From 011e8ec577fd135cbc02993d3ea9840c516d6a1c Mon Sep 17 00:00:00 2001 From: slaren Date: Mon, 22 Jan 2024 23:42:41 +0100 Subject: [PATCH 001/143] llama : fix not enough space in buffer with Qwen (#5086) --- llama.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index 8c906a22f..f6f1ec0f4 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4440,9 +4440,9 @@ static struct ggml_tensor * llm_build_kv( // these nodes are added to the graph together so that they are not reordered // by doing so, the number of splits in the graph is reduced + ggml_build_forward_expand(graph, q_cur); ggml_build_forward_expand(graph, k_cur); ggml_build_forward_expand(graph, v_cur); - ggml_build_forward_expand(graph, q_cur); llm_build_kv_store(ctx, hparams, kv, graph, k_cur, v_cur, n_ctx, n_tokens, kv_head, cb, il); From 125d03a5036a02a983c8e98c2cdc126e061afb8e Mon Sep 17 00:00:00 2001 From: Michael Coppola Date: Tue, 23 Jan 2024 01:51:27 -0500 Subject: [PATCH 002/143] llama.vim : added api key support (#5090) Co-authored-by: Michael Coppola --- examples/llama.vim | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/examples/llama.vim b/examples/llama.vim index f03fadfb7..1b5ad6ba0 100644 --- a/examples/llama.vim +++ b/examples/llama.vim @@ -6,7 +6,7 @@ " Similarly, you could add an insert mode keybind with " inoremap call llama#doLlamaGen() " -" g:llama_api_url and g:llama_overrides can be configured in your .vimrc +" g:llama_api_url, g:llama_api_key and g:llama_overrides can be configured in your .vimrc " let g:llama_api_url = "192.168.1.10:8080" " llama_overrides can also be set through buffer/window scopes. For instance " autocmd filetype python let b:llama_overrides = {"temp": 0.2} @@ -82,6 +82,9 @@ func llama#doLlamaGen() endif let l:querydata.prompt = join(l:buflines, "\n") let l:curlcommand = copy(s:curlcommand) + if exists("g:llama_api_key") + call extend(l:curlcommand, ['--header', 'Authorization: Bearer ' .. g:llama_api_key]) + endif let l:curlcommand[2] = json_encode(l:querydata) let b:job = job_start(l:curlcommand, {"callback": function("s:callbackHandler", [l:cbuffer])}) endfunction From 2bed4aa3f37cb4e39e16e9ec7b595a7738fd5faf Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Tue, 23 Jan 2024 08:11:39 +0100 Subject: [PATCH 003/143] devops : add intel oneapi dockerfile (#5068) Co-authored-by: Xuan Son Nguyen --- .devops/main-intel.Dockerfile | 26 ++++++++++++++++++++++++++ .github/workflows/docker.yml | 1 + CMakeLists.txt | 5 +++++ 3 files changed, 32 insertions(+) create mode 100644 .devops/main-intel.Dockerfile diff --git a/.devops/main-intel.Dockerfile b/.devops/main-intel.Dockerfile new file mode 100644 index 000000000..e1e6acc24 --- /dev/null +++ b/.devops/main-intel.Dockerfile @@ -0,0 +1,26 @@ +ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04 +ARG UBUNTU_VERSION=22.04 + +FROM intel/hpckit:$ONEAPI_VERSION as build + +RUN apt-get update && \ + apt-get install -y git + +WORKDIR /app + +COPY . . + +# for some reasons, "-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DLLAMA_NATIVE=ON" give worse performance +RUN mkdir build && \ + cd build && \ + cmake .. -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx && \ + cmake --build . --config Release --target main server + +FROM ubuntu:$UBUNTU_VERSION as runtime + +COPY --from=build /app/build/bin/main /main +COPY --from=build /app/build/bin/server /server + +ENV LC_ALL=C.utf8 + +ENTRYPOINT [ "/main" ] diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 87904b75e..825b8f503 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -35,6 +35,7 @@ jobs: - { tag: "full-cuda", dockerfile: ".devops/full-cuda.Dockerfile", platforms: "linux/amd64" } - { tag: "light-rocm", dockerfile: ".devops/main-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } - { tag: "full-rocm", dockerfile: ".devops/full-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } + - { tag: "light-intel", dockerfile: ".devops/main-intel.Dockerfile", platforms: "linux/amd64" } steps: - name: Check out the repo uses: actions/checkout@v3 diff --git a/CMakeLists.txt b/CMakeLists.txt index 5a333ff52..af3665129 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -478,6 +478,11 @@ function(get_flags CCID CCVER) if (CCVER VERSION_GREATER_EQUAL 8.1.0) set(CXX_FLAGS ${CXX_FLAGS} -Wextra-semi) endif() + elseif (CCID MATCHES "Intel") + # enable max optimization level when using Intel compiler + set(C_FLAGS -ipo -O3 -static -fp-model=fast -flto -fno-stack-protector) + set(CXX_FLAGS -ipo -O3 -static -fp-model=fast -flto -fno-stack-protector) + add_link_options(-fuse-ld=lld -static-intel) endif() set(GF_C_FLAGS ${C_FLAGS} PARENT_SCOPE) From 89758723c75ba594e401f6513751beeba7ca1d28 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 23 Jan 2024 14:12:57 +0200 Subject: [PATCH 004/143] minor : clean-up some warnings and style (#5094) * minor : clean-up some warnings and style ggml-ci * ggml : add comment --- common/common.cpp | 10 +++---- examples/llava/clip.cpp | 47 ++++++++++++------------------ examples/perplexity/perplexity.cpp | 4 +-- ggml.c | 9 +++--- ggml.h | 1 - llama.cpp | 24 +++++++-------- 6 files changed, 42 insertions(+), 53 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 0a7096171..6b07f1197 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -216,12 +216,10 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { } // store the external file name in params params.prompt_file = argv[i]; - file.seekg(0, std::ios::end); - size_t size = file.tellg(); - file.seekg(0, std::ios::beg); - params.prompt.resize(size); - file.read((char *)params.prompt.data(), size); - fprintf(stderr, "Read %zu bytes from binary file %s\n", size, argv[i]); + std::ostringstream ss; + ss << file.rdbuf(); + params.prompt = ss.str(); + fprintf(stderr, "Read %zu bytes from binary file %s\n", params.prompt.size(), argv[i]); } else if (arg == "-f" || arg == "--file") { if (++i >= argc) { invalid_param = true; diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 6161fd858..4a0338a37 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -2,18 +2,6 @@ // so there might be still unnecessary artifacts hanging around // I'll gradually clean and extend it -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include -#include - #include "clip.h" #include "ggml.h" #include "ggml-alloc.h" @@ -30,6 +18,19 @@ #define STB_IMAGE_IMPLEMENTATION #include "stb_image.h" +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + static std::string format(const char * fmt, ...) { va_list ap; va_list ap2; @@ -217,9 +218,9 @@ static std::string gguf_kv_to_str(const struct gguf_context * ctx_gguf, int i) { static void print_tensor_info(const ggml_tensor* tensor, const char* prefix = "") { size_t tensor_size = ggml_nbytes(tensor); - printf("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%d, %d, %d, %d], type: %d\n", + printf("%s: n_dims = %d, name = %s, tensor_size=%zu, shape:[%" PRId64 ", %" PRId64 ", %" PRId64 ", %" PRId64 "], type = %s\n", prefix, ggml_n_dims(tensor), tensor->name, tensor_size, - tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], tensor->type); + tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], ggml_type_name(tensor->type)); } static projector_type clip_projector_type_from_string(const std::string & name) { @@ -592,7 +593,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 mlp_3 = ggml_cont(ctx0, ggml_permute(ctx0, mlp_3, 1, 0, 2, 3)); mlp_3 = ggml_reshape_4d(ctx0, mlp_3, n_patch, n_patch, mlp_3->ne[1], mlp_3->ne[2]); // stride = 1, padding = 1, bias is nullptr - block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, nullptr, 1, 1, 1, 1, 1, 1); + block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_1_block_0_0_w, mlp_3, 1, 1, 1, 1, 1, 1); // layer norm // // block_1 shape = [1, 2048, 24, 24], ne = [24, 24, 2048, 1] @@ -640,7 +641,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 // block_2 { // stride = 2 - block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_2_block_0_0_w, block_1, nullptr, 2, 2, 1, 1, 1, 1); + block_1 = ggml_conv_depthwise_2d(ctx0, model.mm_model_block_2_block_0_0_w, block_1, 2, 2, 1, 1, 1, 1); // block_1 shape = [1, 2048, 12, 12], ne = [12, 12, 2048, 1] // layer norm @@ -741,18 +742,10 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { { std::map n_type; - uint32_t n_type_max = 0; - enum ggml_type type_max = GGML_TYPE_F32; - for (int i = 0; i < n_tensors; i++) { enum ggml_type type = gguf_get_tensor_type(ctx, i); n_type[type]++; - - if (n_type_max < n_type[type]) { - n_type_max = n_type[type]; - type_max = type; - } } printf("%s: Dumping metadata keys/values. Note: KV overrides do not apply in this output.\n", __func__); @@ -795,14 +788,12 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { size_t tensor_size = ggml_nbytes(cur); buffer_size += tensor_size; if (verbosity >= 3) { - printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%d, %d, %d, %d], type: %d\n", __func__, i, - ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], type); + printf("%s: tensor[%d]: n_dims = %d, name = %s, tensor_size=%zu, offset=%zu, shape:[%" PRIu64 ", %" PRIu64 ", %" PRIu64 ", %" PRIu64 "], type = %s\n", + __func__, i, ggml_n_dims(cur), cur->name, tensor_size, offset, cur->ne[0], cur->ne[1], cur->ne[2], cur->ne[3], ggml_type_name(type)); } } } - - buffer_size += n_tensors * 128 /* CLIP PADDING */; clip_ctx * new_clip = new clip_ctx; diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 1b7f85f49..de6d3eb41 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -1202,11 +1202,11 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) { printf("Final Winogrande score(%d tasks): %.4lf +/- %.4lf\n", n_done, 100*p, sigma); } -static bool deserialize_string(std::istream& in, std::string& str) { +static bool deserialize_string(std::istream & in, std::string & str) { uint32_t size; if (!in.read((char *)&size, sizeof(size)).fail()) { str.resize(size); - if (!in.read((char *)str.data(), size).fail()) return true; + if (!in.read((char *)&str[0], size).fail()) return true; } return false; } diff --git a/ggml.c b/ggml.c index f85045c9c..ca98fde8a 100644 --- a/ggml.c +++ b/ggml.c @@ -5368,14 +5368,12 @@ struct ggml_tensor * ggml_conv_depthwise_2d( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, - struct ggml_tensor * c, int s0, int s1, int p0, int p1, int d0, int d1) { - struct ggml_tensor * new_a = ggml_reshape_4d(ctx, a, a->ne[0], a->ne[1], 1, a->ne[2] * a->ne[3]); struct ggml_tensor * im2col = ggml_im2col(ctx, new_a, ggml_reshape_4d(ctx, b, b->ne[0], b->ne[1], 1, b->ne[2] * b->ne[3]), @@ -9991,7 +9989,7 @@ static void ggml_compute_forward_mul_mat( return; } - const int64_t tgemm0 = ggml_perf_time_us(); + //const int64_t tgemm0 = ggml_perf_time_us(); for (int64_t i13 = 0; i13 < ne13; i13++) { for (int64_t i12 = 0; i12 < ne12; i12++) { const int64_t i03 = i13/r3; @@ -16934,7 +16932,10 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa if (ggml_compute_forward_mul_mat_use_blas(node)) { if (node->src[0]->type != GGML_TYPE_F32) { // here we need memory for fully dequantized matrix from src0 - cur = ggml_type_size(GGML_TYPE_F32)*ggml_nelements(node->src[0]); + // take into account that src0 can be broadcasted into src1[2,3] + cur = ggml_type_size(GGML_TYPE_F32) + * node->src[0]->ne[0]*node->src[0]->ne[1] + * node->src[1]->ne[2]*node->src[1]->ne[3]; } } else #endif diff --git a/ggml.h b/ggml.h index dca7bd9ce..1c4976271 100644 --- a/ggml.h +++ b/ggml.h @@ -1499,7 +1499,6 @@ extern "C" { struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, - struct ggml_tensor * c, int s0, int s1, int p0, diff --git a/llama.cpp b/llama.cpp index f6f1ec0f4..582e82260 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2300,18 +2300,18 @@ struct llama_model_loader { } switch (type_max) { - case GGML_TYPE_F32: ftype = LLAMA_FTYPE_ALL_F32; break; - case GGML_TYPE_F16: ftype = LLAMA_FTYPE_MOSTLY_F16; break; - case GGML_TYPE_Q4_0: ftype = LLAMA_FTYPE_MOSTLY_Q4_0; break; - case GGML_TYPE_Q4_1: ftype = LLAMA_FTYPE_MOSTLY_Q4_1; break; - case GGML_TYPE_Q5_0: ftype = LLAMA_FTYPE_MOSTLY_Q5_0; break; - case GGML_TYPE_Q5_1: ftype = LLAMA_FTYPE_MOSTLY_Q5_1; break; - case GGML_TYPE_Q8_0: ftype = LLAMA_FTYPE_MOSTLY_Q8_0; break; - case GGML_TYPE_Q2_K: ftype = LLAMA_FTYPE_MOSTLY_Q2_K; break; - case GGML_TYPE_Q3_K: ftype = LLAMA_FTYPE_MOSTLY_Q3_K_M; break; - case GGML_TYPE_Q4_K: ftype = LLAMA_FTYPE_MOSTLY_Q4_K_M; break; - case GGML_TYPE_Q5_K: ftype = LLAMA_FTYPE_MOSTLY_Q5_K_M; break; - case GGML_TYPE_Q6_K: ftype = LLAMA_FTYPE_MOSTLY_Q6_K; break; + case GGML_TYPE_F32: ftype = LLAMA_FTYPE_ALL_F32; break; + case GGML_TYPE_F16: ftype = LLAMA_FTYPE_MOSTLY_F16; break; + case GGML_TYPE_Q4_0: ftype = LLAMA_FTYPE_MOSTLY_Q4_0; break; + case GGML_TYPE_Q4_1: ftype = LLAMA_FTYPE_MOSTLY_Q4_1; break; + case GGML_TYPE_Q5_0: ftype = LLAMA_FTYPE_MOSTLY_Q5_0; break; + case GGML_TYPE_Q5_1: ftype = LLAMA_FTYPE_MOSTLY_Q5_1; break; + case GGML_TYPE_Q8_0: ftype = LLAMA_FTYPE_MOSTLY_Q8_0; break; + case GGML_TYPE_Q2_K: ftype = LLAMA_FTYPE_MOSTLY_Q2_K; break; + case GGML_TYPE_Q3_K: ftype = LLAMA_FTYPE_MOSTLY_Q3_K_M; break; + case GGML_TYPE_Q4_K: ftype = LLAMA_FTYPE_MOSTLY_Q4_K_M; break; + case GGML_TYPE_Q5_K: ftype = LLAMA_FTYPE_MOSTLY_Q5_K_M; break; + case GGML_TYPE_Q6_K: ftype = LLAMA_FTYPE_MOSTLY_Q6_K; break; case GGML_TYPE_IQ2_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XXS; break; case GGML_TYPE_IQ2_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XS; break; default: From 9ecdd12e95aee20d6dfaf5f5a0f0ce5ac1fb2747 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Tue, 23 Jan 2024 13:31:56 +0100 Subject: [PATCH 005/143] CUDA: more info when no device code (#5088) --- ggml-cuda.cu | 89 +++++++++++++++++++++++++++++++--------------------- 1 file changed, 54 insertions(+), 35 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index ec3837fb8..7f460449e 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -13,6 +13,10 @@ #include #include +// stringize macro for converting __CUDA_ARCH_LIST__ (list of integers) to string +#define STRINGIZE_IMPL(...) #__VA_ARGS__ +#define STRINGIZE(...) STRINGIZE_IMPL(__VA_ARGS__) + #if defined(GGML_USE_HIPBLAS) #include #include @@ -584,13 +588,28 @@ static cuda_device_capabilities g_device_caps[GGML_CUDA_MAX_DEVICES] = { {0, 0, static cublasHandle_t g_cublas_handles[GGML_CUDA_MAX_DEVICES] = {nullptr}; [[noreturn]] -static __device__ void bad_arch() { - printf("ERROR: ggml-cuda was compiled without support for the current GPU architecture.\n"); +static __device__ void no_device_code( + const char * file_name, const int line, const char * function_name, const int arch, const char * arch_list) { + +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + printf("%s:%d: ERROR: HIP kernel %s has no device code compatible with HIP arch %d.\n", + file_name, line, function_name, arch); + (void) arch_list; +#else + printf("%s:%d: ERROR: CUDA kernel %s has no device code compatible with CUDA arch %d. ggml-cuda.cu was compiled for: %s\n", + file_name, line, function_name, arch, arch_list); +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) __trap(); - (void) bad_arch; // suppress unused function warning + (void) no_device_code; // suppress unused function warning } +#ifdef __CUDA_ARCH__ +#define NO_DEVICE_CODE no_device_code(__FILE__, __LINE__, __FUNCTION__, __CUDA_ARCH__, STRINGIZE(__CUDA_ARCH_LIST__)) +#else +#define NO_DEVICE_CODE GGML_ASSERT(false && "NO_DEVICE_CODE not valid in host code.") +#endif // __CUDA_ARCH__ + static __device__ __forceinline__ float warp_reduce_sum(float x) { #pragma unroll for (int mask = 16; mask > 0; mask >>= 1) { @@ -617,7 +636,7 @@ static __device__ __forceinline__ half2 warp_reduce_sum(half2 a) { return a; #else (void) a; - bad_arch(); + NO_DEVICE_CODE; #endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL } @@ -638,7 +657,7 @@ static __device__ __forceinline__ half2 warp_reduce_max(half2 x) { return x; #else (void) x; - bad_arch(); + NO_DEVICE_CODE; #endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX } @@ -2421,7 +2440,7 @@ static __global__ void dequantize_block_q8_0_f16(const void * __restrict__ vx, h } #else (void) vx; (void) y; (void) k; - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= CC_PASCAL } @@ -2452,7 +2471,7 @@ template static __device__ __forceinline__ float vec_dot_q4_0_q8_1_imp // second part effectively subtracts 8 from each quant value return d4 * (sumi * ds8f.x - (8*vdr/QI4_0) * ds8f.y); #else - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2489,7 +2508,7 @@ template static __device__ __forceinline__ float vec_dot_q4_1_q8_1_imp // scale second part of sum by QI8_1/(vdr * QR4_1) to compensate for multiple threads adding it return sumi * d4d8 + m4s8 / (QI8_1 / (vdr * QR4_1)); #else - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2524,7 +2543,7 @@ template static __device__ __forceinline__ float vec_dot_q5_0_q8_1_imp // second part effectively subtracts 16 from each quant value return d5 * (sumi * ds8f.x - (16*vdr/QI5_0) * ds8f.y); #else - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2569,7 +2588,7 @@ template static __device__ __forceinline__ float vec_dot_q5_1_q8_1_imp return sumi*d5d8 + m5s8 / (QI5_1 / vdr); #else - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2590,7 +2609,7 @@ template static __device__ __forceinline__ float vec_dot_q8_0_q8_1_imp return d8_0*d8_1 * sumi; #else - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2620,7 +2639,7 @@ template static __device__ __forceinline__ float vec_dot_q8_1_q8_1_imp // scale second part of sum by QI8_1/ vdr to compensate for multiple threads adding it return sumi*d8d8 + m8s8 / (QI8_1 / vdr); #else - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2655,7 +2674,7 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmvq( return dm2f.x*sumf_d - dm2f.y*sumf_m; #else - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2692,7 +2711,7 @@ static __device__ __forceinline__ float vec_dot_q2_K_q8_1_impl_mmq( return d8 * (dm2f.x*sumi_d - dm2f.y*sumi_m); #else - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2732,7 +2751,7 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmvq( return d3 * sumf; #else - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2757,7 +2776,7 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1_impl_mmq( return d3*d8 * sumi; #else - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2790,7 +2809,7 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_vmmq( return dm4f.x*sumf_d - dm4f.y*sumf_m; #else - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2823,7 +2842,7 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1_impl_mmq( return dm4f.x*sumf_d - dm4f.y*sumf_m; #else - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2863,7 +2882,7 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_vmmq( return dm5f.x*sumf_d - dm5f.y*sumf_m; #else - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2896,7 +2915,7 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1_impl_mmq( return dm4f.x*sumf_d - dm4f.y*sumf_m; #else - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2926,7 +2945,7 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmvq( return d*sumf; #else - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -2957,7 +2976,7 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1_impl_mmq( return d6 * sumf_d; #else - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A } @@ -3823,7 +3842,7 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1( return dall * sumf_d - dmin * sumf_m; #else - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A #endif @@ -4006,7 +4025,7 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1( return d * sumf_d; #else - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A #endif @@ -4501,7 +4520,7 @@ template static __global__ void (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else (void) vec_dot_q4_0_q8_1_mul_mat; - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= CC_VOLTA } @@ -4570,7 +4589,7 @@ template static __global__ void (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else (void) vec_dot_q4_1_q8_1_mul_mat; - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= CC_VOLTA } @@ -4637,7 +4656,7 @@ template static __global__ void (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else (void) vec_dot_q5_0_q8_1_mul_mat; - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= CC_VOLTA } @@ -4704,7 +4723,7 @@ mul_mat_q5_1( (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else (void) vec_dot_q5_1_q8_1_mul_mat; - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= CC_VOLTA } @@ -4771,7 +4790,7 @@ template static __global__ void (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else (void) vec_dot_q8_0_q8_1_mul_mat; - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= CC_VOLTA } @@ -4838,7 +4857,7 @@ mul_mat_q2_K( (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else (void) vec_dot_q2_K_q8_1_mul_mat; - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= CC_VOLTA } @@ -4907,7 +4926,7 @@ template static __global__ void (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else (void) vec_dot_q3_K_q8_1_mul_mat; - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= CC_VOLTA } @@ -4976,7 +4995,7 @@ template static __global__ void (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else (void) vec_dot_q4_K_q8_1_mul_mat; - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= CC_VOLTA } @@ -5043,7 +5062,7 @@ mul_mat_q5_K( (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else (void) vec_dot_q5_K_q8_1_mul_mat; - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= CC_VOLTA } @@ -5112,7 +5131,7 @@ template static __global__ void (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); #else (void) vec_dot_q6_K_q8_1_mul_mat; - bad_arch(); + NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= CC_VOLTA } @@ -5835,7 +5854,7 @@ static __global__ void soft_max_f16(const float * x, const float * y, float * ds } #else (void) x; (void) y; (void) dst; (void) ncols_par; (void) nrows_y; (void) scale; - bad_arch(); + NO_DEVICE_CODE; #endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) && __CUDA_ARCH__ >= CC_PASCAL && CUDART_VERSION >= CUDART_HMAX } From 44879ee885f48ecf4675dd216b373dce0a6f3690 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Tue, 23 Jan 2024 15:17:20 +0200 Subject: [PATCH 006/143] Additional KL-divergence statistics (#5081) * perplexity: add top-token probability * perplexity: add additional KL-divergence statistics * perplexity: a better organized KL-divergence statistics output --------- Co-authored-by: Iwan Kawrakow --- examples/perplexity/perplexity.cpp | 71 +++++++++++++++++++++++++----- 1 file changed, 61 insertions(+), 10 deletions(-) diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index de6d3eb41..8d2204969 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -222,13 +222,18 @@ struct kl_divergence_result { double sum_kld2 = 0; double sum_nll_diff = 0; double sum_nll_diff2 = 0; + size_t n_same_top = 0; size_t count = 0; }; -static void log_softmax(int n_vocab, const float * logits, const uint16_t * base_log_prob, int tok, kl_divergence_result & kld) { +static double log_softmax(int n_vocab, const float * logits, const uint16_t * base_log_prob, int tok, kl_divergence_result & kld) { float max_logit = logits[0]; + int imax = 0; for (int i = 1; i < n_vocab; ++i) { - max_logit = std::max(max_logit, logits[i]); + if (logits[i] > max_logit) { + max_logit = logits[i]; + imax = i; + } } double sum_exp = 0.0; for (int i = 0; i < n_vocab; ++i) { @@ -247,8 +252,14 @@ static void log_softmax(int n_vocab, const float * logits, const uint16_t * base kld.sum_nll_diff2 += nll*nll; max_logit += log_sum_exp; double sum = 0; + int imax_base = -1; + float p_log_base_max = 0; for (int i = 0; i < n_vocab; ++i) { const float p_log_base = scale*base_log_prob[i] + min_log_prob; + if (i == 0 || p_log_base > p_log_base_max) { + p_log_base_max = p_log_base; + imax_base = i; + } if (p_log_base > -16.f) { const float p_base = expf(p_log_base); sum += p_base * (p_log_base - logits[i] + max_logit); @@ -257,14 +268,17 @@ static void log_softmax(int n_vocab, const float * logits, const uint16_t * base kld.sum_kld += sum; kld.sum_kld2 += sum*sum; ++kld.count; + if (imax == imax_base) ++kld.n_same_top; + return sum; } static void process_logits(int n_vocab, const float * logits, const int * tokens, int n_token, - std::vector & workers, const std::vector & base_log_probs, kl_divergence_result & kld) { + std::vector & workers, const std::vector & base_log_probs, kl_divergence_result & kld, + float * kld_values) { std::mutex mutex; const int nv = 2*((n_vocab + 1)/2) + 4; int counter = 0; - auto compute = [&mutex, &counter, &base_log_probs, &kld, n_vocab, logits, tokens, n_token, nv] () { + auto compute = [&mutex, &counter, &base_log_probs, &kld, n_vocab, logits, tokens, n_token, nv, kld_values] () { kl_divergence_result local_kld; while (true) { std::unique_lock lock(mutex); @@ -276,11 +290,13 @@ static void process_logits(int n_vocab, const float * logits, const int * tokens kld.sum_kld2 += local_kld.sum_kld2; kld.sum_nll_diff += local_kld.sum_nll_diff; kld.sum_nll_diff2 += local_kld.sum_nll_diff2; + kld.n_same_top += local_kld.n_same_top; kld.count += local_kld.count; break; } lock.unlock(); - log_softmax(n_vocab, logits + i*n_vocab, base_log_probs.data() + i*nv, tokens[i+1], local_kld); + double v = log_softmax(n_vocab, logits + i*n_vocab, base_log_probs.data() + i*nv, tokens[i+1], local_kld); + kld_values[i] = (float)v; } }; for (auto & w : workers) { @@ -1615,7 +1631,7 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) { in.read((char *)&n_vocab, sizeof(n_vocab)); in.read((char *)&n_chunk, sizeof(n_chunk)); if (in.fail()) { - fprintf(stderr, "%s: failed rwading n_vocab, n_chunk from %s\n", __func__, params.logits_file.c_str()); + fprintf(stderr, "%s: failed reading n_vocab, n_chunk from %s\n", __func__, params.logits_file.c_str()); return; } if (n_vocab != llama_n_vocab(llama_get_model(ctx))) { @@ -1634,6 +1650,7 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) { const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); std::vector log_probs_uint16(size_t(n_ctx - 1 - n_ctx/2) * nv); + std::vector kld_values(size_t(n_ctx - 1 - n_ctx/2)*n_chunk); std::vector logits; if (num_batches > 1) { logits.reserve(n_ctx * n_vocab); @@ -1652,6 +1669,7 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) { }; kl_divergence_result kld; + auto kld_ptr = kld_values.data(); for (int i = 0; i < n_chunk; ++i) { const int start = i * n_ctx; @@ -1705,20 +1723,24 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) { } fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0); - printf("\nchunk PPL ln(PPL(Q)/PPL(base)) KL-Divergence\n"); + printf("\nchunk PPL ln(PPL(Q)/PPL(base)) KL-Divergence Same top\n"); } const int first = n_ctx/2; const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx); process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first, - workers, log_probs_uint16, kld); + workers, log_probs_uint16, kld, kld_ptr); + kld_ptr += n_ctx - 1 - first; auto ppl = mean_and_uncertainty(kld.sum_nll, kld.sum_nll2, kld.count); auto log_ppl_ratio = mean_and_uncertainty(kld.sum_nll_diff, kld.sum_nll_diff2, kld.count); auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count); + auto p_top = 1.*kld.n_same_top/kld.count; + auto d_p_top = sqrt(p_top*(1 - p_top)/(kld.count - 1)); - printf("%4d %10.4lf %10.5lf ± %10.5f %10.5f ± %10.5lf\n", i+1, exp(ppl.first), - log_ppl_ratio.first, log_ppl_ratio.second, kl_div.first, kl_div.second); + printf("%4d %10.4lf %10.5lf ± %10.5f %10.5f ± %10.5lf %.5f ± %.5f\n", i+1, exp(ppl.first), + log_ppl_ratio.first, log_ppl_ratio.second, kl_div.first, kl_div.second, + p_top, d_p_top); fflush(stdout); @@ -1726,6 +1748,35 @@ static void kl_divergence(llama_context * ctx, const gpt_params & params) { } printf("\n"); + if (kld.count < 100) return; // we do not wish to do statistics on so few values + + std::sort(kld_values.begin(), kld_values.end()); + + printf("===== KL-divergence statistics\n"); + auto kl_div = mean_and_uncertainty(kld.sum_kld, kld.sum_kld2, kld.count); + printf("Average: %10.6f ±%10.6lf\n", kl_div.first, kl_div.second); + auto kld_median = kld_values.size()%2 == 0 ? 0.5f*(kld_values[kld_values.size()/2] + kld_values[kld_values.size()/2-1]) + : kld_values[kld_values.size()/2]; + printf("Median : %10.6f\n", kld_median); + + auto percentile = [&kld_values] (float fraction) { + if (fraction <= 0) return kld_values.front(); + if (fraction >= 1) return kld_values.back(); + float p = fraction*(kld_values.size() - 1); + size_t ip = size_t(p); p -= ip; + return (1 - p)*kld_values[ip] + p*kld_values[std::min(ip+1, kld_values.size()-1)]; + }; + + printf("Maximum: %10.6f\n", kld_values.back()); + printf("KLD_99 : %10.6f\n", percentile(0.99f)); + printf("KLD_95 : %10.6f\n", percentile(0.95f)); + printf("KLD_90 : %10.6f\n", percentile(0.90f)); + + printf("Minimum: %10.6f\n", kld_values.front()); + printf("KLD_01 : %10.6f\n", percentile(0.01f)); + printf("KLD_05 : %10.6f\n", percentile(0.05f)); + printf("KLD_10 : %10.6f\n", percentile(0.10f)); + } int main(int argc, char ** argv) { From 26d607608d794efa56df3bdb6043a2f94c1d632c Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 23 Jan 2024 15:50:56 +0200 Subject: [PATCH 007/143] metal : disable support for MUL_MAT F32 x F16 --- ggml-metal.m | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ggml-metal.m b/ggml-metal.m index 912ddc83f..4b3eb4914 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -668,7 +668,8 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const return true; case GGML_OP_MUL_MAT: case GGML_OP_MUL_MAT_ID: - return ctx->support_simdgroup_reduction; + return ctx->support_simdgroup_reduction && + (op->src[0]->type != GGML_TYPE_F32 || op->src[1]->type == GGML_TYPE_F32); case GGML_OP_CPY: case GGML_OP_DUP: case GGML_OP_CONT: From 1387ea21178f9f154944013d4dd9764b54c69deb Mon Sep 17 00:00:00 2001 From: slaren Date: Wed, 24 Jan 2024 12:48:14 +0100 Subject: [PATCH 008/143] llama : pre-allocate input tensors in a separate buffer (#5100) --- ggml-alloc.c | 4 +- llama.cpp | 345 ++++++++++++++++++++++++--------------------------- 2 files changed, 167 insertions(+), 182 deletions(-) diff --git a/ggml-alloc.c b/ggml-alloc.c index 89b85d348..60141a34d 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -109,8 +109,8 @@ void ggml_tallocr_alloc(ggml_tallocr_t alloc, struct ggml_tensor * tensor) { if (block->size >= size) { best_fit_block = alloc->n_free_blocks - 1; } else { - fprintf(stderr, "%s: not enough space in the buffer (needed %zu, largest block available %zu)\n", - __func__, size, max_avail); + fprintf(stderr, "%s: not enough space in the buffer to allocate %s (needed %zu, largest block available %zu)\n", + __func__, tensor->name, size, max_avail); GGML_ASSERT(!"not enough space in the buffer"); return; } diff --git a/llama.cpp b/llama.cpp index 582e82260..114046db9 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1669,6 +1669,9 @@ struct llama_context { for (ggml_backend_t backend : backends) { ggml_backend_free(backend); } + + ggml_backend_buffer_free(buf_input); + ggml_free(ctx_input); } llama_cparams cparams; @@ -1715,8 +1718,14 @@ struct llama_context { // allocator for the input tensors ggml_tallocr * alloc = nullptr; - // temporary buffer for copying data to/from the backend - std::vector> buf_copy; + // input tensors + ggml_backend_buffer_t buf_input = nullptr; + ggml_context * ctx_input = nullptr; + struct ggml_tensor * inp_tokens; // I32 [n_batch] + struct ggml_tensor * inp_embd; // F32 [n_embd, n_batch] + struct ggml_tensor * inp_pos; // I32 [n_batch] + struct ggml_tensor * inp_KQ_mask; // F32 [n_ctx, n_batch] + struct ggml_tensor * inp_K_shift; // I32 [n_ctx] #ifdef GGML_USE_MPI ggml_mpi_context * ctx_mpi = NULL; @@ -4089,22 +4098,24 @@ static struct ggml_tensor * llm_build_inp_embd( const llama_hparams & hparams, const llama_batch & batch, struct ggml_tensor * tok_embd, + struct ggml_tensor * inp_tokens, + struct ggml_tensor * inp_embd, const llm_build_cb & cb) { const int64_t n_embd = hparams.n_embd; struct ggml_tensor * inpL; if (batch.token) { - struct ggml_tensor * inp_tokens = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, batch.n_tokens); + struct ggml_tensor * inp_tokens_v = ggml_view_1d(ctx, inp_tokens, batch.n_tokens, 0); cb(inp_tokens, "inp_tokens", -1); - inpL = ggml_get_rows(ctx, tok_embd, inp_tokens); + inpL = ggml_get_rows(ctx, tok_embd, inp_tokens_v); } else { #ifdef GGML_USE_MPI GGML_ASSERT(false && "not implemented"); #endif - inpL = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens); + inpL = ggml_view_2d(ctx, inp_embd, n_embd, batch.n_tokens, inp_embd->nb[1], 0); } return inpL; @@ -4118,6 +4129,7 @@ static void llm_build_k_shift( const llama_cparams & cparams, const llama_kv_cache & kv, struct ggml_cgraph * graph, + struct ggml_tensor * K_shift, llm_rope_type type, int64_t n_ctx, float freq_base, @@ -4134,9 +4146,6 @@ static void llm_build_k_shift( const float beta_fast = cparams.yarn_beta_fast; const float beta_slow = cparams.yarn_beta_slow; - struct ggml_tensor * K_shift = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, n_ctx); - cb(K_shift, "K_shift", -1); - int rope_type = 0; switch (type) { @@ -4457,6 +4466,7 @@ static struct ggml_tensor * llm_build_kv( struct llm_build_context { const llama_model & model; + const llama_context & lctx; const llama_hparams & hparams; const llama_cparams & cparams; const llama_batch & batch; @@ -4503,6 +4513,7 @@ struct llm_build_context { const llm_build_cb & cb, bool worst_case) : model (lctx.model), + lctx (lctx), hparams (model.hparams), cparams (lctx.cparams), batch (batch), @@ -4563,20 +4574,20 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); cb(inpL, "inp_embd", -1); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); cb(inp_pos, "inp_pos", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -4747,20 +4758,20 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); cb(inpL, "inp_embd", -1); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); cb(inp_pos, "inp_pos", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -4868,20 +4879,20 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); cb(inpL, "inp_embd", -1); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); cb(inp_pos, "inp_pos", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -4990,15 +5001,15 @@ struct llm_build_context { struct ggml_tensor * pos; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); cb(inpL, "inp_embd", -1); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); cb(inp_pos, "inp_pos", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos); @@ -5087,19 +5098,19 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); cb(inpL, "inp_embd", -1); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); cb(inp_pos, "inp_pos", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -5294,11 +5305,11 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); cb(inpL, "inp_embd", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); for (int il = 0; il < n_layer; ++il) { @@ -5384,11 +5395,11 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); cb(inpL, "inp_embd", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); inpL = llm_build_norm(ctx0, inpL, hparams, @@ -5477,11 +5488,11 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); cb(inpL, "inp_embd", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); for (int il = 0; il < n_layer; ++il) { @@ -5573,20 +5584,20 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); cb(inpL, "inp_embd", -1); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); cb(inp_pos, "inp_pos", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -5696,20 +5707,20 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); cb(inpL, "inp_embd", -1); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); cb(inp_pos, "inp_pos", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -5810,20 +5821,20 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); cb(inpL, "inp_embd", -1); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); cb(inp_pos, "inp_pos", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -5931,20 +5942,20 @@ struct llm_build_context { struct ggml_tensor * ffn_output; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); cb(inpL, "inp_embd", -1); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); cb(inp_pos, "inp_pos", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE_NEOX, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -6053,20 +6064,20 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); cb(inpL, "inp_embd", -1); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); cb(inp_pos, "inp_pos", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -6160,15 +6171,15 @@ struct llm_build_context { struct ggml_tensor * pos; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); cb(inpL, "inp_embd", -1); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); cb(inp_pos, "inp_pos", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos); @@ -6258,20 +6269,20 @@ struct llm_build_context { struct ggml_tensor * cur; struct ggml_tensor * inpL; - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, cb); + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); cb(inpL, "inp_embd", -1); // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens); + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); cb(inp_pos, "inp_pos", -1); // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, n_kv, n_tokens, 1); + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); cb(KQ_mask, "KQ_mask", -1); // shift the entire K-cache if needed if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); } for (int il = 0; il < n_layer; ++il) { @@ -6365,15 +6376,7 @@ static struct ggml_cgraph * llama_build_graph( // check if we should build the worst-case graph (for memory measurement) const bool worst_case = ggml_tallocr_is_measure(lctx.alloc); - // keep track of the input that has already been allocated - bool alloc_inp_tokens = false; - bool alloc_inp_embd = false; - bool alloc_inp_pos = false; - bool alloc_inp_KQ_mask = false; - bool alloc_inp_K_shift = false; - // this callback allows us to apply custom logic to each tensor (e.g. ggml-alloc, offloading, etc.) - // TODO: improve handling of input and output tensors, then replace this with ggml_set_name llm_build_cb cb = [&](struct ggml_tensor * cur, const char * name, int il) { if (il >= 0) { ggml_format_name(cur, "%s-%d", name, il); @@ -6381,127 +6384,79 @@ static struct ggml_cgraph * llama_build_graph( ggml_set_name(cur, name); } - if (!lctx.cparams.offload_kqv) { if (strcmp(name, "kqv_merged_cont") == 0) { // all nodes between the KV store and the attention output are run on the CPU ggml_backend_sched_set_node_backend(lctx.sched, cur, lctx.backend_cpu); } } - - // - // allocate input tensors and set input data - // - - if (!alloc_inp_tokens && strcmp(name, "inp_tokens") == 0) { - ggml_tallocr_alloc(lctx.alloc, cur); - - if (!ggml_tallocr_is_measure(lctx.alloc) && batch.token) { - const int64_t n_tokens = cur->ne[0]; - - ggml_backend_tensor_set(cur, batch.token, 0, n_tokens*ggml_element_size(cur)); - } - - alloc_inp_tokens = true; - } - - if (!alloc_inp_embd && strcmp(name, "inp_embd") == 0 && batch.embd) { - ggml_tallocr_alloc(lctx.alloc, cur); - - if (!ggml_tallocr_is_measure(lctx.alloc) && batch.embd) { - const