From 57cd69460f736031a3fc54af1e97c03f80128478 Mon Sep 17 00:00:00 2001 From: Howard Su Date: Sun, 18 Jun 2023 12:29:47 +0800 Subject: [PATCH 001/852] cmake : add CUDA_ARCHITECTURES to new target ggml_static (#1917) --- CMakeLists.txt | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/CMakeLists.txt b/CMakeLists.txt index f5a968533..736771954 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -492,6 +492,10 @@ if (GGML_SOURCES_CUDA) message(STATUS "GGML CUDA sources found, configuring CUDA architecture") set_property(TARGET ggml PROPERTY CUDA_ARCHITECTURES OFF) set_property(TARGET ggml PROPERTY CUDA_SELECT_NVCC_ARCH_FLAGS "Auto") + + set_property(TARGET ggml_static PROPERTY CUDA_ARCHITECTURES OFF) + set_property(TARGET ggml_static PROPERTY CUDA_SELECT_NVCC_ARCH_FLAGS "Auto") + set_property(TARGET llama PROPERTY CUDA_ARCHITECTURES OFF) endif() From ce2c7d72e2d06988b5ddec6811ab923254542077 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 18 Jun 2023 09:09:47 +0300 Subject: [PATCH 002/852] metal : handle buffers larger than device's maxBufferLength (#1826) * metal : handle buffers larger than device's maxBufferLength * metal : print more verbose device info + handle errors * metal : fix prints for overlapping views * metal : minimize view overlap to try to utilize device memory better --- Makefile | 2 +- ggml-metal.h | 5 ++- ggml-metal.m | 98 ++++++++++++++++++++++++++++++++++++++++++---------- ggml.c | 24 +++++++++++-- ggml.h | 5 +-- llama.cpp | 26 ++++++++------ 6 files changed, 125 insertions(+), 35 deletions(-) diff --git a/Makefile b/Makefile index cf590862b..afd06e0a6 100644 --- a/Makefile +++ b/Makefile @@ -252,7 +252,7 @@ $(info ) ggml.o: ggml.c ggml.h ggml-cuda.h $(CC) $(CFLAGS) -c $< -o $@ -llama.o: llama.cpp ggml.h ggml-cuda.h llama.h llama-util.h +llama.o: llama.cpp ggml.h ggml-cuda.h ggml-metal.h llama.h llama-util.h $(CXX) $(CXXFLAGS) -c $< -o $@ common.o: examples/common.cpp examples/common.h diff --git a/ggml-metal.h b/ggml-metal.h index 033c4d86a..b9e50ac74 100644 --- a/ggml-metal.h +++ b/ggml-metal.h @@ -41,12 +41,15 @@ void ggml_metal_free(struct ggml_metal_context * ctx); // - make sure to map all buffers used in the graph before calling ggml_metal_graph_compute // - the mapping is used during computation to determine the arguments of the compute kernels // - you don't need to keep the host memory buffer allocated as it is never accessed by Metal +// - max_size specifies the maximum size of a tensor and is used to create shared views such +// that it is guaranteed that the tensor will fit in at least one of the views // bool ggml_metal_add_buffer( struct ggml_metal_context * ctx, const char * name, void * data, - size_t size); + size_t size, + size_t max_size); // set data from host memory into the device void ggml_metal_set_tensor(struct ggml_metal_context * ctx, struct ggml_tensor * t); diff --git a/ggml-metal.m b/ggml-metal.m index 07da62a25..a7e104dc7 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -183,6 +183,14 @@ struct ggml_metal_context * ggml_metal_init(void) { #undef GGML_METAL_ADD_KERNEL } + fprintf(stderr, "%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); + fprintf(stderr, "%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false"); + if (ctx->device.maxTransferRate != 0) { + fprintf(stderr, "%s: maxTransferRate = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1024.0 / 1024.0); + } else { + fprintf(stderr, "%s: maxTransferRate = built-in GPU\n", __func__); + } + return ctx; } @@ -199,10 +207,13 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { static id ggml_metal_get_buffer(struct ggml_metal_context * ctx, struct ggml_tensor * t, size_t * offs) { //fprintf(stderr, "%s: data tensor '%16s', offs_data = %8ld, offs_eval = %8ld, offs_cach = %8ld\n", __func__, t->name, offs_data, offs_eval, offs_cach); + const int64_t tsize = ggml_nbytes(t); + + // find the view that contains the tensor fully for (int i = 0; i < ctx->n_buffers; ++i) { const int64_t ioffs = (int64_t) t->data - (int64_t) ctx->buffers[i].data; - if (ioffs >= 0 && ioffs < (int64_t) ctx->buffers[i].size) { + if (ioffs >= 0 && ioffs + tsize <= (int64_t) ctx->buffers[i].size) { *offs = (size_t) ioffs; //fprintf(stderr, "%s: '%s' tensor '%16s', offs = %8ld\n", __func__, ctx->buffers[i].name, t->name, *offs); @@ -220,7 +231,8 @@ bool ggml_metal_add_buffer( struct ggml_metal_context * ctx, const char * name, void * data, - size_t size) { + size_t size, + size_t max_size) { if (ctx->n_buffers >= GGML_METAL_MAX_BUFFERS) { fprintf(stderr, "%s: too many buffers\n", __func__); return false; @@ -237,30 +249,68 @@ bool ggml_metal_add_buffer( } } - size_t page_size = getpagesize(); - size_t aligned_size = size; - if ((aligned_size % page_size) != 0) { - aligned_size += (page_size - (aligned_size % page_size)); + const size_t size_page = getpagesize(); + + size_t size_aligned = size; + if ((size_aligned % size_page) != 0) { + size_aligned += (size_page - (size_aligned % size_page)); } - ctx->buffers[ctx->n_buffers].name = name; - ctx->buffers[ctx->n_buffers].data = data; - ctx->buffers[ctx->n_buffers].size = size; + // the buffer fits into the max buffer size allowed by the device + if (size_aligned <= ctx->device.maxBufferLength) { + ctx->buffers[ctx->n_buffers].name = name; + ctx->buffers[ctx->n_buffers].data = data; + ctx->buffers[ctx->n_buffers].size = size; - if (ctx->device.maxBufferLength < aligned_size) { - fprintf(stderr, "%s: buffer '%s' size %zu is larger than buffer maximum of %zu\n", __func__, name, aligned_size, ctx->device.maxBufferLength); - return false; - } - ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:data length:aligned_size options:MTLResourceStorageModeShared deallocator:nil]; + ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil]; - if (ctx->buffers[ctx->n_buffers].metal == nil) { - fprintf(stderr, "%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, aligned_size / 1024.0 / 1024.0); - return false; + if (ctx->buffers[ctx->n_buffers].metal == nil) { + fprintf(stderr, "%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_aligned / 1024.0 / 1024.0); + return false; + } + + fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB", __func__, name, size_aligned / 1024.0 / 1024.0); + + ++ctx->n_buffers; + } else { + // this overlap between the views will guarantee that the tensor with the maximum size will fully fit into + // one of the views + const size_t size_ovlp = ((max_size + size_page - 1) / size_page + 1) * size_page; // round-up 2 pages just in case + const size_t size_step = ctx->device.maxBufferLength - size_ovlp; + const size_t size_view = ctx->device.maxBufferLength; + + for (size_t i = 0; i < size; i += size_step) { + const size_t size_step_aligned = (i + size_view <= size) ? size_view : (size_aligned - i); + + ctx->buffers[ctx->n_buffers].name = name; + ctx->buffers[ctx->n_buffers].data = (void *) ((uint8_t *) data + i); + ctx->buffers[ctx->n_buffers].size = size_step_aligned; + + ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:(void *) ((uint8_t *) data + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil]; + + if (ctx->buffers[ctx->n_buffers].metal == nil) { + fprintf(stderr, "%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_step_aligned / 1024.0 / 1024.0); + return false; + } + + fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB, offs = %12ld", __func__, name, size_step_aligned / 1024.0 / 1024.0, i); + if (i + size_step < size) { + fprintf(stderr, "\n"); + } + + ++ctx->n_buffers; + } } - fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB\n", __func__, name, aligned_size / 1024.0 / 1024.0); + fprintf(stderr, ", (%8.2f / %8.2f)", + ctx->device.currentAllocatedSize / 1024.0 / 1024.0, + ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); - ++ctx->n_buffers; + if (ctx->device.currentAllocatedSize > ctx->device.recommendedMaxWorkingSetSize) { + fprintf(stderr, ", warning: current allocated size is greater than the recommended max working set size\n"); + } else { + fprintf(stderr, "\n"); + } } return true; @@ -909,4 +959,14 @@ void ggml_metal_graph_compute( dispatch_barrier_sync(queue, ^{}); [command_buffers[n_cb - 1] waitUntilCompleted]; + + // check status of command buffers + // needed to detect if the device ran out-of-memory for example (#1881) + for (int i = 0; i < n_cb; i++) { + MTLCommandBufferStatus status = (MTLCommandBufferStatus) [command_buffers[i] status]; + if (status != MTLCommandBufferStatusCompleted) { + fprintf(stderr, "%s: command buffer %d failed with status %lu\n", __func__, i, status); + GGML_ASSERT(false); + } + } } diff --git a/ggml.c b/ggml.c index 0eda7f338..78c365354 100644 --- a/ggml.c +++ b/ggml.c @@ -4154,14 +4154,34 @@ void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc) { ctx->no_alloc = no_alloc; } -void * ggml_get_mem_buffer(struct ggml_context * ctx) { +void * ggml_get_mem_buffer(const struct ggml_context * ctx) { return ctx->mem_buffer; } -size_t ggml_get_mem_size(struct ggml_context * ctx) { +size_t ggml_get_mem_size(const struct ggml_context * ctx) { return ctx->mem_size; } +size_t ggml_get_max_tensor_size(const struct ggml_context * ctx) { + size_t max_size = 0; + + struct ggml_object * obj = ctx->objects_begin; + + while (obj != NULL) { + struct ggml_tensor * tensor = (struct ggml_tensor *) ((char *) ctx->mem_buffer + obj->offs); + + const size_t size = ggml_nbytes(tensor); + + if (max_size < size) { + max_size = size; + } + + obj = obj->next; + } + + return max_size; +} + // IMPORTANT: // when creating "opt" tensors, always save and load the scratch buffer // this is an error prone process, but it is necessary to support inplace diff --git a/ggml.h b/ggml.h index 9b0c846f8..1380c530f 100644 --- a/ggml.h +++ b/ggml.h @@ -500,8 +500,9 @@ extern "C" { GGML_API size_t ggml_set_scratch (struct ggml_context * ctx, struct ggml_scratch scratch); GGML_API void ggml_set_no_alloc(struct ggml_context * ctx, bool no_alloc); - GGML_API void * ggml_get_mem_buffer(struct ggml_context * ctx); - GGML_API size_t ggml_get_mem_size (struct ggml_context * ctx); + GGML_API void * ggml_get_mem_buffer (const struct ggml_context * ctx); + GGML_API size_t ggml_get_mem_size (const struct ggml_context * ctx); + GGML_API size_t ggml_get_max_tensor_size(const struct ggml_context * ctx); GGML_API struct ggml_tensor * ggml_new_tensor( struct ggml_context * ctx, diff --git a/llama.cpp b/llama.cpp index a2916b3e8..c165d3239 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2696,16 +2696,21 @@ struct llama_context * llama_init_from_file( // this allocates all Metal resources and memory buffers ctx->ctx_metal = ggml_metal_init(); - void *data_ptr = NULL; + void * data_ptr = NULL; size_t data_size = 0; + if (params.use_mmap) { - data_ptr = ctx->model.mapping->addr; - data_size= ctx->model.mapping->size; + data_ptr = ctx->model.mapping->addr; + data_size = ctx->model.mapping->size; } else { - data_ptr = ggml_get_mem_buffer(ctx->model.ctx); - data_size= ggml_get_mem_size(ctx->model.ctx); + data_ptr = ggml_get_mem_buffer(ctx->model.ctx); + data_size = ggml_get_mem_size (ctx->model.ctx); } + const size_t max_size = ggml_get_max_tensor_size(ctx->model.ctx); + + printf("%s: max tensor size = %8.2f MB\n", __func__, max_size/1024.0/1024.0); + #define LLAMA_METAL_CHECK_BUF(result) \ if (!(result)) { \ fprintf(stderr, "%s: failed to add buffer\n", __func__); \ @@ -2713,12 +2718,13 @@ struct llama_context * llama_init_from_file( return NULL; \ } - LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "data", data_ptr, data_size)); - LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.addr, ctx->buf_compute.size)); + LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "data", data_ptr, data_size, max_size)); - LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "kv", ctx->model.kv_self.buf.addr, ctx->model.kv_self.buf.size)); - LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr0", ctx->buf_scratch[0].addr, ctx->buf_scratch[0].size)); - LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr1", ctx->buf_scratch[1].addr, ctx->buf_scratch[1].size)); + LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.addr, ctx->buf_compute.size, 0)); + LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "kv", ctx->model.kv_self.buf.addr, ctx->model.kv_self.buf.size, 0)); + + LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr0", ctx->buf_scratch[0].addr, ctx->buf_scratch[0].size, 0)); + LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr1", ctx->buf_scratch[1].addr, ctx->buf_scratch[1].size, 0)); #undef LLAMA_METAL_CHECK_BUF } #endif From 90cc59d6ab1363a5c69c60c4b94db647d3a54a18 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sun, 18 Jun 2023 10:52:10 +0300 Subject: [PATCH 003/852] examples : fix examples/metal (#1920) Co-authored-by: Iwan Kawrakow --- examples/metal/metal.cpp | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/examples/metal/metal.cpp b/examples/metal/metal.cpp index 77aca94a3..cdfe4bfe9 100644 --- a/examples/metal/metal.cpp +++ b/examples/metal/metal.cpp @@ -40,8 +40,10 @@ int main(int argc, char ** argv) { // this allocates all Metal resources and memory buffers auto * ctx_metal = ggml_metal_init(); - ggml_metal_add_buffer(ctx_metal, "data", ggml_get_mem_buffer(ctx_data), ggml_get_mem_size(ctx_data)); - ggml_metal_add_buffer(ctx_metal, "eval", ggml_get_mem_buffer(ctx_eval), ggml_get_mem_size(ctx_eval)); + const size_t max_size_data = ggml_get_max_tensor_size(ctx_data); + const size_t max_size_eval = ggml_get_max_tensor_size(ctx_eval); + ggml_metal_add_buffer(ctx_metal, "data", ggml_get_mem_buffer(ctx_data), ggml_get_mem_size(ctx_data), max_size_data); + ggml_metal_add_buffer(ctx_metal, "eval", ggml_get_mem_buffer(ctx_eval), ggml_get_mem_size(ctx_eval), max_size_eval); // main { From 8ab8ba62eb27cc340be2edf3418e051b1d967416 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sun, 18 Jun 2023 11:13:43 +0300 Subject: [PATCH 004/852] llama : prevent usage of k-quants when tensor size is not a multiple of 256 (#1921) * Fix examples/metal * k-quants: prevent usage when tensor size is not divisible by 256 --------- Co-authored-by: Iwan Kawrakow --- llama.cpp | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/llama.cpp b/llama.cpp index c165d3239..dfbb85a68 100644 --- a/llama.cpp +++ b/llama.cpp @@ -19,6 +19,11 @@ #ifdef GGML_USE_METAL #include "ggml-metal.h" #endif +#ifdef GGML_USE_K_QUANTS +#ifndef QK_K +#define QK_K 256 +#endif +#endif #include #include @@ -2491,6 +2496,17 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s } else { new_type = quantized_type; #ifdef GGML_USE_K_QUANTS + if (quantized_type == GGML_TYPE_Q2_K || quantized_type == GGML_TYPE_Q3_K || quantized_type == GGML_TYPE_Q4_K || + quantized_type == GGML_TYPE_Q5_K || quantized_type == GGML_TYPE_Q6_K) { + int nx = tensor.ne.at(0); + int ny = tensor.ne.at(0); + if (nx % QK_K != 0 || ny % QK_K != 0) { + fprintf(stderr, "\n\n========================= Tensor sizes %d x %d are not divisible by %d\n",nx,ny,QK_K); + fprintf(stderr, "This is required to be able to use k-quants for now!\n"); + fprintf(stderr, "========================================================================================\n\n"); + throw std::runtime_error("Unsupported tensor size encountered\n"); + } + } if (tensor.name == "output.weight") { new_type = GGML_TYPE_Q6_K; } else if (tensor.name.find("attention.wv.weight") != std::string::npos) { From e1886cf4fe0d0f31661dda52a4a9f34bd9b9009a Mon Sep 17 00:00:00 2001 From: Mike Date: Sun, 18 Jun 2023 16:28:26 +0800 Subject: [PATCH 005/852] readme : update Android build instructions (#1922) Add steps for using termux on android devices to prevent common errors. --- README.md | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 7defb7584..e5b3f59b3 100644 --- a/README.md +++ b/README.md @@ -617,7 +617,12 @@ And after 4.45 hours, you will have the final perplexity. #### Building the Project using Android NDK You can easily run `llama.cpp` on Android device with [termux](https://termux.dev/). -First, obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake: + +First, install the essential packages for termux: +``` +pkg install clang wget git cmake +``` +Second, obtain the [Android NDK](https://developer.android.com/ndk) and then build with CMake: ``` $ mkdir build-android $ cd build-android From 8596af427722775f0df4a7c90b9af067ba90d4ef Mon Sep 17 00:00:00 2001 From: l3utterfly Date: Sun, 18 Jun 2023 19:19:16 +0800 Subject: [PATCH 006/852] ggml : fix bug in ggml_compute_forward_add_q_f32 (#1918) --- ggml.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml.c b/ggml.c index 78c365354..037f0bc99 100644 --- a/ggml.c +++ b/ggml.c @@ -7918,7 +7918,7 @@ static void ggml_compute_forward_add_q_f32( void * src0_row = (void *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)); float * src1_row = (float *)((char *) src1->data + (i11*nb11 + i12*nb12 + i13*nb13)); - void * dst_row = (void *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb0)); + void * dst_row = (void *) ((char *) dst->data + ( i1*nb1 + i2*nb2 + i3*nb3)); assert(ne00 % 32 == 0); From 0ede372a51fd8160688e01b587582666c14e94e5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sun, 18 Jun 2023 16:07:09 +0200 Subject: [PATCH 007/852] Fixed incorrectly applying RMS norm twice (#1925) --- llama.cpp | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/llama.cpp b/llama.cpp index dfbb85a68..45360cea3 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1657,11 +1657,7 @@ static bool llama_eval_internal( { cur = ggml_rms_norm(ctx0, inpL); offload_func_nr(cur); - ggml_set_name(cur, "rms_norm_inpL"); - - cur = ggml_rms_norm(ctx0, cur); - offload_func_nr(cur); - ggml_set_name(cur, "rms_norm_after"); + ggml_set_name(cur, "rms_norm_2"); // cur = cur*norm(broadcasted) cur = ggml_mul(ctx0, cur, model.norm); From b24c3049d96557c24782e4d32feaae65f47277af Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sun, 18 Jun 2023 17:41:26 +0200 Subject: [PATCH 008/852] Added tokens per second to info prints (#1928) --- llama.cpp | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/llama.cpp b/llama.cpp index 45360cea3..2105e3279 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3467,9 +3467,12 @@ void llama_print_timings(struct llama_context * ctx) { fprintf(stderr, "\n"); fprintf(stderr, "%s: load time = %8.2f ms\n", __func__, ctx->t_load_us / 1000.0); - fprintf(stderr, "%s: sample time = %8.2f ms / %5d runs (%8.2f ms per token)\n", __func__, 1e-3 * ctx->t_sample_us, n_sample, 1e-3 * ctx->t_sample_us / n_sample); - fprintf(stderr, "%s: prompt eval time = %8.2f ms / %5d tokens (%8.2f ms per token)\n", __func__, 1e-3 * ctx->t_p_eval_us, n_p_eval, 1e-3 * ctx->t_p_eval_us / n_p_eval); - fprintf(stderr, "%s: eval time = %8.2f ms / %5d runs (%8.2f ms per token)\n", __func__, 1e-3 * ctx->t_eval_us, n_eval, 1e-3 * ctx->t_eval_us / n_eval); + fprintf(stderr, "%s: sample time = %8.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n", + __func__, 1e-3 * ctx->t_sample_us, n_sample, 1e-3 * ctx->t_sample_us / n_sample, 1e6 / ctx->t_sample_us * n_sample); + fprintf(stderr, "%s: prompt eval time = %8.2f ms / %5d tokens (%8.2f ms per token, %8.2f tokens per second)\n", + __func__, 1e-3 * ctx->t_p_eval_us, n_p_eval, 1e-3 * ctx->t_p_eval_us / n_p_eval, 1e6 / ctx->t_p_eval_us * n_p_eval); + fprintf(stderr, "%s: eval time = %8.2f ms / %5d runs (%8.2f ms per token, %8.2f tokens per second)\n", + __func__, 1e-3 * ctx->t_eval_us, n_eval, 1e-3 * ctx->t_eval_us / n_eval, 1e6 / ctx->t_eval_us * n_eval); fprintf(stderr, "%s: total time = %8.2f ms\n", __func__, (t_end_us - ctx->t_start_us)/1000.0); } From 16b9cd193965769089881bb8ec012fccca7b37b6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Mon, 19 Jun 2023 10:23:56 +0200 Subject: [PATCH 009/852] Convert vector to f16 for dequantize mul mat vec (#1913) * Convert vector to f16 for dmmv * compile option * Added compilation option description to README * Changed cmake CUDA_ARCHITECTURES from "OFF" to "native" --- CMakeLists.txt | 10 ++- Makefile | 3 + README.md | 9 ++- ggml-cuda.cu | 202 ++++++++++++++++++++++++++++++++++--------------- llama.cpp | 2 +- 5 files changed, 158 insertions(+), 68 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 736771954..dc06365d1 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -70,6 +70,7 @@ set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor") option(LLAMA_CUBLAS "llama: use cuBLAS" OFF) set(LLAMA_CUDA_DMMV_X "32" CACHE STRING "llama: x stride for dmmv CUDA kernels") set(LLAMA_CUDA_DMMV_Y "1" CACHE STRING "llama: y block size for dmmv CUDA kernels") +option(LLAMA_CUDA_DMMV_F16 "llama: use 16 bit floats for dmmv CUDA kernels" OFF) set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K") option(LLAMA_CLBLAST "llama: use CLBlast" OFF) option(LLAMA_METAL "llama: use Metal" OFF) @@ -238,6 +239,9 @@ if (LLAMA_CUBLAS) add_compile_definitions(GGML_USE_CUBLAS) add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X}) add_compile_definitions(GGML_CUDA_DMMV_Y=${LLAMA_CUDA_DMMV_Y}) + if (LLAMA_CUDA_DMMV_F16) + add_compile_definitions(GGML_CUDA_DMMV_F16) + endif() add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER}) if (LLAMA_STATIC) @@ -490,13 +494,13 @@ endif() if (GGML_SOURCES_CUDA) message(STATUS "GGML CUDA sources found, configuring CUDA architecture") - set_property(TARGET ggml PROPERTY CUDA_ARCHITECTURES OFF) + set_property(TARGET ggml PROPERTY CUDA_ARCHITECTURES "native") set_property(TARGET ggml PROPERTY CUDA_SELECT_NVCC_ARCH_FLAGS "Auto") - set_property(TARGET ggml_static PROPERTY CUDA_ARCHITECTURES OFF) + set_property(TARGET ggml_static PROPERTY CUDA_ARCHITECTURES "native") set_property(TARGET ggml_static PROPERTY CUDA_SELECT_NVCC_ARCH_FLAGS "Auto") - set_property(TARGET llama PROPERTY CUDA_ARCHITECTURES OFF) + set_property(TARGET llama PROPERTY CUDA_ARCHITECTURES "native") endif() diff --git a/Makefile b/Makefile index afd06e0a6..5dd676fad 100644 --- a/Makefile +++ b/Makefile @@ -169,6 +169,9 @@ ifdef LLAMA_CUDA_DMMV_Y else NVCCFLAGS += -DGGML_CUDA_DMMV_Y=1 endif # LLAMA_CUDA_DMMV_Y +ifdef LLAMA_CUDA_DMMV_F16 + NVCCFLAGS += -DGGML_CUDA_DMMV_F16 +endif # LLAMA_CUDA_DMMV_F16 ifdef LLAMA_CUDA_KQUANTS_ITER NVCCFLAGS += -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER) else diff --git a/README.md b/README.md index e5b3f59b3..2d05de333 100644 --- a/README.md +++ b/README.md @@ -337,7 +337,14 @@ Building the program with BLAS support may lead to some performance improvements cmake --build . --config Release ``` - The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. + The environment variable [`CUDA_VISIBLE_DEVICES`](https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#env-vars) can be used to specify which GPU(s) will be used. The following compilation options are also available to tweak performance: + + | Option | Legal values | Default | Description | + |-------------------------|------------------------|---------|-------------| + | LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | + | LLAMA_CUDA_DMMV_Y | Positive integer | 1 | Block size in y direction for the CUDA dequantization + mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. | + | LLAMA_CUDA_DMMV_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels. Can improve performance on relatively recent GPUs. | + | LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value 2 1 can improve performance for slow GPUs. | - #### CLBlast diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 16488b9f9..9ebc57aff 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -50,7 +50,15 @@ static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size"); } while (0) #endif // CUDART_VERSION >= 11 -typedef void (*dequantize_kernel_t)(const void * vx, const int ib, const int iqs, float & v0, float & v1); +#ifdef GGML_CUDA_DMMV_F16 +typedef half dfloat; // dequantize float +typedef half2 dfloat2; +#else +typedef float dfloat; // dequantize float +typedef float2 dfloat2; +#endif //GGML_CUDA_DMMV_F16 + +typedef void (*dequantize_kernel_t)(const void * vx, const int ib, const int iqs, dfloat2 & v); typedef void (*to_fp32_cuda_t)(const void * x, float * y, int k, cudaStream_t stream); typedef void (*dot_kernel_k_t)(const void * vx, const int ib, const int iqs, const float * y, float & v); typedef void (*cpy_kernel_t)(const char * cx, char * cdst); @@ -234,82 +242,106 @@ static __global__ void rms_norm_f32(const float * x, float * dst, const int ncol } } -static __device__ void dequantize_q4_0(const void * vx, const int ib, const int iqs, float & v0, float & v1){ +static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const int ib, const int iqs, dfloat2 & v){ const block_q4_0 * x = (const block_q4_0 *) vx; - const float d = x[ib].d; + const dfloat d = x[ib].d; - const uint8_t vui = x[ib].qs[iqs]; + const int vui = x[ib].qs[iqs]; - const int8_t vi0 = vui & 0xF; - const int8_t vi1 = vui >> 4; + v.x = vui & 0xF; + v.y = vui >> 4; - v0 = (vi0 - 8)*d; - v1 = (vi1 - 8)*d; +#ifdef GGML_CUDA_DMMV_F16 + v = __hsub2(v, {8.0f, 8.0f}); + v = __hmul2(v, {d, d}); +#else + v.x = (v.x - 8.0f) * d; + v.y = (v.y - 8.0f) * d; +#endif // GGML_CUDA_DMMV_F16 } -static __device__ void dequantize_q4_1(const void * vx, const int ib, const int iqs, float & v0, float & v1){ +static __device__ __forceinline__ void dequantize_q4_1(const void * vx, const int ib, const int iqs, dfloat2 & v){ const block_q4_1 * x = (const block_q4_1 *) vx; - const float d = x[ib].d; - const float m = x[ib].m; + const dfloat d = x[ib].d; + const dfloat m = x[ib].m; - const uint8_t vui = x[ib].qs[iqs]; + const int vui = x[ib].qs[iqs]; - const int8_t vi0 = vui & 0xF; - const int8_t vi1 = vui >> 4; + v.x = vui & 0xF; + v.y = vui >> 4; - v0 = vi0*d + m; - v1 = vi1*d + m; +#ifdef GGML_CUDA_DMMV_F16 + v = __hmul2(v, {d, d}); + v = __hadd2(v, {m, m}); +#else + v.x = (v.x * d) + m; + v.y = (v.y * d) + m; +#endif // GGML_CUDA_DMMV_F16 } -static __device__ void dequantize_q5_0(const void * vx, const int ib, const int iqs, float & v0, float & v1){ +static __device__ __forceinline__ void dequantize_q5_0(const void * vx, const int ib, const int iqs, dfloat2 & v){ const block_q5_0 * x = (const block_q5_0 *) vx; - const float d = x[ib].d; + const dfloat d = x[ib].d; uint32_t qh; memcpy(&qh, x[ib].qh, sizeof(qh)); - const uint8_t xh_0 = ((qh >> (iqs + 0)) << 4) & 0x10; - const uint8_t xh_1 = ((qh >> (iqs + 12)) ) & 0x10; + const int xh_0 = ((qh >> (iqs + 0)) << 4) & 0x10; + const int xh_1 = ((qh >> (iqs + 12)) ) & 0x10; - const int32_t x0 = ((x[ib].qs[iqs] & 0xf) | xh_0) - 16; - const int32_t x1 = ((x[ib].qs[iqs] >> 4) | xh_1) - 16; + v.x = ((x[ib].qs[iqs] & 0xf) | xh_0); + v.y = ((x[ib].qs[iqs] >> 4) | xh_1); - v0 = x0*d; - v1 = x1*d; +#ifdef GGML_CUDA_DMMV_F16 + v = __hsub2(v, {16.0f, 16.0f}); + v = __hmul2(v, {d, d}); +#else + v.x = (v.x - 16.0f) * d; + v.y = (v.y - 16.0f) * d; +#endif // GGML_CUDA_DMMV_F16 } -static __device__ void dequantize_q5_1(const void * vx, const int ib, const int iqs, float & v0, float & v1){ +static __device__ __forceinline__ void dequantize_q5_1(const void * vx, const int ib, const int iqs, dfloat2 & v){ const block_q5_1 * x = (const block_q5_1 *) vx; - const float d = x[ib].d; - const float m = x[ib].m; + const dfloat d = x[ib].d; + const dfloat m = x[ib].m; uint32_t qh; memcpy(&qh, x[ib].qh, sizeof(qh)); - const uint8_t xh_0 = ((qh >> (iqs + 0)) << 4) & 0x10; - const uint8_t xh_1 = ((qh >> (iqs + 12)) ) & 0x10; + const int xh_0 = ((qh >> (iqs + 0)) << 4) & 0x10; + const int xh_1 = ((qh >> (iqs + 12)) ) & 0x10; - const int32_t x0 = ((x[ib].qs[iqs] & 0xf) | xh_0); - const int32_t x1 = ((x[ib].qs[iqs] >> 4) | xh_1); + v.x = ((x[ib].qs[iqs] & 0xf) | xh_0); + v.y = ((x[ib].qs[iqs] >> 4) | xh_1); - v0 = x0*d + m; - v1 = x1*d + m; +#ifdef GGML_CUDA_DMMV_F16 + v = __hmul2(v, {d, d}); + v = __hadd2(v, {m, m}); +#else + v.x = (v.x * d) + m; + v.y = (v.y * d) + m; +#endif // GGML_CUDA_DMMV_F16 } -static __device__ void dequantize_q8_0(const void * vx, const int ib, const int iqs, float & v0, float & v1){ +static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const int ib, const int iqs, dfloat2 & v){ const block_q8_0 * x = (const block_q8_0 *) vx; - const float d = x[ib].d; + const dfloat d = x[ib].d; - const int8_t vi0 = x[ib].qs[iqs + 0]; - const int8_t vi1 = x[ib].qs[iqs + 1]; + v.x = x[ib].qs[iqs + 0]; + v.y = x[ib].qs[iqs + 1]; - v0 = vi0*d; - v1 = vi1*d; +#ifdef GGML_CUDA_DMMV_F16 + v = __hmul2(v, {d, d}); +#else + v.x *= d; + v.y *= d; +#endif // GGML_CUDA_DMMV_F16 } //================================== k-quants @@ -843,11 +875,12 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * vx, const float } } -static __device__ void convert_f16(const void * vx, const int ib, const int iqs, float & v0, float & v1){ +static __device__ void convert_f16(const void * vx, const int ib, const int iqs, dfloat2 & v){ const half * x = (const half *) vx; - v0 = __half2float(x[ib + iqs + 0]); - v1 = __half2float(x[ib + iqs + 1]); + // automatic half -> float type cast if dfloat == float + v.x = x[ib + iqs + 0]; + v.y = x[ib + iqs + 1]; } template @@ -864,13 +897,15 @@ static __global__ void dequantize_block(const void * vx, float * y, const int k) const int y_offset = qr == 1 ? 1 : qk/2; // dequantize - float & v0 = y[iybs + iqs + 0]; - float & v1 = y[iybs + iqs + y_offset]; - dequantize_kernel(vx, ib, iqs, v0, v1); + dfloat2 v; + dequantize_kernel(vx, ib, iqs, v); + + y[iybs + iqs + 0] = v.x; + y[iybs + iqs + y_offset] = v.y; } template -static __global__ void dequantize_mul_mat_vec(const void * vx, const float * y, float * dst, const int ncols, const int nrows) { +static __global__ void dequantize_mul_mat_vec(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows) { // qk = quantized weights per x block // qr = number of quantized weights per data value in x block const int row = blockIdx.y*blockDim.y + threadIdx.y; @@ -885,7 +920,12 @@ static __global__ void dequantize_mul_mat_vec(const void * vx, const float * y, const int vals_per_iter = iter_stride / WARP_SIZE; // num quantized vals per thread and i iter const int y_offset = qr == 1 ? 1 : qk/2; - float tmp = 0.0f; // partial sum for thread in warp +// partial sum for each thread +#ifdef GGML_CUDA_DMMV_F16 + half2 tmp = {0.0f, 0.0f}; // two sums for f16 to take advantage of half2 intrinsics +#else + float tmp = 0.0f; +#endif // GGML_CUDA_DMMV_F16 for (int i = 0; i < ncols; i += iter_stride) { const int col = i + vals_per_iter*tid; @@ -899,14 +939,21 @@ static __global__ void dequantize_mul_mat_vec(const void * vx, const float * y, // process 2 vals per j iter // dequantize - float v0, v1; - dequantize_kernel(vx, ib, iqs + j/qr, v0, v1); // for qr = 2 the iqs needs to increase by 1 per j iter because 2 weights per data val + dfloat2 v; + dequantize_kernel(vx, ib, iqs + j/qr, v); // matrix multiplication - tmp += v0 * y[iybs + iqs + j/qr + 0]; - tmp += v1 * y[iybs + iqs + j/qr + y_offset]; // for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2 +#ifdef GGML_CUDA_DMMV_F16 + tmp += __hmul2(v, { + y[iybs + iqs + j/qr + 0], + y[iybs + iqs + j/qr + y_offset] + }); +#else + tmp += v.x * y[iybs + iqs + j/qr + 0]; + tmp += v.y * y[iybs + iqs + j/qr + y_offset]; +#endif // GGML_CUDA_DMMV_F16 } } @@ -918,7 +965,11 @@ static __global__ void dequantize_mul_mat_vec(const void * vx, const float * y, } if (tid == 0) { +#ifdef GGML_CUDA_DMMV_F16 + dst[row] = tmp.x + tmp.y; +#else dst[row] = tmp; +#endif // GGML_CUDA_DMMV_F16 } } @@ -1213,7 +1264,7 @@ static void dequantize_row_q6_K_cuda(const void * vx, float * y, const int k, cu dequantize_block_q6_K<<>>(vx, y); } -static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { +static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y; const dim3 block_nums(1, block_num_y, 1); @@ -1222,7 +1273,7 @@ static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const float * y, f <<>>(vx, y, dst, ncols, nrows); } -static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { +static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y; const dim3 block_nums(1, block_num_y, 1); @@ -1231,7 +1282,7 @@ static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const float * y, f <<>>(vx, y, dst, ncols, nrows); } -static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { +static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y; const dim3 block_nums(1, block_num_y, 1); @@ -1240,7 +1291,7 @@ static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const float * y, f <<>>(vx, y, dst, ncols, nrows); } -static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { +static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y; const dim3 block_nums(1, block_num_y, 1); @@ -1249,7 +1300,7 @@ static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const float * y, f <<>>(vx, y, dst, ncols, nrows); } -static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { +static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y; const dim3 block_nums(1, block_num_y, 1); @@ -1299,7 +1350,7 @@ static void convert_fp16_to_fp32_cuda(const void * vx, float * y, const int k, c dequantize_block<1, 1, convert_f16><<>>(vx, y, k); } -static void convert_mul_mat_vec_f16_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { +static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y; const dim3 block_nums(1, block_num_y, 1); @@ -1714,21 +1765,40 @@ inline void ggml_cuda_op_dequantize_mul_mat_vec( const int64_t ne00 = src0->ne[0]; const int64_t nrows = i01_high - i01_low; +// on some GPUs it is faster to convert src1 to half and to use half precision intrinsics +#ifdef GGML_CUDA_DMMV_F16 + size_t ash; + dfloat * src1_dfloat = nullptr; // dfloat == half + + bool src1_convert_f16 = src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 || + src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 || + src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16; + + if (src1_convert_f16) { + src1_dfloat = (half *) ggml_cuda_pool_malloc(ne00*sizeof(half), &ash); + ggml_cpy_f32_f16_cuda((char *) src1_ddf_i, (char *) src1_dfloat, ne00, + ne00, 1, sizeof(float), 0, 0, + ne00, 1, sizeof(half), 0, 0, cudaStream_main); + } +#else + dfloat * src1_dfloat = src1_ddf_i; // dfloat == float, no conversion +#endif // GGML_CUDA_DMMV_F16 + switch (src0->type) { case GGML_TYPE_Q4_0: - dequantize_mul_mat_vec_q4_0_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); + dequantize_mul_mat_vec_q4_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); break; case GGML_TYPE_Q4_1: - dequantize_mul_mat_vec_q4_1_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); + dequantize_mul_mat_vec_q4_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); break; case GGML_TYPE_Q5_0: - dequantize_mul_mat_vec_q5_0_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); + dequantize_mul_mat_vec_q5_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); break; case GGML_TYPE_Q5_1: - dequantize_mul_mat_vec_q5_1_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); + dequantize_mul_mat_vec_q5_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); break; case GGML_TYPE_Q8_0: - dequantize_mul_mat_vec_q8_0_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); + dequantize_mul_mat_vec_q8_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); break; case GGML_TYPE_Q2_K: dequantize_mul_mat_vec_q2_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); @@ -1746,7 +1816,7 @@ inline void ggml_cuda_op_dequantize_mul_mat_vec( dequantize_mul_mat_vec_q6_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); break; case GGML_TYPE_F16: - convert_mul_mat_vec_f16_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); + convert_mul_mat_vec_f16_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); break; default: GGML_ASSERT(false); @@ -1754,6 +1824,12 @@ inline void ggml_cuda_op_dequantize_mul_mat_vec( } CUDA_CHECK(cudaGetLastError()); +#ifdef GGML_CUDA_DMMV_F16 + if (src1_convert_f16) { + ggml_cuda_pool_free(src1_dfloat, ash); + } +#endif // GGML_CUDA_DMMV_F16 + (void) src1; (void) dst; (void) src0_ddf_i; diff --git a/llama.cpp b/llama.cpp index 2105e3279..5401db00e 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1620,7 +1620,7 @@ static bool llama_eval_internal( model.layers[il].w1, cur); offload_func(cur); - ggml_set_name(cur, "result_w2"); + ggml_set_name(cur, "result_w1"); // SILU activation cur = ggml_silu(ctx0, cur); From 1e3abfcef073e73c2b31e8570cb06c5cb2fd1f55 Mon Sep 17 00:00:00 2001 From: Howard Su Date: Mon, 19 Jun 2023 23:10:37 +0800 Subject: [PATCH 010/852] cmake : fix build shared ggml when CUDA is enabled (#1929) Co-authored-by: Georgi Gerganov --- CMakeLists.txt | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/CMakeLists.txt b/CMakeLists.txt index dc06365d1..a598593b6 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -469,6 +469,7 @@ add_library(ggml_static STATIC $) if (BUILD_SHARED_LIBS) set_target_properties(ggml PROPERTIES POSITION_INDEPENDENT_CODE ON) add_library(ggml_shared SHARED $) + target_link_libraries(ggml_shared PUBLIC Threads::Threads ${LLAMA_EXTRA_LIBS}) endif() add_library(llama @@ -500,6 +501,11 @@ if (GGML_SOURCES_CUDA) set_property(TARGET ggml_static PROPERTY CUDA_ARCHITECTURES "native") set_property(TARGET ggml_static PROPERTY CUDA_SELECT_NVCC_ARCH_FLAGS "Auto") + if (BUILD_SHARED_LIBS) + set_property(TARGET ggml_shared PROPERTY CUDA_ARCHITECTURES "native") + set_property(TARGET ggml_shared PROPERTY CUDA_SELECT_NVCC_ARCH_FLAGS "Auto") + endif() + set_property(TARGET llama PROPERTY CUDA_ARCHITECTURES "native") endif() From b97ca431db35ec96a339a721acb1219c1dd78bed Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 19 Jun 2023 18:12:33 +0300 Subject: [PATCH 011/852] ggml : sync latest ggml repo (#1924) * ggml : sync latest ggml repo * ggml : remove unused comments * ggml : asserts --- ggml.c | 801 ++++++++++++++++++++++++++++++++++++++++++++++++++------- ggml.h | 144 ++++++++++- 2 files changed, 844 insertions(+), 101 deletions(-) diff --git a/ggml.c b/ggml.c index 037f0bc99..14e08f9d6 100644 --- a/ggml.c +++ b/ggml.c @@ -112,6 +112,7 @@ typedef void* thread_ret_t; /*#define GGML_PERF*/ #define GGML_DEBUG 0 #define GGML_GELU_FP16 +#define GGML_GELU_QUICK_FP16 #define GGML_SILU_FP16 #define GGML_SOFT_MAX_UNROLL 4 @@ -340,6 +341,9 @@ static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { // precomputed gelu table for f16 (128 KB) static ggml_fp16_t table_gelu_f16[1 << 16]; +// precomputed quick gelu table for f16 (128 KB) +static ggml_fp16_t table_gelu_quick_f16[1 << 16]; + // precomputed silu table for f16 (128 KB) static ggml_fp16_t table_silu_f16[1 << 16]; @@ -1677,14 +1681,17 @@ quantize_fns_t ggml_internal_get_quantize_fn(size_t i) { #define GGML_F32x4_REDUCE_ONE(x) vaddvq_f32(x) #define GGML_F32x4_REDUCE(res, x) \ { \ - for (int i = 0; i < GGML_F32_ARR/2; ++i) { \ - x[2*i] = vaddq_f32(x[2*i], x[2*i+1]); \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = vaddq_f32(x[i], x[offset+i]); \ } \ - for (int i = 0; i < GGML_F32_ARR/4; ++i) { \ - x[4*i] = vaddq_f32(x[4*i], x[4*i+2]); \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = vaddq_f32(x[i], x[offset+i]); \ } \ - for (int i = 0; i < GGML_F32_ARR/8; ++i) { \ - x[8*i] = vaddq_f32(x[8*i], x[8*i+4]); \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = vaddq_f32(x[i], x[offset+i]); \ } \ res = GGML_F32x4_REDUCE_ONE(x[0]); \ } @@ -1715,14 +1722,17 @@ quantize_fns_t ggml_internal_get_quantize_fn(size_t i) { #define GGML_F16x8_MUL vmulq_f16 #define GGML_F16x8_REDUCE(res, x) \ { \ - for (int i = 0; i < GGML_F16_ARR/2; ++i) { \ - x[2*i] = vaddq_f16(x[2*i], x[2*i+1]); \ + int offset = GGML_F16_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = vaddq_f16(x[i], x[offset+i]); \ } \ - for (int i = 0; i < GGML_F16_ARR/4; ++i) { \ - x[4*i] = vaddq_f16(x[4*i], x[4*i+2]); \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = vaddq_f16(x[i], x[offset+i]); \ } \ - for (int i = 0; i < GGML_F16_ARR/8; ++i) { \ - x[8*i] = vaddq_f16(x[8*i], x[8*i+4]); \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = vaddq_f16(x[i], x[offset+i]); \ } \ const float32x4_t t0 = vcvt_f32_f16(vget_low_f16 (x[0])); \ const float32x4_t t1 = vcvt_f32_f16(vget_high_f16(x[0])); \ @@ -1789,14 +1799,17 @@ quantize_fns_t ggml_internal_get_quantize_fn(size_t i) { #define GGML_F32x8_MUL _mm256_mul_ps #define GGML_F32x8_REDUCE(res, x) \ { \ - for (int i = 0; i < GGML_F32_ARR/2; ++i) { \ - x[2*i] = _mm256_add_ps(x[2*i], x[2*i+1]); \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm256_add_ps(x[i], x[offset+i]); \ } \ - for (int i = 0; i < GGML_F32_ARR/4; ++i) { \ - x[4*i] = _mm256_add_ps(x[4*i], x[4*i+2]); \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm256_add_ps(x[i], x[offset+i]); \ } \ - for (int i = 0; i < GGML_F32_ARR/8; ++i) { \ - x[8*i] = _mm256_add_ps(x[8*i], x[8*i+4]); \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm256_add_ps(x[i], x[offset+i]); \ } \ const __m128 t0 = _mm_add_ps(_mm256_castps256_ps128(x[0]), \ _mm256_extractf128_ps(x[0], 1)); \ @@ -1886,14 +1899,17 @@ static inline void __avx_f32cx8_store(ggml_fp16_t *x, __m256 y) { #define GGML_F32x4_MUL vec_mul #define GGML_F32x4_REDUCE(res, x) \ { \ - for (int i = 0; i < GGML_F32_ARR/2; ++i) { \ - x[2*i] = vec_add(x[2*i], x[2*i+1]); \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = vec_add(x[i], x[offset+i]); \ } \ - for (int i = 0; i < GGML_F32_ARR/4; ++i) { \ - x[4*i] = vec_add(x[4*i], x[4*i+2]); \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = vec_add(x[i], x[offset+i]); \ } \ - for (int i = 0; i < GGML_F32_ARR/8; ++i) { \ - x[8*i] = vec_add(x[8*i], x[8*i+4]); \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = vec_add(x[i], x[offset+i]); \ } \ res = vec_extract(x[0], 0) + \ vec_extract(x[0], 1) + \ @@ -1949,14 +1965,17 @@ static inline void __avx_f32cx8_store(ggml_fp16_t *x, __m256 y) { #define GGML_F32x4_MUL wasm_f32x4_mul #define GGML_F32x4_REDUCE(res, x) \ { \ - for (int i = 0; i < GGML_F32_ARR/2; ++i) { \ - x[2*i] = wasm_f32x4_add(x[2*i], x[2*i+1]); \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ } \ - for (int i = 0; i < GGML_F32_ARR/4; ++i) { \ - x[4*i] = wasm_f32x4_add(x[4*i], x[4*i+2]); \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ } \ - for (int i = 0; i < GGML_F32_ARR/8; ++i) { \ - x[8*i] = wasm_f32x4_add(x[8*i], x[8*i+4]); \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ } \ res = wasm_f32x4_extract_lane(x[0], 0) + \ wasm_f32x4_extract_lane(x[0], 1) + \ @@ -2011,14 +2030,17 @@ inline static void __wasm_f16x4_store(ggml_fp16_t * p, v128_t x) { #define GGML_F16x4_MUL wasm_f32x4_mul #define GGML_F16x4_REDUCE(res, x) \ { \ - for (int i = 0; i < GGML_F16_ARR/2; ++i) { \ - x[2*i] = wasm_f32x4_add(x[2*i], x[2*i+1]); \ + int offset = GGML_F16_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ } \ - for (int i = 0; i < GGML_F16_ARR/4; ++i) { \ - x[4*i] = wasm_f32x4_add(x[4*i], x[4*i+2]); \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ } \ - for (int i = 0; i < GGML_F16_ARR/8; ++i) { \ - x[8*i] = wasm_f32x4_add(x[8*i], x[8*i+4]); \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = wasm_f32x4_add(x[i], x[offset+i]); \ } \ res = wasm_f32x4_extract_lane(x[0], 0) + \ wasm_f32x4_extract_lane(x[0], 1) + \ @@ -2060,14 +2082,17 @@ inline static void __wasm_f16x4_store(ggml_fp16_t * p, v128_t x) { #define GGML_F32x4_MUL _mm_mul_ps #define GGML_F32x4_REDUCE(res, x) \ { \ - for (int i = 0; i < GGML_F32_ARR/2; ++i) { \ - x[2*i] = _mm_add_ps(x[2*i], x[2*i+1]); \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm_add_ps(x[i], x[offset+i]); \ } \ - for (int i = 0; i < GGML_F32_ARR/4; ++i) { \ - x[4*i] = _mm_add_ps(x[4*i], x[4*i+2]); \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm_add_ps(x[i], x[offset+i]); \ } \ - for (int i = 0; i < GGML_F32_ARR/8; ++i) { \ - x[8*i] = _mm_add_ps(x[8*i], x[8*i+4]); \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = _mm_add_ps(x[i], x[offset+i]); \ } \ const __m128 t0 = _mm_hadd_ps(x[0], x[0]); \ res = _mm_cvtss_f32(_mm_hadd_ps(t0, t0)); \ @@ -3356,6 +3381,7 @@ inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; } static const float GELU_COEF_A = 0.044715f; +static const float GELU_QUICK_COEF = -1.702f; static const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; inline static float ggml_gelu_f32(float x) { @@ -3386,6 +3412,34 @@ inline static void ggml_vec_gelu_f32(const int n, float * y, const float * x) { } #endif +inline static float ggml_gelu_quick_f32(float x) { + return x*(1.0f/(1.0f+expf(GELU_QUICK_COEF*x))); +} + +//inline static void ggml_vec_gelu_quick_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { +// const uint16_t * i16 = (const uint16_t *) x; +// for (int i = 0; i < n; ++i) { +// y[i] = table_gelu_quick_f16[i16[i]]; +// } +//} + +#ifdef GGML_GELU_QUICK_FP16 +inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) { + uint16_t t; + for (int i = 0; i < n; ++i) { + ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); + memcpy(&t, &fp16, sizeof(uint16_t)); + y[i] = GGML_FP16_TO_FP32(table_gelu_quick_f16[t]); + } +} +#else +inline static void ggml_vec_gelu_quick_f32(const int n, float * y, const float * x) { + for (int i = 0; i < n; ++i) { + y[i] = ggml_gelu_quick_f32(x[i]); + } +} +#endif + // Sigmoid Linear Unit (SiLU) function inline static float ggml_silu_f32(float x) { return x/(1.0f + expf(-x)); @@ -3616,6 +3670,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "STEP", "RELU", "GELU", + "GELU_QUICK", "SILU", "SILU_BACK", "NORM", @@ -3644,12 +3699,15 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "ROPE_BACK", "ALIBI", "CLAMP", - "CONV_1D_1S", - "CONV_1D_2S", + "CONV_1D_S1_PH", + "CONV_1D_S2_PH", + "CONV_2D_SK_P0", "FLASH_ATTN", "FLASH_FF", "FLASH_ATTN_BACK", + "WIN_PART", + "WIN_UNPART", "MAP_UNARY", "MAP_BINARY", @@ -3658,7 +3716,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "CROSS_ENTROPY_LOSS_BACK", }; -static_assert(GGML_OP_COUNT == 57, "GGML_OP_COUNT != 57"); +static_assert(GGML_OP_COUNT == 61, "GGML_OP_COUNT != 61"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -3684,6 +3742,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "step(x)", "relu(x)", "gelu(x)", + "gelu_quick(x)", "silu(x)", "silu_back(x)", "norm(x)", @@ -3712,12 +3771,15 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "rope_back(x)", "alibi(x)", "clamp(x)", - "conv_1d_1s(x)", - "conv_1d_2s(x)", + "conv_1d_s1_ph(x)", + "conv_1d_s2_ph(x)", + "conv_2d_sk_p0(x)", "flash_attn(x)", "flash_ff(x)", "flash_attn_back(x)", + "win_part(x)", + "win_unpart(x)", "f(x)", "f(x,y)", @@ -3726,7 +3788,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "cross_entropy_loss_back(x,y)", }; -static_assert(GGML_OP_COUNT == 57, "GGML_OP_COUNT != 57"); +static_assert(GGML_OP_COUNT == 61, "GGML_OP_COUNT != 61"); static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN"); static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN"); @@ -4017,7 +4079,7 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { // initialize time system (required on Windows) ggml_time_init(); - // initialize GELU, SILU and EXP F32 tables + // initialize GELU, Quick GELU, SILU and EXP F32 tables { const uint64_t t_start = ggml_time_us(); UNUSED(t_start); @@ -4027,13 +4089,14 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { memcpy(&ii, &ui, sizeof(ii)); const float f = table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(ii); table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f)); + table_gelu_quick_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_quick_f32(f)); table_silu_f16[i] = GGML_FP32_TO_FP16(ggml_silu_f32(f)); table_exp_f16[i] = GGML_FP32_TO_FP16(expf(f)); } const uint64_t t_end = ggml_time_us(); UNUSED(t_end); - GGML_PRINT_DEBUG("%s: GELU, SILU and EXP tables initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f); + GGML_PRINT_DEBUG("%s: GELU, Quick GELU, SILU and EXP tables initialized in %f ms\n", __func__, (t_end - t_start)/1000.0f); } // initialize g_state @@ -4665,9 +4728,10 @@ const char * ggml_get_name(const struct ggml_tensor * tensor) { return tensor->name; } -void ggml_set_name(struct ggml_tensor * tensor, const char * name) { +struct ggml_tensor * ggml_set_name(struct ggml_tensor * tensor, const char * name) { strncpy(tensor->name, name, sizeof(tensor->name)); tensor->name[sizeof(tensor->name) - 1] = '\0'; + return tensor; } struct ggml_tensor * ggml_view_tensor( @@ -5446,6 +5510,40 @@ struct ggml_tensor * ggml_gelu_inplace( return ggml_gelu_impl(ctx, a, true); } +// ggml_gelu_quick + +struct ggml_tensor * ggml_gelu_quick_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + bool inplace) { + bool is_node = false; + + if (!inplace && (a->grad)) { + is_node = true; + } + + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + result->op = GGML_OP_GELU_QUICK; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src0 = a; + result->src1 = NULL; + + return result; +} + +struct ggml_tensor * ggml_gelu_quick( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_gelu_quick_impl(ctx, a, false); +} + +struct ggml_tensor * ggml_gelu_quick_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_gelu_quick_impl(ctx, a, true); +} + // ggml_silu struct ggml_tensor * ggml_silu_impl( @@ -6645,7 +6743,7 @@ struct ggml_tensor * ggml_clamp( ggml_scratch_save(ctx); - struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 3); + struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, 2); ((float *) b->data)[0] = min; ((float *) b->data)[1] = max; @@ -6660,9 +6758,9 @@ struct ggml_tensor * ggml_clamp( return result; } -// ggml_conv_1d_1s +// ggml_conv_1d_s1_ph -struct ggml_tensor * ggml_conv_1d_1s( +struct ggml_tensor * ggml_conv_1d_s1_ph( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { @@ -6679,7 +6777,7 @@ struct ggml_tensor * ggml_conv_1d_1s( const int64_t ne[4] = { b->ne[0], a->ne[2], 1, 1, }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); - result->op = GGML_OP_CONV_1D_1S; + result->op = GGML_OP_CONV_1D_S1_PH; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; @@ -6687,9 +6785,9 @@ struct ggml_tensor * ggml_conv_1d_1s( return result; } -// ggml_conv_1d_2s +// ggml_conv_1d_s2_ph -struct ggml_tensor * ggml_conv_1d_2s( +struct ggml_tensor * ggml_conv_1d_s2_ph( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b) { @@ -6706,7 +6804,35 @@ struct ggml_tensor * ggml_conv_1d_2s( const int64_t ne[4] = { b->ne[0]/2, a->ne[2], 1, 1, }; struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); - result->op = GGML_OP_CONV_1D_2S; + result->op = GGML_OP_CONV_1D_S2_PH; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src0 = a; + result->src1 = b; + + return result; +} + +// ggml_conv_2d_sk_p0 + +struct ggml_tensor * ggml_conv_2d_sk_p0( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b) { + GGML_ASSERT(b->ne[3] == 1); + GGML_ASSERT(a->ne[2] == b->ne[2]); + GGML_ASSERT(b->ne[0] % a->ne[0] == 0); + GGML_ASSERT(b->ne[1] % a->ne[1] == 0); + bool is_node = false; + + if (a->grad || b->grad) { + GGML_ASSERT(false); // TODO: implement backward + is_node = true; + } + + const int64_t ne[4] = { b->ne[0]/a->ne[0], b->ne[1]/a->ne[1], a->ne[3], 1, }; + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + + result->op = GGML_OP_CONV_2D_SK_P0; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; @@ -6840,6 +6966,89 @@ struct ggml_tensor * ggml_flash_attn_back( return result; } +// ggml_win_part + +struct ggml_tensor * ggml_win_part( + struct ggml_context * ctx, + struct ggml_tensor * a, + int w) { + GGML_ASSERT(a->ne[3] == 1); + GGML_ASSERT(a->type == GGML_TYPE_F32); + + bool is_node = false; + + if (a->grad) { + GGML_ASSERT(false); // TODO: implement backward + is_node = true; + } + + // padding + const int px = (w - a->ne[1]%w)%w; + const int py = (w - a->ne[2]%w)%w; + + const int npx = (px + a->ne[1])/w; + const int npy = (py + a->ne[2])/w; + const int np = npx*npy; + + const int64_t ne[4] = { a->ne[0], w, w, np, }; + + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + + ggml_scratch_save(ctx); + + struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 3); + + ((int32_t *) b->data)[0] = npx; + ((int32_t *) b->data)[1] = npy; + ((int32_t *) b->data)[2] = w; + + ggml_scratch_load(ctx); + + result->op = GGML_OP_WIN_PART; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src0 = a; + result->src1 = NULL; + result->opt[0] = b; + + return result; +} + +// ggml_win_unpart + +struct ggml_tensor * ggml_win_unpart( + struct ggml_context * ctx, + struct ggml_tensor * a, + int w0, + int h0, + int w) { + GGML_ASSERT(a->type == GGML_TYPE_F32); + + bool is_node = false; + + if (a->grad) { + GGML_ASSERT(false); // TODO: implement backward + is_node = true; + } + + const int64_t ne[4] = { a->ne[0], w0, h0, 1, }; + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne); + + ggml_scratch_save(ctx); + + struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 1); + + ((int32_t *) b->data)[0] = w; + + ggml_scratch_load(ctx); + + result->op = GGML_OP_WIN_UNPART; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src0 = a; + result->src1 = NULL; + result->opt[0] = b; + + return result; +} // ggml_map_unary @@ -9479,8 +9688,65 @@ static void ggml_compute_forward_gelu( GGML_ASSERT(false); } break; } +} - //printf("XXXXXXXX gelu\n"); +// ggml_compute_forward_gelu_quick + +static void ggml_compute_forward_gelu_quick_f32( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(dst)); + GGML_ASSERT(ggml_are_same_shape(src0, dst)); + + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + const int ith = params->ith; + const int nth = params->nth; + + const int nc = src0->ne[0]; + const int nr = ggml_nrows(src0); + + // rows per thread + const int dr = (nr + nth - 1)/nth; + + // row range for this thread + const int ir0 = dr*ith; + const int ir1 = MIN(ir0 + dr, nr); + + for (int i1 = ir0; i1 < ir1; i1++) { + ggml_vec_gelu_quick_f32(nc, + (float *) ((char *) dst->data + i1*( dst->nb[1])), + (float *) ((char *) src0->data + i1*(src0->nb[1]))); + +#ifndef NDEBUG + for (int k = 0; k < nc; k++) { + const float x = ((float *) ((char *) dst->data + i1*( dst->nb[1])))[k]; + UNUSED(x); + assert(!isnan(x)); + assert(!isinf(x)); + } +#endif + } +} + +static void ggml_compute_forward_gelu_quick( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_gelu_quick_f32(params, src0, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } } // ggml_compute_forward_silu @@ -10878,7 +11144,7 @@ static void ggml_compute_forward_set_f32( const int im2 = (ne12 == 0 ? 0 : ne12-1); const int im3 = (ne13 == 0 ? 0 : ne13-1); - GGML_ASSERT(offset + im0*nb0 + im1*nb1 + im2*nb2 + im3*nb3 < ggml_nbytes(dst)); + GGML_ASSERT(offset + im0*nb0 + im1*nb1 + im2*nb2 + im3*nb3 <= ggml_nbytes(dst)); GGML_ASSERT(nb10 == sizeof(float)); @@ -11599,8 +11865,9 @@ static void ggml_compute_forward_alibi_f32( const struct ggml_tensor * src1, struct ggml_tensor * dst) { assert(params->ith == 0); - assert(src1->type == GGML_TYPE_I32); - assert(ggml_nelements(src1) == 3); + + GGML_ASSERT(src1->type == GGML_TYPE_I32); + GGML_ASSERT(ggml_nelements(src1) == 3); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; @@ -11663,8 +11930,9 @@ static void ggml_compute_forward_alibi_f16( const struct ggml_tensor * src1, struct ggml_tensor * dst) { assert(params->ith == 0); - assert(src1->type == GGML_TYPE_I32); - assert(ggml_nelements(src1) == 3); + + GGML_ASSERT(src1->type == GGML_TYPE_I32); + GGML_ASSERT(ggml_nelements(src1) == 3); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; @@ -11766,15 +12034,16 @@ static void ggml_compute_forward_clamp_f32( const struct ggml_tensor * src1, struct ggml_tensor * dst) { assert(params->ith == 0); - assert(src1->type == GGML_TYPE_I32); - assert(ggml_nelements(src1) == 2); + + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(ggml_nelements(src1) == 2); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; } - const int min = ((float *) src1->data)[0]; - const int max = ((float *) src1->data)[1]; + const float min = ((float *) src1->data)[0]; + const float max = ((float *) src1->data)[1]; const int ith = params->ith; const int nth = params->nth; @@ -12332,9 +12601,9 @@ static void ggml_compute_forward_rope_back( } } -// ggml_compute_forward_conv_1d_1s +// ggml_compute_forward_conv_1d_s1_ph -static void ggml_compute_forward_conv_1d_1s_f16_f32( +static void ggml_compute_forward_conv_1d_s1_ph_f16_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, @@ -12454,7 +12723,7 @@ static void ggml_compute_forward_conv_1d_1s_f16_f32( } } -static void ggml_compute_forward_conv_1d_1s_f32( +static void ggml_compute_forward_conv_1d_s1_ph_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, @@ -12574,7 +12843,7 @@ static void ggml_compute_forward_conv_1d_1s_f32( } } -static void ggml_compute_forward_conv_1d_1s( +static void ggml_compute_forward_conv_1d_s1_ph( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, @@ -12582,11 +12851,11 @@ static void ggml_compute_forward_conv_1d_1s( switch (src0->type) { case GGML_TYPE_F16: { - ggml_compute_forward_conv_1d_1s_f16_f32(params, src0, src1, dst); + ggml_compute_forward_conv_1d_s1_ph_f16_f32(params, src0, src1, dst); } break; case GGML_TYPE_F32: { - ggml_compute_forward_conv_1d_1s_f32(params, src0, src1, dst); + ggml_compute_forward_conv_1d_s1_ph_f32(params, src0, src1, dst); } break; default: { @@ -12595,9 +12864,9 @@ static void ggml_compute_forward_conv_1d_1s( } } -// ggml_compute_forward_conv_1d_2s +// ggml_compute_forward_conv_1d_s2_ph -static void ggml_compute_forward_conv_1d_2s_f16_f32( +static void ggml_compute_forward_conv_1d_s2_ph_f16_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, @@ -12717,7 +12986,7 @@ static void ggml_compute_forward_conv_1d_2s_f16_f32( } } -static void ggml_compute_forward_conv_1d_2s_f32( +static void ggml_compute_forward_conv_1d_s2_ph_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, @@ -12837,7 +13106,7 @@ static void ggml_compute_forward_conv_1d_2s_f32( } } -static void ggml_compute_forward_conv_1d_2s( +static void ggml_compute_forward_conv_1d_s2_ph( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, @@ -12845,11 +13114,148 @@ static void ggml_compute_forward_conv_1d_2s( switch (src0->type) { case GGML_TYPE_F16: { - ggml_compute_forward_conv_1d_2s_f16_f32(params, src0, src1, dst); + ggml_compute_forward_conv_1d_s2_ph_f16_f32(params, src0, src1, dst); } break; case GGML_TYPE_F32: { - ggml_compute_forward_conv_1d_2s_f32(params, src0, src1, dst); + ggml_compute_forward_conv_1d_s2_ph_f32(params, src0, src1, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + +// ggml_compute_forward_conv_2d_sk_p0 + +static void ggml_compute_forward_conv_2d_sk_p0_f16_f32( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + const struct ggml_tensor * src1, + struct ggml_tensor * dst) { + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + int64_t t0 = ggml_perf_time_us(); + UNUSED(t0); + + const int ne00 = src0->ne[0]; + const int ne01 = src0->ne[1]; + const int ne02 = src0->ne[2]; + //const int ne03 = src0->ne[3]; + + const int ne10 = src1->ne[0]; + //const int ne11 = src1->ne[1]; + const int ne12 = src1->ne[2]; + //const int ne13 = src1->ne[3]; + + const int ne0 = dst->ne[0]; + const int ne1 = dst->ne[1]; + const int ne2 = dst->ne[2]; + //const int ne3 = dst->ne[3]; + //const int ne = ne0*ne1*ne2*ne3; + + const int nb00 = src0->nb[0]; + //const int nb01 = src0->nb[1]; + //const int nb02 = src0->nb[2]; + const int nb03 = src0->nb[3]; + + const int nb10 = src1->nb[0]; + //const int nb11 = src1->nb[1]; + const int nb12 = src1->nb[2]; + //const int nb13 = src1->nb[3]; + + //const int nb0 = dst->nb[0]; + //const int nb1 = dst->nb[1]; + const int nb2 = dst->nb[2]; + //const int nb3 = dst->nb[3]; + + const int ith = params->ith; + const int nth = params->nth; + + const int nk0 = ne00; + const int nk1 = ne01; + + // size of the convolution row - the kernel size unrolled across all channels + // round-up so it is more suitable for SIMD + const int ew0 = ggml_up32(nk0*nk1*ne02); + + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb10 == sizeof(float)); + + if (params->type == GGML_TASK_INIT) { + // TODO: fix this memset (wsize is overestimated) + memset(params->wdata, 0, params->wsize); + + // prepare source data (src1) + { + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; + + for (int i12 = 0; i12 < ne12; i12++) { + const float * const src = (float *)((char *) src1->data + i12*nb12); + ggml_fp16_t * dst_data = wdata; + + for (int i1 = 0; i1 < ne1; i1++) { + for (int i0 = 0; i0 < ne0; i0++) { + for (int ik1 = 0; ik1 < nk1; ik1++) { + for (int ik0 = 0; ik0 < nk0; ik0++) { + dst_data[(i1*ne0 + i0)*ew0 + i12*(nk0*nk1) + ik1*nk0 + ik0] = + GGML_FP32_TO_FP16(src[(i1*nk1 + ik1)*ne10 + (i0*nk0 + ik0)]); + } + } + } + } + } + } + + return; + } + + if (params->type == GGML_TASK_FINALIZE) { + return; + } + + // total patches in dst + const int np = ne2; + + // patches per thread + const int dp = (np + nth - 1)/nth; + + // patch range for this thread + const int ip0 = dp*ith; + const int ip1 = MIN(ip0 + dp, np); + + ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; + + for (int i2 = ip0; i2 < ip1; i2++) { + float * dst_data = (float *)((char *) dst->data + i2*nb2); + + for (int i1 = 0; i1 < ne1; ++i1) { + for (int i0 = 0; i0 < ne0; ++i0) { + ggml_vec_dot_f16(ew0, dst_data + i1*ne0 + i0, + (ggml_fp16_t *) ((char *) src0->data + i2*nb03), + (ggml_fp16_t *) wdata + (i1*ne0 + i0)*ew0); + } + } + } +} + +static void ggml_compute_forward_conv_2d_sk_p0( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + const struct ggml_tensor * src1, + struct ggml_tensor * dst) { + switch (src0->type) { + case GGML_TYPE_F16: + { + ggml_compute_forward_conv_2d_sk_p0_f16_f32(params, src0, src1, dst); + } break; + case GGML_TYPE_F32: + { + //ggml_compute_forward_conv_2d_sk_p0_f32(params, src0, src1, dst); + GGML_ASSERT(false); } break; default: { @@ -13952,6 +14358,145 @@ static void ggml_compute_forward_flash_attn_back( } } +// ggml_compute_forward_win_part + +static void ggml_compute_forward_win_part_f32( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + const struct ggml_tensor * opt0, + struct ggml_tensor * dst) { + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + const int64_t ne00 = src0->ne[0]; UNUSED(ne00); + const int64_t ne01 = src0->ne[1]; + const int64_t ne02 = src0->ne[2]; + const int64_t ne03 = src0->ne[3]; UNUSED(ne03); + + const int64_t ne0 = dst->ne[0]; + const int64_t ne1 = dst->ne[1]; + const int64_t ne2 = dst->ne[2]; + const int64_t ne3 = dst->ne[3]; UNUSED(ne3); + + const int32_t nep0 = ((const int32_t *)(opt0->data))[0]; + const int32_t nep1 = ((const int32_t *)(opt0->data))[1]; + const int32_t w = ((const int32_t *)(opt0->data))[2]; + + assert(ne00 == ne0); + assert(ne3 == nep0*nep1); + + // TODO: optimize / multi-thread + for (int py = 0; py < nep1; ++py) { + for (int px = 0; px < nep0; ++px) { + const int64_t i3 = py*nep0 + px; + for (int64_t i2 = 0; i2 < ne2; ++i2) { + for (int64_t i1 = 0; i1 < ne1; ++i1) { + for (int64_t i0 = 0; i0 < ne0; ++i0) { + const int64_t i02 = py*w + i2; + const int64_t i01 = px*w + i1; + const int64_t i00 = i0; + + const int64_t i = i3*ne2*ne1*ne0 + i2*ne1*ne0 + i1*ne0 + i0; + const int64_t j = i02*ne01*ne00 + i01*ne00 + i00; + + if (py*w + i2 >= ne02 || px*w + i1 >= ne01) { + ((float *) dst->data)[i] = 0.0f; + } else { + ((float *) dst->data)[i] = ((float *) src0->data)[j]; + } + } + } + } + } + } +} + +static void ggml_compute_forward_win_part( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + const struct ggml_tensor * opt0, + struct ggml_tensor * dst) { + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_win_part_f32(params, src0, opt0, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + +// ggml_compute_forward_win_unpart + +static void ggml_compute_forward_win_unpart_f32( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + const struct ggml_tensor * opt0, + struct ggml_tensor * dst) { + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + const int64_t ne02 = src0->ne[2]; + //const int64_t ne03 = src0->ne[3]; + + const int64_t ne0 = dst->ne[0]; + const int64_t ne1 = dst->ne[1]; + const int64_t ne2 = dst->ne[2]; + + const int32_t w = ((const int32_t *)(opt0->data))[0]; + + // padding + const int px = (w - ne1%w)%w; + //const int py = (w - ne2%w)%w; + + const int npx = (px + ne1)/w; + //const int npy = (py + ne2)/w; + + assert(ne0 == ne00); + + // TODO: optimize / multi-thread + for (int64_t i2 = 0; i2 < ne2; ++i2) { + for (int64_t i1 = 0; i1 < ne1; ++i1) { + for (int64_t i0 = 0; i0 < ne0; ++i0) { + const int ip2 = i2/w; + const int ip1 = i1/w; + + const int64_t i02 = i2%w; + const int64_t i01 = i1%w; + const int64_t i00 = i0; + + const int64_t i = (ip2*npx + ip1)*ne02*ne01*ne00 + i02*ne01*ne00 + i01*ne00 + i00; + const int64_t j = i2*ne1*ne0 + i1*ne0 + i0; + + ((float *) dst->data)[j] = ((float *) src0->data)[i]; + } + } + } +} + +static void ggml_compute_forward_win_unpart( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + const struct ggml_tensor * opt0, + struct ggml_tensor * dst) { + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_win_unpart_f32(params, src0, opt0, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + // ggml_compute_forward_map_unary static void ggml_compute_forward_map_unary_f32( @@ -14424,6 +14969,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_gelu(params, tensor->src0, tensor); } break; + case GGML_OP_GELU_QUICK: + { + ggml_compute_forward_gelu_quick(params, tensor->src0, tensor); + } break; case GGML_OP_SILU: { ggml_compute_forward_silu(params, tensor->src0, tensor); @@ -14528,19 +15077,23 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_clamp(params, tensor->src0, tensor->src1, tensor); } break; - case GGML_OP_CONV_1D_1S: + case GGML_OP_CONV_1D_S1_PH: { - ggml_compute_forward_conv_1d_1s(params, tensor->src0, tensor->src1, tensor); + ggml_compute_forward_conv_1d_s1_ph(params, tensor->src0, tensor->src1, tensor); } break; - case GGML_OP_CONV_1D_2S: + case GGML_OP_CONV_1D_S2_PH: { - ggml_compute_forward_conv_1d_2s(params, tensor->src0, tensor->src1, tensor); + ggml_compute_forward_conv_1d_s2_ph(params, tensor->src0, tensor->src1, tensor); + } break; + case GGML_OP_CONV_2D_SK_P0: + { + ggml_compute_forward_conv_2d_sk_p0(params, tensor->src0, tensor->src1, tensor); } break; case GGML_OP_FLASH_ATTN: { - int32_t t = ggml_get_i32_1d(tensor->opt[1], 0); + const int32_t t = ggml_get_i32_1d(tensor->opt[1], 0); GGML_ASSERT(t == 0 || t == 1); - bool masked = t != 0; + const bool masked = t != 0; ggml_compute_forward_flash_attn(params, tensor->src0, tensor->src1, tensor->opt[0], masked, tensor); } break; case GGML_OP_FLASH_FF: @@ -14554,6 +15107,14 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm bool masked = t != 0; ggml_compute_forward_flash_attn_back(params, tensor->src0, tensor->src1, tensor->opt[0], tensor->opt[1], masked, tensor); } break; + case GGML_OP_WIN_PART: + { + ggml_compute_forward_win_part(params, tensor->src0, tensor->opt[0], tensor); + } break; + case GGML_OP_WIN_UNPART: + { + ggml_compute_forward_win_unpart(params, tensor->src0, tensor->opt[0], tensor); + } break; case GGML_OP_MAP_UNARY: { const ggml_unary_op_f32_t fun = *((ggml_unary_op_f32_t *)tensor->opt[0]->data); @@ -14825,6 +15386,10 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { GGML_ASSERT(false); // TODO: not implemented } break; + case GGML_OP_GELU_QUICK: + { + GGML_ASSERT(false); // TODO: not implemented + } break; case GGML_OP_ALIBI: { GGML_ASSERT(false); // TODO: not implemented @@ -15187,11 +15752,15 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor // noop } } break; - case GGML_OP_CONV_1D_1S: + case GGML_OP_CONV_1D_S1_PH: { GGML_ASSERT(false); // TODO: not implemented } break; - case GGML_OP_CONV_1D_2S: + case GGML_OP_CONV_1D_S2_PH: + { + GGML_ASSERT(false); // TODO: not implemented + } break; + case GGML_OP_CONV_2D_SK_P0: { GGML_ASSERT(false); // TODO: not implemented } break; @@ -15360,6 +15929,8 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { GGML_ASSERT(false); // not supported } break; + case GGML_OP_WIN_PART: + case GGML_OP_WIN_UNPART: case GGML_OP_MAP_UNARY: case GGML_OP_MAP_BINARY: { @@ -15768,6 +16339,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) } break; case GGML_OP_MUL: case GGML_OP_GELU: + case GGML_OP_GELU_QUICK: case GGML_OP_SILU: case GGML_OP_SILU_BACK: case GGML_OP_NORM: @@ -15874,8 +16446,8 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) { node->n_tasks = 1; //TODO } break; - case GGML_OP_CONV_1D_1S: - case GGML_OP_CONV_1D_2S: + case GGML_OP_CONV_1D_S1_PH: + case GGML_OP_CONV_1D_S2_PH: { node->n_tasks = n_threads; @@ -15902,6 +16474,41 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) GGML_ASSERT(false); } + work_size = MAX(work_size, cur); + } break; + case GGML_OP_CONV_2D_SK_P0: + { + node->n_tasks = n_threads; + + GGML_ASSERT(node->src1->ne[3] == 1); + + const int64_t ne00 = node->src0->ne[0]; // W + const int64_t ne01 = node->src0->ne[1]; // H + const int64_t ne02 = node->src0->ne[2]; // C + const int64_t ne03 = node->src0->ne[3]; // N + + const int64_t ne10 = node->src1->ne[0]; // W + const int64_t ne11 = node->src1->ne[1]; // H + const int64_t ne12 = node->src1->ne[2]; // C + + const int64_t nk = ne00*ne01; + + UNUSED(ne02); + UNUSED(ne03); + UNUSED(nk); + + size_t cur = 0; + + if (node->src0->type == GGML_TYPE_F16 && + node->src1->type == GGML_TYPE_F32) { + cur = sizeof(ggml_fp16_t)*(ne10*ne11*ne12); + } else if (node->src0->type == GGML_TYPE_F32 && + node->src1->type == GGML_TYPE_F32) { + cur = sizeof(float)* (ne10*ne11*ne12); + } else { + GGML_ASSERT(false); + } + work_size = MAX(work_size, cur); } break; case GGML_OP_FLASH_ATTN: @@ -15963,6 +16570,8 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) work_size = MAX(work_size, cur); } break; + case GGML_OP_WIN_PART: + case GGML_OP_WIN_UNPART: case GGML_OP_MAP_UNARY: case GGML_OP_MAP_BINARY: { @@ -16495,16 +17104,20 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** if (!*ctx_data) { fprintf(stderr, "%s: failed to create ggml context\n", __func__); + fclose(fin); return result; } } data = ggml_new_tensor_1d(*ctx_data, GGML_TYPE_I8, fsize); - const size_t ret = fread(data->data, sizeof(char), fsize, fin); - if (ret != fsize) { - fprintf(stderr, "%s: failed to read %s\n", __func__, fname); - return result; + { + const size_t ret = fread(data->data, sizeof(char), fsize, fin); + if (ret != fsize) { + fprintf(stderr, "%s: failed to read %s\n", __func__, fname); + fclose(fin); + return result; + } } fclose(fin); diff --git a/ggml.h b/ggml.h index 1380c530f..18c78551f 100644 --- a/ggml.h +++ b/ggml.h @@ -303,6 +303,7 @@ extern "C" { GGML_OP_STEP, GGML_OP_RELU, GGML_OP_GELU, + GGML_OP_GELU_QUICK, GGML_OP_SILU, GGML_OP_SILU_BACK, GGML_OP_NORM, // normalize @@ -331,12 +332,15 @@ extern "C" { GGML_OP_ROPE_BACK, GGML_OP_ALIBI, GGML_OP_CLAMP, - GGML_OP_CONV_1D_1S, - GGML_OP_CONV_1D_2S, + GGML_OP_CONV_1D_S1_PH, + GGML_OP_CONV_1D_S2_PH, + GGML_OP_CONV_2D_SK_P0, GGML_OP_FLASH_ATTN, GGML_OP_FLASH_FF, GGML_OP_FLASH_ATTN_BACK, + GGML_OP_WIN_PART, + GGML_OP_WIN_UNPART, GGML_OP_MAP_UNARY, GGML_OP_MAP_BINARY, @@ -557,8 +561,8 @@ extern "C" { GGML_API void * ggml_get_data (const struct ggml_tensor * tensor); GGML_API float * ggml_get_data_f32(const struct ggml_tensor * tensor); - GGML_API const char * ggml_get_name(const struct ggml_tensor * tensor); - GGML_API void ggml_set_name(struct ggml_tensor * tensor, const char * name); + GGML_API const char * ggml_get_name(const struct ggml_tensor * tensor); + GGML_API struct ggml_tensor * ggml_set_name(struct ggml_tensor * tensor, const char * name); // // operations on tensors with backpropagation @@ -611,24 +615,47 @@ extern "C" { struct ggml_tensor * a, struct ggml_tensor * b); + GGML_API struct ggml_tensor * ggml_sub_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b); + GGML_API struct ggml_tensor * ggml_mul( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); + GGML_API struct ggml_tensor * ggml_mul_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b); + GGML_API struct ggml_tensor * ggml_div( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); + GGML_API struct ggml_tensor * ggml_div_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b); + GGML_API struct ggml_tensor * ggml_sqr( struct ggml_context * ctx, struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_sqr_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_sqrt( struct ggml_context * ctx, struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_sqrt_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_log( struct ggml_context * ctx, struct ggml_tensor * a); @@ -668,31 +695,67 @@ extern "C" { struct ggml_context * ctx, struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_abs_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_sgn( struct ggml_context * ctx, struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_sgn_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_neg( struct ggml_context * ctx, struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_neg_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_step( struct ggml_context * ctx, struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_step_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_relu( struct ggml_context * ctx, struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_relu_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a); + // TODO: double-check this computation is correct GGML_API struct ggml_tensor * ggml_gelu( struct ggml_context * ctx, struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_gelu_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_gelu_quick( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_gelu_quick_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_silu( struct ggml_context * ctx, struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_silu_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a); + // a - x // b - dy GGML_API struct ggml_tensor * ggml_silu_back( @@ -706,10 +769,18 @@ extern "C" { struct ggml_context * ctx, struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_norm_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_rms_norm( struct ggml_context * ctx, struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_rms_norm_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a); + // a - x // b - dy GGML_API struct ggml_tensor * ggml_rms_norm_back( @@ -999,16 +1070,55 @@ extern "C" { float min, float max); - // padding = 1 + // TODO: implement general-purpose convolutions + // GGML_API struct ggml_tensor * ggml_conv_1d( + // struct ggml_context * ctx, + // struct ggml_tensor * a, + // struct ggml_tensor * b, + // int s0 + // int p0, + // int d0); + // + // GGML_API struct ggml_tensor * ggml_conv_2d( + // struct ggml_context * ctx, + // struct ggml_tensor * a, + // struct ggml_tensor * b, + // int s0, + // int s1, + // int p0, + // int p1, + // int d0, + // int d1); + + // padding = half // TODO: we don't support extra parameters for now // that's why we are hard-coding the stride, padding, and dilation // not great .. - GGML_API struct ggml_tensor * ggml_conv_1d_1s( + // example: + // a: 3 80 768 1 + // b: 3000 80 1 1 + // res: 3000 768 1 1 + // used in whisper + GGML_API struct ggml_tensor * ggml_conv_1d_s1_ph( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); - GGML_API struct ggml_tensor * ggml_conv_1d_2s( + // used in whisper + GGML_API struct ggml_tensor * ggml_conv_1d_s2_ph( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b); + + // kernel size is a->ne[0] x a->ne[1] + // stride is equal to kernel size + // padding is zero + // example: + // a: 16 16 3 768 + // b: 1024 1024 3 1 + // res: 64 64 768 1 + // used in sam + GGML_API struct ggml_tensor * ggml_conv_2d_sk_p0( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b); @@ -1036,6 +1146,26 @@ extern "C" { struct ggml_tensor * c0, struct ggml_tensor * c1); + // partition into non-overlapping windows with padding if needed + // example: + // a: 768 64 64 1 + // w: 14 + // res: 768 14 14 25 + // used in sam + GGML_API struct ggml_tensor * ggml_win_part( + struct ggml_context * ctx, + struct ggml_tensor * a, + int w); + + // reverse of ggml_win_part + // used in sam + GGML_API struct ggml_tensor * ggml_win_unpart( + struct ggml_context * ctx, + struct ggml_tensor * a, + int w0, + int h0, + int w); + // Mapping operations typedef void (*ggml_unary_op_f32_t)(const int, float *, const float *); typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *); From ca7c3f4da5d144d4cd1dd44903552e6ba49b8ec8 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Mon, 19 Jun 2023 18:14:09 +0300 Subject: [PATCH 012/852] cuda : faster k-quants on older GPUs (#1930) * k_quants: hopefully much faster Q4_K on older GPUs On the GTX-1660 that I have available to represent "old GPUs", token prediction drops from 65.5 ms/tok to 41.5 ms/tok! * k_quants: hopefully much faster Q3_K on older GPUs On the GTX-1660 that I have available to represent "old GPUs", token prediction drops from 60.3 ms/tok to 41.0 ms/tok! * k_quants: faster Q2_K on older GPUs It looks like I didn't need to change anything compared to what we already had, so this is just adding clarifying comments. But I now measure 36.3 ms/tok on the GTX-1660, instead fo the 47.2 ms/tok that I have written in the faster k-quants PR. * k_quants: faster Q5_K on older GPUs 68.5 ms/tok -> 62.0 ms/tok on GTX-1660. For some reason the same access pattern that leads to such resounding success for Q2_K to Q4_K did not work at all for Q5_K. It is also more difficult to measure because for Q5_K_S we only have 32 layers on the GTX-1660, so output, tok embeddings and kv cache are done on the CPU. --------- Co-authored-by: Iwan Kawrakow --- ggml-cuda.cu | 83 +++++++++++++++++++++++++++++++--------------------- 1 file changed, 50 insertions(+), 33 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 9ebc57aff..36a251ecc 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -515,15 +515,15 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * vx, const float const block_q2_K * x = (const block_q2_K *)vx + ib0; - const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 - const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 + const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...15 + const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1 const int step = 16/K_QUANTS_PER_ITERATION; - const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... - const int in = tid - step*im; // 0...7 + const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... + const int in = tid - step*im; // 0...15 or 0...7 - const int l0 = K_QUANTS_PER_ITERATION*in; // 0...14 in steps of 4 + const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15 or 0...14 in steps of 2 const int q_offset = 32*im + l0; const int s_offset = 8*im; const int y_offset = 128*im + l0; @@ -578,27 +578,30 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * vx, const float } } -static __global__ void dequantize_mul_mat_vec_q3_k(const void * vx, const float * yy, float * dst, const int ncols) { +static __global__ void dequantize_mul_mat_vec_q3_k(const void * vx, const float * yy, float * dst, const int ncols, int nrows) { const uint16_t kmask1 = 0x0303; const uint16_t kmask2 = 0x0f0f; - const int row = blockIdx.x; + const int row = blockIdx.y*blockDim.y + threadIdx.y; + if (row > nrows) return; + const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; const block_q3_K * x = (const block_q3_K *)vx + ib0; - const int tid = threadIdx.x/2; // 0...15 - const int ix = threadIdx.x%2; // 0, 1 + const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1 - const int n = 2; // iterations in the inner loop - const int im = tid/8; // 0 or 1. 0 computes 0..., 1 computes 128... - const int in = tid - 8*im; // 0...7 + const int n = K_QUANTS_PER_ITERATION; // iterations in the inner loop + const int step = 16/K_QUANTS_PER_ITERATION; + const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... + const int in = tid - step*im; // 0....15 or 0...7 const uint8_t m = 1 << (4*im); - const int l0 = n*in; // 0...28 in steps of 4 + const int l0 = n*in; // 0...15 or 0...14 in steps of 2 const int q_offset = 32*im + l0; const int y_offset = 128*im + l0; @@ -609,7 +612,7 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * vx, const float float tmp = 0; // partial sum for thread in warp - for (int i = ix; i < num_blocks_per_row; i += 2) { + for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { const float * y = yy + i * QK_K + y_offset; const uint8_t * q = x[i].qs + q_offset; @@ -650,22 +653,25 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * vx, const float } } -static __global__ void dequantize_mul_mat_vec_q4_k(const void * vx, const float * yy, float * dst, const int ncols) { +static __global__ void dequantize_mul_mat_vec_q4_k(const void * vx, const float * yy, float * dst, const int ncols, int nrows) { const uint16_t kmask1 = 0x3f3f; const uint16_t kmask2 = 0x0f0f; const uint16_t kmask3 = 0xc0c0; - const int row = blockIdx.x; + const int row = blockIdx.y*blockDim.y + threadIdx.y; + if (row > nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; - const int tid = threadIdx.x/2; // 0...15 - const int ix = threadIdx.x%2; + const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1 - const int il = tid/4; // 0...3 - const int ir = tid - 4*il;// 0...3 - const int n = 4; + const int step = 8/K_QUANTS_PER_ITERATION; // 8 or 4 + + const int il = tid/step; // 0...3 + const int ir = tid - step*il; // 0...7 or 0...3 + const int n = 2 * K_QUANTS_PER_ITERATION; // 2 or 4 const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 const int in = il%2; @@ -681,7 +687,7 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * vx, const float float tmp = 0; // partial sum for thread in warp - for (int i = ix; i < num_blocks_per_row; i += 2) { + for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { const uint8_t * q1 = x[i].qs + q_offset; const uint8_t * q2 = q1 + 64; @@ -736,7 +742,7 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * vx, const float const int il = tid/4; // 0...3 const int ir = tid - 4*il;// 0...3 - const int n = 4; + const int n = 2; const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 const int in = il%2; @@ -775,11 +781,16 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * vx, const float float4 sum = {0.f, 0.f, 0.f, 0.f}; float smin = 0; for (int l = 0; l < n; ++l) { - sum.x += y1[l+ 0] * ((ql1[l] & 0xF) + (qh[l] & (hm1 << 0) ? 16 : 0)); - sum.y += y1[l+32] * ((ql1[l] >> 4) + (qh[l] & (hm1 << 1) ? 16 : 0)); - sum.z += y2[l+ 0] * ((ql2[l] & 0xF) + (qh[l] & (hm2 << 0) ? 16 : 0)); - sum.w += y2[l+32] * ((ql2[l] >> 4) + (qh[l] & (hm2 << 1) ? 16 : 0)); - smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7]; + sum.x += y1[l+ 0] * ((ql1[l+ 0] & 0xF) + (qh[l+ 0] & (hm1 << 0) ? 16 : 0)) + + y1[l+16] * ((ql1[l+16] & 0xF) + (qh[l+16] & (hm1 << 0) ? 16 : 0)); + sum.y += y1[l+32] * ((ql1[l+ 0] >> 4) + (qh[l+ 0] & (hm1 << 1) ? 16 : 0)) + + y1[l+48] * ((ql1[l+16] >> 4) + (qh[l+16] & (hm1 << 1) ? 16 : 0)); + sum.z += y2[l+ 0] * ((ql2[l+ 0] & 0xF) + (qh[l+ 0] & (hm2 << 0) ? 16 : 0)) + + y2[l+16] * ((ql2[l+16] & 0xF) + (qh[l+16] & (hm2 << 0) ? 16 : 0)); + sum.w += y2[l+32] * ((ql2[l+ 0] >> 4) + (qh[l+ 0] & (hm2 << 1) ? 16 : 0)) + + y2[l+48] * ((ql2[l+16] >> 4) + (qh[l+16] & (hm2 << 1) ? 16 : 0)); + smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3] + + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7]; } tmp += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin; @@ -1311,7 +1322,7 @@ static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, static void dequantize_mul_mat_vec_q2_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % QK_K == 0); - const int ny = 2; + const int ny = 2; // very slightly faster than 1 even when K_QUANTS_PER_ITERATION = 2 const int block_num_y = (nrows + ny - 1) / ny; const dim3 block_nums(1, block_num_y, 1); const dim3 block_dims(32, ny, 1); @@ -1320,14 +1331,20 @@ static void dequantize_mul_mat_vec_q2_K_cuda(const void * vx, const float * y, f static void dequantize_mul_mat_vec_q3_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % QK_K == 0); - const dim3 block_dims(32, 1, 1); - dequantize_mul_mat_vec_q3_k<<>>(vx, y, dst, ncols); + const int ny = 2 / K_QUANTS_PER_ITERATION; + const int block_num_y = (nrows + ny - 1) / ny; + const dim3 block_nums(1, block_num_y, 1); + const dim3 block_dims(32, ny, 1); + dequantize_mul_mat_vec_q3_k<<>>(vx, y, dst, ncols, nrows); } static void dequantize_mul_mat_vec_q4_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % QK_K == 0); - const dim3 block_dims(32, 1, 1); - dequantize_mul_mat_vec_q4_k<<>>(vx, y, dst, ncols); + const int ny = 2 / K_QUANTS_PER_ITERATION; + const int block_num_y = (nrows + ny - 1) / ny; + const dim3 block_nums(1, block_num_y, 1); + const dim3 block_dims(32, ny, 1); + dequantize_mul_mat_vec_q4_k<<>>(vx, y, dst, ncols, nrows); } static void dequantize_mul_mat_vec_q5_K_cuda(const void * vx, const float * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { From cb40dfca694b5cb849837548fd69932117c78362 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Mon, 19 Jun 2023 18:17:03 +0300 Subject: [PATCH 013/852] llama : only use Q6_K for output weights if tensor size is multiple of 256 (#1932) * Only use Q6_K for output weights if tensor size is multiple of 256 * Fixed copy/paste mistake --------- Co-authored-by: Iwan Kawrakow --- llama.cpp | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/llama.cpp b/llama.cpp index 5401db00e..dad31cbcb 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2495,7 +2495,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s if (quantized_type == GGML_TYPE_Q2_K || quantized_type == GGML_TYPE_Q3_K || quantized_type == GGML_TYPE_Q4_K || quantized_type == GGML_TYPE_Q5_K || quantized_type == GGML_TYPE_Q6_K) { int nx = tensor.ne.at(0); - int ny = tensor.ne.at(0); + int ny = tensor.ne.at(1); if (nx % QK_K != 0 || ny % QK_K != 0) { fprintf(stderr, "\n\n========================= Tensor sizes %d x %d are not divisible by %d\n",nx,ny,QK_K); fprintf(stderr, "This is required to be able to use k-quants for now!\n"); @@ -2504,7 +2504,11 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s } } if (tensor.name == "output.weight") { - new_type = GGML_TYPE_Q6_K; + int nx = tensor.ne.at(0); + int ny = tensor.ne.at(1); + if (nx % QK_K == 0 && ny % QK_K == 0) { + new_type = GGML_TYPE_Q6_K; + } } else if (tensor.name.find("attention.wv.weight") != std::string::npos) { if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K; else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K; From 23fc5c219a9aebd57c8af3fac454062cc4622980 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 19 Jun 2023 18:18:34 +0300 Subject: [PATCH 014/852] cmake : fix trailing whitespaces --- CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index a598593b6..2846d9b94 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -505,7 +505,7 @@ if (GGML_SOURCES_CUDA) set_property(TARGET ggml_shared PROPERTY CUDA_ARCHITECTURES "native") set_property(TARGET ggml_shared PROPERTY CUDA_SELECT_NVCC_ARCH_FLAGS "Auto") endif() - + set_property(TARGET llama PROPERTY CUDA_ARCHITECTURES "native") endif() From ba4e85a8339b9dd7cdffad31838235f2fe45a8ea Mon Sep 17 00:00:00 2001 From: l3utterfly Date: Mon, 19 Jun 2023 23:20:06 +0800 Subject: [PATCH 015/852] llama : use aligned memory during ggml_init call from loading saved sessions (#1934) * fixed issue: memory is not guaranteed to be aligned properly during ggml_init call from loading saved sessions * - removed commented out old code from fix - updated another instance of same issue below original --- llama.cpp | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/llama.cpp b/llama.cpp index dad31cbcb..4a7d01b32 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3126,9 +3126,7 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) { if (kv_size) { const size_t elt_size = ggml_element_size(kv_self.k); - char buffer[4096]; - - ggml_context * cpy_ctx = ggml_init({ sizeof(buffer), buffer, /* no_alloc */ true }); + ggml_context * cpy_ctx = ggml_init({ 4096, NULL, /* no_alloc */ true }); ggml_cgraph gf{}; gf.n_threads = 1; @@ -3234,9 +3232,7 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { const size_t elt_size = ggml_element_size(kv_self.k); - char buffer[4096]; - - ggml_context * cpy_ctx = ggml_init({ sizeof(buffer), buffer, /* no_alloc */ true }); + ggml_context * cpy_ctx = ggml_init({ 4096, NULL, /* no_alloc */ true }); ggml_cgraph gf{}; gf.n_threads = 1; From 18b35625c3c19c64b7818a12460ba5ddb006dfdc Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 19 Jun 2023 20:43:30 +0300 Subject: [PATCH 016/852] ggml : fix bug in LBFGS optimizer (found by ggml tests) --- ggml.c | 1 - 1 file changed, 1 deletion(-) diff --git a/ggml.c b/ggml.c index 14e08f9d6..4319683f5 100644 --- a/ggml.c +++ b/ggml.c @@ -18237,7 +18237,6 @@ GGML_API void ggml_opt_init( ggml_set_zero(opt->lbfgs.g); ggml_set_zero(opt->lbfgs.gp); ggml_set_zero(opt->lbfgs.d); - ggml_set_zero(opt->lbfgs.pf); if (opt->lbfgs.pf) { ggml_set_zero(opt->lbfgs.pf); } From 20568fe60f00155fa25e92eb3a7f6b911d557967 Mon Sep 17 00:00:00 2001 From: Henri Vasserman Date: Tue, 20 Jun 2023 01:12:39 +0300 Subject: [PATCH 017/852] [Fix] Reenable server embedding endpoint (#1937) * Add back embedding feature * Update README --- examples/server/README.md | 13 +++++++++-- examples/server/server.cpp | 44 +++++++++++++++++++++++++++++++++++++- 2 files changed, 54 insertions(+), 3 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 474a28b20..fa95c0044 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -21,6 +21,7 @@ Command line options: - `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`. - `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`. - `--port`: Set the port to listen. Default: `8080`. +- `--embedding`: Enable embedding extraction, Default: disabled. ## Build @@ -119,14 +120,14 @@ node . `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.9). - `n_predict`: Set the number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. (default: 128, -1 = infinity). + `n_predict`: Set the number of tokens to predict when generating text. **Note:** May exceed the set limit slightly if the last token is a partial multibyte character. When 0, no tokens will be generated but the prompt is evaluated into the cache. (default: 128, -1 = infinity). `n_keep`: Specify the number of tokens from the initial prompt to retain when the model resets its internal context. By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the initial prompt. `stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`. - `prompt`: Provide a prompt. Internally, the prompt is compared, and it detects if a part has already been evaluated, and the remaining part will be evaluate. + `prompt`: Provide a prompt. Internally, the prompt is compared, and it detects if a part has already been evaluated, and the remaining part will be evaluate. A space is inserted in the front like main.cpp does. `stop`: Specify a JSON array of stopping strings. These words will not be included in the completion, so make sure to add them to the prompt for the next iteration (default: []). @@ -163,6 +164,14 @@ node . `content`: Set the text to tokenize. + Note that the special `BOS` token is not added in fron of the text and also a space character is not inserted automatically as it is for `/completion`. + +- **POST** `/embedding`: Generate embedding of a given text just as [the embedding example](../embedding) does. + + *Options:* + + `content`: Set the text to process. + ## More examples ### Interactive mode diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 12d4e2fa4..c0984aadb 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -254,6 +254,11 @@ struct llama_server_context { n_past += n_eval; } + if (params.n_predict == 0) { + has_next_token = false; + return llama_token_eos(); + } + // out of user input, sample next token const float temp = params.temp; const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k; @@ -419,6 +424,19 @@ struct llama_server_context { return token_text; } + + std::vector getEmbedding() { + static const int n_embd = llama_n_embd(ctx); + if (!params.embedding) { + LOG_WARNING("embedding disabled", { + { "params.embedding", params.embedding }, + }); + return std::vector(n_embd, 0.0f); + } + const float * data = llama_get_embeddings(ctx); + std::vector embedding(data, data + n_embd); + return embedding; + } }; static void server_print_usage(const char * argv0, const gpt_params & params, @@ -457,6 +475,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, fprintf(stderr, " --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str()); fprintf(stderr, " --port PORT port to listen (default (default: %d)\n", sparams.port); fprintf(stderr, " -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); + fprintf(stderr, " --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); fprintf(stderr, "\n"); } @@ -603,6 +622,8 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, params.use_mlock = true; } else if (arg == "--no-mmap") { params.use_mmap = false; + } else if (arg == "--embedding") { + params.embedding = true; } else { fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); server_print_usage(argv[0], default_params, default_sparams); @@ -646,6 +667,12 @@ static json format_generation_settings(llama_server_context & llama) { }; } +static json format_embedding_response(llama_server_context & llama) { + return json { + { "embedding", llama.getEmbedding() }, + }; +} + static json format_final_response(llama_server_context & llama, const std::string & content) { return json { { "content", content }, @@ -881,12 +908,27 @@ int main(int argc, char ** argv) { svr.Post("/tokenize", [&llama](const Request & req, Response & res) { const json body = json::parse(req.body); - const std::string content = body["content"].get(); + const std::string content = body.value("content", ""); const std::vector tokens = llama_tokenize(llama.ctx, content, false); const json data = format_tokenizer_response(tokens); return res.set_content(data.dump(), "application/json"); }); + svr.Post("/embedding", [&llama](const Request & req, Response & res) { + const json body = json::parse(req.body); + + llama.rewind(); + llama_reset_timings(llama.ctx); + llama.params.prompt = body.value("content", ""); + llama.params.n_predict = 0; + llama.loadPrompt(); + llama.beginCompletion(); + llama.doCompletion(); + + const json data = format_embedding_response(llama); + return res.set_content(data.dump(), "application/json"); + }); + svr.set_logger(log_server_request); svr.set_exception_handler([](const Request &, Response & res, std::exception_ptr ep) { From aacdbd40562684665b6f7b8ba6695b7a2088bbb0 Mon Sep 17 00:00:00 2001 From: Ettore Di Giacinto Date: Tue, 20 Jun 2023 03:24:39 +0200 Subject: [PATCH 018/852] llama : fix params struct slignment (#1936) * Workaround struct misalignment during value-copy Signed-off-by: mudler * Move booleans at the bottom of the structure Signed-off-by: mudler * Add comment Signed-off-by: mudler --------- Signed-off-by: mudler --- llama.cpp | 6 +++--- llama.h | 17 ++++++++--------- 2 files changed, 11 insertions(+), 12 deletions(-) diff --git a/llama.cpp b/llama.cpp index 4a7d01b32..e597f5048 100644 --- a/llama.cpp +++ b/llama.cpp @@ -925,21 +925,21 @@ static bool kv_cache_init( struct llama_context_params llama_context_default_params() { struct llama_context_params result = { + /*.seed =*/ -1, /*.n_ctx =*/ 512, /*.n_batch =*/ 512, /*.gpu_layers =*/ 0, /*.main_gpu =*/ 0, /*.tensor_split =*/ {0}, + /*.progress_callback =*/ nullptr, + /*.progress_callback_user_data =*/ nullptr, /*.low_vram =*/ false, - /*.seed =*/ -1, /*.f16_kv =*/ true, /*.logits_all =*/ false, /*.vocab_only =*/ false, /*.use_mmap =*/ true, /*.use_mlock =*/ false, /*.embedding =*/ false, - /*.progress_callback =*/ nullptr, - /*.progress_callback_user_data =*/ nullptr, }; return result; diff --git a/llama.h b/llama.h index 1241ba6c0..0de530d45 100644 --- a/llama.h +++ b/llama.h @@ -71,28 +71,27 @@ extern "C" { typedef void (*llama_progress_callback)(float progress, void *ctx); - struct llama_context_params { + struct llama_context_params { + int seed; // RNG seed, -1 for random int n_ctx; // text context int n_batch; // prompt processing batch size int n_gpu_layers; // number of layers to store in VRAM int main_gpu; // the GPU that is used for scratch and small tensors float tensor_split[LLAMA_MAX_DEVICES]; // how to split layers across multiple GPUs - bool low_vram; // if true, reduce VRAM usage at the cost of performance - int seed; // RNG seed, -1 for random + // called with a progress value between 0 and 1, pass NULL to disable + llama_progress_callback progress_callback; + // context pointer passed to the progress callback + void * progress_callback_user_data; + // Keep the booleans together to avoid misalignment during copy-by-value. + bool low_vram; // if true, reduce VRAM usage at the cost of performance bool f16_kv; // use fp16 for KV cache bool logits_all; // the llama_eval() call computes all logits, not just the last one bool vocab_only; // only load the vocabulary, no weights bool use_mmap; // use mmap if possible bool use_mlock; // force system to keep model in RAM bool embedding; // embedding mode only - - // called with a progress value between 0 and 1, pass NULL to disable - llama_progress_callback progress_callback; - // context pointer passed to the progress callback - void * progress_callback_user_data; }; - // model file types enum llama_ftype { LLAMA_FTYPE_ALL_F32 = 0, From 2322ec223a21625dfe9bd73ee677444a98a24ac9 Mon Sep 17 00:00:00 2001 From: Xiake Sun Date: Tue, 20 Jun 2023 05:42:40 -0700 Subject: [PATCH 019/852] Fix typo (#1949) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 2d05de333..8136e7064 100644 --- a/README.md +++ b/README.md @@ -378,7 +378,7 @@ Building the program with BLAS support may lead to some performance improvements ```sh git clone https://github.com/CNugteren/CLBlast.git mkdir CLBlast/build - cd CLBLast/build + cd CLBlast/build cmake .. -DBUILD_SHARED_LIBS=OFF -DTUNERS=OFF cmake --build . --config Release cmake --install . --prefix /some/path From 049aa16b8c5c6d086246e4e6b9feb18de4fbd663 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 20 Jun 2023 19:05:54 +0300 Subject: [PATCH 020/852] readme : add link to p1 --- README.md | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/README.md b/README.md index 8136e7064..67012adab 100644 --- a/README.md +++ b/README.md @@ -9,12 +9,8 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ **Hot topics:** +- p1 : LLM-based code completion engine at the edge : https://github.com/ggml-org/p1/discussions/1 - Roadmap June 2023: https://github.com/ggerganov/llama.cpp/discussions/1729 -- GPU support with Metal (Apple Silicon): https://github.com/ggerganov/llama.cpp/pull/1642 -- High-quality 2,3,4,5,6-bit quantization: https://github.com/ggerganov/llama.cpp/pull/1684 -- Multi-GPU support: https://github.com/ggerganov/llama.cpp/pull/1607 -- Training LLaMA models from scratch: https://github.com/ggerganov/llama.cpp/pull/1652 -- CPU threading improvements: https://github.com/ggerganov/llama.cpp/pull/1632
Table of Contents From fb98254f99d769fcbbf20966ef386abdb48ef601 Mon Sep 17 00:00:00 2001 From: Rahul Vivek Nair <68507071+RahulVivekNair@users.noreply.github.com> Date: Thu, 22 Jun 2023 03:18:43 +0530 Subject: [PATCH 021/852] Fix typo in README.md (#1961) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 67012adab..ace588606 100644 --- a/README.md +++ b/README.md @@ -340,7 +340,7 @@ Building the program with BLAS support may lead to some performance improvements | LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | | LLAMA_CUDA_DMMV_Y | Positive integer | 1 | Block size in y direction for the CUDA dequantization + mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. | | LLAMA_CUDA_DMMV_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels. Can improve performance on relatively recent GPUs. | - | LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value 2 1 can improve performance for slow GPUs. | + | LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | - #### CLBlast From bbca06e26949686d61a5126332680ba3cccf235c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Wed, 21 Jun 2023 23:49:25 +0200 Subject: [PATCH 022/852] cmake: revert CUDA arch default to 52, 61 if f16 (#1959) --- CMakeLists.txt | 25 +++++++++---------------- 1 file changed, 9 insertions(+), 16 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 2846d9b94..cc7560a7a 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -250,6 +250,15 @@ if (LLAMA_CUBLAS) set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} CUDA::cudart CUDA::cublas CUDA::cublasLt) endif() + if (NOT DEFINED CMAKE_CUDA_ARCHITECTURES) + if (LLAMA_CUDA_DMMV_F16) + set(CMAKE_CUDA_ARCHITECTURES "61") # needed for f16 CUDA intrinsics + else() + set(CMAKE_CUDA_ARCHITECTURES "52") # lowest CUDA 12 standard + endif() + endif() + message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}") + else() message(WARNING "cuBLAS not found") endif() @@ -493,22 +502,6 @@ if (BUILD_SHARED_LIBS) endif() endif() -if (GGML_SOURCES_CUDA) - message(STATUS "GGML CUDA sources found, configuring CUDA architecture") - set_property(TARGET ggml PROPERTY CUDA_ARCHITECTURES "native") - set_property(TARGET ggml PROPERTY CUDA_SELECT_NVCC_ARCH_FLAGS "Auto") - - set_property(TARGET ggml_static PROPERTY CUDA_ARCHITECTURES "native") - set_property(TARGET ggml_static PROPERTY CUDA_SELECT_NVCC_ARCH_FLAGS "Auto") - - if (BUILD_SHARED_LIBS) - set_property(TARGET ggml_shared PROPERTY CUDA_ARCHITECTURES "native") - set_property(TARGET ggml_shared PROPERTY CUDA_SELECT_NVCC_ARCH_FLAGS "Auto") - endif() - - set_property(TARGET llama PROPERTY CUDA_ARCHITECTURES "native") -endif() - # # programs, examples and tests From 7487137227eb32ed9b12156338b865cb29b2dfd1 Mon Sep 17 00:00:00 2001 From: Erik Scholz Date: Thu, 22 Jun 2023 14:20:47 +0200 Subject: [PATCH 023/852] rework convert.py to read hyper-parameters from config.json (#1958) * Read hyper-parameters from HuggingFace-transformer config.json, if they exist, and fall back to guessing, like before otherwise. This allows converting open_llama 3B and other non-standard model designs. --- convert.py | 91 +++++++++++++++++++++++++++++++++++++++++------------- 1 file changed, 69 insertions(+), 22 deletions(-) diff --git a/convert.py b/convert.py index 265c41fa0..de6c39c67 100644 --- a/convert.py +++ b/convert.py @@ -130,6 +130,14 @@ TENSORS_LIST = make_tensors_list() TENSORS_SET = set(TENSORS_LIST) +def find_n_mult(n_ff: int, n_embd: int) -> int: + # hardcoded magic range + for n_mult in range(256, 1, -1): + calc_ff = (((8*n_embd) // 3 + n_mult - 1) // n_mult)*n_mult + if calc_ff == n_ff: + return n_mult + return 1 + @dataclass class Params: n_vocab: int @@ -137,21 +145,61 @@ class Params: n_mult: int n_head: int n_layer: int - file_type: GGMLFileType @staticmethod - def guessed(model: 'LazyModel', file_type: GGMLFileType) -> 'Params': - n_vocab, n_embd = model["tok_embeddings.weight"].shape + def guessed(model: 'LazyModel') -> 'Params': + # try transformer naming first + n_vocab, n_embd = model["model.embed_tokens.weight"].shape if "model.embed_tokens.weight" in model else model["tok_embeddings.weight"].shape + + # try transformer naming first + if "model.layers.0.self_attn.q_proj.weight" in model: + n_layer=next(i for i in itertools.count() if f"model.layers.{i}.self_attn.q_proj.weight" not in model) + else: + n_layer=next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model) + + n_head=n_embd // 128 # guessed return Params( n_vocab=n_vocab, n_embd=n_embd, n_mult=256, - n_head=n_embd // 128, - n_layer=next(i for i in itertools.count() if f"layers.{i}.attention.wq.weight" not in model), - file_type=file_type, + n_head=n_head, + n_layer=n_layer, ) + @staticmethod + def loadHFTransformerJson(model: 'LazyModel', config_path: 'Path') -> 'Params': + config = json.load(open(config_path)) + + n_vocab = config["vocab_size"]; + n_embd = config["hidden_size"]; + n_head = config["num_attention_heads"]; + n_layer = config["num_hidden_layers"]; + n_ff = config["intermediate_size"]; + + n_mult = find_n_mult(n_ff, n_embd); + + return Params( + n_vocab=n_vocab, + n_embd=n_embd, + n_mult=n_mult, + n_head=n_head, + n_layer=n_layer, + ) + + @staticmethod + def load(model_plus: 'ModelPlus') -> 'Params': + orig_config_path = model_plus.paths[0].parent / "params.json" + hf_transformer_config_path = model_plus.paths[0].parent / "config.json" + + if hf_transformer_config_path.exists(): + params = Params.loadHFTransformerJson(model_plus.model, hf_transformer_config_path) + else: + params = Params.guessed(model_plus.model) + + print(f'params: n_vocab:{params.n_vocab} n_embd:{params.n_embd} n_mult:{params.n_mult} n_head:{params.n_head} n_layer:{params.n_layer}') + return params + class SentencePieceVocab: def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]) -> None: @@ -595,18 +643,17 @@ def permute_lazy(lazy_tensor: LazyTensor, n_head: int) -> LazyTensor: return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description) -def convert_transformers_to_orig(model: LazyModel) -> LazyModel: +def convert_transformers_to_orig(model: LazyModel, params: Params) -> LazyModel: out: LazyModel = {} out["tok_embeddings.weight"] = model["model.embed_tokens.weight"] out["norm.weight"] = model["model.norm.weight"] out["output.weight"] = model["lm_head.weight"] - n_head = model["model.layers.0.self_attn.q_proj.weight"].shape[1] // 128 for i in itertools.count(): if f"model.layers.{i}.self_attn.q_proj.weight" not in model: break - out[f"layers.{i}.attention.wq.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], n_head) - out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], n_head) + out[f"layers.{i}.attention.wq.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head) + out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head) out[f"layers.{i}.attention.wv.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"] out[f"layers.{i}.attention.wo.weight"] = model[f"model.layers.{i}.self_attn.o_proj.weight"] @@ -920,7 +967,7 @@ class OutputFile: def __init__(self, fname_out: Path) -> None: self.fout = open(fname_out, "wb") - def write_file_header(self, params: Params) -> None: + def write_file_header(self, params: Params, file_type: GGMLFileType) -> None: self.fout.write(b"ggjt"[::-1]) # magic values = [ 1, # file version @@ -930,7 +977,7 @@ class OutputFile: params.n_head, params.n_layer, params.n_embd // params.n_head, # rot (obsolete) - params.file_type.value, + file_type.value, ] self.fout.write(struct.pack("i" * len(values), *values)) @@ -958,10 +1005,10 @@ class OutputFile: of.fout.close() @staticmethod - def write_all(fname_out: Path, params: Params, model: LazyModel, vocab: Vocab) -> None: + def write_all(fname_out: Path, params: Params, file_type: GGMLFileType, model: LazyModel, vocab: Vocab) -> None: check_vocab_size(params, vocab) of = OutputFile(fname_out) - of.write_file_header(params) + of.write_file_header(params, file_type) print("Writing vocab...") of.write_vocab(vocab) @@ -997,11 +1044,11 @@ def pick_output_type(model: LazyModel, output_type_str: Optional[str]) -> GGMLFi raise Exception(f"Unexpected combination of types: {name_to_type}") -def do_necessary_conversions(model: LazyModel) -> LazyModel: +def do_necessary_conversions(model: LazyModel, params: Params) -> LazyModel: model = handle_quantization(model) if "lm_head.weight" in model: - model = convert_transformers_to_orig(model) + model = convert_transformers_to_orig(model, params) model = filter_and_sort_tensors(model) return model @@ -1107,14 +1154,14 @@ def load_vocab(path: Path) -> SentencePieceVocab: return SentencePieceVocab(path, added_tokens_path if added_tokens_path.exists() else None) -def default_outfile(model_paths: List[Path], params: Params) -> Path: +def default_outfile(model_paths: List[Path], file_type: GGMLFileType) -> Path: namestr = { GGMLFileType.AllF32: "f32", GGMLFileType.MostlyF16: "f16", GGMLFileType.MostlyQ4_0: "q4_0", GGMLFileType.MostlyQ4_1: "q4_1", GGMLFileType.PerLayerIsQ4_1: "q4_1", - }[params.file_type] + }[file_type] ret = model_paths[0].parent / f"ggml-model-{namestr}.bin" if ret in model_paths: sys.stderr.write( @@ -1164,13 +1211,13 @@ def main(args_in: Optional[List[str]] = None) -> None: else: vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent vocab = load_vocab(vocab_dir) + params = Params.load(model_plus) model = model_plus.model - model = do_necessary_conversions(model) + model = do_necessary_conversions(model, params) output_type = pick_output_type(model, args.outtype) model = convert_to_output_type(model, output_type) - params = Params.guessed(model, output_type) - outfile = args.outfile or default_outfile(model_plus.paths, params) - OutputFile.write_all(outfile, params, model, vocab) + outfile = args.outfile or default_outfile(model_plus.paths, output_type) + OutputFile.write_all(outfile, params, output_type, model, vocab) print(f"Wrote {outfile}") From d7b7484f74d486f77feb4c0b7af7e1718ed91651 Mon Sep 17 00:00:00 2001 From: eiery <19350831+eiery@users.noreply.github.com> Date: Fri, 23 Jun 2023 04:38:01 -0400 Subject: [PATCH 024/852] Add OpenLLaMA instructions to the README (#1954) * add openllama to readme --- README.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/README.md b/README.md index ace588606..b09498be6 100644 --- a/README.md +++ b/README.md @@ -29,6 +29,7 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++
  • Quantization
  • Interactive mode
  • Instruction mode with Alpaca
    • +
  • Using OpenLLaMA
  • Using GPT4All
  • Using Pygmalion 7B & Metharme 7B
  • Obtaining the Facebook LLaMA original model and Stanford Alpaca model data
    • @@ -543,6 +544,13 @@ cadaver, cauliflower, cabbage (vegetable), catalpa (tree) and Cailleach. > ``` +### Using [OpenLLaMA](https://github.com/openlm-research/open_llama) + +OpenLLaMA is an openly licensed reproduction of Meta's original LLaMA model. It uses the same architecture and is a drop-in replacement for the original LLaMA weights. + +- Download the [3B](https://huggingface.co/openlm-research/open_llama_3b), [7B](https://huggingface.co/openlm-research/open_llama_7b), or [13B](https://huggingface.co/openlm-research/open_llama_13b) model from Hugging Face. +- Convert the model to ggml FP16 format using `python convert.py ` + ### Using [GPT4All](https://github.com/nomic-ai/gpt4all) - Obtain the `tokenizer.model` file from LLaMA model and put it to `models` From 527b6fba1d237befb324fd846bda7418c0fa394d Mon Sep 17 00:00:00 2001 From: Didzis Gosko Date: Sat, 24 Jun 2023 11:47:58 +0300 Subject: [PATCH 025/852] llama : make model stateless and context stateful (llama_state) (#1797) * llama : make model stateless and context stateful * llama : minor cleanup * llama : update internal API declaration * Apply suggestions from code review fix style Co-authored-by: Georgi Gerganov * Missing model memory release * Fix style * Add deprecated warning for public API function llama_init_from_file * Update public API use cases: move away from deprecated llama_init_from_file * Deprecate public API function llama_apply_lora_from_file --------- Co-authored-by: Georgi Gerganov --- examples/common.cpp | 24 ++- examples/common.h | 3 +- examples/embedding/embedding.cpp | 6 +- examples/main/main.cpp | 8 +- examples/perplexity/perplexity.cpp | 6 +- examples/quantize-stats/quantize-stats.cpp | 15 +- examples/save-load-state/save-load-state.cpp | 29 ++- examples/server/server.cpp | 9 +- examples/simple/simple.cpp | 8 +- .../train-text-from-scratch.cpp | 5 +- llama.cpp | 172 ++++++++++++------ llama.h | 35 +++- tests/test-tokenizer-0.cpp | 16 +- 13 files changed, 244 insertions(+), 92 deletions(-) diff --git a/examples/common.cpp b/examples/common.cpp index fed24e027..6ac484555 100644 --- a/examples/common.cpp +++ b/examples/common.cpp @@ -536,7 +536,7 @@ std::vector llama_tokenize(struct llama_context * ctx, const std::s return res; } -struct llama_context * llama_init_from_gpt_params(const gpt_params & params) { +std::tuple llama_init_from_gpt_params(const gpt_params & params) { auto lparams = llama_context_default_params(); lparams.n_ctx = params.n_ctx; @@ -552,25 +552,33 @@ struct llama_context * llama_init_from_gpt_params(const gpt_params & params) { lparams.logits_all = params.perplexity; lparams.embedding = params.embedding; - llama_context * lctx = llama_init_from_file(params.model.c_str(), lparams); - - if (lctx == NULL) { + llama_model * model = llama_load_model_from_file(params.model.c_str(), lparams); + if (model == NULL) { fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str()); - return NULL; + return std::make_tuple(nullptr, nullptr); + } + + llama_context * lctx = llama_new_context_with_model(model, lparams); + if (lctx == NULL) { + fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str()); + llama_free_model(model); + return std::make_tuple(nullptr, nullptr); } if (!params.lora_adapter.empty()) { - int err = llama_apply_lora_from_file(lctx, + int err = llama_model_apply_lora_from_file(model, params.lora_adapter.c_str(), params.lora_base.empty() ? NULL : params.lora_base.c_str(), params.n_threads); if (err != 0) { fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__); - return NULL; + llama_free(lctx); + llama_free_model(model); + return std::make_tuple(nullptr, nullptr); } } - return lctx; + return std::make_tuple(model, lctx); } void console_init(console_state & con_st) { diff --git a/examples/common.h b/examples/common.h index 6c2953cb2..713320179 100644 --- a/examples/common.h +++ b/examples/common.h @@ -9,6 +9,7 @@ #include #include #include +#include #if !defined (_WIN32) #include @@ -95,7 +96,7 @@ std::vector llama_tokenize(struct llama_context * ctx, const std::s // Model utils // -struct llama_context * llama_init_from_gpt_params(const gpt_params & params); +std::tuple llama_init_from_gpt_params(const gpt_params & params); // // Console utils diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index 860f99f67..369eac1d1 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -37,11 +37,12 @@ int main(int argc, char ** argv) { llama_init_backend(); + llama_model * model; llama_context * ctx; // load the model - ctx = llama_init_from_gpt_params(params); - if (ctx == NULL) { + std::tie(model, ctx) = llama_init_from_gpt_params(params); + if (model == NULL) { fprintf(stderr, "%s: error: unable to load model\n", __func__); return 1; } @@ -90,6 +91,7 @@ int main(int argc, char ** argv) { llama_print_timings(ctx); llama_free(ctx); + llama_free_model(model); return 0; } diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 941312f9c..c1e6bf126 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -107,12 +107,13 @@ int main(int argc, char ** argv) { llama_init_backend(); + llama_model * model; llama_context * ctx; g_ctx = &ctx; // load the model and apply lora adapter, if any - ctx = llama_init_from_gpt_params(params); - if (ctx == NULL) { + std::tie(model, ctx) = llama_init_from_gpt_params(params); + if (model == NULL) { fprintf(stderr, "%s: error: unable to load model\n", __func__); return 1; } @@ -139,6 +140,7 @@ int main(int argc, char ** argv) { llama_print_timings(ctx); llama_free(ctx); + llama_free_model(model); return 0; } @@ -147,6 +149,7 @@ int main(int argc, char ** argv) { if (params.export_cgraph) { llama_eval_export(ctx, "llama.ggml"); llama_free(ctx); + llama_free_model(model); return 0; } @@ -666,6 +669,7 @@ int main(int argc, char ** argv) { llama_print_timings(ctx); llama_free(ctx); + llama_free_model(model); return 0; } diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index ae8cfe0af..b59f5971e 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -149,11 +149,12 @@ int main(int argc, char ** argv) { llama_init_backend(); + llama_model * model; llama_context * ctx; // load the model and apply lora adapter, if any - ctx = llama_init_from_gpt_params(params); - if (ctx == NULL) { + std::tie(model, ctx) = llama_init_from_gpt_params(params); + if (model == NULL) { fprintf(stderr, "%s: error: unable to load model\n", __func__); return 1; } @@ -169,6 +170,7 @@ int main(int argc, char ** argv) { llama_print_timings(ctx); llama_free(ctx); + llama_free_model(model); return 0; } diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp index 6b8018ee2..9cea472de 100644 --- a/examples/quantize-stats/quantize-stats.cpp +++ b/examples/quantize-stats/quantize-stats.cpp @@ -320,6 +320,7 @@ int main(int argc, char ** argv) { fprintf(stderr, "Loading model\n"); const int64_t t_main_start_us = ggml_time_us(); + llama_model * model; llama_context * ctx; { @@ -330,10 +331,18 @@ int main(int argc, char ** argv) { lparams.f16_kv = false; lparams.use_mlock = false; - ctx = llama_init_from_file(params.model.c_str(), lparams); + model = llama_load_model_from_file(params.model.c_str(), lparams); + + if (model == NULL) { + fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str()); + return 1; + } + + ctx = llama_new_context_with_model(model, lparams); if (ctx == NULL) { - fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str()); + fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str()); + llama_free_model(model); return 1; } } @@ -357,6 +366,7 @@ int main(int argc, char ** argv) { fprintf(stderr, "%s: error: Quantization should be tested with a float model, " "this model contains already quantized layers (%s is type %d)\n", __func__, kv_tensor.first.c_str(), kv_tensor.second->type); llama_free(ctx); + llama_free_model(model); return 1; } included_layers++; @@ -415,6 +425,7 @@ int main(int argc, char ** argv) { llama_free(ctx); + llama_free_model(model); // report timing { const int64_t t_main_end_us = ggml_time_us(); diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index da4d37ad0..4c8688503 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -35,12 +35,22 @@ int main(int argc, char ** argv) { auto last_n_tokens_data = std::vector(params.repeat_last_n, 0); // init - auto ctx = llama_init_from_file(params.model.c_str(), lparams); + auto model = llama_load_model_from_file(params.model.c_str(), lparams); + if (model == nullptr) { + return 1; + } + auto ctx = llama_new_context_with_model(model, lparams); + if (ctx == nullptr) { + llama_free_model(model); + return 1; + } auto tokens = std::vector(params.n_ctx); auto n_prompt_tokens = llama_tokenize(ctx, params.prompt.c_str(), tokens.data(), int(tokens.size()), true); if (n_prompt_tokens < 1) { fprintf(stderr, "%s : failed to tokenize prompt\n", __func__); + llama_free(ctx); + llama_free_model(model); return 1; } @@ -84,6 +94,8 @@ int main(int argc, char ** argv) { printf("%s", next_token_str); if (llama_eval(ctx, &next_token, 1, n_past, params.n_threads)) { fprintf(stderr, "\n%s : failed to evaluate\n", __func__); + llama_free(ctx); + llama_free_model(model); return 1; } n_past += 1; @@ -91,23 +103,27 @@ int main(int argc, char ** argv) { printf("\n\n"); - // free old model + // free old context llama_free(ctx); - // load new model - auto ctx2 = llama_init_from_file(params.model.c_str(), lparams); + // make new context + auto ctx2 = llama_new_context_with_model(model, lparams); // Load state (rng, logits, embedding and kv_cache) from file { FILE *fp_read = fopen("dump_state.bin", "rb"); if (state_size != llama_get_state_size(ctx2)) { fprintf(stderr, "\n%s : failed to validate state size\n", __func__); + llama_free(ctx2); + llama_free_model(model); return 1; } const size_t ret = fread(state_mem, 1, state_size, fp_read); if (ret != state_size) { fprintf(stderr, "\n%s : failed to read state\n", __func__); + llama_free(ctx2); + llama_free_model(model); return 1; } @@ -138,6 +154,8 @@ int main(int argc, char ** argv) { printf("%s", next_token_str); if (llama_eval(ctx2, &next_token, 1, n_past, params.n_threads)) { fprintf(stderr, "\n%s : failed to evaluate\n", __func__); + llama_free(ctx2); + llama_free_model(model); return 1; } n_past += 1; @@ -145,5 +163,8 @@ int main(int argc, char ** argv) { printf("\n\n"); + llama_free(ctx2); + llama_free_model(model); + return 0; } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index c0984aadb..de22d3013 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -115,6 +115,7 @@ struct llama_server_context { std::vector embd; std::vector last_n_tokens; + llama_model * model = nullptr; llama_context * ctx = nullptr; gpt_params params; @@ -130,6 +131,10 @@ struct llama_server_context { llama_free(ctx); ctx = nullptr; } + if (model) { + llama_free_model(model); + model = nullptr; + } } void rewind() { @@ -150,8 +155,8 @@ struct llama_server_context { bool loadModel(const gpt_params & params_) { params = params_; - ctx = llama_init_from_gpt_params(params); - if (ctx == nullptr) { + std::tie(model, ctx) = llama_init_from_gpt_params(params); + if (model == nullptr) { LOG_ERROR("unable to load model", { { "model", params_.model } }); return false; } diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp index 76f991cdc..fc45c9340 100644 --- a/examples/simple/simple.cpp +++ b/examples/simple/simple.cpp @@ -68,11 +68,12 @@ int main(int argc, char ** argv) llama_init_backend(); - llama_context * ctx ; + llama_model * model; + llama_context * ctx; - ctx = llama_init_from_gpt_params( params ); + std::tie(model, ctx) = llama_init_from_gpt_params( params ); - if ( ctx == NULL ) + if ( model == NULL ) { fprintf( stderr , "%s: error: unable to load model\n" , __func__ ); return 1; @@ -170,6 +171,7 @@ int main(int argc, char ** argv) } // wend of main loop llama_free( ctx ); + llama_free_model( model ); return 0; } diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index 7ec85951a..61c829e5c 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -3054,7 +3054,8 @@ int main(int argc, char ** argv) { struct llama_context_params llama_params = llama_context_default_params(); llama_params.vocab_only = true; - struct llama_context * lctx = llama_init_from_file(params.fn_vocab_model, llama_params); + struct llama_model * lmodel = llama_load_model_from_file(params.fn_vocab_model, llama_params); + struct llama_context * lctx = llama_new_context_with_model(lmodel, llama_params); struct llama_vocab vocab; { @@ -3395,6 +3396,8 @@ int main(int argc, char ** argv) { delete[] compute_addr; delete[] compute_buf_0; delete[] compute_buf_1; + llama_free(lctx); + llama_free_model(lmodel); ggml_free(model.ctx); return 0; diff --git a/llama.cpp b/llama.cpp index e597f5048..a528eef4a 100644 --- a/llama.cpp +++ b/llama.cpp @@ -182,6 +182,19 @@ struct llama_kv_cache { } }; +struct llama_vocab { + using id = int32_t; + using token = std::string; + + struct token_score { + token tok; + float score; + }; + + std::unordered_map token_to_id; + std::vector id_to_token; +}; + struct llama_model { e_model type = MODEL_UNKNOWN; @@ -198,10 +211,6 @@ struct llama_model { // context struct ggml_context * ctx = NULL; - // key + value cache for the self attention - // TODO: move to llama_state - struct llama_kv_cache kv_self; - // the model memory buffer llama_ctx_buffer buf; @@ -215,6 +224,11 @@ struct llama_model { // for quantize-stats only std::vector> tensors_by_name; + int64_t t_load_us = 0; + int64_t t_start_us = 0; + + llama_vocab vocab; + ~llama_model() { if (ctx) { ggml_free(ctx); @@ -233,24 +247,11 @@ struct llama_model { } }; -struct llama_vocab { - using id = int32_t; - using token = std::string; - - struct token_score { - token tok; - float score; - }; - - std::unordered_map token_to_id; - std::vector id_to_token; -}; - struct llama_context { + llama_context(const llama_model & model, const llama_vocab & vocab) : model(model), vocab(vocab), t_load_us(model.t_load_us), t_start_us(model.t_start_us) {} + std::mt19937 rng; - int64_t t_load_us = 0; - int64_t t_start_us = 0; bool has_evaluated_once = false; int64_t t_sample_us = 0; @@ -261,8 +262,16 @@ struct llama_context { int32_t n_eval = 0; // number of eval calls int32_t n_p_eval = 0; // number of tokens in eval calls for the prompt (with batch size > 1) - llama_model model; - llama_vocab vocab; + const llama_model & model; + const llama_vocab & vocab; + + bool model_owner = false; + + int64_t t_load_us; + int64_t t_start_us; + + // key + value cache for the self attention + struct llama_kv_cache kv_self; size_t mem_per_token = 0; @@ -1033,7 +1042,8 @@ static const char *llama_model_type_name(e_model type) { static void llama_model_load_internal( const std::string & fname, - llama_context & lctx, + llama_model & model, + llama_vocab & vocab, int n_ctx, int n_batch, int n_gpu_layers, @@ -1047,12 +1057,11 @@ static void llama_model_load_internal( llama_progress_callback progress_callback, void * progress_callback_user_data) { - lctx.t_start_us = ggml_time_us(); + model.t_start_us = ggml_time_us(); std::unique_ptr ml(new llama_model_loader(fname, use_mmap, vocab_only)); - lctx.vocab = std::move(ml->file_loaders.at(0)->vocab); - auto & model = lctx.model; + vocab = std::move(ml->file_loaders.at(0)->vocab); model.hparams = ml->file_loaders.at(0)->hparams; model.n_gpu_layers = n_gpu_layers; llama_file_version file_version = ml->file_loaders.at(0)->file_version; @@ -1122,15 +1131,15 @@ static void llama_model_load_internal( // create the ggml context { - lctx.model.buf.resize(ctx_size); + model.buf.resize(ctx_size); if (use_mlock) { - lctx.model.mlock_buf.init(lctx.model.buf.addr); - lctx.model.mlock_buf.grow_to(lctx.model.buf.size); + model.mlock_buf.init(model.buf.addr); + model.mlock_buf.grow_to(model.buf.size); } struct ggml_init_params params = { - /*.mem_size =*/ lctx.model.buf.size, - /*.mem_buffer =*/ lctx.model.buf.addr, + /*.mem_size =*/ model.buf.size, + /*.mem_buffer =*/ model.buf.addr, /*.no_alloc =*/ ml->use_mmap, }; @@ -1311,7 +1320,7 @@ static void llama_model_load_internal( } #endif - ml->load_all_data(progress_callback, progress_callback_user_data, use_mlock ? &lctx.model.mlock_mmap : NULL); + ml->load_all_data(progress_callback, progress_callback_user_data, use_mlock ? &model.mlock_mmap : NULL); if (progress_callback) { progress_callback(1.0f, progress_callback_user_data); @@ -1321,12 +1330,13 @@ static void llama_model_load_internal( // loading time will be recalculate after the first eval, so // we take page faults deferred by mmap() into consideration - lctx.t_load_us = ggml_time_us() - lctx.t_start_us; + model.t_load_us = ggml_time_us() - model.t_start_us; } static bool llama_model_load( const std::string & fname, - llama_context & lctx, + llama_model & model, + llama_vocab & vocab, int n_ctx, int n_batch, int n_gpu_layers, @@ -1340,7 +1350,7 @@ static bool llama_model_load( llama_progress_callback progress_callback, void *progress_callback_user_data) { try { - llama_model_load_internal(fname, lctx, n_ctx, n_batch, n_gpu_layers, main_gpu, tensor_split, low_vram, memory_type, + llama_model_load_internal(fname, model, vocab, n_ctx, n_batch, n_gpu_layers, main_gpu, tensor_split, low_vram, memory_type, use_mmap, use_mlock, vocab_only, progress_callback, progress_callback_user_data); return true; } catch (const std::exception & err) { @@ -1378,7 +1388,7 @@ static bool llama_eval_internal( const auto & model = lctx.model; const auto & hparams = model.hparams; - const auto & kv_self = model.kv_self; + const auto & kv_self = lctx.kv_self; LLAMA_ASSERT(!!kv_self.ctx); @@ -1726,7 +1736,7 @@ static bool llama_eval_internal( //memcpy(embd_w.data(), ggml_get_data(cur), sizeof(float)*n_vocab*N); // update kv token count - lctx.model.kv_self.n = n_past + N; + lctx.kv_self.n = n_past + N; // extract logits { @@ -2634,12 +2644,39 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s // interface implementation // -struct llama_context * llama_init_from_file( +struct llama_model * llama_load_model_from_file( const char * path_model, struct llama_context_params params) { ggml_time_init(); - llama_context * ctx = new llama_context; + llama_model * model = new llama_model; + + ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32; + + if (!llama_model_load(path_model, *model, model->vocab, params.n_ctx, params.n_batch, params.n_gpu_layers, + params.main_gpu, params.tensor_split, params.low_vram, memory_type, params.use_mmap, params.use_mlock, + params.vocab_only, params.progress_callback, params.progress_callback_user_data)) { + delete model; + fprintf(stderr, "%s: failed to load model\n", __func__); + return nullptr; + } + + return model; +} + +void llama_free_model(struct llama_model * model) { + delete model; +} + +struct llama_context * llama_new_context_with_model( + struct llama_model * model, + struct llama_context_params params) { + + if (!model) { + return nullptr; + } + + llama_context * ctx = new llama_context(*model, model->vocab); if (params.seed < 0) { params.seed = time(NULL); @@ -2667,24 +2704,16 @@ struct llama_context * llama_init_from_file( ggml_type memory_type = params.f16_kv ? GGML_TYPE_F16 : GGML_TYPE_F32; - if (!llama_model_load(path_model, *ctx, params.n_ctx, params.n_batch, params.n_gpu_layers, params.main_gpu, - params.tensor_split, params.low_vram, memory_type, params.use_mmap, params.use_mlock, - params.vocab_only, params.progress_callback, params.progress_callback_user_data)) { - fprintf(stderr, "%s: failed to load model\n", __func__); - llama_free(ctx); - return nullptr; - } - // reserve memory for context buffers if (!params.vocab_only) { - if (!kv_cache_init(ctx->model.hparams, ctx->model.kv_self, memory_type, ctx->model.hparams.n_ctx, params.n_gpu_layers)) { + if (!kv_cache_init(ctx->model.hparams, ctx->kv_self, memory_type, ctx->model.hparams.n_ctx, params.n_gpu_layers)) { fprintf(stderr, "%s: kv_cache_init() failed for self-attention cache\n", __func__); llama_free(ctx); return nullptr; } { - const size_t memory_size = ggml_nbytes(ctx->model.kv_self.k) + ggml_nbytes(ctx->model.kv_self.v); + const size_t memory_size = ggml_nbytes(ctx->kv_self.k) + ggml_nbytes(ctx->kv_self.v); fprintf(stderr, "%s: kv self size = %7.2f MB\n", __func__, memory_size / 1024.0 / 1024.0); } @@ -2736,8 +2765,8 @@ struct llama_context * llama_init_from_file( LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "data", data_ptr, data_size, max_size)); - LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.addr, ctx->buf_compute.size, 0)); - LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "kv", ctx->model.kv_self.buf.addr, ctx->model.kv_self.buf.size, 0)); + LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "eval", ctx->buf_compute.addr, ctx->buf_compute.size, 0)); + LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "kv", ctx->kv_self.buf.addr, ctx->kv_self.buf.size, 0)); LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr0", ctx->buf_scratch[0].addr, ctx->buf_scratch[0].size, 0)); LLAMA_METAL_CHECK_BUF(ggml_metal_add_buffer(ctx->ctx_metal, "scr1", ctx->buf_scratch[1].addr, ctx->buf_scratch[1].size, 0)); @@ -2748,7 +2777,23 @@ struct llama_context * llama_init_from_file( return ctx; } +struct llama_context * llama_init_from_file( + const char * path_model, + struct llama_context_params params) { + + struct llama_model * model = llama_load_model_from_file(path_model, params); + if (!model) { + return nullptr; + } + struct llama_context * ctx = llama_new_context_with_model(model, params); + ctx->model_owner = true; + return ctx; +} + void llama_free(struct llama_context * ctx) { + if (ctx->model_owner) { + delete &ctx->model; + } delete ctx; } @@ -2765,11 +2810,9 @@ int llama_model_quantize( } } -int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char * path_lora, const char * path_base_model, int n_threads) { +int llama_apply_lora_from_file_internal(const struct llama_model & model, const char * path_lora, const char * path_base_model, int n_threads) { fprintf(stderr, "%s: applying lora adapter from '%s' - please wait ...\n", __func__, path_lora); - auto & model = ctx->model; - const int64_t t_start_lora_us = ggml_time_us(); auto fin = std::ifstream(path_lora, std::ios::binary); @@ -3012,7 +3055,16 @@ int llama_apply_lora_from_file_internal(struct llama_context * ctx, const char * int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lora, const char * path_base_model, int n_threads) { try { - return llama_apply_lora_from_file_internal(ctx, path_lora, path_base_model, n_threads); + return llama_apply_lora_from_file_internal(ctx->model, path_lora, path_base_model, n_threads); + } catch (const std::exception & err) { + fprintf(stderr, "%s: failed to apply lora adapter: %s\n", __func__, err.what()); + return 1; + } +} + +int llama_model_apply_lora_from_file(const struct llama_model * model, const char * path_lora, const char * path_base_model, int n_threads) { + try { + return llama_apply_lora_from_file_internal(*model, path_lora, path_base_model, n_threads); } catch (const std::exception & err) { fprintf(stderr, "%s: failed to apply lora adapter: %s\n", __func__, err.what()); return 1; @@ -3020,7 +3072,7 @@ int llama_apply_lora_from_file(struct llama_context * ctx, const char * path_lor } int llama_get_kv_cache_token_count(const struct llama_context * ctx) { - return ctx->model.kv_self.n; + return ctx->kv_self.n; } #define LLAMA_MAX_RNG_STATE (64*1024) @@ -3045,7 +3097,7 @@ size_t llama_get_state_size(const struct llama_context * ctx) { const size_t s_embedding = ctx->embedding.size() * sizeof(float); const size_t s_kv_size = sizeof(size_t); const size_t s_kv_ntok = sizeof(int); - const size_t s_kv = ctx->model.kv_self.buf.size; + const size_t s_kv = ctx->kv_self.buf.size; const size_t s_total = ( + s_rng_size @@ -3111,7 +3163,7 @@ size_t llama_copy_state_data(struct llama_context * ctx, uint8_t * dst) { // copy kv cache { - const auto & kv_self = ctx->model.kv_self; + const auto & kv_self = ctx->kv_self; const auto & hparams = ctx->model.hparams; const int n_layer = hparams.n_layer; const int n_embd = hparams.n_embd; @@ -3215,7 +3267,7 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { // set kv cache { - const auto & kv_self = ctx->model.kv_self; + const auto & kv_self = ctx->kv_self; const auto & hparams = ctx->model.hparams; const int n_layer = hparams.n_layer; const int n_embd = hparams.n_embd; @@ -3259,7 +3311,7 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { ggml_free(cpy_ctx); } - ctx->model.kv_self.n = kv_ntok; + ctx->kv_self.n = kv_ntok; } const size_t nread = inp - src; @@ -3506,6 +3558,6 @@ const char * llama_print_system_info(void) { } // For internal test use -std::vector>& llama_internal_get_tensor_map(struct llama_context * ctx) { +const std::vector>& llama_internal_get_tensor_map(struct llama_context * ctx) { return ctx->model.tensors_by_name; } diff --git a/llama.h b/llama.h index 0de530d45..a833a7f4d 100644 --- a/llama.h +++ b/llama.h @@ -26,6 +26,14 @@ # define LLAMA_API #endif +#ifdef __GNUC__ +# define DEPRECATED(func, hint) func __attribute__((deprecated(hint))) +#elif defined(_MSC_VER) +# define DEPRECATED(func, hint) __declspec(deprecated(hint)) func +#else +# define DEPRECATED(func, hint) func +#endif + #define LLAMA_FILE_MAGIC_GGJT 0x67676a74u // 'ggjt' #define LLAMA_FILE_MAGIC_GGLA 0x67676c61u // 'ggla' #define LLAMA_FILE_MAGIC_GGMF 0x67676d66u // 'ggmf' @@ -53,6 +61,7 @@ extern "C" { // TODO: show sample usage // + struct llama_model; struct llama_context; typedef int llama_token; @@ -136,12 +145,23 @@ extern "C" { LLAMA_API int64_t llama_time_us(); + LLAMA_API struct llama_model * llama_load_model_from_file( + const char * path_model, + struct llama_context_params params); + + LLAMA_API void llama_free_model(struct llama_model * model); + + LLAMA_API struct llama_context * llama_new_context_with_model( + struct llama_model * model, + struct llama_context_params params); + // Various functions for loading a ggml llama model. // Allocate (almost) all memory needed for the model. // Return NULL on failure - LLAMA_API struct llama_context * llama_init_from_file( + LLAMA_API DEPRECATED(struct llama_context * llama_init_from_file( const char * path_model, - struct llama_context_params params); + struct llama_context_params params), + "please use llama_load_model_from_file combined with llama_new_context_with_model instead"); // Frees all allocated memory LLAMA_API void llama_free(struct llama_context * ctx); @@ -158,8 +178,15 @@ extern "C" { // The model needs to be reloaded before applying a new adapter, otherwise the adapter // will be applied on top of the previous one // Returns 0 on success - LLAMA_API int llama_apply_lora_from_file( + LLAMA_API DEPRECATED(int llama_apply_lora_from_file( struct llama_context * ctx, + const char * path_lora, + const char * path_base_model, + int n_threads), + "please use llama_model_apply_lora_from_file instead"); + + LLAMA_API int llama_model_apply_lora_from_file( + const struct llama_model * model, const char * path_lora, const char * path_base_model, int n_threads); @@ -310,7 +337,7 @@ extern "C" { #include struct ggml_tensor; -std::vector>& llama_internal_get_tensor_map(struct llama_context * ctx); +const std::vector>& llama_internal_get_tensor_map(struct llama_context * ctx); #endif diff --git a/tests/test-tokenizer-0.cpp b/tests/test-tokenizer-0.cpp index ab1538a0c..20abe7100 100644 --- a/tests/test-tokenizer-0.cpp +++ b/tests/test-tokenizer-0.cpp @@ -28,6 +28,7 @@ int main(int argc, char **argv) { fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str()); + llama_model * model; llama_context * ctx; // load the vocab @@ -36,10 +37,18 @@ int main(int argc, char **argv) { lparams.vocab_only = true; - ctx = llama_init_from_file(fname.c_str(), lparams); + model = llama_load_model_from_file(fname.c_str(), lparams); + + if (model == NULL) { + fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str()); + return 1; + } + + ctx = llama_new_context_with_model(model, lparams); if (ctx == NULL) { fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str()); + llama_free_model(model); return 1; } } @@ -48,6 +57,8 @@ int main(int argc, char **argv) { if (n_vocab != 32000) { fprintf(stderr, "%s : expected 32000 tokens, got %d\n", __func__, n_vocab); + llama_free_model(model); + llama_free(ctx); return 2; } @@ -77,10 +88,13 @@ int main(int argc, char **argv) { } fprintf(stderr, "\n"); + llama_free_model(model); + llama_free(ctx); return 3; } } + llama_free_model(model); llama_free(ctx); return 0; From b061ba9e2a7a2c335a200df8c11aed5e31e4ccbb Mon Sep 17 00:00:00 2001 From: Alex Renda Date: Sat, 24 Jun 2023 03:15:01 -0700 Subject: [PATCH 026/852] llama : fix top-p sampling to match the canonical definition (#1953) * Fix top-p sampling to match the standard definition (smallest set that has probability mass at least p, not largest set with probability mass less than p) * top-p: correct gt to gte * add test for correct top-p behavior --- llama.cpp | 7 ++++--- tests/test-sampling.cpp | 1 + 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/llama.cpp b/llama.cpp index a528eef4a..ac22a48f8 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2015,9 +2015,10 @@ void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * can for (size_t i = 0; i < candidates->size; ++i) { cum_sum += candidates->data[i].p; - // Check if the running sum is greater than p or if we have kept at least min_keep tokens - if (cum_sum > p && i >= min_keep) { - last_idx = i; + // Check if the running sum is at least p or if we have kept at least min_keep tokens + // we set the last index to i+1 to indicate that the current iterate should be included in the set + if (cum_sum >= p && i + 1 >= min_keep) { + last_idx = i + 1; break; } } diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp index 5d693f7b5..64f9455d7 100644 --- a/tests/test-sampling.cpp +++ b/tests/test-sampling.cpp @@ -181,6 +181,7 @@ int main(void) { test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f}, 0); test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f}, 0.7f); + test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f}, 0.8f); test_top_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f, 0.3f, 0.2f, 0.1f}, 1); test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f}, 0.25f); From 235b610d650cbfed6dbd5d671f750d35fc18cd7d Mon Sep 17 00:00:00 2001 From: Alberto <57916483+albbus-stack@users.noreply.github.com> Date: Sat, 24 Jun 2023 12:32:13 +0200 Subject: [PATCH 027/852] readme : fixed termux instructions (#1973) --- README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index b09498be6..10462c6b0 100644 --- a/README.md +++ b/README.md @@ -680,12 +680,13 @@ Upon completion of the aforementioned steps, you will have successfully compiled ``` GGML_OPENCL_PLATFORM=0 GGML_OPENCL_DEVICE=0 -export LD_LIBRARY_PATH=/system/vendor/lib64:$LD_LIBRARY_PATH -./main (...) +export LD_LIBRARY_PATH=/vendor/lib64:$LD_LIBRARY_PATH ``` For easy and swift re-execution, consider documenting this final part in a .sh script file. This will enable you to rerun the process with minimal hassle. +Place your desired model into the `/llama.cpp/models/` directory and execute the `./main (...)` script. + ### Docker #### Prerequisites From 11da1a85cd69af84b5861134738c7e9e20907470 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 24 Jun 2023 13:38:18 +0300 Subject: [PATCH 028/852] readme : fix whitespaces --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 10462c6b0..6aa6ce319 100644 --- a/README.md +++ b/README.md @@ -685,7 +685,7 @@ export LD_LIBRARY_PATH=/vendor/lib64:$LD_LIBRARY_PATH For easy and swift re-execution, consider documenting this final part in a .sh script file. This will enable you to rerun the process with minimal hassle. -Place your desired model into the `/llama.cpp/models/` directory and execute the `./main (...)` script. +Place your desired model into the `/llama.cpp/models/` directory and execute the `./main (...)` script. ### Docker From f2c754e1c38936fdde74e4848ac468a696eb73c6 Mon Sep 17 00:00:00 2001 From: slaren Date: Sat, 24 Jun 2023 12:57:18 +0200 Subject: [PATCH 029/852] ggml : improve ggml_graph_dump_dot, add ggml_format_name (#1978) * Improve ggml_graph_dump_dot, add ggml_format_name * add more automatic names to view ops * fix name of copies --- ggml.c | 135 ++++++++++++++++++++++++++++++++++++++++----------------- ggml.h | 1 + 2 files changed, 97 insertions(+), 39 deletions(-) diff --git a/ggml.c b/ggml.c index 4319683f5..ef9e8585d 100644 --- a/ggml.c +++ b/ggml.c @@ -24,6 +24,7 @@ #include #include #include +#include #ifdef GGML_USE_METAL #include @@ -4734,10 +4735,19 @@ struct ggml_tensor * ggml_set_name(struct ggml_tensor * tensor, const char * nam return tensor; } +struct ggml_tensor * ggml_format_name(struct ggml_tensor * tensor, const char * fmt, ...) { + va_list args; + va_start(args, fmt); + vsnprintf(tensor->name, sizeof(tensor->name), fmt, args); + va_end(args); + return tensor; +} + struct ggml_tensor * ggml_view_tensor( struct ggml_context * ctx, const struct ggml_tensor * src) { struct ggml_tensor * result = ggml_new_tensor_impl(ctx, src->type, src->n_dims, src->ne, src->data); + ggml_format_name(result, "%s (view)", src->name); result->nb[0] = src->nb[0]; result->nb[1] = src->nb[1]; @@ -5899,6 +5909,11 @@ struct ggml_tensor * ggml_cpy_impl( // make a view of the destination struct ggml_tensor * result = ggml_view_tensor(ctx, b); + if (strlen(b->name) > 0) { + ggml_format_name(result, "%s (copy of %s)", b->name, a->name); + } else { + ggml_format_name(result, "%s (copy)", a->name); + } result->op = GGML_OP_CPY; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; @@ -5935,6 +5950,7 @@ struct ggml_tensor * ggml_cont_impl( } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + ggml_format_name(result, "%s (cont)", a->name); result->op = GGML_OP_CONT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; @@ -5978,6 +5994,7 @@ struct ggml_tensor * ggml_reshape( } struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, b->n_dims, b->ne, a->data); + ggml_format_name(result, "%s (reshaped)", a->name); result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; @@ -6002,6 +6019,7 @@ struct ggml_tensor * ggml_reshape_1d( const int64_t ne[1] = { ne0 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 1, ne, a->data); + ggml_format_name(result, "%s (reshaped)", a->name); result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; @@ -6027,6 +6045,7 @@ struct ggml_tensor * ggml_reshape_2d( const int64_t ne[2] = { ne0, ne1 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, a->data); + ggml_format_name(result, "%s (reshaped)", a->name); result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; @@ -6053,6 +6072,7 @@ struct ggml_tensor * ggml_reshape_3d( const int64_t ne[3] = { ne0, ne1, ne2 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, a->data); + ggml_format_name(result, "%s (reshaped)", a->name); result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; @@ -6081,6 +6101,7 @@ struct ggml_tensor * ggml_reshape_4d( const int64_t ne[4] = { ne0, ne1, ne2, ne3 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 4, ne, a->data); + ggml_format_name(result, "%s (reshaped)", a->name); result->op = GGML_OP_RESHAPE; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; @@ -6105,10 +6126,12 @@ struct ggml_tensor * ggml_view_1d( } struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 1, &ne0, (char *) a->data + offset); + ggml_format_name(result, "%s (view)", a->name); ggml_scratch_save(ctx); struct ggml_tensor * offs = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 2); + ggml_set_name(offs, "offset"); memcpy(offs->data, &offset, 2*sizeof(int32_t)); ggml_scratch_load(ctx); @@ -6141,10 +6164,12 @@ struct ggml_tensor * ggml_view_2d( const int64_t ne[GGML_MAX_DIMS] = { ne0, ne1, 1, 1 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, (char *) a->data + offset); + ggml_format_name(result, "%s (view)", a->name); ggml_scratch_save(ctx); struct ggml_tensor * offs = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 2); + ggml_set_name(offs, "offset"); memcpy(offs->data, &offset, 2*sizeof(int32_t)); ggml_scratch_load(ctx); @@ -6183,10 +6208,12 @@ struct ggml_tensor * ggml_view_3d( const int64_t ne[GGML_MAX_DIMS] = { ne0, ne1, ne2, 1 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, (char *) a->data + offset); + ggml_format_name(result, "%s (view)", a->name); ggml_scratch_save(ctx); struct ggml_tensor * offs = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 2); + ggml_set_name(offs, "offset"); memcpy(offs->data, &offset, 2*sizeof(int32_t)); ggml_scratch_load(ctx); @@ -6227,10 +6254,12 @@ struct ggml_tensor * ggml_view_4d( const int64_t ne[GGML_MAX_DIMS] = { ne0, ne1, ne2, ne3 }; struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 4, ne, (char *) a->data + offset); + ggml_format_name(result, "%s (view)", a->name); ggml_scratch_save(ctx); struct ggml_tensor * offs = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 2); + ggml_set_name(offs, "offset"); memcpy(offs->data, &offset, 2*sizeof(int32_t)); ggml_scratch_load(ctx); @@ -6276,6 +6305,7 @@ struct ggml_tensor * ggml_permute( } struct ggml_tensor * result = ggml_view_tensor(ctx, a); + ggml_format_name(result, "%s (permuted)", a->name); int ne[GGML_MAX_DIMS]; int nb[GGML_MAX_DIMS]; @@ -6335,6 +6365,7 @@ struct ggml_tensor * ggml_transpose( } struct ggml_tensor * result = ggml_view_tensor(ctx, a); + ggml_format_name(result, "%s (transposed)", a->name); result->ne[0] = a->ne[1]; result->ne[1] = a->ne[0]; @@ -16004,7 +16035,7 @@ static void ggml_visit_parents(struct ggml_cgraph * cgraph, struct ggml_tensor * GGML_ASSERT(cgraph->n_leafs < GGML_MAX_NODES); if (strlen(node->name) == 0) { - snprintf(node->name, sizeof(node->name), "leaf_%d", cgraph->n_leafs); + ggml_format_name(node, "leaf_%d", cgraph->n_leafs); } cgraph->leafs[cgraph->n_leafs] = node; @@ -16013,7 +16044,7 @@ static void ggml_visit_parents(struct ggml_cgraph * cgraph, struct ggml_tensor * GGML_ASSERT(cgraph->n_nodes < GGML_MAX_NODES); if (strlen(node->name) == 0) { - snprintf(node->name, sizeof(node->name), "node_%d", cgraph->n_nodes); + ggml_format_name(node, "node_%d", cgraph->n_nodes); } cgraph->nodes[cgraph->n_nodes] = node; @@ -17397,6 +17428,26 @@ static struct ggml_tensor * ggml_graph_get_parent(const struct ggml_cgraph * cgr return NULL; } +static void ggml_graph_dump_dot_node_edge(FILE * fp, const struct ggml_cgraph * gb, struct ggml_tensor * node, struct ggml_tensor * parent, const char * label) { + struct ggml_tensor * gparent = ggml_graph_get_parent(gb, node); + struct ggml_tensor * gparent0 = ggml_graph_get_parent(gb, parent); + fprintf(fp, " \"%p\":%s -> \"%p\":%s [ arrowhead = %s; style = %s; label = \"%s\"; ]\n", + gparent0 ? (void *) gparent0 : (void *) parent, + gparent0 ? "g" : "x", + gparent ? (void *) gparent : (void *) node, + gparent ? "g" : "x", + gparent ? "empty" : "vee", + gparent ? "dashed" : "solid", + label); +} + +static void ggml_graph_dump_dot_leaf_edge(FILE * fp, struct ggml_tensor * node, struct ggml_tensor * parent, const char * label) { + fprintf(fp, " \"%p\":%s -> \"%p\":%s [ label = \"%s\"; ]\n", + (void *) parent, "x", + (void *) node, "x", + label); +} + void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph * gf, const char * filename) { char color[16]; @@ -17432,7 +17483,9 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph (void *) node, color); if (strlen(node->name) > 0) { - fprintf(fp, "%s |", node->name); + fprintf(fp, "%s (%s)|", node->name, ggml_type_name(node->type)); + } else { + fprintf(fp, "(%s)|", ggml_type_name(node->type)); } if (node->n_dims == 2) { @@ -17441,7 +17494,6 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph fprintf(fp, "%d [%" PRId64 ", %" PRId64 ", %" PRId64 "] | %s", i, node->ne[0], node->ne[1], node->ne[2], GGML_OP_SYMBOL[node->op]); } - if (node->grad) { fprintf(fp, " | %s\"; ]\n", GGML_OP_SYMBOL[node->grad->op]); } else { @@ -17460,18 +17512,29 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph (void *) node, color); if (strlen(node->name) > 0) { - fprintf(fp, "%s | ", node->name); + fprintf(fp, "%s (%s)|", node->name, ggml_type_name(node->type)); + } else { + fprintf(fp, "(%s)|", ggml_type_name(node->type)); } - if (ggml_nelements(node) == 1) { - if (node->type == GGML_TYPE_I8 || node->type == GGML_TYPE_I16 || node->type == GGML_TYPE_I32) { - fprintf(fp, "%d", ggml_get_i32_1d(node, 0)); + + fprintf(fp, "CONST %d [%" PRId64 ", %" PRId64 "]", i, node->ne[0], node->ne[1]); + if (ggml_nelements(node) < 5) { + fprintf(fp, " | ("); + for (int j = 0; j < ggml_nelements(node); j++) { + if (node->type == GGML_TYPE_I8 || node->type == GGML_TYPE_I16 || node->type == GGML_TYPE_I32) { + fprintf(fp, "%d", ggml_get_i32_1d(node, j)); + } + else if (node->type == GGML_TYPE_F32 || node->type == GGML_TYPE_F16) { + fprintf(fp, "%.1e", (double)ggml_get_f32_1d(node, j)); + } + else { + fprintf(fp, "#"); + } + if (j < ggml_nelements(node) - 1) { + fprintf(fp, ", "); + } } - else { - fprintf(fp, "%.1e", (double)ggml_get_f32_1d(node, 0)); - } - } - else { - fprintf(fp, "CONST %d [%" PRId64 ", %" PRId64 "]", i, node->ne[0], node->ne[1]); + fprintf(fp, ")"); } fprintf(fp, "\"; ]\n"); } @@ -17479,30 +17542,20 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph for (int i = 0; i < gb->n_nodes; i++) { struct ggml_tensor * node = gb->nodes[i]; - struct ggml_tensor * parent = ggml_graph_get_parent(gb, node); - if (node->src0) { - struct ggml_tensor * parent0 = ggml_graph_get_parent(gb, node->src0); - - fprintf(fp, " \"%p\":%s -> \"%p\":%s [ arrowhead = %s; style = %s; label = \"x\"; ]\n", - parent0 ? (void *) parent0 : (void *) node->src0, - parent0 ? "g" : "x", - parent ? (void *) parent : (void *) node, - parent ? "g" : "x", - parent ? "empty" : "vee", - parent ? "dashed" : "solid"); + ggml_graph_dump_dot_node_edge(fp, gb, node, node->src0, "x"); } if (node->src1) { - struct ggml_tensor * parent1 = ggml_graph_get_parent(gb, node->src1); + ggml_graph_dump_dot_node_edge(fp, gb, node, node->src1, "y"); + } - fprintf(fp, " \"%p\":%s -> \"%p\":%s [ arrowhead = %s; style = %s; label = \"y\"; ]\n", - parent1 ? (void *) parent1 : (void *) node->src1, - parent1 ? "g" : "x", - parent ? (void *) parent : (void *) node, - parent ? "g" : "x", - parent ? "empty" : "vee", - parent ? "dashed" : "solid"); + for (int j = 0; j < GGML_MAX_OPT; j++) { + if (node->opt[j]) { + char label[16]; + snprintf(label, sizeof(label), "opt %d", j); + ggml_graph_dump_dot_node_edge(fp, gb, node, node->opt[j], label); + } } } @@ -17510,15 +17563,19 @@ void ggml_graph_dump_dot(const struct ggml_cgraph * gb, const struct ggml_cgraph struct ggml_tensor * node = gb->leafs[i]; if (node->src0) { - fprintf(fp, " \"%p\":%s -> \"%p\":%s [ label = \"x\"; ]\n", - (void *) node->src0, "x", - (void *) node, "x"); + ggml_graph_dump_dot_leaf_edge(fp, node, node->src0, "x"); } if (node->src1) { - fprintf(fp, " \"%p\":%s -> \"%p\":%s [ label = \"y\"; ]\n", - (void *) node->src1, "x", - (void *) node, "x"); + ggml_graph_dump_dot_leaf_edge(fp, node, node->src1, "y"); + } + + for (int j = 0; j < GGML_MAX_OPT; j++) { + if (node->opt[j]) { + char label[16]; + snprintf(label, sizeof(label), "opt %d", j); + ggml_graph_dump_dot_leaf_edge(fp, node, node->opt[j], label); + } } } diff --git a/ggml.h b/ggml.h index 18c78551f..4b6b72845 100644 --- a/ggml.h +++ b/ggml.h @@ -563,6 +563,7 @@ extern "C" { GGML_API const char * ggml_get_name(const struct ggml_tensor * tensor); GGML_API struct ggml_tensor * ggml_set_name(struct ggml_tensor * tensor, const char * name); + GGML_API struct ggml_tensor * ggml_format_name(struct ggml_tensor * tensor, const char * fmt, ...); // // operations on tensors with backpropagation From c943d823c14cef33092205ca3944de6fdf7abf99 Mon Sep 17 00:00:00 2001 From: AN Long Date: Sat, 24 Jun 2023 19:02:06 +0800 Subject: [PATCH 030/852] convert : fix invalid params in write_vocab_only (#1975) --- convert.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/convert.py b/convert.py index de6c39c67..e340d2273 100644 --- a/convert.py +++ b/convert.py @@ -998,9 +998,9 @@ class OutputFile: def write_vocab_only(fname_out: Path, vocab: Vocab) -> None: of = OutputFile(fname_out) params = Params(n_vocab=vocab.vocab_size, n_embd=0, n_mult=0, - n_head=1, n_layer=0, file_type=GGMLFileType.AllF32) + n_head=1, n_layer=0) of = OutputFile(fname_out) - of.write_file_header(params) + of.write_file_header(params, file_type=GGMLFileType.AllF32) of.write_vocab(vocab) of.fout.close() From fdd18609113862dc6eb34dfc44a093d54c59ff1f Mon Sep 17 00:00:00 2001 From: Rowan Hart Date: Sat, 24 Jun 2023 04:07:08 -0700 Subject: [PATCH 031/852] flake : fix ggml-metal.metal path and run nixfmt (#1974) --- flake.nix | 50 ++++++++++++++++++++++++++------------------------ 1 file changed, 26 insertions(+), 24 deletions(-) diff --git a/flake.nix b/flake.nix index bba3d71f7..cebb47b94 100644 --- a/flake.nix +++ b/flake.nix @@ -9,27 +9,33 @@ inherit (pkgs.stdenv) isAarch64 isDarwin; inherit (pkgs.lib) optionals; isM1 = isAarch64 && isDarwin; - osSpecific = - if isM1 then with pkgs.darwin.apple_sdk_11_0.frameworks; [ Accelerate MetalKit MetalPerformanceShaders MetalPerformanceShadersGraph ] - else if isDarwin then with pkgs.darwin.apple_sdk.frameworks; [ Accelerate CoreGraphics CoreVideo ] - else [ ]; - pkgs = import nixpkgs { - inherit system; - }; - llama-python = pkgs.python310.withPackages (ps: with ps; [ - numpy - sentencepiece - ]); - in - { + osSpecific = if isM1 then + with pkgs.darwin.apple_sdk_11_0.frameworks; [ + Accelerate + MetalKit + MetalPerformanceShaders + MetalPerformanceShadersGraph + ] + else if isDarwin then + with pkgs.darwin.apple_sdk.frameworks; [ + Accelerate + CoreGraphics + CoreVideo + ] + else + [ ]; + pkgs = import nixpkgs { inherit system; }; + llama-python = + pkgs.python310.withPackages (ps: with ps; [ numpy sentencepiece ]); + in { packages.default = pkgs.stdenv.mkDerivation { name = "llama.cpp"; src = ./.; - postPatch = - if isM1 then '' - substituteInPlace ./ggml-metal.m \ - --replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/ggml-metal.metal\";" - '' else ""; + postPatch = if isM1 then '' + substituteInPlace ./ggml-metal.m \ + --replace '[bundle pathForResource:@"ggml-metal" ofType:@"metal"];' "@\"$out/bin/ggml-metal.metal\";" + '' else + ""; nativeBuildInputs = with pkgs; [ cmake ]; buildInputs = osSpecific; cmakeFlags = [ "-DLLAMA_BUILD_SERVER=ON" ] ++ (optionals isM1 [ @@ -62,11 +68,7 @@ }; apps.default = self.apps.${system}.llama; devShells.default = pkgs.mkShell { - packages = with pkgs; [ - cmake - llama-python - ] ++ osSpecific; + packages = with pkgs; [ cmake llama-python ] ++ osSpecific; }; - } - ); + }); } From 65bdd52a867539691007f85c5508146d507f72c1 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 24 Jun 2023 19:40:18 +0300 Subject: [PATCH 032/852] tests : sync test-grad0 from ggml --- tests/test-grad0.c | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/tests/test-grad0.c b/tests/test-grad0.c index c8c2c0f71..b5a499c1d 100644 --- a/tests/test-grad0.c +++ b/tests/test-grad0.c @@ -1,3 +1,4 @@ +#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnigns on Windows #include "ggml.h" #include @@ -5,6 +6,10 @@ #include #include +#if defined(_MSC_VER) +#pragma warning(disable: 4244 4267) // possible loss of data +#endif + #define MAX_NARGS 3 #undef MIN @@ -197,8 +202,23 @@ bool check_gradient( float max_error_abs, float max_error_rel) { + static int n_threads = -1; + if (n_threads < 0) { + n_threads = GGML_DEFAULT_N_THREADS; + + const char *env = getenv("GGML_N_THREADS"); + if (env) { + n_threads = atoi(env); + } + + printf("GGML_N_THREADS = %d\n", n_threads); + } + struct ggml_cgraph gf = ggml_build_forward (f); + gf.n_threads = n_threads; + struct ggml_cgraph gb = ggml_build_backward(ctx0, &gf, false); + gb.n_threads = n_threads; ggml_graph_compute(ctx0, &gf); ggml_graph_reset (&gf); From 5ec8dd5a3c6a9a109351d2257bb9d53869bd0a94 Mon Sep 17 00:00:00 2001 From: Robyn Date: Sun, 25 Jun 2023 04:10:29 +1000 Subject: [PATCH 033/852] #1869 Fix null reference errors when training from scratch with CUDA (#1907) * #1869 Fix null reference errors when training from scratch with CUDA build Calling ggml_compute_forward when node->src0 was null was causing train-text-from-scratch.exe to terminate unexpectedly. * ggml : do not dereference src0 if NULL --------- Co-authored-by: Georgi Gerganov --- ggml-cuda.cu | 2 +- ggml.c | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 36a251ecc..010682edb 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -2635,7 +2635,7 @@ void ggml_cuda_free_scratch() { bool ggml_cuda_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor){ ggml_cuda_func_t func; const bool any_on_device = tensor->backend == GGML_BACKEND_GPU - || tensor->src0->backend == GGML_BACKEND_GPU || tensor->src0->backend == GGML_BACKEND_GPU_SPLIT + || (tensor->src0 != nullptr && (tensor->src0->backend == GGML_BACKEND_GPU || tensor->src0->backend == GGML_BACKEND_GPU_SPLIT)) || (tensor->src1 != nullptr && tensor->src1->backend == GGML_BACKEND_GPU); switch (tensor->op) { diff --git a/ggml.c b/ggml.c index ef9e8585d..7104be01b 100644 --- a/ggml.c +++ b/ggml.c @@ -14911,7 +14911,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm if (skip_cpu) { return; } - GGML_ASSERT(tensor->src0->backend == GGML_BACKEND_CPU); + GGML_ASSERT(tensor->src0 == NULL || tensor->src0->backend == GGML_BACKEND_CPU); GGML_ASSERT(tensor->src1 == NULL || tensor->src1->backend == GGML_BACKEND_CPU); #endif // GGML_USE_CUBLAS From e65ca7e14ac76c4046091da39d41a9017abaa9b3 Mon Sep 17 00:00:00 2001 From: sjinzh Date: Sun, 25 Jun 2023 13:45:44 +0800 Subject: [PATCH 034/852] zig : upgrade build system support (#1981) * upgrade zig build system support * zig : add new line at the end of the file --------- Co-authored-by: Georgi Gerganov --- build.zig | 87 +++++++++++++++++++++++++++---------------------------- 1 file changed, 42 insertions(+), 45 deletions(-) diff --git a/build.zig b/build.zig index 306127ffe..49c159ebf 100644 --- a/build.zig +++ b/build.zig @@ -1,61 +1,58 @@ const std = @import("std"); +// Zig Version: 0.11.0-dev.3379+629f0d23b pub fn build(b: *std.build.Builder) void { const target = b.standardTargetOptions(.{}); - const optimize = b.standardReleaseOptions(); - const want_lto = b.option(bool, "lto", "Want -fLTO"); - - const lib = b.addStaticLibrary("llama", null); - lib.want_lto = want_lto; - lib.setTarget(target); - lib.setBuildMode(optimize); + const optimize = b.standardOptimizeOption(.{}); + const lib = b.addStaticLibrary(.{ + .name = "llama", + .target = target, + .optimize = optimize, + }); + lib.linkLibC(); lib.linkLibCpp(); lib.addIncludePath("."); - lib.addIncludePath("examples"); + lib.addIncludePath("./examples"); lib.addCSourceFiles(&.{ "ggml.c", }, &.{"-std=c11"}); lib.addCSourceFiles(&.{ "llama.cpp", }, &.{"-std=c++11"}); - lib.install(); + b.installArtifact(lib); - const build_args = .{ .b = b, .lib = lib, .target = target, .optimize = optimize, .want_lto = want_lto }; + const examples = .{ + "main", + "baby-llama", + "embedding", + // "metal", + "perplexity", + "quantize", + "quantize-stats", + "save-load-state", + // "server", + "simple", + "train-text-from-scratch", + }; - const exe = build_example("main", build_args); - _ = build_example("quantize", build_args); - _ = build_example("perplexity", build_args); - _ = build_example("embedding", build_args); - - // create "zig build run" command for ./main - - const run_cmd = exe.run(); - run_cmd.step.dependOn(b.getInstallStep()); - if (b.args) |args| { - run_cmd.addArgs(args); + inline for (examples) |example_name| { + const exe = b.addExecutable(.{ + .name = example_name, + .target = target, + .optimize = optimize, + }); + exe.addIncludePath("."); + exe.addIncludePath("./examples"); + exe.addCSourceFiles(&.{ + std.fmt.comptimePrint("examples/{s}/{s}.cpp", .{example_name, example_name}), + "examples/common.cpp", + }, &.{"-std=c++11"}); + exe.linkLibrary(lib); + b.installArtifact(exe); + const run_cmd = b.addRunArtifact(exe); + run_cmd.step.dependOn(b.getInstallStep()); + if (b.args) |args| run_cmd.addArgs(args); + const run_step = b.step("run_" ++ example_name, "Run the app"); + run_step.dependOn(&run_cmd.step); } - - const run_step = b.step("run", "Run the app"); - run_step.dependOn(&run_cmd.step); -} - -fn build_example(comptime name: []const u8, args: anytype) *std.build.LibExeObjStep { - const b = args.b; - const lib = args.lib; - const want_lto = args.want_lto; - - const exe = b.addExecutable(name, null); - exe.want_lto = want_lto; - lib.setTarget(args.target); - lib.setBuildMode(args.optimize); - exe.addIncludePath("."); - exe.addIncludePath("examples"); - exe.addCSourceFiles(&.{ - std.fmt.comptimePrint("examples/{s}/{s}.cpp", .{name, name}), - "examples/common.cpp", - }, &.{"-std=c++11"}); - exe.linkLibrary(lib); - exe.install(); - - return exe; } From 66a2555ba6cab954c56d653b29c27bfbbacfbfb1 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 25 Jun 2023 09:07:03 +0300 Subject: [PATCH 035/852] readme : add Azure CI discussion link --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 6aa6ce319..3a71e16db 100644 --- a/README.md +++ b/README.md @@ -9,6 +9,7 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ **Hot topics:** +- Azure CI brainstorming: https://github.com/ggerganov/llama.cpp/discussions/1985 - p1 : LLM-based code completion engine at the edge : https://github.com/ggml-org/p1/discussions/1 - Roadmap June 2023: https://github.com/ggerganov/llama.cpp/discussions/1729 From c2a08f87b8d180115d04b8688f383d1b2761b16d Mon Sep 17 00:00:00 2001 From: anon998 <131767832+anon998@users.noreply.github.com> Date: Sun, 25 Jun 2023 08:48:36 +0000 Subject: [PATCH 036/852] fix server sampling: top k sampler first (#1977) Co-authored-by: anon --- examples/server/server.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index de22d3013..79df5e847 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -325,10 +325,10 @@ struct llama_server_context { id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu); } else { // Temperature sampling + llama_sample_top_k(ctx, &candidates_p, top_k, 1); llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1); llama_sample_typical(ctx, &candidates_p, typical_p, 1); llama_sample_top_p(ctx, &candidates_p, top_p, 1); - llama_sample_top_k(ctx, &candidates_p, top_k, 1); llama_sample_temperature(ctx, &candidates_p, temp); id = llama_sample_token(ctx, &candidates_p); } From bd34cdde38f8fd661890ddd5f57ca30bf279877b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 25 Jun 2023 14:25:08 +0300 Subject: [PATCH 037/852] ggml : sync latest ggml (custom operators) --- ggml.c | 369 ++++++++++++++++++++++++++++++++++++++++++++++++++++----- ggml.h | 60 +++++++++- 2 files changed, 397 insertions(+), 32 deletions(-) diff --git a/ggml.c b/ggml.c index 7104be01b..e3f0c939c 100644 --- a/ggml.c +++ b/ggml.c @@ -1,5 +1,5 @@ -// Defines CLOCK_MONOTONIC on Linux -#define _GNU_SOURCE +#define _GNU_SOURCE // Defines CLOCK_MONOTONIC on Linux +#define _CRT_SECURE_NO_DEPRECATE // Disables ridiculous "unsafe" warnigns on Windows #include "ggml.h" @@ -131,6 +131,34 @@ typedef void* thread_ret_t; #define GGML_MEM_ALIGN 16 #endif +// +// logging +// + +#if (GGML_DEBUG >= 1) +#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__) +#else +#define GGML_PRINT_DEBUG(...) +#endif + +#if (GGML_DEBUG >= 5) +#define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__) +#else +#define GGML_PRINT_DEBUG_5(...) +#endif + +#if (GGML_DEBUG >= 10) +#define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__) +#else +#define GGML_PRINT_DEBUG_10(...) +#endif + +#define GGML_PRINT(...) printf(__VA_ARGS__) + +// +// end of logging block +// + #if defined(_MSC_VER) || defined(__MINGW32__) #define GGML_ALIGNED_MALLOC(size) _aligned_malloc(size, GGML_MEM_ALIGN) #define GGML_ALIGNED_FREE(ptr) _aligned_free(ptr) @@ -144,6 +172,17 @@ inline static void* ggml_aligned_malloc(size_t size) { #endif if (result != 0) { // Handle allocation failure + const char *error_desc = "unknown allocation error"; + switch (result) { + case EINVAL: + error_desc = "invalid alignment value"; + break; + case ENOMEM: + error_desc = "insufficient memory"; + break; + } + GGML_PRINT("%s: %s (attempted to allocate %6.2f MB)\n", + __func__, error_desc, size/(1024.0*1024.0)); return NULL; } return aligned_memory; @@ -3530,30 +3569,6 @@ inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x *s = 1.f/(*s); } -// -// logging -// - -#if (GGML_DEBUG >= 1) -#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__) -#else -#define GGML_PRINT_DEBUG(...) -#endif - -#if (GGML_DEBUG >= 5) -#define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__) -#else -#define GGML_PRINT_DEBUG_5(...) -#endif - -#if (GGML_DEBUG >= 10) -#define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__) -#else -#define GGML_PRINT_DEBUG_10(...) -#endif - -#define GGML_PRINT(...) printf(__VA_ARGS__) - // // data types // @@ -3713,11 +3728,15 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "MAP_UNARY", "MAP_BINARY", + "MAP_CUSTOM1", + "MAP_CUSTOM2", + "MAP_CUSTOM3", + "CROSS_ENTROPY_LOSS", "CROSS_ENTROPY_LOSS_BACK", }; -static_assert(GGML_OP_COUNT == 61, "GGML_OP_COUNT != 61"); +static_assert(GGML_OP_COUNT == 64, "GGML_OP_COUNT != 64"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -3785,11 +3804,15 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "f(x)", "f(x,y)", + "custom(x)", + "custom(x,y)", + "custom(x,y,z)", + "cross_entropy_loss(x,y)", "cross_entropy_loss_back(x,y)", }; -static_assert(GGML_OP_COUNT == 61, "GGML_OP_COUNT != 61"); +static_assert(GGML_OP_COUNT == 64, "GGML_OP_COUNT != 64"); static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN"); static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN"); @@ -7094,9 +7117,14 @@ struct ggml_tensor * ggml_map_unary_impl_f32( is_node = true; } + struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + ggml_scratch_save(ctx); + struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t)); *((void (**)(void))addr_tensor->data) = (void (*)(void))fun; - struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + ggml_scratch_load(ctx); result->op = GGML_OP_MAP_UNARY; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; @@ -7136,9 +7164,14 @@ struct ggml_tensor * ggml_map_binary_impl_f32( is_node = true; } + struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + ggml_scratch_save(ctx); + struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t)); *((void (**)(void))addr_tensor->data) = (void (*)(void))fun; - struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + ggml_scratch_load(ctx); result->op = GGML_OP_MAP_BINARY; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; @@ -7165,6 +7198,150 @@ struct ggml_tensor * ggml_map_binary_inplace_f32( return ggml_map_binary_impl_f32(ctx, a, b, fun, true); } +// ggml_map_custom1 + +struct ggml_tensor * ggml_map_custom1_impl_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + const ggml_custom1_op_f32_t fun, + bool inplace) { + bool is_node = false; + + if (!inplace && a->grad) { + is_node = true; + } + + struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + ggml_scratch_save(ctx); + + struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t)); + *((void (**)(void))addr_tensor->data) = (void (*)(void))fun; + + ggml_scratch_load(ctx); + + result->op = GGML_OP_MAP_CUSTOM1; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src0 = a; + result->opt[0] = addr_tensor; + + return result; +} + +struct ggml_tensor * ggml_map_custom1_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + const ggml_custom1_op_f32_t fun) { + return ggml_map_custom1_impl_f32(ctx, a, fun, false); +} + +struct ggml_tensor * ggml_map_custom1_inplace_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + const ggml_custom1_op_f32_t fun) { + return ggml_map_custom1_impl_f32(ctx, a, fun, true); +} + +// ggml_map_custom2 + +struct ggml_tensor * ggml_map_custom2_impl_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + const ggml_custom2_op_f32_t fun, + bool inplace) { + bool is_node = false; + + if (!inplace && (a->grad || b->grad)) { + is_node = true; + } + + struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + ggml_scratch_save(ctx); + + struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t)); + *((void (**)(void))addr_tensor->data) = (void (*)(void))fun; + + ggml_scratch_load(ctx); + + result->op = GGML_OP_MAP_CUSTOM2; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src0 = a; + result->src1 = b; + result->opt[0] = addr_tensor; + + return result; +} + +struct ggml_tensor * ggml_map_custom2_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + const ggml_custom2_op_f32_t fun) { + return ggml_map_custom2_impl_f32(ctx, a, b, fun, false); +} + +struct ggml_tensor * ggml_map_custom2_inplace_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + const ggml_custom2_op_f32_t fun) { + return ggml_map_custom2_impl_f32(ctx, a, b, fun, true); +} + +// ggml_map_custom3 + +struct ggml_tensor * ggml_map_custom3_impl_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + const ggml_custom3_op_f32_t fun, + bool inplace) { + bool is_node = false; + + if (!inplace && (a->grad || b->grad || c->grad)) { + is_node = true; + } + + struct ggml_tensor *result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + ggml_scratch_save(ctx); + + struct ggml_tensor * addr_tensor = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, sizeof(void *) / sizeof(int32_t)); + *((void (**)(void))addr_tensor->data) = (void (*)(void))fun; + + ggml_scratch_load(ctx); + + result->op = GGML_OP_MAP_CUSTOM3; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src0 = a; + result->src1 = b; + result->opt[0] = addr_tensor; + result->opt[1] = c; + + return result; +} + +struct ggml_tensor * ggml_map_custom3_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + const ggml_custom3_op_f32_t fun) { + return ggml_map_custom3_impl_f32(ctx, a, b, c, fun, false); +} + +struct ggml_tensor * ggml_map_custom3_inplace_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + const ggml_custom3_op_f32_t fun) { + return ggml_map_custom3_impl_f32(ctx, a, b, c, fun, true); +} + // ggml_cross_entropy_loss struct ggml_tensor * ggml_cross_entropy_loss( @@ -14621,6 +14798,114 @@ static void ggml_compute_forward_map_binary( } } +// ggml_compute_forward_map_custom1 + +static void ggml_compute_forward_map_custom1_f32( + const struct ggml_compute_params * params, + const struct ggml_tensor * a, + struct ggml_tensor * dst, + const ggml_custom1_op_f32_t fun) { + assert(params->ith == 0); + + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + fun(dst, a); +} + + +static void ggml_compute_forward_map_custom1( + const struct ggml_compute_params * params, + const struct ggml_tensor * a, + struct ggml_tensor * dst, + const ggml_custom1_op_f32_t fun) { + switch (a->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_map_custom1_f32(params, a, dst, fun); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + +// ggml_compute_forward_map_custom2 + +static void ggml_compute_forward_map_custom2_f32( + const struct ggml_compute_params * params, + const struct ggml_tensor * a, + const struct ggml_tensor * b, + struct ggml_tensor * dst, + const ggml_custom2_op_f32_t fun) { + assert(params->ith == 0); + + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + fun(dst, a, b); +} + + +static void ggml_compute_forward_map_custom2( + const struct ggml_compute_params * params, + const struct ggml_tensor * a, + const struct ggml_tensor * b, + struct ggml_tensor * dst, + const ggml_custom2_op_f32_t fun) { + switch (a->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_map_custom2_f32(params, a, b, dst, fun); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + +// ggml_compute_forward_map_custom3 + +static void ggml_compute_forward_map_custom3_f32( + const struct ggml_compute_params * params, + const struct ggml_tensor * a, + const struct ggml_tensor * b, + const struct ggml_tensor * c, + struct ggml_tensor * dst, + const ggml_custom3_op_f32_t fun) { + assert(params->ith == 0); + + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + fun(dst, a, b, c); +} + + +static void ggml_compute_forward_map_custom3( + const struct ggml_compute_params * params, + const struct ggml_tensor * a, + const struct ggml_tensor * b, + const struct ggml_tensor * c, + struct ggml_tensor * dst, + const ggml_custom3_op_f32_t fun) { + switch (a->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_map_custom3_f32(params, a, b, c, dst, fun); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + // ggml_compute_forward_cross_entropy_loss static void ggml_compute_forward_cross_entropy_loss_f32( @@ -15158,6 +15443,24 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm ggml_compute_forward_map_binary(params, tensor->src0, tensor->src1, tensor, fun); } break; + case GGML_OP_MAP_CUSTOM1: + { + const ggml_custom1_op_f32_t fun = *((ggml_custom1_op_f32_t *)tensor->opt[0]->data); + ggml_compute_forward_map_custom1(params, tensor->src0, tensor, fun); + } + break; + case GGML_OP_MAP_CUSTOM2: + { + const ggml_custom2_op_f32_t fun = *((ggml_custom2_op_f32_t *)tensor->opt[0]->data); + ggml_compute_forward_map_custom2(params, tensor->src0, tensor->src1, tensor, fun); + } + break; + case GGML_OP_MAP_CUSTOM3: + { + const ggml_custom3_op_f32_t fun = *((ggml_custom3_op_f32_t *)tensor->opt[0]->data); + ggml_compute_forward_map_custom3(params, tensor->src0, tensor->src1, tensor->opt[1], tensor, fun); + } + break; case GGML_OP_CROSS_ENTROPY_LOSS: { ggml_compute_forward_cross_entropy_loss(params, tensor->src0, tensor->src1, tensor); @@ -15964,6 +16267,9 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor case GGML_OP_WIN_UNPART: case GGML_OP_MAP_UNARY: case GGML_OP_MAP_BINARY: + case GGML_OP_MAP_CUSTOM1: + case GGML_OP_MAP_CUSTOM2: + case GGML_OP_MAP_CUSTOM3: { GGML_ASSERT(false); // not supported } break; @@ -16605,6 +16911,9 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) case GGML_OP_WIN_UNPART: case GGML_OP_MAP_UNARY: case GGML_OP_MAP_BINARY: + case GGML_OP_MAP_CUSTOM1: + case GGML_OP_MAP_CUSTOM2: + case GGML_OP_MAP_CUSTOM3: { node->n_tasks = 1; } break; diff --git a/ggml.h b/ggml.h index 4b6b72845..5ebd9c46c 100644 --- a/ggml.h +++ b/ggml.h @@ -345,6 +345,10 @@ extern "C" { GGML_OP_MAP_UNARY, GGML_OP_MAP_BINARY, + GGML_OP_MAP_CUSTOM1, + GGML_OP_MAP_CUSTOM2, + GGML_OP_MAP_CUSTOM3, + GGML_OP_CROSS_ENTROPY_LOSS, GGML_OP_CROSS_ENTROPY_LOSS_BACK, @@ -1167,21 +1171,73 @@ extern "C" { int h0, int w); - // Mapping operations - typedef void (*ggml_unary_op_f32_t)(const int, float *, const float *); + // custom operators + + typedef void (*ggml_unary_op_f32_t) (const int, float *, const float *); typedef void (*ggml_binary_op_f32_t)(const int, float *, const float *, const float *); + typedef void (*ggml_custom1_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *); + typedef void (*ggml_custom2_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *); + typedef void (*ggml_custom3_op_f32_t)(struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *, const struct ggml_tensor *); + GGML_API struct ggml_tensor * ggml_map_unary_f32( struct ggml_context * ctx, struct ggml_tensor * a, ggml_unary_op_f32_t fun); + GGML_API struct ggml_tensor * ggml_map_unary_inplace_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + ggml_unary_op_f32_t fun); + GGML_API struct ggml_tensor * ggml_map_binary_f32( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, ggml_binary_op_f32_t fun); + GGML_API struct ggml_tensor * ggml_map_binary_inplace_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + ggml_binary_op_f32_t fun); + + GGML_API struct ggml_tensor * ggml_map_custom1_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + ggml_custom1_op_f32_t fun); + + GGML_API struct ggml_tensor * ggml_map_custom1_inplace_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + ggml_custom1_op_f32_t fun); + + GGML_API struct ggml_tensor * ggml_map_custom2_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + ggml_custom2_op_f32_t fun); + + GGML_API struct ggml_tensor * ggml_map_custom2_inplace_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + ggml_custom2_op_f32_t fun); + + GGML_API struct ggml_tensor * ggml_map_custom3_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + ggml_custom3_op_f32_t fun); + + GGML_API struct ggml_tensor * ggml_map_custom3_inplace_f32( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + ggml_custom3_op_f32_t fun); + // loss function GGML_API struct ggml_tensor * ggml_cross_entropy_loss( From 447ccbe8c39332fcdd0d98a041b6e2ff6f06219d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 25 Jun 2023 16:08:12 +0300 Subject: [PATCH 038/852] readme : add new roadmap + manifesto --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 3a71e16db..ad1a5cfc0 100644 --- a/README.md +++ b/README.md @@ -5,13 +5,15 @@ [![Actions Status](https://github.com/ggerganov/llama.cpp/workflows/CI/badge.svg)](https://github.com/ggerganov/llama.cpp/actions) [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) +[Roadmap](https://github.com/users/ggerganov/projects/7) / [Manifesto](https://github.com/ggerganov/llama.cpp/discussions/205) / [ggml](https://github.com/ggerganov/ggml) + Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ **Hot topics:** +- New roadmap: https://github.com/users/ggerganov/projects/7 - Azure CI brainstorming: https://github.com/ggerganov/llama.cpp/discussions/1985 - p1 : LLM-based code completion engine at the edge : https://github.com/ggml-org/p1/discussions/1 -- Roadmap June 2023: https://github.com/ggerganov/llama.cpp/discussions/1729
    Table of Contents From cbebf61ca7584e9709265395f0127ae7fc0f1882 Mon Sep 17 00:00:00 2001 From: Howard Su Date: Mon, 26 Jun 2023 23:15:47 +0800 Subject: [PATCH 039/852] Fix assert when free invalid cuda pointer (#2005) Fix assert via initializing extra structure always. CUDA error 1 at C:\GPT\llama.cpp\ggml-cuda.cu:2536: invalid argument --- ggml-cuda.cu | 1 + 1 file changed, 1 insertion(+) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 010682edb..5e2fbc724 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -2553,6 +2553,7 @@ void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch) { tensor->backend = GGML_BACKEND_GPU; struct ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu; + memset(extra, 0, sizeof(*extra)); const bool inplace = (tensor->src0 != nullptr && tensor->src0->data == tensor->data) || tensor->op == GGML_OP_VIEW; From 6769e944c727c63612dcafbef52009d21ae00fff Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Mon, 26 Jun 2023 19:43:07 +0300 Subject: [PATCH 040/852] k-quants : support for super-block size of 64 (#2001) * k_quants: WIP super-blocks with 64 weights * k_quants: WIP super-blocks with 64 weights Q6_K scalar and AVX2 works * k_quants: WIP super-blocks with 64 weights Q4_K scalar and AVX2 works * k_quants: WIP super-blocks with 64 weights Q2_K scalar and AVX2 works. Q2_K is way too slow (it is actually slower than the scalar implementation) * k_quants: WIP super-blocks with 64 weights Q3_K scalar and AVX2 works. * k_quants: WIP super-blocks with 64 weights Q5_K scalar and AVX2 works, and with that all k_quants are done on AVX2 and scalar * k_quants: WIP super-blocks with 64 weights Q6_K working on CUDA. Cannot make it run quite as gast as with super-blocks with 256 weigths: 8% slower on 4080, 20% slower on the 1660 (but there we fit 1 less layer on the GPU because pf the larger model size), so some fraction of these 20% is due to that, * k_quants: WIP super-blocks with 64 weights Q4_K working on CUDA. ~10% slower on GTX-1660, 16% slower on 4080. * k_quants: WIP super-blocks with 64 weights Q2_K working on CUDA. ~3% slower on GTX-1660, 10% slower on 4080. * k_quants: WIP super-blocks with 64 weights Q3_K working on CUDA. * k_quants: WIP super-blocks with 64 weights Q5_K working on CUDA, and with this CUDA is done. * k_quants: WIP super-blocks with 64 weights Q6_K working on ARM_NEON * k_quants: WIP super-blocks with 64 weights Q4_K working on ARM_NEON, but quite a bit slower than 256 weights * k_quants: WIP super-blocks with 64 weights Q2_K working on ARM_NEON, but quite a bit slower than 256 weights * k_quants: WIP super-blocks with 64 weights Q3_K working on ARM_NEON, but quite a bit slower than 256 weights. * k_quants: WIP super-blocks with 64 weights Q5_K working on ARM_NEON, but quite a bit slower than 256 weights. With that, we have full support for ARM_NEON, although performance is not quite there. * k_quants: WIP super-blocks with 64 weights Slightly more efficient Q3_K and Q5_K * k_quants: WIP super-blocks with 64 weights Another small improvement for Q3_K and Q5_K on ARM_NEON * k_quants: WIP super-blocks with 64 weights Yet another speedup for Q5_K on ARM_NEON. We are now within 10% of the QK_K = 256 version. * k_quants: WIP super-blocks with 64 weights * We are able to pass preprocessor macros to the Metal compiler * Q6_K works and is actually slightly more efficient than the QK_K = 256 version (25.2 ms vs 25.8 ms) * k_quants: WIP super-blocks with 64 weights Q4_K works on Metal and is actually slightly faster than QK_K = 256 (21.95 ms vs 24.0 ms). * k_quants: WIP super-blocks with 64 weights Q2_K works on Metal and is very slightly faster than QK_K = 256 (23.8 ms vs 24.2 ms). * k_quants: WIP super-blocks with 64 weights Q3_K works on Metal and is slightly faster than QK_K = 256 (26.6 ms vs 28.3 ms). * k_quants: WIP super-blocks with 64 weights Q5_K works on Metal and is slightly faster than QK_K = 256 (23.7 ms vs 26.3 ms). * k_quants: call them _K, not _k, also on Metal * k_quants: correctly define QK_K in llama.cpp * Fixed bug in q4_K quantization added with the 64-block addition * Simplify via lambda * k_quants: swicth Q3_K to 4-bit scales when QK_K = 64 Otherwise there isn't much benefit from this quantization type. There is some very slight loss in accuracy, but we reduce size by ~7%. E.g., for OpenLLaMA-3B, Q3_K_S perplexity is 8.6131 with 8-bit scales and 8.6352 with 4-bit, while file size decreases from 1.53G to 1.44G. * k_quants: switch Q4_K to 4-bit scales when QK_K = 64 Here the loss in accuracy is greater than for Q3_K, but the Q4_K points still move further to the left on the perplexity vs size curve. * k_quants: forgot to add the Metal changes in last commit * k_quants: change Q5_K to be type 0 when QK_K = 64 Still needs AVX2 implementation * k_quants: AVX2 implementation for new 64-weight Q5_K * k_quants: 10% faster ARM_NEON Q5_K dot product * k_quants: fixed issue caused by merging with master --------- Co-authored-by: Iwan Kawrakow --- CMakeLists.txt | 14 +- Makefile | 9 +- ggml-cuda.cu | 370 ++++++++++++--- ggml-metal.m | 66 +-- ggml-metal.metal | 414 +++++++++++++---- k_quants.c | 1140 +++++++++++++++++++++++++++++++++++++++++++++- k_quants.h | 51 ++- llama.cpp | 17 +- 8 files changed, 1880 insertions(+), 201 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index cc7560a7a..ffda74a70 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -75,6 +75,7 @@ set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for option(LLAMA_CLBLAST "llama: use CLBlast" OFF) option(LLAMA_METAL "llama: use Metal" OFF) option(LLAMA_K_QUANTS "llama: use k-quants" ON) +option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF) option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE}) option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE}) @@ -225,6 +226,14 @@ if (LLAMA_BLAS) endif() endif() +if (LLAMA_K_QUANTS) + set(GGML_SOURCES_EXTRA ${GGML_SOURCES_EXTRA} k_quants.c k_quants.h) + add_compile_definitions(GGML_USE_K_QUANTS) + if (LLAMA_QKK_64) + add_compile_definitions(GGML_QKK_64) + endif() +endif() + if (LLAMA_CUBLAS) cmake_minimum_required(VERSION 3.17) @@ -289,11 +298,6 @@ if (LLAMA_METAL) ) endif() -if (LLAMA_K_QUANTS) - set(GGML_SOURCES_EXTRA ${GGML_SOURCES_EXTRA} k_quants.c k_quants.h) - add_compile_definitions(GGML_USE_K_QUANTS) -endif() - if (LLAMA_CLBLAST) find_package(CLBlast) if (CLBlast_FOUND) diff --git a/Makefile b/Makefile index 5dd676fad..bda11791d 100644 --- a/Makefile +++ b/Makefile @@ -43,8 +43,11 @@ endif # keep standard at C11 and C++11 # -Ofast tends to produce faster code, but may not be available for some compilers. -#OPT = -Ofast +ifdef LLAMA_FAST +OPT = -Ofast +else OPT = -O3 +endif CFLAGS = -I. $(OPT) -std=c11 -fPIC CXXFLAGS = -I. -I./examples $(OPT) -std=c++11 -fPIC LDFLAGS = @@ -131,6 +134,10 @@ ifndef LLAMA_NO_K_QUANTS CFLAGS += -DGGML_USE_K_QUANTS CXXFLAGS += -DGGML_USE_K_QUANTS OBJS += k_quants.o +ifdef LLAMA_QKK_64 + CFLAGS += -DGGML_QKK_64 + CXXFLAGS += -DGGML_QKK_64 +endif endif ifndef LLAMA_NO_ACCELERATE diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 5e2fbc724..c34e96abf 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -117,7 +117,13 @@ static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 blo //================================= k-quants +#ifdef GGML_QKK_64 +#define QK_K 64 +#define K_SCALE_SIZE 4 +#else #define QK_K 256 +#define K_SCALE_SIZE 12 +#endif typedef struct { uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits @@ -128,13 +134,25 @@ typedef struct { static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding"); typedef struct { - uint8_t hmask[QK_K/8]; - uint8_t qs[QK_K/4]; // nibbles / quants - uint8_t scales[3*QK_K/64]; - half d; + uint8_t hmask[QK_K/8]; // quants - high bit + uint8_t qs[QK_K/4]; // quants - low 2 bits +#ifdef GGML_QKK_64 + uint8_t scales[2]; // scales, quantized with 8 bits +#else + uint8_t scales[K_SCALE_SIZE]; // scales, quantized with 6 bits +#endif + half d; // super-block scale } block_q3_K; -static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + 11 * QK_K / 64, "wrong q3_K block size/padding"); +//static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + K_SCALE_SIZE, "wrong q3_K block size/padding"); +#ifdef GGML_QKK_64 +typedef struct { + half d[2]; // super-block scales/mins + uint8_t scales[2]; // 4-bit block scales/mins + uint8_t qs[QK_K/2]; // 4--bit quants +} block_q4_K; +static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding"); +#else typedef struct { half d; // super-block scale for quantized scales half dmin; // super-block scale for quantized mins @@ -142,15 +160,26 @@ typedef struct { uint8_t qs[QK_K/2]; // 4--bit quants } block_q4_K; static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2, "wrong q4_K block size/padding"); +#endif +#ifdef GGML_QKK_64 typedef struct { - half d; // super-block scale for quantized scales - half dmin; // super-block scale for quantized mins - uint8_t scales[3*QK_K/64]; // scales, quantized with 6 bits + half d; // super-block scale + int8_t scales[QK_K/16]; // block scales + uint8_t qh[QK_K/8]; // quants, high bit + uint8_t qs[QK_K/2]; // quants, low 4 bits +} block_q5_K; +static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding"); +#else +typedef struct { + half d; // super-block scale for quantized scales + half dmin; // super-block scale for quantized mins + uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits uint8_t qh[QK_K/8]; // quants, high bit uint8_t qs[QK_K/2]; // quants, low 4 bits } block_q5_K; -static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2 + QK_K/8, "wrong q5_K block size/padding"); +static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding"); +#endif typedef struct { uint8_t ql[QK_K/2]; // quants, lower 4 bits @@ -349,13 +378,14 @@ static __device__ __forceinline__ void dequantize_q8_0(const void * vx, const in static __global__ void dequantize_block_q2_K(const void * vx, float * yy) { const int i = blockIdx.x; + const block_q2_K * x = (const block_q2_K *) vx; + const int tid = threadIdx.x; +#if QK_K == 256 const int n = tid/32; const int l = tid - 32*n; const int is = 8*n + l/16; - const block_q2_K * x = (const block_q2_K *) vx; - const uint8_t q = x[i].qs[32*n + l]; float * y = yy + i*QK_K + 128*n; @@ -365,21 +395,32 @@ static __global__ void dequantize_block_q2_K(const void * vx, float * yy) { y[l+32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 2) & 3) - dmin * (x[i].scales[is+2] >> 4); y[l+64] = dall * (x[i].scales[is+4] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+4] >> 4); y[l+96] = dall * (x[i].scales[is+6] & 0xF) * ((q >> 6) & 3) - dmin * (x[i].scales[is+6] >> 4); +#else + const int is = tid/16; // 0 or 1 + const int il = tid%16; // 0...15 + const uint8_t q = x[i].qs[il] >> (2*is); + float * y = yy + i*QK_K + 16*is + il; + float dall = x[i].d; + float dmin = x[i].dmin; + y[ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4); + y[32] = dall * (x[i].scales[is+2] & 0xF) * ((q >> 4) & 3) - dmin * (x[i].scales[is+2] >> 4); +#endif } static __global__ void dequantize_block_q3_K(const void * vx, float * yy) { - int r = threadIdx.x/4; - int i = blockIdx.x; - int tid = r/2; - int is0 = r%2; - int l0 = 16*is0 + 4*(threadIdx.x%4); - int n = tid / 4; - int j = tid - 4*n; - + const int i = blockIdx.x; const block_q3_K * x = (const block_q3_K *) vx; +#if QK_K == 256 + const int r = threadIdx.x/4; + const int tid = r/2; + const int is0 = r%2; + const int l0 = 16*is0 + 4*(threadIdx.x%4); + const int n = tid / 4; + const int j = tid - 4*n; + uint8_t m = 1 << (4*n + j); int is = 8*n + 2*j + is0; int shift = 2*j; @@ -396,9 +437,31 @@ static __global__ void dequantize_block_q3_K(const void * vx, float * yy) { const uint8_t * hm = x[i].hmask; for (int l = l0; l < l0+4; ++l) y[l] = dl * ((int8_t)((q[l] >> shift) & 3) - ((hm[l] & m) ? 0 : 4)); +#else + const int tid = threadIdx.x; + const int is = tid/16; // 0 or 1 + const int il = tid%16; // 0...15 + const int im = il/8; // 0...1 + const int in = il%8; // 0...7 + + float * y = yy + i*QK_K + 16*is + il; + + const uint8_t q = x[i].qs[il] >> (2*is); + const uint8_t h = x[i].hmask[in] >> (2*is + im); + const float d = (float)x[i].d; + + if (is == 0) { + y[ 0] = d * ((x[i].scales[0] & 0xF) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4)); + y[32] = d * ((x[i].scales[1] & 0xF) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4)); + } else { + y[ 0] = d * ((x[i].scales[0] >> 4) - 8) * ((int8_t)((q >> 0) & 3) - ((h >> 0) & 1 ? 0 : 4)); + y[32] = d * ((x[i].scales[1] >> 4) - 8) * ((int8_t)((q >> 4) & 3) - ((h >> 4) & 1 ? 0 : 4)); + } +#endif } +#if QK_K == 256 static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) { if (j < 4) { d = q[j] & 63; m = q[j + 4] & 63; @@ -407,19 +470,14 @@ static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4); } } +#endif static __global__ void dequantize_block_q4_K(const void * vx, float * yy) { const block_q4_K * x = (const block_q4_K *) vx; const int i = blockIdx.x; - //// assume 64 threads - this is very slightly better than the one below - //const int tid = threadIdx.x; - //const int il = tid/16; - //const int ir = tid%16; - //const int is = 2*il; - //const int n = 2; - +#if QK_K == 256 // assume 32 threads const int tid = threadIdx.x; const int il = tid/8; @@ -443,6 +501,15 @@ static __global__ void dequantize_block_q4_K(const void * vx, float * yy) { y[l + 0] = d1 * (q[l] & 0xF) - m1; y[l +32] = d2 * (q[l] >> 4) - m2; } +#else + const int tid = threadIdx.x; + const uint8_t * q = x[i].qs; + float * y = yy + i*QK_K; + const float d = (float)x[i].d[0]; + const float m = (float)x[i].d[1]; + y[tid+ 0] = d * (x[i].scales[0] & 0xF) * (q[tid] & 0xF) - m * (x[i].scales[0] >> 4); + y[tid+32] = d * (x[i].scales[1] & 0xF) * (q[tid] >> 4) - m * (x[i].scales[1] >> 4); +#endif } static __global__ void dequantize_block_q5_K(const void * vx, float * yy) { @@ -450,6 +517,7 @@ static __global__ void dequantize_block_q5_K(const void * vx, float * yy) { const int i = blockIdx.x; +#if QK_K == 256 // assume 64 threads - this is very slightly better than the one below const int tid = threadIdx.x; const int il = tid/16; // il is in 0...3 @@ -476,12 +544,25 @@ static __global__ void dequantize_block_q5_K(const void * vx, float * yy) { hm <<= 1; y[32] = d2 * ((ql[ 0] >> 4) + (qh[ 0] & hm ? 16 : 0)) - m2; y[33] = d2 * ((ql[ 1] >> 4) + (qh[ 1] & hm ? 16 : 0)) - m2; +#else + const int tid = threadIdx.x; + const uint8_t q = x[i].qs[tid]; + const int im = tid/8; // 0...3 + const int in = tid%8; // 0...7 + const int is = tid/16; // 0 or 1 + const uint8_t h = x[i].qh[in] >> im; + const float d = x[i].d; + float * y = yy + i*QK_K + tid; + y[ 0] = d * x[i].scales[is+0] * ((q & 0xF) - ((h >> 0) & 1 ? 0 : 16)); + y[32] = d * x[i].scales[is+2] * ((q >> 4) - ((h >> 4) & 1 ? 0 : 16)); +#endif } static __global__ void dequantize_block_q6_K(const void * vx, float * yy) { const block_q6_K * x = (const block_q6_K *) vx; const int i = blockIdx.x; +#if QK_K == 256 // assume 64 threads - this is very slightly better than the one below const int tid = threadIdx.x; @@ -501,6 +582,24 @@ static __global__ void dequantize_block_q6_K(const void * vx, float * yy) { y[32] = d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32); y[64] = d * sc[4] * ((int8_t)((ql[ 0] >> 4) | (((qh >> 4) & 3) << 4)) - 32); y[96] = d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32); +#else + + // assume 32 threads + const int tid = threadIdx.x; + const int ip = tid/16; // 0 or 1 + const int il = tid - 16*ip; // 0...15 + + float * y = yy + i*QK_K + 16*ip + il; + + const float d = x[i].d; + + const uint8_t ql = x[i].ql[16*ip + il]; + const uint8_t qh = x[i].qh[il] >> (2*ip); + const int8_t * sc = x[i].scales; + + y[ 0] = d * sc[ip+0] * ((int8_t)((ql & 0xF) | (((qh >> 0) & 3) << 4)) - 32); + y[32] = d * sc[ip+2] * ((int8_t)((ql >> 4) | (((qh >> 4) & 3) << 4)) - 32); +#endif } static __global__ void dequantize_mul_mat_vec_q2_k(const void * vx, const float * yy, float * dst, const int ncols, int nrows) { @@ -515,6 +614,9 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * vx, const float const block_q2_K * x = (const block_q2_K *)vx + ib0; + float tmp = 0; // partial sum for thread in warp + +#if QK_K == 256 const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...15 const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1 @@ -528,8 +630,6 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * vx, const float const int s_offset = 8*im; const int y_offset = 128*im + l0; - float tmp = 0; // partial sum for thread in warp - uint32_t aux[4]; const uint8_t * d = (const uint8_t *)aux; const uint8_t * m = (const uint8_t *)(aux + 2); @@ -565,6 +665,39 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * vx, const float tmp += dall * sum1 - dmin * sum2; } +#else + const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7 + const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3 + const int offset = tid * K_QUANTS_PER_ITERATION; + + uint32_t uaux[2]; + const uint8_t * d = (const uint8_t *)uaux; + + for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { + + const float * y = yy + i * QK_K + offset; + const uint8_t * q = x[i].qs + offset; + const uint32_t * s = (const uint32_t *)x[i].scales; + + uaux[0] = s[0] & 0x0f0f0f0f; + uaux[1] = (s[0] >> 4) & 0x0f0f0f0f; + + const half2 * dh = (const half2 *)&x[i].d; + + const float2 dall = __half22float2(dh[0]); + + float sum1 = 0, sum2 = 0; + for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { + const uint8_t ql = q[l]; + sum1 += y[l+ 0] * d[0] * ((ql >> 0) & 3) + + y[l+16] * d[1] * ((ql >> 2) & 3) + + y[l+32] * d[2] * ((ql >> 4) & 3) + + y[l+48] * d[3] * ((ql >> 6) & 3); + sum2 += y[l+0] * d[4] + y[l+16] * d[5] + y[l+32] * d[6] + y[l+48] * d[7]; + } + tmp += dall.x * sum1 - dall.y * sum2; + } +#endif // sum up partial sums and write back result __syncthreads(); @@ -573,16 +706,13 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * vx, const float tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); } - if (tid == 0) { + if (threadIdx.x == 0) { dst[row] = tmp; } } static __global__ void dequantize_mul_mat_vec_q3_k(const void * vx, const float * yy, float * dst, const int ncols, int nrows) { - const uint16_t kmask1 = 0x0303; - const uint16_t kmask2 = 0x0f0f; - const int row = blockIdx.y*blockDim.y + threadIdx.y; if (row > nrows) return; @@ -591,6 +721,13 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * vx, const float const block_q3_K * x = (const block_q3_K *)vx + ib0; + float tmp = 0; // partial sum for thread in warp + +#if QK_K == 256 + + const uint16_t kmask1 = 0x0303; + const uint16_t kmask2 = 0x0f0f; + const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1 @@ -610,8 +747,6 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * vx, const float const uint16_t s_shift = 4*im; - float tmp = 0; // partial sum for thread in warp - for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { const float * y = yy + i * QK_K + y_offset; @@ -640,6 +775,34 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * vx, const float tmp += d * sum; } +#else + + const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7 + const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0....1 or 0...3 + const int offset = tid * K_QUANTS_PER_ITERATION; // 0...15 or 0...14 + const int in = offset/8; // 0 or 1 + const int im = offset%8; // 0...7 + + for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { + + const float * y = yy + i * QK_K + offset; + const uint8_t * q = x[i].qs + offset; + const uint8_t * s = x[i].scales; + + const float dall = (float)x[i].d; + + float sum = 0; + for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { + const uint8_t hl = x[i].hmask[im+l] >> in; + const uint8_t ql = q[l]; + sum += y[l+ 0] * dall * ((s[0] & 0xF) - 8) * ((int8_t)((ql >> 0) & 3) - ((hl >> 0) & 1 ? 0 : 4)) + + y[l+16] * dall * ((s[0] >> 4) - 8) * ((int8_t)((ql >> 2) & 3) - ((hl >> 2) & 1 ? 0 : 4)) + + y[l+32] * dall * ((s[1] & 0xF) - 8) * ((int8_t)((ql >> 4) & 3) - ((hl >> 4) & 1 ? 0 : 4)) + + y[l+48] * dall * ((s[1] >> 4) - 8) * ((int8_t)((ql >> 6) & 3) - ((hl >> 6) & 1 ? 0 : 4)); + } + tmp += sum; + } +#endif // sum up partial sums and write back result __syncthreads(); @@ -648,22 +811,25 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * vx, const float tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); } - if (tid == 0) { + if (threadIdx.x == 0) { dst[row] = tmp; } } static __global__ void dequantize_mul_mat_vec_q4_k(const void * vx, const float * yy, float * dst, const int ncols, int nrows) { - const uint16_t kmask1 = 0x3f3f; - const uint16_t kmask2 = 0x0f0f; - const uint16_t kmask3 = 0xc0c0; - const int row = blockIdx.y*blockDim.y + threadIdx.y; if (row > nrows) return; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; + const block_q4_K * x = (const block_q4_K *)vx + ib0; + +#if QK_K == 256 + const uint16_t kmask1 = 0x3f3f; + const uint16_t kmask2 = 0x0f0f; + const uint16_t kmask3 = 0xc0c0; + const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1 @@ -683,8 +849,6 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * vx, const float uint16_t aux[4]; const uint8_t * sc = (const uint8_t *)aux; - const block_q4_K * x = (const block_q4_K *)vx + ib0; - float tmp = 0; // partial sum for thread in warp for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { @@ -713,6 +877,36 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * vx, const float tmp += dall * (s.x * sc[0] + s.y * sc[1] + s.z * sc[4] + s.w * sc[5]) - dmin * smin; } +#else + const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 + const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); + + const int step = tid * K_QUANTS_PER_ITERATION; + + uint16_t aux16[2]; + const uint8_t * s = (const uint8_t *)aux16; + + float tmp = 0; + + for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { + const uint8_t * q = x[i].qs + step; + const float * y = yy + i*QK_K + step; + const uint16_t * a = (const uint16_t *)x[i].scales; + aux16[0] = a[0] & 0x0f0f; + aux16[1] = (a[0] >> 4) & 0x0f0f; + const float d = (float)x[i].d[0]; + const float m = (float)x[i].d[1]; + float sum = 0.f; + for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) { + sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2]) + + y[j+16] * (d * s[0] * (q[j+16] & 0xF) - m * s[2]) + + y[j+32] * (d * s[1] * (q[j+ 0] >> 4) - m * s[3]) + + y[j+48] * (d * s[1] * (q[j+16] >> 4) - m * s[3]); + } + tmp += sum; + } + +#endif // sum up partial sums and write back result __syncthreads(); @@ -728,15 +922,19 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * vx, const float static __global__ void dequantize_mul_mat_vec_q5_k(const void * vx, const float * yy, float * dst, const int ncols) { - const uint16_t kmask1 = 0x3f3f; - const uint16_t kmask2 = 0x0f0f; - const uint16_t kmask3 = 0xc0c0; - - //const int row = blockIdx.x*blockDim.y + threadIdx.y; const int row = blockIdx.x; const int num_blocks_per_row = ncols / QK_K; const int ib0 = row*num_blocks_per_row; + const block_q5_K * x = (const block_q5_K *)vx + ib0; + + float tmp = 0; // partial sum for thread in warp + +#if QK_K == 256 + const uint16_t kmask1 = 0x3f3f; + const uint16_t kmask2 = 0x0f0f; + const uint16_t kmask3 = 0xc0c0; + const int tid = threadIdx.x/2; // 0...15 const int ix = threadIdx.x%2; @@ -757,10 +955,6 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * vx, const float uint16_t aux[4]; const uint8_t * sc = (const uint8_t *)aux; - const block_q5_K * x = (const block_q5_K *)vx + ib0; - - float tmp = 0; // partial sum for thread in warp - for (int i = ix; i < num_blocks_per_row; i += 2) { const uint8_t * ql1 = x[i].qs + q_offset; @@ -793,9 +987,32 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * vx, const float + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7]; } tmp += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin; - } +#else + const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...15 + const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); + const int step = tid * K_QUANTS_PER_ITERATION; + const int im = step/8; + const int in = step%8; + + for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { + const uint8_t * q = x[i].qs + step; + const int8_t * s = x[i].scales; + const float * y = yy + i*QK_K + step; + const float d = x[i].d; + float sum = 0.f; + for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) { + const uint8_t h = x[i].qh[in+j] >> im; + sum += y[j+ 0] * d * s[0] * ((q[j+ 0] & 0xF) - ((h >> 0) & 1 ? 0 : 16)) + + y[j+16] * d * s[1] * ((q[j+16] & 0xF) - ((h >> 2) & 1 ? 0 : 16)) + + y[j+32] * d * s[2] * ((q[j+ 0] >> 4) - ((h >> 4) & 1 ? 0 : 16)) + + y[j+48] * d * s[3] * ((q[j+16] >> 4) - ((h >> 6) & 1 ? 0 : 16)); + } + tmp += sum; + } +#endif + // sum up partial sums and write back result __syncthreads(); #pragma unroll @@ -803,7 +1020,7 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * vx, const float tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); } - if (tid == 0) { + if (threadIdx.x == 0) { dst[row] = tmp; } } @@ -820,6 +1037,8 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * vx, const float const block_q6_K * x = (const block_q6_K *)vx + ib0; +#if QK_K == 256 + const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 @@ -874,6 +1093,37 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * vx, const float } +#else + + const int tid = threadIdx.x/(2*K_QUANTS_PER_ITERATION); // 0...7 + const int ix = threadIdx.x%(2*K_QUANTS_PER_ITERATION); // 0...3 + + const int step = tid * K_QUANTS_PER_ITERATION; + + float tmp = 0; // partial sum for thread in warp + + for (int i = ix; i < num_blocks_per_row; i += 2*K_QUANTS_PER_ITERATION) { + + const float * y = yy + i * QK_K + step; + const uint8_t * ql = x[i].ql + step; + const uint8_t * qh = x[i].qh + step; + const int8_t * s = x[i].scales; + + const float d = x[i+0].d; + + float sum = 0; + for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) { + sum += y[j+ 0] * s[0] * d * ((int8_t)((ql[j+ 0] & 0xF) | ((qh[j] & 0x03) << 4)) - 32) + + y[j+16] * s[1] * d * ((int8_t)((ql[j+16] & 0xF) | ((qh[j] & 0x0c) << 2)) - 32) + + y[j+32] * s[2] * d * ((int8_t)((ql[j+ 0] >> 4) | ((qh[j] & 0x30) >> 0)) - 32) + + y[j+48] * s[3] * d * ((int8_t)((ql[j+16] >> 4) | ((qh[j] & 0xc0) >> 2)) - 32); + } + tmp += sum; + + } + +#endif + // sum up partial sums and write back result __syncthreads(); #pragma unroll @@ -1252,12 +1502,20 @@ static void dequantize_row_q8_0_cuda(const void * vx, float * y, const int k, cu static void dequantize_row_q2_K_cuda(const void * vx, float * y, const int k, cudaStream_t stream) { const int nb = k / QK_K; +#if QK_K == 256 dequantize_block_q2_K<<>>(vx, y); +#else + dequantize_block_q2_K<<>>(vx, y); +#endif } static void dequantize_row_q3_K_cuda(const void * vx, float * y, const int k, cudaStream_t stream) { const int nb = k / QK_K; +#if QK_K == 256 dequantize_block_q3_K<<>>(vx, y); +#else + dequantize_block_q3_K<<>>(vx, y); +#endif } static void dequantize_row_q4_K_cuda(const void * vx, float * y, const int k, cudaStream_t stream) { @@ -1267,12 +1525,20 @@ static void dequantize_row_q4_K_cuda(const void * vx, float * y, const int k, cu static void dequantize_row_q5_K_cuda(const void * vx, float * y, const int k, cudaStream_t stream) { const int nb = k / QK_K; +#if QK_K == 256 dequantize_block_q5_K<<>>(vx, y); +#else + dequantize_block_q5_K<<>>(vx, y); +#endif } static void dequantize_row_q6_K_cuda(const void * vx, float * y, const int k, cudaStream_t stream) { const int nb = k / QK_K; +#if QK_K == 256 dequantize_block_q6_K<<>>(vx, y); +#else + dequantize_block_q6_K<<>>(vx, y); +#endif } static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { diff --git a/ggml-metal.m b/ggml-metal.m index a7e104dc7..7551231b9 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -51,21 +51,21 @@ struct ggml_metal_context { GGML_METAL_DECL_KERNEL(get_rows_f16); GGML_METAL_DECL_KERNEL(get_rows_q4_0); GGML_METAL_DECL_KERNEL(get_rows_q4_1); - GGML_METAL_DECL_KERNEL(get_rows_q2_k); - GGML_METAL_DECL_KERNEL(get_rows_q3_k); - GGML_METAL_DECL_KERNEL(get_rows_q4_k); - GGML_METAL_DECL_KERNEL(get_rows_q5_k); - GGML_METAL_DECL_KERNEL(get_rows_q6_k); + GGML_METAL_DECL_KERNEL(get_rows_q2_K); + GGML_METAL_DECL_KERNEL(get_rows_q3_K); + GGML_METAL_DECL_KERNEL(get_rows_q4_K); + GGML_METAL_DECL_KERNEL(get_rows_q5_K); + GGML_METAL_DECL_KERNEL(get_rows_q6_K); GGML_METAL_DECL_KERNEL(rms_norm); GGML_METAL_DECL_KERNEL(norm); GGML_METAL_DECL_KERNEL(mul_mat_f16_f32); GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32); GGML_METAL_DECL_KERNEL(mul_mat_q4_1_f32); - GGML_METAL_DECL_KERNEL(mul_mat_q2_k_f32); - GGML_METAL_DECL_KERNEL(mul_mat_q3_k_f32); - GGML_METAL_DECL_KERNEL(mul_mat_q4_k_f32); - GGML_METAL_DECL_KERNEL(mul_mat_q5_k_f32); - GGML_METAL_DECL_KERNEL(mul_mat_q6_k_f32); + GGML_METAL_DECL_KERNEL(mul_mat_q2_K_f32); + GGML_METAL_DECL_KERNEL(mul_mat_q3_K_f32); + GGML_METAL_DECL_KERNEL(mul_mat_q4_K_f32); + GGML_METAL_DECL_KERNEL(mul_mat_q5_K_f32); + GGML_METAL_DECL_KERNEL(mul_mat_q6_K_f32); GGML_METAL_DECL_KERNEL(rope); GGML_METAL_DECL_KERNEL(alibi_f32); GGML_METAL_DECL_KERNEL(cpy_f32_f16); @@ -132,7 +132,13 @@ struct ggml_metal_context * ggml_metal_init(void) { exit(1); } +#ifdef GGML_QKK_64 + MTLCompileOptions* options = [MTLCompileOptions new]; + options.preprocessorMacros = @{ @"QK_K" : @(64) }; + ctx->library = [ctx->device newLibraryWithSource:src options:options error:&error]; +#else ctx->library = [ctx->device newLibraryWithSource:src options:nil error:&error]; +#endif if (error) { fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]); exit(1); @@ -159,21 +165,21 @@ struct ggml_metal_context * ggml_metal_init(void) { GGML_METAL_ADD_KERNEL(get_rows_f16); GGML_METAL_ADD_KERNEL(get_rows_q4_0); GGML_METAL_ADD_KERNEL(get_rows_q4_1); - GGML_METAL_ADD_KERNEL(get_rows_q2_k); - GGML_METAL_ADD_KERNEL(get_rows_q3_k); - GGML_METAL_ADD_KERNEL(get_rows_q4_k); - GGML_METAL_ADD_KERNEL(get_rows_q5_k); - GGML_METAL_ADD_KERNEL(get_rows_q6_k); + GGML_METAL_ADD_KERNEL(get_rows_q2_K); + GGML_METAL_ADD_KERNEL(get_rows_q3_K); + GGML_METAL_ADD_KERNEL(get_rows_q4_K); + GGML_METAL_ADD_KERNEL(get_rows_q5_K); + GGML_METAL_ADD_KERNEL(get_rows_q6_K); GGML_METAL_ADD_KERNEL(rms_norm); GGML_METAL_ADD_KERNEL(norm); GGML_METAL_ADD_KERNEL(mul_mat_f16_f32); GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32); GGML_METAL_ADD_KERNEL(mul_mat_q4_1_f32); - GGML_METAL_ADD_KERNEL(mul_mat_q2_k_f32); - GGML_METAL_ADD_KERNEL(mul_mat_q3_k_f32); - GGML_METAL_ADD_KERNEL(mul_mat_q4_k_f32); - GGML_METAL_ADD_KERNEL(mul_mat_q5_k_f32); - GGML_METAL_ADD_KERNEL(mul_mat_q6_k_f32); + GGML_METAL_ADD_KERNEL(mul_mat_q2_K_f32); + GGML_METAL_ADD_KERNEL(mul_mat_q3_K_f32); + GGML_METAL_ADD_KERNEL(mul_mat_q4_K_f32); + GGML_METAL_ADD_KERNEL(mul_mat_q5_K_f32); + GGML_METAL_ADD_KERNEL(mul_mat_q6_K_f32); GGML_METAL_ADD_KERNEL(rope); GGML_METAL_ADD_KERNEL(alibi_f32); GGML_METAL_ADD_KERNEL(cpy_f32_f16); @@ -662,7 +668,7 @@ void ggml_metal_graph_compute( nth0 = 4; nth1 = 16; - [encoder setComputePipelineState:ctx->pipeline_mul_mat_q2_k_f32]; + [encoder setComputePipelineState:ctx->pipeline_mul_mat_q2_K_f32]; } break; case GGML_TYPE_Q3_K: { @@ -671,7 +677,7 @@ void ggml_metal_graph_compute( nth0 = 4; nth1 = 16; - [encoder setComputePipelineState:ctx->pipeline_mul_mat_q3_k_f32]; + [encoder setComputePipelineState:ctx->pipeline_mul_mat_q3_K_f32]; } break; case GGML_TYPE_Q4_K: { @@ -680,7 +686,7 @@ void ggml_metal_graph_compute( nth0 = 4; nth1 = 16; - [encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_k_f32]; + [encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_K_f32]; } break; case GGML_TYPE_Q5_K: { @@ -689,7 +695,7 @@ void ggml_metal_graph_compute( nth0 = 4; nth1 = 16; - [encoder setComputePipelineState:ctx->pipeline_mul_mat_q5_k_f32]; + [encoder setComputePipelineState:ctx->pipeline_mul_mat_q5_K_f32]; } break; case GGML_TYPE_Q6_K: { @@ -698,7 +704,7 @@ void ggml_metal_graph_compute( nth0 = 4; nth1 = 16; - [encoder setComputePipelineState:ctx->pipeline_mul_mat_q6_k_f32]; + [encoder setComputePipelineState:ctx->pipeline_mul_mat_q6_K_f32]; } break; default: { @@ -750,11 +756,11 @@ void ggml_metal_graph_compute( case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_get_rows_f16]; break; case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break; case GGML_TYPE_Q4_1: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_1]; break; - case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q2_k]; break; - case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q3_k]; break; - case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_k]; break; - case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_k]; break; - case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q6_k]; break; + case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q2_K]; break; + case GGML_TYPE_Q3_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q3_K]; break; + case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_K]; break; + case GGML_TYPE_Q5_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q5_K]; break; + case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q6_K]; break; default: GGML_ASSERT(false && "not implemented"); } diff --git a/ggml-metal.metal b/ggml-metal.metal index d1e49222d..e62fe6842 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -428,7 +428,7 @@ kernel void kernel_mul_mat_q4_0_f32( } threadgroup_barrier(mem_flags::mem_threadgroup); if (ith == 0) { - for (uint i = 16; i < nth; i += 16) sum[0] += sum[i]; + for (int i = 16; i < nth; i += 16) sum[0] += sum[i]; dst[r1*ne0 + r0] = sum[0]; } } @@ -497,7 +497,7 @@ kernel void kernel_mul_mat_q4_1_f32( } threadgroup_barrier(mem_flags::mem_threadgroup); if (ith == 0) { - for (int i = 16; i < nth; i += 16) sum[0] += sum[i]; + for (uint i = 16; i < nth; i += 16) sum[0] += sum[i]; dst[r1*ne0 + r0] = sum[0]; } } @@ -775,47 +775,76 @@ kernel void kernel_cpy_f32_f32( //============================================ k-quants ====================================================== +#ifndef QK_K #define QK_K 256 +#else +static_assert(QK_K == 256 || QK_K == 64, "QK_K must be 256 or 64"); +#endif + +#if QK_K == 256 +#define K_SCALE_SIZE 12 +#else +#define K_SCALE_SIZE 4 +#endif typedef struct { uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits uint8_t qs[QK_K/4]; // quants half d; // super-block scale for quantized scales half dmin; // super-block scale for quantized mins -} block_q2_k; +} block_q2_K; // 84 bytes / block typedef struct { uint8_t hmask[QK_K/8]; // quants - high bit uint8_t qs[QK_K/4]; // quants - low 2 bits - uint8_t scales[3*QK_K/64]; // scales, quantized with 6 bits - half d; // super-block scale -} block_q3_k; -// 110 bytes / block +#if QK_K == 64 + uint8_t scales[2]; +#else + uint8_t scales[K_SCALE_SIZE]; // scales, quantized with 6 bits +#endif + half d; // super-block scale +} block_q3_K; +#if QK_K == 64 +typedef struct { + half d[2]; // super-block scales/mins + uint8_t scales[2]; + uint8_t qs[QK_K/2]; // 4-bit quants +} block_q4_K; +#else typedef struct { half d; // super-block scale for quantized scales half dmin; // super-block scale for quantized mins - uint8_t scales[3*QK_K/64]; // scales and mins, quantized with 6 bits + uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits uint8_t qs[QK_K/2]; // 4--bit quants -} block_q4_k; -// 144 bytes / block +} block_q4_K; +#endif +#if QK_K == 64 +typedef struct { + half d; // super-block scales/mins + int8_t scales[QK_K/16]; // 8-bit block scales + uint8_t qh[QK_K/8]; // quants, high bit + uint8_t qs[QK_K/2]; // quants, low 4 bits +} block_q5_K; +#else typedef struct { half d; // super-block scale for quantized scales half dmin; // super-block scale for quantized mins uint8_t scales[3*QK_K/64]; // scales and mins, quantized with 6 bits uint8_t qh[QK_K/8]; // quants, high bit uint8_t qs[QK_K/2]; // quants, low 4 bits -} block_q5_k; +} block_q5_K; // 176 bytes / block +#endif typedef struct { uint8_t ql[QK_K/2]; // quants, lower 4 bits uint8_t qh[QK_K/4]; // quants, upper 2 bits int8_t scales[QK_K/16]; // scales, quantized with 8 bits half d; // super-block scale -} block_q6_k; +} block_q6_K; // 210 bytes / block static inline uchar4 get_scale_min_k4(int j, device const uint8_t * q) { @@ -836,7 +865,7 @@ static inline uchar4 get_scale_min_k4(int j, device const uint8_t * q) { //========================================== dequantization ============================= -static void dequantize_row_q2_k(device const block_q2_k * x, device float * y, int k) { +static void dequantize_row_q2_K(device const block_q2_K * x, device float * y, int k) { assert(k % QK_K == 0); const int nb = k / QK_K; @@ -847,6 +876,7 @@ static void dequantize_row_q2_k(device const block_q2_k * x, device float * y, i device const uint8_t * q = x[i].qs; +#if QK_K == 256 int is = 0; float dl, ml; for (int n = 0; n < QK_K; n += 128) { @@ -865,14 +895,29 @@ static void dequantize_row_q2_k(device const block_q2_k * x, device float * y, i } q += 32; } +#else + float dl1 = d * (x[i].scales[0] & 0xF), ml1 = min * (x[i].scales[0] >> 4); + float dl2 = d * (x[i].scales[1] & 0xF), ml2 = min * (x[i].scales[1] >> 4); + float dl3 = d * (x[i].scales[2] & 0xF), ml3 = min * (x[i].scales[2] >> 4); + float dl4 = d * (x[i].scales[3] & 0xF), ml4 = min * (x[i].scales[3] >> 4); + for (int l = 0; l < 16; ++l) { + y[l+ 0] = dl1 * ((q[l] >> 0) & 3) - ml1; + y[l+16] = dl2 * ((q[l] >> 2) & 3) - ml2; + y[l+32] = dl3 * ((q[l] >> 4) & 3) - ml3; + y[l+48] = dl4 * ((q[l] >> 6) & 3) - ml4; + } + y += QK_K; +#endif } } -static void dequantize_row_q3_k(device const block_q3_k * x, device float * y, int k) { +static void dequantize_row_q3_K(device const block_q3_K * x, device float * y, int k) { assert(k % QK_K == 0); const int nb = k / QK_K; +#if QK_K == 256 + const uint16_t kmask1 = 0x0303; const uint16_t kmask2 = 0x0f0f; @@ -918,22 +963,49 @@ static void dequantize_row_q3_k(device const block_q3_k * x, device float * y, i } q += 32; } - } +#else + for (int i = 0; i < nb; i++) { + + const float d_all = (float)(x[i].d); + + device const uint8_t * q = x[i].qs; + device const uint8_t * hm = x[i].hmask; + + const float d1 = d_all * ((x[i].scales[0] & 0xF) - 8); + const float d2 = d_all * ((x[i].scales[0] >> 4) - 8); + const float d3 = d_all * ((x[i].scales[1] & 0xF) - 8); + const float d4 = d_all * ((x[i].scales[1] >> 4) - 8); + + for (int l = 0; l < 8; ++l) { + uint8_t h = hm[l]; + y[l+ 0] = d1 * ((int8_t)((q[l+0] >> 0) & 3) - ((h & 0x01) ? 0 : 4)); + y[l+ 8] = d1 * ((int8_t)((q[l+8] >> 0) & 3) - ((h & 0x02) ? 0 : 4)); + y[l+16] = d2 * ((int8_t)((q[l+0] >> 2) & 3) - ((h & 0x04) ? 0 : 4)); + y[l+24] = d2 * ((int8_t)((q[l+8] >> 2) & 3) - ((h & 0x08) ? 0 : 4)); + y[l+32] = d3 * ((int8_t)((q[l+0] >> 4) & 3) - ((h & 0x10) ? 0 : 4)); + y[l+40] = d3 * ((int8_t)((q[l+8] >> 4) & 3) - ((h & 0x20) ? 0 : 4)); + y[l+48] = d4 * ((int8_t)((q[l+0] >> 6) & 3) - ((h & 0x40) ? 0 : 4)); + y[l+56] = d4 * ((int8_t)((q[l+8] >> 6) & 3) - ((h & 0x80) ? 0 : 4)); + } + y += QK_K; + } +#endif } -static void dequantize_row_q4_k(device const block_q4_k * x, device float * y, int k) { +static void dequantize_row_q4_K(device const block_q4_K * x, device float * y, int k) { assert(k % QK_K == 0); const int nb = k / QK_K; - for (int i = 0; i < nb; i++) { + device const uint8_t * q = x[i].qs; + +#if QK_K == 256 const float d = x[i].d; const float min = x[i].dmin; - device const uint8_t * q = x[i].qs; device const uint8_t * scales = x[i].scales; int is = 0; @@ -945,14 +1017,29 @@ static void dequantize_row_q4_k(device const block_q4_k * x, device float * y, i for (int l = 0; l < 32; ++l) *y++ = d2 * (q[l] >> 4) - m2; q += 32; is += 2; } +#else + device const uint8_t * s = x[i].scales; + device const half2 * dh = (device const half2 *)x[i].d; + const float2 d = (float2)dh[0]; + const float d1 = d[0] * (s[0] & 0xF); + const float d2 = d[0] * (s[1] & 0xF); + const float m1 = d[1] * (s[0] >> 4); + const float m2 = d[1] * (s[1] >> 4); + for (int l = 0; l < 32; ++l) { + y[l+ 0] = d1 * (q[l] & 0xF) - m1; + y[l+32] = d2 * (q[l] >> 4) - m2; + } + y += QK_K; +#endif } } -static void dequantize_row_q5_k(device const block_q5_k * x, device float * y, int k) { +static void dequantize_row_q5_K(device const block_q5_K * x, device float * y, int k) { assert(k % QK_K == 0); const int nb = k / QK_K; +#if QK_K == 256 for (int i = 0; i < nb; i++) { const float d = (float)(x[i].d); @@ -973,10 +1060,32 @@ static void dequantize_row_q5_k(device const block_q5_k * x, device float * y, i u1 <<= 2; u2 <<= 2; } } +#else + for (int i = 0; i < nb; i++) { + + const float d = (float)x[i].d; + + device const uint8_t * ql = x[i].qs; + device const uint8_t * qh = x[i].qh; + device const int8_t * sc = x[i].scales; + + for (int l = 0; l < 8; ++l) { + y[l+ 0] = d * sc[0] * ((ql[l+ 0] & 0xF) - (qh[l] & 0x01 ? 0 : 16)); + y[l+ 8] = d * sc[0] * ((ql[l+ 8] & 0xF) - (qh[l] & 0x02 ? 0 : 16)); + y[l+16] = d * sc[1] * ((ql[l+16] & 0xF) - (qh[l] & 0x04 ? 0 : 16)); + y[l+24] = d * sc[1] * ((ql[l+24] & 0xF) - (qh[l] & 0x08 ? 0 : 16)); + y[l+32] = d * sc[2] * ((ql[l+ 0] >> 4) - (qh[l] & 0x10 ? 0 : 16)); + y[l+40] = d * sc[2] * ((ql[l+ 8] >> 4) - (qh[l] & 0x20 ? 0 : 16)); + y[l+48] = d * sc[3] * ((ql[l+16] >> 4) - (qh[l] & 0x40 ? 0 : 16)); + y[l+56] = d * sc[3] * ((ql[l+24] >> 4) - (qh[l] & 0x80 ? 0 : 16)); + } + y += QK_K; + } +#endif } -static void dequantize_row_q6_k(device const block_q6_k * x, device float * y, int k) { +static void dequantize_row_q6_K(device const block_q6_K * x, device float * y, int k) { assert(k % QK_K == 0); const int nb = k / QK_K; @@ -988,6 +1097,7 @@ static void dequantize_row_q6_k(device const block_q6_k * x, device float * y, i const float d = x[i].d; +#if QK_K == 256 for (int n = 0; n < QK_K; n += 128) { for (int l = 0; l < 32; ++l) { int is = l/16; @@ -1005,10 +1115,23 @@ static void dequantize_row_q6_k(device const block_q6_k * x, device float * y, i qh += 32; sc += 8; } +#else + for (int l = 0; l < 16; ++l) { + const int8_t q1 = (int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32; + const int8_t q2 = (int8_t)((ql[l+16] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32; + const int8_t q3 = (int8_t)((ql[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32; + const int8_t q4 = (int8_t)((ql[l+16] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32; + y[l+ 0] = d * sc[0] * q1; + y[l+16] = d * sc[1] * q2; + y[l+32] = d * sc[2] * q3; + y[l+48] = d * sc[3] * q4; + } + y += 64; +#endif } } -kernel void kernel_get_rows_q2_k( +kernel void kernel_get_rows_q2_K( device const void * src0, device const int * src1, device float * dst, @@ -1019,12 +1142,12 @@ kernel void kernel_get_rows_q2_k( const int i = tpig; const int r = ((device int32_t *) src1)[i]; - dequantize_row_q2_k( - (device const block_q2_k *) ((device char *) src0 + r*nb01), + dequantize_row_q2_K( + (device const block_q2_K *) ((device char *) src0 + r*nb01), (device float *) ((device char *) dst + i*nb1), ne00); } -kernel void kernel_get_rows_q3_k( +kernel void kernel_get_rows_q3_K( device const void * src0, device const int * src1, device float * dst, @@ -1035,12 +1158,12 @@ kernel void kernel_get_rows_q3_k( const int i = tpig; const int r = ((device int32_t *) src1)[i]; - dequantize_row_q3_k( - (device const block_q3_k *) ((device char *) src0 + r*nb01), + dequantize_row_q3_K( + (device const block_q3_K *) ((device char *) src0 + r*nb01), (device float *) ((device char *) dst + i*nb1), ne00); } -kernel void kernel_get_rows_q4_k( +kernel void kernel_get_rows_q4_K( device const void * src0, device const int * src1, device float * dst, @@ -1051,12 +1174,12 @@ kernel void kernel_get_rows_q4_k( const int i = tpig; const int r = ((device int32_t *) src1)[i]; - dequantize_row_q4_k( - (device const block_q4_k *) ((device char *) src0 + r*nb01), + dequantize_row_q4_K( + (device const block_q4_K *) ((device char *) src0 + r*nb01), (device float *) ((device char *) dst + i*nb1), ne00); } -kernel void kernel_get_rows_q5_k( +kernel void kernel_get_rows_q5_K( device const void * src0, device const int * src1, device float * dst, @@ -1067,12 +1190,12 @@ kernel void kernel_get_rows_q5_k( const int i = tpig; const int r = ((device int32_t *) src1)[i]; - dequantize_row_q5_k( - (device const block_q5_k *) ((device char *) src0 + r*nb01), + dequantize_row_q5_K( + (device const block_q5_K *) ((device char *) src0 + r*nb01), (device float *) ((device char *) dst + i*nb1), ne00); } -kernel void kernel_get_rows_q6_k( +kernel void kernel_get_rows_q6_K( device const void * src0, device const int * src1, device float * dst, @@ -1083,14 +1206,14 @@ kernel void kernel_get_rows_q6_k( const int i = tpig; const int r = ((device int32_t *) src1)[i]; - dequantize_row_q6_k( - (device const block_q6_k *) ((device char *) src0 + r*nb01), + dequantize_row_q6_K( + (device const block_q6_K *) ((device char *) src0 + r*nb01), (device float *) ((device char *) dst + i*nb1), ne00); } //====================================== dot products ========================= -kernel void kernel_mul_mat_q2_k_f32( +kernel void kernel_mul_mat_q2_K_f32( device const void * src0, device const float * src1, device float * dst, @@ -1107,12 +1230,15 @@ kernel void kernel_mul_mat_q2_k_f32( const int64_t r0 = tgpig.x; const int64_t r1 = tgpig.y; - device const block_q2_k * x = (device const block_q2_k *) src0 + r0*nb; + device const block_q2_K * x = (device const block_q2_K *) src0 + r0*nb; device const float * yy = (device const float *) src1 + r1*ne10; const int nth = tptg.x*tptg.y; const int ith = tptg.y*tpitg.x + tpitg.y; + float sumf = 0; + +#if QK_K == 256 const int tid = tpitg.y; // 0...16 const int il = tid/4; // 0...3 const int ir = tid%4; // 0...3 @@ -1125,9 +1251,6 @@ kernel void kernel_mul_mat_q2_k_f32( const int y_offset = 64*il + n*ir; const int q_offset = 32*ip + n*ir; - sum[ith] = 0.0f; - - float sumf = 0; for (int i = tpitg.x; i < nb; i += tptg.x) { device const uint8_t * q = x[i].qs + q_offset; @@ -1140,7 +1263,6 @@ kernel void kernel_mul_mat_q2_k_f32( device const float * y = yy + i*QK_K + y_offset; - //float4 s = {0.f, 0.f, 0.f, 0.f}; float2 s = {0.f, 0.f}; float smin = 0; for (int l = 0; l < n; ++l) { @@ -1155,25 +1277,38 @@ kernel void kernel_mul_mat_q2_k_f32( sumf += dall * (s[0] * d1 + s[1] * d2) - dmin * smin; } +#else + const int il = 4 * tpitg.x; + + uint32_t aux[2]; + thread const uint8_t * d = (thread const uint8_t *)aux; + thread const uint8_t * m = (thread const uint8_t *)aux + 4; + + for (int i = tpitg.y; i < nb; i += tptg.y) { + + device const uint8_t * q = x[i].qs + il; + device const float * y = yy + i*QK_K + il; + + const float dall = (float)x[i].d; + const float dmin = (float)x[i].dmin; + + device const uint32_t * a = (device const uint32_t *)x[i].scales; + aux[0] = a[0] & 0x0f0f0f0f; + aux[1] = (a[0] >> 4) & 0x0f0f0f0f; + + for (int l = 0; l < 4; ++l) { + sumf += y[l+ 0] * (dall * d[0] * ((q[l] >> 0) & 3) - dmin * m[0]) + + y[l+16] * (dall * d[1] * ((q[l] >> 2) & 3) - dmin * m[1]) + + y[l+32] * (dall * d[2] * ((q[l] >> 4) & 3) - dmin * m[2]) + + y[l+48] * (dall * d[3] * ((q[l] >> 6) & 3) - dmin * m[3]); + } + } +#endif + sum[ith] = sumf; - //int mask1 = (ith%4 == 0); - //int mask2 = (ith%16 == 0); - - //threadgroup_barrier(mem_flags::mem_threadgroup); - //for (int i = 1; i < 4; ++i) sum[ith] += mask1 * sum[ith + i]; - //threadgroup_barrier(mem_flags::mem_threadgroup); - //for (int i = 4; i < 16; i += 4) sum[ith] += mask2 * sum[ith + i]; - //threadgroup_barrier(mem_flags::mem_threadgroup); - //if (ith == 0) { - // for (int i = 16; i < nth; i += 16) sum[0] += sum[i]; - // dst[r1*ne0 + r0] = sum[0]; - //} - // // Accumulate the sum from all threads in the threadgroup - // This version is slightly faster than the commented out one below, - // which I copy-pasted from ggerganov's q4_0 dot product for metal. // threadgroup_barrier(mem_flags::mem_threadgroup); if (ith%4 == 0) { @@ -1190,7 +1325,7 @@ kernel void kernel_mul_mat_q2_k_f32( } } -kernel void kernel_mul_mat_q3_k_f32( +kernel void kernel_mul_mat_q3_K_f32( device const void * src0, device const float * src1, device float * dst, @@ -1203,23 +1338,25 @@ kernel void kernel_mul_mat_q3_k_f32( uint2 tpitg[[thread_position_in_threadgroup]], uint2 tptg[[threads_per_threadgroup]]) { - const uint16_t kmask1 = 0x0303; - const uint16_t kmask2 = 0x0f0f; - - const uint8_t m3 = 3; - const int8_t m4 = 4; - const int nb = ne00/QK_K; const int64_t r0 = tgpig.x; const int64_t r1 = tgpig.y; - device const block_q3_k * x = (device const block_q3_k *) src0 + r0*nb; + device const block_q3_K * x = (device const block_q3_K *) src0 + r0*nb; device const float * yy = (device const float *) src1 + r1*ne10; const int nth = tptg.x*tptg.y; const int ith = tptg.y*tpitg.x + tpitg.y; +#if QK_K == 256 + + const uint8_t m3 = 3; + const int8_t m4 = 4; + + const uint16_t kmask1 = 0x0303; + const uint16_t kmask2 = 0x0f0f; + const int tid = tpitg.y; // expecting 16 const int ip = tid/8; // 0 or 1 const int il = tid/2 - 4*ip; // 0...3 @@ -1273,6 +1410,39 @@ kernel void kernel_mul_mat_q3_k_f32( //sum[ith] = sumf; sum[ith] = sumf1 - 32.f*sumf2; +#else + const int il = 4 * tpitg.x; // 0, 4, 8, 12 + const int im = il/8; // 0, 0, 1, 1 + const int in = il%8; // 0, 4, 0, 4 + + float sumf = 0; + + for (int i = tpitg.y; i < nb; i += tptg.y) { + + const float d_all = (float)(x[i].d); + + device const uint8_t * q = x[i].qs + il; + device const uint8_t * h = x[i].hmask + in; + device const float * y = yy + i * QK_K + il; + + const float d1 = d_all * ((x[i].scales[0] & 0xF) - 8); + const float d2 = d_all * ((x[i].scales[0] >> 4) - 8); + const float d3 = d_all * ((x[i].scales[1] & 0xF) - 8); + const float d4 = d_all * ((x[i].scales[1] >> 4) - 8); + + for (int l = 0; l < 4; ++l) { + const uint8_t hm = h[l] >> im; + sumf += y[l+ 0] * d1 * ((int8_t)((q[l+0] >> 0) & 3) - ((hm & 0x01) ? 0 : 4)) + + y[l+16] * d2 * ((int8_t)((q[l+0] >> 2) & 3) - ((hm & 0x04) ? 0 : 4)) + + y[l+32] * d3 * ((int8_t)((q[l+0] >> 4) & 3) - ((hm & 0x10) ? 0 : 4)) + + y[l+48] * d4 * ((int8_t)((q[l+0] >> 6) & 3) - ((hm & 0x40) ? 0 : 4)); + } + + } + + sum[ith] = sumf; + +#endif // // Accumulate the sum from all threads in the threadgroup @@ -1293,7 +1463,7 @@ kernel void kernel_mul_mat_q3_k_f32( } -kernel void kernel_mul_mat_q4_k_f32( +kernel void kernel_mul_mat_q4_K_f32( device const void * src0, device const float * src1, device float * dst, @@ -1305,21 +1475,25 @@ kernel void kernel_mul_mat_q4_k_f32( uint2 tpitg[[thread_position_in_threadgroup]], uint2 tptg[[threads_per_threadgroup]]) { - const uint16_t kmask1 = 0x3f3f; - const uint16_t kmask2 = 0x0f0f; - const uint16_t kmask3 = 0xc0c0; - const int nb = ne00/QK_K; const int64_t r0 = tgpig.x; const int64_t r1 = tgpig.y; - device const block_q4_k * x = (device const block_q4_k *) src0 + r0*nb; - device const float * yy = (device const float *) src1 + r1*ne10; - const int nth = tptg.x*tptg.y; const int ith = tptg.y*tpitg.x + tpitg.y; + device const block_q4_K * x = (device const block_q4_K *) src0 + r0*nb; + device const float * yy = (device const float *) src1 + r1*ne10; + + float sumf = 0; + +#if QK_K == 256 + + const uint16_t kmask1 = 0x3f3f; + const uint16_t kmask2 = 0x0f0f; + const uint16_t kmask3 = 0xc0c0; + const int tid = tpitg.y; // 0...16 const int il = tid/4; // 0...3 const int ir = tid - 4*il;// 0...3 @@ -1332,11 +1506,8 @@ kernel void kernel_mul_mat_q4_k_f32( const int q_offset = 32*im + l0; const int y_offset = 64*im + l0; - sum[ith] = 0.0f; - uchar2 sc1, sc2, sc3, sc4; - float sumf = 0; for (int i = tpitg.x; i < nb; i += tptg.x) { device const uint8_t * q1 = (x + i)->qs + q_offset; @@ -1365,6 +1536,30 @@ kernel void kernel_mul_mat_q4_k_f32( sumf += dall * (s[0] * sc1[0] + s[1] * sc1[1] + s[2] * sc3[0] + s[3] * sc3[1]) - dmin * smin; } +#else + uint16_t aux16[2]; + thread const uint8_t * scales = (thread const uint8_t *)aux16; + + const int il = 4*tpitg.x; + + for (int i = tpitg.y; i < nb; i += tptg.y) { + + device const uint8_t * q = x[i].qs + il; + device const float * y = yy + i * QK_K + il; + + const float d = (float)x[i].d[0]; + const float m = (float)x[i].d[1]; + + device const uint16_t * a = (device const uint16_t *)x[i].scales; + aux16[0] = a[0] & 0x0f0f; + aux16[1] = (a[0] >> 4) & 0x0f0f; + + for (int l = 0; l < 4; ++l) { + sumf += d * scales[0] * (y[l+ 0] * (q[l] & 0xF) + y[l+16] * (q[l+16] & 0xF)) - m * scales[2] * (y[l+ 0] + y[l+16]) + + d * scales[1] * (y[l+32] * (q[l] >> 4) + y[l+48] * (q[l+16] >> 4)) - m * scales[3] * (y[l+32] + y[l+48]); + } + } +#endif sum[ith] = sumf; @@ -1401,7 +1596,7 @@ kernel void kernel_mul_mat_q4_k_f32( //} } -kernel void kernel_mul_mat_q5_k_f32( +kernel void kernel_mul_mat_q5_K_f32( device const void * src0, device const float * src1, device float * dst, @@ -1413,21 +1608,25 @@ kernel void kernel_mul_mat_q5_k_f32( uint2 tpitg[[thread_position_in_threadgroup]], uint2 tptg[[threads_per_threadgroup]]) { - const uint16_t kmask1 = 0x3f3f; - const uint16_t kmask2 = 0x0f0f; - const uint16_t kmask3 = 0xc0c0; - const int nb = ne00/QK_K; const int64_t r0 = tgpig.x; const int64_t r1 = tgpig.y; - device const block_q5_k * x = (device const block_q5_k *) src0 + r0*nb; + device const block_q5_K * x = (device const block_q5_K *) src0 + r0*nb; device const float * yy = (device const float *) src1 + r1*ne10; const int nth = tptg.x*tptg.y; const int ith = tptg.y*tpitg.x + tpitg.y; + float sumf = 0; + +#if QK_K == 256 + + const uint16_t kmask1 = 0x3f3f; + const uint16_t kmask2 = 0x0f0f; + const uint16_t kmask3 = 0xc0c0; + const int tid = tpitg.y; // 0...16 const int il = tid/4; // 0...3 const int ir = tid - 4*il;// 0...3 @@ -1447,7 +1646,6 @@ kernel void kernel_mul_mat_q5_k_f32( uchar2 sc1, sc2, sc3, sc4; - float sumf = 0; for (int i = tpitg.x; i < nb; i += tptg.x) { device const uint8_t * q1 = (x + i)->qs + q_offset; @@ -1479,6 +1677,28 @@ kernel void kernel_mul_mat_q5_k_f32( sumf += dall * (s[0] * sc1[0] + s[1] * sc1[1] + s[2] * sc3[0] + s[3] * sc3[1]) - dmin * smin; } +#else + const int il = 4 * tpitg.x; // 0, 4, 8, 12 + const int im = il/8; // 0, 0, 1, 1 + const int in = il%8; // 0, 4, 0, 4 + + for (int i = tpitg.y; i < nb; i += tptg.y) { + + const float d = (float)x[i].d; + device const uint8_t * q = x[i].qs + il; + device const uint8_t * h = x[i].qh + in; + device const int8_t * s = x[i].scales; + device const float * y = yy + i*QK_K + il; + + for (int l = 0; l < 4; ++l) { + const uint8_t hl = h[l] >> im; + sumf += y[l+ 0] * d * s[0] * ((q[l+ 0] & 0xF) - (hl & 0x01 ? 0 : 16)) + + y[l+16] * d * s[1] * ((q[l+16] & 0xF) - (hl & 0x04 ? 0 : 16)) + + y[l+32] * d * s[2] * ((q[l+ 0] >> 4) - (hl & 0x10 ? 0 : 16)) + + y[l+48] * d * s[3] * ((q[l+16] >> 4) - (hl & 0x40 ? 0 : 16)); + } + } +#endif sum[ith] = sumf; // @@ -1500,7 +1720,7 @@ kernel void kernel_mul_mat_q5_k_f32( } -kernel void kernel_mul_mat_q6_k_f32( +kernel void kernel_mul_mat_q6_K_f32( device const void * src0, device const float * src1, device float * dst, @@ -1522,12 +1742,15 @@ kernel void kernel_mul_mat_q6_k_f32( const int64_t r0 = tgpig.x; const int64_t r1 = tgpig.y; - device const block_q6_k * x = (device const block_q6_k *) src0 + r0*nb; + device const block_q6_K * x = (device const block_q6_K *) src0 + r0*nb; device const float * yy = (device const float *) src1 + r1*ne10; const int nth = tptg.x*tptg.y; const int ith = tptg.y*tpitg.x + tpitg.y; + float sumf = 0; + +#if QK_K == 256 // Note: we absolutely assume that tptg.y = 16 and QK_K = 256! const int iqs = 16 * tpitg.y; const int ip = iqs / 128; // 0 or 1 @@ -1540,7 +1763,6 @@ kernel void kernel_mul_mat_q6_k_f32( const int q_offset_l = 64*ip + l0; const int q_offset_h = 32*ip + l0; - float sumf = 0; for (int i = tpitg.x; i < nb; i += tptg.x) { device const uint8_t * ql = x[i].ql + q_offset_l; @@ -1562,6 +1784,28 @@ kernel void kernel_mul_mat_q6_k_f32( sumf += dall * (sums[0] * sc[0] + sums[1] * sc[2] + sums[2] * sc[4] + sums[3] * sc[6]); } +#else + const int il = 4*tpitg.x; // 0, 4, 8, 12 + + for (int i = tpitg.y; i < nb; i += tptg.y) { + device const float * y = yy + i * QK_K + il; + device const uint8_t * ql = x[i].ql + il; + device const uint8_t * qh = x[i].qh + il; + device const int8_t * s = x[i].scales; + + const float d = x[i].d; + + float4 sums = {0.f, 0.f, 0.f, 0.f}; + for (int l = 0; l < 4; ++l) { + sums[0] += y[l+ 0] * ((int8_t)((ql[l+ 0] & 0xF) | ((qh[l] & kmask1) << 4)) - 32); + sums[1] += y[l+16] * ((int8_t)((ql[l+16] & 0xF) | ((qh[l] & kmask2) << 2)) - 32); + sums[2] += y[l+32] * ((int8_t)((ql[l+ 0] >> 4) | ((qh[l] & kmask3) >> 0)) - 32); + sums[3] += y[l+48] * ((int8_t)((ql[l+16] >> 4) | ((qh[l] & kmask4) >> 2)) - 32); + } + sumf += d * (sums[0] * s[0] + sums[1] * s[1] + sums[2] * s[2] + sums[3] * s[3]); + } + +#endif sum[ith] = sumf; diff --git a/k_quants.c b/k_quants.c index a48c82171..46dd884b0 100644 --- a/k_quants.c +++ b/k_quants.c @@ -261,6 +261,7 @@ static float make_qkx1_quants(int n, int nmax, const float * restrict x, uint8_t return scale; } +#if QK_K == 256 static inline void get_scale_min_k4(int j, const uint8_t * restrict q, uint8_t * restrict d, uint8_t * restrict m) { if (j < 4) { *d = q[j] & 63; *m = q[j + 4] & 63; @@ -269,6 +270,7 @@ static inline void get_scale_min_k4(int j, const uint8_t * restrict q, uint8_t * *m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4); } } +#endif //========================- 2-bit (de)-quantization @@ -330,11 +332,17 @@ void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict } } +#if QK_K == 256 for (int j = 0; j < QK_K; j += 128) { for (int l = 0; l < 32; ++l) { y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6); } } +#else + for (int l = 0; l < 16; ++l) { + y[i].qs[l] = L[l] | (L[l + 16] << 2) | (L[l + 32] << 4) | (L[l + 48] << 6); + } +#endif x += QK_K; @@ -352,6 +360,7 @@ void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int const uint8_t * q = x[i].qs; +#if QK_K == 256 int is = 0; float dl, ml; for (int n = 0; n < QK_K; n += 128) { @@ -370,7 +379,19 @@ void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int } q += 32; } - +#else + float dl1 = d * (x[i].scales[0] & 0xF), ml1 = min * (x[i].scales[0] >> 4); + float dl2 = d * (x[i].scales[1] & 0xF), ml2 = min * (x[i].scales[1] >> 4); + float dl3 = d * (x[i].scales[2] & 0xF), ml3 = min * (x[i].scales[2] >> 4); + float dl4 = d * (x[i].scales[3] & 0xF), ml4 = min * (x[i].scales[3] >> 4); + for (int l = 0; l < 16; ++l) { + y[l+ 0] = dl1 * ((int8_t)((q[l] >> 0) & 3)) - ml1; + y[l+16] = dl2 * ((int8_t)((q[l] >> 2) & 3)) - ml2; + y[l+32] = dl3 * ((int8_t)((q[l] >> 4) & 3)) - ml3; + y[l+48] = dl4 * ((int8_t)((q[l] >> 6) & 3)) - ml4; + } + y += QK_K; +#endif } } @@ -412,6 +433,7 @@ void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict } } +#if QK_K == 256 memset(y[i].scales, 0, 12); if (max_scale) { float iscale = -32.f/max_scale; @@ -445,9 +467,39 @@ void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict L[16*j + ii] = l + 4; } } +#else + if (max_scale) { + float iscale = -8.f/max_scale; + for (int j = 0; j < QK_K/16; j+=2) { + int l1 = nearest_int(iscale*scales[j]); + l1 = 8 + MAX(-8, MIN(7, l1)); + int l2 = nearest_int(iscale*scales[j+1]); + l2 = 8 + MAX(-8, MIN(7, l2)); + y[i].scales[j/2] = l1 | (l2 << 4); + } + y[i].d = ggml_fp32_to_fp16(1/iscale); + } else { + for (int j = 0; j < QK_K/16; j+=2) { + y[i].scales[j/2] = 0; + } + y[i].d = ggml_fp32_to_fp16(0.f); + } + for (int j = 0; j < QK_K/16; ++j) { + int s = j%2 == 0 ? y[i].scales[j/2] & 0xF : y[i].scales[j/2] >> 4; + float d = ggml_fp16_to_fp32(y[i].d) * (s - 8); + if (!d) { + continue; + } + for (int ii = 0; ii < 16; ++ii) { + int l = nearest_int(x[16*j + ii]/d); + l = MAX(-4, MIN(3, l)); + L[16*j + ii] = l + 4; + } + } +#endif memset(y[i].hmask, 0, QK_K/8); - // We put the high-bit for the 1st 32 quants into bit 0, the next 32 into bit 1, etc. + // We put the high-bit for the 1st 8 quants into bit 0, the next 8 into bit 1, etc. int m = 0; uint8_t hm = 1; for (int j = 0; j < QK_K; ++j) { @@ -459,19 +511,25 @@ void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict m = 0; hm <<= 1; } } +#if QK_K == 256 for (int j = 0; j < QK_K; j += 128) { for (int l = 0; l < 32; ++l) { y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6); } } +#else + for (int l = 0; l < 16; ++l) { + y[i].qs[l] = L[l] | (L[l + 16] << 2) | (L[l + 32] << 4) | (L[l + 48] << 6); + } +#endif x += QK_K; } } +#if QK_K == 256 void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k) { assert(k % QK_K == 0); - assert(QK_K == 256); const int nb = k / QK_K; const uint32_t kmask1 = 0x03030303; @@ -519,6 +577,39 @@ void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int } } +#else +void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k) { + assert(k % QK_K == 0); + assert(QK_K == 64); + const int nb = k / QK_K; + + for (int i = 0; i < nb; i++) { + + const float d_all = ggml_fp16_to_fp32(x[i].d); + + const uint8_t * restrict q = x[i].qs; + const uint8_t * restrict hm = x[i].hmask; + + const float d1 = d_all * ((x[i].scales[0] & 0xF) - 8); + const float d2 = d_all * ((x[i].scales[0] >> 4) - 8); + const float d3 = d_all * ((x[i].scales[1] & 0xF) - 8); + const float d4 = d_all * ((x[i].scales[1] >> 4) - 8); + + for (int l=0; l<8; ++l) { + uint8_t h = hm[l]; + y[l+ 0] = d1 * ((int8_t)((q[l+0] >> 0) & 3) - ((h & 0x01) ? 0 : 4)); + y[l+ 8] = d1 * ((int8_t)((q[l+8] >> 0) & 3) - ((h & 0x02) ? 0 : 4)); + y[l+16] = d2 * ((int8_t)((q[l+0] >> 2) & 3) - ((h & 0x04) ? 0 : 4)); + y[l+24] = d2 * ((int8_t)((q[l+8] >> 2) & 3) - ((h & 0x08) ? 0 : 4)); + y[l+32] = d3 * ((int8_t)((q[l+0] >> 4) & 3) - ((h & 0x10) ? 0 : 4)); + y[l+40] = d3 * ((int8_t)((q[l+8] >> 4) & 3) - ((h & 0x20) ? 0 : 4)); + y[l+48] = d4 * ((int8_t)((q[l+0] >> 6) & 3) - ((h & 0x40) ? 0 : 4)); + y[l+56] = d4 * ((int8_t)((q[l+8] >> 6) & 3) - ((h & 0x80) ? 0 : 4)); + } + y += QK_K; + } +} +#endif void quantize_row_q3_K(const float * restrict x, void * restrict vy, int k) { quantize_row_q3_K_reference(x, vy, k); @@ -563,6 +654,7 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict } } +#if QK_K == 256 float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f; float inv_min = max_min > 0 ? 63.f/max_min : 0.f; for (int j = 0; j < QK_K/32; ++j) { @@ -594,9 +686,43 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict L[32*j + ii] = l; } } +#else + const float s_factor = 15.f; + float inv_scale = max_scale > 0 ? s_factor/max_scale : 0.f; + float inv_min = max_min > 0 ? s_factor/max_min : 0.f; + int d1 = nearest_int(inv_scale*scales[0]); + int m1 = nearest_int(inv_min*mins[0]); + int d2 = nearest_int(inv_scale*scales[1]); + int m2 = nearest_int(inv_min*mins[1]); + y[i].scales[0] = d1 | (m1 << 4); + y[i].scales[1] = d2 | (m2 << 4); + y[i].d[0] = ggml_fp32_to_fp16(max_scale/s_factor); + y[i].d[1] = ggml_fp32_to_fp16(max_min/s_factor); + + float sumlx = 0; + int suml2 = 0; + for (int j = 0; j < QK_K/32; ++j) { + const uint8_t sd = y[i].scales[j] & 0xF; + const uint8_t sm = y[i].scales[j] >> 4; + const float d = ggml_fp16_to_fp32(y[i].d[0]) * sd; + if (!d) continue; + const float m = ggml_fp16_to_fp32(y[i].d[1]) * sm; + for (int ii = 0; ii < 32; ++ii) { + int l = nearest_int((x[32*j + ii] + m)/d); + l = MAX(0, MIN(15, l)); + L[32*j + ii] = l; + sumlx += (x[32*j + ii] + m)*l*sd; + suml2 += l*l*sd*sd; + } + } + if (suml2) { + y[i].d[0] = ggml_fp32_to_fp16(sumlx/suml2); + } +#endif uint8_t * q = y[i].qs; for (int j = 0; j < QK_K; j += 64) { - for (int l = 0; l < 32; ++l) *q++ = L[j + l] | (L[j + l + 32] << 4); + for (int l = 0; l < 32; ++l) q[l] = L[j + l] | (L[j + l + 32] << 4); + q += 32; } x += QK_K; @@ -610,11 +736,13 @@ void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int for (int i = 0; i < nb; i++) { - const float d = ggml_fp16_to_fp32(x[i].d); - const float min = ggml_fp16_to_fp32(x[i].dmin); - const uint8_t * q = x[i].qs; +#if QK_K == 256 + + const float d = ggml_fp16_to_fp32(x[i].d); + const float min = ggml_fp16_to_fp32(x[i].dmin); + int is = 0; uint8_t sc, m; for (int j = 0; j < QK_K; j += 64) { @@ -626,6 +754,17 @@ void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int for (int l = 0; l < 32; ++l) *y++ = d2 * (q[l] >> 4) - m2; q += 32; is += 2; } +#else + const float dall = ggml_fp16_to_fp32(x[i].d[0]); + const float mall = ggml_fp16_to_fp32(x[i].d[1]); + const float d1 = dall * (x[i].scales[0] & 0xF), m1 = mall * (x[i].scales[0] >> 4); + const float d2 = dall * (x[i].scales[1] & 0xF), m2 = mall * (x[i].scales[1] >> 4); + for (int l = 0; l < 32; ++l) { + y[l+ 0] = d1 * (q[l] & 0xF) - m1; + y[l+32] = d2 * (q[l] >> 4) - m2; + } + y += QK_K; +#endif } } @@ -653,12 +792,19 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict assert(k % QK_K == 0); const int nb = k / QK_K; +#if QK_K == 256 uint8_t L[QK_K]; float mins[QK_K/32]; float scales[QK_K/32]; +#else + int8_t L[QK_K]; + float scales[QK_K/16]; +#endif for (int i = 0; i < nb; i++) { +#if QK_K == 256 + float max_scale = 0; // as we are deducting the min, scales are always positive float max_min = 0; for (int j = 0; j < QK_K/32; ++j) { @@ -725,6 +871,52 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict m1 <<= 2; m2 <<= 2; ql += 32; } +#else + float max_scale = 0, amax = 0; + for (int j = 0; j < QK_K/16; ++j) { + scales[j] = make_qx_quants(16, 16, x + 16*j, L + 16*j, 1); + float abs_scale = fabsf(scales[j]); + if (abs_scale > amax) { + amax = abs_scale; + max_scale = scales[j]; + } + } + + float iscale = -128.f/max_scale; + for (int j = 0; j < QK_K/16; ++j) { + int l = nearest_int(iscale*scales[j]); + y[i].scales[j] = MAX(-128, MIN(127, l)); + } + y[i].d = ggml_fp32_to_fp16(1/iscale); + + for (int j = 0; j < QK_K/16; ++j) { + const float d = ggml_fp16_to_fp32(y[i].d) * y[i].scales[j]; + if (!d) continue; + for (int ii = 0; ii < 16; ++ii) { + int l = nearest_int(x[16*j + ii]/d); + l = MAX(-16, MIN(15, l)); + L[16*j + ii] = l + 16; + } + } + + uint8_t * restrict qh = y[i].qh; + uint8_t * restrict ql = y[i].qs; + memset(qh, 0, QK_K/8); + + for (int j = 0; j < 32; ++j) { + int jm = j%8; + int is = j/8; + int l1 = L[j]; + if (l1 > 15) { + l1 -= 16; qh[jm] |= (1 << is); + } + int l2 = L[j + 32]; + if (l2 > 15) { + l2 -= 16; qh[jm] |= (1 << (4 + is)); + } + ql[j] = l1 | (l2 << 4); + } +#endif x += QK_K; @@ -737,12 +929,14 @@ void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int for (int i = 0; i < nb; i++) { - const float d = ggml_fp16_to_fp32(x[i].d); - const float min = ggml_fp16_to_fp32(x[i].dmin); - const uint8_t * ql = x[i].qs; const uint8_t * qh = x[i].qh; +#if QK_K == 256 + + const float d = ggml_fp16_to_fp32(x[i].d); + const float min = ggml_fp16_to_fp32(x[i].dmin); + int is = 0; uint8_t sc, m; uint8_t u1 = 1, u2 = 2; @@ -756,6 +950,21 @@ void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int ql += 32; is += 2; u1 <<= 2; u2 <<= 2; } +#else + float d = ggml_fp16_to_fp32(x[i].d); + const int8_t * restrict s = x[i].scales; + for (int l = 0; l < 8; ++l) { + y[l+ 0] = d * s[0] * ((ql[l+ 0] & 0xF) - (qh[l] & 0x01 ? 0 : 16)); + y[l+ 8] = d * s[0] * ((ql[l+ 8] & 0xF) - (qh[l] & 0x02 ? 0 : 16)); + y[l+16] = d * s[1] * ((ql[l+16] & 0xF) - (qh[l] & 0x04 ? 0 : 16)); + y[l+24] = d * s[1] * ((ql[l+24] & 0xF) - (qh[l] & 0x08 ? 0 : 16)); + y[l+32] = d * s[2] * ((ql[l+ 0] >> 4) - (qh[l] & 0x10 ? 0 : 16)); + y[l+40] = d * s[2] * ((ql[l+ 8] >> 4) - (qh[l] & 0x20 ? 0 : 16)); + y[l+48] = d * s[3] * ((ql[l+16] >> 4) - (qh[l] & 0x40 ? 0 : 16)); + y[l+56] = d * s[3] * ((ql[l+24] >> 4) - (qh[l] & 0x80 ? 0 : 16)); + } + y += QK_K; +#endif } } @@ -823,6 +1032,7 @@ void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict uint8_t * restrict ql = y[i].ql; uint8_t * restrict qh = y[i].qh; +#if QK_K == 256 for (int j = 0; j < QK_K; j += 128) { for (int l = 0; l < 32; ++l) { const uint8_t q1 = L[j + l + 0] & 0xF; @@ -836,6 +1046,16 @@ void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict ql += 64; qh += 32; } +#else + for (int l = 0; l < 32; ++l) { + const uint8_t q1 = L[l + 0] & 0xF; + const uint8_t q2 = L[l + 32] & 0xF; + ql[l] = q1 | (q2 << 4); + } + for (int l = 0; l < 16; ++l) { + qh[l] = (L[l] >> 4) | ((L[l + 16] >> 4) << 2) | ((L[l + 32] >> 4) << 4) | ((L[l + 48] >> 4) << 6); + } +#endif x += QK_K; @@ -854,6 +1074,7 @@ void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int const uint8_t * restrict qh = x[i].qh; const int8_t * restrict sc = x[i].scales; +#if QK_K == 256 for (int n = 0; n < QK_K; n += 128) { for (int l = 0; l < 32; ++l) { int is = l/16; @@ -871,6 +1092,19 @@ void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int qh += 32; sc += 8; } +#else + for (int l = 0; l < 16; ++l) { + const int8_t q1 = (int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32; + const int8_t q2 = (int8_t)((ql[l+16] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32; + const int8_t q3 = (int8_t)((ql[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32; + const int8_t q4 = (int8_t)((ql[l+16] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32; + y[l+ 0] = d * sc[0] * q1; + y[l+16] = d * sc[1] * q2; + y[l+32] = d * sc[2] * q3; + y[l+48] = d * sc[3] * q4; + } + y += 64; +#endif } } @@ -1002,6 +1236,7 @@ static inline __m128i get_scale_shuffle(int i) { } #endif +#if QK_K == 256 void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { const block_q2_K * restrict x = vx; @@ -1201,6 +1436,168 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri #endif } +#else + +void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { + + const block_q2_K * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#ifdef __ARM_NEON + + const uint8x16_t m3 = vdupq_n_u8(0x3); + const int32x4_t vzero = vdupq_n_s32(0); + + int8x16x4_t q2bytes; + + uint32_t aux32[2]; + const uint8_t * scales = (const uint8_t *)aux32; + + float sum = 0; + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * (float)x[i].d; + const float dmin = -y[i].d * (float)x[i].dmin; + + const uint8_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + const uint32_t * restrict sc = (const uint32_t *)x[i].scales; + + aux32[0] = sc[0] & 0x0f0f0f0f; + aux32[1] = (sc[0] >> 4) & 0x0f0f0f0f; + + sum += dmin * (scales[4] * y[i].bsums[0] + scales[5] * y[i].bsums[1] + scales[6] * y[i].bsums[2] + scales[7] * y[i].bsums[3]); + + int isum1 = 0, isum2 = 0; + + const uint8x16_t q2bits = vld1q_u8(q2); + + const int8x16x4_t q8bytes = vld1q_s8_x4(q8); + + q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(q2bits, m3)); + q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 2), m3)); + q2bytes.val[2] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 4), m3)); + q2bytes.val[3] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 6), m3)); + +#if defined(__ARM_FEATURE_DOTPROD) + isum1 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * scales[0]; + isum2 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * scales[1]; + isum1 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[2], q8bytes.val[2])) * scales[2]; + isum2 += vaddvq_s32(vdotq_s32(vzero, q2bytes.val[3], q8bytes.val[3])) * scales[3]; +#else + const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q2bytes.val[0]), vget_low_s8 (q8bytes.val[0])), + vmull_s8(vget_high_s8(q2bytes.val[0]), vget_high_s8(q8bytes.val[0]))); + const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q2bytes.val[1]), vget_low_s8 (q8bytes.val[1])), + vmull_s8(vget_high_s8(q2bytes.val[1]), vget_high_s8(q8bytes.val[1]))); + isum1 += vaddvq_s16(p1) * scales[0]; + isum2 += vaddvq_s16(p2) * scales[1]; + + const int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q2bytes.val[2]), vget_low_s8 (q8bytes.val[2])), + vmull_s8(vget_high_s8(q2bytes.val[2]), vget_high_s8(q8bytes.val[2]))); + const int16x8_t p4 = vaddq_s16(vmull_s8(vget_low_s8 (q2bytes.val[3]), vget_low_s8 (q8bytes.val[3])), + vmull_s8(vget_high_s8(q2bytes.val[3]), vget_high_s8(q8bytes.val[3]))); + isum1 += vaddvq_s16(p3) * scales[2]; + isum2 += vaddvq_s16(p4) * scales[3]; +#endif + sum += d * (isum1 + isum2); + + } + + *s = sum; + +#elif defined __AVX2__ + + const __m256i m3 = _mm256_set1_epi8(3); + + __m256 acc = _mm256_setzero_ps(); + + uint32_t ud, um; + const uint8_t * restrict db = (const uint8_t *)&ud; + const uint8_t * restrict mb = (const uint8_t *)&um; + + float summs = 0; + + // TODO: optimize this + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); + const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin); + + const uint8_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + const uint32_t * restrict sc = (const uint32_t *)x[i].scales; + ud = (sc[0] >> 0) & 0x0f0f0f0f; + um = (sc[0] >> 4) & 0x0f0f0f0f; + + int32_t smin = mb[0] * y[i].bsums[0] + mb[1] * y[i].bsums[1] + mb[2] * y[i].bsums[2] + mb[3] * y[i].bsums[3]; + summs += dmin * smin; + + const __m128i q2bits = _mm_loadu_si128((const __m128i*)q2); + const __m256i q2_0 = _mm256_and_si256(_mm256_set_m128i(_mm_srli_epi16(q2bits, 2), q2bits), m3); + const __m256i q2_1 = _mm256_and_si256(_mm256_set_m128i(_mm_srli_epi16(q2bits, 6), _mm_srli_epi16(q2bits, 4)), m3); + + const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); + const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); + + const __m256i p0 = _mm256_maddubs_epi16(q2_0, q8_0); + const __m256i p1 = _mm256_maddubs_epi16(q2_1, q8_1); + + const __m256i p_0 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p0, 0)); + const __m256i p_1 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p0, 1)); + const __m256i p_2 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p1, 0)); + const __m256i p_3 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p1, 1)); + + acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[0]), _mm256_cvtepi32_ps(p_0), acc); + acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[1]), _mm256_cvtepi32_ps(p_1), acc); + acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[2]), _mm256_cvtepi32_ps(p_2), acc); + acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[3]), _mm256_cvtepi32_ps(p_3), acc); + } + + *s = hsum_float_8(acc) + summs; + +#else + + float sumf = 0; + + int isum[4]; + + for (int i = 0; i < nb; ++i) { + + const uint8_t * q2 = x[i].qs; + const int8_t * q8 = y[i].qs; + const uint8_t * sc = x[i].scales; + + int summs = 0; + for (int j = 0; j < QK_K/16; ++j) { + summs += y[i].bsums[j] * (sc[j] >> 4); + } + + const float dall = y[i].d * ggml_fp16_to_fp32(x[i].d); + const float dmin = y[i].d * ggml_fp16_to_fp32(x[i].dmin); + + isum[0] = isum[1] = isum[2] = isum[3] = 0; + for (int l = 0; l < 16; ++l) { + isum[0] += q8[l+ 0] * ((q2[l] >> 0) & 3); + isum[1] += q8[l+16] * ((q2[l] >> 2) & 3); + isum[2] += q8[l+32] * ((q2[l] >> 4) & 3); + isum[3] += q8[l+48] * ((q2[l] >> 6) & 3); + } + for (int l = 0; l < 4; ++l) { + isum[l] *= (sc[l] & 0xF); + } + sumf += dall * (isum[0] + isum[1] + isum[2] + isum[3]) - dmin * summs; + } + *s = sumf; +#endif +} +#endif + +#if QK_K == 256 void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { assert(n % QK_K == 0); @@ -1501,6 +1898,206 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri } +#else + +void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { + assert(n % QK_K == 0); + + const block_q3_K * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#ifdef __ARM_NEON + +#ifdef __ARM_FEATURE_DOTPROD + const int32x4_t vzero = vdupq_n_s32(0); +#endif + + const uint8x16_t m3b = vdupq_n_u8(0x3); + const uint8x16_t mh = vdupq_n_u8(4); + + int8x16x4_t q3bytes; + + uint16_t aux16[2]; + int8_t * scales = (int8_t *)aux16; + + float sum = 0; + + for (int i = 0; i < nb; ++i) { + + uint8x16x4_t q3h; + + const uint8x8_t hbits = vld1_u8(x[i].hmask); + const uint8x16_t q3bits = vld1q_u8(x[i].qs); + const int8x16x4_t q8bytes = vld1q_s8_x4(y[i].qs); + + const uint16_t a = *(const uint16_t *)x[i].scales; + aux16[0] = a & 0x0f0f; + aux16[1] = (a >> 4) & 0x0f0f; + + for (int j = 0; j < 4; ++j) scales[j] -= 8; + + int32_t isum = -4*(scales[0] * y[i].bsums[0] + scales[2] * y[i].bsums[1] + scales[1] * y[i].bsums[2] + scales[3] * y[i].bsums[3]); + + const float d = y[i].d * (float)x[i].d; + + const uint8x16_t htmp = vcombine_u8(hbits, vshr_n_u8(hbits, 1)); + q3h.val[0] = vandq_u8(mh, vshlq_n_u8(htmp, 2)); + q3h.val[1] = vandq_u8(mh, htmp); + q3h.val[2] = vandq_u8(mh, vshrq_n_u8(htmp, 2)); + q3h.val[3] = vandq_u8(mh, vshrq_n_u8(htmp, 4)); + + q3bytes.val[0] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q3bits, m3b), q3h.val[0])); + q3bytes.val[1] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(vshrq_n_u8(q3bits, 2), m3b), q3h.val[1])); + q3bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(vshrq_n_u8(q3bits, 4), m3b), q3h.val[2])); + q3bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q3bits, 6), q3h.val[3])); + +#if defined(__ARM_FEATURE_DOTPROD) + isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[0], q8bytes.val[0])) * scales[0]; + isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[1], q8bytes.val[1])) * scales[2]; + isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[2], q8bytes.val[2])) * scales[1]; + isum += vaddvq_s32(vdotq_s32(vzero, q3bytes.val[3], q8bytes.val[3])) * scales[3]; +#else + const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[0]), vget_low_s8 (q8bytes.val[0])), + vmull_s8(vget_high_s8(q3bytes.val[0]), vget_high_s8(q8bytes.val[0]))); + const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[1]), vget_low_s8 (q8bytes.val[1])), + vmull_s8(vget_high_s8(q3bytes.val[1]), vget_high_s8(q8bytes.val[1]))); + const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[2]), vget_low_s8 (q8bytes.val[2])), + vmull_s8(vget_high_s8(q3bytes.val[2]), vget_high_s8(q8bytes.val[2]))); + const int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q3bytes.val[3]), vget_low_s8 (q8bytes.val[3])), + vmull_s8(vget_high_s8(q3bytes.val[3]), vget_high_s8(q8bytes.val[3]))); + isum += vaddvq_s16(p0) * scales[0] + vaddvq_s16(p1) * scales[2] + vaddvq_s16(p2) * scales[1] + vaddvq_s16(p3) * scales[3]; +#endif + + sum += d * isum; + + } + + *s = sum; + +#elif defined __AVX2__ + + const __m256i m3 = _mm256_set1_epi8(3); + const __m256i m1 = _mm256_set1_epi8(1); + + __m256 acc = _mm256_setzero_ps(); + + uint64_t aux64; + + uint16_t aux16[2]; + const int8_t * aux8 = (const int8_t *)aux16; + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); + + const uint8_t * restrict q3 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + const uint16_t a = *(const uint16_t *)x[i].scales; + aux16[0] = a & 0x0f0f; + aux16[1] = (a >> 4) & 0x0f0f; + + const __m256i scale_0 = _mm256_set_m128i(_mm_set1_epi16(aux8[2] - 8), _mm_set1_epi16(aux8[0] - 8)); + const __m256i scale_1 = _mm256_set_m128i(_mm_set1_epi16(aux8[3] - 8), _mm_set1_epi16(aux8[1] - 8)); + + memcpy(&aux64, x[i].hmask, 8); + + const __m128i haux = _mm_set_epi64x(aux64 >> 1, aux64 >> 0); + __m256i q3h_0 = _mm256_set_m128i(_mm_srli_epi16(haux, 2), haux); + __m256i q3h_1 = _mm256_srli_epi16(q3h_0, 4); + q3h_0 = _mm256_slli_epi16(_mm256_andnot_si256(q3h_0, m1), 2); + q3h_1 = _mm256_slli_epi16(_mm256_andnot_si256(q3h_1, m1), 2); + + // load low 2 bits + const __m128i q3bits = _mm_loadu_si128((const __m128i*)q3); + + // prepare low and high bits + const __m256i q3aux = _mm256_set_m128i(_mm_srli_epi16(q3bits, 2), q3bits); + const __m256i q3l_0 = _mm256_and_si256(q3aux, m3); + const __m256i q3l_1 = _mm256_and_si256(_mm256_srli_epi16(q3aux, 4), m3); + + // load Q8 quants + const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); + const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); + + // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use _mm256_maddubs_epi16, + // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, + // and 2 if the high bit was set) + const __m256i q8s_0 = _mm256_maddubs_epi16(q3h_0, q8_0); + const __m256i q8s_1 = _mm256_maddubs_epi16(q3h_1, q8_1); + + __m256i p16_0 = _mm256_maddubs_epi16(q3l_0, q8_0); + __m256i p16_1 = _mm256_maddubs_epi16(q3l_1, q8_1); + + p16_0 = _mm256_sub_epi16(p16_0, q8s_0); + p16_1 = _mm256_sub_epi16(p16_1, q8s_1); + + // multiply with scales + p16_0 = _mm256_madd_epi16(scale_0, p16_0); + p16_1 = _mm256_madd_epi16(scale_1, p16_1); + + p16_0 = _mm256_add_epi32(p16_0, p16_1); + + // multiply with block scale and accumulate + acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(p16_0), acc); + + } + + *s = hsum_float_8(acc); + +#else + + int8_t aux8[QK_K]; + int16_t aux16[8]; + float sums [8]; + int32_t aux32[8]; + int32_t scales[4]; + memset(sums, 0, 8*sizeof(float)); + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict hm = x[i].hmask; + const int8_t * restrict q8 = y[i].qs; + int8_t * restrict a = aux8; + for (int l = 0; l < 8; ++l) { + a[l+ 0] = (int8_t)((q3[l+0] >> 0) & 3) - (hm[l] & 0x01 ? 0 : 4); + a[l+ 8] = (int8_t)((q3[l+8] >> 0) & 3) - (hm[l] & 0x02 ? 0 : 4); + a[l+16] = (int8_t)((q3[l+0] >> 2) & 3) - (hm[l] & 0x04 ? 0 : 4); + a[l+24] = (int8_t)((q3[l+8] >> 2) & 3) - (hm[l] & 0x08 ? 0 : 4); + a[l+32] = (int8_t)((q3[l+0] >> 4) & 3) - (hm[l] & 0x10 ? 0 : 4); + a[l+40] = (int8_t)((q3[l+8] >> 4) & 3) - (hm[l] & 0x20 ? 0 : 4); + a[l+48] = (int8_t)((q3[l+0] >> 6) & 3) - (hm[l] & 0x40 ? 0 : 4); + a[l+56] = (int8_t)((q3[l+8] >> 6) & 3) - (hm[l] & 0x80 ? 0 : 4); + } + + scales[0] = (x[i].scales[0] & 0xF) - 8; + scales[1] = (x[i].scales[0] >> 4) - 8; + scales[2] = (x[i].scales[1] & 0xF) - 8; + scales[3] = (x[i].scales[1] >> 4) - 8; + + memset(aux32, 0, 8*sizeof(int32_t)); + for (int j = 0; j < QK_K/16; ++j) { + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] += q8[l] * a[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux32[l] += scales[j] * aux16[l]; + } + const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d; + for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; + } + for (int l = 0; l < 8; ++l) sumf += sums[l]; + *s = sumf; + +#endif + +} +#endif + +#if QK_K == 256 void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { assert(n % QK_K == 0); @@ -1614,9 +2211,6 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin); - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - memcpy(utmp, x[i].scales, 12); utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); const uint32_t uaux = utmp[1] & kmask1; @@ -1624,6 +2218,9 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri utmp[2] = uaux; utmp[0] &= kmask1; + const uint8_t * restrict q4 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + const __m256i mins_and_scales = _mm256_cvtepu8_epi16(_mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0])); const __m256i q8sums = _mm256_loadu_si256((const __m256i*)y[i].bsums); @@ -1726,7 +2323,176 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri *s = sumf; #endif } +#else +void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { + assert(n % QK_K == 0); + const block_q4_K * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#ifdef __ARM_NEON + + const uint8x16_t m4b = vdupq_n_u8(0xf); + +#ifdef __ARM_FEATURE_DOTPROD + const int32x4_t mzero = vdupq_n_s32(0); +#endif + + float sumf = 0; + + int8x16x2_t q4bytes; + int8x16x4_t q8bytes; + + float sum_mins = 0.f; + + uint16_t aux16[2]; + const uint8_t * restrict scales = (const uint8_t *)aux16; + + for (int i = 0; i < nb; ++i) { + + const uint8_t * restrict q4 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + const uint16_t * restrict a = (const uint16_t *)x[i].scales; + aux16[0] = a[0] & 0x0f0f; + aux16[1] = (a[0] >> 4) & 0x0f0f; + + const int32_t summi = scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3]); + sum_mins += y[i].d * (float)x[i].d[1] * summi; + + const float d = y[i].d * (float)x[i].d[0]; + + const uint8x16x2_t q4bits = vld1q_u8_x2(q4); + +#ifdef __ARM_FEATURE_DOTPROD + q8bytes = vld1q_s8_x4(q8); + q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); + q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); + + const int32x4_t p1 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); + const int32_t sumi1 = vaddvq_s32(p1) * scales[0]; + + q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4)); + q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4)); + + const int32x4_t p2 = vdotq_s32(vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[2]), q4bytes.val[1], q8bytes.val[3]); + const int32_t sumi2 = vaddvq_s32(p2) * scales[1]; + +#else + q8bytes = vld1q_s8_x4(q8); + q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); + q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); + const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[0]), vget_low_s8 (q8bytes.val[0])), + vmull_s8(vget_high_s8(q4bytes.val[0]), vget_high_s8(q8bytes.val[0]))); + const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[1]), vget_low_s8 (q8bytes.val[1])), + vmull_s8(vget_high_s8(q4bytes.val[1]), vget_high_s8(q8bytes.val[1]))); + int32_t sumi1 = vaddvq_s16(vaddq_s16(p0, p1)) * scales[0]; + + q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4)); + q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4)); + const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[0]), vget_low_s8 (q8bytes.val[2])), + vmull_s8(vget_high_s8(q4bytes.val[0]), vget_high_s8(q8bytes.val[2]))); + const int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q4bytes.val[1]), vget_low_s8 (q8bytes.val[3])), + vmull_s8(vget_high_s8(q4bytes.val[1]), vget_high_s8(q8bytes.val[3]))); + int32_t sumi2 = vaddvq_s16(vaddq_s16(p2, p3)) * scales[1]; + +#endif + sumf += d * (sumi1 + sumi2); + + } + + *s = sumf - sum_mins; + +#elif defined __AVX2__ + + const __m256i m4 = _mm256_set1_epi8(0xF); + + __m256 acc = _mm256_setzero_ps(); + + float summs = 0; + + uint16_t aux16[2]; + const uint8_t * scales = (const uint8_t *)aux16; + + for (int i = 0; i < nb; ++i) { + + const float d = ggml_fp16_to_fp32(x[i].d[0]) * y[i].d; + const float m = ggml_fp16_to_fp32(x[i].d[1]) * y[i].d; + const __m256 vd = _mm256_set1_ps(d); + + const uint16_t * a = (const uint16_t *)x[i].scales; + aux16[0] = a[0] & 0x0f0f; + aux16[1] = (a[0] >> 4) & 0x0f0f; + + summs += m * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3])); + + const uint8_t * restrict q4 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + const __m256i q4bits = _mm256_loadu_si256((const __m256i*)q4); + const __m256i q4l = _mm256_and_si256(q4bits, m4); + const __m256i q4h = _mm256_and_si256(_mm256_srli_epi16(q4bits, 4), m4); + + const __m256i q8l = _mm256_loadu_si256((const __m256i*)(q8+ 0)); + const __m256i q8h = _mm256_loadu_si256((const __m256i*)(q8+32)); + + const __m256i p16l = _mm256_maddubs_epi16(q4l, q8l); + const __m256i p16h = _mm256_maddubs_epi16(q4h, q8h); + + const __m256i p32l = _mm256_madd_epi16(_mm256_set1_epi16(scales[0]), p16l); + acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(p32l), acc); + + const __m256i p32h = _mm256_madd_epi16(_mm256_set1_epi16(scales[1]), p16h); + acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(p32h), acc); + + } + + *s = hsum_float_8(acc) - summs; + +#else + + uint8_t aux8[QK_K]; + int16_t aux16[16]; + float sums [8]; + memset(sums, 0, 8*sizeof(float)); + + uint16_t s16[2]; + const uint8_t * restrict scales = (const uint8_t *)s16; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const uint8_t * restrict q4 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + uint8_t * restrict a = aux8; + for (int l = 0; l < 32; ++l) a[l+ 0] = q4[l] & 0xF; + for (int l = 0; l < 32; ++l) a[l+32] = q4[l] >> 4; + + const uint16_t * restrict b = (const uint16_t *)x[i].scales; + s16[0] = b[0] & 0x0f0f; + s16[1] = (b[0] >> 4) & 0x0f0f; + + sumf -= y[i].d * ggml_fp16_to_fp32(x[i].d[1]) * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3])); + + const float d = y[i].d * ggml_fp16_to_fp32(x[i].d[0]); + + for (int j = 0; j < QK_K/32; ++j) { + for (int l = 0; l < 16; ++l) aux16[l] = q8[l] * a[l]; + q8 += 16; a += 16; + for (int l = 0; l < 16; ++l) aux16[l] += q8[l] * a[l]; + q8 += 16; a += 16; + const float dl = d * scales[j]; + for (int l = 0; l < 8; ++l) sums[l] += dl * (aux16[l] + aux16[l+8]); + } + } + for (int l = 0; l < 8; ++l) sumf += sums[l]; + *s = sumf; +#endif +} +#endif + +#if QK_K == 256 void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { assert(n % QK_K == 0); @@ -1840,18 +2606,23 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri for (int i = 0; i < nb; ++i) { - const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); - const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin); - const uint8_t * restrict q5 = x[i].qs; const int8_t * restrict q8 = y[i].qs; +#if QK_K == 256 + const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); + const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin); + memcpy(utmp, x[i].scales, 12); utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); const uint32_t uaux = utmp[1] & kmask1; utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); utmp[2] = uaux; utmp[0] &= kmask1; +#else + // TODO + const float d = 0, dmin = 0; +#endif const __m256i mins_and_scales = _mm256_cvtepu8_epi16(_mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0])); @@ -1972,8 +2743,169 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri #endif } +#else + +void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { + assert(n % QK_K == 0); + + const block_q5_K * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#ifdef __ARM_NEON + + const uint8x16_t m4b = vdupq_n_u8(0xf); + const int32x4_t mzero = vdupq_n_s32(0); + const uint8x16_t mh = vdupq_n_u8(16); + + int8x16x4_t q5bytes; + uint8x16x4_t q5h; + + float sumf = 0; + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * (float)x[i].d; + const int8_t * sc = x[i].scales; + + const uint8_t * restrict q5 = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + + const uint8x8_t qhbits = vld1_u8(qh); + + const uint8x16x2_t q5bits = vld1q_u8_x2(q5); + const int8x16x4_t q8bytes = vld1q_s8_x4(q8); + + const uint8x16_t htmp = vcombine_u8(qhbits, vshr_n_u8(qhbits, 1)); + q5h.val[0] = vbicq_u8(mh, vshlq_n_u8(htmp, 4)); + q5h.val[1] = vbicq_u8(mh, vshlq_n_u8(htmp, 2)); + q5h.val[2] = vbicq_u8(mh, htmp); + q5h.val[3] = vbicq_u8(mh, vshrq_n_u8(htmp, 2)); + + q5bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q5bits.val[0], m4b)), vreinterpretq_s8_u8(q5h.val[0])); + q5bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q5bits.val[1], m4b)), vreinterpretq_s8_u8(q5h.val[1])); + q5bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(q5bits.val[0], 4)), vreinterpretq_s8_u8(q5h.val[2])); + q5bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(q5bits.val[1], 4)), vreinterpretq_s8_u8(q5h.val[3])); + +#if defined(__ARM_FEATURE_DOTPROD) + + int32_t sumi1 = sc[0] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0])); + int32_t sumi2 = sc[1] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[1], q8bytes.val[1])); + int32_t sumi3 = sc[2] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2])); + int32_t sumi4 = sc[3] * vaddvq_s32(vdotq_s32(mzero, q5bytes.val[3], q8bytes.val[3])); + + sumf += d * (sumi1 + sumi2 + sumi3 + sumi4); + +#else + + const int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[0]), vget_low_s8 (q8bytes.val[0])), + vmull_s8(vget_high_s8(q5bytes.val[0]), vget_high_s8(q8bytes.val[0]))); + const int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[1]), vget_low_s8 (q8bytes.val[1])), + vmull_s8(vget_high_s8(q5bytes.val[1]), vget_high_s8(q8bytes.val[1]))); + int32_t sumi = sc[0] * vaddvq_s16(p0) + sc[1] * vaddvq_s16(p1); + + const int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[2]), vget_low_s8 (q8bytes.val[2])), + vmull_s8(vget_high_s8(q5bytes.val[2]), vget_high_s8(q8bytes.val[2]))); + const int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q5bytes.val[3]), vget_low_s8 (q8bytes.val[3])), + vmull_s8(vget_high_s8(q5bytes.val[3]), vget_high_s8(q8bytes.val[3]))); + sumi += sc[2] * vaddvq_s16(p2) + sc[3] * vaddvq_s16(p3); + + sumf += d*sumi; +#endif + + } + + *s = sumf; + +#elif defined __AVX2__ + + const __m256i m4 = _mm256_set1_epi8(0xF); + const __m256i mone = _mm256_set1_epi8(1); + + __m256 acc = _mm256_setzero_ps(); + + for (int i = 0; i < nb; ++i) { + + const uint8_t * restrict q5 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); + + const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5); + + const __m256i scale_l = _mm256_set_m128i(_mm_set1_epi16(x[i].scales[1]), _mm_set1_epi16(x[i].scales[0])); + const __m256i scale_h = _mm256_set_m128i(_mm_set1_epi16(x[i].scales[3]), _mm_set1_epi16(x[i].scales[2])); + + int64_t aux64; + memcpy(&aux64, x[i].qh, 8); + const __m128i haux128 = _mm_set_epi64x(aux64 >> 1, aux64); + const __m256i haux256 = _mm256_set_m128i(_mm_srli_epi16(haux128, 2), haux128); + + const __m256i q5h_0 = _mm256_slli_epi16(_mm256_andnot_si256(haux256, mone), 4); + const __m256i q5h_1 = _mm256_slli_epi16(_mm256_andnot_si256(_mm256_srli_epi16(haux256, 4), mone), 4); + + const __m256i q5l_0 = _mm256_and_si256(q5bits, m4); + const __m256i q5l_1 = _mm256_and_si256(_mm256_srli_epi16(q5bits, 4), m4); + + const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); + const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); + + const __m256i p16_0 = _mm256_madd_epi16(scale_l, _mm256_maddubs_epi16(q5l_0, q8_0)); + const __m256i p16_1 = _mm256_madd_epi16(scale_h, _mm256_maddubs_epi16(q5l_1, q8_1)); + const __m256i s16_0 = _mm256_madd_epi16(scale_l, _mm256_maddubs_epi16(q5h_0, q8_0)); + const __m256i s16_1 = _mm256_madd_epi16(scale_h, _mm256_maddubs_epi16(q5h_1, q8_1)); + + const __m256i dot = _mm256_sub_epi32(_mm256_add_epi32(p16_0, p16_1), _mm256_add_epi32(s16_0, s16_1)); + + acc = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(dot), acc); + + } + + *s = hsum_float_8(acc); + +#else + uint8_t aux8[QK_K]; + int16_t aux16[16]; + float sums [8]; + memset(sums, 0, 8*sizeof(float)); + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const uint8_t * restrict q4 = x[i].qs; + const uint8_t * restrict hm = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + uint8_t * restrict a = aux8; + for (int l = 0; l < 32; ++l) { + a[l+ 0] = q4[l] & 0xF; + a[l+32] = q4[l] >> 4; + } + for (int is = 0; is < 8; ++is) { + uint8_t m = 1 << is; + for (int l = 0; l < 8; ++l) a[8*is + l] -= (hm[l] & m ? 0 : 16); + } + + const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); + const int8_t * restrict sc = x[i].scales; + + for (int j = 0; j < QK_K/16; ++j) { + const float dl = d * sc[j]; + for (int l = 0; l < 16; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) sums[l] += dl * (aux16[l] + aux16[8+l]); + q8 += 16; a += 16; + } + } + for (int l = 0; l < 8; ++l) sumf += sums[l]; + *s = sumf; +#endif +} +#endif + + +#if QK_K == 256 void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { assert(n % QK_K == 0); @@ -2242,3 +3174,179 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri *s = sumf; #endif } + +#else + +void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { + assert(n % QK_K == 0); + + const block_q6_K * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#ifdef __ARM_NEON + + float sum = 0; + + const uint8x16_t m4b = vdupq_n_u8(0xF); + const int32x4_t vzero = vdupq_n_s32(0); + const int8x16_t m32s = vdupq_n_s8(32); + + const uint8x16_t mone = vdupq_n_u8(3); + + int8x16x4_t q6bytes; + uint8x16x4_t q6h; + + for (int i = 0; i < nb; ++i) { + + const float d_all = (float)x[i].d; + + const uint8_t * restrict q6 = x[i].ql; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + + const int8_t * restrict scale = x[i].scales; + + int32_t isum = 0; + + uint8x16_t qhbits = vld1q_u8(qh); + uint8x16x2_t q6bits = vld1q_u8_x2(q6); + int8x16x4_t q8bytes = vld1q_s8_x4(q8); + + q6h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits), 4); + uint8x16_t shifted = vshrq_n_u8(qhbits, 2); + q6h.val[1] = vshlq_n_u8(vandq_u8(mone, shifted), 4); + shifted = vshrq_n_u8(qhbits, 4); + q6h.val[2] = vshlq_n_u8(vandq_u8(mone, shifted), 4); + shifted = vshrq_n_u8(qhbits, 6); + q6h.val[3] = vshlq_n_u8(vandq_u8(mone, shifted), 4); + + q6bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[0], m4b), q6h.val[0])), m32s); + q6bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[1], m4b), q6h.val[1])), m32s); + q6bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[0], 4), q6h.val[2])), m32s); + q6bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[1], 4), q6h.val[3])), m32s); + +#if defined(__ARM_FEATURE_DOTPROD) + + isum += vaddvq_s32(vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + + vaddvq_s32(vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + + vaddvq_s32(vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + + vaddvq_s32(vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; +#else + + int16x8_t p0 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[0]), vget_low_s8 (q8bytes.val[0])), + vmull_s8(vget_high_s8(q6bytes.val[0]), vget_high_s8(q8bytes.val[0]))); + int16x8_t p1 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[1]), vget_low_s8 (q8bytes.val[1])), + vmull_s8(vget_high_s8(q6bytes.val[1]), vget_high_s8(q8bytes.val[1]))); + isum += vaddvq_s16(p0) * scale[0] + vaddvq_s16(p1) * scale[1]; + + int16x8_t p2 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[2]), vget_low_s8 (q8bytes.val[2])), + vmull_s8(vget_high_s8(q6bytes.val[2]), vget_high_s8(q8bytes.val[2]))); + int16x8_t p3 = vaddq_s16(vmull_s8(vget_low_s8 (q6bytes.val[3]), vget_low_s8 (q8bytes.val[3])), + vmull_s8(vget_high_s8(q6bytes.val[3]), vget_high_s8(q8bytes.val[3]))); + isum += vaddvq_s16(p2) * scale[2] + vaddvq_s16(p3) * scale[3]; +#endif + + sum += isum * d_all * y[i].d; + + } + *s = sum; + +#elif defined __AVX2__ + + const __m256i m4 = _mm256_set1_epi8(0xF); + const __m256i m2 = _mm256_set1_epi8(3); + const __m256i m32s = _mm256_set1_epi8(32); + + __m256 acc = _mm256_setzero_ps(); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); + + const uint8_t * restrict q4 = x[i].ql; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + + const __m64 scales_1 = _mm_set1_pi8(x[i].scales[0]); + const __m64 scales_2 = _mm_set1_pi8(x[i].scales[1]); + const __m64 scales_3 = _mm_set1_pi8(x[i].scales[2]); + const __m64 scales_4 = _mm_set1_pi8(x[i].scales[3]); + + __m256i sumi = _mm256_setzero_si256(); + + const __m128i scale_0 = _mm_set_epi64(scales_2, scales_1); + const __m128i scale_1 = _mm_set_epi64(scales_4, scales_3); + + const __m256i q4bits1 = _mm256_loadu_si256((const __m256i*)q4); + const __m128i q4bitsH = _mm_loadu_si128((const __m128i*)qh); + + const __m256i q4h_0 = _mm256_slli_epi16(_mm256_and_si256(_mm256_set_m128i(_mm_srli_epi16(q4bitsH, 2), q4bitsH), m2), 4); + const __m256i q4h_1 = _mm256_slli_epi16(_mm256_and_si256(_mm256_set_m128i(_mm_srli_epi16(q4bitsH, 6), _mm_srli_epi16(q4bitsH, 4)), m2), 4); + + const __m256i q4_0 = _mm256_or_si256(_mm256_and_si256(q4bits1, m4), q4h_0); + const __m256i q4_1 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits1, 4), m4), q4h_1); + + const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); + const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); + + __m256i q8s_0 = _mm256_maddubs_epi16(m32s, q8_0); + __m256i q8s_1 = _mm256_maddubs_epi16(m32s, q8_1); + + __m256i p16_0 = _mm256_maddubs_epi16(q4_0, q8_0); + __m256i p16_1 = _mm256_maddubs_epi16(q4_1, q8_1); + + p16_0 = _mm256_sub_epi16(p16_0, q8s_0); + p16_1 = _mm256_sub_epi16(p16_1, q8s_1); + + p16_0 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_0), p16_0); + p16_1 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_1), p16_1); + + sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_1)); + + acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc); + } + + *s = hsum_float_8(acc); + +#else + + int8_t aux8[QK_K]; + int16_t aux16[8]; + float sums [8]; + int32_t aux32[8]; + memset(sums, 0, 8*sizeof(float)); + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const uint8_t * restrict q4 = x[i].ql; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + memset(aux32, 0, 8*sizeof(int32_t)); + int8_t * restrict a = aux8; + for (int l = 0; l < 16; ++l) { + a[l+ 0] = (int8_t)((q4[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32; + a[l+16] = (int8_t)((q4[l+16] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32; + a[l+32] = (int8_t)((q4[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32; + a[l+48] = (int8_t)((q4[l+16] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32; + } + int is = 0; + for (int j = 0; j < QK_K/16; ++j) { + int scale = x[i].scales[is++]; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; + for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; + q8 += 8; a += 8; + } + const float d = ggml_fp16_to_fp32(x[i].d) * y[i].d; + for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; + } + for (int l = 0; l < 8; ++l) sumf += sums[l]; + *s = sumf; +#endif +} + +#endif diff --git a/k_quants.h b/k_quants.h index 10a0baac7..6abe3d7b8 100644 --- a/k_quants.h +++ b/k_quants.h @@ -7,7 +7,13 @@ #include // Super-block size +#ifdef GGML_QKK_64 +#define QK_K 64 +#define K_SCALE_SIZE 4 +#else #define QK_K 256 +#define K_SCALE_SIZE 12 +#endif // // Super-block quantization structures @@ -29,38 +35,67 @@ static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "w // weight is represented as x = a * q // 16 blocks of 16 elemenets each // Effectively 3.4375 bits per weight +#ifdef GGML_QKK_64 typedef struct { uint8_t hmask[QK_K/8]; // quants - high bit uint8_t qs[QK_K/4]; // quants - low 2 bits - uint8_t scales[3*QK_K/64]; // scales, quantized with 6 bits + uint8_t scales[2]; ggml_fp16_t d; // super-block scale } block_q3_K; -static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + 11 * QK_K / 64, "wrong q3_K block size/padding"); +static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding"); +#else +typedef struct { + uint8_t hmask[QK_K/8]; // quants - high bit + uint8_t qs[QK_K/4]; // quants - low 2 bits + uint8_t scales[12]; // scales, quantized with 6 bits + ggml_fp16_t d; // super-block scale +} block_q3_K; +static_assert(sizeof(block_q3_K) == sizeof(ggml_fp16_t) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding"); +#endif // 4-bit quantization // 16 blocks of 32 elements each // weight is represented as x = a * q + b // Effectively 4.5 bits per weight +#ifdef GGML_QKK_64 +typedef struct { + ggml_fp16_t d[2]; // super-block scales/mins + uint8_t scales[2]; // 4-bit block scales/mins + uint8_t qs[QK_K/2]; // 4--bit quants +} block_q4_K; +static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding"); +#else typedef struct { ggml_fp16_t d; // super-block scale for quantized scales ggml_fp16_t dmin; // super-block scale for quantized mins - uint8_t scales[3*QK_K/64]; // scales and mins, quantized with 6 bits + uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits uint8_t qs[QK_K/2]; // 4--bit quants } block_q4_K; -static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2, "wrong q4_K block size/padding"); +static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding"); +#endif // 5-bit quantization // 16 blocks of 32 elements each // weight is represented as x = a * q + b // Effectively 5.5 bits per weight +#ifdef GGML_QKK_64 typedef struct { - ggml_fp16_t d; // super-block scale for quantized scales - ggml_fp16_t dmin; // super-block scale for quantized mins - uint8_t scales[3*QK_K/64]; // scales and mins, quantized with 6 bits + ggml_fp16_t d; // super-block scale + int8_t scales[QK_K/16]; // 8-bit block scales uint8_t qh[QK_K/8]; // quants, high bit uint8_t qs[QK_K/2]; // quants, low 4 bits } block_q5_K; -static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + 3*QK_K/64 + QK_K/2 + QK_K/8, "wrong q5_K block size/padding"); +static_assert(sizeof(block_q5_K) == sizeof(ggml_fp16_t) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding"); +#else +typedef struct { + ggml_fp16_t d; // super-block scale for quantized scales + ggml_fp16_t dmin; // super-block scale for quantized mins + uint8_t scales[K_SCALE_SIZE]; // scales and mins, quantized with 6 bits + uint8_t qh[QK_K/8]; // quants, high bit + uint8_t qs[QK_K/2]; // quants, low 4 bits +} block_q5_K; +static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_fp16_t) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding"); +#endif // 6-bit quantization // weight is represented as x = a * q diff --git a/llama.cpp b/llama.cpp index ac22a48f8..c41c2a8a3 100644 --- a/llama.cpp +++ b/llama.cpp @@ -21,9 +21,13 @@ #endif #ifdef GGML_USE_K_QUANTS #ifndef QK_K +#ifdef GGML_QKK_64 +#define QK_K 64 +#else #define QK_K 256 #endif #endif +#endif #include #include @@ -2470,6 +2474,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s std::vector workers; std::mutex mutex; + auto use_more_bits = [] (int i_layer, int num_layers) -> bool { + return i_layer < num_layers/8 || i_layer >= 7*num_layers/8 || (i_layer - num_layers/8)%3 == 2; + }; + size_t idx = 0; for (llama_load_tensor & tensor : model_loader->tensors_map.tensors) { llama_buffer read_data; @@ -2524,15 +2532,16 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K; else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K; else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) && - (i_attention_wv < n_attention_wv/8 || i_attention_wv >= 7*n_attention_wv/8 || - (i_attention_wv - n_attention_wv/8)%3 == 2)) new_type = GGML_TYPE_Q6_K; + use_more_bits(i_attention_wv, n_attention_wv)) new_type = GGML_TYPE_Q6_K; + else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) && + (i_attention_wv < n_attention_wv/8 || i_attention_wv >= 7*n_attention_wv/8)) new_type = GGML_TYPE_Q6_K; ++i_attention_wv; } else if (tensor.name.find("feed_forward.w2.weight") != std::string::npos) { if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K; else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K; else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) && - (i_feed_forward_w2 < n_feed_forward_w2/8 || i_feed_forward_w2 >= 7*n_feed_forward_w2/8 || - (i_feed_forward_w2 - n_feed_forward_w2/8)%3 == 2)) new_type = GGML_TYPE_Q6_K; + use_more_bits(i_feed_forward_w2, n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K; + //else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && i_feed_forward_w2 < n_feed_forward_w2/8) new_type = GGML_TYPE_Q6_K; ++i_feed_forward_w2; } else if (tensor.name.find("attention.wo.weight") != std::string::npos) { if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q4_K; From 412c60e4739367144e51e59add5dc7749d084115 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 26 Jun 2023 19:45:09 +0300 Subject: [PATCH 041/852] readme : add link to new k-quants for visibility --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index ad1a5cfc0..670f35eca 100644 --- a/README.md +++ b/README.md @@ -11,6 +11,7 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ **Hot topics:** +- k-quants now support super-block size of 64: https://github.com/ggerganov/llama.cpp/pull/2001 - New roadmap: https://github.com/users/ggerganov/projects/7 - Azure CI brainstorming: https://github.com/ggerganov/llama.cpp/discussions/1985 - p1 : LLM-based code completion engine at the edge : https://github.com/ggml-org/p1/discussions/1 From 5743ca80928d8410754ec64a5673d5c2dd6cfbb7 Mon Sep 17 00:00:00 2001 From: katsu560 <118887472+katsu560@users.noreply.github.com> Date: Tue, 27 Jun 2023 01:46:07 +0900 Subject: [PATCH 042/852] k-quants : add AVX support to dot functions (#1916) * k_quants : add AVX support * k_quants : apply review comments --- k_quants.c | 547 +++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 547 insertions(+) diff --git a/k_quants.c b/k_quants.c index 46dd884b0..923467d7c 100644 --- a/k_quants.c +++ b/k_quants.c @@ -1393,6 +1393,112 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri *s = hsum_float_8(acc); +#elif defined __AVX__ + + const __m128i m3 = _mm_set1_epi8(0x3); + const __m128i m4 = _mm_set1_epi8(0xF); + const __m128i m2 = _mm_set1_epi8(0x2); + + __m256 acc = _mm256_setzero_ps(); + + for (int i = 0; i < nb; ++i) { + + const float dall = y[i].d * ggml_fp16_to_fp32(x[i].d); + const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin); + + const uint8_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + // load mins and scales from block_q2_K.scales[QK_K/16] + const __m128i mins_and_scales = _mm_loadu_si128((const __m128i*)x[i].scales); + const __m128i scales16 = _mm_and_si128(mins_and_scales, m4); + const __m128i mins16 = _mm_and_si128(_mm_srli_epi16(mins_and_scales, 4), m4); + const __m128i mins_0 = _mm_cvtepi8_epi16(mins16); + const __m128i mins_1 = _mm_cvtepi8_epi16(_mm_unpackhi_epi64(mins16, mins16)); + + // summs = y[i].bsums * (x[i].scales >> 4) in 16bits*8*2 to 32bits*4*2 + const __m128i summs_0 = _mm_madd_epi16(mins_0, _mm_loadu_si128((const __m128i*)&y[i].bsums[0])); + const __m128i summs_1 = _mm_madd_epi16(mins_1, _mm_loadu_si128((const __m128i*)&y[i].bsums[8])); + + // sumf += -dmin * summs in 32bits*8 + acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dmin), _mm256_cvtepi32_ps(_mm256_set_m128i(summs_1, summs_0))), acc); + + const __m128i scales_0 = _mm_cvtepi8_epi16(scales16); + const __m128i scales_1 = _mm_cvtepi8_epi16(_mm_unpackhi_epi64(scales16, scales16)); + const __m128i scales[2] = { scales_0, scales_1 }; + + __m128i sumi_0 = _mm_setzero_si128(); + __m128i sumi_1 = _mm_setzero_si128(); + + for (int j = 0; j < QK_K/128; ++j) { + + // load Q8 quants int8*16*8 from block_q8_K.qs[QK_K] + const __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_2 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_3 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_4 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_5 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_6 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_7 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + + // load 2bits*16*8 from block_q2_K.qs[QK_K/4] + __m128i q2bits = _mm_loadu_si128((const __m128i*)q2); q2 += 16; + const __m128i q2_0 = _mm_and_si128(q2bits, m3); + const __m128i q2_2 = _mm_and_si128(_mm_srli_epi16(q2bits, 2), m3); + const __m128i q2_4 = _mm_and_si128(_mm_srli_epi16(q2bits, 4), m3); + const __m128i q2_6 = _mm_and_si128(_mm_srli_epi16(q2bits, 6), m3); + q2bits = _mm_loadu_si128((const __m128i*)q2); q2 += 16; + const __m128i q2_1 = _mm_and_si128(q2bits, m3); + const __m128i q2_3 = _mm_and_si128(_mm_srli_epi16(q2bits, 2), m3); + const __m128i q2_5 = _mm_and_si128(_mm_srli_epi16(q2bits, 4), m3); + const __m128i q2_7 = _mm_and_si128(_mm_srli_epi16(q2bits, 6), m3); + + // isuml = q8[l] * ((q2[l] >> shift) & 3) in 8bits*16*8 to 16bits*8*8 + __m128i p0 = _mm_maddubs_epi16(q2_0, q8_0); + __m128i p1 = _mm_maddubs_epi16(q2_1, q8_1); + __m128i p2 = _mm_maddubs_epi16(q2_2, q8_2); + __m128i p3 = _mm_maddubs_epi16(q2_3, q8_3); + __m128i p4 = _mm_maddubs_epi16(q2_4, q8_4); + __m128i p5 = _mm_maddubs_epi16(q2_5, q8_5); + __m128i p6 = _mm_maddubs_epi16(q2_6, q8_6); + __m128i p7 = _mm_maddubs_epi16(q2_7, q8_7); + + // isum += (x[i].scales[is++] & 0xF) * isuml in 16bits*8*8 to 32bits*4*8 + __m128i shuffle = _mm_set1_epi16(0x0100); + p0 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p0); + shuffle = _mm_add_epi16(shuffle, m2); + p1 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p1); + shuffle = _mm_add_epi16(shuffle, m2); + p2 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p2); + shuffle = _mm_add_epi16(shuffle, m2); + p3 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p3); + shuffle = _mm_add_epi16(shuffle, m2); + p4 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p4); + shuffle = _mm_add_epi16(shuffle, m2); + p5 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p5); + shuffle = _mm_add_epi16(shuffle, m2); + p6 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p6); + shuffle = _mm_add_epi16(shuffle, m2); + p7 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p7); + + p0 = _mm_add_epi32(p0, p1); + p2 = _mm_add_epi32(p2, p3); + p4 = _mm_add_epi32(p4, p5); + p6 = _mm_add_epi32(p6, p7); + + // isum in 32bits*4*2 + sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p0, p2)); + sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p4, p6)); + } + + // sumf += dall * isum - dmin * summs in 32bits + __m256i sumi = _mm256_set_m128i(sumi_1, sumi_0); + acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&dall), _mm256_cvtepi32_ps(sumi)), acc); + } + + *s = hsum_float_8(acc); + #else float sumf = 0; @@ -1831,6 +1937,147 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri *s = hsum_float_8(acc); +#elif defined __AVX__ + + const __m128i m3 = _mm_set1_epi8(3); + const __m128i mone = _mm_set1_epi8(1); + const __m128i m32 = _mm_set1_epi8(32); + const __m128i m2 = _mm_set1_epi8(2); + + __m256 acc = _mm256_setzero_ps(); + + uint32_t *aux; + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); + + const uint8_t * restrict q3 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + // Set up scales + aux = (uint32_t *)x[i].scales; + __m128i scales128 = _mm_set_epi32( + ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4), + ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4), + (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4), + (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4)); + scales128 = _mm_sub_epi8(scales128, m32); + const __m128i scales_0 = _mm_cvtepi8_epi16(scales128); + const __m128i scales_1 = _mm_cvtepi8_epi16(_mm_unpackhi_epi64(scales128, scales128)); + const __m128i scales[2] = { scales_0, scales_1 }; + + // high bit *128*2 from block_q3_K.hmask[QK_K/8] + const __m128i hbits_0 = _mm_loadu_si128((const __m128i*)&x[i].hmask[0]); + const __m128i hbits_1 = _mm_loadu_si128((const __m128i*)&x[i].hmask[16]); + + // integer accumulator + __m128i sumi_0 = _mm_setzero_si128(); + __m128i sumi_1 = _mm_setzero_si128(); + + for (int j = 0; j < QK_K/128; ++j) { + // load low 2 bits *64*2 from block_q3_K.qs[QK_K/4] + const __m128i q3bits_0 = _mm_loadu_si128((const __m128i*)q3); q3 += 16; + const __m128i q3bits_1 = _mm_loadu_si128((const __m128i*)q3); q3 += 16; + + // prepare low and high bits + const int bit = j << 2; + const __m128i q3l_0 = _mm_and_si128(q3bits_0, m3); + const __m128i q3l_1 = _mm_and_si128(q3bits_1, m3); + const __m128i q3h_0 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit)), bit), 2); + const __m128i q3h_1 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit)), bit), 2); + + const __m128i q3l_2 = _mm_and_si128(_mm_srli_epi16(q3bits_0, 2), m3); + const __m128i q3l_3 = _mm_and_si128(_mm_srli_epi16(q3bits_1, 2), m3); + const __m128i q3h_2 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit+1)), bit+1), 2); + const __m128i q3h_3 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit+1)), bit+1), 2); + + const __m128i q3l_4 = _mm_and_si128(_mm_srli_epi16(q3bits_0, 4), m3); + const __m128i q3l_5 = _mm_and_si128(_mm_srli_epi16(q3bits_1, 4), m3); + const __m128i q3h_4 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit+2)), bit+2), 2); + const __m128i q3h_5 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit+2)), bit+2), 2); + + const __m128i q3l_6 = _mm_and_si128(_mm_srli_epi16(q3bits_0, 6), m3); + const __m128i q3l_7 = _mm_and_si128(_mm_srli_epi16(q3bits_1, 6), m3); + const __m128i q3h_6 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit+3)), bit+3), 2); + const __m128i q3h_7 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_1, _mm_slli_epi16(mone, bit+3)), bit+3), 2); + + // load Q8 quants from block_q8_K.qs[QK_K] + const __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_2 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_3 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_4 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_5 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_6 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_7 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + + // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use _mm256_maddubs_epi16, + // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, + // and 2 if the high bit was set) + __m128i q8s_0 = _mm_maddubs_epi16(q3h_0, q8_0); + __m128i q8s_1 = _mm_maddubs_epi16(q3h_1, q8_1); + __m128i q8s_2 = _mm_maddubs_epi16(q3h_2, q8_2); + __m128i q8s_3 = _mm_maddubs_epi16(q3h_3, q8_3); + __m128i q8s_4 = _mm_maddubs_epi16(q3h_4, q8_4); + __m128i q8s_5 = _mm_maddubs_epi16(q3h_5, q8_5); + __m128i q8s_6 = _mm_maddubs_epi16(q3h_6, q8_6); + __m128i q8s_7 = _mm_maddubs_epi16(q3h_7, q8_7); + + __m128i p16_0 = _mm_maddubs_epi16(q3l_0, q8_0); + __m128i p16_1 = _mm_maddubs_epi16(q3l_1, q8_1); + __m128i p16_2 = _mm_maddubs_epi16(q3l_2, q8_2); + __m128i p16_3 = _mm_maddubs_epi16(q3l_3, q8_3); + __m128i p16_4 = _mm_maddubs_epi16(q3l_4, q8_4); + __m128i p16_5 = _mm_maddubs_epi16(q3l_5, q8_5); + __m128i p16_6 = _mm_maddubs_epi16(q3l_6, q8_6); + __m128i p16_7 = _mm_maddubs_epi16(q3l_7, q8_7); + + p16_0 = _mm_sub_epi16(p16_0, q8s_0); + p16_1 = _mm_sub_epi16(p16_1, q8s_1); + p16_2 = _mm_sub_epi16(p16_2, q8s_2); + p16_3 = _mm_sub_epi16(p16_3, q8s_3); + p16_4 = _mm_sub_epi16(p16_4, q8s_4); + p16_5 = _mm_sub_epi16(p16_5, q8s_5); + p16_6 = _mm_sub_epi16(p16_6, q8s_6); + p16_7 = _mm_sub_epi16(p16_7, q8s_7); + + // multiply with scales + __m128i shuffle = _mm_set1_epi16(0x0100); + p16_0 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_0); + shuffle = _mm_add_epi16(shuffle, m2); + p16_1 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_1); + shuffle = _mm_add_epi16(shuffle, m2); + p16_2 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_2); + shuffle = _mm_add_epi16(shuffle, m2); + p16_3 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_3); + shuffle = _mm_add_epi16(shuffle, m2); + p16_4 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_4); + shuffle = _mm_add_epi16(shuffle, m2); + p16_5 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_5); + shuffle = _mm_add_epi16(shuffle, m2); + p16_6 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_6); + shuffle = _mm_add_epi16(shuffle, m2); + p16_7 = _mm_madd_epi16(_mm_shuffle_epi8(scales[j], shuffle), p16_7); + + // accumulate + p16_0 = _mm_add_epi32(p16_0, p16_1); + p16_2 = _mm_add_epi32(p16_2, p16_3); + p16_4 = _mm_add_epi32(p16_4, p16_5); + p16_6 = _mm_add_epi32(p16_6, p16_7); + sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2)); + sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_4, p16_6)); + + } + + // multiply with block scale and accumulate + __m256i sumi = _mm256_set_m128i(sumi_1, sumi_0); + acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi)), acc); + + } + + *s = hsum_float_8(acc); + #else // scalar version // This function is written like this so the compiler can manage to vectorize most of it @@ -2264,6 +2511,88 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri *s = hsum_float_8(acc) + _mm_cvtss_f32(acc_m); +#elif defined __AVX__ + + const __m128i m4 = _mm_set1_epi8(0xF); + const __m128i m2 = _mm_set1_epi8(0x2); + + __m256 acc = _mm256_setzero_ps(); + __m128 acc_m = _mm_setzero_ps(); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); + const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin); + + const uint8_t * restrict q4 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + const __m128i utmps = _mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0]); + const __m128i scales = _mm_cvtepu8_epi16(utmps); + const __m128i mins = _mm_cvtepu8_epi16(_mm_unpackhi_epi64(utmps, utmps)); + + const __m128i q8sums_0 = _mm_loadu_si128((const __m128i*)&y[i].bsums[0]); + const __m128i q8sums_1 = _mm_loadu_si128((const __m128i*)&y[i].bsums[8]); + const __m128i q8s = _mm_hadd_epi16(q8sums_0, q8sums_1); + const __m128i prod = _mm_madd_epi16(mins, q8s); + acc_m = _mm_add_ps(_mm_mul_ps(_mm_set1_ps(dmin), _mm_cvtepi32_ps(prod)), acc_m); + + __m128i sumi_0 = _mm_setzero_si128(); + __m128i sumi_1 = _mm_setzero_si128(); + + __m128i shuffle = _mm_set1_epi16(0x0100); + for (int j = 0; j < QK_K/64; ++j) { + + const __m128i scale_l = _mm_shuffle_epi8(scales, shuffle); + shuffle = _mm_add_epi16(shuffle, m2); + const __m128i scale_h = _mm_shuffle_epi8(scales, shuffle); + shuffle = _mm_add_epi16(shuffle, m2); + + __m128i q4bits = _mm_loadu_si128((const __m128i*)q4); q4 += 16; + const __m128i q4l_0 = _mm_and_si128(q4bits, m4); + const __m128i q4h_0 = _mm_and_si128(_mm_srli_epi16(q4bits, 4), m4); + q4bits = _mm_loadu_si128((const __m128i*)q4); q4 += 16; + const __m128i q4l_1 = _mm_and_si128(q4bits, m4); + const __m128i q4h_1 = _mm_and_si128(_mm_srli_epi16(q4bits, 4), m4); + + const __m128i q8l_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + __m128i p16l = _mm_maddubs_epi16(q4l_0, q8l_0); + p16l = _mm_madd_epi16(scale_l, p16l); + sumi_0 = _mm_add_epi32(sumi_0, p16l); + const __m128i q8l_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + p16l = _mm_maddubs_epi16(q4l_1, q8l_1); + p16l = _mm_madd_epi16(scale_l, p16l); + sumi_1 = _mm_add_epi32(sumi_1, p16l); + + const __m128i q8h_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + __m128i p16h = _mm_maddubs_epi16(q4h_0, q8h_0); + p16h = _mm_madd_epi16(scale_h, p16h); + sumi_0 = _mm_add_epi32(sumi_0, p16h); + const __m128i q8h_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + p16h = _mm_maddubs_epi16(q4h_1, q8h_1); + p16h = _mm_madd_epi16(scale_h, p16h); + sumi_1 = _mm_add_epi32(sumi_1, p16h); + + } + + __m256 vd = _mm256_set1_ps(d); + __m256i sumi = _mm256_set_m128i(sumi_1, sumi_0); + acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(sumi)), acc); + + } + + acc_m = _mm_add_ps(acc_m, _mm_movehl_ps(acc_m, acc_m)); + acc_m = _mm_add_ss(acc_m, _mm_movehdup_ps(acc_m)); + + *s = hsum_float_8(acc) + _mm_cvtss_f32(acc_m); + #else @@ -2679,6 +3008,106 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri *s = hsum_float_8(acc) + summs; +#elif defined __AVX__ + + const __m128i m4 = _mm_set1_epi8(0xF); + const __m128i mzero = _mm_setzero_si128(); + const __m128i mone = _mm_set1_epi8(1); + const __m128i m2 = _mm_set1_epi8(2); + + __m256 acc = _mm256_setzero_ps(); + + float summs = 0.f; + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); + const float dmin = -y[i].d * ggml_fp16_to_fp32(x[i].dmin); + + const uint8_t * restrict q5 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; + + const __m128i utmps = _mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0]); + const __m128i scales = _mm_cvtepu8_epi16(utmps); + const __m128i mins = _mm_cvtepu8_epi16(_mm_unpackhi_epi64(utmps, utmps)); + + const __m128i q8sums_0 = _mm_loadu_si128((const __m128i*)&y[i].bsums[0]); + const __m128i q8sums_1 = _mm_loadu_si128((const __m128i*)&y[i].bsums[8]); + const __m128i q8s = _mm_hadd_epi16(q8sums_0, q8sums_1); + const __m128i prod = _mm_madd_epi16(mins, q8s); + const __m128i hsum = _mm_hadd_epi32(_mm_hadd_epi32(prod, mzero), mzero); + summs += dmin * _mm_extract_epi32(hsum, 0); + + const __m128i hbits_0 = _mm_loadu_si128((const __m128i*)&x[i].qh[0]); + const __m128i hbits_1 = _mm_loadu_si128((const __m128i*)&x[i].qh[16]); + __m128i hmask = mone; + + __m128i sumi_0 = _mm_setzero_si128(); + __m128i sumi_1 = _mm_setzero_si128(); + + int bit = 0; + + __m128i shuffle = _mm_set1_epi16(0x0100); + for (int j = 0; j < QK_K/64; ++j) { + + const __m128i scale_0 = _mm_shuffle_epi8(scales, shuffle); + shuffle = _mm_add_epi16(shuffle, m2); + const __m128i scale_1 = _mm_shuffle_epi8(scales, shuffle); + shuffle = _mm_add_epi16(shuffle, m2); + + const __m128i q5bits_0 = _mm_loadu_si128((const __m128i*)q5); q5 += 16; + const __m128i q5bits_1 = _mm_loadu_si128((const __m128i*)q5); q5 += 16; + + __m128i q5l_0 = _mm_and_si128(q5bits_0, m4); + __m128i q5l_1 = _mm_and_si128(q5bits_1, m4); + __m128i q5h_0 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_0, hmask), bit), 4); + __m128i q5h_1 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_1, hmask), bit++), 4); + __m128i q5_0 = _mm_add_epi8(q5l_0, q5h_0); + __m128i q5_1 = _mm_add_epi8(q5l_1, q5h_1); + hmask = _mm_slli_epi16(hmask, 1); + + __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + __m128i p16_0 = _mm_maddubs_epi16(q5_0, q8_0); + __m128i p16_1 = _mm_maddubs_epi16(q5_1, q8_1); + p16_0 = _mm_madd_epi16(scale_0, p16_0); + p16_1 = _mm_madd_epi16(scale_0, p16_1); + + q5l_0 = _mm_and_si128(_mm_srli_epi16(q5bits_0, 4), m4); + q5l_1 = _mm_and_si128(_mm_srli_epi16(q5bits_1, 4), m4); + q5h_0 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_0, hmask), bit), 4); + q5h_1 = _mm_slli_epi16(_mm_srli_epi16(_mm_and_si128(hbits_1, hmask), bit++), 4); + q5_0 = _mm_add_epi8(q5l_0, q5h_0); + q5_1 = _mm_add_epi8(q5l_1, q5h_1); + hmask = _mm_slli_epi16(hmask, 1); + + q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + __m128i p16_2 = _mm_maddubs_epi16(q5_0, q8_0); + __m128i p16_3 = _mm_maddubs_epi16(q5_1, q8_1); + p16_2 = _mm_madd_epi16(scale_1, p16_2); + p16_3 = _mm_madd_epi16(scale_1, p16_3); + + sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2)); + sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_1, p16_3)); + + } + + __m256 vd = _mm256_set1_ps(d); + __m256i sumi = _mm256_set_m128i(sumi_1, sumi_0); + acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(sumi)), acc); + + } + + *s = hsum_float_8(acc) + summs; + #else const uint8_t * scales = (const uint8_t*)&utmp[0]; @@ -3130,6 +3559,124 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri *s = hsum_float_8(acc); +#elif defined __AVX__ + + const __m128i m4 = _mm_set1_epi8(0xF); + const __m128i m3 = _mm_set1_epi8(3); + const __m128i m32s = _mm_set1_epi8(32); + const __m128i m2 = _mm_set1_epi8(2); + + __m256 acc = _mm256_setzero_ps(); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * ggml_fp16_to_fp32(x[i].d); + + const uint8_t * restrict q4 = x[i].ql; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + + const __m128i scales = _mm_loadu_si128((const __m128i*)x[i].scales); + + __m128i sumi_0 = _mm_setzero_si128(); + __m128i sumi_1 = _mm_setzero_si128(); + + __m128i shuffle = _mm_set_epi64x(0x0101010101010101, 0x0000000000000000); + for (int j = 0; j < QK_K/128; ++j) { + + const __m128i q4bitsH_0 = _mm_loadu_si128((const __m128i*)qh); qh += 16; + const __m128i q4bitsH_1 = _mm_loadu_si128((const __m128i*)qh); qh += 16; + + const __m128i q4h_0 = _mm_slli_epi16(_mm_and_si128(q4bitsH_0, m3), 4); + const __m128i q4h_1 = _mm_slli_epi16(_mm_and_si128(q4bitsH_1, m3), 4); + const __m128i q4h_2 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_0, 2), m3), 4); + const __m128i q4h_3 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_1, 2), m3), 4); + const __m128i q4h_4 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_0, 4), m3), 4); + const __m128i q4h_5 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_1, 4), m3), 4); + const __m128i q4h_6 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_0, 6), m3), 4); + const __m128i q4h_7 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH_1, 6), m3), 4); + + const __m128i q4bits1_0 = _mm_loadu_si128((const __m128i*)q4); q4 += 16; + const __m128i q4bits1_1 = _mm_loadu_si128((const __m128i*)q4); q4 += 16; + const __m128i q4bits2_0 = _mm_loadu_si128((const __m128i*)q4); q4 += 16; + const __m128i q4bits2_1 = _mm_loadu_si128((const __m128i*)q4); q4 += 16; + + const __m128i q4_0 = _mm_or_si128(_mm_and_si128(q4bits1_0, m4), q4h_0); + const __m128i q4_1 = _mm_or_si128(_mm_and_si128(q4bits1_1, m4), q4h_1); + const __m128i q4_2 = _mm_or_si128(_mm_and_si128(q4bits2_0, m4), q4h_2); + const __m128i q4_3 = _mm_or_si128(_mm_and_si128(q4bits2_1, m4), q4h_3); + const __m128i q4_4 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits1_0, 4), m4), q4h_4); + const __m128i q4_5 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits1_1, 4), m4), q4h_5); + const __m128i q4_6 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits2_0, 4), m4), q4h_6); + const __m128i q4_7 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(q4bits2_1, 4), m4), q4h_7); + + const __m128i q8_0 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_1 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_2 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_3 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_4 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_5 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_6 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + const __m128i q8_7 = _mm_loadu_si128((const __m128i*)q8); q8 += 16; + + __m128i q8s_0 = _mm_maddubs_epi16(m32s, q8_0); + __m128i q8s_1 = _mm_maddubs_epi16(m32s, q8_1); + __m128i q8s_2 = _mm_maddubs_epi16(m32s, q8_2); + __m128i q8s_3 = _mm_maddubs_epi16(m32s, q8_3); + __m128i q8s_4 = _mm_maddubs_epi16(m32s, q8_4); + __m128i q8s_5 = _mm_maddubs_epi16(m32s, q8_5); + __m128i q8s_6 = _mm_maddubs_epi16(m32s, q8_6); + __m128i q8s_7 = _mm_maddubs_epi16(m32s, q8_7); + + __m128i p16_0 = _mm_maddubs_epi16(q4_0, q8_0); + __m128i p16_1 = _mm_maddubs_epi16(q4_1, q8_1); + __m128i p16_2 = _mm_maddubs_epi16(q4_2, q8_2); + __m128i p16_3 = _mm_maddubs_epi16(q4_3, q8_3); + __m128i p16_4 = _mm_maddubs_epi16(q4_4, q8_4); + __m128i p16_5 = _mm_maddubs_epi16(q4_5, q8_5); + __m128i p16_6 = _mm_maddubs_epi16(q4_6, q8_6); + __m128i p16_7 = _mm_maddubs_epi16(q4_7, q8_7); + + p16_0 = _mm_sub_epi16(p16_0, q8s_0); + p16_1 = _mm_sub_epi16(p16_1, q8s_1); + p16_2 = _mm_sub_epi16(p16_2, q8s_2); + p16_3 = _mm_sub_epi16(p16_3, q8s_3); + p16_4 = _mm_sub_epi16(p16_4, q8s_4); + p16_5 = _mm_sub_epi16(p16_5, q8s_5); + p16_6 = _mm_sub_epi16(p16_6, q8s_6); + p16_7 = _mm_sub_epi16(p16_7, q8s_7); + + const __m128i scale_0 = _mm_shuffle_epi8(scales, shuffle); + shuffle = _mm_add_epi8(shuffle, m2); + const __m128i scale_1 = _mm_shuffle_epi8(scales, shuffle); + shuffle = _mm_add_epi8(shuffle, m2); + const __m128i scale_2 = _mm_shuffle_epi8(scales, shuffle); + shuffle = _mm_add_epi8(shuffle, m2); + const __m128i scale_3 = _mm_shuffle_epi8(scales, shuffle); + shuffle = _mm_add_epi8(shuffle, m2); + + p16_0 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_0), p16_0); + p16_1 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_0, scale_0)), p16_1); + p16_2 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_1), p16_2); + p16_3 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_1, scale_1)), p16_3); + p16_4 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_2), p16_4); + p16_5 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_2, scale_2)), p16_5); + p16_6 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_3), p16_6); + p16_7 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_3, scale_3)), p16_7); + + sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2)); + sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_1, p16_3)); + sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_4, p16_6)); + sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_5, p16_7)); + + } + + __m256i sumi = _mm256_set_m128i(sumi_1, sumi_0); + acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi)), acc); + } + + *s = hsum_float_8(acc); + #else int8_t aux8[QK_K]; From a84ab1da8dc6a59a5b67420ae1322f09503ffc72 Mon Sep 17 00:00:00 2001 From: katsu560 <118887472+katsu560@users.noreply.github.com> Date: Tue, 27 Jun 2023 01:47:02 +0900 Subject: [PATCH 043/852] tests : fix quantize perf (#1990) * fix test quantize perf * avoid the global state --- tests/test-quantize-perf.cpp | 71 ++++++++++++++++++++++++++++++------ 1 file changed, 59 insertions(+), 12 deletions(-) diff --git a/tests/test-quantize-perf.cpp b/tests/test-quantize-perf.cpp index 600375771..c0e361e92 100644 --- a/tests/test-quantize-perf.cpp +++ b/tests/test-quantize-perf.cpp @@ -21,6 +21,7 @@ #define QK 32 #define WARMUP 5 #define ITERATIONS 10 +#define MAX_ITERATIONS 100000000 #define L1_SIZE 32*128 #define L2_SIZE 32*2048 @@ -36,9 +37,9 @@ struct quantize_perf_params { bool op_dequantize_row_q = false; bool op_quantize_row_q_dot = false; bool op_vec_dot_q = false; + int64_t iterations = ITERATIONS; }; - #if defined(__x86_64__) || defined(__i386__) #include @@ -75,7 +76,7 @@ void * align_with_offset(void * ptr, int offset) { return (char *) std::align(MAX_ALIGNMENT, MAX_ALIGNMENT, ptr, dummy_size) + offset; } -void benchmark_function(size_t size, size_t q_size, std::function function) { +void benchmark_function(size_t size, size_t q_size, int64_t iterations, std::function function) { int64_t min_time_us = INT64_MAX; int64_t total_time_us = 0; int64_t min_time_cycles = INT64_MAX; @@ -86,7 +87,7 @@ void benchmark_function(size_t size, size_t q_size, std::function } - for (int i = 0; i < ITERATIONS; i++) { + for (int i = 0; i < iterations; i++) { const int64_t start_time = ggml_time_us(); const int64_t start_cycles = cpu_cycles(); @@ -102,9 +103,38 @@ void benchmark_function(size_t size, size_t q_size, std::function } printf(" min cycles/%d vals : %9.2f\n", QK, QK * min_time_cycles / (float) size); - printf(" avg cycles/%d vals : %9.2f\n", QK, QK * total_time_cycles / (float) (size * ITERATIONS)); - printf(" float32 throughput : %9.2f GB/s\n", gigabytes_per_second(4 * size * ITERATIONS, total_time_us)); - printf(" quantized throughput : %9.2f GB/s\n", gigabytes_per_second(q_size * ITERATIONS, total_time_us)); + printf(" avg cycles/%d vals : %9.2f\n", QK, QK * total_time_cycles / (float) (size * iterations)); + printf(" float32 throughput : %9.2f GB/s\n", gigabytes_per_second(4 * size * iterations, total_time_us)); + printf(" quantized throughput : %9.2f GB/s\n", gigabytes_per_second(q_size * iterations, total_time_us)); +} + +void usage(char * argv[]) { + printf("Benchmark quantization specific functions on synthetic data\n"); + printf("\n"); + printf("usage: %s [options]\n", argv[0]); + printf("\n"); + printf("options: (default)\n"); + printf(" -h, --help show this help message and exit\n"); + printf(" --size SIZE set test size, divisible by 32 (L1_SIZE:%d)\n", L1_SIZE); + printf(" -3 use size as L1, L2, L3 sizes (L1:%d L2:%d L3:%d)\n", L1_SIZE, L2_SIZE, L3_SIZE); + printf(" -4 use size as L1, L2, L3, MEM sizes (L1:%d L2:%d L3:%d MEM:%d)\n", L1_SIZE, L2_SIZE, L3_SIZE, MEM_SIZE); + printf(" --op OP set test opration as quantize_row_q_reference, quantize_row_q, dequantize_row_q,\n"); + printf(" quantize_row_q_dot, vec_dot_q (all)\n"); + printf(" --type TYPE set test type as"); + for (int i = 0; i < GGML_TYPE_COUNT; i++) { + ggml_type type = (ggml_type) i; + quantize_fns_t qfns = ggml_internal_get_quantize_fn(type); + if (ggml_type_name(type) != NULL) { + if (qfns.quantize_row_q && qfns.dequantize_row_q) { + printf(" %s", ggml_type_name(type)); + } + } + } + printf(" (all)\n"); + printf(" --alignment-offset OFFSET\n"); + printf(" set alignment offset as OFFSET (0)\n"); + printf(" -i NUM, --iterations NUM\n"); + printf(" set test iteration number (%d)\n", ITERATIONS); } int main(int argc, char * argv[]) { @@ -178,6 +208,21 @@ int main(int argc, char * argv[]) { break; } params.alignment_offset = alignment; + } else if ((arg == "-i") || (arg == "--iterations")) { + if (++i >= argc) { + invalid_param = true; + break; + } + int number = std::stoi(argv[i]); + if (number < 0 || number > MAX_ITERATIONS) { + fprintf(stderr, "error: iterations must be less than %d\n", MAX_ITERATIONS); + invalid_param = true; + break; + } + params.iterations = number; + } else if ((arg == "-h") || (arg == "--help")) { + usage(argv); + return 1; } else { fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); return 1; @@ -213,6 +258,8 @@ int main(int argc, char * argv[]) { generate_data(0, largest, test_data1); generate_data(1, largest, test_data2); + int64_t iterations = params.iterations; + // Initialize GGML, ensures float conversion tables are initialized struct ggml_init_params ggml_params = { @@ -225,7 +272,7 @@ int main(int argc, char * argv[]) { for (int i = 0; i < GGML_TYPE_COUNT; i++) { ggml_type type = (ggml_type) i; quantize_fns_t qfns = ggml_internal_get_quantize_fn(i); - if (!params.include_types.empty() && std::find(params.include_types.begin(), params.include_types.end(), ggml_type_name(type)) == params.include_types.end()) { + if (!params.include_types.empty() && ggml_type_name(type) && std::find(params.include_types.begin(), params.include_types.end(), ggml_type_name(type)) == params.include_types.end()) { continue; } @@ -241,7 +288,7 @@ int main(int argc, char * argv[]) { return test_q1[0]; }; size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type); - benchmark_function(size, quantized_size, quantize_fn); + benchmark_function(size, quantized_size, iterations, quantize_fn); } printf("\n"); } @@ -255,7 +302,7 @@ int main(int argc, char * argv[]) { return test_q1[0]; }; size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type); - benchmark_function(size, quantized_size, quantize_fn); + benchmark_function(size, quantized_size, iterations, quantize_fn); } printf("\n"); } @@ -270,7 +317,7 @@ int main(int argc, char * argv[]) { return test_out[0]; }; size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type); - benchmark_function(size, quantized_size, quantize_fn); + benchmark_function(size, quantized_size, iterations, quantize_fn); } printf("\n"); } @@ -284,7 +331,7 @@ int main(int argc, char * argv[]) { return test_q1[0]; }; size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type); - benchmark_function(size, quantized_size, quantize_fn); + benchmark_function(size, quantized_size, iterations, quantize_fn); } printf("\n"); } @@ -301,7 +348,7 @@ int main(int argc, char * argv[]) { return result; }; size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type); - benchmark_function(size, quantized_size, quantize_fn); + benchmark_function(size, quantized_size, iterations, quantize_fn); } printf("\n"); } From 9225baef71407d799a6f7f563b77fd7f82791416 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 26 Jun 2023 20:10:52 +0300 Subject: [PATCH 044/852] k-quants : fix indentation --- k_quants.c | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/k_quants.c b/k_quants.c index 923467d7c..c576fd7a7 100644 --- a/k_quants.c +++ b/k_quants.c @@ -1981,7 +1981,8 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri const __m128i q3bits_1 = _mm_loadu_si128((const __m128i*)q3); q3 += 16; // prepare low and high bits - const int bit = j << 2; + const int bit = j << 2; + const __m128i q3l_0 = _mm_and_si128(q3bits_0, m3); const __m128i q3l_1 = _mm_and_si128(q3bits_1, m3); const __m128i q3h_0 = _mm_slli_epi16(_mm_srli_epi16(_mm_andnot_si128(hbits_0, _mm_slli_epi16(mone, bit)), bit), 2); From b853d456018b10820686362af41b2f2f75f1eec6 Mon Sep 17 00:00:00 2001 From: zrm Date: Mon, 26 Jun 2023 13:57:59 -0400 Subject: [PATCH 045/852] ggml : add NUMA support (#1556) * detect NUMA systems and pin work threads to nodes (linux) * disable mmap prefetch/readahead for NUMA systems * avoid sending finalize op to thread pool if it does nothing * silence robot * fix args * make --numa a param * recommendation that n_nodes evenly divide n_threads did not warrant such aggressive enforcement * lower synchronization overhead * statically allocate * move numa state to g_state * add description for --numa * ggml : minor style changes * ggml : minor style + try fix sanitizer build * llama : allow to initialize backend with NUMA support * llama : avoid ggml include in llama-util.h * ggml : style / formatting * ggml : fix handling of ops with n_threads > n_tasks > 1 * server : utilize numa parameter --------- Co-authored-by: Georgi Gerganov --- examples/common.cpp | 5 + examples/common.h | 1 + examples/embedding/embedding.cpp | 2 +- examples/main/README.md | 4 + examples/main/main.cpp | 2 +- examples/perplexity/perplexity.cpp | 2 +- examples/quantize/quantize.cpp | 2 +- examples/server/server.cpp | 2 +- examples/simple/simple.cpp | 2 +- ggml.c | 513 ++++++++++++++++------------- ggml.h | 3 + llama-util.h | 24 +- llama.cpp | 10 +- llama.h | 3 +- 14 files changed, 339 insertions(+), 236 deletions(-) diff --git a/examples/common.cpp b/examples/common.cpp index 6ac484555..002302734 100644 --- a/examples/common.cpp +++ b/examples/common.cpp @@ -343,6 +343,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { params.use_mmap = false; } else if (arg == "--mtest") { params.mem_test = true; + } else if (arg == "--numa") { + params.numa = true; } else if (arg == "--export") { params.export_cgraph = true; } else if (arg == "--verbose-prompt") { @@ -488,6 +490,9 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { if (llama_mmap_supported()) { fprintf(stderr, " --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } + fprintf(stderr, " --numa attempt optimizations that help on some NUMA systems\n"); + fprintf(stderr, " if run without this previously, it is recommended to drop the system page cache before using this\n"); + fprintf(stderr, " see https://github.com/ggerganov/llama.cpp/issues/1437\n"); #ifdef LLAMA_SUPPORTS_GPU_OFFLOAD fprintf(stderr, " -ngl N, --n-gpu-layers N\n"); fprintf(stderr, " number of layers to store in VRAM\n"); diff --git a/examples/common.h b/examples/common.h index 713320179..9d213d6d0 100644 --- a/examples/common.h +++ b/examples/common.h @@ -76,6 +76,7 @@ struct gpt_params { bool use_mmap = true; // use mmap for faster loads bool use_mlock = false; // use mlock to keep model in memory bool mem_test = false; // compute maximum memory usage + bool numa = false; // attempt optimizations that help on some NUMA systems bool export_cgraph = false; // export the computation graph bool verbose_prompt = false; // print prompt tokens before generation }; diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index 369eac1d1..3cd5bb794 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -35,7 +35,7 @@ int main(int argc, char ** argv) { params.prompt = gpt_random_prompt(rng); } - llama_init_backend(); + llama_init_backend(params.numa); llama_model * model; llama_context * ctx; diff --git a/examples/main/README.md b/examples/main/README.md index b6d3212fe..9ba1eb384 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -262,6 +262,10 @@ These options help improve the performance and memory usage of the LLaMA models. - `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed. However, if the model is larger than your total amount of RAM or if your system is low on available memory, using mmap might increase the risk of pageouts, negatively impacting performance. Disabling mmap results in slower load times but may reduce pageouts if you're not using `--mlock`. Note that if the model is larger than the total amount of RAM, turning off mmap would prevent the model from loading at all. +### NUMA support + +- `--numa`: Attempt optimizations that help on some systems with non-uniform memory access. This currently consists of pinning an equal proportion of the threads to the cores on each NUMA node, and disabling prefetch and readahead for mmap. The latter causes mapped pages to be faulted in on first access instead of all at once, and in combination with pinning threads to NUMA nodes, more of the pages end up on the NUMA node where they are used. Note that if the model is already in the system page cache, for example because of a previous run without this option, this will have little effect unless you drop the page cache first. This can be done by rebooting the system or on Linux by writing '3' to '/proc/sys/vm/drop\_caches' as root. + ### Memory Float 32 - `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. This doubles the context memory requirement and cached prompt file size but does not appear to increase generation quality in a measurable way. Not recommended. diff --git a/examples/main/main.cpp b/examples/main/main.cpp index c1e6bf126..bcdc98d61 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -105,7 +105,7 @@ int main(int argc, char ** argv) { params.prompt = gpt_random_prompt(rng); } - llama_init_backend(); + llama_init_backend(params.numa); llama_model * model; llama_context * ctx; diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index b59f5971e..f8a6cb516 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -147,7 +147,7 @@ int main(int argc, char ** argv) { params.prompt = gpt_random_prompt(rng); } - llama_init_backend(); + llama_init_backend(params.numa); llama_model * model; llama_context * ctx; diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index 4e8e6f523..1eb0f75d6 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -180,7 +180,7 @@ int main(int argc, char ** argv) { usage(argv[0]); } - llama_init_backend(); + llama_init_backend(false); // parse command line arguments const std::string fname_inp = argv[arg_idx]; diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 79df5e847..998d55eac 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -789,7 +789,7 @@ int main(int argc, char ** argv) { params.model_alias = params.model; } - llama_init_backend(); + llama_init_backend(params.numa); LOG_INFO("build info", { { "build", BUILD_NUMBER }, diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp index fc45c9340..2d913cebb 100644 --- a/examples/simple/simple.cpp +++ b/examples/simple/simple.cpp @@ -66,7 +66,7 @@ int main(int argc, char ** argv) // Init LLM : //--------------------------------- - llama_init_backend(); + llama_init_backend(params.numa); llama_model * model; llama_context * ctx; diff --git a/ggml.c b/ggml.c index e3f0c939c..4d51e31ed 100644 --- a/ggml.c +++ b/ggml.c @@ -91,6 +91,11 @@ static int sched_yield (void) { #include typedef void* thread_ret_t; + +#include +#include +#include + #endif // __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512 @@ -119,6 +124,30 @@ typedef void* thread_ret_t; #define GGML_SOFT_MAX_UNROLL 4 #define GGML_VEC_DOT_UNROLL 2 +// +// logging +// + +#if (GGML_DEBUG >= 1) +#define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__) +#else +#define GGML_PRINT_DEBUG(...) +#endif + +#if (GGML_DEBUG >= 5) +#define GGML_PRINT_DEBUG_5(...) printf(__VA_ARGS__) +#else +#define GGML_PRINT_DEBUG_5(...) +#endif + +#if (GGML_DEBUG >= 10) +#define GGML_PRINT_DEBUG_10(...) printf(__VA_ARGS__) +#else +#define GGML_PRINT_DEBUG_10(...) +#endif + +#define GGML_PRINT(...) printf(__VA_ARGS__) + #ifdef GGML_USE_ACCELERATE // uncomment to use vDSP for soft max computation // note: not sure if it is actually faster @@ -459,7 +488,6 @@ void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, size_t n) { } } - // // timing // @@ -522,6 +550,7 @@ int64_t ggml_cycles_per_ms(void) { #define ggml_perf_cycles_per_ms() 0 #endif + // // cache line // @@ -3843,12 +3872,31 @@ struct ggml_context_container { struct ggml_context context; }; +// +// NUMA support +// + +#define GGML_NUMA_MAX_NODES 8 +#define GGML_NUMA_MAX_CPUS 512 + +struct ggml_numa_node { + uint32_t cpus[GGML_NUMA_MAX_CPUS]; // hardware threads on this node + uint32_t n_cpus; +}; + +struct ggml_numa_nodes { + struct ggml_numa_node nodes[GGML_NUMA_MAX_NODES]; + uint32_t n_nodes; + uint32_t total_cpus; // hardware threads on system +}; + // // ggml state // struct ggml_state { struct ggml_context_container contexts[GGML_MAX_CONTEXTS]; + struct ggml_numa_nodes numa; }; // global state @@ -3873,6 +3921,75 @@ inline static void ggml_critical_section_end(void) { atomic_fetch_sub(&g_state_barrier, 1); } +void ggml_numa_init(void) { + if (g_state.numa.n_nodes > 0) { + fprintf(stderr, "ggml_numa_init: NUMA already initialized\n"); + + return; + } + +#ifdef __linux__ + struct stat st; + char path[256]; + int rv; + + // enumerate nodes + while (g_state.numa.n_nodes < GGML_NUMA_MAX_NODES) { + rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u", g_state.numa.n_nodes); + GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); + if (stat(path, &st) != 0) { break; } + ++g_state.numa.n_nodes; + } + + // enumerate CPUs + while (g_state.numa.total_cpus < GGML_NUMA_MAX_CPUS) { + rv = snprintf(path, sizeof(path), "/sys/devices/system/cpu/cpu%u", g_state.numa.total_cpus); + GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); + if (stat(path, &st) != 0) { break; } + ++g_state.numa.total_cpus; + } + + GGML_PRINT_DEBUG("found %u numa nodes, %u CPUs\n", g_state.numa.n_nodes, g_state.numa.total_cpus); + + if (g_state.numa.n_nodes < 1 || g_state.numa.total_cpus < 1) { + g_state.numa.n_nodes = 0; + return; + } + + for (uint32_t n = 0; n < g_state.numa.n_nodes; ++n) { + struct ggml_numa_node * node = &g_state.numa.nodes[n]; + GGML_PRINT_DEBUG("CPUs on node %u:", n); + node->n_cpus = 0; + for (uint32_t c = 0; c < g_state.numa.total_cpus; ++c) { + rv = snprintf(path, sizeof(path), "/sys/devices/system/node/node%u/cpu%u", n, c); + GGML_ASSERT(rv > 0 && (unsigned)rv < sizeof(path)); + if (stat(path, &st) == 0) { + node->cpus[node->n_cpus++] = c; + GGML_PRINT_DEBUG(" %u", c); + } + } + GGML_PRINT_DEBUG("\n"); + } + + if (ggml_is_numa()) { + FILE *fptr = fopen("/proc/sys/kernel/numa_balancing", "r"); + if (fptr != NULL) { + char buf[42]; + if (fgets(buf, sizeof(buf), fptr) && strncmp(buf, "0\n", sizeof(buf)) != 0) { + GGML_PRINT("WARNING: /proc/sys/kernel/numa_balancing is enabled, this has been observed to impair performance\n"); + } + fclose(fptr); + } + } +#else + // TODO +#endif +} + +bool ggml_is_numa(void) { + return g_state.numa.n_nodes > 1; +} + //////////////////////////////////////////////////////////////////////////////// void ggml_print_object(const struct ggml_object * obj) { @@ -4129,6 +4246,10 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { g_state = (struct ggml_state) { /*.contexts =*/ { { 0 } }, + /*.numa =*/ { + .n_nodes = 0, + .total_cpus = 0, + }, }; for (int i = 0; i < GGML_MAX_CONTEXTS; ++i) { @@ -16504,68 +16625,172 @@ typedef pthread_t ggml_thread_t; #endif +#ifdef __linux__ +void set_numa_thread_affinity(int thread_n, int n_threads) { + if (!ggml_is_numa()) { + return; + } + + // run thread on node_num thread_n / (threads per node) + const int node_num = thread_n / ((n_threads + g_state.numa.n_nodes - 1) / g_state.numa.n_nodes); + struct ggml_numa_node * node = &g_state.numa.nodes[node_num]; + size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus); + + cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus); + CPU_ZERO_S(setsize, cpus); + for (size_t i = 0; i < node->n_cpus; ++i) { + CPU_SET_S(node->cpus[i], setsize, cpus); + } + + int rv = pthread_setaffinity_np(pthread_self(), setsize, cpus); + if (rv) { + fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", + strerror(rv)); + } + + CPU_FREE(cpus); +} + +void clear_numa_thread_affinity(void) { + if (!ggml_is_numa()) { + return; + } + + size_t setsize = CPU_ALLOC_SIZE(g_state.numa.total_cpus); + + cpu_set_t * cpus = CPU_ALLOC(g_state.numa.total_cpus); + CPU_ZERO_S(setsize, cpus); + for (unsigned i = 0; i < g_state.numa.total_cpus; ++i) { + CPU_SET_S(i, setsize, cpus); + } + + int rv = pthread_setaffinity_np(pthread_self(), setsize, cpus); + if (rv) { + fprintf(stderr, "warning: pthread_setaffinity_np() failed: %s\n", + strerror(rv)); + } + + CPU_FREE(cpus); +} +#else +// TODO: Windows etc. +// (the linux implementation may also work on BSD, someone should test) +void set_numa_thread_affinity(int thread_n, int n_threads) { UNUSED(thread_n); UNUSED(n_threads); } +void clear_numa_thread_affinity(void) {} +#endif + struct ggml_compute_state_shared { - ggml_lock_t spin; + struct ggml_cgraph * cgraph; + + int64_t perf_node_start_cycles; + int64_t perf_node_start_time_us; int n_threads; // synchronization primitives - atomic_int n_ready; - atomic_bool has_work; - atomic_bool stop; // stop all threads + atomic_int n_active; // num active threads + atomic_int node_n; // active graph node }; struct ggml_compute_state { ggml_thread_t thrd; - - struct ggml_compute_params params; - struct ggml_tensor * node; - + int ith; struct ggml_compute_state_shared * shared; }; +static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const struct ggml_compute_state_shared * st) { + int64_t cycles_cur = ggml_perf_cycles() - st->perf_node_start_cycles; + int64_t time_us_cur = ggml_perf_time_us() - st->perf_node_start_time_us; + + node->perf_runs++; + node->perf_cycles += cycles_cur; + node->perf_time_us += time_us_cur; +} + static thread_ret_t ggml_graph_compute_thread(void * data) { struct ggml_compute_state * state = (struct ggml_compute_state *) data; + struct ggml_cgraph * cgraph = state->shared->cgraph; const int n_threads = state->shared->n_threads; + set_numa_thread_affinity(state->ith, n_threads); + + int node_n = -1; while (true) { - if (atomic_fetch_add(&state->shared->n_ready, 1) == n_threads - 1) { - atomic_store(&state->shared->has_work, false); - } else { - while (atomic_load(&state->shared->has_work)) { - if (atomic_load(&state->shared->stop)) { - return 0; + if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) { + // all other threads are finished and spinning + // do finalize and init here so we don't have synchronize again + struct ggml_compute_params params = { + /*.type =*/ GGML_TASK_FINALIZE, + /*.ith =*/ 0, + /*.nth =*/ 0, + /*.wsize =*/ cgraph->work ? ggml_nbytes(cgraph->work) : 0, + /*.wdata =*/ cgraph->work ? cgraph->work->data : NULL, + }; + + if (node_n != -1) { + /* FINALIZE */ + struct ggml_tensor * node = state->shared->cgraph->nodes[node_n]; + params.nth = node->n_tasks; + ggml_compute_forward(¶ms, node); + ggml_graph_compute_perf_stats_node(node, state->shared); + } + + // distribute new work or execute it direct if 1T + while (++node_n < cgraph->n_nodes) { + GGML_PRINT_DEBUG_5("%s: %d/%d\n", __func__, node_n, cgraph->n_nodes); + + struct ggml_tensor * node = cgraph->nodes[node_n]; + + state->shared->perf_node_start_cycles = ggml_perf_cycles(); + state->shared->perf_node_start_time_us = ggml_perf_time_us(); + + /* INIT */ + params.type = GGML_TASK_INIT; + params.nth = node->n_tasks; + ggml_compute_forward(¶ms, node); + + if (node->n_tasks == 1) { + // TODO: maybe push node_n to the atomic but if other threads see n_tasks is 1, + // they do something more efficient than spinning (?) + params.type = GGML_TASK_COMPUTE; + ggml_compute_forward(¶ms, node); + + params.type = GGML_TASK_FINALIZE; + ggml_compute_forward(¶ms, node); + ggml_graph_compute_perf_stats_node(node, state->shared); + } else { + break; } - ggml_lock_lock (&state->shared->spin); - ggml_lock_unlock(&state->shared->spin); } - } - atomic_fetch_sub(&state->shared->n_ready, 1); - - // wait for work - while (!atomic_load(&state->shared->has_work)) { - if (atomic_load(&state->shared->stop)) { - return 0; - } - ggml_lock_lock (&state->shared->spin); - ggml_lock_unlock(&state->shared->spin); + atomic_store(&state->shared->n_active, n_threads); + atomic_store(&state->shared->node_n, node_n); + } else { + // wait for other threads to finish + const int last = node_n; + do { + sched_yield(); + node_n = atomic_load(&state->shared->node_n); + } while (node_n == last); } // check if we should stop - if (atomic_load(&state->shared->stop)) { - break; - } + if (node_n >= cgraph->n_nodes) break; - if (state->node) { - if (state->params.ith < state->params.nth) { - ggml_compute_forward(&state->params, state->node); - } + /* COMPUTE */ + struct ggml_tensor * node = cgraph->nodes[node_n]; - state->node = NULL; - } else { - break; + struct ggml_compute_params params = { + /*.type =*/ GGML_TASK_COMPUTE, + /*.ith =*/ state->ith, + /*.nth =*/ node->n_tasks, + /*.wsize =*/ cgraph->work ? ggml_nbytes(cgraph->work) : 0, + /*.wdata =*/ cgraph->work ? cgraph->work->data : NULL, + }; + + if (state->ith < node->n_tasks) { + ggml_compute_forward(¶ms, node); } } @@ -16576,39 +16801,14 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) const int n_threads = cgraph->n_threads; struct ggml_compute_state_shared state_shared = { - /*.spin =*/ GGML_LOCK_INITIALIZER, - /*.n_threads =*/ n_threads, - /*.n_ready =*/ 0, - /*.has_work =*/ false, - /*.stop =*/ false, + /*.cgraph =*/ cgraph, + /*.perf_node_start_cycles =*/ 0, + /*.perf_node_start_time_us =*/ 0, + /*.n_threads =*/ n_threads, + /*.n_active =*/ n_threads, + /*.node_n =*/ -1, }; - struct ggml_compute_state * workers = n_threads > 1 ? alloca(sizeof(struct ggml_compute_state)*(n_threads - 1)) : NULL; - - // create thread pool - if (n_threads > 1) { - ggml_lock_init(&state_shared.spin); - - atomic_store(&state_shared.has_work, true); - - for (int j = 0; j < n_threads - 1; j++) { - workers[j] = (struct ggml_compute_state) { - .thrd = 0, - .params = { - .type = GGML_TASK_COMPUTE, - .ith = j + 1, - .nth = n_threads, - .wsize = cgraph->work ? ggml_nbytes(cgraph->work) : 0, - .wdata = cgraph->work ? cgraph->work->data : NULL, - }, - .node = NULL, - .shared = &state_shared, - }; - - int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]); - GGML_ASSERT(rc == 0); - UNUSED(rc); - } - } + struct ggml_compute_state * workers = alloca(sizeof(struct ggml_compute_state)*n_threads); // initialize tasks + work buffer { @@ -16752,7 +16952,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) } break; case GGML_OP_SCALE: { - node->n_tasks = n_threads; + node->n_tasks = 1; } break; case GGML_OP_SET: case GGML_OP_CONT: @@ -16956,166 +17156,37 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) } } + // create thread pool + if (n_threads > 1) { + for (int j = 1; j < n_threads; ++j) { + workers[j] = (struct ggml_compute_state) { + .thrd = 0, + .ith = j, + .shared = &state_shared, + }; + + const int rc = ggml_thread_create(&workers[j].thrd, NULL, ggml_graph_compute_thread, &workers[j]); + GGML_ASSERT(rc == 0); + } + } + workers[0].ith = 0; + workers[0].shared = &state_shared; + const int64_t perf_start_cycles = ggml_perf_cycles(); const int64_t perf_start_time_us = ggml_perf_time_us(); - for (int i = 0; i < cgraph->n_nodes; i++) { - GGML_PRINT_DEBUG_5("%s: %d/%d\n", __func__, i, cgraph->n_nodes); + // this is a work thread too + ggml_graph_compute_thread(&workers[0]); - struct ggml_tensor * node = cgraph->nodes[i]; - - // TODO: this could be used to avoid unnecessary computations, but it needs to be improved - //if (node->grad == NULL && node->perf_runs > 0) { - // continue; - //} - - const int64_t perf_node_start_cycles = ggml_perf_cycles(); - const int64_t perf_node_start_time_us = ggml_perf_time_us(); - - // INIT - struct ggml_compute_params params = { - /*.type =*/ GGML_TASK_INIT, - /*.ith =*/ 0, - /*.nth =*/ node->n_tasks, - /*.wsize =*/ cgraph->work ? ggml_nbytes(cgraph->work) : 0, - /*.wdata =*/ cgraph->work ? cgraph->work->data : NULL, - }; - - ggml_compute_forward(¶ms, node); - - // COMPUTE - if (node->n_tasks > 1) { - if (atomic_fetch_add(&state_shared.n_ready, 1) == n_threads - 1) { - atomic_store(&state_shared.has_work, false); - } - - while (atomic_load(&state_shared.has_work)) { - ggml_lock_lock (&state_shared.spin); - ggml_lock_unlock(&state_shared.spin); - } - - // launch thread pool - for (int j = 0; j < n_threads - 1; j++) { - workers[j].params = (struct ggml_compute_params) { - .type = GGML_TASK_COMPUTE, - .ith = j + 1, - .nth = node->n_tasks, - .wsize = cgraph->work ? ggml_nbytes(cgraph->work) : 0, - .wdata = cgraph->work ? cgraph->work->data : NULL, - }; - workers[j].node = node; - } - - atomic_fetch_sub(&state_shared.n_ready, 1); - - while (atomic_load(&state_shared.n_ready) > 0) { - ggml_lock_lock (&state_shared.spin); - ggml_lock_unlock(&state_shared.spin); - } - - atomic_store(&state_shared.has_work, true); - } - - params.type = GGML_TASK_COMPUTE; - ggml_compute_forward(¶ms, node); - - // wait for thread pool - if (node->n_tasks > 1) { - if (atomic_fetch_add(&state_shared.n_ready, 1) == n_threads - 1) { - atomic_store(&state_shared.has_work, false); - } - - while (atomic_load(&state_shared.has_work)) { - ggml_lock_lock (&state_shared.spin); - ggml_lock_unlock(&state_shared.spin); - } - - atomic_fetch_sub(&state_shared.n_ready, 1); - - while (atomic_load(&state_shared.n_ready) != 0) { - ggml_lock_lock (&state_shared.spin); - ggml_lock_unlock(&state_shared.spin); - } - } - - // FINALIZE - if (node->n_tasks > 1) { - if (atomic_fetch_add(&state_shared.n_ready, 1) == n_threads - 1) { - atomic_store(&state_shared.has_work, false); - } - - while (atomic_load(&state_shared.has_work)) { - ggml_lock_lock (&state_shared.spin); - ggml_lock_unlock(&state_shared.spin); - } - - // launch thread pool - for (int j = 0; j < n_threads - 1; j++) { - workers[j].params = (struct ggml_compute_params) { - .type = GGML_TASK_FINALIZE, - .ith = j + 1, - .nth = node->n_tasks, - .wsize = cgraph->work ? ggml_nbytes(cgraph->work) : 0, - .wdata = cgraph->work ? cgraph->work->data : NULL, - }; - workers[j].node = node; - } - - atomic_fetch_sub(&state_shared.n_ready, 1); - - while (atomic_load(&state_shared.n_ready) > 0) { - ggml_lock_lock (&state_shared.spin); - ggml_lock_unlock(&state_shared.spin); - } - - atomic_store(&state_shared.has_work, true); - } - - params.type = GGML_TASK_FINALIZE; - ggml_compute_forward(¶ms, node); - - // wait for thread pool - if (node->n_tasks > 1) { - if (atomic_fetch_add(&state_shared.n_ready, 1) == n_threads - 1) { - atomic_store(&state_shared.has_work, false); - } - - while (atomic_load(&state_shared.has_work)) { - ggml_lock_lock (&state_shared.spin); - ggml_lock_unlock(&state_shared.spin); - } - - atomic_fetch_sub(&state_shared.n_ready, 1); - - while (atomic_load(&state_shared.n_ready) != 0) { - ggml_lock_lock (&state_shared.spin); - ggml_lock_unlock(&state_shared.spin); - } - } - - // performance stats (node) - { - int64_t perf_cycles_cur = ggml_perf_cycles() - perf_node_start_cycles; - int64_t perf_time_us_cur = ggml_perf_time_us() - perf_node_start_time_us; - - node->perf_runs++; - node->perf_cycles += perf_cycles_cur; - node->perf_time_us += perf_time_us_cur; - } - } + // don't leave affinity set on the main thread + clear_numa_thread_affinity(); // join thread pool if (n_threads > 1) { - atomic_store(&state_shared.stop, true); - atomic_store(&state_shared.has_work, true); - - for (int j = 0; j < n_threads - 1; j++) { - int rc = ggml_thread_join(workers[j].thrd, NULL); + for (int j = 1; j < n_threads; j++) { + const int rc = ggml_thread_join(workers[j].thrd, NULL); GGML_ASSERT(rc == 0); - UNUSED(rc); } - - ggml_lock_destroy(&state_shared.spin); } // performance stats (graph) diff --git a/ggml.h b/ggml.h index 5ebd9c46c..6b106b1c3 100644 --- a/ggml.h +++ b/ggml.h @@ -469,6 +469,9 @@ extern "C" { GGML_API int64_t ggml_cycles(void); GGML_API int64_t ggml_cycles_per_ms(void); + GGML_API void ggml_numa_init(void); // call once for better performance on NUMA systems + GGML_API bool ggml_is_numa(void); // true if init detected that system has >1 NUMA node + GGML_API void ggml_print_object (const struct ggml_object * obj); GGML_API void ggml_print_objects(const struct ggml_context * ctx); diff --git a/llama-util.h b/llama-util.h index 4f8a4296a..042ebe43c 100644 --- a/llama-util.h +++ b/llama-util.h @@ -172,12 +172,14 @@ struct llama_mmap { #ifdef _POSIX_MAPPED_FILES static constexpr bool SUPPORTED = true; - llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */) { + llama_mmap(struct llama_file * file, size_t prefetch = (size_t) -1 /* -1 = max value */, bool numa = false) { size = file->size; int fd = fileno(file->fp); int flags = MAP_SHARED; + // prefetch/readahead impairs performance on NUMA systems + if (numa) { prefetch = 0; } #ifdef __linux__ - flags |= MAP_POPULATE; + if (prefetch) { flags |= MAP_POPULATE; } #endif addr = mmap(NULL, file->size, PROT_READ, flags, fd, 0); if (addr == MAP_FAILED) { @@ -191,6 +193,14 @@ struct llama_mmap { strerror(errno)); } } + if (numa) { + // advise the kernel not to use readahead + // (because the next page might not belong on the same node) + if (madvise(addr, file->size, MADV_RANDOM)) { + fprintf(stderr, "warning: madvise(.., MADV_RANDOM) failed: %s\n", + strerror(errno)); + } + } } ~llama_mmap() { @@ -199,7 +209,9 @@ struct llama_mmap { #elif defined(_WIN32) static constexpr bool SUPPORTED = true; - llama_mmap(struct llama_file * file, bool prefetch = true) { + llama_mmap(struct llama_file * file, bool prefetch = true, bool numa = false) { + (void) numa; + size = file->size; HANDLE hFile = (HANDLE) _get_osfhandle(_fileno(file->fp)); @@ -244,8 +256,10 @@ struct llama_mmap { #else static constexpr bool SUPPORTED = false; - llama_mmap(struct llama_file *, bool prefetch = true) { - (void)prefetch; + llama_mmap(struct llama_file *, bool prefetch = true, bool numa = false) { + (void) prefetch; + (void) numa; + throw std::runtime_error(std::string("mmap not supported")); } #endif diff --git a/llama.cpp b/llama.cpp index c41c2a8a3..1a15844bc 100644 --- a/llama.cpp +++ b/llama.cpp @@ -774,7 +774,7 @@ struct llama_model_loader { } if (use_mmap) { - mapping.reset(new llama_mmap(&file_loaders.at(0)->file, prefetch_size)); + mapping.reset(new llama_mmap(&file_loaders.at(0)->file, prefetch_size, ggml_is_numa())); if (lmlock) { lmlock->init(mapping->addr); } @@ -977,7 +977,7 @@ bool llama_mlock_supported() { return llama_mlock::SUPPORTED; } -void llama_init_backend() { +void llama_init_backend(bool numa) { ggml_time_init(); // needed to initialize f16 tables @@ -986,6 +986,10 @@ void llama_init_backend() { struct ggml_context * ctx = ggml_init(params); ggml_free(ctx); } + + if (numa) { + ggml_numa_init(); + } } int64_t llama_time_us() { @@ -2899,7 +2903,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const // maybe this should in llama_model_loader if (model_loader->use_mmap) { - model_loader->mapping.reset(new llama_mmap(&model_loader->file_loaders.at(0)->file, /* prefetch */ 0)); + model_loader->mapping.reset(new llama_mmap(&model_loader->file_loaders.at(0)->file, /* prefetch */ 0, ggml_is_numa())); } } diff --git a/llama.h b/llama.h index a833a7f4d..76239be25 100644 --- a/llama.h +++ b/llama.h @@ -140,8 +140,9 @@ extern "C" { // TODO: not great API - very likely to change // Initialize the llama + ggml backend + // If numa is true, use NUMA optimizations // Call once at the start of the program - LLAMA_API void llama_init_backend(); + LLAMA_API void llama_init_backend(bool numa); LLAMA_API int64_t llama_time_us(); From c824d2e368d193d9f564ff29880a51cda9f90527 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 26 Jun 2023 21:03:59 +0300 Subject: [PATCH 046/852] ggml : avoid conv 2d kernel round up --- ggml.c | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/ggml.c b/ggml.c index 4d51e31ed..c179bee93 100644 --- a/ggml.c +++ b/ggml.c @@ -13508,8 +13508,7 @@ static void ggml_compute_forward_conv_2d_sk_p0_f16_f32( const int nk1 = ne01; // size of the convolution row - the kernel size unrolled across all channels - // round-up so it is more suitable for SIMD - const int ew0 = ggml_up32(nk0*nk1*ne02); + const int ew0 = nk0*nk1*ne02; GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb10 == sizeof(float)); From aa777abbb73655c4e1e9237b7c0ad66745e8e48c Mon Sep 17 00:00:00 2001 From: Gustavo Rocha Dias <91472747+gustrd@users.noreply.github.com> Date: Mon, 26 Jun 2023 16:34:45 -0300 Subject: [PATCH 047/852] readme : LD_LIBRARY_PATH complement for some Android devices when building with CLBlast inside Termux (#2007) * docs - Alternative way to build at Android, with CLBlast. * doc - LD_LIBRARY_PATH complement for some Android devices when building with CLBlast inside Termux. * doc- fix typo --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 670f35eca..69f42bd00 100644 --- a/README.md +++ b/README.md @@ -687,6 +687,8 @@ GGML_OPENCL_DEVICE=0 export LD_LIBRARY_PATH=/vendor/lib64:$LD_LIBRARY_PATH ``` +(Note: some Android devices, like the Zenfone 8, need the following command instead - "export LD_LIBRARY_PATH=/system/vendor/lib64:$LD_LIBRARY_PATH". Source: https://www.reddit.com/r/termux/comments/kc3ynp/opencl_working_in_termux_more_in_comments/ ) + For easy and swift re-execution, consider documenting this final part in a .sh script file. This will enable you to rerun the process with minimal hassle. Place your desired model into the `/llama.cpp/models/` directory and execute the `./main (...)` script. From eaa6ca5a61b8c9501df9ebe3d264f45b75a5f8aa Mon Sep 17 00:00:00 2001 From: David Yang Date: Tue, 27 Jun 2023 03:45:32 +0800 Subject: [PATCH 048/852] ggml : increase max tensor name + clean up compiler warnings in train-text (#1988) * Clean up compiler warnings in train-text Some brackets to disambiguate order of operations * Increase GGML_MAX_NAME Avoiding strncpy danger in train-text-from-scratch and reducing potential future name length issues --- .../train-text-from-scratch.cpp | 23 +++++-------------- ggml.h | 2 +- 2 files changed, 7 insertions(+), 18 deletions(-) diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index 61c829e5c..5c6fd5738 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -294,20 +294,9 @@ void init_model(struct my_llama_model * model) { ggml_set_name(layer.ffn_norm, (layers_i + ".ffn_norm.weight").c_str()); - // 'layers.10.feed_forward.w1.weight' has length of 32. - // ggml_tensor->name only has 32 characters, but we need one more for the '\0' terminator. - // ggml_set_name will set the last character to '\0', so we can only store 'layers.10.feed_forward.w1.weigh'. - // when saving llama compatible model the tensors names will miss a character. - // ggml_set_name(layer.w1, (layers_i + ".feed_forward.w1.weight").c_str()); - // ggml_set_name(layer.w2, (layers_i + ".feed_forward.w2.weight").c_str()); - // ggml_set_name(layer.w3, (layers_i + ".feed_forward.w3.weight").c_str()); - - strncpy(layer.w1->name, (layers_i + ".feed_forward.w1.weight").c_str(), sizeof(layer.w1->name)); - strncpy(layer.w2->name, (layers_i + ".feed_forward.w2.weight").c_str(), sizeof(layer.w2->name)); - strncpy(layer.w3->name, (layers_i + ".feed_forward.w3.weight").c_str(), sizeof(layer.w3->name)); - layer.w1->padding[0] = 0; - layer.w2->padding[0] = 0; - layer.w3->padding[0] = 0; + ggml_format_name(layer.w1, "%s.feed_forward.w1.weight", layers_i.c_str()); + ggml_format_name(layer.w2, "%s.feed_forward.w2.weight", layers_i.c_str()); + ggml_format_name(layer.w3, "%s.feed_forward.w3.weight", layers_i.c_str()); } } @@ -2368,7 +2357,7 @@ void write_tensor(struct llama_file * file, struct ggml_tensor * tensor) { file->write_u32(0); file->write_u32(0); file->write_u32(GGML_TYPE_F32); - file->seek(0-file->tell() & 31, SEEK_CUR); + file->seek((0-file->tell()) & 31, SEEK_CUR); return; } const char * name = ggml_get_name(tensor); @@ -2383,7 +2372,7 @@ void write_tensor(struct llama_file * file, struct ggml_tensor * tensor) { file->write_u32(tensor->type); file->write_raw(ne, sizeof(ne[0]) * nd); file->write_raw(name, name_len); - file->seek(0-file->tell() & 31, SEEK_CUR); + file->seek((0-file->tell()) & 31, SEEK_CUR); file->write_raw(tensor->data, ggml_nbytes(tensor)); } @@ -2404,7 +2393,7 @@ void read_tensor(struct llama_file * file, struct ggml_tensor * tensor) { std::string name = file->read_string(name_len); GGML_ASSERT(strncmp(ggml_get_name(tensor), name.c_str(), sizeof(tensor->name)-1) == 0); - file->seek(0-file->tell() & 31, SEEK_CUR); + file->seek((0-file->tell()) & 31, SEEK_CUR); file->read_raw(tensor->data, ggml_nbytes(tensor)); } diff --git a/ggml.h b/ggml.h index 6b106b1c3..08025e57a 100644 --- a/ggml.h +++ b/ggml.h @@ -198,7 +198,7 @@ #define GGML_MAX_PARAMS 256 #define GGML_MAX_CONTEXTS 64 #define GGML_MAX_OPT 4 -#define GGML_MAX_NAME 32 +#define GGML_MAX_NAME 48 #define GGML_DEFAULT_N_THREADS 4 #define GGML_ASSERT(x) \ From d38e45157862b58a1824387e64860d68ca3533a7 Mon Sep 17 00:00:00 2001 From: Roman Parykin Date: Mon, 26 Jun 2023 22:47:59 +0300 Subject: [PATCH 049/852] readme : add Scala 3 bindings repo (#2010) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 69f42bd00..ee56988c7 100644 --- a/README.md +++ b/README.md @@ -93,6 +93,7 @@ as the main playground for developing new features for the [ggml](https://github - Node.js: [hlhr202/llama-node](https://github.com/hlhr202/llama-node) - Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb) - C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp) +- Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s) **UI:** From d9779021bd59ed96daae75e820a5ac5da47ca8ff Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 27 Jun 2023 00:06:51 +0300 Subject: [PATCH 050/852] ggml : add support for ChatGLM RoPE --- ggml.c | 82 ++++++++++++++++++++++++++++++++++++++++++++++++++-------- ggml.h | 7 +++-- 2 files changed, 76 insertions(+), 13 deletions(-) diff --git a/ggml.c b/ggml.c index c179bee93..92faf03f7 100644 --- a/ggml.c +++ b/ggml.c @@ -6778,6 +6778,7 @@ struct ggml_tensor * ggml_rope_impl( int n_past, int n_dims, int mode, + int n_ctx, bool inplace) { GGML_ASSERT(n_past >= 0); bool is_node = false; @@ -6790,11 +6791,12 @@ struct ggml_tensor * ggml_rope_impl( ggml_scratch_save(ctx); - struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 3); + struct ggml_tensor * b = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 4); ((int32_t *) b->data)[0] = n_past; ((int32_t *) b->data)[1] = n_dims; ((int32_t *) b->data)[2] = mode; + ((int32_t *) b->data)[3] = n_ctx; ggml_scratch_load(ctx); @@ -6811,8 +6813,9 @@ struct ggml_tensor * ggml_rope( struct ggml_tensor * a, int n_past, int n_dims, - int mode) { - return ggml_rope_impl(ctx, a, n_past, n_dims, mode, false); + int mode, + int n_ctx) { + return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, false); } struct ggml_tensor * ggml_rope_inplace( @@ -6820,8 +6823,9 @@ struct ggml_tensor * ggml_rope_inplace( struct ggml_tensor * a, int n_past, int n_dims, - int mode) { - return ggml_rope_impl(ctx, a, n_past, n_dims, mode, true); + int mode, + int n_ctx) { + return ggml_rope_impl(ctx, a, n_past, n_dims, mode, n_ctx, true); } // ggml_rope_back @@ -12440,7 +12444,7 @@ static void ggml_compute_forward_rope_f32( const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(src1->type == GGML_TYPE_I32); - GGML_ASSERT(ggml_nelements(src1) == 3); + GGML_ASSERT(ggml_nelements(src1) == 4); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; @@ -12449,6 +12453,7 @@ static void ggml_compute_forward_rope_f32( const int n_past = ((int32_t *) src1->data)[0]; const int n_dims = ((int32_t *) src1->data)[1]; const int mode = ((int32_t *) src1->data)[2]; + const int n_ctx = ((int32_t *) src1->data)[3]; assert(n_past >= 0); @@ -12493,6 +12498,7 @@ static void ggml_compute_forward_rope_f32( const float theta_scale = powf(10000.0, -2.0f/n_dims); const bool is_neox = mode & 2; + const bool is_glm = mode & 4; for (int64_t i3 = 0; i3 < ne3; i3++) { for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) { @@ -12503,7 +12509,32 @@ static void ggml_compute_forward_rope_f32( float theta = (float)p; - if (!is_neox) { + if (is_glm) { + theta = MIN(p, n_ctx - 2); + float block_theta = MAX(p - (n_ctx - 2), 0); + for (int64_t i0 = 0; i0 < ne0 / 4; i0++) { + const float cos_theta = cosf(theta); + const float sin_theta = sinf(theta); + const float cos_block_theta = cosf(block_theta); + const float sin_block_theta = sinf(block_theta); + + theta *= theta_scale; + block_theta *= theta_scale; + + const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + const float x0 = src[0]; + const float x1 = src[n_dims/2]; + const float x2 = src[n_dims]; + const float x3 = src[n_dims/2*3]; + + dst_data[0] = x0*cos_theta - x1*sin_theta; + dst_data[n_dims/2] = x0*sin_theta + x1*cos_theta; + dst_data[n_dims] = x2*cos_block_theta - x3*sin_block_theta; + dst_data[n_dims/2*3] = x2*sin_block_theta + x3*cos_block_theta; + } + } else if (!is_neox) { for (int64_t i0 = 0; i0 < ne0; i0 += 2) { const float cos_theta = cosf(theta); const float sin_theta = sinf(theta); @@ -12553,7 +12584,7 @@ static void ggml_compute_forward_rope_f16( const struct ggml_tensor * src1, struct ggml_tensor * dst) { GGML_ASSERT(src1->type == GGML_TYPE_I32); - GGML_ASSERT(ggml_nelements(src1) == 3); + GGML_ASSERT(ggml_nelements(src1) == 4); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; @@ -12562,6 +12593,7 @@ static void ggml_compute_forward_rope_f16( const int n_past = ((int32_t *) src1->data)[0]; const int n_dims = ((int32_t *) src1->data)[1]; const int mode = ((int32_t *) src1->data)[2]; + const int n_ctx = ((int32_t *) src1->data)[3]; assert(n_past >= 0); @@ -12606,6 +12638,7 @@ static void ggml_compute_forward_rope_f16( const float theta_scale = powf(10000.0, -2.0f/n_dims); const bool is_neox = mode & 2; + const bool is_glm = mode & 4; for (int64_t i3 = 0; i3 < ne3; i3++) { for (int64_t i2 = ((mode & 1) == 0 ? 0 : n_past); i2 < ne2; i2++) { @@ -12616,7 +12649,32 @@ static void ggml_compute_forward_rope_f16( float theta = (float)p; - if (!is_neox) { + if (is_glm) { + theta = MIN(p, n_ctx - 2); + float block_theta = MAX(p - (n_ctx - 2), 0); + for (int64_t i0 = 0; i0 < ne0 / 4; i0++) { + const float cos_theta = cosf(theta); + const float sin_theta = sinf(theta); + const float cos_block_theta = cosf(block_theta); + const float sin_block_theta = sinf(block_theta); + + theta *= theta_scale; + block_theta *= theta_scale; + + const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); + ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + const float x0 = GGML_FP16_TO_FP32(src[0]); + const float x1 = GGML_FP16_TO_FP32(src[n_dims/2]); + const float x2 = GGML_FP16_TO_FP32(src[n_dims]); + const float x3 = GGML_FP16_TO_FP32(src[n_dims/2*3]); + + dst_data[0] = GGML_FP32_TO_FP16(x0*cos_theta - x1*sin_theta); + dst_data[n_dims/2] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); + dst_data[n_dims] = GGML_FP32_TO_FP16(x2*cos_block_theta - x3*sin_block_theta); + dst_data[n_dims/2*3] = GGML_FP32_TO_FP16(x2*sin_block_theta + x3*cos_block_theta); + } + } if (!is_neox) { for (int64_t i0 = 0; i0 < ne0; i0 += 2) { const float cos_theta = cosf(theta); const float sin_theta = sinf(theta); @@ -16189,17 +16247,19 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { if (src0->grad) { assert(src1->type == GGML_TYPE_I32); - assert(ggml_nelements(src1) == 3); + assert(ggml_nelements(src1) == 4); const int n_past = ((int32_t *) src1->data)[0]; const int n_dims = ((int32_t *) src1->data)[1]; const int mode = ((int32_t *) src1->data)[2]; + const int n_ctx = ((int32_t *) src1->data)[3]; src0->grad = ggml_add_impl(ctx, src0->grad, ggml_rope(ctx, tensor->grad, n_past, n_dims, - mode), + mode, + n_ctx), inplace); } if (src1->grad) { diff --git a/ggml.h b/ggml.h index 08025e57a..459913222 100644 --- a/ggml.h +++ b/ggml.h @@ -1036,13 +1036,15 @@ extern "C" { // rotary position embedding // if mode & 1 == 1, skip n_past elements // if mode & 2 == 1, GPT-NeoX style + // if mode & 4 == 1, ChatGLM style // TODO: avoid creating a new tensor every time GGML_API struct ggml_tensor * ggml_rope( struct ggml_context * ctx, struct ggml_tensor * a, int n_past, int n_dims, - int mode); + int mode, + int n_ctx); // in-place, returns view(a) GGML_API struct ggml_tensor * ggml_rope_inplace( @@ -1050,7 +1052,8 @@ extern "C" { struct ggml_tensor * a, int n_past, int n_dims, - int mode); + int mode, + int n_ctx); // rotary position embedding backward, i.e compute dx from dy // a - dy From 181e8d975528a4e27eabb8ae6e9865f9ceae4b37 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 27 Jun 2023 00:37:13 +0300 Subject: [PATCH 051/852] llama : fix rope usage after ChatGLM change --- .../train-text-from-scratch.cpp | 20 +++++++++---------- llama.cpp | 4 ++-- 2 files changed, 12 insertions(+), 12 deletions(-) diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index 5c6fd5738..a05881d16 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -443,8 +443,8 @@ struct ggml_tensor * forward( // wk shape [n_embd, n_embd, 1, 1] // Qcur shape [n_embd/n_head, n_head, N, 1] // Kcur shape [n_embd/n_head, n_head, N, 1] - struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0); - struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0); + struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0); + struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0); // store key and value to memory { @@ -700,8 +700,8 @@ struct ggml_tensor * forward_batch( // wk shape [n_embd, n_embd, 1, 1] // Qcur shape [n_embd/n_head, n_head, N, n_batch] // Kcur shape [n_embd/n_head, n_head, N, n_batch] - struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0); - struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0); + struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0); + struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0); assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch); assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch); @@ -985,8 +985,8 @@ struct ggml_tensor * forward_batch_wo_cache( // wk shape [n_embd, n_embd, 1, 1] // Qcur shape [n_embd/n_head, n_head, N, n_batch] // Kcur shape [n_embd/n_head, n_head, N, n_batch] - struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0); - struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0); + struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0); + struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0); assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch); assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch); @@ -1207,8 +1207,8 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn( // compute Q and K and RoPE them // wq shape [n_embd, n_embd, 1, 1] // wk shape [n_embd, n_embd, 1, 1] - struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0); - struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0); + struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0); + struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0); assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch); assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch); @@ -1607,10 +1607,10 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train( use_buf(-1); struct ggml_tensor * t04 = expand(gf, ggml_mul (ctx0, t02, t03)); assert_shape_2d(t04, n_embd, N*n_batch); use_buf(-1); struct ggml_tensor * t05 = expand(gf, ggml_mul_mat (ctx0, layer.wq, t04)); assert_shape_2d(t05, n_embd, N*n_batch); use_buf(-1); struct ggml_tensor * t06 = expand(gf, ggml_reshape_4d (ctx0, t05, n_embd/n_head, n_head, N, n_batch)); assert_shape_4d(t06, n_embd/n_head, n_head, N, n_batch); - use_buf(-1); struct ggml_tensor * t07 = expand(gf, ggml_rope_inplace (ctx0, t06, n_past, n_rot, rope_mode)); assert_shape_4d(t07, n_embd/n_head, n_head, N, n_batch); + use_buf(-1); struct ggml_tensor * t07 = expand(gf, ggml_rope_inplace (ctx0, t06, n_past, n_rot, rope_mode, 0)); assert_shape_4d(t07, n_embd/n_head, n_head, N, n_batch); use_buf(-1); struct ggml_tensor * t08 = expand(gf, ggml_mul_mat (ctx0, layer.wk, t04)); assert_shape_2d(t08, n_embd, N*n_batch); use_buf(-1); struct ggml_tensor * t09 = expand(gf, ggml_reshape_4d (ctx0, t08, n_embd/n_head, n_head, N, n_batch)); assert_shape_4d(t09, n_embd/n_head, n_head, N, n_batch); - use_buf(-1); struct ggml_tensor * t10 = expand(gf, ggml_rope_inplace (ctx0, t09, n_past, n_rot, rope_mode)); assert_shape_4d(t10, n_embd/n_head, n_head, N, n_batch); + use_buf(-1); struct ggml_tensor * t10 = expand(gf, ggml_rope_inplace (ctx0, t09, n_past, n_rot, rope_mode, 0)); assert_shape_4d(t10, n_embd/n_head, n_head, N, n_batch); use_buf(-1); struct ggml_tensor * t11 = expand(gf, ggml_mul_mat (ctx0, t04, layer.wv)); assert_shape_2d(t11, N*n_batch, n_embd); use_buf(-1); struct ggml_tensor * t12 = expand(gf, ggml_reshape_4d (ctx0, t11, N, n_batch, n_embd/n_head, n_head)); assert_shape_4d(t12, N, n_batch, n_embd/n_head, n_head); use_buf(-1); struct ggml_tensor * t13 = expand(gf, ggml_permute (ctx0, t07, 0, 2, 1, 3)); assert_shape_4d(t13, n_embd/n_head, N, n_head, n_batch); diff --git a/llama.cpp b/llama.cpp index 1a15844bc..2482bdd18 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1491,11 +1491,11 @@ static bool llama_eval_internal( offload_func_kq(tmpq); ggml_set_name(tmpq, "tmpq"); - struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, N), n_past, n_rot, 0); + struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd/n_head, n_head, N), n_past, n_rot, 0, 0); offload_func_kq(Kcur); ggml_set_name(Kcur, "Kcur"); - struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, N), n_past, n_rot, 0); + struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd/n_head, n_head, N), n_past, n_rot, 0, 0); offload_func_kq(Qcur); ggml_set_name(Qcur, "Qcur"); From 0be54f75a6c3e9a09ea71bdfcdabf9a996a0549b Mon Sep 17 00:00:00 2001 From: Howard Su Date: Tue, 27 Jun 2023 13:07:13 +0800 Subject: [PATCH 052/852] baby-llama : fix build after ggml_rope change (#2016) --- examples/baby-llama/baby-llama.cpp | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/baby-llama/baby-llama.cpp b/examples/baby-llama/baby-llama.cpp index 50e14c4ac..212f54d32 100644 --- a/examples/baby-llama/baby-llama.cpp +++ b/examples/baby-llama/baby-llama.cpp @@ -566,8 +566,8 @@ struct ggml_tensor * forward( // wk shape [n_embd, n_embd, 1, 1] // Qcur shape [n_embd/n_head, n_head, N, 1] // Kcur shape [n_embd/n_head, n_head, N, 1] - struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0); - struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0); + struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0); + struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0); // store key and value to memory { @@ -823,8 +823,8 @@ struct ggml_tensor * forward_batch( // wk shape [n_embd, n_embd, 1, 1] // Qcur shape [n_embd/n_head, n_head, N, n_batch] // Kcur shape [n_embd/n_head, n_head, N, n_batch] - struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0); - struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0); + struct ggml_tensor * Qcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0); + struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0); assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch); assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch); @@ -1116,7 +1116,7 @@ struct ggml_tensor * forward_lora( model->layers[il].wqb, cur)), n_embd/n_head, n_head, N), - n_past, n_rot, 0); + n_past, n_rot, 0, 0); struct ggml_tensor * Kcur = ggml_rope(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, @@ -1125,7 +1125,7 @@ struct ggml_tensor * forward_lora( model->layers[il].wkb, cur)), n_embd/n_head, n_head, N), - n_past, n_rot, 0); + n_past, n_rot, 0, 0); // store key and value to memory { From 9d23589d638dc74577d5ff880e6d4248b795f12e Mon Sep 17 00:00:00 2001 From: Erik Scholz Date: Tue, 27 Jun 2023 19:06:33 +0200 Subject: [PATCH 053/852] fix pthreads setaffinity usage on android (#2020) --- ggml.c | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ggml.c b/ggml.c index 92faf03f7..684caaa37 100644 --- a/ggml.c +++ b/ggml.c @@ -16684,7 +16684,8 @@ typedef pthread_t ggml_thread_t; #endif -#ifdef __linux__ +// Android's libc implementation "bionic" does not support setting affinity +#if defined(__linux__) && !defined(__BIONIC__) void set_numa_thread_affinity(int thread_n, int n_threads) { if (!ggml_is_numa()) { return; From cfa0750bc9dbc2d957a91b8ed09ab0035d8f3d4e Mon Sep 17 00:00:00 2001 From: ningshanwutuobang Date: Wed, 28 Jun 2023 23:53:37 +0800 Subject: [PATCH 054/852] llama : support input embeddings directly (#1910) * add interface for float input * fixed inpL shape and type * add examples of input floats * add test example for embd input * fixed sampling * add free for context * fixed add end condition for generating * add examples for llava.py * add READMD for llava.py * add READMD for llava.py * add example of PandaGPT * refactor the interface and fixed the styles * add cmake build for embd-input * add cmake build for embd-input * Add MiniGPT-4 example * change the order of the args of llama_eval_internal * fix ci error --- .gitignore | 3 +- Makefile | 11 +- convert-lora-to-ggml.py | 6 +- examples/CMakeLists.txt | 1 + examples/embd-input/.gitignore | 4 + examples/embd-input/CMakeLists.txt | 15 ++ examples/embd-input/README.md | 63 +++++++ examples/embd-input/embd-input-lib.cpp | 220 ++++++++++++++++++++++++ examples/embd-input/embd-input-test.cpp | 35 ++++ examples/embd-input/embd-input.h | 30 ++++ examples/embd-input/embd_input.py | 71 ++++++++ examples/embd-input/llava.py | 70 ++++++++ examples/embd-input/minigpt4.py | 128 ++++++++++++++ examples/embd-input/panda_gpt.py | 98 +++++++++++ llama.cpp | 70 ++++++-- llama.h | 8 + 16 files changed, 811 insertions(+), 22 deletions(-) create mode 100644 examples/embd-input/.gitignore create mode 100644 examples/embd-input/CMakeLists.txt create mode 100644 examples/embd-input/README.md create mode 100644 examples/embd-input/embd-input-lib.cpp create mode 100644 examples/embd-input/embd-input-test.cpp create mode 100644 examples/embd-input/embd-input.h create mode 100644 examples/embd-input/embd_input.py create mode 100644 examples/embd-input/llava.py create mode 100644 examples/embd-input/minigpt4.py create mode 100644 examples/embd-input/panda_gpt.py diff --git a/.gitignore b/.gitignore index e7bfd52e3..4fccec31b 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,6 @@ *.o *.a +*.so .DS_Store .build/ .cache/ @@ -39,8 +40,8 @@ models/* /vdot /server /Pipfile +/embd-input-test /libllama.so - build-info.h arm_neon.h compile_commands.json diff --git a/Makefile b/Makefile index bda11791d..03f38bdba 100644 --- a/Makefile +++ b/Makefile @@ -1,5 +1,5 @@ # Define the default target now so that it is always the first target -BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple +BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple libembdinput.so embd-input-test ifdef LLAMA_BUILD_SERVER BUILD_TARGETS += server @@ -272,7 +272,7 @@ libllama.so: llama.o ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) clean: - rm -vf *.o *.so main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server vdot train-text-from-scratch build-info.h + rm -vf *.o *.so main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server vdot train-text-from-scratch embd-input-test build-info.h # # Examples @@ -305,6 +305,13 @@ save-load-state: examples/save-load-state/save-load-state.cpp build-info.h ggml. server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp build-info.h ggml.o llama.o common.o $(OBJS) $(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) +libembdinput.so: examples/embd-input/embd-input.h examples/embd-input/embd-input-lib.cpp build-info.h ggml.o llama.o common.o $(OBJS) + $(CXX) --shared $(CXXFLAGS) $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) + + +embd-input-test: libembdinput.so examples/embd-input/embd-input-test.cpp build-info.h ggml.o llama.o common.o $(OBJS) + $(CXX) $(CXXFLAGS) $(filter-out %.so,$(filter-out %.h,$(filter-out %.hpp,$^))) -o $@ $(LDFLAGS) -L. -lembdinput + train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp build-info.h ggml.o llama.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) diff --git a/convert-lora-to-ggml.py b/convert-lora-to-ggml.py index 9090e8d6d..f43c836f5 100644 --- a/convert-lora-to-ggml.py +++ b/convert-lora-to-ggml.py @@ -113,6 +113,10 @@ with open(output_path, "wb") as fout: write_file_header(fout, params) for k, v in model.items(): + if k.endswith(".default.weight"): + k = k.replace(".default.weight", ".weight") + if k in ["llama_proj.weight", "llama_proj.bias"]: + continue if k.endswith("lora_A.weight"): if v.dtype != torch.float16 and v.dtype != torch.float32: v = v.float() @@ -120,7 +124,7 @@ with open(output_path, "wb") as fout: else: v = v.float() - t = v.numpy() + t = v.detach().numpy() tname = translate_tensor_name(k) print(f"{k} => {tname} {t.shape} {t.dtype} {t.nbytes/1024/1024:.2f}MB") write_tensor_header(fout, tname, t.shape, t.dtype) diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index cf9c4a223..161960bb8 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -39,6 +39,7 @@ else() add_subdirectory(baby-llama) add_subdirectory(train-text-from-scratch) add_subdirectory(simple) + add_subdirectory(embd-input) if (LLAMA_METAL) add_subdirectory(metal) endif() diff --git a/examples/embd-input/.gitignore b/examples/embd-input/.gitignore new file mode 100644 index 000000000..87ef68771 --- /dev/null +++ b/examples/embd-input/.gitignore @@ -0,0 +1,4 @@ +PandaGPT +MiniGPT-4 +*.pth + diff --git a/examples/embd-input/CMakeLists.txt b/examples/embd-input/CMakeLists.txt new file mode 100644 index 000000000..2b623953e --- /dev/null +++ b/examples/embd-input/CMakeLists.txt @@ -0,0 +1,15 @@ +set(TARGET embdinput) +add_library(${TARGET} embd-input-lib.cpp embd-input.h) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) +if(TARGET BUILD_INFO) + add_dependencies(${TARGET} BUILD_INFO) +endif() + +set(TARGET embd-input-test) +add_executable(${TARGET} embd-input-test.cpp) +target_link_libraries(${TARGET} PRIVATE common llama embdinput ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) +if(TARGET BUILD_INFO) + add_dependencies(${TARGET} BUILD_INFO) +endif() diff --git a/examples/embd-input/README.md b/examples/embd-input/README.md new file mode 100644 index 000000000..02d028f26 --- /dev/null +++ b/examples/embd-input/README.md @@ -0,0 +1,63 @@ +### Examples for input embedding directly + +## Requirement +build `libembdinput.so` +run the following comman in main dir (../../). +``` +make +``` + +## [LLaVA](https://github.com/haotian-liu/LLaVA/) example (llava.py) + +1. Obtian LLaVA model (following https://github.com/haotian-liu/LLaVA/ , use https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/). +2. Convert it to ggml format. +3. `llava_projection.pth` is [pytorch_model-00003-of-00003.bin](https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/blob/main/pytorch_model-00003-of-00003.bin). + +``` +import torch + +bin_path = "../LLaVA-13b-delta-v1-1/pytorch_model-00003-of-00003.bin" +pth_path = "./examples/embd_input/llava_projection.pth" + +dic = torch.load(bin_path) +used_key = ["model.mm_projector.weight","model.mm_projector.bias"] +torch.save({k: dic[k] for k in used_key}, pth_path) +``` +4. Check the path of LLaVA model and `llava_projection.pth` in `llava.py`. + + +## [PandaGPT](https://github.com/yxuansu/PandaGPT) example (panda_gpt.py) + +1. Obtian PandaGPT lora model from https://github.com/yxuansu/PandaGPT. Rename the file to `adapter_model.bin`. Use [convert-lora-to-ggml.py](../../convert-lora-to-ggml.py) to convert it to ggml format. +The `adapter_config.json` is +``` +{ + "peft_type": "LORA", + "fan_in_fan_out": false, + "bias": null, + "modules_to_save": null, + "r": 32, + "lora_alpha": 32, + "lora_dropout": 0.1, + "target_modules": ["q_proj", "k_proj", "v_proj", "o_proj"] +} +``` +2. Papare the `vicuna` v0 model. +3. Obtain the [ImageBind](https://dl.fbaipublicfiles.com/imagebind/imagebind_huge.pth) model. +4. Clone the PandaGPT source. +``` +git clone https://github.com/yxuansu/PandaGPT +``` +5. Install the requirement of PandaGPT. +6. Check the path of PandaGPT source, ImageBind model, lora model and vicuna model in panda_gpt.py. + +## [MiniGPT-4](https://github.com/Vision-CAIR/MiniGPT-4/) example (minigpt4.py) + +1. Obtain MiniGPT-4 model from https://github.com/Vision-CAIR/MiniGPT-4/ and put it in `embd-input`. +2. Clone the MiniGPT-4 source. +``` +git clone https://github.com/Vision-CAIR/MiniGPT-4/ +``` +3. Install the requirement of PandaGPT. +4. Papare the `vicuna` v0 model. +5. Check the path of MiniGPT-4 source, MiniGPT-4 model and vicuna model in `minigpt4.py`. diff --git a/examples/embd-input/embd-input-lib.cpp b/examples/embd-input/embd-input-lib.cpp new file mode 100644 index 000000000..37de52ad6 --- /dev/null +++ b/examples/embd-input/embd-input-lib.cpp @@ -0,0 +1,220 @@ +// Defines sigaction on msys: +#ifndef _GNU_SOURCE +#define _GNU_SOURCE +#endif + +#include "embd-input.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +static llama_context ** g_ctx; + +extern "C" { + +struct MyModel* create_mymodel(int argc, char ** argv) { + gpt_params params; + + if (gpt_params_parse(argc, argv, params) == false) { + return nullptr; + } + + fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); + + if (params.seed < 0) { + params.seed = time(NULL); + } + fprintf(stderr, "%s: seed = %d\n", __func__, params.seed); + + llama_init_backend(params.numa); + + llama_model * model; + llama_context * ctx; + + g_ctx = &ctx; + + // load the model and apply lora adapter, if any + std::tie(model, ctx) = llama_init_from_gpt_params(params); + if (model == NULL) { + fprintf(stderr, "%s: error: unable to load model\n", __func__); + return nullptr; + } + + // print system information + { + fprintf(stderr, "\n"); + fprintf(stderr, "system_info: n_threads = %d / %d | %s\n", + params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info()); + } + struct MyModel * ret = new MyModel(); + ret->ctx = ctx; + ret->params = params; + ret->n_past = 0; + // printf("ctx: %d\n", ret->ctx); + return ret; +} + +void free_mymodel(struct MyModel * mymodel) { + llama_context * ctx = mymodel->ctx; + llama_print_timings(ctx); + llama_free(ctx); + delete mymodel; +} + + +bool eval_float(void * model, float * input, int N){ + MyModel * mymodel = (MyModel*)model; + llama_context * ctx = mymodel->ctx; + gpt_params params = mymodel->params; + int n_emb = llama_n_embd(ctx); + int n_past = mymodel->n_past; + int n_batch = N; // params.n_batch; + + for (int i = 0; i < (int) N; i += n_batch) { + int n_eval = (int) N - i; + if (n_eval > n_batch) { + n_eval = n_batch; + } + if (llama_eval_embd(ctx, (input+i*n_emb), n_eval, n_past, params.n_threads)) { + fprintf(stderr, "%s : failed to eval\n", __func__); + return false; + } + n_past += n_eval; + } + mymodel->n_past = n_past; + return true; +} + +bool eval_tokens(void * model, std::vector tokens) { + MyModel * mymodel = (MyModel* )model; + llama_context * ctx; + ctx = mymodel->ctx; + gpt_params params = mymodel->params; + int n_past = mymodel->n_past; + for (int i = 0; i < (int) tokens.size(); i += params.n_batch) { + int n_eval = (int) tokens.size() - i; + if (n_eval > params.n_batch) { + n_eval = params.n_batch; + } + if (llama_eval(ctx, &tokens[i], n_eval, n_past, params.n_threads)) { + fprintf(stderr, "%s : failed to eval\n", __func__); + return false; + } + n_past += n_eval; + } + mymodel->n_past = n_past; + return true; +} + +bool eval_id(struct MyModel* mymodel, int id) { + std::vector tokens; + tokens.push_back(id); + return eval_tokens(mymodel, tokens); +} + +bool eval_string(struct MyModel * mymodel,const char* str){ + llama_context * ctx = mymodel->ctx; + std::string str2 = str; + std::vector embd_inp = ::llama_tokenize(ctx, str2, true); + eval_tokens(mymodel, embd_inp); + return true; +} + +llama_token sampling_id(struct MyModel* mymodel) { + llama_context* ctx = mymodel->ctx; + gpt_params params = mymodel->params; + // int n_ctx = llama_n_ctx(ctx); + + // out of user input, sample next token + const float temp = params.temp; + const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k; + const float top_p = params.top_p; + const float tfs_z = params.tfs_z; + const float typical_p = params.typical_p; + // const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n; + // const float repeat_penalty = params.repeat_penalty; + // const float alpha_presence = params.presence_penalty; + // const float alpha_frequency = params.frequency_penalty; + const int mirostat = params.mirostat; + const float mirostat_tau = params.mirostat_tau; + const float mirostat_eta = params.mirostat_eta; + // const bool penalize_nl = params.penalize_nl; + + llama_token id = 0; + { + auto logits = llama_get_logits(ctx); + auto n_vocab = llama_n_vocab(ctx); + + // Apply params.logit_bias map + for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) { + logits[it->first] += it->second; + } + + std::vector candidates; + candidates.reserve(n_vocab); + for (llama_token token_id = 0; token_id < n_vocab; token_id++) { + candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); + } + + llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; + + // TODO: Apply penalties + // float nl_logit = logits[llama_token_nl()]; + // auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx); + // llama_sample_repetition_penalty(ctx, &candidates_p, + // last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, + // last_n_repeat, repeat_penalty); + // llama_sample_frequency_and_presence_penalties(ctx, &candidates_p, + // last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, + // last_n_repeat, alpha_frequency, alpha_presence); + // if (!penalize_nl) { + // logits[llama_token_nl()] = nl_logit; + // } + + if (temp <= 0) { + // Greedy sampling + id = llama_sample_token_greedy(ctx, &candidates_p); + } else { + if (mirostat == 1) { + static float mirostat_mu = 2.0f * mirostat_tau; + const int mirostat_m = 100; + llama_sample_temperature(ctx, &candidates_p, temp); + id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); + } else if (mirostat == 2) { + static float mirostat_mu = 2.0f * mirostat_tau; + llama_sample_temperature(ctx, &candidates_p, temp); + id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu); + } else { + // Temperature sampling + llama_sample_top_k(ctx, &candidates_p, top_k, 1); + llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1); + llama_sample_typical(ctx, &candidates_p, typical_p, 1); + llama_sample_top_p(ctx, &candidates_p, top_p, 1); + llama_sample_temperature(ctx, &candidates_p, temp); + id = llama_sample_token(ctx, &candidates_p); + } + } + } + + return id; +} + +const char * sampling(struct MyModel * mymodel) { + llama_context * ctx = mymodel->ctx; + int id = sampling_id(mymodel); + std::string ret; + if (id == llama_token_eos()) ret = ""; + else ret = llama_token_to_str(ctx, id); + eval_id(mymodel, id); + return ret.c_str(); +} + +} diff --git a/examples/embd-input/embd-input-test.cpp b/examples/embd-input/embd-input-test.cpp new file mode 100644 index 000000000..e5e040f62 --- /dev/null +++ b/examples/embd-input/embd-input-test.cpp @@ -0,0 +1,35 @@ +#include "embd-input.h" +#include +#include +#include + +int main(int argc, char** argv) { + + auto mymodel = create_mymodel(argc, argv); + int N = 10; + int max_tgt_len = 500; + int n_embd = llama_n_embd(mymodel->ctx); + + // add random float embd to test evaluation + float * data = new float[N*n_embd]; + std::default_random_engine e; + std::uniform_real_distribution u(0,1); + for (int i=0;iparams.prompt.c_str()); + const char* tmp; + for (int i=0; i")==0) break; + printf("%s", tmp); + fflush(stdout); + } + printf("\n"); + free_mymodel(mymodel); + return 0; +} diff --git a/examples/embd-input/embd-input.h b/examples/embd-input/embd-input.h new file mode 100644 index 000000000..4fefabd42 --- /dev/null +++ b/examples/embd-input/embd-input.h @@ -0,0 +1,30 @@ +#ifndef _EMBD_INPUT_H_ +#define _EMBD_INPUT_H_ 1 + +#include "common.h" +#include "llama.h" +#include "build-info.h" + + +extern "C" { + +typedef struct MyModel { + llama_context* ctx; + gpt_params params; + int n_past = 0; +} MyModel; + + +struct MyModel* create_mymodel(int argc, char ** argv); + +bool eval_float(void* model, float* input, int N); +bool eval_tokens(void* model, std::vector tokens); +bool eval_id(struct MyModel* mymodel, int id); +bool eval_string(struct MyModel* mymodel, const char* str); +const char* sampling(struct MyModel* mymodel); +llama_token sampling_id(struct MyModel* mymodel); +void free_mymodel(struct MyModel* mymodel); + +} + +#endif diff --git a/examples/embd-input/embd_input.py b/examples/embd-input/embd_input.py new file mode 100644 index 000000000..be2896614 --- /dev/null +++ b/examples/embd-input/embd_input.py @@ -0,0 +1,71 @@ +import ctypes +from ctypes import cdll, c_char_p, c_void_p, POINTER, c_float, c_int +import numpy as np +import os + +libc = cdll.LoadLibrary("./libembdinput.so") +libc.sampling.restype=c_char_p +libc.create_mymodel.restype=c_void_p +libc.eval_string.argtypes=[c_void_p, c_char_p] +libc.sampling.argtypes=[c_void_p] +libc.eval_float.argtypes=[c_void_p, POINTER(c_float), c_int] + + +class MyModel: + def __init__(self, args): + argc = len(args) + c_str = [c_char_p(i.encode()) for i in args] + args_c = (c_char_p * argc)(*c_str) + self.model = c_void_p(libc.create_mymodel(argc, args_c)) + self.max_tgt_len = 512 + self.print_string_eval = True + + def __del__(self): + libc.free_mymodel(self.model) + + def eval_float(self, x): + libc.eval_float(self.model, x.astype(np.float32).ctypes.data_as(POINTER(c_float)), x.shape[1]) + + def eval_string(self, x): + libc.eval_string(self.model, x.encode()) # c_char_p(x.encode())) + if self.print_string_eval: + print(x) + + def eval_token(self, x): + libc.eval_id(self.model, x) + + def sampling(self): + s = libc.sampling(self.model) + return s + + def stream_generate(self, end=""): + ret = b"" + end = end.encode() + for _ in range(self.max_tgt_len): + tmp = self.sampling() + ret += tmp + yield tmp + if ret.endswith(end): + break + + def generate_with_print(self, end=""): + ret = b"" + for i in self.stream_generate(end=end): + ret += i + print(i.decode(errors="replace"), end="", flush=True) + print("") + return ret.decode(errors="replace") + + + def generate(self, end=""): + text = b"".join(self.stream_generate(end=end)) + return text.decode(errors="replace") + +if __name__ == "__main__": + model = MyModel(["main", "--model", "../llama.cpp/models/ggml-vic13b-q4_1.bin", "-c", "2048"]) + model.eval_string("""user: what is the color of the flag of UN?""") + x = np.random.random((5120,10))# , dtype=np.float32) + model.eval_float(x) + model.eval_string("""assistant:""") + for i in model.generate(): + print(i.decode(errors="replace"), end="", flush=True) diff --git a/examples/embd-input/llava.py b/examples/embd-input/llava.py new file mode 100644 index 000000000..2f20cb722 --- /dev/null +++ b/examples/embd-input/llava.py @@ -0,0 +1,70 @@ +import sys +import os +sys.path.insert(0, os.path.dirname(__file__)) +from embd_input import MyModel +import numpy as np +from torch import nn +import torch +from transformers import CLIPVisionModel, CLIPImageProcessor +from PIL import Image + +# model parameters from 'liuhaotian/LLaVA-13b-delta-v1-1' +vision_tower = "openai/clip-vit-large-patch14" +select_hidden_state_layer = -2 +# (vision_config.image_size // vision_config.patch_size) ** 2 +image_token_len = (224//14)**2 + +class Llava: + def __init__(self, args): + self.image_processor = CLIPImageProcessor.from_pretrained(vision_tower) + self.vision_tower = CLIPVisionModel.from_pretrained(vision_tower) + self.mm_projector = nn.Linear(1024, 5120) + self.model = MyModel(["main", *args]) + + def load_projection(self, path): + state = torch.load(path) + self.mm_projector.load_state_dict({ + "weight": state["model.mm_projector.weight"], + "bias": state["model.mm_projector.bias"]}) + + def chat(self, question): + self.model.eval_string("user: ") + self.model.eval_string(question) + self.model.eval_string("\nassistant: ") + return self.model.generate_with_print() + + def chat_with_image(self, image, question): + with torch.no_grad(): + embd_image = self.image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0] + image_forward_out = self.vision_tower(embd_image.unsqueeze(0), output_hidden_states=True) + select_hidden_state = image_forward_out.hidden_states[select_hidden_state_layer] + image_feature = select_hidden_state[:, 1:] + embd_image = self.mm_projector(image_feature) + embd_image = embd_image.cpu().numpy()[0] + self.model.eval_string("user: ") + self.model.eval_token(32003-2) # im_start + self.model.eval_float(embd_image.T) + for i in range(image_token_len-embd_image.shape[0]): + self.model.eval_token(32003-3) # im_patch + self.model.eval_token(32003-1) # im_end + self.model.eval_string(question) + self.model.eval_string("\nassistant: ") + return self.model.generate_with_print() + + +if __name__=="__main__": + # model form liuhaotian/LLaVA-13b-delta-v1-1 + a = Llava(["--model", "./models/ggml-llava-13b-v1.1.bin", "-c", "2048"]) + # Extract from https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/blob/main/pytorch_model-00003-of-00003.bin. + # Also here can use pytorch_model-00003-of-00003.bin directly. + a.load_projection(os.path.join( + os.path.dirname(__file__) , + "llava_projetion.pth")) + respose = a.chat_with_image( + Image.open("./media/llama1-logo.png").convert('RGB'), + "what is the text in the picture?") + respose + a.chat("what is the color of it?") + + + diff --git a/examples/embd-input/minigpt4.py b/examples/embd-input/minigpt4.py new file mode 100644 index 000000000..8e98f8517 --- /dev/null +++ b/examples/embd-input/minigpt4.py @@ -0,0 +1,128 @@ +import sys +import os +sys.path.insert(0, os.path.dirname(__file__)) +from embd_input import MyModel +import numpy as np +from torch import nn +import torch +from PIL import Image + +minigpt4_path = os.path.join(os.path.dirname(__file__), "MiniGPT-4") +sys.path.insert(0, minigpt4_path) +from minigpt4.models.blip2 import Blip2Base +from minigpt4.processors.blip_processors import Blip2ImageEvalProcessor + + +class MiniGPT4(Blip2Base): + """ + MiniGPT4 model from https://github.com/Vision-CAIR/MiniGPT-4 + """ + def __init__(self, + args, + vit_model="eva_clip_g", + q_former_model="https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth", + img_size=224, + drop_path_rate=0, + use_grad_checkpoint=False, + vit_precision="fp32", + freeze_vit=True, + freeze_qformer=True, + num_query_token=32, + llama_model="", + prompt_path="", + prompt_template="", + max_txt_len=32, + end_sym='\n', + low_resource=False, # use 8 bit and put vit in cpu + device_8bit=0 + ): + super().__init__() + self.img_size = img_size + self.low_resource = low_resource + self.preprocessor = Blip2ImageEvalProcessor(img_size) + + print('Loading VIT') + self.visual_encoder, self.ln_vision = self.init_vision_encoder( + vit_model, img_size, drop_path_rate, use_grad_checkpoint, vit_precision + ) + print('Loading VIT Done') + print('Loading Q-Former') + self.Qformer, self.query_tokens = self.init_Qformer( + num_query_token, self.visual_encoder.num_features + ) + self.Qformer.cls = None + self.Qformer.bert.embeddings.word_embeddings = None + self.Qformer.bert.embeddings.position_embeddings = None + for layer in self.Qformer.bert.encoder.layer: + layer.output = None + layer.intermediate = None + self.load_from_pretrained(url_or_filename=q_former_model) + print('Loading Q-Former Done') + self.llama_proj = nn.Linear( + self.Qformer.config.hidden_size, 5120 # self.llama_model.config.hidden_size + ) + self.max_txt_len = max_txt_len + self.end_sym = end_sym + self.model = MyModel(["main", *args]) + # system promt + self.model.eval_string("Give the following image: ImageContent. " + "You will be able to see the image once I provide it to you. Please answer my questions." + "###") + + def encode_img(self, image): + image = self.preprocessor(image) + image = image.unsqueeze(0) + device = image.device + if self.low_resource: + self.vit_to_cpu() + image = image.to("cpu") + + with self.maybe_autocast(): + image_embeds = self.ln_vision(self.visual_encoder(image)).to(device) + image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device) + + query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1) + query_output = self.Qformer.bert( + query_embeds=query_tokens, + encoder_hidden_states=image_embeds, + encoder_attention_mask=image_atts, + return_dict=True, + ) + + inputs_llama = self.llama_proj(query_output.last_hidden_state) + # atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(image.device) + return inputs_llama + + def load_projection(self, path): + state = torch.load(path)["model"] + self.llama_proj.load_state_dict({ + "weight": state["llama_proj.weight"], + "bias": state["llama_proj.bias"]}) + + def chat(self, question): + self.model.eval_string("Human: ") + self.model.eval_string(question) + self.model.eval_string("\n### Assistant:") + return self.model.generate_with_print(end="###") + + def chat_with_image(self, image, question): + with torch.no_grad(): + embd_image = self.encode_img(image) + embd_image = embd_image.cpu().numpy()[0] + self.model.eval_string("Human: ") + self.model.eval_float(embd_image.T) + self.model.eval_string(" ") + self.model.eval_string(question) + self.model.eval_string("\n### Assistant:") + return self.model.generate_with_print(end="###") + + +if __name__=="__main__": + a = MiniGPT4(["--model", "./models/ggml-vicuna-13b-v0-q4_1.bin", "-c", "2048"]) + a.load_projection(os.path.join( + os.path.dirname(__file__) , + "pretrained_minigpt4.pth")) + respose = a.chat_with_image( + Image.open("./media/llama1-logo.png").convert('RGB'), + "what is the text in the picture?") + a.chat("what is the color of it?") diff --git a/examples/embd-input/panda_gpt.py b/examples/embd-input/panda_gpt.py new file mode 100644 index 000000000..0cfac5f32 --- /dev/null +++ b/examples/embd-input/panda_gpt.py @@ -0,0 +1,98 @@ +import sys +import os +sys.path.insert(0, os.path.dirname(__file__)) +from embd_input import MyModel +import numpy as np +from torch import nn +import torch + +# use PandaGPT path +panda_gpt_path = os.path.join(os.path.dirname(__file__), "PandaGPT") +imagebind_ckpt_path = "./models/panda_gpt/" + +sys.path.insert(0, os.path.join(panda_gpt_path,"code","model")) +from ImageBind.models import imagebind_model +from ImageBind import data + +ModalityType = imagebind_model.ModalityType +max_tgt_len = 400 + +class PandaGPT: + def __init__(self, args): + self.visual_encoder,_ = imagebind_model.imagebind_huge(pretrained=True, store_path=imagebind_ckpt_path) + self.visual_encoder.eval() + self.llama_proj = nn.Linear(1024, 5120) # self.visual_hidden_size, 5120) + self.max_tgt_len = max_tgt_len + self.model = MyModel(["main", *args]) + self.generated_text = "" + self.device = "cpu" + + def load_projection(self, path): + state = torch.load(path, map_location="cpu") + self.llama_proj.load_state_dict({ + "weight": state["llama_proj.weight"], + "bias": state["llama_proj.bias"]}) + + def eval_inputs(self, inputs): + self.model.eval_string("") + embds = self.extract_multimoal_feature(inputs) + for i in embds: + self.model.eval_float(i.T) + self.model.eval_string(" ") + + def chat(self, question): + return self.chat_with_image(None, question) + + def chat_with_image(self, inputs, question): + if self.generated_text == "": + self.model.eval_string("###") + self.model.eval_string(" Human: ") + if inputs: + self.eval_inputs(inputs) + self.model.eval_string(question) + self.model.eval_string("\n### Assistant:") + ret = self.model.generate_with_print(end="###") + self.generated_text += ret + return ret + + def extract_multimoal_feature(self, inputs): + features = [] + for key in ["image", "audio", "video", "thermal"]: + if key + "_paths" in inputs: + embeds = self.encode_data(key, inputs[key+"_paths"]) + features.append(embeds) + return features + + def encode_data(self, data_type, data_paths): + + type_map = { + "image": ModalityType.VISION, + "audio": ModalityType.AUDIO, + "video": ModalityType.VISION, + "thermal": ModalityType.THERMAL, + } + load_map = { + "image": data.load_and_transform_vision_data, + "audio": data.load_and_transform_audio_data, + "video": data.load_and_transform_video_data, + "thermal": data.load_and_transform_thermal_data + } + + load_function = load_map[data_type] + key = type_map[data_type] + + inputs = {key: load_function(data_paths, self.device)} + with torch.no_grad(): + embeddings = self.visual_encoder(inputs) + embeds = embeddings[key] + embeds = self.llama_proj(embeds).cpu().numpy() + return embeds + + +if __name__=="__main__": + a = PandaGPT(["--model", "./models/ggml-vicuna-13b-v0-q4_1.bin", "-c", "2048", "--lora", "./models/panda_gpt/ggml-adapter-model.bin","--temp", "0"]) + a.load_projection("./models/panda_gpt/adapter_model.bin") + a.chat_with_image( + {"image_paths": ["./media/llama1-logo.png"]}, + "what is the text in the picture? 'llama' or 'lambda'?") + a.chat("what is the color of it?") diff --git a/llama.cpp b/llama.cpp index 2482bdd18..5a142aba6 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1369,22 +1369,26 @@ static bool llama_model_load( // evaluate the transformer // -// - lctx: llama context -// - tokens: new batch of tokens to process -// - n_past: the context size so far -// - n_threads: number of threads to use -// - cgraph_fname: filename of the exported computation graph +// - lctx: llama context +// - tokens: new batch of tokens to process +// - embd embeddings input +// - n_tokens number of tokens +// - n_past: the context size so far +// - n_threads: number of threads to use // static bool llama_eval_internal( - llama_context & lctx, - const llama_token * tokens, - const int n_tokens, - const int n_past, - const int n_threads, + llama_context & lctx, + const llama_token * tokens, + const float * embd, + const int n_tokens, + const int n_past, + const int n_threads, const char * cgraph_fname) { + LLAMA_ASSERT((!tokens && embd) || (tokens && !embd)); + // enforce that the first token is BOS - if (n_past == 0 && tokens[0] != llama_token_bos()) { + if (tokens && n_past == 0 && tokens[0] != llama_token_bos()) { fprintf(stderr, "%s: first token must be BOS\n", __func__); return false; } @@ -1424,12 +1428,18 @@ static bool llama_eval_internal( ggml_cgraph gf = {}; gf.n_threads = N >= 32 && ggml_cpu_has_blas() && !ggml_cpu_has_gpublas() ? 1 : n_threads; - struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); - ggml_set_name(embd, "embd"); - memcpy(embd->data, tokens, N*ggml_element_size(embd)); - struct ggml_tensor * cur; - struct ggml_tensor * inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd); + struct ggml_tensor * inpL; + + if (tokens) { + struct ggml_tensor * embd = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); + ggml_set_name(embd, "embd"); + memcpy(embd->data, tokens, N*ggml_element_size(embd)); + inpL = ggml_get_rows(ctx0, model.tok_embeddings, embd); + } else { + inpL = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N); + memcpy(inpL->data, embd, N * n_embd * ggml_element_size(inpL)); + } const int i_gpu_start = n_layer - n_gpu_layers; (void) i_gpu_start; @@ -2654,6 +2664,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s } } + + // // interface implementation // @@ -3421,7 +3433,29 @@ int llama_eval( int n_tokens, int n_past, int n_threads) { - if (!llama_eval_internal(*ctx, tokens, n_tokens, n_past, n_threads, nullptr)) { + if (!llama_eval_internal(*ctx, tokens, nullptr, n_tokens, n_past, n_threads, nullptr)) { + fprintf(stderr, "%s: failed to eval\n", __func__); + return 1; + } + + // get a more accurate load time, upon first eval + // TODO: fix this + if (!ctx->has_evaluated_once) { + ctx->t_load_us = ggml_time_us() - ctx->t_start_us; + ctx->has_evaluated_once = true; + } + + return 0; +} + + +int llama_eval_embd( + struct llama_context * ctx, + const float * embd, + int n_tokens, + int n_past, + int n_threads) { + if (!llama_eval_internal(*ctx, nullptr, embd, n_tokens, n_past, n_threads, nullptr)) { fprintf(stderr, "%s: failed to eval\n", __func__); return 1; } @@ -3442,7 +3476,7 @@ int llama_eval_export(struct llama_context * ctx, const char * fname) { const std::vector tmp(n_batch, llama_token_bos()); - if (!llama_eval_internal(*ctx, tmp.data(), tmp.size(), n_ctx, 1, fname)) { + if (!llama_eval_internal(*ctx, tmp.data(), nullptr, tmp.size(), n_ctx, 1, fname)) { fprintf(stderr, "%s: failed to eval\n", __func__); return 1; } diff --git a/llama.h b/llama.h index 76239be25..c2f2e5331 100644 --- a/llama.h +++ b/llama.h @@ -226,6 +226,14 @@ extern "C" { int n_past, int n_threads); + // Same as llama_eval, but use float matrix input directly. + LLAMA_API int llama_eval_embd( + struct llama_context * ctx, + const float * embd, + int n_tokens, + int n_past, + int n_threads); + // Export a static computation graph for context of 511 and batch size of 1 // NOTE: since this functionality is mostly for debugging and demonstration purposes, we hardcode these // parameters here to keep things simple From 7f9753fa1263c4eded9a3de19778562f0e1093d7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Wed, 28 Jun 2023 18:35:54 +0200 Subject: [PATCH 055/852] CUDA GPU acceleration for LoRAs + f16 models (#1970) --- examples/common.cpp | 7 ------ ggml-cuda.cu | 53 +++++++++++++++++++++++++++++++++++---------- ggml-cuda.h | 1 + llama.cpp | 36 +++++++++++++++++++++++++++++- 4 files changed, 78 insertions(+), 19 deletions(-) diff --git a/examples/common.cpp b/examples/common.cpp index 002302734..5addd10a1 100644 --- a/examples/common.cpp +++ b/examples/common.cpp @@ -416,13 +416,6 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { exit(1); } -#ifdef GGML_USE_CUBLAS - if (!params.lora_adapter.empty() && params.n_gpu_layers > 0) { - fprintf(stderr, "%s: error: the simultaneous use of LoRAs and GPU acceleration is not supported", __func__); - exit(1); - } -#endif // GGML_USE_CUBLAS - if (escape_prompt) { process_escapes(params.prompt); } diff --git a/ggml-cuda.cu b/ggml-cuda.cu index c34e96abf..be75cb792 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -223,6 +223,15 @@ static __global__ void add_f32(const float * x, const float * y, float * dst, co dst[i] = x[i] + y[i]; } +static __global__ void add_f16_f32_f16(const half * x, const float * y, half * dst, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + + if (i >= k) { + return; + } + dst[i] = __hadd(x[i], __float2half(y[i])); +} + static __global__ void mul_f32(const float * x, const float * y, float * dst, const int kx, const int ky) { const int i = blockDim.x*blockIdx.x + threadIdx.x; @@ -1459,6 +1468,11 @@ static void add_f32_cuda(const float * x, const float * y, float * dst, const in add_f32<<>>(x, y, dst, k); } +static void add_f16_f32_f16_cuda(const half * x, const float * y, half * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_ADD_BLOCK_SIZE - 1) / CUDA_ADD_BLOCK_SIZE; + add_f16_f32_f16<<>>(x, y, dst, k); +} + static void mul_f32_cuda(const float * x, const float * y, float * dst, const int kx, const int ky, cudaStream_t stream) { const int num_blocks = (kx + CUDA_MUL_BLOCK_SIZE - 1) / CUDA_MUL_BLOCK_SIZE; mul_f32<<>>(x, y, dst, kx, ky); @@ -1941,7 +1955,7 @@ inline void ggml_cuda_op_add( float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1, cudaStream_t & cudaStream_main){ - GGML_ASSERT(src0_ddf_i != nullptr); + GGML_ASSERT(src0_ddq_i != nullptr || src0_ddf_i != nullptr); GGML_ASSERT(src1_ddf_i != nullptr); GGML_ASSERT(dst_ddf_i != nullptr); @@ -1949,7 +1963,13 @@ inline void ggml_cuda_op_add( const int64_t i01_diff = i01_high - i01_low; // compute - add_f32_cuda(src0_ddf_i, src1_ddf_i, dst_ddf_i, ne0*i01_diff, cudaStream_main); + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + add_f32_cuda(src0_ddf_i, src1_ddf_i, dst_ddf_i, ne0*i01_diff, cudaStream_main); + } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { + add_f16_f32_f16_cuda((half *) src0_ddq_i, src1_ddf_i, (half *) dst_ddf_i, ne0*i01_diff, cudaStream_main); + } else { + GGML_ASSERT(false); + } CUDA_CHECK(cudaGetLastError()); (void) src1; @@ -2547,8 +2567,14 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm } void ggml_cuda_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32); - ggml_cuda_op(src0, src1, dst, ggml_cuda_op_add, true, true); + // ggml_cuda_add permits f16 dst even though this could in theory cause problems with the pointer arithmetic in ggml_cuda_op. + // Due to flatten_rows == true this does in practice not make a difference however. + // Better solution would be nice but right now that would require disproportionate changes. + GGML_ASSERT( + (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16) && + src1->type == GGML_TYPE_F32 && + (dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16)); + ggml_cuda_op(src0, src1, dst, ggml_cuda_op_add, false, true); } void ggml_cuda_mul(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -2801,7 +2827,7 @@ void ggml_cuda_free_data(struct ggml_tensor * tensor) { delete extra; } -void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch) { +void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch, bool force_inplace) { if (scratch && g_scratch_size == 0) { return; } @@ -2810,11 +2836,11 @@ void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch) { if (tensor->src0 != nullptr && tensor->src0->backend == GGML_BACKEND_CPU) { const ggml_op src0_op = tensor->src0->op; if (src0_op == GGML_OP_RESHAPE || src0_op == GGML_OP_TRANSPOSE || src0_op == GGML_OP_VIEW) { - ggml_cuda_assign_buffers_impl(tensor->src0, scratch); + ggml_cuda_assign_buffers_impl(tensor->src0, scratch, force_inplace); } } if (tensor->op == GGML_OP_CPY && tensor->src1->backend == GGML_BACKEND_CPU) { - ggml_cuda_assign_buffers_impl(tensor->src1, scratch); + ggml_cuda_assign_buffers_impl(tensor->src1, scratch, force_inplace); } tensor->backend = GGML_BACKEND_GPU; @@ -2822,11 +2848,12 @@ void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch) { memset(extra, 0, sizeof(*extra)); const bool inplace = (tensor->src0 != nullptr && tensor->src0->data == tensor->data) || - tensor->op == GGML_OP_VIEW; + tensor->op == GGML_OP_VIEW || + force_inplace; const size_t size = ggml_nbytes(tensor); CUDA_CHECK(cudaSetDevice(g_main_device)); - if (inplace && tensor->src0->backend == GGML_BACKEND_GPU) { + if (inplace && (tensor->src0->backend == GGML_BACKEND_GPU || tensor->src0->backend == GGML_BACKEND_GPU_SPLIT)) { struct ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src0->extra; char * src0_ddc = (char *) src0_extra->data_device[g_main_device]; size_t offset = 0; @@ -2865,11 +2892,15 @@ void ggml_cuda_assign_buffers_impl(struct ggml_tensor * tensor, bool scratch) { } void ggml_cuda_assign_buffers(struct ggml_tensor * tensor) { - ggml_cuda_assign_buffers_impl(tensor, true); + ggml_cuda_assign_buffers_impl(tensor, true, false); } void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor) { - ggml_cuda_assign_buffers_impl(tensor, false); + ggml_cuda_assign_buffers_impl(tensor, false, false); +} + +void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor) { + ggml_cuda_assign_buffers_impl(tensor, false, true); } void ggml_cuda_set_main_device(int main_device) { diff --git a/ggml-cuda.h b/ggml-cuda.h index d32b44842..7a65a3558 100644 --- a/ggml-cuda.h +++ b/ggml-cuda.h @@ -29,6 +29,7 @@ void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor); void ggml_cuda_free_data(struct ggml_tensor * tensor); void ggml_cuda_assign_buffers(struct ggml_tensor * tensor); void ggml_cuda_assign_buffers_no_scratch(struct ggml_tensor * tensor); +void ggml_cuda_assign_buffers_force_inplace(struct ggml_tensor * tensor); void ggml_cuda_set_main_device(int main_device); void ggml_cuda_set_scratch_size(size_t scratch_size); void ggml_cuda_free_scratch(void); diff --git a/llama.cpp b/llama.cpp index 5a142aba6..5f3761b0e 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2976,7 +2976,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const return false; } } - ggml_tensor* lora_tensor; + ggml_tensor * lora_tensor; if (n_dims == 2) { lora_tensor = ggml_new_tensor_2d(lora_ctx, wtype, ne[0], ne[1]); } @@ -2984,6 +2984,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const fprintf(stderr, "%s: unsupported tensor dimension %d\n", __func__, n_dims); return 1; } + ggml_set_name(lora_tensor, "lora_tensor"); // load tensor data size_t offset = fin.tellg(); @@ -2999,6 +3000,21 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const lora_tensors.find(base_name + ".loraB") != lora_tensors.end()) { ggml_tensor * dest_t = model_tensors[base_name]; + + offload_func_t offload_func = llama_nop; + offload_func_t offload_func_force_inplace = llama_nop; + +#ifdef GGML_USE_CUBLAS + if (dest_t->backend == GGML_BACKEND_GPU || dest_t->backend == GGML_BACKEND_GPU_SPLIT) { + if (dest_t->type != GGML_TYPE_F16) { + throw std::runtime_error(format( + "%s: error: the simultaneous use of LoRAs and GPU acceleration is only supported for f16 models", __func__)); + } + offload_func = ggml_cuda_assign_buffers; + offload_func_force_inplace = ggml_cuda_assign_buffers_force_inplace; + } +#endif // GGML_USE_CUBLAS + ggml_tensor * base_t; if (model_loader) { // load from base model @@ -3026,7 +3042,12 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const } ggml_tensor * loraA = lora_tensors[base_name + ".loraA"]; + GGML_ASSERT(loraA->type == GGML_TYPE_F32); + ggml_set_name(loraA, "loraA"); + ggml_tensor * loraB = lora_tensors[base_name + ".loraB"]; + GGML_ASSERT(loraB->type == GGML_TYPE_F32); + ggml_set_name(loraB, "loraB"); if (base_t->ne[0] != loraA->ne[1] || base_t->ne[1] != loraB->ne[1]) { fprintf(stderr, "%s: incompatible tensor dimensions (%" PRId64 " and %" PRId64 ");" @@ -3036,19 +3057,32 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const // w = w + BA*s ggml_tensor * BA = ggml_mul_mat(lora_ctx, loraA, loraB); + offload_func(BA); + ggml_set_name(BA, "BA"); if (scaling != 1.0f) { ggml_tensor * scale_tensor = ggml_new_f32(lora_ctx, scaling); + ggml_set_name(scale_tensor, "scale_tensor"); + BA = ggml_scale_inplace(lora_ctx, BA, scale_tensor); + offload_func(BA); + ggml_set_name(BA, "BA_scaled"); } ggml_tensor * r; if (base_t == dest_t) { r = ggml_add_inplace(lora_ctx, dest_t, BA); + offload_func_force_inplace(r); + ggml_set_name(r, "r_add_inplace"); } else { r = ggml_add(lora_ctx, base_t, BA); + offload_func(r); + ggml_set_name(r, "r_add"); + r = ggml_cpy(lora_ctx, r, dest_t); + offload_func(r); + ggml_set_name(r, "r_cpy"); } struct ggml_cgraph gf = ggml_build_forward(r); From b922bc351b69770cec2d35d2aa50fa052b95ca93 Mon Sep 17 00:00:00 2001 From: Howard Su Date: Wed, 28 Jun 2023 10:13:02 -0700 Subject: [PATCH 056/852] llama : remove shards weight file support (#2000) * Remove multiple shards * Remove multiple file loaders * Remove llama_load_tensor_shard class * Simplify load logic * Remove dead code guess_n_parts function * Remove vocab_only from constructor of llama_model_loader * Remove alignment_prevents_mmap which is not more needed. * Remove useless check --- llama.cpp | 233 ++++++++---------------------------------------------- 1 file changed, 35 insertions(+), 198 deletions(-) diff --git a/llama.cpp b/llama.cpp index 5f3761b0e..47e11d03c 100644 --- a/llama.cpp +++ b/llama.cpp @@ -364,96 +364,14 @@ static size_t llama_calc_tensor_size(const std::vector & ne, enum ggml return size / ggml_blck_size(type); } -struct llama_load_tensor_shard { - std::vector ne; - size_t size; - enum ggml_type type; - size_t file_idx; - size_t file_off; - - void calc_size() { - size = llama_calc_tensor_size(ne, type); - } -}; - -enum llama_split_type { - SPLIT_NONE, - SPLIT_BY_COLUMNS, - SPLIT_BY_ROWS -}; - struct llama_load_tensor { - std::vector shards; - std::string name; enum ggml_type type = GGML_TYPE_F32; - llama_split_type split_type = SPLIT_NONE; std::vector ne; + size_t file_off; size_t size; struct ggml_tensor * ggml_tensor = NULL; uint8_t * data; - - llama_load_tensor(const std::string & name) : name(name) {} - - void calc_all() { - calc_type(); - calc_split_type(); - calc_ne(); - calc_size(); - } - - void calc_type() { - const auto & first_shard = shards.at(0); - for (const auto & shard : shards) { - if (shard.type != first_shard.type) { - throw std::runtime_error(format("inconsistent tensor shard type in '%s'", name.c_str())); - } - } - type = first_shard.type; - } - - void calc_split_type() { - if (shards.at(0).ne.size() == 1 || // 1D tensors are just duplicated in every file - shards.size() == 1) { // only one file? - split_type = SPLIT_NONE; - } else if (name.find("tok_embeddings.") == 0 || - name.find(".attention.wo.weight") != std::string::npos || - name.find(".feed_forward.w2.weight") != std::string::npos) { - split_type = SPLIT_BY_COLUMNS; - } else { - split_type = SPLIT_BY_ROWS; - } - } - - void calc_ne() { - const auto & first_shard = shards.at(0); - for (const auto & shard : shards) { - if (shard.ne != first_shard.ne) { - throw std::runtime_error(format("inconsistent tensor shard shape in '%s': first was %s, other was %s", - name.c_str(), llama_format_tensor_shape(first_shard.ne).c_str(), llama_format_tensor_shape(shard.ne).c_str())); - } - } - ne = first_shard.ne; - LLAMA_ASSERT(shards.size() <= UINT32_MAX); - uint32_t n_shards = (uint32_t) shards.size(); - switch (split_type) { - case SPLIT_NONE: - ne = first_shard.ne; - break; - case SPLIT_BY_COLUMNS: - ne = {checked_mul(first_shard.ne[0], n_shards), - first_shard.ne[1]}; - break; - case SPLIT_BY_ROWS: - ne = {first_shard.ne[0], - checked_mul(first_shard.ne[1], n_shards)}; - break; - } - } - - void calc_size() { - size = llama_calc_tensor_size(ne, type); - } }; struct llama_load_tensors_map { @@ -476,13 +394,13 @@ struct llama_file_loader { llama_hparams hparams; llama_vocab vocab; - llama_file_loader(const char * fname, size_t file_idx, llama_load_tensors_map & tensors_map) + llama_file_loader(const char * fname, llama_load_tensors_map & tensors_map) : file(fname, "rb") { fprintf(stderr, "llama.cpp: loading model from %s\n", fname); read_magic(); read_hparams(); read_vocab(); - read_tensor_metadata(file_idx, tensors_map); + read_tensor_metadata(tensors_map); } void read_magic() { uint32_t magic = file.read_u32(); @@ -539,19 +457,19 @@ struct llama_file_loader { tok_score.score = score; } } - void read_tensor_metadata(size_t file_idx, llama_load_tensors_map & tensors_map) { + void read_tensor_metadata(llama_load_tensors_map & tensors_map) { while (file.tell() < file.size) { - llama_load_tensor_shard shard; + llama_load_tensor tensor; uint32_t n_dims = file.read_u32(); uint32_t name_len = file.read_u32(); - shard.type = (enum ggml_type) file.read_u32(); - shard.ne.resize(n_dims); - file.read_raw(shard.ne.data(), sizeof(shard.ne[0]) * n_dims); + tensor.type = (enum ggml_type) file.read_u32(); + tensor.ne.resize(n_dims); + file.read_raw(tensor.ne.data(), sizeof(tensor.ne[0]) * n_dims); std::string name = file.read_string(name_len); if (n_dims < 1 || n_dims > 2) { throw std::runtime_error(format("llama.cpp: tensor '%s' should not be %u-dimensional", name.c_str(), n_dims)); } - switch (shard.type) { + switch (tensor.type) { case GGML_TYPE_F32: case GGML_TYPE_F16: case GGML_TYPE_Q4_0: @@ -566,30 +484,20 @@ struct llama_file_loader { case GGML_TYPE_Q6_K: break; default: { - throw std::runtime_error(format("unrecognized tensor type %u\n", shard.type)); + throw std::runtime_error(format("unrecognized tensor type %u\n", tensor.type)); } } - if (file_version >= LLAMA_FILE_VERSION_GGJT_V1) { - // skip to the next multiple of 32 bytes - file.seek(-static_cast(file.tell()) & 31, SEEK_CUR); - } - shard.file_idx = file_idx; - shard.file_off = file.tell(); + // skip to the next multiple of 32 bytes + file.seek(-static_cast(file.tell()) & 31, SEEK_CUR); - shard.calc_size(); - file.seek(shard.size, SEEK_CUR); + tensor.file_off = file.tell(); + tensor.name = name; + tensor.size = llama_calc_tensor_size(tensor.ne, tensor.type); + file.seek(tensor.size, SEEK_CUR); - auto it = tensors_map.name_to_idx.find(name); - size_t idx; - if (it != tensors_map.name_to_idx.end()) { - idx = it->second; - } else { - tensors_map.tensors.emplace_back(name); - idx = tensors_map.tensors.size() - 1; - tensors_map.name_to_idx.emplace(name, idx); - } - tensors_map.tensors.at(idx).shards.push_back(shard); + tensors_map.tensors.push_back(tensor); + tensors_map.name_to_idx[name] = tensors_map.tensors.size() - 1; } } }; @@ -659,56 +567,19 @@ struct llama_file_saver { }; struct llama_model_loader { - std::vector> file_loaders; + std::unique_ptr file_loader; llama_load_tensors_map tensors_map; bool use_mmap; size_t num_ggml_tensors_created = 0; struct ggml_context * ggml_ctx = NULL; std::unique_ptr mapping; - llama_model_loader(const std::string & fname_base, bool use_mmap, bool vocab_only) { - auto * first_file = new llama_file_loader(fname_base.c_str(), 0, tensors_map); - file_loaders.emplace_back(first_file); - uint32_t n_parts = vocab_only ? 1 : guess_n_parts(); - for (uint32_t i = 1; i < n_parts; i++) { - std::string fname = fname_base + "." + std::to_string(i); - auto * ith_file = new llama_file_loader(fname.c_str(), i, tensors_map); - file_loaders.emplace_back(ith_file); - if (ith_file->hparams != first_file->hparams) { - throw std::runtime_error(format("llama.cpp: hparams inconsistent between files")); - } - } + llama_model_loader(const std::string & fname_base, bool use_mmap) { + file_loader = std::unique_ptr(new llama_file_loader(fname_base.c_str(), tensors_map)); if (!llama_mmap::SUPPORTED) { use_mmap = false; } - if (use_mmap && alignment_prevents_mmap()) { - fprintf(stderr, "llama.cpp: can't use mmap because tensors are not aligned; convert to new format to avoid this\n"); - use_mmap = false; - } this->use_mmap = use_mmap; - for (llama_load_tensor & lt : tensors_map.tensors) { - lt.calc_all(); - } - } - - bool alignment_prevents_mmap() { - for (const llama_load_tensor & lt : tensors_map.tensors) { - for (const llama_load_tensor_shard & shard : lt.shards) { - if (shard.file_off & 3) { - return true; - } - } - } - return false; - } - - uint32_t guess_n_parts() const { - auto it = tensors_map.name_to_idx.find("tok_embeddings.weight"); - if (it == tensors_map.name_to_idx.end()) { - throw std::runtime_error(std::string("missing tok_embeddings.weight")); - } - const llama_load_tensor & lt = tensors_map.tensors.at(it->second); - return file_loaders.at(0)->hparams.n_embd / lt.shards.at(0).ne.at(0); } void calc_sizes(size_t * ctx_size_p, size_t * mmapped_size_p) const { @@ -774,7 +645,7 @@ struct llama_model_loader { } if (use_mmap) { - mapping.reset(new llama_mmap(&file_loaders.at(0)->file, prefetch_size, ggml_is_numa())); + mapping.reset(new llama_mmap(&file_loader->file, prefetch_size, ggml_is_numa())); if (lmlock) { lmlock->init(mapping->addr); } @@ -830,45 +701,13 @@ struct llama_model_loader { void load_data_for(llama_load_tensor & lt) { if (use_mmap) { - LLAMA_ASSERT(lt.shards.size() == 1); - lt.data = (uint8_t *) mapping->addr + lt.shards.at(0).file_off; - } else if (lt.split_type == SPLIT_NONE) { - llama_file & file = file_loaders.at(lt.shards.at(0).file_idx)->file; - file.seek(lt.shards.at(0).file_off, SEEK_SET); + lt.data = (uint8_t *) mapping->addr + lt.file_off; + } else { + llama_file & file = file_loader->file; + file.seek(lt.file_off, SEEK_SET); file.read_raw(lt.data, lt.size); - } else if (lt.split_type == SPLIT_BY_ROWS) { - size_t offset = 0; - for (llama_load_tensor_shard & shard : lt.shards) { - llama_file & file = file_loaders.at(shard.file_idx)->file; - file.seek(shard.file_off, SEEK_SET); - file.read_raw(lt.data + offset, shard.size); - offset += shard.size; - } - LLAMA_ASSERT(offset == lt.size); - } else if (lt.split_type == SPLIT_BY_COLUMNS) { - // Let's load the data into temporary buffers to ensure the OS performs large loads. - std::vector tmp_bufs(lt.shards.size()); - for (size_t i = 0; i < lt.shards.size(); i++) { - llama_load_tensor_shard & shard = lt.shards.at(i); - llama_file & file = file_loaders.at(shard.file_idx)->file; - file.seek(shard.file_off, SEEK_SET); - tmp_bufs.at(i).resize(shard.size); - file.read_raw(tmp_bufs.at(i).addr, shard.size); - } - // Then reshape. - size_t num_rows = lt.ne.at(1); - size_t per_shard_row_size = lt.shards.at(0).size / num_rows; - size_t out_offset = 0; - for (size_t row = 0; row < num_rows; row++) { - for (llama_buffer & tmp_buf : tmp_bufs) { - memcpy(lt.data + out_offset, - tmp_buf.addr + row * per_shard_row_size, - per_shard_row_size); - out_offset += per_shard_row_size; - } - } - LLAMA_ASSERT(out_offset == lt.size); } + if (0) { print_checksum(lt); } @@ -1067,12 +906,12 @@ static void llama_model_load_internal( model.t_start_us = ggml_time_us(); - std::unique_ptr ml(new llama_model_loader(fname, use_mmap, vocab_only)); + std::unique_ptr ml(new llama_model_loader(fname, use_mmap)); - vocab = std::move(ml->file_loaders.at(0)->vocab); - model.hparams = ml->file_loaders.at(0)->hparams; + vocab = std::move(ml->file_loader->vocab); + model.hparams = ml->file_loader->hparams; model.n_gpu_layers = n_gpu_layers; - llama_file_version file_version = ml->file_loaders.at(0)->file_version; + llama_file_version file_version = ml->file_loader->file_version; auto & hparams = model.hparams; { @@ -1106,7 +945,6 @@ static void llama_model_load_internal( fprintf(stderr, "%s: n_rot = %u\n", __func__, hparams.n_rot); fprintf(stderr, "%s: ftype = %u (%s)\n", __func__, hparams.ftype, llama_ftype_name(hparams.ftype)); fprintf(stderr, "%s: n_ff = %u\n", __func__, n_ff); - fprintf(stderr, "%s: n_parts = %zu\n", __func__, ml->file_loaders.size()); fprintf(stderr, "%s: model size = %s\n", __func__, llama_model_type_name(model.type)); } @@ -2461,9 +2299,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s nthread = std::thread::hardware_concurrency(); } - std::unique_ptr model_loader(new llama_model_loader(fname_inp, /*use_mmap*/ false, - /*vocab_only*/ false)); - llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loaders.at(0).get(), params->ftype); + std::unique_ptr model_loader(new llama_model_loader(fname_inp, /*use_mmap*/ false)); + llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loader.get(), params->ftype); #ifdef GGML_USE_K_QUANTS int n_attention_wv = 0; @@ -2897,7 +2734,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const llama_buffer base_buf; if (path_base_model) { fprintf(stderr, "%s: loading base model from '%s'\n", __func__, path_base_model); - model_loader.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true, /*vocab_only*/ false)); + model_loader.reset(new llama_model_loader(path_base_model, /*use_mmap*/ true)); size_t ctx_size; size_t mmapped_size; @@ -2915,7 +2752,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const // maybe this should in llama_model_loader if (model_loader->use_mmap) { - model_loader->mapping.reset(new llama_mmap(&model_loader->file_loaders.at(0)->file, /* prefetch */ 0, ggml_is_numa())); + model_loader->mapping.reset(new llama_mmap(&model_loader->file_loader->file, /* prefetch */ 0, ggml_is_numa())); } } From 6432aabb6dc887436e4d57414b63116189c3b13b Mon Sep 17 00:00:00 2001 From: "Salvador E. Tropea" Date: Wed, 28 Jun 2023 14:26:26 -0300 Subject: [PATCH 057/852] cuda : fix missing const qualifier in casts (#2027) --- ggml-cuda.cu | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index be75cb792..5f05d9181 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -1244,7 +1244,7 @@ static __global__ void dequantize_mul_mat_vec(const void * vx, const dfloat * y, } static __global__ void mul_mat_p021_f16_f32(const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x, const int nchannels_x) { - const half * x = (half *) vx; + const half * x = (const half *) vx; const int row_x = blockDim.y*blockIdx.y + threadIdx.y; const int channel = blockDim.z*blockIdx.z + threadIdx.z; @@ -1294,7 +1294,7 @@ static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x, const int row_stride_x, const int nchannels_x, const int channel_stride_x) { - const half * x = (half *) vx; + const half * x = (const half *) vx; const int row_x = blockDim.y*blockIdx.y + threadIdx.y; const int channel = blockDim.z*blockIdx.z + threadIdx.z; @@ -1337,14 +1337,14 @@ static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous } static __device__ void cpy_1_f32_f32(const char * cxi, char * cdsti) { - const float * xi = (float *) cxi; + const float * xi = (const float *) cxi; float * dsti = (float *) cdsti; *dsti = *xi; } static __device__ void cpy_1_f32_f16(const char * cxi, char * cdsti) { - const float * xi = (float *) cxi; + const float * xi = (const float *) cxi; half * dsti = (half *) cdsti; *dsti = __float2half(*xi); From 5b351e94d041742cd50ffcf2d44718d63bab398a Mon Sep 17 00:00:00 2001 From: "Salvador E. Tropea" Date: Wed, 28 Jun 2023 14:27:31 -0300 Subject: [PATCH 058/852] cuda : remove nchannels_x argument from mul_mat_vec_nc_f16_f32 (#2028) - Not used --- ggml-cuda.cu | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 5f05d9181..4e0d3dbde 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -1292,7 +1292,7 @@ static __global__ void mul_mat_p021_f16_f32(const void * vx, const float * y, fl static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous const void * vx, const float * y, float * dst, const int ncols_x, const int nrows_x, - const int row_stride_x, const int nchannels_x, const int channel_stride_x) { + const int row_stride_x, const int channel_stride_x) { const half * x = (const half *) vx; @@ -1698,7 +1698,7 @@ static void ggml_mul_mat_vec_nc_f16_f32_cuda( const dim3 block_nums(1, nrows_x, nchannels_x); const dim3 block_dims(WARP_SIZE, 1, 1); mul_mat_vec_nc_f16_f32<<>> - (vx, y, dst, ncols_x, nrows_x, row_stride_x, nchannels_x, channel_stride_x); + (vx, y, dst, ncols_x, nrows_x, row_stride_x, channel_stride_x); } static void ggml_cpy_f32_f32_cuda( From d3494bb86bf7ad5b0b60aae0220ea576f273b5c0 Mon Sep 17 00:00:00 2001 From: m3ndax Date: Wed, 28 Jun 2023 20:39:08 +0200 Subject: [PATCH 059/852] llama : replacing auto &kv with const auto &kv (#2041) * Replacing auto &kv with const auto &kv * Create codacy.yml * Delete codacy.yml --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 47e11d03c..ef80b4e8b 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2723,7 +2723,7 @@ int llama_apply_lora_from_file_internal(const struct llama_model & model, const // create a name -> tensor map of the model to accelerate lookups std::unordered_map model_tensors; - for (auto & kv: model.tensors_by_name) { + for (const auto & kv: model.tensors_by_name) { model_tensors.insert(kv); } From 96a712ca1b7f427e3bd7ffc0c70b2105cfc7fbf1 Mon Sep 17 00:00:00 2001 From: LostRuins <39025047+LostRuins@users.noreply.github.com> Date: Thu, 29 Jun 2023 11:56:43 +0800 Subject: [PATCH 060/852] Porting the improved K-Quant CUDA kernels to OpenCL (#1966) * Added broken new q4k quant * xx + ib0 * Fix q2_k fast kernel * Use preprocessor for QK_K * Add q6_k fast matmul kernel * ported q3k speedup successfully * ported q2k and q5k speedups * remove old dot kernels and template * fixed global const struct types * fixing address spaces * fixed string too long CI issue --------- Co-authored-by: 0cc4m --- ggml-opencl.cpp | 545 ++++++++++++++++++++++++++++++++---------------- 1 file changed, 361 insertions(+), 184 deletions(-) diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp index 95f4cec6d..fed4ffb0c 100644 --- a/ggml-opencl.cpp +++ b/ggml-opencl.cpp @@ -21,11 +21,19 @@ #define CL_DMMV_BLOCK_SIZE 32 +#ifndef K_QUANTS_PER_ITERATION +#define K_QUANTS_PER_ITERATION 1 +#else +static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2"); +#endif + #define MULTILINE_QUOTE(...) #__VA_ARGS__ static std::string program_source = MULTILINE_QUOTE( typedef char int8_t; typedef uchar uint8_t; +typedef short int16_t; +typedef ushort uint16_t; typedef int int32_t; typedef uint uint32_t; @@ -175,7 +183,9 @@ void convert_f16(__global half* x, const int ib, const int iqs, float* v0, float *v0 = vload_half(0, &x[ib + 0]); *v1 = vload_half(0, &x[ib + 1]); } +); +static std::string k_quants_source = MULTILINE_QUOTE( inline void get_scale_min_k4(int j, const __global uint8_t *q, uint8_t *d, uint8_t *m) { if (j < 4) @@ -199,7 +209,7 @@ __kernel void dequantize_block_q2_K(__global const struct block_q2_K *x, __globa const int is = 8 * n + l / 16; const uint8_t q = x[i].qs[32 * n + l]; - __global float *y = yy + i * 256 + 128 * n; + __global float *y = yy + i * QK_K + 128 * n; const float dall = vload_half(0, &x[i].d); const float dmin = vload_half(0, &x[i].dmin); @@ -231,7 +241,7 @@ __kernel void dequantize_block_q3_K(__global const struct block_q3_K *x, __globa float d_all = vload_half(0, &x[i].d); float dl = d_all * (us - 32); - __global float *y = yy + i * 256 + 128 * n + 32 * j; + __global float *y = yy + i * QK_K + 128 * n + 32 * j; const __global uint8_t *q = x[i].qs + 32 * n; const __global uint8_t *hm = x[i].hmask; @@ -248,7 +258,7 @@ __kernel void dequantize_block_q4_K(__global const struct block_q4_K *x, __globa const int is = 2 * il; const int n = 4; - __global float *y = yy + i * 256 + 64 * il + n * ir; + __global float *y = yy + i * QK_K + 64 * il + n * ir; const float dall = vload_half(0, &x[i].d); const float dmin = vload_half(0, &x[i].dmin); @@ -277,7 +287,7 @@ __kernel void dequantize_block_q5_K(__global const struct block_q5_K *x, __globa const int ir = tid % 16; const int is = 2 * il; - __global float *y = yy + i * 256 + 64 * il + 2 * ir; + __global float *y = yy + i * QK_K + 64 * il + 2 * ir; const float dall = vload_half(0, &x[i].d); const float dmin = vload_half(0, &x[i].dmin); @@ -309,7 +319,7 @@ __kernel void dequantize_block_q6_K(__global const struct block_q6_K *x, __globa const int il = tid - 32 * ip; const int is = 8 * ip + il / 16; - __global float *y = yy + i * 256 + 128 * ip + il; + __global float *y = yy + i * QK_K + 128 * ip + il; const float d = vload_half(0, &x[i].d); @@ -323,161 +333,383 @@ __kernel void dequantize_block_q6_K(__global const struct block_q6_K *x, __globa y[96] = d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh >> 6) & 3) << 4)) - 32); } +__kernel void dequantize_mul_mat_vec_q2_K(__global const struct block_q2_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) { -void vec_dot_q2_K(__global const struct block_q2_K* x, const int ib, const int iqs, const __global float *yy, float *result) { + const int row = get_group_id(0); - int n = iqs / 128; - int r = iqs - 128 * n; - int l = r / 8; + const int num_blocks_per_row = ncols / QK_K; + const int ib0 = row*num_blocks_per_row; - __global const float *y = yy + 128 * n + l; - __global const uint8_t *q = x[ib].qs + 32 * n + l; - __global const uint8_t *s = x[ib].scales + 8 * n; + __global const struct block_q2_K * x = xx + ib0; - const float dall = vload_half(0, &x[ib].d); - const float dmin = vload_half(0, &x[ib].dmin); + const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION; // 0...31 or 0...15 + const int ix = get_local_id(0)%K_QUANTS_PER_ITERATION; // 0 or 0,1 - float sum = y[ 0] * (dall * ((s[0] & 0xF) * ((q[ 0] >> 0) & 3)) - dmin * (s[0] >> 4)) - + y[ 32] * (dall * ((s[2] & 0xF) * ((q[ 0] >> 2) & 3)) - dmin * (s[2] >> 4)) - + y[ 64] * (dall * ((s[4] & 0xF) * ((q[ 0] >> 4) & 3)) - dmin * (s[4] >> 4)) - + y[ 96] * (dall * ((s[6] & 0xF) * ((q[ 0] >> 6) & 3)) - dmin * (s[6] >> 4)) - + y[ 16] * (dall * ((s[1] & 0xF) * ((q[16] >> 0) & 3)) - dmin * (s[1] >> 4)) - + y[ 48] * (dall * ((s[3] & 0xF) * ((q[16] >> 2) & 3)) - dmin * (s[3] >> 4)) - + y[ 80] * (dall * ((s[5] & 0xF) * ((q[16] >> 4) & 3)) - dmin * (s[5] >> 4)) - + y[112] * (dall * ((s[7] & 0xF) * ((q[16] >> 6) & 3)) - dmin * (s[7] >> 4)); + const int step = 16/K_QUANTS_PER_ITERATION; - *result = sum; -} + const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... + const int in = tid - step*im; // 0...15 or 0...7 -void vec_dot_q3_K(__global const struct block_q3_K* x, const int ib, const int iqs, const __global float *yy, float *result) { + const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15 or 0...14 in steps of 2 + const int q_offset = 32*im + l0; + const int s_offset = 8*im; + const int y_offset = 128*im + l0; - const uint32_t kmask1 = 0x03030303; - const uint32_t kmask2 = 0x0f0f0f0f; + tmp[16 * ix + tid] = 0; - uint32_t aux[3]; - uint32_t utmp[4]; + uint32_t aux[4]; + const uint8_t * d = (const uint8_t *)aux; + const uint8_t * m = (const uint8_t *)(aux + 2); - int n = iqs/128; - int r = iqs - 128*n; - int l = r/8; + for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { - __global const float * y = yy + 128*n + l; - __global const uint8_t * q = x[ib].qs + 32*n + l; - __global const uint8_t * hm = x[ib].hmask + l; - const int8_t * s = (const int8_t *)utmp + 8*n; + __global const float * y = yy + i * QK_K + y_offset; + __global const uint8_t * q = x[i].qs + q_offset; - aux[0] = x[ib].scales[0] | x[ib].scales[1] << 8 | x[ib].scales[2] << 16 | x[ib].scales[3] << 24; - aux[1] = x[ib].scales[4] | x[ib].scales[5] << 8 | x[ib].scales[6] << 16 | x[ib].scales[7] << 24; - aux[2] = x[ib].scales[8] | x[ib].scales[9] << 8 | x[ib].scales[10] << 16 | x[ib].scales[11] << 24; + const float dall = vload_half(0, &x[i].d); + const float dmin = vload_half(0, &x[i].dmin); - utmp[3] = ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4); - utmp[2] = ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4); - utmp[1] = (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4); - utmp[0] = (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4); + __global const uint32_t * a = (__global const uint32_t *)(x[i].scales + s_offset); + aux[0] = a[0] & 0x0f0f0f0f; + aux[1] = a[1] & 0x0f0f0f0f; + aux[2] = (a[0] >> 4) & 0x0f0f0f0f; + aux[3] = (a[1] >> 4) & 0x0f0f0f0f; - const float dall = vload_half(0, &x[ib].d); - const uint8_t m = 1 << (4*n); + float sum1 = 0, sum2 = 0; + for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { + sum1 += y[l+ 0] * d[0] * ((q[l+ 0] >> 0) & 3) + + y[l+32] * d[2] * ((q[l+ 0] >> 2) & 3) + + y[l+64] * d[4] * ((q[l+ 0] >> 4) & 3) + + y[l+96] * d[6] * ((q[l+ 0] >> 6) & 3) + + y[l+16] * d[1] * ((q[l+16] >> 0) & 3) + + y[l+48] * d[3] * ((q[l+16] >> 2) & 3) + + y[l+80] * d[5] * ((q[l+16] >> 4) & 3) + +y[l+112] * d[7] * ((q[l+16] >> 6) & 3); + sum2 += y[l+ 0] * m[0] + y[l+32] * m[2] + y[l+64] * m[4] + y[ l+96] * m[6] + + y[l+16] * m[1] + y[l+48] * m[3] + y[l+80] * m[5] + y[l+112] * m[7]; - float sum = y[ 0] * (s[0] - 32) * (((q[ 0] >> 0) & 3) - (hm[ 0] & (m << 0) ? 0 : 4)) - + y[ 32] * (s[2] - 32) * (((q[ 0] >> 2) & 3) - (hm[ 0] & (m << 1) ? 0 : 4)) - + y[ 64] * (s[4] - 32) * (((q[ 0] >> 4) & 3) - (hm[ 0] & (m << 2) ? 0 : 4)) - + y[ 96] * (s[6] - 32) * (((q[ 0] >> 6) & 3) - (hm[ 0] & (m << 3) ? 0 : 4)) - + y[ 16] * (s[1] - 32) * (((q[16] >> 0) & 3) - (hm[16] & (m << 0) ? 0 : 4)) - + y[ 48] * (s[3] - 32) * (((q[16] >> 2) & 3) - (hm[16] & (m << 1) ? 0 : 4)) - + y[ 80] * (s[5] - 32) * (((q[16] >> 4) & 3) - (hm[16] & (m << 2) ? 0 : 4)) - + y[112] * (s[7] - 32) * (((q[16] >> 6) & 3) - (hm[16] & (m << 3) ? 0 : 4)); + } + tmp[16 * ix + tid] += dall * sum1 - dmin * sum2; - *result = sum * dall; - -} - -void vec_dot_q4_K(__global const struct block_q4_K* x, const int ib, const int iqs, const __global float *yy, float *result) { - - const int j = iqs / 64; // j is in 0...3 - const int ir = (iqs - 64*j)/2; // ir is in 0...28 in steps of 4 - const int is = 2*j; // is is in 0...6 in steps of 2 - - __global const float * y = yy + 64*j + ir; - __global const uint8_t * q = x[ib].qs + 32*j + ir; - - const float dall = vload_half(0, &x[ib].d); - const float dmin = vload_half(0, &x[ib].dmin); - - uint8_t sc, m; - get_scale_min_k4(is + 0, x[ib].scales, &sc, &m); - const float d1 = dall * sc; - const float m1 = dmin * m; - get_scale_min_k4(is + 1, x[ib].scales, &sc, &m); - const float d2 = dall * sc; - const float m2 = dmin * m; - - float sum = 0; - for (int k = 0; k < 4; ++k) { - sum += y[k + 0] * (d1 * (q[k] & 0xF) - m1); - sum += y[k + 32] * (d2 * (q[k] >> 4) - m2); } - *result = sum; + // sum up partial sums and write back result + barrier(CLK_LOCAL_MEM_FENCE); + for (int s=16; s>0; s>>=1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(CLK_LOCAL_MEM_FENCE); + } + if (tid == 0) { + dst[row] = tmp[0]; + } } -void vec_dot_q5_K(__global const struct block_q5_K* x, const int ib, const int iqs, const __global float *yy, float *result) { +__kernel void dequantize_mul_mat_vec_q3_K(__global const struct block_q3_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) { + const uint16_t kmask1 = 0x0303; + const uint16_t kmask2 = 0x0f0f; - const int j = iqs / 64; - const int ir = (iqs - 64*j)/2; - const int is = 2*j; + const int row = get_group_id(0); - __global const float * y = yy + 64*j + ir; - __global const uint8_t * ql = x[ib].qs + 32*j + ir; - __global const uint8_t * qh = x[ib].qh + ir; + const int num_blocks_per_row = ncols / QK_K; + const int ib0 = row*num_blocks_per_row; - const float dall = vload_half(0, &x[ib].d); - const float dmin = vload_half(0, &x[ib].dmin); + __global const struct block_q3_K * x = xx + ib0; - uint8_t sc, m; - get_scale_min_k4(is + 0, x[ib].scales, &sc, &m); - const float d1 = dall * sc; - const float m1 = dmin * m; - get_scale_min_k4(is + 1, x[ib].scales, &sc, &m); - const float d2 = dall * sc; - const float m2 = dmin * m; + const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const int ix = get_local_id(0)%K_QUANTS_PER_ITERATION; // 0 or 0,1 + + const int n = K_QUANTS_PER_ITERATION; // iterations in the inner loop + const int step = 16/K_QUANTS_PER_ITERATION; + const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... + const int in = tid - step*im; // 0....15 or 0...7 + + const uint8_t m = 1 << (4*im); + + const int l0 = n*in; // 0...15 or 0...14 in steps of 2 + const int q_offset = 32*im + l0; + const int y_offset = 128*im + l0; + + uint16_t utmp[4]; + const int8_t * s = (const int8_t *)utmp; + + const uint16_t s_shift = 4*im; + + tmp[16 * ix + tid] = 0; + + for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { + + __global const float * y = yy + i * QK_K + y_offset; + __global const uint8_t * q = x[i].qs + q_offset; + __global const uint8_t * h = x[i].hmask + l0; + + __global const uint16_t * a = (__global const uint16_t *)x[i].scales; + utmp[0] = ((a[0] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 0)) & kmask1) << 4); + utmp[1] = ((a[1] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 0)) & kmask1) << 4); + utmp[2] = ((a[2] >> s_shift) & kmask2) | (((a[4] >> (s_shift + 2)) & kmask1) << 4); + utmp[3] = ((a[3] >> s_shift) & kmask2) | (((a[5] >> (s_shift + 2)) & kmask1) << 4); + + const float d = vload_half(0, &x[i].d); + + float sum = 0; + for (int l = 0; l < n; ++l) { + sum += y[l+ 0] * (s[0] - 32) * (((q[l] >> 0) & 3) - (h[l] & (m << 0) ? 0 : 4)) + + y[l+32] * (s[2] - 32) * (((q[l] >> 2) & 3) - (h[l] & (m << 1) ? 0 : 4)) + + y[l+64] * (s[4] - 32) * (((q[l] >> 4) & 3) - (h[l] & (m << 2) ? 0 : 4)) + + y[l+96] * (s[6] - 32) * (((q[l] >> 6) & 3) - (h[l] & (m << 3) ? 0 : 4)); + sum += y[l+16] * (s[1] - 32) * (((q[l+16] >> 0) & 3) - (h[l+16] & (m << 0) ? 0 : 4)) + + y[l+48] * (s[3] - 32) * (((q[l+16] >> 2) & 3) - (h[l+16] & (m << 1) ? 0 : 4)) + + y[l+80] * (s[5] - 32) * (((q[l+16] >> 4) & 3) - (h[l+16] & (m << 2) ? 0 : 4)) + + y[l+112] * (s[7] - 32) * (((q[l+16] >> 6) & 3) - (h[l+16] & (m << 3) ? 0 : 4)); + } + tmp[16 * ix + tid] += d * sum; - uint8_t hm = 1 << is; - float sum = 0; - for (int k = 0; k < 4; ++k) { - sum += y[k + 0] * (d1 * ((ql[k] & 0xF) + (qh[k] & hm ? 16 : 0)) - m1); } - hm <<= 1; - for (int k = 0; k < 4; ++k) { - sum += y[k + 32] * (d2 * ((ql[k] >> 4) + (qh[k] & hm ? 16 : 0)) - m2); - } - *result = sum; + // sum up partial sums and write back result + barrier(CLK_LOCAL_MEM_FENCE); + for (int s=16; s>0; s>>=1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(CLK_LOCAL_MEM_FENCE); + } + if (tid == 0) { + dst[row] = tmp[0]; + } } -void vec_dot_q6_K(__global const struct block_q6_K* x, const int ib, const int iqs, const __global float *yy, float *result) { +__kernel void dequantize_mul_mat_vec_q4_K(__global const struct block_q4_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) { + //to rename it later, just to test now + const uint16_t kmask1 = 0x3f3f; + const uint16_t kmask2 = 0x0f0f; + const uint16_t kmask3 = 0xc0c0; - const int ip = iqs / 128; // 0 or 1 - const int il = (iqs - 128*ip)/8; // 0...15 - const int is = 8*ip; + const int row = get_group_id(0); + const int num_blocks_per_row = ncols / QK_K; + const int ib0 = row*num_blocks_per_row; - __global const float * y = yy + 128*ip + il; + const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION; // 0...15 + const int ix = get_local_id(0)%K_QUANTS_PER_ITERATION; - const float d = vload_half(0, &x[ib].d); + const int step = 8/K_QUANTS_PER_ITERATION; - __global const uint8_t * ql = x[ib].ql + 64*ip + il; - __global const uint8_t * qh = x[ib].qh + 32*ip + il; - __global const int8_t * sc = x[ib].scales + is; + const int il = tid/step; // 0...3 + const int ir = tid - step*il;// 0...3 + const int n = 2*K_QUANTS_PER_ITERATION; - *result = y[ 0] * d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh[ 0] >> 0) & 3) << 4)) - 32) - + y[ 32] * d * sc[2] * ((int8_t)((ql[32] & 0xF) | (((qh[ 0] >> 2) & 3) << 4)) - 32) - + y[ 64] * d * sc[4] * ((int8_t)((ql[ 0] >> 4) | (((qh[ 0] >> 4) & 3) << 4)) - 32) - + y[ 96] * d * sc[6] * ((int8_t)((ql[32] >> 4) | (((qh[ 0] >> 6) & 3) << 4)) - 32) - + y[ 16] * d * sc[1] * ((int8_t)((ql[16] & 0xF) | (((qh[16] >> 0) & 3) << 4)) - 32) - + y[ 48] * d * sc[3] * ((int8_t)((ql[48] & 0xF) | (((qh[16] >> 2) & 3) << 4)) - 32) - + y[ 80] * d * sc[5] * ((int8_t)((ql[16] >> 4) | (((qh[16] >> 4) & 3) << 4)) - 32) - + y[112] * d * sc[7] * ((int8_t)((ql[48] >> 4) | (((qh[16] >> 6) & 3) << 4)) - 32); + const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 + const int in = il%2; + const int l0 = n*(2*ir + in); + const int q_offset = 32*im + l0; + const int y_offset = 64*im + l0; + + uint16_t aux[4]; + const uint8_t * sc = (const uint8_t *)aux; + + __global const struct block_q4_K * x = xx + ib0; + + tmp[16 * ix + tid] = 0; + + for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { + + __global const uint8_t * q1 = x[i].qs + q_offset; + __global const uint8_t * q2 = q1 + 64; + __global const float * y1 = yy + i*QK_K + y_offset; + __global const float * y2 = y1 + 128; + + const float dall = vload_half(0, &x[i].d); + const float dmin = vload_half(0, &x[i].dmin); + + __global const uint16_t * a = (__global const uint16_t *)x[i].scales; + aux[0] = a[im+0] & kmask1; + aux[1] = a[im+2] & kmask1; + aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2); + aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2); + + float4 s = (float4)(0.f); + float smin = 0; + for (int l = 0; l < n; ++l) { + s.x += y1[l] * (q1[l] & 0xF); s.y += y1[l+32] * (q1[l] >> 4); + s.z += y2[l] * (q2[l] & 0xF); s.w += y2[l+32] * (q2[l] >> 4); + smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7]; + } + tmp[16 * ix + tid] += dall * (s.x * sc[0] + s.y * sc[1] + s.z * sc[4] + s.w * sc[5]) - dmin * smin; + + } + + // sum up partial sums and write back result + barrier(CLK_LOCAL_MEM_FENCE); + for (int s=16; s>0; s>>=1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(CLK_LOCAL_MEM_FENCE); + } + if (tid == 0) { + dst[row] = tmp[0]; + } +} + +__kernel void dequantize_mul_mat_vec_q5_K(__global const struct block_q5_K * xx, __local float* tmp, __global float* yy, __global float* dst, const int ncols) { + + const uint16_t kmask1 = 0x3f3f; + const uint16_t kmask2 = 0x0f0f; + const uint16_t kmask3 = 0xc0c0; + + const int row = get_group_id(0); + const int num_blocks_per_row = ncols / QK_K; + const int ib0 = row*num_blocks_per_row; + + const int tid = get_local_id(0)/2; // 0...15 + const int ix = get_local_id(0)%2; + + const int il = tid/4; // 0...3 + const int ir = tid - 4*il;// 0...3 + const int n = 2; + + const int im = il/2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 + const int in = il%2; + + const int l0 = n*(2*ir + in); + const int q_offset = 32*im + l0; + const int y_offset = 64*im + l0; + + const uint8_t hm1 = 1 << (2*im); + const uint8_t hm2 = hm1 << 4; + + uint16_t aux[4]; + const uint8_t * sc = (const uint8_t *)aux; + + __global const struct block_q5_K * x = xx + ib0; + + tmp[16 * ix + tid] = 0; + + for (int i = ix; i < num_blocks_per_row; i += 2) { + + __global const uint8_t * ql1 = x[i].qs + q_offset; + __global const uint8_t * ql2 = ql1 + 64; + __global const uint8_t * qh = x[i].qh + l0; + __global const float * y1 = yy + i*QK_K + y_offset; + __global const float * y2 = y1 + 128; + + const float dall = vload_half(0, &x[i].d); + const float dmin = vload_half(0, &x[i].dmin); + + __global const uint16_t * a = (__global const uint16_t *)x[i].scales; + aux[0] = a[im+0] & kmask1; + aux[1] = a[im+2] & kmask1; + aux[2] = ((a[im+4] >> 0) & kmask2) | ((a[im+0] & kmask3) >> 2); + aux[3] = ((a[im+4] >> 4) & kmask2) | ((a[im+2] & kmask3) >> 2); + + float4 sum = (float4)(0.f); + float smin = 0; + for (int l = 0; l < n; ++l) { + sum.x += y1[l+ 0] * ((ql1[l+ 0] & 0xF) + (qh[l+ 0] & (hm1 << 0) ? 16 : 0)) + + y1[l+16] * ((ql1[l+16] & 0xF) + (qh[l+16] & (hm1 << 0) ? 16 : 0)); + sum.y += y1[l+32] * ((ql1[l+ 0] >> 4) + (qh[l+ 0] & (hm1 << 1) ? 16 : 0)) + + y1[l+48] * ((ql1[l+16] >> 4) + (qh[l+16] & (hm1 << 1) ? 16 : 0)); + sum.z += y2[l+ 0] * ((ql2[l+ 0] & 0xF) + (qh[l+ 0] & (hm2 << 0) ? 16 : 0)) + + y2[l+16] * ((ql2[l+16] & 0xF) + (qh[l+16] & (hm2 << 0) ? 16 : 0)); + sum.w += y2[l+32] * ((ql2[l+ 0] >> 4) + (qh[l+ 0] & (hm2 << 1) ? 16 : 0)) + + y2[l+48] * ((ql2[l+16] >> 4) + (qh[l+16] & (hm2 << 1) ? 16 : 0)); + smin += (y1[l] + y1[l+16]) * sc[2] + (y1[l+32] + y1[l+48]) * sc[3] + + (y2[l] + y2[l+16]) * sc[6] + (y2[l+32] + y2[l+48]) * sc[7]; + } + tmp[16 * ix + tid] += dall * (sum.x * sc[0] + sum.y * sc[1] + sum.z * sc[4] + sum.w * sc[5]) - dmin * smin; + + } + + // sum up partial sums and write back result + barrier(CLK_LOCAL_MEM_FENCE); + for (int s=16; s>0; s>>=1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(CLK_LOCAL_MEM_FENCE); + } + if (tid == 0) { + dst[row] = tmp[0]; + } +} + +__kernel void dequantize_mul_mat_vec_q6_K(__global const struct block_q6_K * xx, __local float* tmp, __global const float * yy, __global float * dst, const int ncols) { + + const int row = get_group_id(0); + + const int num_blocks_per_row = ncols / QK_K; + const int ib0 = row*num_blocks_per_row; + + __global const struct block_q6_K * x = xx + ib0; + + const int tid = get_local_id(0)/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const int ix = get_local_id(0)%K_QUANTS_PER_ITERATION; // 0 or 0, 1 + + const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 + + const int im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... + const int in = tid - step*im; // 0...15 or 0...7 + +#if K_QUANTS_PER_ITERATION == 1 + const int l0 = K_QUANTS_PER_ITERATION*in; // 0...15 + const int is = 0; +#else + const int l0 = 4 * in; // 0, 4, 8, ..., 28 + const int is = in / 4; +#endif + const int ql_offset = 64*im + l0; + const int qh_offset = 32*im + l0; + const int s_offset = 8*im + is; + const int y_offset = 128*im + l0; + + tmp[16 * ix + tid] = 0; // partial sum for thread in warp + + for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { + + __global const float * y = yy + i * QK_K + y_offset; + __global const uint8_t * ql = x[i].ql + ql_offset; + __global const uint8_t * qh = x[i].qh + qh_offset; + __global const int8_t * s = x[i].scales + s_offset; + + const float d = vload_half(0, &x[i].d); + +#if K_QUANTS_PER_ITERATION == 1 + float sum = y[ 0] * s[0] * d * ((int8_t)((ql[ 0] & 0xF) | ((qh[ 0] & 0x03) << 4)) - 32) + + y[16] * s[1] * d * ((int8_t)((ql[16] & 0xF) | ((qh[16] & 0x03) << 4)) - 32) + + y[32] * s[2] * d * ((int8_t)((ql[32] & 0xF) | ((qh[ 0] & 0x0c) << 2)) - 32) + + y[48] * s[3] * d * ((int8_t)((ql[48] & 0xF) | ((qh[16] & 0x0c) << 2)) - 32) + + y[64] * s[4] * d * ((int8_t)((ql[ 0] >> 4) | ((qh[ 0] & 0x30) >> 0)) - 32) + + y[80] * s[5] * d * ((int8_t)((ql[16] >> 4) | ((qh[16] & 0x30) >> 0)) - 32) + + y[96] * s[6] * d * ((int8_t)((ql[32] >> 4) | ((qh[ 0] & 0xc0) >> 2)) - 32) + +y[112] * s[7] * d * ((int8_t)((ql[48] >> 4) | ((qh[16] & 0xc0) >> 2)) - 32); + tmp[16 * ix + tid] += sum; +#else + float sum = 0; + for (int l = 0; l < 4; ++l) { + sum += y[l+ 0] * s[0] * d * ((int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32) + + y[l+32] * s[2] * d * ((int8_t)((ql[l+32] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32) + + y[l+64] * s[4] * d * ((int8_t)((ql[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32) + + y[l+96] * s[6] * d * ((int8_t)((ql[l+32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32); + } + tmp[16 * ix + tid] += sum; +#endif + + } + + // sum up partial sums and write back result + barrier(CLK_LOCAL_MEM_FENCE); + for (int s=16; s>0; s>>=1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(CLK_LOCAL_MEM_FENCE); + } + if (tid == 0) { + dst[row] = tmp[0]; + } } ); @@ -549,44 +781,6 @@ __kernel void KERNEL_NAME(__global X_TYPE* x, __local float* tmp, __global float } ); -std::string dequant_mul_mat_vec_k_template = MULTILINE_QUOTE( -__kernel void KERNEL_NAME(__global X_TYPE* x, __local float* tmp, __global float* y, __global float* dst, const int ncols) { - const int block_size = get_local_size(0); - const int row = get_group_id(0); - const int tid = get_local_id(0); - - const int iter_stride = 256; - const int vals_per_iter = iter_stride / block_size; - const int num_blocks_per_row = ncols / 256; - const int ib0 = row*num_blocks_per_row; - - tmp[tid] = 0; - - for (int i = 0; i < ncols; i += iter_stride) { - const int col = i + vals_per_iter*tid; - const int ib = ib0 + col/256; // x block index - const int iqs = col%256; // x quant index - const int iybs = col - col%256; // y block start index - - // dequantize - float v; - DOT_KERNEL(x, ib, iqs, y + iybs, &v); - tmp[tid] += v; - } - - // sum up partial sums and write back result - barrier(CLK_LOCAL_MEM_FENCE); - for (int s=block_size/2; s>0; s>>=1) { - if (tid < s) { - tmp[tid] += tmp[tid + s]; - } - barrier(CLK_LOCAL_MEM_FENCE); - } - if (tid == 0) { - dst[row] = tmp[0]; - } -} -); std::string mul_template = MULTILINE_QUOTE( __kernel void KERNEL_NAME(__global TYPE* x, const int x_offset, __global TYPE* y, const int y_offset, __global TYPE* dst, const int dst_offset, const int ky) { @@ -649,18 +843,6 @@ std::array mul_str_values = { "mul_f32", "float" }; -std::array dmmv_k_str_keys = { - "KERNEL_NAME", "X_TYPE", "DOT_KERNEL" -}; - -std::array dmmv_k_str_values = { - "dequantize_mul_mat_vec_q2_K", "struct block_q2_K", "vec_dot_q2_K", - "dequantize_mul_mat_vec_q3_K", "struct block_q3_K", "vec_dot_q3_K", - "dequantize_mul_mat_vec_q4_K", "struct block_q4_K", "vec_dot_q4_K", - "dequantize_mul_mat_vec_q5_K", "struct block_q5_K", "vec_dot_q5_K", - "dequantize_mul_mat_vec_q6_K", "struct block_q6_K", "vec_dot_q6_K", -}; - std::string& replace(std::string& s, const std::string& from, const std::string& to) { size_t pos = 0; while ((pos = s.find(from, pos)) != std::string::npos) { @@ -673,6 +855,7 @@ std::string& replace(std::string& s, const std::string& from, const std::string& std::string generate_kernels() { std::stringstream src; src << program_source << '\n'; + src << k_quants_source << '\n'; for (size_t i = 0; i < dequant_str_values.size(); i += dequant_str_keys.size()) { std::string dequant_kernel = dequant_template; std::string dmmv_kernel = dequant_mul_mat_vec_template; @@ -690,13 +873,6 @@ std::string generate_kernels() { } src << mul_kernel << '\n'; } - for (size_t i = 0; i < dmmv_k_str_values.size(); i += dmmv_k_str_keys.size()) { - std::string dmmv_k_kernel = dequant_mul_mat_vec_k_template; - for (size_t j = 0; j < dmmv_k_str_keys.size(); j++) { - replace(dmmv_k_kernel, dmmv_k_str_keys[j], dmmv_k_str_values[i + j]); - } - src << dmmv_k_kernel << '\n'; - } return src.str(); } @@ -729,10 +905,11 @@ static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, co exit(1); } - const char* compile_opts = "-cl-mad-enable -cl-unsafe-math-optimizations -cl-finite-math-only -cl-fast-relaxed-math " - "-DQK4_0=32 -DQR4_0=2 -DQK4_1=32 -DQR4_1=2 -DQK5_0=32 -DQR5_0=2 -DQK5_1=32 -DQR5_1=2 -DQK8_0=32 -DQR8_0=1"; + std::string compile_opts = "-cl-mad-enable -cl-unsafe-math-optimizations -cl-finite-math-only -cl-fast-relaxed-math " + "-DQK4_0=32 -DQR4_0=2 -DQK4_1=32 -DQR4_1=2 -DQK5_0=32 -DQR5_0=2 -DQK5_1=32 -DQR5_1=2 -DQK8_0=32 -DQR8_0=1 " + "-DQK_K=256 -DK_QUANTS_PER_ITERATION=" + std::to_string(K_QUANTS_PER_ITERATION); - err = clBuildProgram(p, 0, NULL, compile_opts, NULL, NULL); + err = clBuildProgram(p, 0, NULL, compile_opts.c_str(), NULL, NULL); if(err < 0) { clGetProgramBuildInfo(p, dev, CL_PROGRAM_BUILD_LOG, 0, NULL, &log_size); From b8c8dda75fdf5fdea49c80af36818e7c30fe0ddf Mon Sep 17 00:00:00 2001 From: Howard Su Date: Thu, 29 Jun 2023 21:15:15 +0800 Subject: [PATCH 061/852] Use unsigned for random seed (#2006) * Use unsigned for random seed. Keep -1 as the value to use a time based seed. Co-authored-by: Georgi Gerganov --- examples/common.cpp | 2 +- examples/common.h | 2 +- examples/embedding/embedding.cpp | 4 ++-- examples/main/README.md | 2 +- examples/main/main.cpp | 4 ++-- examples/perplexity/perplexity.cpp | 4 ++-- examples/server/README.md | 2 +- .../train-text-from-scratch.cpp | 6 +++--- llama.cpp | 8 ++++---- llama.h | 14 ++++++++------ 10 files changed, 25 insertions(+), 23 deletions(-) diff --git a/examples/common.cpp b/examples/common.cpp index 5addd10a1..3278a0643 100644 --- a/examples/common.cpp +++ b/examples/common.cpp @@ -110,7 +110,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { invalid_param = true; break; } - params.seed = std::stoi(argv[i]); + params.seed = std::stoul(argv[i]); } else if (arg == "-t" || arg == "--threads") { if (++i >= argc) { invalid_param = true; diff --git a/examples/common.h b/examples/common.h index 9d213d6d0..66e567291 100644 --- a/examples/common.h +++ b/examples/common.h @@ -22,7 +22,7 @@ int32_t get_num_physical_cores(); struct gpt_params { - int32_t seed = -1; // RNG seed + uint32_t seed = -1; // RNG seed int32_t n_threads = get_num_physical_cores(); int32_t n_predict = -1; // new tokens to predict int32_t n_ctx = 512; // context size diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index 3cd5bb794..2b7eb39c5 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -24,11 +24,11 @@ int main(int argc, char ** argv) { fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); - if (params.seed < 0) { + if (params.seed == LLAMA_DEFAULT_SEED) { params.seed = time(NULL); } - fprintf(stderr, "%s: seed = %d\n", __func__, params.seed); + fprintf(stderr, "%s: seed = %u\n", __func__, params.seed); std::mt19937 rng(params.seed); if (params.random_prompt) { diff --git a/examples/main/README.md b/examples/main/README.md index 9ba1eb384..375386130 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -242,7 +242,7 @@ Example usage: `--logit-bias 29905-inf` ### RNG Seed -- `-s SEED, --seed SEED`: Set the random number generator (RNG) seed (default: -1, < 0 = random seed). +- `-s SEED, --seed SEED`: Set the random number generator (RNG) seed (default: -1, -1 = random seed). The RNG seed is used to initialize the random number generator that influences the text generation process. By setting a specific seed value, you can obtain consistent and reproducible results across multiple runs with the same input and settings. This can be helpful for testing, debugging, or comparing the effects of different options on the generated text to see when they diverge. If the seed is set to a value less than 0, a random seed will be used, which will result in different outputs on each run. diff --git a/examples/main/main.cpp b/examples/main/main.cpp index bcdc98d61..3a171925b 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -94,11 +94,11 @@ int main(int argc, char ** argv) { fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); - if (params.seed < 0) { + if (params.seed == LLAMA_DEFAULT_SEED) { params.seed = time(NULL); } - fprintf(stderr, "%s: seed = %d\n", __func__, params.seed); + fprintf(stderr, "%s: seed = %u\n", __func__, params.seed); std::mt19937 rng(params.seed); if (params.random_prompt) { diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index f8a6cb516..dd54ed3c4 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -136,11 +136,11 @@ int main(int argc, char ** argv) { fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); - if (params.seed < 0) { + if (params.seed == LLAMA_DEFAULT_SEED) { params.seed = time(NULL); } - fprintf(stderr, "%s: seed = %d\n", __func__, params.seed); + fprintf(stderr, "%s: seed = %u\n", __func__, params.seed); std::mt19937 rng(params.seed); if (params.random_prompt) { diff --git a/examples/server/README.md b/examples/server/README.md index fa95c0044..ba4b2fec9 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -152,7 +152,7 @@ node . `mirostat_eta`: Set the Mirostat learning rate, parameter eta (default: 0.1). - `seed`: Set the random number generator (RNG) seed (default: -1, < 0 = random seed). + `seed`: Set the random number generator (RNG) seed (default: -1, -1 = random seed). `ignore_eos`: Ignore end of stream token and continue generating (default: false). diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index a05881d16..05bfa8016 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -2768,7 +2768,7 @@ void train_print_usage(int /*argc*/, char ** argv, const struct train_params * p fprintf(stderr, " --checkpoint-in FNAME path from which to load training checkpoint (default '%s')\n", params->fn_checkpoint_in); fprintf(stderr, " --checkpoint-out FNAME path to save training checkpoint (default '%s')\n", params->fn_checkpoint_out); fprintf(stderr, " --model-out FNAME path to save ggml model (default '%s')\n", params->fn_model_out); - fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for < 0)\n"); + fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for -1)\n"); fprintf(stderr, " -c N, --ctx N Context size used during training (default %d)\n", params->n_ctx); fprintf(stderr, " --embd N Embedding size used for new models (default %d)\n", params->n_embd); fprintf(stderr, " --mult N Mult size used for new models, influences feedforward size. (default %d)\n", params->n_mult); @@ -3034,10 +3034,10 @@ int main(int argc, char ** argv) { return 1; } - if (params.seed < 0) { + if (params.seed == LLAMA_DEFAULT_SEED) { params.seed = time(NULL); } - printf("%s: seed: %d\n", __func__, params.seed); + printf("%s: seed: %u\n", __func__, params.seed); srand(params.seed); struct llama_context_params llama_params = llama_context_default_params(); diff --git a/llama.cpp b/llama.cpp index ef80b4e8b..049f73e44 100644 --- a/llama.cpp +++ b/llama.cpp @@ -777,7 +777,7 @@ static bool kv_cache_init( struct llama_context_params llama_context_default_params() { struct llama_context_params result = { - /*.seed =*/ -1, + /*.seed =*/ LLAMA_DEFAULT_SEED, /*.n_ctx =*/ 512, /*.n_batch =*/ 512, /*.gpu_layers =*/ 0, @@ -2541,7 +2541,7 @@ struct llama_context * llama_new_context_with_model( llama_context * ctx = new llama_context(*model, model->vocab); - if (params.seed < 0) { + if (params.seed == LLAMA_DEFAULT_SEED) { params.seed = time(NULL); } @@ -2974,8 +2974,8 @@ int llama_get_kv_cache_token_count(const struct llama_context * ctx) { #define LLAMA_MAX_RNG_STATE (64*1024) -void llama_set_rng_seed(struct llama_context * ctx, int seed) { - if (seed < 0) { +void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed) { + if (seed == LLAMA_DEFAULT_SEED) { seed = time(NULL); } ctx->rng.seed(seed); diff --git a/llama.h b/llama.h index c2f2e5331..5bb1964bd 100644 --- a/llama.h +++ b/llama.h @@ -46,6 +46,8 @@ #define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN #define LLAMA_SESSION_VERSION 1 +#define LLAMA_DEFAULT_SEED 0xFFFFFFFF + #if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) // Defined when llama.cpp is compiled with support for offloading model layers to GPU. #define LLAMA_SUPPORTS_GPU_OFFLOAD @@ -81,11 +83,11 @@ extern "C" { typedef void (*llama_progress_callback)(float progress, void *ctx); struct llama_context_params { - int seed; // RNG seed, -1 for random - int n_ctx; // text context - int n_batch; // prompt processing batch size - int n_gpu_layers; // number of layers to store in VRAM - int main_gpu; // the GPU that is used for scratch and small tensors + uint32_t seed; // RNG seed, -1 for random + int32_t n_ctx; // text context + int32_t n_batch; // prompt processing batch size + int32_t n_gpu_layers; // number of layers to store in VRAM + int32_t main_gpu; // the GPU that is used for scratch and small tensors float tensor_split[LLAMA_MAX_DEVICES]; // how to split layers across multiple GPUs // called with a progress value between 0 and 1, pass NULL to disable llama_progress_callback progress_callback; @@ -196,7 +198,7 @@ extern "C" { LLAMA_API int llama_get_kv_cache_token_count(const struct llama_context * ctx); // Sets the current rng seed. - LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, int seed); + LLAMA_API void llama_set_rng_seed(struct llama_context * ctx, uint32_t seed); // Returns the maximum size in bytes of the state (rng, logits, embedding // and kv_cache) - will often be smaller after compacting tokens From b1ca8f36a9cdbcee5f5c425df717611a1040a897 Mon Sep 17 00:00:00 2001 From: Qingyou Meng Date: Sat, 1 Jul 2023 23:42:43 +0800 Subject: [PATCH 062/852] ggml : disable GGML_TASK_INIT and GGML_TASK_FINALIZE by default (#1995) Will not be scheduled unless explicitly enabled. --- ggml.c | 61 +++++++++++++++++++++++++++++++++++++++++++++++++--------- ggml.h | 3 +++ 2 files changed, 55 insertions(+), 9 deletions(-) diff --git a/ggml.c b/ggml.c index 684caaa37..75cc44baa 100644 --- a/ggml.c +++ b/ggml.c @@ -3846,6 +3846,40 @@ static_assert(GGML_OP_COUNT == 64, "GGML_OP_COUNT != 64"); static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN"); static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN"); +// WARN: +// Mis-confguration can lead to problem that's hard to reason about: +// * At best it crash or talks nosense. +// * At worst it talks slightly difference but hard to perceive. +// +// An op has to enable INIT or FINALIZE when any of it's branch needs that pass. +// Take care about compile options (e.g., GGML_USE_xxx). +static bool GGML_OP_HAS_INIT [GGML_OP_COUNT] = { 0 }; +static bool GGML_OP_HAS_FINALIZE[GGML_OP_COUNT] = { 0 }; +static void ggml_setup_op_has_task_pass(void) { + { // INIT + bool * I = GGML_OP_HAS_INIT; + + I[GGML_OP_ACC ] = true; + I[GGML_OP_MUL_MAT ] = true; + I[GGML_OP_OUT_PROD ] = true; + I[GGML_OP_SET ] = true; + I[GGML_OP_GET_ROWS_BACK ] = true; + I[GGML_OP_DIAG_MASK_INF ] = true; + I[GGML_OP_DIAG_MASK_ZERO ] = true; + I[GGML_OP_CONV_1D_S1_PH ] = true; + I[GGML_OP_CONV_1D_S2_PH ] = true; + I[GGML_OP_CONV_2D_SK_P0 ] = true; + I[GGML_OP_FLASH_ATTN_BACK ] = true; + I[GGML_OP_CROSS_ENTROPY_LOSS ] = true; + } + + { // FINALIZE + bool * F = GGML_OP_HAS_FINALIZE; + + F[GGML_OP_CROSS_ENTROPY_LOSS ] = true; + } +} + // // ggml context // @@ -4267,6 +4301,8 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { ggml_cl_init(); #endif + ggml_setup_op_has_task_pass(); + is_first_call = false; } @@ -16791,9 +16827,11 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { if (node_n != -1) { /* FINALIZE */ struct ggml_tensor * node = state->shared->cgraph->nodes[node_n]; - params.nth = node->n_tasks; - ggml_compute_forward(¶ms, node); - ggml_graph_compute_perf_stats_node(node, state->shared); + if (GGML_OP_HAS_FINALIZE[node->op]) { + params.nth = node->n_tasks; + ggml_compute_forward(¶ms, node); + ggml_graph_compute_perf_stats_node(node, state->shared); + } } // distribute new work or execute it direct if 1T @@ -16805,10 +16843,13 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { state->shared->perf_node_start_cycles = ggml_perf_cycles(); state->shared->perf_node_start_time_us = ggml_perf_time_us(); + params.nth = node->n_tasks; + /* INIT */ - params.type = GGML_TASK_INIT; - params.nth = node->n_tasks; - ggml_compute_forward(¶ms, node); + if (GGML_OP_HAS_INIT[node->op]) { + params.type = GGML_TASK_INIT; + ggml_compute_forward(¶ms, node); + } if (node->n_tasks == 1) { // TODO: maybe push node_n to the atomic but if other threads see n_tasks is 1, @@ -16816,9 +16857,11 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { params.type = GGML_TASK_COMPUTE; ggml_compute_forward(¶ms, node); - params.type = GGML_TASK_FINALIZE; - ggml_compute_forward(¶ms, node); - ggml_graph_compute_perf_stats_node(node, state->shared); + if (GGML_OP_HAS_FINALIZE[node->op]) { + params.type = GGML_TASK_FINALIZE; + ggml_compute_forward(¶ms, node); + ggml_graph_compute_perf_stats_node(node, state->shared); + } } else { break; } diff --git a/ggml.h b/ggml.h index 459913222..11b51f8bd 100644 --- a/ggml.h +++ b/ggml.h @@ -444,6 +444,9 @@ extern "C" { // compute types + + // NOTE: the INIT or FINALIZE pass is not scheduled unless explicitly enabled. + // This behavior was changed since https://github.com/ggerganov/llama.cpp/pull/1995. enum ggml_task_type { GGML_TASK_INIT = 0, GGML_TASK_COMPUTE, From 04606a159947566b27810508433e6ca5dbc684ba Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 1 Jul 2023 18:45:44 +0300 Subject: [PATCH 063/852] train : fix compile warning --- examples/train-text-from-scratch/train-text-from-scratch.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index 05bfa8016..c50eeb343 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -2671,7 +2671,8 @@ struct train_params { const char * fn_checkpoint_out; const char * fn_model_out; - int seed; + uint32_t seed; + int n_ctx; int n_embd; int n_mult; From 79f634a19d1c32a6cfb1befc21551ee684fced6b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 1 Jul 2023 18:46:00 +0300 Subject: [PATCH 064/852] embd-input : fix returning ptr to temporary --- examples/embd-input/embd-input-lib.cpp | 9 ++++++--- examples/embd-input/embd-input.h | 4 +--- 2 files changed, 7 insertions(+), 6 deletions(-) diff --git a/examples/embd-input/embd-input-lib.cpp b/examples/embd-input/embd-input-lib.cpp index 37de52ad6..570e273fc 100644 --- a/examples/embd-input/embd-input-lib.cpp +++ b/examples/embd-input/embd-input-lib.cpp @@ -210,9 +210,12 @@ llama_token sampling_id(struct MyModel* mymodel) { const char * sampling(struct MyModel * mymodel) { llama_context * ctx = mymodel->ctx; int id = sampling_id(mymodel); - std::string ret; - if (id == llama_token_eos()) ret = ""; - else ret = llama_token_to_str(ctx, id); + static std::string ret; + if (id == llama_token_eos()) { + ret = ""; + } else { + ret = llama_token_to_str(ctx, id); + } eval_id(mymodel, id); return ret.c_str(); } diff --git a/examples/embd-input/embd-input.h b/examples/embd-input/embd-input.h index 4fefabd42..efb5ba5e2 100644 --- a/examples/embd-input/embd-input.h +++ b/examples/embd-input/embd-input.h @@ -5,7 +5,6 @@ #include "llama.h" #include "build-info.h" - extern "C" { typedef struct MyModel { @@ -14,14 +13,13 @@ typedef struct MyModel { int n_past = 0; } MyModel; - struct MyModel* create_mymodel(int argc, char ** argv); bool eval_float(void* model, float* input, int N); bool eval_tokens(void* model, std::vector tokens); bool eval_id(struct MyModel* mymodel, int id); bool eval_string(struct MyModel* mymodel, const char* str); -const char* sampling(struct MyModel* mymodel); +const char * sampling(struct MyModel* mymodel); llama_token sampling_id(struct MyModel* mymodel); void free_mymodel(struct MyModel* mymodel); From cb44dbc7de287b3d17772cfb1aa49d55e082ce5b Mon Sep 17 00:00:00 2001 From: Rand Xie Date: Sun, 2 Jul 2023 00:02:58 +0800 Subject: [PATCH 065/852] llama : catch llama_load_session_file_internal exceptions (#2022) * convert checks in llama_load_session_file to throw and handle them * make llama_load_session_file_internal static * address feedbacks to avoid using exceptions --- llama.cpp | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/llama.cpp b/llama.cpp index 049f73e44..3a7a0d5da 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3219,7 +3219,7 @@ size_t llama_set_state_data(struct llama_context * ctx, uint8_t * src) { return nread; } -bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) { +static bool llama_load_session_file_internal(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) { llama_file file(path_session, "rb"); // sanity checks @@ -3269,8 +3269,15 @@ bool llama_load_session_file(struct llama_context * ctx, const char * path_sessi llama_set_state_data(ctx, state_data.data()); } +} - return true; +bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) { + try { + return llama_load_session_file_internal(ctx, path_session, tokens_out, n_token_capacity, n_token_count_out); + } catch (const std::exception & err) { + fprintf(stderr, "error loading session file: %s\n", err.what()); + return false; + } } bool llama_save_session_file(struct llama_context * ctx, const char * path_session, const llama_token * tokens, size_t n_token_count) { From 463f2f4c4f8dd5ca9594b7d65849f346f0effe05 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 1 Jul 2023 19:05:09 +0300 Subject: [PATCH 066/852] llama : fix return value of llama_load_session_file_internal (#2022) --- llama.cpp | 2 ++ 1 file changed, 2 insertions(+) diff --git a/llama.cpp b/llama.cpp index 3a7a0d5da..69c2ab01b 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3269,6 +3269,8 @@ static bool llama_load_session_file_internal(struct llama_context * ctx, const c llama_set_state_data(ctx, state_data.data()); } + + return true; } bool llama_load_session_file(struct llama_context * ctx, const char * path_session, llama_token * tokens_out, size_t n_token_capacity, size_t * n_token_count_out) { From 471aab6e4cb89d8ef6d043f1bc93acb6eb78ab67 Mon Sep 17 00:00:00 2001 From: Judd Date: Sun, 2 Jul 2023 01:00:25 +0800 Subject: [PATCH 067/852] convert : add support of baichuan-7b (#2055) Co-authored-by: Judd --- README.md | 1 + convert.py | 41 ++++++++++++++++++++++++++++++++++++----- 2 files changed, 37 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index ee56988c7..e890dc9c2 100644 --- a/README.md +++ b/README.md @@ -85,6 +85,7 @@ as the main playground for developing new features for the [ggml](https://github - [X] [OpenBuddy 🐶 (Multilingual)](https://github.com/OpenBuddy/OpenBuddy) - [X] [Pygmalion 7B / Metharme 7B](#using-pygmalion-7b--metharme-7b) - [X] [WizardLM](https://github.com/nlpxucan/WizardLM) +- [X] [Baichuan-7B](https://huggingface.co/baichuan-inc/baichuan-7B) **Bindings:** diff --git a/convert.py b/convert.py index e340d2273..142692776 100644 --- a/convert.py +++ b/convert.py @@ -136,7 +136,7 @@ def find_n_mult(n_ff: int, n_embd: int) -> int: calc_ff = (((8*n_embd) // 3 + n_mult - 1) // n_mult)*n_mult if calc_ff == n_ff: return n_mult - return 1 + raise Exception(f"failed to find n_mult for (n_ff={n_ff}, n_embd={n_embd}).") @dataclass class Params: @@ -321,6 +321,10 @@ class Tensor(metaclass=ABCMeta): @abstractmethod def permute(self, n_head: int) -> 'Tensor': ... @abstractmethod + def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': ... + @abstractmethod + def part(self, n_part: int) -> 'UnquantizedTensor': ... + @abstractmethod def to_ggml(self) -> 'GGMLCompatibleTensor': ... @@ -345,6 +349,14 @@ class UnquantizedTensor(Tensor): def to_ggml(self) -> 'UnquantizedTensor': return self + def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': + r = self.ndarray.shape[0] // 3 + return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head)) + + def part(self, n_part: int) -> 'UnquantizedTensor': + r = self.ndarray.shape[0] // 3 + return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...]) + def permute(self, n_head: int) -> 'UnquantizedTensor': return UnquantizedTensor(permute(self.ndarray, n_head)) @@ -642,6 +654,19 @@ def permute_lazy(lazy_tensor: LazyTensor, n_head: int) -> LazyTensor: return lazy_tensor.load().permute(n_head) return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description) +def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int) -> LazyTensor: + def load() -> Tensor: + return lazy_tensor.load().permute_part(n_part, n_head) + s = lazy_tensor.shape.copy() + s[0] = s[0] // 3 + return LazyTensor(load, s, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description) + +def part_lazy(lazy_tensor: LazyTensor, n_part: int) -> LazyTensor: + def load() -> Tensor: + return lazy_tensor.load().part(n_part) + s = lazy_tensor.shape.copy() + s[0] = s[0] // 3 + return LazyTensor(load, s, lazy_tensor.data_type, 'part ' + lazy_tensor.description) def convert_transformers_to_orig(model: LazyModel, params: Params) -> LazyModel: out: LazyModel = {} @@ -650,11 +675,17 @@ def convert_transformers_to_orig(model: LazyModel, params: Params) -> LazyModel: out["output.weight"] = model["lm_head.weight"] for i in itertools.count(): - if f"model.layers.{i}.self_attn.q_proj.weight" not in model: + if f"model.layers.{i}.self_attn.q_proj.weight" in model: + out[f"layers.{i}.attention.wq.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head) + out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head) + out[f"layers.{i}.attention.wv.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"] + elif f"model.layers.{i}.self_attn.W_pack.weight" in model: + out[f"layers.{i}.attention.wq.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head) + out[f"layers.{i}.attention.wk.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head) + out[f"layers.{i}.attention.wv.weight"] = part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 2) + else: break - out[f"layers.{i}.attention.wq.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.q_proj.weight"], params.n_head) - out[f"layers.{i}.attention.wk.weight"] = permute_lazy(model[f"model.layers.{i}.self_attn.k_proj.weight"], params.n_head) - out[f"layers.{i}.attention.wv.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"] + out[f"layers.{i}.attention.wo.weight"] = model[f"model.layers.{i}.self_attn.o_proj.weight"] out[f"layers.{i}.feed_forward.w1.weight"] = model[f"model.layers.{i}.mlp.gate_proj.weight"] From 2f8cd979ecd1fa582852e7136e92ff8990b98fd8 Mon Sep 17 00:00:00 2001 From: Aaron Miller Date: Sat, 1 Jul 2023 11:14:59 -0700 Subject: [PATCH 068/852] metal : release buffers when freeing metal context (#2062) --- ggml-metal.m | 4 +++- llama.cpp | 8 +++++++- 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 7551231b9..fd69c41fe 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -202,7 +202,9 @@ struct ggml_metal_context * ggml_metal_init(void) { void ggml_metal_free(struct ggml_metal_context * ctx) { fprintf(stderr, "%s: deallocating\n", __func__); - + for (int i = 0; i < ctx->n_buffers; ++i) { + [ctx->buffers[i].metal release]; + } free(ctx); } diff --git a/llama.cpp b/llama.cpp index 69c2ab01b..561accf88 100644 --- a/llama.cpp +++ b/llama.cpp @@ -253,7 +253,13 @@ struct llama_model { struct llama_context { llama_context(const llama_model & model, const llama_vocab & vocab) : model(model), vocab(vocab), t_load_us(model.t_load_us), t_start_us(model.t_start_us) {} - +#ifdef GGML_USE_METAL + ~llama_context() { + if (ctx_metal) { + ggml_metal_free(ctx_metal); + } + } +#endif std::mt19937 rng; bool has_evaluated_once = false; From b2132270678c473f7cd9ba871b03d694126bc33a Mon Sep 17 00:00:00 2001 From: Daniel Drake Date: Sat, 1 Jul 2023 20:31:44 +0200 Subject: [PATCH 069/852] cmake : don't force -mcpu=native on aarch64 (#2063) It's currently not possible to cross-compile llama.cpp for aarch64 because CMakeLists.txt forces -mcpu=native for that target. -mcpu=native doesn't make sense if your build host is not the target architecture, and clang rejects it for that reason, aborting the build. This can be easily reproduced using the current Android NDK to build for aarch64 on an x86_64 host. If there is not a specific CPU-tuning target for aarch64 then -mcpu should be omitted completely. I think that makes sense, there is not enough variance in the aarch64 instruction set to warrant a fixed -mcpu optimization at this point. And if someone is building natively and wishes to enable any possible optimizations for the host device, then there is already the LLAMA_NATIVE option available. Fixes #495. --- CMakeLists.txt | 5 ----- 1 file changed, 5 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index ffda74a70..34a897327 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -386,11 +386,6 @@ if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm" OR ${CMAKE_SYSTEM_PROCESSOR} MATCHES if (MSVC) # TODO: arm msvc? else() - if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64") - # Apple M1, M2, etc. - # Raspberry Pi 3, 4, Zero 2 (64-bit) - add_compile_options(-mcpu=native) - endif() if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv6") # Raspberry Pi 1, Zero add_compile_options(-mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access) From befb3a35627432473f143c90994557d78ff5bc67 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 1 Jul 2023 21:47:26 +0200 Subject: [PATCH 070/852] Test-based VRAM scratch size + context adjustment (#2056) --- llama.cpp | 38 +++++++++++++++++++++++++++++++++++--- 1 file changed, 35 insertions(+), 3 deletions(-) diff --git a/llama.cpp b/llama.cpp index 561accf88..a869bbac8 100644 --- a/llama.cpp +++ b/llama.cpp @@ -66,6 +66,7 @@ enum e_model { MODEL_65B, }; +static const size_t kB = 1024; static const size_t MB = 1024*1024; // computed for n_ctx == 2048 @@ -129,6 +130,34 @@ static const std::map & MEM_REQ_EVAL() return k_sizes; } +// amount of VRAM needed per batch size to hold temporary results +// the values for 3b and 65b are not derived from testing but instead chosen conservatively +static const std::map & VRAM_REQ_SCRATCH_BASE() +{ + static std::map k_sizes = { + { MODEL_3B, 512ull * kB }, + { MODEL_7B, 512ull * kB }, + { MODEL_13B, 640ull * kB }, + { MODEL_30B, 768ull * kB }, + { MODEL_65B, 1536ull * kB }, + }; + return k_sizes; +} + +// amount of VRAM needed per batch size and context to hold temporary results +// the values for 3b and 65b are not derived from testing but instead chosen conservatively +static const std::map & VRAM_REQ_SCRATCH_PER_CONTEXT() +{ + static std::map k_sizes = { + { MODEL_3B, 128ull }, + { MODEL_7B, 128ull }, + { MODEL_13B, 160ull }, + { MODEL_30B, 208ull }, + { MODEL_65B, 416ull }, + }; + return k_sizes; +} + // default hparams (LLaMA 7B) struct llama_hparams { uint32_t n_vocab = 32000; @@ -1118,11 +1147,14 @@ static void llama_model_load_internal( fprintf(stderr, "%s: not allocating a VRAM scratch buffer due to low VRAM option\n", __func__); ggml_cuda_set_scratch_size(0); // disable scratch } else { - vram_scratch = n_batch * MB; + const size_t vram_scratch_base = VRAM_REQ_SCRATCH_BASE().at(model.type); + const size_t vram_scratch_per_context = VRAM_REQ_SCRATCH_PER_CONTEXT().at(model.type); + vram_scratch = n_batch * (vram_scratch_base + n_ctx * vram_scratch_per_context); ggml_cuda_set_scratch_size(vram_scratch); if (n_gpu_layers > 0) { - fprintf(stderr, "%s: allocating batch_size x 1 MB = %zd MB VRAM for the scratch buffer\n", - __func__, vram_scratch / MB); + fprintf(stderr, "%s: allocating batch_size x (%zd kB + n_ctx x %zd B) = %zd MB VRAM for the scratch buffer\n", + __func__, vram_scratch_base / kB, vram_scratch_per_context, + (vram_scratch + MB - 1) / MB); // round up } } #endif // GGML_USE_CUBLAS From 0bc2cdfc875fa7877d8e01c8bb17066f99c08f21 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 1 Jul 2023 21:49:44 +0200 Subject: [PATCH 071/852] Better CUDA synchronization logic (#2057) --- ggml-cuda.cu | 63 ++++++++++++++++++++++++++++++++++++++-------------- ggml-cuda.h | 4 ---- 2 files changed, 46 insertions(+), 21 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 4e0d3dbde..50df20edd 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -214,6 +214,11 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_ static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2"); #endif +struct ggml_tensor_extra_gpu { + void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors + cudaEvent_t events[GGML_CUDA_MAX_DEVICES]; // events for synchronizing multiple GPUs +}; + static __global__ void add_f32(const float * x, const float * y, float * dst, const int k) { const int i = blockDim.x*blockIdx.x + threadIdx.x; @@ -1970,7 +1975,6 @@ inline void ggml_cuda_op_add( } else { GGML_ASSERT(false); } - CUDA_CHECK(cudaGetLastError()); (void) src1; (void) dst; @@ -2002,7 +2006,6 @@ inline void ggml_cuda_op_mul( // compute mul_f32_cuda(src0_ddf_i01, src1_ddf_i01, dst_ddf_i01, ne00, ne10, cudaStream_main); - CUDA_CHECK(cudaGetLastError()); } (void) dst; @@ -2023,7 +2026,6 @@ inline void ggml_cuda_op_silu( // compute silu_f32_cuda(src0_ddf_i, dst_ddf_i, ne00*i01_diff, cudaStream_main); - CUDA_CHECK(cudaGetLastError()); (void) src1; (void) dst; @@ -2046,7 +2048,6 @@ inline void ggml_cuda_op_rms_norm( // compute rms_norm_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, cudaStream_main); - CUDA_CHECK(cudaGetLastError()); (void) src1; (void) dst; @@ -2125,7 +2126,6 @@ inline void ggml_cuda_op_dequantize_mul_mat_vec( GGML_ASSERT(false); break; } - CUDA_CHECK(cudaGetLastError()); #ifdef GGML_CUDA_DMMV_F16 if (src1_convert_f16) { @@ -2202,7 +2202,6 @@ inline void ggml_cuda_op_rope( // compute rope_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, p, theta_scale, cudaStream_main); - CUDA_CHECK(cudaGetLastError()); (void) dst; (void) src0_ddq_i; @@ -2226,7 +2225,6 @@ inline void ggml_cuda_op_diag_mask_inf( // compute diag_mask_inf_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, ne01, n_past, cudaStream_main); - CUDA_CHECK(cudaGetLastError()); (void) dst; (void) src0_ddq_i; @@ -2248,7 +2246,6 @@ inline void ggml_cuda_op_soft_max( // compute soft_max_f32_cuda(src0_ddf_i, dst_ddf_i, ne00, i01_diff, cudaStream_main); - CUDA_CHECK(cudaGetLastError()); (void) src1; (void) dst; @@ -2344,10 +2341,11 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm size_t src1_asf[GGML_CUDA_MAX_DEVICES] = {0}; size_t dst_asf[GGML_CUDA_MAX_DEVICES] = {0}; - // if multiple GPUs are used they need to wait for the main GPU to finish + // if multiple devices are used they need to wait for the main device + // here an event is recorded that signifies that the main device has finished calculating the input data if (split && g_device_count > 1) { CUDA_CHECK(cudaSetDevice(g_main_device)); - CUDA_CHECK(cudaDeviceSynchronize()); + CUDA_CHECK(cudaEventRecord(src0_extra->events[g_main_device], g_cudaStreams_main[g_main_device])); } for (int id = 0; id < g_device_count; ++id) { @@ -2373,6 +2371,12 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm int64_t row_diff = row_high - row_low; cudaSetDevice(id); + cudaStream_t cudaStream_main = g_cudaStreams_main[id]; + + // wait for main GPU data if necessary + if (split && id != g_main_device) { + CUDA_CHECK(cudaStreamWaitEvent(cudaStream_main, src0_extra->events[g_main_device])); + } if (src0_on_device && src0_is_contiguous) { if (src0_is_f32) { @@ -2448,8 +2452,6 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm } const int64_t i11 = i13*ne12 + i12; - cudaStream_t cudaStream_main = g_cudaStreams_main[id]; - // for split tensors the data begins at i0 == i0_offset_low char * src0_ddq_i = src0_ddq[id] + (i0 - i0_offset_low)*src0_stride*src0_ts/src0_bs; float * src0_ddf_i = src0_ddf[id] + (i0 - i0_offset_low)*src0_stride; @@ -2509,6 +2511,7 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm // do the computation op(src0, src1, dst, src0_ddq_i, src0_ddf_i, src1_ddf_i, dst_ddf_i, i02, i01_low, i01_high, i11, cudaStream_main); + CUDA_CHECK(cudaGetLastError()); // copy dst to host or other device if necessary if (!dst_on_device) { @@ -2538,6 +2541,11 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm CUDA_CHECK(cudaMemcpyAsync(dhf_dst_i, dst_ddf_i, dst_stride*sizeof(float), kind, cudaStream_main)); } } + + // signify to main device that other device is done + if (split && g_device_count > 1 && id != g_main_device) { + CUDA_CHECK(cudaEventRecord(src0_extra->events[id], cudaStream_main)); + } } } } @@ -2549,7 +2557,6 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm } CUDA_CHECK(cudaSetDevice(id)); - CUDA_CHECK(cudaDeviceSynchronize()); if (src0_asq[id] > 0) { ggml_cuda_pool_free(src0_ddq[id], src0_asq[id]); @@ -2564,6 +2571,21 @@ static void ggml_cuda_op(const ggml_tensor * src0, const ggml_tensor * src1, ggm ggml_cuda_pool_free(dst_ddf[id], dst_asf[id]); } } + + // main device waits for all other devices to be finished + if (split && g_device_count > 1) { + CUDA_CHECK(cudaSetDevice(g_main_device)); + for (int id = 0; id < g_device_count; ++id) { + if (id != g_main_device) { + CUDA_CHECK(cudaStreamWaitEvent(g_cudaStreams_main[g_main_device], src0_extra->events[id])); + } + } + } + + if (dst->backend == GGML_BACKEND_CPU) { + CUDA_CHECK(cudaSetDevice(g_main_device)); + CUDA_CHECK(cudaDeviceSynchronize()); + } } void ggml_cuda_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -2803,6 +2825,10 @@ void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) { cudaMemcpy(buf, buf_host, size, cudaMemcpyHostToDevice); extra->data_device[id] = buf; + + if (backend == GGML_BACKEND_GPU_SPLIT) { + CUDA_CHECK(cudaEventCreateWithFlags(&extra->events[id], cudaEventDisableTiming)); + } } tensor->extra = extra; @@ -2816,12 +2842,15 @@ void ggml_cuda_free_data(struct ggml_tensor * tensor) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; for (int id = 0; id < g_device_count; ++id) { - if (extra->data_device[id] == nullptr) { - continue; + if (extra->data_device[id] != nullptr) { + CUDA_CHECK(cudaSetDevice(id)); + CUDA_CHECK(cudaFree(extra->data_device[id])); } - CUDA_CHECK(cudaSetDevice(id)); - CUDA_CHECK(cudaFree(extra->data_device[id])); + if (extra->events[id] != nullptr) { + CUDA_CHECK(cudaSetDevice(id)); + CUDA_CHECK(cudaEventDestroy(extra->events[id])); + } } delete extra; diff --git a/ggml-cuda.h b/ggml-cuda.h index 7a65a3558..3c1e8deb6 100644 --- a/ggml-cuda.h +++ b/ggml-cuda.h @@ -8,10 +8,6 @@ extern "C" { #define GGML_CUDA_MAX_DEVICES 16 -struct ggml_tensor_extra_gpu { - void * data_device[GGML_CUDA_MAX_DEVICES]; // 1 pointer for each device for split tensors -}; - void ggml_init_cublas(void); void ggml_cuda_set_tensor_split(const float * tensor_split); From 46088f72318981341a2d646f12f6eee6aec06d65 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 2 Jul 2023 09:46:46 +0300 Subject: [PATCH 072/852] ggml : fix build with OpenBLAS (close #2066) --- ggml.c | 31 ++++++++++++++++--------------- 1 file changed, 16 insertions(+), 15 deletions(-) diff --git a/ggml.c b/ggml.c index 75cc44baa..afeb72ff0 100644 --- a/ggml.c +++ b/ggml.c @@ -3855,28 +3855,29 @@ static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size // Take care about compile options (e.g., GGML_USE_xxx). static bool GGML_OP_HAS_INIT [GGML_OP_COUNT] = { 0 }; static bool GGML_OP_HAS_FINALIZE[GGML_OP_COUNT] = { 0 }; + static void ggml_setup_op_has_task_pass(void) { { // INIT - bool * I = GGML_OP_HAS_INIT; + bool * p = GGML_OP_HAS_INIT; - I[GGML_OP_ACC ] = true; - I[GGML_OP_MUL_MAT ] = true; - I[GGML_OP_OUT_PROD ] = true; - I[GGML_OP_SET ] = true; - I[GGML_OP_GET_ROWS_BACK ] = true; - I[GGML_OP_DIAG_MASK_INF ] = true; - I[GGML_OP_DIAG_MASK_ZERO ] = true; - I[GGML_OP_CONV_1D_S1_PH ] = true; - I[GGML_OP_CONV_1D_S2_PH ] = true; - I[GGML_OP_CONV_2D_SK_P0 ] = true; - I[GGML_OP_FLASH_ATTN_BACK ] = true; - I[GGML_OP_CROSS_ENTROPY_LOSS ] = true; + p[GGML_OP_ACC ] = true; + p[GGML_OP_MUL_MAT ] = true; + p[GGML_OP_OUT_PROD ] = true; + p[GGML_OP_SET ] = true; + p[GGML_OP_GET_ROWS_BACK ] = true; + p[GGML_OP_DIAG_MASK_INF ] = true; + p[GGML_OP_DIAG_MASK_ZERO ] = true; + p[GGML_OP_CONV_1D_S1_PH ] = true; + p[GGML_OP_CONV_1D_S2_PH ] = true; + p[GGML_OP_CONV_2D_SK_P0 ] = true; + p[GGML_OP_FLASH_ATTN_BACK ] = true; + p[GGML_OP_CROSS_ENTROPY_LOSS ] = true; } { // FINALIZE - bool * F = GGML_OP_HAS_FINALIZE; + bool * p = GGML_OP_HAS_FINALIZE; - F[GGML_OP_CROSS_ENTROPY_LOSS ] = true; + p[GGML_OP_CROSS_ENTROPY_LOSS ] = true; } } From d7d2e6a0f0c74f7a570dae384dfff371ac744d2a Mon Sep 17 00:00:00 2001 From: WangHaoranRobin <56047610+WangHaoranRobin@users.noreply.github.com> Date: Mon, 3 Jul 2023 05:38:44 +0800 Subject: [PATCH 073/852] server: add option to output probabilities for completion (#1962) * server: add option to output probabilities for completion * server: fix issue when handling probability output for incomplete tokens for multibyte character generation * server: fix llama_sample_top_k order * examples/common.h: put all bool variables in gpt_params together --- examples/common.h | 3 +- examples/server/server.cpp | 150 +++++++++++++++++++++++++++++-------- 2 files changed, 122 insertions(+), 31 deletions(-) diff --git a/examples/common.h b/examples/common.h index 66e567291..96f2228f8 100644 --- a/examples/common.h +++ b/examples/common.h @@ -31,7 +31,7 @@ struct gpt_params { int32_t n_gpu_layers = 0; // number of layers to store in VRAM int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs - bool low_vram = 0; // if true, reduce VRAM usage at the cost of performance + int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. // sampling parameters std::unordered_map logit_bias; // logit bias for specific tokens @@ -59,6 +59,7 @@ struct gpt_params { std::string lora_adapter = ""; // lora adapter path std::string lora_base = ""; // base model path for the lora adapter + bool low_vram = false; // if true, reduce VRAM usage at the cost of performance bool memory_f16 = true; // use f16 instead of f32 for memory kv bool random_prompt = false; // do not randomize prompt if none provided bool use_color = false; // use color to distinguish generations and inputs diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 998d55eac..e4ddbe986 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -26,6 +26,17 @@ struct server_params { int32_t write_timeout = 600; }; +// completion token output with probabilities +struct completion_token_output { + struct token_prob { + llama_token tok; + float prob; + }; + + std::vector probs; + llama_token tok; +}; + static size_t common_part(const std::vector & a, const std::vector & b) { size_t i; for (i = 0; i < a.size() && i < b.size() && a[i] == b[i]; i++) {} @@ -86,6 +97,40 @@ static void server_log(const char * level, const char * function, int line, fflush(stdout); } +// format incomplete utf-8 multibyte character for output +static std::string tokens_to_output_formatted_string(const llama_context * ctx, const llama_token token) { + std::string out = token == -1 ? "" : llama_token_to_str(ctx, token); + // if first bit is 1, meaning it's a partial character + if (out.size() > 0 && (out[0] & 0x80) == 0x80) { + std::stringstream ss; + ss<< std::hex << (out[0] & 0xff); + std::string res ( ss.str() ); + out = "byte: \\x" + res; + } + return out; +} + +// convert a vector of completion_token_output to json +static json probs_vector_to_json(const llama_context * ctx, const std::vector probs) { + json out = json::array(); + for (const auto & prob : probs) { + json probs_for_token = json::array(); + for (const auto & p : prob.probs) { + std::string tok_str = tokens_to_output_formatted_string(ctx, p.tok); + probs_for_token.push_back(json { + { "tok_str", tok_str }, + { "prob", p.prob }, + }); + } + std::string tok_str = tokens_to_output_formatted_string(ctx, prob.tok); + out.push_back(json { + {"content", tok_str}, + {"probs", probs_for_token}, + }); + } + return out; +} + static bool server_verbose = false; #if SERVER_VERBOSE != 1 @@ -107,6 +152,7 @@ struct llama_server_context { bool stream = false; bool has_next_token = false; std::string generated_text; + std::vector generated_token_probs; size_t num_tokens_predicted = 0; size_t n_past = 0; @@ -142,6 +188,7 @@ struct llama_server_context { num_tokens_predicted = 0; generated_text = ""; generated_text.reserve(params.n_ctx); + generated_token_probs.clear(); truncated = false; stopped_eos = false; stopped_word = false; @@ -221,8 +268,9 @@ struct llama_server_context { llama_set_rng_seed(ctx, params.seed); } - llama_token nextToken() { - llama_token result = -1; + completion_token_output nextToken() { + completion_token_output result; + result.tok = -1; if (embd.size() >= (size_t)params.n_ctx) { // Reset context @@ -261,7 +309,8 @@ struct llama_server_context { if (params.n_predict == 0) { has_next_token = false; - return llama_token_eos(); + result.tok = llama_token_eos(); + return result; } // out of user input, sample next token @@ -278,7 +327,7 @@ struct llama_server_context { const float mirostat_tau = params.mirostat_tau; const float mirostat_eta = params.mirostat_eta; const bool penalize_nl = params.penalize_nl; - llama_token id = 0; + const int32_t n_probs = params.n_probs; { auto * logits = llama_get_logits(ctx); @@ -312,35 +361,42 @@ struct llama_server_context { if (temp <= 0) { // Greedy sampling - id = llama_sample_token_greedy(ctx, &candidates_p); + result.tok = llama_sample_token_greedy(ctx, &candidates_p); + if (n_probs > 0) { + llama_sample_softmax(ctx, &candidates_p); + } } else { if (mirostat == 1) { static float mirostat_mu = 2.0f * mirostat_tau; const int mirostat_m = 100; llama_sample_temperature(ctx, &candidates_p, temp); - id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); + result.tok = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); } else if (mirostat == 2) { static float mirostat_mu = 2.0f * mirostat_tau; llama_sample_temperature(ctx, &candidates_p, temp); - id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu); + result.tok = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu); } else { // Temperature sampling - llama_sample_top_k(ctx, &candidates_p, top_k, 1); - llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1); - llama_sample_typical(ctx, &candidates_p, typical_p, 1); - llama_sample_top_p(ctx, &candidates_p, top_p, 1); + size_t min_keep = std::max(1, n_probs); + llama_sample_top_k(ctx, &candidates_p, top_k, min_keep); + llama_sample_tail_free(ctx, &candidates_p, tfs_z, min_keep); + llama_sample_typical(ctx, &candidates_p, typical_p, min_keep); + llama_sample_top_p(ctx, &candidates_p, top_p, min_keep); llama_sample_temperature(ctx, &candidates_p, temp); - id = llama_sample_token(ctx, &candidates_p); + result.tok = llama_sample_token(ctx, &candidates_p); } } + + for (size_t i = 0; i < std::min(candidates_p.size, (size_t) n_probs); ++i) { + result.probs.push_back({candidates_p.data[i].id, candidates_p.data[i].p}); + } last_n_tokens.erase(last_n_tokens.begin()); - last_n_tokens.push_back(id); + last_n_tokens.push_back(result.tok); num_tokens_predicted++; } // add it to the context - embd.push_back(id); - result = id; + embd.push_back(result.tok); // decrement remaining sampling budget --n_remain; @@ -382,12 +438,16 @@ struct llama_server_context { return stop_pos; } - std::string doCompletion() { - const llama_token token = nextToken(); + completion_token_output doCompletion() { + const completion_token_output token_with_probs = nextToken(); - const std::string token_text = token == -1 ? "" : llama_token_to_str(ctx, token); + const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(ctx, token_with_probs.tok); generated_text += token_text; + if (params.n_probs > 0) { + generated_token_probs.push_back(token_with_probs); + } + if (multibyte_pending > 0) { multibyte_pending -= token_text.size(); } else if (token_text.size() == 1) { @@ -416,8 +476,8 @@ struct llama_server_context { } LOG_VERBOSE("next token", { - { "token", token }, - { "token_text", llama_token_to_str(ctx, token) }, + { "token", token_with_probs.tok }, + { "token_text", tokens_to_output_formatted_string(ctx, token_with_probs.tok) }, { "has_next_token", has_next_token }, { "n_remain", n_remain }, { "num_tokens_predicted", num_tokens_predicted }, @@ -427,7 +487,7 @@ struct llama_server_context { { "stopping_word", stopping_word }, }); - return token_text; + return token_with_probs; } std::vector getEmbedding() { @@ -669,6 +729,7 @@ static json format_generation_settings(llama_server_context & llama) { { "ignore_eos", ignore_eos }, { "stream", llama.stream }, { "logit_bias", llama.params.logit_bias }, + { "n_probs", llama.params.n_probs }, }; } @@ -678,8 +739,9 @@ static json format_embedding_response(llama_server_context & llama) { }; } -static json format_final_response(llama_server_context & llama, const std::string & content) { - return json { +static json format_final_response(llama_server_context & llama, const std::string & content, const std::vector & probs) { + + json res = json { { "content", content }, { "stop", true }, { "model", llama.params.model_alias }, @@ -692,13 +754,25 @@ static json format_final_response(llama_server_context & llama, const std::strin { "stopped_limit", llama.stopped_limit }, { "stopping_word", llama.stopping_word }, }; + + if (llama.params.n_probs > 0) { + res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs); + } + + return res; } -static json format_partial_response(const std::string & content) { - return json { +static json format_partial_response(llama_server_context & llama, const std::string & content, const std::vector & probs) { + json res = json { { "content", content }, { "stop", false }, }; + + if (llama.params.n_probs > 0) { + res["completion_probabilities"] = probs_vector_to_json(llama.ctx, probs); + } + + return res; } static json format_tokenizer_response(const std::vector & tokens) { @@ -728,6 +802,7 @@ static void parse_options_completion(const json & body, llama_server_context & l llama.params.n_keep = body.value("n_keep", default_params.n_keep); llama.params.seed = body.value("seed", default_params.seed); llama.params.prompt = body.value("prompt", default_params.prompt); + llama.params.n_probs = body.value("n_probs", default_params.n_probs); llama.params.logit_bias.clear(); if (body.value("ignore_eos", false)) { @@ -830,7 +905,8 @@ int main(int argc, char ** argv) { size_t stop_pos = std::string::npos; while (llama.has_next_token) { - const std::string token_text = llama.doCompletion(); + const completion_token_output token_with_probs = llama.doCompletion(); + const std::string token_text = llama_token_to_str(llama.ctx, token_with_probs.tok); stop_pos = llama.findStoppingStrings(llama.generated_text, token_text.size(), STOP_FULL); @@ -844,7 +920,7 @@ int main(int argc, char ** argv) { llama.generated_text.end()); } - const json data = format_final_response(llama, llama.generated_text); + const json data = format_final_response(llama, llama.generated_text, llama.generated_token_probs); llama_print_timings(llama.ctx); @@ -853,9 +929,11 @@ int main(int argc, char ** argv) { } else { const auto chunked_content_provider = [&](size_t, DataSink & sink) { size_t sent_count = 0; + size_t sent_token_probs_index = 0; while (llama.has_next_token) { - const std::string token_text = llama.doCompletion(); + const completion_token_output token_with_probs = llama.doCompletion(); + const std::string token_text = llama_token_to_str(llama.ctx, token_with_probs.tok); if (llama.multibyte_pending > 0) { continue; } @@ -878,10 +956,22 @@ int main(int argc, char ** argv) { const std::string to_send = llama.generated_text.substr(pos, stop_pos); sent_count += to_send.size(); + std::vector probs_output = {}; + + if (llama.params.n_probs > 0) { + const std::vector to_send_toks = llama_tokenize(llama.ctx, to_send, false); + size_t probs_pos = std::min(sent_token_probs_index, llama.generated_token_probs.size()); + size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), llama.generated_token_probs.size()); + if (probs_pos < probs_stop_pos) { + probs_output = std::vector(llama.generated_token_probs.begin() + probs_pos, llama.generated_token_probs.begin() + probs_stop_pos); + } + sent_token_probs_index = probs_stop_pos; + } + const json data = llama.has_next_token - ? format_partial_response(to_send) + ? format_partial_response(llama, to_send, probs_output) // Generation is done, send extra information. - : format_final_response(llama, to_send); + : format_final_response(llama, to_send, llama.generated_token_probs); const std::string str = "data: " + From 55dbb915cc2a95048f56e667b09dfad38d840421 Mon Sep 17 00:00:00 2001 From: Howard Su Date: Mon, 3 Jul 2023 19:58:58 +0800 Subject: [PATCH 074/852] [llama] No need to check file version when loading vocab score (#2079) --- llama.cpp | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/llama.cpp b/llama.cpp index a869bbac8..f48a6ca79 100644 --- a/llama.cpp +++ b/llama.cpp @@ -481,9 +481,7 @@ struct llama_file_loader { std::string word = file.read_string(len); float score = 0.0f; - if (file_version >= LLAMA_FILE_VERSION_GGMF_V1) { - file.read_raw(&score, sizeof(score)); - } + file.read_raw(&score, sizeof(score)); vocab.token_to_id[word] = i; From cc45a7feb8412e84ff292207621412fffc0d3d51 Mon Sep 17 00:00:00 2001 From: Howard Su Date: Tue, 4 Jul 2023 02:43:55 +0800 Subject: [PATCH 075/852] Fix crash of test-tokenizer-0 under Debug build (#2064) * Fix crash of test-tokenizer-0 under Debug build * Change per comment --- ggml-cuda.cu | 2 +- llama.cpp | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 50df20edd..0b12a9e76 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -2835,7 +2835,7 @@ void ggml_cuda_transform_tensor(void * data, struct ggml_tensor * tensor) { } void ggml_cuda_free_data(struct ggml_tensor * tensor) { - if (tensor->backend != GGML_BACKEND_GPU && tensor->backend != GGML_BACKEND_GPU_SPLIT) { + if (!tensor || (tensor->backend != GGML_BACKEND_GPU && tensor->backend != GGML_BACKEND_GPU_SPLIT) ) { return; } diff --git a/llama.cpp b/llama.cpp index f48a6ca79..7419b03b6 100644 --- a/llama.cpp +++ b/llama.cpp @@ -194,8 +194,8 @@ struct llama_layer { }; struct llama_kv_cache { - struct ggml_tensor * k; - struct ggml_tensor * v; + struct ggml_tensor * k = NULL; + struct ggml_tensor * v = NULL; struct ggml_context * ctx = NULL; From 1cf14ccef12e19c5a5b0b17ab456242d1f8c7fdd Mon Sep 17 00:00:00 2001 From: Henri Vasserman Date: Tue, 4 Jul 2023 00:05:23 +0300 Subject: [PATCH 076/852] fix server crashes (#2076) --- examples/server/server.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index e4ddbe986..3bf985957 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -906,7 +906,7 @@ int main(int argc, char ** argv) { while (llama.has_next_token) { const completion_token_output token_with_probs = llama.doCompletion(); - const std::string token_text = llama_token_to_str(llama.ctx, token_with_probs.tok); + const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(llama.ctx, token_with_probs.tok); stop_pos = llama.findStoppingStrings(llama.generated_text, token_text.size(), STOP_FULL); @@ -933,7 +933,7 @@ int main(int argc, char ** argv) { while (llama.has_next_token) { const completion_token_output token_with_probs = llama.doCompletion(); - const std::string token_text = llama_token_to_str(llama.ctx, token_with_probs.tok); + const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(llama.ctx, token_with_probs.tok); if (llama.multibyte_pending > 0) { continue; } From 14a2cc71f62e45803ae70890ffbdeb0a172e6210 Mon Sep 17 00:00:00 2001 From: Govlzkoy Date: Tue, 4 Jul 2023 07:50:00 +0800 Subject: [PATCH 077/852] [ggml] fix index for ne03 value in ggml_cl_mul_f32 (#2088) --- ggml-opencl.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp index fed4ffb0c..fa0bdbefb 100644 --- a/ggml-opencl.cpp +++ b/ggml-opencl.cpp @@ -1376,7 +1376,7 @@ static void ggml_cl_mul_f32(const ggml_tensor * src0, const ggml_tensor * src1, const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[2]; + const int64_t ne03 = src0->ne[3]; const int64_t ne0 = ne00 * ne01 * ne02 * ne03; const int64_t ne10 = src1->ne[0]; const int64_t ne11 = src1->ne[1]; From 698efad5fbbf326f01288649b123eff5f79b417e Mon Sep 17 00:00:00 2001 From: Erik Scholz Date: Tue, 4 Jul 2023 01:50:12 +0200 Subject: [PATCH 078/852] CI: make the brew update temporarily optional. (#2092) until they decide to fix the brew installation in the macos runners. see the open issues. eg https://github.com/actions/runner-images/pull/7710 --- .github/workflows/build.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index b87ea76bc..aec43bd92 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -111,6 +111,7 @@ jobs: - name: Dependencies id: depends + continue-on-error: true run: | brew update @@ -129,6 +130,7 @@ jobs: - name: Dependencies id: depends + continue-on-error: true run: | brew update From 23c7c6fc9182b041f006b86ea1e7f99911ecf344 Mon Sep 17 00:00:00 2001 From: ZhouYuChen Date: Tue, 4 Jul 2023 20:15:16 +0800 Subject: [PATCH 079/852] Update Makefile: clean simple (#2097) --- Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 03f38bdba..b289d97ed 100644 --- a/Makefile +++ b/Makefile @@ -272,7 +272,7 @@ libllama.so: llama.o ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) clean: - rm -vf *.o *.so main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server vdot train-text-from-scratch embd-input-test build-info.h + rm -vf *.o *.so main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch embd-input-test build-info.h # # Examples From acc111caf93fc6681450924df9f99679c384c59e Mon Sep 17 00:00:00 2001 From: Henri Vasserman Date: Tue, 4 Jul 2023 15:38:04 +0300 Subject: [PATCH 080/852] Allow old Make to build server. (#2098) Also make server build by default. Tested with Make 3.82 --- Makefile | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/Makefile b/Makefile index b289d97ed..8966a3590 100644 --- a/Makefile +++ b/Makefile @@ -1,11 +1,5 @@ # Define the default target now so that it is always the first target -BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple libembdinput.so embd-input-test - -ifdef LLAMA_BUILD_SERVER - BUILD_TARGETS += server - LLAMA_SERVER_VERBOSE ?= 1 -server: private CXXFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE) -endif +BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch simple server libembdinput.so embd-input-test default: $(BUILD_TARGETS) @@ -61,6 +55,10 @@ else CXXFLAGS += -DNDEBUG endif +ifdef LLAMA_SERVER_VERBOSE + CXXFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE) +endif + # warnings CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar From 7ee76e45afae7f9a7a53e93393accfb5b36684e1 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tobias=20L=C3=BCtke?= Date: Tue, 4 Jul 2023 10:05:27 -0400 Subject: [PATCH 081/852] Simple webchat for server (#1998) * expose simple web interface on root domain * embed index and add --path for choosing static dir * allow server to multithread because web browsers send a lot of garbage requests we want the server to multithread when serving 404s for favicon's etc. To avoid blowing up llama we just take a mutex when it's invoked. * let's try this with the xxd tool instead and see if msvc is happier with that * enable server in Makefiles * add /completion.js file to make it easy to use the server from js * slightly nicer css * rework state management into session, expose historyTemplate to settings --------- Co-authored-by: Georgi Gerganov --- CMakeLists.txt | 2 +- examples/server/completion.js.hpp | 193 +++ examples/server/deps.sh | 22 + examples/server/index.html.hpp | 846 ++++++++++++ examples/server/index.js.hpp | 1851 ++++++++++++++++++++++++++ examples/server/public/completion.js | 81 ++ examples/server/public/index.html | 359 +++++ examples/server/public/index.js | 1 + examples/server/server.cpp | 69 +- 9 files changed, 3416 insertions(+), 8 deletions(-) create mode 100644 examples/server/completion.js.hpp create mode 100755 examples/server/deps.sh create mode 100644 examples/server/index.html.hpp create mode 100644 examples/server/index.js.hpp create mode 100644 examples/server/public/completion.js create mode 100644 examples/server/public/index.html create mode 100644 examples/server/public/index.js diff --git a/CMakeLists.txt b/CMakeLists.txt index 34a897327..4ac0f6f4e 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -79,7 +79,7 @@ option(LLAMA_QKK_64 "llama: use super-block size of 64 option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE}) option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE}) -option(LLAMA_BUILD_SERVER "llama: build server example" OFF) +option(LLAMA_BUILD_SERVER "llama: build server example" ON) # # Build info header diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp new file mode 100644 index 000000000..002830cad --- /dev/null +++ b/examples/server/completion.js.hpp @@ -0,0 +1,193 @@ +unsigned char completion_js[] = { + 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x44, + 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x73, 0x20, 0x3d, 0x20, 0x7b, 0x0a, + 0x20, 0x20, 0x73, 0x74, 0x72, 0x65, 0x61, 0x6d, 0x3a, 0x20, 0x74, 0x72, + 0x75, 0x65, 0x2c, 0x0a, 0x20, 0x20, 0x6e, 0x5f, 0x70, 0x72, 0x65, 0x64, + 0x69, 0x63, 0x74, 0x3a, 0x20, 0x35, 0x30, 0x30, 0x2c, 0x0a, 0x20, 0x20, + 0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x3a, + 0x20, 0x30, 0x2e, 0x32, 0x2c, 0x0a, 0x20, 0x20, 0x73, 0x74, 0x6f, 0x70, + 0x3a, 0x20, 0x5b, 0x22, 0x3c, 0x2f, 0x73, 0x3e, 0x22, 0x5d, 0x0a, 0x7d, + 0x3b, 0x0a, 0x0a, 0x2f, 0x2a, 0x2a, 0x0a, 0x20, 0x2a, 0x20, 0x54, 0x68, + 0x69, 0x73, 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, + 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x65, 0x73, 0x20, 0x74, 0x68, + 0x65, 0x20, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x20, 0x74, 0x65, 0x78, 0x74, + 0x20, 0x75, 0x73, 0x69, 0x6e, 0x67, 0x20, 0x61, 0x20, 0x6c, 0x6c, 0x61, + 0x6d, 0x61, 0x20, 0x64, 0x69, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x61, 0x72, + 0x79, 0x2e, 0x0a, 0x20, 0x2a, 0x20, 0x40, 0x70, 0x61, 0x72, 0x61, 0x6d, + 0x20, 0x7b, 0x6f, 0x62, 0x6a, 0x65, 0x63, 0x74, 0x7d, 0x20, 0x70, 0x61, + 0x72, 0x61, 0x6d, 0x73, 0x20, 0x2d, 0x20, 0x54, 0x68, 0x65, 0x20, 0x70, + 0x61, 0x72, 0x61, 0x6d, 0x65, 0x74, 0x65, 0x72, 0x73, 0x20, 0x66, 0x6f, + 0x72, 0x20, 0x74, 0x68, 0x65, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, + 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x72, 0x65, 0x71, 0x75, 0x65, 0x73, 0x74, + 0x2e, 0x0a, 0x20, 0x2a, 0x20, 0x40, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x20, + 0x7b, 0x6f, 0x62, 0x6a, 0x65, 0x63, 0x74, 0x7d, 0x20, 0x63, 0x6f, 0x6e, + 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x20, 0x2d, 0x20, 0x61, 0x6e, + 0x20, 0x69, 0x6e, 0x73, 0x74, 0x61, 0x6e, 0x63, 0x65, 0x20, 0x6f, 0x66, + 0x20, 0x41, 0x62, 0x6f, 0x72, 0x74, 0x43, 0x6f, 0x6e, 0x74, 0x72, 0x6f, + 0x6c, 0x6c, 0x65, 0x72, 0x20, 0x69, 0x66, 0x20, 0x79, 0x6f, 0x75, 0x20, + 0x6e, 0x65, 0x65, 0x64, 0x20, 0x6f, 0x6e, 0x65, 0x2c, 0x20, 0x6f, 0x72, + 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x2e, 0x0a, 0x20, 0x2a, 0x20, 0x40, 0x70, + 0x61, 0x72, 0x61, 0x6d, 0x20, 0x7b, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, + 0x6f, 0x6e, 0x7d, 0x20, 0x63, 0x61, 0x6c, 0x6c, 0x62, 0x61, 0x63, 0x6b, + 0x20, 0x2d, 0x20, 0x54, 0x68, 0x65, 0x20, 0x63, 0x61, 0x6c, 0x6c, 0x62, + 0x61, 0x63, 0x6b, 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x20, 0x74, 0x6f, 0x20, 0x63, 0x61, 0x6c, 0x6c, 0x20, 0x77, 0x68, 0x65, + 0x6e, 0x20, 0x74, 0x68, 0x65, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, + 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x69, 0x73, 0x20, 0x64, 0x6f, 0x6e, 0x65, + 0x2e, 0x0a, 0x20, 0x2a, 0x20, 0x40, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, + 0x73, 0x20, 0x7b, 0x73, 0x74, 0x72, 0x69, 0x6e, 0x67, 0x7d, 0x20, 0x74, + 0x68, 0x65, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x65, 0x64, + 0x20, 0x74, 0x65, 0x78, 0x74, 0x20, 0x61, 0x73, 0x20, 0x61, 0x20, 0x73, + 0x74, 0x72, 0x69, 0x6e, 0x67, 0x2e, 0x20, 0x49, 0x64, 0x65, 0x61, 0x6c, + 0x6c, 0x79, 0x20, 0x69, 0x67, 0x6e, 0x6f, 0x72, 0x65, 0x64, 0x2c, 0x20, + 0x61, 0x6e, 0x64, 0x20, 0x79, 0x6f, 0x75, 0x20, 0x67, 0x65, 0x74, 0x20, + 0x61, 0x74, 0x20, 0x69, 0x74, 0x20, 0x76, 0x69, 0x61, 0x20, 0x74, 0x68, + 0x65, 0x20, 0x63, 0x61, 0x6c, 0x6c, 0x62, 0x61, 0x63, 0x6b, 0x2e, 0x0a, + 0x20, 0x2a, 0x2f, 0x0a, 0x65, 0x78, 0x70, 0x6f, 0x72, 0x74, 0x20, 0x63, + 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x43, 0x6f, + 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x65, 0x20, 0x3d, 0x20, 0x61, 0x73, 0x79, + 0x6e, 0x63, 0x20, 0x28, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2c, 0x20, + 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2c, 0x20, + 0x63, 0x61, 0x6c, 0x6c, 0x62, 0x61, 0x63, 0x6b, 0x29, 0x20, 0x3d, 0x3e, + 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x21, 0x63, 0x6f, + 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x29, 0x20, 0x7b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, + 0x65, 0x72, 0x20, 0x3d, 0x20, 0x6e, 0x65, 0x77, 0x20, 0x41, 0x62, 0x6f, + 0x72, 0x74, 0x43, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, + 0x28, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x63, 0x6f, + 0x6e, 0x73, 0x74, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, + 0x6f, 0x6e, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x20, 0x3d, 0x20, 0x7b, + 0x20, 0x2e, 0x2e, 0x2e, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x44, 0x65, 0x66, + 0x61, 0x75, 0x6c, 0x74, 0x73, 0x2c, 0x20, 0x2e, 0x2e, 0x2e, 0x70, 0x61, + 0x72, 0x61, 0x6d, 0x73, 0x20, 0x7d, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x2f, + 0x2f, 0x20, 0x77, 0x65, 0x20, 0x75, 0x73, 0x65, 0x20, 0x66, 0x65, 0x74, + 0x63, 0x68, 0x20, 0x64, 0x69, 0x72, 0x65, 0x63, 0x74, 0x6c, 0x79, 0x20, + 0x68, 0x65, 0x72, 0x65, 0x20, 0x62, 0x65, 0x63, 0x61, 0x73, 0x75, 0x65, + 0x20, 0x74, 0x68, 0x65, 0x20, 0x62, 0x75, 0x69, 0x6c, 0x74, 0x20, 0x69, + 0x6e, 0x20, 0x66, 0x65, 0x74, 0x63, 0x68, 0x45, 0x76, 0x65, 0x6e, 0x74, + 0x53, 0x6f, 0x75, 0x72, 0x63, 0x65, 0x20, 0x64, 0x6f, 0x65, 0x73, 0x20, + 0x6e, 0x6f, 0x74, 0x20, 0x73, 0x75, 0x70, 0x70, 0x6f, 0x72, 0x74, 0x20, + 0x50, 0x4f, 0x53, 0x54, 0x0a, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, + 0x20, 0x72, 0x65, 0x73, 0x70, 0x6f, 0x6e, 0x73, 0x65, 0x20, 0x3d, 0x20, + 0x61, 0x77, 0x61, 0x69, 0x74, 0x20, 0x66, 0x65, 0x74, 0x63, 0x68, 0x28, + 0x22, 0x2f, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, + 0x22, 0x2c, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x65, 0x74, + 0x68, 0x6f, 0x64, 0x3a, 0x20, 0x27, 0x50, 0x4f, 0x53, 0x54, 0x27, 0x2c, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x62, 0x6f, 0x64, 0x79, 0x3a, 0x20, 0x4a, + 0x53, 0x4f, 0x4e, 0x2e, 0x73, 0x74, 0x72, 0x69, 0x6e, 0x67, 0x69, 0x66, + 0x79, 0x28, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, + 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x29, 0x2c, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x68, 0x65, 0x61, 0x64, 0x65, 0x72, 0x73, 0x3a, 0x20, 0x7b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x27, 0x43, 0x6f, 0x6e, 0x6e, 0x65, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x27, 0x3a, 0x20, 0x27, 0x6b, 0x65, 0x65, + 0x70, 0x2d, 0x61, 0x6c, 0x69, 0x76, 0x65, 0x27, 0x2c, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x27, 0x43, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, + 0x2d, 0x54, 0x79, 0x70, 0x65, 0x27, 0x3a, 0x20, 0x27, 0x61, 0x70, 0x70, + 0x6c, 0x69, 0x63, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x2f, 0x6a, 0x73, 0x6f, + 0x6e, 0x27, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x27, 0x41, + 0x63, 0x63, 0x65, 0x70, 0x74, 0x27, 0x3a, 0x20, 0x27, 0x74, 0x65, 0x78, + 0x74, 0x2f, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x2d, 0x73, 0x74, 0x72, 0x65, + 0x61, 0x6d, 0x27, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x2c, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x3a, 0x20, 0x63, + 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x73, 0x69, + 0x67, 0x6e, 0x61, 0x6c, 0x2c, 0x0a, 0x20, 0x20, 0x7d, 0x29, 0x3b, 0x0a, + 0x0a, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x72, 0x65, 0x61, + 0x64, 0x65, 0x72, 0x20, 0x3d, 0x20, 0x72, 0x65, 0x73, 0x70, 0x6f, 0x6e, + 0x73, 0x65, 0x2e, 0x62, 0x6f, 0x64, 0x79, 0x2e, 0x67, 0x65, 0x74, 0x52, + 0x65, 0x61, 0x64, 0x65, 0x72, 0x28, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x63, + 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x64, 0x65, 0x63, 0x6f, 0x64, 0x65, 0x72, + 0x20, 0x3d, 0x20, 0x6e, 0x65, 0x77, 0x20, 0x54, 0x65, 0x78, 0x74, 0x44, + 0x65, 0x63, 0x6f, 0x64, 0x65, 0x72, 0x28, 0x29, 0x3b, 0x0a, 0x0a, 0x20, + 0x20, 0x6c, 0x65, 0x74, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, + 0x20, 0x3d, 0x20, 0x22, 0x22, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x74, 0x72, + 0x79, 0x20, 0x7b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x65, 0x74, + 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x20, 0x3d, 0x20, 0x74, 0x72, 0x75, 0x65, + 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x77, 0x68, 0x69, 0x6c, 0x65, + 0x20, 0x28, 0x63, 0x6f, 0x6e, 0x74, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x72, 0x65, + 0x73, 0x75, 0x6c, 0x74, 0x20, 0x3d, 0x20, 0x61, 0x77, 0x61, 0x69, 0x74, + 0x20, 0x72, 0x65, 0x61, 0x64, 0x65, 0x72, 0x2e, 0x72, 0x65, 0x61, 0x64, + 0x28, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, + 0x20, 0x28, 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, 0x2e, 0x64, 0x6f, 0x6e, + 0x65, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x62, 0x72, 0x65, 0x61, 0x6b, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, + 0x2f, 0x20, 0x73, 0x73, 0x65, 0x20, 0x61, 0x6e, 0x73, 0x77, 0x65, 0x72, + 0x73, 0x20, 0x69, 0x6e, 0x20, 0x74, 0x68, 0x65, 0x20, 0x66, 0x6f, 0x72, + 0x6d, 0x20, 0x6d, 0x75, 0x6c, 0x74, 0x69, 0x70, 0x6c, 0x65, 0x20, 0x6c, + 0x69, 0x6e, 0x65, 0x73, 0x20, 0x6f, 0x66, 0x3a, 0x20, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x5c, 0x6e, 0x20, 0x77, 0x69, 0x74, 0x68, 0x20, 0x64, 0x61, + 0x74, 0x61, 0x20, 0x61, 0x6c, 0x77, 0x61, 0x79, 0x73, 0x20, 0x70, 0x72, + 0x65, 0x73, 0x65, 0x6e, 0x74, 0x20, 0x61, 0x73, 0x20, 0x61, 0x20, 0x6b, + 0x65, 0x79, 0x2e, 0x20, 0x69, 0x6e, 0x20, 0x6f, 0x75, 0x72, 0x20, 0x63, + 0x61, 0x73, 0x65, 0x20, 0x77, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x2f, 0x2f, 0x20, 0x6d, 0x61, 0x69, 0x6e, 0x6c, 0x79, 0x20, 0x63, + 0x61, 0x72, 0x65, 0x20, 0x61, 0x62, 0x6f, 0x75, 0x74, 0x20, 0x74, 0x68, + 0x65, 0x20, 0x64, 0x61, 0x74, 0x61, 0x3a, 0x20, 0x6b, 0x65, 0x79, 0x20, + 0x68, 0x65, 0x72, 0x65, 0x2c, 0x20, 0x77, 0x68, 0x69, 0x63, 0x68, 0x20, + 0x77, 0x65, 0x20, 0x65, 0x78, 0x70, 0x65, 0x63, 0x74, 0x20, 0x61, 0x73, + 0x20, 0x6a, 0x73, 0x6f, 0x6e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x74, 0x65, 0x78, 0x74, 0x20, 0x3d, + 0x20, 0x64, 0x65, 0x63, 0x6f, 0x64, 0x65, 0x72, 0x2e, 0x64, 0x65, 0x63, + 0x6f, 0x64, 0x65, 0x28, 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x29, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x70, 0x61, 0x72, 0x73, 0x65, 0x20, 0x61, + 0x6c, 0x6c, 0x20, 0x73, 0x73, 0x65, 0x20, 0x65, 0x76, 0x65, 0x6e, 0x74, + 0x73, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x61, 0x64, 0x64, 0x20, 0x74, 0x68, + 0x65, 0x6d, 0x20, 0x74, 0x6f, 0x20, 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, + 0x20, 0x72, 0x65, 0x67, 0x65, 0x78, 0x20, 0x3d, 0x20, 0x2f, 0x5e, 0x28, + 0x5c, 0x53, 0x2b, 0x29, 0x3a, 0x5c, 0x73, 0x28, 0x2e, 0x2a, 0x29, 0x24, + 0x2f, 0x67, 0x6d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x66, + 0x6f, 0x72, 0x20, 0x28, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6d, 0x61, + 0x74, 0x63, 0x68, 0x20, 0x6f, 0x66, 0x20, 0x74, 0x65, 0x78, 0x74, 0x2e, + 0x6d, 0x61, 0x74, 0x63, 0x68, 0x41, 0x6c, 0x6c, 0x28, 0x72, 0x65, 0x67, + 0x65, 0x78, 0x29, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, 0x5b, 0x6d, 0x61, + 0x74, 0x63, 0x68, 0x5b, 0x31, 0x5d, 0x5d, 0x20, 0x3d, 0x20, 0x6d, 0x61, + 0x74, 0x63, 0x68, 0x5b, 0x32, 0x5d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, + 0x20, 0x73, 0x69, 0x6e, 0x63, 0x65, 0x20, 0x77, 0x65, 0x20, 0x6b, 0x6e, + 0x6f, 0x77, 0x20, 0x74, 0x68, 0x69, 0x73, 0x20, 0x69, 0x73, 0x20, 0x6c, + 0x6c, 0x61, 0x6d, 0x61, 0x2e, 0x63, 0x70, 0x70, 0x2c, 0x20, 0x6c, 0x65, + 0x74, 0x27, 0x73, 0x20, 0x6a, 0x75, 0x73, 0x74, 0x20, 0x64, 0x65, 0x63, + 0x6f, 0x64, 0x65, 0x20, 0x74, 0x68, 0x65, 0x20, 0x6a, 0x73, 0x6f, 0x6e, + 0x20, 0x69, 0x6e, 0x20, 0x64, 0x61, 0x74, 0x61, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, 0x2e, 0x64, 0x61, + 0x74, 0x61, 0x20, 0x3d, 0x20, 0x4a, 0x53, 0x4f, 0x4e, 0x2e, 0x70, 0x61, + 0x72, 0x73, 0x65, 0x28, 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, 0x2e, 0x64, + 0x61, 0x74, 0x61, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x20, 0x2b, 0x3d, 0x20, 0x72, + 0x65, 0x73, 0x75, 0x6c, 0x74, 0x2e, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x63, + 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x63, 0x61, 0x6c, 0x6c, 0x61, 0x63, + 0x6b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, + 0x63, 0x61, 0x6c, 0x6c, 0x62, 0x61, 0x63, 0x6b, 0x29, 0x20, 0x7b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x74, + 0x20, 0x3d, 0x20, 0x63, 0x61, 0x6c, 0x6c, 0x62, 0x61, 0x63, 0x6b, 0x28, + 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, 0x29, 0x20, 0x21, 0x3d, 0x20, 0x66, + 0x61, 0x6c, 0x73, 0x65, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, + 0x69, 0x66, 0x20, 0x77, 0x65, 0x20, 0x67, 0x6f, 0x74, 0x20, 0x61, 0x20, + 0x73, 0x74, 0x6f, 0x70, 0x20, 0x74, 0x6f, 0x6b, 0x65, 0x6e, 0x20, 0x66, + 0x72, 0x6f, 0x6d, 0x20, 0x73, 0x65, 0x72, 0x76, 0x65, 0x72, 0x2c, 0x20, + 0x77, 0x65, 0x20, 0x77, 0x69, 0x6c, 0x6c, 0x20, 0x62, 0x72, 0x65, 0x61, + 0x6b, 0x20, 0x68, 0x65, 0x72, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x69, 0x66, 0x20, 0x28, 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, 0x2e, + 0x64, 0x61, 0x74, 0x61, 0x2e, 0x73, 0x74, 0x6f, 0x70, 0x29, 0x20, 0x7b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x62, 0x72, 0x65, + 0x61, 0x6b, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x7d, 0x20, 0x63, 0x61, + 0x74, 0x63, 0x68, 0x20, 0x28, 0x65, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, 0x65, 0x2e, 0x65, 0x72, + 0x72, 0x6f, 0x72, 0x28, 0x22, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x20, 0x65, + 0x72, 0x72, 0x6f, 0x72, 0x3a, 0x20, 0x22, 0x2c, 0x20, 0x65, 0x29, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x74, 0x68, 0x72, 0x6f, 0x77, 0x20, 0x65, + 0x3b, 0x0a, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x66, 0x69, 0x6e, 0x61, + 0x6c, 0x6c, 0x79, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, + 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x61, 0x62, 0x6f, + 0x72, 0x74, 0x28, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, + 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x63, 0x6f, 0x6e, 0x74, + 0x65, 0x6e, 0x74, 0x3b, 0x0a, 0x7d, 0x0a +}; +unsigned int completion_js_len = 2275; diff --git a/examples/server/deps.sh b/examples/server/deps.sh new file mode 100755 index 000000000..cf995162a --- /dev/null +++ b/examples/server/deps.sh @@ -0,0 +1,22 @@ +#!