int64_t n_embd = cur->ne[0]; - const int64_t n_tokens = cur->ne[1]; - - ggml_backend_tensor_set(cur, batch.embd, 0, n_tokens*n_embd*ggml_element_size(cur)); - } - - alloc_inp_embd = true; - } - - if (!alloc_inp_pos && strcmp(name, "inp_pos") == 0) { - ggml_tallocr_alloc(lctx.alloc, cur); - - if (!ggml_tallocr_is_measure(lctx.alloc) && batch.pos) { - const int64_t n_tokens = cur->ne[0]; - - static_assert(std::is_same::value, "llama_pos must be int32_t"); - ggml_backend_tensor_set(cur, batch.pos, 0, n_tokens*ggml_element_size(cur)); - } - - alloc_inp_pos = true; - } - - if (!alloc_inp_KQ_mask && strcmp(name, "KQ_mask") == 0) { - ggml_tallocr_alloc(lctx.alloc, cur); - - if (!ggml_tallocr_is_measure(lctx.alloc)) { - const int64_t n_kv = cur->ne[0]; - const int64_t n_tokens = cur->ne[1]; - - float * data; - if (ggml_backend_buffer_is_host(cur->buffer)) { - data = (float *) cur->data; - } else { - lctx.buf_copy.resize(ggml_nbytes(cur)); - data = (float *) lctx.buf_copy.data(); - } - - for (int h = 0; h < 1; ++h) { - for (int j = 0; j < n_tokens; ++j) { - const llama_pos pos = batch.pos[j]; - const llama_seq_id seq_id = batch.seq_id[j][0]; - - for (int i = 0; i < n_kv; ++i) { - float f; - if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) { - f = -INFINITY; - } else { - f = 0; - } - data[h*(n_kv*n_tokens) + j*n_kv + i] = f; - } - } - } - - if (data != cur->data) { - ggml_backend_tensor_set(cur, data, 0, ggml_nbytes(cur)); - } - } - - alloc_inp_KQ_mask = true; - } - - if (!alloc_inp_K_shift && strcmp(name, "K_shift") == 0) { - ggml_tallocr_alloc(lctx.alloc, cur); - - if (!ggml_tallocr_is_measure(lctx.alloc)) { - const int64_t n_ctx = cur->ne[0]; - - int32_t * data; - if (ggml_backend_buffer_is_host(cur->buffer)) { - data = (int32_t *) cur->data; - } else { - lctx.buf_copy.resize(ggml_nbytes(cur)); - data = (int32_t *) lctx.buf_copy.data(); - } - - for (int i = 0; i < n_ctx; ++i) { - data[i] = lctx.kv_self.cells[i].delta; - } - - if (data != cur->data) { - ggml_backend_tensor_set(cur, data, 0, ggml_nbytes(cur)); - } - } - - alloc_inp_K_shift = true; - } }; struct ggml_cgraph * result = NULL; struct llm_build_context llm(lctx, batch, cb, worst_case); + // + // set input data + // + + if (!ggml_tallocr_is_measure(lctx.alloc)) { + if (batch.token) { + const int64_t n_tokens = batch.n_tokens; + + ggml_backend_tensor_set(lctx.inp_tokens, batch.token, 0, n_tokens*ggml_element_size(lctx.inp_tokens)); + } + + if (batch.embd) { + const int64_t n_embd = llm.n_embd; + const int64_t n_tokens = batch.n_tokens; + + ggml_backend_tensor_set(lctx.inp_embd, batch.embd, 0, n_tokens*n_embd*ggml_element_size(lctx.inp_embd)); + } + + if (batch.pos) { + const int64_t n_tokens = batch.n_tokens; + + ggml_backend_tensor_set(lctx.inp_pos, batch.pos, 0, n_tokens*ggml_element_size(lctx.inp_pos)); + } + + { + const int64_t n_kv = llm.n_kv; + const int64_t n_tokens = batch.n_tokens; + + GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_KQ_mask->buffer)); + float * data = (float *) lctx.inp_KQ_mask->data; + + for (int h = 0; h < 1; ++h) { + for (int j = 0; j < n_tokens; ++j) { + const llama_pos pos = batch.pos[j]; + const llama_seq_id seq_id = batch.seq_id[j][0]; + + for (int i = 0; i < n_kv; ++i) { + float f; + if (!lctx.kv_self.cells[i].has_seq_id(seq_id) || lctx.kv_self.cells[i].pos > pos) { + f = -INFINITY; + } else { + f = 0; + } + data[h*(n_kv*n_tokens) + j*n_kv + i] = f; + } + } + } + } + + if (llm.do_rope_shift) { + const int64_t n_ctx = llm.n_ctx; + + GGML_ASSERT(ggml_backend_buffer_is_host(lctx.inp_K_shift->buffer)); + int32_t * data = (int32_t *) lctx.inp_K_shift->data; + + for (int i = 0; i < n_ctx; ++i) { + data[i] = lctx.kv_self.cells[i].delta; + } + } + } + llm.init(); switch (model.arch) { @@ -9964,6 +9919,35 @@ struct llama_context * llama_new_context_with_model( ctx->embedding.resize(hparams.n_embd); } + // graph inputs + { + ggml_init_params init_params = { + /* .mem_size */ ggml_tensor_overhead()*5, + /* .mem_buffer */ nullptr, + /* .no_alloc */ true, + }; + ctx->ctx_input = ggml_init(init_params); + + ctx->inp_tokens = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch); + ctx->inp_embd = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, hparams.n_embd, cparams.n_batch); + ctx->inp_pos = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_batch); + ctx->inp_KQ_mask = ggml_new_tensor_2d(ctx->ctx_input, GGML_TYPE_F32, cparams.n_ctx, cparams.n_batch); + ctx->inp_K_shift = ggml_new_tensor_1d(ctx->ctx_input, GGML_TYPE_I32, cparams.n_ctx); + + ggml_set_name(ctx->inp_tokens, "inp_tokens"); + ggml_set_name(ctx->inp_embd, "inp_embd"); + ggml_set_name(ctx->inp_pos, "inp_pos"); + ggml_set_name(ctx->inp_KQ_mask, "inp_KQ_mask"); + ggml_set_name(ctx->inp_K_shift, "inp_K_shift"); + + ctx->buf_input = ggml_backend_alloc_ctx_tensors_from_buft(ctx->ctx_input, llama_default_buffer_type_cpu(true)); + + LLAMA_LOG_INFO("%s: %10s input buffer size = %8.2f MiB\n", __func__, + ggml_backend_buffer_name(ctx->buf_input), + ggml_backend_buffer_get_size(ctx->buf_input) / 1024.0 / 1024.0); + } + + // scheduler and compute buffers { // buffer types used for the compute buffer of each backend std::vector backend_buft; @@ -9990,9 +9974,6 @@ struct llama_context * llama_new_context_with_model( // initialize scheduler with the worst-case graph ggml_backend_sched_init_measure(ctx->sched, gf); - // note: the number of splits during measure is higher than during inference due to the kv shift - int n_splits = ggml_backend_sched_get_n_splits(ctx->sched); - LLAMA_LOG_INFO("%s: graph splits (measure): %d\n", __func__, n_splits); ctx->alloc = ggml_backend_sched_get_tallocr(ctx->sched, ctx->backend_cpu); for (ggml_backend_t backend : ctx->backends) { @@ -10001,6 +9982,10 @@ struct llama_context * llama_new_context_with_model( ggml_backend_buffer_name(buf), ggml_backend_buffer_get_size(buf) / 1024.0 / 1024.0); } + + // note: the number of splits during measure is higher than during inference due to the kv shift + int n_splits = ggml_backend_sched_get_n_splits(ctx->sched); + LLAMA_LOG_INFO("%s: graph splits (measure): %d\n", __func__, n_splits); } } From bf63d695b804b1c995c7ae4427a8a86936ea6d25 Mon Sep 17 00:00:00 2001 From: Michael Hueschen Date: Mon, 22 Jan 2024 03:17:05 -0700 Subject: [PATCH 009/143] nix: add cc to devShell LD_LIBRARY_PATH this fixes the error I encountered when trying to run the convert.py script in a venv: ``` $ nix develop [...]$ source .venv/bin/activate (.venv) [...]$ pip3 install -r requirements.txt <... clipped ...> [...]$ python3 ./convert.py Traceback (most recent call last): File "/home/mhueschen/projects-reference/llama.cpp/./convert.py", line 40, in from sentencepiece import SentencePieceProcessor File "/home/mhueschen/projects-reference/llama.cpp/.venv/lib/python3.11/site-packages/sentencepiece/__init__.py", line 13, in from . import _sentencepiece ImportError: libstdc++.so.6: cannot open shared object file: No such file or directory ``` however, I am not sure this is the cleanest way to address this linker issue... --- .devops/nix/package.nix | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix index c25d99f01..91ddb8890 100644 --- a/.devops/nix/package.nix +++ b/.devops/nix/package.nix @@ -221,10 +221,16 @@ effectiveStdenv.mkDerivation ( ; shell = mkShell { + NIX_LD_LIBRARY_PATH = lib.makeLibraryPath [ + effectiveStdenv.cc.cc + ]; name = "shell-${finalAttrs.finalPackage.name}"; description = "contains numpy and sentencepiece"; buildInputs = [ llama-python ]; inputsFrom = [ finalAttrs.finalPackage ]; + shellHook = '' + export LD_LIBRARY_PATH=$NIX_LD_LIBRARY_PATH + ''; }; shell-extra = mkShell { From c9b316c78fba31e65879a2ec91cbafd341b88cce Mon Sep 17 00:00:00 2001 From: Michael Hueschen Date: Mon, 22 Jan 2024 16:44:10 -0700 Subject: [PATCH 010/143] nix-shell: use addToSearchPath thx to @SomeoneSerge for the suggestion! --- .devops/nix/package.nix | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix index 91ddb8890..a868a9a61 100644 --- a/.devops/nix/package.nix +++ b/.devops/nix/package.nix @@ -221,15 +221,12 @@ effectiveStdenv.mkDerivation ( ; shell = mkShell { - NIX_LD_LIBRARY_PATH = lib.makeLibraryPath [ - effectiveStdenv.cc.cc - ]; name = "shell-${finalAttrs.finalPackage.name}"; description = "contains numpy and sentencepiece"; buildInputs = [ llama-python ]; inputsFrom = [ finalAttrs.finalPackage ]; shellHook = '' - export LD_LIBRARY_PATH=$NIX_LD_LIBRARY_PATH + addToSearchPath "LD_LIBRARY_PATH" "${lib.getLib effectiveStdenv.cc.cc}/lib" ''; }; From cd4fddb29f81d6a1f6d51a0c016bc6b486d68def Mon Sep 17 00:00:00 2001 From: Engininja2 <139037756+Engininja2@users.noreply.github.com> Date: Wed, 24 Jan 2024 16:18:15 -0600 Subject: [PATCH 011/143] cuda : fix 2-bit quants on amd hip (#5105) * cuda : fix 2-bit quants on amd hip * use __low2float intrinsic function for new quants --- ggml-cuda.cu | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 7f460449e..05e5d18ab 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -4283,7 +4283,7 @@ static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1( q8 += 8; aux32 >>= 7; } - const float d = (float)bq2->d * (0.5f + aux32) * (float)bq8_1[ib32].ds.x * 0.25f; + const float d = (float)bq2->d * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.25f; return d * sumi; #else // iqs is 0...15 @@ -4294,7 +4294,7 @@ static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1( const uint8_t * grid1 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+0]); const uint8_t * grid2 = (const uint8_t *)(iq2xxs_grid + aux8[2*il+1]); const uint32_t aux32 = q2[2] | (q2[3] << 16); - const float d = (float)bq2->d * (0.5f + (aux32 >> 28)) * (float)bq8_1[ib32].ds.x * 0.25f; + const float d = (float)bq2->d * (0.5f + (aux32 >> 28)) * __low2float(bq8_1[ib32].ds) * 0.25f; const uint8_t signs1 = ksigns_iq2xs[(aux32 >> 14*il) & 127]; const uint8_t signs2 = ksigns_iq2xs[(aux32 >> (14*il + 7)) & 127]; const int8_t * q8 = bq8_1[ib32].qs + 16*il; @@ -4339,7 +4339,7 @@ static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1( } q8 += 8; } - const float d = (float)bq2->d * (float)bq8_1[ib32].ds.x * 0.25f; + const float d = (float)bq2->d * __low2float(bq8_1[ib32].ds) * 0.25f; return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2); #else assert(false); From ddc5a5033f948dc7ab0a3a6ec2d914d13c274077 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 25 Jan 2024 11:26:17 +0200 Subject: [PATCH 012/143] metal : show compile log messages --- ggml-metal.m | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 4b3eb4914..60fef1a19 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -277,6 +277,10 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { NSURL * libURL = [NSURL fileURLWithPath:libPath]; GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [libPath UTF8String]); ctx->library = [ctx->device newLibraryWithURL:libURL error:&error]; + if (error) { + GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]); + return NULL; + } } else { GGML_METAL_LOG_INFO("%s: default.metallib not found, loading from source\n", __func__); @@ -315,13 +319,12 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { //[options setFastMathEnabled:false]; ctx->library = [ctx->device newLibraryWithSource:src options:options error:&error]; + if (error) { + GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]); + return NULL; + } } } - - if (error) { - GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]); - return NULL; - } } // print MTL GPU family: From faa3526a1eba458120987ed8269e5616385a76f4 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Thu, 25 Jan 2024 17:58:53 +0200 Subject: [PATCH 013/143] Fix Q3_K_XS for MoE models (#5113) Co-authored-by: Iwan Kawrakow --- llama.cpp | 45 +++++++++++++++++++++++++-------------------- 1 file changed, 25 insertions(+), 20 deletions(-) diff --git a/llama.cpp b/llama.cpp index 114046db9..6a7506e85 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8829,6 +8829,23 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty 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; }; + const int n_expert = std::max(1, (int)qs.model.hparams.n_expert); + auto layer_info = [n_expert] (int i_layer, int n_layer, const char * name) { + if (n_expert > 1) { + // Believe it or not, "experts" in the FFN of Mixtral-8x7B are not consecutive, but iccasionally randomly + // sprinkled in the model. Hence, simply dividing i_ffn_down by n_expert does not work + // for getting the current layer as I initially thought, and we need to resort to parsing the + // tensor name. + n_layer /= n_expert; + if (sscanf(name, "blk.%d.", &i_layer) != 1) { + throw std::runtime_error(format("Failed to determine layer for tensor %s", name)); + } + if (i_layer < 0 || i_layer >= n_layer) { + throw std::runtime_error(format("Bad layer %d for tensor %s. Must be in [0, %d)", i_layer, name, n_layer)); + } + } + return std::make_pair(i_layer, n_layer); + }; if (name == tn(LLM_TENSOR_OUTPUT, "weight")) { int nx = tensor->ne[0]; @@ -8890,24 +8907,8 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty new_type = GGML_TYPE_Q2_K; } } else if (name.find("ffn_down") != std::string::npos) { - const int n_expert = std::max(1, (int)qs.model.hparams.n_expert); - int i_layer, n_layer; - if (n_expert == 1) { - i_layer = qs.i_ffn_down; - n_layer = qs.n_ffn_down; - } else { - // Believe it or not, "experts" in the FFN of Mixtral-8x7B are not consecutive, but iccasionally randomly - // sprinkled in the model. Hence, simply dividing i_ffn_down by n_expert does not work - // for getting the current layer as I initially thought, and we need to resort to parsing the - // tensor name. - n_layer = qs.n_ffn_down / n_expert; - if (sscanf(name.c_str(), "blk.%d.ffn_down", &i_layer) != 1) { - throw std::runtime_error(format("Failed to determine layer for tensor %s", name.c_str())); - } - if (i_layer < 0 || i_layer >= n_layer) { - throw std::runtime_error(format("Bad layer %d for tensor %s. Must be in [0, %d)", i_layer, name.c_str(), n_layer)); - } - } + auto info = layer_info(qs.i_ffn_down, qs.n_ffn_down, name.c_str()); + int i_layer = info.first, n_layer = info.second; if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) new_type = GGML_TYPE_Q3_K; else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS) { if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K; @@ -8963,13 +8964,17 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty else if (ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) new_type = GGML_TYPE_Q6_K; } else if (name.find("ffn_gate") != std::string::npos) { - if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS && !use_more_bits(qs.i_ffn_gate, qs.n_ffn_gate)) { + auto info = layer_info(qs.i_ffn_gate, qs.n_ffn_gate, name.c_str()); + int i_layer = info.first, n_layer = info.second; + if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS && !use_more_bits(i_layer, n_layer)) { new_type = GGML_TYPE_Q2_K; } ++qs.i_ffn_gate; } else if (name.find("ffn_up") != std::string::npos) { - if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS && !use_more_bits(qs.i_ffn_up, qs.n_ffn_up)) { + auto info = layer_info(qs.i_ffn_up, qs.n_ffn_up, name.c_str()); + int i_layer = info.first, n_layer = info.second; + if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS && !use_more_bits(i_layer, n_layer)) { new_type = GGML_TYPE_Q2_K; } ++qs.i_ffn_up; From 256d1bb0ddce6a0a21f5a7503019bdd5c1933cba Mon Sep 17 00:00:00 2001 From: Valentin Konovalov Date: Thu, 25 Jan 2024 12:05:51 -0500 Subject: [PATCH 014/143] android : use release cmake build type by default (#5123) --- examples/llama.android/app/build.gradle.kts | 1 + 1 file changed, 1 insertion(+) diff --git a/examples/llama.android/app/build.gradle.kts b/examples/llama.android/app/build.gradle.kts index 7815a8025..aadbe22c9 100644 --- a/examples/llama.android/app/build.gradle.kts +++ b/examples/llama.android/app/build.gradle.kts @@ -30,6 +30,7 @@ android { } externalNativeBuild { cmake { + arguments += "-DCMAKE_BUILD_TYPE=Release" cppFlags += listOf() arguments += listOf() } From d292f4f2047963f558dd516f1baaa71793e9acf2 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Thu, 25 Jan 2024 14:51:24 -0500 Subject: [PATCH 015/143] examples : make pydantic scripts pass mypy and support py3.8 (#5099) --- .../pydantic-models-to-grammar-examples.py | 43 +---- examples/pydantic_models_to_grammar.py | 166 +++++++++--------- 2 files changed, 88 insertions(+), 121 deletions(-) diff --git a/examples/pydantic-models-to-grammar-examples.py b/examples/pydantic-models-to-grammar-examples.py index cbf376652..160966649 100644 --- a/examples/pydantic-models-to-grammar-examples.py +++ b/examples/pydantic-models-to-grammar-examples.py @@ -1,14 +1,14 @@ # Function calling example using pydantic models. import datetime +import importlib import json from enum import Enum -from typing import Union, Optional +from typing import Optional, Union import requests from pydantic import BaseModel, Field - -import importlib -from pydantic_models_to_grammar import generate_gbnf_grammar_and_documentation, convert_dictionary_to_pydantic_model, add_run_method_to_dynamic_model, create_dynamic_model_from_function +from pydantic_models_to_grammar import (add_run_method_to_dynamic_model, convert_dictionary_to_pydantic_model, + create_dynamic_model_from_function, generate_gbnf_grammar_and_documentation) # Function to get completion on the llama.cpp server with grammar. @@ -35,7 +35,7 @@ class SendMessageToUser(BaseModel): print(self.message) -# Enum for the calculator function. +# Enum for the calculator tool. class MathOperation(Enum): ADD = "add" SUBTRACT = "subtract" @@ -43,7 +43,7 @@ class MathOperation(Enum): DIVIDE = "divide" -# Very simple calculator tool for the agent. +# Simple pydantic calculator tool for the agent that can add, subtract, multiply, and divide. Docstring and description of fields will be used in system prompt. class Calculator(BaseModel): """ Perform a math operation on two numbers. @@ -148,37 +148,6 @@ def get_current_datetime(output_format: Optional[str] = None): return datetime.datetime.now().strftime(output_format) -# Enum for the calculator tool. -class MathOperation(Enum): - ADD = "add" - SUBTRACT = "subtract" - MULTIPLY = "multiply" - DIVIDE = "divide" - - - -# Simple pydantic calculator tool for the agent that can add, subtract, multiply, and divide. Docstring and description of fields will be used in system prompt. -class Calculator(BaseModel): - """ - Perform a math operation on two numbers. - """ - number_one: Union[int, float] = Field(..., description="First number.") - operation: MathOperation = Field(..., description="Math operation to perform.") - number_two: Union[int, float] = Field(..., description="Second number.") - - def run(self): - if self.operation == MathOperation.ADD: - return self.number_one + self.number_two - elif self.operation == MathOperation.SUBTRACT: - return self.number_one - self.number_two - elif self.operation == MathOperation.MULTIPLY: - return self.number_one * self.number_two - elif self.operation == MathOperation.DIVIDE: - return self.number_one / self.number_two - else: - raise ValueError("Unknown operation.") - - # Example function to get the weather def get_current_weather(location, unit): """Get the current weather in a given location""" diff --git a/examples/pydantic_models_to_grammar.py b/examples/pydantic_models_to_grammar.py index 848c1c367..9acc7cc6d 100644 --- a/examples/pydantic_models_to_grammar.py +++ b/examples/pydantic_models_to_grammar.py @@ -1,15 +1,21 @@ +from __future__ import annotations + import inspect import json +import re from copy import copy -from inspect import isclass, getdoc -from types import NoneType +from enum import Enum +from inspect import getdoc, isclass +from typing import TYPE_CHECKING, Any, Callable, List, Optional, Union, get_args, get_origin, get_type_hints from docstring_parser import parse -from pydantic import BaseModel, create_model, Field -from typing import Any, Type, List, get_args, get_origin, Tuple, Union, Optional, _GenericAlias -from enum import Enum -from typing import get_type_hints, Callable -import re +from pydantic import BaseModel, Field, create_model + +if TYPE_CHECKING: + from types import GenericAlias +else: + # python 3.8 compat + from typing import _GenericAlias as GenericAlias class PydanticDataType(Enum): @@ -43,7 +49,7 @@ class PydanticDataType(Enum): SET = "set" -def map_pydantic_type_to_gbnf(pydantic_type: Type[Any]) -> str: +def map_pydantic_type_to_gbnf(pydantic_type: type[Any]) -> str: if isclass(pydantic_type) and issubclass(pydantic_type, str): return PydanticDataType.STRING.value elif isclass(pydantic_type) and issubclass(pydantic_type, bool): @@ -57,22 +63,22 @@ def map_pydantic_type_to_gbnf(pydantic_type: Type[Any]) -> str: elif isclass(pydantic_type) and issubclass(pydantic_type, BaseModel): return format_model_and_field_name(pydantic_type.__name__) - elif get_origin(pydantic_type) == list: + elif get_origin(pydantic_type) is list: element_type = get_args(pydantic_type)[0] return f"{map_pydantic_type_to_gbnf(element_type)}-list" - elif get_origin(pydantic_type) == set: + elif get_origin(pydantic_type) is set: element_type = get_args(pydantic_type)[0] return f"{map_pydantic_type_to_gbnf(element_type)}-set" - elif get_origin(pydantic_type) == Union: + elif get_origin(pydantic_type) is Union: union_types = get_args(pydantic_type) union_rules = [map_pydantic_type_to_gbnf(ut) for ut in union_types] return f"union-{'-or-'.join(union_rules)}" - elif get_origin(pydantic_type) == Optional: + elif get_origin(pydantic_type) is Optional: element_type = get_args(pydantic_type)[0] return f"optional-{map_pydantic_type_to_gbnf(element_type)}" elif isclass(pydantic_type): return f"{PydanticDataType.CUSTOM_CLASS.value}-{format_model_and_field_name(pydantic_type.__name__)}" - elif get_origin(pydantic_type) == dict: + elif get_origin(pydantic_type) is dict: key_type, value_type = get_args(pydantic_type) return f"custom-dict-key-type-{format_model_and_field_name(map_pydantic_type_to_gbnf(key_type))}-value-type-{format_model_and_field_name(map_pydantic_type_to_gbnf(value_type))}" else: @@ -106,7 +112,6 @@ def get_members_structure(cls, rule_name): return f"{cls.__name__.lower()} ::= " + " | ".join(members) if cls.__annotations__ and cls.__annotations__ != {}: result = f'{rule_name} ::= "{{"' - type_list_rules = [] # Modify this comprehension members = [ f' "\\"{name}\\"" ":" {map_pydantic_type_to_gbnf(param_type)}' @@ -116,27 +121,25 @@ def get_members_structure(cls, rule_name): result += '"," '.join(members) result += ' "}"' - return result, type_list_rules - elif rule_name == "custom-class-any": + return result + if rule_name == "custom-class-any": result = f"{rule_name} ::= " result += "value" - type_list_rules = [] - return result, type_list_rules - else: - init_signature = inspect.signature(cls.__init__) - parameters = init_signature.parameters - result = f'{rule_name} ::= "{{"' - type_list_rules = [] - # Modify this comprehension too - members = [ - f' "\\"{name}\\"" ":" {map_pydantic_type_to_gbnf(param.annotation)}' - for name, param in parameters.items() - if name != "self" and param.annotation != inspect.Parameter.empty - ] + return result - result += '", "'.join(members) - result += ' "}"' - return result, type_list_rules + init_signature = inspect.signature(cls.__init__) + parameters = init_signature.parameters + result = f'{rule_name} ::= "{{"' + # Modify this comprehension too + members = [ + f' "\\"{name}\\"" ":" {map_pydantic_type_to_gbnf(param.annotation)}' + for name, param in parameters.items() + if name != "self" and param.annotation != inspect.Parameter.empty + ] + + result += '", "'.join(members) + result += ' "}"' + return result def regex_to_gbnf(regex_pattern: str) -> str: @@ -269,7 +272,7 @@ def generate_gbnf_float_rules(max_digit=None, min_digit=None, max_precision=None def generate_gbnf_rule_for_type( model_name, field_name, field_type, is_optional, processed_models, created_rules, field_info=None -) -> Tuple[str, list]: +) -> tuple[str, list[str]]: """ Generate GBNF rule for a given field type. @@ -283,7 +286,7 @@ def generate_gbnf_rule_for_type( :param field_info: Additional information about the field (optional). :return: Tuple containing the GBNF type and a list of additional rules. - :rtype: Tuple[str, list] + :rtype: tuple[str, list] """ rules = [] @@ -321,8 +324,7 @@ def generate_gbnf_rule_for_type( gbnf_type, rules = model_name + "-" + field_name, rules elif gbnf_type.startswith("custom-class-"): - nested_model_rules, field_types = get_members_structure(field_type, gbnf_type) - rules.append(nested_model_rules) + rules.append(get_members_structure(field_type, gbnf_type)) elif gbnf_type.startswith("custom-dict-"): key_type, value_type = get_args(field_type) @@ -341,14 +343,14 @@ def generate_gbnf_rule_for_type( union_rules = [] for union_type in union_types: - if isinstance(union_type, _GenericAlias): + if isinstance(union_type, GenericAlias): union_gbnf_type, union_rules_list = generate_gbnf_rule_for_type( model_name, field_name, union_type, False, processed_models, created_rules ) union_rules.append(union_gbnf_type) rules.extend(union_rules_list) - elif not issubclass(union_type, NoneType): + elif not issubclass(union_type, type(None)): union_gbnf_type, union_rules_list = generate_gbnf_rule_for_type( model_name, field_name, union_type, False, processed_models, created_rules ) @@ -424,14 +426,10 @@ def generate_gbnf_rule_for_type( else: gbnf_type, rules = gbnf_type, [] - if gbnf_type not in created_rules: - return gbnf_type, rules - else: - if gbnf_type in created_rules: - return gbnf_type, rules + return gbnf_type, rules -def generate_gbnf_grammar(model: Type[BaseModel], processed_models: set, created_rules: dict) -> (list, bool, bool): +def generate_gbnf_grammar(model: type[BaseModel], processed_models: set[type[BaseModel]], created_rules: dict[str, list[str]]) -> tuple[list[str], bool]: """ Generate GBnF Grammar @@ -452,7 +450,7 @@ def generate_gbnf_grammar(model: Type[BaseModel], processed_models: set, created ``` """ if model in processed_models: - return [] + return [], False processed_models.add(model) model_name = format_model_and_field_name(model.__name__) @@ -518,7 +516,7 @@ def generate_gbnf_grammar(model: Type[BaseModel], processed_models: set, created def generate_gbnf_grammar_from_pydantic_models( - models: List[Type[BaseModel]], outer_object_name: str = None, outer_object_content: str = None, + models: list[type[BaseModel]], outer_object_name: str | None = None, outer_object_content: str | None = None, list_of_outputs: bool = False ) -> str: """ @@ -528,7 +526,7 @@ def generate_gbnf_grammar_from_pydantic_models( * grammar. Args: - models (List[Type[BaseModel]]): A list of Pydantic models to generate the grammar from. + models (list[type[BaseModel]]): A list of Pydantic models to generate the grammar from. outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. list_of_outputs (str, optional): Allows a list of output objects @@ -543,9 +541,9 @@ def generate_gbnf_grammar_from_pydantic_models( # root ::= UserModel | PostModel # ... """ - processed_models = set() + processed_models: set[type[BaseModel]] = set() all_rules = [] - created_rules = {} + created_rules: dict[str, list[str]] = {} if outer_object_name is None: for model in models: model_rules, _ = generate_gbnf_grammar(model, processed_models, created_rules) @@ -608,7 +606,7 @@ def get_primitive_grammar(grammar): Returns: str: GBNF primitive grammar string. """ - type_list = [] + type_list: list[type[object]] = [] if "string-list" in grammar: type_list.append(str) if "boolean-list" in grammar: @@ -666,14 +664,14 @@ triple-quotes ::= "'''" """ def generate_markdown_documentation( - pydantic_models: List[Type[BaseModel]], model_prefix="Model", fields_prefix="Fields", + pydantic_models: list[type[BaseModel]], model_prefix="Model", fields_prefix="Fields", documentation_with_field_description=True ) -> str: """ Generate markdown documentation for a list of Pydantic models. Args: - pydantic_models (List[Type[BaseModel]]): List of Pydantic model classes. + pydantic_models (list[type[BaseModel]]): list of Pydantic model classes. model_prefix (str): Prefix for the model section. fields_prefix (str): Prefix for the fields section. documentation_with_field_description (bool): Include field descriptions in the documentation. @@ -731,7 +729,7 @@ def generate_markdown_documentation( def generate_field_markdown( - field_name: str, field_type: Type[Any], model: Type[BaseModel], depth=1, + field_name: str, field_type: type[Any], model: type[BaseModel], depth=1, documentation_with_field_description=True ) -> str: """ @@ -739,8 +737,8 @@ def generate_field_markdown( Args: field_name (str): Name of the field. - field_type (Type[Any]): Type of the field. - model (Type[BaseModel]): Pydantic model class. + field_type (type[Any]): Type of the field. + model (type[BaseModel]): Pydantic model class. depth (int): Indentation depth in the documentation. documentation_with_field_description (bool): Include field descriptions in the documentation. @@ -798,7 +796,7 @@ def generate_field_markdown( return field_text -def format_json_example(example: dict, depth: int) -> str: +def format_json_example(example: dict[str, Any], depth: int) -> str: """ Format a JSON example into a readable string with indentation. @@ -819,14 +817,14 @@ def format_json_example(example: dict, depth: int) -> str: def generate_text_documentation( - pydantic_models: List[Type[BaseModel]], model_prefix="Model", fields_prefix="Fields", + pydantic_models: list[type[BaseModel]], model_prefix="Model", fields_prefix="Fields", documentation_with_field_description=True ) -> str: """ Generate text documentation for a list of Pydantic models. Args: - pydantic_models (List[Type[BaseModel]]): List of Pydantic model classes. + pydantic_models (list[type[BaseModel]]): List of Pydantic model classes. model_prefix (str): Prefix for the model section. fields_prefix (str): Prefix for the fields section. documentation_with_field_description (bool): Include field descriptions in the documentation. @@ -885,7 +883,7 @@ def generate_text_documentation( def generate_field_text( - field_name: str, field_type: Type[Any], model: Type[BaseModel], depth=1, + field_name: str, field_type: type[Any], model: type[BaseModel], depth=1, documentation_with_field_description=True ) -> str: """ @@ -893,8 +891,8 @@ def generate_field_text( Args: field_name (str): Name of the field. - field_type (Type[Any]): Type of the field. - model (Type[BaseModel]): Pydantic model class. + field_type (type[Any]): Type of the field. + model (type[BaseModel]): Pydantic model class. depth (int): Indentation depth in the documentation. documentation_with_field_description (bool): Include field descriptions in the documentation. @@ -1017,8 +1015,8 @@ def generate_and_save_gbnf_grammar_and_documentation( pydantic_model_list, grammar_file_path="./generated_grammar.gbnf", documentation_file_path="./generated_grammar_documentation.md", - outer_object_name: str = None, - outer_object_content: str = None, + outer_object_name: str | None = None, + outer_object_content: str | None = None, model_prefix: str = "Output Model", fields_prefix: str = "Output Fields", list_of_outputs: bool = False, @@ -1053,8 +1051,8 @@ def generate_and_save_gbnf_grammar_and_documentation( def generate_gbnf_grammar_and_documentation( pydantic_model_list, - outer_object_name: str = None, - outer_object_content: str = None, + outer_object_name: str | None = None, + outer_object_content: str | None = None, model_prefix: str = "Output Model", fields_prefix: str = "Output Fields", list_of_outputs: bool = False, @@ -1086,9 +1084,9 @@ def generate_gbnf_grammar_and_documentation( def generate_gbnf_grammar_and_documentation_from_dictionaries( - dictionaries: List[dict], - outer_object_name: str = None, - outer_object_content: str = None, + dictionaries: list[dict[str, Any]], + outer_object_name: str | None = None, + outer_object_content: str | None = None, model_prefix: str = "Output Model", fields_prefix: str = "Output Fields", list_of_outputs: bool = False, @@ -1098,7 +1096,7 @@ def generate_gbnf_grammar_and_documentation_from_dictionaries( Generate GBNF grammar and documentation from a list of dictionaries. Args: - dictionaries (List[dict]): List of dictionaries representing Pydantic models. + dictionaries (list[dict]): List of dictionaries representing Pydantic models. outer_object_name (str): Outer object name for the GBNF grammar. If None, no outer object will be generated. Eg. "function" for function calling. outer_object_content (str): Content for the outer rule in the GBNF grammar. Eg. "function_parameters" or "params" for function calling. model_prefix (str): Prefix for the model section in the documentation. @@ -1120,7 +1118,7 @@ def generate_gbnf_grammar_and_documentation_from_dictionaries( return grammar, documentation -def create_dynamic_model_from_function(func: Callable): +def create_dynamic_model_from_function(func: Callable[..., Any]): """ Creates a dynamic Pydantic model from a given function's type hints and adds the function as a 'run' method. @@ -1135,6 +1133,7 @@ def create_dynamic_model_from_function(func: Callable): sig = inspect.signature(func) # Parse the docstring + assert func.__doc__ is not None docstring = parse(func.__doc__) dynamic_fields = {} @@ -1157,7 +1156,6 @@ def create_dynamic_model_from_function(func: Callable): f"Parameter '{param.name}' in function '{func.__name__}' lacks a description in the docstring") # Add parameter details to the schema - param_doc = next((d for d in docstring.params if d.arg_name == param.name), None) param_docs.append((param.name, param_doc)) if param.default == inspect.Parameter.empty: default_value = ... @@ -1166,10 +1164,10 @@ def create_dynamic_model_from_function(func: Callable): dynamic_fields[param.name] = ( param.annotation if param.annotation != inspect.Parameter.empty else str, default_value) # Creating the dynamic model - dynamic_model = create_model(f"{func.__name__}", **dynamic_fields) + dynamic_model = create_model(f"{func.__name__}", **dynamic_fields) # type: ignore[call-overload] - for param_doc in param_docs: - dynamic_model.model_fields[param_doc[0]].description = param_doc[1].description + for name, param_doc in param_docs: + dynamic_model.model_fields[name].description = param_doc.description dynamic_model.__doc__ = docstring.short_description @@ -1182,16 +1180,16 @@ def create_dynamic_model_from_function(func: Callable): return dynamic_model -def add_run_method_to_dynamic_model(model: Type[BaseModel], func: Callable): +def add_run_method_to_dynamic_model(model: type[BaseModel], func: Callable[..., Any]): """ Add a 'run' method to a dynamic Pydantic model, using the provided function. Args: - model (Type[BaseModel]): Dynamic Pydantic model class. + model (type[BaseModel]): Dynamic Pydantic model class. func (Callable): Function to be added as a 'run' method to the model. Returns: - Type[BaseModel]: Pydantic model class with the added 'run' method. + type[BaseModel]: Pydantic model class with the added 'run' method. """ def run_method_wrapper(self): @@ -1204,15 +1202,15 @@ def add_run_method_to_dynamic_model(model: Type[BaseModel], func: Callable): return model -def create_dynamic_models_from_dictionaries(dictionaries: List[dict]): +def create_dynamic_models_from_dictionaries(dictionaries: list[dict[str, Any]]): """ Create a list of dynamic Pydantic model classes from a list of dictionaries. Args: - dictionaries (List[dict]): List of dictionaries representing model structures. + dictionaries (list[dict]): List of dictionaries representing model structures. Returns: - List[Type[BaseModel]]: List of generated dynamic Pydantic model classes. + list[type[BaseModel]]: List of generated dynamic Pydantic model classes. """ dynamic_models = [] for func in dictionaries: @@ -1249,7 +1247,7 @@ def list_to_enum(enum_name, values): return Enum(enum_name, {value: value for value in values}) -def convert_dictionary_to_pydantic_model(dictionary: dict, model_name: str = "CustomModel") -> Type[BaseModel]: +def convert_dictionary_to_pydantic_model(dictionary: dict[str, Any], model_name: str = "CustomModel") -> type[Any]: """ Convert a dictionary to a Pydantic model class. @@ -1258,9 +1256,9 @@ def convert_dictionary_to_pydantic_model(dictionary: dict, model_name: str = "Cu model_name (str): Name of the generated Pydantic model. Returns: - Type[BaseModel]: Generated Pydantic model class. + type[BaseModel]: Generated Pydantic model class. """ - fields = {} + fields: dict[str, Any] = {} if "properties" in dictionary: for field_name, field_data in dictionary.get("properties", {}).items(): @@ -1277,7 +1275,7 @@ def convert_dictionary_to_pydantic_model(dictionary: dict, model_name: str = "Cu if items != {}: array = {"properties": items} array_type = convert_dictionary_to_pydantic_model(array, f"{model_name}_{field_name}_items") - fields[field_name] = (List[array_type], ...) + fields[field_name] = (List[array_type], ...) # type: ignore[valid-type] else: fields[field_name] = (list, ...) elif field_type == "object": From 5eaf9964fc797d4585c214db32a463d557f3ed33 Mon Sep 17 00:00:00 2001 From: l3utterfly Date: Fri, 26 Jan 2024 05:06:22 +0900 Subject: [PATCH 016/143] llama : dynamic temperature sampling (#4972) * implemented dynamic temperature sampling from koboldcpp * removed trailing whitespace * removed unused temp parameter in llama_sample_entropy * exposed exponent_val in dynamic temp sampler * added debug check for printf statements * use nullptr in llama_sample_softmax call during llama_sample_entropy this avoids counting the time taken stats twice Co-authored-by: Georgi Gerganov * return earlier if there is only 1 candiate (i.e. max_entropy == 0) * reformat 't' case in llama_sample_queue Co-authored-by: Jared Van Bortel * check for one or zero candidates case in llama_sample_entropy --------- Co-authored-by: Georgi Gerganov Co-authored-by: Jared Van Bortel --- common/sampling.cpp | 12 +++++++- common/sampling.h | 2 ++ llama.cpp | 67 +++++++++++++++++++++++++++++++++++++++++++++ llama.h | 8 ++++++ 4 files changed, 88 insertions(+), 1 deletion(-) diff --git a/common/sampling.cpp b/common/sampling.cpp index dd1ffeb1b..efd7eab6e 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -129,6 +129,8 @@ static void sampler_queue( const int n_vocab = llama_n_vocab(llama_get_model(ctx_main)); const float temp = params.temp; + const float dynatemp_range = params.dynatemp_range; + const float dynatemp_exponent = params.dynatemp_exponent; const int32_t top_k = params.top_k <= 0 ? n_vocab : params.top_k; const float top_p = params.top_p; const float min_p = params.min_p; @@ -143,7 +145,15 @@ static void sampler_queue( case 'y': llama_sample_typical (ctx_main, &cur_p, typical_p, min_keep); break; case 'p': llama_sample_top_p (ctx_main, &cur_p, top_p, min_keep); break; case 'm': llama_sample_min_p (ctx_main, &cur_p, min_p, min_keep); break; - case 't': llama_sample_temp (ctx_main, &cur_p, temp); break; + case 't': + if (dynatemp_range > 0) { + float dynatemp_min = std::max(0.0f, temp - dynatemp_range); + float dynatemp_max = std::max(0.0f, temp + dynatemp_range); + llama_sample_entropy(ctx_main, &cur_p, dynatemp_min, dynatemp_max, dynatemp_exponent); + } else { + llama_sample_temp(ctx_main, &cur_p, temp); + } + break; default : break; } } diff --git a/common/sampling.h b/common/sampling.h index 2ee180376..88899c094 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -18,6 +18,8 @@ typedef struct llama_sampling_params { float tfs_z = 1.00f; // 1.0 = disabled float typical_p = 1.00f; // 1.0 = disabled float temp = 0.80f; // <= 0.0 to sample greedily, 0.0 to not output probabilities + float dynatemp_range = 0.00f; // 0.0 = disabled + float dynatemp_exponent = 1.00f; // controls how entropy maps to temperature in dynamic temperature sampler int32_t penalty_last_n = 64; // last n tokens to penalize (0 = disable penalty, -1 = context size) float penalty_repeat = 1.10f; // 1.0 = disabled float penalty_freq = 0.00f; // 0.0 = disabled diff --git a/llama.cpp b/llama.cpp index 6a7506e85..823d42d7f 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8151,6 +8151,73 @@ void llama_sample_typical(struct llama_context * ctx, llama_token_data_array * c } } +void llama_sample_entropy(struct llama_context * ctx, llama_token_data_array * candidates_p, float min_temp, float max_temp, float exponent_val) { + const int64_t t_start_sample_us = ggml_time_us(); + + // no need to do anything if there is only one (or zero) candidates + if(candidates_p->size <= 1) { + return; + } + + // Calculate maximum possible entropy + float max_entropy = -logf(1.0f / candidates_p->size); + + llama_sample_softmax(nullptr, candidates_p); + + // Calculate entropy of the softmax probabilities + float entropy = 0.0f; + for (size_t i = 0; i < candidates_p->size; ++i) { + float prob = candidates_p->data[i].p; + if (prob > 0.0f) { // Ensure no log(0) + entropy -= prob * logf(prob); + } + } + + // Normalize the entropy (max_entropy cannot be 0 here because we checked candidates_p->size != 1 above) + float normalized_entropy = entropy / max_entropy; + + // Map the normalized entropy to the desired temperature range using the power function + float dyn_temp = min_temp + (max_temp - min_temp) * powf(normalized_entropy, exponent_val); + +#ifdef DEBUG + LLAMA_LOG_INFO("Your text maxtemp value is: %f\n", max_temp); + LLAMA_LOG_INFO("Entropy: %f\n", entropy); + LLAMA_LOG_INFO("Max Possible Entropy: %f\n", max_entropy); + LLAMA_LOG_INFO("Normalized Entropy: %f\n", normalized_entropy); + LLAMA_LOG_INFO("Exponent: %f\n", exponent_val); + LLAMA_LOG_INFO("Dynamic Temperature (dyn_temp): %f\n", dyn_temp); +#endif + + // Apply the dynamically calculated temperature scaling + for (size_t i = 0; i < candidates_p->size; ++i) { + candidates_p->data[i].logit /= dyn_temp; + } + + // Re-compute softmax probabilities after scaling logits with dynamic temperature + double max_l_double = candidates_p->data[0].logit; + double cum_sum_double = 0.0; + for (size_t i = 0; i < candidates_p->size; ++i) { + double p = exp(candidates_p->data[i].logit - max_l_double); + candidates_p->data[i].p = p; // Store the scaled probability + cum_sum_double += p; + } + for (size_t i = 0; i < candidates_p->size; ++i) { + candidates_p->data[i].p /= cum_sum_double; // Re-normalize the probabilities + } + +#ifdef DEBUG + // Print the updated top 25 probabilities after temperature scaling + LLAMA_LOG_INFO("\nUpdated Top 25 Probabilities After Dynamic Temperature Scaling (in percentages):\n"); + for (size_t i = 0; i < 25 && i < candidates_p->size; ++i) { + LLAMA_LOG_INFO("Token %zu: %f%%\n", i + 1, candidates_p->data[i].p * 100.0f); + } +#endif + + if (ctx) { + ctx->t_sample_us += ggml_time_us() - t_start_sample_us; + } +} + void llama_sample_temp(struct llama_context * ctx, llama_token_data_array * candidates_p, float temp) { const int64_t t_start_sample_us = ggml_time_us(); diff --git a/llama.h b/llama.h index bb6054557..7b3634aa6 100644 --- a/llama.h +++ b/llama.h @@ -775,6 +775,14 @@ extern "C" { float p, size_t min_keep); + /// @details Dynamic temperature implementation described in the paper https://arxiv.org/abs/2309.02772. + LLAMA_API void llama_sample_entropy( + struct llama_context * ctx, + llama_token_data_array * candidates_p, + float min_temp, + float max_temp, + float exponent_val); + LLAMA_API void llama_sample_temp( struct llama_context * ctx, llama_token_data_array * candidates, From fe54033b69b83164cabb5f3ed92dc0ff7ea47605 Mon Sep 17 00:00:00 2001 From: XiaotaoChen Date: Fri, 26 Jan 2024 04:14:32 +0800 Subject: [PATCH 017/143] readme : add MobileVLM 1.7B/3B to the supported models list (#5107) Co-authored-by: Chenxiaotao03 --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index cbfba01bc..c2a33022c 100644 --- a/README.md +++ b/README.md @@ -112,6 +112,7 @@ as the main playground for developing new features for the [ggml](https://github - [x] [Bakllava](https://huggingface.co/models?search=SkunkworksAI/Bakllava) - [x] [Obsidian](https://huggingface.co/NousResearch/Obsidian-3B-V0.5) - [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V) +- [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM) **Bindings:** From 1182cf4d4f6ee383b92695c2e3fe438086dcdba7 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Fri, 26 Jan 2024 09:14:39 +0200 Subject: [PATCH 018/143] Another bucket sort (#5109) * Initial bucket sort * Bucket sort: slightly better version * Bucket sort: another minor improvement --------- Co-authored-by: Iwan Kawrakow --- llama.cpp | 53 ++++++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 50 insertions(+), 3 deletions(-) diff --git a/llama.cpp b/llama.cpp index 823d42d7f..b03b67e16 100644 --- a/llama.cpp +++ b/llama.cpp @@ -7956,10 +7956,57 @@ void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * can auto comp = [](const llama_token_data & a, const llama_token_data & b) { return a.logit > b.logit; }; - if (k == (int) candidates->size) { - std::sort(candidates->data, candidates->data + candidates->size, comp); - } else { + if (k <= 128) { std::partial_sort(candidates->data, candidates->data + k, candidates->data + candidates->size, comp); + } else { + constexpr int nbuckets = 128; + constexpr float bucket_low = -10.0f; + constexpr float bucket_high = 10.0f; + constexpr float bucket_scale = nbuckets/(bucket_high - bucket_low); + constexpr float bucker_inter = -bucket_low * bucket_scale; + + std::vector bucket_idx(candidates->size); + std::vector histo(nbuckets, 0); + + for (int i = 0; i < (int)candidates->size; ++i) { + const float val = candidates->data[i].logit; + int ib = int(bucket_scale * val + bucker_inter); //nbuckets * (val - bucket_low) / (bucket_high - bucket_low); + ib = std::max(0, std::min(nbuckets-1, ib)); + bucket_idx[i] = ib; + ++histo[ib]; + } + int nhave = 0; + int ib = nbuckets - 1; + for ( ; ib >= 0; --ib) { + nhave += histo[ib]; + if (nhave >= k) break; + } + std::vector tmp_tokens(nhave); + auto ptr = tmp_tokens.data(); + std::vector bucket_ptrs; + bucket_ptrs.reserve(nbuckets - ib); + for (int j = nbuckets - 1; j >= ib; --j) { + bucket_ptrs.push_back(ptr); + ptr += histo[j]; + } + for (int i = 0; i < (int)candidates->size; ++i) { + int j = bucket_idx[i]; + if (j >= ib) { + *bucket_ptrs[nbuckets-1-j]++ = candidates->data[i]; + } + } + + ptr = tmp_tokens.data(); + int ndone = 0; + for (int j = nbuckets-1; j > ib; --j) { + std::sort(ptr, ptr + histo[j], comp); + ptr += histo[j]; + ndone += histo[j]; + } + std::partial_sort(ptr, ptr + k - ndone, ptr + histo[ib], comp); + + std::memcpy(candidates->data, tmp_tokens.data(), k*sizeof(llama_token_data)); + } candidates->sorted = true; } From aad0b01d7380a7cdfe0dd42307b18c7b6bac9575 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 26 Jan 2024 10:52:33 +0200 Subject: [PATCH 019/143] readme : update hot topics --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c2a33022c..76e48ce8a 100644 --- a/README.md +++ b/README.md @@ -10,11 +10,11 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ ### Hot topics +- ⚠️ Incoming backends: https://github.com/ggerganov/llama.cpp/discussions/5138 - New SOTA quantized models, including pure 2-bits: https://huggingface.co/ikawrakow - Collecting Apple Silicon performance stats: - M-series: https://github.com/ggerganov/llama.cpp/discussions/4167 - A-series: https://github.com/ggerganov/llama.cpp/discussions/4508 -- Added Mixtral support: https://github.com/ggerganov/llama.cpp/pull/4406 - Looking for contributions to improve and maintain the `server` example: https://github.com/ggerganov/llama.cpp/issues/4216 ---- From 38b431de232d1b736b5af19b8c7d72f7075a70bc Mon Sep 17 00:00:00 2001 From: Riceball LEE Date: Fri, 26 Jan 2024 17:10:28 +0800 Subject: [PATCH 020/143] gguf : fix "general.alignment" type in gguf_reader.py (#5136) --- gguf-py/gguf/gguf_reader.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/gguf-py/gguf/gguf_reader.py b/gguf-py/gguf/gguf_reader.py index 8682765ed..5b6d4ba6b 100644 --- a/gguf-py/gguf/gguf_reader.py +++ b/gguf-py/gguf/gguf_reader.py @@ -107,7 +107,7 @@ class GGUFReader: offs, tensors_fields = self._build_tensors_fields(offs, tensor_count) new_align = self.fields.get('general.alignment') if new_align is not None: - if new_align.types != [GGUFValueType.UINT64]: + if new_align.types != [GGUFValueType.UINT32]: raise ValueError('Bad type for general.alignment field') self.alignment = new_align.parts[-1][0] padding = offs % self.alignment From 6dd3c28c9cd1ef74b49d79f47d668759346a3c6c Mon Sep 17 00:00:00 2001 From: Paul Tsochantaris Date: Fri, 26 Jan 2024 12:16:07 +0000 Subject: [PATCH 021/143] metal : remove unused `n_buffers` and `buffers` (#5129) --- ggml-metal.m | 73 ++++++++++++---------------------------------------- 1 file changed, 16 insertions(+), 57 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 60fef1a19..ab3c84f7f 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -26,15 +26,6 @@ #define GGML_METAL_MAX_KERNELS 256 -struct ggml_metal_buffer { - const char * name; - - void * data; - size_t size; - - id metal; -}; - struct ggml_metal_kernel { id function; id pipeline; @@ -172,9 +163,6 @@ struct ggml_metal_context { dispatch_queue_t d_queue; - int n_buffers; - struct ggml_metal_buffer buffers[GGML_METAL_MAX_BUFFERS]; - struct ggml_metal_kernel kernels[GGML_METAL_MAX_KERNELS]; bool support_simdgroup_reduction; @@ -242,24 +230,20 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { // Show all the Metal device instances in the system NSArray * devices = MTLCopyAllDevices(); for (id device in devices) { - NSString * s = [device name]; - GGML_METAL_LOG_INFO("%s: found device: %s\n", __func__, [s UTF8String]); + GGML_METAL_LOG_INFO("%s: found device: %s\n", __func__, [[device name] UTF8String]); } [devices release]; // since it was created by a *Copy* C method #endif // Pick and show default Metal device id device = MTLCreateSystemDefaultDevice(); - NSString * s = [device name]; - GGML_METAL_LOG_INFO("%s: picking default device: %s\n", __func__, [s UTF8String]); + GGML_METAL_LOG_INFO("%s: picking default device: %s\n", __func__, [[device name] 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->queue = [ctx->device newCommandQueue]; - ctx->n_buffers = 0; - ctx->d_queue = dispatch_queue_create("ggml-metal", DISPATCH_QUEUE_CONCURRENT); // load library @@ -534,10 +518,6 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { static void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_LOG_INFO("%s: deallocating\n", __func__); - for (int i = 0; i < ctx->n_buffers; ++i) { - [ctx->buffers[i].metal release]; - } - for (int i = 0; i < GGML_METAL_MAX_KERNELS; ++i) { if (ctx->kernels[i].pipeline) { [ctx->kernels[i].pipeline release]; @@ -580,51 +560,30 @@ struct ggml_backend_metal_buffer_context { // the assumption is that there is 1-to-1 mapping between the host and device memory buffers, so we can find the // 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) { +static id ggml_metal_get_buffer(struct ggml_tensor * t, size_t * offs) { //GGML_METAL_LOG_INFO("%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); ggml_backend_buffer_t buffer = t->view_src ? t->view_src->buffer : t->buffer; - // compatibility with ggml-backend - if (buffer && buffer->buft == ggml_backend_metal_buffer_type()) { - struct ggml_backend_metal_buffer_context * buf_ctx = (struct ggml_backend_metal_buffer_context *) buffer->context; - - // find the view that contains the tensor fully - for (int i = 0; i < buf_ctx->n_buffers; ++i) { - const int64_t ioffs = (int64_t) t->data - (int64_t) buf_ctx->buffers[i].data; - - //GGML_METAL_LOG_INFO("ioffs = %10ld, tsize = %10ld, sum = %10ld, buf_ctx->buffers[%d].size = %10ld\n", ioffs, tsize, ioffs + tsize, i, buf_ctx->buffers[i].size); - if (ioffs >= 0 && ioffs + tsize <= (int64_t) buf_ctx->buffers[i].size) { - *offs = (size_t) ioffs; - - //GGML_METAL_LOG_INFO("%s: tensor '%16s', offs = %8ld\n", __func__, t->name, *offs); - - return buf_ctx->buffers[i].metal; - } - } - - GGML_METAL_LOG_ERROR("%s: error: tensor '%s' buffer is nil\n", __func__, t->name); - - return nil; - } + struct ggml_backend_metal_buffer_context * buf_ctx = (struct ggml_backend_metal_buffer_context *) buffer->context; // 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; + for (int i = 0; i < buf_ctx->n_buffers; ++i) { + const int64_t ioffs = (int64_t) t->data - (int64_t) buf_ctx->buffers[i].data; - //GGML_METAL_LOG_INFO("ioffs = %10ld, tsize = %10ld, sum = %10ld, ctx->buffers[%d].size = %10ld, name = %s\n", ioffs, tsize, ioffs + tsize, i, ctx->buffers[i].size, ctx->buffers[i].name); - if (ioffs >= 0 && ioffs + tsize <= (int64_t) ctx->buffers[i].size) { + //GGML_METAL_LOG_INFO("ioffs = %10ld, tsize = %10ld, sum = %10ld, buf_ctx->buffers[%d].size = %10ld\n", ioffs, tsize, ioffs + tsize, i, buf_ctx->buffers[i].size); + if (ioffs >= 0 && ioffs + tsize <= (int64_t) buf_ctx->buffers[i].size) { *offs = (size_t) ioffs; - //GGML_METAL_LOG_INFO("%s: '%s' tensor '%16s', offs = %8ld\n", __func__, ctx->buffers[i].name, t->name, *offs); + //GGML_METAL_LOG_INFO("%s: tensor '%16s', offs = %8ld\n", __func__, t->name, *offs); - return ctx->buffers[i].metal; + return buf_ctx->buffers[i].metal; } } - GGML_METAL_LOG_ERROR("%s: error: buffer is nil\n", __func__); + GGML_METAL_LOG_ERROR("%s: error: tensor '%s' buffer is nil\n", __func__, t->name); return nil; } @@ -817,9 +776,9 @@ static bool ggml_metal_graph_compute( const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT; const enum ggml_type dstt = dst ? dst->type : GGML_TYPE_COUNT; - id id_src0 = src0 ? ggml_metal_get_buffer(ctx, src0, &offs_src0) : nil; - id id_src1 = src1 ? ggml_metal_get_buffer(ctx, src1, &offs_src1) : nil; - id id_dst = dst ? ggml_metal_get_buffer(ctx, dst, &offs_dst) : nil; + id id_src0 = src0 ? ggml_metal_get_buffer(src0, &offs_src0) : nil; + id id_src1 = src1 ? ggml_metal_get_buffer(src1, &offs_src1) : nil; + id id_dst = dst ? ggml_metal_get_buffer(dst, &offs_dst) : nil; //GGML_METAL_LOG_INFO("%s: op - %s\n", __func__, ggml_op_name(dst->op)); //if (src0) { @@ -1601,7 +1560,7 @@ static bool ggml_metal_graph_compute( struct ggml_tensor * src_cur = dst->src[2 + (j % n_as)]; size_t offs_src_cur = 0; - id id_src_cur = ggml_metal_get_buffer(ctx, src_cur, &offs_src_cur); + id id_src_cur = ggml_metal_get_buffer(src_cur, &offs_src_cur); [encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:19 + j]; } @@ -1746,7 +1705,7 @@ static bool ggml_metal_graph_compute( struct ggml_tensor * src_cur = dst->src[2 + (j % n_as)]; size_t offs_src_cur = 0; - id id_src_cur = ggml_metal_get_buffer(ctx, src_cur, &offs_src_cur); + id id_src_cur = ggml_metal_get_buffer(src_cur, &offs_src_cur); [encoder setBuffer:id_src_cur offset:offs_src_cur atIndex:23 + j]; } From 413e7b0559f922bd4de5e9eec548829d111651b1 Mon Sep 17 00:00:00 2001 From: crasm Date: Fri, 26 Jan 2024 07:18:00 -0500 Subject: [PATCH 022/143] ci : add model tests + script wrapper (#4586) * scripts : add lib.sh and lib_test.sh * scripts : stub out new ci-run.sh script * scripts : switch to PascalCase for functions This looks a little odd at first, but I find it very useful as a convention to know if a command is part of our code vs a builtin. * scripts : add some fancy conversion from snake_case to PascalCase * Add venv to ci/run.sh * Revert scripts work * scripts : add wrapper script for local use of ci/run.sh * Simplify .gitignore for tests, clang-tidy fixes * Label all ctest tests * ci : ctest uses -L main * Attempt at writing ctest_with_model * Update test-model-load-cancel * ci : add ctest_with_model for debug and release ggml-ci * Fix gg_get_model function ggml-ci * got stuck on CMake * Add get_model.cpp to tests/CMakeLists.txt ggml-ci * Fix README.md output for ctest_with_model ggml-ci * workflows : use `-L main` for all ctest ggml-ci * Fixes * GG_RUN_CTEST_MODELFILE => LLAMACPP_TESTMODELFILE * Always show warning rather than failing if model file variable is not set * scripts : update usage text for ci-run.sh --- .github/workflows/build.yml | 12 ++--- .gitignore | 19 +------- Makefile | 7 ++- ci/run.sh | 81 +++++++++++++++++++++++++++----- scripts/ci-run.sh | 50 ++++++++++++++++++++ tests/.gitignore | 2 + tests/CMakeLists.txt | 14 ++++-- tests/get-model.cpp | 21 +++++++++ tests/get-model.h | 2 + tests/test-autorelease.cpp | 12 ++--- tests/test-model-load-cancel.cpp | 27 +++++++++++ 11 files changed, 199 insertions(+), 48 deletions(-) create mode 100755 scripts/ci-run.sh create mode 100644 tests/.gitignore create mode 100644 tests/get-model.cpp create mode 100644 tests/get-model.h create mode 100644 tests/test-model-load-cancel.cpp diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index c3aa6f992..d22a041a6 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -72,7 +72,7 @@ jobs: id: cmake_test run: | cd build - ctest --verbose --timeout 900 + ctest -L main --verbose --timeout 900 ubuntu-latest-cmake-sanitizer: runs-on: ubuntu-latest @@ -107,7 +107,7 @@ jobs: id: cmake_test run: | cd build - ctest --verbose --timeout 900 + ctest -L main --verbose --timeout 900 ubuntu-latest-cmake-mpi: runs-on: ubuntu-latest @@ -141,7 +141,7 @@ jobs: id: cmake_test run: | cd build - ctest --verbose + ctest -L main --verbose # TODO: build with LLAMA_NO_METAL because test-backend-ops fail on "Apple Paravirtual device" and I don't know # how to debug it. @@ -202,7 +202,7 @@ jobs: id: cmake_test run: | cd build - ctest --verbose --timeout 900 + ctest -L main --verbose --timeout 900 macOS-latest-cmake-ios: runs-on: macos-latest @@ -394,7 +394,7 @@ jobs: if: ${{ matrix.build != 'clblast' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }} # not all machines have native AVX-512 run: | cd build - ctest -C Release --verbose --timeout 900 + ctest -L main -C Release --verbose --timeout 900 - name: Test (Intel SDE) id: cmake_test_sde @@ -406,7 +406,7 @@ jobs: 7z x "-o${env:RUNNER_TEMP}" $env:RUNNER_TEMP/sde.tar $sde = $(join-path $env:RUNNER_TEMP sde-external-${env:SDE_VERSION}-win/sde.exe) cd build - & $sde -future -- ctest -C Release --verbose --timeout 900 + & $sde -future -- ctest -L main -C Release --verbose --timeout 900 - name: Determine tag name id: tag diff --git a/.gitignore b/.gitignore index 5ab81445d..cb0069bfb 100644 --- a/.gitignore +++ b/.gitignore @@ -27,7 +27,7 @@ lcov-report/ gcovr-report/ -build*/ +build* out/ tmp/ @@ -89,20 +89,3 @@ examples/jeopardy/results.txt poetry.lock 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-llama -/tests/test-tokenizer-0-falcon -/tests/test-tokenizer-1-llama -/tests/test-tokenizer-1-bpe -/tests/test-rope -/tests/test-backend-ops -/tests/test-autorelease diff --git a/Makefile b/Makefile index a8658a596..82c89e87a 100644 --- a/Makefile +++ b/Makefile @@ -9,7 +9,7 @@ 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-llama tests/test-tokenizer-1-bpe tests/test-rope \ - tests/test-backend-ops tests/test-autorelease + tests/test-backend-ops tests/test-model-load-cancel tests/test-autorelease # Code coverage output files COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report @@ -748,5 +748,8 @@ tests/test-c.o: tests/test-c.c llama.h tests/test-backend-ops: tests/test-backend-ops.cpp ggml.o $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -tests/test-autorelease: tests/test-autorelease.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) +tests/test-model-load-cancel: tests/test-model-load-cancel.cpp ggml.o llama.o tests/get-model.cpp $(COMMON_DEPS) $(OBJS) + $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + +tests/test-autorelease: tests/test-autorelease.cpp ggml.o llama.o tests/get-model.cpp $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) diff --git a/ci/run.sh b/ci/run.sh index 791b17a19..2427e55a2 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -22,9 +22,9 @@ mkdir -p "$2" OUT=$(realpath "$1") MNT=$(realpath "$2") -rm -v $OUT/*.log -rm -v $OUT/*.exit -rm -v $OUT/*.md +rm -f "$OUT/*.log" +rm -f "$OUT/*.exit" +rm -f "$OUT/*.md" sd=`dirname $0` cd $sd/../ @@ -94,7 +94,7 @@ function gg_run_ctest_debug { (time cmake -DCMAKE_BUILD_TYPE=Debug ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log - (time ctest --output-on-failure -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log + (time ctest --output-on-failure -L main -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log set +e } @@ -123,9 +123,9 @@ function gg_run_ctest_release { (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log if [ -z ${GG_BUILD_LOW_PERF} ]; then - (time ctest --output-on-failure ) 2>&1 | tee -a $OUT/${ci}-ctest.log + (time ctest --output-on-failure -L main ) 2>&1 | tee -a $OUT/${ci}-ctest.log else - (time ctest --output-on-failure -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log + (time ctest --output-on-failure -L main -E test-opt ) 2>&1 | tee -a $OUT/${ci}-ctest.log fi set +e @@ -141,6 +141,61 @@ function gg_sum_ctest_release { gg_printf '```\n' } +function gg_get_model { + local gguf_3b="$MNT/models/open-llama/3B-v2/ggml-model-f16.gguf" + local gguf_7b="$MNT/models/open-llama/7B-v2/ggml-model-f16.gguf" + if [[ -s $gguf_3b ]]; then + echo -n "$gguf_3b" + elif [[ -s $gguf_7b ]]; then + echo -n "$gguf_7b" + else + echo >&2 "No model found. Can't run gg_run_ctest_with_model." + exit 1 + fi +} + +function gg_run_ctest_with_model_debug { + cd ${SRC} + + local model; model=$(gg_get_model) + cd build-ci-debug + set -e + (LLAMACPP_TEST_MODELFILE="$model" time ctest --output-on-failure -L model) 2>&1 | tee -a $OUT/${ci}-ctest.log + set +e + cd .. +} + +function gg_run_ctest_with_model_release { + cd ${SRC} + + local model; model=$(gg_get_model) + cd build-ci-release + set -e + (LLAMACPP_TEST_MODELFILE="$model" time ctest --output-on-failure -L model) 2>&1 | tee -a $OUT/${ci}-ctest.log + set +e + cd .. +} + +function gg_sum_ctest_with_model_debug { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Runs ctest with model files in debug mode\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '```\n' + gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)" + gg_printf '```\n' +} + +function gg_sum_ctest_with_model_release { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Runs ctest with model files in release mode\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '```\n' + gg_printf '%s\n' "$(cat $OUT/${ci}-ctest.log)" + gg_printf '```\n' +} + # open_llama_3b_v2 function gg_run_open_llama_3b_v2 { @@ -183,8 +238,6 @@ function gg_run_open_llama_3b_v2 { wiki_test_60="${path_wiki}/wiki.test-60.raw" - ./bin/test-autorelease ${model_f16} - ./bin/quantize ${model_f16} ${model_q8_0} q8_0 ./bin/quantize ${model_f16} ${model_q4_0} q4_0 ./bin/quantize ${model_f16} ${model_q4_1} q4_1 @@ -507,14 +560,18 @@ function gg_sum_open_llama_7b_v2 { ## main if [ -z ${GG_BUILD_LOW_PERF} ]; then + # Create symlink: ./llama.cpp/models-mnt -> $MNT/models/models-mnt rm -rf ${SRC}/models-mnt - mnt_models=${MNT}/models mkdir -p ${mnt_models} ln -sfn ${mnt_models} ${SRC}/models-mnt - python3 -m pip install -r ${SRC}/requirements.txt - python3 -m pip install --editable gguf-py + # Create a fresh python3 venv and enter it + python3 -m venv "$MNT/venv" + source "$MNT/venv/bin/activate" + + pip install -r ${SRC}/requirements.txt --disable-pip-version-check + pip install --editable gguf-py --disable-pip-version-check fi ret=0 @@ -529,6 +586,8 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then else test $ret -eq 0 && gg_run open_llama_7b_v2 fi + test $ret -eq 0 && gg_run ctest_with_model_debug + test $ret -eq 0 && gg_run ctest_with_model_release fi fi diff --git a/scripts/ci-run.sh b/scripts/ci-run.sh new file mode 100755 index 000000000..06b5d9c6e --- /dev/null +++ b/scripts/ci-run.sh @@ -0,0 +1,50 @@ +#!/bin/bash +set -euo pipefail +this=$(realpath "$0"); readonly this +cd "$(dirname "$this")" +shellcheck "$this" + +if (( $# != 1 && $# != 2 )); then + cat >&2 <<'EOF' +usage: + ci-run.sh [] + +This script wraps ci/run.sh: +* If is a ramdisk, you can reduce writes to your SSD. If is not a ramdisk, keep in mind that total writes will increase by the size of . + (openllama_3b_v2: quantized models are about 30GB) +* Persistent model and data files are synced to and from , + excluding generated .gguf files. + (openllama_3b_v2: persistent files are about 6.6GB) +* defaults to ~/.cache/llama.cpp +EOF + exit 1 +fi + +cd .. # => llama.cpp repo root + +tmp="$1" +mkdir -p "$tmp" +tmp=$(realpath "$tmp") +echo >&2 "Using tmp=$tmp" + +cache="${2-$HOME/.cache/llama.cpp}" +mkdir -p "$cache" +cache=$(realpath "$cache") +echo >&2 "Using cache=$cache" + +_sync() { + local from="$1"; shift + local to="$1"; shift + + echo >&2 "Syncing from $from to $to" + mkdir -p "$from" "$to" + rsync -a "$from" "$to" --delete-during "$@" +} + +_sync "$(realpath .)/" "$tmp/llama.cpp" +_sync "$cache/ci-mnt/models/" "$tmp/llama.cpp/ci-mnt/models/" + +cd "$tmp/llama.cpp" +bash ci/run.sh ci-out ci-mnt + +_sync 'ci-mnt/models/' "$cache/ci-mnt/models/" --exclude='*.gguf' -P diff --git a/tests/.gitignore b/tests/.gitignore new file mode 100644 index 000000000..59be43b99 --- /dev/null +++ b/tests/.gitignore @@ -0,0 +1,2 @@ +* +!*.* diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index d7aaab843..3e40a78cd 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -1,6 +1,6 @@ function(llama_build_executable source) get_filename_component(TEST_TARGET ${source} NAME_WE) - add_executable(${TEST_TARGET} ${source}) + add_executable(${TEST_TARGET} ${source} get-model.cpp) install(TARGETS ${TEST_TARGET} RUNTIME) target_link_libraries(${TEST_TARGET} PRIVATE common) endfunction() @@ -8,14 +8,20 @@ endfunction() function(llama_test_executable name source) get_filename_component(TEST_TARGET ${source} NAME_WE) add_test(NAME ${name} COMMAND $ ${ARGN}) + set_property(TEST ${name} PROPERTY LABELS "main") endfunction() function(llama_build_and_test_executable source) + llama_build_and_test_executable_with_label(${source} "main") +endfunction() + +function(llama_build_and_test_executable_with_label source label) get_filename_component(TEST_TARGET ${source} NAME_WE) - add_executable(${TEST_TARGET} ${source}) + add_executable(${TEST_TARGET} ${source} get-model.cpp) install(TARGETS ${TEST_TARGET} RUNTIME) target_link_libraries(${TEST_TARGET} PRIVATE common) add_test(NAME ${TEST_TARGET} COMMAND $ ${ARGN}) + set_property(TEST ${TEST_TARGET} PROPERTY LABELS ${label}) endfunction() # llama_build_and_test_executable(test-double-float.cpp) # SLOW @@ -49,10 +55,12 @@ llama_build_and_test_executable(test-llama-grammar.cpp) llama_build_and_test_executable(test-grad0.cpp) # llama_build_and_test_executable(test-opt.cpp) # SLOW llama_build_and_test_executable(test-backend-ops.cpp) -llama_build_and_test_executable(test-autorelease.cpp) llama_build_and_test_executable(test-rope.cpp) +llama_build_and_test_executable_with_label(test-model-load-cancel.cpp "model") +llama_build_and_test_executable_with_label(test-autorelease.cpp "model") + # dummy executable - not installed get_filename_component(TEST_TARGET test-c.c NAME_WE) add_executable(${TEST_TARGET} test-c.c) diff --git a/tests/get-model.cpp b/tests/get-model.cpp new file mode 100644 index 000000000..4edb685f0 --- /dev/null +++ b/tests/get-model.cpp @@ -0,0 +1,21 @@ +#include +#include +#include + +#include "get-model.h" + +char * get_model_or_exit(int argc, char *argv[]) { + char * model_path; + if (argc > 1) { + model_path = argv[1]; + + } else { + model_path = getenv("LLAMACPP_TEST_MODELFILE"); + if (!model_path || strlen(model_path) == 0) { + fprintf(stderr, "\033[33mWARNING: No model file provided. Skipping this test. Set LLAMACPP_TEST_MODELFILE= to silence this warning and run this test.\n\033[0m"); + exit(EXIT_SUCCESS); + } + } + + return model_path; +} diff --git a/tests/get-model.h b/tests/get-model.h new file mode 100644 index 000000000..81a3a0fef --- /dev/null +++ b/tests/get-model.h @@ -0,0 +1,2 @@ +#pragma once +char * get_model_or_exit(int, char*[]); diff --git a/tests/test-autorelease.cpp b/tests/test-autorelease.cpp index 289c6ba6c..36a23c0bb 100644 --- a/tests/test-autorelease.cpp +++ b/tests/test-autorelease.cpp @@ -5,19 +5,15 @@ #include #include "llama.h" +#include "get-model.h" // This creates a new context inside a pthread and then tries to exit cleanly. int main(int argc, char ** argv) { - if (argc < 2) { - printf("Usage: %s model.gguf\n", argv[0]); - return 0; // intentionally return success - } + auto * model_path = get_model_or_exit(argc, argv); - const std::string fname = argv[1]; - - std::thread([&fname]() { + std::thread([&model_path]() { llama_backend_init(false); - auto * model = llama_load_model_from_file(fname.c_str(), llama_model_default_params()); + auto * model = llama_load_model_from_file(model_path, llama_model_default_params()); auto * ctx = llama_new_context_with_model(model, llama_context_default_params()); llama_free(ctx); llama_free_model(model); diff --git a/tests/test-model-load-cancel.cpp b/tests/test-model-load-cancel.cpp new file mode 100644 index 000000000..7ea4bbacc --- /dev/null +++ b/tests/test-model-load-cancel.cpp @@ -0,0 +1,27 @@ +#include "llama.h" +#include "get-model.h" + +#include + +int main(int argc, char *argv[] ) { + auto * model_path = get_model_or_exit(argc, argv); + auto * file = fopen(model_path, "r"); + if (file == nullptr) { + fprintf(stderr, "no model at '%s' found\n", model_path); + return EXIT_FAILURE; + } + + fprintf(stderr, "using '%s'\n", model_path); + fclose(file); + + llama_backend_init(false); + auto params = llama_model_params{}; + params.use_mmap = false; + params.progress_callback = [](float progress, void * ctx){ + (void) ctx; + return progress > 0.50; + }; + auto * model = llama_load_model_from_file(model_path, params); + llama_backend_free(); + return model == nullptr ? EXIT_SUCCESS : EXIT_FAILURE; +} From 48c857aa10aea73210a4a72da3f1a6f99269e75d Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Fri, 26 Jan 2024 13:42:20 +0100 Subject: [PATCH 023/143] server : refactored the task processing logic (#5065) * server: add llama_server_queue struct * server: add llama_server_response_event * server: add comments * server: move all mutexes away from server.cpp * server: correct multitask response * server: only add back deferred tasks when one slot is available * server: fix a race condition cause by "request_completion" --- Makefile | 2 +- examples/server/CMakeLists.txt | 2 +- examples/server/oai.hpp | 208 ++++++++ examples/server/server.cpp | 849 ++++++--------------------------- examples/server/utils.hpp | 507 ++++++++++++++++++++ 5 files changed, 876 insertions(+), 692 deletions(-) create mode 100644 examples/server/oai.hpp create mode 100644 examples/server/utils.hpp diff --git a/Makefile b/Makefile index 82c89e87a..b8858b412 100644 --- a/Makefile +++ b/Makefile @@ -619,7 +619,7 @@ embedding: examples/embedding/embedding.cpp ggml.o llama.o $(C save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -server: examples/server/server.cpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) +server: examples/server/server.cpp examples/server/oai.hpp examples/server/utils.hpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2) -Wno-cast-qual gguf: examples/gguf/gguf.cpp ggml.o $(OBJS) diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index 81709e448..cc13b2d63 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -1,7 +1,7 @@ set(TARGET server) option(LLAMA_SERVER_VERBOSE "Build verbose logging option for Server" ON) include_directories(${CMAKE_CURRENT_SOURCE_DIR}) -add_executable(${TARGET} server.cpp json.hpp httplib.h) +add_executable(${TARGET} server.cpp oai.hpp utils.hpp json.hpp httplib.h) install(TARGETS ${TARGET} RUNTIME) target_compile_definitions(${TARGET} PRIVATE SERVER_VERBOSE=$ diff --git a/examples/server/oai.hpp b/examples/server/oai.hpp new file mode 100644 index 000000000..bc5db6eef --- /dev/null +++ b/examples/server/oai.hpp @@ -0,0 +1,208 @@ +#pragma once + +#include +#include +#include +#include +#include +#include + +#include "json.hpp" +#include "utils.hpp" + +#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613" + +using json = nlohmann::json; + +inline static json oaicompat_completion_params_parse( + const json &body /* openai api json semantics */) +{ + json llama_params; + + llama_params["__oaicompat"] = true; + + // Map OpenAI parameters to llama.cpp parameters + // + // For parameters that are defined by the OpenAI documentation (e.g. + // temperature), we explicitly specify OpenAI's intended default; we + // need to do that because sometimes OpenAI disagrees with llama.cpp + // + // https://platform.openai.com/docs/api-reference/chat/create + llama_sampling_params default_sparams; + llama_params["model"] = json_value(body, "model", std::string("unknown")); + llama_params["prompt"] = format_chatml(body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt' + llama_params["cache_prompt"] = json_value(body, "cache_prompt", false); + llama_params["temperature"] = json_value(body, "temperature", 0.0); + llama_params["top_k"] = json_value(body, "top_k", default_sparams.top_k); + llama_params["top_p"] = json_value(body, "top_p", 1.0); + llama_params["n_predict"] = json_value(body, "max_tokens", -1); + llama_params["logit_bias"] = json_value(body, "logit_bias",json::object()); + llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0); + llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0); + llama_params["seed"] = json_value(body, "seed", LLAMA_DEFAULT_SEED); + llama_params["stream"] = json_value(body, "stream", false); + llama_params["mirostat"] = json_value(body, "mirostat", default_sparams.mirostat); + llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", default_sparams.mirostat_tau); + llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", default_sparams.mirostat_eta); + llama_params["penalize_nl"] = json_value(body, "penalize_nl", default_sparams.penalize_nl); + llama_params["typical_p"] = json_value(body, "typical_p", default_sparams.typical_p); + llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", default_sparams.penalty_last_n); + llama_params["ignore_eos"] = json_value(body, "ignore_eos", false); + llama_params["tfs_z"] = json_value(body, "tfs_z", default_sparams.tfs_z); + + if (body.count("grammar") != 0) { + llama_params["grammar"] = json_value(body, "grammar", json::object()); + } + + // Handle 'stop' field + if (body.contains("stop") && body["stop"].is_string()) { + llama_params["stop"] = json::array({body["stop"].get()}); + } else { + llama_params["stop"] = json_value(body, "stop", json::array()); + } + + // Ensure there is ChatML-specific end sequence among stop words + llama_params["stop"].push_back("<|im_end|>"); + + return llama_params; +} + +inline static json format_final_response_oaicompat(const json &request, const task_result &response, bool streaming = false) +{ + json result = response.result_json; + + bool stopped_word = result.count("stopped_word") != 0; + bool stopped_eos = json_value(result, "stopped_eos", false); + int num_tokens_predicted = json_value(result, "tokens_predicted", 0); + int num_prompt_tokens = json_value(result, "tokens_evaluated", 0); + std::string content = json_value(result, "content", std::string("")); + + std::string finish_reason = "length"; + if (stopped_word || stopped_eos) { + finish_reason = "stop"; + } + + json choices = + streaming ? json::array({json{{"finish_reason", finish_reason}, + {"index", 0}, + {"delta", json::object()}}}) + : json::array({json{{"finish_reason", finish_reason}, + {"index", 0}, + {"message", json{{"content", content}, + {"role", "assistant"}}}}}); + + std::time_t t = std::time(0); + + json res = + json{{"choices", choices}, + {"created", t}, + {"model", + json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, + {"object", streaming ? "chat.completion.chunk" : "chat.completion"}, + {"usage", + json{{"completion_tokens", num_tokens_predicted}, + {"prompt_tokens", num_prompt_tokens}, + {"total_tokens", num_tokens_predicted + num_prompt_tokens}}}, + {"id", gen_chatcmplid()}}; + + if (server_verbose) { + res["__verbose"] = result; + } + + if (result.contains("completion_probabilities")) { + res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array()); + } + + return res; +} + +// return value is vector as there is one case where we might need to generate two responses +inline static std::vector format_partial_response_oaicompat(const task_result &response) { + json result = response.result_json; + + if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) { + return std::vector({response.result_json}); + } + + bool first = json_value(result, "oaicompat_token_ctr", 0) == 0; + std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL)); + + bool stopped_word = json_value(result, "stopped_word", false); + bool stopped_eos = json_value(result, "stopped_eos", false); + bool stopped_limit = json_value(result, "stopped_limit", false); + std::string content = json_value(result, "content", std::string("")); + + std::string finish_reason; + if (stopped_word || stopped_eos) { + finish_reason = "stop"; + } + if (stopped_limit) { + finish_reason = "length"; + } + + std::time_t t = std::time(0); + + json choices; + + if (!finish_reason.empty()) { + choices = json::array({json{{"finish_reason", finish_reason}, + {"index", 0}, + {"delta", json::object()}}}); + } else { + if (first) { + if (content.empty()) { + choices = json::array({json{{"finish_reason", nullptr}, + {"index", 0}, + {"delta", json{{"role", "assistant"}}}}}); + } else { + // We have to send this as two updates to conform to openai behavior + json initial_ret = json{{"choices", json::array({json{ + {"finish_reason", nullptr}, + {"index", 0}, + {"delta", json{ + {"role", "assistant"} + }}}})}, + {"created", t}, + {"id", gen_chatcmplid()}, + {"model", modelname}, + {"object", "chat.completion.chunk"}}; + + json second_ret = json{ + {"choices", json::array({json{{"finish_reason", nullptr}, + {"index", 0}, + {"delta", json{ + {"content", content}}} + }})}, + {"created", t}, + {"id", gen_chatcmplid()}, + {"model", modelname}, + {"object", "chat.completion.chunk"}}; + + return std::vector({initial_ret, second_ret}); + } + } else { + // Some idiosyncrasy in task processing logic makes several trailing calls + // with empty content, we ignore these at the calee site. + if (content.empty()) { + return std::vector({json::object()}); + } + + choices = json::array({json{ + {"finish_reason", nullptr}, + {"index", 0}, + {"delta", + json{ + {"content", content}, + }}, + }}); + } + } + + json ret = json{{"choices", choices}, + {"created", t}, + {"id", gen_chatcmplid()}, + {"model", modelname}, + {"object", "chat.completion.chunk"}}; + + return std::vector({ret}); +} diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 0462fbd24..392836132 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1,6 +1,8 @@ #include "common.h" #include "llama.h" #include "grammar-parser.h" +#include "utils.hpp" +#include "oai.hpp" #include "../llava/clip.h" @@ -23,17 +25,10 @@ #include #include -#include #include #include #include -#ifndef SERVER_VERBOSE -#define SERVER_VERBOSE 1 -#endif - -#define DEFAULT_OAICOMPAT_MODEL "gpt-3.5-turbo-0613" - using json = nlohmann::json; struct server_params @@ -46,197 +41,7 @@ struct server_params int32_t write_timeout = 600; }; -static bool server_verbose = false; - -#if SERVER_VERBOSE != 1 -#define LOG_VERBOSE(MSG, ...) -#else -#define LOG_VERBOSE(MSG, ...) \ - do \ - { \ - if (server_verbose) \ - { \ - server_log("VERBOSE", __func__, __LINE__, MSG, __VA_ARGS__); \ - } \ - } while (0) -#endif - -#define LOG_ERROR( MSG, ...) server_log("ERROR", __func__, __LINE__, MSG, __VA_ARGS__) -#define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__) -#define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__) - -json oaicompat_completion_params_parse(const json &body); -std::string format_chatml(std::vector messages); - - -// -// base64 utils (TODO: move to common in the future) -// - -static const std::string base64_chars = - "ABCDEFGHIJKLMNOPQRSTUVWXYZ" - "abcdefghijklmnopqrstuvwxyz" - "0123456789+/"; - -static inline bool is_base64(uint8_t c) -{ - return (isalnum(c) || (c == '+') || (c == '/')); -} - -static std::vector base64_decode(const std::string & encoded_string) -{ - int i = 0; - int j = 0; - int in_ = 0; - - int in_len = encoded_string.size(); - - uint8_t char_array_4[4]; - uint8_t char_array_3[3]; - - std::vector ret; - - while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_])) - { - char_array_4[i++] = encoded_string[in_]; in_++; - if (i == 4) - { - for (i = 0; i <4; i++) - { - char_array_4[i] = base64_chars.find(char_array_4[i]); - } - - char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4); - char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); - char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; - - for (i = 0; (i < 3); i++) - { - ret.push_back(char_array_3[i]); - } - i = 0; - } - } - - if (i) - { - for (j = i; j <4; j++) - { - char_array_4[j] = 0; - } - - for (j = 0; j <4; j++) - { - char_array_4[j] = base64_chars.find(char_array_4[j]); - } - - char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4); - char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); - char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; - - for (j = 0; (j < i - 1); j++) - { - ret.push_back(char_array_3[j]); - } - } - - return ret; -} - -// -// parallel -// - -enum server_state { - SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet - SERVER_STATE_READY, // Server is ready and model is loaded - SERVER_STATE_ERROR // An error occurred, load_model failed -}; - -enum task_type { - TASK_TYPE_COMPLETION, - TASK_TYPE_CANCEL, -}; - -struct task_server { - int id; - int target_id; - task_type type; - json data; - bool infill_mode = false; - bool embedding_mode = false; - int multitask_id = -1; -}; - -struct task_result { - int id; - int multitask_id = -1; - bool stop; - bool error; - json result_json; -}; - -struct task_multi { - int id; - std::set subtasks_remaining{}; - std::vector results{}; -}; - -// TODO: can become bool if we can't find use of more states -enum slot_state -{ - IDLE, - PROCESSING, -}; - -enum slot_command -{ - NONE, - LOAD_PROMPT, - RELEASE, -}; - -struct slot_params -{ - bool stream = true; - bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt - - uint32_t seed = -1; // RNG seed - int32_t n_keep = 0; // number of tokens to keep from initial prompt - int32_t n_predict = -1; // new tokens to predict - - std::vector antiprompt; - - json input_prefix; - json input_suffix; -}; - -struct slot_image -{ - int32_t id; - - bool request_encode_image = false; - float * image_embedding = nullptr; - int32_t image_tokens = 0; - - clip_image_u8 * img_data; - - std::string prefix_prompt; // before of this image -}; - -// completion token output with probabilities -struct completion_token_output -{ - struct token_prob - { - llama_token tok; - float prob; - }; - - std::vector probs; - llama_token tok; - std::string text_to_send; -}; +bool server_verbose = false; static size_t common_part(const std::vector &a, const std::vector &b) { @@ -292,28 +97,6 @@ static std::string tokens_to_str(llama_context *ctx, Iter begin, Iter end) return ret; } -static void server_log(const char *level, const char *function, int line, - const char *message, const nlohmann::ordered_json &extra) -{ - nlohmann::ordered_json log - { - {"timestamp", time(nullptr)}, - {"level", level}, - {"function", function}, - {"line", line}, - {"message", message}, - }; - - if (!extra.empty()) - { - log.merge_patch(extra); - } - - const std::string str = log.dump(-1, ' ', false, json::error_handler_t::replace); - printf("%.