/bin/bash +# Download and update deps for binary + +# get the directory of this script file +DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" +PUBLIC=$DIR/public +OUTPUT=$DIR/templats.hpp + +echo "// Generated file, do not edit" > $OUTPUT +echo "" > $OUTPUT + +echo "download js bundle files" +curl https://npm.reversehttp.com/@preact/signals-core,@preact/signals,htm/preact,preact,preact/hooks > $PUBLIC/index.js +echo >> $PUBLIC/index.js # add newline + +FILES=$(ls $PUBLIC) + +for FILE in $FILES; do + func=$(echo $FILE | tr '.' '_') + echo "generate $FILE.hpp ($func)" + xxd -n $func -i $PUBLIC/$FILE > $DIR/$FILE.hpp +done diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp new file mode 100644 index 000000000..832e9a3bb --- /dev/null +++ b/examples/server/index.html.hpp @@ -0,0 +1,846 @@ +unsigned char index_html[] = { + 0x3c, 0x68, 0x74, 0x6d, 0x6c, 0x3e, 0x0a, 0x0a, 0x3c, 0x68, 0x65, 0x61, + 0x64, 0x3e, 0x0a, 0x20, 0x20, 0x3c, 0x6d, 0x65, 0x74, 0x61, 0x20, 0x63, + 0x68, 0x61, 0x72, 0x73, 0x65, 0x74, 0x3d, 0x22, 0x55, 0x54, 0x46, 0x2d, + 0x38, 0x22, 0x3e, 0x0a, 0x20, 0x20, 0x3c, 0x6d, 0x65, 0x74, 0x61, 0x20, + 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x76, 0x69, 0x65, 0x77, 0x70, 0x6f, + 0x72, 0x74, 0x22, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x3d, + 0x22, 0x77, 0x69, 0x64, 0x74, 0x68, 0x3d, 0x64, 0x65, 0x76, 0x69, 0x63, + 0x65, 0x2d, 0x77, 0x69, 0x64, 0x74, 0x68, 0x2c, 0x20, 0x69, 0x6e, 0x69, + 0x74, 0x69, 0x61, 0x6c, 0x2d, 0x73, 0x63, 0x61, 0x6c, 0x65, 0x3d, 0x31, + 0x2c, 0x20, 0x6d, 0x61, 0x78, 0x69, 0x6d, 0x75, 0x6d, 0x2d, 0x73, 0x63, + 0x61, 0x6c, 0x65, 0x3d, 0x31, 0x22, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, + 0x3c, 0x74, 0x69, 0x74, 0x6c, 0x65, 0x3e, 0x6c, 0x6c, 0x61, 0x6d, 0x61, + 0x2e, 0x63, 0x70, 0x70, 0x20, 0x2d, 0x20, 0x63, 0x68, 0x61, 0x74, 0x3c, + 0x2f, 0x74, 0x69, 0x74, 0x6c, 0x65, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x3c, + 0x73, 0x74, 0x79, 0x6c, 0x65, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x62, 0x6f, 0x64, 0x79, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x62, 0x61, 0x63, 0x6b, 0x67, 0x72, 0x6f, 0x75, 0x6e, 0x64, 0x2d, + 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x3a, 0x20, 0x23, 0x66, 0x66, 0x66, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6c, 0x6f, 0x72, + 0x3a, 0x20, 0x23, 0x30, 0x30, 0x30, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x66, 0x6f, 0x6e, 0x74, 0x2d, 0x66, 0x61, 0x6d, 0x69, 0x6c, + 0x79, 0x3a, 0x20, 0x73, 0x79, 0x73, 0x74, 0x65, 0x6d, 0x2d, 0x75, 0x69, + 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x66, 0x6f, 0x6e, 0x74, + 0x2d, 0x73, 0x69, 0x7a, 0x65, 0x3a, 0x20, 0x39, 0x30, 0x25, 0x3b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x23, + 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x20, 0x7b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, 0x72, 0x67, 0x69, 0x6e, + 0x3a, 0x20, 0x30, 0x65, 0x6d, 0x20, 0x61, 0x75, 0x74, 0x6f, 0x3b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x64, 0x69, 0x73, 0x70, 0x6c, 0x61, + 0x79, 0x3a, 0x20, 0x66, 0x6c, 0x65, 0x78, 0x3b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x66, 0x6c, 0x65, 0x78, 0x2d, 0x64, 0x69, 0x72, 0x65, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x3a, 0x20, 0x63, 0x6f, 0x6c, 0x75, 0x6d, + 0x6e, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6a, 0x75, 0x73, + 0x74, 0x69, 0x66, 0x79, 0x2d, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, + 0x3a, 0x20, 0x73, 0x70, 0x61, 0x63, 0x65, 0x2d, 0x62, 0x65, 0x74, 0x77, + 0x65, 0x65, 0x6e, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x68, + 0x65, 0x69, 0x67, 0x68, 0x74, 0x3a, 0x20, 0x31, 0x30, 0x30, 0x25, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x68, 0x65, 0x61, 0x64, 0x65, 0x72, 0x2c, 0x20, 0x66, 0x6f, 0x6f, 0x74, + 0x65, 0x72, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, + 0x65, 0x78, 0x74, 0x2d, 0x61, 0x6c, 0x69, 0x67, 0x6e, 0x3a, 0x20, 0x63, + 0x65, 0x6e, 0x74, 0x65, 0x72, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, + 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, 0x69, 0x6e, 0x20, 0x7b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, 0x72, 0x67, 0x69, + 0x6e, 0x3a, 0x20, 0x33, 0x70, 0x78, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x64, 0x69, 0x73, 0x70, 0x6c, 0x61, 0x79, 0x3a, 0x20, 0x66, + 0x6c, 0x65, 0x78, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x66, + 0x6c, 0x65, 0x78, 0x2d, 0x64, 0x69, 0x72, 0x65, 0x63, 0x74, 0x69, 0x6f, + 0x6e, 0x3a, 0x20, 0x63, 0x6f, 0x6c, 0x75, 0x6d, 0x6e, 0x3b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x6a, 0x75, 0x73, 0x74, 0x69, 0x66, 0x79, + 0x2d, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x3a, 0x20, 0x73, 0x70, + 0x61, 0x63, 0x65, 0x2d, 0x62, 0x65, 0x74, 0x77, 0x65, 0x65, 0x6e, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x67, 0x61, 0x70, 0x3a, 0x20, + 0x31, 0x65, 0x6d, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x66, 0x6c, 0x65, 0x78, 0x2d, 0x67, 0x72, 0x6f, 0x77, 0x3a, 0x20, 0x31, + 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6f, 0x76, 0x65, 0x72, + 0x66, 0x6c, 0x6f, 0x77, 0x2d, 0x79, 0x3a, 0x20, 0x61, 0x75, 0x74, 0x6f, + 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x62, 0x6f, 0x72, + 0x64, 0x65, 0x72, 0x3a, 0x20, 0x31, 0x70, 0x78, 0x20, 0x73, 0x6f, 0x6c, + 0x69, 0x64, 0x20, 0x23, 0x63, 0x63, 0x63, 0x3b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x62, 0x6f, 0x72, 0x64, 0x65, 0x72, 0x2d, 0x72, 0x61, + 0x64, 0x69, 0x75, 0x73, 0x3a, 0x20, 0x35, 0x70, 0x78, 0x3b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x70, 0x61, 0x64, 0x64, 0x69, 0x6e, 0x67, + 0x3a, 0x20, 0x30, 0x2e, 0x35, 0x65, 0x6d, 0x3b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x62, 0x6f, 0x64, 0x79, + 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, 0x78, + 0x2d, 0x77, 0x69, 0x64, 0x74, 0x68, 0x3a, 0x20, 0x36, 0x30, 0x30, 0x70, + 0x78, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x69, 0x6e, + 0x2d, 0x77, 0x69, 0x64, 0x74, 0x68, 0x3a, 0x20, 0x33, 0x30, 0x30, 0x70, + 0x78, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x69, 0x6e, + 0x65, 0x2d, 0x68, 0x65, 0x69, 0x67, 0x68, 0x74, 0x3a, 0x20, 0x31, 0x2e, + 0x32, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, 0x72, + 0x67, 0x69, 0x6e, 0x3a, 0x20, 0x30, 0x20, 0x61, 0x75, 0x74, 0x6f, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x70, 0x61, 0x64, 0x64, 0x69, + 0x6e, 0x67, 0x3a, 0x20, 0x30, 0x20, 0x30, 0x2e, 0x35, 0x65, 0x6d, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x70, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6f, 0x76, + 0x65, 0x72, 0x66, 0x6c, 0x6f, 0x77, 0x2d, 0x77, 0x72, 0x61, 0x70, 0x3a, + 0x20, 0x62, 0x72, 0x65, 0x61, 0x6b, 0x2d, 0x77, 0x6f, 0x72, 0x64, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x77, 0x6f, 0x72, 0x64, 0x2d, + 0x77, 0x72, 0x61, 0x70, 0x3a, 0x20, 0x62, 0x72, 0x65, 0x61, 0x6b, 0x2d, + 0x77, 0x6f, 0x72, 0x64, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x68, 0x79, 0x70, 0x68, 0x65, 0x6e, 0x73, 0x3a, 0x20, 0x61, 0x75, 0x74, + 0x6f, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, 0x72, + 0x67, 0x69, 0x6e, 0x2d, 0x74, 0x6f, 0x70, 0x3a, 0x20, 0x30, 0x2e, 0x35, + 0x65, 0x6d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, + 0x72, 0x67, 0x69, 0x6e, 0x2d, 0x62, 0x6f, 0x74, 0x74, 0x6f, 0x6d, 0x3a, + 0x20, 0x30, 0x2e, 0x35, 0x65, 0x6d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x23, 0x77, 0x72, 0x69, 0x74, + 0x65, 0x20, 0x66, 0x6f, 0x72, 0x6d, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x6d, 0x61, 0x72, 0x67, 0x69, 0x6e, 0x3a, 0x20, 0x31, + 0x65, 0x6d, 0x20, 0x30, 0x20, 0x30, 0x20, 0x30, 0x3b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x64, 0x69, 0x73, 0x70, 0x6c, 0x61, 0x79, 0x3a, + 0x20, 0x66, 0x6c, 0x65, 0x78, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x66, 0x6c, 0x65, 0x78, 0x2d, 0x64, 0x69, 0x72, 0x65, 0x63, 0x74, + 0x69, 0x6f, 0x6e, 0x3a, 0x20, 0x63, 0x6f, 0x6c, 0x75, 0x6d, 0x6e, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x67, 0x61, 0x70, 0x3a, 0x20, + 0x30, 0x2e, 0x35, 0x65, 0x6d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x61, 0x6c, 0x69, 0x67, 0x6e, 0x2d, 0x69, 0x74, 0x65, 0x6d, 0x73, + 0x3a, 0x20, 0x73, 0x74, 0x72, 0x65, 0x74, 0x63, 0x68, 0x3b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, + 0x69, 0x67, 0x68, 0x74, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x64, 0x69, 0x73, 0x70, 0x6c, 0x61, 0x79, 0x3a, 0x20, 0x66, 0x6c, + 0x65, 0x78, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x66, 0x6c, + 0x65, 0x78, 0x2d, 0x64, 0x69, 0x72, 0x65, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x3a, 0x20, 0x72, 0x6f, 0x77, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x67, 0x61, 0x70, 0x3a, 0x20, 0x30, 0x2e, 0x35, 0x65, 0x6d, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6a, 0x75, 0x73, 0x74, 0x69, + 0x66, 0x79, 0x2d, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x3a, 0x20, + 0x66, 0x6c, 0x65, 0x78, 0x2d, 0x65, 0x6e, 0x64, 0x3b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x66, 0x69, 0x65, + 0x6c, 0x64, 0x73, 0x65, 0x74, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x62, 0x6f, 0x72, 0x64, 0x65, 0x72, 0x3a, 0x20, 0x6e, 0x6f, + 0x6e, 0x65, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x70, 0x61, + 0x64, 0x64, 0x69, 0x6e, 0x67, 0x3a, 0x20, 0x30, 0x3b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, 0x72, 0x67, 0x69, 0x6e, 0x3a, 0x20, + 0x30, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x74, 0x65, 0x78, 0x74, 0x61, 0x72, 0x65, 0x61, 0x20, 0x7b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x70, 0x61, 0x64, 0x64, 0x69, + 0x6e, 0x67, 0x3a, 0x20, 0x35, 0x70, 0x78, 0x3b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x66, 0x6c, 0x65, 0x78, 0x2d, 0x67, 0x72, 0x6f, 0x77, + 0x3a, 0x20, 0x31, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x77, + 0x69, 0x64, 0x74, 0x68, 0x3a, 0x20, 0x31, 0x30, 0x30, 0x25, 0x3b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x70, + 0x72, 0x65, 0x20, 0x63, 0x6f, 0x64, 0x65, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x64, 0x69, 0x73, 0x70, 0x6c, 0x61, 0x79, 0x3a, + 0x20, 0x62, 0x6c, 0x6f, 0x63, 0x6b, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x62, 0x61, 0x63, 0x6b, 0x67, 0x72, 0x6f, 0x75, 0x6e, 0x64, + 0x2d, 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x3a, 0x20, 0x23, 0x32, 0x32, 0x32, + 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6c, 0x6f, + 0x72, 0x3a, 0x20, 0x23, 0x64, 0x64, 0x64, 0x3b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x64, 0x65, 0x20, + 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x66, 0x6f, 0x6e, 0x74, + 0x2d, 0x66, 0x61, 0x6d, 0x69, 0x6c, 0x79, 0x3a, 0x20, 0x6d, 0x6f, 0x6e, + 0x6f, 0x73, 0x70, 0x61, 0x63, 0x65, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x70, 0x61, 0x64, 0x64, 0x69, 0x6e, 0x67, 0x3a, 0x20, 0x30, + 0x2e, 0x31, 0x65, 0x6d, 0x20, 0x30, 0x2e, 0x33, 0x65, 0x6d, 0x3b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x62, 0x6f, 0x72, 0x64, 0x65, 0x72, + 0x2d, 0x72, 0x61, 0x64, 0x69, 0x75, 0x73, 0x3a, 0x20, 0x33, 0x70, 0x78, + 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, 0x20, 0x6c, 0x61, + 0x62, 0x65, 0x6c, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x6d, 0x61, 0x72, 0x67, 0x69, 0x6e, 0x3a, 0x20, 0x30, 0x2e, 0x35, 0x65, + 0x6d, 0x20, 0x30, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x64, + 0x69, 0x73, 0x70, 0x6c, 0x61, 0x79, 0x3a, 0x20, 0x62, 0x6c, 0x6f, 0x63, + 0x6b, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x3c, + 0x2f, 0x73, 0x74, 0x79, 0x6c, 0x65, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x3c, + 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, + 0x22, 0x6d, 0x6f, 0x64, 0x75, 0x6c, 0x65, 0x22, 0x3e, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x69, 0x6d, 0x70, 0x6f, 0x72, 0x74, 0x20, 0x7b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x2c, 0x20, 0x68, + 0x2c, 0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x2c, 0x20, 0x65, 0x66, + 0x66, 0x65, 0x63, 0x74, 0x2c, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x75, 0x74, + 0x65, 0x64, 0x2c, 0x20, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x2c, 0x20, + 0x75, 0x73, 0x65, 0x53, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x2c, 0x20, 0x75, + 0x73, 0x65, 0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x2c, 0x20, 0x75, 0x73, + 0x65, 0x52, 0x65, 0x66, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x20, 0x66, + 0x72, 0x6f, 0x6d, 0x20, 0x27, 0x2f, 0x69, 0x6e, 0x64, 0x65, 0x78, 0x2e, + 0x6a, 0x73, 0x27, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x69, 0x6d, + 0x70, 0x6f, 0x72, 0x74, 0x20, 0x7b, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, + 0x43, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x65, 0x20, 0x7d, 0x20, 0x66, + 0x72, 0x6f, 0x6d, 0x20, 0x27, 0x2f, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, + 0x74, 0x69, 0x6f, 0x6e, 0x2e, 0x6a, 0x73, 0x27, 0x3b, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x73, 0x65, 0x73, + 0x73, 0x69, 0x6f, 0x6e, 0x20, 0x3d, 0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, + 0x6c, 0x28, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x70, 0x72, + 0x6f, 0x6d, 0x70, 0x74, 0x3a, 0x20, 0x22, 0x54, 0x68, 0x69, 0x73, 0x20, + 0x69, 0x73, 0x20, 0x61, 0x20, 0x63, 0x6f, 0x6e, 0x76, 0x65, 0x72, 0x73, + 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x62, 0x65, 0x74, 0x77, 0x65, 0x65, + 0x6e, 0x20, 0x75, 0x73, 0x65, 0x72, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x6c, + 0x6c, 0x61, 0x6d, 0x61, 0x2c, 0x20, 0x61, 0x20, 0x66, 0x72, 0x69, 0x65, + 0x6e, 0x64, 0x6c, 0x79, 0x20, 0x63, 0x68, 0x61, 0x74, 0x62, 0x6f, 0x74, + 0x2e, 0x20, 0x72, 0x65, 0x73, 0x70, 0x6f, 0x6e, 0x64, 0x20, 0x69, 0x6e, + 0x20, 0x6d, 0x61, 0x72, 0x6b, 0x64, 0x6f, 0x77, 0x6e, 0x2e, 0x22, 0x2c, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, + 0x61, 0x74, 0x65, 0x3a, 0x20, 0x22, 0x7b, 0x7b, 0x70, 0x72, 0x6f, 0x6d, + 0x70, 0x74, 0x7d, 0x7d, 0x5c, 0x6e, 0x5c, 0x6e, 0x7b, 0x7b, 0x68, 0x69, + 0x73, 0x74, 0x6f, 0x72, 0x79, 0x7d, 0x7d, 0x5c, 0x6e, 0x7b, 0x7b, 0x63, + 0x68, 0x61, 0x72, 0x7d, 0x7d, 0x3a, 0x22, 0x2c, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x54, 0x65, + 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x3a, 0x20, 0x22, 0x7b, 0x7b, 0x6e, + 0x61, 0x6d, 0x65, 0x7d, 0x7d, 0x3a, 0x20, 0x7b, 0x7b, 0x6d, 0x65, 0x73, + 0x73, 0x61, 0x67, 0x65, 0x7d, 0x7d, 0x22, 0x2c, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, + 0x74, 0x3a, 0x20, 0x5b, 0x5d, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x74, 0x79, 0x70, 0x65, 0x3a, 0x20, 0x22, 0x63, 0x68, 0x61, 0x74, + 0x22, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x68, 0x61, + 0x72, 0x3a, 0x20, 0x22, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x22, 0x2c, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x75, 0x73, 0x65, 0x72, 0x3a, 0x20, + 0x22, 0x55, 0x73, 0x65, 0x72, 0x22, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x7d, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, + 0x74, 0x20, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, + 0x55, 0x70, 0x64, 0x61, 0x74, 0x65, 0x20, 0x3d, 0x20, 0x28, 0x74, 0x72, + 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x29, 0x20, 0x3d, 0x3e, + 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x65, 0x73, + 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, + 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, + 0x2e, 0x2e, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, + 0x6c, 0x75, 0x65, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, + 0x20, 0x63, 0x68, 0x61, 0x74, 0x53, 0x74, 0x61, 0x72, 0x74, 0x65, 0x64, + 0x20, 0x3d, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x75, 0x74, 0x65, 0x64, 0x28, + 0x28, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, + 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x72, 0x61, 0x6e, + 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, + 0x68, 0x20, 0x3e, 0x20, 0x30, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, + 0x20, 0x3d, 0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x28, 0x7b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6e, 0x5f, 0x70, 0x72, 0x65, 0x64, + 0x69, 0x63, 0x74, 0x3a, 0x20, 0x34, 0x30, 0x30, 0x2c, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, + 0x75, 0x72, 0x65, 0x3a, 0x20, 0x30, 0x2e, 0x37, 0x2c, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x6c, + 0x61, 0x73, 0x74, 0x5f, 0x6e, 0x3a, 0x20, 0x32, 0x35, 0x36, 0x2c, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, + 0x5f, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x3a, 0x20, 0x31, 0x2e, + 0x31, 0x38, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x6f, + 0x70, 0x5f, 0x6b, 0x3a, 0x20, 0x34, 0x30, 0x2c, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x74, 0x6f, 0x70, 0x5f, 0x70, 0x3a, 0x20, 0x30, 0x2e, + 0x35, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x29, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x6f, 0x6e, + 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x20, 0x3d, 0x20, 0x73, 0x69, + 0x67, 0x6e, 0x61, 0x6c, 0x28, 0x6e, 0x75, 0x6c, 0x6c, 0x29, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x67, 0x65, 0x6e, + 0x65, 0x72, 0x61, 0x74, 0x69, 0x6e, 0x67, 0x20, 0x3d, 0x20, 0x63, 0x6f, + 0x6d, 0x70, 0x75, 0x74, 0x65, 0x64, 0x28, 0x28, 0x29, 0x20, 0x3d, 0x3e, + 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, + 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x3d, 0x20, 0x6e, 0x75, 0x6c, + 0x6c, 0x20, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, + 0x73, 0x69, 0x6d, 0x70, 0x6c, 0x65, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, + 0x61, 0x74, 0x65, 0x20, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x74, 0x65, + 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x20, 0x3d, 0x20, 0x28, 0x73, 0x74, + 0x72, 0x2c, 0x20, 0x65, 0x78, 0x74, 0x72, 0x61, 0x53, 0x65, 0x74, 0x74, + 0x69, 0x6e, 0x67, 0x73, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x65, 0x74, 0x20, 0x73, 0x65, 0x74, + 0x74, 0x69, 0x6e, 0x67, 0x73, 0x20, 0x3d, 0x20, 0x73, 0x65, 0x73, 0x73, + 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x65, 0x78, 0x74, + 0x72, 0x61, 0x53, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x29, 0x20, + 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x65, + 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x20, 0x3d, 0x20, 0x7b, 0x20, 0x2e, + 0x2e, 0x2e, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x2c, 0x20, + 0x2e, 0x2e, 0x2e, 0x65, 0x78, 0x74, 0x72, 0x61, 0x53, 0x65, 0x74, 0x74, + 0x69, 0x6e, 0x67, 0x73, 0x20, 0x7d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, + 0x74, 0x75, 0x72, 0x6e, 0x20, 0x53, 0x74, 0x72, 0x69, 0x6e, 0x67, 0x28, + 0x73, 0x74, 0x72, 0x29, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, + 0x41, 0x6c, 0x6c, 0x28, 0x2f, 0x5c, 0x7b, 0x5c, 0x7b, 0x28, 0x2e, 0x2a, + 0x3f, 0x29, 0x5c, 0x7d, 0x5c, 0x7d, 0x2f, 0x67, 0x2c, 0x20, 0x28, 0x5f, + 0x2c, 0x20, 0x6b, 0x65, 0x79, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x74, 0x65, + 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, 0x73, 0x65, 0x74, 0x74, 0x69, + 0x6e, 0x67, 0x73, 0x5b, 0x6b, 0x65, 0x79, 0x5d, 0x29, 0x29, 0x3b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, + 0x2f, 0x20, 0x73, 0x65, 0x6e, 0x64, 0x20, 0x6d, 0x65, 0x73, 0x73, 0x61, + 0x67, 0x65, 0x20, 0x74, 0x6f, 0x20, 0x73, 0x65, 0x72, 0x76, 0x65, 0x72, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, + 0x68, 0x61, 0x74, 0x20, 0x3d, 0x20, 0x61, 0x73, 0x79, 0x6e, 0x63, 0x20, + 0x28, 0x6d, 0x73, 0x67, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x63, 0x6f, 0x6e, + 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, 0x65, 0x2e, 0x6c, 0x6f, 0x67, + 0x28, 0x27, 0x61, 0x6c, 0x72, 0x65, 0x61, 0x64, 0x79, 0x20, 0x72, 0x75, + 0x6e, 0x6e, 0x69, 0x6e, 0x67, 0x2e, 0x2e, 0x2e, 0x27, 0x29, 0x3b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, + 0x72, 0x6e, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, + 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, + 0x20, 0x6e, 0x65, 0x77, 0x20, 0x41, 0x62, 0x6f, 0x72, 0x74, 0x43, 0x6f, + 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x28, 0x29, 0x3b, 0x0a, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x72, 0x61, 0x6e, 0x73, + 0x63, 0x72, 0x69, 0x70, 0x74, 0x55, 0x70, 0x64, 0x61, 0x74, 0x65, 0x28, + 0x5b, 0x2e, 0x2e, 0x2e, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, + 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, + 0x72, 0x69, 0x70, 0x74, 0x2c, 0x20, 0x5b, 0x22, 0x7b, 0x7b, 0x75, 0x73, + 0x65, 0x72, 0x7d, 0x7d, 0x22, 0x2c, 0x20, 0x6d, 0x73, 0x67, 0x5d, 0x5d, + 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, + 0x73, 0x74, 0x20, 0x70, 0x61, 0x79, 0x6c, 0x6f, 0x61, 0x64, 0x20, 0x3d, + 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, 0x73, 0x65, + 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, + 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x2c, 0x20, 0x7b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x65, 0x73, 0x73, + 0x61, 0x67, 0x65, 0x3a, 0x20, 0x6d, 0x73, 0x67, 0x2c, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, + 0x79, 0x3a, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, + 0x69, 0x70, 0x74, 0x2e, 0x66, 0x6c, 0x61, 0x74, 0x4d, 0x61, 0x70, 0x28, + 0x28, 0x5b, 0x6e, 0x61, 0x6d, 0x65, 0x2c, 0x20, 0x6d, 0x65, 0x73, 0x73, + 0x61, 0x67, 0x65, 0x5d, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x74, 0x65, 0x6d, + 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, + 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x68, 0x69, 0x73, 0x74, + 0x6f, 0x72, 0x79, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x2c, + 0x20, 0x7b, 0x6e, 0x61, 0x6d, 0x65, 0x2c, 0x20, 0x6d, 0x65, 0x73, 0x73, + 0x61, 0x67, 0x65, 0x7d, 0x29, 0x29, 0x2e, 0x6a, 0x6f, 0x69, 0x6e, 0x28, + 0x22, 0x5c, 0x6e, 0x22, 0x29, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x7d, 0x29, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x6c, 0x65, 0x74, 0x20, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, + 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x20, 0x3d, 0x20, 0x27, 0x27, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, + 0x20, 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x20, 0x3d, 0x20, 0x73, + 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, + 0x2e, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x0a, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, + 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, + 0x20, 0x3d, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x2e, 0x2e, 0x2e, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x3a, 0x20, 0x70, 0x61, + 0x79, 0x6c, 0x6f, 0x61, 0x64, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x73, 0x74, 0x6f, 0x70, 0x3a, 0x20, 0x5b, 0x22, 0x3c, + 0x2f, 0x73, 0x3e, 0x22, 0x2c, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, + 0x74, 0x65, 0x28, 0x22, 0x7b, 0x7b, 0x63, 0x68, 0x61, 0x72, 0x7d, 0x7d, + 0x3a, 0x22, 0x29, 0x2c, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, + 0x65, 0x28, 0x22, 0x7b, 0x7b, 0x75, 0x73, 0x65, 0x72, 0x7d, 0x7d, 0x3a, + 0x22, 0x29, 0x5d, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, + 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x61, 0x77, 0x61, 0x69, + 0x74, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x43, 0x6f, 0x6d, 0x70, 0x6c, + 0x65, 0x74, 0x65, 0x28, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x50, 0x61, 0x72, + 0x61, 0x6d, 0x73, 0x2c, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, + 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x20, 0x28, + 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x29, 0x20, 0x3d, 0x3e, 0x20, + 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, + 0x6e, 0x73, 0x74, 0x20, 0x64, 0x61, 0x74, 0x61, 0x20, 0x3d, 0x20, 0x6d, + 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x2e, 0x64, 0x61, 0x74, 0x61, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x75, 0x72, + 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x20, + 0x2b, 0x3d, 0x20, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x63, 0x6f, 0x6e, 0x74, + 0x65, 0x6e, 0x74, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x2f, 0x2f, 0x20, 0x72, 0x65, 0x6d, 0x6f, 0x76, 0x65, 0x20, 0x6c, + 0x65, 0x61, 0x64, 0x69, 0x6e, 0x67, 0x20, 0x77, 0x68, 0x69, 0x74, 0x65, + 0x73, 0x70, 0x61, 0x63, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, + 0x73, 0x61, 0x67, 0x65, 0x20, 0x3d, 0x20, 0x63, 0x75, 0x72, 0x72, 0x65, + 0x6e, 0x74, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x2e, 0x72, 0x65, + 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x5e, 0x5c, 0x73, 0x2b, 0x2f, + 0x2c, 0x20, 0x22, 0x22, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, + 0x74, 0x55, 0x70, 0x64, 0x61, 0x74, 0x65, 0x28, 0x5b, 0x2e, 0x2e, 0x2e, + 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x2c, 0x20, 0x5b, 0x22, 0x7b, + 0x7b, 0x63, 0x68, 0x61, 0x72, 0x7d, 0x7d, 0x22, 0x2c, 0x20, 0x63, 0x75, + 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, + 0x5d, 0x5d, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x69, 0x66, 0x20, 0x28, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x73, 0x74, + 0x6f, 0x70, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, 0x65, 0x2e, + 0x6c, 0x6f, 0x67, 0x28, 0x22, 0x2d, 0x2d, 0x3e, 0x22, 0x2c, 0x20, 0x64, + 0x61, 0x74, 0x61, 0x2c, 0x20, 0x27, 0x20, 0x72, 0x65, 0x73, 0x70, 0x6f, + 0x6e, 0x73, 0x65, 0x20, 0x77, 0x61, 0x73, 0x3a, 0x27, 0x2c, 0x20, 0x63, + 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, + 0x65, 0x2c, 0x20, 0x27, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, + 0x70, 0x74, 0x20, 0x73, 0x74, 0x61, 0x74, 0x65, 0x3a, 0x27, 0x2c, 0x20, + 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x2e, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, + 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x29, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, + 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, + 0x6e, 0x75, 0x6c, 0x6c, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, + 0x6e, 0x20, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x49, 0x6e, 0x70, + 0x75, 0x74, 0x28, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6d, 0x65, 0x73, 0x73, 0x61, + 0x67, 0x65, 0x20, 0x3d, 0x20, 0x75, 0x73, 0x65, 0x53, 0x69, 0x67, 0x6e, + 0x61, 0x6c, 0x28, 0x22, 0x22, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x73, 0x74, 0x6f, 0x70, + 0x20, 0x3d, 0x20, 0x28, 0x65, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x65, 0x2e, 0x70, 0x72, + 0x65, 0x76, 0x65, 0x6e, 0x74, 0x44, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, + 0x28, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x69, 0x66, 0x20, 0x28, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, + 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x20, 0x7b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, + 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x2e, 0x61, 0x62, 0x6f, 0x72, 0x74, 0x28, 0x29, 0x3b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, + 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x20, 0x3d, 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x3b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x72, 0x65, 0x73, 0x65, 0x74, 0x20, + 0x3d, 0x20, 0x28, 0x65, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x74, 0x6f, 0x70, 0x28, + 0x65, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x55, 0x70, + 0x64, 0x61, 0x74, 0x65, 0x28, 0x5b, 0x5d, 0x29, 0x3b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x73, 0x75, 0x62, 0x6d, 0x69, + 0x74, 0x20, 0x3d, 0x20, 0x28, 0x65, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x74, 0x6f, + 0x70, 0x28, 0x65, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x63, 0x68, 0x61, 0x74, 0x28, 0x6d, 0x65, 0x73, 0x73, 0x61, + 0x67, 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x3b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x65, 0x73, 0x73, 0x61, + 0x67, 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x22, + 0x22, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, + 0x65, 0x6e, 0x74, 0x65, 0x72, 0x53, 0x75, 0x62, 0x6d, 0x69, 0x74, 0x73, + 0x20, 0x3d, 0x20, 0x28, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x29, 0x20, 0x3d, + 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x69, 0x66, 0x20, 0x28, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x2e, 0x77, 0x68, + 0x69, 0x63, 0x68, 0x20, 0x3d, 0x3d, 0x3d, 0x20, 0x31, 0x33, 0x20, 0x26, + 0x26, 0x20, 0x21, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x2e, 0x73, 0x68, 0x69, + 0x66, 0x74, 0x4b, 0x65, 0x79, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x75, 0x62, 0x6d, 0x69, + 0x74, 0x28, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x29, 0x3b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, + 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x66, 0x6f, 0x72, + 0x6d, 0x20, 0x6f, 0x6e, 0x73, 0x75, 0x62, 0x6d, 0x69, 0x74, 0x3d, 0x24, + 0x7b, 0x73, 0x75, 0x62, 0x6d, 0x69, 0x74, 0x7d, 0x3e, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, + 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x3c, 0x74, 0x65, 0x78, 0x74, 0x61, 0x72, 0x65, 0x61, 0x20, 0x74, 0x79, + 0x70, 0x65, 0x3d, 0x22, 0x74, 0x65, 0x78, 0x74, 0x22, 0x20, 0x72, 0x6f, + 0x77, 0x73, 0x3d, 0x32, 0x20, 0x6f, 0x6e, 0x6b, 0x65, 0x79, 0x70, 0x72, + 0x65, 0x73, 0x73, 0x3d, 0x24, 0x7b, 0x65, 0x6e, 0x74, 0x65, 0x72, 0x53, + 0x75, 0x62, 0x6d, 0x69, 0x74, 0x73, 0x7d, 0x20, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, + 0x7d, 0x22, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, + 0x7b, 0x28, 0x65, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x6d, 0x65, 0x73, 0x73, + 0x61, 0x67, 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, + 0x65, 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x7d, 0x20, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x68, 0x6f, 0x6c, + 0x64, 0x65, 0x72, 0x3d, 0x22, 0x53, 0x61, 0x79, 0x20, 0x73, 0x6f, 0x6d, + 0x65, 0x74, 0x68, 0x69, 0x6e, 0x67, 0x2e, 0x2e, 0x2e, 0x22, 0x2f, 0x3e, + 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x20, 0x63, 0x6c, + 0x61, 0x73, 0x73, 0x3d, 0x22, 0x72, 0x69, 0x67, 0x68, 0x74, 0x22, 0x3e, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, + 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, + 0x22, 0x73, 0x75, 0x62, 0x6d, 0x69, 0x74, 0x22, 0x20, 0x64, 0x69, 0x73, + 0x61, 0x62, 0x6c, 0x65, 0x64, 0x3d, 0x24, 0x7b, 0x21, 0x67, 0x65, 0x6e, + 0x65, 0x72, 0x61, 0x74, 0x69, 0x6e, 0x67, 0x2e, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x7d, 0x20, 0x3e, 0x53, 0x65, 0x6e, 0x64, 0x3c, 0x2f, 0x62, 0x75, + 0x74, 0x74, 0x6f, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x3c, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x20, + 0x6f, 0x6e, 0x63, 0x6c, 0x69, 0x63, 0x6b, 0x3d, 0x24, 0x7b, 0x73, 0x74, + 0x6f, 0x70, 0x7d, 0x20, 0x64, 0x69, 0x73, 0x61, 0x62, 0x6c, 0x65, 0x64, + 0x3d, 0x24, 0x7b, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6e, + 0x67, 0x7d, 0x3e, 0x53, 0x74, 0x6f, 0x70, 0x3c, 0x2f, 0x62, 0x75, 0x74, + 0x74, 0x6f, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x3c, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x20, 0x6f, + 0x6e, 0x63, 0x6c, 0x69, 0x63, 0x6b, 0x3d, 0x24, 0x7b, 0x72, 0x65, 0x73, + 0x65, 0x74, 0x7d, 0x3e, 0x52, 0x65, 0x73, 0x65, 0x74, 0x3c, 0x2f, 0x62, + 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x6f, + 0x72, 0x6d, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, + 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x43, 0x68, 0x61, 0x74, 0x4c, 0x6f, 0x67, + 0x20, 0x3d, 0x20, 0x28, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x29, 0x20, 0x3d, + 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, + 0x6e, 0x73, 0x74, 0x20, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x73, + 0x20, 0x3d, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, + 0x69, 0x70, 0x74, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, + 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, + 0x65, 0x72, 0x20, 0x3d, 0x20, 0x75, 0x73, 0x65, 0x52, 0x65, 0x66, 0x28, + 0x6e, 0x75, 0x6c, 0x6c, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x75, 0x73, 0x65, 0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x28, 0x28, + 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x73, 0x63, 0x72, 0x6f, 0x6c, 0x6c, + 0x20, 0x74, 0x6f, 0x20, 0x62, 0x6f, 0x74, 0x74, 0x6f, 0x6d, 0x20, 0x28, + 0x69, 0x66, 0x20, 0x6e, 0x65, 0x65, 0x64, 0x65, 0x64, 0x29, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x63, + 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x2e, 0x63, 0x75, 0x72, + 0x72, 0x65, 0x6e, 0x74, 0x20, 0x26, 0x26, 0x20, 0x63, 0x6f, 0x6e, 0x74, + 0x61, 0x69, 0x6e, 0x65, 0x72, 0x2e, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, + 0x74, 0x2e, 0x73, 0x63, 0x72, 0x6f, 0x6c, 0x6c, 0x48, 0x65, 0x69, 0x67, + 0x68, 0x74, 0x20, 0x3c, 0x3d, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, + 0x6e, 0x65, 0x72, 0x2e, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x2e, + 0x73, 0x63, 0x72, 0x6f, 0x6c, 0x6c, 0x54, 0x6f, 0x70, 0x20, 0x2b, 0x20, + 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x2e, 0x63, 0x75, + 0x72, 0x72, 0x65, 0x6e, 0x74, 0x2e, 0x6f, 0x66, 0x66, 0x73, 0x65, 0x74, + 0x48, 0x65, 0x69, 0x67, 0x68, 0x74, 0x20, 0x2b, 0x20, 0x33, 0x30, 0x30, + 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x2e, + 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x2e, 0x73, 0x63, 0x72, 0x6f, + 0x6c, 0x6c, 0x54, 0x6f, 0x28, 0x30, 0x2c, 0x20, 0x63, 0x6f, 0x6e, 0x74, + 0x61, 0x69, 0x6e, 0x65, 0x72, 0x2e, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, + 0x74, 0x2e, 0x73, 0x63, 0x72, 0x6f, 0x6c, 0x6c, 0x48, 0x65, 0x69, 0x67, + 0x68, 0x74, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x2c, 0x20, 0x5b, + 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x73, 0x5d, 0x29, 0x0a, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, + 0x63, 0x68, 0x61, 0x74, 0x4c, 0x69, 0x6e, 0x65, 0x20, 0x3d, 0x20, 0x28, + 0x5b, 0x75, 0x73, 0x65, 0x72, 0x2c, 0x20, 0x6d, 0x73, 0x67, 0x5d, 0x29, + 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, + 0x6c, 0x60, 0x3c, 0x70, 0x20, 0x6b, 0x65, 0x79, 0x3d, 0x24, 0x7b, 0x6d, + 0x73, 0x67, 0x7d, 0x3e, 0x3c, 0x73, 0x74, 0x72, 0x6f, 0x6e, 0x67, 0x3e, + 0x24, 0x7b, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, 0x75, + 0x73, 0x65, 0x72, 0x29, 0x7d, 0x3a, 0x3c, 0x2f, 0x73, 0x74, 0x72, 0x6f, + 0x6e, 0x67, 0x3e, 0x20, 0x3c, 0x24, 0x7b, 0x4d, 0x61, 0x72, 0x6b, 0x64, + 0x6f, 0x77, 0x6e, 0x7d, 0x20, 0x74, 0x65, 0x78, 0x74, 0x3d, 0x24, 0x7b, + 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, 0x6d, 0x73, 0x67, + 0x29, 0x7d, 0x20, 0x2f, 0x3e, 0x3c, 0x2f, 0x70, 0x3e, 0x60, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, + 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x3c, 0x73, 0x65, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x69, 0x64, 0x3d, + 0x22, 0x63, 0x68, 0x61, 0x74, 0x22, 0x20, 0x72, 0x65, 0x66, 0x3d, 0x24, + 0x7b, 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x7d, 0x3e, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, + 0x7b, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x73, 0x2e, 0x66, 0x6c, + 0x61, 0x74, 0x4d, 0x61, 0x70, 0x28, 0x63, 0x68, 0x61, 0x74, 0x4c, 0x69, + 0x6e, 0x65, 0x29, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x2f, 0x73, 0x65, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x3e, 0x60, + 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x3b, 0x0a, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x43, 0x6f, 0x6e, 0x66, + 0x69, 0x67, 0x46, 0x6f, 0x72, 0x6d, 0x20, 0x3d, 0x20, 0x28, 0x70, 0x72, + 0x6f, 0x70, 0x73, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x75, 0x70, + 0x64, 0x61, 0x74, 0x65, 0x53, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x20, + 0x3d, 0x20, 0x28, 0x65, 0x6c, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x73, 0x65, + 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, + 0x3d, 0x20, 0x7b, 0x20, 0x2e, 0x2e, 0x2e, 0x73, 0x65, 0x73, 0x73, 0x69, + 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x20, 0x5b, 0x65, + 0x6c, 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, 0x6e, 0x61, 0x6d, + 0x65, 0x5d, 0x3a, 0x20, 0x65, 0x6c, 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, + 0x74, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x7d, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x75, 0x70, + 0x64, 0x61, 0x74, 0x65, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x20, 0x3d, + 0x20, 0x28, 0x65, 0x6c, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x70, 0x61, 0x72, + 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, + 0x7b, 0x20, 0x2e, 0x2e, 0x2e, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, + 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x20, 0x5b, 0x65, 0x6c, 0x2e, 0x74, + 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, 0x6e, 0x61, 0x6d, 0x65, 0x5d, 0x3a, + 0x20, 0x65, 0x6c, 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x75, 0x70, 0x64, 0x61, 0x74, + 0x65, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x46, 0x6c, 0x6f, 0x61, 0x74, + 0x20, 0x3d, 0x20, 0x28, 0x65, 0x6c, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x70, + 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, + 0x3d, 0x20, 0x7b, 0x20, 0x2e, 0x2e, 0x2e, 0x70, 0x61, 0x72, 0x61, 0x6d, + 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x20, 0x5b, 0x65, 0x6c, + 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, 0x6e, 0x61, 0x6d, 0x65, + 0x5d, 0x3a, 0x20, 0x70, 0x61, 0x72, 0x73, 0x65, 0x46, 0x6c, 0x6f, 0x61, + 0x74, 0x28, 0x65, 0x6c, 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, + 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, + 0x74, 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x66, 0x6f, 0x72, 0x6d, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x66, 0x69, 0x65, 0x6c, 0x64, + 0x73, 0x65, 0x74, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, + 0x22, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x22, 0x3e, 0x50, 0x72, 0x6f, + 0x6d, 0x70, 0x74, 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x3c, 0x74, 0x65, 0x78, 0x74, 0x61, 0x72, 0x65, 0x61, 0x20, + 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, 0x74, 0x65, 0x78, 0x74, 0x22, 0x20, + 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, + 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x73, + 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, + 0x2e, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x7d, 0x22, 0x20, 0x72, 0x6f, + 0x77, 0x73, 0x3d, 0x34, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, + 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x53, 0x65, 0x73, + 0x73, 0x69, 0x6f, 0x6e, 0x7d, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, + 0x76, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, + 0x75, 0x73, 0x65, 0x72, 0x22, 0x3e, 0x55, 0x73, 0x65, 0x72, 0x20, 0x6e, + 0x61, 0x6d, 0x65, 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x3c, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x20, 0x74, 0x79, 0x70, + 0x65, 0x3d, 0x22, 0x74, 0x65, 0x78, 0x74, 0x22, 0x20, 0x6e, 0x61, 0x6d, + 0x65, 0x3d, 0x22, 0x75, 0x73, 0x65, 0x72, 0x22, 0x20, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, + 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x75, 0x73, 0x65, 0x72, + 0x7d, 0x22, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, + 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x53, 0x65, 0x73, 0x73, 0x69, + 0x6f, 0x6e, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, + 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, + 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, 0x62, + 0x6f, 0x74, 0x22, 0x3e, 0x42, 0x6f, 0x74, 0x20, 0x6e, 0x61, 0x6d, 0x65, + 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, + 0x69, 0x6e, 0x70, 0x75, 0x74, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, + 0x74, 0x65, 0x78, 0x74, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, + 0x63, 0x68, 0x61, 0x72, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, + 0x22, 0x24, 0x7b, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x63, 0x68, 0x61, 0x72, 0x7d, 0x22, 0x20, + 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, 0x70, + 0x64, 0x61, 0x74, 0x65, 0x53, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x7d, + 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, 0x62, + 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, 0x74, 0x65, 0x6d, 0x70, + 0x6c, 0x61, 0x74, 0x65, 0x22, 0x3e, 0x50, 0x72, 0x6f, 0x6d, 0x70, 0x74, + 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x3c, 0x2f, 0x6c, + 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x74, 0x65, 0x78, + 0x74, 0x61, 0x72, 0x65, 0x61, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x74, 0x65, + 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, + 0x3d, 0x22, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x22, 0x20, + 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x73, 0x65, 0x73, + 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, + 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x7d, 0x22, 0x20, 0x72, 0x6f, + 0x77, 0x73, 0x3d, 0x34, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, + 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x53, 0x65, 0x73, + 0x73, 0x69, 0x6f, 0x6e, 0x7d, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, + 0x76, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, + 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x22, 0x3e, 0x43, 0x68, + 0x61, 0x74, 0x20, 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x20, 0x74, + 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x3c, 0x2f, 0x6c, 0x61, 0x62, + 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x74, 0x65, 0x78, 0x74, 0x61, + 0x72, 0x65, 0x61, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x74, 0x65, 0x6d, 0x70, + 0x6c, 0x61, 0x74, 0x65, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, + 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x54, 0x65, 0x6d, 0x70, 0x6c, + 0x61, 0x74, 0x65, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, + 0x24, 0x7b, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, + 0x6c, 0x75, 0x65, 0x2e, 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x54, + 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x7d, 0x22, 0x20, 0x72, 0x6f, + 0x77, 0x73, 0x3d, 0x31, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, + 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x53, 0x65, 0x73, + 0x73, 0x69, 0x6f, 0x6e, 0x7d, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, + 0x76, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, + 0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x22, + 0x3e, 0x54, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, + 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, + 0x69, 0x6e, 0x70, 0x75, 0x74, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, + 0x72, 0x61, 0x6e, 0x67, 0x65, 0x22, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x74, + 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x22, 0x20, + 0x6d, 0x69, 0x6e, 0x3d, 0x22, 0x30, 0x2e, 0x30, 0x22, 0x20, 0x6d, 0x61, + 0x78, 0x3d, 0x22, 0x31, 0x2e, 0x30, 0x22, 0x20, 0x73, 0x74, 0x65, 0x70, + 0x3d, 0x22, 0x30, 0x2e, 0x30, 0x31, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, + 0x3d, 0x22, 0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, + 0x65, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, + 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, + 0x2e, 0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, + 0x7d, 0x22, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, + 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x50, 0x61, 0x72, 0x61, 0x6d, + 0x73, 0x46, 0x6c, 0x6f, 0x61, 0x74, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x24, 0x7b, 0x70, 0x61, 0x72, + 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x65, + 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x7d, 0x3c, 0x2f, + 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, + 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, + 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, 0x6e, 0x50, + 0x72, 0x65, 0x64, 0x69, 0x63, 0x74, 0x22, 0x3e, 0x50, 0x72, 0x65, 0x64, + 0x69, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x73, 0x3c, 0x2f, 0x6c, 0x61, 0x62, + 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x69, 0x6e, 0x70, 0x75, 0x74, + 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, 0x72, 0x61, 0x6e, 0x67, 0x65, + 0x22, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x6e, 0x50, 0x72, 0x65, 0x64, 0x69, + 0x63, 0x74, 0x22, 0x20, 0x6d, 0x69, 0x6e, 0x3d, 0x22, 0x31, 0x22, 0x20, + 0x6d, 0x61, 0x78, 0x3d, 0x22, 0x32, 0x30, 0x34, 0x38, 0x22, 0x20, 0x73, + 0x74, 0x65, 0x70, 0x3d, 0x22, 0x31, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, + 0x3d, 0x22, 0x6e, 0x5f, 0x70, 0x72, 0x65, 0x64, 0x69, 0x63, 0x74, 0x22, + 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x70, 0x61, + 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x6e, + 0x5f, 0x70, 0x72, 0x65, 0x64, 0x69, 0x63, 0x74, 0x7d, 0x22, 0x20, 0x6f, + 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, + 0x61, 0x74, 0x65, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x46, 0x6c, 0x6f, + 0x61, 0x74, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x73, 0x70, + 0x61, 0x6e, 0x3e, 0x24, 0x7b, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, + 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x6e, 0x5f, 0x70, 0x72, 0x65, 0x64, + 0x69, 0x63, 0x74, 0x7d, 0x3c, 0x2f, 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, + 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, + 0x6f, 0x72, 0x3d, 0x22, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x70, + 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x22, 0x3e, 0x50, 0x65, 0x6e, 0x61, + 0x6c, 0x69, 0x7a, 0x65, 0x20, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x20, + 0x73, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x63, 0x65, 0x3c, 0x2f, 0x6c, 0x61, + 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x69, 0x6e, 0x70, 0x75, + 0x74, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, 0x72, 0x61, 0x6e, 0x67, + 0x65, 0x22, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x72, 0x65, 0x70, 0x65, 0x61, + 0x74, 0x5f, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x22, 0x20, 0x6d, + 0x69, 0x6e, 0x3d, 0x22, 0x30, 0x2e, 0x30, 0x22, 0x20, 0x6d, 0x61, 0x78, + 0x3d, 0x22, 0x32, 0x2e, 0x30, 0x22, 0x20, 0x73, 0x74, 0x65, 0x70, 0x3d, + 0x22, 0x30, 0x2e, 0x30, 0x31, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, + 0x22, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x70, 0x65, 0x6e, 0x61, + 0x6c, 0x74, 0x79, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, + 0x24, 0x7b, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x2e, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x70, 0x65, + 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x7d, 0x22, 0x20, 0x6f, 0x6e, 0x69, 0x6e, + 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, + 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x46, 0x6c, 0x6f, 0x61, 0x74, 0x7d, + 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x73, 0x70, 0x61, 0x6e, 0x3e, + 0x24, 0x7b, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x2e, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x70, 0x65, + 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x7d, 0x3c, 0x2f, 0x73, 0x70, 0x61, 0x6e, + 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, + 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, + 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, + 0x5f, 0x6c, 0x61, 0x73, 0x74, 0x5f, 0x6e, 0x22, 0x3e, 0x43, 0x6f, 0x6e, + 0x73, 0x69, 0x64, 0x65, 0x72, 0x20, 0x4e, 0x20, 0x74, 0x6f, 0x6b, 0x65, + 0x6e, 0x73, 0x20, 0x66, 0x6f, 0x72, 0x20, 0x70, 0x65, 0x6e, 0x61, 0x6c, + 0x69, 0x7a, 0x65, 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x3c, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x20, 0x74, 0x79, 0x70, + 0x65, 0x3d, 0x22, 0x72, 0x61, 0x6e, 0x67, 0x65, 0x22, 0x20, 0x69, 0x64, + 0x3d, 0x22, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x6c, 0x61, 0x73, + 0x74, 0x5f, 0x6e, 0x22, 0x20, 0x6d, 0x69, 0x6e, 0x3d, 0x22, 0x30, 0x2e, + 0x30, 0x22, 0x20, 0x6d, 0x61, 0x78, 0x3d, 0x22, 0x32, 0x30, 0x34, 0x38, + 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x72, 0x65, 0x70, 0x65, + 0x61, 0x74, 0x5f, 0x6c, 0x61, 0x73, 0x74, 0x5f, 0x6e, 0x22, 0x20, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x70, 0x61, 0x72, 0x61, + 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x72, 0x65, 0x70, + 0x65, 0x61, 0x74, 0x5f, 0x6c, 0x61, 0x73, 0x74, 0x5f, 0x6e, 0x7d, 0x22, + 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, + 0x70, 0x64, 0x61, 0x74, 0x65, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x46, + 0x6c, 0x6f, 0x61, 0x74, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, + 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x24, 0x7b, 0x70, 0x61, 0x72, 0x61, 0x6d, + 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x72, 0x65, 0x70, 0x65, + 0x61, 0x74, 0x5f, 0x6c, 0x61, 0x73, 0x74, 0x5f, 0x6e, 0x7d, 0x3c, 0x2f, + 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, + 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x3c, 0x2f, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, 0x3e, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x6f, + 0x72, 0x6d, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, + 0x4d, 0x61, 0x72, 0x6b, 0x64, 0x6f, 0x77, 0x6e, 0x20, 0x3d, 0x20, 0x28, + 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, + 0x0a, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6d, 0x64, 0x20, + 0x3d, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x74, 0x65, 0x78, + 0x74, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, + 0x63, 0x65, 0x28, 0x2f, 0x5e, 0x23, 0x7b, 0x31, 0x2c, 0x36, 0x7d, 0x20, + 0x28, 0x2e, 0x2a, 0x29, 0x24, 0x2f, 0x67, 0x69, 0x6d, 0x2c, 0x20, 0x27, + 0x3c, 0x68, 0x33, 0x3e, 0x24, 0x31, 0x3c, 0x2f, 0x68, 0x33, 0x3e, 0x27, + 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, + 0x63, 0x65, 0x28, 0x2f, 0x5c, 0x2a, 0x5c, 0x2a, 0x28, 0x2e, 0x2a, 0x3f, + 0x29, 0x5c, 0x2a, 0x5c, 0x2a, 0x2f, 0x67, 0x2c, 0x20, 0x27, 0x3c, 0x73, + 0x74, 0x72, 0x6f, 0x6e, 0x67, 0x3e, 0x24, 0x31, 0x3c, 0x2f, 0x73, 0x74, + 0x72, 0x6f, 0x6e, 0x67, 0x3e, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x5f, 0x5f, + 0x28, 0x2e, 0x2a, 0x3f, 0x29, 0x5f, 0x5f, 0x2f, 0x67, 0x2c, 0x20, 0x27, + 0x3c, 0x73, 0x74, 0x72, 0x6f, 0x6e, 0x67, 0x3e, 0x24, 0x31, 0x3c, 0x2f, + 0x73, 0x74, 0x72, 0x6f, 0x6e, 0x67, 0x3e, 0x27, 0x29, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, + 0x5c, 0x2a, 0x28, 0x2e, 0x2a, 0x3f, 0x29, 0x5c, 0x2a, 0x2f, 0x67, 0x2c, + 0x20, 0x27, 0x3c, 0x65, 0x6d, 0x3e, 0x24, 0x31, 0x3c, 0x2f, 0x65, 0x6d, + 0x3e, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, 0x70, + 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x5f, 0x28, 0x2e, 0x2a, 0x3f, 0x29, + 0x5f, 0x2f, 0x67, 0x2c, 0x20, 0x27, 0x3c, 0x65, 0x6d, 0x3e, 0x24, 0x31, + 0x3c, 0x2f, 0x65, 0x6d, 0x3e, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x60, 0x60, + 0x60, 0x2e, 0x2a, 0x3f, 0x5c, 0x6e, 0x28, 0x5b, 0x5c, 0x73, 0x5c, 0x53, + 0x5d, 0x2a, 0x3f, 0x29, 0x60, 0x60, 0x60, 0x2f, 0x67, 0x2c, 0x20, 0x27, + 0x3c, 0x70, 0x72, 0x65, 0x3e, 0x3c, 0x63, 0x6f, 0x64, 0x65, 0x3e, 0x24, + 0x31, 0x3c, 0x2f, 0x63, 0x6f, 0x64, 0x65, 0x3e, 0x3c, 0x2f, 0x70, 0x72, + 0x65, 0x3e, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, + 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x60, 0x28, 0x2e, 0x2a, 0x3f, + 0x29, 0x60, 0x2f, 0x67, 0x2c, 0x20, 0x27, 0x3c, 0x63, 0x6f, 0x64, 0x65, + 0x3e, 0x24, 0x31, 0x3c, 0x2f, 0x63, 0x6f, 0x64, 0x65, 0x3e, 0x27, 0x29, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, + 0x65, 0x28, 0x2f, 0x5c, 0x6e, 0x2f, 0x67, 0x69, 0x6d, 0x2c, 0x20, 0x27, + 0x3c, 0x62, 0x72, 0x20, 0x2f, 0x3e, 0x27, 0x29, 0x3b, 0x0a, 0x20, 0x20, + 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, + 0x3c, 0x73, 0x70, 0x61, 0x6e, 0x20, 0x64, 0x61, 0x6e, 0x67, 0x65, 0x72, + 0x6f, 0x75, 0x73, 0x6c, 0x79, 0x53, 0x65, 0x74, 0x49, 0x6e, 0x6e, 0x65, + 0x72, 0x48, 0x54, 0x4d, 0x4c, 0x3d, 0x24, 0x7b, 0x7b, 0x20, 0x5f, 0x5f, + 0x68, 0x74, 0x6d, 0x6c, 0x3a, 0x20, 0x6d, 0x64, 0x20, 0x7d, 0x7d, 0x20, + 0x2f, 0x3e, 0x60, 0x3b, 0x0a, 0x7d, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x41, 0x70, + 0x70, 0x28, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x29, 0x20, 0x7b, 0x0a, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, + 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x64, 0x69, 0x76, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x63, 0x6f, + 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x22, 0x3e, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x68, 0x65, 0x61, 0x64, 0x65, + 0x72, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x68, 0x31, 0x3e, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x2e, 0x63, + 0x70, 0x70, 0x3c, 0x2f, 0x68, 0x31, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x68, 0x65, 0x61, 0x64, 0x65, 0x72, + 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, + 0x6d, 0x61, 0x69, 0x6e, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x63, 0x6f, 0x6e, + 0x74, 0x65, 0x6e, 0x74, 0x22, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x24, 0x7b, 0x63, 0x68, 0x61, 0x74, + 0x53, 0x74, 0x61, 0x72, 0x74, 0x65, 0x64, 0x2e, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x20, 0x3f, 0x20, 0x43, 0x68, 0x61, 0x74, 0x4c, 0x6f, 0x67, 0x20, + 0x3a, 0x20, 0x43, 0x6f, 0x6e, 0x66, 0x69, 0x67, 0x46, 0x6f, 0x72, 0x6d, + 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x2f, 0x6d, 0x61, 0x69, 0x6e, 0x3e, 0x0a, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x66, 0x6f, 0x6f, 0x74, 0x65, + 0x72, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x77, 0x72, 0x69, 0x74, 0x65, 0x22, + 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x3c, 0x24, 0x7b, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x49, 0x6e, + 0x70, 0x75, 0x74, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x6f, 0x6f, 0x74, 0x65, 0x72, + 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, + 0x66, 0x6f, 0x6f, 0x74, 0x65, 0x72, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x70, 0x3e, 0x50, 0x6f, 0x77, + 0x65, 0x72, 0x65, 0x64, 0x20, 0x62, 0x79, 0x20, 0x3c, 0x61, 0x20, 0x68, + 0x72, 0x65, 0x66, 0x3d, 0x22, 0x68, 0x74, 0x74, 0x70, 0x73, 0x3a, 0x2f, + 0x2f, 0x67, 0x69, 0x74, 0x68, 0x75, 0x62, 0x2e, 0x63, 0x6f, 0x6d, 0x2f, + 0x67, 0x67, 0x65, 0x72, 0x67, 0x61, 0x6e, 0x6f, 0x76, 0x2f, 0x6c, 0x6c, + 0x61, 0x6d, 0x61, 0x2e, 0x63, 0x70, 0x70, 0x22, 0x3e, 0x6c, 0x6c, 0x61, + 0x6d, 0x61, 0x2e, 0x63, 0x70, 0x70, 0x3c, 0x2f, 0x61, 0x3e, 0x20, 0x61, + 0x6e, 0x64, 0x20, 0x3c, 0x61, 0x20, 0x68, 0x72, 0x65, 0x66, 0x3d, 0x22, + 0x68, 0x74, 0x74, 0x70, 0x73, 0x3a, 0x2f, 0x2f, 0x67, 0x67, 0x6d, 0x6c, + 0x2e, 0x61, 0x69, 0x22, 0x3e, 0x67, 0x67, 0x6d, 0x6c, 0x2e, 0x61, 0x69, + 0x3c, 0x2f, 0x61, 0x3e, 0x3c, 0x2f, 0x70, 0x3e, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x6f, 0x6f, 0x74, 0x65, + 0x72, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, + 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x28, 0x68, 0x28, 0x41, 0x70, 0x70, + 0x29, 0x2c, 0x20, 0x64, 0x6f, 0x63, 0x75, 0x6d, 0x65, 0x6e, 0x74, 0x2e, + 0x62, 0x6f, 0x64, 0x79, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x3c, 0x2f, 0x73, + 0x63, 0x72, 0x69, 0x70, 0x74, 0x3e, 0x0a, 0x3c, 0x2f, 0x68, 0x65, 0x61, + 0x64, 0x3e, 0x0a, 0x0a, 0x3c, 0x62, 0x6f, 0x64, 0x79, 0x3e, 0x0a, 0x3c, + 0x2f, 0x62, 0x6f, 0x64, 0x79, 0x3e, 0x0a, 0x0a, 0x3c, 0x2f, 0x68, 0x74, + 0x6d, 0x6c, 0x3e, 0x0a +}; +unsigned int index_html_len = 10108; diff --git a/examples/server/index.js.hpp b/examples/server/index.js.hpp new file mode 100644 index 000000000..a3b5be6d8 --- /dev/null +++ b/examples/server/index.js.hpp @@ -0,0 +1,1851 @@ +unsigned char index_js[] = { + 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x74, 0x28, 0x29, + 0x7b, 0x74, 0x68, 0x72, 0x6f, 0x77, 0x20, 0x6e, 0x65, 0x77, 0x20, 0x45, + 0x72, 0x72, 0x6f, 0x72, 0x28, 0x22, 0x43, 0x79, 0x63, 0x6c, 0x65, 0x20, + 0x64, 0x65, 0x74, 0x65, 0x63, 0x74, 0x65, 0x64, 0x22, 0x29, 0x7d, 0x66, + 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x6e, 0x28, 0x29, 0x7b, + 0x69, 0x66, 0x28, 0x6f, 0x3e, 0x31, 0x29, 0x7b, 0x6f, 0x2d, 0x2d, 0x3b, + 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x7d, 0x6c, 0x65, 0x74, 0x20, 0x74, + 0x2c, 0x6e, 0x3d, 0x21, 0x31, 0x3b, 0x77, 0x68, 0x69, 0x6c, 0x65, 0x28, + 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x5f, 0x29, 0x7b, + 0x6c, 0x65, 0x74, 0x20, 0x69, 0x3d, 0x5f, 0x3b, 0x5f, 0x3d, 0x76, 0x6f, + 0x69, 0x64, 0x20, 0x30, 0x3b, 0x72, 0x2b, 0x2b, 0x3b, 0x77, 0x68, 0x69, + 0x6c, 0x65, 0x28, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, + 0x69, 0x29, 0x7b, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x5f, 0x3d, 0x69, + 0x2e, 0x6f, 0x3b, 0x69, 0x2e, 0x6f, 0x3d, 0x76, 0x6f, 0x69, 0x64, 0x20, + 0x30, 0x3b, 0x69, 0x2e, 0x66, 0x26, 0x3d, 0x2d, 0x33, 0x3b, 0x69, 0x66, + 0x28, 0x21, 0x28, 0x38, 0x26, 0x69, 0x2e, 0x66, 0x29, 0x26, 0x26, 0x63, + 0x28, 0x69, 0x29, 0x29, 0x74, 0x72, 0x79, 0x7b, 0x69, 0x2e, 0x63, 0x28, + 0x29, 0x7d, 0x63, 0x61, 0x74, 0x63, 0x68, 0x28, 0x65, 0x29, 0x7b, 0x69, + 0x66, 0x28, 0x21, 0x6e, 0x29, 0x7b, 0x74, 0x3d, 0x65, 0x3b, 0x6e, 0x3d, + 0x21, 0x30, 0x7d, 0x7d, 0x69, 0x3d, 0x5f, 0x7d, 0x7d, 0x72, 0x3d, 0x30, + 0x3b, 0x6f, 0x2d, 0x2d, 0x3b, 0x69, 0x66, 0x28, 0x6e, 0x29, 0x74, 0x68, + 0x72, 0x6f, 0x77, 0x20, 0x74, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, + 0x6f, 0x6e, 0x20, 0x65, 0x28, 0x74, 0x29, 0x7b, 0x69, 0x66, 0x28, 0x6f, + 0x3e, 0x30, 0x29, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x74, 0x28, + 0x29, 0x3b, 0x6f, 0x2b, 0x2b, 0x3b, 0x74, 0x72, 0x79, 0x7b, 0x72, 0x65, + 0x74, 0x75, 0x72, 0x6e, 0x20, 0x74, 0x28, 0x29, 0x7d, 0x66, 0x69, 0x6e, + 0x61, 0x6c, 0x6c, 0x79, 0x7b, 0x6e, 0x28, 0x29, 0x7d, 0x7d, 0x6c, 0x65, + 0x74, 0x20, 0x69, 0x2c, 0x5f, 0x2c, 0x6f, 0x3d, 0x30, 0x2c, 0x72, 0x3d, + 0x30, 0x2c, 0x75, 0x3d, 0x30, 0x3b, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, + 0x6f, 0x6e, 0x20, 0x6c, 0x28, 0x74, 0x29, 0x7b, 0x69, 0x66, 0x28, 0x76, + 0x6f, 0x69, 0x64, 0x20, 0x30, 0x3d, 0x3d, 0x3d, 0x69, 0x29, 0x72, 0x65, + 0x74, 0x75, 0x72, 0x6e, 0x3b, 0x6c, 0x65, 0x74, 0x20, 0x6e, 0x3d, 0x74, + 0x2e, 0x6e, 0x3b, 0x69, 0x66, 0x28, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, + 0x3d, 0x3d, 0x3d, 0x6e, 0x7c, 0x7c, 0x6e, 0x2e, 0x74, 0x21, 0x3d, 0x3d, + 0x69, 0x29, 0x7b, 0x6e, 0x3d, 0x7b, 0x69, 0x3a, 0x30, 0x2c, 0x53, 0x3a, + 0x74, 0x2c, 0x70, 0x3a, 0x69, 0x2e, 0x73, 0x2c, 0x6e, 0x3a, 0x76, 0x6f, + 0x69, 0x64, 0x20, 0x30, 0x2c, 0x74, 0x3a, 0x69, 0x2c, 0x65, 0x3a, 0x76, + 0x6f, 0x69, 0x64, 0x20, 0x30, 0x2c, 0x78, 0x3a, 0x76, 0x6f, 0x69, 0x64, + 0x20, 0x30, 0x2c, 0x72, 0x3a, 0x6e, 0x7d, 0x3b, 0x69, 0x66, 0x28, 0x76, + 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x69, 0x2e, 0x73, 0x29, + 0x69, 0x2e, 0x73, 0x2e, 0x6e, 0x3d, 0x6e, 0x3b, 0x69, 0x2e, 0x73, 0x3d, + 0x6e, 0x3b, 0x74, 0x2e, 0x6e, 0x3d, 0x6e, 0x3b, 0x69, 0x66, 0x28, 0x33, + 0x32, 0x26, 0x69, 0x2e, 0x66, 0x29, 0x74, 0x2e, 0x53, 0x28, 0x6e, 0x29, + 0x3b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x6e, 0x7d, 0x65, 0x6c, + 0x73, 0x65, 0x20, 0x69, 0x66, 0x28, 0x2d, 0x31, 0x3d, 0x3d, 0x3d, 0x6e, + 0x2e, 0x69, 0x29, 0x7b, 0x6e, 0x2e, 0x69, 0x3d, 0x30, 0x3b, 0x69, 0x66, + 0x28, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x6e, 0x2e, + 0x6e, 0x29, 0x7b, 0x6e, 0x2e, 0x6e, 0x2e, 0x70, 0x3d, 0x6e, 0x2e, 0x70, + 0x3b, 0x69, 0x66, 0x28, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, + 0x3d, 0x6e, 0x2e, 0x70, 0x29, 0x6e, 0x2e, 0x70, 0x2e, 0x6e, 0x3d, 0x6e, + 0x2e, 0x6e, 0x3b, 0x6e, 0x2e, 0x70, 0x3d, 0x69, 0x2e, 0x73, 0x3b, 0x6e, + 0x2e, 0x6e, 0x3d, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x3b, 0x69, 0x2e, + 0x73, 0x2e, 0x6e, 0x3d, 0x6e, 0x3b, 0x69, 0x2e, 0x73, 0x3d, 0x6e, 0x7d, + 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x6e, 0x7d, 0x7d, 0x66, 0x75, + 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x66, 0x28, 0x74, 0x29, 0x7b, + 0x74, 0x68, 0x69, 0x73, 0x2e, 0x76, 0x3d, 0x74, 0x3b, 0x74, 0x68, 0x69, + 0x73, 0x2e, 0x69, 0x3d, 0x30, 0x3b, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x6e, + 0x3d, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x3b, 0x74, 0x68, 0x69, 0x73, + 0x2e, 0x74, 0x3d, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x7d, 0x66, 0x2e, + 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x74, 0x79, 0x70, 0x65, 0x2e, 0x68, 0x3d, + 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x29, 0x7b, 0x72, + 0x65, 0x74, 0x75, 0x72, 0x6e, 0x21, 0x30, 0x7d, 0x3b, 0x66, 0x2e, 0x70, + 0x72, 0x6f, 0x74, 0x6f, 0x74, 0x79, 0x70, 0x65, 0x2e, 0x53, 0x3d, 0x66, + 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x29, 0x7b, 0x69, + 0x66, 0x28, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x74, 0x21, 0x3d, 0x3d, 0x74, + 0x26, 0x26, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x3d, 0x3d, 0x3d, 0x74, + 0x2e, 0x65, 0x29, 0x7b, 0x74, 0x2e, 0x78, 0x3d, 0x74, 0x68, 0x69, 0x73, + 0x2e, 0x74, 0x3b, 0x69, 0x66, 0x28, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, + 0x21, 0x3d, 0x3d, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x74, 0x29, 0x74, 0x68, + 0x69, 0x73, 0x2e, 0x74, 0x2e, 0x65, 0x3d, 0x74, 0x3b, 0x74, 0x68, 0x69, + 0x73, 0x2e, 0x74, 0x3d, 0x74, 0x7d, 0x7d, 0x3b, 0x66, 0x2e, 0x70, 0x72, + 0x6f, 0x74, 0x6f, 0x74, 0x79, 0x70, 0x65, 0x2e, 0x55, 0x3d, 0x66, 0x75, + 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x29, 0x7b, 0x69, 0x66, + 0x28, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x74, 0x68, + 0x69, 0x73, 0x2e, 0x74, 0x29, 0x7b, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, + 0x6e, 0x3d, 0x74, 0x2e, 0x65, 0x2c, 0x65, 0x3d, 0x74, 0x2e, 0x78, 0x3b, + 0x69, 0x66, 0x28, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, + 0x6e, 0x29, 0x7b, 0x6e, 0x2e, 0x78, 0x3d, 0x65, 0x3b, 0x74, 0x2e, 0x65, + 0x3d, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x7d, 0x69, 0x66, 0x28, 0x76, + 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x65, 0x29, 0x7b, 0x65, + 0x2e, 0x65, 0x3d, 0x6e, 0x3b, 0x74, 0x2e, 0x78, 0x3d, 0x76, 0x6f, 0x69, + 0x64, 0x20, 0x30, 0x7d, 0x69, 0x66, 0x28, 0x74, 0x3d, 0x3d, 0x3d, 0x74, + 0x68, 0x69, 0x73, 0x2e, 0x74, 0x29, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x74, + 0x3d, 0x65, 0x7d, 0x7d, 0x3b, 0x66, 0x2e, 0x70, 0x72, 0x6f, 0x74, 0x6f, + 0x74, 0x79, 0x70, 0x65, 0x2e, 0x73, 0x75, 0x62, 0x73, 0x63, 0x72, 0x69, + 0x62, 0x65, 0x3d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, + 0x74, 0x29, 0x7b, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6e, 0x3d, 0x74, + 0x68, 0x69, 0x73, 0x3b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x62, + 0x28, 0x28, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x29, + 0x7b, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x65, 0x3d, 0x6e, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x69, 0x3d, 0x33, 0x32, 0x26, 0x74, 0x68, + 0x69, 0x73, 0x2e, 0x66, 0x3b, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x66, 0x26, + 0x3d, 0x2d, 0x33, 0x33, 0x3b, 0x74, 0x72, 0x79, 0x7b, 0x74, 0x28, 0x65, + 0x29, 0x7d, 0x66, 0x69, 0x6e, 0x61, 0x6c, 0x6c, 0x79, 0x7b, 0x74, 0x68, + 0x69, 0x73, 0x2e, 0x66, 0x7c, 0x3d, 0x69, 0x7d, 0x7d, 0x29, 0x29, 0x7d, + 0x3b, 0x66, 0x2e, 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x74, 0x79, 0x70, 0x65, + 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x4f, 0x66, 0x3d, 0x66, 0x75, 0x6e, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x29, 0x7b, 0x72, 0x65, 0x74, 0x75, + 0x72, 0x6e, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x7d, 0x3b, 0x66, 0x2e, 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x74, 0x79, + 0x70, 0x65, 0x2e, 0x74, 0x6f, 0x53, 0x74, 0x72, 0x69, 0x6e, 0x67, 0x3d, + 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x29, 0x7b, 0x72, + 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x2b, 0x22, 0x22, 0x7d, 0x3b, 0x66, 0x2e, 0x70, + 0x72, 0x6f, 0x74, 0x6f, 0x74, 0x79, 0x70, 0x65, 0x2e, 0x74, 0x6f, 0x4a, + 0x53, 0x4f, 0x4e, 0x3d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x28, 0x29, 0x7b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x74, 0x68, + 0x69, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x7d, 0x3b, 0x66, 0x2e, + 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x74, 0x79, 0x70, 0x65, 0x2e, 0x70, 0x65, + 0x65, 0x6b, 0x3d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, + 0x29, 0x7b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x74, 0x68, 0x69, + 0x73, 0x2e, 0x76, 0x7d, 0x3b, 0x4f, 0x62, 0x6a, 0x65, 0x63, 0x74, 0x2e, + 0x64, 0x65, 0x66, 0x69, 0x6e, 0x65, 0x50, 0x72, 0x6f, 0x70, 0x65, 0x72, + 0x74, 0x79, 0x28, 0x66, 0x2e, 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x74, 0x79, + 0x70, 0x65, 0x2c, 0x22, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x22, 0x2c, 0x7b, + 0x67, 0x65, 0x74, 0x28, 0x29, 0x7b, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, + 0x74, 0x3d, 0x6c, 0x28, 0x74, 0x68, 0x69, 0x73, 0x29, 0x3b, 0x69, 0x66, + 0x28, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x74, 0x29, + 0x74, 0x2e, 0x69, 0x3d, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x69, 0x3b, 0x72, + 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x76, + 0x7d, 0x2c, 0x73, 0x65, 0x74, 0x28, 0x65, 0x29, 0x7b, 0x69, 0x66, 0x28, + 0x69, 0x20, 0x69, 0x6e, 0x73, 0x74, 0x61, 0x6e, 0x63, 0x65, 0x6f, 0x66, + 0x20, 0x70, 0x29, 0x21, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x28, 0x29, 0x7b, 0x74, 0x68, 0x72, 0x6f, 0x77, 0x20, 0x6e, 0x65, 0x77, + 0x20, 0x45, 0x72, 0x72, 0x6f, 0x72, 0x28, 0x22, 0x43, 0x6f, 0x6d, 0x70, + 0x75, 0x74, 0x65, 0x64, 0x20, 0x63, 0x61, 0x6e, 0x6e, 0x6f, 0x74, 0x20, + 0x68, 0x61, 0x76, 0x65, 0x20, 0x73, 0x69, 0x64, 0x65, 0x2d, 0x65, 0x66, + 0x66, 0x65, 0x63, 0x74, 0x73, 0x22, 0x29, 0x7d, 0x28, 0x29, 0x3b, 0x69, + 0x66, 0x28, 0x65, 0x21, 0x3d, 0x3d, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x76, + 0x29, 0x7b, 0x69, 0x66, 0x28, 0x72, 0x3e, 0x31, 0x30, 0x30, 0x29, 0x74, + 0x28, 0x29, 0x3b, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x76, 0x3d, 0x65, 0x3b, + 0x74, 0x68, 0x69, 0x73, 0x2e, 0x69, 0x2b, 0x2b, 0x3b, 0x75, 0x2b, 0x2b, + 0x3b, 0x6f, 0x2b, 0x2b, 0x3b, 0x74, 0x72, 0x79, 0x7b, 0x66, 0x6f, 0x72, + 0x28, 0x6c, 0x65, 0x74, 0x20, 0x74, 0x3d, 0x74, 0x68, 0x69, 0x73, 0x2e, + 0x74, 0x3b, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x74, + 0x3b, 0x74, 0x3d, 0x74, 0x2e, 0x78, 0x29, 0x74, 0x2e, 0x74, 0x2e, 0x4e, + 0x28, 0x29, 0x7d, 0x66, 0x69, 0x6e, 0x61, 0x6c, 0x6c, 0x79, 0x7b, 0x6e, + 0x28, 0x29, 0x7d, 0x7d, 0x7d, 0x7d, 0x29, 0x3b, 0x66, 0x75, 0x6e, 0x63, + 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x73, 0x28, 0x74, 0x29, 0x7b, 0x72, 0x65, + 0x74, 0x75, 0x72, 0x6e, 0x20, 0x6e, 0x65, 0x77, 0x20, 0x66, 0x28, 0x74, + 0x29, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x63, + 0x28, 0x74, 0x29, 0x7b, 0x66, 0x6f, 0x72, 0x28, 0x6c, 0x65, 0x74, 0x20, + 0x6e, 0x3d, 0x74, 0x2e, 0x73, 0x3b, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, + 0x21, 0x3d, 0x3d, 0x6e, 0x3b, 0x6e, 0x3d, 0x6e, 0x2e, 0x6e, 0x29, 0x69, + 0x66, 0x28, 0x6e, 0x2e, 0x53, 0x2e, 0x69, 0x21, 0x3d, 0x3d, 0x6e, 0x2e, + 0x69, 0x7c, 0x7c, 0x21, 0x6e, 0x2e, 0x53, 0x2e, 0x68, 0x28, 0x29, 0x7c, + 0x7c, 0x6e, 0x2e, 0x53, 0x2e, 0x69, 0x21, 0x3d, 0x3d, 0x6e, 0x2e, 0x69, + 0x29, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x21, 0x30, 0x3b, 0x72, 0x65, + 0x74, 0x75, 0x72, 0x6e, 0x21, 0x31, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, + 0x69, 0x6f, 0x6e, 0x20, 0x68, 0x28, 0x74, 0x29, 0x7b, 0x66, 0x6f, 0x72, + 0x28, 0x6c, 0x65, 0x74, 0x20, 0x6e, 0x3d, 0x74, 0x2e, 0x73, 0x3b, 0x76, + 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x6e, 0x3b, 0x6e, 0x3d, + 0x6e, 0x2e, 0x6e, 0x29, 0x7b, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x65, + 0x3d, 0x6e, 0x2e, 0x53, 0x2e, 0x6e, 0x3b, 0x69, 0x66, 0x28, 0x76, 0x6f, + 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x65, 0x29, 0x6e, 0x2e, 0x72, + 0x3d, 0x65, 0x3b, 0x6e, 0x2e, 0x53, 0x2e, 0x6e, 0x3d, 0x6e, 0x3b, 0x6e, + 0x2e, 0x69, 0x3d, 0x2d, 0x31, 0x3b, 0x69, 0x66, 0x28, 0x76, 0x6f, 0x69, + 0x64, 0x20, 0x30, 0x3d, 0x3d, 0x3d, 0x6e, 0x2e, 0x6e, 0x29, 0x7b, 0x74, + 0x2e, 0x73, 0x3d, 0x6e, 0x3b, 0x62, 0x72, 0x65, 0x61, 0x6b, 0x7d, 0x7d, + 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x61, 0x28, + 0x74, 0x29, 0x7b, 0x6c, 0x65, 0x74, 0x20, 0x6e, 0x2c, 0x65, 0x3d, 0x74, + 0x2e, 0x73, 0x3b, 0x77, 0x68, 0x69, 0x6c, 0x65, 0x28, 0x76, 0x6f, 0x69, + 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x65, 0x29, 0x7b, 0x63, 0x6f, 0x6e, + 0x73, 0x74, 0x20, 0x74, 0x3d, 0x65, 0x2e, 0x70, 0x3b, 0x69, 0x66, 0x28, + 0x2d, 0x31, 0x3d, 0x3d, 0x3d, 0x65, 0x2e, 0x69, 0x29, 0x7b, 0x65, 0x2e, + 0x53, 0x2e, 0x55, 0x28, 0x65, 0x29, 0x3b, 0x69, 0x66, 0x28, 0x76, 0x6f, + 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x74, 0x29, 0x74, 0x2e, 0x6e, + 0x3d, 0x65, 0x2e, 0x6e, 0x3b, 0x69, 0x66, 0x28, 0x76, 0x6f, 0x69, 0x64, + 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x65, 0x2e, 0x6e, 0x29, 0x65, 0x2e, 0x6e, + 0x2e, 0x70, 0x3d, 0x74, 0x7d, 0x65, 0x6c, 0x73, 0x65, 0x20, 0x6e, 0x3d, + 0x65, 0x3b, 0x65, 0x2e, 0x53, 0x2e, 0x6e, 0x3d, 0x65, 0x2e, 0x72, 0x3b, + 0x69, 0x66, 0x28, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, + 0x65, 0x2e, 0x72, 0x29, 0x65, 0x2e, 0x72, 0x3d, 0x76, 0x6f, 0x69, 0x64, + 0x20, 0x30, 0x3b, 0x65, 0x3d, 0x74, 0x7d, 0x74, 0x2e, 0x73, 0x3d, 0x6e, + 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x70, 0x28, + 0x74, 0x29, 0x7b, 0x66, 0x2e, 0x63, 0x61, 0x6c, 0x6c, 0x28, 0x74, 0x68, + 0x69, 0x73, 0x2c, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x29, 0x3b, 0x74, + 0x68, 0x69, 0x73, 0x2e, 0x78, 0x3d, 0x74, 0x3b, 0x74, 0x68, 0x69, 0x73, + 0x2e, 0x73, 0x3d, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x3b, 0x74, 0x68, + 0x69, 0x73, 0x2e, 0x67, 0x3d, 0x75, 0x2d, 0x31, 0x3b, 0x74, 0x68, 0x69, + 0x73, 0x2e, 0x66, 0x3d, 0x34, 0x7d, 0x28, 0x70, 0x2e, 0x70, 0x72, 0x6f, + 0x74, 0x6f, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x6e, 0x65, 0x77, 0x20, 0x66, + 0x29, 0x2e, 0x68, 0x3d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x28, 0x29, 0x7b, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x66, 0x26, 0x3d, 0x2d, + 0x33, 0x3b, 0x69, 0x66, 0x28, 0x31, 0x26, 0x74, 0x68, 0x69, 0x73, 0x2e, + 0x66, 0x29, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x21, 0x31, 0x3b, 0x69, + 0x66, 0x28, 0x33, 0x32, 0x3d, 0x3d, 0x28, 0x33, 0x36, 0x26, 0x74, 0x68, + 0x69, 0x73, 0x2e, 0x66, 0x29, 0x29, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, + 0x21, 0x30, 0x3b, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x66, 0x26, 0x3d, 0x2d, + 0x35, 0x3b, 0x69, 0x66, 0x28, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x67, 0x3d, + 0x3d, 0x3d, 0x75, 0x29, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x21, 0x30, + 0x3b, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x67, 0x3d, 0x75, 0x3b, 0x74, 0x68, + 0x69, 0x73, 0x2e, 0x66, 0x7c, 0x3d, 0x31, 0x3b, 0x69, 0x66, 0x28, 0x74, + 0x68, 0x69, 0x73, 0x2e, 0x69, 0x3e, 0x30, 0x26, 0x26, 0x21, 0x63, 0x28, + 0x74, 0x68, 0x69, 0x73, 0x29, 0x29, 0x7b, 0x74, 0x68, 0x69, 0x73, 0x2e, + 0x66, 0x26, 0x3d, 0x2d, 0x32, 0x3b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, + 0x21, 0x30, 0x7d, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x74, 0x3d, 0x69, + 0x3b, 0x74, 0x72, 0x79, 0x7b, 0x68, 0x28, 0x74, 0x68, 0x69, 0x73, 0x29, + 0x3b, 0x69, 0x3d, 0x74, 0x68, 0x69, 0x73, 0x3b, 0x63, 0x6f, 0x6e, 0x73, + 0x74, 0x20, 0x74, 0x3d, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x78, 0x28, 0x29, + 0x3b, 0x69, 0x66, 0x28, 0x31, 0x36, 0x26, 0x74, 0x68, 0x69, 0x73, 0x2e, + 0x66, 0x7c, 0x7c, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x76, 0x21, 0x3d, 0x3d, + 0x74, 0x7c, 0x7c, 0x30, 0x3d, 0x3d, 0x3d, 0x74, 0x68, 0x69, 0x73, 0x2e, + 0x69, 0x29, 0x7b, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x76, 0x3d, 0x74, 0x3b, + 0x74, 0x68, 0x69, 0x73, 0x2e, 0x66, 0x26, 0x3d, 0x2d, 0x31, 0x37, 0x3b, + 0x74, 0x68, 0x69, 0x73, 0x2e, 0x69, 0x2b, 0x2b, 0x7d, 0x7d, 0x63, 0x61, + 0x74, 0x63, 0x68, 0x28, 0x74, 0x29, 0x7b, 0x74, 0x68, 0x69, 0x73, 0x2e, + 0x76, 0x3d, 0x74, 0x3b, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x66, 0x7c, 0x3d, + 0x31, 0x36, 0x3b, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x69, 0x2b, 0x2b, 0x7d, + 0x69, 0x3d, 0x74, 0x3b, 0x61, 0x28, 0x74, 0x68, 0x69, 0x73, 0x29, 0x3b, + 0x74, 0x68, 0x69, 0x73, 0x2e, 0x66, 0x26, 0x3d, 0x2d, 0x32, 0x3b, 0x72, + 0x65, 0x74, 0x75, 0x72, 0x6e, 0x21, 0x30, 0x7d, 0x3b, 0x70, 0x2e, 0x70, + 0x72, 0x6f, 0x74, 0x6f, 0x74, 0x79, 0x70, 0x65, 0x2e, 0x53, 0x3d, 0x66, + 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x29, 0x7b, 0x69, + 0x66, 0x28, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x3d, 0x3d, 0x3d, 0x74, + 0x68, 0x69, 0x73, 0x2e, 0x74, 0x29, 0x7b, 0x74, 0x68, 0x69, 0x73, 0x2e, + 0x66, 0x7c, 0x3d, 0x33, 0x36, 0x3b, 0x66, 0x6f, 0x72, 0x28, 0x6c, 0x65, + 0x74, 0x20, 0x74, 0x3d, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x73, 0x3b, 0x76, + 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x74, 0x3b, 0x74, 0x3d, + 0x74, 0x2e, 0x6e, 0x29, 0x74, 0x2e, 0x53, 0x2e, 0x53, 0x28, 0x74, 0x29, + 0x7d, 0x66, 0x2e, 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x74, 0x79, 0x70, 0x65, + 0x2e, 0x53, 0x2e, 0x63, 0x61, 0x6c, 0x6c, 0x28, 0x74, 0x68, 0x69, 0x73, + 0x2c, 0x74, 0x29, 0x7d, 0x3b, 0x70, 0x2e, 0x70, 0x72, 0x6f, 0x74, 0x6f, + 0x74, 0x79, 0x70, 0x65, 0x2e, 0x55, 0x3d, 0x66, 0x75, 0x6e, 0x63, 0x74, + 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x29, 0x7b, 0x69, 0x66, 0x28, 0x76, 0x6f, + 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x74, 0x68, 0x69, 0x73, 0x2e, + 0x74, 0x29, 0x7b, 0x66, 0x2e, 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x74, 0x79, + 0x70, 0x65, 0x2e, 0x55, 0x2e, 0x63, 0x61, 0x6c, 0x6c, 0x28, 0x74, 0x68, + 0x69, 0x73, 0x2c, 0x74, 0x29, 0x3b, 0x69, 0x66, 0x28, 0x76, 0x6f, 0x69, + 0x64, 0x20, 0x30, 0x3d, 0x3d, 0x3d, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x74, + 0x29, 0x7b, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x66, 0x26, 0x3d, 0x2d, 0x33, + 0x33, 0x3b, 0x66, 0x6f, 0x72, 0x28, 0x6c, 0x65, 0x74, 0x20, 0x74, 0x3d, + 0x74, 0x68, 0x69, 0x73, 0x2e, 0x73, 0x3b, 0x76, 0x6f, 0x69, 0x64, 0x20, + 0x30, 0x21, 0x3d, 0x3d, 0x74, 0x3b, 0x74, 0x3d, 0x74, 0x2e, 0x6e, 0x29, + 0x74, 0x2e, 0x53, 0x2e, 0x55, 0x28, 0x74, 0x29, 0x7d, 0x7d, 0x7d, 0x3b, + 0x70, 0x2e, 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x74, 0x79, 0x70, 0x65, 0x2e, + 0x4e, 0x3d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x29, + 0x7b, 0x69, 0x66, 0x28, 0x21, 0x28, 0x32, 0x26, 0x74, 0x68, 0x69, 0x73, + 0x2e, 0x66, 0x29, 0x29, 0x7b, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x66, 0x7c, + 0x3d, 0x36, 0x3b, 0x66, 0x6f, 0x72, 0x28, 0x6c, 0x65, 0x74, 0x20, 0x74, + 0x3d, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x74, 0x3b, 0x76, 0x6f, 0x69, 0x64, + 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x74, 0x3b, 0x74, 0x3d, 0x74, 0x2e, 0x78, + 0x29, 0x74, 0x2e, 0x74, 0x2e, 0x4e, 0x28, 0x29, 0x7d, 0x7d, 0x3b, 0x70, + 0x2e, 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x74, 0x79, 0x70, 0x65, 0x2e, 0x70, + 0x65, 0x65, 0x6b, 0x3d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x28, 0x29, 0x7b, 0x69, 0x66, 0x28, 0x21, 0x74, 0x68, 0x69, 0x73, 0x2e, + 0x68, 0x28, 0x29, 0x29, 0x74, 0x28, 0x29, 0x3b, 0x69, 0x66, 0x28, 0x31, + 0x36, 0x26, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x66, 0x29, 0x74, 0x68, 0x72, + 0x6f, 0x77, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x76, 0x3b, 0x72, 0x65, + 0x74, 0x75, 0x72, 0x6e, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x76, 0x7d, + 0x3b, 0x4f, 0x62, 0x6a, 0x65, 0x63, 0x74, 0x2e, 0x64, 0x65, 0x66, 0x69, + 0x6e, 0x65, 0x50, 0x72, 0x6f, 0x70, 0x65, 0x72, 0x74, 0x79, 0x28, 0x70, + 0x2e, 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x74, 0x79, 0x70, 0x65, 0x2c, 0x22, + 0x76, 0x61, 0x6c, 0x75, 0x65, 0x22, 0x2c, 0x7b, 0x67, 0x65, 0x74, 0x28, + 0x29, 0x7b, 0x69, 0x66, 0x28, 0x31, 0x26, 0x74, 0x68, 0x69, 0x73, 0x2e, + 0x66, 0x29, 0x74, 0x28, 0x29, 0x3b, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, + 0x6e, 0x3d, 0x6c, 0x28, 0x74, 0x68, 0x69, 0x73, 0x29, 0x3b, 0x74, 0x68, + 0x69, 0x73, 0x2e, 0x68, 0x28, 0x29, 0x3b, 0x69, 0x66, 0x28, 0x76, 0x6f, + 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x6e, 0x29, 0x6e, 0x2e, 0x69, + 0x3d, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x69, 0x3b, 0x69, 0x66, 0x28, 0x31, + 0x36, 0x26, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x66, 0x29, 0x74, 0x68, 0x72, + 0x6f, 0x77, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x76, 0x3b, 0x72, 0x65, + 0x74, 0x75, 0x72, 0x6e, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x76, 0x7d, + 0x7d, 0x29, 0x3b, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, + 0x64, 0x28, 0x74, 0x29, 0x7b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, + 0x6e, 0x65, 0x77, 0x20, 0x70, 0x28, 0x74, 0x29, 0x7d, 0x66, 0x75, 0x6e, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x76, 0x28, 0x74, 0x29, 0x7b, 0x63, + 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x65, 0x3d, 0x74, 0x2e, 0x75, 0x3b, 0x74, + 0x2e, 0x75, 0x3d, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x3b, 0x69, 0x66, + 0x28, 0x22, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x22, 0x3d, + 0x3d, 0x74, 0x79, 0x70, 0x65, 0x6f, 0x66, 0x20, 0x65, 0x29, 0x7b, 0x6f, + 0x2b, 0x2b, 0x3b, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x5f, 0x3d, 0x69, + 0x3b, 0x69, 0x3d, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x3b, 0x74, 0x72, + 0x79, 0x7b, 0x65, 0x28, 0x29, 0x7d, 0x63, 0x61, 0x74, 0x63, 0x68, 0x28, + 0x6e, 0x29, 0x7b, 0x74, 0x2e, 0x66, 0x26, 0x3d, 0x2d, 0x32, 0x3b, 0x74, + 0x2e, 0x66, 0x7c, 0x3d, 0x38, 0x3b, 0x79, 0x28, 0x74, 0x29, 0x3b, 0x74, + 0x68, 0x72, 0x6f, 0x77, 0x20, 0x6e, 0x7d, 0x66, 0x69, 0x6e, 0x61, 0x6c, + 0x6c, 0x79, 0x7b, 0x69, 0x3d, 0x5f, 0x3b, 0x6e, 0x28, 0x29, 0x7d, 0x7d, + 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x79, 0x28, + 0x74, 0x29, 0x7b, 0x66, 0x6f, 0x72, 0x28, 0x6c, 0x65, 0x74, 0x20, 0x6e, + 0x3d, 0x74, 0x2e, 0x73, 0x3b, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, + 0x3d, 0x3d, 0x6e, 0x3b, 0x6e, 0x3d, 0x6e, 0x2e, 0x6e, 0x29, 0x6e, 0x2e, + 0x53, 0x2e, 0x55, 0x28, 0x6e, 0x29, 0x3b, 0x74, 0x2e, 0x78, 0x3d, 0x76, + 0x6f, 0x69, 0x64, 0x20, 0x30, 0x3b, 0x74, 0x2e, 0x73, 0x3d, 0x76, 0x6f, + 0x69, 0x64, 0x20, 0x30, 0x3b, 0x76, 0x28, 0x74, 0x29, 0x7d, 0x66, 0x75, + 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x6d, 0x28, 0x74, 0x29, 0x7b, + 0x69, 0x66, 0x28, 0x69, 0x21, 0x3d, 0x3d, 0x74, 0x68, 0x69, 0x73, 0x29, + 0x74, 0x68, 0x72, 0x6f, 0x77, 0x20, 0x6e, 0x65, 0x77, 0x20, 0x45, 0x72, + 0x72, 0x6f, 0x72, 0x28, 0x22, 0x4f, 0x75, 0x74, 0x2d, 0x6f, 0x66, 0x2d, + 0x6f, 0x72, 0x64, 0x65, 0x72, 0x20, 0x65, 0x66, 0x66, 0x65, 0x63, 0x74, + 0x22, 0x29, 0x3b, 0x61, 0x28, 0x74, 0x68, 0x69, 0x73, 0x29, 0x3b, 0x69, + 0x3d, 0x74, 0x3b, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x66, 0x26, 0x3d, 0x2d, + 0x32, 0x3b, 0x69, 0x66, 0x28, 0x38, 0x26, 0x74, 0x68, 0x69, 0x73, 0x2e, + 0x66, 0x29, 0x79, 0x28, 0x74, 0x68, 0x69, 0x73, 0x29, 0x3b, 0x6e, 0x28, + 0x29, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x67, + 0x28, 0x74, 0x29, 0x7b, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x78, 0x3d, 0x74, + 0x3b, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x75, 0x3d, 0x76, 0x6f, 0x69, 0x64, + 0x20, 0x30, 0x3b, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x73, 0x3d, 0x76, 0x6f, + 0x69, 0x64, 0x20, 0x30, 0x3b, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x6f, 0x3d, + 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x3b, 0x74, 0x68, 0x69, 0x73, 0x2e, + 0x66, 0x3d, 0x33, 0x32, 0x7d, 0x67, 0x2e, 0x70, 0x72, 0x6f, 0x74, 0x6f, + 0x74, 0x79, 0x70, 0x65, 0x2e, 0x63, 0x3d, 0x66, 0x75, 0x6e, 0x63, 0x74, + 0x69, 0x6f, 0x6e, 0x28, 0x29, 0x7b, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, + 0x74, 0x3d, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x53, 0x28, 0x29, 0x3b, 0x74, + 0x72, 0x79, 0x7b, 0x69, 0x66, 0x28, 0x38, 0x26, 0x74, 0x68, 0x69, 0x73, + 0x2e, 0x66, 0x29, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x3b, 0x69, 0x66, + 0x28, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x3d, 0x3d, 0x3d, 0x74, 0x68, + 0x69, 0x73, 0x2e, 0x78, 0x29, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x3b, + 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6e, 0x3d, 0x74, 0x68, 0x69, 0x73, + 0x2e, 0x78, 0x28, 0x29, 0x3b, 0x69, 0x66, 0x28, 0x22, 0x66, 0x75, 0x6e, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x22, 0x3d, 0x3d, 0x74, 0x79, 0x70, 0x65, + 0x6f, 0x66, 0x20, 0x6e, 0x29, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x75, 0x3d, + 0x6e, 0x7d, 0x66, 0x69, 0x6e, 0x61, 0x6c, 0x6c, 0x79, 0x7b, 0x74, 0x28, + 0x29, 0x7d, 0x7d, 0x3b, 0x67, 0x2e, 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x74, + 0x79, 0x70, 0x65, 0x2e, 0x53, 0x3d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, + 0x6f, 0x6e, 0x28, 0x29, 0x7b, 0x69, 0x66, 0x28, 0x31, 0x26, 0x74, 0x68, + 0x69, 0x73, 0x2e, 0x66, 0x29, 0x74, 0x28, 0x29, 0x3b, 0x74, 0x68, 0x69, + 0x73, 0x2e, 0x66, 0x7c, 0x3d, 0x31, 0x3b, 0x74, 0x68, 0x69, 0x73, 0x2e, + 0x66, 0x26, 0x3d, 0x2d, 0x39, 0x3b, 0x76, 0x28, 0x74, 0x68, 0x69, 0x73, + 0x29, 0x3b, 0x68, 0x28, 0x74, 0x68, 0x69, 0x73, 0x29, 0x3b, 0x6f, 0x2b, + 0x2b, 0x3b, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6e, 0x3d, 0x69, 0x3b, + 0x69, 0x3d, 0x74, 0x68, 0x69, 0x73, 0x3b, 0x72, 0x65, 0x74, 0x75, 0x72, + 0x6e, 0x20, 0x6d, 0x2e, 0x62, 0x69, 0x6e, 0x64, 0x28, 0x74, 0x68, 0x69, + 0x73, 0x2c, 0x6e, 0x29, 0x7d, 0x3b, 0x67, 0x2e, 0x70, 0x72, 0x6f, 0x74, + 0x6f, 0x74, 0x79, 0x70, 0x65, 0x2e, 0x4e, 0x3d, 0x66, 0x75, 0x6e, 0x63, + 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x29, 0x7b, 0x69, 0x66, 0x28, 0x21, 0x28, + 0x32, 0x26, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x66, 0x29, 0x29, 0x7b, 0x74, + 0x68, 0x69, 0x73, 0x2e, 0x66, 0x7c, 0x3d, 0x32, 0x3b, 0x74, 0x68, 0x69, + 0x73, 0x2e, 0x6f, 0x3d, 0x5f, 0x3b, 0x5f, 0x3d, 0x74, 0x68, 0x69, 0x73, + 0x7d, 0x7d, 0x3b, 0x67, 0x2e, 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x74, 0x79, + 0x70, 0x65, 0x2e, 0x64, 0x3d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, + 0x6e, 0x28, 0x29, 0x7b, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x66, 0x7c, 0x3d, + 0x38, 0x3b, 0x69, 0x66, 0x28, 0x21, 0x28, 0x31, 0x26, 0x74, 0x68, 0x69, + 0x73, 0x2e, 0x66, 0x29, 0x29, 0x79, 0x28, 0x74, 0x68, 0x69, 0x73, 0x29, + 0x7d, 0x3b, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x62, + 0x28, 0x74, 0x29, 0x7b, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6e, 0x3d, + 0x6e, 0x65, 0x77, 0x20, 0x67, 0x28, 0x74, 0x29, 0x3b, 0x74, 0x72, 0x79, + 0x7b, 0x6e, 0x2e, 0x63, 0x28, 0x29, 0x7d, 0x63, 0x61, 0x74, 0x63, 0x68, + 0x28, 0x74, 0x29, 0x7b, 0x6e, 0x2e, 0x64, 0x28, 0x29, 0x3b, 0x74, 0x68, + 0x72, 0x6f, 0x77, 0x20, 0x74, 0x7d, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, + 0x20, 0x6e, 0x2e, 0x64, 0x2e, 0x62, 0x69, 0x6e, 0x64, 0x28, 0x6e, 0x29, + 0x7d, 0x76, 0x61, 0x72, 0x20, 0x6b, 0x2c, 0x53, 0x2c, 0x78, 0x2c, 0x77, + 0x2c, 0x43, 0x2c, 0x45, 0x2c, 0x55, 0x2c, 0x48, 0x2c, 0x4e, 0x2c, 0x50, + 0x3d, 0x7b, 0x7d, 0x2c, 0x44, 0x3d, 0x5b, 0x5d, 0x2c, 0x24, 0x3d, 0x2f, + 0x61, 0x63, 0x69, 0x74, 0x7c, 0x65, 0x78, 0x28, 0x3f, 0x3a, 0x73, 0x7c, + 0x67, 0x7c, 0x6e, 0x7c, 0x70, 0x7c, 0x24, 0x29, 0x7c, 0x72, 0x70, 0x68, + 0x7c, 0x67, 0x72, 0x69, 0x64, 0x7c, 0x6f, 0x77, 0x73, 0x7c, 0x6d, 0x6e, + 0x63, 0x7c, 0x6e, 0x74, 0x77, 0x7c, 0x69, 0x6e, 0x65, 0x5b, 0x63, 0x68, + 0x5d, 0x7c, 0x7a, 0x6f, 0x6f, 0x7c, 0x5e, 0x6f, 0x72, 0x64, 0x7c, 0x69, + 0x74, 0x65, 0x72, 0x61, 0x2f, 0x69, 0x2c, 0x54, 0x3d, 0x41, 0x72, 0x72, + 0x61, 0x79, 0x2e, 0x69, 0x73, 0x41, 0x72, 0x72, 0x61, 0x79, 0x3b, 0x66, + 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x56, 0x28, 0x74, 0x2c, + 0x6e, 0x29, 0x7b, 0x66, 0x6f, 0x72, 0x28, 0x76, 0x61, 0x72, 0x20, 0x65, + 0x20, 0x69, 0x6e, 0x20, 0x6e, 0x29, 0x74, 0x5b, 0x65, 0x5d, 0x3d, 0x6e, + 0x5b, 0x65, 0x5d, 0x3b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x74, + 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x41, 0x28, + 0x74, 0x29, 0x7b, 0x76, 0x61, 0x72, 0x20, 0x6e, 0x3d, 0x74, 0x2e, 0x70, + 0x61, 0x72, 0x65, 0x6e, 0x74, 0x4e, 0x6f, 0x64, 0x65, 0x3b, 0x6e, 0x26, + 0x26, 0x6e, 0x2e, 0x72, 0x65, 0x6d, 0x6f, 0x76, 0x65, 0x43, 0x68, 0x69, + 0x6c, 0x64, 0x28, 0x74, 0x29, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, + 0x6f, 0x6e, 0x20, 0x46, 0x28, 0x74, 0x2c, 0x6e, 0x2c, 0x65, 0x29, 0x7b, + 0x76, 0x61, 0x72, 0x20, 0x69, 0x2c, 0x5f, 0x2c, 0x6f, 0x2c, 0x72, 0x3d, + 0x7b, 0x7d, 0x3b, 0x66, 0x6f, 0x72, 0x28, 0x6f, 0x20, 0x69, 0x6e, 0x20, + 0x6e, 0x29, 0x22, 0x6b, 0x65, 0x79, 0x22, 0x3d, 0x3d, 0x6f, 0x3f, 0x69, + 0x3d, 0x6e, 0x5b, 0x6f, 0x5d, 0x3a, 0x22, 0x72, 0x65, 0x66, 0x22, 0x3d, + 0x3d, 0x6f, 0x3f, 0x5f, 0x3d, 0x6e, 0x5b, 0x6f, 0x5d, 0x3a, 0x72, 0x5b, + 0x6f, 0x5d, 0x3d, 0x6e, 0x5b, 0x6f, 0x5d, 0x3b, 0x69, 0x66, 0x28, 0x61, + 0x72, 0x67, 0x75, 0x6d, 0x65, 0x6e, 0x74, 0x73, 0x2e, 0x6c, 0x65, 0x6e, + 0x67, 0x74, 0x68, 0x3e, 0x32, 0x26, 0x26, 0x28, 0x72, 0x2e, 0x63, 0x68, + 0x69, 0x6c, 0x64, 0x72, 0x65, 0x6e, 0x3d, 0x61, 0x72, 0x67, 0x75, 0x6d, + 0x65, 0x6e, 0x74, 0x73, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x3e, + 0x33, 0x3f, 0x6b, 0x2e, 0x63, 0x61, 0x6c, 0x6c, 0x28, 0x61, 0x72, 0x67, + 0x75, 0x6d, 0x65, 0x6e, 0x74, 0x73, 0x2c, 0x32, 0x29, 0x3a, 0x65, 0x29, + 0x2c, 0x22, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x22, 0x3d, + 0x3d, 0x74, 0x79, 0x70, 0x65, 0x6f, 0x66, 0x20, 0x74, 0x26, 0x26, 0x6e, + 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x74, 0x2e, 0x64, 0x65, 0x66, 0x61, 0x75, + 0x6c, 0x74, 0x50, 0x72, 0x6f, 0x70, 0x73, 0x29, 0x66, 0x6f, 0x72, 0x28, + 0x6f, 0x20, 0x69, 0x6e, 0x20, 0x74, 0x2e, 0x64, 0x65, 0x66, 0x61, 0x75, + 0x6c, 0x74, 0x50, 0x72, 0x6f, 0x70, 0x73, 0x29, 0x76, 0x6f, 0x69, 0x64, + 0x20, 0x30, 0x3d, 0x3d, 0x3d, 0x72, 0x5b, 0x6f, 0x5d, 0x26, 0x26, 0x28, + 0x72, 0x5b, 0x6f, 0x5d, 0x3d, 0x74, 0x2e, 0x64, 0x65, 0x66, 0x61, 0x75, + 0x6c, 0x74, 0x50, 0x72, 0x6f, 0x70, 0x73, 0x5b, 0x6f, 0x5d, 0x29, 0x3b, + 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x4d, 0x28, 0x74, 0x2c, 0x72, + 0x2c, 0x69, 0x2c, 0x5f, 0x2c, 0x6e, 0x75, 0x6c, 0x6c, 0x29, 0x7d, 0x66, + 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x4d, 0x28, 0x74, 0x2c, + 0x6e, 0x2c, 0x65, 0x2c, 0x69, 0x2c, 0x5f, 0x29, 0x7b, 0x76, 0x61, 0x72, + 0x20, 0x6f, 0x3d, 0x7b, 0x74, 0x79, 0x70, 0x65, 0x3a, 0x74, 0x2c, 0x70, + 0x72, 0x6f, 0x70, 0x73, 0x3a, 0x6e, 0x2c, 0x6b, 0x65, 0x79, 0x3a, 0x65, + 0x2c, 0x72, 0x65, 0x66, 0x3a, 0x69, 0x2c, 0x5f, 0x5f, 0x6b, 0x3a, 0x6e, + 0x75, 0x6c, 0x6c, 0x2c, 0x5f, 0x5f, 0x3a, 0x6e, 0x75, 0x6c, 0x6c, 0x2c, + 0x5f, 0x5f, 0x62, 0x3a, 0x30, 0x2c, 0x5f, 0x5f, 0x65, 0x3a, 0x6e, 0x75, + 0x6c, 0x6c, 0x2c, 0x5f, 0x5f, 0x64, 0x3a, 0x76, 0x6f, 0x69, 0x64, 0x20, + 0x30, 0x2c, 0x5f, 0x5f, 0x63, 0x3a, 0x6e, 0x75, 0x6c, 0x6c, 0x2c, 0x5f, + 0x5f, 0x68, 0x3a, 0x6e, 0x75, 0x6c, 0x6c, 0x2c, 0x63, 0x6f, 0x6e, 0x73, + 0x74, 0x72, 0x75, 0x63, 0x74, 0x6f, 0x72, 0x3a, 0x76, 0x6f, 0x69, 0x64, + 0x20, 0x30, 0x2c, 0x5f, 0x5f, 0x76, 0x3a, 0x6e, 0x75, 0x6c, 0x6c, 0x3d, + 0x3d, 0x5f, 0x3f, 0x2b, 0x2b, 0x78, 0x3a, 0x5f, 0x7d, 0x3b, 0x72, 0x65, + 0x74, 0x75, 0x72, 0x6e, 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x3d, 0x3d, 0x5f, + 0x26, 0x26, 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x53, 0x2e, 0x76, 0x6e, + 0x6f, 0x64, 0x65, 0x26, 0x26, 0x53, 0x2e, 0x76, 0x6e, 0x6f, 0x64, 0x65, + 0x28, 0x6f, 0x29, 0x2c, 0x6f, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, + 0x6f, 0x6e, 0x20, 0x57, 0x28, 0x29, 0x7b, 0x72, 0x65, 0x74, 0x75, 0x72, + 0x6e, 0x7b, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x3a, 0x6e, 0x75, + 0x6c, 0x6c, 0x7d, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x20, 0x4f, 0x28, 0x74, 0x29, 0x7b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, + 0x20, 0x74, 0x2e, 0x63, 0x68, 0x69, 0x6c, 0x64, 0x72, 0x65, 0x6e, 0x7d, + 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x4c, 0x28, 0x74, + 0x2c, 0x6e, 0x29, 0x7b, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x70, 0x72, 0x6f, + 0x70, 0x73, 0x3d, 0x74, 0x2c, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x63, 0x6f, + 0x6e, 0x74, 0x65, 0x78, 0x74, 0x3d, 0x6e, 0x7d, 0x66, 0x75, 0x6e, 0x63, + 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x52, 0x28, 0x74, 0x2c, 0x6e, 0x29, 0x7b, + 0x69, 0x66, 0x28, 0x6e, 0x75, 0x6c, 0x6c, 0x3d, 0x3d, 0x6e, 0x29, 0x72, + 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x74, 0x2e, 0x5f, 0x5f, 0x3f, 0x52, + 0x28, 0x74, 0x2e, 0x5f, 0x5f, 0x2c, 0x74, 0x2e, 0x5f, 0x5f, 0x2e, 0x5f, + 0x5f, 0x6b, 0x2e, 0x69, 0x6e, 0x64, 0x65, 0x78, 0x4f, 0x66, 0x28, 0x74, + 0x29, 0x2b, 0x31, 0x29, 0x3a, 0x6e, 0x75, 0x6c, 0x6c, 0x3b, 0x66, 0x6f, + 0x72, 0x28, 0x76, 0x61, 0x72, 0x20, 0x65, 0x3b, 0x6e, 0x3c, 0x74, 0x2e, + 0x5f, 0x5f, 0x6b, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x3b, 0x6e, + 0x2b, 0x2b, 0x29, 0x69, 0x66, 0x28, 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, + 0x28, 0x65, 0x3d, 0x74, 0x2e, 0x5f, 0x5f, 0x6b, 0x5b, 0x6e, 0x5d, 0x29, + 0x26, 0x26, 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x65, 0x2e, 0x5f, 0x5f, + 0x65, 0x29, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x65, 0x2e, 0x5f, + 0x5f, 0x65, 0x3b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x22, 0x66, 0x75, + 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x22, 0x3d, 0x3d, 0x74, 0x79, 0x70, + 0x65, 0x6f, 0x66, 0x20, 0x74, 0x2e, 0x74, 0x79, 0x70, 0x65, 0x3f, 0x52, + 0x28, 0x74, 0x29, 0x3a, 0x6e, 0x75, 0x6c, 0x6c, 0x7d, 0x66, 0x75, 0x6e, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x49, 0x28, 0x74, 0x29, 0x7b, 0x76, + 0x61, 0x72, 0x20, 0x6e, 0x2c, 0x65, 0x3b, 0x69, 0x66, 0x28, 0x6e, 0x75, + 0x6c, 0x6c, 0x21, 0x3d, 0x28, 0x74, 0x3d, 0x74, 0x2e, 0x5f, 0x5f, 0x29, + 0x26, 0x26, 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x74, 0x2e, 0x5f, 0x5f, + 0x63, 0x29, 0x7b, 0x66, 0x6f, 0x72, 0x28, 0x74, 0x2e, 0x5f, 0x5f, 0x65, + 0x3d, 0x74, 0x2e, 0x5f, 0x5f, 0x63, 0x2e, 0x62, 0x61, 0x73, 0x65, 0x3d, + 0x6e, 0x75, 0x6c, 0x6c, 0x2c, 0x6e, 0x3d, 0x30, 0x3b, 0x6e, 0x3c, 0x74, + 0x2e, 0x5f, 0x5f, 0x6b, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x3b, + 0x6e, 0x2b, 0x2b, 0x29, 0x69, 0x66, 0x28, 0x6e, 0x75, 0x6c, 0x6c, 0x21, + 0x3d, 0x28, 0x65, 0x3d, 0x74, 0x2e, 0x5f, 0x5f, 0x6b, 0x5b, 0x6e, 0x5d, + 0x29, 0x26, 0x26, 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x65, 0x2e, 0x5f, + 0x5f, 0x65, 0x29, 0x7b, 0x74, 0x2e, 0x5f, 0x5f, 0x65, 0x3d, 0x74, 0x2e, + 0x5f, 0x5f, 0x63, 0x2e, 0x62, 0x61, 0x73, 0x65, 0x3d, 0x65, 0x2e, 0x5f, + 0x5f, 0x65, 0x3b, 0x62, 0x72, 0x65, 0x61, 0x6b, 0x7d, 0x72, 0x65, 0x74, + 0x75, 0x72, 0x6e, 0x20, 0x49, 0x28, 0x74, 0x29, 0x7d, 0x7d, 0x66, 0x75, + 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x6a, 0x28, 0x74, 0x29, 0x7b, + 0x28, 0x21, 0x74, 0x2e, 0x5f, 0x5f, 0x64, 0x26, 0x26, 0x28, 0x74, 0x2e, + 0x5f, 0x5f, 0x64, 0x3d, 0x21, 0x30, 0x29, 0x26, 0x26, 0x43, 0x2e, 0x70, + 0x75, 0x73, 0x68, 0x28, 0x74, 0x29, 0x26, 0x26, 0x21, 0x71, 0x2e, 0x5f, + 0x5f, 0x72, 0x2b, 0x2b, 0x7c, 0x7c, 0x45, 0x21, 0x3d, 0x3d, 0x53, 0x2e, + 0x64, 0x65, 0x62, 0x6f, 0x75, 0x6e, 0x63, 0x65, 0x52, 0x65, 0x6e, 0x64, + 0x65, 0x72, 0x69, 0x6e, 0x67, 0x29, 0x26, 0x26, 0x28, 0x28, 0x45, 0x3d, + 0x53, 0x2e, 0x64, 0x65, 0x62, 0x6f, 0x75, 0x6e, 0x63, 0x65, 0x52, 0x65, + 0x6e, 0x64, 0x65, 0x72, 0x69, 0x6e, 0x67, 0x29, 0x7c, 0x7c, 0x55, 0x29, + 0x28, 0x71, 0x29, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x20, 0x71, 0x28, 0x29, 0x7b, 0x76, 0x61, 0x72, 0x20, 0x74, 0x2c, 0x6e, + 0x2c, 0x65, 0x2c, 0x69, 0x2c, 0x5f, 0x2c, 0x6f, 0x2c, 0x72, 0x2c, 0x75, + 0x3b, 0x66, 0x6f, 0x72, 0x28, 0x43, 0x2e, 0x73, 0x6f, 0x72, 0x74, 0x28, + 0x48, 0x29, 0x3b, 0x74, 0x3d, 0x43, 0x2e, 0x73, 0x68, 0x69, 0x66, 0x74, + 0x28, 0x29, 0x3b, 0x29, 0x74, 0x2e, 0x5f, 0x5f, 0x64, 0x26, 0x26, 0x28, + 0x6e, 0x3d, 0x43, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x2c, 0x69, + 0x3d, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x2c, 0x5f, 0x3d, 0x76, 0x6f, + 0x69, 0x64, 0x20, 0x30, 0x2c, 0x72, 0x3d, 0x28, 0x6f, 0x3d, 0x28, 0x65, + 0x3d, 0x74, 0x29, 0x2e, 0x5f, 0x5f, 0x76, 0x29, 0x2e, 0x5f, 0x5f, 0x65, + 0x2c, 0x28, 0x75, 0x3d, 0x65, 0x2e, 0x5f, 0x5f, 0x50, 0x29, 0x26, 0x26, + 0x28, 0x69, 0x3d, 0x5b, 0x5d, 0x2c, 0x28, 0x5f, 0x3d, 0x56, 0x28, 0x7b, + 0x7d, 0x2c, 0x6f, 0x29, 0x29, 0x2e, 0x5f, 0x5f, 0x76, 0x3d, 0x6f, 0x2e, + 0x5f, 0x5f, 0x76, 0x2b, 0x31, 0x2c, 0x6e, 0x74, 0x28, 0x75, 0x2c, 0x6f, + 0x2c, 0x5f, 0x2c, 0x65, 0x2e, 0x5f, 0x5f, 0x6e, 0x2c, 0x76, 0x6f, 0x69, + 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x75, 0x2e, 0x6f, 0x77, 0x6e, 0x65, + 0x72, 0x53, 0x56, 0x47, 0x45, 0x6c, 0x65, 0x6d, 0x65, 0x6e, 0x74, 0x2c, + 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x6f, 0x2e, 0x5f, 0x5f, 0x68, 0x3f, + 0x5b, 0x72, 0x5d, 0x3a, 0x6e, 0x75, 0x6c, 0x6c, 0x2c, 0x69, 0x2c, 0x6e, + 0x75, 0x6c, 0x6c, 0x3d, 0x3d, 0x72, 0x3f, 0x52, 0x28, 0x6f, 0x29, 0x3a, + 0x72, 0x2c, 0x6f, 0x2e, 0x5f, 0x5f, 0x68, 0x29, 0x2c, 0x65, 0x74, 0x28, + 0x69, 0x2c, 0x6f, 0x29, 0x2c, 0x6f, 0x2e, 0x5f, 0x5f, 0x65, 0x21, 0x3d, + 0x72, 0x26, 0x26, 0x49, 0x28, 0x6f, 0x29, 0x29, 0x2c, 0x43, 0x2e, 0x6c, + 0x65, 0x6e, 0x67, 0x74, 0x68, 0x3e, 0x6e, 0x26, 0x26, 0x43, 0x2e, 0x73, + 0x6f, 0x72, 0x74, 0x28, 0x48, 0x29, 0x29, 0x3b, 0x71, 0x2e, 0x5f, 0x5f, + 0x72, 0x3d, 0x30, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x20, 0x42, 0x28, 0x74, 0x2c, 0x6e, 0x2c, 0x65, 0x2c, 0x69, 0x2c, 0x5f, + 0x2c, 0x6f, 0x2c, 0x72, 0x2c, 0x75, 0x2c, 0x6c, 0x2c, 0x66, 0x29, 0x7b, + 0x76, 0x61, 0x72, 0x20, 0x73, 0x2c, 0x63, 0x2c, 0x68, 0x2c, 0x61, 0x2c, + 0x70, 0x2c, 0x64, 0x2c, 0x76, 0x2c, 0x79, 0x3d, 0x69, 0x26, 0x26, 0x69, + 0x2e, 0x5f, 0x5f, 0x6b, 0x7c, 0x7c, 0x44, 0x2c, 0x6d, 0x3d, 0x79, 0x2e, + 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x3b, 0x66, 0x6f, 0x72, 0x28, 0x65, + 0x2e, 0x5f, 0x5f, 0x6b, 0x3d, 0x5b, 0x5d, 0x2c, 0x73, 0x3d, 0x30, 0x3b, + 0x73, 0x3c, 0x6e, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x3b, 0x73, + 0x2b, 0x2b, 0x29, 0x69, 0x66, 0x28, 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, + 0x28, 0x61, 0x3d, 0x65, 0x2e, 0x5f, 0x5f, 0x6b, 0x5b, 0x73, 0x5d, 0x3d, + 0x6e, 0x75, 0x6c, 0x6c, 0x3d, 0x3d, 0x28, 0x61, 0x3d, 0x6e, 0x5b, 0x73, + 0x5d, 0x29, 0x7c, 0x7c, 0x22, 0x62, 0x6f, 0x6f, 0x6c, 0x65, 0x61, 0x6e, + 0x22, 0x3d, 0x3d, 0x74, 0x79, 0x70, 0x65, 0x6f, 0x66, 0x20, 0x61, 0x7c, + 0x7c, 0x22, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x22, 0x3d, + 0x3d, 0x74, 0x79, 0x70, 0x65, 0x6f, 0x66, 0x20, 0x61, 0x3f, 0x6e, 0x75, + 0x6c, 0x6c, 0x3a, 0x22, 0x73, 0x74, 0x72, 0x69, 0x6e, 0x67, 0x22, 0x3d, + 0x3d, 0x74, 0x79, 0x70, 0x65, 0x6f, 0x66, 0x20, 0x61, 0x7c, 0x7c, 0x22, + 0x6e, 0x75, 0x6d, 0x62, 0x65, 0x72, 0x22, 0x3d, 0x3d, 0x74, 0x79, 0x70, + 0x65, 0x6f, 0x66, 0x20, 0x61, 0x7c, 0x7c, 0x22, 0x62, 0x69, 0x67, 0x69, + 0x6e, 0x74, 0x22, 0x3d, 0x3d, 0x74, 0x79, 0x70, 0x65, 0x6f, 0x66, 0x20, + 0x61, 0x3f, 0x4d, 0x28, 0x6e, 0x75, 0x6c, 0x6c, 0x2c, 0x61, 0x2c, 0x6e, + 0x75, 0x6c, 0x6c, 0x2c, 0x6e, 0x75, 0x6c, 0x6c, 0x2c, 0x61, 0x29, 0x3a, + 0x54, 0x28, 0x61, 0x29, 0x3f, 0x4d, 0x28, 0x4f, 0x2c, 0x7b, 0x63, 0x68, + 0x69, 0x6c, 0x64, 0x72, 0x65, 0x6e, 0x3a, 0x61, 0x7d, 0x2c, 0x6e, 0x75, + 0x6c, 0x6c, 0x2c, 0x6e, 0x75, 0x6c, 0x6c, 0x2c, 0x6e, 0x75, 0x6c, 0x6c, + 0x29, 0x3a, 0x61, 0x2e, 0x5f, 0x5f, 0x62, 0x3e, 0x30, 0x3f, 0x4d, 0x28, + 0x61, 0x2e, 0x74, 0x79, 0x70, 0x65, 0x2c, 0x61, 0x2e, 0x70, 0x72, 0x6f, + 0x70, 0x73, 0x2c, 0x61, 0x2e, 0x6b, 0x65, 0x79, 0x2c, 0x61, 0x2e, 0x72, + 0x65, 0x66, 0x3f, 0x61, 0x2e, 0x72, 0x65, 0x66, 0x3a, 0x6e, 0x75, 0x6c, + 0x6c, 0x2c, 0x61, 0x2e, 0x5f, 0x5f, 0x76, 0x29, 0x3a, 0x61, 0x29, 0x29, + 0x7b, 0x69, 0x66, 0x28, 0x61, 0x2e, 0x5f, 0x5f, 0x3d, 0x65, 0x2c, 0x61, + 0x2e, 0x5f, 0x5f, 0x62, 0x3d, 0x65, 0x2e, 0x5f, 0x5f, 0x62, 0x2b, 0x31, + 0x2c, 0x6e, 0x75, 0x6c, 0x6c, 0x3d, 0x3d, 0x3d, 0x28, 0x68, 0x3d, 0x79, + 0x5b, 0x73, 0x5d, 0x29, 0x7c, 0x7c, 0x68, 0x26, 0x26, 0x61, 0x2e, 0x6b, + 0x65, 0x79, 0x3d, 0x3d, 0x68, 0x2e, 0x6b, 0x65, 0x79, 0x26, 0x26, 0x61, + 0x2e, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x3d, 0x3d, 0x68, 0x2e, 0x74, 0x79, + 0x70, 0x65, 0x29, 0x79, 0x5b, 0x73, 0x5d, 0x3d, 0x76, 0x6f, 0x69, 0x64, + 0x20, 0x30, 0x3b, 0x65, 0x6c, 0x73, 0x65, 0x20, 0x66, 0x6f, 0x72, 0x28, + 0x63, 0x3d, 0x30, 0x3b, 0x63, 0x3c, 0x6d, 0x3b, 0x63, 0x2b, 0x2b, 0x29, + 0x7b, 0x69, 0x66, 0x28, 0x28, 0x68, 0x3d, 0x79, 0x5b, 0x63, 0x5d, 0x29, + 0x26, 0x26, 0x61, 0x2e, 0x6b, 0x65, 0x79, 0x3d, 0x3d, 0x68, 0x2e, 0x6b, + 0x65, 0x79, 0x26, 0x26, 0x61, 0x2e, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x3d, + 0x3d, 0x68, 0x2e, 0x74, 0x79, 0x70, 0x65, 0x29, 0x7b, 0x79, 0x5b, 0x63, + 0x5d, 0x3d, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x3b, 0x62, 0x72, 0x65, + 0x61, 0x6b, 0x7d, 0x68, 0x3d, 0x6e, 0x75, 0x6c, 0x6c, 0x7d, 0x6e, 0x74, + 0x28, 0x74, 0x2c, 0x61, 0x2c, 0x68, 0x3d, 0x68, 0x7c, 0x7c, 0x50, 0x2c, + 0x5f, 0x2c, 0x6f, 0x2c, 0x72, 0x2c, 0x75, 0x2c, 0x6c, 0x2c, 0x66, 0x29, + 0x2c, 0x70, 0x3d, 0x61, 0x2e, 0x5f, 0x5f, 0x65, 0x2c, 0x28, 0x63, 0x3d, + 0x61, 0x2e, 0x72, 0x65, 0x66, 0x29, 0x26, 0x26, 0x68, 0x2e, 0x72, 0x65, + 0x66, 0x21, 0x3d, 0x63, 0x26, 0x26, 0x28, 0x76, 0x7c, 0x7c, 0x28, 0x76, + 0x3d, 0x5b, 0x5d, 0x29, 0x2c, 0x68, 0x2e, 0x72, 0x65, 0x66, 0x26, 0x26, + 0x76, 0x2e, 0x70, 0x75, 0x73, 0x68, 0x28, 0x68, 0x2e, 0x72, 0x65, 0x66, + 0x2c, 0x6e, 0x75, 0x6c, 0x6c, 0x2c, 0x61, 0x29, 0x2c, 0x76, 0x2e, 0x70, + 0x75, 0x73, 0x68, 0x28, 0x63, 0x2c, 0x61, 0x2e, 0x5f, 0x5f, 0x63, 0x7c, + 0x7c, 0x70, 0x2c, 0x61, 0x29, 0x29, 0x2c, 0x6e, 0x75, 0x6c, 0x6c, 0x21, + 0x3d, 0x70, 0x3f, 0x28, 0x6e, 0x75, 0x6c, 0x6c, 0x3d, 0x3d, 0x64, 0x26, + 0x26, 0x28, 0x64, 0x3d, 0x70, 0x29, 0x2c, 0x22, 0x66, 0x75, 0x6e, 0x63, + 0x74, 0x69, 0x6f, 0x6e, 0x22, 0x3d, 0x3d, 0x74, 0x79, 0x70, 0x65, 0x6f, + 0x66, 0x20, 0x61, 0x2e, 0x74, 0x79, 0x70, 0x65, 0x26, 0x26, 0x61, 0x2e, + 0x5f, 0x5f, 0x6b, 0x3d, 0x3d, 0x3d, 0x68, 0x2e, 0x5f, 0x5f, 0x6b, 0x3f, + 0x61, 0x2e, 0x5f, 0x5f, 0x64, 0x3d, 0x6c, 0x3d, 0x47, 0x28, 0x61, 0x2c, + 0x6c, 0x2c, 0x74, 0x29, 0x3a, 0x6c, 0x3d, 0x4a, 0x28, 0x74, 0x2c, 0x61, + 0x2c, 0x68, 0x2c, 0x79, 0x2c, 0x70, 0x2c, 0x6c, 0x29, 0x2c, 0x22, 0x66, + 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x22, 0x3d, 0x3d, 0x74, 0x79, + 0x70, 0x65, 0x6f, 0x66, 0x20, 0x65, 0x2e, 0x74, 0x79, 0x70, 0x65, 0x26, + 0x26, 0x28, 0x65, 0x2e, 0x5f, 0x5f, 0x64, 0x3d, 0x6c, 0x29, 0x29, 0x3a, + 0x6c, 0x26, 0x26, 0x68, 0x2e, 0x5f, 0x5f, 0x65, 0x3d, 0x3d, 0x6c, 0x26, + 0x26, 0x6c, 0x2e, 0x70, 0x61, 0x72, 0x65, 0x6e, 0x74, 0x4e, 0x6f, 0x64, + 0x65, 0x21, 0x3d, 0x74, 0x26, 0x26, 0x28, 0x6c, 0x3d, 0x52, 0x28, 0x68, + 0x29, 0x29, 0x7d, 0x66, 0x6f, 0x72, 0x28, 0x65, 0x2e, 0x5f, 0x5f, 0x65, + 0x3d, 0x64, 0x2c, 0x73, 0x3d, 0x6d, 0x3b, 0x73, 0x2d, 0x2d, 0x3b, 0x29, + 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x79, 0x5b, 0x73, 0x5d, 0x26, 0x26, + 0x28, 0x22, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x22, 0x3d, + 0x3d, 0x74, 0x79, 0x70, 0x65, 0x6f, 0x66, 0x20, 0x65, 0x2e, 0x74, 0x79, + 0x70, 0x65, 0x26, 0x26, 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x79, 0x5b, + 0x73, 0x5d, 0x2e, 0x5f, 0x5f, 0x65, 0x26, 0x26, 0x79, 0x5b, 0x73, 0x5d, + 0x2e, 0x5f, 0x5f, 0x65, 0x3d, 0x3d, 0x65, 0x2e, 0x5f, 0x5f, 0x64, 0x26, + 0x26, 0x28, 0x65, 0x2e, 0x5f, 0x5f, 0x64, 0x3d, 0x4b, 0x28, 0x69, 0x29, + 0x2e, 0x6e, 0x65, 0x78, 0x74, 0x53, 0x69, 0x62, 0x6c, 0x69, 0x6e, 0x67, + 0x29, 0x2c, 0x6f, 0x74, 0x28, 0x79, 0x5b, 0x73, 0x5d, 0x2c, 0x79, 0x5b, + 0x73, 0x5d, 0x29, 0x29, 0x3b, 0x69, 0x66, 0x28, 0x76, 0x29, 0x66, 0x6f, + 0x72, 0x28, 0x73, 0x3d, 0x30, 0x3b, 0x73, 0x3c, 0x76, 0x2e, 0x6c, 0x65, + 0x6e, 0x67, 0x74, 0x68, 0x3b, 0x73, 0x2b, 0x2b, 0x29, 0x5f, 0x74, 0x28, + 0x76, 0x5b, 0x73, 0x5d, 0x2c, 0x76, 0x5b, 0x2b, 0x2b, 0x73, 0x5d, 0x2c, + 0x76, 0x5b, 0x2b, 0x2b, 0x73, 0x5d, 0x29, 0x7d, 0x66, 0x75, 0x6e, 0x63, + 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x47, 0x28, 0x74, 0x2c, 0x6e, 0x2c, 0x65, + 0x29, 0x7b, 0x66, 0x6f, 0x72, 0x28, 0x76, 0x61, 0x72, 0x20, 0x69, 0x2c, + 0x5f, 0x3d, 0x74, 0x2e, 0x5f, 0x5f, 0x6b, 0x2c, 0x6f, 0x3d, 0x30, 0x3b, + 0x5f, 0x26, 0x26, 0x6f, 0x3c, 0x5f, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, + 0x68, 0x3b, 0x6f, 0x2b, 0x2b, 0x29, 0x28, 0x69, 0x3d, 0x5f, 0x5b, 0x6f, + 0x5d, 0x29, 0x26, 0x26, 0x28, 0x69, 0x2e, 0x5f, 0x5f, 0x3d, 0x74, 0x2c, + 0x6e, 0x3d, 0x22, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x22, + 0x3d, 0x3d, 0x74, 0x79, 0x70, 0x65, 0x6f, 0x66, 0x20, 0x69, 0x2e, 0x74, + 0x79, 0x70, 0x65, 0x3f, 0x47, 0x28, 0x69, 0x2c, 0x6e, 0x2c, 0x65, 0x29, + 0x3a, 0x4a, 0x28, 0x65, 0x2c, 0x69, 0x2c, 0x69, 0x2c, 0x5f, 0x2c, 0x69, + 0x2e, 0x5f, 0x5f, 0x65, 0x2c, 0x6e, 0x29, 0x29, 0x3b, 0x72, 0x65, 0x74, + 0x75, 0x72, 0x6e, 0x20, 0x6e, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, + 0x6f, 0x6e, 0x20, 0x7a, 0x28, 0x74, 0x2c, 0x6e, 0x29, 0x7b, 0x72, 0x65, + 0x74, 0x75, 0x72, 0x6e, 0x20, 0x6e, 0x3d, 0x6e, 0x7c, 0x7c, 0x5b, 0x5d, + 0x2c, 0x6e, 0x75, 0x6c, 0x6c, 0x3d, 0x3d, 0x74, 0x7c, 0x7c, 0x22, 0x62, + 0x6f, 0x6f, 0x6c, 0x65, 0x61, 0x6e, 0x22, 0x3d, 0x3d, 0x74, 0x79, 0x70, + 0x65, 0x6f, 0x66, 0x20, 0x74, 0x7c, 0x7c, 0x28, 0x54, 0x28, 0x74, 0x29, + 0x3f, 0x74, 0x2e, 0x73, 0x6f, 0x6d, 0x65, 0x28, 0x28, 0x66, 0x75, 0x6e, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x29, 0x7b, 0x7a, 0x28, 0x74, + 0x2c, 0x6e, 0x29, 0x7d, 0x29, 0x29, 0x3a, 0x6e, 0x2e, 0x70, 0x75, 0x73, + 0x68, 0x28, 0x74, 0x29, 0x29, 0x2c, 0x6e, 0x7d, 0x66, 0x75, 0x6e, 0x63, + 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x4a, 0x28, 0x74, 0x2c, 0x6e, 0x2c, 0x65, + 0x2c, 0x69, 0x2c, 0x5f, 0x2c, 0x6f, 0x29, 0x7b, 0x76, 0x61, 0x72, 0x20, + 0x72, 0x2c, 0x75, 0x2c, 0x6c, 0x3b, 0x69, 0x66, 0x28, 0x76, 0x6f, 0x69, + 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x6e, 0x2e, 0x5f, 0x5f, 0x64, 0x29, + 0x72, 0x3d, 0x6e, 0x2e, 0x5f, 0x5f, 0x64, 0x2c, 0x6e, 0x2e, 0x5f, 0x5f, + 0x64, 0x3d, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x3b, 0x65, 0x6c, 0x73, + 0x65, 0x20, 0x69, 0x66, 0x28, 0x6e, 0x75, 0x6c, 0x6c, 0x3d, 0x3d, 0x65, + 0x7c, 0x7c, 0x5f, 0x21, 0x3d, 0x6f, 0x7c, 0x7c, 0x6e, 0x75, 0x6c, 0x6c, + 0x3d, 0x3d, 0x5f, 0x2e, 0x70, 0x61, 0x72, 0x65, 0x6e, 0x74, 0x4e, 0x6f, + 0x64, 0x65, 0x29, 0x74, 0x3a, 0x69, 0x66, 0x28, 0x6e, 0x75, 0x6c, 0x6c, + 0x3d, 0x3d, 0x6f, 0x7c, 0x7c, 0x6f, 0x2e, 0x70, 0x61, 0x72, 0x65, 0x6e, + 0x74, 0x4e, 0x6f, 0x64, 0x65, 0x21, 0x3d, 0x3d, 0x74, 0x29, 0x74, 0x2e, + 0x61, 0x70, 0x70, 0x65, 0x6e, 0x64, 0x43, 0x68, 0x69, 0x6c, 0x64, 0x28, + 0x5f, 0x29, 0x2c, 0x72, 0x3d, 0x6e, 0x75, 0x6c, 0x6c, 0x3b, 0x65, 0x6c, + 0x73, 0x65, 0x7b, 0x66, 0x6f, 0x72, 0x28, 0x75, 0x3d, 0x6f, 0x2c, 0x6c, + 0x3d, 0x30, 0x3b, 0x28, 0x75, 0x3d, 0x75, 0x2e, 0x6e, 0x65, 0x78, 0x74, + 0x53, 0x69, 0x62, 0x6c, 0x69, 0x6e, 0x67, 0x29, 0x26, 0x26, 0x6c, 0x3c, + 0x69, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x3b, 0x6c, 0x2b, 0x3d, + 0x31, 0x29, 0x69, 0x66, 0x28, 0x75, 0x3d, 0x3d, 0x5f, 0x29, 0x62, 0x72, + 0x65, 0x61, 0x6b, 0x20, 0x74, 0x3b, 0x74, 0x2e, 0x69, 0x6e, 0x73, 0x65, + 0x72, 0x74, 0x42, 0x65, 0x66, 0x6f, 0x72, 0x65, 0x28, 0x5f, 0x2c, 0x6f, + 0x29, 0x2c, 0x72, 0x3d, 0x6f, 0x7d, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, + 0x20, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x72, 0x3f, + 0x72, 0x3a, 0x5f, 0x2e, 0x6e, 0x65, 0x78, 0x74, 0x53, 0x69, 0x62, 0x6c, + 0x69, 0x6e, 0x67, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x20, 0x4b, 0x28, 0x74, 0x29, 0x7b, 0x76, 0x61, 0x72, 0x20, 0x6e, 0x2c, + 0x65, 0x2c, 0x69, 0x3b, 0x69, 0x66, 0x28, 0x6e, 0x75, 0x6c, 0x6c, 0x3d, + 0x3d, 0x74, 0x2e, 0x74, 0x79, 0x70, 0x65, 0x7c, 0x7c, 0x22, 0x73, 0x74, + 0x72, 0x69, 0x6e, 0x67, 0x22, 0x3d, 0x3d, 0x74, 0x79, 0x70, 0x65, 0x6f, + 0x66, 0x20, 0x74, 0x2e, 0x74, 0x79, 0x70, 0x65, 0x29, 0x72, 0x65, 0x74, + 0x75, 0x72, 0x6e, 0x20, 0x74, 0x2e, 0x5f, 0x5f, 0x65, 0x3b, 0x69, 0x66, + 0x28, 0x74, 0x2e, 0x5f, 0x5f, 0x6b, 0x29, 0x66, 0x6f, 0x72, 0x28, 0x6e, + 0x3d, 0x74, 0x2e, 0x5f, 0x5f, 0x6b, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, + 0x68, 0x2d, 0x31, 0x3b, 0x6e, 0x3e, 0x3d, 0x30, 0x3b, 0x6e, 0x2d, 0x2d, + 0x29, 0x69, 0x66, 0x28, 0x28, 0x65, 0x3d, 0x74, 0x2e, 0x5f, 0x5f, 0x6b, + 0x5b, 0x6e, 0x5d, 0x29, 0x26, 0x26, 0x28, 0x69, 0x3d, 0x4b, 0x28, 0x65, + 0x29, 0x29, 0x29, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x69, 0x3b, + 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x7d, + 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x51, 0x28, 0x74, + 0x2c, 0x6e, 0x2c, 0x65, 0x2c, 0x69, 0x2c, 0x5f, 0x29, 0x7b, 0x76, 0x61, + 0x72, 0x20, 0x6f, 0x3b, 0x66, 0x6f, 0x72, 0x28, 0x6f, 0x20, 0x69, 0x6e, + 0x20, 0x65, 0x29, 0x22, 0x63, 0x68, 0x69, 0x6c, 0x64, 0x72, 0x65, 0x6e, + 0x22, 0x3d, 0x3d, 0x3d, 0x6f, 0x7c, 0x7c, 0x22, 0x6b, 0x65, 0x79, 0x22, + 0x3d, 0x3d, 0x3d, 0x6f, 0x7c, 0x7c, 0x6f, 0x20, 0x69, 0x6e, 0x20, 0x6e, + 0x7c, 0x7c, 0x59, 0x28, 0x74, 0x2c, 0x6f, 0x2c, 0x6e, 0x75, 0x6c, 0x6c, + 0x2c, 0x65, 0x5b, 0x6f, 0x5d, 0x2c, 0x69, 0x29, 0x3b, 0x66, 0x6f, 0x72, + 0x28, 0x6f, 0x20, 0x69, 0x6e, 0x20, 0x6e, 0x29, 0x5f, 0x26, 0x26, 0x22, + 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x22, 0x21, 0x3d, 0x74, + 0x79, 0x70, 0x65, 0x6f, 0x66, 0x20, 0x6e, 0x5b, 0x6f, 0x5d, 0x7c, 0x7c, + 0x22, 0x63, 0x68, 0x69, 0x6c, 0x64, 0x72, 0x65, 0x6e, 0x22, 0x3d, 0x3d, + 0x3d, 0x6f, 0x7c, 0x7c, 0x22, 0x6b, 0x65, 0x79, 0x22, 0x3d, 0x3d, 0x3d, + 0x6f, 0x7c, 0x7c, 0x22, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x22, 0x3d, 0x3d, + 0x3d, 0x6f, 0x7c, 0x7c, 0x22, 0x63, 0x68, 0x65, 0x63, 0x6b, 0x65, 0x64, + 0x22, 0x3d, 0x3d, 0x3d, 0x6f, 0x7c, 0x7c, 0x65, 0x5b, 0x6f, 0x5d, 0x3d, + 0x3d, 0x3d, 0x6e, 0x5b, 0x6f, 0x5d, 0x7c, 0x7c, 0x59, 0x28, 0x74, 0x2c, + 0x6f, 0x2c, 0x6e, 0x5b, 0x6f, 0x5d, 0x2c, 0x65, 0x5b, 0x6f, 0x5d, 0x2c, + 0x69, 0x29, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, + 0x58, 0x28, 0x74, 0x2c, 0x6e, 0x2c, 0x65, 0x29, 0x7b, 0x22, 0x2d, 0x22, + 0x3d, 0x3d, 0x3d, 0x6e, 0x5b, 0x30, 0x5d, 0x3f, 0x74, 0x2e, 0x73, 0x65, + 0x74, 0x50, 0x72, 0x6f, 0x70, 0x65, 0x72, 0x74, 0x79, 0x28, 0x6e, 0x2c, + 0x6e, 0x75, 0x6c, 0x6c, 0x3d, 0x3d, 0x65, 0x3f, 0x22, 0x22, 0x3a, 0x65, + 0x29, 0x3a, 0x74, 0x5b, 0x6e, 0x5d, 0x3d, 0x6e, 0x75, 0x6c, 0x6c, 0x3d, + 0x3d, 0x65, 0x3f, 0x22, 0x22, 0x3a, 0x22, 0x6e, 0x75, 0x6d, 0x62, 0x65, + 0x72, 0x22, 0x21, 0x3d, 0x74, 0x79, 0x70, 0x65, 0x6f, 0x66, 0x20, 0x65, + 0x7c, 0x7c, 0x24, 0x2e, 0x74, 0x65, 0x73, 0x74, 0x28, 0x6e, 0x29, 0x3f, + 0x65, 0x3a, 0x65, 0x2b, 0x22, 0x70, 0x78, 0x22, 0x7d, 0x66, 0x75, 0x6e, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x59, 0x28, 0x74, 0x2c, 0x6e, 0x2c, + 0x65, 0x2c, 0x69, 0x2c, 0x5f, 0x29, 0x7b, 0x76, 0x61, 0x72, 0x20, 0x6f, + 0x3b, 0x74, 0x3a, 0x69, 0x66, 0x28, 0x22, 0x73, 0x74, 0x79, 0x6c, 0x65, + 0x22, 0x3d, 0x3d, 0x3d, 0x6e, 0x29, 0x69, 0x66, 0x28, 0x22, 0x73, 0x74, + 0x72, 0x69, 0x6e, 0x67, 0x22, 0x3d, 0x3d, 0x74, 0x79, 0x70, 0x65, 0x6f, + 0x66, 0x20, 0x65, 0x29, 0x74, 0x2e, 0x73, 0x74, 0x79, 0x6c, 0x65, 0x2e, + 0x63, 0x73, 0x73, 0x54, 0x65, 0x78, 0x74, 0x3d, 0x65, 0x3b, 0x65, 0x6c, + 0x73, 0x65, 0x7b, 0x69, 0x66, 0x28, 0x22, 0x73, 0x74, 0x72, 0x69, 0x6e, + 0x67, 0x22, 0x3d, 0x3d, 0x74, 0x79, 0x70, 0x65, 0x6f, 0x66, 0x20, 0x69, + 0x26, 0x26, 0x28, 0x74, 0x2e, 0x73, 0x74, 0x79, 0x6c, 0x65, 0x2e, 0x63, + 0x73, 0x73, 0x54, 0x65, 0x78, 0x74, 0x3d, 0x69, 0x3d, 0x22, 0x22, 0x29, + 0x2c, 0x69, 0x29, 0x66, 0x6f, 0x72, 0x28, 0x6e, 0x20, 0x69, 0x6e, 0x20, + 0x69, 0x29, 0x65, 0x26, 0x26, 0x6e, 0x20, 0x69, 0x6e, 0x20, 0x65, 0x7c, + 0x7c, 0x58, 0x28, 0x74, 0x2e, 0x73, 0x74, 0x79, 0x6c, 0x65, 0x2c, 0x6e, + 0x2c, 0x22, 0x22, 0x29, 0x3b, 0x69, 0x66, 0x28, 0x65, 0x29, 0x66, 0x6f, + 0x72, 0x28, 0x6e, 0x20, 0x69, 0x6e, 0x20, 0x65, 0x29, 0x69, 0x26, 0x26, + 0x65, 0x5b, 0x6e, 0x5d, 0x3d, 0x3d, 0x3d, 0x69, 0x5b, 0x6e, 0x5d, 0x7c, + 0x7c, 0x58, 0x28, 0x74, 0x2e, 0x73, 0x74, 0x79, 0x6c, 0x65, 0x2c, 0x6e, + 0x2c, 0x65, 0x5b, 0x6e, 0x5d, 0x29, 0x7d, 0x65, 0x6c, 0x73, 0x65, 0x20, + 0x69, 0x66, 0x28, 0x22, 0x6f, 0x22, 0x3d, 0x3d, 0x3d, 0x6e, 0x5b, 0x30, + 0x5d, 0x26, 0x26, 0x22, 0x6e, 0x22, 0x3d, 0x3d, 0x3d, 0x6e, 0x5b, 0x31, + 0x5d, 0x29, 0x6f, 0x3d, 0x6e, 0x21, 0x3d, 0x3d, 0x28, 0x6e, 0x3d, 0x6e, + 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x43, 0x61, + 0x70, 0x74, 0x75, 0x72, 0x65, 0x24, 0x2f, 0x2c, 0x22, 0x22, 0x29, 0x29, + 0x2c, 0x6e, 0x3d, 0x6e, 0x2e, 0x74, 0x6f, 0x4c, 0x6f, 0x77, 0x65, 0x72, + 0x43, 0x61, 0x73, 0x65, 0x28, 0x29, 0x69, 0x6e, 0x20, 0x74, 0x3f, 0x6e, + 0x2e, 0x74, 0x6f, 0x4c, 0x6f, 0x77, 0x65, 0x72, 0x43, 0x61, 0x73, 0x65, + 0x28, 0x29, 0x2e, 0x73, 0x6c, 0x69, 0x63, 0x65, 0x28, 0x32, 0x29, 0x3a, + 0x6e, 0x2e, 0x73, 0x6c, 0x69, 0x63, 0x65, 0x28, 0x32, 0x29, 0x2c, 0x74, + 0x2e, 0x6c, 0x7c, 0x7c, 0x28, 0x74, 0x2e, 0x6c, 0x3d, 0x7b, 0x7d, 0x29, + 0x2c, 0x74, 0x2e, 0x6c, 0x5b, 0x6e, 0x2b, 0x6f, 0x5d, 0x3d, 0x65, 0x2c, + 0x65, 0x3f, 0x69, 0x7c, 0x7c, 0x74, 0x2e, 0x61, 0x64, 0x64, 0x45, 0x76, + 0x65, 0x6e, 0x74, 0x4c, 0x69, 0x73, 0x74, 0x65, 0x6e, 0x65, 0x72, 0x28, + 0x6e, 0x2c, 0x6f, 0x3f, 0x74, 0x74, 0x3a, 0x5a, 0x2c, 0x6f, 0x29, 0x3a, + 0x74, 0x2e, 0x72, 0x65, 0x6d, 0x6f, 0x76, 0x65, 0x45, 0x76, 0x65, 0x6e, + 0x74, 0x4c, 0x69, 0x73, 0x74, 0x65, 0x6e, 0x65, 0x72, 0x28, 0x6e, 0x2c, + 0x6f, 0x3f, 0x74, 0x74, 0x3a, 0x5a, 0x2c, 0x6f, 0x29, 0x3b, 0x65, 0x6c, + 0x73, 0x65, 0x20, 0x69, 0x66, 0x28, 0x22, 0x64, 0x61, 0x6e, 0x67, 0x65, + 0x72, 0x6f, 0x75, 0x73, 0x6c, 0x79, 0x53, 0x65, 0x74, 0x49, 0x6e, 0x6e, + 0x65, 0x72, 0x48, 0x54, 0x4d, 0x4c, 0x22, 0x21, 0x3d, 0x3d, 0x6e, 0x29, + 0x7b, 0x69, 0x66, 0x28, 0x5f, 0x29, 0x6e, 0x3d, 0x6e, 0x2e, 0x72, 0x65, + 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x78, 0x6c, 0x69, 0x6e, 0x6b, + 0x28, 0x48, 0x7c, 0x3a, 0x68, 0x29, 0x2f, 0x2c, 0x22, 0x68, 0x22, 0x29, + 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x73, 0x4e, + 0x61, 0x6d, 0x65, 0x24, 0x2f, 0x2c, 0x22, 0x73, 0x22, 0x29, 0x3b, 0x65, + 0x6c, 0x73, 0x65, 0x20, 0x69, 0x66, 0x28, 0x22, 0x77, 0x69, 0x64, 0x74, + 0x68, 0x22, 0x21, 0x3d, 0x3d, 0x6e, 0x26, 0x26, 0x22, 0x68, 0x65, 0x69, + 0x67, 0x68, 0x74, 0x22, 0x21, 0x3d, 0x3d, 0x6e, 0x26, 0x26, 0x22, 0x68, + 0x72, 0x65, 0x66, 0x22, 0x21, 0x3d, 0x3d, 0x6e, 0x26, 0x26, 0x22, 0x6c, + 0x69, 0x73, 0x74, 0x22, 0x21, 0x3d, 0x3d, 0x6e, 0x26, 0x26, 0x22, 0x66, + 0x6f, 0x72, 0x6d, 0x22, 0x21, 0x3d, 0x3d, 0x6e, 0x26, 0x26, 0x22, 0x74, + 0x61, 0x62, 0x49, 0x6e, 0x64, 0x65, 0x78, 0x22, 0x21, 0x3d, 0x3d, 0x6e, + 0x26, 0x26, 0x22, 0x64, 0x6f, 0x77, 0x6e, 0x6c, 0x6f, 0x61, 0x64, 0x22, + 0x21, 0x3d, 0x3d, 0x6e, 0x26, 0x26, 0x22, 0x72, 0x6f, 0x77, 0x53, 0x70, + 0x61, 0x6e, 0x22, 0x21, 0x3d, 0x3d, 0x6e, 0x26, 0x26, 0x22, 0x63, 0x6f, + 0x6c, 0x53, 0x70, 0x61, 0x6e, 0x22, 0x21, 0x3d, 0x3d, 0x6e, 0x26, 0x26, + 0x6e, 0x20, 0x69, 0x6e, 0x20, 0x74, 0x29, 0x74, 0x72, 0x79, 0x7b, 0x74, + 0x5b, 0x6e, 0x5d, 0x3d, 0x6e, 0x75, 0x6c, 0x6c, 0x3d, 0x3d, 0x65, 0x3f, + 0x22, 0x22, 0x3a, 0x65, 0x3b, 0x62, 0x72, 0x65, 0x61, 0x6b, 0x20, 0x74, + 0x7d, 0x63, 0x61, 0x74, 0x63, 0x68, 0x28, 0x74, 0x29, 0x7b, 0x7d, 0x22, + 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x22, 0x3d, 0x3d, 0x74, + 0x79, 0x70, 0x65, 0x6f, 0x66, 0x20, 0x65, 0x7c, 0x7c, 0x28, 0x6e, 0x75, + 0x6c, 0x6c, 0x3d, 0x3d, 0x65, 0x7c, 0x7c, 0x21, 0x31, 0x3d, 0x3d, 0x3d, + 0x65, 0x26, 0x26, 0x22, 0x2d, 0x22, 0x21, 0x3d, 0x3d, 0x6e, 0x5b, 0x34, + 0x5d, 0x3f, 0x74, 0x2e, 0x72, 0x65, 0x6d, 0x6f, 0x76, 0x65, 0x41, 0x74, + 0x74, 0x72, 0x69, 0x62, 0x75, 0x74, 0x65, 0x28, 0x6e, 0x29, 0x3a, 0x74, + 0x2e, 0x73, 0x65, 0x74, 0x41, 0x74, 0x74, 0x72, 0x69, 0x62, 0x75, 0x74, + 0x65, 0x28, 0x6e, 0x2c, 0x65, 0x29, 0x29, 0x7d, 0x7d, 0x66, 0x75, 0x6e, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x5a, 0x28, 0x74, 0x29, 0x7b, 0x72, + 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x6c, + 0x5b, 0x74, 0x2e, 0x74, 0x79, 0x70, 0x65, 0x2b, 0x21, 0x31, 0x5d, 0x28, + 0x53, 0x2e, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x3f, 0x53, 0x2e, 0x65, 0x76, + 0x65, 0x6e, 0x74, 0x28, 0x74, 0x29, 0x3a, 0x74, 0x29, 0x7d, 0x66, 0x75, + 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x74, 0x74, 0x28, 0x74, 0x29, + 0x7b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x74, 0x68, 0x69, 0x73, + 0x2e, 0x6c, 0x5b, 0x74, 0x2e, 0x74, 0x79, 0x70, 0x65, 0x2b, 0x21, 0x30, + 0x5d, 0x28, 0x53, 0x2e, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x3f, 0x53, 0x2e, + 0x65, 0x76, 0x65, 0x6e, 0x74, 0x28, 0x74, 0x29, 0x3a, 0x74, 0x29, 0x7d, + 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x6e, 0x74, 0x28, + 0x74, 0x2c, 0x6e, 0x2c, 0x65, 0x2c, 0x69, 0x2c, 0x5f, 0x2c, 0x6f, 0x2c, + 0x72, 0x2c, 0x75, 0x2c, 0x6c, 0x29, 0x7b, 0x76, 0x61, 0x72, 0x20, 0x66, + 0x2c, 0x73, 0x2c, 0x63, 0x2c, 0x68, 0x2c, 0x61, 0x2c, 0x70, 0x2c, 0x64, + 0x2c, 0x76, 0x2c, 0x79, 0x2c, 0x6d, 0x2c, 0x67, 0x2c, 0x62, 0x2c, 0x6b, + 0x2c, 0x78, 0x2c, 0x77, 0x2c, 0x43, 0x3d, 0x6e, 0x2e, 0x74, 0x79, 0x70, + 0x65, 0x3b, 0x69, 0x66, 0x28, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, + 0x3d, 0x3d, 0x6e, 0x2e, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x72, 0x75, 0x63, + 0x74, 0x6f, 0x72, 0x29, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x6e, + 0x75, 0x6c, 0x6c, 0x3b, 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x65, 0x2e, + 0x5f, 0x5f, 0x68, 0x26, 0x26, 0x28, 0x6c, 0x3d, 0x65, 0x2e, 0x5f, 0x5f, + 0x68, 0x2c, 0x75, 0x3d, 0x6e, 0x2e, 0x5f, 0x5f, 0x65, 0x3d, 0x65, 0x2e, + 0x5f, 0x5f, 0x65, 0x2c, 0x6e, 0x2e, 0x5f, 0x5f, 0x68, 0x3d, 0x6e, 0x75, + 0x6c, 0x6c, 0x2c, 0x6f, 0x3d, 0x5b, 0x75, 0x5d, 0x29, 0x2c, 0x28, 0x66, + 0x3d, 0x53, 0x2e, 0x5f, 0x5f, 0x62, 0x29, 0x26, 0x26, 0x66, 0x28, 0x6e, + 0x29, 0x3b, 0x74, 0x72, 0x79, 0x7b, 0x74, 0x3a, 0x69, 0x66, 0x28, 0x22, + 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x22, 0x3d, 0x3d, 0x74, + 0x79, 0x70, 0x65, 0x6f, 0x66, 0x20, 0x43, 0x29, 0x7b, 0x69, 0x66, 0x28, + 0x76, 0x3d, 0x6e, 0x2e, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x2c, 0x79, 0x3d, + 0x28, 0x66, 0x3d, 0x43, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x78, 0x74, + 0x54, 0x79, 0x70, 0x65, 0x29, 0x26, 0x26, 0x69, 0x5b, 0x66, 0x2e, 0x5f, + 0x5f, 0x63, 0x5d, 0x2c, 0x6d, 0x3d, 0x66, 0x3f, 0x79, 0x3f, 0x79, 0x2e, + 0x70, 0x72, 0x6f, 0x70, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3a, + 0x66, 0x2e, 0x5f, 0x5f, 0x3a, 0x69, 0x2c, 0x65, 0x2e, 0x5f, 0x5f, 0x63, + 0x3f, 0x64, 0x3d, 0x28, 0x73, 0x3d, 0x6e, 0x2e, 0x5f, 0x5f, 0x63, 0x3d, + 0x65, 0x2e, 0x5f, 0x5f, 0x63, 0x29, 0x2e, 0x5f, 0x5f, 0x3d, 0x73, 0x2e, + 0x5f, 0x5f, 0x45, 0x3a, 0x28, 0x22, 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x74, + 0x79, 0x70, 0x65, 0x22, 0x69, 0x6e, 0x20, 0x43, 0x26, 0x26, 0x43, 0x2e, + 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x74, 0x79, 0x70, 0x65, 0x2e, 0x72, 0x65, + 0x6e, 0x64, 0x65, 0x72, 0x3f, 0x6e, 0x2e, 0x5f, 0x5f, 0x63, 0x3d, 0x73, + 0x3d, 0x6e, 0x65, 0x77, 0x20, 0x43, 0x28, 0x76, 0x2c, 0x6d, 0x29, 0x3a, + 0x28, 0x6e, 0x2e, 0x5f, 0x5f, 0x63, 0x3d, 0x73, 0x3d, 0x6e, 0x65, 0x77, + 0x20, 0x4c, 0x28, 0x76, 0x2c, 0x6d, 0x29, 0x2c, 0x73, 0x2e, 0x63, 0x6f, + 0x6e, 0x73, 0x74, 0x72, 0x75, 0x63, 0x74, 0x6f, 0x72, 0x3d, 0x43, 0x2c, + 0x73, 0x2e, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x3d, 0x72, 0x74, 0x29, + 0x2c, 0x79, 0x26, 0x26, 0x79, 0x2e, 0x73, 0x75, 0x62, 0x28, 0x73, 0x29, + 0x2c, 0x73, 0x2e, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x3d, 0x76, 0x2c, 0x73, + 0x2e, 0x73, 0x74, 0x61, 0x74, 0x65, 0x7c, 0x7c, 0x28, 0x73, 0x2e, 0x73, + 0x74, 0x61, 0x74, 0x65, 0x3d, 0x7b, 0x7d, 0x29, 0x2c, 0x73, 0x2e, 0x63, + 0x6f, 0x6e, 0x74, 0x65, 0x78, 0x74, 0x3d, 0x6d, 0x2c, 0x73, 0x2e, 0x5f, + 0x5f, 0x6e, 0x3d, 0x69, 0x2c, 0x63, 0x3d, 0x73, 0x2e, 0x5f, 0x5f, 0x64, + 0x3d, 0x21, 0x30, 0x2c, 0x73, 0x2e, 0x5f, 0x5f, 0x68, 0x3d, 0x5b, 0x5d, + 0x2c, 0x73, 0x2e, 0x5f, 0x73, 0x62, 0x3d, 0x5b, 0x5d, 0x29, 0x2c, 0x6e, + 0x75, 0x6c, 0x6c, 0x3d, 0x3d, 0x73, 0x2e, 0x5f, 0x5f, 0x73, 0x26, 0x26, + 0x28, 0x73, 0x2e, 0x5f, 0x5f, 0x73, 0x3d, 0x73, 0x2e, 0x73, 0x74, 0x61, + 0x74, 0x65, 0x29, 0x2c, 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x43, 0x2e, + 0x67, 0x65, 0x74, 0x44, 0x65, 0x72, 0x69, 0x76, 0x65, 0x64, 0x53, 0x74, + 0x61, 0x74, 0x65, 0x46, 0x72, 0x6f, 0x6d, 0x50, 0x72, 0x6f, 0x70, 0x73, + 0x26, 0x26, 0x28, 0x73, 0x2e, 0x5f, 0x5f, 0x73, 0x3d, 0x3d, 0x73, 0x2e, + 0x73, 0x74, 0x61, 0x74, 0x65, 0x26, 0x26, 0x28, 0x73, 0x2e, 0x5f, 0x5f, + 0x73, 0x3d, 0x56, 0x28, 0x7b, 0x7d, 0x2c, 0x73, 0x2e, 0x5f, 0x5f, 0x73, + 0x29, 0x29, 0x2c, 0x56, 0x28, 0x73, 0x2e, 0x5f, 0x5f, 0x73, 0x2c, 0x43, + 0x2e, 0x67, 0x65, 0x74, 0x44, 0x65, 0x72, 0x69, 0x76, 0x65, 0x64, 0x53, + 0x74, 0x61, 0x74, 0x65, 0x46, 0x72, 0x6f, 0x6d, 0x50, 0x72, 0x6f, 0x70, + 0x73, 0x28, 0x76, 0x2c, 0x73, 0x2e, 0x5f, 0x5f, 0x73, 0x29, 0x29, 0x29, + 0x2c, 0x68, 0x3d, 0x73, 0x2e, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x2c, 0x61, + 0x3d, 0x73, 0x2e, 0x73, 0x74, 0x61, 0x74, 0x65, 0x2c, 0x73, 0x2e, 0x5f, + 0x5f, 0x76, 0x3d, 0x6e, 0x2c, 0x63, 0x29, 0x6e, 0x75, 0x6c, 0x6c, 0x3d, + 0x3d, 0x43, 0x2e, 0x67, 0x65, 0x74, 0x44, 0x65, 0x72, 0x69, 0x76, 0x65, + 0x64, 0x53, 0x74, 0x61, 0x74, 0x65, 0x46, 0x72, 0x6f, 0x6d, 0x50, 0x72, + 0x6f, 0x70, 0x73, 0x26, 0x26, 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x73, + 0x2e, 0x63, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x57, 0x69, + 0x6c, 0x6c, 0x4d, 0x6f, 0x75, 0x6e, 0x74, 0x26, 0x26, 0x73, 0x2e, 0x63, + 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x57, 0x69, 0x6c, 0x6c, + 0x4d, 0x6f, 0x75, 0x6e, 0x74, 0x28, 0x29, 0x2c, 0x6e, 0x75, 0x6c, 0x6c, + 0x21, 0x3d, 0x73, 0x2e, 0x63, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, + 0x74, 0x44, 0x69, 0x64, 0x4d, 0x6f, 0x75, 0x6e, 0x74, 0x26, 0x26, 0x73, + 0x2e, 0x5f, 0x5f, 0x68, 0x2e, 0x70, 0x75, 0x73, 0x68, 0x28, 0x73, 0x2e, + 0x63, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x44, 0x69, 0x64, + 0x4d, 0x6f, 0x75, 0x6e, 0x74, 0x29, 0x3b, 0x65, 0x6c, 0x73, 0x65, 0x7b, + 0x69, 0x66, 0x28, 0x6e, 0x75, 0x6c, 0x6c, 0x3d, 0x3d, 0x43, 0x2e, 0x67, + 0x65, 0x74, 0x44, 0x65, 0x72, 0x69, 0x76, 0x65, 0x64, 0x53, 0x74, 0x61, + 0x74, 0x65, 0x46, 0x72, 0x6f, 0x6d, 0x50, 0x72, 0x6f, 0x70, 0x73, 0x26, + 0x26, 0x76, 0x21, 0x3d, 0x3d, 0x68, 0x26, 0x26, 0x6e, 0x75, 0x6c, 0x6c, + 0x21, 0x3d, 0x73, 0x2e, 0x63, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, + 0x74, 0x57, 0x69, 0x6c, 0x6c, 0x52, 0x65, 0x63, 0x65, 0x69, 0x76, 0x65, + 0x50, 0x72, 0x6f, 0x70, 0x73, 0x26, 0x26, 0x73, 0x2e, 0x63, 0x6f, 0x6d, + 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x57, 0x69, 0x6c, 0x6c, 0x52, 0x65, + 0x63, 0x65, 0x69, 0x76, 0x65, 0x50, 0x72, 0x6f, 0x70, 0x73, 0x28, 0x76, + 0x2c, 0x6d, 0x29, 0x2c, 0x21, 0x73, 0x2e, 0x5f, 0x5f, 0x65, 0x26, 0x26, + 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x73, 0x2e, 0x73, 0x68, 0x6f, 0x75, + 0x6c, 0x64, 0x43, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x55, + 0x70, 0x64, 0x61, 0x74, 0x65, 0x26, 0x26, 0x21, 0x31, 0x3d, 0x3d, 0x3d, + 0x73, 0x2e, 0x73, 0x68, 0x6f, 0x75, 0x6c, 0x64, 0x43, 0x6f, 0x6d, 0x70, + 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x55, 0x70, 0x64, 0x61, 0x74, 0x65, 0x28, + 0x76, 0x2c, 0x73, 0x2e, 0x5f, 0x5f, 0x73, 0x2c, 0x6d, 0x29, 0x7c, 0x7c, + 0x6e, 0x2e, 0x5f, 0x5f, 0x76, 0x3d, 0x3d, 0x3d, 0x65, 0x2e, 0x5f, 0x5f, + 0x76, 0x29, 0x7b, 0x66, 0x6f, 0x72, 0x28, 0x6e, 0x2e, 0x5f, 0x5f, 0x76, + 0x21, 0x3d, 0x3d, 0x65, 0x2e, 0x5f, 0x5f, 0x76, 0x26, 0x26, 0x28, 0x73, + 0x2e, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x3d, 0x76, 0x2c, 0x73, 0x2e, 0x73, + 0x74, 0x61, 0x74, 0x65, 0x3d, 0x73, 0x2e, 0x5f, 0x5f, 0x73, 0x2c, 0x73, + 0x2e, 0x5f, 0x5f, 0x64, 0x3d, 0x21, 0x31, 0x29, 0x2c, 0x73, 0x2e, 0x5f, + 0x5f, 0x65, 0x3d, 0x21, 0x31, 0x2c, 0x6e, 0x2e, 0x5f, 0x5f, 0x65, 0x3d, + 0x65, 0x2e, 0x5f, 0x5f, 0x65, 0x2c, 0x6e, 0x2e, 0x5f, 0x5f, 0x6b, 0x3d, + 0x65, 0x2e, 0x5f, 0x5f, 0x6b, 0x2c, 0x6e, 0x2e, 0x5f, 0x5f, 0x6b, 0x2e, + 0x66, 0x6f, 0x72, 0x45, 0x61, 0x63, 0x68, 0x28, 0x28, 0x66, 0x75, 0x6e, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x29, 0x7b, 0x74, 0x26, 0x26, + 0x28, 0x74, 0x2e, 0x5f, 0x5f, 0x3d, 0x6e, 0x29, 0x7d, 0x29, 0x29, 0x2c, + 0x67, 0x3d, 0x30, 0x3b, 0x67, 0x3c, 0x73, 0x2e, 0x5f, 0x73, 0x62, 0x2e, + 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x3b, 0x67, 0x2b, 0x2b, 0x29, 0x73, + 0x2e, 0x5f, 0x5f, 0x68, 0x2e, 0x70, 0x75, 0x73, 0x68, 0x28, 0x73, 0x2e, + 0x5f, 0x73, 0x62, 0x5b, 0x67, 0x5d, 0x29, 0x3b, 0x73, 0x2e, 0x5f, 0x73, + 0x62, 0x3d, 0x5b, 0x5d, 0x2c, 0x73, 0x2e, 0x5f, 0x5f, 0x68, 0x2e, 0x6c, + 0x65, 0x6e, 0x67, 0x74, 0x68, 0x26, 0x26, 0x72, 0x2e, 0x70, 0x75, 0x73, + 0x68, 0x28, 0x73, 0x29, 0x3b, 0x62, 0x72, 0x65, 0x61, 0x6b, 0x20, 0x74, + 0x7d, 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x73, 0x2e, 0x63, 0x6f, 0x6d, + 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x57, 0x69, 0x6c, 0x6c, 0x55, 0x70, + 0x64, 0x61, 0x74, 0x65, 0x26, 0x26, 0x73, 0x2e, 0x63, 0x6f, 0x6d, 0x70, + 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x57, 0x69, 0x6c, 0x6c, 0x55, 0x70, 0x64, + 0x61, 0x74, 0x65, 0x28, 0x76, 0x2c, 0x73, 0x2e, 0x5f, 0x5f, 0x73, 0x2c, + 0x6d, 0x29, 0x2c, 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x73, 0x2e, 0x63, + 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x44, 0x69, 0x64, 0x55, + 0x70, 0x64, 0x61, 0x74, 0x65, 0x26, 0x26, 0x73, 0x2e, 0x5f, 0x5f, 0x68, + 0x2e, 0x70, 0x75, 0x73, 0x68, 0x28, 0x28, 0x66, 0x75, 0x6e, 0x63, 0x74, + 0x69, 0x6f, 0x6e, 0x28, 0x29, 0x7b, 0x73, 0x2e, 0x63, 0x6f, 0x6d, 0x70, + 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x44, 0x69, 0x64, 0x55, 0x70, 0x64, 0x61, + 0x74, 0x65, 0x28, 0x68, 0x2c, 0x61, 0x2c, 0x70, 0x29, 0x7d, 0x29, 0x29, + 0x7d, 0x69, 0x66, 0x28, 0x73, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x78, + 0x74, 0x3d, 0x6d, 0x2c, 0x73, 0x2e, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x3d, + 0x76, 0x2c, 0x73, 0x2e, 0x5f, 0x5f, 0x50, 0x3d, 0x74, 0x2c, 0x62, 0x3d, + 0x53, 0x2e, 0x5f, 0x5f, 0x72, 0x2c, 0x6b, 0x3d, 0x30, 0x2c, 0x22, 0x70, + 0x72, 0x6f, 0x74, 0x6f, 0x74, 0x79, 0x70, 0x65, 0x22, 0x69, 0x6e, 0x20, + 0x43, 0x26, 0x26, 0x43, 0x2e, 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x74, 0x79, + 0x70, 0x65, 0x2e, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x29, 0x7b, 0x66, + 0x6f, 0x72, 0x28, 0x73, 0x2e, 0x73, 0x74, 0x61, 0x74, 0x65, 0x3d, 0x73, + 0x2e, 0x5f, 0x5f, 0x73, 0x2c, 0x73, 0x2e, 0x5f, 0x5f, 0x64, 0x3d, 0x21, + 0x31, 0x2c, 0x62, 0x26, 0x26, 0x62, 0x28, 0x6e, 0x29, 0x2c, 0x66, 0x3d, + 0x73, 0x2e, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x28, 0x73, 0x2e, 0x70, + 0x72, 0x6f, 0x70, 0x73, 0x2c, 0x73, 0x2e, 0x73, 0x74, 0x61, 0x74, 0x65, + 0x2c, 0x73, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x78, 0x74, 0x29, 0x2c, + 0x78, 0x3d, 0x30, 0x3b, 0x78, 0x3c, 0x73, 0x2e, 0x5f, 0x73, 0x62, 0x2e, + 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x3b, 0x78, 0x2b, 0x2b, 0x29, 0x73, + 0x2e, 0x5f, 0x5f, 0x68, 0x2e, 0x70, 0x75, 0x73, 0x68, 0x28, 0x73, 0x2e, + 0x5f, 0x73, 0x62, 0x5b, 0x78, 0x5d, 0x29, 0x3b, 0x73, 0x2e, 0x5f, 0x73, + 0x62, 0x3d, 0x5b, 0x5d, 0x7d, 0x65, 0x6c, 0x73, 0x65, 0x20, 0x64, 0x6f, + 0x7b, 0x73, 0x2e, 0x5f, 0x5f, 0x64, 0x3d, 0x21, 0x31, 0x2c, 0x62, 0x26, + 0x26, 0x62, 0x28, 0x6e, 0x29, 0x2c, 0x66, 0x3d, 0x73, 0x2e, 0x72, 0x65, + 0x6e, 0x64, 0x65, 0x72, 0x28, 0x73, 0x2e, 0x70, 0x72, 0x6f, 0x70, 0x73, + 0x2c, 0x73, 0x2e, 0x73, 0x74, 0x61, 0x74, 0x65, 0x2c, 0x73, 0x2e, 0x63, + 0x6f, 0x6e, 0x74, 0x65, 0x78, 0x74, 0x29, 0x2c, 0x73, 0x2e, 0x73, 0x74, + 0x61, 0x74, 0x65, 0x3d, 0x73, 0x2e, 0x5f, 0x5f, 0x73, 0x7d, 0x77, 0x68, + 0x69, 0x6c, 0x65, 0x28, 0x73, 0x2e, 0x5f, 0x5f, 0x64, 0x26, 0x26, 0x2b, + 0x2b, 0x6b, 0x3c, 0x32, 0x35, 0x29, 0x3b, 0x73, 0x2e, 0x73, 0x74, 0x61, + 0x74, 0x65, 0x3d, 0x73, 0x2e, 0x5f, 0x5f, 0x73, 0x2c, 0x6e, 0x75, 0x6c, + 0x6c, 0x21, 0x3d, 0x73, 0x2e, 0x67, 0x65, 0x74, 0x43, 0x68, 0x69, 0x6c, + 0x64, 0x43, 0x6f, 0x6e, 0x74, 0x65, 0x78, 0x74, 0x26, 0x26, 0x28, 0x69, + 0x3d, 0x56, 0x28, 0x56, 0x28, 0x7b, 0x7d, 0x2c, 0x69, 0x29, 0x2c, 0x73, + 0x2e, 0x67, 0x65, 0x74, 0x43, 0x68, 0x69, 0x6c, 0x64, 0x43, 0x6f, 0x6e, + 0x74, 0x65, 0x78, 0x74, 0x28, 0x29, 0x29, 0x29, 0x2c, 0x63, 0x7c, 0x7c, + 0x6e, 0x75, 0x6c, 0x6c, 0x3d, 0x3d, 0x73, 0x2e, 0x67, 0x65, 0x74, 0x53, + 0x6e, 0x61, 0x70, 0x73, 0x68, 0x6f, 0x74, 0x42, 0x65, 0x66, 0x6f, 0x72, + 0x65, 0x55, 0x70, 0x64, 0x61, 0x74, 0x65, 0x7c, 0x7c, 0x28, 0x70, 0x3d, + 0x73, 0x2e, 0x67, 0x65, 0x74, 0x53, 0x6e, 0x61, 0x70, 0x73, 0x68, 0x6f, + 0x74, 0x42, 0x65, 0x66, 0x6f, 0x72, 0x65, 0x55, 0x70, 0x64, 0x61, 0x74, + 0x65, 0x28, 0x68, 0x2c, 0x61, 0x29, 0x29, 0x2c, 0x42, 0x28, 0x74, 0x2c, + 0x54, 0x28, 0x77, 0x3d, 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x66, 0x26, + 0x26, 0x66, 0x2e, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x3d, 0x3d, 0x4f, 0x26, + 0x26, 0x6e, 0x75, 0x6c, 0x6c, 0x3d, 0x3d, 0x66, 0x2e, 0x6b, 0x65, 0x79, + 0x3f, 0x66, 0x2e, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x2e, 0x63, 0x68, 0x69, + 0x6c, 0x64, 0x72, 0x65, 0x6e, 0x3a, 0x66, 0x29, 0x3f, 0x77, 0x3a, 0x5b, + 0x77, 0x5d, 0x2c, 0x6e, 0x2c, 0x65, 0x2c, 0x69, 0x2c, 0x5f, 0x2c, 0x6f, + 0x2c, 0x72, 0x2c, 0x75, 0x2c, 0x6c, 0x29, 0x2c, 0x73, 0x2e, 0x62, 0x61, + 0x73, 0x65, 0x3d, 0x6e, 0x2e, 0x5f, 0x5f, 0x65, 0x2c, 0x6e, 0x2e, 0x5f, + 0x5f, 0x68, 0x3d, 0x6e, 0x75, 0x6c, 0x6c, 0x2c, 0x73, 0x2e, 0x5f, 0x5f, + 0x68, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x26, 0x26, 0x72, 0x2e, + 0x70, 0x75, 0x73, 0x68, 0x28, 0x73, 0x29, 0x2c, 0x64, 0x26, 0x26, 0x28, + 0x73, 0x2e, 0x5f, 0x5f, 0x45, 0x3d, 0x73, 0x2e, 0x5f, 0x5f, 0x3d, 0x6e, + 0x75, 0x6c, 0x6c, 0x29, 0x2c, 0x73, 0x2e, 0x5f, 0x5f, 0x65, 0x3d, 0x21, + 0x31, 0x7d, 0x65, 0x6c, 0x73, 0x65, 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x3d, + 0x3d, 0x6f, 0x26, 0x26, 0x6e, 0x2e, 0x5f, 0x5f, 0x76, 0x3d, 0x3d, 0x3d, + 0x65, 0x2e, 0x5f, 0x5f, 0x76, 0x3f, 0x28, 0x6e, 0x2e, 0x5f, 0x5f, 0x6b, + 0x3d, 0x65, 0x2e, 0x5f, 0x5f, 0x6b, 0x2c, 0x6e, 0x2e, 0x5f, 0x5f, 0x65, + 0x3d, 0x65, 0x2e, 0x5f, 0x5f, 0x65, 0x29, 0x3a, 0x6e, 0x2e, 0x5f, 0x5f, + 0x65, 0x3d, 0x69, 0x74, 0x28, 0x65, 0x2e, 0x5f, 0x5f, 0x65, 0x2c, 0x6e, + 0x2c, 0x65, 0x2c, 0x69, 0x2c, 0x5f, 0x2c, 0x6f, 0x2c, 0x72, 0x2c, 0x6c, + 0x29, 0x3b, 0x28, 0x66, 0x3d, 0x53, 0x2e, 0x64, 0x69, 0x66, 0x66, 0x65, + 0x64, 0x29, 0x26, 0x26, 0x66, 0x28, 0x6e, 0x29, 0x7d, 0x63, 0x61, 0x74, + 0x63, 0x68, 0x28, 0x74, 0x29, 0x7b, 0x6e, 0x2e, 0x5f, 0x5f, 0x76, 0x3d, + 0x6e, 0x75, 0x6c, 0x6c, 0x2c, 0x28, 0x6c, 0x7c, 0x7c, 0x6e, 0x75, 0x6c, + 0x6c, 0x21, 0x3d, 0x6f, 0x29, 0x26, 0x26, 0x28, 0x6e, 0x2e, 0x5f, 0x5f, + 0x65, 0x3d, 0x75, 0x2c, 0x6e, 0x2e, 0x5f, 0x5f, 0x68, 0x3d, 0x21, 0x21, + 0x6c, 0x2c, 0x6f, 0x5b, 0x6f, 0x2e, 0x69, 0x6e, 0x64, 0x65, 0x78, 0x4f, + 0x66, 0x28, 0x75, 0x29, 0x5d, 0x3d, 0x6e, 0x75, 0x6c, 0x6c, 0x29, 0x2c, + 0x53, 0x2e, 0x5f, 0x5f, 0x65, 0x28, 0x74, 0x2c, 0x6e, 0x2c, 0x65, 0x29, + 0x7d, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x65, + 0x74, 0x28, 0x74, 0x2c, 0x6e, 0x29, 0x7b, 0x53, 0x2e, 0x5f, 0x5f, 0x63, + 0x26, 0x26, 0x53, 0x2e, 0x5f, 0x5f, 0x63, 0x28, 0x6e, 0x2c, 0x74, 0x29, + 0x2c, 0x74, 0x2e, 0x73, 0x6f, 0x6d, 0x65, 0x28, 0x28, 0x66, 0x75, 0x6e, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x6e, 0x29, 0x7b, 0x74, 0x72, 0x79, + 0x7b, 0x74, 0x3d, 0x6e, 0x2e, 0x5f, 0x5f, 0x68, 0x2c, 0x6e, 0x2e, 0x5f, + 0x5f, 0x68, 0x3d, 0x5b, 0x5d, 0x2c, 0x74, 0x2e, 0x73, 0x6f, 0x6d, 0x65, + 0x28, 0x28, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, + 0x29, 0x7b, 0x74, 0x2e, 0x63, 0x61, 0x6c, 0x6c, 0x28, 0x6e, 0x29, 0x7d, + 0x29, 0x29, 0x7d, 0x63, 0x61, 0x74, 0x63, 0x68, 0x28, 0x74, 0x29, 0x7b, + 0x53, 0x2e, 0x5f, 0x5f, 0x65, 0x28, 0x74, 0x2c, 0x6e, 0x2e, 0x5f, 0x5f, + 0x76, 0x29, 0x7d, 0x7d, 0x29, 0x29, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, + 0x69, 0x6f, 0x6e, 0x20, 0x69, 0x74, 0x28, 0x74, 0x2c, 0x6e, 0x2c, 0x65, + 0x2c, 0x69, 0x2c, 0x5f, 0x2c, 0x6f, 0x2c, 0x72, 0x2c, 0x75, 0x29, 0x7b, + 0x76, 0x61, 0x72, 0x20, 0x6c, 0x2c, 0x66, 0x2c, 0x73, 0x2c, 0x63, 0x3d, + 0x65, 0x2e, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x2c, 0x68, 0x3d, 0x6e, 0x2e, + 0x70, 0x72, 0x6f, 0x70, 0x73, 0x2c, 0x61, 0x3d, 0x6e, 0x2e, 0x74, 0x79, + 0x70, 0x65, 0x2c, 0x70, 0x3d, 0x30, 0x3b, 0x69, 0x66, 0x28, 0x22, 0x73, + 0x76, 0x67, 0x22, 0x3d, 0x3d, 0x3d, 0x61, 0x26, 0x26, 0x28, 0x5f, 0x3d, + 0x21, 0x30, 0x29, 0x2c, 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x6f, 0x29, + 0x66, 0x6f, 0x72, 0x28, 0x3b, 0x70, 0x3c, 0x6f, 0x2e, 0x6c, 0x65, 0x6e, + 0x67, 0x74, 0x68, 0x3b, 0x70, 0x2b, 0x2b, 0x29, 0x69, 0x66, 0x28, 0x28, + 0x6c, 0x3d, 0x6f, 0x5b, 0x70, 0x5d, 0x29, 0x26, 0x26, 0x22, 0x73, 0x65, + 0x74, 0x41, 0x74, 0x74, 0x72, 0x69, 0x62, 0x75, 0x74, 0x65, 0x22, 0x69, + 0x6e, 0x20, 0x6c, 0x3d, 0x3d, 0x21, 0x21, 0x61, 0x26, 0x26, 0x28, 0x61, + 0x3f, 0x6c, 0x2e, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x4e, 0x61, 0x6d, 0x65, + 0x3d, 0x3d, 0x3d, 0x61, 0x3a, 0x33, 0x3d, 0x3d, 0x3d, 0x6c, 0x2e, 0x6e, + 0x6f, 0x64, 0x65, 0x54, 0x79, 0x70, 0x65, 0x29, 0x29, 0x7b, 0x74, 0x3d, + 0x6c, 0x2c, 0x6f, 0x5b, 0x70, 0x5d, 0x3d, 0x6e, 0x75, 0x6c, 0x6c, 0x3b, + 0x62, 0x72, 0x65, 0x61, 0x6b, 0x7d, 0x69, 0x66, 0x28, 0x6e, 0x75, 0x6c, + 0x6c, 0x3d, 0x3d, 0x74, 0x29, 0x7b, 0x69, 0x66, 0x28, 0x6e, 0x75, 0x6c, + 0x6c, 0x3d, 0x3d, 0x3d, 0x61, 0x29, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, + 0x20, 0x64, 0x6f, 0x63, 0x75, 0x6d, 0x65, 0x6e, 0x74, 0x2e, 0x63, 0x72, + 0x65, 0x61, 0x74, 0x65, 0x54, 0x65, 0x78, 0x74, 0x4e, 0x6f, 0x64, 0x65, + 0x28, 0x68, 0x29, 0x3b, 0x74, 0x3d, 0x5f, 0x3f, 0x64, 0x6f, 0x63, 0x75, + 0x6d, 0x65, 0x6e, 0x74, 0x2e, 0x63, 0x72, 0x65, 0x61, 0x74, 0x65, 0x45, + 0x6c, 0x65, 0x6d, 0x65, 0x6e, 0x74, 0x4e, 0x53, 0x28, 0x22, 0x68, 0x74, + 0x74, 0x70, 0x3a, 0x2f, 0x2f, 0x77, 0x77, 0x77, 0x2e, 0x77, 0x33, 0x2e, + 0x6f, 0x72, 0x67, 0x2f, 0x32, 0x30, 0x30, 0x30, 0x2f, 0x73, 0x76, 0x67, + 0x22, 0x2c, 0x61, 0x29, 0x3a, 0x64, 0x6f, 0x63, 0x75, 0x6d, 0x65, 0x6e, + 0x74, 0x2e, 0x63, 0x72, 0x65, 0x61, 0x74, 0x65, 0x45, 0x6c, 0x65, 0x6d, + 0x65, 0x6e, 0x74, 0x28, 0x61, 0x2c, 0x68, 0x2e, 0x69, 0x73, 0x26, 0x26, + 0x68, 0x29, 0x2c, 0x6f, 0x3d, 0x6e, 0x75, 0x6c, 0x6c, 0x2c, 0x75, 0x3d, + 0x21, 0x31, 0x7d, 0x69, 0x66, 0x28, 0x6e, 0x75, 0x6c, 0x6c, 0x3d, 0x3d, + 0x3d, 0x61, 0x29, 0x63, 0x3d, 0x3d, 0x3d, 0x68, 0x7c, 0x7c, 0x75, 0x26, + 0x26, 0x74, 0x2e, 0x64, 0x61, 0x74, 0x61, 0x3d, 0x3d, 0x3d, 0x68, 0x7c, + 0x7c, 0x28, 0x74, 0x2e, 0x64, 0x61, 0x74, 0x61, 0x3d, 0x68, 0x29, 0x3b, + 0x65, 0x6c, 0x73, 0x65, 0x7b, 0x69, 0x66, 0x28, 0x6f, 0x3d, 0x6f, 0x26, + 0x26, 0x6b, 0x2e, 0x63, 0x61, 0x6c, 0x6c, 0x28, 0x74, 0x2e, 0x63, 0x68, + 0x69, 0x6c, 0x64, 0x4e, 0x6f, 0x64, 0x65, 0x73, 0x29, 0x2c, 0x66, 0x3d, + 0x28, 0x63, 0x3d, 0x65, 0x2e, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x7c, 0x7c, + 0x50, 0x29, 0x2e, 0x64, 0x61, 0x6e, 0x67, 0x65, 0x72, 0x6f, 0x75, 0x73, + 0x6c, 0x79, 0x53, 0x65, 0x74, 0x49, 0x6e, 0x6e, 0x65, 0x72, 0x48, 0x54, + 0x4d, 0x4c, 0x2c, 0x73, 0x3d, 0x68, 0x2e, 0x64, 0x61, 0x6e, 0x67, 0x65, + 0x72, 0x6f, 0x75, 0x73, 0x6c, 0x79, 0x53, 0x65, 0x74, 0x49, 0x6e, 0x6e, + 0x65, 0x72, 0x48, 0x54, 0x4d, 0x4c, 0x2c, 0x21, 0x75, 0x29, 0x7b, 0x69, + 0x66, 0x28, 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x6f, 0x29, 0x66, 0x6f, + 0x72, 0x28, 0x63, 0x3d, 0x7b, 0x7d, 0x2c, 0x70, 0x3d, 0x30, 0x3b, 0x70, + 0x3c, 0x74, 0x2e, 0x61, 0x74, 0x74, 0x72, 0x69, 0x62, 0x75, 0x74, 0x65, + 0x73, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x3b, 0x70, 0x2b, 0x2b, + 0x29, 0x63, 0x5b, 0x74, 0x2e, 0x61, 0x74, 0x74, 0x72, 0x69, 0x62, 0x75, + 0x74, 0x65, 0x73, 0x5b, 0x70, 0x5d, 0x2e, 0x6e, 0x61, 0x6d, 0x65, 0x5d, + 0x3d, 0x74, 0x2e, 0x61, 0x74, 0x74, 0x72, 0x69, 0x62, 0x75, 0x74, 0x65, + 0x73, 0x5b, 0x70, 0x5d, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3b, 0x28, + 0x73, 0x7c, 0x7c, 0x66, 0x29, 0x26, 0x26, 0x28, 0x73, 0x26, 0x26, 0x28, + 0x66, 0x26, 0x26, 0x73, 0x2e, 0x5f, 0x5f, 0x68, 0x74, 0x6d, 0x6c, 0x3d, + 0x3d, 0x66, 0x2e, 0x5f, 0x5f, 0x68, 0x74, 0x6d, 0x6c, 0x7c, 0x7c, 0x73, + 0x2e, 0x5f, 0x5f, 0x68, 0x74, 0x6d, 0x6c, 0x3d, 0x3d, 0x3d, 0x74, 0x2e, + 0x69, 0x6e, 0x6e, 0x65, 0x72, 0x48, 0x54, 0x4d, 0x4c, 0x29, 0x7c, 0x7c, + 0x28, 0x74, 0x2e, 0x69, 0x6e, 0x6e, 0x65, 0x72, 0x48, 0x54, 0x4d, 0x4c, + 0x3d, 0x73, 0x26, 0x26, 0x73, 0x2e, 0x5f, 0x5f, 0x68, 0x74, 0x6d, 0x6c, + 0x7c, 0x7c, 0x22, 0x22, 0x29, 0x29, 0x7d, 0x69, 0x66, 0x28, 0x51, 0x28, + 0x74, 0x2c, 0x68, 0x2c, 0x63, 0x2c, 0x5f, 0x2c, 0x75, 0x29, 0x2c, 0x73, + 0x29, 0x6e, 0x2e, 0x5f, 0x5f, 0x6b, 0x3d, 0x5b, 0x5d, 0x3b, 0x65, 0x6c, + 0x73, 0x65, 0x20, 0x69, 0x66, 0x28, 0x42, 0x28, 0x74, 0x2c, 0x54, 0x28, + 0x70, 0x3d, 0x6e, 0x2e, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x2e, 0x63, 0x68, + 0x69, 0x6c, 0x64, 0x72, 0x65, 0x6e, 0x29, 0x3f, 0x70, 0x3a, 0x5b, 0x70, + 0x5d, 0x2c, 0x6e, 0x2c, 0x65, 0x2c, 0x69, 0x2c, 0x5f, 0x26, 0x26, 0x22, + 0x66, 0x6f, 0x72, 0x65, 0x69, 0x67, 0x6e, 0x4f, 0x62, 0x6a, 0x65, 0x63, + 0x74, 0x22, 0x21, 0x3d, 0x3d, 0x61, 0x2c, 0x6f, 0x2c, 0x72, 0x2c, 0x6f, + 0x3f, 0x6f, 0x5b, 0x30, 0x5d, 0x3a, 0x65, 0x2e, 0x5f, 0x5f, 0x6b, 0x26, + 0x26, 0x52, 0x28, 0x65, 0x2c, 0x30, 0x29, 0x2c, 0x75, 0x29, 0x2c, 0x6e, + 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x6f, 0x29, 0x66, 0x6f, 0x72, 0x28, 0x70, + 0x3d, 0x6f, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x3b, 0x70, 0x2d, + 0x2d, 0x3b, 0x29, 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x6f, 0x5b, 0x70, + 0x5d, 0x26, 0x26, 0x41, 0x28, 0x6f, 0x5b, 0x70, 0x5d, 0x29, 0x3b, 0x75, + 0x7c, 0x7c, 0x28, 0x22, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x22, 0x69, 0x6e, + 0x20, 0x68, 0x26, 0x26, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, + 0x3d, 0x28, 0x70, 0x3d, 0x68, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, + 0x26, 0x26, 0x28, 0x70, 0x21, 0x3d, 0x3d, 0x74, 0x2e, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x7c, 0x7c, 0x22, 0x70, 0x72, 0x6f, 0x67, 0x72, 0x65, 0x73, + 0x73, 0x22, 0x3d, 0x3d, 0x3d, 0x61, 0x26, 0x26, 0x21, 0x70, 0x7c, 0x7c, + 0x22, 0x6f, 0x70, 0x74, 0x69, 0x6f, 0x6e, 0x22, 0x3d, 0x3d, 0x3d, 0x61, + 0x26, 0x26, 0x70, 0x21, 0x3d, 0x3d, 0x63, 0x2e, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x29, 0x26, 0x26, 0x59, 0x28, 0x74, 0x2c, 0x22, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x22, 0x2c, 0x70, 0x2c, 0x63, 0x2e, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x2c, 0x21, 0x31, 0x29, 0x2c, 0x22, 0x63, 0x68, 0x65, 0x63, 0x6b, + 0x65, 0x64, 0x22, 0x69, 0x6e, 0x20, 0x68, 0x26, 0x26, 0x76, 0x6f, 0x69, + 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x28, 0x70, 0x3d, 0x68, 0x2e, 0x63, + 0x68, 0x65, 0x63, 0x6b, 0x65, 0x64, 0x29, 0x26, 0x26, 0x70, 0x21, 0x3d, + 0x3d, 0x74, 0x2e, 0x63, 0x68, 0x65, 0x63, 0x6b, 0x65, 0x64, 0x26, 0x26, + 0x59, 0x28, 0x74, 0x2c, 0x22, 0x63, 0x68, 0x65, 0x63, 0x6b, 0x65, 0x64, + 0x22, 0x2c, 0x70, 0x2c, 0x63, 0x2e, 0x63, 0x68, 0x65, 0x63, 0x6b, 0x65, + 0x64, 0x2c, 0x21, 0x31, 0x29, 0x29, 0x7d, 0x72, 0x65, 0x74, 0x75, 0x72, + 0x6e, 0x20, 0x74, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x20, 0x5f, 0x74, 0x28, 0x74, 0x2c, 0x6e, 0x2c, 0x65, 0x29, 0x7b, 0x74, + 0x72, 0x79, 0x7b, 0x22, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x22, 0x3d, 0x3d, 0x74, 0x79, 0x70, 0x65, 0x6f, 0x66, 0x20, 0x74, 0x3f, + 0x74, 0x28, 0x6e, 0x29, 0x3a, 0x74, 0x2e, 0x63, 0x75, 0x72, 0x72, 0x65, + 0x6e, 0x74, 0x3d, 0x6e, 0x7d, 0x63, 0x61, 0x74, 0x63, 0x68, 0x28, 0x74, + 0x29, 0x7b, 0x53, 0x2e, 0x5f, 0x5f, 0x65, 0x28, 0x74, 0x2c, 0x65, 0x29, + 0x7d, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x6f, + 0x74, 0x28, 0x74, 0x2c, 0x6e, 0x2c, 0x65, 0x29, 0x7b, 0x76, 0x61, 0x72, + 0x20, 0x69, 0x2c, 0x5f, 0x3b, 0x69, 0x66, 0x28, 0x53, 0x2e, 0x75, 0x6e, + 0x6d, 0x6f, 0x75, 0x6e, 0x74, 0x26, 0x26, 0x53, 0x2e, 0x75, 0x6e, 0x6d, + 0x6f, 0x75, 0x6e, 0x74, 0x28, 0x74, 0x29, 0x2c, 0x28, 0x69, 0x3d, 0x74, + 0x2e, 0x72, 0x65, 0x66, 0x29, 0x26, 0x26, 0x28, 0x69, 0x2e, 0x63, 0x75, + 0x72, 0x72, 0x65, 0x6e, 0x74, 0x26, 0x26, 0x69, 0x2e, 0x63, 0x75, 0x72, + 0x72, 0x65, 0x6e, 0x74, 0x21, 0x3d, 0x3d, 0x74, 0x2e, 0x5f, 0x5f, 0x65, + 0x7c, 0x7c, 0x5f, 0x74, 0x28, 0x69, 0x2c, 0x6e, 0x75, 0x6c, 0x6c, 0x2c, + 0x6e, 0x29, 0x29, 0x2c, 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x28, 0x69, + 0x3d, 0x74, 0x2e, 0x5f, 0x5f, 0x63, 0x29, 0x29, 0x7b, 0x69, 0x66, 0x28, + 0x69, 0x2e, 0x63, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x57, + 0x69, 0x6c, 0x6c, 0x55, 0x6e, 0x6d, 0x6f, 0x75, 0x6e, 0x74, 0x29, 0x74, + 0x72, 0x79, 0x7b, 0x69, 0x2e, 0x63, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, + 0x6e, 0x74, 0x57, 0x69, 0x6c, 0x6c, 0x55, 0x6e, 0x6d, 0x6f, 0x75, 0x6e, + 0x74, 0x28, 0x29, 0x7d, 0x63, 0x61, 0x74, 0x63, 0x68, 0x28, 0x74, 0x29, + 0x7b, 0x53, 0x2e, 0x5f, 0x5f, 0x65, 0x28, 0x74, 0x2c, 0x6e, 0x29, 0x7d, + 0x69, 0x2e, 0x62, 0x61, 0x73, 0x65, 0x3d, 0x69, 0x2e, 0x5f, 0x5f, 0x50, + 0x3d, 0x6e, 0x75, 0x6c, 0x6c, 0x2c, 0x74, 0x2e, 0x5f, 0x5f, 0x63, 0x3d, + 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x7d, 0x69, 0x66, 0x28, 0x69, 0x3d, + 0x74, 0x2e, 0x5f, 0x5f, 0x6b, 0x29, 0x66, 0x6f, 0x72, 0x28, 0x5f, 0x3d, + 0x30, 0x3b, 0x5f, 0x3c, 0x69, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, + 0x3b, 0x5f, 0x2b, 0x2b, 0x29, 0x69, 0x5b, 0x5f, 0x5d, 0x26, 0x26, 0x6f, + 0x74, 0x28, 0x69, 0x5b, 0x5f, 0x5d, 0x2c, 0x6e, 0x2c, 0x65, 0x7c, 0x7c, + 0x22, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x22, 0x21, 0x3d, + 0x74, 0x79, 0x70, 0x65, 0x6f, 0x66, 0x20, 0x74, 0x2e, 0x74, 0x79, 0x70, + 0x65, 0x29, 0x3b, 0x65, 0x7c, 0x7c, 0x6e, 0x75, 0x6c, 0x6c, 0x3d, 0x3d, + 0x74, 0x2e, 0x5f, 0x5f, 0x65, 0x7c, 0x7c, 0x41, 0x28, 0x74, 0x2e, 0x5f, + 0x5f, 0x65, 0x29, 0x2c, 0x74, 0x2e, 0x5f, 0x5f, 0x3d, 0x74, 0x2e, 0x5f, + 0x5f, 0x65, 0x3d, 0x74, 0x2e, 0x5f, 0x5f, 0x64, 0x3d, 0x76, 0x6f, 0x69, + 0x64, 0x20, 0x30, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x20, 0x72, 0x74, 0x28, 0x74, 0x2c, 0x6e, 0x2c, 0x65, 0x29, 0x7b, 0x72, + 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x63, + 0x6f, 0x6e, 0x73, 0x74, 0x72, 0x75, 0x63, 0x74, 0x6f, 0x72, 0x28, 0x74, + 0x2c, 0x65, 0x29, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x20, 0x75, 0x74, 0x28, 0x74, 0x2c, 0x6e, 0x2c, 0x65, 0x29, 0x7b, 0x76, + 0x61, 0x72, 0x20, 0x69, 0x2c, 0x5f, 0x2c, 0x6f, 0x3b, 0x53, 0x2e, 0x5f, + 0x5f, 0x26, 0x26, 0x53, 0x2e, 0x5f, 0x5f, 0x28, 0x74, 0x2c, 0x6e, 0x29, + 0x2c, 0x5f, 0x3d, 0x28, 0x69, 0x3d, 0x22, 0x66, 0x75, 0x6e, 0x63, 0x74, + 0x69, 0x6f, 0x6e, 0x22, 0x3d, 0x3d, 0x74, 0x79, 0x70, 0x65, 0x6f, 0x66, + 0x20, 0x65, 0x29, 0x3f, 0x6e, 0x75, 0x6c, 0x6c, 0x3a, 0x65, 0x26, 0x26, + 0x65, 0x2e, 0x5f, 0x5f, 0x6b, 0x7c, 0x7c, 0x6e, 0x2e, 0x5f, 0x5f, 0x6b, + 0x2c, 0x6f, 0x3d, 0x5b, 0x5d, 0x2c, 0x6e, 0x74, 0x28, 0x6e, 0x2c, 0x74, + 0x3d, 0x28, 0x21, 0x69, 0x26, 0x26, 0x65, 0x7c, 0x7c, 0x6e, 0x29, 0x2e, + 0x5f, 0x5f, 0x6b, 0x3d, 0x46, 0x28, 0x4f, 0x2c, 0x6e, 0x75, 0x6c, 0x6c, + 0x2c, 0x5b, 0x74, 0x5d, 0x29, 0x2c, 0x5f, 0x7c, 0x7c, 0x50, 0x2c, 0x50, + 0x2c, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x6e, 0x2e, + 0x6f, 0x77, 0x6e, 0x65, 0x72, 0x53, 0x56, 0x47, 0x45, 0x6c, 0x65, 0x6d, + 0x65, 0x6e, 0x74, 0x2c, 0x21, 0x69, 0x26, 0x26, 0x65, 0x3f, 0x5b, 0x65, + 0x5d, 0x3a, 0x5f, 0x3f, 0x6e, 0x75, 0x6c, 0x6c, 0x3a, 0x6e, 0x2e, 0x66, + 0x69, 0x72, 0x73, 0x74, 0x43, 0x68, 0x69, 0x6c, 0x64, 0x3f, 0x6b, 0x2e, + 0x63, 0x61, 0x6c, 0x6c, 0x28, 0x6e, 0x2e, 0x63, 0x68, 0x69, 0x6c, 0x64, + 0x4e, 0x6f, 0x64, 0x65, 0x73, 0x29, 0x3a, 0x6e, 0x75, 0x6c, 0x6c, 0x2c, + 0x6f, 0x2c, 0x21, 0x69, 0x26, 0x26, 0x65, 0x3f, 0x65, 0x3a, 0x5f, 0x3f, + 0x5f, 0x2e, 0x5f, 0x5f, 0x65, 0x3a, 0x6e, 0x2e, 0x66, 0x69, 0x72, 0x73, + 0x74, 0x43, 0x68, 0x69, 0x6c, 0x64, 0x2c, 0x69, 0x29, 0x2c, 0x65, 0x74, + 0x28, 0x6f, 0x2c, 0x74, 0x29, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, + 0x6f, 0x6e, 0x20, 0x6c, 0x74, 0x28, 0x74, 0x2c, 0x6e, 0x29, 0x7b, 0x75, + 0x74, 0x28, 0x74, 0x2c, 0x6e, 0x2c, 0x6c, 0x74, 0x29, 0x7d, 0x66, 0x75, + 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x66, 0x74, 0x28, 0x74, 0x2c, + 0x6e, 0x2c, 0x65, 0x29, 0x7b, 0x76, 0x61, 0x72, 0x20, 0x69, 0x2c, 0x5f, + 0x2c, 0x6f, 0x2c, 0x72, 0x2c, 0x75, 0x3d, 0x56, 0x28, 0x7b, 0x7d, 0x2c, + 0x74, 0x2e, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x29, 0x3b, 0x66, 0x6f, 0x72, + 0x28, 0x6f, 0x20, 0x69, 0x6e, 0x20, 0x74, 0x2e, 0x74, 0x79, 0x70, 0x65, + 0x26, 0x26, 0x74, 0x2e, 0x74, 0x79, 0x70, 0x65, 0x2e, 0x64, 0x65, 0x66, + 0x61, 0x75, 0x6c, 0x74, 0x50, 0x72, 0x6f, 0x70, 0x73, 0x26, 0x26, 0x28, + 0x72, 0x3d, 0x74, 0x2e, 0x74, 0x79, 0x70, 0x65, 0x2e, 0x64, 0x65, 0x66, + 0x61, 0x75, 0x6c, 0x74, 0x50, 0x72, 0x6f, 0x70, 0x73, 0x29, 0x2c, 0x6e, + 0x29, 0x22, 0x6b, 0x65, 0x79, 0x22, 0x3d, 0x3d, 0x6f, 0x3f, 0x69, 0x3d, + 0x6e, 0x5b, 0x6f, 0x5d, 0x3a, 0x22, 0x72, 0x65, 0x66, 0x22, 0x3d, 0x3d, + 0x6f, 0x3f, 0x5f, 0x3d, 0x6e, 0x5b, 0x6f, 0x5d, 0x3a, 0x75, 0x5b, 0x6f, + 0x5d, 0x3d, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x3d, 0x3d, 0x3d, 0x6e, + 0x5b, 0x6f, 0x5d, 0x26, 0x26, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, + 0x3d, 0x3d, 0x72, 0x3f, 0x72, 0x5b, 0x6f, 0x5d, 0x3a, 0x6e, 0x5b, 0x6f, + 0x5d, 0x3b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x61, 0x72, 0x67, + 0x75, 0x6d, 0x65, 0x6e, 0x74, 0x73, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, + 0x68, 0x3e, 0x32, 0x26, 0x26, 0x28, 0x75, 0x2e, 0x63, 0x68, 0x69, 0x6c, + 0x64, 0x72, 0x65, 0x6e, 0x3d, 0x61, 0x72, 0x67, 0x75, 0x6d, 0x65, 0x6e, + 0x74, 0x73, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x3e, 0x33, 0x3f, + 0x6b, 0x2e, 0x63, 0x61, 0x6c, 0x6c, 0x28, 0x61, 0x72, 0x67, 0x75, 0x6d, + 0x65, 0x6e, 0x74, 0x73, 0x2c, 0x32, 0x29, 0x3a, 0x65, 0x29, 0x2c, 0x4d, + 0x28, 0x74, 0x2e, 0x74, 0x79, 0x70, 0x65, 0x2c, 0x75, 0x2c, 0x69, 0x7c, + 0x7c, 0x74, 0x2e, 0x6b, 0x65, 0x79, 0x2c, 0x5f, 0x7c, 0x7c, 0x74, 0x2e, + 0x72, 0x65, 0x66, 0x2c, 0x6e, 0x75, 0x6c, 0x6c, 0x29, 0x7d, 0x66, 0x75, + 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x73, 0x74, 0x28, 0x74, 0x2c, + 0x6e, 0x29, 0x7b, 0x76, 0x61, 0x72, 0x20, 0x65, 0x3d, 0x7b, 0x5f, 0x5f, + 0x63, 0x3a, 0x6e, 0x3d, 0x22, 0x5f, 0x5f, 0x63, 0x43, 0x22, 0x2b, 0x4e, + 0x2b, 0x2b, 0x2c, 0x5f, 0x5f, 0x3a, 0x74, 0x2c, 0x43, 0x6f, 0x6e, 0x73, + 0x75, 0x6d, 0x65, 0x72, 0x3a, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, + 0x6e, 0x28, 0x74, 0x2c, 0x6e, 0x29, 0x7b, 0x72, 0x65, 0x74, 0x75, 0x72, + 0x6e, 0x20, 0x74, 0x2e, 0x63, 0x68, 0x69, 0x6c, 0x64, 0x72, 0x65, 0x6e, + 0x28, 0x6e, 0x29, 0x7d, 0x2c, 0x50, 0x72, 0x6f, 0x76, 0x69, 0x64, 0x65, + 0x72, 0x3a, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, + 0x29, 0x7b, 0x76, 0x61, 0x72, 0x20, 0x65, 0x2c, 0x69, 0x3b, 0x72, 0x65, + 0x74, 0x75, 0x72, 0x6e, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x67, 0x65, + 0x74, 0x43, 0x68, 0x69, 0x6c, 0x64, 0x43, 0x6f, 0x6e, 0x74, 0x65, 0x78, + 0x74, 0x7c, 0x7c, 0x28, 0x65, 0x3d, 0x5b, 0x5d, 0x2c, 0x28, 0x69, 0x3d, + 0x7b, 0x7d, 0x29, 0x5b, 0x6e, 0x5d, 0x3d, 0x74, 0x68, 0x69, 0x73, 0x2c, + 0x74, 0x68, 0x69, 0x73, 0x2e, 0x67, 0x65, 0x74, 0x43, 0x68, 0x69, 0x6c, + 0x64, 0x43, 0x6f, 0x6e, 0x74, 0x65, 0x78, 0x74, 0x3d, 0x66, 0x75, 0x6e, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x29, 0x7b, 0x72, 0x65, 0x74, 0x75, + 0x72, 0x6e, 0x20, 0x69, 0x7d, 0x2c, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x73, + 0x68, 0x6f, 0x75, 0x6c, 0x64, 0x43, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, + 0x6e, 0x74, 0x55, 0x70, 0x64, 0x61, 0x74, 0x65, 0x3d, 0x66, 0x75, 0x6e, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x29, 0x7b, 0x74, 0x68, 0x69, + 0x73, 0x2e, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x21, 0x3d, 0x3d, 0x74, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x26, + 0x26, 0x65, 0x2e, 0x73, 0x6f, 0x6d, 0x65, 0x28, 0x28, 0x66, 0x75, 0x6e, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x29, 0x7b, 0x74, 0x2e, 0x5f, + 0x5f, 0x65, 0x3d, 0x21, 0x30, 0x2c, 0x6a, 0x28, 0x74, 0x29, 0x7d, 0x29, + 0x29, 0x7d, 0x2c, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x73, 0x75, 0x62, 0x3d, + 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x29, 0x7b, + 0x65, 0x2e, 0x70, 0x75, 0x73, 0x68, 0x28, 0x74, 0x29, 0x3b, 0x76, 0x61, + 0x72, 0x20, 0x6e, 0x3d, 0x74, 0x2e, 0x63, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, + 0x65, 0x6e, 0x74, 0x57, 0x69, 0x6c, 0x6c, 0x55, 0x6e, 0x6d, 0x6f, 0x75, + 0x6e, 0x74, 0x3b, 0x74, 0x2e, 0x63, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, + 0x6e, 0x74, 0x57, 0x69, 0x6c, 0x6c, 0x55, 0x6e, 0x6d, 0x6f, 0x75, 0x6e, + 0x74, 0x3d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x29, + 0x7b, 0x65, 0x2e, 0x73, 0x70, 0x6c, 0x69, 0x63, 0x65, 0x28, 0x65, 0x2e, + 0x69, 0x6e, 0x64, 0x65, 0x78, 0x4f, 0x66, 0x28, 0x74, 0x29, 0x2c, 0x31, + 0x29, 0x2c, 0x6e, 0x26, 0x26, 0x6e, 0x2e, 0x63, 0x61, 0x6c, 0x6c, 0x28, + 0x74, 0x29, 0x7d, 0x7d, 0x29, 0x2c, 0x74, 0x2e, 0x63, 0x68, 0x69, 0x6c, + 0x64, 0x72, 0x65, 0x6e, 0x7d, 0x7d, 0x3b, 0x72, 0x65, 0x74, 0x75, 0x72, + 0x6e, 0x20, 0x65, 0x2e, 0x50, 0x72, 0x6f, 0x76, 0x69, 0x64, 0x65, 0x72, + 0x2e, 0x5f, 0x5f, 0x3d, 0x65, 0x2e, 0x43, 0x6f, 0x6e, 0x73, 0x75, 0x6d, + 0x65, 0x72, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x78, 0x74, 0x54, 0x79, + 0x70, 0x65, 0x3d, 0x65, 0x7d, 0x6b, 0x3d, 0x44, 0x2e, 0x73, 0x6c, 0x69, + 0x63, 0x65, 0x2c, 0x53, 0x3d, 0x7b, 0x5f, 0x5f, 0x65, 0x3a, 0x66, 0x75, + 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x2c, 0x6e, 0x2c, 0x65, + 0x2c, 0x69, 0x29, 0x7b, 0x66, 0x6f, 0x72, 0x28, 0x76, 0x61, 0x72, 0x20, + 0x5f, 0x2c, 0x6f, 0x2c, 0x72, 0x3b, 0x6e, 0x3d, 0x6e, 0x2e, 0x5f, 0x5f, + 0x3b, 0x29, 0x69, 0x66, 0x28, 0x28, 0x5f, 0x3d, 0x6e, 0x2e, 0x5f, 0x5f, + 0x63, 0x29, 0x26, 0x26, 0x21, 0x5f, 0x2e, 0x5f, 0x5f, 0x29, 0x74, 0x72, + 0x79, 0x7b, 0x69, 0x66, 0x28, 0x28, 0x6f, 0x3d, 0x5f, 0x2e, 0x63, 0x6f, + 0x6e, 0x73, 0x74, 0x72, 0x75, 0x63, 0x74, 0x6f, 0x72, 0x29, 0x26, 0x26, + 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x6f, 0x2e, 0x67, 0x65, 0x74, 0x44, + 0x65, 0x72, 0x69, 0x76, 0x65, 0x64, 0x53, 0x74, 0x61, 0x74, 0x65, 0x46, + 0x72, 0x6f, 0x6d, 0x45, 0x72, 0x72, 0x6f, 0x72, 0x26, 0x26, 0x28, 0x5f, + 0x2e, 0x73, 0x65, 0x74, 0x53, 0x74, 0x61, 0x74, 0x65, 0x28, 0x6f, 0x2e, + 0x67, 0x65, 0x74, 0x44, 0x65, 0x72, 0x69, 0x76, 0x65, 0x64, 0x53, 0x74, + 0x61, 0x74, 0x65, 0x46, 0x72, 0x6f, 0x6d, 0x45, 0x72, 0x72, 0x6f, 0x72, + 0x28, 0x74, 0x29, 0x29, 0x2c, 0x72, 0x3d, 0x5f, 0x2e, 0x5f, 0x5f, 0x64, + 0x29, 0x2c, 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x5f, 0x2e, 0x63, 0x6f, + 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x44, 0x69, 0x64, 0x43, 0x61, + 0x74, 0x63, 0x68, 0x26, 0x26, 0x28, 0x5f, 0x2e, 0x63, 0x6f, 0x6d, 0x70, + 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x44, 0x69, 0x64, 0x43, 0x61, 0x74, 0x63, + 0x68, 0x28, 0x74, 0x2c, 0x69, 0x7c, 0x7c, 0x7b, 0x7d, 0x29, 0x2c, 0x72, + 0x3d, 0x5f, 0x2e, 0x5f, 0x5f, 0x64, 0x29, 0x2c, 0x72, 0x29, 0x72, 0x65, + 0x74, 0x75, 0x72, 0x6e, 0x20, 0x5f, 0x2e, 0x5f, 0x5f, 0x45, 0x3d, 0x5f, + 0x7d, 0x63, 0x61, 0x74, 0x63, 0x68, 0x28, 0x6e, 0x29, 0x7b, 0x74, 0x3d, + 0x6e, 0x7d, 0x74, 0x68, 0x72, 0x6f, 0x77, 0x20, 0x74, 0x7d, 0x7d, 0x2c, + 0x78, 0x3d, 0x30, 0x2c, 0x77, 0x3d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, + 0x6f, 0x6e, 0x28, 0x74, 0x29, 0x7b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, + 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x74, 0x26, 0x26, 0x76, 0x6f, + 0x69, 0x64, 0x20, 0x30, 0x3d, 0x3d, 0x3d, 0x74, 0x2e, 0x63, 0x6f, 0x6e, + 0x73, 0x74, 0x72, 0x75, 0x63, 0x74, 0x6f, 0x72, 0x7d, 0x2c, 0x4c, 0x2e, + 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x74, 0x79, 0x70, 0x65, 0x2e, 0x73, 0x65, + 0x74, 0x53, 0x74, 0x61, 0x74, 0x65, 0x3d, 0x66, 0x75, 0x6e, 0x63, 0x74, + 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x2c, 0x6e, 0x29, 0x7b, 0x76, 0x61, 0x72, + 0x20, 0x65, 0x3b, 0x65, 0x3d, 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x74, + 0x68, 0x69, 0x73, 0x2e, 0x5f, 0x5f, 0x73, 0x26, 0x26, 0x74, 0x68, 0x69, + 0x73, 0x2e, 0x5f, 0x5f, 0x73, 0x21, 0x3d, 0x3d, 0x74, 0x68, 0x69, 0x73, + 0x2e, 0x73, 0x74, 0x61, 0x74, 0x65, 0x3f, 0x74, 0x68, 0x69, 0x73, 0x2e, + 0x5f, 0x5f, 0x73, 0x3a, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x5f, 0x5f, 0x73, + 0x3d, 0x56, 0x28, 0x7b, 0x7d, 0x2c, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x73, + 0x74, 0x61, 0x74, 0x65, 0x29, 0x2c, 0x22, 0x66, 0x75, 0x6e, 0x63, 0x74, + 0x69, 0x6f, 0x6e, 0x22, 0x3d, 0x3d, 0x74, 0x79, 0x70, 0x65, 0x6f, 0x66, + 0x20, 0x74, 0x26, 0x26, 0x28, 0x74, 0x3d, 0x74, 0x28, 0x56, 0x28, 0x7b, + 0x7d, 0x2c, 0x65, 0x29, 0x2c, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x70, 0x72, + 0x6f, 0x70, 0x73, 0x29, 0x29, 0x2c, 0x74, 0x26, 0x26, 0x56, 0x28, 0x65, + 0x2c, 0x74, 0x29, 0x2c, 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x74, 0x26, + 0x26, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x5f, 0x5f, 0x76, 0x26, 0x26, 0x28, + 0x6e, 0x26, 0x26, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x5f, 0x73, 0x62, 0x2e, + 0x70, 0x75, 0x73, 0x68, 0x28, 0x6e, 0x29, 0x2c, 0x6a, 0x28, 0x74, 0x68, + 0x69, 0x73, 0x29, 0x29, 0x7d, 0x2c, 0x4c, 0x2e, 0x70, 0x72, 0x6f, 0x74, + 0x6f, 0x74, 0x79, 0x70, 0x65, 0x2e, 0x66, 0x6f, 0x72, 0x63, 0x65, 0x55, + 0x70, 0x64, 0x61, 0x74, 0x65, 0x3d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, + 0x6f, 0x6e, 0x28, 0x74, 0x29, 0x7b, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x5f, + 0x5f, 0x76, 0x26, 0x26, 0x28, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x5f, 0x5f, + 0x65, 0x3d, 0x21, 0x30, 0x2c, 0x74, 0x26, 0x26, 0x74, 0x68, 0x69, 0x73, + 0x2e, 0x5f, 0x5f, 0x68, 0x2e, 0x70, 0x75, 0x73, 0x68, 0x28, 0x74, 0x29, + 0x2c, 0x6a, 0x28, 0x74, 0x68, 0x69, 0x73, 0x29, 0x29, 0x7d, 0x2c, 0x4c, + 0x2e, 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x74, 0x79, 0x70, 0x65, 0x2e, 0x72, + 0x65, 0x6e, 0x64, 0x65, 0x72, 0x3d, 0x4f, 0x2c, 0x43, 0x3d, 0x5b, 0x5d, + 0x2c, 0x55, 0x3d, 0x22, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x22, 0x3d, 0x3d, 0x74, 0x79, 0x70, 0x65, 0x6f, 0x66, 0x20, 0x50, 0x72, + 0x6f, 0x6d, 0x69, 0x73, 0x65, 0x3f, 0x50, 0x72, 0x6f, 0x6d, 0x69, 0x73, + 0x65, 0x2e, 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x74, 0x79, 0x70, 0x65, 0x2e, + 0x74, 0x68, 0x65, 0x6e, 0x2e, 0x62, 0x69, 0x6e, 0x64, 0x28, 0x50, 0x72, + 0x6f, 0x6d, 0x69, 0x73, 0x65, 0x2e, 0x72, 0x65, 0x73, 0x6f, 0x6c, 0x76, + 0x65, 0x28, 0x29, 0x29, 0x3a, 0x73, 0x65, 0x74, 0x54, 0x69, 0x6d, 0x65, + 0x6f, 0x75, 0x74, 0x2c, 0x48, 0x3d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, + 0x6f, 0x6e, 0x28, 0x74, 0x2c, 0x6e, 0x29, 0x7b, 0x72, 0x65, 0x74, 0x75, + 0x72, 0x6e, 0x20, 0x74, 0x2e, 0x5f, 0x5f, 0x76, 0x2e, 0x5f, 0x5f, 0x62, + 0x2d, 0x6e, 0x2e, 0x5f, 0x5f, 0x76, 0x2e, 0x5f, 0x5f, 0x62, 0x7d, 0x2c, + 0x71, 0x2e, 0x5f, 0x5f, 0x72, 0x3d, 0x30, 0x2c, 0x4e, 0x3d, 0x30, 0x3b, + 0x76, 0x61, 0x72, 0x20, 0x63, 0x74, 0x2c, 0x68, 0x74, 0x2c, 0x61, 0x74, + 0x2c, 0x70, 0x74, 0x2c, 0x64, 0x74, 0x3d, 0x30, 0x2c, 0x76, 0x74, 0x3d, + 0x5b, 0x5d, 0x2c, 0x79, 0x74, 0x3d, 0x5b, 0x5d, 0x2c, 0x6d, 0x74, 0x3d, + 0x53, 0x2e, 0x5f, 0x5f, 0x62, 0x2c, 0x67, 0x74, 0x3d, 0x53, 0x2e, 0x5f, + 0x5f, 0x72, 0x2c, 0x62, 0x74, 0x3d, 0x53, 0x2e, 0x64, 0x69, 0x66, 0x66, + 0x65, 0x64, 0x2c, 0x6b, 0x74, 0x3d, 0x53, 0x2e, 0x5f, 0x5f, 0x63, 0x2c, + 0x53, 0x74, 0x3d, 0x53, 0x2e, 0x75, 0x6e, 0x6d, 0x6f, 0x75, 0x6e, 0x74, + 0x3b, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x78, 0x74, + 0x28, 0x74, 0x2c, 0x6e, 0x29, 0x7b, 0x53, 0x2e, 0x5f, 0x5f, 0x68, 0x26, + 0x26, 0x53, 0x2e, 0x5f, 0x5f, 0x68, 0x28, 0x68, 0x74, 0x2c, 0x74, 0x2c, + 0x64, 0x74, 0x7c, 0x7c, 0x6e, 0x29, 0x2c, 0x64, 0x74, 0x3d, 0x30, 0x3b, + 0x76, 0x61, 0x72, 0x20, 0x65, 0x3d, 0x68, 0x74, 0x2e, 0x5f, 0x5f, 0x48, + 0x7c, 0x7c, 0x28, 0x68, 0x74, 0x2e, 0x5f, 0x5f, 0x48, 0x3d, 0x7b, 0x5f, + 0x5f, 0x3a, 0x5b, 0x5d, 0x2c, 0x5f, 0x5f, 0x68, 0x3a, 0x5b, 0x5d, 0x7d, + 0x29, 0x3b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x74, 0x3e, 0x3d, + 0x65, 0x2e, 0x5f, 0x5f, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x26, + 0x26, 0x65, 0x2e, 0x5f, 0x5f, 0x2e, 0x70, 0x75, 0x73, 0x68, 0x28, 0x7b, + 0x5f, 0x5f, 0x56, 0x3a, 0x79, 0x74, 0x7d, 0x29, 0x2c, 0x65, 0x2e, 0x5f, + 0x5f, 0x5b, 0x74, 0x5d, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, + 0x6e, 0x20, 0x77, 0x74, 0x28, 0x74, 0x29, 0x7b, 0x72, 0x65, 0x74, 0x75, + 0x72, 0x6e, 0x20, 0x64, 0x74, 0x3d, 0x31, 0x2c, 0x43, 0x74, 0x28, 0x49, + 0x74, 0x2c, 0x74, 0x29, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, + 0x6e, 0x20, 0x43, 0x74, 0x28, 0x74, 0x2c, 0x6e, 0x2c, 0x65, 0x29, 0x7b, + 0x76, 0x61, 0x72, 0x20, 0x69, 0x3d, 0x78, 0x74, 0x28, 0x63, 0x74, 0x2b, + 0x2b, 0x2c, 0x32, 0x29, 0x3b, 0x69, 0x66, 0x28, 0x69, 0x2e, 0x74, 0x3d, + 0x74, 0x2c, 0x21, 0x69, 0x2e, 0x5f, 0x5f, 0x63, 0x26, 0x26, 0x28, 0x69, + 0x2e, 0x5f, 0x5f, 0x3d, 0x5b, 0x65, 0x3f, 0x65, 0x28, 0x6e, 0x29, 0x3a, + 0x49, 0x74, 0x28, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x2c, 0x6e, 0x29, + 0x2c, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x29, + 0x7b, 0x76, 0x61, 0x72, 0x20, 0x6e, 0x3d, 0x69, 0x2e, 0x5f, 0x5f, 0x4e, + 0x3f, 0x69, 0x2e, 0x5f, 0x5f, 0x4e, 0x5b, 0x30, 0x5d, 0x3a, 0x69, 0x2e, + 0x5f, 0x5f, 0x5b, 0x30, 0x5d, 0x2c, 0x65, 0x3d, 0x69, 0x2e, 0x74, 0x28, + 0x6e, 0x2c, 0x74, 0x29, 0x3b, 0x6e, 0x21, 0x3d, 0x3d, 0x65, 0x26, 0x26, + 0x28, 0x69, 0x2e, 0x5f, 0x5f, 0x4e, 0x3d, 0x5b, 0x65, 0x2c, 0x69, 0x2e, + 0x5f, 0x5f, 0x5b, 0x31, 0x5d, 0x5d, 0x2c, 0x69, 0x2e, 0x5f, 0x5f, 0x63, + 0x2e, 0x73, 0x65, 0x74, 0x53, 0x74, 0x61, 0x74, 0x65, 0x28, 0x7b, 0x7d, + 0x29, 0x29, 0x7d, 0x5d, 0x2c, 0x69, 0x2e, 0x5f, 0x5f, 0x63, 0x3d, 0x68, + 0x74, 0x2c, 0x21, 0x68, 0x74, 0x2e, 0x75, 0x29, 0x29, 0x7b, 0x76, 0x61, + 0x72, 0x20, 0x5f, 0x3d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x28, 0x74, 0x2c, 0x6e, 0x2c, 0x65, 0x29, 0x7b, 0x69, 0x66, 0x28, 0x21, + 0x69, 0x2e, 0x5f, 0x5f, 0x63, 0x2e, 0x5f, 0x5f, 0x48, 0x29, 0x72, 0x65, + 0x74, 0x75, 0x72, 0x6e, 0x21, 0x30, 0x3b, 0x76, 0x61, 0x72, 0x20, 0x5f, + 0x3d, 0x69, 0x2e, 0x5f, 0x5f, 0x63, 0x2e, 0x5f, 0x5f, 0x48, 0x2e, 0x5f, + 0x5f, 0x2e, 0x66, 0x69, 0x6c, 0x74, 0x65, 0x72, 0x28, 0x28, 0x66, 0x75, + 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x29, 0x7b, 0x72, 0x65, + 0x74, 0x75, 0x72, 0x6e, 0x20, 0x74, 0x2e, 0x5f, 0x5f, 0x63, 0x7d, 0x29, + 0x29, 0x3b, 0x69, 0x66, 0x28, 0x5f, 0x2e, 0x65, 0x76, 0x65, 0x72, 0x79, + 0x28, 0x28, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, + 0x29, 0x7b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x21, 0x74, 0x2e, 0x5f, + 0x5f, 0x4e, 0x7d, 0x29, 0x29, 0x29, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, + 0x21, 0x6f, 0x7c, 0x7c, 0x6f, 0x2e, 0x63, 0x61, 0x6c, 0x6c, 0x28, 0x74, + 0x68, 0x69, 0x73, 0x2c, 0x74, 0x2c, 0x6e, 0x2c, 0x65, 0x29, 0x3b, 0x76, + 0x61, 0x72, 0x20, 0x72, 0x3d, 0x21, 0x31, 0x3b, 0x72, 0x65, 0x74, 0x75, + 0x72, 0x6e, 0x20, 0x5f, 0x2e, 0x66, 0x6f, 0x72, 0x45, 0x61, 0x63, 0x68, + 0x28, 0x28, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, + 0x29, 0x7b, 0x69, 0x66, 0x28, 0x74, 0x2e, 0x5f, 0x5f, 0x4e, 0x29, 0x7b, + 0x76, 0x61, 0x72, 0x20, 0x6e, 0x3d, 0x74, 0x2e, 0x5f, 0x5f, 0x5b, 0x30, + 0x5d, 0x3b, 0x74, 0x2e, 0x5f, 0x5f, 0x3d, 0x74, 0x2e, 0x5f, 0x5f, 0x4e, + 0x2c, 0x74, 0x2e, 0x5f, 0x5f, 0x4e, 0x3d, 0x76, 0x6f, 0x69, 0x64, 0x20, + 0x30, 0x2c, 0x6e, 0x21, 0x3d, 0x3d, 0x74, 0x2e, 0x5f, 0x5f, 0x5b, 0x30, + 0x5d, 0x26, 0x26, 0x28, 0x72, 0x3d, 0x21, 0x30, 0x29, 0x7d, 0x7d, 0x29, + 0x29, 0x2c, 0x21, 0x28, 0x21, 0x72, 0x26, 0x26, 0x69, 0x2e, 0x5f, 0x5f, + 0x63, 0x2e, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x3d, 0x3d, 0x3d, 0x74, 0x29, + 0x26, 0x26, 0x28, 0x21, 0x6f, 0x7c, 0x7c, 0x6f, 0x2e, 0x63, 0x61, 0x6c, + 0x6c, 0x28, 0x74, 0x68, 0x69, 0x73, 0x2c, 0x74, 0x2c, 0x6e, 0x2c, 0x65, + 0x29, 0x29, 0x7d, 0x3b, 0x68, 0x74, 0x2e, 0x75, 0x3d, 0x21, 0x30, 0x3b, + 0x76, 0x61, 0x72, 0x20, 0x6f, 0x3d, 0x68, 0x74, 0x2e, 0x73, 0x68, 0x6f, + 0x75, 0x6c, 0x64, 0x43, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, + 0x55, 0x70, 0x64, 0x61, 0x74, 0x65, 0x2c, 0x72, 0x3d, 0x68, 0x74, 0x2e, + 0x63, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x57, 0x69, 0x6c, + 0x6c, 0x55, 0x70, 0x64, 0x61, 0x74, 0x65, 0x3b, 0x68, 0x74, 0x2e, 0x63, + 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x57, 0x69, 0x6c, 0x6c, + 0x55, 0x70, 0x64, 0x61, 0x74, 0x65, 0x3d, 0x66, 0x75, 0x6e, 0x63, 0x74, + 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x2c, 0x6e, 0x2c, 0x65, 0x29, 0x7b, 0x69, + 0x66, 0x28, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x5f, 0x5f, 0x65, 0x29, 0x7b, + 0x76, 0x61, 0x72, 0x20, 0x69, 0x3d, 0x6f, 0x3b, 0x6f, 0x3d, 0x76, 0x6f, + 0x69, 0x64, 0x20, 0x30, 0x2c, 0x5f, 0x28, 0x74, 0x2c, 0x6e, 0x2c, 0x65, + 0x29, 0x2c, 0x6f, 0x3d, 0x69, 0x7d, 0x72, 0x26, 0x26, 0x72, 0x2e, 0x63, + 0x61, 0x6c, 0x6c, 0x28, 0x74, 0x68, 0x69, 0x73, 0x2c, 0x74, 0x2c, 0x6e, + 0x2c, 0x65, 0x29, 0x7d, 0x2c, 0x68, 0x74, 0x2e, 0x73, 0x68, 0x6f, 0x75, + 0x6c, 0x64, 0x43, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x55, + 0x70, 0x64, 0x61, 0x74, 0x65, 0x3d, 0x5f, 0x7d, 0x72, 0x65, 0x74, 0x75, + 0x72, 0x6e, 0x20, 0x69, 0x2e, 0x5f, 0x5f, 0x4e, 0x7c, 0x7c, 0x69, 0x2e, + 0x5f, 0x5f, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, + 0x45, 0x74, 0x28, 0x74, 0x2c, 0x6e, 0x29, 0x7b, 0x76, 0x61, 0x72, 0x20, + 0x65, 0x3d, 0x78, 0x74, 0x28, 0x63, 0x74, 0x2b, 0x2b, 0x2c, 0x33, 0x29, + 0x3b, 0x21, 0x53, 0x2e, 0x5f, 0x5f, 0x73, 0x26, 0x26, 0x52, 0x74, 0x28, + 0x65, 0x2e, 0x5f, 0x5f, 0x48, 0x2c, 0x6e, 0x29, 0x26, 0x26, 0x28, 0x65, + 0x2e, 0x5f, 0x5f, 0x3d, 0x74, 0x2c, 0x65, 0x2e, 0x69, 0x3d, 0x6e, 0x2c, + 0x68, 0x74, 0x2e, 0x5f, 0x5f, 0x48, 0x2e, 0x5f, 0x5f, 0x68, 0x2e, 0x70, + 0x75, 0x73, 0x68, 0x28, 0x65, 0x29, 0x29, 0x7d, 0x66, 0x75, 0x6e, 0x63, + 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x55, 0x74, 0x28, 0x74, 0x2c, 0x6e, 0x29, + 0x7b, 0x76, 0x61, 0x72, 0x20, 0x65, 0x3d, 0x78, 0x74, 0x28, 0x63, 0x74, + 0x2b, 0x2b, 0x2c, 0x34, 0x29, 0x3b, 0x21, 0x53, 0x2e, 0x5f, 0x5f, 0x73, + 0x26, 0x26, 0x52, 0x74, 0x28, 0x65, 0x2e, 0x5f, 0x5f, 0x48, 0x2c, 0x6e, + 0x29, 0x26, 0x26, 0x28, 0x65, 0x2e, 0x5f, 0x5f, 0x3d, 0x74, 0x2c, 0x65, + 0x2e, 0x69, 0x3d, 0x6e, 0x2c, 0x68, 0x74, 0x2e, 0x5f, 0x5f, 0x68, 0x2e, + 0x70, 0x75, 0x73, 0x68, 0x28, 0x65, 0x29, 0x29, 0x7d, 0x66, 0x75, 0x6e, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x48, 0x74, 0x28, 0x74, 0x29, 0x7b, + 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x64, 0x74, 0x3d, 0x35, 0x2c, + 0x50, 0x74, 0x28, 0x28, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x28, 0x29, 0x7b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x7b, 0x63, 0x75, + 0x72, 0x72, 0x65, 0x6e, 0x74, 0x3a, 0x74, 0x7d, 0x7d, 0x29, 0x2c, 0x5b, + 0x5d, 0x29, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, + 0x4e, 0x74, 0x28, 0x74, 0x2c, 0x6e, 0x2c, 0x65, 0x29, 0x7b, 0x64, 0x74, + 0x3d, 0x36, 0x2c, 0x55, 0x74, 0x28, 0x28, 0x66, 0x75, 0x6e, 0x63, 0x74, + 0x69, 0x6f, 0x6e, 0x28, 0x29, 0x7b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, + 0x22, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x22, 0x3d, 0x3d, + 0x74, 0x79, 0x70, 0x65, 0x6f, 0x66, 0x20, 0x74, 0x3f, 0x28, 0x74, 0x28, + 0x6e, 0x28, 0x29, 0x29, 0x2c, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, + 0x6e, 0x28, 0x29, 0x7b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x74, + 0x28, 0x6e, 0x75, 0x6c, 0x6c, 0x29, 0x7d, 0x29, 0x3a, 0x74, 0x3f, 0x28, + 0x74, 0x2e, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x3d, 0x6e, 0x28, + 0x29, 0x2c, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x29, + 0x7b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x74, 0x2e, 0x63, 0x75, + 0x72, 0x72, 0x65, 0x6e, 0x74, 0x3d, 0x6e, 0x75, 0x6c, 0x6c, 0x7d, 0x29, + 0x3a, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x7d, 0x29, 0x2c, 0x6e, 0x75, + 0x6c, 0x6c, 0x3d, 0x3d, 0x65, 0x3f, 0x65, 0x3a, 0x65, 0x2e, 0x63, 0x6f, + 0x6e, 0x63, 0x61, 0x74, 0x28, 0x74, 0x29, 0x29, 0x7d, 0x66, 0x75, 0x6e, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x50, 0x74, 0x28, 0x74, 0x2c, 0x6e, + 0x29, 0x7b, 0x76, 0x61, 0x72, 0x20, 0x65, 0x3d, 0x78, 0x74, 0x28, 0x63, + 0x74, 0x2b, 0x2b, 0x2c, 0x37, 0x29, 0x3b, 0x72, 0x65, 0x74, 0x75, 0x72, + 0x6e, 0x20, 0x52, 0x74, 0x28, 0x65, 0x2e, 0x5f, 0x5f, 0x48, 0x2c, 0x6e, + 0x29, 0x3f, 0x28, 0x65, 0x2e, 0x5f, 0x5f, 0x56, 0x3d, 0x74, 0x28, 0x29, + 0x2c, 0x65, 0x2e, 0x69, 0x3d, 0x6e, 0x2c, 0x65, 0x2e, 0x5f, 0x5f, 0x68, + 0x3d, 0x74, 0x2c, 0x65, 0x2e, 0x5f, 0x5f, 0x56, 0x29, 0x3a, 0x65, 0x2e, + 0x5f, 0x5f, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, + 0x44, 0x74, 0x28, 0x74, 0x2c, 0x6e, 0x29, 0x7b, 0x72, 0x65, 0x74, 0x75, + 0x72, 0x6e, 0x20, 0x64, 0x74, 0x3d, 0x38, 0x2c, 0x50, 0x74, 0x28, 0x28, + 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x29, 0x7b, 0x72, + 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x74, 0x7d, 0x29, 0x2c, 0x6e, 0x29, + 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x24, 0x74, + 0x28, 0x74, 0x29, 0x7b, 0x76, 0x61, 0x72, 0x20, 0x6e, 0x3d, 0x68, 0x74, + 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x78, 0x74, 0x5b, 0x74, 0x2e, 0x5f, + 0x5f, 0x63, 0x5d, 0x2c, 0x65, 0x3d, 0x78, 0x74, 0x28, 0x63, 0x74, 0x2b, + 0x2b, 0x2c, 0x39, 0x29, 0x3b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, + 0x65, 0x2e, 0x63, 0x3d, 0x74, 0x2c, 0x6e, 0x3f, 0x28, 0x6e, 0x75, 0x6c, + 0x6c, 0x3d, 0x3d, 0x65, 0x2e, 0x5f, 0x5f, 0x26, 0x26, 0x28, 0x65, 0x2e, + 0x5f, 0x5f, 0x3d, 0x21, 0x30, 0x2c, 0x6e, 0x2e, 0x73, 0x75, 0x62, 0x28, + 0x68, 0x74, 0x29, 0x29, 0x2c, 0x6e, 0x2e, 0x70, 0x72, 0x6f, 0x70, 0x73, + 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x3a, 0x74, 0x2e, 0x5f, 0x5f, + 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x54, 0x74, + 0x28, 0x74, 0x2c, 0x6e, 0x29, 0x7b, 0x53, 0x2e, 0x75, 0x73, 0x65, 0x44, + 0x65, 0x62, 0x75, 0x67, 0x56, 0x61, 0x6c, 0x75, 0x65, 0x26, 0x26, 0x53, + 0x2e, 0x75, 0x73, 0x65, 0x44, 0x65, 0x62, 0x75, 0x67, 0x56, 0x61, 0x6c, + 0x75, 0x65, 0x28, 0x6e, 0x3f, 0x6e, 0x28, 0x74, 0x29, 0x3a, 0x74, 0x29, + 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x56, 0x74, + 0x28, 0x74, 0x29, 0x7b, 0x76, 0x61, 0x72, 0x20, 0x6e, 0x3d, 0x78, 0x74, + 0x28, 0x63, 0x74, 0x2b, 0x2b, 0x2c, 0x31, 0x30, 0x29, 0x2c, 0x65, 0x3d, + 0x77, 0x74, 0x28, 0x29, 0x3b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, + 0x6e, 0x2e, 0x5f, 0x5f, 0x3d, 0x74, 0x2c, 0x68, 0x74, 0x2e, 0x63, 0x6f, + 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x44, 0x69, 0x64, 0x43, 0x61, + 0x74, 0x63, 0x68, 0x7c, 0x7c, 0x28, 0x68, 0x74, 0x2e, 0x63, 0x6f, 0x6d, + 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x44, 0x69, 0x64, 0x43, 0x61, 0x74, + 0x63, 0x68, 0x3d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, + 0x74, 0x2c, 0x69, 0x29, 0x7b, 0x6e, 0x2e, 0x5f, 0x5f, 0x26, 0x26, 0x6e, + 0x2e, 0x5f, 0x5f, 0x28, 0x74, 0x2c, 0x69, 0x29, 0x2c, 0x65, 0x5b, 0x31, + 0x5d, 0x28, 0x74, 0x29, 0x7d, 0x29, 0x2c, 0x5b, 0x65, 0x5b, 0x30, 0x5d, + 0x2c, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x29, 0x7b, + 0x65, 0x5b, 0x31, 0x5d, 0x28, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x29, + 0x7d, 0x5d, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, + 0x41, 0x74, 0x28, 0x29, 0x7b, 0x76, 0x61, 0x72, 0x20, 0x74, 0x3d, 0x78, + 0x74, 0x28, 0x63, 0x74, 0x2b, 0x2b, 0x2c, 0x31, 0x31, 0x29, 0x3b, 0x69, + 0x66, 0x28, 0x21, 0x74, 0x2e, 0x5f, 0x5f, 0x29, 0x7b, 0x66, 0x6f, 0x72, + 0x28, 0x76, 0x61, 0x72, 0x20, 0x6e, 0x3d, 0x68, 0x74, 0x2e, 0x5f, 0x5f, + 0x76, 0x3b, 0x6e, 0x75, 0x6c, 0x6c, 0x21, 0x3d, 0x3d, 0x6e, 0x26, 0x26, + 0x21, 0x6e, 0x2e, 0x5f, 0x5f, 0x6d, 0x26, 0x26, 0x6e, 0x75, 0x6c, 0x6c, + 0x21, 0x3d, 0x3d, 0x6e, 0x2e, 0x5f, 0x5f, 0x3b, 0x29, 0x6e, 0x3d, 0x6e, + 0x2e, 0x5f, 0x5f, 0x3b, 0x76, 0x61, 0x72, 0x20, 0x65, 0x3d, 0x6e, 0x2e, + 0x5f, 0x5f, 0x6d, 0x7c, 0x7c, 0x28, 0x6e, 0x2e, 0x5f, 0x5f, 0x6d, 0x3d, + 0x5b, 0x30, 0x2c, 0x30, 0x5d, 0x29, 0x3b, 0x74, 0x2e, 0x5f, 0x5f, 0x3d, + 0x22, 0x50, 0x22, 0x2b, 0x65, 0x5b, 0x30, 0x5d, 0x2b, 0x22, 0x2d, 0x22, + 0x2b, 0x65, 0x5b, 0x31, 0x5d, 0x2b, 0x2b, 0x7d, 0x72, 0x65, 0x74, 0x75, + 0x72, 0x6e, 0x20, 0x74, 0x2e, 0x5f, 0x5f, 0x7d, 0x66, 0x75, 0x6e, 0x63, + 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x46, 0x74, 0x28, 0x29, 0x7b, 0x66, 0x6f, + 0x72, 0x28, 0x76, 0x61, 0x72, 0x20, 0x74, 0x3b, 0x74, 0x3d, 0x76, 0x74, + 0x2e, 0x73, 0x68, 0x69, 0x66, 0x74, 0x28, 0x29, 0x3b, 0x29, 0x69, 0x66, + 0x28, 0x74, 0x2e, 0x5f, 0x5f, 0x50, 0x26, 0x26, 0x74, 0x2e, 0x5f, 0x5f, + 0x48, 0x29, 0x74, 0x72, 0x79, 0x7b, 0x74, 0x2e, 0x5f, 0x5f, 0x48, 0x2e, + 0x5f, 0x5f, 0x68, 0x2e, 0x66, 0x6f, 0x72, 0x45, 0x61, 0x63, 0x68, 0x28, + 0x4f, 0x74, 0x29, 0x2c, 0x74, 0x2e, 0x5f, 0x5f, 0x48, 0x2e, 0x5f, 0x5f, + 0x68, 0x2e, 0x66, 0x6f, 0x72, 0x45, 0x61, 0x63, 0x68, 0x28, 0x4c, 0x74, + 0x29, 0x2c, 0x74, 0x2e, 0x5f, 0x5f, 0x48, 0x2e, 0x5f, 0x5f, 0x68, 0x3d, + 0x5b, 0x5d, 0x7d, 0x63, 0x61, 0x74, 0x63, 0x68, 0x28, 0x75, 0x29, 0x7b, + 0x74, 0x2e, 0x5f, 0x5f, 0x48, 0x2e, 0x5f, 0x5f, 0x68, 0x3d, 0x5b, 0x5d, + 0x2c, 0x53, 0x2e, 0x5f, 0x5f, 0x65, 0x28, 0x75, 0x2c, 0x74, 0x2e, 0x5f, + 0x5f, 0x76, 0x29, 0x7d, 0x7d, 0x53, 0x2e, 0x5f, 0x5f, 0x62, 0x3d, 0x66, + 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x29, 0x7b, 0x68, + 0x74, 0x3d, 0x6e, 0x75, 0x6c, 0x6c, 0x2c, 0x6d, 0x74, 0x26, 0x26, 0x6d, + 0x74, 0x28, 0x74, 0x29, 0x7d, 0x2c, 0x53, 0x2e, 0x5f, 0x5f, 0x72, 0x3d, + 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x29, 0x7b, + 0x67, 0x74, 0x26, 0x26, 0x67, 0x74, 0x28, 0x74, 0x29, 0x2c, 0x63, 0x74, + 0x3d, 0x30, 0x3b, 0x76, 0x61, 0x72, 0x20, 0x6e, 0x3d, 0x28, 0x68, 0x74, + 0x3d, 0x74, 0x2e, 0x5f, 0x5f, 0x63, 0x29, 0x2e, 0x5f, 0x5f, 0x48, 0x3b, + 0x6e, 0x26, 0x26, 0x28, 0x61, 0x74, 0x3d, 0x3d, 0x3d, 0x68, 0x74, 0x3f, + 0x28, 0x6e, 0x2e, 0x5f, 0x5f, 0x68, 0x3d, 0x5b, 0x5d, 0x2c, 0x68, 0x74, + 0x2e, 0x5f, 0x5f, 0x68, 0x3d, 0x5b, 0x5d, 0x2c, 0x6e, 0x2e, 0x5f, 0x5f, + 0x2e, 0x66, 0x6f, 0x72, 0x45, 0x61, 0x63, 0x68, 0x28, 0x28, 0x66, 0x75, + 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x29, 0x7b, 0x74, 0x2e, + 0x5f, 0x5f, 0x4e, 0x26, 0x26, 0x28, 0x74, 0x2e, 0x5f, 0x5f, 0x3d, 0x74, + 0x2e, 0x5f, 0x5f, 0x4e, 0x29, 0x2c, 0x74, 0x2e, 0x5f, 0x5f, 0x56, 0x3d, + 0x79, 0x74, 0x2c, 0x74, 0x2e, 0x5f, 0x5f, 0x4e, 0x3d, 0x74, 0x2e, 0x69, + 0x3d, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x7d, 0x29, 0x29, 0x29, 0x3a, + 0x28, 0x6e, 0x2e, 0x5f, 0x5f, 0x68, 0x2e, 0x66, 0x6f, 0x72, 0x45, 0x61, + 0x63, 0x68, 0x28, 0x4f, 0x74, 0x29, 0x2c, 0x6e, 0x2e, 0x5f, 0x5f, 0x68, + 0x2e, 0x66, 0x6f, 0x72, 0x45, 0x61, 0x63, 0x68, 0x28, 0x4c, 0x74, 0x29, + 0x2c, 0x6e, 0x2e, 0x5f, 0x5f, 0x68, 0x3d, 0x5b, 0x5d, 0x2c, 0x63, 0x74, + 0x3d, 0x30, 0x29, 0x29, 0x2c, 0x61, 0x74, 0x3d, 0x68, 0x74, 0x7d, 0x2c, + 0x53, 0x2e, 0x64, 0x69, 0x66, 0x66, 0x65, 0x64, 0x3d, 0x66, 0x75, 0x6e, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x29, 0x7b, 0x62, 0x74, 0x26, + 0x26, 0x62, 0x74, 0x28, 0x74, 0x29, 0x3b, 0x76, 0x61, 0x72, 0x20, 0x6e, + 0x3d, 0x74, 0x2e, 0x5f, 0x5f, 0x63, 0x3b, 0x6e, 0x26, 0x26, 0x6e, 0x2e, + 0x5f, 0x5f, 0x48, 0x26, 0x26, 0x28, 0x6e, 0x2e, 0x5f, 0x5f, 0x48, 0x2e, + 0x5f, 0x5f, 0x68, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x26, 0x26, + 0x28, 0x31, 0x21, 0x3d, 0x3d, 0x76, 0x74, 0x2e, 0x70, 0x75, 0x73, 0x68, + 0x28, 0x6e, 0x29, 0x26, 0x26, 0x70, 0x74, 0x3d, 0x3d, 0x3d, 0x53, 0x2e, + 0x72, 0x65, 0x71, 0x75, 0x65, 0x73, 0x74, 0x41, 0x6e, 0x69, 0x6d, 0x61, + 0x74, 0x69, 0x6f, 0x6e, 0x46, 0x72, 0x61, 0x6d, 0x65, 0x7c, 0x7c, 0x28, + 0x28, 0x70, 0x74, 0x3d, 0x53, 0x2e, 0x72, 0x65, 0x71, 0x75, 0x65, 0x73, + 0x74, 0x41, 0x6e, 0x69, 0x6d, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x46, 0x72, + 0x61, 0x6d, 0x65, 0x29, 0x7c, 0x7c, 0x57, 0x74, 0x29, 0x28, 0x46, 0x74, + 0x29, 0x29, 0x2c, 0x6e, 0x2e, 0x5f, 0x5f, 0x48, 0x2e, 0x5f, 0x5f, 0x2e, + 0x66, 0x6f, 0x72, 0x45, 0x61, 0x63, 0x68, 0x28, 0x28, 0x66, 0x75, 0x6e, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x29, 0x7b, 0x74, 0x2e, 0x69, + 0x26, 0x26, 0x28, 0x74, 0x2e, 0x5f, 0x5f, 0x48, 0x3d, 0x74, 0x2e, 0x69, + 0x29, 0x2c, 0x74, 0x2e, 0x5f, 0x5f, 0x56, 0x21, 0x3d, 0x3d, 0x79, 0x74, + 0x26, 0x26, 0x28, 0x74, 0x2e, 0x5f, 0x5f, 0x3d, 0x74, 0x2e, 0x5f, 0x5f, + 0x56, 0x29, 0x2c, 0x74, 0x2e, 0x69, 0x3d, 0x76, 0x6f, 0x69, 0x64, 0x20, + 0x30, 0x2c, 0x74, 0x2e, 0x5f, 0x5f, 0x56, 0x3d, 0x79, 0x74, 0x7d, 0x29, + 0x29, 0x29, 0x2c, 0x61, 0x74, 0x3d, 0x68, 0x74, 0x3d, 0x6e, 0x75, 0x6c, + 0x6c, 0x7d, 0x2c, 0x53, 0x2e, 0x5f, 0x5f, 0x63, 0x3d, 0x66, 0x75, 0x6e, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x2c, 0x6e, 0x29, 0x7b, 0x6e, + 0x2e, 0x73, 0x6f, 0x6d, 0x65, 0x28, 0x28, 0x66, 0x75, 0x6e, 0x63, 0x74, + 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x29, 0x7b, 0x74, 0x72, 0x79, 0x7b, 0x74, + 0x2e, 0x5f, 0x5f, 0x68, 0x2e, 0x66, 0x6f, 0x72, 0x45, 0x61, 0x63, 0x68, + 0x28, 0x4f, 0x74, 0x29, 0x2c, 0x74, 0x2e, 0x5f, 0x5f, 0x68, 0x3d, 0x74, + 0x2e, 0x5f, 0x5f, 0x68, 0x2e, 0x66, 0x69, 0x6c, 0x74, 0x65, 0x72, 0x28, + 0x28, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x29, + 0x7b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x21, 0x74, 0x2e, 0x5f, 0x5f, + 0x7c, 0x7c, 0x4c, 0x74, 0x28, 0x74, 0x29, 0x7d, 0x29, 0x29, 0x7d, 0x63, + 0x61, 0x74, 0x63, 0x68, 0x28, 0x73, 0x29, 0x7b, 0x6e, 0x2e, 0x73, 0x6f, + 0x6d, 0x65, 0x28, 0x28, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x28, 0x74, 0x29, 0x7b, 0x74, 0x2e, 0x5f, 0x5f, 0x68, 0x26, 0x26, 0x28, + 0x74, 0x2e, 0x5f, 0x5f, 0x68, 0x3d, 0x5b, 0x5d, 0x29, 0x7d, 0x29, 0x29, + 0x2c, 0x6e, 0x3d, 0x5b, 0x5d, 0x2c, 0x53, 0x2e, 0x5f, 0x5f, 0x65, 0x28, + 0x73, 0x2c, 0x74, 0x2e, 0x5f, 0x5f, 0x76, 0x29, 0x7d, 0x7d, 0x29, 0x29, + 0x2c, 0x6b, 0x74, 0x26, 0x26, 0x6b, 0x74, 0x28, 0x74, 0x2c, 0x6e, 0x29, + 0x7d, 0x2c, 0x53, 0x2e, 0x75, 0x6e, 0x6d, 0x6f, 0x75, 0x6e, 0x74, 0x3d, + 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x29, 0x7b, + 0x53, 0x74, 0x26, 0x26, 0x53, 0x74, 0x28, 0x74, 0x29, 0x3b, 0x76, 0x61, + 0x72, 0x20, 0x6e, 0x2c, 0x65, 0x3d, 0x74, 0x2e, 0x5f, 0x5f, 0x63, 0x3b, + 0x65, 0x26, 0x26, 0x65, 0x2e, 0x5f, 0x5f, 0x48, 0x26, 0x26, 0x28, 0x65, + 0x2e, 0x5f, 0x5f, 0x48, 0x2e, 0x5f, 0x5f, 0x2e, 0x66, 0x6f, 0x72, 0x45, + 0x61, 0x63, 0x68, 0x28, 0x28, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, + 0x6e, 0x28, 0x74, 0x29, 0x7b, 0x74, 0x72, 0x79, 0x7b, 0x4f, 0x74, 0x28, + 0x74, 0x29, 0x7d, 0x63, 0x61, 0x74, 0x63, 0x68, 0x28, 0x74, 0x29, 0x7b, + 0x6e, 0x3d, 0x74, 0x7d, 0x7d, 0x29, 0x29, 0x2c, 0x65, 0x2e, 0x5f, 0x5f, + 0x48, 0x3d, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x2c, 0x6e, 0x26, 0x26, + 0x53, 0x2e, 0x5f, 0x5f, 0x65, 0x28, 0x6e, 0x2c, 0x65, 0x2e, 0x5f, 0x5f, + 0x76, 0x29, 0x29, 0x7d, 0x3b, 0x76, 0x61, 0x72, 0x20, 0x4d, 0x74, 0x3d, + 0x22, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x22, 0x3d, 0x3d, + 0x74, 0x79, 0x70, 0x65, 0x6f, 0x66, 0x20, 0x72, 0x65, 0x71, 0x75, 0x65, + 0x73, 0x74, 0x41, 0x6e, 0x69, 0x6d, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x46, + 0x72, 0x61, 0x6d, 0x65, 0x3b, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, + 0x6e, 0x20, 0x57, 0x74, 0x28, 0x74, 0x29, 0x7b, 0x76, 0x61, 0x72, 0x20, + 0x6e, 0x2c, 0x65, 0x3d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x28, 0x29, 0x7b, 0x63, 0x6c, 0x65, 0x61, 0x72, 0x54, 0x69, 0x6d, 0x65, + 0x6f, 0x75, 0x74, 0x28, 0x69, 0x29, 0x2c, 0x4d, 0x74, 0x26, 0x26, 0x63, + 0x61, 0x6e, 0x63, 0x65, 0x6c, 0x41, 0x6e, 0x69, 0x6d, 0x61, 0x74, 0x69, + 0x6f, 0x6e, 0x46, 0x72, 0x61, 0x6d, 0x65, 0x28, 0x6e, 0x29, 0x2c, 0x73, + 0x65, 0x74, 0x54, 0x69, 0x6d, 0x65, 0x6f, 0x75, 0x74, 0x28, 0x74, 0x29, + 0x7d, 0x2c, 0x69, 0x3d, 0x73, 0x65, 0x74, 0x54, 0x69, 0x6d, 0x65, 0x6f, + 0x75, 0x74, 0x28, 0x65, 0x2c, 0x31, 0x30, 0x30, 0x29, 0x3b, 0x4d, 0x74, + 0x26, 0x26, 0x28, 0x6e, 0x3d, 0x72, 0x65, 0x71, 0x75, 0x65, 0x73, 0x74, + 0x41, 0x6e, 0x69, 0x6d, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x46, 0x72, 0x61, + 0x6d, 0x65, 0x28, 0x65, 0x29, 0x29, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, + 0x69, 0x6f, 0x6e, 0x20, 0x4f, 0x74, 0x28, 0x74, 0x29, 0x7b, 0x76, 0x61, + 0x72, 0x20, 0x6e, 0x3d, 0x68, 0x74, 0x2c, 0x65, 0x3d, 0x74, 0x2e, 0x5f, + 0x5f, 0x63, 0x3b, 0x22, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x22, 0x3d, 0x3d, 0x74, 0x79, 0x70, 0x65, 0x6f, 0x66, 0x20, 0x65, 0x26, + 0x26, 0x28, 0x74, 0x2e, 0x5f, 0x5f, 0x63, 0x3d, 0x76, 0x6f, 0x69, 0x64, + 0x20, 0x30, 0x2c, 0x65, 0x28, 0x29, 0x29, 0x2c, 0x68, 0x74, 0x3d, 0x6e, + 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x4c, 0x74, + 0x28, 0x74, 0x29, 0x7b, 0x76, 0x61, 0x72, 0x20, 0x6e, 0x3d, 0x68, 0x74, + 0x3b, 0x74, 0x2e, 0x5f, 0x5f, 0x63, 0x3d, 0x74, 0x2e, 0x5f, 0x5f, 0x28, + 0x29, 0x2c, 0x68, 0x74, 0x3d, 0x6e, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, + 0x69, 0x6f, 0x6e, 0x20, 0x52, 0x74, 0x28, 0x74, 0x2c, 0x6e, 0x29, 0x7b, + 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x21, 0x74, 0x7c, 0x7c, 0x74, 0x2e, + 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x21, 0x3d, 0x3d, 0x6e, 0x2e, 0x6c, + 0x65, 0x6e, 0x67, 0x74, 0x68, 0x7c, 0x7c, 0x6e, 0x2e, 0x73, 0x6f, 0x6d, + 0x65, 0x28, 0x28, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, + 0x6e, 0x2c, 0x65, 0x29, 0x7b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, + 0x6e, 0x21, 0x3d, 0x3d, 0x74, 0x5b, 0x65, 0x5d, 0x7d, 0x29, 0x29, 0x7d, + 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x49, 0x74, 0x28, + 0x74, 0x2c, 0x6e, 0x29, 0x7b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x22, + 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x22, 0x3d, 0x3d, 0x74, + 0x79, 0x70, 0x65, 0x6f, 0x66, 0x20, 0x6e, 0x3f, 0x6e, 0x28, 0x74, 0x29, + 0x3a, 0x6e, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, + 0x6a, 0x74, 0x28, 0x74, 0x2c, 0x6e, 0x29, 0x7b, 0x53, 0x5b, 0x74, 0x5d, + 0x3d, 0x6e, 0x2e, 0x62, 0x69, 0x6e, 0x64, 0x28, 0x6e, 0x75, 0x6c, 0x6c, + 0x2c, 0x53, 0x5b, 0x74, 0x5d, 0x7c, 0x7c, 0x28, 0x28, 0x29, 0x3d, 0x3e, + 0x7b, 0x7d, 0x29, 0x29, 0x7d, 0x6c, 0x65, 0x74, 0x20, 0x71, 0x74, 0x2c, + 0x42, 0x74, 0x3b, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, + 0x47, 0x74, 0x28, 0x74, 0x29, 0x7b, 0x69, 0x66, 0x28, 0x42, 0x74, 0x29, + 0x42, 0x74, 0x28, 0x29, 0x3b, 0x42, 0x74, 0x3d, 0x74, 0x26, 0x26, 0x74, + 0x2e, 0x53, 0x28, 0x29, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, + 0x6e, 0x20, 0x7a, 0x74, 0x28, 0x7b, 0x64, 0x61, 0x74, 0x61, 0x3a, 0x74, + 0x7d, 0x29, 0x7b, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6e, 0x3d, 0x4b, + 0x74, 0x28, 0x74, 0x29, 0x3b, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, + 0x3d, 0x74, 0x3b, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x65, 0x3d, 0x50, + 0x74, 0x28, 0x28, 0x29, 0x3d, 0x3e, 0x7b, 0x6c, 0x65, 0x74, 0x20, 0x74, + 0x3d, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x5f, 0x5f, 0x76, 0x3b, 0x77, 0x68, + 0x69, 0x6c, 0x65, 0x28, 0x74, 0x3d, 0x74, 0x2e, 0x5f, 0x5f, 0x29, 0x69, + 0x66, 0x28, 0x74, 0x2e, 0x5f, 0x5f, 0x63, 0x29, 0x7b, 0x74, 0x2e, 0x5f, + 0x5f, 0x63, 0x2e, 0x5f, 0x5f, 0x24, 0x66, 0x7c, 0x3d, 0x34, 0x3b, 0x62, + 0x72, 0x65, 0x61, 0x6b, 0x7d, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x5f, 0x5f, + 0x24, 0x75, 0x2e, 0x63, 0x3d, 0x28, 0x29, 0x3d, 0x3e, 0x7b, 0x74, 0x68, + 0x69, 0x73, 0x2e, 0x62, 0x61, 0x73, 0x65, 0x2e, 0x64, 0x61, 0x74, 0x61, + 0x3d, 0x65, 0x2e, 0x70, 0x65, 0x65, 0x6b, 0x28, 0x29, 0x7d, 0x3b, 0x72, + 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x64, 0x28, 0x28, 0x29, 0x3d, 0x3e, + 0x7b, 0x6c, 0x65, 0x74, 0x20, 0x74, 0x3d, 0x6e, 0x2e, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3b, 0x72, 0x65, 0x74, + 0x75, 0x72, 0x6e, 0x20, 0x30, 0x3d, 0x3d, 0x3d, 0x74, 0x3f, 0x30, 0x3a, + 0x21, 0x30, 0x3d, 0x3d, 0x3d, 0x74, 0x3f, 0x22, 0x22, 0x3a, 0x74, 0x7c, + 0x7c, 0x22, 0x22, 0x7d, 0x29, 0x7d, 0x2c, 0x5b, 0x5d, 0x29, 0x3b, 0x72, + 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x7d, 0x7a, 0x74, 0x2e, 0x64, 0x69, 0x73, 0x70, 0x6c, 0x61, 0x79, + 0x4e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x5f, 0x73, 0x74, 0x22, 0x3b, 0x4f, + 0x62, 0x6a, 0x65, 0x63, 0x74, 0x2e, 0x64, 0x65, 0x66, 0x69, 0x6e, 0x65, + 0x50, 0x72, 0x6f, 0x70, 0x65, 0x72, 0x74, 0x69, 0x65, 0x73, 0x28, 0x66, + 0x2e, 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x74, 0x79, 0x70, 0x65, 0x2c, 0x7b, + 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x72, 0x75, 0x63, 0x74, 0x6f, 0x72, 0x3a, + 0x7b, 0x63, 0x6f, 0x6e, 0x66, 0x69, 0x67, 0x75, 0x72, 0x61, 0x62, 0x6c, + 0x65, 0x3a, 0x21, 0x30, 0x2c, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3a, 0x76, + 0x6f, 0x69, 0x64, 0x20, 0x30, 0x7d, 0x2c, 0x74, 0x79, 0x70, 0x65, 0x3a, + 0x7b, 0x63, 0x6f, 0x6e, 0x66, 0x69, 0x67, 0x75, 0x72, 0x61, 0x62, 0x6c, + 0x65, 0x3a, 0x21, 0x30, 0x2c, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3a, 0x7a, + 0x74, 0x7d, 0x2c, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x3a, 0x7b, 0x63, 0x6f, + 0x6e, 0x66, 0x69, 0x67, 0x75, 0x72, 0x61, 0x62, 0x6c, 0x65, 0x3a, 0x21, + 0x30, 0x2c, 0x67, 0x65, 0x74, 0x28, 0x29, 0x7b, 0x72, 0x65, 0x74, 0x75, + 0x72, 0x6e, 0x7b, 0x64, 0x61, 0x74, 0x61, 0x3a, 0x74, 0x68, 0x69, 0x73, + 0x7d, 0x7d, 0x7d, 0x2c, 0x5f, 0x5f, 0x62, 0x3a, 0x7b, 0x63, 0x6f, 0x6e, + 0x66, 0x69, 0x67, 0x75, 0x72, 0x61, 0x62, 0x6c, 0x65, 0x3a, 0x21, 0x30, + 0x2c, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3a, 0x31, 0x7d, 0x7d, 0x29, 0x3b, + 0x6a, 0x74, 0x28, 0x22, 0x5f, 0x5f, 0x62, 0x22, 0x2c, 0x28, 0x74, 0x2c, + 0x6e, 0x29, 0x3d, 0x3e, 0x7b, 0x69, 0x66, 0x28, 0x22, 0x73, 0x74, 0x72, + 0x69, 0x6e, 0x67, 0x22, 0x3d, 0x3d, 0x74, 0x79, 0x70, 0x65, 0x6f, 0x66, + 0x20, 0x6e, 0x2e, 0x74, 0x79, 0x70, 0x65, 0x29, 0x7b, 0x6c, 0x65, 0x74, + 0x20, 0x74, 0x2c, 0x65, 0x3d, 0x6e, 0x2e, 0x70, 0x72, 0x6f, 0x70, 0x73, + 0x3b, 0x66, 0x6f, 0x72, 0x28, 0x6c, 0x65, 0x74, 0x20, 0x69, 0x20, 0x69, + 0x6e, 0x20, 0x65, 0x29, 0x7b, 0x69, 0x66, 0x28, 0x22, 0x63, 0x68, 0x69, + 0x6c, 0x64, 0x72, 0x65, 0x6e, 0x22, 0x3d, 0x3d, 0x3d, 0x69, 0x29, 0x63, + 0x6f, 0x6e, 0x74, 0x69, 0x6e, 0x75, 0x65, 0x3b, 0x6c, 0x65, 0x74, 0x20, + 0x5f, 0x3d, 0x65, 0x5b, 0x69, 0x5d, 0x3b, 0x69, 0x66, 0x28, 0x5f, 0x20, + 0x69, 0x6e, 0x73, 0x74, 0x61, 0x6e, 0x63, 0x65, 0x6f, 0x66, 0x20, 0x66, + 0x29, 0x7b, 0x69, 0x66, 0x28, 0x21, 0x74, 0x29, 0x6e, 0x2e, 0x5f, 0x5f, + 0x6e, 0x70, 0x3d, 0x74, 0x3d, 0x7b, 0x7d, 0x3b, 0x74, 0x5b, 0x69, 0x5d, + 0x3d, 0x5f, 0x3b, 0x65, 0x5b, 0x69, 0x5d, 0x3d, 0x5f, 0x2e, 0x70, 0x65, + 0x65, 0x6b, 0x28, 0x29, 0x7d, 0x7d, 0x7d, 0x74, 0x28, 0x6e, 0x29, 0x7d, + 0x29, 0x3b, 0x6a, 0x74, 0x28, 0x22, 0x5f, 0x5f, 0x72, 0x22, 0x2c, 0x28, + 0x74, 0x2c, 0x6e, 0x29, 0x3d, 0x3e, 0x7b, 0x47, 0x74, 0x28, 0x29, 0x3b, + 0x6c, 0x65, 0x74, 0x20, 0x65, 0x2c, 0x69, 0x3d, 0x6e, 0x2e, 0x5f, 0x5f, + 0x63, 0x3b, 0x69, 0x66, 0x28, 0x69, 0x29, 0x7b, 0x69, 0x2e, 0x5f, 0x5f, + 0x24, 0x66, 0x26, 0x3d, 0x2d, 0x32, 0x3b, 0x65, 0x3d, 0x69, 0x2e, 0x5f, + 0x5f, 0x24, 0x75, 0x3b, 0x69, 0x66, 0x28, 0x76, 0x6f, 0x69, 0x64, 0x20, + 0x30, 0x3d, 0x3d, 0x3d, 0x65, 0x29, 0x69, 0x2e, 0x5f, 0x5f, 0x24, 0x75, + 0x3d, 0x65, 0x3d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, + 0x74, 0x29, 0x7b, 0x6c, 0x65, 0x74, 0x20, 0x6e, 0x3b, 0x62, 0x28, 0x28, + 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x29, 0x7b, 0x6e, + 0x3d, 0x74, 0x68, 0x69, 0x73, 0x7d, 0x29, 0x29, 0x3b, 0x6e, 0x2e, 0x63, + 0x3d, 0x28, 0x29, 0x3d, 0x3e, 0x7b, 0x69, 0x2e, 0x5f, 0x5f, 0x24, 0x66, + 0x7c, 0x3d, 0x31, 0x3b, 0x69, 0x2e, 0x73, 0x65, 0x74, 0x53, 0x74, 0x61, + 0x74, 0x65, 0x28, 0x7b, 0x7d, 0x29, 0x7d, 0x3b, 0x72, 0x65, 0x74, 0x75, + 0x72, 0x6e, 0x20, 0x6e, 0x7d, 0x28, 0x29, 0x7d, 0x71, 0x74, 0x3d, 0x69, + 0x3b, 0x47, 0x74, 0x28, 0x65, 0x29, 0x3b, 0x74, 0x28, 0x6e, 0x29, 0x7d, + 0x29, 0x3b, 0x6a, 0x74, 0x28, 0x22, 0x5f, 0x5f, 0x65, 0x22, 0x2c, 0x28, + 0x74, 0x2c, 0x6e, 0x2c, 0x65, 0x2c, 0x69, 0x29, 0x3d, 0x3e, 0x7b, 0x47, + 0x74, 0x28, 0x29, 0x3b, 0x71, 0x74, 0x3d, 0x76, 0x6f, 0x69, 0x64, 0x20, + 0x30, 0x3b, 0x74, 0x28, 0x6e, 0x2c, 0x65, 0x2c, 0x69, 0x29, 0x7d, 0x29, + 0x3b, 0x6a, 0x74, 0x28, 0x22, 0x64, 0x69, 0x66, 0x66, 0x65, 0x64, 0x22, + 0x2c, 0x28, 0x74, 0x2c, 0x6e, 0x29, 0x3d, 0x3e, 0x7b, 0x47, 0x74, 0x28, + 0x29, 0x3b, 0x71, 0x74, 0x3d, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x3b, + 0x6c, 0x65, 0x74, 0x20, 0x65, 0x3b, 0x69, 0x66, 0x28, 0x22, 0x73, 0x74, + 0x72, 0x69, 0x6e, 0x67, 0x22, 0x3d, 0x3d, 0x74, 0x79, 0x70, 0x65, 0x6f, + 0x66, 0x20, 0x6e, 0x2e, 0x74, 0x79, 0x70, 0x65, 0x26, 0x26, 0x28, 0x65, + 0x3d, 0x6e, 0x2e, 0x5f, 0x5f, 0x65, 0x29, 0x29, 0x7b, 0x6c, 0x65, 0x74, + 0x20, 0x74, 0x3d, 0x6e, 0x2e, 0x5f, 0x5f, 0x6e, 0x70, 0x2c, 0x69, 0x3d, + 0x6e, 0x2e, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x3b, 0x69, 0x66, 0x28, 0x74, + 0x29, 0x7b, 0x6c, 0x65, 0x74, 0x20, 0x6e, 0x3d, 0x65, 0x2e, 0x55, 0x3b, + 0x69, 0x66, 0x28, 0x6e, 0x29, 0x66, 0x6f, 0x72, 0x28, 0x6c, 0x65, 0x74, + 0x20, 0x65, 0x20, 0x69, 0x6e, 0x20, 0x6e, 0x29, 0x7b, 0x6c, 0x65, 0x74, + 0x20, 0x69, 0x3d, 0x6e, 0x5b, 0x65, 0x5d, 0x3b, 0x69, 0x66, 0x28, 0x76, + 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x69, 0x26, 0x26, 0x21, + 0x28, 0x65, 0x20, 0x69, 0x6e, 0x20, 0x74, 0x29, 0x29, 0x7b, 0x69, 0x2e, + 0x64, 0x28, 0x29, 0x3b, 0x6e, 0x5b, 0x65, 0x5d, 0x3d, 0x76, 0x6f, 0x69, + 0x64, 0x20, 0x30, 0x7d, 0x7d, 0x65, 0x6c, 0x73, 0x65, 0x7b, 0x6e, 0x3d, + 0x7b, 0x7d, 0x3b, 0x65, 0x2e, 0x55, 0x3d, 0x6e, 0x7d, 0x66, 0x6f, 0x72, + 0x28, 0x6c, 0x65, 0x74, 0x20, 0x5f, 0x20, 0x69, 0x6e, 0x20, 0x74, 0x29, + 0x7b, 0x6c, 0x65, 0x74, 0x20, 0x6f, 0x3d, 0x6e, 0x5b, 0x5f, 0x5d, 0x2c, + 0x72, 0x3d, 0x74, 0x5b, 0x5f, 0x5d, 0x3b, 0x69, 0x66, 0x28, 0x76, 0x6f, + 0x69, 0x64, 0x20, 0x30, 0x3d, 0x3d, 0x3d, 0x6f, 0x29, 0x7b, 0x6f, 0x3d, + 0x4a, 0x74, 0x28, 0x65, 0x2c, 0x5f, 0x2c, 0x72, 0x2c, 0x69, 0x29, 0x3b, + 0x6e, 0x5b, 0x5f, 0x5d, 0x3d, 0x6f, 0x7d, 0x65, 0x6c, 0x73, 0x65, 0x20, + 0x6f, 0x2e, 0x6f, 0x28, 0x72, 0x2c, 0x69, 0x29, 0x7d, 0x7d, 0x7d, 0x74, + 0x28, 0x6e, 0x29, 0x7d, 0x29, 0x3b, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, + 0x6f, 0x6e, 0x20, 0x4a, 0x74, 0x28, 0x74, 0x2c, 0x6e, 0x2c, 0x65, 0x2c, + 0x69, 0x29, 0x7b, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x5f, 0x3d, 0x6e, + 0x20, 0x69, 0x6e, 0x20, 0x74, 0x26, 0x26, 0x76, 0x6f, 0x69, 0x64, 0x20, + 0x30, 0x3d, 0x3d, 0x3d, 0x74, 0x2e, 0x6f, 0x77, 0x6e, 0x65, 0x72, 0x53, + 0x56, 0x47, 0x45, 0x6c, 0x65, 0x6d, 0x65, 0x6e, 0x74, 0x2c, 0x6f, 0x3d, + 0x73, 0x28, 0x65, 0x29, 0x3b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x7b, + 0x6f, 0x3a, 0x28, 0x74, 0x2c, 0x6e, 0x29, 0x3d, 0x3e, 0x7b, 0x6f, 0x2e, + 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x74, 0x3b, 0x69, 0x3d, 0x6e, 0x7d, + 0x2c, 0x64, 0x3a, 0x62, 0x28, 0x28, 0x29, 0x3d, 0x3e, 0x7b, 0x63, 0x6f, + 0x6e, 0x73, 0x74, 0x20, 0x65, 0x3d, 0x6f, 0x2e, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3b, 0x69, 0x66, 0x28, 0x69, + 0x5b, 0x6e, 0x5d, 0x21, 0x3d, 0x3d, 0x65, 0x29, 0x7b, 0x69, 0x5b, 0x6e, + 0x5d, 0x3d, 0x65, 0x3b, 0x69, 0x66, 0x28, 0x5f, 0x29, 0x74, 0x5b, 0x6e, + 0x5d, 0x3d, 0x65, 0x3b, 0x65, 0x6c, 0x73, 0x65, 0x20, 0x69, 0x66, 0x28, + 0x65, 0x29, 0x74, 0x2e, 0x73, 0x65, 0x74, 0x41, 0x74, 0x74, 0x72, 0x69, + 0x62, 0x75, 0x74, 0x65, 0x28, 0x6e, 0x2c, 0x65, 0x29, 0x3b, 0x65, 0x6c, + 0x73, 0x65, 0x20, 0x74, 0x2e, 0x72, 0x65, 0x6d, 0x6f, 0x76, 0x65, 0x41, + 0x74, 0x74, 0x72, 0x69, 0x62, 0x75, 0x74, 0x65, 0x28, 0x6e, 0x29, 0x7d, + 0x7d, 0x29, 0x7d, 0x7d, 0x6a, 0x74, 0x28, 0x22, 0x75, 0x6e, 0x6d, 0x6f, + 0x75, 0x6e, 0x74, 0x22, 0x2c, 0x28, 0x74, 0x2c, 0x6e, 0x29, 0x3d, 0x3e, + 0x7b, 0x69, 0x66, 0x28, 0x22, 0x73, 0x74, 0x72, 0x69, 0x6e, 0x67, 0x22, + 0x3d, 0x3d, 0x74, 0x79, 0x70, 0x65, 0x6f, 0x66, 0x20, 0x6e, 0x2e, 0x74, + 0x79, 0x70, 0x65, 0x29, 0x7b, 0x6c, 0x65, 0x74, 0x20, 0x74, 0x3d, 0x6e, + 0x2e, 0x5f, 0x5f, 0x65, 0x3b, 0x69, 0x66, 0x28, 0x74, 0x29, 0x7b, 0x63, + 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6e, 0x3d, 0x74, 0x2e, 0x55, 0x3b, 0x69, + 0x66, 0x28, 0x6e, 0x29, 0x7b, 0x74, 0x2e, 0x55, 0x3d, 0x76, 0x6f, 0x69, + 0x64, 0x20, 0x30, 0x3b, 0x66, 0x6f, 0x72, 0x28, 0x6c, 0x65, 0x74, 0x20, + 0x74, 0x20, 0x69, 0x6e, 0x20, 0x6e, 0x29, 0x7b, 0x6c, 0x65, 0x74, 0x20, + 0x65, 0x3d, 0x6e, 0x5b, 0x74, 0x5d, 0x3b, 0x69, 0x66, 0x28, 0x65, 0x29, + 0x65, 0x2e, 0x64, 0x28, 0x29, 0x7d, 0x7d, 0x7d, 0x7d, 0x65, 0x6c, 0x73, + 0x65, 0x7b, 0x6c, 0x65, 0x74, 0x20, 0x74, 0x3d, 0x6e, 0x2e, 0x5f, 0x5f, + 0x63, 0x3b, 0x69, 0x66, 0x28, 0x74, 0x29, 0x7b, 0x63, 0x6f, 0x6e, 0x73, + 0x74, 0x20, 0x6e, 0x3d, 0x74, 0x2e, 0x5f, 0x5f, 0x24, 0x75, 0x3b, 0x69, + 0x66, 0x28, 0x6e, 0x29, 0x7b, 0x74, 0x2e, 0x5f, 0x5f, 0x24, 0x75, 0x3d, + 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x3b, 0x6e, 0x2e, 0x64, 0x28, 0x29, + 0x7d, 0x7d, 0x7d, 0x74, 0x28, 0x6e, 0x29, 0x7d, 0x29, 0x3b, 0x6a, 0x74, + 0x28, 0x22, 0x5f, 0x5f, 0x68, 0x22, 0x2c, 0x28, 0x74, 0x2c, 0x6e, 0x2c, + 0x65, 0x2c, 0x69, 0x29, 0x3d, 0x3e, 0x7b, 0x69, 0x66, 0x28, 0x69, 0x3c, + 0x33, 0x29, 0x6e, 0x2e, 0x5f, 0x5f, 0x24, 0x66, 0x7c, 0x3d, 0x32, 0x3b, + 0x74, 0x28, 0x6e, 0x2c, 0x65, 0x2c, 0x69, 0x29, 0x7d, 0x29, 0x3b, 0x4c, + 0x2e, 0x70, 0x72, 0x6f, 0x74, 0x6f, 0x74, 0x79, 0x70, 0x65, 0x2e, 0x73, + 0x68, 0x6f, 0x75, 0x6c, 0x64, 0x43, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, + 0x6e, 0x74, 0x55, 0x70, 0x64, 0x61, 0x74, 0x65, 0x3d, 0x66, 0x75, 0x6e, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x2c, 0x6e, 0x29, 0x7b, 0x63, + 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x65, 0x3d, 0x74, 0x68, 0x69, 0x73, 0x2e, + 0x5f, 0x5f, 0x24, 0x75, 0x3b, 0x69, 0x66, 0x28, 0x21, 0x28, 0x65, 0x26, + 0x26, 0x76, 0x6f, 0x69, 0x64, 0x20, 0x30, 0x21, 0x3d, 0x3d, 0x65, 0x2e, + 0x73, 0x7c, 0x7c, 0x34, 0x26, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x5f, 0x5f, + 0x24, 0x66, 0x29, 0x29, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x21, 0x30, + 0x3b, 0x69, 0x66, 0x28, 0x33, 0x26, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x5f, + 0x5f, 0x24, 0x66, 0x29, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x21, 0x30, + 0x3b, 0x66, 0x6f, 0x72, 0x28, 0x6c, 0x65, 0x74, 0x20, 0x69, 0x20, 0x69, + 0x6e, 0x20, 0x6e, 0x29, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x21, 0x30, + 0x3b, 0x66, 0x6f, 0x72, 0x28, 0x6c, 0x65, 0x74, 0x20, 0x69, 0x20, 0x69, + 0x6e, 0x20, 0x74, 0x29, 0x69, 0x66, 0x28, 0x22, 0x5f, 0x5f, 0x73, 0x6f, + 0x75, 0x72, 0x63, 0x65, 0x22, 0x21, 0x3d, 0x3d, 0x69, 0x26, 0x26, 0x74, + 0x5b, 0x69, 0x5d, 0x21, 0x3d, 0x3d, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x70, + 0x72, 0x6f, 0x70, 0x73, 0x5b, 0x69, 0x5d, 0x29, 0x72, 0x65, 0x74, 0x75, + 0x72, 0x6e, 0x21, 0x30, 0x3b, 0x66, 0x6f, 0x72, 0x28, 0x6c, 0x65, 0x74, + 0x20, 0x69, 0x20, 0x69, 0x6e, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x70, + 0x72, 0x6f, 0x70, 0x73, 0x29, 0x69, 0x66, 0x28, 0x21, 0x28, 0x69, 0x20, + 0x69, 0x6e, 0x20, 0x74, 0x29, 0x29, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, + 0x21, 0x30, 0x3b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x21, 0x31, 0x7d, + 0x3b, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x4b, 0x74, + 0x28, 0x74, 0x29, 0x7b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x50, + 0x74, 0x28, 0x28, 0x29, 0x3d, 0x3e, 0x73, 0x28, 0x74, 0x29, 0x2c, 0x5b, + 0x5d, 0x29, 0x7d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, + 0x51, 0x74, 0x28, 0x74, 0x29, 0x7b, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, + 0x6e, 0x3d, 0x48, 0x74, 0x28, 0x74, 0x29, 0x3b, 0x6e, 0x2e, 0x63, 0x75, + 0x72, 0x72, 0x65, 0x6e, 0x74, 0x3d, 0x74, 0x3b, 0x71, 0x74, 0x2e, 0x5f, + 0x5f, 0x24, 0x66, 0x7c, 0x3d, 0x34, 0x3b, 0x72, 0x65, 0x74, 0x75, 0x72, + 0x6e, 0x20, 0x50, 0x74, 0x28, 0x28, 0x29, 0x3d, 0x3e, 0x64, 0x28, 0x28, + 0x29, 0x3d, 0x3e, 0x6e, 0x2e, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, + 0x28, 0x29, 0x29, 0x2c, 0x5b, 0x5d, 0x29, 0x7d, 0x66, 0x75, 0x6e, 0x63, + 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x58, 0x74, 0x28, 0x74, 0x29, 0x7b, 0x63, + 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6e, 0x3d, 0x48, 0x74, 0x28, 0x74, 0x29, + 0x3b, 0x6e, 0x2e, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x3d, 0x74, + 0x3b, 0x45, 0x74, 0x28, 0x28, 0x29, 0x3d, 0x3e, 0x62, 0x28, 0x28, 0x29, + 0x3d, 0x3e, 0x6e, 0x2e, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x28, + 0x29, 0x29, 0x2c, 0x5b, 0x5d, 0x29, 0x7d, 0x76, 0x61, 0x72, 0x20, 0x59, + 0x74, 0x3d, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, + 0x2c, 0x6e, 0x2c, 0x65, 0x2c, 0x69, 0x29, 0x7b, 0x76, 0x61, 0x72, 0x20, + 0x5f, 0x3b, 0x6e, 0x5b, 0x30, 0x5d, 0x3d, 0x30, 0x3b, 0x66, 0x6f, 0x72, + 0x28, 0x76, 0x61, 0x72, 0x20, 0x6f, 0x3d, 0x31, 0x3b, 0x6f, 0x3c, 0x6e, + 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x3b, 0x6f, 0x2b, 0x2b, 0x29, + 0x7b, 0x76, 0x61, 0x72, 0x20, 0x72, 0x3d, 0x6e, 0x5b, 0x6f, 0x2b, 0x2b, + 0x5d, 0x2c, 0x75, 0x3d, 0x6e, 0x5b, 0x6f, 0x5d, 0x3f, 0x28, 0x6e, 0x5b, + 0x30, 0x5d, 0x7c, 0x3d, 0x72, 0x3f, 0x31, 0x3a, 0x32, 0x2c, 0x65, 0x5b, + 0x6e, 0x5b, 0x6f, 0x2b, 0x2b, 0x5d, 0x5d, 0x29, 0x3a, 0x6e, 0x5b, 0x2b, + 0x2b, 0x6f, 0x5d, 0x3b, 0x33, 0x3d, 0x3d, 0x3d, 0x72, 0x3f, 0x69, 0x5b, + 0x30, 0x5d, 0x3d, 0x75, 0x3a, 0x34, 0x3d, 0x3d, 0x3d, 0x72, 0x3f, 0x69, + 0x5b, 0x31, 0x5d, 0x3d, 0x4f, 0x62, 0x6a, 0x65, 0x63, 0x74, 0x2e, 0x61, + 0x73, 0x73, 0x69, 0x67, 0x6e, 0x28, 0x69, 0x5b, 0x31, 0x5d, 0x7c, 0x7c, + 0x7b, 0x7d, 0x2c, 0x75, 0x29, 0x3a, 0x35, 0x3d, 0x3d, 0x3d, 0x72, 0x3f, + 0x28, 0x69, 0x5b, 0x31, 0x5d, 0x3d, 0x69, 0x5b, 0x31, 0x5d, 0x7c, 0x7c, + 0x7b, 0x7d, 0x29, 0x5b, 0x6e, 0x5b, 0x2b, 0x2b, 0x6f, 0x5d, 0x5d, 0x3d, + 0x75, 0x3a, 0x36, 0x3d, 0x3d, 0x3d, 0x72, 0x3f, 0x69, 0x5b, 0x31, 0x5d, + 0x5b, 0x6e, 0x5b, 0x2b, 0x2b, 0x6f, 0x5d, 0x5d, 0x2b, 0x3d, 0x75, 0x2b, + 0x22, 0x22, 0x3a, 0x72, 0x3f, 0x28, 0x5f, 0x3d, 0x74, 0x2e, 0x61, 0x70, + 0x70, 0x6c, 0x79, 0x28, 0x75, 0x2c, 0x59, 0x74, 0x28, 0x74, 0x2c, 0x75, + 0x2c, 0x65, 0x2c, 0x5b, 0x22, 0x22, 0x2c, 0x6e, 0x75, 0x6c, 0x6c, 0x5d, + 0x29, 0x29, 0x2c, 0x69, 0x2e, 0x70, 0x75, 0x73, 0x68, 0x28, 0x5f, 0x29, + 0x2c, 0x75, 0x5b, 0x30, 0x5d, 0x3f, 0x6e, 0x5b, 0x30, 0x5d, 0x7c, 0x3d, + 0x32, 0x3a, 0x28, 0x6e, 0x5b, 0x6f, 0x2d, 0x32, 0x5d, 0x3d, 0x30, 0x2c, + 0x6e, 0x5b, 0x6f, 0x5d, 0x3d, 0x5f, 0x29, 0x29, 0x3a, 0x69, 0x2e, 0x70, + 0x75, 0x73, 0x68, 0x28, 0x75, 0x29, 0x7d, 0x72, 0x65, 0x74, 0x75, 0x72, + 0x6e, 0x20, 0x69, 0x7d, 0x2c, 0x5a, 0x74, 0x3d, 0x6e, 0x65, 0x77, 0x20, + 0x4d, 0x61, 0x70, 0x3b, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x20, 0x74, 0x6e, 0x28, 0x74, 0x29, 0x7b, 0x76, 0x61, 0x72, 0x20, 0x6e, + 0x3d, 0x5a, 0x74, 0x2e, 0x67, 0x65, 0x74, 0x28, 0x74, 0x68, 0x69, 0x73, + 0x29, 0x3b, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x6e, 0x7c, 0x7c, + 0x28, 0x6e, 0x3d, 0x6e, 0x65, 0x77, 0x20, 0x4d, 0x61, 0x70, 0x2c, 0x5a, + 0x74, 0x2e, 0x73, 0x65, 0x74, 0x28, 0x74, 0x68, 0x69, 0x73, 0x2c, 0x6e, + 0x29, 0x29, 0x2c, 0x28, 0x6e, 0x3d, 0x59, 0x74, 0x28, 0x74, 0x68, 0x69, + 0x73, 0x2c, 0x6e, 0x2e, 0x67, 0x65, 0x74, 0x28, 0x74, 0x29, 0x7c, 0x7c, + 0x28, 0x6e, 0x2e, 0x73, 0x65, 0x74, 0x28, 0x74, 0x2c, 0x6e, 0x3d, 0x66, + 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x29, 0x7b, 0x66, + 0x6f, 0x72, 0x28, 0x76, 0x61, 0x72, 0x20, 0x6e, 0x2c, 0x65, 0x2c, 0x69, + 0x3d, 0x31, 0x2c, 0x5f, 0x3d, 0x22, 0x22, 0x2c, 0x6f, 0x3d, 0x22, 0x22, + 0x2c, 0x72, 0x3d, 0x5b, 0x30, 0x5d, 0x2c, 0x75, 0x3d, 0x66, 0x75, 0x6e, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x28, 0x74, 0x29, 0x7b, 0x31, 0x3d, 0x3d, + 0x3d, 0x69, 0x26, 0x26, 0x28, 0x74, 0x7c, 0x7c, 0x28, 0x5f, 0x3d, 0x5f, + 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x5e, 0x5c, + 0x73, 0x2a, 0x5c, 0x6e, 0x5c, 0x73, 0x2a, 0x7c, 0x5c, 0x73, 0x2a, 0x5c, + 0x6e, 0x5c, 0x73, 0x2a, 0x24, 0x2f, 0x67, 0x2c, 0x22, 0x22, 0x29, 0x29, + 0x29, 0x3f, 0x72, 0x2e, 0x70, 0x75, 0x73, 0x68, 0x28, 0x30, 0x2c, 0x74, + 0x2c, 0x5f, 0x29, 0x3a, 0x33, 0x3d, 0x3d, 0x3d, 0x69, 0x26, 0x26, 0x28, + 0x74, 0x7c, 0x7c, 0x5f, 0x29, 0x3f, 0x28, 0x72, 0x2e, 0x70, 0x75, 0x73, + 0x68, 0x28, 0x33, 0x2c, 0x74, 0x2c, 0x5f, 0x29, 0x2c, 0x69, 0x3d, 0x32, + 0x29, 0x3a, 0x32, 0x3d, 0x3d, 0x3d, 0x69, 0x26, 0x26, 0x22, 0x2e, 0x2e, + 0x2e, 0x22, 0x3d, 0x3d, 0x3d, 0x5f, 0x26, 0x26, 0x74, 0x3f, 0x72, 0x2e, + 0x70, 0x75, 0x73, 0x68, 0x28, 0x34, 0x2c, 0x74, 0x2c, 0x30, 0x29, 0x3a, + 0x32, 0x3d, 0x3d, 0x3d, 0x69, 0x26, 0x26, 0x5f, 0x26, 0x26, 0x21, 0x74, + 0x3f, 0x72, 0x2e, 0x70, 0x75, 0x73, 0x68, 0x28, 0x35, 0x2c, 0x30, 0x2c, + 0x21, 0x30, 0x2c, 0x5f, 0x29, 0x3a, 0x69, 0x3e, 0x3d, 0x35, 0x26, 0x26, + 0x28, 0x28, 0x5f, 0x7c, 0x7c, 0x21, 0x74, 0x26, 0x26, 0x35, 0x3d, 0x3d, + 0x3d, 0x69, 0x29, 0x26, 0x26, 0x28, 0x72, 0x2e, 0x70, 0x75, 0x73, 0x68, + 0x28, 0x69, 0x2c, 0x30, 0x2c, 0x5f, 0x2c, 0x65, 0x29, 0x2c, 0x69, 0x3d, + 0x36, 0x29, 0x2c, 0x74, 0x26, 0x26, 0x28, 0x72, 0x2e, 0x70, 0x75, 0x73, + 0x68, 0x28, 0x69, 0x2c, 0x74, 0x2c, 0x30, 0x2c, 0x65, 0x29, 0x2c, 0x69, + 0x3d, 0x36, 0x29, 0x29, 0x2c, 0x5f, 0x3d, 0x22, 0x22, 0x7d, 0x2c, 0x6c, + 0x3d, 0x30, 0x3b, 0x6c, 0x3c, 0x74, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, + 0x68, 0x3b, 0x6c, 0x2b, 0x2b, 0x29, 0x7b, 0x6c, 0x26, 0x26, 0x28, 0x31, + 0x3d, 0x3d, 0x3d, 0x69, 0x26, 0x26, 0x75, 0x28, 0x29, 0x2c, 0x75, 0x28, + 0x6c, 0x29, 0x29, 0x3b, 0x66, 0x6f, 0x72, 0x28, 0x76, 0x61, 0x72, 0x20, + 0x66, 0x3d, 0x30, 0x3b, 0x66, 0x3c, 0x74, 0x5b, 0x6c, 0x5d, 0x2e, 0x6c, + 0x65, 0x6e, 0x67, 0x74, 0x68, 0x3b, 0x66, 0x2b, 0x2b, 0x29, 0x6e, 0x3d, + 0x74, 0x5b, 0x6c, 0x5d, 0x5b, 0x66, 0x5d, 0x2c, 0x31, 0x3d, 0x3d, 0x3d, + 0x69, 0x3f, 0x22, 0x3c, 0x22, 0x3d, 0x3d, 0x3d, 0x6e, 0x3f, 0x28, 0x75, + 0x28, 0x29, 0x2c, 0x72, 0x3d, 0x5b, 0x72, 0x5d, 0x2c, 0x69, 0x3d, 0x33, + 0x29, 0x3a, 0x5f, 0x2b, 0x3d, 0x6e, 0x3a, 0x34, 0x3d, 0x3d, 0x3d, 0x69, + 0x3f, 0x22, 0x2d, 0x2d, 0x22, 0x3d, 0x3d, 0x3d, 0x5f, 0x26, 0x26, 0x22, + 0x3e, 0x22, 0x3d, 0x3d, 0x3d, 0x6e, 0x3f, 0x28, 0x69, 0x3d, 0x31, 0x2c, + 0x5f, 0x3d, 0x22, 0x22, 0x29, 0x3a, 0x5f, 0x3d, 0x6e, 0x2b, 0x5f, 0x5b, + 0x30, 0x5d, 0x3a, 0x6f, 0x3f, 0x6e, 0x3d, 0x3d, 0x3d, 0x6f, 0x3f, 0x6f, + 0x3d, 0x22, 0x22, 0x3a, 0x5f, 0x2b, 0x3d, 0x6e, 0x3a, 0x27, 0x22, 0x27, + 0x3d, 0x3d, 0x3d, 0x6e, 0x7c, 0x7c, 0x22, 0x27, 0x22, 0x3d, 0x3d, 0x3d, + 0x6e, 0x3f, 0x6f, 0x3d, 0x6e, 0x3a, 0x22, 0x3e, 0x22, 0x3d, 0x3d, 0x3d, + 0x6e, 0x3f, 0x28, 0x75, 0x28, 0x29, 0x2c, 0x69, 0x3d, 0x31, 0x29, 0x3a, + 0x69, 0x26, 0x26, 0x28, 0x22, 0x3d, 0x22, 0x3d, 0x3d, 0x3d, 0x6e, 0x3f, + 0x28, 0x69, 0x3d, 0x35, 0x2c, 0x65, 0x3d, 0x5f, 0x2c, 0x5f, 0x3d, 0x22, + 0x22, 0x29, 0x3a, 0x22, 0x2f, 0x22, 0x3d, 0x3d, 0x3d, 0x6e, 0x26, 0x26, + 0x28, 0x69, 0x3c, 0x35, 0x7c, 0x7c, 0x22, 0x3e, 0x22, 0x3d, 0x3d, 0x3d, + 0x74, 0x5b, 0x6c, 0x5d, 0x5b, 0x66, 0x2b, 0x31, 0x5d, 0x29, 0x3f, 0x28, + 0x75, 0x28, 0x29, 0x2c, 0x33, 0x3d, 0x3d, 0x3d, 0x69, 0x26, 0x26, 0x28, + 0x72, 0x3d, 0x72, 0x5b, 0x30, 0x5d, 0x29, 0x2c, 0x69, 0x3d, 0x72, 0x2c, + 0x28, 0x72, 0x3d, 0x72, 0x5b, 0x30, 0x5d, 0x29, 0x2e, 0x70, 0x75, 0x73, + 0x68, 0x28, 0x32, 0x2c, 0x30, 0x2c, 0x69, 0x29, 0x2c, 0x69, 0x3d, 0x30, + 0x29, 0x3a, 0x22, 0x20, 0x22, 0x3d, 0x3d, 0x3d, 0x6e, 0x7c, 0x7c, 0x22, + 0x5c, 0x74, 0x22, 0x3d, 0x3d, 0x3d, 0x6e, 0x7c, 0x7c, 0x22, 0x5c, 0x6e, + 0x22, 0x3d, 0x3d, 0x3d, 0x6e, 0x7c, 0x7c, 0x22, 0x5c, 0x72, 0x22, 0x3d, + 0x3d, 0x3d, 0x6e, 0x3f, 0x28, 0x75, 0x28, 0x29, 0x2c, 0x69, 0x3d, 0x32, + 0x29, 0x3a, 0x5f, 0x2b, 0x3d, 0x6e, 0x29, 0x2c, 0x33, 0x3d, 0x3d, 0x3d, + 0x69, 0x26, 0x26, 0x22, 0x21, 0x2d, 0x2d, 0x22, 0x3d, 0x3d, 0x3d, 0x5f, + 0x26, 0x26, 0x28, 0x69, 0x3d, 0x34, 0x2c, 0x72, 0x3d, 0x72, 0x5b, 0x30, + 0x5d, 0x29, 0x7d, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x75, 0x28, + 0x29, 0x2c, 0x72, 0x7d, 0x28, 0x74, 0x29, 0x29, 0x2c, 0x6e, 0x29, 0x2c, + 0x61, 0x72, 0x67, 0x75, 0x6d, 0x65, 0x6e, 0x74, 0x73, 0x2c, 0x5b, 0x5d, + 0x29, 0x29, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x3e, 0x31, 0x3f, + 0x6e, 0x3a, 0x6e, 0x5b, 0x30, 0x5d, 0x7d, 0x76, 0x61, 0x72, 0x20, 0x6e, + 0x6e, 0x3d, 0x74, 0x6e, 0x2e, 0x62, 0x69, 0x6e, 0x64, 0x28, 0x46, 0x29, + 0x3b, 0x65, 0x78, 0x70, 0x6f, 0x72, 0x74, 0x7b, 0x4c, 0x20, 0x61, 0x73, + 0x20, 0x43, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x2c, 0x4f, + 0x20, 0x61, 0x73, 0x20, 0x46, 0x72, 0x61, 0x67, 0x6d, 0x65, 0x6e, 0x74, + 0x2c, 0x66, 0x20, 0x61, 0x73, 0x20, 0x53, 0x69, 0x67, 0x6e, 0x61, 0x6c, + 0x2c, 0x65, 0x20, 0x61, 0x73, 0x20, 0x62, 0x61, 0x74, 0x63, 0x68, 0x2c, + 0x66, 0x74, 0x20, 0x61, 0x73, 0x20, 0x63, 0x6c, 0x6f, 0x6e, 0x65, 0x45, + 0x6c, 0x65, 0x6d, 0x65, 0x6e, 0x74, 0x2c, 0x64, 0x20, 0x61, 0x73, 0x20, + 0x63, 0x6f, 0x6d, 0x70, 0x75, 0x74, 0x65, 0x64, 0x2c, 0x73, 0x74, 0x20, + 0x61, 0x73, 0x20, 0x63, 0x72, 0x65, 0x61, 0x74, 0x65, 0x43, 0x6f, 0x6e, + 0x74, 0x65, 0x78, 0x74, 0x2c, 0x46, 0x20, 0x61, 0x73, 0x20, 0x63, 0x72, + 0x65, 0x61, 0x74, 0x65, 0x45, 0x6c, 0x65, 0x6d, 0x65, 0x6e, 0x74, 0x2c, + 0x57, 0x20, 0x61, 0x73, 0x20, 0x63, 0x72, 0x65, 0x61, 0x74, 0x65, 0x52, + 0x65, 0x66, 0x2c, 0x62, 0x20, 0x61, 0x73, 0x20, 0x65, 0x66, 0x66, 0x65, + 0x63, 0x74, 0x2c, 0x46, 0x20, 0x61, 0x73, 0x20, 0x68, 0x2c, 0x6e, 0x6e, + 0x20, 0x61, 0x73, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x2c, 0x6c, 0x74, 0x20, + 0x61, 0x73, 0x20, 0x68, 0x79, 0x64, 0x72, 0x61, 0x74, 0x65, 0x2c, 0x77, + 0x20, 0x61, 0x73, 0x20, 0x69, 0x73, 0x56, 0x61, 0x6c, 0x69, 0x64, 0x45, + 0x6c, 0x65, 0x6d, 0x65, 0x6e, 0x74, 0x2c, 0x53, 0x20, 0x61, 0x73, 0x20, + 0x6f, 0x70, 0x74, 0x69, 0x6f, 0x6e, 0x73, 0x2c, 0x75, 0x74, 0x20, 0x61, + 0x73, 0x20, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x2c, 0x73, 0x20, 0x61, + 0x73, 0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x2c, 0x7a, 0x20, 0x61, + 0x73, 0x20, 0x74, 0x6f, 0x43, 0x68, 0x69, 0x6c, 0x64, 0x41, 0x72, 0x72, + 0x61, 0x79, 0x2c, 0x44, 0x74, 0x20, 0x61, 0x73, 0x20, 0x75, 0x73, 0x65, + 0x43, 0x61, 0x6c, 0x6c, 0x62, 0x61, 0x63, 0x6b, 0x2c, 0x51, 0x74, 0x20, + 0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x43, 0x6f, 0x6d, 0x70, 0x75, 0x74, + 0x65, 0x64, 0x2c, 0x24, 0x74, 0x20, 0x61, 0x73, 0x20, 0x75, 0x73, 0x65, + 0x43, 0x6f, 0x6e, 0x74, 0x65, 0x78, 0x74, 0x2c, 0x54, 0x74, 0x20, 0x61, + 0x73, 0x20, 0x75, 0x73, 0x65, 0x44, 0x65, 0x62, 0x75, 0x67, 0x56, 0x61, + 0x6c, 0x75, 0x65, 0x2c, 0x45, 0x74, 0x20, 0x61, 0x73, 0x20, 0x75, 0x73, + 0x65, 0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x2c, 0x56, 0x74, 0x20, 0x61, + 0x73, 0x20, 0x75, 0x73, 0x65, 0x45, 0x72, 0x72, 0x6f, 0x72, 0x42, 0x6f, + 0x75, 0x6e, 0x64, 0x61, 0x72, 0x79, 0x2c, 0x41, 0x74, 0x20, 0x61, 0x73, + 0x20, 0x75, 0x73, 0x65, 0x49, 0x64, 0x2c, 0x4e, 0x74, 0x20, 0x61, 0x73, + 0x20, 0x75, 0x73, 0x65, 0x49, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x69, + 0x76, 0x65, 0x48, 0x61, 0x6e, 0x64, 0x6c, 0x65, 0x2c, 0x55, 0x74, 0x20, + 0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x4c, 0x61, 0x79, 0x6f, 0x75, 0x74, + 0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x2c, 0x50, 0x74, 0x20, 0x61, 0x73, + 0x20, 0x75, 0x73, 0x65, 0x4d, 0x65, 0x6d, 0x6f, 0x2c, 0x43, 0x74, 0x20, + 0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x52, 0x65, 0x64, 0x75, 0x63, 0x65, + 0x72, 0x2c, 0x48, 0x74, 0x20, 0x61, 0x73, 0x20, 0x75, 0x73, 0x65, 0x52, + 0x65, 0x66, 0x2c, 0x4b, 0x74, 0x20, 0x61, 0x73, 0x20, 0x75, 0x73, 0x65, + 0x53, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x2c, 0x58, 0x74, 0x20, 0x61, 0x73, + 0x20, 0x75, 0x73, 0x65, 0x53, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x45, 0x66, + 0x66, 0x65, 0x63, 0x74, 0x2c, 0x77, 0x74, 0x20, 0x61, 0x73, 0x20, 0x75, + 0x73, 0x65, 0x53, 0x74, 0x61, 0x74, 0x65, 0x7d, 0x3b, 0x0a +}; +unsigned int index_js_len = 22174; diff --git a/examples/server/public/completion.js b/examples/server/public/completion.js new file mode 100644 index 000000000..4f5005cfb --- /dev/null +++ b/examples/server/public/completion.js @@ -0,0 +1,81 @@ +const paramDefaults = { + stream: true, + n_predict: 500, + temperature: 0.2, + stop: [""] +}; + +/** + * This function completes the input text using a llama dictionary. + * @param {object} params - The parameters for the completion request. + * @param {object} controller - an instance of AbortController if you need one, or null. + * @param {function} callback - The callback function to call when the completion is done. + * @returns {string} the completed text as a string. Ideally ignored, and you get at it via the callback. + */ +export const llamaComplete = async (params, controller, callback) => { + if (!controller) { + controller = new AbortController(); + } + const completionParams = { ...paramDefaults, ...params }; + + // we use fetch directly here becasue the built in fetchEventSource does not support POST + const response = await fetch("/completion", { + method: 'POST', + body: JSON.stringify(completionParams), + headers: { + 'Connection': 'keep-alive', + 'Content-Type': 'application/json', + 'Accept': 'text/event-stream' + }, + signal: controller.signal, + }); + + const reader = response.body.getReader(); + const decoder = new TextDecoder(); + + let content = ""; + + try { + + let cont = true; + + while (cont) { + const result = await reader.read(); + if (result.done) { + break; + } + + // sse answers in the form multiple lines of: value\n with data always present as a key. in our case we + // mainly care about the data: key here, which we expect as json + const text = decoder.decode(result.value); + + // parse all sse events and add them to result + const regex = /^(\S+):\s(.*)$/gm; + for (const match of text.matchAll(regex)) { + result[match[1]] = match[2] + } + + // since we know this is llama.cpp, let's just decode the json in data + result.data = JSON.parse(result.data); + content += result.data.content; + + // callack + if (callback) { + cont = callback(result) != false; + } + + // if we got a stop token from server, we will break here + if (result.data.stop) { + break; + } + } + } catch (e) { + console.error("llama error: ", e); + throw e; + } + finally { + controller.abort(); + } + + return content; +} diff --git a/examples/server/public/index.html b/examples/server/public/index.html new file mode 100644 index 000000000..6393e2e75 --- /dev/null +++ b/examples/server/public/index.html @@ -0,0 +1,359 @@ + + + + + + llama.cpp - chat + + + + + + + + + + diff --git a/examples/server/public/index.js b/examples/server/public/index.js new file mode 100644 index 000000000..4fa725a90 --- /dev/null +++ b/examples/server/public/index.js @@ -0,0 +1 @@ +function t(){throw new Error("Cycle detected")}function n(){if(o>1){o--;return}let t,n=!1;while(void 0!==_){let i=_;_=void 0;r++;while(void 0!==i){const _=i.o;i.o=void 0;i.f&=-3;if(!(8&i.f)&&c(i))try{i.c()}catch(e){if(!n){t=e;n=!0}}i=_}}r=0;o--;if(n)throw t}function e(t){if(o>0)return t();o++;try{return t()}finally{n()}}let i,_,o=0,r=0,u=0;function l(t){if(void 0===i)return;let n=t.n;if(void 0===n||n.t!==i){n={i:0,S:t,p:i.s,n:void 0,t:i,e:void 0,x:void 0,r:n};if(void 0!==i.s)i.s.n=n;i.s=n;t.n=n;if(32&i.f)t.S(n);return n}else if(-1===n.i){n.i=0;if(void 0!==n.n){n.n.p=n.p;if(void 0!==n.p)n.p.n=n.n;n.p=i.s;n.n=void 0;i.s.n=n;i.s=n}return n}}function f(t){this.v=t;this.i=0;this.n=void 0;this.t=void 0}f.prototype.h=function(){return!0};f.prototype.S=function(t){if(this.t!==t&&void 0===t.e){t.x=this.t;if(void 0!==this.t)this.t.e=t;this.t=t}};f.prototype.U=function(t){if(void 0!==this.t){const n=t.e,e=t.x;if(void 0!==n){n.x=e;t.e=void 0}if(void 0!==e){e.e=n;t.x=void 0}if(t===this.t)this.t=e}};f.prototype.subscribe=function(t){const n=this;return b((function(){const e=n.value,i=32&this.f;this.f&=-33;try{t(e)}finally{this.f|=i}}))};f.prototype.valueOf=function(){return this.value};f.prototype.toString=function(){return this.value+""};f.prototype.toJSON=function(){return this.value};f.prototype.peek=function(){return this.v};Object.defineProperty(f.prototype,"value",{get(){const t=l(this);if(void 0!==t)t.i=this.i;return this.v},set(e){if(i instanceof p)!function(){throw new Error("Computed cannot have side-effects")}();if(e!==this.v){if(r>100)t();this.v=e;this.i++;u++;o++;try{for(let t=this.t;void 0!==t;t=t.x)t.t.N()}finally{n()}}}});function s(t){return new f(t)}function c(t){for(let n=t.s;void 0!==n;n=n.n)if(n.S.i!==n.i||!n.S.h()||n.S.i!==n.i)return!0;return!1}function h(t){for(let n=t.s;void 0!==n;n=n.n){const e=n.S.n;if(void 0!==e)n.r=e;n.S.n=n;n.i=-1;if(void 0===n.n){t.s=n;break}}}function a(t){let n,e=t.s;while(void 0!==e){const t=e.p;if(-1===e.i){e.S.U(e);if(void 0!==t)t.n=e.n;if(void 0!==e.n)e.n.p=t}else n=e;e.S.n=e.r;if(void 0!==e.r)e.r=void 0;e=t}t.s=n}function p(t){f.call(this,void 0);this.x=t;this.s=void 0;this.g=u-1;this.f=4}(p.prototype=new f).h=function(){this.f&=-3;if(1&this.f)return!1;if(32==(36&this.f))return!0;this.f&=-5;if(this.g===u)return!0;this.g=u;this.f|=1;if(this.i>0&&!c(this)){this.f&=-2;return!0}const t=i;try{h(this);i=this;const t=this.x();if(16&this.f||this.v!==t||0===this.i){this.v=t;this.f&=-17;this.i++}}catch(t){this.v=t;this.f|=16;this.i++}i=t;a(this);this.f&=-2;return!0};p.prototype.S=function(t){if(void 0===this.t){this.f|=36;for(let t=this.s;void 0!==t;t=t.n)t.S.S(t)}f.prototype.S.call(this,t)};p.prototype.U=function(t){if(void 0!==this.t){f.prototype.U.call(this,t);if(void 0===this.t){this.f&=-33;for(let t=this.s;void 0!==t;t=t.n)t.S.U(t)}}};p.prototype.N=function(){if(!(2&this.f)){this.f|=6;for(let t=this.t;void 0!==t;t=t.x)t.t.N()}};p.prototype.peek=function(){if(!this.h())t();if(16&this.f)throw this.v;return this.v};Object.defineProperty(p.prototype,"value",{get(){if(1&this.f)t();const n=l(this);this.h();if(void 0!==n)n.i=this.i;if(16&this.f)throw this.v;return this.v}});function d(t){return new p(t)}function v(t){const e=t.u;t.u=void 0;if("function"==typeof e){o++;const _=i;i=void 0;try{e()}catch(n){t.f&=-2;t.f|=8;y(t);throw n}finally{i=_;n()}}}function y(t){for(let n=t.s;void 0!==n;n=n.n)n.S.U(n);t.x=void 0;t.s=void 0;v(t)}function m(t){if(i!==this)throw new Error("Out-of-order effect");a(this);i=t;this.f&=-2;if(8&this.f)y(this);n()}function g(t){this.x=t;this.u=void 0;this.s=void 0;this.o=void 0;this.f=32}g.prototype.c=function(){const t=this.S();try{if(8&this.f)return;if(void 0===this.x)return;const n=this.x();if("function"==typeof n)this.u=n}finally{t()}};g.prototype.S=function(){if(1&this.f)t();this.f|=1;this.f&=-9;v(this);h(this);o++;const n=i;i=this;return m.bind(this,n)};g.prototype.N=function(){if(!(2&this.f)){this.f|=2;this.o=_;_=this}};g.prototype.d=function(){this.f|=8;if(!(1&this.f))y(this)};function b(t){const n=new g(t);try{n.c()}catch(t){n.d();throw t}return n.d.bind(n)}var k,S,x,w,C,E,U,H,N,P={},D=[],$=/acit|ex(?:s|g|n|p|$)|rph|grid|ows|mnc|ntw|ine[ch]|zoo|^ord|itera/i,T=Array.isArray;function V(t,n){for(var e in n)t[e]=n[e];return t}function A(t){var n=t.parentNode;n&&n.removeChild(t)}function F(t,n,e){var i,_,o,r={};for(o in n)"key"==o?i=n[o]:"ref"==o?_=n[o]:r[o]=n[o];if(arguments.length>2&&(r.children=arguments.length>3?k.call(arguments,2):e),"function"==typeof t&&null!=t.defaultProps)for(o in t.defaultProps)void 0===r[o]&&(r[o]=t.defaultProps[o]);return M(t,r,i,_,null)}function M(t,n,e,i,_){var o={type:t,props:n,key:e,ref:i,__k:null,__:null,__b:0,__e:null,__d:void 0,__c:null,__h:null,constructor:void 0,__v:null==_?++x:_};return null==_&&null!=S.vnode&&S.vnode(o),o}function W(){return{current:null}}function O(t){return t.children}function L(t,n){this.props=t,this.context=n}function R(t,n){if(null==n)return t.__?R(t.__,t.__.__k.indexOf(t)+1):null;for(var e;nn&&C.sort(H));q.__r=0}function B(t,n,e,i,_,o,r,u,l,f){var s,c,h,a,p,d,v,y=i&&i.__k||D,m=y.length;for(e.__k=[],s=0;s0?M(a.type,a.props,a.key,a.ref?a.ref:null,a.__v):a)){if(a.__=e,a.__b=e.__b+1,null===(h=y[s])||h&&a.key==h.key&&a.type===h.type)y[s]=void 0;else for(c=0;c=0;n--)if((e=t.__k[n])&&(i=K(e)))return i;return null}function Q(t,n,e,i,_){var o;for(o in e)"children"===o||"key"===o||o in n||Y(t,o,null,e[o],i);for(o in n)_&&"function"!=typeof n[o]||"children"===o||"key"===o||"value"===o||"checked"===o||e[o]===n[o]||Y(t,o,n[o],e[o],i)}function X(t,n,e){"-"===n[0]?t.setProperty(n,null==e?"":e):t[n]=null==e?"":"number"!=typeof e||$.test(n)?e:e+"px"}function Y(t,n,e,i,_){var o;t:if("style"===n)if("string"==typeof e)t.style.cssText=e;else{if("string"==typeof i&&(t.style.cssText=i=""),i)for(n in i)e&&n in e||X(t.style,n,"");if(e)for(n in e)i&&e[n]===i[n]||X(t.style,n,e[n])}else if("o"===n[0]&&"n"===n[1])o=n!==(n=n.replace(/Capture$/,"")),n=n.toLowerCase()in t?n.toLowerCase().slice(2):n.slice(2),t.l||(t.l={}),t.l[n+o]=e,e?i||t.addEventListener(n,o?tt:Z,o):t.removeEventListener(n,o?tt:Z,o);else if("dangerouslySetInnerHTML"!==n){if(_)n=n.replace(/xlink(H|:h)/,"h").replace(/sName$/,"s");else if("width"!==n&&"height"!==n&&"href"!==n&&"list"!==n&&"form"!==n&&"tabIndex"!==n&&"download"!==n&&"rowSpan"!==n&&"colSpan"!==n&&n in t)try{t[n]=null==e?"":e;break t}catch(t){}"function"==typeof e||(null==e||!1===e&&"-"!==n[4]?t.removeAttribute(n):t.setAttribute(n,e))}}function Z(t){return this.l[t.type+!1](S.event?S.event(t):t)}function tt(t){return this.l[t.type+!0](S.event?S.event(t):t)}function nt(t,n,e,i,_,o,r,u,l){var f,s,c,h,a,p,d,v,y,m,g,b,k,x,w,C=n.type;if(void 0!==n.constructor)return null;null!=e.__h&&(l=e.__h,u=n.__e=e.__e,n.__h=null,o=[u]),(f=S.__b)&&f(n);try{t:if("function"==typeof C){if(v=n.props,y=(f=C.contextType)&&i[f.__c],m=f?y?y.props.value:f.__:i,e.__c?d=(s=n.__c=e.__c).__=s.__E:("prototype"in C&&C.prototype.render?n.__c=s=new C(v,m):(n.__c=s=new L(v,m),s.constructor=C,s.render=rt),y&&y.sub(s),s.props=v,s.state||(s.state={}),s.context=m,s.__n=i,c=s.__d=!0,s.__h=[],s._sb=[]),null==s.__s&&(s.__s=s.state),null!=C.getDerivedStateFromProps&&(s.__s==s.state&&(s.__s=V({},s.__s)),V(s.__s,C.getDerivedStateFromProps(v,s.__s))),h=s.props,a=s.state,s.__v=n,c)null==C.getDerivedStateFromProps&&null!=s.componentWillMount&&s.componentWillMount(),null!=s.componentDidMount&&s.__h.push(s.componentDidMount);else{if(null==C.getDerivedStateFromProps&&v!==h&&null!=s.componentWillReceiveProps&&s.componentWillReceiveProps(v,m),!s.__e&&null!=s.shouldComponentUpdate&&!1===s.shouldComponentUpdate(v,s.__s,m)||n.__v===e.__v){for(n.__v!==e.__v&&(s.props=v,s.state=s.__s,s.__d=!1),s.__e=!1,n.__e=e.__e,n.__k=e.__k,n.__k.forEach((function(t){t&&(t.__=n)})),g=0;g2&&(u.children=arguments.length>3?k.call(arguments,2):e),M(t.type,u,i||t.key,_||t.ref,null)}function st(t,n){var e={__c:n="__cC"+N++,__:t,Consumer:function(t,n){return t.children(n)},Provider:function(t){var e,i;return this.getChildContext||(e=[],(i={})[n]=this,this.getChildContext=function(){return i},this.shouldComponentUpdate=function(t){this.props.value!==t.value&&e.some((function(t){t.__e=!0,j(t)}))},this.sub=function(t){e.push(t);var n=t.componentWillUnmount;t.componentWillUnmount=function(){e.splice(e.indexOf(t),1),n&&n.call(t)}}),t.children}};return e.Provider.__=e.Consumer.contextType=e}k=D.slice,S={__e:function(t,n,e,i){for(var _,o,r;n=n.__;)if((_=n.__c)&&!_.__)try{if((o=_.constructor)&&null!=o.getDerivedStateFromError&&(_.setState(o.getDerivedStateFromError(t)),r=_.__d),null!=_.componentDidCatch&&(_.componentDidCatch(t,i||{}),r=_.__d),r)return _.__E=_}catch(n){t=n}throw t}},x=0,w=function(t){return null!=t&&void 0===t.constructor},L.prototype.setState=function(t,n){var e;e=null!=this.__s&&this.__s!==this.state?this.__s:this.__s=V({},this.state),"function"==typeof t&&(t=t(V({},e),this.props)),t&&V(e,t),null!=t&&this.__v&&(n&&this._sb.push(n),j(this))},L.prototype.forceUpdate=function(t){this.__v&&(this.__e=!0,t&&this.__h.push(t),j(this))},L.prototype.render=O,C=[],U="function"==typeof Promise?Promise.prototype.then.bind(Promise.resolve()):setTimeout,H=function(t,n){return t.__v.__b-n.__v.__b},q.__r=0,N=0;var ct,ht,at,pt,dt=0,vt=[],yt=[],mt=S.__b,gt=S.__r,bt=S.diffed,kt=S.__c,St=S.unmount;function xt(t,n){S.__h&&S.__h(ht,t,dt||n),dt=0;var e=ht.__H||(ht.__H={__:[],__h:[]});return t>=e.__.length&&e.__.push({__V:yt}),e.__[t]}function wt(t){return dt=1,Ct(It,t)}function Ct(t,n,e){var i=xt(ct++,2);if(i.t=t,!i.__c&&(i.__=[e?e(n):It(void 0,n),function(t){var n=i.__N?i.__N[0]:i.__[0],e=i.t(n,t);n!==e&&(i.__N=[e,i.__[1]],i.__c.setState({}))}],i.__c=ht,!ht.u)){var _=function(t,n,e){if(!i.__c.__H)return!0;var _=i.__c.__H.__.filter((function(t){return t.__c}));if(_.every((function(t){return!t.__N})))return!o||o.call(this,t,n,e);var r=!1;return _.forEach((function(t){if(t.__N){var n=t.__[0];t.__=t.__N,t.__N=void 0,n!==t.__[0]&&(r=!0)}})),!(!r&&i.__c.props===t)&&(!o||o.call(this,t,n,e))};ht.u=!0;var o=ht.shouldComponentUpdate,r=ht.componentWillUpdate;ht.componentWillUpdate=function(t,n,e){if(this.__e){var i=o;o=void 0,_(t,n,e),o=i}r&&r.call(this,t,n,e)},ht.shouldComponentUpdate=_}return i.__N||i.__}function Et(t,n){var e=xt(ct++,3);!S.__s&&Rt(e.__H,n)&&(e.__=t,e.i=n,ht.__H.__h.push(e))}function Ut(t,n){var e=xt(ct++,4);!S.__s&&Rt(e.__H,n)&&(e.__=t,e.i=n,ht.__h.push(e))}function Ht(t){return dt=5,Pt((function(){return{current:t}}),[])}function Nt(t,n,e){dt=6,Ut((function(){return"function"==typeof t?(t(n()),function(){return t(null)}):t?(t.current=n(),function(){return t.current=null}):void 0}),null==e?e:e.concat(t))}function Pt(t,n){var e=xt(ct++,7);return Rt(e.__H,n)?(e.__V=t(),e.i=n,e.__h=t,e.__V):e.__}function Dt(t,n){return dt=8,Pt((function(){return t}),n)}function $t(t){var n=ht.context[t.__c],e=xt(ct++,9);return e.c=t,n?(null==e.__&&(e.__=!0,n.sub(ht)),n.props.value):t.__}function Tt(t,n){S.useDebugValue&&S.useDebugValue(n?n(t):t)}function Vt(t){var n=xt(ct++,10),e=wt();return n.__=t,ht.componentDidCatch||(ht.componentDidCatch=function(t,i){n.__&&n.__(t,i),e[1](t)}),[e[0],function(){e[1](void 0)}]}function At(){var t=xt(ct++,11);if(!t.__){for(var n=ht.__v;null!==n&&!n.__m&&null!==n.__;)n=n.__;var e=n.__m||(n.__m=[0,0]);t.__="P"+e[0]+"-"+e[1]++}return t.__}function Ft(){for(var t;t=vt.shift();)if(t.__P&&t.__H)try{t.__H.__h.forEach(Ot),t.__H.__h.forEach(Lt),t.__H.__h=[]}catch(u){t.__H.__h=[],S.__e(u,t.__v)}}S.__b=function(t){ht=null,mt&&mt(t)},S.__r=function(t){gt&>(t),ct=0;var n=(ht=t.__c).__H;n&&(at===ht?(n.__h=[],ht.__h=[],n.__.forEach((function(t){t.__N&&(t.__=t.__N),t.__V=yt,t.__N=t.i=void 0}))):(n.__h.forEach(Ot),n.__h.forEach(Lt),n.__h=[],ct=0)),at=ht},S.diffed=function(t){bt&&bt(t);var n=t.__c;n&&n.__H&&(n.__H.__h.length&&(1!==vt.push(n)&&pt===S.requestAnimationFrame||((pt=S.requestAnimationFrame)||Wt)(Ft)),n.__H.__.forEach((function(t){t.i&&(t.__H=t.i),t.__V!==yt&&(t.__=t.__V),t.i=void 0,t.__V=yt}))),at=ht=null},S.__c=function(t,n){n.some((function(t){try{t.__h.forEach(Ot),t.__h=t.__h.filter((function(t){return!t.__||Lt(t)}))}catch(s){n.some((function(t){t.__h&&(t.__h=[])})),n=[],S.__e(s,t.__v)}})),kt&&kt(t,n)},S.unmount=function(t){St&&St(t);var n,e=t.__c;e&&e.__H&&(e.__H.__.forEach((function(t){try{Ot(t)}catch(t){n=t}})),e.__H=void 0,n&&S.__e(n,e.__v))};var Mt="function"==typeof requestAnimationFrame;function Wt(t){var n,e=function(){clearTimeout(i),Mt&&cancelAnimationFrame(n),setTimeout(t)},i=setTimeout(e,100);Mt&&(n=requestAnimationFrame(e))}function Ot(t){var n=ht,e=t.__c;"function"==typeof e&&(t.__c=void 0,e()),ht=n}function Lt(t){var n=ht;t.__c=t.__(),ht=n}function Rt(t,n){return!t||t.length!==n.length||n.some((function(n,e){return n!==t[e]}))}function It(t,n){return"function"==typeof n?n(t):n}function jt(t,n){S[t]=n.bind(null,S[t]||(()=>{}))}let qt,Bt;function Gt(t){if(Bt)Bt();Bt=t&&t.S()}function zt({data:t}){const n=Kt(t);n.value=t;const e=Pt(()=>{let t=this.__v;while(t=t.__)if(t.__c){t.__c.__$f|=4;break}this.__$u.c=()=>{this.base.data=e.peek()};return d(()=>{let t=n.value.value;return 0===t?0:!0===t?"":t||""})},[]);return e.value}zt.displayName="_st";Object.defineProperties(f.prototype,{constructor:{configurable:!0,value:void 0},type:{configurable:!0,value:zt},props:{configurable:!0,get(){return{data:this}}},__b:{configurable:!0,value:1}});jt("__b",(t,n)=>{if("string"==typeof n.type){let t,e=n.props;for(let i in e){if("children"===i)continue;let _=e[i];if(_ instanceof f){if(!t)n.__np=t={};t[i]=_;e[i]=_.peek()}}}t(n)});jt("__r",(t,n)=>{Gt();let e,i=n.__c;if(i){i.__$f&=-2;e=i.__$u;if(void 0===e)i.__$u=e=function(t){let n;b((function(){n=this}));n.c=()=>{i.__$f|=1;i.setState({})};return n}()}qt=i;Gt(e);t(n)});jt("__e",(t,n,e,i)=>{Gt();qt=void 0;t(n,e,i)});jt("diffed",(t,n)=>{Gt();qt=void 0;let e;if("string"==typeof n.type&&(e=n.__e)){let t=n.__np,i=n.props;if(t){let n=e.U;if(n)for(let e in n){let i=n[e];if(void 0!==i&&!(e in t)){i.d();n[e]=void 0}}else{n={};e.U=n}for(let _ in t){let o=n[_],r=t[_];if(void 0===o){o=Jt(e,_,r,i);n[_]=o}else o.o(r,i)}}}t(n)});function Jt(t,n,e,i){const _=n in t&&void 0===t.ownerSVGElement,o=s(e);return{o:(t,n)=>{o.value=t;i=n},d:b(()=>{const e=o.value.value;if(i[n]!==e){i[n]=e;if(_)t[n]=e;else if(e)t.setAttribute(n,e);else t.removeAttribute(n)}})}}jt("unmount",(t,n)=>{if("string"==typeof n.type){let t=n.__e;if(t){const n=t.U;if(n){t.U=void 0;for(let t in n){let e=n[t];if(e)e.d()}}}}else{let t=n.__c;if(t){const n=t.__$u;if(n){t.__$u=void 0;n.d()}}}t(n)});jt("__h",(t,n,e,i)=>{if(i<3)n.__$f|=2;t(n,e,i)});L.prototype.shouldComponentUpdate=function(t,n){const e=this.__$u;if(!(e&&void 0!==e.s||4&this.__$f))return!0;if(3&this.__$f)return!0;for(let i in n)return!0;for(let i in t)if("__source"!==i&&t[i]!==this.props[i])return!0;for(let i in this.props)if(!(i in t))return!0;return!1};function Kt(t){return Pt(()=>s(t),[])}function Qt(t){const n=Ht(t);n.current=t;qt.__$f|=4;return Pt(()=>d(()=>n.current()),[])}function Xt(t){const n=Ht(t);n.current=t;Et(()=>b(()=>n.current()),[])}var Yt=function(t,n,e,i){var _;n[0]=0;for(var o=1;o=5&&((_||!t&&5===i)&&(r.push(i,0,_,e),i=6),t&&(r.push(i,t,0,e),i=6)),_=""},l=0;l"===n?(i=1,_=""):_=n+_[0]:o?n===o?o="":_+=n:'"'===n||"'"===n?o=n:">"===n?(u(),i=1):i&&("="===n?(i=5,e=_,_=""):"/"===n&&(i<5||">"===t[l][f+1])?(u(),3===i&&(r=r[0]),i=r,(r=r[0]).push(2,0,i),i=0):" "===n||"\t"===n||"\n"===n||"\r"===n?(u(),i=2):_+=n),3===i&&"!--"===_&&(i=4,r=r[0])}return u(),r}(t)),n),arguments,[])).length>1?n:n[0]}var nn=tn.bind(F);export{L as Component,O as Fragment,f as Signal,e as batch,ft as cloneElement,d as computed,st as createContext,F as createElement,W as createRef,b as effect,F as h,nn as html,lt as hydrate,w as isValidElement,S as options,ut as render,s as signal,z as toChildArray,Dt as useCallback,Qt as useComputed,$t as useContext,Tt as useDebugValue,Et as useEffect,Vt as useErrorBoundary,At as useId,Nt as useImperativeHandle,Ut as useLayoutEffect,Pt as useMemo,Ct as useReducer,Ht as useRef,Kt as useSignal,Xt as useSignalEffect,wt as useState}; diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 3bf985957..043e49750 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2,8 +2,6 @@ #include "llama.h" #include "build-info.h" -// single thread -#define CPPHTTPLIB_THREAD_POOL_COUNT 1 #ifndef NDEBUG // crash the server in debug mode, otherwise send an http 500 error #define CPPHTTPLIB_NO_EXCEPTIONS 1 @@ -12,6 +10,11 @@ #include "httplib.h" #include "json.hpp" +// auto generated files (update with ./deps.sh) +#include "index.html.hpp" +#include "index.js.hpp" +#include "completion.js.hpp" + #ifndef SERVER_VERBOSE #define SERVER_VERBOSE 1 #endif @@ -21,6 +24,7 @@ using json = nlohmann::json; struct server_params { std::string hostname = "127.0.0.1"; + std::string public_path = "examples/server/public"; int32_t port = 8080; int32_t read_timeout = 600; int32_t write_timeout = 600; @@ -172,6 +176,12 @@ struct llama_server_context { std::string stopping_word; int32_t multibyte_pending = 0; + std::mutex mutex; + + std::unique_lock lock() { + return std::unique_lock(mutex); + } + ~llama_server_context() { if (ctx) { llama_free(ctx); @@ -539,6 +549,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, fprintf(stderr, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); fprintf(stderr, " --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str()); fprintf(stderr, " --port PORT port to listen (default (default: %d)\n", sparams.port); + fprintf(stderr, " --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str()); fprintf(stderr, " -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); fprintf(stderr, " --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); fprintf(stderr, "\n"); @@ -565,6 +576,12 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, break; } sparams.hostname = argv[i]; + } else if (arg == "--path") { + if (++i >= argc) { + invalid_param = true; + break; + } + sparams.public_path = argv[i]; } else if (arg == "--timeout" || arg == "-to") { if (++i >= argc) { invalid_param = true; @@ -839,17 +856,24 @@ static void parse_options_completion(const json & body, llama_server_context & l LOG_VERBOSE("completion parameters parsed", format_generation_settings(llama)); } + static void log_server_request(const Request & req, const Response & res) { LOG_INFO("request", { { "remote_addr", req.remote_addr }, { "remote_port", req.remote_port }, { "status", res.status }, + { "method", req.method }, { "path", req.path }, + { "params", req.params }, + }); + + LOG_VERBOSE("request", { { "request", req.body }, { "response", res.body }, }); } + int main(int argc, char ** argv) { // own arguments required by this example gpt_params params; @@ -884,16 +908,34 @@ int main(int argc, char ** argv) { Server svr; svr.set_default_headers({ + { "Server", "llama.cpp" }, { "Access-Control-Allow-Origin", "*" }, { "Access-Control-Allow-Headers", "content-type" } }); + // this is only called if no index.js is found in the public --path + svr.Get("/index.js", [](const Request &, Response & res) { + res.set_content(reinterpret_cast(&index_js), index_js_len, "text/javascript"); + return false; + }); + + // this is only called if no index.html is found in the public --path svr.Get("/", [](const Request &, Response & res) { - res.set_content("

    llama.cpp server works

    ", "text/html"); + res.set_content(reinterpret_cast(&index_html), index_html_len, "text/html"); + return false; + }); + + // this is only called if no index.html is found in the public --path + svr.Get("/completion.js", [](const Request &, Response & res) { + res.set_content(reinterpret_cast(&completion_js), completion_js_len, "application/javascript"); + return false; }); svr.Post("/completion", [&llama](const Request & req, Response & res) { + auto lock = llama.lock(); + llama.rewind(); + llama_reset_timings(llama.ctx); parse_options_completion(json::parse(req.body), llama); @@ -1002,6 +1044,8 @@ int main(int argc, char ** argv) { }); svr.Post("/tokenize", [&llama](const Request & req, Response & res) { + auto lock = llama.lock(); + const json body = json::parse(req.body); const std::string content = body.value("content", ""); const std::vector tokens = llama_tokenize(llama.ctx, content, false); @@ -1010,6 +1054,8 @@ int main(int argc, char ** argv) { }); svr.Post("/embedding", [&llama](const Request & req, Response & res) { + auto lock = llama.lock(); + const json body = json::parse(req.body); llama.rewind(); @@ -1040,18 +1086,27 @@ int main(int argc, char ** argv) { res.status = 500; }); + svr.set_error_handler([](const Request &, Response & res) { + res.set_content("File Not Found", "text/plain"); + res.status = 404; + }); + + // set timeouts and change hostname and port svr.set_read_timeout(sparams.read_timeout); svr.set_write_timeout(sparams.write_timeout); if (!svr.bind_to_port(sparams.hostname, sparams.port)) { - LOG_ERROR("couldn't bind to server socket", { - { "hostname", sparams.hostname }, - { "port", sparams.port }, - }); + fprintf(stderr, "\ncouldn't bind to server socket: hostname=%s port=%d\n\n", sparams.hostname.c_str(), sparams.port); return 1; } + // Set the base directory for serving static files + svr.set_base_dir(sparams.public_path); + + // to make it ctrl+clickable: + fprintf(stdout, "\nllama server listening at http://%s:%d\n\n", sparams.hostname.c_str(), sparams.port); + LOG_INFO("HTTP server listening", { { "hostname", sparams.hostname }, { "port", sparams.port }, From f257fd255044decffad93dee2502875ce66ad80c Mon Sep 17 00:00:00 2001 From: jwj7140 <32943891+jwj7140@users.noreply.github.com> Date: Wed, 5 Jul 2023 03:06:12 +0900 Subject: [PATCH 082/852] Add an API example using server.cpp similar to OAI. (#2009) * add api_like_OAI.py * add evaluated token count to server * add /v1/ endpoints binding --- examples/server/README.md | 16 +++ examples/server/api_like_OAI.py | 219 ++++++++++++++++++++++++++++++++ examples/server/server.cpp | 14 +- 3 files changed, 244 insertions(+), 5 deletions(-) create mode 100755 examples/server/api_like_OAI.py diff --git a/examples/server/README.md b/examples/server/README.md index ba4b2fec9..4ed226e04 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -190,3 +190,19 @@ Run with bash: ```sh bash chat.sh ``` + +### API like OAI + +API example using Python Flask: [api_like_OAI.py](api_like_OAI.py) +This example must be used with server.cpp + +```sh +python api_like_OAI.py +``` + +After running the API server, you can use it in Python by setting the API base URL. +```python +openai.api_base = "http://:port" +``` + +Then you can utilize llama.cpp as an OpenAI's **chat.completion** or **text_completion** API diff --git a/examples/server/api_like_OAI.py b/examples/server/api_like_OAI.py new file mode 100755 index 000000000..aa325a03e --- /dev/null +++ b/examples/server/api_like_OAI.py @@ -0,0 +1,219 @@ +import argparse +from flask import Flask, jsonify, request, Response +import urllib.parse +import requests +import time +import json + + +app = Flask(__name__) + +parser = argparse.ArgumentParser(description="An example of using server.cpp with a similar API to OAI. It must be used together with server.cpp.") +parser.add_argument("--chat-prompt", type=str, help="the top prompt in chat completions(default: 'A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n')", default='A chat between a curious user and an artificial intelligence assistant. The assistant follows the given rules no matter what.\\n') +parser.add_argument("--user-name", type=str, help="USER name in chat completions(default: '\\nUSER: ')", default="\\nUSER: ") +parser.add_argument("--ai-name", type=str, help="ASSISTANT name in chat completions(default: '\\nASSISTANT: ')", default="\\nASSISTANT: ") +parser.add_argument("--system-name", type=str, help="SYSTEM name in chat completions(default: '\\nASSISTANT's RULE: ')", default="\\nASSISTANT's RULE: ") +parser.add_argument("--stop", type=str, help="the end of response in chat completions(default: '')", default="") +parser.add_argument("--llama-api", type=str, help="Set the address of server.cpp in llama.cpp(default: http://127.0.0.1:8080)", default='http://127.0.0.1:8080') +parser.add_argument("--api-key", type=str, help="Set the api key to allow only few user(default: NULL)", default="") +parser.add_argument("--host", type=str, help="Set the ip address to listen.(default: 127.0.0.1)", default='127.0.0.1') +parser.add_argument("--port", type=int, help="Set the port to listen.(default: 8081)", default=8081) + +args = parser.parse_args() + +def is_present(json, key): + try: + buf = json[key] + except KeyError: + return False + return True + + + +#convert chat to prompt +def convert_chat(messages): + prompt = "" + args.chat_prompt.replace("\\n", "\n") + + system_n = args.system_name.replace("\\n", "\n") + user_n = args.user_name.replace("\\n", "\n") + ai_n = args.ai_name.replace("\\n", "\n") + stop = args.stop.replace("\\n", "\n") + + + for line in messages: + if (line["role"] == "system"): + prompt += f"{system_n}{line['content']}" + if (line["role"] == "user"): + prompt += f"{user_n}{line['content']}" + if (line["role"] == "assistant"): + prompt += f"{ai_n}{line['content']}{stop}" + prompt += ai_n.rstrip() + + return prompt + +def make_postData(body, chat=False, stream=False): + postData = {} + if (chat): + postData["prompt"] = convert_chat(body["messages"]) + else: + postData["prompt"] = body["prompt"] + if(is_present(body, "temperature")): postData["temperature"] = body["temperature"] + if(is_present(body, "top_k")): postData["top_k"] = body["top_k"] + if(is_present(body, "top_p")): postData["top_p"] = body["top_p"] + if(is_present(body, "max_tokens")): postData["n_predict"] = body["max_tokens"] + if(is_present(body, "presence_penalty")): postData["presence_penalty"] = body["presence_penalty"] + if(is_present(body, "frequency_penalty")): postData["frequency_penalty"] = body["frequency_penalty"] + if(is_present(body, "repeat_penalty")): postData["repeat_penalty"] = body["repeat_penalty"] + if(is_present(body, "mirostat")): postData["mirostat"] = body["mirostat"] + if(is_present(body, "mirostat_tau")): postData["mirostat_tau"] = body["mirostat_tau"] + if(is_present(body, "mirostat_eta")): postData["mirostat_eta"] = body["mirostat_eta"] + if(is_present(body, "seed")): postData["seed"] = body["seed"] + if(is_present(body, "logit_bias")): postData["logit_bias"] = [[int(token), body["logit_bias"][token]] for token in body["logit_bias"].keys()] + if (args.stop != ""): + postData["stop"] = [args.stop] + else: + postData["stop"] = [] + if(is_present(body, "stop")): postData["stop"] += body["stop"] + postData["n_keep"] = -1 + postData["stream"] = stream + + return postData + +def make_resData(data, chat=False, promptToken=[]): + resData = { + "id": "chatcmpl" if (chat) else "cmpl", + "object": "chat.completion" if (chat) else "text_completion", + "created": int(time.time()), + "truncated": data["truncated"], + "model": "LLaMA_CPP", + "usage": { + "prompt_tokens": data["tokens_evaluated"], + "completion_tokens": data["tokens_predicted"], + "total_tokens": data["tokens_evaluated"] + data["tokens_predicted"] + } + } + if (len(promptToken) != 0): + resData["promptToken"] = promptToken + if (chat): + #only one choice is supported + resData["choices"] = [{ + "index": 0, + "message": { + "role": "assistant", + "content": data["content"], + }, + "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" + }] + else: + #only one choice is supported + resData["choices"] = [{ + "text": data["content"], + "index": 0, + "logprobs": None, + "finish_reason": "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" + }] + return resData + +def make_resData_stream(data, chat=False, time_now = 0, start=False): + resData = { + "id": "chatcmpl" if (chat) else "cmpl", + "object": "chat.completion.chunk" if (chat) else "text_completion.chunk", + "created": time_now, + "model": "LLaMA_CPP", + "choices": [ + { + "finish_reason": None, + "index": 0 + } + ] + } + if (chat): + if (start): + resData["choices"][0]["delta"] = { + "role": "assistant" + } + else: + resData["choices"][0]["delta"] = { + "content": data["content"] + } + if (data["stop"]): + resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" + else: + resData["choices"][0]["text"] = data["content"] + if (data["stop"]): + resData["choices"][0]["finish_reason"] = "stop" if (data["stopped_eos"] or data["stopped_word"]) else "length" + + return resData + + +@app.route('/chat/completions', methods=['POST']) +@app.route('/v1/chat/completions', methods=['POST']) +def chat_completions(): + if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key): + return Response(status=403) + body = request.get_json() + stream = False + tokenize = False + if(is_present(body, "stream")): stream = body["stream"] + if(is_present(body, "tokenize")): tokenize = body["tokenize"] + postData = make_postData(body, chat=True, stream=stream) + + promptToken = [] + if (tokenize): + tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json() + promptToken = tokenData["tokens"] + + if (not stream): + data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData)) + print(data.json()) + resData = make_resData(data.json(), chat=True, promptToken=promptToken) + return jsonify(resData) + else: + def generate(): + data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True) + time_now = int(time.time()) + resData = make_resData_stream({}, chat=True, time_now=time_now, start=True) + yield 'data: {}\n'.format(json.dumps(resData)) + for line in data.iter_lines(): + if line: + decoded_line = line.decode('utf-8') + resData = make_resData_stream(json.loads(decoded_line[6:]), chat=True, time_now=time_now) + yield 'data: {}\n'.format(json.dumps(resData)) + return Response(generate(), mimetype='text/event-stream') + + +@app.route('/completions', methods=['POST']) +@app.route('/v1/completions', methods=['POST']) +def completion(): + if (args.api_key != "" and request.headers["Authorization"].split()[1] != args.api_key): + return Response(status=403) + body = request.get_json() + stream = False + tokenize = False + if(is_present(body, "stream")): stream = body["stream"] + if(is_present(body, "tokenize")): tokenize = body["tokenize"] + postData = make_postData(body, chat=False, stream=stream) + + promptToken = [] + if (tokenize): + tokenData = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/tokenize"), data=json.dumps({"content": postData["prompt"]})).json() + promptToken = tokenData["tokens"] + + if (not stream): + data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData)) + print(data.json()) + resData = make_resData(data.json(), chat=False, promptToken=promptToken) + return jsonify(resData) + else: + def generate(): + data = requests.request("POST", urllib.parse.urljoin(args.llama_api, "/completion"), data=json.dumps(postData), stream=True) + time_now = int(time.time()) + for line in data.iter_lines(): + if line: + decoded_line = line.decode('utf-8') + resData = make_resData_stream(json.loads(decoded_line[6:]), chat=False, time_now=time_now) + yield 'data: {}\n'.format(json.dumps(resData)) + return Response(generate(), mimetype='text/event-stream') + +if __name__ == '__main__': + app.run(args.host, port=args.port) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 043e49750..a835c3988 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -158,6 +158,7 @@ struct llama_server_context { std::string generated_text; std::vector generated_token_probs; + size_t num_prompt_tokens = 0; size_t num_tokens_predicted = 0; size_t n_past = 0; size_t n_remain = 0; @@ -195,6 +196,7 @@ struct llama_server_context { void rewind() { params.antiprompt.clear(); + num_prompt_tokens = 0; num_tokens_predicted = 0; generated_text = ""; generated_text.reserve(params.n_ctx); @@ -226,17 +228,18 @@ struct llama_server_context { void loadPrompt() { params.prompt.insert(0, 1, ' '); // always add a first space std::vector prompt_tokens = ::llama_tokenize(ctx, params.prompt, true); + num_prompt_tokens = prompt_tokens.size(); if (params.n_keep < 0) { - params.n_keep = (int)prompt_tokens.size(); + params.n_keep = (int)num_prompt_tokens; } params.n_keep = std::min(params.n_ctx - 4, params.n_keep); // if input prompt is too big, truncate like normal - if (prompt_tokens.size() >= (size_t)params.n_ctx) { + if (num_prompt_tokens>= (size_t)params.n_ctx) { const int n_left = (params.n_ctx - params.n_keep) / 2; std::vector new_tokens(prompt_tokens.begin(), prompt_tokens.begin() + params.n_keep); - const int erased_blocks = (prompt_tokens.size() - params.n_keep - n_left - 1) / n_left; + const int erased_blocks = (num_prompt_tokens - params.n_keep - n_left - 1) / n_left; new_tokens.insert(new_tokens.end(), prompt_tokens.begin() + params.n_keep + erased_blocks * n_left, prompt_tokens.end()); std::copy(prompt_tokens.end() - params.n_ctx, prompt_tokens.end(), last_n_tokens.begin()); @@ -250,7 +253,7 @@ struct llama_server_context { truncated = true; prompt_tokens = new_tokens; } else { - const size_t ps = prompt_tokens.size(); + const size_t ps = num_prompt_tokens; std::fill(last_n_tokens.begin(), last_n_tokens.end() - ps, 0); std::copy(prompt_tokens.begin(), prompt_tokens.end(), last_n_tokens.end() - ps); } @@ -258,7 +261,7 @@ struct llama_server_context { // compare the evaluated prompt with the new prompt n_past = common_part(embd, prompt_tokens); embd = prompt_tokens; - if (n_past == prompt_tokens.size()) { + if (n_past == num_prompt_tokens) { // we have to evaluate at least 1 token to generate logits. n_past--; } @@ -763,6 +766,7 @@ static json format_final_response(llama_server_context & llama, const std::strin { "stop", true }, { "model", llama.params.model_alias }, { "tokens_predicted", llama.num_tokens_predicted }, + { "tokens_evaluated", llama.num_prompt_tokens }, { "generation_settings", format_generation_settings(llama) }, { "prompt", llama.params.prompt }, { "truncated", llama.truncated }, From ed9a54e5129a11c2a5b555e1dc65e875e3c37b4f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 4 Jul 2023 21:54:11 +0300 Subject: [PATCH 083/852] ggml : sync latest (new ops, macros, refactoring) (#2106) - add ggml_argmax() - add ggml_tanh() - add ggml_elu() - refactor ggml_conv_1d() and variants - refactor ggml_conv_2d() and variants - add helper macros to reduce code duplication in ggml.c --- ggml.c | 1512 ++++++++++++++---------------------------- ggml.h | 118 ++-- scripts/sync-ggml.sh | 11 +- 3 files changed, 589 insertions(+), 1052 deletions(-) diff --git a/ggml.c b/ggml.c index afeb72ff0..88cbed7d5 100644 --- a/ggml.c +++ b/ggml.c @@ -220,9 +220,27 @@ inline static void* ggml_aligned_malloc(size_t size) { #define GGML_ALIGNED_FREE(ptr) free(ptr) #endif -#define UNUSED(x) (void)(x) +#define UNUSED GGML_UNUSED #define SWAP(x, y, T) do { T SWAP = x; x = y; y = SWAP; } while (0) +// +// tensor access macros +// + +#define GGML_TENSOR_UNARY_OP_LOCALS \ + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); \ + GGML_TENSOR_LOCALS(size_t, nb0, src0, nb); \ + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); \ + GGML_TENSOR_LOCALS(size_t, nb, dst, nb); + +#define GGML_TENSOR_BINARY_OP_LOCALS \ + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); \ + GGML_TENSOR_LOCALS(size_t, nb0, src0, nb); \ + GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne); \ + GGML_TENSOR_LOCALS(size_t, nb1, src1, nb); \ + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); \ + GGML_TENSOR_LOCALS(size_t, nb, dst, nb); + #if defined(GGML_USE_ACCELERATE) #include #if defined(GGML_USE_CLBLAST) // allow usage of CLBlast alongside Accelerate functions @@ -3447,6 +3465,8 @@ inline static void ggml_vec_log_f32 (const int n, float * y, const float * x) { inline static void ggml_vec_abs_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = fabsf(x[i]); } inline static void ggml_vec_sgn_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : ((x[i] < 0.f) ? -1.f : 0.f); } inline static void ggml_vec_step_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? 1.f : 0.f; } +inline static void ggml_vec_tanh_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = tanhf(x[i]); } +inline static void ggml_vec_elu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : expf(x[i])-1; } inline static void ggml_vec_relu_f32 (const int n, float * y, const float * x) { for (int i = 0; i < n; ++i) y[i] = (x[i] > 0.f) ? x[i] : 0.f; } static const float GELU_COEF_A = 0.044715f; @@ -3598,6 +3618,16 @@ inline static void ggml_vec_norm_inv_f32(const int n, float * s, const float * x *s = 1.f/(*s); } +inline static void ggml_vec_argmax_f32(const int n, int * s, const float * x) { + float max = -INFINITY; + int idx = 0; + for (int i = 0; i < n; ++i) { + max = MAX(max, x[i]); + if (max == x[i]) { idx = i; } + } + *s = idx; +} + // // data types // @@ -3707,12 +3737,15 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "SUM", "SUM_ROWS", "MEAN", + "ARGMAX", "REPEAT", "REPEAT_BACK", "ABS", "SGN", "NEG", "STEP", + "TANH", + "ELU", "RELU", "GELU", "GELU_QUICK", @@ -3744,9 +3777,8 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "ROPE_BACK", "ALIBI", "CLAMP", - "CONV_1D_S1_PH", - "CONV_1D_S2_PH", - "CONV_2D_SK_P0", + "CONV_1D", + "CONV_2D", "FLASH_ATTN", "FLASH_FF", @@ -3765,7 +3797,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "CROSS_ENTROPY_LOSS_BACK", }; -static_assert(GGML_OP_COUNT == 64, "GGML_OP_COUNT != 64"); +static_assert(GGML_OP_COUNT == 66, "GGML_OP_COUNT != 66"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -3783,12 +3815,15 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "Σx", "Σx_k", "Σx/n", + "argmax(x)", "repeat(x)", "repeat_back(x)", "abs(x)", "sgn(x)", "-x", "step(x)", + "tanh(x)", + "elu(x)", "relu(x)", "gelu(x)", "gelu_quick(x)", @@ -3820,9 +3855,8 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "rope_back(x)", "alibi(x)", "clamp(x)", - "conv_1d_s1_ph(x)", - "conv_1d_s2_ph(x)", - "conv_2d_sk_p0(x)", + "conv_1d(x)", + "conv_2d(x)", "flash_attn(x)", "flash_ff(x)", @@ -3841,7 +3875,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "cross_entropy_loss_back(x,y)", }; -static_assert(GGML_OP_COUNT == 64, "GGML_OP_COUNT != 64"); +static_assert(GGML_OP_COUNT == 66, "GGML_OP_COUNT != 66"); static_assert(sizeof(struct ggml_object)%GGML_MEM_ALIGN == 0, "ggml_object size must be a multiple of GGML_MEM_ALIGN"); static_assert(sizeof(struct ggml_tensor)%GGML_MEM_ALIGN == 0, "ggml_tensor size must be a multiple of GGML_MEM_ALIGN"); @@ -3867,9 +3901,8 @@ static void ggml_setup_op_has_task_pass(void) { p[GGML_OP_GET_ROWS_BACK ] = true; p[GGML_OP_DIAG_MASK_INF ] = true; p[GGML_OP_DIAG_MASK_ZERO ] = true; - p[GGML_OP_CONV_1D_S1_PH ] = true; - p[GGML_OP_CONV_1D_S2_PH ] = true; - p[GGML_OP_CONV_2D_SK_P0 ] = true; + p[GGML_OP_CONV_1D ] = true; + p[GGML_OP_CONV_2D ] = true; p[GGML_OP_FLASH_ATTN_BACK ] = true; p[GGML_OP_CROSS_ENTROPY_LOSS ] = true; } @@ -5440,6 +5473,30 @@ struct ggml_tensor * ggml_mean( return result; } +// ggml_argmax + +struct ggml_tensor * ggml_argmax( + struct ggml_context * ctx, + struct ggml_tensor * a) { + GGML_ASSERT(ggml_is_matrix(a)); + bool is_node = false; + + if (a->grad) { + GGML_ASSERT(false); + is_node = true; + } + + int64_t ne[GGML_MAX_DIMS] = { a->ne[1], 1, 1, 1 }; + struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_I32, a->n_dims, ne); + + result->op = GGML_OP_ARGMAX; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src0 = a; + result->src1 = NULL; + + return result; +} + // ggml_repeat struct ggml_tensor * ggml_repeat( @@ -5633,6 +5690,74 @@ struct ggml_tensor * ggml_step_inplace( return ggml_step_impl(ctx, a, true); } +// ggml_tanh + +struct ggml_tensor * ggml_tanh_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + bool inplace) { + bool is_node = false; + + if (!inplace && (a->grad)) { + is_node = true; + } + + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + result->op = GGML_OP_TANH; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src0 = a; + result->src1 = NULL; + + return result; +} + +struct ggml_tensor * ggml_tanh( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_tanh_impl(ctx, a, false); +} + +struct ggml_tensor * ggml_tanh_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_tanh_impl(ctx, a, true); +} + +// ggml_elu + +struct ggml_tensor * ggml_elu_impl( + struct ggml_context * ctx, + struct ggml_tensor * a, + bool inplace) { + bool is_node = false; + + if (!inplace && (a->grad)) { + is_node = true; + } + + struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + + result->op = GGML_OP_ELU; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; + result->src0 = a; + result->src1 = NULL; + + return result; +} + +struct ggml_tensor * ggml_elu( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_elu_impl(ctx, a, false); +} + +struct ggml_tensor * ggml_elu_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a) { + return ggml_elu_impl(ctx, a, true); +} + // ggml_relu struct ggml_tensor * ggml_relu_impl( @@ -6874,6 +6999,8 @@ struct ggml_tensor * ggml_rope_back( int n_dims, int mode) { GGML_ASSERT(n_past >= 0); + GGML_ASSERT((mode & 4) == 0 && "ggml_rope_back() for ChatGLM not implemented yet"); + bool is_node = false; if (a->grad) { @@ -6974,15 +7101,21 @@ struct ggml_tensor * ggml_clamp( return result; } -// ggml_conv_1d_s1_ph +// ggml_conv_1d -struct ggml_tensor * ggml_conv_1d_s1_ph( +static int64_t ggml_calc_conv_output_size(int64_t ins, int64_t ks, int s, int p, int d) { + return (ins + 2 * p - d * (ks - 1) - 1) / s + 1; +} + +GGML_API struct ggml_tensor * ggml_conv_1d( struct ggml_context * ctx, struct ggml_tensor * a, - struct ggml_tensor * b) { + struct ggml_tensor * b, + int s0, + int p0, + int d0) { GGML_ASSERT(ggml_is_matrix(b)); GGML_ASSERT(a->ne[1] == b->ne[1]); - GGML_ASSERT(a->ne[3] == 1); bool is_node = false; if (a->grad || b->grad) { @@ -6990,54 +7123,43 @@ struct ggml_tensor * ggml_conv_1d_s1_ph( is_node = true; } - const int64_t ne[4] = { b->ne[0], a->ne[2], 1, 1, }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); + const int64_t ne[4] = { + ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0), + a->ne[2], 1, 1, + }; + struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); - result->op = GGML_OP_CONV_1D_S1_PH; + ggml_scratch_save(ctx); + struct ggml_tensor* c = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 3); + ((int32_t*)c->data)[0] = s0; + ((int32_t*)c->data)[1] = p0; + ((int32_t*)c->data)[2] = d0; + ggml_scratch_load(ctx); + + result->op = GGML_OP_CONV_1D; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; + result->opt[0] = c; return result; } -// ggml_conv_1d_s2_ph +// ggml_conv_2d -struct ggml_tensor * ggml_conv_1d_s2_ph( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b) { - GGML_ASSERT(ggml_is_matrix(b)); - GGML_ASSERT(a->ne[1] == b->ne[1]); - GGML_ASSERT(a->ne[3] == 1); - bool is_node = false; +struct ggml_tensor* ggml_conv_2d( + struct ggml_context* ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int s0, + int s1, + int p0, + int p1, + int d0, + int d1) { - if (a->grad || b->grad) { - GGML_ASSERT(false); // TODO: implement backward - is_node = true; - } - - const int64_t ne[4] = { b->ne[0]/2, a->ne[2], 1, 1, }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 2, ne); - - result->op = GGML_OP_CONV_1D_S2_PH; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; - result->src0 = a; - result->src1 = b; - - return result; -} - -// ggml_conv_2d_sk_p0 - -struct ggml_tensor * ggml_conv_2d_sk_p0( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b) { GGML_ASSERT(b->ne[3] == 1); GGML_ASSERT(a->ne[2] == b->ne[2]); - GGML_ASSERT(b->ne[0] % a->ne[0] == 0); - GGML_ASSERT(b->ne[1] % a->ne[1] == 0); bool is_node = false; if (a->grad || b->grad) { @@ -7045,15 +7167,42 @@ struct ggml_tensor * ggml_conv_2d_sk_p0( is_node = true; } - const int64_t ne[4] = { b->ne[0]/a->ne[0], b->ne[1]/a->ne[1], a->ne[3], 1, }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + const int64_t ne[4] = { + ggml_calc_conv_output_size(b->ne[0], a->ne[0], s0, p0, d0), + ggml_calc_conv_output_size(b->ne[1], a->ne[1], s1, p1, d1), + a->ne[3], 1, + }; + struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); - result->op = GGML_OP_CONV_2D_SK_P0; + ggml_scratch_save(ctx); + struct ggml_tensor* c = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, 6); + ((int32_t*)c->data)[0] = s0; + ((int32_t*)c->data)[1] = s1; + ((int32_t*)c->data)[2] = p0; + ((int32_t*)c->data)[3] = p1; + ((int32_t*)c->data)[4] = d0; + ((int32_t*)c->data)[5] = d1; + ggml_scratch_load(ctx); + + result->op = GGML_OP_CONV_2D; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src0 = a; result->src1 = b; + result->opt[0] = c; return result; + +} + +// ggml_conv_1d_ph + +struct ggml_tensor* ggml_conv_1d_ph( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int s, + int d) { + return ggml_conv_1d(ctx, a, b, s, a->ne[0] / 2, d); } // ggml_flash_attn @@ -7603,25 +7752,7 @@ static void ggml_compute_forward_dup_f16( return; } - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; const int ith = params->ith; // thread index const int nth = params->nth; // number of threads @@ -7892,25 +8023,7 @@ static void ggml_compute_forward_dup_f32( return; } - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; const int ith = params->ith; // thread index const int nth = params->nth; // number of threads @@ -8208,24 +8321,8 @@ static void ggml_compute_forward_add_f32( const int nth = params->nth; const int nr = ggml_nrows(src0); - const int64_t ne0 = src0->ne[0]; - const int64_t ne1 = src0->ne[1]; - const int64_t ne2 = src0->ne[2]; - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb10 = src1->nb[0]; - const size_t nb11 = src1->nb[1]; - const size_t nb12 = src1->nb[2]; - const size_t nb13 = src1->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); @@ -8294,28 +8391,12 @@ static void ggml_compute_forward_add_f16_f32( const int nth = params->nth; const int nr = ggml_nrows(src0); - const int64_t ne0 = src0->ne[0]; - const int64_t ne1 = src0->ne[1]; - const int64_t ne2 = src0->ne[2]; - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb10 = src1->nb[0]; - const size_t nb11 = src1->nb[1]; - const size_t nb12 = src1->nb[2]; - const size_t nb13 = src1->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F16); + GGML_ASSERT(dst->type == GGML_TYPE_F16); GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); @@ -8364,24 +8445,8 @@ static void ggml_compute_forward_add_f16_f16( const int nth = params->nth; const int nr = ggml_nrows(src0); - const int64_t ne0 = src0->ne[0]; - const int64_t ne1 = src0->ne[1]; - const int64_t ne2 = src0->ne[2]; - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb10 = src1->nb[0]; - const size_t nb11 = src1->nb[1]; - const size_t nb12 = src1->nb[2]; - const size_t nb13 = src1->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F16); @@ -8431,25 +8496,8 @@ static void ggml_compute_forward_add_q_f32( } const int nr = ggml_nrows(src0); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - //const int64_t ne03 = src0->ne[3]; - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb10 = src1->nb[0]; - const size_t nb11 = src1->nb[1]; - const size_t nb12 = src1->nb[2]; - const size_t nb13 = src1->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; @@ -8570,19 +8618,8 @@ static void ggml_compute_forward_add1_f32( const int nth = params->nth; const int nr = ggml_nrows(src0); - const int64_t ne0 = src0->ne[0]; - const int64_t ne1 = src0->ne[1]; - const int64_t ne2 = src0->ne[2]; - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); @@ -8636,23 +8673,12 @@ static void ggml_compute_forward_add1_f16_f32( const int nth = params->nth; const int nr = ggml_nrows(src0); - const int64_t ne0 = src0->ne[0]; - const int64_t ne1 = src0->ne[1]; - const int64_t ne2 = src0->ne[2]; - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F16); + GGML_ASSERT(dst->type == GGML_TYPE_F16); GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); @@ -8697,23 +8723,12 @@ static void ggml_compute_forward_add1_f16_f16( const int nth = params->nth; const int nr = ggml_nrows(src0); - const int64_t ne0 = src0->ne[0]; - const int64_t ne1 = src0->ne[1]; - const int64_t ne2 = src0->ne[2]; - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F16); - GGML_ASSERT(dst->type == GGML_TYPE_F16); + GGML_ASSERT(dst->type == GGML_TYPE_F16); GGML_ASSERT( nb0 == sizeof(ggml_fp16_t)); GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); @@ -8758,19 +8773,8 @@ static void ggml_compute_forward_add1_q_f32( const int nth = params->nth; const int nr = ggml_nrows(src0); - const int64_t ne0 = src0->ne[0]; - const int64_t ne1 = src0->ne[1]; - const int64_t ne2 = src0->ne[2]; - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; const enum ggml_type type = src0->type; dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q; @@ -8902,15 +8906,8 @@ static void ggml_compute_forward_acc_f32( const int nr = ggml_nrows(src1); const int nc = src1->ne[0]; - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - - const size_t nb10 = src1->nb[0]; - const size_t nb11 = src1->nb[1]; - const size_t nb12 = src1->nb[2]; - const size_t nb13 = src1->nb[3]; + GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne); + GGML_TENSOR_LOCALS(size_t, nb1, src1, nb); // src0 and dst as viewed during acc const size_t nb0 = ggml_element_size(src0); @@ -8999,24 +8996,8 @@ static void ggml_compute_forward_sub_f32( } const int nr = ggml_nrows(src0); - const int64_t ne0 = src0->ne[0]; - const int64_t ne1 = src0->ne[1]; - const int64_t ne2 = src0->ne[2]; - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb10 = src1->nb[0]; - const size_t nb11 = src1->nb[1]; - const size_t nb12 = src1->nb[2]; - const size_t nb13 = src1->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); @@ -9106,29 +9087,7 @@ static void ggml_compute_forward_mul_f32( const int64_t nr = ggml_nrows(src0); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb10 = src1->nb[0]; - const size_t nb11 = src1->nb[1]; - const size_t nb12 = src1->nb[2]; - const size_t nb13 = src1->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); @@ -9216,24 +9175,8 @@ static void ggml_compute_forward_div_f32( } const int nr = ggml_nrows(src0); - const int64_t ne0 = src0->ne[0]; - const int64_t ne1 = src0->ne[1]; - const int64_t ne2 = src0->ne[2]; - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb10 = src1->nb[0]; - const size_t nb11 = src1->nb[1]; - const size_t nb12 = src1->nb[2]; - const size_t nb13 = src1->nb[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; GGML_ASSERT( nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); @@ -9440,14 +9383,8 @@ static void ggml_compute_forward_sum_f32( assert(ggml_is_scalar(dst)); assert(src0->nb[0] == sizeof(float)); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); + GGML_TENSOR_LOCALS(size_t, nb0, src0, nb); ggml_float sum = 0; ggml_float row_sum = 0; @@ -9496,29 +9433,13 @@ static void ggml_compute_forward_sum_rows_f32( GGML_ASSERT(src0->nb[0] == sizeof(float)); GGML_ASSERT(dst->nb[0] == sizeof(float)); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; + GGML_TENSOR_UNARY_OP_LOCALS; GGML_ASSERT(ne0 == 1); GGML_ASSERT(ne1 == ne01); GGML_ASSERT(ne2 == ne02); GGML_ASSERT(ne3 == ne03); - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; - for (int64_t i3 = 0; i3 < ne03; i3++) { for (int64_t i2 = 0; i2 < ne02; i2++) { for (int64_t i1 = 0; i1 < ne01; i1++) { @@ -9562,19 +9483,7 @@ static void ggml_compute_forward_mean_f32( assert(src0->nb[0] == sizeof(float)); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; + GGML_TENSOR_UNARY_OP_LOCALS; assert(ne0 == 1); assert(ne1 == ne01); @@ -9586,10 +9495,6 @@ static void ggml_compute_forward_mean_f32( UNUSED(ne2); UNUSED(ne3); - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; - for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { for (int64_t i01 = 0; i01 < ne01; i01++) { @@ -9619,6 +9524,52 @@ static void ggml_compute_forward_mean( } } +// ggml_compute_forward_argmax + +static void ggml_compute_forward_argmax_f32( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + assert(params->ith == 0); + + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + assert(src0->nb[0] == sizeof(float)); + assert(dst->nb[0] == sizeof(float)); + + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + + const size_t nb01 = src0->nb[1]; + const size_t nb0 = dst->nb[0]; + + for (int64_t i1 = 0; i1 < ne01; i1++) { + float * src = (float *) ((char *) src0->data + i1*nb01); + int32_t * dst_ = (int32_t *) ((char *) dst->data + i1*nb0); + int v = 0; + ggml_vec_argmax_f32(ne00, &v, src); + dst_[0] = v; + } +} + +static void ggml_compute_forward_argmax( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_argmax_f32(params, src0, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + // ggml_compute_forward_repeat static void ggml_compute_forward_repeat_f32( @@ -9632,25 +9583,7 @@ static void ggml_compute_forward_repeat_f32( return; } - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; - - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; // guaranteed to be an integer due to the check in ggml_can_repeat const int nr0 = (int)(ne0/ne00); @@ -9711,25 +9644,7 @@ static void ggml_compute_forward_repeat_back_f32( return; } - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; - - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; // guaranteed to be an integer due to the check in ggml_can_repeat const int nr0 = (int)(ne00/ne0); @@ -9959,6 +9874,90 @@ static void ggml_compute_forward_step( } } +// ggml_compute_forward_tanh + +static void ggml_compute_forward_tanh_f32( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + assert(params->ith == 0); + assert(ggml_are_same_shape(src0, dst)); + + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + assert(dst->nb[0] == sizeof(float)); + assert(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + ggml_vec_tanh_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_tanh( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_tanh_f32(params, src0, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + +// ggml_compute_forward_elu + +static void ggml_compute_forward_elu_f32( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + assert(params->ith == 0); + assert(ggml_are_same_shape(src0, dst)); + + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return; + } + + const int n = ggml_nrows(src0); + const int nc = src0->ne[0]; + + assert(dst->nb[0] == sizeof(float)); + assert(src0->nb[0] == sizeof(float)); + + for (int i = 0; i < n; i++) { + ggml_vec_elu_f32(nc, + (float *) ((char *) dst->data + i*( dst->nb[1])), + (float *) ((char *) src0->data + i*(src0->nb[1]))); + } +} + +static void ggml_compute_forward_elu( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + struct ggml_tensor * dst) { + switch (src0->type) { + case GGML_TYPE_F32: + { + ggml_compute_forward_elu_f32(params, src0, dst); + } break; + default: + { + GGML_ASSERT(false); + } break; + } +} + // ggml_compute_forward_relu static void ggml_compute_forward_relu_f32( @@ -10260,18 +10259,7 @@ static void ggml_compute_forward_norm_f32( const int ith = params->ith; const int nth = params->nth; - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; const float eps = 1e-5f; // TODO: make this a parameter @@ -10337,18 +10325,7 @@ static void ggml_compute_forward_rms_norm_f32( const int ith = params->ith; const int nth = params->nth; - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; const float eps = 1e-6f; // TODO: make this a parameter @@ -10413,22 +10390,7 @@ static void ggml_compute_forward_rms_norm_back_f32( const int ith = params->ith; const int nth = params->nth; - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const size_t nb11 = src1->nb[1]; - const size_t nb12 = src1->nb[2]; - const size_t nb13 = src1->nb[3]; - - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; const float eps = 1e-6f; // TODO: make this a parameter @@ -10624,41 +10586,7 @@ static void ggml_compute_forward_mul_mat_f32( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) - const int64_t ne10 = src1->ne[0]; -#endif - const int64_t ne11 = src1->ne[1]; -#ifndef NDEBUG - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const int nb00 = src0->nb[0]; -#endif - const int nb01 = src0->nb[1]; - const int nb02 = src0->nb[2]; - const int nb03 = src0->nb[3]; - -#ifndef NDEBUG - const int nb10 = src1->nb[0]; -#endif - const int nb11 = src1->nb[1]; - const int nb12 = src1->nb[2]; - const int nb13 = src1->nb[3]; - - const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; @@ -10795,37 +10723,10 @@ static void ggml_compute_forward_mul_mat_f16_f32( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; + GGML_TENSOR_BINARY_OP_LOCALS; - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; //const int64_t ne = ne0*ne1*ne2*ne3; - const int nb00 = src0->nb[0]; - const int nb01 = src0->nb[1]; - const int nb02 = src0->nb[2]; - const int nb03 = src0->nb[3]; - - const int nb10 = src1->nb[0]; - const int nb11 = src1->nb[1]; - const int nb12 = src1->nb[2]; - const int nb13 = src1->nb[3]; - - const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; - const int ith = params->ith; const int nth = params->nth; @@ -10995,35 +10896,7 @@ static void ggml_compute_forward_mul_mat_q_f32( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const int nb00 = src0->nb[0]; - const int nb01 = src0->nb[1]; - const int nb02 = src0->nb[2]; - const int nb03 = src0->nb[3]; - - const int nb10 = src1->nb[0]; - const int nb11 = src1->nb[1]; - const int nb12 = src1->nb[2]; - const int nb13 = src1->nb[3]; - - const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; @@ -11039,7 +10912,7 @@ static void ggml_compute_forward_mul_mat_q_f32( enum ggml_type const vec_dot_type = quantize_fns[type].vec_dot_type; // we don't support permuted src0 or src1 - GGML_ASSERT(nb00 == (int) GGML_TYPE_SIZE[type]); + GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]); GGML_ASSERT(nb10 == sizeof(float)); // dst cannot be transposed or permuted @@ -11233,35 +11106,7 @@ static void ggml_compute_forward_out_prod_f32( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; - - const int64_t ne10 = src1->ne[0]; - //const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const int nb00 = src0->nb[0]; - const int nb01 = src0->nb[1]; - const int nb02 = src0->nb[2]; - const int nb03 = src0->nb[3]; - - const int nb10 = src1->nb[0]; - const int nb11 = src1->nb[1]; - const int nb12 = src1->nb[2]; - const int nb13 = src1->nb[3]; - - const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; @@ -11496,15 +11341,8 @@ static void ggml_compute_forward_set_f32( const int nr = ggml_nrows(src1); const int nc = src1->ne[0]; - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - const int64_t ne12 = src1->ne[2]; - const int64_t ne13 = src1->ne[3]; - - const size_t nb10 = src1->nb[0]; - const size_t nb11 = src1->nb[1]; - const size_t nb12 = src1->nb[2]; - const size_t nb13 = src1->nb[3]; + GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne); + GGML_TENSOR_LOCALS(size_t, nb1, src1, nb); // src0 and dst as viewed during set const size_t nb0 = ggml_element_size(src0); @@ -11895,29 +11733,14 @@ static void ggml_compute_forward_diag_f32( // TODO: handle transposed/permuted matrices - const int ne00 = src0->ne[0]; - const int ne01 = src0->ne[1]; - const int ne02 = src0->ne[2]; - const int ne03 = src0->ne[3]; - const int ne0 = dst->ne[0]; - const int ne1 = dst->ne[1]; - const int ne2 = dst->ne[2]; - const int ne3 = dst->ne[3]; + GGML_TENSOR_UNARY_OP_LOCALS; + GGML_ASSERT(ne00 == ne0); GGML_ASSERT(ne00 == ne1); GGML_ASSERT(ne01 == 1); GGML_ASSERT(ne02 == ne2); GGML_ASSERT(ne03 == ne3); - const int nb00 = src0->nb[0]; - //const int nb01 = src0->nb[1]; - const int nb02 = src0->nb[2]; - const int nb03 = src0->nb[3]; - const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; - GGML_ASSERT(nb00 == sizeof(float)); GGML_ASSERT(nb0 == sizeof(float)); @@ -12494,20 +12317,7 @@ static void ggml_compute_forward_rope_f32( assert(n_past >= 0); - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); //printf("n_past = %d, ne2 = %d\n", n_past, ne2); @@ -12634,20 +12444,7 @@ static void ggml_compute_forward_rope_f16( assert(n_past >= 0); - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; + GGML_TENSOR_UNARY_OP_LOCALS; //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); //printf("n_past = %d, ne2 = %d\n", n_past, ne2); @@ -12800,21 +12597,7 @@ static void ggml_compute_forward_rope_back_f32( assert(n_past >= 0); - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; - + GGML_TENSOR_UNARY_OP_LOCALS; //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); //printf("n_past = %d, ne2 = %d\n", n_past, ne2); @@ -12913,21 +12696,7 @@ static void ggml_compute_forward_rope_back_f16( assert(n_past >= 0); - const size_t nb00 = src0->nb[0]; - const size_t nb01 = src0->nb[1]; - const size_t nb02 = src0->nb[2]; - const size_t nb03 = src0->nb[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const size_t nb0 = dst->nb[0]; - const size_t nb1 = dst->nb[1]; - const size_t nb2 = dst->nb[2]; - const size_t nb3 = dst->nb[3]; - + GGML_TENSOR_UNARY_OP_LOCALS; //printf("ne0: %d, ne1: %d, ne2: %d, ne3: %d\n", ne0, ne1, ne2, ne3); //printf("n_past = %d, ne2 = %d\n", n_past, ne2); @@ -13025,7 +12794,7 @@ static void ggml_compute_forward_rope_back( } } -// ggml_compute_forward_conv_1d_s1_ph +// ggml_compute_forward_conv_1d static void ggml_compute_forward_conv_1d_s1_ph_f16_f32( const struct ggml_compute_params * params, @@ -13039,36 +12808,7 @@ static void ggml_compute_forward_conv_1d_s1_ph_f16_f32( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - //const int64_t ne03 = src0->ne[3]; - - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - //const int64_t ne12 = src1->ne[2]; - //const int64_t ne13 = src1->ne[3]; - - //const int64_t ne0 = dst->ne[0]; - //const int64_t ne1 = dst->ne[1]; - //const int64_t ne2 = dst->ne[2]; - //const int64_t ne3 = dst->ne[3]; - //const int64_t ne = ne0*ne1*ne2*ne3; - - const int nb00 = src0->nb[0]; - const int nb01 = src0->nb[1]; - const int nb02 = src0->nb[2]; - //const int nb03 = src0->nb[3]; - - const int nb10 = src1->nb[0]; - const int nb11 = src1->nb[1]; - //const int nb12 = src1->nb[2]; - //const int nb13 = src1->nb[3]; - - //const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - //const int nb2 = dst->nb[2]; - //const int nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; @@ -13159,36 +12899,7 @@ static void ggml_compute_forward_conv_1d_s1_ph_f32( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - //const int64_t ne03 = src0->ne[3]; - - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - //const int64_t ne12 = src1->ne[2]; - //const int64_t ne13 = src1->ne[3]; - - //const int64_t ne0 = dst->ne[0]; - //const int64_t ne1 = dst->ne[1]; - //const int64_t ne2 = dst->ne[2]; - //const int64_t ne3 = dst->ne[3]; - //const int64_t ne = ne0*ne1*ne2*ne3; - - const int nb00 = src0->nb[0]; - const int nb01 = src0->nb[1]; - const int nb02 = src0->nb[2]; - //const int nb03 = src0->nb[3]; - - const int nb10 = src1->nb[0]; - const int nb11 = src1->nb[1]; - //const int nb12 = src1->nb[2]; - //const int nb13 = src1->nb[3]; - - //const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - //const int nb2 = dst->nb[2]; - //const int nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; @@ -13288,8 +12999,6 @@ static void ggml_compute_forward_conv_1d_s1_ph( } } -// ggml_compute_forward_conv_1d_s2_ph - static void ggml_compute_forward_conv_1d_s2_ph_f16_f32( const struct ggml_compute_params * params, const struct ggml_tensor * src0, @@ -13302,36 +13011,7 @@ static void ggml_compute_forward_conv_1d_s2_ph_f16_f32( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - //const int64_t ne03 = src0->ne[3]; - - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - //const int64_t ne12 = src1->ne[2]; - //const int64_t ne13 = src1->ne[3]; - - //const int64_t ne0 = dst->ne[0]; - //const int64_t ne1 = dst->ne[1]; - //const int64_t ne2 = dst->ne[2]; - //const int64_t ne3 = dst->ne[3]; - //const int64_t ne = ne0*ne1*ne2*ne3; - - const int nb00 = src0->nb[0]; - const int nb01 = src0->nb[1]; - const int nb02 = src0->nb[2]; - //const int nb03 = src0->nb[3]; - - const int nb10 = src1->nb[0]; - const int nb11 = src1->nb[1]; - //const int nb12 = src1->nb[2]; - //const int nb13 = src1->nb[3]; - - //const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - //const int nb2 = dst->nb[2]; - //const int nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; @@ -13422,36 +13102,7 @@ static void ggml_compute_forward_conv_1d_s2_ph_f32( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - //const int64_t ne03 = src0->ne[3]; - - const int64_t ne10 = src1->ne[0]; - const int64_t ne11 = src1->ne[1]; - //const int64_t ne12 = src1->ne[2]; - //const int64_t ne13 = src1->ne[3]; - - //const int64_t ne0 = dst->ne[0]; - //const int64_t ne1 = dst->ne[1]; - //const int64_t ne2 = dst->ne[2]; - //const int64_t ne3 = dst->ne[3]; - //const int64_t ne = ne0*ne1*ne2*ne3; - - const int nb00 = src0->nb[0]; - const int nb01 = src0->nb[1]; - const int nb02 = src0->nb[2]; - //const int nb03 = src0->nb[3]; - - const int nb10 = src1->nb[0]; - const int nb11 = src1->nb[1]; - //const int nb12 = src1->nb[2]; - //const int nb13 = src1->nb[3]; - - //const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - //const int nb2 = dst->nb[2]; - //const int nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; @@ -13551,6 +13202,28 @@ static void ggml_compute_forward_conv_1d_s2_ph( } } +// ggml_compute_forward_conv_1d + +static void ggml_compute_forward_conv_1d( + const struct ggml_compute_params * params, + const struct ggml_tensor * src0, + const struct ggml_tensor * src1, + const struct ggml_tensor * opt0, + struct ggml_tensor * dst) { + const int32_t s0 = ((const int32_t*)(opt0->data))[0]; + const int32_t p0 = ((const int32_t*)(opt0->data))[1]; + const int32_t d0 = ((const int32_t*)(opt0->data))[2]; + GGML_ASSERT(d0 == 1); // dilation not supported + GGML_ASSERT(p0 == src0->ne[0]/2); // only half padding supported + if (s0 == 1) { + ggml_compute_forward_conv_1d_s1_ph(params, src0, src1, dst); + } else if (s0 == 2) { + ggml_compute_forward_conv_1d_s2_ph(params, src0, src1, dst); + } else { + GGML_ASSERT(false); // only stride 1 and 2 supported + }; +} + // ggml_compute_forward_conv_2d_sk_p0 static void ggml_compute_forward_conv_2d_sk_p0_f16_f32( @@ -13565,36 +13238,7 @@ static void ggml_compute_forward_conv_2d_sk_p0_f16_f32( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int ne00 = src0->ne[0]; - const int ne01 = src0->ne[1]; - const int ne02 = src0->ne[2]; - //const int ne03 = src0->ne[3]; - - const int ne10 = src1->ne[0]; - //const int ne11 = src1->ne[1]; - const int ne12 = src1->ne[2]; - //const int ne13 = src1->ne[3]; - - const int ne0 = dst->ne[0]; - const int ne1 = dst->ne[1]; - const int ne2 = dst->ne[2]; - //const int ne3 = dst->ne[3]; - //const int ne = ne0*ne1*ne2*ne3; - - const int nb00 = src0->nb[0]; - //const int nb01 = src0->nb[1]; - //const int nb02 = src0->nb[2]; - const int nb03 = src0->nb[3]; - - const int nb10 = src1->nb[0]; - //const int nb11 = src1->nb[1]; - const int nb12 = src1->nb[2]; - //const int nb13 = src1->nb[3]; - - //const int nb0 = dst->nb[0]; - //const int nb1 = dst->nb[1]; - const int nb2 = dst->nb[2]; - //const int nb3 = dst->nb[3]; + GGML_TENSOR_BINARY_OP_LOCALS; const int ith = params->ith; const int nth = params->nth; @@ -13687,6 +13331,34 @@ static void ggml_compute_forward_conv_2d_sk_p0( } } +// ggml_compute_forward_conv_2d + +static void ggml_compute_forward_conv_2d( + const struct ggml_compute_params* params, + const struct ggml_tensor* src0, + const struct ggml_tensor* src1, + const struct ggml_tensor* opt0, + struct ggml_tensor* dst) { + const int32_t s0 = ((const int32_t*)(opt0->data))[0]; + const int32_t s1 = ((const int32_t*)(opt0->data))[1]; + const int32_t p0 = ((const int32_t*)(opt0->data))[2]; + const int32_t p1 = ((const int32_t*)(opt0->data))[3]; + const int32_t d0 = ((const int32_t*)(opt0->data))[4]; + const int32_t d1 = ((const int32_t*)(opt0->data))[5]; + GGML_ASSERT(d0 == 1); // dilation not supported + GGML_ASSERT(d1 == 1); + GGML_ASSERT(p0 == 0); // padding not supported + GGML_ASSERT(p1 == 0); + + if (s0 == src0->ne[0] && s1 == src0->ne[1]) { + ggml_compute_forward_conv_2d_sk_p0(params, src0, src1, dst); + } + else { + GGML_ASSERT(false); // only stride equal to kernel size is supported + }; +} + + // ggml_compute_forward_flash_attn static void ggml_compute_forward_flash_attn_f32( @@ -13699,45 +13371,14 @@ static void ggml_compute_forward_flash_attn_f32( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t neq0 = q->ne[0]; - const int64_t neq1 = q->ne[1]; - const int64_t neq2 = q->ne[2]; - const int64_t neq3 = q->ne[3]; - - const int64_t nek0 = k->ne[0]; - const int64_t nek1 = k->ne[1]; - //const int64_t nek2 = k->ne[2]; - //const int64_t nek3 = k->ne[3]; - - //const int64_t nev0 = v->ne[0]; - const int64_t nev1 = v->ne[1]; - //const int64_t nev2 = v->ne[2]; - //const int64_t nev3 = v->ne[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - //const int64_t ne2 = dst->ne[2]; - //const int64_t ne3 = dst->ne[3]; - - const int nbk0 = k->nb[0]; - const int nbk1 = k->nb[1]; - const int nbk2 = k->nb[2]; - const int nbk3 = k->nb[3]; - - const int nbq0 = q->nb[0]; - const int nbq1 = q->nb[1]; - const int nbq2 = q->nb[2]; - const int nbq3 = q->nb[3]; - - const int nbv0 = v->nb[0]; - const int nbv1 = v->nb[1]; - const int nbv2 = v->nb[2]; - const int nbv3 = v->nb[3]; - - const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; + GGML_TENSOR_LOCALS(int64_t, neq, q, ne); + GGML_TENSOR_LOCALS(size_t, nbq, q, nb); + GGML_TENSOR_LOCALS(int64_t, nek, k, ne); + GGML_TENSOR_LOCALS(size_t, nbk, k, nb); + GGML_TENSOR_LOCALS(int64_t, nev, v, ne); + GGML_TENSOR_LOCALS(size_t, nbv, v, nb); + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); + GGML_TENSOR_LOCALS(size_t, nb, dst, nb); const int ith = params->ith; const int nth = params->nth; @@ -13908,45 +13549,14 @@ static void ggml_compute_forward_flash_attn_f16( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t neq0 = q->ne[0]; - const int64_t neq1 = q->ne[1]; - const int64_t neq2 = q->ne[2]; - const int64_t neq3 = q->ne[3]; - - const int64_t nek0 = k->ne[0]; - const int64_t nek1 = k->ne[1]; - //const int64_t nek2 = k->ne[2]; - //const int64_t nek3 = k->ne[3]; - - //const int64_t nev0 = v->ne[0]; - const int64_t nev1 = v->ne[1]; - //const int64_t nev2 = v->ne[2]; - //const int64_t nev3 = v->ne[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - //const int64_t ne2 = dst->ne[2]; - //const int64_t ne3 = dst->ne[3]; - - const int nbk0 = k->nb[0]; - const int nbk1 = k->nb[1]; - const int nbk2 = k->nb[2]; - const int nbk3 = k->nb[3]; - - const int nbq0 = q->nb[0]; - const int nbq1 = q->nb[1]; - const int nbq2 = q->nb[2]; - const int nbq3 = q->nb[3]; - - const int nbv0 = v->nb[0]; - const int nbv1 = v->nb[1]; - const int nbv2 = v->nb[2]; - const int nbv3 = v->nb[3]; - - const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; + GGML_TENSOR_LOCALS(int64_t, neq, q, ne); + GGML_TENSOR_LOCALS(size_t, nbq, q, nb); + GGML_TENSOR_LOCALS(int64_t, nek, k, ne); + GGML_TENSOR_LOCALS(size_t, nbk, k, nb); + GGML_TENSOR_LOCALS(int64_t, nev, v, ne); + GGML_TENSOR_LOCALS(size_t, nbv, v, nb); + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); + GGML_TENSOR_LOCALS(size_t, nb, dst, nb); const int ith = params->ith; const int nth = params->nth; @@ -14180,65 +13790,18 @@ static void ggml_compute_forward_flash_ff_f16( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t nea0 = a->ne[0]; - const int64_t nea1 = a->ne[1]; - const int64_t nea2 = a->ne[2]; - const int64_t nea3 = a->ne[3]; - - const int64_t neb00 = b0->ne[0]; - const int64_t neb01 = b0->ne[1]; - //const int64_t neb02 = b0->ne[2]; - //const int64_t neb03 = b0->ne[3]; - - const int64_t neb10 = b1->ne[0]; - const int64_t neb11 = b1->ne[1]; - //const int64_t neb12 = b1->ne[2]; - //const int64_t neb13 = b1->ne[3]; - - const int64_t nec00 = c0->ne[0]; - const int64_t nec01 = c0->ne[1]; - //const int64_t nec02 = c0->ne[2]; - //const int64_t nec03 = c0->ne[3]; - - const int64_t nec10 = c1->ne[0]; - const int64_t nec11 = c1->ne[1]; - //const int64_t nec12 = c1->ne[2]; - //const int64_t nec13 = c1->ne[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - //const int64_t ne3 = dst->ne[3]; - - const int nba0 = a->nb[0]; - const int nba1 = a->nb[1]; - const int nba2 = a->nb[2]; - const int nba3 = a->nb[3]; - - const int nbb00 = b0->nb[0]; - const int nbb01 = b0->nb[1]; - const int nbb02 = b0->nb[2]; - const int nbb03 = b0->nb[3]; - - const int nbb10 = b1->nb[0]; - //const int nbb11 = b1->nb[1]; - //const int nbb12 = b1->nb[2]; - //const int nbb13 = b1->nb[3]; - - const int nbc00 = c0->nb[0]; - const int nbc01 = c0->nb[1]; - const int nbc02 = c0->nb[2]; - const int nbc03 = c0->nb[3]; - - const int nbc10 = c1->nb[0]; - //const int nbc11 = c1->nb[1]; - //const int nbc12 = c1->nb[2]; - //const int nbc13 = c1->nb[3]; - - const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; + GGML_TENSOR_LOCALS(int64_t, nea, a, ne); + GGML_TENSOR_LOCALS(size_t, nba, a, nb); + GGML_TENSOR_LOCALS(int64_t, neb0, b0, ne); + GGML_TENSOR_LOCALS(size_t, nbb0, b0, nb); + GGML_TENSOR_LOCALS(int64_t, neb1, b1, ne); + GGML_TENSOR_LOCALS(size_t, nbb1, b1, nb); + GGML_TENSOR_LOCALS(int64_t, nec0, c0, ne); + GGML_TENSOR_LOCALS(size_t, nbc0, c0, nb); + GGML_TENSOR_LOCALS(int64_t, nec1, c1, ne); + GGML_TENSOR_LOCALS(size_t, nbc1, c1, nb); + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); + GGML_TENSOR_LOCALS(size_t, nb, dst, nb); const int ith = params->ith; const int nth = params->nth; @@ -14386,55 +13949,16 @@ static void ggml_compute_forward_flash_attn_back_f32( int64_t t0 = ggml_perf_time_us(); UNUSED(t0); - const int64_t neq0 = q->ne[0]; - const int64_t neq1 = q->ne[1]; - const int64_t neq2 = q->ne[2]; - const int64_t neq3 = q->ne[3]; - - const int64_t nek0 = k->ne[0]; - const int64_t nek1 = k->ne[1]; - //const int64_t nek2 = k->ne[2]; - //const int64_t nek3 = k->ne[3]; - - const int64_t nev0 = v->ne[0]; - const int64_t nev1 = v->ne[1]; - //const int64_t nev2 = v->ne[2]; - //const int64_t nev3 = v->ne[3]; - - const int64_t ned0 = d->ne[0]; - const int64_t ned1 = d->ne[1]; - //const int64_t ned2 = d->ne[2]; - //const int64_t ned3 = d->ne[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; - - const int nbk0 = k->nb[0]; - const int nbk1 = k->nb[1]; - const int nbk2 = k->nb[2]; - const int nbk3 = k->nb[3]; - - const int nbq0 = q->nb[0]; - const int nbq1 = q->nb[1]; - const int nbq2 = q->nb[2]; - const int nbq3 = q->nb[3]; - - const int nbv0 = v->nb[0]; - const int nbv1 = v->nb[1]; - const int nbv2 = v->nb[2]; - const int nbv3 = v->nb[3]; - - const int nbd0 = d->nb[0]; - const int nbd1 = d->nb[1]; - const int nbd2 = d->nb[2]; - const int nbd3 = d->nb[3]; - - const int nb0 = dst->nb[0]; - const int nb1 = dst->nb[1]; - const int nb2 = dst->nb[2]; - const int nb3 = dst->nb[3]; + GGML_TENSOR_LOCALS(int64_t, neq, q, ne); + GGML_TENSOR_LOCALS(size_t, nbq, q, nb); + GGML_TENSOR_LOCALS(int64_t, nek, k, ne); + GGML_TENSOR_LOCALS(size_t, nbk, k, nb); + GGML_TENSOR_LOCALS(int64_t, nev, v, ne); + GGML_TENSOR_LOCALS(size_t, nbv, v, nb); + GGML_TENSOR_LOCALS(int64_t, ned, d, ne); + GGML_TENSOR_LOCALS(size_t, nbd, d, nb); + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); + GGML_TENSOR_LOCALS(size_t, nb, dst, nb); const int ith = params->ith; const int nth = params->nth; @@ -14792,15 +14316,8 @@ static void ggml_compute_forward_win_part_f32( return; } - const int64_t ne00 = src0->ne[0]; UNUSED(ne00); - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - const int64_t ne03 = src0->ne[3]; UNUSED(ne03); - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; - const int64_t ne3 = dst->ne[3]; UNUSED(ne3); + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); const int32_t nep0 = ((const int32_t *)(opt0->data))[0]; const int32_t nep1 = ((const int32_t *)(opt0->data))[1]; @@ -14863,14 +14380,8 @@ static void ggml_compute_forward_win_unpart_f32( return; } - const int64_t ne00 = src0->ne[0]; - const int64_t ne01 = src0->ne[1]; - const int64_t ne02 = src0->ne[2]; - //const int64_t ne03 = src0->ne[3]; - - const int64_t ne0 = dst->ne[0]; - const int64_t ne1 = dst->ne[1]; - const int64_t ne2 = dst->ne[2]; + GGML_TENSOR_LOCALS(int64_t, ne0, src0, ne); + GGML_TENSOR_LOCALS(int64_t, ne, dst, ne); const int32_t w = ((const int32_t *)(opt0->data))[0]; @@ -15468,6 +14979,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_mean(params, tensor->src0, tensor); } break; + case GGML_OP_ARGMAX: + { + ggml_compute_forward_argmax(params, tensor->src0, tensor); + } break; case GGML_OP_REPEAT: { ggml_compute_forward_repeat(params, tensor->src0, tensor); @@ -15492,6 +15007,14 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_step(params, tensor->src0, tensor); } break; + case GGML_OP_TANH: + { + ggml_compute_forward_tanh(params, tensor->src0, tensor); + } break; + case GGML_OP_ELU: + { + ggml_compute_forward_elu(params, tensor->src0, tensor); + } break; case GGML_OP_RELU: { ggml_compute_forward_relu(params, tensor->src0, tensor); @@ -15608,17 +15131,13 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_clamp(params, tensor->src0, tensor->src1, tensor); } break; - case GGML_OP_CONV_1D_S1_PH: + case GGML_OP_CONV_1D: { - ggml_compute_forward_conv_1d_s1_ph(params, tensor->src0, tensor->src1, tensor); + ggml_compute_forward_conv_1d(params, tensor->src0, tensor->src1, tensor->opt[0], tensor); } break; - case GGML_OP_CONV_1D_S2_PH: + case GGML_OP_CONV_2D: { - ggml_compute_forward_conv_1d_s2_ph(params, tensor->src0, tensor->src1, tensor); - } break; - case GGML_OP_CONV_2D_SK_P0: - { - ggml_compute_forward_conv_2d_sk_p0(params, tensor->src0, tensor->src1, tensor); + ggml_compute_forward_conv_2d(params, tensor->src0, tensor->src1, tensor->opt[0], tensor); } break; case GGML_OP_FLASH_ATTN: { @@ -15867,6 +15386,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor } } break; case GGML_OP_MEAN: + case GGML_OP_ARGMAX: { GGML_ASSERT(false); // TODO: implement } break; @@ -15920,6 +15440,14 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor // noop } } break; + case GGML_OP_TANH: + { + GGML_ASSERT(false); // TODO: not implemented + } break; + case GGML_OP_ELU: + { + GGML_ASSERT(false); // TODO: not implemented + } break; case GGML_OP_RELU: { if (src0->grad) { @@ -15939,14 +15467,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { GGML_ASSERT(false); // TODO: not implemented } break; - case GGML_OP_ALIBI: - { - GGML_ASSERT(false); // TODO: not implemented - } break; - case GGML_OP_CLAMP: - { - GGML_ASSERT(false); // TODO: not implemented - } break; case GGML_OP_SILU: { // necessary for llama @@ -16263,7 +15783,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor // necessary for llama if (src0->grad) { assert(src1->type == GGML_TYPE_I32); - assert(ggml_nelements(src1) == 3); + assert(ggml_nelements(src1) == 4); const int n_past = ((int32_t *) src1->data)[0]; const int n_dims = ((int32_t *) src1->data)[1]; const int mode = ((int32_t *) src1->data)[2]; @@ -16303,15 +15823,19 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor // noop } } break; - case GGML_OP_CONV_1D_S1_PH: + case GGML_OP_ALIBI: { GGML_ASSERT(false); // TODO: not implemented } break; - case GGML_OP_CONV_1D_S2_PH: + case GGML_OP_CLAMP: { GGML_ASSERT(false); // TODO: not implemented } break; - case GGML_OP_CONV_2D_SK_P0: + case GGML_OP_CONV_1D: + { + GGML_ASSERT(false); // TODO: not implemented + } break; + case GGML_OP_CONV_2D: { GGML_ASSERT(false); // TODO: not implemented } break; @@ -16968,12 +16492,15 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) case GGML_OP_SUM: case GGML_OP_SUM_ROWS: case GGML_OP_MEAN: + case GGML_OP_ARGMAX: case GGML_OP_REPEAT: case GGML_OP_REPEAT_BACK: case GGML_OP_ABS: case GGML_OP_SGN: case GGML_OP_NEG: case GGML_OP_STEP: + case GGML_OP_TANH: + case GGML_OP_ELU: case GGML_OP_RELU: { node->n_tasks = 1; @@ -17087,8 +16614,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) { node->n_tasks = 1; //TODO } break; - case GGML_OP_CONV_1D_S1_PH: - case GGML_OP_CONV_1D_S2_PH: + case GGML_OP_CONV_1D: { node->n_tasks = n_threads; @@ -17117,7 +16643,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) work_size = MAX(work_size, cur); } break; - case GGML_OP_CONV_2D_SK_P0: + case GGML_OP_CONV_2D: { node->n_tasks = n_threads; @@ -17479,13 +17005,6 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) { fwrite(&nb, sizeof(uint64_t), 1, fout); } - // store the pointer address - { - const uint64_t ptr = (uint64_t) tensor->data; - - fwrite(&ptr, sizeof(uint64_t), 1, fout); - } - fwrite(tensor->name, sizeof(char), GGML_MAX_NAME, fout); // dump the data @@ -17519,13 +17038,6 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) { fwrite(&nb, sizeof(uint64_t), 1, fout); } - // store the pointer address - { - const uint64_t ptr = (uint64_t) tensor->data; - - fwrite(&ptr, sizeof(uint64_t), 1, fout); - } - fwrite(tensor->name, sizeof(char), GGML_MAX_NAME, fout); // output the op arguments @@ -17710,8 +17222,6 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** tensor->op = (enum ggml_op) op; - uint64_t ptr_cur = *(const uint64_t *) ptr; ptr += sizeof(ptr_cur); - memcpy(tensor->name, ptr, GGML_MAX_NAME); ptr += GGML_MAX_NAME; tensor->data = (void *) ptr; @@ -17757,8 +17267,6 @@ struct ggml_cgraph ggml_graph_import(const char * fname, struct ggml_context ** nb[j] = nb_cur; } - uint64_t ptr_cur = *(const uint64_t *) ptr; ptr += sizeof(ptr_cur); // TODO: not yet used - const char * ptr_name = ptr; ptr += GGML_MAX_NAME; const int32_t * ptr_arg_idx = (const int32_t *) ptr; ptr += (2 + GGML_MAX_OPT)*sizeof(int32_t); diff --git a/ggml.h b/ggml.h index 11b51f8bd..0af96c76b 100644 --- a/ggml.h +++ b/ggml.h @@ -201,6 +201,8 @@ #define GGML_MAX_NAME 48 #define GGML_DEFAULT_N_THREADS 4 +#define GGML_UNUSED(x) (void)(x) + #define GGML_ASSERT(x) \ do { \ if (!(x)) { \ @@ -209,6 +211,30 @@ } \ } while (0) +// used to copy the number of elements and stride in bytes of tensors into local variables. +// main purpose is to reduce code duplication and improve readability. +// +// example: +// +// GGML_TENSOR_LOCALS(int64_t, ne1, src1, ne); +// GGML_TENSOR_LOCALS(size_t, nb1, src1, nb); +// +#define GGML_TENSOR_LOCALS_1(type, prefix, pointer, array) \ + const type prefix##0 = (pointer)->array[0]; \ + GGML_UNUSED(prefix##0); +#define GGML_TENSOR_LOCALS_2(type, prefix, pointer, array) \ + GGML_TENSOR_LOCALS_1 (type, prefix, pointer, array) \ + const type prefix##1 = (pointer)->array[1]; \ + GGML_UNUSED(prefix##1); +#define GGML_TENSOR_LOCALS_3(type, prefix, pointer, array) \ + GGML_TENSOR_LOCALS_2 (type, prefix, pointer, array) \ + const type prefix##2 = (pointer)->array[2]; \ + GGML_UNUSED(prefix##2); +#define GGML_TENSOR_LOCALS(type, prefix, pointer, array) \ + GGML_TENSOR_LOCALS_3 (type, prefix, pointer, array) \ + const type prefix##3 = (pointer)->array[3]; \ + GGML_UNUSED(prefix##3); + #ifdef __cplusplus extern "C" { #endif @@ -295,12 +321,15 @@ extern "C" { GGML_OP_SUM, GGML_OP_SUM_ROWS, GGML_OP_MEAN, + GGML_OP_ARGMAX, GGML_OP_REPEAT, GGML_OP_REPEAT_BACK, GGML_OP_ABS, GGML_OP_SGN, GGML_OP_NEG, GGML_OP_STEP, + GGML_OP_TANH, + GGML_OP_ELU, GGML_OP_RELU, GGML_OP_GELU, GGML_OP_GELU_QUICK, @@ -332,9 +361,8 @@ extern "C" { GGML_OP_ROPE_BACK, GGML_OP_ALIBI, GGML_OP_CLAMP, - GGML_OP_CONV_1D_S1_PH, - GGML_OP_CONV_1D_S2_PH, - GGML_OP_CONV_2D_SK_P0, + GGML_OP_CONV_1D, + GGML_OP_CONV_2D, GGML_OP_FLASH_ATTN, GGML_OP_FLASH_FF, @@ -690,6 +718,11 @@ extern "C" { struct ggml_context * ctx, struct ggml_tensor * a); + // argmax along rows + GGML_API struct ggml_tensor * ggml_argmax( + struct ggml_context * ctx, + struct ggml_tensor * a); + // if a is the same shape as b, and a is not parameter, return a // otherwise, return a new tensor: repeat(a) to fit in b GGML_API struct ggml_tensor * ggml_repeat( @@ -734,6 +767,22 @@ extern "C" { struct ggml_context * ctx, struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_tanh( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_tanh_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_elu( + struct ggml_context * ctx, + struct ggml_tensor * a); + + GGML_API struct ggml_tensor * ggml_elu_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a); + GGML_API struct ggml_tensor * ggml_relu( struct ggml_context * ctx, struct ggml_tensor * a); @@ -1084,58 +1133,33 @@ extern "C" { float min, float max); - // TODO: implement general-purpose convolutions - // GGML_API struct ggml_tensor * ggml_conv_1d( - // struct ggml_context * ctx, - // struct ggml_tensor * a, - // struct ggml_tensor * b, - // int s0 - // int p0, - // int d0); - // - // GGML_API struct ggml_tensor * ggml_conv_2d( - // struct ggml_context * ctx, - // struct ggml_tensor * a, - // struct ggml_tensor * b, - // int s0, - // int s1, - // int p0, - // int p1, - // int d0, - // int d1); - - // padding = half - // TODO: we don't support extra parameters for now - // that's why we are hard-coding the stride, padding, and dilation - // not great .. - // example: - // a: 3 80 768 1 - // b: 3000 80 1 1 - // res: 3000 768 1 1 - // used in whisper - GGML_API struct ggml_tensor * ggml_conv_1d_s1_ph( + GGML_API struct ggml_tensor * ggml_conv_1d( struct ggml_context * ctx, struct ggml_tensor * a, - struct ggml_tensor * b); + struct ggml_tensor * b, + int s0, // stride + int p0, // padding + int d0); // dilation - // used in whisper - GGML_API struct ggml_tensor * ggml_conv_1d_s2_ph( + GGML_API struct ggml_tensor * ggml_conv_2d( struct ggml_context * ctx, struct ggml_tensor * a, - struct ggml_tensor * b); + struct ggml_tensor * b, + int s0, + int s1, + int p0, + int p1, + int d0, + int d1); - // kernel size is a->ne[0] x a->ne[1] - // stride is equal to kernel size - // padding is zero - // example: - // a: 16 16 3 768 - // b: 1024 1024 3 1 - // res: 64 64 768 1 - // used in sam - GGML_API struct ggml_tensor * ggml_conv_2d_sk_p0( + // conv_1d with padding = half + // alias for ggml_conv_1d(a, b, s, a->ne[0]/2, d) + GGML_API struct ggml_tensor* ggml_conv_1d_ph( struct ggml_context * ctx, struct ggml_tensor * a, - struct ggml_tensor * b); + struct ggml_tensor * b, + int s, + int d); GGML_API struct ggml_tensor * ggml_flash_attn( struct ggml_context * ctx, diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh index e6e39ff8f..574e5180b 100755 --- a/scripts/sync-ggml.sh +++ b/scripts/sync-ggml.sh @@ -1,6 +1,11 @@ #!/bin/bash -cp -rpv ../ggml/src/ggml.c ./ggml.c -cp -rpv ../ggml/src/ggml-cuda.cu ./ggml-cuda.cu -cp -rpv ../ggml/src/ggml-cuda.h ./ggml-cuda.h +cp -rpv ../ggml/src/ggml.c ./ggml.c +cp -rpv ../ggml/src/ggml-cuda.h ./ggml-cuda.h +cp -rpv ../ggml/src/ggml-cuda.cu ./ggml-cuda.cu +cp -rpv ../ggml/src/ggml-opencl.h ./ggml-opencl.h +cp -rpv ../ggml/src/ggml-opencl.cpp ./ggml-opencl.cpp +cp -rpv ../ggml/src/ggml-metal.h ./ggml-metal.h +cp -rpv ../ggml/src/ggml-metal.m ./ggml-metal.m +cp -rpv ../ggml/src/ggml-metal.metal ./ggml-metal.metal cp -rpv ../ggml/include/ggml/ggml.h ./ggml.h From b472f3fca558b6335adbd87210ed58cfb5da37cb Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 4 Jul 2023 22:25:22 +0300 Subject: [PATCH 084/852] readme : add link web chat PR --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index e890dc9c2..6c2bb392e 100644 --- a/README.md +++ b/README.md @@ -11,6 +11,7 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ **Hot topics:** +- Simple web chat example: https://github.com/ggerganov/llama.cpp/pull/1998 - k-quants now support super-block size of 64: https://github.com/ggerganov/llama.cpp/pull/2001 - New roadmap: https://github.com/users/ggerganov/projects/7 - Azure CI brainstorming: https://github.com/ggerganov/llama.cpp/discussions/1985 From 7f0e9a775ecc4c6ade271c217f63d6dc93e79eaa Mon Sep 17 00:00:00 2001 From: Nigel Bosch Date: Tue, 4 Jul 2023 18:33:33 -0500 Subject: [PATCH 085/852] embd-input: Fix input embedding example unsigned int seed (#2105) --- examples/embd-input/embd-input-lib.