*s\n", (int)str.size(), str.data()); - 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) { @@ -355,15 +138,6 @@ static json probs_vector_to_json(const llama_context *ctx, const std::vector -static T json_value(const json &body, const std::string &key, const T &default_value) -{ - // Fallback null to default value - return body.contains(key) && !body.at(key).is_null() - ? body.value(key, default_value) - : default_value; -} - struct llama_client_slot { int id; @@ -491,7 +265,7 @@ struct llama_client_slot } void release() { - if (state == IDLE || state == PROCESSING) + if (state == PROCESSING) { t_token_generation = (ggml_time_us() - t_start_genereration) / 1e3; command = RELEASE; @@ -539,7 +313,6 @@ struct llama_server_context bool all_slots_are_idle = false; bool add_bos_token = true; - int32_t id_gen; int32_t n_ctx; // total context for all clients / slots // system prompt @@ -554,13 +327,8 @@ struct llama_server_context // slots / clients std::vector slots; - std::vector queue_tasks; - std::vector queue_results; - std::vector queue_multitasks; - std::mutex mutex_tasks; // also guards id_gen, and queue_multitasks - std::condition_variable condition_tasks; - std::mutex mutex_results; - std::condition_variable condition_results; + llama_server_queue queue_tasks; + llama_server_response queue_results; ~llama_server_context() { @@ -619,8 +387,6 @@ struct llama_server_context } void initialize() { - id_gen = 0; - // create slots all_slots_are_idle = true; @@ -1183,39 +949,13 @@ struct llama_server_context void send_error(task_server& task, const std::string &error) { LOG_TEE("task %i - error: %s\n", task.id, error.c_str()); - std::unique_lock lock(mutex_results); task_result res; res.id = task.id; res.multitask_id = task.multitask_id; res.stop = false; res.error = true; res.result_json = { { "content", error } }; - queue_results.push_back(res); - condition_results.notify_all(); - } - - void add_multi_task(int id, std::vector& sub_ids) - { - std::lock_guard lock(mutex_tasks); - task_multi multi; - multi.id = id; - std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end())); - queue_multitasks.push_back(multi); - condition_tasks.notify_one(); - } - - void update_multi_task(int multitask_id, int subtask_id, task_result& result) - { - std::lock_guard lock(mutex_tasks); - for (auto& multitask : queue_multitasks) - { - if (multitask.id == multitask_id) - { - multitask.subtasks_remaining.erase(subtask_id); - multitask.results.push_back(result); - condition_tasks.notify_one(); - } - } + queue_results.send(res); } json get_model_props() @@ -1261,7 +1001,6 @@ struct llama_server_context void send_partial_response(llama_client_slot &slot, completion_token_output tkn) { - std::unique_lock lock(mutex_results); task_result res; res.id = slot.task_id; res.multitask_id = slot.multitask_id; @@ -1296,13 +1035,11 @@ struct llama_server_context res.result_json["model"] = slot.oaicompat_model; } - queue_results.push_back(res); - condition_results.notify_all(); + queue_results.send(res); } void send_final_response(llama_client_slot &slot) { - std::unique_lock lock(mutex_results); task_result res; res.id = slot.task_id; res.multitask_id = slot.multitask_id; @@ -1351,22 +1088,11 @@ struct llama_server_context res.result_json["model"] = slot.oaicompat_model; } - queue_results.push_back(res); - condition_results.notify_all(); - - // done with results, unlock - lock.unlock(); - - // parent multitask, if any, needs to be updated - if (slot.multitask_id != -1) - { - update_multi_task(slot.multitask_id, slot.task_id, res); - } + queue_results.send(res); } void send_embedding(llama_client_slot &slot) { - std::unique_lock lock(mutex_results); task_result res; res.id = slot.task_id; res.multitask_id = slot.multitask_id; @@ -1393,15 +1119,13 @@ struct llama_server_context {"embedding", embedding }, }; } - queue_results.push_back(res); - condition_results.notify_all(); + queue_results.send(res); } - int request_completion(json data, bool infill, bool embedding, int multitask_id) + void request_completion(int task_id, json data, bool infill, bool embedding, int multitask_id) { - std::unique_lock lock(mutex_tasks); task_server task; - task.id = id_gen++; + task.id = task_id; task.target_id = 0; task.data = std::move(data); task.infill_mode = infill; @@ -1412,47 +1136,11 @@ struct llama_server_context // when a completion task's prompt array is not a singleton, we split it into multiple requests if (task.data.count("prompt") && task.data.at("prompt").size() > 1) { - lock.unlock(); // entering new func scope - return split_multiprompt_task(task); + split_multiprompt_task(task_id, task); } // otherwise, it's a single-prompt task, we actually queue it - queue_tasks.push_back(task); - condition_tasks.notify_one(); - return task.id; - } - - task_result next_result(int task_id) - { - while (true) - { - std::unique_lock lock(mutex_results); - condition_results.wait(lock, [&]{ - return !queue_results.empty(); - }); - - for (int i = 0; i < (int) queue_results.size(); i++) - { - // for now, tasks that have associated parent multitasks just get erased once multitask picks up the result - if (queue_results[i].multitask_id == task_id) - { - update_multi_task(task_id, queue_results[i].id, queue_results[i]); - queue_results.erase(queue_results.begin() + i); - continue; - } - - if (queue_results[i].id == task_id) - { - assert(queue_results[i].multitask_id == -1); - task_result res = queue_results[i]; - queue_results.erase(queue_results.begin() + i); - return res; - } - } - } - - // never reached - //return task_result{-1, false, false, {}}; + queue_tasks.post(task); } // for multiple images processing @@ -1525,150 +1213,117 @@ struct llama_server_context void request_cancel(int task_id) { - std::unique_lock lock(mutex_tasks); task_server task; - task.id = id_gen++; task.type = TASK_TYPE_CANCEL; task.target_id = task_id; - queue_tasks.push_back(task); - condition_tasks.notify_one(); + queue_tasks.post(task); } - int split_multiprompt_task(task_server& multiprompt_task) + void split_multiprompt_task(int multitask_id, task_server& multiprompt_task) { int prompt_count = multiprompt_task.data.at("prompt").size(); assert(prompt_count > 1); - int multitask_id = id_gen++; + // generate all the ID for subtask std::vector subtask_ids(prompt_count); for (int i = 0; i < prompt_count; i++) + { + subtask_ids[i] = queue_tasks.get_new_id(); + } + + // queue up the multitask so we can track its subtask progression + queue_tasks.add_multitask(multitask_id, subtask_ids); + + // add subtasks + for (int i = 0; i < prompt_count; i++) { json subtask_data = multiprompt_task.data; subtask_data["prompt"] = subtask_data["prompt"][i]; // subtasks inherit everything else (infill mode, embedding mode, etc.) - subtask_ids[i] = request_completion(subtask_data, multiprompt_task.infill_mode, multiprompt_task.embedding_mode, multitask_id); + request_completion(subtask_ids[i], subtask_data, multiprompt_task.infill_mode, multiprompt_task.embedding_mode, multitask_id); } - - // queue up the multitask so we can track its subtask progression - add_multi_task(multitask_id, subtask_ids); - return multitask_id; } - void process_tasks() + void process_single_task(task_server& task) { - std::unique_lock lock(mutex_tasks); - std::vector deferred_tasks; - while (!queue_tasks.empty()) + switch (task.type) { - task_server task = queue_tasks.front(); - queue_tasks.erase(queue_tasks.begin()); - switch (task.type) - { - case TASK_TYPE_COMPLETION: { - llama_client_slot *slot = get_slot(json_value(task.data, "slot_id", -1)); - if (slot == nullptr) - { - // if no slot is available, we defer this task for processing later - deferred_tasks.push_back(task); - break; - } - - if (task.data.contains("system_prompt")) - { - if (!all_slots_are_idle) { - send_error(task, "system prompt can only be updated when all slots are idle"); - break; - } - process_system_prompt_data(task.data["system_prompt"]); - - // reset cache_tokens for all slots - for (llama_client_slot &slot : slots) - { - slot.cache_tokens.clear(); - } - } - - slot->reset(); - - slot->infill = task.infill_mode; - slot->embedding = task.embedding_mode; - slot->task_id = task.id; - slot->multitask_id = task.multitask_id; - - if (!launch_slot_with_data(slot, task.data)) - { - // send error result - send_error(task, "internal_error"); - break; - } - } break; - case TASK_TYPE_CANCEL: { // release slot linked with the task id - for (auto & slot : slots) - { - if (slot.task_id == task.target_id) - { - slot.release(); - break; - } - } - } break; - } - } - - // add all the deferred tasks back the the queue - for (task_server &task : deferred_tasks) - { - queue_tasks.push_back(task); - } - - // remove finished multitasks from the queue of multitasks, and add the corresponding result to the result queue - std::vector agg_results; - auto queue_iterator = queue_multitasks.begin(); - while (queue_iterator != queue_multitasks.end()) - { - if (queue_iterator->subtasks_remaining.empty()) - { - // all subtasks done == multitask is done - task_result aggregate_result; - aggregate_result.id = queue_iterator->id; - aggregate_result.stop = true; - aggregate_result.error = false; - - // collect json results into one json result - std::vector result_jsons; - for (auto& subres : queue_iterator->results) + case TASK_TYPE_COMPLETION: { + llama_client_slot *slot = get_slot(json_value(task.data, "slot_id", -1)); + if (slot == nullptr) { - result_jsons.push_back(subres.result_json); - aggregate_result.error = aggregate_result.error && subres.error; + // if no slot is available, we defer this task for processing later + LOG_VERBOSE("no slot is available", {}); + queue_tasks.defer(task); + break; } - aggregate_result.result_json = json{ "results", result_jsons }; + if (task.data.contains("system_prompt")) + { + if (!all_slots_are_idle) { + send_error(task, "system prompt can only be updated when all slots are idle"); + break; + } + process_system_prompt_data(task.data["system_prompt"]); - agg_results.push_back(aggregate_result); + // reset cache_tokens for all slots + for (llama_client_slot &slot : slots) + { + slot.cache_tokens.clear(); + } + } - condition_results.notify_all(); + slot->reset(); - queue_iterator = queue_multitasks.erase(queue_iterator); - } - else - { - ++queue_iterator; - } + slot->infill = task.infill_mode; + slot->embedding = task.embedding_mode; + slot->task_id = task.id; + slot->multitask_id = task.multitask_id; + + if (!launch_slot_with_data(slot, task.data)) + { + // send error result + send_error(task, "internal_error"); + break; + } + } break; + case TASK_TYPE_CANCEL: { // release slot linked with the task id + for (auto & slot : slots) + { + if (slot.task_id == task.target_id) + { + slot.release(); + break; + } + } + } break; + case TASK_TYPE_NEXT_RESPONSE: { + // do nothing + } break; } + } - // done with tasks, unlock - lock.unlock(); + void on_finish_multitask(task_multi& multitask) + { + // all subtasks done == multitask is done + task_result result; + result.id = multitask.id; + result.stop = true; + result.error = false; - // copy aggregate results of complete multi-tasks to the results queue - std::lock_guard lock_results(mutex_results); - queue_results.insert(queue_results.end(), agg_results.begin(), agg_results.end()); + // collect json results into one json result + std::vector result_jsons; + for (auto& subres : multitask.results) + { + result_jsons.push_back(subres.result_json); + result.error = result.error && subres.error; + } + result.result_json = json{ { "results", result_jsons } }; + queue_results.send(result); } bool update_slots() { - // attend tasks - process_tasks(); - if (system_need_update) { LOG_TEE("updating system prompt\n"); @@ -1684,10 +1339,12 @@ struct llama_server_context LOG_TEE("all slots are idle and system prompt is empty, clear the KV cache\n"); kv_cache_clear(); } - std::unique_lock lock(mutex_tasks); - condition_tasks.wait(lock, [&]{ - return !queue_tasks.empty(); - }); + return true; + } else { + task_server task; + task.type = TASK_TYPE_NEXT_RESPONSE; + task.target_id = -1; + queue_tasks.post(task); } for (llama_client_slot &slot : slots) @@ -1732,6 +1389,7 @@ struct llama_server_context slot.t_last_used = ggml_time_us(); LOG_TEE("slot %d released (%d tokens in cache)\n", slot.id, (int) slot.cache_tokens.size()); + queue_tasks.notify_slot_changed(); continue; } @@ -1997,6 +1655,10 @@ struct llama_server_context } return true; } + + void run_on_all_tasks_finished() { + update_slots(); + } }; static void server_print_usage(const char *argv0, const gpt_params ¶ms, @@ -2541,239 +2203,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } } -static std::string random_string() -{ - static const std::string str("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"); - - std::random_device rd; - std::mt19937 generator(rd()); - - std::string result(32, ' '); - - for (int i = 0; i < 32; ++i) { - result[i] = str[generator() % str.size()]; - } - - return result; -} - -static std::string gen_chatcmplid() -{ - std::stringstream chatcmplid; - chatcmplid << "chatcmpl-" << random_string(); - return chatcmplid.str(); -} - -std::string format_chatml(std::vector messages) -{ - std::ostringstream chatml_msgs; - - for (auto it = messages.begin(); it != messages.end(); ++it) { - chatml_msgs << "<|im_start|>" - << json_value(*it, "role", std::string("user")) << '\n'; - chatml_msgs << json_value(*it, "content", std::string("")) - << "<|im_end|>\n"; - } - - chatml_msgs << "<|im_start|>assistant" << '\n'; - - return chatml_msgs.str(); -} - /* llama.cpp completion api semantics */ -json oaicompat_completion_params_parse( - const json &body /* openai api json semantics */) -{ - json llama_params; - - llama_params["__oaicompat"] = true; - - // Map OpenAI parameters to llama.cpp parameters - // - // For parameters that are defined by the OpenAI documentation (e.g. - // temperature), we explicitly specify OpenAI's intended default; we - // need to do that because sometimes OpenAI disagrees with llama.cpp - // - // https://platform.openai.com/docs/api-reference/chat/create - llama_sampling_params default_sparams; - llama_params["model"] = json_value(body, "model", std::string("unknown")); - llama_params["prompt"] = format_chatml(body["messages"]); // OpenAI 'messages' to llama.cpp 'prompt' - llama_params["cache_prompt"] = json_value(body, "cache_prompt", false); - llama_params["temperature"] = json_value(body, "temperature", 0.0); - llama_params["top_k"] = json_value(body, "top_k", default_sparams.top_k); - llama_params["top_p"] = json_value(body, "top_p", 1.0); - llama_params["n_predict"] = json_value(body, "max_tokens", -1); - llama_params["logit_bias"] = json_value(body, "logit_bias",json::object()); - llama_params["frequency_penalty"] = json_value(body, "frequency_penalty", 0.0); - llama_params["presence_penalty"] = json_value(body, "presence_penalty", 0.0); - llama_params["seed"] = json_value(body, "seed", LLAMA_DEFAULT_SEED); - llama_params["stream"] = json_value(body, "stream", false); - llama_params["mirostat"] = json_value(body, "mirostat", default_sparams.mirostat); - llama_params["mirostat_tau"] = json_value(body, "mirostat_tau", default_sparams.mirostat_tau); - llama_params["mirostat_eta"] = json_value(body, "mirostat_eta", default_sparams.mirostat_eta); - llama_params["penalize_nl"] = json_value(body, "penalize_nl", default_sparams.penalize_nl); - llama_params["typical_p"] = json_value(body, "typical_p", default_sparams.typical_p); - llama_params["repeat_last_n"] = json_value(body, "repeat_last_n", default_sparams.penalty_last_n); - llama_params["ignore_eos"] = json_value(body, "ignore_eos", false); - llama_params["tfs_z"] = json_value(body, "tfs_z", default_sparams.tfs_z); - - if (body.count("grammar") != 0) { - llama_params["grammar"] = json_value(body, "grammar", json::object()); - } - - // Handle 'stop' field - if (body.contains("stop") && body["stop"].is_string()) { - llama_params["stop"] = json::array({body["stop"].get()}); - } else { - llama_params["stop"] = json_value(body, "stop", json::array()); - } - - // Ensure there is ChatML-specific end sequence among stop words - llama_params["stop"].push_back("<|im_end|>"); - - return llama_params; -} - -static json format_final_response_oaicompat(const json &request, const task_result &response, bool streaming = false) -{ - json result = response.result_json; - - bool stopped_word = result.count("stopped_word") != 0; - bool stopped_eos = json_value(result, "stopped_eos", false); - int num_tokens_predicted = json_value(result, "tokens_predicted", 0); - int num_prompt_tokens = json_value(result, "tokens_evaluated", 0); - std::string content = json_value(result, "content", std::string("")); - - std::string finish_reason = "length"; - if (stopped_word || stopped_eos) { - finish_reason = "stop"; - } - - json choices = - streaming ? json::array({json{{"finish_reason", finish_reason}, - {"index", 0}, - {"delta", json::object()}}}) - : json::array({json{{"finish_reason", finish_reason}, - {"index", 0}, - {"message", json{{"content", content}, - {"role", "assistant"}}}}}); - - std::time_t t = std::time(0); - - json res = - json{{"choices", choices}, - {"created", t}, - {"model", - json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, - {"object", streaming ? "chat.completion.chunk" : "chat.completion"}, - {"usage", - json{{"completion_tokens", num_tokens_predicted}, - {"prompt_tokens", num_prompt_tokens}, - {"total_tokens", num_tokens_predicted + num_prompt_tokens}}}, - {"id", gen_chatcmplid()}}; - - if (server_verbose) { - res["__verbose"] = result; - } - - if (result.contains("completion_probabilities")) { - res["completion_probabilities"] = json_value(result, "completion_probabilities", json::array()); - } - - return res; -} - -// return value is vector as there is one case where we might need to generate two responses -static std::vector format_partial_response_oaicompat(const task_result &response) { - json result = response.result_json; - - if (!result.contains("model") || !result.contains("oaicompat_token_ctr")) { - return std::vector({response.result_json}); - } - - bool first = json_value(result, "oaicompat_token_ctr", 0) == 0; - std::string modelname = json_value(result, "model", std::string(DEFAULT_OAICOMPAT_MODEL)); - - bool stopped_word = json_value(result, "stopped_word", false); - bool stopped_eos = json_value(result, "stopped_eos", false); - bool stopped_limit = json_value(result, "stopped_limit", false); - std::string content = json_value(result, "content", std::string("")); - - std::string finish_reason; - if (stopped_word || stopped_eos) { - finish_reason = "stop"; - } - if (stopped_limit) { - finish_reason = "length"; - } - - std::time_t t = std::time(0); - - json choices; - - if (!finish_reason.empty()) { - choices = json::array({json{{"finish_reason", finish_reason}, - {"index", 0}, - {"delta", json::object()}}}); - } else { - if (first) { - if (content.empty()) { - choices = json::array({json{{"finish_reason", nullptr}, - {"index", 0}, - {"delta", json{{"role", "assistant"}}}}}); - } else { - // We have to send this as two updates to conform to openai behavior - json initial_ret = json{{"choices", json::array({json{ - {"finish_reason", nullptr}, - {"index", 0}, - {"delta", json{ - {"role", "assistant"} - }}}})}, - {"created", t}, - {"id", gen_chatcmplid()}, - {"model", modelname}, - {"object", "chat.completion.chunk"}}; - - json second_ret = json{ - {"choices", json::array({json{{"finish_reason", nullptr}, - {"index", 0}, - {"delta", json{ - {"content", content}}} - }})}, - {"created", t}, - {"id", gen_chatcmplid()}, - {"model", modelname}, - {"object", "chat.completion.chunk"}}; - - return std::vector({initial_ret, second_ret}); - } - } else { - // Some idiosyncrasy in task processing logic makes several trailing calls - // with empty content, we ignore these at the calee site. - if (content.empty()) { - return std::vector({json::object()}); - } - - choices = json::array({json{ - {"finish_reason", nullptr}, - {"index", 0}, - {"delta", - json{ - {"content", content}, - }}, - }}); - } - } - - json ret = json{{"choices", choices}, - {"created", t}, - {"id", gen_chatcmplid()}, - {"model", modelname}, - {"object", "chat.completion.chunk"}}; - - return std::vector({ret}); -} - static json format_partial_response( llama_server_context &llama, llama_client_slot *slot, const std::string &content, const std::vector &probs ) { @@ -3069,10 +2499,12 @@ int main(int argc, char **argv) return; } json data = json::parse(req.body); - const int task_id = llama.request_completion(data, false, false, -1); + const int task_id = llama.queue_tasks.get_new_id(); + llama.queue_results.add_waiting_task_id(task_id); + llama.request_completion(task_id, data, false, false, -1); if (!json_value(data, "stream", false)) { std::string completion_text; - task_result result = llama.next_result(task_id); + task_result result = llama.queue_results.recv(task_id); if (!result.error && result.stop) { res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); } @@ -3080,14 +2512,14 @@ int main(int argc, char **argv) { res.status = 404; res.set_content(result.result_json["content"], "text/plain; charset=utf-8"); - return; } + llama.queue_results.remove_waiting_task_id(task_id); } else { const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) { while (true) { - task_result result = llama.next_result(task_id); + task_result result = llama.queue_results.recv(task_id); if (!result.error) { const std::string str = "data: " + @@ -3098,6 +2530,7 @@ int main(int argc, char **argv) }); if (!sink.write(str.c_str(), str.size())) { + llama.queue_results.remove_waiting_task_id(task_id); return false; } if (result.stop) { @@ -3113,11 +2546,14 @@ int main(int argc, char **argv) }); if (!sink.write(str.c_str(), str.size())) { + llama.queue_results.remove_waiting_task_id(task_id); return false; } break; } } + + llama.queue_results.remove_waiting_task_id(task_id); sink.done(); return true; }; @@ -3126,6 +2562,7 @@ int main(int argc, char **argv) { // cancel llama.request_cancel(task_id); + llama.queue_results.remove_waiting_task_id(task_id); }; res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); @@ -3162,11 +2599,13 @@ int main(int argc, char **argv) } json data = oaicompat_completion_params_parse(json::parse(req.body)); - const int task_id = llama.request_completion(data, false, false, -1); + const int task_id = llama.queue_tasks.get_new_id(); + llama.queue_results.add_waiting_task_id(task_id); + llama.request_completion(task_id, data, false, false, -1); if (!json_value(data, "stream", false)) { std::string completion_text; - task_result result = llama.next_result(task_id); + task_result result = llama.queue_results.recv(task_id); if (!result.error && result.stop) { json oaicompat_result = format_final_response_oaicompat(data, result); @@ -3177,12 +2616,12 @@ int main(int argc, char **argv) } else { res.status = 500; res.set_content(result.result_json["content"], "text/plain; charset=utf-8"); - return; } + llama.queue_results.remove_waiting_task_id(task_id); } else { const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink &sink) { while (true) { - task_result llama_result = llama.next_result(task_id); + task_result llama_result = llama.queue_results.recv(task_id); if (!llama_result.error) { std::vector result_array = format_partial_response_oaicompat( llama_result); @@ -3195,6 +2634,7 @@ int main(int argc, char **argv) "\n\n"; LOG_VERBOSE("data stream", {{"to_send", str}}); if (!sink.write(str.c_str(), str.size())) { + llama.queue_results.remove_waiting_task_id(task_id); return false; } } @@ -3210,18 +2650,21 @@ int main(int argc, char **argv) "\n\n"; LOG_VERBOSE("data stream", {{"to_send", str}}); if (!sink.write(str.c_str(), str.size())) { + llama.queue_results.remove_waiting_task_id(task_id); return false; } break; } } sink.done(); + llama.queue_results.remove_waiting_task_id(task_id); return true; }; auto on_complete = [task_id, &llama](bool) { // cancel request llama.request_cancel(task_id); + llama.queue_results.remove_waiting_task_id(task_id); }; res.set_chunked_content_provider("text/event-stream", chunked_content_provider, on_complete); @@ -3235,10 +2678,12 @@ int main(int argc, char **argv) return; } json data = json::parse(req.body); - const int task_id = llama.request_completion(data, true, false, -1); + const int task_id = llama.queue_tasks.get_new_id(); + llama.queue_results.add_waiting_task_id(task_id); + llama.request_completion(task_id, data, true, false, -1); if (!json_value(data, "stream", false)) { std::string completion_text; - task_result result = llama.next_result(task_id); + task_result result = llama.queue_results.recv(task_id); if (!result.error && result.stop) { res.set_content(result.result_json.dump(-1, ' ', false, json::error_handler_t::replace), "application/json; charset=utf-8"); @@ -3247,13 +2692,13 @@ int main(int argc, char **argv) { res.status = 404; res.set_content(result.result_json["content"], "text/plain; charset=utf-8"); - return; } + llama.queue_results.remove_waiting_task_id(task_id); } else { const auto chunked_content_provider = [task_id, &llama](size_t, httplib::DataSink & sink) { while (true) { - task_result result = llama.next_result(task_id); + task_result result = llama.queue_results.recv(task_id); if (!result.error) { const std::string str = "data: " + @@ -3264,6 +2709,7 @@ int main(int argc, char **argv) }); if (!sink.write(str.c_str(), str.size())) { + llama.queue_results.remove_waiting_task_id(task_id); return false; } if (result.stop) @@ -3277,8 +2723,8 @@ int main(int argc, char **argv) } } + llama.queue_results.remove_waiting_task_id(task_id); sink.done(); - return true; }; @@ -3352,23 +2798,46 @@ int main(int argc, char **argv) image_data = ""; } - const int task_id = llama.request_completion({ {"prompt", prompt}, { "n_predict", 0}, {"image_data", image_data} }, false, true, -1); - task_result result = llama.next_result(task_id); + // create and queue the task + const int task_id = llama.queue_tasks.get_new_id(); + llama.queue_results.add_waiting_task_id(task_id); + llama.request_completion(task_id, { {"prompt", prompt}, { "n_predict", 0}, {"image_data", image_data} }, false, true, -1); + + // get the result + task_result result = llama.queue_results.recv(task_id); + llama.queue_results.remove_waiting_task_id(task_id); + + // send the result return res.set_content(result.result_json.dump(), "application/json; charset=utf-8"); }); // GG: if I put the main loop inside a thread, it crashes on the first request when build in Debug!? // "Bus error: 10" - this is on macOS, it does not crash on Linux //std::thread t2([&]() - { + /*{ bool running = true; while (running) { running = llama.update_slots(); } - } + }*/ //); + llama.queue_tasks.on_new_task(std::bind( + &llama_server_context::process_single_task, &llama, std::placeholders::_1)); + llama.queue_tasks.on_finish_multitask(std::bind( + &llama_server_context::on_finish_multitask, &llama, std::placeholders::_1)); + llama.queue_tasks.on_all_tasks_finished(std::bind( + &llama_server_context::run_on_all_tasks_finished, &llama)); + llama.queue_results.on_multitask_update(std::bind( + &llama_server_queue::update_multitask, + &llama.queue_tasks, + std::placeholders::_1, + std::placeholders::_2, + std::placeholders::_3 + )); + llama.queue_tasks.start_loop(); + t.join(); llama_backend_free(); diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp new file mode 100644 index 000000000..e2b6065f7 --- /dev/null +++ b/examples/server/utils.hpp @@ -0,0 +1,507 @@ +#pragma once + +#include +#include +#include +#include +#include +#include + +#include "json.hpp" + +#include "../llava/clip.h" + +using json = nlohmann::json; + +extern bool server_verbose; + +#ifndef SERVER_VERBOSE +#define SERVER_VERBOSE 1 +#endif + +#if SERVER_VERBOSE != 1 +#define LOG_VERBOSE(MSG, ...) +#else +#define LOG_VERBOSE(MSG, ...) \ + do \ + { \ + if (server_verbose) \ + { \ + server_log("VERBOSE", __func__, __LINE__, MSG, __VA_ARGS__); \ + } \ + } while (0) +#endif + +#define LOG_ERROR( MSG, ...) server_log("ERROR", __func__, __LINE__, MSG, __VA_ARGS__) +#define LOG_WARNING(MSG, ...) server_log("WARNING", __func__, __LINE__, MSG, __VA_ARGS__) +#define LOG_INFO( MSG, ...) server_log("INFO", __func__, __LINE__, MSG, __VA_ARGS__) + +// +// parallel +// + +enum server_state { + SERVER_STATE_LOADING_MODEL, // Server is starting up, model not fully loaded yet + SERVER_STATE_READY, // Server is ready and model is loaded + SERVER_STATE_ERROR // An error occurred, load_model failed +}; + +enum task_type { + TASK_TYPE_COMPLETION, + TASK_TYPE_CANCEL, + TASK_TYPE_NEXT_RESPONSE +}; + +struct task_server { + int id = -1; // to be filled by llama_server_queue + int target_id; + task_type type; + json data; + bool infill_mode = false; + bool embedding_mode = false; + int multitask_id = -1; +}; + +struct task_result { + int id; + int multitask_id = -1; + bool stop; + bool error; + json result_json; +}; + +struct task_multi { + int id; + std::set subtasks_remaining{}; + std::vector results{}; +}; + +// TODO: can become bool if we can't find use of more states +enum slot_state +{ + IDLE, + PROCESSING, +}; + +enum slot_command +{ + NONE, + LOAD_PROMPT, + RELEASE, +}; + +struct slot_params +{ + bool stream = true; + bool cache_prompt = false; // remember the prompt to avoid reprocessing all prompt + + uint32_t seed = -1; // RNG seed + int32_t n_keep = 0; // number of tokens to keep from initial prompt + int32_t n_predict = -1; // new tokens to predict + + std::vector antiprompt; + + json input_prefix; + json input_suffix; +}; + +struct slot_image +{ + int32_t id; + + bool request_encode_image = false; + float * image_embedding = nullptr; + int32_t image_tokens = 0; + + clip_image_u8 * img_data; + + std::string prefix_prompt; // before of this image +}; + +// completion token output with probabilities +struct completion_token_output +{ + struct token_prob + { + llama_token tok; + float prob; + }; + + std::vector probs; + llama_token tok; + std::string text_to_send; +}; + +static inline void server_log(const char *level, const char *function, int line, + const char *message, const nlohmann::ordered_json &extra) +{ + nlohmann::ordered_json log + { + {"timestamp", time(nullptr)}, + {"level", level}, + {"function", function}, + {"line", line}, + {"message", message}, + }; + + if (!extra.empty()) + { + log.merge_patch(extra); + } + + const std::string str = log.dump(-1, ' ', false, json::error_handler_t::replace); + printf("%.*s\n", (int)str.size(), str.data()); + fflush(stdout); +} + +// +// server utils +// + +template +static T json_value(const json &body, const std::string &key, const T &default_value) +{ + // Fallback null to default value + return body.contains(key) && !body.at(key).is_null() + ? body.value(key, default_value) + : default_value; +} + +inline std::string format_chatml(std::vector messages) +{ + std::ostringstream chatml_msgs; + + for (auto it = messages.begin(); it != messages.end(); ++it) { + chatml_msgs << "<|im_start|>" + << json_value(*it, "role", std::string("user")) << '\n'; + chatml_msgs << json_value(*it, "content", std::string("")) + << "<|im_end|>\n"; + } + + chatml_msgs << "<|im_start|>assistant" << '\n'; + + return chatml_msgs.str(); +} + +// +// work queue utils +// + +struct llama_server_queue { + int id = 0; + std::mutex mutex_tasks; + // queues + std::vector queue_tasks; + std::vector queue_tasks_deferred; + std::vector queue_multitasks; + std::condition_variable condition_tasks; + // callback functions + std::function callback_new_task; + std::function callback_finish_multitask; + std::function callback_all_task_finished; + + // Add a new task to the end of the queue + int post(task_server task) { + std::unique_lock lock(mutex_tasks); + if (task.id == -1) { + task.id = id++; + } + queue_tasks.push_back(std::move(task)); + condition_tasks.notify_one(); + return task.id; + } + + // Add a new task, but defer until one slot is available + void defer(task_server task) { + std::unique_lock lock(mutex_tasks); + queue_tasks_deferred.push_back(std::move(task)); + } + + // Get the next id for creating anew task + int get_new_id() { + std::unique_lock lock(mutex_tasks); + return id++; + } + + // Register function to process a new task + void on_new_task(std::function callback) { + callback_new_task = callback; + } + + // Register function to process a multitask + void on_finish_multitask(std::function callback) { + callback_finish_multitask = callback; + } + + // Register the function to be called when the batch of tasks is finished + void on_all_tasks_finished(std::function callback) { + callback_all_task_finished = callback; + } + + // Call when the state of one slot is changed + void notify_slot_changed() { + // move deferred tasks back to main loop + std::unique_lock lock(mutex_tasks); + for (auto & task : queue_tasks_deferred) { + queue_tasks.push_back(std::move(task)); + } + queue_tasks_deferred.clear(); + } + + // Start the main loop. This call is blocking + void start_loop() { + while (true) { + // new task arrived + LOG_VERBOSE("have new task", {}); + { + while (true) + { + std::unique_lock lock(mutex_tasks); + if (queue_tasks.empty()) { + lock.unlock(); + break; + } + task_server task = queue_tasks.front(); + queue_tasks.erase(queue_tasks.begin()); + lock.unlock(); + LOG_VERBOSE("callback_new_task", {}); + callback_new_task(task); + } + LOG_VERBOSE("callback_all_task_finished", {}); + // process and update all the multitasks + auto queue_iterator = queue_multitasks.begin(); + while (queue_iterator != queue_multitasks.end()) + { + if (queue_iterator->subtasks_remaining.empty()) + { + // all subtasks done == multitask is done + task_multi current_multitask = *queue_iterator; + callback_finish_multitask(current_multitask); + // remove this multitask + queue_iterator = queue_multitasks.erase(queue_iterator); + } + else + { + ++queue_iterator; + } + } + // all tasks in the current loop is finished + callback_all_task_finished(); + } + LOG_VERBOSE("wait for new task", {}); + // wait for new task + { + std::unique_lock lock(mutex_tasks); + if (queue_tasks.empty()) { + condition_tasks.wait(lock, [&]{ + return !queue_tasks.empty(); + }); + } + } + } + } + + // + // functions to manage multitasks + // + + // add a multitask by specifying the id of all subtask (subtask is a task_server) + void add_multitask(int multitask_id, std::vector& sub_ids) + { + std::lock_guard lock(mutex_tasks); + task_multi multi; + multi.id = multitask_id; + std::copy(sub_ids.begin(), sub_ids.end(), std::inserter(multi.subtasks_remaining, multi.subtasks_remaining.end())); + queue_multitasks.push_back(multi); + } + + // updatethe remaining subtasks, while appending results to multitask + void update_multitask(int multitask_id, int subtask_id, task_result& result) + { + std::lock_guard lock(mutex_tasks); + for (auto& multitask : queue_multitasks) + { + if (multitask.id == multitask_id) + { + multitask.subtasks_remaining.erase(subtask_id); + multitask.results.push_back(result); + } + } + } +}; + +struct llama_server_response { + typedef std::function callback_multitask_t; + callback_multitask_t callback_update_multitask; + // for keeping track of all tasks waiting for the result + std::set waiting_task_ids; + // the main result queue + std::vector queue_results; + std::mutex mutex_results; + std::condition_variable condition_results; + + void add_waiting_task_id(int task_id) { + std::unique_lock lock(mutex_results); + waiting_task_ids.insert(task_id); + } + + void remove_waiting_task_id(int task_id) { + std::unique_lock lock(mutex_results); + waiting_task_ids.erase(task_id); + } + + // This function blocks the thread until there is a response for this task_id + task_result recv(int task_id) { + while (true) + { + std::unique_lock lock(mutex_results); + condition_results.wait(lock, [&]{ + return !queue_results.empty(); + }); + LOG_VERBOSE("condition_results unblock", {}); + + for (int i = 0; i < (int) queue_results.size(); i++) + { + if (queue_results[i].id == task_id) + { + assert(queue_results[i].multitask_id == -1); + task_result res = queue_results[i]; + queue_results.erase(queue_results.begin() + i); + return res; + } + } + } + + // should never reach here + } + + // Register the function to update multitask + void on_multitask_update(callback_multitask_t callback) { + callback_update_multitask = callback; + } + + // Send a new result to a waiting task_id + void send(task_result result) { + std::unique_lock lock(mutex_results); + LOG_VERBOSE("send new result", {}); + for (auto& task_id : waiting_task_ids) { + // LOG_TEE("waiting task id %i \n", task_id); + // for now, tasks that have associated parent multitasks just get erased once multitask picks up the result + if (result.multitask_id == task_id) + { + LOG_VERBOSE("callback_update_multitask", {}); + callback_update_multitask(task_id, result.id, result); + continue; + } + + if (result.id == task_id) + { + LOG_VERBOSE("queue_results.push_back", {}); + queue_results.push_back(result); + condition_results.notify_one(); + return; + } + } + } +}; + +// +// base64 utils (TODO: move to common in the future) +// + +static const std::string base64_chars = + "ABCDEFGHIJKLMNOPQRSTUVWXYZ" + "abcdefghijklmnopqrstuvwxyz" + "0123456789+/"; + +static inline bool is_base64(uint8_t c) +{ + return (isalnum(c) || (c == '+') || (c == '/')); +} + +static inline std::vector base64_decode(const std::string & encoded_string) +{ + int i = 0; + int j = 0; + int in_ = 0; + + int in_len = encoded_string.size(); + + uint8_t char_array_4[4]; + uint8_t char_array_3[3]; + + std::vector ret; + + while (in_len-- && (encoded_string[in_] != '=') && is_base64(encoded_string[in_])) + { + char_array_4[i++] = encoded_string[in_]; in_++; + if (i == 4) + { + for (i = 0; i <4; i++) + { + char_array_4[i] = base64_chars.find(char_array_4[i]); + } + + char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4); + char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); + char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; + + for (i = 0; (i < 3); i++) + { + ret.push_back(char_array_3[i]); + } + i = 0; + } + } + + if (i) + { + for (j = i; j <4; j++) + { + char_array_4[j] = 0; + } + + for (j = 0; j <4; j++) + { + char_array_4[j] = base64_chars.find(char_array_4[j]); + } + + char_array_3[0] = ((char_array_4[0] ) << 2) + ((char_array_4[1] & 0x30) >> 4); + char_array_3[1] = ((char_array_4[1] & 0xf) << 4) + ((char_array_4[2] & 0x3c) >> 2); + char_array_3[2] = ((char_array_4[2] & 0x3) << 6) + char_array_4[3]; + + for (j = 0; (j < i - 1); j++) + { + ret.push_back(char_array_3[j]); + } + } + + return ret; +} + +// +// random string / id +// + +static std::string random_string() +{ + static const std::string str("0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"); + + std::random_device rd; + std::mt19937 generator(rd()); + + std::string result(32, ' '); + + for (int i = 0; i < 32; ++i) { + result[i] = str[generator() % str.size()]; + } + + return result; +} + +static std::string gen_chatcmplid() +{ + std::stringstream chatcmplid; + chatcmplid << "chatcmpl-" << random_string(); + return chatcmplid.str(); +} From 3b7c914de25c6851396d7f9178249f1ed278120e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 26 Jan 2024 14:48:15 +0200 Subject: [PATCH 024/143] tests : gitignore test-c.o --- tests/.gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/.gitignore b/tests/.gitignore index 59be43b99..092dce742 100644 --- a/tests/.gitignore +++ b/tests/.gitignore @@ -1,2 +1,3 @@ * !*.* +test-c.o From 5f1925a8cef81eb9b372faaae34b0dd76d5361d4 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 26 Jan 2024 17:09:44 +0200 Subject: [PATCH 025/143] scripts : move run-with-preset.py from root to scripts folder --- run_with_preset.py => scripts/run-with-preset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) rename run_with_preset.py => scripts/run-with-preset.py (98%) diff --git a/run_with_preset.py b/scripts/run-with-preset.py similarity index 98% rename from run_with_preset.py rename to scripts/run-with-preset.py index 9b4d7ecbe..a18252730 100755 --- a/run_with_preset.py +++ b/scripts/run-with-preset.py @@ -46,7 +46,7 @@ Formatting considerations: - 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 ...] [-- ...]" +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.") From 7032f4f6349c17a8352f9f93f7d2122f45469e59 Mon Sep 17 00:00:00 2001 From: snadampal <87143774+snadampal@users.noreply.github.com> Date: Fri, 26 Jan 2024 11:17:59 -0600 Subject: [PATCH 026/143] ggml : update softmax n_task calculation (#5126) updated the n_task calculation to use max number of threads possible. This has improved the prompt eval performance by around 5% for DOT kernels and by around 10% for MMLA kernels on AWS Graviton3. --- ggml.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml.c b/ggml.c index ca98fde8a..ef6fd8caf 100644 --- a/ggml.c +++ b/ggml.c @@ -16597,7 +16597,7 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads) { } break; case GGML_OP_SOFT_MAX: { - n_tasks = MIN(MIN(4, n_threads), ggml_nrows(node->src[0])); + n_tasks = MIN(n_threads, ggml_nrows(node->src[0])); } break; case GGML_OP_CONV_TRANSPOSE_1D: { From 15b4538ff29b280a395a1406d711497d8eaa2564 Mon Sep 17 00:00:00 2001 From: slaren Date: Fri, 26 Jan 2024 18:18:26 +0100 Subject: [PATCH 027/143] ggml-alloc : add 10% margin to the buffer sizes (#5149) --- ggml-alloc.c | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/ggml-alloc.c b/ggml-alloc.c index 60141a34d..95a93c99d 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -335,7 +335,9 @@ bool ggml_tallocr_is_measure(ggml_tallocr_t alloc) { } size_t ggml_tallocr_max_size(ggml_tallocr_t alloc) { - return alloc->max_size; + // FIXME: changes in the tensor sizes compared to the measure graph may cause allocations to fail + // to avoid this, we add a 10% margin to the buffer size + return alloc->max_size + alloc->max_size/10; } // graph allocator From 62fead3ea0a30c8d424f4a8373fa14165c7c707f Mon Sep 17 00:00:00 2001 From: slaren Date: Fri, 26 Jan 2024 18:59:43 +0100 Subject: [PATCH 028/143] cuda : fix tensor size calculation for non-split buffer (#5145) --- ggml-backend.c | 4 +++- ggml-cuda.cu | 19 +++++-------------- 2 files changed, 8 insertions(+), 15 deletions(-) diff --git a/ggml-backend.c b/ggml-backend.c index 423512def..3fff5fc87 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -30,7 +30,9 @@ size_t ggml_backend_buft_get_alignment(ggml_backend_buffer_type_t buft) { GGML_CALL size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor) { // get_alloc_size is optional, defaults to ggml_nbytes if (buft->iface.get_alloc_size) { - return buft->iface.get_alloc_size(buft, tensor); + size_t size = buft->iface.get_alloc_size(buft, tensor); + assert(size >= ggml_nbytes(tensor)); + return size; } return ggml_nbytes(tensor); } diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 05e5d18ab..0d599e20a 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -9790,8 +9790,8 @@ static void ggml_cuda_mul_mat_id(const ggml_tensor * src0, const ggml_tensor * s // TODO: mmq/mmv support #endif - const int64_t nb11 = src1->nb[1]; - const int64_t nb1 = dst->nb[1]; + const size_t nb11 = src1->nb[1]; + const size_t nb1 = dst->nb[1]; const struct ggml_tensor * ids = src0; const int32_t id = ((int32_t *) dst->op_params)[0]; @@ -10304,15 +10304,11 @@ GGML_CALL static void ggml_backend_cuda_buffer_init_tensor(ggml_backend_buffer_t if (ggml_is_quantized(tensor->type)) { // initialize padding to 0 to avoid possible NaN values - int64_t row_low = 0; - int64_t row_high = ggml_nrows(tensor); - int64_t nrows_split = row_high - row_low; - - size_t original_size = ggml_nbytes_split(tensor, nrows_split); + size_t original_size = ggml_nbytes(tensor); size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor); if (padded_size > original_size && tensor->view_src == nullptr) { - CUDA_CHECK(cudaMemsetAsync((char *)tensor->data + original_size, 0, padded_size - original_size, g_cudaStreams[ctx->device][0])); + CUDA_CHECK(cudaMemset((char *)tensor->data + original_size, 0, padded_size - original_size)); } } } @@ -10415,12 +10411,7 @@ GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alignment(ggml_backend } GGML_CALL static size_t ggml_backend_cuda_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { - int64_t row_low = 0; - int64_t row_high = ggml_nrows(tensor); - int64_t nrows_split = row_high - row_low; - - size_t size = ggml_nbytes_split(tensor, nrows_split); - + size_t size = ggml_nbytes(tensor); int64_t ne0 = tensor->ne[0]; if (ggml_is_quantized(tensor->type)) { From bbe7c56c9993af86aa2d84cbe1fd69e1b4300cea Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Fri, 26 Jan 2024 15:34:06 -0500 Subject: [PATCH 029/143] cmake : pass CPU architecture flags to nvcc (#5146) --- CMakeLists.txt | 74 ++++++++++++++++++++++++++------------------------ 1 file changed, 39 insertions(+), 35 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index af3665129..2b2ae532e 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -466,17 +466,17 @@ function(get_flags CCID CCVER) (CCID STREQUAL "Clang" AND CCVER VERSION_GREATER_EQUAL 3.8.0) OR (CCID STREQUAL "AppleClang" AND CCVER VERSION_GREATER_EQUAL 7.3.0) ) - set(C_FLAGS ${C_FLAGS} -Wdouble-promotion) + list(APPEND C_FLAGS -Wdouble-promotion) endif() elseif (CCID STREQUAL "GNU") set(C_FLAGS -Wdouble-promotion) set(CXX_FLAGS -Wno-array-bounds) if (CCVER VERSION_GREATER_EQUAL 7.1.0) - set(CXX_FLAGS ${CXX_FLAGS} -Wno-format-truncation) + list(APPEND CXX_FLAGS -Wno-format-truncation) endif() if (CCVER VERSION_GREATER_EQUAL 8.1.0) - set(CXX_FLAGS ${CXX_FLAGS} -Wextra-semi) + list(APPEND CXX_FLAGS -Wextra-semi) endif() elseif (CCID MATCHES "Intel") # enable max optimization level when using Intel compiler @@ -510,16 +510,18 @@ if (LLAMA_ALL_WARNINGS) endif() endif() +set(CUDA_CXX_FLAGS "") + if (LLAMA_CUBLAS) set(CUDA_FLAGS ${CXX_FLAGS} -use_fast_math) if (NOT MSVC) - set(CUDA_FLAGS ${CUDA_FLAGS} -Wno-pedantic) + list(APPEND CUDA_FLAGS -Wno-pedantic) endif() if (LLAMA_ALL_WARNINGS AND NOT MSVC) set(NVCC_CMD ${CMAKE_CUDA_COMPILER} .c) if (NOT CMAKE_CUDA_HOST_COMPILER STREQUAL "") - set(NVCC_CMD ${NVCC_CMD} -ccbin ${CMAKE_CUDA_HOST_COMPILER}) + list(APPEND NVCC_CMD -ccbin ${CMAKE_CUDA_HOST_COMPILER}) endif() execute_process( @@ -547,13 +549,8 @@ if (LLAMA_CUBLAS) message("-- CUDA host compiler is ${CUDA_CCID} ${CUDA_CCVER}") get_flags(${CUDA_CCID} ${CUDA_CCVER}) - list(JOIN GF_CXX_FLAGS " " CUDA_CXX_FLAGS) # pass host compiler flags as a single argument - if (NOT CUDA_CXX_FLAGS STREQUAL "") - set(CUDA_FLAGS ${CUDA_FLAGS} -Xcompiler ${CUDA_CXX_FLAGS}) - endif() + list(APPEND CUDA_CXX_FLAGS ${GF_CXX_FLAGS}) # This is passed to -Xcompiler later endif() - - add_compile_options("$<$:${CUDA_FLAGS}>") endif() if (WIN32) @@ -618,12 +615,7 @@ if (NOT MSVC) endif() endif() -function(add_compile_option_cpp ARG) - # Adds a compile option to C/C++ only, but not for Cuda. - # Use, e.g., for CPU-architecture flags. - add_compile_options($<$:${ARG}>) - add_compile_options($<$:${ARG}>) -endfunction() +set(ARCH_FLAGS "") if ((${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm") OR (${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64") OR ("${CMAKE_GENERATOR_PLATFORM_LWR}" MATCHES "arm64")) message(STATUS "ARM detected") @@ -636,19 +628,19 @@ if ((${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm") OR (${CMAKE_SYSTEM_PROCESSOR} MATC else() check_cxx_compiler_flag(-mfp16-format=ieee COMPILER_SUPPORTS_FP16_FORMAT_I3E) if (NOT "${COMPILER_SUPPORTS_FP16_FORMAT_I3E}" STREQUAL "") - add_compile_options(-mfp16-format=ieee) + list(APPEND ARCH_FLAGS -mfp16-format=ieee) endif() if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv6") # Raspberry Pi 1, Zero - add_compile_options(-mfpu=neon-fp-armv8 -mno-unaligned-access) + list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access) endif() if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv7") # Raspberry Pi 2 - add_compile_options(-mfpu=neon-fp-armv8 -mno-unaligned-access -funsafe-math-optimizations) + list(APPEND ARCH_FLAGS -mfpu=neon-fp-armv8 -mno-unaligned-access -funsafe-math-optimizations) endif() if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "armv8") # Raspberry Pi 3, 4, Zero 2 (32-bit) - add_compile_options(-mno-unaligned-access) + list(APPEND ARCH_FLAGS -mno-unaligned-access) endif() endif() elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GENERATOR_PLATFORM_LWR}" MATCHES "^(x86_64|i686|amd64|x64)$" ) @@ -659,7 +651,7 @@ elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GE include(cmake/FindSIMD.cmake) endif () if (LLAMA_AVX512) - add_compile_option_cpp(/arch:AVX512) + list(APPEND ARCH_FLAGS /arch:AVX512) # MSVC has no compile-time flags enabling specific # AVX512 extensions, neither it defines the # macros corresponding to the extensions. @@ -673,49 +665,61 @@ elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GE add_compile_definitions($<$:__AVX512VNNI__>) endif() elseif (LLAMA_AVX2) - add_compile_option_cpp(/arch:AVX2) + list(APPEND ARCH_FLAGS /arch:AVX2) elseif (LLAMA_AVX) - add_compile_option_cpp(/arch:AVX) + list(APPEND ARCH_FLAGS /arch:AVX) endif() else() if (LLAMA_NATIVE) - add_compile_option_cpp(-march=native) + list(APPEND ARCH_FLAGS -march=native) endif() if (LLAMA_F16C) - add_compile_option_cpp(-mf16c) + list(APPEND ARCH_FLAGS -mf16c) endif() if (LLAMA_FMA) - add_compile_option_cpp(-mfma) + list(APPEND ARCH_FLAGS -mfma) endif() if (LLAMA_AVX) - add_compile_option_cpp(-mavx) + list(APPEND ARCH_FLAGS -mavx) endif() if (LLAMA_AVX2) - add_compile_option_cpp(-mavx2) + list(APPEND ARCH_FLAGS -mavx2) endif() if (LLAMA_AVX512) - add_compile_option_cpp(-mavx512f) - add_compile_option_cpp(-mavx512bw) + list(APPEND ARCH_FLAGS -mavx512f) + list(APPEND ARCH_FLAGS -mavx512bw) endif() if (LLAMA_AVX512_VBMI) - add_compile_option_cpp(-mavx512vbmi) + list(APPEND ARCH_FLAGS -mavx512vbmi) endif() if (LLAMA_AVX512_VNNI) - add_compile_option_cpp(-mavx512vnni) + list(APPEND ARCH_FLAGS -mavx512vnni) endif() endif() elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64") message(STATUS "PowerPC detected") if (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64le") - add_compile_options(-mcpu=powerpc64le) + list(APPEND ARCH_FLAGS -mcpu=powerpc64le) else() - add_compile_options(-mcpu=native -mtune=native) + list(APPEND ARCH_FLAGS -mcpu=native -mtune=native) #TODO: Add targets for Power8/Power9 (Altivec/VSX) and Power10(MMA) and query for big endian systems (ppc64/le/be) endif() else() message(STATUS "Unknown architecture") endif() +add_compile_options("$<$:${ARCH_FLAGS}>") +add_compile_options("$<$:${ARCH_FLAGS}>") + +if (LLAMA_CUBLAS) + list(APPEND CUDA_CXX_FLAGS ${ARCH_FLAGS}) + list(JOIN CUDA_CXX_FLAGS " " CUDA_CXX_FLAGS_JOINED) # pass host compiler flags as a single argument + if (NOT CUDA_CXX_FLAGS_JOINED STREQUAL "") + list(APPEND CUDA_FLAGS -Xcompiler ${CUDA_CXX_FLAGS_JOINED}) + endif() + add_compile_options("$<$:${CUDA_FLAGS}>") +endif() + if (MINGW) # Target Windows 8 for PrefetchVirtualMemory add_compile_definitions(_WIN32_WINNT=${LLAMA_WIN_VER}) From a1d6df129bcd3d42cda38c09217d8d4ec4ea3bdd Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Fri, 26 Jan 2024 23:07:32 +0100 Subject: [PATCH 030/143] Add OpenCL add kernel (#5151) * Add OpenCL add kernel * Put add kernel into different string to stay within MSVC string length limit, disable float16 support due to bad results --- ggml-opencl.cpp | 87 +++++++++++++++++++++++++++++++++++++++++++++++-- ggml-opencl.h | 1 + ggml.c | 11 +++++++ 3 files changed, 96 insertions(+), 3 deletions(-) diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp index 2bb93638f..bf9ad964f 100644 --- a/ggml-opencl.cpp +++ b/ggml-opencl.cpp @@ -714,7 +714,6 @@ __kernel void dequantize_mul_mat_vec_q6_K(__global const struct block_q6_K * xx, dst[row] = tmp[0]; } } - ); @@ -784,6 +783,7 @@ __kernel void KERNEL_NAME(__global X_TYPE* x, __local float* tmp, __global float dst[row] = tmp[0]; } } + ); @@ -799,6 +799,18 @@ __kernel void KERNEL_NAME(__global TYPE* x, const int x_offset, __global TYPE* y } ); +std::string add_template = MULTILINE_QUOTE( +__kernel void add_f32(__global float * x, const int x_offset, __global float * y, const int y_offset, __global float * dst, const int dst_offset, const int ky) { + const int i = get_group_id(0)*get_local_size(0) + get_local_id(0); + + if (i >= get_global_size(0)) { + return; + } + + dst[dst_offset + i] = x[x_offset + i] + y[y_offset + i%ky]; +} +); + #define CL_CHECK(err) \ do { \ cl_int err_ = (err); \ @@ -878,6 +890,7 @@ static std::string generate_kernels() { } src << mul_kernel << '\n'; } + src << add_template << '\n'; return src.str(); } @@ -893,6 +906,7 @@ static cl_kernel dequantize_mul_mat_vec_q4_0_cl, dequantize_mul_mat_vec_q4_1_cl, static cl_kernel dequantize_block_q2_k_cl, dequantize_block_q3_k_cl, dequantize_block_q4_k_cl, dequantize_block_q5_k_cl, dequantize_block_q6_k_cl; static cl_kernel dequantize_mul_mat_vec_q2_K_cl, dequantize_mul_mat_vec_q3_K_cl, dequantize_mul_mat_vec_q4_K_cl, dequantize_mul_mat_vec_q5_K_cl, dequantize_mul_mat_vec_q6_K_cl; static cl_kernel mul_f32_cl; +static cl_kernel add_f32_cl; static bool fp16_support; static cl_program build_program_from_source(cl_context ctx, cl_device_id dev, const char* program_buffer) { @@ -1100,9 +1114,10 @@ void ggml_cl_init(void) { char *ext_buffer = (char *)alloca(ext_str_size + 1); clGetDeviceInfo(device, CL_DEVICE_EXTENSIONS, ext_str_size, ext_buffer, NULL); ext_buffer[ext_str_size] = '\0'; // ensure it is null terminated + // Disabled due to faulty outputs // Check if ext_buffer contains cl_khr_fp16 - fp16_support = strstr(ext_buffer, "cl_khr_fp16") != NULL; - fprintf(stderr, "ggml_opencl: device FP16 support: %s\n", fp16_support ? "true" : "false"); + fp16_support = false; // strstr(ext_buffer, "cl_khr_fp16") != NULL; + // fprintf(stderr, "ggml_opencl: device FP16 support: %s\n", fp16_support ? "true" : "false"); cl_context_properties properties[] = { (intptr_t)CL_CONTEXT_PLATFORM, (intptr_t)platform, 0 @@ -1150,6 +1165,8 @@ void ggml_cl_init(void) { // mul kernel CL_CHECK((mul_f32_cl = clCreateKernel(program, "mul_f32", &err), err)); + + CL_CHECK((add_f32_cl = clCreateKernel(program, "add_f32", &err), err)); } static cl_kernel* ggml_get_to_fp32_cl(ggml_type type) { @@ -1458,6 +1475,70 @@ void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src ggml_cl_mul_f32(src0, src1, dst); } +static void ggml_cl_add_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(src1->backend == GGML_BACKEND_GPU); + 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 int nb2 = dst->nb[2]; + const int nb3 = dst->nb[3]; + size_t x_size; + size_t d_size; + + cl_mem d_X = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &x_size); // src0 + cl_mem d_Y = (cl_mem) src1->extra; // src1 is already on device, broadcasted. + cl_mem d_D = ggml_cl_pool_malloc(ne00 * ne01 * sizeof(float), &d_size); // dst + + + for (int64_t i03 = 0; i03 < ne03; i03++) { + for (int64_t i02 = 0; i02 < ne02; i02++) { + cl_event ev; + + // copy src0 to device + CL_CHECK(ggml_cl_h2d_tensor_2d(queue, d_X, 0, src0, i03, i02, &ev)); + + const int64_t i13 = i03%ne13; + const int64_t i12 = i02%ne12; + const int i1 = i13*ne12*ne11 + i12*ne11; + + cl_int x_offset = 0; + cl_int y_offset = i1*ne10; + cl_int d_offset = 0; + + size_t global = ne00 * ne01; + cl_int ky = ne10 * ne11; + + CL_CHECK(clSetKernelArg(add_f32_cl, 0, sizeof(cl_mem), &d_X)); + CL_CHECK(clSetKernelArg(add_f32_cl, 1, sizeof(cl_int), &x_offset)); + CL_CHECK(clSetKernelArg(add_f32_cl, 2, sizeof(cl_mem), &d_Y)); + CL_CHECK(clSetKernelArg(add_f32_cl, 3, sizeof(cl_int), &y_offset)); + CL_CHECK(clSetKernelArg(add_f32_cl, 4, sizeof(cl_mem), &d_D)); + CL_CHECK(clSetKernelArg(add_f32_cl, 5, sizeof(cl_int), &d_offset)); + CL_CHECK(clSetKernelArg(add_f32_cl, 6, sizeof(cl_int), &ky)); + CL_CHECK(clEnqueueNDRangeKernel(queue, add_f32_cl, 1, NULL, &global, NULL, 1, &ev, NULL)); + + CL_CHECK(clReleaseEvent(ev)); + CL_CHECK(clFinish(queue)); + + // copy dst to host + float * d = (float *) ((char *) dst->data + i02*nb2 + i03*nb3); + CL_CHECK(clEnqueueReadBuffer(queue, d_D, true, 0, sizeof(float) * ne00*ne01, d, 0, NULL, NULL)); + } + } + ggml_cl_pool_free(d_X, x_size); + ggml_cl_pool_free(d_D, d_size); +} + +void ggml_cl_add(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { + GGML_ASSERT(src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32); + ggml_cl_add_f32(src0, src1, dst); +} + static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; diff --git a/ggml-opencl.h b/ggml-opencl.h index 919b00d63..257a6be6a 100644 --- a/ggml-opencl.h +++ b/ggml-opencl.h @@ -10,6 +10,7 @@ extern "C" { GGML_API void ggml_cl_init(void); GGML_API void ggml_cl_mul(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); +GGML_API void ggml_cl_add(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); GGML_API bool ggml_cl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, const struct ggml_tensor * dst); GGML_API size_t ggml_cl_mul_mat_get_wsize(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); GGML_API void ggml_cl_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst, void * wdata, size_t wsize); diff --git a/ggml.c b/ggml.c index ef6fd8caf..8f57003e0 100644 --- a/ggml.c +++ b/ggml.c @@ -7207,6 +7207,17 @@ static void ggml_compute_forward_add_f32( const int ith = params->ith; const int nth = params->nth; +#ifdef GGML_USE_CLBLAST + if (src1->backend == GGML_BACKEND_GPU) { + // TODO: OpenCL kernel support full broadcast + GGML_ASSERT(ggml_can_repeat_rows(src1, src0)); + if (ith == 0) { + ggml_cl_add(src0, src1, dst); + } + return; + } +#endif + const int nr = ggml_nrows(src0); GGML_TENSOR_BINARY_OP_LOCALS From ec903c034131848da9222536ff18da07ec0882a0 Mon Sep 17 00:00:00 2001 From: Maximilian Winter Date: Sat, 27 Jan 2024 14:38:05 +0100 Subject: [PATCH 031/143] server : add self-extend support (#5104) * Ported self extension to server example * Update server.cpp * Fixed prompt caching without self extend * Update server.cpp * Added description to server readme. * Update server.cpp * Update server.cpp * Update server.cpp * Update server.cpp * Update README.md * Changed descriptions * server : formatting * Update examples/server/server.cpp Co-authored-by: Georgi Gerganov * Update examples/server/server.cpp Co-authored-by: Georgi Gerganov * Update server.cpp * Update server.cpp --------- Co-authored-by: Georgi Gerganov --- examples/server/README.md | 3 +- examples/server/server.cpp | 172 +++++++++++++++++++++++++++++++------ 2 files changed, 147 insertions(+), 28 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index fd3034b99..1c92a2041 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -30,7 +30,8 @@ Command line options: - `-cb`, `--cont-batching`: enable continuous batching (a.k.a dynamic batching) (default: disabled) - `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load "a system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime) - `--mmproj MMPROJ_FILE`: Path to a multimodal projector file for LLaVA. - +- `--grp-attn-n`: Set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w` +- `--grp-attn-w`: Set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n` ## Build server is build alongside everything else from the root of the project diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 392836132..af63f2f6f 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -184,6 +184,12 @@ struct llama_client_slot struct llama_sampling_params sparams; llama_sampling_context *ctx_sampling = nullptr; + int32_t ga_i = 0; // group-attention state + int32_t ga_n = 1;// group-attention factor + int32_t ga_w = 512; // group-attention width + + int32_t n_past_se = 0; // self-extend + // multimodal std::vector images; @@ -212,7 +218,8 @@ struct llama_client_slot sent_count = 0; sent_token_probs_index = 0; infill = false; - + ga_i = 0; + n_past_se = 0; generated_token_probs.clear(); for (slot_image & img : images) @@ -399,9 +406,26 @@ struct llama_server_context slot.id = i; slot.n_ctx = n_ctx_slot; - slot.reset(); LOG_TEE(" -> Slot %i - max context: %i\n", slot.id, n_ctx_slot); + + const int ga_n = params.grp_attn_n; + const int ga_w = params.grp_attn_w; + + if (ga_n != 1) { + GGML_ASSERT(ga_n > 0 && "ga_n must be positive"); // NOLINT + GGML_ASSERT(ga_w % ga_n == 0 && "ga_w must be a multiple of ga_n"); // NOLINT + //GGML_ASSERT(n_ctx_train % ga_w == 0 && "n_ctx_train must be a multiple of ga_w"); // NOLINT + //GGML_ASSERT(n_ctx >= n_ctx_train * ga_n && "n_ctx must be at least n_ctx_train * ga_n"); // NOLINT + LOG_TEE(" -> Slot %i - self-extend: ga_n = %d, ga_w = %d\n", slot.id, ga_n, ga_w); + } + + slot.ga_i = 0; + slot.ga_n = ga_n; + slot.ga_w = ga_w; + + slot.reset(); + slots.push_back(slot); } @@ -1349,32 +1373,35 @@ struct llama_server_context for (llama_client_slot &slot : slots) { - if (slot.is_processing() && slot.cache_tokens.size() >= (size_t) slot.n_ctx) + if (slot.ga_n == 1) { - // Shift context - const int n_left = slot.n_past - slot.params.n_keep - 1; - const int n_discard = n_left / 2; - - LOG_TEE("slot %d: context shift - n_keep = %d, n_left = %d, n_discard = %d\n", slot.id, slot.params.n_keep, n_left, n_discard); - llama_kv_cache_seq_rm (ctx, slot.id, slot.params.n_keep + 1 , slot.params.n_keep + n_discard + 1); - llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, slot.n_past, -n_discard); - - for (size_t i = slot.params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++) + if (slot.is_processing() && slot.cache_tokens.size() >= (size_t) slot.n_ctx) { - slot.cache_tokens[i - n_discard] = slot.cache_tokens[i]; + // Shift context + const int n_left = slot.n_past - slot.params.n_keep - 1; + const int n_discard = n_left / 2; + + LOG_TEE("slot %d: context shift - n_keep = %d, n_left = %d, n_discard = %d\n", slot.id, slot.params.n_keep, n_left, n_discard); + llama_kv_cache_seq_rm (ctx, slot.id, slot.params.n_keep + 1 , slot.params.n_keep + n_discard + 1); + llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, slot.n_past, -n_discard); + + for (size_t i = slot.params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++) + { + slot.cache_tokens[i - n_discard] = slot.cache_tokens[i]; + } + + slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard); + + slot.n_past -= n_discard; + + slot.truncated = true; + + LOG_VERBOSE("context shift", { + { "n_ctx", n_ctx }, + { "n_keep", params.n_keep }, + { "n_left", n_left }, + }); } - - slot.cache_tokens.resize(slot.cache_tokens.size() - n_discard); - - slot.n_past -= n_discard; - - slot.truncated = true; - - LOG_VERBOSE("context shift", { - {"n_ctx", n_ctx}, - {"n_keep", params.n_keep}, - {"n_left", n_left}, - }); } } @@ -1401,7 +1428,8 @@ struct llama_server_context slot.i_batch = batch.n_tokens; - llama_batch_add(batch, slot.sampled, system_tokens.size() + slot.n_past, { slot.id }, true); + const int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past; + llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id }, true); slot.n_past += 1; } @@ -1499,6 +1527,8 @@ struct llama_server_context llama_sampling_reset(slot.ctx_sampling); slot.n_past = 0; + slot.n_past_se = 0; + slot.ga_i = 0; slot.num_prompt_tokens_processed = slot.num_prompt_tokens; } else @@ -1512,6 +1542,25 @@ struct llama_server_context slot.n_past = common_part(slot.cache_tokens, prompt_tokens); slot.num_prompt_tokens_processed = slot.num_prompt_tokens - slot.n_past; + if (slot.ga_n != 1) + { + int ga_i = 0; + int32_t ga_n = slot.ga_n; + int32_t ga_w = slot.ga_w; + int32_t slot_npast = 0; + for (int k = 0; k < slot.n_past; ++k) + { + while (slot_npast >= ga_i + ga_w) { + const int bd = (ga_w/ga_n)*(ga_n - 1); + slot_npast -= bd; + ga_i += ga_w/ga_n; + } + slot_npast++; + } + slot.n_past_se = slot_npast; + slot.ga_i = ga_i; + } + LOG_TEE("slot %d : in cache: %i tokens | to process: %i tokens\n", slot.id, slot.n_past, slot.num_prompt_tokens_processed); } @@ -1526,6 +1575,10 @@ struct llama_server_context // we have to evaluate at least 1 token to generate logits. LOG_TEE("slot %d : we have to evaluate at least 1 token to generate logits\n", slot.id); slot.n_past--; + if (slot.ga_i > 0) + { + slot.n_past_se--; + } } LOG_VERBOSE("prompt ingested", { @@ -1538,9 +1591,22 @@ struct llama_server_context // process the prefix of first image std::vector prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, add_bos_token) : prompt_tokens; + int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past; + int ga_i = slot.ga_i; + int32_t ga_n = slot.ga_n; + int32_t ga_w = slot.ga_w; for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past) { - llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot.n_past, { slot.id }, false); + if (slot.ga_n != 1) + { + while (slot_npast >= ga_i + ga_w) { + const int bd = (ga_w/ga_n)*(ga_n - 1); + slot_npast -= bd; + ga_i += ga_w/ga_n; + } + } + llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot_npast, {slot.id }, false); + slot_npast += 1; } if (has_images && !ingest_images(slot, n_batch)) @@ -1570,6 +1636,36 @@ struct llama_server_context for (int32_t i = 0; i < (int32_t) batch.n_tokens; i += n_batch) { const int32_t n_tokens = std::min(n_batch, (int32_t) (batch.n_tokens - i)); + + for (auto & slot : slots) + { + if (slot.ga_n != 1) + { + // context extension via Self-Extend + while (slot.n_past_se >= slot.ga_i + slot.ga_w) + { + const int ib = (slot.ga_n * slot.ga_i) / slot.ga_w; + const int bd = (slot.ga_w / slot.ga_n) * (slot.ga_n - 1); + const int dd = (slot.ga_w / slot.ga_n) - ib * bd - slot.ga_w; + + LOG_TEE("\n"); + LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i, slot.n_past_se, ib * bd, slot.ga_i + ib * bd, slot.n_past_se + ib * bd); + LOG_TEE("div: [%6d, %6d] / %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w, slot.ga_n, (slot.ga_i + ib * bd) / slot.ga_n, (slot.ga_i + ib * bd + slot.ga_w) / slot.ga_n); + LOG_TEE("shift: [%6d, %6d] + %6d -> [%6d, %6d]\n", slot.ga_i + ib * bd + slot.ga_w, slot.n_past_se + ib * bd, dd, slot.ga_i + ib * bd + slot.ga_w + dd, slot.n_past_se + ib * bd + dd); + + llama_kv_cache_seq_shift(ctx, slot.id, slot.ga_i, slot.n_past_se, ib * bd); + llama_kv_cache_seq_div(ctx, slot.id, slot.ga_i + ib * bd, slot.ga_i + ib * bd + slot.ga_w,slot.ga_n); + llama_kv_cache_seq_shift(ctx, slot.id, slot.ga_i + ib * bd + slot.ga_w,slot.n_past_se + ib * bd, dd); + + slot.n_past_se -= bd; + + slot.ga_i += slot.ga_w / slot.ga_n; + + LOG_TEE("\nn_past_old = %d, n_past = %d, ga_i = %d\n\n", slot.n_past_se + bd, slot.n_past_se, slot.ga_i); + } + slot.n_past_se += n_tokens; + } + } llama_batch batch_view = { n_tokens, @@ -1583,6 +1679,7 @@ struct llama_server_context }; const int ret = llama_decode(ctx, batch_view); + if (ret != 0) { if (n_batch == 1 || ret < 0) @@ -1728,6 +1825,8 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" --override-kv KEY=TYPE:VALUE\n"); printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); + printf(" -gan N, --grp-attn-n N Set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`"); + printf(" -gaw N, --grp-attn-w N Set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`"); printf("\n"); } @@ -1913,6 +2012,25 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } params.n_threads = std::stoi(argv[i]); } + else if (arg == "--grp-attn-n" || arg == "-gan") + { + if (++i >= argc) { + invalid_param = true; + break; + } + + params.grp_attn_n = std::stoi(argv[i]); + } + else if (arg == "--grp-attn-w" || arg == "-gaw") + { + if (++i >= argc) + { + invalid_param = true; + break; + } + + params.grp_attn_w = std::stoi(argv[i]); + } else if (arg == "--threads-batch" || arg == "-tb") { if (++i >= argc) From 35a2ee914308c85ab5cb576467381443ad23f0ac Mon Sep 17 00:00:00 2001 From: Michael Klimenko Date: Sat, 27 Jan 2024 15:25:55 +0100 Subject: [PATCH 032/143] Remove unused data and add fixes (#5154) * Remove unused data and add fixes * Add missing file * Address review comments * Replace the scope of vq allocation --- common/sampling.cpp | 1 + examples/infill/infill.cpp | 2 +- examples/llava/clip.cpp | 1 - examples/server/server.cpp | 2 +- pocs/vdot/vdot.cpp | 1 - tests/test-backend-ops.cpp | 1 - tests/test-llama-grammar.cpp | 1 - 7 files changed, 3 insertions(+), 6 deletions(-) diff --git a/common/sampling.cpp b/common/sampling.cpp index efd7eab6e..e8675a8c0 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -13,6 +13,7 @@ struct llama_sampling_context * llama_sampling_init(const struct llama_sampling_ // will be empty (default) if there are parse errors if (result->parsed_grammar.rules.empty()) { fprintf(stderr, "%s: failed to parse grammar\n", __func__); + delete result; return nullptr; } diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index 4a7827876..72fb133b4 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -241,7 +241,7 @@ int main(int argc, char ** argv) { LOG("add_bos: %d\n", add_bos); bool suff_rm_leading_spc = params.escape; - if (suff_rm_leading_spc && params.input_suffix.find_first_of(" ") == 0 && params.input_suffix.size() > 1) { + if (suff_rm_leading_spc && params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) { params.input_suffix.erase(0, 1); suff_rm_leading_spc = false; } diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index 4a0338a37..f2cd86afe 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -1277,7 +1277,6 @@ bool clip_model_quantize(const char * fname_inp, const char * fname_out, const i ".