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/embd-input/embd-input-lib.cpp b/examples/embd-input/embd-input-lib.cpp index 570e273fc..5fa4942be 100644 --- a/examples/embd-input/embd-input-lib.cpp +++ b/examples/embd-input/embd-input-lib.cpp @@ -29,7 +29,7 @@ struct MyModel* create_mymodel(int argc, char ** argv) { fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); - if (params.seed < 0) { + if (params.seed == LLAMA_DEFAULT_SEED) { params.seed = time(NULL); } fprintf(stderr, "%s: seed = %d\n", __func__, params.seed); From 9e4475f5cf639315f61ed7b8da6258bb0c7c5ca9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Wed, 5 Jul 2023 08:58:05 +0200 Subject: [PATCH 086/852] Fixed OpenCL offloading prints (#2082) --- llama.cpp | 15 ++++++++++++--- 1 file changed, 12 insertions(+), 3 deletions(-) diff --git a/llama.cpp b/llama.cpp index 7419b03b6..83e93efc1 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1156,6 +1156,7 @@ static void llama_model_load_internal( } } #endif // GGML_USE_CUBLAS + #if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) const int n_gpu = std::min(n_gpu_layers, int(hparams.n_layer)); @@ -1164,6 +1165,10 @@ static void llama_model_load_internal( fprintf(stderr, "%s: offloading non-repeating layers to GPU\n", __func__); } size_t vram_kv_cache = 0; + +#ifdef GGML_USE_CUBLAS + const int max_backend_supported_layers = hparams.n_layer + 3; + const int max_offloadable_layers = low_vram ? hparams.n_layer + 1 : hparams.n_layer + 3; if (n_gpu_layers > (int) hparams.n_layer + 1) { if (low_vram) { fprintf(stderr, "%s: cannot offload v cache to GPU due to low VRAM option\n", __func__); @@ -1180,14 +1185,18 @@ static void llama_model_load_internal( vram_kv_cache += MEM_REQ_KV_SELF().at(model.type) / 2; } } - const int max_offloadable_layers = low_vram ? hparams.n_layer + 1 : hparams.n_layer + 3; +#elif defined(GGML_USE_CLBLAST) + const int max_backend_supported_layers = hparams.n_layer + 1; + const int max_offloadable_layers = hparams.n_layer + 1; +#endif // GGML_USE_CUBLAS + fprintf(stderr, "%s: offloaded %d/%d layers to GPU\n", - __func__, std::min(n_gpu_layers, max_offloadable_layers), hparams.n_layer + 3); + __func__, std::min(n_gpu_layers, max_offloadable_layers), max_backend_supported_layers); fprintf(stderr, "%s: total VRAM used: %zu MB\n", __func__, (vram_weights + vram_scratch + vram_kv_cache + MB - 1) / MB); // round up #else (void) n_gpu_layers; -#endif +#endif // defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) } // populate `tensors_by_name` From 051c70dcd55709c9cbbfa849af035951fe720433 Mon Sep 17 00:00:00 2001 From: Howard Su Date: Wed, 5 Jul 2023 18:31:23 +0800 Subject: [PATCH 087/852] llama: Don't double count the sampling time (#2107) --- llama.cpp | 20 +++++++++----------- 1 file changed, 9 insertions(+), 11 deletions(-) diff --git a/llama.cpp b/llama.cpp index 83e93efc1..e04fbfc0a 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1905,10 +1905,10 @@ void llama_sample_top_p(struct llama_context * ctx, llama_token_data_array * can return; } - const int64_t t_start_sample_us = ggml_time_us(); - llama_sample_softmax(ctx, candidates); + const int64_t t_start_sample_us = ggml_time_us(); + // Compute the cumulative probabilities float cum_sum = 0.0f; size_t last_idx = candidates->size; @@ -1937,9 +1937,8 @@ void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * return; } - const int64_t t_start_sample_us = ggml_time_us(); - llama_sample_softmax(nullptr, candidates); + const int64_t t_start_sample_us = ggml_time_us(); // Compute the first and second derivatives std::vector first_derivatives(candidates->size - 1); @@ -1991,11 +1990,11 @@ void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * c return; } - const int64_t t_start_sample_us = ggml_time_us(); - // Compute the softmax of logits and calculate entropy llama_sample_softmax(nullptr, candidates); + const int64_t t_start_sample_us = ggml_time_us(); + float entropy = 0.0f; for (size_t i = 0; i < candidates->size; ++i) { entropy += -candidates->data[i].p * logf(candidates->data[i].p); @@ -2164,13 +2163,11 @@ llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_ if (ctx) { ctx->t_sample_us += ggml_time_us() - t_start_sample_us; - ctx->n_sample++; } return X; } llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_token_data_array * candidates, float tau, float eta, float * mu) { - assert(ctx); int64_t t_start_sample_us; t_start_sample_us = ggml_time_us(); @@ -2185,13 +2182,14 @@ llama_token llama_sample_token_mirostat_v2(struct llama_context * ctx, llama_tok candidates->size = 1; } + if (ctx) { + ctx->t_sample_us += ggml_time_us() - t_start_sample_us; + } + // Normalize the probabilities of the remaining words llama_sample_softmax(ctx, candidates); // Sample the next word X from the remaining words - if (ctx) { - ctx->t_sample_us += ggml_time_us() - t_start_sample_us; - } llama_token X = llama_sample_token(ctx, candidates); t_start_sample_us = ggml_time_us(); From 924dd22fd3ba93e097f8d19ba5cda919ca2fe2fb Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Wed, 5 Jul 2023 14:19:42 +0200 Subject: [PATCH 088/852] Quantized dot products for CUDA mul mat vec (#2067) --- CMakeLists.txt | 13 +- Makefile | 13 +- README.md | 3 +- ggml-cuda.cu | 491 ++++++++++++++++++++++++++++++++++++++++--------- 4 files changed, 427 insertions(+), 93 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 4ac0f6f4e..a2404548f 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -68,8 +68,9 @@ option(LLAMA_ACCELERATE "llama: enable Accelerate framework option(LLAMA_BLAS "llama: use BLAS" OFF) set(LLAMA_BLAS_VENDOR "Generic" CACHE STRING "llama: BLAS library vendor") option(LLAMA_CUBLAS "llama: use cuBLAS" OFF) +option(LLAMA_CUDA_FORCE_DMMV "llama: use dmmv instead of mmvq CUDA kernels" OFF) set(LLAMA_CUDA_DMMV_X "32" CACHE STRING "llama: x stride for dmmv CUDA kernels") -set(LLAMA_CUDA_DMMV_Y "1" CACHE STRING "llama: y block size for dmmv CUDA kernels") +set(LLAMA_CUDA_MMV_Y "1" CACHE STRING "llama: y block size for mmv CUDA kernels") option(LLAMA_CUDA_DMMV_F16 "llama: use 16 bit floats for dmmv CUDA kernels" OFF) set(LLAMA_CUDA_KQUANTS_ITER "2" CACHE STRING "llama: iters./thread per block for Q2_K/Q6_K") option(LLAMA_CLBLAST "llama: use CLBlast" OFF) @@ -246,8 +247,14 @@ if (LLAMA_CUBLAS) set(GGML_SOURCES_CUDA ggml-cuda.cu ggml-cuda.h) add_compile_definitions(GGML_USE_CUBLAS) + if (LLAMA_CUDA_FORCE_DMMV) + add_compile_definitions(GGML_CUDA_FORCE_DMMV) + endif() add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X}) - add_compile_definitions(GGML_CUDA_DMMV_Y=${LLAMA_CUDA_DMMV_Y}) + add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y}) + if (DEFINED LLAMA_CUDA_DMMV_Y) + add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_DMMV_Y}) # for backwards compatibility + endif() if (LLAMA_CUDA_DMMV_F16) add_compile_definitions(GGML_CUDA_DMMV_F16) endif() @@ -263,7 +270,7 @@ if (LLAMA_CUBLAS) if (LLAMA_CUDA_DMMV_F16) set(CMAKE_CUDA_ARCHITECTURES "61") # needed for f16 CUDA intrinsics else() - set(CMAKE_CUDA_ARCHITECTURES "52") # lowest CUDA 12 standard + set(CMAKE_CUDA_ARCHITECTURES "52;61") # lowest CUDA 12 standard + lowest for integer intrinsics endif() endif() message(STATUS "Using CUDA architectures: ${CMAKE_CUDA_ARCHITECTURES}") diff --git a/Makefile b/Makefile index 8966a3590..71415664b 100644 --- a/Makefile +++ b/Makefile @@ -164,16 +164,21 @@ ifdef LLAMA_CUBLAS OBJS += ggml-cuda.o NVCC = nvcc NVCCFLAGS = --forward-unknown-to-host-compiler -arch=native +ifdef LLAMA_CUDA_FORCE_DMMV + NVCCFLAGS += -DGGML_CUDA_FORCE_DMMV +endif # LLAMA_CUDA_FORCE_DMMV ifdef LLAMA_CUDA_DMMV_X NVCCFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X) else NVCCFLAGS += -DGGML_CUDA_DMMV_X=32 endif # LLAMA_CUDA_DMMV_X -ifdef LLAMA_CUDA_DMMV_Y - NVCCFLAGS += -DGGML_CUDA_DMMV_Y=$(LLAMA_CUDA_DMMV_Y) +ifdef LLAMA_CUDA_MMV_Y + NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y) +else ifdef LLAMA_CUDA_DMMV_Y + NVCCFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_DMMV_Y) # for backwards compatibility else - NVCCFLAGS += -DGGML_CUDA_DMMV_Y=1 -endif # LLAMA_CUDA_DMMV_Y + NVCCFLAGS += -DGGML_CUDA_MMV_Y=1 +endif # LLAMA_CUDA_MMV_Y ifdef LLAMA_CUDA_DMMV_F16 NVCCFLAGS += -DGGML_CUDA_DMMV_F16 endif # LLAMA_CUDA_DMMV_F16 diff --git a/README.md b/README.md index 6c2bb392e..32f17c2d1 100644 --- a/README.md +++ b/README.md @@ -345,8 +345,9 @@ Building the program with BLAS support may lead to some performance improvements | Option | Legal values | Default | Description | |-------------------------|------------------------|---------|-------------| + | LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 7.0/Turing/RTX 2000 or higher). Does not affect k-quants. | | LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | - | LLAMA_CUDA_DMMV_Y | Positive integer | 1 | Block size in y direction for the CUDA dequantization + mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. | + | LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. Does not affect k-quants. | | LLAMA_CUDA_DMMV_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels. Can improve performance on relatively recent GPUs. | | LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 0b12a9e76..7965ff741 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -70,9 +70,11 @@ typedef void (*ggml_cuda_op_t)( // QK = number of values after dequantization // QR = QK / number of values before dequantization +// QI = number of 32 bit integers before dequantization #define QK4_0 32 #define QR4_0 2 +#define QI4_0 4 typedef struct { half d; // delta uint8_t qs[QK4_0 / 2]; // nibbles / quants @@ -81,6 +83,7 @@ static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 #define QK4_1 32 #define QR4_1 2 +#define QI4_1 4 typedef struct { half d; // delta half m; // min @@ -90,6 +93,7 @@ static_assert(sizeof(block_q4_1) == sizeof(ggml_fp16_t) * 2 + QK4_1 / 2, "wrong #define QK5_0 32 #define QR5_0 2 +#define QI5_0 4 typedef struct { half d; // delta uint8_t qh[4]; // 5-th bit of quants @@ -99,6 +103,7 @@ static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5 #define QK5_1 32 #define QR5_1 2 +#define QI5_1 4 typedef struct { half d; // delta half m; // min @@ -109,12 +114,25 @@ static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + #define QK8_0 32 #define QR8_0 1 +#define QI8_0 8 typedef struct { half d; // delta int8_t qs[QK8_0]; // quants } block_q8_0; static_assert(sizeof(block_q8_0) == sizeof(ggml_fp16_t) + QK8_0, "wrong q8_0 block size/padding"); +#define QK8_1 32 +#define QR8_1 1 +#define QI8_1 8 +typedef struct { + half d; // delta + half s; // unquantized sum + int8_t qs[QK8_0]; // quants +} block_q8_1; +static_assert(sizeof(block_q8_1) == 2*sizeof(ggml_fp16_t) + QK8_0, "wrong q8_1 block size/padding"); + +typedef float (*vec_dot_q_cuda_t)(const void * vbq, const block_q8_1 * bq8_1, const int iqs); + //================================= k-quants #ifdef GGML_QKK_64 @@ -198,14 +216,15 @@ static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_ #define CUDA_SCALE_BLOCK_SIZE 256 #define CUDA_ROPE_BLOCK_SIZE 256 #define CUDA_DIAG_MASK_INF_BLOCK_SIZE 32 +#define CUDA_QUANTIZE_BLOCK_SIZE 256 #define CUDA_DEQUANTIZE_BLOCK_SIZE 256 // dmmv = dequantize_mul_mat_vec #ifndef GGML_CUDA_DMMV_X #define GGML_CUDA_DMMV_X 32 #endif -#ifndef GGML_CUDA_DMMV_Y -#define GGML_CUDA_DMMV_Y 1 +#ifndef GGML_CUDA_MMV_Y +#define GGML_CUDA_MMV_Y 1 #endif #ifndef K_QUANTS_PER_ITERATION @@ -270,7 +289,6 @@ static __global__ void rms_norm_f32(const float * x, float * dst, const int ncol } // sum up partial sums - __syncthreads(); #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); @@ -714,7 +732,6 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * vx, const float #endif // sum up partial sums and write back result - __syncthreads(); #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); @@ -819,7 +836,6 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * vx, const float #endif // sum up partial sums and write back result - __syncthreads(); #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); @@ -923,7 +939,6 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * vx, const float #endif // sum up partial sums and write back result - __syncthreads(); #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); @@ -1028,7 +1043,6 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * vx, const float #endif // sum up partial sums and write back result - __syncthreads(); #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); @@ -1139,7 +1153,6 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * vx, const float #endif // sum up partial sums and write back result - __syncthreads(); #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); @@ -1158,6 +1171,41 @@ static __device__ void convert_f16(const void * vx, const int ib, const int iqs, v.y = x[ib + iqs + 1]; } +static __global__ void quantize_q8_1(const float * x, void * vy, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + + if (i >= k) { + return; + } + + block_q8_1 * y = (block_q8_1 *) vy; + + const int ib = i / QK8_0; // block index + const int iqs = i % QK8_0; // quant index + + const float xi = x[i]; + float amax = fabsf(xi); + float sum = xi; + +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + amax = fmaxf(amax, __shfl_xor_sync(0xffffffff, amax, mask, 32)); + sum += __shfl_xor_sync(0xffffffff, sum, mask, 32); + } + + const float d = amax / 127; + const int8_t q = amax == 0.0f ? 0 : roundf(xi / d); + + y[ib].qs[iqs] = q; + + if (iqs > 0) { + return; + } + + y[ib].d = d; + y[ib].s = sum; +} + template static __global__ void dequantize_block(const void * vx, float * y, const int k) { const int i = blockDim.x*blockIdx.x + 2*threadIdx.x; @@ -1179,6 +1227,182 @@ static __global__ void dequantize_block(const void * vx, float * y, const int k) y[iybs + iqs + y_offset] = v.y; } +static __device__ __forceinline__ float vec_dot_q4_0_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) { +#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics + const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq; + + int vi; + memcpy(&vi, &bq4_0->qs[sizeof(int) * (iqs + 0)], sizeof(int)); + const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]); + const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI4_0)]); + + const float d = __half2float(bq4_0->d) * __half2float(bq8_1->d); + + // subtract 8 from each quantized value + const int vi0 = __vsub4((vi >> 0) & 0x0F0F0F0F, 0x08080808); + const int vi1 = __vsub4((vi >> 4) & 0x0F0F0F0F, 0x08080808); + + // SIMD dot product of quantized values + int sumi = __dp4a(vi0, ui0, 0); + sumi = __dp4a(vi1, ui1, sumi); + + return sumi*d; +#else + return 0.0f; // only to satisfy the compiler +#endif // __CUDA_ARCH__ >= 600 +} + +static __device__ __forceinline__ float vec_dot_q4_1_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) { +#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics + const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq; + + const int vi = *((int *) &bq4_1->qs[sizeof(int) * (iqs + 0)]); + const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]); + const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI4_1)]); + + const float d = __half2float(bq4_1->d) * __half2float(bq8_1->d); + const float m = bq4_1->m; + const float s = bq8_1->s; + + const int vi0 = (vi >> 0) & 0x0F0F0F0F; + const int vi1 = (vi >> 4) & 0x0F0F0F0F; + + // SIMD dot product of quantized values + int sumi = __dp4a(vi0, ui0, 0); + sumi = __dp4a(vi1, ui1, sumi); + + return sumi*d + m*s / QI4_1; // scale sum by QI4_1 because there are QI4_1 threads working on this block +#else + return 0.0f; // only to satisfy the compiler +#endif // __CUDA_ARCH__ >= 600 +} + +static __device__ __forceinline__ float vec_dot_q5_0_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) { +#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics + const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq; + + int qs; + memcpy(&qs, &bq5_0->qs[sizeof(int) * (iqs + 0)], sizeof(int)); + const int qh0 = bq5_0->qh[iqs/2 + 0] >> 4*(iqs%2); + const int qh1 = bq5_0->qh[iqs/2 + 2] >> 4*(iqs%2); + const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]); + const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI5_0)]); + + const float d = __half2float(bq5_0->d) * __half2float(bq8_1->d); + + int vi0 = (qs >> 0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh0 as 5th bits + vi0 |= (qh0 << 4) & 0x00000010; // 1 -> 5 + vi0 |= (qh0 << 11) & 0x00001000; // 2 -> 13 + vi0 |= (qh0 << 18) & 0x00100000; // 3 -> 21 + vi0 |= (qh0 << 25) & 0x10000000; // 4 -> 29 + vi0 = __vsub4(vi0, 0x10101010); // subtract 16 from quantized values + int sumi = __dp4a(vi0, ui0, 0); // SIMD dot product of quantized values + + int vi1 = (qs >> 4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh1 as 5th bits + vi1 |= (qh1 << 4) & 0x00000010; // 1 -> 5 + vi1 |= (qh1 << 11) & 0x00001000; // 2 -> 13 + vi1 |= (qh1 << 18) & 0x00100000; // 3 -> 21 + vi1 |= (qh1 << 25) & 0x10000000; // 4 -> 29 + vi1 = __vsub4(vi1, 0x10101010); // subtract 16 from quantized values + sumi = __dp4a(vi1, ui1, sumi); // SIMD dot product of quantized values + + return sumi*d; +#else + return 0.0f; // only to satisfy the compiler +#endif // __CUDA_ARCH__ >= 600 +} + +static __device__ __forceinline__ float vec_dot_q5_1_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) { +#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics + const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq; + + const int qs = *((int *) &bq5_1->qs[sizeof(int) * (iqs + 0)]); + const int qh0 = bq5_1->qh[iqs/2 + 0] >> 4*(iqs%2); + const int qh1 = bq5_1->qh[iqs/2 + 2] >> 4*(iqs%2); + const int ui0 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]); + const int ui1 = *((int *) &bq8_1->qs[sizeof(int) * (iqs + QI5_1)]); + + const float d = __half2float(bq5_1->d) * __half2float(bq8_1->d); + const float m = bq5_1->m; + const float s = bq8_1->s; + + int vi0 = (qs >> 0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh0 as 5th bits + vi0 |= (qh0 << 4) & 0x00000010; // 1 -> 5 + vi0 |= (qh0 << 11) & 0x00001000; // 2 -> 13 + vi0 |= (qh0 << 18) & 0x00100000; // 3 -> 21 + vi0 |= (qh0 << 25) & 0x10000000; // 4 -> 29 + int sumi = __dp4a(vi0, ui0, 0); // SIMD dot product of quantized values + + int vi1 = (qs >> 4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh1 as 5th bits + vi1 |= (qh1 << 4) & 0x00000010; // 1 -> 5 + vi1 |= (qh1 << 11) & 0x00001000; // 2 -> 13 + vi1 |= (qh1 << 18) & 0x00100000; // 3 -> 21 + vi1 |= (qh1 << 25) & 0x10000000; // 4 -> 29 + sumi = __dp4a(vi1, ui1, sumi); // SIMD dot product of quantized values + + return sumi*d + m*s / QI5_1; // scale sum by QI5_1 because there are QI5_1 threads working on this block +#else + return 0.0f; // only to satisfy the compiler +#endif // __CUDA_ARCH__ >= 600 +} + +static __device__ __forceinline__ float vec_dot_q8_0_q8_1(const void * vbq, const block_q8_1 * bq8_1, const int iqs) { +#if __CUDA_ARCH__ >= 600 // lowest compute capability for integer intrinsics + const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq; + + int vi; + memcpy(&vi, &bq8_0->qs[sizeof(int) * (iqs + 0)], sizeof(int)); + const int ui = *((int *) &bq8_1->qs[sizeof(int) * (iqs + 0)]); + + const float d = __half2float(bq8_0->d) * __half2float(bq8_1->d); + + // SIMD dot product of quantized values + int sumi = __dp4a(vi, ui, 0); + + return sumi*d; +#else + return 0.0f; // only to satisfy the compiler +#endif // __CUDA_ARCH__ >= 600 +} + +template +static __global__ void mul_mat_vec_q(const void * vx, const void * vy, float * dst, const int ncols, const int nrows) { + const int row = blockIdx.y*blockDim.y + threadIdx.y; + + if (row >= nrows) { + return; + } + + const int blocks_per_row = ncols / qk; + const int blocks_per_warp = WARP_SIZE / qi; + +// partial sum for each thread + float tmp = 0.0f; + + const block_q_t * x = (const block_q_t *) vx; + const block_q8_1 * y = (const block_q8_1 *) vy; + + for (int i = 0; i < blocks_per_row; i += blocks_per_warp) { + const int ibx = row*blocks_per_row + i + threadIdx.x / qi; // x block index + + const int iby = i + threadIdx.x / qi; // y block index + + const int iqs = threadIdx.x % qi; // x block quant index when casting the quants to int + + tmp += vec_dot_q_cuda(&x[ibx], &y[iby], iqs); + } + + // sum up partial sums and write back result +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); + } + + if (threadIdx.x == 0) { + dst[row] = tmp; + } +} + template static __global__ void dequantize_mul_mat_vec(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows) { // qk = quantized weights per x block @@ -1233,7 +1457,6 @@ static __global__ void dequantize_mul_mat_vec(const void * vx, const dfloat * y, } // sum up partial sums and write back result - __syncthreads(); #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); @@ -1284,7 +1507,6 @@ static __global__ void mul_mat_p021_f16_f32(const void * vx, const float * y, fl const int idst = channel*nrows_dst + row_dst; // sum up partial sums and write back result - __syncthreads(); #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); @@ -1330,7 +1552,6 @@ static __global__ void mul_mat_vec_nc_f16_f32( // nc == non-contiguous } // sum up partial sums and write back result - __syncthreads(); #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); @@ -1440,7 +1661,6 @@ static __global__ void soft_max_f32(const float * x, float * dst, const int ncol } // sum up partial sums - __syncthreads(); #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); @@ -1494,6 +1714,11 @@ static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, con rms_norm_f32<<>>(x, dst, ncols); } +static void quantize_row_q8_1_cuda(const float * x, void * vy, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_QUANTIZE_BLOCK_SIZE - 1) / CUDA_QUANTIZE_BLOCK_SIZE; + quantize_q8_1<<>>(x, vy, k); +} + static void dequantize_row_q4_0_cuda(const void * vx, float * y, const int k, cudaStream_t stream) { const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE; dequantize_block<<>>(vx, y, k); @@ -1562,45 +1787,45 @@ static void dequantize_row_q6_K_cuda(const void * vx, float * y, const int k, cu static void dequantize_mul_mat_vec_q4_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); - const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y; + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const dim3 block_nums(1, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); } static void dequantize_mul_mat_vec_q4_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); - const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y; + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const dim3 block_nums(1, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); } static void dequantize_mul_mat_vec_q5_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); - const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y; + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const dim3 block_nums(1, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); } static void dequantize_mul_mat_vec_q5_1_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); - const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y; + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const dim3 block_nums(1, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); } static void dequantize_mul_mat_vec_q8_0_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); - const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y; + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const dim3 block_nums(1, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec <<>>(vx, y, dst, ncols, nrows); } @@ -1647,6 +1872,51 @@ static void dequantize_mul_mat_vec_q6_K_cuda(const void * vx, const float * y, f dequantize_mul_mat_vec_q6_k<<>>(vx, y, dst, ncols, nrows); } +static void mul_mat_vec_q4_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; + const dim3 block_nums(1, block_num_y, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); + mul_mat_vec_q + <<>>(vx, vy, dst, ncols, nrows); +} + +static void mul_mat_vec_q4_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; + const dim3 block_nums(1, block_num_y, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); + mul_mat_vec_q + <<>>(vx, vy, dst, ncols, nrows); +} + +static void mul_mat_vec_q5_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; + const dim3 block_nums(1, block_num_y, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); + mul_mat_vec_q + <<>>(vx, vy, dst, ncols, nrows); +} + +static void mul_mat_vec_q5_1_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; + const dim3 block_nums(1, block_num_y, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); + mul_mat_vec_q + <<>>(vx, vy, dst, ncols, nrows); +} + +static void mul_mat_vec_q8_0_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; + const dim3 block_nums(1, block_num_y, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); + mul_mat_vec_q + <<>>(vx, vy, dst, ncols, nrows); +} + static void convert_fp16_to_fp32_cuda(const void * vx, float * y, const int k, cudaStream_t stream) { const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE; dequantize_block<1, 1, convert_f16><<>>(vx, y, k); @@ -1654,9 +1924,9 @@ static void convert_fp16_to_fp32_cuda(const void * vx, float * y, const int k, c static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % GGML_CUDA_DMMV_X == 0); - const int block_num_y = (nrows + GGML_CUDA_DMMV_Y - 1) / GGML_CUDA_DMMV_Y; + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const dim3 block_nums(1, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_DMMV_Y, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); dequantize_mul_mat_vec<1, 1, convert_f16> <<>>(vx, y, dst, ncols, nrows); } @@ -1822,6 +2092,7 @@ static size_t g_scratch_offset = 0; static int g_device_count = -1; static int g_main_device = 0; +static int g_compute_capabilities[GGML_CUDA_MAX_DEVICES]; static float g_tensor_split[GGML_CUDA_MAX_DEVICES] = {0}; static cublasHandle_t g_cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr}; @@ -1839,9 +2110,12 @@ void ggml_init_cublas() { for (int id = 0; id < g_device_count; ++id) { cudaDeviceProp prop; CUDA_CHECK(cudaGetDeviceProperties(&prop, id)); - fprintf(stderr, " Device %d: %s\n", id, prop.name); + fprintf(stderr, " Device %d: %s, compute capability %d.%d\n", id, prop.name, prop.major, prop.minor); + g_tensor_split[id] = total_vram; total_vram += prop.totalGlobalMem; + + g_compute_capabilities[id] = 100*prop.major + 10*prop.minor; } for (int id = 0; id < g_device_count; ++id) { g_tensor_split[id] /= total_vram; @@ -2057,7 +2331,7 @@ inline void ggml_cuda_op_rms_norm( (void) i1; } -inline void ggml_cuda_op_dequantize_mul_mat_vec( +inline void ggml_cuda_op_mul_mat_vec( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, char * src0_ddq_i, float * src0_ddf_i, float * src1_ddf_i, float * dst_ddf_i, int64_t i02, int64_t i01_low, int64_t i01_high, int i1, cudaStream_t & cudaStream_main){ @@ -2069,69 +2343,116 @@ inline void ggml_cuda_op_dequantize_mul_mat_vec( const int64_t ne00 = src0->ne[0]; const int64_t nrows = i01_high - i01_low; -// on some GPUs it is faster to convert src1 to half and to use half precision intrinsics -#ifdef GGML_CUDA_DMMV_F16 - size_t ash; - dfloat * src1_dfloat = nullptr; // dfloat == half - - bool src1_convert_f16 = src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 || - src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 || - src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16; - - if (src1_convert_f16) { - src1_dfloat = (half *) ggml_cuda_pool_malloc(ne00*sizeof(half), &ash); - ggml_cpy_f32_f16_cuda((char *) src1_ddf_i, (char *) src1_dfloat, ne00, - ne00, 1, sizeof(float), 0, 0, - ne00, 1, sizeof(half), 0, 0, cudaStream_main); - } +#ifdef GGML_CUDA_FORCE_DMMV + const bool use_mul_mat_vec_q = false; #else - dfloat * src1_dfloat = src1_ddf_i; // dfloat == float, no conversion + int id; + CUDA_CHECK(cudaGetDevice(&id)); + + const bool mul_mat_vec_q_implemented = src0->type == GGML_TYPE_Q4_0 || + src0->type == GGML_TYPE_Q4_1 || + src0->type == GGML_TYPE_Q5_0 || + src0->type == GGML_TYPE_Q5_1 || + src0->type == GGML_TYPE_Q8_0; + + // The integer intrinsics used in mul_mat_vec_q are available with compute capability 6. + // However, they have bad performance with Pascal cards. + // Therefore, in a multi GPU setting decide at runtime which GPUs should use mul_mat_vec_q. + const bool use_mul_mat_vec_q = g_compute_capabilities[id] >= 700 && mul_mat_vec_q_implemented; +#endif + + if (use_mul_mat_vec_q) { + size_t as; + void * src1_q8_1 = ggml_cuda_pool_malloc(ne00*sizeof(block_q8_1)/QK8_1, &as); + quantize_row_q8_1_cuda(src1_ddf_i, src1_q8_1, ne00, cudaStream_main); + + switch (src0->type) { + case GGML_TYPE_Q4_0: + mul_mat_vec_q4_0_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q4_1: + mul_mat_vec_q4_1_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q5_0: + mul_mat_vec_q5_0_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q5_1: + mul_mat_vec_q5_1_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q8_0: + mul_mat_vec_q8_0_q8_1_cuda(src0_ddq_i, src1_q8_1, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + default: + GGML_ASSERT(false); + break; + } + + ggml_cuda_pool_free(src1_q8_1, as); + } else { + // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics +#ifdef GGML_CUDA_DMMV_F16 + size_t ash; + dfloat * src1_dfloat = nullptr; // dfloat == half + + bool src1_convert_f16 = src0->type == GGML_TYPE_Q4_0 || src0->type == GGML_TYPE_Q4_1 || + src0->type == GGML_TYPE_Q5_0 || src0->type == GGML_TYPE_Q5_1 || + src0->type == GGML_TYPE_Q8_0 || src0->type == GGML_TYPE_F16; + + if (src1_convert_f16) { + src1_dfloat = (half *) ggml_cuda_pool_malloc(ne00*sizeof(half), &ash); + ggml_cpy_f32_f16_cuda((char *) src1_ddf_i, (char *) src1_dfloat, ne00, + ne00, 1, sizeof(float), 0, 0, + ne00, 1, sizeof(half), 0, 0, cudaStream_main); + } +#else + dfloat * src1_dfloat = src1_ddf_i; // dfloat == float, no conversion #endif // GGML_CUDA_DMMV_F16 - switch (src0->type) { - case GGML_TYPE_Q4_0: - dequantize_mul_mat_vec_q4_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); - break; - case GGML_TYPE_Q4_1: - dequantize_mul_mat_vec_q4_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); - break; - case GGML_TYPE_Q5_0: - dequantize_mul_mat_vec_q5_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); - break; - case GGML_TYPE_Q5_1: - dequantize_mul_mat_vec_q5_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); - break; - case GGML_TYPE_Q8_0: - dequantize_mul_mat_vec_q8_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); - break; - case GGML_TYPE_Q2_K: - dequantize_mul_mat_vec_q2_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); - break; - case GGML_TYPE_Q3_K: - dequantize_mul_mat_vec_q3_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); - break; - case GGML_TYPE_Q4_K: - dequantize_mul_mat_vec_q4_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); - break; - case GGML_TYPE_Q5_K: - dequantize_mul_mat_vec_q5_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); - break; - case GGML_TYPE_Q6_K: - dequantize_mul_mat_vec_q6_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); - break; - case GGML_TYPE_F16: - convert_mul_mat_vec_f16_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); - break; - default: - GGML_ASSERT(false); - break; - } + switch (src0->type) { + case GGML_TYPE_Q4_0: + dequantize_mul_mat_vec_q4_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q4_1: + dequantize_mul_mat_vec_q4_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q5_0: + dequantize_mul_mat_vec_q5_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q5_1: + dequantize_mul_mat_vec_q5_1_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q8_0: + dequantize_mul_mat_vec_q8_0_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q2_K: + dequantize_mul_mat_vec_q2_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q3_K: + dequantize_mul_mat_vec_q3_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q4_K: + dequantize_mul_mat_vec_q4_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q5_K: + dequantize_mul_mat_vec_q5_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_Q6_K: + dequantize_mul_mat_vec_q6_K_cuda(src0_ddq_i, src1_ddf_i, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + case GGML_TYPE_F16: + convert_mul_mat_vec_f16_cuda(src0_ddq_i, src1_dfloat, dst_ddf_i, ne00, nrows, cudaStream_main); + break; + default: + GGML_ASSERT(false); + break; + } #ifdef GGML_CUDA_DMMV_F16 - if (src1_convert_f16) { - ggml_cuda_pool_free(src1_dfloat, ash); - } + if (src1_convert_f16) { + ggml_cuda_pool_free(src1_dfloat, ash); + } #endif // GGML_CUDA_DMMV_F16 + } (void) src1; (void) dst; @@ -2701,8 +3022,8 @@ void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_ }else if (src0->type == GGML_TYPE_F32) { ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, true, false); } else if (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) { - if (src1->ne[1] == 1 && src0->ne[0] % GGML_CUDA_DMMV_X == 0 && src0->ne[1] % GGML_CUDA_DMMV_Y == 0) { - ggml_cuda_op(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false, false); + if (src1->ne[1] == 1 && src0->ne[0] % GGML_CUDA_DMMV_X == 0) { + ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_vec, false, false); } else { ggml_cuda_op(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, true, false); } From 8567c76b5326e862be0755a8dc1dd988223fcae3 Mon Sep 17 00:00:00 2001 From: Jesse Jojo Johnson Date: Wed, 5 Jul 2023 15:13:35 +0000 Subject: [PATCH 089/852] Update server instructions for web front end (#2103) Co-authored-by: Jesse Johnson --- examples/server/README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 4ed226e04..160614ba8 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -1,6 +1,6 @@ # llama.cpp/example/server -This example demonstrates a simple HTTP API server to interact with llama.cpp. +This example demonstrates a simple HTTP API server and a simple web front end to interact with llama.cpp. Command line options: @@ -21,6 +21,7 @@ Command line options: - `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`. - `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`. - `--port`: Set the port to listen. Default: `8080`. +- `--public`: path from which to serve static files (default examples/server/public) - `--embedding`: Enable embedding extraction, Default: disabled. ## Build @@ -59,7 +60,7 @@ server.exe -m models\7B\ggml-model.bin -c 2048 ``` The above command will start a server that by default listens on `127.0.0.1:8080`. -You can consume the endpoints with Postman or NodeJS with axios library. +You can consume the endpoints with Postman or NodeJS with axios library. You can visit the web front end at the same url. ## Testing with CURL From 1b107b8550dced48dc5f41184640061354226b96 Mon Sep 17 00:00:00 2001 From: Stephan Walter Date: Wed, 5 Jul 2023 16:13:06 +0000 Subject: [PATCH 090/852] ggml : generalize `quantize_fns` for simpler FP16 handling (#1237) * Generalize quantize_fns for simpler FP16 handling * Remove call to ggml_cuda_mul_mat_get_wsize * ci : disable FMA for mac os actions --------- Co-authored-by: Georgi Gerganov --- .github/workflows/build.yml | 3 +- examples/quantize-stats/quantize-stats.cpp | 14 +- ggml.c | 588 ++++----------------- ggml.h | 31 +- llama.cpp | 10 +- pocs/vdot/q8dot.cpp | 6 +- pocs/vdot/vdot.cpp | 13 +- tests/test-quantize-fns.cpp | 30 +- tests/test-quantize-perf.cpp | 25 +- 9 files changed, 172 insertions(+), 548 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index aec43bd92..12481e8be 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -137,9 +137,10 @@ jobs: - name: Build id: cmake_build run: | + sysctl -a mkdir build cd build - cmake -DLLAMA_AVX2=OFF .. + cmake -DLLAMA_AVX2=OFF -DLLAMA_FMA=OFF .. cmake --build . --config Release - name: Test diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp index 9cea472de..6aa06ec8f 100644 --- a/examples/quantize-stats/quantize-stats.cpp +++ b/examples/quantize-stats/quantize-stats.cpp @@ -147,7 +147,7 @@ void test_roundtrip_on_chunk( const ggml_tensor * layer, int64_t offset, int64_t chunk_size, - const quantize_fns_t & qfns, + const ggml_type_traits_t & qfns, bool use_reference, float * input_scratch, char * quantized_scratch, @@ -163,11 +163,11 @@ void test_roundtrip_on_chunk( } if (use_reference) { - qfns.quantize_row_q_reference(input_scratch, quantized_scratch, chunk_size); + qfns.from_float_reference(input_scratch, quantized_scratch, chunk_size); } else { - qfns.quantize_row_q(input_scratch, quantized_scratch, chunk_size); + qfns.from_float(input_scratch, quantized_scratch, chunk_size); } - qfns.dequantize_row_q(quantized_scratch, output_scratch, chunk_size); + qfns.to_float(quantized_scratch, output_scratch, chunk_size); update_error_stats(chunk_size, input_scratch, output_scratch, stats); } @@ -177,7 +177,7 @@ void test_roundtrip_on_chunk( void test_roundtrip_on_layer( std::string & name, bool print_layer_stats, - const quantize_fns_t & qfns, + const ggml_type_traits_t & qfns, bool use_reference, const ggml_tensor * layer, std::vector & input_scratch, @@ -388,8 +388,8 @@ int main(int argc, char ** argv) { if (!params.include_types.empty() && std::find(params.include_types.begin(), params.include_types.end(), i) == params.include_types.end()) { continue; } - quantize_fns_t qfns = ggml_internal_get_quantize_fn(i); - if (qfns.quantize_row_q && qfns.dequantize_row_q) { + ggml_type_traits_t qfns = ggml_internal_get_type_traits(type); + if (qfns.from_float && qfns.to_float) { if (params.verbose) { printf("testing %s ...\n", ggml_type_name(type)); } diff --git a/ggml.c b/ggml.c index 88cbed7d5..635c32eb5 100644 --- a/ggml.c +++ b/ggml.c @@ -481,14 +481,14 @@ ggml_fp16_t ggml_fp32_to_fp16(float x) { return GGML_FP32_TO_FP16(x); } -void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, size_t n) { - for (size_t i = 0; i < n; i++) { +void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int n) { + for (int i = 0; i < n; i++) { y[i] = GGML_FP16_TO_FP32(x[i]); } } -void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, size_t n) { - size_t i = 0; +void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n) { + int i = 0; #if defined(__F16C__) for (; i + 7 < n; i += 8) { __m256 x_vec = _mm256_loadu_ps(x + i); @@ -1627,109 +1627,112 @@ static void dequantize_row_q8_0(const void * restrict vx, float * restrict y, in } } +static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y); +static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y); static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * restrict vx, const void * restrict vy); -static const quantize_fns_t quantize_fns[GGML_TYPE_COUNT] = { +static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { + [GGML_TYPE_F32] = { + .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f32, + .vec_dot_type = GGML_TYPE_F32, + }, + [GGML_TYPE_F16] = { + .to_float = (ggml_to_float_t) ggml_fp16_to_fp32_row, + .from_float = (ggml_from_float_t) ggml_fp32_to_fp16_row, + .from_float_reference = (ggml_from_float_t) ggml_fp32_to_fp16_row, + .vec_dot = (ggml_vec_dot_t) ggml_vec_dot_f16, + .vec_dot_type = GGML_TYPE_F16, + }, [GGML_TYPE_Q4_0] = { - .dequantize_row_q = (dequantize_row_q_t) dequantize_row_q4_0, - .quantize_row_q = quantize_row_q4_0, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_0_reference, - .quantize_row_q_dot = quantize_row_q8_0, - .vec_dot_q = ggml_vec_dot_q4_0_q8_0, + .to_float = (ggml_to_float_t) dequantize_row_q4_0, + .from_float = quantize_row_q4_0, + .from_float_reference = (ggml_from_float_t) quantize_row_q4_0_reference, + .vec_dot = ggml_vec_dot_q4_0_q8_0, .vec_dot_type = GGML_TYPE_Q8_0, }, [GGML_TYPE_Q4_1] = { - .dequantize_row_q = (dequantize_row_q_t)dequantize_row_q4_1, - .quantize_row_q = quantize_row_q4_1, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_1_reference, - .quantize_row_q_dot = quantize_row_q8_1, - .vec_dot_q = ggml_vec_dot_q4_1_q8_1, + .to_float = (ggml_to_float_t) dequantize_row_q4_1, + .from_float = quantize_row_q4_1, + .from_float_reference = (ggml_from_float_t) quantize_row_q4_1_reference, + .vec_dot = ggml_vec_dot_q4_1_q8_1, .vec_dot_type = GGML_TYPE_Q8_1, }, [GGML_TYPE_Q5_0] = { - .dequantize_row_q = (dequantize_row_q_t) dequantize_row_q5_0, - .quantize_row_q = quantize_row_q5_0, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q5_0_reference, - .quantize_row_q_dot = quantize_row_q8_0, - .vec_dot_q = ggml_vec_dot_q5_0_q8_0, + .to_float = (ggml_to_float_t) dequantize_row_q5_0, + .from_float = quantize_row_q5_0, + .from_float_reference = (ggml_from_float_t) quantize_row_q5_0_reference, + .vec_dot = ggml_vec_dot_q5_0_q8_0, .vec_dot_type = GGML_TYPE_Q8_0, }, [GGML_TYPE_Q5_1] = { - .dequantize_row_q = (dequantize_row_q_t) dequantize_row_q5_1, - .quantize_row_q = quantize_row_q5_1, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q5_1_reference, - .quantize_row_q_dot = quantize_row_q8_1, - .vec_dot_q = ggml_vec_dot_q5_1_q8_1, + .to_float = (ggml_to_float_t) dequantize_row_q5_1, + .from_float = quantize_row_q5_1, + .from_float_reference = (ggml_from_float_t) quantize_row_q5_1_reference, + .vec_dot = ggml_vec_dot_q5_1_q8_1, .vec_dot_type = GGML_TYPE_Q8_1, }, [GGML_TYPE_Q8_0] = { - .dequantize_row_q = dequantize_row_q8_0, - .quantize_row_q = quantize_row_q8_0, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q8_0_reference, - .quantize_row_q_dot = quantize_row_q8_0, - .vec_dot_q = ggml_vec_dot_q8_0_q8_0, + .to_float = dequantize_row_q8_0, + .from_float = quantize_row_q8_0, + .from_float_reference = (ggml_from_float_t) quantize_row_q8_0_reference, + .vec_dot = ggml_vec_dot_q8_0_q8_0, .vec_dot_type = GGML_TYPE_Q8_0, }, [GGML_TYPE_Q8_1] = { - .dequantize_row_q = NULL, // TODO - .quantize_row_q = quantize_row_q8_1, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q8_1_reference, - .quantize_row_q_dot = quantize_row_q8_1, - .vec_dot_q = NULL, // TODO + .from_float = quantize_row_q8_1, + .from_float_reference = (ggml_from_float_t) quantize_row_q8_1_reference, .vec_dot_type = GGML_TYPE_Q8_1, }, #ifdef GGML_USE_K_QUANTS [GGML_TYPE_Q2_K] = { - .dequantize_row_q = (dequantize_row_q_t) dequantize_row_q2_K, - .quantize_row_q = quantize_row_q2_K, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q2_K_reference, - .quantize_row_q_dot = quantize_row_q8_K, - .vec_dot_q = ggml_vec_dot_q2_K_q8_K, + .to_float = (ggml_to_float_t) dequantize_row_q2_K, + .from_float = quantize_row_q2_K, + .from_float_reference = (ggml_from_float_t) quantize_row_q2_K_reference, + .vec_dot = ggml_vec_dot_q2_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, }, [GGML_TYPE_Q3_K] = { - .dequantize_row_q = (dequantize_row_q_t) dequantize_row_q3_K, - .quantize_row_q = quantize_row_q3_K, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q3_K_reference, - .quantize_row_q_dot = quantize_row_q8_K, - .vec_dot_q = ggml_vec_dot_q3_K_q8_K, + .to_float = (ggml_to_float_t) dequantize_row_q3_K, + .from_float = quantize_row_q3_K, + .from_float_reference = (ggml_from_float_t) quantize_row_q3_K_reference, + .vec_dot = ggml_vec_dot_q3_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, }, [GGML_TYPE_Q4_K] = { - .dequantize_row_q = (dequantize_row_q_t) dequantize_row_q4_K, - .quantize_row_q = quantize_row_q4_K, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q4_K_reference, - .quantize_row_q_dot = quantize_row_q8_K, - .vec_dot_q = ggml_vec_dot_q4_K_q8_K, + .to_float = (ggml_to_float_t) dequantize_row_q4_K, + .from_float = quantize_row_q4_K, + .from_float_reference = (ggml_from_float_t) quantize_row_q4_K_reference, + .vec_dot = ggml_vec_dot_q4_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, }, [GGML_TYPE_Q5_K] = { - .dequantize_row_q = (dequantize_row_q_t) dequantize_row_q5_K, - .quantize_row_q = quantize_row_q5_K, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q5_K_reference, - .quantize_row_q_dot = quantize_row_q8_K, - .vec_dot_q = ggml_vec_dot_q5_K_q8_K, + .to_float = (ggml_to_float_t) dequantize_row_q5_K, + .from_float = quantize_row_q5_K, + .from_float_reference = (ggml_from_float_t) quantize_row_q5_K_reference, + .vec_dot = ggml_vec_dot_q5_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, }, [GGML_TYPE_Q6_K] = { - .dequantize_row_q = (dequantize_row_q_t) dequantize_row_q6_K, - .quantize_row_q = quantize_row_q6_K, - .quantize_row_q_reference = (quantize_row_q_t) quantize_row_q6_K_reference, - .quantize_row_q_dot = quantize_row_q8_K, - .vec_dot_q = ggml_vec_dot_q6_K_q8_K, + .to_float = (ggml_to_float_t) dequantize_row_q6_K, + .from_float = quantize_row_q6_K, + .from_float_reference = (ggml_from_float_t) quantize_row_q6_K_reference, + .vec_dot = ggml_vec_dot_q6_K_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, }, + [GGML_TYPE_Q8_K] = { + .from_float = quantize_row_q8_K, + } #endif }; // For internal test use -quantize_fns_t ggml_internal_get_quantize_fn(size_t i) { +ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type i) { GGML_ASSERT(i < GGML_TYPE_COUNT); - return quantize_fns[i]; + return type_traits[i]; } @@ -2275,7 +2278,7 @@ inline static void ggml_vec_neg_f32 (const int n, float * y, const float * x) inline static void ggml_vec_mul_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]*y[i]; } inline static void ggml_vec_div_f32 (const int n, float * z, const float * x, const float * y) { for (int i = 0; i < n; ++i) z[i] = x[i]/y[i]; } -inline static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y) { +static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y) { #ifdef GGML_SIMD float sumf = 0.0f; const int np = (n & ~(GGML_F32_STEP - 1)); @@ -2312,7 +2315,7 @@ inline static void ggml_vec_dot_f32(const int n, float * restrict s, const float *s = sumf; } -inline static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y) { +static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y) { ggml_float sumf = 0.0; #if defined(GGML_SIMD) @@ -7825,8 +7828,8 @@ static void ggml_compute_forward_dup_f16( id += ne00 * (ne01 - ir1); } } - } else if (ggml_is_quantized(dst->type)) { - quantize_row_q_t const quantize_row_q = quantize_fns[dst->type].quantize_row_q; + } else if (type_traits[dst->type].from_float) { + ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float; float * src0_f32 = (float *) params->wdata + (ne00 + CACHE_LINE_SIZE_F32) * ith; size_t id = 0; @@ -8078,26 +8081,8 @@ static void ggml_compute_forward_dup_f32( id += rs * (ne01 - ir1); } } - } else if (dst->type == GGML_TYPE_F16) { - size_t id = 0; - ggml_fp16_t * dst_ptr = (ggml_fp16_t *) dst->data; - - for (int i03 = 0; i03 < ne03; i03++) { - for (int i02 = 0; i02 < ne02; i02++) { - id += ne00 * ir0; - for (int i01 = ir0; i01 < ir1; i01++) { - for (int i00 = 0; i00 < ne00; i00++) { - const float * src0_ptr = (float *) ((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); - - dst_ptr[id] = GGML_FP32_TO_FP16(*src0_ptr); - id++; - } - } - id += ne00 * (ne01 - ir1); - } - } - } else if (ggml_is_quantized(dst->type)) { - quantize_row_q_t const quantize_row_q = quantize_fns[dst->type].quantize_row_q; + } else if (type_traits[dst->type].from_float) { + ggml_from_float_t const quantize_row_q = type_traits[dst->type].from_float; size_t id = 0; size_t rs = nb0 * (ne00 / GGML_BLCK_SIZE[dst->type]); @@ -8503,8 +8488,8 @@ static void ggml_compute_forward_add_q_f32( const int nth = params->nth; const enum ggml_type type = src0->type; - dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q; - quantize_row_q_t const quantize_row_q = quantize_fns[type].quantize_row_q; + ggml_to_float_t const dequantize_row_q = type_traits[type].to_float; + ggml_from_float_t const quantize_row_q = type_traits[type].from_float; // we don't support permuted src0 or src1 GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]); @@ -8777,8 +8762,8 @@ static void ggml_compute_forward_add1_q_f32( GGML_TENSOR_UNARY_OP_LOCALS; const enum ggml_type type = src0->type; - dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q; - quantize_row_q_t const quantize_row_q = quantize_fns[type].quantize_row_q; + ggml_to_float_t const dequantize_row_q = type_traits[type].to_float; + ggml_from_float_t const quantize_row_q = type_traits[type].from_float; // we don't support permuted src0 GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]); @@ -10578,317 +10563,7 @@ static bool ggml_compute_forward_mul_mat_use_blas( } #endif -static void ggml_compute_forward_mul_mat_f32( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - - GGML_TENSOR_BINARY_OP_LOCALS; - - const int ith = params->ith; - const int nth = params->nth; - - assert(ne02 == ne12); - assert(ne03 == ne13); - assert(ne2 == ne12); - assert(ne3 == ne13); - - // we don't support permuted src0 or src1 - assert(nb00 == sizeof(float)); - assert(nb10 == sizeof(float)); - - // dst cannot be transposed or permuted - assert(nb0 == sizeof(float)); - assert(nb0 <= nb1); - assert(nb1 <= nb2); - assert(nb2 <= nb3); - - assert(ne0 == ne01); - assert(ne1 == ne11); - assert(ne2 == ne02); - assert(ne3 == ne03); - - // nb01 >= nb00 - src0 is not transposed - // compute by src0 rows - -#if defined(GGML_USE_CLBLAST) - if (ggml_cl_can_mul_mat(src0, src1, dst)) { - if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) { - ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize); - } - return; - } -#endif - -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) - if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) { - if (params->ith != 0) { - return; - } - - if (params->type == GGML_TASK_INIT) { - return; - } - - if (params->type == GGML_TASK_FINALIZE) { - return; - } - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - const float * x = (float *) ((char *) src0->data + i02*nb02 + i03*nb03); - const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13); - float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); - - cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, - ne11, ne01, ne10, - 1.0f, y, ne10, - x, ne00, - 0.0f, d, ne01); - } - } - //printf("CBLAS F32 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3); - - return; - } -#endif - - if (params->type == GGML_TASK_INIT) { - return; - } - - if (params->type == GGML_TASK_FINALIZE) { - return; - } - - // parallelize by src0 rows using ggml_vec_dot_f32 - - // total rows in src0 - const int nr = ne01*ne02*ne03; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 indices - const int i03 = ir/(ne02*ne01); - const int i02 = (ir - i03*ne02*ne01)/ne01; - const int i01 = (ir - i03*ne02*ne01 - i02*ne01); - - for (int64_t ic = 0; ic < ne11; ++ic) { - // src1 indices - const int i13 = i03; - const int i12 = i02; - const int i11 = ic; - - // dst indices - const int i0 = i01; - const int i1 = i11; - const int i2 = i02; - const int i3 = i03; - - ggml_vec_dot_f32(ne00, - (float *) ((char *) dst->data + (i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), - (float *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)), - (float *) ((char *) src1->data + (i11*nb11 + i12*nb12 + i13*nb13))); - } - } - - //int64_t t1 = ggml_perf_time_us(); - //static int64_t acc = 0; - //acc += t1 - t0; - //if (t1 - t0 > 10) { - // printf("\n"); - // printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03); - // printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03); - // printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13); - // printf("nb10 = %5d, nb11 = %5d, nb12 = %5d, nb13 = %5d\n", nb10, nb11, nb12, nb13); - - // printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc); - //} -} - -static void ggml_compute_forward_mul_mat_f16_f32( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - - GGML_TENSOR_BINARY_OP_LOCALS; - - //const int64_t ne = ne0*ne1*ne2*ne3; - - const int ith = params->ith; - const int nth = params->nth; - - GGML_ASSERT(ne02 == ne12); - GGML_ASSERT(ne03 == ne13); - GGML_ASSERT(ne2 == ne12); - GGML_ASSERT(ne3 == ne13); - - // TODO: we don't support permuted src0 - GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - GGML_ASSERT(ne0 == ne01); - GGML_ASSERT(ne1 == ne11); - GGML_ASSERT(ne2 == ne02); - GGML_ASSERT(ne3 == ne03); - - // nb01 >= nb00 - src0 is not transposed - // compute by src0 rows - -#if defined(GGML_USE_CLBLAST) - if (ggml_cl_can_mul_mat(src0, src1, dst)) { - if (params->ith == 0 && params->type == GGML_TASK_COMPUTE) { - ggml_cl_mul_mat(src0, src1, dst, params->wdata, params->wsize); - } - return; - } -#endif - -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) - if (ggml_compute_forward_mul_mat_use_blas(src0, src1, dst)) { - GGML_ASSERT(nb10 == sizeof(float)); - - if (params->ith != 0) { - return; - } - - if (params->type == GGML_TASK_INIT) { - return; - } - - if (params->type == GGML_TASK_FINALIZE) { - return; - } - - for (int64_t i03 = 0; i03 < ne03; i03++) { - for (int64_t i02 = 0; i02 < ne02; i02++) { - float * const wdata = params->wdata; - { - size_t id = 0; - for (int64_t i01 = 0; i01 < ne01; ++i01) { - for (int64_t i00 = 0; i00 < ne00; ++i00) { - wdata[id++] = GGML_FP16_TO_FP32(*(ggml_fp16_t *) ((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00)); - } - } - - assert(id*sizeof(float) <= params->wsize); - } - - const float * x = wdata; - const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13); - - float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); - - // zT = y * xT - cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, - ne11, ne01, ne10, - 1.0f, y, ne10, - x, ne00, - 0.0f, d, ne01); - } - } - - /*printf("CBLAS F16 = %f ms, %d x %d x %d x %d\n", (ggml_perf_time_us() - t0)/1000.0, ne0, ne1, ne2, ne3);*/ - - return; - } -#endif - - if (params->type == GGML_TASK_INIT) { - ggml_fp16_t * const wdata = params->wdata; - - size_t id = 0; - for (int64_t i13 = 0; i13 < ne13; ++i13) { - for (int64_t i12 = 0; i12 < ne12; ++i12) { - for (int64_t i11 = 0; i11 < ne11; ++i11) { - for (int64_t i10 = 0; i10 < ne10; ++i10) { - wdata[id++] = GGML_FP32_TO_FP16(*(float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11 + i10*nb10)); - } - } - } - } - - GGML_ASSERT(id*sizeof(ggml_fp16_t) <= params->wsize); - - return; - } - - if (params->type == GGML_TASK_FINALIZE) { - return; - } - - // fp16 -> half the size, so divide by 2 - // TODO: do not support transposed src1 - assert(nb10/2 == sizeof(ggml_fp16_t)); - - // parallelize by src0 rows using ggml_vec_dot_f16 - - // total rows in src0 - const int nr = ne01*ne02*ne03; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - ggml_fp16_t * wdata = params->wdata; - - for (int ir = ir0; ir < ir1; ++ir) { - // src0 indices - const int i03 = ir/(ne02*ne01); - const int i02 = (ir - i03*ne02*ne01)/ne01; - const int i01 = (ir - i03*ne02*ne01 - i02*ne01); - - const int i13 = i03; - const int i12 = i02; - - const int i0 = i01; - const int i2 = i02; - const int i3 = i03; - - ggml_fp16_t * src0_row = (ggml_fp16_t *) ((char *) src0->data + (i01*nb01 + i02*nb02 + i03*nb03)); - ggml_fp16_t * src1_col = wdata + ( 0 + i12*ne11 + i13*ne12*ne11)*ne00; - - float * dst_col = (float *) ((char *) dst->data + (i0*nb0 + 0*nb1 + i2*nb2 + i3*nb3)); - - for (int64_t ic = 0; ic < ne11; ++ic) { - ggml_vec_dot_f16(ne00, &dst_col[ic*ne0], src0_row, src1_col + ic*ne00); - } - } - - //int64_t t1 = ggml_time_us(); - //static int64_t acc = 0; - //acc += t1 - t0; - //if (t1 - t0 > 10) { - // printf("\n"); - // printf("ne00 = %5d, ne01 = %5d, ne02 = %5d, ne03 = %5d\n", ne00, ne01, ne02, ne03); - // printf("nb00 = %5d, nb01 = %5d, nb02 = %5d, nb03 = %5d\n", nb00, nb01, nb02, nb03); - // printf("ne10 = %5d, ne11 = %5d, ne12 = %5d, ne13 = %5d\n", ne10, ne11, ne12, ne13); - - // printf("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX task %d/%d: %d us, acc = %d\n", ith, nth, (int) (t1 - t0), (int) acc); - //} -} - -static void ggml_compute_forward_mul_mat_q_f32( +static void ggml_compute_forward_mul_mat( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, @@ -10907,9 +10582,10 @@ static void ggml_compute_forward_mul_mat_q_f32( GGML_ASSERT(ne3 == ne13); const enum ggml_type type = src0->type; - quantize_row_q_t const quantize_row_q_dot = quantize_fns[type].quantize_row_q_dot; - vec_dot_q_t const vec_dot_q = quantize_fns[type].vec_dot_q; - enum ggml_type const vec_dot_type = quantize_fns[type].vec_dot_type; + + ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot; + enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type; + ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float; // we don't support permuted src0 or src1 GGML_ASSERT(nb00 == GGML_TYPE_SIZE[type]); @@ -10952,27 +10628,27 @@ static void ggml_compute_forward_mul_mat_q_f32( return; } - float * const wdata = params->wdata; - dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q; - for (int64_t i03 = 0; i03 < ne03; i03++) { for (int64_t i02 = 0; i02 < ne02; i02++) { + const void * x = (char *) src0->data + i03*nb03 + i02*nb02; const float * y = (float *) ((char *) src1->data + i02*nb12 + i03*nb13); float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); - { + if (type != GGML_TYPE_F32) { + float * const wdata = params->wdata; + ggml_to_float_t const to_float = type_traits[type].to_float; + size_t id = 0; for (int64_t i01 = 0; i01 < ne01; ++i01) { - dequantize_row_q((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01, wdata + id, ne00); + to_float((char *) src0->data + i03*nb03 + i02*nb02 + i01*nb01, wdata + id, ne00); id += ne00; } assert(id*sizeof(float) <= params->wsize); + x = wdata; } - const float * x = wdata; - cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasTrans, ne11, ne01, ne10, 1.0f, y, ne10, @@ -10988,14 +10664,16 @@ static void ggml_compute_forward_mul_mat_q_f32( #endif if (params->type == GGML_TASK_INIT) { - char * wdata = params->wdata; - const size_t row_size = ne10*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type]; + if (src1->type != vec_dot_type) { + char * wdata = params->wdata; + const size_t row_size = ne10*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type]; - for (int64_t i13 = 0; i13 < ne13; ++i13) { - for (int64_t i12 = 0; i12 < ne12; ++i12) { - for (int64_t i11 = 0; i11 < ne11; ++i11) { - quantize_row_q_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10); - wdata += row_size; + for (int64_t i13 = 0; i13 < ne13; ++i13) { + for (int64_t i12 = 0; i12 < ne12; ++i12) { + for (int64_t i11 = 0; i11 < ne11; ++i11) { + from_float_to_vec_dot((float *)((char *) src1->data + i13*nb13 + i12*nb12 + i11*nb11), (void *) wdata, ne10); + wdata += row_size; + } } } } @@ -11019,7 +10697,7 @@ static void ggml_compute_forward_mul_mat_q_f32( const int ir0 = dr*ith; const int ir1 = MIN(ir0 + dr, nr); - void * wdata = params->wdata; + void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; const size_t row_size = ne00*GGML_TYPE_SIZE[vec_dot_type]/GGML_BLCK_SIZE[vec_dot_type]; for (int ir = ir0; ir < ir1; ++ir) { @@ -11043,7 +10721,7 @@ static void ggml_compute_forward_mul_mat_q_f32( assert(ne00 % 32 == 0); for (int64_t ic = 0; ic < ne11; ++ic) { - vec_dot_q(ne00, &dst_col[ic*ne0], src0_row, (void *) (src1_col + ic*row_size)); + vec_dot(ne00, &dst_col[ic*ne0], src0_row, (void *) (src1_col + ic*row_size)); } } @@ -11060,40 +10738,6 @@ static void ggml_compute_forward_mul_mat_q_f32( //} } -static void ggml_compute_forward_mul_mat( - const struct ggml_compute_params * params, - const struct ggml_tensor * src0, - const struct ggml_tensor * src1, - struct ggml_tensor * dst) { - switch (src0->type) { - case GGML_TYPE_Q4_0: - case GGML_TYPE_Q4_1: - case GGML_TYPE_Q5_0: - case GGML_TYPE_Q5_1: - case GGML_TYPE_Q8_0: - case GGML_TYPE_Q8_1: - case GGML_TYPE_Q2_K: - case GGML_TYPE_Q3_K: - case GGML_TYPE_Q4_K: - case GGML_TYPE_Q5_K: - case GGML_TYPE_Q6_K: - { - ggml_compute_forward_mul_mat_q_f32(params, src0, src1, dst); - } break; - case GGML_TYPE_F16: - { - ggml_compute_forward_mul_mat_f16_f32(params, src0, src1, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_mul_mat_f32(params, src0, src1, dst); - } break; - default: - { - GGML_ASSERT(false); - } break; - } -} // ggml_compute_forward_out_prod @@ -11483,7 +11127,7 @@ static void ggml_compute_forward_get_rows_q( const int nc = src0->ne[0]; const int nr = ggml_nelements(src1); const enum ggml_type type = src0->type; - dequantize_row_q_t const dequantize_row_q = quantize_fns[type].dequantize_row_q; + ggml_to_float_t const dequantize_row_q = type_traits[type].to_float; assert( dst->ne[0] == nc); assert( dst->ne[1] == nr); @@ -16529,6 +16173,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) //printf("nr0 = %8d, nr1 = %8d, nr0*nr1 = %8d, n_tasks = %d\n", nr0, nr1, nr0*nr1, node->n_tasks); size_t cur = 0; + const enum ggml_type vec_dot_type = type_traits[node->src0->type].vec_dot_type; #if defined(GGML_USE_CUBLAS) if (ggml_cuda_can_mul_mat(node->src0, node->src1, node)) { @@ -16544,37 +16189,18 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) } else #endif - if (node->src0->type == GGML_TYPE_F16 && node->src1->type == GGML_TYPE_F32) { #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) - if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) { - node->n_tasks = 1; // TODO: this actually is doing nothing - // the threads are still spinning + if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) { + node->n_tasks = 1; // TODO: this actually is doing nothing + // the threads are still spinning + if (node->src0->type != GGML_TYPE_F32) { // here we need memory just for single 2D matrix from src0 cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]); - } else { - cur = GGML_TYPE_SIZE[GGML_TYPE_F16]*ggml_nelements(node->src1); } -#else - cur = GGML_TYPE_SIZE[GGML_TYPE_F16]*ggml_nelements(node->src1); + } else #endif - } else if (node->src0->type == GGML_TYPE_F32 && node->src1->type == GGML_TYPE_F32) { - cur = 0; -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) - if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) { - node->n_tasks = 1; - } -#endif - } else if (ggml_is_quantized(node->src0->type) && node->src1->type == GGML_TYPE_F32) { -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) - if (ggml_compute_forward_mul_mat_use_blas(node->src0, node->src1, node)) { - node->n_tasks = 1; - cur = GGML_TYPE_SIZE[GGML_TYPE_F32]*(node->src0->ne[0]*node->src0->ne[1]); - } else -#endif - { - const enum ggml_type type_q = quantize_fns[node->src0->type].vec_dot_type; - cur = GGML_TYPE_SIZE[type_q]*ggml_nelements(node->src1)/GGML_BLCK_SIZE[type_q]; - } + if (node->src1->type != vec_dot_type) { + cur = GGML_TYPE_SIZE[vec_dot_type]*ggml_nelements(node->src1)/GGML_BLCK_SIZE[vec_dot_type]; } else { GGML_ASSERT(false); } diff --git a/ggml.h b/ggml.h index 0af96c76b..24ca8ae22 100644 --- a/ggml.h +++ b/ggml.h @@ -250,8 +250,8 @@ extern "C" { GGML_API float ggml_fp16_to_fp32(ggml_fp16_t x); GGML_API ggml_fp16_t ggml_fp32_to_fp16(float x); - GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, size_t n); - GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, size_t n); + GGML_API void ggml_fp16_to_fp32_row(const ggml_fp16_t * x, float * y, int n); + GGML_API void ggml_fp32_to_fp16_row(const float * x, ggml_fp16_t * y, int n); struct ggml_object; struct ggml_context; @@ -1514,26 +1514,19 @@ extern "C" { // Internal types and functions exposed for tests and benchmarks // -#ifdef __cplusplus - // restrict not standard in C++ -#define GGML_RESTRICT -#else -#define GGML_RESTRICT restrict -#endif - typedef void (*dequantize_row_q_t)(const void * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); - typedef void (*quantize_row_q_t) (const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); - typedef void (*vec_dot_q_t) (const int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT x, const void * GGML_RESTRICT y); + typedef void (*ggml_to_float_t)(const void * x, float * y, int k); + typedef void (*ggml_from_float_t)(const float * x, void * y, int k); + typedef void (*ggml_vec_dot_t)(const int n, float * s, const void * x, const void * y); typedef struct { - dequantize_row_q_t dequantize_row_q; - quantize_row_q_t quantize_row_q; - quantize_row_q_t quantize_row_q_reference; - quantize_row_q_t quantize_row_q_dot; - vec_dot_q_t vec_dot_q; - enum ggml_type vec_dot_type; - } quantize_fns_t; + ggml_to_float_t to_float; + ggml_from_float_t from_float; + ggml_from_float_t from_float_reference; + ggml_vec_dot_t vec_dot; + enum ggml_type vec_dot_type; + } ggml_type_traits_t; - quantize_fns_t ggml_internal_get_quantize_fn(size_t i); + ggml_type_traits_t ggml_internal_get_type_traits(enum ggml_type i); #ifdef __cplusplus } diff --git a/llama.cpp b/llama.cpp index e04fbfc0a..7a866cb79 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2257,10 +2257,10 @@ static void llama_convert_tensor_internal(const llama_load_tensor & tensor, llam } float * f32_output = (float *) output.addr; - quantize_fns_t qtype; + ggml_type_traits_t qtype; if (ggml_is_quantized(tensor.type)) { - qtype = ggml_internal_get_quantize_fn(tensor.type); - if (qtype.dequantize_row_q == NULL) { + qtype = ggml_internal_get_type_traits(tensor.type); + if (qtype.to_float == NULL) { throw std::runtime_error(format("type %s unsupported for integer quantization: no dequantization available", ggml_type_name(tensor.type))); } } else if (tensor.type != GGML_TYPE_F16) { @@ -2271,7 +2271,7 @@ static void llama_convert_tensor_internal(const llama_load_tensor & tensor, llam if (tensor.type == GGML_TYPE_F16) { ggml_fp16_to_fp32_row((ggml_fp16_t *)tensor.data, f32_output, nelements); } else if (ggml_is_quantized(tensor.type)) { - qtype.dequantize_row_q(tensor.data, f32_output, nelements); + qtype.to_float(tensor.data, f32_output, nelements); } else { LLAMA_ASSERT(false); // unreachable } @@ -2296,7 +2296,7 @@ static void llama_convert_tensor_internal(const llama_load_tensor & tensor, llam if (typ == GGML_TYPE_F16) { ggml_fp16_to_fp32_row((ggml_fp16_t *)inbuf, outbuf, nels); } else { - qtype.dequantize_row_q(inbuf, outbuf, nels); + qtype.to_float(inbuf, outbuf, nels); } }; workers.push_back(std::thread(compute, tensor.type, tensor.data + in_buff_offs, f32_output + out_buff_offs, thr_elems)); diff --git a/pocs/vdot/q8dot.cpp b/pocs/vdot/q8dot.cpp index 5748c8ac2..4e0e02357 100644 --- a/pocs/vdot/q8dot.cpp +++ b/pocs/vdot/q8dot.cpp @@ -136,7 +136,7 @@ int main(int argc, char** argv) { auto ggml_type = type == 0 ? GGML_TYPE_Q4_0 : GGML_TYPE_Q4_1; - auto funcs = ggml_internal_get_quantize_fn(ggml_type); + auto funcs = ggml_internal_get_type_traits(ggml_type); Stat simple, ggml; @@ -156,8 +156,8 @@ int main(int argc, char** argv) { t1 = std::chrono::high_resolution_clock::now(); float fs; - if (type == 0) funcs.vec_dot_q(kVecSize * QK4_1, &fs, x40.data(), y.data()); - else funcs.vec_dot_q(kVecSize * QK4_1, &fs, x41.data(), y.data()); + if (type == 0) funcs.vec_dot(kVecSize * QK4_1, &fs, x40.data(), y.data()); + else funcs.vec_dot(kVecSize * QK4_1, &fs, x41.data(), y.data()); t2 = std::chrono::high_resolution_clock::now(); t = 1e-3*std::chrono::duration_cast(t2-t1).count(); if (iloop > 3) ggml.addResult(fs, t); diff --git a/pocs/vdot/vdot.cpp b/pocs/vdot/vdot.cpp index 7b18090d6..48758cda8 100644 --- a/pocs/vdot/vdot.cpp +++ b/pocs/vdot/vdot.cpp @@ -235,7 +235,7 @@ int main(int argc, char** argv) { int n4 = useQ4_1 ? kVecSize / QK4_1 : kVecSize / QK4_0; n4 = 64*((n4 + 63)/64); int n8 = kVecSize / QK8_0; n8 = 64*((n8 + 63)/64); - auto funcs = useQ4_1 ? ggml_internal_get_quantize_fn(GGML_TYPE_Q4_1) : ggml_internal_get_quantize_fn(GGML_TYPE_Q4_0); + auto funcs = useQ4_1 ? ggml_internal_get_type_traits(GGML_TYPE_Q4_1) : ggml_internal_get_type_traits(GGML_TYPE_Q4_0); std::vector q40; std::vector q41; @@ -261,9 +261,9 @@ int main(int argc, char** argv) { // Note, we do not include this in the timing as in practical application // we already have the quantized model weights. if (useQ4_1) { - funcs.quantize_row_q(x1.data(), q41.data(), kVecSize); + funcs.from_float(x1.data(), q41.data(), kVecSize); } else { - funcs.quantize_row_q(x1.data(), q40.data(), kVecSize); + funcs.from_float(x1.data(), q40.data(), kVecSize); } // Now measure time the dot product needs using the "scalar" version above @@ -282,9 +282,10 @@ int main(int argc, char** argv) { dot_q4_q8(kVecSize, &result, q40.data(), q8.data()); } else { - funcs.quantize_row_q_dot(y1.data(), q8.data(), kVecSize); - if (useQ4_1) funcs.vec_dot_q(kVecSize, &result, q41.data(), q8.data()); - else funcs.vec_dot_q(kVecSize, &result, q40.data(), q8.data()); + auto vdot = ggml_internal_get_type_traits(funcs.vec_dot_type); + vdot.from_float(y1.data(), q8.data(), kVecSize); + if (useQ4_1) funcs.vec_dot(kVecSize, &result, q41.data(), q8.data()); + else funcs.vec_dot(kVecSize, &result, q40.data(), q8.data()); } sumq += result; t2 = std::chrono::high_resolution_clock::now(); diff --git a/tests/test-quantize-fns.cpp b/tests/test-quantize-fns.cpp index c40f1b29c..8d3c162d2 100644 --- a/tests/test-quantize-fns.cpp +++ b/tests/test-quantize-fns.cpp @@ -40,26 +40,26 @@ float array_rmse(const float * a1, const float * a2, size_t n) { } // Total quantization error on test data -float total_quantization_error(quantize_fns_t & qfns, size_t test_size, const float * test_data) { +float total_quantization_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data) { std::vector tmp_q(2*test_size); std::vector tmp_out(test_size); - qfns.quantize_row_q(test_data, tmp_q.data(), test_size); - qfns.dequantize_row_q(tmp_q.data(), tmp_out.data(), test_size); + qfns.from_float(test_data, tmp_q.data(), test_size); + qfns.to_float(tmp_q.data(), tmp_out.data(), test_size); return array_rmse(test_data, tmp_out.data(), test_size); } // Total quantization error on test data -float reference_quantization_error(quantize_fns_t & qfns, size_t test_size, const float * test_data) { +float reference_quantization_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data) { std::vector tmp_q(2*test_size); std::vector tmp_out(test_size); std::vector tmp_out_ref(test_size); - qfns.quantize_row_q(test_data, tmp_q.data(), test_size); - qfns.dequantize_row_q(tmp_q.data(), tmp_out.data(), test_size); + qfns.from_float(test_data, tmp_q.data(), test_size); + qfns.to_float(tmp_q.data(), tmp_out.data(), test_size); - qfns.quantize_row_q_reference(test_data, tmp_q.data(), test_size); - qfns.dequantize_row_q(tmp_q.data(), tmp_out_ref.data(), test_size); + qfns.from_float_reference(test_data, tmp_q.data(), test_size); + qfns.to_float(tmp_q.data(), tmp_out_ref.data(), test_size); return array_rmse(tmp_out.data(), tmp_out_ref.data(), test_size); } @@ -73,15 +73,17 @@ float dot_product(const float * a1, const float * a2, size_t test_size) { } // Total dot product error -float dot_product_error(quantize_fns_t & qfns, size_t test_size, const float * test_data1, const float *test_data2) { +float dot_product_error(ggml_type_traits_t & qfns, size_t test_size, const float * test_data1, const float *test_data2) { std::vector tmp_q1(2*test_size); std::vector tmp_q2(2*test_size); - qfns.quantize_row_q (test_data1, tmp_q1.data(), test_size); - qfns.quantize_row_q_dot(test_data2, tmp_q2.data(), test_size); + auto vdot = ggml_internal_get_type_traits(qfns.vec_dot_type); + + qfns.from_float(test_data1, tmp_q1.data(), test_size); + vdot.from_float(test_data2, tmp_q2.data(), test_size); float result = INFINITY; - qfns.vec_dot_q(test_size, &result, tmp_q1.data(), tmp_q2.data()); + qfns.vec_dot(test_size, &result, tmp_q1.data(), tmp_q2.data()); const float dot_ref = dot_product(test_data1, test_data2, test_size); @@ -123,9 +125,9 @@ int main(int argc, char * argv[]) { for (int i = 0; i < GGML_TYPE_COUNT; i++) { ggml_type type = (ggml_type) i; - quantize_fns_t qfns = ggml_internal_get_quantize_fn(i); + ggml_type_traits_t qfns = ggml_internal_get_type_traits(type); - if (qfns.quantize_row_q && qfns.dequantize_row_q) { + if (qfns.from_float && qfns.to_float) { const float total_error = total_quantization_error(qfns, test_size, test_data.data()); const float max_quantization_error = type == GGML_TYPE_Q2_K ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS : diff --git a/tests/test-quantize-perf.cpp b/tests/test-quantize-perf.cpp index c0e361e92..0bb9537f6 100644 --- a/tests/test-quantize-perf.cpp +++ b/tests/test-quantize-perf.cpp @@ -123,9 +123,9 @@ void usage(char * argv[]) { printf(" --type TYPE set test type as"); for (int i = 0; i < GGML_TYPE_COUNT; i++) { ggml_type type = (ggml_type) i; - quantize_fns_t qfns = ggml_internal_get_quantize_fn(type); + ggml_type_traits_t qfns = ggml_internal_get_type_traits(type); if (ggml_type_name(type) != NULL) { - if (qfns.quantize_row_q && qfns.dequantize_row_q) { + if (qfns.from_float && qfns.to_float) { printf(" %s", ggml_type_name(type)); } } @@ -271,12 +271,12 @@ int main(int argc, char * argv[]) { for (int i = 0; i < GGML_TYPE_COUNT; i++) { ggml_type type = (ggml_type) i; - quantize_fns_t qfns = ggml_internal_get_quantize_fn(i); + ggml_type_traits_t qfns = ggml_internal_get_type_traits(type); if (!params.include_types.empty() && ggml_type_name(type) && std::find(params.include_types.begin(), params.include_types.end(), ggml_type_name(type)) == params.include_types.end()) { continue; } - if (qfns.quantize_row_q && qfns.dequantize_row_q) { + if (qfns.from_float && qfns.to_float) { printf("%s\n", ggml_type_name(type)); if (params.op_quantize_row_q_reference) { @@ -284,7 +284,7 @@ int main(int argc, char * argv[]) { for (size_t size : params.test_sizes) { printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024)); auto quantize_fn = [&](void ) { - qfns.quantize_row_q_reference(test_data1, test_q1, size); + qfns.from_float_reference(test_data1, test_q1, size); return test_q1[0]; }; size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type); @@ -298,7 +298,7 @@ int main(int argc, char * argv[]) { for (size_t size : params.test_sizes) { printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024)); auto quantize_fn = [&](void ) { - qfns.quantize_row_q(test_data1, test_q1, size); + qfns.from_float(test_data1, test_q1, size); return test_q1[0]; }; size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type); @@ -309,11 +309,11 @@ int main(int argc, char * argv[]) { if (params.op_dequantize_row_q) { printf(" dequantize_row_q\n"); - qfns.quantize_row_q(test_data1, test_q1, largest); + qfns.from_float(test_data1, test_q1, largest); for (size_t size : params.test_sizes) { printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024)); auto quantize_fn = [&](void ) { - qfns.dequantize_row_q(test_q1, test_out, size); + qfns.to_float(test_q1, test_out, size); return test_out[0]; }; size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type); @@ -327,7 +327,8 @@ int main(int argc, char * argv[]) { for (size_t size : params.test_sizes) { printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024)); auto quantize_fn = [&](void ) { - qfns.quantize_row_q_dot(test_data1, test_q1, size); + auto vdot = ggml_internal_get_type_traits(qfns.vec_dot_type); + vdot.from_float(test_data1, test_q1, size); return test_q1[0]; }; size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type); @@ -338,13 +339,13 @@ int main(int argc, char * argv[]) { if (params.op_vec_dot_q) { printf(" vec_dot_q\n"); - qfns.quantize_row_q(test_data1, test_q1, largest); - qfns.quantize_row_q(test_data2, test_q2, largest); + qfns.from_float(test_data1, test_q1, largest); + qfns.from_float(test_data2, test_q2, largest); for (size_t size : params.test_sizes) { printf(" %zu values (%.2f MB)\n", size, 4*size/(float)(1024*1024)); auto quantize_fn = [&](void ) { float result; - qfns.vec_dot_q(size, &result, test_q1, test_q2); + qfns.vec_dot(size, &result, test_q1, test_q2); return result; }; size_t quantized_size = size / ggml_blck_size(type) * ggml_type_size(type); From 1b6efeab829f3eeda5b39bd47624bb60b3531b88 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 5 Jul 2023 20:20:05 +0300 Subject: [PATCH 091/852] tests : fix test-grad0 --- scripts/sync-ggml.sh | 5 ++++- tests/test-grad0.c | 2 +- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh index 574e5180b..02ea6ec15 100755 --- a/scripts/sync-ggml.sh +++ b/scripts/sync-ggml.sh @@ -8,4 +8,7 @@ cp -rpv ../ggml/src/ggml-opencl.cpp ./ggml-opencl.cpp cp -rpv ../ggml/src/ggml-metal.h ./ggml-metal.h cp -rpv ../ggml/src/ggml-metal.m ./ggml-metal.m cp -rpv ../ggml/src/ggml-metal.metal ./ggml-metal.metal -cp -rpv ../ggml/include/ggml/ggml.h ./ggml.h +cp -rpv ../ggml/include/ggml/ggml.h ./ggml.h + +cp -rpv ../ggml/tests/test-opt.c ./tests/test-opt.c +cp -rpv ../ggml/tests/test-grad0.c ./tests/test-grad0.c diff --git a/tests/test-grad0.c b/tests/test-grad0.c index b5a499c1d..a3e25214b 100644 --- a/tests/test-grad0.c +++ b/tests/test-grad0.c @@ -1154,7 +1154,7 @@ int main(int argc, const char ** argv) { continue; } - struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], n_past, n_rot, mode)); + struct ggml_tensor * f = ggml_sum(ctx0, ggml_rope(ctx0, x[0], n_past, n_rot, mode, 0)); GGML_PRINT_DEBUG("rope: n_past: %d n_rot: %d mode: %d\n", n_past, n_rot, mode); check_gradient("rope", ctx0, x, f, ndims, nargs, 1e-2f, 1e-3f, INFINITY); From ec326d350c72afd23709a409944728a607188cc0 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 5 Jul 2023 20:44:11 +0300 Subject: [PATCH 092/852] ggml : fix bug introduced in #1237 --- ggml.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml.c b/ggml.c index 635c32eb5..d257c3d65 100644 --- a/ggml.c +++ b/ggml.c @@ -16202,7 +16202,7 @@ void ggml_graph_compute(struct ggml_context * ctx, struct ggml_cgraph * cgraph) if (node->src1->type != vec_dot_type) { cur = GGML_TYPE_SIZE[vec_dot_type]*ggml_nelements(node->src1)/GGML_BLCK_SIZE[vec_dot_type]; } else { - GGML_ASSERT(false); + cur = 0; } work_size = MAX(work_size, cur); From 983b555e9ddb36703cee4d22642afe958de093b7 Mon Sep 17 00:00:00 2001 From: Jesse Jojo Johnson Date: Wed, 5 Jul 2023 18:03:19 +0000 Subject: [PATCH 093/852] Update Server Instructions (#2113) * Update server instructions for web front end * Update server README * Remove duplicate OAI instructions * Fix duplicate text --------- Co-authored-by: Jesse Johnson --- examples/server/README.md | 26 +++++++++++++++++++++++++- 1 file changed, 25 insertions(+), 1 deletion(-) diff --git a/examples/server/README.md b/examples/server/README.md index 160614ba8..037412d76 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -21,7 +21,7 @@ Command line options: - `-to N`, `--timeout N`: Server read/write timeout in seconds. Default `600`. - `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`. - `--port`: Set the port to listen. Default: `8080`. -- `--public`: path from which to serve static files (default examples/server/public) +- `--path`: path from which to serve static files (default examples/server/public) - `--embedding`: Enable embedding extraction, Default: disabled. ## Build @@ -207,3 +207,27 @@ openai.api_base = "http://:port" ``` Then you can utilize llama.cpp as an OpenAI's **chat.completion** or **text_completion** API + +### Extending the Web Front End + +The default location for the static files is `examples/server/public`. You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method. A simple example is below: + +``` + + + 
    +      
+
    + + +``` From 31cfbb1013a482e89c72146e2063ac4362becae7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Tobias=20L=C3=BCtke?= Date: Wed, 5 Jul 2023 16:51:13 -0400 Subject: [PATCH 094/852] Expose generation timings from server & update completions.js (#2116) * use javascript generators as much cleaner API Also add ways to access completion as promise and EventSource * export llama_timings as struct and expose them in server * update readme, update baked includes * llama : uniform variable names + struct init --------- Co-authored-by: Georgi Gerganov --- examples/server/README.md | 35 +- examples/server/completion.js.hpp | 548 ++++++--- examples/server/deps.sh | 4 - examples/server/index.html.hpp | 1581 +++++++++++++------------- examples/server/public/completion.js | 119 +- examples/server/public/index.html | 129 ++- examples/server/server.cpp | 821 ++++++++----- llama.cpp | 32 +- llama.h | 15 + 9 files changed, 1921 insertions(+), 1363 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 037412d76..c5139c16b 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -26,20 +26,17 @@ Command line options: ## Build -Build llama.cpp with server from repository root with either make or CMake. +server is build alongside everything else from the root of the project - Using `make`: ```bash - LLAMA_BUILD_SERVER=1 make + make ``` - Using `CMake`: ```bash - mkdir build-server - cd build-server - cmake -DLLAMA_BUILD_SERVER=ON .. cmake --build . --config Release ``` @@ -208,24 +205,30 @@ openai.api_base = "http://:port" Then you can utilize llama.cpp as an OpenAI's **chat.completion** or **text_completion** API -### Extending the Web Front End +### Extending or building alternative Web Front End -The default location for the static files is `examples/server/public`. You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method. A simple example is below: +The default location for the static files is `examples/server/public`. You can extend the front end by running the server binary with `--path` set to `./your-directory` and importing `/completion.js` to get access to the llamaComplete() method. -``` +Read the documentation in `/completion.js` to see convenient ways to access llama. + +A simple example is below: + +```html
    diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp index 002830cad..f399fb19a 100644 --- a/examples/server/completion.js.hpp +++ b/examples/server/completion.js.hpp @@ -7,187 +7,369 @@ unsigned char completion_js[] = { 0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x3a, 0x20, 0x30, 0x2e, 0x32, 0x2c, 0x0a, 0x20, 0x20, 0x73, 0x74, 0x6f, 0x70, 0x3a, 0x20, 0x5b, 0x22, 0x3c, 0x2f, 0x73, 0x3e, 0x22, 0x5d, 0x0a, 0x7d, - 0x3b, 0x0a, 0x0a, 0x2f, 0x2a, 0x2a, 0x0a, 0x20, 0x2a, 0x20, 0x54, 0x68, - 0x69, 0x73, 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, - 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x65, 0x73, 0x20, 0x74, 0x68, - 0x65, 0x20, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x20, 0x74, 0x65, 0x78, 0x74, - 0x20, 0x75, 0x73, 0x69, 0x6e, 0x67, 0x20, 0x61, 0x20, 0x6c, 0x6c, 0x61, - 0x6d, 0x61, 0x20, 0x64, 0x69, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x61, 0x72, - 0x79, 0x2e, 0x0a, 0x20, 0x2a, 0x20, 0x40, 0x70, 0x61, 0x72, 0x61, 0x6d, - 0x20, 0x7b, 0x6f, 0x62, 0x6a, 0x65, 0x63, 0x74, 0x7d, 0x20, 0x70, 0x61, - 0x72, 0x61, 0x6d, 0x73, 0x20, 0x2d, 0x20, 0x54, 0x68, 0x65, 0x20, 0x70, - 0x61, 0x72, 0x61, 0x6d, 0x65, 0x74, 0x65, 0x72, 0x73, 0x20, 0x66, 0x6f, - 0x72, 0x20, 0x74, 0x68, 0x65, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, - 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x72, 0x65, 0x71, 0x75, 0x65, 0x73, 0x74, - 0x2e, 0x0a, 0x20, 0x2a, 0x20, 0x40, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x20, - 0x7b, 0x6f, 0x62, 0x6a, 0x65, 0x63, 0x74, 0x7d, 0x20, 0x63, 0x6f, 0x6e, - 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x20, 0x2d, 0x20, 0x61, 0x6e, - 0x20, 0x69, 0x6e, 0x73, 0x74, 0x61, 0x6e, 0x63, 0x65, 0x20, 0x6f, 0x66, - 0x20, 0x41, 0x62, 0x6f, 0x72, 0x74, 0x43, 0x6f, 0x6e, 0x74, 0x72, 0x6f, - 0x6c, 0x6c, 0x65, 0x72, 0x20, 0x69, 0x66, 0x20, 0x79, 0x6f, 0x75, 0x20, - 0x6e, 0x65, 0x65, 0x64, 0x20, 0x6f, 0x6e, 0x65, 0x2c, 0x20, 0x6f, 0x72, - 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x2e, 0x0a, 0x20, 0x2a, 0x20, 0x40, 0x70, - 0x61, 0x72, 0x61, 0x6d, 0x20, 0x7b, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, - 0x6f, 0x6e, 0x7d, 0x20, 0x63, 0x61, 0x6c, 0x6c, 0x62, 0x61, 0x63, 0x6b, - 0x20, 0x2d, 0x20, 0x54, 0x68, 0x65, 0x20, 0x63, 0x61, 0x6c, 0x6c, 0x62, - 0x61, 0x63, 0x6b, 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, - 0x20, 0x74, 0x6f, 0x20, 0x63, 0x61, 0x6c, 0x6c, 0x20, 0x77, 0x68, 0x65, - 0x6e, 0x20, 0x74, 0x68, 0x65, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, - 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x69, 0x73, 0x20, 0x64, 0x6f, 0x6e, 0x65, - 0x2e, 0x0a, 0x20, 0x2a, 0x20, 0x40, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, - 0x73, 0x20, 0x7b, 0x73, 0x74, 0x72, 0x69, 0x6e, 0x67, 0x7d, 0x20, 0x74, - 0x68, 0x65, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x65, 0x64, - 0x20, 0x74, 0x65, 0x78, 0x74, 0x20, 0x61, 0x73, 0x20, 0x61, 0x20, 0x73, - 0x74, 0x72, 0x69, 0x6e, 0x67, 0x2e, 0x20, 0x49, 0x64, 0x65, 0x61, 0x6c, - 0x6c, 0x79, 0x20, 0x69, 0x67, 0x6e, 0x6f, 0x72, 0x65, 0x64, 0x2c, 0x20, - 0x61, 0x6e, 0x64, 0x20, 0x79, 0x6f, 0x75, 0x20, 0x67, 0x65, 0x74, 0x20, - 0x61, 0x74, 0x20, 0x69, 0x74, 0x20, 0x76, 0x69, 0x61, 0x20, 0x74, 0x68, - 0x65, 0x20, 0x63, 0x61, 0x6c, 0x6c, 0x62, 0x61, 0x63, 0x6b, 0x2e, 0x0a, - 0x20, 0x2a, 0x2f, 0x0a, 0x65, 0x78, 0x70, 0x6f, 0x72, 0x74, 0x20, 0x63, - 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x43, 0x6f, - 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x65, 0x20, 0x3d, 0x20, 0x61, 0x73, 0x79, - 0x6e, 0x63, 0x20, 0x28, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2c, 0x20, - 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2c, 0x20, - 0x63, 0x61, 0x6c, 0x6c, 0x62, 0x61, 0x63, 0x6b, 0x29, 0x20, 0x3d, 0x3e, - 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x21, 0x63, 0x6f, - 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x29, 0x20, 0x7b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, - 0x65, 0x72, 0x20, 0x3d, 0x20, 0x6e, 0x65, 0x77, 0x20, 0x41, 0x62, 0x6f, - 0x72, 0x74, 0x43, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, - 0x28, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x63, 0x6f, - 0x6e, 0x73, 0x74, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, - 0x6f, 0x6e, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x20, 0x3d, 0x20, 0x7b, - 0x20, 0x2e, 0x2e, 0x2e, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x44, 0x65, 0x66, - 0x61, 0x75, 0x6c, 0x74, 0x73, 0x2c, 0x20, 0x2e, 0x2e, 0x2e, 0x70, 0x61, - 0x72, 0x61, 0x6d, 0x73, 0x20, 0x7d, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x2f, - 0x2f, 0x20, 0x77, 0x65, 0x20, 0x75, 0x73, 0x65, 0x20, 0x66, 0x65, 0x74, - 0x63, 0x68, 0x20, 0x64, 0x69, 0x72, 0x65, 0x63, 0x74, 0x6c, 0x79, 0x20, - 0x68, 0x65, 0x72, 0x65, 0x20, 0x62, 0x65, 0x63, 0x61, 0x73, 0x75, 0x65, - 0x20, 0x74, 0x68, 0x65, 0x20, 0x62, 0x75, 0x69, 0x6c, 0x74, 0x20, 0x69, - 0x6e, 0x20, 0x66, 0x65, 0x74, 0x63, 0x68, 0x45, 0x76, 0x65, 0x6e, 0x74, - 0x53, 0x6f, 0x75, 0x72, 0x63, 0x65, 0x20, 0x64, 0x6f, 0x65, 0x73, 0x20, - 0x6e, 0x6f, 0x74, 0x20, 0x73, 0x75, 0x70, 0x70, 0x6f, 0x72, 0x74, 0x20, - 0x50, 0x4f, 0x53, 0x54, 0x0a, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, - 0x20, 0x72, 0x65, 0x73, 0x70, 0x6f, 0x6e, 0x73, 0x65, 0x20, 0x3d, 0x20, - 0x61, 0x77, 0x61, 0x69, 0x74, 0x20, 0x66, 0x65, 0x74, 0x63, 0x68, 0x28, - 0x22, 0x2f, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, - 0x22, 0x2c, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x65, 0x74, - 0x68, 0x6f, 0x64, 0x3a, 0x20, 0x27, 0x50, 0x4f, 0x53, 0x54, 0x27, 0x2c, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x62, 0x6f, 0x64, 0x79, 0x3a, 0x20, 0x4a, - 0x53, 0x4f, 0x4e, 0x2e, 0x73, 0x74, 0x72, 0x69, 0x6e, 0x67, 0x69, 0x66, - 0x79, 0x28, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, - 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x29, 0x2c, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x68, 0x65, 0x61, 0x64, 0x65, 0x72, 0x73, 0x3a, 0x20, 0x7b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x27, 0x43, 0x6f, 0x6e, 0x6e, 0x65, - 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x27, 0x3a, 0x20, 0x27, 0x6b, 0x65, 0x65, - 0x70, 0x2d, 0x61, 0x6c, 0x69, 0x76, 0x65, 0x27, 0x2c, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x27, 0x43, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, - 0x2d, 0x54, 0x79, 0x70, 0x65, 0x27, 0x3a, 0x20, 0x27, 0x61, 0x70, 0x70, - 0x6c, 0x69, 0x63, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x2f, 0x6a, 0x73, 0x6f, - 0x6e, 0x27, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x27, 0x41, - 0x63, 0x63, 0x65, 0x70, 0x74, 0x27, 0x3a, 0x20, 0x27, 0x74, 0x65, 0x78, - 0x74, 0x2f, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x2d, 0x73, 0x74, 0x72, 0x65, - 0x61, 0x6d, 0x27, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x2c, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x3a, 0x20, 0x63, - 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x73, 0x69, - 0x67, 0x6e, 0x61, 0x6c, 0x2c, 0x0a, 0x20, 0x20, 0x7d, 0x29, 0x3b, 0x0a, - 0x0a, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x72, 0x65, 0x61, - 0x64, 0x65, 0x72, 0x20, 0x3d, 0x20, 0x72, 0x65, 0x73, 0x70, 0x6f, 0x6e, - 0x73, 0x65, 0x2e, 0x62, 0x6f, 0x64, 0x79, 0x2e, 0x67, 0x65, 0x74, 0x52, - 0x65, 0x61, 0x64, 0x65, 0x72, 0x28, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x63, - 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x64, 0x65, 0x63, 0x6f, 0x64, 0x65, 0x72, - 0x20, 0x3d, 0x20, 0x6e, 0x65, 0x77, 0x20, 0x54, 0x65, 0x78, 0x74, 0x44, - 0x65, 0x63, 0x6f, 0x64, 0x65, 0x72, 0x28, 0x29, 0x3b, 0x0a, 0x0a, 0x20, - 0x20, 0x6c, 0x65, 0x74, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, - 0x20, 0x3d, 0x20, 0x22, 0x22, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x74, 0x72, - 0x79, 0x20, 0x7b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x65, 0x74, - 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x20, 0x3d, 0x20, 0x74, 0x72, 0x75, 0x65, - 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x77, 0x68, 0x69, 0x6c, 0x65, - 0x20, 0x28, 0x63, 0x6f, 0x6e, 0x74, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x72, 0x65, - 0x73, 0x75, 0x6c, 0x74, 0x20, 0x3d, 0x20, 0x61, 0x77, 0x61, 0x69, 0x74, - 0x20, 0x72, 0x65, 0x61, 0x64, 0x65, 0x72, 0x2e, 0x72, 0x65, 0x61, 0x64, - 0x28, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, - 0x20, 0x28, 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, 0x2e, 0x64, 0x6f, 0x6e, - 0x65, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x62, 0x72, 0x65, 0x61, 0x6b, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, - 0x2f, 0x20, 0x73, 0x73, 0x65, 0x20, 0x61, 0x6e, 0x73, 0x77, 0x65, 0x72, - 0x73, 0x20, 0x69, 0x6e, 0x20, 0x74, 0x68, 0x65, 0x20, 0x66, 0x6f, 0x72, - 0x6d, 0x20, 0x6d, 0x75, 0x6c, 0x74, 0x69, 0x70, 0x6c, 0x65, 0x20, 0x6c, - 0x69, 0x6e, 0x65, 0x73, 0x20, 0x6f, 0x66, 0x3a, 0x20, 0x76, 0x61, 0x6c, - 0x75, 0x65, 0x5c, 0x6e, 0x20, 0x77, 0x69, 0x74, 0x68, 0x20, 0x64, 0x61, - 0x74, 0x61, 0x20, 0x61, 0x6c, 0x77, 0x61, 0x79, 0x73, 0x20, 0x70, 0x72, - 0x65, 0x73, 0x65, 0x6e, 0x74, 0x20, 0x61, 0x73, 0x20, 0x61, 0x20, 0x6b, - 0x65, 0x79, 0x2e, 0x20, 0x69, 0x6e, 0x20, 0x6f, 0x75, 0x72, 0x20, 0x63, - 0x61, 0x73, 0x65, 0x20, 0x77, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x2f, 0x2f, 0x20, 0x6d, 0x61, 0x69, 0x6e, 0x6c, 0x79, 0x20, 0x63, - 0x61, 0x72, 0x65, 0x20, 0x61, 0x62, 0x6f, 0x75, 0x74, 0x20, 0x74, 0x68, - 0x65, 0x20, 0x64, 0x61, 0x74, 0x61, 0x3a, 0x20, 0x6b, 0x65, 0x79, 0x20, - 0x68, 0x65, 0x72, 0x65, 0x2c, 0x20, 0x77, 0x68, 0x69, 0x63, 0x68, 0x20, - 0x77, 0x65, 0x20, 0x65, 0x78, 0x70, 0x65, 0x63, 0x74, 0x20, 0x61, 0x73, - 0x20, 0x6a, 0x73, 0x6f, 0x6e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x74, 0x65, 0x78, 0x74, 0x20, 0x3d, - 0x20, 0x64, 0x65, 0x63, 0x6f, 0x64, 0x65, 0x72, 0x2e, 0x64, 0x65, 0x63, - 0x6f, 0x64, 0x65, 0x28, 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, 0x2e, 0x76, - 0x61, 0x6c, 0x75, 0x65, 0x29, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x70, 0x61, 0x72, 0x73, 0x65, 0x20, 0x61, - 0x6c, 0x6c, 0x20, 0x73, 0x73, 0x65, 0x20, 0x65, 0x76, 0x65, 0x6e, 0x74, - 0x73, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x61, 0x64, 0x64, 0x20, 0x74, 0x68, - 0x65, 0x6d, 0x20, 0x74, 0x6f, 0x20, 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, - 0x20, 0x72, 0x65, 0x67, 0x65, 0x78, 0x20, 0x3d, 0x20, 0x2f, 0x5e, 0x28, - 0x5c, 0x53, 0x2b, 0x29, 0x3a, 0x5c, 0x73, 0x28, 0x2e, 0x2a, 0x29, 0x24, - 0x2f, 0x67, 0x6d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x66, - 0x6f, 0x72, 0x20, 0x28, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6d, 0x61, - 0x74, 0x63, 0x68, 0x20, 0x6f, 0x66, 0x20, 0x74, 0x65, 0x78, 0x74, 0x2e, - 0x6d, 0x61, 0x74, 0x63, 0x68, 0x41, 0x6c, 0x6c, 0x28, 0x72, 0x65, 0x67, - 0x65, 0x78, 0x29, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, 0x5b, 0x6d, 0x61, - 0x74, 0x63, 0x68, 0x5b, 0x31, 0x5d, 0x5d, 0x20, 0x3d, 0x20, 0x6d, 0x61, - 0x74, 0x63, 0x68, 0x5b, 0x32, 0x5d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, - 0x20, 0x73, 0x69, 0x6e, 0x63, 0x65, 0x20, 0x77, 0x65, 0x20, 0x6b, 0x6e, - 0x6f, 0x77, 0x20, 0x74, 0x68, 0x69, 0x73, 0x20, 0x69, 0x73, 0x20, 0x6c, - 0x6c, 0x61, 0x6d, 0x61, 0x2e, 0x63, 0x70, 0x70, 0x2c, 0x20, 0x6c, 0x65, - 0x74, 0x27, 0x73, 0x20, 0x6a, 0x75, 0x73, 0x74, 0x20, 0x64, 0x65, 0x63, - 0x6f, 0x64, 0x65, 0x20, 0x74, 0x68, 0x65, 0x20, 0x6a, 0x73, 0x6f, 0x6e, - 0x20, 0x69, 0x6e, 0x20, 0x64, 0x61, 0x74, 0x61, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, 0x2e, 0x64, 0x61, - 0x74, 0x61, 0x20, 0x3d, 0x20, 0x4a, 0x53, 0x4f, 0x4e, 0x2e, 0x70, 0x61, - 0x72, 0x73, 0x65, 0x28, 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, 0x2e, 0x64, - 0x61, 0x74, 0x61, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x20, 0x2b, 0x3d, 0x20, 0x72, - 0x65, 0x73, 0x75, 0x6c, 0x74, 0x2e, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x63, - 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x63, 0x61, 0x6c, 0x6c, 0x61, 0x63, - 0x6b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, - 0x63, 0x61, 0x6c, 0x6c, 0x62, 0x61, 0x63, 0x6b, 0x29, 0x20, 0x7b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x74, - 0x20, 0x3d, 0x20, 0x63, 0x61, 0x6c, 0x6c, 0x62, 0x61, 0x63, 0x6b, 0x28, - 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, 0x29, 0x20, 0x21, 0x3d, 0x20, 0x66, - 0x61, 0x6c, 0x73, 0x65, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, - 0x69, 0x66, 0x20, 0x77, 0x65, 0x20, 0x67, 0x6f, 0x74, 0x20, 0x61, 0x20, - 0x73, 0x74, 0x6f, 0x70, 0x20, 0x74, 0x6f, 0x6b, 0x65, 0x6e, 0x20, 0x66, - 0x72, 0x6f, 0x6d, 0x20, 0x73, 0x65, 0x72, 0x76, 0x65, 0x72, 0x2c, 0x20, - 0x77, 0x65, 0x20, 0x77, 0x69, 0x6c, 0x6c, 0x20, 0x62, 0x72, 0x65, 0x61, - 0x6b, 0x20, 0x68, 0x65, 0x72, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x69, 0x66, 0x20, 0x28, 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, 0x2e, - 0x64, 0x61, 0x74, 0x61, 0x2e, 0x73, 0x74, 0x6f, 0x70, 0x29, 0x20, 0x7b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x62, 0x72, 0x65, - 0x61, 0x6b, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x7d, 0x20, 0x63, 0x61, - 0x74, 0x63, 0x68, 0x20, 0x28, 0x65, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, 0x65, 0x2e, 0x65, 0x72, - 0x72, 0x6f, 0x72, 0x28, 0x22, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x20, 0x65, - 0x72, 0x72, 0x6f, 0x72, 0x3a, 0x20, 0x22, 0x2c, 0x20, 0x65, 0x29, 0x3b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x74, 0x68, 0x72, 0x6f, 0x77, 0x20, 0x65, - 0x3b, 0x0a, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x66, 0x69, 0x6e, 0x61, - 0x6c, 0x6c, 0x79, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, - 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x61, 0x62, 0x6f, - 0x72, 0x74, 0x28, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, - 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x63, 0x6f, 0x6e, 0x74, - 0x65, 0x6e, 0x74, 0x3b, 0x0a, 0x7d, 0x0a + 0x3b, 0x0a, 0x0a, 0x6c, 0x65, 0x74, 0x20, 0x67, 0x65, 0x6e, 0x65, 0x72, + 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, + 0x67, 0x73, 0x20, 0x3d, 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x3b, 0x0a, 0x0a, + 0x0a, 0x2f, 0x2f, 0x20, 0x43, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x65, + 0x73, 0x20, 0x74, 0x68, 0x65, 0x20, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, + 0x20, 0x61, 0x73, 0x20, 0x61, 0x20, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, + 0x74, 0x6f, 0x72, 0x2e, 0x20, 0x52, 0x65, 0x63, 0x6f, 0x6d, 0x6d, 0x65, + 0x6e, 0x64, 0x65, 0x64, 0x20, 0x66, 0x6f, 0x72, 0x20, 0x6d, 0x6f, 0x73, + 0x74, 0x20, 0x75, 0x73, 0x65, 0x20, 0x63, 0x61, 0x73, 0x65, 0x73, 0x2e, + 0x0a, 0x2f, 0x2f, 0x0a, 0x2f, 0x2f, 0x20, 0x45, 0x78, 0x61, 0x6d, 0x70, + 0x6c, 0x65, 0x3a, 0x0a, 0x2f, 0x2f, 0x0a, 0x2f, 0x2f, 0x20, 0x20, 0x20, + 0x20, 0x69, 0x6d, 0x70, 0x6f, 0x72, 0x74, 0x20, 0x7b, 0x20, 0x6c, 0x6c, + 0x61, 0x6d, 0x61, 0x20, 0x7d, 0x20, 0x66, 0x72, 0x6f, 0x6d, 0x20, 0x27, + 0x2f, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x2e, + 0x6a, 0x73, 0x27, 0x0a, 0x2f, 0x2f, 0x0a, 0x2f, 0x2f, 0x20, 0x20, 0x20, + 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x72, 0x65, 0x71, 0x75, 0x65, + 0x73, 0x74, 0x20, 0x3d, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x28, 0x22, + 0x54, 0x65, 0x6c, 0x6c, 0x20, 0x6d, 0x65, 0x20, 0x61, 0x20, 0x6a, 0x6f, + 0x6b, 0x65, 0x22, 0x2c, 0x20, 0x7b, 0x6e, 0x5f, 0x70, 0x72, 0x65, 0x64, + 0x69, 0x63, 0x74, 0x3a, 0x20, 0x38, 0x30, 0x30, 0x7d, 0x29, 0x0a, 0x2f, + 0x2f, 0x20, 0x20, 0x20, 0x20, 0x66, 0x6f, 0x72, 0x20, 0x61, 0x77, 0x61, + 0x69, 0x74, 0x20, 0x28, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x68, + 0x75, 0x6e, 0x6b, 0x20, 0x6f, 0x66, 0x20, 0x72, 0x65, 0x71, 0x75, 0x65, + 0x73, 0x74, 0x29, 0x20, 0x7b, 0x0a, 0x2f, 0x2f, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x64, 0x6f, 0x63, 0x75, 0x6d, 0x65, 0x6e, 0x74, 0x2e, 0x77, + 0x72, 0x69, 0x74, 0x65, 0x28, 0x63, 0x68, 0x75, 0x6e, 0x6b, 0x2e, 0x64, + 0x61, 0x74, 0x61, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x29, + 0x0a, 0x2f, 0x2f, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x2f, 0x2f, 0x0a, + 0x65, 0x78, 0x70, 0x6f, 0x72, 0x74, 0x20, 0x61, 0x73, 0x79, 0x6e, 0x63, + 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x2a, 0x20, 0x6c, + 0x6c, 0x61, 0x6d, 0x61, 0x28, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x2c, + 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x20, 0x3d, 0x20, 0x7b, 0x7d, + 0x2c, 0x20, 0x63, 0x6f, 0x6e, 0x66, 0x69, 0x67, 0x20, 0x3d, 0x20, 0x7b, + 0x7d, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x6c, 0x65, 0x74, 0x20, 0x63, + 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x20, 0x3d, 0x20, + 0x63, 0x6f, 0x6e, 0x66, 0x69, 0x67, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x72, + 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x69, 0x66, + 0x20, 0x28, 0x21, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, + 0x72, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, + 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x20, 0x3d, 0x20, 0x6e, 0x65, + 0x77, 0x20, 0x41, 0x62, 0x6f, 0x72, 0x74, 0x43, 0x6f, 0x6e, 0x74, 0x72, + 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x28, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x7d, + 0x0a, 0x0a, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x6f, + 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x50, 0x61, 0x72, 0x61, + 0x6d, 0x73, 0x20, 0x3d, 0x20, 0x7b, 0x20, 0x2e, 0x2e, 0x2e, 0x70, 0x61, + 0x72, 0x61, 0x6d, 0x44, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x73, 0x2c, + 0x20, 0x2e, 0x2e, 0x2e, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2c, 0x20, + 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x20, 0x7d, 0x3b, 0x0a, 0x0a, 0x20, + 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x72, 0x65, 0x73, 0x70, 0x6f, + 0x6e, 0x73, 0x65, 0x20, 0x3d, 0x20, 0x61, 0x77, 0x61, 0x69, 0x74, 0x20, + 0x66, 0x65, 0x74, 0x63, 0x68, 0x28, 0x22, 0x2f, 0x63, 0x6f, 0x6d, 0x70, + 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x22, 0x2c, 0x20, 0x7b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x6d, 0x65, 0x74, 0x68, 0x6f, 0x64, 0x3a, 0x20, 0x27, + 0x50, 0x4f, 0x53, 0x54, 0x27, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x62, + 0x6f, 0x64, 0x79, 0x3a, 0x20, 0x4a, 0x53, 0x4f, 0x4e, 0x2e, 0x73, 0x74, + 0x72, 0x69, 0x6e, 0x67, 0x69, 0x66, 0x79, 0x28, 0x63, 0x6f, 0x6d, 0x70, + 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, + 0x29, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x68, 0x65, 0x61, 0x64, 0x65, + 0x72, 0x73, 0x3a, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x27, 0x43, 0x6f, 0x6e, 0x6e, 0x65, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x27, + 0x3a, 0x20, 0x27, 0x6b, 0x65, 0x65, 0x70, 0x2d, 0x61, 0x6c, 0x69, 0x76, + 0x65, 0x27, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x27, 0x43, + 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x2d, 0x54, 0x79, 0x70, 0x65, 0x27, + 0x3a, 0x20, 0x27, 0x61, 0x70, 0x70, 0x6c, 0x69, 0x63, 0x61, 0x74, 0x69, + 0x6f, 0x6e, 0x2f, 0x6a, 0x73, 0x6f, 0x6e, 0x27, 0x2c, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x27, 0x41, 0x63, 0x63, 0x65, 0x70, 0x74, 0x27, + 0x3a, 0x20, 0x27, 0x74, 0x65, 0x78, 0x74, 0x2f, 0x65, 0x76, 0x65, 0x6e, + 0x74, 0x2d, 0x73, 0x74, 0x72, 0x65, 0x61, 0x6d, 0x27, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x7d, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x73, 0x69, 0x67, + 0x6e, 0x61, 0x6c, 0x3a, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, + 0x6c, 0x65, 0x72, 0x2e, 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x2c, 0x0a, + 0x20, 0x20, 0x7d, 0x29, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x63, 0x6f, 0x6e, + 0x73, 0x74, 0x20, 0x72, 0x65, 0x61, 0x64, 0x65, 0x72, 0x20, 0x3d, 0x20, + 0x72, 0x65, 0x73, 0x70, 0x6f, 0x6e, 0x73, 0x65, 0x2e, 0x62, 0x6f, 0x64, + 0x79, 0x2e, 0x67, 0x65, 0x74, 0x52, 0x65, 0x61, 0x64, 0x65, 0x72, 0x28, + 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x64, + 0x65, 0x63, 0x6f, 0x64, 0x65, 0x72, 0x20, 0x3d, 0x20, 0x6e, 0x65, 0x77, + 0x20, 0x54, 0x65, 0x78, 0x74, 0x44, 0x65, 0x63, 0x6f, 0x64, 0x65, 0x72, + 0x28, 0x29, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x6c, 0x65, 0x74, 0x20, 0x63, + 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x20, 0x3d, 0x20, 0x22, 0x22, 0x3b, + 0x0a, 0x0a, 0x20, 0x20, 0x74, 0x72, 0x79, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x6c, 0x65, 0x74, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x20, 0x3d, + 0x20, 0x74, 0x72, 0x75, 0x65, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x77, 0x68, 0x69, 0x6c, 0x65, 0x20, 0x28, 0x63, 0x6f, 0x6e, 0x74, 0x29, + 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, + 0x73, 0x74, 0x20, 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, 0x20, 0x3d, 0x20, + 0x61, 0x77, 0x61, 0x69, 0x74, 0x20, 0x72, 0x65, 0x61, 0x64, 0x65, 0x72, + 0x2e, 0x72, 0x65, 0x61, 0x64, 0x28, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x72, 0x65, 0x73, 0x75, 0x6c, + 0x74, 0x2e, 0x64, 0x6f, 0x6e, 0x65, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x62, 0x72, 0x65, 0x61, 0x6b, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x73, 0x73, 0x65, 0x20, 0x61, + 0x6e, 0x73, 0x77, 0x65, 0x72, 0x73, 0x20, 0x69, 0x6e, 0x20, 0x74, 0x68, + 0x65, 0x20, 0x66, 0x6f, 0x72, 0x6d, 0x20, 0x6d, 0x75, 0x6c, 0x74, 0x69, + 0x70, 0x6c, 0x65, 0x20, 0x6c, 0x69, 0x6e, 0x65, 0x73, 0x20, 0x6f, 0x66, + 0x3a, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x5c, 0x6e, 0x20, 0x77, 0x69, + 0x74, 0x68, 0x20, 0x64, 0x61, 0x74, 0x61, 0x20, 0x61, 0x6c, 0x77, 0x61, + 0x79, 0x73, 0x20, 0x70, 0x72, 0x65, 0x73, 0x65, 0x6e, 0x74, 0x20, 0x61, + 0x73, 0x20, 0x61, 0x20, 0x6b, 0x65, 0x79, 0x2e, 0x20, 0x69, 0x6e, 0x20, + 0x6f, 0x75, 0x72, 0x20, 0x63, 0x61, 0x73, 0x65, 0x20, 0x77, 0x65, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x6d, 0x61, 0x69, + 0x6e, 0x6c, 0x79, 0x20, 0x63, 0x61, 0x72, 0x65, 0x20, 0x61, 0x62, 0x6f, + 0x75, 0x74, 0x20, 0x74, 0x68, 0x65, 0x20, 0x64, 0x61, 0x74, 0x61, 0x3a, + 0x20, 0x6b, 0x65, 0x79, 0x20, 0x68, 0x65, 0x72, 0x65, 0x2c, 0x20, 0x77, + 0x68, 0x69, 0x63, 0x68, 0x20, 0x77, 0x65, 0x20, 0x65, 0x78, 0x70, 0x65, + 0x63, 0x74, 0x20, 0x61, 0x73, 0x20, 0x6a, 0x73, 0x6f, 0x6e, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x74, + 0x65, 0x78, 0x74, 0x20, 0x3d, 0x20, 0x64, 0x65, 0x63, 0x6f, 0x64, 0x65, + 0x72, 0x2e, 0x64, 0x65, 0x63, 0x6f, 0x64, 0x65, 0x28, 0x72, 0x65, 0x73, + 0x75, 0x6c, 0x74, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x3b, 0x0a, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x70, 0x61, + 0x72, 0x73, 0x65, 0x20, 0x61, 0x6c, 0x6c, 0x20, 0x73, 0x73, 0x65, 0x20, + 0x65, 0x76, 0x65, 0x6e, 0x74, 0x73, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x61, + 0x64, 0x64, 0x20, 0x74, 0x68, 0x65, 0x6d, 0x20, 0x74, 0x6f, 0x20, 0x72, + 0x65, 0x73, 0x75, 0x6c, 0x74, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x72, 0x65, 0x67, 0x65, 0x78, 0x20, + 0x3d, 0x20, 0x2f, 0x5e, 0x28, 0x5c, 0x53, 0x2b, 0x29, 0x3a, 0x5c, 0x73, + 0x28, 0x2e, 0x2a, 0x29, 0x24, 0x2f, 0x67, 0x6d, 0x3b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x66, 0x6f, 0x72, 0x20, 0x28, 0x63, 0x6f, 0x6e, + 0x73, 0x74, 0x20, 0x6d, 0x61, 0x74, 0x63, 0x68, 0x20, 0x6f, 0x66, 0x20, + 0x74, 0x65, 0x78, 0x74, 0x2e, 0x6d, 0x61, 0x74, 0x63, 0x68, 0x41, 0x6c, + 0x6c, 0x28, 0x72, 0x65, 0x67, 0x65, 0x78, 0x29, 0x29, 0x20, 0x7b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x73, 0x75, + 0x6c, 0x74, 0x5b, 0x6d, 0x61, 0x74, 0x63, 0x68, 0x5b, 0x31, 0x5d, 0x5d, + 0x20, 0x3d, 0x20, 0x6d, 0x61, 0x74, 0x63, 0x68, 0x5b, 0x32, 0x5d, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x73, 0x69, 0x6e, 0x63, 0x65, 0x20, + 0x77, 0x65, 0x20, 0x6b, 0x6e, 0x6f, 0x77, 0x20, 0x74, 0x68, 0x69, 0x73, + 0x20, 0x69, 0x73, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x2e, 0x63, 0x70, + 0x70, 0x2c, 0x20, 0x6c, 0x65, 0x74, 0x27, 0x73, 0x20, 0x6a, 0x75, 0x73, + 0x74, 0x20, 0x64, 0x65, 0x63, 0x6f, 0x64, 0x65, 0x20, 0x74, 0x68, 0x65, + 0x20, 0x6a, 0x73, 0x6f, 0x6e, 0x20, 0x69, 0x6e, 0x20, 0x64, 0x61, 0x74, + 0x61, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x73, 0x75, + 0x6c, 0x74, 0x2e, 0x64, 0x61, 0x74, 0x61, 0x20, 0x3d, 0x20, 0x4a, 0x53, + 0x4f, 0x4e, 0x2e, 0x70, 0x61, 0x72, 0x73, 0x65, 0x28, 0x72, 0x65, 0x73, + 0x75, 0x6c, 0x74, 0x2e, 0x64, 0x61, 0x74, 0x61, 0x29, 0x3b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, + 0x20, 0x2b, 0x3d, 0x20, 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, 0x2e, 0x64, + 0x61, 0x74, 0x61, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x3b, + 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x79, + 0x69, 0x65, 0x6c, 0x64, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x79, + 0x69, 0x65, 0x6c, 0x64, 0x20, 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, 0x3b, + 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x69, + 0x66, 0x20, 0x77, 0x65, 0x20, 0x67, 0x6f, 0x74, 0x20, 0x61, 0x20, 0x73, + 0x74, 0x6f, 0x70, 0x20, 0x74, 0x6f, 0x6b, 0x65, 0x6e, 0x20, 0x66, 0x72, + 0x6f, 0x6d, 0x20, 0x73, 0x65, 0x72, 0x76, 0x65, 0x72, 0x2c, 0x20, 0x77, + 0x65, 0x20, 0x77, 0x69, 0x6c, 0x6c, 0x20, 0x62, 0x72, 0x65, 0x61, 0x6b, + 0x20, 0x68, 0x65, 0x72, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x69, 0x66, 0x20, 0x28, 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, 0x2e, 0x64, + 0x61, 0x74, 0x61, 0x2e, 0x73, 0x74, 0x6f, 0x70, 0x29, 0x20, 0x7b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, + 0x72, 0x65, 0x73, 0x75, 0x6c, 0x74, 0x2e, 0x64, 0x61, 0x74, 0x61, 0x2e, + 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, + 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x29, 0x20, 0x7b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x67, 0x65, 0x6e, + 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, + 0x69, 0x6e, 0x67, 0x73, 0x20, 0x3d, 0x20, 0x72, 0x65, 0x73, 0x75, 0x6c, + 0x74, 0x2e, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x67, 0x65, 0x6e, 0x65, 0x72, + 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, + 0x67, 0x73, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x62, 0x72, + 0x65, 0x61, 0x6b, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x7d, 0x20, 0x63, + 0x61, 0x74, 0x63, 0x68, 0x20, 0x28, 0x65, 0x29, 0x20, 0x7b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x65, 0x2e, 0x6e, 0x61, 0x6d, + 0x65, 0x20, 0x21, 0x3d, 0x3d, 0x20, 0x27, 0x41, 0x62, 0x6f, 0x72, 0x74, + 0x45, 0x72, 0x72, 0x6f, 0x72, 0x27, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, 0x65, 0x2e, + 0x65, 0x72, 0x72, 0x6f, 0x72, 0x28, 0x22, 0x6c, 0x6c, 0x61, 0x6d, 0x61, + 0x20, 0x65, 0x72, 0x72, 0x6f, 0x72, 0x3a, 0x20, 0x22, 0x2c, 0x20, 0x65, + 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x74, 0x68, 0x72, 0x6f, 0x77, 0x20, 0x65, 0x3b, 0x0a, 0x20, 0x20, + 0x7d, 0x0a, 0x20, 0x20, 0x66, 0x69, 0x6e, 0x61, 0x6c, 0x6c, 0x79, 0x20, + 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, + 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x61, 0x62, 0x6f, 0x72, 0x74, 0x28, 0x29, + 0x3b, 0x0a, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x72, 0x65, 0x74, + 0x75, 0x72, 0x6e, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x3b, + 0x0a, 0x7d, 0x0a, 0x0a, 0x2f, 0x2f, 0x20, 0x43, 0x61, 0x6c, 0x6c, 0x20, + 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x2c, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, + 0x6e, 0x20, 0x61, 0x6e, 0x20, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x20, 0x74, + 0x61, 0x72, 0x67, 0x65, 0x74, 0x20, 0x74, 0x68, 0x61, 0x74, 0x20, 0x79, + 0x6f, 0x75, 0x20, 0x63, 0x61, 0x6e, 0x20, 0x73, 0x75, 0x62, 0x63, 0x72, + 0x69, 0x62, 0x65, 0x20, 0x74, 0x6f, 0x0a, 0x2f, 0x2f, 0x0a, 0x2f, 0x2f, + 0x20, 0x45, 0x78, 0x61, 0x6d, 0x70, 0x6c, 0x65, 0x3a, 0x0a, 0x2f, 0x2f, + 0x0a, 0x2f, 0x2f, 0x20, 0x20, 0x20, 0x20, 0x69, 0x6d, 0x70, 0x6f, 0x72, + 0x74, 0x20, 0x7b, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x45, 0x76, 0x65, + 0x6e, 0x74, 0x54, 0x61, 0x72, 0x67, 0x65, 0x74, 0x20, 0x7d, 0x20, 0x66, + 0x72, 0x6f, 0x6d, 0x20, 0x27, 0x2f, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, + 0x74, 0x69, 0x6f, 0x6e, 0x2e, 0x6a, 0x73, 0x27, 0x0a, 0x2f, 0x2f, 0x0a, + 0x2f, 0x2f, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, + 0x63, 0x6f, 0x6e, 0x6e, 0x20, 0x3d, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, + 0x45, 0x76, 0x65, 0x6e, 0x74, 0x54, 0x61, 0x72, 0x67, 0x65, 0x74, 0x28, + 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x29, 0x0a, 0x2f, 0x2f, 0x20, 0x20, + 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x6e, 0x2e, 0x61, 0x64, 0x64, 0x45, 0x76, + 0x65, 0x6e, 0x74, 0x4c, 0x69, 0x73, 0x74, 0x65, 0x6e, 0x65, 0x72, 0x28, + 0x22, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x22, 0x2c, 0x20, 0x28, + 0x63, 0x68, 0x75, 0x6e, 0x6b, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, + 0x2f, 0x2f, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x64, 0x6f, 0x63, 0x75, + 0x6d, 0x65, 0x6e, 0x74, 0x2e, 0x77, 0x72, 0x69, 0x74, 0x65, 0x28, 0x63, + 0x68, 0x75, 0x6e, 0x6b, 0x2e, 0x64, 0x65, 0x74, 0x61, 0x69, 0x6c, 0x2e, + 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x29, 0x0a, 0x2f, 0x2f, 0x20, + 0x20, 0x20, 0x20, 0x7d, 0x29, 0x0a, 0x2f, 0x2f, 0x0a, 0x65, 0x78, 0x70, + 0x6f, 0x72, 0x74, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6c, 0x6c, + 0x61, 0x6d, 0x61, 0x45, 0x76, 0x65, 0x6e, 0x74, 0x54, 0x61, 0x72, 0x67, + 0x65, 0x74, 0x20, 0x3d, 0x20, 0x28, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, + 0x2c, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x20, 0x3d, 0x20, 0x7b, + 0x7d, 0x2c, 0x20, 0x63, 0x6f, 0x6e, 0x66, 0x69, 0x67, 0x20, 0x3d, 0x20, + 0x7b, 0x7d, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x63, + 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x54, 0x61, + 0x72, 0x67, 0x65, 0x74, 0x20, 0x3d, 0x20, 0x6e, 0x65, 0x77, 0x20, 0x45, + 0x76, 0x65, 0x6e, 0x74, 0x54, 0x61, 0x72, 0x67, 0x65, 0x74, 0x28, 0x29, + 0x3b, 0x0a, 0x20, 0x20, 0x28, 0x61, 0x73, 0x79, 0x6e, 0x63, 0x20, 0x28, + 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x6c, + 0x65, 0x74, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x20, 0x3d, + 0x20, 0x22, 0x22, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x66, 0x6f, 0x72, + 0x20, 0x61, 0x77, 0x61, 0x69, 0x74, 0x20, 0x28, 0x63, 0x6f, 0x6e, 0x73, + 0x74, 0x20, 0x63, 0x68, 0x75, 0x6e, 0x6b, 0x20, 0x6f, 0x66, 0x20, 0x6c, + 0x6c, 0x61, 0x6d, 0x61, 0x28, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x2c, + 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2c, 0x20, 0x63, 0x6f, 0x6e, + 0x66, 0x69, 0x67, 0x29, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x63, 0x68, 0x75, 0x6e, 0x6b, 0x2e, + 0x64, 0x61, 0x74, 0x61, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x20, + 0x2b, 0x3d, 0x20, 0x63, 0x68, 0x75, 0x6e, 0x6b, 0x2e, 0x64, 0x61, 0x74, + 0x61, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x3b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x65, 0x76, 0x65, 0x6e, 0x74, + 0x54, 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, 0x64, 0x69, 0x73, 0x70, 0x61, + 0x74, 0x63, 0x68, 0x45, 0x76, 0x65, 0x6e, 0x74, 0x28, 0x6e, 0x65, 0x77, + 0x20, 0x43, 0x75, 0x73, 0x74, 0x6f, 0x6d, 0x45, 0x76, 0x65, 0x6e, 0x74, + 0x28, 0x22, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x22, 0x2c, 0x20, + 0x7b, 0x20, 0x64, 0x65, 0x74, 0x61, 0x69, 0x6c, 0x3a, 0x20, 0x63, 0x68, + 0x75, 0x6e, 0x6b, 0x2e, 0x64, 0x61, 0x74, 0x61, 0x20, 0x7d, 0x29, 0x29, + 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x63, 0x68, 0x75, 0x6e, + 0x6b, 0x2e, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x67, 0x65, 0x6e, 0x65, 0x72, + 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, + 0x67, 0x73, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x54, 0x61, 0x72, 0x67, 0x65, + 0x74, 0x2e, 0x64, 0x69, 0x73, 0x70, 0x61, 0x74, 0x63, 0x68, 0x45, 0x76, + 0x65, 0x6e, 0x74, 0x28, 0x6e, 0x65, 0x77, 0x20, 0x43, 0x75, 0x73, 0x74, + 0x6f, 0x6d, 0x45, 0x76, 0x65, 0x6e, 0x74, 0x28, 0x22, 0x67, 0x65, 0x6e, + 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, + 0x69, 0x6e, 0x67, 0x73, 0x22, 0x2c, 0x20, 0x7b, 0x20, 0x64, 0x65, 0x74, + 0x61, 0x69, 0x6c, 0x3a, 0x20, 0x63, 0x68, 0x75, 0x6e, 0x6b, 0x2e, 0x64, + 0x61, 0x74, 0x61, 0x2e, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, + 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x20, + 0x7d, 0x29, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x63, + 0x68, 0x75, 0x6e, 0x6b, 0x2e, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x74, 0x69, + 0x6d, 0x69, 0x6e, 0x67, 0x73, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x54, 0x61, + 0x72, 0x67, 0x65, 0x74, 0x2e, 0x64, 0x69, 0x73, 0x70, 0x61, 0x74, 0x63, + 0x68, 0x45, 0x76, 0x65, 0x6e, 0x74, 0x28, 0x6e, 0x65, 0x77, 0x20, 0x43, + 0x75, 0x73, 0x74, 0x6f, 0x6d, 0x45, 0x76, 0x65, 0x6e, 0x74, 0x28, 0x22, + 0x74, 0x69, 0x6d, 0x69, 0x6e, 0x67, 0x73, 0x22, 0x2c, 0x20, 0x7b, 0x20, + 0x64, 0x65, 0x74, 0x61, 0x69, 0x6c, 0x3a, 0x20, 0x63, 0x68, 0x75, 0x6e, + 0x6b, 0x2e, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x74, 0x69, 0x6d, 0x69, 0x6e, + 0x67, 0x73, 0x20, 0x7d, 0x29, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x54, 0x61, 0x72, 0x67, 0x65, + 0x74, 0x2e, 0x64, 0x69, 0x73, 0x70, 0x61, 0x74, 0x63, 0x68, 0x45, 0x76, + 0x65, 0x6e, 0x74, 0x28, 0x6e, 0x65, 0x77, 0x20, 0x43, 0x75, 0x73, 0x74, + 0x6f, 0x6d, 0x45, 0x76, 0x65, 0x6e, 0x74, 0x28, 0x22, 0x64, 0x6f, 0x6e, + 0x65, 0x22, 0x2c, 0x20, 0x7b, 0x20, 0x64, 0x65, 0x74, 0x61, 0x69, 0x6c, + 0x3a, 0x20, 0x7b, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x20, + 0x7d, 0x20, 0x7d, 0x29, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x7d, 0x29, 0x28, + 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, + 0x65, 0x76, 0x65, 0x6e, 0x74, 0x54, 0x61, 0x72, 0x67, 0x65, 0x74, 0x3b, + 0x0a, 0x7d, 0x0a, 0x0a, 0x2f, 0x2f, 0x20, 0x43, 0x61, 0x6c, 0x6c, 0x20, + 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x2c, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, + 0x6e, 0x20, 0x61, 0x20, 0x70, 0x72, 0x6f, 0x6d, 0x69, 0x73, 0x65, 0x20, + 0x74, 0x68, 0x61, 0x74, 0x20, 0x72, 0x65, 0x73, 0x6f, 0x6c, 0x76, 0x65, + 0x73, 0x20, 0x74, 0x6f, 0x20, 0x74, 0x68, 0x65, 0x20, 0x63, 0x6f, 0x6d, + 0x70, 0x6c, 0x65, 0x74, 0x65, 0x64, 0x20, 0x74, 0x65, 0x78, 0x74, 0x2e, + 0x20, 0x54, 0x68, 0x69, 0x73, 0x20, 0x64, 0x6f, 0x65, 0x73, 0x20, 0x6e, + 0x6f, 0x74, 0x20, 0x73, 0x75, 0x70, 0x70, 0x6f, 0x72, 0x74, 0x20, 0x73, + 0x74, 0x72, 0x65, 0x61, 0x6d, 0x69, 0x6e, 0x67, 0x0a, 0x2f, 0x2f, 0x0a, + 0x2f, 0x2f, 0x20, 0x45, 0x78, 0x61, 0x6d, 0x70, 0x6c, 0x65, 0x3a, 0x0a, + 0x2f, 0x2f, 0x0a, 0x2f, 0x2f, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x6c, + 0x61, 0x6d, 0x61, 0x50, 0x72, 0x6f, 0x6d, 0x69, 0x73, 0x65, 0x28, 0x70, + 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x29, 0x2e, 0x74, 0x68, 0x65, 0x6e, 0x28, + 0x28, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x29, 0x20, 0x3d, 0x3e, + 0x20, 0x7b, 0x0a, 0x2f, 0x2f, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x64, 0x6f, 0x63, 0x75, 0x6d, 0x65, 0x6e, 0x74, 0x2e, 0x77, 0x72, 0x69, + 0x74, 0x65, 0x28, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x29, 0x0a, + 0x2f, 0x2f, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x29, 0x0a, 0x2f, 0x2f, + 0x0a, 0x2f, 0x2f, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6f, 0x72, 0x0a, 0x2f, + 0x2f, 0x0a, 0x2f, 0x2f, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, + 0x73, 0x74, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x20, 0x3d, + 0x20, 0x61, 0x77, 0x61, 0x69, 0x74, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, + 0x50, 0x72, 0x6f, 0x6d, 0x69, 0x73, 0x65, 0x28, 0x70, 0x72, 0x6f, 0x6d, + 0x70, 0x74, 0x29, 0x0a, 0x2f, 0x2f, 0x20, 0x20, 0x20, 0x20, 0x20, 0x64, + 0x6f, 0x63, 0x75, 0x6d, 0x65, 0x6e, 0x74, 0x2e, 0x77, 0x72, 0x69, 0x74, + 0x65, 0x28, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x29, 0x0a, 0x2f, + 0x2f, 0x0a, 0x65, 0x78, 0x70, 0x6f, 0x72, 0x74, 0x20, 0x63, 0x6f, 0x6e, + 0x73, 0x74, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x50, 0x72, 0x6f, 0x6d, + 0x69, 0x73, 0x65, 0x20, 0x3d, 0x20, 0x28, 0x70, 0x72, 0x6f, 0x6d, 0x70, + 0x74, 0x2c, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x20, 0x3d, 0x20, + 0x7b, 0x7d, 0x2c, 0x20, 0x63, 0x6f, 0x6e, 0x66, 0x69, 0x67, 0x20, 0x3d, + 0x20, 0x7b, 0x7d, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x6e, 0x65, 0x77, 0x20, 0x50, + 0x72, 0x6f, 0x6d, 0x69, 0x73, 0x65, 0x28, 0x61, 0x73, 0x79, 0x6e, 0x63, + 0x20, 0x28, 0x72, 0x65, 0x73, 0x6f, 0x6c, 0x76, 0x65, 0x2c, 0x20, 0x72, + 0x65, 0x6a, 0x65, 0x63, 0x74, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x6c, 0x65, 0x74, 0x20, 0x63, 0x6f, 0x6e, 0x74, + 0x65, 0x6e, 0x74, 0x20, 0x3d, 0x20, 0x22, 0x22, 0x3b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x74, 0x72, 0x79, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x66, 0x6f, 0x72, 0x20, 0x61, 0x77, 0x61, 0x69, 0x74, 0x20, + 0x28, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x68, 0x75, 0x6e, 0x6b, + 0x20, 0x6f, 0x66, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x28, 0x70, 0x72, + 0x6f, 0x6d, 0x70, 0x74, 0x2c, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, + 0x2c, 0x20, 0x63, 0x6f, 0x6e, 0x66, 0x69, 0x67, 0x29, 0x29, 0x20, 0x7b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, + 0x74, 0x65, 0x6e, 0x74, 0x20, 0x2b, 0x3d, 0x20, 0x63, 0x68, 0x75, 0x6e, + 0x6b, 0x2e, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, + 0x6e, 0x74, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x73, 0x6f, 0x6c, 0x76, + 0x65, 0x28, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x29, 0x3b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x7d, 0x20, 0x63, 0x61, 0x74, 0x63, 0x68, 0x20, + 0x28, 0x65, 0x72, 0x72, 0x6f, 0x72, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x6a, 0x65, 0x63, 0x74, 0x28, 0x65, + 0x72, 0x72, 0x6f, 0x72, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, + 0x0a, 0x20, 0x20, 0x7d, 0x29, 0x3b, 0x0a, 0x7d, 0x3b, 0x0a, 0x0a, 0x2f, + 0x2a, 0x2a, 0x0a, 0x20, 0x2a, 0x20, 0x28, 0x64, 0x65, 0x70, 0x72, 0x65, + 0x63, 0x61, 0x74, 0x65, 0x64, 0x29, 0x0a, 0x20, 0x2a, 0x2f, 0x0a, 0x65, + 0x78, 0x70, 0x6f, 0x72, 0x74, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, + 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x43, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, + 0x65, 0x20, 0x3d, 0x20, 0x61, 0x73, 0x79, 0x6e, 0x63, 0x20, 0x28, 0x70, + 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2c, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72, + 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2c, 0x20, 0x63, 0x61, 0x6c, 0x6c, 0x62, + 0x61, 0x63, 0x6b, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x66, 0x6f, 0x72, 0x20, 0x61, 0x77, 0x61, 0x69, 0x74, 0x20, 0x28, 0x63, + 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x68, 0x75, 0x6e, 0x6b, 0x20, 0x6f, + 0x66, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x28, 0x70, 0x61, 0x72, 0x61, + 0x6d, 0x73, 0x2e, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x2c, 0x20, 0x70, + 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2c, 0x20, 0x7b, 0x20, 0x63, 0x6f, 0x6e, + 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x20, 0x7d, 0x29, 0x29, 0x20, + 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x61, 0x6c, 0x6c, 0x62, 0x61, + 0x63, 0x6b, 0x28, 0x63, 0x68, 0x75, 0x6e, 0x6b, 0x29, 0x3b, 0x0a, 0x20, + 0x20, 0x7d, 0x0a, 0x7d, 0x0a, 0x0a, 0x2f, 0x2f, 0x20, 0x47, 0x65, 0x74, + 0x20, 0x74, 0x68, 0x65, 0x20, 0x6d, 0x6f, 0x64, 0x65, 0x6c, 0x20, 0x69, + 0x6e, 0x66, 0x6f, 0x20, 0x66, 0x72, 0x6f, 0x6d, 0x20, 0x74, 0x68, 0x65, + 0x20, 0x73, 0x65, 0x72, 0x76, 0x65, 0x72, 0x2e, 0x20, 0x54, 0x68, 0x69, + 0x73, 0x20, 0x69, 0x73, 0x20, 0x75, 0x73, 0x65, 0x66, 0x75, 0x6c, 0x20, + 0x66, 0x6f, 0x72, 0x20, 0x67, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x20, + 0x74, 0x68, 0x65, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x78, 0x74, 0x20, + 0x77, 0x69, 0x6e, 0x64, 0x6f, 0x77, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x73, + 0x6f, 0x20, 0x6f, 0x6e, 0x2e, 0x0a, 0x65, 0x78, 0x70, 0x6f, 0x72, 0x74, + 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, + 0x4d, 0x6f, 0x64, 0x65, 0x6c, 0x49, 0x6e, 0x66, 0x6f, 0x20, 0x3d, 0x20, + 0x61, 0x73, 0x79, 0x6e, 0x63, 0x20, 0x28, 0x29, 0x20, 0x3d, 0x3e, 0x20, + 0x7b, 0x0a, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x21, 0x67, 0x65, 0x6e, + 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, 0x74, 0x74, + 0x69, 0x6e, 0x67, 0x73, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, + 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x20, 0x3d, 0x20, 0x61, 0x77, + 0x61, 0x69, 0x74, 0x20, 0x66, 0x65, 0x74, 0x63, 0x68, 0x28, 0x22, 0x2f, + 0x6d, 0x6f, 0x64, 0x65, 0x6c, 0x2e, 0x6a, 0x73, 0x6f, 0x6e, 0x22, 0x29, + 0x2e, 0x74, 0x68, 0x65, 0x6e, 0x28, 0x72, 0x20, 0x3d, 0x3e, 0x20, 0x72, + 0x2e, 0x6a, 0x73, 0x6f, 0x6e, 0x28, 0x29, 0x29, 0x3b, 0x0a, 0x20, 0x20, + 0x7d, 0x0a, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x67, + 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x73, 0x65, + 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x7d, 0x0a }; -unsigned int completion_js_len = 2275; +unsigned int completion_js_len = 4462; diff --git a/examples/server/deps.sh b/examples/server/deps.sh index cf995162a..1e9fe964b 100755 --- a/examples/server/deps.sh +++ b/examples/server/deps.sh @@ -4,10 +4,6 @@ # get the directory of this script file DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )" PUBLIC=$DIR/public -OUTPUT=$DIR/templats.hpp - -echo "// Generated file, do not edit" > $OUTPUT -echo "" > $OUTPUT echo "download js bundle files" curl https://npm.reversehttp.com/@preact/signals-core,@preact/signals,htm/preact,preact,preact/hooks > $PUBLIC/index.js diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 832e9a3bb..42707fad9 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -13,138 +13,143 @@ unsigned char index_html[] = { 0x3c, 0x74, 0x69, 0x74, 0x6c, 0x65, 0x3e, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x2e, 0x63, 0x70, 0x70, 0x20, 0x2d, 0x20, 0x63, 0x68, 0x61, 0x74, 0x3c, 0x2f, 0x74, 0x69, 0x74, 0x6c, 0x65, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x3c, - 0x73, 0x74, 0x79, 0x6c, 0x65, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x62, 0x6f, 0x64, 0x79, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x62, 0x61, 0x63, 0x6b, 0x67, 0x72, 0x6f, 0x75, 0x6e, 0x64, 0x2d, - 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x3a, 0x20, 0x23, 0x66, 0x66, 0x66, 0x3b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6c, 0x6f, 0x72, - 0x3a, 0x20, 0x23, 0x30, 0x30, 0x30, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x66, 0x6f, 0x6e, 0x74, 0x2d, 0x66, 0x61, 0x6d, 0x69, 0x6c, - 0x79, 0x3a, 0x20, 0x73, 0x79, 0x73, 0x74, 0x65, 0x6d, 0x2d, 0x75, 0x69, - 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x66, 0x6f, 0x6e, 0x74, - 0x2d, 0x73, 0x69, 0x7a, 0x65, 0x3a, 0x20, 0x39, 0x30, 0x25, 0x3b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x23, - 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x20, 0x7b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, 0x72, 0x67, 0x69, 0x6e, - 0x3a, 0x20, 0x30, 0x65, 0x6d, 0x20, 0x61, 0x75, 0x74, 0x6f, 0x3b, 0x0a, + 0x73, 0x74, 0x79, 0x6c, 0x65, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x62, + 0x6f, 0x64, 0x79, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x62, 0x61, 0x63, 0x6b, 0x67, 0x72, 0x6f, 0x75, 0x6e, 0x64, 0x2d, 0x63, + 0x6f, 0x6c, 0x6f, 0x72, 0x3a, 0x20, 0x23, 0x66, 0x66, 0x66, 0x3b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x3a, + 0x20, 0x23, 0x30, 0x30, 0x30, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x66, 0x6f, 0x6e, 0x74, 0x2d, 0x66, 0x61, 0x6d, 0x69, 0x6c, 0x79, + 0x3a, 0x20, 0x73, 0x79, 0x73, 0x74, 0x65, 0x6d, 0x2d, 0x75, 0x69, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x66, 0x6f, 0x6e, 0x74, 0x2d, + 0x73, 0x69, 0x7a, 0x65, 0x3a, 0x20, 0x39, 0x30, 0x25, 0x3b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x23, 0x63, + 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x20, 0x7b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, 0x72, 0x67, 0x69, 0x6e, 0x3a, + 0x20, 0x30, 0x65, 0x6d, 0x20, 0x61, 0x75, 0x74, 0x6f, 0x3b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x64, 0x69, 0x73, 0x70, 0x6c, 0x61, 0x79, + 0x3a, 0x20, 0x66, 0x6c, 0x65, 0x78, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x66, 0x6c, 0x65, 0x78, 0x2d, 0x64, 0x69, 0x72, 0x65, 0x63, + 0x74, 0x69, 0x6f, 0x6e, 0x3a, 0x20, 0x63, 0x6f, 0x6c, 0x75, 0x6d, 0x6e, + 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6a, 0x75, 0x73, 0x74, + 0x69, 0x66, 0x79, 0x2d, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x3a, + 0x20, 0x73, 0x70, 0x61, 0x63, 0x65, 0x2d, 0x62, 0x65, 0x74, 0x77, 0x65, + 0x65, 0x6e, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x68, 0x65, + 0x69, 0x67, 0x68, 0x74, 0x3a, 0x20, 0x31, 0x30, 0x30, 0x25, 0x3b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x6d, + 0x61, 0x69, 0x6e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x6d, 0x61, 0x72, 0x67, 0x69, 0x6e, 0x3a, 0x20, 0x33, 0x70, 0x78, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x64, 0x69, 0x73, 0x70, 0x6c, + 0x61, 0x79, 0x3a, 0x20, 0x66, 0x6c, 0x65, 0x78, 0x3b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x66, 0x6c, 0x65, 0x78, 0x2d, 0x64, 0x69, 0x72, + 0x65, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x3a, 0x20, 0x63, 0x6f, 0x6c, 0x75, + 0x6d, 0x6e, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6a, 0x75, + 0x73, 0x74, 0x69, 0x66, 0x79, 0x2d, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, + 0x74, 0x3a, 0x20, 0x73, 0x70, 0x61, 0x63, 0x65, 0x2d, 0x62, 0x65, 0x74, + 0x77, 0x65, 0x65, 0x6e, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x67, 0x61, 0x70, 0x3a, 0x20, 0x31, 0x65, 0x6d, 0x3b, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x66, 0x6c, 0x65, 0x78, 0x2d, 0x67, 0x72, + 0x6f, 0x77, 0x3a, 0x20, 0x31, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x6f, 0x76, 0x65, 0x72, 0x66, 0x6c, 0x6f, 0x77, 0x2d, 0x79, 0x3a, + 0x20, 0x61, 0x75, 0x74, 0x6f, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x62, 0x6f, 0x72, 0x64, 0x65, 0x72, 0x3a, 0x20, 0x31, 0x70, + 0x78, 0x20, 0x73, 0x6f, 0x6c, 0x69, 0x64, 0x20, 0x23, 0x63, 0x63, 0x63, + 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x62, 0x6f, 0x72, 0x64, + 0x65, 0x72, 0x2d, 0x72, 0x61, 0x64, 0x69, 0x75, 0x73, 0x3a, 0x20, 0x35, + 0x70, 0x78, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x70, 0x61, + 0x64, 0x64, 0x69, 0x6e, 0x67, 0x3a, 0x20, 0x30, 0x2e, 0x35, 0x65, 0x6d, + 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x62, 0x6f, 0x64, 0x79, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x6d, 0x61, 0x78, 0x2d, 0x77, 0x69, 0x64, 0x74, 0x68, 0x3a, + 0x20, 0x36, 0x30, 0x30, 0x70, 0x78, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x6d, 0x69, 0x6e, 0x2d, 0x77, 0x69, 0x64, 0x74, 0x68, 0x3a, + 0x20, 0x33, 0x30, 0x30, 0x70, 0x78, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x6c, 0x69, 0x6e, 0x65, 0x2d, 0x68, 0x65, 0x69, 0x67, 0x68, + 0x74, 0x3a, 0x20, 0x31, 0x2e, 0x32, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x6d, 0x61, 0x72, 0x67, 0x69, 0x6e, 0x3a, 0x20, 0x30, 0x20, + 0x61, 0x75, 0x74, 0x6f, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x70, 0x61, 0x64, 0x64, 0x69, 0x6e, 0x67, 0x3a, 0x20, 0x30, 0x20, 0x30, + 0x2e, 0x35, 0x65, 0x6d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x70, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x6f, 0x76, 0x65, 0x72, 0x66, 0x6c, 0x6f, 0x77, 0x2d, + 0x77, 0x72, 0x61, 0x70, 0x3a, 0x20, 0x62, 0x72, 0x65, 0x61, 0x6b, 0x2d, + 0x77, 0x6f, 0x72, 0x64, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x77, 0x6f, 0x72, 0x64, 0x2d, 0x77, 0x72, 0x61, 0x70, 0x3a, 0x20, 0x62, + 0x72, 0x65, 0x61, 0x6b, 0x2d, 0x77, 0x6f, 0x72, 0x64, 0x3b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x68, 0x79, 0x70, 0x68, 0x65, 0x6e, 0x73, + 0x3a, 0x20, 0x61, 0x75, 0x74, 0x6f, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x6d, 0x61, 0x72, 0x67, 0x69, 0x6e, 0x2d, 0x74, 0x6f, 0x70, + 0x3a, 0x20, 0x30, 0x2e, 0x35, 0x65, 0x6d, 0x3b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x6d, 0x61, 0x72, 0x67, 0x69, 0x6e, 0x2d, 0x62, 0x6f, + 0x74, 0x74, 0x6f, 0x6d, 0x3a, 0x20, 0x30, 0x2e, 0x35, 0x65, 0x6d, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x23, 0x77, 0x72, 0x69, 0x74, 0x65, 0x20, 0x66, 0x6f, 0x72, 0x6d, 0x20, + 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, 0x72, 0x67, + 0x69, 0x6e, 0x3a, 0x20, 0x31, 0x65, 0x6d, 0x20, 0x30, 0x20, 0x30, 0x20, + 0x30, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x64, 0x69, 0x73, + 0x70, 0x6c, 0x61, 0x79, 0x3a, 0x20, 0x66, 0x6c, 0x65, 0x78, 0x3b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x66, 0x6c, 0x65, 0x78, 0x2d, 0x64, + 0x69, 0x72, 0x65, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x3a, 0x20, 0x63, 0x6f, + 0x6c, 0x75, 0x6d, 0x6e, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x67, 0x61, 0x70, 0x3a, 0x20, 0x30, 0x2e, 0x35, 0x65, 0x6d, 0x3b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x61, 0x6c, 0x69, 0x67, 0x6e, 0x2d, + 0x69, 0x74, 0x65, 0x6d, 0x73, 0x3a, 0x20, 0x73, 0x74, 0x72, 0x65, 0x74, + 0x63, 0x68, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x2e, 0x72, 0x69, 0x67, 0x68, 0x74, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x64, 0x69, 0x73, 0x70, 0x6c, 0x61, 0x79, 0x3a, 0x20, 0x66, 0x6c, 0x65, 0x78, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x66, 0x6c, 0x65, 0x78, 0x2d, 0x64, 0x69, 0x72, 0x65, - 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x3a, 0x20, 0x63, 0x6f, 0x6c, 0x75, 0x6d, - 0x6e, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6a, 0x75, 0x73, - 0x74, 0x69, 0x66, 0x79, 0x2d, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, - 0x3a, 0x20, 0x73, 0x70, 0x61, 0x63, 0x65, 0x2d, 0x62, 0x65, 0x74, 0x77, - 0x65, 0x65, 0x6e, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x68, - 0x65, 0x69, 0x67, 0x68, 0x74, 0x3a, 0x20, 0x31, 0x30, 0x30, 0x25, 0x3b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x68, 0x65, 0x61, 0x64, 0x65, 0x72, 0x2c, 0x20, 0x66, 0x6f, 0x6f, 0x74, - 0x65, 0x72, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, - 0x65, 0x78, 0x74, 0x2d, 0x61, 0x6c, 0x69, 0x67, 0x6e, 0x3a, 0x20, 0x63, - 0x65, 0x6e, 0x74, 0x65, 0x72, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, - 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, 0x69, 0x6e, 0x20, 0x7b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, 0x72, 0x67, 0x69, - 0x6e, 0x3a, 0x20, 0x33, 0x70, 0x78, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x64, 0x69, 0x73, 0x70, 0x6c, 0x61, 0x79, 0x3a, 0x20, 0x66, - 0x6c, 0x65, 0x78, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x66, - 0x6c, 0x65, 0x78, 0x2d, 0x64, 0x69, 0x72, 0x65, 0x63, 0x74, 0x69, 0x6f, - 0x6e, 0x3a, 0x20, 0x63, 0x6f, 0x6c, 0x75, 0x6d, 0x6e, 0x3b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x6a, 0x75, 0x73, 0x74, 0x69, 0x66, 0x79, - 0x2d, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x3a, 0x20, 0x73, 0x70, - 0x61, 0x63, 0x65, 0x2d, 0x62, 0x65, 0x74, 0x77, 0x65, 0x65, 0x6e, 0x3b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x67, 0x61, 0x70, 0x3a, 0x20, - 0x31, 0x65, 0x6d, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x66, 0x6c, 0x65, 0x78, 0x2d, 0x67, 0x72, 0x6f, 0x77, 0x3a, 0x20, 0x31, - 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6f, 0x76, 0x65, 0x72, - 0x66, 0x6c, 0x6f, 0x77, 0x2d, 0x79, 0x3a, 0x20, 0x61, 0x75, 0x74, 0x6f, - 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x62, 0x6f, 0x72, - 0x64, 0x65, 0x72, 0x3a, 0x20, 0x31, 0x70, 0x78, 0x20, 0x73, 0x6f, 0x6c, - 0x69, 0x64, 0x20, 0x23, 0x63, 0x63, 0x63, 0x3b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x62, 0x6f, 0x72, 0x64, 0x65, 0x72, 0x2d, 0x72, 0x61, - 0x64, 0x69, 0x75, 0x73, 0x3a, 0x20, 0x35, 0x70, 0x78, 0x3b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x70, 0x61, 0x64, 0x64, 0x69, 0x6e, 0x67, - 0x3a, 0x20, 0x30, 0x2e, 0x35, 0x65, 0x6d, 0x3b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x62, 0x6f, 0x64, 0x79, - 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, 0x78, - 0x2d, 0x77, 0x69, 0x64, 0x74, 0x68, 0x3a, 0x20, 0x36, 0x30, 0x30, 0x70, - 0x78, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x69, 0x6e, - 0x2d, 0x77, 0x69, 0x64, 0x74, 0x68, 0x3a, 0x20, 0x33, 0x30, 0x30, 0x70, - 0x78, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x69, 0x6e, - 0x65, 0x2d, 0x68, 0x65, 0x69, 0x67, 0x68, 0x74, 0x3a, 0x20, 0x31, 0x2e, - 0x32, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, 0x72, - 0x67, 0x69, 0x6e, 0x3a, 0x20, 0x30, 0x20, 0x61, 0x75, 0x74, 0x6f, 0x3b, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x3a, 0x20, 0x72, 0x6f, 0x77, 0x3b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x67, 0x61, 0x70, 0x3a, 0x20, 0x30, + 0x2e, 0x35, 0x65, 0x6d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x6a, 0x75, 0x73, 0x74, 0x69, 0x66, 0x79, 0x2d, 0x63, 0x6f, 0x6e, 0x74, + 0x65, 0x6e, 0x74, 0x3a, 0x20, 0x66, 0x6c, 0x65, 0x78, 0x2d, 0x65, 0x6e, + 0x64, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, 0x20, 0x7b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x62, 0x6f, 0x72, 0x64, 0x65, + 0x72, 0x3a, 0x20, 0x6e, 0x6f, 0x6e, 0x65, 0x3b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x70, 0x61, 0x64, 0x64, 0x69, 0x6e, 0x67, 0x3a, 0x20, + 0x30, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, 0x72, + 0x67, 0x69, 0x6e, 0x3a, 0x20, 0x30, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x74, 0x65, 0x78, 0x74, 0x61, + 0x72, 0x65, 0x61, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x70, 0x61, 0x64, 0x64, 0x69, 0x6e, 0x67, 0x3a, 0x20, 0x35, 0x70, 0x78, + 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x66, 0x6c, 0x65, 0x78, + 0x2d, 0x67, 0x72, 0x6f, 0x77, 0x3a, 0x20, 0x31, 0x3b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x77, 0x69, 0x64, 0x74, 0x68, 0x3a, 0x20, 0x31, + 0x30, 0x30, 0x25, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x70, 0x72, 0x65, 0x20, 0x63, 0x6f, 0x64, 0x65, + 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x64, 0x69, 0x73, + 0x70, 0x6c, 0x61, 0x79, 0x3a, 0x20, 0x62, 0x6c, 0x6f, 0x63, 0x6b, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x62, 0x61, 0x63, 0x6b, 0x67, + 0x72, 0x6f, 0x75, 0x6e, 0x64, 0x2d, 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x3a, + 0x20, 0x23, 0x32, 0x32, 0x32, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x3a, 0x20, 0x23, 0x64, 0x64, 0x64, + 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x63, 0x6f, 0x64, 0x65, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x66, 0x6f, 0x6e, 0x74, 0x2d, 0x66, 0x61, 0x6d, 0x69, 0x6c, 0x79, + 0x3a, 0x20, 0x6d, 0x6f, 0x6e, 0x6f, 0x73, 0x70, 0x61, 0x63, 0x65, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x70, 0x61, 0x64, 0x64, 0x69, - 0x6e, 0x67, 0x3a, 0x20, 0x30, 0x20, 0x30, 0x2e, 0x35, 0x65, 0x6d, 0x3b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x70, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6f, 0x76, - 0x65, 0x72, 0x66, 0x6c, 0x6f, 0x77, 0x2d, 0x77, 0x72, 0x61, 0x70, 0x3a, - 0x20, 0x62, 0x72, 0x65, 0x61, 0x6b, 0x2d, 0x77, 0x6f, 0x72, 0x64, 0x3b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x77, 0x6f, 0x72, 0x64, 0x2d, - 0x77, 0x72, 0x61, 0x70, 0x3a, 0x20, 0x62, 0x72, 0x65, 0x61, 0x6b, 0x2d, - 0x77, 0x6f, 0x72, 0x64, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x68, 0x79, 0x70, 0x68, 0x65, 0x6e, 0x73, 0x3a, 0x20, 0x61, 0x75, 0x74, - 0x6f, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, 0x72, - 0x67, 0x69, 0x6e, 0x2d, 0x74, 0x6f, 0x70, 0x3a, 0x20, 0x30, 0x2e, 0x35, - 0x65, 0x6d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, - 0x72, 0x67, 0x69, 0x6e, 0x2d, 0x62, 0x6f, 0x74, 0x74, 0x6f, 0x6d, 0x3a, - 0x20, 0x30, 0x2e, 0x35, 0x65, 0x6d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x23, 0x77, 0x72, 0x69, 0x74, - 0x65, 0x20, 0x66, 0x6f, 0x72, 0x6d, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x6d, 0x61, 0x72, 0x67, 0x69, 0x6e, 0x3a, 0x20, 0x31, - 0x65, 0x6d, 0x20, 0x30, 0x20, 0x30, 0x20, 0x30, 0x3b, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x64, 0x69, 0x73, 0x70, 0x6c, 0x61, 0x79, 0x3a, - 0x20, 0x66, 0x6c, 0x65, 0x78, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x66, 0x6c, 0x65, 0x78, 0x2d, 0x64, 0x69, 0x72, 0x65, 0x63, 0x74, - 0x69, 0x6f, 0x6e, 0x3a, 0x20, 0x63, 0x6f, 0x6c, 0x75, 0x6d, 0x6e, 0x3b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x67, 0x61, 0x70, 0x3a, 0x20, - 0x30, 0x2e, 0x35, 0x65, 0x6d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x61, 0x6c, 0x69, 0x67, 0x6e, 0x2d, 0x69, 0x74, 0x65, 0x6d, 0x73, - 0x3a, 0x20, 0x73, 0x74, 0x72, 0x65, 0x74, 0x63, 0x68, 0x3b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, - 0x69, 0x67, 0x68, 0x74, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x64, 0x69, 0x73, 0x70, 0x6c, 0x61, 0x79, 0x3a, 0x20, 0x66, 0x6c, - 0x65, 0x78, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x66, 0x6c, - 0x65, 0x78, 0x2d, 0x64, 0x69, 0x72, 0x65, 0x63, 0x74, 0x69, 0x6f, 0x6e, - 0x3a, 0x20, 0x72, 0x6f, 0x77, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x67, 0x61, 0x70, 0x3a, 0x20, 0x30, 0x2e, 0x35, 0x65, 0x6d, 0x3b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6a, 0x75, 0x73, 0x74, 0x69, - 0x66, 0x79, 0x2d, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x3a, 0x20, - 0x66, 0x6c, 0x65, 0x78, 0x2d, 0x65, 0x6e, 0x64, 0x3b, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x66, 0x69, 0x65, - 0x6c, 0x64, 0x73, 0x65, 0x74, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x62, 0x6f, 0x72, 0x64, 0x65, 0x72, 0x3a, 0x20, 0x6e, 0x6f, - 0x6e, 0x65, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x70, 0x61, - 0x64, 0x64, 0x69, 0x6e, 0x67, 0x3a, 0x20, 0x30, 0x3b, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, 0x72, 0x67, 0x69, 0x6e, 0x3a, 0x20, - 0x30, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x74, 0x65, 0x78, 0x74, 0x61, 0x72, 0x65, 0x61, 0x20, 0x7b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x70, 0x61, 0x64, 0x64, 0x69, - 0x6e, 0x67, 0x3a, 0x20, 0x35, 0x70, 0x78, 0x3b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x66, 0x6c, 0x65, 0x78, 0x2d, 0x67, 0x72, 0x6f, 0x77, - 0x3a, 0x20, 0x31, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x77, - 0x69, 0x64, 0x74, 0x68, 0x3a, 0x20, 0x31, 0x30, 0x30, 0x25, 0x3b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x70, - 0x72, 0x65, 0x20, 0x63, 0x6f, 0x64, 0x65, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x6e, 0x67, 0x3a, 0x20, 0x30, 0x2e, 0x31, 0x65, 0x6d, 0x20, 0x30, 0x2e, + 0x33, 0x65, 0x6d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x62, + 0x6f, 0x72, 0x64, 0x65, 0x72, 0x2d, 0x72, 0x61, 0x64, 0x69, 0x75, 0x73, + 0x3a, 0x20, 0x33, 0x70, 0x78, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, + 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, + 0x65, 0x74, 0x20, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, 0x7b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x61, 0x72, 0x67, 0x69, 0x6e, 0x3a, + 0x20, 0x30, 0x2e, 0x35, 0x65, 0x6d, 0x20, 0x30, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x64, 0x69, 0x73, 0x70, 0x6c, 0x61, 0x79, 0x3a, 0x20, 0x62, 0x6c, 0x6f, 0x63, 0x6b, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x62, 0x61, 0x63, 0x6b, 0x67, 0x72, 0x6f, 0x75, 0x6e, 0x64, - 0x2d, 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x3a, 0x20, 0x23, 0x32, 0x32, 0x32, - 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6c, 0x6f, - 0x72, 0x3a, 0x20, 0x23, 0x64, 0x64, 0x64, 0x3b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x64, 0x65, 0x20, - 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x66, 0x6f, 0x6e, 0x74, - 0x2d, 0x66, 0x61, 0x6d, 0x69, 0x6c, 0x79, 0x3a, 0x20, 0x6d, 0x6f, 0x6e, - 0x6f, 0x73, 0x70, 0x61, 0x63, 0x65, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x70, 0x61, 0x64, 0x64, 0x69, 0x6e, 0x67, 0x3a, 0x20, 0x30, - 0x2e, 0x31, 0x65, 0x6d, 0x20, 0x30, 0x2e, 0x33, 0x65, 0x6d, 0x3b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x62, 0x6f, 0x72, 0x64, 0x65, 0x72, - 0x2d, 0x72, 0x61, 0x64, 0x69, 0x75, 0x73, 0x3a, 0x20, 0x33, 0x70, 0x78, + 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x68, 0x65, 0x61, 0x64, 0x65, + 0x72, 0x2c, 0x20, 0x66, 0x6f, 0x6f, 0x74, 0x65, 0x72, 0x20, 0x7b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x65, 0x78, 0x74, 0x2d, 0x61, + 0x6c, 0x69, 0x67, 0x6e, 0x3a, 0x20, 0x63, 0x65, 0x6e, 0x74, 0x65, 0x72, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, 0x20, 0x6c, 0x61, - 0x62, 0x65, 0x6c, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x6d, 0x61, 0x72, 0x67, 0x69, 0x6e, 0x3a, 0x20, 0x30, 0x2e, 0x35, 0x65, - 0x6d, 0x20, 0x30, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x64, - 0x69, 0x73, 0x70, 0x6c, 0x61, 0x79, 0x3a, 0x20, 0x62, 0x6c, 0x6f, 0x63, - 0x6b, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x3c, + 0x20, 0x66, 0x6f, 0x6f, 0x74, 0x65, 0x72, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x66, 0x6f, 0x6e, 0x74, 0x2d, 0x73, 0x69, 0x7a, + 0x65, 0x3a, 0x20, 0x38, 0x30, 0x25, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x3a, 0x20, 0x23, 0x38, 0x38, + 0x38, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x3c, 0x2f, 0x73, 0x74, 0x79, 0x6c, 0x65, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x3c, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, 0x6d, 0x6f, 0x64, 0x75, 0x6c, 0x65, 0x22, 0x3e, 0x0a, 0x20, 0x20, @@ -159,440 +164,432 @@ unsigned char index_html[] = { 0x72, 0x6f, 0x6d, 0x20, 0x27, 0x2f, 0x69, 0x6e, 0x64, 0x65, 0x78, 0x2e, 0x6a, 0x73, 0x27, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x69, 0x6d, 0x70, 0x6f, 0x72, 0x74, 0x20, 0x7b, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, - 0x43, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x65, 0x20, 0x7d, 0x20, 0x66, - 0x72, 0x6f, 0x6d, 0x20, 0x27, 0x2f, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, - 0x74, 0x69, 0x6f, 0x6e, 0x2e, 0x6a, 0x73, 0x27, 0x3b, 0x0a, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x73, 0x65, 0x73, - 0x73, 0x69, 0x6f, 0x6e, 0x20, 0x3d, 0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, - 0x6c, 0x28, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x70, 0x72, - 0x6f, 0x6d, 0x70, 0x74, 0x3a, 0x20, 0x22, 0x54, 0x68, 0x69, 0x73, 0x20, - 0x69, 0x73, 0x20, 0x61, 0x20, 0x63, 0x6f, 0x6e, 0x76, 0x65, 0x72, 0x73, - 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x62, 0x65, 0x74, 0x77, 0x65, 0x65, - 0x6e, 0x20, 0x75, 0x73, 0x65, 0x72, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x6c, - 0x6c, 0x61, 0x6d, 0x61, 0x2c, 0x20, 0x61, 0x20, 0x66, 0x72, 0x69, 0x65, - 0x6e, 0x64, 0x6c, 0x79, 0x20, 0x63, 0x68, 0x61, 0x74, 0x62, 0x6f, 0x74, - 0x2e, 0x20, 0x72, 0x65, 0x73, 0x70, 0x6f, 0x6e, 0x64, 0x20, 0x69, 0x6e, - 0x20, 0x6d, 0x61, 0x72, 0x6b, 0x64, 0x6f, 0x77, 0x6e, 0x2e, 0x22, 0x2c, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, - 0x61, 0x74, 0x65, 0x3a, 0x20, 0x22, 0x7b, 0x7b, 0x70, 0x72, 0x6f, 0x6d, - 0x70, 0x74, 0x7d, 0x7d, 0x5c, 0x6e, 0x5c, 0x6e, 0x7b, 0x7b, 0x68, 0x69, - 0x73, 0x74, 0x6f, 0x72, 0x79, 0x7d, 0x7d, 0x5c, 0x6e, 0x7b, 0x7b, 0x63, - 0x68, 0x61, 0x72, 0x7d, 0x7d, 0x3a, 0x22, 0x2c, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x54, 0x65, - 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x3a, 0x20, 0x22, 0x7b, 0x7b, 0x6e, - 0x61, 0x6d, 0x65, 0x7d, 0x7d, 0x3a, 0x20, 0x7b, 0x7b, 0x6d, 0x65, 0x73, - 0x73, 0x61, 0x67, 0x65, 0x7d, 0x7d, 0x22, 0x2c, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, - 0x74, 0x3a, 0x20, 0x5b, 0x5d, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x74, 0x79, 0x70, 0x65, 0x3a, 0x20, 0x22, 0x63, 0x68, 0x61, 0x74, - 0x22, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x68, 0x61, - 0x72, 0x3a, 0x20, 0x22, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x22, 0x2c, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x75, 0x73, 0x65, 0x72, 0x3a, 0x20, - 0x22, 0x55, 0x73, 0x65, 0x72, 0x22, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x7d, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, - 0x74, 0x20, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, - 0x55, 0x70, 0x64, 0x61, 0x74, 0x65, 0x20, 0x3d, 0x20, 0x28, 0x74, 0x72, - 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x29, 0x20, 0x3d, 0x3e, - 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x65, 0x73, - 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, - 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, - 0x2e, 0x2e, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, - 0x6c, 0x75, 0x65, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, - 0x20, 0x63, 0x68, 0x61, 0x74, 0x53, 0x74, 0x61, 0x72, 0x74, 0x65, 0x64, - 0x20, 0x3d, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x75, 0x74, 0x65, 0x64, 0x28, - 0x28, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, - 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x72, 0x61, 0x6e, - 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, - 0x68, 0x20, 0x3e, 0x20, 0x30, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, - 0x20, 0x3d, 0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x28, 0x7b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6e, 0x5f, 0x70, 0x72, 0x65, 0x64, - 0x69, 0x63, 0x74, 0x3a, 0x20, 0x34, 0x30, 0x30, 0x2c, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, - 0x75, 0x72, 0x65, 0x3a, 0x20, 0x30, 0x2e, 0x37, 0x2c, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x6c, - 0x61, 0x73, 0x74, 0x5f, 0x6e, 0x3a, 0x20, 0x32, 0x35, 0x36, 0x2c, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, - 0x5f, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x3a, 0x20, 0x31, 0x2e, - 0x31, 0x38, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x6f, - 0x70, 0x5f, 0x6b, 0x3a, 0x20, 0x34, 0x30, 0x2c, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x74, 0x6f, 0x70, 0x5f, 0x70, 0x3a, 0x20, 0x30, 0x2e, - 0x35, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x29, 0x0a, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x6f, 0x6e, - 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x20, 0x3d, 0x20, 0x73, 0x69, - 0x67, 0x6e, 0x61, 0x6c, 0x28, 0x6e, 0x75, 0x6c, 0x6c, 0x29, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x67, 0x65, 0x6e, - 0x65, 0x72, 0x61, 0x74, 0x69, 0x6e, 0x67, 0x20, 0x3d, 0x20, 0x63, 0x6f, - 0x6d, 0x70, 0x75, 0x74, 0x65, 0x64, 0x28, 0x28, 0x29, 0x20, 0x3d, 0x3e, - 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, - 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x3d, 0x20, 0x6e, 0x75, 0x6c, - 0x6c, 0x20, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, - 0x73, 0x69, 0x6d, 0x70, 0x6c, 0x65, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, - 0x61, 0x74, 0x65, 0x20, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x74, 0x65, - 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x20, 0x3d, 0x20, 0x28, 0x73, 0x74, - 0x72, 0x2c, 0x20, 0x65, 0x78, 0x74, 0x72, 0x61, 0x53, 0x65, 0x74, 0x74, - 0x69, 0x6e, 0x67, 0x73, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x65, 0x74, 0x20, 0x73, 0x65, 0x74, - 0x74, 0x69, 0x6e, 0x67, 0x73, 0x20, 0x3d, 0x20, 0x73, 0x65, 0x73, 0x73, - 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x65, 0x78, 0x74, - 0x72, 0x61, 0x53, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x29, 0x20, - 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x65, - 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x20, 0x3d, 0x20, 0x7b, 0x20, 0x2e, - 0x2e, 0x2e, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x2c, 0x20, - 0x2e, 0x2e, 0x2e, 0x65, 0x78, 0x74, 0x72, 0x61, 0x53, 0x65, 0x74, 0x74, - 0x69, 0x6e, 0x67, 0x73, 0x20, 0x7d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, - 0x74, 0x75, 0x72, 0x6e, 0x20, 0x53, 0x74, 0x72, 0x69, 0x6e, 0x67, 0x28, - 0x73, 0x74, 0x72, 0x29, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, - 0x41, 0x6c, 0x6c, 0x28, 0x2f, 0x5c, 0x7b, 0x5c, 0x7b, 0x28, 0x2e, 0x2a, - 0x3f, 0x29, 0x5c, 0x7d, 0x5c, 0x7d, 0x2f, 0x67, 0x2c, 0x20, 0x28, 0x5f, - 0x2c, 0x20, 0x6b, 0x65, 0x79, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x74, 0x65, - 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, 0x73, 0x65, 0x74, 0x74, 0x69, - 0x6e, 0x67, 0x73, 0x5b, 0x6b, 0x65, 0x79, 0x5d, 0x29, 0x29, 0x3b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, - 0x2f, 0x20, 0x73, 0x65, 0x6e, 0x64, 0x20, 0x6d, 0x65, 0x73, 0x73, 0x61, - 0x67, 0x65, 0x20, 0x74, 0x6f, 0x20, 0x73, 0x65, 0x72, 0x76, 0x65, 0x72, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, - 0x68, 0x61, 0x74, 0x20, 0x3d, 0x20, 0x61, 0x73, 0x79, 0x6e, 0x63, 0x20, - 0x28, 0x6d, 0x73, 0x67, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x63, 0x6f, 0x6e, + 0x20, 0x7d, 0x20, 0x66, 0x72, 0x6f, 0x6d, 0x20, 0x27, 0x2f, 0x63, 0x6f, + 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x2e, 0x6a, 0x73, 0x27, + 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, + 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x20, 0x3d, 0x20, 0x73, + 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x28, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x3a, 0x20, 0x22, 0x54, + 0x68, 0x69, 0x73, 0x20, 0x69, 0x73, 0x20, 0x61, 0x20, 0x63, 0x6f, 0x6e, + 0x76, 0x65, 0x72, 0x73, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x62, 0x65, + 0x74, 0x77, 0x65, 0x65, 0x6e, 0x20, 0x75, 0x73, 0x65, 0x72, 0x20, 0x61, + 0x6e, 0x64, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x2c, 0x20, 0x61, 0x20, + 0x66, 0x72, 0x69, 0x65, 0x6e, 0x64, 0x6c, 0x79, 0x20, 0x63, 0x68, 0x61, + 0x74, 0x62, 0x6f, 0x74, 0x2e, 0x20, 0x72, 0x65, 0x73, 0x70, 0x6f, 0x6e, + 0x64, 0x20, 0x69, 0x6e, 0x20, 0x73, 0x69, 0x6d, 0x70, 0x6c, 0x65, 0x20, + 0x6d, 0x61, 0x72, 0x6b, 0x64, 0x6f, 0x77, 0x6e, 0x2e, 0x22, 0x2c, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, + 0x74, 0x65, 0x3a, 0x20, 0x22, 0x7b, 0x7b, 0x70, 0x72, 0x6f, 0x6d, 0x70, + 0x74, 0x7d, 0x7d, 0x5c, 0x6e, 0x5c, 0x6e, 0x7b, 0x7b, 0x68, 0x69, 0x73, + 0x74, 0x6f, 0x72, 0x79, 0x7d, 0x7d, 0x5c, 0x6e, 0x7b, 0x7b, 0x63, 0x68, + 0x61, 0x72, 0x7d, 0x7d, 0x3a, 0x22, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x54, 0x65, 0x6d, + 0x70, 0x6c, 0x61, 0x74, 0x65, 0x3a, 0x20, 0x22, 0x7b, 0x7b, 0x6e, 0x61, + 0x6d, 0x65, 0x7d, 0x7d, 0x3a, 0x20, 0x7b, 0x7b, 0x6d, 0x65, 0x73, 0x73, + 0x61, 0x67, 0x65, 0x7d, 0x7d, 0x22, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, + 0x3a, 0x20, 0x5b, 0x5d, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x74, 0x79, 0x70, 0x65, 0x3a, 0x20, 0x22, 0x63, 0x68, 0x61, 0x74, 0x22, + 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x68, 0x61, 0x72, + 0x3a, 0x20, 0x22, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x22, 0x2c, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x75, 0x73, 0x65, 0x72, 0x3a, 0x20, 0x22, + 0x55, 0x73, 0x65, 0x72, 0x22, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, + 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, + 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x20, 0x3d, 0x20, 0x73, 0x69, + 0x67, 0x6e, 0x61, 0x6c, 0x28, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x6e, 0x5f, 0x70, 0x72, 0x65, 0x64, 0x69, 0x63, 0x74, 0x3a, 0x20, + 0x34, 0x30, 0x30, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, + 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x3a, 0x20, + 0x30, 0x2e, 0x37, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, + 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x6c, 0x61, 0x73, 0x74, 0x5f, 0x6e, + 0x3a, 0x20, 0x32, 0x35, 0x36, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x70, 0x65, 0x6e, 0x61, + 0x6c, 0x74, 0x79, 0x3a, 0x20, 0x31, 0x2e, 0x31, 0x38, 0x2c, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x6f, 0x70, 0x5f, 0x6b, 0x3a, 0x20, + 0x34, 0x30, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x6f, + 0x70, 0x5f, 0x70, 0x3a, 0x20, 0x30, 0x2e, 0x35, 0x2c, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x7d, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, + 0x6e, 0x73, 0x74, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x53, 0x74, 0x61, + 0x74, 0x73, 0x20, 0x3d, 0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x28, + 0x6e, 0x75, 0x6c, 0x6c, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, + 0x6e, 0x73, 0x74, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, + 0x65, 0x72, 0x20, 0x3d, 0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x28, + 0x6e, 0x75, 0x6c, 0x6c, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, + 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, + 0x69, 0x6e, 0x67, 0x20, 0x3d, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x75, 0x74, + 0x65, 0x64, 0x28, 0x28, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, - 0x65, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, 0x65, 0x2e, 0x6c, 0x6f, 0x67, - 0x28, 0x27, 0x61, 0x6c, 0x72, 0x65, 0x61, 0x64, 0x79, 0x20, 0x72, 0x75, - 0x6e, 0x6e, 0x69, 0x6e, 0x67, 0x2e, 0x2e, 0x2e, 0x27, 0x29, 0x3b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, - 0x72, 0x6e, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, - 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, - 0x20, 0x6e, 0x65, 0x77, 0x20, 0x41, 0x62, 0x6f, 0x72, 0x74, 0x43, 0x6f, - 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x28, 0x29, 0x3b, 0x0a, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x72, 0x61, 0x6e, 0x73, - 0x63, 0x72, 0x69, 0x70, 0x74, 0x55, 0x70, 0x64, 0x61, 0x74, 0x65, 0x28, - 0x5b, 0x2e, 0x2e, 0x2e, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, - 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, - 0x72, 0x69, 0x70, 0x74, 0x2c, 0x20, 0x5b, 0x22, 0x7b, 0x7b, 0x75, 0x73, - 0x65, 0x72, 0x7d, 0x7d, 0x22, 0x2c, 0x20, 0x6d, 0x73, 0x67, 0x5d, 0x5d, - 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, - 0x73, 0x74, 0x20, 0x70, 0x61, 0x79, 0x6c, 0x6f, 0x61, 0x64, 0x20, 0x3d, - 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, 0x73, 0x65, - 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, - 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x2c, 0x20, 0x7b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x65, 0x73, 0x73, - 0x61, 0x67, 0x65, 0x3a, 0x20, 0x6d, 0x73, 0x67, 0x2c, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, - 0x79, 0x3a, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, + 0x65, 0x20, 0x3d, 0x3d, 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x20, 0x29, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x68, + 0x61, 0x74, 0x53, 0x74, 0x61, 0x72, 0x74, 0x65, 0x64, 0x20, 0x3d, 0x20, + 0x63, 0x6f, 0x6d, 0x70, 0x75, 0x74, 0x65, 0x64, 0x28, 0x28, 0x29, 0x20, + 0x3d, 0x3e, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, - 0x69, 0x70, 0x74, 0x2e, 0x66, 0x6c, 0x61, 0x74, 0x4d, 0x61, 0x70, 0x28, - 0x28, 0x5b, 0x6e, 0x61, 0x6d, 0x65, 0x2c, 0x20, 0x6d, 0x65, 0x73, 0x73, - 0x61, 0x67, 0x65, 0x5d, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x74, 0x65, 0x6d, - 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, - 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x68, 0x69, 0x73, 0x74, - 0x6f, 0x72, 0x79, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x2c, - 0x20, 0x7b, 0x6e, 0x61, 0x6d, 0x65, 0x2c, 0x20, 0x6d, 0x65, 0x73, 0x73, - 0x61, 0x67, 0x65, 0x7d, 0x29, 0x29, 0x2e, 0x6a, 0x6f, 0x69, 0x6e, 0x28, - 0x22, 0x5c, 0x6e, 0x22, 0x29, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x7d, 0x29, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x6c, 0x65, 0x74, 0x20, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, - 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x20, 0x3d, 0x20, 0x27, 0x27, 0x3b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, - 0x20, 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x20, 0x3d, 0x20, 0x73, - 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, - 0x2e, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x0a, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, - 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, - 0x20, 0x3d, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x2e, 0x2e, 0x2e, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, + 0x69, 0x70, 0x74, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x20, 0x3e, + 0x20, 0x30, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, + 0x73, 0x74, 0x20, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, + 0x74, 0x55, 0x70, 0x64, 0x61, 0x74, 0x65, 0x20, 0x3d, 0x20, 0x28, 0x74, + 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x29, 0x20, 0x3d, + 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x65, + 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, + 0x3d, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x2e, 0x2e, 0x2e, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x3a, 0x20, 0x70, 0x61, - 0x79, 0x6c, 0x6f, 0x61, 0x64, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x73, 0x74, 0x6f, 0x70, 0x3a, 0x20, 0x5b, 0x22, 0x3c, - 0x2f, 0x73, 0x3e, 0x22, 0x2c, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, - 0x74, 0x65, 0x28, 0x22, 0x7b, 0x7b, 0x63, 0x68, 0x61, 0x72, 0x7d, 0x7d, - 0x3a, 0x22, 0x29, 0x2c, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, - 0x65, 0x28, 0x22, 0x7b, 0x7b, 0x75, 0x73, 0x65, 0x72, 0x7d, 0x7d, 0x3a, - 0x22, 0x29, 0x5d, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, - 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x61, 0x77, 0x61, 0x69, - 0x74, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x43, 0x6f, 0x6d, 0x70, 0x6c, - 0x65, 0x74, 0x65, 0x28, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x50, 0x61, 0x72, - 0x61, 0x6d, 0x73, 0x2c, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, - 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x20, 0x28, - 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x29, 0x20, 0x3d, 0x3e, 0x20, - 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, - 0x6e, 0x73, 0x74, 0x20, 0x64, 0x61, 0x74, 0x61, 0x20, 0x3d, 0x20, 0x6d, - 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x2e, 0x64, 0x61, 0x74, 0x61, 0x3b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x75, 0x72, - 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x20, - 0x2b, 0x3d, 0x20, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x63, 0x6f, 0x6e, 0x74, - 0x65, 0x6e, 0x74, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x2f, 0x2f, 0x20, 0x72, 0x65, 0x6d, 0x6f, 0x76, 0x65, 0x20, 0x6c, - 0x65, 0x61, 0x64, 0x69, 0x6e, 0x67, 0x20, 0x77, 0x68, 0x69, 0x74, 0x65, - 0x73, 0x70, 0x61, 0x63, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, - 0x73, 0x61, 0x67, 0x65, 0x20, 0x3d, 0x20, 0x63, 0x75, 0x72, 0x72, 0x65, - 0x6e, 0x74, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x2e, 0x72, 0x65, - 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x5e, 0x5c, 0x73, 0x2b, 0x2f, - 0x2c, 0x20, 0x22, 0x22, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, - 0x74, 0x55, 0x70, 0x64, 0x61, 0x74, 0x65, 0x28, 0x5b, 0x2e, 0x2e, 0x2e, - 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x2c, 0x20, 0x5b, 0x22, 0x7b, - 0x7b, 0x63, 0x68, 0x61, 0x72, 0x7d, 0x7d, 0x22, 0x2c, 0x20, 0x63, 0x75, - 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, - 0x5d, 0x5d, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x69, 0x66, 0x20, 0x28, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x73, 0x74, - 0x6f, 0x70, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, 0x65, 0x2e, - 0x6c, 0x6f, 0x67, 0x28, 0x22, 0x2d, 0x2d, 0x3e, 0x22, 0x2c, 0x20, 0x64, - 0x61, 0x74, 0x61, 0x2c, 0x20, 0x27, 0x20, 0x72, 0x65, 0x73, 0x70, 0x6f, - 0x6e, 0x73, 0x65, 0x20, 0x77, 0x61, 0x73, 0x3a, 0x27, 0x2c, 0x20, 0x63, - 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, - 0x65, 0x2c, 0x20, 0x27, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, - 0x70, 0x74, 0x20, 0x73, 0x74, 0x61, 0x74, 0x65, 0x3a, 0x27, 0x2c, 0x20, - 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, - 0x65, 0x2e, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, - 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x29, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x73, + 0x69, 0x6d, 0x70, 0x6c, 0x65, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, + 0x74, 0x65, 0x20, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x74, 0x65, 0x6d, + 0x70, 0x6c, 0x61, 0x74, 0x65, 0x20, 0x3d, 0x20, 0x28, 0x73, 0x74, 0x72, + 0x2c, 0x20, 0x65, 0x78, 0x74, 0x72, 0x61, 0x53, 0x65, 0x74, 0x74, 0x69, + 0x6e, 0x67, 0x73, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x6c, 0x65, 0x74, 0x20, 0x73, 0x65, 0x74, 0x74, + 0x69, 0x6e, 0x67, 0x73, 0x20, 0x3d, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, + 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x65, 0x78, 0x74, 0x72, + 0x61, 0x53, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x29, 0x20, 0x7b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x65, 0x74, + 0x74, 0x69, 0x6e, 0x67, 0x73, 0x20, 0x3d, 0x20, 0x7b, 0x20, 0x2e, 0x2e, + 0x2e, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x2c, 0x20, 0x2e, + 0x2e, 0x2e, 0x65, 0x78, 0x74, 0x72, 0x61, 0x53, 0x65, 0x74, 0x74, 0x69, + 0x6e, 0x67, 0x73, 0x20, 0x7d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, + 0x75, 0x72, 0x6e, 0x20, 0x53, 0x74, 0x72, 0x69, 0x6e, 0x67, 0x28, 0x73, + 0x74, 0x72, 0x29, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x41, + 0x6c, 0x6c, 0x28, 0x2f, 0x5c, 0x7b, 0x5c, 0x7b, 0x28, 0x2e, 0x2a, 0x3f, + 0x29, 0x5c, 0x7d, 0x5c, 0x7d, 0x2f, 0x67, 0x2c, 0x20, 0x28, 0x5f, 0x2c, + 0x20, 0x6b, 0x65, 0x79, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x74, 0x65, 0x6d, + 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, + 0x67, 0x73, 0x5b, 0x6b, 0x65, 0x79, 0x5d, 0x29, 0x29, 0x3b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, + 0x20, 0x73, 0x65, 0x6e, 0x64, 0x20, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, + 0x65, 0x20, 0x74, 0x6f, 0x20, 0x73, 0x65, 0x72, 0x76, 0x65, 0x72, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x68, + 0x61, 0x74, 0x20, 0x3d, 0x20, 0x61, 0x73, 0x79, 0x6e, 0x63, 0x20, 0x28, + 0x6d, 0x73, 0x67, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x63, 0x6f, 0x6e, 0x74, + 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, + 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, + 0x27, 0x61, 0x6c, 0x72, 0x65, 0x61, 0x64, 0x79, 0x20, 0x72, 0x75, 0x6e, + 0x6e, 0x69, 0x6e, 0x67, 0x2e, 0x2e, 0x2e, 0x27, 0x29, 0x3b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, + 0x6e, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, - 0x6e, 0x75, 0x6c, 0x6c, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, - 0x6e, 0x20, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x49, 0x6e, 0x70, - 0x75, 0x74, 0x28, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6d, 0x65, 0x73, 0x73, 0x61, - 0x67, 0x65, 0x20, 0x3d, 0x20, 0x75, 0x73, 0x65, 0x53, 0x69, 0x67, 0x6e, - 0x61, 0x6c, 0x28, 0x22, 0x22, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x73, 0x74, 0x6f, 0x70, - 0x20, 0x3d, 0x20, 0x28, 0x65, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x65, 0x2e, 0x70, 0x72, - 0x65, 0x76, 0x65, 0x6e, 0x74, 0x44, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, - 0x28, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x69, 0x66, 0x20, 0x28, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, - 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x20, 0x7b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, - 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, - 0x75, 0x65, 0x2e, 0x61, 0x62, 0x6f, 0x72, 0x74, 0x28, 0x29, 0x3b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, - 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, - 0x75, 0x65, 0x20, 0x3d, 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x3b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x72, 0x65, 0x73, 0x65, 0x74, 0x20, + 0x6e, 0x65, 0x77, 0x20, 0x41, 0x62, 0x6f, 0x72, 0x74, 0x43, 0x6f, 0x6e, + 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x28, 0x29, 0x3b, 0x0a, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, + 0x72, 0x69, 0x70, 0x74, 0x55, 0x70, 0x64, 0x61, 0x74, 0x65, 0x28, 0x5b, + 0x2e, 0x2e, 0x2e, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, + 0x69, 0x70, 0x74, 0x2c, 0x20, 0x5b, 0x22, 0x7b, 0x7b, 0x75, 0x73, 0x65, + 0x72, 0x7d, 0x7d, 0x22, 0x2c, 0x20, 0x6d, 0x73, 0x67, 0x5d, 0x5d, 0x29, + 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, + 0x74, 0x20, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x20, 0x3d, 0x20, 0x74, + 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, 0x73, 0x65, 0x73, 0x73, + 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x65, + 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x2c, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, + 0x65, 0x3a, 0x20, 0x6d, 0x73, 0x67, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x3a, + 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x2e, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, + 0x74, 0x2e, 0x66, 0x6c, 0x61, 0x74, 0x4d, 0x61, 0x70, 0x28, 0x28, 0x5b, + 0x6e, 0x61, 0x6d, 0x65, 0x2c, 0x20, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, + 0x65, 0x5d, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, + 0x61, 0x74, 0x65, 0x28, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, + 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, + 0x79, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x2c, 0x20, 0x7b, + 0x6e, 0x61, 0x6d, 0x65, 0x2c, 0x20, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, + 0x65, 0x7d, 0x29, 0x29, 0x2e, 0x6a, 0x6f, 0x69, 0x6e, 0x28, 0x22, 0x5c, + 0x6e, 0x22, 0x29, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, + 0x29, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x65, + 0x74, 0x20, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, + 0x73, 0x61, 0x67, 0x65, 0x20, 0x3d, 0x20, 0x27, 0x27, 0x3b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x68, + 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x20, 0x3d, 0x20, 0x73, 0x65, 0x73, + 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, + 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6c, + 0x6c, 0x61, 0x6d, 0x61, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x20, 0x3d, + 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, + 0x2e, 0x2e, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x73, 0x74, 0x6f, 0x70, 0x3a, 0x20, 0x5b, 0x22, 0x3c, 0x2f, 0x73, 0x3e, + 0x22, 0x2c, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, + 0x22, 0x7b, 0x7b, 0x63, 0x68, 0x61, 0x72, 0x7d, 0x7d, 0x3a, 0x22, 0x29, + 0x2c, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, 0x22, + 0x7b, 0x7b, 0x75, 0x73, 0x65, 0x72, 0x7d, 0x7d, 0x3a, 0x22, 0x29, 0x5d, + 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x66, 0x6f, 0x72, 0x20, 0x61, 0x77, 0x61, + 0x69, 0x74, 0x20, 0x28, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x68, + 0x75, 0x6e, 0x6b, 0x20, 0x6f, 0x66, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, + 0x28, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x2c, 0x20, 0x6c, 0x6c, 0x61, + 0x6d, 0x61, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2c, 0x20, 0x7b, 0x20, + 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x3a, 0x20, + 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x20, 0x7d, 0x29, 0x29, 0x20, 0x7b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, + 0x20, 0x64, 0x61, 0x74, 0x61, 0x20, 0x3d, 0x20, 0x63, 0x68, 0x75, 0x6e, + 0x6b, 0x2e, 0x64, 0x61, 0x74, 0x61, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, + 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x20, 0x2b, 0x3d, 0x20, 0x64, 0x61, + 0x74, 0x61, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x3b, 0x0a, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, + 0x72, 0x65, 0x6d, 0x6f, 0x76, 0x65, 0x20, 0x6c, 0x65, 0x61, 0x64, 0x69, + 0x6e, 0x67, 0x20, 0x77, 0x68, 0x69, 0x74, 0x65, 0x73, 0x70, 0x61, 0x63, + 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x75, + 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, + 0x20, 0x3d, 0x20, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, + 0x73, 0x73, 0x61, 0x67, 0x65, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, + 0x65, 0x28, 0x2f, 0x5e, 0x5c, 0x73, 0x2b, 0x2f, 0x2c, 0x20, 0x22, 0x22, + 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, + 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x55, 0x70, 0x64, + 0x61, 0x74, 0x65, 0x28, 0x5b, 0x2e, 0x2e, 0x2e, 0x68, 0x69, 0x73, 0x74, + 0x6f, 0x72, 0x79, 0x2c, 0x20, 0x5b, 0x22, 0x7b, 0x7b, 0x63, 0x68, 0x61, + 0x72, 0x7d, 0x7d, 0x22, 0x2c, 0x20, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, + 0x74, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x5d, 0x5d, 0x29, 0x0a, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, + 0x28, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x73, 0x74, 0x6f, 0x70, 0x29, 0x20, + 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, + 0x22, 0x43, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x20, + 0x66, 0x69, 0x6e, 0x69, 0x73, 0x68, 0x65, 0x64, 0x3a, 0x20, 0x27, 0x22, + 0x2c, 0x20, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, + 0x73, 0x61, 0x67, 0x65, 0x2c, 0x20, 0x22, 0x27, 0x2c, 0x20, 0x73, 0x75, + 0x6d, 0x6d, 0x61, 0x72, 0x79, 0x3a, 0x20, 0x22, 0x2c, 0x20, 0x64, 0x61, + 0x74, 0x61, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x69, 0x66, 0x20, 0x28, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x74, 0x69, 0x6d, + 0x69, 0x6e, 0x67, 0x73, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x53, + 0x74, 0x61, 0x74, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, + 0x20, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x74, 0x69, 0x6d, 0x69, 0x6e, 0x67, + 0x73, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, + 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x6e, + 0x75, 0x6c, 0x6c, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x20, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x49, 0x6e, 0x70, 0x75, + 0x74, 0x28, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, + 0x65, 0x20, 0x3d, 0x20, 0x75, 0x73, 0x65, 0x53, 0x69, 0x67, 0x6e, 0x61, + 0x6c, 0x28, 0x22, 0x22, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x73, 0x74, 0x6f, 0x70, 0x20, 0x3d, 0x20, 0x28, 0x65, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x74, 0x6f, 0x70, 0x28, - 0x65, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x55, 0x70, - 0x64, 0x61, 0x74, 0x65, 0x28, 0x5b, 0x5d, 0x29, 0x3b, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x73, 0x75, 0x62, 0x6d, 0x69, - 0x74, 0x20, 0x3d, 0x20, 0x28, 0x65, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x74, 0x6f, - 0x70, 0x28, 0x65, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x63, 0x68, 0x61, 0x74, 0x28, 0x6d, 0x65, 0x73, 0x73, 0x61, - 0x67, 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x3b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x65, 0x73, 0x73, 0x61, - 0x67, 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x22, - 0x22, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, - 0x65, 0x6e, 0x74, 0x65, 0x72, 0x53, 0x75, 0x62, 0x6d, 0x69, 0x74, 0x73, - 0x20, 0x3d, 0x20, 0x28, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x29, 0x20, 0x3d, - 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x69, 0x66, 0x20, 0x28, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x2e, 0x77, 0x68, - 0x69, 0x63, 0x68, 0x20, 0x3d, 0x3d, 0x3d, 0x20, 0x31, 0x33, 0x20, 0x26, - 0x26, 0x20, 0x21, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x2e, 0x73, 0x68, 0x69, - 0x66, 0x74, 0x4b, 0x65, 0x79, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x75, 0x62, 0x6d, 0x69, - 0x74, 0x28, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x29, 0x3b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x65, 0x2e, 0x70, 0x72, 0x65, + 0x76, 0x65, 0x6e, 0x74, 0x44, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x28, + 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, + 0x66, 0x20, 0x28, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, + 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x20, 0x7b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, + 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x2e, 0x61, 0x62, 0x6f, 0x72, 0x74, 0x28, 0x29, 0x3b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, + 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x20, 0x3d, 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, - 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x66, 0x6f, 0x72, - 0x6d, 0x20, 0x6f, 0x6e, 0x73, 0x75, 0x62, 0x6d, 0x69, 0x74, 0x3d, 0x24, - 0x7b, 0x73, 0x75, 0x62, 0x6d, 0x69, 0x74, 0x7d, 0x3e, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, + 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, + 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x72, 0x65, 0x73, 0x65, 0x74, 0x20, 0x3d, + 0x20, 0x28, 0x65, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x74, 0x6f, 0x70, 0x28, 0x65, + 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, + 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x55, 0x70, 0x64, + 0x61, 0x74, 0x65, 0x28, 0x5b, 0x5d, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x73, 0x75, 0x62, 0x6d, 0x69, 0x74, + 0x20, 0x3d, 0x20, 0x28, 0x65, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x74, 0x6f, 0x70, + 0x28, 0x65, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x63, 0x68, 0x61, 0x74, 0x28, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, + 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x3b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, + 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x22, 0x22, + 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x65, + 0x6e, 0x74, 0x65, 0x72, 0x53, 0x75, 0x62, 0x6d, 0x69, 0x74, 0x73, 0x20, + 0x3d, 0x20, 0x28, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x29, 0x20, 0x3d, 0x3e, + 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, + 0x66, 0x20, 0x28, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x2e, 0x77, 0x68, 0x69, + 0x63, 0x68, 0x20, 0x3d, 0x3d, 0x3d, 0x20, 0x31, 0x33, 0x20, 0x26, 0x26, + 0x20, 0x21, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x2e, 0x73, 0x68, 0x69, 0x66, + 0x74, 0x4b, 0x65, 0x79, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x75, 0x62, 0x6d, 0x69, 0x74, + 0x28, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, + 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x66, 0x6f, 0x72, 0x6d, + 0x20, 0x6f, 0x6e, 0x73, 0x75, 0x62, 0x6d, 0x69, 0x74, 0x3d, 0x24, 0x7b, + 0x73, 0x75, 0x62, 0x6d, 0x69, 0x74, 0x7d, 0x3e, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x74, 0x65, 0x78, 0x74, 0x61, 0x72, 0x65, 0x61, 0x20, 0x74, + 0x79, 0x70, 0x65, 0x3d, 0x22, 0x74, 0x65, 0x78, 0x74, 0x22, 0x20, 0x72, + 0x6f, 0x77, 0x73, 0x3d, 0x32, 0x20, 0x6f, 0x6e, 0x6b, 0x65, 0x79, 0x70, + 0x72, 0x65, 0x73, 0x73, 0x3d, 0x24, 0x7b, 0x65, 0x6e, 0x74, 0x65, 0x72, + 0x53, 0x75, 0x62, 0x6d, 0x69, 0x74, 0x73, 0x7d, 0x20, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, + 0x65, 0x7d, 0x22, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, + 0x24, 0x7b, 0x28, 0x65, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x6d, 0x65, 0x73, + 0x73, 0x61, 0x67, 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, + 0x20, 0x65, 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, 0x76, 0x61, + 0x6c, 0x75, 0x65, 0x7d, 0x20, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x68, 0x6f, + 0x6c, 0x64, 0x65, 0x72, 0x3d, 0x22, 0x53, 0x61, 0x79, 0x20, 0x73, 0x6f, + 0x6d, 0x65, 0x74, 0x68, 0x69, 0x6e, 0x67, 0x2e, 0x2e, 0x2e, 0x22, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x3c, 0x74, 0x65, 0x78, 0x74, 0x61, 0x72, 0x65, 0x61, 0x20, 0x74, 0x79, - 0x70, 0x65, 0x3d, 0x22, 0x74, 0x65, 0x78, 0x74, 0x22, 0x20, 0x72, 0x6f, - 0x77, 0x73, 0x3d, 0x32, 0x20, 0x6f, 0x6e, 0x6b, 0x65, 0x79, 0x70, 0x72, - 0x65, 0x73, 0x73, 0x3d, 0x24, 0x7b, 0x65, 0x6e, 0x74, 0x65, 0x72, 0x53, - 0x75, 0x62, 0x6d, 0x69, 0x74, 0x73, 0x7d, 0x20, 0x76, 0x61, 0x6c, 0x75, - 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, - 0x7d, 0x22, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, - 0x7b, 0x28, 0x65, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x6d, 0x65, 0x73, 0x73, - 0x61, 0x67, 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, - 0x65, 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, 0x76, 0x61, 0x6c, - 0x75, 0x65, 0x7d, 0x20, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x68, 0x6f, 0x6c, - 0x64, 0x65, 0x72, 0x3d, 0x22, 0x53, 0x61, 0x79, 0x20, 0x73, 0x6f, 0x6d, - 0x65, 0x74, 0x68, 0x69, 0x6e, 0x67, 0x2e, 0x2e, 0x2e, 0x22, 0x2f, 0x3e, - 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x20, 0x63, 0x6c, 0x61, 0x73, 0x73, 0x3d, 0x22, 0x72, 0x69, 0x67, 0x68, 0x74, 0x22, 0x3e, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x20, 0x74, 0x79, 0x70, + 0x65, 0x3d, 0x22, 0x73, 0x75, 0x62, 0x6d, 0x69, 0x74, 0x22, 0x20, 0x64, + 0x69, 0x73, 0x61, 0x62, 0x6c, 0x65, 0x64, 0x3d, 0x24, 0x7b, 0x21, 0x67, + 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6e, 0x67, 0x2e, 0x76, 0x61, + 0x6c, 0x75, 0x65, 0x7d, 0x20, 0x3e, 0x53, 0x65, 0x6e, 0x64, 0x3c, 0x2f, + 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x62, 0x75, 0x74, + 0x74, 0x6f, 0x6e, 0x20, 0x6f, 0x6e, 0x63, 0x6c, 0x69, 0x63, 0x6b, 0x3d, + 0x24, 0x7b, 0x73, 0x74, 0x6f, 0x70, 0x7d, 0x20, 0x64, 0x69, 0x73, 0x61, + 0x62, 0x6c, 0x65, 0x64, 0x3d, 0x24, 0x7b, 0x67, 0x65, 0x6e, 0x65, 0x72, + 0x61, 0x74, 0x69, 0x6e, 0x67, 0x7d, 0x3e, 0x53, 0x74, 0x6f, 0x70, 0x3c, + 0x2f, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x62, 0x75, + 0x74, 0x74, 0x6f, 0x6e, 0x20, 0x6f, 0x6e, 0x63, 0x6c, 0x69, 0x63, 0x6b, + 0x3d, 0x24, 0x7b, 0x72, 0x65, 0x73, 0x65, 0x74, 0x7d, 0x3e, 0x52, 0x65, + 0x73, 0x65, 0x74, 0x3c, 0x2f, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, - 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, - 0x22, 0x73, 0x75, 0x62, 0x6d, 0x69, 0x74, 0x22, 0x20, 0x64, 0x69, 0x73, - 0x61, 0x62, 0x6c, 0x65, 0x64, 0x3d, 0x24, 0x7b, 0x21, 0x67, 0x65, 0x6e, - 0x65, 0x72, 0x61, 0x74, 0x69, 0x6e, 0x67, 0x2e, 0x76, 0x61, 0x6c, 0x75, - 0x65, 0x7d, 0x20, 0x3e, 0x53, 0x65, 0x6e, 0x64, 0x3c, 0x2f, 0x62, 0x75, - 0x74, 0x74, 0x6f, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x3c, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x20, - 0x6f, 0x6e, 0x63, 0x6c, 0x69, 0x63, 0x6b, 0x3d, 0x24, 0x7b, 0x73, 0x74, - 0x6f, 0x70, 0x7d, 0x20, 0x64, 0x69, 0x73, 0x61, 0x62, 0x6c, 0x65, 0x64, - 0x3d, 0x24, 0x7b, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6e, - 0x67, 0x7d, 0x3e, 0x53, 0x74, 0x6f, 0x70, 0x3c, 0x2f, 0x62, 0x75, 0x74, - 0x74, 0x6f, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x3c, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x20, 0x6f, - 0x6e, 0x63, 0x6c, 0x69, 0x63, 0x6b, 0x3d, 0x24, 0x7b, 0x72, 0x65, 0x73, - 0x65, 0x74, 0x7d, 0x3e, 0x52, 0x65, 0x73, 0x65, 0x74, 0x3c, 0x2f, 0x62, - 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x6f, - 0x72, 0x6d, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, - 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x43, 0x68, 0x61, 0x74, 0x4c, 0x6f, 0x67, - 0x20, 0x3d, 0x20, 0x28, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x29, 0x20, 0x3d, - 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, - 0x6e, 0x73, 0x74, 0x20, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x73, - 0x20, 0x3d, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, - 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, - 0x69, 0x70, 0x74, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, - 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, - 0x65, 0x72, 0x20, 0x3d, 0x20, 0x75, 0x73, 0x65, 0x52, 0x65, 0x66, 0x28, - 0x6e, 0x75, 0x6c, 0x6c, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x75, 0x73, 0x65, 0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x28, 0x28, - 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x73, 0x63, 0x72, 0x6f, 0x6c, 0x6c, - 0x20, 0x74, 0x6f, 0x20, 0x62, 0x6f, 0x74, 0x74, 0x6f, 0x6d, 0x20, 0x28, - 0x69, 0x66, 0x20, 0x6e, 0x65, 0x65, 0x64, 0x65, 0x64, 0x29, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x63, - 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x2e, 0x63, 0x75, 0x72, - 0x72, 0x65, 0x6e, 0x74, 0x20, 0x26, 0x26, 0x20, 0x63, 0x6f, 0x6e, 0x74, - 0x61, 0x69, 0x6e, 0x65, 0x72, 0x2e, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, - 0x74, 0x2e, 0x73, 0x63, 0x72, 0x6f, 0x6c, 0x6c, 0x48, 0x65, 0x69, 0x67, - 0x68, 0x74, 0x20, 0x3c, 0x3d, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, - 0x6e, 0x65, 0x72, 0x2e, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x2e, - 0x73, 0x63, 0x72, 0x6f, 0x6c, 0x6c, 0x54, 0x6f, 0x70, 0x20, 0x2b, 0x20, - 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x2e, 0x63, 0x75, - 0x72, 0x72, 0x65, 0x6e, 0x74, 0x2e, 0x6f, 0x66, 0x66, 0x73, 0x65, 0x74, - 0x48, 0x65, 0x69, 0x67, 0x68, 0x74, 0x20, 0x2b, 0x20, 0x33, 0x30, 0x30, - 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x2e, - 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x2e, 0x73, 0x63, 0x72, 0x6f, - 0x6c, 0x6c, 0x54, 0x6f, 0x28, 0x30, 0x2c, 0x20, 0x63, 0x6f, 0x6e, 0x74, - 0x61, 0x69, 0x6e, 0x65, 0x72, 0x2e, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, - 0x74, 0x2e, 0x73, 0x63, 0x72, 0x6f, 0x6c, 0x6c, 0x48, 0x65, 0x69, 0x67, - 0x68, 0x74, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x2c, 0x20, 0x5b, - 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x73, 0x5d, 0x29, 0x0a, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, - 0x63, 0x68, 0x61, 0x74, 0x4c, 0x69, 0x6e, 0x65, 0x20, 0x3d, 0x20, 0x28, - 0x5b, 0x75, 0x73, 0x65, 0x72, 0x2c, 0x20, 0x6d, 0x73, 0x67, 0x5d, 0x29, - 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, - 0x6c, 0x60, 0x3c, 0x70, 0x20, 0x6b, 0x65, 0x79, 0x3d, 0x24, 0x7b, 0x6d, - 0x73, 0x67, 0x7d, 0x3e, 0x3c, 0x73, 0x74, 0x72, 0x6f, 0x6e, 0x67, 0x3e, - 0x24, 0x7b, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, 0x75, - 0x73, 0x65, 0x72, 0x29, 0x7d, 0x3a, 0x3c, 0x2f, 0x73, 0x74, 0x72, 0x6f, - 0x6e, 0x67, 0x3e, 0x20, 0x3c, 0x24, 0x7b, 0x4d, 0x61, 0x72, 0x6b, 0x64, - 0x6f, 0x77, 0x6e, 0x7d, 0x20, 0x74, 0x65, 0x78, 0x74, 0x3d, 0x24, 0x7b, - 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, 0x6d, 0x73, 0x67, - 0x29, 0x7d, 0x20, 0x2f, 0x3e, 0x3c, 0x2f, 0x70, 0x3e, 0x60, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, - 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x3c, 0x73, 0x65, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x69, 0x64, 0x3d, - 0x22, 0x63, 0x68, 0x61, 0x74, 0x22, 0x20, 0x72, 0x65, 0x66, 0x3d, 0x24, - 0x7b, 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x7d, 0x3e, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, - 0x7b, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x73, 0x2e, 0x66, 0x6c, - 0x61, 0x74, 0x4d, 0x61, 0x70, 0x28, 0x63, 0x68, 0x61, 0x74, 0x4c, 0x69, - 0x6e, 0x65, 0x29, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x3c, 0x2f, 0x73, 0x65, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x3e, 0x60, - 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x3b, 0x0a, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x43, 0x6f, 0x6e, 0x66, - 0x69, 0x67, 0x46, 0x6f, 0x72, 0x6d, 0x20, 0x3d, 0x20, 0x28, 0x70, 0x72, + 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x6f, 0x72, 0x6d, 0x3e, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x43, + 0x68, 0x61, 0x74, 0x4c, 0x6f, 0x67, 0x20, 0x3d, 0x20, 0x28, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x75, 0x70, - 0x64, 0x61, 0x74, 0x65, 0x53, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x20, - 0x3d, 0x20, 0x28, 0x65, 0x6c, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x73, 0x65, - 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, - 0x3d, 0x20, 0x7b, 0x20, 0x2e, 0x2e, 0x2e, 0x73, 0x65, 0x73, 0x73, 0x69, - 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x20, 0x5b, 0x65, - 0x6c, 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, 0x6e, 0x61, 0x6d, - 0x65, 0x5d, 0x3a, 0x20, 0x65, 0x6c, 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, - 0x74, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x7d, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x75, 0x70, - 0x64, 0x61, 0x74, 0x65, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x20, 0x3d, - 0x20, 0x28, 0x65, 0x6c, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x70, 0x61, 0x72, - 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, - 0x7b, 0x20, 0x2e, 0x2e, 0x2e, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, + 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6d, 0x65, + 0x73, 0x73, 0x61, 0x67, 0x65, 0x73, 0x20, 0x3d, 0x20, 0x73, 0x65, 0x73, + 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, + 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x3b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, + 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x20, 0x3d, 0x20, 0x75, + 0x73, 0x65, 0x52, 0x65, 0x66, 0x28, 0x6e, 0x75, 0x6c, 0x6c, 0x29, 0x0a, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x75, 0x73, 0x65, 0x45, 0x66, + 0x66, 0x65, 0x63, 0x74, 0x28, 0x28, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, + 0x73, 0x63, 0x72, 0x6f, 0x6c, 0x6c, 0x20, 0x74, 0x6f, 0x20, 0x62, 0x6f, + 0x74, 0x74, 0x6f, 0x6d, 0x20, 0x28, 0x69, 0x66, 0x20, 0x6e, 0x65, 0x65, + 0x64, 0x65, 0x64, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x69, 0x66, 0x20, 0x28, 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, + 0x65, 0x72, 0x2e, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x20, 0x26, + 0x26, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x2e, + 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x2e, 0x73, 0x63, 0x72, 0x6f, + 0x6c, 0x6c, 0x48, 0x65, 0x69, 0x67, 0x68, 0x74, 0x20, 0x3c, 0x3d, 0x20, + 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x2e, 0x63, 0x75, + 0x72, 0x72, 0x65, 0x6e, 0x74, 0x2e, 0x73, 0x63, 0x72, 0x6f, 0x6c, 0x6c, + 0x54, 0x6f, 0x70, 0x20, 0x2b, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, + 0x6e, 0x65, 0x72, 0x2e, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x2e, + 0x6f, 0x66, 0x66, 0x73, 0x65, 0x74, 0x48, 0x65, 0x69, 0x67, 0x68, 0x74, + 0x20, 0x2b, 0x20, 0x33, 0x30, 0x30, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x74, + 0x61, 0x69, 0x6e, 0x65, 0x72, 0x2e, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, + 0x74, 0x2e, 0x73, 0x63, 0x72, 0x6f, 0x6c, 0x6c, 0x54, 0x6f, 0x28, 0x30, + 0x2c, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x2e, + 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x2e, 0x73, 0x63, 0x72, 0x6f, + 0x6c, 0x6c, 0x48, 0x65, 0x69, 0x67, 0x68, 0x74, 0x29, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x7d, 0x2c, 0x20, 0x5b, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, + 0x65, 0x73, 0x5d, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x68, 0x61, 0x74, 0x4c, 0x69, + 0x6e, 0x65, 0x20, 0x3d, 0x20, 0x28, 0x5b, 0x75, 0x73, 0x65, 0x72, 0x2c, + 0x20, 0x6d, 0x73, 0x67, 0x5d, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, + 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x3c, 0x70, 0x20, 0x6b, + 0x65, 0x79, 0x3d, 0x24, 0x7b, 0x6d, 0x73, 0x67, 0x7d, 0x3e, 0x3c, 0x73, + 0x74, 0x72, 0x6f, 0x6e, 0x67, 0x3e, 0x24, 0x7b, 0x74, 0x65, 0x6d, 0x70, + 0x6c, 0x61, 0x74, 0x65, 0x28, 0x75, 0x73, 0x65, 0x72, 0x29, 0x7d, 0x3a, + 0x3c, 0x2f, 0x73, 0x74, 0x72, 0x6f, 0x6e, 0x67, 0x3e, 0x20, 0x3c, 0x24, + 0x7b, 0x4d, 0x61, 0x72, 0x6b, 0x64, 0x6f, 0x77, 0x6e, 0x69, 0x73, 0x68, + 0x7d, 0x20, 0x74, 0x65, 0x78, 0x74, 0x3d, 0x24, 0x7b, 0x74, 0x65, 0x6d, + 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, 0x6d, 0x73, 0x67, 0x29, 0x7d, 0x20, + 0x2f, 0x3e, 0x3c, 0x2f, 0x70, 0x3e, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x7d, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x73, 0x65, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x63, 0x68, + 0x61, 0x74, 0x22, 0x20, 0x72, 0x65, 0x66, 0x3d, 0x24, 0x7b, 0x63, 0x6f, + 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x7d, 0x3e, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x6d, 0x65, + 0x73, 0x73, 0x61, 0x67, 0x65, 0x73, 0x2e, 0x66, 0x6c, 0x61, 0x74, 0x4d, + 0x61, 0x70, 0x28, 0x63, 0x68, 0x61, 0x74, 0x4c, 0x69, 0x6e, 0x65, 0x29, + 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, + 0x73, 0x65, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x3e, 0x60, 0x3b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x7d, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, + 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x43, 0x6f, 0x6e, 0x66, 0x69, 0x67, 0x46, + 0x6f, 0x72, 0x6d, 0x20, 0x3d, 0x20, 0x28, 0x70, 0x72, 0x6f, 0x70, 0x73, + 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x75, 0x70, 0x64, 0x61, 0x74, + 0x65, 0x53, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x20, 0x3d, 0x20, 0x28, + 0x65, 0x6c, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, + 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x7b, + 0x20, 0x2e, 0x2e, 0x2e, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x20, 0x5b, 0x65, 0x6c, 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, 0x6e, 0x61, 0x6d, 0x65, 0x5d, 0x3a, 0x20, 0x65, 0x6c, 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x75, 0x70, 0x64, 0x61, 0x74, - 0x65, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x46, 0x6c, 0x6f, 0x61, 0x74, - 0x20, 0x3d, 0x20, 0x28, 0x65, 0x6c, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x70, - 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, - 0x3d, 0x20, 0x7b, 0x20, 0x2e, 0x2e, 0x2e, 0x70, 0x61, 0x72, 0x61, 0x6d, - 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x20, 0x5b, 0x65, 0x6c, - 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, 0x6e, 0x61, 0x6d, 0x65, - 0x5d, 0x3a, 0x20, 0x70, 0x61, 0x72, 0x73, 0x65, 0x46, 0x6c, 0x6f, 0x61, - 0x74, 0x28, 0x65, 0x6c, 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, - 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, - 0x74, 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x3c, 0x66, 0x6f, 0x72, 0x6d, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x66, 0x69, 0x65, 0x6c, 0x64, - 0x73, 0x65, 0x74, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, - 0x22, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x22, 0x3e, 0x50, 0x72, 0x6f, - 0x6d, 0x70, 0x74, 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x3c, 0x74, 0x65, 0x78, 0x74, 0x61, 0x72, 0x65, 0x61, 0x20, - 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, 0x74, 0x65, 0x78, 0x74, 0x22, 0x20, - 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, - 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x73, - 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, - 0x2e, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x7d, 0x22, 0x20, 0x72, 0x6f, - 0x77, 0x73, 0x3d, 0x34, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, - 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x53, 0x65, 0x73, - 0x73, 0x69, 0x6f, 0x6e, 0x7d, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, - 0x76, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, - 0x75, 0x73, 0x65, 0x72, 0x22, 0x3e, 0x55, 0x73, 0x65, 0x72, 0x20, 0x6e, - 0x61, 0x6d, 0x65, 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x3c, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x20, 0x74, 0x79, 0x70, - 0x65, 0x3d, 0x22, 0x74, 0x65, 0x78, 0x74, 0x22, 0x20, 0x6e, 0x61, 0x6d, - 0x65, 0x3d, 0x22, 0x75, 0x73, 0x65, 0x72, 0x22, 0x20, 0x76, 0x61, 0x6c, - 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, - 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x75, 0x73, 0x65, 0x72, - 0x7d, 0x22, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, - 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x53, 0x65, 0x73, 0x73, 0x69, - 0x6f, 0x6e, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, - 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, + 0x65, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x20, 0x3d, 0x20, 0x28, 0x65, + 0x6c, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, + 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x7b, 0x20, 0x2e, + 0x2e, 0x2e, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x2c, 0x20, 0x5b, 0x65, 0x6c, 0x2e, 0x74, 0x61, 0x72, 0x67, + 0x65, 0x74, 0x2e, 0x6e, 0x61, 0x6d, 0x65, 0x5d, 0x3a, 0x20, 0x65, 0x6c, + 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, + 0x6e, 0x73, 0x74, 0x20, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x50, 0x61, + 0x72, 0x61, 0x6d, 0x73, 0x46, 0x6c, 0x6f, 0x61, 0x74, 0x20, 0x3d, 0x20, + 0x28, 0x65, 0x6c, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x70, 0x61, 0x72, 0x61, + 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x7b, + 0x20, 0x2e, 0x2e, 0x2e, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x20, 0x5b, 0x65, 0x6c, 0x2e, 0x74, 0x61, + 0x72, 0x67, 0x65, 0x74, 0x2e, 0x6e, 0x61, 0x6d, 0x65, 0x5d, 0x3a, 0x20, + 0x70, 0x61, 0x72, 0x73, 0x65, 0x46, 0x6c, 0x6f, 0x61, 0x74, 0x28, 0x65, + 0x6c, 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x29, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, + 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x66, + 0x6f, 0x72, 0x6d, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x3c, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, + 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, + 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, 0x70, 0x72, + 0x6f, 0x6d, 0x70, 0x74, 0x22, 0x3e, 0x50, 0x72, 0x6f, 0x6d, 0x70, 0x74, + 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, - 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, 0x62, - 0x6f, 0x74, 0x22, 0x3e, 0x42, 0x6f, 0x74, 0x20, 0x6e, 0x61, 0x6d, 0x65, + 0x74, 0x65, 0x78, 0x74, 0x61, 0x72, 0x65, 0x61, 0x20, 0x74, 0x79, 0x70, + 0x65, 0x3d, 0x22, 0x74, 0x65, 0x78, 0x74, 0x22, 0x20, 0x6e, 0x61, 0x6d, + 0x65, 0x3d, 0x22, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x22, 0x20, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x73, 0x65, 0x73, 0x73, + 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x70, 0x72, + 0x6f, 0x6d, 0x70, 0x74, 0x7d, 0x22, 0x20, 0x72, 0x6f, 0x77, 0x73, 0x3d, + 0x34, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, + 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x53, 0x65, 0x73, 0x73, 0x69, 0x6f, + 0x6e, 0x7d, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, + 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, 0x75, 0x73, 0x65, + 0x72, 0x22, 0x3e, 0x55, 0x73, 0x65, 0x72, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, 0x74, 0x65, 0x78, 0x74, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, - 0x63, 0x68, 0x61, 0x72, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, + 0x75, 0x73, 0x65, 0x72, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, - 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x63, 0x68, 0x61, 0x72, 0x7d, 0x22, 0x20, + 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x75, 0x73, 0x65, 0x72, 0x7d, 0x22, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x53, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, @@ -600,247 +597,303 @@ unsigned char index_html[] = { 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, 0x62, - 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, 0x74, 0x65, 0x6d, 0x70, - 0x6c, 0x61, 0x74, 0x65, 0x22, 0x3e, 0x50, 0x72, 0x6f, 0x6d, 0x70, 0x74, - 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x3c, 0x2f, 0x6c, + 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, 0x62, 0x6f, 0x74, 0x22, + 0x3e, 0x42, 0x6f, 0x74, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x74, 0x65, 0x78, - 0x74, 0x61, 0x72, 0x65, 0x61, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x74, 0x65, - 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, - 0x3d, 0x22, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x22, 0x20, - 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x73, 0x65, 0x73, - 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, - 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x7d, 0x22, 0x20, 0x72, 0x6f, - 0x77, 0x73, 0x3d, 0x34, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, - 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x53, 0x65, 0x73, - 0x73, 0x69, 0x6f, 0x6e, 0x7d, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, - 0x76, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, - 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x22, 0x3e, 0x43, 0x68, - 0x61, 0x74, 0x20, 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x20, 0x74, - 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x3c, 0x2f, 0x6c, 0x61, 0x62, - 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x74, 0x65, 0x78, 0x74, 0x61, - 0x72, 0x65, 0x61, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x74, 0x65, 0x6d, 0x70, - 0x6c, 0x61, 0x74, 0x65, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, - 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x54, 0x65, 0x6d, 0x70, 0x6c, - 0x61, 0x74, 0x65, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, - 0x24, 0x7b, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, - 0x6c, 0x75, 0x65, 0x2e, 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x54, - 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x7d, 0x22, 0x20, 0x72, 0x6f, - 0x77, 0x73, 0x3d, 0x31, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, - 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x53, 0x65, 0x73, - 0x73, 0x69, 0x6f, 0x6e, 0x7d, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, - 0x76, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, - 0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x22, - 0x3e, 0x54, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, - 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, - 0x69, 0x6e, 0x70, 0x75, 0x74, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, - 0x72, 0x61, 0x6e, 0x67, 0x65, 0x22, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x74, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x69, 0x6e, 0x70, + 0x75, 0x74, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, 0x74, 0x65, 0x78, + 0x74, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x63, 0x68, 0x61, + 0x72, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, + 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x2e, 0x63, 0x68, 0x61, 0x72, 0x7d, 0x22, 0x20, 0x6f, 0x6e, 0x69, + 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, + 0x65, 0x53, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x7d, 0x20, 0x2f, 0x3e, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, + 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, + 0x66, 0x6f, 0x72, 0x3d, 0x22, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, + 0x65, 0x22, 0x3e, 0x50, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x20, 0x74, 0x65, + 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, + 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x74, 0x65, 0x78, 0x74, 0x61, 0x72, + 0x65, 0x61, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x74, 0x65, 0x6d, 0x70, 0x6c, + 0x61, 0x74, 0x65, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x74, + 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x22, 0x20, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, + 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x65, 0x6d, 0x70, + 0x6c, 0x61, 0x74, 0x65, 0x7d, 0x22, 0x20, 0x72, 0x6f, 0x77, 0x73, 0x3d, + 0x34, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, + 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x53, 0x65, 0x73, 0x73, 0x69, 0x6f, + 0x6e, 0x7d, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, + 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, 0x74, 0x65, 0x6d, + 0x70, 0x6c, 0x61, 0x74, 0x65, 0x22, 0x3e, 0x43, 0x68, 0x61, 0x74, 0x20, + 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x20, 0x74, 0x65, 0x6d, 0x70, + 0x6c, 0x61, 0x74, 0x65, 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x3c, 0x74, 0x65, 0x78, 0x74, 0x61, 0x72, 0x65, 0x61, + 0x20, 0x69, 0x64, 0x3d, 0x22, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, + 0x65, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x68, 0x69, 0x73, + 0x74, 0x6f, 0x72, 0x79, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, + 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x73, + 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, + 0x2e, 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x54, 0x65, 0x6d, 0x70, + 0x6c, 0x61, 0x74, 0x65, 0x7d, 0x22, 0x20, 0x72, 0x6f, 0x77, 0x73, 0x3d, + 0x31, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, + 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x53, 0x65, 0x73, 0x73, 0x69, 0x6f, + 0x6e, 0x7d, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, + 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, 0x74, 0x65, 0x6d, + 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x22, 0x3e, 0x54, 0x65, + 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x3c, 0x2f, 0x6c, + 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x69, 0x6e, 0x70, + 0x75, 0x74, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, 0x72, 0x61, 0x6e, + 0x67, 0x65, 0x22, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x74, 0x65, 0x6d, 0x70, + 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x22, 0x20, 0x6d, 0x69, 0x6e, + 0x3d, 0x22, 0x30, 0x2e, 0x30, 0x22, 0x20, 0x6d, 0x61, 0x78, 0x3d, 0x22, + 0x31, 0x2e, 0x30, 0x22, 0x20, 0x73, 0x74, 0x65, 0x70, 0x3d, 0x22, 0x30, + 0x2e, 0x30, 0x31, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x22, 0x20, - 0x6d, 0x69, 0x6e, 0x3d, 0x22, 0x30, 0x2e, 0x30, 0x22, 0x20, 0x6d, 0x61, - 0x78, 0x3d, 0x22, 0x31, 0x2e, 0x30, 0x22, 0x20, 0x73, 0x74, 0x65, 0x70, - 0x3d, 0x22, 0x30, 0x2e, 0x30, 0x31, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, - 0x3d, 0x22, 0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, - 0x65, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, - 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, - 0x2e, 0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, - 0x7d, 0x22, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, - 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x50, 0x61, 0x72, 0x61, 0x6d, - 0x73, 0x46, 0x6c, 0x6f, 0x61, 0x74, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x3c, 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x24, 0x7b, 0x70, 0x61, 0x72, + 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x65, - 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x7d, 0x3c, 0x2f, - 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, - 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, - 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, 0x6e, 0x50, - 0x72, 0x65, 0x64, 0x69, 0x63, 0x74, 0x22, 0x3e, 0x50, 0x72, 0x65, 0x64, - 0x69, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x73, 0x3c, 0x2f, 0x6c, 0x61, 0x62, - 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x69, 0x6e, 0x70, 0x75, 0x74, - 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, 0x72, 0x61, 0x6e, 0x67, 0x65, - 0x22, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x6e, 0x50, 0x72, 0x65, 0x64, 0x69, - 0x63, 0x74, 0x22, 0x20, 0x6d, 0x69, 0x6e, 0x3d, 0x22, 0x31, 0x22, 0x20, - 0x6d, 0x61, 0x78, 0x3d, 0x22, 0x32, 0x30, 0x34, 0x38, 0x22, 0x20, 0x73, - 0x74, 0x65, 0x70, 0x3d, 0x22, 0x31, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, - 0x3d, 0x22, 0x6e, 0x5f, 0x70, 0x72, 0x65, 0x64, 0x69, 0x63, 0x74, 0x22, - 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x70, 0x61, - 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x6e, - 0x5f, 0x70, 0x72, 0x65, 0x64, 0x69, 0x63, 0x74, 0x7d, 0x22, 0x20, 0x6f, - 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, - 0x61, 0x74, 0x65, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x46, 0x6c, 0x6f, - 0x61, 0x74, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x73, 0x70, - 0x61, 0x6e, 0x3e, 0x24, 0x7b, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, - 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x6e, 0x5f, 0x70, 0x72, 0x65, 0x64, - 0x69, 0x63, 0x74, 0x7d, 0x3c, 0x2f, 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, - 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, - 0x6f, 0x72, 0x3d, 0x22, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x70, - 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x22, 0x3e, 0x50, 0x65, 0x6e, 0x61, - 0x6c, 0x69, 0x7a, 0x65, 0x20, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x20, - 0x73, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x63, 0x65, 0x3c, 0x2f, 0x6c, 0x61, - 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x69, 0x6e, 0x70, 0x75, - 0x74, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, 0x72, 0x61, 0x6e, 0x67, - 0x65, 0x22, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x72, 0x65, 0x70, 0x65, 0x61, - 0x74, 0x5f, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x22, 0x20, 0x6d, - 0x69, 0x6e, 0x3d, 0x22, 0x30, 0x2e, 0x30, 0x22, 0x20, 0x6d, 0x61, 0x78, - 0x3d, 0x22, 0x32, 0x2e, 0x30, 0x22, 0x20, 0x73, 0x74, 0x65, 0x70, 0x3d, - 0x22, 0x30, 0x2e, 0x30, 0x31, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, - 0x22, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x70, 0x65, 0x6e, 0x61, - 0x6c, 0x74, 0x79, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, - 0x24, 0x7b, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, - 0x75, 0x65, 0x2e, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x70, 0x65, - 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x7d, 0x22, 0x20, 0x6f, 0x6e, 0x69, 0x6e, + 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x7d, 0x22, 0x20, + 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, 0x70, + 0x64, 0x61, 0x74, 0x65, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x46, 0x6c, + 0x6f, 0x61, 0x74, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x73, + 0x70, 0x61, 0x6e, 0x3e, 0x24, 0x7b, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, + 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x65, 0x6d, 0x70, 0x65, + 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x7d, 0x3c, 0x2f, 0x73, 0x70, 0x61, + 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, + 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, 0x62, 0x65, + 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, 0x6e, 0x50, 0x72, 0x65, 0x64, + 0x69, 0x63, 0x74, 0x22, 0x3e, 0x50, 0x72, 0x65, 0x64, 0x69, 0x63, 0x74, + 0x69, 0x6f, 0x6e, 0x73, 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x3c, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x20, 0x74, 0x79, + 0x70, 0x65, 0x3d, 0x22, 0x72, 0x61, 0x6e, 0x67, 0x65, 0x22, 0x20, 0x69, + 0x64, 0x3d, 0x22, 0x6e, 0x50, 0x72, 0x65, 0x64, 0x69, 0x63, 0x74, 0x22, + 0x20, 0x6d, 0x69, 0x6e, 0x3d, 0x22, 0x31, 0x22, 0x20, 0x6d, 0x61, 0x78, + 0x3d, 0x22, 0x32, 0x30, 0x34, 0x38, 0x22, 0x20, 0x73, 0x74, 0x65, 0x70, + 0x3d, 0x22, 0x31, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x6e, + 0x5f, 0x70, 0x72, 0x65, 0x64, 0x69, 0x63, 0x74, 0x22, 0x20, 0x76, 0x61, + 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x70, 0x61, 0x72, 0x61, 0x6d, + 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x6e, 0x5f, 0x70, 0x72, + 0x65, 0x64, 0x69, 0x63, 0x74, 0x7d, 0x22, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x46, 0x6c, 0x6f, 0x61, 0x74, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x24, 0x7b, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, - 0x75, 0x65, 0x2e, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x70, 0x65, - 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x7d, 0x3c, 0x2f, 0x73, 0x70, 0x61, 0x6e, - 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, - 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, - 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, - 0x5f, 0x6c, 0x61, 0x73, 0x74, 0x5f, 0x6e, 0x22, 0x3e, 0x43, 0x6f, 0x6e, - 0x73, 0x69, 0x64, 0x65, 0x72, 0x20, 0x4e, 0x20, 0x74, 0x6f, 0x6b, 0x65, - 0x6e, 0x73, 0x20, 0x66, 0x6f, 0x72, 0x20, 0x70, 0x65, 0x6e, 0x61, 0x6c, - 0x69, 0x7a, 0x65, 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, + 0x75, 0x65, 0x2e, 0x6e, 0x5f, 0x70, 0x72, 0x65, 0x64, 0x69, 0x63, 0x74, + 0x7d, 0x3c, 0x2f, 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, + 0x69, 0x76, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x3c, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x20, 0x74, 0x79, 0x70, - 0x65, 0x3d, 0x22, 0x72, 0x61, 0x6e, 0x67, 0x65, 0x22, 0x20, 0x69, 0x64, - 0x3d, 0x22, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x6c, 0x61, 0x73, - 0x74, 0x5f, 0x6e, 0x22, 0x20, 0x6d, 0x69, 0x6e, 0x3d, 0x22, 0x30, 0x2e, - 0x30, 0x22, 0x20, 0x6d, 0x61, 0x78, 0x3d, 0x22, 0x32, 0x30, 0x34, 0x38, - 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x72, 0x65, 0x70, 0x65, - 0x61, 0x74, 0x5f, 0x6c, 0x61, 0x73, 0x74, 0x5f, 0x6e, 0x22, 0x20, 0x76, - 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x70, 0x61, 0x72, 0x61, - 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x72, 0x65, 0x70, - 0x65, 0x61, 0x74, 0x5f, 0x6c, 0x61, 0x73, 0x74, 0x5f, 0x6e, 0x7d, 0x22, - 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, - 0x70, 0x64, 0x61, 0x74, 0x65, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x46, - 0x6c, 0x6f, 0x61, 0x74, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, + 0x22, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x70, 0x65, 0x6e, 0x61, + 0x6c, 0x74, 0x79, 0x22, 0x3e, 0x50, 0x65, 0x6e, 0x61, 0x6c, 0x69, 0x7a, + 0x65, 0x20, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x20, 0x73, 0x65, 0x71, + 0x75, 0x65, 0x6e, 0x63, 0x65, 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, + 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x3c, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x20, 0x74, + 0x79, 0x70, 0x65, 0x3d, 0x22, 0x72, 0x61, 0x6e, 0x67, 0x65, 0x22, 0x20, + 0x69, 0x64, 0x3d, 0x22, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x70, + 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x22, 0x20, 0x6d, 0x69, 0x6e, 0x3d, + 0x22, 0x30, 0x2e, 0x30, 0x22, 0x20, 0x6d, 0x61, 0x78, 0x3d, 0x22, 0x32, + 0x2e, 0x30, 0x22, 0x20, 0x73, 0x74, 0x65, 0x70, 0x3d, 0x22, 0x30, 0x2e, + 0x30, 0x31, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x72, 0x65, + 0x70, 0x65, 0x61, 0x74, 0x5f, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, + 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x70, + 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, + 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x70, 0x65, 0x6e, 0x61, 0x6c, + 0x74, 0x79, 0x7d, 0x22, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, + 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x50, 0x61, 0x72, + 0x61, 0x6d, 0x73, 0x46, 0x6c, 0x6f, 0x61, 0x74, 0x7d, 0x20, 0x2f, 0x3e, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x3c, 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x24, 0x7b, 0x70, + 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, + 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x70, 0x65, 0x6e, 0x61, 0x6c, + 0x74, 0x79, 0x7d, 0x3c, 0x2f, 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, - 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x24, 0x7b, 0x70, 0x61, 0x72, 0x61, 0x6d, - 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x72, 0x65, 0x70, 0x65, - 0x61, 0x74, 0x5f, 0x6c, 0x61, 0x73, 0x74, 0x5f, 0x6e, 0x7d, 0x3c, 0x2f, - 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, - 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x3c, 0x2f, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, 0x3e, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x6f, - 0x72, 0x6d, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, - 0x4d, 0x61, 0x72, 0x6b, 0x64, 0x6f, 0x77, 0x6e, 0x20, 0x3d, 0x20, 0x28, - 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, - 0x0a, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6d, 0x64, 0x20, - 0x3d, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x74, 0x65, 0x78, - 0x74, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, - 0x63, 0x65, 0x28, 0x2f, 0x5e, 0x23, 0x7b, 0x31, 0x2c, 0x36, 0x7d, 0x20, - 0x28, 0x2e, 0x2a, 0x29, 0x24, 0x2f, 0x67, 0x69, 0x6d, 0x2c, 0x20, 0x27, - 0x3c, 0x68, 0x33, 0x3e, 0x24, 0x31, 0x3c, 0x2f, 0x68, 0x33, 0x3e, 0x27, - 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, - 0x63, 0x65, 0x28, 0x2f, 0x5c, 0x2a, 0x5c, 0x2a, 0x28, 0x2e, 0x2a, 0x3f, - 0x29, 0x5c, 0x2a, 0x5c, 0x2a, 0x2f, 0x67, 0x2c, 0x20, 0x27, 0x3c, 0x73, + 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, + 0x72, 0x3d, 0x22, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x6c, 0x61, + 0x73, 0x74, 0x5f, 0x6e, 0x22, 0x3e, 0x43, 0x6f, 0x6e, 0x73, 0x69, 0x64, + 0x65, 0x72, 0x20, 0x4e, 0x20, 0x74, 0x6f, 0x6b, 0x65, 0x6e, 0x73, 0x20, + 0x66, 0x6f, 0x72, 0x20, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x69, 0x7a, 0x65, + 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, + 0x69, 0x6e, 0x70, 0x75, 0x74, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, + 0x72, 0x61, 0x6e, 0x67, 0x65, 0x22, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x72, + 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x6c, 0x61, 0x73, 0x74, 0x5f, 0x6e, + 0x22, 0x20, 0x6d, 0x69, 0x6e, 0x3d, 0x22, 0x30, 0x2e, 0x30, 0x22, 0x20, + 0x6d, 0x61, 0x78, 0x3d, 0x22, 0x32, 0x30, 0x34, 0x38, 0x22, 0x20, 0x6e, + 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, + 0x6c, 0x61, 0x73, 0x74, 0x5f, 0x6e, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, + 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, + 0x5f, 0x6c, 0x61, 0x73, 0x74, 0x5f, 0x6e, 0x7d, 0x22, 0x20, 0x6f, 0x6e, + 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, + 0x74, 0x65, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x46, 0x6c, 0x6f, 0x61, + 0x74, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x73, 0x70, 0x61, + 0x6e, 0x3e, 0x24, 0x7b, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, + 0x6c, 0x61, 0x73, 0x74, 0x5f, 0x6e, 0x7d, 0x3c, 0x2f, 0x73, 0x70, 0x61, + 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, + 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, 0x3e, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x6f, 0x72, 0x6d, 0x3e, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x70, 0x6f, + 0x6f, 0x72, 0x20, 0x6d, 0x61, 0x6e, 0x73, 0x20, 0x6d, 0x61, 0x72, 0x6b, + 0x64, 0x6f, 0x77, 0x6e, 0x20, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, + 0x6d, 0x65, 0x6e, 0x74, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, + 0x73, 0x74, 0x20, 0x4d, 0x61, 0x72, 0x6b, 0x64, 0x6f, 0x77, 0x6e, 0x69, + 0x73, 0x68, 0x20, 0x3d, 0x20, 0x28, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, + 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6d, 0x64, 0x20, 0x3d, 0x20, + 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x74, 0x65, 0x78, 0x74, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, 0x70, + 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x5e, 0x23, 0x7b, 0x31, 0x2c, 0x36, + 0x7d, 0x20, 0x28, 0x2e, 0x2a, 0x29, 0x24, 0x2f, 0x67, 0x69, 0x6d, 0x2c, + 0x20, 0x27, 0x3c, 0x68, 0x33, 0x3e, 0x24, 0x31, 0x3c, 0x2f, 0x68, 0x33, + 0x3e, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x5c, 0x2a, + 0x5c, 0x2a, 0x28, 0x2e, 0x2a, 0x3f, 0x29, 0x5c, 0x2a, 0x5c, 0x2a, 0x2f, + 0x67, 0x2c, 0x20, 0x27, 0x3c, 0x73, 0x74, 0x72, 0x6f, 0x6e, 0x67, 0x3e, + 0x24, 0x31, 0x3c, 0x2f, 0x73, 0x74, 0x72, 0x6f, 0x6e, 0x67, 0x3e, 0x27, + 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, + 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x5f, 0x5f, 0x28, 0x2e, + 0x2a, 0x3f, 0x29, 0x5f, 0x5f, 0x2f, 0x67, 0x2c, 0x20, 0x27, 0x3c, 0x73, 0x74, 0x72, 0x6f, 0x6e, 0x67, 0x3e, 0x24, 0x31, 0x3c, 0x2f, 0x73, 0x74, 0x72, 0x6f, 0x6e, 0x67, 0x3e, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x5f, 0x5f, - 0x28, 0x2e, 0x2a, 0x3f, 0x29, 0x5f, 0x5f, 0x2f, 0x67, 0x2c, 0x20, 0x27, - 0x3c, 0x73, 0x74, 0x72, 0x6f, 0x6e, 0x67, 0x3e, 0x24, 0x31, 0x3c, 0x2f, - 0x73, 0x74, 0x72, 0x6f, 0x6e, 0x67, 0x3e, 0x27, 0x29, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, + 0x28, 0x2f, 0x5c, 0x2a, 0x28, 0x2e, 0x2a, 0x3f, 0x29, 0x5c, 0x2a, 0x2f, + 0x67, 0x2c, 0x20, 0x27, 0x3c, 0x65, 0x6d, 0x3e, 0x24, 0x31, 0x3c, 0x2f, + 0x65, 0x6d, 0x3e, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, - 0x5c, 0x2a, 0x28, 0x2e, 0x2a, 0x3f, 0x29, 0x5c, 0x2a, 0x2f, 0x67, 0x2c, - 0x20, 0x27, 0x3c, 0x65, 0x6d, 0x3e, 0x24, 0x31, 0x3c, 0x2f, 0x65, 0x6d, - 0x3e, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, 0x70, - 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x5f, 0x28, 0x2e, 0x2a, 0x3f, 0x29, - 0x5f, 0x2f, 0x67, 0x2c, 0x20, 0x27, 0x3c, 0x65, 0x6d, 0x3e, 0x24, 0x31, - 0x3c, 0x2f, 0x65, 0x6d, 0x3e, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x60, 0x60, - 0x60, 0x2e, 0x2a, 0x3f, 0x5c, 0x6e, 0x28, 0x5b, 0x5c, 0x73, 0x5c, 0x53, - 0x5d, 0x2a, 0x3f, 0x29, 0x60, 0x60, 0x60, 0x2f, 0x67, 0x2c, 0x20, 0x27, - 0x3c, 0x70, 0x72, 0x65, 0x3e, 0x3c, 0x63, 0x6f, 0x64, 0x65, 0x3e, 0x24, - 0x31, 0x3c, 0x2f, 0x63, 0x6f, 0x64, 0x65, 0x3e, 0x3c, 0x2f, 0x70, 0x72, - 0x65, 0x3e, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, - 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x60, 0x28, 0x2e, 0x2a, 0x3f, - 0x29, 0x60, 0x2f, 0x67, 0x2c, 0x20, 0x27, 0x3c, 0x63, 0x6f, 0x64, 0x65, - 0x3e, 0x24, 0x31, 0x3c, 0x2f, 0x63, 0x6f, 0x64, 0x65, 0x3e, 0x27, 0x29, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, - 0x65, 0x28, 0x2f, 0x5c, 0x6e, 0x2f, 0x67, 0x69, 0x6d, 0x2c, 0x20, 0x27, - 0x3c, 0x62, 0x72, 0x20, 0x2f, 0x3e, 0x27, 0x29, 0x3b, 0x0a, 0x20, 0x20, + 0x5f, 0x28, 0x2e, 0x2a, 0x3f, 0x29, 0x5f, 0x2f, 0x67, 0x2c, 0x20, 0x27, + 0x3c, 0x65, 0x6d, 0x3e, 0x24, 0x31, 0x3c, 0x2f, 0x65, 0x6d, 0x3e, 0x27, + 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, + 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x60, 0x60, 0x60, 0x2e, + 0x2a, 0x3f, 0x5c, 0x6e, 0x28, 0x5b, 0x5c, 0x73, 0x5c, 0x53, 0x5d, 0x2a, + 0x3f, 0x29, 0x60, 0x60, 0x60, 0x2f, 0x67, 0x2c, 0x20, 0x27, 0x3c, 0x70, + 0x72, 0x65, 0x3e, 0x3c, 0x63, 0x6f, 0x64, 0x65, 0x3e, 0x24, 0x31, 0x3c, + 0x2f, 0x63, 0x6f, 0x64, 0x65, 0x3e, 0x3c, 0x2f, 0x70, 0x72, 0x65, 0x3e, + 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, + 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x60, 0x28, 0x2e, + 0x2a, 0x3f, 0x29, 0x60, 0x2f, 0x67, 0x2c, 0x20, 0x27, 0x3c, 0x63, 0x6f, + 0x64, 0x65, 0x3e, 0x24, 0x31, 0x3c, 0x2f, 0x63, 0x6f, 0x64, 0x65, 0x3e, + 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, + 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x5c, 0x6e, 0x2f, + 0x67, 0x69, 0x6d, 0x2c, 0x20, 0x27, 0x3c, 0x62, 0x72, 0x20, 0x2f, 0x3e, + 0x27, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, + 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x3c, 0x73, + 0x70, 0x61, 0x6e, 0x20, 0x64, 0x61, 0x6e, 0x67, 0x65, 0x72, 0x6f, 0x75, + 0x73, 0x6c, 0x79, 0x53, 0x65, 0x74, 0x49, 0x6e, 0x6e, 0x65, 0x72, 0x48, + 0x54, 0x4d, 0x4c, 0x3d, 0x24, 0x7b, 0x7b, 0x20, 0x5f, 0x5f, 0x68, 0x74, + 0x6d, 0x6c, 0x3a, 0x20, 0x6d, 0x64, 0x20, 0x7d, 0x7d, 0x20, 0x2f, 0x3e, + 0x60, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x3b, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x4d, 0x6f, 0x64, + 0x65, 0x6c, 0x47, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, + 0x49, 0x6e, 0x66, 0x6f, 0x20, 0x3d, 0x20, 0x28, 0x70, 0x61, 0x72, 0x61, + 0x6d, 0x73, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x21, 0x6c, 0x6c, 0x61, 0x6d, + 0x61, 0x53, 0x74, 0x61, 0x74, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, + 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, - 0x3c, 0x73, 0x70, 0x61, 0x6e, 0x20, 0x64, 0x61, 0x6e, 0x67, 0x65, 0x72, - 0x6f, 0x75, 0x73, 0x6c, 0x79, 0x53, 0x65, 0x74, 0x49, 0x6e, 0x6e, 0x65, - 0x72, 0x48, 0x54, 0x4d, 0x4c, 0x3d, 0x24, 0x7b, 0x7b, 0x20, 0x5f, 0x5f, - 0x68, 0x74, 0x6d, 0x6c, 0x3a, 0x20, 0x6d, 0x64, 0x20, 0x7d, 0x7d, 0x20, - 0x2f, 0x3e, 0x60, 0x3b, 0x0a, 0x7d, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x41, 0x70, - 0x70, 0x28, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x29, 0x20, 0x7b, 0x0a, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, - 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x3c, 0x64, 0x69, 0x76, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x63, 0x6f, - 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x22, 0x3e, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x68, 0x65, 0x61, 0x64, 0x65, - 0x72, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x3c, 0x68, 0x31, 0x3e, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x2e, 0x63, - 0x70, 0x70, 0x3c, 0x2f, 0x68, 0x31, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x68, 0x65, 0x61, 0x64, 0x65, 0x72, - 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, - 0x6d, 0x61, 0x69, 0x6e, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x63, 0x6f, 0x6e, - 0x74, 0x65, 0x6e, 0x74, 0x22, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x24, 0x7b, 0x63, 0x68, 0x61, 0x74, - 0x53, 0x74, 0x61, 0x72, 0x74, 0x65, 0x64, 0x2e, 0x76, 0x61, 0x6c, 0x75, - 0x65, 0x20, 0x3f, 0x20, 0x43, 0x68, 0x61, 0x74, 0x4c, 0x6f, 0x67, 0x20, - 0x3a, 0x20, 0x43, 0x6f, 0x6e, 0x66, 0x69, 0x67, 0x46, 0x6f, 0x72, 0x6d, - 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x3c, 0x2f, 0x6d, 0x61, 0x69, 0x6e, 0x3e, 0x0a, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x66, 0x6f, 0x6f, 0x74, 0x65, - 0x72, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x77, 0x72, 0x69, 0x74, 0x65, 0x22, - 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x3c, 0x24, 0x7b, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x49, 0x6e, - 0x70, 0x75, 0x74, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x6f, 0x6f, 0x74, 0x65, 0x72, - 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, - 0x66, 0x6f, 0x6f, 0x74, 0x65, 0x72, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x70, 0x3e, 0x50, 0x6f, 0x77, - 0x65, 0x72, 0x65, 0x64, 0x20, 0x62, 0x79, 0x20, 0x3c, 0x61, 0x20, 0x68, - 0x72, 0x65, 0x66, 0x3d, 0x22, 0x68, 0x74, 0x74, 0x70, 0x73, 0x3a, 0x2f, - 0x2f, 0x67, 0x69, 0x74, 0x68, 0x75, 0x62, 0x2e, 0x63, 0x6f, 0x6d, 0x2f, - 0x67, 0x67, 0x65, 0x72, 0x67, 0x61, 0x6e, 0x6f, 0x76, 0x2f, 0x6c, 0x6c, - 0x61, 0x6d, 0x61, 0x2e, 0x63, 0x70, 0x70, 0x22, 0x3e, 0x6c, 0x6c, 0x61, - 0x6d, 0x61, 0x2e, 0x63, 0x70, 0x70, 0x3c, 0x2f, 0x61, 0x3e, 0x20, 0x61, - 0x6e, 0x64, 0x20, 0x3c, 0x61, 0x20, 0x68, 0x72, 0x65, 0x66, 0x3d, 0x22, - 0x68, 0x74, 0x74, 0x70, 0x73, 0x3a, 0x2f, 0x2f, 0x67, 0x67, 0x6d, 0x6c, - 0x2e, 0x61, 0x69, 0x22, 0x3e, 0x67, 0x67, 0x6d, 0x6c, 0x2e, 0x61, 0x69, - 0x3c, 0x2f, 0x61, 0x3e, 0x3c, 0x2f, 0x70, 0x3e, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x6f, 0x6f, 0x74, 0x65, - 0x72, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, - 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x3b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x28, 0x68, 0x28, 0x41, 0x70, 0x70, - 0x29, 0x2c, 0x20, 0x64, 0x6f, 0x63, 0x75, 0x6d, 0x65, 0x6e, 0x74, 0x2e, - 0x62, 0x6f, 0x64, 0x79, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x3c, 0x2f, 0x73, - 0x63, 0x72, 0x69, 0x70, 0x74, 0x3e, 0x0a, 0x3c, 0x2f, 0x68, 0x65, 0x61, - 0x64, 0x3e, 0x0a, 0x0a, 0x3c, 0x62, 0x6f, 0x64, 0x79, 0x3e, 0x0a, 0x3c, - 0x2f, 0x62, 0x6f, 0x64, 0x79, 0x3e, 0x0a, 0x0a, 0x3c, 0x2f, 0x68, 0x74, - 0x6d, 0x6c, 0x3e, 0x0a + 0x3c, 0x73, 0x70, 0x61, 0x6e, 0x2f, 0x3e, 0x60, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, + 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x73, 0x70, 0x61, + 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x24, 0x7b, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x53, 0x74, 0x61, 0x74, + 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x70, 0x72, 0x65, 0x64, + 0x69, 0x63, 0x74, 0x65, 0x64, 0x5f, 0x70, 0x65, 0x72, 0x5f, 0x74, 0x6f, + 0x6b, 0x65, 0x6e, 0x5f, 0x6d, 0x73, 0x2e, 0x74, 0x6f, 0x46, 0x69, 0x78, + 0x65, 0x64, 0x28, 0x29, 0x7d, 0x6d, 0x73, 0x20, 0x70, 0x65, 0x72, 0x20, + 0x74, 0x6f, 0x6b, 0x65, 0x6e, 0x2c, 0x20, 0x24, 0x7b, 0x6c, 0x6c, 0x61, + 0x6d, 0x61, 0x53, 0x74, 0x61, 0x74, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x2e, 0x70, 0x72, 0x65, 0x64, 0x69, 0x63, 0x74, 0x65, 0x64, 0x5f, + 0x70, 0x65, 0x72, 0x5f, 0x73, 0x65, 0x63, 0x6f, 0x6e, 0x64, 0x2e, 0x74, + 0x6f, 0x46, 0x69, 0x78, 0x65, 0x64, 0x28, 0x32, 0x29, 0x7d, 0x20, 0x74, + 0x6f, 0x6b, 0x65, 0x6e, 0x73, 0x20, 0x70, 0x65, 0x72, 0x20, 0x73, 0x65, + 0x63, 0x6f, 0x6e, 0x64, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x2f, 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x20, 0x41, 0x70, 0x70, 0x28, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x29, 0x20, + 0x7b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, + 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x20, 0x69, + 0x64, 0x3d, 0x22, 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, + 0x22, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x68, 0x65, 0x61, 0x64, 0x65, 0x72, 0x3e, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x68, + 0x31, 0x3e, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x2e, 0x63, 0x70, 0x70, 0x3c, + 0x2f, 0x68, 0x31, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x68, 0x65, 0x61, 0x64, 0x65, 0x72, 0x3e, + 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x3c, 0x6d, 0x61, 0x69, 0x6e, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x63, 0x6f, + 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x22, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x24, 0x7b, 0x63, + 0x68, 0x61, 0x74, 0x53, 0x74, 0x61, 0x72, 0x74, 0x65, 0x64, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3f, 0x20, 0x43, 0x68, 0x61, 0x74, 0x4c, + 0x6f, 0x67, 0x20, 0x3a, 0x20, 0x43, 0x6f, 0x6e, 0x66, 0x69, 0x67, 0x46, + 0x6f, 0x72, 0x6d, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x6d, 0x61, 0x69, 0x6e, + 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x73, 0x65, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x69, 0x64, + 0x3d, 0x22, 0x77, 0x72, 0x69, 0x74, 0x65, 0x22, 0x3e, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x24, + 0x7b, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x49, 0x6e, 0x70, 0x75, + 0x74, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x73, 0x65, 0x63, 0x74, 0x69, 0x6f, + 0x6e, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x3c, 0x66, 0x6f, 0x6f, 0x74, 0x65, 0x72, 0x3e, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, + 0x70, 0x3e, 0x3c, 0x24, 0x7b, 0x4d, 0x6f, 0x64, 0x65, 0x6c, 0x47, 0x65, + 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x49, 0x6e, 0x66, 0x6f, + 0x7d, 0x20, 0x2f, 0x3e, 0x3c, 0x2f, 0x70, 0x3e, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x70, 0x3e, + 0x50, 0x6f, 0x77, 0x65, 0x72, 0x65, 0x64, 0x20, 0x62, 0x79, 0x20, 0x3c, + 0x61, 0x20, 0x68, 0x72, 0x65, 0x66, 0x3d, 0x22, 0x68, 0x74, 0x74, 0x70, + 0x73, 0x3a, 0x2f, 0x2f, 0x67, 0x69, 0x74, 0x68, 0x75, 0x62, 0x2e, 0x63, + 0x6f, 0x6d, 0x2f, 0x67, 0x67, 0x65, 0x72, 0x67, 0x61, 0x6e, 0x6f, 0x76, + 0x2f, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x2e, 0x63, 0x70, 0x70, 0x22, 0x3e, + 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x2e, 0x63, 0x70, 0x70, 0x3c, 0x2f, 0x61, + 0x3e, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x3c, 0x61, 0x20, 0x68, 0x72, 0x65, + 0x66, 0x3d, 0x22, 0x68, 0x74, 0x74, 0x70, 0x73, 0x3a, 0x2f, 0x2f, 0x67, + 0x67, 0x6d, 0x6c, 0x2e, 0x61, 0x69, 0x22, 0x3e, 0x67, 0x67, 0x6d, 0x6c, + 0x2e, 0x61, 0x69, 0x3c, 0x2f, 0x61, 0x3e, 0x2e, 0x3c, 0x2f, 0x70, 0x3e, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, + 0x2f, 0x66, 0x6f, 0x6f, 0x74, 0x65, 0x72, 0x3e, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x3b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x6e, 0x64, + 0x65, 0x72, 0x28, 0x68, 0x28, 0x41, 0x70, 0x70, 0x29, 0x2c, 0x20, 0x64, + 0x6f, 0x63, 0x75, 0x6d, 0x65, 0x6e, 0x74, 0x2e, 0x62, 0x6f, 0x64, 0x79, + 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x3c, 0x2f, 0x73, 0x63, 0x72, 0x69, 0x70, + 0x74, 0x3e, 0x0a, 0x3c, 0x2f, 0x68, 0x65, 0x61, 0x64, 0x3e, 0x0a, 0x0a, + 0x3c, 0x62, 0x6f, 0x64, 0x79, 0x3e, 0x0a, 0x3c, 0x2f, 0x62, 0x6f, 0x64, + 0x79, 0x3e, 0x0a, 0x0a, 0x3c, 0x2f, 0x68, 0x74, 0x6d, 0x6c, 0x3e, 0x0a }; -unsigned int index_html_len = 10108; +unsigned int index_html_len = 10752; diff --git a/examples/server/public/completion.js b/examples/server/public/completion.js index 4f5005cfb..a43d5a7d5 100644 --- a/examples/server/public/completion.js +++ b/examples/server/public/completion.js @@ -5,20 +5,29 @@ const paramDefaults = { stop: [""] }; -/** - * This function completes the input text using a llama dictionary. - * @param {object} params - The parameters for the completion request. - * @param {object} controller - an instance of AbortController if you need one, or null. - * @param {function} callback - The callback function to call when the completion is done. - * @returns {string} the completed text as a string. Ideally ignored, and you get at it via the callback. - */ -export const llamaComplete = async (params, controller, callback) => { +let generation_settings = null; + + +// Completes the prompt as a generator. Recommended for most use cases. +// +// Example: +// +// import { llama } from '/completion.js' +// +// const request = llama("Tell me a joke", {n_predict: 800}) +// for await (const chunk of request) { +// document.write(chunk.data.content) +// } +// +export async function* llama(prompt, params = {}, config = {}) { + let controller = config.controller; + if (!controller) { controller = new AbortController(); } - const completionParams = { ...paramDefaults, ...params }; - // we use fetch directly here becasue the built in fetchEventSource does not support POST + const completionParams = { ...paramDefaults, ...params, prompt }; + const response = await fetch("/completion", { method: 'POST', body: JSON.stringify(completionParams), @@ -36,7 +45,6 @@ export const llamaComplete = async (params, controller, callback) => { let content = ""; try { - let cont = true; while (cont) { @@ -59,18 +67,21 @@ export const llamaComplete = async (params, controller, callback) => { result.data = JSON.parse(result.data); content += result.data.content; - // callack - if (callback) { - cont = callback(result) != false; - } + // yield + yield result; // if we got a stop token from server, we will break here if (result.data.stop) { + if (result.data.generation_settings) { + generation_settings = result.data.generation_settings; + } break; } } } catch (e) { - console.error("llama error: ", e); + if (e.name !== 'AbortError') { + console.error("llama error: ", e); + } throw e; } finally { @@ -79,3 +90,79 @@ export const llamaComplete = async (params, controller, callback) => { return content; } + +// Call llama, return an event target that you can subcribe to +// +// Example: +// +// import { llamaEventTarget } from '/completion.js' +// +// const conn = llamaEventTarget(prompt) +// conn.addEventListener("message", (chunk) => { +// document.write(chunk.detail.content) +// }) +// +export const llamaEventTarget = (prompt, params = {}, config = {}) => { + const eventTarget = new EventTarget(); + (async () => { + let content = ""; + for await (const chunk of llama(prompt, params, config)) { + if (chunk.data) { + content += chunk.data.content; + eventTarget.dispatchEvent(new CustomEvent("message", { detail: chunk.data })); + } + if (chunk.data.generation_settings) { + eventTarget.dispatchEvent(new CustomEvent("generation_settings", { detail: chunk.data.generation_settings })); + } + if (chunk.data.timings) { + eventTarget.dispatchEvent(new CustomEvent("timings", { detail: chunk.data.timings })); + } + } + eventTarget.dispatchEvent(new CustomEvent("done", { detail: { content } })); + })(); + return eventTarget; +} + +// Call llama, return a promise that resolves to the completed text. This does not support streaming +// +// Example: +// +// llamaPromise(prompt).then((content) => { +// document.write(content) +// }) +// +// or +// +// const content = await llamaPromise(prompt) +// document.write(content) +// +export const llamaPromise = (prompt, params = {}, config = {}) => { + return new Promise(async (resolve, reject) => { + let content = ""; + try { + for await (const chunk of llama(prompt, params, config)) { + content += chunk.data.content; + } + resolve(content); + } catch (error) { + reject(error); + } + }); +}; + +/** + * (deprecated) + */ +export const llamaComplete = async (params, controller, callback) => { + for await (const chunk of llama(params.prompt, params, { controller })) { + callback(chunk); + } +} + +// Get the model info from the server. This is useful for getting the context window and so on. +export const llamaModelInfo = async () => { + if (!generation_settings) { + generation_settings = await fetch("/model.json").then(r => r.json()); + } + return generation_settings; +} diff --git a/examples/server/public/index.html b/examples/server/public/index.html index 6393e2e75..8ace0b0af 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -6,7 +6,6 @@ llama.cpp - chat +
    +
    diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 1e6d10c1d..025b385cc 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -124,8 +124,9 @@ static void server_log(const char *level, const char *function, int line, static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token) { std::string out = token == -1 ? "" : llama_token_to_str(ctx, token); - // if first bit is 1, meaning it's a partial character - if (out.size() > 0 && (out[0] & 0x80) == 0x80) + // if the size is 1 and first bit is 1, meaning it's a partial character + // (size > 1 meaning it's already a known token) + if (out.size() == 1 && (out[0] & 0x80) == 0x80) { std::stringstream ss; ss << std::hex << (out[0] & 0xff); @@ -1321,59 +1322,86 @@ int main(int argc, char **argv) while (llama.has_next_token) { const completion_token_output token_with_probs = llama.doCompletion(); - const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(llama.ctx, token_with_probs.tok); - if (llama.multibyte_pending > 0) { + if (token_with_probs.tok == -1 || llama.multibyte_pending > 0) { continue; } + const std::string token_text = llama_token_to_str(llama.ctx, token_with_probs.tok); size_t pos = std::min(sent_count, llama.generated_text.size()); const std::string str_test = llama.generated_text.substr(pos); + bool is_stop_full = false; size_t stop_pos = llama.findStoppingStrings(str_test, token_text.size(), STOP_FULL); if (stop_pos != std::string::npos) { + is_stop_full = true; llama.generated_text.erase( llama.generated_text.begin() + pos + stop_pos, llama.generated_text.end()); pos = std::min(sent_count, llama.generated_text.size()); } else { + is_stop_full = false; stop_pos = llama.findStoppingStrings(str_test, token_text.size(), STOP_PARTIAL); } - const std::string to_send = llama.generated_text.substr(pos, stop_pos); - sent_count += to_send.size(); + if ( + stop_pos == std::string::npos || + // Send rest of the text if we are at the end of the generation + (!llama.has_next_token && !is_stop_full && stop_pos > 0) + ) { + const std::string to_send = llama.generated_text.substr(pos, std::string::npos); - std::vector probs_output = {}; + sent_count += to_send.size(); - if (llama.params.n_probs > 0) { - const std::vector to_send_toks = llama_tokenize(llama.ctx, to_send, false); - size_t probs_pos = std::min(sent_token_probs_index, llama.generated_token_probs.size()); - size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), llama.generated_token_probs.size()); - if (probs_pos < probs_stop_pos) { - probs_output = std::vector(llama.generated_token_probs.begin() + probs_pos, llama.generated_token_probs.begin() + probs_stop_pos); + std::vector probs_output = {}; + + if (llama.params.n_probs > 0) { + const std::vector to_send_toks = llama_tokenize(llama.ctx, to_send, false); + size_t probs_pos = std::min(sent_token_probs_index, llama.generated_token_probs.size()); + size_t probs_stop_pos = std::min(sent_token_probs_index + to_send_toks.size(), llama.generated_token_probs.size()); + if (probs_pos < probs_stop_pos) { + probs_output = std::vector(llama.generated_token_probs.begin() + probs_pos, llama.generated_token_probs.begin() + probs_stop_pos); + } + sent_token_probs_index = probs_stop_pos; + } + + const json data = format_partial_response(llama, to_send, probs_output); + + const std::string str = + "data: " + + data.dump(-1, ' ', false, json::error_handler_t::replace) + + "\n\n"; + + LOG_VERBOSE("data stream", { + { "to_send", str } + }); + + if (!sink.write(str.data(), str.size())) { + LOG_VERBOSE("stream closed", {}); + llama_print_timings(llama.ctx); + return false; } - sent_token_probs_index = probs_stop_pos; } - const json data = llama.has_next_token - ? format_partial_response(llama, to_send, probs_output) - // Generation is done, send extra information. - : format_final_response(llama, to_send, llama.generated_token_probs); + if (!llama.has_next_token) { + // Generation is done, send extra information. + const json data = format_final_response(llama, "", llama.generated_token_probs); - const std::string str = - "data: " + - data.dump(-1, ' ', false, json::error_handler_t::replace) + - "\n\n"; + const std::string str = + "data: " + + data.dump(-1, ' ', false, json::error_handler_t::replace) + + "\n\n"; - LOG_VERBOSE("data stream", { - { "to_send", str } - }); + LOG_VERBOSE("data stream", { + { "to_send", str } + }); - if (!sink.write(str.data(), str.size())) { - LOG_VERBOSE("stream closed", {}); - llama_print_timings(llama.ctx); - return false; + if (!sink.write(str.data(), str.size())) { + LOG_VERBOSE("stream closed", {}); + llama_print_timings(llama.ctx); + return false; + } } } From 12e2e33a977af73e75885eeee91c5575a77f4e5f Mon Sep 17 00:00:00 2001 From: slaren Date: Fri, 25 Aug 2023 14:08:53 +0200 Subject: [PATCH 362/852] convert.py : export rope freq_base when converting CodeLlama from an HF model (#2773) --- convert.py | 34 ++++++++++++++++++---------------- 1 file changed, 18 insertions(+), 16 deletions(-) diff --git a/convert.py b/convert.py index 10276bf63..e58ea46e0 100755 --- a/convert.py +++ b/convert.py @@ -160,13 +160,14 @@ class Params: def loadHFTransformerJson(model: 'LazyModel', config_path: 'Path') -> 'Params': config = json.load(open(config_path)) - n_vocab = config["vocab_size"] - n_embd = config["hidden_size"] - n_layer = config["num_hidden_layers"] - n_ff = config["intermediate_size"] - n_head = config["num_attention_heads"] - n_head_kv = config["num_key_value_heads"] if "num_key_value_heads" in config else n_head - f_norm_eps = config["rms_norm_eps"] + n_vocab = config["vocab_size"] + n_embd = config["hidden_size"] + n_layer = config["num_hidden_layers"] + n_ff = config["intermediate_size"] + n_head = config["num_attention_heads"] + n_head_kv = config["num_key_value_heads"] if "num_key_value_heads" in config else n_head + f_norm_eps = config["rms_norm_eps"] + f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None n_mult = Params.find_n_mult(n_ff, n_embd) @@ -179,15 +180,16 @@ class Params: "Suggestion: provide 'config.json' of the model in the same directory containing model files.") return Params( - n_vocab = n_vocab, - n_embd = n_embd, - n_mult = n_mult, - n_layer = n_layer, - n_ctx = n_ctx, - n_ff = n_ff, - n_head = n_head, - n_head_kv = n_head_kv, - f_norm_eps = f_norm_eps, + n_vocab = n_vocab, + n_embd = n_embd, + n_mult = n_mult, + n_layer = n_layer, + n_ctx = n_ctx, + n_ff = n_ff, + n_head = n_head, + n_head_kv = n_head_kv, + f_norm_eps = f_norm_eps, + f_rope_freq_base = f_rope_freq_base, ) # LLaMA v2 70B params.json From 154725c5436808e5c519685d0279e850596dbe62 Mon Sep 17 00:00:00 2001 From: slaren Date: Fri, 25 Aug 2023 15:16:19 +0200 Subject: [PATCH 363/852] llama-bench : add model sizes (#2771) * llama-bench : add model sizes * more compact markdown output * back to GiB * adjust column sizes --- examples/llama-bench/llama-bench.cpp | 60 ++++++++++++++++++++++++---- llama.cpp | 18 ++++++++- llama.h | 6 ++- 3 files changed, 74 insertions(+), 10 deletions(-) diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 7a2811584..d0fe6d90d 100755 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -441,6 +441,8 @@ struct test { static const std::string gpu_info; std::string model_filename; std::string model_type; + uint64_t model_size; + uint64_t model_n_params; int n_batch; int n_threads; bool f32_kv; @@ -457,8 +459,10 @@ struct test { test(const cmd_params_instance & inst, const llama_model * lmodel, const llama_context * ctx) { model_filename = inst.model; char buf[128]; - llama_model_type(lmodel, buf, sizeof(buf)); + llama_model_desc(lmodel, buf, sizeof(buf)); model_type = buf; + model_size = llama_model_size(lmodel); + model_n_params = llama_model_n_params(lmodel); n_batch = inst.n_batch; n_threads = inst.n_threads; f32_kv = inst.f32_kv; @@ -524,7 +528,7 @@ struct test { "build_commit", "build_number", "cuda", "opencl", "metal", "gpu_blas", "blas", "cpu_info", "gpu_info", - "model_filename", "model_type", + "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_threads", "f16_kv", "n_gpu_layers", "main_gpu", "mul_mat_q", "low_vram", "tensor_split", "n_prompt", "n_gen", "test_time", @@ -538,6 +542,7 @@ struct test { static field_type get_field_type(const std::string & field) { if (field == "build_number" || field == "n_batch" || field == "n_threads" || + field == "model_size" || field == "model_n_params" || field == "n_gpu_layers" || field == "main_gpu" || field == "n_prompt" || field == "n_gen" || field == "avg_ns" || field == "stddev_ns") { @@ -573,7 +578,7 @@ struct test { build_commit, std::to_string(build_number), std::to_string(cuda), std::to_string(opencl), std::to_string(metal), std::to_string(gpu_blas), std::to_string(blas), cpu_info, gpu_info, - model_filename, model_type, + model_filename, model_type, std::to_string(model_size), std::to_string(model_n_params), std::to_string(n_batch), std::to_string(n_threads), std::to_string(!f32_kv), std::to_string(n_gpu_layers), std::to_string(main_gpu), std::to_string(mul_mat_q), std::to_string(low_vram), tensor_split_str, std::to_string(n_prompt), std::to_string(n_gen), test_time, @@ -709,8 +714,15 @@ struct markdown_printer : public printer { return -30; } if (field == "t/s") { - return 15; + return 16; } + if (field == "size" || field == "params") { + return 10; + } + if (field == "n_gpu_layers") { + return 3; + } + int width = std::max((int)field.length(), 10); if (test::get_field_type(field) == test::STRING) { @@ -719,9 +731,28 @@ struct markdown_printer : public printer { return width; } + static std::string get_field_display_name(const std::string & field) { + if (field == "n_gpu_layers") { + return "ngl"; + } + if (field == "n_threads") { + return "threads"; + } + if (field == "mul_mat_q") { + return "mmq"; + } + if (field == "tensor_split") { + return "ts"; + } + return field; + } + void print_header(const cmd_params & params) override { // select fields to print - fields = { "model", "backend" }; + fields.push_back("model"); + fields.push_back("size"); + fields.push_back("params"); + fields.push_back("backend"); bool is_cpu_backend = test::get_backend() == "CPU" || test::get_backend() == "BLAS"; if (!is_cpu_backend) { fields.push_back("n_gpu_layers"); @@ -752,7 +783,7 @@ struct markdown_printer : public printer { fprintf(fout, "|"); for (const auto & field : fields) { - fprintf(fout, " %*s |", get_field_width(field), field.c_str()); + fprintf(fout, " %*s |", get_field_width(field), get_field_display_name(field).c_str()); } fprintf(fout, "\n"); fprintf(fout, "|"); @@ -769,12 +800,26 @@ struct markdown_printer : public printer { fprintf(fout, "|"); for (const auto & field : fields) { std::string value; + char buf[128]; if (field == "model") { value = t.model_type; + } else if (field == "size") { + if (t.model_size < 1024*1024*1024) { + snprintf(buf, sizeof(buf), "%.2f MiB", t.model_size / 1024.0 / 1024.0); + } else { + snprintf(buf, sizeof(buf), "%.2f GiB", t.model_size / 1024.0 / 1024.0 / 1024.0); + } + value = buf; + } else if (field == "params") { + if (t.model_n_params < 1000*1000*1000) { + snprintf(buf, sizeof(buf), "%.2f M", t.model_n_params / 1e6); + } else { + snprintf(buf, sizeof(buf), "%.2f B", t.model_n_params / 1e9); + } + value = buf; } else if (field == "backend") { value = test::get_backend(); } else if (field == "test") { - char buf[128]; if (t.n_prompt > 0 && t.n_gen == 0) { snprintf(buf, sizeof(buf), "pp %d", t.n_prompt); } else if (t.n_gen > 0 && t.n_prompt == 0) { @@ -785,7 +830,6 @@ struct markdown_printer : public printer { } value = buf; } else if (field == "t/s") { - char buf[128]; snprintf(buf, sizeof(buf), "%.2f ± %.2f", t.avg_ts(), t.stdev_ts()); value = buf; } else if (vmap.find(field) != vmap.end()) { diff --git a/llama.cpp b/llama.cpp index d12b6d1cb..4529ac822 100644 --- a/llama.cpp +++ b/llama.cpp @@ -5297,13 +5297,29 @@ int llama_model_n_embd(const struct llama_model * model) { return model->hparams.n_embd; } -int llama_model_type(const struct llama_model * model, char * buf, size_t buf_size) { +int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) { return snprintf(buf, buf_size, "%s %s %s", model->name.c_str(), llama_model_type_name(model->type), llama_model_ftype_name(model->ftype).c_str()); } +uint64_t llama_model_size(const struct llama_model * model) { + uint64_t size = 0; + for (const auto & it : model->tensors_by_name) { + size += ggml_nbytes(it.second); + } + return size; +} + +uint64_t llama_model_n_params(const struct llama_model * model) { + uint64_t nparams = 0; + for (const auto & it : model->tensors_by_name) { + nparams += ggml_nelements(it.second); + } + return nparams; +} + int llama_model_quantize( const char * fname_inp, const char * fname_out, diff --git a/llama.h b/llama.h index 2bcf94e0f..d47468172 100644 --- a/llama.h +++ b/llama.h @@ -254,7 +254,11 @@ extern "C" { LLAMA_API int llama_model_n_embd (const struct llama_model * model); // Get a string describing the model type - LLAMA_API int llama_model_type(const struct llama_model * model, char * buf, size_t buf_size); + LLAMA_API int llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size); + // Returns the total size of all the tensors in the model in bytes + LLAMA_API uint64_t llama_model_size(const struct llama_model * model); + // Returns the total number of parameters in the model + LLAMA_API uint64_t llama_model_n_params(const struct llama_model * model); // Returns 0 on success LLAMA_API int llama_model_quantize( From 28b2c996ca0ab90a5669946084f13443ec98e241 Mon Sep 17 00:00:00 2001 From: Nigel Bosch Date: Fri, 25 Aug 2023 09:41:52 -0500 Subject: [PATCH 364/852] convert.py : Get rope scale from HuggingFace models (#2772) * Get rope scale from HF models * Save rope scale only for linear scaling * Rewrite for clarity --- convert.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/convert.py b/convert.py index e58ea46e0..4f3e92798 100755 --- a/convert.py +++ b/convert.py @@ -105,6 +105,7 @@ class Params: f_norm_eps: float f_rope_freq_base: Optional[float] = None + f_rope_scale: Optional[float] = None ftype: Optional[GGMLFileType] = None @@ -169,6 +170,11 @@ class Params: f_norm_eps = config["rms_norm_eps"] f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None + if "rope_scaling" in config and config["rope_scaling"].get("type") == "linear": + f_rope_scale = config["rope_scaling"].get("factor") + else: + f_rope_scale = None + n_mult = Params.find_n_mult(n_ff, n_embd) if "max_sequence_length" in config: @@ -190,6 +196,7 @@ class Params: n_head_kv = n_head_kv, f_norm_eps = f_norm_eps, f_rope_freq_base = f_rope_freq_base, + f_rope_scale = f_rope_scale, ) # LLaMA v2 70B params.json @@ -773,6 +780,9 @@ class OutputFile: if params.f_rope_freq_base: self.gguf.add_rope_freq_base(params.f_rope_freq_base) + if params.f_rope_scale: + self.gguf.add_rope_scale_linear(params.f_rope_scale) + if params.ftype: self.gguf.add_file_type(params.ftype) From c82742ac9cd96fd34aa961978805c1d8a361d589 Mon Sep 17 00:00:00 2001 From: Matt Pulver Date: Fri, 25 Aug 2023 11:18:48 -0400 Subject: [PATCH 365/852] llama : add llama_beam_search() (#2267) * Add llama_beam_search(). * Add '// Beam search' heading to llama.{h,cpp} after llama_grammar_accept_token(). * Add space around * pointers and & references. * Add spaces around comparison and assignment operators. * Prefer west const. * Use llama_ prefix for structs in global namespace. * Delete obsolete comment from an earlier revision. * Change eos to eob in llama_beam and llama_beam_view structs. --- common/common.h | 1 + examples/CMakeLists.txt | 1 + examples/beam_search/CMakeLists.txt | 8 + examples/beam_search/beam_search.cpp | 188 ++++++++++++++++++++ examples/server/server.cpp | 90 ++++++++-- llama.cpp | 251 +++++++++++++++++++++++++++ llama.h | 37 ++++ 7 files changed, 563 insertions(+), 13 deletions(-) create mode 100644 examples/beam_search/CMakeLists.txt create mode 100644 examples/beam_search/beam_search.cpp diff --git a/common/common.h b/common/common.h index 17d271e67..ce61265f8 100644 --- a/common/common.h +++ b/common/common.h @@ -28,6 +28,7 @@ struct gpt_params { int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors float tensor_split[LLAMA_MAX_DEVICES] = {0}; // how split tensors should be distributed across GPUs int32_t n_probs = 0; // if greater than 0, output the probabilities of top n_probs tokens. + int32_t n_beams = 0; // if non-zero then use beam search of given width. float rope_freq_base = 10000.0f; // RoPE base frequency float rope_freq_scale = 1.0f; // RoPE frequency scaling factor diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index d2176c910..94b785224 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -25,6 +25,7 @@ else() add_subdirectory(simple) add_subdirectory(embd-input) add_subdirectory(llama-bench) + add_subdirectory(beam_search) if (LLAMA_METAL) add_subdirectory(metal) endif() diff --git a/examples/beam_search/CMakeLists.txt b/examples/beam_search/CMakeLists.txt new file mode 100644 index 000000000..b29e01092 --- /dev/null +++ b/examples/beam_search/CMakeLists.txt @@ -0,0 +1,8 @@ +set(TARGET beam_search) +add_executable(${TARGET} beam_search.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) +if(TARGET BUILD_INFO) + add_dependencies(${TARGET} BUILD_INFO) +endif() diff --git a/examples/beam_search/beam_search.cpp b/examples/beam_search/beam_search.cpp new file mode 100644 index 000000000..1c04fabc2 --- /dev/null +++ b/examples/beam_search/beam_search.cpp @@ -0,0 +1,188 @@ +#ifndef _GNU_SOURCE +#define _GNU_SOURCE +#endif + +#include "common.h" +#include "llama.h" +#include "build-info.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) +#include +#include +#elif defined (_WIN32) +#define WIN32_LEAN_AND_MEAN +#define NOMINMAX +#include +#include +#endif + +// Used for debugging to print out beam tokens. +struct ostream_beam_view { + llama_context * ctx; + llama_beam_view beam_view; +}; +std::ostream& operator<<(std::ostream& os, const ostream_beam_view & obv) { + os << "p(" << obv.beam_view.p << ") eob(" << std::boolalpha << obv.beam_view.eob << ") tokens("; + for (size_t i = 0 ; i < obv.beam_view.n_tokens ; ++i) { + os << llama_token_to_str(obv.ctx, obv.beam_view.tokens[i]); + } + return os << ')'; +} + +// Put here anything you want back in beam_search_callback(). +struct beam_search_callback_data { + llama_context * ctx; + std::vector response; +}; + +// In this case, end-of-beam (eob) is equivalent to end-of-sentence (eos) but this need not always be the same. +// For example, eob can be flagged due to maximum token length, stop words, etc. +bool is_at_eob(const beam_search_callback_data & callback_data, const llama_token * tokens, const size_t n_tokens) { + return n_tokens && tokens[n_tokens-1] == llama_token_eos(callback_data.ctx); +} + +// Function matching type llama_beam_search_callback_fn_t. +// Custom callback example is called each time the beams lengths increase: +// * Show progress by printing ',' following by number of convergent beam tokens if any. +// * When all beams converge to a common prefix, they are made available in beams_state.beams[0]. +// This is also called when the stop condition is met. +// Collect tokens into std::vector response which is pointed to by callback_data. +void beam_search_callback(void * callback_data_ptr, llama_beams_state beams_state) { + auto& callback_data = *static_cast(callback_data_ptr); + // Mark beams as EOS as needed. + for (size_t i = 0 ; i < beams_state.n_beams ; ++i) { + llama_beam_view& beam_view = beams_state.beam_views[i]; + if (!beam_view.eob && is_at_eob(callback_data, beam_view.tokens, beam_view.n_tokens)) { + beam_view.eob = true; + } + } + printf(","); // Show progress + if (const size_t n = beams_state.common_prefix_length) { + callback_data.response.resize(callback_data.response.size() + n); + assert(0u < beams_state.n_beams); + const llama_token * tokens = beams_state.beam_views[0].tokens; + std::copy(tokens, tokens + n, callback_data.response.end() - n); + printf("%lu", n); + } + fflush(stdout); +#if 1 // DEBUG: print current beams for this iteration + std::cout << "\n\nCurrent beams (last_call=" << beams_state.last_call << "):\n"; + for (size_t i = 0 ; i < beams_state.n_beams ; ++i) { + std::cout << "beams["< 3 ) + { + params.prompt = argv[3]; + } + + if ( params.prompt.empty() ) + { + params.prompt = "### Request:\nHow many countries are there?\n\n### Response:\n"; + } + + //--------------------------------- + // Init LLM : + //--------------------------------- + + llama_backend_init(params.numa); + + llama_model * model; + llama_context * ctx; + + std::tie(model, ctx) = llama_init_from_gpt_params( params ); + + if ( model == NULL ) + { + fprintf( stderr , "%s: error: unable to load model\n" , __func__ ); + return 1; + } + + //--------------------------------- + // Tokenize the prompt : + //--------------------------------- + + std::vector tokens_list = llama_tokenize(ctx, params.prompt, true); + + const size_t max_context_size = llama_n_ctx( ctx ); + const size_t max_tokens_list_size = max_context_size - 4 ; + + if (tokens_list.size() > max_tokens_list_size) + { + fprintf( stderr , "%s: error: prompt too long (%lu tokens, max %lu)\n" , + __func__ , tokens_list.size() , max_tokens_list_size ); + return 1; + } + + fprintf( stderr, "\n\n" ); + + // Print the tokens from the prompt : + + for( auto id : tokens_list ) + { + std::cout << llama_token_to_str(ctx, id); + } + std::cout << std::flush; + + int n_past = llama_get_kv_cache_token_count(ctx); + if (llama_eval(ctx, tokens_list.data(), tokens_list.size(), n_past, params.n_threads)) + { + fprintf(stderr, "%s : failed to eval prompt.\n" , __func__ ); + return 1; + } + n_past += tokens_list.size(); + + beam_search_callback_data callback_data{ctx, {}}; + size_t const beam_width = static_cast(params.n_beams); + int const n_predict = 256; + llama_beam_search(ctx, beam_search_callback, &callback_data, beam_width, n_past, n_predict, params.n_threads); + + std::cout << "\n\n"; + for (llama_token const token_id : callback_data.response) { + std::cout << llama_token_to_str(ctx,token_id); + } + std::cout << std::endl; + + llama_free( ctx ); + llama_free_model( model ); + + llama_backend_free(); + + return 0; +} diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 025b385cc..3300553f9 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1209,6 +1209,62 @@ static void log_server_request(const Request &req, const Response &res) }); } +bool is_at_eob(llama_server_context & server_context, const llama_token * tokens, const size_t n_tokens) { + return n_tokens && tokens[n_tokens-1] == llama_token_eos(server_context.ctx); +} + +// Function matching type llama_beam_search_callback_fn_t. +// Custom callback example is called each time the beams lengths increase: +// * Show progress by printing ',' following by number of convergent beam tokens if any. +// * When all beams converge to a common prefix, they are made available in beams_state.beams[0]. +// This is also called when the stop condition is met. +// Collect tokens into std::vector response which is pointed to by callback_data. +void beam_search_callback(void * callback_data, llama_beams_state beams_state) { + auto & llama = *static_cast(callback_data); + // Mark beams as EOS as needed. + for (size_t i = 0 ; i < beams_state.n_beams ; ++i) { + llama_beam_view& beam_view = beams_state.beam_views[i]; + if (!beam_view.eob && is_at_eob(llama, beam_view.tokens, beam_view.n_tokens)) { + beam_view.eob = true; + } + } + printf(","); // Show progress + if (const size_t n = beams_state.common_prefix_length) { + llama.generated_token_probs.resize(llama.generated_token_probs.size() + n); + assert(0u < beams_state.n_beams); + const llama_token * tokens = beams_state.beam_views[0].tokens; + const auto map = [](llama_token tok) { return completion_token_output{{},tok}; }; + std::transform(tokens, tokens + n, llama.generated_token_probs.end() - n, map); + printf("%lu", n); + } + fflush(stdout); +#if 0 // DEBUG: print current beams for this iteration + std::cout << "\n\nCurrent beams:\n"; + for (size_t i=0 ; i < beams_state.n_beams ; ++i) { + std::cout << "beams["<t_sample_us += ggml_time_us() - t_start_sample_us; } +// +// Beam search +// + +struct llama_beam { + std::vector tokens; + float p; // Cumulative beam probability (renormalized relative to all beams) + bool eob; // Initialize end-of-beam to false. Callback sets this to true. + // Sort beams by probability. In case of ties, prefer beams at eob. + bool operator<(const llama_beam & rhs) const { + return std::make_pair(p, eob) < std::make_pair(rhs.p, rhs.eob); + } + // Shift off first n tokens and discard them. + void shift_tokens(const size_t n) { + if (n) { + std::copy(tokens.begin() + n, tokens.end(), tokens.begin()); + tokens.resize(tokens.size() - n); + } + } + llama_beam_view view() const { return {tokens.data(), tokens.size(), p, eob}; } +}; + +// A struct for calculating logit-related info. +struct llama_logit_info { + const float * const logits; + const int n_vocab; + const float max_l; + const float normalizer; + struct sum_exp { + float max_l; + float operator()(float sum, float l) const { return sum + std::exp(l - max_l); } + }; + llama_logit_info(llama_context * ctx) + : logits(llama_get_logits(ctx)) + , n_vocab(llama_n_vocab(ctx)) + , max_l(*std::max_element(logits, logits + n_vocab)) + , normalizer(1.0f / std::accumulate(logits, logits + n_vocab, 0.0f, sum_exp{max_l})) + { } + llama_token_data get_token_data(const llama_token token_id) const { + constexpr auto p = std::numeric_limits::quiet_NaN(); // never used + return {token_id, logits[token_id], p}; + } + // Return top k token_data by logit. + std::vector top_k(size_t k) { + std::vector min_heap; // min-heap by logit + const llama_token k_min = std::min(static_cast(k), n_vocab); + min_heap.reserve(k_min); + for (llama_token token_id = 0 ; token_id < k_min ; ++token_id) { + min_heap.push_back(get_token_data(token_id)); + } + auto comp = [](const llama_token_data & a, const llama_token_data & b) { return a.logit > b.logit; }; + std::make_heap(min_heap.begin(), min_heap.end(), comp); + for (llama_token token_id = k_min ; token_id < n_vocab ; ++token_id) { + if (min_heap.front().logit < logits[token_id]) { + std::pop_heap(min_heap.begin(), min_heap.end(), comp); + min_heap.back().id = token_id; + min_heap.back().logit = logits[token_id]; + std::push_heap(min_heap.begin(), min_heap.end(), comp); + } + } + return min_heap; + } + float probability_from_logit(float logit) { + return normalizer * std::exp(logit - max_l); + } +}; + +struct llama_beam_search_data { + llama_context * ctx; + size_t n_beams; + int n_past; + int n_predict; + int n_threads; + std::vector beams; + std::vector next_beams; + + // Re-calculated on each loop iteration + size_t common_prefix_length; + + // Used to communicate to/from callback on beams state. + std::vector beam_views; + + llama_beam_search_data(llama_context * ctx, size_t n_beams, int n_past, int n_predict, int n_threads) + : ctx(ctx) + , n_beams(n_beams) + , n_past(n_past) + , n_predict(n_predict) + , n_threads(n_threads) + , beam_views(n_beams) { + beams.reserve(n_beams); + next_beams.reserve(n_beams); + } + + // Collapse beams to a single beam given by index. + void collapse_beams(const size_t beam_idx) { + if (0u < beam_idx) { + std::swap(beams[0], beams[beam_idx]); + } + beams.resize(1); + } + + // Min-heaps are used to efficiently collect the top-k elements (k=n_beams). + // The repetative patterns below reflect the 2 stages of heaps: + // * Gather elements until the vector is full, then call std::make_heap() on it. + // * If the heap is full and a new element is found that should be included, pop the + // least element to the back(), replace it with the new, then push it into the heap. + void fill_next_beams_by_top_probabilities(llama_beam & beam) { + // Min-heaps use a greater-than comparator. + const auto comp = [](const llama_beam & a, const llama_beam & b) { return a.p > b.p; }; + if (beam.eob) { + // beam is at end-of-sentence, so just copy it to next_beams if its probability is high enough. + if (next_beams.size() < n_beams) { + next_beams.push_back(std::move(beam)); + if (next_beams.size() == n_beams) { + std::make_heap(next_beams.begin(), next_beams.end(), comp); + } + } else if (next_beams.front().p < beam.p) { + std::pop_heap(next_beams.begin(), next_beams.end(), comp); + next_beams.back() = std::move(beam); + std::push_heap(next_beams.begin(), next_beams.end(), comp); + } + } else { + // beam is not at end-of-sentence, so branch with next top_k tokens. + if (!beam.tokens.empty()) { + llama_eval(ctx, beam.tokens.data(), beam.tokens.size(), n_past, n_threads); + } + llama_logit_info logit_info(ctx); + std::vector next_tokens = logit_info.top_k(n_beams); + size_t i=0; + if (next_beams.size() < n_beams) { + for (; next_beams.size() < n_beams ; ++i) { + llama_beam next_beam = beam; + next_beam.tokens.push_back(next_tokens[i].id); + next_beam.p *= logit_info.probability_from_logit(next_tokens[i].logit); + next_beams.push_back(std::move(next_beam)); + } + std::make_heap(next_beams.begin(), next_beams.end(), comp); + } else { + for (; next_beams.front().p == 0.0f ; ++i) { + std::pop_heap(next_beams.begin(), next_beams.end(), comp); + next_beams.back() = beam; + next_beams.back().tokens.push_back(next_tokens[i].id); + next_beams.back().p *= logit_info.probability_from_logit(next_tokens[i].logit); + std::push_heap(next_beams.begin(), next_beams.end(), comp); + } + } + for (; i < n_beams ; ++i) { + const float next_p = beam.p * logit_info.probability_from_logit(next_tokens[i].logit); + if (next_beams.front().p < next_p) { + std::pop_heap(next_beams.begin(), next_beams.end(), comp); + next_beams.back() = beam; + next_beams.back().tokens.push_back(next_tokens[i].id); + next_beams.back().p = next_p; + std::push_heap(next_beams.begin(), next_beams.end(), comp); + } + } + } + } + + // Find common_prefix_length based on beams. + // Requires beams is not empty. + size_t find_common_prefix_length() { + size_t common_prefix_length = beams[0].tokens.size(); + for (size_t i = 1 ; i < beams.size() ; ++i) { + common_prefix_length = std::min(common_prefix_length, beams[i].tokens.size()); + for (size_t j = 0 ; j < common_prefix_length ; ++j) { + if (beams[0].tokens[j] != beams[i].tokens[j]) { + common_prefix_length = j; + break; + } + } + } + return common_prefix_length; + } + + // Construct beams_state to send back to caller via the callback function. + // Side effect: set common_prefix_length = find_common_prefix_length(); + llama_beams_state get_beams_state(const bool last_call) { + for (size_t i = 0 ; i < beams.size() ; ++i) { + beam_views[i] = beams[i].view(); + } + common_prefix_length = find_common_prefix_length(); + return {beam_views.data(), beams.size(), common_prefix_length, last_call}; + } + + // Loop: + // * while i < n_predict, AND + // * any of the beams have not yet reached end-of-beam (eob), AND + // * the highest probability beam(s) (plural in case of ties) are not at end-of-sentence + // (since all other beam probabilities can only decrease) + void loop(const llama_beam_search_callback_fn_t callback, void * const callback_data) { + beams.push_back({{}, 1.0f, false}); // Start with one empty beam w/ probability = 1.0 and !eob. + const auto not_eob = [](const llama_beam & beam) { return !beam.eob; }; + for (int i = 0 ; i < n_predict && std::any_of(beams.begin(),beams.end(),not_eob) && + !beams[top_beam_index()].eob ; ++i) { + callback(callback_data, get_beams_state(false)); // Sets common_prefix_length + update_beams_from_beam_views(); // Update values (p,eob) that callback may have changed. + if (common_prefix_length) { + llama_eval(ctx, beams[0].tokens.data(), common_prefix_length, n_past, n_threads); + n_past += common_prefix_length; + } + // Zero-out next_beam probabilities to place them last in following min-heap. + std::for_each(next_beams.begin(), next_beams.end(), [](llama_beam & beam) { beam.p = 0.0f; }); + for (llama_beam & beam : beams) { + beam.shift_tokens(common_prefix_length); + fill_next_beams_by_top_probabilities(beam); + } + // next_beams become the beams of next/final iteration. Swap them to re-use memory. + beams.swap(next_beams); + renormalize_beam_probabilities(beams); + } + collapse_beams(top_beam_index()); + callback(callback_data, get_beams_state(true)); + } + + // As beams grow, the cumulative probabilities decrease. + // Renormalize them to avoid floating point underflow. + static void renormalize_beam_probabilities(std::vector & beams) { + const auto sum_p = [](float sum, llama_beam & beam) { return sum + beam.p; }; + const float inv_sum = 1.0f / std::accumulate(beams.begin(), beams.end(), 0.0f, sum_p); + std::for_each(beams.begin(), beams.end(), [=](llama_beam & beam) { beam.p *= inv_sum; }); + } + + // Assumes beams is non-empty. Uses llama_beam::operator<() for ordering. + size_t top_beam_index() { + return std::max_element(beams.begin(), beams.end()) - beams.begin(); + } + + // Copy (p,eob) for each beam which may have been changed by the callback. + void update_beams_from_beam_views() { + for (size_t i = 0 ; i < beams.size() ; ++i) { + beams[i].p = beam_views[i].p; + beams[i].eob = beam_views[i].eob; + } + } +}; + +void llama_beam_search(llama_context * ctx, + llama_beam_search_callback_fn_t callback, void * callback_data, + size_t n_beams, int n_past, int n_predict, int n_threads) { + assert(ctx); + const int64_t t_start_sample_us = ggml_time_us(); + + llama_beam_search_data beam_search_data(ctx, n_beams, n_past, n_predict, n_threads); + + beam_search_data.loop(callback, callback_data); + + ctx->t_sample_us += ggml_time_us() - t_start_sample_us; + ctx->n_sample++; +} + // // quantization // diff --git a/llama.h b/llama.h index d47468172..86737200f 100644 --- a/llama.h +++ b/llama.h @@ -469,6 +469,43 @@ extern "C" { /// @details Accepts the sampled token into the grammar LLAMA_API void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar * grammar, llama_token token); + // + // Beam search + // + + struct llama_beam_view { + const llama_token * tokens; + size_t n_tokens; + float p; // Cumulative beam probability (renormalized relative to all beams) + bool eob; // Callback should set this to true when a beam is at end-of-beam. + }; + + // Passed to beam_search_callback function. + // Whenever 0 < common_prefix_length, this number of tokens should be copied from any of the beams + // (e.g. beams[0]) as they will be removed (shifted) from all beams in all subsequent callbacks. + // These pointers are valid only during the synchronous callback, so should not be saved. + struct llama_beams_state { + llama_beam_view * beam_views; + size_t n_beams; // Number of elements in beam_views[]. + size_t common_prefix_length; // Current max length of prefix tokens shared by all beams. + bool last_call; // True iff this is the last callback invocation. + }; + + // Type of pointer to the beam_search_callback function. + // void* callback_data is any custom data passed to llama_beam_search, that is subsequently + // passed back to beam_search_callback. This avoids having to use global variables in the callback. + typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, llama_beams_state); + + /// @details Deterministically returns entire sentence constructed by a beam search. + /// @param ctx Pointer to the llama_context. + /// @param callback Invoked for each iteration of the beam_search loop, passing in beams_state. + /// @param callback_data A pointer that is simply passed back to callback. + /// @param n_beams Number of beams to use. + /// @param n_past Number of tokens already evaluated. + /// @param n_predict Maximum number of tokens to predict. EOS may occur earlier. + /// @param n_threads Number of threads as passed to llama_eval(). + LLAMA_API void llama_beam_search(struct llama_context * ctx, llama_beam_search_callback_fn_t callback, void * callback_data, size_t n_beams, int n_past, int n_predict, int n_threads); + // Performance information LLAMA_API struct llama_timings llama_get_timings(struct llama_context * ctx); LLAMA_API void llama_print_timings(struct llama_context * ctx); From d046dcee081118c9071bbc63dacdb359a58c467a Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Fri, 25 Aug 2023 19:05:02 +0300 Subject: [PATCH 366/852] Faster perplexity computation (#2786) Co-authored-by: Iwan Kawrakow --- examples/perplexity/perplexity.cpp | 67 +++++++++++++++++++++++++----- 1 file changed, 56 insertions(+), 11 deletions(-) diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index a7bd9db2a..18635932b 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -6,6 +6,8 @@ #include #include #include +#include +#include #if defined(_MSC_VER) #pragma warning(disable: 4244 4267) // possible loss of data @@ -27,6 +29,40 @@ std::vector softmax(const std::vector& logits) { return probs; } +float log_softmax(int n_vocab, const float * logits, int tok) { + float max_logit = logits[0]; + for (int i = 1; i < n_vocab; ++i) max_logit = std::max(max_logit, logits[i]); + double sum_exp = 0.0; + for (int i = 0; i < n_vocab; ++i) sum_exp += expf(logits[i] - max_logit); + return logits[tok] - max_logit - log(sum_exp); +} + +void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token, std::vector& workers, + double& nll, double& nll2) { + + std::mutex mutex; + int counter = 0; + auto compute = [&mutex, &counter, &nll, &nll2, n_vocab, logits, tokens, n_token] () { + double local_nll = 0, local_nll2 = 0; + while (true) { + std::unique_lock lock(mutex); + int i = counter++; + if (i >= n_token) { + nll += local_nll; nll2 += local_nll2; + break; + } + lock.unlock(); + double v = -log_softmax(n_vocab, logits + i*n_vocab, tokens[i+1]); + local_nll += v; + local_nll2 += v*v; + } + }; + for (auto& w : workers) w = std::thread(compute); + compute(); + for (auto& w : workers) w.join(); + +} + void perplexity_v2(llama_context * ctx, const gpt_params & params) { // Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research // Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw` @@ -166,9 +202,12 @@ void perplexity(llama_context * ctx, const gpt_params & params) { int count = 0; double nll = 0.0; + double nll2 = 0.0; fprintf(stderr, "%s: calculating perplexity over %d chunks, batch_size=%d\n", __func__, n_chunk, n_batch); + std::vector workers(std::thread::hardware_concurrency() - 1); + for (int i = 0; i < n_chunk; ++i) { const int start = i * params.n_ctx; const int end = start + params.n_ctx; @@ -228,26 +267,32 @@ void perplexity(llama_context * ctx, const gpt_params & params) { // Example, we have a context window of 512, we will compute perplexity for each of the // last 256 tokens. Then, we split the input up into context window size chunks to // process the entire prompt. - for (int j = std::min(512, params.n_ctx / 2); j < params.n_ctx - 1; ++j) { - // Calculate probability of next token, given the previous ones. - const std::vector tok_logits( - logits.begin() + (j + 0) * n_vocab, - logits.begin() + (j + 1) * n_vocab); + const int first = std::min(512, params.n_ctx/2); + process_logits(n_vocab, logits.data() + first*n_vocab, tokens.data() + start + first, params.n_ctx - 1 - first, workers, nll, nll2); + count += params.n_ctx - first - 1; - const float prob = softmax(tok_logits)[tokens[start + j + 1]]; - - nll += -std::log(prob); - ++count; - } // perplexity is e^(average negative log-likelihood) if (params.ppl_output_type == 0) { printf("[%d]%.4lf,", i + 1, std::exp(nll / count)); } else { - printf("%8d %.4lf\n", i*params.n_ctx, std::exp(nll / count)); + double av = nll/count; + double av2 = nll2/count - av*av; + if (av2 > 0) av2 = sqrt(av2/(count-1)); + printf("%8d %.4lf %4lf %4lf\n", i*params.n_ctx, std::exp(nll / count), av, av2); } fflush(stdout); } printf("\n"); + nll2 /= count; + nll /= count; + nll2 -= nll * nll; + if (nll2 > 0) { + nll2 = sqrt(nll2/(count-1)); + double ppl = exp(nll); + printf("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl); + } else { + printf("Unexpected negative standard deviation of log(prob)\n"); + } } std::vector hellaswag_evaluate_tokens(llama_context * ctx, const std::vector& tokens, int n_past, int n_batch, From 232caf3c1581a6cb023571780ff41dc2d66d1ca0 Mon Sep 17 00:00:00 2001 From: Marcus Dunn <51931484+MarcusDunn@users.noreply.github.com> Date: Fri, 25 Aug 2023 09:17:15 -0700 Subject: [PATCH 367/852] llama : fix struct decl (#2790) --- llama.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.h b/llama.h index 86737200f..b77dd7735 100644 --- a/llama.h +++ b/llama.h @@ -485,7 +485,7 @@ extern "C" { // (e.g. beams[0]) as they will be removed (shifted) from all beams in all subsequent callbacks. // These pointers are valid only during the synchronous callback, so should not be saved. struct llama_beams_state { - llama_beam_view * beam_views; + struct llama_beam_view * beam_views; size_t n_beams; // Number of elements in beam_views[]. size_t common_prefix_length; // Current max length of prefix tokens shared by all beams. bool last_call; // True iff this is the last callback invocation. From bae5c5f679e043371bc2b4dffff8d4964d6cb953 Mon Sep 17 00:00:00 2001 From: lon <114724657+longregen@users.noreply.github.com> Date: Sat, 26 Aug 2023 10:07:43 +0200 Subject: [PATCH 368/852] examples : skip unnecessary external lib in server README.md how-to (#2804) --- examples/server/README.md | 23 ++++++++++------------- 1 file changed, 10 insertions(+), 13 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 77997f98d..7105e9020 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -77,34 +77,31 @@ You need to have [Node.js](https://nodejs.org/en) installed. ```bash mkdir llama-client cd llama-client -npm init -npm install axios ``` Create a index.js file and put inside this: ```javascript -const axios = require("axios"); - const prompt = `Building a website can be done in 10 simple steps:`; async function Test() { - let result = await axios.post("http://127.0.0.1:8080/completion", { - prompt, - n_predict: 512, - }); - - // the response is received until completion finish - console.log(result.data.content); + let response = await fetch("http://127.0.0.1:8080/completion", { + method: 'POST', + body: JSON.stringify({ + prompt, + n_predict: 512, + }) + }) + console.log((await response.json()).content) } -Test(); +Test() ``` And run it: ```bash -node . +node index.js ``` ## API Endpoints From 2ba83c8685177faea3399db9564f9c52df75c366 Mon Sep 17 00:00:00 2001 From: klosax <131523366+klosax@users.noreply.github.com> Date: Sat, 26 Aug 2023 13:45:53 +0200 Subject: [PATCH 369/852] Fix spm whitespaces (#2806) * llama.cpp : fix spm whitespace escaping + clean up * main.cpp : spm - add whitespace in front of prompt * test-tokenizer-0.cpp : spm - add whitespace in front of prompt --- examples/main/main.cpp | 17 ++++++++++---- llama.cpp | 48 +++++++++++--------------------------- tests/test-tokenizer-0.cpp | 3 ++- 3 files changed, 27 insertions(+), 41 deletions(-) diff --git a/examples/main/main.cpp b/examples/main/main.cpp index cb8747c2b..4665b82fe 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -189,12 +189,19 @@ int main(int argc, char ** argv) { } } - const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM; + // Add BOS if SPM tokenizer + const bool add_bos = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM; // tokenize the prompt std::vector embd_inp; + + if (llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM) { + // Add a space in front of the first character to match OG llama tokenizer behavior + params.prompt.insert(0, 1, ' '); + } + if (params.interactive_first || params.instruct || !params.prompt.empty() || session_tokens.empty()) { - embd_inp = ::llama_tokenize(ctx, params.prompt, is_spm); + embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos); } else { embd_inp = session_tokens; } @@ -210,9 +217,9 @@ int main(int argc, char ** argv) { int original_prompt_len = 0; if (ctx_guidance) { params.cfg_negative_prompt.insert(0, 1, ' '); - guidance_inp = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, is_spm); + guidance_inp = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, add_bos); - std::vector original_inp = ::llama_tokenize(ctx, params.prompt, is_spm); + std::vector original_inp = ::llama_tokenize(ctx, params.prompt, add_bos); original_prompt_len = original_inp.size(); guidance_offset = (int)guidance_inp.size() - original_prompt_len; } @@ -259,7 +266,7 @@ int main(int argc, char ** argv) { } // prefix & suffix for instruct mode - const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", is_spm); + const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", add_bos); const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false); // in instruct mode, we inject a prefix and a suffix to each input by the user diff --git a/llama.cpp b/llama.cpp index 7d8b9a0ac..b0a3b5768 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1635,7 +1635,7 @@ static void llm_load_hparams( } // TODO: This should probably be in llama.h -static std::vector llama_tokenize_internal(const llama_vocab & vocab, const std::string & raw_text, bool bos, bool escape); +static std::vector llama_tokenize_internal(const llama_vocab & vocab, const std::string & raw_text, bool bos); static void llm_load_vocab( llama_model_loader & ml, @@ -1737,7 +1737,7 @@ static void llm_load_vocab( } // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n' - vocab.linefeed_id = llama_tokenize_internal(vocab, "\n", false, false)[0]; + vocab.linefeed_id = llama_tokenize_internal(vocab, "\n", false)[0]; // special tokens GGUF_GET_KEY(ctx, vocab.special_bos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_BOS_ID)); @@ -3027,14 +3027,8 @@ static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) { } static std::string llama_escape_whitespace(const std::string& text) { - std::string result = "\xe2\x96\x81"; - for (size_t offs = 0; offs < text.length(); ++offs) { - if (text[offs] == ' ') { - result += "\xe2\x96\x81"; - } else { - result += text[offs]; - } - } + std::string result = text; + replace_all(result, " ", "\xe2\x96\x81"); return result; } @@ -3219,7 +3213,7 @@ struct llm_bigram_bpe { }; struct llm_tokenizer_bpe { - llm_tokenizer_bpe(const llama_vocab & vocab, bool g2ws): vocab(vocab) { flag_g2ws = g2ws; } + llm_tokenizer_bpe(const llama_vocab & vocab): vocab(vocab) {} void tokenize(const std::string & text, std::vector & output) { int final_prev_index = -1; @@ -3371,8 +3365,6 @@ private: return words; } - bool flag_g2ws = false; - const llama_vocab & vocab; std::vector symbols; @@ -3381,39 +3373,26 @@ private: llm_bigram_bpe::queue work_queue; }; -static std::vector llama_tokenize_internal(const llama_vocab & vocab, const std::string & raw_text, bool bos, bool escape) { +static std::vector llama_tokenize_internal(const llama_vocab & vocab, const std::string & raw_text, bool bos) { std::vector output; if (raw_text.empty()) { return output; } + if (bos && vocab.special_bos_id != -1) { + output.push_back(vocab.special_bos_id); + } + switch (vocab.type) { case LLAMA_VOCAB_TYPE_SPM: { llm_tokenizer_spm tokenizer(vocab); - - if (bos) { - output.push_back(vocab.special_bos_id); - } - - std::string text; - if (escape) { - text = llama_escape_whitespace(raw_text); - } else { - text = raw_text; - } - - tokenizer.tokenize(text, output); + tokenizer.tokenize(llama_escape_whitespace(raw_text), output); } break; case LLAMA_VOCAB_TYPE_BPE: { - llm_tokenizer_bpe tokenizer(vocab, escape); - - if (bos && vocab.special_bos_id != -1) { - output.push_back(vocab.special_bos_id); - } - + llm_tokenizer_bpe tokenizer(vocab); tokenizer.tokenize(raw_text, output); } break; }; @@ -6095,8 +6074,7 @@ int llama_tokenize_with_model( llama_token * tokens, int n_max_tokens, bool add_bos) { - auto escape = llama_vocab_get_type(model->vocab) == LLAMA_VOCAB_TYPE_SPM; - auto res = llama_tokenize_internal(model->vocab, text, add_bos, escape); + auto res = llama_tokenize_internal(model->vocab, text, add_bos); if (n_max_tokens < (int) res.size()) { LLAMA_LOG_ERROR("%s: too many tokens\n", __func__); diff --git a/tests/test-tokenizer-0.cpp b/tests/test-tokenizer-0.cpp index f3ee851a3..7e9ac9188 100644 --- a/tests/test-tokenizer-0.cpp +++ b/tests/test-tokenizer-0.cpp @@ -100,7 +100,8 @@ int main(int argc, char **argv) { bool success = true; for (const auto & test_kv : k_tests()) { - std::vector res = llama_tokenize(ctx, test_kv.first, true); + // Add a space in front of the first character to match OG llama tokenizer behavior + std::vector res = llama_tokenize(ctx, " " + test_kv.first, true); fprintf(stderr, "%s : '%s' tokenized to '%s'\n", __func__, test_kv.first.c_str(), unescape_whitespace(ctx, res).c_str()); From a2ca4e9de9da45ed0bb1c34935d5ec80cebc22d5 Mon Sep 17 00:00:00 2001 From: Nigel Bosch Date: Sat, 26 Aug 2023 07:11:17 -0500 Subject: [PATCH 370/852] Handle null rope scaling value (#2793) --- convert.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/convert.py b/convert.py index 4f3e92798..d44e5a8c4 100755 --- a/convert.py +++ b/convert.py @@ -170,7 +170,8 @@ class Params: f_norm_eps = config["rms_norm_eps"] f_rope_freq_base = config["rope_theta"] if "rope_theta" in config else None - if "rope_scaling" in config and config["rope_scaling"].get("type") == "linear": + rope_scaling = config.get("rope_scaling") + if isinstance(rope_scaling, dict) and rope_scaling.get("type") == "linear": f_rope_scale = config["rope_scaling"].get("factor") else: f_rope_scale = None From f305bad11e10ad09e396faed2e37f4f845f5d566 Mon Sep 17 00:00:00 2001 From: Volodymyr Vitvitskyi <72226+signalpillar@users.noreply.github.com> Date: Sat, 26 Aug 2023 14:25:39 +0100 Subject: [PATCH 371/852] flake : build llama.cpp on Intel with nix (#2795) Problem ------- `nix build` fails with missing `Accelerate.h`. Changes ------- - Fix build of the llama.cpp with nix for Intel: add the same SDK frameworks as for ARM - Add `quantize` app to the output of nix flake - Extend nix devShell with llama-python so we can use convertScript Testing ------- Testing the steps with nix: 1. `nix build` Get the model and then 2. `nix develop` and then `python convert.py models/llama-2-7b.ggmlv3.q4_0.bin` 3. `nix run llama.cpp#quantize -- open_llama_7b/ggml-model-f16.gguf ./models/ggml-model-q4_0.bin 2` 4. `nix run llama.cpp#llama -- -m models/ggml-model-q4_0.bin -p "What is nix?" -n 400 --temp 0.8 -e -t 8` Co-authored-by: Volodymyr Vitvitskyi --- flake.nix | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/flake.nix b/flake.nix index 616b90252..d454cedc3 100644 --- a/flake.nix +++ b/flake.nix @@ -21,6 +21,12 @@ CoreGraphics CoreVideo ] + else if isDarwin then + with pkgs.darwin.apple_sdk.frameworks; [ + Accelerate + CoreGraphics + CoreVideo + ] else with pkgs; [ openblas ] ); @@ -80,8 +86,13 @@ type = "app"; program = "${self.packages.${system}.default}/bin/llama"; }; + apps.quantize = { + type = "app"; + program = "${self.packages.${system}.default}/bin/quantize"; + }; apps.default = self.apps.${system}.llama; devShells.default = pkgs.mkShell { + buildInputs = [ llama-python ]; packages = nativeBuildInputs ++ osSpecific; }; }); From 771551a793c9976ed9cdfe7b8c69536af32af9f9 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sat, 26 Aug 2023 16:48:53 +0300 Subject: [PATCH 372/852] Fix HellaSwag (#2805) Co-authored-by: Iwan Kawrakow --- examples/perplexity/perplexity.cpp | 20 +++++++++++++++++--- 1 file changed, 17 insertions(+), 3 deletions(-) diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 18635932b..fd89852d6 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -351,6 +351,7 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) { fprintf(stderr, "%s : loaded %zu tasks from prompt.\n", __func__, hs_task_count); const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM; + fprintf(stderr, "================================= is_spm = %d\n", is_spm); // This is needed as usual for LLaMA models const bool add_bos = is_spm; @@ -406,6 +407,8 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) { double acc = 0.0f; const int n_vocab = llama_n_vocab(ctx); + std::vector> ending_tokens(4); + std::vector tok_logits(n_vocab); for (size_t task_idx = 0; task_idx < hs_task_count; task_idx++) { @@ -413,11 +416,21 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) { std::vector context_embd = ::llama_tokenize(ctx, hs_data[task_idx].context, add_bos); size_t context_size = context_embd.size(); + for (int i = 0; i < 4; ++i) { + ending_tokens[i] = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[i], add_bos); + for (int k = 0; k < int(context_size); ++k) { + if (ending_tokens[i][k] != context_embd[k]) { + fprintf(stderr, "Oops: ending %d of task %d differs from context at position %d\n",i,int(task_idx),k); + break; + } + } + } + // Do the 1st ending // In this case we include the context when evaluating - auto query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[0], add_bos); + //auto query_embd = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[0], add_bos); + auto query_embd = ending_tokens[0]; auto query_size = query_embd.size(); - //printf("First query: %d\n",(int)query_size); // Stop if query wont fit the ctx window if (query_size > (size_t)params.n_ctx) { @@ -462,7 +475,8 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) { for (size_t ending_idx = 1; ending_idx < 4; ending_idx++) { // Tokenize the query - query_embd = ::llama_tokenize(ctx, hs_data[task_idx].ending[ending_idx], false); + query_embd.resize(ending_tokens[ending_idx].size() - context_size); + std::memcpy(query_embd.data(), ending_tokens[ending_idx].data() + context_size, query_embd.size()*sizeof(int)); query_size = query_embd.size(); // Stop if query wont fit the ctx window From 7592375403a0bd0456d5ec2cdf8350e591f04fb0 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sat, 26 Aug 2023 17:27:49 +0300 Subject: [PATCH 373/852] Better perplexity for 2- and 3-bit quantization for LLaMA-v2-70B (#2807) * Better perplexity for 2- and 3-bit quantization for the 70B model * PR comment --------- Co-authored-by: Iwan Kawrakow --- llama.cpp | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/llama.cpp b/llama.cpp index b0a3b5768..52fcaceff 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4653,6 +4653,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s std::unique_ptr ml(new llama_model_loader(fname_inp, /*use_mmap*/ false)); + llama_model model; + llm_load_arch(*ml, model); + llm_load_hparams(*ml, model, 0, 0, 0); + const size_t align = GGUF_DEFAULT_ALIGNMENT; struct gguf_context * ctx_out = gguf_init_empty(); @@ -4678,6 +4682,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s ++n_feed_forward_w2; } } + if (n_attention_wv != n_feed_forward_w2 || (uint32_t)n_attention_wv != model.hparams.n_layer) { + LLAMA_LOG_WARN("%s ============ Strange model: n_attention_wv = %d, n_feed_forward_w2 = %d, hparams.n_layer = %d\n", + __func__, n_attention_wv, n_feed_forward_w2, model.hparams.n_layer); + } int i_attention_wv = 0; int i_feed_forward_w2 = 0; @@ -4769,6 +4777,12 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && i_attention_wv < 4) new_type = GGML_TYPE_Q5_K; else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) && (i_attention_wv < n_attention_wv/8 || i_attention_wv >= 7*n_attention_wv/8)) new_type = GGML_TYPE_Q6_K; + if (model.type == MODEL_70B) { + // In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is + // 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with + // nearly negligible increase in model size by quantizing this tensor with more bits: + if (new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K) new_type = GGML_TYPE_Q5_K; + } ++i_attention_wv; } else if (name.find("ffn_down.weight") != std::string::npos) { if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K; From 04f4b1eb10f3e25750ca3e530265ce2841730e6b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 26 Aug 2023 17:37:35 +0300 Subject: [PATCH 374/852] k-quants : remove unnecessary tensor shape restrictions (#2811) --- llama.cpp | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/llama.cpp b/llama.cpp index 52fcaceff..59105db1c 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4762,8 +4762,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s if (name == tn(LLM_TENSOR_OUTPUT, "weight")) { int nx = tensor->ne[0]; - int ny = tensor->ne[1]; - if (nx % QK_K == 0 && ny % QK_K == 0) { + if (nx % QK_K == 0) { new_type = GGML_TYPE_Q6_K; } } else if (name.find("attn_v.weight") != std::string::npos) { @@ -4812,8 +4811,8 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K) { int nx = tensor->ne[0]; int ny = tensor->ne[1]; - if (nx % QK_K != 0 || ny % QK_K != 0) { - LLAMA_LOG_INFO("\n\nTensor sizes %d x %d are not divisible by %d, required for k-quants.\n",nx,ny,QK_K); + if (nx % QK_K != 0) { + LLAMA_LOG_WARN("\n\n%s : tensor cols %d x %d are not divisible by %d, required for k-quants\n", __func__, nx, ny, QK_K); convert_incompatible_tensor = true; } } From 50526f37eba0b28336700890242ff282b949cd83 Mon Sep 17 00:00:00 2001 From: Cebtenzzre Date: Sat, 26 Aug 2023 12:53:52 -0400 Subject: [PATCH 375/852] llama : use std::abs in llama_sample_tail_free (#2800) Plain 'abs' casts the input to int. --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 59105db1c..2b88485a8 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3887,7 +3887,7 @@ void llama_sample_tail_free(struct llama_context * ctx, llama_token_data_array * // Calculate absolute value of second derivatives for (size_t i = 0; i < second_derivatives.size(); ++i) { - second_derivatives[i] = abs(second_derivatives[i]); + second_derivatives[i] = std::abs(second_derivatives[i]); } // Normalize the second derivatives From 72f895c923ba98b8f2af294440206f35915c0501 Mon Sep 17 00:00:00 2001 From: "Dr. Tom Murphy VII Ph.D" <499244+tom7@users.noreply.github.com> Date: Sat, 26 Aug 2023 14:12:56 -0400 Subject: [PATCH 376/852] main : fix bug (penalize_nl=false doesn't work) + suppress warning on mingw (#1528) * Fix bug in main.cpp where penalize_nl=false has no effect. It modifies the underlying logits array, but at this point we are already working on the candidates copy. * Suppress redefinition warning for NOMINMAX on mingw. In my installation, this macro is already defined by /usr/lib/gcc/x86_64-w64-mingw32/11/include/c++/x86_64-w64-mingw32/bits/os_defines.h:45. * main : fix indentation * main : pass ctx to llama_token_nl() --------- Co-authored-by: Georgi Gerganov --- examples/main/main.cpp | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 4665b82fe..11d7a7e4f 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -604,7 +604,12 @@ int main(int argc, char ** argv) { last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, last_n_repeat, alpha_frequency, alpha_presence); if (!penalize_nl) { - logits[llama_token_nl(ctx)] = nl_logit; + for (size_t idx = 0; idx < candidates_p.size; idx++) { + if (candidates_p.data[idx].id == llama_token_nl(ctx)) { + candidates_p.data[idx].logit = nl_logit; + break; + } + } } if (grammar != NULL) { From 741ca7dd1cec0a0349494742b9083d6ef4cd73c5 Mon Sep 17 00:00:00 2001 From: Cebtenzzre Date: Sat, 26 Aug 2023 14:17:51 -0400 Subject: [PATCH 377/852] llama : move #includes out of _GNU_SOURCE conditional (#2817) --- llama.cpp | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/llama.cpp b/llama.cpp index 2b88485a8..62889b3ed 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1,9 +1,6 @@ // Defines fileno on msys: #ifndef _GNU_SOURCE #define _GNU_SOURCE -#include -#include -#include #endif #include "llama.h" @@ -62,6 +59,9 @@ #include #include #include +#include +#include +#include #include #include #include From 61d1a2895eeca55e0c8b7018492f6ab9c90cff78 Mon Sep 17 00:00:00 2001 From: Tungsten842 Date: Sat, 26 Aug 2023 20:19:44 +0200 Subject: [PATCH 378/852] flake.nix : add rocm support and cleanup (#2808) --- flake.lock | 12 ++++++------ flake.nix | 43 +++++++++++++++++++++++-------------------- 2 files changed, 29 insertions(+), 26 deletions(-) diff --git a/flake.lock b/flake.lock index 33164e096..a7777d05d 100644 --- a/flake.lock +++ b/flake.lock @@ -5,11 +5,11 @@ "systems": "systems" }, "locked": { - "lastModified": 1685518550, - "narHash": "sha256-o2d0KcvaXzTrPRIo0kOLV0/QXHhDQ5DTi+OxcjO8xqY=", + "lastModified": 1692799911, + "narHash": "sha256-3eihraek4qL744EvQXsK1Ha6C3CR7nnT8X2qWap4RNk=", "owner": "numtide", "repo": "flake-utils", - "rev": "a1720a10a6cfe8234c0e93907ffe81be440f4cef", + "rev": "f9e7cf818399d17d347f847525c5a5a8032e4e44", "type": "github" }, "original": { @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1685931219, - "narHash": "sha256-8EWeOZ6LKQfgAjB/USffUSELPRjw88A+xTcXnOUvO5M=", + "lastModified": 1692913444, + "narHash": "sha256-1SvMQm2DwofNxXVtNWWtIcTh7GctEVrS/Xel/mdc6iY=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "7409480d5c8584a1a83c422530419efe4afb0d19", + "rev": "18324978d632ffc55ef1d928e81630c620f4f447", "type": "github" }, "original": { diff --git a/flake.nix b/flake.nix index d454cedc3..02095411e 100644 --- a/flake.nix +++ b/flake.nix @@ -6,6 +6,9 @@ outputs = { self, nixpkgs, flake-utils }: flake-utils.lib.eachDefaultSystem (system: let + name = "llama.cpp"; + src = ./.; + meta.mainProgram = "llama"; inherit (pkgs.stdenv) isAarch32 isAarch64 isDarwin; buildInputs = with pkgs; [ openmpi ]; osSpecific = with pkgs; buildInputs ++ @@ -31,7 +34,7 @@ with pkgs; [ openblas ] ); pkgs = import nixpkgs { inherit system; }; - nativeBuildInputs = with pkgs; [ cmake pkgconfig ]; + nativeBuildInputs = with pkgs; [ cmake ninja pkgconfig ]; llama-python = pkgs.python3.withPackages (ps: with ps; [ numpy sentencepiece ]); postPatch = '' @@ -44,35 +47,35 @@ mv $out/bin/server $out/bin/llama-server ''; cmakeFlags = [ "-DLLAMA_BUILD_SERVER=ON" "-DLLAMA_MPI=ON" "-DBUILD_SHARED_LIBS=ON" "-DCMAKE_SKIP_BUILD_RPATH=ON" ]; - in { + in + { packages.default = pkgs.stdenv.mkDerivation { - name = "llama.cpp"; - src = ./.; - postPatch = postPatch; - nativeBuildInputs = nativeBuildInputs; - buildInputs = osSpecific; + inherit name src meta postPatch nativeBuildInputs buildInputs postInstall; cmakeFlags = cmakeFlags ++ (if isAarch64 && isDarwin then [ - "-DCMAKE_C_FLAGS=-D__ARM_FEATURE_DOTPROD=1" - "-DLLAMA_METAL=ON" - ] else [ - "-DLLAMA_BLAS=ON" - "-DLLAMA_BLAS_VENDOR=OpenBLAS" + "-DCMAKE_C_FLAGS=-D__ARM_FEATURE_DOTPROD=1" + "-DLLAMA_METAL=ON" + ] else [ + "-DLLAMA_BLAS=ON" + "-DLLAMA_BLAS_VENDOR=OpenBLAS" ]); - postInstall = postInstall; - meta.mainProgram = "llama"; }; packages.opencl = pkgs.stdenv.mkDerivation { - name = "llama.cpp"; - src = ./.; - postPatch = postPatch; - nativeBuildInputs = nativeBuildInputs; + inherit name src meta postPatch nativeBuildInputs postInstall; buildInputs = with pkgs; buildInputs ++ [ clblast ]; cmakeFlags = cmakeFlags ++ [ "-DLLAMA_CLBLAST=ON" ]; - postInstall = postInstall; - meta.mainProgram = "llama"; + }; + packages.rocm = pkgs.stdenv.mkDerivation { + inherit name src meta postPatch nativeBuildInputs postInstall; + buildInputs = with pkgs; buildInputs ++ [ hip hipblas rocblas ]; + cmakeFlags = cmakeFlags ++ [ + "-DLLAMA_HIPBLAS=1" + "-DCMAKE_C_COMPILER=hipcc" + "-DCMAKE_CXX_COMPILER=hipcc" + "-DCMAKE_POSITION_INDEPENDENT_CODE=ON" + ]; }; apps.llama-server = { type = "app"; From c7d92e6dfec3f54849f3a0ba373054d29f321ea2 Mon Sep 17 00:00:00 2001 From: Tim Miller Date: Sun, 27 Aug 2023 03:27:07 +0900 Subject: [PATCH 379/852] llama : use Unicode Escape Sequence to replace encoded characters (#2814) The use of special characters within source files can break compiling on some computers with different region and language settings. Using Unicode escape sequences should allow for the code to be compiled on all setups without needing to change your computers settings or switch regions. --- llama.cpp | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/llama.cpp b/llama.cpp index 62889b3ed..05c54c213 100644 --- a/llama.cpp +++ b/llama.cpp @@ -955,10 +955,10 @@ struct llama_vocab { id linefeed_id = 13; int find_bpe_rank(std::string token_left, std::string token_right) const { - replace_all(token_left, " ", "Ġ"); - replace_all(token_left, "\n", "Ċ"); - replace_all(token_right, " ", "Ġ"); - replace_all(token_right, "\n", "Ċ"); + replace_all(token_left, " ", "\u0120"); + replace_all(token_left, "\n", "\u010A"); + replace_all(token_right, " ", "\u0120"); + replace_all(token_right, "\n", "\u010A"); auto it = bpe_ranks.find(std::make_pair(token_left, token_right)); if (it == bpe_ranks.end()) { From 730d9c681e339b76407659344e5a2cd50af7d7d5 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Sat, 26 Aug 2023 14:13:36 -0600 Subject: [PATCH 380/852] convert.py : advanced option (#2753) * Allow convert.py to convert to q8_0 Fix issue with bounded_parallel_map and greedy consuming iterator Display elapsed time during conversion * Add --concurrency option Minor improvements to help text Clean up bounded_parallel_map function a bit * Massive speed improvement thanks to Cebtenzzre * Refactor types --- convert.py | 206 ++++++++++++++++++++++++++++++++++------------------- 1 file changed, 133 insertions(+), 73 deletions(-) diff --git a/convert.py b/convert.py index d44e5a8c4..a15e6ccd2 100755 --- a/convert.py +++ b/convert.py @@ -3,6 +3,7 @@ import gguf import argparse import concurrent.futures +from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor import copy import enum import faulthandler @@ -17,13 +18,14 @@ import re import signal import struct import sys +import time import zipfile import numpy as np from abc import ABCMeta, abstractmethod from dataclasses import dataclass from pathlib import Path -from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Literal, Optional, Sequence, Tuple, TypeVar, Union) +from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Generator, Iterable, List, Literal, Optional, Sequence, Set, Tuple, TypeVar, Union) from sentencepiece import SentencePieceProcessor # type: ignore if TYPE_CHECKING: @@ -37,30 +39,70 @@ NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]' ARCH=gguf.MODEL_ARCH.LLAMA NAMES=gguf.MODEL_TENSOR_NAMES[ARCH] +DEFAULT_CONCURRENCY = 8 # # data types # @dataclass(frozen=True) -class UnquantizedDataType: +class DataType: name: str + dtype: 'np.dtype[Any]' + valid_conversions: List[str] -DT_F16 = UnquantizedDataType('F16') -DT_F32 = UnquantizedDataType('F32') -DT_I32 = UnquantizedDataType('I32') -DT_BF16 = UnquantizedDataType('BF16') + def elements_to_bytes(self, n_elements: int) -> int: + return n_elements * self.dtype.itemsize -DataType = Union[UnquantizedDataType] +@dataclass(frozen=True) +class UnquantizedDataType(DataType): + pass -DATA_TYPE_TO_NUMPY: Dict[DataType, 'np.dtype[Any]'] = { - DT_BF16: np.dtype(np.uint16), - DT_F16: np.dtype(np.float16), - DT_F32: np.dtype(np.float32), - DT_I32: np.dtype(np.int32), -} +DT_F16 = UnquantizedDataType('F16', dtype = np.dtype(np.float16), valid_conversions = ['F32', 'Q8_0']) +DT_F32 = UnquantizedDataType('F32', dtype = np.dtype(np.float32), valid_conversions = ['F16', 'Q8_0']) +DT_I32 = UnquantizedDataType('I32', dtype = np.dtype(np.int16), valid_conversions = []) +DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_conversions = ['F32', 'F16', 'Q8_0']) -NUMPY_TYPE_TO_DATA_TYPE: Dict['np.dtype[Any]', DataType] = \ - {dtype: data_type for (data_type, dtype) in DATA_TYPE_TO_NUMPY.items()} +@dataclass(frozen=True) +class QuantizedDataType(DataType): + block_size: int + quantized_dtype: 'np.dtype[Any]' + ggml_type: gguf.GGMLQuantizationType + + def quantize(self, arr: NDArray) -> NDArray: + raise NotImplementedError(f'Quantization for {self.name} not implemented') + + def elements_to_bytes(self, n_elements: int) -> int: + assert n_elements % self.block_size == 0, f'Invalid number of elements {n_elements} for {self.name} with block size {self.block_size}' + return self.quantized_dtype.itemsize * (n_elements // self.block_size) + +@dataclass(frozen=True) +class Q8_0QuantizedDataType(QuantizedDataType): + # Mini Q8_0 quantization in Python! + def quantize(self, arr: NDArray) -> NDArray: + assert arr.size % self.block_size == 0 and arr.size != 0, f'Bad array size {arr.size}' + assert arr.dtype == np.float32, f'Bad array type {arr.dtype}' + n_blocks = arr.size // self.block_size + blocks = arr.reshape((n_blocks, self.block_size)) + # Much faster implementation of block quantization contributed by @Cebtenzzre + def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[Tuple[Any, Any]]: + d = abs(blocks).max(axis = 1) / np.float32(127) + with np.errstate(divide = 'ignore'): + qs = (blocks / d[:, None]).round() + qs[d == 0] = 0 + yield from zip(d, qs) + return np.fromiter(quantize_blocks_q8_0(blocks), count = n_blocks, dtype = self.quantized_dtype) + +DT_Q8_0 = Q8_0QuantizedDataType('Q8_0', + dtype = np.dtype(np.float32), valid_conversions = [], + ggml_type = gguf.GGMLQuantizationType.Q8_0, block_size = 32, + quantized_dtype = np.dtype([('d', ' DataType: - if len(tensor.shape) == 1: - # 1D tensors are always F32. - return DT_F32 - elif self == GGMLFileType.AllF32: - return DT_F32 - elif self == GGMLFileType.MostlyF16: - return DT_F16 - else: + dt = GGML_FILE_TYPE_TO_DATA_TYPE.get(self) + if dt is None: raise ValueError(self) + # 1D tensors are always F32. + return dt if len(tensor.shape) > 1 else DT_F32 +GGML_FILE_TYPE_TO_DATA_TYPE: Dict[GGMLFileType, DataType] = { + GGMLFileType.AllF32 : DT_F32, + GGMLFileType.MostlyF16 : DT_F16, + GGMLFileType.MostlyQ8_0: DT_Q8_0, +} # # hparams loading @@ -415,7 +459,7 @@ class UnquantizedTensor(Tensor): self.data_type = NUMPY_TYPE_TO_DATA_TYPE[ndarray.dtype] def astype(self, data_type: DataType) -> Tensor: - dtype = DATA_TYPE_TO_NUMPY[data_type] + dtype = data_type.dtype if self.data_type == DT_BF16: self.ndarray = bf16_to_fp32(self.ndarray) return UnquantizedTensor(self.ndarray.astype(dtype)) @@ -454,22 +498,6 @@ def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, conv GGMLCompatibleTensor = Union[UnquantizedTensor] -class DeferredPermutedTensor(Tensor): - def __init__(self, base: Tensor, n_head: int, n_head_kv: int) -> None: - self.base = base - self.n_head = n_head - self.data_type = self.base.data_type - - def astype(self, data_type: DataType) -> Tensor: - return self.base.astype(data_type).permute(self.n_head, self.n_head_kv) - - def to_ggml(self) -> GGMLCompatibleTensor: - return self.base.to_ggml().permute(self.n_head, self.n_head_kv) - - def permute(self, n_head: int, n_head_kv: int) -> Tensor: - raise Exception("shouldn't permute twice") - - @dataclass class LazyTensor: _load: Callable[[], Tensor] @@ -479,7 +507,9 @@ class LazyTensor: def load(self) -> Tensor: ret = self._load() - assert ret.data_type == self.data_type, (self.data_type, ret.data_type, self.description) + # Should be okay if it maps to the same numpy type? + assert ret.data_type == self.data_type or (self.data_type.dtype == ret.data_type.dtype), \ + (self.data_type, ret.data_type, self.description) return ret def astype(self, data_type: DataType) -> 'LazyTensor': @@ -490,8 +520,8 @@ class LazyTensor: return LazyTensor(load, self.shape, data_type, f'convert({data_type}) {self.description}') def validate_conversion_to(self, data_type: DataType) -> None: - if data_type == self.data_type: - return + if data_type != self.data_type and data_type.name not in self.data_type.valid_conversions: + raise ValueError(f'Cannot validate conversion from {self.data_type} to {data_type}.') LazyModel = Dict[str, LazyTensor] @@ -617,9 +647,7 @@ class LazyUnpickler(pickle.Unpickler): info = self.zip_file.getinfo(filename) def load(offset: int, elm_count: int) -> NDArray: - dtype = DATA_TYPE_TO_NUMPY.get(data_type) - if dtype is None: - raise Exception("tensor stored in unsupported format") + dtype = data_type.dtype fp = self.zip_file.open(info) fp.seek(offset * dtype.itemsize) size = elm_count * dtype.itemsize @@ -683,7 +711,7 @@ def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus: def convert(info: Dict[str, Any]) -> LazyTensor: data_type = SAFETENSORS_DATA_TYPES[info['dtype']] - numpy_dtype = DATA_TYPE_TO_NUMPY[data_type] + numpy_dtype = data_type.dtype shape: List[int] = info['shape'] begin, end = info['data_offsets'] assert 0 <= begin <= end <= len(byte_buf) @@ -723,23 +751,35 @@ def lazy_load_file(path: Path) -> ModelPlus: In = TypeVar('In') Out = TypeVar('Out') -def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int) -> Iterable[Out]: +def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int, max_workers: Optional[int] = None, factory: Callable = ThreadPoolExecutor) -> Iterable[Out]: '''Parallel map, but with backpressure. If the caller doesn't call `next` fast enough, this will stop calling `func` at some point rather than letting results pile up in memory. Specifically, there is a max of one output value buffered per thread.''' - with concurrent.futures.ThreadPoolExecutor() as executor: + if concurrency < 2: + yield from map(func, iterable) + # Not reached. + iterable = iter(iterable) + with factory(max_workers = max_workers) as executor: futures: List[concurrent.futures.Future[Out]] = [] - items_rev = list(iterable)[::-1] - for i in range(min(concurrency, len(items_rev))): - futures.append(executor.submit(func, items_rev.pop())) + done = False + for _ in range(concurrency): + try: + futures.append(executor.submit(func, next(iterable))) + except StopIteration: + done = True + break + while futures: result = futures.pop(0).result() - if items_rev: - futures.append(executor.submit(func, items_rev.pop())) + while not done and len(futures) < concurrency: + try: + futures.append(executor.submit(func, next(iterable))) + except StopIteration: + done = True + break yield result - def check_vocab_size(params: Params, vocab: Vocab) -> None: if params.n_vocab != vocab.vocab_size: assert isinstance(vocab, BpeVocab) or isinstance(vocab, SentencePieceVocab) @@ -804,12 +844,11 @@ class OutputFile: self.gguf.add_token_types(toktypes) def add_tensor_info(self, name: str, tensor: LazyTensor) -> None: - n_elements = 1 - for dim in tensor.shape: - n_elements *= dim - data_type = DATA_TYPE_TO_NUMPY[tensor.data_type] - data_nbytes = n_elements * data_type.itemsize - self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes) + n_elements = int(np.prod(tensor.shape)) + raw_dtype = getattr(tensor.data_type, 'ggml_type', None) + data_type = getattr(tensor.data_type, 'quantized_type', None) or tensor.data_type.dtype + data_nbytes = tensor.data_type.elements_to_bytes(n_elements) + self.gguf.add_tensor_info(name, tensor.shape, data_type, data_nbytes, raw_dtype = raw_dtype) def write_meta(self) -> None: self.gguf.write_header_to_file() @@ -835,7 +874,20 @@ class OutputFile: of.close() @staticmethod - def write_all(fname_out: Path, params: Params, model: LazyModel, vocab: Vocab) -> None: + def do_item(item: Tuple[str, LazyTensor]) -> Tuple[DataType, NDArray]: + name, lazy_tensor = item + tensor = lazy_tensor.load().to_ggml() + return (lazy_tensor.data_type, tensor.ndarray) + + @staticmethod + def maybe_do_quantize(item: Tuple[DataType, NDArray]) -> NDArray: + dt, arr = item + if not isinstance(dt, QuantizedDataType): + return arr + return dt.quantize(arr) + + @staticmethod + def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, concurrency: int = DEFAULT_CONCURRENCY) -> None: check_vocab_size(params, vocab) of = OutputFile(fname_out) @@ -851,16 +903,19 @@ class OutputFile: of.write_meta() of.write_tensor_info() - def do_item(item: Tuple[str, LazyTensor]) -> NDArray: - name, lazy_tensor = item - return lazy_tensor.load().to_ggml().ndarray - # tensor data - ndarrays = bounded_parallel_map(do_item, model.items(), concurrency=8) + ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency = concurrency) + if ftype == GGMLFileType.MostlyQ8_0: + ndarrays = bounded_parallel_map(OutputFile.maybe_do_quantize, ndarrays_inner, concurrency = concurrency, max_workers = concurrency, factory = ProcessPoolExecutor) + else: + ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner) + + start = time.time() for i, ((name, lazy_tensor), ndarray) in enumerate(zip(model.items(), ndarrays)): + elapsed = time.time() - start size = ' x '.join(f"{dim:6d}" for dim in lazy_tensor.shape) padi = len(str(len(model))) - print(f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type}") + print(f"[{i+1:{padi}d}/{len(model)}] Writing tensor {name:38s} | size {size:16} | type {lazy_tensor.data_type.name:4} | T+{int(elapsed):4}") of.gguf.write_tensor_data(ndarray) of.close() @@ -872,6 +927,8 @@ def pick_output_type(model: LazyModel, output_type_str: Optional[str]) -> GGMLFi return GGMLFileType.AllF32 if output_type_str == "f16" or (output_type_str is None and wq_type in (DT_F16, DT_BF16)): return GGMLFileType.MostlyF16 + if output_type_str == "q8_0": + return GGMLFileType.MostlyQ8_0 name_to_type = {name: lazy_tensor.data_type for (name, lazy_tensor) in model.items()} @@ -918,7 +975,7 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel: print(f"skipping tensor {name_new}") continue else: - print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type} | {lazy_tensor.shape}") + print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}") out[name_new] = lazy_tensor return out @@ -1023,6 +1080,7 @@ def default_outfile(model_paths: List[Path], file_type: GGMLFileType) -> Path: namestr = { GGMLFileType.AllF32: "f32", GGMLFileType.MostlyF16: "f16", + GGMLFileType.MostlyQ8_0:"q8_0", }[file_type] ret = model_paths[0].parent / f"ggml-model-{namestr}.gguf" if ret in model_paths: @@ -1046,12 +1104,13 @@ def main(args_in: Optional[List[str]] = None) -> None: parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model") parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file") parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") - parser.add_argument("--outtype", choices=["f32", "f16"], help="output format (default: based on input)") + parser.add_argument("--outtype", choices=["f32", "f16", "q8_0"], help="output format - note: q8_0 may be very slow (default: f16 or f32 based on input)") parser.add_argument("--vocab-dir", type=Path, help="directory containing tokenizer.model, if separate from model file") parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.pth, *.pt, *.bin)") parser.add_argument("--vocabtype", choices=["spm", "bpe"], help="vocab format (default: spm)", default="spm") parser.add_argument("--ctx", type=int, help="model training context (default: based on input)") + parser.add_argument("--concurrency", type=int, help=f"concurrency used for conversion (default: {DEFAULT_CONCURRENCY})", default = DEFAULT_CONCURRENCY) args = parser.parse_args(args_in) if args.dump_single: @@ -1073,6 +1132,7 @@ def main(args_in: Optional[List[str]] = None) -> None: params.ftype = { "f32": GGMLFileType.AllF32, "f16": GGMLFileType.MostlyF16, + "q8_0": GGMLFileType.MostlyQ8_0, }[args.outtype] print(f"params = {params}") @@ -1104,7 +1164,7 @@ def main(args_in: Optional[List[str]] = None) -> None: params.ftype = ftype print(f"Writing {outfile}, format {ftype}") - OutputFile.write_all(outfile, params, model, vocab) + OutputFile.write_all(outfile, ftype, params, model, vocab, concurrency = args.concurrency) print(f"Wrote {outfile}") From c1ac54b77aaba10d029084d152be786102010eb2 Mon Sep 17 00:00:00 2001 From: Bruce MacDonald Date: Sat, 26 Aug 2023 16:11:45 -0700 Subject: [PATCH 381/852] server : add `/detokenize` endpoint (#2802) * Add a /detokenize endpoint to the example server * remove trailing white-space --- examples/server/README.md | 6 ++++++ examples/server/server.cpp | 21 +++++++++++++++++++++ 2 files changed, 27 insertions(+) diff --git a/examples/server/README.md b/examples/server/README.md index 7105e9020..517608046 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -164,6 +164,12 @@ node index.js Note that the special `BOS` token is not added in front of the text and also a space character is not inserted automatically as it is for `/completion`. +- **POST** `/detokenize`: Convert tokens to text. + + *Options:* + + `tokens`: Set the tokens to detokenize. + - **POST** `/embedding`: Generate embedding of a given text just as [the embedding example](../embedding) does. *Options:* diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 3300553f9..a4b4d6418 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1104,6 +1104,12 @@ static json format_tokenizer_response(const std::vector &tokens) {"tokens", tokens}}; } +static json format_detokenized_response(std::string content) +{ + return json{ + {"content", content}}; +} + template static T json_value(const json &body, const std::string &key, const T &default_value) { @@ -1501,6 +1507,21 @@ int main(int argc, char **argv) const json data = format_tokenizer_response(tokens); return res.set_content(data.dump(), "application/json"); }); + svr.Post("/detokenize", [&llama](const Request &req, Response &res) + { + auto lock = llama.lock(); + + const json body = json::parse(req.body); + std::string content; + if (body.count("tokens") != 0) + { + const std::vector tokens = body["tokens"]; + content = tokens_to_str(llama.ctx, tokens.cbegin(), tokens.cend()); + } + + const json data = format_detokenized_response(content); + return res.set_content(data.dump(), "application/json"); }); + svr.Post("/embedding", [&llama](const Request &req, Response &res) { auto lock = llama.lock(); From 789c8c945a2814e1487e18e68823d9926e3b1454 Mon Sep 17 00:00:00 2001 From: slaren Date: Sun, 27 Aug 2023 09:03:27 +0200 Subject: [PATCH 382/852] ci : add LoRA test to CI (#2650) * ci : add lora test ggml-ci * move lora summary to the top, add lora logs ggml-ci * ci : decrease CPU ppl runs to 2 to avoide 20 min timeout ggml-ci * add 7b lora test use 1 thread for CUDA generation tests ggml-ci * add test with q8_0 (cpu only) ggml-ci --------- Co-authored-by: Georgi Gerganov --- ci/run.sh | 140 +++++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 118 insertions(+), 22 deletions(-) diff --git a/ci/run.sh b/ci/run.sh index e1486e7c1..942b2e00c 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -196,17 +196,17 @@ function gg_run_open_llama_3b_v2 { (time ./bin/main --model ${model_q5_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log (time ./bin/main --model ${model_q6_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - (time ./bin/perplexity --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + (time ./bin/perplexity --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log function check_ppl { qnt="$1" @@ -233,6 +233,48 @@ function gg_run_open_llama_3b_v2 { check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + # lora + function compare_ppl { + qnt="$1" + ppl1=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) + ppl2=$(echo "$3" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) + + if [ $(echo "$ppl1 < $ppl2" | bc) -eq 1 ]; then + printf ' - %s @ %s (FAIL: %s > %s)\n' "$qnt" "$ppl" "$ppl1" "$ppl2" + return 20 + fi + + printf ' - %s @ %s %s OK\n' "$qnt" "$ppl1" "$ppl2" + return 0 + } + + path_lora="../models-mnt/open-llama/3B-v2/lora" + path_shakespeare="../models-mnt/shakespeare" + + shakespeare="${path_shakespeare}/shakespeare.txt" + lora_shakespeare="${path_lora}/ggml-adapter-model.bin" + + gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/adapter_config.json + gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/adapter_model.bin + gg_wget ${path_shakespeare} https://huggingface.co/slaren/open_llama_3b_v2_shakespeare_lora/resolve/main/shakespeare.txt + + python3 ../convert-lora-to-ggml.py ${path_lora} + + # f16 + (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log + (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-f16.log + compare_ppl "f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-f16.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log + + # q8_0 + (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log + (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0.log + compare_ppl "q8_0 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log + + # q8_0 + f16 lora-base + (time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -c 128 -b 128 --chunks 2 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log + compare_ppl "q8_0 / f16 base shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log + + set +e } @@ -242,6 +284,7 @@ function gg_sum_open_llama_3b_v2 { gg_printf 'OpenLLaMA 3B-v2:\n' gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)" + gg_printf '- lora:\n%s\n' "$(cat $OUT/${ci}-lora-ppl.log)" gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)" gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)" gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)" @@ -253,6 +296,11 @@ function gg_sum_open_llama_3b_v2 { gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)" gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)" gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)" + gg_printf '- shakespeare (f16):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-f16.log)" + gg_printf '- shakespeare (f16 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log)" + gg_printf '- shakespeare (q8_0):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log)" + gg_printf '- shakespeare (q8_0 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log)" + gg_printf '- shakespeare (q8_0 / f16 base lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log)" } # open_llama_7b_v2 @@ -310,17 +358,17 @@ function gg_run_open_llama_7b_v2 { ./bin/quantize ${model_f16} ${model_q5_k} q5_k ./bin/quantize ${model_f16} ${model_q6_k} q6_k - (time ./bin/main --model ${model_f16} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/main --model ${model_q8_0} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/main --model ${model_q4_0} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/main --model ${model_q4_1} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/main --model ${model_q5_0} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/main --model ${model_q5_1} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/main --model ${model_q2_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/main --model ${model_q3_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/main --model ${model_q4_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/main --model ${model_q5_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/main --model ${model_q6_k} -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + (time ./bin/main --model ${model_f16} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/main --model ${model_q8_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/main --model ${model_q4_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/main --model ${model_q4_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/main --model ${model_q5_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/main --model ${model_q5_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/main --model ${model_q2_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/main --model ${model_q3_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/main --model ${model_q4_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/main --model ${model_q5_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/main --model ${model_q6_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log (time ./bin/perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log @@ -359,6 +407,48 @@ function gg_run_open_llama_7b_v2 { check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + # lora + function compare_ppl { + qnt="$1" + ppl1=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) + ppl2=$(echo "$3" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) + + if [ $(echo "$ppl1 < $ppl2" | bc) -eq 1 ]; then + printf ' - %s @ %s (FAIL: %s > %s)\n' "$qnt" "$ppl" "$ppl1" "$ppl2" + return 20 + fi + + printf ' - %s @ %s %s OK\n' "$qnt" "$ppl1" "$ppl2" + return 0 + } + + path_lora="../models-mnt/open-llama/7B-v2/lora" + path_shakespeare="../models-mnt/shakespeare" + + shakespeare="${path_shakespeare}/shakespeare.txt" + lora_shakespeare="${path_lora}/ggml-adapter-model.bin" + + gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/adapter_config.json + gg_wget ${path_lora} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/adapter_model.bin + gg_wget ${path_shakespeare} https://huggingface.co/slaren/open_llama_7b_v2_shakespeare_lora/resolve/main/shakespeare.txt + + python3 ../convert-lora-to-ggml.py ${path_lora} + + # f16 + (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-f16.log + (time ./bin/perplexity --model ${model_f16} -f ${shakespeare} --lora ${lora_shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-f16.log + compare_ppl "f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-f16.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log + + # currently not supported by the CUDA backend + # q8_0 + #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-q8_0.log + #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0.log + #compare_ppl "q8_0 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log + + # q8_0 + f16 lora-base + #(time ./bin/perplexity --model ${model_q8_0} -f ${shakespeare} --lora ${lora_shakespeare} --lora-base ${model_f16} -t 1 -ngl 999 -c 2048 -b 512 --chunks 3 ) 2>&1 | tee -a $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log + #compare_ppl "q8_0 / f16 shakespeare" "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log | grep "^\[1\]")" "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-lora-ppl.log + set +e } @@ -368,6 +458,7 @@ function gg_sum_open_llama_7b_v2 { gg_printf 'OpenLLaMA 7B-v2:\n' gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)" + gg_printf '- lora:\n%s\n' "$(cat $OUT/${ci}-lora-ppl.log)" gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)" gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)" gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)" @@ -379,6 +470,11 @@ function gg_sum_open_llama_7b_v2 { gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)" gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)" gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)" + gg_printf '- shakespeare (f16):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-f16.log)" + gg_printf '- shakespeare (f16 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-f16.log)" + #gg_printf '- shakespeare (q8_0):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-q8_0.log)" + #gg_printf '- shakespeare (q8_0 lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0.log)" + #gg_printf '- shakespeare (q8_0 / f16 base lora):\n```\n%s\n```\n' "$(cat $OUT/${ci}-ppl-shakespeare-lora-q8_0-f16.log)" } ## main From 1591e2e590762011b43b10a9b6e04f13f98f2aa5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Przemys=C5=82aw=20Pawe=C5=82czyk?= Date: Sun, 27 Aug 2023 10:10:25 +0200 Subject: [PATCH 383/852] ggml : detect SSSE3 (#2825) * ggml : add ggml_cpu_has_ssse3 * llama : show SSSE3 in system info --- ggml.c | 8 ++++++++ ggml.h | 1 + llama.cpp | 1 + 3 files changed, 10 insertions(+) diff --git a/ggml.c b/ggml.c index 8cb5c404f..394fb459f 100644 --- a/ggml.c +++ b/ggml.c @@ -20516,6 +20516,14 @@ int ggml_cpu_has_sse3(void) { #endif } +int ggml_cpu_has_ssse3(void) { +#if defined(__SSSE3__) + return 1; +#else + return 0; +#endif +} + int ggml_cpu_has_vsx(void) { #if defined(__POWER9_VECTOR__) return 1; diff --git a/ggml.h b/ggml.h index 421c0df60..b418153ba 100644 --- a/ggml.h +++ b/ggml.h @@ -1944,6 +1944,7 @@ extern "C" { GGML_API int ggml_cpu_has_clblast (void); GGML_API int ggml_cpu_has_gpublas (void); GGML_API int ggml_cpu_has_sse3 (void); + GGML_API int ggml_cpu_has_ssse3 (void); GGML_API int ggml_cpu_has_vsx (void); // diff --git a/llama.cpp b/llama.cpp index 05c54c213..e956c0163 100644 --- a/llama.cpp +++ b/llama.cpp @@ -6194,6 +6194,7 @@ const char * llama_print_system_info(void) { s += "WASM_SIMD = " + std::to_string(ggml_cpu_has_wasm_simd()) + " | "; s += "BLAS = " + std::to_string(ggml_cpu_has_blas()) + " | "; s += "SSE3 = " + std::to_string(ggml_cpu_has_sse3()) + " | "; + s += "SSSE3 = " + std::to_string(ggml_cpu_has_ssse3()) + " | "; s += "VSX = " + std::to_string(ggml_cpu_has_vsx()) + " | "; return s.c_str(); From edd4c1481708fcd788b0e423268304fd26e2b125 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 27 Aug 2023 14:19:19 +0300 Subject: [PATCH 384/852] llama : more tokenizer fixes (#2810) * tests : write a Python tokenizer test (wip) * llama : prefix input text for tokenization with whitespace * llama : distinguish pieces from decoded text + fix detokenization * common : add comments * examples : no longer manually add leading space when tokenizing * tests : use Python to generate tokenizer tests for C++ * tests : add option to tokenize text files ggml-ci * tests : add test-tokenizer-1.py * llama.cpp : fix LF token * hellaswag : move the concat space for clarity * tests : add falcon tests (py + cpp, currently do not pass Unicode) ggml-ci * common : temporary separate llama_detokenize calls for SPM and BPE --------- Co-authored-by: klosax <131523366+klosax@users.noreply.github.com> --- common/common.cpp | 39 +++- common/common.h | 22 ++- examples/beam_search/beam_search.cpp | 6 +- examples/embd-input/embd-input-lib.cpp | 2 +- examples/embedding/embedding.cpp | 5 +- examples/main/main.cpp | 20 +- examples/perplexity/perplexity.cpp | 4 +- examples/save-load-state/save-load-state.cpp | 4 +- examples/server/server.cpp | 16 +- examples/simple/simple.cpp | 4 +- .../train-text-from-scratch.cpp | 4 +- llama.cpp | 60 +++--- llama.h | 10 +- tests/CMakeLists.txt | 6 +- tests/test-tokenizer-0-falcon.cpp | 178 +++++++++++++++++ tests/test-tokenizer-0-falcon.py | 83 ++++++++ tests/test-tokenizer-0-llama.cpp | 182 ++++++++++++++++++ tests/test-tokenizer-0-llama.py | 95 +++++++++ tests/test-tokenizer-0.cpp | 141 -------------- tests/test-tokenizer-1.cpp | 14 +- 20 files changed, 671 insertions(+), 224 deletions(-) create mode 100644 tests/test-tokenizer-0-falcon.cpp create mode 100644 tests/test-tokenizer-0-falcon.py create mode 100644 tests/test-tokenizer-0-llama.cpp create mode 100644 tests/test-tokenizer-0-llama.py delete mode 100644 tests/test-tokenizer-0.cpp diff --git a/common/common.cpp b/common/common.cpp index ff19ec4e5..0d91a6a35 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -733,12 +733,12 @@ std::vector llama_tokenize( return result; } -std::string llama_token_to_str(const struct llama_context * ctx, llama_token token) { +std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token) { std::vector result(8, 0); - const int n_tokens = llama_token_to_str(ctx, token, result.data(), result.size()); + const int n_tokens = llama_token_to_piece(ctx, token, result.data(), result.size()); if (n_tokens < 0) { result.resize(-n_tokens); - int check = llama_token_to_str(ctx, token, result.data(), result.size()); + int check = llama_token_to_piece(ctx, token, result.data(), result.size()); GGML_ASSERT(check == -n_tokens); } else { result.resize(n_tokens); @@ -746,3 +746,36 @@ std::string llama_token_to_str(const struct llama_context * ctx, llama_token tok return std::string(result.data(), result.size()); } + +std::string llama_detokenize_spm(llama_context * ctx, const std::vector & tokens) { + const llama_token bos_id = llama_token_bos(ctx); + + std::string piece; + std::string result; + + for (size_t i = 0; i < tokens.size(); ++i) { + piece = llama_token_to_piece(ctx, tokens[i]); + + // remove the leading space of the first non-BOS token + if (((tokens[0] == bos_id && i == 1) || (tokens[0] != bos_id && i == 0)) && piece[0] == ' ') { + piece = piece.substr(1); + } + + result += piece; + } + + return result; +} + +std::string llama_detokenize_bpe(llama_context * ctx, const std::vector & tokens) { + std::string piece; + std::string result; + + for (size_t i = 0; i < tokens.size(); ++i) { + piece = llama_token_to_piece(ctx, tokens[i]); + + result += piece; + } + + return result; +} diff --git a/common/common.h b/common/common.h index ce61265f8..97fda2be7 100644 --- a/common/common.h +++ b/common/common.h @@ -116,11 +116,31 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param // Vocab utils // +// tokenizes a string into a vector of tokens +// should work similar to Python's `tokenizer.encode` std::vector llama_tokenize( struct llama_context * ctx, const std::string & text, bool add_bos); -std::string llama_token_to_str( +// tokenizes a token into a piece +// should work similar to Python's `tokenizer.id_to_piece` +std::string llama_token_to_piece( const struct llama_context * ctx, llama_token token); + +// TODO: these should be moved in llama.h C-style API under single `llama_detokenize` function +// that takes into account the tokenizer type and decides how to handle the leading space +// +// detokenizes a vector of tokens into a string +// should work similar to Python's `tokenizer.decode` +// removes the leading space from the first non-BOS token +std::string llama_detokenize_spm( + llama_context * ctx, + const std::vector & tokens); + +// detokenizes a vector of tokens into a string +// should work similar to Python's `tokenizer.decode` +std::string llama_detokenize_bpe( + llama_context * ctx, + const std::vector & tokens); diff --git a/examples/beam_search/beam_search.cpp b/examples/beam_search/beam_search.cpp index 1c04fabc2..42c7c7254 100644 --- a/examples/beam_search/beam_search.cpp +++ b/examples/beam_search/beam_search.cpp @@ -35,7 +35,7 @@ struct ostream_beam_view { std::ostream& operator<<(std::ostream& os, const ostream_beam_view & obv) { os << "p(" << obv.beam_view.p << ") eob(" << std::boolalpha << obv.beam_view.eob << ") tokens("; for (size_t i = 0 ; i < obv.beam_view.n_tokens ; ++i) { - os << llama_token_to_str(obv.ctx, obv.beam_view.tokens[i]); + os << llama_token_to_piece(obv.ctx, obv.beam_view.tokens[i]); } return os << ')'; } @@ -156,7 +156,7 @@ int main(int argc, char ** argv) for( auto id : tokens_list ) { - std::cout << llama_token_to_str(ctx, id); + std::cout << llama_token_to_piece(ctx, id); } std::cout << std::flush; @@ -175,7 +175,7 @@ int main(int argc, char ** argv) std::cout << "\n\n"; for (llama_token const token_id : callback_data.response) { - std::cout << llama_token_to_str(ctx,token_id); + std::cout << llama_token_to_piece(ctx,token_id); } std::cout << std::endl; diff --git a/examples/embd-input/embd-input-lib.cpp b/examples/embd-input/embd-input-lib.cpp index 8a6ad882e..036bdb398 100644 --- a/examples/embd-input/embd-input-lib.cpp +++ b/examples/embd-input/embd-input-lib.cpp @@ -214,7 +214,7 @@ const char * sampling(struct MyModel * mymodel) { if (id == llama_token_eos(ctx)) { ret = ""; } else { - ret = llama_token_to_str(ctx, id); + ret = llama_token_to_piece(ctx, id); } eval_id(mymodel, id); return ret.c_str(); diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index 38395c75b..93d583b5c 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -56,9 +56,6 @@ int main(int argc, char ** argv) { int n_past = 0; - // Add a space in front of the first character to match OG llama tokenizer behavior - params.prompt.insert(0, 1, ' '); - // tokenize the prompt auto embd_inp = ::llama_tokenize(ctx, params.prompt, true); @@ -67,7 +64,7 @@ int main(int argc, char ** argv) { fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str()); fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size()); for (int i = 0; i < (int) embd_inp.size(); i++) { - fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i]).c_str()); + fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str()); } fprintf(stderr, "\n"); } diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 11d7a7e4f..3ce57f436 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -195,11 +195,6 @@ int main(int argc, char ** argv) { // tokenize the prompt std::vector embd_inp; - if (llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM) { - // Add a space in front of the first character to match OG llama tokenizer behavior - params.prompt.insert(0, 1, ' '); - } - if (params.interactive_first || params.instruct || !params.prompt.empty() || session_tokens.empty()) { embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos); } else { @@ -216,7 +211,6 @@ int main(int argc, char ** argv) { int guidance_offset = 0; int original_prompt_len = 0; if (ctx_guidance) { - params.cfg_negative_prompt.insert(0, 1, ' '); guidance_inp = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, add_bos); std::vector original_inp = ::llama_tokenize(ctx, params.prompt, add_bos); @@ -285,7 +279,7 @@ int main(int argc, char ** argv) { fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str()); fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size()); for (int i = 0; i < (int) embd_inp.size(); i++) { - fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_str(ctx, embd_inp[i]).c_str()); + fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str()); } if (ctx_guidance) { @@ -293,14 +287,14 @@ int main(int argc, char ** argv) { fprintf(stderr, "%s: negative prompt: '%s'\n", __func__, params.cfg_negative_prompt.c_str()); fprintf(stderr, "%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size()); for (int i = 0; i < (int) guidance_inp.size(); i++) { - fprintf(stderr, "%6d -> '%s'\n", guidance_inp[i], llama_token_to_str(ctx, guidance_inp[i]).c_str()); + fprintf(stderr, "%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str()); } } if (params.n_keep > 0) { fprintf(stderr, "%s: static prompt based on n_keep: '", __func__); for (int i = 0; i < params.n_keep; i++) { - fprintf(stderr, "%s", llama_token_to_str(ctx, embd_inp[i]).c_str()); + fprintf(stderr, "%s", llama_token_to_piece(ctx, embd_inp[i]).c_str()); } fprintf(stderr, "'\n"); } @@ -456,7 +450,7 @@ int main(int argc, char ** argv) { //printf("\n---\n"); //printf("resetting: '"); //for (int i = 0; i < (int) embd.size(); i++) { - // printf("%s", llama_token_to_str(ctx, embd[i])); + // printf("%s", llama_token_to_piece(ctx, embd[i])); //} //printf("'\n"); //printf("\n---\n"); @@ -509,7 +503,7 @@ int main(int argc, char ** argv) { input_size = embd_guidance.size(); //fprintf(stderr, "\n---------------------\n"); //for (int i = 0; i < (int) embd_guidance.size(); i++) { - //fprintf(stderr, "%s", llama_token_to_str(ctx, embd_guidance[i])); + //fprintf(stderr, "%s", llama_token_to_piece(ctx, embd_guidance[i])); //} //fprintf(stderr, "\n---------------------\n"); } else { @@ -673,7 +667,7 @@ int main(int argc, char ** argv) { // display text if (input_echo) { for (auto id : embd) { - printf("%s", llama_token_to_str(ctx, id).c_str()); + printf("%s", llama_token_to_piece(ctx, id).c_str()); } fflush(stdout); } @@ -689,7 +683,7 @@ int main(int argc, char ** argv) { if (params.antiprompt.size()) { std::string last_output; for (auto id : last_n_tokens) { - last_output += llama_token_to_str(ctx, id); + last_output += llama_token_to_piece(ctx, id); } is_antiprompt = false; diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index fd89852d6..b596d0626 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -392,7 +392,7 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) { hs_data[i].context = prompt_lines[idx*6]; hs_data[i].gold_ending_idx = std::stoi( prompt_lines[idx*6+1] ); for (size_t j=0; j < 4; j++) { - hs_data[i].ending[j] = " " + prompt_lines[idx*6+2+j]; + hs_data[i].ending[j] = prompt_lines[idx*6+2+j]; } // Delete the selected random example from the prompt @@ -417,7 +417,7 @@ void hellaswag_score(llama_context * ctx, const gpt_params & params) { size_t context_size = context_embd.size(); for (int i = 0; i < 4; ++i) { - ending_tokens[i] = ::llama_tokenize(ctx, hs_data[task_idx].context + hs_data[task_idx].ending[i], add_bos); + ending_tokens[i] = ::llama_tokenize(ctx, hs_data[task_idx].context + " " + hs_data[task_idx].ending[i], add_bos); for (int k = 0; k < int(context_size); ++k) { if (ending_tokens[i][k] != context_embd[k]) { fprintf(stderr, "Oops: ending %d of task %d differs from context at position %d\n",i,int(task_idx),k); diff --git a/examples/save-load-state/save-load-state.cpp b/examples/save-load-state/save-load-state.cpp index 3db61b754..573bc4ef9 100644 --- a/examples/save-load-state/save-load-state.cpp +++ b/examples/save-load-state/save-load-state.cpp @@ -87,7 +87,7 @@ int main(int argc, char ** argv) { } llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; auto next_token = llama_sample_token(ctx, &candidates_p); - auto next_token_str = llama_token_to_str(ctx, next_token); + auto next_token_str = llama_token_to_piece(ctx, next_token); last_n_tokens_data.push_back(next_token); printf("%s", next_token_str.c_str()); @@ -147,7 +147,7 @@ int main(int argc, char ** argv) { } llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; auto next_token = llama_sample_token(ctx2, &candidates_p); - auto next_token_str = llama_token_to_str(ctx2, next_token); + auto next_token_str = llama_token_to_piece(ctx2, next_token); last_n_tokens_data.push_back(next_token); printf("%s", next_token_str.c_str()); diff --git a/examples/server/server.cpp b/examples/server/server.cpp index a4b4d6418..89a3311f5 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -94,7 +94,7 @@ static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end) std::string ret; for (; begin != end; ++begin) { - ret += llama_token_to_str(ctx, *begin); + ret += llama_token_to_piece(ctx, *begin); } return ret; } @@ -123,7 +123,7 @@ static void server_log(const char *level, const char *function, int line, // format incomplete utf-8 multibyte character for output static std::string tokens_to_output_formatted_string(const llama_context *ctx, const llama_token token) { - std::string out = token == -1 ? "" : llama_token_to_str(ctx, token); + std::string out = token == -1 ? "" : llama_token_to_piece(ctx, token); // if the size is 1 and first bit is 1, meaning it's a partial character // (size > 1 meaning it's already a known token) if (out.size() == 1 && (out[0] & 0x80) == 0x80) @@ -286,7 +286,6 @@ struct llama_server_context std::vector p; if (first) { - s.insert(0, 1, ' '); // add a space if it's the first p = ::llama_tokenize(ctx, s, add_bos); first = false; } @@ -309,7 +308,6 @@ struct llama_server_context else { auto s = json_prompt.template get(); - s.insert(0, 1, ' '); // always add a first space prompt_tokens = ::llama_tokenize(ctx, s, add_bos); } @@ -566,7 +564,7 @@ struct llama_server_context if (!embd.empty() && embd.back() == llama_token_eos(ctx)) { - // stopping_word = llama_token_to_str(ctx, embd.back()); + // stopping_word = llama_token_to_piece(ctx, embd.back()); has_next_token = false; stopped_eos = true; LOG_VERBOSE("eos token found", {}); @@ -613,7 +611,7 @@ struct llama_server_context { const completion_token_output token_with_probs = nextToken(); - const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(ctx, token_with_probs.tok); + const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(ctx, token_with_probs.tok); generated_text += token_text; if (params.n_probs > 0) @@ -1254,7 +1252,7 @@ void beam_search_callback(void * callback_data, llama_beams_state beams_state) { struct token_translator { llama_context * ctx; - std::string operator()(llama_token tok) const { return llama_token_to_str(ctx, tok); } + std::string operator()(llama_token tok) const { return llama_token_to_piece(ctx, tok); } std::string operator()(completion_token_output cto) const { return (*this)(cto.tok); } }; @@ -1364,7 +1362,7 @@ int main(int argc, char **argv) while (llama.has_next_token) { const completion_token_output token_with_probs = llama.doCompletion(); - const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_str(llama.ctx, token_with_probs.tok); + const std::string token_text = token_with_probs.tok == -1 ? "" : llama_token_to_piece(llama.ctx, token_with_probs.tok); stop_pos = llama.findStoppingStrings(llama.generated_text, token_text.size(), STOP_FULL); @@ -1395,7 +1393,7 @@ int main(int argc, char **argv) if (token_with_probs.tok == -1 || llama.multibyte_pending > 0) { continue; } - const std::string token_text = llama_token_to_str(llama.ctx, token_with_probs.tok); + const std::string token_text = llama_token_to_piece(llama.ctx, token_with_probs.tok); size_t pos = std::min(sent_count, llama.generated_text.size()); diff --git a/examples/simple/simple.cpp b/examples/simple/simple.cpp index 132f7fbf9..4ee85faca 100644 --- a/examples/simple/simple.cpp +++ b/examples/simple/simple.cpp @@ -63,7 +63,7 @@ int main(int argc, char ** argv) { fprintf(stderr, "\n\n"); for (auto id : tokens_list) { - fprintf(stderr, "%s", llama_token_to_str(ctx, id).c_str()); + fprintf(stderr, "%s", llama_token_to_piece(ctx, id).c_str()); } fflush(stderr); @@ -112,7 +112,7 @@ int main(int argc, char ** argv) { } // print the new token : - printf("%s", llama_token_to_str(ctx, new_token_id).c_str()); + printf("%s", llama_token_to_piece(ctx, new_token_id).c_str()); fflush(stdout); // push this new token for next evaluation diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index 79b117df7..12d153417 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -1964,7 +1964,7 @@ void print_matrix(struct ggml_tensor * probs) { void print_token(struct llama_context * ctx, llama_token token) { - printf("%s", llama_token_to_str(ctx, token).c_str()); + printf("%s", llama_token_to_piece(ctx, token).c_str()); } void print_tokens(struct llama_context* ctx, struct ggml_tensor * tokens) { @@ -2202,7 +2202,7 @@ int tokenize_file(struct llama_context * lctx, const char * filename, std::vecto const char * in = buf.data(); const char * end = buf.data() + buf.size(); for (int i = 0; i < (int) out.size(); ++i) { - std::string s = llama_token_to_str(lctx, out[i]); + std::string s = llama_token_to_piece(lctx, out[i]); int len = s.length(); if (in >= end) { printf("%s: unexpected end of original text.\n", __func__); diff --git a/llama.cpp b/llama.cpp index e956c0163..2a8af4ee9 100644 --- a/llama.cpp +++ b/llama.cpp @@ -796,12 +796,12 @@ static void llama_nop(struct ggml_tensor * tensor) { // don't offload by default (void) tensor; } -static std::string llama_token_to_text(const struct llama_context * ctx, llama_token token) { +static std::string llama_token_to_str(const struct llama_context * ctx, llama_token token) { std::vector result(8, 0); - const int n_tokens = llama_token_to_str(ctx, token, result.data(), result.size()); + const int n_tokens = llama_token_to_piece(ctx, token, result.data(), result.size()); if (n_tokens < 0) { result.resize(-n_tokens); - int check = llama_token_to_str(ctx, token, result.data(), result.size()); + int check = llama_token_to_piece(ctx, token, result.data(), result.size()); GGML_ASSERT(check == -n_tokens); } else { result.resize(n_tokens); @@ -1635,7 +1635,8 @@ static void llm_load_hparams( } // TODO: This should probably be in llama.h -static std::vector llama_tokenize_internal(const llama_vocab & vocab, const std::string & raw_text, bool bos); +static std::vector llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos); +static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch); static void llm_load_vocab( llama_model_loader & ml, @@ -1737,7 +1738,11 @@ static void llm_load_vocab( } // determine the newline token: LLaMA "<0x0A>" == 10 == '\n', Falcon 193 == '\n' - vocab.linefeed_id = llama_tokenize_internal(vocab, "\n", false)[0]; + if (vocab.type == LLAMA_VOCAB_TYPE_SPM) { + vocab.linefeed_id = llama_byte_to_token(vocab, '\n'); + } else { + vocab.linefeed_id = llama_tokenize_internal(vocab, "\n", false)[0]; + } // special tokens GGUF_GET_KEY(ctx, vocab.special_bos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_BOS_ID)); @@ -3026,10 +3031,8 @@ static llama_token llama_byte_to_token(const llama_vocab & vocab, uint8_t ch) { return vocab.token_to_id.at(buf); } -static std::string llama_escape_whitespace(const std::string& text) { - std::string result = text; - replace_all(result, " ", "\xe2\x96\x81"); - return result; +static void llama_escape_whitespace(std::string & text) { + replace_all(text, " ", "\xe2\x96\x81"); } static void llama_unescape_whitespace(std::string & word) { @@ -3373,22 +3376,31 @@ private: llm_bigram_bpe::queue work_queue; }; -static std::vector llama_tokenize_internal(const llama_vocab & vocab, const std::string & raw_text, bool bos) { +static std::vector llama_tokenize_internal(const llama_vocab & vocab, std::string raw_text, bool bos) { std::vector output; - if (raw_text.empty()) { - return output; - } + // OG tokenizer behavior: + // + // tokenizer.encode('', add_bos=True) returns [1] + // tokenizer.encode('', add_bos=False) returns [] if (bos && vocab.special_bos_id != -1) { output.push_back(vocab.special_bos_id); } + if (raw_text.empty()) { + return output; + } + switch (vocab.type) { case LLAMA_VOCAB_TYPE_SPM: { + // without adding this leading whitespace, we do not get the same results as the original tokenizer + raw_text = " " + raw_text; + llm_tokenizer_spm tokenizer(vocab); - tokenizer.tokenize(llama_escape_whitespace(raw_text), output); + llama_escape_whitespace(raw_text); + tokenizer.tokenize(raw_text, output); } break; case LLAMA_VOCAB_TYPE_BPE: { @@ -4078,16 +4090,16 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c std::vector candidates_grammar; for (size_t i = 0; i < candidates->size; ++i) { - const llama_token id = candidates->data[i].id; - const std::string text = llama_token_to_text(ctx, id); + const llama_token id = candidates->data[i].id; + const std::string piece = llama_token_to_str(ctx, id); if (id == eos) { if (!allow_eos) { candidates->data[i].logit = -INFINITY; } - } else if (text.empty() || text[0] == 0) { + } else if (piece.empty() || piece[0] == 0) { candidates->data[i].logit = -INFINITY; } else { - candidates_decoded.push_back(decode_utf8(text.c_str(), grammar->partial_utf8)); + candidates_decoded.push_back(decode_utf8(piece.c_str(), grammar->partial_utf8)); candidates_grammar.push_back({ i, candidates_decoded.back().first.data(), candidates_decoded.back().second }); } } @@ -4291,10 +4303,10 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar GGML_ASSERT(false); } - const std::string text = llama_token_to_text(ctx, token); + const std::string piece = llama_token_to_str(ctx, token); // Note terminating 0 in decoded string - const auto decoded = decode_utf8(text.c_str(), grammar->partial_utf8); + const auto decoded = decode_utf8(piece.c_str(), grammar->partial_utf8); const auto & code_points = decoded.first; for (auto it = code_points.begin(), end = code_points.end() - 1; it != end; ++it) { grammar->stacks = llama_grammar_accept(grammar->rules, grammar->stacks, *it); @@ -6101,12 +6113,12 @@ int llama_tokenize_with_model( return res.size(); } -int llama_token_to_str(const struct llama_context * ctx, llama_token token, char * buf, int length) { - return llama_token_to_str_with_model(&ctx->model, token, buf, length); +int llama_token_to_piece(const struct llama_context * ctx, llama_token token, char * buf, int length) { + return llama_token_to_piece_with_model(&ctx->model, token, buf, length); } -// does not write null-terminator to str -int llama_token_to_str_with_model(const struct llama_model * model, llama_token token, char * buf, int length) { +// does not write null-terminator to buf +int llama_token_to_piece_with_model(const struct llama_model * model, llama_token token, char * buf, int length) { if (0 <= token && token < llama_model_n_vocab(model)) { if (llama_is_normal_token(model->vocab, token)) { std::string result = model->vocab.id_to_token[token].text; diff --git a/llama.h b/llama.h index b77dd7735..b084fe23c 100644 --- a/llama.h +++ b/llama.h @@ -381,15 +381,17 @@ extern "C" { int n_max_tokens, bool add_bos); - // Token Id -> String. Uses the vocabulary in the provided context - // Does not write null terminator to the buffer - LLAMA_API int llama_token_to_str( + // Token Id -> Piece. + // Uses the vocabulary in the provided context. + // Does not write null terminator to the buffer. + // User code is responsible to remove the leading whitespace of the first non-BOS token when decoding multiple tokens. + LLAMA_API int llama_token_to_piece( const struct llama_context * ctx, llama_token token, char * buf, int length); - LLAMA_API int llama_token_to_str_with_model( + LLAMA_API int llama_token_to_piece_with_model( const struct llama_model * model, llama_token token, char * buf, diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 2afaf86b1..ca1f39d31 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -25,8 +25,10 @@ endfunction() llama_build_and_test_executable(test-quantize-fns.cpp) llama_build_and_test_executable(test-quantize-perf.cpp) llama_build_and_test_executable(test-sampling.cpp) -llama_build_executable(test-tokenizer-0.cpp) -llama_test_executable (test-tokenizer-0.llama test-tokenizer-0.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf) +llama_build_executable(test-tokenizer-0-llama.cpp) +llama_test_executable (test-tokenizer-0-llama test-tokenizer-0-llama.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-llama.gguf) +llama_build_executable(test-tokenizer-0-falcon.cpp) +#llama_test_executable (test-tokenizer-0-falcon test-tokenizer-0-falcon.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf) llama_build_executable(test-tokenizer-1.cpp) # test-tokenizer-1 requires a BPE vocab. re-enable when we have one. #llama_test_executable (test-tokenizer-1.llama test-tokenizer-1.cpp ${CMAKE_CURRENT_SOURCE_DIR}/../models/ggml-vocab-falcon.gguf) diff --git a/tests/test-tokenizer-0-falcon.cpp b/tests/test-tokenizer-0-falcon.cpp new file mode 100644 index 000000000..836fb8ad2 --- /dev/null +++ b/tests/test-tokenizer-0-falcon.cpp @@ -0,0 +1,178 @@ +#include "llama.h" +#include "common.h" + +#include +#include +#include +#include +#include + +// generate using test-tokenizer-0-falcon.py +static const std::map> & k_tests() { + static std::map> _k_tests = { + { "" , { }, }, + { " " , { 204, }, }, + { " " , { 258, }, }, + { " " , { 466, }, }, + { "\t" , { 192, }, }, + { "\n" , { 193, }, }, + { "\t\n" , { 19125, }, }, + { "Hello world" , { 9856, 1079, }, }, + { " Hello world" , { 23090, 1079, }, }, + { "Hello World" , { 9856, 2889, }, }, + { " Hello World" , { 23090, 2889, }, }, + { " Hello World!" , { 23090, 2889, 12, }, }, + { "Hello, world!" , { 9856, 23, 1079, 12, }, }, + { " Hello, world!" , { 23090, 23, 1079, 12, }, }, + { " this is 🦙.cpp" , { 414, 304, 3346, 111, 231, 25, 29247, }, }, + { "w048 7tuijk dsdfhu" , { 98, 55866, 204, 34, 16682, 7149, 36190, 6869, 11481, }, }, + { "нещо на Български" , { 150, 133, 6207, 151, 215, 150, 134, 5052, 133, 6279, 5052, 223, 151, 216, 49679, 123, 53110, 47043, 7795, }, }, + { "កាន់តែពិសេសអាចខលចេញ" , { 38154, 206, 38154, 126, 38154, 225, 167, 237, 217, 38154, 221, 167, 237, 208, 38154, 228, 38154, 127, 38154, 237, 167, 237, 207, 38154, 237, 38154, 107, 38154, 126, 38154, 211, 38154, 207, 38154, 233, 38154, 211, 167, 237, 207, 38154, 215, }, }, + { "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", { 2571, 232, 206, 204, 19, 11003, 20, 8196, 126, 283, 219, 48778, 116, 13392, 204, 19, 51831, 732, 63209, 1741, 7955, 522, 20, 22438, 211, 204, 19, 7927, 53360, 325, 504, 701, 946, 10930, 20, }, }, + { "Hello" , { 9856, }, }, + { " Hello" , { 23090, }, }, + { " Hello" , { 204, 23090, }, }, + { " Hello" , { 258, 23090, }, }, + { " Hello" , { 466, 23090, }, }, + { " Hello\n Hello" , { 466, 23090, 742, 23090, }, }, + }; + + return _k_tests; +} + +int main(int argc, char **argv) { + if (argc < 2) { + fprintf(stderr, "Usage: %s vocab-file [text-file]\n", argv[0]); + return 1; + } + + const std::string fname = argv[1]; + + std::string fname_text; + if (argc > 2) { + fname_text = argv[2]; + } + + fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str()); + + llama_model * model; + llama_context * ctx; + + llama_backend_init(false); + + // load the vocab + { + auto lparams = llama_context_default_params(); + + lparams.vocab_only = true; + + model = llama_load_model_from_file(fname.c_str(), lparams); + + if (model == NULL) { + fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str()); + return 1; + } + + ctx = llama_new_context_with_model(model, lparams); + + if (ctx == NULL) { + fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str()); + llama_free_model(model); + return 1; + } + } + + if (llama_vocab_type(ctx) != LLAMA_VOCAB_TYPE_BPE) { + fprintf(stderr, "%s : error: vocab type is not SPM\n", __func__); + llama_free_model(model); + llama_free(ctx); + return 2; + } + + bool success = true; + + for (const auto & test_kv : k_tests()) { + const std::vector res = llama_tokenize(ctx, test_kv.first, false); + + printf("\n"); + printf("src: '%s'\n", test_kv.first.c_str()); + printf("res: '%s'\n", llama_detokenize_bpe(ctx, res).c_str()); + printf("tok: "); + for (const auto & tok : res) { + printf("%d ", tok); + } + printf("\n"); + + bool correct = res.size() == test_kv.second.size(); + + for (int i = 0; i < (int) res.size() && correct; ++i) { + if (test_kv.second[i] != res[i]) { + correct = false; + } + } + + if (!correct) { + fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str()); + fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__, + llama_detokenize_bpe(ctx, res).c_str(), + llama_detokenize_bpe(ctx, test_kv.second).c_str()); + fprintf(stderr, "%s : expected tokens: ", __func__); + for (const auto & t : test_kv.second) { + fprintf(stderr, "%6d, ", t); + } + fprintf(stderr, "\n"); + fprintf(stderr, "%s : got tokens: ", __func__); + for (const auto & t : res) { + fprintf(stderr, "%6d, ", t); + } + fprintf(stderr, "\n"); + + success = false; + } + } + + if (!fname_text.empty()) { + fprintf(stderr, "%s : tokenizing: '%s'\n", __func__, fname_text.c_str()); + + std::string text; + { + std::ifstream ifs(fname_text); + if (!ifs) { + fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_text.c_str()); + return 1; + } + text = std::string(std::istreambuf_iterator(ifs), std::istreambuf_iterator()); + } + + fprintf(stderr, "%s : text size: %zu\n", __func__, text.size()); + + const std::vector res = llama_tokenize(ctx, text, true); + + fprintf(stderr, "%s : tokens: %zu\n", __func__, res.size()); + + { + const std::string fname_out = fname_text + ".tokcpp"; + + std::ofstream ofs(fname_out); + if (!ofs) { + fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_out.c_str()); + return 1; + } + + for (const auto & tok : res) { + ofs << tok << " "; + } + + ofs << "\n"; + } + + fprintf(stderr, "%s : tokens written to '%s'\n", __func__, (fname_text + ".tokcpp").c_str()); + } + + llama_free_model(model); + llama_free(ctx); + + llama_backend_free(); + + return success ? 0 : 3; +} diff --git a/tests/test-tokenizer-0-falcon.py b/tests/test-tokenizer-0-falcon.py new file mode 100644 index 000000000..9c8c1c7d1 --- /dev/null +++ b/tests/test-tokenizer-0-falcon.py @@ -0,0 +1,83 @@ +# tests with BPE tokenizer + +import os +import sys +import argparse + +from transformers import AutoTokenizer + +parser = argparse.ArgumentParser() +parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file") +parser.add_argument("--fname-tok", help="path to a text file to tokenize") +args = parser.parse_args() + +dir_tokenizer = args.dir_tokenizer + +tokenizer = AutoTokenizer.from_pretrained(dir_tokenizer) + +tests = [ + "", + " ", + " ", + " ", + "\t", + "\n", + "\t\n", + "Hello world", + " Hello world", + "Hello World", + " Hello World", + " Hello World!", + "Hello, world!", + " Hello, world!", + " this is 🦙.cpp", + "w048 7tuijk dsdfhu", + "нещо на Български", + "កាន់តែពិសេសអាចខលចេញ", + "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", + "Hello", + " Hello", + " Hello", + " Hello", + " Hello", + " Hello\n Hello", + ] + +for text in tests: + print('text: ', text) + print(tokenizer.encode(text)) + print(tokenizer.decode(tokenizer.encode(text))) + +print("\n\ntests for C++:\n") +for text in tests: + res = tokenizer.encode(text) + + k = text.replace('\n', '\\n') + k = k.replace('\t', '\\t') + k = '"' + k + '"' + print("{ %-24s, { " % k, end='') + for x in res: + print("%7d," % x, end='') + print(" }, },") + +print(tokenizer.encode('hello')) +print(tokenizer.encode('world')) +print(tokenizer.encode(' world')) +print(tokenizer.encode('hello world')) + +fname_tok = args.fname_tok +if fname_tok: + print('tokenizing file: ', fname_tok) + fname_out = fname_tok + '.tok' + with open(fname_tok, 'r') as f: + lines = f.readlines() + s = ''.join(lines) + res = tokenizer.encode(s) + # write to file + with open(fname_out, 'w') as f: + for x in res: + f.write(str(x) + ' ') + f.write('\n') + print('len(res): ', len(res)) + print('len(lines): ', len(lines)) + print('results written to: ', fname_out) diff --git a/tests/test-tokenizer-0-llama.cpp b/tests/test-tokenizer-0-llama.cpp new file mode 100644 index 000000000..8630742c6 --- /dev/null +++ b/tests/test-tokenizer-0-llama.cpp @@ -0,0 +1,182 @@ +#include "llama.h" +#include "common.h" + +#include +#include +#include +#include +#include + +// generate using test-tokenizer-0-llama.py +static const std::map> & k_tests() { + static std::map> _k_tests = { + { "" , { }, }, + { " " , { 259, }, }, + { " " , { 1678, }, }, + { " " , { 268, }, }, + { "\t" , { 29871, 12, }, }, + { "\n" , { 29871, 13, }, }, + { "\t\n" , { 29871, 12, 13, }, }, + { "Hello world" , { 15043, 3186, }, }, + { " Hello world" , { 29871, 15043, 3186, }, }, + { "Hello World" , { 15043, 2787, }, }, + { " Hello World" , { 29871, 15043, 2787, }, }, + { " Hello World!" , { 29871, 15043, 2787, 29991, }, }, + { "Hello, world!" , { 15043, 29892, 3186, 29991, }, }, + { " Hello, world!" , { 29871, 15043, 29892, 3186, 29991, }, }, + { " this is 🦙.cpp" , { 29871, 445, 338, 29871, 243, 162, 169, 156, 29889, 8223, }, }, + { "w048 7tuijk dsdfhu" , { 281, 29900, 29946, 29947, 29871, 29955, 9161, 13535, 18031, 2176, 6905, }, }, + { "нещо на Български" , { 1538, 4851, 665, 1386, 29713, 1305, }, }, + { "កាន់តែពិសេសអាចខលចេញ" , { 29871, 31849, 31324, 31934, 228, 162, 142, 228, 161, 146, 228, 162, 133, 228, 161, 153, 228, 161, 186, 31708, 228, 162, 132, 31708, 228, 161, 165, 31324, 228, 161, 136, 228, 161, 132, 228, 161, 158, 228, 161, 136, 228, 162, 132, 228, 161, 140, }, }, + { "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", { 29871, 243, 162, 157, 131, 313, 8945, 29897, 29871, 243, 162, 155, 185, 30722, 243, 162, 143, 174, 30598, 313, 20787, 953, 3848, 275, 16125, 630, 29897, 29871, 31681, 313, 6194, 953, 29877, 2397, 393, 756, 967, 1914, 5993, 29897, }, }, + { "Hello" , { 15043, }, }, + { " Hello" , { 29871, 15043, }, }, + { " Hello" , { 259, 15043, }, }, + { " Hello" , { 1678, 15043, }, }, + { " Hello" , { 268, 15043, }, }, + { " Hello\n Hello" , { 268, 15043, 13, 1678, 15043, }, }, + }; + + return _k_tests; +} + +int main(int argc, char **argv) { + if (argc < 2) { + fprintf(stderr, "Usage: %s vocab-file [text-file]\n", argv[0]); + return 1; + } + + const std::string fname = argv[1]; + + std::string fname_text; + if (argc > 2) { + fname_text = argv[2]; + } + + fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str()); + + llama_model * model; + llama_context * ctx; + + llama_backend_init(false); + + // load the vocab + { + auto lparams = llama_context_default_params(); + + lparams.vocab_only = true; + + model = llama_load_model_from_file(fname.c_str(), lparams); + + if (model == NULL) { + fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str()); + return 1; + } + + ctx = llama_new_context_with_model(model, lparams); + + if (ctx == NULL) { + fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str()); + llama_free_model(model); + return 1; + } + } + + if (llama_vocab_type(ctx) != LLAMA_VOCAB_TYPE_SPM) { + fprintf(stderr, "%s : error: vocab type is not SPM\n", __func__); + llama_free_model(model); + llama_free(ctx); + return 2; + } + + bool success = true; + + for (const auto & test_kv : k_tests()) { + const std::vector res_bos = llama_tokenize(ctx, test_kv.first, true); + const std::vector res_nobos = llama_tokenize(ctx, test_kv.first, false); + + printf("\n"); + printf("src: '%s'\n", test_kv.first.c_str()); + printf("res: '%s'\n", llama_detokenize_spm(ctx, res_bos).c_str()); + printf("tok: "); + for (const auto & tok : res_bos) { + printf("%d ", tok); + } + printf("\n"); + + bool correct = res_nobos.size() == test_kv.second.size() && res_bos.size() == res_nobos.size() + 1 && res_bos[0] == 1; + + for (int i = 0; i < (int) res_nobos.size() && correct; ++i) { + if (test_kv.second[i] != res_bos[i + 1]) { + correct = false; + } + if (test_kv.second[i] != res_nobos[i]) { + correct = false; + } + } + + if (!correct) { + fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str()); + fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__, + llama_detokenize_spm(ctx, res_nobos).c_str(), + llama_detokenize_spm(ctx, test_kv.second).c_str()); + fprintf(stderr, "%s : expected tokens: ", __func__); + for (const auto & t : test_kv.second) { + fprintf(stderr, "%6d, ", t); + } + fprintf(stderr, "\n"); + fprintf(stderr, "%s : got tokens: ", __func__); + for (const auto & t : res_nobos) { + fprintf(stderr, "%6d, ", t); + } + fprintf(stderr, "\n"); + + success = false; + } + } + + if (!fname_text.empty()) { + fprintf(stderr, "%s : tokenizing: '%s'\n", __func__, fname_text.c_str()); + + std::string text; + { + std::ifstream ifs(fname_text); + if (!ifs) { + fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_text.c_str()); + return 1; + } + text = std::string(std::istreambuf_iterator(ifs), std::istreambuf_iterator()); + } + + fprintf(stderr, "%s : text size: %zu\n", __func__, text.size()); + + const std::vector res = llama_tokenize(ctx, text, true); + + fprintf(stderr, "%s : tokens: %zu\n", __func__, res.size()); + + { + const std::string fname_out = fname_text + ".tokcpp"; + + std::ofstream ofs(fname_out); + if (!ofs) { + fprintf(stderr, "%s : error: could not open file '%s'\n", __func__, fname_out.c_str()); + return 1; + } + + for (const auto & tok : res) { + ofs << tok << " "; + } + + ofs << "\n"; + } + + fprintf(stderr, "%s : tokens written to '%s'\n", __func__, (fname_text + ".tokcpp").c_str()); + } + + llama_free_model(model); + llama_free(ctx); + + llama_backend_free(); + + return success ? 0 : 3; +} diff --git a/tests/test-tokenizer-0-llama.py b/tests/test-tokenizer-0-llama.py new file mode 100644 index 000000000..bc164ee29 --- /dev/null +++ b/tests/test-tokenizer-0-llama.py @@ -0,0 +1,95 @@ +# tests with SPM tokenizer + +import os +import sys +import argparse + +from sentencepiece import SentencePieceProcessor + +parser = argparse.ArgumentParser() +parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file") +parser.add_argument("--fname-tok", help="path to a text file to tokenize") +args = parser.parse_args() + +dir_tokenizer = args.dir_tokenizer + +tokenizer = SentencePieceProcessor(dir_tokenizer + '/tokenizer.model') + +tests = [ + "", + " ", + " ", + " ", + "\t", + "\n", + "\t\n", + "Hello world", + " Hello world", + "Hello World", + " Hello World", + " Hello World!", + "Hello, world!", + " Hello, world!", + " this is 🦙.cpp", + "w048 7tuijk dsdfhu", + "нещо на Български", + "កាន់តែពិសេសអាចខលចេញ", + "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", + "Hello", + " Hello", + " Hello", + " Hello", + " Hello", + " Hello\n Hello", + ] + + +for text in tests: + print('text: ', text) + print('\nwith bos:') + print(tokenizer.encode(text, add_bos=True)) + print(tokenizer.decode(tokenizer.encode(text, add_bos=True))) + print('\nwithout bos:') + print(tokenizer.encode(text, add_bos=False)) + print(tokenizer.decode(tokenizer.encode(text, add_bos=False))) + +print("'" + tokenizer.id_to_piece(15043) + "'") # '_Hello' +print("'" + tokenizer.id_to_piece(29871) + "'") # '_' +print("'" + tokenizer.decode([15043]) + "'") # 'Hello' +print("'" + tokenizer.decode([15043, 15043]) + "'") # 'Hello Hello' +print("'" + tokenizer.decode([29871, 15043]) + "'") # ' Hello' +print("'" + tokenizer.decode([29871, 15043, 29871, 15043]) + "'") # ' Hello Hello' + +print("\n\ntests for C++:\n") +for text in tests: + res = tokenizer.encode(text, add_bos=False) + + k = text.replace('\n', '\\n') + k = k.replace('\t', '\\t') + k = '"' + k + '"' + print("{ %-24s, { " % k, end='') + for x in res: + print("%7d," % x, end='') + print(" }, },") + +print(tokenizer.encode('hello')) +print(tokenizer.encode('world')) +print(tokenizer.encode(' world')) +print(tokenizer.encode('hello world')) + +fname_tok = args.fname_tok +if fname_tok: + print('tokenizing file: ', fname_tok) + fname_out = fname_tok + '.tok' + with open(fname_tok, 'r') as f: + lines = f.readlines() + s = ''.join(lines) + res = tokenizer.encode(s, add_bos=True) + # write to file + with open(fname_out, 'w') as f: + for x in res: + f.write(str(x) + ' ') + f.write('\n') + print('len(res): ', len(res)) + print('len(lines): ', len(lines)) + print('results written to: ', fname_out) diff --git a/tests/test-tokenizer-0.cpp b/tests/test-tokenizer-0.cpp deleted file mode 100644 index 7e9ac9188..000000000 --- a/tests/test-tokenizer-0.cpp +++ /dev/null @@ -1,141 +0,0 @@ -#include "llama.h" -#include "common.h" - -#include -#include -#include -#include - -static std::string unescape_whitespace(llama_context* ctx, const std::vector& tokens) { - std::string result; - for (size_t i = 0; i < tokens.size(); ++i) { - result += llama_token_to_str(ctx, tokens[i]); - } - return result; -} - -static const std::map> & k_tests() { - static std::map> _k_tests = { - { " ", {1, 259, }, }, - { " ", { 1, 1678, }, }, - { " ", { 1, 268, }, }, - { "\t", { 1, 29871, 12, }, }, - { "\n", { 1, 29871, 13, }, }, - { "\t\n", { 1, 29871, 12, 13, }, }, - { "Hello world", { 1, 15043, 3186, }, }, - { " Hello world", { 1, 29871, 15043, 3186, }, }, - { "Hello World", { 1, 15043, 2787, }, }, - { " Hello World", { 1, 29871, 15043, 2787, }, }, - { " Hello World!", { 1, 29871, 15043, 2787, 29991, }, }, - { " this is 🦙.cpp", { 1, 29871, 445, 338, 29871, 243, 162, 169, 156, 29889, 8223, }, }, - { "w048 7tuijk dsdfhu", { 1, 281, 29900, 29946, 29947, 29871, 29955, 9161, 13535, 18031, 2176, 6905, }, }, - { "нещо на Български", { 1, 1538, 4851, 665, 1386, 29713, 1305, }, }, - { "កាន់តែពិសេសអាចខលចេញ", { 1, 29871, 31849, 31324, 31934, 228, 162, 142, 228, 161, - 146, 228, 162, 133, 228, 161, 153, 228, 161, 186, - 31708, 228, 162, 132, 31708, 228, 161, 165, 31324, 228, - 161, 136, 228, 161, 132, 228, 161, 158, 228, 161, - 136, 228, 162, 132, 228, 161, 140, }, }, - { "🚀 (normal) 😶‍🌫️ (multiple emojis concatenated) ✅ (only emoji that has its own token)", - { 1, 29871, 243, 162, 157, 131, 313, 8945, 29897, 29871, - 243, 162, 155, 185, 30722, 243, 162, 143, 174, 30598, - 313, 20787, 953, 3848, 275, 16125, 630, 29897, 29871, 31681, - 313, 6194, 953, 29877, 2397, 393, 756, 967, 1914, 5993, 29897, }, }, - { "Hello", { 1, 15043 }, }, - { " Hello", { 1, 29871, 15043 }, }, - { " Hello", { 1, 259, 15043 }, }, - { " Hello", { 1, 1678, 15043 }, }, - { " Hello", { 1, 268, 15043 }, }, - { " Hello\n Hello", { 1, 268, 15043, 13, 1678, 15043 }, }, - }; - - return _k_tests; -} - -int main(int argc, char **argv) { - if (argc < 2) { - fprintf(stderr, "Usage: %s \n", argv[0]); - return 1; - } - - const std::string fname = argv[1]; - - fprintf(stderr, "%s : reading vocab from: '%s'\n", __func__, fname.c_str()); - - llama_model * model; - llama_context * ctx; - - llama_backend_init(false); - - // load the vocab - { - auto lparams = llama_context_default_params(); - - lparams.vocab_only = true; - - model = llama_load_model_from_file(fname.c_str(), lparams); - - if (model == NULL) { - fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str()); - return 1; - } - - ctx = llama_new_context_with_model(model, lparams); - - if (ctx == NULL) { - fprintf(stderr, "%s: error: failed to load vocab '%s'\n", __func__, fname.c_str()); - llama_free_model(model); - return 1; - } - } - - const int n_vocab = llama_n_vocab(ctx); - - if (n_vocab != 32000) { - fprintf(stderr, "%s : expected 32000 tokens, got %d\n", __func__, n_vocab); - llama_free_model(model); - llama_free(ctx); - return 2; - } - - bool success = true; - - for (const auto & test_kv : k_tests()) { - // Add a space in front of the first character to match OG llama tokenizer behavior - std::vector res = llama_tokenize(ctx, " " + test_kv.first, true); - fprintf(stderr, "%s : '%s' tokenized to '%s'\n", - __func__, test_kv.first.c_str(), unescape_whitespace(ctx, res).c_str()); - - bool correct = res.size() == test_kv.second.size(); - - for (int i = 0; i < (int) res.size() && correct; ++i) { - if (res[i] != test_kv.second[i]) { - correct = false; - } - } - - if (!correct) { - fprintf(stderr, "%s : failed test: '%s'\n", __func__, test_kv.first.c_str()); - fprintf(stderr, "%s : detokenized to: '%s' instead of '%s'\n", __func__, - unescape_whitespace(ctx, res).c_str(), unescape_whitespace(ctx, test_kv.second).c_str()); - fprintf(stderr, "%s : expected tokens: ", __func__); - for (const auto & t : test_kv.second) { - fprintf(stderr, "%6d, ", t); - } - fprintf(stderr, "\n"); - fprintf(stderr, "%s : got tokens: ", __func__); - for (const auto & t : res) { - fprintf(stderr, "%6d, ", t); - } - fprintf(stderr, "\n"); - - success = false; - } - } - - llama_free_model(model); - llama_free(ctx); - - llama_backend_free(); - - return success ? 0 : 3; -} diff --git a/tests/test-tokenizer-1.cpp b/tests/test-tokenizer-1.cpp index bd607d12b..ce4f2898c 100644 --- a/tests/test-tokenizer-1.cpp +++ b/tests/test-tokenizer-1.cpp @@ -22,14 +22,6 @@ static std::string escape_whitespace(const std::string& text) { return result; } -static std::string unescape_whitespace(llama_context * ctx, const std::vector & tokens) { - std::string result; - for (size_t i = 0; i < tokens.size(); ++i) { - result += llama_token_to_str(ctx, tokens[i]); - } - return result; -} - int main(int argc, char **argv) { if (argc < 2) { fprintf(stderr, "Usage: %s \n", argv[0]); @@ -72,13 +64,13 @@ int main(int argc, char **argv) { const int n_vocab = llama_n_vocab(ctx); for (int i = 0; i < n_vocab; ++i) { - std::string forward = llama_token_to_str(ctx, i); + std::string forward = llama_token_to_piece(ctx, i); std::vector tokens = llama_tokenize(ctx, forward, false); if (tokens.size() == 1) { if (i != tokens[0]) { - std::string backward = llama_token_to_str(ctx, tokens[0]); + std::string backward = llama_token_to_piece(ctx, tokens[0]); fprintf(stderr, "%s : error: token %d is string %s but bpe returns token %d %s\n", - __func__, i, llama_token_to_str(ctx, i).c_str(), tokens[0], backward.c_str()); + __func__, i, llama_token_to_piece(ctx, i).c_str(), tokens[0], backward.c_str()); return 2; } } From d0cee0d36d5be95a0d9088b674dbb27354107221 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 27 Aug 2023 14:19:54 +0300 Subject: [PATCH 385/852] gguf : add 64-bit support (GGUF v2) (#2821) * gguf : bump version to 2 * gguf : add support for 64-bit (no backwards comp yet) * gguf : v1 backwards comp * gguf.py : bump GGUF version * gguf.py : uint64_t on all lengths, sizes and counts, enums still uint32_t * gguf.py : string lengths uint32_t * gguf : update all counts to 64-bit * gguf.py : string len uint64_t and n_dims uint32_t * gguf : fix typo * llama.cpp : print gguf version --------- Co-authored-by: klosax <131523366+klosax@users.noreply.github.com> --- examples/gguf/gguf.cpp | 3 + ggml.c | 137 +++++++++++++++++++++++++++++++++++------ ggml.h | 11 +++- gguf-py/gguf/gguf.py | 36 ++++++++--- llama.cpp | 4 +- 5 files changed, 164 insertions(+), 27 deletions(-) diff --git a/examples/gguf/gguf.cpp b/examples/gguf/gguf.cpp index dee00df87..cda517bde 100644 --- a/examples/gguf/gguf.cpp +++ b/examples/gguf/gguf.cpp @@ -30,6 +30,9 @@ bool gguf_ex_write(const std::string & fname) { gguf_set_val_u32 (ctx, "some.parameter.uint32", 0x12345678); gguf_set_val_i32 (ctx, "some.parameter.int32", -0x12345679); gguf_set_val_f32 (ctx, "some.parameter.float32", 0.123456789f); + gguf_set_val_u64 (ctx, "some.parameter.uint64", 0x123456789abcdef0ull); + gguf_set_val_i64 (ctx, "some.parameter.int64", -0x123456789abcdef1ll); + gguf_set_val_f64 (ctx, "some.parameter.float64", 0.1234567890123456789); gguf_set_val_bool(ctx, "some.parameter.bool", true); gguf_set_val_str (ctx, "some.parameter.string", "hello world"); diff --git a/ggml.c b/ggml.c index 394fb459f..855d519bf 100644 --- a/ggml.c +++ b/ggml.c @@ -19394,7 +19394,7 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i //////////////////////////////////////////////////////////////////////////////// struct gguf_str { - uint32_t n; + uint64_t n; // GGUFv2 char * data; }; @@ -19408,9 +19408,12 @@ static const size_t GGUF_TYPE_SIZE[GGUF_TYPE_COUNT] = { [GGUF_TYPE_FLOAT32] = sizeof(float), [GGUF_TYPE_BOOL] = sizeof(bool), [GGUF_TYPE_STRING] = sizeof(struct gguf_str), + [GGUF_TYPE_UINT64] = sizeof(uint64_t), + [GGUF_TYPE_INT64] = sizeof(int64_t), + [GGUF_TYPE_FLOAT64] = sizeof(double), [GGUF_TYPE_ARRAY] = 0, // undefined }; -static_assert(GGUF_TYPE_COUNT == 10, "GGUF_TYPE_COUNT != 10"); +static_assert(GGUF_TYPE_COUNT == 13, "GGUF_TYPE_COUNT != 13"); static const char * GGUF_TYPE_NAME[GGUF_TYPE_COUNT] = { [GGUF_TYPE_UINT8] = "u8", @@ -19423,8 +19426,11 @@ static const char * GGUF_TYPE_NAME[GGUF_TYPE_COUNT] = { [GGUF_TYPE_BOOL] = "bool", [GGUF_TYPE_STRING] = "str", [GGUF_TYPE_ARRAY] = "arr", + [GGUF_TYPE_UINT64] = "u64", + [GGUF_TYPE_INT64] = "i64", + [GGUF_TYPE_FLOAT64] = "f64", }; -static_assert(GGUF_TYPE_COUNT == 10, "GGUF_TYPE_COUNT != 10"); +static_assert(GGUF_TYPE_COUNT == 13, "GGUF_TYPE_COUNT != 13"); union gguf_value { uint8_t uint8; @@ -19434,6 +19440,9 @@ union gguf_value { uint32_t uint32; int32_t int32; float float32; + uint64_t uint64; + int64_t int64; + double float64; bool bool_; struct gguf_str str; @@ -19441,7 +19450,7 @@ union gguf_value { struct { enum gguf_type type; - uint32_t n; + uint64_t n; // GGUFv2 void * data; } arr; }; @@ -19449,8 +19458,6 @@ union gguf_value { struct gguf_kv { struct gguf_str key; - uint32_t n_bytes; // TODO: is this actually needed? - enum gguf_type type; union gguf_value value; }; @@ -19458,15 +19465,15 @@ struct gguf_kv { struct gguf_header { uint32_t magic; uint32_t version; - uint32_t n_tensors; - uint32_t n_kv; + uint64_t n_tensors; // GGUFv2 + uint64_t n_kv; // GGUFv2 }; struct gguf_tensor_info { struct gguf_str name; uint32_t n_dims; - uint32_t ne[GGML_MAX_DIMS]; + uint64_t ne[GGML_MAX_DIMS]; enum ggml_type type; @@ -19497,19 +19504,32 @@ static bool gguf_fread_el(FILE * file, void * dst, size_t size, size_t * offset) return n == size; } -static bool gguf_fread_str(FILE * file, struct gguf_str * p, size_t * offset) { +// NOTE: temporary handling of GGUFv1 >> remove after Oct 2023 +static bool gguf_fread_str_cur(FILE * file, struct gguf_str * p, size_t * offset) { p->n = 0; p->data = NULL; bool ok = true; - // TODO: how to avoid mallocs for strings? ok = ok && gguf_fread_el(file, &p->n, sizeof(p->n), offset); p->data = calloc(p->n + 1, 1); ok = ok && gguf_fread_el(file, p->data, p->n, offset); return ok; } +static bool gguf_fread_str_v1(FILE * file, struct gguf_str * p, size_t * offset) { + p->n = 0; + p->data = NULL; + + bool ok = true; + + uint32_t n = 0; + ok = ok && gguf_fread_el(file, &n, sizeof(n), offset); p->data = calloc(n + 1, 1); p->n = n; + ok = ok && gguf_fread_el(file, p->data, p->n, offset); + + return ok; +} + struct gguf_context * gguf_init_empty(void) { struct gguf_context * ctx = GGML_ALIGNED_MALLOC(sizeof(struct gguf_context)); @@ -19565,8 +19585,21 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p ctx->data = NULL; ok = ok && gguf_fread_el(file, &ctx->header.version, sizeof(ctx->header.version), &offset); - ok = ok && gguf_fread_el(file, &ctx->header.n_tensors, sizeof(ctx->header.n_tensors), &offset); - ok = ok && gguf_fread_el(file, &ctx->header.n_kv, sizeof(ctx->header.n_kv), &offset); + + if (ctx->header.version == 1) { + // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023 + uint32_t n_tensors = 0; + uint32_t n_kv = 0; + + ok = ok && gguf_fread_el(file, &n_tensors, sizeof(n_tensors), &offset); + ok = ok && gguf_fread_el(file, &n_kv, sizeof(n_kv), &offset); + + ctx->header.n_tensors = n_tensors; + ctx->header.n_kv = n_kv; + } else { + ok = ok && gguf_fread_el(file, &ctx->header.n_tensors, sizeof(ctx->header.n_tensors), &offset); + ok = ok && gguf_fread_el(file, &ctx->header.n_kv, sizeof(ctx->header.n_kv), &offset); + } if (!ok) { fprintf(stderr, "%s: failed to read header\n", __func__); @@ -19576,6 +19609,12 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p } } + // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023 + bool (* gguf_fread_str)(FILE *, struct gguf_str *, size_t *) = gguf_fread_str_cur; + if (ctx->header.version == 1) { + gguf_fread_str = gguf_fread_str_v1; + } + // read the kv pairs { ctx->kv = GGML_ALIGNED_MALLOC(ctx->header.n_kv * sizeof(struct gguf_kv)); @@ -19585,9 +19624,8 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p //fprintf(stderr, "%s: reading kv %d\n", __func__, i); - ok = ok && gguf_fread_str(file, &kv->key, &offset); - //ok = ok && gguf_fread_el (file, &kv->n_bytes, sizeof(kv->n_bytes), &offset); - ok = ok && gguf_fread_el (file, &kv->type, sizeof(kv->type), &offset); + ok = ok && gguf_fread_str(file, &kv->key, &offset); + ok = ok && gguf_fread_el (file, &kv->type, sizeof(kv->type), &offset); //fprintf(stderr, "%s: reading kv with key %s\n", __func__, kv->key.data); @@ -19599,12 +19637,23 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p case GGUF_TYPE_UINT32: ok = ok && gguf_fread_el (file, &kv->value.uint32, sizeof(kv->value.uint32), &offset); break; case GGUF_TYPE_INT32: ok = ok && gguf_fread_el (file, &kv->value.int32, sizeof(kv->value.int32), &offset); break; case GGUF_TYPE_FLOAT32: ok = ok && gguf_fread_el (file, &kv->value.float32, sizeof(kv->value.float32), &offset); break; + case GGUF_TYPE_UINT64: ok = ok && gguf_fread_el (file, &kv->value.uint64, sizeof(kv->value.uint64), &offset); break; + case GGUF_TYPE_INT64: ok = ok && gguf_fread_el (file, &kv->value.int64, sizeof(kv->value.int64), &offset); break; + case GGUF_TYPE_FLOAT64: ok = ok && gguf_fread_el (file, &kv->value.float64, sizeof(kv->value.float64), &offset); break; case GGUF_TYPE_BOOL: ok = ok && gguf_fread_el (file, &kv->value.bool_, sizeof(kv->value.bool_), &offset); break; case GGUF_TYPE_STRING: ok = ok && gguf_fread_str(file, &kv->value.str, &offset); break; case GGUF_TYPE_ARRAY: { ok = ok && gguf_fread_el(file, &kv->value.arr.type, sizeof(kv->value.arr.type), &offset); - ok = ok && gguf_fread_el(file, &kv->value.arr.n, sizeof(kv->value.arr.n), &offset); + + if (ctx->header.version == 1) { + // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023 + uint32_t n = 0; + ok = ok && gguf_fread_el(file, &n, sizeof(n), &offset); + kv->value.arr.n = n; + } else { + ok = ok && gguf_fread_el(file, &kv->value.arr.n, sizeof(kv->value.arr.n), &offset); + } switch (kv->value.arr.type) { case GGUF_TYPE_UINT8: @@ -19614,6 +19663,9 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p case GGUF_TYPE_UINT32: case GGUF_TYPE_INT32: case GGUF_TYPE_FLOAT32: + case GGUF_TYPE_UINT64: + case GGUF_TYPE_INT64: + case GGUF_TYPE_FLOAT64: case GGUF_TYPE_BOOL: { kv->value.arr.data = malloc(kv->value.arr.n * GGUF_TYPE_SIZE[kv->value.arr.type]); @@ -19660,7 +19712,14 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p ok = ok && gguf_fread_str(file, &info->name, &offset); ok = ok && gguf_fread_el (file, &info->n_dims, sizeof(info->n_dims), &offset); for (uint32_t j = 0; j < info->n_dims; ++j) { - ok = ok && gguf_fread_el(file, &info->ne[j], sizeof(info->ne[j]), &offset); + if (ctx->header.version == 1) { + // NOTE: temporary handling of GGUFv1 >> remove after Oct 2023 + uint32_t t = 0; + ok = ok && gguf_fread_el(file, &t, sizeof(t), &offset); + info->ne[j] = t; + } else { + ok = ok && gguf_fread_el(file, &info->ne[j], sizeof(info->ne[j]), &offset); + } } ok = ok && gguf_fread_el (file, &info->type, sizeof(info->type), &offset); ok = ok && gguf_fread_el (file, &info->offset, sizeof(info->offset), &offset); @@ -19954,6 +20013,18 @@ float gguf_get_val_f32(struct gguf_context * ctx, int i) { return ctx->kv[i].value.float32; } +uint64_t gguf_get_val_u64(struct gguf_context * ctx, int i) { + return ctx->kv[i].value.uint64; +} + +int64_t gguf_get_val_i64(struct gguf_context * ctx, int i) { + return ctx->kv[i].value.int64; +} + +double gguf_get_val_f64(struct gguf_context * ctx, int i) { + return ctx->kv[i].value.float64; +} + bool gguf_get_val_bool(struct gguf_context * ctx, int i) { return ctx->kv[i].value.bool_; } @@ -20056,6 +20127,27 @@ void gguf_set_val_f32(struct gguf_context * ctx, const char * key, float val) { ctx->kv[idx].value.float32 = val; } +void gguf_set_val_u64(struct gguf_context * ctx, const char * key, uint64_t val) { + const int idx = gguf_get_or_add_key(ctx, key); + + ctx->kv[idx].type = GGUF_TYPE_UINT64; + ctx->kv[idx].value.uint64 = val; +} + +void gguf_set_val_i64(struct gguf_context * ctx, const char * key, int64_t val) { + const int idx = gguf_get_or_add_key(ctx, key); + + ctx->kv[idx].type = GGUF_TYPE_INT64; + ctx->kv[idx].value.int64 = val; +} + +void gguf_set_val_f64(struct gguf_context * ctx, const char * key, double val) { + const int idx = gguf_get_or_add_key(ctx, key); + + ctx->kv[idx].type = GGUF_TYPE_FLOAT64; + ctx->kv[idx].value.float64 = val; +} + void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool val) { const int idx = gguf_get_or_add_key(ctx, key); @@ -20106,6 +20198,9 @@ void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src) { case GGUF_TYPE_UINT32: gguf_set_val_u32 (ctx, src->kv[i].key.data, src->kv[i].value.uint32); break; case GGUF_TYPE_INT32: gguf_set_val_i32 (ctx, src->kv[i].key.data, src->kv[i].value.int32); break; case GGUF_TYPE_FLOAT32: gguf_set_val_f32 (ctx, src->kv[i].key.data, src->kv[i].value.float32); break; + case GGUF_TYPE_UINT64: gguf_set_val_u64 (ctx, src->kv[i].key.data, src->kv[i].value.uint64); break; + case GGUF_TYPE_INT64: gguf_set_val_i64 (ctx, src->kv[i].key.data, src->kv[i].value.int64); break; + case GGUF_TYPE_FLOAT64: gguf_set_val_f64 (ctx, src->kv[i].key.data, src->kv[i].value.float64); break; case GGUF_TYPE_BOOL: gguf_set_val_bool(ctx, src->kv[i].key.data, src->kv[i].value.bool_); break; case GGUF_TYPE_STRING: gguf_set_val_str (ctx, src->kv[i].key.data, src->kv[i].value.str.data); break; case GGUF_TYPE_ARRAY: @@ -20267,6 +20362,9 @@ static void gguf_write_to_buf(struct gguf_context * ctx, struct gguf_buf * buf, case GGUF_TYPE_UINT32: gguf_bwrite_el (buf, &kv->value.uint32, sizeof(kv->value.uint32) ); break; case GGUF_TYPE_INT32: gguf_bwrite_el (buf, &kv->value.int32, sizeof(kv->value.int32) ); break; case GGUF_TYPE_FLOAT32: gguf_bwrite_el (buf, &kv->value.float32, sizeof(kv->value.float32)); break; + case GGUF_TYPE_UINT64: gguf_bwrite_el (buf, &kv->value.uint64, sizeof(kv->value.uint64) ); break; + case GGUF_TYPE_INT64: gguf_bwrite_el (buf, &kv->value.int64, sizeof(kv->value.int64) ); break; + case GGUF_TYPE_FLOAT64: gguf_bwrite_el (buf, &kv->value.float64, sizeof(kv->value.float64)); break; case GGUF_TYPE_BOOL: gguf_bwrite_el (buf, &kv->value.bool_, sizeof(kv->value.bool_) ); break; case GGUF_TYPE_STRING: gguf_bwrite_str(buf, &kv->value.str ); break; case GGUF_TYPE_ARRAY: @@ -20282,6 +20380,9 @@ static void gguf_write_to_buf(struct gguf_context * ctx, struct gguf_buf * buf, case GGUF_TYPE_UINT32: case GGUF_TYPE_INT32: case GGUF_TYPE_FLOAT32: + case GGUF_TYPE_UINT64: + case GGUF_TYPE_INT64: + case GGUF_TYPE_FLOAT64: case GGUF_TYPE_BOOL: { gguf_bwrite_el(buf, kv->value.arr.data, kv->value.arr.n * GGUF_TYPE_SIZE[kv->value.arr.type]); diff --git a/ggml.h b/ggml.h index b418153ba..792ca6e42 100644 --- a/ggml.h +++ b/ggml.h @@ -216,7 +216,7 @@ #define GGML_EXIT_ABORTED 1 #define GGUF_MAGIC 0x46554747 // "GGUF" -#define GGUF_VERSION 1 +#define GGUF_VERSION 2 #define GGUF_DEFAULT_ALIGNMENT 32 @@ -1827,6 +1827,9 @@ extern "C" { GGUF_TYPE_BOOL = 7, GGUF_TYPE_STRING = 8, GGUF_TYPE_ARRAY = 9, + GGUF_TYPE_UINT64 = 10, + GGUF_TYPE_INT64 = 11, + GGUF_TYPE_FLOAT64 = 12, GGUF_TYPE_COUNT, // marks the end of the enum }; @@ -1867,6 +1870,9 @@ extern "C" { GGML_API uint32_t gguf_get_val_u32 (struct gguf_context * ctx, int i); GGML_API int32_t gguf_get_val_i32 (struct gguf_context * ctx, int i); GGML_API float gguf_get_val_f32 (struct gguf_context * ctx, int i); + GGML_API uint64_t gguf_get_val_u64 (struct gguf_context * ctx, int i); + GGML_API int64_t gguf_get_val_i64 (struct gguf_context * ctx, int i); + GGML_API double gguf_get_val_f64 (struct gguf_context * ctx, int i); GGML_API bool gguf_get_val_bool(struct gguf_context * ctx, int i); GGML_API const char * gguf_get_val_str (struct gguf_context * ctx, int i); GGML_API int gguf_get_arr_n (struct gguf_context * ctx, int i); @@ -1886,6 +1892,9 @@ extern "C" { GGML_API void gguf_set_val_u32 (struct gguf_context * ctx, const char * key, uint32_t val); GGML_API void gguf_set_val_i32 (struct gguf_context * ctx, const char * key, int32_t val); GGML_API void gguf_set_val_f32 (struct gguf_context * ctx, const char * key, float val); + GGML_API void gguf_set_val_u64 (struct gguf_context * ctx, const char * key, uint64_t val); + GGML_API void gguf_set_val_i64 (struct gguf_context * ctx, const char * key, int64_t val); + GGML_API void gguf_set_val_f64 (struct gguf_context * ctx, const char * key, double val); GGML_API void gguf_set_val_bool(struct gguf_context * ctx, const char * key, bool val); GGML_API void gguf_set_val_str (struct gguf_context * ctx, const char * key, const char * val); GGML_API void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_type type, const void * data, int n); diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py index f4db7001b..838a2c0f8 100644 --- a/gguf-py/gguf/gguf.py +++ b/gguf-py/gguf/gguf.py @@ -13,7 +13,7 @@ from typing import Any, IO, List, Optional # GGUF_MAGIC = 0x46554747 -GGUF_VERSION = 1 +GGUF_VERSION = 2 GGUF_DEFAULT_ALIGNMENT = 32 # general @@ -365,6 +365,9 @@ class GGUFValueType(IntEnum): BOOL = 7 STRING = 8 ARRAY = 9 + UINT64 = 10 + INT64 = 11 + FLOAT64 = 12 @staticmethod def get_type(val): @@ -378,6 +381,7 @@ class GGUFValueType(IntEnum): return GGUFValueType.BOOL elif isinstance(val, int): return GGUFValueType.INT32 + # TODO: need help with 64-bit types in Python else: print("Unknown type: "+str(type(val))) sys.exit() @@ -400,8 +404,8 @@ class GGUFWriter: def write_header_to_file(self): self.fout.write(struct.pack(" Date: Sun, 27 Aug 2023 14:44:35 +0300 Subject: [PATCH 386/852] readme : update hot topics --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index 95471fdbb..f15a583b0 100644 --- a/README.md +++ b/README.md @@ -11,6 +11,10 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ ### Hot topics +- ## IMPORTANT: Tokenizer fixes and API change (developers and projects using `llama.cpp` built-in tokenization must read): https://github.com/ggerganov/llama.cpp/pull/2810 + +- ## GGUFv2 adds support for 64-bit sizes + backwards compatible: https://github.com/ggerganov/llama.cpp/pull/2821 + - Added support for Falcon models: https://github.com/ggerganov/llama.cpp/pull/2717 - A new file format has been introduced: [GGUF](https://github.com/ggerganov/llama.cpp/pull/2398) From a6d1189fdd4c1ab4ba23f9d777f8950901dcffb2 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sun, 27 Aug 2023 15:19:59 +0300 Subject: [PATCH 387/852] k_quants tuning for Falcon-7b (#2816) * Make ggml-cuda.cu build with QK_K = 64 Using LLAMA_CUDA_FORCE_DMMV = ON and -nommq it runs and produces a meaningful result. * k_quants tuning for Falcon-7b --------- Co-authored-by: Iwan Kawrakow --- ggml-cuda.cu | 25 +++++++++++++++++-------- llama.cpp | 43 ++++++++++++++++++++++++++++++++++--------- 2 files changed, 51 insertions(+), 17 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 83d53c13c..d83aefc9a 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -306,11 +306,11 @@ typedef struct { #define QI4_K (QK_K / (4*QR4_K)) #ifdef GGML_QKK_64 typedef struct { - half d[2]; // super-block scales/mins + half dm[2]; // super-block scales/mins uint8_t scales[2]; // 4-bit block scales/mins uint8_t qs[QK_K/2]; // 4--bit quants } block_q4_K; -static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_fp16_t) + QK_K/2 + 2, "wrong q4_K block size/padding"); +static_assert(sizeof(block_q4_K) == sizeof(half2) + QK_K/2 + 2, "wrong q4_K block size/padding"); #else typedef struct { half2 dm; // super-block scale for quantized scales/mins @@ -737,8 +737,8 @@ static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, float const int tid = threadIdx.x; const uint8_t * q = x[i].qs; float * y = yy + i*QK_K; - const float d = (float)x[i].d[0]; - const float m = (float)x[i].d[1]; + const float d = (float)x[i].dm[0]; + const float m = (float)x[i].dm[1]; y[tid+ 0] = d * (x[i].scales[0] & 0xF) * (q[tid] & 0xF) - m * (x[i].scales[0] >> 4); y[tid+32] = d * (x[i].scales[1] & 0xF) * (q[tid] >> 4) - m * (x[i].scales[1] >> 4); #endif @@ -1155,8 +1155,8 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, const uint16_t * a = (const uint16_t *)x[i].scales; aux16[0] = a[0] & 0x0f0f; aux16[1] = (a[0] >> 4) & 0x0f0f; - const float d = (float)x[i].d[0]; - const float m = (float)x[i].d[1]; + const float d = (float)x[i].dm[0]; + const float m = (float)x[i].dm[1]; float sum = 0.f; for (int j = 0; j < K_QUANTS_PER_ITERATION; ++j) { sum += y[j+ 0] * (d * s[0] * (q[j+ 0] & 0xF) - m * s[2]) @@ -2845,8 +2845,8 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1( aux16[0] = a[0] & 0x0f0f; aux16[1] = (a[0] >> 4) & 0x0f0f; - const float dall = bq4_K->d[0]; - const float dmin = bq4_K->d[1]; + const float dall = bq4_K->dm[0]; + const float dmin = bq4_K->dm[1]; const float d8_1 = __low2float(bq8_1[0].ds); const float d8_2 = __low2float(bq8_1[1].ds); @@ -2929,7 +2929,11 @@ template static __device__ __forceinlin const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd; +#if QK_K == 256 x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm; +#else + x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = {bxi->dm[0], bxi->dm[1]}; +#endif } #pragma unroll @@ -3119,7 +3123,9 @@ template static __device__ __forceinlin const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd; +#if QK_K == 256 x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm; +#endif } #pragma unroll @@ -4709,6 +4715,8 @@ static void ggml_mul_mat_q3_K_q8_1_cuda( const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { +#if QK_K == 256 + int id; CUDA_CHECK(cudaGetDevice(&id)); const int compute_capability = g_compute_capabilities[id]; @@ -4740,6 +4748,7 @@ static void ggml_mul_mat_q3_K_q8_1_cuda( mul_mat_q3_K<<>> (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); } +#endif } static void ggml_mul_mat_q4_K_q8_1_cuda( diff --git a/llama.cpp b/llama.cpp index df103a6e5..e9868f5d0 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4776,7 +4776,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s if (name == tn(LLM_TENSOR_OUTPUT, "weight")) { int nx = tensor->ne[0]; - if (nx % QK_K == 0) { + if (model.arch == LLM_ARCH_FALCON || nx % QK_K != 0) { + new_type = GGML_TYPE_Q8_0; + } + else if (new_type != GGML_TYPE_Q8_0) { new_type = GGML_TYPE_Q6_K; } } else if (name.find("attn_v.weight") != std::string::npos) { @@ -4800,17 +4803,39 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s } else if (name.find("ffn_down.weight") != std::string::npos) { if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K; else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) { - new_type = i_feed_forward_w2 < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K; + new_type = i_feed_forward_w2 < 2 ? GGML_TYPE_Q5_K + : model.arch != LLM_ARCH_FALCON || use_more_bits(i_feed_forward_w2, n_feed_forward_w2) ? GGML_TYPE_Q4_K + : GGML_TYPE_Q3_K; + } + else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) { + new_type = model.arch == LLM_ARCH_FALCON ? GGML_TYPE_Q4_K : GGML_TYPE_Q5_K; + } + else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) { + if (model.arch == LLM_ARCH_FALCON) { + new_type = i_feed_forward_w2 < 2 ? GGML_TYPE_Q6_K : + use_more_bits(i_feed_forward_w2, n_feed_forward_w2) ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K; + } else { + if (use_more_bits(i_feed_forward_w2, n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K; + } + } + else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M && use_more_bits(i_feed_forward_w2, n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && model.arch != LLM_ARCH_FALCON && i_feed_forward_w2 < 4) { + new_type = GGML_TYPE_Q5_K; } - else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K; - else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) && - use_more_bits(i_feed_forward_w2, n_feed_forward_w2)) new_type = GGML_TYPE_Q6_K; - else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && i_feed_forward_w2 < 4) new_type = GGML_TYPE_Q5_K; ++i_feed_forward_w2; } else if (name.find("attn_output.weight") != std::string::npos) { - if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K; - else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) new_type = GGML_TYPE_Q4_K; - else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K; + if (model.arch != LLM_ARCH_FALCON) { + if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) new_type = GGML_TYPE_Q4_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q5_K; + } else { + if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K; + } + } + else if (name.find("attn_qkv.weight") != std::string::npos) { + if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_L) new_type = GGML_TYPE_Q4_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) new_type = GGML_TYPE_Q5_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) new_type = GGML_TYPE_Q6_K; } else if (name.find("ffn_gate.weight") != std::string::npos || name.find("ffn_up.weight") != std::string::npos) { if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K; From 25423e9185b7c2a1881ed8f85cc752a12370be9d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 27 Aug 2023 15:24:40 +0300 Subject: [PATCH 388/852] scripts : helper convert script --- scripts/convert-gg.sh | 26 ++++++++++++++++++++++++++ scripts/qnt-all.sh | 2 ++ scripts/run-all-perf.sh | 2 ++ scripts/run-all-ppl.sh | 2 ++ 4 files changed, 32 insertions(+) create mode 100755 scripts/convert-gg.sh diff --git a/scripts/convert-gg.sh b/scripts/convert-gg.sh new file mode 100755 index 000000000..01fda16fd --- /dev/null +++ b/scripts/convert-gg.sh @@ -0,0 +1,26 @@ +#!/bin/bash + +set -e + +# LLaMA v1 +python3 convert.py ../llama1/7B --outfile models/llama-7b/ggml-model-f16.gguf --outtype f16 +python3 convert.py ../llama1/13B --outfile models/llama-13b/ggml-model-f16.gguf --outtype f16 +python3 convert.py ../llama1/30B --outfile models/llama-30b/ggml-model-f16.gguf --outtype f16 +python3 convert.py ../llama1/65B --outfile models/llama-65b/ggml-model-f16.gguf --outtype f16 + +# LLaMA v2 +python3 convert.py ../llama2/llama-2-7b --outfile models/llama-7b-v2/ggml-model-f16.gguf --outtype f16 +python3 convert.py ../llama2/llama-2-13b --outfile models/llama-13b-v2/ggml-model-f16.gguf --outtype f16 +python3 convert.py ../llama2/llama-2-70b --outfile models/llama-70b-v2/ggml-model-f16.gguf --outtype f16 + +# Code Llama +python3 convert.py ../codellama/CodeLlama-7b/ --outfile models/codellama-7b/ggml-model-f16.gguf --outtype f16 +python3 convert.py ../codellama/CodeLlama-13b/ --outfile models/codellama-13b/ggml-model-f16.gguf --outtype f16 +python3 convert.py ../codellama/CodeLlama-34b/ --outfile models/codellama-34b/ggml-model-f16.gguf --outtype f16 + +# Falcon +python3 convert-falcon-hf-to-gguf.py ../falcon/falcon-7b 1 +mv -v ../falcon/falcon-7b/ggml-model-f16.gguf models/falcon-7b/ggml-model-f16.gguf + +python3 convert-falcon-hf-to-gguf.py ../falcon/falcon-40b 1 +mv -v ../falcon/falcon-40b/ggml-model-f16.gguf models/falcon-40b/ggml-model-f16.gguf diff --git a/scripts/qnt-all.sh b/scripts/qnt-all.sh index 1b3d07da5..1376e4194 100755 --- a/scripts/qnt-all.sh +++ b/scripts/qnt-all.sh @@ -20,6 +20,8 @@ fi model="$1" out="../tmp/results-${model}" +set -e + mkdir -p ${out} for q in ${qnt[@]}; do diff --git a/scripts/run-all-perf.sh b/scripts/run-all-perf.sh index 91a6d853f..7391e3dd5 100755 --- a/scripts/run-all-perf.sh +++ b/scripts/run-all-perf.sh @@ -20,6 +20,8 @@ fi model="$1" out="../tmp/results-${model}" +set -e + mkdir -p ${out} mstr="" diff --git a/scripts/run-all-ppl.sh b/scripts/run-all-ppl.sh index 366d0866c..f643ca3ae 100755 --- a/scripts/run-all-ppl.sh +++ b/scripts/run-all-ppl.sh @@ -17,6 +17,8 @@ if [ ! -z "$3" ]; then args="$3" fi +set -e + model="$1" out="../tmp/results-${model}" From da7455d0467b5f5cc2e45d0dcffaf098df13db63 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 27 Aug 2023 15:52:34 +0300 Subject: [PATCH 389/852] readme : fix headings --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index f15a583b0..bf3eb0b76 100644 --- a/README.md +++ b/README.md @@ -11,9 +11,9 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ ### Hot topics -- ## IMPORTANT: Tokenizer fixes and API change (developers and projects using `llama.cpp` built-in tokenization must read): https://github.com/ggerganov/llama.cpp/pull/2810 +- #### IMPORTANT: Tokenizer fixes and API change (developers and projects using `llama.cpp` built-in tokenization must read): https://github.com/ggerganov/llama.cpp/pull/2810 -- ## GGUFv2 adds support for 64-bit sizes + backwards compatible: https://github.com/ggerganov/llama.cpp/pull/2821 +- GGUFv2 adds support for 64-bit sizes + backwards compatible: https://github.com/ggerganov/llama.cpp/pull/2821 - Added support for Falcon models: https://github.com/ggerganov/llama.cpp/pull/2717 From eaa13a48ff4136f01c1cdb79cacd61b67ec53095 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 27 Aug 2023 16:40:48 +0300 Subject: [PATCH 390/852] falcon : fix CUDA inference by making K and Q contiguous (#2830) * falcon : fix CUDA inference by making K and Q contiguous ggml-ci * cuda : add assert to guard from non-cont ropes --- ggml-cuda.cu | 2 ++ llama.cpp | 10 ++++++---- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index d83aefc9a..d76a25dc2 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -6337,9 +6337,11 @@ void ggml_cuda_soft_max(const ggml_tensor * src0, const ggml_tensor * src1, ggml void ggml_cuda_rope(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { GGML_ASSERT(src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32); + GGML_ASSERT(ggml_is_contiguous(src0)); // TODO: this restriction is temporary until non-cont support is implemented const int mode = ((int32_t *) dst->op_params)[2]; const bool is_glm = mode & 4; + ggml_cuda_op(src0, src1, dst, ggml_cuda_op_rope, true, !is_glm); // flatten support not implemented for glm } diff --git a/llama.cpp b/llama.cpp index e9868f5d0..0d12d9cca 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2642,18 +2642,20 @@ static struct ggml_cgraph * llm_build_falcon( const size_t wsize = ggml_type_size(cur->type); - struct ggml_tensor * tmpq = ggml_view_3d( + // TODO: these 2 ggml_conts are technically not needed, but we add them until CUDA support for + // non-contiguous views is added for the rope operator + struct ggml_tensor * tmpq = ggml_cont(ctx0, ggml_view_3d( ctx0, cur, n_embd_head, n_head, N, wsize * n_embd_head, wsize * n_embd_head * (n_head + 2 * n_head_kv), - 0); + 0)); offload_func_kq(tmpq); - struct ggml_tensor * tmpk = ggml_view_3d( + struct ggml_tensor * tmpk = ggml_cont(ctx0, ggml_view_3d( ctx0, cur, n_embd_head, n_head_kv, N, wsize * n_embd_head, wsize * n_embd_head * (n_head + 2 * n_head_kv), - wsize * n_embd_head * n_head); + wsize * n_embd_head * n_head)); offload_func_kq(tmpk); struct ggml_tensor * tmpv = ggml_view_3d( From 463173a6c0ff353055eb90665794884c888c790f Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sun, 27 Aug 2023 16:50:33 +0300 Subject: [PATCH 391/852] llama : speedup tokenization (#2831) * Speedup tokenization On current master it takes ~3.2 seconds to tokenize Wikitext. With this change it becomes ~525 ms. * Fixit: it was missing the piece after the last found occurence --------- Co-authored-by: Iwan Kawrakow --- examples/perplexity/perplexity.cpp | 4 ++++ llama.cpp | 15 ++++++++++----- 2 files changed, 14 insertions(+), 5 deletions(-) diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index b596d0626..ebafa0c29 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -190,10 +190,14 @@ void perplexity(llama_context * ctx, const gpt_params & params) { const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM; const bool add_bos = is_spm; + auto tim1 = std::chrono::high_resolution_clock::now(); fprintf(stderr, "%s: tokenizing the input ..\n", __func__); auto tokens = ::llama_tokenize(ctx, params.prompt, add_bos); + auto tim2 = std::chrono::high_resolution_clock::now(); + fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast(tim2-tim1).count()); + const int n_chunk_max = tokens.size() / params.n_ctx; const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max); diff --git a/llama.cpp b/llama.cpp index 0d12d9cca..0bb8fcd6e 100644 --- a/llama.cpp +++ b/llama.cpp @@ -114,12 +114,17 @@ static size_t utf8_len(char src) { } void replace_all(std::string & s, const std::string & search, const std::string & replace) { - for (size_t pos = 0; ; pos += replace.length()) { - pos = s.find(search, pos); - if (pos == std::string::npos) break; - s.erase(pos, search.length()); - s.insert(pos, replace); + std::string result; + for (size_t pos = 0; ; pos += search.length()) { + auto new_pos = s.find(search, pos); + if (new_pos == std::string::npos) { + result += s.substr(pos, s.size() - pos); + break; + } + result += s.substr(pos, new_pos - pos) + replace; + pos = new_pos; } + s = std::move(result); } static void zeros(std::ofstream & file, size_t n) { From 230d46c723edf5999752e4cb67fd94edb19ef9c7 Mon Sep 17 00:00:00 2001 From: Olivier Chafik Date: Sun, 27 Aug 2023 15:13:31 +0100 Subject: [PATCH 392/852] examples : update llama2.c converter to read vocab and write models in GGUF format (#2751) * llama2.c: direct gguf output (WIP) * Simplify vector building logic * llama2.c gguf conversion: fix token types in converter * llama2.c: support copying vocab from a llama gguf model file * llama2.c: update default path for vocab model + readme * llama2.c: use defines for gguf keys * llama2.c: escape whitespaces w/ U+2581 in vocab converter the llama.cpp way * llama2.c converter: cleanups + take n_ff from config --- examples/convert-llama2c-to-ggml/README.md | 8 +- .../convert-llama2c-to-ggml.cpp | 340 +++++++++++------- 2 files changed, 220 insertions(+), 128 deletions(-) diff --git a/examples/convert-llama2c-to-ggml/README.md b/examples/convert-llama2c-to-ggml/README.md index fd561fcbc..0f37d295b 100644 --- a/examples/convert-llama2c-to-ggml/README.md +++ b/examples/convert-llama2c-to-ggml/README.md @@ -12,18 +12,14 @@ usage: ./convert-llama2c-to-ggml [options] options: -h, --help show this help message and exit - --copy-vocab-from-model FNAME model path from which to copy vocab (default 'tokenizer.bin') + --copy-vocab-from-model FNAME path of gguf llama model or llama2.c vocabulary from which to copy vocab (default 'models/7B/ggml-model-f16.gguf') --llama2c-model FNAME [REQUIRED] model path from which to load Karpathy's llama2.c model --llama2c-output-model FNAME model path to save the converted llama2.c model (default ak_llama_model.bin') ``` An example command using a model from [karpathy/tinyllamas](https://huggingface.co/karpathy/tinyllamas) is as follows: -`$ ./convert-llama2c-to-ggml --copy-vocab-from-model ../llama2.c/tokenizer.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.ggmlv3.bin` - -For now the generated model is in the legacy GGJTv3 format, so you need to convert it to gguf manually: - -`$ python ./convert-llama-ggmlv3-to-gguf.py --eps 1e-5 --input stories42M.ggmlv3.bin --output stories42M.gguf.bin` +`$ ./convert-llama2c-to-ggml --copy-vocab-from-model llama-2-7b-chat.gguf.q2_K.bin --llama2c-model stories42M.bin --llama2c-output-model stories42M.gguf.bin` Now you can use the model with a command like: diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp index f8a58dc7a..51d90ea6a 100644 --- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp +++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp @@ -10,9 +10,48 @@ #include #include #include +#include #include #include +// GGUF keys & tensor names. + +#define KV_GENERAL_ARCHITECTURE "general.architecture" +#define KV_GENERAL_NAME "general.name" + +#define KV_TOKENIZER_MODEL "tokenizer.ggml.model" +#define KV_TOKENIZER_LIST "tokenizer.ggml.tokens" +#define KV_TOKENIZER_TOKEN_TYPE "tokenizer.ggml.token_type" +#define KV_TOKENIZER_SCORES "tokenizer.ggml.scores" +#define KV_TOKENIZER_BOS_ID "tokenizer.ggml.bos_token_id" +#define KV_TOKENIZER_EOS_ID "tokenizer.ggml.eos_token_id" +#define KV_TOKENIZER_UNK_ID "tokenizer.ggml.unknown_token_id" +#define KV_TOKENIZER_SEP_ID "tokenizer.ggml.seperator_token_id" +#define KV_TOKENIZER_PAD_ID "tokenizer.ggml.padding_token_id" +#define KV_TOKENIZER_HF_JSON "tokenizer.huggingface.json" + +#define KV_CONTEXT_LENGTH "llama.context_length" +#define KV_EMBEDDING_LENGTH "llama.embedding_length" +#define KV_BLOCK_COUNT "llama.block_count" +#define KV_FEED_FORWARD_LENGTH "llama.feed_forward_length" +#define KV_ATTENTION_HEAD_COUNT "llama.attention.head_count" +#define KV_ATTENTION_HEAD_COUNT_KV "llama.attention.head_count_kv" +#define KV_ATTENTION_LAYERNORM_RMS_EPS "llama.attention.layer_norm_rms_epsilon" +#define KV_ROPE_DIMENSION_COUNT "llama.rope.dimension_count" + +#define TN_TOKEN_EMBD "token_embd.weight" +#define TN_OUTPUT_NORM "output_norm.weight" +#define TN_OUTPUT "output.weight" +#define TN_ATTN_NORM "blk.%d.attn_norm.weight" +#define TN_ATTN_Q "blk.%d.attn_q.weight" +#define TN_ATTN_K "blk.%d.attn_k.weight" +#define TN_ATTN_V "blk.%d.attn_v.weight" +#define TN_ATTN_OUTPUT "blk.%d.attn_output.weight" +#define TN_FFN_NORM "blk.%d.ffn_norm.weight" +#define TN_FFN_GATE "blk.%d.ffn_gate.weight" +#define TN_FFN_DOWN "blk.%d.ffn_down.weight" +#define TN_FFN_UP "blk.%d.ffn_up.weight" + #if defined(_MSC_VER) #pragma warning(disable: 4244 4267) // possible loss of data #endif @@ -20,6 +59,11 @@ #define LLAMA_FILE_MAGIC_GGJT 0x67676a74u // 'ggjt' #define LLAMA_FILE_VERSION_GGJT_V3 3 +#define TOKENIZER_NAME "llama" +#define UNKNOWN_TOKEN_ID 0 +#define BOS_TOKEN_ID 1 +#define EOS_TOKEN_ID 2 + //////////////////////////////////////// llama2.c model structs and functions to load models, alloc memory etc. typedef struct { int dim; // transformer dimension @@ -183,6 +227,7 @@ struct my_llama_hparams { uint32_t n_vocab = 32000; uint32_t n_ctx = 512; // this is provided as user input? uint32_t n_embd = 4096; + uint32_t n_ff = 11008; uint32_t n_mult = 4; uint32_t n_head = 32; uint32_t n_layer = 32; @@ -214,6 +259,8 @@ struct my_llama_layer { struct my_llama_model { struct ggml_context * ctx = NULL; + std::string name; + my_llama_hparams hparams; struct ggml_tensor * tok_embeddings; @@ -276,18 +323,13 @@ struct train_params { int mem_compute1_gb; }; -uint32_t get_n_ff(const struct my_llama_hparams* hparams) { - const uint32_t n_ff = ((2*(4*hparams->n_embd)/3 + hparams->n_mult - 1)/hparams->n_mult)*hparams->n_mult; - return n_ff; -} - void print_params(struct my_llama_hparams * params) { printf("%s: n_vocab: %d\n", __func__, params->n_vocab); printf("%s: n_ctx: %d\n", __func__, params->n_ctx); printf("%s: n_embd: %d\n", __func__, params->n_embd); printf("%s: n_mult: %d\n", __func__, params->n_mult); printf("%s: n_head: %d\n", __func__, params->n_head); - printf("%s: n_ff: %d\n", __func__, get_n_ff(params)); + printf("%s: n_ff: %d\n", __func__, params->n_ff); printf("%s: n_layer: %d\n", __func__, params->n_layer); printf("%s: n_rot: %d\n", __func__, params->n_rot); } @@ -299,7 +341,7 @@ void init_model(struct my_llama_model * model) { const uint32_t n_layer = hparams.n_layer; const uint32_t n_vocab = hparams.n_vocab; - const uint32_t n_ff = get_n_ff(&hparams); + const uint32_t n_ff = hparams.n_ff; struct ggml_context * ctx = model->ctx; model->train_its = 0; @@ -481,21 +523,6 @@ struct llama_file { return std::string(chars.data(), len); } - void write_raw(const void * ptr, size_t size) { - if (size == 0) { - return; - } - errno = 0; - size_t ret = std::fwrite(ptr, size, 1, fp); - if (ret != 1) { - throw std::runtime_error(format("write error: %s", strerror(errno))); - } - } - - void write_u32(std::uint32_t val) { - write_raw(&val, sizeof(val)); - } - ~llama_file() { if (fp) { std::fclose(fp); @@ -503,30 +530,6 @@ struct llama_file { } }; -void write_tensor(struct llama_file * file, struct ggml_tensor * tensor) { - if (tensor == NULL) { - file->write_u32(0); - file->write_u32(0); - file->write_u32(GGML_TYPE_F32); - file->seek((0-file->tell()) & 31, SEEK_CUR); - return; - } - const char * name = ggml_get_name(tensor); - uint32_t name_len = strlen(name); - uint32_t nd = tensor->n_dims; - uint32_t ne[4] = { (uint32_t)tensor->ne[0], - (uint32_t)tensor->ne[1], - (uint32_t)tensor->ne[2], - (uint32_t)tensor->ne[3] }; - file->write_u32(nd); - file->write_u32(name_len); - file->write_u32(tensor->type); - file->write_raw(ne, sizeof(ne[0]) * nd); - file->write_raw(name, name_len); - file->seek((0-file->tell()) & 31, SEEK_CUR); - file->write_raw(tensor->data, ggml_nbytes(tensor)); -} - bool is_ggml_file(const char *filename) { llama_file file(filename, "rb"); if (file.size < 4) { @@ -536,48 +539,96 @@ bool is_ggml_file(const char *filename) { return magic == GGUF_MAGIC; } +static std::string llama_escape_whitespaces(const std::string& text) { + std::ostringstream out; + for (char c : text) { + if (c == ' ') out << "\xe2\x96\x81"; + else out << c; + } + return out.str(); +} + void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) { -#pragma message("TODO: implement reading vocabulary using gguf") -// // heuristic to infer whether vocab is from ggml or from llama2.c vocabulary -// if (is_ggml_file(filename)) { -// -// struct llama_context_params llama_params = llama_context_default_params(); -// llama_params.vocab_only = true; -// -// struct llama_model * lmodel = llama_load_model_from_file(filename, llama_params); -// struct llama_context * lctx = llama_new_context_with_model(lmodel, llama_params); -// -// const int n_vocab = llama_n_vocab(lctx); -// vocab->id_to_token.resize(n_vocab); -// for (int i=0; iid_to_token[i].text = llama_token_get_text(lctx, i); -// vocab->id_to_token[i].score = llama_token_get_score(lctx, i); -// vocab->id_to_token[i].type = llama_token_get_type(lctx, i); -// vocab->token_to_id.emplace(vocab->id_to_token[i].text, i); -// } -// llama_free(lctx); -// llama_free_model(lmodel); -// } else - { // assume llama2.c vocabulary - printf("Assuming llama2.c vocabulary since %s is not a ggml file\n", filename); + if (is_ggml_file(filename)) { + struct ggml_context * ctx_data = NULL; + + struct gguf_init_params params = { + /*.no_alloc = */ false, + /*.ctx = */ &ctx_data, + }; + + struct gguf_context * ctx = gguf_init_from_file(filename, params); + GGML_ASSERT(ctx != NULL); + + const int model_idx = gguf_find_key(ctx, KV_TOKENIZER_MODEL); + GGML_ASSERT(model_idx >= 0); + std::string tokenizer_name = gguf_get_val_str(ctx, model_idx); + GGML_ASSERT(tokenizer_name == TOKENIZER_NAME); + + const int token_idx = gguf_find_key(ctx, KV_TOKENIZER_LIST); + GGML_ASSERT(token_idx >= 0); + + const int score_idx = gguf_find_key(ctx, KV_TOKENIZER_SCORES); + GGML_ASSERT(score_idx >= 0); + const float * scores = (const float * ) gguf_get_arr_data(ctx, score_idx); + + const int toktype_idx = gguf_find_key(ctx, KV_TOKENIZER_TOKEN_TYPE); + GGML_ASSERT(toktype_idx >= 0); + const int * toktypes = (const int * ) gguf_get_arr_data(ctx, toktype_idx); + + const uint32_t n_vocab = gguf_get_arr_n(ctx, token_idx); + + vocab->id_to_token.resize(n_vocab); + + for (uint32_t i = 0; i < n_vocab; i++) { + std::string word = gguf_get_arr_str(ctx, token_idx, i); + + vocab->token_to_id[word] = i; + + auto & token_data = vocab->id_to_token[i]; + token_data.text = std::move(word); + token_data.score = scores[i]; + token_data.type = (llama_token_type) toktypes[i]; + } + ggml_free(ctx_data); + gguf_free(ctx); + } else { + // assume llama2.c vocabulary + printf("Assuming llama2.c vocabulary since %s is not a gguf file\n", filename); llama_file file(filename, "rb"); const int n_vocab = config->vocab_size; /* uint32_t max_token_length = */ file.read_u32(); // unused vocab->id_to_token.resize(n_vocab); - for (int i=0; i single byte tokens. - char byte_val; - if (sscanf(text.c_str(), "<0x%02hhX>", &byte_val) == 1) { - char cstr[2] = { byte_val, 0 }; - text = cstr; + + unsigned char byte_val; + llama_vocab::ttype type = LLAMA_TOKEN_TYPE_NORMAL; + if (id == UNKNOWN_TOKEN_ID) { + text = ""; + type = LLAMA_TOKEN_TYPE_UNKNOWN; + } else if (id == BOS_TOKEN_ID) { + text = ""; + type = LLAMA_TOKEN_TYPE_CONTROL; + } else if (id == EOS_TOKEN_ID) { + text = ""; + type = LLAMA_TOKEN_TYPE_CONTROL; + } else if (text.empty()) { + type = LLAMA_TOKEN_TYPE_CONTROL; + } else if (sscanf(text.c_str(), "<0x%02hhX>", &byte_val) == 1) { + // Text of byte tokens is already in the expected format. + type = LLAMA_TOKEN_TYPE_BYTE; + } else { + type = LLAMA_TOKEN_TYPE_NORMAL; } - vocab->id_to_token[i].text = text; - vocab->id_to_token[i].score = score; - vocab->id_to_token[i].type = LLAMA_TOKEN_TYPE_UNDEFINED; - vocab->token_to_id.emplace(text, i); + text = llama_escape_whitespaces(text); + + vocab->id_to_token[id].text = text; + vocab->id_to_token[id].score = score; + vocab->id_to_token[id].type = type; + vocab->token_to_id.emplace(text, id); } } } @@ -619,33 +670,6 @@ void stuff_karpathy_weights_into_gg(struct ggml_tensor * gg_weights, float * kar } void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * model, TransformerWeights* w, const char * filename) { - struct llama_file file(filename, "wb"); - if (file.fp == NULL) { - return; - } - -#pragma message("TODO: implement file saving using gguf") - // write_magic - file.write_u32(LLAMA_FILE_MAGIC_GGJT); // magic - file.write_u32(LLAMA_FILE_VERSION_GGJT_V3); // version - // write_hparams - file.write_u32(model->hparams.n_vocab); - file.write_u32(model->hparams.n_embd); - file.write_u32(model->hparams.n_mult); - file.write_u32(model->hparams.n_head); - file.write_u32(model->hparams.n_layer); - file.write_u32(model->hparams.n_rot); - file.write_u32(LLAMA_FTYPE_ALL_F32); - - // write_vocab - for now we are just writing the existing BPE voc. assuming karpathy's vocabulary is the same. idk. - uint32_t n_vocab = model->hparams.n_vocab; - for (uint32_t i = 0; i < n_vocab; i++) { - const auto & token_data = vocab->id_to_token.at(i); - file.write_u32((uint32_t) token_data.text.size()); - file.write_raw(token_data.text.data(), token_data.text.size()); - file.write_raw(&token_data.score, sizeof(token_data.score)); - } - // stuff AK weights into GG weights one by one. // w->token_embedding_table -> model->tok_embeddings // float* -> struct ggml_tensor @@ -658,8 +682,7 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod // for rms-att-weight int row_length = model->hparams.n_embd; const auto & hparams = model->hparams; - //int n_ff = model->hparams.n_embd; - int n_ff = get_n_ff(&hparams); + int n_ff = model->hparams.n_ff; for (uint32_t i = 0; i < model->hparams.n_layer; ++i){ auto & layer = model->layers[i]; @@ -677,28 +700,91 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod stuff_karpathy_weights_into_gg(layer.w2 , &w->w2[i*n_ff*row_length]); stuff_karpathy_weights_into_gg(layer.w3 , &w->w3[i*row_length*n_ff]); } + + struct gguf_context * ctx = gguf_init_empty(); + + std::vector tokens; + std::vector scores; + std::vector token_types; + for (const llama_vocab::token_data & token_data : vocab->id_to_token) { + tokens.push_back(token_data.text.c_str()); + scores.push_back(token_data.score); + token_types.push_back(token_data.type); + } + gguf_set_arr_str(ctx, KV_TOKENIZER_LIST, tokens.data(), tokens.size()); + gguf_set_arr_data(ctx, KV_TOKENIZER_SCORES, GGUF_TYPE_FLOAT32, scores.data(), scores.size()); + gguf_set_arr_data(ctx, KV_TOKENIZER_TOKEN_TYPE, GGUF_TYPE_INT32, token_types.data(), token_types.size()); + + gguf_set_val_str(ctx, KV_TOKENIZER_MODEL, TOKENIZER_NAME); + + gguf_set_val_str(ctx, KV_GENERAL_ARCHITECTURE, "llama"); + gguf_set_val_str(ctx, KV_GENERAL_NAME, "llama"); + + // special tokens + gguf_set_val_u32(ctx, KV_TOKENIZER_UNK_ID, UNKNOWN_TOKEN_ID); + gguf_set_val_u32(ctx, KV_TOKENIZER_BOS_ID, BOS_TOKEN_ID); + gguf_set_val_u32(ctx, KV_TOKENIZER_EOS_ID, EOS_TOKEN_ID); + gguf_set_val_u32(ctx, KV_TOKENIZER_SEP_ID, -1); + gguf_set_val_u32(ctx, KV_TOKENIZER_PAD_ID, -1); + + gguf_set_val_u32(ctx, KV_CONTEXT_LENGTH, model->hparams.n_ctx); + gguf_set_val_u32(ctx, KV_EMBEDDING_LENGTH, model->hparams.n_embd); + gguf_set_val_u32(ctx, KV_FEED_FORWARD_LENGTH, model->hparams.n_ff); + gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT, model->hparams.n_head); + // n_head_kv is optional, default to n_head + // gguf_set_val_u32(ctx, KV_ATTENTION_HEAD_COUNT_KV, ...); + gguf_set_val_u32(ctx, KV_BLOCK_COUNT, model->hparams.n_layer); + gguf_set_val_u32(ctx, KV_ROPE_DIMENSION_COUNT, model->hparams.n_rot); + gguf_set_val_f32(ctx, KV_ATTENTION_LAYERNORM_RMS_EPS, 1e-5f); + // write tensors - write_tensor(&file, model->tok_embeddings); - write_tensor(&file, model->norm); - write_tensor(&file, model->output); // ? + ggml_set_name(model->tok_embeddings, TN_TOKEN_EMBD); + gguf_add_tensor(ctx, model->tok_embeddings); + + ggml_set_name(model->norm, TN_OUTPUT_NORM); + gguf_add_tensor(ctx, model->norm); + + ggml_set_name(model->output, TN_OUTPUT); + gguf_add_tensor(ctx, model->output); + for (uint32_t i = 0; i < model->hparams.n_layer; ++i) { auto & layer = model->layers[i]; - write_tensor(&file, layer.attention_norm); - write_tensor(&file, layer.wq); - write_tensor(&file, layer.wk); - write_tensor(&file, layer.wv); - write_tensor(&file, layer.wo); - write_tensor(&file, layer.ffn_norm); - write_tensor(&file, layer.w1); - write_tensor(&file, layer.w2); - write_tensor(&file, layer.w3); + ggml_format_name(layer.wq, TN_ATTN_Q, i); + gguf_add_tensor(ctx, layer.wq); + + ggml_format_name(layer.wk, TN_ATTN_K, i); + gguf_add_tensor(ctx, layer.wk); + + ggml_format_name(layer.wv, TN_ATTN_V, i); + gguf_add_tensor(ctx, layer.wv); + + ggml_format_name(layer.wo, TN_ATTN_OUTPUT, i); + gguf_add_tensor(ctx, layer.wo); + + ggml_format_name(layer.attention_norm, TN_ATTN_NORM, i); + gguf_add_tensor(ctx, layer.attention_norm); + + ggml_format_name(layer.w1, TN_FFN_GATE, i); + gguf_add_tensor(ctx, layer.w1); + + ggml_format_name(layer.w2, TN_FFN_DOWN, i); + gguf_add_tensor(ctx, layer.w2); + + ggml_format_name(layer.w3, TN_FFN_UP, i); + gguf_add_tensor(ctx, layer.w3); + + ggml_format_name(layer.ffn_norm, TN_FFN_NORM, i); + gguf_add_tensor(ctx, layer.ffn_norm); } + + gguf_write_to_file(ctx, filename, false); + gguf_free(ctx); } struct train_params get_default_train_params() { struct train_params params; - params.fn_vocab_model = "tokenizer.bin"; + params.fn_vocab_model = "models/7B/ggml-model-f16.gguf"; params.fn_llama2c_output_model = "ak_llama_model.bin"; params.fn_train_data = "shakespeare.txt"; params.fn_checkpoint_in = "checkpoint.bin"; @@ -751,7 +837,7 @@ void print_usage(int /*argc*/, char ** argv, const struct train_params * params) fprintf(stderr, "\n"); fprintf(stderr, "options:\n"); fprintf(stderr, " -h, --help show this help message and exit\n"); - fprintf(stderr, " --copy-vocab-from-model FNAME llama2.c vocabulary or ggmlv3 model path from which to copy vocab (default '%s')\n", params->fn_vocab_model); + fprintf(stderr, " --copy-vocab-from-model FNAME path of gguf llama model or llama2.c vocabulary from which to copy vocab (default '%s')\n", params->fn_vocab_model); fprintf(stderr, " --llama2c-model FNAME [REQUIRED] model path from which to load Karpathy's llama2.c model\n"); fprintf(stderr, " --llama2c-output-model FNAME model path to save the converted llama2.c model (default %s')\n", params->fn_llama2c_output_model); fprintf(stderr, "\n"); @@ -812,6 +898,14 @@ bool params_parse(int argc, char ** argv, struct train_params * params) { return true; } +std::string basename(const std::string &path) { + size_t pos = path.find_last_of("/"); + if (pos == std::string::npos) { + return path; + } + return path.substr(pos + 1); +} + int main(int argc, char ** argv) { struct train_params params = get_default_train_params(); if (!params_parse(argc, argv, ¶ms)) { @@ -840,6 +934,7 @@ int main(int argc, char ** argv) { model.hparams.n_vocab = config.vocab_size; //llama_n_vocab(lctx); model.hparams.n_ctx = params.n_ctx; model.hparams.n_embd = config.dim; //params.n_embd; + model.hparams.n_ff = config.hidden_dim; model.hparams.n_mult = 32;//params.n_mult; model.hparams.n_head = config.n_heads; //params.n_head; model.hparams.n_layer = config.n_layers; //params.n_layer; @@ -853,6 +948,7 @@ int main(int argc, char ** argv) { model.ctx = ggml_init(lcparams); init_model(&model); + model.name = basename(params.fn_llama2c_model); save_as_llama_model(&vocab, &model, &weights, params.fn_llama2c_output_model); printf("Saving llama.c model file %s in ggml format at %s\n", params.fn_llama2c_model, params.fn_llama2c_output_model); From c10704d01e21e3dbe4d6ca1026ebff85349dd239 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 27 Aug 2023 18:55:41 +0300 Subject: [PATCH 393/852] llama : fix MPI threads (close #2827) --- llama.cpp | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 0bb8fcd6e..72d2d1de0 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2845,7 +2845,6 @@ static bool llama_eval_internal( GGML_ASSERT(n_tokens > 0); GGML_ASSERT(n_past >= 0); - GGML_ASSERT(n_threads > 0); // TODO: keep the values of n_batch and n_ctx // GGML_ASSERT(n_tokens <= n_batch); // GGML_ASSERT(n_past + n_tokens <= n_ctx); @@ -2856,6 +2855,8 @@ static bool llama_eval_internal( ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads); #endif + GGML_ASSERT(n_threads > 0); + const int N = n_tokens; const auto & model = lctx.model; From 103cfafc774f6feb3172b5d4d39681c965b17eba Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 27 Aug 2023 21:50:22 +0300 Subject: [PATCH 394/852] gguf : fix strings to not be null-terminated (#2839) * gguf : fix strings to not be null-terminated ggml-ci * gguf : fix gguf_add_tensor name --- ggml.c | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/ggml.c b/ggml.c index 855d519bf..767c19ae2 100644 --- a/ggml.c +++ b/ggml.c @@ -19524,8 +19524,8 @@ static bool gguf_fread_str_v1(FILE * file, struct gguf_str * p, size_t * offset) bool ok = true; uint32_t n = 0; - ok = ok && gguf_fread_el(file, &n, sizeof(n), offset); p->data = calloc(n + 1, 1); p->n = n; - ok = ok && gguf_fread_el(file, p->data, p->n, offset); + ok = ok && gguf_fread_el(file, &n, sizeof(n), offset); p->data = calloc(n + 1, 1); p->n = n; + ok = ok && gguf_fread_el(file, p->data, p->n, offset); return ok; } @@ -20071,7 +20071,7 @@ static int gguf_get_or_add_key(struct gguf_context * ctx, const char * key) { const int n_kv = gguf_get_n_kv(ctx); ctx->kv = realloc(ctx->kv, (n_kv + 1) * sizeof(struct gguf_kv)); - ctx->kv[n_kv].key.n = strlen(key) + 1; + ctx->kv[n_kv].key.n = strlen(key); ctx->kv[n_kv].key.data = strdup(key); ctx->header.n_kv++; @@ -20159,7 +20159,7 @@ void gguf_set_val_str(struct gguf_context * ctx, const char * key, const char * const int idx = gguf_get_or_add_key(ctx, key); ctx->kv[idx].type = GGUF_TYPE_STRING; - ctx->kv[idx].value.str.n = strlen(val) + 1; + ctx->kv[idx].value.str.n = strlen(val); ctx->kv[idx].value.str.data = strdup(val); } @@ -20182,7 +20182,7 @@ void gguf_set_arr_str(struct gguf_context * ctx, const char * key, const char ** ctx->kv[idx].value.arr.data = malloc(n*sizeof(struct gguf_str)); for (int i = 0; i < n; i++) { struct gguf_str * str = &((struct gguf_str *)ctx->kv[idx].value.arr.data)[i]; - str->n = strlen(data[i]) + 1; + str->n = strlen(data[i]); str->data = strdup(data[i]); } } @@ -20229,7 +20229,7 @@ void gguf_add_tensor( const int idx = ctx->header.n_tensors; ctx->infos = realloc(ctx->infos, (idx + 1)*sizeof(struct gguf_tensor_info)); - ctx->infos[idx].name.n = strlen(tensor->name) + 1; + ctx->infos[idx].name.n = strlen(tensor->name); ctx->infos[idx].name.data = strdup(tensor->name); for (int i = 0; i < GGML_MAX_DIMS; ++i) { From 3e8ff47af620a31e0810c58a41e4b089145982ef Mon Sep 17 00:00:00 2001 From: JohnnyB Date: Mon, 28 Aug 2023 07:31:24 +0100 Subject: [PATCH 395/852] devops : added systemd units and set versioning to use date. (#2835) * Corrections and systemd units * Missing dependency clblast --- ....srpm.spec => llama-cpp-clblast.srpm.spec} | 44 ++++++++++++++---- ...s.srpm.spec => llama-cpp-cublas.srpm.spec} | 26 ++++++++++- .devops/llama-cpp.srpm.spec | 45 +++++++++++++++---- 3 files changed, 96 insertions(+), 19 deletions(-) rename .devops/{lamma-cpp-clblast.srpm.spec => llama-cpp-clblast.srpm.spec} (56%) rename .devops/{lamma-cpp-cublas.srpm.spec => llama-cpp-cublas.srpm.spec} (71%) diff --git a/.devops/lamma-cpp-clblast.srpm.spec b/.devops/llama-cpp-clblast.srpm.spec similarity index 56% rename from .devops/lamma-cpp-clblast.srpm.spec rename to .devops/llama-cpp-clblast.srpm.spec index 739c68281..076f29695 100644 --- a/.devops/lamma-cpp-clblast.srpm.spec +++ b/.devops/llama-cpp-clblast.srpm.spec @@ -13,12 +13,13 @@ # It is up to the user to install the correct vendor-specific support. Name: llama.cpp-clblast -Version: master +Version: %( date "+%%Y%%m%%d" ) Release: 1%{?dist} -Summary: OpenCL Inference of LLaMA model in pure C/C++ +Summary: OpenCL Inference of LLaMA model in C/C++ License: MIT Source0: https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz -BuildRequires: coreutils make gcc-c++ git mesa-libOpenCL-devel +BuildRequires: coreutils make gcc-c++ git mesa-libOpenCL-devel clblast-devel +Requires: clblast URL: https://github.com/ggerganov/llama.cpp %define debug_package %{nil} @@ -35,18 +36,43 @@ make -j LLAMA_CLBLAST=1 %install mkdir -p %{buildroot}%{_bindir}/ -cp -p main %{buildroot}%{_bindir}/llamacppclblast -cp -p server %{buildroot}%{_bindir}/llamacppclblastserver -cp -p simple %{buildroot}%{_bindir}/llamacppclblastsimple +cp -p main %{buildroot}%{_bindir}/llamaclblast +cp -p server %{buildroot}%{_bindir}/llamaclblastserver +cp -p simple %{buildroot}%{_bindir}/llamaclblastsimple + +mkdir -p %{buildroot}/usr/lib/systemd/system +%{__cat} < %{buildroot}/usr/lib/systemd/system/llamaclblast.service +[Unit] +Description=Llama.cpp server, CPU only (no GPU support in this build). +After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target + +[Service] +Type=simple +EnvironmentFile=/etc/sysconfig/llama +ExecStart=/usr/bin/llamaclblastserver $LLAMA_ARGS +ExecReload=/bin/kill -s HUP $MAINPID +Restart=never + +[Install] +WantedBy=default.target +EOF + +mkdir -p %{buildroot}/etc/sysconfig +%{__cat} < %{buildroot}/etc/sysconfig/llama +LLAMA_ARGS="-m /opt/llama2/ggml-model-f32.bin" +EOF %clean rm -rf %{buildroot} rm -rf %{_builddir}/* %files -%{_bindir}/llamacppclblast -%{_bindir}/llamacppclblastserver -%{_bindir}/llamacppclblastsimple +%{_bindir}/llamaclblast +%{_bindir}/llamaclblastserver +%{_bindir}/llamaclblastsimple +/usr/lib/systemd/system/llamaclblast.service +%config /etc/sysconfig/llama + %pre diff --git a/.devops/lamma-cpp-cublas.srpm.spec b/.devops/llama-cpp-cublas.srpm.spec similarity index 71% rename from .devops/lamma-cpp-cublas.srpm.spec rename to .devops/llama-cpp-cublas.srpm.spec index 75d32fbe7..f847ebb1e 100644 --- a/.devops/lamma-cpp-cublas.srpm.spec +++ b/.devops/llama-cpp-cublas.srpm.spec @@ -13,7 +13,7 @@ # It is up to the user to install the correct vendor-specific support. Name: llama.cpp-cublas -Version: master +Version: %( date "+%%Y%%m%%d" ) Release: 1%{?dist} Summary: CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL) License: MIT @@ -40,6 +40,28 @@ cp -p main %{buildroot}%{_bindir}/llamacppcublas cp -p server %{buildroot}%{_bindir}/llamacppcublasserver cp -p simple %{buildroot}%{_bindir}/llamacppcublassimple +mkdir -p %{buildroot}/usr/lib/systemd/system +%{__cat} < %{buildroot}/usr/lib/systemd/system/llamacublas.service +[Unit] +Description=Llama.cpp server, CPU only (no GPU support in this build). +After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target + +[Service] +Type=simple +EnvironmentFile=/etc/sysconfig/llama +ExecStart=/usr/bin/llamacppcublasserver $LLAMA_ARGS +ExecReload=/bin/kill -s HUP $MAINPID +Restart=never + +[Install] +WantedBy=default.target +EOF + +mkdir -p %{buildroot}/etc/sysconfig +%{__cat} < %{buildroot}/etc/sysconfig/llama +LLAMA_ARGS="-m /opt/llama2/ggml-model-f32.bin" +EOF + %clean rm -rf %{buildroot} rm -rf %{_builddir}/* @@ -48,6 +70,8 @@ rm -rf %{_builddir}/* %{_bindir}/llamacppcublas %{_bindir}/llamacppcublasserver %{_bindir}/llamacppcublassimple +/usr/lib/systemd/system/llamacublas.service +%config /etc/sysconfig/llama %pre diff --git a/.devops/llama-cpp.srpm.spec b/.devops/llama-cpp.srpm.spec index c65251a5a..446213d69 100644 --- a/.devops/llama-cpp.srpm.spec +++ b/.devops/llama-cpp.srpm.spec @@ -6,6 +6,7 @@ # Notes for llama.cpp: # 1. Tags are currently based on hash - which will not sort asciibetically. # We need to declare standard versioning if people want to sort latest releases. +# In the meantime, YYYYMMDD format will be used. # 2. Builds for CUDA/OpenCL support are separate, with different depenedencies. # 3. NVidia's developer repo must be enabled with nvcc, cublas, clblas, etc installed. # Example: https://developer.download.nvidia.com/compute/cuda/repos/fedora37/x86_64/cuda-fedora37.repo @@ -13,12 +14,13 @@ # It is up to the user to install the correct vendor-specific support. Name: llama.cpp -Version: master +Version: %( date "+%%Y%%m%%d" ) Release: 1%{?dist} Summary: CPU Inference of LLaMA model in pure C/C++ (no CUDA/OpenCL) License: MIT Source0: https://github.com/ggerganov/llama.cpp/archive/refs/heads/master.tar.gz -BuildRequires: coreutils make gcc-c++ git +BuildRequires: coreutils make gcc-c++ git libstdc++-devel +Requires: libstdc++ URL: https://github.com/ggerganov/llama.cpp %define debug_package %{nil} @@ -26,27 +28,52 @@ URL: https://github.com/ggerganov/llama.cpp %description CPU inference for Meta's Lllama2 models using default options. +Models are not included in this package and must be downloaded separately. %prep -%autosetup +%setup -n llama.cpp-master %build make -j %install mkdir -p %{buildroot}%{_bindir}/ -cp -p main %{buildroot}%{_bindir}/llamacpp -cp -p server %{buildroot}%{_bindir}/llamacppserver -cp -p simple %{buildroot}%{_bindir}/llamacppsimple +cp -p main %{buildroot}%{_bindir}/llama +cp -p server %{buildroot}%{_bindir}/llamaserver +cp -p simple %{buildroot}%{_bindir}/llamasimple + +mkdir -p %{buildroot}/usr/lib/systemd/system +%{__cat} < %{buildroot}/usr/lib/systemd/system/llama.service +[Unit] +Description=Llama.cpp server, CPU only (no GPU support in this build). +After=syslog.target network.target local-fs.target remote-fs.target nss-lookup.target + +[Service] +Type=simple +EnvironmentFile=/etc/sysconfig/llama +ExecStart=/usr/bin/llamaserver $LLAMA_ARGS +ExecReload=/bin/kill -s HUP $MAINPID +Restart=never + +[Install] +WantedBy=default.target +EOF + +mkdir -p %{buildroot}/etc/sysconfig +%{__cat} < %{buildroot}/etc/sysconfig/llama +LLAMA_ARGS="-m /opt/llama2/ggml-model-f32.bin" +EOF %clean rm -rf %{buildroot} rm -rf %{_builddir}/* %files -%{_bindir}/llamacpp -%{_bindir}/llamacppserver -%{_bindir}/llamacppsimple +%{_bindir}/llama +%{_bindir}/llamaserver +%{_bindir}/llamasimple +/usr/lib/systemd/system/llama.service +%config /etc/sysconfig/llama %pre From ebcee207b6058b7f695bb5c203ad87b1066a9790 Mon Sep 17 00:00:00 2001 From: Cebtenzzre Date: Mon, 28 Aug 2023 02:32:25 -0400 Subject: [PATCH 396/852] quantize : make output filename optional again (#2823) * quantize : make output filename optional again * quantize : fix path parsing on Windows suggested by @slaren --- examples/quantize/quantize.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index d172f645a..df9a214fc 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -100,7 +100,7 @@ int main(int argc, char ** argv) { } } - if (argc - arg_idx < 3) { + if (argc - arg_idx < 2) { usage(argv[0]); } @@ -114,7 +114,7 @@ int main(int argc, char ** argv) { std::string ftype_str; if (try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) { std::string fpath; - const size_t pos = fname_inp.find_last_of('/'); + const size_t pos = fname_inp.find_last_of("/\\"); if (pos != std::string::npos) { fpath = fname_inp.substr(0, pos + 1); } From f55538c3ccba9b926846ef862fa830cea08c433e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 28 Aug 2023 10:59:08 +0300 Subject: [PATCH 397/852] metal : fix memory leak (#2762) * metal : fix memory leak * metal : fix encoders memory leak * metal : clean up more memory resources * metal : fix more leaks * metal : reuse dispatch queue + autoreleasepool * metal : reuse array for command buffers and encoders * ggml : assert for odd number of blocks on ARM 15M tinyllama is an example --- ggml-metal.h | 1 + ggml-metal.m | 100 +++++++++++++++++++++++++++++++++++++++++---------- ggml.c | 11 +++--- 3 files changed, 88 insertions(+), 24 deletions(-) diff --git a/ggml-metal.h b/ggml-metal.h index 00202b787..fca28d37e 100644 --- a/ggml-metal.h +++ b/ggml-metal.h @@ -24,6 +24,7 @@ // max memory buffers that can be mapped to the device #define GGML_METAL_MAX_BUFFERS 16 +#define GGML_METAL_MAX_COMMAND_BUFFERS 32 struct ggml_tensor; struct ggml_cgraph; diff --git a/ggml-metal.m b/ggml-metal.m index 06eb3872e..ad2ee8cf5 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -33,12 +33,15 @@ struct ggml_metal_buffer { struct ggml_metal_context { int n_cb; - float * logits; - id device; id queue; id library; + id command_buffers [GGML_METAL_MAX_COMMAND_BUFFERS]; + id command_encoders[GGML_METAL_MAX_COMMAND_BUFFERS]; + + dispatch_queue_t d_queue; + int n_buffers; struct ggml_metal_buffer buffers[GGML_METAL_MAX_BUFFERS]; @@ -114,12 +117,13 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context)); - ctx->n_cb = n_cb; + ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS); ctx->device = MTLCreateSystemDefaultDevice(); ctx->queue = [ctx->device newCommandQueue]; ctx->n_buffers = 0; ctx->concur_list_len = 0; + ctx->d_queue = dispatch_queue_create("llama.cpp", DISPATCH_QUEUE_CONCURRENT); #if 0 // compile from source string and show compile log @@ -239,9 +243,67 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { void ggml_metal_free(struct ggml_metal_context * ctx) { fprintf(stderr, "%s: deallocating\n", __func__); +#define GGML_METAL_DEL_KERNEL(name) \ + [ctx->function_##name release]; \ + [ctx->pipeline_##name release]; + + GGML_METAL_DEL_KERNEL(add); + GGML_METAL_DEL_KERNEL(add_row); + GGML_METAL_DEL_KERNEL(mul); + GGML_METAL_DEL_KERNEL(mul_row); + GGML_METAL_DEL_KERNEL(scale); + GGML_METAL_DEL_KERNEL(silu); + GGML_METAL_DEL_KERNEL(relu); + GGML_METAL_DEL_KERNEL(gelu); + GGML_METAL_DEL_KERNEL(soft_max); + GGML_METAL_DEL_KERNEL(diag_mask_inf); + GGML_METAL_DEL_KERNEL(get_rows_f16); + GGML_METAL_DEL_KERNEL(get_rows_q4_0); + GGML_METAL_DEL_KERNEL(get_rows_q4_1); + GGML_METAL_DEL_KERNEL(get_rows_q8_0); + GGML_METAL_DEL_KERNEL(get_rows_q2_K); + GGML_METAL_DEL_KERNEL(get_rows_q3_K); + GGML_METAL_DEL_KERNEL(get_rows_q4_K); + GGML_METAL_DEL_KERNEL(get_rows_q5_K); + GGML_METAL_DEL_KERNEL(get_rows_q6_K); + GGML_METAL_DEL_KERNEL(rms_norm); + GGML_METAL_DEL_KERNEL(norm); + GGML_METAL_DEL_KERNEL(mul_mat_f16_f32); + GGML_METAL_DEL_KERNEL(mul_mat_q4_0_f32); + GGML_METAL_DEL_KERNEL(mul_mat_q4_1_f32); + GGML_METAL_DEL_KERNEL(mul_mat_q8_0_f32); + GGML_METAL_DEL_KERNEL(mul_mat_q2_K_f32); + GGML_METAL_DEL_KERNEL(mul_mat_q3_K_f32); + GGML_METAL_DEL_KERNEL(mul_mat_q4_K_f32); + GGML_METAL_DEL_KERNEL(mul_mat_q5_K_f32); + GGML_METAL_DEL_KERNEL(mul_mat_q6_K_f32); + GGML_METAL_DEL_KERNEL(mul_mm_f16_f32); + GGML_METAL_DEL_KERNEL(mul_mm_q4_0_f32); + GGML_METAL_DEL_KERNEL(mul_mm_q8_0_f32); + GGML_METAL_DEL_KERNEL(mul_mm_q4_1_f32); + GGML_METAL_DEL_KERNEL(mul_mm_q2_K_f32); + GGML_METAL_DEL_KERNEL(mul_mm_q3_K_f32); + GGML_METAL_DEL_KERNEL(mul_mm_q4_K_f32); + GGML_METAL_DEL_KERNEL(mul_mm_q5_K_f32); + GGML_METAL_DEL_KERNEL(mul_mm_q6_K_f32); + GGML_METAL_DEL_KERNEL(rope); + GGML_METAL_DEL_KERNEL(alibi_f32); + GGML_METAL_DEL_KERNEL(cpy_f32_f16); + GGML_METAL_DEL_KERNEL(cpy_f32_f32); + GGML_METAL_DEL_KERNEL(cpy_f16_f16); + +#undef GGML_METAL_DEL_KERNEL + for (int i = 0; i < ctx->n_buffers; ++i) { [ctx->buffers[i].metal release]; } + + [ctx->library release]; + [ctx->queue release]; + [ctx->device release]; + + dispatch_release(ctx->d_queue); + free(ctx); } @@ -261,7 +323,7 @@ void ggml_metal_host_free(void * data) { } void ggml_metal_set_n_cb(struct ggml_metal_context * ctx, int n_cb) { - ctx->n_cb = n_cb; + ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS); } int ggml_metal_if_optimized(struct ggml_metal_context * ctx) { @@ -507,6 +569,8 @@ void ggml_metal_graph_compute( struct ggml_cgraph * gf) { metal_printf("%s: evaluating graph\n", __func__); + @autoreleasepool { + // if there is ctx->concur_list, dispatch concurrently // else fallback to serial dispatch MTLComputePassDescriptor * edesc = MTLComputePassDescriptor.computePassDescriptor; @@ -521,29 +585,25 @@ void ggml_metal_graph_compute( const int n_cb = ctx->n_cb; - NSMutableArray * command_buffers = [NSMutableArray arrayWithCapacity:n_cb]; - for (int i = 0; i < n_cb; ++i) { - command_buffers[i] = [ctx->queue commandBuffer]; + ctx->command_buffers[i] = [ctx->queue commandBuffer]; // enqueue the command buffers in order to specify their execution order - [command_buffers[i] enqueue]; - } + [ctx->command_buffers[i] enqueue]; - // TODO: is this the best way to start threads? - dispatch_queue_t queue = dispatch_queue_create("llama.cpp", DISPATCH_QUEUE_CONCURRENT); + ctx->command_encoders[i] = [ctx->command_buffers[i] computeCommandEncoderWithDescriptor: edesc]; + } for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) { const int n_nodes_per_cb = (n_nodes + n_cb - 1) / n_cb; - dispatch_async(queue, ^{ + dispatch_async(ctx->d_queue, ^{ size_t offs_src0 = 0; size_t offs_src1 = 0; size_t offs_dst = 0; - id command_buffer = command_buffers[cb_idx]; - - id encoder = [command_buffer computeCommandEncoderWithDescriptor: edesc]; + id command_buffer = ctx->command_buffers[cb_idx]; + id encoder = ctx->command_encoders[cb_idx]; const int node_start = (cb_idx + 0) * n_nodes_per_cb; const int node_end = MIN((cb_idx == n_cb - 1) ? n_nodes : (cb_idx + 1) * n_nodes_per_cb, n_nodes); @@ -1117,17 +1177,19 @@ void ggml_metal_graph_compute( } // wait for all threads to finish - dispatch_barrier_sync(queue, ^{}); - - [command_buffers[n_cb - 1] waitUntilCompleted]; + dispatch_barrier_sync(ctx->d_queue, ^{}); // check status of command buffers // needed to detect if the device ran out-of-memory for example (#1881) for (int i = 0; i < n_cb; i++) { - MTLCommandBufferStatus status = (MTLCommandBufferStatus) [command_buffers[i] status]; + [ctx->command_buffers[i] waitUntilCompleted]; + + MTLCommandBufferStatus status = (MTLCommandBufferStatus) [ctx->command_buffers[i] status]; if (status != MTLCommandBufferStatusCompleted) { fprintf(stderr, "%s: command buffer %d failed with status %lu\n", __func__, i, status); GGML_ASSERT(false); } } + + } } diff --git a/ggml.c b/ggml.c index 767c19ae2..54f426bc0 100644 --- a/ggml.c +++ b/ggml.c @@ -2436,7 +2436,6 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * const int nb = n / qk; assert(n % qk == 0); - assert(nb % 2 == 0); const block_q4_0 * restrict x = vx; const block_q8_0 * restrict y = vy; @@ -2445,6 +2444,7 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * float32x4_t sumv0 = vdupq_n_f32(0.0f); float32x4_t sumv1 = vdupq_n_f32(0.0f); + GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb for (int i = 0; i < nb; i += 2) { const block_q4_0 * restrict x0 = &x[i + 0]; const block_q4_0 * restrict x1 = &x[i + 1]; @@ -2623,6 +2623,7 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * } // Main loop + GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb for (int i = 2; i < nb; i+=2) { _mm_prefetch(&x[i] + sizeof(block_q4_0), _MM_HINT_T0); _mm_prefetch(&y[i] + sizeof(block_q8_0), _MM_HINT_T0); @@ -2706,7 +2707,6 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * const int nb = n / qk; assert(n % qk == 0); - assert(nb % 2 == 0); const block_q4_1 * restrict x = vx; const block_q8_1 * restrict y = vy; @@ -2718,6 +2718,7 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * float summs = 0; + GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb for (int i = 0; i < nb; i += 2) { const block_q4_1 * restrict x0 = &x[i + 0]; const block_q4_1 * restrict x1 = &x[i + 1]; @@ -2832,7 +2833,6 @@ static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * const int nb = n / qk; assert(n % qk == 0); - assert(nb % 2 == 0); assert(qk == QK5_0); const block_q5_0 * restrict x = vx; @@ -2848,6 +2848,7 @@ static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * uint64_t tmp0[4]; uint64_t tmp1[4]; + GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb for (int i = 0; i < nb; i += 2) { const block_q5_0 * restrict x0 = &x[i]; const block_q5_0 * restrict x1 = &x[i + 1]; @@ -3072,7 +3073,6 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * const int nb = n / qk; assert(n % qk == 0); - assert(nb % 2 == 0); assert(qk == QK5_1); const block_q5_1 * restrict x = vx; @@ -3091,6 +3091,7 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * uint64_t tmp0[4]; uint64_t tmp1[4]; + GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb for (int i = 0; i < nb; i += 2) { const block_q5_1 * restrict x0 = &x[i]; const block_q5_1 * restrict x1 = &x[i + 1]; @@ -3328,7 +3329,6 @@ static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * const int nb = n / qk; assert(n % qk == 0); - assert(nb % 2 == 0); const block_q8_0 * restrict x = vx; const block_q8_0 * restrict y = vy; @@ -3337,6 +3337,7 @@ static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * float32x4_t sumv0 = vdupq_n_f32(0.0f); float32x4_t sumv1 = vdupq_n_f32(0.0f); + GGML_ASSERT(nb % 2 == 0); // TODO: handle odd nb for (int i = 0; i < nb; i += 2) { const block_q8_0 * restrict x0 = &x[i + 0]; const block_q8_0 * restrict x1 = &x[i + 1]; From dd0dc366dab10e8df28d3924e7f313b5c695e908 Mon Sep 17 00:00:00 2001 From: igarnier Date: Mon, 28 Aug 2023 10:19:59 +0200 Subject: [PATCH 398/852] llama.h : add missing struct keyword for C compat in callback type (#2847) --- llama.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.h b/llama.h index b084fe23c..7bb681d61 100644 --- a/llama.h +++ b/llama.h @@ -496,7 +496,7 @@ extern "C" { // Type of pointer to the beam_search_callback function. // void* callback_data is any custom data passed to llama_beam_search, that is subsequently // passed back to beam_search_callback. This avoids having to use global variables in the callback. - typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, llama_beams_state); + typedef void (*llama_beam_search_callback_fn_t)(void * callback_data, struct llama_beams_state); /// @details Deterministically returns entire sentence constructed by a beam search. /// @param ctx Pointer to the llama_context. From 92b1bbd2ec43c82ec0530ba3c8758846c5790c75 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Mon, 28 Aug 2023 13:23:55 +0200 Subject: [PATCH 399/852] CUDA: fix RoPE asserts, block sizes (#2833) --- ggml-cuda.cu | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index d76a25dc2..5fd625630 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -4908,8 +4908,8 @@ static void scale_f32_cuda(const float * x, float * dst, const float scale, cons static void rope_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0, const float p_delta, const int p_delta_rows, const float theta_scale, cudaStream_t stream) { - GGML_ASSERT(nrows % 2 == 0); // GG: is this assert really needed? I don't see why - const dim3 block_dims(1, 2*CUDA_ROPE_BLOCK_SIZE, 1); + GGML_ASSERT(ncols % 2 == 0); + const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1); const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE); const dim3 block_nums(nrows, num_blocks_x, 1); rope_f32<<>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale); @@ -4917,7 +4917,8 @@ static void rope_f32_cuda(const float * x, float * dst, const int ncols, const i static void rope_neox_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float p0, const float p_delta, const int p_delta_rows, const float theta_scale, cudaStream_t stream) { - const dim3 block_dims(1, 2*CUDA_ROPE_BLOCK_SIZE, 1); + GGML_ASSERT(ncols % 2 == 0); + const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1); const int num_blocks_x = (ncols + 2*CUDA_ROPE_BLOCK_SIZE - 1) / (2*CUDA_ROPE_BLOCK_SIZE); const dim3 block_nums(nrows, num_blocks_x, 1); rope_neox_f32<<>>(x, dst, ncols, p0, p_delta, p_delta_rows, theta_scale); From 35feac6560387cf0484371af3d9b12bff678e0b9 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 28 Aug 2023 14:24:53 +0300 Subject: [PATCH 400/852] ggml : sync (mem align to header + conv_transpose_2d fixes + ggml_alloc) (#2852) * ggml : sync (mem align to header + conv_transpose_2d fixes) ggml-ci * ggml-alloc : minor fix * ggml-alloc : sync more fixes --- ggml-alloc.c | 6 +++--- ggml.c | 22 ++++++++-------------- ggml.h | 18 +++++++++++++----- 3 files changed, 24 insertions(+), 22 deletions(-) diff --git a/ggml-alloc.c b/ggml-alloc.c index 1ef011654..140e9a2a7 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -268,7 +268,7 @@ struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment) /*.parse_seq = */ {0}, /*.parse_seq_len = */ 0, #ifdef GGML_ALLOCATOR_DEBUG - /*.allocated_tensors = */ = {0}, + /*.allocated_tensors = */ {0}, #endif }; @@ -297,7 +297,7 @@ struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) { /*.parse_seq = */ {0}, /*.parse_seq_len = */ 0, #ifdef GGML_ALLOCATOR_DEBUG - /*.allocated_tensors = */ = {0}, + /*.allocated_tensors = */ {0}, #endif }; @@ -556,7 +556,7 @@ static size_t ggml_allocator_alloc_graph_tensors_n( struct ggml_tensor * view_src = get_view_source(parent); struct hash_node * view_src_hn = hash_get(ht, view_src); view_src_hn->n_views -= 1; - AT_PRINTF("view_src %s\n", view_src->name); + AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src_hn->n_children, view_src_hn->n_views); if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src->data != node->data) { ggml_allocator_free_tensor(alloc, view_src); } diff --git a/ggml.c b/ggml.c index 54f426bc0..dadb30757 100644 --- a/ggml.c +++ b/ggml.c @@ -157,12 +157,6 @@ typedef void * thread_ret_t; //#define GGML_SOFT_MAX_ACCELERATE #endif -#if UINTPTR_MAX == 0xFFFFFFFF - #define GGML_MEM_ALIGN 4 -#else - #define GGML_MEM_ALIGN 16 -#endif - // // logging // @@ -7098,11 +7092,13 @@ struct ggml_tensor * ggml_conv_transpose_2d_p0( }; struct ggml_tensor* result = ggml_new_tensor(ctx, GGML_TYPE_F32, 4, ne); + + ggml_set_op_params_i32(result, 0, stride); + result->op = GGML_OP_CONV_TRANSPOSE_2D; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; - result->src[2] = ggml_new_i32(ctx, stride); return result; } @@ -13498,7 +13494,6 @@ static void ggml_compute_forward_conv_transpose_2d( const struct ggml_compute_params * params, const struct ggml_tensor * src0, const struct ggml_tensor * src1, - const struct ggml_tensor * opt0, struct ggml_tensor * dst) { GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); @@ -13558,7 +13553,7 @@ static void ggml_compute_forward_conv_transpose_2d( return; } - const int32_t stride = ((const int32_t*)(opt0->data))[0]; + const int32_t stride = ggml_get_op_params_i32(dst, 0); // total patches in dst const int np = ne2; @@ -13571,7 +13566,7 @@ static void ggml_compute_forward_conv_transpose_2d( const int ip1 = MIN(ip0 + dp, np); ggml_fp16_t * const wdata = (ggml_fp16_t *) params->wdata + 0; - ggml_fp16_t * const wdata_src = (ggml_fp16_t *) params->wdata + nk; + ggml_fp16_t * const wdata_src = wdata + nk; for (int i2 = ip0; i2 < ip1; i2++) { // Cout float * dst_data = (float *)((char *) dst->data + i2*nb2); @@ -13583,9 +13578,8 @@ static void ggml_compute_forward_conv_transpose_2d( for (int i00 = 0; i00 < ne00; i00++) { float v = 0; ggml_vec_dot_f16(ne03, &v, - (ggml_fp16_t *) wdata_src + i1n, - (ggml_fp16_t *) wdata_kernel + i01*ne00*ne03 + i00*ne03); - + wdata_src + i1n, + wdata_kernel + i01*ne00*ne03 + i00*ne03); dst_data[(i11*stride + i01)*ne0 + i10*stride + i00] += v; } } @@ -15732,7 +15726,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm } break; case GGML_OP_CONV_TRANSPOSE_2D: { - ggml_compute_forward_conv_transpose_2d(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor); + ggml_compute_forward_conv_transpose_2d(params, tensor->src[0], tensor->src[1], tensor); } break; case GGML_OP_POOL_1D: { diff --git a/ggml.h b/ggml.h index 792ca6e42..4ef3d5253 100644 --- a/ggml.h +++ b/ggml.h @@ -130,13 +130,16 @@ // The data of the tensor is accessed via the "data" pointer. For example: // // { -// struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 2, 3); +// const int nx = 2; +// const int ny = 3; // -// // a[2, 1] = 1.0f; -// *(float *) ((char *) a->data + 2*a->nb[1] + 1*a->nb[0]) = 1.0f; +// struct ggml_tensor * a = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nx, ny); // -// // a[0, 2] = 2.0f; -// *(float *) ((char *) a->data + 0*a->nb[1] + 2*a->nb[0]) = 2.0f; +// for (int y = 0; y < ny; y++) { +// for (int x = 0; x < nx; x++) { +// *(float *) ((char *) a->data + y*a->nb[1] + x*a->nb[0]) = x + y; +// } +// } // // ... // } @@ -211,6 +214,11 @@ #define GGML_MAX_OP_PARAMS 32 #define GGML_DEFAULT_N_THREADS 4 +#if UINTPTR_MAX == 0xFFFFFFFF + #define GGML_MEM_ALIGN 4 +#else + #define GGML_MEM_ALIGN 16 +#endif #define GGML_EXIT_SUCCESS 0 #define GGML_EXIT_ABORTED 1 From 3af6b86301ddfb11bb68e91dfc030b611b0d8426 Mon Sep 17 00:00:00 2001 From: Ronny Brendel Date: Mon, 28 Aug 2023 14:51:08 +0200 Subject: [PATCH 401/852] ggml : tiny ggml_vec_dot_q4_K_q8_K AVX2 improvement (#2819) --- k_quants.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/k_quants.c b/k_quants.c index 82bf81697..3a9b1dafd 100644 --- a/k_quants.c +++ b/k_quants.c @@ -2694,13 +2694,13 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri const __m256i q8l = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; __m256i p16l = _mm256_maddubs_epi16(q4l, q8l); p16l = _mm256_madd_epi16(scale_l, p16l); - sumi = _mm256_add_epi32(sumi, p16l); const __m256i q8h = _mm256_loadu_si256((const __m256i*)q8); q8 += 32; __m256i p16h = _mm256_maddubs_epi16(q4h, q8h); p16h = _mm256_madd_epi16(scale_h, p16h); - sumi = _mm256_add_epi32(sumi, p16h); + const __m256i sumj = _mm256_add_epi32(p16l, p16h); + sumi = _mm256_add_epi32(sumi, sumj); } __m256 vd = _mm256_set1_ps(d); From be475f60af1a54e8de81466ccc907d080cf6df1a Mon Sep 17 00:00:00 2001 From: grahameth <96447521+grahameth@users.noreply.github.com> Date: Mon, 28 Aug 2023 17:38:12 +0200 Subject: [PATCH 402/852] llama.cpp : fix wrong vsnprintf call in MS compiler (#2856) Co-authored-by: grahameth <-> --- llama.cpp | 4 ---- 1 file changed, 4 deletions(-) diff --git a/llama.cpp b/llama.cpp index 72d2d1de0..da8ff64d0 100644 --- a/llama.cpp +++ b/llama.cpp @@ -6257,10 +6257,6 @@ void llama_log_set(llama_log_callback log_callback, void * user_data) { g_state.log_callback_user_data = user_data; } -#if defined(_MSC_VER) && !defined(vsnprintf) -#define vsnprintf _vsnprintf -#endif - static void llama_log_internal_v(llama_log_level level, const char * format, va_list args) { va_list args_copy; va_copy(args_copy, args); From 75fafcbcccc280a5b3883bc76d0a2dabf474d094 Mon Sep 17 00:00:00 2001 From: alonfaraj Date: Mon, 28 Aug 2023 18:38:35 +0300 Subject: [PATCH 403/852] make : fix tests build (#2855) * makefile: - fix test name - add missing tests build * editorconfig : fixes --------- Co-authored-by: Georgi Gerganov --- .gitignore | 5 ++++- Makefile | 14 ++++++++++---- 2 files changed, 14 insertions(+), 5 deletions(-) diff --git a/.gitignore b/.gitignore index e5faab774..7a3f3fff4 100644 --- a/.gitignore +++ b/.gitignore @@ -63,10 +63,13 @@ poetry.toml # Test binaries tests/test-grammar-parser +tests/test-llama-grammar tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling -tests/test-tokenizer-0 +tests/test-tokenizer-0-llama +tests/test-tokenizer-0-falcon +tests/test-tokenizer-1 diff --git a/Makefile b/Makefile index a3400e491..e60821dd5 100644 --- a/Makefile +++ b/Makefile @@ -2,7 +2,7 @@ BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple server embd-input-test gguf llama-bench # Binaries only useful for tests -TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0 +TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama tests/test-tokenizer-0-falcon tests/test-tokenizer-1 default: $(BUILD_TARGETS) @@ -442,10 +442,10 @@ benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.h ggml.o vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS) $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) -tests/test-llama-grammar: tests/test-llama-grammar.cpp build-info.h ggml.o llama.o common.o $(OBJS) +tests/test-llama-grammar: tests/test-llama-grammar.cpp build-info.h ggml.o common.o grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS) -tests/test-grammar-parser: tests/test-grammar-parser.cpp build-info.h ggml.o llama.o common.o $(OBJS) +tests/test-grammar-parser: tests/test-grammar-parser.cpp build-info.h ggml.o llama.o common.o grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS) tests/test-double-float: tests/test-double-float.cpp build-info.h ggml.o llama.o common.o $(OBJS) @@ -466,5 +466,11 @@ tests/test-quantize-perf: tests/test-quantize-perf.cpp build-info.h ggml.o llama tests/test-sampling: tests/test-sampling.cpp build-info.h ggml.o llama.o common.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS) -tests/test-tokenizer-0: tests/test-tokenizer-0.cpp build-info.h ggml.o llama.o common.o $(OBJS) +tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp build-info.h ggml.o llama.o common.o $(OBJS) + $(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS) + +tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp build-info.h ggml.o llama.o common.o $(OBJS) + $(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS) + +tests/test-tokenizer-1: tests/test-tokenizer-1.cpp build-info.h ggml.o llama.o common.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS) From 6b73ef120114beb5664ea94aab48d07ed248ee52 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Mon, 28 Aug 2023 17:59:39 +0200 Subject: [PATCH 404/852] YAML result logging + preset script (#2657) --- common/common.cpp | 331 +++++++++++++++++++++++++++-- common/common.h | 18 ++ examples/main/main.cpp | 78 ++++++- examples/perplexity/perplexity.cpp | 141 +++++++++--- examples/server/server.cpp | 2 +- llama.cpp | 29 +++ llama.h | 3 + run_with_preset.py | 140 ++++++++++++ 8 files changed, 700 insertions(+), 42 deletions(-) create mode 100755 run_with_preset.py diff --git a/common/common.cpp b/common/common.cpp index 0d91a6a35..4a0d43c13 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1,15 +1,20 @@ #include "common.h" +#include "build-info.h" +#include "llama.h" -#include -#include -#include -#include -#include -#include #include -#include -#include +#include +#include +#include +#include +#include +#include +#include #include +#include +#include +#include +#include #if defined(__APPLE__) && defined(__MACH__) #include @@ -19,11 +24,14 @@ #if defined(_WIN32) #define WIN32_LEAN_AND_MEAN #define NOMINMAX +#include +#include #include #include #include #else #include +#include #include #endif @@ -93,7 +101,6 @@ void process_escapes(std::string& input) { bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { bool invalid_param = false; - bool escape_prompt = false; std::string arg; gpt_params default_params; const std::string arg_prefix = "--"; @@ -125,8 +132,8 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } params.prompt = argv[i]; - } else if (arg == "-e") { - escape_prompt = true; + } else if (arg == "-e" || arg == "--escape") { + params.escape = true; } else if (arg == "--prompt-cache") { if (++i >= argc) { invalid_param = true; @@ -415,6 +422,16 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } params.antiprompt.push_back(argv[i]); + } else if (arg == "-ld" || arg == "--logdir") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.logdir = argv[i]; + + if (params.logdir.back() != DIRECTORY_SEPARATOR) { + params.logdir += DIRECTORY_SEPARATOR; + } } else if (arg == "--perplexity") { params.perplexity = true; } else if (arg == "--ppl-stride") { @@ -520,7 +537,7 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { exit(1); } - if (escape_prompt) { + if (params.escape) { process_escapes(params.prompt); process_escapes(params.input_prefix); process_escapes(params.input_suffix); @@ -546,7 +563,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { fprintf(stdout, " -t N, --threads N number of threads to use during computation (default: %d)\n", params.n_threads); fprintf(stdout, " -p PROMPT, --prompt PROMPT\n"); fprintf(stdout, " prompt to start generation with (default: empty)\n"); - fprintf(stdout, " -e process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n"); + fprintf(stdout, " -e, --escape process prompt escapes sequences (\\n, \\r, \\t, \\', \\\", \\\\)\n"); fprintf(stdout, " --prompt-cache FNAME file to cache prompt state for faster startup (default: none)\n"); fprintf(stdout, " --prompt-cache-all if specified, saves user input and generations to cache as well.\n"); fprintf(stdout, " not supported with --interactive or other interactive options\n"); @@ -627,6 +644,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { fprintf(stdout, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); fprintf(stdout, " -m FNAME, --model FNAME\n"); fprintf(stdout, " model path (default: %s)\n", params.model.c_str()); + fprintf(stdout, " -ld LOGDIR, --logdir LOGDIR\n"); + fprintf(stdout, " path under which to save YAML logs (no logging if unset)\n"); fprintf(stdout, "\n"); } @@ -779,3 +798,289 @@ std::string llama_detokenize_bpe(llama_context * ctx, const std::vector> converter; + std::wstring wpath = converter.from_bytes(path); + + // if the path already exists, check whether it's a directory + const DWORD attributes = GetFileAttributesW(wpath.c_str()); + if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) { + return true; + } + + size_t pos_slash = 0; + + // process path from front to back, procedurally creating directories + while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) { + const std::wstring subpath = wpath.substr(0, pos_slash); + const wchar_t * test = subpath.c_str(); + + const bool success = CreateDirectoryW(test, NULL); + if (!success) { + const DWORD error = GetLastError(); + + // if the path already exists, ensure that it's a directory + if (error == ERROR_ALREADY_EXISTS) { + const DWORD attributes = GetFileAttributesW(subpath.c_str()); + if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) { + return false; + } + } else { + return false; + } + } + + pos_slash += 1; + } + + return true; +#else + // if the path already exists, check whether it's a directory + struct stat info; + if (stat(path.c_str(), &info) == 0) { + return S_ISDIR(info.st_mode); + } + + size_t pos_slash = 1; // skip leading slashes for directory creation + + // process path from front to back, procedurally creating directories + while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) { + const std::string subpath = path.substr(0, pos_slash); + struct stat info; + + // if the path already exists, ensure that it's a directory + if (stat(subpath.c_str(), &info) == 0) { + if (!S_ISDIR(info.st_mode)) { + return false; + } + } else { + // create parent directories + const int ret = mkdir(subpath.c_str(), 0755); + if (ret != 0) { + return false; + } + } + + pos_slash += 1; + } + + return true; +#endif // _WIN32 +} + +void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector & data) { + if (data.empty()) { + fprintf(stream, "%s:\n", prop_name); + return; + } + + fprintf(stream, "%s: [", prop_name); + for (size_t i = 0; i < data.size() - 1; ++i) { + fprintf(stream, "%e, ", data[i]); + } + fprintf(stream, "%e]\n", data.back()); +} + +void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector & data) { + if (data.empty()) { + fprintf(stream, "%s:\n", prop_name); + return; + } + + fprintf(stream, "%s: [", prop_name); + for (size_t i = 0; i < data.size() - 1; ++i) { + fprintf(stream, "%d, ", data[i]); + } + fprintf(stream, "%d]\n", data.back()); +} + +void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data) { + std::string data_str(data == NULL ? "" : data); + + if (data_str.empty()) { + fprintf(stream, "%s:\n", prop_name); + return; + } + + size_t pos_start = 0; + size_t pos_found = 0; + + if (!data_str.empty() && (std::isspace(data_str[0]) || std::isspace(data_str.back()))) { + data_str = std::regex_replace(data_str, std::regex("\n"), "\\n"); + data_str = std::regex_replace(data_str, std::regex("\""), "\\\""); + data_str = "\"" + data_str + "\""; + fprintf(stream, "%s: %s\n", prop_name, data_str.c_str()); + return; + } + + if (data_str.find('\n') == std::string::npos) { + fprintf(stream, "%s: %s\n", prop_name, data_str.c_str()); + return; + } + + fprintf(stream, "%s: |\n", prop_name); + while ((pos_found = data_str.find('\n', pos_start)) != std::string::npos) { + fprintf(stream, " %s\n", data_str.substr(pos_start, pos_found-pos_start).c_str()); + pos_start = pos_found + 1; + } +} + +std::string get_sortable_timestamp() { + using clock = std::chrono::system_clock; + + const clock::time_point current_time = clock::now(); + const time_t as_time_t = clock::to_time_t(current_time); + char timestamp_no_ns[100]; + std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t)); + + const int64_t ns = std::chrono::duration_cast( + current_time.time_since_epoch() % 1000000000).count(); + char timestamp_ns[10]; + snprintf(timestamp_ns, 11, "%09ld", ns); + + return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns); +} + +void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const llama_context * lctx, + const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc) { + fprintf(stream, "build_commit: %s\n", BUILD_COMMIT); + fprintf(stream, "build_number: %d\n", BUILD_NUMBER); + fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false"); + fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false"); + fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false"); + fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false"); + fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false"); + fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false"); + fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false"); + fprintf(stream, "cpu_has_cublas: %s\n", ggml_cpu_has_cublas() ? "true" : "false"); + fprintf(stream, "cpu_has_clblast: %s\n", ggml_cpu_has_clblast() ? "true" : "false"); + fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false"); + fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false"); + fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false"); + fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false"); + fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false"); + fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false"); + fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false"); + fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false"); + fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false"); + +#ifdef NDEBUG + fprintf(stream, "debug: false\n"); +#else + fprintf(stream, "debug: true\n"); +#endif // NDEBUG + + fprintf(stream, "model_desc: %s\n", model_desc); + fprintf(stream, "n_vocab: %d # output size of the final layer, 32001 for some models\n", llama_n_vocab(lctx)); + +#ifdef __OPTIMIZE__ + fprintf(stream, "optimize: true\n"); +#else + fprintf(stream, "optimize: false\n"); +#endif // __OPTIMIZE__ + + fprintf(stream, "time: %s\n", timestamp.c_str()); + + fprintf(stream, "\n"); + fprintf(stream, "###############\n"); + fprintf(stream, "# User Inputs #\n"); + fprintf(stream, "###############\n"); + fprintf(stream, "\n"); + + fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str()); + fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch); + dump_string_yaml_multiline(stream, "cfg_negative_prompt", params.cfg_negative_prompt.c_str()); + fprintf(stream, "cfg_scale: %f # default: 1.0\n", params.cfg_scale); + fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks); + fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false"); + fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx); + fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false"); + fprintf(stream, "export: %s # default: false\n", params.export_cgraph ? "true" : "false"); + fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n"); + fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", params.frequency_penalty); + dump_string_yaml_multiline(stream, "grammar", params.grammar.c_str()); + fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n"); + fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false"); + fprintf(stream, "hellaswag_tasks: %ld # default: 400\n", params.hellaswag_tasks); + + const auto logit_bias_eos = params.logit_bias.find(llama_token_eos(lctx)); + const bool ignore_eos = logit_bias_eos != params.logit_bias.end() && logit_bias_eos->second == -INFINITY; + fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false"); + + dump_string_yaml_multiline(stream, "in_prefix", params.input_prefix.c_str()); + fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false"); + dump_string_yaml_multiline(stream, "in_suffix", params.input_prefix.c_str()); + fprintf(stream, "instruct: %s # default: false\n", params.instruct ? "true" : "false"); + fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false"); + fprintf(stream, "interactive_first: %s # default: false\n", params.interactive_first ? "true" : "false"); + fprintf(stream, "keep: %d # default: 0\n", params.n_keep); + fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str()); + + fprintf(stream, "logit_bias:\n"); + for (std::pair lb : params.logit_bias) { + if (ignore_eos && lb.first == logit_bias_eos->first) { + continue; + } + fprintf(stream, " %d: %f", lb.first, lb.second); + } + + fprintf(stream, "lora: %s\n", params.lora_adapter.c_str()); + fprintf(stream, "lora_base: %s\n", params.lora_base.c_str()); + fprintf(stream, "low_vram: %s # default: false\n", params.low_vram ? "true" : "false"); + fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu); + fprintf(stream, "memory_f32: %s # default: false\n", !params.memory_f16 ? "true" : "false"); + fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", params.mirostat); + fprintf(stream, "mirostat_ent: %f # default: 5.0\n", params.mirostat_tau); + fprintf(stream, "mirostat_lr: %f # default: 0.1\n", params.mirostat_eta); + fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false"); + fprintf(stream, "model: %s # default: models/7B/ggml-model.bin\n", params.model.c_str()); + fprintf(stream, "mtest: %s # default: false\n", params.mem_test ? "true" : "false"); + fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false"); + fprintf(stream, "n_gpu_layers: %d # default: 0\n", params.n_gpu_layers); + fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict); + fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", params.n_probs); + fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false"); + fprintf(stream, "no_mul_mat_q: %s # default: false\n", !params.mul_mat_q ? "true" : "false"); + fprintf(stream, "no_penalize_nl: %s # default: false\n", !params.penalize_nl ? "true" : "false"); + fprintf(stream, "numa: %s # default: false\n", params.numa ? "true" : "false"); + fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type); + fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride); + fprintf(stream, "presence_penalty: %f # default: 0.0\n", params.presence_penalty); + dump_string_yaml_multiline(stream, "prompt", params.prompt.c_str()); + fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str()); + fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false"); + fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false"); + dump_vector_int_yaml(stream, "prompt_tokens", prompt_tokens); + fprintf(stream, "random_prompt: %s # default: false\n", params.random_prompt ? "true" : "false"); + fprintf(stream, "repeat_penalty: %f # default: 1.1\n", params.repeat_penalty); + + fprintf(stream, "reverse_prompt:\n"); + for (std::string ap : params.antiprompt) { + size_t pos = 0; + while ((pos = ap.find('\n', pos)) != std::string::npos) { + ap.replace(pos, 1, "\\n"); + pos += 1; + } + + fprintf(stream, " - %s\n", ap.c_str()); + } + + fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base); + fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale); + fprintf(stream, "seed: %d # default: -1 (random seed)\n", params.seed); + fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false"); + fprintf(stream, "temp: %f # default: 0.8\n", params.temp); + + const std::vector tensor_split_vector(params.tensor_split, params.tensor_split + LLAMA_MAX_DEVICES); + dump_vector_float_yaml(stream, "tensor_split", tensor_split_vector); + + fprintf(stream, "tfs: %f # default: 1.0\n", params.tfs_z); + fprintf(stream, "threads: %d # default: %d\n", params.n_threads, std::thread::hardware_concurrency()); + fprintf(stream, "top_k: %d # default: 40\n", params.top_k); + fprintf(stream, "top_p: %f # default: 0.95\n", params.top_p); + fprintf(stream, "typical_p: %f # default: 1.0\n", params.typical_p); + fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false"); +} diff --git a/common/common.h b/common/common.h index 97fda2be7..c15373144 100644 --- a/common/common.h +++ b/common/common.h @@ -11,6 +11,12 @@ #include #include +#ifdef _WIN32 +#define DIRECTORY_SEPARATOR '\\' +#else +#define DIRECTORY_SEPARATOR '/' +#endif // _WIN32 + // // CLI argument parsing // @@ -61,6 +67,7 @@ struct gpt_params { std::string input_suffix = ""; // string to suffix user inputs with std::string grammar = ""; // optional BNF-like grammar to constrain sampling std::vector antiprompt; // string upon seeing which more user input is prompted + std::string logdir = ""; // directory in which to save YAML log files std::string lora_adapter = ""; // lora adapter path std::string lora_base = ""; // base model path for the lora adapter @@ -82,6 +89,7 @@ struct gpt_params { bool prompt_cache_ro = false; // open the prompt cache read-only and do not update it bool embedding = false; // get only sentence embedding + bool escape = false; // escape "\n", "\r", "\t", "\'", "\"", and "\\" bool interactive_first = false; // wait for user input immediately bool multiline_input = false; // reverse the usage of `\` bool simple_io = false; // improves compatibility with subprocesses and limited consoles @@ -144,3 +152,13 @@ std::string llama_detokenize_spm( std::string llama_detokenize_bpe( llama_context * ctx, const std::vector & tokens); + +bool create_directory_with_parents(const std::string & path); +void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector & data); +void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector & data); +void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data); +std::string get_sortable_timestamp(); + +void dump_non_result_info_yaml( + FILE * stream, const gpt_params & params, const llama_context * lctx, + const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc); diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 3ce57f436..89cc4f602 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -17,6 +17,7 @@ #include #include #include +#include #include #include @@ -36,9 +37,57 @@ #pragma warning(disable: 4244 4267) // possible loss of data #endif -static llama_context ** g_ctx; +static llama_context ** g_ctx; +static llama_model ** g_model; +static gpt_params * g_params; +static std::vector * g_input_tokens; +static std::ostringstream * g_output_ss; +static std::vector * g_output_tokens; static bool is_interacting = false; +void write_logfile( + const llama_context * ctx, const gpt_params & params, const llama_model * model, + const std::vector input_tokens, const std::string output, const std::vector output_tokens) { + + if (params.logdir.empty()) { + return; + } + + const std::string timestamp = get_sortable_timestamp(); + + const bool success = create_directory_with_parents(params.logdir); + if (!success) { + fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n", + __func__, params.logdir.c_str()); + return; + } + + const std::string logfile_path = params.logdir + timestamp + ".yml"; + FILE * logfile = fopen(logfile_path.c_str(), "w"); + + if (logfile == NULL) { + fprintf(stderr, "%s: failed to open logfile %s\n", __func__, logfile_path.c_str()); + return; + } + + fprintf(logfile, "binary: main\n"); + char model_desc[128]; + llama_model_desc(model, model_desc, sizeof(model_desc)); + dump_non_result_info_yaml(logfile, params, ctx, timestamp, input_tokens, model_desc); + + fprintf(logfile, "\n"); + fprintf(logfile, "######################\n"); + fprintf(logfile, "# Generation Results #\n"); + fprintf(logfile, "######################\n"); + fprintf(logfile, "\n"); + + dump_string_yaml_multiline(logfile, "output", output.c_str()); + dump_vector_int_yaml(logfile, "output_tokens", output_tokens); + + llama_dump_timing_info_yaml(logfile, ctx); + fclose(logfile); +} + #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) void sigint_handler(int signo) { if (signo == SIGINT) { @@ -48,6 +97,7 @@ void sigint_handler(int signo) { console::cleanup(); printf("\n"); llama_print_timings(*g_ctx); + write_logfile(*g_ctx, *g_params, *g_model, *g_input_tokens, g_output_ss->str(), *g_output_tokens); _exit(130); } } @@ -56,6 +106,7 @@ void sigint_handler(int signo) { int main(int argc, char ** argv) { gpt_params params; + g_params = ¶ms; if (gpt_params_parse(argc, argv, params) == false) { return 1; @@ -116,6 +167,7 @@ int main(int argc, char ** argv) { llama_model * model; llama_context * ctx; llama_context * ctx_guidance = NULL; + g_model = &model; g_ctx = &ctx; // load the model and apply lora adapter, if any @@ -397,6 +449,10 @@ int main(int argc, char ** argv) { int n_session_consumed = 0; int n_past_guidance = 0; + std::vector input_tokens; g_input_tokens = &input_tokens; + std::vector output_tokens; g_output_tokens = &output_tokens; + std::ostringstream output_ss; g_output_ss = &output_ss; + // the first thing we will do is to output the prompt, so set color accordingly console::set_display(console::prompt); @@ -667,7 +723,15 @@ int main(int argc, char ** argv) { // display text if (input_echo) { for (auto id : embd) { - printf("%s", llama_token_to_piece(ctx, id).c_str()); + const std::string token_str = llama_token_to_piece(ctx, id); + printf("%s", token_str.c_str()); + + if (embd.size() > 1) { + input_tokens.push_back(id); + } else { + output_tokens.push_back(id); + output_ss << token_str; + } } fflush(stdout); } @@ -761,6 +825,8 @@ int main(int argc, char ** argv) { printf("%s", params.input_suffix.c_str()); } + const size_t original_size = embd_inp.size(); + // instruct mode: insert instruction prefix if (params.instruct && !is_antiprompt) { n_consumed = embd_inp.size(); @@ -775,6 +841,12 @@ int main(int argc, char ** argv) { embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end()); } + for (size_t i = original_size; i < embd_inp.size(); ++i) { + const llama_token token = embd_inp[i]; + output_tokens.push_back(token); + output_ss << llama_token_to_piece(ctx, token); + } + n_remain -= line_inp.size(); } @@ -817,6 +889,8 @@ int main(int argc, char ** argv) { } llama_print_timings(ctx); + write_logfile(ctx, params, model, input_tokens, output_ss.str(), output_tokens); + if (ctx_guidance) { llama_free(ctx_guidance); } llama_free(ctx); llama_free_model(model); diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index ebafa0c29..aeb774c5f 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -3,16 +3,79 @@ #include "build-info.h" #include +#include +#include #include #include -#include #include #include +#include #if defined(_MSC_VER) #pragma warning(disable: 4244 4267) // possible loss of data #endif +struct results_perplexity { + std::vector tokens; + double ppl_value; + std::vector logits; + std::vector probs; +}; + +struct results_log_softmax { + double log_softmax; + float logit; + float prob; +}; + +void write_logfile(const llama_context * ctx, const gpt_params & params, + const llama_model * model, const struct results_perplexity & results) { + + if (params.logdir.empty()) { + return; + } + + if (params.hellaswag) { + fprintf(stderr, "%s: warning: logging results is not implemented for HellaSwag. No files will be written.\n", __func__); + return; + } + + const std::string timestamp = get_sortable_timestamp(); + + const bool success = create_directory_with_parents(params.logdir); + if (!success) { + fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n", + __func__, params.logdir.c_str()); + return; + } + + const std::string logfile_path = params.logdir + timestamp + ".yml"; + FILE * logfile = fopen(logfile_path.c_str(), "w"); + + if (logfile == NULL) { + fprintf(stderr, "%s: failed to open logfile %s\n", __func__, logfile_path.c_str()); + return; + } + + fprintf(logfile, "binary: main\n"); + char model_desc[128]; + llama_model_desc(model, model_desc, sizeof(model_desc)); + dump_non_result_info_yaml(logfile, params, ctx, timestamp, results.tokens, model_desc); + + fprintf(logfile, "\n"); + fprintf(logfile, "######################\n"); + fprintf(logfile, "# Perplexity Results #\n"); + fprintf(logfile, "######################\n"); + fprintf(logfile, "\n"); + + dump_vector_float_yaml(logfile, "logits", results.logits); + fprintf(logfile, "ppl_value: %f\n", results.ppl_value); + dump_vector_float_yaml(logfile, "probs", results.probs); + + llama_dump_timing_info_yaml(logfile, ctx); + fclose(logfile); +} + std::vector softmax(const std::vector& logits) { std::vector probs(logits.size()); float max_logit = logits[0]; @@ -29,20 +92,20 @@ std::vector softmax(const std::vector& logits) { return probs; } -float log_softmax(int n_vocab, const float * logits, int tok) { +results_log_softmax log_softmax(int n_vocab, const float * logits, int tok) { float max_logit = logits[0]; for (int i = 1; i < n_vocab; ++i) max_logit = std::max(max_logit, logits[i]); double sum_exp = 0.0; for (int i = 0; i < n_vocab; ++i) sum_exp += expf(logits[i] - max_logit); - return logits[tok] - max_logit - log(sum_exp); + return {logits[tok] - max_logit - log(sum_exp), logits[tok], expf(logits[tok] - max_logit) / (float) sum_exp}; } -void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token, std::vector& workers, - double& nll, double& nll2) { +void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token, std::vector & workers, + double & nll, double & nll2, float * logit_history, float * prob_history) { std::mutex mutex; int counter = 0; - auto compute = [&mutex, &counter, &nll, &nll2, n_vocab, logits, tokens, n_token] () { + auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () { double local_nll = 0, local_nll2 = 0; while (true) { std::unique_lock lock(mutex); @@ -52,34 +115,43 @@ void process_logits(int n_vocab, const float * logits, const int * tokens, int n break; } lock.unlock(); - double v = -log_softmax(n_vocab, logits + i*n_vocab, tokens[i+1]); + const results_log_softmax results = log_softmax(n_vocab, logits + i*n_vocab, tokens[i+1]); + const double v = -results.log_softmax; local_nll += v; local_nll2 += v*v; + + logit_history[i] = results.logit; + prob_history[i] = results.prob; } }; - for (auto& w : workers) w = std::thread(compute); + for (auto & w : workers) w = std::thread(compute); compute(); - for (auto& w : workers) w.join(); + for (auto & w : workers) w.join(); } -void perplexity_v2(llama_context * ctx, const gpt_params & params) { +results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & params) { // Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research // Run `./perplexity -m models/7B/ggml-model-q4_0.bin -f wiki.test.raw` // Output: `perplexity: 13.5106 [114/114]` // BOS tokens will be added for each chunk before eval - if (params.ppl_stride <= 0) { - fprintf(stderr, "%s: stride is %d but must be greater than zero!\n",__func__,params.ppl_stride); - return; - } - const bool is_spm = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM; const bool add_bos = is_spm; fprintf(stderr, "%s: tokenizing the input ..\n", __func__); - auto tokens = ::llama_tokenize(ctx, params.prompt, add_bos); + std::vector tokens = ::llama_tokenize(ctx, params.prompt, add_bos); + std::vector logit_history; + std::vector prob_history; + + logit_history.resize(tokens.size()); + prob_history.resize(tokens.size()); + + if (params.ppl_stride <= 0) { + fprintf(stderr, "%s: stride is %d but must be greater than zero!\n",__func__,params.ppl_stride); + return {tokens, -1, logit_history, prob_history}; + } const int calc_chunk = params.n_ctx; @@ -88,7 +160,7 @@ void perplexity_v2(llama_context * ctx, const gpt_params & params) { if (int(tokens.size()) <= calc_chunk) { fprintf(stderr, "%s: there are only %zu tokens, this is not enough for a context size of %d and stride %d\n",__func__, tokens.size(), params.n_ctx, params.ppl_stride); - return; + return {tokens, -1, logit_history, prob_history}; } const int n_chunk_max = (tokens.size() - calc_chunk + params.ppl_stride - 1) / params.ppl_stride; @@ -120,7 +192,7 @@ void perplexity_v2(llama_context * ctx, const gpt_params & params) { //fprintf(stderr, " Batch %d: starts at %d, size is %d, n_past is %d\n",j,batch_start,batch_size,j * n_batch); if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads)) { //fprintf(stderr, "%s : failed to eval\n", __func__); - return; + return {tokens, -1, logit_history, prob_history}; } // save original token and restore it after eval @@ -161,6 +233,8 @@ void perplexity_v2(llama_context * ctx, const gpt_params & params) { logits.begin() + (j + 1) * n_vocab); const float prob = softmax(tok_logits)[tokens[start + j + 1]]; + logit_history[start + j + 1] = tok_logits[tokens[start + j + 1]]; + prob_history[start + j + 1] = prob; nll += -std::log(prob); ++count; @@ -174,12 +248,14 @@ void perplexity_v2(llama_context * ctx, const gpt_params & params) { fflush(stdout); } printf("\n"); + + return {tokens, std::exp(nll / count), logit_history, prob_history}; } -void perplexity(llama_context * ctx, const gpt_params & params) { +results_perplexity perplexity(llama_context * ctx, const gpt_params & params) { + if (params.ppl_stride > 0) { - perplexity_v2(ctx, params); - return; + return perplexity_v2(ctx, params); } // Download: https://s3.amazonaws.com/research.metamind.io/wikitext/wikitext-2-raw-v1.zip?ref=salesforce-research @@ -193,11 +269,17 @@ void perplexity(llama_context * ctx, const gpt_params & params) { auto tim1 = std::chrono::high_resolution_clock::now(); fprintf(stderr, "%s: tokenizing the input ..\n", __func__); - auto tokens = ::llama_tokenize(ctx, params.prompt, add_bos); + std::vector tokens = ::llama_tokenize(ctx, params.prompt, add_bos); auto tim2 = std::chrono::high_resolution_clock::now(); fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast(tim2-tim1).count()); + std::vector logit_history; + logit_history.resize(tokens.size()); + + std::vector prob_history; + prob_history.resize(tokens.size()); + const int n_chunk_max = tokens.size() / params.n_ctx; const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max); @@ -236,7 +318,7 @@ void perplexity(llama_context * ctx, const gpt_params & params) { if (llama_eval(ctx, tokens.data() + batch_start, batch_size, j * n_batch, params.n_threads)) { fprintf(stderr, "%s : failed to eval\n", __func__); - return; + return {tokens, -1, logit_history, prob_history}; } // restore the original token in case it was set to BOS @@ -272,7 +354,8 @@ void perplexity(llama_context * ctx, const gpt_params & params) { // last 256 tokens. Then, we split the input up into context window size chunks to // process the entire prompt. const int first = std::min(512, params.n_ctx/2); - process_logits(n_vocab, logits.data() + first*n_vocab, tokens.data() + start + first, params.n_ctx - 1 - first, workers, nll, nll2); + process_logits(n_vocab, logits.data() + first*n_vocab, tokens.data() + start + first, params.n_ctx - 1 - first, + workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first); count += params.n_ctx - first - 1; // perplexity is e^(average negative log-likelihood) @@ -287,16 +370,19 @@ void perplexity(llama_context * ctx, const gpt_params & params) { fflush(stdout); } printf("\n"); + nll2 /= count; nll /= count; + const double ppl = exp(nll); nll2 -= nll * nll; if (nll2 > 0) { nll2 = sqrt(nll2/(count-1)); - double ppl = exp(nll); printf("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl); } else { printf("Unexpected negative standard deviation of log(prob)\n"); } + + return {tokens, ppl, logit_history, prob_history}; } std::vector hellaswag_evaluate_tokens(llama_context * ctx, const std::vector& tokens, int n_past, int n_batch, @@ -604,13 +690,16 @@ int main(int argc, char ** argv) { params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info()); } + struct results_perplexity results; if (params.hellaswag) { hellaswag_score(ctx, params); } else { - perplexity(ctx, params); + results = perplexity(ctx, params); } llama_print_timings(ctx); + write_logfile(ctx, params, model, results); + llama_free(ctx); llama_free_model(model); diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 89a3311f5..b485a5ead 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -719,7 +719,7 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, fprintf(stdout, " -ts SPLIT --tensor-split SPLIT\n"); fprintf(stdout, " how to split tensors across multiple GPUs, comma-separated list of proportions, e.g. 3,1\n"); fprintf(stdout, " -mg i, --main-gpu i the GPU to use for scratch and small tensors\n"); - fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n"); + fprintf(stdout, " -lv, --low-vram don't allocate VRAM scratch buffer\n"); fprintf(stdout, " -nommq, --no-mul-mat-q\n"); fprintf(stdout, " use cuBLAS instead of custom mul_mat_q CUDA kernels.\n"); fprintf(stdout, " Not recommended since this is both slower and uses more VRAM.\n"); diff --git a/llama.cpp b/llama.cpp index da8ff64d0..11697ee65 100644 --- a/llama.cpp +++ b/llama.cpp @@ -6247,6 +6247,35 @@ const char * llama_print_system_info(void) { return s.c_str(); } +void llama_dump_timing_info_yaml(FILE * stream, const llama_context * ctx) { + + fprintf(stream, "\n"); + fprintf(stream, "###########\n"); + fprintf(stream, "# Timings #\n"); + fprintf(stream, "###########\n"); + fprintf(stream, "\n"); + + fprintf(stream, "mst_eval: %.2f # ms / token during generation\n", + 1.0e-3 * ctx->t_eval_us / ctx->n_eval); + fprintf(stream, "mst_p_eval: %.2f # ms / token during prompt processing\n", + 1.0e-3 * ctx->t_p_eval_us / ctx->n_p_eval); + fprintf(stream, "mst_sample: %.2f # ms / token during sampling\n", + 1.0e-3 * ctx->t_sample_us / ctx->n_sample); + fprintf(stream, "n_eval: %d # number of tokens generated (excluding the first one)\n", ctx->n_eval); + fprintf(stream, "n_p_eval: %d # number of tokens processed in batches at the beginning\n", ctx->n_p_eval); + fprintf(stream, "n_sample: %d # number of sampled tokens\n", ctx->n_sample); + fprintf(stream, "t_eval_us: %ld # total microseconds spent generating tokens\n", ctx->t_eval_us); + fprintf(stream, "t_load_us: %ld # total microseconds spent loading the model\n", ctx->t_load_us); + fprintf(stream, "t_p_eval_us: %ld # total microseconds spent prompt processing\n", ctx->t_p_eval_us); + fprintf(stream, "t_sample_us: %ld # total microseconds spent sampling\n", ctx->t_sample_us); + fprintf(stream, "ts_eval: %.2f # tokens / second during generation\n", + 1.0e6 * ctx->n_eval / ctx->t_eval_us); + fprintf(stream, "ts_p_eval: %.2f # tokens / second during prompt processing\n", + 1.0e6 * ctx->n_p_eval / ctx->t_p_eval_us); + fprintf(stream, "ts_sample: %.2f # tokens / second during sampling\n", + 1.0e6 * ctx->n_sample / ctx->t_sample_us); +} + // For internal test use const std::vector>& llama_internal_get_tensor_map(struct llama_context * ctx) { return ctx->model.tensors_by_name; diff --git a/llama.h b/llama.h index 7bb681d61..b38d3be20 100644 --- a/llama.h +++ b/llama.h @@ -10,6 +10,7 @@ #endif // GGML_USE_CUBLAS #include #include +#include #include #ifdef LLAMA_SHARED @@ -520,6 +521,8 @@ extern "C" { // If this is not called, or NULL is supplied, everything is output on stderr. LLAMA_API void llama_log_set(llama_log_callback log_callback, void * user_data); + LLAMA_API void llama_dump_timing_info_yaml(FILE * stream, const llama_context * ctx); + #ifdef __cplusplus } #endif diff --git a/run_with_preset.py b/run_with_preset.py new file mode 100755 index 000000000..8f90f52a9 --- /dev/null +++ b/run_with_preset.py @@ -0,0 +1,140 @@ +#!/usr/bin/env python3 + +import argparse +import os +import subprocess +import sys + +import yaml + +CLI_ARGS_MAIN_PERPLEXITY = [ + "batch-size", "cfg-negative-prompt", "cfg-scale", "chunks", "color", "ctx-size", "escape", + "export", "file", "frequency-penalty", "grammar", "grammar-file", "hellaswag", + "hellaswag-tasks", "ignore-eos", "in-prefix", "in-prefix-bos", "in-suffix", "instruct", + "interactive", "interactive-first", "keep", "logdir", "logit-bias", "lora", "lora-base", + "low-vram", "main-gpu", "memory-f32", "mirostat", "mirostat-ent", "mirostat-lr", "mlock", + "model", "mtest", "multiline-input", "n-gpu-layers", "n-predict", "no-mmap", "no-mul-mat-q", + "np-penalize-nl", "numa", "ppl-output-type", "ppl-stride", "presence-penalty", "prompt", + "prompt-cache", "prompt-cache-all", "prompt-cache-ro", "random-prompt", "repeat-last-n", + "repeat-penalty", "reverse-prompt", "rope-freq-base", "rope-freq-scale", "rope-scale", "seed", + "simple-io", "tensor-split", "threads", "temp", "tfs", "top-k", "top-p", "typical", + "verbose-prompt" +] + +CLI_ARGS_LLAMA_BENCH = [ + "batch-size", "memory-f32", "low-vram", "model", "mul-mat-q", "n-gen", "n-gpu-layers", + "n-prompt", "output", "repetitions", "tensor-split", "threads", "verbose" +] + +CLI_ARGS_SERVER = [ + "alias", "batch-size", "ctx-size", "embedding", "host", "memory-f32", "lora", "lora-base", + "low-vram", "main-gpu", "mlock", "model", "n-gpu-layers", "n-probs", "no-mmap", "no-mul-mat-q", + "numa", "path", "port", "rope-freq-base", "timeout", "rope-freq-scale", "tensor-split", + "threads", "verbose" +] + +description = """Run llama.cpp binaries with presets from YAML file(s). +To specify which binary should be run, specify the "binary" property (main, perplexity, llama-bench, and server are supported). +To get a preset file template, run a llama.cpp binary with the "--logdir" CLI argument. + +Formatting considerations: +- The YAML property names are the same as the CLI argument names of the corresponding binary. +- Properties must use the long name of their corresponding llama.cpp CLI arguments. +- Like the llama.cpp binaries the property names do not differentiate between hyphens and underscores. +- Flags must be defined as ": true" to be effective. +- To define the logit_bias property, the expected format is ": " in the "logit_bias" namespace. +- To define multiple "reverse_prompt" properties simultaneously the expected format is a list of strings. +- To define a tensor split, pass a list of floats. +""" +usage = "run_with_preset.py [-h] [yaml_files ...] [-- ...]" +epilog = (" -- specify additional CLI ars to be passed to the binary (override all preset files). " + "Unknown args will be ignored.") + +parser = argparse.ArgumentParser( + description=description, usage=usage, epilog=epilog, formatter_class=argparse.RawTextHelpFormatter) +parser.add_argument("-bin", "--binary", help="The binary to run.") +parser.add_argument("yaml_files", nargs="*", + help="Arbitrary number of YAML files from which to read preset values. " + "If two files specify the same values the later one will be used.") + +known_args, unknown_args = parser.parse_known_args() + +if not known_args.yaml_files and not unknown_args: + parser.print_help() + sys.exit(0) + +props = dict() + +for yaml_file in known_args.yaml_files: + with open(yaml_file, "r") as f: + props.update(yaml.load(f, yaml.SafeLoader)) + +props = {prop.replace("_", "-"): val for prop, val in props.items()} + +binary = props.pop("binary", "main") +if known_args.binary: + binary = known_args.binary + +if os.path.exists(f"./{binary}"): + binary = f"./{binary}" + +if binary.lower().endswith("main") or binary.lower().endswith("perplexity"): + cli_args = CLI_ARGS_MAIN_PERPLEXITY +elif binary.lower().endswith("llama-bench"): + cli_args = CLI_ARGS_LLAMA_BENCH +elif binary.lower().endswith("server"): + cli_args = CLI_ARGS_SERVER +else: + print(f"Unknown binary: {binary}") + sys.exit(1) + +command_list = [binary] + +for cli_arg in cli_args: + value = props.pop(cli_arg, None) + + if not value or value == -1: + continue + + if cli_arg == "logit-bias": + for token, bias in value.items(): + command_list.append("--logit-bias") + command_list.append(f"{token}{bias:+}") + continue + + if cli_arg == "reverse-prompt" and not isinstance(value, str): + for rp in value: + command_list.append("--reverse-prompt") + command_list.append(str(rp)) + continue + + command_list.append(f"--{cli_arg}") + + if cli_arg == "tensor-split": + command_list.append(",".join([str(v) for v in value])) + continue + + value = str(value) + + if value != "True": + command_list.append(str(value)) + +num_unused = len(props) +if num_unused > 10: + print(f"The preset file contained a total of {num_unused} unused properties.") +elif num_unused > 0: + print("The preset file contained the following unused properties:") + for prop, value in props.items(): + print(f" {prop}: {value}") + +command_list += unknown_args + +sp = subprocess.Popen(command_list) + +while sp.returncode is None: + try: + sp.wait() + except KeyboardInterrupt: + pass + +sys.exit(sp.returncode) From 43033b7bb4858da4f591715b3babdf906c9b7cbc Mon Sep 17 00:00:00 2001 From: slaren Date: Mon, 28 Aug 2023 19:19:18 +0200 Subject: [PATCH 405/852] llama-bench : set locale to utf8 (#2832) --- examples/llama-bench/llama-bench.cpp | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index d0fe6d90d..bf3a487ab 100755 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -3,6 +3,9 @@ #include #include #include +#include +#include +#include #include #include #include @@ -10,7 +13,6 @@ #include #include #include -#include #include #include @@ -916,6 +918,9 @@ static void llama_null_log_callback(enum llama_log_level level, const char * tex } int main(int argc, char ** argv) { + // try to set locale for unicode characters in markdown + setlocale(LC_CTYPE, ".UTF-8"); + #if !defined(NDEBUG) fprintf(stderr, "warning: asserts enabled, performance may be affected\n"); #endif From 44c117f41ee01c5ac8fb86bba041f08d8b87b46d Mon Sep 17 00:00:00 2001 From: xaedes Date: Mon, 28 Aug 2023 21:51:47 +0200 Subject: [PATCH 406/852] train : mem usage and other improvements (#2439) * fix track_max_mem in forward_batch_wo_cache_flash_attn_train * remove unnecessary Adam(W) optimizer tensors. reduces optimizer memory overhead from 7*modelsize to 2*modelsize. additionally allows to optimize models with more than 2^31 parameters by replacing int with int64_t. bumps training checkpoint file version, but old checkpoints can still be read. new version with less tensors is saved. * add gradient clipping to AdamW * Fix reset of unused g->nodes and g->grads to NULL * implement gradient checkpointing for training reduces memory overhead from O(n_layer) to O(sqrt(n_layer)) as explained in readme of https://github.com/cybertronai/gradient-checkpointing * remove unused compute buffer 3 * add and use function ggml_build_backward_expand to avoid stack overflows with large maximum number of nodes GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep); * change AdamW decay parameter to work like the torch AdamW decay parameter It is now relative to Adam learning rate `alpha*sched`. Before that it was relative to `sched` only. `alpha` being the maximum learning rate and `sched` being a scaling parameter in [0..1] * change default AdamW weight decay parameter used in training to 0.1 as used in nanoGPT * change default AdamW weight decay parameter defined in ggml to 0.0, making Adam default instead of AdamW btw: the default weight decay parameter for torch.optim.AdamW is 0.01 * bug fixes for cross entropy loss ggml_cross_entropy_loss: sums where not correctly added in workload of each thread ggml_cross_entropy_loss_back: simplify backward process, reducing numerical issues guard usage of exp f16 lookup in cross entropy by #define GGML_CROSS_ENTROPY_EXP_FP16 cross entropy loss is only used once during training, but it is quite sensitive to numerical errors introduced by exp-f16-lookup. so exp-f16-lookup for cross entropy loss is disabled by default, trading better gradients for very slightly worse runtime performance. * fix test-grad0 for cross_entropy_loss the second argument to cross_entropy_loss must sum up to 1 for each row * fix test-grad0 for soft_max dont use only sum as aggregation, because sum of softmax is always 1 -> finite differences should not work instead use sum(log(soft_max()*(1-eps)+eps)); use eps to avoid log(0) * improve finite differences of test-grad0 by using double instead of float * change cross_entropy_loss to output average over all rows this helps keeping the loss and gradients in a sane range * improve gradient checkpointing sqrt(n_layers) is only the best checkpoint step when mem size of checkpoints and mem size of layers are equal. since layers require more memory than the single-tensor-checkpoint we use, the optimal values are compute different: ``` given: n, u, v objective: minimize(a*u+b*v) where a*b=n, a>0, b>0 b=n/a minimize(a*u+v*n/a) diff(a*u+v*n/a, a) = u - (v*n/a)/a diff(a*u+v*n/a, a) == 0 u - (v*n/a)/a == 0 u == v*n/(a*a) u*a*a = v*n a*a = v*n/u a = sqrt(n*v/u) ``` this change results in more checkpoints, requiring less layers to store between checkpoints, overall improving memory usage. * disable gradient checkpointing debug output * llama : fix rope usage in train-text-from-scratch after ChatGLM change * add more training parameters: --enable-restart N Only for Adam optimizer. Enable restarts of cos-decay --disable-restart N Only for Adam optimizer. Disable restarts of cos-decay --opt-past N Number of optimization iterations to track for delta convergence test. Disabled when zero. --opt-delta N Maximum delta for delta convergence test. Disabled when <= zero. --opt-max-no-improvement N Maximum number of optimization iterations with no improvement. Disabled when <= zero. --adam-epsf N AdamW epsilon for convergence test. Disabled when <= zero. --adam-min-alpha N Adam minimum learning rate alpha, usually 0.1 * alpha * replace memcpy with reshape operation so that the graph is not cut at the input this makes it possible to store other values into the input tensor and then simply recompute the graph without rebuilding it * remove unused function argument from get_example_targets_batch * measure and print total training time * add optimization callback to ggml_opt_resume_g this callback is called before each iteration with custom data and pointer to learning schedule parameter (only used in Adam(W)). can be used for dynamic learning schedule and setting input data for batches before each iteration * use optimization callback in training allows dynamic learning schedule and different batch data for each iteration without relying on low n_iter and high n_examples parameters reduces runtime by avoiding restart of optimization function and improves training convergence by providing a different batch for each iteration * add minimum number of tensor dimensions to apply weight decay (default 2) this allows to not apply weight decay to bias parameters * rename training parameter cos-decay-alpha to cos-decay-min and clarify that adam-min-alpha also applies to warmup * fix increase of model.train_samples and model.train_tokens now that each optimizer iteration gets its own batch we need to multiply by number of opt iterations * change sampling parameters for prediction after training to defaults of common.h and clarify what is context for prediction and what are generated tokens * tighten abs error bounds for cross_entropy_loss in test-grad0 * add conditional compilation of using F16 exp in flash attention uncomment `// #define GGML_FLASH_ATTN_EXP_FP16` to enable usage of f16 exp in flash attention * tighten abs error bounds for flash_attn in test-grad0 * tighten abs error bounds for sqrt in test-grad0 * remove out-commented vectorized code of opt_adam the vectorized code might be bit faster for low number of parameters, but it had a big memory usage overhead * ggml : update ggml_rms_norm_back with configurable eps * llama training : fix ggml_rms_norm_back calls to pass configurable eps * remove trailing whitespace * add train function using automatic gradient checkpointing backward pass and allocator * in train function replace add_inplace by regular add because using add_inplace seems to result in different gradients * don't use allocate hash_map on context because the context has no_alloc=True when using memory allocator resulting in NULL data pointers * correctly clone reshape and permute operations by also cloning tensor->nb values * fix variable name and add missing type cast * terminate recursive tensor cloning when reaching tensor without src tensors * correctly clone view tensors by setting data pointers without this the checkpointing would only work when being used together with memory allocator * fix variable names * swap arguments to commutative ops to be the same as in `forward_batch_wo_cache_flash_attn` * add input tensors as checkpoints so that recursive tensor cloning of gradient checkpointing terminates on input tensors * fix variable name and add missing boolean negation * make sure some tensors are not reallocated by inserting new temporary nodes depending on them: output and parameter gradient tensors need to be available at the end of the graph execution parameter gradient tensors also need to be available before the graph execution because they are set to zero before each optimizer iteration checkpoint tensors are allocated all together to reduce memory allocator fragmentation afterwards, in addition to the temporary nodes, we also need to reset the temporary leafs * fix ASSERT to work with zero layers * add training options whether to use allocator and/or unified training function * integrate unified training function which may use memory allocator the unified training function also supports arguments whether to use flash attention and/or gradient checkpointing * format name of cloned tensors with " (clone)" suffix * set names for tensors in unified train function for easier debugging * allocate graph on context using ggml_new_graph * remove handwritten training functions * remove unused training parameters "use_scratch" and "use_unified" * remove trailing whitespace * remove unused train params: mem_compute1_gb & mem_compute2_gb mem_compute_gb is used for compute when automatic memory allocator is not enabled, otherwise it can be very small to only hold the tensor definitions mem_compute0_gb is used for automatic memory allocator (as long as measurement of max required size is not implemented) * remove unused forward_batch function * add debug asserts in ggml_allocr_alloc to some common pitfalls when using this function directly * only use ggml_allocr_alloc when tensor has NULL data and is no view * fix test when to create temporary backward graph temporary backward graph is only necessary when using checkpointing * fix memory "leak" in optimizers each iteration a new cplan with new memory for work data was allocated. now cplan creation only happens at the start of optimization, with each iteration reusing the cplan and its work data. * reverse order of for loop in ggml_build_backward_expand to save memory when using gradient checkpointing and allocator with this loop order gradient checkpointing with allocator on 16 layer model saves 13% memory; 2 layer memory it saves 2% memory. the computation results are the same * add missing lctx argument to get_example_targets_batch * implement llama model file saving using gguf checkpoint loading and saving disabled, to be replaced by loading and saving via gguf * implement loading/saving of checkpointing files using GGUF * bug fixes * add checkpoint file version for future compatibility * update readme with gguf filenames * save & load opt->just_initialized value * add first draft for checkpoint conversion script * add gguf arch and ftype * save opt parameter counter as uint64 * add gguf key and tensor names for optimizer and training * add layer_norm_rms_eps to checkpoint convert script * use same GGUF_GET_KEY macro as in llama.cpp * use norm_rms_eps, and rope parameters and command line options to set them * fix memory corruption bug in gguf ctx->kv and ctx->infos was reallocated using not-aligned realloc, but freed with aligned free. to fix this a GGML_ALIGNED_REALLOC was added, but there is no posix_memalign_realloc function. so on non-windows and non-mingw32 platforms we fall back to aligned malloc, followed by copying and freeing the old data. * add gguf example cmake file * bug fixes in tokenize_file * bug fixes in load_llama_model_gguf * bug fix: init model when no checkpoint was loaded * bug fix in read_tensor_by_name * bug fix in load_opt_context_gguf * avoid printing lots of spaced on the unusual case that loss gets nan * set name of tensors with empty name from what was read from gguf * remove trailing whitespace * print data checksums before saving and after loading to verify correctness * bug fixes for convert-train-checkpoint-to-gguf * temporarily add code to write old checkpoint files used to verify that old checkpoint files are correctly converted to gguf * bug fixes for convert-train-checkpoint-to-gguf.py loading checkpoints with opt_version=0 * remove code used to verify correctness of checkpoint file conversion * remove trailing whitespace * remove prediction related code use main for prediction, it is better optimized * update train-text-from-scratch README.md * fix non-windows GGML_ALIGNED_REALLOC * add missing blank line at end of file * remove GGML_ALIGNED_REALLOC and use normal malloc/realloc/free for gguf ctx->kv & ctx->infos * train : fix compile warnings --------- Co-authored-by: Georgi Gerganov --- common/common.cpp | 5 +- .../convert-llama2c-to-ggml.cpp | 1 - examples/gguf/CMakeLists.txt | 5 + examples/train-text-from-scratch/README.md | 14 +- .../convert-train-checkpoint-to-gguf.py | 492 +++ .../train-text-from-scratch.cpp | 3502 ++++++----------- ggml-alloc.c | 4 + ggml.c | 335 +- ggml.h | 29 +- llama.cpp | 9 +- tests/test-grad0.cpp | 52 +- 11 files changed, 1940 insertions(+), 2508 deletions(-) create mode 100644 examples/gguf/CMakeLists.txt create mode 100644 examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py diff --git a/common/common.cpp b/common/common.cpp index 4a0d43c13..90fe2e84e 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -15,6 +15,7 @@ #include #include #include +#include #if defined(__APPLE__) && defined(__MACH__) #include @@ -938,8 +939,8 @@ std::string get_sortable_timestamp() { const int64_t ns = std::chrono::duration_cast( current_time.time_since_epoch() % 1000000000).count(); - char timestamp_ns[10]; - snprintf(timestamp_ns, 11, "%09ld", ns); + char timestamp_ns[11]; + snprintf(timestamp_ns, 11, "%09" PRId64, ns); return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns); } diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp index 51d90ea6a..e9e070b1f 100644 --- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp +++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp @@ -681,7 +681,6 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod // for rms-att-weight int row_length = model->hparams.n_embd; - const auto & hparams = model->hparams; int n_ff = model->hparams.n_ff; for (uint32_t i = 0; i < model->hparams.n_layer; ++i){ diff --git a/examples/gguf/CMakeLists.txt b/examples/gguf/CMakeLists.txt new file mode 100644 index 000000000..7d1806af3 --- /dev/null +++ b/examples/gguf/CMakeLists.txt @@ -0,0 +1,5 @@ +set(TARGET gguf) +add_executable(${TARGET} gguf.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/train-text-from-scratch/README.md b/examples/train-text-from-scratch/README.md index 726ec47c0..f4ffcd987 100644 --- a/examples/train-text-from-scratch/README.md +++ b/examples/train-text-from-scratch/README.md @@ -8,15 +8,15 @@ wget https://raw.githubusercontent.com/brunoklein99/deep-learning-notes/master/s # train ./bin/train-text-from-scratch \ - --vocab-model ../models/ggml-vocab.bin \ + --vocab-model ../models/ggml-vocab-llama.gguf \ --ctx 64 --embd 256 --head 8 --layer 16 \ - --checkpoint-in chk-shakespeare-256x16.bin \ - --checkpoint-out chk-shakespeare-256x16.bin \ - --model-out ggml-shakespeare-256x16-f32.bin \ + --checkpoint-in chk-shakespeare-256x16.gguf \ + --checkpoint-out chk-shakespeare-256x16.gguf \ + --model-out ggml-shakespeare-256x16-f32.gguf \ --train-data "shakespeare.txt" \ - -t 6 -b 16 -n 32 --seed 1 --adam-iter 16 \ - --print-details-interval 0 --predict 16 --use-flash + -t 6 -b 16 --seed 1 --adam-iter 256 \ + --no-checkpointing # predict -./bin/main -m ggml-shakespeare-256x16-f32.bin +./bin/main -m ggml-shakespeare-256x16-f32.gguf ``` diff --git a/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py b/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py new file mode 100644 index 000000000..01b3ee92a --- /dev/null +++ b/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py @@ -0,0 +1,492 @@ +#!/usr/bin/env python3 +# train-text-from-scratch checkpoint --> gguf conversion + +import argparse +import gguf +import os +import struct +import sys +import numpy as np +from pathlib import Path + +# gguf constants +LLM_KV_OPTIMIZER_TYPE = "optimizer.type" +LLM_KV_OPTIMIZER_TYPE_ADAM = "adam" +LLM_KV_OPTIMIZER_TYPE_LBFGS = "lbfgs" +LLM_KV_OPTIMIZER_FILE_VERSION = "optimizer.file_version" +LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT = "optimizer.convergence_past_count" +LLM_KV_OPTIMIZER_PARAMETER_COUNT = "optimizer.parameter_count" +LLM_KV_OPTIMIZER_ITERATION_COUNT = "optimizer.iteration_count" +LLM_KV_OPTIMIZER_JUST_INITIALIZED = "optimizer.just_initialized" +LLM_KV_OPTIMIZER_ADAM_BEST_LOSS = "optimizer.adam.best_loss" +LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS = "optimizer.adam.previous_loss" +LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT = "optimizer.adam.no_improvement_count" +LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT = "optimizer.lbfgs.approx_hessian_count" +LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS = "optimizer.lbfgs.best_loss" +LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP = "optimizer.lbfgs.line_search_step" +LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J = "optimizer.lbfgs.line_search_j" +LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K = "optimizer.lbfgs.line_search_k" +LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END = "optimizer.lbfgs.line_search_end" +LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT = "optimizer.lbfgs.no_improvement_count" + +LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS = "optimizer.adam.first_moments" +LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS = "optimizer.adam.second_moments" +LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES = "optimizer.adam.past_loss_values" + +LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS = "optimizer.lbfgs.current_parameters" +LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS = "optimizer.lbfgs.previous_parameters" +LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS = "optimizer.lbfgs.current_gradients" +LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS = "optimizer.lbfgs.previous_gradients" +LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION = "optimizer.lbfgs.search_direction" +LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES = "optimizer.lbfgs.past_loss_values" +LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA = "optimizer.lbfgs.memory_alpha" +LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS = "optimizer.lbfgs.memory_ys" +LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S = "optimizer.lbfgs.memory_s" +LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y = "optimizer.lbfgs.memory_y" + +LLM_KV_TRAINING_FILE_VERSION = "training.file_version" +LLM_KV_TRAINING_ITERATION_COUNT = "training.iteration_count" +LLM_KV_TRAINING_SAMPLE_COUNT = "training.sample_count" +LLM_KV_TRAINING_TOKEN_COUNT = "training.token_count" + +class Tensor: + def __init__(self, dtype='f', ne=None): + if ne is None: + ne = [] + self.dtype = dtype + self.ne = ne + self.nbytes = 0 + if self.dtype == 'f': + if len(self.ne) == 0: + self.nbytes = 0 + else: + self.nbytes = int(np.product(self.ne)) * 4 + else: + raise ValueError(f"Unhandled data type '{self.dtype}'") + + def load(self, data, offset): + nd = struct.unpack(' 0 else []) + + self.lbfgs_x = Tensor('f', [self.nx]) + self.lbfgs_xp = Tensor('f', [self.nx]) + self.lbfgs_g = Tensor('f', [self.nx]) + self.lbfgs_gp = Tensor('f', [self.nx]) + self.lbfgs_d = Tensor('f', [self.nx]) + self.lbfgs_pf = Tensor('f', [self.past] if self.past > 0 else []) + self.lbfgs_lmal = Tensor('f', [self.lbfgs_m]) + self.lbfgs_lmys = Tensor('f', [self.lbfgs_m]) + self.lbfgs_lms = Tensor('f', [self.nx, self.lbfgs_m]) + self.lbfgs_lmy = Tensor('f', [self.nx, self.lbfgs_m]) + + if self.type == 0: + # these tensors are stored, but we don't need their data + x = Tensor('f', [self.nx]) + g = Tensor('f', [self.nx]) + g2 = Tensor('f', [self.nx]) + mh = Tensor('f', [self.nx]) + vh = Tensor('f', [self.nx]) + + offset = x.load(data, offset) + offset = g.load(data, offset) + offset = g2.load(data, offset) + offset = self.adam_m.load(data, offset) + offset = self.adam_v.load(data, offset) + offset = mh.load(data, offset) + offset = vh.load(data, offset) + offset = self.adam_pf.load(data, offset) + + self.adam_fx_best = struct.unpack(' 0 else []) + + self.lbfgs_x = Tensor('f', [self.nx]) + self.lbfgs_xp = Tensor('f', [self.nx]) + self.lbfgs_g = Tensor('f', [self.nx]) + self.lbfgs_gp = Tensor('f', [self.nx]) + self.lbfgs_d = Tensor('f', [self.nx]) + self.lbfgs_pf = Tensor('f', [self.past] if self.past > 0 else []) + self.lbfgs_lmal = Tensor('f', [self.lbfgs_m]) + self.lbfgs_lmys = Tensor('f', [self.lbfgs_m]) + self.lbfgs_lms = Tensor('f', [self.nx, self.lbfgs_m]) + self.lbfgs_lmy = Tensor('f', [self.nx, self.lbfgs_m]) + + # forgot to save type in version 1: + # guess self.type from number of remaining bytes + size_type_0 = 12 + sum([t.max_storage_size() for t in + [self.adam_m, self.adam_v] + +([self.adam_pf] if (self.past > 0) else [])]) + size_type_1 = 24 + sum([t.max_storage_size() for t in + [self.lbfgs_x, self.lbfgs_xp, self.lbfgs_g, + self.lbfgs_gp, self.lbfgs_d, self.lbfgs_pf, + self.lbfgs_lmal, self.lbfgs_lmys, + self.lbfgs_lms, self.lbfgs_lmy] + +([self.lbfgs_pf] if (self.past > 0) else [])]) + # due to alignment padding the size might not by exact + # but the difference in size for both types is significant, + # so we can just use whichever is closest + remaining = len(data) - offset + if abs(remaining - size_type_0) < abs(remaining - size_type_1): + self.type = 0 + else: + self.type = 1 + + if self.type == 0: + offset = self.adam_m.load(data, offset) + offset = self.adam_v.load(data, offset) + offset = self.adam_pf.load(data,offset) + + self.adam_fx_best = struct.unpack(' 0: + self.adam_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES) + + elif self.type == 1: + gguf_writer.add_string(LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS) + gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, self.lbfgs_m) + gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS, self.lbfgs_fx_best) + gguf_writer.add_float32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP, self.lbfgs_step) + gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J, self.lbfgs_j) + gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K, self.lbfgs_k) + gguf_writer.add_int32(LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END, self.lbfgs_end) + gguf_writer.add_uint32(LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT, self.lbfgs_n_no_improvement) + + self.lbfgs_x.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS) + self.lbfgs_xp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS) + self.lbfgs_g.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS) + self.lbfgs_gp.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS) + self.lbfgs_d.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION) + if self.past > 0: + self.lbfgs_pf.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES) + self.lbfgs_lmal.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA) + self.lbfgs_lmys.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS) + self.lbfgs_lms.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S) + self.lbfgs_lmy.save_gguf(gguf_writer, name=LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y) + else: + raise ValueError('Unknown optimizer type') + +class ModelParams: + def __init__(self): + pass + + def load(self, data, offset): + self.n_vocab = struct.unpack(' @@ -17,8 +18,6 @@ #pragma warning(disable: 4244 4267) // possible loss of data #endif -static const float rms_norm_eps = 1e-5f; - struct random_normal_distribution { std::mt19937 gen; std::normal_distribution rd; @@ -63,17 +62,6 @@ float frand_uniform(struct random_uniform_distribution * rnd) { return rnd->rd(rnd->gen); } -void ggml_graph_compute_helper(std::vector & buf, ggml_cgraph * graph, int n_threads) { - struct ggml_cplan plan = ggml_graph_plan(graph, n_threads); - - if (plan.work_size > 0) { - buf.resize(plan.work_size); - plan.work_data = buf.data(); - } - - ggml_graph_compute(graph, &plan); -} - struct ggml_tensor * randomize_tensor_normal(struct ggml_tensor * tensor, struct random_normal_distribution * rnd) { float scale = 1.0f; // xavier switch (tensor->n_dims) { @@ -167,29 +155,20 @@ struct ggml_tensor * randomize_tensor_uniform(struct ggml_tensor * tensor, struc return tensor; } -struct llama_vocab { - using id = int32_t; - using token = std::string; - using ttype = llama_token_type; - - struct token_data { - token text; - float score; - ttype type; - }; - - std::unordered_map token_to_id; - std::vector id_to_token; -}; - struct my_llama_hparams { uint32_t n_vocab = 32000; - uint32_t n_ctx = 512; // this is provided as user input? + uint32_t n_ctx = 512; uint32_t n_embd = 4096; - uint32_t n_mult = 4; uint32_t n_head = 32; uint32_t n_layer = 32; uint32_t n_rot = 64; + uint32_t n_ff = 11008; + + // float f_norm_eps = 1e-5; // falcon + float f_norm_rms_eps = 1e-5; // llama + + float rope_freq_base = 10000.0f; + float rope_freq_scale = 1.0f; bool operator!=(const my_llama_hparams& other) const { return memcmp(this, &other, sizeof(my_llama_hparams)); @@ -215,17 +194,6 @@ struct my_llama_layer { struct ggml_tensor * w3; }; -struct my_llama_kv_cache { - struct ggml_context * ctx = NULL; - - struct ggml_tensor * k; - struct ggml_tensor * v; - - // llama_ctx_buffer buf; - - int n; // number of tokens currently in the cache -}; - struct my_llama_model { struct ggml_context * ctx = NULL; @@ -243,18 +211,91 @@ struct my_llama_model { uint32_t train_tokens = 0; }; -uint32_t get_n_ff(const struct my_llama_hparams* hparams) { - const uint32_t n_ff = ((2*(4*hparams->n_embd)/3 + hparams->n_mult - 1)/hparams->n_mult)*hparams->n_mult; - return n_ff; -} +// gguf constants +const char * LLM_KV_OPTIMIZER_TYPE = "optimizer.type"; +const char * LLM_KV_OPTIMIZER_TYPE_ADAM = "adam"; +const char * LLM_KV_OPTIMIZER_TYPE_LBFGS = "lbfgs"; +const char * LLM_KV_OPTIMIZER_FILE_VERSION = "optimizer.file_version"; +const char * LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT = "optimizer.convergence_past_count"; +const char * LLM_KV_OPTIMIZER_PARAMETER_COUNT = "optimizer.parameter_count"; +const char * LLM_KV_OPTIMIZER_ITERATION_COUNT = "optimizer.iteration_count"; +const char * LLM_KV_OPTIMIZER_JUST_INITIALIZED = "optimizer.just_initialized"; +const char * LLM_KV_OPTIMIZER_ADAM_BEST_LOSS = "optimizer.adam.best_loss"; +const char * LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS = "optimizer.adam.previous_loss"; +const char * LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT = "optimizer.adam.no_improvement_count"; +const char * LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT = "optimizer.lbfgs.approx_hessian_count"; +const char * LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS = "optimizer.lbfgs.best_loss"; +const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP = "optimizer.lbfgs.line_search_step"; +const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J = "optimizer.lbfgs.line_search_j"; +const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K = "optimizer.lbfgs.line_search_k"; +const char * LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END = "optimizer.lbfgs.line_search_end"; +const char * LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT = "optimizer.lbfgs.no_improvement_count"; + +const char * LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS = "optimizer.adam.first_moments"; +const char * LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS = "optimizer.adam.second_moments"; +const char * LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES = "optimizer.adam.past_loss_values"; + +const char * LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS = "optimizer.lbfgs.current_parameters"; +const char * LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS = "optimizer.lbfgs.previous_parameters"; +const char * LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS = "optimizer.lbfgs.current_gradients"; +const char * LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS = "optimizer.lbfgs.previous_gradients"; +const char * LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION = "optimizer.lbfgs.search_direction"; +const char * LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES = "optimizer.lbfgs.past_loss_values"; +const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA = "optimizer.lbfgs.memory_alpha"; +const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS = "optimizer.lbfgs.memory_ys"; +const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S = "optimizer.lbfgs.memory_s"; +const char * LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y = "optimizer.lbfgs.memory_y"; + +const char * LLM_KV_TRAINING_FILE_VERSION = "training.file_version"; +const char * LLM_KV_TRAINING_ITERATION_COUNT = "training.iteration_count"; +const char * LLM_KV_TRAINING_SAMPLE_COUNT = "training.sample_count"; +const char * LLM_KV_TRAINING_TOKEN_COUNT = "training.token_count"; + +// gguf constants (sync with gguf.py) + +const char * LLM_KV_GENERAL_ARCHITECTURE = "general.architecture"; +const char * LLM_KV_GENERAL_FILE_TYPE = "general.file_type"; + +const char * LLM_KV_CONTEXT_LENGTH = "%s.context_length"; +const char * LLM_KV_EMBEDDING_LENGTH = "%s.embedding_length"; +const char * LLM_KV_BLOCK_COUNT = "%s.block_count"; +const char * LLM_KV_FEED_FORWARD_LENGTH = "%s.feed_forward_length"; +const char * LLM_KV_ATTENTION_HEAD_COUNT = "%s.attention.head_count"; +const char * LLM_KV_ATTENTION_LAYERNORM_RMS_EPS = "%s.attention.layer_norm_rms_epsilon"; +const char * LLM_KV_ROPE_DIMENSION_COUNT = "%s.rope.dimension_count"; +const char * LLM_KV_ROPE_FREQ_BASE = "%s.rope.freq_base"; // TODO load in llama.cpp +const char * LLM_KV_ROPE_SCALE_LINEAR = "%s.rope.scale_linear"; + +const char * LLM_KV_TOKENIZER_MODEL = "tokenizer.ggml.model"; +const char * LLM_KV_TOKENIZER_LIST = "tokenizer.ggml.tokens"; +const char * LLM_KV_TOKENIZER_TOKEN_TYPE = "tokenizer.ggml.token_type"; +const char * LLM_KV_TOKENIZER_SCORES = "tokenizer.ggml.scores"; +const char * LLM_KV_TOKENIZER_MERGES = "tokenizer.ggml.merges"; +const char * LLM_KV_TOKENIZER_BOS_ID = "tokenizer.ggml.bos_token_id"; +const char * LLM_KV_TOKENIZER_EOS_ID = "tokenizer.ggml.eos_token_id"; +const char * LLM_KV_TOKENIZER_UNK_ID = "tokenizer.ggml.unknown_token_id"; +const char * LLM_KV_TOKENIZER_SEP_ID = "tokenizer.ggml.seperator_token_id"; +const char * LLM_KV_TOKENIZER_PAD_ID = "tokenizer.ggml.padding_token_id"; + +const char * LLM_TENSOR_TOKEN_EMBD = "token_embd"; +const char * LLM_TENSOR_OUTPUT_NORM = "output_norm"; +const char * LLM_TENSOR_OUTPUT = "output"; +const char * LLM_TENSOR_ATTN_NORM = "blk.%d.attn_norm"; +const char * LLM_TENSOR_ATTN_Q = "blk.%d.attn_q"; +const char * LLM_TENSOR_ATTN_K = "blk.%d.attn_k"; +const char * LLM_TENSOR_ATTN_V = "blk.%d.attn_v"; +const char * LLM_TENSOR_ATTN_OUT = "blk.%d.attn_output"; +const char * LLM_TENSOR_FFN_NORM = "blk.%d.ffn_norm"; +const char * LLM_TENSOR_FFN_GATE = "blk.%d.ffn_gate"; +const char * LLM_TENSOR_FFN_DOWN = "blk.%d.ffn_down"; +const char * LLM_TENSOR_FFN_UP = "blk.