*weight", }; - std::vector read_data(512); std::vector work(512); std::vector conv_buf(512); std::vector hist_all(1 << 4, 0); diff --git a/examples/server/server.cpp b/examples/server/server.cpp index af63f2f6f..f58a2acaa 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -681,7 +681,7 @@ struct llama_server_context while ((pos = prompt.find(pattern, pos)) != std::string::npos) { size_t end_prefix = pos; pos += pattern.length(); - size_t end_pos = prompt.find("]", pos); + size_t end_pos = prompt.find(']', pos); if (end_pos != std::string::npos) { std::string image_id = prompt.substr(pos, end_pos - pos); diff --git a/pocs/vdot/vdot.cpp b/pocs/vdot/vdot.cpp index e96372c4b..73ffcd1ca 100644 --- a/pocs/vdot/vdot.cpp +++ b/pocs/vdot/vdot.cpp @@ -243,7 +243,6 @@ int main(int argc, char** argv) { if (useQ4_1) q41.resize(n4); else q40.resize(n4); std::vector q8(n8); - std::vector H(16, 0); double sumt = 0, sumt2 = 0, maxt = 0; double sumqt = 0, sumqt2 = 0, maxqt = 0; double sum = 0, sumq = 0, exactSum = 0; diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 55ce14e0d..e3c656f56 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -102,7 +102,6 @@ static std::vector tensor_to_float(const ggml_tensor * t) { } else if (t->type == GGML_TYPE_I8) { tv.push_back((float)*(int8_t *) &buf[i]); } else if (quantized) { - std::vector vq(ggml_blck_size(t->type)); tt.to_float(&buf[i], vq.data(), ggml_blck_size(t->type)); tv.insert(tv.end(), vq.begin(), vq.end()); } else { diff --git a/tests/test-llama-grammar.cpp b/tests/test-llama-grammar.cpp index 73dd33dd2..78fc41117 100644 --- a/tests/test-llama-grammar.cpp +++ b/tests/test-llama-grammar.cpp @@ -190,7 +190,6 @@ int main() index++; } - std::vector> next_stacks; std::vector next_candidates; next_candidates.resize(24); From e9764230054e01553bdead6f2bfd8e001869599d Mon Sep 17 00:00:00 2001 From: Judd Date: Fri, 26 Jan 2024 21:04:01 +0800 Subject: [PATCH 033/143] ggml : check ggml_add src1 type (ggml/708) Co-authored-by: Judd --- ggml.c | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/ggml.c b/ggml.c index 8f57003e0..11a3114e7 100644 --- a/ggml.c +++ b/ggml.c @@ -7498,7 +7498,12 @@ static void ggml_compute_forward_add( switch (src0->type) { case GGML_TYPE_F32: { - ggml_compute_forward_add_f32(params, src0, src1, dst); + if (src1->type == GGML_TYPE_F32) { + ggml_compute_forward_add_f32(params, src0, src1, dst); + } + else { + GGML_ASSERT(false); + } } break; case GGML_TYPE_F16: { From 753eafed0ebd07af6903771327a1786a7c02cf98 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 27 Jan 2024 16:59:20 +0200 Subject: [PATCH 034/143] sync : ggml --- examples/server/utils.hpp | 1 + scripts/sync-ggml.last | 2 +- 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/server/utils.hpp b/examples/server/utils.hpp index e2b6065f7..70cce0721 100644 --- a/examples/server/utils.hpp +++ b/examples/server/utils.hpp @@ -249,6 +249,7 @@ struct llama_server_queue { } // Start the main loop. This call is blocking + [[noreturn]] void start_loop() { while (true) { // new task arrived diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 4d52d946b..efde0069f 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -6c1ce0bd591a430c1d3f6797d905194581c878c1 +c2448f88d17395452a587d0176d19ed87e0f7ce1 From 6db2b41a76ee78d5efdd5c3cddd5d7ad3f646855 Mon Sep 17 00:00:00 2001 From: John <78893154+cmp-nct@users.noreply.github.com> Date: Sat, 27 Jan 2024 16:09:18 +0100 Subject: [PATCH 035/143] llava : support for Yi-VL and fix for mobileVLM (#5093) * Support for Yi-VL, templating fix for mobileVLM * ws * Update examples/llava/clip.cpp Co-authored-by: Georgi Gerganov * Update llava-cli.cpp * Update clip.cpp bugfix for new conversions --------- Co-authored-by: Georgi Gerganov --- examples/llava/clip.cpp | 70 ++++++++++++++++++++++++++++++++---- examples/llava/llava-cli.cpp | 32 +++++++++++++++-- 2 files changed, 92 insertions(+), 10 deletions(-) diff --git a/examples/llava/clip.cpp b/examples/llava/clip.cpp index f2cd86afe..9129052a2 100644 --- a/examples/llava/clip.cpp +++ b/examples/llava/clip.cpp @@ -98,6 +98,7 @@ static std::string format(const char * fmt, ...) { enum projector_type { PROJECTOR_TYPE_MLP, + PROJECTOR_TYPE_MLP_NORM, PROJECTOR_TYPE_LDP, PROJECTOR_TYPE_UNKNOWN, }; @@ -304,10 +305,18 @@ struct clip_vision_model { struct ggml_tensor * projection; // LLaVA projection - struct ggml_tensor * mm_0_w; - struct ggml_tensor * mm_0_b; - struct ggml_tensor * mm_2_w; - struct ggml_tensor * mm_2_b; + struct ggml_tensor * mm_0_w = NULL; + struct ggml_tensor * mm_0_b = NULL; + struct ggml_tensor * mm_2_w = NULL; + struct ggml_tensor * mm_2_b = NULL; + + // Yi type models with mlp+normalization projection + struct ggml_tensor * mm_1_w = NULL; // Yi type models have 0, 1, 3, 4 + struct ggml_tensor * mm_1_b = NULL; + struct ggml_tensor * mm_3_w = NULL; + struct ggml_tensor * mm_3_b = NULL; + struct ggml_tensor * mm_4_w = NULL; + struct ggml_tensor * mm_4_b = NULL; // MobileVLM projection struct ggml_tensor * mm_model_mlp_1_w; @@ -460,6 +469,7 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 // pre-layernorm { embeddings = ggml_norm(ctx0, embeddings, eps); + ggml_set_name(embeddings, "pre_ln"); embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.pre_ln_w), model.pre_ln_b); } @@ -575,6 +585,27 @@ static ggml_cgraph * clip_image_build_graph(clip_ctx * ctx, const clip_image_f32 embeddings = ggml_mul_mat(ctx0, model.mm_2_w, embeddings); embeddings = ggml_add(ctx0, embeddings, model.mm_2_b); + + } else if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) { + embeddings = ggml_mul_mat(ctx0, model.mm_0_w, embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_0_b); + // ggml_tensor_printf(embeddings, "mm_0_w",0,true,false); + // First LayerNorm + embeddings = ggml_norm(ctx0, embeddings, eps); + embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_1_w), + model.mm_1_b); + + // GELU activation + embeddings = ggml_gelu(ctx0, embeddings); + + // Second linear layer + embeddings = ggml_mul_mat(ctx0, model.mm_3_w, embeddings); + embeddings = ggml_add(ctx0, embeddings, model.mm_3_b); + + // Second LayerNorm + embeddings = ggml_norm(ctx0, embeddings, eps); + embeddings = ggml_add(ctx0, ggml_mul(ctx0, embeddings, model.mm_4_w), + model.mm_4_b); } else if (ctx->proj_type == PROJECTOR_TYPE_LDP) { // MobileVLM projector @@ -808,6 +839,11 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { else { new_clip->proj_type = PROJECTOR_TYPE_MLP; } + if (new_clip->proj_type == PROJECTOR_TYPE_MLP) { + if (gguf_find_tensor(ctx, format(TN_LLAVA_PROJ, 3, "weight").c_str()) != -1) { + new_clip->proj_type = PROJECTOR_TYPE_MLP_NORM; + } + } } #ifdef GGML_USE_CUBLAS @@ -956,11 +992,29 @@ struct clip_ctx * clip_model_load(const char * fname, const int verbosity = 1) { vision_model.pre_ln_b = get_tensor(new_clip->ctx_data, format(TN_LN_PRE, "v", "bias")); // LLaVA projection - if (new_clip->proj_type == PROJECTOR_TYPE_MLP) { + if (new_clip->proj_type == PROJECTOR_TYPE_MLP || new_clip->proj_type == PROJECTOR_TYPE_MLP_NORM) { vision_model.mm_0_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "weight")); vision_model.mm_0_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 0, "bias")); - vision_model.mm_2_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight")); - vision_model.mm_2_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias")); + try { + // Yi-type llava + vision_model.mm_1_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 1, "weight")); + vision_model.mm_1_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 1, "bias")); + } catch (std::runtime_error & e) { } + try { + // missing in Yi-type llava + vision_model.mm_2_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "weight")); + vision_model.mm_2_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 2, "bias")); + } catch (std::runtime_error & e) { } + try { + // Yi-type llava + vision_model.mm_3_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 3, "weight")); + vision_model.mm_3_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 3, "bias")); + } catch (std::runtime_error & e) { } + try { + // Yi-type llava + vision_model.mm_4_w = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "weight")); + vision_model.mm_4_b = get_tensor(new_clip->ctx_data, format(TN_LLAVA_PROJ, 4, "bias")); + } catch (std::runtime_error & e) { } } else if (new_clip->proj_type == PROJECTOR_TYPE_LDP) { // MobileVLM projection @@ -1432,6 +1486,8 @@ int clip_n_mmproj_embd(const struct clip_ctx * ctx) { } else if (ctx->proj_type == PROJECTOR_TYPE_MLP) { return ctx->vision_model.mm_2_b->ne[0]; + } else if (ctx->proj_type == PROJECTOR_TYPE_MLP_NORM) { + return ctx->vision_model.mm_3_b->ne[0]; } else { std::string proj_type = PROJECTOR_TYPE_NAMES[ctx->proj_type]; diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index d94795fe3..6ac70ba69 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -148,10 +148,35 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ const int max_tgt_len = params->n_predict < 0 ? 256 : params->n_predict; const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx_llava->ctx_llama)); - // llava chat format is "\nUSER:\n\nASSISTANT:" - eval_string(ctx_llava->ctx_llama, "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:", params->n_batch, &n_past, add_bos); + std::string system_prompt, user_prompt; + size_t image_pos = prompt.find(""); + if (image_pos != std::string::npos) { + // new templating mode: Provide the full prompt including system message and use as a placeholder for the image + + system_prompt = prompt.substr(0, image_pos); + user_prompt = prompt.substr(image_pos + std::string("").length()); + // We replace \n with actual newlines in user_prompt, just in case -e was not used in templating string + size_t pos = 0; + while ((pos = user_prompt.find("\\n", pos)) != std::string::npos) { + user_prompt.replace(pos, 2, "\n"); + pos += 1; // Advance past the replaced newline + } + while ((pos = system_prompt.find("\\n", pos)) != std::string::npos) { + system_prompt.replace(pos, 2, "\n"); + pos += 1; // Advance past the replaced newline + } + + printf("system_prompt: %s\n", system_prompt.c_str()); + printf("user_prompt: %s\n", user_prompt.c_str()); + } else { + // llava-1.5 native mode + system_prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.\nUSER:"; + user_prompt = prompt + "\nASSISTANT:"; + } + + eval_string(ctx_llava->ctx_llama, system_prompt.c_str(), params->n_batch, &n_past, add_bos); llava_eval_image_embed(ctx_llava->ctx_llama, image_embed, params->n_batch, &n_past); - eval_string(ctx_llava->ctx_llama, (prompt + "\nASSISTANT:").c_str(), params->n_batch, &n_past, false); + eval_string(ctx_llava->ctx_llama, user_prompt.c_str(), params->n_batch, &n_past, false); // generate the response @@ -162,6 +187,7 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ for (int i = 0; i < max_tgt_len; i++) { const char * tmp = sample(ctx_sampling, ctx_llava->ctx_llama, &n_past); if (strcmp(tmp, "") == 0) break; + if (strstr(tmp, "###")) break; // Yi-VL behavior printf("%s", tmp); fflush(stdout); From 39baaf55a160909bb9428bd981014218761a20cb Mon Sep 17 00:00:00 2001 From: Kyle Mistele Date: Sun, 28 Jan 2024 01:55:31 -0600 Subject: [PATCH 036/143] docker : add server-first container images (#5157) * feat: add Dockerfiles for each platform that user ./server instead of ./main * feat: update .github/workflows/docker.yml to build server-first docker containers * doc: add information about running the server with Docker to README.md * doc: add information about running with docker to the server README * doc: update n-gpu-layers to show correct GPU usage * fix(doc): update container tag from `server` to `server-cuda` for README example on running server container with CUDA --- .devops/server-cuda.Dockerfile | 32 +++++++++++++++++++++++ .devops/server-intel.Dockerfile | 25 ++++++++++++++++++ .devops/server-rocm.Dockerfile | 45 +++++++++++++++++++++++++++++++++ .devops/server.Dockerfile | 20 +++++++++++++++ .github/workflows/docker.yml | 4 +++ README.md | 14 +++++++++- examples/server/README.md | 8 ++++++ 7 files changed, 147 insertions(+), 1 deletion(-) create mode 100644 .devops/server-cuda.Dockerfile create mode 100644 .devops/server-intel.Dockerfile create mode 100644 .devops/server-rocm.Dockerfile create mode 100644 .devops/server.Dockerfile diff --git a/.devops/server-cuda.Dockerfile b/.devops/server-cuda.Dockerfile new file mode 100644 index 000000000..4f83904bc --- /dev/null +++ b/.devops/server-cuda.Dockerfile @@ -0,0 +1,32 @@ +ARG UBUNTU_VERSION=22.04 +# This needs to generally match the container host's environment. +ARG CUDA_VERSION=11.7.1 +# Target the CUDA build image +ARG BASE_CUDA_DEV_CONTAINER=nvidia/cuda:${CUDA_VERSION}-devel-ubuntu${UBUNTU_VERSION} +# Target the CUDA runtime image +ARG BASE_CUDA_RUN_CONTAINER=nvidia/cuda:${CUDA_VERSION}-runtime-ubuntu${UBUNTU_VERSION} + +FROM ${BASE_CUDA_DEV_CONTAINER} as build + +# Unless otherwise specified, we make a fat build. +ARG CUDA_DOCKER_ARCH=all + +RUN apt-get update && \ + apt-get install -y build-essential git + +WORKDIR /app + +COPY . . + +# Set nvcc architecture +ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH} +# Enable cuBLAS +ENV LLAMA_CUBLAS=1 + +RUN make + +FROM ${BASE_CUDA_RUN_CONTAINER} as runtime + +COPY --from=build /app/server /server + +ENTRYPOINT [ "/server" ] diff --git a/.devops/server-intel.Dockerfile b/.devops/server-intel.Dockerfile new file mode 100644 index 000000000..e343d278c --- /dev/null +++ b/.devops/server-intel.Dockerfile @@ -0,0 +1,25 @@ +ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04 +ARG UBUNTU_VERSION=22.04 + +FROM intel/hpckit:$ONEAPI_VERSION as build + +RUN apt-get update && \ + apt-get install -y git + +WORKDIR /app + +COPY . . + +# for some reasons, "-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DLLAMA_NATIVE=ON" give worse performance +RUN mkdir build && \ + cd build && \ + cmake .. -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx && \ + cmake --build . --config Release --target main server + +FROM ubuntu:$UBUNTU_VERSION as runtime + +COPY --from=build /app/build/bin/server /server + +ENV LC_ALL=C.utf8 + +ENTRYPOINT [ "/server" ] diff --git a/.devops/server-rocm.Dockerfile b/.devops/server-rocm.Dockerfile new file mode 100644 index 000000000..e9a31647c --- /dev/null +++ b/.devops/server-rocm.Dockerfile @@ -0,0 +1,45 @@ +ARG UBUNTU_VERSION=22.04 + +# This needs to generally match the container host's environment. +ARG ROCM_VERSION=5.6 + +# Target the CUDA build image +ARG BASE_ROCM_DEV_CONTAINER=rocm/dev-ubuntu-${UBUNTU_VERSION}:${ROCM_VERSION}-complete + +FROM ${BASE_ROCM_DEV_CONTAINER} as build + +# Unless otherwise specified, we make a fat build. +# List from https://github.com/ggerganov/llama.cpp/pull/1087#issuecomment-1682807878 +# This is mostly tied to rocBLAS supported archs. +ARG ROCM_DOCKER_ARCH=\ + gfx803 \ + gfx900 \ + gfx906 \ + gfx908 \ + gfx90a \ + gfx1010 \ + gfx1030 \ + gfx1100 \ + gfx1101 \ + gfx1102 + +COPY requirements.txt requirements.txt +COPY requirements requirements + +RUN pip install --upgrade pip setuptools wheel \ + && pip install -r requirements.txt + +WORKDIR /app + +COPY . . + +# Set nvcc architecture +ENV GPU_TARGETS=${ROCM_DOCKER_ARCH} +# Enable ROCm +ENV LLAMA_HIPBLAS=1 +ENV CC=/opt/rocm/llvm/bin/clang +ENV CXX=/opt/rocm/llvm/bin/clang++ + +RUN make + +ENTRYPOINT [ "/app/server" ] diff --git a/.devops/server.Dockerfile b/.devops/server.Dockerfile new file mode 100644 index 000000000..134588fe2 --- /dev/null +++ b/.devops/server.Dockerfile @@ -0,0 +1,20 @@ +ARG UBUNTU_VERSION=22.04 + +FROM ubuntu:$UBUNTU_VERSION as build + +RUN apt-get update && \ + apt-get install -y build-essential git + +WORKDIR /app + +COPY . . + +RUN make + +FROM ubuntu:$UBUNTU_VERSION as runtime + +COPY --from=build /app/server /server + +ENV LC_ALL=C.utf8 + +ENTRYPOINT [ "/server" ] diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 825b8f503..94f9161fc 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -28,14 +28,18 @@ jobs: config: - { tag: "light", dockerfile: ".devops/main.Dockerfile", platforms: "linux/amd64,linux/arm64" } - { tag: "full", dockerfile: ".devops/full.Dockerfile", platforms: "linux/amd64,linux/arm64" } + - { tag: "server", dockerfile: ".devops/server.Dockerfile", platforms: "linux/amd64,linux/arm64" } # NOTE(canardletter): The CUDA builds on arm64 are very slow, so I # have disabled them for now until the reason why # is understood. - { tag: "light-cuda", dockerfile: ".devops/main-cuda.Dockerfile", platforms: "linux/amd64" } - { tag: "full-cuda", dockerfile: ".devops/full-cuda.Dockerfile", platforms: "linux/amd64" } + - { tag: "server-cuda", dockerfile: ".devops/server-cuda.Dockerfile", platforms: "linux/amd64" } - { tag: "light-rocm", dockerfile: ".devops/main-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } - { tag: "full-rocm", dockerfile: ".devops/full-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } + - { tag: "server-rocm", dockerfile: ".devops/server-rocm.Dockerfile", platforms: "linux/amd64,linux/arm64" } - { tag: "light-intel", dockerfile: ".devops/main-intel.Dockerfile", platforms: "linux/amd64" } + - { tag: "server-intel", dockerfile: ".devops/server-intel.Dockerfile", platforms: "linux/amd64" } steps: - name: Check out the repo uses: actions/checkout@v3 diff --git a/README.md b/README.md index 76e48ce8a..cd95f8144 100644 --- a/README.md +++ b/README.md @@ -931,17 +931,20 @@ Place your desired model into the `~/llama.cpp/models/` directory and execute th * Create a folder to store big models & intermediate files (ex. /llama/models) #### Images -We have two Docker images available for this project: +We have three Docker images available for this project: 1. `ghcr.io/ggerganov/llama.cpp:full`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. (platforms: `linux/amd64`, `linux/arm64`) 2. `ghcr.io/ggerganov/llama.cpp:light`: This image only includes the main executable file. (platforms: `linux/amd64`, `linux/arm64`) +3. `ghcr.io/ggerganov/llama.cpp:server`: This image only includes the server executabhle file. (platforms: `linux/amd64`, `linux/arm64`) Additionally, there the following images, similar to the above: - `ghcr.io/ggerganov/llama.cpp:full-cuda`: Same as `full` but compiled with CUDA support. (platforms: `linux/amd64`) - `ghcr.io/ggerganov/llama.cpp:light-cuda`: Same as `light` but compiled with CUDA support. (platforms: `linux/amd64`) +- `ghcr.io/ggerganov/llama.cpp:server-cuda`: Same as `server` but compiled with CUDA support. (platforms: `linux/amd64`) - `ghcr.io/ggerganov/llama.cpp:full-rocm`: Same as `full` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) - `ghcr.io/ggerganov/llama.cpp:light-rocm`: Same as `light` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) +- `ghcr.io/ggerganov/llama.cpp:server-rocm`: Same as `server` but compiled with ROCm support. (platforms: `linux/amd64`, `linux/arm64`) The GPU enabled images are not currently tested by CI beyond being built. They are not built with any variation from the ones in the Dockerfiles defined in [.devops/](.devops/) and the GitHub Action defined in [.github/workflows/docker.yml](.github/workflows/docker.yml). If you need different settings (for example, a different CUDA or ROCm library, you'll need to build the images locally for now). @@ -967,6 +970,12 @@ or with a light image: docker run -v /path/to/models:/models ghcr.io/ggerganov/llama.cpp:light -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 ``` +or with a server image: + +```bash +docker run -v /path/to/models:/models -p 8000:8000 ghcr.io/ggerganov/llama.cpp:server -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 +``` + ### Docker With CUDA Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia-container-toolkit) properly installed on Linux, or is using a GPU enabled cloud, `cuBLAS` should be accessible inside the container. @@ -976,6 +985,7 @@ Assuming one has the [nvidia-container-toolkit](https://github.com/NVIDIA/nvidia ```bash docker build -t local/llama.cpp:full-cuda -f .devops/full-cuda.Dockerfile . docker build -t local/llama.cpp:light-cuda -f .devops/main-cuda.Dockerfile . +docker build -t local/llama.cpp:server-cuda -f .devops/server-cuda.Dockerfile . ``` You may want to pass in some different `ARGS`, depending on the CUDA environment supported by your container host, as well as the GPU architecture. @@ -989,6 +999,7 @@ The resulting images, are essentially the same as the non-CUDA images: 1. `local/llama.cpp:full-cuda`: This image includes both the main executable file and the tools to convert LLaMA models into ggml and convert into 4-bit quantization. 2. `local/llama.cpp:light-cuda`: This image only includes the main executable file. +3. `local/llama.cpp:server-cuda`: This image only includes the server executable file. #### Usage @@ -997,6 +1008,7 @@ After building locally, Usage is similar to the non-CUDA examples, but you'll ne ```bash docker run --gpus all -v /path/to/models:/models local/llama.cpp:full-cuda --run -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1 docker run --gpus all -v /path/to/models:/models local/llama.cpp:light-cuda -m /models/7B/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 512 --n-gpu-layers 1 +docker run --gpus all -v /path/to/models:/models local/llama.cpp:server-cuda -m /models/7B/ggml-model-q4_0.gguf --port 8000 --host 0.0.0.0 -n 512 --n-gpu-layers 1 ``` ### Contributing diff --git a/examples/server/README.md b/examples/server/README.md index 1c92a2041..dce4ec47c 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -66,6 +66,14 @@ server.exe -m models\7B\ggml-model.gguf -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 visit the web front end at the same url. +### Docker: +```bash +docker run -p 8080:8080 -v /path/to/models:/models ggerganov/llama.cpp:server -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 + +# or, with CUDA: +docker run -p 8080:8080 -v /path/to/models:/models --gpus all ggerganov/llama.cpp:server-cuda -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 --n-gpu-layers 99 +``` + ## Testing with CURL Using [curl](https://curl.se/). On Windows `curl.exe` should be available in the base OS. From f2e69d28c01303ca9dc79907f89ef120a6ac4a92 Mon Sep 17 00:00:00 2001 From: sharpHL <132747147+sharpHL@users.noreply.github.com> Date: Sun, 28 Jan 2024 16:00:30 +0800 Subject: [PATCH 037/143] llama : add support for Orion-14B (#5118) * add support for Orion-14B(https://huggingface.co/OrionStarAI/Orion-14B-Chat) * flake8 support * Update llama.cpp Co-authored-by: Georgi Gerganov * Update llama.cpp Co-authored-by: Georgi Gerganov * Update llama.cpp Co-authored-by: Georgi Gerganov * Update llama.cpp Co-authored-by: Georgi Gerganov * Update llama.cpp Co-authored-by: slaren * Update llama.cpp * Update llama.cpp --------- Co-authored-by: lixiaopu Co-authored-by: Georgi Gerganov Co-authored-by: slaren --- convert-hf-to-gguf.py | 81 +++++++++++++++++ gguf-py/gguf/constants.py | 24 ++++- llama.cpp | 187 ++++++++++++++++++++++++++++++++++++++ 3 files changed, 291 insertions(+), 1 deletion(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 7a0a8c3db..6ab7f486e 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -201,6 +201,8 @@ class Model: return PlamoModel if model_architecture == "CodeShellForCausalLM": return CodeShellModel + if model_architecture == "OrionForCausalLM": + return OrionModel return Model def _is_model_safetensors(self) -> bool: @@ -250,6 +252,8 @@ class Model: return gguf.MODEL_ARCH.PLAMO if arch == "CodeShellForCausalLM": return gguf.MODEL_ARCH.CODESHELL + if arch == "OrionForCausalLM": + return gguf.MODEL_ARCH.ORION raise NotImplementedError(f'Architecture "{arch}" not supported!') @@ -572,6 +576,83 @@ class MPTModel(Model): self.gguf_writer.add_tensor("output.weight", data) +class OrionModel(Model): + def set_vocab(self): + self._set_vocab_sentencepiece() + + def set_gguf_parameters(self): + block_count = self.hparams["num_hidden_layers"] + head_count = self.hparams["num_attention_heads"] + head_count_kv = self.hparams.get("num_key_value_heads", head_count) + hf_repo = self.hparams.get("_name_or_path", "") + + ctx_length = 0 + if "max_sequence_length" in self.hparams: + ctx_length = self.hparams["max_sequence_length"] + elif "max_position_embeddings" in self.hparams: + ctx_length = self.hparams["max_position_embeddings"] + elif "model_max_length" in self.hparams: + ctx_length = self.hparams["model_max_length"] + else: + print("gguf: can not find ctx length parameter.") + sys.exit() + + self.gguf_writer.add_file_type(self.ftype) + self.gguf_writer.add_name(self.dir_model.name) + self.gguf_writer.add_source_hf_repo(hf_repo) + self.gguf_writer.add_tensor_data_layout("Meta AI original pth") + self.gguf_writer.add_context_length(ctx_length) + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_head_count(head_count) + self.gguf_writer.add_head_count_kv(head_count_kv) + self.gguf_writer.add_layer_norm_eps(self.hparams["rms_norm_eps"]) + + def write_tensors(self): + # Collect tensors from generator object + model_kv = dict(self.get_tensors()) + block_count = self.hparams["num_hidden_layers"] + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + + for name, data_torch in model_kv.items(): + # we don't need these + if name.endswith(".rotary_emb.inv_freq"): + continue + + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + data = data_torch.squeeze().numpy() + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.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 self.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 self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{name} -> {new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + self.gguf_writer.add_tensor(new_name, data) + + class BaichuanModel(Model): def set_vocab(self): self._set_vocab_sentencepiece() diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 2d9c33c7d..f5c933a41 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -101,6 +101,7 @@ class MODEL_ARCH(IntEnum): PHI2 = auto() PLAMO = auto() CODESHELL = auto() + ORION = auto() class MODEL_TENSOR(IntEnum): @@ -151,6 +152,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.PHI2: "phi2", MODEL_ARCH.PLAMO: "plamo", MODEL_ARCH.CODESHELL: "codeshell", + MODEL_ARCH.ORION: "orion", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -427,7 +429,23 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_NORM, MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, - ] + ], + MODEL_ARCH.ORION: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], # TODO } @@ -452,6 +470,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], + MODEL_ARCH.ORION: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], } # diff --git a/llama.cpp b/llama.cpp index b03b67e16..4cd0f16eb 100644 --- a/llama.cpp +++ b/llama.cpp @@ -196,6 +196,7 @@ enum llm_arch { LLM_ARCH_PHI2, LLM_ARCH_PLAMO, LLM_ARCH_CODESHELL, + LLM_ARCH_ORION, LLM_ARCH_UNKNOWN, }; @@ -217,6 +218,7 @@ static std::map LLM_ARCH_NAMES = { { LLM_ARCH_PHI2, "phi2" }, { LLM_ARCH_PLAMO, "plamo" }, { LLM_ARCH_CODESHELL, "codeshell" }, + { LLM_ARCH_ORION, "orion" }, }; enum llm_kv { @@ -641,6 +643,25 @@ static std::map> LLM_TENSOR_NAMES = { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_ORION, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ROPE_FREQS, "rope_freqs" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_UNKNOWN, @@ -1332,6 +1353,7 @@ enum e_model { MODEL_7B, MODEL_8B, MODEL_13B, + MODEL_14B, MODEL_15B, MODEL_30B, MODEL_34B, @@ -2683,6 +2705,7 @@ static const char * llama_model_type_name(e_model type) { case MODEL_7B: return "7B"; case MODEL_8B: return "8B"; case MODEL_13B: return "13B"; + case MODEL_14B: return "14B"; case MODEL_15B: return "15B"; case MODEL_30B: return "30B"; case MODEL_34B: return "34B"; @@ -2950,7 +2973,15 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_ORION: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + switch (hparams.n_layer) { + case 40: model.type = e_model::MODEL_14B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -3933,6 +3964,38 @@ static bool llm_load_tensors( layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); } } break; + case LLM_ARCH_ORION: + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + { + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + } + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}); + + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}); + + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + } + } break; + + default: throw std::runtime_error("unknown architecture"); } @@ -4563,6 +4626,126 @@ struct llm_build_context { ctx0 = nullptr; } } + struct ggml_cgraph * build_orion() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); + cb(inpL, "inp_embd", -1); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); + cb(inp_pos, "inp_pos", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); + cb(KQ_mask, "KQ_mask", -1); + + // shift the entire K-cache if needed + if (do_rope_shift) { + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); + } + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, model.layers[il].attn_norm_b, + LLM_NORM, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + // if (model.layers[il].bq) { + // Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); + // cb(Qcur, "Qcur", il); + // } + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + // if (model.layers[il].bk) { + // Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); + // cb(Kcur, "Kcur", il); + // } + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + // if (model.layers[il].bv) { + // Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); + // cb(Vcur, "Vcur", il); + // } + + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + model.layers[il].wo, NULL, + Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + cb(cur, "kqv_out", il); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, model.layers[il].ffn_norm_b, + LLM_NORM, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, model.output_norm_b, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } + + struct ggml_cgraph * build_llama() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); @@ -6520,6 +6703,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_codeshell(); } break; + case LLM_ARCH_ORION: + { + result = llm.build_orion(); + } break; default: GGML_ASSERT(false); } From af4980bfedfd8df43b9e4cd1442895e85fee37bc Mon Sep 17 00:00:00 2001 From: Marcus Dunn <51931484+MarcusDunn@users.noreply.github.com> Date: Sun, 28 Jan 2024 00:30:44 -0800 Subject: [PATCH 038/143] readme : add link to rust bindings (#5148) * added link to another set of rust bindings with brief note on differences. * fixed link name --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index cd95f8144..44898d2f2 100644 --- a/README.md +++ b/README.md @@ -122,7 +122,8 @@ as the main playground for developing new features for the [ggml](https://github - Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp) - JS/TS (llama.cpp server client): [lgrammel/modelfusion](https://modelfusion.dev/integration/model-provider/llamacpp) - Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb) -- Rust: [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp) +- Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp) +- Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs) - 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) From b2b2bf988c098851b4f3831f0cf38394bff75121 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sun, 28 Jan 2024 09:35:14 +0100 Subject: [PATCH 039/143] Tests for min_p, sampling queue (#5147) --- llama.cpp | 5 ++ tests/test-sampling.cpp | 169 ++++++++++++++++++++++++++++++++++++---- 2 files changed, 159 insertions(+), 15 deletions(-) diff --git a/llama.cpp b/llama.cpp index 4cd0f16eb..391c956ec 100644 --- a/llama.cpp +++ b/llama.cpp @@ -8133,6 +8133,11 @@ void llama_sample_softmax(struct llama_context * ctx, llama_token_data_array * c } void llama_sample_top_k(struct llama_context * ctx, llama_token_data_array * candidates, int32_t k, size_t min_keep) { + // TODO: move bucket sort to separate function so that top_p/tail_free/typical/softmax first is equally fast + // if (k >= (int32_t)candidates->size) { + // return; + // } + const int64_t t_start_sample_us = ggml_time_us(); k = std::max(k, (int) min_keep); diff --git a/tests/test-sampling.cpp b/tests/test-sampling.cpp index 32e58941c..c3b3d6629 100644 --- a/tests/test-sampling.cpp +++ b/tests/test-sampling.cpp @@ -5,11 +5,10 @@ #undef NDEBUG #endif -#include -#include -#include -#include #include +#include +#include +#include static void dump(const llama_token_data_array * candidates) { for (size_t i = 0; i < candidates->size; i++) { @@ -20,11 +19,11 @@ static void dump(const llama_token_data_array * candidates) { #define DUMP(__candidates) do { printf("%s:%d (%s)\n", __FILE__, __LINE__, __func__); dump((__candidates)); printf("-\n"); } while(0) static void test_top_k(const std::vector & probs, const std::vector & expected_probs, int k) { - size_t n_vocab = probs.size(); + const size_t n_vocab = probs.size(); std::vector candidates; candidates.reserve(n_vocab); for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { - float logit = log(probs[token_id]); + const float logit = logf(probs[token_id]); candidates.emplace_back(llama_token_data{token_id, logit, 0.0f}); } @@ -41,11 +40,11 @@ static void test_top_k(const std::vector & probs, const std::vector & probs, const std::vector & expected_probs, float p) { - size_t n_vocab = probs.size(); + const size_t n_vocab = probs.size(); std::vector candidates; candidates.reserve(n_vocab); for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { - float logit = log(probs[token_id]); + const float logit = logf(probs[token_id]); candidates.emplace_back(llama_token_data{token_id, logit, 0.0f}); } @@ -62,11 +61,11 @@ static void test_top_p(const std::vector & probs, const std::vector & probs, const std::vector & expected_probs, float z) { - size_t n_vocab = probs.size(); + const size_t n_vocab = probs.size(); std::vector candidates; candidates.reserve(n_vocab); for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { - float logit = log(probs[token_id]); + const float logit = logf(probs[token_id]); candidates.emplace_back(llama_token_data{token_id, logit, 0.0f}); } @@ -81,12 +80,33 @@ static void test_tfs(const std::vector & probs, const std::vector } } -static void test_typical(const std::vector & probs, const std::vector & expected_probs, float p) { - size_t n_vocab = probs.size(); +static void test_min_p(const std::vector & probs, const std::vector & expected_probs, float p) { + const size_t n_vocab = probs.size(); std::vector candidates; candidates.reserve(n_vocab); for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { - float logit = log(probs[token_id]); + const float logit = logf(probs[token_id]); + candidates.emplace_back(llama_token_data{token_id, logit, 0.0f}); + } + + llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; + DUMP(&candidates_p); + llama_sample_min_p(nullptr, &candidates_p, p, 1); + DUMP(&candidates_p); + llama_sample_softmax(nullptr, &candidates_p); + + GGML_ASSERT(candidates_p.size == expected_probs.size()); + for (size_t i = 0; i < candidates_p.size; i++) { + GGML_ASSERT(fabs(candidates_p.data[i].p - expected_probs[i]) < 1e-3); + } +} + +static void test_typical(const std::vector & probs, const std::vector & expected_probs, float p) { + const size_t n_vocab = probs.size(); + std::vector candidates; + candidates.reserve(n_vocab); + for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { + const float logit = logf(probs[token_id]); candidates.emplace_back(llama_token_data{token_id, logit, 0.0f}); } @@ -107,11 +127,11 @@ static void test_repetition_penalties( ) { GGML_ASSERT(probs.size() == expected_probs.size()); - size_t n_vocab = probs.size(); + const size_t n_vocab = probs.size(); std::vector candidates; candidates.reserve(n_vocab); for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { - float logit = log(probs[token_id]); + const float logit = logf(probs[token_id]); candidates.emplace_back(llama_token_data{token_id, logit, 0.0f}); } @@ -128,6 +148,88 @@ static void test_repetition_penalties( } } +static void test_sampler_queue( + const size_t n_vocab, const std::string samplers_sequence, const int top_k, const float top_p, const float min_p +) { + std::vector candidates; + candidates.reserve(n_vocab); + for (llama_token token_id = 0; token_id < (llama_token)n_vocab; token_id++) { + const float logit = logf(token_id); + candidates.emplace_back(llama_token_data{token_id, logit, 0.0f}); + } + + llama_token_data_array candidates_p = { candidates.data(), candidates.size(), false }; + + llama_token min_token_id = 0; + const llama_token max_token_id = n_vocab-1; + + for (auto s : samplers_sequence) { + switch (s){ + case 'k': llama_sample_top_k (nullptr, &candidates_p, top_k, 1); break; + case 'f': GGML_ASSERT(false && "tail_free test not implemented"); break; + case 'y': GGML_ASSERT(false && "typical test not implemented"); break; + case 'p': llama_sample_top_p (nullptr, &candidates_p, top_p, 1); break; + case 'm': llama_sample_min_p (nullptr, &candidates_p, min_p, 1); break; + case 't': GGML_ASSERT(false && "temperature test not implemented"); break; + default : GGML_ASSERT(false && "Unknown sampler"); break; + } + + llama_sample_softmax(nullptr, &candidates_p); // make sure tokens are sorted for tests + + const int size = candidates_p.size; + + if (s == 'k') { + const int expected_size = std::min(size, top_k); + min_token_id = std::max(min_token_id, (llama_token)(n_vocab - top_k)); + + GGML_ASSERT(size == expected_size); + GGML_ASSERT(candidates_p.data[0].id == max_token_id); + GGML_ASSERT(candidates_p.data[expected_size-1].id == min_token_id); + } else if (s == 'p') { + const int softmax_divisor = n_vocab * (n_vocab-1) / 2 - min_token_id * (min_token_id-1) / 2; + const int softmax_numerator_target = ceilf(top_p * softmax_divisor); + + min_token_id = n_vocab; + int expected_size = 0; + int cumsum = 0; + do { // do-while because always at least one token is sampled + min_token_id--; + expected_size++; + + cumsum += min_token_id; + } while (cumsum < softmax_numerator_target); + + // token 0 has p == 0, need special consideration for cumsum because top_p immediately returns + if (min_token_id == 1) { + min_token_id--; + expected_size += 1; + } + + GGML_ASSERT(size == expected_size); + GGML_ASSERT(candidates_p.data[0].id == max_token_id); + GGML_ASSERT(candidates_p.data[expected_size-1].id == min_token_id); + } else if (s == 'm') { + int expected_size = ceilf((1.0f-min_p) * n_vocab); + expected_size = std::max(expected_size, 1); + expected_size = std::min(expected_size, size); + + min_token_id = floorf(min_p * n_vocab); + min_token_id = std::max(min_token_id, 1); + min_token_id = std::max(min_token_id, (llama_token)(n_vocab - size)); + min_token_id = std::min(min_token_id, (llama_token)(n_vocab - 1)); + + GGML_ASSERT(size == expected_size); + GGML_ASSERT(candidates_p.data[0].id == max_token_id); + GGML_ASSERT(candidates_p.data[expected_size-1].id == min_token_id); + } else { + GGML_ASSERT(false); + } + } + + printf("Sampler queue %3s OK with n_vocab=%05ld top_k=%05d top_p=%f min_p=%f\n", + samplers_sequence.c_str(), n_vocab, top_k, top_p, min_p); +} + int main(void) { ggml_time_init(); @@ -139,6 +241,15 @@ int main(void) { 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_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/1.0f, 0.3f/1.0f, 0.2f/1.0f, 0.1f/1.0f}, 0.00f); + test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/1.0f, 0.3f/1.0f, 0.2f/1.0f, 0.1f/1.0f}, 0.24f); + test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.9f, 0.3f/0.9f, 0.2f/0.9f}, 0.26f); + test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.9f, 0.3f/0.9f, 0.2f/0.9f}, 0.49f); + test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.7f, 0.3f/0.7f}, 0.51f); + test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.7f, 0.3f/0.7f}, 0.74f); + test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f}, 0.76f); + test_min_p({0.1f, 0.2f, 0.3f, 0.4f}, {0.4f/0.4f}, 1.00f); + test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f}, 0.25f); test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.75f); test_tfs({0.1f, 0.15f, 0.2f, 0.25f, 0.3f}, {0.3f, 0.25f}, 0.99f); @@ -154,6 +265,34 @@ int main(void) { test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2}, {0.499966f, 0.499966f, 0.000023f, 0.000023f, 0.000023f}, 1.0f, 5.0f, 5.0f); test_repetition_penalties({0.2f, 0.2f, 0.2f, 0.2f, 0.2f}, {0, 1, 2, 0, 0}, {0.499977f, 0.499977f, 0.000023f, 0.000023f, 0.000000f}, 1.0f, 5.0f, 5.0f); + test_sampler_queue(10000, "k", 10000, 1.0f, 1.0f); + test_sampler_queue(10000, "k", 1, 1.0f, 1.0f); + test_sampler_queue(10000, "p", 10000, 1.0f, 1.0f); + test_sampler_queue(10000, "p", 10000, 0.0f, 1.0f); + test_sampler_queue(10000, "m", 10000, 1.0f, 1.0f); + test_sampler_queue(10000, "m", 10000, 1.0f, 1e-12); + + test_sampler_queue(10000, "k", 100, 1.0000f, 1.0f); + test_sampler_queue(10000, "p", 10000, 0.0002f, 1.0f); + test_sampler_queue(10000, "p", 10000, 0.8000f, 1.0f); + test_sampler_queue(10000, "m", 10000, 1.0000f, 9997.9f/9999.0f); + test_sampler_queue(10000, "m", 10000, 1.0000f, 0.1f); + + test_sampler_queue(10000, "kp", 100, 0.8f, 0.1f); + test_sampler_queue(10000, "km", 100, 0.8f, 0.1f); + test_sampler_queue(10000, "pk", 100, 0.8f, 0.1f); + test_sampler_queue(10000, "pm", 100, 0.8f, 0.1f); + test_sampler_queue(10000, "mk", 100, 0.8f, 0.1f); + test_sampler_queue(10000, "mp", 100, 0.8f, 9997.9f/9999.0f); + test_sampler_queue(10000, "mp", 100, 0.8f, 0.1f); + + test_sampler_queue(10000, "kpm", 100, 0.8f, 0.1f); + test_sampler_queue(10000, "kmp", 100, 0.8f, 0.1f); + test_sampler_queue(10000, "pkm", 100, 0.8f, 0.1f); + test_sampler_queue(10000, "pmk", 100, 0.8f, 0.1f); + test_sampler_queue(10000, "mkp", 100, 0.8f, 0.1f); + test_sampler_queue(10000, "mpk", 100, 0.8f, 0.1f); + printf("OK\n"); return 0; From 9241c3a2ace544aef708334e54bbdddb90208ee8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sun, 28 Jan 2024 09:59:49 +0100 Subject: [PATCH 040/143] Apply min_p to unsorted tokens (#5115) --- llama.cpp | 54 +++++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 45 insertions(+), 9 deletions(-) diff --git a/llama.cpp b/llama.cpp index 391c956ec..45eeadbe8 100644 --- a/llama.cpp +++ b/llama.cpp @@ -52,6 +52,7 @@ #include #include #include +#include #include #include #include @@ -8246,21 +8247,56 @@ void llama_sample_min_p(struct llama_context * ctx, llama_token_data_array * can return; } - llama_sample_softmax(ctx, candidates); - const int64_t t_start_sample_us = ggml_time_us(); - float scale = candidates->data[0].p; // scale by max prob - size_t i = 1; // first token always matches + bool min_p_applied = false; - for (; i < candidates->size; ++i) { - if (candidates->data[i].p < p * scale && i >= min_keep) { - break; // prob too small + // if the candidates aren't sorted, try the unsorted implementation first + if (!candidates->sorted) { + std::vector filtered_tokens; + + float max_logit = -FLT_MAX; + for (size_t i = 0; i < candidates->size; ++i) { + max_logit = std::max(max_logit, candidates->data[i].logit); + } + const float min_logit = max_logit + logf(p); // min logit for p_i >= p * p_max + + for (size_t i = 0; i < candidates->size; ++i) { + if (candidates->data[i].logit >= min_logit) { + filtered_tokens.push_back(candidates->data[i]); + } + } + + // if we have enough values the operation was a success + if (filtered_tokens.size() >= min_keep) { + memcpy(candidates->data, filtered_tokens.data(), filtered_tokens.size()*sizeof(llama_token_data)); + candidates->size = filtered_tokens.size(); + min_p_applied = true; } } - // Resize the output vector to keep only the matching tokens - candidates->size = i; + // if the candidates are sorted or the unsorted implementation failed, use this implementation + if (!min_p_applied) { + // Sort the logits in descending order + if (!candidates->sorted) { + std::sort(candidates->data, candidates->data + candidates->size, [](const llama_token_data & a, const llama_token_data & b) { + return a.logit > b.logit; + }); + candidates->sorted = true; + } + + const float min_logit = candidates->data[0].logit + logf(p); // min logit for p_i >= p * p_max + size_t i = 1; // first token always matches + + for (; i < candidates->size; ++i) { + if (candidates->data[i].logit < min_logit && i >= min_keep) { + break; // prob too small + } + } + + // Resize the output vector to keep only the matching tokens + candidates->size = i; + } if (ctx) { ctx->t_sample_us += ggml_time_us() - t_start_sample_us; From b764b8f1d079ba44d912801ce6d29bd0d94d51cf Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 28 Jan 2024 16:54:54 +0200 Subject: [PATCH 041/143] flake.lock: Update (#5162) --- flake.