%d.ffn_up"; void print_params(struct my_llama_hparams * params) { printf("%s: n_vocab: %d\n", __func__, params->n_vocab); printf("%s: n_ctx: %d\n", __func__, params->n_ctx); printf("%s: n_embd: %d\n", __func__, params->n_embd); - printf("%s: n_mult: %d\n", __func__, params->n_mult); printf("%s: n_head: %d\n", __func__, params->n_head); - printf("%s: n_ff: %d\n", __func__, get_n_ff(params)); + printf("%s: n_ff: %d\n", __func__, params->n_ff); printf("%s: n_layer: %d\n", __func__, params->n_layer); printf("%s: n_rot: %d\n", __func__, params->n_rot); } @@ -265,8 +306,7 @@ void init_model(struct my_llama_model * model) { const uint32_t n_embd = hparams.n_embd; const uint32_t n_layer = hparams.n_layer; const uint32_t n_vocab = hparams.n_vocab; - - const uint32_t n_ff = get_n_ff(&hparams); + const uint32_t n_ff = hparams.n_ff; struct ggml_context * ctx = model->ctx; @@ -274,20 +314,31 @@ void init_model(struct my_llama_model * model) { model->train_samples = 0; model->train_tokens = 0; + std::vector tn_buf; + tn_buf.resize(GGML_MAX_NAME); + auto tn = [&tn_buf](const char * key) -> const char * { + snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", key); + return tn_buf.data(); + }; + auto tni = [&tn_buf](const char * key, int bid) -> const char * { + snprintf(tn_buf.data(), tn_buf.size(), key, bid); + std::string s = tn_buf.data(); + snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", s.c_str()); + return tn_buf.data(); + }; + model->tok_embeddings = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); model->norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); model->output = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_vocab); - ggml_set_name(model->tok_embeddings, "tok_embeddings.weight"); - ggml_set_name(model->norm, "norm.weight"); - ggml_set_name(model->output, "output.weight"); + ggml_set_name(model->tok_embeddings, tn(LLM_TENSOR_TOKEN_EMBD)); + ggml_set_name(model->norm, tn(LLM_TENSOR_OUTPUT_NORM)); + ggml_set_name(model->output, tn(LLM_TENSOR_OUTPUT)); model->layers.resize(n_layer); for (uint32_t i = 0; i < n_layer; ++i) { auto & layer = model->layers[i]; - std::string layers_i = "layers." + std::to_string(i); - layer.attention_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_embd); layer.wq = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_embd); @@ -301,18 +352,18 @@ void init_model(struct my_llama_model * model) { layer.w2 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_ff, n_embd); layer.w3 = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, n_ff); - ggml_set_name(layer.attention_norm, (layers_i + ".attention_norm.weight").c_str()); + ggml_set_name(layer.attention_norm, tni(LLM_TENSOR_ATTN_NORM, i)); - ggml_set_name(layer.wq, (layers_i + ".attention.wq.weight").c_str()); - ggml_set_name(layer.wk, (layers_i + ".attention.wk.weight").c_str()); - ggml_set_name(layer.wv, (layers_i + ".attention.wv.weight").c_str()); - ggml_set_name(layer.wo, (layers_i + ".attention.wo.weight").c_str()); + ggml_set_name(layer.wq, tni(LLM_TENSOR_ATTN_Q, i)); + ggml_set_name(layer.wk, tni(LLM_TENSOR_ATTN_K, i)); + ggml_set_name(layer.wv, tni(LLM_TENSOR_ATTN_V, i)); + ggml_set_name(layer.wo, tni(LLM_TENSOR_ATTN_OUT, i)); - ggml_set_name(layer.ffn_norm, (layers_i + ".ffn_norm.weight").c_str()); + ggml_set_name(layer.ffn_norm, tni(LLM_TENSOR_FFN_NORM, i)); - ggml_format_name(layer.w1, "%s.feed_forward.w1.weight", layers_i.c_str()); - ggml_format_name(layer.w2, "%s.feed_forward.w2.weight", layers_i.c_str()); - ggml_format_name(layer.w3, "%s.feed_forward.w3.weight", layers_i.c_str()); + ggml_set_name(layer.w1, tni(LLM_TENSOR_FFN_GATE, i)); + ggml_set_name(layer.w2, tni(LLM_TENSOR_FFN_DOWN, i)); + ggml_set_name(layer.w3, tni(LLM_TENSOR_FFN_UP, i)); } } @@ -371,267 +422,6 @@ void randomize_model(struct my_llama_model * model, int seed, float mean, float } } -bool init_kv_cache(struct my_llama_kv_cache* cache, struct my_llama_model * model, int n_batch) { - const auto & hparams = model->hparams; - - const uint32_t n_ctx = hparams.n_ctx; - const uint32_t n_embd = hparams.n_embd; - const uint32_t n_layer = hparams.n_layer; - - const int64_t n_mem = n_layer*n_ctx*n_batch; - const int64_t n_elements = n_embd*n_mem; - - // cache.buf.resize(2u*n_elements*ggml_type_size(wtype) + 2u*MB); - - // struct ggml_init_params params; - // params.mem_size = cache.buf.size; - // params.mem_buffer = cache.buf.addr; - // params.no_alloc = false; - if (!cache->ctx) { - struct ggml_init_params params; - params.mem_size = 2u*n_elements*ggml_type_size(GGML_TYPE_F32) + 2u*1024*1024; - params.mem_buffer = NULL; - params.no_alloc = false; - - cache->ctx = ggml_init(params); - - if (!cache->ctx) { - fprintf(stderr, "%s: failed to allocate memory for kv cache\n", __func__); - return false; - } - } - - cache->k = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements); - cache->v = ggml_new_tensor_1d(cache->ctx, GGML_TYPE_F32, n_elements); - - return true; -} - -struct ggml_tensor * forward( - struct my_llama_model * model, - struct my_llama_kv_cache * cache, - struct ggml_context * ctx0, - struct ggml_cgraph * gf, - struct ggml_tensor * tokens_input, - const int n_tokens, - const int n_past) { - - const int N = n_tokens; - - struct my_llama_kv_cache& kv_self = *cache; - const auto & hparams = model->hparams; - const int n_ctx = hparams.n_ctx; - const int n_embd = hparams.n_embd; - const int n_layer = hparams.n_layer; - const int n_head = hparams.n_head; - const int n_rot = hparams.n_rot; - - struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N); - memcpy(tokens->data, tokens_input->data, N*ggml_element_size(tokens)); - - struct ggml_tensor * kc = kv_self.k; - struct ggml_tensor * vc = kv_self.v; - - // inpL shape [n_embd,N,1,1] - struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens); - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * inpSA = inpL; - - struct ggml_tensor * cur; - - // lctx.use_buf(ctx0, 0); - - // norm - { - // cur shape [n_embd,N,1,1] - cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps); - - // cur = attention_norm*cur - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model->layers[il].attention_norm, cur), - cur); - } - - // self-attention - { - // compute Q and K and RoPE them - // wq shape [n_embd, n_embd, 1, 1] - // wk shape [n_embd, n_embd, 1, 1] - // Qcur shape [n_embd/n_head, n_head, N, 1] - // Kcur shape [n_embd/n_head, n_head, N, 1] - struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0); - struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_3d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N), n_past, n_rot, 0, 0); - - // store key and value to memory - { - // compute the transposed [N, n_embd] V matrix - // wv shape [n_embd, n_embd, 1, 1] - // Vcur shape [n_embd, N, 1, 1] - struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_transpose(ctx0, ggml_reshape_2d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wv, cur), n_embd, N))); - - // kv_self.k shape [n_embd * n_ctx * n_layer, 1] - // kv_self.v shape [n_embd * n_ctx * n_layer, 1] - // k shape [n_embd * N, 1] == kv_self.k[:,n_past:n_past+N,il,0] - // v shape [N, n_embd, 1, 1] == kv_self.v[:,n_past:n_past+N,il,0] - - /* { - struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past)); - struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd, - ( n_ctx)*ggml_element_size(kv_self.v), - (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v)); - - // important: storing RoPE-ed version of K in the KV cache! - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k)); - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v)); - } //*/ - - kc = ggml_set_1d_inplace(ctx0, kc, ggml_reshape_1d(ctx0, Kcur, n_embd*N), (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past)); - vc = ggml_set_2d_inplace(ctx0, vc, Vcur, ( n_ctx)*ggml_element_size(kv_self.v), - (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v)); - } - - // Qcur shape [n_embd/n_head, n_head, N, 1] - // Q shape [n_embd/n_head, N, n_head, 1] - struct ggml_tensor * Q = - ggml_permute(ctx0, - Qcur, - 0, 2, 1, 3); - - // kv_self.k shape [n_embd * n_ctx * n_layer, 1] - // K shape [n_embd/n_head, n_past + N, n_head, 1] - struct ggml_tensor * K = - ggml_permute(ctx0, - ggml_reshape_3d(ctx0, - ggml_view_1d(ctx0, kc, (n_past + N)*n_embd, il*n_ctx*ggml_element_size(kc)*n_embd), - n_embd/n_head, n_head, n_past + N), - 0, 2, 1, 3); - - // K * Q - // KQ shape [n_past + N, N, n_head, 1] - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - - // KQ_scaled = KQ / sqrt(n_embd/n_head) - // KQ_scaled shape [n_past + N, N, n_head, 1] - struct ggml_tensor * KQ_scaled = - ggml_scale(ctx0, - KQ, - ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head))); - - // KQ_masked = mask_past(KQ_scaled) - // KQ_masked shape [n_past + N, N, n_head, 1] - struct ggml_tensor * KQ_masked = ggml_diag_mask_inf(ctx0, KQ_scaled, n_past); - - // KQ = soft_max(KQ_masked) - // KQ_soft_max shape [n_past + N, N, n_head, 1] - struct ggml_tensor * KQ_soft_max = ggml_soft_max(ctx0, KQ_masked); - - // split cached V into n_head heads - //// V shape [n_past + N, n_embd/n_head, n_head, 1] - // V shape [n_past + N, n_embd/n_head, n_head, 1] == kv_self.v[:,:(n_past+N),il,1] - struct ggml_tensor * V = - ggml_view_3d(ctx0, vc, - n_past + N, n_embd/n_head, n_head, - n_ctx*ggml_element_size(vc), - n_ctx*ggml_element_size(vc)*n_embd/n_head, - il*n_ctx*ggml_element_size(vc)*n_embd); - - // KQV shape [n_embd/n_head, N, n_head, 1] - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max); - - // KQV_merged = KQV.permute(0, 2, 1, 3) - // KQV_merged shape [n_embd/n_head, n_head, N, 1] - struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - // KQV_merged shape - - // cur = KQV_merged.contiguous().view(n_embd, N) - // cur shape [n_embd,N,1,1] - cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N); - // cur = ggml_cpy(ctx0, - // KQV_merged, - // ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N)); - - // projection (no bias) - // cur shape [n_embd,N,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].wo, - cur); - } - - // lctx.use_buf(ctx0, 1); - - // inpFF shape [n_embd,N,1,1] - struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpSA); - - // feed-forward network - { - // norm - { - // cur shape [n_embd,N,1,1] - cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps); - - // cur = ffn_norm*cur - // cur shape [n_embd,N,1,1] - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model->layers[il].ffn_norm, cur), - cur); - } - - // tmp shape [n_ff,N,1,1] - struct ggml_tensor * tmp = ggml_mul_mat(ctx0, - model->layers[il].w3, - cur); - - // cur shape [n_ff,N,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].w1, - cur); - - // SILU activation - // cur shape [n_ff,N,1,1] - cur = ggml_silu(ctx0, cur); - - // cur shape [n_ff,N,1,1] - cur = ggml_mul(ctx0, cur, tmp); - - // cur shape [n_embd,N,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].w2, - cur); - } - - // cur shape [n_embd,N,1,1] - cur = ggml_add(ctx0, cur, inpFF); - - // input for next layer - // inpL shape [n_embd,N,1,1] - inpL = cur; - } - - // norm - { - - // inpL shape [n_embd,N,1,1] - inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps); - - // inpL = norm*inpL - // inpL shape [n_embd,N,1,1] - inpL = ggml_mul(ctx0, - ggml_repeat(ctx0, model->norm, inpL), - inpL); - - //embeddings = inpL; - } - - // lm_head - // inpL shape [n_vocab,N,1,1] - inpL = ggml_mul_mat(ctx0, model->output, inpL); - - // run the computation - ggml_build_forward_expand(gf, inpL); - - return inpL; -} - void assert_shape_1d(struct ggml_tensor * tensor, int64_t ne0) { GGML_ASSERT(tensor->n_dims == 1); GGML_ASSERT(tensor->ne[0] == ne0); @@ -658,786 +448,222 @@ void assert_shape_4d(struct ggml_tensor * tensor, int64_t ne0, int64_t ne1, int6 GGML_ASSERT(tensor->ne[3] == ne3); } -struct ggml_tensor * forward_batch( - struct my_llama_model * model, - struct my_llama_kv_cache * cache, - struct ggml_context * ctx0, - struct ggml_cgraph * gf, - struct ggml_tensor * tokens_input, - const int n_tokens, - const int n_past, - const int n_batch) { - - const int N = n_tokens; - - struct my_llama_kv_cache& kv_self = *cache; - const auto & hparams = model->hparams; - const int n_ctx = hparams.n_ctx; - const int n_vocab = hparams.n_vocab; - const int n_embd = hparams.n_embd; - const int n_layer = hparams.n_layer; - const int n_head = hparams.n_head; - const int n_rot = hparams.n_rot; - const int n_ff = get_n_ff(&hparams); - - struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N*n_batch); - memcpy(tokens->data, tokens_input->data, ggml_element_size(tokens)*N*n_batch); - - struct ggml_tensor * kc = kv_self.k; - struct ggml_tensor * vc = kv_self.v; - - // inpL shape [n_embd,N*n_batch,1] - struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens); - assert_shape_2d(inpL, n_embd, N*n_batch); - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * inpSA = inpL; - - struct ggml_tensor * cur; - - // lctx.use_buf(ctx0, 0); - - // norm - { - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps); - assert_shape_2d(cur, n_embd, N*n_batch); - - // cur = attention_norm*cur - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model->layers[il].attention_norm, cur), - cur); - assert_shape_2d(cur, n_embd, N*n_batch); - } - - // self-attention - { - // compute Q and K and RoPE them - // wq shape [n_embd, n_embd, 1, 1] - // wk shape [n_embd, n_embd, 1, 1] - // Qcur shape [n_embd/n_head, n_head, N, n_batch] - // Kcur shape [n_embd/n_head, n_head, N, n_batch] - struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0); - struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0); - assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch); - assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch); - - // store key and value to memory - { - // compute the transposed [N, n_embd] V matrix - // wv shape [n_embd, n_embd, 1, 1] - // Vcur shape [N, n_embd, n_batch, 1] - struct ggml_tensor * Vcur = ggml_cont(ctx0, - ggml_permute(ctx0, - ggml_reshape_3d(ctx0, - ggml_mul_mat(ctx0, - model->layers[il].wv, - cur), - n_embd, N, n_batch), - 1, 0, 2, 3)); - assert_shape_3d(Vcur, N, n_embd, n_batch); - - // kv_self.k shape [n_embd * n_ctx * n_batch * n_layer] - // kv_self.v shape [n_ctx * n_embd * n_batch * n_layer] - // k shape [n_embd * N, n_batch] == kv_self.k[:,n_past:n_past+N,:,il] - // v shape [N, n_embd, n_batch, 1] == kv_self.v[:,n_past:n_past+N,:,il] - - /* { - struct ggml_tensor * k = ggml_view_1d(ctx0, kv_self.k, N*n_embd, (ggml_element_size(kv_self.k)*n_embd)*(il*n_ctx + n_past)); - struct ggml_tensor * v = ggml_view_2d(ctx0, kv_self.v, N, n_embd, - ( n_ctx)*ggml_element_size(kv_self.v), - (il*n_ctx)*ggml_element_size(kv_self.v)*n_embd + n_past*ggml_element_size(kv_self.v)); - - // important: storing RoPE-ed version of K in the KV cache! - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Kcur, k)); - ggml_build_forward_expand(gf, ggml_cpy(ctx0, Vcur, v)); - } //*/ - - kc = ggml_set_2d_inplace(ctx0, kc, - ggml_reshape_2d(ctx0, Kcur, n_embd*N, n_batch), - ggml_element_size(kc)*n_embd*n_ctx, - (ggml_element_size(kc)*n_embd)*(il*n_batch*n_ctx + n_past)); - vc = ggml_set_2d_inplace(ctx0, vc, - ggml_reshape_2d(ctx0, Vcur, N*n_embd, n_batch), - ggml_element_size(vc)*n_ctx*n_embd, - ggml_element_size(vc)*(n_past + il*n_embd*n_batch*n_ctx)); - - assert_shape_1d(kc, n_embd * n_ctx * n_batch * n_layer); - assert_shape_1d(vc, n_embd * n_ctx * n_batch * n_layer); - } - - // Qcur shape [n_embd/n_head, n_head, N, n_batch] - // Q shape [n_embd/n_head, N, n_head, n_batch] - struct ggml_tensor * Q = - ggml_permute(ctx0, - Qcur, - 0, 2, 1, 3); - assert_shape_4d(Q, n_embd/n_head, N, n_head, n_batch); - - // kv_self.k shape [n_embd * n_ctx * n_batch * n_layer] - // K shape [n_embd/n_head, n_past + N, n_head, n_batch] - struct ggml_tensor * K = - ggml_permute(ctx0, - ggml_reshape_4d(ctx0, - ggml_view_3d(ctx0, - kc, - n_embd, - (n_past + N), - n_batch, - n_embd*ggml_element_size(kc), - n_ctx*n_embd*ggml_element_size(kc), - il*n_batch*n_ctx*n_embd*ggml_element_size(kc)), - n_embd/n_head, n_head, n_past + N, n_batch), - 0, 2, 1, 3); - assert_shape_4d(K, n_embd/n_head, n_past + N, n_head, n_batch); - - // K * Q - // KQ shape [n_past + N, N, n_head, n_batch] - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - assert_shape_4d(KQ, n_past + N, N, n_head, n_batch); - - // KQ_scaled = KQ / sqrt(n_embd/n_head) - // KQ_scaled shape [n_past + N, N, n_head, n_batch] - struct ggml_tensor * KQ_scaled = - ggml_scale_inplace(ctx0, - KQ, - ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head))); - assert_shape_4d(KQ_scaled, n_past + N, N, n_head, n_batch); - - // KQ_masked = mask_past(KQ_scaled) - // KQ_masked shape [n_past + N, N, n_head, n_batch] - struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past); - assert_shape_4d(KQ_masked, n_past + N, N, n_head, n_batch); - - // KQ = soft_max(KQ_masked) - // KQ_soft_max shape [n_past + N, N, n_head, n_batch] - struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked); - assert_shape_4d(KQ_soft_max, n_past + N, N, n_head, n_batch); - - // split cached V into n_head heads - // kv_self.v shape [n_ctx * n_embd * n_batch * n_layer] - // V shape [n_past + N, n_embd/n_head, n_head, n_batch] == kv_self.v[:(n_past+N),:,:,il] - struct ggml_tensor * V = - ggml_view_4d(ctx0, vc, - n_past + N, n_embd/n_head, n_head, n_batch, - ggml_element_size(vc)*n_ctx, - ggml_element_size(vc)*n_ctx*n_embd/n_head, - ggml_element_size(vc)*n_ctx*n_embd, - il*n_batch*n_ctx*n_embd*ggml_element_size(vc)); - assert_shape_4d(V, n_past + N, n_embd/n_head, n_head, n_batch); - - // KQV shape [n_embd/n_head, N, n_head, n_batch] - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max); - assert_shape_4d(KQV, n_embd/n_head, N, n_head, n_batch); - - // KQV_merged = KQV.permute(0, 2, 1, 3) - // KQV_merged shape [n_embd/n_head, n_head, N, n_batch] - struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - assert_shape_4d(KQV_merged, n_embd/n_head, n_head, N, n_batch); - // KQV_merged shape - - // cur = KQV_merged.contiguous().view(n_embd, N) - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N*n_batch); - assert_shape_2d(cur, n_embd, N*n_batch); - // cur = ggml_cpy(ctx0, - // KQV_merged, - // ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, N)); - - // projection (no bias) - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].wo, - cur); - assert_shape_2d(cur, n_embd, N*n_batch); - } - - // lctx.use_buf(ctx0, 1); - - // inpFF shape [n_embd,N*n_batch,1,1] - struct ggml_tensor * inpFF = ggml_add_inplace(ctx0, cur, inpSA); - assert_shape_2d(inpFF, n_embd, N*n_batch); - - // feed-forward network - { - // norm - { - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps); - assert_shape_2d(cur, n_embd, N*n_batch); - - // cur = ffn_norm*cur - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model->layers[il].ffn_norm, cur), - cur); - assert_shape_2d(cur, n_embd, N*n_batch); - } - - // tmp shape [n_ff,N*n_batch,1,1] - struct ggml_tensor * tmp = ggml_mul_mat(ctx0, - model->layers[il].w3, - cur); - assert_shape_2d(tmp, n_ff, N*n_batch); - - // cur shape [n_ff,N*n_batch,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].w1, - cur); - assert_shape_2d(cur, n_ff, N*n_batch); - - // SILU activation - // cur shape [n_ff,N*n_batch,1,1] - cur = ggml_silu(ctx0, cur); - assert_shape_2d(cur, n_ff, N*n_batch); - - // cur shape [n_ff,N*n_batch,1,1] - cur = ggml_mul(ctx0, cur, tmp); - assert_shape_2d(cur, n_ff, N*n_batch); - - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].w2, - cur); - assert_shape_2d(cur, n_embd, N*n_batch); - } - - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_add_inplace(ctx0, cur, inpFF); - assert_shape_2d(cur, n_embd, N*n_batch); - - // input for next layer - // inpL shape [n_embd,N*n_batch,1,1] - inpL = cur; - assert_shape_2d(inpL, n_embd, N*n_batch); - } - - // norm - { - - // inpL shape [n_embd,N*n_batch,1,1] - inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps); - assert_shape_2d(inpL, n_embd, N*n_batch); - - // inpL = norm*inpL - // inpL shape [n_embd,N*n_batch,1,1] - inpL = ggml_mul(ctx0, - ggml_repeat(ctx0, model->norm, inpL), - inpL); - - assert_shape_2d(inpL, n_embd, N*n_batch); - - //embeddings = inpL; - } - - // lm_head - // inpL shape [n_vocab,N*n_batch,1,1] - inpL = ggml_mul_mat(ctx0, model->output, inpL); - assert_shape_2d(inpL, n_vocab, N*n_batch); - - { - // inpL shape [n_vocab,N,n_batch,1] - inpL = ggml_reshape_3d(ctx0, - inpL, - n_vocab, N, n_batch); - assert_shape_3d(inpL, n_vocab, N, n_batch); - } - - // run the computation - ggml_build_forward_expand(gf, inpL); - - return inpL; +static size_t hash(void * p) { + return (size_t)p % GGML_GRAPH_HASHTABLE_SIZE; } -struct ggml_tensor * forward_batch_wo_cache( - struct my_llama_model * model, - struct ggml_context * ctx0, - struct ggml_cgraph * gf, - struct ggml_tensor * tokens_input, - const int n_tokens, - const int n_batch) { +static size_t hash_find(void * hash_table[], void * p) { + size_t h = hash(p); - const int n_past = 0; - const int N = n_tokens; - - const auto & hparams = model->hparams; - //const int n_ctx = hparams.n_ctx; - const int n_vocab = hparams.n_vocab; - const int n_embd = hparams.n_embd; - const int n_layer = hparams.n_layer; - const int n_head = hparams.n_head; - const int n_rot = hparams.n_rot; - const int n_ff = get_n_ff(&hparams); - - struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N*n_batch); - memcpy(tokens->data, tokens_input->data, ggml_element_size(tokens)*N*n_batch); - - // inpL shape [n_embd,N*n_batch,1] - struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens); - assert_shape_2d(inpL, n_embd, N*n_batch); - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * inpSA = inpL; - - struct ggml_tensor * cur; - - // lctx.use_buf(ctx0, 0); - - // norm - { - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps); - assert_shape_2d(cur, n_embd, N*n_batch); - - // cur = attention_norm*cur - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model->layers[il].attention_norm, cur), - cur); - assert_shape_2d(cur, n_embd, N*n_batch); + // linear probing + size_t i = h; + while (hash_table[i] != NULL && hash_table[i] != p) { + i = (i + 1) % GGML_GRAPH_HASHTABLE_SIZE; + if (i == h) { + // visited all hash table entries -> not found + return GGML_GRAPH_HASHTABLE_SIZE; } - - // self-attention - { - // compute Q and K and RoPE them - // wq shape [n_embd, n_embd, 1, 1] - // wk shape [n_embd, n_embd, 1, 1] - // Qcur shape [n_embd/n_head, n_head, N, n_batch] - // Kcur shape [n_embd/n_head, n_head, N, n_batch] - struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0); - struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0); - assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch); - assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch); - - // Vcur shape [N, n_batch, n_embd/n_head, n_head] - struct ggml_tensor * Vcur = ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, cur, model->layers[il].wv), N, n_batch, n_embd/n_head, n_head); - assert_shape_4d(Vcur, N, n_batch, n_embd/n_head, n_head); - - // Qcur shape [n_embd/n_head, n_head, N, n_batch] - // Q shape [n_embd/n_head, N, n_head, n_batch] - struct ggml_tensor * Q = - ggml_permute(ctx0, - Qcur, - 0, 2, 1, 3); - assert_shape_4d(Q, n_embd/n_head, N, n_head, n_batch); - - // kv_self.k shape [n_embd * n_ctx * n_batch * n_layer] - // K shape [n_embd/n_head, N, n_head, n_batch] - struct ggml_tensor * K = - ggml_permute(ctx0, - Kcur, - 0, 2, 1, 3); - assert_shape_4d(K, n_embd/n_head, N, n_head, n_batch); - - // K * Q - // KQ shape [N, N, n_head, n_batch] - struct ggml_tensor * KQ = ggml_mul_mat(ctx0, K, Q); - assert_shape_4d(KQ, N, N, n_head, n_batch); - - // KQ_scaled = KQ / sqrt(n_embd/n_head) - // KQ_scaled shape [N, N, n_head, n_batch] - struct ggml_tensor * KQ_scaled = - ggml_scale_inplace(ctx0, - KQ, - ggml_new_f32(ctx0, 1.0f/sqrtf(float(n_embd)/n_head))); - assert_shape_4d(KQ_scaled, N, N, n_head, n_batch); - - // KQ_masked = mask_past(KQ_scaled) - // KQ_masked shape [N, N, n_head, n_batch] - struct ggml_tensor * KQ_masked = ggml_diag_mask_inf_inplace(ctx0, KQ_scaled, n_past); - assert_shape_4d(KQ_masked, N, N, n_head, n_batch); - - // KQ = soft_max(KQ_masked) - // KQ_soft_max shape [N, N, n_head, n_batch] - struct ggml_tensor * KQ_soft_max = ggml_soft_max_inplace(ctx0, KQ_masked); - assert_shape_4d(KQ_soft_max, N, N, n_head, n_batch); - - // Vcur shape [N, n_batch, n_embd/n_head, n_head] - // V shape [N, n_embd/n_head, n_head, n_batch] - struct ggml_tensor * V = - ggml_permute(ctx0, - Vcur, - 0, 3, 1, 2); - assert_shape_4d(V, N, n_embd/n_head, n_head, n_batch); - - // KQV shape [n_embd/n_head, N, n_head, n_batch] - struct ggml_tensor * KQV = ggml_mul_mat(ctx0, V, KQ_soft_max); - assert_shape_4d(KQV, n_embd/n_head, N, n_head, n_batch); - - // KQV_merged = KQV.permute(0, 2, 1, 3) - // KQV_merged shape [n_embd/n_head, n_head, N, n_batch] - struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - assert_shape_4d(KQV_merged, n_embd/n_head, n_head, N, n_batch); - // KQV_merged shape - - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N*n_batch); - assert_shape_2d(cur, n_embd, N*n_batch); - - // projection (no bias) - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].wo, - cur); - assert_shape_2d(cur, n_embd, N*n_batch); - } - - // lctx.use_buf(ctx0, 1); - - // inpFF shape [n_embd,N*n_batch,1,1] - struct ggml_tensor * inpFF = ggml_add_inplace(ctx0, cur, inpSA); - assert_shape_2d(inpFF, n_embd, N*n_batch); - - // feed-forward network - { - // norm - { - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps); - assert_shape_2d(cur, n_embd, N*n_batch); - - // cur = ffn_norm*cur - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model->layers[il].ffn_norm, cur), - cur); - assert_shape_2d(cur, n_embd, N*n_batch); - } - - // tmp shape [n_ff,N*n_batch,1,1] - struct ggml_tensor * tmp = ggml_mul_mat(ctx0, - model->layers[il].w3, - cur); - assert_shape_2d(tmp, n_ff, N*n_batch); - - // cur shape [n_ff,N*n_batch,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].w1, - cur); - assert_shape_2d(cur, n_ff, N*n_batch); - - // SILU activation - // cur shape [n_ff,N*n_batch,1,1] - cur = ggml_silu(ctx0, cur); - assert_shape_2d(cur, n_ff, N*n_batch); - - // cur shape [n_ff,N*n_batch,1,1] - cur = ggml_mul(ctx0, cur, tmp); - assert_shape_2d(cur, n_ff, N*n_batch); - - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_mul_mat(ctx0, - model->layers[il].w2, - cur); - assert_shape_2d(cur, n_embd, N*n_batch); - } - - // cur shape [n_embd,N*n_batch,1,1] - cur = ggml_add_inplace(ctx0, cur, inpFF); - assert_shape_2d(cur, n_embd, N*n_batch); - - // input for next layer - // inpL shape [n_embd,N*n_batch,1,1] - inpL = cur; - assert_shape_2d(inpL, n_embd, N*n_batch); } - - // norm - { - - // inpL shape [n_embd,N*n_batch,1,1] - inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps); - assert_shape_2d(inpL, n_embd, N*n_batch); - - // inpL = norm*inpL - // inpL shape [n_embd,N*n_batch,1,1] - inpL = ggml_mul(ctx0, - ggml_repeat(ctx0, model->norm, inpL), - inpL); - - assert_shape_2d(inpL, n_embd, N*n_batch); - - //embeddings = inpL; - } - - // lm_head - // inpL shape [n_vocab,N*n_batch,1,1] - inpL = ggml_mul_mat(ctx0, model->output, inpL); - assert_shape_2d(inpL, n_vocab, N*n_batch); - - { - // inpL shape [n_vocab,N,n_batch,1] - inpL = ggml_reshape_3d(ctx0, - inpL, - n_vocab, N, n_batch); - assert_shape_3d(inpL, n_vocab, N, n_batch); - } - - // run the computation - ggml_build_forward_expand(gf, inpL); - - return inpL; + return i; } -struct ggml_tensor * forward_batch_wo_cache_flash_attn( - struct my_llama_model * model, - struct ggml_context * ctx0, - struct ggml_cgraph * gf, - struct ggml_tensor * tokens_input, - const int n_tokens, - const int n_batch) { +static bool hash_insert(void * hash_table[], void * p) { + //size_t h = hash(p); + size_t i = hash_find(hash_table, p); - const int n_past = 0; - const int N = n_tokens; + GGML_ASSERT(i < GGML_GRAPH_HASHTABLE_SIZE); // assert that not full - const auto & hparams = model->hparams; - //const int n_ctx = hparams.n_ctx; - const int n_vocab = hparams.n_vocab; - const int n_embd = hparams.n_embd; - const int n_layer = hparams.n_layer; - const int n_head = hparams.n_head; - const int n_rot = hparams.n_rot; - const int n_ff = get_n_ff(&hparams); - - struct ggml_tensor * tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N*n_batch); - memcpy(tokens->data, tokens_input->data, ggml_element_size(tokens)*N*n_batch); - - struct ggml_tensor * inpL = ggml_get_rows(ctx0, model->tok_embeddings, tokens); - assert_shape_2d(inpL, n_embd, N*n_batch); - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * inpSA = inpL; - - struct ggml_tensor * cur; - - // norm - { - cur = ggml_rms_norm(ctx0, inpL, rms_norm_eps); - assert_shape_2d(cur, n_embd, N*n_batch); - - // cur = attention_norm*cur - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model->layers[il].attention_norm, cur), - cur); - assert_shape_2d(cur, n_embd, N*n_batch); - } - - // self-attention - { - // compute Q and K and RoPE them - // wq shape [n_embd, n_embd, 1, 1] - // wk shape [n_embd, n_embd, 1, 1] - struct ggml_tensor * Qcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wq, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0); - struct ggml_tensor * Kcur = ggml_rope_inplace(ctx0, ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, model->layers[il].wk, cur), n_embd/n_head, n_head, N, n_batch), n_past, n_rot, 0, 0); - assert_shape_4d(Qcur, n_embd/n_head, n_head, N, n_batch); - assert_shape_4d(Kcur, n_embd/n_head, n_head, N, n_batch); - - struct ggml_tensor * Vcur = ggml_reshape_4d(ctx0, ggml_mul_mat(ctx0, cur, model->layers[il].wv), N, n_batch, n_embd/n_head, n_head); - assert_shape_4d(Vcur, N, n_batch, n_embd/n_head, n_head); - - struct ggml_tensor * Q = - ggml_permute(ctx0, - Qcur, - 0, 2, 1, 3); - assert_shape_4d(Q, n_embd/n_head, N, n_head, n_batch); - - struct ggml_tensor * K = - ggml_permute(ctx0, - Kcur, - 0, 2, 1, 3); - assert_shape_4d(K, n_embd/n_head, N, n_head, n_batch); - - struct ggml_tensor * V = - ggml_permute(ctx0, - Vcur, - 0, 3, 1, 2); - assert_shape_4d(V, N, n_embd/n_head, n_head, n_batch); - - bool masked = true; - struct ggml_tensor * KQV = ggml_flash_attn(ctx0, Q, K, V, masked); - assert_shape_4d(KQV, n_embd/n_head, N, n_head, n_batch); - - struct ggml_tensor * KQV_merged = ggml_permute(ctx0, KQV, 0, 2, 1, 3); - assert_shape_4d(KQV_merged, n_embd/n_head, n_head, N, n_batch); - cur = ggml_reshape_2d(ctx0, ggml_cont(ctx0, KQV_merged), n_embd, N*n_batch); - assert_shape_2d(cur, n_embd, N*n_batch); - - // projection (no bias) - cur = ggml_mul_mat(ctx0, - model->layers[il].wo, - cur); - assert_shape_2d(cur, n_embd, N*n_batch); - } - - struct ggml_tensor * inpFF = ggml_add_inplace(ctx0, cur, inpSA); - assert_shape_2d(inpFF, n_embd, N*n_batch); - - // feed-forward network - { - // norm - { - cur = ggml_rms_norm(ctx0, inpFF, rms_norm_eps); - assert_shape_2d(cur, n_embd, N*n_batch); - - // cur = ffn_norm*cur - cur = ggml_mul(ctx0, - ggml_repeat(ctx0, model->layers[il].ffn_norm, cur), - cur); - assert_shape_2d(cur, n_embd, N*n_batch); - } - - struct ggml_tensor * tmp = ggml_mul_mat(ctx0, - model->layers[il].w3, - cur); - assert_shape_2d(tmp, n_ff, N*n_batch); - - cur = ggml_mul_mat(ctx0, - model->layers[il].w1, - cur); - assert_shape_2d(cur, n_ff, N*n_batch); - - // SILU activation - cur = ggml_silu(ctx0, cur); - assert_shape_2d(cur, n_ff, N*n_batch); - - cur = ggml_mul(ctx0, cur, tmp); - assert_shape_2d(cur, n_ff, N*n_batch); - - cur = ggml_mul_mat(ctx0, - model->layers[il].w2, - cur); - assert_shape_2d(cur, n_embd, N*n_batch); - } - - cur = ggml_add_inplace(ctx0, cur, inpFF); - assert_shape_2d(cur, n_embd, N*n_batch); - - // input for next layer - inpL = cur; - assert_shape_2d(inpL, n_embd, N*n_batch); + if (hash_table[i] == p) { + return true; } - // norm - { - - inpL = ggml_rms_norm(ctx0, inpL, rms_norm_eps); - assert_shape_2d(inpL, n_embd, N*n_batch); - - // inpL = norm*inpL - inpL = ggml_mul(ctx0, - ggml_repeat(ctx0, model->norm, inpL), - inpL); - - assert_shape_2d(inpL, n_embd, N*n_batch); - } - - // lm_head - inpL = ggml_mul_mat(ctx0, model->output, inpL); - assert_shape_2d(inpL, n_vocab, N*n_batch); - - { - inpL = ggml_reshape_3d(ctx0, - inpL, - n_vocab, N, n_batch); - assert_shape_3d(inpL, n_vocab, N, n_batch); - } - - // run the computation - ggml_build_forward_expand(gf, inpL); - - return inpL; + // insert + GGML_ASSERT(hash_table[i] == NULL); + hash_table[i] = p; + return false; } -// expand the graph nodes without creating leafs. -struct ggml_tensor * expand(struct ggml_cgraph * g, struct ggml_tensor * t) { - // check if already visited - for (int i = 0; i < g->n_nodes; i++) { - if (g->nodes[i] == t) { - return t; - } - } - - for (int i = 0; i < g->n_leafs; i++) { - if (g->leafs[i] == t) { - return t; - } - } - - for (int i = 0; i < GGML_MAX_SRC; ++i) { - if (t->src[i]) { - expand(g, t->src[i]); - } - } - - GGML_ASSERT(g->n_nodes < GGML_MAX_NODES); - - if (strlen(t->name) == 0) { - snprintf(t->name, sizeof(t->name), "node_%d", g->n_nodes); - } - - g->nodes[g->n_nodes] = t; - g->grads[g->n_nodes] = t->grad; - g->n_nodes++; - return t; +static bool hash_contains(void * hash_table[], void * p) { + size_t i = hash_find(hash_table, p); + return (i < GGML_GRAPH_HASHTABLE_SIZE) && (hash_table[i] == p); } -void graph_set_leafs_grads(struct ggml_cgraph * g) { - // moves leaf nodes to g->leafs. - // i.e. g->n_nodes might change. - int n_nodes = 0; - for (int i = 0; i < g->n_nodes; ++i) { - struct ggml_tensor * node = g->nodes[i]; - const bool is_leaf = node->op == GGML_OP_NONE && node->grad == NULL; - if (is_leaf) { - GGML_ASSERT(g->n_leafs < GGML_MAX_NODES); +struct hash_map { + void * keys[GGML_GRAPH_HASHTABLE_SIZE]; + void * vals[GGML_GRAPH_HASHTABLE_SIZE]; +}; +//static const size_t HASH_MAP_SIZE = sizeof(struct hash_map); - if (strlen(node->name) == 0) { - snprintf(node->name, sizeof(node->name), "leaf_%d", g->n_leafs); - } - - g->leafs[g->n_leafs] = node; - g->n_leafs++; - } else { - GGML_ASSERT(n_nodes < GGML_MAX_NODES); - - if (strlen(node->name) == 0) { - snprintf(node->name, sizeof(node->name), "node_%d", n_nodes); - } - - g->nodes[n_nodes] = node; - g->grads[n_nodes] = node->grad; - n_nodes++; - } +struct hash_map * new_hash_map() { + struct hash_map * result = new struct hash_map; + for (int i=0; ikeys[i] = NULL; + result->vals[i] = NULL; } - for (int i=n_nodes; i < g->n_nodes; ++i) { - g->nodes[n_nodes] = NULL; - g->grads[n_nodes] = NULL; - } - g->n_nodes = n_nodes; + return result; +}; + +void free_hash_map(struct hash_map * map) { + delete map; } -struct ggml_tensor * forward_batch_wo_cache_flash_attn_train( - struct my_llama_model * model, - struct ggml_context * ctx0, +static bool ggml_is_view(struct ggml_tensor * t) { + return t->op == GGML_OP_RESHAPE || t->op == GGML_OP_VIEW || t->op == GGML_OP_TRANSPOSE || + t->op == GGML_OP_PERMUTE || t->op == GGML_OP_CPY; +} + +static struct ggml_tensor * get_view_parent(struct ggml_tensor * t) { + switch (t->op) { + case GGML_OP_PERMUTE: + case GGML_OP_RESHAPE: + case GGML_OP_TRANSPOSE: + case GGML_OP_VIEW: + return t->src[0]; + case GGML_OP_CPY: + return t->src[1]; + default: + return NULL; + } +} + +static struct ggml_tensor * get_view_source(struct ggml_tensor * t) { + struct ggml_tensor * parent = t; + do { + parent = get_view_parent(parent); + } while (ggml_is_view(parent)); + return parent; +} + +struct ggml_tensor * ggml_recompute_graph_node( + struct ggml_context * ctx, + struct ggml_cgraph * graph, + struct hash_map * replacements, + struct ggml_tensor * node) { + + if (node == NULL) { + return NULL; + } + + if (node->is_param) { + return node; + } + + if (!hash_contains(graph->visited_hash_table, node)) { + return node; + } + + int count_children = 0; + for (int k = 0; k < GGML_MAX_SRC; ++k) { + if (node->src[k]) { + ++count_children; + } + } + + if (count_children == 0) { + return node; + } + + size_t i = hash_find(replacements->keys, node); + GGML_ASSERT(i < GGML_GRAPH_HASHTABLE_SIZE); // assert that not full + if (replacements->keys[i] == node) { + return (struct ggml_tensor *) replacements->vals[i]; + } + + struct ggml_tensor * clone = ggml_new_tensor(ctx, node->type, node->n_dims, node->ne); + + // insert clone into replacements + GGML_ASSERT(replacements->keys[i] == NULL); // assert that we don't overwrite + replacements->keys[i] = node; + replacements->vals[i] = clone; + + clone->op = node->op; + clone->grad = node->grad; + clone->is_param = node->is_param; + clone->extra = node->extra; + for (int k = 0; k < GGML_MAX_DIMS; ++k) { + clone->nb[k] = node->nb[k]; + } + for (int k = 0; k < GGML_MAX_SRC; ++k) { + clone->src[k] = ggml_recompute_graph_node(ctx, graph, replacements, node->src[k]); + } + if (ggml_is_view(clone)) { + struct ggml_tensor * source = get_view_source(clone); + GGML_ASSERT(source != NULL); + clone->data = source->data; + } + + GGML_ASSERT(sizeof(node->op_params) == sizeof(int32_t) * (GGML_MAX_OP_PARAMS / sizeof(int32_t))); + GGML_ASSERT(sizeof(node->name) == GGML_MAX_NAME); + memcpy(clone->op_params, node->op_params, sizeof(node->op_params)); + ggml_format_name(clone, "%s (clone)", ggml_get_name(node)); + + return clone; +}; + +void ggml_build_backward_gradient_checkpointing( + struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, + struct ggml_cgraph * gb_tmp, + struct ggml_tensor * * checkpoints, + int n_checkpoints) { + *gb_tmp = *gf; + ggml_build_backward_expand(ctx, gf, gb_tmp, true); + + if (n_checkpoints <= 0) { + *gb = *gb_tmp; + return; + } + + struct hash_map * replacements = new_hash_map(); + + // insert checkpoints in replacements + for (int i = 0; i < n_checkpoints; ++i) { + size_t k = hash_find(replacements->keys, checkpoints[i]); + GGML_ASSERT(k < GGML_GRAPH_HASHTABLE_SIZE); // assert that not full + GGML_ASSERT(replacements->keys[k] == NULL); // assert that we don't overwrite + replacements->keys[k] = checkpoints[i]; + replacements->vals[k] = checkpoints[i]; + } + + *gb = *gf; + // rewrite gb_tmp->nodes[gf->n_nodes:gb_tmp->n_nodes], + // replacing references to gb_tmp->nodes[0:gf->n_nodes] ( == gf->nodes[0:gf->n_nodes]), + // by recomputing them from checkpoints + for (int i = gf->n_nodes; in_nodes; ++i) { + struct ggml_tensor * node = gb_tmp->nodes[i]; + for (int k = 0; k < GGML_MAX_SRC; ++k) { + // insert new tensors recomputing src, reusing already made replacements, + // remember replacements: remember new tensors with mapping from corresponding gf nodes + // recurse for input tensors, + // unless (i.e. terminating when) input tensors are checkpoints + node->src[k] = ggml_recompute_graph_node(ctx, gf, replacements, node->src[k]); + } + // insert rewritten backward node with replacements made into resulting backward graph gb + ggml_build_forward_expand(gb, node); + } + + free_hash_map(replacements); +} + +struct ggml_tensor * llama_build_train_graphs( + struct my_llama_model * model, + struct ggml_allocr * alloc, + struct ggml_context * ctx, + struct ggml_cgraph * gf, + struct ggml_cgraph * gb, + struct ggml_cgraph * gb_tmp, struct ggml_tensor * * logits, struct ggml_tensor * tokens_input, struct ggml_tensor * targets, - void * compute_buf_0, - void * compute_buf_1, - size_t size_buf_0, - size_t size_buf_1, const int n_tokens, - const int n_batch) { - - ggml_set_scratch(ctx0, { 0, 0, nullptr, }); + const int n_batch, + const bool enable_flash_attn, + const bool enable_checkpointing) { + ggml_set_scratch(ctx, { 0, 0, nullptr, }); const int n_past = 0; const int N = n_tokens; - - gf->n_nodes = 0; - gf->n_leafs = 0; - gf->perf_runs = 0; - gf->perf_cycles = 0; - gf->perf_time_us = 0; - const auto & hparams = model->hparams; const int n_ctx = hparams.n_ctx; const int n_vocab = hparams.n_vocab; @@ -1445,476 +671,162 @@ struct ggml_tensor * forward_batch_wo_cache_flash_attn_train( const int n_layer = hparams.n_layer; const int n_head = hparams.n_head; const int n_rot = hparams.n_rot; - const int n_ff = get_n_ff(&hparams); - const int rope_mode = 0; + const int n_ff = hparams.n_ff; + const float f_norm_rms_eps = hparams.f_norm_rms_eps; + const float rope_freq_base = hparams.rope_freq_base; + const float rope_freq_scale = hparams.rope_freq_scale; - int last_buf = -1; - size_t buf_offs[2] = { 0, 0 }; - size_t buf_size[2] = { size_buf_0, - size_buf_1 }; - void * buf_data[2] = { compute_buf_0, - compute_buf_1 }; - auto use_buf = [ctx0, &last_buf, &buf_offs, &buf_size, &buf_data] (int buf) { - size_t last_offs = 0; - last_offs = ggml_set_scratch(ctx0, { 0, 0, nullptr, }); - if (last_buf >= 0) { - buf_offs[last_buf] = last_offs; - } - if (buf >= 0) { - size_t offs = buf_offs[buf]; - size_t size = buf_size[buf]; - void * data = buf_data[buf]; - ggml_set_scratch(ctx0, { offs, size, data, }); - } - last_buf = buf; - }; - - bool track_max_mem = false; - size_t buf_maxs[2] = { 0, 0 }; - - auto clr_buf = [ctx0, &last_buf, &buf_offs, &buf_size, &buf_data, &buf_maxs, track_max_mem] (int buf) { - if (buf < 0) return; - if (track_max_mem) { - size_t last_offs = 0; - last_offs = ggml_set_scratch(ctx0, { 0, 0, nullptr, }); - if (last_buf >= 0) { - buf_offs[last_buf] = last_offs; - buf_maxs[last_buf] = std::max(buf_maxs[last_buf], buf_offs[last_buf]); - } - } - buf_offs[buf] = 0; - if (track_max_mem && last_buf >= 0) { - size_t offs = buf_offs[last_buf]; - size_t size = buf_size[last_buf]; - void * data = buf_data[last_buf]; - ggml_set_scratch(ctx0, { offs, size, data, }); + auto set_name = [](struct ggml_tensor * t, const char * n) { + ggml_set_name(t, n); + if (t->grad) { + ggml_format_name(t->grad, "%s->grad", n); } }; + // rope has so much parameters that we make a custom function for it + auto rope = [ctx, n_rot, n_ctx, rope_freq_base, rope_freq_scale] + (struct ggml_tensor * t) -> struct ggml_tensor * { + // not capturing these, to silcence warnings + const int n_past = 0; + const int rope_mode = 0; - auto view__q = [ctx0, n_embd, n_head, N, n_batch] (struct ggml_tensor * t) -> struct ggml_tensor * { - int64_t ne0 = n_embd/n_head; - int64_t ne1 = N; - int64_t ne2 = n_head; - int64_t ne3 = n_batch; - size_t nb0 = ggml_element_size(t); - size_t nb1 = nb0*ne0; - size_t nb2 = nb1*ne1; - size_t nb3 = nb2*ne2; - size_t offset = 0; - return ggml_view_4d(ctx0, t, ne0, ne1, ne2, ne3, nb1, nb2, nb3, offset); + return ggml_rope_custom(ctx, + t, n_past, n_rot, rope_mode, n_ctx, + rope_freq_base, rope_freq_scale); }; - auto view__k = [ctx0, n_embd, n_head, N, n_batch] (struct ggml_tensor * t) -> struct ggml_tensor * { - int64_t ne0 = n_embd/n_head; - int64_t ne1 = N; - int64_t ne2 = n_head; - int64_t ne3 = n_batch; - size_t nb0 = ggml_element_size(t); - size_t nb1 = nb0*ne0; - size_t nb2 = nb1*ne1; - size_t nb3 = nb2*ne2; - size_t offset = nb3*ne3; - return ggml_view_4d(ctx0, t, ne0, ne1, ne2, ne3, nb1, nb2, nb3, offset); - }; + set_name(tokens_input, "tokens_input"); + set_name(targets, "targets"); - auto view__v = [ctx0, n_embd, n_head, N, n_batch] (struct ggml_tensor * t) -> struct ggml_tensor * { - int64_t ne0 = N; - int64_t ne1 = n_embd/n_head; - int64_t ne2 = n_head; - int64_t ne3 = n_batch; - size_t nb0 = ggml_element_size(t); - size_t nb1 = nb0*ne0; - size_t nb2 = nb1*ne1; - size_t nb3 = nb2*ne2; - size_t offset = 2*nb3*ne3; - return ggml_view_4d(ctx0, t, ne0, ne1, ne2, ne3, nb1, nb2, nb3, offset); - }; - - auto add_or_set = [ctx0] (struct ggml_tensor * a, struct ggml_tensor * b) -> struct ggml_tensor * { - if (a == NULL) { - return b; - } else { - return ggml_add_inplace(ctx0, a, b); - } - }; - - use_buf(-1); - - model->tok_embeddings->grad = NULL; - model->norm->grad = NULL; - model->output->grad = NULL; - - for (int il = 0; il < n_layer; ++il) { - struct my_llama_layer & layer = model->layers[il]; - layer.attention_norm->grad = NULL; - layer.wq->grad = NULL; - layer.wk->grad = NULL; - layer.wv->grad = NULL; - layer.wo->grad = NULL; - layer.ffn_norm->grad = NULL; - layer.w1->grad = NULL; - layer.w2->grad = NULL; - layer.w3->grad = NULL; - } - - clr_buf(0); - clr_buf(1); - - use_buf(-1); - - struct ggml_tensor * t00 = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, N*n_batch); assert_shape_1d(t00, N*n_batch); - memcpy(t00->data, tokens_input->data, ggml_element_size(t00)*N*n_batch); - - use_buf(-1); - - struct ggml_tensor * t01 = expand(gf, ggml_get_rows(ctx0, model->tok_embeddings, t00)); assert_shape_2d(t01, n_embd, N*n_batch); - - // need to remember these for the backward pass - std::vector t02L; t02L.resize(n_layer, NULL); - std::vector t03L; t03L.resize(n_layer, NULL); - std::vector t04L; t04L.resize(n_layer, NULL); - std::vector t05L; t05L.resize(n_layer, NULL); - std::vector t06L; t06L.resize(n_layer, NULL); - std::vector t07L; t07L.resize(n_layer, NULL); - std::vector t08L; t08L.resize(n_layer, NULL); - std::vector t09L; t09L.resize(n_layer, NULL); - std::vector t10L; t10L.resize(n_layer, NULL); - std::vector t11L; t11L.resize(n_layer, NULL); - std::vector t12L; t12L.resize(n_layer, NULL); - std::vector t13L; t13L.resize(n_layer, NULL); - std::vector t14L; t14L.resize(n_layer, NULL); - std::vector t15L; t15L.resize(n_layer, NULL); - std::vector t16L; t16L.resize(n_layer, NULL); - std::vector t17L; t17L.resize(n_layer, NULL); - std::vector t18L; t18L.resize(n_layer, NULL); - std::vector t19L; t19L.resize(n_layer, NULL); - std::vector t20L; t20L.resize(n_layer, NULL); - std::vector t21L; t21L.resize(n_layer, NULL); - std::vector t22L; t22L.resize(n_layer, NULL); - std::vector t23L; t23L.resize(n_layer, NULL); - std::vector t24L; t24L.resize(n_layer, NULL); - std::vector t25L; t25L.resize(n_layer, NULL); - std::vector t26L; t26L.resize(n_layer, NULL); - std::vector t27L; t27L.resize(n_layer, NULL); - std::vector t28L; t28L.resize(n_layer, NULL); - std::vector t29L; t29L.resize(n_layer, NULL); - std::vector t30L; t30L.resize(n_layer, NULL); + GGML_ASSERT(tokens_input->type == GGML_TYPE_I32); + struct ggml_tensor * t00 = ggml_reshape_1d(ctx, tokens_input, N*n_batch); set_name(t00, "t00"); assert_shape_1d(t00, N*n_batch); + struct ggml_tensor * t01 = ggml_get_rows(ctx, model->tok_embeddings, t00); set_name(t01, "t01"); assert_shape_2d(t01, n_embd, N*n_batch); struct ggml_tensor * cur = t01; + std::vector checkpoints; + checkpoints.push_back(tokens_input); + checkpoints.push_back(targets); + checkpoints.push_back(t00); + checkpoints.push_back(t01); + + struct ggml_tensor * kv_scale; + if (!enable_flash_attn) { + kv_scale = ggml_new_f32(ctx, 1.0f/sqrtf(float(n_embd)/n_head)); + } + for (int il = 0; il < n_layer; ++il) { - clr_buf(0); struct my_llama_layer & layer = model->layers[il]; - // tensors with values necessary for backward pass are in persistent buf(-1) - // other tensors with buf(0) and buf(1) are only temporary needed, and their memory reused after layer is completed. - use_buf(-1); struct ggml_tensor * t02 = expand(gf, ggml_rms_norm (ctx0, cur, rms_norm_eps)); assert_shape_2d(t02, n_embd, N*n_batch); - use_buf( 0); struct ggml_tensor * t03 = expand(gf, ggml_repeat (ctx0, layer.attention_norm, t02)); assert_shape_2d(t03, n_embd, N*n_batch); - use_buf(-1); struct ggml_tensor * t04 = expand(gf, ggml_mul (ctx0, t02, t03)); assert_shape_2d(t04, n_embd, N*n_batch); - use_buf(-1); struct ggml_tensor * t05 = expand(gf, ggml_mul_mat (ctx0, layer.wq, t04)); assert_shape_2d(t05, n_embd, N*n_batch); - use_buf(-1); struct ggml_tensor * t06 = expand(gf, ggml_reshape_4d (ctx0, t05, n_embd/n_head, n_head, N, n_batch)); assert_shape_4d(t06, n_embd/n_head, n_head, N, n_batch); - use_buf(-1); struct ggml_tensor * t07 = expand(gf, ggml_rope_inplace (ctx0, t06, n_past, n_rot, rope_mode, 0)); assert_shape_4d(t07, n_embd/n_head, n_head, N, n_batch); - use_buf(-1); struct ggml_tensor * t08 = expand(gf, ggml_mul_mat (ctx0, layer.wk, t04)); assert_shape_2d(t08, n_embd, N*n_batch); - use_buf(-1); struct ggml_tensor * t09 = expand(gf, ggml_reshape_4d (ctx0, t08, n_embd/n_head, n_head, N, n_batch)); assert_shape_4d(t09, n_embd/n_head, n_head, N, n_batch); - use_buf(-1); struct ggml_tensor * t10 = expand(gf, ggml_rope_inplace (ctx0, t09, n_past, n_rot, rope_mode, 0)); assert_shape_4d(t10, n_embd/n_head, n_head, N, n_batch); - use_buf(-1); struct ggml_tensor * t11 = expand(gf, ggml_mul_mat (ctx0, t04, layer.wv)); assert_shape_2d(t11, N*n_batch, n_embd); - use_buf(-1); struct ggml_tensor * t12 = expand(gf, ggml_reshape_4d (ctx0, t11, N, n_batch, n_embd/n_head, n_head)); assert_shape_4d(t12, N, n_batch, n_embd/n_head, n_head); - use_buf(-1); struct ggml_tensor * t13 = expand(gf, ggml_permute (ctx0, t07, 0, 2, 1, 3)); assert_shape_4d(t13, n_embd/n_head, N, n_head, n_batch); - use_buf(-1); struct ggml_tensor * t14 = expand(gf, ggml_permute (ctx0, t10, 0, 2, 1, 3)); assert_shape_4d(t14, n_embd/n_head, N, n_head, n_batch); - use_buf(-1); struct ggml_tensor * t15 = expand(gf, ggml_permute (ctx0, t12, 0, 3, 1, 2)); assert_shape_4d(t15, N, n_embd/n_head, n_head, n_batch); - use_buf(-1); struct ggml_tensor * t16 = expand(gf, ggml_flash_attn (ctx0, t13, t14, t15, true)); assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch); - use_buf( 0); struct ggml_tensor * t17 = expand(gf, ggml_permute (ctx0, t16, 0, 2, 1, 3)); assert_shape_4d(t17, n_embd/n_head, n_head, N, n_batch); - use_buf(-1); struct ggml_tensor * t18 = expand(gf, ggml_cont (ctx0, t17)); assert_shape_4d(t18, n_embd/n_head, n_head, N, n_batch); - use_buf(-1); struct ggml_tensor * t19 = expand(gf, ggml_reshape_2d (ctx0, t18, n_embd, N*n_batch)); assert_shape_2d(t19, n_embd, N*n_batch); - use_buf( 0); struct ggml_tensor * t20 = expand(gf, ggml_mul_mat (ctx0, layer.wo, t19)); assert_shape_2d(t20, n_embd, N*n_batch); - use_buf(-1); struct ggml_tensor * t21 = expand(gf, ggml_add (ctx0, t20, cur)); assert_shape_2d(t21, n_embd, N*n_batch); - use_buf(-1); struct ggml_tensor * t22 = expand(gf, ggml_rms_norm (ctx0, t21, rms_norm_eps)); assert_shape_2d(t22, n_embd, N*n_batch); - use_buf( 0); struct ggml_tensor * t23 = expand(gf, ggml_repeat (ctx0, layer.ffn_norm, t22)); assert_shape_2d(t23, n_embd, N*n_batch); - use_buf(-1); struct ggml_tensor * t24 = expand(gf, ggml_mul (ctx0, t23, t22)); assert_shape_2d(t24, n_embd, N*n_batch); - use_buf(-1); struct ggml_tensor * t25 = expand(gf, ggml_mul_mat (ctx0, layer.w3, t24)); assert_shape_2d(t25, n_ff, N*n_batch); - use_buf(-1); struct ggml_tensor * t26 = expand(gf, ggml_mul_mat (ctx0, layer.w1, t24)); assert_shape_2d(t26, n_ff, N*n_batch); - use_buf(-1); struct ggml_tensor * t27 = expand(gf, ggml_silu (ctx0, t26)); assert_shape_2d(t27, n_ff, N*n_batch); - use_buf(-1); struct ggml_tensor * t28 = expand(gf, ggml_mul (ctx0, t27, t25)); assert_shape_2d(t28, n_ff, N*n_batch); - use_buf( 0); struct ggml_tensor * t29 = expand(gf, ggml_mul_mat (ctx0, layer.w2, t28)); assert_shape_2d(t29, n_embd, N*n_batch); - use_buf(-1); struct ggml_tensor * t30 = expand(gf, ggml_add (ctx0, t21, t29)); assert_shape_2d(t30, n_embd, N*n_batch); - t02L[il] = t02; - t03L[il] = t03; - t04L[il] = t04; - t05L[il] = t05; - t06L[il] = t06; - t07L[il] = t07; - t08L[il] = t08; - t09L[il] = t09; - t10L[il] = t10; - t11L[il] = t11; - t12L[il] = t12; - t13L[il] = t13; - t14L[il] = t14; - t15L[il] = t15; - t16L[il] = t16; - t17L[il] = t17; - t18L[il] = t18; - t19L[il] = t19; - t20L[il] = t20; - t21L[il] = t21; - t22L[il] = t22; - t23L[il] = t23; - t24L[il] = t24; - t25L[il] = t25; - t26L[il] = t26; - t27L[il] = t27; - t28L[il] = t28; - t29L[il] = t29; - t30L[il] = t30; - - cur = t30; - } - clr_buf(0); - use_buf(0); - struct ggml_tensor * t31 = expand(gf, ggml_rms_norm (ctx0, cur, rms_norm_eps)); assert_shape_2d(t31, n_embd, N*n_batch); - struct ggml_tensor * t32 = expand(gf, ggml_repeat (ctx0, model->norm, t31)); assert_shape_2d(t32, n_embd, N*n_batch); - struct ggml_tensor * t33 = expand(gf, ggml_mul (ctx0, t32, t31)); assert_shape_2d(t33, n_embd, N*n_batch); - use_buf(-1); - struct ggml_tensor * t34 = expand(gf, ggml_mul_mat (ctx0, model->output, t33)); assert_shape_2d(t34, n_vocab, N*n_batch); - struct ggml_tensor * t35 = expand(gf, ggml_reshape_3d(ctx0, t34, n_vocab, N, n_batch)); assert_shape_3d(t35, n_vocab, N, n_batch); - struct ggml_tensor * t36 = expand(gf, ggml_cross_entropy_loss(ctx0, t35, targets)); assert_shape_1d(t36, 1); - - { - /* - tok_embeddings | grad_tok_embeddings = ggml_get_rows_back(grad_t01, t00) - L0_att_norm | grad_L0_att_norm = ggml_repeat_back(grad_t03L0, L0_att_norm.shape) - L0_wq | grad_L0_wq = ggml_out_prod(t04L0, grad_t05L0) - L0_wk | grad_L0_wk = ggml_out_prod(t04L0, grad_t08L0) - L0_wv | grad_L0_wv = ggml_out_prod(t04L0, ggml_transpose(grad_t11L0)) - L0_wo | grad_L0_wo = ggml_out_prod(t19L0, grad_t20L0) - L0_ffn_norm | grad_L0_ffn_norm = ggml_repeat_back(grad_t23L0, L0_ffn_norm.shape) - L0_w1 | grad_L0_w1 = ggml_out_prod(t24L0, grad_t26L0) - L0_w2 | grad_L0_w2 = ggml_out_prod(t28L0, grad_t29L0) - L0_w3 | grad_L0_w3 = ggml_out_prod(t24L0, grad_t25L0) - L1_att_norm | grad_L1_att_norm = ggml_repeat_back(grad_t03L1, L1_att_norm.shape) - L1_wq | grad_L1_wq = ggml_out_prod(t04L1, grad_t05L1) - L1_wk | grad_L1_wk = ggml_out_prod(t04L1, grad_t08L1) - L1_wv | grad_L1_wv = ggml_out_prod(t04L1, ggml_transpose(grad_t11L1)) - L1_wo | grad_L1_wo = ggml_out_prod(t19L1, grad_t20L1) - L1_ffn_norm | grad_L1_ffn_norm = ggml_repeat_back(grad_t23L1, L1_ffn_norm.shape) - L1_w1 | grad_L1_w1 = ggml_out_prod(t24L1, grad_t26L1) - L1_w2 | grad_L1_w2 = ggml_out_prod(t28L1, grad_t29L1) - L1_w3 | grad_L1_w3 = ggml_out_prod(t24L1, grad_t25L1) - norm | grad_norm = ggml_repeat_back(grad_t32, norm.shape) - output | grad_output = ggml_out_prod(t33, grad_t34) - | - t01 = ggml_get_rows(tok_embeddings, t00) | grad_t01 = grad_t21L0 + ggml_rms_norm_back(t01, grad_t02L0) - for layer: | - t02L0*= ggml_rms_norm (t01) | grad_t02L0 = ggml_mul(grad_t04L0, t03L0) - t03L0 = ggml_repeat (L0_att_norm, t02L0_shape) | grad_t03L0 = ggml_mul(grad_t04L0, t02L0) - t04L0*= ggml_mul (t02L0, t03L0) | grad_t04L0 = ggml_out_prod(L0_wv, grad_t11L0) + ggml_out_prod(L0_wk, ggml_transpose(grad_t08L0)) + ggml_out_prod(L0_wq, ggml_transpose(grad_t05L0)) - t05L0 = ggml_mul_mat (L0_wq, t04L0) | grad_t05L0 = ggml_reshape(grad_t06L0, t05L0_shape) - t06L0 = ggml_reshape_4d (t05L0, n_embd/n_head, n_head, N, n_batch) | grad_t06L0 = ggml_rope_back(grad_t07L0) - t07L0 = ggml_rope_inplace (t06L0) | grad_t07L0 = ggml_permute_back(grad_t13L0, 0, 2, 1, 3) = ggml_permute(grad_t13L0, 0, 2, 1, 3) - t08L0 = ggml_mul_mat (L0_wk, t04L0) | grad_t08L0 = ggml_reshape(grad_t09L0, t08L0_shape) - t09L0 = ggml_reshape_4d (t08L0, n_embd/n_head, n_head, N, n_batch) | grad_t09L0 = ggml_rope_back(grad_t10L0) - t10L0 = ggml_rope_inplace (t09L0) | grad_t10L0 = ggml_permute_back(grad_t14L0, 0, 2, 1, 3) = ggml_permute(grad_t14L0, 0, 2, 1, 3) - t11L0 = ggml_mul_mat (t04L0, L0_wv) | grad_t11L0 = ggml_reshape(grad_t12L0, t11L0_shape) - t12L0 = ggml_reshape_4d (t11L0, N, n_batch, n_embd/n_head, n_head) | grad_t12L0 = ggml_permute_back(grad_t15L0, 0, 3, 1, 2) = ggml_permute(grad_t15L0, 0, 2, 3, 1) - t13L0*= ggml_permute (t07L0, 0, 2, 1, 3) | grad_t13L0 = view__q(ggml_flash_attn_back(t13L0, t14L0, t15L0, grad_t16L0)) - t14L0*= ggml_permute (t10L0, 0, 2, 1, 3) | grad_t14L0 = view__k(ggml_flash_attn_back(t13L0, t14L0, t15L0, grad_t16L0)) - t15L0*= ggml_permute (t12L0, 0, 3, 1, 2) | grad_t15L0 = view__v(ggml_flash_attn_back(t13L0, t14L0, t15L0, grad_t16L0)) - t16L0 = ggml_flash_attn (t13L0, t14L0, t15L0) | grad_t16L0 = ggml_permute_back(grad_t17L0, 0, 2, 1, 3) = ggml_permute(grad_t17L0, 0, 2, 1, 3) - t17L0 = ggml_permute (t16L0, 0, 2, 1, 3) | grad_t17L0 = grad_t18L0 - t18L0 = ggml_cont (t17L0) | grad_t18L0 = ggml_reshape(grad_t19L0, t18L0_shape) - t19L0*= ggml_reshape_2d (t18L0, n_embd, N*n_batch) | grad_t19L0 = ggml_out_prod(L0_wo, ggml_transpose(grad_t20L0)) - t20L0 = ggml_mul_mat (L0_wo, t19L0) | grad_t20L0 = grad_t21L0 - t21L0*= ggml_add (t20L0, t01) | grad_t21L0 = grad_t30L0 + ggml_rms_norm_back(t21L0, grad_t22L0) - t22L0*= ggml_rms_norm (t21L0) | grad_t22L0 = ggml_mul(grad_t24L0, t23L0) - t23L0 = ggml_repeat (L0_ffn_norm, t22L0_shape) | grad_t23L0 = ggml_mul(grad_t24L0, t22L0) - t24L0*= ggml_mul (t23L0, t22L0) | grad_t24L0 = ggml_out_prod(L0_w1, ggml_transpose(grad_t26L0)) + ggml_out_prod(L0_w3, ggml_transpose(grad_t25L0)) - t25L0*= ggml_mul_mat (L0_w3, t24L0) | grad_t25L0 = ggml_mul(grad_t28L0, t27L0) - t26L0*= ggml_mul_mat (L0_w1, t24L0) | grad_t26L0 = ggml_silu_back(t26L0, grad_t27L0) - t27L0*= ggml_silu (t26L0) | grad_t27L0 = ggml_mul(grad_t28L0, t25L0) - t28L0*= ggml_mul (t27L0, t25L0) | grad_t28L0 = ggml_out_prod(L0_w2, ggml_transpose(grad_t29L0)) - t29L0 = ggml_mul_mat (L0_w2, t28L0) | grad_t29L0 = grad_t30L0 - t30L0*= ggml_add (t21L0, t29L0) | grad_t30L0 = ggml_rms_norm_back(t30L0, grad_t02L1) + grad_t21L1 - ^ - t02L1*= ggml_rms_norm (t30L0) | grad_t02L1 = ggml_mul(grad_t04L1, t03L1) - t03L1 = ggml_repeat (L1_att_norm, t02L1_shape) | grad_t03L1 = ggml_mul(grad_t04L1, t02L1) - t04L1*= ggml_mul (t02L1, t03L1) | grad_t04L1 = ggml_out_prod(L1_wv, grad_t11L1) + ggml_out_prod(L1_wk, ggml_transpose(grad_t08L1)) + ggml_out_prod(L1_wq, ggml_transpose(grad_t05L1)) - t05L1 = ggml_mul_mat (L1_wq, t04L1) | grad_t05L1 = ggml_reshape(grad_t06L1, t05L1_shape) - t06L1 = ggml_reshape_4d (t05L1, n_embd/n_head, n_head, N, n_batch) | grad_t06L1 = ggml_rope_back(grad_t07L1) - t07L1 = ggml_rope_inplace (t06L1) | grad_t07L1 = ggml_permute_back(grad_t13L1, 0, 2, 1, 3) = ggml_permute(grad_t13L1, 0, 2, 1, 3) - t08L1 = ggml_mul_mat (L1_wk, t04L1) | grad_t08L1 = ggml_reshape(grad_t09L1, t08L1_shape) - t09L1 = ggml_reshape_4d (t08L1, n_embd/n_head, n_head, N, n_batch) | grad_t09L1 = ggml_rope_back(grad_t10L1) - t10L1 = ggml_rope_inplace (t09L1) | grad_t10L1 = ggml_permute_back(grad_t14L1, 0, 2, 1, 3) = ggml_permute(grad_t14L1, 0, 2, 1, 3) - t11L1 = ggml_mul_mat (t04L1, L1_wv) | grad_t11L1 = ggml_reshape(grad_t12L1, t11L1_shape) - t12L1 = ggml_reshape_4d (t11L1, N, n_batch, n_embd/n_head, n_head) | grad_t12L1 = ggml_permute_back(grad_t15L1, 0, 3, 1, 2) = ggml_permute(grad_t15L1, 0, 2, 3, 1) - t13L1*= ggml_permute (t07L1, 0, 2, 1, 3) | grad_t13L1 = view__q(ggml_flash_attn_back(t13L1, t14L1, t15L1, grad_t16L1)) - t14L1*= ggml_permute (t10L1, 0, 2, 1, 3) | grad_t14L1 = view__k(ggml_flash_attn_back(t13L1, t14L1, t15L1, grad_t16L1)) - t15L1*= ggml_permute (t12L1, 0, 3, 1, 2) | grad_t15L1 = view__v(ggml_flash_attn_back(t13L1, t14L1, t15L1, grad_t16L1)) - t16L1 = ggml_flash_attn (t13L1, t14L1, t15L1) | grad_t16L1 = ggml_permute_back(grad_t17L1, 0, 2, 1, 3) = ggml_permute(grad_t17L1, 0, 2, 1, 3) - t17L1 = ggml_permute (t16L1, 0, 2, 1, 3) | grad_t17L1 = grad_t18L1 - t18L1 = ggml_cont (t17L1) | grad_t18L1 = ggml_reshape(grad_t19L1, t18L1_shape) - t19L1*= ggml_reshape_2d (t18L1, n_embd, N*n_batch) | grad_t19L1 = ggml_out_prod(L1_wo, ggml_transpose(grad_t20L1)) - t20L1 = ggml_mul_mat (L1_wo, t19L1) | grad_t20L1 = grad_t21L1 - t21L1*= ggml_add (t20L1, t30L0) | grad_t21L1 = grad_t30L1 + ggml_rms_norm_back(t21L1, grad_t22L1) - t22L1*= ggml_rms_norm (t21L1) | grad_t22L1 = ggml_mul(grad_t24L1, t23L1) - t23L1 = ggml_repeat (L1_ffn_norm, t22L1_shape) | grad_t23L1 = ggml_mul(grad_t24L1, t22L1) - t24L1*= ggml_mul (t23L1, t22L1) | grad_t24L1 = ggml_out_prod(L1_w1, ggml_transpose(grad_t26L1)) + ggml_out_prod(L1_w3, ggml_transpose(grad_t25L1)) - t25L1*= ggml_mul_mat (L1_w3, t24L1) | grad_t25L1 = ggml_mul(grad_t28L1, t27L1) - t26L1*= ggml_mul_mat (L1_w1, t24L1) | grad_t26L1 = ggml_silu_back(t26L1, grad_t27L1) - t27L1*= ggml_silu (t26L1) | grad_t27L1 = ggml_mul(grad_t28L1, t25L1) - t28L1*= ggml_mul (t27L1, t25L1) | grad_t28L1 = ggml_out_prod(L1_w2, ggml_transpose(grad_t29L1)) - t29L1 = ggml_mul_mat (L1_w2, t28L1) | grad_t29L1 = grad_t30L1 - t30L1*= ggml_add (t21L1, t29L1) | grad_t30L1 = ggml_rms_norm_back(t30L1, grad_t31) - ^ - t31 = ggml_rms_norm (t30L1) | grad_t31 = ggml_mul(grad_t33, t32) - t32 = ggml_repeat (norm, t31.shape) | grad_t32 = ggml_mul(grad_t33, t31) - t33 = ggml_mul (t32, t31) | grad_t33 = ggml_out_prod(output, ggml_transpose(grad_t34)) - t34 = ggml_mul_mat (output, t33) | grad_t34 = ggml_reshape(grad_t35, t34.shape) - t35 = ggml_reshape_3d (t34, n_vocab, N, n_batch) | grad_t35 = ggml_cross_entropy_loss_back(t35, targets, grad_t36) - t36 = ggml_cross_entropy_loss(t35, targets) | grad_t36 = 1 (optimizer) - tensors marked with * need to be stored until grad computation - tensors during grad computation are all temporary - */ - } - - *gb = *gf; - - // t36->grad gets set to one by optimizer, so we need the tensor. - // initialize it with 1.0f to make sure. - use_buf(-1); - t36->grad = expand(gb, ggml_new_f32(ctx0, 1.0f)); - - use_buf(0); - t35->grad = expand(gb, ggml_cross_entropy_loss_back(ctx0, t35, targets, t36->grad)); assert_shape_3d(t35->grad, n_vocab, N, n_batch); - t34->grad = expand(gb, ggml_reshape_2d (ctx0, t35->grad, n_vocab, N*n_batch)); assert_shape_2d(t34->grad, n_vocab, N*n_batch); - t33->grad = expand(gb, ggml_out_prod (ctx0, model->output, ggml_transpose(ctx0, t34->grad))); assert_shape_2d(t33->grad, n_embd, N*n_batch); - t32->grad = expand(gb, ggml_mul (ctx0, t33->grad, t31)); assert_shape_2d(t32->grad, n_embd, N*n_batch); - - use_buf(-1); - - model->norm->grad = expand(gb, add_or_set(model->norm->grad, ggml_repeat_back(ctx0, t32->grad, model->norm))); assert_shape_1d(model->norm->grad, n_embd); - model->output->grad = expand(gb, add_or_set(model->output->grad, ggml_out_prod(ctx0, t33, t34->grad))); assert_shape_2d(model->output->grad, n_embd, n_vocab); - - clr_buf(1); - use_buf(1); - t31->grad = expand(gb, ggml_mul(ctx0, t33->grad, t32)); assert_shape_2d(t31->grad, n_embd, N*n_batch); - - struct ggml_tensor * back_layer_inp = t31; - struct ggml_tensor * grad_layer_inp = NULL; - - for (int k = 0; k < n_layer; ++k) { - int il = n_layer-1-k; - struct my_llama_layer & layer = model->layers[il]; - - struct ggml_tensor * t02 = t02L[il]; - struct ggml_tensor * t03 = t03L[il]; - struct ggml_tensor * t04 = t04L[il]; - struct ggml_tensor * t05 = t05L[il]; - struct ggml_tensor * t06 = t06L[il]; - struct ggml_tensor * t07 = t07L[il]; - struct ggml_tensor * t08 = t08L[il]; - struct ggml_tensor * t09 = t09L[il]; - struct ggml_tensor * t10 = t10L[il]; - struct ggml_tensor * t11 = t11L[il]; - struct ggml_tensor * t12 = t12L[il]; - struct ggml_tensor * t13 = t13L[il]; - struct ggml_tensor * t14 = t14L[il]; - struct ggml_tensor * t15 = t15L[il]; - struct ggml_tensor * t16 = t16L[il]; - struct ggml_tensor * t17 = t17L[il]; - struct ggml_tensor * t18 = t18L[il]; - struct ggml_tensor * t19 = t19L[il]; - struct ggml_tensor * t20 = t20L[il]; - struct ggml_tensor * t21 = t21L[il]; - struct ggml_tensor * t22 = t22L[il]; - struct ggml_tensor * t23 = t23L[il]; - struct ggml_tensor * t24 = t24L[il]; - struct ggml_tensor * t25 = t25L[il]; - struct ggml_tensor * t26 = t26L[il]; - struct ggml_tensor * t27 = t27L[il]; - struct ggml_tensor * t28 = t28L[il]; - struct ggml_tensor * t29 = t29L[il]; - struct ggml_tensor * t30 = t30L[il]; - - clr_buf(0); - use_buf(0); - t30->grad = expand(gb, ggml_rms_norm_back(ctx0, t30, back_layer_inp->grad)); assert_shape_2d(t30->grad, n_embd, N*n_batch); - if (grad_layer_inp) { - t30->grad = expand(gb, ggml_add(ctx0, t30->grad, grad_layer_inp->grad)); assert_shape_2d(t30->grad, n_embd, N*n_batch); + struct ggml_tensor * t02 = ggml_rms_norm (ctx, cur, f_norm_rms_eps); set_name(t02, "t02"); assert_shape_2d(t02, n_embd, N*n_batch); + struct ggml_tensor * t03 = ggml_repeat (ctx, layer.attention_norm, t02); set_name(t03, "t03"); assert_shape_2d(t03, n_embd, N*n_batch); + struct ggml_tensor * t04 = ggml_mul (ctx, t03, t02); set_name(t04, "t04"); assert_shape_2d(t04, n_embd, N*n_batch); + struct ggml_tensor * t05 = ggml_mul_mat (ctx, layer.wq, t04); set_name(t05, "t05"); assert_shape_2d(t05, n_embd, N*n_batch); + struct ggml_tensor * t06 = ggml_reshape_4d (ctx, t05, n_embd/n_head, n_head, N, n_batch); set_name(t06, "t06"); assert_shape_4d(t06, n_embd/n_head, n_head, N, n_batch); + struct ggml_tensor * t07 = rope (t06); set_name(t07, "t07"); assert_shape_4d(t07, n_embd/n_head, n_head, N, n_batch); + struct ggml_tensor * t08 = ggml_mul_mat (ctx, layer.wk, t04); set_name(t08, "t08"); assert_shape_2d(t08, n_embd, N*n_batch); + struct ggml_tensor * t09 = ggml_reshape_4d (ctx, t08, n_embd/n_head, n_head, N, n_batch); set_name(t09, "t09"); assert_shape_4d(t09, n_embd/n_head, n_head, N, n_batch); + struct ggml_tensor * t10 = rope (t09); set_name(t10, "t10"); assert_shape_4d(t10, n_embd/n_head, n_head, N, n_batch); + struct ggml_tensor * t11 = ggml_mul_mat (ctx, t04, layer.wv); set_name(t11, "t11"); assert_shape_2d(t11, N*n_batch, n_embd); + struct ggml_tensor * t12 = ggml_reshape_4d (ctx, t11, N, n_batch, n_embd/n_head, n_head); set_name(t12, "t12"); assert_shape_4d(t12, N, n_batch, n_embd/n_head, n_head); + struct ggml_tensor * t13 = ggml_permute (ctx, t07, 0, 2, 1, 3); set_name(t13, "t13"); assert_shape_4d(t13, n_embd/n_head, N, n_head, n_batch); + struct ggml_tensor * t14 = ggml_permute (ctx, t10, 0, 2, 1, 3); set_name(t14, "t14"); assert_shape_4d(t14, n_embd/n_head, N, n_head, n_batch); + struct ggml_tensor * t15 = ggml_permute (ctx, t12, 0, 3, 1, 2); set_name(t15, "t15"); assert_shape_4d(t15, N, n_embd/n_head, n_head, n_batch); + struct ggml_tensor * t16; + if (enable_flash_attn) { + t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, "t16"); assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch); + } else { + struct ggml_tensor * t16_0 = ggml_mul_mat (ctx, t14, t13); set_name(t16_0, "t16_0"); assert_shape_4d(t16_0, N, N, n_head, n_batch); + struct ggml_tensor * t16_1 = ggml_scale_inplace (ctx, t16_0, kv_scale); set_name(t16_1, "t16_1"); assert_shape_4d(t16_1, N, N, n_head, n_batch); + struct ggml_tensor * t16_2 = ggml_diag_mask_inf_inplace(ctx, t16_1, n_past); set_name(t16_2, "t16_2"); assert_shape_4d(t16_2, N, N, n_head, n_batch); + struct ggml_tensor * t16_3 = ggml_soft_max_inplace (ctx, t16_2); set_name(t16_3, "t16_3"); assert_shape_4d(t16_3, N, N, n_head, n_batch); + t16 = ggml_mul_mat(ctx, t15, t16_3); set_name(t16, "t16"); assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch); } - clr_buf(1); - t29->grad = t30->grad; assert_shape_2d(t29->grad, n_embd, N*n_batch); - t28->grad = expand(gb, ggml_out_prod(ctx0, layer.w2, ggml_transpose(ctx0, t29->grad))); assert_shape_2d(t28->grad, n_ff, N*n_batch); - t27->grad = expand(gb, ggml_mul(ctx0, t28->grad, t25)); assert_shape_2d(t27->grad, n_ff, N*n_batch); - t26->grad = expand(gb, ggml_silu_back(ctx0, t26, t27->grad)); assert_shape_2d(t26->grad, n_ff, N*n_batch); - t25->grad = expand(gb, ggml_mul(ctx0, t28->grad, t27)); assert_shape_2d(t25->grad, n_ff, N*n_batch); - t24->grad = expand(gb, ggml_add_inplace(ctx0, - ggml_out_prod(ctx0, layer.w1, ggml_transpose(ctx0, t26->grad)), - ggml_out_prod(ctx0, layer.w3, ggml_transpose(ctx0, t25->grad)))); assert_shape_2d(t24->grad, n_embd, N*n_batch); - t23->grad = expand(gb, ggml_mul(ctx0, t24->grad, t22)); assert_shape_2d(t23->grad, n_embd, N*n_batch); - t22->grad = expand(gb, ggml_mul(ctx0, t24->grad, ggml_repeat(ctx0, layer.ffn_norm, t24->grad))); assert_shape_2d(t22->grad, n_embd, N*n_batch); - use_buf(1); - t21->grad = expand(gb, ggml_add(ctx0, t30->grad, ggml_rms_norm_back(ctx0, t21, t22->grad))); assert_shape_2d(t21->grad, n_embd, N*n_batch); - grad_layer_inp = t21; - use_buf(0); - t20->grad = t21->grad; assert_shape_2d(t20->grad, n_embd, N*n_batch); - t19->grad = expand(gb, ggml_out_prod(ctx0, layer.wo, ggml_transpose(ctx0, t20->grad))); assert_shape_2d(t19->grad, n_embd, N*n_batch); - t18->grad = expand(gb, ggml_reshape_4d(ctx0, t19->grad, n_embd/n_head, n_head, N, n_batch)); assert_shape_4d(t18->grad, n_embd/n_head, n_head, N, n_batch); - t17->grad = t18->grad; assert_shape_4d(t17->grad, n_embd/n_head, n_head, N, n_batch); - t16->grad = expand(gb, ggml_permute(ctx0, t17->grad, 0, 2, 1, 3)); assert_shape_4d(t16->grad, n_embd/n_head, N, n_head, n_batch); - struct ggml_tensor * flash_attn = expand(gb, ggml_flash_attn_back(ctx0, t13, t14, t15, t16->grad, true)); assert_shape_4d(flash_attn, n_embd/n_head, N*3, n_head, n_batch); - t15->grad = expand(gb, view__v(flash_attn)); assert_shape_4d(t15->grad, N, n_embd/n_head, n_head, n_batch); - t14->grad = expand(gb, view__k(flash_attn)); assert_shape_4d(t14->grad, n_embd/n_head, N, n_head, n_batch); - t13->grad = expand(gb, view__q(flash_attn)); assert_shape_4d(t13->grad, n_embd/n_head, N, n_head, n_batch); - t12->grad = expand(gb, ggml_permute(ctx0, t15->grad, 0, 2, 3, 1)); assert_shape_4d(t12->grad, N, n_batch, n_embd/n_head, n_head); - t11->grad = expand(gb, ggml_reshape_2d(ctx0, ggml_cont(ctx0, t12->grad), N*n_batch, n_embd)); assert_shape_2d(t11->grad, N*n_batch, n_embd); - t10->grad = expand(gb, ggml_permute(ctx0, t14->grad, 0, 2, 1, 3)); assert_shape_4d(t10->grad, n_embd/n_head, n_head, N, n_batch); - t09->grad = expand(gb, ggml_rope_back(ctx0, t10->grad, n_past, n_rot, rope_mode, n_ctx, 10000.0f, 1.0f, 0.0f, false)); assert_shape_4d(t09->grad, n_embd/n_head, n_head, N, n_batch); - t08->grad = expand(gb, ggml_reshape_2d(ctx0, t09->grad, n_embd, N*n_batch)); assert_shape_2d(t08->grad, n_embd, N*n_batch); - t07->grad = expand(gb, ggml_permute(ctx0, t13->grad, 0, 2, 1, 3)); assert_shape_4d(t07->grad, n_embd/n_head, n_head, N, n_batch); - t06->grad = expand(gb, ggml_rope_back(ctx0, t07->grad, n_past, n_rot, rope_mode, n_ctx, 10000.0f, 1.0f, 0.0f, false)); assert_shape_4d(t06->grad, n_embd/n_head, n_head, N, n_batch); - t05->grad = expand(gb, ggml_reshape_2d(ctx0, t06->grad, n_embd, N*n_batch)); assert_shape_2d(t05->grad, n_embd, N*n_batch); - t04->grad = expand(gb, ggml_add_inplace(ctx0, - ggml_add_inplace(ctx0, - ggml_out_prod(ctx0, layer.wv, t11->grad), - ggml_out_prod(ctx0, layer.wk, ggml_transpose(ctx0, t08->grad))), - ggml_out_prod(ctx0, layer.wq, ggml_transpose(ctx0, t05->grad)))); assert_shape_2d(t04->grad, n_embd, N*n_batch); - t03->grad = expand(gb, ggml_mul(ctx0, t04->grad, t02)); assert_shape_2d(t04->grad, n_embd, N*n_batch); - use_buf(1); - t02->grad = expand(gb, ggml_mul(ctx0, t04->grad, ggml_repeat(ctx0, layer.attention_norm, t02))); assert_shape_2d(t02->grad, n_embd, N*n_batch); - back_layer_inp = t02; - // use_buf(0); - - use_buf(-1); - layer.attention_norm->grad = expand(gb, add_or_set(layer.attention_norm->grad, ggml_repeat_back(ctx0, t03->grad, layer.attention_norm))); assert_shape_1d(layer.attention_norm->grad, n_embd); - layer.wq->grad = expand(gb, add_or_set(layer.wq->grad, ggml_out_prod(ctx0, t04, t05->grad))); assert_shape_2d(layer.wq->grad, n_embd, n_embd); - layer.wk->grad = expand(gb, add_or_set(layer.wk->grad, ggml_out_prod(ctx0, t04, t08->grad))); assert_shape_2d(layer.wk->grad, n_embd, n_embd); - layer.wv->grad = expand(gb, add_or_set(layer.wv->grad, ggml_out_prod(ctx0, t04, ggml_transpose(ctx0, t11->grad)))); assert_shape_2d(layer.wv->grad, n_embd, n_embd); - layer.wo->grad = expand(gb, add_or_set(layer.wo->grad, ggml_out_prod(ctx0, t19, t20->grad))); assert_shape_2d(layer.wo->grad, n_embd, n_embd); - layer.ffn_norm->grad = expand(gb, add_or_set(layer.ffn_norm->grad, ggml_repeat_back(ctx0, t23->grad, layer.ffn_norm))); assert_shape_1d(layer.ffn_norm->grad, n_embd); - layer.w1->grad = expand(gb, add_or_set(layer.w1->grad, ggml_out_prod(ctx0, t24, t26->grad))); assert_shape_2d(layer.w1->grad, n_embd, n_ff); - layer.w2->grad = expand(gb, add_or_set(layer.w2->grad, ggml_out_prod(ctx0, t28, t29->grad))); assert_shape_2d(layer.w2->grad, n_ff, n_embd); - layer.w3->grad = expand(gb, add_or_set(layer.w3->grad, ggml_out_prod(ctx0, t24, t25->grad))); assert_shape_2d(layer.w3->grad, n_embd, n_ff); - // use_buf(0); + struct ggml_tensor * t17 = ggml_permute (ctx, t16, 0, 2, 1, 3); set_name(t17, "t17"); assert_shape_4d(t17, n_embd/n_head, n_head, N, n_batch); + struct ggml_tensor * t18 = ggml_cont (ctx, t17); set_name(t18, "t18"); assert_shape_4d(t18, n_embd/n_head, n_head, N, n_batch); + struct ggml_tensor * t19 = ggml_reshape_2d (ctx, t18, n_embd, N*n_batch); set_name(t19, "t19"); assert_shape_2d(t19, n_embd, N*n_batch); + struct ggml_tensor * t20 = ggml_mul_mat (ctx, layer.wo, t19); set_name(t20, "t20"); assert_shape_2d(t20, n_embd, N*n_batch); + struct ggml_tensor * t21 = ggml_add (ctx, t20, cur); set_name(t21, "t21"); assert_shape_2d(t21, n_embd, N*n_batch); + struct ggml_tensor * t22 = ggml_rms_norm (ctx, t21, f_norm_rms_eps); set_name(t22, "t22"); assert_shape_2d(t22, n_embd, N*n_batch); + struct ggml_tensor * t23 = ggml_repeat (ctx, layer.ffn_norm, t22); set_name(t23, "t23"); assert_shape_2d(t23, n_embd, N*n_batch); + struct ggml_tensor * t24 = ggml_mul (ctx, t23, t22); set_name(t24, "t24"); assert_shape_2d(t24, n_embd, N*n_batch); + struct ggml_tensor * t25 = ggml_mul_mat (ctx, layer.w3, t24); set_name(t25, "t25"); assert_shape_2d(t25, n_ff, N*n_batch); + struct ggml_tensor * t26 = ggml_mul_mat (ctx, layer.w1, t24); set_name(t26, "t26"); assert_shape_2d(t26, n_ff, N*n_batch); + struct ggml_tensor * t27 = ggml_silu (ctx, t26); set_name(t27, "t27"); assert_shape_2d(t27, n_ff, N*n_batch); + struct ggml_tensor * t28 = ggml_mul (ctx, t27, t25); set_name(t28, "t28"); assert_shape_2d(t28, n_ff, N*n_batch); + struct ggml_tensor * t29 = ggml_mul_mat (ctx, layer.w2, t28); set_name(t29, "t29"); assert_shape_2d(t29, n_embd, N*n_batch); + struct ggml_tensor * t30 = ggml_add (ctx, t29, t21); set_name(t30, "t30"); assert_shape_2d(t30, n_embd, N*n_batch); + cur = t30; + checkpoints.push_back(cur); + } + struct ggml_tensor * t31 = ggml_rms_norm (ctx, cur, f_norm_rms_eps); set_name(t31, "t31"); assert_shape_2d(t31, n_embd, N*n_batch); + struct ggml_tensor * t32 = ggml_repeat (ctx, model->norm, t31); set_name(t32, "t32"); assert_shape_2d(t32, n_embd, N*n_batch); + struct ggml_tensor * t33 = ggml_mul (ctx, t32, t31); set_name(t33, "t33"); assert_shape_2d(t33, n_embd, N*n_batch); + struct ggml_tensor * t34 = ggml_mul_mat (ctx, model->output, t33); set_name(t34, "t34"); assert_shape_2d(t34, n_vocab, N*n_batch); + struct ggml_tensor * t35 = ggml_reshape_3d (ctx, t34, n_vocab, N, n_batch); set_name(t35, "t35"); assert_shape_3d(t35, n_vocab, N, n_batch); + struct ggml_tensor * t36 = ggml_cross_entropy_loss(ctx, t35, targets); set_name(t36, "t36"); assert_shape_1d(t36, 1); + + checkpoints.push_back(t31); + checkpoints.push_back(t32); + checkpoints.push_back(t33); + checkpoints.push_back(t34); + checkpoints.push_back(t35); + checkpoints.push_back(t36); + + ggml_build_forward_expand(gf, t36); + + if (enable_checkpointing) { + ggml_build_backward_gradient_checkpointing(ctx, gf, gb, gb_tmp, checkpoints.data(), (int) checkpoints.size()); + } else { + *gb = *gf; + ggml_build_backward_expand(ctx, gf, gb, true); + } + + if (alloc) { + // make sure some tensors are not reallocated by inserting new temporary nodes depending on them + int n_leafs_before = gb->n_leafs; + int n_nodes_before = gb->n_nodes; + struct ggml_tensor * one = ggml_new_f32(ctx, 1.0f); + // output tensors + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t35, one)); + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36, one)); + // input gradient + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, t36->grad, one)); + GGML_ASSERT(t36->grad->data == NULL && !ggml_is_view(t36->grad)); + ggml_allocr_alloc(alloc, t36->grad); + // gradient tensors (will be set to zero by ggml_graph_reset) + // pinning these produces large unnecessary memory overhead, which will be resolved by PR 2632 + for (int i = 0; i < gf->n_nodes; ++i) { + if (!gf->grads[i]) continue; + if (gf->grads[i]->data == NULL && !ggml_is_view(gf->grads[i])) { + ggml_allocr_alloc(alloc, gf->grads[i]); + } + ggml_build_forward_expand(gb, ggml_scale_inplace(ctx, gf->grads[i], one)); + } + // allocating checkpoints in one block to reduce memory fragmentation + // note: they will be freed in reverse order + for (int i = 0; i < (int) checkpoints.size(); ++i) { + if (checkpoints[i]->data == NULL && !ggml_is_view(checkpoints[i])) { + ggml_allocr_alloc(alloc, checkpoints[i]); + } + } + + //int n_leafs_after = gb->n_leafs; + //int n_nodes_after = gb->n_nodes; + + ggml_allocr_alloc_graph(alloc, gb); + + // remove the additional nodes and leafs + for (int i = n_leafs_before; i < gb->n_leafs; ++i) { + gb->leafs[i] = NULL; + } + for (int i = n_nodes_before; i < gb->n_nodes; ++i) { + gb->nodes[i] = NULL; + } + gb->n_leafs = n_leafs_before; + gb->n_nodes = n_nodes_before; } - clr_buf(0); - use_buf(0); - t01->grad = expand(gb, ggml_add_inplace(ctx0, grad_layer_inp->grad, ggml_rms_norm_back(ctx0, t01, back_layer_inp->grad))); assert_shape_2d(t01->grad, n_embd, N*n_batch); - use_buf(-1); - model->tok_embeddings->grad = expand(gb, ggml_get_rows_back(ctx0, t01->grad, t00, model->tok_embeddings)); assert_shape_2d(model->tok_embeddings->grad, n_embd, n_vocab); - // clr_buf(1); - // clr_buf(0); *logits = t35; - - if (track_max_mem) { - printf("%s: max size compute buf0: %zu\n", __func__, buf_maxs[0]); - printf("%s: max size compute buf1: %zu\n", __func__, buf_maxs[1]); - } - - // now that all grads are created, set the graph leafs and grads - graph_set_leafs_grads(gf); - graph_set_leafs_grads(gb); - return t36; } @@ -1962,42 +874,6 @@ void print_matrix(struct ggml_tensor * probs) { } } - -void print_token(struct llama_context * ctx, llama_token token) { - printf("%s", llama_token_to_piece(ctx, token).c_str()); -} - -void print_tokens(struct llama_context* ctx, struct ggml_tensor * tokens) { - for (int i=0; ine[0]; ++i) { - int token = ggml_get_i32_1d(tokens, i); - print_token(ctx, token); - } -} - -void print_tokens_batch(struct llama_context* ctx, struct ggml_tensor * tokens) { - for (int i1=0; i1ne[1]; ++i1) { - //int num_newline = 0; - for (int i0=0; i0ne[0]; ++i0) { - int token = get_i32_2d(tokens, i0, i1); - print_token(ctx, token); - // bool isnl = (token == llama_token_nl()); - // if (isnl) { - // ++num_newline; - // } - // if (isnl) { - // if (num_newline < 2) { - // print_token(ctx, token); - // } else { - // printf("\\n"); - // } - // } else { - // print_token(ctx, token); - // } - } - printf("\n--\n"); - } -} - void get_example_targets(struct llama_context * lctx, const int * train_samples, size_t n_train_samples, const llama_token * train_data, size_t n_train_data, int example_id, struct ggml_tensor * tokens_input, struct ggml_tensor * target_logits, struct ggml_tensor * target_probs) { int n_tokens = tokens_input->ne[0]; int n_vocab = target_logits->ne[0]; @@ -2033,51 +909,27 @@ void get_example_targets_batch(struct llama_context * lctx, const int * train_sa ggml_set_f32(target_logits, -1.0f/n_vocab); ggml_set_f32(target_probs, 0.0f); + // printf("%s: example_id=%d n_batch=%d n_train_samples=%zu\n", __func__, example_id, n_batch, n_train_samples); for (int k=0; kne[0]; - int n_vocab = target_logits->ne[0]; - for (int i=0; i chars(len); - read_raw(chars.data(), len); - return std::string(chars.data(), len); - } - - void write_raw(const void * ptr, size_t size) { - if (size == 0) { - return; - } - errno = 0; - size_t ret = std::fwrite(ptr, size, 1, fp); - if (ret != 1) { - throw std::runtime_error(format("write error: %s", strerror(errno))); - } - } - - void write_u32(std::uint32_t val) { - write_raw(&val, sizeof(val)); - } - - ~llama_file() { - if (fp) { - std::fclose(fp); - } - } -}; - int tokenize_file(struct llama_context * lctx, const char * filename, std::vector& out) { - struct llama_file f(filename, "rb"); + FILE * fp = std::fopen(filename, "rb"); + if (fp == NULL) { + return 0; + } + +#ifdef _WIN32 + GGML_ASSERT(_fseeki64(fp, (__int64) 0, SEEK_END) == 0); +#else + GGML_ASSERT(std::fseek(fp, (long) 0, SEEK_END) == 0); +#endif + + size_t size = 0; +#ifdef _WIN32 + __int64 ret = _ftelli64(fp); + size = ret; +#else + long ret = std::ftell(fp); + size = ret; +#endif + +#ifdef _WIN32 + GGML_ASSERT(_fseeki64(fp, (__int64) 0, SEEK_SET) == 0); +#else + GGML_ASSERT(std::fseek(fp, (long) 0, SEEK_SET) == 0); +#endif std::vector buf; - buf.resize(f.size+1); + buf.resize(size+1); + out.resize(size+1); - f.read_raw(buf.data(), f.size); - buf[f.size] = '\0'; + if (std::fread(buf.data(), size, 1, fp) != 1) { + throw std::runtime_error(std::string("unexpectedly reached end of file")); + } + if (ferror(fp)) { + throw std::runtime_error(format("read error: %s", strerror(errno))); + } + + buf[size] = '\0'; int n_tokens = llama_tokenize(lctx, buf.data(), out.data(), out.size(), false); if (n_tokens < 0) { out.resize(-n_tokens); - llama_tokenize(lctx, buf.data(), out.data(), out.size(), false); + n_tokens = llama_tokenize(lctx, buf.data(), out.data(), out.size(), false); } + GGML_ASSERT(n_tokens >= 0); + out.resize(n_tokens); bool verify = false; if (verify) { @@ -2238,438 +1040,466 @@ void shuffle_ints(int * begin, int * end) { }); } -struct my_llama_sampler_params { - float temp = 0.0f; // <= 0.0 disabled - int top_k = 20; // <= 0 to use vocab size - float top_p = 0.95f; // 1.0 = disabled - float tfs_z = 1.00f; // 1.0 = disabled - float typical_p = 1.00f; // 1.0 = disabled - int repeat_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size) - float repeat_penalty = 1.0f; // 1.0 = disabled - float alpha_presence = 0.0f; // 0.0 = disabled - float alpha_frequency = 0.0f; // 0.0 = disabled - int mirostat = 0; // 0 = disabled, 1 = mirostat, 2 = mirostat 2.0 - float mirostat_tau = 5.00f; // target entropy - float mirostat_eta = 0.10f; // learning rate - bool penalize_nl = true; // consider newlines as a repeatable token -}; - -struct my_llama_sampler { - struct llama_context * ctx = NULL; - my_llama_sampler_params params; - - int n_vocab = 0; - int n_ctx = 0; - - float mirostat_mu; - - std::vector candidates; - llama_token_data_array candidates_p; - -}; - -void init_sampler(struct my_llama_sampler * sampler, struct llama_context * ctx) { - sampler->ctx = ctx; - sampler->n_vocab = llama_n_vocab(sampler->ctx); - sampler->n_ctx = llama_n_ctx(sampler->ctx); - sampler->mirostat_mu = 2.0f * sampler->params.mirostat_tau; +#define GGUF_GET_KEY(ctx, dst, func, type, req, key) \ +{ \ + const std::string skey(key); \ + const int kid = gguf_find_key(ctx, skey.c_str()); \ + if (kid >= 0) { \ + enum gguf_type ktype = gguf_get_kv_type(ctx, kid); \ + if (ktype != (type)) { \ + throw std::runtime_error(format("key %s has wrong type: %s", skey.c_str(), gguf_type_name(ktype))); \ + } \ + (dst) = func(ctx, kid); \ + } else if (req) { \ + throw std::runtime_error(format("key not found in model: %s", skey.c_str())); \ + } \ } -llama_token sample(struct my_llama_sampler * sampler, float * logits, const llama_token * last_tokens, int n_last_tokens) { - GGML_ASSERT(sampler->ctx != NULL); - struct llama_context * ctx = sampler->ctx; +bool are_same_layout(struct ggml_tensor * a, struct ggml_tensor * b) { + GGML_ASSERT(a != NULL); + GGML_ASSERT(b != NULL); + GGML_ASSERT(a->type == b->type); + GGML_ASSERT(ggml_are_same_shape(a, b)); + GGML_ASSERT(ggml_is_contiguous(a) && ggml_is_contiguous(b)); - sampler->candidates.resize(sampler->n_vocab); - for (llama_token token_id = 0; token_id < sampler->n_vocab; ++token_id) { - sampler->candidates[token_id].id = token_id; - sampler->candidates[token_id].logit = logits[token_id]; - sampler->candidates[token_id].p = 0.0; + return true; +} + +void read_tensor_by_name(struct ggml_tensor * dst, struct ggml_context * ctx, const char * name) { + if (dst == NULL) { + return; } + struct ggml_tensor * t = ggml_get_tensor(ctx, name); + GGML_ASSERT(are_same_layout(dst, t)); + memcpy(dst->data, t->data, ggml_nbytes(t)); - llama_token_data_array * candidates_p = & sampler->candidates_p; - - candidates_p->data = sampler->candidates.data(); - candidates_p->size = sampler->candidates.size(); - candidates_p->sorted = false; - - const auto params = sampler->params; - - // Apply penalties - const float nl_logit = logits[llama_token_nl(ctx)]; - - const int n_last = std::min(std::min(n_last_tokens, params.repeat_last_n), sampler->n_ctx); - - llama_sample_repetition_penalty( - ctx, - candidates_p, - last_tokens + n_last_tokens - n_last, - n_last, - params.repeat_penalty); - llama_sample_frequency_and_presence_penalties( - ctx, - candidates_p, - last_tokens + n_last_tokens - n_last, - n_last, - params.alpha_frequency, - params.alpha_presence); - - if (!params.penalize_nl) { - logits[llama_token_nl(ctx)] = nl_logit; + if (strlen(ggml_get_name(dst)) == 0) { + ggml_set_name(dst, name); } +} - llama_token token = 0; - if (params.temp <= 0) { - // Greedy sampling - token = llama_sample_token_greedy(ctx, candidates_p); +void load_opt_context_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct ggml_opt_context * opt) { + // NOTE: gguf_context must be initialized with f_ggml_ctx and no_alloc=false, otherwise tensor data can not be read + + uint32_t file_version; + GGUF_GET_KEY(fctx, file_version, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_FILE_VERSION); + GGML_ASSERT(file_version == 0); + + GGUF_GET_KEY(fctx, opt->params.past, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT); + GGUF_GET_KEY(fctx, opt->iter, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_ITERATION_COUNT); + GGUF_GET_KEY(fctx, opt->just_initialized, gguf_get_val_bool, GGUF_TYPE_BOOL, true, LLM_KV_OPTIMIZER_JUST_INITIALIZED); + + uint64_t nx; + GGUF_GET_KEY(fctx, nx, gguf_get_val_u64, GGUF_TYPE_UINT64, true, LLM_KV_OPTIMIZER_PARAMETER_COUNT); + opt->nx = (size_t) nx; + + // don't call ggml_opt_init until optimizer type and optimizer specific parameters are know + + std::string opt_type; + GGUF_GET_KEY(fctx, opt_type, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_OPTIMIZER_TYPE); + if (opt_type == LLM_KV_OPTIMIZER_TYPE_ADAM) { + opt->params.type = GGML_OPT_ADAM; + + GGUF_GET_KEY(fctx, opt->adam.fx_best, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_ADAM_BEST_LOSS); + GGUF_GET_KEY(fctx, opt->adam.fx_prev, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS); + GGUF_GET_KEY(fctx, opt->adam.n_no_improvement, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT); + + GGML_ASSERT(opt->ctx != NULL); + ggml_opt_init(opt->ctx, opt, opt->params, opt->nx); + + read_tensor_by_name(opt->adam.m, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS); + read_tensor_by_name(opt->adam.v, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS); + read_tensor_by_name(opt->adam.pf, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES); + } else if (opt_type == LLM_KV_OPTIMIZER_TYPE_LBFGS) { + opt->params.type = GGML_OPT_LBFGS; + + GGUF_GET_KEY(fctx, opt->params.lbfgs.m, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT); + GGUF_GET_KEY(fctx, opt->lbfgs.fx_best, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS); + GGUF_GET_KEY(fctx, opt->lbfgs.step, gguf_get_val_f32, GGUF_TYPE_FLOAT32, true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP); + GGUF_GET_KEY(fctx, opt->lbfgs.j, gguf_get_val_i32, GGUF_TYPE_INT32, true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J); + GGUF_GET_KEY(fctx, opt->lbfgs.k, gguf_get_val_i32, GGUF_TYPE_INT32, true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K); + GGUF_GET_KEY(fctx, opt->lbfgs.end, gguf_get_val_i32, GGUF_TYPE_INT32, true, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END); + GGUF_GET_KEY(fctx, opt->lbfgs.n_no_improvement, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT); + + GGML_ASSERT(opt->ctx != NULL); + ggml_opt_init(opt->ctx, opt, opt->params, opt->nx); + + read_tensor_by_name(opt->lbfgs.x, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS); + read_tensor_by_name(opt->lbfgs.xp, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS); + read_tensor_by_name(opt->lbfgs.g, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS); + read_tensor_by_name(opt->lbfgs.gp, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS); + read_tensor_by_name(opt->lbfgs.d, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION); + read_tensor_by_name(opt->lbfgs.pf, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES); + read_tensor_by_name(opt->lbfgs.lmal, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA); + read_tensor_by_name(opt->lbfgs.lmys, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS); + read_tensor_by_name(opt->lbfgs.lms, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S); + read_tensor_by_name(opt->lbfgs.lmy, f_ggml_ctx, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y); } else { - if (params.mirostat == 1) { - int mirostat_m = 100; - llama_sample_temperature(ctx, candidates_p, params.temp); - token = llama_sample_token_mirostat(ctx, candidates_p, params.mirostat_tau, params.mirostat_eta, mirostat_m, &sampler->mirostat_mu); - } else if (params.mirostat == 2) { - llama_sample_temperature(ctx, candidates_p, params.temp); - token = llama_sample_token_mirostat_v2(ctx, candidates_p, params.mirostat_tau, params.mirostat_eta, &sampler->mirostat_mu); - } else { - // Temperature sampling - llama_sample_top_k (ctx, candidates_p, params.top_k, 1); - llama_sample_tail_free (ctx, candidates_p, params.tfs_z, 1); - llama_sample_typical (ctx, candidates_p, params.typical_p, 1); - - llama_sample_top_p (ctx, candidates_p, params.top_p, 1); - llama_sample_temperature (ctx, candidates_p, params.temp); - token = llama_sample_token(ctx, candidates_p); - } - } - return token; -} - -void set_logits_masked(struct ggml_tensor * logits, std::vector& mask, float value) { - GGML_ASSERT(logits->ne[0] == (int64_t) mask.size()); - for (int i2 = 0; i2 < logits->ne[2]; ++i2) { - for (int i1 = 0; i1 < logits->ne[1]; ++i1) { - for (int i0 = 0; i0 < logits->ne[0]; ++i0) { - if (!mask[i0]) continue; - float * ptr = (float *) ((char *) logits->data + i2*logits->nb[2] + i1*logits->nb[1] + i0*logits->nb[0]); - *ptr = value; - } - } + throw std::runtime_error("unknown optimizer type\n"); } } -void write_tensor(struct llama_file * file, struct ggml_tensor * tensor) { - if (tensor == NULL) { - file->write_u32(0); - file->write_u32(0); - file->write_u32(GGML_TYPE_F32); - file->seek((0-file->tell()) & 31, SEEK_CUR); - return; - } - const char * name = ggml_get_name(tensor); - uint32_t name_len = strlen(name); - uint32_t nd = tensor->n_dims; - uint32_t ne[4] = { (uint32_t)tensor->ne[0], - (uint32_t)tensor->ne[1], - (uint32_t)tensor->ne[2], - (uint32_t)tensor->ne[3] }; - file->write_u32(nd); - file->write_u32(name_len); - file->write_u32(tensor->type); - file->write_raw(ne, sizeof(ne[0]) * nd); - file->write_raw(name, name_len); - file->seek((0-file->tell()) & 31, SEEK_CUR); - file->write_raw(tensor->data, ggml_nbytes(tensor)); -} - -void read_tensor(struct llama_file * file, struct ggml_tensor * tensor) { - int32_t nd = file->read_u32(); - GGML_ASSERT(nd == tensor->n_dims); - - uint32_t name_len = file->read_u32(); - enum ggml_type type = (enum ggml_type) file->read_u32(); - GGML_ASSERT(type == tensor->type); - - uint32_t ne[4]; - file->read_raw(ne, sizeof(ne[0]) * nd); - for (int i=0; ine[i]); - } - - std::string name = file->read_string(name_len); - GGML_ASSERT(strncmp(ggml_get_name(tensor), name.c_str(), sizeof(tensor->name)-1) == 0); - - file->seek((0-file->tell()) & 31, SEEK_CUR); - file->read_raw(tensor->data, ggml_nbytes(tensor)); -} - -void write_opt_context(struct llama_file * file, struct ggml_opt_context * opt) { - const uint32_t version = 0; - GGML_ASSERT(opt->nx >= 0); - GGML_ASSERT(opt->iter >= 0); - file->write_u32(version); - file->write_raw(&opt->params, sizeof(opt->params)); - file->write_raw(&opt->nx, sizeof(opt->nx)); - file->write_raw(&opt->iter, sizeof(opt->iter)); - file->write_u32((uint32_t) opt->just_initialized); - switch (opt->params.type) { - case GGML_OPT_ADAM: - { - GGML_ASSERT(opt->adam.x != NULL); - write_tensor(file, opt->adam.x); - write_tensor(file, opt->adam.g1); - write_tensor(file, opt->adam.g2); - write_tensor(file, opt->adam.m); - write_tensor(file, opt->adam.v); - write_tensor(file, opt->adam.mh); - write_tensor(file, opt->adam.vh); - write_tensor(file, opt->adam.pf); - file->write_raw(&opt->adam.fx_best, sizeof(opt->adam.fx_best)); - file->write_raw(&opt->adam.fx_prev, sizeof(opt->adam.fx_prev)); - file->write_raw(&opt->adam.n_no_improvement, sizeof(opt->adam.n_no_improvement)); - } break; - case GGML_OPT_LBFGS: - { - GGML_ASSERT(opt->adam.x != NULL); - write_tensor(file, opt->lbfgs.x); - write_tensor(file, opt->lbfgs.xp); - write_tensor(file, opt->lbfgs.g); - write_tensor(file, opt->lbfgs.gp); - write_tensor(file, opt->lbfgs.d); - write_tensor(file, opt->lbfgs.pf); - write_tensor(file, opt->lbfgs.lmal); - write_tensor(file, opt->lbfgs.lmys); - write_tensor(file, opt->lbfgs.lms); - write_tensor(file, opt->lbfgs.lmy); - file->write_raw(&opt->lbfgs.fx_best, sizeof(opt->lbfgs.fx_best)); - file->write_raw(&opt->lbfgs.step, sizeof(opt->lbfgs.step)); - file->write_raw(&opt->lbfgs.j, sizeof(opt->lbfgs.j)); - file->write_raw(&opt->lbfgs.k, sizeof(opt->lbfgs.k)); - file->write_raw(&opt->lbfgs.end, sizeof(opt->lbfgs.end)); - file->write_raw(&opt->lbfgs.n_no_improvement, sizeof(opt->lbfgs.n_no_improvement)); - } break; - } -} - -void read_opt_context(struct llama_file * file, struct ggml_context * ctx, struct ggml_opt_context * opt) { - uint32_t version = file->read_u32(); - GGML_ASSERT(version == 0); - - file->read_raw(&opt->params, sizeof(opt->params)); - file->read_raw(&opt->nx, sizeof(opt->nx)); - ggml_opt_init(ctx, opt, opt->params, opt->nx); - - file->read_raw(&opt->iter, sizeof(opt->iter)); - opt->just_initialized = (bool) file->read_u32(); +void save_opt_context_gguf(struct gguf_context * fctx, struct ggml_opt_context * opt) { + gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_FILE_VERSION, 0); + gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_CONVERGENCE_PAST_COUNT, opt->params.past); + gguf_set_val_u64(fctx, LLM_KV_OPTIMIZER_PARAMETER_COUNT, (uint64_t) opt->nx); + gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_ITERATION_COUNT, opt->iter); + gguf_set_val_bool(fctx, LLM_KV_OPTIMIZER_JUST_INITIALIZED, opt->just_initialized); switch (opt->params.type) { case GGML_OPT_ADAM: { - read_tensor(file, opt->adam.x); - read_tensor(file, opt->adam.g1); - read_tensor(file, opt->adam.g2); - read_tensor(file, opt->adam.m); - read_tensor(file, opt->adam.v); - read_tensor(file, opt->adam.mh); - read_tensor(file, opt->adam.vh); - if (opt->adam.pf) { read_tensor(file, opt->adam.pf); } - file->read_raw(&opt->adam.fx_best, sizeof(opt->adam.fx_best)); - file->read_raw(&opt->adam.fx_prev, sizeof(opt->adam.fx_prev)); - file->read_raw(&opt->adam.n_no_improvement, sizeof(opt->adam.n_no_improvement)); + gguf_set_val_str(fctx, LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_ADAM); + gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_ADAM_BEST_LOSS, opt->adam.fx_best); + gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_ADAM_PREVIOUS_LOSS, opt->adam.fx_prev); + gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_ADAM_NO_IMPROVEMENT_COUNT, opt->adam.n_no_improvement); + + ggml_set_name(opt->adam.m, LLM_TENSOR_OPTIMIZER_ADAM_FIRST_MOMENTS); + ggml_set_name(opt->adam.v, LLM_TENSOR_OPTIMIZER_ADAM_SECOND_MOMENTS); + if (opt->adam.pf) { + ggml_set_name(opt->adam.pf, LLM_TENSOR_OPTIMIZER_ADAM_PAST_LOSS_VALUES); + } + + gguf_add_tensor(fctx, opt->adam.m); + gguf_add_tensor(fctx, opt->adam.v); + if (opt->adam.pf) { + gguf_add_tensor(fctx, opt->adam.pf); + } } break; case GGML_OPT_LBFGS: { - GGML_ASSERT(opt->adam.x != NULL); - read_tensor(file, opt->lbfgs.x); - read_tensor(file, opt->lbfgs.xp); - read_tensor(file, opt->lbfgs.g); - read_tensor(file, opt->lbfgs.gp); - read_tensor(file, opt->lbfgs.d); - if (opt->lbfgs.pf) { read_tensor(file, opt->lbfgs.pf); } - read_tensor(file, opt->lbfgs.lmal); - read_tensor(file, opt->lbfgs.lmys); - read_tensor(file, opt->lbfgs.lms); - read_tensor(file, opt->lbfgs.lmy); - file->read_raw(&opt->lbfgs.fx_best, sizeof(opt->lbfgs.fx_best)); - file->read_raw(&opt->lbfgs.step, sizeof(opt->lbfgs.step)); - file->read_raw(&opt->lbfgs.j, sizeof(opt->lbfgs.j)); - file->read_raw(&opt->lbfgs.k, sizeof(opt->lbfgs.k)); - file->read_raw(&opt->lbfgs.end, sizeof(opt->lbfgs.end)); - file->read_raw(&opt->lbfgs.n_no_improvement, sizeof(opt->lbfgs.n_no_improvement)); + gguf_set_val_str(fctx, LLM_KV_OPTIMIZER_TYPE, LLM_KV_OPTIMIZER_TYPE_LBFGS); + gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_LBFGS_APPROX_HESSIAN_COUNT, opt->params.lbfgs.m); + gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_LBFGS_BEST_LOSS, opt->lbfgs.fx_best); + gguf_set_val_f32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_STEP, opt->lbfgs.step); + gguf_set_val_i32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_J, opt->lbfgs.j); + gguf_set_val_i32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_K, opt->lbfgs.k); + gguf_set_val_i32(fctx, LLM_KV_OPTIMIZER_LBFGS_LINE_SEARCH_END, opt->lbfgs.end); + gguf_set_val_u32(fctx, LLM_KV_OPTIMIZER_LBFGS_NO_IMPROVEMENT_COUNT, opt->lbfgs.n_no_improvement); + + ggml_set_name(opt->lbfgs.x, LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_PARAMETERS); + ggml_set_name(opt->lbfgs.xp, LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_PARAMETERS); + ggml_set_name(opt->lbfgs.g, LLM_TENSOR_OPTIMIZER_LBFGS_CURRENT_GRADIENTS); + ggml_set_name(opt->lbfgs.gp, LLM_TENSOR_OPTIMIZER_LBFGS_PREVIOUS_GRADIENTS); + ggml_set_name(opt->lbfgs.d, LLM_TENSOR_OPTIMIZER_LBFGS_SEARCH_DIRECTION); + if (opt->lbfgs.pf) { + ggml_set_name(opt->lbfgs.pf, LLM_TENSOR_OPTIMIZER_LBFGS_PAST_LOSS_VALUES); + } + ggml_set_name(opt->lbfgs.lmal, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_ALPHA); + ggml_set_name(opt->lbfgs.lmys, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_YS); + ggml_set_name(opt->lbfgs.lms, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_S); + ggml_set_name(opt->lbfgs.lmy, LLM_TENSOR_OPTIMIZER_LBFGS_MEMORY_Y); + + gguf_add_tensor(fctx, opt->lbfgs.x); + gguf_add_tensor(fctx, opt->lbfgs.xp); + gguf_add_tensor(fctx, opt->lbfgs.g); + gguf_add_tensor(fctx, opt->lbfgs.gp); + gguf_add_tensor(fctx, opt->lbfgs.d); + if (opt->lbfgs.pf) { + gguf_add_tensor(fctx, opt->lbfgs.pf); + } + gguf_add_tensor(fctx, opt->lbfgs.lmal); + gguf_add_tensor(fctx, opt->lbfgs.lmys); + gguf_add_tensor(fctx, opt->lbfgs.lms); + gguf_add_tensor(fctx, opt->lbfgs.lmy); } break; } } -void save_checkpoint(struct my_llama_model * model, struct ggml_opt_context * opt, const char * filename) { - struct llama_file file(filename, "wb"); - if (file.fp == NULL) { - return; +void load_llama_model_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model) { + // NOTE: gguf_context must be initialized with f_ggml_ctx and no_alloc=false, otherwise tensor data can not be read + std::string arch; + + std::vector keybuf; + keybuf.resize(512); + auto kv = [&arch, &keybuf](const char * key) -> const char * { + snprintf(keybuf.data(), keybuf.size(), key, arch.c_str()); + return keybuf.data(); + }; + + std::vector tn_buf; + tn_buf.resize(GGML_MAX_NAME); + auto tn = [&tn_buf](const char * key) -> const char * { + snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", key); + return tn_buf.data(); + }; + auto tni = [&tn_buf](const char * key, int bid) -> const char * { + snprintf(tn_buf.data(), tn_buf.size(), key, bid); + std::string s = tn_buf.data(); + snprintf(tn_buf.data(), tn_buf.size(), "%s.weight", s.c_str()); + return tn_buf.data(); + }; + + GGUF_GET_KEY(fctx, arch, gguf_get_val_str, GGUF_TYPE_STRING, true, LLM_KV_GENERAL_ARCHITECTURE); + GGML_ASSERT(arch == "llama"); + + uint32_t ftype_u; + GGUF_GET_KEY(fctx, ftype_u, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_GENERAL_FILE_TYPE); + GGML_ASSERT((enum llama_ftype) ftype_u == LLAMA_FTYPE_ALL_F32); + + // n_ctx was not saved in earlier checkpoint file versions, so we make it optional here + GGUF_GET_KEY(fctx, model->hparams.n_ctx, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_CONTEXT_LENGTH)); + + GGUF_GET_KEY(fctx, model->hparams.n_embd, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_EMBEDDING_LENGTH)); + GGUF_GET_KEY(fctx, model->hparams.n_ff, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_FEED_FORWARD_LENGTH)); + GGUF_GET_KEY(fctx, model->hparams.n_head, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_ATTENTION_HEAD_COUNT)); + GGUF_GET_KEY(fctx, model->hparams.n_layer, gguf_get_val_u32, GGUF_TYPE_UINT32, true, kv(LLM_KV_BLOCK_COUNT)); + + model->hparams.n_rot = model->hparams.n_embd / model->hparams.n_head; + GGUF_GET_KEY(fctx, model->hparams.n_rot, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ROPE_DIMENSION_COUNT)); + + float rope_freq_scale = 1.0f; + GGUF_GET_KEY(fctx, model->hparams.f_norm_rms_eps, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS)); + GGUF_GET_KEY(fctx, model->hparams.rope_freq_base, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_FREQ_BASE)); + GGUF_GET_KEY(fctx, rope_freq_scale, gguf_get_val_f32, GGUF_TYPE_FLOAT32, false, kv(LLM_KV_ROPE_SCALE_LINEAR)); + if (rope_freq_scale != 1.0f) { + model->hparams.rope_freq_scale = 1.0f / rope_freq_scale; } - const uint32_t magic = 'ggcp'; - const uint32_t version = 0; + init_model(model); - file.write_u32(magic); - file.write_u32(version); - file.write_u32(model->train_its); - file.write_u32(model->train_samples); - file.write_u32(model->train_tokens); - file.write_u32(model->hparams.n_vocab); - file.write_u32(model->hparams.n_embd); - file.write_u32(model->hparams.n_mult); - file.write_u32(model->hparams.n_head); - file.write_u32(model->hparams.n_layer); - file.write_u32(model->hparams.n_rot); - - write_tensor(&file, model->tok_embeddings); - write_tensor(&file, model->norm); - write_tensor(&file, model->output); + read_tensor_by_name(model->tok_embeddings, f_ggml_ctx, tn(LLM_TENSOR_TOKEN_EMBD)); + read_tensor_by_name(model->norm, f_ggml_ctx, tn(LLM_TENSOR_OUTPUT_NORM)); + read_tensor_by_name(model->output, f_ggml_ctx, tn(LLM_TENSOR_OUTPUT)); for (uint32_t i = 0; i < model->hparams.n_layer; ++i) { auto & layer = model->layers[i]; - write_tensor(&file, layer.attention_norm); - write_tensor(&file, layer.wq); - write_tensor(&file, layer.wk); - write_tensor(&file, layer.wv); - write_tensor(&file, layer.wo); - write_tensor(&file, layer.ffn_norm); - write_tensor(&file, layer.w1); - write_tensor(&file, layer.w2); - write_tensor(&file, layer.w3); + read_tensor_by_name(layer.attention_norm, f_ggml_ctx, tni(LLM_TENSOR_ATTN_NORM, i)); + read_tensor_by_name(layer.wq, f_ggml_ctx, tni(LLM_TENSOR_ATTN_Q, i)); + read_tensor_by_name(layer.wk, f_ggml_ctx, tni(LLM_TENSOR_ATTN_K, i)); + read_tensor_by_name(layer.wv, f_ggml_ctx, tni(LLM_TENSOR_ATTN_V, i)); + read_tensor_by_name(layer.wo, f_ggml_ctx, tni(LLM_TENSOR_ATTN_OUT, i)); + read_tensor_by_name(layer.ffn_norm, f_ggml_ctx, tni(LLM_TENSOR_FFN_NORM, i)); + read_tensor_by_name(layer.w1, f_ggml_ctx, tni(LLM_TENSOR_FFN_GATE, i)); + read_tensor_by_name(layer.w2, f_ggml_ctx, tni(LLM_TENSOR_FFN_DOWN, i)); + read_tensor_by_name(layer.w3, f_ggml_ctx, tni(LLM_TENSOR_FFN_UP, i)); } - - write_opt_context(&file, opt); } -bool load_checkpoint(struct my_llama_model * model, struct ggml_opt_context * opt, const char * filename, bool init) { - struct llama_file file(filename, "rb"); +void save_llama_model_gguf(struct gguf_context * fctx, const char * fn_vocab_model, struct my_llama_model * model) { + const char * arch = "llama"; + enum llama_ftype ftype = LLAMA_FTYPE_ALL_F32; - uint32_t magic; - uint32_t version; + std::vector keybuf; + keybuf.resize(512); + auto kv = [arch, &keybuf](const char * key) -> const char * { + snprintf(keybuf.data(), keybuf.size(), key, arch); + return keybuf.data(); + }; - uint32_t train_its = 0; - uint32_t train_samples = 0; - uint32_t train_tokens = 0; + // set arch + gguf_set_val_str(fctx, LLM_KV_GENERAL_ARCHITECTURE, arch); + gguf_set_val_u32(fctx, LLM_KV_GENERAL_FILE_TYPE, ftype); - if (file.fp) { - printf("%s: Loading model from '%s'.\n", __func__, filename); - magic = file.read_u32(); - GGML_ASSERT(magic == 'ggcp'); - version = file.read_u32(); - GGML_ASSERT(version == 0); - train_its = file.read_u32(); - train_samples = file.read_u32(); - train_tokens = file.read_u32(); - model->hparams.n_vocab = file.read_u32(); - model->hparams.n_embd = file.read_u32(); - model->hparams.n_mult = file.read_u32(); - model->hparams.n_head = file.read_u32(); - model->hparams.n_layer = file.read_u32(); - model->hparams.n_rot = file.read_u32(); - print_params(&model->hparams); - } + // set hparams + gguf_set_val_u32(fctx, kv(LLM_KV_CONTEXT_LENGTH), model->hparams.n_ctx ); + gguf_set_val_u32(fctx, kv(LLM_KV_EMBEDDING_LENGTH), model->hparams.n_embd ); + gguf_set_val_u32(fctx, kv(LLM_KV_FEED_FORWARD_LENGTH), model->hparams.n_ff ); + gguf_set_val_u32(fctx, kv(LLM_KV_ATTENTION_HEAD_COUNT), model->hparams.n_head ); + gguf_set_val_u32(fctx, kv(LLM_KV_BLOCK_COUNT), model->hparams.n_layer ); + gguf_set_val_u32(fctx, kv(LLM_KV_ROPE_DIMENSION_COUNT), model->hparams.n_rot ); - if (init) { - init_model(model); - } + gguf_set_val_f32(fctx, kv(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS), model->hparams.f_norm_rms_eps ); + gguf_set_val_f32(fctx, kv(LLM_KV_ROPE_FREQ_BASE), model->hparams.rope_freq_base ); // TODO load in llama.cpp + gguf_set_val_f32(fctx, kv(LLM_KV_ROPE_SCALE_LINEAR), 1.0f / model->hparams.rope_freq_scale ); - if (file.fp) { - model->train_its = train_its; - model->train_samples = train_samples; - model->train_tokens = train_tokens; - } + // set vocab by copying from vocab_model gguf file + { + struct gguf_init_params params = { + /*.no_alloc = */ false, + /*.ctx = */ NULL, + }; + struct gguf_context * vctx = gguf_init_from_file(fn_vocab_model, params); - printf("%s: Training iterations: %u.\n", __func__, model->train_its); - printf("%s: Training samples: %u.\n", __func__, model->train_samples); - printf("%s: Training tokens: %u.\n", __func__, model->train_tokens); + const int token_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_LIST)); + if (token_idx == -1) { + throw std::runtime_error("cannot find tokenizer vocab in model file\n"); + } + const uint32_t n_vocab = gguf_get_arr_n(vctx, token_idx); - if (file.fp) { - read_tensor(&file, model->tok_embeddings); - read_tensor(&file, model->norm); - read_tensor(&file, model->output); - - for (uint32_t i = 0; i < model->hparams.n_layer; ++i) { - auto & layer = model->layers[i]; - - read_tensor(&file, layer.attention_norm); - read_tensor(&file, layer.wq); - read_tensor(&file, layer.wk); - read_tensor(&file, layer.wv); - read_tensor(&file, layer.wo); - read_tensor(&file, layer.ffn_norm); - read_tensor(&file, layer.w1); - read_tensor(&file, layer.w2); - read_tensor(&file, layer.w3); + const int score_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_SCORES)); + if (score_idx == -1) { + throw std::runtime_error("cannot find tokenizer scores in model file\n"); } - read_opt_context(&file, model->ctx, opt); + const float * scores = (const float * ) gguf_get_arr_data(vctx, score_idx); + + const int toktype_idx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE)); + if (toktype_idx == -1) { + throw std::runtime_error("cannot find token type list in GGUF file\n"); + } + + const int * toktypes = (const int * ) gguf_get_arr_data(vctx, toktype_idx); + + std::string tokenizer_name; + GGUF_GET_KEY(vctx, tokenizer_name, gguf_get_val_str, GGUF_TYPE_STRING, true, kv(LLM_KV_TOKENIZER_MODEL)); + + gguf_set_val_str(fctx, kv(LLM_KV_TOKENIZER_MODEL), tokenizer_name.c_str()); + gguf_set_arr_data(fctx, kv(LLM_KV_TOKENIZER_SCORES), GGUF_TYPE_FLOAT32, scores, n_vocab); + gguf_set_arr_data(fctx, kv(LLM_KV_TOKENIZER_TOKEN_TYPE), GGUF_TYPE_INT32, toktypes, n_vocab); + + int32_t special_bos_id = 1; + int32_t special_eos_id = 2; + int32_t special_unk_id = 0; + int32_t special_sep_id = -1; + int32_t special_pad_id = -1; + if (tokenizer_name == "llama") { + // default special tokens + special_bos_id = 1; + special_eos_id = 2; + special_unk_id = 0; + special_sep_id = -1; + special_pad_id = -1; + } else if (tokenizer_name == "gpt2") { + // read and copy bpe merges + const int merges_keyidx = gguf_find_key(vctx, kv(LLM_KV_TOKENIZER_MERGES)); + if (merges_keyidx == -1) { + throw std::runtime_error("cannot find tokenizer merges in model file\n"); + } + + const int n_merges = gguf_get_arr_n(vctx, merges_keyidx); + + std::vector merges; + merges.resize(n_merges); + for (int i = 0; i < n_merges; i++) { + merges[i] = gguf_get_arr_str(vctx, merges_keyidx, i); + } + gguf_set_arr_str(fctx, kv(LLM_KV_TOKENIZER_MERGES), merges.data(), n_merges); + + // default special tokens + special_bos_id = 11; + special_eos_id = 11; + special_unk_id = -1; + special_sep_id = -1; + special_pad_id = -1; + } else { + fprintf(stderr, "%s: unknown tokenizer: '%s'", __func__, tokenizer_name.c_str()); + fprintf(stderr, "%s: using default tokenizer: 'llama'", __func__); + } + + std::vector tokens; + tokens.resize(n_vocab); + for (uint32_t i = 0; i < n_vocab; i++) { + tokens[i] = gguf_get_arr_str(vctx, token_idx, i); + } + gguf_set_arr_str(fctx, kv(LLM_KV_TOKENIZER_LIST), tokens.data(), n_vocab); + + GGUF_GET_KEY(vctx, special_bos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_BOS_ID)); + GGUF_GET_KEY(vctx, special_eos_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_EOS_ID)); + GGUF_GET_KEY(vctx, special_unk_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_UNK_ID)); + GGUF_GET_KEY(vctx, special_sep_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_SEP_ID)); + GGUF_GET_KEY(vctx, special_pad_id, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_TOKENIZER_PAD_ID)); + + gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_BOS_ID), special_bos_id); + gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_EOS_ID), special_eos_id); + gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_UNK_ID), special_unk_id); + gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_SEP_ID), special_sep_id); + gguf_set_val_u32(fctx, kv(LLM_KV_TOKENIZER_PAD_ID), special_pad_id); + + gguf_free(vctx); } - return (file.fp != NULL); + // add tensors + gguf_add_tensor(fctx, model->tok_embeddings); + gguf_add_tensor(fctx, model->norm); + gguf_add_tensor(fctx, model->output); + for (uint32_t i = 0; i < model->hparams.n_layer; ++i) { + auto & layer = model->layers[i]; + + + gguf_add_tensor(fctx, layer.attention_norm); + gguf_add_tensor(fctx, layer.wq); + gguf_add_tensor(fctx, layer.wk); + gguf_add_tensor(fctx, layer.wv); + gguf_add_tensor(fctx, layer.wo); + gguf_add_tensor(fctx, layer.ffn_norm); + gguf_add_tensor(fctx, layer.w1); + gguf_add_tensor(fctx, layer.w2); + gguf_add_tensor(fctx, layer.w3); + } } -void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * model, const char * filename) { - struct llama_file file(filename, "wb"); - if (file.fp == NULL) { - return; +void save_llama_model_file(const char * filename, const char * fn_vocab_model, struct my_llama_model * model) { + struct gguf_context * fctx = gguf_init_empty(); + + save_llama_model_gguf(fctx, fn_vocab_model, model); + + // write file + const bool only_meta = false; + gguf_write_to_file(fctx, filename, only_meta); + gguf_free(fctx); +} + +void load_checkpoint_gguf(struct gguf_context * fctx, struct ggml_context * f_ggml_ctx, struct my_llama_model * model, struct ggml_opt_context * opt) { + load_llama_model_gguf(fctx, f_ggml_ctx, model); + + uint32_t file_version; + GGUF_GET_KEY(fctx, file_version, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_FILE_VERSION); + GGML_ASSERT(file_version == 0); + + GGUF_GET_KEY(fctx, model->train_its, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_ITERATION_COUNT); + GGUF_GET_KEY(fctx, model->train_samples, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_SAMPLE_COUNT); + GGUF_GET_KEY(fctx, model->train_tokens, gguf_get_val_u32, GGUF_TYPE_UINT32, true, LLM_KV_TRAINING_TOKEN_COUNT); + + load_opt_context_gguf(fctx, f_ggml_ctx, opt); +} + +void save_checkpoint_gguf(struct gguf_context * fctx, const char * fn_vocab_model, struct my_llama_model * model, struct ggml_opt_context * opt) { + save_llama_model_gguf(fctx, fn_vocab_model, model); + + gguf_set_val_u32(fctx, LLM_KV_TRAINING_FILE_VERSION, 0); + gguf_set_val_u32(fctx, LLM_KV_TRAINING_ITERATION_COUNT, model->train_its); + gguf_set_val_u32(fctx, LLM_KV_TRAINING_SAMPLE_COUNT, model->train_samples); + gguf_set_val_u32(fctx, LLM_KV_TRAINING_TOKEN_COUNT, model->train_tokens); + + save_opt_context_gguf(fctx, opt); +} + +bool load_checkpoint_file(const char * filename, struct my_llama_model * model, struct ggml_opt_context * opt) { + struct ggml_context * f_ggml_ctx; + struct gguf_init_params params; + params.no_alloc = false; + params.ctx = &f_ggml_ctx; + struct gguf_context * fctx = gguf_init_from_file(filename, params); + if (fctx == NULL) { + return false; } -#pragma message("TODO: implement file saving using gguf") - (void) vocab; - (void) model; -// // write_magic -// file.write_u32(LLAMA_FILE_MAGIC); // magic -// file.write_u32(LLAMA_FILE_VERSION); // version -// // write_hparams -// file.write_u32(model->hparams.n_vocab); -// file.write_u32(model->hparams.n_embd); -// file.write_u32(model->hparams.n_mult); -// file.write_u32(model->hparams.n_head); -// file.write_u32(model->hparams.n_layer); -// file.write_u32(model->hparams.n_rot); -// file.write_u32(LLAMA_FTYPE_ALL_F32); -// // write_vocab -// uint32_t n_vocab = model->hparams.n_vocab; -// for (uint32_t i = 0; i < n_vocab; i++) { -// const auto & token_data = vocab->id_to_token.at(i); -// file.write_u32((uint32_t) token_data.tok.size()); -// file.write_raw(token_data.tok.data(), token_data.tok.size()); -// file.write_raw(&token_data.score, sizeof(token_data.score)); -// } -// // write tensors -// write_tensor(&file, model->tok_embeddings); -// write_tensor(&file, model->norm); -// write_tensor(&file, model->output); -// for (uint32_t i = 0; i < model->hparams.n_layer; ++i) { -// auto & layer = model->layers[i]; -// -// write_tensor(&file, layer.attention_norm); -// write_tensor(&file, layer.wq); -// write_tensor(&file, layer.wk); -// write_tensor(&file, layer.wv); -// write_tensor(&file, layer.wo); -// write_tensor(&file, layer.ffn_norm); -// write_tensor(&file, layer.w1); -// write_tensor(&file, layer.w2); -// write_tensor(&file, layer.w3); -// } + load_checkpoint_gguf(fctx, f_ggml_ctx, model, opt); + + return true; } -float cosine_decay(const int decay_steps, const float alpha, int step) { +void save_checkpoint_file(const char * filename, const char * fn_vocab_model, struct my_llama_model * model, struct ggml_opt_context * opt) { + struct gguf_context * fctx = gguf_init_empty(); + + save_checkpoint_gguf(fctx, fn_vocab_model, model, opt); + + // write file + const bool only_meta = false; + gguf_write_to_file(fctx, filename, only_meta); + gguf_free(fctx); +} + +float cosine_decay(const int decay_steps, const float minimum, int step) { if (step > decay_steps) { step = decay_steps; } const float cosine_decay = 0.50f*(1.0f + cosf(3.14159265359f*step/decay_steps)); - const float decay = (1 - alpha)*cosine_decay + alpha; + const float decay = (1 - minimum)*cosine_decay + minimum; return decay; } -float cosine_decay_restart(int decay_steps, const float alpha, int step, float restart_step_mult) { - while (step > decay_steps) { - step -= decay_steps; - decay_steps = (int) restart_step_mult * decay_steps; +float cosine_decay_restart(int decay_steps, const float minimum, int step, float restart_step_mult, bool enable_restart) { + if (enable_restart) { + while (step > decay_steps) { + step -= decay_steps; + decay_steps = (int) restart_step_mult * decay_steps; + } } - return cosine_decay(decay_steps, alpha, step); + return cosine_decay(decay_steps, minimum, step); } struct train_params { @@ -2683,39 +1513,51 @@ struct train_params { int n_ctx; int n_embd; - int n_mult; int n_head; int n_layer; - int n_rotmax; + int n_ff; int n_threads; int n_batch; int n_examples; - int n_predict; + + float f_norm_rms_eps; + float rope_freq_base; + float rope_freq_scale; int print_info_interval; - int print_details_interval; bool samples_start_after_nl; bool use_adam; bool use_flash; - bool use_scratch; + bool use_checkpointing; + bool use_alloc; // only adam int warmup; int cos_decay_steps; float cos_decay_restart; - float cos_decay_alpha; + float cos_decay_min; + bool enable_restart; + + int opt_past; + float opt_delta; + int opt_max_no_improvement; int lbfgs_n_iter; int adam_n_iter; float adam_alpha; + float adam_min_alpha; float adam_decay; + int adam_decay_min_ndim; + float adam_beta1; + float adam_beta2; + float adam_gclip; + float adam_eps_f; int mem_model_gb; int mem_compute_gb; int mem_compute0_gb; - int mem_compute1_gb; }; struct train_params get_default_train_params() { @@ -2730,40 +1572,51 @@ struct train_params get_default_train_params() { params.n_ctx = 128; params.n_embd = 256; - params.n_mult = 256; params.n_head = 8; params.n_layer = 16; - params.n_rotmax = 64; + params.n_ff = 768; params.n_threads = 6; params.n_batch = 8; - params.n_examples = 8; - params.n_predict = 1024; + params.n_examples = 1; + + params.f_norm_rms_eps = 1e-5; + params.rope_freq_base = 10000.0f; + params.rope_freq_scale = 1.0f; params.print_info_interval = 1; - params.print_details_interval = 2; params.samples_start_after_nl = false; params.use_adam = true; params.use_flash = true; - params.use_scratch = true; + params.use_checkpointing = true; + params.use_alloc = true; + + params.opt_past = 0; + params.opt_delta = 1e-5f; + params.opt_max_no_improvement = 0; // only adam params.warmup = 100; params.cos_decay_steps = 1000; params.cos_decay_restart = 1.1f; - params.cos_decay_alpha = 0.0f; + params.cos_decay_min = 0.1f; + params.enable_restart = false; - params.lbfgs_n_iter = 16; - params.adam_n_iter = 16; - params.adam_alpha = 1e-3f; - params.adam_decay = 1e-3f; + params.lbfgs_n_iter = 256; + params.adam_n_iter = 256; + params.adam_alpha = 1e-3f; + params.adam_min_alpha = 0; + params.adam_decay = 1e-1f; + params.adam_decay_min_ndim = 2; + params.adam_beta1 = 0.9f; + params.adam_beta2 = 0.999f; + params.adam_gclip = 1.0f; + params.adam_eps_f = 0.0f; - params.mem_model_gb = 2; + params.mem_model_gb = 2; params.mem_compute_gb = 24; params.mem_compute0_gb = 8; - params.mem_compute1_gb = 2; - return params; } @@ -2780,35 +1633,47 @@ void train_print_usage(int /*argc*/, char ** argv, const struct train_params * p fprintf(stderr, " -s SEED, --seed SEED RNG seed (default: -1, use random seed for -1)\n"); fprintf(stderr, " -c N, --ctx N Context size used during training (default %d)\n", params->n_ctx); fprintf(stderr, " --embd N Embedding size used for new models (default %d)\n", params->n_embd); - fprintf(stderr, " --mult N Mult size used for new models, influences feedforward size. (default %d)\n", params->n_mult); + fprintf(stderr, " --ff N Feedforward size used for new models. (default %d)\n", params->n_ff); fprintf(stderr, " --head N Number of heads for new models (default %d)\n", params->n_head); fprintf(stderr, " --layer N Number of layers for new models (default %d)\n", params->n_layer); - fprintf(stderr, " --rotmax N Maximal number Rope dimensions for new models (default %d)\n", params->n_rotmax); + fprintf(stderr, " --norm-rms-eps F RMS-Norm epsilon value (default %f)\n", params->f_norm_rms_eps); + fprintf(stderr, " --rope-freq-base F Frequency base for ROPE (default %f)\n", params->rope_freq_base); + fprintf(stderr, " --rope-freq-scale F Frequency scale for ROPE (default %f)\n", params->rope_freq_scale); fprintf(stderr, " -t N, --threads N Number of threads (default %d)\n", params->n_threads); fprintf(stderr, " -b N, --batch N Parallel batch size (default %d)\n", params->n_batch); fprintf(stderr, " -n N, --examples N Number of examples to train (default %d)\n", params->n_examples); - fprintf(stderr, " --predict N Number of tokens to generate after training (default %d)\n", params->n_predict); fprintf(stderr, " --print-info-interval N Print infos during training each N examples (default %d)\n", params->print_info_interval); - fprintf(stderr, " --print-details-interval N Print details during training each N examples (default %d)\n", params->print_details_interval); fprintf(stderr, " --samples-after-nl Training samples start after newlines. (default %s)\n", params->samples_start_after_nl ? "on" : "off"); fprintf(stderr, " --use-lbfgs Use LBFGS optimizer instead of default Adam\n"); fprintf(stderr, " --use-adam Use Adam optimizer (default)\n"); - fprintf(stderr, " --no-flash Don't use flash attention.\n"); + fprintf(stderr, " --no-flash Don't use flash attention \n"); fprintf(stderr, " --use-flash Use flash attention (default)\n"); - fprintf(stderr, " --no-scratch Don't use scratch buffers\n"); - fprintf(stderr, " --use-scratch Use scratch buffers (default)\n"); - fprintf(stderr, " --warmup N Number of warmup steps (default %d)\n", params->warmup); - fprintf(stderr, " --cos-decay-steps N Number of cosine decay steps (default %d)\n", params->cos_decay_steps); - fprintf(stderr, " --cos-decay-restart N Increase of cosine decay steps after restart (default %f)\n", params->cos_decay_restart); - fprintf(stderr, " --cos-decay-alpha N Cosine decay alpha (default %f)\n", params->cos_decay_alpha); - fprintf(stderr, " --lbfgs-iter N Maximum number of LBFGS optimization iterations for each batch (default %d)\n", params->lbfgs_n_iter); + fprintf(stderr, " --no-checkpointing Don't use gradient checkpointing\n"); + fprintf(stderr, " --use-checkpointing Use gradient checkpointing (default)\n"); + fprintf(stderr, " --no-alloc Don't use allocator\n"); + fprintf(stderr, " --use-alloc Use allocator (default)\n"); + fprintf(stderr, " --warmup N Only for Adam optimizer. Number of warmup steps (default %d)\n", params->warmup); + fprintf(stderr, " --cos-decay-steps N Only for Adam optimizer. Number of cosine decay steps (default %d)\n", params->cos_decay_steps); + fprintf(stderr, " --cos-decay-restart N Only for Adam optimizer. Increase of cosine decay steps after restart (default %f)\n", params->cos_decay_restart); + fprintf(stderr, " --cos-decay-min N Only for Adam optimizer. Cosine decay minimum (default %f)\n", params->cos_decay_min); + fprintf(stderr, " --enable-restart N Only for Adam optimizer. Enable restarts of cos-decay %s\n", params->enable_restart ? "(default)" : ""); + fprintf(stderr, " --disable-restart N Only for Adam optimizer. Disable restarts of cos-decay %s\n", !params->enable_restart ? "(default)" : ""); + fprintf(stderr, " --opt-past N Number of optimization iterations to track for delta convergence test. Disabled when zero. (default %d)\n", params->opt_past); + fprintf(stderr, " --opt-delta N Maximum delta for delta convergence test. Disabled when <= zero. (default %f)\n", params->opt_delta); + fprintf(stderr, " --opt-max-no-improvement N Maximum number of optimization iterations with no improvement. Disabled when <= zero. (default %d)\n", params->opt_max_no_improvement); + fprintf(stderr, " --adam-epsf N AdamW epsilon for convergence test. Disabled when <= zero. (default %f)\n", params->adam_eps_f); fprintf(stderr, " --adam-iter N Maximum number of Adam optimization iterations for each batch (default %d)\n", params->adam_n_iter); fprintf(stderr, " --adam-alpha N Adam learning rate alpha (default %f)\n", params->adam_alpha); + fprintf(stderr, " --adam-min-alpha N Adam minimum learning rate alpha - including warmup phase (default %f)\n", params->adam_min_alpha); fprintf(stderr, " --adam-decay N AdamW weight decay. Values greater zero enable AdamW instead of regular Adam. (default %f)\n", params->adam_decay); + fprintf(stderr, " --adam-decay-min-ndim N Minimum number of tensor dimensions to apply AdamW weight decay. Weight decay is not applied to tensors with less n_dims. (default %d)\n", params->adam_decay_min_ndim); + fprintf(stderr, " --adam-beta1 N AdamW beta1 in interval [0,1). How much to smooth the first moment of gradients. (default %f)\n", params->adam_beta1); + fprintf(stderr, " --adam-beta2 N AdamW beta2 in interval [0,1). How much to smooth the second moment of gradients. (default %f)\n", params->adam_beta2); + fprintf(stderr, " --adam-gclip N AdamW gradient clipping. Disabled when zero. (default %f)\n", params->adam_gclip); + fprintf(stderr, " --lbfgs-iter N Maximum number of LBFGS optimization iterations for each batch (default %d)\n", params->lbfgs_n_iter); fprintf(stderr, " --mem-model N Memory to allocate for model and cache in gigabytes. (default %d)\n", params->mem_model_gb); fprintf(stderr, " --mem-compute N Memory to allocate for compute in gigabytes. (default %d)\n", params->mem_compute_gb); - fprintf(stderr, " --mem-compute0 N Memory to allocate for compute in gigabytes. (default %d)\n", params->mem_compute0_gb); - fprintf(stderr, " --mem-compute1 N Memory to allocate for compute in gigabytes. (default %d)\n", params->mem_compute1_gb); + fprintf(stderr, " --mem-compute0 N Memory to allocate for automatic memory allocator in gigabytes. (default %d)\n", params->mem_compute0_gb); fprintf(stderr, "\n"); } @@ -2872,12 +1737,12 @@ bool train_params_parse(int argc, char ** argv, struct train_params * params) { break; } params->n_embd = std::stoi(argv[i]); - } else if (arg == "--mult") { + } else if (arg == "--ff") { if (++i >= argc) { invalid_param = true; break; } - params->n_mult = std::stoi(argv[i]); + params->n_ff = std::stoi(argv[i]); } else if (arg == "--head") { if (++i >= argc) { invalid_param = true; @@ -2890,12 +1755,24 @@ bool train_params_parse(int argc, char ** argv, struct train_params * params) { break; } params->n_layer = std::stoi(argv[i]); - } else if (arg == "--rotmax") { + } else if (arg == "--norm-rms-eps") { if (++i >= argc) { invalid_param = true; break; } - params->n_rotmax = std::stoi(argv[i]); + params->f_norm_rms_eps = std::stof(argv[i]); + } else if (arg == "--rope-freq-base") { + if (++i >= argc) { + invalid_param = true; + break; + } + params->rope_freq_base = std::stof(argv[i]); + } else if (arg == "--rope-freq-scale") { + if (++i >= argc) { + invalid_param = true; + break; + } + params->rope_freq_scale = std::stof(argv[i]); } else if (arg == "-t" || arg == "--threads") { if (++i >= argc) { invalid_param = true; @@ -2914,24 +1791,12 @@ bool train_params_parse(int argc, char ** argv, struct train_params * params) { break; } params->n_examples = std::stoi(argv[i]); - } else if (arg == "--predict") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->n_predict = std::stoi(argv[i]); } else if (arg == "--print-info-interval") { if (++i >= argc) { invalid_param = true; break; } params->print_info_interval = std::stoi(argv[i]); - } else if (arg == "--print-details-interval") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->print_details_interval = std::stoi(argv[i]); } else if (arg == "--samples-after-nl") { params->samples_start_after_nl = true; } else if (arg == "--use-lbfgs") { @@ -2942,10 +1807,14 @@ bool train_params_parse(int argc, char ** argv, struct train_params * params) { params->use_flash = false; } else if (arg == "--use-flash") { params->use_flash = true; - } else if (arg == "--no-scratch") { - params->use_scratch = false; - } else if (arg == "--use-scratch") { - params->use_scratch = true; + } else if (arg == "--no-checkpointing") { + params->use_checkpointing = false; + } else if (arg == "--use-checkpointing") { + params->use_checkpointing = true; + } else if (arg == "--no-alloc") { + params->use_alloc = false; + } else if (arg == "--use-alloc") { + params->use_alloc = true; } else if (arg == "--warmup") { if (++i >= argc) { invalid_param = true; @@ -2964,18 +1833,40 @@ bool train_params_parse(int argc, char ** argv, struct train_params * params) { break; } params->cos_decay_restart = std::stof(argv[i]); - } else if (arg == "--cos-decay-alpha") { + } else if (arg == "--cos-decay-min") { if (++i >= argc) { invalid_param = true; break; } - params->cos_decay_alpha = std::stof(argv[i]); - } else if (arg == "--lbfgs-iter") { + params->cos_decay_min = std::stof(argv[i]); + } else if (arg == "--enable-restart") { + params->enable_restart = true; + } else if (arg == "--disable-restart") { + params->enable_restart = false; + } else if (arg == "--opt-past") { if (++i >= argc) { invalid_param = true; break; } - params->lbfgs_n_iter = std::stoi(argv[i]); + params->opt_past = std::stoi(argv[i]); + } else if (arg == "--opt-delta") { + if (++i >= argc) { + invalid_param = true; + break; + } + params->opt_delta = std::stof(argv[i]); + } else if (arg == "--opt-max-no-improvement") { + if (++i >= argc) { + invalid_param = true; + break; + } + params->opt_max_no_improvement = std::stoi(argv[i]); + } else if (arg == "--adam-epsf") { + if (++i >= argc) { + invalid_param = true; + break; + } + params->adam_eps_f = std::stof(argv[i]); } else if (arg == "--adam-iter") { if (++i >= argc) { invalid_param = true; @@ -2988,12 +1879,48 @@ bool train_params_parse(int argc, char ** argv, struct train_params * params) { break; } params->adam_alpha = std::stof(argv[i]); + } else if (arg == "--adam-min-alpha") { + if (++i >= argc) { + invalid_param = true; + break; + } + params->adam_min_alpha = std::stof(argv[i]); } else if (arg == "--adam-decay") { if (++i >= argc) { invalid_param = true; break; } params->adam_decay = std::stof(argv[i]); + } else if (arg == "--adam-decay-min-ndim") { + if (++i >= argc) { + invalid_param = true; + break; + } + params->adam_decay_min_ndim = std::stoi(argv[i]); + } else if (arg == "--adam-beta1") { + if (++i >= argc) { + invalid_param = true; + break; + } + params->adam_beta1 = std::stof(argv[i]); + } else if (arg == "--adam-beta2") { + if (++i >= argc) { + invalid_param = true; + break; + } + params->adam_beta2 = std::stof(argv[i]); + } else if (arg == "--adam-gclip") { + if (++i >= argc) { + invalid_param = true; + break; + } + params->adam_gclip = std::stof(argv[i]); + } else if (arg == "--lbfgs-iter") { + if (++i >= argc) { + invalid_param = true; + break; + } + params->lbfgs_n_iter = std::stoi(argv[i]); } else if (arg == "--mem-model") { if (++i >= argc) { invalid_param = true; @@ -3012,12 +1939,6 @@ bool train_params_parse(int argc, char ** argv, struct train_params * params) { break; } params->mem_compute0_gb = std::stoi(argv[i]); - } else if (arg == "--mem-compute1") { - if (++i >= argc) { - invalid_param = true; - break; - } - params->mem_compute1_gb = std::stoi(argv[i]); } else if (arg == "-h" || arg == "--help") { train_print_usage(argc, argv, &default_params); exit(0); @@ -3036,6 +1957,63 @@ bool train_params_parse(int argc, char ** argv, struct train_params * params) { return true; } +struct opt_callback_data { + struct train_params * params; + struct ggml_opt_context * opt; + struct llama_context * lctx; + llama_token * tokens_data; + size_t tokens_size; + int * samples_data; + size_t samples_size; + int shuffle_countdown; + struct ggml_tensor * tokens_input; + struct ggml_tensor * target_logits; + struct ggml_tensor * target_probs; +}; + +void opt_callback(void * vdata, float * sched) { + struct opt_callback_data * data = (struct opt_callback_data *) vdata; + struct train_params * params = data->params; + struct ggml_opt_context * opt = data->opt; + int n_batch = params->n_batch; + + *sched = (opt->iter < params->warmup) + ? (float) opt->iter / (float) params->warmup + : cosine_decay_restart( + params->cos_decay_steps, + params->cos_decay_min, + opt->iter - params->warmup, + params->cos_decay_restart, + params->enable_restart); + float min_sched = params->adam_min_alpha / params->adam_alpha; + *sched = min_sched + *sched * (1.0f - min_sched); + + int impr_plot = std::isnan(opt->loss_after) ? 0 : -(int)(1 + (opt->loss_before - opt->loss_after) * 10.0f + 0.5f); + printf("%s: iter=%*d, sched=%f loss0=%f loss=%f | improvement: %*d>\n", __func__, 6, opt->iter, *sched, opt->loss_before, opt->loss_after, impr_plot, (int)0); + + if (data->shuffle_countdown < n_batch) { + printf("%s: reshuffle samples\n", __func__); + shuffle_ints(data->samples_data, data->samples_data + data->samples_size); + for (int i = 0; i < (int) data->samples_size; ++i) { + GGML_ASSERT(data->samples_data[i]+params->n_ctx-1 < (int) data->tokens_size); + } + data->shuffle_countdown = data->samples_size; + } + + get_example_targets_batch( + data->lctx, + data->samples_data, + data->samples_size, + data->tokens_data, + data->tokens_size, + opt->iter, + data->tokens_input, + data->target_logits, + data->target_probs); + + data->shuffle_countdown -= n_batch; +} + int main(int argc, char ** argv) { struct train_params params = get_default_train_params(); @@ -3055,18 +2033,6 @@ int main(int argc, char ** argv) { struct llama_model * lmodel = llama_load_model_from_file(params.fn_vocab_model, llama_params); struct llama_context * lctx = llama_new_context_with_model(lmodel, llama_params); - struct llama_vocab vocab; - { - const int n_vocab = llama_n_vocab(lctx); - vocab.id_to_token.resize(n_vocab); - for (int i=0; i train_tokens; if (tokenize_file(lctx, params.fn_train_data, train_tokens) < 0) { @@ -3078,10 +2044,14 @@ int main(int argc, char ** argv) { model.hparams.n_vocab = llama_n_vocab(lctx); model.hparams.n_ctx = params.n_ctx; model.hparams.n_embd = params.n_embd; - model.hparams.n_mult = params.n_mult; model.hparams.n_head = params.n_head; model.hparams.n_layer = params.n_layer; - model.hparams.n_rot = std::min((uint32_t)params.n_rotmax, model.hparams.n_embd / model.hparams.n_head); + model.hparams.n_ff = params.n_ff; + // llama.cpp requires n_rot to be exactly n_embd / n_head + model.hparams.n_rot = model.hparams.n_embd / model.hparams.n_head; + model.hparams.f_norm_rms_eps = params.f_norm_rms_eps; + model.hparams.rope_freq_base = params.rope_freq_base; + model.hparams.rope_freq_scale = params.rope_freq_scale; print_params(&model.hparams); @@ -3103,19 +2073,12 @@ int main(int argc, char ** argv) { } printf("%s: number of unique tokens: %d\n", __func__, n_unique_tokens); - struct my_llama_kv_cache kv_self; - - struct ggml_init_params lcparams; lcparams.mem_size = 1024ll*1024ll*1024ll*((size_t) params.mem_model_gb); lcparams.mem_buffer = NULL; lcparams.no_alloc = false; model.ctx = ggml_init(lcparams); - kv_self.ctx = model.ctx; - - my_llama_sampler sampler; - int n_tokens = model.hparams.n_ctx; int n_vocab = model.hparams.n_vocab; @@ -3126,24 +2089,38 @@ int main(int argc, char ** argv) { struct ggml_opt_params opt_params_adam = ggml_opt_default_params(GGML_OPT_ADAM); struct ggml_opt_params opt_params_lbfgs = ggml_opt_default_params(GGML_OPT_LBFGS); - opt_params_adam.print_forward_graph = false; + opt_params_adam.print_forward_graph = false; opt_params_adam.print_backward_graph = false; - opt_params_adam.n_threads = params.n_threads; - opt_params_adam.adam.n_iter = params.adam_n_iter; - opt_params_adam.adam.sched = 1.0f; - opt_params_adam.adam.alpha = params.adam_alpha; - opt_params_adam.adam.decay = params.adam_decay; + opt_params_adam.n_threads = params.n_threads; + opt_params_adam.past = params.opt_past; + opt_params_adam.delta = params.opt_delta; + opt_params_adam.max_no_improvement = params.opt_max_no_improvement; + opt_params_adam.adam.n_iter = params.adam_n_iter; + opt_params_adam.adam.sched = 1.0f; + opt_params_adam.adam.alpha = params.adam_alpha; + opt_params_adam.adam.decay = params.adam_decay; + opt_params_adam.adam.decay_min_ndim = params.adam_decay_min_ndim; + opt_params_adam.adam.beta1 = params.adam_beta1; + opt_params_adam.adam.beta2 = params.adam_beta2; + opt_params_adam.adam.gclip = params.adam_gclip; + opt_params_adam.adam.eps_f = params.adam_eps_f; - opt_params_lbfgs.print_forward_graph = false; + opt_params_lbfgs.print_forward_graph = false; opt_params_lbfgs.print_backward_graph = false; - opt_params_lbfgs.n_threads = params.n_threads; - opt_params_lbfgs.lbfgs.n_iter = params.lbfgs_n_iter; + opt_params_lbfgs.n_threads = params.n_threads; + opt_params_adam.past = params.opt_past; + opt_params_adam.delta = params.opt_delta; + opt_params_adam.max_no_improvement = params.opt_max_no_improvement; + opt_params_lbfgs.lbfgs.n_iter = params.lbfgs_n_iter; opt->ctx = model.ctx; opt->params = params.use_adam ? opt_params_adam : opt_params_lbfgs; printf("%s: init model\n", __func__); - bool existed = load_checkpoint(&model, opt, params.fn_checkpoint_in, true); + bool existed = load_checkpoint_file(params.fn_checkpoint_in, &model, opt); + if (!existed) { + init_model(&model); + } set_param_model(&model); opt->params = params.use_adam ? opt_params_adam : opt_params_lbfgs; @@ -3156,11 +2133,7 @@ int main(int argc, char ** argv) { randomize_model(&model, params.seed, 0.0f, 1.0f, -1.0f, +1.0f); } - init_kv_cache(&kv_self, &model, 1); - // init_kv_cache(&kv_self, &model, n_batch); - init_sampler(&sampler, lctx); - - printf("used_mem model+cache: %zu bytes\n", ggml_used_mem(model.ctx)); + printf("used_mem model: %zu bytes\n", ggml_used_mem(model.ctx)); // ggml_print_tensor_objects(model.ctx); // TODO: use std::vector intead of "new" @@ -3168,9 +2141,13 @@ int main(int argc, char ** argv) { uint8_t * compute_addr = new uint8_t[compute_size]; size_t size_buf_0 = 1024ll*1024ll*1024ll*((size_t) params.mem_compute0_gb); - size_t size_buf_1 = 1024ll*1024ll*1024ll*((size_t) params.mem_compute1_gb); uint8_t * compute_buf_0 = new uint8_t[size_buf_0]; - uint8_t * compute_buf_1 = new uint8_t[size_buf_1]; + + ggml_allocr * alloc = NULL; + if (params.use_alloc) { + static const size_t tensor_alignment = 32; + alloc = ggml_allocr_new(compute_buf_0, size_buf_0, tensor_alignment); + } GGML_ASSERT(n_tokens < (int) train_tokens.size()); std::vector train_samples; @@ -3185,10 +2162,23 @@ int main(int argc, char ** argv) { GGML_ASSERT(train_samples[i]+n_tokens-1 < (int) train_tokens.size()); } - std::vector work_buffer; - printf("%s: begin training\n", __func__); + struct opt_callback_data opt_cb_data; + opt_cb_data.params = ¶ms; + opt_cb_data.opt = opt; + opt_cb_data.lctx = lctx; + opt_cb_data.tokens_data = train_tokens.data(); + opt_cb_data.tokens_size = train_tokens.size(); + opt_cb_data.samples_data = train_samples.data(); + opt_cb_data.samples_size = train_samples.size(); + opt_cb_data.shuffle_countdown = train_samples.size(); + opt_cb_data.tokens_input = NULL; + opt_cb_data.target_logits = NULL; + opt_cb_data.target_probs = NULL; + + int64_t t0 = ggml_time_ms(); + for (int ex = 0; ex < params.n_examples; ++ex) { if (ex*n_batch >= (int) train_samples.size()) { shuffle_ints(train_samples.data(), train_samples.data() + train_samples.size()); @@ -3198,198 +2188,110 @@ int main(int argc, char ** argv) { } struct ggml_init_params cparams = { - /*.mem_size =*/ compute_size, - /*.mem_buffer =*/ compute_addr, - /*.no_alloc =*/ false, + compute_size, // mem_size + compute_addr, // mem_buffer + false, // no_alloc }; struct ggml_context * ctx0 = ggml_init(cparams); - struct ggml_tensor * after_opt_best_samples = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_tokens, n_batch); + ggml_set_no_alloc(ctx0, false); + + // don't use alloc for input tensors, so we can safely fill them with data + //struct ggml_tensor * after_opt_best_samples = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_tokens, n_batch); //struct ggml_tensor * after_opt_probs = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_vocab, n_tokens, n_batch); struct ggml_tensor * tokens_input = ggml_new_tensor_2d(ctx0, GGML_TYPE_I32, n_tokens, n_batch); struct ggml_tensor * target_logits = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_vocab, n_tokens, n_batch); struct ggml_tensor * target_probs = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_vocab, n_tokens, n_batch); + ggml_set_no_alloc(ctx0, (alloc != NULL)); + + if (alloc) { + ggml_allocr_reset(alloc); + } + + opt_cb_data.tokens_input = tokens_input; + opt_cb_data.target_logits = target_logits; + opt_cb_data.target_probs = target_probs; + int n_past = 0; - struct ggml_tensor * gfbuf = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / ggml_type_size(GGML_TYPE_I32) + (sizeof(struct ggml_cgraph) % ggml_type_size(GGML_TYPE_I32) ? 1 : 0)); - struct ggml_tensor * gbbuf = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, sizeof(struct ggml_cgraph) / ggml_type_size(GGML_TYPE_I32) + (sizeof(struct ggml_cgraph) % ggml_type_size(GGML_TYPE_I32) ? 1 : 0)); - - memset(gfbuf->data, 0, ggml_nbytes(gfbuf)); - memset(gbbuf->data, 0, ggml_nbytes(gbbuf)); - - struct ggml_cgraph * gf = (struct ggml_cgraph *) gfbuf->data; - struct ggml_cgraph * gb = (struct ggml_cgraph *) gbbuf->data; - - - get_example_targets_batch(lctx, train_samples.data(), train_samples.size(), train_tokens.data(), train_tokens.size(), ex, tokens_input, target_logits, target_probs); + struct ggml_cgraph * gf = ggml_new_graph(ctx0); + struct ggml_cgraph * gb = ggml_new_graph(ctx0); + struct ggml_cgraph * gb_tmp = params.use_checkpointing + ? ggml_new_graph(ctx0) + : NULL; GGML_ASSERT(n_past == 0); struct ggml_tensor * loss = NULL; struct ggml_tensor * logits = NULL; - if (params.use_scratch) { - loss = forward_batch_wo_cache_flash_attn_train( - &model, ctx0, - gf, gb, - &logits, tokens_input, target_probs, - compute_buf_0, compute_buf_1, - size_buf_0, size_buf_1, - n_tokens, n_batch); - } else if (params.use_flash) { - logits = forward_batch_wo_cache_flash_attn(&model, ctx0, gf, tokens_input, n_tokens, n_batch); - loss = cross_entropy_loss(ctx0, logits, target_probs); - ggml_build_forward_expand(gf, loss); - *gb = ggml_build_backward(ctx0, gf, true); - } else { - logits = forward_batch_wo_cache(&model, ctx0, gf, tokens_input, n_tokens, n_batch); - loss = cross_entropy_loss(ctx0, logits, target_probs); - ggml_build_forward_expand(gf, loss); - *gb = ggml_build_backward(ctx0, gf, true); - } - - ggml_graph_compute_helper(work_buffer, gf, params.n_threads); + loss = llama_build_train_graphs( + &model, alloc, ctx0, + gf, gb, gb_tmp, + &logits, tokens_input, target_probs, + n_tokens, n_batch, + params.use_flash, + params.use_checkpointing + ); size_t used_mem_before_opt = ggml_used_mem(ctx0); - float error_before_opt = ggml_get_f32_1d(loss, 0); - opt->params.adam.sched = (opt->iter < params.warmup) ? (float) opt->iter / (float) params.warmup : cosine_decay_restart( params.cos_decay_steps, - params.cos_decay_alpha, + params.cos_decay_min, opt->iter - params.warmup, - params.cos_decay_restart); + params.cos_decay_restart, + params.enable_restart); + + float min_sched = params.adam_min_alpha / params.adam_alpha; + opt->params.adam.sched = min_sched + opt->params.adam.sched * (1.0f - min_sched); printf("%s: opt->params.adam.sched %.5f\n", __func__, opt->params.adam.sched); - ggml_opt_resume_g(ctx0, opt, loss, gf, gb); + ggml_opt_resume_g(ctx0, opt, loss, gf, gb, &opt_callback, (void *) &opt_cb_data); size_t used_mem_after_opt = ggml_used_mem(ctx0); + int n_iter = params.use_adam ? params.adam_n_iter : params.lbfgs_n_iter; model.train_its = opt->iter; - model.train_samples += n_batch; - model.train_tokens += n_batch * n_tokens; - - ggml_graph_compute_helper(work_buffer, gf, params.n_threads); - - float error_after_opt = ggml_get_f32_1d(loss, 0); + model.train_samples += n_batch * n_iter; + model.train_tokens += n_batch * n_tokens * n_iter; if (params.print_info_interval > 0 && ex % params.print_info_interval == 0) { printf("Example %d, opt iter %d\n", ex, opt->iter); - printf("error_before_opt: %.6f\n", error_before_opt); - printf("error_after_opt: %.6f\n", error_after_opt); + printf("error_before_opt: %.6f\n", opt->loss_before); + printf("error_after_opt: %.6f\n", opt->loss_after); printf("used_mem_before_opt: %zu bytes\n", used_mem_before_opt); printf("used_mem_after_opt: %zu bytes\n", used_mem_after_opt); } - if (params.print_details_interval > 0 && ex % params.print_details_interval == 0) { - // set_logits_masked(logits, token_notavail, -1e9); - for (int i=0; idata + i*logits->nb[2] + k*logits->nb[1]), - (llama_token *) ((char *) tokens_input->data + i*tokens_input->nb[1]), - k); - * ((int32_t *) ((char *) after_opt_best_samples->data + i*after_opt_best_samples->nb[1] + k*after_opt_best_samples->nb[0])) = token; - } - } - - // printf("probabilities after optimization:\n"); - // print_matrix(after_opt_probs); - printf("Example:\n---\n"); - print_tokens_batch(lctx, tokens_input); - printf("\n---\n"); - - // printf("best samples after optimization:\n---\n"); - printf("samples after optimization:\n---\n"); - print_tokens_batch(lctx, after_opt_best_samples); - printf("\n---\n"); - } - ggml_free(ctx0); } + int64_t t1 = ggml_time_ms(); + int64_t d = t1-t0; + double dd = (double) d * 1e-3; + printf("%s: total training time=%f seconds\n", __func__, dd); + if (params.n_examples > 0) { - save_checkpoint(&model, opt, params.fn_checkpoint_out); + save_checkpoint_file(params.fn_checkpoint_out, params.fn_vocab_model, &model, opt); } if (strlen(params.fn_model_out) > 0) { - save_as_llama_model(&vocab, &model, params.fn_model_out); + save_llama_model_file(params.fn_model_out, params.fn_vocab_model, &model); } - { - int n_gen = params.n_predict; - int sample_ctx = n_tokens - n_tokens/8; - - sampler.params.temp = 0.2f; - sampler.params.repeat_penalty = 1.1f; - sampler.params.mirostat = 2; - init_sampler(&sampler, lctx); - - printf("Generating %d tokens.\n", n_gen); - - struct ggml_tensor * tokens_input = ggml_new_tensor_1d(model.ctx, GGML_TYPE_I32, n_tokens); - struct ggml_tensor * target_logits = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, n_vocab, n_tokens); - struct ggml_tensor * target_probs = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, n_vocab, n_tokens); - - get_example_targets(lctx, train_samples.data(), train_samples.size(), train_tokens.data(), train_tokens.size(), rand()%train_samples.size(), tokens_input, target_logits, target_probs); - for (int i=sample_ctx; idata + (sample_ctx-1)*logits->nb[1]), - (llama_token *) tokens_input->data, - sample_ctx-1); - //int token = ggml_get_i32_1d(best_samples, sample_ctx-1); - - // print_row(probs, sample_at); - print_token(lctx, token); - - lshift_examples(tokens_input, target_logits, target_probs, 1); - ggml_set_i32_1d(tokens_input, 0, 0); - ggml_set_i32_1d(tokens_input, sample_ctx-1, token); - - ggml_free(ctx0); - } + if (alloc) { + ggml_allocr_free(alloc); } delete[] compute_addr; delete[] compute_buf_0; - delete[] compute_buf_1; - + ggml_free(model.ctx); llama_free(lctx); llama_free_model(lmodel); - ggml_free(model.ctx); - return 0; } diff --git a/ggml-alloc.c b/ggml-alloc.c index 140e9a2a7..63beb1d4e 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -107,6 +107,10 @@ static size_t ggml_allocator_get_alloc_size(struct ggml_allocr * alloc, struct g } void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { +#ifdef GGML_ALLOCATOR_DEBUG + GGML_ASSERT(ggml_is_view(tensor) == false); // views generally get data pointer from one of their sources + GGML_ASSERT(tensor->data == NULL); // avoid allocating tensor which already has memory allocated +#endif size_t size = ggml_allocator_get_alloc_size(alloc, tensor); size = aligned_offset(NULL, size, alloc->alignment); diff --git a/ggml.c b/ggml.c index dadb30757..9a787863d 100644 --- a/ggml.c +++ b/ggml.c @@ -123,6 +123,8 @@ typedef void * thread_ret_t; #define GGML_GELU_FP16 #define GGML_GELU_QUICK_FP16 #define GGML_SILU_FP16 +// #define GGML_CROSS_ENTROPY_EXP_FP16 +// #define GGML_FLASH_ATTN_EXP_FP16 #define GGML_SOFT_MAX_UNROLL 4 #define GGML_VEC_DOT_UNROLL 2 @@ -186,8 +188,8 @@ typedef void * thread_ret_t; // #if defined(_MSC_VER) || defined(__MINGW32__) -#define GGML_ALIGNED_MALLOC(size) _aligned_malloc(size, GGML_MEM_ALIGN) -#define GGML_ALIGNED_FREE(ptr) _aligned_free(ptr) +#define GGML_ALIGNED_MALLOC(size) _aligned_malloc(size, GGML_MEM_ALIGN) +#define GGML_ALIGNED_FREE(ptr) _aligned_free(ptr) #else inline static void * ggml_aligned_malloc(size_t size) { void * aligned_memory = NULL; @@ -212,8 +214,8 @@ inline static void * ggml_aligned_malloc(size_t size) { } return aligned_memory; } -#define GGML_ALIGNED_MALLOC(size) ggml_aligned_malloc(size) -#define GGML_ALIGNED_FREE(ptr) free(ptr) +#define GGML_ALIGNED_MALLOC(size) ggml_aligned_malloc(size) +#define GGML_ALIGNED_FREE(ptr) free(ptr) #endif #define UNUSED GGML_UNUSED @@ -5857,7 +5859,8 @@ struct ggml_tensor * ggml_rms_norm_inplace( struct ggml_tensor * ggml_rms_norm_back( struct ggml_context * ctx, struct ggml_tensor * a, - struct ggml_tensor * b) { + struct ggml_tensor * b, + float eps) { bool is_node = false; if (a->grad) { @@ -5867,6 +5870,8 @@ struct ggml_tensor * ggml_rms_norm_back( struct ggml_tensor * result = ggml_dup_tensor(ctx, a); + ggml_set_op_params(result, &eps, sizeof(eps)); + result->op = GGML_OP_RMS_NORM_BACK; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; @@ -9443,6 +9448,8 @@ static void ggml_compute_forward_div_f32( #ifdef GGML_USE_ACCELERATE + UNUSED(ggml_vec_div_f32); + vDSP_vdiv( (float *) ((char *) src1->data + i3*nb13 + i2*nb12 + i1*nb11), 1, (float *) ((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01), 1, @@ -10749,7 +10756,8 @@ static void ggml_compute_forward_rms_norm_back_f32( GGML_TENSOR_BINARY_OP_LOCALS; - const float eps = 1e-6f; // TODO: make this a parameter + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); // TODO: optimize for (int64_t i03 = 0; i03 < ne03; i03++) { @@ -12139,6 +12147,7 @@ static void ggml_compute_forward_soft_max_back_f32( // dx = J * dy // dxk = sum_i(Jki * dyi) // dxk = sum_i(-yk*yi * dyi) - (-yk*yk)*dyk + (yk - yk*yk)*dyk + // dxk = sum_i(-yk*yi * dyi) + yk*yk*dyk + yk*dyk - yk*yk*dyk // dxk = sum_i(-yk*yi * dyi) + yk*dyk // dxk = -yk * sum_i(yi * dyi) + yk*dyk // dxk = -yk * dot(y, dy) + yk*dyk @@ -13929,7 +13938,7 @@ static void ggml_compute_forward_flash_attn_f32( vvexpf(S, S, &Mup); ggml_vec_sum_f32(Mup, &sum, S); #else - uint16_t scvt[GGML_SOFT_MAX_UNROLL]; + uint16_t scvt[GGML_SOFT_MAX_UNROLL]; UNUSED(scvt); ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 }; for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) { @@ -13939,9 +13948,13 @@ static void ggml_compute_forward_flash_attn_f32( if (SS[j] == -INFINITY) { SS[j] = 0.0f; } else { +#ifndef GGML_FLASH_ATTN_EXP_FP16 + const float val = expf(SS[j] - max); +#else ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max); memcpy(&scvt[j], &s, sizeof(uint16_t)); const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]); +#endif sump[j] += (ggml_float)val; SS[j] = val; } @@ -14519,7 +14532,7 @@ static void ggml_compute_forward_flash_attn_back_f32( vvexpf(SM, SM, &Mup); ggml_vec_sum_f32(Mup, &sum, SM); #else - uint16_t scvt[GGML_SOFT_MAX_UNROLL]; + uint16_t scvt[GGML_SOFT_MAX_UNROLL]; UNUSED(scvt); ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 }; for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) { @@ -14530,9 +14543,13 @@ static void ggml_compute_forward_flash_attn_back_f32( if (SR[j] == -INFINITY) { SW[j] = 0.0f; } else { +#ifndef GGML_FLASH_ATTN_EXP_FP16 + const float val = expf(SR[j] - max); +#else ggml_fp16_t s = GGML_FP32_TO_FP16(SR[j] - max); memcpy(&scvt[j], &s, sizeof(uint16_t)); const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt[j]]); +#endif sump[j] += (ggml_float)val; SW[j] = val; } @@ -15270,6 +15287,8 @@ static void ggml_compute_forward_cross_entropy_loss_f32( const int nc = src0->ne[0]; const int nr = ggml_nrows(src0); + GGML_ASSERT(params->wsize >= sizeof(float) * (nth + nth * nc)); + if (params->type == GGML_TASK_INIT) { if (ith == 0) { memset(sums, 0, sizeof(float) * (nth + nth * nc)); @@ -15281,7 +15300,7 @@ static void ggml_compute_forward_cross_entropy_loss_f32( if (ith == 0) { float * dp = (float *) dst->data; ggml_vec_sum_f32(nth, dp, sums); - dp[0] *= -1.0f; + dp[0] *= -1.0f / (float) nr; } return; } @@ -15298,7 +15317,7 @@ static void ggml_compute_forward_cross_entropy_loss_f32( for (int i1 = ir0; i1 < ir1; i1++) { float * s0 = (float *)((char *) src0->data + i1*src0->nb[1]); float * s1 = (float *)((char *) src1->data + i1*src1->nb[1]); - float * st = (float *) params->wdata + nth + ith*nc; + float * st = ((float *) params->wdata) + nth + ith*nc; #ifndef NDEBUG for (int i = 0; i < nc; ++i) { @@ -15313,15 +15332,19 @@ static void ggml_compute_forward_cross_entropy_loss_f32( float max = -INFINITY; ggml_vec_max_f32(nc, &max, s0); - uint16_t scvt; + uint16_t scvt; UNUSED(scvt); for (int i = 0; i < nc; i++) { if (s0[i] == -INFINITY) { st[i] = 0.0f; } else { - // const float val = (s0[i] == -INFINITY) ? 0.0 : exp(s0[i] - max); +#ifndef GGML_CROSS_ENTROPY_EXP_FP16 + const float s = s0[i] - max; + const float val = expf(s); +#else ggml_fp16_t s = GGML_FP32_TO_FP16(s0[i] - max); memcpy(&scvt, &s, sizeof(scvt)); const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]); +#endif sum += (ggml_float)val; st[i] = val; } @@ -15337,7 +15360,9 @@ static void ggml_compute_forward_cross_entropy_loss_f32( ggml_vec_log_f32(nc, st, st); ggml_vec_mul_f32(nc, st, st, s1); - ggml_vec_sum_f32(nc, sums + ith, st); + float st_sum = 0; + ggml_vec_sum_f32(nc, &st_sum, st); + sums[ith] += st_sum; #ifndef NDEBUG for (int i = 0; i < nc; ++i) { @@ -15387,7 +15412,7 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32( return; } - const float eps = 1e-9f; + const double eps = 1e-9; // TODO: handle transposed/permuted matrices const int64_t nc = src0->ne[0]; @@ -15406,7 +15431,6 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32( float * ds0 = (float *)((char *) dst->data + i1*dst->nb[1]); float * s0 = (float *)((char *) src0->data + i1*src0->nb[1]); float * s1 = (float *)((char *) src1->data + i1*src1->nb[1]); - float * sm = (float *) params->wdata + ith*nc; #ifndef NDEBUG for (int i = 0; i < nc; ++i) { @@ -15415,54 +15439,6 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32( assert(!isnan(s1[i])); } #endif - // step by step explanation: - { - //float * sums = (float *) params->wdata; - - // forward pass with annotated gradients from backward pass - // (built by going in reverse operation order, adding to gradients of current operation args) - // st0 = exp(s0-max(s0)) grad[st0] = grad[st1]*(1.0 - eps)/sum - // from softmax_back: grad[s0] = st1_k * (grad[st1]_k - dot(st1, grad[st1])) - // ggml_vec_scale_f32(nc, st, sum); // st1 = st0*/sum = softmax(s0) grad[st1] = grad[st2]*(1.0 - eps) - // ggml_vec_scale_f32(nc, st, (1.0f - eps)); // st2 = st1*(1.0 - eps) grad[st2] = grad[st3] - // ggml_vec_add1_f32(nc, st, st, eps); // st3 = st2 + eps grad[st3] = grad[st4]/st3 - // ggml_vec_log_f32(nc, st, st); // st4 = log(st3) grad[st4] = grad[st5] * s1 - // ggml_vec_mul_f32(nc, st, st, s1); // st5 = st4 * s1 grad[st5] = grad[sums[ith]] - // ggml_vec_sum_f32(nc, sums + ith, st); // sums[ith] = st5 grad[sums[ith]] = grad[cross_entropy_loss] = -grad[cel] - - // substitute into grad[st1], because we can reuse softmax_back from this point on - // grad[st1] = -grad[cel]*s1*(1.0 - eps)/(eps + softmax(s0)*(1.0 - eps)) - // postorder: - // grad[st1] := softmax(s0) - // grad[st1] := grad[st1]*(1.0 - eps) - // grad[st1] := grad[st1] + eps - // grad[st1] := s1 / grad[st1] - // grad[st1] := grad[st1]*(1.0-eps)*-grad[cel] - - // src0 gradients by going through softmax_back - // grad[s0] = st1_k * (grad[st1]_k - dot(st1, grad[st1])) - // from softmax_back: - // dxk = yk * (dyk - dot(y, dy)) - // dot_y_dy := dot(y, dy) - // dx := dy - // dx := dx - dot_y_dy - // dx := dx * y - // postorder: - // dot_st1_dst1 := dot(st1, grad[st1]) - // grad[s0] := grad[st1] - // grad[s0] := grad[s0] - dot_st1_dst1 - // grad[s0] := grad[s0] * st1 - - // prepend postorder from grad[st1] directly using grad[s0] as memory location, as we will grad[s0] := grad[st1] - // sm := softmax(s0) - // grad[s0] := sm*(1.0 - eps) - // grad[s0] := grad[s0] + eps - // grad[s0] := s1 / grad[s0] - // grad[s0] := grad[s0]*(1.0-eps)*-grad[cel] - // dot_st1_dst1 := dot(sm, grad[s0]) - // grad[s0] := grad[s0] - dot_st1_dst1 - // grad[s0] := grad[s0] * sm - } // soft_max ggml_float sum = 0.0; @@ -15470,39 +15446,37 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32( float max = -INFINITY; ggml_vec_max_f32(nc, &max, s0); - uint16_t scvt; + uint16_t scvt; UNUSED(scvt); for (int i = 0; i < nc; i++) { if (s0[i] == -INFINITY) { - sm[i] = 0.0f; + ds0[i] = 0.0f; } else { - // const float val = (s0[i] == -INFINITY) ? 0.0 : exp(s0[i] - max); +#ifndef GGML_CROSS_ENTROPY_EXP_FP16 + const float s = s0[i] - max; + const float val = expf(s); +#else ggml_fp16_t s = GGML_FP32_TO_FP16(s0[i] - max); memcpy(&scvt, &s, sizeof(scvt)); const float val = GGML_FP16_TO_FP32(table_exp_f16[scvt]); +#endif sum += (ggml_float)val; - sm[i] = val; + ds0[i] = val; } } assert(sum > 0.0); - sum = 1.0/sum; + sum = (1.0 - eps)/sum; } - float dot_st1_dst1 = 0; - ggml_vec_scale_f32(nc, sm, sum); - ggml_vec_cpy_f32 (nc, ds0, sm); - ggml_vec_scale_f32(nc, ds0, (1.0f - eps)); - ggml_vec_add1_f32 (nc, ds0, ds0, eps); - ggml_vec_div_f32 (nc, ds0, s1, ds0); - ggml_vec_scale_f32(nc, ds0, -(1.0f - eps)*d[0]); - ggml_vec_dot_f32 (nc, &dot_st1_dst1, sm, ds0); - ggml_vec_acc1_f32 (nc, ds0, -dot_st1_dst1); - ggml_vec_mul_f32 (nc, ds0, ds0, sm); + // grad(src0) = (softmax(src0) - src1) * grad(cross_entropy_loss(src0, src1)) / nr + ggml_vec_scale_f32(nc, ds0, sum); + ggml_vec_add1_f32(nc, ds0, ds0, eps); + ggml_vec_sub_f32(nc, ds0, ds0, s1); + ggml_vec_scale_f32(nc, ds0, d[0] / (float) nr); + #ifndef NDEBUG for (int i = 0; i < nc; ++i) { - assert(!isnan(sm[i])); - assert(!isinf(sm[i])); assert(!isnan(ds0[i])); assert(!isinf(ds0[i])); } @@ -16057,9 +16031,12 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { // necessary for llama if (src0->grad) { + float eps; + memcpy(&eps, tensor->op_params, sizeof(float)); + src0->grad = ggml_add_impl(ctx, src0->grad, - ggml_rms_norm_back(ctx, src0, tensor->grad), + ggml_rms_norm_back(ctx, src0, tensor->grad, eps), inplace); } } break; @@ -16827,9 +16804,7 @@ struct ggml_cgraph ggml_build_forward(struct ggml_tensor * tensor) { return result; } -struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep) { - struct ggml_cgraph result = *gf; - +void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep) { GGML_ASSERT(gf->n_nodes > 0); // if we are keeping the gradient graph, we have to detach the gradient nodes from the original graph @@ -16853,15 +16828,19 @@ struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cg } } - for (int i = gf->n_nodes - 1; i >= 0; i--) { + for (int i = 0; i < gf->n_nodes; i++) { struct ggml_tensor * node = gf->nodes[i]; if (node->is_param) { GGML_PRINT_DEBUG("%s: found root node %p\n", __func__, (void *) node); - ggml_build_forward_expand(&result, node->grad); + ggml_build_forward_expand(gb, node->grad); } } +} +struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep) { + struct ggml_cgraph result = *gf; + ggml_build_backward_expand(ctx, gf, &result, keep); return result; } @@ -17537,10 +17516,6 @@ struct ggml_cplan ggml_graph_plan(struct ggml_cgraph * cgraph, int n_threads) { case GGML_OP_CROSS_ENTROPY_LOSS_BACK: { n_tasks = n_threads; - - size_t cur = ggml_type_size(node->type)*node->src[0]->ne[0]*n_tasks; - - work_size = MAX(work_size, cur); } break; case GGML_OP_NONE: { @@ -18418,14 +18393,16 @@ static enum ggml_opt_result ggml_opt_adam( struct ggml_opt_params params, struct ggml_tensor * f, struct ggml_cgraph * gf, - struct ggml_cgraph * gb) { + struct ggml_cgraph * gb, + ggml_opt_callback callback, + void * callback_data) { GGML_ASSERT(ggml_is_scalar(f)); // these will store the parameters we want to optimize struct ggml_tensor * ps[GGML_MAX_PARAMS]; int np = 0; - int nx = 0; + int64_t nx = 0; for (int i = 0; i < gf->n_nodes; ++i) { if (gf->nodes[i]->is_param) { GGML_PRINT_DEBUG("found param %d: grad->op = %d\n", np, gf->nodes[i]->grad->op); @@ -18444,31 +18421,32 @@ static enum ggml_opt_result ggml_opt_adam( } // constants - const float sched = params.adam.sched; - const float decay = params.adam.decay * sched; - const float alpha = params.adam.alpha * sched; + float sched = params.adam.sched; + const float alpha = params.adam.alpha; + const float decay = params.adam.decay * alpha; const float beta1 = params.adam.beta1; const float beta2 = params.adam.beta2; const float eps = params.adam.eps; + const float gclip = params.adam.gclip; + const int decay_min_ndim = params.adam.decay_min_ndim; - float * x = opt->adam.x->data; // view of the parameters - float * g1 = opt->adam.g1->data; // gradient - float * g2 = opt->adam.g2->data; // gradient squared float * m = opt->adam.m->data; // first moment float * v = opt->adam.v->data; // second moment - float * mh = opt->adam.mh->data; // first moment hat - float * vh = opt->adam.vh->data; // second moment hat float * pf = params.past > 0 ? opt->adam.pf->data : NULL; // past function values - // update view - ggml_opt_get_params(np, ps, x); + if (callback) { + callback(callback_data, &sched); + } // compute the function value ggml_graph_reset (gf); ggml_set_f32 (f->grad, 1.0f); - ggml_graph_compute_with_ctx(ctx, gb, params.n_threads); + struct ggml_cplan cplan = ggml_graph_plan(gb, params.n_threads); + struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size); + cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs; + ggml_graph_compute(gb, &cplan); opt->adam.fx_prev = ggml_get_f32_1d(f, 0); opt->adam.fx_best = opt->adam.fx_prev; @@ -18476,6 +18454,9 @@ static enum ggml_opt_result ggml_opt_adam( pf[opt->iter % params.past] = opt->adam.fx_prev; } + opt->loss_before = opt->adam.fx_prev; + opt->loss_after = opt->adam.fx_prev; + // initialize if (opt->just_initialized) { opt->adam.n_no_improvement = 0; @@ -18508,50 +18489,55 @@ static enum ggml_opt_result ggml_opt_adam( UNUSED(t_start_cpu); { - // update the gradient - ggml_opt_get_grad(np, ps, g1); + float gnorm = 1.0f; + if (gclip > 0.0f) { + // gradient clipping + ggml_float sum = 0.0; + for (int p = 0; p < np; ++p) { + const int64_t ne = ggml_nelements(ps[p]); + for (int64_t j = 0; j < ne; ++j) { + float g = ggml_get_f32_1d(ps[p]->grad, j); + sum += (ggml_float)(g*g); + } + } + ggml_float norm = sqrt(sum); + if (norm > (ggml_float) gclip) { + gnorm = (float) ((ggml_float) gclip / norm); + } + } + const float beta1h = alpha*sched/(1.0f - powf(beta1, opt->iter)); + const float beta2h = 1.0f/(1.0f - powf(beta2, opt->iter)); + int64_t i = 0; + for (int p = 0; p < np; ++p) { + const int64_t ne = ggml_nelements(ps[p]); + const float p_decay = ((ps[p]->n_dims >= decay_min_ndim) ? decay : 0.0f) * sched; + for (int64_t j = 0; j < ne; ++j) { + float x = ggml_get_f32_1d(ps[p], j); + float g = ggml_get_f32_1d(ps[p]->grad, j)*gnorm; + m[i] = m[i]*beta1 + g*(1.0f - beta1); + v[i] = v[i]*beta2 + g*g*(1.0f - beta2); + float mh = m[i]*beta1h; + float vh = v[i]*beta2h; + vh = sqrtf(vh) + eps; + x = x*(1.0f - p_decay) - mh/vh; + ggml_set_f32_1d(ps[p], j, x); + ++i; + } + } + } - // m_t = beta1*m_t-1 + (1 - beta1)*g_t - ggml_vec_scale_f32(nx, m, beta1); - ggml_vec_mad_f32 (nx, m, g1, 1.0f - beta1); - - // g2 = g1^2 - ggml_vec_sqr_f32 (nx, g2, g1); - - // v_t = beta2*v_t-1 + (1 - beta2)*g_t^2 - ggml_vec_scale_f32(nx, v, beta2); - ggml_vec_mad_f32 (nx, v, g2, 1.0f - beta2); - - // m^hat = m_t / (1 - beta1^t) - // v^hat = v_t / (1 - beta2^t) - // x_t = x_t-1 - sched*(alpha*m^hat/(sqrt(v^hat) + eps) + decay*x_t-1) - // x_t = x_t-1 - sched*alpha*m^hat/(sqrt(v^hat) + eps) - sched*decay*x_t-1 - // x_t = x_t-1*(1-sched*decay) - sched*alpha*m^hat/(sqrt(v^hat) + eps) - // x_t = x_t-1*(1-sched*decay) + sched*decay*(-alpha/decay)*m^hat/(sqrt(v^hat) + eps) - // x_t = mix(x_t-1, (-alpha/decay)*m^hat/(sqrt(v^hat) + eps), sched*decay) - ggml_vec_cpy_f32 (nx, mh, m); - ggml_vec_cpy_f32 (nx, vh, v); - - ggml_vec_scale_f32(nx, mh, alpha/(1.0f - powf(beta1, opt->iter))); - ggml_vec_scale_f32(nx, vh, 1.0f/(1.0f - powf(beta2, opt->iter))); - - ggml_vec_sqrt_f32 (nx, vh, vh); - ggml_vec_acc1_f32 (nx, vh, eps); - - ggml_vec_div_f32 (nx, mh, mh, vh); - ggml_vec_scale_f32(nx, x, 1.0f - decay); - ggml_vec_sub_f32 (nx, x, x, mh); - - // update the parameters - ggml_opt_set_params(np, ps, x); + if (callback) { + callback(callback_data, &sched); } ggml_graph_reset (gf); ggml_set_f32 (f->grad, 1.0f); - ggml_graph_compute_with_ctx(ctx, gb, params.n_threads); + ggml_graph_compute(gb, &cplan); const float fx = ggml_get_f32_1d(f, 0); + opt->loss_after = fx; + // check convergence if (fabsf(fx - fx_prev[0])/fx < params.adam.eps_f) { @@ -18620,7 +18606,6 @@ struct ggml_lbfgs_iteration_data { }; static enum ggml_opt_result linesearch_backtracking( - struct ggml_context * ctx, const struct ggml_opt_params * params, int nx, float * x, @@ -18632,8 +18617,11 @@ static enum ggml_opt_result linesearch_backtracking( struct ggml_tensor * f, struct ggml_cgraph * gf, struct ggml_cgraph * gb, + struct ggml_cplan * cplan, const int np, - struct ggml_tensor * ps[]) { + struct ggml_tensor * ps[], + ggml_opt_callback callback, + void * callback_data) { int count = 0; float width = 0.0f; @@ -18662,6 +18650,12 @@ static enum ggml_opt_result linesearch_backtracking( dgtest = params->lbfgs.ftol*dginit; while (true) { + if (callback) { + // LBFG-S does not support learning rate -> ignore learning schedule + float sched = 0; + callback(callback_data, &sched); + } + ggml_vec_cpy_f32(nx, x, xp); ggml_vec_mad_f32(nx, x, d, *step); @@ -18672,7 +18666,7 @@ static enum ggml_opt_result linesearch_backtracking( ggml_graph_reset (gf); ggml_set_f32 (f->grad, 1.0f); - ggml_graph_compute_with_ctx(ctx, gb, params->n_threads); + ggml_graph_compute(gb, cplan); ggml_opt_get_grad(np, ps, g); @@ -18732,7 +18726,9 @@ static enum ggml_opt_result ggml_opt_lbfgs( struct ggml_opt_params params, struct ggml_tensor * f, struct ggml_cgraph * gf, - struct ggml_cgraph * gb) { + struct ggml_cgraph * gb, + ggml_opt_callback callback, + void * callback_data) { if (params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_WOLFE || params.lbfgs.linesearch == GGML_LINESEARCH_BACKTRACKING_STRONG_WOLFE) { if (params.lbfgs.wolfe <= params.lbfgs.ftol || 1.f <= params.lbfgs.wolfe) { @@ -18764,6 +18760,10 @@ static enum ggml_opt_result ggml_opt_lbfgs( opt->iter = iter; } + struct ggml_cplan cplan = ggml_graph_plan(gb, params.n_threads); + struct ggml_object * obj = ggml_new_object(ctx, GGML_OBJECT_WORK_BUFFER, cplan.work_size); + cplan.work_data = (uint8_t *)ctx->mem_buffer + obj->offs; + float * x = opt->lbfgs.x->data; // current parameters float * xp = opt->lbfgs.xp->data; // previous parameters float * g = opt->lbfgs.g->data; // current gradient @@ -18785,6 +18785,12 @@ static enum ggml_opt_result ggml_opt_lbfgs( float * lm_s = opt->lbfgs.lms->data; float * lm_y = opt->lbfgs.lmy->data; + if (callback) { + // LBFG-S does not support learning rate -> ignore learning schedule + float sched = 0; + callback(callback_data, &sched); + } + // evaluate the function value and its gradient { ggml_opt_set_params(np, ps, x); @@ -18792,11 +18798,14 @@ static enum ggml_opt_result ggml_opt_lbfgs( ggml_graph_reset (gf); ggml_set_f32 (f->grad, 1.0f); - ggml_graph_compute_with_ctx(ctx, gb, params.n_threads); + ggml_graph_compute(gb, &cplan); ggml_opt_get_grad(np, ps, g); fx = ggml_get_f32_1d(f, 0); + + opt->loss_before = fx; + opt->loss_after = fx; } // search direction = -gradient @@ -18851,7 +18860,7 @@ static enum ggml_opt_result ggml_opt_lbfgs( ggml_vec_cpy_f32(nx, xp, x); ggml_vec_cpy_f32(nx, gp, g); - ls = linesearch_backtracking(ctx, ¶ms, nx, x, &fx, g, d, step, xp, f, gf, gb, np, ps); + ls = linesearch_backtracking(¶ms, nx, x, &fx, g, d, step, xp, f, gf, gb, &cplan, np, ps, callback, callback_data); if (ls < 0) { // linesearch failed - go back to the previous point and return @@ -18861,6 +18870,8 @@ static enum ggml_opt_result ggml_opt_lbfgs( return ls; } + opt->loss_after = fx; + ggml_vec_norm_f32(nx, &xnorm, x); ggml_vec_norm_f32(nx, &gnorm, g); @@ -18918,7 +18929,7 @@ static enum ggml_opt_result ggml_opt_lbfgs( // ys = y^t \cdot s -> 1 / \rho. // yy = y^t \cdot y. // - ggml_vec_dot_f32(nx, &ys, &lm_y[end[0]*nx], &lm_s[end[0] *nx]); + ggml_vec_dot_f32(nx, &ys, &lm_y[end[0]*nx], &lm_s[end[0]*nx]); ggml_vec_dot_f32(nx, &yy, &lm_y[end[0]*nx], &lm_y[end[0]*nx]); lm_ys[end[0]] = ys; @@ -18981,13 +18992,15 @@ struct ggml_opt_params ggml_opt_default_params(enum ggml_opt_type type) { .adam = { .n_iter = 10000, .sched = 1.000f, - .decay = 0.001f, + .decay = 0.0f, + .decay_min_ndim = 2, .alpha = 0.001f, .beta1 = 0.9f, .beta2 = 0.999f, .eps = 1e-8f, .eps_f = 1e-5f, .eps_g = 1e-3f, + .gclip = 0.0f, }, }; } break; @@ -19037,23 +19050,13 @@ GGML_API void ggml_opt_init( switch (opt->params.type) { case GGML_OPT_ADAM: { - opt->adam.x = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx); - opt->adam.g1 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx); - opt->adam.g2 = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx); opt->adam.m = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx); opt->adam.v = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx); - opt->adam.mh = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx); - opt->adam.vh = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, nx); opt->adam.pf = params.past > 0 ? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, params.past) : NULL; - ggml_set_zero(opt->adam.x); - ggml_set_zero(opt->adam.g1); - ggml_set_zero(opt->adam.g2); ggml_set_zero(opt->adam.m); ggml_set_zero(opt->adam.v); - ggml_set_zero(opt->adam.mh); - ggml_set_zero(opt->adam.vh); if (opt->adam.pf) { ggml_set_zero(opt->adam.pf); } @@ -19137,7 +19140,7 @@ enum ggml_opt_result ggml_opt_resume( *gf = ggml_build_forward (f); *gb = ggml_build_backward(ctx, gf, true); - return ggml_opt_resume_g(ctx, opt, f, gf, gb); + return ggml_opt_resume_g(ctx, opt, f, gf, gb, NULL, NULL); } enum ggml_opt_result ggml_opt_resume_g( @@ -19145,7 +19148,9 @@ enum ggml_opt_result ggml_opt_resume_g( struct ggml_opt_context * opt, struct ggml_tensor * f, struct ggml_cgraph * gf, - struct ggml_cgraph * gb) { + struct ggml_cgraph * gb, + ggml_opt_callback callback, + void * callback_data) { // build forward + backward compute graphs enum ggml_opt_result result = GGML_OPT_OK; @@ -19153,11 +19158,11 @@ enum ggml_opt_result ggml_opt_resume_g( switch (opt->params.type) { case GGML_OPT_ADAM: { - result = ggml_opt_adam(ctx, opt, opt->params, f, gf, gb); + result = ggml_opt_adam(ctx, opt, opt->params, f, gf, gb, callback, callback_data); } break; case GGML_OPT_LBFGS: { - result = ggml_opt_lbfgs(ctx, opt, opt->params, f, gf, gb); + result = ggml_opt_lbfgs(ctx, opt, opt->params, f, gf, gb, callback, callback_data); } break; } @@ -19612,7 +19617,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p // read the kv pairs { - ctx->kv = GGML_ALIGNED_MALLOC(ctx->header.n_kv * sizeof(struct gguf_kv)); + ctx->kv = malloc(ctx->header.n_kv * sizeof(struct gguf_kv)); for (uint32_t i = 0; i < ctx->header.n_kv; ++i) { struct gguf_kv * kv = &ctx->kv[i]; @@ -19695,7 +19700,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p // read the tensor infos { - ctx->infos = GGML_ALIGNED_MALLOC(ctx->header.n_tensors * sizeof(struct gguf_tensor_info)); + ctx->infos = malloc(ctx->header.n_tensors * sizeof(struct gguf_tensor_info)); for (uint32_t i = 0; i < ctx->header.n_tensors; ++i) { struct gguf_tensor_info * info = &ctx->infos[i]; @@ -19896,7 +19901,7 @@ void gguf_free(struct gguf_context * ctx) { } } - GGML_ALIGNED_FREE(ctx->kv); + free(ctx->kv); } if (ctx->infos) { @@ -19908,7 +19913,7 @@ void gguf_free(struct gguf_context * ctx) { } } - GGML_ALIGNED_FREE(ctx->infos); + free(ctx->infos); } GGML_ALIGNED_FREE(ctx); diff --git a/ggml.h b/ggml.h index 4ef3d5253..8b410cc85 100644 --- a/ggml.h +++ b/ggml.h @@ -952,11 +952,11 @@ extern "C" { // a - x // b - dy - // TODO: update with configurable eps GGML_API struct ggml_tensor * ggml_rms_norm_back( struct ggml_context * ctx, struct ggml_tensor * a, - struct ggml_tensor * b); + struct ggml_tensor * b, + float eps); // A: n columns, m rows // B: n columns, p rows (i.e. we transpose it internally) @@ -1612,7 +1612,8 @@ extern "C" { struct ggml_tensor * tensor); - GGML_API void ggml_build_forward_expand(struct ggml_cgraph * cgraph, struct ggml_tensor * tensor); + GGML_API void ggml_build_forward_expand (struct ggml_cgraph * cgraph, struct ggml_tensor * tensor); + GGML_API void ggml_build_backward_expand(struct ggml_context * ctx, struct ggml_cgraph * gf, struct ggml_cgraph * gb, bool keep); GGML_API struct ggml_cgraph ggml_build_forward (struct ggml_tensor * tensor); GGML_API struct ggml_cgraph ggml_build_backward(struct ggml_context * ctx, struct ggml_cgraph * gf, bool keep); @@ -1677,6 +1678,8 @@ extern "C" { GGML_LINESEARCH_INVALID_PARAMETERS, }; + typedef void (*ggml_opt_callback)(void * data, float * sched); + // optimization parameters // // see ggml.c (ggml_opt_default_params) for default values @@ -1712,12 +1715,14 @@ extern "C" { float sched; // schedule multiplier (fixed, decay or warmup) float decay; // weight decay for AdamW, use 0.0f to disable + int decay_min_ndim; // minimum number of tensor dimension to apply weight decay float alpha; // learning rate float beta1; float beta2; float eps; // epsilon for numerical stability float eps_f; // epsilon for convergence test float eps_g; // epsilon for convergence test + float gclip; // gradient clipping } adam; // LBFGS parameters @@ -1745,14 +1750,12 @@ extern "C" { bool just_initialized; + float loss_before; + float loss_after; + struct { - struct ggml_tensor * x; // view of the parameters - struct ggml_tensor * g1; // gradient - struct ggml_tensor * g2; // gradient squared struct ggml_tensor * m; // first moment struct ggml_tensor * v; // second moment - struct ggml_tensor * mh; // first moment hat - struct ggml_tensor * vh; // second moment hat struct ggml_tensor * pf; // past function values float fx_best; float fx_prev; @@ -1789,10 +1792,10 @@ extern "C" { // initialize optimizer context GGML_API void ggml_opt_init( - struct ggml_context * ctx, + struct ggml_context * ctx, struct ggml_opt_context * opt, - struct ggml_opt_params params, - int64_t nx); + struct ggml_opt_params params, + int64_t nx); // continue optimizing the function defined by the tensor f GGML_API enum ggml_opt_result ggml_opt_resume( @@ -1806,7 +1809,9 @@ extern "C" { struct ggml_opt_context * opt, struct ggml_tensor * f, struct ggml_cgraph * gf, - struct ggml_cgraph * gb); + struct ggml_cgraph * gb, + ggml_opt_callback callback, + void * callback_data); // // quantization diff --git a/llama.cpp b/llama.cpp index 11697ee65..7cb468538 100644 --- a/llama.cpp +++ b/llama.cpp @@ -6248,7 +6248,6 @@ const char * llama_print_system_info(void) { } void llama_dump_timing_info_yaml(FILE * stream, const llama_context * ctx) { - fprintf(stream, "\n"); fprintf(stream, "###########\n"); fprintf(stream, "# Timings #\n"); @@ -6264,10 +6263,10 @@ void llama_dump_timing_info_yaml(FILE * stream, const llama_context * ctx) { fprintf(stream, "n_eval: %d # number of tokens generated (excluding the first one)\n", ctx->n_eval); fprintf(stream, "n_p_eval: %d # number of tokens processed in batches at the beginning\n", ctx->n_p_eval); fprintf(stream, "n_sample: %d # number of sampled tokens\n", ctx->n_sample); - fprintf(stream, "t_eval_us: %ld # total microseconds spent generating tokens\n", ctx->t_eval_us); - fprintf(stream, "t_load_us: %ld # total microseconds spent loading the model\n", ctx->t_load_us); - fprintf(stream, "t_p_eval_us: %ld # total microseconds spent prompt processing\n", ctx->t_p_eval_us); - fprintf(stream, "t_sample_us: %ld # total microseconds spent sampling\n", ctx->t_sample_us); + fprintf(stream, "t_eval_us: %" PRId64 " # total microseconds spent generating tokens\n", ctx->t_eval_us); + fprintf(stream, "t_load_us: %" PRId64 " # total microseconds spent loading the model\n", ctx->t_load_us); + fprintf(stream, "t_p_eval_us: %" PRId64 " # total microseconds spent prompt processing\n", ctx->t_p_eval_us); + fprintf(stream, "t_sample_us: %" PRId64 " # total microseconds spent sampling\n", ctx->t_sample_us); fprintf(stream, "ts_eval: %.2f # tokens / second during generation\n", 1.0e6 * ctx->n_eval / ctx->t_eval_us); fprintf(stream, "ts_p_eval: %.2f # tokens / second during prompt processing\n", diff --git a/tests/test-grad0.cpp b/tests/test-grad0.cpp index 75a698d73..468cde66a 100644 --- a/tests/test-grad0.cpp +++ b/tests/test-grad0.cpp @@ -275,14 +275,14 @@ static bool check_gradient( ggml_graph_compute_with_ctx(ctx0, &gf, n_threads); - const float f0 = ggml_get_f32_1d(f, 0); + const double f0 = ggml_get_f32_1d(f, 0); ggml_set_f32_1d(x[i], k, xm); ggml_graph_compute_with_ctx(ctx0, &gf, n_threads); - const float f1 = ggml_get_f32_1d(f, 0); - const float g0 = (f0 - f1)/(2.0f*eps); + const double f1 = ggml_get_f32_1d(f, 0); + const double g0 = (f0 - f1)/(2.0*(double) eps); ggml_set_f32_1d(x[i], k, x0); @@ -292,10 +292,10 @@ static bool check_gradient( ggml_graph_compute_with_ctx(ctx0, &gb, n_threads); - const float g1 = ggml_get_f32_1d(x[i]->grad, k); + const double g1 = ggml_get_f32_1d(x[i]->grad, k); - const float error_abs = fabsf(g0 - g1); - const float error_rel = g0 != 0 ? fabsf(g0 - g1)/fabsf(g0) : 0; + const double error_abs = fabs(g0 - g1); + const double error_rel = g0 != 0 ? fabs(g0 - g1)/fabs(g0) : 0; if (error_abs > max_error_abs || error_rel > max_error_rel) { printf("%s: ndims=%d, i=%d, k=%d, x0=%f, xm=%f, xp=%f, f0=%f, f1=%f, g0=%f, g1=%f, eps=%f, error_abs=%f, error_rel=%f\n", @@ -531,7 +531,7 @@ int main(int argc, const char ** argv) { struct ggml_tensor * f = ggml_sum(ctx0, ggml_sqrt(ctx0, x[0])); - check_gradient("sqrt", ctx0, x, f, ndims, nargs, 1e-3f, INFINITY, 1e-1f); + check_gradient("sqrt", ctx0, x, f, ndims, nargs, 1e-3f, 2e-2f, 1e-1f); } } @@ -1345,9 +1345,18 @@ int main(int argc, const char ** argv) { x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f); ggml_set_param(ctx0, x[0]); - struct ggml_tensor * f = ggml_sum(ctx0, ggml_soft_max(ctx0, x[0])); + float eps = 1e-6f; + // dont use only sum as aggregation, because sum of softmax is always 1 -> finite differences should not work + // instead use sum(log(soft_max()*(1-eps)+eps)); use eps to avoid log(0) + struct ggml_tensor * f = ggml_sum(ctx0, + ggml_log(ctx0, + ggml_add1(ctx0, + ggml_scale(ctx0, + ggml_soft_max(ctx0, x[0]), + ggml_new_f32(ctx0, 1.0f - eps)), + ggml_new_f32(ctx0, eps)))); - check_gradient("softmax", ctx0, x, f, ndims, nargs, 1e-3f, 1e-3f, INFINITY); + check_gradient("softmax", ctx0, x, f, ndims, nargs, 1e-3f, 2e-1f, INFINITY); } } @@ -1358,15 +1367,26 @@ int main(int argc, const char ** argv) { int64_t ne2[4]; get_random_dims(ne2, 4); - for (int ndims = 1; ndims <= 3; ++ndims) { - x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -1.0f, 1.0f); + for (int ndims = 1; ndims <= 4; ++ndims) { + x[0] = get_random_tensor_f32(ctx0, ndims, ne2, -0.1f, 0.1f); x[1] = get_random_tensor_f32(ctx0, ndims, ne2, 0.0f, 1.0f); + // the second argument to cross_entropy_loss must sum up to 1 for each row + int nr = ggml_nrows(x[1]); + int nc = ggml_nelements(x[1]) / nr; + for (int ir = 0; ir < nr; ++ir) { + float sum = 0; + for (int ic = 0; ic < nc; ++ic) { + sum += ((float *) x[1]->data)[ic + ir*nc]; + } + for (int ic = 0; ic < nc; ++ic) { + ((float *) x[1]->data)[ic + ir*nc] /= sum; + } + } ggml_set_param(ctx0, x[0]); - struct ggml_tensor * f = ggml_sum(ctx0, ggml_cross_entropy_loss(ctx0, x[0], x[1])); + struct ggml_tensor * f = ggml_cross_entropy_loss(ctx0, x[0], x[1]); - check_gradient("cross_entropy_loss", ctx0, x, f, ndims, nargs, 1e-1f, 1e-2f, INFINITY); - // finite differences regularly fails! + check_gradient("cross_entropy_loss", ctx0, x, f, ndims, nargs, 1e-4f, 1e-3f, INFINITY); } } @@ -1473,7 +1493,7 @@ int main(int argc, const char ** argv) { struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0))); - check_gradient("flash_attn f32", ctx0, x, f, ndims, nargs, 1.5e-4f, INFINITY, 3.5f); + check_gradient("flash_attn f32", ctx0, x, f, ndims, nargs, 1.5e-4f, 1e-3f, INFINITY); } } } @@ -1514,7 +1534,7 @@ int main(int argc, const char ** argv) { struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0))); - check_gradient("flash_attn f16", ctx0, x, f, ndims, nargs, 1.5e-4f, INFINITY, 3.5f); + check_gradient("flash_attn f16", ctx0, x, f, ndims, nargs, 1.5e-4f, 1e-3f, INFINITY); } } } From 95b6e5212f5e4e1419de1d833d7f8d788f9f2227 Mon Sep 17 00:00:00 2001 From: Marcus Dunn <51931484+MarcusDunn@users.noreply.github.com> Date: Mon, 28 Aug 2023 23:33:27 -0700 Subject: [PATCH 407/852] added `struct` to llama_dump_timing_info_yaml's `llama_context` (#2857) fixes C compat. --- llama.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.h b/llama.h index b38d3be20..6e5e1df63 100644 --- a/llama.h +++ b/llama.h @@ -521,7 +521,7 @@ extern "C" { // If this is not called, or NULL is supplied, everything is output on stderr. LLAMA_API void llama_log_set(llama_log_callback log_callback, void * user_data); - LLAMA_API void llama_dump_timing_info_yaml(FILE * stream, const llama_context * ctx); + LLAMA_API void llama_dump_timing_info_yaml(FILE * stream, const struct llama_context * ctx); #ifdef __cplusplus } From 611363ac791435497e66278dfe31ac8a4e11fa4f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 29 Aug 2023 10:50:30 +0300 Subject: [PATCH 408/852] scripts : add pipefail --- scripts/qnt-all.sh | 1 + scripts/run-all-perf.sh | 1 + scripts/run-all-ppl.sh | 1 + 3 files changed, 3 insertions(+) diff --git a/scripts/qnt-all.sh b/scripts/qnt-all.sh index 1376e4194..b4c2a159e 100755 --- a/scripts/qnt-all.sh +++ b/scripts/qnt-all.sh @@ -20,6 +20,7 @@ fi model="$1" out="../tmp/results-${model}" +set -o pipefail set -e mkdir -p ${out} diff --git a/scripts/run-all-perf.sh b/scripts/run-all-perf.sh index 7391e3dd5..6384e364d 100755 --- a/scripts/run-all-perf.sh +++ b/scripts/run-all-perf.sh @@ -20,6 +20,7 @@ fi model="$1" out="../tmp/results-${model}" +set -o pipefail set -e mkdir -p ${out} diff --git a/scripts/run-all-ppl.sh b/scripts/run-all-ppl.sh index f643ca3ae..e04d61d7f 100755 --- a/scripts/run-all-ppl.sh +++ b/scripts/run-all-ppl.sh @@ -17,6 +17,7 @@ if [ ! -z "$3" ]; then args="$3" fi +set -o pipefail set -e model="$1" From 3a007648f230ea37d6cca5e63850f04ebb12d2cf Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 29 Aug 2023 11:33:46 +0300 Subject: [PATCH 409/852] metal : add option to disable debug logs (close #2764) --- CMakeLists.txt | 2 +- Makefile | 2 +- ggml-metal.m | 71 +++++++++++++++++++++++--------------------------- 3 files changed, 35 insertions(+), 40 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index ba008bcc6..1eae2d670 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -301,7 +301,7 @@ if (LLAMA_METAL) set(GGML_SOURCES_METAL ggml-metal.m ggml-metal.h) add_compile_definitions(GGML_USE_METAL) - add_compile_definitions(GGML_METAL_NDEBUG) + #add_compile_definitions(GGML_METAL_NDEBUG) # get full path to the file #add_compile_definitions(GGML_METAL_DIR_KERNELS="${CMAKE_CURRENT_SOURCE_DIR}/") diff --git a/Makefile b/Makefile index e60821dd5..a64374e7d 100644 --- a/Makefile +++ b/Makefile @@ -305,7 +305,7 @@ ggml-cuda.o: ggml-cuda.cu ggml-cuda.h endif # LLAMA_HIPBLAS ifdef LLAMA_METAL - CFLAGS += -DGGML_USE_METAL -DGGML_METAL_NDEBUG + CFLAGS += -DGGML_USE_METAL #-DGGML_METAL_NDEBUG CXXFLAGS += -DGGML_USE_METAL LDFLAGS += -framework Foundation -framework Metal -framework MetalKit OBJS += ggml-metal.o diff --git a/ggml-metal.m b/ggml-metal.m index ad2ee8cf5..e929c4b07 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -11,6 +11,7 @@ #define MIN(a, b) ((a) < (b) ? (a) : (b)) #define MAX(a, b) ((a) > (b) ? (a) : (b)) +// TODO: temporary - reuse llama.cpp logging #ifdef GGML_METAL_NDEBUG #define metal_printf(...) #else @@ -113,7 +114,7 @@ static NSString * const msl_library_source = @"see metal.metal"; @end struct ggml_metal_context * ggml_metal_init(int n_cb) { - fprintf(stderr, "%s: allocating\n", __func__); + metal_printf("%s: allocating\n", __func__); struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context)); @@ -132,7 +133,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { ctx->library = [ctx->device newLibraryWithSource:msl_library_source options:nil error:&error]; if (error) { - fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]); + metal_printf("%s: error: %s\n", __func__, [[error description] UTF8String]); return NULL; } } @@ -146,11 +147,11 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { //NSString * path = [[NSBundle mainBundle] pathForResource:@"../../examples/metal/metal" ofType:@"metal"]; NSBundle * bundle = [NSBundle bundleForClass:[GGMLMetalClass class]]; NSString * path = [bundle pathForResource:@"ggml-metal" ofType:@"metal"]; - fprintf(stderr, "%s: loading '%s'\n", __func__, [path UTF8String]); + metal_printf("%s: loading '%s'\n", __func__, [path UTF8String]); NSString * src = [NSString stringWithContentsOfFile:path encoding:NSUTF8StringEncoding error:&error]; if (error) { - fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]); + metal_printf("%s: error: %s\n", __func__, [[error description] UTF8String]); return NULL; } @@ -162,7 +163,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { ctx->library = [ctx->device newLibraryWithSource:src options:nil error:&error]; #endif if (error) { - fprintf(stderr, "%s: error: %s\n", __func__, [[error description] UTF8String]); + metal_printf("%s: error: %s\n", __func__, [[error description] UTF8String]); return NULL; } } @@ -174,11 +175,11 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { #define GGML_METAL_ADD_KERNEL(name) \ ctx->function_##name = [ctx->library newFunctionWithName:@"kernel_"#name]; \ ctx->pipeline_##name = [ctx->device newComputePipelineStateWithFunction:ctx->function_##name error:&error]; \ - fprintf(stderr, "%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name, \ + metal_printf("%s: loaded %-32s %16p | th_max = %4d | th_width = %4d\n", __func__, "kernel_"#name, (void *) ctx->pipeline_##name, \ (int) ctx->pipeline_##name.maxTotalThreadsPerThreadgroup, \ (int) ctx->pipeline_##name.threadExecutionWidth); \ if (error) { \ - fprintf(stderr, "%s: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \ + metal_printf("%s: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \ return NULL; \ } @@ -230,19 +231,19 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { #undef GGML_METAL_ADD_KERNEL } - fprintf(stderr, "%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); - fprintf(stderr, "%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false"); + metal_printf("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); + metal_printf("%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false"); if (ctx->device.maxTransferRate != 0) { - fprintf(stderr, "%s: maxTransferRate = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1024.0 / 1024.0); + metal_printf("%s: maxTransferRate = %8.2f MB/s\n", __func__, ctx->device.maxTransferRate / 1024.0 / 1024.0); } else { - fprintf(stderr, "%s: maxTransferRate = built-in GPU\n", __func__); + metal_printf("%s: maxTransferRate = built-in GPU\n", __func__); } return ctx; } void ggml_metal_free(struct ggml_metal_context * ctx) { - fprintf(stderr, "%s: deallocating\n", __func__); + metal_printf("%s: deallocating\n", __func__); #define GGML_METAL_DEL_KERNEL(name) \ [ctx->function_##name release]; \ [ctx->pipeline_##name release]; @@ -311,7 +312,7 @@ void * ggml_metal_host_malloc(size_t n) { void * data = NULL; const int result = posix_memalign((void **) &data, getpagesize(), n); if (result != 0) { - fprintf(stderr, "%s: error: posix_memalign failed\n", __func__); + metal_printf("%s: error: posix_memalign failed\n", __func__); return NULL; } @@ -339,7 +340,7 @@ int * ggml_metal_get_concur_list(struct ggml_metal_context * ctx) { // Metal buffer based on the host memory pointer // static id ggml_metal_get_buffer(struct ggml_metal_context * ctx, struct ggml_tensor * t, size_t * offs) { - //fprintf(stderr, "%s: data tensor '%16s', offs_data = %8ld, offs_eval = %8ld, offs_cach = %8ld\n", __func__, t->name, offs_data, offs_eval, offs_cach); + //metal_printf("%s: data tensor '%16s', offs_data = %8ld, offs_eval = %8ld, offs_cach = %8ld\n", __func__, t->name, offs_data, offs_eval, offs_cach); const int64_t tsize = ggml_nbytes(t); @@ -350,13 +351,13 @@ static id ggml_metal_get_buffer(struct ggml_metal_context * ctx, stru if (ioffs >= 0 && ioffs + tsize <= (int64_t) ctx->buffers[i].size) { *offs = (size_t) ioffs; - //fprintf(stderr, "%s: '%s' tensor '%16s', offs = %8ld\n", __func__, ctx->buffers[i].name, t->name, *offs); + //metal_printf("%s: '%s' tensor '%16s', offs = %8ld\n", __func__, ctx->buffers[i].name, t->name, *offs); return ctx->buffers[i].metal; } } - fprintf(stderr, "%s: error: buffer is nil\n", __func__); + metal_printf("%s: error: buffer is nil\n", __func__); return nil; } @@ -368,7 +369,7 @@ bool ggml_metal_add_buffer( size_t size, size_t max_size) { if (ctx->n_buffers >= GGML_METAL_MAX_BUFFERS) { - fprintf(stderr, "%s: too many buffers\n", __func__); + metal_printf("%s: too many buffers\n", __func__); return false; } @@ -378,7 +379,7 @@ bool ggml_metal_add_buffer( const int64_t ioffs = (int64_t) data - (int64_t) ctx->buffers[i].data; if (ioffs >= 0 && ioffs < (int64_t) ctx->buffers[i].size) { - fprintf(stderr, "%s: error: buffer '%s' overlaps with '%s'\n", __func__, name, ctx->buffers[i].name); + metal_printf("%s: error: buffer '%s' overlaps with '%s'\n", __func__, name, ctx->buffers[i].name); return false; } } @@ -399,11 +400,11 @@ bool ggml_metal_add_buffer( ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:data length:size_aligned options:MTLResourceStorageModeShared deallocator:nil]; if (ctx->buffers[ctx->n_buffers].metal == nil) { - fprintf(stderr, "%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_aligned / 1024.0 / 1024.0); + metal_printf("%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_aligned / 1024.0 / 1024.0); return false; } - fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB", __func__, name, size_aligned / 1024.0 / 1024.0); + metal_printf("%s: allocated '%-16s' buffer, size = %8.2f MB", __func__, name, size_aligned / 1024.0 / 1024.0); ++ctx->n_buffers; } else { @@ -423,27 +424,27 @@ bool ggml_metal_add_buffer( ctx->buffers[ctx->n_buffers].metal = [ctx->device newBufferWithBytesNoCopy:(void *) ((uint8_t *) data + i) length:size_step_aligned options:MTLResourceStorageModeShared deallocator:nil]; if (ctx->buffers[ctx->n_buffers].metal == nil) { - fprintf(stderr, "%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_step_aligned / 1024.0 / 1024.0); + metal_printf("%s: failed to allocate '%-16s' buffer, size = %8.2f MB\n", __func__, name, size_step_aligned / 1024.0 / 1024.0); return false; } - fprintf(stderr, "%s: allocated '%-16s' buffer, size = %8.2f MB, offs = %12ld", __func__, name, size_step_aligned / 1024.0 / 1024.0, i); + metal_printf("%s: allocated '%-16s' buffer, size = %8.2f MB, offs = %12ld", __func__, name, size_step_aligned / 1024.0 / 1024.0, i); if (i + size_step < size) { - fprintf(stderr, "\n"); + metal_printf("\n"); } ++ctx->n_buffers; } } - fprintf(stderr, ", (%8.2f / %8.2f)", + metal_printf(", (%8.2f / %8.2f)", ctx->device.currentAllocatedSize / 1024.0 / 1024.0, ctx->device.recommendedMaxWorkingSetSize / 1024.0 / 1024.0); if (ctx->device.currentAllocatedSize > ctx->device.recommendedMaxWorkingSetSize) { - fprintf(stderr, ", warning: current allocated size is greater than the recommended max working set size\n"); + metal_printf(", warning: current allocated size is greater than the recommended max working set size\n"); } else { - fprintf(stderr, "\n"); + metal_printf("\n"); } } @@ -453,8 +454,6 @@ bool ggml_metal_add_buffer( void ggml_metal_set_tensor( struct ggml_metal_context * ctx, struct ggml_tensor * t) { - metal_printf("%s: set input for tensor '%s'\n", __func__, t->name); - size_t offs; id id_dst = ggml_metal_get_buffer(ctx, t, &offs); @@ -464,8 +463,6 @@ void ggml_metal_set_tensor( void ggml_metal_get_tensor( struct ggml_metal_context * ctx, struct ggml_tensor * t) { - metal_printf("%s: extract results for tensor '%s'\n", __func__, t->name); - size_t offs; id id_src = ggml_metal_get_buffer(ctx, t, &offs); @@ -560,15 +557,13 @@ void ggml_metal_graph_find_concurrency( } if (ctx->concur_list_len > GGML_MAX_CONCUR) { - fprintf(stderr, "%s: too many elements for metal ctx->concur_list!\n", __func__); + metal_printf("%s: too many elements for metal ctx->concur_list!\n", __func__); } } void ggml_metal_graph_compute( struct ggml_metal_context * ctx, struct ggml_cgraph * gf) { - metal_printf("%s: evaluating graph\n", __func__); - @autoreleasepool { // if there is ctx->concur_list, dispatch concurrently @@ -616,7 +611,7 @@ void ggml_metal_graph_compute( continue; } - metal_printf("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op)); + //metal_printf("%s: encoding node %3d, op = %8s\n", __func__, i, ggml_op_name(gf->nodes[i]->op)); struct ggml_tensor * src0 = gf->nodes[i]->src[0]; struct ggml_tensor * src1 = gf->nodes[i]->src[1]; @@ -764,7 +759,7 @@ void ggml_metal_graph_compute( } break; default: { - fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op)); + metal_printf("%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op)); GGML_ASSERT(false); } } break; @@ -923,7 +918,7 @@ void ggml_metal_graph_compute( } break; default: { - fprintf(stderr, "Asserting on type %d\n",(int)src0t); + metal_printf("Asserting on type %d\n",(int)src0t); GGML_ASSERT(false && "not implemented"); } }; @@ -1161,7 +1156,7 @@ void ggml_metal_graph_compute( } break; default: { - fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op)); + metal_printf("%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op)); GGML_ASSERT(false); } } @@ -1186,7 +1181,7 @@ void ggml_metal_graph_compute( MTLCommandBufferStatus status = (MTLCommandBufferStatus) [ctx->command_buffers[i] status]; if (status != MTLCommandBufferStatusCompleted) { - fprintf(stderr, "%s: command buffer %d failed with status %lu\n", __func__, i, status); + metal_printf("%s: command buffer %d failed with status %lu\n", __func__, i, status); GGML_ASSERT(false); } } From d4b5e16c32ba9c5fa6bbd035e80a99c113050cde Mon Sep 17 00:00:00 2001 From: Cebtenzzre Date: Tue, 29 Aug 2023 04:42:41 -0400 Subject: [PATCH 410/852] make : fix clang tests build, add missing examples (#2859) * make : do not pass headers to the compiler This fixes building tests with clang. * make : add missing examples * make : fix build-info.h dependencies --- Makefile | 49 ++++++++++++++++++++++++++++++++----------------- 1 file changed, 32 insertions(+), 17 deletions(-) diff --git a/Makefile b/Makefile index a64374e7d..02ba3e36d 100644 --- a/Makefile +++ b/Makefile @@ -1,5 +1,5 @@ # Define the default target now so that it is always the first target -BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple server embd-input-test gguf llama-bench +BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple save-load-state server embd-input-test gguf llama-bench baby-llama beam_search # Binaries only useful for tests TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama tests/test-tokenizer-0-falcon tests/test-tokenizer-1 @@ -356,7 +356,7 @@ OBJS += ggml-alloc.o llama.o: llama.cpp ggml.h ggml-alloc.h ggml-cuda.h ggml-metal.h llama.h $(CXX) $(CXXFLAGS) -c $< -o $@ -common.o: common/common.cpp common/common.h +common.o: common/common.cpp common/common.h build-info.h $(CXX) $(CXXFLAGS) -c $< -o $@ console.o: common/console.cpp common/console.h @@ -369,7 +369,7 @@ libllama.so: llama.o ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) clean: - rm -vf *.o *.so *.dll main quantize quantize-stats perplexity embedding benchmark-matmult save-load-state server simple vdot train-text-from-scratch convert-llama2c-to-ggml embd-input-test gguf llama-bench build-info.h $(TEST_TARGETS) + rm -vf *.o *.so *.dll benchmark-matmult build-info.h $(BUILD_TARGETS) $(TEST_TARGETS) # # Examples @@ -409,18 +409,33 @@ $(LIB_PRE)embdinput$(DSO_EXT): examples/embd-input/embd-input.h examples/embd-in embd-input-test: $(LIB_PRE)embdinput$(DSO_EXT) examples/embd-input/embd-input-test.cpp build-info.h ggml.o llama.o common.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %$(DSO_EXT),$(filter-out %.h,$(filter-out %.hpp,$^))) -o $@ $(LDFLAGS) -L. -lembdinput -gguf: examples/gguf/gguf.cpp build-info.h ggml.o llama.o $(OBJS) +gguf: examples/gguf/gguf.cpp ggml.o llama.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp build-info.h ggml.o llama.o common.o $(OBJS) +train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp ggml.o llama.o common.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp build-info.h ggml.o llama.o $(OBJS) +convert-llama2c-to-ggml: examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp ggml.o llama.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) llama-bench: examples/llama-bench/llama-bench.cpp build-info.h ggml.o llama.o common.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) +baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o common.o $(OBJS) + $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + +beam_search: examples/beam_search/beam_search.cpp build-info.h ggml.o llama.o common.o $(OBJS) + $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + +ifneq '' '$(or $(filter clean,$(MAKECMDGOALS)),$(LLAMA_METAL))' +BUILD_TARGETS += metal +endif + +ifdef LLAMA_METAL +metal: examples/metal/metal.cpp ggml.o $(OBJS) + $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) +endif + build-info.h: $(wildcard .git/index) scripts/build-info.sh @sh scripts/build-info.sh > $@.tmp @if ! cmp -s $@.tmp $@; then \ @@ -443,34 +458,34 @@ vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS) $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) tests/test-llama-grammar: tests/test-llama-grammar.cpp build-info.h ggml.o common.o grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) tests/test-grammar-parser: tests/test-grammar-parser.cpp build-info.h ggml.o llama.o common.o grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) tests/test-double-float: tests/test-double-float.cpp build-info.h ggml.o llama.o common.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) tests/test-grad0: tests/test-grad0.cpp build-info.h ggml.o llama.o common.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) tests/test-opt: tests/test-opt.cpp build-info.h ggml.o llama.o common.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) tests/test-quantize-fns: tests/test-quantize-fns.cpp build-info.h ggml.o llama.o common.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) tests/test-quantize-perf: tests/test-quantize-perf.cpp build-info.h ggml.o llama.o common.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) tests/test-sampling: tests/test-sampling.cpp build-info.h ggml.o llama.o common.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp build-info.h ggml.o llama.o common.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp build-info.h ggml.o llama.o common.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) tests/test-tokenizer-1: tests/test-tokenizer-1.cpp build-info.h ggml.o llama.o common.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.txt,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) From 74e0caeb82fc9db77fa2cc93070bb919a9a935dd Mon Sep 17 00:00:00 2001 From: Jhen-Jie Hong Date: Tue, 29 Aug 2023 17:30:10 +0800 Subject: [PATCH 411/852] readme : add react-native binding (#2869) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index bf3eb0b76..8d54a558d 100644 --- a/README.md +++ b/README.md @@ -113,6 +113,7 @@ as the main playground for developing new features for the [ggml](https://github - C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp) - Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s) - Clojure: [phronmophobic/llama.clj](https://github.com/phronmophobic/llama.clj) +- React Native: [mybigday/llama.rn](https://github.com/mybigday/llama.rn) **UI:** From bcce96ba4dd95482824700c4ce2455fe8c49055a Mon Sep 17 00:00:00 2001 From: jameswu2014 <545426914@qq.com> Date: Tue, 29 Aug 2023 17:48:41 +0800 Subject: [PATCH 412/852] convert.py : fix baichuan7B support (#2870) * [Fix]: convert.py support baichuan7B * convert.py : fix trailing whitespaces --------- Co-authored-by: Georgi Gerganov --- convert.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/convert.py b/convert.py index a15e6ccd2..3f0a1c932 100755 --- a/convert.py +++ b/convert.py @@ -469,7 +469,7 @@ class UnquantizedTensor(Tensor): def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': r = self.ndarray.shape[0] // 3 - return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head)) + return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head, n_head)) def part(self, n_part: int) -> 'UnquantizedTensor': r = self.ndarray.shape[0] // 3 @@ -952,9 +952,10 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel: #tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"] elif f"model.layers.{i}.self_attn.W_pack.weight" in model: print(f"Unpacking and permuting layer {i}") - tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head, params.n_head) - tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head, params.n_head_kv) + tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head) + tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head) tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = part_lazy (model[f"model.layers.{i}.self_attn.W_pack.weight"], 2) + del tmp[f"model.layers.{i}.self_attn.W_pack.weight"] else: break From 53885d7256909ec3e2176cdc2477f3986c15ec69 Mon Sep 17 00:00:00 2001 From: maddes8cht <55592906+maddes8cht@users.noreply.github.com> Date: Tue, 29 Aug 2023 15:51:02 +0200 Subject: [PATCH 413/852] py : fix "usage" messages (#2873) convert-to-gguf python scripts --- convert-falcon-hf-to-gguf.py | 2 +- convert-gptneox-hf-to-gguf.py | 2 +- convert-llama-7b-pth-to-gguf.py | 2 +- convert-llama-hf-to-gguf.py | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/convert-falcon-hf-to-gguf.py b/convert-falcon-hf-to-gguf.py index 411cbf682..168bcf17f 100755 --- a/convert-falcon-hf-to-gguf.py +++ b/convert-falcon-hf-to-gguf.py @@ -48,7 +48,7 @@ def count_model_parts(dir_model: str) -> int: if len(sys.argv) < 3: - print("Usage: convert-h5-to-ggml.py dir-model ftype\n") + print(f"Usage: python {sys.argv[0]} dir-model ftype\n") print(" ftype == 0 -> float32") print(" ftype == 1 -> float16") sys.exit(1) diff --git a/convert-gptneox-hf-to-gguf.py b/convert-gptneox-hf-to-gguf.py index 6eeff5bb1..d9c42d76b 100755 --- a/convert-gptneox-hf-to-gguf.py +++ b/convert-gptneox-hf-to-gguf.py @@ -50,7 +50,7 @@ def count_model_parts(dir_model: str) -> int: if len(sys.argv) < 3: - print("Usage: convert-h5-to-ggml.py dir-model ftype\n") + print(f"Usage: python {sys.argv[0]} dir-model ftype\n") print(" ftype == 0 -> float32") print(" ftype == 1 -> float16") sys.exit(1) diff --git a/convert-llama-7b-pth-to-gguf.py b/convert-llama-7b-pth-to-gguf.py index f103f5f61..2ab082383 100755 --- a/convert-llama-7b-pth-to-gguf.py +++ b/convert-llama-7b-pth-to-gguf.py @@ -32,7 +32,7 @@ def count_model_parts(dir_model: str) -> int: if len(sys.argv) < 3: - print("Usage: convert-h5-to-ggml.py dir-model ftype\n") + print(f"Usage: python {sys.argv[0]} dir-model ftype\n") print(" ftype == 0 -> float32") print(" ftype == 1 -> float16") diff --git a/convert-llama-hf-to-gguf.py b/convert-llama-hf-to-gguf.py index 08fde238b..b00810dbb 100755 --- a/convert-llama-hf-to-gguf.py +++ b/convert-llama-hf-to-gguf.py @@ -44,7 +44,7 @@ def count_model_parts(dir_model: str) -> int: if len(sys.argv) < 3: - print("Usage: convert-h5-to-ggml.py dir-model ftype\n") + print(f"Usage: python {sys.argv[0]} dir-model ftype\n") print(" ftype == 0 -> float32") print(" ftype == 1 -> float16") From e37e69dcc3d52f21222a63cafed2a71b3f6b53c6 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Tue, 29 Aug 2023 23:55:03 +0300 Subject: [PATCH 414/852] 10X faster BPE tokenizer (#2876) * 10X faster BPE tokenizer * Remove comment that no longer applies --------- Co-authored-by: Iwan Kawrakow --- llama.cpp | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/llama.cpp b/llama.cpp index 7cb468538..fcd6f276a 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3211,7 +3211,7 @@ private: struct llm_bigram_bpe { struct comparator { - bool operator()(llm_bigram_bpe & l, llm_bigram_bpe & r) { + bool operator()(const llm_bigram_bpe & l, const llm_bigram_bpe & r) const { return l.rank > r.rank || (l.rank == r.rank && l.left > r.left); } }; @@ -3359,23 +3359,22 @@ private: } // probably not 100% correct - // TODO: this is quite slow - how to make it more efficient? - static std::vector bpe_gpt2_preprocess(std::string text) { + static std::vector bpe_gpt2_preprocess(const std::string & text) { std::vector words; // ref: https://github.com/openai/gpt-2/blob/a74da5d99abaaba920de8131d64da2862a8f213b/src/encoder.py#L53 const std::string pattern = R"('s|'t|'re|'ve|'m|'ll|'d| ?[[:alpha:]]+| ?[[:digit:]]+| ?[^\s[:alpha:][:digit:]]+|\s+(?!\S)|\s+)"; const std::regex re(pattern); - std::smatch m; - while (std::regex_search(text, m, re)) { - for (auto x : m) { - words.push_back(x); - } - text = m.suffix(); + auto words_begin = std::sregex_iterator(text.begin(), text.end(), re); + auto words_end = std::sregex_iterator(); + auto n_words = std::distance(words_begin, words_end); + words.reserve(n_words); + for (auto it = words_begin; it != words_end; ++it) { + words.push_back(it->str()); } - return words; + } const llama_vocab & vocab; From fa3582f509a2715e80a473e79f88dcd1ebff44c2 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Tue, 29 Aug 2023 23:55:45 +0300 Subject: [PATCH 415/852] Tell users attmepting to run perplexity with too few tokens to use more (#2882) Closes #2858 Co-authored-by: Iwan Kawrakow --- examples/perplexity/perplexity.cpp | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index aeb774c5f..7c02b6d40 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -142,6 +142,14 @@ results_perplexity perplexity_v2(llama_context * ctx, const gpt_params & params) fprintf(stderr, "%s: tokenizing the input ..\n", __func__); std::vector tokens = ::llama_tokenize(ctx, params.prompt, add_bos); + + if (int(tokens.size()) < 2*params.n_ctx) { + fprintf(stderr, "%s: you need at least %d tokens to evaluate perplexity with a context of %d\n",__func__,2*params.n_ctx, + params.n_ctx); + fprintf(stderr, "%s: the data file you provided tokenizes to only %zu tokens\n",__func__,tokens.size()); + return {std::move(tokens), 0., {}, {}}; + } + std::vector logit_history; std::vector prob_history; @@ -274,6 +282,13 @@ results_perplexity perplexity(llama_context * ctx, const gpt_params & params) { auto tim2 = std::chrono::high_resolution_clock::now(); fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast(tim2-tim1).count()); + if (int(tokens.size()) < 2*params.n_ctx) { + fprintf(stderr, "%s: you need at least %d tokens to evaluate perplexity with a context of %d\n",__func__,2*params.n_ctx, + params.n_ctx); + fprintf(stderr, "%s: the data file you provided tokenizes to only %zu tokens\n",__func__,tokens.size()); + return {std::move(tokens), 0., {}, {}}; + } + std::vector logit_history; logit_history.resize(tokens.size()); From c03a243abf9f30889f31fefdfa94fe9d7034820c Mon Sep 17 00:00:00 2001 From: slaren Date: Tue, 29 Aug 2023 23:17:34 +0200 Subject: [PATCH 416/852] remove outdated references to -eps and -gqa from README (#2881) --- README.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/README.md b/README.md index 8d54a558d..a880fd29f 100644 --- a/README.md +++ b/README.md @@ -729,8 +729,6 @@ python3 convert.py pygmalion-7b/ --outtype q4_1 - [LLaMA 2 7B chat](https://huggingface.co/TheBloke/Llama-2-7B-chat-GGML) - [LLaMA 2 13B chat](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGML) - [LLaMA 2 70B chat](https://huggingface.co/TheBloke/Llama-2-70B-chat-GGML) -- Specify `-eps 1e-5` for best generation quality -- Specify `-gqa 8` for 70B models to work ### Verifying the model files From 06abf8eebabe086ca4003dee2754ab45032cd3fd Mon Sep 17 00:00:00 2001 From: slaren Date: Tue, 29 Aug 2023 23:24:42 +0200 Subject: [PATCH 417/852] ggml : add view_src and view_offs to ggml_tensor for views (#2874) * ggml : add view_src and view_offs * update ggml-alloc to use view_src * update ggml_diag_mask to work correctly with automatic inplace * exclude other ops that set an inplace flag from automatic inplace --- ggml-alloc.c | 53 ++---------- ggml.c | 233 +++++++++++++++++++++++---------------------------- ggml.h | 5 +- 3 files changed, 113 insertions(+), 178 deletions(-) diff --git a/ggml-alloc.c b/ggml-alloc.c index 63beb1d4e..f07a4a217 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -321,8 +321,7 @@ bool ggml_allocr_is_measure(struct ggml_allocr * alloc) { //////////// compute graph allocator static bool ggml_is_view(struct ggml_tensor * t) { - return t->op == GGML_OP_RESHAPE || t->op == GGML_OP_VIEW || t->op == GGML_OP_TRANSPOSE || - t->op == GGML_OP_PERMUTE || t->op == GGML_OP_CPY; + return t->view_src != NULL; } static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml_tensor * b) { @@ -340,28 +339,6 @@ static bool ggml_are_same_layout(const struct ggml_tensor * a, const struct ggml return true; } -static struct ggml_tensor * get_view_parent(struct ggml_tensor * t) { - switch (t->op) { - case GGML_OP_PERMUTE: - case GGML_OP_RESHAPE: - case GGML_OP_TRANSPOSE: - case GGML_OP_VIEW: - return t->src[0]; - case GGML_OP_CPY: - return t->src[1]; - default: - return NULL; - } -} - -static struct ggml_tensor * get_view_source(struct ggml_tensor * t) { - struct ggml_tensor * parent = t; - do { - parent = get_view_parent(parent); - } while (ggml_is_view(parent)); - return parent; -} - static bool ggml_op_can_inplace(enum ggml_op op) { switch (op) { case GGML_OP_SCALE: @@ -369,7 +346,6 @@ static bool ggml_op_can_inplace(enum ggml_op op) { case GGML_OP_DIAG_MASK_INF: case GGML_OP_ADD: case GGML_OP_ADD1: - case GGML_OP_ACC: case GGML_OP_SUB: case GGML_OP_MUL: case GGML_OP_DIV: @@ -379,7 +355,6 @@ static bool ggml_op_can_inplace(enum ggml_op op) { case GGML_OP_UNARY: case GGML_OP_ROPE: case GGML_OP_RMS_NORM: - case GGML_OP_SET: case GGML_OP_SOFT_MAX: case GGML_OP_CONT: return true; @@ -393,24 +368,8 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) struct hash_node * ht = alloc->hash_table; if (node->data == NULL) { if (ggml_is_view(node)) { - size_t offset; - switch(node->op) { - case GGML_OP_VIEW: - memcpy(&offset, node->op_params, sizeof(size_t)); - node->data = (char *) node->src[0]->data + offset; - break; - case GGML_OP_PERMUTE: - case GGML_OP_RESHAPE: - case GGML_OP_TRANSPOSE: - node->data = node->src[0]->data; - break; - case GGML_OP_CPY: - node->data = node->src[1]->data; - break; - default: - GGML_ASSERT(!"unknown view op"); - break; - } + assert(node->view_src->data != NULL); + node->data = (char *)node->view_src->data + node->view_offs; } else { // see if we can reuse a parent's buffer (inplace) if (ggml_op_can_inplace(node->op)) { @@ -430,7 +389,7 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) struct hash_node * p_hn = hash_get(ht, parent); if (parent->data != NULL && p_hn->n_children == 1 && p_hn->n_views == 0 && ggml_are_same_layout(node, parent)) { if (ggml_is_view(parent)) { - struct ggml_tensor * view_src = get_view_source(parent); + struct ggml_tensor * view_src = parent->view_src; struct hash_node * view_src_hn = hash_get(ht, view_src); if (view_src_hn->n_views == 1 && view_src_hn->n_children == 0 && view_src->data == parent->data) { // TODO: the offset of the view parent must be kept to ensure that the op doesn't overwrite @@ -472,7 +431,7 @@ static size_t ggml_allocator_alloc_graph_tensors_n( struct ggml_tensor * node = gf->nodes[i]; if (ggml_is_view(node)) { - struct ggml_tensor * view_src = get_view_source(node); + struct ggml_tensor * view_src = node->view_src; hash_get(ht, view_src)->n_views += 1; } @@ -557,7 +516,7 @@ static size_t ggml_allocator_alloc_graph_tensors_n( if (p_hn->n_children == 0 && p_hn->n_views == 0) { if (ggml_is_view(parent)) { - struct ggml_tensor * view_src = get_view_source(parent); + struct ggml_tensor * view_src = parent->view_src; struct hash_node * view_src_hn = hash_get(ht, view_src); view_src_hn->n_views -= 1; AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src_hn->n_children, view_src_hn->n_views); diff --git a/ggml.c b/ggml.c index 9a787863d..46ce4a581 100644 --- a/ggml.c +++ b/ggml.c @@ -4104,16 +4104,11 @@ int64_t ggml_nrows(const struct ggml_tensor * tensor) { } size_t ggml_nbytes(const struct ggml_tensor * tensor) { - static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); - - // this should handle cases where the tensor is not contiguous in memory - // probaby just: - // - // return tensor->ne[3]*tensor->nb[3] - // - // is enough, but just in case, adding the second part - - return MAX(tensor->ne[3]*tensor->nb[3], (ggml_nelements(tensor)*ggml_type_size(tensor->type))/ggml_blck_size(tensor->type)); + size_t nbytes = tensor->ne[0]*tensor->nb[0]/ggml_blck_size(tensor->type); + for (int i = 1; i < GGML_MAX_DIMS; ++i) { + nbytes += (tensor->ne[i] - 1)*tensor->nb[i]; + } + return nbytes; } size_t ggml_nbytes_pad(const struct ggml_tensor * tensor) { @@ -4567,36 +4562,51 @@ static struct ggml_tensor * ggml_new_tensor_impl( enum ggml_type type, int n_dims, const int64_t * ne, - void * data) { + struct ggml_tensor * view_src, + size_t view_offs) { assert(n_dims >= 1 && n_dims <= GGML_MAX_DIMS); - size_t data_size = 0; + // find the base tensor and absolute offset + if (view_src != NULL && view_src->view_src != NULL) { + view_offs += view_src->view_offs; + view_src = view_src->view_src; + } - if (data == NULL && !ctx->no_alloc) { - data_size += ggml_type_size(type)*(ne[0]/ggml_blck_size(type)); - for (int i = 1; i < n_dims; i++) { - data_size *= ne[i]; + size_t data_size = ggml_type_size(type)*(ne[0]/ggml_blck_size(type)); + for (int i = 1; i < n_dims; i++) { + data_size *= ne[i]; + } + + GGML_ASSERT(view_src == NULL || data_size + view_offs <= ggml_nbytes(view_src)); + + void * data = view_src != NULL ? view_src->data : NULL; + if (data != NULL) { + data = (char *) data + view_offs; + } + + size_t obj_alloc_size = 0; + + if (view_src == NULL && ctx->no_alloc == false) { + if (ctx->scratch.data != NULL) { + // allocate tensor data in the scratch buffer + if (ctx->scratch.offs + data_size > ctx->scratch.size) { + GGML_PRINT("%s: not enough space in the scratch memory pool (needed %zu, available %zu)\n", + __func__, ctx->scratch.offs + data_size, ctx->scratch.size); + assert(false); + return NULL; + } + + data = (char * const) ctx->scratch.data + ctx->scratch.offs; + + ctx->scratch.offs += data_size; + } else { + // allocate tensor data in the context's memory pool + obj_alloc_size = data_size; } } - if (ctx->scratch.data != NULL && data == NULL) { - // allocate tensor data in the scratch buffer - if (ctx->scratch.offs + data_size > ctx->scratch.size) { - GGML_PRINT("%s: not enough space in the scratch memory pool (needed %zu, available %zu)\n", - __func__, ctx->scratch.offs + data_size, ctx->scratch.size); - assert(false); - return NULL; - } - - data = (char * const) ctx->scratch.data + ctx->scratch.offs; - - ctx->scratch.offs += data_size; - - data_size = 0; - } - - struct ggml_object * const obj_new = ggml_new_object(ctx, GGML_OBJECT_TENSOR, GGML_TENSOR_SIZE + data_size); + struct ggml_object * const obj_new = ggml_new_object(ctx, GGML_OBJECT_TENSOR, GGML_TENSOR_SIZE + obj_alloc_size); // TODO: for recoverable errors, we would need to free the data allocated from the scratch buffer here @@ -4616,7 +4626,9 @@ static struct ggml_tensor * ggml_new_tensor_impl( /*.perf_runs =*/ 0, /*.perf_cycles =*/ 0, /*.perf_time_us =*/ 0, - /*.data =*/ (data == NULL && !ctx->no_alloc) ? (void *)(result + 1) : data, + /*.view_src =*/ view_src, + /*.view_offs =*/ view_offs, + /*.data =*/ obj_alloc_size > 0 ? (void *)(result + 1) : data, /*.name =*/ { 0 }, /*.extra =*/ NULL, /*.padding =*/ { 0 }, @@ -4640,28 +4652,12 @@ static struct ggml_tensor * ggml_new_tensor_impl( return result; } -static void ggml_set_op_params(struct ggml_tensor * tensor, const void * params, size_t params_size) { - GGML_ASSERT(tensor != NULL); // silence -Warray-bounds warnings - assert(params_size <= GGML_MAX_OP_PARAMS); - memcpy(tensor->op_params, params, params_size); -} - -static int32_t ggml_get_op_params_i32(const struct ggml_tensor * tensor, uint32_t i) { - assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t)); - return ((const int32_t *)(tensor->op_params))[i]; -} - -static void ggml_set_op_params_i32(struct ggml_tensor * tensor, uint32_t i, int32_t value) { - assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t)); - ((int32_t *)(tensor->op_params))[i] = value; -} - struct ggml_tensor * ggml_new_tensor( struct ggml_context * ctx, enum ggml_type type, int n_dims, const int64_t * ne) { - return ggml_new_tensor_impl(ctx, type, n_dims, ne, NULL); + return ggml_new_tensor_impl(ctx, type, n_dims, ne, NULL, 0); } struct ggml_tensor * ggml_new_tensor_1d( @@ -4726,7 +4722,23 @@ struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value) { } struct ggml_tensor * ggml_dup_tensor(struct ggml_context * ctx, const struct ggml_tensor * src) { - return ggml_new_tensor_impl(ctx, src->type, src->n_dims, src->ne, NULL); + return ggml_new_tensor(ctx, src->type, src->n_dims, src->ne); +} + +static void ggml_set_op_params(struct ggml_tensor * tensor, const void * params, size_t params_size) { + GGML_ASSERT(tensor != NULL); // silence -Warray-bounds warnings + assert(params_size <= GGML_MAX_OP_PARAMS); + memcpy(tensor->op_params, params, params_size); +} + +static int32_t ggml_get_op_params_i32(const struct ggml_tensor * tensor, uint32_t i) { + assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t)); + return ((const int32_t *)(tensor->op_params))[i]; +} + +static void ggml_set_op_params_i32(struct ggml_tensor * tensor, uint32_t i, int32_t value) { + assert(i < GGML_MAX_OP_PARAMS / sizeof(int32_t)); + ((int32_t *)(tensor->op_params))[i] = value; } struct ggml_tensor * ggml_set_zero(struct ggml_tensor * tensor) { @@ -5012,14 +5024,13 @@ struct ggml_tensor * ggml_format_name(struct ggml_tensor * tensor, const char * struct ggml_tensor * ggml_view_tensor( struct ggml_context * ctx, - const struct ggml_tensor * src) { - struct ggml_tensor * result = ggml_new_tensor_impl(ctx, src->type, src->n_dims, src->ne, src->data); + struct ggml_tensor * src) { + struct ggml_tensor * result = ggml_new_tensor_impl(ctx, src->type, src->n_dims, src->ne, src, 0); ggml_format_name(result, "%s (view)", src->name); - result->nb[0] = src->nb[0]; - result->nb[1] = src->nb[1]; - result->nb[2] = src->nb[2]; - result->nb[3] = src->nb[3]; + for (int i = 0; i < GGML_MAX_DIMS; i++) { + result->nb[i] = src->nb[i]; + } return result; } @@ -5592,7 +5603,7 @@ struct ggml_tensor * ggml_repeat_back( // ggml_concat -struct ggml_tensor* ggml_concat( +struct ggml_tensor * ggml_concat( struct ggml_context* ctx, struct ggml_tensor* a, struct ggml_tensor* b) { @@ -6201,7 +6212,7 @@ struct ggml_tensor * ggml_reshape( //GGML_ASSERT(false); } - struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, b->n_dims, b->ne, a->data); + struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, b->n_dims, b->ne, a, 0); ggml_format_name(result, "%s (reshaped)", a->name); result->op = GGML_OP_RESHAPE; @@ -6225,7 +6236,7 @@ struct ggml_tensor * ggml_reshape_1d( } const int64_t ne[1] = { ne0 }; - struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 1, ne, a->data); + struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 1, ne, a, 0); ggml_format_name(result, "%s (reshaped)", a->name); result->op = GGML_OP_RESHAPE; @@ -6250,7 +6261,7 @@ struct ggml_tensor * ggml_reshape_2d( } const int64_t ne[2] = { ne0, ne1 }; - struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, a->data); + struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 2, ne, a, 0); ggml_format_name(result, "%s (reshaped)", a->name); result->op = GGML_OP_RESHAPE; @@ -6276,7 +6287,7 @@ struct ggml_tensor * ggml_reshape_3d( } const int64_t ne[3] = { ne0, ne1, ne2 }; - struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, a->data); + struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 3, ne, a, 0); ggml_format_name(result, "%s (reshaped)", a->name); result->op = GGML_OP_RESHAPE; @@ -6286,7 +6297,6 @@ struct ggml_tensor * ggml_reshape_3d( return result; } - struct ggml_tensor * ggml_reshape_4d( struct ggml_context * ctx, struct ggml_tensor * a, @@ -6304,7 +6314,7 @@ struct ggml_tensor * ggml_reshape_4d( } const int64_t ne[4] = { ne0, ne1, ne2, ne3 }; - struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 4, ne, a->data); + struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, 4, ne, a, 0); ggml_format_name(result, "%s (reshaped)", a->name); result->op = GGML_OP_RESHAPE; @@ -6314,34 +6324,12 @@ struct ggml_tensor * ggml_reshape_4d( return result; } -// ggml_view_1d - -static struct ggml_tensor * ggml_view_tensor_offset( +static struct ggml_tensor * ggml_view_impl( struct ggml_context * ctx, struct ggml_tensor * a, int n_dims, const int64_t * ne, size_t offset) { - // don't calculate an offset from an unallocated tensor - void * data = NULL; - if (a->data != NULL) { - data = (char *) a->data + offset; - } - - struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, n_dims, ne, data); - - ggml_format_name(result, "%s (view)", a->name); - - ggml_set_op_params(result, &offset, sizeof(offset)); - - return result; -} - -struct ggml_tensor * ggml_view_1d( - struct ggml_context * ctx, - struct ggml_tensor * a, - int64_t ne0, - size_t offset) { bool is_node = false; @@ -6349,7 +6337,10 @@ struct ggml_tensor * ggml_view_1d( is_node = true; } - struct ggml_tensor * result = ggml_view_tensor_offset(ctx, a, 1, &ne0, offset); + struct ggml_tensor * result = ggml_new_tensor_impl(ctx, a->type, n_dims, ne, a, offset); + ggml_format_name(result, "%s (view)", a->name); + + ggml_set_op_params(result, &offset, sizeof(offset)); result->op = GGML_OP_VIEW; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; @@ -6358,6 +6349,19 @@ struct ggml_tensor * ggml_view_1d( return result; } +// ggml_view_1d + +struct ggml_tensor * ggml_view_1d( + struct ggml_context * ctx, + struct ggml_tensor * a, + int64_t ne0, + size_t offset) { + + struct ggml_tensor * result = ggml_view_impl(ctx, a, 1, &ne0, offset); + + return result; +} + // ggml_view_2d struct ggml_tensor * ggml_view_2d( @@ -6368,24 +6372,14 @@ struct ggml_tensor * ggml_view_2d( size_t nb1, size_t offset) { - bool is_node = false; + const int64_t ne[2] = { ne0, ne1 }; - if (a->grad) { - is_node = true; - } - - const int64_t ne[GGML_MAX_DIMS] = { ne0, ne1, 1, 1 }; - - struct ggml_tensor * result = ggml_view_tensor_offset(ctx, a, 2, ne, offset); + struct ggml_tensor * result = ggml_view_impl(ctx, a, 2, ne, offset); result->nb[1] = nb1; result->nb[2] = result->nb[1]*ne1; result->nb[3] = result->nb[2]; - result->op = GGML_OP_VIEW; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; - result->src[0] = a; - return result; } @@ -6401,24 +6395,14 @@ struct ggml_tensor * ggml_view_3d( size_t nb2, size_t offset) { - bool is_node = false; + const int64_t ne[3] = { ne0, ne1, ne2 }; - if (a->grad) { - is_node = true; - } - - const int64_t ne[GGML_MAX_DIMS] = { ne0, ne1, ne2, 1 }; - - struct ggml_tensor * result = ggml_view_tensor_offset(ctx, a, 3, ne, offset); + struct ggml_tensor * result = ggml_view_impl(ctx, a, 3, ne, offset); result->nb[1] = nb1; result->nb[2] = nb2; result->nb[3] = result->nb[2]*ne2; - result->op = GGML_OP_VIEW; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; - result->src[0] = a; - return result; } @@ -6436,24 +6420,14 @@ struct ggml_tensor * ggml_view_4d( size_t nb3, size_t offset) { - bool is_node = false; + const int64_t ne[4] = { ne0, ne1, ne2, ne3 }; - if (a->grad) { - is_node = true; - } - - const int64_t ne[GGML_MAX_DIMS] = { ne0, ne1, ne2, ne3 }; - - struct ggml_tensor * result = ggml_view_tensor_offset(ctx, a, 4, ne, offset); + struct ggml_tensor * result = ggml_view_impl(ctx, a, 4, ne, offset); result->nb[1] = nb1; result->nb[2] = nb2; result->nb[3] = nb3; - result->op = GGML_OP_VIEW; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; - result->src[0] = a; - return result; } @@ -6640,7 +6614,7 @@ static struct ggml_tensor * ggml_diag_mask_inf_impl( struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - int32_t params[] = { n_past, inplace ? 1 : 0 }; + int32_t params[] = { n_past }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_DIAG_MASK_INF; @@ -6657,7 +6631,6 @@ struct ggml_tensor * ggml_diag_mask_inf( return ggml_diag_mask_inf_impl(ctx, a, n_past, false); } - struct ggml_tensor * ggml_diag_mask_inf_inplace( struct ggml_context * ctx, struct ggml_tensor * a, @@ -6680,7 +6653,7 @@ static struct ggml_tensor * ggml_diag_mask_zero_impl( struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); - int32_t params[] = { n_past, inplace ? 1 : 0 }; + int32_t params[] = { n_past }; ggml_set_op_params(result, params, sizeof(params)); result->op = GGML_OP_DIAG_MASK_ZERO; @@ -11935,8 +11908,8 @@ static void ggml_compute_forward_diag_mask_f32( const int ith = params->ith; const int nth = params->nth; - const int n_past = ((int32_t *) dst->op_params)[0]; - const bool inplace = (bool)((int32_t *) dst->op_params)[1]; + const int n_past = ((int32_t *) dst->op_params)[0]; + const bool inplace = src0->data == dst->data; GGML_ASSERT(n_past >= 0); diff --git a/ggml.h b/ggml.h index 8b410cc85..c936823d6 100644 --- a/ggml.h +++ b/ggml.h @@ -479,6 +479,9 @@ extern "C" { int64_t perf_cycles; int64_t perf_time_us; + struct ggml_tensor * view_src; + size_t view_offs; + void * data; char name[GGML_MAX_NAME]; @@ -661,7 +664,7 @@ extern "C" { GGML_API struct ggml_tensor * ggml_new_f32(struct ggml_context * ctx, float value); GGML_API struct ggml_tensor * ggml_dup_tensor (struct ggml_context * ctx, const struct ggml_tensor * src); - GGML_API struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, const struct ggml_tensor * src); + GGML_API struct ggml_tensor * ggml_view_tensor(struct ggml_context * ctx, struct ggml_tensor * src); GGML_API struct ggml_tensor * ggml_get_tensor(struct ggml_context * ctx, const char * name); From 849408957c687cde4ab32c147107f643fc55130b Mon Sep 17 00:00:00 2001 From: Cebtenzzre Date: Wed, 30 Aug 2023 02:20:26 -0400 Subject: [PATCH 418/852] tests : add a C compliance test (#2848) * tests : add a C compliance test * make : build C compliance test by default * make : fix clean and make sure C test fails on clang * make : move -Werror=implicit-int to CFLAGS --- CMakeLists.txt | 1 + Makefile | 9 ++++++--- tests/CMakeLists.txt | 5 +++++ tests/test-c.c | 3 +++ 4 files changed, 15 insertions(+), 3 deletions(-) create mode 100644 tests/test-c.c diff --git a/CMakeLists.txt b/CMakeLists.txt index 1eae2d670..d6c1b3b33 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -402,6 +402,7 @@ if (LLAMA_ALL_WARNINGS) -Wstrict-prototypes -Wpointer-arith -Wmissing-prototypes + -Werror=implicit-int ) set(cxx_flags -Wall diff --git a/Makefile b/Makefile index 02ba3e36d..44e68b7fc 100644 --- a/Makefile +++ b/Makefile @@ -1,5 +1,5 @@ # Define the default target now so that it is always the first target -BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple save-load-state server embd-input-test gguf llama-bench baby-llama beam_search +BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple save-load-state server embd-input-test gguf llama-bench baby-llama beam_search tests/test-c.o # Binaries only useful for tests TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama tests/test-tokenizer-0-falcon tests/test-tokenizer-1 @@ -64,7 +64,7 @@ endif # warnings CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith \ - -Wmissing-prototypes + -Wmissing-prototypes -Werror=implicit-int CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar # OS specific @@ -369,7 +369,7 @@ libllama.so: llama.o ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) clean: - rm -vf *.o *.so *.dll benchmark-matmult build-info.h $(BUILD_TARGETS) $(TEST_TARGETS) + rm -vf *.o tests/*.o *.so *.dll benchmark-matmult build-info.h $(BUILD_TARGETS) $(TEST_TARGETS) # # Examples @@ -489,3 +489,6 @@ tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp build-info.h ggml tests/test-tokenizer-1: tests/test-tokenizer-1.cpp build-info.h ggml.o llama.o common.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + +tests/test-c.o: tests/test-c.c llama.h + $(CC) $(CFLAGS) -c $(filter-out %.h,$^) -o $@ diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index ca1f39d31..483210d7b 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -37,3 +37,8 @@ llama_build_and_test_executable(test-grammar-parser.cpp) llama_build_and_test_executable(test-llama-grammar.cpp) llama_build_and_test_executable(test-grad0.cpp) # SLOW # llama_build_and_test_executable(test-opt.cpp) # SLOW + +# dummy executable - not installed +get_filename_component(TEST_TARGET test-c.c NAME_WE) +add_executable(${TEST_TARGET} test-c.c) +target_link_libraries(${TEST_TARGET} PRIVATE llama) diff --git a/tests/test-c.c b/tests/test-c.c new file mode 100644 index 000000000..a05071080 --- /dev/null +++ b/tests/test-c.c @@ -0,0 +1,3 @@ +#include "llama.h" + +int main(void) {} From 8341a25957b319a03d4a811176cd5ad7f2b0fbd4 Mon Sep 17 00:00:00 2001 From: staviq Date: Wed, 30 Aug 2023 08:29:32 +0200 Subject: [PATCH 419/852] main : log file (#2748) * initial, base LOG macro * add *.log to .gitignore * added basic log file handler * reverted log auto endline to better mimic printf * remove atomics and add dynamic log target * log_enable/disable, LOG_TEE, basic usage doc * update .gitignore * mv include to common, params, help msg * log tostring helpers, token vectors pretty prints * main: replaced fprintf/LOG_TEE, some trace logging * LOG_DISABLE_LOGS compile flag, wrapped f in macros * fix LOG_TEELN and configchecker * stub LOG_DUMP_CMDLINE for WIN32 for now * fix msvc * cleanup main.cpp:273 * fix stray whitespace after master sync * log : fix compile warnings - do not use C++20 stuff - use PRIu64 to print uint64_t - avoid string copies by using const ref - fix ", ##__VA_ARGS__" warnings - compare strings with == and != * log : do not append to existing log + disable file line func by default * log : try to fix Windows build * main : wip logs * main : add trace log * review: macro f lowercase, str append to sstream * review: simplify ifs and str comparisons * fix MSVC, formatting, FMT/VAL placeholders * review: if/else cleanup * review: if/else cleanup (2) * replace _ prefix with _impl suffix --------- Co-authored-by: Georgi Gerganov --- .gitignore | 1 + Makefile | 7 +- common/common.cpp | 22 ++ common/common.h | 3 + common/log.h | 643 +++++++++++++++++++++++++++++++++++++++++ examples/chat.sh | 2 +- examples/main/main.cpp | 288 +++++++++++------- 7 files changed, 859 insertions(+), 107 deletions(-) create mode 100644 common/log.h diff --git a/.gitignore b/.gitignore index 7a3f3fff4..54ea2b522 100644 --- a/.gitignore +++ b/.gitignore @@ -5,6 +5,7 @@ *.bin *.exe *.dll +*.log .DS_Store .build/ .cache/ diff --git a/Makefile b/Makefile index 44e68b7fc..c8b8a92d7 100644 --- a/Makefile +++ b/Makefile @@ -326,6 +326,11 @@ k_quants.o: k_quants.c k_quants.h $(CC) $(CFLAGS) -c $< -o $@ endif # LLAMA_NO_K_QUANTS +ifdef LLAMA_DISABLE_LOGS + CFLAGS += -DLOG_DISABLE_LOGS + CXXFLAGS += -DLOG_DISABLE_LOGS +endif # LLAMA_DISABLE_LOGS + # # Print build information # @@ -356,7 +361,7 @@ OBJS += ggml-alloc.o llama.o: llama.cpp ggml.h ggml-alloc.h ggml-cuda.h ggml-metal.h llama.h $(CXX) $(CXXFLAGS) -c $< -o $@ -common.o: common/common.cpp common/common.h build-info.h +common.o: common/common.cpp common/common.h build-info.h common/log.h $(CXX) $(CXXFLAGS) -c $< -o $@ console.o: common/console.cpp common/console.h diff --git a/common/common.cpp b/common/common.cpp index 90fe2e84e..ed09fc27d 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -480,6 +480,9 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { } } else if (arg == "-h" || arg == "--help") { gpt_print_usage(argc, argv, default_params); +#ifndef LOG_DISABLE_LOGS + log_print_usage(); +#endif // LOG_DISABLE_LOGS exit(0); } else if (arg == "--random-prompt") { params.random_prompt = true; @@ -519,6 +522,25 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { std::istreambuf_iterator(), std::back_inserter(params.grammar) ); +#ifndef LOG_DISABLE_LOGS + // Parse args for logging parameters + } else if ( log_param_single_parse( argv[i] ) ) { + // Do nothing, log_param_single_parse automatically does it's thing + // and returns if a match was found and parsed. + } else if ( log_param_pair_parse( /*check_but_dont_parse*/ true, argv[i] ) ) { + // We have a matching known parameter requiring an argument, + // now we need to check if there is anything after this argv + // and flag invalid_param or parse it. + if (++i >= argc) { + invalid_param = true; + break; + } + if( !log_param_pair_parse( /*check_but_dont_parse*/ false, argv[i-1], argv[i]) ) { + invalid_param = true; + break; + } + // End of Parse args for logging parameters +#endif // LOG_DISABLE_LOGS } else { fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); gpt_print_usage(argc, argv, default_params); diff --git a/common/common.h b/common/common.h index c15373144..5a379688e 100644 --- a/common/common.h +++ b/common/common.h @@ -4,6 +4,9 @@ #include "llama.h" +#define LOG_NO_FILE_LINE_FUNCTION +#include "log.h" + #include #include #include diff --git a/common/log.h b/common/log.h new file mode 100644 index 000000000..c1364187d --- /dev/null +++ b/common/log.h @@ -0,0 +1,643 @@ +#pragma once + +#include +#include +#include +#include +#include +#include +#include +#include + +// -------------------------------- +// +// Basic usage: +// +// -------- +// +// The LOG() and LOG_TEE() macros are ready to go by default +// they do not require any initialization. +// +// LOGLN() and LOG_TEELN() are variants which automatically +// include \n character at the end of the log string. +// +// LOG() behaves exactly like printf, by default writing to a logfile. +// LOG_TEE() additionally, prints to the screen too ( mimics Unix tee command ). +// +// Default logfile is named +// "llama..log" +// Default LOG_TEE() secondary output target is +// stderr +// +// Logs can be dynamically disabled or enabled using functions: +// log_disable() +// and +// log_enable() +// +// A log target can be changed with: +// log_set_target( string ) +// creating and opening, or re-opening a file by string filename +// or +// log_set_target( FILE* ) +// allowing to point at stderr, stdout, or any valid FILE* file handler. +// +// -------- +// +// End of Basic usage. +// +// -------------------------------- + +// Specifies a log target. +// default uses log_handler() with "llama.log" log file +// this can be changed, by defining LOG_TARGET +// like so: +// +// #define LOG_TARGET (a valid FILE*) +// #include "log.h" +// +// or it can be simply redirected to stdout or stderr +// like so: +// +// #define LOG_TARGET stderr +// #include "log.h" +// +// The log target can also be redirected to a diffrent function +// like so: +// +// #define LOG_TARGET log_handler_diffrent() +// #include "log.h" +// +// FILE* log_handler_diffrent() +// { +// return stderr; +// } +// +// or: +// +// #define LOG_TARGET log_handler_another_one("somelog.log") +// #include "log.h" +// +// FILE* log_handler_another_one(char*filename) +// { +// static FILE* logfile = nullptr; +// (...) +// if( !logfile ) +// { +// fopen(...) +// } +// (...) +// return logfile +// } +// +#ifndef LOG_TARGET + #define LOG_TARGET log_handler() +#endif + +#ifndef LOG_TEE_TARGET + #define LOG_TEE_TARGET stderr +#endif + +// Utility to obtain "pid" like unique process id and use it when creating log files. +inline std::string log_get_pid() +{ + static std::string pid; + if (pid.empty()) + { + // std::this_thread::get_id() is the most portable way of obtaining a "process id" + // it's not the same as "pid" but is unique enough to solve multiple instances + // trying to write to the same log. + std::stringstream ss; + ss << std::this_thread::get_id(); + pid = ss.str(); + } + + return pid; +} + +// Utility function for generating log file names with unique id based on thread id. +// invocation with log_filename_generator( "llama", "log" ) creates a string "llama..log" +// where the number is a runtime id of the current thread. + +#define log_filename_generator(log_file_basename, log_file_extension) log_filename_generator_impl(log_file_basename, log_file_extension) + +// INTERNAL, DO NOT USE +inline std::string log_filename_generator_impl(const std::string & log_file_basename, const std::string & log_file_extension) +{ + std::stringstream buf; + + buf << log_file_basename; + buf << "."; + buf << log_get_pid(); + buf << "."; + buf << log_file_extension; + + return buf.str(); +} + +#ifndef LOG_DEFAULT_FILE_NAME + #define LOG_DEFAULT_FILE_NAME log_filename_generator("llama", "log") +#endif + +// Utility for turning #define values into string literals +// so we can have a define for stderr and +// we can print "stderr" instead of literal stderr, etc. +#define LOG_STRINGIZE1(s) #s +#define LOG_STRINGIZE(s) LOG_STRINGIZE1(s) + +#define LOG_TEE_TARGET_STRING LOG_STRINGIZE(LOG_TEE_TARGET) + +// Allows disabling timestamps. +// in order to disable, define LOG_NO_TIMESTAMPS +// like so: +// +// #define LOG_NO_TIMESTAMPS +// #include "log.h" +// +#ifndef LOG_NO_TIMESTAMPS + #ifndef _WIN32 + #define LOG_TIMESTAMP_FMT "[%" PRIu64 "] " + #define LOG_TIMESTAMP_VAL , (std::chrono::duration_cast>(std::chrono::system_clock::now().time_since_epoch())).count() + #else + #define LOG_TIMESTAMP_FMT "[%" PRIu64 "] " + #define LOG_TIMESTAMP_VAL , (std::chrono::duration_cast>(std::chrono::system_clock::now().time_since_epoch())).count() + #endif +#else + #define LOG_TIMESTAMP_FMT "%s" + #define LOG_TIMESTAMP_VAL ,"" +#endif + +#ifdef LOG_TEE_TIMESTAMPS + #ifndef _WIN32 + #define LOG_TEE_TIMESTAMP_FMT "[%" PRIu64 "] " + #define LOG_TEE_TIMESTAMP_VAL , (std::chrono::duration_cast>(std::chrono::system_clock::now().time_since_epoch())).count() + #else + #define LOG_TEE_TIMESTAMP_FMT "[%" PRIu64 "] " + #define LOG_TEE_TIMESTAMP_VAL , (std::chrono::duration_cast>(std::chrono::system_clock::now().time_since_epoch())).count() + #endif +#else + #define LOG_TEE_TIMESTAMP_FMT "%s" + #define LOG_TEE_TIMESTAMP_VAL ,"" +#endif + +// Allows disabling file/line/function prefix +// in order to disable, define LOG_NO_FILE_LINE_FUNCTION +// like so: +// +// #define LOG_NO_FILE_LINE_FUNCTION +// #include "log.h" +// +#ifndef LOG_NO_FILE_LINE_FUNCTION + #ifndef _WIN32 + #define LOG_FLF_FMT "[%24s:%5d][%24s] " + #define LOG_FLF_VAL , __FILE__, __LINE__, __FUNCTION__ + #else + #define LOG_FLF_FMT "[%24s:%5ld][%24s] " + #define LOG_FLF_VAL , __FILE__, __LINE__, __FUNCTION__ + #endif +#else + #define LOG_FLF_FMT "%s" + #define LOG_FLF_VAL ,"" +#endif + +#ifdef LOG_TEE_FILE_LINE_FUNCTION + #ifndef _WIN32 + #define LOG_TEE_FLF_FMT "[%24s:%5d][%24s] " + #define LOG_TEE_FLF_VAL , __FILE__, __LINE__, __FUNCTION__ + #else + #define LOG_TEE_FLF_FMT "[%24s:%5ld][%24s] " + #define LOG_TEE_FLF_VAL , __FILE__, __LINE__, __FUNCTION__ + #endif +#else + #define LOG_TEE_FLF_FMT "%s" + #define LOG_TEE_FLF_VAL ,"" +#endif + +// Utility for synchronizing log configuration state +// since std::optional was introduced only in c++17 +enum LogTriState +{ + LogTriStateSame, + LogTriStateFalse, + LogTriStateTrue +}; + +// INTERNAL, DO NOT USE +// USE LOG() INSTEAD +// +#ifndef _WIN32 + #define LOG_IMPL(str, ...) \ + { \ + if (LOG_TARGET != nullptr) \ + { \ + fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str "%s" LOG_TIMESTAMP_VAL LOG_FLF_VAL, __VA_ARGS__); \ + fflush(LOG_TARGET); \ + } \ + } +#else + #define LOG_IMPL(str, ...) \ + { \ + if (LOG_TARGET != nullptr) \ + { \ + fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str "%s" LOG_TIMESTAMP_VAL LOG_FLF_VAL "", ##__VA_ARGS__); \ + fflush(LOG_TARGET); \ + } \ + } +#endif + +// INTERNAL, DO NOT USE +// USE LOG_TEE() INSTEAD +// +#ifndef _WIN32 + #define LOG_TEE_IMPL(str, ...) \ + { \ + if (LOG_TARGET != nullptr) \ + { \ + fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str "%s" LOG_TIMESTAMP_VAL LOG_FLF_VAL, __VA_ARGS__); \ + fflush(LOG_TARGET); \ + } \ + if (LOG_TARGET != nullptr && LOG_TARGET != stdout && LOG_TARGET != stderr && LOG_TEE_TARGET != nullptr) \ + { \ + fprintf(LOG_TEE_TARGET, LOG_TEE_TIMESTAMP_FMT LOG_TEE_FLF_FMT str "%s" LOG_TEE_TIMESTAMP_VAL LOG_TEE_FLF_VAL, __VA_ARGS__); \ + fflush(LOG_TEE_TARGET); \ + } \ + } +#else + #define LOG_TEE_IMPL(str, ...) \ + { \ + if (LOG_TARGET != nullptr) \ + { \ + fprintf(LOG_TARGET, LOG_TIMESTAMP_FMT LOG_FLF_FMT str "%s" LOG_TIMESTAMP_VAL LOG_FLF_VAL "", ##__VA_ARGS__); \ + fflush(LOG_TARGET); \ + } \ + if (LOG_TARGET != nullptr && LOG_TARGET != stdout && LOG_TARGET != stderr && LOG_TEE_TARGET != nullptr) \ + { \ + fprintf(LOG_TEE_TARGET, LOG_TEE_TIMESTAMP_FMT LOG_TEE_FLF_FMT str "%s" LOG_TEE_TIMESTAMP_VAL LOG_TEE_FLF_VAL "", ##__VA_ARGS__); \ + fflush(LOG_TEE_TARGET); \ + } \ + } +#endif + +// The '\0' as a last argument, is a trick to bypass the silly +// "warning: ISO C++11 requires at least one argument for the "..." in a variadic macro" +// so we can have a single macro which can be called just like printf. + +// Main LOG macro. +// behaves like printf, and supports arguments the exact same way. +// +#ifndef _WIN32 + #define LOG(...) LOG_IMPL(__VA_ARGS__, "") +#else + #define LOG(str, ...) LOG_IMPL("%s" str, "", __VA_ARGS__, "") +#endif + +// Main TEE macro. +// does the same as LOG +// and +// simultaneously writes stderr. +// +// Secondary target can be changed just like LOG_TARGET +// by defining LOG_TEE_TARGET +// +#ifndef _WIN32 + #define LOG_TEE(...) LOG_TEE_IMPL(__VA_ARGS__, "") +#else + #define LOG_TEE(str, ...) LOG_TEE_IMPL("%s" str, "", __VA_ARGS__, "") +#endif + +// LOG macro variants with auto endline. +#ifndef _WIN32 + #define LOGLN(...) LOG_IMPL(__VA_ARGS__, "\n") + #define LOG_TEELN(...) LOG_TEE_IMPL(__VA_ARGS__, "\n") +#else + #define LOGLN(str, ...) LOG_IMPL("%s" str, "", __VA_ARGS__, "\n") + #define LOG_TEELN(str, ...) LOG_TEE_IMPL("%s" str, "", __VA_ARGS__, "\n") +#endif + +// INTERNAL, DO NOT USE +inline FILE *log_handler1_impl(bool change = false, LogTriState disable = LogTriStateSame, const std::string & filename = LOG_DEFAULT_FILE_NAME, FILE *target = nullptr) +{ + static bool _initialized{false}; + static bool _disabled{(filename.empty() && target == nullptr)}; + static std::string log_current_filename{filename}; + static FILE *log_current_target{target}; + static FILE *logfile = nullptr; + + if (change) + { + if (disable == LogTriStateTrue) + { + // Disable primary target + _disabled = true; + } + // If previously disabled, only enable, and keep previous target + else if (disable == LogTriStateFalse) + { + _disabled = false; + } + // Otherwise, process the arguments + else if (log_current_filename != filename || log_current_target != target) + { + _initialized = false; + } + } + + if (_initialized) + { + if (_disabled) + { + // Log is disabled + return nullptr; + } + + // with fallback in case something went wrong + return logfile ? logfile : stderr; + } + + // do the (re)initialization + if (target != nullptr) + { + if (logfile != nullptr && logfile != stdout && logfile != stderr) + { + fclose(logfile); + } + + log_current_filename = LOG_DEFAULT_FILE_NAME; + log_current_target = target; + + logfile = target; + } + else + { + if (log_current_filename != filename) + { + if (logfile != nullptr && logfile != stdout && logfile != stderr) + { + fclose(logfile); + } + } + + logfile = fopen(filename.c_str(), "w"); + } + + if (!logfile) + { + // Verify whether the file was opened, otherwise fallback to stderr + logfile = stderr; + + fprintf(stderr, "Failed to open logfile '%s' with error '%s'\n", filename.c_str(), std::strerror(errno)); + fflush(stderr); + + // At this point we let the init flag be to true below, and let the target fallback to stderr + // otherwise we would repeatedly fopen() which was already unsuccessful + } + + _initialized = true; + + return logfile ? logfile : stderr; +} + +// INTERNAL, DO NOT USE +inline FILE *log_handler2_impl(bool change = false, LogTriState disable = LogTriStateSame, FILE *target = nullptr, const std::string & filename = LOG_DEFAULT_FILE_NAME) +{ + return log_handler1_impl(change, disable, filename, target); +} + +// Disables logs entirely at runtime. +// Makes LOG() and LOG_TEE() produce no output, +// untill enabled back. +#define log_disable() log_disable_impl() + +// INTERNAL, DO NOT USE +inline FILE *log_disable_impl() +{ + return log_handler1_impl(true, LogTriStateTrue); +} + +// Enables logs at runtime. +#define log_enable() log_enable_impl() + +// INTERNAL, DO NOT USE +inline FILE *log_enable_impl() +{ + return log_handler1_impl(true, LogTriStateFalse); +} + +// Sets target fir logs, either by a file name or FILE* pointer (stdout, stderr, or any valid FILE*) +#define log_set_target(target) log_set_target_impl(target) + +// INTERNAL, DO NOT USE +inline FILE *log_set_target_impl(const std::string & filename) { return log_handler1_impl(true, LogTriStateSame, filename); } +inline FILE *log_set_target_impl(FILE *target) { return log_handler2_impl(true, LogTriStateSame, target); } + +// INTERNAL, DO NOT USE +inline FILE *log_handler() { return log_handler1_impl(); } + +inline void log_test() +{ + log_disable(); + LOG("01 Hello World to nobody, because logs are disabled!\n") + log_enable(); + LOG("02 Hello World to default output, which is \"%s\" ( Yaaay, arguments! )!\n", LOG_STRINGIZE(LOG_TARGET)) + LOG_TEE("03 Hello World to **both** default output and " LOG_TEE_TARGET_STRING "!\n") + log_set_target(stderr); + LOG("04 Hello World to stderr!\n") + LOG_TEE("05 Hello World TEE with double printing to stderr prevented!\n") + log_set_target(LOG_DEFAULT_FILE_NAME); + LOG("06 Hello World to default log file!\n") + log_set_target(stdout); + LOG("07 Hello World to stdout!\n") + log_set_target(LOG_DEFAULT_FILE_NAME); + LOG("08 Hello World to default log file again!\n") + log_disable(); + LOG("09 Hello World _1_ into the void!\n") + log_enable(); + LOG("10 Hello World back from the void ( you should not see _1_ in the log or the output )!\n") + log_disable(); + log_set_target("llama.anotherlog.log"); + LOG("11 Hello World _2_ to nobody, new target was selected but logs are still disabled!\n") + log_enable(); + LOG("12 Hello World this time in a new file ( you should not see _2_ in the log or the output )?\n") + log_set_target("llama.yetanotherlog.log"); + LOG("13 Hello World this time in yet new file?\n") + log_set_target(log_filename_generator("llama_autonamed", "log")); + LOG("14 Hello World in log with generated filename!\n") +#ifdef _WIN32 + LOG_TEE("15 Hello msvc TEE without arguments\n") + LOG_TEE("16 Hello msvc TEE with (%d)(%s) arguments\n", 1, "test") + LOG_TEELN("17 Hello msvc TEELN without arguments\n") + LOG_TEELN("18 Hello msvc TEELN with (%d)(%s) arguments\n", 1, "test") + LOG("19 Hello msvc LOG without arguments\n") + LOG("20 Hello msvc LOG with (%d)(%s) arguments\n", 1, "test") + LOGLN("21 Hello msvc LOGLN without arguments\n") + LOGLN("22 Hello msvc LOGLN with (%d)(%s) arguments\n", 1, "test") +#endif +} + +inline bool log_param_single_parse(const std::string & param) +{ + if ( param == "--log-test") + { + log_test(); + return true; + } + + if ( param == "--log-disable") + { + log_disable(); + return true; + } + + if ( param == "--log-enable") + { + log_enable(); + return true; + } + + return false; +} + +inline bool log_param_pair_parse(bool check_but_dont_parse, const std::string & param, const std::string & next = std::string()) +{ + if ( param == "--log-file") + { + if (!check_but_dont_parse) + { + log_set_target(log_filename_generator(next.empty() ? "unnamed" : next, "log")); + } + + return true; + } + + return false; +} + +inline void log_print_usage() +{ + fprintf(stdout, "log options:\n"); + /* format + fprintf(stdout, " -h, --help show this help message and exit\n");*/ + /* spacing + fprintf(stdout, "__-param----------------Description\n");*/ + fprintf(stdout, " --log-test Run simple logging test\n"); + fprintf(stdout, " --log-disable Disable trace logs\n"); + fprintf(stdout, " --log-enable Enable trace logs\n"); + fprintf(stdout, " --log-file Specify a log filename (without extension)\n"); + fprintf(stdout, " Log file will be tagged with unique ID and written as \"..log\"\n"); /* */ +} + +#define log_dump_cmdline(argc, argv) log_dump_cmdline_impl(argc, argv) + +// INTERNAL, DO NOT USE +inline void log_dump_cmdline_impl(int argc, char **argv) +{ + std::stringstream buf; + for (int i = 0; i < argc; ++i) + { + if (std::string(argv[i]).find(' ') != std::string::npos) + { + buf << " \"" << argv[i] <<"\""; + } + else + { + buf << " " << argv[i]; + } + } + LOGLN("Cmd:%s", buf.str().c_str()) +} + +#define log_tostr(var) log_var_to_string_impl(var).c_str() + +inline std::string log_var_to_string_impl(bool var) +{ + return var ? "true" : "false"; +} + +inline std::string log_var_to_string_impl(std::string var) +{ + return var; +} + +inline std::string log_var_to_string_impl(const std::vector & var) +{ + std::stringstream buf; + buf << "[ "; + bool first = true; + for (auto e : var) + { + if (first) + { + first = false; + } + else + { + buf << ", "; + } + buf << std::to_string(e); + } + buf << " ]"; + + return buf.str(); +} + +#define LOG_TOKENS_TOSTR_PRETTY(ctx, tokens) \ + [&tokens, &ctx]() \ + { \ + std::stringstream buf; \ + buf << "[ "; \ + \ + bool first = true; \ + for (const auto &token : tokens) \ + { \ + if (!first) \ + buf << ", "; \ + else \ + first = false; \ + \ + auto detokenized = llama_token_to_piece(ctx, token); \ + \ + detokenized.erase( \ + std::remove_if( \ + detokenized.begin(), \ + detokenized.end(), \ + [](const unsigned char c) { return !std::isprint(c); }), \ + detokenized.end()); \ + \ + buf \ + << "'" << detokenized << "'" \ + << ":" << std::to_string(token); \ + } \ + buf << " ]"; \ + \ + return buf.str(); \ + }() \ + .c_str() + +#ifdef LOG_DISABLE_LOGS + +#undef LOG +#define LOG(...) // dummy stub +#undef LOGLN +#define LOGLN(...) // dummy stub + +#undef LOG_TEE +#define LOG_TEE(...) fprintf(stderr, __VA_ARGS__); // convert to normal fprintf + +#undef LOG_TEELN +#define LOG_TEELN(...) fprintf(stderr, __VA_ARGS__); // convert to normal fprintf + +#undef LOG_DISABLE +#define LOG_DISABLE() // dummy stub + +#undef LOG_ENABLE +#define LOG_ENABLE() // dummy stub + +#undef LOG_ENABLE +#define LOG_ENABLE() // dummy stub + +#undef LOG_SET_TARGET +#define LOG_SET_TARGET(...) // dummy stub + +#undef LOG_DUMP_CMDLINE +#define LOG_DUMP_CMDLINE(...) // dummy stub + +#endif // LOG_DISABLE_LOGS diff --git a/examples/chat.sh b/examples/chat.sh index 9a928ef05..d567acecd 100755 --- a/examples/chat.sh +++ b/examples/chat.sh @@ -11,6 +11,6 @@ cd .. # # "--keep 48" is based on the contents of prompts/chat-with-bob.txt # -./main -m ./models/7B/ggml-model-q4_0.bin -c 512 -b 1024 -n 256 --keep 48 \ +./main -m ./models/llama-7b/ggml-model-q4_0.gguf -c 512 -b 1024 -n 256 --keep 48 \ --repeat_penalty 1.0 --color -i \ -r "User:" -f prompts/chat-with-bob.txt diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 89cc4f602..7117db4b0 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -4,6 +4,7 @@ #endif #include "common.h" + #include "console.h" #include "llama.h" #include "build-info.h" @@ -112,6 +113,15 @@ int main(int argc, char ** argv) { return 1; } +#ifndef LOG_DISABLE_LOGS + log_set_target(log_filename_generator("main", "log")); + LOG_TEE("Log start\n"); + log_dump_cmdline(argc,argv); +#endif // LOG_DISABLE_LOGS + + // TODO: Dump params ? + //LOG("Params perplexity: %s\n", LOG_TOSTR(params.perplexity)); + // save choice to use color for later // (note for later: this is a slightly awkward choice) console::init(params.simple_io, params.use_color); @@ -134,34 +144,35 @@ int main(int argc, char ** argv) { } if (params.rope_freq_base != 10000.0) { - fprintf(stderr, "%s: warning: changing RoPE frequency base to %g (default 10000.0)\n", __func__, params.rope_freq_base); + LOG_TEE("%s: warning: changing RoPE frequency base to %g (default 10000.0)\n", __func__, params.rope_freq_base); } if (params.rope_freq_scale != 1.0) { - fprintf(stderr, "%s: warning: scaling RoPE frequency by %g (default 1.0)\n", __func__, params.rope_freq_scale); + LOG_TEE("%s: warning: scaling RoPE frequency by %g (default 1.0)\n", __func__, params.rope_freq_scale); } if (params.n_ctx > 2048) { // TODO: determine the actual max context of the model (e.g. 4096 for LLaMA v2) and use that instead of 2048 - fprintf(stderr, "%s: warning: base model only supports context sizes no greater than 2048 tokens (%d specified)\n", __func__, params.n_ctx); + LOG_TEE("%s: warning: base model only supports context sizes no greater than 2048 tokens (%d specified)\n", __func__, params.n_ctx); } else if (params.n_ctx < 8) { - fprintf(stderr, "%s: warning: minimum context size is 8, using minimum size.\n", __func__); + LOG_TEE("%s: warning: minimum context size is 8, using minimum size.\n", __func__); params.n_ctx = 8; } - fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); + LOG_TEE("%s: build = %d (%s)\n", __func__, BUILD_NUMBER, BUILD_COMMIT); if (params.seed == LLAMA_DEFAULT_SEED) { params.seed = time(NULL); } - fprintf(stderr, "%s: seed = %u\n", __func__, params.seed); + LOG_TEE("%s: seed = %u\n", __func__, params.seed); std::mt19937 rng(params.seed); if (params.random_prompt) { params.prompt = gpt_random_prompt(rng); } + LOG("%s: llama backend init\n", __func__); llama_backend_init(params.numa); llama_model * model; @@ -171,6 +182,7 @@ int main(int argc, char ** argv) { g_ctx = &ctx; // load the model and apply lora adapter, if any + LOG("%s: load the model and apply lora adapter, if any\n", __func__); std::tie(model, ctx) = llama_init_from_gpt_params(params); if (params.cfg_scale > 1.f) { struct llama_context_params lparams = llama_context_params_from_gpt_params(params); @@ -178,14 +190,14 @@ int main(int argc, char ** argv) { } if (model == NULL) { - fprintf(stderr, "%s: error: unable to load model\n", __func__); + LOG_TEE("%s: error: unable to load model\n", __func__); return 1; } // print system information { - fprintf(stderr, "\n"); - fprintf(stderr, "system_info: n_threads = %d / %d | %s\n", + LOG_TEE("\n"); + LOG_TEE("system_info: n_threads = %d / %d | %s\n", params.n_threads, std::thread::hardware_concurrency(), llama_print_system_info()); } @@ -193,7 +205,7 @@ int main(int argc, char ** argv) { // uncomment the "used_mem" line in llama.cpp to see the results if (params.mem_test) { { - fprintf(stderr, "%s: testing memory usage for n_batch = %d, n_ctx = %d\n", __func__, params.n_batch, params.n_ctx); + LOG_TEE("%s: testing memory usage for n_batch = %d, n_ctx = %d\n", __func__, params.n_batch, params.n_ctx); const std::vector tmp(params.n_batch, llama_token_bos(ctx)); llama_eval(ctx, tmp.data(), tmp.size(), params.n_ctx, params.n_threads); @@ -219,7 +231,7 @@ int main(int argc, char ** argv) { std::vector session_tokens; if (!path_session.empty()) { - fprintf(stderr, "%s: attempting to load saved session from '%s'\n", __func__, path_session.c_str()); + LOG_TEE("%s: attempting to load saved session from '%s'\n", __func__, path_session.c_str()); // fopen to check for existing session FILE * fp = std::fopen(path_session.c_str(), "rb"); @@ -229,33 +241,38 @@ int main(int argc, char ** argv) { session_tokens.resize(params.n_ctx); size_t n_token_count_out = 0; if (!llama_load_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.capacity(), &n_token_count_out)) { - fprintf(stderr, "%s: error: failed to load session file '%s'\n", __func__, path_session.c_str()); + LOG_TEE("%s: error: failed to load session file '%s'\n", __func__, path_session.c_str()); return 1; } session_tokens.resize(n_token_count_out); llama_set_rng_seed(ctx, params.seed); - fprintf(stderr, "%s: loaded a session with prompt size of %d tokens\n", __func__, (int) session_tokens.size()); + LOG_TEE("%s: loaded a session with prompt size of %d tokens\n", __func__, (int) session_tokens.size()); } else { - fprintf(stderr, "%s: session file does not exist, will create\n", __func__); + LOG_TEE("%s: session file does not exist, will create\n", __func__); } } - // Add BOS if SPM tokenizer const bool add_bos = llama_vocab_type(ctx) == LLAMA_VOCAB_TYPE_SPM; + LOG("add_bos: %d\n", add_bos); - // tokenize the prompt std::vector embd_inp; if (params.interactive_first || params.instruct || !params.prompt.empty() || session_tokens.empty()) { + LOG("tokenize the prompt\n"); embd_inp = ::llama_tokenize(ctx, params.prompt, add_bos); } else { + LOG("use session tokens\n"); embd_inp = session_tokens; } + LOG("prompt: \"%s\"\n", log_tostr(params.prompt)); + LOG("tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp)); + // Should not run without any tokens if (embd_inp.empty()) { embd_inp.push_back(llama_token_bos(ctx)); + LOG("embd_inp was considered empty and bos was added: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_inp)); } // Tokenize negative prompt @@ -263,23 +280,31 @@ int main(int argc, char ** argv) { int guidance_offset = 0; int original_prompt_len = 0; if (ctx_guidance) { + LOG("cfg_negative_prompt: \"%s\"\n", log_tostr(params.cfg_negative_prompt)); + guidance_inp = ::llama_tokenize(ctx_guidance, params.cfg_negative_prompt, add_bos); + LOG("guidance_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_guidance, guidance_inp)); std::vector original_inp = ::llama_tokenize(ctx, params.prompt, add_bos); + LOG("original_inp tokenized: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, original_inp)); + original_prompt_len = original_inp.size(); guidance_offset = (int)guidance_inp.size() - original_prompt_len; + LOG("original_prompt_len: %s", log_tostr(original_prompt_len)); + LOG("guidance_offset: %s", log_tostr(guidance_offset)); } const int n_ctx = llama_n_ctx(ctx); + LOG("n_ctx: %d\n", n_ctx); if ((int) embd_inp.size() > n_ctx - 4) { - fprintf(stderr, "%s: error: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4); + LOG_TEE("%s: error: prompt is too long (%d tokens, max %d)\n", __func__, (int) embd_inp.size(), n_ctx - 4); return 1; } // debug message about similarity of saved session, if applicable size_t n_matching_session_tokens = 0; - if (session_tokens.size()) { + if (session_tokens.size() > 0) { for (llama_token id : session_tokens) { if (n_matching_session_tokens >= embd_inp.size() || id != embd_inp[n_matching_session_tokens]) { break; @@ -287,22 +312,27 @@ int main(int argc, char ** argv) { n_matching_session_tokens++; } if (params.prompt.empty() && n_matching_session_tokens == embd_inp.size()) { - fprintf(stderr, "%s: using full prompt from session file\n", __func__); + LOG_TEE("%s: using full prompt from session file\n", __func__); } else if (n_matching_session_tokens >= embd_inp.size()) { - fprintf(stderr, "%s: session file has exact match for prompt!\n", __func__); + LOG_TEE("%s: session file has exact match for prompt!\n", __func__); } else if (n_matching_session_tokens < (embd_inp.size() / 2)) { - fprintf(stderr, "%s: warning: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n", + LOG_TEE("%s: warning: session file has low similarity to prompt (%zu / %zu tokens); will mostly be reevaluated\n", __func__, n_matching_session_tokens, embd_inp.size()); } else { - fprintf(stderr, "%s: session file matches %zu / %zu tokens of prompt\n", + LOG_TEE("%s: session file matches %zu / %zu tokens of prompt\n", __func__, n_matching_session_tokens, embd_inp.size()); } } + LOGLN( + "recalculate the cached logits (check): embd_inp.empty() %s, n_matching_session_tokens %zu, embd_inp.size() %zu, session_tokens.size() %zu, embd_inp.size() %zu", + log_tostr(embd_inp.empty()), n_matching_session_tokens, embd_inp.size(), session_tokens.size(), embd_inp.size()); + // if we will use the cache for the full prompt without reaching the end of the cache, force // reevaluation of the last token token to recalculate the cached logits - if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() && - session_tokens.size() > embd_inp.size()) { + if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() && session_tokens.size() > embd_inp.size()) { + LOGLN("recalculate the cached logits (do): session_tokens.resize( %zu )", embd_inp.size() - 1); + session_tokens.resize(embd_inp.size() - 1); } @@ -315,6 +345,9 @@ int main(int argc, char ** argv) { const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", add_bos); const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false); + LOG("inp_pfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_pfx)); + LOG("inp_sfx: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, inp_sfx)); + // in instruct mode, we inject a prefix and a suffix to each input by the user if (params.instruct) { params.interactive_first = true; @@ -327,30 +360,30 @@ int main(int argc, char ** argv) { } if (params.verbose_prompt) { - fprintf(stderr, "\n"); - fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str()); - fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size()); + LOG_TEE("\n"); + LOG_TEE("%s: prompt: '%s'\n", __func__, params.prompt.c_str()); + LOG_TEE("%s: number of tokens in prompt = %zu\n", __func__, embd_inp.size()); for (int i = 0; i < (int) embd_inp.size(); i++) { - fprintf(stderr, "%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str()); + LOG_TEE("%6d -> '%s'\n", embd_inp[i], llama_token_to_piece(ctx, embd_inp[i]).c_str()); } if (ctx_guidance) { - fprintf(stderr, "\n"); - fprintf(stderr, "%s: negative prompt: '%s'\n", __func__, params.cfg_negative_prompt.c_str()); - fprintf(stderr, "%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size()); + LOG_TEE("\n"); + LOG_TEE("%s: negative prompt: '%s'\n", __func__, params.cfg_negative_prompt.c_str()); + LOG_TEE("%s: number of tokens in negative prompt = %zu\n", __func__, guidance_inp.size()); for (int i = 0; i < (int) guidance_inp.size(); i++) { - fprintf(stderr, "%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str()); + LOG_TEE("%6d -> '%s'\n", guidance_inp[i], llama_token_to_piece(ctx, guidance_inp[i]).c_str()); } } if (params.n_keep > 0) { - fprintf(stderr, "%s: static prompt based on n_keep: '", __func__); + LOG_TEE("%s: static prompt based on n_keep: '", __func__); for (int i = 0; i < params.n_keep; i++) { - fprintf(stderr, "%s", llama_token_to_piece(ctx, embd_inp[i]).c_str()); + LOG_TEE("%s", llama_token_to_piece(ctx, embd_inp[i]).c_str()); } - fprintf(stderr, "'\n"); + LOG_TEE("'\n"); } - fprintf(stderr, "\n"); + LOG_TEE("\n"); } if (params.interactive) { @@ -367,30 +400,30 @@ int main(int argc, char ** argv) { SetConsoleCtrlHandler(reinterpret_cast(console_ctrl_handler), true); #endif - fprintf(stderr, "%s: interactive mode on.\n", __func__); + LOG_TEE("%s: interactive mode on.\n", __func__); if (params.antiprompt.size()) { - for (auto antiprompt : params.antiprompt) { - fprintf(stderr, "Reverse prompt: '%s'\n", antiprompt.c_str()); + for (const auto & antiprompt : params.antiprompt) { + LOG_TEE("Reverse prompt: '%s'\n", antiprompt.c_str()); } } if (params.input_prefix_bos) { - fprintf(stderr, "Input prefix with BOS\n"); + LOG_TEE("Input prefix with BOS\n"); } if (!params.input_prefix.empty()) { - fprintf(stderr, "Input prefix: '%s'\n", params.input_prefix.c_str()); + LOG_TEE("Input prefix: '%s'\n", params.input_prefix.c_str()); } if (!params.input_suffix.empty()) { - fprintf(stderr, "Input suffix: '%s'\n", params.input_suffix.c_str()); + LOG_TEE("Input suffix: '%s'\n", params.input_suffix.c_str()); } } - fprintf(stderr, "sampling: repeat_last_n = %d, repeat_penalty = %f, presence_penalty = %f, frequency_penalty = %f, top_k = %d, tfs_z = %f, top_p = %f, typical_p = %f, temp = %f, mirostat = %d, mirostat_lr = %f, mirostat_ent = %f\n", + LOG_TEE("sampling: repeat_last_n = %d, repeat_penalty = %f, presence_penalty = %f, frequency_penalty = %f, top_k = %d, tfs_z = %f, top_p = %f, typical_p = %f, temp = %f, mirostat = %d, mirostat_lr = %f, mirostat_ent = %f\n", params.repeat_last_n, params.repeat_penalty, params.presence_penalty, params.frequency_penalty, params.top_k, params.tfs_z, params.top_p, params.typical_p, params.temp, params.mirostat, params.mirostat_eta, params.mirostat_tau); - fprintf(stderr, "generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep); - fprintf(stderr, "\n\n"); + LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep); + LOG_TEE("\n\n"); grammar_parser::parse_state parsed_grammar; llama_grammar * grammar = NULL; @@ -400,14 +433,14 @@ int main(int argc, char ** argv) { if (parsed_grammar.rules.empty()) { return 1; } - fprintf(stderr, "%s: grammar:\n", __func__); + LOG_TEE("%s: grammar:\n", __func__); grammar_parser::print_grammar(stderr, parsed_grammar); - fprintf(stderr, "\n"); + LOG_TEE("\n"); { auto it = params.logit_bias.find(llama_token_eos(ctx)); if (it != params.logit_bias.end() && it->second == -INFINITY) { - fprintf(stderr, "%s: warning: EOS token is disabled, which will cause most grammars to fail\n", __func__); + LOG_TEE("%s: warning: EOS token is disabled, which will cause most grammars to fail\n", __func__); } } @@ -430,11 +463,11 @@ int main(int argc, char ** argv) { " - To return control without starting a new line, end your input with '/'.\n" " - If you want to submit another line, end your input with '\\'.\n"; } - fprintf(stderr, "== Running in interactive mode. ==\n" + LOG_TEE("== Running in interactive mode. ==\n"); #if defined (__unix__) || (defined (__APPLE__) && defined (__MACH__)) || defined (_WIN32) - " - Press Ctrl+C to interject at any time.\n" + LOG_TEE( " - Press Ctrl+C to interject at any time.\n"); #endif - "%s\n", control_message); + LOG_TEE( "%s\n", control_message); is_interacting = params.interactive_first; } @@ -459,8 +492,9 @@ int main(int argc, char ** argv) { std::vector embd; std::vector embd_guidance; - // do one empty run to warm up the model { + LOG("warming up the model with an empty run\n"); + const std::vector tmp = { llama_token_bos(ctx), }; llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads); llama_reset_timings(ctx); @@ -471,15 +505,17 @@ int main(int argc, char ** argv) { if (embd.size() > 0) { // Note: n_ctx - 4 here is to match the logic for commandline prompt handling via // --prompt or --file which uses the same value. - auto max_embd_size = n_ctx - 4; + int max_embd_size = n_ctx - 4; + // Ensure the input doesn't exceed the context size by truncating embd if necessary. - if ((int)embd.size() > max_embd_size) { - auto skipped_tokens = embd.size() - max_embd_size; + if ((int) embd.size() > max_embd_size) { + const int skipped_tokens = (int) embd.size() - max_embd_size; + embd.resize(max_embd_size); + console::set_display(console::error); - printf("<>", skipped_tokens, skipped_tokens != 1 ? "s" : ""); + printf("<>", skipped_tokens, skipped_tokens != 1 ? "s" : ""); console::set_display(console::reset); fflush(stdout); - embd.resize(max_embd_size); } // infinite text generation via context swapping @@ -488,28 +524,26 @@ int main(int argc, char ** argv) { // - take half of the last (n_ctx - n_keep) tokens and recompute the logits in batches if (n_past + (int) embd.size() + std::max(0, guidance_offset) > n_ctx) { if (params.n_predict == -2) { - fprintf(stderr, "\n\n%s: context full, stopping generation\n", __func__); + LOG_TEE("\n\n%s: context full and n_predict == -%d => stopping\n", __func__, params.n_predict); break; } const int n_left = n_past - params.n_keep; + LOG("context full, swapping: n_past = %d, n_left = %d, n_ctx = %d, n_keep = %d\n", n_past, n_left, n_ctx, params.n_keep); + // always keep the first token - BOS - n_past = std::max(1, params.n_keep); + n_past = std::max(1, params.n_keep); n_past_guidance = std::max(1, params.n_keep + guidance_offset); + LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance); + // insert n_left/2 tokens at the start of embd from last_n_tokens embd.insert(embd.begin(), last_n_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_n_tokens.end() - embd.size()); - // stop saving session if we run out of context - path_session.clear(); + LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd)); - //printf("\n---\n"); - //printf("resetting: '"); - //for (int i = 0; i < (int) embd.size(); i++) { - // printf("%s", llama_token_to_piece(ctx, embd[i])); - //} - //printf("'\n"); - //printf("\n---\n"); + LOG("clear session path\n"); + path_session.clear(); } // try to reuse a matching prefix from the loaded session instead of re-eval (via n_past) @@ -539,7 +573,7 @@ int main(int argc, char ** argv) { if (ctx_guidance) { int input_size = 0; - llama_token* input_buf = NULL; + llama_token * input_buf = NULL; if (n_past_guidance < (int) guidance_inp.size()) { // Guidance context should have the same data with these modifications: @@ -555,22 +589,19 @@ int main(int argc, char ** argv) { ); } - input_buf = embd_guidance.data(); + input_buf = embd_guidance.data(); input_size = embd_guidance.size(); - //fprintf(stderr, "\n---------------------\n"); - //for (int i = 0; i < (int) embd_guidance.size(); i++) { - //fprintf(stderr, "%s", llama_token_to_piece(ctx, embd_guidance[i])); - //} - //fprintf(stderr, "\n---------------------\n"); + + LOG("guidance context: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd_guidance)); } else { - input_buf = embd.data(); + input_buf = embd.data(); input_size = embd.size(); } for (int i = 0; i < input_size; i += params.n_batch) { int n_eval = std::min(input_size - i, params.n_batch); if (llama_eval(ctx_guidance, input_buf + i, n_eval, n_past_guidance, params.n_threads)) { - fprintf(stderr, "%s : failed to eval\n", __func__); + LOG_TEE("%s : failed to eval\n", __func__); return 1; } @@ -583,11 +614,17 @@ int main(int argc, char ** argv) { if (n_eval > params.n_batch) { n_eval = params.n_batch; } + + LOG("eval: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd)); + if (llama_eval(ctx, &embd[i], n_eval, n_past, params.n_threads)) { - fprintf(stderr, "%s : failed to eval\n", __func__); + LOG_TEE("%s : failed to eval\n", __func__); return 1; } + n_past += n_eval; + + LOG("n_past = %d\n", n_past); } if (embd.size() > 0 && !path_session.empty()) { @@ -600,7 +637,6 @@ int main(int argc, char ** argv) { embd_guidance.clear(); if ((int) embd_inp.size() <= n_consumed && !is_interacting) { - // out of user input, sample next token const float temp = params.temp; const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k; const float top_p = params.top_p; @@ -619,6 +655,8 @@ int main(int argc, char ** argv) { if (!path_session.empty() && need_to_save_session && !params.prompt_cache_ro) { need_to_save_session = false; llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size()); + + LOG("saved session to %s\n", path_session.c_str()); } llama_token id = 0; @@ -638,55 +676,68 @@ int main(int argc, char ** argv) { candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); } - llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; + llama_token_data_array cur_p = { candidates.data(), candidates.size(), false }; if (ctx_guidance) { - llama_sample_classifier_free_guidance(ctx, &candidates_p, ctx_guidance, params.cfg_scale); + llama_sample_classifier_free_guidance(ctx, &cur_p, ctx_guidance, params.cfg_scale); } // Apply penalties float nl_logit = logits[llama_token_nl(ctx)]; auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx); - llama_sample_repetition_penalty(ctx, &candidates_p, + llama_sample_repetition_penalty(ctx, &cur_p, last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, last_n_repeat, repeat_penalty); - llama_sample_frequency_and_presence_penalties(ctx, &candidates_p, + llama_sample_frequency_and_presence_penalties(ctx, &cur_p, last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, last_n_repeat, alpha_frequency, alpha_presence); if (!penalize_nl) { - for (size_t idx = 0; idx < candidates_p.size; idx++) { - if (candidates_p.data[idx].id == llama_token_nl(ctx)) { - candidates_p.data[idx].logit = nl_logit; + for (size_t idx = 0; idx < cur_p.size; idx++) { + if (cur_p.data[idx].id == llama_token_nl(ctx)) { + cur_p.data[idx].logit = nl_logit; break; } } } if (grammar != NULL) { - llama_sample_grammar(ctx, &candidates_p, grammar); + llama_sample_grammar(ctx, &cur_p, grammar); } if (temp <= 0) { // Greedy sampling - id = llama_sample_token_greedy(ctx, &candidates_p); + id = llama_sample_token_greedy(ctx, &cur_p); } else { if (mirostat == 1) { static float mirostat_mu = 2.0f * mirostat_tau; const int mirostat_m = 100; - llama_sample_temperature(ctx, &candidates_p, temp); - id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); + llama_sample_temperature(ctx, &cur_p, temp); + id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); } else if (mirostat == 2) { static float mirostat_mu = 2.0f * mirostat_tau; - llama_sample_temperature(ctx, &candidates_p, temp); - id = llama_sample_token_mirostat_v2(ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu); + llama_sample_temperature(ctx, &cur_p, temp); + id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu); } else { // Temperature sampling - llama_sample_top_k(ctx, &candidates_p, top_k, 1); - llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1); - llama_sample_typical(ctx, &candidates_p, typical_p, 1); - llama_sample_top_p(ctx, &candidates_p, top_p, 1); - llama_sample_temperature(ctx, &candidates_p, temp); - id = llama_sample_token(ctx, &candidates_p); + llama_sample_top_k (ctx, &cur_p, top_k, 1); + llama_sample_tail_free (ctx, &cur_p, tfs_z, 1); + llama_sample_typical (ctx, &cur_p, typical_p, 1); + llama_sample_top_p (ctx, &cur_p, top_p, 1); + llama_sample_temperature(ctx, &cur_p, temp); + + { + const int n_top = 10; + LOG("top %d candidates:\n", n_top); + + for (int i = 0; i < n_top; i++) { + const llama_token id = cur_p.data[i].id; + LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx, id).c_str(), cur_p.data[i].p); + } + } + + id = llama_sample_token(ctx, &cur_p); + + LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx, id).c_str()); } } // printf("`%d`", candidates_p.size); @@ -697,9 +748,10 @@ int main(int argc, char ** argv) { last_n_tokens.erase(last_n_tokens.begin()); last_n_tokens.push_back(id); + + LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, last_n_tokens)); } - // add it to the context embd.push_back(id); // echo this to console @@ -707,8 +759,11 @@ int main(int argc, char ** argv) { // decrement remaining sampling budget --n_remain; + + LOG("n_remain: %d\n", n_remain); } else { // some user input remains from prompt or interaction, forward it to processing + LOG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed); while ((int) embd_inp.size() > n_consumed) { embd.push_back(embd_inp[n_consumed]); last_n_tokens.erase(last_n_tokens.begin()); @@ -736,13 +791,12 @@ int main(int argc, char ** argv) { fflush(stdout); } // reset color to default if we there is no pending user input - if (input_echo && (int)embd_inp.size() == n_consumed) { + if (input_echo && (int) embd_inp.size() == n_consumed) { console::set_display(console::reset); } // if not currently processing queued inputs; if ((int) embd_inp.size() <= n_consumed) { - // check for reverse prompt if (params.antiprompt.size()) { std::string last_output; @@ -760,7 +814,7 @@ int main(int argc, char ** argv) { ? last_output.length() - static_cast(antiprompt.length() + extra_padding) : 0; - if (last_output.find(antiprompt.c_str(), search_start_pos) != std::string::npos) { + if (last_output.find(antiprompt, search_start_pos) != std::string::npos) { if (params.interactive) { is_interacting = true; console::set_display(console::user_input); @@ -770,10 +824,16 @@ int main(int argc, char ** argv) { break; } } + + if (is_antiprompt) { + LOG("found antiprompt: %s\n", last_output.c_str()); + } } // deal with end of text token in interactive mode if (last_n_tokens.back() == llama_token_eos(ctx)) { + LOG("found EOS token\n"); + if (params.interactive) { if (params.antiprompt.size() != 0) { // tokenize and inject first reverse prompt @@ -792,16 +852,20 @@ int main(int argc, char ** argv) { } if (n_past > 0 && is_interacting) { + LOG("waiting for user input\n"); + if (params.instruct) { printf("\n> "); } if (params.input_prefix_bos) { + LOG("adding input prefix BOS token\n"); embd_inp.push_back(llama_token_bos(ctx)); } std::string buffer; if (!params.input_prefix.empty()) { + LOG("appending input prefix: '%s'\n", params.input_prefix.c_str()); buffer += params.input_prefix; printf("%s", buffer.c_str()); } @@ -821,23 +885,30 @@ int main(int argc, char ** argv) { if (buffer.length() > 1) { // append input suffix if any if (!params.input_suffix.empty()) { + LOG("appending input suffix: '%s'\n", params.input_suffix.c_str()); buffer += params.input_suffix; printf("%s", params.input_suffix.c_str()); } + LOG("buffer: '%s'\n", buffer.c_str()); + const size_t original_size = embd_inp.size(); // instruct mode: insert instruction prefix if (params.instruct && !is_antiprompt) { + LOG("inserting instruction prefix\n"); n_consumed = embd_inp.size(); embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end()); } - auto line_inp = ::llama_tokenize(ctx, buffer, false); + const auto line_inp = ::llama_tokenize(ctx, buffer, false); + LOG("input tokens: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, line_inp)); + embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end()); // instruct mode: insert response suffix if (params.instruct) { + LOG("inserting instruction suffix\n"); embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end()); } @@ -848,6 +919,9 @@ int main(int argc, char ** argv) { } n_remain -= line_inp.size(); + LOG("n_remain: %d\n", n_remain); + } else { + LOG("empty line, passing control back\n"); } input_echo = false; // do not echo this again @@ -871,7 +945,7 @@ int main(int argc, char ** argv) { // end of text token if (!embd.empty() && embd.back() == llama_token_eos(ctx) && !(params.instruct || params.interactive)) { - fprintf(stderr, " [end of text]\n"); + LOG_TEE(" [end of text]\n"); break; } @@ -884,7 +958,7 @@ int main(int argc, char ** argv) { } if (!path_session.empty() && params.prompt_cache_all && !params.prompt_cache_ro) { - fprintf(stderr, "\n%s: saving final output to session file '%s'\n", __func__, path_session.c_str()); + LOG_TEE("\n%s: saving final output to session file '%s'\n", __func__, path_session.c_str()); llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(), session_tokens.size()); } @@ -900,5 +974,9 @@ int main(int argc, char ** argv) { } llama_backend_free(); +#ifndef LOG_DISABLE_LOGS + LOG_TEE("Log end\n") +#endif // LOG_DISABLE_LOGS + return 0; } From ad9ddcff6ef322db5cf13785bd7c856b610d242e Mon Sep 17 00:00:00 2001 From: chaihahaha Date: Wed, 30 Aug 2023 14:50:55 +0800 Subject: [PATCH 420/852] llm.vim : stop generation at multiple linebreaks, bind to (#2879) --- examples/llm.vim | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/llm.vim b/examples/llm.vim index 594a28549..d580a3d00 100644 --- a/examples/llm.vim +++ b/examples/llm.vim @@ -8,7 +8,7 @@ function! Llm() let buffer_content = join(getline(1, '$'), "\n") " Create the JSON payload - let json_payload = {"temp":0.72,"top_k":100,"top_p":0.73,"repeat_penalty":1.100000023841858,"n_predict":10,"stream": v:false} + let json_payload = {"temp":0.72,"top_k":100,"top_p":0.73,"repeat_penalty":1.100000023841858,"n_predict":256,"stop": ["\n\n\n"],"stream": v:false} let json_payload.prompt = buffer_content " Define the curl command @@ -25,3 +25,4 @@ function! Llm() endfunction command! Llm call Llm() +noremap :Llm From dc07dc492ef9640bbb82904d7c7679f7bdcf6d76 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Wed, 30 Aug 2023 02:25:50 -0600 Subject: [PATCH 421/852] convert : various script cleanups/fixes + merges and special token handling (#2842) * convert: Fix permute calls and method/func definitions * Cleanups for gguf-py * Minor types cleanups. * Initial implementation of handling merges and special tokens * convert: Handle special tokens and merges in vocab only mode convert: Vocab only mode no longer requires loading model tensors * gguf: Refactor tensor name mapping * convert: Fix type hint for special_token_types in SpecialVocab * Use common special vocab handling in various conversion scripts * First pass at implementing suggested changes * Second pass * gguf: SpecialVocab: Fix issue with special token content not in a dict gguf: SpecialVocab: Allow skipping handling of merges * convert-falcon-hf-to-gguf: Support --vocab-only option, bail out if no tokenizer.json * convert-gptneox-hf-to-gguf and convert: Only handle merges for BPE tokenizer * gguf: SpecialVocab: Actually set load_merges in object * Uniform args parsing and vocab only mode for convert examples * convert.py: Set gpt2 as tokenizer model when using BPE * Squish last type warning in gguf.py - yay! --- convert-falcon-hf-to-gguf.py | 170 +++++----- convert-gptneox-hf-to-gguf.py | 175 ++++------ convert-llama-7b-pth-to-gguf.py | 200 +++++------- convert-llama-ggmlv3-to-gguf.py | 28 +- convert-llama-hf-to-gguf.py | 203 +++++------- convert-lora-to-ggml.py | 6 +- convert.py | 142 ++++---- gguf-py/gguf/gguf.py | 551 +++++++++++++++++++------------- gguf-py/gguf/py.typed | 0 gguf-py/pyproject.toml | 1 + 10 files changed, 728 insertions(+), 748 deletions(-) create mode 100644 gguf-py/gguf/py.typed diff --git a/convert-falcon-hf-to-gguf.py b/convert-falcon-hf-to-gguf.py index 168bcf17f..0fdea70e1 100755 --- a/convert-falcon-hf-to-gguf.py +++ b/convert-falcon-hf-to-gguf.py @@ -8,6 +8,7 @@ import struct import json import numpy as np import torch +import argparse from typing import Any, List from pathlib import Path @@ -32,11 +33,10 @@ def bytes_to_unicode(): bs.append(b) cs.append(2**8+n) n += 1 - cs = [chr(n) for n in cs] - return dict(zip(bs, cs)) + return dict(zip(bs, (chr(n) for n in cs))) -def count_model_parts(dir_model: str) -> int: +def count_model_parts(dir_model: Path) -> int: num_parts = 0 for filename in os.listdir(dir_model): if filename.startswith("pytorch_model-"): @@ -47,17 +47,22 @@ def count_model_parts(dir_model: str) -> int: return num_parts -if len(sys.argv) < 3: - print(f"Usage: python {sys.argv[0]} dir-model ftype\n") - print(" ftype == 0 -> float32") - print(" ftype == 1 -> float16") +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Convert a Falcon model to a GGML compatible file") + parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") + parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") + parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)") + parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1) + return parser.parse_args() + +args = parse_args() + +dir_model = args.model +ftype = args.ftype +if not dir_model.is_dir(): + print(f'Error: {args.model} is not a directory', file = sys.stderr) sys.exit(1) - -# output in the same directory as the model -dir_model = sys.argv[1] -last_dir = os.path.basename(os.path.normpath(dir_model)) - # possible tensor data types # ftype == 0 -> float32 # ftype == 1 -> float16 @@ -65,25 +70,21 @@ last_dir = os.path.basename(os.path.normpath(dir_model)) # map from ftype to string ftype_str = ["f32", "f16"] -ftype = 1 -if len(sys.argv) > 2: - ftype = int(sys.argv[2]) - if ftype < 0 or ftype > 1: - print("Invalid ftype: " + str(ftype)) +if args.outfile is not None: + fname_out = args.outfile +else: + # output in the same directory as the model by default + fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' - sys.exit(1) +print("gguf: loading model "+dir_model.name) -fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf" - -print("gguf: loading model "+last_dir) - -with open(dir_model + "/config.json", "r", encoding="utf-8") as f: +with open(dir_model / "config.json", "r", encoding="utf-8") as f: hparams = json.load(f) if hparams["architectures"][0] != "RWForCausalLM": print("Model architecture not supported: " + hparams["architectures"][0]) - sys.exit() + sys.exit(1) # get number of model parts num_parts = count_model_parts(dir_model) @@ -113,77 +114,58 @@ gguf_writer.add_file_type(ftype) print("gguf: get tokenizer metadata") -tokens: List[str] = [] +tokens: List[bytearray] = [] scores: List[float] = [] toktypes: List[int] = [] -merges: List[str] = [] +tokenizer_json_file = dir_model / 'tokenizer.json' +if not tokenizer_json_file.is_file(): + print(f'Error: Missing {tokenizer_json_file}', file = sys.stderr) + sys.exit(1) -if Path(dir_model + "/tokenizer.json").is_file(): - # gpt2 tokenizer - gguf_writer.add_tokenizer_model("gpt2") +# gpt2 tokenizer +gguf_writer.add_tokenizer_model("gpt2") - print("gguf: get gpt2 tokenizer merges") +with open(tokenizer_json_file, "r", encoding="utf-8") as f: + tokenizer_json = json.load(f) - with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f: - tokenizer_json = json.load(f) - merges = tokenizer_json["model"]["merges"] +print("gguf: get gpt2 tokenizer vocab") - gguf_writer.add_token_merges(merges) +vocab_size = len(tokenizer_json["model"]["vocab"]) - print("gguf: get gpt2 tokenizer vocab") +# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py +tokenizer = AutoTokenizer.from_pretrained(dir_model) - vocab_size = len(tokenizer_json["model"]["vocab"]) +reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} +byte_encoder = bytes_to_unicode() +byte_decoder = {v: k for k, v in byte_encoder.items()} - # ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py - tokenizer = AutoTokenizer.from_pretrained(dir_model) +for i in range(vocab_size): + if i in reverse_vocab: + try: + text = bytearray([byte_decoder[c] for c in reverse_vocab[i]]) + except KeyError: + text = bytearray() + for c in reverse_vocab[i]: + if ord(c) < 256: # single byte character + text.append(byte_decoder[ord(c)]) + else: # multibyte special token character + text.extend(c.encode('utf-8')) + else: + print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.") + pad_token = f"[PAD{i}]".encode("utf8") + text = bytearray(pad_token) - reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} - byte_encoder = bytes_to_unicode() - byte_decoder = {v: k for k, v in byte_encoder.items()} + tokens.append(text) + scores.append(0.0) # dymmy + toktypes.append(gguf.TokenType.NORMAL) # dummy - for i in range(vocab_size): - if i in reverse_vocab: - try: - text = bytearray([byte_decoder[c] for c in reverse_vocab[i]]) - except KeyError: - text = bytearray() - for c in reverse_vocab[i]: - if ord(c) < 256: # single byte character - text.append(byte_decoder[ord(c)]) - else: # multibyte special token character - text.extend(c.encode('utf-8')) - else: - print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.") - pad_token = f"[PAD{i}]".encode("utf8") - text = bytearray(pad_token) - - tokens.append(text) - scores.append(0.0) # dymmy - toktypes.append(gguf.TokenType.NORMAL) # dummy - - gguf_writer.add_token_list(tokens) - gguf_writer.add_token_scores(scores) - gguf_writer.add_token_types(toktypes) - -print("gguf: get special token ids") -# Look for special tokens in config.json - -if "bos_token_id" in hparams and hparams["bos_token_id"] != None: - gguf_writer.add_bos_token_id(hparams["bos_token_id"]) - -if "eos_token_id" in hparams and hparams["eos_token_id"] != None: - gguf_writer.add_eos_token_id(hparams["eos_token_id"]) - -if "unk_token_id" in hparams and hparams["unk_token_id"] != None: - gguf_writer.add_unk_token_id(hparams["unk_token_id"]) - -if "sep_token_id" in hparams and hparams["sep_token_id"] != None: - gguf_writer.add_sep_token_id(hparams["sep_token_id"]) - -if "pad_token_id" in hparams and hparams["pad_token_id"] != None: - gguf_writer.add_pad_token_id(hparams["pad_token_id"]) +gguf_writer.add_token_list(tokens) +gguf_writer.add_token_scores(scores) +gguf_writer.add_token_types(toktypes) +special_vocab = gguf.SpecialVocab(dir_model, load_merges = True) +special_vocab.add_to_gguf(gguf_writer) # TENSORS @@ -199,15 +181,17 @@ head_dim = hparams["hidden_size"] // n_head print("gguf: get tensor metadata") if num_parts == 0: - part_names = ("pytorch_model.bin",) + part_names = iter(("pytorch_model.bin",)) else: part_names = ( f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) ) for part_name in part_names: + if args.vocab_only: + break print("gguf: loading model part '" + part_name + "'") - model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu") + model_part = torch.load(dir_model / part_name, map_location="cpu") for name in model_part.keys(): data = model_part[name] @@ -238,11 +222,8 @@ for part_name in part_names: data = data.squeeze().numpy() # map tensor names - if name.endswith(".weight") and name[:-7] in tensor_map: - name = tensor_map[name[:-7]] + ".weight" - elif name.endswith(".bias") and name[:-5] in tensor_map: - name = tensor_map[name[:-5]] + ".bias" - else: + new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) + if new_name is None: print("Can not map tensor '" + name + "'") sys.exit() @@ -261,19 +242,20 @@ for part_name in part_names: if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) - print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) + print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) - gguf_writer.add_tensor(name, data) + gguf_writer.add_tensor(new_name, data) print("gguf: write header") gguf_writer.write_header_to_file() print("gguf: write metadata") gguf_writer.write_kv_data_to_file() -print("gguf: write tensors") -gguf_writer.write_tensors_to_file() +if not args.vocab_only: + print("gguf: write tensors") + gguf_writer.write_tensors_to_file() gguf_writer.close() -print("gguf: model successfully exported to '" + fname_out + "'") +print(f"gguf: model successfully exported to '{fname_out}'") print("") diff --git a/convert-gptneox-hf-to-gguf.py b/convert-gptneox-hf-to-gguf.py index d9c42d76b..38e71e03b 100755 --- a/convert-gptneox-hf-to-gguf.py +++ b/convert-gptneox-hf-to-gguf.py @@ -8,6 +8,7 @@ import struct import json import numpy as np import torch +import argparse from typing import Any, List from pathlib import Path @@ -34,11 +35,10 @@ def bytes_to_unicode(): bs.append(b) cs.append(2**8+n) n += 1 - cs = [chr(n) for n in cs] - return dict(zip(bs, cs)) + return dict(zip(bs, (chr(n) for n in cs))) -def count_model_parts(dir_model: str) -> int: +def count_model_parts(dir_model: Path) -> int: num_parts = 0 for filename in os.listdir(dir_model): if filename.startswith("pytorch_model-"): @@ -49,17 +49,22 @@ def count_model_parts(dir_model: str) -> int: return num_parts -if len(sys.argv) < 3: - print(f"Usage: python {sys.argv[0]} dir-model ftype\n") - print(" ftype == 0 -> float32") - print(" ftype == 1 -> float16") +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Convert a GPT-NeoX model to a GGML compatible file") + parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") + parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") + parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)") + parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1) + return parser.parse_args() + +args = parse_args() + +dir_model = args.model +ftype = args.ftype +if not dir_model.is_dir(): + print(f'Error: {args.model} is not a directory', file = sys.stderr) sys.exit(1) - -# output in the same directory as the model -dir_model = sys.argv[1] -last_dir = os.path.basename(os.path.normpath(dir_model)) - # possible tensor data types # ftype == 0 -> float32 # ftype == 1 -> float16 @@ -67,19 +72,15 @@ last_dir = os.path.basename(os.path.normpath(dir_model)) # map from ftype to string ftype_str = ["f32", "f16"] -ftype = 1 -if len(sys.argv) > 2: - ftype = int(sys.argv[2]) - if ftype < 0 or ftype > 1: - print("Invalid ftype: " + str(ftype)) +if args.outfile is not None: + fname_out = args.outfile +else: + # output in the same directory as the model by default + fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' - sys.exit(1) +print("gguf: loading model "+dir_model.name) -fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf" - -print("gguf: loading model "+last_dir) - -with open(dir_model + "/config.json", "r", encoding="utf-8") as f: +with open(dir_model / "config.json", "r", encoding="utf-8") as f: hparams = json.load(f) if hparams["architectures"][0] != "GPTNeoXForCausalLM": @@ -97,7 +98,7 @@ print("gguf: get model metadata") block_count = hparams["num_hidden_layers"] -gguf_writer.add_name(last_dir) +gguf_writer.add_name(dir_model.name) gguf_writer.add_context_length(hparams["max_position_embeddings"]) gguf_writer.add_embedding_length(hparams["hidden_size"]) gguf_writer.add_block_count(block_count) @@ -111,86 +112,52 @@ gguf_writer.add_layer_norm_eps(hparams["layer_norm_eps"]) print("gguf: get tokenizer metadata") -tokens: List[str] = [] -merges: List[str] = [] +tokens: List[bytearray] = [] +tokenizer_json_file = dir_model / 'tokenizer.json' +if not tokenizer_json_file.is_file(): + print(f'Error: Missing {tokenizer_json_file}', file = sys.stderr) + sys.exit(1) -if Path(dir_model + "/tokenizer.json").is_file(): - # gpt2 tokenizer - gguf_writer.add_tokenizer_model("gpt2") +# gpt2 tokenizer +gguf_writer.add_tokenizer_model("gpt2") - print("gguf: get gpt2 tokenizer merges") +with open(tokenizer_json_file, "r", encoding="utf-8") as f: + tokenizer_json = json.load(f) - with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f: - tokenizer_json = json.load(f) - merges = tokenizer_json["model"]["merges"] +print("gguf: get gpt2 tokenizer vocab") - gguf_writer.add_token_merges(merges) +vocab_size = len(tokenizer_json["model"]["vocab"]) - print("gguf: get gpt2 tokenizer vocab") +# ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py +tokenizer = AutoTokenizer.from_pretrained(dir_model) - vocab_size = len(tokenizer_json["model"]["vocab"]) +reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} +byte_encoder = bytes_to_unicode() +byte_decoder = {v: k for k, v in byte_encoder.items()} - # ref: https://github.com/cmp-nct/ggllm.cpp/blob/master/falcon_convert.py - tokenizer = AutoTokenizer.from_pretrained(dir_model) +for i in range(vocab_size): + if i in reverse_vocab: + try: + text = bytearray([byte_decoder[c] for c in reverse_vocab[i]]) + except KeyError: + text = bytearray() + for c in reverse_vocab[i]: + if ord(c) < 256: # single byte character + text.append(byte_decoder[ord(c)]) + else: # multibyte special token character + text.extend(c.encode('utf-8')) + else: + print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.") + pad_token = f"[PAD{i}]".encode("utf8") + text = bytearray(pad_token) - reverse_vocab = {id: encoded_tok for encoded_tok, id in tokenizer.vocab.items()} - byte_encoder = bytes_to_unicode() - byte_decoder = {v: k for k, v in byte_encoder.items()} + tokens.append(text) - for i in range(vocab_size): - if i in reverse_vocab: - try: - text = bytearray([byte_decoder[c] for c in reverse_vocab[i]]) - except KeyError: - text = bytearray() - for c in reverse_vocab[i]: - if ord(c) < 256: # single byte character - text.append(byte_decoder[ord(c)]) - else: # multibyte special token character - text.extend(c.encode('utf-8')) - else: - print(f"Key {i} not in tokenizer vocabulary. Padding with an arbitrary token.") - pad_token = f"[PAD{i}]".encode("utf8") - text = bytearray(pad_token) - - tokens.append(text) - - gguf_writer.add_token_list(tokens) - - if "added_tokens" in tokenizer_json and Path(dir_model + "/tokenizer_config.json").is_file(): - print("gguf: get special token ids") - - with open(dir_model + "/tokenizer_config.json", "r", encoding="utf-8") as f: - tokenizer_config = json.load(f) - - # find special token ids - - if "bos_token" in tokenizer_config: - for key in tokenizer_json["added_tokens"]: - if key["content"] == tokenizer_config["bos_token"]: - gguf_writer.add_bos_token_id(key["id"]) - - if "eos_token" in tokenizer_config: - for key in tokenizer_json["added_tokens"]: - if key["content"] == tokenizer_config["eos_token"]: - gguf_writer.add_eos_token_id(key["id"]) - - if "unk_token" in tokenizer_config: - for key in tokenizer_json["added_tokens"]: - if key["content"] == tokenizer_config["unk_token"]: - gguf_writer.add_unk_token_id(key["id"]) - - if "sep_token" in tokenizer_config: - for key in tokenizer_json["added_tokens"]: - if key["content"] == tokenizer_config["sep_token"]: - gguf_writer.add_sep_token_id(key["id"]) - - if "pad_token" in tokenizer_config: - for key in tokenizer_json["added_tokens"]: - if key["content"] == tokenizer_config["pad_token"]: - gguf_writer.add_pad_token_id(key["id"]) +gguf_writer.add_token_list(tokens) +special_vocab = gguf.SpecialVocab(dir_model, load_merges = True) +special_vocab.add_to_gguf(gguf_writer) # TENSORS @@ -200,13 +167,15 @@ tensor_map = gguf.get_tensor_name_map(ARCH,block_count) print("gguf: get tensor metadata") if num_parts == 0: - part_names = ("pytorch_model.bin",) + part_names = iter(("pytorch_model.bin",)) else: part_names = ( f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) ) for part_name in part_names: + if args.vocab_only: + break print("gguf: loading model part '" + part_name + "'") model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu") @@ -226,11 +195,8 @@ for part_name in part_names: data = data.squeeze().numpy() # map tensor names - if name.endswith(".weight") and name[:-7] in tensor_map: - name = tensor_map[name[:-7]] + ".weight" - elif name.endswith(".bias") and name[:-5] in tensor_map: - name = tensor_map[name[:-5]] + ".bias" - else: + new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) + if new_name is None: print("Can not map tensor '" + name + "'") sys.exit() @@ -249,19 +215,20 @@ for part_name in part_names: if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) - print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) + print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) - gguf_writer.add_tensor(name, data) + gguf_writer.add_tensor(new_name, data) print("gguf: write header") gguf_writer.write_header_to_file() print("gguf: write metadata") gguf_writer.write_kv_data_to_file() -print("gguf: write tensors") -gguf_writer.write_tensors_to_file() +if not args.vocab_only: + print("gguf: write tensors") + gguf_writer.write_tensors_to_file() gguf_writer.close() -print("gguf: model successfully exported to '" + fname_out + "'") +print(f"gguf: model successfully exported to '{fname_out}'") print("") diff --git a/convert-llama-7b-pth-to-gguf.py b/convert-llama-7b-pth-to-gguf.py index 2ab082383..6e973a116 100755 --- a/convert-llama-7b-pth-to-gguf.py +++ b/convert-llama-7b-pth-to-gguf.py @@ -10,8 +10,9 @@ import struct import json import numpy as np import torch +import argparse -from typing import Any, List +from typing import Any, List, TypeAlias from pathlib import Path from sentencepiece import SentencePieceProcessor @@ -20,7 +21,7 @@ from sentencepiece import SentencePieceProcessor NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]' -def count_model_parts(dir_model: str) -> int: +def count_model_parts(dir_model: Path) -> int: num_parts = 0 for filename in os.listdir(dir_model): if filename.startswith("consolidated."): @@ -31,19 +32,22 @@ def count_model_parts(dir_model: str) -> int: return num_parts -if len(sys.argv) < 3: - print(f"Usage: python {sys.argv[0]} dir-model ftype\n") - print(" ftype == 0 -> float32") - print(" ftype == 1 -> float16") +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Convert a PyTorch 7B LLaMA model to a GGML compatible file") + parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") + parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") + parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)") + parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1) + return parser.parse_args() +args = parse_args() + +dir_model = args.model +ftype = args.ftype +if not dir_model.is_dir(): + print(f'Error: {args.model} is not a directory', file = sys.stderr) sys.exit(1) - -# output in the same directory as the model -dir_model = sys.argv[1] -last_dir = os.path.basename(os.path.normpath(dir_model)) - - # possible tensor data types # ftype == 0 -> float32 # ftype == 1 -> float16 @@ -51,19 +55,15 @@ last_dir = os.path.basename(os.path.normpath(dir_model)) # map from ftype to string ftype_str = ["f32", "f16"] -ftype = 1 -if len(sys.argv) > 2: - ftype = int(sys.argv[2]) - if ftype < 0 or ftype > 1: - print("Invalid ftype: " + str(ftype)) +if args.outfile is not None: + fname_out = args.outfile +else: + # output in the same directory as the model by default + fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' - sys.exit(1) +print("gguf: loading model "+dir_model.name) -fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf" - -print("gguf: loading model "+last_dir) - -with open(dir_model + "/config.json", "r", encoding="utf-8") as f: +with open(dir_model / "config.json", "r", encoding="utf-8") as f: hparams = json.load(f) if hparams["architectures"][0] != "LlamaForCausalLM": @@ -107,7 +107,7 @@ else: sys.exit() -gguf_writer.add_name(last_dir) +gguf_writer.add_name(dir_model.name) gguf_writer.add_source_hf_repo(hf_repo) gguf_writer.add_tensor_data_layout("Meta AI original pth") gguf_writer.add_context_length(ctx_length) @@ -133,109 +133,60 @@ tokens: List[bytes] = [] scores: List[float] = [] toktypes: List[int] = [] -if Path(dir_model + "/tokenizer.model").is_file(): - # vocab type sentencepiece - print("gguf: get sentencepiece tokenizer vocab and scores") +tokenizer_model_file = dir_model / 'tokenizer.model' +if not tokenizer_model_file.is_file(): + print(f'Error: Missing {tokenizer_model_file}', file = sys.stderr) + sys.exit(1) - tokenizer = SentencePieceProcessor(dir_model + "/tokenizer.model") +# vocab type sentencepiece +print("gguf: get sentencepiece tokenizer vocab and scores") - for i in range(tokenizer.vocab_size()): - text: bytes - score: float +tokenizer = SentencePieceProcessor(str(tokenizer_model_file)) - piece = tokenizer.id_to_piece(i) - text = piece.encode("utf-8") - score = tokenizer.get_score(i) +for i in range(tokenizer.vocab_size()): + text: bytes + score: float - toktype = 1 # defualt to normal token type - if tokenizer.is_unknown(i): - toktype = 2 - if tokenizer.is_control(i): - toktype = 3 + piece = tokenizer.id_to_piece(i) + text = piece.encode("utf-8") + score = tokenizer.get_score(i) - # toktype = 4 is user-defined = tokens from added_tokens.json + toktype = 1 # defualt to normal token type + if tokenizer.is_unknown(i): + toktype = 2 + if tokenizer.is_control(i): + toktype = 3 - if tokenizer.is_unused(i): - toktype = 5 - if tokenizer.is_byte(i): - toktype = 6 + # toktype = 4 is user-defined = tokens from added_tokens.json - tokens.append(text) - scores.append(score) - toktypes.append(toktype) + if tokenizer.is_unused(i): + toktype = 5 + if tokenizer.is_byte(i): + toktype = 6 - if Path(dir_model + "/added_tokens.json").is_file(): - with open(dir_model + "/added_tokens.json", "r", encoding="utf-8") as f: - addtokens_json = json.load(f) + tokens.append(text) + scores.append(score) + toktypes.append(toktype) - print("gguf: get added tokens") +added_tokens_file = dir_model / 'added_tokens.json' +if added_tokens_file.is_file(): + with open(added_tokens_file, "r", encoding="utf-8") as f: + addtokens_json = json.load(f) - for key in addtokens_json: - tokens.append( key.encode("utf-8") ) - scores.append(-1000.0) - toktypes.append(4) # user-defined token type + print("gguf: get added tokens") - gguf_writer.add_tokenizer_model("llama") - gguf_writer.add_token_list(tokens) - gguf_writer.add_token_scores(scores) - gguf_writer.add_token_types(toktypes) + for key in addtokens_json: + tokens.append( key.encode("utf-8") ) + scores.append(-1000.0) + toktypes.append(4) # user-defined token type +gguf_writer.add_tokenizer_model("llama") +gguf_writer.add_token_list(tokens) +gguf_writer.add_token_scores(scores) +gguf_writer.add_token_types(toktypes) -print("gguf: get special token ids") - -if Path(dir_model + "/tokenizer.json").is_file(): - # Look for special tokens in tokenizer.json if it exists - - with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f: - tokenizer = json.load(f) - - if "added_tokens" in tokenizer and Path(dir_model + "/tokenizer_config.json").is_file(): - - with open(dir_model + "/tokenizer_config.json", "r", encoding="utf-8") as f: - tokenizer_config = json.load(f) - - if "bos_token" in tokenizer_config and tokenizer_config["bos_token"] != None: - for key in tokenizer["added_tokens"]: - if key["content"] == tokenizer_config["bos_token"]["content"]: - gguf_writer.add_bos_token_id(key["id"]) - - if "eos_token" in tokenizer_config and tokenizer_config["eos_token"] != None: - for key in tokenizer["added_tokens"]: - if key["content"] == tokenizer_config["eos_token"]["content"]: - gguf_writer.add_eos_token_id(key["id"]) - - if "unk_token" in tokenizer_config and tokenizer_config["unk_token"] != None: - for key in tokenizer["added_tokens"]: - if key["content"] == tokenizer_config["unk_token"]["content"]: - gguf_writer.add_unk_token_id(key["id"]) - - if "sep_token" in tokenizer_config and tokenizer_config["sep_token"] != None: - for key in tokenizer["added_tokens"]: - if key["content"] == tokenizer_config["sep_token"]["content"]: - gguf_writer.add_sep_token_id(key["id"]) - - if "pad_token" in tokenizer_config and tokenizer_config["pad_token"] != None: - for key in tokenizer["added_tokens"]: - if key["content"] == tokenizer_config["pad_token"]["content"]: - gguf_writer.add_pad_token_id(key["id"]) -else: - # If no tokenizer.json: Look for special tokens in config.json - - if "bos_token_id" in hparams and hparams["bos_token_id"] != None: - gguf_writer.add_bos_token_id(hparams["bos_token_id"]) - - if "eos_token_id" in hparams and hparams["eos_token_id"] != None: - gguf_writer.add_eos_token_id(hparams["eos_token_id"]) - - if "unk_token_id" in hparams and hparams["unk_token_id"] != None: - gguf_writer.add_unk_token_id(hparams["unk_token_id"]) - - if "sep_token_id" in hparams and hparams["sep_token_id"] != None: - gguf_writer.add_sep_token_id(hparams["sep_token_id"]) - - if "pad_token_id" in hparams and hparams["pad_token_id"] != None: - gguf_writer.add_pad_token_id(hparams["pad_token_id"]) - +special_vocab = gguf.SpecialVocab(dir_model) +special_vocab.add_to_gguf(gguf_writer) # TENSORS @@ -247,6 +198,8 @@ print("gguf: get tensor metadata") part_names = (f"consolidated.