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/flake.lock b/flake.lock index 1b253cb44..95e41f333 100644 --- a/flake.lock +++ b/flake.lock @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1705677747, - "narHash": "sha256-eyM3okYtMgYDgmYukoUzrmuoY4xl4FUujnsv/P6I/zI=", + "lastModified": 1706191920, + "narHash": "sha256-eLihrZAPZX0R6RyM5fYAWeKVNuQPYjAkCUBr+JNvtdE=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "bbe7d8f876fbbe7c959c90ba2ae2852220573261", + "rev": "ae5c332cbb5827f6b1f02572496b141021de335f", "type": "github" }, "original": { From 0f648573dde61c510560f68244f70ece7e60d8c1 Mon Sep 17 00:00:00 2001 From: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> Date: Sun, 28 Jan 2024 21:26:23 +0530 Subject: [PATCH 042/143] ggml : add unified SYCL backend for Intel GPUs (#2690) * first update for migration * update init_cublas * add debug functio, commit all help code * step 1 * step 2 * step3 add fp16, slower 31->28 * add GGML_LIST_DEVICE function * step 5 format device and print * step6, enhance error check, remove CUDA macro, enhance device id to fix none-zero id issue * support main device is non-zero * step7 add debug for code path, rm log * step 8, rename all macro & func from cuda by sycl * fix error of select non-zero device, format device list * ren ggml-sycl.hpp -> ggml-sycl.h * clear CMAKE to rm unused lib and options * correct queue: rm dtct:get_queue * add print tensor function to debug * fix error: wrong result in 658746bb26702e50f2c59c0e4ada8e9da6010481 * summary dpct definition in one header file to replace folder:dpct * refactor device log * mv dpct definition from folder dpct to ggml-sycl.h * update readme, refactor build script * fix build with sycl * set nthread=1 when sycl, increase performance * add run script, comment debug code * add ls-sycl-device tool * add ls-sycl-device, rm unused files * rm rear space * dos2unix * Update README_sycl.md * fix return type * remove sycl version from include path * restore rm code to fix hang issue * add syc and link for sycl readme * rm original sycl code before refactor * fix code err * add know issue for pvc hang issue * enable SYCL_F16 support * align pr4766 * check for sycl blas, better performance * cleanup 1 * remove extra endif * add build&run script, clean CMakefile, update guide by review comments * rename macro to intel hardware * editor config format * format fixes * format fixes * editor format fix * Remove unused headers * skip build sycl tool for other code path * replace tab by space * fix blas matmul function * fix mac build * restore hip dependency * fix conflict * ren as review comments * mv internal function to .cpp file * export funciton print_sycl_devices(), mv class dpct definition to source file * update CI/action for sycl code, fix CI error of repeat/dup * fix action ID format issue * rm unused strategy * enable llama_f16 in ci * fix conflict * fix build break on MacOS, due to CI of MacOS depend on external ggml, instead of internal ggml * fix ci cases for unsupported data type * revert unrelated changed in cuda cmake remove useless nommq fix typo of GGML_USE_CLBLAS_SYCL * revert hip cmake changes * fix indent * add prefix in func name * revert no mmq * rm cpu blas duplicate * fix no_new_line * fix src1->type==F16 bug. * pass batch offset for F16 src1 * fix batch error * fix wrong code * revert sycl checking in test-sampling * pass void as arguments of ggml_backend_sycl_print_sycl_devices * remove extra blank line in test-sampling * revert setting n_threads in sycl * implement std::isinf for icpx with fast math. * Update ci/run.sh Co-authored-by: Georgi Gerganov * Update examples/sycl/run-llama2.sh Co-authored-by: Georgi Gerganov * Update examples/sycl/run-llama2.sh Co-authored-by: Georgi Gerganov * Update CMakeLists.txt Co-authored-by: Georgi Gerganov * Update CMakeLists.txt Co-authored-by: Georgi Gerganov * Update CMakeLists.txt Co-authored-by: Georgi Gerganov * Update CMakeLists.txt Co-authored-by: Georgi Gerganov * add copyright and MIT license declare * update the cmd example --------- Co-authored-by: jianyuzh Co-authored-by: luoyu-intel Co-authored-by: Meng, Hengyu Co-authored-by: Georgi Gerganov --- .github/workflows/build.yml | 41 + CMakeLists.txt | 46 +- README.md | 11 +- README_sycl.md | 252 + ci/README.md | 4 + ci/run.sh | 11 + common/common.cpp | 26 +- examples/CMakeLists.txt | 3 + examples/server/server.cpp | 6 +- examples/sycl/CMakeLists.txt | 9 + examples/sycl/README.md | 47 + examples/sycl/build.sh | 20 + examples/sycl/ls-sycl-device.cpp | 11 + examples/sycl/run-llama2.sh | 19 + ggml-backend.c | 5 + ggml-sycl.cpp | 15197 +++++++++++++++++++++++++++++ ggml-sycl.h | 27 + ggml.c | 22 +- ggml.h | 1 + llama.cpp | 16 + llama.h | 5 +- tests/test-backend-ops.cpp | 9 +- 22 files changed, 15764 insertions(+), 24 deletions(-) create mode 100644 README_sycl.md create mode 100644 examples/sycl/CMakeLists.txt create mode 100644 examples/sycl/README.md create mode 100755 examples/sycl/build.sh create mode 100644 examples/sycl/ls-sycl-device.cpp create mode 100755 examples/sycl/run-llama2.sh create mode 100644 ggml-sycl.cpp create mode 100644 ggml-sycl.h diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index d22a041a6..e5e435a70 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -143,6 +143,47 @@ jobs: cd build ctest -L main --verbose + ubuntu-22-cmake-sycl: + runs-on: ubuntu-22.04 + + continue-on-error: true + + steps: + - uses: actions/checkout@v2 + + - name: add oneAPI to apt + shell: bash + run: | + cd /tmp + wget https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB + sudo apt-key add GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB + rm GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB + sudo add-apt-repository "deb https://apt.repos.intel.com/oneapi all main" + + - name: install oneAPI dpcpp compiler + shell: bash + run: | + sudo apt update + sudo apt install intel-oneapi-compiler-dpcpp-cpp + + - name: install oneAPI MKL library + shell: bash + run: | + sudo apt install intel-oneapi-mkl-devel + + - name: Clone + id: checkout + uses: actions/checkout@v3 + + - name: Build + id: cmake_build + run: | + source /opt/intel/oneapi/setvars.sh + mkdir build + cd build + cmake -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx .. + cmake --build . --config Release -j $(nproc) + # TODO: build with LLAMA_NO_METAL because test-backend-ops fail on "Apple Paravirtual device" and I don't know # how to debug it. # ref: https://github.com/ggerganov/llama.cpp/actions/runs/7131777249/job/19420981052#step:5:1124 diff --git a/CMakeLists.txt b/CMakeLists.txt index 2b2ae532e..8b530e3ed 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,5 +1,6 @@ cmake_minimum_required(VERSION 3.14) # for add_link_options and implicit target directories. project("llama.cpp" C CXX) +include(CheckIncludeFileCXX) set(CMAKE_EXPORT_COMPILE_COMMANDS ON) @@ -103,6 +104,8 @@ option(LLAMA_METAL_NDEBUG "llama: disable Metal debugging" option(LLAMA_METAL_SHADER_DEBUG "llama: compile Metal with -fno-fast-math" OFF) option(LLAMA_MPI "llama: use MPI" OFF) option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF) +option(LLAMA_SYCL "llama: use SYCL" OFF) +option(LLAMA_SYCL_F16 "llama: use 16 bit floats for sycl calculations" OFF) option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE}) option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE}) @@ -121,8 +124,12 @@ include(${CMAKE_CURRENT_SOURCE_DIR}/scripts/build-info.cmake) # # Compile flags # +if (LLAMA_SYCL) + set(CMAKE_CXX_STANDARD 17) +else() + set(CMAKE_CXX_STANDARD 11) +endif() -set(CMAKE_CXX_STANDARD 11) set(CMAKE_CXX_STANDARD_REQUIRED true) set(CMAKE_C_STANDARD 11) set(CMAKE_C_STANDARD_REQUIRED true) @@ -454,6 +461,32 @@ if (LLAMA_HIPBLAS) endif() endif() + +if (LLAMA_SYCL) + if ( NOT DEFINED ENV{ONEAPI_ROOT}) + message(FATAL_ERROR "Not detect ENV {ONEAPI_ROOT}, please install oneAPI & source it, like: source /opt/intel/oneapi/setvars.sh") + endif() + #todo: AOT + + find_package(IntelSYCL REQUIRED) + if (LLAMA_SYCL_F16) + add_compile_definitions(GGML_SYCL_F16) + endif() + add_compile_definitions(GGML_USE_SYCL) + + add_compile_options(-I./) #include DPCT + add_compile_options(-I/${SYCL_INCLUDE_DIR}) + + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-narrowing") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -O3") + set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -fsycl -L${MKLROOT}/lib") + + set(GGML_HEADERS_SYCL ggml.h ggml-sycl.h) + set(GGML_SOURCES_SYCL ggml-sycl.cpp) + + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} sycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread) +endif() + function(get_flags CCID CCVER) set(C_FLAGS "") set(CXX_FLAGS "") @@ -479,10 +512,12 @@ function(get_flags CCID CCVER) list(APPEND CXX_FLAGS -Wextra-semi) endif() elseif (CCID MATCHES "Intel") - # enable max optimization level when using Intel compiler - set(C_FLAGS -ipo -O3 -static -fp-model=fast -flto -fno-stack-protector) - set(CXX_FLAGS -ipo -O3 -static -fp-model=fast -flto -fno-stack-protector) - add_link_options(-fuse-ld=lld -static-intel) + if (NOT LLAMA_SYCL) + # enable max optimization level when using Intel compiler + set(C_FLAGS -ipo -O3 -static -fp-model=fast -flto -fno-stack-protector) + set(CXX_FLAGS -ipo -O3 -static -fp-model=fast -flto -fno-stack-protector) + add_link_options(-fuse-ld=lld -static-intel) + endif() endif() set(GF_C_FLAGS ${C_FLAGS} PARENT_SCOPE) @@ -799,6 +834,7 @@ add_library(ggml OBJECT ${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL} ${GGML_SOURCES_MPI} ${GGML_HEADERS_MPI} ${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA} + ${GGML_SOURCES_SYCL} ${GGML_HEADERS_SYCL} ) target_include_directories(ggml PUBLIC . ${LLAMA_EXTRA_INCLUDES}) diff --git a/README.md b/README.md index 44898d2f2..ecad95e58 100644 --- a/README.md +++ b/README.md @@ -63,7 +63,7 @@ The main goal of `llama.cpp` is to run the LLaMA model using 4-bit integer quant - AVX, AVX2 and AVX512 support for x86 architectures - Mixed F16 / F32 precision - 2-bit, 3-bit, 4-bit, 5-bit, 6-bit and 8-bit integer quantization support -- CUDA, Metal and OpenCL GPU backend support +- CUDA, Metal, OpenCL, SYCL GPU backend support The original implementation of `llama.cpp` was [hacked in an evening](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022). Since then, the project has improved significantly thanks to many contributions. This project is mainly for educational purposes and serves @@ -599,6 +599,15 @@ Building the program with BLAS support may lead to some performance improvements You can get a list of platforms and devices from the `clinfo -l` command, etc. +- #### SYCL + + SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators. + + llama.cpp based on SYCL is used to support Intel GPU (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU). + + For detailed info, please refer to [llama.cpp for SYCL](README_sycl.md). + + ### Prepare Data & Run ```bash diff --git a/README_sycl.md b/README_sycl.md new file mode 100644 index 000000000..d5a1818f5 --- /dev/null +++ b/README_sycl.md @@ -0,0 +1,252 @@ +# llama.cpp for SYCL + +[Background](#background) + +[OS](#os) + +[Intel GPU](#intel-gpu) + +[Linux](#linux) + +[Environment Variable](#environment-variable) + +[Known Issue](#known-issue) + +[Todo](#todo) + +## Background + +SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators—such as CPUs, GPUs, and FPGAs. It is a single-source embedded domain-specific language based on pure C++17. + +oneAPI is a specification that is open and standards-based, supporting multiple architecture types including but not limited to GPU, CPU, and FPGA. The spec has both direct programming and API-based programming paradigms. + +Intel uses the SYCL as direct programming language to support CPU, GPUs and FPGAs. + +To avoid to re-invent the wheel, this code refer other code paths in llama.cpp (like OpenBLAS, cuBLAS, CLBlast). We use a open-source tool [SYCLomatic](https://github.com/oneapi-src/SYCLomatic) (Commercial release [Intel® DPC++ Compatibility Tool](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compatibility-tool.html)) migrate to SYCL. + +The llama.cpp for SYCL is used to support Intel GPUs. + +For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). + +## OS + +|OS|Status|Verified| +|-|-|-| +|Linux|Support|Ubuntu 22.04| +|Windows|Ongoing| | + + +## Intel GPU + +|Intel GPU| Status | Verified Model| +|-|-|-| +|Intel Data Center Max Series| Support| Max 1550| +|Intel Data Center Flex Series| Support| Flex 170| +|Intel Arc Series| Support| Arc 770| +|Intel built-in Arc GPU| Support| built-in Arc GPU in Meteor Lake| +|Intel iGPU| Support| iGPU in i5-1250P, i7-1165G7| + + +## Linux + +### Setup Environment + +1. Install Intel GPU driver. + +a. Please install Intel GPU driver by official guide: [Install GPU Drivers](https://dgpu-docs.intel.com/driver/installation.html). + +Note: for iGPU, please install the client GPU driver. + +b. Add user to group: video, render. + +``` +sudo usermod -aG render username +sudo usermod -aG video username +``` + +Note: re-login to enable it. + +c. Check + +``` +sudo apt install clinfo +sudo clinfo -l +``` + +Output (example): + +``` +Platform #0: Intel(R) OpenCL Graphics + `-- Device #0: Intel(R) Arc(TM) A770 Graphics + + +Platform #0: Intel(R) OpenCL HD Graphics + `-- Device #0: Intel(R) Iris(R) Xe Graphics [0x9a49] +``` + +2. Install Intel® oneAPI Base toolkit. + + +a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html). + +Recommend to install to default folder: **/opt/intel/oneapi**. + +Following guide use the default folder as example. If you use other folder, please modify the following guide info with your folder. + +b. Check + +``` +source /opt/intel/oneapi/setvars.sh + +sycl-ls +``` + +There should be one or more level-zero devices. Like **[ext_oneapi_level_zero:gpu:0]**. + +Output (example): +``` +[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.10.0.17_160000] +[opencl:cpu:1] Intel(R) OpenCL, 13th Gen Intel(R) Core(TM) i7-13700K OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000] +[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Arc(TM) A770 Graphics OpenCL 3.0 NEO [23.30.26918.50] +[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Arc(TM) A770 Graphics 1.3 [1.3.26918] + +``` + +2. Build locally: + +``` +mkdir -p build +cd build +source /opt/intel/oneapi/setvars.sh + +#for FP16 +#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON # faster for long-prompt inference + +#for FP32 +cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx + +#build example/main only +#cmake --build . --config Release --target main + +#build all binary +cmake --build . --config Release -v + +``` + +or + +``` +./examples/sycl/build.sh +``` + +Note: + +- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only. + +### Run + +1. Put model file to folder **models** + +2. Enable oneAPI running environment + +``` +source /opt/intel/oneapi/setvars.sh +``` + +3. List device ID + +Run without parameter: + +``` +./build/bin/ls-sycl-device + +or + +./build/bin/main +``` + +Check the ID in startup log, like: + +``` +found 4 SYCL devices: + Device 0: Intel(R) Arc(TM) A770 Graphics, compute capability 1.3, + max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136 + Device 1: Intel(R) FPGA Emulation Device, compute capability 1.2, + max compute_units 24, max work group size 67108864, max sub group size 64, global mem size 67065057280 + Device 2: 13th Gen Intel(R) Core(TM) i7-13700K, compute capability 3.0, + max compute_units 24, max work group size 8192, max sub group size 64, global mem size 67065057280 + Device 3: Intel(R) Arc(TM) A770 Graphics, compute capability 3.0, + max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136 + +``` + +|Attribute|Note| +|-|-| +|compute capability 1.3|Level-zero running time, recommended | +|compute capability 3.0|OpenCL running time, slower than level-zero in most cases| + +4. Set device ID and execute llama.cpp + +Set device ID = 0 by **GGML_SYCL_DEVICE=0** + +``` +GGML_SYCL_DEVICE=0 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 +``` +or run by script: + +``` +./examples/sycl/run_llama2.sh +``` + +Note: + +- By default, mmap is used to read model file. In some cases, it leads to the hang issue. Recommend to use parameter **--no-mmap** to disable mmap() to skip this issue. + + +5. Check the device ID in output + +Like： +``` +Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device +``` + + +## Environment Variable + +#### Build + +|Name|Value|Function| +|-|-|-| +|LLAMA_SYCL|ON (mandatory)|Enable build with SYCL code path.
For FP32/FP16, LLAMA_SYCL=ON is mandatory.| +|LLAMA_SYCL_F16|ON (optional)|Enable FP16 build with SYCL code path. Faster for long-prompt inference.
For FP32, not set it.| +|CMAKE_C_COMPILER|icx|Use icx compiler for SYCL code path| +|CMAKE_CXX_COMPILER|icpx|use icpx for SYCL code path| + +#### Running + + +|Name|Value|Function| +|-|-|-| +|GGML_SYCL_DEVICE|0 (default) or 1|Set the device id used. Check the device ids by default running output| +|GGML_SYCL_DEBUG|0 (default) or 1|Enable log function by macro: GGML_SYCL_DEBUG| + +## Known Issue + +- Error: `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`. + + Miss to enable oneAPI running environment. + + Install oneAPI base toolkit and enable it by: `source /opt/intel/oneapi/setvars.sh`. + + +- Hang during startup + + llama.cpp use mmap as default way to read model file and copy to GPU. In some system, memcpy will be abnormal and block. + + Solution: add **--no-mmap**. + +## Todo + +- Support to build in Windows. + +- Support multiple cards. diff --git a/ci/README.md b/ci/README.md index 65cfe63eb..406470519 100644 --- a/ci/README.md +++ b/ci/README.md @@ -22,4 +22,8 @@ bash ./ci/run.sh ./tmp/results ./tmp/mnt # with CUDA support GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt + +# with SYCL support +source /opt/intel/oneapi/setvars.sh +GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt ``` diff --git a/ci/run.sh b/ci/run.sh index 2427e55a2..82fe247a5 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -10,6 +10,9 @@ # # with CUDA support # GG_BUILD_CUDA=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt # +# # with SYCL support +# GG_BUILD_SYCL=1 bash ./ci/run.sh ./tmp/results ./tmp/mnt +# if [ -z "$2" ]; then echo "usage: $0 " @@ -40,6 +43,14 @@ if [ ! -z ${GG_BUILD_CUDA} ]; then CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_CUBLAS=1" fi +if [ ! -z ${GG_BUILD_SYCL} ]; then + if [ -z ${ONEAPI_ROOT} ]; then + echo "Not detected ONEAPI_ROOT, please install oneAPI base toolkit and enable it by:\n source /opt/intel/oneapi/setvars.sh" + exit 1 + fi + + CMAKE_EXTRA="${CMAKE_EXTRA} -DLLAMA_SYCL=1 DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON" +fi ## helpers # download a file if it does not exist or if it is outdated diff --git a/common/common.cpp b/common/common.cpp index 6b07f1197..288013676 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -42,6 +42,10 @@ #pragma warning(disable: 4244 4267) // possible loss of data #endif +#if (defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)) +#define GGML_USE_CUBLAS_SYCL +#endif + int32_t get_num_physical_cores() { #ifdef __linux__ // enumerate the set of thread siblings, num entries is num cores @@ -599,9 +603,9 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } params.main_gpu = std::stoi(argv[i]); -#ifndef GGML_USE_CUBLAS - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting the main GPU has no effect.\n"); -#endif // GGML_USE_CUBLAS +#ifndef GGML_USE_CUBLAS_SYCL + fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL. Setting the main GPU has no effect.\n"); +#endif // GGML_USE_CUBLAS_SYCL } else if (arg == "--split-mode" || arg == "-sm") { if (++i >= argc) { invalid_param = true; @@ -618,9 +622,10 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { invalid_param = true; break; } -#ifndef GGML_USE_CUBLAS - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting the split mode has no effect.\n"); -#endif // GGML_USE_CUBLAS +#ifndef GGML_USE_CUBLAS_SYCL + fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL. Setting the split mode has no effect.\n"); +#endif // GGML_USE_CUBLAS_SYCL + } else if (arg == "--tensor-split" || arg == "-ts") { if (++i >= argc) { invalid_param = true; @@ -643,9 +648,9 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { params.tensor_split[i] = 0.0f; } } -#ifndef GGML_USE_CUBLAS - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS. Setting a tensor split has no effect.\n"); -#endif // GGML_USE_CUBLAS +#ifndef GGML_USE_CUBLAS_SYCL + fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL. Setting a tensor split has no effect.\n"); +#endif // GGML_USE_CUBLAS_SYCL } else if (arg == "--no-mmap") { params.use_mmap = false; } else if (arg == "--numa") { @@ -1007,7 +1012,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); printf(" or for intermediate results and KV (with split-mode = row) (default: %d)\n", params.main_gpu); -#endif +#endif // LLAMA_SUPPORTS_GPU_OFFLOAD printf(" --verbose-prompt print a verbose prompt before generation (default: %s)\n", params.verbose_prompt ? "true" : "false"); printf(" --no-display-prompt don't print prompt at generation (default: %s)\n", !params.display_prompt ? "true" : "false"); printf(" -gan N, --grp-attn-n N\n"); @@ -1514,7 +1519,6 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l 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"); diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index f67d74c55..68ad89964 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -23,6 +23,9 @@ else() add_subdirectory(infill) add_subdirectory(llama-bench) add_subdirectory(llava) + if (LLAMA_SYCL) + add_subdirectory(sycl) + endif() add_subdirectory(main) add_subdirectory(tokenize) add_subdirectory(parallel) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index f58a2acaa..a48582ad9 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2099,7 +2099,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, invalid_param = true; break; } -#ifdef GGML_USE_CUBLAS +#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL) std::string arg_next = argv[i]; // split string by , and / @@ -2125,7 +2125,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } else if (arg == "--no-mul-mat-q" || arg == "-nommq") { -#ifdef GGML_USE_CUBLAS +#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL) params.mul_mat_q = false; #else LOG_WARNING("warning: llama.cpp was compiled without cuBLAS. Disabling mul_mat_q kernels has no effect.\n", {}); @@ -2138,7 +2138,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, invalid_param = true; break; } -#ifdef GGML_USE_CUBLAS +#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL) params.main_gpu = std::stoi(argv[i]); #else LOG_WARNING("llama.cpp was compiled without cuBLAS. It is not possible to set a main GPU.", {}); diff --git a/examples/sycl/CMakeLists.txt b/examples/sycl/CMakeLists.txt new file mode 100644 index 000000000..69cf8932e --- /dev/null +++ b/examples/sycl/CMakeLists.txt @@ -0,0 +1,9 @@ +# MIT license +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: MIT + +set(TARGET ls-sycl-device) +add_executable(${TARGET} ls-sycl-device.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_17) diff --git a/examples/sycl/README.md b/examples/sycl/README.md new file mode 100644 index 000000000..b46f17f39 --- /dev/null +++ b/examples/sycl/README.md @@ -0,0 +1,47 @@ +# llama.cpp/example/sycl + +This example program provide the tools for llama.cpp for SYCL on Intel GPU. + +## Tool + +|Tool Name| Function|Status| +|-|-|-| +|ls-sycl-device| List all SYCL devices with ID, compute capability, max work group size, ect.|Support| + +### ls-sycl-device + +List all SYCL devices with ID, compute capability, max work group size, ect. + +1. Build the llama.cpp for SYCL for all targets. + +2. Enable oneAPI running environment + +``` +source /opt/intel/oneapi/setvars.sh +``` + +3. Execute + +``` +./build/bin/ls-sycl-device +``` + +Check the ID in startup log, like: + +``` +found 4 SYCL devices: + Device 0: Intel(R) Arc(TM) A770 Graphics, compute capability 1.3, + max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136 + Device 1: Intel(R) FPGA Emulation Device, compute capability 1.2, + max compute_units 24, max work group size 67108864, max sub group size 64, global mem size 67065057280 + Device 2: 13th Gen Intel(R) Core(TM) i7-13700K, compute capability 3.0, + max compute_units 24, max work group size 8192, max sub group size 64, global mem size 67065057280 + Device 3: Intel(R) Arc(TM) A770 Graphics, compute capability 3.0, + max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136 + +``` + +|Attribute|Note| +|-|-| +|compute capability 1.3|Level-zero running time, recommended | +|compute capability 3.0|OpenCL running time, slower than level-zero in most cases| diff --git a/examples/sycl/build.sh b/examples/sycl/build.sh new file mode 100755 index 000000000..26ad2f7da --- /dev/null +++ b/examples/sycl/build.sh @@ -0,0 +1,20 @@ + +# MIT license +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: MIT + +mkdir -p build +cd build +source /opt/intel/oneapi/setvars.sh + +#for FP16 +#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON # faster for long-prompt inference + +#for FP32 +cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx + +#build example/main only +#cmake --build . --config Release --target main + +#build all binary +cmake --build . --config Release -v diff --git a/examples/sycl/ls-sycl-device.cpp b/examples/sycl/ls-sycl-device.cpp new file mode 100644 index 000000000..42847154a --- /dev/null +++ b/examples/sycl/ls-sycl-device.cpp @@ -0,0 +1,11 @@ +/*MIT license + Copyright (C) 2024 Intel Corporation + SPDX-License-Identifier: MIT +*/ + +#include "ggml-sycl.h" + +int main(int argc, char ** argv) { + ggml_backend_sycl_print_sycl_devices(); + return 0; +} diff --git a/examples/sycl/run-llama2.sh b/examples/sycl/run-llama2.sh new file mode 100755 index 000000000..f5f4c1e98 --- /dev/null +++ b/examples/sycl/run-llama2.sh @@ -0,0 +1,19 @@ +#!/bin/bash + +# MIT license +# Copyright (C) 2024 Intel Corporation +# SPDX-License-Identifier: MIT + +INPUT2="Building a website can be done in 10 simple steps:\nStep 1:" +source /opt/intel/oneapi/setvars.sh + +if [ $# -gt 0 ]; then + export GGML_SYCL_DEVICE=$1 +else + export GGML_SYCL_DEVICE=0 +fi +echo GGML_SYCL_DEVICE=$GGML_SYCL_DEVICE +#export GGML_SYCL_DEBUG=1 +./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 400 -e -ngl 33 -s 0 +#./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "${INPUT2}" -n 5 -e -ngl 33 -t 1 -s 0 + diff --git a/ggml-backend.c b/ggml-backend.c index 3fff5fc87..897a4cb5c 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -339,6 +339,11 @@ GGML_CALL static void ggml_backend_registry_init(void) { ggml_backend_cuda_reg_devices(); #endif +#ifdef GGML_USE_SYCL + extern void ggml_backend_sycl_reg_devices(void); + ggml_backend_sycl_reg_devices(); +#endif + #ifdef GGML_USE_METAL extern GGML_CALL ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data); extern GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void); diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp new file mode 100644 index 000000000..9764d9c35 --- /dev/null +++ b/ggml-sycl.cpp @@ -0,0 +1,15197 @@ +/*MIT license + Copyright (C) 2024 Intel Corporation + SPDX-License-Identifier: MIT +*/ + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include + + +#include +#include + +#include "ggml-sycl.h" +#include "ggml.h" +#include "ggml-backend-impl.h" + +/* +Following definition copied from DPCT head files, which are used by ggml-sycl.cpp +*/ +// COPY from DPCT head files +#include +#include +#include + +#if defined(__linux__) +#include +#elif defined(_WIN64) +#ifndef NOMINMAX +#define NOMINMAX +#endif +#include +#else +#error "Only support Windows and Linux." +#endif + +#if defined(__linux__) +#include +#include +#endif +#if defined(_WIN64) +#ifndef NOMINMAX +#define NOMINMAX +#endif +#include +#endif + +#define DPCT_COMPATIBILITY_TEMP (900) + +#if defined(_MSC_VER) +#define __dpct_align__(n) __declspec(align(n)) +#define __dpct_inline__ __forceinline +#else +#define __dpct_align__(n) __attribute__((aligned(n))) +#define __dpct_inline__ __inline__ __attribute__((always_inline)) +#endif + +#if defined(_MSC_VER) +#define __dpct_noinline__ __declspec(noinline) +#else +#define __dpct_noinline__ __attribute__((noinline)) +#endif + +namespace dpct +{ + typedef sycl::queue *queue_ptr; + typedef sycl::event *event_ptr; + typedef char *device_ptr; + typedef uint8_t byte_t; + typedef sycl::buffer buffer_t; + + /// SYCL default exception handler + inline auto exception_handler = [](sycl::exception_list exceptions) + { + for (std::exception_ptr const &e : exceptions) + { + try + { + std::rethrow_exception(e); + } + catch (sycl::exception const &e) + { + std::cerr << "Caught asynchronous SYCL exception:" << std::endl + << e.what() << std::endl + << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + } + } + }; + + enum error_code + { + success = 0, + default_error = 999 + }; + + enum memcpy_direction + { + host_to_host, + host_to_device, + device_to_host, + device_to_device, + automatic + }; + + enum memory_region + { + global = 0, // device global memory + constant, // device constant memory + local, // device local memory + shared, // memory which can be accessed by host and device + }; + + enum class library_data_t : unsigned char + { + real_float = 0, + complex_float, + real_double, + complex_double, + real_half, + complex_half, + real_bfloat16, + complex_bfloat16, + real_int4, + complex_int4, + real_uint4, + complex_uint4, + real_int8, + complex_int8, + real_uint8, + complex_uint8, + real_int16, + complex_int16, + real_uint16, + complex_uint16, + real_int32, + complex_int32, + real_uint32, + complex_uint32, + real_int64, + complex_int64, + real_uint64, + complex_uint64, + real_int8_4, + real_int8_32, + real_uint8_4, + library_data_t_size + }; + + template + struct DataType + { + using T2 = T; + }; + template + struct DataType> + { + using T2 = std::complex; + }; + + static void destroy_event(event_ptr event) + { + delete event; + } + + static inline unsigned int get_tid() + { +#if defined(__linux__) + return syscall(SYS_gettid); +#elif defined(_WIN64) + return GetCurrentThreadId(); +#else +#error "Only support Windows and Linux." +#endif + } + + namespace detail + { + static void get_version(const sycl::device &dev, int &major, int &minor) + { + // Version string has the following format: + // a. OpenCL + // b. + std::string ver; + ver = dev.get_info(); + std::string::size_type i = 0; + while (i < ver.size()) + { + if (isdigit(ver[i])) + break; + i++; + } + major = std::stoi(&(ver[i])); + while (i < ver.size()) + { + if (ver[i] == '.') + break; + i++; + } + i++; + minor = std::stoi(&(ver[i])); + } + + template + class generic_error_type + { + public: + generic_error_type() = default; + generic_error_type(T value) : value{value} {} + operator T() const { return value; } + + private: + T value; + }; + + } // namespace detail + + /// Pitched 2D/3D memory data. + class pitched_data + { + public: + pitched_data() : pitched_data(nullptr, 0, 0, 0) {} + pitched_data(void *data, size_t pitch, size_t x, size_t y) + : _data(data), _pitch(pitch), _x(x), _y(y) {} + + void *get_data_ptr() { return _data; } + void set_data_ptr(void *data) { _data = data; } + + size_t get_pitch() { return _pitch; } + void set_pitch(size_t pitch) { _pitch = pitch; } + + size_t get_x() { return _x; } + void set_x(size_t x) { _x = x; }; + + size_t get_y() { return _y; } + void set_y(size_t y) { _y = y; } + + private: + void *_data; + size_t _pitch, _x, _y; + }; + + class device_info + { + public: + // get interface + const char *get_name() const { return _name; } + char *get_name() { return _name; } + template , + std::enable_if_t> || + std::is_same_v, + int> = 0> + auto get_max_work_item_sizes() const + { + if constexpr (std::is_same_v>) + return sycl::range<3>(_max_work_item_sizes_i[0], + _max_work_item_sizes_i[1], + _max_work_item_sizes_i[2]); + else + { + return _max_work_item_sizes_i; + } + } + template , + std::enable_if_t> || + std::is_same_v, + int> = 0> + auto get_max_work_item_sizes() + { + if constexpr (std::is_same_v>) + return sycl::range<3>(_max_work_item_sizes_i[0], + _max_work_item_sizes_i[1], + _max_work_item_sizes_i[2]); + else + { + return _max_work_item_sizes_i; + } + } + bool get_host_unified_memory() const { return _host_unified_memory; } + int get_major_version() const { return _major; } + int get_minor_version() const { return _minor; } + int get_integrated() const { return _integrated; } + int get_max_clock_frequency() const { return _frequency; } + int get_max_compute_units() const { return _max_compute_units; } + int get_max_work_group_size() const { return _max_work_group_size; } + int get_max_sub_group_size() const { return _max_sub_group_size; } + int get_max_work_items_per_compute_unit() const + { + return _max_work_items_per_compute_unit; + } + int get_max_register_size_per_work_group() const + { + return _max_register_size_per_work_group; + } + template || + std::is_same_v, + int> = 0> + auto get_max_nd_range_size() const + { + if constexpr (std::is_same_v) + return _max_nd_range_size; + else + return _max_nd_range_size_i; + } + template || + std::is_same_v, + int> = 0> + auto get_max_nd_range_size() + { + if constexpr (std::is_same_v) + return _max_nd_range_size; + else + return _max_nd_range_size_i; + } + size_t get_global_mem_size() const { return _global_mem_size; } + size_t get_local_mem_size() const { return _local_mem_size; } + /// Returns the maximum clock rate of device's global memory in kHz. If + /// compiler does not support this API then returns default value 3200000 kHz. + unsigned int get_memory_clock_rate() const { return _memory_clock_rate; } + /// Returns the maximum bus width between device and memory in bits. If + /// compiler does not support this API then returns default value 64 bits. + unsigned int get_memory_bus_width() const { return _memory_bus_width; } + uint32_t get_device_id() const { return _device_id; } + std::array get_uuid() const { return _uuid; } + /// Returns global memory cache size in bytes. + unsigned int get_global_mem_cache_size() const + { + return _global_mem_cache_size; + } + + // set interface + void set_name(const char *name) + { + size_t length = strlen(name); + if (length < 256) + { + std::memcpy(_name, name, length + 1); + } + else + { + std::memcpy(_name, name, 255); + _name[255] = '\0'; + } + } + void set_max_work_item_sizes(const sycl::range<3> max_work_item_sizes) + { + for (int i = 0; i < 3; ++i) + _max_work_item_sizes_i[i] = max_work_item_sizes[i]; + } + [[deprecated]] void + set_max_work_item_sizes(const sycl::id<3> max_work_item_sizes) + { + for (int i = 0; i < 3; ++i) + { + _max_work_item_sizes_i[i] = max_work_item_sizes[i]; + } + } + void set_host_unified_memory(bool host_unified_memory) + { + _host_unified_memory = host_unified_memory; + } + void set_major_version(int major) { _major = major; } + void set_minor_version(int minor) { _minor = minor; } + void set_integrated(int integrated) { _integrated = integrated; } + void set_max_clock_frequency(int frequency) { _frequency = frequency; } + void set_max_compute_units(int max_compute_units) + { + _max_compute_units = max_compute_units; + } + void set_global_mem_size(size_t global_mem_size) + { + _global_mem_size = global_mem_size; + } + void set_local_mem_size(size_t local_mem_size) + { + _local_mem_size = local_mem_size; + } + void set_max_work_group_size(int max_work_group_size) + { + _max_work_group_size = max_work_group_size; + } + void set_max_sub_group_size(int max_sub_group_size) + { + _max_sub_group_size = max_sub_group_size; + } + void + set_max_work_items_per_compute_unit(int max_work_items_per_compute_unit) + { + _max_work_items_per_compute_unit = max_work_items_per_compute_unit; + } + void set_max_nd_range_size(int max_nd_range_size[]) + { + for (int i = 0; i < 3; i++) + { + _max_nd_range_size[i] = max_nd_range_size[i]; + _max_nd_range_size_i[i] = max_nd_range_size[i]; + } + } + void set_memory_clock_rate(unsigned int memory_clock_rate) + { + _memory_clock_rate = memory_clock_rate; + } + void set_memory_bus_width(unsigned int memory_bus_width) + { + _memory_bus_width = memory_bus_width; + } + void + set_max_register_size_per_work_group(int max_register_size_per_work_group) + { + _max_register_size_per_work_group = max_register_size_per_work_group; + } + void set_device_id(uint32_t device_id) + { + _device_id = device_id; + } + void set_uuid(std::array uuid) + { + _uuid = std::move(uuid); + } + void set_global_mem_cache_size(unsigned int global_mem_cache_size) + { + _global_mem_cache_size = global_mem_cache_size; + } + + private: + char _name[256]; + int _max_work_item_sizes_i[3]; + bool _host_unified_memory = false; + int _major; + int _minor; + int _integrated = 0; + int _frequency; + // Set estimated value 3200000 kHz as default value. + unsigned int _memory_clock_rate = 3200000; + // Set estimated value 64 bits as default value. + unsigned int _memory_bus_width = 64; + unsigned int _global_mem_cache_size; + int _max_compute_units; + int _max_work_group_size; + int _max_sub_group_size; + int _max_work_items_per_compute_unit; + int _max_register_size_per_work_group; + size_t _global_mem_size; + size_t _local_mem_size; + size_t _max_nd_range_size[3]; + int _max_nd_range_size_i[3]; + uint32_t _device_id; + std::array _uuid; + }; + + static int get_major_version(const sycl::device &dev) + { + int major, minor; + detail::get_version(dev, major, minor); + return major; + } + + static int get_minor_version(const sycl::device &dev) + { + int major, minor; + detail::get_version(dev, major, minor); + return minor; + } + + static void get_device_info(device_info &out, const sycl::device &dev) + { + device_info prop; + prop.set_name(dev.get_info().c_str()); + + int major, minor; + detail::get_version(dev, major, minor); + prop.set_major_version(major); + prop.set_minor_version(minor); + + prop.set_max_work_item_sizes( +#if (__SYCL_COMPILER_VERSION && __SYCL_COMPILER_VERSION < 20220902) + // oneAPI DPC++ compiler older than 2022/09/02, where max_work_item_sizes + // is an enum class element + dev.get_info()); +#else + // SYCL 