{n:02}.pth" for n in range(0, num_parts)) for part_name in part_names: + if args.vocab_only: + break print("gguf: loading model part '" + part_name + "'") model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu") @@ -266,11 +219,8 @@ for part_name in part_names: data = data.squeeze().numpy() # map tensor names - if name.endswith(".weight") and name[:-7] in tensor_map: - name = tensor_map[name[:-7]] + ".weight" - elif name.endswith(".bias") and name[:-5] in tensor_map: - name = tensor_map[name[:-5]] + ".bias" - else: + new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) + if new_name is None: print("Can not map tensor '" + name + "'") sys.exit() @@ -289,20 +239,20 @@ for part_name in part_names: if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) - print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) + print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) - gguf_writer.add_tensor(name, data) + gguf_writer.add_tensor(new_name, data) print("gguf: write header") gguf_writer.write_header_to_file() print("gguf: write metadata") gguf_writer.write_kv_data_to_file() -print("gguf: write tensors") -gguf_writer.write_tensors_to_file() +if not args.vocab_only: + print("gguf: write tensors") + gguf_writer.write_tensors_to_file() gguf_writer.close() - -print("gguf: model successfully exported to '" + fname_out + "'") +print(f"gguf: model successfully exported to '{fname_out}'") print("") diff --git a/convert-llama-ggmlv3-to-gguf.py b/convert-llama-ggmlv3-to-gguf.py index 3bf93627d..c8e7f1761 100755 --- a/convert-llama-ggmlv3-to-gguf.py +++ b/convert-llama-ggmlv3-to-gguf.py @@ -75,7 +75,7 @@ class Tensor: self.dims = () self.dtype = None self.start_offset = 0 - self.len_bytes = 0 + self.len_bytes = np.int64(0) def load(self, data, offset): orig_offset = offset @@ -134,13 +134,14 @@ class GGMLV3Model: return offset class GGMLToGGUF: - def __init__(self, ggml_model, data, cfg, params_override = None, vocab_override = None): + def __init__(self, ggml_model, data, cfg, params_override = None, vocab_override = None, special_vocab = None): hp = ggml_model.hyperparameters self.model = ggml_model self.data = data self.cfg = cfg self.params_override = params_override self.vocab_override = vocab_override + self.special_vocab = special_vocab if params_override is not None: n_kv_head = params_override.n_head_kv else: @@ -162,6 +163,8 @@ class GGMLToGGUF: gguf_writer = gguf.GGUFWriter(self.cfg.output, gguf.MODEL_ARCH_NAMES[gguf.MODEL_ARCH.LLAMA], use_temp_file = False) self.add_params(gguf_writer) self.add_vocab(gguf_writer) + if self.special_vocab is not None: + self.special_vocab.add_to_gguf(gguf_writer) self.add_tensors(gguf_writer) print(" gguf: write header") gguf_writer.write_header_to_file() @@ -259,20 +262,13 @@ class GGMLToGGUF: gguf_writer.add_eos_token_id(2) def add_tensors(self, gguf_writer): - nm = self.name_map + tensor_map = self.name_map data = self.data print(f'* Adding {len(self.model.tensors)} tensor(s)') for tensor in self.model.tensors: name = str(tensor.name, 'UTF-8') - if name.endswith('.weight'): - name = name[:-7] - suffix = '.weight' - elif name.endswith('.bias'): - name = name[:-5] - suffix = '.bias' - mapped_name = nm.get(name) + mapped_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) assert mapped_name is not None, f'Bad name {name}' - mapped_name += suffix tempdims = list(tensor.dims[:]) if len(tempdims) > 1: temp = tempdims[1] @@ -302,8 +298,10 @@ def handle_metadata(cfg, hp): else: raise ValueError('Unable to load metadata') vocab = convert.load_vocab(cfg.vocab_dir if cfg.vocab_dir is not None else cfg.model_metadata_dir, cfg.vocabtype) + # FIXME: Respect cfg.vocab_dir? + svocab = gguf.SpecialVocab(cfg.model_metadata_dir) convert.check_vocab_size(params, vocab) - return (params, vocab) + return (params, vocab, svocab) def handle_args(): parser = argparse.ArgumentParser(description = 'Convert GGMLv3 models to GGUF') @@ -330,14 +328,16 @@ def main(): print(f'* GGML model hyperparameters: {model.hyperparameters}') vocab_override = None params_override = None + special_vocab = None if cfg.model_metadata_dir is not None: - (params_override, vocab_override) = handle_metadata(cfg, model.hyperparameters) + (params_override, vocab_override, special_vocab) = handle_metadata(cfg, model.hyperparameters) print('!! Note: When overriding params the --gqa, --eps and --context-length options are ignored.') print(f'* Overriding params: {params_override}') print(f'* Overriding vocab: {vocab_override}') + print(f'* Special vocab: {special_vocab}') else: print('\n=== WARNING === Special tokens may not be converted correctly. Use --model-metadata-dir if possible === WARNING ===\n') - converter = GGMLToGGUF(model, data, cfg, params_override = params_override, vocab_override = vocab_override) + converter = GGMLToGGUF(model, data, cfg, params_override = params_override, vocab_override = vocab_override, special_vocab = special_vocab) converter.save() print(f'* Successful completion. Output saved to: {cfg.output}') diff --git a/convert-llama-hf-to-gguf.py b/convert-llama-hf-to-gguf.py index b00810dbb..ab94b5eab 100755 --- a/convert-llama-hf-to-gguf.py +++ b/convert-llama-hf-to-gguf.py @@ -8,8 +8,9 @@ import struct import json import numpy as np import torch +import argparse -from typing import Any, List, Optional +from typing import Any, List, Optional, TypeAlias from pathlib import Path from sentencepiece import SentencePieceProcessor @@ -43,40 +44,38 @@ def count_model_parts(dir_model: str) -> int: return num_parts -if len(sys.argv) < 3: - print(f"Usage: python {sys.argv[0]} dir-model ftype\n") - print(" ftype == 0 -> float32") - print(" ftype == 1 -> float16") +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser(description="Convert a HuggingFace LLaMA model to a GGML compatible file") + parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") + parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") + parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)") + parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1) + return parser.parse_args() +args = parse_args() + +dir_model = args.model +ftype = args.ftype +if not dir_model.is_dir(): + print(f'Error: {args.model} is not a directory', file = sys.stderr) sys.exit(1) - -# output in the same directory as the model -dir_model = sys.argv[1] -last_dir = os.path.basename(os.path.normpath(dir_model)) - - # possible tensor data types # ftype == 0 -> float32 # ftype == 1 -> float16 - # map from ftype to string ftype_str = ["f32", "f16"] -ftype = 1 -if len(sys.argv) > 2: - ftype = int(sys.argv[2]) - if ftype < 0 or ftype > 1: - print("Invalid ftype: " + str(ftype)) +if args.outfile is not None: + fname_out = args.outfile +else: + # output in the same directory as the model by default + fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' - sys.exit(1) +print("gguf: loading model "+dir_model.name) -fname_out = sys.argv[1] + "/ggml-model-" + ftype_str[ftype] + ".gguf" - -print("gguf: loading model "+last_dir) - -with open(dir_model + "/config.json", "r", encoding="utf-8") as f: +with open(dir_model / "config.json", "r", encoding="utf-8") as f: hparams = json.load(f) if hparams["architectures"][0] != "LlamaForCausalLM": @@ -115,7 +114,7 @@ else: sys.exit() -gguf_writer.add_name(last_dir) +gguf_writer.add_name(dir_model.name) gguf_writer.add_source_hf_repo(hf_repo) gguf_writer.add_tensor_data_layout("Meta AI original pth") gguf_writer.add_context_length(ctx_length) @@ -141,110 +140,61 @@ tokens: List[bytes] = [] scores: List[float] = [] toktypes: List[int] = [] -if Path(dir_model + "/tokenizer.model").is_file(): - # vocab type sentencepiece - print("gguf: get sentencepiece tokenizer vocab, scores and token types") +tokenizer_model_file = dir_model / 'tokenizer.model' +if not tokenizer_model_file.is_file(): + print(f'Error: Missing {tokenizer_model_file}', file = sys.stderr) + sys.exit(1) - tokenizer = SentencePieceProcessor(dir_model + "/tokenizer.model") +# vocab type sentencepiece +print("gguf: get sentencepiece tokenizer vocab, scores and token types") - for i in range(tokenizer.vocab_size()): - text: bytes - score: float +tokenizer = SentencePieceProcessor(str(tokenizer_model_file)) - piece = tokenizer.id_to_piece(i) - text = piece.encode("utf-8") - score = tokenizer.get_score(i) +for i in range(tokenizer.vocab_size()): + text: bytes + score: float - toktype = 1 # defualt to normal token type - if tokenizer.is_unknown(i): - toktype = 2 - if tokenizer.is_control(i): - toktype = 3 + piece = tokenizer.id_to_piece(i) + text = piece.encode("utf-8") + score = tokenizer.get_score(i) - # toktype = 4 is user-defined = tokens from added_tokens.json + toktype = 1 # defualt to normal token type + if tokenizer.is_unknown(i): + toktype = 2 + if tokenizer.is_control(i): + toktype = 3 - if tokenizer.is_unused(i): - toktype = 5 - if tokenizer.is_byte(i): - toktype = 6 + # toktype = 4 is user-defined = tokens from added_tokens.json - tokens.append(text) - scores.append(score) - toktypes.append(toktype) + if tokenizer.is_unused(i): + toktype = 5 + if tokenizer.is_byte(i): + toktype = 6 - if Path(dir_model + "/added_tokens.json").is_file(): - with open(dir_model + "/added_tokens.json", "r", encoding="utf-8") as f: - addtokens_json = json.load(f) + tokens.append(text) + scores.append(score) + toktypes.append(toktype) - print("gguf: get added tokens") +added_tokens_file = dir_model / 'added_tokens.json' +if added_tokens_file.is_file(): + with open(added_tokens_file, "r", encoding="utf-8") as f: + addtokens_json = json.load(f) - for key in addtokens_json: - tokens.append( key.encode("utf-8") ) - scores.append(-1000.0) - toktypes.append(4) # user-defined token type + print("gguf: get added tokens") + + for key in addtokens_json: + tokens.append( key.encode("utf-8") ) + scores.append(-1000.0) + toktypes.append(4) # user-defined token type - gguf_writer.add_tokenizer_model("llama") - gguf_writer.add_token_list(tokens) - gguf_writer.add_token_scores(scores) - gguf_writer.add_token_types(toktypes) - - -print("gguf: get special token ids") - -if Path(dir_model + "/tokenizer.json").is_file(): - # Look for special tokens in tokenizer.json if it exists - - with open(dir_model + "/tokenizer.json", "r", encoding="utf-8") as f: - tokenizer = json.load(f) - - if "added_tokens" in tokenizer and Path(dir_model + "/tokenizer_config.json").is_file(): - - with open(dir_model + "/tokenizer_config.json", "r", encoding="utf-8") as f: - tokenizer_config = json.load(f) - - if "bos_token" in tokenizer_config and tokenizer_config["bos_token"] != None: - for key in tokenizer["added_tokens"]: - if key["content"] == tokenizer_config["bos_token"]["content"]: - gguf_writer.add_bos_token_id(key["id"]) - - if "eos_token" in tokenizer_config and tokenizer_config["eos_token"] != None: - for key in tokenizer["added_tokens"]: - if key["content"] == tokenizer_config["eos_token"]["content"]: - gguf_writer.add_eos_token_id(key["id"]) - - if "unk_token" in tokenizer_config and tokenizer_config["unk_token"] != None: - for key in tokenizer["added_tokens"]: - if key["content"] == tokenizer_config["unk_token"]["content"]: - gguf_writer.add_unk_token_id(key["id"]) - - if "sep_token" in tokenizer_config and tokenizer_config["sep_token"] != None: - for key in tokenizer["added_tokens"]: - if key["content"] == tokenizer_config["sep_token"]["content"]: - gguf_writer.add_sep_token_id(key["id"]) - - if "pad_token" in tokenizer_config and tokenizer_config["pad_token"] != None: - for key in tokenizer["added_tokens"]: - if key["content"] == tokenizer_config["pad_token"]["content"]: - gguf_writer.add_pad_token_id(key["id"]) -else: - # If no tokenizer.json: Look for special tokens in config.json - - if "bos_token_id" in hparams and hparams["bos_token_id"] != None: - gguf_writer.add_bos_token_id(hparams["bos_token_id"]) - - if "eos_token_id" in hparams and hparams["eos_token_id"] != None: - gguf_writer.add_eos_token_id(hparams["eos_token_id"]) - - if "unk_token_id" in hparams and hparams["unk_token_id"] != None: - gguf_writer.add_unk_token_id(hparams["unk_token_id"]) - - if "sep_token_id" in hparams and hparams["sep_token_id"] != None: - gguf_writer.add_sep_token_id(hparams["sep_token_id"]) - - if "pad_token_id" in hparams and hparams["pad_token_id"] != None: - gguf_writer.add_pad_token_id(hparams["pad_token_id"]) +gguf_writer.add_tokenizer_model("llama") +gguf_writer.add_token_list(tokens) +gguf_writer.add_token_scores(scores) +gguf_writer.add_token_types(toktypes) +special_vocab = gguf.SpecialVocab(dir_model) +special_vocab.add_to_gguf(gguf_writer) # TENSORS @@ -254,13 +204,15 @@ tensor_map = gguf.get_tensor_name_map(ARCH,block_count) print("gguf: get tensor metadata") if num_parts == 0: - part_names = ("pytorch_model.bin",) + part_names = iter(("pytorch_model.bin",)) else: part_names = ( f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) ) for part_name in part_names: + if args.vocab_only: + break print("gguf: loading model part '" + part_name + "'") model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu") @@ -286,11 +238,8 @@ for part_name in part_names: data = reverse_hf_permute(data, head_count, head_count_kv) # map tensor names - if name.endswith(".weight") and name[:-7] in tensor_map: - name = tensor_map[name[:-7]] + ".weight" - elif name.endswith(".bias") and name[:-5] in tensor_map: - name = tensor_map[name[:-5]] + ".bias" - else: + new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) + if new_name is None: print("Can not map tensor '" + name + "'") sys.exit() @@ -309,20 +258,20 @@ for part_name in part_names: if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: data = data.astype(np.float16) - print(name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) + print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) - gguf_writer.add_tensor(name, data) + gguf_writer.add_tensor(new_name, data) print("gguf: write header") gguf_writer.write_header_to_file() print("gguf: write metadata") gguf_writer.write_kv_data_to_file() -print("gguf: write tensors") -gguf_writer.write_tensors_to_file() +if not args.vocab_only: + print("gguf: write tensors") + gguf_writer.write_tensors_to_file() gguf_writer.close() - -print("gguf: model successfully exported to '" + fname_out + "'") +print(f"gguf: model successfully exported to '{fname_out}'") print("") diff --git a/convert-lora-to-ggml.py b/convert-lora-to-ggml.py index a94a7d0af..a00339b47 100755 --- a/convert-lora-to-ggml.py +++ b/convert-lora-to-ggml.py @@ -4,7 +4,7 @@ import os import re import struct import sys -from typing import Any, Dict, Sequence, TextIO +from typing import Any, Dict, Sequence, BinaryIO import numpy as np import torch @@ -46,7 +46,7 @@ def translate_tensor_name(t: str) -> str: sys.exit(1) -def write_file_header(fout: TextIO, params: Dict[str, Any]) -> None: +def write_file_header(fout: BinaryIO, params: Dict[str, Any]) -> None: fout.write(b"ggla"[::-1]) # magic (ggml lora) fout.write(struct.pack("i", 1)) # file version fout.write(struct.pack("i", params["r"])) @@ -60,7 +60,7 @@ def write_file_header(fout: TextIO, params: Dict[str, Any]) -> None: def write_tensor_header( - self, name: str, shape: Sequence[int], data_type: np.dtype + self, name: str, shape: Sequence[int], data_type: np.dtype[Any] ) -> None: sname = name.encode("utf-8") fout.write( diff --git a/convert.py b/convert.py index 3f0a1c932..448b6f0f3 100755 --- a/convert.py +++ b/convert.py @@ -25,7 +25,7 @@ import numpy as np from abc import ABCMeta, abstractmethod from dataclasses import dataclass from pathlib import Path -from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Generator, Iterable, List, Literal, Optional, Sequence, Set, Tuple, TypeVar, Union) +from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Generator, Iterable, List, Literal, Optional, Sequence, Set, Tuple, Type, TypeVar, Union) from sentencepiece import SentencePieceProcessor # type: ignore if TYPE_CHECKING: @@ -299,8 +299,10 @@ class Params: params = Params.loadHFTransformerJson(model_plus.model, hf_config_path) elif orig_config_path.exists(): params = Params.loadOriginalParamsJson(model_plus.model, orig_config_path) - else: + elif model_plus.format != 'none': params = Params.guessed(model_plus.model) + else: + raise ValueError('Cannot guess params when model format is none') params.path_model = model_plus.paths[0].parent @@ -353,7 +355,7 @@ class BpeVocab: yield from self.added_tokens() def __repr__(self) -> str: - return f"BpeVocab with {self.vocab_size_base} base tokens and {len(self.added_tokens_list)} added tokens>" + return f"" class SentencePieceVocab: @@ -416,7 +418,6 @@ class SentencePieceVocab: Vocab = Union[BpeVocab, SentencePieceVocab] - # # data loading # TODO: reuse (probably move to gguf.py?) @@ -439,14 +440,14 @@ class Tensor(metaclass=ABCMeta): @abstractmethod def permute(self, n_head: int, n_head_kv: int) -> 'Tensor': ... @abstractmethod - def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': ... + def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> 'UnquantizedTensor': ... @abstractmethod def part(self, n_part: int) -> 'UnquantizedTensor': ... @abstractmethod def to_ggml(self) -> 'GGMLCompatibleTensor': ... -def bf16_to_fp32(bf16_arr: np.ndarray) -> np.ndarray: +def bf16_to_fp32(bf16_arr: np.ndarray[Any, np.dtype[np.uint16]]) -> NDArray: assert bf16_arr.dtype == np.uint16, f"Input array should be of dtype uint16, but got {bf16_arr.dtype}" fp32_arr = bf16_arr.astype(np.uint32) << 16 return fp32_arr.view(np.float32) @@ -467,9 +468,9 @@ class UnquantizedTensor(Tensor): def to_ggml(self) -> 'UnquantizedTensor': return self - def permute_part(self, n_part: int, n_head: int) -> 'UnquantizedTensor': + def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> 'UnquantizedTensor': r = self.ndarray.shape[0] // 3 - return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head, n_head)) + return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head, n_head_kv)) def part(self, n_part: int) -> 'UnquantizedTensor': r = self.ndarray.shape[0] // 3 @@ -531,7 +532,7 @@ LazyModel = Dict[str, LazyTensor] class ModelPlus: model: LazyModel paths: List[Path] # Where this was read from. - format: Literal['ggml', 'torch', 'safetensors'] + format: Literal['ggml', 'torch', 'safetensors', 'none'] vocab: Optional[Vocab] # For GGML models (which have vocab built in), the vocab. @@ -597,12 +598,12 @@ def permute_lazy(lazy_tensor: LazyTensor, n_head: int, n_head_kv: int) -> LazyTe return lazy_tensor.load().permute(n_head, n_head_kv) return LazyTensor(load, lazy_tensor.shape, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description) -def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int) -> LazyTensor: +def permute_part_lazy(lazy_tensor: LazyTensor, n_part: int, n_head: int, n_head_kv: int) -> LazyTensor: def load() -> Tensor: - return lazy_tensor.load().permute_part(n_part, n_head) + return lazy_tensor.load().permute_part(n_part, n_head, n_head_kv) s = lazy_tensor.shape.copy() s[0] = s[0] // 3 - return LazyTensor(load, s, lazy_tensor.data_type, f'permute({n_head}) ' + lazy_tensor.description) + return LazyTensor(load, s, lazy_tensor.data_type, f'permute({n_head}, {n_head_kv}) ' + lazy_tensor.description) def part_lazy(lazy_tensor: LazyTensor, n_part: int) -> LazyTensor: def load() -> Tensor: @@ -657,7 +658,7 @@ class LazyUnpickler(pickle.Unpickler): description = f'storage data_type={data_type} path-in-zip={filename} path={self.zip_file.filename}' return LazyStorage(load=load, kind=pid[1], description=description) - # @staticmethod + @staticmethod def lazy_rebuild_tensor_v2(storage: Any, storage_offset: Any, size: Any, stride: Any, # pyright: ignore[reportSelfClsParameterName] requires_grad: Any, backward_hooks: Any, metadata: Any = None) -> LazyTensor: @@ -669,13 +670,15 @@ class LazyUnpickler(pickle.Unpickler): description = f'pickled storage_offset={storage_offset} in {storage.description}' return LazyTensor(load, list(size), storage.kind.data_type, description) - # @staticmethod + @staticmethod def rebuild_from_type_v2(func, new_type, args, state): return func(*args) - CLASSES: Dict[Any, Any] = { - ('torch._tensor', '_rebuild_from_type_v2'): rebuild_from_type_v2, - ('torch._utils', '_rebuild_tensor_v2'): lazy_rebuild_tensor_v2, + CLASSES: Dict[Tuple[str, str], Any] = { + # getattr used here as a workaround for mypy not being smart enough to detrmine + # the staticmethods have a __func__ attribute. + ('torch._tensor', '_rebuild_from_type_v2'): getattr(rebuild_from_type_v2, '__func__'), + ('torch._utils', '_rebuild_tensor_v2'): getattr(lazy_rebuild_tensor_v2, '__func__'), ('torch', 'BFloat16Storage'): LazyStorageKind(DT_BF16), ('torch', 'HalfStorage'): LazyStorageKind(DT_F16), ('torch', 'FloatStorage'): LazyStorageKind(DT_F32), @@ -751,7 +754,7 @@ def lazy_load_file(path: Path) -> ModelPlus: In = TypeVar('In') Out = TypeVar('Out') -def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int, max_workers: Optional[int] = None, factory: Callable = ThreadPoolExecutor) -> Iterable[Out]: +def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int, max_workers: Optional[int] = None, use_processpool_executor: bool = False) -> Iterable[Out]: '''Parallel map, but with backpressure. If the caller doesn't call `next` fast enough, this will stop calling `func` at some point rather than letting results pile up in memory. Specifically, there is a max of one @@ -760,7 +763,12 @@ def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], conc yield from map(func, iterable) # Not reached. iterable = iter(iterable) - with factory(max_workers = max_workers) as executor: + executor_class: Union[Type[ThreadPoolExecutor], Type[ProcessPoolExecutor]] + if use_processpool_executor: + executor_class = ProcessPoolExecutor + else: + executor_class = ThreadPoolExecutor + with executor_class(max_workers = max_workers) as executor: futures: List[concurrent.futures.Future[Out]] = [] done = False for _ in range(concurrency): @@ -838,11 +846,19 @@ class OutputFile: scores.append(score) toktypes.append(toktype) - self.gguf.add_tokenizer_model("llama") + if isinstance(vocab, SentencePieceVocab): + self.gguf.add_tokenizer_model("llama") + elif isinstance(vocab, BpeVocab): + self.gguf.add_tokenizer_model("gpt2") + else: + raise ValueError(f'Unknown vocab type: Not BpeVocab or SentencePieceVocab') self.gguf.add_token_list(tokens) self.gguf.add_token_scores(scores) self.gguf.add_token_types(toktypes) + def add_meta_special_vocab(self, svocab: gguf.SpecialVocab) -> None: + svocab.add_to_gguf(self.gguf) + def add_tensor_info(self, name: str, tensor: LazyTensor) -> None: n_elements = int(np.prod(tensor.shape)) raw_dtype = getattr(tensor.data_type, 'ggml_type', None) @@ -861,7 +877,7 @@ class OutputFile: self.gguf.close() @staticmethod - def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab) -> None: + def write_vocab_only(fname_out: Path, params: Params, vocab: Vocab, svocab: gguf.SpecialVocab) -> None: check_vocab_size(params, vocab) of = OutputFile(fname_out) @@ -869,6 +885,8 @@ class OutputFile: # meta data of.add_meta_arch(params) of.add_meta_vocab(vocab) + of.add_meta_special_vocab(svocab) + of.write_meta() of.close() @@ -887,7 +905,7 @@ class OutputFile: return dt.quantize(arr) @staticmethod - def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, concurrency: int = DEFAULT_CONCURRENCY) -> None: + def write_all(fname_out: Path, ftype: GGMLFileType, params: Params, model: LazyModel, vocab: Vocab, svocab: gguf.SpecialVocab, concurrency: int = DEFAULT_CONCURRENCY) -> None: check_vocab_size(params, vocab) of = OutputFile(fname_out) @@ -895,6 +913,7 @@ class OutputFile: # meta data of.add_meta_arch(params) of.add_meta_vocab(vocab) + of.add_meta_special_vocab(svocab) # tensor info for name, lazy_tensor in model.items(): @@ -906,7 +925,7 @@ class OutputFile: # tensor data ndarrays_inner = bounded_parallel_map(OutputFile.do_item, model.items(), concurrency = concurrency) if ftype == GGMLFileType.MostlyQ8_0: - ndarrays = bounded_parallel_map(OutputFile.maybe_do_quantize, ndarrays_inner, concurrency = concurrency, max_workers = concurrency, factory = ProcessPoolExecutor) + ndarrays = bounded_parallel_map(OutputFile.maybe_do_quantize, ndarrays_inner, concurrency = concurrency, max_workers = concurrency, use_processpool_executor = True) else: ndarrays = map(OutputFile.maybe_do_quantize, ndarrays_inner) @@ -939,7 +958,8 @@ def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyM for (name, tensor) in model.items()} def convert_model_names(model: LazyModel, params: Params) -> LazyModel: - tmap = gguf.get_tensor_name_map(ARCH, params.n_layer) + tmap = gguf.TensorNameMap(ARCH, params.n_layer) + should_skip: Set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, [])) tmp = model @@ -952,8 +972,8 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel: #tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = model[f"model.layers.{i}.self_attn.v_proj.weight"] elif f"model.layers.{i}.self_attn.W_pack.weight" in model: print(f"Unpacking and permuting layer {i}") - tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head) - tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head) + tmp[f"model.layers.{i}.self_attn.q_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 0, params.n_head, params.n_head) + tmp[f"model.layers.{i}.self_attn.k_proj.weight"] = permute_part_lazy(model[f"model.layers.{i}.self_attn.W_pack.weight"], 1, params.n_head, params.n_head_kv) tmp[f"model.layers.{i}.self_attn.v_proj.weight"] = part_lazy (model[f"model.layers.{i}.self_attn.W_pack.weight"], 2) del tmp[f"model.layers.{i}.self_attn.W_pack.weight"] else: @@ -961,23 +981,16 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel: out: LazyModel = {} for name, lazy_tensor in model.items(): - name_new = name - - if name in tmap: - name_new = tmap[name] - elif name.endswith(".weight") and name[:-7] in tmap: - name_new = tmap[name[:-7]] + ".weight" - elif name.endswith(".bias") and name[:-5] in tmap: - name_new = tmap[name[:-5]] + ".bias" - else: + tensor_type, name_new = tmap.get_type_and_name(name, try_suffixes = (".weight", ".bias")) or (None, None) + if name_new is None: raise Exception(f"Unexpected tensor name: {name}") - if gguf.should_skip_tensor_TMP(ARCH, params.n_layer, name_new): + if tensor_type in should_skip: print(f"skipping tensor {name_new}") continue - else: - print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}") - out[name_new] = lazy_tensor + + print(f"{name:48s} -> {name_new:40s} | {lazy_tensor.data_type.name:6s} | {lazy_tensor.shape}") + out[name_new] = lazy_tensor return out @@ -1117,8 +1130,16 @@ def main(args_in: Optional[List[str]] = None) -> None: if args.dump_single: model_plus = lazy_load_file(args.model) do_dump_model(model_plus) + return - model_plus = load_some_model(args.model) + if not args.vocab_only: + model_plus = load_some_model(args.model) + else: + model_plus = ModelPlus(model = {}, paths = [args.model / 'dummy'], format = 'none', vocab = None) + + if args.dump: + do_dump_model(model_plus) + return params = Params.load(model_plus) if params.n_ctx == -1: @@ -1140,33 +1161,34 @@ def main(args_in: Optional[List[str]] = None) -> None: vocab: Vocab if args.vocab_only: - vocab = load_vocab(args.vocab_dir or args.model, args.vocabtype) assert args.outfile, "need --outfile if using --vocab-only" + # FIXME: Try to respect vocab_dir somehow? + vocab = load_vocab(args.vocab_dir or args.model, args.vocabtype) + special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent, load_merges = args.vocabtype == 'bpe') outfile = args.outfile - OutputFile.write_vocab_only(outfile, params, vocab) + OutputFile.write_vocab_only(outfile, params, vocab, special_vocab) print(f"Wrote {outfile}") + return + + if model_plus.vocab is not None and args.vocab_dir is None: + vocab = model_plus.vocab else: - if args.dump: - do_dump_model(model_plus) - return + vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent + vocab = load_vocab(vocab_dir, args.vocabtype) + # FIXME: Try to respect vocab_dir somehow? + special_vocab = gguf.SpecialVocab(model_plus.paths[0].parent, load_merges = args.vocabtype == 'bpe') - if model_plus.vocab is not None and args.vocab_dir is None: - vocab = model_plus.vocab - else: - vocab_dir = args.vocab_dir if args.vocab_dir else model_plus.paths[0].parent - vocab = load_vocab(vocab_dir, args.vocabtype) + model = model_plus.model + model = convert_model_names(model, params) + ftype = pick_output_type(model, args.outtype) + model = convert_to_output_type(model, ftype) + outfile = args.outfile or default_outfile(model_plus.paths, ftype) - model = model_plus.model - model = convert_model_names(model, params) - ftype = pick_output_type(model, args.outtype) - model = convert_to_output_type(model, ftype) - outfile = args.outfile or default_outfile(model_plus.paths, ftype) + params.ftype = ftype + print(f"Writing {outfile}, format {ftype}") - params.ftype = ftype - print(f"Writing {outfile}, format {ftype}") - - OutputFile.write_all(outfile, ftype, params, model, vocab, concurrency = args.concurrency) - print(f"Wrote {outfile}") + OutputFile.write_all(outfile, ftype, params, model, vocab, special_vocab, concurrency = args.concurrency) + print(f"Wrote {outfile}") if __name__ == '__main__': diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py index 838a2c0f8..de3edbc99 100644 --- a/gguf-py/gguf/gguf.py +++ b/gguf-py/gguf/gguf.py @@ -4,9 +4,13 @@ import sys import struct import tempfile import numpy as np +import json +import os +from pathlib import Path from enum import IntEnum, auto -from typing import Any, IO, List, Optional +from io import BufferedWriter +from typing import Any, BinaryIO, Callable, IO, Dict, List, Optional, Sequence, Tuple, Union # # constants @@ -71,35 +75,35 @@ KEY_TOKENIZER_RWKV = "tokenizer.rwkv.world" class MODEL_ARCH(IntEnum): - LLAMA = auto() - FALCON = auto() - GPT2 = auto() - GPTJ = auto() - GPTNEOX = auto() - MPT = auto() + LLAMA : int = auto() + FALCON : int = auto() + GPT2 : int = auto() + GPTJ : int = auto() + GPTNEOX: int = auto() + MPT : int = auto() class MODEL_TENSOR(IntEnum): - TOKEN_EMBD = auto() - POS_EMBD = auto() - OUTPUT = auto() - OUTPUT_NORM = auto() - ROPE_FREQS = auto() - ATTN_Q = auto() - ATTN_K = auto() - ATTN_V = auto() - ATTN_QKV = auto() - ATTN_OUT = auto() - ATTN_NORM = auto() - ATTN_NORM_2 = auto() - ATTN_ROT_EMBD = auto() - FFN_GATE = auto() - FFN_DOWN = auto() - FFN_UP = auto() - FFN_NORM = auto() + TOKEN_EMBD : int = auto() + POS_EMBD : int = auto() + OUTPUT : int = auto() + OUTPUT_NORM : int = auto() + ROPE_FREQS : int = auto() + ATTN_Q : int = auto() + ATTN_K : int = auto() + ATTN_V : int = auto() + ATTN_QKV : int = auto() + ATTN_OUT : int = auto() + ATTN_NORM : int = auto() + ATTN_NORM_2 : int = auto() + ATTN_ROT_EMBD: int = auto() + FFN_GATE : int = auto() + FFN_DOWN : int = auto() + FFN_UP : int = auto() + FFN_NORM : int = auto() -MODEL_ARCH_NAMES = { +MODEL_ARCH_NAMES: Dict[MODEL_ARCH, str] = { MODEL_ARCH.LLAMA: "llama", MODEL_ARCH.FALCON: "falcon", MODEL_ARCH.GPT2: "gpt2", @@ -108,7 +112,7 @@ MODEL_ARCH_NAMES = { MODEL_ARCH.MPT: "mpt", } -MODEL_TENSOR_NAMES = { +MODEL_TENSOR_NAMES: Dict[MODEL_ARCH, Dict[MODEL_TENSOR, str]] = { MODEL_ARCH.LLAMA: { MODEL_TENSOR.TOKEN_EMBD: "token_embd", MODEL_TENSOR.OUTPUT_NORM: "output_norm", @@ -154,7 +158,7 @@ MODEL_TENSOR_NAMES = { } # tensors that will not be serialized -MODEL_TENSOR_SKIP = { +MODEL_TENSOR_SKIP: Dict[MODEL_ARCH, List[MODEL_TENSOR]] = { MODEL_ARCH.LLAMA: [ MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, @@ -162,167 +166,198 @@ MODEL_TENSOR_SKIP = { } -# TODO: the following helper functions should be removed -# instead, get_tensor_name_map should return tuples of (name, MODEL_TENSOR) -# however, my Python is very bad, and I couldn't figure out how to do this, hence these functions -# REMOVE -def should_skip_tensor_TMP(arch: MODEL_ARCH, n_blocks: int, name: str) -> bool: - for skip in MODEL_TENSOR_SKIP.get(arch, []): - for i in range(n_blocks): - if name == MODEL_TENSOR_NAMES[arch][skip].format(bid=i): - return True +class TensorNameMap: + mappings_cfg: Dict[MODEL_TENSOR, Tuple[str, ...]] = { + # Token embeddings + MODEL_TENSOR.TOKEN_EMBD: ( + "gpt_neox.embed_in", # gptneox + "transformer.wte", # gpt2 mpt + "transformer.word_embeddings", # falcon + "model.embed_tokens", # llama-hf + "tok_embeddings", # llama-pth + ), - return False + # Position embeddings + MODEL_TENSOR.POS_EMBD: ( + "transformer.wpe", # gpt2 + ), + # Output + MODEL_TENSOR.OUTPUT: ( + "embed_out", # gptneox + "lm_head", # gpt2 mpt falcon llama-hf + "output", # llama-pth + ), -def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> dict: - tensor_map = {} + # Output norm + MODEL_TENSOR.OUTPUT_NORM: ( + "gpt_neox.final_layer_norm", # gptneox + "transformer.ln_f", # gpt2 falcon + "model.norm", # llama-hf + "norm", # llama-pth + ), - # Token embeddings - mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.TOKEN_EMBD, None) + # Rope frequencies + MODEL_TENSOR.ROPE_FREQS: ( + "rope.freqs", # llama-pth + ), + } - tensor_map["gpt_neox.embed_in"] = mapped_to # gptneox - tensor_map["transformer.wte"] = mapped_to # gpt2 mpt - tensor_map["transformer.word_embeddings"] = mapped_to # falcon - tensor_map["model.embed_tokens"] = mapped_to # llama-hf - tensor_map["tok_embeddings"] = mapped_to # llama-pth - - # Position embeddings - mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.POS_EMBD, None) - - tensor_map["transformer.wpe"] = mapped_to # gpt2 - - # Output - mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.OUTPUT, None) - - tensor_map["embed_out"] = mapped_to # gptneox - tensor_map["lm_head"] = mapped_to # gpt2 mpt falcon llama-hf - tensor_map["output"] = mapped_to # llama-pth - - # Output norm - mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.OUTPUT_NORM, None) - - tensor_map["gpt_neox.final_layer_norm"] = mapped_to # gptneox - tensor_map["transformer.ln_f"] = mapped_to # gpt2 falcon - tensor_map["transformer.norm_f"] = mapped_to # mpt - tensor_map["model.norm"] = mapped_to # llama-hf - tensor_map["norm"] = mapped_to # llama-pth - - # Rope frequencies - mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ROPE_FREQS, None) - - tensor_map["rope.freqs"] = mapped_to # llama-pth - - # Attention and feed-forward blocks - for i in range(0, n_blocks): + block_mappings_cfg: Dict[MODEL_TENSOR, Tuple[str, ...]] = { # Attention norm - # TODO: is there are simpler way to write these 2 lines in Python? - mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_NORM, None) - mapped_to = mapped_to.format(bid=i) if mapped_to else None - - tensor_map["gpt_neox.layers."+str(i)+".input_layernorm"] = mapped_to # gptneox - tensor_map["transformer.h."+str(i)+".ln_1"] = mapped_to # gpt2 - tensor_map["transformer.blocks."+str(i)+".norm_1"] = mapped_to # mpt - tensor_map["transformer.h."+str(i)+".input_layernorm"] = mapped_to # falcon7b - tensor_map["transformer.h."+str(i)+".ln_mlp"] = mapped_to # falcon40b - tensor_map["model.layers."+str(i)+".input_layernorm"] = mapped_to # llama-hf - tensor_map["layers."+str(i)+".attention_norm"] = mapped_to # llama-pth + MODEL_TENSOR.ATTN_NORM: ( + "gpt_neox.layers.{bid}.input_layernorm", # gptneox + "transformer.h.{bid}.ln_1", # gpt2 + "transformer.blocks.{bid}.norm_1", # mpt + "transformer.h.{bid}.input_layernorm", # falcon7b + "transformer.h.{bid}.ln_mlp", # falcon40b + "model.layers.{bid}.input_layernorm", # llama-hf + "layers.{bid}.attention_norm", # llama-pth + ), # Attention norm 2 - mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_NORM_2, None) - mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None - - tensor_map["transformer.h."+str(i)+".ln_attn"] = mapped_to # falcon40b + MODEL_TENSOR.ATTN_NORM_2: ( + "transformer.h.{bid}.ln_attn", # falcon40b + ), # Attention query-key-value - mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_QKV, None) - mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None - - tensor_map["gpt_neox.layers."+str(i)+".attention.query_key_value"] = mapped_to # gptneox - tensor_map["transformer.h."+str(i)+".attn.c_attn"] = mapped_to # gpt2 - tensor_map["transformer.blocks."+str(i)+".attn.Wqkv"] = mapped_to # mpt - tensor_map["transformer.h."+str(i)+".self_attention.query_key_value"] = mapped_to # falcon + MODEL_TENSOR.ATTN_QKV: ( + "gpt_neox.layers.{bid}.attention.query_key_value", # gptneox + "transformer.h.{bid}.attn.c_attn", # gpt2 + "transformer.blocks.{bid}.attn.Wqkv", # mpt + "transformer.h.{bid}.self_attention.query_key_value", # falcon + ), # Attention query - mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_Q, None) - mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None - - tensor_map["model.layers."+str(i)+".self_attn.q_proj"] = mapped_to # llama-hf - tensor_map["layers."+str(i)+".attention.wq"] = mapped_to # llama-pth + MODEL_TENSOR.ATTN_Q: ( + "model.layers.{bid}.self_attn.q_proj", # llama-hf + "layers.{bid}.attention.wq", # llama-pth + ), # Attention key - mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_K, None) - mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None - - tensor_map["model.layers."+str(i)+".self_attn.k_proj"] = mapped_to # llama-hf - tensor_map["layers."+str(i)+".attention.wk"] = mapped_to # llama-pth + MODEL_TENSOR.ATTN_K: ( + "model.layers.{bid}.self_attn.k_proj", # llama-hf + "layers.{bid}.attention.wk", # llama-pth + ), # Attention value - mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_V, None) - mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None - - tensor_map["model.layers."+str(i)+".self_attn.v_proj"] = mapped_to # llama-hf - tensor_map["layers."+str(i)+".attention.wv"] = mapped_to # llama-pth + MODEL_TENSOR.ATTN_V: ( + "model.layers.{bid}.self_attn.v_proj", # llama-hf + "layers.{bid}.attention.wv", # llama-pth + ), # Attention output - mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_OUT, None) - mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None - - tensor_map["gpt_neox.layers."+str(i)+".attention.dense"] = mapped_to # gptneox - tensor_map["transformer.h."+str(i)+".attn.c_proj"] = mapped_to # gpt2 - tensor_map["transformer.blocks."+str(i)+".attn.out_proj"] = mapped_to # mpt - tensor_map["transformer.h."+str(i)+".self_attention.dense"] = mapped_to # falcon - tensor_map["model.layers."+str(i)+".self_attn.o_proj"] = mapped_to # llama-hf - tensor_map["layers."+str(i)+".attention.wo"] = mapped_to # llama-pth + MODEL_TENSOR.ATTN_OUT: ( + "gpt_neox.layers.{bid}.attention.dense", # gptneox + "transformer.h.{bid}.attn.c_proj", # gpt2 + "transformer.blocks.{bid}.attn.out_proj", # mpt + "transformer.h.{bid}.self_attention.dense", # falcon + "model.layers.{bid}.self_attn.o_proj", # llama-hf + "layers.{bid}.attention.wo", # llama-pth + ), # Rotary embeddings - mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.ATTN_ROT_EMBD, None) - mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None - - tensor_map["model.layers."+str(i)+".self_attn.rotary_emb.inv_freq"] = mapped_to # llama-hf - tensor_map["layers."+str(i)+".attention.inner_attention.rope.freqs"] = mapped_to # llama-pth + MODEL_TENSOR.ATTN_ROT_EMBD: ( + "model.layers.{bid}.self_attn.rotary_emb.inv_freq", # llama-hf + "layers.{bid}.attention.inner_attention.rope.freqs", # llama-pth + ), # Feed-forward norm - mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_NORM, None) - mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None - - tensor_map["gpt_neox.layers."+str(i)+".post_attention_layernorm"] = mapped_to # gptneox - tensor_map["transformer.h."+str(i)+".ln_2"] = mapped_to # gpt2 - tensor_map["transformer.blocks."+str(i)+".norm_2"] = mapped_to # mpt - tensor_map["model.layers."+str(i)+".post_attention_layernorm"] = mapped_to # llama-hf - tensor_map["layers."+str(i)+".ffn_norm"] = mapped_to # llama-pth + MODEL_TENSOR.FFN_NORM: ( + "gpt_neox.layers.{bid}.post_attention_layernorm", # gptneox + "transformer.h.{bid}.ln_2", # gpt2 + "transformer.blocks.{bid}.norm_2", # mpt + "model.layers.{bid}.post_attention_layernorm", # llama-hf + "layers.{bid}.ffn_norm", # llama-pth + ), # Feed-forward up - mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_UP, None) - mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None - - tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_h_to_4h"] = mapped_to # gptneox - tensor_map["transformer.h."+str(i)+".mlp.c_fc"] = mapped_to # gpt2 - tensor_map["transformer.blocks."+str(i)+".ffn.up_proj"] = mapped_to # mpt - tensor_map["transformer.h."+str(i)+".mlp.dense_h_to_4h"] = mapped_to # falcon - tensor_map["model.layers."+str(i)+".mlp.up_proj"] = mapped_to # llama-hf - tensor_map["layers."+str(i)+".feed_forward.w3"] = mapped_to # llama-pth + MODEL_TENSOR.FFN_UP: ( + "gpt_neox.layers.{bid}.mlp.dense_h_to_4h", # gptneox + "transformer.h.{bid}.mlp.c_fc", # gpt2 + "transformer.blocks.{bid}.ffn.up_proj", # mpt + "transformer.h.{bid}.mlp.dense_h_to_4h", # falcon + "model.layers.{bid}.mlp.up_proj", # llama-hf + "layers.{bid}.feed_forward.w3", # llama-pth + ), # Feed-forward gate - mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_GATE, None) - mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None - - tensor_map["model.layers."+str(i)+".mlp.gate_proj"] = mapped_to # llama-hf - tensor_map["layers."+str(i)+".feed_forward.w1"] = mapped_to # llama-pth + MODEL_TENSOR.FFN_GATE: ( + "model.layers.{bid}.mlp.gate_proj", # llama-hf + "layers.{bid}.feed_forward.w1", # llama-pth + ), # Feed-forward down - mapped_to = MODEL_TENSOR_NAMES[arch].get(MODEL_TENSOR.FFN_DOWN, None) - mapped_to = mapped_to.format(bid=i) if mapped_to is not None else None + MODEL_TENSOR.FFN_DOWN: ( + "gpt_neox.layers.{bid}.mlp.dense_4h_to_h", # gptneox + "transformer.h.{bid}.mlp.c_proj", # gpt2 + "transformer.blocks.{bid}.ffn.down_proj", # mpt + "transformer.h.{bid}.mlp.dense_4h_to_h", # falcon + "model.layers.{bid}.mlp.down_proj", # llama-hf + "layers.{bid}.feed_forward.w2", # llama-pth + ), + } - tensor_map["gpt_neox.layers."+str(i)+".mlp.dense_4h_to_h"] = mapped_to # gptneox - tensor_map["transformer.h."+str(i)+".mlp.c_proj"] = mapped_to # gpt2 - tensor_map["transformer.blocks."+str(i)+".ffn.down_proj"] = mapped_to # mpt - tensor_map["transformer.h."+str(i)+".mlp.dense_4h_to_h"] = mapped_to # falcon - tensor_map["model.layers."+str(i)+".mlp.down_proj"] = mapped_to # llama-hf - tensor_map["layers."+str(i)+".feed_forward.w2"] = mapped_to # llama-pth + mapping: Dict[str, Tuple[MODEL_TENSOR, str]] - return tensor_map + tensor_names: Dict[MODEL_TENSOR, str] + def __init__(self, arch: MODEL_ARCH, n_blocks: int): + mapping = self.mapping = {} + tensor_names = self.tensor_names = MODEL_TENSOR_NAMES[arch] + for tensor, keys in self.mappings_cfg.items(): + tensor_name = tensor_names.get(tensor) + if tensor_name is None: + continue + for key in keys: + mapping[key] = (tensor, tensor_name) + for bid in range(n_blocks): + for tensor, keys in self.block_mappings_cfg.items(): + tensor_name = tensor_names.get(tensor) + if tensor_name is None: + continue + tensor_name = tensor_name.format(bid = bid) + for key in keys: + key = key.format(bid = bid) + mapping[key] = (tensor, tensor_name) + + def get_type_and_name(self, key: str, try_suffixes: Sequence[str]) -> Optional[Tuple[MODEL_TENSOR, str]]: + result = self.mapping.get(key) + if result is not None: + return result + for suffix in try_suffixes: + if key.endswith(suffix): + result = self.mapping.get(key[:-len(suffix)]) + if result is not None: + return (result[0], result[1] + suffix) + return None + + def get_name(self, key: str, try_suffixes: Sequence[str]) -> Optional[str]: + result = self.get_type_and_name(key, try_suffixes = try_suffixes) + if result is None: + return None + return result[1] + + def get_type(self, key: str, try_suffixes: Sequence[str]) -> Optional[MODEL_TENSOR]: + result = self.get_type_and_name(key, try_suffixes = try_suffixes) + if result is None: + return None + return result[0] + + def __getitem__(self, key: str) -> str: + try: + return self.mapping[key][1] + except KeyError: + raise KeyError(key) + + def __contains__(self, key: str) -> bool: + return key in self.mapping + + def __repr__(self) -> str: + return repr(self.mapping) + +def get_tensor_name_map(arch: MODEL_ARCH, n_blocks: int) -> TensorNameMap: + return TensorNameMap(arch, n_blocks) class TokenType(IntEnum): NORMAL = 1 @@ -388,15 +423,21 @@ class GGUFValueType(IntEnum): class GGUFWriter: - def __init__(self, path: str, arch: str, use_temp_file = True): + fout: BufferedWriter + arch: str + offset_tensor = 0 + data_alignment = GGUF_DEFAULT_ALIGNMENT + kv_data = b"" + kv_data_count = 0 + ti_data = b"" + ti_data_count = 0 + use_temp_file: bool + temp_file: Optional[tempfile.SpooledTemporaryFile[bytes]] = None + tensors: List[Tuple[np.ndarray[Any, Any], int]] + + def __init__(self, path: Union[os.PathLike[str], str], arch: str, use_temp_file = True): self.fout = open(path, "wb") self.arch = arch - self.offset_tensor = 0 - self.data_alignment = GGUF_DEFAULT_ALIGNMENT - self.kv_data = b"" - self.kv_data_count = 0 - self.ti_data = b"" - self.ti_data_count = 0 self.add_architecture() self.use_temp_file = use_temp_file self.tensors = [] @@ -470,14 +511,27 @@ class GGUFWriter: self.add_key(key) self.add_val(val, GGUFValueType.STRING) - def add_array(self, key: str, val: list): - if not isinstance(val, list): - raise ValueError("Value must be a list for array type") + def add_array(self, key: str, val: Sequence[Any]): + if not isinstance(val, Sequence): + raise ValueError("Value must be a sequence for array type") self.add_key(key) self.add_val(val, GGUFValueType.ARRAY) - def add_val(self: str, val: Any, vtype: GGUFValueType = None, add_vtype: bool = True): + _simple_value_packing = { + GGUFValueType.UINT8: " 0: + ltype = GGUFValueType.get_type(val[0]) + if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]): + raise ValueError("All items in a GGUF array should be of the same type") + self.kv_data += struct.pack(" int: return ((x + n - 1) // n) * n - def add_tensor_info(self, name: str, tensor_shape: np.ndarray, tensor_dtype: np.dtype, tensor_nbytes: int, raw_dtype: Optional[GGMLQuantizationType] = None): + def add_tensor_info(self, name: str, tensor_shape: Sequence[int], tensor_dtype: Union[np.dtype[np.float16], np.dtype[np.float32]], tensor_nbytes: int, raw_dtype: Optional[GGMLQuantizationType] = None): assert raw_dtype is not None or tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now" encoded_name = name.encode("utf8") @@ -544,16 +580,18 @@ class GGUFWriter: self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment) self.ti_data_count += 1 - def add_tensor(self, name: str, tensor: np.ndarray, raw_shape: Optional[np.ndarray] = None, raw_dtype: Optional[GGMLQuantizationType] = None): - if self.use_temp_file and not hasattr(self, "temp_file"): - self.temp_file = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256*1024*1024) - self.temp_file.seek(0) + def add_tensor(self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Optional[Sequence[int]] = None, raw_dtype: Optional[GGMLQuantizationType] = None): + if self.use_temp_file and self.temp_file is None: + fp = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256*1024*1024) + fp.seek(0) + self.temp_file = fp - self.add_tensor_info(name, raw_shape if raw_shape is not None else tensor.shape, tensor.dtype, tensor.nbytes, raw_dtype = raw_dtype) + shape: Sequence[int] = raw_shape if raw_shape is not None else tensor.shape + self.add_tensor_info(name, shape, tensor.dtype, tensor.nbytes, raw_dtype = raw_dtype) pad = GGUFWriter.ggml_pad(tensor.nbytes, self.data_alignment) - tensor.nbytes - if not self.use_temp_file: + if self.temp_file is None: self.tensors.append((tensor, pad)) return @@ -562,25 +600,22 @@ class GGUFWriter: if pad != 0: self.temp_file.write(bytes([0] * pad)) - def write_tensor_data(self, tensor: np.ndarray): - pad = GGUFWriter.ggml_pad(self.fout.tell(), self.data_alignment) - self.fout.tell() + def write_padding(self, fp: BinaryIO, n: int, align: Optional[int] = None): + pad = GGUFWriter.ggml_pad(n, align if align is not None else self.data_alignment) - n if pad != 0: - self.fout.write(bytes([0] * pad)) + fp.write(bytes([0] * pad)) + def write_tensor_data(self, tensor: np.ndarray[Any, Any]): + self.write_padding(self.fout, self.fout.tell()) tensor.tofile(self.fout) - - pad = GGUFWriter.ggml_pad(tensor.nbytes, self.data_alignment) - tensor.nbytes - if pad != 0: - self.fout.write(bytes([0] * pad)) + self.write_padding(self.fout, tensor.nbytes) def write_tensors_to_file(self): self.write_ti_data_to_file() - pad = GGUFWriter.ggml_pad(self.fout.tell(), self.data_alignment) - self.fout.tell() - if pad != 0: - self.fout.write(bytes([0] * pad)) + self.write_padding(self.fout, self.fout.tell()) - if not self.use_temp_file: + if self.temp_file is None: for (currtensor, currpad) in self.tensors: currtensor.tofile(self.fout) if currpad != 0: @@ -654,10 +689,6 @@ class GGUFWriter: self.add_bool( KEY_USE_PARALLEL_RESIDUAL.format(arch=self.arch), use) - def add_tensor_data_layout(self, layout: str): - self.add_string( - KEY_TENSOR_DATA_LAYOUT.format(arch=self.arch), layout) - def add_head_count(self, count: int): self.add_uint32( KEY_ATTENTION_HEAD_COUNT.format(arch=self.arch), count) @@ -695,16 +726,16 @@ class GGUFWriter: def add_tokenizer_model(self, model: str): self.add_string(KEY_TOKENIZER_MODEL, model) - def add_token_list(self, tokens: List): + def add_token_list(self, tokens: Union[Sequence[str], Sequence[bytes], Sequence[bytearray]]): self.add_array(KEY_TOKENIZER_LIST, tokens) - def add_token_merges(self, merges: List): + def add_token_merges(self, merges: Union[Sequence[str], Sequence[bytes], Sequence[bytearray]]): self.add_array(KEY_TOKENIZER_MERGES, merges) - def add_token_types(self, types: List[int]): + def add_token_types(self, types: Union[Sequence[TokenType], Sequence[int]]): self.add_array(KEY_TOKENIZER_TOKEN_TYPE, types) - def add_token_scores(self, scores: List[float]): + def add_token_scores(self, scores: Sequence[float]): self.add_array(KEY_TOKENIZER_SCORES, scores) def add_bos_token_id(self, id: int): @@ -723,6 +754,84 @@ class GGUFWriter: self.add_uint32(KEY_TOKENIZER_PAD_ID, id) +class SpecialVocab: + load_merges: bool = False + merges: List[str] = [] + special_token_types: Tuple[str, ...] = tuple(('bos', 'eos', 'unk', 'sep', 'pad')) + special_token_ids: Dict[str, int] = {} + + def __init__(self, path: Path, load_merges: bool = False, special_token_types: Optional[Tuple[str, ...]] = None): + self.special_token_ids = {} + self.load_merges = load_merges + if special_token_types is not None: + self.special_token_types = special_token_types + self.load(path) + + def load(self, path: Path): + if not self.try_load_from_tokenizer_json(path): + self.try_load_from_config_json(path) + + def try_load_from_tokenizer_json(self, path: Path) -> bool: + tokenizer_file = path / 'tokenizer.json' + if not tokenizer_file.is_file(): + return False + with open(tokenizer_file, 'r', encoding = 'utf-8') as f: + tokenizer = json.load(f) + if self.load_merges: + merges = tokenizer.get('model', {}).get('merges') + if isinstance(merges, list) and len(merges) > 0 and isinstance(merges[0], str): + self.merges = merges + tokenizer_config_file = path / 'tokenizer_config.json' + added_tokens = tokenizer.get('added_tokens') + if added_tokens is None or not tokenizer_config_file.is_file(): + return True + with open(tokenizer_config_file, 'r', encoding = 'utf-8') as f: + tokenizer_config = json.load(f) + for typ in self.special_token_types: + entry = tokenizer_config.get(f'{typ}_token') + if isinstance(entry, str): + tc_content = entry + elif isinstance(entry, dict): + entry_content = entry.get('content') + if not isinstance(entry_content, str): + continue + tc_content = entry_content + else: + continue + for maybe_token_id in (atok.get('id') for atok in added_tokens if atok.get('content') == tc_content): + if isinstance(maybe_token_id, int): + self.special_token_ids[typ] = maybe_token_id + break + return True + + def try_load_from_config_json(self, path: Path) -> bool: + config_file = path / 'config.json' + if not config_file.is_file(): + return False + with open(config_file, 'r', encoding = 'utf-8') as f: + config = json.load(f) + for typ in self.special_token_types: + maybe_token_id = config.get(f'{typ}_token_id') + if isinstance(maybe_token_id, int): + self.special_token_ids[typ] = maybe_token_id + return True + + def add_to_gguf(self, gw: GGUFWriter): + if len(self.merges) > 0: + print(f'gguf: Adding {len(self.merges)} merge(s).') + gw.add_token_merges(self.merges) + for typ, tokid in self.special_token_ids.items(): + handler: Optional[Callable[[int], None]] = getattr(gw, f'add_{typ}_token_id', None) + if handler is None: + print(f'gguf: WARNING: No handler for special token type {typ} with id {tokid} - skipping') + continue + print(f'gguf: Setting special token type {typ} to {tokid}') + handler(tokid) + + def __repr__(self): + return f'' + + # Example usage: if __name__ == "__main__": # Example usage with a file diff --git a/gguf-py/gguf/py.typed b/gguf-py/gguf/py.typed new file mode 100644 index 000000000..e69de29bb diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index cc70e28b7..c66b069f9 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -5,6 +5,7 @@ description = "Write ML models in GGUF for GGML" authors = ["GGML "] packages = [ {include = "gguf"}, + {include = "gguf/py.typed"}, ] readme = "README.md" homepage = "https://ggml.ai" From 35092fb54712d032860f3976a6fc1ae1f84a4a28 Mon Sep 17 00:00:00 2001 From: Gilad S Date: Wed, 30 Aug 2023 11:40:12 +0300 Subject: [PATCH 422/852] docs : add `node-llama-cpp` to `README.md` (#2885) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a880fd29f..d727b0554 100644 --- a/README.md +++ b/README.md @@ -107,7 +107,7 @@ as the main playground for developing new features for the [ggml](https://github - Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python) - Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp) -- Node.js: [hlhr202/llama-node](https://github.com/hlhr202/llama-node) +- Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp), [hlhr202/llama-node](https://github.com/hlhr202/llama-node) - Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb) - Rust: [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp) - C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp) From 950929442070874d45561d2a4b68b010457767de Mon Sep 17 00:00:00 2001 From: alonfaraj Date: Wed, 30 Aug 2023 12:42:51 +0300 Subject: [PATCH 423/852] make : add test and update CI (#2897) * build ci: run make test * makefile: - add all - add test * enable tests/test-tokenizer-0-llama * fix path to model * remove gcc-8 from macos build test * Update Makefile * Update Makefile --- .github/workflows/build.yml | 12 ++++++++++++ Makefile | 17 +++++++++++++++++ 2 files changed, 29 insertions(+) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 10320ad1f..20fd8c2b5 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -41,6 +41,12 @@ jobs: run: | CC=gcc-8 make + - name: Test + id: make_test + run: | + CC=gcc-8 make tests + make test + ubuntu-latest-cmake: runs-on: ubuntu-latest @@ -157,6 +163,12 @@ jobs: run: | make + - name: Test + id: make_test + run: | + make tests + make test + macOS-latest-cmake: runs-on: macos-latest diff --git a/Makefile b/Makefile index c8b8a92d7..bd2d92869 100644 --- a/Makefile +++ b/Makefile @@ -6,6 +6,23 @@ TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-dou default: $(BUILD_TARGETS) +test: + @echo "Running tests..." + @for test_target in $(TEST_TARGETS); do \ + if [ "$$test_target" = "tests/test-tokenizer-0-llama" ]; then \ + ./$$test_target $(CURDIR)/models/ggml-vocab-llama.gguf; \ + elif [ "$$test_target" = "tests/test-tokenizer-0-falcon" ]; then \ + continue; \ + elif [ "$$test_target" = "tests/test-tokenizer-1" ]; then \ + continue; \ + else \ + ./$$test_target; \ + fi; \ + done + @echo "All tests have been run." + +all: $(BUILD_TARGETS) $(TEST_TARGETS) + ifndef UNAME_S UNAME_S := $(shell uname -s) endif From 0d1c706181cd31e7f368dd14eeb16c1a2569e4df Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=2E=20Yusuf=20Sar=C4=B1g=C3=B6z?= Date: Wed, 30 Aug 2023 12:47:40 +0300 Subject: [PATCH 424/852] gguf : add workflow for Pypi publishing (#2896) * gguf : add workflow for Pypi publishing * gguf : add workflow for Pypi publishing * fix trailing whitespace --- .github/workflows/gguf-publish.yml | 43 ++++++++++++++++++++++++++++++ gguf-py/README.md | 23 +++++++++++++--- 2 files changed, 63 insertions(+), 3 deletions(-) create mode 100644 .github/workflows/gguf-publish.yml diff --git a/.github/workflows/gguf-publish.yml b/.github/workflows/gguf-publish.yml new file mode 100644 index 000000000..a6289e335 --- /dev/null +++ b/.github/workflows/gguf-publish.yml @@ -0,0 +1,43 @@ +# This workflow will upload a Python Package using Twine when a GGUF release is created +# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries + +# See `gguf-py/README.md` for how to make a release. + +# This workflow uses actions that are not certified by GitHub. +# They are provided by a third-party and are governed by +# separate terms of service, privacy policy, and support +# documentation. + +name: Upload Python Package + +on: + workflow_dispatch: + push: + # Pattern matched against refs/tags + tags: + - 'gguf-v*' # Push events to every version tag + + +jobs: + deploy: + + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v2 + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: '3.9.x' + - name: Install dependencies + run: | + cd gguf-py + python -m pip install poetry + poetry install + + - name: Build package + run: poetry build + - name: Publish package + uses: pypa/gh-action-pypi-publish@release/v1 + with: + password: ${{ secrets.PYPI_API_TOKEN }} diff --git a/gguf-py/README.md b/gguf-py/README.md index 03ad306ec..ffe25c495 100644 --- a/gguf-py/README.md +++ b/gguf-py/README.md @@ -27,8 +27,25 @@ In this case, upgrade Pip to the latest: pip install --upgrade pip ``` -## Publishing -To publish the package, you need to have `twine` and `build` installed: +## Automatic publishing with CI + +There's a GitHub workflow to make a release automatically upon creation of tags in a specified format. + +1. Bump the version in `pyproject.toml`. +2. Create a tag named `gguf-vx.x.x` where `x.x.x` is the semantic version number. + +```sh +git tag -a gguf-v1.0.0 -m "Version 1.0 release" +``` + +3. Push the tags. + +```sh +git push origin --tags +``` + +## Manual publishing +If you want to publish the package manually for any reason, you need to have `twine` and `build` installed: ```sh pip install build twine @@ -36,7 +53,7 @@ pip install build twine Then, folow these steps to release a new version: -1. Update the version in `pyproject.toml`. +1. Bump the version in `pyproject.toml`. 2. Build the package: ```sh From c90d135eb433cf0d40fb95e46a48d1391d2352b5 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 30 Aug 2023 12:52:46 +0300 Subject: [PATCH 425/852] examples : fix underscore in beam-search + .gitignore (close #2900) --- .gitignore | 3 +++ Makefile | 4 ++-- examples/CMakeLists.txt | 2 +- examples/{beam_search => beam-search}/CMakeLists.txt | 4 ++-- .../beam_search.cpp => beam-search/beam-search.cpp} | 0 5 files changed, 8 insertions(+), 5 deletions(-) rename examples/{beam_search => beam-search}/CMakeLists.txt (78%) rename examples/{beam_search/beam_search.cpp => beam-search/beam-search.cpp} (100%) diff --git a/.gitignore b/.gitignore index 54ea2b522..8b5f45a2d 100644 --- a/.gitignore +++ b/.gitignore @@ -42,6 +42,9 @@ models-mnt /gguf-llama-simple /libllama.so /llama-bench +/baby-llama +/beam-search +/save-load-state build-info.h arm_neon.h compile_commands.json diff --git a/Makefile b/Makefile index bd2d92869..b750540fe 100644 --- a/Makefile +++ b/Makefile @@ -1,5 +1,5 @@ # Define the default target now so that it is always the first target -BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple save-load-state server embd-input-test gguf llama-bench baby-llama beam_search tests/test-c.o +BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-text-from-scratch convert-llama2c-to-ggml simple save-load-state server embd-input-test gguf llama-bench baby-llama beam-search tests/test-c.o # Binaries only useful for tests TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama tests/test-tokenizer-0-falcon tests/test-tokenizer-1 @@ -446,7 +446,7 @@ llama-bench: examples/llama-bench/llama-bench.cpp build-info.h ggml.o llama.o co baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o common.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -beam_search: examples/beam_search/beam_search.cpp build-info.h ggml.o llama.o common.o $(OBJS) +beam-search: examples/beam-search/beam-search.cpp build-info.h ggml.o llama.o common.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) ifneq '' '$(or $(filter clean,$(MAKECMDGOALS)),$(LLAMA_METAL))' diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 94b785224..6e65eb087 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -25,7 +25,7 @@ else() add_subdirectory(simple) add_subdirectory(embd-input) add_subdirectory(llama-bench) - add_subdirectory(beam_search) + add_subdirectory(beam-search) if (LLAMA_METAL) add_subdirectory(metal) endif() diff --git a/examples/beam_search/CMakeLists.txt b/examples/beam-search/CMakeLists.txt similarity index 78% rename from examples/beam_search/CMakeLists.txt rename to examples/beam-search/CMakeLists.txt index b29e01092..e44a74975 100644 --- a/examples/beam_search/CMakeLists.txt +++ b/examples/beam-search/CMakeLists.txt @@ -1,5 +1,5 @@ -set(TARGET beam_search) -add_executable(${TARGET} beam_search.cpp) +set(TARGET beam-search) +add_executable(${TARGET} beam-search.cpp) install(TARGETS ${TARGET} RUNTIME) target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) target_compile_features(${TARGET} PRIVATE cxx_std_11) diff --git a/examples/beam_search/beam_search.cpp b/examples/beam-search/beam-search.cpp similarity index 100% rename from examples/beam_search/beam_search.cpp rename to examples/beam-search/beam-search.cpp From b532a69b2fd08067f34f32f37a2fd9b37678a34a Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 30 Aug 2023 13:29:40 +0300 Subject: [PATCH 426/852] convert.py : use dir name to name the llama --- convert.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/convert.py b/convert.py index 448b6f0f3..a7f4c2d75 100755 --- a/convert.py +++ b/convert.py @@ -811,10 +811,12 @@ class OutputFile: def add_meta_arch(self, params: Params) -> None: name = "LLaMA" + + # TODO: better logic to determine model name if (params.n_ctx == 4096): name = "LLaMA v2" - if params.path_model: - name = str(params.path_model.parent).split('/')[-1] + elif params.path_model: + name = str(params.path_model.parent).split('/')[-1] self.gguf.add_name (name) self.gguf.add_context_length (params.n_ctx) @@ -839,8 +841,7 @@ class OutputFile: tokens = [] scores = [] toktypes = [] - # NOTE: `all_tokens` returns the the base vocabulary and added tokens - # TODO: add special tokens? + # NOTE: `all_tokens` returns the base vocabulary and added tokens for text, score, toktype in vocab.all_tokens(): tokens.append(text) scores.append(score) From 71d6975559acfd6c8407a4ef8275a9979c737765 Mon Sep 17 00:00:00 2001 From: Henri Vasserman Date: Wed, 30 Aug 2023 19:14:53 +0300 Subject: [PATCH 427/852] [Docker] fix tools.sh argument passing. (#2884) * [Docker] fix tools.sh argument passing. This should allow passing multiple arguments to containers with the full image that are using the tools.sh frontend. Fix from https://github.com/ggerganov/llama.cpp/issues/2535#issuecomment-1697091734 --- .devops/tools.sh | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/.devops/tools.sh b/.devops/tools.sh index 2787c21fe..9d999315f 100755 --- a/.devops/tools.sh +++ b/.devops/tools.sh @@ -7,15 +7,12 @@ arg1="$1" # Shift the arguments to remove the first one shift -# Join the remaining arguments into a single string -arg2="$@" - if [[ "$arg1" == '--convert' || "$arg1" == '-c' ]]; then - python3 ./convert.py "$arg2" + python3 ./convert.py "$@" elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then - ./quantize "$arg2" + ./quantize "$@" elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then - ./main "$arg2" + ./main "$@" elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then echo "Converting PTH to GGML..." for i in `ls $1/$2/ggml-model-f16.bin*`; do @@ -27,7 +24,7 @@ elif [[ "$arg1" == '--all-in-one' || "$arg1" == '-a' ]]; then fi done elif [[ "$arg1" == '--server' || "$arg1" == '-s' ]]; then - ./server "$arg2" + ./server "$@" else echo "Unknown command: $arg1" echo "Available commands: " From 8afe2280009ecbfc9de2c93b8f41283dc810609a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Wed, 30 Aug 2023 21:46:19 +0200 Subject: [PATCH 428/852] CUDA: mul_mat_q=true llama_context_params default (#2912) --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index fcd6f276a..95ee6ffe4 100644 --- a/llama.cpp +++ b/llama.cpp @@ -5287,7 +5287,7 @@ struct llama_context_params llama_context_default_params() { /*.progress_callback =*/ nullptr, /*.progress_callback_user_data =*/ nullptr, /*.low_vram =*/ false, - /*.mul_mat_q =*/ false, + /*.mul_mat_q =*/ true, /*.f16_kv =*/ true, /*.logits_all =*/ false, /*.vocab_only =*/ false, From 92d0b751a77a089e650983e9f1564ef4d31b32b9 Mon Sep 17 00:00:00 2001 From: Cebtenzzre Date: Thu, 31 Aug 2023 01:02:23 -0400 Subject: [PATCH 429/852] convert : fix python 3.8 support, modernize type annotations (#2916) * convert : fix python 3.8 support * convert : sort imports * convert : fix required parameters in convert-llama-ggmlv3-to-gguf * convert : fix mypy errors in convert-llama-ggmlv3-to-gguf * convert : use PEP 585 generics and PEP 604 unions Now that we have `from __future__ import annotations`, we can use this modern syntax in Python 3.7 instead of restricting support to Python 3.9 or 3.10 respectively. * gguf.py : a tuple is already a tuple * add mypy.ini * convert : add necessary `type: ignore` comments * gguf-py: bump version --- convert-falcon-hf-to-gguf.py | 25 +++--- convert-gptneox-hf-to-gguf.py | 22 ++--- convert-llama-7b-pth-to-gguf.py | 31 ++++--- convert-llama-ggmlv3-to-gguf.py | 18 ++-- convert-llama-hf-to-gguf.py | 33 +++---- convert-lora-to-ggml.py | 8 +- convert.py | 149 ++++++++++++++++---------------- gguf-py/gguf/gguf.py | 68 ++++++++------- gguf-py/pyproject.toml | 2 +- mypy.ini | 5 ++ 10 files changed, 193 insertions(+), 168 deletions(-) create mode 100644 mypy.ini diff --git a/convert-falcon-hf-to-gguf.py b/convert-falcon-hf-to-gguf.py index 0fdea70e1..ec786ff67 100755 --- a/convert-falcon-hf-to-gguf.py +++ b/convert-falcon-hf-to-gguf.py @@ -1,18 +1,21 @@ #!/usr/bin/env python3 # HF falcon--> gguf conversion -import gguf -import os -import sys -import struct +from __future__ import annotations + +import argparse import json +import os +import struct +import sys +from pathlib import Path +from typing import Any + +import gguf import numpy as np import torch -import argparse +from transformers import AutoTokenizer # type: ignore[import] -from typing import Any, List -from pathlib import Path -from transformers import AutoTokenizer def bytes_to_unicode(): # ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py @@ -114,9 +117,9 @@ gguf_writer.add_file_type(ftype) print("gguf: get tokenizer metadata") -tokens: List[bytearray] = [] -scores: List[float] = [] -toktypes: List[int] = [] +tokens: list[bytearray] = [] +scores: list[float] = [] +toktypes: list[int] = [] tokenizer_json_file = dir_model / 'tokenizer.json' if not tokenizer_json_file.is_file(): diff --git a/convert-gptneox-hf-to-gguf.py b/convert-gptneox-hf-to-gguf.py index 38e71e03b..852123d99 100755 --- a/convert-gptneox-hf-to-gguf.py +++ b/convert-gptneox-hf-to-gguf.py @@ -1,18 +1,20 @@ #!/usr/bin/env python3 # HF gptneox--> gguf conversion -import gguf -import os -import sys -import struct +from __future__ import annotations + +import argparse import json +import os +import struct +import sys +from pathlib import Path +from typing import Any + +import gguf import numpy as np import torch -import argparse - -from typing import Any, List -from pathlib import Path -from transformers import AutoTokenizer +from transformers import AutoTokenizer # type: ignore[import] # ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py @@ -112,7 +114,7 @@ gguf_writer.add_layer_norm_eps(hparams["layer_norm_eps"]) print("gguf: get tokenizer metadata") -tokens: List[bytearray] = [] +tokens: list[bytearray] = [] tokenizer_json_file = dir_model / 'tokenizer.json' if not tokenizer_json_file.is_file(): diff --git a/convert-llama-7b-pth-to-gguf.py b/convert-llama-7b-pth-to-gguf.py index 6e973a116..6574c11dd 100755 --- a/convert-llama-7b-pth-to-gguf.py +++ b/convert-llama-7b-pth-to-gguf.py @@ -3,22 +3,25 @@ # Only models with a single datafile are supported, like 7B # HF files required in the model dir: config.json tokenizer_config.json tokenizer.json tokenizer.model -import gguf -import os -import sys -import struct +from __future__ import annotations + +import argparse import json +import os +import struct +import sys +from pathlib import Path +from typing import TYPE_CHECKING, Any + +import gguf import numpy as np import torch -import argparse +from sentencepiece import SentencePieceProcessor # type: ignore[import] -from typing import Any, List, TypeAlias -from pathlib import Path -from sentencepiece import SentencePieceProcessor +if TYPE_CHECKING: + from typing import TypeAlias -#NDArray = np.ndarray[Any, Any] -# compatible with python < 3.9 -NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]' +NDArray: TypeAlias = 'np.ndarray[Any, Any]' def count_model_parts(dir_model: Path) -> int: @@ -129,9 +132,9 @@ if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in print("gguf: get tokenizer metadata") -tokens: List[bytes] = [] -scores: List[float] = [] -toktypes: List[int] = [] +tokens: list[bytes] = [] +scores: list[float] = [] +toktypes: list[int] = [] tokenizer_model_file = dir_model / 'tokenizer.model' if not tokenizer_model_file.is_file(): diff --git a/convert-llama-ggmlv3-to-gguf.py b/convert-llama-ggmlv3-to-gguf.py index c8e7f1761..3f39bc39e 100755 --- a/convert-llama-ggmlv3-to-gguf.py +++ b/convert-llama-ggmlv3-to-gguf.py @@ -1,10 +1,14 @@ #!/usr/bin/env python3 -import sys, struct, math, argparse +from __future__ import annotations + +import argparse +import math +import struct +import sys from pathlib import Path -import numpy as np - import gguf +import numpy as np # Note: Does not support GGML_QKK_64 QK_K = 256 @@ -72,7 +76,7 @@ class Vocab: class Tensor: def __init__(self): self.name = None - self.dims = () + self.dims: tuple[int, ...] = () self.dtype = None self.start_offset = 0 self.len_bytes = np.int64(0) @@ -119,7 +123,7 @@ class GGMLV3Model: offset += hp.load(data, offset) vocab = Vocab() offset += vocab.load(data, offset, hp.n_vocab) - tensors = [] + tensors: list[Tensor] = [] tensor_map = {} while offset < len(data): tensor = Tensor() @@ -305,8 +309,8 @@ def handle_metadata(cfg, hp): def handle_args(): parser = argparse.ArgumentParser(description = 'Convert GGMLv3 models to GGUF') - parser.add_argument('--input', '-i', type = Path, help = 'Input GGMLv3 filename') - parser.add_argument('--output', '-o', type = Path, help ='Output GGUF filename') + parser.add_argument('--input', '-i', type = Path, required = True, help = 'Input GGMLv3 filename') + parser.add_argument('--output', '-o', type = Path, required = True, help ='Output GGUF filename') parser.add_argument('--name', help = 'Set model name') parser.add_argument('--desc', help = 'Set model description') parser.add_argument('--gqa', type = int, default = 1, help = 'grouped-query attention factor (use 8 for LLaMA2 70B)') diff --git a/convert-llama-hf-to-gguf.py b/convert-llama-hf-to-gguf.py index ab94b5eab..c453c83c3 100755 --- a/convert-llama-hf-to-gguf.py +++ b/convert-llama-hf-to-gguf.py @@ -1,28 +1,31 @@ #!/usr/bin/env python3 # HF llama --> gguf conversion -import gguf -import os -import sys -import struct +from __future__ import annotations + +import argparse import json +import os +import struct +import sys +from pathlib import Path +from typing import TYPE_CHECKING, Any + +import gguf import numpy as np import torch -import argparse +from sentencepiece import SentencePieceProcessor # type: ignore[import] -from typing import Any, List, Optional, TypeAlias -from pathlib import Path -from sentencepiece import SentencePieceProcessor +if TYPE_CHECKING: + from typing import TypeAlias -#NDArray = np.ndarray[Any, Any] -# compatible with python < 3.9 -NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]' +NDArray: TypeAlias = 'np.ndarray[Any, Any]' # reverse HF permute back to original pth layout # https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py -def reverse_hf_permute(weights: NDArray, n_head: int, n_kv_head: Optional[int] = None) -> NDArray: +def reverse_hf_permute(weights: NDArray, n_head: int, n_kv_head: int | None = None) -> NDArray: if n_kv_head is not None and n_head != n_kv_head: n_head //= n_kv_head @@ -136,9 +139,9 @@ if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in print("gguf: get tokenizer metadata") -tokens: List[bytes] = [] -scores: List[float] = [] -toktypes: List[int] = [] +tokens: list[bytes] = [] +scores: list[float] = [] +toktypes: list[int] = [] tokenizer_model_file = dir_model / 'tokenizer.model' if not tokenizer_model_file.is_file(): diff --git a/convert-lora-to-ggml.py b/convert-lora-to-ggml.py index a00339b47..a937410dd 100755 --- a/convert-lora-to-ggml.py +++ b/convert-lora-to-ggml.py @@ -1,15 +1,17 @@ #!/usr/bin/env python3 +from __future__ import annotations + import json import os import re import struct import sys -from typing import Any, Dict, Sequence, BinaryIO +from typing import Any, BinaryIO, Sequence import numpy as np import torch -NUMPY_TYPE_TO_FTYPE: Dict[str, int] = {"float32": 0, "float16": 1} +NUMPY_TYPE_TO_FTYPE: dict[str, int] = {"float32": 0, "float16": 1} HF_SUBLAYER_TO_GGML = { @@ -46,7 +48,7 @@ def translate_tensor_name(t: str) -> str: sys.exit(1) -def write_file_header(fout: BinaryIO, params: Dict[str, Any]) -> None: +def write_file_header(fout: BinaryIO, params: dict[str, Any]) -> None: fout.write(b"ggla"[::-1]) # magic (ggml lora) fout.write(struct.pack("i", 1)) # file version fout.write(struct.pack("i", params["r"])) diff --git a/convert.py b/convert.py index a7f4c2d75..9a39edb99 100755 --- a/convert.py +++ b/convert.py @@ -1,9 +1,8 @@ #!/usr/bin/env python3 +from __future__ import annotations -import gguf import argparse import concurrent.futures -from concurrent.futures import ThreadPoolExecutor, ProcessPoolExecutor import copy import enum import faulthandler @@ -20,21 +19,23 @@ import struct import sys import time import zipfile -import numpy as np - from abc import ABCMeta, abstractmethod +from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor from dataclasses import dataclass from pathlib import Path -from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Generator, Iterable, List, Literal, Optional, Sequence, Set, Tuple, Type, TypeVar, Union) -from sentencepiece import SentencePieceProcessor # type: ignore +from typing import IO, TYPE_CHECKING, Any, Callable, Generator, Iterable, Literal, Sequence, TypeVar + +import gguf +import numpy as np +from sentencepiece import SentencePieceProcessor # type: ignore[import] if TYPE_CHECKING: - from typing_extensions import TypeAlias + from typing import TypeAlias if hasattr(faulthandler, 'register') and hasattr(signal, 'SIGUSR1'): faulthandler.register(signal.SIGUSR1) -NDArray: 'TypeAlias' = 'np.ndarray[Any, Any]' +NDArray: TypeAlias = 'np.ndarray[Any, Any]' ARCH=gguf.MODEL_ARCH.LLAMA NAMES=gguf.MODEL_TENSOR_NAMES[ARCH] @@ -47,8 +48,8 @@ DEFAULT_CONCURRENCY = 8 @dataclass(frozen=True) class DataType: name: str - dtype: 'np.dtype[Any]' - valid_conversions: List[str] + dtype: np.dtype[Any] + valid_conversions: list[str] def elements_to_bytes(self, n_elements: int) -> int: return n_elements * self.dtype.itemsize @@ -65,7 +66,7 @@ DT_BF16 = UnquantizedDataType('BF16', dtype = np.dtype(np.uint16), valid_convers @dataclass(frozen=True) class QuantizedDataType(DataType): block_size: int - quantized_dtype: 'np.dtype[Any]' + quantized_dtype: np.dtype[Any] ggml_type: gguf.GGMLQuantizationType def quantize(self, arr: NDArray) -> NDArray: @@ -84,7 +85,7 @@ class Q8_0QuantizedDataType(QuantizedDataType): n_blocks = arr.size // self.block_size blocks = arr.reshape((n_blocks, self.block_size)) # Much faster implementation of block quantization contributed by @Cebtenzzre - def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[Tuple[Any, Any]]: + def quantize_blocks_q8_0(blocks: NDArray) -> Iterable[tuple[Any, Any]]: d = abs(blocks).max(axis = 1) / np.float32(127) with np.errstate(divide = 'ignore'): qs = (blocks / d[:, None]).round() @@ -98,13 +99,13 @@ DT_Q8_0 = Q8_0QuantizedDataType('Q8_0', quantized_dtype = np.dtype([('d', ' DataType: + def type_for_tensor(self, name: str, tensor: LazyTensor) -> DataType: dt = GGML_FILE_TYPE_TO_DATA_TYPE.get(self) if dt is None: raise ValueError(self) # 1D tensors are always F32. return dt if len(tensor.shape) > 1 else DT_F32 -GGML_FILE_TYPE_TO_DATA_TYPE: Dict[GGMLFileType, DataType] = { +GGML_FILE_TYPE_TO_DATA_TYPE: dict[GGMLFileType, DataType] = { GGMLFileType.AllF32 : DT_F32, GGMLFileType.MostlyF16 : DT_F16, GGMLFileType.MostlyQ8_0: DT_Q8_0, @@ -148,13 +149,13 @@ class Params: n_head_kv: int f_norm_eps: float - f_rope_freq_base: Optional[float] = None - f_rope_scale: Optional[float] = None + f_rope_freq_base: float | None = None + f_rope_scale: float | None = None - ftype: Optional[GGMLFileType] = None + ftype: GGMLFileType | None = None # path to the directory containing the model files - path_model: Optional['Path'] = None + path_model: Path | None = None @staticmethod def find_n_mult(n_ff: int, n_embd: int) -> int: @@ -166,7 +167,7 @@ class Params: raise Exception(f"failed to find n_mult for (n_ff={n_ff}, n_embd={n_embd}).") @staticmethod - def guessed(model: 'LazyModel') -> 'Params': + def guessed(model: LazyModel) -> Params: # try transformer naming first n_vocab, n_embd = model["model.embed_tokens.weight"].shape if "model.embed_tokens.weight" in model else model["tok_embeddings.weight"].shape @@ -202,7 +203,7 @@ class Params: ) @staticmethod - def loadHFTransformerJson(model: 'LazyModel', config_path: 'Path') -> 'Params': + def loadHFTransformerJson(model: LazyModel, config_path: Path) -> Params: config = json.load(open(config_path)) n_vocab = config["vocab_size"] @@ -247,7 +248,7 @@ class Params: # LLaMA v2 70B params.json # {"dim": 8192, "multiple_of": 4096, "ffn_dim_multiplier": 1.3, "n_heads": 64, "n_kv_heads": 8, "n_layers": 80, "norm_eps": 1e-05, "vocab_size": -1 @staticmethod - def loadOriginalParamsJson(model: 'LazyModel', config_path: 'Path') -> 'Params': + def loadOriginalParamsJson(model: LazyModel, config_path: Path) -> Params: config = json.load(open(config_path)) n_vocab = config["vocab_size"] if "vocab_size" in config else -1 @@ -291,7 +292,7 @@ class Params: ) @staticmethod - def load(model_plus: 'ModelPlus') -> 'Params': + def load(model_plus: ModelPlus) -> Params: hf_config_path = model_plus.paths[0].parent / "config.json" orig_config_path = model_plus.paths[0].parent / "params.json" @@ -314,9 +315,9 @@ class Params: # class BpeVocab: - def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]) -> None: + def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None: self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read()) - added_tokens: Dict[str, int] + added_tokens: dict[str, int] if fname_added_tokens is not None: added_tokens = json.load(open(fname_added_tokens, encoding="utf-8")) else: @@ -335,9 +336,9 @@ class BpeVocab: self.fname_tokenizer = fname_tokenizer self.fname_added_tokens = fname_added_tokens - def bpe_tokens(self) -> Iterable[Tuple[bytes, float, gguf.TokenType]]: + def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: tokenizer = self.bpe_tokenizer - from transformers.models.gpt2 import tokenization_gpt2 + from transformers.models.gpt2 import tokenization_gpt2 # type: ignore[import] byte_encoder = tokenization_gpt2.bytes_to_unicode() byte_decoder = {v: k for k, v in byte_encoder.items()} for i, item in enumerate(tokenizer): @@ -345,12 +346,12 @@ class BpeVocab: score: float = -i yield text, score, gguf.TokenType.USER_DEFINED - def added_tokens(self) -> Iterable[Tuple[bytes, float, gguf.TokenType]]: + def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: for text in self.added_tokens_list: score = -1000.0 yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED - def all_tokens(self) -> Iterable[Tuple[bytes, float, gguf.TokenType]]: + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: yield from self.bpe_tokens() yield from self.added_tokens() @@ -359,9 +360,9 @@ class BpeVocab: class SentencePieceVocab: - def __init__(self, fname_tokenizer: Path, fname_added_tokens: Optional[Path]) -> None: + def __init__(self, fname_tokenizer: Path, fname_added_tokens: Path | None) -> None: self.sentencepiece_tokenizer = SentencePieceProcessor(str(fname_tokenizer)) - added_tokens: Dict[str, int] + added_tokens: dict[str, int] if fname_added_tokens is not None: added_tokens = json.load(open(fname_added_tokens, encoding="utf-8")) else: @@ -380,7 +381,7 @@ class SentencePieceVocab: self.fname_tokenizer = fname_tokenizer self.fname_added_tokens = fname_added_tokens - def sentencepiece_tokens(self) -> Iterable[Tuple[bytes, float, gguf.TokenType]]: + def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: tokenizer = self.sentencepiece_tokenizer for i in range(tokenizer.vocab_size()): piece = tokenizer.id_to_piece(i) @@ -404,19 +405,19 @@ class SentencePieceVocab: yield text, score, toktype - def added_tokens(self) -> Iterable[Tuple[bytes, float, gguf.TokenType]]: + def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: for text in self.added_tokens_list: score = -1000.0 yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED - def all_tokens(self) -> Iterable[Tuple[bytes, float, gguf.TokenType]]: + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: yield from self.sentencepiece_tokens() yield from self.added_tokens() def __repr__(self) -> str: return f"" -Vocab = Union[BpeVocab, SentencePieceVocab] +Vocab: TypeAlias = 'BpeVocab | SentencePieceVocab' # # data loading @@ -436,15 +437,15 @@ class Tensor(metaclass=ABCMeta): data_type: DataType @abstractmethod - def astype(self, data_type: DataType) -> 'Tensor': ... + def astype(self, data_type: DataType) -> Tensor: ... @abstractmethod - def permute(self, n_head: int, n_head_kv: int) -> 'Tensor': ... + def permute(self, n_head: int, n_head_kv: int) -> Tensor: ... @abstractmethod - def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> 'UnquantizedTensor': ... + def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor: ... @abstractmethod - def part(self, n_part: int) -> 'UnquantizedTensor': ... + def part(self, n_part: int) -> UnquantizedTensor: ... @abstractmethod - def to_ggml(self) -> 'GGMLCompatibleTensor': ... + def to_ggml(self) -> GGMLCompatibleTensor: ... def bf16_to_fp32(bf16_arr: np.ndarray[Any, np.dtype[np.uint16]]) -> NDArray: @@ -465,22 +466,22 @@ class UnquantizedTensor(Tensor): self.ndarray = bf16_to_fp32(self.ndarray) return UnquantizedTensor(self.ndarray.astype(dtype)) - def to_ggml(self) -> 'UnquantizedTensor': + def to_ggml(self) -> UnquantizedTensor: return self - def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> 'UnquantizedTensor': + def permute_part(self, n_part: int, n_head: int, n_head_kv: int) -> UnquantizedTensor: r = self.ndarray.shape[0] // 3 return UnquantizedTensor(permute(self.ndarray[r * n_part : r * n_part + r, ...], n_head, n_head_kv)) - def part(self, n_part: int) -> 'UnquantizedTensor': + def part(self, n_part: int) -> UnquantizedTensor: r = self.ndarray.shape[0] // 3 return UnquantizedTensor(self.ndarray[r * n_part : r * n_part + r, ...]) - def permute(self, n_head: int, n_head_kv: int) -> 'UnquantizedTensor': + def permute(self, n_head: int, n_head_kv: int) -> UnquantizedTensor: return UnquantizedTensor(permute(self.ndarray, n_head, n_head_kv)) -def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, convert: bool = False) -> NDArray: +def load_unquantized(lazy_tensor: LazyTensor, expected_dtype: Any = None, convert: bool = False) -> NDArray: tensor = lazy_tensor.load() assert isinstance(tensor, UnquantizedTensor) @@ -496,13 +497,13 @@ def load_unquantized(lazy_tensor: 'LazyTensor', expected_dtype: Any = None, conv return tensor.ndarray -GGMLCompatibleTensor = Union[UnquantizedTensor] +GGMLCompatibleTensor = UnquantizedTensor @dataclass class LazyTensor: _load: Callable[[], Tensor] - shape: List[int] + shape: list[int] data_type: DataType description: str @@ -513,7 +514,7 @@ class LazyTensor: (self.data_type, ret.data_type, self.description) return ret - def astype(self, data_type: DataType) -> 'LazyTensor': + def astype(self, data_type: DataType) -> LazyTensor: self.validate_conversion_to(data_type) def load() -> Tensor: @@ -525,24 +526,24 @@ class LazyTensor: raise ValueError(f'Cannot validate conversion from {self.data_type} to {data_type}.') -LazyModel = Dict[str, LazyTensor] +LazyModel = dict[str, LazyTensor] @dataclass class ModelPlus: model: LazyModel - paths: List[Path] # Where this was read from. + paths: list[Path] # Where this was read from. format: Literal['ggml', 'torch', 'safetensors', 'none'] - vocab: Optional[Vocab] # For GGML models (which have vocab built in), the vocab. + vocab: Vocab | None # For GGML models (which have vocab built in), the vocab. -def merge_sharded(models: List[LazyModel]) -> LazyModel: +def merge_sharded(models: list[LazyModel]) -> LazyModel: # Original LLaMA models have each file contain one part of each tensor. # Use a dict instead of a set to preserve order. names = {name: None for model in models for name in model} def convert(name: str) -> LazyTensor: - lazy_tensors: List[LazyTensor] = [model[name] for model in models] + lazy_tensors: list[LazyTensor] = [model[name] for model in models] if len(lazy_tensors) == 1: # only one file; don't go through this procedure since there might # be quantized tensors @@ -570,7 +571,7 @@ def merge_sharded(models: List[LazyModel]) -> LazyModel: return {name: convert(name) for name in names} -def merge_multifile_models(models_plus: List[ModelPlus]) -> ModelPlus: +def merge_multifile_models(models_plus: list[ModelPlus]) -> ModelPlus: formats = set(mp.format for mp in models_plus) assert len(formats) == 1, "different formats?" format = formats.pop() @@ -674,7 +675,7 @@ class LazyUnpickler(pickle.Unpickler): def rebuild_from_type_v2(func, new_type, args, state): return func(*args) - CLASSES: Dict[Tuple[str, str], Any] = { + CLASSES: dict[tuple[str, str], Any] = { # getattr used here as a workaround for mypy not being smart enough to detrmine # the staticmethods have a __func__ attribute. ('torch._tensor', '_rebuild_from_type_v2'): getattr(rebuild_from_type_v2, '__func__'), @@ -707,15 +708,15 @@ def lazy_load_torch_file(outer_fp: IO[bytes], path: Path) -> ModelPlus: def lazy_load_safetensors_file(fp: IO[bytes], path: Path) -> ModelPlus: header_size, = struct.unpack(' LazyTensor: + def convert(info: dict[str, Any]) -> LazyTensor: data_type = SAFETENSORS_DATA_TYPES[info['dtype']] numpy_dtype = data_type.dtype - shape: List[int] = info['shape'] + shape: list[int] = info['shape'] begin, end = info['data_offsets'] assert 0 <= begin <= end <= len(byte_buf) assert end - begin == math.prod(shape) * numpy_dtype.itemsize @@ -754,7 +755,7 @@ def lazy_load_file(path: Path) -> ModelPlus: In = TypeVar('In') Out = TypeVar('Out') -def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int, max_workers: Optional[int] = None, use_processpool_executor: bool = False) -> Iterable[Out]: +def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], concurrency: int, max_workers: int | None = None, use_processpool_executor: bool = False) -> Iterable[Out]: '''Parallel map, but with backpressure. If the caller doesn't call `next` fast enough, this will stop calling `func` at some point rather than letting results pile up in memory. Specifically, there is a max of one @@ -763,13 +764,13 @@ def bounded_parallel_map(func: Callable[[In], Out], iterable: Iterable[In], conc yield from map(func, iterable) # Not reached. iterable = iter(iterable) - executor_class: Union[Type[ThreadPoolExecutor], Type[ProcessPoolExecutor]] + executor_class: type[ThreadPoolExecutor] | type[ProcessPoolExecutor] if use_processpool_executor: executor_class = ProcessPoolExecutor else: executor_class = ThreadPoolExecutor with executor_class(max_workers = max_workers) as executor: - futures: List[concurrent.futures.Future[Out]] = [] + futures: list[concurrent.futures.Future[Out]] = [] done = False for _ in range(concurrency): try: @@ -893,13 +894,13 @@ class OutputFile: of.close() @staticmethod - def do_item(item: Tuple[str, LazyTensor]) -> Tuple[DataType, NDArray]: + def do_item(item: tuple[str, LazyTensor]) -> tuple[DataType, NDArray]: name, lazy_tensor = item tensor = lazy_tensor.load().to_ggml() return (lazy_tensor.data_type, tensor.ndarray) @staticmethod - def maybe_do_quantize(item: Tuple[DataType, NDArray]) -> NDArray: + def maybe_do_quantize(item: tuple[DataType, NDArray]) -> NDArray: dt, arr = item if not isinstance(dt, QuantizedDataType): return arr @@ -940,7 +941,7 @@ class OutputFile: of.close() -def pick_output_type(model: LazyModel, output_type_str: Optional[str]) -> GGMLFileType: +def pick_output_type(model: LazyModel, output_type_str: str | None) -> GGMLFileType: wq_type = model[NAMES[gguf.MODEL_TENSOR.ATTN_Q].format(bid=0)+".weight"].data_type if output_type_str == "f32" or (output_type_str is None and wq_type == DT_F32): @@ -960,7 +961,7 @@ def convert_to_output_type(model: LazyModel, output_type: GGMLFileType) -> LazyM def convert_model_names(model: LazyModel, params: Params) -> LazyModel: tmap = gguf.TensorNameMap(ARCH, params.n_layer) - should_skip: Set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, [])) + should_skip: set[gguf.MODEL_TENSOR] = set(gguf.MODEL_TENSOR_SKIP.get(ARCH, [])) tmp = model @@ -995,12 +996,12 @@ def convert_model_names(model: LazyModel, params: Params) -> LazyModel: return out -def nth_multifile_path(path: Path, n: int) -> Optional[Path]: +def nth_multifile_path(path: Path, n: int) -> Path | None: '''Given any path belonging to a multi-file model (e.g. foo.bin.1), return the nth path in the model. ''' # Support the following patterns: - patterns: List[Tuple[str, str]] = [ + patterns: list[tuple[str, str]] = [ # - x.00.pth, x.01.pth, etc. (r'\.[0-9]{2}\.pth$', f'.{n:02}.pth'), # - x-00001-of-00002.bin, x-00002-of-00002.bin, etc. @@ -1016,11 +1017,11 @@ def nth_multifile_path(path: Path, n: int) -> Optional[Path]: return None -def find_multifile_paths(path: Path) -> List[Path]: +def find_multifile_paths(path: Path) -> list[Path]: '''Given any path belonging to a multi-file model (e.g. foo.bin.1), return the whole list of paths in the model. ''' - ret: List[Path] = [] + ret: list[Path] = [] for i in itertools.count(): nth_path = nth_multifile_path(path, i) if nth_path is None: @@ -1051,7 +1052,7 @@ def load_some_model(path: Path) -> ModelPlus: path = files[0] paths = find_multifile_paths(path) - models_plus: List[ModelPlus] = [] + models_plus: list[ModelPlus] = [] for path in paths: print(f"Loading model file {path}") models_plus.append(lazy_load_file(path)) @@ -1060,7 +1061,7 @@ def load_some_model(path: Path) -> ModelPlus: return model_plus -def load_vocab(path: Path, vocabtype: Optional[str]) -> Union[BpeVocab, SentencePieceVocab]: +def load_vocab(path: Path, vocabtype: str | None) -> Vocab: # Be extra-friendly and accept either a file or a directory. Also, if it's # a directory, it might be the model directory, and tokenizer.model might # be in the parent of that. @@ -1091,7 +1092,7 @@ def load_vocab(path: Path, vocabtype: Optional[str]) -> Union[BpeVocab, Sentence raise ValueError(f"Unsupported vocabulary type {vocabtype}") -def default_outfile(model_paths: List[Path], file_type: GGMLFileType) -> Path: +def default_outfile(model_paths: list[Path], file_type: GGMLFileType) -> Path: namestr = { GGMLFileType.AllF32: "f32", GGMLFileType.MostlyF16: "f16", @@ -1114,7 +1115,7 @@ def do_dump_model(model_plus: ModelPlus) -> None: print(f"{name}: shape={lazy_tensor.shape} type={lazy_tensor.data_type}; {lazy_tensor.description}") -def main(args_in: Optional[List[str]] = None) -> None: +def main(args_in: list[str] | None = None) -> None: parser = argparse.ArgumentParser(description="Convert a LLaMa model to a GGML compatible file") parser.add_argument("--dump", action="store_true", help="don't convert, just show what's in the model") parser.add_argument("--dump-single", action="store_true", help="don't convert, just show what's in a single model file") diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py index de3edbc99..b1bc4205b 100644 --- a/gguf-py/gguf/gguf.py +++ b/gguf-py/gguf/gguf.py @@ -1,16 +1,18 @@ #!/usr/bin/env python3 -import shutil -import sys -import struct -import tempfile -import numpy as np +from __future__ import annotations + import json import os -from pathlib import Path - +import shutil +import struct +import sys +import tempfile from enum import IntEnum, auto from io import BufferedWriter -from typing import Any, BinaryIO, Callable, IO, Dict, List, Optional, Sequence, Tuple, Union +from pathlib import Path +from typing import IO, Any, BinaryIO, Callable, Sequence + +import numpy as np # # constants @@ -103,7 +105,7 @@ class MODEL_TENSOR(IntEnum): FFN_NORM : int = auto() -MODEL_ARCH_NAMES: Dict[MODEL_ARCH, str] = { +MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.LLAMA: "llama", MODEL_ARCH.FALCON: "falcon", MODEL_ARCH.GPT2: "gpt2", @@ -112,7 +114,7 @@ MODEL_ARCH_NAMES: Dict[MODEL_ARCH, str] = { MODEL_ARCH.MPT: "mpt", } -MODEL_TENSOR_NAMES: Dict[MODEL_ARCH, Dict[MODEL_TENSOR, str]] = { +MODEL_TENSOR_NAMES: dict[MODEL_ARCH, dict[MODEL_TENSOR, str]] = { MODEL_ARCH.LLAMA: { MODEL_TENSOR.TOKEN_EMBD: "token_embd", MODEL_TENSOR.OUTPUT_NORM: "output_norm", @@ -158,7 +160,7 @@ MODEL_TENSOR_NAMES: Dict[MODEL_ARCH, Dict[MODEL_TENSOR, str]] = { } # tensors that will not be serialized -MODEL_TENSOR_SKIP: Dict[MODEL_ARCH, List[MODEL_TENSOR]] = { +MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_ARCH.LLAMA: [ MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, @@ -167,7 +169,7 @@ MODEL_TENSOR_SKIP: Dict[MODEL_ARCH, List[MODEL_TENSOR]] = { class TensorNameMap: - mappings_cfg: Dict[MODEL_TENSOR, Tuple[str, ...]] = { + mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { # Token embeddings MODEL_TENSOR.TOKEN_EMBD: ( "gpt_neox.embed_in", # gptneox @@ -203,7 +205,7 @@ class TensorNameMap: ), } - block_mappings_cfg: Dict[MODEL_TENSOR, Tuple[str, ...]] = { + block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = { # Attention norm MODEL_TENSOR.ATTN_NORM: ( "gpt_neox.layers.{bid}.input_layernorm", # gptneox @@ -298,9 +300,9 @@ class TensorNameMap: ), } - mapping: Dict[str, Tuple[MODEL_TENSOR, str]] + mapping: dict[str, tuple[MODEL_TENSOR, str]] - tensor_names: Dict[MODEL_TENSOR, str] + tensor_names: dict[MODEL_TENSOR, str] def __init__(self, arch: MODEL_ARCH, n_blocks: int): mapping = self.mapping = {} @@ -321,7 +323,7 @@ class TensorNameMap: key = key.format(bid = bid) mapping[key] = (tensor, tensor_name) - def get_type_and_name(self, key: str, try_suffixes: Sequence[str]) -> Optional[Tuple[MODEL_TENSOR, str]]: + def get_type_and_name(self, key: str, try_suffixes: Sequence[str]) -> tuple[MODEL_TENSOR, str] | None: result = self.mapping.get(key) if result is not None: return result @@ -332,13 +334,13 @@ class TensorNameMap: return (result[0], result[1] + suffix) return None - def get_name(self, key: str, try_suffixes: Sequence[str]) -> Optional[str]: + def get_name(self, key: str, try_suffixes: Sequence[str]) -> str | None: result = self.get_type_and_name(key, try_suffixes = try_suffixes) if result is None: return None return result[1] - def get_type(self, key: str, try_suffixes: Sequence[str]) -> Optional[MODEL_TENSOR]: + def get_type(self, key: str, try_suffixes: Sequence[str]) -> MODEL_TENSOR | None: result = self.get_type_and_name(key, try_suffixes = try_suffixes) if result is None: return None @@ -432,10 +434,10 @@ class GGUFWriter: ti_data = b"" ti_data_count = 0 use_temp_file: bool - temp_file: Optional[tempfile.SpooledTemporaryFile[bytes]] = None - tensors: List[Tuple[np.ndarray[Any, Any], int]] + temp_file: tempfile.SpooledTemporaryFile[bytes] | None = None + tensors: list[tuple[np.ndarray[Any, Any], int]] - def __init__(self, path: Union[os.PathLike[str], str], arch: str, use_temp_file = True): + def __init__(self, path: os.PathLike[str] | str, arch: str, use_temp_file = True): self.fout = open(path, "wb") self.arch = arch self.add_architecture() @@ -531,7 +533,7 @@ class GGUFWriter: GGUFValueType.FLOAT64: " int: return ((x + n - 1) // n) * n - def add_tensor_info(self, name: str, tensor_shape: Sequence[int], tensor_dtype: Union[np.dtype[np.float16], np.dtype[np.float32]], tensor_nbytes: int, raw_dtype: Optional[GGMLQuantizationType] = None): + def add_tensor_info(self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype[np.float16] | np.dtype[np.float32], tensor_nbytes: int, raw_dtype: GGMLQuantizationType | None = None): assert raw_dtype is not None or tensor_dtype in (np.float32, np.float16), "Only F32 and F16 tensors are supported for now" encoded_name = name.encode("utf8") @@ -580,7 +582,7 @@ class GGUFWriter: self.offset_tensor += GGUFWriter.ggml_pad(tensor_nbytes, self.data_alignment) self.ti_data_count += 1 - def add_tensor(self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Optional[Sequence[int]] = None, raw_dtype: Optional[GGMLQuantizationType] = None): + def add_tensor(self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None, raw_dtype: GGMLQuantizationType | None = None): if self.use_temp_file and self.temp_file is None: fp = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256*1024*1024) fp.seek(0) @@ -600,7 +602,7 @@ class GGUFWriter: if pad != 0: self.temp_file.write(bytes([0] * pad)) - def write_padding(self, fp: BinaryIO, n: int, align: Optional[int] = None): + def write_padding(self, fp: BinaryIO, n: int, align: int | None = None): pad = GGUFWriter.ggml_pad(n, align if align is not None else self.data_alignment) - n if pad != 0: fp.write(bytes([0] * pad)) @@ -726,13 +728,13 @@ class GGUFWriter: def add_tokenizer_model(self, model: str): self.add_string(KEY_TOKENIZER_MODEL, model) - def add_token_list(self, tokens: Union[Sequence[str], Sequence[bytes], Sequence[bytearray]]): + def add_token_list(self, tokens: Sequence[str] | Sequence[bytes] | Sequence[bytearray]): self.add_array(KEY_TOKENIZER_LIST, tokens) - def add_token_merges(self, merges: Union[Sequence[str], Sequence[bytes], Sequence[bytearray]]): + def add_token_merges(self, merges: Sequence[str] | Sequence[bytes] | Sequence[bytearray]): self.add_array(KEY_TOKENIZER_MERGES, merges) - def add_token_types(self, types: Union[Sequence[TokenType], Sequence[int]]): + def add_token_types(self, types: Sequence[TokenType] | Sequence[int]): self.add_array(KEY_TOKENIZER_TOKEN_TYPE, types) def add_token_scores(self, scores: Sequence[float]): @@ -756,11 +758,11 @@ class GGUFWriter: class SpecialVocab: load_merges: bool = False - merges: List[str] = [] - special_token_types: Tuple[str, ...] = tuple(('bos', 'eos', 'unk', 'sep', 'pad')) - special_token_ids: Dict[str, int] = {} + merges: list[str] = [] + special_token_types: tuple[str, ...] = ('bos', 'eos', 'unk', 'sep', 'pad') + special_token_ids: dict[str, int] = {} - def __init__(self, path: Path, load_merges: bool = False, special_token_types: Optional[Tuple[str, ...]] = None): + def __init__(self, path: Path, load_merges: bool = False, special_token_types: tuple[str, ...] | None = None): self.special_token_ids = {} self.load_merges = load_merges if special_token_types is not None: @@ -821,7 +823,7 @@ class SpecialVocab: print(f'gguf: Adding {len(self.merges)} merge(s).') gw.add_token_merges(self.merges) for typ, tokid in self.special_token_ids.items(): - handler: Optional[Callable[[int], None]] = getattr(gw, f'add_{typ}_token_id', None) + handler: Callable[[int], None] | None = getattr(gw, f'add_{typ}_token_id', None) if handler is None: print(f'gguf: WARNING: No handler for special token type {typ} with id {tokid} - skipping') continue diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index c66b069f9..26f792b14 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "gguf" -version = "0.2.1" +version = "0.3.1" description = "Write ML models in GGUF for GGML" authors = ["GGML "] packages = [ diff --git a/mypy.ini b/mypy.ini new file mode 100644 index 000000000..55c168f2d --- /dev/null +++ b/mypy.ini @@ -0,0 +1,5 @@ +[mypy] +strict = true +allow_untyped_calls = true +allow_untyped_defs = true +allow_incomplete_defs = true From e8422de39e4aa2f7e50574124b060a80607e654a Mon Sep 17 00:00:00 2001 From: DannyDaemonic Date: Thu, 31 Aug 2023 04:21:45 -0700 Subject: [PATCH 430/852] @vxiiduu's fix for PrefetchVirtualMemory (#2930) Reimplement fix for `PrefetchVirtualMemory`. Co-authored-by: vxiiduu <73044267+vxiiduu@users.noreply.github.com> --- llama.cpp | 27 ++++++++++++++++----------- 1 file changed, 16 insertions(+), 11 deletions(-) diff --git a/llama.cpp b/llama.cpp index 95ee6ffe4..98a5da963 100644 --- a/llama.cpp +++ b/llama.cpp @@ -611,20 +611,25 @@ struct llama_mmap { throw std::runtime_error(format("MapViewOfFile failed: %s", llama_format_win_err(error).c_str())); } - #if _WIN32_WINNT >= _WIN32_WINNT_WIN8 if (prefetch) { - // Advise the kernel to preload the mapped memory - WIN32_MEMORY_RANGE_ENTRY range; - range.VirtualAddress = addr; - range.NumberOfBytes = (SIZE_T)size; - if (!PrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) { - fprintf(stderr, "warning: PrefetchVirtualMemory failed: %s\n", - llama_format_win_err(GetLastError()).c_str()); + // PrefetchVirtualMemory is only present on Windows 8 and above, so we dynamically load it + BOOL (WINAPI *pPrefetchVirtualMemory) (HANDLE, ULONG_PTR, PWIN32_MEMORY_RANGE_ENTRY, ULONG); + HMODULE hKernel32 = GetModuleHandleW(L"kernel32.dll"); + + // may fail on pre-Windows 8 systems + pPrefetchVirtualMemory = reinterpret_cast (GetProcAddress(hKernel32, "PrefetchVirtualMemory")); + + if (pPrefetchVirtualMemory) { + // advise the kernel to preload the mapped memory + WIN32_MEMORY_RANGE_ENTRY range; + range.VirtualAddress = addr; + range.NumberOfBytes = (SIZE_T)size; + if (!pPrefetchVirtualMemory(GetCurrentProcess(), 1, &range, 0)) { + fprintf(stderr, "warning: PrefetchVirtualMemory failed: %s\n", + llama_format_win_err(GetLastError()).c_str()); + } } } - #else - #pragma message("warning: You are building for pre-Windows 8; prefetch not supported") - #endif // _WIN32_WINNT >= _WIN32_WINNT_WIN8 } ~llama_mmap() { From aeefac4ff760acea5afe66fbfe8d7eca1937b79c Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Thu, 31 Aug 2023 16:49:24 -0600 Subject: [PATCH 431/852] scripts: Use local gguf package when running from repo (#2927) * scripts: Use local gguf when running from repo --- convert-falcon-hf-to-gguf.py | 5 ++++- convert-gptneox-hf-to-gguf.py | 5 ++++- convert-llama-ggmlv3-to-gguf.py | 6 +++++- convert.py | 6 +++++- .../convert-train-checkpoint-to-gguf.py | 5 ++++- 5 files changed, 22 insertions(+), 5 deletions(-) diff --git a/convert-falcon-hf-to-gguf.py b/convert-falcon-hf-to-gguf.py index ec786ff67..271e58972 100755 --- a/convert-falcon-hf-to-gguf.py +++ b/convert-falcon-hf-to-gguf.py @@ -11,11 +11,14 @@ import sys from pathlib import Path from typing import Any -import gguf import numpy as np import torch from transformers import AutoTokenizer # type: ignore[import] +if 'NO_LOCAL_GGUF' not in os.environ: + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) +import gguf + def bytes_to_unicode(): # ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py diff --git a/convert-gptneox-hf-to-gguf.py b/convert-gptneox-hf-to-gguf.py index 852123d99..b9c8b4607 100755 --- a/convert-gptneox-hf-to-gguf.py +++ b/convert-gptneox-hf-to-gguf.py @@ -11,11 +11,14 @@ import sys from pathlib import Path from typing import Any -import gguf import numpy as np import torch from transformers import AutoTokenizer # type: ignore[import] +if 'NO_LOCAL_GGUF' not in os.environ: + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) +import gguf + # ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py diff --git a/convert-llama-ggmlv3-to-gguf.py b/convert-llama-ggmlv3-to-gguf.py index 3f39bc39e..08ba0c490 100755 --- a/convert-llama-ggmlv3-to-gguf.py +++ b/convert-llama-ggmlv3-to-gguf.py @@ -7,9 +7,13 @@ import struct import sys from pathlib import Path -import gguf import numpy as np +import os +if 'NO_LOCAL_GGUF' not in os.environ: + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) +import gguf + # Note: Does not support GGML_QKK_64 QK_K = 256 # Items here are (block size, type size) diff --git a/convert.py b/convert.py index 9a39edb99..5cc3f6e66 100755 --- a/convert.py +++ b/convert.py @@ -25,10 +25,14 @@ from dataclasses import dataclass from pathlib import Path from typing import IO, TYPE_CHECKING, Any, Callable, Generator, Iterable, Literal, Sequence, TypeVar -import gguf import numpy as np from sentencepiece import SentencePieceProcessor # type: ignore[import] +import os +if 'NO_LOCAL_GGUF' not in os.environ: + sys.path.insert(1, str(Path(__file__).parent / 'gguf-py' / 'gguf')) +import gguf + if TYPE_CHECKING: from typing import TypeAlias diff --git a/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py b/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py index 01b3ee92a..a527d6153 100644 --- a/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py +++ b/examples/train-text-from-scratch/convert-train-checkpoint-to-gguf.py @@ -2,13 +2,16 @@ # train-text-from-scratch checkpoint --> gguf conversion import argparse -import gguf import os import struct import sys import numpy as np from pathlib import Path +if 'NO_LOCAL_GGUF' not in os.environ: + sys.path.insert(1, str(Path(__file__).parent / '..' / '..' / 'gguf-py' / 'gguf')) +import gguf + # gguf constants LLM_KV_OPTIMIZER_TYPE = "optimizer.type" LLM_KV_OPTIMIZER_TYPE_ADAM = "adam" From 528134dd0267838d9c0250cf1d9621631dff09b2 Mon Sep 17 00:00:00 2001 From: slaren Date: Fri, 1 Sep 2023 01:32:09 +0200 Subject: [PATCH 432/852] remove convert-llama-7b-pth-to-gguf.py and convert-llama-hf-to-gguf.py (#2906) --- convert-llama-7b-pth-to-gguf.py | 261 ----------------------------- convert-llama-hf-to-gguf.py | 280 -------------------------------- 2 files changed, 541 deletions(-) delete mode 100755 convert-llama-7b-pth-to-gguf.py delete mode 100755 convert-llama-hf-to-gguf.py diff --git a/convert-llama-7b-pth-to-gguf.py b/convert-llama-7b-pth-to-gguf.py deleted file mode 100755 index 6574c11dd..000000000 --- a/convert-llama-7b-pth-to-gguf.py +++ /dev/null @@ -1,261 +0,0 @@ -#!/usr/bin/env python3 -# 7b pth llama --> gguf conversion -# Only models with a single datafile are supported, like 7B -# HF files required in the model dir: config.json tokenizer_config.json tokenizer.json tokenizer.model - -from __future__ import annotations - -import argparse -import json -import os -import struct -import sys -from pathlib import Path -from typing import TYPE_CHECKING, Any - -import gguf -import numpy as np -import torch -from sentencepiece import SentencePieceProcessor # type: ignore[import] - -if TYPE_CHECKING: - from typing import TypeAlias - -NDArray: TypeAlias = 'np.ndarray[Any, Any]' - - -def count_model_parts(dir_model: Path) -> int: - num_parts = 0 - for filename in os.listdir(dir_model): - if filename.startswith("consolidated."): - num_parts += 1 - - if num_parts > 0: - print("gguf: found " + str(num_parts) + " model parts") - return num_parts - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Convert a PyTorch 7B LLaMA model to a GGML compatible file") - parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") - parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") - parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)") - parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1) - return parser.parse_args() - -args = parse_args() - -dir_model = args.model -ftype = args.ftype -if not dir_model.is_dir(): - print(f'Error: {args.model} is not a directory', file = sys.stderr) - sys.exit(1) - -# possible tensor data types -# ftype == 0 -> float32 -# ftype == 1 -> float16 - -# map from ftype to string -ftype_str = ["f32", "f16"] - -if args.outfile is not None: - fname_out = args.outfile -else: - # output in the same directory as the model by default - fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' - -print("gguf: loading model "+dir_model.name) - -with open(dir_model / "config.json", "r", encoding="utf-8") as f: - hparams = json.load(f) - -if hparams["architectures"][0] != "LlamaForCausalLM": - print("Model architecture not supported: " + hparams["architectures"][0]) - sys.exit() - -# get number of model parts -num_parts = count_model_parts(dir_model) - -if num_parts > 1: - print("gguf: Only models with a single datafile are supported.") - - sys.exit() - -ARCH=gguf.MODEL_ARCH.LLAMA -gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) - - -print("gguf: get model metadata") - -block_count = hparams["num_hidden_layers"] -head_count = hparams["num_attention_heads"] - -if "num_key_value_heads" in hparams: - head_count_kv = hparams["num_key_value_heads"] -else: - head_count_kv = head_count - -if "_name_or_path" in hparams: - hf_repo = hparams["_name_or_path"] -else: - hf_repo = "" - -if "max_sequence_length" in hparams: - ctx_length = hparams["max_sequence_length"] -elif "max_position_embeddings" in hparams: - ctx_length = hparams["max_position_embeddings"] -else: - print("gguf: can not find ctx length parameter.") - - sys.exit() - - -gguf_writer.add_name(dir_model.name) -gguf_writer.add_source_hf_repo(hf_repo) -gguf_writer.add_tensor_data_layout("Meta AI original pth") -gguf_writer.add_context_length(ctx_length) -gguf_writer.add_embedding_length(hparams["hidden_size"]) -gguf_writer.add_block_count(block_count) -gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) -gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"]) -gguf_writer.add_head_count(head_count) -gguf_writer.add_head_count_kv(head_count_kv) -gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"]) - -if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in hparams["rope_scaling"]: - if "type" in hparams["rope_scaling"]: - if hparams["rope_scaling"]["type"] == "linear": - gguf_writer.add_rope_scale_linear(hparams["rope_scaling"]["factor"]) - - -# TOKENIZATION - -print("gguf: get tokenizer metadata") - -tokens: list[bytes] = [] -scores: list[float] = [] -toktypes: list[int] = [] - -tokenizer_model_file = dir_model / 'tokenizer.model' -if not tokenizer_model_file.is_file(): - print(f'Error: Missing {tokenizer_model_file}', file = sys.stderr) - sys.exit(1) - -# vocab type sentencepiece -print("gguf: get sentencepiece tokenizer vocab and scores") - -tokenizer = SentencePieceProcessor(str(tokenizer_model_file)) - -for i in range(tokenizer.vocab_size()): - text: bytes - score: float - - piece = tokenizer.id_to_piece(i) - text = piece.encode("utf-8") - score = tokenizer.get_score(i) - - toktype = 1 # defualt to normal token type - if tokenizer.is_unknown(i): - toktype = 2 - if tokenizer.is_control(i): - toktype = 3 - - # toktype = 4 is user-defined = tokens from added_tokens.json - - if tokenizer.is_unused(i): - toktype = 5 - if tokenizer.is_byte(i): - toktype = 6 - - tokens.append(text) - scores.append(score) - toktypes.append(toktype) - -added_tokens_file = dir_model / 'added_tokens.json' -if added_tokens_file.is_file(): - with open(added_tokens_file, "r", encoding="utf-8") as f: - addtokens_json = json.load(f) - - print("gguf: get added tokens") - - for key in addtokens_json: - tokens.append( key.encode("utf-8") ) - scores.append(-1000.0) - toktypes.append(4) # user-defined token type - -gguf_writer.add_tokenizer_model("llama") -gguf_writer.add_token_list(tokens) -gguf_writer.add_token_scores(scores) -gguf_writer.add_token_types(toktypes) - -special_vocab = gguf.SpecialVocab(dir_model) -special_vocab.add_to_gguf(gguf_writer) - -# TENSORS - -tensor_map = gguf.get_tensor_name_map(ARCH,block_count) - -# tensor info -print("gguf: get tensor metadata") - -part_names = (f"consolidated.{n:02}.pth" for n in range(0, num_parts)) - -for part_name in part_names: - if args.vocab_only: - break - print("gguf: loading model part '" + part_name + "'") - model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu") - - for name in model_part.keys(): - data = model_part[name] - - # we don't need these - if name == "rope.freqs": - continue - - old_dtype = data.dtype - - # convert any unsupported data types to float32 - if data.dtype != torch.float16 and data.dtype != torch.float32: - data = data.to(torch.float32) - - data = data.squeeze().numpy() - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) - if new_name is None: - print("Can not map tensor '" + name + "'") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) - - gguf_writer.add_tensor(new_name, data) - - -print("gguf: write header") -gguf_writer.write_header_to_file() -print("gguf: write metadata") -gguf_writer.write_kv_data_to_file() -if not args.vocab_only: - print("gguf: write tensors") - gguf_writer.write_tensors_to_file() - -gguf_writer.close() - -print(f"gguf: model successfully exported to '{fname_out}'") -print("") diff --git a/convert-llama-hf-to-gguf.py b/convert-llama-hf-to-gguf.py deleted file mode 100755 index c453c83c3..000000000 --- a/convert-llama-hf-to-gguf.py +++ /dev/null @@ -1,280 +0,0 @@ -#!/usr/bin/env python3 -# HF llama --> gguf conversion - -from __future__ import annotations - -import argparse -import json -import os -import struct -import sys -from pathlib import Path -from typing import TYPE_CHECKING, Any - -import gguf -import numpy as np -import torch -from sentencepiece import SentencePieceProcessor # type: ignore[import] - -if TYPE_CHECKING: - from typing import TypeAlias - -NDArray: TypeAlias = 'np.ndarray[Any, Any]' - -# reverse HF permute back to original pth layout -# https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py - - -def reverse_hf_permute(weights: NDArray, n_head: int, n_kv_head: int | None = None) -> NDArray: - if n_kv_head is not None and n_head != n_kv_head: - n_head //= n_kv_head - - return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) - .swapaxes(1, 2) - .reshape(weights.shape)) - - -def count_model_parts(dir_model: str) -> int: - num_parts = 0 - - for filename in os.listdir(dir_model): - if filename.startswith("pytorch_model-"): - num_parts += 1 - - if num_parts > 0: - print("gguf: found " + str(num_parts) + " model parts") - - return num_parts - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Convert a HuggingFace LLaMA model to a GGML compatible file") - parser.add_argument("--vocab-only", action="store_true", help="extract only the vocab") - parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") - parser.add_argument("model", type=Path, help="directory containing model file, or model file itself (*.bin)") - parser.add_argument("ftype", type=int, choices=[0, 1], help="output format - use 0 for float32, 1 for float16", default = 1) - return parser.parse_args() - -args = parse_args() - -dir_model = args.model -ftype = args.ftype -if not dir_model.is_dir(): - print(f'Error: {args.model} is not a directory', file = sys.stderr) - sys.exit(1) - -# possible tensor data types -# ftype == 0 -> float32 -# ftype == 1 -> float16 - -# map from ftype to string -ftype_str = ["f32", "f16"] - -if args.outfile is not None: - fname_out = args.outfile -else: - # output in the same directory as the model by default - fname_out = dir_model / f'ggml-model-{ftype_str[ftype]}.gguf' - -print("gguf: loading model "+dir_model.name) - -with open(dir_model / "config.json", "r", encoding="utf-8") as f: - hparams = json.load(f) - -if hparams["architectures"][0] != "LlamaForCausalLM": - print("Model architecture not supported: " + hparams["architectures"][0]) - - sys.exit() - -# get number of model parts -num_parts = count_model_parts(dir_model) - -ARCH=gguf.MODEL_ARCH.LLAMA -gguf_writer = gguf.GGUFWriter(fname_out, gguf.MODEL_ARCH_NAMES[ARCH]) - -print("gguf: get model metadata") - -block_count = hparams["num_hidden_layers"] -head_count = hparams["num_attention_heads"] - -if "num_key_value_heads" in hparams: - head_count_kv = hparams["num_key_value_heads"] -else: - head_count_kv = head_count - -if "_name_or_path" in hparams: - hf_repo = hparams["_name_or_path"] -else: - hf_repo = "" - -if "max_sequence_length" in hparams: - ctx_length = hparams["max_sequence_length"] -elif "max_position_embeddings" in hparams: - ctx_length = hparams["max_position_embeddings"] -else: - print("gguf: can not find ctx length parameter.") - - sys.exit() - - -gguf_writer.add_name(dir_model.name) -gguf_writer.add_source_hf_repo(hf_repo) -gguf_writer.add_tensor_data_layout("Meta AI original pth") -gguf_writer.add_context_length(ctx_length) -gguf_writer.add_embedding_length(hparams["hidden_size"]) -gguf_writer.add_block_count(block_count) -gguf_writer.add_feed_forward_length(hparams["intermediate_size"]) -gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"]) -gguf_writer.add_head_count(head_count) -gguf_writer.add_head_count_kv(head_count_kv) -gguf_writer.add_layer_norm_rms_eps(hparams["rms_norm_eps"]) - -if "rope_scaling" in hparams and hparams["rope_scaling"] != None and "factor" in hparams["rope_scaling"]: - if "type" in hparams["rope_scaling"]: - if hparams["rope_scaling"]["type"] == "linear": - gguf_writer.add_rope_scale_linear(hparams["rope_scaling"]["factor"]) - - -# TOKENIZATION - -print("gguf: get tokenizer metadata") - -tokens: list[bytes] = [] -scores: list[float] = [] -toktypes: list[int] = [] - -tokenizer_model_file = dir_model / 'tokenizer.model' -if not tokenizer_model_file.is_file(): - print(f'Error: Missing {tokenizer_model_file}', file = sys.stderr) - sys.exit(1) - -# vocab type sentencepiece -print("gguf: get sentencepiece tokenizer vocab, scores and token types") - -tokenizer = SentencePieceProcessor(str(tokenizer_model_file)) - -for i in range(tokenizer.vocab_size()): - text: bytes - score: float - - piece = tokenizer.id_to_piece(i) - text = piece.encode("utf-8") - score = tokenizer.get_score(i) - - toktype = 1 # defualt to normal token type - if tokenizer.is_unknown(i): - toktype = 2 - if tokenizer.is_control(i): - toktype = 3 - - # toktype = 4 is user-defined = tokens from added_tokens.json - - if tokenizer.is_unused(i): - toktype = 5 - if tokenizer.is_byte(i): - toktype = 6 - - tokens.append(text) - scores.append(score) - toktypes.append(toktype) - -added_tokens_file = dir_model / 'added_tokens.json' -if added_tokens_file.is_file(): - with open(added_tokens_file, "r", encoding="utf-8") as f: - addtokens_json = json.load(f) - - print("gguf: get added tokens") - - for key in addtokens_json: - tokens.append( key.encode("utf-8") ) - scores.append(-1000.0) - toktypes.append(4) # user-defined token type - - -gguf_writer.add_tokenizer_model("llama") -gguf_writer.add_token_list(tokens) -gguf_writer.add_token_scores(scores) -gguf_writer.add_token_types(toktypes) - -special_vocab = gguf.SpecialVocab(dir_model) -special_vocab.add_to_gguf(gguf_writer) - -# TENSORS - -tensor_map = gguf.get_tensor_name_map(ARCH,block_count) - -# tensor info -print("gguf: get tensor metadata") - -if num_parts == 0: - part_names = iter(("pytorch_model.bin",)) -else: - part_names = ( - f"pytorch_model-{n:05}-of-{num_parts:05}.bin" for n in range(1, num_parts + 1) - ) - -for part_name in part_names: - if args.vocab_only: - break - print("gguf: loading model part '" + part_name + "'") - model_part = torch.load(f"{dir_model}/{part_name}", map_location="cpu") - - for name in model_part.keys(): - data = model_part[name] - - # we don't need these - if name.endswith(".rotary_emb.inv_freq"): - continue - - old_dtype = data.dtype - - # convert any unsupported data types to float32 - if data.dtype != torch.float16 and data.dtype != torch.float32: - data = data.to(torch.float32) - - data = data.squeeze().numpy() - - # reverse permute these - if name.endswith(".q_proj.weight"): - data = reverse_hf_permute(data, head_count) - if name.endswith(".k_proj.weight"): - data = reverse_hf_permute(data, head_count, head_count_kv) - - # map tensor names - new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) - if new_name is None: - print("Can not map tensor '" + name + "'") - sys.exit() - - n_dims = len(data.shape) - data_dtype = data.dtype - - # if f32 desired, convert any float16 to float32 - if ftype == 0 and data_dtype == np.float16: - data = data.astype(np.float32) - - # TODO: Why cant we use these float16 as-is? There should be not reason to store float16 as float32 - if ftype == 1 and data_dtype == np.float16 and n_dims == 1: - data = data.astype(np.float32) - - # if f16 desired, convert any float32 2-dim weight tensors to float16 - if ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: - data = data.astype(np.float16) - - print(new_name + ", n_dims = " + str(n_dims) + ", " + str(old_dtype) + " --> " + str(data.dtype)) - - gguf_writer.add_tensor(new_name, data) - - -print("gguf: write header") -gguf_writer.write_header_to_file() -print("gguf: write metadata") -gguf_writer.write_kv_data_to_file() -if not args.vocab_only: - print("gguf: write tensors") - gguf_writer.write_tensors_to_file() - -gguf_writer.close() - -print(f"gguf: model successfully exported to '{fname_out}'") -print("") From bce1fef328941499dc0acb76cc7fd7ac90449c2f Mon Sep 17 00:00:00 2001 From: Cebtenzzre Date: Thu, 31 Aug 2023 22:13:51 -0400 Subject: [PATCH 433/852] convert : fix another python 3.8 issue (#2949) --- convert.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/convert.py b/convert.py index 5cc3f6e66..6c89b5ecc 100755 --- a/convert.py +++ b/convert.py @@ -530,7 +530,7 @@ class LazyTensor: raise ValueError(f'Cannot validate conversion from {self.data_type} to {data_type}.') -LazyModel = dict[str, LazyTensor] +LazyModel: TypeAlias = 'dict[str, LazyTensor]' @dataclass From e8d91589258f9204397a7ac5f9b3c857835c98f8 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Fri, 1 Sep 2023 11:15:57 +0300 Subject: [PATCH 434/852] metal: somewhat faster f16 x f32 matrix multiply kernel (#2951) * Somewhat faster f16 x f32 matrix multiply kernel * Better use 32 thread groups for f16 x f32 --------- Co-authored-by: Iwan Kawrakow --- ggml-metal.m | 2 +- ggml-metal.metal | 38 ++++++++++++++++++++++++++++---------- 2 files changed, 29 insertions(+), 11 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index e929c4b07..8c3c64f53 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -840,7 +840,7 @@ void ggml_metal_graph_compute( switch (src0t) { case GGML_TYPE_F16: { - nth0 = 64; + nth0 = 32; nth1 = 1; [encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32]; } break; diff --git a/ggml-metal.metal b/ggml-metal.metal index 82e1a0c7a..02db5323e 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -528,24 +528,42 @@ kernel void kernel_mul_mat_f16_f32( device const half * x = (device const half *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02); device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); - sum[tpitg.x] = 0.0f; + uint ith = tpitg.x; + uint nth = tptg.x; - for (int i = tpitg.x; i < ne00; i += tptg.x) { - sum[tpitg.x] += (float) x[i] * (float) y[i]; + sum[ith] = 0.0f; + + for (int i = ith; i < ne00; i += nth) { + sum[ith] += (float) x[i] * (float) y[i]; } // accumulate the sum from all threads in the threadgroup threadgroup_barrier(mem_flags::mem_threadgroup); - for (uint i = tptg.x/2; i > 0; i /= 2) { - if (tpitg.x < i) { - sum[tpitg.x] += sum[tpitg.x + i]; - } - threadgroup_barrier(mem_flags::mem_threadgroup); + if (ith%4 == 0) { + for (int i = 1; i < 4; ++i) sum[ith] += sum[ith + i]; } - - if (tpitg.x == 0) { + threadgroup_barrier(mem_flags::mem_threadgroup); + if (ith%16 == 0) { + for (int i = 4; i < 16; i += 4) sum[ith] += sum[ith + i]; + } + threadgroup_barrier(mem_flags::mem_threadgroup); + if (ith == 0) { + for (int i = 16; i < nth; i += 16) sum[0] += sum[i]; dst[im*ne1*ne0 + r1*ne0 + r0] = sum[0]; } + + // Original implementation. Left behind commented out for now + //threadgroup_barrier(mem_flags::mem_threadgroup); + //for (uint i = tptg.x/2; i > 0; i /= 2) { + // if (tpitg.x < i) { + // sum[tpitg.x] += sum[tpitg.x + i]; + // } + // threadgroup_barrier(mem_flags::mem_threadgroup); + //} + // + //if (tpitg.x == 0) { + // dst[im*ne1*ne0 + r1*ne0 + r0] = sum[0]; + //} } kernel void kernel_alibi_f32( From 18705a30ef3d6a89e1d7c6cb8cfe8633f760cb53 Mon Sep 17 00:00:00 2001 From: Cebtenzzre Date: Fri, 1 Sep 2023 05:03:49 -0400 Subject: [PATCH 435/852] llama2c : fix segfault and alloc-dealloc-mismatch (#2913) * llama2c : fix segfault if vocab is not found * llama2c : fix mismatch between new[] and delete * llama2c : fix basename on Windows * llama2c : use a destructor to prevent memory leaks --- .../convert-llama2c-to-ggml.cpp | 43 ++++++++++--------- 1 file changed, 23 insertions(+), 20 deletions(-) diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp index e9e070b1f..0b03c9d2b 100644 --- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp +++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp @@ -75,7 +75,7 @@ typedef struct { int seq_len; // max sequence length } Config; -typedef struct { +struct TransformerWeights { // token embedding table float* token_embedding_table; // (vocab_size, dim) // weights for rmsnorms @@ -97,7 +97,22 @@ typedef struct { // float* freq_cis_imag; // (seq_len, dim/2) // (optional) classifier weights for the logits, on the last layer float* wcls; -} TransformerWeights; + + ~TransformerWeights() { + delete[] token_embedding_table; + delete[] rms_att_weight; + delete[] rms_ffn_weight; + delete[] wq; + delete[] wk; + delete[] wv; + delete[] wo; + delete[] w1; + delete[] w2; + delete[] w3; + delete[] rms_final_weight; + delete[] wcls; + } +}; void malloc_weights(TransformerWeights* w, Config* p, bool shared_weights) { // we calloc instead of malloc to keep valgrind happy @@ -173,21 +188,6 @@ int checkpoint_init_weights(TransformerWeights *w, Config* p, FILE* f, bool shar return 0; } -void free_weights(TransformerWeights* w) { - delete w->token_embedding_table; - delete w->rms_att_weight; - delete w->rms_ffn_weight; - delete w->wq; - delete w->wk; - delete w->wv; - delete w->wo; - delete w->w1; - delete w->w2; - delete w->w3; - delete w->rms_final_weight; - if (w->wcls) delete w->wcls; -} - void print_sample_weights(TransformerWeights *w){ printf("----- Quick print of first of the weight vales of all the variables\n"); printf("%f\n", w->token_embedding_table[0]); @@ -596,6 +596,10 @@ void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) // assume llama2.c vocabulary printf("Assuming llama2.c vocabulary since %s is not a gguf file\n", filename); llama_file file(filename, "rb"); + if (!file.fp) { + fprintf(stderr, "error: %s: %s\n", strerror(errno), filename); + exit(1); + } const int n_vocab = config->vocab_size; /* uint32_t max_token_length = */ file.read_u32(); // unused vocab->id_to_token.resize(n_vocab); @@ -898,7 +902,7 @@ bool params_parse(int argc, char ** argv, struct train_params * params) { } std::string basename(const std::string &path) { - size_t pos = path.find_last_of("/"); + size_t pos = path.find_last_of("/\\"); if (pos == std::string::npos) { return path; } @@ -911,7 +915,7 @@ int main(int argc, char ** argv) { return 1; } Config config; - TransformerWeights weights; + TransformerWeights weights = {}; { FILE *file = fopen(params.fn_llama2c_model, "rb"); if (!file) { printf("Unable to open the checkpoint file %s!\n", params.fn_llama2c_model); return 1; } @@ -953,6 +957,5 @@ int main(int argc, char ** argv) { printf("Saving llama.c model file %s in ggml format at %s\n", params.fn_llama2c_model, params.fn_llama2c_output_model); ggml_free(model.ctx); - free_weights(&weights); return 0; } From 4dcd47d71df8ca4edcc31302744bd93f0c31298e Mon Sep 17 00:00:00 2001 From: staviq Date: Fri, 1 Sep 2023 11:07:06 +0200 Subject: [PATCH 436/852] logs : fix mingw-like builds (fixes #2898) (#2911) * fix mingw-like builds * formatting * make LOG_COMPAT easier to override and extend * simplify win detection * fix for #2940 --- Makefile | 10 +++++----- common/log.h | 20 ++++++++++---------- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/Makefile b/Makefile index b750540fe..b56df3d8a 100644 --- a/Makefile +++ b/Makefile @@ -79,6 +79,11 @@ ifdef LLAMA_SERVER_VERBOSE CXXFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE) endif +ifdef LLAMA_DISABLE_LOGS + CFLAGS += -DLOG_DISABLE_LOGS + CXXFLAGS += -DLOG_DISABLE_LOGS +endif # LLAMA_DISABLE_LOGS + # warnings CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith \ -Wmissing-prototypes -Werror=implicit-int @@ -343,11 +348,6 @@ k_quants.o: k_quants.c k_quants.h $(CC) $(CFLAGS) -c $< -o $@ endif # LLAMA_NO_K_QUANTS -ifdef LLAMA_DISABLE_LOGS - CFLAGS += -DLOG_DISABLE_LOGS - CXXFLAGS += -DLOG_DISABLE_LOGS -endif # LLAMA_DISABLE_LOGS - # # Print build information # diff --git a/common/log.h b/common/log.h index c1364187d..bf9fafd68 100644 --- a/common/log.h +++ b/common/log.h @@ -154,7 +154,7 @@ inline std::string log_filename_generator_impl(const std::string & log_file_base // #include "log.h" // #ifndef LOG_NO_TIMESTAMPS - #ifndef _WIN32 + #ifndef _MSC_VER #define LOG_TIMESTAMP_FMT "[%" PRIu64 "] " #define LOG_TIMESTAMP_VAL , (std::chrono::duration_cast>(std::chrono::system_clock::now().time_since_epoch())).count() #else @@ -167,7 +167,7 @@ inline std::string log_filename_generator_impl(const std::string & log_file_base #endif #ifdef LOG_TEE_TIMESTAMPS - #ifndef _WIN32 + #ifndef _MSC_VER #define LOG_TEE_TIMESTAMP_FMT "[%" PRIu64 "] " #define LOG_TEE_TIMESTAMP_VAL , (std::chrono::duration_cast>(std::chrono::system_clock::now().time_since_epoch())).count() #else @@ -187,7 +187,7 @@ inline std::string log_filename_generator_impl(const std::string & log_file_base // #include "log.h" // #ifndef LOG_NO_FILE_LINE_FUNCTION - #ifndef _WIN32 + #ifndef _MSC_VER #define LOG_FLF_FMT "[%24s:%5d][%24s] " #define LOG_FLF_VAL , __FILE__, __LINE__, __FUNCTION__ #else @@ -200,7 +200,7 @@ inline std::string log_filename_generator_impl(const std::string & log_file_base #endif #ifdef LOG_TEE_FILE_LINE_FUNCTION - #ifndef _WIN32 + #ifndef _MSC_VER #define LOG_TEE_FLF_FMT "[%24s:%5d][%24s] " #define LOG_TEE_FLF_VAL , __FILE__, __LINE__, __FUNCTION__ #else @@ -224,7 +224,7 @@ enum LogTriState // INTERNAL, DO NOT USE // USE LOG() INSTEAD // -#ifndef _WIN32 +#ifndef _MSC_VER #define LOG_IMPL(str, ...) \ { \ if (LOG_TARGET != nullptr) \ @@ -247,7 +247,7 @@ enum LogTriState // INTERNAL, DO NOT USE // USE LOG_TEE() INSTEAD // -#ifndef _WIN32 +#ifndef _MSC_VER #define LOG_TEE_IMPL(str, ...) \ { \ if (LOG_TARGET != nullptr) \ @@ -284,7 +284,7 @@ enum LogTriState // Main LOG macro. // behaves like printf, and supports arguments the exact same way. // -#ifndef _WIN32 +#ifndef _MSC_VER #define LOG(...) LOG_IMPL(__VA_ARGS__, "") #else #define LOG(str, ...) LOG_IMPL("%s" str, "", __VA_ARGS__, "") @@ -298,14 +298,14 @@ enum LogTriState // Secondary target can be changed just like LOG_TARGET // by defining LOG_TEE_TARGET // -#ifndef _WIN32 +#ifndef _MSC_VER #define LOG_TEE(...) LOG_TEE_IMPL(__VA_ARGS__, "") #else #define LOG_TEE(str, ...) LOG_TEE_IMPL("%s" str, "", __VA_ARGS__, "") #endif // LOG macro variants with auto endline. -#ifndef _WIN32 +#ifndef _MSC_VER #define LOGLN(...) LOG_IMPL(__VA_ARGS__, "\n") #define LOG_TEELN(...) LOG_TEE_IMPL(__VA_ARGS__, "\n") #else @@ -461,7 +461,7 @@ inline void log_test() LOG("13 Hello World this time in yet new file?\n") log_set_target(log_filename_generator("llama_autonamed", "log")); LOG("14 Hello World in log with generated filename!\n") -#ifdef _WIN32 +#ifdef _MSC_VER LOG_TEE("15 Hello msvc TEE without arguments\n") LOG_TEE("16 Hello msvc TEE with (%d)(%s) arguments\n", 1, "test") LOG_TEELN("17 Hello msvc TEELN without arguments\n") From 13268c533177a4dc76bce0b465645d74f0d51d55 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 1 Sep 2023 13:42:41 +0300 Subject: [PATCH 437/852] metal : slight speed-up for add and mul kernels (#2917) --- ggml-metal.m | 20 ++++++++++++++++---- ggml-metal.metal | 32 ++++++++++++++++---------------- 2 files changed, 32 insertions(+), 20 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 8c3c64f53..4267db9be 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -680,6 +680,12 @@ void ggml_metal_graph_compute( } break; case GGML_OP_ADD: { + GGML_ASSERT(ggml_is_contiguous(src0)); + + // utilize float4 + GGML_ASSERT(ne00 % 4 == 0); + const int64_t nb = ne00/4; + if (ggml_nelements(src1) == ne10) { // src1 is a row [encoder setComputePipelineState:ctx->pipeline_add_row]; @@ -689,14 +695,20 @@ void ggml_metal_graph_compute( [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; + [encoder setBytes:&nb length:sizeof(nb) atIndex:3]; - const int64_t n = ggml_nelements(dst); + const int64_t n = ggml_nelements(dst)/4; [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; case GGML_OP_MUL: { + GGML_ASSERT(ggml_is_contiguous(src0)); + + // utilize float4 + GGML_ASSERT(ne00 % 4 == 0); + const int64_t nb = ne00/4; + if (ggml_nelements(src1) == ne10) { // src1 is a row [encoder setComputePipelineState:ctx->pipeline_mul_row]; @@ -706,9 +718,9 @@ void ggml_metal_graph_compute( [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:3]; + [encoder setBytes:&nb length:sizeof(nb) atIndex:3]; - const int64_t n = ggml_nelements(dst); + const int64_t n = ggml_nelements(dst)/4; [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; diff --git a/ggml-metal.metal b/ggml-metal.metal index 02db5323e..8cdf0b9d2 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -25,9 +25,9 @@ typedef struct { } block_q8_0; kernel void kernel_add( - device const float * src0, - device const float * src1, - device float * dst, + device const float4 * src0, + device const float4 * src1, + device float4 * dst, uint tpig[[thread_position_in_grid]]) { dst[tpig] = src0[tpig] + src1[tpig]; } @@ -35,18 +35,18 @@ kernel void kernel_add( // assumption: src1 is a row // broadcast src1 into src0 kernel void kernel_add_row( - device const float * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, + device const float4 * src0, + device const float4 * src1, + device float4 * dst, + constant int64_t & nb, uint tpig[[thread_position_in_grid]]) { - dst[tpig] = src0[tpig] + src1[tpig % ne00]; + dst[tpig] = src0[tpig] + src1[tpig % nb]; } kernel void kernel_mul( - device const float * src0, - device const float * src1, - device float * dst, + device const float4 * src0, + device const float4 * src1, + device float4 * dst, uint tpig[[thread_position_in_grid]]) { dst[tpig] = src0[tpig] * src1[tpig]; } @@ -54,12 +54,12 @@ kernel void kernel_mul( // assumption: src1 is a row // broadcast src1 into src0 kernel void kernel_mul_row( - device const float * src0, - device const float * src1, - device float * dst, - constant int64_t & ne00, + device const float4 * src0, + device const float4 * src1, + device float4 * dst, + constant int64_t & nb, uint tpig[[thread_position_in_grid]]) { - dst[tpig] = src0[tpig] * src1[tpig % ne00]; + dst[tpig] = src0[tpig] * src1[tpig % nb]; } kernel void kernel_scale( From 5aec2cfaac386eb09aebb75b805860828f00de91 Mon Sep 17 00:00:00 2001 From: Tameem <113388789+AhmadTameem@users.noreply.github.com> Date: Fri, 1 Sep 2023 18:27:40 +0500 Subject: [PATCH 438/852] ggml : add RISC-V vector intrinsics support (#2929) * added support for RISCV CFLAGS & native compile + cross compile options * Add RISC-V Vector Intrinsics Support Added RVV intrinsics for following ggml_vec_dot_q4_0_q8_0 ggml_vec_dot_q4_1_q8_1 ggml_vec_dot_q5_0_q8_0 ggml_vec_dot_q5_1_q8_1 ggml_vec_dot_q8_0_q8_0 Co-authored-by: Sharafat Signed-off-by: Ahmad Tameem --------- Signed-off-by: Ahmad Tameem Co-authored-by: moiz.hussain Co-authored-by: Sharafat --- Makefile | 13 ++++ ggml.c | 227 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 240 insertions(+) diff --git a/Makefile b/Makefile index b56df3d8a..8f73297f4 100644 --- a/Makefile +++ b/Makefile @@ -35,6 +35,11 @@ ifndef UNAME_M UNAME_M := $(shell uname -m) endif +ifdef RISCV_CROSS_COMPILE +CC := riscv64-unknown-linux-gnu-gcc +CXX := riscv64-unknown-linux-gnu-g++ +endif + CCV := $(shell $(CC) --version | head -n 1) CXXV := $(shell $(CXX) --version | head -n 1) @@ -150,6 +155,9 @@ endif # Architecture specific # TODO: probably these flags need to be tweaked on some architectures # feel free to update the Makefile for your architecture and send a pull request or issue + +ifndef RISCV + ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64)) # Use all CPU extensions that are available: CFLAGS += -march=native -mtune=native @@ -198,6 +206,11 @@ ifneq ($(filter ppc64%,$(UNAME_M)),) endif endif +else + CFLAGS += -march=rv64gcv -mabi=lp64d + CXXFLAGS += -march=rv64gcv -mabi=lp64d +endif + ifndef LLAMA_NO_K_QUANTS CFLAGS += -DGGML_USE_K_QUANTS CXXFLAGS += -DGGML_USE_K_QUANTS diff --git a/ggml.c b/ggml.c index 46ce4a581..cf3955f7f 100644 --- a/ggml.c +++ b/ggml.c @@ -301,6 +301,10 @@ typedef double ggml_float; #endif #endif +#ifdef __riscv_v_intrinsic +#include +#endif + #ifdef __F16C__ #ifdef _MSC_VER @@ -2677,6 +2681,41 @@ static void ggml_vec_dot_q4_0_q8_0(const int n, float * restrict s, const void * } *s = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3); +#elif defined(__riscv_v_intrinsic) + float sumf = 0.0; + + size_t vl = __riscv_vsetvl_e8m1(qk/2); + + for (int i = 0; i < nb; i++) { + vuint8m1_t tx = __riscv_vle8_v_u8m1(x[i].qs, vl); + + vint8m1_t y0 = __riscv_vle8_v_i8m1(y[i].qs, vl); + vint8m1_t y1 = __riscv_vle8_v_i8m1(y[i].qs+16, vl); + + vuint8m1_t x_a = __riscv_vand_vx_u8m1(tx, 0x0F, vl); + vuint8m1_t x_l = __riscv_vsrl_vx_u8m1(tx, 0x04, vl); + + vint8m1_t x_ai = __riscv_vreinterpret_v_u8m1_i8m1(x_a); + vint8m1_t x_li = __riscv_vreinterpret_v_u8m1_i8m1(x_l); + + vint8m1_t v0 = __riscv_vsub_vx_i8m1(x_ai, 8, vl); + vint8m1_t v1 = __riscv_vsub_vx_i8m1(x_li, 8, vl); + + vint16m2_t vec_mul1 = __riscv_vwmul_vv_i16m2(v0, y0, vl); + vint16m2_t vec_mul2 = __riscv_vwmul_vv_i16m2(v1, y1, vl); + + vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); + + vint32m1_t vs1 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul1, vec_zero, vl); + vint32m1_t vs2 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul2, vec_zero, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(vs1); + sumi += __riscv_vmv_x_s_i32m1_i32(vs2); + + sumf += sumi*GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d); + } + + *s = sumf; #else // scalar float sumf = 0.0; @@ -2803,6 +2842,38 @@ static void ggml_vec_dot_q4_1_q8_1(const int n, float * restrict s, const void * } *s = hsum_float_8(acc) + summs; +#elif defined(__riscv_v_intrinsic) + float sumf = 0.0; + + size_t vl = __riscv_vsetvl_e8m1(qk/2); + + for (int i = 0; i < nb; i++) { + vuint8m1_t tx = __riscv_vle8_v_u8m1(x[i].qs, vl); + + vint8m1_t y0 = __riscv_vle8_v_i8m1(y[i].qs, vl); + vint8m1_t y1 = __riscv_vle8_v_i8m1(y[i].qs+16, vl); + + vuint8m1_t x_a = __riscv_vand_vx_u8m1(tx, 0x0F, vl); + vuint8m1_t x_l = __riscv_vsrl_vx_u8m1(tx, 0x04, vl); + + vint8m1_t v0 = __riscv_vreinterpret_v_u8m1_i8m1(x_a); + vint8m1_t v1 = __riscv_vreinterpret_v_u8m1_i8m1(x_l); + + vint16m2_t vec_mul1 = __riscv_vwmul_vv_i16m2(v0, y0, vl); + vint16m2_t vec_mul2 = __riscv_vwmul_vv_i16m2(v1, y1, vl); + + vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); + + vint32m1_t vs1 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul1, vec_zero, vl); + vint32m1_t vs2 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul2, vec_zero, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(vs1); + sumi += __riscv_vmv_x_s_i32m1_i32(vs2); + + sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s; + } + + *s = sumf; #else // scalar float sumf = 0.0; @@ -3037,6 +3108,76 @@ static void ggml_vec_dot_q5_0_q8_0(const int n, float * restrict s, const void * } *s = hsum_float_8(acc); +#elif defined(__riscv_v_intrinsic) + float sumf = 0.0; + + uint32_t qh; + + // These temp values are for masking and shift operations + uint32_t temp_1[16] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}; + uint32_t temp_2[16] = {0x1, 0x2, 0x4, 0x8, 0x10, 0x20, 0x40, 0x80, + 0x100, 0x200, 0x400, 0x800, 0x1000, 0x2000, 0x4000, 0x8000}; + + size_t vl = __riscv_vsetvl_e8m1(qk/2); + + for (int i = 0; i < nb; i++) { + memcpy(&qh, x[i].qh, sizeof(uint32_t)); + + // temporary registers + vuint32m4_t vt_1 = __riscv_vle32_v_u32m4(temp_2, vl); + vuint32m4_t vt_2 = __riscv_vle32_v_u32m4(temp_1, vl); + vuint32m4_t vt_3 = __riscv_vsll_vx_u32m4(vt_1, 16, vl); + vuint32m4_t vt_4 = __riscv_vadd_vx_u32m4(vt_2, 12, vl); + + // ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4; + vuint32m4_t xha_0 = __riscv_vand_vx_u32m4(vt_1, qh, vl); + vuint32m4_t xhr_0 = __riscv_vsrl_vv_u32m4(xha_0, vt_2, vl); + vuint32m4_t xhl_0 = __riscv_vsll_vx_u32m4(xhr_0, 4, vl); + + // ((qh & (1u << (j + 16))) >> (j + 12)); + vuint32m4_t xha_1 = __riscv_vand_vx_u32m4(vt_3, qh, vl); + vuint32m4_t xhl_1 = __riscv_vsrl_vv_u32m4(xha_1, vt_4, vl); + + // narrowing + vuint16m2_t xhc_0 = __riscv_vncvt_x_x_w_u16m2(xhl_0, vl); + vuint8m1_t xh_0 = __riscv_vncvt_x_x_w_u8m1(xhc_0, vl); + + vuint16m2_t xhc_1 = __riscv_vncvt_x_x_w_u16m2(xhl_1, vl); + vuint8m1_t xh_1 = __riscv_vncvt_x_x_w_u8m1(xhc_1, vl); + + // load + vuint8m1_t tx = __riscv_vle8_v_u8m1(x[i].qs, vl); + + vint8m1_t y0 = __riscv_vle8_v_i8m1(y[i].qs, vl); + vint8m1_t y1 = __riscv_vle8_v_i8m1(y[i].qs+16, vl); + + vuint8m1_t x_at = __riscv_vand_vx_u8m1(tx, 0x0F, vl); + vuint8m1_t x_lt = __riscv_vsrl_vx_u8m1(tx, 0x04, vl); + + vuint8m1_t x_a = __riscv_vor_vv_u8m1(x_at, xh_0, vl); + vuint8m1_t x_l = __riscv_vor_vv_u8m1(x_lt, xh_1, vl); + + vint8m1_t x_ai = __riscv_vreinterpret_v_u8m1_i8m1(x_a); + vint8m1_t x_li = __riscv_vreinterpret_v_u8m1_i8m1(x_l); + + vint8m1_t v0 = __riscv_vsub_vx_i8m1(x_ai, 16, vl); + vint8m1_t v1 = __riscv_vsub_vx_i8m1(x_li, 16, vl); + + vint16m2_t vec_mul1 = __riscv_vwmul_vv_i16m2(v0, y0, vl); + vint16m2_t vec_mul2 = __riscv_vwmul_vv_i16m2(v1, y1, vl); + + vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); + + vint32m1_t vs1 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul1, vec_zero, vl); + vint32m1_t vs2 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul2, vec_zero, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(vs1); + sumi += __riscv_vmv_x_s_i32m1_i32(vs2); + + sumf += (GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)) * sumi; + } + + *s = sumf; #else // scalar float sumf = 0.0; @@ -3293,6 +3434,72 @@ static void ggml_vec_dot_q5_1_q8_1(const int n, float * restrict s, const void * } *s = hsum_float_8(acc) + summs; +#elif defined(__riscv_v_intrinsic) + float sumf = 0.0; + + uint32_t qh; + + // These temp values are for shift operations + uint32_t temp_1[16] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15}; + + size_t vl = __riscv_vsetvl_e8m1(qk/2); + + for (int i = 0; i < nb; i++) { + memcpy(&qh, x[i].qh, sizeof(uint32_t)); + + // temporary registers + vuint32m4_t vt_1 = __riscv_vle32_v_u32m4(temp_1, vl); + vuint32m4_t vt_2 = __riscv_vadd_vx_u32m4(vt_1, 12, vl); + + // load qh + vuint32m4_t vqh = __riscv_vmv_v_x_u32m4(qh, vl); + + // ((qh >> (j + 0)) << 4) & 0x10; + vuint32m4_t xhr_0 = __riscv_vsrl_vv_u32m4(vqh, vt_1, vl); + vuint32m4_t xhl_0 = __riscv_vsll_vx_u32m4(xhr_0, 4, vl); + vuint32m4_t xha_0 = __riscv_vand_vx_u32m4(xhl_0, 0x10, vl); + + // ((qh >> (j + 12)) ) & 0x10; + vuint32m4_t xhr_1 = __riscv_vsrl_vv_u32m4(vqh, vt_2, vl); + vuint32m4_t xha_1 = __riscv_vand_vx_u32m4(xhr_1, 0x10, vl); + + // narrowing + vuint16m2_t xhc_0 = __riscv_vncvt_x_x_w_u16m2(xha_0, vl); + vuint8m1_t xh_0 = __riscv_vncvt_x_x_w_u8m1(xhc_0, vl); + + vuint16m2_t xhc_1 = __riscv_vncvt_x_x_w_u16m2(xha_1, vl); + vuint8m1_t xh_1 = __riscv_vncvt_x_x_w_u8m1(xhc_1, vl); + + // load + vuint8m1_t tx = __riscv_vle8_v_u8m1(x[i].qs, vl); + + vint8m1_t y0 = __riscv_vle8_v_i8m1(y[i].qs, vl); + vint8m1_t y1 = __riscv_vle8_v_i8m1(y[i].qs+16, vl); + + vuint8m1_t x_at = __riscv_vand_vx_u8m1(tx, 0x0F, vl); + vuint8m1_t x_lt = __riscv_vsrl_vx_u8m1(tx, 0x04, vl); + + vuint8m1_t x_a = __riscv_vor_vv_u8m1(x_at, xh_0, vl); + vuint8m1_t x_l = __riscv_vor_vv_u8m1(x_lt, xh_1, vl); + + vint8m1_t v0 = __riscv_vreinterpret_v_u8m1_i8m1(x_a); + vint8m1_t v1 = __riscv_vreinterpret_v_u8m1_i8m1(x_l); + + vint16m2_t vec_mul1 = __riscv_vwmul_vv_i16m2(v0, y0, vl); + vint16m2_t vec_mul2 = __riscv_vwmul_vv_i16m2(v1, y1, vl); + + vint32m1_t vec_zero = __riscv_vmv_v_x_i32m1(0, vl); + + vint32m1_t vs1 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul1, vec_zero, vl); + vint32m1_t vs2 = __riscv_vwredsum_vs_i16m2_i32m1(vec_mul2, vec_zero, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(vs1); + sumi += __riscv_vmv_x_s_i32m1_i32(vs2); + + sumf += (GGML_FP16_TO_FP32(x[i].d)*y[i].d)*sumi + GGML_FP16_TO_FP32(x[i].m)*y[i].s; + } + + *s = sumf; #else // scalar float sumf = 0.0; @@ -3404,6 +3611,26 @@ static void ggml_vec_dot_q8_0_q8_0(const int n, float * restrict s, const void * } *s = hsum_float_8(acc); +#elif defined(__riscv_v_intrinsic) + float sumf = 0.0; + size_t vl = __riscv_vsetvl_e8m1(qk); + + for (int i = 0; i < nb; i++) { + // load elements + vint8m1_t bx = __riscv_vle8_v_i8m1(x[i].qs, vl); + vint8m1_t by = __riscv_vle8_v_i8m1(y[i].qs, vl); + + vint16m2_t vw_mul = __riscv_vwmul_vv_i16m2(bx, by, vl); + + vint32m1_t v_zero = __riscv_vmv_v_x_i32m1(0, vl); + vint32m1_t v_sum = __riscv_vwredsum_vs_i16m2_i32m1(vw_mul, v_zero, vl); + + int sumi = __riscv_vmv_x_s_i32m1_i32(v_sum); + + sumf += sumi*(GGML_FP16_TO_FP32(x[i].d)*GGML_FP16_TO_FP32(y[i].d)); + } + + *s = sumf; #else // scalar float sumf = 0.0; From d8d6977f48f1fa402ade38ad32c5b5fb1358d059 Mon Sep 17 00:00:00 2001 From: Ben Siraphob Date: Fri, 1 Sep 2023 09:32:14 -0400 Subject: [PATCH 439/852] examples : add C grammar (#2357) --- grammars/c.gbnf | 42 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) create mode 100644 grammars/c.gbnf diff --git a/grammars/c.gbnf b/grammars/c.gbnf new file mode 100644 index 000000000..4a0331dd2 --- /dev/null +++ b/grammars/c.gbnf @@ -0,0 +1,42 @@ +root ::= (declaration)* + +declaration ::= dataType identifier "(" parameter? ")" "{" statement* "}" + +dataType ::= "int" ws | "float" ws | "char" ws +identifier ::= [a-zA-Z_] [a-zA-Z_0-9]* + +parameter ::= dataType identifier + +statement ::= + ( dataType identifier ws "=" ws expression ";" ) | + ( identifier ws "=" ws expression ";" ) | + ( identifier ws "(" argList? ")" ";" ) | + ( "return" ws expression ";" ) | + ( "while" "(" condition ")" "{" statement* "}" ) | + ( "for" "(" forInit ";" ws condition ";" ws forUpdate ")" "{" statement* "}" ) | + ( "if" "(" condition ")" "{" statement* "}" ("else" "{" statement* "}")? ) | + ( singleLineComment ) | + ( multiLineComment ) + +forInit ::= dataType identifier ws "=" ws expression | identifier ws "=" ws expression +forUpdate ::= identifier ws "=" ws expression + +condition ::= expression relationOperator expression +relationOperator ::= ("<=" | "<" | "==" | "!=" | ">=" | ">") + +expression ::= term (("+" | "-") term)* +term ::= factor(("*" | "/") factor)* + +factor ::= identifier | number | unaryTerm | funcCall | parenExpression +unaryTerm ::= "-" factor +funcCall ::= identifier "(" argList? ")" +parenExpression ::= "(" ws expression ws ")" + +argList ::= expression ("," ws expression)* + +number ::= [0-9]+ + +singleLineComment ::= "//" [^\n]* "\n" +multiLineComment ::= "/*" ( [^*] | ("*" [^/]) )* "*/" + +ws ::= ([ \t\n]+) From ef156499721c67748cde01a5436cb6f0648bb4b4 Mon Sep 17 00:00:00 2001 From: Cebtenzzre Date: Fri, 1 Sep 2023 09:34:50 -0400 Subject: [PATCH 440/852] build : fix most gcc and clang warnings (#2861) * fix most gcc and clang warnings * baby-llama : remove commented opt_params_adam * fix some MinGW warnings * fix more MinGW warnings --- CMakeLists.txt | 5 +++++ Makefile | 7 ++++++- common/common.cpp | 6 ++++-- common/console.cpp | 1 + examples/baby-llama/baby-llama.cpp | 5 ----- examples/beam-search/beam-search.cpp | 8 +++++--- examples/server/server.cpp | 8 +++++--- k_quants.c | 8 ++------ llama.cpp | 4 ++-- 9 files changed, 30 insertions(+), 22 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index d6c1b3b33..1b7cce9f1 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -403,6 +403,7 @@ if (LLAMA_ALL_WARNINGS) -Wpointer-arith -Wmissing-prototypes -Werror=implicit-int + -Wno-unused-function ) set(cxx_flags -Wall @@ -412,6 +413,10 @@ if (LLAMA_ALL_WARNINGS) -Wno-unused-function -Wno-multichar ) + if (CMAKE_CXX_COMPILER_ID STREQUAL "GNU") + # g++ only + set(cxx_flags ${cxx_flags} -Wno-format-truncation) + endif() else() # todo : msvc endif() diff --git a/Makefile b/Makefile index 8f73297f4..ef1eef6ac 100644 --- a/Makefile +++ b/Makefile @@ -91,9 +91,14 @@ endif # LLAMA_DISABLE_LOGS # warnings CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith \ - -Wmissing-prototypes -Werror=implicit-int + -Wmissing-prototypes -Werror=implicit-int -Wno-unused-function CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar +ifeq '' '$(findstring clang++,$(CXX))' + # g++ only + CXXFLAGS += -Wno-format-truncation +endif + # OS specific # TODO: support Windows ifeq ($(UNAME_S),Linux) diff --git a/common/common.cpp b/common/common.cpp index ed09fc27d..41fc59ced 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -24,7 +24,9 @@ #if defined(_WIN32) #define WIN32_LEAN_AND_MEAN -#define NOMINMAX +#ifndef NOMINMAX +# define NOMINMAX +#endif #include #include #include @@ -1027,7 +1029,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l dump_string_yaml_multiline(stream, "grammar", params.grammar.c_str()); fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n"); fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false"); - fprintf(stream, "hellaswag_tasks: %ld # default: 400\n", params.hellaswag_tasks); + fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks); const auto logit_bias_eos = params.logit_bias.find(llama_token_eos(lctx)); const bool ignore_eos = logit_bias_eos != params.logit_bias.end() && logit_bias_eos->second == -INFINITY; diff --git a/common/console.cpp b/common/console.cpp index 8efa2a674..23545e5be 100644 --- a/common/console.cpp +++ b/common/console.cpp @@ -235,6 +235,7 @@ namespace console { int estimateWidth(char32_t codepoint) { #if defined(_WIN32) + (void)codepoint; return 1; #else return wcwidth(codepoint); diff --git a/examples/baby-llama/baby-llama.cpp b/examples/baby-llama/baby-llama.cpp index 6fa55b319..a99ece9a6 100644 --- a/examples/baby-llama/baby-llama.cpp +++ b/examples/baby-llama/baby-llama.cpp @@ -1617,15 +1617,10 @@ int main(int argc, char ** argv) { float error_before_opt = ggml_get_f32_1d(e, 0); - struct ggml_opt_params opt_params_adam = ggml_opt_default_params(GGML_OPT_ADAM); struct ggml_opt_params opt_params_lbfgs = ggml_opt_default_params(GGML_OPT_LBFGS); - opt_params_adam.print_forward_graph = false; - opt_params_adam.print_backward_graph = false; opt_params_lbfgs.print_forward_graph = false; opt_params_lbfgs.print_backward_graph = false; - opt_params_adam.adam.n_iter = 16; opt_params_lbfgs.lbfgs.n_iter = 16; - // ggml_opt(ctx0, opt_params_adam, e); ggml_opt(ctx0, opt_params_lbfgs, e); // ggml_build_forward_expand(&gf, e); diff --git a/examples/beam-search/beam-search.cpp b/examples/beam-search/beam-search.cpp index 42c7c7254..4d021434b 100644 --- a/examples/beam-search/beam-search.cpp +++ b/examples/beam-search/beam-search.cpp @@ -22,7 +22,9 @@ #include #elif defined (_WIN32) #define WIN32_LEAN_AND_MEAN -#define NOMINMAX +#ifndef NOMINMAX +# define NOMINMAX +#endif #include #include #endif @@ -73,7 +75,7 @@ void beam_search_callback(void * callback_data_ptr, llama_beams_state beams_stat assert(0u < beams_state.n_beams); const llama_token * tokens = beams_state.beam_views[0].tokens; std::copy(tokens, tokens + n, callback_data.response.end() - n); - printf("%lu", n); + printf("%zu", n); } fflush(stdout); #if 1 // DEBUG: print current beams for this iteration @@ -145,7 +147,7 @@ int main(int argc, char ** argv) if (tokens_list.size() > max_tokens_list_size) { - fprintf( stderr , "%s: error: prompt too long (%lu tokens, max %lu)\n" , + fprintf( stderr , "%s: error: prompt too long (%zu tokens, max %zu)\n" , __func__ , tokens_list.size() , max_tokens_list_size ); return 1; } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index b485a5ead..09eac2ec2 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -17,6 +17,8 @@ #include "completion.js.hpp" #include "json-schema-to-grammar.mjs.hpp" +#include + #ifndef SERVER_VERBOSE #define SERVER_VERBOSE 1 #endif @@ -1038,7 +1040,7 @@ static json format_timings(llama_server_context &llama) { const auto timings = llama_get_timings(llama.ctx); - assert(timings.n_eval == llama.num_tokens_predicted); + assert(timings.n_eval == ptrdiff_t(llama.num_tokens_predicted)); return json{ {"prompt_n", timings.n_p_eval}, @@ -1239,7 +1241,7 @@ void beam_search_callback(void * callback_data, llama_beams_state beams_state) { const llama_token * tokens = beams_state.beam_views[0].tokens; const auto map = [](llama_token tok) { return completion_token_output{{},tok}; }; std::transform(tokens, tokens + n, llama.generated_token_probs.end() - n, map); - printf("%lu", n); + printf("%zu", n); } fflush(stdout); #if 0 // DEBUG: print current beams for this iteration @@ -1548,7 +1550,7 @@ int main(int argc, char **argv) svr.set_exception_handler([](const Request &, Response &res, std::exception_ptr ep) { - const auto * fmt = "500 Internal Server Error\n%s"; + const char fmt[] = "500 Internal Server Error\n%s"; char buf[BUFSIZ]; try { std::rethrow_exception(std::move(ep)); diff --git a/k_quants.c b/k_quants.c index 3a9b1dafd..3deeaedf7 100644 --- a/k_quants.c +++ b/k_quants.c @@ -183,13 +183,9 @@ static float make_qkx1_quants(int n, int nmax, const float * restrict x, uint8_t int ntry, float alpha) { float min = x[0]; float max = x[0]; - float sum_x = 0; - float sum_x2 = 0; for (int i = 1; i < n; ++i) { if (x[i] < min) min = x[i]; if (x[i] > max) max = x[i]; - sum_x += x[i]; - sum_x2 += x[i]*x[i]; } if (max == min) { for (int i = 0; i < n; ++i) L[i] = 0; @@ -2060,7 +2056,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri __m256 acc = _mm256_setzero_ps(); - uint32_t *aux; + const uint32_t *aux; for (int i = 0; i < nb; ++i) { @@ -2070,7 +2066,7 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri const int8_t * restrict q8 = y[i].qs; // Set up scales - aux = (uint32_t *)x[i].scales; + aux = (const uint32_t *)x[i].scales; __m128i scales128 = _mm_set_epi32( ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4), ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4), diff --git a/llama.cpp b/llama.cpp index 98a5da963..5ca119238 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3600,7 +3600,7 @@ static void llama_grammar_advance_stack( std::vector> & new_stacks) { if (stack.empty()) { - new_stacks.push_back(stack); + new_stacks.emplace_back(stack); return; } @@ -3637,7 +3637,7 @@ static void llama_grammar_advance_stack( } case LLAMA_GRETYPE_CHAR: case LLAMA_GRETYPE_CHAR_NOT: - new_stacks.push_back(stack); + new_stacks.emplace_back(stack); break; default: // end of alternate (LLAMA_GRETYPE_END, LLAMA_GRETYPE_ALT) or middle of char range From 49bb9cbe0f598bc43be539b0df8eafb2130cfad3 Mon Sep 17 00:00:00 2001 From: Konstantin Herud Date: Fri, 1 Sep 2023 15:36:14 +0200 Subject: [PATCH 441/852] docs : add java-llama.cpp to README.md (#2935) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index d727b0554..4e6a0957d 100644 --- a/README.md +++ b/README.md @@ -114,6 +114,7 @@ as the main playground for developing new features for the [ggml](https://github - Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s) - Clojure: [phronmophobic/llama.clj](https://github.com/phronmophobic/llama.clj) - React Native: [mybigday/llama.rn](https://github.com/mybigday/llama.rn) +- Java: [kherud/java-llama.cpp](https://github.com/kherud/java-llama.cpp) **UI:** From ee8654bcd0146708988a703e54406d5b553712ea Mon Sep 17 00:00:00 2001 From: m3ndax Date: Fri, 1 Sep 2023 15:47:27 +0200 Subject: [PATCH 442/852] minor : add const qualifiers (#2853) * made the methods const # Conflicts: # examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp * made method const * Update convert-llama2c-to-ggml.cpp removed write_raw and write_u32 * llama2c : remove misleading const --------- Co-authored-by: Georgi Gerganov --- examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp | 2 +- llama.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp index 0b03c9d2b..0ee7adc52 100644 --- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp +++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp @@ -637,7 +637,7 @@ void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) } } -void stuff_karpathy_weights_into_gg(struct ggml_tensor * gg_weights, float * karpathy_weights){ +void stuff_karpathy_weights_into_gg(struct ggml_tensor * gg_weights, const float * karpathy_weights) { int ct; switch (gg_weights->n_dims){ case 1: diff --git a/llama.cpp b/llama.cpp index 5ca119238..23b251caf 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4393,7 +4393,7 @@ struct llama_logit_info { } return min_heap; } - float probability_from_logit(float logit) { + float probability_from_logit(float logit) const { return normalizer * std::exp(logit - max_l); } }; From 6c9c23429bf4e4fcaaddbebadc4638558430a7f2 Mon Sep 17 00:00:00 2001 From: Cebtenzzre Date: Fri, 1 Sep 2023 09:53:14 -0400 Subject: [PATCH 443/852] make : use unaligned vector moves on MinGW (#2945) Fixes #2922 --- Makefile | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/Makefile b/Makefile index ef1eef6ac..23f050c0d 100644 --- a/Makefile +++ b/Makefile @@ -177,6 +177,14 @@ ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64)) #CXXFLAGS += -mssse3 endif +# The stack is only 16-byte aligned on Windows, so don't let gcc emit aligned moves. +# https://gcc.gnu.org/bugzilla/show_bug.cgi?id=54412 +# https://github.com/ggerganov/llama.cpp/issues/2922 +ifneq '' '$(findstring mingw,$(shell $(CC) -dumpmachine))' + CFLAGS += -Xassembler -muse-unaligned-vector-move + CXXFLAGS += -Xassembler -muse-unaligned-vector-move +endif + ifneq ($(filter aarch64%,$(UNAME_M)),) # Apple M1, M2, etc. # Raspberry Pi 3, 4, Zero 2 (64-bit) From 0d5893668625456c94bbadfddc53fc69cd51c223 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 1 Sep 2023 17:00:40 +0300 Subject: [PATCH 444/852] llama2c : rename function --- .../convert-llama2c-to-ggml.cpp | 28 +++++++++---------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp index 0ee7adc52..9e856c21a 100644 --- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp +++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp @@ -637,7 +637,7 @@ void load_vocab(const char *filename, Config *config, struct llama_vocab *vocab) } } -void stuff_karpathy_weights_into_gg(struct ggml_tensor * gg_weights, const float * karpathy_weights) { +void convert_weights_ak_to_gg(struct ggml_tensor * gg_weights, const float * karpathy_weights) { int ct; switch (gg_weights->n_dims){ case 1: @@ -674,13 +674,13 @@ void stuff_karpathy_weights_into_gg(struct ggml_tensor * gg_weights, const float } void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * model, TransformerWeights* w, const char * filename) { - // stuff AK weights into GG weights one by one. + // convert AK weights into GG weights one by one. // w->token_embedding_table -> model->tok_embeddings // float* -> struct ggml_tensor - stuff_karpathy_weights_into_gg(model->tok_embeddings, w->token_embedding_table); - stuff_karpathy_weights_into_gg(model->output, w->wcls ? w->wcls : w->token_embedding_table); + convert_weights_ak_to_gg(model->tok_embeddings, w->token_embedding_table); + convert_weights_ak_to_gg(model->output, w->wcls ? w->wcls : w->token_embedding_table); - stuff_karpathy_weights_into_gg(model->norm, w->rms_final_weight); + convert_weights_ak_to_gg(model->norm, w->rms_final_weight); //print_row(model->norm, 0); // for rms-att-weight @@ -690,18 +690,18 @@ void save_as_llama_model(struct llama_vocab * vocab, struct my_llama_model * mod for (uint32_t i = 0; i < model->hparams.n_layer; ++i){ auto & layer = model->layers[i]; // 1d - stuff_karpathy_weights_into_gg(layer.attention_norm, &w->rms_att_weight[i*row_length]); - stuff_karpathy_weights_into_gg(layer.ffn_norm , &w->rms_ffn_weight[i*row_length]); + convert_weights_ak_to_gg(layer.attention_norm, &w->rms_att_weight[i*row_length]); + convert_weights_ak_to_gg(layer.ffn_norm , &w->rms_ffn_weight[i*row_length]); // from 3d matrix layer x dim x dim to 2d matrix dim x dim - stuff_karpathy_weights_into_gg(layer.wq , &w->wq[i*row_length*row_length]); - stuff_karpathy_weights_into_gg(layer.wk , &w->wk[i*row_length*row_length]); - stuff_karpathy_weights_into_gg(layer.wv , &w->wv[i*row_length*row_length]); - stuff_karpathy_weights_into_gg(layer.wo , &w->wo[i*row_length*row_length]); + convert_weights_ak_to_gg(layer.wq , &w->wq[i*row_length*row_length]); + convert_weights_ak_to_gg(layer.wk , &w->wk[i*row_length*row_length]); + convert_weights_ak_to_gg(layer.wv , &w->wv[i*row_length*row_length]); + convert_weights_ak_to_gg(layer.wo , &w->wo[i*row_length*row_length]); - stuff_karpathy_weights_into_gg(layer.w1 , &w->w1[i*row_length*n_ff]); - stuff_karpathy_weights_into_gg(layer.w2 , &w->w2[i*n_ff*row_length]); - stuff_karpathy_weights_into_gg(layer.w3 , &w->w3[i*row_length*n_ff]); + convert_weights_ak_to_gg(layer.w1 , &w->w1[i*row_length*n_ff]); + convert_weights_ak_to_gg(layer.w2 , &w->w2[i*n_ff*row_length]); + convert_weights_ak_to_gg(layer.w3 , &w->w3[i*row_length*n_ff]); } struct gguf_context * ctx = gguf_init_empty(); From 5d6f19f16b2173afe2d5c6aee2f5c9fc31038eba Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Fri, 1 Sep 2023 08:02:48 -0600 Subject: [PATCH 445/852] Allow quantize to only copy tensors, some other improvements (#2931) * Allow quantize tool to only copy tensors to allow repackaging models. * Slightly better logic when requantizing. * Change help message to go to `stdout`. --- examples/quantize/quantize.cpp | 24 +++++++++++++++++++----- llama.cpp | 25 +++++++++++++++++-------- llama.h | 1 + 3 files changed, 37 insertions(+), 13 deletions(-) diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index df9a214fc..c174be069 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -35,6 +35,8 @@ static const std::vector QUANT_OPTIONS = { { "Q8_0", LLAMA_FTYPE_MOSTLY_Q8_0, " 6.70G, +0.0004 ppl @ LLaMA-v1-7B", }, { "F16", LLAMA_FTYPE_MOSTLY_F16, "13.00G @ 7B", }, { "F32", LLAMA_FTYPE_ALL_F32, "26.00G @ 7B", }, + // Note: Ensure COPY comes after F32 to avoid ftype 0 from matching. + { "COPY", LLAMA_FTYPE_ALL_F32, "only copy tensors, no quantizing", }, }; @@ -71,12 +73,17 @@ bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftype, std: // ./quantize [--allow-requantize] [--leave-output-tensor] models/llama/ggml-model.gguf [models/llama/ggml-model-quant.gguf] type [nthreads] // void usage(const char * executable) { - fprintf(stderr, "usage: %s [--help] [--allow-requantize] [--leave-output-tensor] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable); - fprintf(stderr, " --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n"); - fprintf(stderr, " --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n"); - fprintf(stderr, "\nAllowed quantization types:\n"); + printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable); + printf(" --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n"); + printf(" --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n"); + printf("\nAllowed quantization types:\n"); for (auto & it : QUANT_OPTIONS) { - printf(" %2d or %-6s : %s\n", it.ftype, it.name.c_str(), it.desc.c_str()); + if (it.name != "COPY") { + printf(" %2d or ", it.ftype); + } else { + printf(" "); + } + printf("%-6s : %s\n", it.name.c_str(), it.desc.c_str()); } exit(1); } @@ -121,6 +128,9 @@ int main(int argc, char ** argv) { // export as [inp path]/ggml-model-[ftype].gguf fname_out = fpath + "ggml-model-" + ftype_str + ".gguf"; arg_idx++; + if (ftype_str == "COPY") { + params.only_copy = true; + } } else { fname_out = argv[arg_idx]; @@ -133,6 +143,10 @@ int main(int argc, char ** argv) { if (!try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) { fprintf(stderr, "%s: invalid ftype '%s'\n", __func__, argv[3]); return 1; + } else { + if (ftype_str == "COPY") { + params.only_copy = true; + } } arg_idx++; } diff --git a/llama.cpp b/llama.cpp index 23b251caf..3114d3311 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4683,6 +4683,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s llm_load_arch(*ml, model); llm_load_hparams(*ml, model, 0, 0, 0); + if (params->only_copy) { + ftype = model.ftype; + } + const size_t align = GGUF_DEFAULT_ALIGNMENT; struct gguf_context * ctx_out = gguf_init_empty(); @@ -4769,18 +4773,13 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s // quantize only 2D tensors quantize &= (tensor->n_dims == 2); quantize &= params->quantize_output_tensor || name != "output.weight"; - quantize &= quantized_type != tensor->type; + quantize &= !params->only_copy; enum ggml_type new_type; void * new_data; size_t new_size; - if (!quantize) { - new_type = tensor->type; - new_data = tensor->data; - new_size = ggml_nbytes(tensor); - LLAMA_LOG_INFO("size = %8.3f MB\n", ggml_nbytes(tensor)/1024.0/1024.0); - } else { + if (quantize) { new_type = quantized_type; #ifdef GGML_USE_K_QUANTS // TODO: avoid hardcoded tensor names - use the TN_* constants @@ -4879,7 +4878,16 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s } } #endif - + // If we've decided to quantize to the same type the tensor is already + // in then there's nothing to do. + quantize = tensor->type != new_type; + } + if (!quantize) { + new_type = tensor->type; + new_data = tensor->data; + new_size = ggml_nbytes(tensor); + LLAMA_LOG_INFO("size = %8.3f MB\n", ggml_nbytes(tensor)/1024.0/1024.0); + } else { const size_t nelements = ggml_nelements(tensor); float * f32_data; @@ -5310,6 +5318,7 @@ struct llama_model_quantize_params llama_model_quantize_default_params() { /*.ftype =*/ LLAMA_FTYPE_MOSTLY_Q5_1, /*.allow_requantize =*/ false, /*.quantize_output_tensor =*/ true, + /*.only_copy =*/ false, }; return result; diff --git a/llama.h b/llama.h index 6e5e1df63..422f28527 100644 --- a/llama.h +++ b/llama.h @@ -164,6 +164,7 @@ extern "C" { enum llama_ftype ftype; // quantize to this llama_ftype bool allow_requantize; // allow quantizing non-f32/f16 tensors bool quantize_output_tensor; // quantize output.weight + bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored } llama_model_quantize_params; // grammar types From 69fdbb9abc8907dd2a9ffdd840cba92d678a660a Mon Sep 17 00:00:00 2001 From: ZHAOKAI WANG Date: Fri, 1 Sep 2023 22:06:44 +0800 Subject: [PATCH 446/852] readme : quick start command fix (#2908) * quick start command fix * quick start win command fix --- examples/main/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/main/README.md b/examples/main/README.md index d555afdcc..2773fe976 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -34,7 +34,7 @@ For an interactive experience, try this command: #### Unix-based systems (Linux, macOS, etc.): ```bash -./main -m models/7B/ggml-model.bin -n -1 --color -r "User:" --in-prefix " " \ +./main -m models/7B/ggml-model.bin -n -1 --color -r "User:" --in-prefix " " -i -p \ 'User: Hi AI: Hello. I am an AI chatbot. Would you like to talk? User: Sure! @@ -45,7 +45,7 @@ User:' #### Windows: ```powershell -main.exe -m models\7B\ggml-model.bin -n -1 --color -r "User:" --in-prefix " " -e --prompt "User: Hi\nAI: Hello. I am an AI chatbot. Would you like to talk?\nUser: Sure!\nAI: What would you like to talk about?\nUser:" +main.exe -m models\7B\ggml-model.bin -n -1 --color -r "User:" --in-prefix " " -i -e -p "User: Hi\nAI: Hello. I am an AI chatbot. Would you like to talk?\nUser: Sure!\nAI: What would you like to talk about?\nUser:" ``` The following command generates "infinite" text from a starting prompt (you can use `Ctrl-C` to stop it): From f04d0028444bc9b3d4225fba47e19d4c3aeb3741 Mon Sep 17 00:00:00 2001 From: Engininja2 <139037756+Engininja2@users.noreply.github.com> Date: Fri, 1 Sep 2023 15:33:19 -0600 Subject: [PATCH 447/852] cuda : vsubss4 for older versions of ROCm/clang (#2942) --- ggml-cuda.cu | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 5fd625630..8357f32f7 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -81,12 +81,29 @@ #if defined(GGML_USE_HIPBLAS) #define __CUDA_ARCH__ 1300 +#ifndef __has_builtin + #define __has_builtin(x) 0 +#endif + typedef int8_t int8x4_t __attribute__((ext_vector_type(4))); static __device__ __forceinline__ int __vsubss4(const int a, const int b) { const int8x4_t va = reinterpret_cast(a); const int8x4_t vb = reinterpret_cast(b); +#if __has_builtin(__builtin_elementwise_sub_sat) const int8x4_t c = __builtin_elementwise_sub_sat(va, vb); return reinterpret_cast(c); +#else + int8x4_t c; + int16_t tmp; +#pragma unroll + for (int i = 0; i < 4; i++) { + tmp = va[i] - vb[i]; + if(tmp > std::numeric_limits::max()) tmp = std::numeric_limits::max(); + if(tmp < std::numeric_limits::min()) tmp = std::numeric_limits::min(); + c[i] = tmp; + } + return reinterpret_cast(c); +#endif // __has_builtin(__builtin_elementwise_sub_sat) } static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) { From 571083f508266c4eb5cb5457d836df5dd3c173ce Mon Sep 17 00:00:00 2001 From: Jhen-Jie Hong Date: Sat, 2 Sep 2023 08:31:46 +0800 Subject: [PATCH 448/852] server : avoid aniprompt in probabilities of final response (#2849) --- examples/server/server.cpp | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 09eac2ec2..94def943b 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1379,7 +1379,13 @@ int main(int argc, char **argv) } } - const json data = format_final_response(llama, llama.generated_text, llama.generated_token_probs); + auto probs = llama.generated_token_probs; + if (llama.params.n_probs > 0 && llama.stopped_word) { + const std::vector stop_word_toks = llama_tokenize(llama.ctx, llama.stopping_word, false); + probs = std::vector(llama.generated_token_probs.begin(), llama.generated_token_probs.end() - stop_word_toks.size()); + } + + const json data = format_final_response(llama, llama.generated_text, probs); llama_print_timings(llama.ctx); @@ -1456,7 +1462,11 @@ int main(int argc, char **argv) if (!llama.has_next_token) { // Generation is done, send extra information. - const json data = format_final_response(llama, "", llama.generated_token_probs); + const json data = format_final_response( + llama, + "", + std::vector(llama.generated_token_probs.begin(), llama.generated_token_probs.begin() + sent_token_probs_index) + ); const std::string str = "data: " + From 21f3d1be867b4d7be07c26f5da6e4bc69bcf4d27 Mon Sep 17 00:00:00 2001 From: Jhen-Jie Hong Date: Sat, 2 Sep 2023 20:23:45 +0800 Subject: [PATCH 449/852] k-quants : fix build on armv7 (android only) (#2920) * k-quants : fix build on armv7 * ggml : cleanup unused arm32 specific impl * k-quants : avoid some unused vzero / mzero define * ggml-alloc : use 4g for MEASURE_MAX_SIZE in 32-bit arm --- ggml-alloc.c | 7 +++++++ ggml.c | 46 ---------------------------------------------- k_quants.c | 40 +++++++++++++++++++++++++++++++++++----- 3 files changed, 42 insertions(+), 51 deletions(-) diff --git a/ggml-alloc.c b/ggml-alloc.c index f07a4a217..459f121ca 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -284,7 +284,14 @@ struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment) // address and size of the buffer when measuring // it needs to be large enough to fit all the tensors, but it cannot overlap with other existing buffers static void * const MEASURE_BASE_ADDR = (void *) 0x1000; +#if defined(__ARM_NEON) && !defined(__aarch64__) +// 32-bit +// TODO: Use for 32-bit x86 as well +static const size_t MEASURE_MAX_SIZE = (1ULL<<32) - 1; // 4 GB +#else +// 64-bit static const size_t MEASURE_MAX_SIZE = 1ULL<<40; // 1 TB +#endif struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) { struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr) /* + n_free_blocks * sizeof(struct free_block) */); diff --git a/ggml.c b/ggml.c index cf3955f7f..38b1155c1 100644 --- a/ggml.c +++ b/ggml.c @@ -817,46 +817,6 @@ static inline float hsum_float_4x4(const __m128 a, const __m128 b, const __m128 #if !defined(__aarch64__) -inline static uint16_t vaddvq_u8(uint8x16_t v) { - return - (uint16_t)vgetq_lane_u8(v, 0) + (uint16_t)vgetq_lane_u8(v, 1) + - (uint16_t)vgetq_lane_u8(v, 2) + (uint16_t)vgetq_lane_u8(v, 3) + - (uint16_t)vgetq_lane_u8(v, 4) + (uint16_t)vgetq_lane_u8(v, 5) + - (uint16_t)vgetq_lane_u8(v, 6) + (uint16_t)vgetq_lane_u8(v, 7) + - (uint16_t)vgetq_lane_u8(v, 8) + (uint16_t)vgetq_lane_u8(v, 9) + - (uint16_t)vgetq_lane_u8(v, 10) + (uint16_t)vgetq_lane_u8(v, 11) + - (uint16_t)vgetq_lane_u8(v, 12) + (uint16_t)vgetq_lane_u8(v, 13) + - (uint16_t)vgetq_lane_u8(v, 14) + (uint16_t)vgetq_lane_u8(v, 15); -} - -inline static int16_t vaddvq_s8(int8x16_t v) { - return - (int16_t)vgetq_lane_s8(v, 0) + (int16_t)vgetq_lane_s8(v, 1) + - (int16_t)vgetq_lane_s8(v, 2) + (int16_t)vgetq_lane_s8(v, 3) + - (int16_t)vgetq_lane_s8(v, 4) + (int16_t)vgetq_lane_s8(v, 5) + - (int16_t)vgetq_lane_s8(v, 6) + (int16_t)vgetq_lane_s8(v, 7) + - (int16_t)vgetq_lane_s8(v, 8) + (int16_t)vgetq_lane_s8(v, 9) + - (int16_t)vgetq_lane_s8(v, 10) + (int16_t)vgetq_lane_s8(v, 11) + - (int16_t)vgetq_lane_s8(v, 12) + (int16_t)vgetq_lane_s8(v, 13) + - (int16_t)vgetq_lane_s8(v, 14) + (int16_t)vgetq_lane_s8(v, 15); -} - -inline static int32_t vaddvq_s16(int16x8_t v) { - return - (int32_t)vgetq_lane_s16(v, 0) + (int32_t)vgetq_lane_s16(v, 1) + - (int32_t)vgetq_lane_s16(v, 2) + (int32_t)vgetq_lane_s16(v, 3) + - (int32_t)vgetq_lane_s16(v, 4) + (int32_t)vgetq_lane_s16(v, 5) + - (int32_t)vgetq_lane_s16(v, 6) + (int32_t)vgetq_lane_s16(v, 7); -} - -inline static uint32_t vaddvq_u16(uint16x8_t v) { - return - (uint32_t)vgetq_lane_u16(v, 0) + (uint32_t)vgetq_lane_u16(v, 1) + - (uint32_t)vgetq_lane_u16(v, 2) + (uint32_t)vgetq_lane_u16(v, 3) + - (uint32_t)vgetq_lane_u16(v, 4) + (uint32_t)vgetq_lane_u16(v, 5) + - (uint32_t)vgetq_lane_u16(v, 6) + (uint32_t)vgetq_lane_u16(v, 7); -} - inline static int32_t vaddvq_s32(int32x4_t v) { return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3); } @@ -865,12 +825,6 @@ inline static float vaddvq_f32(float32x4_t v) { return vgetq_lane_f32(v, 0) + vgetq_lane_f32(v, 1) + vgetq_lane_f32(v, 2) + vgetq_lane_f32(v, 3); } -inline static float vminvq_f32(float32x4_t v) { - return - MIN(MIN(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)), - MIN(vgetq_lane_f32(v, 2), vgetq_lane_f32(v, 3))); -} - inline static float vmaxvq_f32(float32x4_t v) { return MAX(MAX(vgetq_lane_f32(v, 0), vgetq_lane_f32(v, 1)), diff --git a/k_quants.c b/k_quants.c index 3deeaedf7..4accd2480 100644 --- a/k_quants.c +++ b/k_quants.c @@ -13,6 +13,26 @@ // #include +#if !defined(__aarch64__) +inline static int32_t vaddvq_s16(int16x8_t v) { + return + (int32_t)vgetq_lane_s16(v, 0) + (int32_t)vgetq_lane_s16(v, 1) + + (int32_t)vgetq_lane_s16(v, 2) + (int32_t)vgetq_lane_s16(v, 3) + + (int32_t)vgetq_lane_s16(v, 4) + (int32_t)vgetq_lane_s16(v, 5) + + (int32_t)vgetq_lane_s16(v, 6) + (int32_t)vgetq_lane_s16(v, 7); +} + +inline static int16x8_t vpaddq_s16(int16x8_t a, int16x8_t b) { + int16x4_t a0 = vpadd_s16(vget_low_s16(a), vget_high_s16(a)); + int16x4_t b0 = vpadd_s16(vget_low_s16(b), vget_high_s16(b)); + return vcombine_s16(a0, b0); +} + +inline static int32_t vaddvq_s32(int32x4_t v) { + return vgetq_lane_s32(v, 0) + vgetq_lane_s32(v, 1) + vgetq_lane_s32(v, 2) + vgetq_lane_s32(v, 3); +} +#endif + #else #ifdef __wasm_simd128__ @@ -1302,7 +1322,9 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri const uint8x16_t m3 = vdupq_n_u8(0x3); const uint8x16_t m4 = vdupq_n_u8(0xF); +#if defined(__ARM_FEATURE_DOTPROD) const int32x4_t vzero = vdupq_n_s32(0); +#endif int8x16x2_t q2bytes; uint8_t aux[16]; @@ -1608,7 +1630,9 @@ void ggml_vec_dot_q2_K_q8_K(const int n, float * restrict s, const void * restri #ifdef __ARM_NEON const uint8x16_t m3 = vdupq_n_u8(0x3); +#if defined(__ARM_FEATURE_DOTPROD) const int32x4_t vzero = vdupq_n_s32(0); +#endif int8x16x4_t q2bytes; @@ -2592,8 +2616,6 @@ void ggml_vec_dot_q4_K_q8_K(const int n, float * restrict s, const void * restri const uint8_t * restrict q4 = x[i].qs; const int8_t * restrict q8 = y[i].qs; - //int32x4_t isum = mzero; - int32_t sumi1 = 0; int32_t sumi2 = 0; @@ -3092,9 +3114,11 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri #ifdef __ARM_NEON const uint8x16_t m4b = vdupq_n_u8(0xf); - const int32x4_t mzero = vdupq_n_s32(0); const uint8x16_t mone = vdupq_n_u8(1); const uint8x16_t mtwo = vdupq_n_u8(2); +#if defined(__ARM_FEATURE_DOTPROD) + const int32x4_t mzero = vdupq_n_s32(0); +#endif int8x16x4_t q5bytes; @@ -3437,8 +3461,10 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri #ifdef __ARM_NEON const uint8x16_t m4b = vdupq_n_u8(0xf); - const int32x4_t mzero = vdupq_n_s32(0); const uint8x16_t mh = vdupq_n_u8(16); +#if defined(__ARM_FEATURE_DOTPROD) + const int32x4_t mzero = vdupq_n_s32(0); +#endif int8x16x4_t q5bytes; uint8x16x4_t q5h; @@ -3656,7 +3682,9 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri float sum = 0; const uint8x16_t m4b = vdupq_n_u8(0xF); +#if defined(__ARM_FEATURE_DOTPROD) const int32x4_t vzero = vdupq_n_s32(0); +#endif //const int8x16_t m32s = vdupq_n_s8(32); const uint8x16_t mone = vdupq_n_u8(3); @@ -4045,8 +4073,10 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri float sum = 0; const uint8x16_t m4b = vdupq_n_u8(0xF); - const int32x4_t vzero = vdupq_n_s32(0); const int8x16_t m32s = vdupq_n_s8(32); +#if defined(__ARM_FEATURE_DOTPROD) + const int32x4_t vzero = vdupq_n_s32(0); +#endif const uint8x16_t mone = vdupq_n_u8(3); From 8b56b4f2c396eae1f4417e5a859557fed989e0ee Mon Sep 17 00:00:00 2001 From: Karsten Weiss Date: Sat, 2 Sep 2023 14:29:09 +0200 Subject: [PATCH 450/852] metal : show all Metal device instances in the system (#2952) * ggml_metal_init: Show all Metal device instances in the system Also show the default Metal device that was picked. * Update ggml-metal.m --------- Co-authored-by: Georgi Gerganov --- ggml-metal.m | 18 ++++++++++++++++-- 1 file changed, 16 insertions(+), 2 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 4267db9be..88e7e1356 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -116,10 +116,24 @@ static NSString * const msl_library_source = @"see metal.metal"; struct ggml_metal_context * ggml_metal_init(int n_cb) { metal_printf("%s: allocating\n", __func__); - struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context)); + // Show all the Metal device instances in the system + NSArray * devices = MTLCopyAllDevices(); + id device; + NSString * s; + for (device in devices) { + s = [device name]; + metal_printf("%s: found device: %s\n", __func__, [s UTF8String]); + } + // Pick and show default Metal device + device = MTLCreateSystemDefaultDevice(); + s = [device name]; + metal_printf("%s: picking default device: %s\n", __func__, [s UTF8String]); + + // Configure context + struct ggml_metal_context * ctx = malloc(sizeof(struct ggml_metal_context)); + ctx->device = device; ctx->n_cb = MIN(n_cb, GGML_METAL_MAX_BUFFERS); - ctx->device = MTLCreateSystemDefaultDevice(); ctx->queue = [ctx->device newCommandQueue]; ctx->n_buffers = 0; ctx->concur_list_len = 0; From 52315a421674ff64305dbf082f69e4ec77f0a3f3 Mon Sep 17 00:00:00 2001 From: bandoti <141645996+bandoti@users.noreply.github.com> Date: Sat, 2 Sep 2023 09:53:18 -0300 Subject: [PATCH 451/852] readme : update clblast instructions (#2903) * Update Windows CLBlast instructions * Update Windows CLBlast instructions * Remove trailing whitespace --- README.md | 40 +++++++++++++++++++++++++++++++++++----- 1 file changed, 35 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 4e6a0957d..5eda5f006 100644 --- a/README.md +++ b/README.md @@ -464,6 +464,8 @@ Building the program with BLAS support may lead to some performance improvements You will need the [OpenCL SDK](https://github.com/KhronosGroup/OpenCL-SDK). - For Ubuntu or Debian, the packages `opencl-headers`, `ocl-icd` may be needed. + - For Windows, a pre-built SDK is available on the [OpenCL Releases](https://github.com/KhronosGroup/OpenCL-SDK/releases) page. + -
    Installing the OpenCL SDK from source @@ -481,10 +483,27 @@ Building the program with BLAS support may lead to some performance improvements ```
    - Installing CLBlast: it may be found in your operating system's packages. + ##### Installing CLBlast + + Pre-built CLBlast binaries may be found on the [CLBlast Releases](https://github.com/CNugteren/CLBlast/releases) page. For Unix variants, it may also be found in your operating system's packages. + + Alternatively, they may be built from source. -
    - If not, then installing from source: + Windows: + + ```cmd + set OPENCL_SDK_ROOT="C:/OpenCL-SDK-v2023.04.17-Win-x64" + git clone https://github.com/CNugteren/CLBlast.git + mkdir CLBlast\build + cd CLBlast\build + cmake .. -DBUILD_SHARED_LIBS=OFF -DOVERRIDE_MSVC_FLAGS_TO_MT=OFF -DTUNERS=OFF -DOPENCL_ROOT=%OPENCL_SDK_ROOT% -G "Visual Studio 17 2022" -A x64 + cmake --build . --config Release + cmake --install . --prefix C:/CLBlast + ``` + + -
    + Unix: ```sh git clone https://github.com/CNugteren/CLBlast.git @@ -498,21 +517,32 @@ Building the program with BLAS support may lead to some performance improvements Where `/some/path` is where the built library will be installed (default is `/usr/local`).
    - Building: + ##### Building Llama with CLBlast - Build with make: ```sh make LLAMA_CLBLAST=1 ``` - - CMake: + - CMake (Unix): ```sh mkdir build cd build cmake .. -DLLAMA_CLBLAST=ON -DCLBlast_dir=/some/path cmake --build . --config Release ``` + - CMake (Windows): + ```cmd + set CL_BLAST_CMAKE_PKG="C:/CLBlast/lib/cmake/CLBlast" + git clone https://github.com/ggerganov/llama.cpp + cd llama.cpp + mkdir build + cd build + cmake .. -DBUILD_SHARED_LIBS=OFF -DLLAMA_CLBLAST=ON -DCMAKE_PREFIX_PATH=%CL_BLAST_CMAKE_PKG% -G "Visual Studio 17 2022" -A x64 + cmake --build . --config Release + cmake --install . --prefix C:/LlamaCPP + ``` - Running: + ##### Running Llama with CLBlast The CLBlast build supports `--gpu-layers|-ngl` like the CUDA version does. From 3358c381f6251bf6e65855e1c93bfaa9ec82ddb3 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Sat, 2 Sep 2023 11:53:55 -0600 Subject: [PATCH 452/852] logging: Fix creating empty file even when disabled (#2966) * logging: Fix creating empty file even when disabled * Minor formatting fix Co-authored-by: staviq --------- Co-authored-by: staviq --- common/log.h | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/common/log.h b/common/log.h index bf9fafd68..0b9b01052 100644 --- a/common/log.h +++ b/common/log.h @@ -341,14 +341,14 @@ inline FILE *log_handler1_impl(bool change = false, LogTriState disable = LogTri } } + if (_disabled) + { + // Log is disabled + return nullptr; + } + if (_initialized) { - if (_disabled) - { - // Log is disabled - return nullptr; - } - // with fallback in case something went wrong return logfile ? logfile : stderr; } From bc054af97ac68a4b726e972cb283eb9565253ed5 Mon Sep 17 00:00:00 2001 From: Cebtenzzre Date: Sun, 3 Sep 2023 01:26:59 -0400 Subject: [PATCH 453/852] make : support overriding CFLAGS/CXXFLAGS/CPPFLAGS/LDFLAGS (#2886) * make : remove unused -DGGML_BIG_ENDIAN * make : put preprocessor stuff in CPPFLAGS * make : pass Raspberry Pi arch flags to g++ as well * make : support overriding CFLAGS/CXXFLAGS/CPPFLAGS/LDFLAGS * make : fix inverted conditional --- Makefile | 150 ++++++++++++++++++++++++------------------------------- 1 file changed, 66 insertions(+), 84 deletions(-) diff --git a/Makefile b/Makefile index 23f050c0d..e214970f8 100644 --- a/Makefile +++ b/Makefile @@ -67,21 +67,21 @@ OPT = -Ofast else OPT = -O3 endif -CFLAGS = -I. $(OPT) -std=c11 -fPIC -CXXFLAGS = -I. -I./common $(OPT) -std=c++11 -fPIC -LDFLAGS = +MK_CPPFLAGS = -I. -Icommon +MK_CFLAGS = $(CPPFLAGS) $(OPT) -std=c11 -fPIC +MK_CXXFLAGS = $(CPPFLAGS) $(OPT) -std=c++11 -fPIC +MK_LDFLAGS = ifdef LLAMA_DEBUG - CFLAGS += -O0 -g - CXXFLAGS += -O0 -g - LDFLAGS += -g + MK_CFLAGS += -O0 -g + MK_CXXFLAGS += -O0 -g + MK_LDFLAGS += -g else - CFLAGS += -DNDEBUG - CXXFLAGS += -DNDEBUG + MK_CPPFLAGS += -DNDEBUG endif ifdef LLAMA_SERVER_VERBOSE - CXXFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE) + MK_CPPFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE) endif ifdef LLAMA_DISABLE_LOGS @@ -90,9 +90,9 @@ ifdef LLAMA_DISABLE_LOGS endif # LLAMA_DISABLE_LOGS # warnings -CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith \ - -Wmissing-prototypes -Werror=implicit-int -Wno-unused-function -CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar +MK_CFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wdouble-promotion -Wshadow -Wstrict-prototypes -Wpointer-arith \ + -Wmissing-prototypes -Werror=implicit-int -Wno-unused-function +MK_CXXFLAGS += -Wall -Wextra -Wpedantic -Wcast-qual -Wno-unused-function -Wno-multichar ifeq '' '$(findstring clang++,$(CXX))' # g++ only @@ -101,29 +101,9 @@ endif # OS specific # TODO: support Windows -ifeq ($(UNAME_S),Linux) - CFLAGS += -pthread - CXXFLAGS += -pthread -endif -ifeq ($(UNAME_S),Darwin) - CFLAGS += -pthread - CXXFLAGS += -pthread -endif -ifeq ($(UNAME_S),FreeBSD) - CFLAGS += -pthread - CXXFLAGS += -pthread -endif -ifeq ($(UNAME_S),NetBSD) - CFLAGS += -pthread - CXXFLAGS += -pthread -endif -ifeq ($(UNAME_S),OpenBSD) - CFLAGS += -pthread - CXXFLAGS += -pthread -endif -ifeq ($(UNAME_S),Haiku) - CFLAGS += -pthread - CXXFLAGS += -pthread +ifneq '' '$(filter $(UNAME_S),Linux Darwin FreeBSD NetBSD OpenBSD Haiku)' + MK_CFLAGS += -pthread + MK_CXXFLAGS += -pthread endif # detect Windows @@ -149,12 +129,11 @@ ifeq ($(_WIN32),1) endif ifdef LLAMA_GPROF - CFLAGS += -pg - CXXFLAGS += -pg + MK_CFLAGS += -pg + MK_CXXFLAGS += -pg endif ifdef LLAMA_PERF - CFLAGS += -DGGML_PERF - CXXFLAGS += -DGGML_PERF + MK_CPPFLAGS += -DGGML_PERF endif # Architecture specific @@ -165,16 +144,16 @@ ifndef RISCV ifeq ($(UNAME_M),$(filter $(UNAME_M),x86_64 i686 amd64)) # Use all CPU extensions that are available: - CFLAGS += -march=native -mtune=native - CXXFLAGS += -march=native -mtune=native + MK_CFLAGS += -march=native -mtune=native + MK_CXXFLAGS += -march=native -mtune=native # Usage AVX-only - #CFLAGS += -mfma -mf16c -mavx - #CXXFLAGS += -mfma -mf16c -mavx + #MK_CFLAGS += -mfma -mf16c -mavx + #MK_CXXFLAGS += -mfma -mf16c -mavx # Usage SSSE3-only (Not is SSE3!) - #CFLAGS += -mssse3 - #CXXFLAGS += -mssse3 + #MK_CFLAGS += -mssse3 + #MK_CXXFLAGS += -mssse3 endif # The stack is only 16-byte aligned on Windows, so don't let gcc emit aligned moves. @@ -188,34 +167,33 @@ endif ifneq ($(filter aarch64%,$(UNAME_M)),) # Apple M1, M2, etc. # Raspberry Pi 3, 4, Zero 2 (64-bit) - CFLAGS += -mcpu=native - CXXFLAGS += -mcpu=native + MK_CFLAGS += -mcpu=native + MK_CXXFLAGS += -mcpu=native endif ifneq ($(filter armv6%,$(UNAME_M)),) # Raspberry Pi 1, Zero - CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access + MK_CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access + MK_CXXFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access endif ifneq ($(filter armv7%,$(UNAME_M)),) # Raspberry Pi 2 - CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations + MK_CFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations + MK_CXXFLAGS += -mfpu=neon-fp-armv8 -mfp16-format=ieee -mno-unaligned-access -funsafe-math-optimizations endif ifneq ($(filter armv8%,$(UNAME_M)),) # Raspberry Pi 3, 4, Zero 2 (32-bit) - CFLAGS += -mfp16-format=ieee -mno-unaligned-access + MK_CFLAGS += -mfp16-format=ieee -mno-unaligned-access + MK_CXXFLAGS += -mfp16-format=ieee -mno-unaligned-access endif ifneq ($(filter ppc64%,$(UNAME_M)),) POWER9_M := $(shell grep "POWER9" /proc/cpuinfo) ifneq (,$(findstring POWER9,$(POWER9_M))) - CFLAGS += -mcpu=power9 - CXXFLAGS += -mcpu=power9 - endif - # Require c++23's std::byteswap for big-endian support. - ifeq ($(UNAME_M),ppc64) - CXXFLAGS += -std=c++23 -DGGML_BIG_ENDIAN + MK_CFLAGS += -mcpu=power9 + MK_CXXFLAGS += -mcpu=power9 endif endif @@ -225,12 +203,10 @@ else endif ifndef LLAMA_NO_K_QUANTS - CFLAGS += -DGGML_USE_K_QUANTS - CXXFLAGS += -DGGML_USE_K_QUANTS + MK_CPPFLAGS += -DGGML_USE_K_QUANTS OBJS += k_quants.o ifdef LLAMA_QKK_64 - CFLAGS += -DGGML_QKK_64 - CXXFLAGS += -DGGML_QKK_64 + MK_CPPFLAGS += -DGGML_QKK_64 endif endif @@ -238,31 +214,32 @@ ifndef LLAMA_NO_ACCELERATE # Mac M1 - include Accelerate framework. # `-framework Accelerate` works on Mac Intel as well, with negliable performance boost (as of the predict time). ifeq ($(UNAME_S),Darwin) - CFLAGS += -DGGML_USE_ACCELERATE - LDFLAGS += -framework Accelerate + MK_CPPFLAGS += -DGGML_USE_ACCELERATE + MK_LDFLAGS += -framework Accelerate endif endif # LLAMA_NO_ACCELERATE ifdef LLAMA_MPI - CFLAGS += -DGGML_USE_MPI -Wno-cast-qual - CXXFLAGS += -DGGML_USE_MPI -Wno-cast-qual + MK_CPPFLAGS += -DGGML_USE_MPI + MK_CFLAGS += -Wno-cast-qual + MK_CXXFLAGS += -Wno-cast-qual OBJS += ggml-mpi.o endif # LLAMA_MPI ifdef LLAMA_OPENBLAS - CFLAGS += -DGGML_USE_OPENBLAS $(shell pkg-config --cflags openblas) - LDFLAGS += $(shell pkg-config --libs openblas) + MK_CPPFLAGS += -DGGML_USE_OPENBLAS $(shell pkg-config --cflags-only-I openblas) + MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas) + MK_LDFLAGS += $(shell pkg-config --libs openblas) endif # LLAMA_OPENBLAS ifdef LLAMA_BLIS - CFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/blis -I/usr/include/blis - LDFLAGS += -lblis -L/usr/local/lib + MK_CPPFLAGS += -DGGML_USE_OPENBLAS -I/usr/local/include/blis -I/usr/include/blis + MK_LDFLAGS += -lblis -L/usr/local/lib endif # LLAMA_BLIS ifdef LLAMA_CUBLAS - CFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include - CXXFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include - LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib + MK_CPPFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include + MK_LDFLAGS += -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib OBJS += ggml-cuda.o NVCCFLAGS = --forward-unknown-to-host-compiler -use_fast_math ifdef LLAMA_CUDA_NVCC @@ -313,14 +290,15 @@ endif # LLAMA_CUBLAS ifdef LLAMA_CLBLAST - CFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags clblast OpenCL) - CXXFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags clblast OpenCL) + MK_CPPFLAGS += -DGGML_USE_CLBLAST $(shell pkg-config --cflags-only-I clblast OpenCL) + MK_CFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL) + MK_CXXFLAGS += $(shell pkg-config --cflags-only-other clblast OpenCL) # Mac provides OpenCL as a framework ifeq ($(UNAME_S),Darwin) - LDFLAGS += -lclblast -framework OpenCL + MK_LDFLAGS += -lclblast -framework OpenCL else - LDFLAGS += $(shell pkg-config --libs clblast OpenCL) + MK_LDFLAGS += $(shell pkg-config --libs clblast OpenCL) endif OBJS += ggml-opencl.o @@ -335,10 +313,9 @@ ifdef LLAMA_HIPBLAS LLAMA_CUDA_DMMV_X ?= 32 LLAMA_CUDA_MMV_Y ?= 1 LLAMA_CUDA_KQUANTS_ITER ?= 2 - CFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS - CXXFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS - LDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib - LDFLAGS += -lhipblas -lamdhip64 -lrocblas + MK_CPPFLAGS += -DGGML_USE_HIPBLAS -DGGML_USE_CUBLAS + MK_LDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib + MK_LDFLAGS += -lhipblas -lamdhip64 -lrocblas HIPFLAGS += $(addprefix --offload-arch=,$(GPU_TARGETS)) HIPFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X) HIPFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y) @@ -353,10 +330,9 @@ ggml-cuda.o: ggml-cuda.cu ggml-cuda.h endif # LLAMA_HIPBLAS ifdef LLAMA_METAL - CFLAGS += -DGGML_USE_METAL #-DGGML_METAL_NDEBUG - CXXFLAGS += -DGGML_USE_METAL - LDFLAGS += -framework Foundation -framework Metal -framework MetalKit - OBJS += ggml-metal.o + MK_CPPFLAGS += -DGGML_USE_METAL #-DGGML_METAL_NDEBUG + MK_LDFLAGS += -framework Foundation -framework Metal -framework MetalKit + OBJS += ggml-metal.o endif # LLAMA_METAL ifdef LLAMA_METAL @@ -369,11 +345,17 @@ ggml-mpi.o: ggml-mpi.c ggml-mpi.h $(CC) $(CFLAGS) -c $< -o $@ endif # LLAMA_MPI -ifdef LLAMA_NO_K_QUANTS +ifndef LLAMA_NO_K_QUANTS k_quants.o: k_quants.c k_quants.h $(CC) $(CFLAGS) -c $< -o $@ endif # LLAMA_NO_K_QUANTS +# combine build flags with cmdline overrides +override CPPFLAGS := $(MK_CPPFLAGS) $(CPPFLAGS) +override CFLAGS := $(MK_CFLAGS) $(CFLAGS) +override CXXFLAGS := $(MK_CXXFLAGS) $(CXXFLAGS) +override LDFLAGS := $(MK_LDFLAGS) $(LDFLAGS) + # # Print build information # From 2753415afdaf22a18c49608bd9d93cfffc05d435 Mon Sep 17 00:00:00 2001 From: kchro3 <62481661+kchro3@users.noreply.github.com> Date: Sat, 2 Sep 2023 22:27:25 -0700 Subject: [PATCH 454/852] swift : add missing c file to Package.swift (#2978) --- Package.swift | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Package.swift b/Package.swift index 73d027c70..2c07710cd 100644 --- a/Package.swift +++ b/Package.swift @@ -12,7 +12,7 @@ let package = Package( name: "llama", path: ".", exclude: ["ggml-metal.metal"], - sources: ["ggml.c", "llama.cpp"], + sources: ["ggml.c", "llama.cpp", "ggml-alloc.c"], publicHeadersPath: "spm-headers", cSettings: [.unsafeFlags(["-Wno-shorten-64-to-32"]), .define("GGML_USE_ACCELERATE")], linkerSettings: [ From c42f0ec6b344e14bd81c8612ab1445b3ff77358b Mon Sep 17 00:00:00 2001 From: momonga <115213907+mmnga@users.noreply.github.com> Date: Sun, 3 Sep 2023 14:36:28 +0900 Subject: [PATCH 455/852] examples : fix gpt-neox (#2943) Co-authored-by: mmnga --- examples/gptneox-wip/gptneox-main.cpp | 13 ++++---- llama.cpp | 46 +++++++++++++++++++++++++-- 2 files changed, 51 insertions(+), 8 deletions(-) diff --git a/examples/gptneox-wip/gptneox-main.cpp b/examples/gptneox-wip/gptneox-main.cpp index 04af50245..6291523f2 100644 --- a/examples/gptneox-wip/gptneox-main.cpp +++ b/examples/gptneox-wip/gptneox-main.cpp @@ -660,9 +660,10 @@ bool gpt_neox_model_load(const std::string & fname, gpt_neox_model & model, gpt2 ggml_tensor * gpt_neox_ff( const gpt_neox_block &block, ggml_context * ctx0, - ggml_tensor * inp) { + ggml_tensor * inp, + const gpt_neox_hparams &hparams) { - ggml_tensor * cur = ggml_norm(ctx0, inp); + ggml_tensor * cur = ggml_norm(ctx0, inp, hparams.norm_eps); cur = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, block.ln_2_g, cur), cur), ggml_repeat(ctx0, block.ln_2_b, cur)); cur = ggml_mul_mat(ctx0, block.c_mlp_fc_w, cur); @@ -753,7 +754,7 @@ bool gpt_neox_eval( // self-attention { { - cur = ggml_norm(ctx0, inpL); + cur = ggml_norm(ctx0, inpL, hparams.norm_eps); cur = ggml_add(ctx0, ggml_mul(ctx0, ggml_repeat(ctx0, model.blocks[il].ln_1_g, cur), cur), @@ -844,7 +845,7 @@ bool gpt_neox_eval( if (hparams.par_res == 0) { struct ggml_tensor * inpFF = ggml_add(ctx0, cur, inpL); - cur = gpt_neox_ff(model.blocks[il], ctx0, inpFF); + cur = gpt_neox_ff(model.blocks[il], ctx0, inpFF, hparams); // input for next layer inpL = ggml_add(ctx0, cur, inpFF); @@ -853,7 +854,7 @@ bool gpt_neox_eval( // this is independent of the self-attention result, so it could be done in parallel to the self-attention // note here we pass inpL instead of cur - cur = gpt_neox_ff(model.blocks[il], ctx0, inpL); + cur = gpt_neox_ff(model.blocks[il], ctx0, inpL, hparams); // layer input + FF cur = ggml_add(ctx0, cur, inpFF); @@ -867,7 +868,7 @@ bool gpt_neox_eval( // norm { - inpL = ggml_norm(ctx0, inpL); + inpL = ggml_norm(ctx0, inpL, hparams.norm_eps); // inpL = ln_f_g*inpL + ln_f_b inpL = ggml_add(ctx0, diff --git a/llama.cpp b/llama.cpp index 3114d3311..2b0cf30f6 100644 --- a/llama.cpp +++ b/llama.cpp @@ -325,6 +325,44 @@ static std::map> LLM_TENSOR_NAMES = { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_GPT2, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + }, + }, + { + LLM_ARCH_GPTJ, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + }, + }, + { + LLM_ARCH_GPTNEOX, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, + { + LLM_ARCH_MPT, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + }, + }, + { + LLM_ARCH_UNKNOWN, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + }, + }, }; static llm_arch llm_arch_from_string(const std::string & name) { @@ -1605,9 +1643,13 @@ static void llm_load_hparams( GGUF_GET_KEY(ctx, hparams.n_rot, gguf_get_val_u32, GGUF_TYPE_UINT32, false, kv(LLM_KV_ROPE_DIMENSION_COUNT)); - if (hparams.n_rot != hparams.n_embd / hparams.n_head) { - throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd / hparams.n_head)); + if (model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON) { + if (hparams.n_rot != hparams.n_embd / hparams.n_head) { + throw std::runtime_error(format("invalid n_rot: %u, expected %u", hparams.n_rot, hparams.n_embd / hparams.n_head)); + } } + // gpt-neox n_rot = rotary_pct * (n_embd / n_head) + // gpt-j n_rot = rotary_dim } // arch-specific KVs From 340af42f09a80e32f4998857b4f0543e41124525 Mon Sep 17 00:00:00 2001 From: Ido S Date: Sun, 3 Sep 2023 08:50:51 +0300 Subject: [PATCH 456/852] docs : add `catai` to `README.md` (#2967) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 5eda5f006..0cfd94db4 100644 --- a/README.md +++ b/README.md @@ -120,6 +120,7 @@ as the main playground for developing new features for the [ggml](https://github - [nat/openplayground](https://github.com/nat/openplayground) - [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) +- [withcatai/catai](https://github.com/withcatai/catai) --- From cff7b0bf07cb46e1ad4fd199f6bdeb538925c8c4 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Sat, 2 Sep 2023 23:52:13 -0600 Subject: [PATCH 457/852] convert.py : BPE fixes (#2938) * convert.py: BPE fixes? * Remove unnecessary conditional in addl token error handling --- convert.py | 32 ++++++++++++++++++++++++++++---- 1 file changed, 28 insertions(+), 4 deletions(-) diff --git a/convert.py b/convert.py index 6c89b5ecc..5a7483b43 100755 --- a/convert.py +++ b/convert.py @@ -323,15 +323,27 @@ class BpeVocab: self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read()) added_tokens: dict[str, int] if fname_added_tokens is not None: + # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab. added_tokens = json.load(open(fname_added_tokens, encoding="utf-8")) else: - added_tokens = {} + # Fall back to trying to find the added tokens in tokenizer.json + tokenizer_json_file = fname_tokenizer.parent / 'tokenizer.json' + if not tokenizer_json_file.is_file(): + added_tokens = {} + else: + tokenizer_json = json.load(open(tokenizer_json_file, encoding="utf-8")) + added_tokens = dict( + (item['content'], item['id']) + for item in tokenizer_json.get('added_tokens', []) + # Added tokens here can be duplicates of the main vocabulary. + if item['content'] not in self.bpe_tokenizer ) vocab_size: int = len(self.bpe_tokenizer) expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) actual_ids = sorted(added_tokens.values()) if expected_ids != actual_ids: - raise Exception(f"Expected added token IDs to be sequential and start at {len(added_tokens)}; got {actual_ids}") + expected_end_id = vocab_size + len(actual_ids) - 1 + raise Exception(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range {vocab_size} - {expected_end_id}; got {actual_ids}") items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1]) self.added_tokens_list = [text for (text, idx) in items] @@ -345,10 +357,22 @@ class BpeVocab: from transformers.models.gpt2 import tokenization_gpt2 # type: ignore[import] byte_encoder = tokenization_gpt2.bytes_to_unicode() byte_decoder = {v: k for k, v in byte_encoder.items()} + score = 0.0 for i, item in enumerate(tokenizer): text: bytes = item.encode("utf-8") - score: float = -i - yield text, score, gguf.TokenType.USER_DEFINED + # FIXME: These shouldn't be hardcoded, but it's probably better than the current behavior? + if i <= 258 and text.startswith(b'<') and text.endswith(b'>'): + if i == 0 and text == b'': + toktype = gguf.TokenType.UNKNOWN + elif i == 1 or i == 2: + toktype = gguf.TokenType.CONTROL + elif i >= 3 and text.startswith(b'<0x'): + toktype = gguf.TokenType.BYTE + else: + toktype = gguf.TokenType.NORMAL + else: + toktype = gguf.TokenType.NORMAL + yield text, score, toktype def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: for text in self.added_tokens_list: From 6a31a3bd9806c85ed08266f6ab65181da0f30d03 Mon Sep 17 00:00:00 2001 From: kchro3 <62481661+kchro3@users.noreply.github.com> Date: Sat, 2 Sep 2023 23:21:05 -0700 Subject: [PATCH 458/852] swift : add support for k-quants (#2983) --- Package.swift | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/Package.swift b/Package.swift index 2c07710cd..96f52c4f0 100644 --- a/Package.swift +++ b/Package.swift @@ -12,9 +12,18 @@ let package = Package( name: "llama", path: ".", exclude: ["ggml-metal.metal"], - sources: ["ggml.c", "llama.cpp", "ggml-alloc.c"], + sources: [ + "ggml.c", + "llama.cpp", + "ggml-alloc.c", + "k_quants.c" + ], publicHeadersPath: "spm-headers", - cSettings: [.unsafeFlags(["-Wno-shorten-64-to-32"]), .define("GGML_USE_ACCELERATE")], + cSettings: [ + .unsafeFlags(["-Wno-shorten-64-to-32"]), + .define("GGML_USE_K_QUANTS"), + .define("GGML_USE_ACCELERATE") + ], linkerSettings: [ .linkedFramework("Accelerate") ] From ca82cf7bac0c91d03e3d320b3a865dd006f854ac Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sun, 3 Sep 2023 11:06:22 +0300 Subject: [PATCH 459/852] metal : more optimizations (#2959) * Very minor speedup via simd-group synchronization in f16 x f32 * Another very minor speedup on metal * Quite significant PP speedup on metal * Another attempt * Minor * Massive improvement for TG for fp16 * ~4-5% improvement for Q8_0 TG on metal --------- Co-authored-by: Iwan Kawrakow Co-authored-by: Georgi Gerganov --- ggml-metal.m | 22 +++-- ggml-metal.metal | 226 ++++++++++++++++++++++++++++++----------------- 2 files changed, 163 insertions(+), 85 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 88e7e1356..d0d23442e 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -76,6 +76,7 @@ struct ggml_metal_context { GGML_METAL_DECL_KERNEL(rms_norm); GGML_METAL_DECL_KERNEL(norm); GGML_METAL_DECL_KERNEL(mul_mat_f16_f32); + GGML_METAL_DECL_KERNEL(mul_mat_f16_f32_1row); GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32); GGML_METAL_DECL_KERNEL(mul_mat_q4_1_f32); GGML_METAL_DECL_KERNEL(mul_mat_q8_0_f32); @@ -219,6 +220,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(rms_norm); GGML_METAL_ADD_KERNEL(norm); GGML_METAL_ADD_KERNEL(mul_mat_f16_f32); + GGML_METAL_ADD_KERNEL(mul_mat_f16_f32_1row); GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32); GGML_METAL_ADD_KERNEL(mul_mat_q4_1_f32); GGML_METAL_ADD_KERNEL(mul_mat_q8_0_f32); @@ -284,6 +286,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_DEL_KERNEL(rms_norm); GGML_METAL_DEL_KERNEL(norm); GGML_METAL_DEL_KERNEL(mul_mat_f16_f32); + GGML_METAL_DEL_KERNEL(mul_mat_f16_f32_1row); GGML_METAL_DEL_KERNEL(mul_mat_q4_0_f32); GGML_METAL_DEL_KERNEL(mul_mat_q4_1_f32); GGML_METAL_DEL_KERNEL(mul_mat_q8_0_f32); @@ -868,7 +871,11 @@ void ggml_metal_graph_compute( { nth0 = 32; nth1 = 1; - [encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32]; + if (ne11 * ne12 < 4) { + [encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32_1row]; + } else { + [encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32]; + } } break; case GGML_TYPE_Q4_0: { @@ -920,8 +927,8 @@ void ggml_metal_graph_compute( GGML_ASSERT(ne02 == 1); GGML_ASSERT(ne12 == 1); - nth0 = 2; - nth1 = 32; + nth0 = 4; //1; + nth1 = 8; //32; [encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_K_f32]; } break; case GGML_TYPE_Q5_K: @@ -969,9 +976,12 @@ void ggml_metal_graph_compute( [encoder setBytes:&gqa length:sizeof(gqa) atIndex:17]; if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q8_0 || - src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_Q4_K) { + src0t == GGML_TYPE_Q2_K) {// || src0t == GGML_TYPE_Q4_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } + else if (src0t == GGML_TYPE_Q4_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } else if (src0t == GGML_TYPE_Q3_K) { #ifdef GGML_QKK_64 [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; @@ -985,8 +995,8 @@ void ggml_metal_graph_compute( else if (src0t == GGML_TYPE_Q6_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else { - [encoder setThreadgroupMemoryLength:nth0*sizeof(float) atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + int64_t ny = (ne11 + 3)/4; + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } } } break; diff --git a/ggml-metal.metal b/ggml-metal.metal index 8cdf0b9d2..3fa311b40 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -133,19 +133,24 @@ kernel void kernel_soft_max( threadgroup_barrier(mem_flags::mem_threadgroup); } - // broadcast - if (tpitg[0] == 0) { - buf[0] = buf[0]; - } + //// broadcast - not needed. There is a threadgroup barrier above in the last iteration of + // the loop, and when that is done, buf[0] has the correct (synchronized) value + //if (tpitg[0] == 0) { + // buf[0] = buf[0]; + //} - threadgroup_barrier(mem_flags::mem_threadgroup); + //threadgroup_barrier(mem_flags::mem_threadgroup); const float max = buf[0]; // parallel sum buf[tpitg[0]] = 0.0f; for (int i00 = tpitg[0]; i00 < ne00; i00 += ntg[0]) { - buf[tpitg[0]] += exp(psrc0[i00] - max); + const float exp_psrc0 = exp(psrc0[i00] - max); + buf[tpitg[0]] += exp_psrc0; + // Remember the result of exp here. exp is expensive, so we really do not + // whish to compute it twice. + pdst[i00] = exp_psrc0; } // reduce @@ -157,17 +162,18 @@ kernel void kernel_soft_max( threadgroup_barrier(mem_flags::mem_threadgroup); } - // broadcast - if (tpitg[0] == 0) { - buf[0] = buf[0]; - } + // broadcast - not needed, see above + //// broadcast + //if (tpitg[0] == 0) { + // buf[0] = buf[0]; + //} - threadgroup_barrier(mem_flags::mem_threadgroup); + //threadgroup_barrier(mem_flags::mem_threadgroup); const float sum = buf[0]; for (int i00 = tpitg[0]; i00 < ne00; i00 += ntg[0]) { - pdst[i00] = exp(psrc0[i00] - max) / sum; + pdst[i00] /= sum; } } @@ -214,25 +220,27 @@ kernel void kernel_norm( } threadgroup_barrier(mem_flags::mem_threadgroup); } - // broadcast - if (tpitg == 0) { - sum[0] /= ne00; - } - threadgroup_barrier(mem_flags::mem_threadgroup); + //// broadcast + //if (tpitg == 0) { + // sum[0] /= ne00; + //} + //threadgroup_barrier(mem_flags::mem_threadgroup); const float mean = sum[0]; - // recenter + // recenter and VARIANCE device float * y = dst + tgpig*ne00; - for (int i00 = tpitg; i00 < ne00; i00 += ntg) { - y[i00] = x[i00] - mean; - } - - // VARIANCE - // parallel sum sum[tpitg] = 0.0f; for (int i00 = tpitg; i00 < ne00; i00 += ntg) { + y[i00] = x[i00] - mean; sum[tpitg] += y[i00] * y[i00]; } + + //// VARIANCE + //// parallel sum + //sum[tpitg] = 0.0f; + //for (int i00 = tpitg; i00 < ne00; i00 += ntg) { + // sum[tpitg] += y[i00] * y[i00]; + //} // reduce threadgroup_barrier(mem_flags::mem_threadgroup); for (uint i = ntg/2; i > 0; i /= 2) { @@ -241,11 +249,11 @@ kernel void kernel_norm( } threadgroup_barrier(mem_flags::mem_threadgroup); } - // broadcast - if (tpitg == 0) { - sum[0] /= ne00; - } - threadgroup_barrier(mem_flags::mem_threadgroup); + //// broadcast + //if (tpitg == 0) { + // sum[0] /= ne00; + //} + //threadgroup_barrier(mem_flags::mem_threadgroup); const float variance = sum[0]; const float scale = 1.0f/sqrt(variance + eps); @@ -435,6 +443,8 @@ kernel void kernel_mul_mat_q4_1_f32( mul_vec_q_n_f32(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,gqa,tgpig,tiisg,sgitg); } +#define NB_Q8_0 8 + kernel void kernel_mul_mat_q8_0_f32( device const void * src0, device const float * src1, @@ -463,30 +473,30 @@ kernel void kernel_mul_mat_q8_0_f32( device const block_q8_0 * x = (device const block_q8_0 *) src0 + offset0; device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; - float yl[16]; + float yl[NB_Q8_0]; float sumf[nr]={0.f}; - const int ix = tiisg/2; - const int il = tiisg%2; + const int ix = tiisg/4; + const int il = tiisg%4; - device const float * yb = y + ix * QK8_0 + 16*il; + device const float * yb = y + ix * QK8_0 + NB_Q8_0*il; - // each thread in a SIMD group deals with half a block. - for (int ib = ix; ib < nb; ib += nw/2) { - for (int i = 0; i < 16; ++i) { + // each thread in a SIMD group deals with NB_Q8_0 quants at a time + for (int ib = ix; ib < nb; ib += nw/4) { + for (int i = 0; i < NB_Q8_0; ++i) { yl[i] = yb[i]; } for (int row = 0; row < nr; row++) { - device const int8_t * qs = x[ib+row*nb].qs + 16*il; + device const int8_t * qs = x[ib+row*nb].qs + NB_Q8_0*il; float sumq = 0.f; - for (int iq = 0; iq < 16; ++iq) { + for (int iq = 0; iq < NB_Q8_0; ++iq) { sumq += qs[iq] * yl[iq]; } sumf[row] += sumq*x[ib+row*nb].d; } - yb += QK8_0 * 16; + yb += NB_Q8_0 * nw; } for (int row = 0; row < nr; ++row) { @@ -497,6 +507,60 @@ kernel void kernel_mul_mat_q8_0_f32( } } +kernel void kernel_mul_mat_f16_f32_1row( + device const char * src0, + device const char * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]]) { + + const int64_t r0 = tgpig.x; + const int64_t r1 = tgpig.y; + const int64_t im = tgpig.z; + + device const half * x = (device const half *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02); + device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); + + float sumf = 0; + if (ne00 < 128) { + for (int i = tiisg; i < ne00; i += 32) { + sumf += (float) x[i] * (float) y[i]; + } + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } + } else { + device const half4 * x4 = (device const half4 *) x; + device const float4 * y4 = (device const float4 *) y; + for (int i = tiisg; i < ne00/4; i += 32) { + for (int k = 0; k < 4; ++k) sumf += (float)x4[i][k] * y4[i][k]; + } + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + for (int i = 4*(ne00/4); i < ne00; ++i) sumf += (float) x[i] * y[i]; + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } + } + +} + +#define N_F16_F32 4 + kernel void kernel_mul_mat_f16_f32( device const char * src0, device const char * src1, @@ -515,55 +579,58 @@ kernel void kernel_mul_mat_f16_f32( constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, - threadgroup float * sum [[threadgroup(0)]], uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpig[[thread_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 tptg[[threads_per_threadgroup]]) { + uint tiisg[[thread_index_in_simdgroup]]) { const int64_t r0 = tgpig.x; - const int64_t r1 = tgpig.y; + const int64_t rb = N_F16_F32*tgpig.y; const int64_t im = tgpig.z; - device const half * x = (device const half *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02); - device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); + device const half * x = (device const half *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02); - uint ith = tpitg.x; - uint nth = tptg.x; + if (ne00 < 128) { + for (int row = 0; row < N_F16_F32; ++row) { + int r1 = rb + row; + if (r1 >= ne11) { + break; + } - sum[ith] = 0.0f; + device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); - for (int i = ith; i < ne00; i += nth) { - sum[ith] += (float) x[i] * (float) y[i]; + float sumf = 0; + for (int i = tiisg; i < ne00; i += 32) { + sumf += (float) x[i] * (float) y[i]; + } + + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } + } + } else { + device const half4 * x4 = (device const half4 *)x; + for (int row = 0; row < N_F16_F32; ++row) { + int r1 = rb + row; + if (r1 >= ne11) { + break; + } + + device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); + device const float4 * y4 = (device const float4 *) y; + + float sumf = 0; + for (int i = tiisg; i < ne00/4; i += 32) { + for (int k = 0; k < 4; ++k) sumf += (float) x4[i][k] * y4[i][k]; + } + + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + for (int i = 4*(ne00/4); i < ne00; ++i) sumf += (float) x[i] * y[i]; + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } + } } - // accumulate the sum from all threads in the threadgroup - threadgroup_barrier(mem_flags::mem_threadgroup); - if (ith%4 == 0) { - for (int i = 1; i < 4; ++i) sum[ith] += sum[ith + i]; - } - threadgroup_barrier(mem_flags::mem_threadgroup); - if (ith%16 == 0) { - for (int i = 4; i < 16; i += 4) sum[ith] += sum[ith + i]; - } - threadgroup_barrier(mem_flags::mem_threadgroup); - if (ith == 0) { - for (int i = 16; i < nth; i += 16) sum[0] += sum[i]; - dst[im*ne1*ne0 + r1*ne0 + r0] = sum[0]; - } - - // Original implementation. Left behind commented out for now - //threadgroup_barrier(mem_flags::mem_threadgroup); - //for (uint i = tptg.x/2; i > 0; i /= 2) { - // if (tpitg.x < i) { - // sum[tpitg.x] += sum[tpitg.x + i]; - // } - // threadgroup_barrier(mem_flags::mem_threadgroup); - //} - // - //if (tpitg.x == 0) { - // dst[im*ne1*ne0 + r1*ne0 + r0] = sum[0]; - //} } kernel void kernel_alibi_f32( @@ -1262,7 +1329,8 @@ kernel void kernel_mul_mat_q4_K_f32( const int r0 = tgpig.x; const int r1 = tgpig.y; const int r2 = tgpig.z; - const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; + //const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; + const int first_row = r0 * N_DST; const int ib_row = first_row * nb; const uint offset0 = r2/gqa*(nb*ne0); device const block_q4_K * x = (device const block_q4_K *) src0 + ib_row + offset0; From 6460f758dbd472653296044d36bed8c4554988f5 Mon Sep 17 00:00:00 2001 From: Wentai Zhang Date: Sun, 3 Sep 2023 16:46:44 +0800 Subject: [PATCH 460/852] opencl : fix a bug in ggml_cl_pool_malloc() for ggml_cl_mul_mat_f32() (#2955) Co-authored-by: Wentai Zhang --- ggml-opencl.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp index eb214a836..3d50a7f08 100644 --- a/ggml-opencl.cpp +++ b/ggml-opencl.cpp @@ -1493,7 +1493,7 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr if (src0->backend == GGML_BACKEND_GPU) { // NOLINT d_X = (cl_mem) src0->data; } else { - d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size); + d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size); } cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size); cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size); From afc43d5f82588d2ed71ea104e8262f5e5da13980 Mon Sep 17 00:00:00 2001 From: Alon Date: Sun, 3 Sep 2023 11:48:49 +0300 Subject: [PATCH 461/852] cov : add Code Coverage and codecov.io integration (#2928) * update .gitignore * makefile: add coverage support (lcov, gcovr) * add code-coverage workflow * update code coverage workflow * wun on ubuntu 20.04 * use gcc-8 * check why the job hang * add env vars * add LLAMA_CODE_COVERAGE=1 again * - add CODECOV_TOKEN - add missing make lcov-report * install lcov * update make file -pb flag * remove unused GGML_NITER from workflows * wrap coverage output files in COV_TARGETS --- .github/workflows/build.yml | 1 - .github/workflows/code-coverage.yml | 36 +++++++++++++++++++++++++++++ .gitignore | 7 ++++++ Makefile | 22 +++++++++++++++++- 4 files changed, 64 insertions(+), 2 deletions(-) create mode 100644 .github/workflows/code-coverage.yml diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 20fd8c2b5..9d0a6c222 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -18,7 +18,6 @@ on: env: BRANCH_NAME: ${{ github.head_ref || github.ref_name }} GGML_NLOOP: 3 - GGML_NITER: 1 GGML_N_THREADS: 1 jobs: diff --git a/.github/workflows/code-coverage.yml b/.github/workflows/code-coverage.yml new file mode 100644 index 000000000..392db8a08 --- /dev/null +++ b/.github/workflows/code-coverage.yml @@ -0,0 +1,36 @@ +name: Code Coverage +on: [push, pull_request] + +env: + GGML_NLOOP: 3 + GGML_N_THREADS: 1 + +jobs: + run: + runs-on: ubuntu-20.04 + steps: + - name: Checkout + uses: actions/checkout@v3 + + - name: Dependencies + run: | + sudo apt-get update + sudo apt-get install build-essential gcc-8 lcov + + - name: Build + run: CC=gcc-8 make -j LLAMA_CODE_COVERAGE=1 tests + + - name: Run tests + run: CC=gcc-8 make test + + - name: Generate coverage report + run: | + make coverage + make lcov-report + + - name: Upload coverage to Codecov + uses: codecov/codecov-action@v3 + env: + CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} + with: + files: lcov-report/coverage.info diff --git a/.gitignore b/.gitignore index 8b5f45a2d..f9244fadc 100644 --- a/.gitignore +++ b/.gitignore @@ -6,6 +6,10 @@ *.exe *.dll *.log +*.gcov +*.gcno +*.gcda +*.dot .DS_Store .build/ .cache/ @@ -17,6 +21,9 @@ .vs/ .vscode/ +lcov-report/ +gcovr-report/ + build*/ out/ tmp/ diff --git a/Makefile b/Makefile index e214970f8..c042bf0e5 100644 --- a/Makefile +++ b/Makefile @@ -4,6 +4,9 @@ BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-tex # Binaries only useful for tests TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama tests/test-tokenizer-0-falcon tests/test-tokenizer-1 +# Code coverage output files +COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report + default: $(BUILD_TARGETS) test: @@ -23,6 +26,18 @@ test: all: $(BUILD_TARGETS) $(TEST_TARGETS) +coverage: ## Run code coverage + gcov -pb tests/*.cpp + +lcov-report: coverage ## Generate lcov report + mkdir -p lcov-report + lcov --capture --directory . --output-file lcov-report/coverage.info + genhtml lcov-report/coverage.info --output-directory lcov-report + +gcovr-report: coverage ## Generate gcovr report + mkdir -p gcovr-report + gcovr --root . --html --html-details --output gcovr-report/coverage.html + ifndef UNAME_S UNAME_S := $(shell uname -s) endif @@ -84,6 +99,11 @@ ifdef LLAMA_SERVER_VERBOSE MK_CPPFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE) endif + +ifdef LLAMA_CODE_COVERAGE + CXXFLAGS += -fprofile-arcs -ftest-coverage -dumpbase '' +endif + ifdef LLAMA_DISABLE_LOGS CFLAGS += -DLOG_DISABLE_LOGS CXXFLAGS += -DLOG_DISABLE_LOGS @@ -399,7 +419,7 @@ libllama.so: llama.o ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) clean: - rm -vf *.o tests/*.o *.so *.dll benchmark-matmult build-info.h $(BUILD_TARGETS) $(TEST_TARGETS) + rm -vrf *.o tests/*.o *.so *.dll benchmark-matmult build-info.h *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) # # Examples From d9151e6f570eb20bfd54427bd8a337d9b1a08018 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 3 Sep 2023 12:40:56 +0300 Subject: [PATCH 462/852] metal : revert 6af0bab until we fix it This restores the generated text to be the same as before #2959 --- ggml-metal.metal | 74 ++++++++++++------------------------------------ 1 file changed, 18 insertions(+), 56 deletions(-) diff --git a/ggml-metal.metal b/ggml-metal.metal index 3fa311b40..1d324e466 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -536,27 +536,14 @@ kernel void kernel_mul_mat_f16_f32_1row( device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); float sumf = 0; - if (ne00 < 128) { - for (int i = tiisg; i < ne00; i += 32) { - sumf += (float) x[i] * (float) y[i]; - } - float all_sum = simd_sum(sumf); - if (tiisg == 0) { - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; - } - } else { - device const half4 * x4 = (device const half4 *) x; - device const float4 * y4 = (device const float4 *) y; - for (int i = tiisg; i < ne00/4; i += 32) { - for (int k = 0; k < 4; ++k) sumf += (float)x4[i][k] * y4[i][k]; - } - float all_sum = simd_sum(sumf); - if (tiisg == 0) { - for (int i = 4*(ne00/4); i < ne00; ++i) sumf += (float) x[i] * y[i]; - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; - } + for (int i = tiisg; i < ne00; i += 32) { + sumf += (float) x[i] * (float) y[i]; } + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } } #define N_F16_F32 4 @@ -588,49 +575,24 @@ kernel void kernel_mul_mat_f16_f32( device const half * x = (device const half *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02); - if (ne00 < 128) { - for (int row = 0; row < N_F16_F32; ++row) { - int r1 = rb + row; - if (r1 >= ne11) { - break; - } - - device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); - - float sumf = 0; - for (int i = tiisg; i < ne00; i += 32) { - sumf += (float) x[i] * (float) y[i]; - } - - float all_sum = simd_sum(sumf); - if (tiisg == 0) { - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; - } + for (int row = 0; row < N_F16_F32; ++row) { + int r1 = rb + row; + if (r1 >= ne11) { + break; } - } else { - device const half4 * x4 = (device const half4 *)x; - for (int row = 0; row < N_F16_F32; ++row) { - int r1 = rb + row; - if (r1 >= ne11) { - break; - } - device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); - device const float4 * y4 = (device const float4 *) y; + device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); - float sumf = 0; - for (int i = tiisg; i < ne00/4; i += 32) { - for (int k = 0; k < 4; ++k) sumf += (float) x4[i][k] * y4[i][k]; - } + float sumf = 0; + for (int i = tiisg; i < ne00; i += 32) { + sumf += (float) x[i] * (float) y[i]; + } - float all_sum = simd_sum(sumf); - if (tiisg == 0) { - for (int i = 4*(ne00/4); i < ne00; ++i) sumf += (float) x[i] * y[i]; - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; - } + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; } } - } kernel void kernel_alibi_f32( From 37301347767d555d0a66c043ce4ef6ead8e61c55 Mon Sep 17 00:00:00 2001 From: opparco Date: Sun, 3 Sep 2023 19:18:09 +0900 Subject: [PATCH 463/852] llama : fix bpe tokenize from byte (#2889) --- llama.cpp | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/llama.cpp b/llama.cpp index 2b0cf30f6..c97c1462f 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3366,9 +3366,15 @@ struct llm_tokenizer_bpe { std::string byte_str(1, *j); auto token_multibyte = vocab.token_to_id.find(byte_str); if (token_multibyte == vocab.token_to_id.end()) { - fprintf(stderr,"ERROR: byte not found in vocab: '%s'\n", byte_str.c_str()); + try { + llama_token token_byte = llama_byte_to_token(vocab, *j); + output.push_back(token_byte); + } catch (const std::out_of_range & err) { + fprintf(stderr,"ERROR: byte not found in vocab: '%s'\n", byte_str.c_str()); + } + } else { + output.push_back((*token_multibyte).second); } - output.push_back((*token_multibyte).second); } } else { output.push_back((*token).second); From 73a12a6344d5da4d8e2eba5d12221b8bc6895931 Mon Sep 17 00:00:00 2001 From: Alon Date: Sun, 3 Sep 2023 13:19:01 +0300 Subject: [PATCH 464/852] cov : disable comment in PRs (#2989) --- codecov.yml | 14 ++++++++++++++ 1 file changed, 14 insertions(+) create mode 100644 codecov.yml diff --git a/codecov.yml b/codecov.yml new file mode 100644 index 000000000..a301c5b2c --- /dev/null +++ b/codecov.yml @@ -0,0 +1,14 @@ +comment: off + +coverage: + status: + project: + default: + target: auto + threshold: 0 + base: auto + patch: + default: + target: auto + threshold: 0 + base: auto From b7f2aa9e512c3be2e863d877cbb1056d7c4a03f8 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 3 Sep 2023 13:23:33 +0300 Subject: [PATCH 465/852] metal : restore 363f0bf and fix reduce in F16_F32 kernels (#2986) --- ggml-metal.metal | 76 ++++++++++++++++++++++++++++++++++++------------ 1 file changed, 57 insertions(+), 19 deletions(-) diff --git a/ggml-metal.metal b/ggml-metal.metal index 1d324e466..119fcbeb6 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -536,14 +536,27 @@ kernel void kernel_mul_mat_f16_f32_1row( device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); float sumf = 0; - for (int i = tiisg; i < ne00; i += 32) { - sumf += (float) x[i] * (float) y[i]; + if (ne00 < 128) { + for (int i = tiisg; i < ne00; i += 32) { + sumf += (float) x[i] * (float) y[i]; + } + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } + } else { + device const half4 * x4 = (device const half4 *) x; + device const float4 * y4 = (device const float4 *) y; + for (int i = tiisg; i < ne00/4; i += 32) { + for (int k = 0; k < 4; ++k) sumf += (float)x4[i][k] * y4[i][k]; + } + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) x[i] * y[i]; + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } } - float all_sum = simd_sum(sumf); - if (tiisg == 0) { - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; - } } #define N_F16_F32 4 @@ -570,29 +583,54 @@ kernel void kernel_mul_mat_f16_f32( uint tiisg[[thread_index_in_simdgroup]]) { const int64_t r0 = tgpig.x; - const int64_t rb = N_F16_F32*tgpig.y; + const int64_t rb = tgpig.y*N_F16_F32; const int64_t im = tgpig.z; device const half * x = (device const half *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02); - for (int row = 0; row < N_F16_F32; ++row) { - int r1 = rb + row; - if (r1 >= ne11) { - break; + if (ne00 < 128) { + for (int row = 0; row < N_F16_F32; ++row) { + int r1 = rb + row; + if (r1 >= ne11) { + break; + } + + device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); + + float sumf = 0; + for (int i = tiisg; i < ne00; i += 32) { + sumf += (float) x[i] * (float) y[i]; + } + + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } } + } else { + device const half4 * x4 = (device const half4 *)x; + for (int row = 0; row < N_F16_F32; ++row) { + int r1 = rb + row; + if (r1 >= ne11) { + break; + } - device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); + device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); + device const float4 * y4 = (device const float4 *) y; - float sumf = 0; - for (int i = tiisg; i < ne00; i += 32) { - sumf += (float) x[i] * (float) y[i]; - } + float sumf = 0; + for (int i = tiisg; i < ne00/4; i += 32) { + for (int k = 0; k < 4; ++k) sumf += (float) x4[i][k] * y4[i][k]; + } - float all_sum = simd_sum(sumf); - if (tiisg == 0) { - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) x[i] * y[i]; + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } } } + } kernel void kernel_alibi_f32( From 6519e9c99cffbad19b31bcba86df48c500628c09 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Sun, 3 Sep 2023 04:38:43 -0600 Subject: [PATCH 466/852] gguf(python): Fix special vocab handling when id < 0 (#2984) --- gguf-py/gguf/gguf.py | 4 ++-- gguf-py/pyproject.toml | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py index b1bc4205b..d377cd56d 100644 --- a/gguf-py/gguf/gguf.py +++ b/gguf-py/gguf/gguf.py @@ -801,7 +801,7 @@ class SpecialVocab: else: continue for maybe_token_id in (atok.get('id') for atok in added_tokens if atok.get('content') == tc_content): - if isinstance(maybe_token_id, int): + if isinstance(maybe_token_id, int) and maybe_token_id >= 0: self.special_token_ids[typ] = maybe_token_id break return True @@ -814,7 +814,7 @@ class SpecialVocab: config = json.load(f) for typ in self.special_token_types: maybe_token_id = config.get(f'{typ}_token_id') - if isinstance(maybe_token_id, int): + if isinstance(maybe_token_id, int) and maybe_token_id >= 0: self.special_token_ids[typ] = maybe_token_id return True diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index 26f792b14..8da60de1b 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "gguf" -version = "0.3.1" +version = "0.3.2" description = "Write ML models in GGUF for GGML" authors = ["GGML "] packages = [ From 8f429fa5111901f9646cf998643ac5310846d487 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 3 Sep 2023 13:42:56 +0300 Subject: [PATCH 467/852] perplexity : fix ETA by warming up the model with an empty run --- common/common.cpp | 8 ++++++++ examples/main/main.cpp | 8 -------- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 41fc59ced..a1c3dc780 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -752,6 +752,14 @@ std::tuple llama_init_from_gpt_par params.logit_bias[llama_token_eos(lctx)] = -INFINITY; } + { + LOG("warming up the model with an empty run\n"); + + const std::vector tmp = { llama_token_bos(lctx), }; + llama_eval(lctx, tmp.data(), tmp.size(), 0, params.n_threads); + llama_reset_timings(lctx); + } + return std::make_tuple(model, lctx); } diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 7117db4b0..db98312ca 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -492,14 +492,6 @@ int main(int argc, char ** argv) { std::vector embd; std::vector embd_guidance; - { - LOG("warming up the model with an empty run\n"); - - const std::vector tmp = { llama_token_bos(ctx), }; - llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads); - llama_reset_timings(ctx); - } - while ((n_remain != 0 && !is_antiprompt) || params.interactive) { // predict if (embd.size() > 0) { From 47068e517004d90f13c16352bb3b4cafd53a00cd Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 3 Sep 2023 15:12:08 +0300 Subject: [PATCH 468/852] speculative : PoC for speeding-up inference via speculative sampling (#2926) * speculative : initial example * speculative : print encoding speed * speculative : add --draft CLI arg --- common/common.cpp | 140 ++++++++++++++++ common/common.h | 36 +++++ examples/CMakeLists.txt | 1 + examples/main/main.cpp | 136 +++------------- examples/speculative/CMakeLists.txt | 8 + examples/speculative/speculative.cpp | 234 +++++++++++++++++++++++++++ 6 files changed, 440 insertions(+), 115 deletions(-) create mode 100644 examples/speculative/CMakeLists.txt create mode 100644 examples/speculative/speculative.cpp diff --git a/common/common.cpp b/common/common.cpp index a1c3dc780..313821375 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -305,6 +305,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } params.n_keep = std::stoi(argv[i]); + } else if (arg == "--draft") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.n_draft = std::stoi(argv[i]); } else if (arg == "--chunks") { if (++i >= argc) { invalid_param = true; @@ -317,6 +323,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } params.model = argv[i]; + } else if (arg == "-md" || arg == "--model-draft") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.model_draft = argv[i]; } else if (arg == "-a" || arg == "--alias") { if (++i >= argc) { invalid_param = true; @@ -638,6 +650,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { fprintf(stdout, " --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n"); fprintf(stdout, " --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks); fprintf(stdout, " --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep); + fprintf(stdout, " --draft N number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft); fprintf(stdout, " --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks); if (llama_mlock_supported()) { fprintf(stdout, " --mlock force system to keep model in RAM rather than swapping or compressing\n"); @@ -669,6 +682,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { fprintf(stdout, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); fprintf(stdout, " -m FNAME, --model FNAME\n"); fprintf(stdout, " model path (default: %s)\n", params.model.c_str()); + fprintf(stdout, " -md FNAME, --model-draft FNAME\n"); + fprintf(stdout, " draft model for speculative decoding (default: %s)\n", params.model.c_str()); fprintf(stdout, " -ld LOGDIR, --logdir LOGDIR\n"); fprintf(stdout, " path under which to save YAML logs (no logging if unset)\n"); fprintf(stdout, "\n"); @@ -832,6 +847,130 @@ std::string llama_detokenize_bpe(llama_context * ctx, const std::vector & last_tokens, + std::vector & candidates, + int idx) { + const int n_ctx = llama_n_ctx(ctx); + const int n_vocab = llama_n_vocab(ctx); + + const float temp = params.temp; + const int32_t top_k = params.top_k <= 0 ? n_vocab : params.top_k; + const float top_p = params.top_p; + const float tfs_z = params.tfs_z; + const float typical_p = params.typical_p; + const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n; + const float repeat_penalty = params.repeat_penalty; + const float alpha_presence = params.presence_penalty; + const float alpha_frequency = params.frequency_penalty; + const int mirostat = params.mirostat; + const float mirostat_tau = params.mirostat_tau; + const float mirostat_eta = params.mirostat_eta; + const bool penalize_nl = params.penalize_nl; + + llama_token id = 0; + + float * logits = llama_get_logits(ctx) + idx * n_vocab; + + // Apply params.logit_bias map + for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) { + logits[it->first] += it->second; + } + + candidates.clear(); + for (llama_token token_id = 0; token_id < n_vocab; token_id++) { + candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); + } + + llama_token_data_array cur_p = { candidates.data(), candidates.size(), false }; + + if (ctx_guidance) { + llama_sample_classifier_free_guidance(ctx, &cur_p, ctx_guidance, params.cfg_scale); + } + + // apply penalties + if (!last_tokens.empty()) { + const float nl_logit = logits[llama_token_nl(ctx)]; + const int last_n_repeat = std::min(std::min((int)last_tokens.size(), repeat_last_n), n_ctx); + + llama_sample_repetition_penalty(ctx, &cur_p, + last_tokens.data() + last_tokens.size() - last_n_repeat, + last_n_repeat, repeat_penalty); + llama_sample_frequency_and_presence_penalties(ctx, &cur_p, + last_tokens.data() + last_tokens.size() - last_n_repeat, + last_n_repeat, alpha_frequency, alpha_presence); + + if (!penalize_nl) { + for (size_t idx = 0; idx < cur_p.size; idx++) { + if (cur_p.data[idx].id == llama_token_nl(ctx)) { + cur_p.data[idx].logit = nl_logit; + break; + } + } + } + } + + if (grammar != NULL) { + llama_sample_grammar(ctx, &cur_p, grammar); + } + + if (temp <= 0) { + // Greedy sampling + id = llama_sample_token_greedy(ctx, &cur_p); + } else { + if (mirostat == 1) { + static float mirostat_mu = 2.0f * mirostat_tau; + const int mirostat_m = 100; + llama_sample_temperature(ctx, &cur_p, temp); + id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); + } else if (mirostat == 2) { + static float mirostat_mu = 2.0f * mirostat_tau; + llama_sample_temperature(ctx, &cur_p, temp); + id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu); + } else { + // Temperature sampling + llama_sample_top_k (ctx, &cur_p, top_k, 1); + llama_sample_tail_free (ctx, &cur_p, tfs_z, 1); + llama_sample_typical (ctx, &cur_p, typical_p, 1); + llama_sample_top_p (ctx, &cur_p, top_p, 1); + llama_sample_temperature(ctx, &cur_p, temp); + + { + const int n_top = 10; + LOG("top %d candidates:\n", n_top); + + for (int i = 0; i < n_top; i++) { + const llama_token id = cur_p.data[i].id; + LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx, id).c_str(), cur_p.data[i].p); + } + } + + id = llama_sample_token(ctx, &cur_p); + + LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx, id).c_str()); + } + } + // printf("`%d`", candidates_p.size); + + if (grammar != NULL) { + llama_grammar_accept_token(ctx, grammar, id); + } + + return id; +} + +// +// YAML utils +// + // returns true if successful, false otherwise bool create_directory_with_parents(const std::string & path) { #ifdef _WIN32 @@ -1070,6 +1209,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "mirostat_lr: %f # default: 0.1\n", params.mirostat_eta); fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false"); fprintf(stream, "model: %s # default: models/7B/ggml-model.bin\n", params.model.c_str()); + fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str()); fprintf(stream, "mtest: %s # default: false\n", params.mem_test ? "true" : "false"); fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false"); fprintf(stream, "n_gpu_layers: %d # default: 0\n", params.n_gpu_layers); diff --git a/common/common.h b/common/common.h index 5a379688e..105fb09e4 100644 --- a/common/common.h +++ b/common/common.h @@ -32,6 +32,7 @@ struct gpt_params { int32_t n_ctx = 512; // context size int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) int32_t n_keep = 0; // number of tokens to keep from initial prompt + int32_t n_draft = 16; // number of tokens to draft during speculative decoding int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) int32_t n_gpu_layers = 0; // number of layers to store in VRAM int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors @@ -63,6 +64,7 @@ struct gpt_params { float cfg_scale = 1.f; // How strong is guidance std::string model = "models/7B/ggml-model-f16.gguf"; // model path + std::string model_draft = ""; // draft model for speculative decoding std::string model_alias = "unknown"; // model alias std::string prompt = ""; std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state @@ -156,6 +158,40 @@ std::string llama_detokenize_bpe( llama_context * ctx, const std::vector & tokens); +// +// Sampling utils +// + +// this is a common sampling function used across the examples for convenience +// it can serve as a starting point for implementing your own sampling function +// +// required: +// - ctx: context to use for sampling +// - params: sampling parameters +// +// optional: +// - ctx_guidance: context to use for classifier-free guidance, ignore if NULL +// - grammar: grammar to use for sampling, ignore if NULL +// - last_tokens: needed for repetition penalty, ignore if empty +// - idx: sample from llama_get_logits(ctx) + idx * n_vocab +// +// returns: +// - token: sampled token +// - candidates: vector of candidate tokens +// +llama_token llama_sample_token( + struct llama_context * ctx, + struct llama_context * ctx_guidance, + struct llama_grammar * grammar, + const struct gpt_params & params, + const std::vector & last_tokens, + std::vector & candidates, + int idx = 0); + +// +// YAML utils +// + bool create_directory_with_parents(const std::string & path); void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector & data); void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector & data); diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 6e65eb087..884c42764 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -23,6 +23,7 @@ else() add_subdirectory(train-text-from-scratch) add_subdirectory(convert-llama2c-to-ggml) add_subdirectory(simple) + add_subdirectory(speculative) add_subdirectory(embd-input) add_subdirectory(llama-bench) add_subdirectory(beam-search) diff --git a/examples/main/main.cpp b/examples/main/main.cpp index db98312ca..922b9a980 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -116,7 +116,7 @@ int main(int argc, char ** argv) { #ifndef LOG_DISABLE_LOGS log_set_target(log_filename_generator("main", "log")); LOG_TEE("Log start\n"); - log_dump_cmdline(argc,argv); + log_dump_cmdline(argc, argv); #endif // LOG_DISABLE_LOGS // TODO: Dump params ? @@ -425,8 +425,9 @@ int main(int argc, char ** argv) { LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep); LOG_TEE("\n\n"); + struct llama_grammar * grammar = NULL; grammar_parser::parse_state parsed_grammar; - llama_grammar * grammar = NULL; + if (!params.grammar.empty()) { parsed_grammar = grammar_parser::parse(params.grammar.c_str()); // will be empty (default) if there are parse errors @@ -450,8 +451,8 @@ int main(int argc, char ** argv) { } // TODO: replace with ring-buffer - std::vector last_n_tokens(n_ctx); - std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0); + std::vector last_tokens(n_ctx); + std::fill(last_tokens.begin(), last_tokens.end(), 0); if (params.interactive) { const char *control_message; @@ -492,6 +493,11 @@ int main(int argc, char ** argv) { std::vector embd; std::vector embd_guidance; + const int n_vocab = llama_n_vocab(ctx); + + std::vector candidates; + candidates.reserve(n_vocab); + while ((n_remain != 0 && !is_antiprompt) || params.interactive) { // predict if (embd.size() > 0) { @@ -529,8 +535,8 @@ int main(int argc, char ** argv) { LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance); - // insert n_left/2 tokens at the start of embd from last_n_tokens - embd.insert(embd.begin(), last_n_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_n_tokens.end() - embd.size()); + // insert n_left/2 tokens at the start of embd from last_tokens + embd.insert(embd.begin(), last_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_tokens.end() - embd.size()); LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd)); @@ -629,20 +635,6 @@ int main(int argc, char ** argv) { embd_guidance.clear(); if ((int) embd_inp.size() <= n_consumed && !is_interacting) { - const float temp = params.temp; - const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k; - const float top_p = params.top_p; - const float tfs_z = params.tfs_z; - const float typical_p = params.typical_p; - const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n; - const float repeat_penalty = params.repeat_penalty; - const float alpha_presence = params.presence_penalty; - const float alpha_frequency = params.frequency_penalty; - const int mirostat = params.mirostat; - const float mirostat_tau = params.mirostat_tau; - const float mirostat_eta = params.mirostat_eta; - const bool penalize_nl = params.penalize_nl; - // optionally save the session on first sample (for faster prompt loading next time) if (!path_session.empty() && need_to_save_session && !params.prompt_cache_ro) { need_to_save_session = false; @@ -651,98 +643,12 @@ int main(int argc, char ** argv) { LOG("saved session to %s\n", path_session.c_str()); } - llama_token id = 0; + const llama_token id = llama_sample_token(ctx, ctx_guidance, grammar, params, last_tokens, candidates); - { - auto logits = llama_get_logits(ctx); - auto n_vocab = llama_n_vocab(ctx); + last_tokens.erase(last_tokens.begin()); + last_tokens.push_back(id); - // Apply params.logit_bias map - for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) { - logits[it->first] += it->second; - } - - std::vector candidates; - candidates.reserve(n_vocab); - for (llama_token token_id = 0; token_id < n_vocab; token_id++) { - candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); - } - - llama_token_data_array cur_p = { candidates.data(), candidates.size(), false }; - - if (ctx_guidance) { - llama_sample_classifier_free_guidance(ctx, &cur_p, ctx_guidance, params.cfg_scale); - } - - // Apply penalties - float nl_logit = logits[llama_token_nl(ctx)]; - auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx); - llama_sample_repetition_penalty(ctx, &cur_p, - last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, - last_n_repeat, repeat_penalty); - llama_sample_frequency_and_presence_penalties(ctx, &cur_p, - last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, - last_n_repeat, alpha_frequency, alpha_presence); - if (!penalize_nl) { - for (size_t idx = 0; idx < cur_p.size; idx++) { - if (cur_p.data[idx].id == llama_token_nl(ctx)) { - cur_p.data[idx].logit = nl_logit; - break; - } - } - } - - if (grammar != NULL) { - llama_sample_grammar(ctx, &cur_p, grammar); - } - - if (temp <= 0) { - // Greedy sampling - id = llama_sample_token_greedy(ctx, &cur_p); - } else { - if (mirostat == 1) { - static float mirostat_mu = 2.0f * mirostat_tau; - const int mirostat_m = 100; - llama_sample_temperature(ctx, &cur_p, temp); - id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); - } else if (mirostat == 2) { - static float mirostat_mu = 2.0f * mirostat_tau; - llama_sample_temperature(ctx, &cur_p, temp); - id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu); - } else { - // Temperature sampling - llama_sample_top_k (ctx, &cur_p, top_k, 1); - llama_sample_tail_free (ctx, &cur_p, tfs_z, 1); - llama_sample_typical (ctx, &cur_p, typical_p, 1); - llama_sample_top_p (ctx, &cur_p, top_p, 1); - llama_sample_temperature(ctx, &cur_p, temp); - - { - const int n_top = 10; - LOG("top %d candidates:\n", n_top); - - for (int i = 0; i < n_top; i++) { - const llama_token id = cur_p.data[i].id; - LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx, id).c_str(), cur_p.data[i].p); - } - } - - id = llama_sample_token(ctx, &cur_p); - - LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx, id).c_str()); - } - } - // printf("`%d`", candidates_p.size); - - if (grammar != NULL) { - llama_grammar_accept_token(ctx, grammar, id); - } - - last_n_tokens.erase(last_n_tokens.begin()); - last_n_tokens.push_back(id); - - LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, last_n_tokens)); - } + LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, last_tokens)); embd.push_back(id); @@ -758,8 +664,8 @@ int main(int argc, char ** argv) { LOG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed); while ((int) embd_inp.size() > n_consumed) { embd.push_back(embd_inp[n_consumed]); - last_n_tokens.erase(last_n_tokens.begin()); - last_n_tokens.push_back(embd_inp[n_consumed]); + last_tokens.erase(last_tokens.begin()); + last_tokens.push_back(embd_inp[n_consumed]); ++n_consumed; if ((int) embd.size() >= params.n_batch) { break; @@ -792,7 +698,7 @@ int main(int argc, char ** argv) { // check for reverse prompt if (params.antiprompt.size()) { std::string last_output; - for (auto id : last_n_tokens) { + for (auto id : last_tokens) { last_output += llama_token_to_piece(ctx, id); } @@ -823,7 +729,7 @@ int main(int argc, char ** argv) { } // deal with end of text token in interactive mode - if (last_n_tokens.back() == llama_token_eos(ctx)) { + if (last_tokens.back() == llama_token_eos(ctx)) { LOG("found EOS token\n"); if (params.interactive) { @@ -925,7 +831,7 @@ int main(int argc, char ** argv) { if (grammar != NULL) { llama_grammar_free(grammar); - std::vector grammar_rules( parsed_grammar.c_rules()); + std::vector grammar_rules(parsed_grammar.c_rules()); grammar = llama_grammar_init( grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); diff --git a/examples/speculative/CMakeLists.txt b/examples/speculative/CMakeLists.txt new file mode 100644 index 000000000..6c5c9456e --- /dev/null +++ b/examples/speculative/CMakeLists.txt @@ -0,0 +1,8 @@ +set(TARGET speculative) +add_executable(${TARGET} speculative.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) +if(TARGET BUILD_INFO) + add_dependencies(${TARGET} BUILD_INFO) +endif() diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp new file mode 100644 index 000000000..f0400c13f --- /dev/null +++ b/examples/speculative/speculative.cpp @@ -0,0 +1,234 @@ +#ifndef _GNU_SOURCE +#define _GNU_SOURCE +#endif + +#include "build-info.h" + +#include "common.h" +#include "llama.h" + +#include +#include +#include +#include + +int main(int argc, char ** argv) { + gpt_params params; + + if (gpt_params_parse(argc, argv, params) == false) { + return 1; + } + + if (params.model_draft.empty()) { + fprintf(stderr, "%s: error: --model-draft is required\n", __func__); + return 1; + } + +#ifndef LOG_DISABLE_LOGS + log_set_target(log_filename_generator("speculative", "log")); + LOG_TEE("Log start\n"); + log_dump_cmdline(argc, argv); +#endif // LOG_DISABLE_LOGS + + // init llama.cpp + llama_backend_init(params.numa); + + llama_model * model_tgt = NULL; + llama_model * model_dft = NULL; + + llama_context * ctx_tgt = NULL; + llama_context * ctx_dft = NULL; + + // load the target model + params.perplexity = true; // HACK: enable logits_all = true + std::tie(model_tgt, ctx_tgt) = llama_init_from_gpt_params(params); + + // load the draft model + params.model = params.model_draft; + std::tie(model_dft, ctx_dft) = llama_init_from_gpt_params(params); + + // tokenize the prompt + std::vector inp; + inp = ::llama_tokenize(ctx_tgt, params.prompt, true); + + const int max_context_size = llama_n_ctx(ctx_tgt); + const int max_tokens_list_size = max_context_size - 4; + + if ((int) inp.size() > max_tokens_list_size) { + fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size); + return 1; + } + + fprintf(stderr, "\n\n"); + + for (auto id : inp) { + fprintf(stderr, "%s", llama_token_to_piece(ctx_tgt, id).c_str()); + } + + fflush(stderr); + + const int n_input = inp.size(); + + const auto t_enc_start = ggml_time_us(); + + // eval the prompt with both models + llama_eval(ctx_tgt, inp.data(), int(inp.size() - 1), 0, params.n_threads); + llama_eval(ctx_tgt, &inp.back(), 1, inp.size() - 1, params.n_threads); + llama_eval(ctx_dft, inp.data(), int(inp.size()), 0, params.n_threads); + + const auto t_enc_end = ggml_time_us(); + + // the 2 models should have the same vocab + const int n_ctx = llama_n_ctx(ctx_tgt); + const int n_vocab = llama_n_vocab(ctx_tgt); + //GGML_ASSERT(n_vocab == llama_n_vocab(ctx_dft)); + + // how many tokens to draft each time + const int n_draft = params.n_draft; + + int n_predict = 0; + int n_drafted = 0; + int n_accept = 0; + + int n_past_tgt = inp.size(); + int n_past_dft = inp.size(); + + std::vector drafted; + + std::vector last_tokens(n_ctx); + std::fill(last_tokens.begin(), last_tokens.end(), 0); + + for (auto & id : inp) { + last_tokens.erase(last_tokens.begin()); + last_tokens.push_back(id); + } + + std::vector candidates; + candidates.reserve(n_vocab); + + // used to determine end of generation + bool has_eos = false; + + const auto t_dec_start = ggml_time_us(); + + while (true) { + LOG("drafted: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_dft, drafted)); + + // sample from the drafted tokens if any + int i_dft = 0; + while (true) { + const llama_token id = llama_sample_token(ctx_tgt, NULL, NULL, params, last_tokens, candidates, i_dft); + + last_tokens.erase(last_tokens.begin()); + last_tokens.push_back(id); + + //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, last_tokens)); + + const std::string token_str = llama_token_to_piece(ctx_tgt, id); + printf("%s", token_str.c_str()); + fflush(stdout); + + if (id == llama_token_eos(ctx_tgt)) { + has_eos = true; + } + + ++n_predict; + + if (i_dft < (int) drafted.size() && id == drafted[i_dft]) { + LOG("drafted token %d accepted\n", id); + ++n_accept; + ++n_past_tgt; + ++n_past_dft; + ++i_dft; + + continue; + } + + // the drafted token was rejected or we are out of drafted tokens + llama_eval(ctx_dft, &id, 1, n_past_dft, params.n_threads); + ++n_past_dft; + + drafted.clear(); + drafted.push_back(id); + + break; + } + + if (n_predict > params.n_predict || has_eos) { + break; + } + + // sample n_draft tokens from the draft model picking the best token + int n_past_cur = n_past_dft; + for (int i = 0; i < n_draft; ++i) { + float * logits = llama_get_logits(ctx_dft); + + candidates.clear(); + for (llama_token token_id = 0; token_id < n_vocab; token_id++) { + candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); + } + + llama_token_data_array cur_p = { candidates.data(), candidates.size(), false }; + + // computes softmax and sorts the candidates + llama_sample_softmax(ctx_dft, &cur_p); + + for (int i = 0; i < 3; ++i) { + LOG(" - draft candidate %d: %d (%.3f)\n", i, cur_p.data[i].id, cur_p.data[i].p); + } + + // too low probability, stop drafting + if (cur_p.data[0].p < 2*cur_p.data[1].p) { + break; + } + + drafted.push_back(cur_p.data[0].id); + ++n_drafted; + + if (i < n_draft - 1) { + // evaluate the drafted token on the draft model + llama_eval(ctx_dft, &drafted.back(), 1, n_past_cur, params.n_threads); + ++n_past_cur; + } + } + + // evaluate the target model on the drafted tokens + llama_eval(ctx_tgt, drafted.data(), drafted.size(), n_past_tgt, params.n_threads); + ++n_past_tgt; + + drafted.erase(drafted.begin()); + } + + auto t_dec_end = ggml_time_us(); + + LOG_TEE("\n\n"); + + LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input, (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f)); + LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f)); + + // TODO: make sure these numbers are computed correctly + LOG_TEE("\n"); + LOG_TEE("n_draft = %d\n", n_draft); + LOG_TEE("n_predict = %d\n", n_predict); + LOG_TEE("n_drafted = %d\n", n_drafted); + LOG_TEE("n_accept = %d\n", n_accept); + LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted); + + LOG_TEE("\ndraft:\n"); + llama_print_timings(ctx_dft); + + LOG_TEE("\ntarget:\n"); + llama_print_timings(ctx_tgt); + + llama_free(ctx_tgt); + llama_free_model(model_tgt); + + llama_free(ctx_dft); + llama_free_model(model_dft); + + llama_backend_free(); + + fprintf(stderr, "\n\n"); + + return 0; +} From cf9b08485c4c2d4d945c6e74fe20f273a38b6104 Mon Sep 17 00:00:00 2001 From: slaren Date: Sun, 3 Sep 2023 20:34:09 +0200 Subject: [PATCH 469/852] ggml-alloc : use virtual memory for measurement (#2973) * ggml-alloc : use virtual memory for measurement * compatibility fixes for MAP_ANONYMOUS * fallback to fixed address for systems without virtual memory --- ggml-alloc.c | 123 ++++++++++++++++++++++++++++++++++++++++----------- 1 file changed, 97 insertions(+), 26 deletions(-) diff --git a/ggml-alloc.c b/ggml-alloc.c index 459f121ca..c1939a4b7 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -1,3 +1,8 @@ +// defines MAP_ANONYMOUS +#ifndef _GNU_SOURCE +#define _GNU_SOURCE +#endif + #include "ggml-alloc.h" #include "ggml.h" #include @@ -6,6 +11,26 @@ #include #include +#ifdef __has_include + #if __has_include() + #include + #if defined(_POSIX_MAPPED_FILES) + #include + #include + #endif + #endif +#endif + +#if defined(_WIN32) + #define WIN32_LEAN_AND_MEAN + #ifndef NOMINMAX + #define NOMINMAX + #endif + #include + #include +#endif + + #define UNUSED(x) (void)(x) #define MAX(a, b) ((a) > (b) ? (a) : (b)) #define GGML_MAX_CONCUR (2*GGML_MAX_NODES) @@ -99,19 +124,24 @@ static void remove_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tens } #endif - -static size_t ggml_allocator_get_alloc_size(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { +static size_t ggml_allocr_get_alloc_size(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { return ggml_nbytes(tensor); UNUSED(alloc); } +// check if a tensor is allocated by this buffer +static bool ggml_allocr_is_own(struct ggml_allocr * alloc, const struct ggml_tensor * tensor) { + void * ptr = tensor->data; + return ptr >= alloc->data && (char *)ptr < (char *)alloc->data + alloc->max_size; +} + void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { #ifdef GGML_ALLOCATOR_DEBUG GGML_ASSERT(ggml_is_view(tensor) == false); // views generally get data pointer from one of their sources GGML_ASSERT(tensor->data == NULL); // avoid allocating tensor which already has memory allocated #endif - size_t size = ggml_allocator_get_alloc_size(alloc, tensor); + size_t size = ggml_allocr_get_alloc_size(alloc, tensor); size = aligned_offset(NULL, size, alloc->alignment); AT_PRINTF("%s: allocating %s (%zu bytes) - ", __func__, tensor->name, size); @@ -177,17 +207,17 @@ void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) } // this is a very naive implementation, but for our case the number of free blocks should be very small -static void ggml_allocator_free_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { +static void ggml_allocr_free_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { void * ptr = tensor->data; - if (ptr < alloc->data || (char*)ptr >= (char*)alloc->data + alloc->max_size) { + if (ggml_allocr_is_own(alloc, tensor) == false) { // the tensor was not allocated in this buffer // this can happen because the graph allocator will try to free weights and other tensors from different buffers // the easiest way to deal with this is just to ignore it return; } - size_t size = ggml_allocator_get_alloc_size(alloc, tensor); + size_t size = ggml_allocr_get_alloc_size(alloc, tensor); size = aligned_offset(NULL, size, alloc->alignment); AT_PRINTF("%s: freeing %s (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, size, alloc->n_free_blocks); @@ -281,24 +311,64 @@ struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment) return alloc; } -// address and size of the buffer when measuring -// it needs to be large enough to fit all the tensors, but it cannot overlap with other existing buffers -static void * const MEASURE_BASE_ADDR = (void *) 0x1000; -#if defined(__ARM_NEON) && !defined(__aarch64__) -// 32-bit -// TODO: Use for 32-bit x86 as well -static const size_t MEASURE_MAX_SIZE = (1ULL<<32) - 1; // 4 GB +// OS specific functions to allocate and free uncommitted virtual memory +static void * alloc_vmem(size_t size) { +#if defined(_WIN32) + return VirtualAlloc(NULL, size, MEM_RESERVE, PAGE_NOACCESS); +#elif defined(_POSIX_MAPPED_FILES) + return mmap(NULL, size, PROT_NONE, MAP_PRIVATE | MAP_ANON, -1, 0); #else -// 64-bit -static const size_t MEASURE_MAX_SIZE = 1ULL<<40; // 1 TB + // use a fixed address for other platforms + uintptr_t base_addr = (uintptr_t)-size - 0x100; + return (void *)base_addr; #endif +} + +static void free_vmem(void * base_addr, size_t size) { +#if defined(_WIN32) + VirtualFree(base_addr, 0, MEM_RELEASE); + UNUSED(size); +#elif defined(_POSIX_MAPPED_FILES) + munmap(base_addr, size); +#else + // nothing to do + UNUSED(base_addr); + UNUSED(size); +#endif +} + +// allocate uncommitted virtual memory to measure the size of the graph +static void alloc_measure_vmem(void ** base_addr, size_t * size) { + // 1TB for 64-bit, 1GB for 32-bit + *size = sizeof(void *) == 4 ? 1ULL<<30 : 1ULL<<40; + do { + *base_addr = alloc_vmem(*size); + if (*base_addr != NULL) { + AT_PRINTF("allocated %.2f GB of virtual memory for measure buffer at %p\n", *size / 1024.0 / 1024.0 / 1024.0, *base_addr); + return; + } + // try again with half the size + *size /= 2; + } while (*size > 0); + + GGML_ASSERT(!"failed to allocate virtual memory for measure buffer"); +} + +static void free_measure_vmem(void * base_addr, size_t size) { + free_vmem(base_addr, size); +} struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) { struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr) /* + n_free_blocks * sizeof(struct free_block) */); + void * base_addr; + size_t size; + + alloc_measure_vmem(&base_addr, &size); + *alloc = (struct ggml_allocr){ - /*.data = */ MEASURE_BASE_ADDR, - /*.size = */ MEASURE_MAX_SIZE, + /*.data = */ base_addr, + /*.size = */ size, /*.alignment = */ alignment, /*.n_free_blocks = */ 0, /*.free_blocks = */ {{0}}, @@ -318,6 +388,9 @@ struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) { } void ggml_allocr_free(struct ggml_allocr * alloc) { + if (alloc->measure) { + free_measure_vmem(alloc->data, alloc->size); + } free(alloc); } @@ -387,8 +460,7 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) } // if the node's data is external, then we cannot re-use it - if ((char *) parent->data < (char *) alloc->data || - (char *) parent->data >= ((char *) alloc->data + alloc->size)) { + if (ggml_allocr_is_own(alloc, parent) == false) { AT_PRINTF("not reusing parent %s for %s as %p is external\n", parent->name, node->name, parent->data); continue; } @@ -422,7 +494,7 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) } } -static size_t ggml_allocator_alloc_graph_tensors_n( +static size_t ggml_allocr_alloc_graph_tensors_n( struct ggml_allocr * alloc, struct ggml_cgraph ** graphs, int n_graphs, struct ggml_tensor *** inputs, struct ggml_tensor *** outputs) { @@ -500,11 +572,10 @@ static size_t ggml_allocator_alloc_graph_tensors_n( AT_PRINTF("\n"); } - // update parents // update immediately if there is no parse_seq // update only at barriers if there is parse_seq - if ((alloc->parse_seq_len==0) || alloc->parse_seq[ind] == -1) { + if ((alloc->parse_seq_len == 0) || alloc->parse_seq[ind] == -1) { int update_start = alloc->parse_seq_len ? last_barrier_pos : ind; int update_end = alloc->parse_seq_len ? ind : ind + 1; for (int i = update_start; i < update_end; i++) { @@ -528,12 +599,12 @@ static size_t ggml_allocator_alloc_graph_tensors_n( view_src_hn->n_views -= 1; AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src_hn->n_children, view_src_hn->n_views); if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src->data != node->data) { - ggml_allocator_free_tensor(alloc, view_src); + ggml_allocr_free_tensor(alloc, view_src); } } else { if (parent->data != node->data) { - ggml_allocator_free_tensor(alloc, parent); + ggml_allocr_free_tensor(alloc, parent); } } } @@ -550,7 +621,7 @@ static size_t ggml_allocator_alloc_graph_tensors_n( for (int i = 0; outputs[g][i] != NULL; i++) { struct ggml_tensor * output = outputs[g][i]; AT_PRINTF("output: %s\n", output->name); - ggml_allocator_free_tensor(alloc, output); + ggml_allocr_free_tensor(alloc, output); } } } @@ -559,5 +630,5 @@ static size_t ggml_allocator_alloc_graph_tensors_n( } size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph) { - return ggml_allocator_alloc_graph_tensors_n(alloc, &graph, 1, NULL, NULL); + return ggml_allocr_alloc_graph_tensors_n(alloc, &graph, 1, NULL, NULL); } From 35195689cd835464779c247b1c22ab9247418fd1 Mon Sep 17 00:00:00 2001 From: Jiahao Li Date: Mon, 4 Sep 2023 14:53:30 +0800 Subject: [PATCH 470/852] 2x faster (rms) norm cuda kernels (3.7% e2e improvement) (#2985) * 2x faster (rms) norm cuda kernels * Fix code style --- ggml-cuda.cu | 89 ++++++++++++++++++++++++++++++++++++++-------------- 1 file changed, 66 insertions(+), 23 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 8357f32f7..d2dbf824e 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -464,58 +464,91 @@ static __global__ void silu_f32(const float * x, float * dst, const int k) { dst[i] = x[i] / (1.0f + expf(-x[i])); } +static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) { +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32); + a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32); + } + return a; +} + +template static __global__ void norm_f32(const float * x, float * dst, const int ncols) { const int row = blockIdx.x*blockDim.y + threadIdx.y; const int tid = threadIdx.x; const float eps = 1e-5f; - float mean = 0.0f; - float var = 0.0f; + float2 mean_var = make_float2(0.f, 0.f); - for (int col = tid; col < ncols; col += WARP_SIZE) { + for (int col = tid; col < ncols; col += block_size) { const float xi = x[row*ncols + col]; - mean += xi; - var += xi * xi; + mean_var.x += xi; + mean_var.y += xi * xi; } // sum up partial sums -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - mean += __shfl_xor_sync(0xffffffff, mean, mask, 32); - var += __shfl_xor_sync(0xffffffff, var, mask, 32); + mean_var = warp_reduce_sum(mean_var); + if (block_size > WARP_SIZE) { + __shared__ float2 s_sum[32]; + int warp_id = threadIdx.x / WARP_SIZE; + int lane_id = threadIdx.x % WARP_SIZE; + if (lane_id == 0) { + s_sum[warp_id] = mean_var; + } + __syncthreads(); + mean_var = s_sum[lane_id]; + mean_var = warp_reduce_sum(mean_var); } - mean /= ncols; - var = var / ncols - mean * mean; - const float inv_var = rsqrtf(var + eps); + const float mean = mean_var.x / ncols; + const float var = mean_var.y / ncols - mean * mean; + const float inv_std = rsqrtf(var + eps); - for (int col = tid; col < ncols; col += WARP_SIZE) { - dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_var; + for (int col = tid; col < ncols; col += block_size) { + dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_std; } } +static __device__ __forceinline__ float warp_reduce_sum(float x) { +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + x += __shfl_xor_sync(0xffffffff, x, mask, 32); + } + return x; +} + +template static __global__ void rms_norm_f32(const float * x, float * dst, const int ncols, const float eps) { const int row = blockIdx.x*blockDim.y + threadIdx.y; const int tid = threadIdx.x; float tmp = 0.0f; // partial sum for thread in warp - for (int col = tid; col < ncols; col += WARP_SIZE) { + for (int col = tid; col < ncols; col += block_size) { const float xi = x[row*ncols + col]; tmp += xi * xi; } // sum up partial sums -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); + tmp = warp_reduce_sum(tmp); + if (block_size > WARP_SIZE) { + __shared__ float s_sum[32]; + int warp_id = threadIdx.x / WARP_SIZE; + int lane_id = threadIdx.x % WARP_SIZE; + if (lane_id == 0) { + s_sum[warp_id] = tmp; + } + __syncthreads(); + tmp = s_sum[lane_id]; + tmp = warp_reduce_sum(tmp); } const float mean = tmp / ncols; const float scale = rsqrtf(mean + eps); - for (int col = tid; col < ncols; col += WARP_SIZE) { + for (int col = tid; col < ncols; col += block_size) { dst[row*ncols + col] = scale * x[row*ncols + col]; } } @@ -4203,14 +4236,24 @@ static void silu_f32_cuda(const float * x, float * dst, const int k, cudaStream_ static void norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % WARP_SIZE == 0); - const dim3 block_dims(WARP_SIZE, 1, 1); - norm_f32<<>>(x, dst, ncols); + if (ncols < 1024) { + const dim3 block_dims(WARP_SIZE, 1, 1); + norm_f32<<>>(x, dst, ncols); + } else { + const dim3 block_dims(1024, 1, 1); + norm_f32<1024><<>>(x, dst, ncols); + } } static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) { GGML_ASSERT(ncols % WARP_SIZE == 0); - const dim3 block_dims(WARP_SIZE, 1, 1); - rms_norm_f32<<>>(x, dst, ncols, eps); + if (ncols < 1024) { + const dim3 block_dims(WARP_SIZE, 1, 1); + rms_norm_f32<<>>(x, dst, ncols, eps); + } else { + const dim3 block_dims(1024, 1, 1); + rms_norm_f32<1024><<>>(x, dst, ncols, eps); + } } static void quantize_row_q8_1_cuda(const float * x, void * vy, const int kx, const int ky, const int kx_padded, cudaStream_t stream) { From e4386f417faf894f6706eec005e24d142b577fcb Mon Sep 17 00:00:00 2001 From: Aarni Koskela Date: Mon, 4 Sep 2023 10:28:55 +0200 Subject: [PATCH 471/852] server : add a subtle loading animation to the edit box (#2466) * editorconfig: add override for the server HTML (which already is 2-space indented) * server: add a subtle loading animation to the edit box --- .editorconfig | 3 + examples/server/index.html.hpp | 3970 +++++++++++++++-------------- examples/server/public/index.html | 41 +- 3 files changed, 2058 insertions(+), 1956 deletions(-) diff --git a/.editorconfig b/.editorconfig index 135a7e4bc..f8245b85c 100644 --- a/.editorconfig +++ b/.editorconfig @@ -17,3 +17,6 @@ indent_style = tab [prompts/*.txt] insert_final_newline = unset + +[examples/server/public/*] +indent_size = 2 diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 84e6f97ba..f30232929 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -210,1024 +210,1120 @@ unsigned char index_html[] = { 0x66, 0x6f, 0x6e, 0x74, 0x2d, 0x73, 0x69, 0x7a, 0x65, 0x3a, 0x20, 0x38, 0x30, 0x25, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x3a, 0x20, 0x23, 0x38, 0x38, 0x38, 0x3b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x40, 0x6d, - 0x65, 0x64, 0x69, 0x61, 0x20, 0x28, 0x70, 0x72, 0x65, 0x66, 0x65, 0x72, - 0x73, 0x2d, 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x2d, 0x73, 0x63, 0x68, 0x65, - 0x6d, 0x65, 0x3a, 0x20, 0x64, 0x61, 0x72, 0x6b, 0x29, 0x20, 0x7b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x70, 0x6f, 0x70, 0x6f, 0x76, - 0x65, 0x72, 0x2d, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x20, 0x7b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x62, 0x61, 0x63, - 0x6b, 0x67, 0x72, 0x6f, 0x75, 0x6e, 0x64, 0x2d, 0x63, 0x6f, 0x6c, 0x6f, - 0x72, 0x3a, 0x20, 0x62, 0x6c, 0x61, 0x63, 0x6b, 0x3b, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, - 0x20, 0x20, 0x3c, 0x2f, 0x73, 0x74, 0x79, 0x6c, 0x65, 0x3e, 0x0a, 0x0a, - 0x20, 0x20, 0x3c, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x20, 0x74, 0x79, - 0x70, 0x65, 0x3d, 0x22, 0x6d, 0x6f, 0x64, 0x75, 0x6c, 0x65, 0x22, 0x3e, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x69, 0x6d, 0x70, 0x6f, 0x72, 0x74, 0x20, - 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x68, 0x74, 0x6d, 0x6c, - 0x2c, 0x20, 0x68, 0x2c, 0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x2c, - 0x20, 0x65, 0x66, 0x66, 0x65, 0x63, 0x74, 0x2c, 0x20, 0x63, 0x6f, 0x6d, - 0x70, 0x75, 0x74, 0x65, 0x64, 0x2c, 0x20, 0x72, 0x65, 0x6e, 0x64, 0x65, - 0x72, 0x2c, 0x20, 0x75, 0x73, 0x65, 0x53, 0x69, 0x67, 0x6e, 0x61, 0x6c, - 0x2c, 0x20, 0x75, 0x73, 0x65, 0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x2c, - 0x20, 0x75, 0x73, 0x65, 0x52, 0x65, 0x66, 0x2c, 0x20, 0x43, 0x6f, 0x6d, - 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, - 0x20, 0x66, 0x72, 0x6f, 0x6d, 0x20, 0x27, 0x2f, 0x69, 0x6e, 0x64, 0x65, - 0x78, 0x2e, 0x6a, 0x73, 0x27, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x69, 0x6d, 0x70, 0x6f, 0x72, 0x74, 0x20, 0x7b, 0x20, 0x6c, 0x6c, 0x61, - 0x6d, 0x61, 0x20, 0x7d, 0x20, 0x66, 0x72, 0x6f, 0x6d, 0x20, 0x27, 0x2f, - 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x2e, 0x6a, - 0x73, 0x27, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x69, 0x6d, 0x70, 0x6f, - 0x72, 0x74, 0x20, 0x7b, 0x20, 0x53, 0x63, 0x68, 0x65, 0x6d, 0x61, 0x43, - 0x6f, 0x6e, 0x76, 0x65, 0x72, 0x74, 0x65, 0x72, 0x20, 0x7d, 0x20, 0x66, - 0x72, 0x6f, 0x6d, 0x20, 0x27, 0x2f, 0x6a, 0x73, 0x6f, 0x6e, 0x2d, 0x73, - 0x63, 0x68, 0x65, 0x6d, 0x61, 0x2d, 0x74, 0x6f, 0x2d, 0x67, 0x72, 0x61, - 0x6d, 0x6d, 0x61, 0x72, 0x2e, 0x6d, 0x6a, 0x73, 0x27, 0x3b, 0x0a, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x73, 0x65, - 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x20, 0x3d, 0x20, 0x73, 0x69, 0x67, 0x6e, - 0x61, 0x6c, 0x28, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x70, - 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x3a, 0x20, 0x22, 0x54, 0x68, 0x69, 0x73, - 0x20, 0x69, 0x73, 0x20, 0x61, 0x20, 0x63, 0x6f, 0x6e, 0x76, 0x65, 0x72, - 0x73, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x62, 0x65, 0x74, 0x77, 0x65, - 0x65, 0x6e, 0x20, 0x55, 0x73, 0x65, 0x72, 0x20, 0x61, 0x6e, 0x64, 0x20, - 0x4c, 0x6c, 0x61, 0x6d, 0x61, 0x2c, 0x20, 0x61, 0x20, 0x66, 0x72, 0x69, - 0x65, 0x6e, 0x64, 0x6c, 0x79, 0x20, 0x63, 0x68, 0x61, 0x74, 0x62, 0x6f, - 0x74, 0x2e, 0x20, 0x4c, 0x6c, 0x61, 0x6d, 0x61, 0x20, 0x69, 0x73, 0x20, - 0x68, 0x65, 0x6c, 0x70, 0x66, 0x75, 0x6c, 0x2c, 0x20, 0x6b, 0x69, 0x6e, - 0x64, 0x2c, 0x20, 0x68, 0x6f, 0x6e, 0x65, 0x73, 0x74, 0x2c, 0x20, 0x67, - 0x6f, 0x6f, 0x64, 0x20, 0x61, 0x74, 0x20, 0x77, 0x72, 0x69, 0x74, 0x69, - 0x6e, 0x67, 0x2c, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x6e, 0x65, 0x76, 0x65, - 0x72, 0x20, 0x66, 0x61, 0x69, 0x6c, 0x73, 0x20, 0x74, 0x6f, 0x20, 0x61, - 0x6e, 0x73, 0x77, 0x65, 0x72, 0x20, 0x61, 0x6e, 0x79, 0x20, 0x72, 0x65, - 0x71, 0x75, 0x65, 0x73, 0x74, 0x73, 0x20, 0x69, 0x6d, 0x6d, 0x65, 0x64, - 0x69, 0x61, 0x74, 0x65, 0x6c, 0x79, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x77, - 0x69, 0x74, 0x68, 0x20, 0x70, 0x72, 0x65, 0x63, 0x69, 0x73, 0x69, 0x6f, - 0x6e, 0x2e, 0x22, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, - 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x3a, 0x20, 0x22, 0x7b, 0x7b, - 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x7d, 0x7d, 0x5c, 0x6e, 0x5c, 0x6e, - 0x7b, 0x7b, 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x7d, 0x7d, 0x5c, - 0x6e, 0x7b, 0x7b, 0x63, 0x68, 0x61, 0x72, 0x7d, 0x7d, 0x3a, 0x22, 0x2c, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x68, 0x69, 0x73, 0x74, 0x6f, - 0x72, 0x79, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x3a, 0x20, - 0x22, 0x7b, 0x7b, 0x6e, 0x61, 0x6d, 0x65, 0x7d, 0x7d, 0x3a, 0x20, 0x7b, - 0x7b, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x7d, 0x7d, 0x22, 0x2c, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x72, 0x61, 0x6e, 0x73, - 0x63, 0x72, 0x69, 0x70, 0x74, 0x3a, 0x20, 0x5b, 0x5d, 0x2c, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3a, 0x20, 0x22, - 0x63, 0x68, 0x61, 0x74, 0x22, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x63, 0x68, 0x61, 0x72, 0x3a, 0x20, 0x22, 0x4c, 0x6c, 0x61, 0x6d, - 0x61, 0x22, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x75, 0x73, - 0x65, 0x72, 0x3a, 0x20, 0x22, 0x55, 0x73, 0x65, 0x72, 0x22, 0x2c, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x7d, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, - 0x20, 0x3d, 0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x28, 0x7b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6e, 0x5f, 0x70, 0x72, 0x65, 0x64, - 0x69, 0x63, 0x74, 0x3a, 0x20, 0x34, 0x30, 0x30, 0x2c, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, - 0x75, 0x72, 0x65, 0x3a, 0x20, 0x30, 0x2e, 0x37, 0x2c, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x6c, - 0x61, 0x73, 0x74, 0x5f, 0x6e, 0x3a, 0x20, 0x32, 0x35, 0x36, 0x2c, 0x20, - 0x2f, 0x2f, 0x20, 0x30, 0x20, 0x3d, 0x20, 0x64, 0x69, 0x73, 0x61, 0x62, - 0x6c, 0x65, 0x20, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x2c, 0x20, - 0x2d, 0x31, 0x20, 0x3d, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x78, 0x74, - 0x20, 0x73, 0x69, 0x7a, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x70, 0x65, 0x6e, 0x61, 0x6c, - 0x74, 0x79, 0x3a, 0x20, 0x31, 0x2e, 0x31, 0x38, 0x2c, 0x20, 0x2f, 0x2f, - 0x20, 0x31, 0x2e, 0x30, 0x20, 0x3d, 0x20, 0x64, 0x69, 0x73, 0x61, 0x62, - 0x6c, 0x65, 0x64, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x6f, - 0x70, 0x5f, 0x6b, 0x3a, 0x20, 0x34, 0x30, 0x2c, 0x20, 0x2f, 0x2f, 0x20, - 0x3c, 0x3d, 0x20, 0x30, 0x20, 0x74, 0x6f, 0x20, 0x75, 0x73, 0x65, 0x20, - 0x76, 0x6f, 0x63, 0x61, 0x62, 0x20, 0x73, 0x69, 0x7a, 0x65, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x6f, 0x70, 0x5f, 0x70, 0x3a, 0x20, - 0x30, 0x2e, 0x35, 0x2c, 0x20, 0x2f, 0x2f, 0x20, 0x31, 0x2e, 0x30, 0x20, - 0x3d, 0x20, 0x64, 0x69, 0x73, 0x61, 0x62, 0x6c, 0x65, 0x64, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x66, 0x73, 0x5f, 0x7a, 0x3a, 0x20, - 0x31, 0x2e, 0x30, 0x2c, 0x20, 0x2f, 0x2f, 0x20, 0x31, 0x2e, 0x30, 0x20, - 0x3d, 0x20, 0x64, 0x69, 0x73, 0x61, 0x62, 0x6c, 0x65, 0x64, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x79, 0x70, 0x69, 0x63, 0x61, 0x6c, - 0x5f, 0x70, 0x3a, 0x20, 0x31, 0x2e, 0x30, 0x2c, 0x20, 0x2f, 0x2f, 0x20, - 0x31, 0x2e, 0x30, 0x20, 0x3d, 0x20, 0x64, 0x69, 0x73, 0x61, 0x62, 0x6c, - 0x65, 0x64, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x70, 0x72, 0x65, - 0x73, 0x65, 0x6e, 0x63, 0x65, 0x5f, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x74, - 0x79, 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x2c, 0x20, 0x2f, 0x2f, 0x20, 0x30, + 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x40, + 0x6b, 0x65, 0x79, 0x66, 0x72, 0x61, 0x6d, 0x65, 0x73, 0x20, 0x6c, 0x6f, + 0x61, 0x64, 0x69, 0x6e, 0x67, 0x2d, 0x62, 0x67, 0x2d, 0x77, 0x69, 0x70, + 0x65, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x30, 0x25, + 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x62, + 0x61, 0x63, 0x6b, 0x67, 0x72, 0x6f, 0x75, 0x6e, 0x64, 0x2d, 0x70, 0x6f, + 0x73, 0x69, 0x74, 0x69, 0x6f, 0x6e, 0x3a, 0x20, 0x30, 0x25, 0x3b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x31, 0x30, 0x30, 0x25, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x62, 0x61, 0x63, 0x6b, 0x67, 0x72, 0x6f, + 0x75, 0x6e, 0x64, 0x2d, 0x70, 0x6f, 0x73, 0x69, 0x74, 0x69, 0x6f, 0x6e, + 0x3a, 0x20, 0x31, 0x30, 0x30, 0x25, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x2e, 0x6c, 0x6f, 0x61, 0x64, 0x69, 0x6e, 0x67, 0x20, + 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2d, 0x2d, 0x6c, 0x6f, + 0x61, 0x64, 0x69, 0x6e, 0x67, 0x2d, 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x2d, + 0x31, 0x3a, 0x20, 0x23, 0x65, 0x65, 0x65, 0x65, 0x65, 0x65, 0x30, 0x30, + 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2d, 0x2d, 0x6c, 0x6f, + 0x61, 0x64, 0x69, 0x6e, 0x67, 0x2d, 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x2d, + 0x32, 0x3a, 0x20, 0x23, 0x65, 0x65, 0x65, 0x65, 0x65, 0x65, 0x66, 0x66, + 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x62, 0x61, 0x63, 0x6b, + 0x67, 0x72, 0x6f, 0x75, 0x6e, 0x64, 0x2d, 0x73, 0x69, 0x7a, 0x65, 0x3a, + 0x20, 0x35, 0x30, 0x25, 0x20, 0x31, 0x30, 0x30, 0x25, 0x3b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x62, 0x61, 0x63, 0x6b, 0x67, 0x72, 0x6f, + 0x75, 0x6e, 0x64, 0x2d, 0x69, 0x6d, 0x61, 0x67, 0x65, 0x3a, 0x20, 0x6c, + 0x69, 0x6e, 0x65, 0x61, 0x72, 0x2d, 0x67, 0x72, 0x61, 0x64, 0x69, 0x65, + 0x6e, 0x74, 0x28, 0x39, 0x30, 0x64, 0x65, 0x67, 0x2c, 0x20, 0x76, 0x61, + 0x72, 0x28, 0x2d, 0x2d, 0x6c, 0x6f, 0x61, 0x64, 0x69, 0x6e, 0x67, 0x2d, + 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x2d, 0x31, 0x29, 0x2c, 0x20, 0x76, 0x61, + 0x72, 0x28, 0x2d, 0x2d, 0x6c, 0x6f, 0x61, 0x64, 0x69, 0x6e, 0x67, 0x2d, + 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x2d, 0x32, 0x29, 0x2c, 0x20, 0x76, 0x61, + 0x72, 0x28, 0x2d, 0x2d, 0x6c, 0x6f, 0x61, 0x64, 0x69, 0x6e, 0x67, 0x2d, + 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x2d, 0x31, 0x29, 0x29, 0x3b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x61, 0x6e, 0x69, 0x6d, 0x61, 0x74, 0x69, + 0x6f, 0x6e, 0x3a, 0x20, 0x6c, 0x6f, 0x61, 0x64, 0x69, 0x6e, 0x67, 0x2d, + 0x62, 0x67, 0x2d, 0x77, 0x69, 0x70, 0x65, 0x20, 0x32, 0x73, 0x20, 0x6c, + 0x69, 0x6e, 0x65, 0x61, 0x72, 0x20, 0x69, 0x6e, 0x66, 0x69, 0x6e, 0x69, + 0x74, 0x65, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x40, 0x6d, 0x65, 0x64, 0x69, 0x61, 0x20, 0x28, 0x70, + 0x72, 0x65, 0x66, 0x65, 0x72, 0x73, 0x2d, 0x63, 0x6f, 0x6c, 0x6f, 0x72, + 0x2d, 0x73, 0x63, 0x68, 0x65, 0x6d, 0x65, 0x3a, 0x20, 0x64, 0x61, 0x72, + 0x6b, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, + 0x6c, 0x6f, 0x61, 0x64, 0x69, 0x6e, 0x67, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2d, 0x2d, 0x6c, 0x6f, 0x61, 0x64, + 0x69, 0x6e, 0x67, 0x2d, 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x2d, 0x31, 0x3a, + 0x20, 0x23, 0x32, 0x32, 0x32, 0x32, 0x32, 0x32, 0x30, 0x30, 0x3b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2d, 0x2d, 0x6c, 0x6f, + 0x61, 0x64, 0x69, 0x6e, 0x67, 0x2d, 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x2d, + 0x32, 0x3a, 0x20, 0x23, 0x32, 0x32, 0x32, 0x32, 0x32, 0x32, 0x66, 0x66, + 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x2e, 0x70, 0x6f, 0x70, 0x6f, 0x76, 0x65, 0x72, + 0x2d, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x20, 0x7b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x62, 0x61, 0x63, 0x6b, 0x67, + 0x72, 0x6f, 0x75, 0x6e, 0x64, 0x2d, 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x3a, + 0x20, 0x62, 0x6c, 0x61, 0x63, 0x6b, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, + 0x3c, 0x2f, 0x73, 0x74, 0x79, 0x6c, 0x65, 0x3e, 0x0a, 0x0a, 0x20, 0x20, + 0x3c, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x20, 0x74, 0x79, 0x70, 0x65, + 0x3d, 0x22, 0x6d, 0x6f, 0x64, 0x75, 0x6c, 0x65, 0x22, 0x3e, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x69, 0x6d, 0x70, 0x6f, 0x72, 0x74, 0x20, 0x7b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x2c, 0x20, + 0x68, 0x2c, 0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x2c, 0x20, 0x65, + 0x66, 0x66, 0x65, 0x63, 0x74, 0x2c, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x75, + 0x74, 0x65, 0x64, 0x2c, 0x20, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x2c, + 0x20, 0x75, 0x73, 0x65, 0x53, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x2c, 0x20, + 0x75, 0x73, 0x65, 0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x2c, 0x20, 0x75, + 0x73, 0x65, 0x52, 0x65, 0x66, 0x2c, 0x20, 0x43, 0x6f, 0x6d, 0x70, 0x6f, + 0x6e, 0x65, 0x6e, 0x74, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x20, 0x66, + 0x72, 0x6f, 0x6d, 0x20, 0x27, 0x2f, 0x69, 0x6e, 0x64, 0x65, 0x78, 0x2e, + 0x6a, 0x73, 0x27, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x69, 0x6d, + 0x70, 0x6f, 0x72, 0x74, 0x20, 0x7b, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, + 0x20, 0x7d, 0x20, 0x66, 0x72, 0x6f, 0x6d, 0x20, 0x27, 0x2f, 0x63, 0x6f, + 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x2e, 0x6a, 0x73, 0x27, + 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x69, 0x6d, 0x70, 0x6f, 0x72, 0x74, + 0x20, 0x7b, 0x20, 0x53, 0x63, 0x68, 0x65, 0x6d, 0x61, 0x43, 0x6f, 0x6e, + 0x76, 0x65, 0x72, 0x74, 0x65, 0x72, 0x20, 0x7d, 0x20, 0x66, 0x72, 0x6f, + 0x6d, 0x20, 0x27, 0x2f, 0x6a, 0x73, 0x6f, 0x6e, 0x2d, 0x73, 0x63, 0x68, + 0x65, 0x6d, 0x61, 0x2d, 0x74, 0x6f, 0x2d, 0x67, 0x72, 0x61, 0x6d, 0x6d, + 0x61, 0x72, 0x2e, 0x6d, 0x6a, 0x73, 0x27, 0x3b, 0x0a, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x73, 0x65, 0x73, 0x73, + 0x69, 0x6f, 0x6e, 0x20, 0x3d, 0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, + 0x28, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x70, 0x72, 0x6f, + 0x6d, 0x70, 0x74, 0x3a, 0x20, 0x22, 0x54, 0x68, 0x69, 0x73, 0x20, 0x69, + 0x73, 0x20, 0x61, 0x20, 0x63, 0x6f, 0x6e, 0x76, 0x65, 0x72, 0x73, 0x61, + 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x62, 0x65, 0x74, 0x77, 0x65, 0x65, 0x6e, + 0x20, 0x55, 0x73, 0x65, 0x72, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x4c, 0x6c, + 0x61, 0x6d, 0x61, 0x2c, 0x20, 0x61, 0x20, 0x66, 0x72, 0x69, 0x65, 0x6e, + 0x64, 0x6c, 0x79, 0x20, 0x63, 0x68, 0x61, 0x74, 0x62, 0x6f, 0x74, 0x2e, + 0x20, 0x4c, 0x6c, 0x61, 0x6d, 0x61, 0x20, 0x69, 0x73, 0x20, 0x68, 0x65, + 0x6c, 0x70, 0x66, 0x75, 0x6c, 0x2c, 0x20, 0x6b, 0x69, 0x6e, 0x64, 0x2c, + 0x20, 0x68, 0x6f, 0x6e, 0x65, 0x73, 0x74, 0x2c, 0x20, 0x67, 0x6f, 0x6f, + 0x64, 0x20, 0x61, 0x74, 0x20, 0x77, 0x72, 0x69, 0x74, 0x69, 0x6e, 0x67, + 0x2c, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x6e, 0x65, 0x76, 0x65, 0x72, 0x20, + 0x66, 0x61, 0x69, 0x6c, 0x73, 0x20, 0x74, 0x6f, 0x20, 0x61, 0x6e, 0x73, + 0x77, 0x65, 0x72, 0x20, 0x61, 0x6e, 0x79, 0x20, 0x72, 0x65, 0x71, 0x75, + 0x65, 0x73, 0x74, 0x73, 0x20, 0x69, 0x6d, 0x6d, 0x65, 0x64, 0x69, 0x61, + 0x74, 0x65, 0x6c, 0x79, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x77, 0x69, 0x74, + 0x68, 0x20, 0x70, 0x72, 0x65, 0x63, 0x69, 0x73, 0x69, 0x6f, 0x6e, 0x2e, + 0x22, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x65, 0x6d, + 0x70, 0x6c, 0x61, 0x74, 0x65, 0x3a, 0x20, 0x22, 0x7b, 0x7b, 0x70, 0x72, + 0x6f, 0x6d, 0x70, 0x74, 0x7d, 0x7d, 0x5c, 0x6e, 0x5c, 0x6e, 0x7b, 0x7b, + 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x7d, 0x7d, 0x5c, 0x6e, 0x7b, + 0x7b, 0x63, 0x68, 0x61, 0x72, 0x7d, 0x7d, 0x3a, 0x22, 0x2c, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, + 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x3a, 0x20, 0x22, 0x7b, + 0x7b, 0x6e, 0x61, 0x6d, 0x65, 0x7d, 0x7d, 0x3a, 0x20, 0x7b, 0x7b, 0x6d, + 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x7d, 0x7d, 0x22, 0x2c, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, + 0x69, 0x70, 0x74, 0x3a, 0x20, 0x5b, 0x5d, 0x2c, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3a, 0x20, 0x22, 0x63, 0x68, + 0x61, 0x74, 0x22, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, + 0x68, 0x61, 0x72, 0x3a, 0x20, 0x22, 0x4c, 0x6c, 0x61, 0x6d, 0x61, 0x22, + 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x75, 0x73, 0x65, 0x72, + 0x3a, 0x20, 0x22, 0x55, 0x73, 0x65, 0x72, 0x22, 0x2c, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x7d, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, + 0x6e, 0x73, 0x74, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x20, 0x3d, + 0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x28, 0x7b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x6e, 0x5f, 0x70, 0x72, 0x65, 0x64, 0x69, 0x63, + 0x74, 0x3a, 0x20, 0x34, 0x30, 0x30, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, + 0x65, 0x3a, 0x20, 0x30, 0x2e, 0x37, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x6c, 0x61, 0x73, + 0x74, 0x5f, 0x6e, 0x3a, 0x20, 0x32, 0x35, 0x36, 0x2c, 0x20, 0x2f, 0x2f, + 0x20, 0x30, 0x20, 0x3d, 0x20, 0x64, 0x69, 0x73, 0x61, 0x62, 0x6c, 0x65, + 0x20, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x2c, 0x20, 0x2d, 0x31, + 0x20, 0x3d, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x78, 0x74, 0x20, 0x73, + 0x69, 0x7a, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, + 0x70, 0x65, 0x61, 0x74, 0x5f, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, + 0x3a, 0x20, 0x31, 0x2e, 0x31, 0x38, 0x2c, 0x20, 0x2f, 0x2f, 0x20, 0x31, 0x2e, 0x30, 0x20, 0x3d, 0x20, 0x64, 0x69, 0x73, 0x61, 0x62, 0x6c, 0x65, - 0x64, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x66, 0x72, 0x65, 0x71, - 0x75, 0x65, 0x6e, 0x63, 0x79, 0x5f, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x74, - 0x79, 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x2c, 0x20, 0x2f, 0x2f, 0x20, 0x30, - 0x2e, 0x30, 0x20, 0x3d, 0x20, 0x64, 0x69, 0x73, 0x61, 0x62, 0x6c, 0x65, - 0x64, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x69, 0x72, 0x6f, - 0x73, 0x74, 0x61, 0x74, 0x3a, 0x20, 0x30, 0x2c, 0x20, 0x2f, 0x2f, 0x20, - 0x30, 0x2f, 0x31, 0x2f, 0x32, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x6d, 0x69, 0x72, 0x6f, 0x73, 0x74, 0x61, 0x74, 0x5f, 0x74, 0x61, 0x75, - 0x3a, 0x20, 0x35, 0x2c, 0x20, 0x2f, 0x2f, 0x20, 0x74, 0x61, 0x72, 0x67, - 0x65, 0x74, 0x20, 0x65, 0x6e, 0x74, 0x72, 0x6f, 0x70, 0x79, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x69, 0x72, 0x6f, 0x73, 0x74, 0x61, - 0x74, 0x5f, 0x65, 0x74, 0x61, 0x3a, 0x20, 0x30, 0x2e, 0x31, 0x2c, 0x20, - 0x2f, 0x2f, 0x20, 0x6c, 0x65, 0x61, 0x72, 0x6e, 0x69, 0x6e, 0x67, 0x20, - 0x72, 0x61, 0x74, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x67, - 0x72, 0x61, 0x6d, 0x6d, 0x61, 0x72, 0x3a, 0x20, 0x27, 0x27, 0x2c, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6e, 0x5f, 0x70, 0x72, 0x6f, 0x62, - 0x73, 0x3a, 0x20, 0x30, 0x2c, 0x20, 0x2f, 0x2f, 0x20, 0x6e, 0x6f, 0x20, - 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x70, - 0x72, 0x6f, 0x62, 0x61, 0x62, 0x69, 0x6c, 0x69, 0x74, 0x69, 0x65, 0x73, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x2f, 0x2a, 0x20, 0x53, 0x54, 0x41, 0x52, 0x54, 0x3a, 0x20, 0x53, - 0x75, 0x70, 0x70, 0x6f, 0x72, 0x74, 0x20, 0x66, 0x6f, 0x72, 0x20, 0x73, - 0x74, 0x6f, 0x72, 0x69, 0x6e, 0x67, 0x20, 0x70, 0x72, 0x6f, 0x6d, 0x70, - 0x74, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x20, - 0x61, 0x6e, 0x64, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x65, 0x74, 0x65, - 0x72, 0x73, 0x20, 0x69, 0x6e, 0x20, 0x62, 0x6f, 0x72, 0x77, 0x73, 0x65, - 0x72, 0x20, 0x4c, 0x6f, 0x63, 0x61, 0x6c, 0x53, 0x74, 0x6f, 0x72, 0x61, - 0x67, 0x65, 0x20, 0x2a, 0x2f, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, - 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x5f, 0x73, - 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, - 0x67, 0x65, 0x4b, 0x65, 0x79, 0x20, 0x3d, 0x20, 0x22, 0x6c, 0x6c, 0x61, - 0x6d, 0x61, 0x63, 0x70, 0x70, 0x5f, 0x73, 0x65, 0x72, 0x76, 0x65, 0x72, - 0x5f, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, - 0x67, 0x65, 0x22, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x66, 0x75, - 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x6c, 0x6f, 0x63, 0x61, 0x6c, - 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x5f, 0x73, 0x65, 0x74, - 0x44, 0x61, 0x74, 0x61, 0x46, 0x72, 0x6f, 0x6d, 0x4f, 0x62, 0x6a, 0x65, - 0x63, 0x74, 0x28, 0x74, 0x61, 0x67, 0x2c, 0x20, 0x63, 0x6f, 0x6e, 0x74, - 0x65, 0x6e, 0x74, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x53, 0x74, 0x6f, 0x72, 0x61, 0x67, - 0x65, 0x2e, 0x73, 0x65, 0x74, 0x49, 0x74, 0x65, 0x6d, 0x28, 0x6c, 0x6f, - 0x63, 0x61, 0x6c, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x5f, - 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x4b, 0x65, 0x79, 0x20, 0x2b, - 0x20, 0x27, 0x2f, 0x27, 0x20, 0x2b, 0x20, 0x74, 0x61, 0x67, 0x2c, 0x20, - 0x4a, 0x53, 0x4f, 0x4e, 0x2e, 0x73, 0x74, 0x72, 0x69, 0x6e, 0x67, 0x69, - 0x66, 0x79, 0x28, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x29, 0x29, - 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x6c, 0x6f, - 0x63, 0x61, 0x6c, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x5f, - 0x73, 0x65, 0x74, 0x44, 0x61, 0x74, 0x61, 0x46, 0x72, 0x6f, 0x6d, 0x52, - 0x61, 0x77, 0x54, 0x65, 0x78, 0x74, 0x28, 0x74, 0x61, 0x67, 0x2c, 0x20, - 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x29, 0x20, 0x7b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x53, 0x74, - 0x6f, 0x72, 0x61, 0x67, 0x65, 0x2e, 0x73, 0x65, 0x74, 0x49, 0x74, 0x65, - 0x6d, 0x28, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x5f, 0x73, 0x74, 0x6f, 0x72, - 0x61, 0x67, 0x65, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x4b, - 0x65, 0x79, 0x20, 0x2b, 0x20, 0x27, 0x2f, 0x27, 0x20, 0x2b, 0x20, 0x74, - 0x61, 0x67, 0x2c, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x29, - 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x6c, 0x6f, - 0x63, 0x61, 0x6c, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x5f, - 0x67, 0x65, 0x74, 0x44, 0x61, 0x74, 0x61, 0x41, 0x73, 0x4f, 0x62, 0x6a, - 0x65, 0x63, 0x74, 0x28, 0x74, 0x61, 0x67, 0x29, 0x20, 0x7b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x69, - 0x74, 0x65, 0x6d, 0x20, 0x3d, 0x20, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x53, - 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x2e, 0x67, 0x65, 0x74, 0x49, 0x74, - 0x65, 0x6d, 0x28, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x5f, 0x73, 0x74, 0x6f, + 0x64, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x6f, 0x70, 0x5f, + 0x6b, 0x3a, 0x20, 0x34, 0x30, 0x2c, 0x20, 0x2f, 0x2f, 0x20, 0x3c, 0x3d, + 0x20, 0x30, 0x20, 0x74, 0x6f, 0x20, 0x75, 0x73, 0x65, 0x20, 0x76, 0x6f, + 0x63, 0x61, 0x62, 0x20, 0x73, 0x69, 0x7a, 0x65, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x74, 0x6f, 0x70, 0x5f, 0x70, 0x3a, 0x20, 0x30, 0x2e, + 0x35, 0x2c, 0x20, 0x2f, 0x2f, 0x20, 0x31, 0x2e, 0x30, 0x20, 0x3d, 0x20, + 0x64, 0x69, 0x73, 0x61, 0x62, 0x6c, 0x65, 0x64, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x74, 0x66, 0x73, 0x5f, 0x7a, 0x3a, 0x20, 0x31, 0x2e, + 0x30, 0x2c, 0x20, 0x2f, 0x2f, 0x20, 0x31, 0x2e, 0x30, 0x20, 0x3d, 0x20, + 0x64, 0x69, 0x73, 0x61, 0x62, 0x6c, 0x65, 0x64, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x74, 0x79, 0x70, 0x69, 0x63, 0x61, 0x6c, 0x5f, 0x70, + 0x3a, 0x20, 0x31, 0x2e, 0x30, 0x2c, 0x20, 0x2f, 0x2f, 0x20, 0x31, 0x2e, + 0x30, 0x20, 0x3d, 0x20, 0x64, 0x69, 0x73, 0x61, 0x62, 0x6c, 0x65, 0x64, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x70, 0x72, 0x65, 0x73, 0x65, + 0x6e, 0x63, 0x65, 0x5f, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x3a, + 0x20, 0x30, 0x2e, 0x30, 0x2c, 0x20, 0x2f, 0x2f, 0x20, 0x30, 0x2e, 0x30, + 0x20, 0x3d, 0x20, 0x64, 0x69, 0x73, 0x61, 0x62, 0x6c, 0x65, 0x64, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x66, 0x72, 0x65, 0x71, 0x75, 0x65, + 0x6e, 0x63, 0x79, 0x5f, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x3a, + 0x20, 0x30, 0x2e, 0x30, 0x2c, 0x20, 0x2f, 0x2f, 0x20, 0x30, 0x2e, 0x30, + 0x20, 0x3d, 0x20, 0x64, 0x69, 0x73, 0x61, 0x62, 0x6c, 0x65, 0x64, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x69, 0x72, 0x6f, 0x73, 0x74, + 0x61, 0x74, 0x3a, 0x20, 0x30, 0x2c, 0x20, 0x2f, 0x2f, 0x20, 0x30, 0x2f, + 0x31, 0x2f, 0x32, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x69, + 0x72, 0x6f, 0x73, 0x74, 0x61, 0x74, 0x5f, 0x74, 0x61, 0x75, 0x3a, 0x20, + 0x35, 0x2c, 0x20, 0x2f, 0x2f, 0x20, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, + 0x20, 0x65, 0x6e, 0x74, 0x72, 0x6f, 0x70, 0x79, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x6d, 0x69, 0x72, 0x6f, 0x73, 0x74, 0x61, 0x74, 0x5f, + 0x65, 0x74, 0x61, 0x3a, 0x20, 0x30, 0x2e, 0x31, 0x2c, 0x20, 0x2f, 0x2f, + 0x20, 0x6c, 0x65, 0x61, 0x72, 0x6e, 0x69, 0x6e, 0x67, 0x20, 0x72, 0x61, + 0x74, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x67, 0x72, 0x61, + 0x6d, 0x6d, 0x61, 0x72, 0x3a, 0x20, 0x27, 0x27, 0x2c, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x6e, 0x5f, 0x70, 0x72, 0x6f, 0x62, 0x73, 0x3a, + 0x20, 0x30, 0x2c, 0x20, 0x2f, 0x2f, 0x20, 0x6e, 0x6f, 0x20, 0x63, 0x6f, + 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x70, 0x72, 0x6f, + 0x62, 0x61, 0x62, 0x69, 0x6c, 0x69, 0x74, 0x69, 0x65, 0x73, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x7d, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, + 0x2a, 0x20, 0x53, 0x54, 0x41, 0x52, 0x54, 0x3a, 0x20, 0x53, 0x75, 0x70, + 0x70, 0x6f, 0x72, 0x74, 0x20, 0x66, 0x6f, 0x72, 0x20, 0x73, 0x74, 0x6f, + 0x72, 0x69, 0x6e, 0x67, 0x20, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x20, + 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x20, 0x61, 0x6e, + 0x64, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x65, 0x74, 0x65, 0x72, 0x73, + 0x20, 0x69, 0x6e, 0x20, 0x62, 0x6f, 0x72, 0x77, 0x73, 0x65, 0x72, 0x20, + 0x4c, 0x6f, 0x63, 0x61, 0x6c, 0x53, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, + 0x20, 0x2a, 0x2f, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, + 0x73, 0x74, 0x20, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, - 0x4b, 0x65, 0x79, 0x20, 0x2b, 0x20, 0x27, 0x2f, 0x27, 0x20, 0x2b, 0x20, - 0x74, 0x61, 0x67, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x69, 0x66, 0x20, 0x28, 0x21, 0x69, 0x74, 0x65, 0x6d, 0x29, 0x20, 0x7b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, - 0x75, 0x72, 0x6e, 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x3b, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x7d, 0x20, 0x65, 0x6c, 0x73, 0x65, 0x20, 0x7b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, - 0x75, 0x72, 0x6e, 0x20, 0x4a, 0x53, 0x4f, 0x4e, 0x2e, 0x70, 0x61, 0x72, - 0x73, 0x65, 0x28, 0x69, 0x74, 0x65, 0x6d, 0x29, 0x3b, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, - 0x6e, 0x20, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x5f, 0x73, 0x74, 0x6f, 0x72, - 0x61, 0x67, 0x65, 0x5f, 0x67, 0x65, 0x74, 0x44, 0x61, 0x74, 0x61, 0x41, - 0x73, 0x52, 0x61, 0x77, 0x54, 0x65, 0x78, 0x74, 0x28, 0x74, 0x61, 0x67, - 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, - 0x6e, 0x73, 0x74, 0x20, 0x69, 0x74, 0x65, 0x6d, 0x20, 0x3d, 0x20, 0x6c, + 0x4b, 0x65, 0x79, 0x20, 0x3d, 0x20, 0x22, 0x6c, 0x6c, 0x61, 0x6d, 0x61, + 0x63, 0x70, 0x70, 0x5f, 0x73, 0x65, 0x72, 0x76, 0x65, 0x72, 0x5f, 0x6c, + 0x6f, 0x63, 0x61, 0x6c, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, + 0x22, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x66, 0x75, 0x6e, 0x63, + 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x5f, 0x73, + 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x5f, 0x73, 0x65, 0x74, 0x44, 0x61, + 0x74, 0x61, 0x46, 0x72, 0x6f, 0x6d, 0x4f, 0x62, 0x6a, 0x65, 0x63, 0x74, + 0x28, 0x74, 0x61, 0x67, 0x2c, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, + 0x74, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x53, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x2e, - 0x67, 0x65, 0x74, 0x49, 0x74, 0x65, 0x6d, 0x28, 0x6c, 0x6f, 0x63, 0x61, + 0x73, 0x65, 0x74, 0x49, 0x74, 0x65, 0x6d, 0x28, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x4b, 0x65, 0x79, 0x20, 0x2b, 0x20, 0x27, - 0x2f, 0x27, 0x20, 0x2b, 0x20, 0x74, 0x61, 0x67, 0x29, 0x3b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x21, 0x69, 0x74, - 0x65, 0x6d, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x6e, 0x75, 0x6c, - 0x6c, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x20, 0x65, - 0x6c, 0x73, 0x65, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x69, 0x74, 0x65, - 0x6d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, - 0x20, 0x63, 0x72, 0x65, 0x61, 0x74, 0x65, 0x20, 0x61, 0x20, 0x63, 0x6f, - 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x20, 0x66, 0x6f, 0x72, 0x20, - 0x75, 0x73, 0x65, 0x72, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, - 0x65, 0x73, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x73, 0x65, 0x74, 0x74, 0x69, - 0x6e, 0x67, 0x73, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, - 0x73, 0x74, 0x20, 0x73, 0x61, 0x76, 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, - 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x20, 0x3d, 0x20, - 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x28, 0x7b, 0x7d, 0x29, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x73, 0x65, 0x6c, - 0x65, 0x63, 0x74, 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, - 0x70, 0x6c, 0x61, 0x74, 0x65, 0x20, 0x3d, 0x20, 0x73, 0x69, 0x67, 0x6e, - 0x61, 0x6c, 0x28, 0x7b, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3a, 0x20, 0x27, - 0x27, 0x2c, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x3a, - 0x20, 0x7b, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x3a, 0x20, - 0x7b, 0x7d, 0x2c, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x3a, 0x20, - 0x7b, 0x7d, 0x20, 0x7d, 0x20, 0x7d, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x2f, 0x2f, 0x20, 0x6c, 0x65, 0x74, 0x27, 0x73, 0x20, 0x69, 0x6d, - 0x70, 0x6f, 0x72, 0x74, 0x20, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x6c, 0x79, - 0x20, 0x73, 0x61, 0x76, 0x65, 0x64, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, - 0x61, 0x74, 0x65, 0x73, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x73, 0x65, 0x74, - 0x74, 0x69, 0x6e, 0x67, 0x73, 0x20, 0x69, 0x66, 0x20, 0x74, 0x68, 0x65, - 0x72, 0x65, 0x20, 0x61, 0x72, 0x65, 0x20, 0x61, 0x6e, 0x79, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x75, 0x73, 0x65, 0x72, 0x20, 0x74, - 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x20, 0x61, 0x6e, 0x64, - 0x20, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x20, 0x61, 0x72, - 0x65, 0x20, 0x73, 0x74, 0x6f, 0x72, 0x65, 0x64, 0x20, 0x69, 0x6e, 0x20, - 0x6f, 0x6e, 0x65, 0x20, 0x6f, 0x62, 0x6a, 0x65, 0x63, 0x74, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x69, 0x6e, 0x20, 0x66, 0x6f, 0x72, - 0x6d, 0x20, 0x6f, 0x66, 0x20, 0x7b, 0x20, 0x22, 0x74, 0x65, 0x6d, 0x70, - 0x6c, 0x61, 0x74, 0x65, 0x6e, 0x61, 0x6d, 0x65, 0x22, 0x3a, 0x20, 0x22, - 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x64, 0x61, 0x74, 0x61, - 0x22, 0x20, 0x7d, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x7b, 0x20, 0x22, 0x73, - 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x74, 0x65, 0x6d, 0x70, 0x6c, - 0x61, 0x74, 0x65, 0x6e, 0x61, 0x6d, 0x65, 0x22, 0x3a, 0x22, 0x73, 0x65, - 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x64, 0x61, 0x74, 0x61, 0x22, 0x20, - 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x6f, - 0x6c, 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, 0x27, 0x49, 0x6d, 0x70, 0x6f, - 0x72, 0x74, 0x69, 0x6e, 0x67, 0x20, 0x73, 0x61, 0x76, 0x65, 0x64, 0x20, - 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x27, 0x29, 0x0a, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x65, 0x74, 0x20, 0x69, 0x6d, 0x70, - 0x6f, 0x72, 0x74, 0x65, 0x64, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, - 0x65, 0x73, 0x20, 0x3d, 0x20, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x5f, 0x73, - 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x5f, 0x67, 0x65, 0x74, 0x44, 0x61, - 0x74, 0x61, 0x41, 0x73, 0x4f, 0x62, 0x6a, 0x65, 0x63, 0x74, 0x28, 0x27, - 0x75, 0x73, 0x65, 0x72, 0x5f, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, - 0x65, 0x73, 0x27, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, - 0x20, 0x28, 0x69, 0x6d, 0x70, 0x6f, 0x72, 0x74, 0x65, 0x64, 0x54, 0x65, - 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x29, 0x20, 0x7b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x73, 0x61, 0x76, 0x65, - 0x64, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x20, - 0x77, 0x65, 0x72, 0x65, 0x20, 0x73, 0x75, 0x63, 0x63, 0x65, 0x73, 0x73, - 0x66, 0x75, 0x6c, 0x79, 0x20, 0x69, 0x6d, 0x70, 0x6f, 0x72, 0x74, 0x65, - 0x64, 0x2e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, - 0x6e, 0x73, 0x6f, 0x6c, 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, 0x27, 0x50, - 0x72, 0x6f, 0x63, 0x65, 0x73, 0x73, 0x69, 0x6e, 0x67, 0x20, 0x73, 0x61, - 0x76, 0x65, 0x64, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, - 0x73, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x75, 0x70, 0x64, 0x61, 0x74, 0x69, - 0x6e, 0x67, 0x20, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x20, 0x74, - 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x27, 0x29, 0x0a, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x63, 0x6f, 0x6e, 0x73, 0x6f, - 0x6c, 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, 0x69, 0x6d, 0x70, 0x6f, 0x72, - 0x74, 0x65, 0x64, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, - 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x61, 0x76, - 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, - 0x74, 0x65, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, - 0x69, 0x6d, 0x70, 0x6f, 0x72, 0x74, 0x65, 0x64, 0x54, 0x65, 0x6d, 0x70, - 0x6c, 0x61, 0x74, 0x65, 0x73, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x2f, 0x2f, 0x6f, 0x76, 0x65, 0x72, 0x72, 0x69, 0x64, 0x65, - 0x20, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x20, 0x74, 0x65, 0x6d, - 0x70, 0x6c, 0x61, 0x74, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x73, 0x61, 0x76, 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, - 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, - 0x2e, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x20, 0x3d, 0x20, 0x7b, - 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x3a, 0x20, 0x73, 0x65, - 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2c, - 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x3a, 0x20, 0x70, 0x61, 0x72, - 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x7d, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x5f, - 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x5f, 0x73, 0x65, 0x74, 0x44, - 0x61, 0x74, 0x61, 0x46, 0x72, 0x6f, 0x6d, 0x4f, 0x62, 0x6a, 0x65, 0x63, - 0x74, 0x28, 0x27, 0x75, 0x73, 0x65, 0x72, 0x5f, 0x74, 0x65, 0x6d, 0x70, - 0x6c, 0x61, 0x74, 0x65, 0x73, 0x27, 0x2c, 0x20, 0x73, 0x61, 0x76, 0x65, - 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, - 0x65, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x0a, 0x20, 0x20, + 0x2f, 0x27, 0x20, 0x2b, 0x20, 0x74, 0x61, 0x67, 0x2c, 0x20, 0x4a, 0x53, + 0x4f, 0x4e, 0x2e, 0x73, 0x74, 0x72, 0x69, 0x6e, 0x67, 0x69, 0x66, 0x79, + 0x28, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x29, 0x29, 0x3b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x66, + 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x6c, 0x6f, 0x63, 0x61, + 0x6c, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x5f, 0x73, 0x65, + 0x74, 0x44, 0x61, 0x74, 0x61, 0x46, 0x72, 0x6f, 0x6d, 0x52, 0x61, 0x77, + 0x54, 0x65, 0x78, 0x74, 0x28, 0x74, 0x61, 0x67, 0x2c, 0x20, 0x63, 0x6f, + 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x53, 0x74, 0x6f, 0x72, + 0x61, 0x67, 0x65, 0x2e, 0x73, 0x65, 0x74, 0x49, 0x74, 0x65, 0x6d, 0x28, + 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, + 0x65, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x4b, 0x65, 0x79, + 0x20, 0x2b, 0x20, 0x27, 0x2f, 0x27, 0x20, 0x2b, 0x20, 0x74, 0x61, 0x67, + 0x2c, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x29, 0x3b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x66, + 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x6c, 0x6f, 0x63, 0x61, + 0x6c, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x5f, 0x67, 0x65, + 0x74, 0x44, 0x61, 0x74, 0x61, 0x41, 0x73, 0x4f, 0x62, 0x6a, 0x65, 0x63, + 0x74, 0x28, 0x74, 0x61, 0x67, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x69, 0x74, 0x65, + 0x6d, 0x20, 0x3d, 0x20, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x53, 0x74, 0x6f, + 0x72, 0x61, 0x67, 0x65, 0x2e, 0x67, 0x65, 0x74, 0x49, 0x74, 0x65, 0x6d, + 0x28, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, + 0x67, 0x65, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x4b, 0x65, + 0x79, 0x20, 0x2b, 0x20, 0x27, 0x2f, 0x27, 0x20, 0x2b, 0x20, 0x74, 0x61, + 0x67, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, + 0x20, 0x28, 0x21, 0x69, 0x74, 0x65, 0x6d, 0x29, 0x20, 0x7b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, + 0x6e, 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x20, 0x65, 0x6c, 0x73, 0x65, 0x20, 0x7b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x6e, 0x6f, 0x20, 0x73, - 0x61, 0x76, 0x65, 0x64, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, - 0x65, 0x73, 0x20, 0x64, 0x65, 0x74, 0x65, 0x63, 0x74, 0x65, 0x64, 0x2e, - 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, - 0x6f, 0x6c, 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, 0x27, 0x49, 0x6e, 0x69, - 0x74, 0x69, 0x61, 0x6c, 0x69, 0x7a, 0x69, 0x6e, 0x67, 0x20, 0x4c, 0x6f, - 0x63, 0x61, 0x6c, 0x53, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x20, 0x61, - 0x6e, 0x64, 0x20, 0x73, 0x61, 0x76, 0x69, 0x6e, 0x67, 0x20, 0x64, 0x65, - 0x66, 0x61, 0x75, 0x6c, 0x74, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, - 0x74, 0x65, 0x27, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x73, 0x61, 0x76, 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, - 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, - 0x20, 0x3d, 0x20, 0x7b, 0x20, 0x22, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, - 0x74, 0x22, 0x3a, 0x20, 0x7b, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, - 0x6e, 0x3a, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, - 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, - 0x3a, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, - 0x75, 0x65, 0x20, 0x7d, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, - 0x67, 0x65, 0x5f, 0x73, 0x65, 0x74, 0x44, 0x61, 0x74, 0x61, 0x46, 0x72, - 0x6f, 0x6d, 0x4f, 0x62, 0x6a, 0x65, 0x63, 0x74, 0x28, 0x27, 0x75, 0x73, - 0x65, 0x72, 0x5f, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, - 0x27, 0x2c, 0x20, 0x73, 0x61, 0x76, 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, - 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x2e, 0x76, 0x61, - 0x6c, 0x75, 0x65, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, - 0x20, 0x75, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, - 0x65, 0x52, 0x65, 0x73, 0x65, 0x74, 0x54, 0x6f, 0x44, 0x65, 0x66, 0x61, - 0x75, 0x6c, 0x74, 0x28, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, 0x65, 0x2e, 0x6c, 0x6f, - 0x67, 0x28, 0x27, 0x52, 0x65, 0x73, 0x65, 0x74, 0x69, 0x6e, 0x67, 0x20, - 0x74, 0x68, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x20, 0x74, 0x6f, - 0x20, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x27, 0x29, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x65, 0x6c, 0x65, 0x63, 0x74, 0x65, - 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, - 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x6e, 0x61, 0x6d, 0x65, - 0x20, 0x3d, 0x20, 0x27, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x27, - 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x65, 0x6c, 0x65, - 0x63, 0x74, 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, - 0x6c, 0x61, 0x74, 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x64, - 0x61, 0x74, 0x61, 0x20, 0x3d, 0x20, 0x73, 0x61, 0x76, 0x65, 0x64, 0x55, - 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, - 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x5b, 0x27, 0x64, 0x65, 0x66, 0x61, - 0x75, 0x6c, 0x74, 0x27, 0x5d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, - 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, - 0x6f, 0x6e, 0x20, 0x75, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, - 0x61, 0x74, 0x65, 0x41, 0x70, 0x70, 0x6c, 0x79, 0x28, 0x74, 0x29, 0x20, - 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x65, 0x73, 0x73, - 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, - 0x74, 0x2e, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x73, 0x65, 0x73, 0x73, 0x69, - 0x6f, 0x6e, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x70, 0x61, - 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, - 0x20, 0x74, 0x2e, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x70, 0x61, 0x72, 0x61, - 0x6d, 0x73, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, + 0x6e, 0x20, 0x4a, 0x53, 0x4f, 0x4e, 0x2e, 0x70, 0x61, 0x72, 0x73, 0x65, + 0x28, 0x69, 0x74, 0x65, 0x6d, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, - 0x75, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, - 0x52, 0x65, 0x73, 0x65, 0x74, 0x54, 0x6f, 0x44, 0x65, 0x66, 0x61, 0x75, - 0x6c, 0x74, 0x41, 0x6e, 0x64, 0x41, 0x70, 0x70, 0x6c, 0x79, 0x28, 0x29, - 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x75, 0x73, 0x65, - 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x52, 0x65, 0x73, - 0x65, 0x74, 0x54, 0x6f, 0x44, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x28, - 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x75, 0x73, 0x65, 0x72, - 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x41, 0x70, 0x70, 0x6c, - 0x79, 0x28, 0x73, 0x65, 0x6c, 0x65, 0x63, 0x74, 0x65, 0x64, 0x55, 0x73, - 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x2e, 0x76, - 0x61, 0x6c, 0x75, 0x65, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, - 0x6e, 0x20, 0x75, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, - 0x74, 0x65, 0x4c, 0x6f, 0x61, 0x64, 0x41, 0x6e, 0x64, 0x41, 0x70, 0x70, - 0x6c, 0x79, 0x41, 0x75, 0x74, 0x6f, 0x73, 0x61, 0x76, 0x65, 0x64, 0x28, - 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, - 0x20, 0x67, 0x65, 0x74, 0x20, 0x61, 0x75, 0x74, 0x6f, 0x73, 0x61, 0x76, - 0x65, 0x64, 0x20, 0x6c, 0x61, 0x73, 0x74, 0x20, 0x75, 0x73, 0x65, 0x64, - 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x6c, 0x65, 0x74, 0x20, 0x6c, 0x61, 0x73, 0x74, - 0x55, 0x73, 0x65, 0x64, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, + 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, + 0x65, 0x5f, 0x67, 0x65, 0x74, 0x44, 0x61, 0x74, 0x61, 0x41, 0x73, 0x52, + 0x61, 0x77, 0x54, 0x65, 0x78, 0x74, 0x28, 0x74, 0x61, 0x67, 0x29, 0x20, + 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, + 0x74, 0x20, 0x69, 0x74, 0x65, 0x6d, 0x20, 0x3d, 0x20, 0x6c, 0x6f, 0x63, + 0x61, 0x6c, 0x53, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x2e, 0x67, 0x65, + 0x74, 0x49, 0x74, 0x65, 0x6d, 0x28, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x5f, + 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x5f, 0x73, 0x74, 0x6f, 0x72, + 0x61, 0x67, 0x65, 0x4b, 0x65, 0x79, 0x20, 0x2b, 0x20, 0x27, 0x2f, 0x27, + 0x20, 0x2b, 0x20, 0x74, 0x61, 0x67, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x21, 0x69, 0x74, 0x65, 0x6d, + 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x20, 0x65, 0x6c, 0x73, + 0x65, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x69, 0x74, 0x65, 0x6d, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x63, + 0x72, 0x65, 0x61, 0x74, 0x65, 0x20, 0x61, 0x20, 0x63, 0x6f, 0x6e, 0x74, + 0x61, 0x69, 0x6e, 0x65, 0x72, 0x20, 0x66, 0x6f, 0x72, 0x20, 0x75, 0x73, + 0x65, 0x72, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, + 0x20, 0x61, 0x6e, 0x64, 0x20, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, + 0x73, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, + 0x20, 0x73, 0x61, 0x76, 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, + 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x20, 0x3d, 0x20, 0x73, 0x69, + 0x67, 0x6e, 0x61, 0x6c, 0x28, 0x7b, 0x7d, 0x29, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x73, 0x65, 0x6c, 0x65, 0x63, + 0x74, 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, + 0x61, 0x74, 0x65, 0x20, 0x3d, 0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, + 0x28, 0x7b, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3a, 0x20, 0x27, 0x27, 0x2c, + 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x3a, 0x20, 0x7b, + 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x3a, 0x20, 0x7b, 0x7d, + 0x2c, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x3a, 0x20, 0x7b, 0x7d, + 0x20, 0x7d, 0x20, 0x7d, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, + 0x2f, 0x20, 0x6c, 0x65, 0x74, 0x27, 0x73, 0x20, 0x69, 0x6d, 0x70, 0x6f, + 0x72, 0x74, 0x20, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x6c, 0x79, 0x20, 0x73, + 0x61, 0x76, 0x65, 0x64, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, + 0x65, 0x73, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x73, 0x65, 0x74, 0x74, 0x69, + 0x6e, 0x67, 0x73, 0x20, 0x69, 0x66, 0x20, 0x74, 0x68, 0x65, 0x72, 0x65, + 0x20, 0x61, 0x72, 0x65, 0x20, 0x61, 0x6e, 0x79, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x2f, 0x2f, 0x20, 0x75, 0x73, 0x65, 0x72, 0x20, 0x74, 0x65, 0x6d, + 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x73, + 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x20, 0x61, 0x72, 0x65, 0x20, + 0x73, 0x74, 0x6f, 0x72, 0x65, 0x64, 0x20, 0x69, 0x6e, 0x20, 0x6f, 0x6e, + 0x65, 0x20, 0x6f, 0x62, 0x6a, 0x65, 0x63, 0x74, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x2f, 0x2f, 0x20, 0x69, 0x6e, 0x20, 0x66, 0x6f, 0x72, 0x6d, 0x20, + 0x6f, 0x66, 0x20, 0x7b, 0x20, 0x22, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, + 0x74, 0x65, 0x6e, 0x61, 0x6d, 0x65, 0x22, 0x3a, 0x20, 0x22, 0x74, 0x65, + 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x64, 0x61, 0x74, 0x61, 0x22, 0x20, + 0x7d, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x7b, 0x20, 0x22, 0x73, 0x65, 0x74, + 0x74, 0x69, 0x6e, 0x67, 0x73, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, + 0x65, 0x6e, 0x61, 0x6d, 0x65, 0x22, 0x3a, 0x22, 0x73, 0x65, 0x74, 0x74, + 0x69, 0x6e, 0x67, 0x73, 0x64, 0x61, 0x74, 0x61, 0x22, 0x20, 0x7d, 0x0a, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, 0x65, + 0x2e, 0x6c, 0x6f, 0x67, 0x28, 0x27, 0x49, 0x6d, 0x70, 0x6f, 0x72, 0x74, + 0x69, 0x6e, 0x67, 0x20, 0x73, 0x61, 0x76, 0x65, 0x64, 0x20, 0x74, 0x65, + 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x27, 0x29, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x6c, 0x65, 0x74, 0x20, 0x69, 0x6d, 0x70, 0x6f, 0x72, + 0x74, 0x65, 0x64, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x20, 0x3d, 0x20, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x5f, 0x67, 0x65, 0x74, 0x44, 0x61, 0x74, 0x61, 0x41, 0x73, 0x4f, 0x62, 0x6a, 0x65, 0x63, 0x74, 0x28, 0x27, 0x75, 0x73, 0x65, 0x72, 0x5f, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, - 0x5f, 0x6c, 0x61, 0x73, 0x74, 0x27, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x6c, 0x61, 0x73, 0x74, 0x55, - 0x73, 0x65, 0x64, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x29, - 0x20, 0x7b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x27, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, + 0x69, 0x6d, 0x70, 0x6f, 0x72, 0x74, 0x65, 0x64, 0x54, 0x65, 0x6d, 0x70, + 0x6c, 0x61, 0x74, 0x65, 0x73, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x73, 0x61, 0x76, 0x65, 0x64, 0x20, + 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x20, 0x77, 0x65, + 0x72, 0x65, 0x20, 0x73, 0x75, 0x63, 0x63, 0x65, 0x73, 0x73, 0x66, 0x75, + 0x6c, 0x79, 0x20, 0x69, 0x6d, 0x70, 0x6f, 0x72, 0x74, 0x65, 0x64, 0x2e, + 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, + 0x6f, 0x6c, 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, 0x27, 0x50, 0x72, 0x6f, + 0x63, 0x65, 0x73, 0x73, 0x69, 0x6e, 0x67, 0x20, 0x73, 0x61, 0x76, 0x65, + 0x64, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x20, + 0x61, 0x6e, 0x64, 0x20, 0x75, 0x70, 0x64, 0x61, 0x74, 0x69, 0x6e, 0x67, + 0x20, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x20, 0x74, 0x65, 0x6d, + 0x70, 0x6c, 0x61, 0x74, 0x65, 0x27, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, 0x65, + 0x2e, 0x6c, 0x6f, 0x67, 0x28, 0x69, 0x6d, 0x70, 0x6f, 0x72, 0x74, 0x65, + 0x64, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x29, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x61, 0x76, 0x65, 0x64, + 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, + 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x69, 0x6d, + 0x70, 0x6f, 0x72, 0x74, 0x65, 0x64, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, + 0x74, 0x65, 0x73, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x2f, 0x2f, 0x6f, 0x76, 0x65, 0x72, 0x72, 0x69, 0x64, 0x65, 0x20, 0x64, + 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, + 0x61, 0x74, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x61, + 0x76, 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, + 0x61, 0x74, 0x65, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x64, + 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x20, 0x3d, 0x20, 0x7b, 0x20, 0x73, + 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x3a, 0x20, 0x73, 0x65, 0x73, 0x73, + 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x20, 0x70, + 0x61, 0x72, 0x61, 0x6d, 0x73, 0x3a, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, + 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x7d, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x5f, 0x73, 0x74, + 0x6f, 0x72, 0x61, 0x67, 0x65, 0x5f, 0x73, 0x65, 0x74, 0x44, 0x61, 0x74, + 0x61, 0x46, 0x72, 0x6f, 0x6d, 0x4f, 0x62, 0x6a, 0x65, 0x63, 0x74, 0x28, + 0x27, 0x75, 0x73, 0x65, 0x72, 0x5f, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, + 0x74, 0x65, 0x73, 0x27, 0x2c, 0x20, 0x73, 0x61, 0x76, 0x65, 0x64, 0x55, + 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, + 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x7d, 0x20, 0x65, 0x6c, 0x73, 0x65, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x6e, 0x6f, 0x20, 0x73, 0x61, 0x76, + 0x65, 0x64, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, + 0x20, 0x64, 0x65, 0x74, 0x65, 0x63, 0x74, 0x65, 0x64, 0x2e, 0x0a, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, + 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, 0x27, 0x49, 0x6e, 0x69, 0x74, 0x69, + 0x61, 0x6c, 0x69, 0x7a, 0x69, 0x6e, 0x67, 0x20, 0x4c, 0x6f, 0x63, 0x61, + 0x6c, 0x53, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x20, 0x61, 0x6e, 0x64, + 0x20, 0x73, 0x61, 0x76, 0x69, 0x6e, 0x67, 0x20, 0x64, 0x65, 0x66, 0x61, + 0x75, 0x6c, 0x74, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, + 0x27, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x61, + 0x76, 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, + 0x61, 0x74, 0x65, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, + 0x20, 0x7b, 0x20, 0x22, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x22, + 0x3a, 0x20, 0x7b, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x3a, + 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x2c, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x3a, 0x20, + 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, + 0x20, 0x7d, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6c, + 0x6f, 0x63, 0x61, 0x6c, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, + 0x5f, 0x73, 0x65, 0x74, 0x44, 0x61, 0x74, 0x61, 0x46, 0x72, 0x6f, 0x6d, + 0x4f, 0x62, 0x6a, 0x65, 0x63, 0x74, 0x28, 0x27, 0x75, 0x73, 0x65, 0x72, + 0x5f, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x27, 0x2c, + 0x20, 0x73, 0x61, 0x76, 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, + 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x75, + 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x52, + 0x65, 0x73, 0x65, 0x74, 0x54, 0x6f, 0x44, 0x65, 0x66, 0x61, 0x75, 0x6c, + 0x74, 0x28, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, - 0x27, 0x41, 0x75, 0x74, 0x6f, 0x73, 0x61, 0x76, 0x65, 0x64, 0x20, 0x74, - 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x20, 0x66, 0x6f, 0x75, 0x6e, - 0x64, 0x2c, 0x20, 0x72, 0x65, 0x73, 0x74, 0x6f, 0x72, 0x69, 0x6e, 0x67, - 0x27, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x27, 0x52, 0x65, 0x73, 0x65, 0x74, 0x69, 0x6e, 0x67, 0x20, 0x74, 0x68, + 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x20, 0x74, 0x6f, 0x20, 0x64, + 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x73, 0x65, 0x6c, 0x65, 0x63, 0x74, 0x65, 0x64, 0x55, + 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x2e, + 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x6e, 0x61, 0x6d, 0x65, 0x20, 0x3d, + 0x20, 0x27, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x27, 0x3b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x65, 0x6c, 0x65, 0x63, 0x74, + 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, + 0x74, 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x64, 0x61, 0x74, + 0x61, 0x20, 0x3d, 0x20, 0x73, 0x61, 0x76, 0x65, 0x64, 0x55, 0x73, 0x65, + 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x5b, 0x27, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, + 0x74, 0x27, 0x5d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x20, 0x75, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, + 0x65, 0x41, 0x70, 0x70, 0x6c, 0x79, 0x28, 0x74, 0x29, 0x20, 0x7b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, + 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x74, 0x2e, + 0x64, 0x61, 0x74, 0x61, 0x2e, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, + 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x70, 0x61, 0x72, 0x61, + 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x74, + 0x2e, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, + 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x75, 0x73, + 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x52, 0x65, + 0x73, 0x65, 0x74, 0x54, 0x6f, 0x44, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, + 0x41, 0x6e, 0x64, 0x41, 0x70, 0x70, 0x6c, 0x79, 0x28, 0x29, 0x20, 0x7b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x75, 0x73, 0x65, 0x72, 0x54, + 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x52, 0x65, 0x73, 0x65, 0x74, + 0x54, 0x6f, 0x44, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x28, 0x29, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x75, 0x73, 0x65, 0x72, 0x54, 0x65, + 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x41, 0x70, 0x70, 0x6c, 0x79, 0x28, 0x73, 0x65, 0x6c, 0x65, 0x63, 0x74, 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x2e, 0x76, 0x61, 0x6c, - 0x75, 0x65, 0x20, 0x3d, 0x20, 0x6c, 0x61, 0x73, 0x74, 0x55, 0x73, 0x65, - 0x64, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x65, 0x6c, 0x73, 0x65, 0x20, 0x7b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, 0x65, 0x2e, - 0x6c, 0x6f, 0x67, 0x28, 0x27, 0x4e, 0x6f, 0x20, 0x61, 0x75, 0x74, 0x6f, - 0x73, 0x61, 0x76, 0x65, 0x64, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, - 0x74, 0x65, 0x20, 0x66, 0x6f, 0x75, 0x6e, 0x64, 0x2c, 0x20, 0x75, 0x73, - 0x69, 0x6e, 0x67, 0x20, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x20, - 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x27, 0x29, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x6e, 0x6f, - 0x20, 0x61, 0x75, 0x74, 0x6f, 0x73, 0x61, 0x76, 0x65, 0x64, 0x20, 0x6c, - 0x61, 0x73, 0x74, 0x20, 0x75, 0x73, 0x65, 0x64, 0x20, 0x74, 0x65, 0x6d, - 0x70, 0x6c, 0x61, 0x74, 0x65, 0x20, 0x77, 0x61, 0x73, 0x20, 0x66, 0x6f, - 0x75, 0x6e, 0x64, 0x2c, 0x20, 0x73, 0x6f, 0x20, 0x6c, 0x6f, 0x61, 0x64, - 0x20, 0x66, 0x72, 0x6f, 0x6d, 0x20, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, - 0x74, 0x2e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x75, 0x65, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x75, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, - 0x52, 0x65, 0x73, 0x65, 0x74, 0x54, 0x6f, 0x44, 0x65, 0x66, 0x61, 0x75, - 0x6c, 0x74, 0x28, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, - 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, - 0x6f, 0x6c, 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, 0x27, 0x41, 0x70, 0x70, - 0x6c, 0x79, 0x69, 0x6e, 0x67, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, - 0x74, 0x65, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, - 0x2f, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, - 0x20, 0x69, 0x6e, 0x74, 0x65, 0x72, 0x6e, 0x61, 0x6c, 0x20, 0x64, 0x61, - 0x74, 0x61, 0x20, 0x66, 0x72, 0x6f, 0x6d, 0x20, 0x74, 0x65, 0x6d, 0x70, - 0x6c, 0x61, 0x74, 0x65, 0x73, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x75, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, - 0x65, 0x41, 0x70, 0x70, 0x6c, 0x79, 0x28, 0x73, 0x65, 0x6c, 0x65, 0x63, - 0x74, 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, - 0x61, 0x74, 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, - 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, - 0x73, 0x61, 0x76, 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, - 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, - 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x63, 0x6f, 0x6e, 0x73, - 0x6f, 0x6c, 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, 0x73, 0x65, 0x6c, 0x65, - 0x63, 0x74, 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, - 0x6c, 0x61, 0x74, 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x0a, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, - 0x6e, 0x20, 0x75, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, - 0x74, 0x65, 0x41, 0x75, 0x74, 0x6f, 0x73, 0x61, 0x76, 0x65, 0x28, 0x29, - 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, - 0x73, 0x6f, 0x6c, 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, 0x27, 0x54, 0x65, - 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x20, 0x41, 0x75, 0x74, 0x6f, 0x73, - 0x61, 0x76, 0x65, 0x2e, 0x2e, 0x2e, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x73, 0x65, 0x6c, 0x65, 0x63, - 0x74, 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, - 0x61, 0x74, 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x6e, 0x61, - 0x6d, 0x65, 0x20, 0x3d, 0x3d, 0x20, 0x27, 0x64, 0x65, 0x66, 0x61, 0x75, - 0x6c, 0x74, 0x27, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x77, 0x65, 0x20, 0x64, 0x6f, 0x6e, - 0x27, 0x74, 0x20, 0x77, 0x61, 0x6e, 0x74, 0x20, 0x74, 0x6f, 0x20, 0x73, - 0x61, 0x76, 0x65, 0x20, 0x6f, 0x76, 0x65, 0x72, 0x20, 0x64, 0x65, 0x66, - 0x61, 0x75, 0x6c, 0x74, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, - 0x65, 0x2c, 0x20, 0x73, 0x6f, 0x20, 0x6c, 0x65, 0x74, 0x27, 0x73, 0x20, - 0x63, 0x72, 0x65, 0x61, 0x74, 0x65, 0x20, 0x61, 0x20, 0x6e, 0x65, 0x77, - 0x20, 0x6f, 0x6e, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x6c, 0x65, 0x74, 0x20, 0x6e, 0x65, 0x77, 0x54, 0x65, 0x6d, 0x70, - 0x6c, 0x61, 0x74, 0x65, 0x4e, 0x61, 0x6d, 0x65, 0x20, 0x3d, 0x20, 0x27, - 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, - 0x2d, 0x27, 0x20, 0x2b, 0x20, 0x44, 0x61, 0x74, 0x65, 0x2e, 0x6e, 0x6f, - 0x77, 0x28, 0x29, 0x2e, 0x74, 0x6f, 0x53, 0x74, 0x72, 0x69, 0x6e, 0x67, - 0x28, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6c, + 0x4c, 0x6f, 0x61, 0x64, 0x41, 0x6e, 0x64, 0x41, 0x70, 0x70, 0x6c, 0x79, + 0x41, 0x75, 0x74, 0x6f, 0x73, 0x61, 0x76, 0x65, 0x64, 0x28, 0x29, 0x20, + 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x67, + 0x65, 0x74, 0x20, 0x61, 0x75, 0x74, 0x6f, 0x73, 0x61, 0x76, 0x65, 0x64, + 0x20, 0x6c, 0x61, 0x73, 0x74, 0x20, 0x75, 0x73, 0x65, 0x64, 0x20, 0x74, + 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x6c, 0x65, 0x74, 0x20, 0x6c, 0x61, 0x73, 0x74, 0x55, 0x73, + 0x65, 0x64, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x20, 0x3d, + 0x20, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, + 0x67, 0x65, 0x5f, 0x67, 0x65, 0x74, 0x44, 0x61, 0x74, 0x61, 0x41, 0x73, + 0x4f, 0x62, 0x6a, 0x65, 0x63, 0x74, 0x28, 0x27, 0x75, 0x73, 0x65, 0x72, + 0x5f, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x5f, 0x6c, + 0x61, 0x73, 0x74, 0x27, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x69, 0x66, 0x20, 0x28, 0x6c, 0x61, 0x73, 0x74, 0x55, 0x73, 0x65, + 0x64, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x29, 0x20, 0x7b, + 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, + 0x6e, 0x73, 0x6f, 0x6c, 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, 0x27, 0x41, + 0x75, 0x74, 0x6f, 0x73, 0x61, 0x76, 0x65, 0x64, 0x20, 0x74, 0x65, 0x6d, + 0x70, 0x6c, 0x61, 0x74, 0x65, 0x20, 0x66, 0x6f, 0x75, 0x6e, 0x64, 0x2c, + 0x20, 0x72, 0x65, 0x73, 0x74, 0x6f, 0x72, 0x69, 0x6e, 0x67, 0x27, 0x29, + 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x65, + 0x6c, 0x65, 0x63, 0x74, 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, + 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, + 0x20, 0x3d, 0x20, 0x6c, 0x61, 0x73, 0x74, 0x55, 0x73, 0x65, 0x64, 0x54, + 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x65, 0x6c, + 0x73, 0x65, 0x20, 0x7b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, 0x65, 0x2e, 0x6c, 0x6f, + 0x67, 0x28, 0x27, 0x4e, 0x6f, 0x20, 0x61, 0x75, 0x74, 0x6f, 0x73, 0x61, + 0x76, 0x65, 0x64, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, + 0x20, 0x66, 0x6f, 0x75, 0x6e, 0x64, 0x2c, 0x20, 0x75, 0x73, 0x69, 0x6e, + 0x67, 0x20, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x20, 0x74, 0x65, + 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x6e, 0x6f, 0x20, 0x61, + 0x75, 0x74, 0x6f, 0x73, 0x61, 0x76, 0x65, 0x64, 0x20, 0x6c, 0x61, 0x73, + 0x74, 0x20, 0x75, 0x73, 0x65, 0x64, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, + 0x61, 0x74, 0x65, 0x20, 0x77, 0x61, 0x73, 0x20, 0x66, 0x6f, 0x75, 0x6e, + 0x64, 0x2c, 0x20, 0x73, 0x6f, 0x20, 0x6c, 0x6f, 0x61, 0x64, 0x20, 0x66, + 0x72, 0x6f, 0x6d, 0x20, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x2e, + 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x75, 0x73, + 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x52, 0x65, + 0x73, 0x65, 0x74, 0x54, 0x6f, 0x44, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, + 0x28, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, + 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, 0x27, 0x41, 0x70, 0x70, 0x6c, 0x79, + 0x69, 0x6e, 0x67, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, + 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, + 0x61, 0x6e, 0x64, 0x20, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x20, 0x69, + 0x6e, 0x74, 0x65, 0x72, 0x6e, 0x61, 0x6c, 0x20, 0x64, 0x61, 0x74, 0x61, + 0x20, 0x66, 0x72, 0x6f, 0x6d, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, + 0x74, 0x65, 0x73, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x75, + 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x41, + 0x70, 0x70, 0x6c, 0x79, 0x28, 0x73, 0x65, 0x6c, 0x65, 0x63, 0x74, 0x65, + 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, + 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x63, 0x6f, + 0x6e, 0x73, 0x6f, 0x6c, 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, 0x73, 0x61, + 0x76, 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, + 0x61, 0x74, 0x65, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, + 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, 0x73, 0x65, 0x6c, 0x65, 0x63, 0x74, + 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, + 0x74, 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, + 0x75, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, + 0x41, 0x75, 0x74, 0x6f, 0x73, 0x61, 0x76, 0x65, 0x28, 0x29, 0x20, 0x7b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x6f, + 0x6c, 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, 0x27, 0x54, 0x65, 0x6d, 0x70, + 0x6c, 0x61, 0x74, 0x65, 0x20, 0x41, 0x75, 0x74, 0x6f, 0x73, 0x61, 0x76, + 0x65, 0x2e, 0x2e, 0x2e, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x69, 0x66, 0x20, 0x28, 0x73, 0x65, 0x6c, 0x65, 0x63, 0x74, 0x65, + 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, + 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x6e, 0x61, 0x6d, 0x65, + 0x20, 0x3d, 0x3d, 0x20, 0x27, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, + 0x27, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x2f, 0x2f, 0x20, 0x77, 0x65, 0x20, 0x64, 0x6f, 0x6e, 0x27, 0x74, + 0x20, 0x77, 0x61, 0x6e, 0x74, 0x20, 0x74, 0x6f, 0x20, 0x73, 0x61, 0x76, + 0x65, 0x20, 0x6f, 0x76, 0x65, 0x72, 0x20, 0x64, 0x65, 0x66, 0x61, 0x75, + 0x6c, 0x74, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x2c, + 0x20, 0x73, 0x6f, 0x20, 0x6c, 0x65, 0x74, 0x27, 0x73, 0x20, 0x63, 0x72, + 0x65, 0x61, 0x74, 0x65, 0x20, 0x61, 0x20, 0x6e, 0x65, 0x77, 0x20, 0x6f, + 0x6e, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x65, 0x74, 0x20, 0x6e, 0x65, 0x77, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, - 0x74, 0x65, 0x20, 0x3d, 0x20, 0x7b, 0x20, 0x27, 0x6e, 0x61, 0x6d, 0x65, - 0x27, 0x3a, 0x20, 0x6e, 0x65, 0x77, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, - 0x74, 0x65, 0x4e, 0x61, 0x6d, 0x65, 0x2c, 0x20, 0x27, 0x64, 0x61, 0x74, + 0x74, 0x65, 0x4e, 0x61, 0x6d, 0x65, 0x20, 0x3d, 0x20, 0x27, 0x55, 0x73, + 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x2d, 0x27, + 0x20, 0x2b, 0x20, 0x44, 0x61, 0x74, 0x65, 0x2e, 0x6e, 0x6f, 0x77, 0x28, + 0x29, 0x2e, 0x74, 0x6f, 0x53, 0x74, 0x72, 0x69, 0x6e, 0x67, 0x28, 0x29, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x65, 0x74, + 0x20, 0x6e, 0x65, 0x77, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, + 0x20, 0x3d, 0x20, 0x7b, 0x20, 0x27, 0x6e, 0x61, 0x6d, 0x65, 0x27, 0x3a, + 0x20, 0x6e, 0x65, 0x77, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, + 0x4e, 0x61, 0x6d, 0x65, 0x2c, 0x20, 0x27, 0x64, 0x61, 0x74, 0x61, 0x27, + 0x3a, 0x20, 0x7b, 0x20, 0x27, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, + 0x27, 0x3a, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x20, 0x27, 0x70, 0x61, 0x72, 0x61, 0x6d, + 0x73, 0x27, 0x3a, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x20, 0x7d, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, + 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, 0x27, 0x53, 0x61, 0x76, 0x69, 0x6e, + 0x67, 0x20, 0x61, 0x73, 0x20, 0x27, 0x20, 0x2b, 0x20, 0x6e, 0x65, 0x77, + 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x4e, 0x61, 0x6d, 0x65, + 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, + 0x2f, 0x20, 0x73, 0x61, 0x76, 0x65, 0x20, 0x69, 0x6e, 0x20, 0x74, 0x68, + 0x65, 0x20, 0x61, 0x75, 0x74, 0x6f, 0x73, 0x61, 0x76, 0x65, 0x20, 0x73, + 0x6c, 0x6f, 0x74, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, + 0x65, 0x5f, 0x73, 0x65, 0x74, 0x44, 0x61, 0x74, 0x61, 0x46, 0x72, 0x6f, + 0x6d, 0x4f, 0x62, 0x6a, 0x65, 0x63, 0x74, 0x28, 0x27, 0x75, 0x73, 0x65, + 0x72, 0x5f, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x5f, + 0x6c, 0x61, 0x73, 0x74, 0x27, 0x2c, 0x20, 0x6e, 0x65, 0x77, 0x54, 0x65, + 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x61, 0x6e, 0x64, 0x20, + 0x6c, 0x6f, 0x61, 0x64, 0x20, 0x69, 0x74, 0x20, 0x62, 0x61, 0x63, 0x6b, + 0x20, 0x61, 0x6e, 0x64, 0x20, 0x61, 0x70, 0x70, 0x6c, 0x79, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x75, 0x73, 0x65, 0x72, 0x54, + 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x4c, 0x6f, 0x61, 0x64, 0x41, + 0x6e, 0x64, 0x41, 0x70, 0x70, 0x6c, 0x79, 0x41, 0x75, 0x74, 0x6f, 0x73, + 0x61, 0x76, 0x65, 0x64, 0x28, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x7d, 0x20, 0x65, 0x6c, 0x73, 0x65, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x5f, + 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x5f, 0x73, 0x65, 0x74, 0x44, + 0x61, 0x74, 0x61, 0x46, 0x72, 0x6f, 0x6d, 0x4f, 0x62, 0x6a, 0x65, 0x63, + 0x74, 0x28, 0x27, 0x75, 0x73, 0x65, 0x72, 0x5f, 0x74, 0x65, 0x6d, 0x70, + 0x6c, 0x61, 0x74, 0x65, 0x73, 0x5f, 0x6c, 0x61, 0x73, 0x74, 0x27, 0x2c, + 0x20, 0x7b, 0x20, 0x27, 0x6e, 0x61, 0x6d, 0x65, 0x27, 0x3a, 0x20, 0x73, + 0x65, 0x6c, 0x65, 0x63, 0x74, 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, + 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x2e, 0x6e, 0x61, 0x6d, 0x65, 0x2c, 0x20, 0x27, 0x64, 0x61, 0x74, 0x61, 0x27, 0x3a, 0x20, 0x7b, 0x20, 0x27, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x27, 0x3a, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x20, 0x27, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x27, 0x3a, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, - 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x7d, 0x20, 0x7d, 0x0a, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, - 0x6f, 0x6c, 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, 0x27, 0x53, 0x61, 0x76, - 0x69, 0x6e, 0x67, 0x20, 0x61, 0x73, 0x20, 0x27, 0x20, 0x2b, 0x20, 0x6e, - 0x65, 0x77, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x4e, 0x61, - 0x6d, 0x65, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x2f, 0x2f, 0x20, 0x73, 0x61, 0x76, 0x65, 0x20, 0x69, 0x6e, 0x20, - 0x74, 0x68, 0x65, 0x20, 0x61, 0x75, 0x74, 0x6f, 0x73, 0x61, 0x76, 0x65, - 0x20, 0x73, 0x6c, 0x6f, 0x74, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x6c, 0x6f, 0x63, 0x61, 0x6c, 0x5f, 0x73, 0x74, 0x6f, 0x72, - 0x61, 0x67, 0x65, 0x5f, 0x73, 0x65, 0x74, 0x44, 0x61, 0x74, 0x61, 0x46, - 0x72, 0x6f, 0x6d, 0x4f, 0x62, 0x6a, 0x65, 0x63, 0x74, 0x28, 0x27, 0x75, - 0x73, 0x65, 0x72, 0x5f, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, - 0x73, 0x5f, 0x6c, 0x61, 0x73, 0x74, 0x27, 0x2c, 0x20, 0x6e, 0x65, 0x77, - 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x29, 0x0a, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x61, 0x6e, - 0x64, 0x20, 0x6c, 0x6f, 0x61, 0x64, 0x20, 0x69, 0x74, 0x20, 0x62, 0x61, - 0x63, 0x6b, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x61, 0x70, 0x70, 0x6c, 0x79, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x75, 0x73, 0x65, - 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x4c, 0x6f, 0x61, - 0x64, 0x41, 0x6e, 0x64, 0x41, 0x70, 0x70, 0x6c, 0x79, 0x41, 0x75, 0x74, - 0x6f, 0x73, 0x61, 0x76, 0x65, 0x64, 0x28, 0x29, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x7d, 0x20, 0x65, 0x6c, 0x73, 0x65, 0x20, 0x7b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x6f, 0x63, 0x61, - 0x6c, 0x5f, 0x73, 0x74, 0x6f, 0x72, 0x61, 0x67, 0x65, 0x5f, 0x73, 0x65, - 0x74, 0x44, 0x61, 0x74, 0x61, 0x46, 0x72, 0x6f, 0x6d, 0x4f, 0x62, 0x6a, - 0x65, 0x63, 0x74, 0x28, 0x27, 0x75, 0x73, 0x65, 0x72, 0x5f, 0x74, 0x65, - 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x5f, 0x6c, 0x61, 0x73, 0x74, - 0x27, 0x2c, 0x20, 0x7b, 0x20, 0x27, 0x6e, 0x61, 0x6d, 0x65, 0x27, 0x3a, - 0x20, 0x73, 0x65, 0x6c, 0x65, 0x63, 0x74, 0x65, 0x64, 0x55, 0x73, 0x65, - 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x2e, 0x76, 0x61, - 0x6c, 0x75, 0x65, 0x2e, 0x6e, 0x61, 0x6d, 0x65, 0x2c, 0x20, 0x27, 0x64, - 0x61, 0x74, 0x61, 0x27, 0x3a, 0x20, 0x7b, 0x20, 0x27, 0x73, 0x65, 0x73, - 0x73, 0x69, 0x6f, 0x6e, 0x27, 0x3a, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, - 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x20, 0x27, 0x70, - 0x61, 0x72, 0x61, 0x6d, 0x73, 0x27, 0x3a, 0x20, 0x70, 0x61, 0x72, 0x61, - 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x7d, 0x20, 0x7d, - 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, - 0x73, 0x6f, 0x6c, 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, 0x27, 0x43, 0x68, - 0x65, 0x63, 0x6b, 0x69, 0x6e, 0x67, 0x20, 0x66, 0x6f, 0x72, 0x20, 0x61, - 0x75, 0x74, 0x6f, 0x73, 0x61, 0x76, 0x65, 0x64, 0x20, 0x6c, 0x61, 0x73, - 0x74, 0x20, 0x75, 0x73, 0x65, 0x64, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, - 0x61, 0x74, 0x65, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x75, 0x73, - 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x4c, 0x6f, - 0x61, 0x64, 0x41, 0x6e, 0x64, 0x41, 0x70, 0x70, 0x6c, 0x79, 0x41, 0x75, - 0x74, 0x6f, 0x73, 0x61, 0x76, 0x65, 0x64, 0x28, 0x29, 0x0a, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x2f, 0x2a, 0x20, 0x45, 0x4e, 0x44, 0x3a, 0x20, 0x53, - 0x75, 0x70, 0x70, 0x6f, 0x72, 0x74, 0x20, 0x66, 0x6f, 0x72, 0x20, 0x73, - 0x74, 0x6f, 0x72, 0x69, 0x6e, 0x67, 0x20, 0x70, 0x72, 0x6f, 0x6d, 0x70, - 0x74, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x20, - 0x61, 0x6e, 0x64, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x65, 0x74, 0x65, - 0x72, 0x73, 0x20, 0x69, 0x6e, 0x20, 0x62, 0x72, 0x6f, 0x77, 0x73, 0x65, - 0x72, 0x73, 0x20, 0x4c, 0x6f, 0x63, 0x61, 0x6c, 0x53, 0x74, 0x6f, 0x72, - 0x61, 0x67, 0x65, 0x20, 0x2a, 0x2f, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x53, - 0x74, 0x61, 0x74, 0x73, 0x20, 0x3d, 0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, - 0x6c, 0x28, 0x6e, 0x75, 0x6c, 0x6c, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, - 0x6c, 0x6c, 0x65, 0x72, 0x20, 0x3d, 0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, - 0x6c, 0x28, 0x6e, 0x75, 0x6c, 0x6c, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x67, 0x65, 0x6e, 0x65, 0x72, - 0x61, 0x74, 0x69, 0x6e, 0x67, 0x20, 0x3d, 0x20, 0x63, 0x6f, 0x6d, 0x70, - 0x75, 0x74, 0x65, 0x64, 0x28, 0x28, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x63, - 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, - 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x3d, 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x20, - 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, - 0x63, 0x68, 0x61, 0x74, 0x53, 0x74, 0x61, 0x72, 0x74, 0x65, 0x64, 0x20, - 0x3d, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x75, 0x74, 0x65, 0x64, 0x28, 0x28, - 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, - 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x72, 0x61, 0x6e, 0x73, - 0x63, 0x72, 0x69, 0x70, 0x74, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, - 0x20, 0x3e, 0x20, 0x30, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, - 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, - 0x69, 0x70, 0x74, 0x55, 0x70, 0x64, 0x61, 0x74, 0x65, 0x20, 0x3d, 0x20, - 0x28, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x29, - 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, - 0x65, 0x20, 0x3d, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x2e, 0x2e, 0x2e, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, - 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, - 0x70, 0x74, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, - 0x20, 0x73, 0x69, 0x6d, 0x70, 0x6c, 0x65, 0x20, 0x74, 0x65, 0x6d, 0x70, - 0x6c, 0x61, 0x74, 0x65, 0x20, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x74, - 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x20, 0x3d, 0x20, 0x28, 0x73, - 0x74, 0x72, 0x2c, 0x20, 0x65, 0x78, 0x74, 0x72, 0x61, 0x53, 0x65, 0x74, - 0x74, 0x69, 0x6e, 0x67, 0x73, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x65, 0x74, 0x20, 0x73, 0x65, - 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x20, 0x3d, 0x20, 0x73, 0x65, 0x73, - 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x65, 0x78, - 0x74, 0x72, 0x61, 0x53, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x29, - 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, - 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x20, 0x3d, 0x20, 0x7b, 0x20, - 0x2e, 0x2e, 0x2e, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x2c, - 0x20, 0x2e, 0x2e, 0x2e, 0x65, 0x78, 0x74, 0x72, 0x61, 0x53, 0x65, 0x74, - 0x74, 0x69, 0x6e, 0x67, 0x73, 0x20, 0x7d, 0x3b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, - 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x53, 0x74, 0x72, 0x69, 0x6e, 0x67, - 0x28, 0x73, 0x74, 0x72, 0x29, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, - 0x65, 0x41, 0x6c, 0x6c, 0x28, 0x2f, 0x5c, 0x7b, 0x5c, 0x7b, 0x28, 0x2e, - 0x2a, 0x3f, 0x29, 0x5c, 0x7d, 0x5c, 0x7d, 0x2f, 0x67, 0x2c, 0x20, 0x28, - 0x5f, 0x2c, 0x20, 0x6b, 0x65, 0x79, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x74, - 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, 0x73, 0x65, 0x74, 0x74, - 0x69, 0x6e, 0x67, 0x73, 0x5b, 0x6b, 0x65, 0x79, 0x5d, 0x29, 0x29, 0x3b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x2f, 0x2f, 0x20, 0x73, 0x65, 0x6e, 0x64, 0x20, 0x6d, 0x65, 0x73, 0x73, - 0x61, 0x67, 0x65, 0x20, 0x74, 0x6f, 0x20, 0x73, 0x65, 0x72, 0x76, 0x65, - 0x72, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, - 0x63, 0x68, 0x61, 0x74, 0x20, 0x3d, 0x20, 0x61, 0x73, 0x79, 0x6e, 0x63, - 0x20, 0x28, 0x6d, 0x73, 0x67, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x63, 0x6f, - 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, - 0x75, 0x65, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, 0x65, 0x2e, 0x6c, 0x6f, - 0x67, 0x28, 0x27, 0x61, 0x6c, 0x72, 0x65, 0x61, 0x64, 0x79, 0x20, 0x72, - 0x75, 0x6e, 0x6e, 0x69, 0x6e, 0x67, 0x2e, 0x2e, 0x2e, 0x27, 0x29, 0x3b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, - 0x75, 0x72, 0x6e, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72, - 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, - 0x3d, 0x20, 0x6e, 0x65, 0x77, 0x20, 0x41, 0x62, 0x6f, 0x72, 0x74, 0x43, - 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x28, 0x29, 0x3b, - 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x72, 0x61, 0x6e, - 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x55, 0x70, 0x64, 0x61, 0x74, 0x65, - 0x28, 0x5b, 0x2e, 0x2e, 0x2e, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, - 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x72, 0x61, 0x6e, 0x73, - 0x63, 0x72, 0x69, 0x70, 0x74, 0x2c, 0x20, 0x5b, 0x22, 0x7b, 0x7b, 0x75, - 0x73, 0x65, 0x72, 0x7d, 0x7d, 0x22, 0x2c, 0x20, 0x6d, 0x73, 0x67, 0x5d, - 0x5d, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, - 0x6e, 0x73, 0x74, 0x20, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x20, 0x3d, - 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, 0x73, 0x65, - 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, - 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x2c, 0x20, 0x7b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x65, 0x73, 0x73, - 0x61, 0x67, 0x65, 0x3a, 0x20, 0x6d, 0x73, 0x67, 0x2c, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, - 0x79, 0x3a, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, - 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, - 0x69, 0x70, 0x74, 0x2e, 0x66, 0x6c, 0x61, 0x74, 0x4d, 0x61, 0x70, 0x28, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x28, - 0x5b, 0x6e, 0x61, 0x6d, 0x65, 0x2c, 0x20, 0x64, 0x61, 0x74, 0x61, 0x5d, - 0x29, 0x20, 0x3d, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, - 0x65, 0x28, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, - 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x68, 0x69, 0x73, 0x74, 0x6f, - 0x72, 0x79, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x2c, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6e, 0x61, 0x6d, 0x65, - 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, - 0x65, 0x3a, 0x20, 0x41, 0x72, 0x72, 0x61, 0x79, 0x2e, 0x69, 0x73, 0x41, - 0x72, 0x72, 0x61, 0x79, 0x28, 0x64, 0x61, 0x74, 0x61, 0x29, 0x20, 0x3f, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x64, 0x61, 0x74, 0x61, 0x2e, - 0x6d, 0x61, 0x70, 0x28, 0x6d, 0x73, 0x67, 0x20, 0x3d, 0x3e, 0x20, 0x6d, - 0x73, 0x67, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x29, 0x2e, - 0x6a, 0x6f, 0x69, 0x6e, 0x28, 0x27, 0x27, 0x29, 0x2e, 0x72, 0x65, 0x70, - 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x5e, 0x5c, 0x73, 0x2f, 0x2c, 0x20, - 0x27, 0x27, 0x29, 0x20, 0x3a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x64, 0x61, 0x74, 0x61, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x29, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x29, 0x2e, 0x6a, 0x6f, - 0x69, 0x6e, 0x28, 0x22, 0x5c, 0x6e, 0x22, 0x29, 0x2c, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x7d, 0x29, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x75, 0x72, - 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x73, - 0x20, 0x3d, 0x20, 0x5b, 0x5d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x68, 0x69, 0x73, 0x74, 0x6f, - 0x72, 0x79, 0x20, 0x3d, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, - 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x72, 0x61, 0x6e, 0x73, - 0x63, 0x72, 0x69, 0x70, 0x74, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, - 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x20, 0x3d, 0x20, 0x7b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x2e, 0x2e, 0x70, 0x61, - 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x74, 0x6f, 0x70, - 0x3a, 0x20, 0x5b, 0x22, 0x3c, 0x2f, 0x73, 0x3e, 0x22, 0x2c, 0x20, 0x74, - 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, 0x22, 0x7b, 0x7b, 0x63, - 0x68, 0x61, 0x72, 0x7d, 0x7d, 0x3a, 0x22, 0x29, 0x2c, 0x20, 0x74, 0x65, - 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, 0x22, 0x7b, 0x7b, 0x75, 0x73, - 0x65, 0x72, 0x7d, 0x7d, 0x3a, 0x22, 0x29, 0x5d, 0x2c, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x66, 0x6f, 0x72, 0x20, 0x61, 0x77, 0x61, 0x69, 0x74, 0x20, 0x28, - 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x68, 0x75, 0x6e, 0x6b, 0x20, - 0x6f, 0x66, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x28, 0x70, 0x72, 0x6f, - 0x6d, 0x70, 0x74, 0x2c, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x50, 0x61, - 0x72, 0x61, 0x6d, 0x73, 0x2c, 0x20, 0x7b, 0x20, 0x63, 0x6f, 0x6e, 0x74, - 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x3a, 0x20, 0x63, 0x6f, 0x6e, 0x74, - 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, - 0x20, 0x7d, 0x29, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x64, 0x61, 0x74, - 0x61, 0x20, 0x3d, 0x20, 0x63, 0x68, 0x75, 0x6e, 0x6b, 0x2e, 0x64, 0x61, - 0x74, 0x61, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x69, 0x66, 0x20, 0x28, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x73, 0x74, - 0x6f, 0x70, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x77, 0x68, 0x69, 0x6c, 0x65, 0x20, 0x28, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, 0x73, 0x61, - 0x67, 0x65, 0x73, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x20, 0x3e, - 0x20, 0x30, 0x20, 0x26, 0x26, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, - 0x74, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x73, 0x5b, 0x63, 0x75, - 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, - 0x73, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x20, 0x2d, 0x20, 0x31, - 0x5d, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x2e, 0x6d, 0x61, - 0x74, 0x63, 0x68, 0x28, 0x2f, 0x5c, 0x6e, 0x24, 0x2f, 0x29, 0x20, 0x21, - 0x3d, 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x75, 0x72, - 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x73, - 0x2e, 0x70, 0x6f, 0x70, 0x28, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x7d, 0x20, 0x7d, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x6f, + 0x6c, 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, 0x27, 0x43, 0x68, 0x65, 0x63, + 0x6b, 0x69, 0x6e, 0x67, 0x20, 0x66, 0x6f, 0x72, 0x20, 0x61, 0x75, 0x74, + 0x6f, 0x73, 0x61, 0x76, 0x65, 0x64, 0x20, 0x6c, 0x61, 0x73, 0x74, 0x20, + 0x75, 0x73, 0x65, 0x64, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, + 0x65, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x75, 0x73, 0x65, 0x72, + 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x4c, 0x6f, 0x61, 0x64, + 0x41, 0x6e, 0x64, 0x41, 0x70, 0x70, 0x6c, 0x79, 0x41, 0x75, 0x74, 0x6f, + 0x73, 0x61, 0x76, 0x65, 0x64, 0x28, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x2f, 0x2a, 0x20, 0x45, 0x4e, 0x44, 0x3a, 0x20, 0x53, 0x75, 0x70, + 0x70, 0x6f, 0x72, 0x74, 0x20, 0x66, 0x6f, 0x72, 0x20, 0x73, 0x74, 0x6f, + 0x72, 0x69, 0x6e, 0x67, 0x20, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x20, + 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x73, 0x20, 0x61, 0x6e, + 0x64, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x65, 0x74, 0x65, 0x72, 0x73, + 0x20, 0x69, 0x6e, 0x20, 0x62, 0x72, 0x6f, 0x77, 0x73, 0x65, 0x72, 0x73, + 0x20, 0x4c, 0x6f, 0x63, 0x61, 0x6c, 0x53, 0x74, 0x6f, 0x72, 0x61, 0x67, + 0x65, 0x20, 0x2a, 0x2f, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, + 0x6e, 0x73, 0x74, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x53, 0x74, 0x61, + 0x74, 0x73, 0x20, 0x3d, 0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x28, + 0x6e, 0x75, 0x6c, 0x6c, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, + 0x6e, 0x73, 0x74, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, + 0x65, 0x72, 0x20, 0x3d, 0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x28, + 0x6e, 0x75, 0x6c, 0x6c, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, + 0x2f, 0x20, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x6c, 0x79, 0x20, + 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6e, 0x67, 0x20, 0x61, + 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x3f, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x67, + 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6e, 0x67, 0x20, 0x3d, 0x20, + 0x63, 0x6f, 0x6d, 0x70, 0x75, 0x74, 0x65, 0x64, 0x28, 0x28, 0x29, 0x20, + 0x3d, 0x3e, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, + 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x21, 0x3d, 0x20, 0x6e, + 0x75, 0x6c, 0x6c, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, + 0x20, 0x68, 0x61, 0x73, 0x20, 0x74, 0x68, 0x65, 0x20, 0x75, 0x73, 0x65, + 0x72, 0x20, 0x73, 0x74, 0x61, 0x72, 0x74, 0x65, 0x64, 0x20, 0x61, 0x20, + 0x63, 0x68, 0x61, 0x74, 0x3f, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, + 0x6e, 0x73, 0x74, 0x20, 0x63, 0x68, 0x61, 0x74, 0x53, 0x74, 0x61, 0x72, + 0x74, 0x65, 0x64, 0x20, 0x3d, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x75, 0x74, + 0x65, 0x64, 0x28, 0x28, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x73, 0x65, 0x73, + 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, + 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x2e, 0x6c, 0x65, + 0x6e, 0x67, 0x74, 0x68, 0x20, 0x3e, 0x20, 0x30, 0x29, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x74, 0x72, 0x61, + 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x55, 0x70, 0x64, 0x61, 0x74, + 0x65, 0x20, 0x3d, 0x20, 0x28, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, + 0x69, 0x70, 0x74, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, + 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x2e, 0x2e, 0x73, 0x65, 0x73, + 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x72, 0x61, 0x6e, + 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x73, 0x69, 0x6d, 0x70, 0x6c, 0x65, 0x20, + 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x20, 0x72, 0x65, 0x70, + 0x6c, 0x61, 0x63, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, + 0x73, 0x74, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x20, + 0x3d, 0x20, 0x28, 0x73, 0x74, 0x72, 0x2c, 0x20, 0x65, 0x78, 0x74, 0x72, + 0x61, 0x53, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x29, 0x20, 0x3d, + 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x65, + 0x74, 0x20, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x20, 0x3d, + 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, + 0x20, 0x28, 0x65, 0x78, 0x74, 0x72, 0x61, 0x53, 0x65, 0x74, 0x74, 0x69, + 0x6e, 0x67, 0x73, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x20, + 0x3d, 0x20, 0x7b, 0x20, 0x2e, 0x2e, 0x2e, 0x73, 0x65, 0x74, 0x74, 0x69, + 0x6e, 0x67, 0x73, 0x2c, 0x20, 0x2e, 0x2e, 0x2e, 0x65, 0x78, 0x74, 0x72, + 0x61, 0x53, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x20, 0x7d, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x53, 0x74, + 0x72, 0x69, 0x6e, 0x67, 0x28, 0x73, 0x74, 0x72, 0x29, 0x2e, 0x72, 0x65, + 0x70, 0x6c, 0x61, 0x63, 0x65, 0x41, 0x6c, 0x6c, 0x28, 0x2f, 0x5c, 0x7b, + 0x5c, 0x7b, 0x28, 0x2e, 0x2a, 0x3f, 0x29, 0x5c, 0x7d, 0x5c, 0x7d, 0x2f, + 0x67, 0x2c, 0x20, 0x28, 0x5f, 0x2c, 0x20, 0x6b, 0x65, 0x79, 0x29, 0x20, + 0x3d, 0x3e, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, + 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 0x5b, 0x6b, 0x65, 0x79, + 0x5d, 0x29, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x73, 0x65, 0x6e, 0x64, 0x20, + 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x20, 0x74, 0x6f, 0x20, 0x73, + 0x65, 0x72, 0x76, 0x65, 0x72, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, + 0x6e, 0x73, 0x74, 0x20, 0x63, 0x68, 0x61, 0x74, 0x20, 0x3d, 0x20, 0x61, + 0x73, 0x79, 0x6e, 0x63, 0x20, 0x28, 0x6d, 0x73, 0x67, 0x29, 0x20, 0x3d, + 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, + 0x20, 0x28, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, + 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, + 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, 0x27, 0x61, 0x6c, 0x72, 0x65, 0x61, + 0x64, 0x79, 0x20, 0x72, 0x75, 0x6e, 0x6e, 0x69, 0x6e, 0x67, 0x2e, 0x2e, + 0x2e, 0x27, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x3b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, + 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, + 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x6e, 0x65, 0x77, 0x20, 0x41, 0x62, + 0x6f, 0x72, 0x74, 0x43, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, + 0x72, 0x28, 0x29, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x55, 0x70, + 0x64, 0x61, 0x74, 0x65, 0x28, 0x5b, 0x2e, 0x2e, 0x2e, 0x73, 0x65, 0x73, + 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, + 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x2c, 0x20, 0x5b, + 0x22, 0x7b, 0x7b, 0x75, 0x73, 0x65, 0x72, 0x7d, 0x7d, 0x22, 0x2c, 0x20, + 0x6d, 0x73, 0x67, 0x5d, 0x5d, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x70, 0x72, 0x6f, 0x6d, + 0x70, 0x74, 0x20, 0x3d, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, + 0x65, 0x28, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, + 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, + 0x2c, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x3a, 0x20, 0x6d, 0x73, 0x67, + 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x68, 0x69, + 0x73, 0x74, 0x6f, 0x72, 0x79, 0x3a, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, + 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x72, 0x61, + 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x2e, 0x66, 0x6c, 0x61, 0x74, + 0x4d, 0x61, 0x70, 0x28, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x28, 0x5b, 0x6e, 0x61, 0x6d, 0x65, 0x2c, 0x20, 0x64, + 0x61, 0x74, 0x61, 0x5d, 0x29, 0x20, 0x3d, 0x3e, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x65, 0x6d, + 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x65, 0x73, + 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x68, + 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, + 0x74, 0x65, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x6e, 0x61, 0x6d, 0x65, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x65, + 0x73, 0x73, 0x61, 0x67, 0x65, 0x3a, 0x20, 0x41, 0x72, 0x72, 0x61, 0x79, + 0x2e, 0x69, 0x73, 0x41, 0x72, 0x72, 0x61, 0x79, 0x28, 0x64, 0x61, 0x74, + 0x61, 0x29, 0x20, 0x3f, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x64, + 0x61, 0x74, 0x61, 0x2e, 0x6d, 0x61, 0x70, 0x28, 0x6d, 0x73, 0x67, 0x20, + 0x3d, 0x3e, 0x20, 0x6d, 0x73, 0x67, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, + 0x6e, 0x74, 0x29, 0x2e, 0x6a, 0x6f, 0x69, 0x6e, 0x28, 0x27, 0x27, 0x29, + 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x5e, 0x5c, + 0x73, 0x2f, 0x2c, 0x20, 0x27, 0x27, 0x29, 0x20, 0x3a, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x64, 0x61, 0x74, 0x61, 0x2c, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x29, 0x2e, 0x6a, 0x6f, 0x69, 0x6e, 0x28, 0x22, 0x5c, 0x6e, 0x22, 0x29, + 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x29, 0x3b, 0x0a, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, + 0x20, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, 0x73, + 0x61, 0x67, 0x65, 0x73, 0x20, 0x3d, 0x20, 0x5b, 0x5d, 0x3b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x68, + 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x20, 0x3d, 0x20, 0x73, 0x65, 0x73, + 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, + 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6c, + 0x6c, 0x61, 0x6d, 0x61, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x20, 0x3d, + 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, + 0x2e, 0x2e, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x73, 0x74, 0x6f, 0x70, 0x3a, 0x20, 0x5b, 0x22, 0x3c, 0x2f, 0x73, 0x3e, + 0x22, 0x2c, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, + 0x22, 0x7b, 0x7b, 0x63, 0x68, 0x61, 0x72, 0x7d, 0x7d, 0x3a, 0x22, 0x29, + 0x2c, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, 0x22, + 0x7b, 0x7b, 0x75, 0x73, 0x65, 0x72, 0x7d, 0x7d, 0x3a, 0x22, 0x29, 0x5d, + 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x66, 0x6f, 0x72, 0x20, 0x61, 0x77, 0x61, + 0x69, 0x74, 0x20, 0x28, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x68, + 0x75, 0x6e, 0x6b, 0x20, 0x6f, 0x66, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, + 0x28, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x2c, 0x20, 0x6c, 0x6c, 0x61, + 0x6d, 0x61, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2c, 0x20, 0x7b, 0x20, + 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x3a, 0x20, + 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x20, 0x7d, 0x29, 0x29, 0x20, 0x7b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, + 0x20, 0x64, 0x61, 0x74, 0x61, 0x20, 0x3d, 0x20, 0x63, 0x68, 0x75, 0x6e, + 0x6b, 0x2e, 0x64, 0x61, 0x74, 0x61, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x64, 0x61, 0x74, + 0x61, 0x2e, 0x73, 0x74, 0x6f, 0x70, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x77, 0x68, 0x69, 0x6c, + 0x65, 0x20, 0x28, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, + 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x73, 0x2e, 0x6c, 0x65, 0x6e, 0x67, + 0x74, 0x68, 0x20, 0x3e, 0x20, 0x30, 0x20, 0x26, 0x26, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x75, + 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, + 0x73, 0x5b, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, + 0x73, 0x61, 0x67, 0x65, 0x73, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, + 0x20, 0x2d, 0x20, 0x31, 0x5d, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, + 0x74, 0x2e, 0x6d, 0x61, 0x74, 0x63, 0x68, 0x28, 0x2f, 0x5c, 0x6e, 0x24, + 0x2f, 0x29, 0x20, 0x21, 0x3d, 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x29, 0x20, 0x7b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, 0x73, + 0x61, 0x67, 0x65, 0x73, 0x2e, 0x70, 0x6f, 0x70, 0x28, 0x29, 0x3b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x72, + 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x55, 0x70, 0x64, 0x61, + 0x74, 0x65, 0x28, 0x5b, 0x2e, 0x2e, 0x2e, 0x68, 0x69, 0x73, 0x74, 0x6f, + 0x72, 0x79, 0x2c, 0x20, 0x5b, 0x22, 0x7b, 0x7b, 0x63, 0x68, 0x61, 0x72, + 0x7d, 0x7d, 0x22, 0x2c, 0x20, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, + 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x73, 0x5d, 0x5d, 0x29, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, + 0x6e, 0x73, 0x6f, 0x6c, 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, 0x22, 0x43, + 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x66, 0x69, + 0x6e, 0x69, 0x73, 0x68, 0x65, 0x64, 0x3a, 0x20, 0x27, 0x22, 0x2c, 0x20, + 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, 0x73, 0x61, + 0x67, 0x65, 0x73, 0x2e, 0x6d, 0x61, 0x70, 0x28, 0x6d, 0x73, 0x67, 0x20, + 0x3d, 0x3e, 0x20, 0x6d, 0x73, 0x67, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, + 0x6e, 0x74, 0x29, 0x2e, 0x6a, 0x6f, 0x69, 0x6e, 0x28, 0x27, 0x27, 0x29, + 0x2c, 0x20, 0x22, 0x27, 0x2c, 0x20, 0x73, 0x75, 0x6d, 0x6d, 0x61, 0x72, + 0x79, 0x3a, 0x20, 0x22, 0x2c, 0x20, 0x64, 0x61, 0x74, 0x61, 0x29, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x20, 0x65, + 0x6c, 0x73, 0x65, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, + 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x73, 0x2e, 0x70, 0x75, 0x73, 0x68, + 0x28, 0x64, 0x61, 0x74, 0x61, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x55, 0x70, 0x64, 0x61, 0x74, 0x65, 0x28, 0x5b, 0x2e, 0x2e, 0x2e, 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x2c, 0x20, 0x5b, 0x22, 0x7b, 0x7b, 0x63, 0x68, 0x61, 0x72, 0x7d, 0x7d, 0x22, 0x2c, 0x20, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x73, 0x5d, 0x5d, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x6f, 0x6c, - 0x65, 0x2e, 0x6c, 0x6f, 0x67, 0x28, 0x22, 0x43, 0x6f, 0x6d, 0x70, 0x6c, - 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x66, 0x69, 0x6e, 0x69, 0x73, 0x68, - 0x65, 0x64, 0x3a, 0x20, 0x27, 0x22, 0x2c, 0x20, 0x63, 0x75, 0x72, 0x72, - 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x73, 0x2e, - 0x6d, 0x61, 0x70, 0x28, 0x6d, 0x73, 0x67, 0x20, 0x3d, 0x3e, 0x20, 0x6d, - 0x73, 0x67, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x29, 0x2e, - 0x6a, 0x6f, 0x69, 0x6e, 0x28, 0x27, 0x27, 0x29, 0x2c, 0x20, 0x22, 0x27, - 0x2c, 0x20, 0x73, 0x75, 0x6d, 0x6d, 0x61, 0x72, 0x79, 0x3a, 0x20, 0x22, - 0x2c, 0x20, 0x64, 0x61, 0x74, 0x61, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x20, 0x65, 0x6c, 0x73, 0x65, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x64, 0x61, 0x74, 0x61, 0x2e, + 0x74, 0x69, 0x6d, 0x69, 0x6e, 0x67, 0x73, 0x29, 0x20, 0x7b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x6c, 0x61, + 0x6d, 0x61, 0x53, 0x74, 0x61, 0x74, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x20, 0x3d, 0x20, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x74, 0x69, 0x6d, + 0x69, 0x6e, 0x67, 0x73, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72, + 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, + 0x3d, 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, + 0x69, 0x6f, 0x6e, 0x20, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x49, + 0x6e, 0x70, 0x75, 0x74, 0x28, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6d, 0x65, 0x73, + 0x73, 0x61, 0x67, 0x65, 0x20, 0x3d, 0x20, 0x75, 0x73, 0x65, 0x53, 0x69, + 0x67, 0x6e, 0x61, 0x6c, 0x28, 0x22, 0x22, 0x29, 0x0a, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x73, 0x74, + 0x6f, 0x70, 0x20, 0x3d, 0x20, 0x28, 0x65, 0x29, 0x20, 0x3d, 0x3e, 0x20, + 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x65, 0x2e, + 0x70, 0x72, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x44, 0x65, 0x66, 0x61, 0x75, + 0x6c, 0x74, 0x28, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, + 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, 0x73, 0x61, - 0x67, 0x65, 0x73, 0x2e, 0x70, 0x75, 0x73, 0x68, 0x28, 0x64, 0x61, 0x74, - 0x61, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x61, 0x62, 0x6f, 0x72, 0x74, 0x28, 0x29, + 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x72, 0x65, 0x73, 0x65, + 0x74, 0x20, 0x3d, 0x20, 0x28, 0x65, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x74, 0x6f, + 0x70, 0x28, 0x65, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, - 0x55, 0x70, 0x64, 0x61, 0x74, 0x65, 0x28, 0x5b, 0x2e, 0x2e, 0x2e, 0x68, - 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x2c, 0x20, 0x5b, 0x22, 0x7b, 0x7b, - 0x63, 0x68, 0x61, 0x72, 0x7d, 0x7d, 0x22, 0x2c, 0x20, 0x63, 0x75, 0x72, - 0x72, 0x65, 0x6e, 0x74, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x73, - 0x5d, 0x5d, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, - 0x66, 0x20, 0x28, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x74, 0x69, 0x6d, 0x69, - 0x6e, 0x67, 0x73, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x53, 0x74, - 0x61, 0x74, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, - 0x64, 0x61, 0x74, 0x61, 0x2e, 0x74, 0x69, 0x6d, 0x69, 0x6e, 0x67, 0x73, - 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, + 0x55, 0x70, 0x64, 0x61, 0x74, 0x65, 0x28, 0x5b, 0x5d, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, - 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x6e, 0x75, - 0x6c, 0x6c, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, - 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x49, 0x6e, 0x70, 0x75, 0x74, - 0x28, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, - 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, - 0x20, 0x3d, 0x20, 0x75, 0x73, 0x65, 0x53, 0x69, 0x67, 0x6e, 0x61, 0x6c, - 0x28, 0x22, 0x22, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x73, 0x74, 0x6f, 0x70, 0x20, 0x3d, - 0x20, 0x28, 0x65, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x65, 0x2e, 0x70, 0x72, 0x65, 0x76, - 0x65, 0x6e, 0x74, 0x44, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x28, 0x29, - 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, - 0x20, 0x28, 0x63, 0x6f, 0x6e, 0x74, 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, - 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x74, - 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, - 0x2e, 0x61, 0x62, 0x6f, 0x72, 0x74, 0x28, 0x29, 0x3b, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x74, - 0x72, 0x6f, 0x6c, 0x6c, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, - 0x20, 0x3d, 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x3b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, - 0x6e, 0x73, 0x74, 0x20, 0x72, 0x65, 0x73, 0x65, 0x74, 0x20, 0x3d, 0x20, - 0x28, 0x65, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x74, 0x6f, 0x70, 0x28, 0x65, 0x29, - 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x72, - 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x55, 0x70, 0x64, 0x61, - 0x74, 0x65, 0x28, 0x5b, 0x5d, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, - 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x73, 0x75, 0x62, 0x6d, 0x69, 0x74, 0x20, - 0x3d, 0x20, 0x28, 0x65, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x74, 0x6f, 0x70, 0x28, - 0x65, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x63, 0x68, 0x61, 0x74, 0x28, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, - 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, - 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x22, 0x22, 0x3b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x65, 0x6e, - 0x74, 0x65, 0x72, 0x53, 0x75, 0x62, 0x6d, 0x69, 0x74, 0x73, 0x20, 0x3d, - 0x20, 0x28, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x29, 0x20, 0x3d, 0x3e, 0x20, - 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, - 0x20, 0x28, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x2e, 0x77, 0x68, 0x69, 0x63, - 0x68, 0x20, 0x3d, 0x3d, 0x3d, 0x20, 0x31, 0x33, 0x20, 0x26, 0x26, 0x20, - 0x21, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x2e, 0x73, 0x68, 0x69, 0x66, 0x74, - 0x4b, 0x65, 0x79, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x75, 0x62, 0x6d, 0x69, 0x74, 0x28, - 0x65, 0x76, 0x65, 0x6e, 0x74, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, - 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x66, 0x6f, 0x72, 0x6d, 0x20, - 0x6f, 0x6e, 0x73, 0x75, 0x62, 0x6d, 0x69, 0x74, 0x3d, 0x24, 0x7b, 0x73, - 0x75, 0x62, 0x6d, 0x69, 0x74, 0x7d, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, + 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x73, 0x75, 0x62, + 0x6d, 0x69, 0x74, 0x20, 0x3d, 0x20, 0x28, 0x65, 0x29, 0x20, 0x3d, 0x3e, + 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, + 0x74, 0x6f, 0x70, 0x28, 0x65, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x63, 0x68, 0x61, 0x74, 0x28, 0x6d, 0x65, 0x73, + 0x73, 0x61, 0x67, 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x65, 0x73, + 0x73, 0x61, 0x67, 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, + 0x20, 0x22, 0x22, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, + 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, + 0x74, 0x20, 0x65, 0x6e, 0x74, 0x65, 0x72, 0x53, 0x75, 0x62, 0x6d, 0x69, + 0x74, 0x73, 0x20, 0x3d, 0x20, 0x28, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x29, + 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x2e, + 0x77, 0x68, 0x69, 0x63, 0x68, 0x20, 0x3d, 0x3d, 0x3d, 0x20, 0x31, 0x33, + 0x20, 0x26, 0x26, 0x20, 0x21, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x2e, 0x73, + 0x68, 0x69, 0x66, 0x74, 0x4b, 0x65, 0x79, 0x29, 0x20, 0x7b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x75, 0x62, + 0x6d, 0x69, 0x74, 0x28, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x29, 0x3b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, + 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x66, + 0x6f, 0x72, 0x6d, 0x20, 0x6f, 0x6e, 0x73, 0x75, 0x62, 0x6d, 0x69, 0x74, + 0x3d, 0x24, 0x7b, 0x73, 0x75, 0x62, 0x6d, 0x69, 0x74, 0x7d, 0x3e, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, + 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x3c, 0x74, 0x65, 0x78, 0x74, 0x61, 0x72, 0x65, + 0x61, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6c, 0x61, 0x73, 0x73, 0x4e, 0x61, + 0x6d, 0x65, 0x3d, 0x24, 0x7b, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, + 0x69, 0x6e, 0x67, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3f, 0x20, + 0x22, 0x6c, 0x6f, 0x61, 0x64, 0x69, 0x6e, 0x67, 0x22, 0x20, 0x3a, 0x20, + 0x6e, 0x75, 0x6c, 0x6c, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6f, 0x6e, 0x69, + 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x28, 0x65, 0x29, 0x20, 0x3d, + 0x3e, 0x20, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x2e, 0x76, 0x61, + 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x65, 0x2e, 0x74, 0x61, 0x72, 0x67, + 0x65, 0x74, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x3c, 0x74, 0x65, 0x78, 0x74, 0x61, 0x72, 0x65, 0x61, 0x20, 0x74, 0x79, - 0x70, 0x65, 0x3d, 0x22, 0x74, 0x65, 0x78, 0x74, 0x22, 0x20, 0x72, 0x6f, - 0x77, 0x73, 0x3d, 0x32, 0x20, 0x6f, 0x6e, 0x6b, 0x65, 0x79, 0x70, 0x72, - 0x65, 0x73, 0x73, 0x3d, 0x24, 0x7b, 0x65, 0x6e, 0x74, 0x65, 0x72, 0x53, - 0x75, 0x62, 0x6d, 0x69, 0x74, 0x73, 0x7d, 0x20, 0x76, 0x61, 0x6c, 0x75, - 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, - 0x7d, 0x22, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, - 0x7b, 0x28, 0x65, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x6d, 0x65, 0x73, 0x73, - 0x61, 0x67, 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, - 0x65, 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, 0x76, 0x61, 0x6c, - 0x75, 0x65, 0x7d, 0x20, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x68, 0x6f, 0x6c, - 0x64, 0x65, 0x72, 0x3d, 0x22, 0x53, 0x61, 0x79, 0x20, 0x73, 0x6f, 0x6d, - 0x65, 0x74, 0x68, 0x69, 0x6e, 0x67, 0x2e, 0x2e, 0x2e, 0x22, 0x2f, 0x3e, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, - 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x20, 0x63, 0x6c, 0x61, - 0x73, 0x73, 0x3d, 0x22, 0x72, 0x69, 0x67, 0x68, 0x74, 0x22, 0x3e, 0x0a, + 0x20, 0x6f, 0x6e, 0x6b, 0x65, 0x79, 0x70, 0x72, 0x65, 0x73, 0x73, 0x3d, + 0x24, 0x7b, 0x65, 0x6e, 0x74, 0x65, 0x72, 0x53, 0x75, 0x62, 0x6d, 0x69, + 0x74, 0x73, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x70, 0x6c, 0x61, 0x63, 0x65, + 0x68, 0x6f, 0x6c, 0x64, 0x65, 0x72, 0x3d, 0x22, 0x53, 0x61, 0x79, 0x20, + 0x73, 0x6f, 0x6d, 0x65, 0x74, 0x68, 0x69, 0x6e, 0x67, 0x2e, 0x2e, 0x2e, + 0x22, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x6f, 0x77, 0x73, 0x3d, 0x32, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x3c, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x20, 0x74, 0x79, 0x70, 0x65, - 0x3d, 0x22, 0x73, 0x75, 0x62, 0x6d, 0x69, 0x74, 0x22, 0x20, 0x64, 0x69, - 0x73, 0x61, 0x62, 0x6c, 0x65, 0x64, 0x3d, 0x24, 0x7b, 0x21, 0x67, 0x65, - 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6e, 0x67, 0x2e, 0x76, 0x61, 0x6c, - 0x75, 0x65, 0x7d, 0x20, 0x3e, 0x53, 0x65, 0x6e, 0x64, 0x3c, 0x2f, 0x62, - 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x62, 0x75, 0x74, 0x74, - 0x6f, 0x6e, 0x20, 0x6f, 0x6e, 0x63, 0x6c, 0x69, 0x63, 0x6b, 0x3d, 0x24, - 0x7b, 0x73, 0x74, 0x6f, 0x70, 0x7d, 0x20, 0x64, 0x69, 0x73, 0x61, 0x62, - 0x6c, 0x65, 0x64, 0x3d, 0x24, 0x7b, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, - 0x74, 0x69, 0x6e, 0x67, 0x7d, 0x3e, 0x53, 0x74, 0x6f, 0x70, 0x3c, 0x2f, + 0x20, 0x20, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, 0x74, 0x65, 0x78, + 0x74, 0x22, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, + 0x22, 0x24, 0x7b, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x7d, 0x22, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x20, + 0x63, 0x6c, 0x61, 0x73, 0x73, 0x3d, 0x22, 0x72, 0x69, 0x67, 0x68, 0x74, + 0x22, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x3c, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x20, 0x74, + 0x79, 0x70, 0x65, 0x3d, 0x22, 0x73, 0x75, 0x62, 0x6d, 0x69, 0x74, 0x22, + 0x20, 0x64, 0x69, 0x73, 0x61, 0x62, 0x6c, 0x65, 0x64, 0x3d, 0x24, 0x7b, + 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6e, 0x67, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x7d, 0x3e, 0x53, 0x65, 0x6e, 0x64, 0x3c, 0x2f, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x20, 0x6f, 0x6e, 0x63, 0x6c, 0x69, 0x63, 0x6b, 0x3d, - 0x24, 0x7b, 0x72, 0x65, 0x73, 0x65, 0x74, 0x7d, 0x3e, 0x52, 0x65, 0x73, - 0x65, 0x74, 0x3c, 0x2f, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x3e, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, - 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x3c, 0x2f, 0x66, 0x6f, 0x72, 0x6d, 0x3e, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x43, 0x68, - 0x61, 0x74, 0x4c, 0x6f, 0x67, 0x20, 0x3d, 0x20, 0x28, 0x70, 0x72, 0x6f, - 0x70, 0x73, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6d, 0x65, 0x73, - 0x73, 0x61, 0x67, 0x65, 0x73, 0x20, 0x3d, 0x20, 0x73, 0x65, 0x73, 0x73, - 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x72, - 0x61, 0x6e, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x3b, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x6f, - 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x20, 0x3d, 0x20, 0x75, 0x73, - 0x65, 0x52, 0x65, 0x66, 0x28, 0x6e, 0x75, 0x6c, 0x6c, 0x29, 0x0a, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x75, 0x73, 0x65, 0x45, 0x66, 0x66, - 0x65, 0x63, 0x74, 0x28, 0x28, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x73, - 0x63, 0x72, 0x6f, 0x6c, 0x6c, 0x20, 0x74, 0x6f, 0x20, 0x62, 0x6f, 0x74, - 0x74, 0x6f, 0x6d, 0x20, 0x28, 0x69, 0x66, 0x20, 0x6e, 0x65, 0x65, 0x64, - 0x65, 0x64, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x70, 0x61, 0x72, 0x65, 0x6e, 0x74, - 0x20, 0x3d, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, - 0x2e, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x2e, 0x70, 0x61, 0x72, - 0x65, 0x6e, 0x74, 0x45, 0x6c, 0x65, 0x6d, 0x65, 0x6e, 0x74, 0x3b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, - 0x70, 0x61, 0x72, 0x65, 0x6e, 0x74, 0x20, 0x26, 0x26, 0x20, 0x70, 0x61, + 0x24, 0x7b, 0x73, 0x74, 0x6f, 0x70, 0x7d, 0x20, 0x64, 0x69, 0x73, 0x61, + 0x62, 0x6c, 0x65, 0x64, 0x3d, 0x24, 0x7b, 0x21, 0x67, 0x65, 0x6e, 0x65, + 0x72, 0x61, 0x74, 0x69, 0x6e, 0x67, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, + 0x7d, 0x3e, 0x53, 0x74, 0x6f, 0x70, 0x3c, 0x2f, 0x62, 0x75, 0x74, 0x74, + 0x6f, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x3c, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x20, + 0x6f, 0x6e, 0x63, 0x6c, 0x69, 0x63, 0x6b, 0x3d, 0x24, 0x7b, 0x72, 0x65, + 0x73, 0x65, 0x74, 0x7d, 0x3e, 0x52, 0x65, 0x73, 0x65, 0x74, 0x3c, 0x2f, + 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, + 0x6f, 0x72, 0x6d, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x43, 0x68, 0x61, 0x74, 0x4c, 0x6f, + 0x67, 0x20, 0x3d, 0x20, 0x28, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x29, 0x20, + 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, + 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, + 0x73, 0x20, 0x3d, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, + 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x63, + 0x72, 0x69, 0x70, 0x74, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, + 0x6e, 0x65, 0x72, 0x20, 0x3d, 0x20, 0x75, 0x73, 0x65, 0x52, 0x65, 0x66, + 0x28, 0x6e, 0x75, 0x6c, 0x6c, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x75, 0x73, 0x65, 0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x28, + 0x28, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x73, 0x63, 0x72, 0x6f, 0x6c, + 0x6c, 0x20, 0x74, 0x6f, 0x20, 0x62, 0x6f, 0x74, 0x74, 0x6f, 0x6d, 0x20, + 0x28, 0x69, 0x66, 0x20, 0x6e, 0x65, 0x65, 0x64, 0x65, 0x64, 0x29, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, + 0x74, 0x20, 0x70, 0x61, 0x72, 0x65, 0x6e, 0x74, 0x20, 0x3d, 0x20, 0x63, + 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x2e, 0x63, 0x75, 0x72, + 0x72, 0x65, 0x6e, 0x74, 0x2e, 0x70, 0x61, 0x72, 0x65, 0x6e, 0x74, 0x45, + 0x6c, 0x65, 0x6d, 0x65, 0x6e, 0x74, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x70, 0x61, 0x72, 0x65, + 0x6e, 0x74, 0x20, 0x26, 0x26, 0x20, 0x70, 0x61, 0x72, 0x65, 0x6e, 0x74, + 0x2e, 0x73, 0x63, 0x72, 0x6f, 0x6c, 0x6c, 0x48, 0x65, 0x69, 0x67, 0x68, + 0x74, 0x20, 0x3c, 0x3d, 0x20, 0x70, 0x61, 0x72, 0x65, 0x6e, 0x74, 0x2e, + 0x73, 0x63, 0x72, 0x6f, 0x6c, 0x6c, 0x54, 0x6f, 0x70, 0x20, 0x2b, 0x20, + 0x70, 0x61, 0x72, 0x65, 0x6e, 0x74, 0x2e, 0x6f, 0x66, 0x66, 0x73, 0x65, + 0x74, 0x48, 0x65, 0x69, 0x67, 0x68, 0x74, 0x20, 0x2b, 0x20, 0x33, 0x30, + 0x30, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x70, 0x61, 0x72, 0x65, 0x6e, 0x74, 0x2e, 0x73, 0x63, + 0x72, 0x6f, 0x6c, 0x6c, 0x54, 0x6f, 0x28, 0x30, 0x2c, 0x20, 0x70, 0x61, 0x72, 0x65, 0x6e, 0x74, 0x2e, 0x73, 0x63, 0x72, 0x6f, 0x6c, 0x6c, 0x48, - 0x65, 0x69, 0x67, 0x68, 0x74, 0x20, 0x3c, 0x3d, 0x20, 0x70, 0x61, 0x72, - 0x65, 0x6e, 0x74, 0x2e, 0x73, 0x63, 0x72, 0x6f, 0x6c, 0x6c, 0x54, 0x6f, - 0x70, 0x20, 0x2b, 0x20, 0x70, 0x61, 0x72, 0x65, 0x6e, 0x74, 0x2e, 0x6f, - 0x66, 0x66, 0x73, 0x65, 0x74, 0x48, 0x65, 0x69, 0x67, 0x68, 0x74, 0x20, - 0x2b, 0x20, 0x33, 0x30, 0x30, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x70, 0x61, 0x72, 0x65, 0x6e, - 0x74, 0x2e, 0x73, 0x63, 0x72, 0x6f, 0x6c, 0x6c, 0x54, 0x6f, 0x28, 0x30, - 0x2c, 0x20, 0x70, 0x61, 0x72, 0x65, 0x6e, 0x74, 0x2e, 0x73, 0x63, 0x72, - 0x6f, 0x6c, 0x6c, 0x48, 0x65, 0x69, 0x67, 0x68, 0x74, 0x29, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x7d, 0x2c, 0x20, 0x5b, 0x6d, 0x65, 0x73, 0x73, 0x61, - 0x67, 0x65, 0x73, 0x5d, 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x68, 0x61, 0x74, 0x4c, - 0x69, 0x6e, 0x65, 0x20, 0x3d, 0x20, 0x28, 0x5b, 0x75, 0x73, 0x65, 0x72, - 0x2c, 0x20, 0x64, 0x61, 0x74, 0x61, 0x5d, 0x2c, 0x20, 0x69, 0x6e, 0x64, - 0x65, 0x78, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x65, 0x74, 0x20, 0x6d, 0x65, 0x73, - 0x73, 0x61, 0x67, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x69, 0x73, 0x41, 0x72, 0x72, - 0x61, 0x79, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x20, 0x3d, 0x20, - 0x41, 0x72, 0x72, 0x61, 0x79, 0x2e, 0x69, 0x73, 0x41, 0x72, 0x72, 0x61, - 0x79, 0x28, 0x64, 0x61, 0x74, 0x61, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x70, 0x61, 0x72, 0x61, - 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x6e, 0x5f, 0x70, - 0x72, 0x6f, 0x62, 0x73, 0x20, 0x3e, 0x20, 0x30, 0x20, 0x26, 0x26, 0x20, - 0x69, 0x73, 0x41, 0x72, 0x72, 0x61, 0x79, 0x4d, 0x65, 0x73, 0x73, 0x61, - 0x67, 0x65, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x20, - 0x3d, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x3c, 0x24, 0x7b, 0x50, 0x72, - 0x6f, 0x62, 0x61, 0x62, 0x69, 0x6c, 0x69, 0x74, 0x69, 0x65, 0x73, 0x7d, - 0x20, 0x64, 0x61, 0x74, 0x61, 0x3d, 0x24, 0x7b, 0x64, 0x61, 0x74, 0x61, - 0x7d, 0x20, 0x2f, 0x3e, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x7d, 0x20, 0x65, 0x6c, 0x73, 0x65, 0x20, 0x7b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, - 0x73, 0x74, 0x20, 0x74, 0x65, 0x78, 0x74, 0x20, 0x3d, 0x20, 0x69, 0x73, - 0x41, 0x72, 0x72, 0x61, 0x79, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, - 0x20, 0x3f, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x6d, 0x61, 0x70, 0x28, - 0x6d, 0x73, 0x67, 0x20, 0x3d, 0x3e, 0x20, 0x6d, 0x73, 0x67, 0x2e, 0x63, - 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x29, 0x2e, 0x6a, 0x6f, 0x69, 0x6e, - 0x28, 0x27, 0x27, 0x29, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, - 0x28, 0x2f, 0x5e, 0x5c, 0x73, 0x2b, 0x2f, 0x2c, 0x20, 0x27, 0x27, 0x29, - 0x20, 0x3a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x64, 0x61, 0x74, 0x61, 0x3b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6d, 0x65, 0x73, 0x73, 0x61, - 0x67, 0x65, 0x20, 0x3d, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x3c, 0x24, - 0x7b, 0x4d, 0x61, 0x72, 0x6b, 0x64, 0x6f, 0x77, 0x6e, 0x69, 0x73, 0x68, - 0x7d, 0x20, 0x74, 0x65, 0x78, 0x74, 0x3d, 0x24, 0x7b, 0x74, 0x65, 0x6d, - 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, 0x74, 0x65, 0x78, 0x74, 0x29, 0x7d, - 0x20, 0x2f, 0x3e, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, - 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x3c, - 0x70, 0x20, 0x6b, 0x65, 0x79, 0x3d, 0x24, 0x7b, 0x69, 0x6e, 0x64, 0x65, - 0x78, 0x7d, 0x3e, 0x3c, 0x73, 0x74, 0x72, 0x6f, 0x6e, 0x67, 0x3e, 0x24, - 0x7b, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, 0x75, 0x73, - 0x65, 0x72, 0x29, 0x7d, 0x3a, 0x3c, 0x2f, 0x73, 0x74, 0x72, 0x6f, 0x6e, - 0x67, 0x3e, 0x20, 0x24, 0x7b, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, - 0x7d, 0x3c, 0x2f, 0x70, 0x3e, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x7d, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, - 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x73, 0x65, 0x63, - 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x63, 0x68, 0x61, - 0x74, 0x22, 0x20, 0x72, 0x65, 0x66, 0x3d, 0x24, 0x7b, 0x63, 0x6f, 0x6e, - 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x7d, 0x3e, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x6d, 0x65, 0x73, - 0x73, 0x61, 0x67, 0x65, 0x73, 0x2e, 0x66, 0x6c, 0x61, 0x74, 0x4d, 0x61, - 0x70, 0x28, 0x63, 0x68, 0x61, 0x74, 0x4c, 0x69, 0x6e, 0x65, 0x29, 0x7d, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x73, - 0x65, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x3e, 0x60, 0x3b, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x7d, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, - 0x6e, 0x73, 0x74, 0x20, 0x43, 0x6f, 0x6e, 0x66, 0x69, 0x67, 0x46, 0x6f, - 0x72, 0x6d, 0x20, 0x3d, 0x20, 0x28, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x29, - 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x65, 0x69, 0x67, 0x68, 0x74, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, + 0x2c, 0x20, 0x5b, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x73, 0x5d, + 0x29, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, + 0x73, 0x74, 0x20, 0x63, 0x68, 0x61, 0x74, 0x4c, 0x69, 0x6e, 0x65, 0x20, + 0x3d, 0x20, 0x28, 0x5b, 0x75, 0x73, 0x65, 0x72, 0x2c, 0x20, 0x64, 0x61, + 0x74, 0x61, 0x5d, 0x2c, 0x20, 0x69, 0x6e, 0x64, 0x65, 0x78, 0x29, 0x20, + 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x6c, 0x65, 0x74, 0x20, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, + 0x73, 0x74, 0x20, 0x69, 0x73, 0x41, 0x72, 0x72, 0x61, 0x79, 0x4d, 0x65, + 0x73, 0x73, 0x61, 0x67, 0x65, 0x20, 0x3d, 0x20, 0x41, 0x72, 0x72, 0x61, + 0x79, 0x2e, 0x69, 0x73, 0x41, 0x72, 0x72, 0x61, 0x79, 0x28, 0x64, 0x61, + 0x74, 0x61, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x69, 0x66, 0x20, 0x28, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x6e, 0x5f, 0x70, 0x72, 0x6f, 0x62, 0x73, + 0x20, 0x3e, 0x20, 0x30, 0x20, 0x26, 0x26, 0x20, 0x69, 0x73, 0x41, 0x72, + 0x72, 0x61, 0x79, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x29, 0x20, + 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x20, 0x3d, 0x20, 0x68, 0x74, + 0x6d, 0x6c, 0x60, 0x3c, 0x24, 0x7b, 0x50, 0x72, 0x6f, 0x62, 0x61, 0x62, + 0x69, 0x6c, 0x69, 0x74, 0x69, 0x65, 0x73, 0x7d, 0x20, 0x64, 0x61, 0x74, + 0x61, 0x3d, 0x24, 0x7b, 0x64, 0x61, 0x74, 0x61, 0x7d, 0x20, 0x2f, 0x3e, + 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x20, + 0x65, 0x6c, 0x73, 0x65, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x74, + 0x65, 0x78, 0x74, 0x20, 0x3d, 0x20, 0x69, 0x73, 0x41, 0x72, 0x72, 0x61, + 0x79, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x20, 0x3f, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x64, + 0x61, 0x74, 0x61, 0x2e, 0x6d, 0x61, 0x70, 0x28, 0x6d, 0x73, 0x67, 0x20, + 0x3d, 0x3e, 0x20, 0x6d, 0x73, 0x67, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, + 0x6e, 0x74, 0x29, 0x2e, 0x6a, 0x6f, 0x69, 0x6e, 0x28, 0x27, 0x27, 0x29, + 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x5e, 0x5c, + 0x73, 0x2b, 0x2f, 0x2c, 0x20, 0x27, 0x27, 0x29, 0x20, 0x3a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x64, + 0x61, 0x74, 0x61, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x20, 0x3d, + 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x3c, 0x24, 0x7b, 0x4d, 0x61, 0x72, + 0x6b, 0x64, 0x6f, 0x77, 0x6e, 0x69, 0x73, 0x68, 0x7d, 0x20, 0x74, 0x65, + 0x78, 0x74, 0x3d, 0x24, 0x7b, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, + 0x65, 0x28, 0x74, 0x65, 0x78, 0x74, 0x29, 0x7d, 0x20, 0x2f, 0x3e, 0x60, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, + 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x3c, 0x70, 0x20, 0x6b, 0x65, + 0x79, 0x3d, 0x24, 0x7b, 0x69, 0x6e, 0x64, 0x65, 0x78, 0x7d, 0x3e, 0x3c, + 0x73, 0x74, 0x72, 0x6f, 0x6e, 0x67, 0x3e, 0x24, 0x7b, 0x74, 0x65, 0x6d, + 0x70, 0x6c, 0x61, 0x74, 0x65, 0x28, 0x75, 0x73, 0x65, 0x72, 0x29, 0x7d, + 0x3a, 0x3c, 0x2f, 0x73, 0x74, 0x72, 0x6f, 0x6e, 0x67, 0x3e, 0x20, 0x24, + 0x7b, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x7d, 0x3c, 0x2f, 0x70, + 0x3e, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x3b, 0x0a, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, + 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x3c, 0x73, 0x65, 0x63, 0x74, 0x69, 0x6f, 0x6e, + 0x20, 0x69, 0x64, 0x3d, 0x22, 0x63, 0x68, 0x61, 0x74, 0x22, 0x20, 0x72, + 0x65, 0x66, 0x3d, 0x24, 0x7b, 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, + 0x65, 0x72, 0x7d, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x24, 0x7b, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, + 0x73, 0x2e, 0x66, 0x6c, 0x61, 0x74, 0x4d, 0x61, 0x70, 0x28, 0x63, 0x68, + 0x61, 0x74, 0x4c, 0x69, 0x6e, 0x65, 0x29, 0x7d, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x73, 0x65, 0x63, 0x74, 0x69, + 0x6f, 0x6e, 0x3e, 0x60, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x3b, + 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, + 0x43, 0x6f, 0x6e, 0x66, 0x69, 0x67, 0x46, 0x6f, 0x72, 0x6d, 0x20, 0x3d, + 0x20, 0x28, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x29, 0x20, 0x3d, 0x3e, 0x20, + 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, + 0x74, 0x20, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x53, 0x65, 0x73, 0x73, + 0x69, 0x6f, 0x6e, 0x20, 0x3d, 0x20, 0x28, 0x65, 0x6c, 0x29, 0x20, 0x3d, + 0x3e, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, + 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x7b, 0x20, 0x2e, 0x2e, 0x2e, 0x73, + 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, + 0x2c, 0x20, 0x5b, 0x65, 0x6c, 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, + 0x2e, 0x6e, 0x61, 0x6d, 0x65, 0x5d, 0x3a, 0x20, 0x65, 0x6c, 0x2e, 0x74, + 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, + 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, + 0x74, 0x20, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x50, 0x61, 0x72, 0x61, + 0x6d, 0x73, 0x20, 0x3d, 0x20, 0x28, 0x65, 0x6c, 0x29, 0x20, 0x3d, 0x3e, + 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x20, 0x3d, 0x20, 0x7b, 0x20, 0x2e, 0x2e, 0x2e, 0x70, 0x61, 0x72, + 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x20, 0x5b, + 0x65, 0x6c, 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, 0x6e, 0x61, + 0x6d, 0x65, 0x5d, 0x3a, 0x20, 0x65, 0x6c, 0x2e, 0x74, 0x61, 0x72, 0x67, + 0x65, 0x74, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x7d, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x75, + 0x70, 0x64, 0x61, 0x74, 0x65, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x46, + 0x6c, 0x6f, 0x61, 0x74, 0x20, 0x3d, 0x20, 0x28, 0x65, 0x6c, 0x29, 0x20, + 0x3d, 0x3e, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, + 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x7b, 0x20, 0x2e, 0x2e, 0x2e, 0x70, + 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2c, + 0x20, 0x5b, 0x65, 0x6c, 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, + 0x6e, 0x61, 0x6d, 0x65, 0x5d, 0x3a, 0x20, 0x70, 0x61, 0x72, 0x73, 0x65, + 0x46, 0x6c, 0x6f, 0x61, 0x74, 0x28, 0x65, 0x6c, 0x2e, 0x74, 0x61, 0x72, + 0x67, 0x65, 0x74, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x20, 0x7d, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, + 0x20, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x50, 0x61, 0x72, 0x61, 0x6d, + 0x73, 0x49, 0x6e, 0x74, 0x20, 0x3d, 0x20, 0x28, 0x65, 0x6c, 0x29, 0x20, + 0x3d, 0x3e, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, + 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x7b, 0x20, 0x2e, 0x2e, 0x2e, 0x70, + 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2c, + 0x20, 0x5b, 0x65, 0x6c, 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, + 0x6e, 0x61, 0x6d, 0x65, 0x5d, 0x3a, 0x20, 0x4d, 0x61, 0x74, 0x68, 0x2e, + 0x66, 0x6c, 0x6f, 0x6f, 0x72, 0x28, 0x70, 0x61, 0x72, 0x73, 0x65, 0x46, + 0x6c, 0x6f, 0x61, 0x74, 0x28, 0x65, 0x6c, 0x2e, 0x74, 0x61, 0x72, 0x67, + 0x65, 0x74, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x29, 0x29, 0x20, 0x7d, + 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, + 0x74, 0x20, 0x67, 0x72, 0x61, 0x6d, 0x6d, 0x61, 0x72, 0x4a, 0x73, 0x6f, + 0x6e, 0x53, 0x63, 0x68, 0x65, 0x6d, 0x61, 0x50, 0x72, 0x6f, 0x70, 0x4f, + 0x72, 0x64, 0x65, 0x72, 0x20, 0x3d, 0x20, 0x73, 0x69, 0x67, 0x6e, 0x61, + 0x6c, 0x28, 0x27, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, - 0x53, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x20, 0x3d, 0x20, 0x28, 0x65, - 0x6c, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, - 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x7b, 0x20, - 0x2e, 0x2e, 0x2e, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, - 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x20, 0x5b, 0x65, 0x6c, 0x2e, 0x74, 0x61, - 0x72, 0x67, 0x65, 0x74, 0x2e, 0x6e, 0x61, 0x6d, 0x65, 0x5d, 0x3a, 0x20, + 0x47, 0x72, 0x61, 0x6d, 0x6d, 0x61, 0x72, 0x4a, 0x73, 0x6f, 0x6e, 0x53, + 0x63, 0x68, 0x65, 0x6d, 0x61, 0x50, 0x72, 0x6f, 0x70, 0x4f, 0x72, 0x64, + 0x65, 0x72, 0x20, 0x3d, 0x20, 0x28, 0x65, 0x6c, 0x29, 0x20, 0x3d, 0x3e, + 0x20, 0x67, 0x72, 0x61, 0x6d, 0x6d, 0x61, 0x72, 0x4a, 0x73, 0x6f, 0x6e, + 0x53, 0x63, 0x68, 0x65, 0x6d, 0x61, 0x50, 0x72, 0x6f, 0x70, 0x4f, 0x72, + 0x64, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x65, 0x6c, 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, 0x76, 0x61, - 0x6c, 0x75, 0x65, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, - 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x20, 0x3d, 0x20, 0x28, 0x65, 0x6c, - 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, - 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x7b, 0x20, 0x2e, 0x2e, - 0x2e, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, - 0x65, 0x2c, 0x20, 0x5b, 0x65, 0x6c, 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, - 0x74, 0x2e, 0x6e, 0x61, 0x6d, 0x65, 0x5d, 0x3a, 0x20, 0x65, 0x6c, 0x2e, - 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, - 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, - 0x73, 0x74, 0x20, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x50, 0x61, 0x72, - 0x61, 0x6d, 0x73, 0x46, 0x6c, 0x6f, 0x61, 0x74, 0x20, 0x3d, 0x20, 0x28, - 0x65, 0x6c, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, - 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x7b, 0x20, - 0x2e, 0x2e, 0x2e, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, - 0x6c, 0x75, 0x65, 0x2c, 0x20, 0x5b, 0x65, 0x6c, 0x2e, 0x74, 0x61, 0x72, - 0x67, 0x65, 0x74, 0x2e, 0x6e, 0x61, 0x6d, 0x65, 0x5d, 0x3a, 0x20, 0x70, - 0x61, 0x72, 0x73, 0x65, 0x46, 0x6c, 0x6f, 0x61, 0x74, 0x28, 0x65, 0x6c, - 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, 0x76, 0x61, 0x6c, 0x75, - 0x65, 0x29, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, - 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x50, - 0x61, 0x72, 0x61, 0x6d, 0x73, 0x49, 0x6e, 0x74, 0x20, 0x3d, 0x20, 0x28, - 0x65, 0x6c, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, - 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x7b, 0x20, - 0x2e, 0x2e, 0x2e, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, - 0x6c, 0x75, 0x65, 0x2c, 0x20, 0x5b, 0x65, 0x6c, 0x2e, 0x74, 0x61, 0x72, - 0x67, 0x65, 0x74, 0x2e, 0x6e, 0x61, 0x6d, 0x65, 0x5d, 0x3a, 0x20, 0x4d, - 0x61, 0x74, 0x68, 0x2e, 0x66, 0x6c, 0x6f, 0x6f, 0x72, 0x28, 0x70, 0x61, - 0x72, 0x73, 0x65, 0x46, 0x6c, 0x6f, 0x61, 0x74, 0x28, 0x65, 0x6c, 0x2e, - 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, - 0x29, 0x29, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x67, 0x72, 0x61, 0x6d, 0x6d, 0x61, - 0x72, 0x4a, 0x73, 0x6f, 0x6e, 0x53, 0x63, 0x68, 0x65, 0x6d, 0x61, 0x50, - 0x72, 0x6f, 0x70, 0x4f, 0x72, 0x64, 0x65, 0x72, 0x20, 0x3d, 0x20, 0x73, - 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x28, 0x27, 0x27, 0x29, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x75, 0x70, - 0x64, 0x61, 0x74, 0x65, 0x47, 0x72, 0x61, 0x6d, 0x6d, 0x61, 0x72, 0x4a, + 0x6c, 0x75, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, + 0x6e, 0x73, 0x74, 0x20, 0x63, 0x6f, 0x6e, 0x76, 0x65, 0x72, 0x74, 0x4a, + 0x53, 0x4f, 0x4e, 0x53, 0x63, 0x68, 0x65, 0x6d, 0x61, 0x47, 0x72, 0x61, + 0x6d, 0x6d, 0x61, 0x72, 0x20, 0x3d, 0x20, 0x28, 0x29, 0x20, 0x3d, 0x3e, + 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, + 0x72, 0x79, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x73, 0x63, 0x68, + 0x65, 0x6d, 0x61, 0x20, 0x3d, 0x20, 0x4a, 0x53, 0x4f, 0x4e, 0x2e, 0x70, + 0x61, 0x72, 0x73, 0x65, 0x28, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, + 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x67, 0x72, 0x61, 0x6d, 0x6d, 0x61, + 0x72, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x6f, 0x6e, 0x76, 0x65, + 0x72, 0x74, 0x65, 0x72, 0x20, 0x3d, 0x20, 0x6e, 0x65, 0x77, 0x20, 0x53, + 0x63, 0x68, 0x65, 0x6d, 0x61, 0x43, 0x6f, 0x6e, 0x76, 0x65, 0x72, 0x74, + 0x65, 0x72, 0x28, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x67, 0x72, 0x61, 0x6d, 0x6d, 0x61, 0x72, 0x4a, 0x73, 0x6f, 0x6e, 0x53, 0x63, 0x68, 0x65, 0x6d, 0x61, 0x50, 0x72, 0x6f, - 0x70, 0x4f, 0x72, 0x64, 0x65, 0x72, 0x20, 0x3d, 0x20, 0x28, 0x65, 0x6c, - 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x67, 0x72, 0x61, 0x6d, 0x6d, 0x61, 0x72, - 0x4a, 0x73, 0x6f, 0x6e, 0x53, 0x63, 0x68, 0x65, 0x6d, 0x61, 0x50, 0x72, - 0x6f, 0x70, 0x4f, 0x72, 0x64, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, - 0x65, 0x20, 0x3d, 0x20, 0x65, 0x6c, 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, - 0x74, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, 0x6f, 0x6e, 0x76, - 0x65, 0x72, 0x74, 0x4a, 0x53, 0x4f, 0x4e, 0x53, 0x63, 0x68, 0x65, 0x6d, - 0x61, 0x47, 0x72, 0x61, 0x6d, 0x6d, 0x61, 0x72, 0x20, 0x3d, 0x20, 0x28, - 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x74, 0x72, 0x79, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, - 0x20, 0x73, 0x63, 0x68, 0x65, 0x6d, 0x61, 0x20, 0x3d, 0x20, 0x4a, 0x53, - 0x4f, 0x4e, 0x2e, 0x70, 0x61, 0x72, 0x73, 0x65, 0x28, 0x70, 0x61, 0x72, - 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x67, 0x72, - 0x61, 0x6d, 0x6d, 0x61, 0x72, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x63, - 0x6f, 0x6e, 0x76, 0x65, 0x72, 0x74, 0x65, 0x72, 0x20, 0x3d, 0x20, 0x6e, - 0x65, 0x77, 0x20, 0x53, 0x63, 0x68, 0x65, 0x6d, 0x61, 0x43, 0x6f, 0x6e, - 0x76, 0x65, 0x72, 0x74, 0x65, 0x72, 0x28, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x67, 0x72, 0x61, 0x6d, - 0x6d, 0x61, 0x72, 0x4a, 0x73, 0x6f, 0x6e, 0x53, 0x63, 0x68, 0x65, 0x6d, - 0x61, 0x50, 0x72, 0x6f, 0x70, 0x4f, 0x72, 0x64, 0x65, 0x72, 0x2e, 0x76, - 0x61, 0x6c, 0x75, 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x73, 0x70, 0x6c, 0x69, - 0x74, 0x28, 0x27, 0x2c, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, - 0x64, 0x75, 0x63, 0x65, 0x28, 0x28, 0x61, 0x63, 0x63, 0x2c, 0x20, 0x63, - 0x75, 0x72, 0x2c, 0x20, 0x69, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x28, 0x7b, - 0x2e, 0x2e, 0x2e, 0x61, 0x63, 0x63, 0x2c, 0x20, 0x5b, 0x63, 0x75, 0x72, - 0x2e, 0x74, 0x72, 0x69, 0x6d, 0x28, 0x29, 0x5d, 0x3a, 0x20, 0x69, 0x7d, - 0x29, 0x2c, 0x20, 0x7b, 0x7d, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x76, 0x65, 0x72, 0x74, - 0x65, 0x72, 0x2e, 0x76, 0x69, 0x73, 0x69, 0x74, 0x28, 0x73, 0x63, 0x68, - 0x65, 0x6d, 0x61, 0x2c, 0x20, 0x27, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, - 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x7b, 0x0a, + 0x70, 0x4f, 0x72, 0x64, 0x65, 0x72, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x2e, 0x73, 0x70, 0x6c, 0x69, 0x74, 0x28, 0x27, 0x2c, + 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, 0x64, 0x75, 0x63, 0x65, + 0x28, 0x28, 0x61, 0x63, 0x63, 0x2c, 0x20, 0x63, 0x75, 0x72, 0x2c, 0x20, + 0x69, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x28, 0x7b, 0x2e, 0x2e, 0x2e, 0x61, + 0x63, 0x63, 0x2c, 0x20, 0x5b, 0x63, 0x75, 0x72, 0x2e, 0x74, 0x72, 0x69, + 0x6d, 0x28, 0x29, 0x5d, 0x3a, 0x20, 0x69, 0x7d, 0x29, 0x2c, 0x20, 0x7b, + 0x7d, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x63, 0x6f, 0x6e, 0x76, 0x65, 0x72, 0x74, 0x65, 0x72, 0x2e, 0x76, + 0x69, 0x73, 0x69, 0x74, 0x28, 0x73, 0x63, 0x68, 0x65, 0x6d, 0x61, 0x2c, + 0x20, 0x27, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, + 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x2e, 0x2e, 0x70, + 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2c, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x67, 0x72, 0x61, 0x6d, 0x6d, 0x61, 0x72, 0x3a, 0x20, 0x63, 0x6f, + 0x6e, 0x76, 0x65, 0x72, 0x74, 0x65, 0x72, 0x2e, 0x66, 0x6f, 0x72, 0x6d, + 0x61, 0x74, 0x47, 0x72, 0x61, 0x6d, 0x6d, 0x61, 0x72, 0x28, 0x29, 0x2c, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x20, 0x63, + 0x61, 0x74, 0x63, 0x68, 0x20, 0x28, 0x65, 0x29, 0x20, 0x7b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x61, 0x6c, 0x65, + 0x72, 0x74, 0x28, 0x60, 0x43, 0x6f, 0x6e, 0x76, 0x65, 0x72, 0x74, 0x20, + 0x66, 0x61, 0x69, 0x6c, 0x65, 0x64, 0x3a, 0x20, 0x24, 0x7b, 0x65, 0x2e, + 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x7d, 0x60, 0x29, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x46, 0x6c, 0x6f, 0x61, 0x74, 0x46, + 0x69, 0x65, 0x6c, 0x64, 0x20, 0x3d, 0x20, 0x28, 0x7b, 0x6c, 0x61, 0x62, + 0x65, 0x6c, 0x2c, 0x20, 0x6d, 0x61, 0x78, 0x2c, 0x20, 0x6d, 0x69, 0x6e, + 0x2c, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x2c, 0x20, 0x73, 0x74, 0x65, 0x70, + 0x2c, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x7d, 0x29, 0x20, 0x3d, 0x3e, + 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, + 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, + 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, + 0x6f, 0x72, 0x3d, 0x22, 0x24, 0x7b, 0x6e, 0x61, 0x6d, 0x65, 0x7d, 0x22, + 0x3e, 0x24, 0x7b, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x7d, 0x3c, 0x2f, 0x6c, + 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x69, 0x6e, 0x70, 0x75, 0x74, + 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, 0x72, 0x61, 0x6e, 0x67, 0x65, + 0x22, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x24, 0x7b, 0x6e, 0x61, 0x6d, 0x65, + 0x7d, 0x22, 0x20, 0x6d, 0x69, 0x6e, 0x3d, 0x22, 0x24, 0x7b, 0x6d, 0x69, + 0x6e, 0x7d, 0x22, 0x20, 0x6d, 0x61, 0x78, 0x3d, 0x22, 0x24, 0x7b, 0x6d, + 0x61, 0x78, 0x7d, 0x22, 0x20, 0x73, 0x74, 0x65, 0x70, 0x3d, 0x22, 0x24, + 0x7b, 0x73, 0x74, 0x65, 0x70, 0x7d, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, + 0x3d, 0x22, 0x24, 0x7b, 0x6e, 0x61, 0x6d, 0x65, 0x7d, 0x22, 0x20, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x7d, 0x22, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, + 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x50, 0x61, 0x72, 0x61, + 0x6d, 0x73, 0x46, 0x6c, 0x6f, 0x61, 0x74, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x2e, 0x2e, 0x2e, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, - 0x6c, 0x75, 0x65, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x67, 0x72, 0x61, 0x6d, 0x6d, 0x61, 0x72, - 0x3a, 0x20, 0x63, 0x6f, 0x6e, 0x76, 0x65, 0x72, 0x74, 0x65, 0x72, 0x2e, - 0x66, 0x6f, 0x72, 0x6d, 0x61, 0x74, 0x47, 0x72, 0x61, 0x6d, 0x6d, 0x61, - 0x72, 0x28, 0x29, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x7d, 0x20, 0x63, 0x61, 0x74, 0x63, 0x68, 0x20, 0x28, 0x65, 0x29, - 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x61, 0x6c, 0x65, 0x72, 0x74, 0x28, 0x60, 0x43, 0x6f, 0x6e, 0x76, - 0x65, 0x72, 0x74, 0x20, 0x66, 0x61, 0x69, 0x6c, 0x65, 0x64, 0x3a, 0x20, - 0x24, 0x7b, 0x65, 0x2e, 0x6d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, 0x7d, - 0x60, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x46, 0x6c, - 0x6f, 0x61, 0x74, 0x46, 0x69, 0x65, 0x6c, 0x64, 0x20, 0x3d, 0x20, 0x28, - 0x7b, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x2c, 0x20, 0x6d, 0x61, 0x78, 0x2c, - 0x20, 0x6d, 0x69, 0x6e, 0x2c, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x2c, 0x20, - 0x73, 0x74, 0x65, 0x70, 0x2c, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x7d, - 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, - 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, 0x62, - 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, 0x24, 0x7b, 0x6e, 0x61, - 0x6d, 0x65, 0x7d, 0x22, 0x3e, 0x24, 0x7b, 0x6c, 0x61, 0x62, 0x65, 0x6c, - 0x7d, 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x69, - 0x6e, 0x70, 0x75, 0x74, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, 0x72, - 0x61, 0x6e, 0x67, 0x65, 0x22, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x24, 0x7b, - 0x6e, 0x61, 0x6d, 0x65, 0x7d, 0x22, 0x20, 0x6d, 0x69, 0x6e, 0x3d, 0x22, - 0x24, 0x7b, 0x6d, 0x69, 0x6e, 0x7d, 0x22, 0x20, 0x6d, 0x61, 0x78, 0x3d, - 0x22, 0x24, 0x7b, 0x6d, 0x61, 0x78, 0x7d, 0x22, 0x20, 0x73, 0x74, 0x65, - 0x70, 0x3d, 0x22, 0x24, 0x7b, 0x73, 0x74, 0x65, 0x70, 0x7d, 0x22, 0x20, - 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x6e, 0x61, 0x6d, 0x65, - 0x7d, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, - 0x76, 0x61, 0x6c, 0x75, 0x65, 0x7d, 0x22, 0x20, 0x6f, 0x6e, 0x69, 0x6e, - 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, - 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x46, 0x6c, 0x6f, 0x61, 0x74, 0x7d, + 0x3c, 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x24, 0x7b, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x7d, 0x3c, 0x2f, 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, + 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x3b, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x49, + 0x6e, 0x74, 0x46, 0x69, 0x65, 0x6c, 0x64, 0x20, 0x3d, 0x20, 0x28, 0x7b, + 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x2c, 0x20, 0x6d, 0x61, 0x78, 0x2c, 0x20, + 0x6d, 0x69, 0x6e, 0x2c, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x2c, 0x20, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x7d, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, + 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, + 0x22, 0x24, 0x7b, 0x6e, 0x61, 0x6d, 0x65, 0x7d, 0x22, 0x3e, 0x24, 0x7b, + 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x7d, 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, + 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x3c, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x20, 0x74, 0x79, + 0x70, 0x65, 0x3d, 0x22, 0x72, 0x61, 0x6e, 0x67, 0x65, 0x22, 0x20, 0x69, + 0x64, 0x3d, 0x22, 0x24, 0x7b, 0x6e, 0x61, 0x6d, 0x65, 0x7d, 0x22, 0x20, + 0x6d, 0x69, 0x6e, 0x3d, 0x22, 0x24, 0x7b, 0x6d, 0x69, 0x6e, 0x7d, 0x22, + 0x20, 0x6d, 0x61, 0x78, 0x3d, 0x22, 0x24, 0x7b, 0x6d, 0x61, 0x78, 0x7d, + 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x6e, 0x61, + 0x6d, 0x65, 0x7d, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, + 0x24, 0x7b, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x7d, 0x22, 0x20, 0x6f, 0x6e, + 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, + 0x74, 0x65, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x49, 0x6e, 0x74, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x24, 0x7b, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x7d, 0x3c, 0x2f, 0x73, 0x70, 0x61, 0x6e, @@ -1235,1038 +1331,1008 @@ unsigned char index_html[] = { 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, - 0x73, 0x74, 0x20, 0x49, 0x6e, 0x74, 0x46, 0x69, 0x65, 0x6c, 0x64, 0x20, - 0x3d, 0x20, 0x28, 0x7b, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x2c, 0x20, 0x6d, - 0x61, 0x78, 0x2c, 0x20, 0x6d, 0x69, 0x6e, 0x2c, 0x20, 0x6e, 0x61, 0x6d, - 0x65, 0x2c, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x7d, 0x29, 0x20, 0x3d, - 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, - 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, - 0x66, 0x6f, 0x72, 0x3d, 0x22, 0x24, 0x7b, 0x6e, 0x61, 0x6d, 0x65, 0x7d, - 0x22, 0x3e, 0x24, 0x7b, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x7d, 0x3c, 0x2f, - 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x69, 0x6e, 0x70, 0x75, - 0x74, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, 0x72, 0x61, 0x6e, 0x67, - 0x65, 0x22, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x24, 0x7b, 0x6e, 0x61, 0x6d, - 0x65, 0x7d, 0x22, 0x20, 0x6d, 0x69, 0x6e, 0x3d, 0x22, 0x24, 0x7b, 0x6d, - 0x69, 0x6e, 0x7d, 0x22, 0x20, 0x6d, 0x61, 0x78, 0x3d, 0x22, 0x24, 0x7b, - 0x6d, 0x61, 0x78, 0x7d, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, - 0x24, 0x7b, 0x6e, 0x61, 0x6d, 0x65, 0x7d, 0x22, 0x20, 0x76, 0x61, 0x6c, - 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x7d, - 0x22, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, - 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, - 0x49, 0x6e, 0x74, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x73, 0x70, 0x61, - 0x6e, 0x3e, 0x24, 0x7b, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x7d, 0x3c, 0x2f, - 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x7d, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x75, 0x73, 0x65, 0x72, 0x54, - 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x52, 0x65, 0x73, 0x65, 0x74, - 0x20, 0x3d, 0x20, 0x28, 0x65, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x65, 0x2e, 0x70, 0x72, - 0x65, 0x76, 0x65, 0x6e, 0x74, 0x44, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, - 0x28, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x75, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, - 0x52, 0x65, 0x73, 0x65, 0x74, 0x54, 0x6f, 0x44, 0x65, 0x66, 0x61, 0x75, - 0x6c, 0x74, 0x41, 0x6e, 0x64, 0x41, 0x70, 0x70, 0x6c, 0x79, 0x28, 0x29, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x55, 0x73, - 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x52, 0x65, - 0x73, 0x65, 0x74, 0x42, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x20, 0x3d, 0x20, - 0x28, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x73, 0x65, 0x6c, 0x65, - 0x63, 0x74, 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, - 0x6c, 0x61, 0x74, 0x65, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x6e, - 0x61, 0x6d, 0x65, 0x20, 0x3d, 0x3d, 0x20, 0x27, 0x64, 0x65, 0x66, 0x61, - 0x75, 0x6c, 0x74, 0x27, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, - 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x62, 0x75, 0x74, 0x74, - 0x6f, 0x6e, 0x20, 0x64, 0x69, 0x73, 0x61, 0x62, 0x6c, 0x65, 0x64, 0x3e, - 0x55, 0x73, 0x69, 0x6e, 0x67, 0x20, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, - 0x74, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x3c, 0x2f, - 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, - 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x3c, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x20, 0x6f, 0x6e, - 0x63, 0x6c, 0x69, 0x63, 0x6b, 0x3d, 0x24, 0x7b, 0x75, 0x73, 0x65, 0x72, + 0x73, 0x74, 0x20, 0x75, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, + 0x61, 0x74, 0x65, 0x52, 0x65, 0x73, 0x65, 0x74, 0x20, 0x3d, 0x20, 0x28, + 0x65, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x65, 0x2e, 0x70, 0x72, 0x65, 0x76, 0x65, 0x6e, + 0x74, 0x44, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x28, 0x29, 0x3b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x75, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x52, 0x65, 0x73, 0x65, - 0x74, 0x7d, 0x3e, 0x52, 0x65, 0x73, 0x65, 0x74, 0x20, 0x61, 0x6c, 0x6c, - 0x20, 0x74, 0x6f, 0x20, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x3c, - 0x2f, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x7d, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x75, - 0x73, 0x65, 0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x28, 0x28, 0x29, 0x20, - 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x2f, 0x2f, 0x20, 0x61, 0x75, 0x74, 0x6f, 0x73, 0x61, 0x76, 0x65, - 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x20, 0x6f, 0x6e, - 0x20, 0x65, 0x76, 0x65, 0x72, 0x79, 0x20, 0x63, 0x68, 0x61, 0x6e, 0x67, - 0x65, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x75, 0x73, - 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x41, 0x75, - 0x74, 0x6f, 0x73, 0x61, 0x76, 0x65, 0x28, 0x29, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x7d, 0x2c, 0x20, 0x5b, 0x73, 0x65, 0x73, 0x73, 0x69, - 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x20, 0x70, 0x61, - 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x5d, 0x29, - 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, - 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x66, 0x6f, 0x72, 0x6d, 0x3e, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x66, - 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, 0x3e, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x24, 0x7b, + 0x74, 0x54, 0x6f, 0x44, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x41, 0x6e, + 0x64, 0x41, 0x70, 0x70, 0x6c, 0x79, 0x28, 0x29, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, + 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x52, 0x65, 0x73, 0x65, 0x74, 0x42, + 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x20, 0x3d, 0x20, 0x28, 0x29, 0x20, 0x3d, + 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x69, 0x66, 0x20, 0x28, 0x73, 0x65, 0x6c, 0x65, 0x63, 0x74, 0x65, 0x64, 0x55, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, - 0x52, 0x65, 0x73, 0x65, 0x74, 0x42, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x7d, - 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x3c, 0x2f, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, 0x3e, - 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x3c, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, 0x3e, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, - 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, 0x62, 0x65, - 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, 0x70, 0x72, 0x6f, 0x6d, 0x70, - 0x74, 0x22, 0x3e, 0x50, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x3c, 0x2f, 0x6c, - 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x74, 0x65, 0x78, - 0x74, 0x61, 0x72, 0x65, 0x61, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, - 0x74, 0x65, 0x78, 0x74, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, - 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, - 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, - 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x70, 0x72, 0x6f, 0x6d, 0x70, - 0x74, 0x7d, 0x22, 0x20, 0x72, 0x6f, 0x77, 0x73, 0x3d, 0x34, 0x20, 0x6f, - 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, - 0x61, 0x74, 0x65, 0x53, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x7d, 0x2f, - 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x69, 0x65, - 0x6c, 0x64, 0x73, 0x65, 0x74, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x6e, 0x61, 0x6d, 0x65, 0x20, + 0x3d, 0x3d, 0x20, 0x27, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x27, + 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, + 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x3c, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x20, 0x64, + 0x69, 0x73, 0x61, 0x62, 0x6c, 0x65, 0x64, 0x3e, 0x55, 0x73, 0x69, 0x6e, + 0x67, 0x20, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x20, 0x74, 0x65, + 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x3c, 0x2f, 0x62, 0x75, 0x74, 0x74, + 0x6f, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, + 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x62, + 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x20, 0x6f, 0x6e, 0x63, 0x6c, 0x69, 0x63, + 0x6b, 0x3d, 0x24, 0x7b, 0x75, 0x73, 0x65, 0x72, 0x54, 0x65, 0x6d, 0x70, + 0x6c, 0x61, 0x74, 0x65, 0x52, 0x65, 0x73, 0x65, 0x74, 0x7d, 0x3e, 0x52, + 0x65, 0x73, 0x65, 0x74, 0x20, 0x61, 0x6c, 0x6c, 0x20, 0x74, 0x6f, 0x20, + 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, 0x3c, 0x2f, 0x62, 0x75, 0x74, + 0x74, 0x6f, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x3b, 0x0a, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x75, 0x73, 0x65, 0x45, 0x66, + 0x66, 0x65, 0x63, 0x74, 0x28, 0x28, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, + 0x61, 0x75, 0x74, 0x6f, 0x73, 0x61, 0x76, 0x65, 0x20, 0x74, 0x65, 0x6d, + 0x70, 0x6c, 0x61, 0x74, 0x65, 0x20, 0x6f, 0x6e, 0x20, 0x65, 0x76, 0x65, + 0x72, 0x79, 0x20, 0x63, 0x68, 0x61, 0x6e, 0x67, 0x65, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x75, 0x73, 0x65, 0x72, 0x54, 0x65, + 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x41, 0x75, 0x74, 0x6f, 0x73, 0x61, + 0x76, 0x65, 0x28, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, + 0x2c, 0x20, 0x5b, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x2c, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, + 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x5d, 0x29, 0x0a, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, + 0x74, 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x66, 0x6f, 0x72, 0x6d, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x66, 0x69, 0x65, 0x6c, 0x64, - 0x73, 0x65, 0x74, 0x20, 0x63, 0x6c, 0x61, 0x73, 0x73, 0x3d, 0x22, 0x74, - 0x77, 0x6f, 0x22, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, - 0x22, 0x75, 0x73, 0x65, 0x72, 0x22, 0x3e, 0x55, 0x73, 0x65, 0x72, 0x20, - 0x6e, 0x61, 0x6d, 0x65, 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, + 0x73, 0x65, 0x74, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x24, 0x7b, 0x55, 0x73, 0x65, 0x72, + 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x52, 0x65, 0x73, 0x65, + 0x74, 0x42, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x7d, 0x2f, 0x3e, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, + 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, 0x3e, 0x0a, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x66, 0x69, 0x65, + 0x6c, 0x64, 0x73, 0x65, 0x74, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x3c, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x20, 0x74, 0x79, - 0x70, 0x65, 0x3d, 0x22, 0x74, 0x65, 0x78, 0x74, 0x22, 0x20, 0x6e, 0x61, - 0x6d, 0x65, 0x3d, 0x22, 0x75, 0x73, 0x65, 0x72, 0x22, 0x20, 0x76, 0x61, - 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x73, 0x65, 0x73, 0x73, 0x69, - 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x75, 0x73, 0x65, - 0x72, 0x7d, 0x22, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, - 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x53, 0x65, 0x73, 0x73, - 0x69, 0x6f, 0x6e, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, - 0x76, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, + 0x72, 0x3d, 0x22, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x22, 0x3e, 0x50, + 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, + 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x3c, 0x74, 0x65, 0x78, 0x74, 0x61, 0x72, 0x65, + 0x61, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, 0x74, 0x65, 0x78, 0x74, + 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x70, 0x72, 0x6f, 0x6d, + 0x70, 0x74, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, + 0x7b, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x2e, 0x70, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x7d, 0x22, 0x20, + 0x72, 0x6f, 0x77, 0x73, 0x3d, 0x34, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, + 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x53, + 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x7d, 0x2f, 0x3e, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, + 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, + 0x74, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x3c, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, 0x20, + 0x63, 0x6c, 0x61, 0x73, 0x73, 0x3d, 0x22, 0x74, 0x77, 0x6f, 0x22, 0x3e, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, + 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, 0x75, 0x73, 0x65, + 0x72, 0x22, 0x3e, 0x55, 0x73, 0x65, 0x72, 0x20, 0x6e, 0x61, 0x6d, 0x65, + 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, + 0x69, 0x6e, 0x70, 0x75, 0x74, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, + 0x74, 0x65, 0x78, 0x74, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, + 0x75, 0x73, 0x65, 0x72, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, + 0x22, 0x24, 0x7b, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x75, 0x73, 0x65, 0x72, 0x7d, 0x22, 0x20, + 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, 0x70, + 0x64, 0x61, 0x74, 0x65, 0x53, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x7d, + 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, 0x62, + 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, 0x62, 0x6f, 0x74, 0x22, + 0x3e, 0x42, 0x6f, 0x74, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3c, 0x2f, 0x6c, + 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x69, 0x6e, 0x70, + 0x75, 0x74, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, 0x74, 0x65, 0x78, + 0x74, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x63, 0x68, 0x61, + 0x72, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, + 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x2e, 0x63, 0x68, 0x61, 0x72, 0x7d, 0x22, 0x20, 0x6f, 0x6e, 0x69, + 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, + 0x65, 0x53, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x7d, 0x20, 0x2f, 0x3e, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x69, 0x65, 0x6c, + 0x64, 0x73, 0x65, 0x74, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, + 0x65, 0x74, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, - 0x62, 0x6f, 0x74, 0x22, 0x3e, 0x42, 0x6f, 0x74, 0x20, 0x6e, 0x61, 0x6d, + 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x22, 0x3e, 0x50, 0x72, + 0x6f, 0x6d, 0x70, 0x74, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x3c, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, - 0x22, 0x74, 0x65, 0x78, 0x74, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, - 0x22, 0x63, 0x68, 0x61, 0x72, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, - 0x3d, 0x22, 0x24, 0x7b, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, - 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x63, 0x68, 0x61, 0x72, 0x7d, 0x22, - 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, - 0x70, 0x64, 0x61, 0x74, 0x65, 0x53, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, - 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, - 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, 0x3e, 0x0a, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x66, 0x69, - 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x3c, 0x74, 0x65, 0x78, 0x74, 0x61, 0x72, 0x65, 0x61, 0x20, 0x69, 0x64, + 0x3d, 0x22, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x22, 0x20, + 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, + 0x74, 0x65, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, + 0x7b, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x2e, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x7d, + 0x22, 0x20, 0x72, 0x6f, 0x77, 0x73, 0x3d, 0x34, 0x20, 0x6f, 0x6e, 0x69, + 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, + 0x65, 0x53, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x7d, 0x2f, 0x3e, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, - 0x22, 0x3e, 0x50, 0x72, 0x6f, 0x6d, 0x70, 0x74, 0x20, 0x74, 0x65, 0x6d, - 0x70, 0x6c, 0x61, 0x74, 0x65, 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, + 0x22, 0x3e, 0x43, 0x68, 0x61, 0x74, 0x20, 0x68, 0x69, 0x73, 0x74, 0x6f, + 0x72, 0x79, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x3c, + 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x74, + 0x65, 0x78, 0x74, 0x61, 0x72, 0x65, 0x61, 0x20, 0x69, 0x64, 0x3d, 0x22, + 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x22, 0x20, 0x6e, 0x61, + 0x6d, 0x65, 0x3d, 0x22, 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x54, + 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x22, 0x20, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, + 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x68, 0x69, 0x73, 0x74, + 0x6f, 0x72, 0x79, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x7d, + 0x22, 0x20, 0x72, 0x6f, 0x77, 0x73, 0x3d, 0x31, 0x20, 0x6f, 0x6e, 0x69, + 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, + 0x65, 0x53, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x7d, 0x2f, 0x3e, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x3c, 0x74, 0x65, 0x78, 0x74, 0x61, 0x72, 0x65, - 0x61, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, - 0x74, 0x65, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x74, 0x65, - 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, - 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x73, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, - 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x65, 0x6d, 0x70, 0x6c, - 0x61, 0x74, 0x65, 0x7d, 0x22, 0x20, 0x72, 0x6f, 0x77, 0x73, 0x3d, 0x34, - 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, - 0x70, 0x64, 0x61, 0x74, 0x65, 0x53, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, - 0x7d, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, 0x62, - 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, 0x74, 0x65, 0x6d, 0x70, - 0x6c, 0x61, 0x74, 0x65, 0x22, 0x3e, 0x43, 0x68, 0x61, 0x74, 0x20, 0x68, - 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x20, 0x74, 0x65, 0x6d, 0x70, 0x6c, - 0x61, 0x74, 0x65, 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x3c, 0x74, 0x65, 0x78, 0x74, 0x61, 0x72, 0x65, 0x61, 0x20, - 0x69, 0x64, 0x3d, 0x22, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, - 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x68, 0x69, 0x73, 0x74, - 0x6f, 0x72, 0x79, 0x54, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, 0x22, - 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x73, 0x65, - 0x73, 0x73, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, - 0x68, 0x69, 0x73, 0x74, 0x6f, 0x72, 0x79, 0x54, 0x65, 0x6d, 0x70, 0x6c, - 0x61, 0x74, 0x65, 0x7d, 0x22, 0x20, 0x72, 0x6f, 0x77, 0x73, 0x3d, 0x31, - 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, - 0x70, 0x64, 0x61, 0x74, 0x65, 0x53, 0x65, 0x73, 0x73, 0x69, 0x6f, 0x6e, - 0x7d, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, 0x62, - 0x65, 0x6c, 0x20, 0x66, 0x6f, 0x72, 0x3d, 0x22, 0x74, 0x65, 0x6d, 0x70, - 0x6c, 0x61, 0x74, 0x65, 0x22, 0x3e, 0x47, 0x72, 0x61, 0x6d, 0x6d, 0x61, - 0x72, 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x3c, 0x74, 0x65, 0x78, 0x74, 0x61, 0x72, 0x65, 0x61, 0x20, 0x69, 0x64, - 0x3d, 0x22, 0x67, 0x72, 0x61, 0x6d, 0x6d, 0x61, 0x72, 0x22, 0x20, 0x6e, - 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x67, 0x72, 0x61, 0x6d, 0x6d, 0x61, 0x72, - 0x22, 0x20, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x68, 0x6f, 0x6c, 0x64, 0x65, - 0x72, 0x3d, 0x22, 0x55, 0x73, 0x65, 0x20, 0x67, 0x62, 0x6e, 0x66, 0x20, - 0x6f, 0x72, 0x20, 0x4a, 0x53, 0x4f, 0x4e, 0x20, 0x53, 0x63, 0x68, 0x65, - 0x6d, 0x61, 0x2b, 0x63, 0x6f, 0x6e, 0x76, 0x65, 0x72, 0x74, 0x22, 0x20, - 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x70, 0x61, 0x72, - 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x67, 0x72, - 0x61, 0x6d, 0x6d, 0x61, 0x72, 0x7d, 0x22, 0x20, 0x72, 0x6f, 0x77, 0x73, - 0x3d, 0x34, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, - 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x50, 0x61, 0x72, 0x61, 0x6d, - 0x73, 0x7d, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x69, 0x6e, 0x70, 0x75, - 0x74, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, 0x74, 0x65, 0x78, 0x74, - 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x70, 0x72, 0x6f, 0x70, - 0x2d, 0x6f, 0x72, 0x64, 0x65, 0x72, 0x22, 0x20, 0x70, 0x6c, 0x61, 0x63, - 0x65, 0x68, 0x6f, 0x6c, 0x64, 0x65, 0x72, 0x3d, 0x22, 0x6f, 0x72, 0x64, - 0x65, 0x72, 0x3a, 0x20, 0x70, 0x72, 0x6f, 0x70, 0x31, 0x2c, 0x70, 0x72, - 0x6f, 0x70, 0x32, 0x2c, 0x70, 0x72, 0x6f, 0x70, 0x33, 0x22, 0x20, 0x6f, + 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x20, 0x66, + 0x6f, 0x72, 0x3d, 0x22, 0x74, 0x65, 0x6d, 0x70, 0x6c, 0x61, 0x74, 0x65, + 0x22, 0x3e, 0x47, 0x72, 0x61, 0x6d, 0x6d, 0x61, 0x72, 0x3c, 0x2f, 0x6c, + 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x74, 0x65, 0x78, + 0x74, 0x61, 0x72, 0x65, 0x61, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x67, 0x72, + 0x61, 0x6d, 0x6d, 0x61, 0x72, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, + 0x22, 0x67, 0x72, 0x61, 0x6d, 0x6d, 0x61, 0x72, 0x22, 0x20, 0x70, 0x6c, + 0x61, 0x63, 0x65, 0x68, 0x6f, 0x6c, 0x64, 0x65, 0x72, 0x3d, 0x22, 0x55, + 0x73, 0x65, 0x20, 0x67, 0x62, 0x6e, 0x66, 0x20, 0x6f, 0x72, 0x20, 0x4a, + 0x53, 0x4f, 0x4e, 0x20, 0x53, 0x63, 0x68, 0x65, 0x6d, 0x61, 0x2b, 0x63, + 0x6f, 0x6e, 0x76, 0x65, 0x72, 0x74, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x3d, 0x22, 0x24, 0x7b, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, + 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x67, 0x72, 0x61, 0x6d, 0x6d, 0x61, + 0x72, 0x7d, 0x22, 0x20, 0x72, 0x6f, 0x77, 0x73, 0x3d, 0x34, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, - 0x61, 0x74, 0x65, 0x47, 0x72, 0x61, 0x6d, 0x6d, 0x61, 0x72, 0x4a, 0x73, - 0x6f, 0x6e, 0x53, 0x63, 0x68, 0x65, 0x6d, 0x61, 0x50, 0x72, 0x6f, 0x70, - 0x4f, 0x72, 0x64, 0x65, 0x72, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, + 0x61, 0x74, 0x65, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x7d, 0x2f, 0x3e, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x3c, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x20, 0x74, 0x79, + 0x70, 0x65, 0x3d, 0x22, 0x74, 0x65, 0x78, 0x74, 0x22, 0x20, 0x6e, 0x61, + 0x6d, 0x65, 0x3d, 0x22, 0x70, 0x72, 0x6f, 0x70, 0x2d, 0x6f, 0x72, 0x64, + 0x65, 0x72, 0x22, 0x20, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x68, 0x6f, 0x6c, + 0x64, 0x65, 0x72, 0x3d, 0x22, 0x6f, 0x72, 0x64, 0x65, 0x72, 0x3a, 0x20, + 0x70, 0x72, 0x6f, 0x70, 0x31, 0x2c, 0x70, 0x72, 0x6f, 0x70, 0x32, 0x2c, + 0x70, 0x72, 0x6f, 0x70, 0x33, 0x22, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, + 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x47, + 0x72, 0x61, 0x6d, 0x6d, 0x61, 0x72, 0x4a, 0x73, 0x6f, 0x6e, 0x53, 0x63, + 0x68, 0x65, 0x6d, 0x61, 0x50, 0x72, 0x6f, 0x70, 0x4f, 0x72, 0x64, 0x65, + 0x72, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x62, 0x75, 0x74, + 0x74, 0x6f, 0x6e, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, 0x62, 0x75, + 0x74, 0x74, 0x6f, 0x6e, 0x22, 0x20, 0x6f, 0x6e, 0x63, 0x6c, 0x69, 0x63, + 0x6b, 0x3d, 0x24, 0x7b, 0x63, 0x6f, 0x6e, 0x76, 0x65, 0x72, 0x74, 0x4a, + 0x53, 0x4f, 0x4e, 0x53, 0x63, 0x68, 0x65, 0x6d, 0x61, 0x47, 0x72, 0x61, + 0x6d, 0x6d, 0x61, 0x72, 0x7d, 0x3e, 0x43, 0x6f, 0x6e, 0x76, 0x65, 0x72, + 0x74, 0x20, 0x4a, 0x53, 0x4f, 0x4e, 0x20, 0x53, 0x63, 0x68, 0x65, 0x6d, + 0x61, 0x3c, 0x2f, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x3e, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, + 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, + 0x65, 0x74, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x3c, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, + 0x20, 0x63, 0x6c, 0x61, 0x73, 0x73, 0x3d, 0x22, 0x74, 0x77, 0x6f, 0x22, + 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x24, 0x7b, 0x49, 0x6e, 0x74, 0x46, 0x69, 0x65, 0x6c, 0x64, + 0x28, 0x7b, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3a, 0x20, 0x22, 0x50, 0x72, + 0x65, 0x64, 0x69, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x73, 0x22, 0x2c, 0x20, + 0x6d, 0x61, 0x78, 0x3a, 0x20, 0x32, 0x30, 0x34, 0x38, 0x2c, 0x20, 0x6d, + 0x69, 0x6e, 0x3a, 0x20, 0x2d, 0x31, 0x2c, 0x20, 0x6e, 0x61, 0x6d, 0x65, + 0x3a, 0x20, 0x22, 0x6e, 0x5f, 0x70, 0x72, 0x65, 0x64, 0x69, 0x63, 0x74, + 0x22, 0x2c, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3a, 0x20, 0x70, 0x61, + 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x6e, + 0x5f, 0x70, 0x72, 0x65, 0x64, 0x69, 0x63, 0x74, 0x7d, 0x29, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x3c, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x20, 0x74, 0x79, 0x70, 0x65, - 0x3d, 0x22, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x22, 0x20, 0x6f, 0x6e, - 0x63, 0x6c, 0x69, 0x63, 0x6b, 0x3d, 0x24, 0x7b, 0x63, 0x6f, 0x6e, 0x76, - 0x65, 0x72, 0x74, 0x4a, 0x53, 0x4f, 0x4e, 0x53, 0x63, 0x68, 0x65, 0x6d, - 0x61, 0x47, 0x72, 0x61, 0x6d, 0x6d, 0x61, 0x72, 0x7d, 0x3e, 0x43, 0x6f, - 0x6e, 0x76, 0x65, 0x72, 0x74, 0x20, 0x4a, 0x53, 0x4f, 0x4e, 0x20, 0x53, - 0x63, 0x68, 0x65, 0x6d, 0x61, 0x3c, 0x2f, 0x62, 0x75, 0x74, 0x74, 0x6f, - 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x69, - 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x66, 0x69, 0x65, 0x6c, - 0x64, 0x73, 0x65, 0x74, 0x20, 0x63, 0x6c, 0x61, 0x73, 0x73, 0x3d, 0x22, - 0x74, 0x77, 0x6f, 0x22, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x49, 0x6e, 0x74, 0x46, - 0x69, 0x65, 0x6c, 0x64, 0x28, 0x7b, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3a, - 0x20, 0x22, 0x50, 0x72, 0x65, 0x64, 0x69, 0x63, 0x74, 0x69, 0x6f, 0x6e, - 0x73, 0x22, 0x2c, 0x20, 0x6d, 0x61, 0x78, 0x3a, 0x20, 0x32, 0x30, 0x34, - 0x38, 0x2c, 0x20, 0x6d, 0x69, 0x6e, 0x3a, 0x20, 0x2d, 0x31, 0x2c, 0x20, - 0x6e, 0x61, 0x6d, 0x65, 0x3a, 0x20, 0x22, 0x6e, 0x5f, 0x70, 0x72, 0x65, - 0x64, 0x69, 0x63, 0x74, 0x22, 0x2c, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, - 0x3a, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, - 0x75, 0x65, 0x2e, 0x6e, 0x5f, 0x70, 0x72, 0x65, 0x64, 0x69, 0x63, 0x74, + 0x24, 0x7b, 0x46, 0x6c, 0x6f, 0x61, 0x74, 0x46, 0x69, 0x65, 0x6c, 0x64, + 0x28, 0x7b, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3a, 0x20, 0x22, 0x54, 0x65, + 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x22, 0x2c, 0x20, + 0x6d, 0x61, 0x78, 0x3a, 0x20, 0x31, 0x2e, 0x35, 0x2c, 0x20, 0x6d, 0x69, + 0x6e, 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x2c, 0x20, 0x6e, 0x61, 0x6d, 0x65, + 0x3a, 0x20, 0x22, 0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, + 0x72, 0x65, 0x22, 0x2c, 0x20, 0x73, 0x74, 0x65, 0x70, 0x3a, 0x20, 0x30, + 0x2e, 0x30, 0x31, 0x2c, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3a, 0x20, + 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, + 0x2e, 0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x7d, 0x29, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x46, 0x6c, 0x6f, 0x61, 0x74, 0x46, 0x69, 0x65, 0x6c, 0x64, 0x28, 0x7b, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3a, - 0x20, 0x22, 0x54, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, 0x74, 0x75, 0x72, - 0x65, 0x22, 0x2c, 0x20, 0x6d, 0x61, 0x78, 0x3a, 0x20, 0x31, 0x2e, 0x35, - 0x2c, 0x20, 0x6d, 0x69, 0x6e, 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x2c, 0x20, - 0x6e, 0x61, 0x6d, 0x65, 0x3a, 0x20, 0x22, 0x74, 0x65, 0x6d, 0x70, 0x65, - 0x72, 0x61, 0x74, 0x75, 0x72, 0x65, 0x22, 0x2c, 0x20, 0x73, 0x74, 0x65, - 0x70, 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x31, 0x2c, 0x20, 0x76, 0x61, 0x6c, - 0x75, 0x65, 0x3a, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, - 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x65, 0x6d, 0x70, 0x65, 0x72, 0x61, - 0x74, 0x75, 0x72, 0x65, 0x7d, 0x29, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x46, 0x6c, - 0x6f, 0x61, 0x74, 0x46, 0x69, 0x65, 0x6c, 0x64, 0x28, 0x7b, 0x6c, 0x61, - 0x62, 0x65, 0x6c, 0x3a, 0x20, 0x22, 0x50, 0x65, 0x6e, 0x61, 0x6c, 0x69, - 0x7a, 0x65, 0x20, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x20, 0x73, 0x65, - 0x71, 0x75, 0x65, 0x6e, 0x63, 0x65, 0x22, 0x2c, 0x20, 0x6d, 0x61, 0x78, - 0x3a, 0x20, 0x32, 0x2e, 0x30, 0x2c, 0x20, 0x6d, 0x69, 0x6e, 0x3a, 0x20, - 0x30, 0x2e, 0x30, 0x2c, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3a, 0x20, 0x22, - 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x70, 0x65, 0x6e, 0x61, 0x6c, - 0x74, 0x79, 0x22, 0x2c, 0x20, 0x73, 0x74, 0x65, 0x70, 0x3a, 0x20, 0x30, - 0x2e, 0x30, 0x31, 0x2c, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3a, 0x20, - 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, - 0x2e, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x70, 0x65, 0x6e, 0x61, - 0x6c, 0x74, 0x79, 0x7d, 0x29, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x49, 0x6e, 0x74, - 0x46, 0x69, 0x65, 0x6c, 0x64, 0x28, 0x7b, 0x6c, 0x61, 0x62, 0x65, 0x6c, - 0x3a, 0x20, 0x22, 0x43, 0x6f, 0x6e, 0x73, 0x69, 0x64, 0x65, 0x72, 0x20, - 0x4e, 0x20, 0x74, 0x6f, 0x6b, 0x65, 0x6e, 0x73, 0x20, 0x66, 0x6f, 0x72, - 0x20, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x69, 0x7a, 0x65, 0x22, 0x2c, 0x20, - 0x6d, 0x61, 0x78, 0x3a, 0x20, 0x32, 0x30, 0x34, 0x38, 0x2c, 0x20, 0x6d, - 0x69, 0x6e, 0x3a, 0x20, 0x30, 0x2c, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3a, - 0x20, 0x22, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x6c, 0x61, 0x73, - 0x74, 0x5f, 0x6e, 0x22, 0x2c, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3a, - 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, - 0x65, 0x2e, 0x72, 0x65, 0x70, 0x65, 0x61, 0x74, 0x5f, 0x6c, 0x61, 0x73, - 0x74, 0x5f, 0x6e, 0x7d, 0x29, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x49, 0x6e, 0x74, - 0x46, 0x69, 0x65, 0x6c, 0x64, 0x28, 0x7b, 0x6c, 0x61, 0x62, 0x65, 0x6c, - 0x3a, 0x20, 0x22, 0x54, 0x6f, 0x70, 0x2d, 0x4b, 0x20, 0x73, 0x61, 0x6d, - 0x70, 0x6c, 0x69, 0x6e, 0x67, 0x22, 0x2c, 0x20, 0x6d, 0x61, 0x78, 0x3a, - 0x20, 0x31, 0x30, 0x30, 0x2c, 0x20, 0x6d, 0x69, 0x6e, 0x3a, 0x20, 0x2d, - 0x31, 0x2c, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3a, 0x20, 0x22, 0x74, 0x6f, - 0x70, 0x5f, 0x6b, 0x22, 0x2c, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3a, - 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, - 0x65, 0x2e, 0x74, 0x6f, 0x70, 0x5f, 0x6b, 0x7d, 0x29, 0x7d, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, - 0x7b, 0x46, 0x6c, 0x6f, 0x61, 0x74, 0x46, 0x69, 0x65, 0x6c, 0x64, 0x28, - 0x7b, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3a, 0x20, 0x22, 0x54, 0x6f, 0x70, - 0x2d, 0x50, 0x20, 0x73, 0x61, 0x6d, 0x70, 0x6c, 0x69, 0x6e, 0x67, 0x22, - 0x2c, 0x20, 0x6d, 0x61, 0x78, 0x3a, 0x20, 0x31, 0x2e, 0x30, 0x2c, 0x20, - 0x6d, 0x69, 0x6e, 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x2c, 0x20, 0x6e, 0x61, - 0x6d, 0x65, 0x3a, 0x20, 0x22, 0x74, 0x6f, 0x70, 0x5f, 0x70, 0x22, 0x2c, + 0x20, 0x22, 0x50, 0x65, 0x6e, 0x61, 0x6c, 0x69, 0x7a, 0x65, 0x20, 0x72, + 0x65, 0x70, 0x65, 0x61, 0x74, 0x20, 0x73, 0x65, 0x71, 0x75, 0x65, 0x6e, + 0x63, 0x65, 0x22, 0x2c, 0x20, 0x6d, 0x61, 0x78, 0x3a, 0x20, 0x32, 0x2e, + 0x30, 0x2c, 0x20, 0x6d, 0x69, 0x6e, 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x2c, + 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3a, 0x20, 0x22, 0x72, 0x65, 0x70, 0x65, + 0x61, 0x74, 0x5f, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x22, 0x2c, 0x20, 0x73, 0x74, 0x65, 0x70, 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x31, 0x2c, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3a, 0x20, 0x70, 0x61, 0x72, 0x61, - 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x6f, 0x70, - 0x5f, 0x70, 0x7d, 0x29, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, - 0x65, 0x74, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x3c, 0x64, 0x65, 0x74, 0x61, 0x69, 0x6c, 0x73, 0x3e, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x3c, 0x73, 0x75, 0x6d, 0x6d, 0x61, 0x72, 0x79, 0x3e, 0x4d, 0x6f, 0x72, - 0x65, 0x20, 0x6f, 0x70, 0x74, 0x69, 0x6f, 0x6e, 0x73, 0x3c, 0x2f, 0x73, - 0x75, 0x6d, 0x6d, 0x61, 0x72, 0x79, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x66, 0x69, 0x65, - 0x6c, 0x64, 0x73, 0x65, 0x74, 0x20, 0x63, 0x6c, 0x61, 0x73, 0x73, 0x3d, - 0x22, 0x74, 0x77, 0x6f, 0x22, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x46, - 0x6c, 0x6f, 0x61, 0x74, 0x46, 0x69, 0x65, 0x6c, 0x64, 0x28, 0x7b, 0x6c, - 0x61, 0x62, 0x65, 0x6c, 0x3a, 0x20, 0x22, 0x54, 0x46, 0x53, 0x2d, 0x5a, - 0x22, 0x2c, 0x20, 0x6d, 0x61, 0x78, 0x3a, 0x20, 0x31, 0x2e, 0x30, 0x2c, - 0x20, 0x6d, 0x69, 0x6e, 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x2c, 0x20, 0x6e, - 0x61, 0x6d, 0x65, 0x3a, 0x20, 0x22, 0x74, 0x66, 0x73, 0x5f, 0x7a, 0x22, - 0x2c, 0x20, 0x73, 0x74, 0x65, 0x70, 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x31, + 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x72, 0x65, 0x70, + 0x65, 0x61, 0x74, 0x5f, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x7d, + 0x29, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x24, 0x7b, 0x49, 0x6e, 0x74, 0x46, 0x69, 0x65, 0x6c, + 0x64, 0x28, 0x7b, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3a, 0x20, 0x22, 0x43, + 0x6f, 0x6e, 0x73, 0x69, 0x64, 0x65, 0x72, 0x20, 0x4e, 0x20, 0x74, 0x6f, + 0x6b, 0x65, 0x6e, 0x73, 0x20, 0x66, 0x6f, 0x72, 0x20, 0x70, 0x65, 0x6e, + 0x61, 0x6c, 0x69, 0x7a, 0x65, 0x22, 0x2c, 0x20, 0x6d, 0x61, 0x78, 0x3a, + 0x20, 0x32, 0x30, 0x34, 0x38, 0x2c, 0x20, 0x6d, 0x69, 0x6e, 0x3a, 0x20, + 0x30, 0x2c, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3a, 0x20, 0x22, 0x72, 0x65, + 0x70, 0x65, 0x61, 0x74, 0x5f, 0x6c, 0x61, 0x73, 0x74, 0x5f, 0x6e, 0x22, 0x2c, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3a, 0x20, 0x70, 0x61, 0x72, - 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x66, - 0x73, 0x5f, 0x7a, 0x7d, 0x29, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x46, - 0x6c, 0x6f, 0x61, 0x74, 0x46, 0x69, 0x65, 0x6c, 0x64, 0x28, 0x7b, 0x6c, - 0x61, 0x62, 0x65, 0x6c, 0x3a, 0x20, 0x22, 0x54, 0x79, 0x70, 0x69, 0x63, - 0x61, 0x6c, 0x20, 0x50, 0x22, 0x2c, 0x20, 0x6d, 0x61, 0x78, 0x3a, 0x20, - 0x31, 0x2e, 0x30, 0x2c, 0x20, 0x6d, 0x69, 0x6e, 0x3a, 0x20, 0x30, 0x2e, - 0x30, 0x2c, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3a, 0x20, 0x22, 0x74, 0x79, - 0x70, 0x69, 0x63, 0x61, 0x6c, 0x5f, 0x70, 0x22, 0x2c, 0x20, 0x73, 0x74, + 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x72, 0x65, + 0x70, 0x65, 0x61, 0x74, 0x5f, 0x6c, 0x61, 0x73, 0x74, 0x5f, 0x6e, 0x7d, + 0x29, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x24, 0x7b, 0x49, 0x6e, 0x74, 0x46, 0x69, 0x65, 0x6c, + 0x64, 0x28, 0x7b, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3a, 0x20, 0x22, 0x54, + 0x6f, 0x70, 0x2d, 0x4b, 0x20, 0x73, 0x61, 0x6d, 0x70, 0x6c, 0x69, 0x6e, + 0x67, 0x22, 0x2c, 0x20, 0x6d, 0x61, 0x78, 0x3a, 0x20, 0x31, 0x30, 0x30, + 0x2c, 0x20, 0x6d, 0x69, 0x6e, 0x3a, 0x20, 0x2d, 0x31, 0x2c, 0x20, 0x6e, + 0x61, 0x6d, 0x65, 0x3a, 0x20, 0x22, 0x74, 0x6f, 0x70, 0x5f, 0x6b, 0x22, + 0x2c, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3a, 0x20, 0x70, 0x61, 0x72, + 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x6f, + 0x70, 0x5f, 0x6b, 0x7d, 0x29, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x46, 0x6c, 0x6f, + 0x61, 0x74, 0x46, 0x69, 0x65, 0x6c, 0x64, 0x28, 0x7b, 0x6c, 0x61, 0x62, + 0x65, 0x6c, 0x3a, 0x20, 0x22, 0x54, 0x6f, 0x70, 0x2d, 0x50, 0x20, 0x73, + 0x61, 0x6d, 0x70, 0x6c, 0x69, 0x6e, 0x67, 0x22, 0x2c, 0x20, 0x6d, 0x61, + 0x78, 0x3a, 0x20, 0x31, 0x2e, 0x30, 0x2c, 0x20, 0x6d, 0x69, 0x6e, 0x3a, + 0x20, 0x30, 0x2e, 0x30, 0x2c, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3a, 0x20, + 0x22, 0x74, 0x6f, 0x70, 0x5f, 0x70, 0x22, 0x2c, 0x20, 0x73, 0x74, 0x65, + 0x70, 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x31, 0x2c, 0x20, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x3a, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x6f, 0x70, 0x5f, 0x70, 0x7d, 0x29, + 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x3c, 0x2f, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, 0x3e, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, + 0x65, 0x74, 0x61, 0x69, 0x6c, 0x73, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x73, 0x75, 0x6d, + 0x6d, 0x61, 0x72, 0x79, 0x3e, 0x4d, 0x6f, 0x72, 0x65, 0x20, 0x6f, 0x70, + 0x74, 0x69, 0x6f, 0x6e, 0x73, 0x3c, 0x2f, 0x73, 0x75, 0x6d, 0x6d, 0x61, + 0x72, 0x79, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x3c, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, + 0x74, 0x20, 0x63, 0x6c, 0x61, 0x73, 0x73, 0x3d, 0x22, 0x74, 0x77, 0x6f, + 0x22, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x46, 0x6c, 0x6f, 0x61, 0x74, + 0x46, 0x69, 0x65, 0x6c, 0x64, 0x28, 0x7b, 0x6c, 0x61, 0x62, 0x65, 0x6c, + 0x3a, 0x20, 0x22, 0x54, 0x46, 0x53, 0x2d, 0x5a, 0x22, 0x2c, 0x20, 0x6d, + 0x61, 0x78, 0x3a, 0x20, 0x31, 0x2e, 0x30, 0x2c, 0x20, 0x6d, 0x69, 0x6e, + 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x2c, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3a, + 0x20, 0x22, 0x74, 0x66, 0x73, 0x5f, 0x7a, 0x22, 0x2c, 0x20, 0x73, 0x74, 0x65, 0x70, 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x31, 0x2c, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3a, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, - 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x79, 0x70, 0x69, 0x63, 0x61, - 0x6c, 0x5f, 0x70, 0x7d, 0x29, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x46, - 0x6c, 0x6f, 0x61, 0x74, 0x46, 0x69, 0x65, 0x6c, 0x64, 0x28, 0x7b, 0x6c, - 0x61, 0x62, 0x65, 0x6c, 0x3a, 0x20, 0x22, 0x50, 0x72, 0x65, 0x73, 0x65, - 0x6e, 0x63, 0x65, 0x20, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x22, - 0x2c, 0x20, 0x6d, 0x61, 0x78, 0x3a, 0x20, 0x31, 0x2e, 0x30, 0x2c, 0x20, - 0x6d, 0x69, 0x6e, 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x2c, 0x20, 0x6e, 0x61, - 0x6d, 0x65, 0x3a, 0x20, 0x22, 0x70, 0x72, 0x65, 0x73, 0x65, 0x6e, 0x63, - 0x65, 0x5f, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x22, 0x2c, 0x20, - 0x73, 0x74, 0x65, 0x70, 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x31, 0x2c, 0x20, - 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3a, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, - 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x70, 0x72, 0x65, 0x73, - 0x65, 0x6e, 0x63, 0x65, 0x5f, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, - 0x7d, 0x29, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x46, 0x6c, 0x6f, 0x61, - 0x74, 0x46, 0x69, 0x65, 0x6c, 0x64, 0x28, 0x7b, 0x6c, 0x61, 0x62, 0x65, - 0x6c, 0x3a, 0x20, 0x22, 0x46, 0x72, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x63, - 0x79, 0x20, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x22, 0x2c, 0x20, - 0x6d, 0x61, 0x78, 0x3a, 0x20, 0x31, 0x2e, 0x30, 0x2c, 0x20, 0x6d, 0x69, - 0x6e, 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x2c, 0x20, 0x6e, 0x61, 0x6d, 0x65, - 0x3a, 0x20, 0x22, 0x66, 0x72, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x63, 0x79, - 0x5f, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x22, 0x2c, 0x20, 0x73, - 0x74, 0x65, 0x70, 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x31, 0x2c, 0x20, 0x76, - 0x61, 0x6c, 0x75, 0x65, 0x3a, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, - 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x66, 0x72, 0x65, 0x71, 0x75, - 0x65, 0x6e, 0x63, 0x79, 0x5f, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, - 0x7d, 0x29, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, - 0x65, 0x74, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x3c, 0x68, 0x72, 0x20, 0x2f, 0x3e, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, - 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, 0x20, 0x63, 0x6c, 0x61, - 0x73, 0x73, 0x3d, 0x22, 0x74, 0x68, 0x72, 0x65, 0x65, 0x22, 0x3e, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x3c, 0x69, 0x6e, 0x70, 0x75, - 0x74, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, 0x72, 0x61, 0x64, 0x69, - 0x6f, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x6d, 0x69, 0x72, - 0x6f, 0x73, 0x74, 0x61, 0x74, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, - 0x3d, 0x22, 0x30, 0x22, 0x20, 0x63, 0x68, 0x65, 0x63, 0x6b, 0x65, 0x64, - 0x3d, 0x24, 0x7b, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, - 0x6c, 0x75, 0x65, 0x2e, 0x6d, 0x69, 0x72, 0x6f, 0x73, 0x74, 0x61, 0x74, - 0x20, 0x3d, 0x3d, 0x20, 0x30, 0x7d, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, - 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x50, - 0x61, 0x72, 0x61, 0x6d, 0x73, 0x49, 0x6e, 0x74, 0x7d, 0x20, 0x2f, 0x3e, - 0x20, 0x6e, 0x6f, 0x20, 0x4d, 0x69, 0x72, 0x6f, 0x73, 0x74, 0x61, 0x74, - 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x3c, 0x69, 0x6e, 0x70, - 0x75, 0x74, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, 0x72, 0x61, 0x64, - 0x69, 0x6f, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x6d, 0x69, - 0x72, 0x6f, 0x73, 0x74, 0x61, 0x74, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, - 0x65, 0x3d, 0x22, 0x31, 0x22, 0x20, 0x63, 0x68, 0x65, 0x63, 0x6b, 0x65, - 0x64, 0x3d, 0x24, 0x7b, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, - 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x6d, 0x69, 0x72, 0x6f, 0x73, 0x74, 0x61, - 0x74, 0x20, 0x3d, 0x3d, 0x20, 0x31, 0x7d, 0x20, 0x6f, 0x6e, 0x69, 0x6e, - 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, - 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x49, 0x6e, 0x74, 0x7d, 0x20, 0x2f, - 0x3e, 0x20, 0x4d, 0x69, 0x72, 0x6f, 0x73, 0x74, 0x61, 0x74, 0x20, 0x76, - 0x31, 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x3c, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x3c, 0x69, 0x6e, - 0x70, 0x75, 0x74, 0x20, 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, 0x72, 0x61, - 0x64, 0x69, 0x6f, 0x22, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x6d, - 0x69, 0x72, 0x6f, 0x73, 0x74, 0x61, 0x74, 0x22, 0x20, 0x76, 0x61, 0x6c, - 0x75, 0x65, 0x3d, 0x22, 0x32, 0x22, 0x20, 0x63, 0x68, 0x65, 0x63, 0x6b, - 0x65, 0x64, 0x3d, 0x24, 0x7b, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, - 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x6d, 0x69, 0x72, 0x6f, 0x73, 0x74, - 0x61, 0x74, 0x20, 0x3d, 0x3d, 0x20, 0x32, 0x7d, 0x20, 0x6f, 0x6e, 0x69, - 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, - 0x65, 0x50, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x49, 0x6e, 0x74, 0x7d, 0x20, - 0x2f, 0x3e, 0x20, 0x4d, 0x69, 0x72, 0x6f, 0x73, 0x74, 0x61, 0x74, 0x20, - 0x76, 0x32, 0x3c, 0x2f, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, - 0x46, 0x6c, 0x6f, 0x61, 0x74, 0x46, 0x69, 0x65, 0x6c, 0x64, 0x28, 0x7b, - 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3a, 0x20, 0x22, 0x4d, 0x69, 0x72, 0x6f, - 0x73, 0x74, 0x61, 0x74, 0x20, 0x74, 0x61, 0x75, 0x22, 0x2c, 0x20, 0x6d, - 0x61, 0x78, 0x3a, 0x20, 0x31, 0x30, 0x2e, 0x30, 0x2c, 0x20, 0x6d, 0x69, - 0x6e, 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x2c, 0x20, 0x6e, 0x61, 0x6d, 0x65, - 0x3a, 0x20, 0x22, 0x6d, 0x69, 0x72, 0x6f, 0x73, 0x74, 0x61, 0x74, 0x5f, - 0x74, 0x61, 0x75, 0x22, 0x2c, 0x20, 0x73, 0x74, 0x65, 0x70, 0x3a, 0x20, + 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x66, 0x73, 0x5f, 0x7a, 0x7d, + 0x29, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x46, 0x6c, 0x6f, 0x61, 0x74, + 0x46, 0x69, 0x65, 0x6c, 0x64, 0x28, 0x7b, 0x6c, 0x61, 0x62, 0x65, 0x6c, + 0x3a, 0x20, 0x22, 0x54, 0x79, 0x70, 0x69, 0x63, 0x61, 0x6c, 0x20, 0x50, + 0x22, 0x2c, 0x20, 0x6d, 0x61, 0x78, 0x3a, 0x20, 0x31, 0x2e, 0x30, 0x2c, + 0x20, 0x6d, 0x69, 0x6e, 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x2c, 0x20, 0x6e, + 0x61, 0x6d, 0x65, 0x3a, 0x20, 0x22, 0x74, 0x79, 0x70, 0x69, 0x63, 0x61, + 0x6c, 0x5f, 0x70, 0x22, 0x2c, 0x20, 0x73, 0x74, 0x65, 0x70, 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x31, 0x2c, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3a, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, - 0x65, 0x2e, 0x6d, 0x69, 0x72, 0x6f, 0x73, 0x74, 0x61, 0x74, 0x5f, 0x74, - 0x61, 0x75, 0x7d, 0x29, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x46, 0x6c, - 0x6f, 0x61, 0x74, 0x46, 0x69, 0x65, 0x6c, 0x64, 0x28, 0x7b, 0x6c, 0x61, - 0x62, 0x65, 0x6c, 0x3a, 0x20, 0x22, 0x4d, 0x69, 0x72, 0x6f, 0x73, 0x74, - 0x61, 0x74, 0x20, 0x65, 0x74, 0x61, 0x22, 0x2c, 0x20, 0x6d, 0x61, 0x78, - 0x3a, 0x20, 0x31, 0x2e, 0x30, 0x2c, 0x20, 0x6d, 0x69, 0x6e, 0x3a, 0x20, - 0x30, 0x2e, 0x30, 0x2c, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3a, 0x20, 0x22, - 0x6d, 0x69, 0x72, 0x6f, 0x73, 0x74, 0x61, 0x74, 0x5f, 0x65, 0x74, 0x61, - 0x22, 0x2c, 0x20, 0x73, 0x74, 0x65, 0x70, 0x3a, 0x20, 0x30, 0x2e, 0x30, - 0x31, 0x2c, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3a, 0x20, 0x70, 0x61, - 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x6d, - 0x69, 0x72, 0x6f, 0x73, 0x74, 0x61, 0x74, 0x5f, 0x65, 0x74, 0x61, 0x7d, + 0x65, 0x2e, 0x74, 0x79, 0x70, 0x69, 0x63, 0x61, 0x6c, 0x5f, 0x70, 0x7d, 0x29, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, - 0x74, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x3c, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, - 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x49, 0x6e, 0x74, 0x46, 0x69, 0x65, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x46, 0x6c, 0x6f, 0x61, 0x74, + 0x46, 0x69, 0x65, 0x6c, 0x64, 0x28, 0x7b, 0x6c, 0x61, 0x62, 0x65, 0x6c, + 0x3a, 0x20, 0x22, 0x50, 0x72, 0x65, 0x73, 0x65, 0x6e, 0x63, 0x65, 0x20, + 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x22, 0x2c, 0x20, 0x6d, 0x61, + 0x78, 0x3a, 0x20, 0x31, 0x2e, 0x30, 0x2c, 0x20, 0x6d, 0x69, 0x6e, 0x3a, + 0x20, 0x30, 0x2e, 0x30, 0x2c, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3a, 0x20, + 0x22, 0x70, 0x72, 0x65, 0x73, 0x65, 0x6e, 0x63, 0x65, 0x5f, 0x70, 0x65, + 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x22, 0x2c, 0x20, 0x73, 0x74, 0x65, 0x70, + 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x31, 0x2c, 0x20, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x3a, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, + 0x6c, 0x75, 0x65, 0x2e, 0x70, 0x72, 0x65, 0x73, 0x65, 0x6e, 0x63, 0x65, + 0x5f, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x7d, 0x29, 0x7d, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x24, 0x7b, 0x46, 0x6c, 0x6f, 0x61, 0x74, 0x46, 0x69, 0x65, 0x6c, 0x64, 0x28, 0x7b, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3a, 0x20, 0x22, - 0x53, 0x68, 0x6f, 0x77, 0x20, 0x50, 0x72, 0x6f, 0x62, 0x61, 0x62, 0x69, - 0x6c, 0x69, 0x74, 0x69, 0x65, 0x73, 0x22, 0x2c, 0x20, 0x6d, 0x61, 0x78, - 0x3a, 0x20, 0x31, 0x30, 0x2c, 0x20, 0x6d, 0x69, 0x6e, 0x3a, 0x20, 0x30, - 0x2c, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3a, 0x20, 0x22, 0x6e, 0x5f, 0x70, - 0x72, 0x6f, 0x62, 0x73, 0x22, 0x2c, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, + 0x46, 0x72, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x63, 0x79, 0x20, 0x70, 0x65, + 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x22, 0x2c, 0x20, 0x6d, 0x61, 0x78, 0x3a, + 0x20, 0x31, 0x2e, 0x30, 0x2c, 0x20, 0x6d, 0x69, 0x6e, 0x3a, 0x20, 0x30, + 0x2e, 0x30, 0x2c, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3a, 0x20, 0x22, 0x66, + 0x72, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x63, 0x79, 0x5f, 0x70, 0x65, 0x6e, + 0x61, 0x6c, 0x74, 0x79, 0x22, 0x2c, 0x20, 0x73, 0x74, 0x65, 0x70, 0x3a, + 0x20, 0x30, 0x2e, 0x30, 0x31, 0x2c, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3a, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, - 0x75, 0x65, 0x2e, 0x6e, 0x5f, 0x70, 0x72, 0x6f, 0x62, 0x73, 0x7d, 0x29, + 0x75, 0x65, 0x2e, 0x66, 0x72, 0x65, 0x71, 0x75, 0x65, 0x6e, 0x63, 0x79, + 0x5f, 0x70, 0x65, 0x6e, 0x61, 0x6c, 0x74, 0x79, 0x7d, 0x29, 0x7d, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x3c, 0x2f, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, 0x3e, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x3c, 0x68, 0x72, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x66, 0x69, 0x65, 0x6c, + 0x64, 0x73, 0x65, 0x74, 0x20, 0x63, 0x6c, 0x61, 0x73, 0x73, 0x3d, 0x22, + 0x74, 0x68, 0x72, 0x65, 0x65, 0x22, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, + 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, 0x62, + 0x65, 0x6c, 0x3e, 0x3c, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x20, 0x74, 0x79, + 0x70, 0x65, 0x3d, 0x22, 0x72, 0x61, 0x64, 0x69, 0x6f, 0x22, 0x20, 0x6e, + 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x6d, 0x69, 0x72, 0x6f, 0x73, 0x74, 0x61, + 0x74, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x30, 0x22, + 0x20, 0x63, 0x68, 0x65, 0x63, 0x6b, 0x65, 0x64, 0x3d, 0x24, 0x7b, 0x70, + 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, + 0x6d, 0x69, 0x72, 0x6f, 0x73, 0x74, 0x61, 0x74, 0x20, 0x3d, 0x3d, 0x20, + 0x30, 0x7d, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, 0x24, + 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x50, 0x61, 0x72, 0x61, 0x6d, + 0x73, 0x49, 0x6e, 0x74, 0x7d, 0x20, 0x2f, 0x3e, 0x20, 0x6e, 0x6f, 0x20, + 0x4d, 0x69, 0x72, 0x6f, 0x73, 0x74, 0x61, 0x74, 0x3c, 0x2f, 0x6c, 0x61, + 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, 0x61, + 0x62, 0x65, 0x6c, 0x3e, 0x3c, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x20, 0x74, + 0x79, 0x70, 0x65, 0x3d, 0x22, 0x72, 0x61, 0x64, 0x69, 0x6f, 0x22, 0x20, + 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x6d, 0x69, 0x72, 0x6f, 0x73, 0x74, + 0x61, 0x74, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, 0x31, + 0x22, 0x20, 0x63, 0x68, 0x65, 0x63, 0x6b, 0x65, 0x64, 0x3d, 0x24, 0x7b, + 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, + 0x2e, 0x6d, 0x69, 0x72, 0x6f, 0x73, 0x74, 0x61, 0x74, 0x20, 0x3d, 0x3d, + 0x20, 0x31, 0x7d, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x3d, + 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x50, 0x61, 0x72, 0x61, + 0x6d, 0x73, 0x49, 0x6e, 0x74, 0x7d, 0x20, 0x2f, 0x3e, 0x20, 0x4d, 0x69, + 0x72, 0x6f, 0x73, 0x74, 0x61, 0x74, 0x20, 0x76, 0x31, 0x3c, 0x2f, 0x6c, + 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6c, + 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x3c, 0x69, 0x6e, 0x70, 0x75, 0x74, 0x20, + 0x74, 0x79, 0x70, 0x65, 0x3d, 0x22, 0x72, 0x61, 0x64, 0x69, 0x6f, 0x22, + 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, 0x6d, 0x69, 0x72, 0x6f, 0x73, + 0x74, 0x61, 0x74, 0x22, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3d, 0x22, + 0x32, 0x22, 0x20, 0x63, 0x68, 0x65, 0x63, 0x6b, 0x65, 0x64, 0x3d, 0x24, + 0x7b, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x2e, 0x6d, 0x69, 0x72, 0x6f, 0x73, 0x74, 0x61, 0x74, 0x20, 0x3d, + 0x3d, 0x20, 0x32, 0x7d, 0x20, 0x6f, 0x6e, 0x69, 0x6e, 0x70, 0x75, 0x74, + 0x3d, 0x24, 0x7b, 0x75, 0x70, 0x64, 0x61, 0x74, 0x65, 0x50, 0x61, 0x72, + 0x61, 0x6d, 0x73, 0x49, 0x6e, 0x74, 0x7d, 0x20, 0x2f, 0x3e, 0x20, 0x4d, + 0x69, 0x72, 0x6f, 0x73, 0x74, 0x61, 0x74, 0x20, 0x76, 0x32, 0x3c, 0x2f, + 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, + 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x46, 0x6c, 0x6f, 0x61, + 0x74, 0x46, 0x69, 0x65, 0x6c, 0x64, 0x28, 0x7b, 0x6c, 0x61, 0x62, 0x65, + 0x6c, 0x3a, 0x20, 0x22, 0x4d, 0x69, 0x72, 0x6f, 0x73, 0x74, 0x61, 0x74, + 0x20, 0x74, 0x61, 0x75, 0x22, 0x2c, 0x20, 0x6d, 0x61, 0x78, 0x3a, 0x20, + 0x31, 0x30, 0x2e, 0x30, 0x2c, 0x20, 0x6d, 0x69, 0x6e, 0x3a, 0x20, 0x30, + 0x2e, 0x30, 0x2c, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3a, 0x20, 0x22, 0x6d, + 0x69, 0x72, 0x6f, 0x73, 0x74, 0x61, 0x74, 0x5f, 0x74, 0x61, 0x75, 0x22, + 0x2c, 0x20, 0x73, 0x74, 0x65, 0x70, 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x31, + 0x2c, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3a, 0x20, 0x70, 0x61, 0x72, + 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x6d, 0x69, + 0x72, 0x6f, 0x73, 0x74, 0x61, 0x74, 0x5f, 0x74, 0x61, 0x75, 0x7d, 0x29, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, - 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x3c, 0x2f, 0x64, 0x65, 0x74, 0x61, 0x69, 0x6c, 0x73, 0x3e, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x6f, 0x72, - 0x6d, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, - 0x6e, 0x73, 0x74, 0x20, 0x70, 0x72, 0x6f, 0x62, 0x43, 0x6f, 0x6c, 0x6f, - 0x72, 0x20, 0x3d, 0x20, 0x28, 0x70, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, - 0x20, 0x72, 0x20, 0x3d, 0x20, 0x4d, 0x61, 0x74, 0x68, 0x2e, 0x66, 0x6c, - 0x6f, 0x6f, 0x72, 0x28, 0x31, 0x39, 0x32, 0x20, 0x2a, 0x20, 0x28, 0x31, - 0x20, 0x2d, 0x20, 0x70, 0x29, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x67, 0x20, 0x3d, 0x20, - 0x4d, 0x61, 0x74, 0x68, 0x2e, 0x66, 0x6c, 0x6f, 0x6f, 0x72, 0x28, 0x31, - 0x39, 0x32, 0x20, 0x2a, 0x20, 0x70, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x60, 0x72, - 0x67, 0x62, 0x61, 0x28, 0x24, 0x7b, 0x72, 0x7d, 0x2c, 0x24, 0x7b, 0x67, - 0x7d, 0x2c, 0x30, 0x2c, 0x30, 0x2e, 0x33, 0x29, 0x60, 0x3b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, - 0x6e, 0x73, 0x74, 0x20, 0x50, 0x72, 0x6f, 0x62, 0x61, 0x62, 0x69, 0x6c, - 0x69, 0x74, 0x69, 0x65, 0x73, 0x20, 0x3d, 0x20, 0x28, 0x70, 0x61, 0x72, - 0x61, 0x6d, 0x73, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x70, - 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x6d, - 0x61, 0x70, 0x28, 0x6d, 0x73, 0x67, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, - 0x74, 0x20, 0x7b, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, - 0x6f, 0x6e, 0x5f, 0x70, 0x72, 0x6f, 0x62, 0x61, 0x62, 0x69, 0x6c, 0x69, - 0x74, 0x69, 0x65, 0x73, 0x20, 0x7d, 0x20, 0x3d, 0x20, 0x6d, 0x73, 0x67, - 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, - 0x20, 0x28, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x21, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, - 0x5f, 0x70, 0x72, 0x6f, 0x62, 0x61, 0x62, 0x69, 0x6c, 0x69, 0x74, 0x69, - 0x65, 0x73, 0x20, 0x7c, 0x7c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, - 0x6f, 0x6e, 0x5f, 0x70, 0x72, 0x6f, 0x62, 0x61, 0x62, 0x69, 0x6c, 0x69, - 0x74, 0x69, 0x65, 0x73, 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x20, - 0x3d, 0x3d, 0x3d, 0x20, 0x30, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x29, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x6d, - 0x73, 0x67, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x0a, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, + 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x46, 0x6c, 0x6f, 0x61, 0x74, 0x46, + 0x69, 0x65, 0x6c, 0x64, 0x28, 0x7b, 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3a, + 0x20, 0x22, 0x4d, 0x69, 0x72, 0x6f, 0x73, 0x74, 0x61, 0x74, 0x20, 0x65, + 0x74, 0x61, 0x22, 0x2c, 0x20, 0x6d, 0x61, 0x78, 0x3a, 0x20, 0x31, 0x2e, + 0x30, 0x2c, 0x20, 0x6d, 0x69, 0x6e, 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x2c, + 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3a, 0x20, 0x22, 0x6d, 0x69, 0x72, 0x6f, + 0x73, 0x74, 0x61, 0x74, 0x5f, 0x65, 0x74, 0x61, 0x22, 0x2c, 0x20, 0x73, + 0x74, 0x65, 0x70, 0x3a, 0x20, 0x30, 0x2e, 0x30, 0x31, 0x2c, 0x20, 0x76, + 0x61, 0x6c, 0x75, 0x65, 0x3a, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, + 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x6d, 0x69, 0x72, 0x6f, 0x73, + 0x74, 0x61, 0x74, 0x5f, 0x65, 0x74, 0x61, 0x7d, 0x29, 0x7d, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, + 0x2f, 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, 0x3e, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, + 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, 0x3e, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x24, 0x7b, 0x49, 0x6e, 0x74, 0x46, 0x69, 0x65, 0x6c, 0x64, 0x28, 0x7b, + 0x6c, 0x61, 0x62, 0x65, 0x6c, 0x3a, 0x20, 0x22, 0x53, 0x68, 0x6f, 0x77, + 0x20, 0x50, 0x72, 0x6f, 0x62, 0x61, 0x62, 0x69, 0x6c, 0x69, 0x74, 0x69, + 0x65, 0x73, 0x22, 0x2c, 0x20, 0x6d, 0x61, 0x78, 0x3a, 0x20, 0x31, 0x30, + 0x2c, 0x20, 0x6d, 0x69, 0x6e, 0x3a, 0x20, 0x30, 0x2c, 0x20, 0x6e, 0x61, + 0x6d, 0x65, 0x3a, 0x20, 0x22, 0x6e, 0x5f, 0x70, 0x72, 0x6f, 0x62, 0x73, + 0x22, 0x2c, 0x20, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3a, 0x20, 0x70, 0x61, + 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x6e, + 0x5f, 0x70, 0x72, 0x6f, 0x62, 0x73, 0x7d, 0x29, 0x7d, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, + 0x66, 0x69, 0x65, 0x6c, 0x64, 0x73, 0x65, 0x74, 0x3e, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x65, + 0x74, 0x61, 0x69, 0x6c, 0x73, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x6f, 0x72, 0x6d, 0x3e, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, + 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, + 0x70, 0x72, 0x6f, 0x62, 0x43, 0x6f, 0x6c, 0x6f, 0x72, 0x20, 0x3d, 0x20, + 0x28, 0x70, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x72, 0x20, 0x3d, + 0x20, 0x4d, 0x61, 0x74, 0x68, 0x2e, 0x66, 0x6c, 0x6f, 0x6f, 0x72, 0x28, + 0x31, 0x39, 0x32, 0x20, 0x2a, 0x20, 0x28, 0x31, 0x20, 0x2d, 0x20, 0x70, + 0x29, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, + 0x6e, 0x73, 0x74, 0x20, 0x67, 0x20, 0x3d, 0x20, 0x4d, 0x61, 0x74, 0x68, + 0x2e, 0x66, 0x6c, 0x6f, 0x6f, 0x72, 0x28, 0x31, 0x39, 0x32, 0x20, 0x2a, + 0x20, 0x70, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, + 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x60, 0x72, 0x67, 0x62, 0x61, 0x28, + 0x24, 0x7b, 0x72, 0x7d, 0x2c, 0x24, 0x7b, 0x67, 0x7d, 0x2c, 0x30, 0x2c, + 0x30, 0x2e, 0x33, 0x29, 0x60, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, + 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, + 0x50, 0x72, 0x6f, 0x62, 0x61, 0x62, 0x69, 0x6c, 0x69, 0x74, 0x69, 0x65, + 0x73, 0x20, 0x3d, 0x20, 0x28, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x29, + 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, + 0x73, 0x2e, 0x64, 0x61, 0x74, 0x61, 0x2e, 0x6d, 0x61, 0x70, 0x28, 0x6d, + 0x73, 0x67, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x7b, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x70, 0x72, 0x6f, 0x62, 0x61, 0x62, 0x69, 0x6c, 0x69, 0x74, 0x69, 0x65, 0x73, - 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x20, 0x3e, 0x20, 0x31, 0x29, - 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x2f, 0x2f, 0x20, 0x4e, 0x6f, 0x74, 0x20, 0x66, 0x6f, 0x72, 0x20, - 0x62, 0x79, 0x74, 0x65, 0x20, 0x70, 0x61, 0x69, 0x72, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, + 0x20, 0x7d, 0x20, 0x3d, 0x20, 0x6d, 0x73, 0x67, 0x3b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x21, 0x63, 0x6f, + 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x70, 0x72, 0x6f, + 0x62, 0x61, 0x62, 0x69, 0x6c, 0x69, 0x74, 0x69, 0x65, 0x73, 0x20, 0x7c, + 0x7c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x70, 0x72, 0x6f, 0x62, 0x61, 0x62, 0x69, 0x6c, 0x69, 0x74, 0x69, 0x65, 0x73, - 0x5b, 0x30, 0x5d, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x2e, - 0x73, 0x74, 0x61, 0x72, 0x74, 0x73, 0x57, 0x69, 0x74, 0x68, 0x28, 0x27, - 0x62, 0x79, 0x74, 0x65, 0x3a, 0x20, 0x5c, 0x5c, 0x27, 0x29, 0x29, 0x20, + 0x2e, 0x6c, 0x65, 0x6e, 0x67, 0x74, 0x68, 0x20, 0x3d, 0x3d, 0x3d, 0x20, + 0x30, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x29, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x6d, 0x73, 0x67, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, - 0x73, 0x70, 0x6c, 0x69, 0x74, 0x44, 0x61, 0x74, 0x61, 0x20, 0x3d, 0x20, - 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x70, + 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x63, 0x6f, 0x6d, 0x70, + 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x70, 0x72, 0x6f, 0x62, 0x61, + 0x62, 0x69, 0x6c, 0x69, 0x74, 0x69, 0x65, 0x73, 0x2e, 0x6c, 0x65, 0x6e, + 0x67, 0x74, 0x68, 0x20, 0x3e, 0x20, 0x31, 0x29, 0x20, 0x7b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, + 0x4e, 0x6f, 0x74, 0x20, 0x66, 0x6f, 0x72, 0x20, 0x62, 0x79, 0x74, 0x65, + 0x20, 0x70, 0x61, 0x69, 0x72, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x63, 0x6f, 0x6d, 0x70, + 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x70, 0x72, 0x6f, 0x62, 0x61, + 0x62, 0x69, 0x6c, 0x69, 0x74, 0x69, 0x65, 0x73, 0x5b, 0x30, 0x5d, 0x2e, + 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x2e, 0x73, 0x74, 0x61, 0x72, + 0x74, 0x73, 0x57, 0x69, 0x74, 0x68, 0x28, 0x27, 0x62, 0x79, 0x74, 0x65, + 0x3a, 0x20, 0x5c, 0x5c, 0x27, 0x29, 0x29, 0x20, 0x72, 0x65, 0x74, 0x75, + 0x72, 0x6e, 0x20, 0x6d, 0x73, 0x67, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, + 0x6e, 0x74, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x73, 0x70, 0x6c, 0x69, + 0x74, 0x44, 0x61, 0x74, 0x61, 0x20, 0x3d, 0x20, 0x63, 0x6f, 0x6d, 0x70, + 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x70, 0x72, 0x6f, 0x62, 0x61, + 0x62, 0x69, 0x6c, 0x69, 0x74, 0x69, 0x65, 0x73, 0x2e, 0x6d, 0x61, 0x70, + 0x28, 0x70, 0x72, 0x6f, 0x62, 0x20, 0x3d, 0x3e, 0x20, 0x28, 0x7b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x3a, 0x20, 0x70, 0x72, 0x6f, + 0x62, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x2c, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, + 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x70, 0x72, + 0x6f, 0x62, 0x61, 0x62, 0x69, 0x6c, 0x69, 0x74, 0x69, 0x65, 0x73, 0x3a, + 0x20, 0x5b, 0x70, 0x72, 0x6f, 0x62, 0x5d, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x29, 0x29, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, + 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x3c, 0x24, 0x7b, 0x50, 0x72, 0x6f, 0x62, 0x61, 0x62, 0x69, 0x6c, 0x69, 0x74, 0x69, 0x65, 0x73, - 0x2e, 0x6d, 0x61, 0x70, 0x28, 0x70, 0x72, 0x6f, 0x62, 0x20, 0x3d, 0x3e, - 0x20, 0x28, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x3a, - 0x20, 0x70, 0x72, 0x6f, 0x62, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, - 0x74, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, - 0x6e, 0x5f, 0x70, 0x72, 0x6f, 0x62, 0x61, 0x62, 0x69, 0x6c, 0x69, 0x74, - 0x69, 0x65, 0x73, 0x3a, 0x20, 0x5b, 0x70, 0x72, 0x6f, 0x62, 0x5d, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x29, - 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, - 0x3c, 0x24, 0x7b, 0x50, 0x72, 0x6f, 0x62, 0x61, 0x62, 0x69, 0x6c, 0x69, - 0x74, 0x69, 0x65, 0x73, 0x7d, 0x20, 0x64, 0x61, 0x74, 0x61, 0x3d, 0x24, - 0x7b, 0x73, 0x70, 0x6c, 0x69, 0x74, 0x44, 0x61, 0x74, 0x61, 0x7d, 0x20, - 0x2f, 0x3e, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, - 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x7b, 0x20, 0x70, 0x72, 0x6f, 0x62, 0x73, - 0x2c, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x20, 0x7d, 0x20, - 0x3d, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, - 0x5f, 0x70, 0x72, 0x6f, 0x62, 0x61, 0x62, 0x69, 0x6c, 0x69, 0x74, 0x69, - 0x65, 0x73, 0x5b, 0x30, 0x5d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x66, 0x6f, 0x75, 0x6e, - 0x64, 0x20, 0x3d, 0x20, 0x70, 0x72, 0x6f, 0x62, 0x73, 0x2e, 0x66, 0x69, - 0x6e, 0x64, 0x28, 0x70, 0x20, 0x3d, 0x3e, 0x20, 0x70, 0x2e, 0x74, 0x6f, - 0x6b, 0x5f, 0x73, 0x74, 0x72, 0x20, 0x3d, 0x3d, 0x3d, 0x20, 0x6d, 0x73, - 0x67, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x29, 0x0a, 0x20, + 0x7d, 0x20, 0x64, 0x61, 0x74, 0x61, 0x3d, 0x24, 0x7b, 0x73, 0x70, 0x6c, + 0x69, 0x74, 0x44, 0x61, 0x74, 0x61, 0x7d, 0x20, 0x2f, 0x3e, 0x60, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, - 0x20, 0x70, 0x43, 0x6f, 0x6c, 0x6f, 0x72, 0x20, 0x3d, 0x20, 0x66, 0x6f, - 0x75, 0x6e, 0x64, 0x20, 0x3f, 0x20, 0x70, 0x72, 0x6f, 0x62, 0x43, 0x6f, - 0x6c, 0x6f, 0x72, 0x28, 0x66, 0x6f, 0x75, 0x6e, 0x64, 0x2e, 0x70, 0x72, - 0x6f, 0x62, 0x29, 0x20, 0x3a, 0x20, 0x27, 0x74, 0x72, 0x61, 0x6e, 0x73, - 0x70, 0x61, 0x72, 0x65, 0x6e, 0x74, 0x27, 0x0a, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x70, - 0x6f, 0x70, 0x6f, 0x76, 0x65, 0x72, 0x43, 0x68, 0x69, 0x6c, 0x64, 0x72, - 0x65, 0x6e, 0x20, 0x3d, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, - 0x76, 0x20, 0x63, 0x6c, 0x61, 0x73, 0x73, 0x3d, 0x22, 0x70, 0x72, 0x6f, - 0x62, 0x2d, 0x73, 0x65, 0x74, 0x22, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x70, 0x72, - 0x6f, 0x62, 0x73, 0x2e, 0x6d, 0x61, 0x70, 0x28, 0x28, 0x70, 0x2c, 0x20, - 0x69, 0x6e, 0x64, 0x65, 0x78, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, + 0x20, 0x7b, 0x20, 0x70, 0x72, 0x6f, 0x62, 0x73, 0x2c, 0x20, 0x63, 0x6f, + 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x20, 0x7d, 0x20, 0x3d, 0x20, 0x63, 0x6f, + 0x6d, 0x70, 0x6c, 0x65, 0x74, 0x69, 0x6f, 0x6e, 0x5f, 0x70, 0x72, 0x6f, + 0x62, 0x61, 0x62, 0x69, 0x6c, 0x69, 0x74, 0x69, 0x65, 0x73, 0x5b, 0x30, + 0x5d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, + 0x6e, 0x73, 0x74, 0x20, 0x66, 0x6f, 0x75, 0x6e, 0x64, 0x20, 0x3d, 0x20, + 0x70, 0x72, 0x6f, 0x62, 0x73, 0x2e, 0x66, 0x69, 0x6e, 0x64, 0x28, 0x70, + 0x20, 0x3d, 0x3e, 0x20, 0x70, 0x2e, 0x74, 0x6f, 0x6b, 0x5f, 0x73, 0x74, + 0x72, 0x20, 0x3d, 0x3d, 0x3d, 0x20, 0x6d, 0x73, 0x67, 0x2e, 0x63, 0x6f, + 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x70, 0x43, 0x6f, + 0x6c, 0x6f, 0x72, 0x20, 0x3d, 0x20, 0x66, 0x6f, 0x75, 0x6e, 0x64, 0x20, + 0x3f, 0x20, 0x70, 0x72, 0x6f, 0x62, 0x43, 0x6f, 0x6c, 0x6f, 0x72, 0x28, + 0x66, 0x6f, 0x75, 0x6e, 0x64, 0x2e, 0x70, 0x72, 0x6f, 0x62, 0x29, 0x20, + 0x3a, 0x20, 0x27, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x70, 0x61, 0x72, 0x65, + 0x6e, 0x74, 0x27, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x70, 0x6f, 0x70, 0x6f, 0x76, + 0x65, 0x72, 0x43, 0x68, 0x69, 0x6c, 0x64, 0x72, 0x65, 0x6e, 0x20, 0x3d, + 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x20, 0x63, 0x6c, + 0x61, 0x73, 0x73, 0x3d, 0x22, 0x70, 0x72, 0x6f, 0x62, 0x2d, 0x73, 0x65, + 0x74, 0x22, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x70, 0x72, 0x6f, 0x62, 0x73, 0x2e, + 0x6d, 0x61, 0x70, 0x28, 0x28, 0x70, 0x2c, 0x20, 0x69, 0x6e, 0x64, 0x65, + 0x78, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, + 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, - 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x0a, + 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6b, 0x65, 0x79, 0x3d, 0x24, 0x7b, - 0x69, 0x6e, 0x64, 0x65, 0x78, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x6b, 0x65, 0x79, 0x3d, 0x24, 0x7b, 0x69, 0x6e, 0x64, 0x65, + 0x78, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x69, 0x74, + 0x6c, 0x65, 0x3d, 0x24, 0x7b, 0x60, 0x70, 0x72, 0x6f, 0x62, 0x3a, 0x20, + 0x24, 0x7b, 0x70, 0x2e, 0x70, 0x72, 0x6f, 0x62, 0x7d, 0x60, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x74, 0x69, 0x74, 0x6c, 0x65, 0x3d, 0x24, 0x7b, 0x60, 0x70, 0x72, - 0x6f, 0x62, 0x3a, 0x20, 0x24, 0x7b, 0x70, 0x2e, 0x70, 0x72, 0x6f, 0x62, - 0x7d, 0x60, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x74, - 0x79, 0x6c, 0x65, 0x3d, 0x24, 0x7b, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x74, 0x79, 0x6c, 0x65, 0x3d, + 0x24, 0x7b, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x70, 0x61, 0x64, 0x64, 0x69, 0x6e, 0x67, 0x3a, - 0x20, 0x27, 0x30, 0x2e, 0x33, 0x65, 0x6d, 0x27, 0x2c, 0x0a, 0x20, 0x20, + 0x70, 0x61, 0x64, 0x64, 0x69, 0x6e, 0x67, 0x3a, 0x20, 0x27, 0x30, 0x2e, + 0x33, 0x65, 0x6d, 0x27, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x62, 0x61, 0x63, 0x6b, 0x67, 0x72, - 0x6f, 0x75, 0x6e, 0x64, 0x43, 0x6f, 0x6c, 0x6f, 0x72, 0x3a, 0x20, 0x70, - 0x2e, 0x74, 0x6f, 0x6b, 0x5f, 0x73, 0x74, 0x72, 0x20, 0x3d, 0x3d, 0x3d, - 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x20, 0x3f, 0x20, 0x70, - 0x72, 0x6f, 0x62, 0x43, 0x6f, 0x6c, 0x6f, 0x72, 0x28, 0x70, 0x2e, 0x70, - 0x72, 0x6f, 0x62, 0x29, 0x20, 0x3a, 0x20, 0x27, 0x74, 0x72, 0x61, 0x6e, - 0x73, 0x70, 0x61, 0x72, 0x65, 0x6e, 0x74, 0x27, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x62, 0x61, 0x63, 0x6b, 0x67, 0x72, 0x6f, 0x75, 0x6e, 0x64, + 0x43, 0x6f, 0x6c, 0x6f, 0x72, 0x3a, 0x20, 0x70, 0x2e, 0x74, 0x6f, 0x6b, + 0x5f, 0x73, 0x74, 0x72, 0x20, 0x3d, 0x3d, 0x3d, 0x20, 0x63, 0x6f, 0x6e, + 0x74, 0x65, 0x6e, 0x74, 0x20, 0x3f, 0x20, 0x70, 0x72, 0x6f, 0x62, 0x43, + 0x6f, 0x6c, 0x6f, 0x72, 0x28, 0x70, 0x2e, 0x70, 0x72, 0x6f, 0x62, 0x29, + 0x20, 0x3a, 0x20, 0x27, 0x74, 0x72, 0x61, 0x6e, 0x73, 0x70, 0x61, 0x72, + 0x65, 0x6e, 0x74, 0x27, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, + 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x7d, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3e, 0x0a, + 0x20, 0x20, 0x3c, 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x24, 0x7b, 0x70, 0x2e, + 0x74, 0x6f, 0x6b, 0x5f, 0x73, 0x74, 0x72, 0x7d, 0x3a, 0x20, 0x3c, 0x2f, + 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x73, 0x70, 0x61, 0x6e, 0x3e, - 0x24, 0x7b, 0x70, 0x2e, 0x74, 0x6f, 0x6b, 0x5f, 0x73, 0x74, 0x72, 0x7d, - 0x3a, 0x20, 0x3c, 0x2f, 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x3c, 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x24, 0x7b, - 0x4d, 0x61, 0x74, 0x68, 0x2e, 0x66, 0x6c, 0x6f, 0x6f, 0x72, 0x28, 0x70, - 0x2e, 0x70, 0x72, 0x6f, 0x62, 0x20, 0x2a, 0x20, 0x31, 0x30, 0x30, 0x29, - 0x7d, 0x25, 0x3c, 0x2f, 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, + 0x3c, 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x24, 0x7b, 0x4d, 0x61, 0x74, 0x68, + 0x2e, 0x66, 0x6c, 0x6f, 0x6f, 0x72, 0x28, 0x70, 0x2e, 0x70, 0x72, 0x6f, + 0x62, 0x20, 0x2a, 0x20, 0x31, 0x30, 0x30, 0x29, 0x7d, 0x25, 0x3c, 0x2f, + 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, + 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x29, 0x7d, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, + 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x60, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, + 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x3c, 0x24, 0x7b, 0x50, 0x6f, 0x70, 0x6f, 0x76, 0x65, 0x72, 0x7d, 0x20, + 0x73, 0x74, 0x79, 0x6c, 0x65, 0x3d, 0x24, 0x7b, 0x7b, 0x20, 0x62, 0x61, + 0x63, 0x6b, 0x67, 0x72, 0x6f, 0x75, 0x6e, 0x64, 0x43, 0x6f, 0x6c, 0x6f, + 0x72, 0x3a, 0x20, 0x70, 0x43, 0x6f, 0x6c, 0x6f, 0x72, 0x20, 0x7d, 0x7d, + 0x20, 0x70, 0x6f, 0x70, 0x6f, 0x76, 0x65, 0x72, 0x43, 0x68, 0x69, 0x6c, + 0x64, 0x72, 0x65, 0x6e, 0x3d, 0x24, 0x7b, 0x70, 0x6f, 0x70, 0x6f, 0x76, + 0x65, 0x72, 0x43, 0x68, 0x69, 0x6c, 0x64, 0x72, 0x65, 0x6e, 0x7d, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x7d, 0x29, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x0a, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, - 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x3c, 0x24, 0x7b, 0x50, 0x6f, 0x70, 0x6f, 0x76, - 0x65, 0x72, 0x7d, 0x20, 0x73, 0x74, 0x79, 0x6c, 0x65, 0x3d, 0x24, 0x7b, - 0x7b, 0x20, 0x62, 0x61, 0x63, 0x6b, 0x67, 0x72, 0x6f, 0x75, 0x6e, 0x64, - 0x43, 0x6f, 0x6c, 0x6f, 0x72, 0x3a, 0x20, 0x70, 0x43, 0x6f, 0x6c, 0x6f, - 0x72, 0x20, 0x7d, 0x7d, 0x20, 0x70, 0x6f, 0x70, 0x6f, 0x76, 0x65, 0x72, - 0x43, 0x68, 0x69, 0x6c, 0x64, 0x72, 0x65, 0x6e, 0x3d, 0x24, 0x7b, 0x70, - 0x6f, 0x70, 0x6f, 0x76, 0x65, 0x72, 0x43, 0x68, 0x69, 0x6c, 0x64, 0x72, - 0x65, 0x6e, 0x7d, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x6d, 0x73, 0x67, 0x2e, 0x63, - 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x2e, 0x6d, 0x61, 0x74, 0x63, 0x68, - 0x28, 0x2f, 0x5c, 0x6e, 0x2f, 0x67, 0x69, 0x6d, 0x29, 0x20, 0x3f, 0x20, - 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x3c, 0x62, 0x72, 0x20, 0x2f, 0x3e, 0x60, - 0x20, 0x3a, 0x20, 0x6d, 0x73, 0x67, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, - 0x6e, 0x74, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x3c, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x29, - 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x2f, 0x2f, 0x20, 0x70, 0x6f, 0x6f, 0x72, 0x20, 0x6d, 0x61, 0x6e, - 0x73, 0x20, 0x6d, 0x61, 0x72, 0x6b, 0x64, 0x6f, 0x77, 0x6e, 0x20, 0x72, - 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x6d, 0x65, 0x6e, 0x74, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x4d, 0x61, 0x72, - 0x6b, 0x64, 0x6f, 0x77, 0x6e, 0x69, 0x73, 0x68, 0x20, 0x3d, 0x20, 0x28, - 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, - 0x20, 0x6d, 0x64, 0x20, 0x3d, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, - 0x2e, 0x74, 0x65, 0x78, 0x74, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, - 0x26, 0x2f, 0x67, 0x2c, 0x20, 0x27, 0x26, 0x61, 0x6d, 0x70, 0x3b, 0x27, - 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, - 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x3c, 0x2f, 0x67, 0x2c, - 0x20, 0x27, 0x26, 0x6c, 0x74, 0x3b, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, - 0x65, 0x28, 0x2f, 0x3e, 0x2f, 0x67, 0x2c, 0x20, 0x27, 0x26, 0x67, 0x74, - 0x3b, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x5e, 0x23, - 0x7b, 0x31, 0x2c, 0x36, 0x7d, 0x20, 0x28, 0x2e, 0x2a, 0x29, 0x24, 0x2f, - 0x67, 0x69, 0x6d, 0x2c, 0x20, 0x27, 0x3c, 0x68, 0x33, 0x3e, 0x24, 0x31, - 0x3c, 0x2f, 0x68, 0x33, 0x3e, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, - 0x28, 0x2f, 0x5c, 0x2a, 0x5c, 0x2a, 0x28, 0x2e, 0x2a, 0x3f, 0x29, 0x5c, - 0x2a, 0x5c, 0x2a, 0x2f, 0x67, 0x2c, 0x20, 0x27, 0x3c, 0x73, 0x74, 0x72, - 0x6f, 0x6e, 0x67, 0x3e, 0x24, 0x31, 0x3c, 0x2f, 0x73, 0x74, 0x72, 0x6f, - 0x6e, 0x67, 0x3e, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, - 0x5f, 0x5f, 0x28, 0x2e, 0x2a, 0x3f, 0x29, 0x5f, 0x5f, 0x2f, 0x67, 0x2c, - 0x20, 0x27, 0x3c, 0x73, 0x74, 0x72, 0x6f, 0x6e, 0x67, 0x3e, 0x24, 0x31, - 0x3c, 0x2f, 0x73, 0x74, 0x72, 0x6f, 0x6e, 0x67, 0x3e, 0x27, 0x29, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, 0x70, - 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x5c, 0x2a, 0x28, 0x2e, 0x2a, 0x3f, - 0x29, 0x5c, 0x2a, 0x2f, 0x67, 0x2c, 0x20, 0x27, 0x3c, 0x65, 0x6d, 0x3e, - 0x24, 0x31, 0x3c, 0x2f, 0x65, 0x6d, 0x3e, 0x27, 0x29, 0x0a, 0x20, 0x20, + 0x20, 0x24, 0x7b, 0x6d, 0x73, 0x67, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, + 0x6e, 0x74, 0x2e, 0x6d, 0x61, 0x74, 0x63, 0x68, 0x28, 0x2f, 0x5c, 0x6e, + 0x2f, 0x67, 0x69, 0x6d, 0x29, 0x20, 0x3f, 0x20, 0x68, 0x74, 0x6d, 0x6c, + 0x60, 0x3c, 0x62, 0x72, 0x20, 0x2f, 0x3e, 0x60, 0x20, 0x3a, 0x20, 0x6d, + 0x73, 0x67, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x7d, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, + 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x29, 0x3b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, + 0x70, 0x6f, 0x6f, 0x72, 0x20, 0x6d, 0x61, 0x6e, 0x73, 0x20, 0x6d, 0x61, + 0x72, 0x6b, 0x64, 0x6f, 0x77, 0x6e, 0x20, 0x72, 0x65, 0x70, 0x6c, 0x61, + 0x63, 0x65, 0x6d, 0x65, 0x6e, 0x74, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, + 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x4d, 0x61, 0x72, 0x6b, 0x64, 0x6f, 0x77, + 0x6e, 0x69, 0x73, 0x68, 0x20, 0x3d, 0x20, 0x28, 0x70, 0x61, 0x72, 0x61, + 0x6d, 0x73, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6d, 0x64, 0x20, + 0x3d, 0x20, 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x2e, 0x74, 0x65, 0x78, + 0x74, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, + 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x26, 0x2f, 0x67, 0x2c, + 0x20, 0x27, 0x26, 0x61, 0x6d, 0x70, 0x3b, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, - 0x63, 0x65, 0x28, 0x2f, 0x5f, 0x28, 0x2e, 0x2a, 0x3f, 0x29, 0x5f, 0x2f, + 0x63, 0x65, 0x28, 0x2f, 0x3c, 0x2f, 0x67, 0x2c, 0x20, 0x27, 0x26, 0x6c, + 0x74, 0x3b, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x3e, + 0x2f, 0x67, 0x2c, 0x20, 0x27, 0x26, 0x67, 0x74, 0x3b, 0x27, 0x29, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, 0x70, + 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x5e, 0x23, 0x7b, 0x31, 0x2c, 0x36, + 0x7d, 0x20, 0x28, 0x2e, 0x2a, 0x29, 0x24, 0x2f, 0x67, 0x69, 0x6d, 0x2c, + 0x20, 0x27, 0x3c, 0x68, 0x33, 0x3e, 0x24, 0x31, 0x3c, 0x2f, 0x68, 0x33, + 0x3e, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x5c, 0x2a, + 0x5c, 0x2a, 0x28, 0x2e, 0x2a, 0x3f, 0x29, 0x5c, 0x2a, 0x5c, 0x2a, 0x2f, + 0x67, 0x2c, 0x20, 0x27, 0x3c, 0x73, 0x74, 0x72, 0x6f, 0x6e, 0x67, 0x3e, + 0x24, 0x31, 0x3c, 0x2f, 0x73, 0x74, 0x72, 0x6f, 0x6e, 0x67, 0x3e, 0x27, + 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, + 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x5f, 0x5f, 0x28, 0x2e, + 0x2a, 0x3f, 0x29, 0x5f, 0x5f, 0x2f, 0x67, 0x2c, 0x20, 0x27, 0x3c, 0x73, + 0x74, 0x72, 0x6f, 0x6e, 0x67, 0x3e, 0x24, 0x31, 0x3c, 0x2f, 0x73, 0x74, + 0x72, 0x6f, 0x6e, 0x67, 0x3e, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, + 0x28, 0x2f, 0x5c, 0x2a, 0x28, 0x2e, 0x2a, 0x3f, 0x29, 0x5c, 0x2a, 0x2f, 0x67, 0x2c, 0x20, 0x27, 0x3c, 0x65, 0x6d, 0x3e, 0x24, 0x31, 0x3c, 0x2f, 0x65, 0x6d, 0x3e, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, - 0x60, 0x60, 0x60, 0x2e, 0x2a, 0x3f, 0x5c, 0x6e, 0x28, 0x5b, 0x5c, 0x73, - 0x5c, 0x53, 0x5d, 0x2a, 0x3f, 0x29, 0x60, 0x60, 0x60, 0x2f, 0x67, 0x2c, - 0x20, 0x27, 0x3c, 0x70, 0x72, 0x65, 0x3e, 0x3c, 0x63, 0x6f, 0x64, 0x65, - 0x3e, 0x24, 0x31, 0x3c, 0x2f, 0x63, 0x6f, 0x64, 0x65, 0x3e, 0x3c, 0x2f, - 0x70, 0x72, 0x65, 0x3e, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, - 0x2f, 0x60, 0x28, 0x2e, 0x2a, 0x3f, 0x29, 0x60, 0x2f, 0x67, 0x2c, 0x20, - 0x27, 0x3c, 0x63, 0x6f, 0x64, 0x65, 0x3e, 0x24, 0x31, 0x3c, 0x2f, 0x63, - 0x6f, 0x64, 0x65, 0x3e, 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x2e, 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, - 0x2f, 0x5c, 0x6e, 0x2f, 0x67, 0x69, 0x6d, 0x2c, 0x20, 0x27, 0x3c, 0x62, - 0x72, 0x20, 0x2f, 0x3e, 0x27, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, - 0x6c, 0x60, 0x3c, 0x73, 0x70, 0x61, 0x6e, 0x20, 0x64, 0x61, 0x6e, 0x67, - 0x65, 0x72, 0x6f, 0x75, 0x73, 0x6c, 0x79, 0x53, 0x65, 0x74, 0x49, 0x6e, - 0x6e, 0x65, 0x72, 0x48, 0x54, 0x4d, 0x4c, 0x3d, 0x24, 0x7b, 0x7b, 0x20, - 0x5f, 0x5f, 0x68, 0x74, 0x6d, 0x6c, 0x3a, 0x20, 0x6d, 0x64, 0x20, 0x7d, - 0x7d, 0x20, 0x2f, 0x3e, 0x60, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, - 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, - 0x20, 0x4d, 0x6f, 0x64, 0x65, 0x6c, 0x47, 0x65, 0x6e, 0x65, 0x72, 0x61, - 0x74, 0x69, 0x6f, 0x6e, 0x49, 0x6e, 0x66, 0x6f, 0x20, 0x3d, 0x20, 0x28, - 0x70, 0x61, 0x72, 0x61, 0x6d, 0x73, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x21, - 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x53, 0x74, 0x61, 0x74, 0x73, 0x2e, 0x76, - 0x61, 0x6c, 0x75, 0x65, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, - 0x74, 0x6d, 0x6c, 0x60, 0x3c, 0x73, 0x70, 0x61, 0x6e, 0x2f, 0x3e, 0x60, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, - 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x3c, 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x6c, 0x6c, 0x61, 0x6d, 0x61, - 0x53, 0x74, 0x61, 0x74, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, - 0x70, 0x72, 0x65, 0x64, 0x69, 0x63, 0x74, 0x65, 0x64, 0x5f, 0x70, 0x65, - 0x72, 0x5f, 0x74, 0x6f, 0x6b, 0x65, 0x6e, 0x5f, 0x6d, 0x73, 0x2e, 0x74, - 0x6f, 0x46, 0x69, 0x78, 0x65, 0x64, 0x28, 0x29, 0x7d, 0x6d, 0x73, 0x20, - 0x70, 0x65, 0x72, 0x20, 0x74, 0x6f, 0x6b, 0x65, 0x6e, 0x2c, 0x20, 0x24, - 0x7b, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x53, 0x74, 0x61, 0x74, 0x73, 0x2e, - 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x70, 0x72, 0x65, 0x64, 0x69, 0x63, - 0x74, 0x65, 0x64, 0x5f, 0x70, 0x65, 0x72, 0x5f, 0x73, 0x65, 0x63, 0x6f, - 0x6e, 0x64, 0x2e, 0x74, 0x6f, 0x46, 0x69, 0x78, 0x65, 0x64, 0x28, 0x32, - 0x29, 0x7d, 0x20, 0x74, 0x6f, 0x6b, 0x65, 0x6e, 0x73, 0x20, 0x70, 0x65, - 0x72, 0x20, 0x73, 0x65, 0x63, 0x6f, 0x6e, 0x64, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x73, 0x70, 0x61, 0x6e, 0x3e, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x73, - 0x69, 0x6d, 0x70, 0x6c, 0x65, 0x20, 0x70, 0x6f, 0x70, 0x6f, 0x76, 0x65, - 0x72, 0x20, 0x69, 0x6d, 0x70, 0x6c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, - 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x50, 0x6f, 0x70, 0x6f, 0x76, 0x65, 0x72, - 0x20, 0x3d, 0x20, 0x28, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x29, 0x20, 0x3d, - 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, - 0x6e, 0x73, 0x74, 0x20, 0x69, 0x73, 0x4f, 0x70, 0x65, 0x6e, 0x20, 0x3d, - 0x20, 0x75, 0x73, 0x65, 0x53, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x28, 0x66, - 0x61, 0x6c, 0x73, 0x65, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x70, 0x6f, 0x73, 0x69, 0x74, - 0x69, 0x6f, 0x6e, 0x20, 0x3d, 0x20, 0x75, 0x73, 0x65, 0x53, 0x69, 0x67, - 0x6e, 0x61, 0x6c, 0x28, 0x7b, 0x20, 0x74, 0x6f, 0x70, 0x3a, 0x20, 0x27, - 0x30, 0x70, 0x78, 0x27, 0x2c, 0x20, 0x6c, 0x65, 0x66, 0x74, 0x3a, 0x20, - 0x27, 0x30, 0x70, 0x78, 0x27, 0x20, 0x7d, 0x29, 0x3b, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x62, 0x75, - 0x74, 0x74, 0x6f, 0x6e, 0x52, 0x65, 0x66, 0x20, 0x3d, 0x20, 0x75, 0x73, - 0x65, 0x52, 0x65, 0x66, 0x28, 0x6e, 0x75, 0x6c, 0x6c, 0x29, 0x3b, 0x0a, + 0x5f, 0x28, 0x2e, 0x2a, 0x3f, 0x29, 0x5f, 0x2f, 0x67, 0x2c, 0x20, 0x27, + 0x3c, 0x65, 0x6d, 0x3e, 0x24, 0x31, 0x3c, 0x2f, 0x65, 0x6d, 0x3e, 0x27, + 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x72, + 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x60, 0x60, 0x60, 0x2e, + 0x2a, 0x3f, 0x5c, 0x6e, 0x28, 0x5b, 0x5c, 0x73, 0x5c, 0x53, 0x5d, 0x2a, + 0x3f, 0x29, 0x60, 0x60, 0x60, 0x2f, 0x67, 0x2c, 0x20, 0x27, 0x3c, 0x70, + 0x72, 0x65, 0x3e, 0x3c, 0x63, 0x6f, 0x64, 0x65, 0x3e, 0x24, 0x31, 0x3c, + 0x2f, 0x63, 0x6f, 0x64, 0x65, 0x3e, 0x3c, 0x2f, 0x70, 0x72, 0x65, 0x3e, + 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, + 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x60, 0x28, 0x2e, + 0x2a, 0x3f, 0x29, 0x60, 0x2f, 0x67, 0x2c, 0x20, 0x27, 0x3c, 0x63, 0x6f, + 0x64, 0x65, 0x3e, 0x24, 0x31, 0x3c, 0x2f, 0x63, 0x6f, 0x64, 0x65, 0x3e, + 0x27, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, + 0x72, 0x65, 0x70, 0x6c, 0x61, 0x63, 0x65, 0x28, 0x2f, 0x5c, 0x6e, 0x2f, + 0x67, 0x69, 0x6d, 0x2c, 0x20, 0x27, 0x3c, 0x62, 0x72, 0x20, 0x2f, 0x3e, + 0x27, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, + 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x3c, 0x73, + 0x70, 0x61, 0x6e, 0x20, 0x64, 0x61, 0x6e, 0x67, 0x65, 0x72, 0x6f, 0x75, + 0x73, 0x6c, 0x79, 0x53, 0x65, 0x74, 0x49, 0x6e, 0x6e, 0x65, 0x72, 0x48, + 0x54, 0x4d, 0x4c, 0x3d, 0x24, 0x7b, 0x7b, 0x20, 0x5f, 0x5f, 0x68, 0x74, + 0x6d, 0x6c, 0x3a, 0x20, 0x6d, 0x64, 0x20, 0x7d, 0x7d, 0x20, 0x2f, 0x3e, + 0x60, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x3b, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x4d, 0x6f, 0x64, + 0x65, 0x6c, 0x47, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, + 0x49, 0x6e, 0x66, 0x6f, 0x20, 0x3d, 0x20, 0x28, 0x70, 0x61, 0x72, 0x61, + 0x6d, 0x73, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x21, 0x6c, 0x6c, 0x61, 0x6d, + 0x61, 0x53, 0x74, 0x61, 0x74, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, + 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, + 0x3c, 0x73, 0x70, 0x61, 0x6e, 0x2f, 0x3e, 0x60, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, + 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x73, 0x70, 0x61, + 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x24, 0x7b, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x53, 0x74, 0x61, 0x74, + 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x70, 0x72, 0x65, 0x64, + 0x69, 0x63, 0x74, 0x65, 0x64, 0x5f, 0x70, 0x65, 0x72, 0x5f, 0x74, 0x6f, + 0x6b, 0x65, 0x6e, 0x5f, 0x6d, 0x73, 0x2e, 0x74, 0x6f, 0x46, 0x69, 0x78, + 0x65, 0x64, 0x28, 0x29, 0x7d, 0x6d, 0x73, 0x20, 0x70, 0x65, 0x72, 0x20, + 0x74, 0x6f, 0x6b, 0x65, 0x6e, 0x2c, 0x20, 0x24, 0x7b, 0x6c, 0x6c, 0x61, + 0x6d, 0x61, 0x53, 0x74, 0x61, 0x74, 0x73, 0x2e, 0x76, 0x61, 0x6c, 0x75, + 0x65, 0x2e, 0x70, 0x72, 0x65, 0x64, 0x69, 0x63, 0x74, 0x65, 0x64, 0x5f, + 0x70, 0x65, 0x72, 0x5f, 0x73, 0x65, 0x63, 0x6f, 0x6e, 0x64, 0x2e, 0x74, + 0x6f, 0x46, 0x69, 0x78, 0x65, 0x64, 0x28, 0x32, 0x29, 0x7d, 0x20, 0x74, + 0x6f, 0x6b, 0x65, 0x6e, 0x73, 0x20, 0x70, 0x65, 0x72, 0x20, 0x73, 0x65, + 0x63, 0x6f, 0x6e, 0x64, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x2f, 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x73, 0x69, 0x6d, 0x70, 0x6c, + 0x65, 0x20, 0x70, 0x6f, 0x70, 0x6f, 0x76, 0x65, 0x72, 0x20, 0x69, 0x6d, + 0x70, 0x6c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, + 0x20, 0x50, 0x6f, 0x70, 0x6f, 0x76, 0x65, 0x72, 0x20, 0x3d, 0x20, 0x28, + 0x70, 0x72, 0x6f, 0x70, 0x73, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, + 0x69, 0x73, 0x4f, 0x70, 0x65, 0x6e, 0x20, 0x3d, 0x20, 0x75, 0x73, 0x65, + 0x53, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x28, 0x66, 0x61, 0x6c, 0x73, 0x65, + 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, + 0x73, 0x74, 0x20, 0x70, 0x6f, 0x73, 0x69, 0x74, 0x69, 0x6f, 0x6e, 0x20, + 0x3d, 0x20, 0x75, 0x73, 0x65, 0x53, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x28, + 0x7b, 0x20, 0x74, 0x6f, 0x70, 0x3a, 0x20, 0x27, 0x30, 0x70, 0x78, 0x27, + 0x2c, 0x20, 0x6c, 0x65, 0x66, 0x74, 0x3a, 0x20, 0x27, 0x30, 0x70, 0x78, + 0x27, 0x20, 0x7d, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, + 0x52, 0x65, 0x66, 0x20, 0x3d, 0x20, 0x75, 0x73, 0x65, 0x52, 0x65, 0x66, + 0x28, 0x6e, 0x75, 0x6c, 0x6c, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x70, 0x6f, 0x70, 0x6f, + 0x76, 0x65, 0x72, 0x52, 0x65, 0x66, 0x20, 0x3d, 0x20, 0x75, 0x73, 0x65, + 0x52, 0x65, 0x66, 0x28, 0x6e, 0x75, 0x6c, 0x6c, 0x29, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, - 0x70, 0x6f, 0x70, 0x6f, 0x76, 0x65, 0x72, 0x52, 0x65, 0x66, 0x20, 0x3d, - 0x20, 0x75, 0x73, 0x65, 0x52, 0x65, 0x66, 0x28, 0x6e, 0x75, 0x6c, 0x6c, - 0x29, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, - 0x6e, 0x73, 0x74, 0x20, 0x74, 0x6f, 0x67, 0x67, 0x6c, 0x65, 0x50, 0x6f, - 0x70, 0x6f, 0x76, 0x65, 0x72, 0x20, 0x3d, 0x20, 0x28, 0x29, 0x20, 0x3d, - 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x69, 0x66, 0x20, 0x28, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x52, 0x65, - 0x66, 0x2e, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x29, 0x20, 0x7b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, - 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x72, 0x65, 0x63, 0x74, 0x20, 0x3d, 0x20, - 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x52, 0x65, 0x66, 0x2e, 0x63, 0x75, - 0x72, 0x72, 0x65, 0x6e, 0x74, 0x2e, 0x67, 0x65, 0x74, 0x42, 0x6f, 0x75, - 0x6e, 0x64, 0x69, 0x6e, 0x67, 0x43, 0x6c, 0x69, 0x65, 0x6e, 0x74, 0x52, - 0x65, 0x63, 0x74, 0x28, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x70, 0x6f, 0x73, 0x69, 0x74, 0x69, 0x6f, - 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x7b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x74, 0x6f, 0x70, 0x3a, 0x20, 0x60, 0x24, 0x7b, 0x72, 0x65, 0x63, 0x74, - 0x2e, 0x62, 0x6f, 0x74, 0x74, 0x6f, 0x6d, 0x20, 0x2b, 0x20, 0x77, 0x69, - 0x6e, 0x64, 0x6f, 0x77, 0x2e, 0x73, 0x63, 0x72, 0x6f, 0x6c, 0x6c, 0x59, - 0x7d, 0x70, 0x78, 0x60, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x65, 0x66, 0x74, 0x3a, 0x20, - 0x60, 0x24, 0x7b, 0x72, 0x65, 0x63, 0x74, 0x2e, 0x6c, 0x65, 0x66, 0x74, - 0x20, 0x2b, 0x20, 0x77, 0x69, 0x6e, 0x64, 0x6f, 0x77, 0x2e, 0x73, 0x63, - 0x72, 0x6f, 0x6c, 0x6c, 0x58, 0x7d, 0x70, 0x78, 0x60, 0x2c, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x3b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x73, 0x4f, 0x70, 0x65, 0x6e, - 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x21, 0x69, 0x73, - 0x4f, 0x70, 0x65, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x3b, 0x0a, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x68, 0x61, - 0x6e, 0x64, 0x6c, 0x65, 0x43, 0x6c, 0x69, 0x63, 0x6b, 0x4f, 0x75, 0x74, - 0x73, 0x69, 0x64, 0x65, 0x20, 0x3d, 0x20, 0x28, 0x65, 0x76, 0x65, 0x6e, - 0x74, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x70, 0x6f, 0x70, 0x6f, - 0x76, 0x65, 0x72, 0x52, 0x65, 0x66, 0x2e, 0x63, 0x75, 0x72, 0x72, 0x65, - 0x6e, 0x74, 0x20, 0x26, 0x26, 0x20, 0x21, 0x70, 0x6f, 0x70, 0x6f, 0x76, - 0x65, 0x72, 0x52, 0x65, 0x66, 0x2e, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, - 0x74, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x73, 0x28, 0x65, - 0x76, 0x65, 0x6e, 0x74, 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x29, - 0x20, 0x26, 0x26, 0x20, 0x21, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x52, - 0x65, 0x66, 0x2e, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x2e, 0x63, - 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x73, 0x28, 0x65, 0x76, 0x65, 0x6e, - 0x74, 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x29, 0x29, 0x20, 0x7b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, - 0x73, 0x4f, 0x70, 0x65, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, - 0x3d, 0x20, 0x66, 0x61, 0x6c, 0x73, 0x65, 0x3b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x7d, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x75, - 0x73, 0x65, 0x45, 0x66, 0x66, 0x65, 0x63, 0x74, 0x28, 0x28, 0x29, 0x20, - 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x64, 0x6f, 0x63, 0x75, 0x6d, 0x65, 0x6e, 0x74, 0x2e, 0x61, 0x64, - 0x64, 0x45, 0x76, 0x65, 0x6e, 0x74, 0x4c, 0x69, 0x73, 0x74, 0x65, 0x6e, - 0x65, 0x72, 0x28, 0x27, 0x6d, 0x6f, 0x75, 0x73, 0x65, 0x64, 0x6f, 0x77, - 0x6e, 0x27, 0x2c, 0x20, 0x68, 0x61, 0x6e, 0x64, 0x6c, 0x65, 0x43, 0x6c, - 0x69, 0x63, 0x6b, 0x4f, 0x75, 0x74, 0x73, 0x69, 0x64, 0x65, 0x29, 0x3b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, - 0x75, 0x72, 0x6e, 0x20, 0x28, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x64, 0x6f, - 0x63, 0x75, 0x6d, 0x65, 0x6e, 0x74, 0x2e, 0x72, 0x65, 0x6d, 0x6f, 0x76, - 0x65, 0x45, 0x76, 0x65, 0x6e, 0x74, 0x4c, 0x69, 0x73, 0x74, 0x65, 0x6e, - 0x65, 0x72, 0x28, 0x27, 0x6d, 0x6f, 0x75, 0x73, 0x65, 0x64, 0x6f, 0x77, - 0x6e, 0x27, 0x2c, 0x20, 0x68, 0x61, 0x6e, 0x64, 0x6c, 0x65, 0x43, 0x6c, - 0x69, 0x63, 0x6b, 0x4f, 0x75, 0x74, 0x73, 0x69, 0x64, 0x65, 0x29, 0x3b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x3b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x2c, 0x20, 0x5b, 0x5d, 0x29, - 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, - 0x75, 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x73, 0x70, 0x61, 0x6e, 0x20, - 0x73, 0x74, 0x79, 0x6c, 0x65, 0x3d, 0x24, 0x7b, 0x70, 0x72, 0x6f, 0x70, - 0x73, 0x2e, 0x73, 0x74, 0x79, 0x6c, 0x65, 0x7d, 0x20, 0x72, 0x65, 0x66, - 0x3d, 0x24, 0x7b, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x52, 0x65, 0x66, - 0x7d, 0x20, 0x6f, 0x6e, 0x43, 0x6c, 0x69, 0x63, 0x6b, 0x3d, 0x24, 0x7b, 0x74, 0x6f, 0x67, 0x67, 0x6c, 0x65, 0x50, 0x6f, 0x70, 0x6f, 0x76, 0x65, - 0x72, 0x7d, 0x3e, 0x24, 0x7b, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x2e, 0x63, - 0x68, 0x69, 0x6c, 0x64, 0x72, 0x65, 0x6e, 0x7d, 0x3c, 0x2f, 0x73, 0x70, - 0x61, 0x6e, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x24, 0x7b, 0x69, 0x73, 0x4f, 0x70, 0x65, 0x6e, 0x2e, 0x76, 0x61, 0x6c, - 0x75, 0x65, 0x20, 0x26, 0x26, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x24, - 0x7b, 0x50, 0x6f, 0x72, 0x74, 0x61, 0x6c, 0x7d, 0x20, 0x69, 0x6e, 0x74, - 0x6f, 0x3d, 0x22, 0x23, 0x70, 0x6f, 0x72, 0x74, 0x61, 0x6c, 0x22, 0x3e, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x3c, 0x64, 0x69, 0x76, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x66, 0x3d, - 0x24, 0x7b, 0x70, 0x6f, 0x70, 0x6f, 0x76, 0x65, 0x72, 0x52, 0x65, 0x66, - 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x63, 0x6c, 0x61, 0x73, 0x73, 0x3d, 0x22, 0x70, - 0x6f, 0x70, 0x6f, 0x76, 0x65, 0x72, 0x2d, 0x63, 0x6f, 0x6e, 0x74, 0x65, - 0x6e, 0x74, 0x22, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x73, 0x74, 0x79, 0x6c, 0x65, 0x3d, - 0x24, 0x7b, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x6f, 0x70, 0x3a, + 0x72, 0x20, 0x3d, 0x20, 0x28, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, + 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x52, 0x65, 0x66, 0x2e, 0x63, 0x75, + 0x72, 0x72, 0x65, 0x6e, 0x74, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6e, 0x73, 0x74, + 0x20, 0x72, 0x65, 0x63, 0x74, 0x20, 0x3d, 0x20, 0x62, 0x75, 0x74, 0x74, + 0x6f, 0x6e, 0x52, 0x65, 0x66, 0x2e, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, + 0x74, 0x2e, 0x67, 0x65, 0x74, 0x42, 0x6f, 0x75, 0x6e, 0x64, 0x69, 0x6e, + 0x67, 0x43, 0x6c, 0x69, 0x65, 0x6e, 0x74, 0x52, 0x65, 0x63, 0x74, 0x28, + 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x70, 0x6f, 0x73, 0x69, 0x74, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, - 0x6c, 0x75, 0x65, 0x2e, 0x74, 0x6f, 0x70, 0x2c, 0x0a, 0x20, 0x20, 0x20, + 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x6f, 0x70, 0x3a, + 0x20, 0x60, 0x24, 0x7b, 0x72, 0x65, 0x63, 0x74, 0x2e, 0x62, 0x6f, 0x74, + 0x74, 0x6f, 0x6d, 0x20, 0x2b, 0x20, 0x77, 0x69, 0x6e, 0x64, 0x6f, 0x77, + 0x2e, 0x73, 0x63, 0x72, 0x6f, 0x6c, 0x6c, 0x59, 0x7d, 0x70, 0x78, 0x60, + 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x6c, 0x65, 0x66, 0x74, 0x3a, 0x20, 0x60, 0x24, 0x7b, 0x72, + 0x65, 0x63, 0x74, 0x2e, 0x6c, 0x65, 0x66, 0x74, 0x20, 0x2b, 0x20, 0x77, + 0x69, 0x6e, 0x64, 0x6f, 0x77, 0x2e, 0x73, 0x63, 0x72, 0x6f, 0x6c, 0x6c, + 0x58, 0x7d, 0x70, 0x78, 0x60, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x69, 0x73, 0x4f, 0x70, 0x65, 0x6e, 0x2e, 0x76, 0x61, 0x6c, + 0x75, 0x65, 0x20, 0x3d, 0x20, 0x21, 0x69, 0x73, 0x4f, 0x70, 0x65, 0x6e, + 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x7d, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x68, 0x61, 0x6e, 0x64, 0x6c, 0x65, + 0x43, 0x6c, 0x69, 0x63, 0x6b, 0x4f, 0x75, 0x74, 0x73, 0x69, 0x64, 0x65, + 0x20, 0x3d, 0x20, 0x28, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x29, 0x20, 0x3d, + 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x69, 0x66, 0x20, 0x28, 0x70, 0x6f, 0x70, 0x6f, 0x76, 0x65, 0x72, 0x52, + 0x65, 0x66, 0x2e, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x20, 0x26, + 0x26, 0x20, 0x21, 0x70, 0x6f, 0x70, 0x6f, 0x76, 0x65, 0x72, 0x52, 0x65, + 0x66, 0x2e, 0x63, 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x2e, 0x63, 0x6f, + 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x73, 0x28, 0x65, 0x76, 0x65, 0x6e, 0x74, + 0x2e, 0x74, 0x61, 0x72, 0x67, 0x65, 0x74, 0x29, 0x20, 0x26, 0x26, 0x20, + 0x21, 0x62, 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x52, 0x65, 0x66, 0x2e, 0x63, + 0x75, 0x72, 0x72, 0x65, 0x6e, 0x74, 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x61, + 0x69, 0x6e, 0x73, 0x28, 0x65, 0x76, 0x65, 0x6e, 0x74, 0x2e, 0x74, 0x61, + 0x72, 0x67, 0x65, 0x74, 0x29, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x73, 0x4f, 0x70, 0x65, + 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3d, 0x20, 0x66, 0x61, + 0x6c, 0x73, 0x65, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x3b, 0x0a, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x75, 0x73, 0x65, 0x45, 0x66, + 0x66, 0x65, 0x63, 0x74, 0x28, 0x28, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x64, 0x6f, 0x63, + 0x75, 0x6d, 0x65, 0x6e, 0x74, 0x2e, 0x61, 0x64, 0x64, 0x45, 0x76, 0x65, + 0x6e, 0x74, 0x4c, 0x69, 0x73, 0x74, 0x65, 0x6e, 0x65, 0x72, 0x28, 0x27, + 0x6d, 0x6f, 0x75, 0x73, 0x65, 0x64, 0x6f, 0x77, 0x6e, 0x27, 0x2c, 0x20, + 0x68, 0x61, 0x6e, 0x64, 0x6c, 0x65, 0x43, 0x6c, 0x69, 0x63, 0x6b, 0x4f, + 0x75, 0x74, 0x73, 0x69, 0x64, 0x65, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, + 0x28, 0x29, 0x20, 0x3d, 0x3e, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x64, 0x6f, 0x63, 0x75, 0x6d, 0x65, + 0x6e, 0x74, 0x2e, 0x72, 0x65, 0x6d, 0x6f, 0x76, 0x65, 0x45, 0x76, 0x65, + 0x6e, 0x74, 0x4c, 0x69, 0x73, 0x74, 0x65, 0x6e, 0x65, 0x72, 0x28, 0x27, + 0x6d, 0x6f, 0x75, 0x73, 0x65, 0x64, 0x6f, 0x77, 0x6e, 0x27, 0x2c, 0x20, + 0x68, 0x61, 0x6e, 0x64, 0x6c, 0x65, 0x43, 0x6c, 0x69, 0x63, 0x6b, 0x4f, + 0x75, 0x74, 0x73, 0x69, 0x64, 0x65, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x7d, 0x2c, 0x20, 0x5b, 0x5d, 0x29, 0x3b, 0x0a, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, + 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x3c, 0x73, 0x70, 0x61, 0x6e, 0x20, 0x73, 0x74, 0x79, 0x6c, + 0x65, 0x3d, 0x24, 0x7b, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x2e, 0x73, 0x74, + 0x79, 0x6c, 0x65, 0x7d, 0x20, 0x72, 0x65, 0x66, 0x3d, 0x24, 0x7b, 0x62, + 0x75, 0x74, 0x74, 0x6f, 0x6e, 0x52, 0x65, 0x66, 0x7d, 0x20, 0x6f, 0x6e, + 0x43, 0x6c, 0x69, 0x63, 0x6b, 0x3d, 0x24, 0x7b, 0x74, 0x6f, 0x67, 0x67, + 0x6c, 0x65, 0x50, 0x6f, 0x70, 0x6f, 0x76, 0x65, 0x72, 0x7d, 0x3e, 0x24, + 0x7b, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x2e, 0x63, 0x68, 0x69, 0x6c, 0x64, + 0x72, 0x65, 0x6e, 0x7d, 0x3c, 0x2f, 0x73, 0x70, 0x61, 0x6e, 0x3e, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x69, 0x73, + 0x4f, 0x70, 0x65, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x26, + 0x26, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x24, 0x7b, 0x50, 0x6f, 0x72, + 0x74, 0x61, 0x6c, 0x7d, 0x20, 0x69, 0x6e, 0x74, 0x6f, 0x3d, 0x22, 0x23, + 0x70, 0x6f, 0x72, 0x74, 0x61, 0x6c, 0x22, 0x3e, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, + 0x76, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x66, 0x3d, 0x24, 0x7b, 0x70, 0x6f, + 0x70, 0x6f, 0x76, 0x65, 0x72, 0x52, 0x65, 0x66, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x6c, 0x65, 0x66, 0x74, 0x3a, 0x20, 0x70, 0x6f, 0x73, 0x69, 0x74, - 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x6c, 0x65, - 0x66, 0x74, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x7d, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3e, 0x0a, 0x20, + 0x63, 0x6c, 0x61, 0x73, 0x73, 0x3d, 0x22, 0x70, 0x6f, 0x70, 0x6f, 0x76, + 0x65, 0x72, 0x2d, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x22, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x24, 0x7b, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x2e, 0x70, 0x6f, 0x70, - 0x6f, 0x76, 0x65, 0x72, 0x43, 0x68, 0x69, 0x6c, 0x64, 0x72, 0x65, 0x6e, - 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x24, 0x7b, 0x50, - 0x6f, 0x72, 0x74, 0x61, 0x6c, 0x7d, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x60, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x60, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x3b, 0x0a, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x53, 0x6f, 0x75, 0x72, 0x63, - 0x65, 0x3a, 0x20, 0x70, 0x72, 0x65, 0x61, 0x63, 0x74, 0x2d, 0x70, 0x6f, - 0x72, 0x74, 0x61, 0x6c, 0x20, 0x28, 0x68, 0x74, 0x74, 0x70, 0x73, 0x3a, - 0x2f, 0x2f, 0x67, 0x69, 0x74, 0x68, 0x75, 0x62, 0x2e, 0x63, 0x6f, 0x6d, - 0x2f, 0x64, 0x65, 0x76, 0x65, 0x6c, 0x6f, 0x70, 0x69, 0x74, 0x2f, 0x70, + 0x20, 0x20, 0x73, 0x74, 0x79, 0x6c, 0x65, 0x3d, 0x24, 0x7b, 0x7b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x74, 0x6f, 0x70, 0x3a, 0x20, 0x70, 0x6f, 0x73, + 0x69, 0x74, 0x69, 0x6f, 0x6e, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, + 0x74, 0x6f, 0x70, 0x2c, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x6c, 0x65, 0x66, + 0x74, 0x3a, 0x20, 0x70, 0x6f, 0x73, 0x69, 0x74, 0x69, 0x6f, 0x6e, 0x2e, + 0x76, 0x61, 0x6c, 0x75, 0x65, 0x2e, 0x6c, 0x65, 0x66, 0x74, 0x2c, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x7d, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x70, + 0x72, 0x6f, 0x70, 0x73, 0x2e, 0x70, 0x6f, 0x70, 0x6f, 0x76, 0x65, 0x72, + 0x43, 0x68, 0x69, 0x6c, 0x64, 0x72, 0x65, 0x6e, 0x7d, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, + 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x24, 0x7b, 0x50, 0x6f, 0x72, 0x74, 0x61, + 0x6c, 0x7d, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x60, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x3b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x7d, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x2f, 0x2f, 0x20, 0x53, 0x6f, 0x75, 0x72, 0x63, 0x65, 0x3a, 0x20, 0x70, 0x72, 0x65, 0x61, 0x63, 0x74, 0x2d, 0x70, 0x6f, 0x72, 0x74, 0x61, 0x6c, - 0x2f, 0x62, 0x6c, 0x6f, 0x62, 0x2f, 0x6d, 0x61, 0x73, 0x74, 0x65, 0x72, - 0x2f, 0x73, 0x72, 0x63, 0x2f, 0x70, 0x72, 0x65, 0x61, 0x63, 0x74, 0x2d, - 0x70, 0x6f, 0x72, 0x74, 0x61, 0x6c, 0x2e, 0x6a, 0x73, 0x29, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x2f, 0x2a, 0x2a, 0x20, 0x52, 0x65, 0x64, 0x69, 0x72, - 0x65, 0x63, 0x74, 0x20, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x69, 0x6e, - 0x67, 0x20, 0x6f, 0x66, 0x20, 0x64, 0x65, 0x73, 0x63, 0x65, 0x6e, 0x64, - 0x61, 0x6e, 0x74, 0x73, 0x20, 0x69, 0x6e, 0x74, 0x6f, 0x20, 0x74, 0x68, - 0x65, 0x20, 0x67, 0x69, 0x76, 0x65, 0x6e, 0x20, 0x43, 0x53, 0x53, 0x20, - 0x73, 0x65, 0x6c, 0x65, 0x63, 0x74, 0x6f, 0x72, 0x20, 0x2a, 0x2f, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x63, 0x6c, 0x61, 0x73, 0x73, 0x20, 0x50, 0x6f, - 0x72, 0x74, 0x61, 0x6c, 0x20, 0x65, 0x78, 0x74, 0x65, 0x6e, 0x64, 0x73, - 0x20, 0x43, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x20, 0x7b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x6f, - 0x6e, 0x65, 0x6e, 0x74, 0x44, 0x69, 0x64, 0x55, 0x70, 0x64, 0x61, 0x74, - 0x65, 0x28, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x29, 0x20, 0x7b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x66, 0x6f, 0x72, 0x20, 0x28, - 0x6c, 0x65, 0x74, 0x20, 0x69, 0x20, 0x69, 0x6e, 0x20, 0x70, 0x72, 0x6f, - 0x70, 0x73, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x70, 0x72, 0x6f, 0x70, - 0x73, 0x5b, 0x69, 0x5d, 0x20, 0x21, 0x3d, 0x3d, 0x20, 0x74, 0x68, 0x69, - 0x73, 0x2e, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x5b, 0x69, 0x5d, 0x29, 0x20, - 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x73, 0x65, 0x74, - 0x54, 0x69, 0x6d, 0x65, 0x6f, 0x75, 0x74, 0x28, 0x74, 0x68, 0x69, 0x73, - 0x2e, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x4c, 0x61, 0x79, 0x65, 0x72, - 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, - 0x74, 0x44, 0x69, 0x64, 0x4d, 0x6f, 0x75, 0x6e, 0x74, 0x28, 0x29, 0x20, - 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x68, - 0x69, 0x73, 0x2e, 0x69, 0x73, 0x4d, 0x6f, 0x75, 0x6e, 0x74, 0x65, 0x64, - 0x20, 0x3d, 0x20, 0x74, 0x72, 0x75, 0x65, 0x3b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x72, 0x65, - 0x6e, 0x64, 0x65, 0x72, 0x4c, 0x61, 0x79, 0x65, 0x72, 0x20, 0x3d, 0x20, - 0x74, 0x68, 0x69, 0x73, 0x2e, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x4c, - 0x61, 0x79, 0x65, 0x72, 0x2e, 0x62, 0x69, 0x6e, 0x64, 0x28, 0x74, 0x68, - 0x69, 0x73, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, - 0x4c, 0x61, 0x79, 0x65, 0x72, 0x28, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x63, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x57, 0x69, 0x6c, - 0x6c, 0x55, 0x6e, 0x6d, 0x6f, 0x75, 0x6e, 0x74, 0x28, 0x29, 0x20, 0x7b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x68, 0x69, - 0x73, 0x2e, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x4c, 0x61, 0x79, 0x65, - 0x72, 0x28, 0x66, 0x61, 0x6c, 0x73, 0x65, 0x29, 0x3b, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x69, - 0x73, 0x4d, 0x6f, 0x75, 0x6e, 0x74, 0x65, 0x64, 0x20, 0x3d, 0x20, 0x66, - 0x61, 0x6c, 0x73, 0x65, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x72, - 0x65, 0x6d, 0x6f, 0x74, 0x65, 0x20, 0x26, 0x26, 0x20, 0x74, 0x68, 0x69, - 0x73, 0x2e, 0x72, 0x65, 0x6d, 0x6f, 0x74, 0x65, 0x2e, 0x70, 0x61, 0x72, - 0x65, 0x6e, 0x74, 0x4e, 0x6f, 0x64, 0x65, 0x29, 0x20, 0x74, 0x68, 0x69, - 0x73, 0x2e, 0x72, 0x65, 0x6d, 0x6f, 0x74, 0x65, 0x2e, 0x70, 0x61, 0x72, - 0x65, 0x6e, 0x74, 0x4e, 0x6f, 0x64, 0x65, 0x2e, 0x72, 0x65, 0x6d, 0x6f, - 0x76, 0x65, 0x43, 0x68, 0x69, 0x6c, 0x64, 0x28, 0x74, 0x68, 0x69, 0x73, - 0x2e, 0x72, 0x65, 0x6d, 0x6f, 0x74, 0x65, 0x29, 0x3b, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x66, 0x69, 0x6e, 0x64, 0x4e, 0x6f, 0x64, 0x65, 0x28, 0x6e, 0x6f, - 0x64, 0x65, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x74, 0x79, 0x70, - 0x65, 0x6f, 0x66, 0x20, 0x6e, 0x6f, 0x64, 0x65, 0x20, 0x3d, 0x3d, 0x3d, - 0x20, 0x27, 0x73, 0x74, 0x72, 0x69, 0x6e, 0x67, 0x27, 0x20, 0x3f, 0x20, - 0x64, 0x6f, 0x63, 0x75, 0x6d, 0x65, 0x6e, 0x74, 0x2e, 0x71, 0x75, 0x65, - 0x72, 0x79, 0x53, 0x65, 0x6c, 0x65, 0x63, 0x74, 0x6f, 0x72, 0x28, 0x6e, - 0x6f, 0x64, 0x65, 0x29, 0x20, 0x3a, 0x20, 0x6e, 0x6f, 0x64, 0x65, 0x3b, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x4c, 0x61, - 0x79, 0x65, 0x72, 0x28, 0x73, 0x68, 0x6f, 0x77, 0x20, 0x3d, 0x20, 0x74, - 0x72, 0x75, 0x65, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x21, 0x74, 0x68, 0x69, 0x73, - 0x2e, 0x69, 0x73, 0x4d, 0x6f, 0x75, 0x6e, 0x74, 0x65, 0x64, 0x29, 0x20, - 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x63, 0x6c, 0x65, 0x61, - 0x6e, 0x20, 0x75, 0x70, 0x20, 0x6f, 0x6c, 0x64, 0x20, 0x6e, 0x6f, 0x64, - 0x65, 0x20, 0x69, 0x66, 0x20, 0x6d, 0x6f, 0x76, 0x69, 0x6e, 0x67, 0x20, - 0x62, 0x61, 0x73, 0x65, 0x73, 0x3a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x74, 0x68, 0x69, 0x73, 0x2e, - 0x70, 0x72, 0x6f, 0x70, 0x73, 0x2e, 0x69, 0x6e, 0x74, 0x6f, 0x20, 0x21, - 0x3d, 0x3d, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x69, 0x6e, 0x74, 0x6f, - 0x50, 0x6f, 0x69, 0x6e, 0x74, 0x65, 0x72, 0x29, 0x20, 0x7b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x68, 0x69, - 0x73, 0x2e, 0x69, 0x6e, 0x74, 0x6f, 0x50, 0x6f, 0x69, 0x6e, 0x74, 0x65, - 0x72, 0x20, 0x3d, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x70, 0x72, 0x6f, - 0x70, 0x73, 0x2e, 0x69, 0x6e, 0x74, 0x6f, 0x3b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x74, - 0x68, 0x69, 0x73, 0x2e, 0x69, 0x6e, 0x74, 0x6f, 0x20, 0x26, 0x26, 0x20, - 0x74, 0x68, 0x69, 0x73, 0x2e, 0x72, 0x65, 0x6d, 0x6f, 0x74, 0x65, 0x29, - 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x72, 0x65, 0x6d, 0x6f, - 0x74, 0x65, 0x20, 0x3d, 0x20, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x28, - 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x3c, 0x24, 0x7b, 0x50, 0x6f, 0x72, 0x74, - 0x61, 0x6c, 0x50, 0x72, 0x6f, 0x78, 0x79, 0x7d, 0x20, 0x2f, 0x3e, 0x60, - 0x2c, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x69, 0x6e, 0x74, 0x6f, 0x2c, - 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x72, 0x65, 0x6d, 0x6f, 0x74, 0x65, - 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x69, 0x6e, 0x74, 0x6f, 0x20, 0x3d, - 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x66, 0x69, 0x6e, 0x64, 0x4e, 0x6f, - 0x64, 0x65, 0x28, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x70, 0x72, 0x6f, 0x70, - 0x73, 0x2e, 0x69, 0x6e, 0x74, 0x6f, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x72, 0x65, 0x6d, - 0x6f, 0x74, 0x65, 0x20, 0x3d, 0x20, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, - 0x28, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x24, 0x7b, 0x50, 0x6f, 0x72, 0x74, - 0x61, 0x6c, 0x50, 0x72, 0x6f, 0x78, 0x79, 0x7d, 0x20, 0x63, 0x6f, 0x6e, - 0x74, 0x65, 0x78, 0x74, 0x3d, 0x24, 0x7b, 0x74, 0x68, 0x69, 0x73, 0x2e, - 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x78, 0x74, 0x7d, 0x3e, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, - 0x73, 0x68, 0x6f, 0x77, 0x20, 0x26, 0x26, 0x20, 0x74, 0x68, 0x69, 0x73, - 0x2e, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x2e, 0x63, 0x68, 0x69, 0x6c, 0x64, - 0x72, 0x65, 0x6e, 0x20, 0x7c, 0x7c, 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x7d, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, - 0x2f, 0x24, 0x7b, 0x50, 0x6f, 0x72, 0x74, 0x61, 0x6c, 0x50, 0x72, 0x6f, - 0x78, 0x79, 0x7d, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x60, 0x2c, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x69, 0x6e, 0x74, - 0x6f, 0x2c, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x72, 0x65, 0x6d, 0x6f, - 0x74, 0x65, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, - 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x6e, 0x64, - 0x65, 0x72, 0x28, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x6e, 0x75, - 0x6c, 0x6c, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, - 0x20, 0x68, 0x69, 0x67, 0x68, 0x2d, 0x6f, 0x72, 0x64, 0x65, 0x72, 0x20, - 0x63, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x20, 0x74, 0x68, - 0x61, 0x74, 0x20, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x73, 0x20, 0x69, - 0x74, 0x73, 0x20, 0x66, 0x69, 0x72, 0x73, 0x74, 0x20, 0x63, 0x68, 0x69, - 0x6c, 0x64, 0x20, 0x69, 0x66, 0x20, 0x69, 0x74, 0x20, 0x65, 0x78, 0x69, - 0x73, 0x74, 0x73, 0x2e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, - 0x75, 0x73, 0x65, 0x64, 0x20, 0x61, 0x73, 0x20, 0x61, 0x20, 0x63, 0x6f, - 0x6e, 0x64, 0x69, 0x74, 0x69, 0x6f, 0x6e, 0x61, 0x6c, 0x20, 0x72, 0x65, - 0x6e, 0x64, 0x65, 0x72, 0x69, 0x6e, 0x67, 0x20, 0x70, 0x72, 0x6f, 0x78, - 0x79, 0x2e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6c, 0x61, 0x73, 0x73, - 0x20, 0x50, 0x6f, 0x72, 0x74, 0x61, 0x6c, 0x50, 0x72, 0x6f, 0x78, 0x79, + 0x20, 0x28, 0x68, 0x74, 0x74, 0x70, 0x73, 0x3a, 0x2f, 0x2f, 0x67, 0x69, + 0x74, 0x68, 0x75, 0x62, 0x2e, 0x63, 0x6f, 0x6d, 0x2f, 0x64, 0x65, 0x76, + 0x65, 0x6c, 0x6f, 0x70, 0x69, 0x74, 0x2f, 0x70, 0x72, 0x65, 0x61, 0x63, + 0x74, 0x2d, 0x70, 0x6f, 0x72, 0x74, 0x61, 0x6c, 0x2f, 0x62, 0x6c, 0x6f, + 0x62, 0x2f, 0x6d, 0x61, 0x73, 0x74, 0x65, 0x72, 0x2f, 0x73, 0x72, 0x63, + 0x2f, 0x70, 0x72, 0x65, 0x61, 0x63, 0x74, 0x2d, 0x70, 0x6f, 0x72, 0x74, + 0x61, 0x6c, 0x2e, 0x6a, 0x73, 0x29, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, + 0x2a, 0x2a, 0x20, 0x52, 0x65, 0x64, 0x69, 0x72, 0x65, 0x63, 0x74, 0x20, + 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x69, 0x6e, 0x67, 0x20, 0x6f, 0x66, + 0x20, 0x64, 0x65, 0x73, 0x63, 0x65, 0x6e, 0x64, 0x61, 0x6e, 0x74, 0x73, + 0x20, 0x69, 0x6e, 0x74, 0x6f, 0x20, 0x74, 0x68, 0x65, 0x20, 0x67, 0x69, + 0x76, 0x65, 0x6e, 0x20, 0x43, 0x53, 0x53, 0x20, 0x73, 0x65, 0x6c, 0x65, + 0x63, 0x74, 0x6f, 0x72, 0x20, 0x2a, 0x2f, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x63, 0x6c, 0x61, 0x73, 0x73, 0x20, 0x50, 0x6f, 0x72, 0x74, 0x61, 0x6c, 0x20, 0x65, 0x78, 0x74, 0x65, 0x6e, 0x64, 0x73, 0x20, 0x43, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x67, 0x65, 0x74, 0x43, 0x68, 0x69, 0x6c, 0x64, 0x43, - 0x6f, 0x6e, 0x74, 0x65, 0x78, 0x74, 0x28, 0x29, 0x20, 0x7b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, - 0x6e, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x70, 0x72, 0x6f, 0x70, 0x73, - 0x2e, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x78, 0x74, 0x3b, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x28, 0x7b, 0x20, 0x63, 0x68, 0x69, - 0x6c, 0x64, 0x72, 0x65, 0x6e, 0x20, 0x7d, 0x29, 0x20, 0x7b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, - 0x6e, 0x20, 0x63, 0x68, 0x69, 0x6c, 0x64, 0x72, 0x65, 0x6e, 0x20, 0x7c, - 0x7c, 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, - 0x41, 0x70, 0x70, 0x28, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x29, 0x20, 0x7b, - 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, - 0x72, 0x6e, 0x20, 0x68, 0x74, 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x68, 0x65, - 0x61, 0x64, 0x65, 0x72, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x68, 0x31, 0x3e, 0x6c, 0x6c, - 0x61, 0x6d, 0x61, 0x2e, 0x63, 0x70, 0x70, 0x3c, 0x2f, 0x68, 0x31, 0x3e, - 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, - 0x2f, 0x68, 0x65, 0x61, 0x64, 0x65, 0x72, 0x3e, 0x0a, 0x0a, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6d, 0x61, 0x69, - 0x6e, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, - 0x74, 0x22, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x3c, 0x24, 0x7b, 0x63, 0x68, 0x61, 0x74, 0x53, - 0x74, 0x61, 0x72, 0x74, 0x65, 0x64, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, - 0x20, 0x3f, 0x20, 0x43, 0x68, 0x61, 0x74, 0x4c, 0x6f, 0x67, 0x20, 0x3a, - 0x20, 0x43, 0x6f, 0x6e, 0x66, 0x69, 0x67, 0x46, 0x6f, 0x72, 0x6d, 0x7d, - 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x3c, 0x2f, 0x6d, 0x61, 0x69, 0x6e, 0x3e, 0x0a, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x73, 0x65, - 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x77, 0x72, - 0x69, 0x74, 0x65, 0x22, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x24, 0x7b, 0x4d, 0x65, 0x73, - 0x73, 0x61, 0x67, 0x65, 0x49, 0x6e, 0x70, 0x75, 0x74, 0x7d, 0x20, 0x2f, + 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, + 0x44, 0x69, 0x64, 0x55, 0x70, 0x64, 0x61, 0x74, 0x65, 0x28, 0x70, 0x72, + 0x6f, 0x70, 0x73, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x66, 0x6f, 0x72, 0x20, 0x28, 0x6c, 0x65, 0x74, 0x20, + 0x69, 0x20, 0x69, 0x6e, 0x20, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x29, 0x20, + 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x69, 0x66, 0x20, 0x28, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x5b, 0x69, 0x5d, + 0x20, 0x21, 0x3d, 0x3d, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x70, 0x72, + 0x6f, 0x70, 0x73, 0x5b, 0x69, 0x5d, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, + 0x74, 0x75, 0x72, 0x6e, 0x20, 0x73, 0x65, 0x74, 0x54, 0x69, 0x6d, 0x65, + 0x6f, 0x75, 0x74, 0x28, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x72, 0x65, 0x6e, + 0x64, 0x65, 0x72, 0x4c, 0x61, 0x79, 0x65, 0x72, 0x29, 0x3b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x63, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x44, 0x69, 0x64, + 0x4d, 0x6f, 0x75, 0x6e, 0x74, 0x28, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x69, + 0x73, 0x4d, 0x6f, 0x75, 0x6e, 0x74, 0x65, 0x64, 0x20, 0x3d, 0x20, 0x74, + 0x72, 0x75, 0x65, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, + 0x4c, 0x61, 0x79, 0x65, 0x72, 0x20, 0x3d, 0x20, 0x74, 0x68, 0x69, 0x73, + 0x2e, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x4c, 0x61, 0x79, 0x65, 0x72, + 0x2e, 0x62, 0x69, 0x6e, 0x64, 0x28, 0x74, 0x68, 0x69, 0x73, 0x29, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x68, 0x69, + 0x73, 0x2e, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x4c, 0x61, 0x79, 0x65, + 0x72, 0x28, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, + 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6d, 0x70, + 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x57, 0x69, 0x6c, 0x6c, 0x55, 0x6e, 0x6d, + 0x6f, 0x75, 0x6e, 0x74, 0x28, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x72, 0x65, + 0x6e, 0x64, 0x65, 0x72, 0x4c, 0x61, 0x79, 0x65, 0x72, 0x28, 0x66, 0x61, + 0x6c, 0x73, 0x65, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x69, 0x73, 0x4d, 0x6f, 0x75, + 0x6e, 0x74, 0x65, 0x64, 0x20, 0x3d, 0x20, 0x66, 0x61, 0x6c, 0x73, 0x65, + 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, 0x66, + 0x20, 0x28, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x72, 0x65, 0x6d, 0x6f, 0x74, + 0x65, 0x20, 0x26, 0x26, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x72, 0x65, + 0x6d, 0x6f, 0x74, 0x65, 0x2e, 0x70, 0x61, 0x72, 0x65, 0x6e, 0x74, 0x4e, + 0x6f, 0x64, 0x65, 0x29, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x72, 0x65, + 0x6d, 0x6f, 0x74, 0x65, 0x2e, 0x70, 0x61, 0x72, 0x65, 0x6e, 0x74, 0x4e, + 0x6f, 0x64, 0x65, 0x2e, 0x72, 0x65, 0x6d, 0x6f, 0x76, 0x65, 0x43, 0x68, + 0x69, 0x6c, 0x64, 0x28, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x72, 0x65, 0x6d, + 0x6f, 0x74, 0x65, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x66, 0x69, 0x6e, + 0x64, 0x4e, 0x6f, 0x64, 0x65, 0x28, 0x6e, 0x6f, 0x64, 0x65, 0x29, 0x20, + 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, + 0x74, 0x75, 0x72, 0x6e, 0x20, 0x74, 0x79, 0x70, 0x65, 0x6f, 0x66, 0x20, + 0x6e, 0x6f, 0x64, 0x65, 0x20, 0x3d, 0x3d, 0x3d, 0x20, 0x27, 0x73, 0x74, + 0x72, 0x69, 0x6e, 0x67, 0x27, 0x20, 0x3f, 0x20, 0x64, 0x6f, 0x63, 0x75, + 0x6d, 0x65, 0x6e, 0x74, 0x2e, 0x71, 0x75, 0x65, 0x72, 0x79, 0x53, 0x65, + 0x6c, 0x65, 0x63, 0x74, 0x6f, 0x72, 0x28, 0x6e, 0x6f, 0x64, 0x65, 0x29, + 0x20, 0x3a, 0x20, 0x6e, 0x6f, 0x64, 0x65, 0x3b, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x4c, 0x61, 0x79, 0x65, 0x72, 0x28, + 0x73, 0x68, 0x6f, 0x77, 0x20, 0x3d, 0x20, 0x74, 0x72, 0x75, 0x65, 0x29, + 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, + 0x66, 0x20, 0x28, 0x21, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x69, 0x73, 0x4d, + 0x6f, 0x75, 0x6e, 0x74, 0x65, 0x64, 0x29, 0x20, 0x72, 0x65, 0x74, 0x75, + 0x72, 0x6e, 0x3b, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x2f, 0x2f, 0x20, 0x63, 0x6c, 0x65, 0x61, 0x6e, 0x20, 0x75, 0x70, + 0x20, 0x6f, 0x6c, 0x64, 0x20, 0x6e, 0x6f, 0x64, 0x65, 0x20, 0x69, 0x66, + 0x20, 0x6d, 0x6f, 0x76, 0x69, 0x6e, 0x67, 0x20, 0x62, 0x61, 0x73, 0x65, + 0x73, 0x3a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x69, + 0x66, 0x20, 0x28, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x70, 0x72, 0x6f, 0x70, + 0x73, 0x2e, 0x69, 0x6e, 0x74, 0x6f, 0x20, 0x21, 0x3d, 0x3d, 0x20, 0x74, + 0x68, 0x69, 0x73, 0x2e, 0x69, 0x6e, 0x74, 0x6f, 0x50, 0x6f, 0x69, 0x6e, + 0x74, 0x65, 0x72, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x69, 0x6e, + 0x74, 0x6f, 0x50, 0x6f, 0x69, 0x6e, 0x74, 0x65, 0x72, 0x20, 0x3d, 0x20, + 0x74, 0x68, 0x69, 0x73, 0x2e, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x2e, 0x69, + 0x6e, 0x74, 0x6f, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x69, 0x66, 0x20, 0x28, 0x74, 0x68, 0x69, 0x73, 0x2e, + 0x69, 0x6e, 0x74, 0x6f, 0x20, 0x26, 0x26, 0x20, 0x74, 0x68, 0x69, 0x73, + 0x2e, 0x72, 0x65, 0x6d, 0x6f, 0x74, 0x65, 0x29, 0x20, 0x7b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, + 0x68, 0x69, 0x73, 0x2e, 0x72, 0x65, 0x6d, 0x6f, 0x74, 0x65, 0x20, 0x3d, + 0x20, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x28, 0x68, 0x74, 0x6d, 0x6c, + 0x60, 0x3c, 0x24, 0x7b, 0x50, 0x6f, 0x72, 0x74, 0x61, 0x6c, 0x50, 0x72, + 0x6f, 0x78, 0x79, 0x7d, 0x20, 0x2f, 0x3e, 0x60, 0x2c, 0x20, 0x74, 0x68, + 0x69, 0x73, 0x2e, 0x69, 0x6e, 0x74, 0x6f, 0x2c, 0x20, 0x74, 0x68, 0x69, + 0x73, 0x2e, 0x72, 0x65, 0x6d, 0x6f, 0x74, 0x65, 0x29, 0x3b, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x74, 0x68, 0x69, + 0x73, 0x2e, 0x69, 0x6e, 0x74, 0x6f, 0x20, 0x3d, 0x20, 0x74, 0x68, 0x69, + 0x73, 0x2e, 0x66, 0x69, 0x6e, 0x64, 0x4e, 0x6f, 0x64, 0x65, 0x28, 0x74, + 0x68, 0x69, 0x73, 0x2e, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x2e, 0x69, 0x6e, + 0x74, 0x6f, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x74, 0x68, 0x69, 0x73, 0x2e, 0x72, 0x65, 0x6d, 0x6f, 0x74, 0x65, 0x20, + 0x3d, 0x20, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x28, 0x68, 0x74, 0x6d, + 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x24, 0x7b, 0x50, 0x6f, 0x72, 0x74, 0x61, 0x6c, 0x50, 0x72, + 0x6f, 0x78, 0x79, 0x7d, 0x20, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x78, 0x74, + 0x3d, 0x24, 0x7b, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x63, 0x6f, 0x6e, 0x74, + 0x65, 0x78, 0x74, 0x7d, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x24, 0x7b, 0x73, 0x68, 0x6f, 0x77, + 0x20, 0x26, 0x26, 0x20, 0x74, 0x68, 0x69, 0x73, 0x2e, 0x70, 0x72, 0x6f, + 0x70, 0x73, 0x2e, 0x63, 0x68, 0x69, 0x6c, 0x64, 0x72, 0x65, 0x6e, 0x20, + 0x7c, 0x7c, 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x7d, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x24, 0x7b, 0x50, + 0x6f, 0x72, 0x74, 0x61, 0x6c, 0x50, 0x72, 0x6f, 0x78, 0x79, 0x7d, 0x3e, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x2c, 0x20, + 0x74, 0x68, 0x69, 0x73, 0x2e, 0x69, 0x6e, 0x74, 0x6f, 0x2c, 0x20, 0x74, + 0x68, 0x69, 0x73, 0x2e, 0x72, 0x65, 0x6d, 0x6f, 0x74, 0x65, 0x29, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x28, 0x29, + 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, + 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x6e, 0x75, 0x6c, 0x6c, 0x3b, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x68, 0x69, 0x67, + 0x68, 0x2d, 0x6f, 0x72, 0x64, 0x65, 0x72, 0x20, 0x63, 0x6f, 0x6d, 0x70, + 0x6f, 0x6e, 0x65, 0x6e, 0x74, 0x20, 0x74, 0x68, 0x61, 0x74, 0x20, 0x72, + 0x65, 0x6e, 0x64, 0x65, 0x72, 0x73, 0x20, 0x69, 0x74, 0x73, 0x20, 0x66, + 0x69, 0x72, 0x73, 0x74, 0x20, 0x63, 0x68, 0x69, 0x6c, 0x64, 0x20, 0x69, + 0x66, 0x20, 0x69, 0x74, 0x20, 0x65, 0x78, 0x69, 0x73, 0x74, 0x73, 0x2e, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x2f, 0x2f, 0x20, 0x75, 0x73, 0x65, 0x64, + 0x20, 0x61, 0x73, 0x20, 0x61, 0x20, 0x63, 0x6f, 0x6e, 0x64, 0x69, 0x74, + 0x69, 0x6f, 0x6e, 0x61, 0x6c, 0x20, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, + 0x69, 0x6e, 0x67, 0x20, 0x70, 0x72, 0x6f, 0x78, 0x79, 0x2e, 0x0a, 0x20, + 0x20, 0x20, 0x20, 0x63, 0x6c, 0x61, 0x73, 0x73, 0x20, 0x50, 0x6f, 0x72, + 0x74, 0x61, 0x6c, 0x50, 0x72, 0x6f, 0x78, 0x79, 0x20, 0x65, 0x78, 0x74, + 0x65, 0x6e, 0x64, 0x73, 0x20, 0x43, 0x6f, 0x6d, 0x70, 0x6f, 0x6e, 0x65, + 0x6e, 0x74, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x67, + 0x65, 0x74, 0x43, 0x68, 0x69, 0x6c, 0x64, 0x43, 0x6f, 0x6e, 0x74, 0x65, + 0x78, 0x74, 0x28, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x74, 0x68, + 0x69, 0x73, 0x2e, 0x70, 0x72, 0x6f, 0x70, 0x73, 0x2e, 0x63, 0x6f, 0x6e, + 0x74, 0x65, 0x78, 0x74, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x7d, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x6e, 0x64, + 0x65, 0x72, 0x28, 0x7b, 0x20, 0x63, 0x68, 0x69, 0x6c, 0x64, 0x72, 0x65, + 0x6e, 0x20, 0x7d, 0x29, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x63, 0x68, + 0x69, 0x6c, 0x64, 0x72, 0x65, 0x6e, 0x20, 0x7c, 0x7c, 0x20, 0x6e, 0x75, + 0x6c, 0x6c, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x66, + 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x41, 0x70, 0x70, 0x28, + 0x70, 0x72, 0x6f, 0x70, 0x73, 0x29, 0x20, 0x7b, 0x0a, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x74, 0x75, 0x72, 0x6e, 0x20, 0x68, + 0x74, 0x6d, 0x6c, 0x60, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x68, 0x65, 0x61, 0x64, 0x65, 0x72, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x3c, 0x2f, 0x73, 0x65, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x3e, 0x0a, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x66, - 0x6f, 0x6f, 0x74, 0x65, 0x72, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x70, 0x3e, 0x3c, 0x24, - 0x7b, 0x4d, 0x6f, 0x64, 0x65, 0x6c, 0x47, 0x65, 0x6e, 0x65, 0x72, 0x61, - 0x74, 0x69, 0x6f, 0x6e, 0x49, 0x6e, 0x66, 0x6f, 0x7d, 0x20, 0x2f, 0x3e, + 0x20, 0x20, 0x3c, 0x68, 0x31, 0x3e, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x2e, + 0x63, 0x70, 0x70, 0x3c, 0x2f, 0x68, 0x31, 0x3e, 0x0a, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x68, 0x65, 0x61, + 0x64, 0x65, 0x72, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x3c, 0x6d, 0x61, 0x69, 0x6e, 0x20, 0x69, 0x64, + 0x3d, 0x22, 0x63, 0x6f, 0x6e, 0x74, 0x65, 0x6e, 0x74, 0x22, 0x3e, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x3c, 0x24, 0x7b, 0x63, 0x68, 0x61, 0x74, 0x53, 0x74, 0x61, 0x72, 0x74, + 0x65, 0x64, 0x2e, 0x76, 0x61, 0x6c, 0x75, 0x65, 0x20, 0x3f, 0x20, 0x43, + 0x68, 0x61, 0x74, 0x4c, 0x6f, 0x67, 0x20, 0x3a, 0x20, 0x43, 0x6f, 0x6e, + 0x66, 0x69, 0x67, 0x46, 0x6f, 0x72, 0x6d, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, + 0x6d, 0x61, 0x69, 0x6e, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x73, 0x65, 0x63, 0x74, 0x69, 0x6f, + 0x6e, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x77, 0x72, 0x69, 0x74, 0x65, 0x22, + 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x3c, 0x24, 0x7b, 0x4d, 0x65, 0x73, 0x73, 0x61, 0x67, 0x65, + 0x49, 0x6e, 0x70, 0x75, 0x74, 0x7d, 0x20, 0x2f, 0x3e, 0x0a, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x73, 0x65, + 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x3e, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x66, 0x6f, 0x6f, 0x74, 0x65, + 0x72, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x20, 0x20, 0x3c, 0x70, 0x3e, 0x3c, 0x24, 0x7b, 0x4d, 0x6f, 0x64, + 0x65, 0x6c, 0x47, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, + 0x49, 0x6e, 0x66, 0x6f, 0x7d, 0x20, 0x2f, 0x3e, 0x3c, 0x2f, 0x70, 0x3e, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, + 0x20, 0x3c, 0x70, 0x3e, 0x50, 0x6f, 0x77, 0x65, 0x72, 0x65, 0x64, 0x20, + 0x62, 0x79, 0x20, 0x3c, 0x61, 0x20, 0x68, 0x72, 0x65, 0x66, 0x3d, 0x22, + 0x68, 0x74, 0x74, 0x70, 0x73, 0x3a, 0x2f, 0x2f, 0x67, 0x69, 0x74, 0x68, + 0x75, 0x62, 0x2e, 0x63, 0x6f, 0x6d, 0x2f, 0x67, 0x67, 0x65, 0x72, 0x67, + 0x61, 0x6e, 0x6f, 0x76, 0x2f, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x2e, 0x63, + 0x70, 0x70, 0x22, 0x3e, 0x6c, 0x6c, 0x61, 0x6d, 0x61, 0x2e, 0x63, 0x70, + 0x70, 0x3c, 0x2f, 0x61, 0x3e, 0x20, 0x61, 0x6e, 0x64, 0x20, 0x3c, 0x61, + 0x20, 0x68, 0x72, 0x65, 0x66, 0x3d, 0x22, 0x68, 0x74, 0x74, 0x70, 0x73, + 0x3a, 0x2f, 0x2f, 0x67, 0x67, 0x6d, 0x6c, 0x2e, 0x61, 0x69, 0x22, 0x3e, + 0x67, 0x67, 0x6d, 0x6c, 0x2e, 0x61, 0x69, 0x3c, 0x2f, 0x61, 0x3e, 0x2e, 0x3c, 0x2f, 0x70, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x70, 0x3e, 0x50, 0x6f, 0x77, 0x65, - 0x72, 0x65, 0x64, 0x20, 0x62, 0x79, 0x20, 0x3c, 0x61, 0x20, 0x68, 0x72, - 0x65, 0x66, 0x3d, 0x22, 0x68, 0x74, 0x74, 0x70, 0x73, 0x3a, 0x2f, 0x2f, - 0x67, 0x69, 0x74, 0x68, 0x75, 0x62, 0x2e, 0x63, 0x6f, 0x6d, 0x2f, 0x67, - 0x67, 0x65, 0x72, 0x67, 0x61, 0x6e, 0x6f, 0x76, 0x2f, 0x6c, 0x6c, 0x61, - 0x6d, 0x61, 0x2e, 0x63, 0x70, 0x70, 0x22, 0x3e, 0x6c, 0x6c, 0x61, 0x6d, - 0x61, 0x2e, 0x63, 0x70, 0x70, 0x3c, 0x2f, 0x61, 0x3e, 0x20, 0x61, 0x6e, - 0x64, 0x20, 0x3c, 0x61, 0x20, 0x68, 0x72, 0x65, 0x66, 0x3d, 0x22, 0x68, - 0x74, 0x74, 0x70, 0x73, 0x3a, 0x2f, 0x2f, 0x67, 0x67, 0x6d, 0x6c, 0x2e, - 0x61, 0x69, 0x22, 0x3e, 0x67, 0x67, 0x6d, 0x6c, 0x2e, 0x61, 0x69, 0x3c, - 0x2f, 0x61, 0x3e, 0x2e, 0x3c, 0x2f, 0x70, 0x3e, 0x0a, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x6f, 0x6f, - 0x74, 0x65, 0x72, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, - 0x20, 0x20, 0x60, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x28, 0x68, - 0x28, 0x41, 0x70, 0x70, 0x29, 0x2c, 0x20, 0x64, 0x6f, 0x63, 0x75, 0x6d, - 0x65, 0x6e, 0x74, 0x2e, 0x71, 0x75, 0x65, 0x72, 0x79, 0x53, 0x65, 0x6c, - 0x65, 0x63, 0x74, 0x6f, 0x72, 0x28, 0x27, 0x23, 0x63, 0x6f, 0x6e, 0x74, - 0x61, 0x69, 0x6e, 0x65, 0x72, 0x27, 0x29, 0x29, 0x3b, 0x0a, 0x20, 0x20, - 0x3c, 0x2f, 0x73, 0x63, 0x72, 0x69, 0x70, 0x74, 0x3e, 0x0a, 0x3c, 0x2f, - 0x68, 0x65, 0x61, 0x64, 0x3e, 0x0a, 0x0a, 0x3c, 0x62, 0x6f, 0x64, 0x79, - 0x3e, 0x0a, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x20, 0x69, 0x64, 0x3d, - 0x22, 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x22, 0x3e, - 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x3c, 0x64, 0x69, - 0x76, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x70, 0x6f, 0x72, 0x74, 0x61, 0x6c, - 0x22, 0x3e, 0x3c, 0x2f, 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x3c, 0x2f, 0x62, - 0x6f, 0x64, 0x79, 0x3e, 0x0a, 0x0a, 0x3c, 0x2f, 0x68, 0x74, 0x6d, 0x6c, - 0x3e, 0x0a + 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x66, 0x6f, 0x6f, 0x74, 0x65, 0x72, 0x3e, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x3c, 0x2f, 0x64, + 0x69, 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x60, 0x3b, + 0x0a, 0x20, 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, + 0x72, 0x65, 0x6e, 0x64, 0x65, 0x72, 0x28, 0x68, 0x28, 0x41, 0x70, 0x70, + 0x29, 0x2c, 0x20, 0x64, 0x6f, 0x63, 0x75, 0x6d, 0x65, 0x6e, 0x74, 0x2e, + 0x71, 0x75, 0x65, 0x72, 0x79, 0x53, 0x65, 0x6c, 0x65, 0x63, 0x74, 0x6f, + 0x72, 0x28, 0x27, 0x23, 0x63, 0x6f, 0x6e, 0x74, 0x61, 0x69, 0x6e, 0x65, + 0x72, 0x27, 0x29, 0x29, 0x3b, 0x0a, 0x20, 0x20, 0x3c, 0x2f, 0x73, 0x63, + 0x72, 0x69, 0x70, 0x74, 0x3e, 0x0a, 0x3c, 0x2f, 0x68, 0x65, 0x61, 0x64, + 0x3e, 0x0a, 0x0a, 0x3c, 0x62, 0x6f, 0x64, 0x79, 0x3e, 0x0a, 0x20, 0x20, + 0x3c, 0x64, 0x69, 0x76, 0x20, 0x69, 0x64, 0x3d, 0x22, 0x63, 0x6f, 0x6e, + 0x74, 0x61, 0x69, 0x6e, 0x65, 0x72, 0x22, 0x3e, 0x3c, 0x2f, 0x64, 0x69, + 0x76, 0x3e, 0x0a, 0x20, 0x20, 0x3c, 0x64, 0x69, 0x76, 0x20, 0x69, 0x64, + 0x3d, 0x22, 0x70, 0x6f, 0x72, 0x74, 0x61, 0x6c, 0x22, 0x3e, 0x3c, 0x2f, + 0x64, 0x69, 0x76, 0x3e, 0x0a, 0x3c, 0x2f, 0x62, 0x6f, 0x64, 0x79, 0x3e, + 0x0a, 0x0a, 0x3c, 0x2f, 0x68, 0x74, 0x6d, 0x6c, 0x3e, 0x0a }; -unsigned int index_html_len = 27218; +unsigned int index_html_len = 28018; diff --git a/examples/server/public/index.html b/examples/server/public/index.html index 959a9b9a6..1bf2a8b3a 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -145,7 +145,29 @@ color: #888; } + + @keyframes loading-bg-wipe { + 0% { + background-position: 0%; + } + 100% { + background-position: 100%; + } + } + + .loading { + --loading-color-1: #eeeeee00; + --loading-color-2: #eeeeeeff; + background-size: 50% 100%; + background-image: linear-gradient(90deg, var(--loading-color-1), var(--loading-color-2), var(--loading-color-1)); + animation: loading-bg-wipe 2s linear infinite; + } + @media (prefers-color-scheme: dark) { + .loading { + --loading-color-1: #22222200; + --loading-color-2: #222222ff; + } .popover-content { background-color: black; } @@ -321,7 +343,10 @@ const llamaStats = signal(null) const controller = signal(null) - const generating = computed(() => controller.value == null ) + // currently generating a completion? + const generating = computed(() => controller.value != null) + + // has the user started a chat? const chatStarted = computed(() => session.value.transcript.length > 0) const transcriptUpdate = (transcript) => { @@ -430,11 +455,19 @@ return html`
    -