2020-conformant code, max_work_item_sizes is a struct templated by + // an int + dev.get_info>()); +#endif + prop.set_host_unified_memory(dev.has(sycl::aspect::usm_host_allocations)); + + prop.set_max_clock_frequency( + dev.get_info() * 1000); + + prop.set_max_compute_units( + dev.get_info()); + prop.set_max_work_group_size( + dev.get_info()); + prop.set_global_mem_size(dev.get_info()); + prop.set_local_mem_size(dev.get_info()); + +#if (defined(SYCL_EXT_INTEL_DEVICE_INFO) && SYCL_EXT_INTEL_DEVICE_INFO >= 6) + if (dev.has(sycl::aspect::ext_intel_memory_clock_rate)) + { + unsigned int tmp = + dev.get_info(); + if (tmp != 0) + prop.set_memory_clock_rate(1000 * tmp); + } + if (dev.has(sycl::aspect::ext_intel_memory_bus_width)) + { + prop.set_memory_bus_width( + dev.get_info()); + } + if (dev.has(sycl::aspect::ext_intel_device_id)) + { + prop.set_device_id( + dev.get_info()); + } + if (dev.has(sycl::aspect::ext_intel_device_info_uuid)) + { + prop.set_uuid(dev.get_info()); + } +#elif defined(_MSC_VER) && !defined(__clang__) +#pragma message("get_device_info: querying memory_clock_rate and \ + memory_bus_width are not supported by the compiler used. \ + Use 3200000 kHz as memory_clock_rate default value. \ + Use 64 bits as memory_bus_width default value.") +#else +#warning "get_device_info: querying memory_clock_rate and \ + memory_bus_width are not supported by the compiler used. \ + Use 3200000 kHz as memory_clock_rate default value. \ + Use 64 bits as memory_bus_width default value." +#endif + + size_t max_sub_group_size = 1; + std::vector sub_group_sizes = + dev.get_info(); + + for (const auto &sub_group_size : sub_group_sizes) + { + if (max_sub_group_size < sub_group_size) + max_sub_group_size = sub_group_size; + } + + prop.set_max_sub_group_size(max_sub_group_size); + + prop.set_max_work_items_per_compute_unit( + dev.get_info()); + int max_nd_range_size[] = {0x7FFFFFFF, 0x7FFFFFFF, 0x7FFFFFFF}; + prop.set_max_nd_range_size(max_nd_range_size); + + // Estimates max register size per work group, feel free to update the value + // according to device properties. + prop.set_max_register_size_per_work_group(65536); + + prop.set_global_mem_cache_size( + dev.get_info()); + out = prop; + } + + /// dpct device extension + class device_ext : public sycl::device + { + typedef std::mutex mutex_type; + + public: + device_ext() : sycl::device(), _ctx(*this) {} + ~device_ext() + { + std::lock_guard lock(m_mutex); + clear_queues(); + } + device_ext(const sycl::device &base) : sycl::device(base), _ctx(*this) + { + std::lock_guard lock(m_mutex); + init_queues(); + } + + int is_native_atomic_supported() { return 0; } + int get_major_version() const + { + return dpct::get_major_version(*this); + } + + int get_minor_version() const + { + return dpct::get_minor_version(*this); + } + + int get_max_compute_units() const + { + return get_device_info().get_max_compute_units(); + } + + /// Return the maximum clock frequency of this device in KHz. + int get_max_clock_frequency() const + { + return get_device_info().get_max_clock_frequency(); + } + + int get_integrated() const { return get_device_info().get_integrated(); } + + int get_max_sub_group_size() const + { + return get_device_info().get_max_sub_group_size(); + } + + int get_max_register_size_per_work_group() const + { + return get_device_info().get_max_register_size_per_work_group(); + } + + int get_max_work_group_size() const + { + return get_device_info().get_max_work_group_size(); + } + + int get_mem_base_addr_align() const + { + return get_info(); + } + + size_t get_global_mem_size() const + { + return get_device_info().get_global_mem_size(); + } + + /// Get the number of bytes of free and total memory on the SYCL device. + /// \param [out] free_memory The number of bytes of free memory on the SYCL device. + /// \param [out] total_memory The number of bytes of total memory on the SYCL device. + void get_memory_info(size_t &free_memory, size_t &total_memory) + { +#if (defined(__SYCL_COMPILER_VERSION) && __SYCL_COMPILER_VERSION >= 20221105) + if (!has(sycl::aspect::ext_intel_free_memory)) + { + std::cerr << "get_memory_info: ext_intel_free_memory is not supported." << std::endl; + free_memory = 0; + } + else + { + free_memory = get_info(); + } +#else + std::cerr << "get_memory_info: ext_intel_free_memory is not supported." << std::endl; + free_memory = 0; +#if defined(_MSC_VER) && !defined(__clang__) +#pragma message("Querying the number of bytes of free memory is not supported") +#else +#warning "Querying the number of bytes of free memory is not supported" +#endif +#endif + total_memory = get_device_info().get_global_mem_size(); + } + + void get_device_info(device_info &out) const + { + dpct::get_device_info(out, *this); + } + + device_info get_device_info() const + { + device_info prop; + dpct::get_device_info(prop, *this); + return prop; + } + + void reset() + { + std::lock_guard lock(m_mutex); + clear_queues(); + init_queues(); + } + + sycl::queue &in_order_queue() { return *_q_in_order; } + + sycl::queue &out_of_order_queue() { return *_q_out_of_order; } + + sycl::queue &default_queue() + { +#ifdef DPCT_USM_LEVEL_NONE + return out_of_order_queue(); +#else + return in_order_queue(); +#endif // DPCT_USM_LEVEL_NONE + } + + void queues_wait_and_throw() + { + std::unique_lock lock(m_mutex); + std::vector> current_queues( + _queues); + lock.unlock(); + for (const auto &q : current_queues) + { + q->wait_and_throw(); + } + // Guard the destruct of current_queues to make sure the ref count is safe. + lock.lock(); + } + + sycl::queue *create_queue(bool enable_exception_handler = false) + { +#ifdef DPCT_USM_LEVEL_NONE + return create_out_of_order_queue(enable_exception_handler); +#else + return create_in_order_queue(enable_exception_handler); +#endif // DPCT_USM_LEVEL_NONE + } + + sycl::queue *create_in_order_queue(bool enable_exception_handler = false) + { + std::lock_guard lock(m_mutex); + return create_queue_impl(enable_exception_handler, + sycl::property::queue::in_order()); + } + + sycl::queue *create_out_of_order_queue(bool enable_exception_handler = false) + { + std::lock_guard lock(m_mutex); + return create_queue_impl(enable_exception_handler); + } + + void destroy_queue(sycl::queue *&queue) + { + std::lock_guard lock(m_mutex); + _queues.erase(std::remove_if(_queues.begin(), _queues.end(), + [=](const std::shared_ptr &q) -> bool + { + return q.get() == queue; + }), + _queues.end()); + queue = nullptr; + } + void set_saved_queue(sycl::queue *q) + { + std::lock_guard lock(m_mutex); + _saved_queue = q; + } + sycl::queue *get_saved_queue() const + { + std::lock_guard lock(m_mutex); + return _saved_queue; + } + sycl::context get_context() const { return _ctx; } + + private: + void clear_queues() + { + _queues.clear(); + _q_in_order = _q_out_of_order = _saved_queue = nullptr; + } + + void init_queues() + { + _q_in_order = create_queue_impl(true, sycl::property::queue::in_order()); + _q_out_of_order = create_queue_impl(true); + _saved_queue = &default_queue(); + } + + /// Caller should acquire resource \p m_mutex before calling this function. + template + sycl::queue *create_queue_impl(bool enable_exception_handler, + Properties... properties) + { + sycl::async_handler eh = {}; + if (enable_exception_handler) + { + eh = exception_handler; + } + _queues.push_back(std::make_shared( + _ctx, *this, eh, + sycl::property_list( +#ifdef DPCT_PROFILING_ENABLED + sycl::property::queue::enable_profiling(), +#endif + properties...))); + + return _queues.back().get(); + } + + void get_version(int &major, int &minor) const + { + detail::get_version(*this, major, minor); + } + sycl::queue *_q_in_order, *_q_out_of_order; + sycl::queue *_saved_queue; + sycl::context _ctx; + std::vector> _queues; + mutable mutex_type m_mutex; + }; + + /// device manager + class dev_mgr + { + public: + device_ext ¤t_device() + { + unsigned int dev_id = current_device_id(); + check_id(dev_id); + return *_devs[dev_id]; + } + device_ext &cpu_device() const + { + std::lock_guard lock(m_mutex); + if (_cpu_device == -1) + { + throw std::runtime_error("no valid cpu device"); + } + else + { + return *_devs[_cpu_device]; + } + } + device_ext &get_device(unsigned int id) const + { + std::lock_guard lock(m_mutex); + check_id(id); + return *_devs[id]; + } + unsigned int current_device_id() const + { + std::lock_guard lock(m_mutex); + auto it = _thread2dev_map.find(get_tid()); + if (it != _thread2dev_map.end()) + return it->second; + return DEFAULT_DEVICE_ID; + } + + /// Select device with a device ID. + /// \param [in] id The id of the device which can + /// be obtained through get_device_id(const sycl::device). + void select_device(unsigned int id) + { + std::lock_guard lock(m_mutex); + check_id(id); + _thread2dev_map[get_tid()] = id; + } + unsigned int device_count() { return _devs.size(); } + + unsigned int get_device_id(const sycl::device &dev) + { + unsigned int id = 0; + for (auto dev_item : _devs) + { + if (*dev_item == dev) + { + break; + } + id++; + } + return id; + } + + template + std::enable_if_t< + std::is_invocable_r_v> + select_device(const DeviceSelector &selector = sycl::gpu_selector_v) + { + sycl::device selected_device = sycl::device(selector); + unsigned int selected_device_id = get_device_id(selected_device); + select_device(selected_device_id); + } + + /// Returns the instance of device manager singleton. + static dev_mgr &instance() + { + static dev_mgr d_m; + return d_m; + } + dev_mgr(const dev_mgr &) = delete; + dev_mgr &operator=(const dev_mgr &) = delete; + dev_mgr(dev_mgr &&) = delete; + dev_mgr &operator=(dev_mgr &&) = delete; + + private: + mutable std::recursive_mutex m_mutex; + dev_mgr() + { + sycl::device default_device = + sycl::device(sycl::default_selector_v); + _devs.push_back(std::make_shared(default_device)); + + std::vector sycl_all_devs = + sycl::device::get_devices(sycl::info::device_type::all); + // Collect other devices except for the default device. + if (default_device.is_cpu()) + _cpu_device = 0; + for (auto &dev : sycl_all_devs) + { + if (dev == default_device) + { + continue; + } + _devs.push_back(std::make_shared(dev)); + if (_cpu_device == -1 && dev.is_cpu()) + { + _cpu_device = _devs.size() - 1; + } + } + } + void check_id(unsigned int id) const + { + if (id >= _devs.size()) + { + throw std::runtime_error("invalid device id"); + } + } + std::vector> _devs; + /// DEFAULT_DEVICE_ID is used, if current_device_id() can not find current + /// thread id in _thread2dev_map, which means default device should be used + /// for the current thread. + const unsigned int DEFAULT_DEVICE_ID = 0; + /// thread-id to device-id map. + std::map _thread2dev_map; + int _cpu_device = -1; + }; + + static inline sycl::queue &get_default_queue() + { + return dev_mgr::instance().current_device().default_queue(); + } + + namespace detail + { + enum class pointer_access_attribute + { + host_only = 0, + device_only, + host_device, + end + }; + + static pointer_access_attribute get_pointer_attribute(sycl::queue &q, + const void *ptr) + { +#ifdef DPCT_USM_LEVEL_NONE + return mem_mgr::instance().is_device_ptr(ptr) + ? pointer_access_attribute::device_only + : pointer_access_attribute::host_only; +#else + switch (sycl::get_pointer_type(ptr, q.get_context())) + { + case sycl::usm::alloc::unknown: + return pointer_access_attribute::host_only; + case sycl::usm::alloc::device: + return pointer_access_attribute::device_only; + case sycl::usm::alloc::shared: + case sycl::usm::alloc::host: + return pointer_access_attribute::host_device; + } +#endif + } + + template + inline constexpr std::uint64_t get_type_combination_id(ArgT Val) + { + static_assert((unsigned char)library_data_t::library_data_t_size <= + std::numeric_limits::max() && + "library_data_t size exceeds limit."); + static_assert(std::is_same_v, "Unsupported ArgT"); + return (std::uint64_t)Val; + } + + template + inline constexpr std::uint64_t get_type_combination_id(FirstT FirstVal, + RestT... RestVal) + { + static_assert((std::uint8_t)library_data_t::library_data_t_size <= + std::numeric_limits::max() && + "library_data_t size exceeds limit."); + static_assert(sizeof...(RestT) <= 8 && "Too many parameters"); + static_assert(std::is_same_v, "Unsupported FirstT"); + return get_type_combination_id(RestVal...) << 8 | ((std::uint64_t)FirstVal); + } + + class mem_mgr + { + mem_mgr() + { + // Reserved address space, no real memory allocation happens here. +#if defined(__linux__) + mapped_address_space = + (byte_t *)mmap(nullptr, mapped_region_size, PROT_NONE, + MAP_PRIVATE | MAP_ANONYMOUS, -1, 0); +#elif defined(_WIN64) + mapped_address_space = (byte_t *)VirtualAlloc( + NULL, // NULL specified as the base address parameter + mapped_region_size, // Size of allocation + MEM_RESERVE, // Allocate reserved pages + PAGE_NOACCESS); // Protection = no access +#else +#error "Only support Windows and Linux." +#endif + next_free = mapped_address_space; + }; + + public: + using buffer_id_t = int; + + struct allocation + { + buffer_t buffer; + byte_t *alloc_ptr; + size_t size; + }; + + ~mem_mgr() + { +#if defined(__linux__) + munmap(mapped_address_space, mapped_region_size); +#elif defined(_WIN64) + VirtualFree(mapped_address_space, 0, MEM_RELEASE); +#else +#error "Only support Windows and Linux." +#endif + }; + + mem_mgr(const mem_mgr &) = delete; + mem_mgr &operator=(const mem_mgr &) = delete; + mem_mgr(mem_mgr &&) = delete; + mem_mgr &operator=(mem_mgr &&) = delete; + + /// Allocate + void *mem_alloc(size_t size) + { + if (!size) + return nullptr; + std::lock_guard lock(m_mutex); + if (next_free + size > mapped_address_space + mapped_region_size) + { + throw std::runtime_error("dpct_malloc: out of memory for virtual memory pool"); + } + // Allocation + sycl::range<1> r(size); + buffer_t buf(r); + allocation A{buf, next_free, size}; + // Map allocation to device pointer + void *result = next_free; + m_map.emplace(next_free + size, A); + // Update pointer to the next free space. + next_free += (size + extra_padding + alignment - 1) & ~(alignment - 1); + + return result; + } + + /// Deallocate + void mem_free(const void *ptr) + { + if (!ptr) + return; + std::lock_guard lock(m_mutex); + auto it = get_map_iterator(ptr); + m_map.erase(it); + } + + /// map: device pointer -> allocation(buffer, alloc_ptr, size) + allocation translate_ptr(const void *ptr) + { + std::lock_guard lock(m_mutex); + auto it = get_map_iterator(ptr); + return it->second; + } + + /// Check if the pointer represents device pointer or not. + bool is_device_ptr(const void *ptr) const + { + std::lock_guard lock(m_mutex); + return (mapped_address_space <= ptr) && + (ptr < mapped_address_space + mapped_region_size); + } + + /// Returns the instance of memory manager singleton. + static mem_mgr &instance() + { + static mem_mgr m; + return m; + } + + private: + std::map m_map; + mutable std::mutex m_mutex; + byte_t *mapped_address_space; + byte_t *next_free; + const size_t mapped_region_size = 128ull * 1024 * 1024 * 1024; + const size_t alignment = 256; + /// This padding may be defined to some positive value to debug + /// out of bound accesses. + const size_t extra_padding = 0; + + std::map::iterator get_map_iterator(const void *ptr) + { + auto it = m_map.upper_bound((byte_t *)ptr); + if (it == m_map.end()) + { + // Not a virtual pointer. + throw std::runtime_error("can not get buffer from non-virtual pointer"); + } + const allocation &alloc = it->second; + if (ptr < alloc.alloc_ptr) + { + // Out of bound. + // This may happen if there's a gap between allocations due to alignment + // or extra padding and pointer points to this gap. + throw std::runtime_error("invalid virtual pointer"); + } + return it; + } + }; + + template + class accessor; + template + class memory_traits + { + public: + static constexpr sycl::access::target target = + sycl::access::target::device; + static constexpr sycl::access_mode mode = + (Memory == constant) ? sycl::access_mode::read + : sycl::access_mode::read_write; + static constexpr size_t type_size = sizeof(T); + using element_t = + typename std::conditional::type; + using value_t = typename std::remove_cv::type; + template + using accessor_t = typename std::conditional< + Memory == local, sycl::local_accessor, + sycl::accessor>::type; + using pointer_t = T *; + }; + + static inline void *dpct_malloc(size_t size, sycl::queue &q) + { +#ifdef DPCT_USM_LEVEL_NONE + return mem_mgr::instance().mem_alloc(size * sizeof(byte_t)); +#else + return sycl::malloc_device(size, q.get_device(), q.get_context()); +#endif // DPCT_USM_LEVEL_NONE + } + +#define PITCH_DEFAULT_ALIGN(x) (((x) + 31) & ~(0x1F)) + static inline void *dpct_malloc(size_t &pitch, size_t x, size_t y, size_t z, + sycl::queue &q) + { + pitch = PITCH_DEFAULT_ALIGN(x); + return dpct_malloc(pitch * y * z, q); + } + + /** + * @brief Sets \p value to the first \p size elements starting from \p dev_ptr in \p q. + * @tparam valueT The type of the element to be set. + * @param [in] q The queue in which the operation is done. + * @param [in] dev_ptr Pointer to the virtual device memory address. + * @param [in] value The value to be set. + * @param [in] size Number of elements to be set to the value. + * @return An event representing the memset operation. + */ + template + static inline sycl::event dpct_memset(sycl::queue &q, void *dev_ptr, + valueT value, size_t size) + { +#ifdef DPCT_USM_LEVEL_NONE + auto &mm = mem_mgr::instance(); + assert(mm.is_device_ptr(dev_ptr)); + auto alloc = mm.translate_ptr(dev_ptr); + size_t offset = (valueT *)dev_ptr - (valueT *)alloc.alloc_ptr; + + return q.submit([&](sycl::handler &cgh) + { + auto r = sycl::range<1>(size); + auto o = sycl::id<1>(offset); + auto new_buffer = alloc.buffer.reinterpret( + sycl::range<1>(alloc.size / sizeof(valueT))); + sycl::accessor + acc(new_buffer, cgh, r, o); + cgh.fill(acc, value); }); +#else + return q.fill(dev_ptr, value, size); +#endif // DPCT_USM_LEVEL_NONE + } + + /** + * @brief Sets \p value to the 3D memory region pointed by \p data in \p q. + * @tparam valueT The type of the element to be set. + * @param [in] q The queue in which the operation is done. + * @param [in] data Pointer to the pitched device memory region. + * @param [in] value The value to be set. + * @param [in] size 3D memory region by number of elements. + * @return An event list representing the memset operations. + */ + template + static inline std::vector + dpct_memset(sycl::queue &q, pitched_data data, valueT value, + sycl::range<3> size) + { + std::vector event_list; + size_t slice = data.get_pitch() * data.get_y(); + unsigned char *data_surface = (unsigned char *)data.get_data_ptr(); + for (size_t z = 0; z < size.get(2); ++z) + { + unsigned char *data_ptr = data_surface; + for (size_t y = 0; y < size.get(1); ++y) + { + event_list.push_back(dpct_memset(q, data_ptr, value, size.get(0))); + data_ptr += data.get_pitch(); + } + data_surface += slice; + } + return event_list; + } + + /** + * @brief Sets \p val to the pitched 2D memory region pointed by \p ptr in \p q. + * @tparam valueT The type of the element to be set. + * @param [in] q The queue in which the operation is done. + * @param [in] ptr Pointer to the virtual device memory. + * @param [in] pitch The pitch size by number of elements, including padding. + * @param [in] val The value to be set. + * @param [in] x The width of memory region by number of elements. + * @param [in] y The height of memory region by number of elements. + * @return An event list representing the memset operations. + */ + template + static inline std::vector + dpct_memset(sycl::queue &q, void *ptr, size_t pitch, valueT val, size_t x, + size_t y) + { + return dpct_memset(q, pitched_data(ptr, pitch, x, 1), val, + sycl::range<3>(x, y, 1)); + } + + static memcpy_direction deduce_memcpy_direction(sycl::queue &q, void *to_ptr, + const void *from_ptr, + memcpy_direction dir) + { + switch (dir) + { + case memcpy_direction::host_to_host: + case memcpy_direction::host_to_device: + case memcpy_direction::device_to_host: + case memcpy_direction::device_to_device: + return dir; + case memcpy_direction::automatic: + { + // table[to_attribute][from_attribute] + static const memcpy_direction + direction_table[static_cast(pointer_access_attribute::end)] + [static_cast(pointer_access_attribute::end)] = + {{memcpy_direction::host_to_host, + memcpy_direction::device_to_host, + memcpy_direction::host_to_host}, + {memcpy_direction::host_to_device, + memcpy_direction::device_to_device, + memcpy_direction::device_to_device}, + {memcpy_direction::host_to_host, + memcpy_direction::device_to_device, + memcpy_direction::device_to_device}}; + return direction_table[static_cast(get_pointer_attribute( + q, to_ptr))][static_cast(get_pointer_attribute(q, from_ptr))]; + } + default: + throw std::runtime_error("dpct_memcpy: invalid direction value"); + } + } + + static sycl::event + dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, size_t size, + memcpy_direction direction, + const std::vector &dep_events = {}) + { + if (!size) + return sycl::event{}; +#ifdef DPCT_USM_LEVEL_NONE + auto &mm = mem_mgr::instance(); + auto real_direction = deduce_memcpy_direction(q, to_ptr, from_ptr, direction); + + switch (real_direction) + { + case host_to_host: + return q.submit([&](sycl::handler &cgh) + { + cgh.depends_on(dep_events); + cgh.host_task([=] { std::memcpy(to_ptr, from_ptr, size); }); }); + case host_to_device: + { + auto alloc = mm.translate_ptr(to_ptr); + size_t offset = (byte_t *)to_ptr - alloc.alloc_ptr; + return q.submit([&](sycl::handler &cgh) + { + cgh.depends_on(dep_events); + auto r = sycl::range<1>(size); + auto o = sycl::id<1>(offset); + sycl::accessor + acc(alloc.buffer, cgh, r, o); + cgh.copy(from_ptr, acc); }); + } + case device_to_host: + { + auto alloc = mm.translate_ptr(from_ptr); + size_t offset = (byte_t *)from_ptr - alloc.alloc_ptr; + return q.submit([&](sycl::handler &cgh) + { + cgh.depends_on(dep_events); + auto r = sycl::range<1>(size); + auto o = sycl::id<1>(offset); + sycl::accessor + acc(alloc.buffer, cgh, r, o); + cgh.copy(acc, to_ptr); }); + } + case device_to_device: + { + auto to_alloc = mm.translate_ptr(to_ptr); + auto from_alloc = mm.translate_ptr(from_ptr); + size_t to_offset = (byte_t *)to_ptr - to_alloc.alloc_ptr; + size_t from_offset = (byte_t *)from_ptr - from_alloc.alloc_ptr; + return q.submit([&](sycl::handler &cgh) + { + cgh.depends_on(dep_events); + auto r = sycl::range<1>(size); + auto to_o = sycl::id<1>(to_offset); + auto from_o = sycl::id<1>(from_offset); + sycl::accessor + to_acc(to_alloc.buffer, cgh, r, to_o); + sycl::accessor + from_acc(from_alloc.buffer, cgh, r, from_o); + cgh.copy(from_acc, to_acc); }); + } + default: + throw std::runtime_error("dpct_memcpy: invalid direction value"); + } +#else + return q.memcpy(to_ptr, from_ptr, size, dep_events); +#endif // DPCT_USM_LEVEL_NONE + } + + // Get actual copy range and make sure it will not exceed range. + static inline size_t get_copy_range(sycl::range<3> size, size_t slice, + size_t pitch) + { + return slice * (size.get(2) - 1) + pitch * (size.get(1) - 1) + size.get(0); + } + + static inline size_t get_offset(sycl::id<3> id, size_t slice, + size_t pitch) + { + return slice * id.get(2) + pitch * id.get(1) + id.get(0); + } + + /// copy 3D matrix specified by \p size from 3D matrix specified by \p from_ptr + /// and \p from_range to another specified by \p to_ptr and \p to_range. + static inline std::vector + dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, + sycl::range<3> to_range, sycl::range<3> from_range, + sycl::id<3> to_id, sycl::id<3> from_id, + sycl::range<3> size, memcpy_direction direction, + const std::vector &dep_events = {}) + { + // RAII for host pointer + class host_buffer + { + void *_buf; + size_t _size; + sycl::queue &_q; + const std::vector &_deps; // free operation depends + + public: + host_buffer(size_t size, sycl::queue &q, + const std::vector &deps) + : _buf(std::malloc(size)), _size(size), _q(q), _deps(deps) {} + void *get_ptr() const { return _buf; } + size_t get_size() const { return _size; } + ~host_buffer() + { + if (_buf) + { + _q.submit([&](sycl::handler &cgh) + { + cgh.depends_on(_deps); + cgh.host_task([buf = _buf] { std::free(buf); }); }); + } + } + }; + std::vector event_list; + + size_t to_slice = to_range.get(1) * to_range.get(0), + from_slice = from_range.get(1) * from_range.get(0); + unsigned char *to_surface = + (unsigned char *)to_ptr + get_offset(to_id, to_slice, to_range.get(0)); + const unsigned char *from_surface = + (const unsigned char *)from_ptr + + get_offset(from_id, from_slice, from_range.get(0)); + + if (to_slice == from_slice && to_slice == size.get(1) * size.get(0)) + { + return {dpct_memcpy(q, to_surface, from_surface, to_slice * size.get(2), + direction, dep_events)}; + } + direction = deduce_memcpy_direction(q, to_ptr, from_ptr, direction); + size_t size_slice = size.get(1) * size.get(0); + switch (direction) + { + case host_to_host: + for (size_t z = 0; z < size.get(2); ++z) + { + unsigned char *to_ptr = to_surface; + const unsigned char *from_ptr = from_surface; + if (to_range.get(0) == from_range.get(0) && + to_range.get(0) == size.get(0)) + { + event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size_slice, + direction, dep_events)); + } + else + { + for (size_t y = 0; y < size.get(1); ++y) + { + event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size.get(0), + direction, dep_events)); + to_ptr += to_range.get(0); + from_ptr += from_range.get(0); + } + } + to_surface += to_slice; + from_surface += from_slice; + } + break; + case host_to_device: + { + host_buffer buf(get_copy_range(size, to_slice, to_range.get(0)), q, + event_list); + std::vector host_events; + if (to_slice == size_slice) + { + // Copy host data to a temp host buffer with the shape of target. + host_events = + dpct_memcpy(q, buf.get_ptr(), from_surface, to_range, from_range, + sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size, + host_to_host, dep_events); + } + else + { + // Copy host data to a temp host buffer with the shape of target. + host_events = dpct_memcpy( + q, buf.get_ptr(), from_surface, to_range, from_range, + sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size, host_to_host, + // If has padding data, not sure whether it is useless. So fill temp + // buffer with it. + std::vector{ + dpct_memcpy(q, buf.get_ptr(), to_surface, buf.get_size(), + device_to_host, dep_events)}); + } + // Copy from temp host buffer to device with only one submit. + event_list.push_back(dpct_memcpy(q, to_surface, buf.get_ptr(), + buf.get_size(), host_to_device, + host_events)); + break; + } + case device_to_host: + { + host_buffer buf(get_copy_range(size, from_slice, from_range.get(0)), q, + event_list); + // Copy from host temp buffer to host target with reshaping. + event_list = dpct_memcpy( + q, to_surface, buf.get_ptr(), to_range, from_range, sycl::id<3>(0, 0, 0), + sycl::id<3>(0, 0, 0), size, host_to_host, + // Copy from device to temp host buffer with only one submit. + std::vector{dpct_memcpy(q, buf.get_ptr(), from_surface, + buf.get_size(), + device_to_host, dep_events)}); + break; + } + case device_to_device: +#ifdef DPCT_USM_LEVEL_NONE + { + auto &mm = mem_mgr::instance(); + auto to_alloc = mm.translate_ptr(to_surface); + auto from_alloc = mm.translate_ptr(from_surface); + size_t to_offset = (byte_t *)to_surface - to_alloc.alloc_ptr; + size_t from_offset = (byte_t *)from_surface - from_alloc.alloc_ptr; + event_list.push_back(q.submit([&](sycl::handler &cgh) + { + cgh.depends_on(dep_events); + auto to_o = sycl::id<1>(to_offset); + auto from_o = sycl::id<1>(from_offset); + sycl::accessor + to_acc(to_alloc.buffer, cgh, + get_copy_range(size, to_slice, to_range.get(0)), to_o); + sycl::accessor + from_acc(from_alloc.buffer, cgh, + get_copy_range(size, from_slice, from_range.get(0)), from_o); + cgh.parallel_for( + size, + [=](sycl::id<3> id) { + to_acc[get_offset(id, to_slice, to_range.get(0))] = + from_acc[get_offset(id, from_slice, from_range.get(0))]; + }); })); + } +#else + event_list.push_back(q.submit([&](sycl::handler &cgh) + { + cgh.depends_on(dep_events); + cgh.parallel_for( + size, + [=](sycl::id<3> id) { + to_surface[get_offset(id, to_slice, to_range.get(0))] = + from_surface[get_offset(id, from_slice, from_range.get(0))]; + }); })); +#endif + break; + default: + throw std::runtime_error("dpct_memcpy: invalid direction value"); + } + return event_list; + } + + /// memcpy 2D/3D matrix specified by pitched_data. + static inline std::vector + dpct_memcpy(sycl::queue &q, pitched_data to, sycl::id<3> to_id, + pitched_data from, sycl::id<3> from_id, sycl::range<3> size, + memcpy_direction direction = automatic) + { + return dpct_memcpy(q, to.get_data_ptr(), from.get_data_ptr(), + sycl::range<3>(to.get_pitch(), to.get_y(), 1), + sycl::range<3>(from.get_pitch(), from.get_y(), 1), to_id, from_id, + size, direction); + } + + /// memcpy 2D matrix with pitch. + static inline std::vector + dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, + size_t to_pitch, size_t from_pitch, size_t x, size_t y, + memcpy_direction direction = automatic) + { + return dpct_memcpy(q, to_ptr, from_ptr, sycl::range<3>(to_pitch, y, 1), + sycl::range<3>(from_pitch, y, 1), + sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), + sycl::range<3>(x, y, 1), direction); + } + + namespace deprecated + { + + template + class usm_allocator + { + private: + using Alloc = sycl::usm_allocator; + Alloc _impl; + + public: + using value_type = typename std::allocator_traits::value_type; + using pointer = typename std::allocator_traits::pointer; + using const_pointer = typename std::allocator_traits::const_pointer; + using void_pointer = typename std::allocator_traits::void_pointer; + using const_void_pointer = + typename std::allocator_traits::const_void_pointer; + using reference = typename std::allocator_traits::value_type &; + using const_reference = + const typename std::allocator_traits::value_type &; + using difference_type = + typename std::allocator_traits::difference_type; + using size_type = typename std::allocator_traits::size_type; + using propagate_on_container_copy_assignment = typename std::allocator_traits< + Alloc>::propagate_on_container_copy_assignment; + using propagate_on_container_move_assignment = typename std::allocator_traits< + Alloc>::propagate_on_container_move_assignment; + using propagate_on_container_swap = + typename std::allocator_traits::propagate_on_container_swap; + using is_always_equal = + typename std::allocator_traits::is_always_equal; + + template + struct rebind + { + typedef usm_allocator other; + }; + + usm_allocator() : _impl(dpct::get_default_queue()) {} + ~usm_allocator() {} + usm_allocator(const usm_allocator &other) : _impl(other._impl) {} + usm_allocator(usm_allocator &&other) : _impl(std::move(other._impl)) {} + pointer address(reference r) { return &r; } + const_pointer address(const_reference r) { return &r; } + pointer allocate(size_type cnt, const_void_pointer hint = nullptr) + { + return std::allocator_traits::allocate(_impl, cnt, hint); + } + void deallocate(pointer p, size_type cnt) + { + std::allocator_traits::deallocate(_impl, p, cnt); + } + size_type max_size() const + { + return std::allocator_traits::max_size(_impl); + } + bool operator==(const usm_allocator &other) const { return _impl == other._impl; } + bool operator!=(const usm_allocator &other) const { return _impl != other._impl; } + }; + + } // namespace deprecated + + inline void dpct_free(void *ptr, + const sycl::queue &q) + { + if (ptr) + { +#ifdef DPCT_USM_LEVEL_NONE + detail::mem_mgr::instance().mem_free(ptr); +#else + sycl::free(ptr, q.get_context()); +#endif // DPCT_USM_LEVEL_NONE + } + } + + template + inline auto get_memory(const void *x) + { + T *new_x = reinterpret_cast(const_cast(x)); +#ifdef DPCT_USM_LEVEL_NONE + return dpct::get_buffer>(new_x); +#else + return new_x; +#endif + } + + template + inline typename DataType::T2 get_value(const T *s, sycl::queue &q) + { + using Ty = typename DataType::T2; + Ty s_h; + if (get_pointer_attribute(q, s) == pointer_access_attribute::device_only) + detail::dpct_memcpy(q, (void *)&s_h, (void *)s, sizeof(T), device_to_host) + .wait(); + else + s_h = *reinterpret_cast(s); + return s_h; + } + + } // namespace detail + + template + inline auto get_value(const T *s, sycl::queue &q) + { + return detail::get_value(s, q); + } + + namespace detail + { + template + inline void gemm_impl(sycl::queue &q, oneapi::mkl::transpose a_trans, + oneapi::mkl::transpose b_trans, int m, int n, int k, + const void *alpha, const void *a, int lda, const void *b, + int ldb, const void *beta, void *c, int ldc) + { +#ifndef __INTEL_MKL__ + throw std::runtime_error("The oneAPI Math Kernel Library (oneMKL) Interfaces " + "Project does not support this API."); +#else + Ts alpha_value = dpct::get_value(reinterpret_cast(alpha), q); + Ts beta_value = dpct::get_value(reinterpret_cast(beta), q); + auto data_a = get_memory(a); + auto data_b = get_memory(b); + auto data_c = get_memory(c); + oneapi::mkl::blas::column_major::gemm( + q, a_trans, b_trans, m, n, k, alpha_value, data_a, lda, + data_b, ldb, beta_value, data_c, ldc); +#endif + } + + template + class vectorized_binary + { + public: + inline VecT operator()(VecT a, VecT b, const BinaryOperation binary_op) + { + VecT v4; + for (size_t i = 0; i < v4.size(); ++i) + { + v4[i] = binary_op(a[i], b[i]); + } + return v4; + } + }; + + template + class vectorized_binary< + VecT, BinaryOperation, + std::void_t>> + { + public: + inline VecT operator()(VecT a, VecT b, const BinaryOperation binary_op) + { + return binary_op(a, b).template as(); + } + }; + + template + inline void gemm_batch_impl(sycl::queue &q, oneapi::mkl::transpose a_trans, + oneapi::mkl::transpose b_trans, int m, int n, int k, + const void *alpha, const void **a, int lda, + const void **b, int ldb, const void *beta, void **c, + int ldc, int batch_size) + { + struct matrix_info_t + { + oneapi::mkl::transpose transpose_info[2]; + Ts value_info[2]; + std::int64_t size_info[3]; + std::int64_t ld_info[3]; + std::int64_t groupsize_info; + }; + + Ts alpha_value = dpct::get_value(reinterpret_cast(alpha), q); + Ts beta_value = dpct::get_value(reinterpret_cast(beta), q); + + matrix_info_t *matrix_info = + (matrix_info_t *)std::malloc(sizeof(matrix_info_t)); + matrix_info->transpose_info[0] = a_trans; + matrix_info->transpose_info[1] = b_trans; + matrix_info->value_info[0] = alpha_value; + matrix_info->value_info[1] = beta_value; + matrix_info->size_info[0] = m; + matrix_info->size_info[1] = n; + matrix_info->size_info[2] = k; + matrix_info->ld_info[0] = lda; + matrix_info->ld_info[1] = ldb; + matrix_info->ld_info[2] = ldc; + matrix_info->groupsize_info = batch_size; + + sycl::event e = oneapi::mkl::blas::column_major::gemm_batch( + q, matrix_info->transpose_info, matrix_info->transpose_info + 1, + matrix_info->size_info, matrix_info->size_info + 1, + matrix_info->size_info + 2, matrix_info->value_info, + reinterpret_cast(a), matrix_info->ld_info, + reinterpret_cast(b), matrix_info->ld_info + 1, + matrix_info->value_info + 1, reinterpret_cast(c), + matrix_info->ld_info + 2, 1, &(matrix_info->groupsize_info)); + + q.submit([&](sycl::handler &cgh) + { + cgh.depends_on(e); + cgh.host_task([=] { std::free(matrix_info); }); }); + } + + template + inline void + gemm_batch_impl(sycl::queue &q, oneapi::mkl::transpose a_trans, + oneapi::mkl::transpose b_trans, int m, int n, + int k, const void *alpha, const void *a, int lda, + long long int stride_a, const void *b, int ldb, + long long int stride_b, const void *beta, void *c, + int ldc, long long int stride_c, int batch_size) + { + Ts alpha_value = dpct::get_value(reinterpret_cast(alpha), q); + Ts beta_value = dpct::get_value(reinterpret_cast(beta), q); + auto data_a = get_memory(a); + auto data_b = get_memory(b); + auto data_c = get_memory(c); + oneapi::mkl::blas::column_major::gemm_batch( + q, a_trans, b_trans, m, n, k, alpha_value, data_a, lda, + stride_a, data_b, ldb, stride_b, beta_value, + data_c, ldc, stride_c, batch_size); + } + + } // namespace detail + + template + inline unsigned vectorized_binary(unsigned a, unsigned b, + const BinaryOperation binary_op) + { + sycl::vec v0{a}, v1{b}; + auto v2 = v0.as(); + auto v3 = v1.as(); + auto v4 = + detail::vectorized_binary()(v2, v3, binary_op); + v0 = v4.template as>(); + return v0; + } + + static void async_dpct_memcpy(void *to_ptr, const void *from_ptr, size_t size, + memcpy_direction direction = automatic, + sycl::queue &q = dpct::get_default_queue()) + { + detail::dpct_memcpy(q, to_ptr, from_ptr, size, direction); + } + + static inline unsigned int select_device(unsigned int id) + { + dev_mgr::instance().select_device(id); + return id; + } + + template + T permute_sub_group_by_xor(sycl::sub_group g, T x, unsigned int mask, + int logical_sub_group_size = 32) + { + unsigned int id = g.get_local_linear_id(); + unsigned int start_index = + id / logical_sub_group_size * logical_sub_group_size; + unsigned int target_offset = (id % logical_sub_group_size) ^ mask; + return sycl::select_from_group(g, x, + target_offset < logical_sub_group_size + ? start_index + target_offset + : id); + } + + template + sycl::vec extract_and_sign_or_zero_extend4(T val) + { + return sycl::vec(val) + .template as, int8_t, uint8_t>, 4>>() + .template convert(); + } + + template + using dot_product_acc_t = + std::conditional_t && std::is_unsigned_v, + uint32_t, int32_t>; + + template + inline auto dp4a(T1 a, T2 b, T3 c) + { + dot_product_acc_t res = c; + auto va = extract_and_sign_or_zero_extend4(a); + auto vb = extract_and_sign_or_zero_extend4(b); + res += va[0] * vb[0]; + res += va[1] * vb[1]; + res += va[2] * vb[2]; + res += va[3] * vb[3]; + return res; + } + + struct sub_sat + { + template + auto operator()(const T x, const T y) const + { + return sycl::sub_sat(x, y); + } + }; + + template + inline T vectorized_min(T a, T b) + { + sycl::vec v0{a}, v1{b}; + auto v2 = v0.template as(); + auto v3 = v1.template as(); + auto v4 = sycl::min(v2, v3); + v0 = v4.template as>(); + return v0; + } + + inline float pow(const float a, const int b) { return sycl::pown(a, b); } + inline double pow(const double a, const int b) { return sycl::pown(a, b); } + inline float pow(const float a, const float b) { return sycl::pow(a, b); } + inline double pow(const double a, const double b) { return sycl::pow(a, b); } + template + inline typename std::enable_if_t, T> + pow(const T a, const U b) + { + return sycl::pow(a, static_cast(b)); + } + template + inline typename std::enable_if_t, double> + pow(const T a, const U b) + { + return sycl::pow(static_cast(a), static_cast(b)); + } + + inline double min(const double a, const float b) + { + return sycl::fmin(a, static_cast(b)); + } + inline double min(const float a, const double b) + { + return sycl::fmin(static_cast(a), b); + } + inline float min(const float a, const float b) { return sycl::fmin(a, b); } + inline double min(const double a, const double b) { return sycl::fmin(a, b); } + inline std::uint32_t min(const std::uint32_t a, const std::int32_t b) + { + return sycl::min(a, static_cast(b)); + } + inline std::uint32_t min(const std::int32_t a, const std::uint32_t b) + { + return sycl::min(static_cast(a), b); + } + inline std::int32_t min(const std::int32_t a, const std::int32_t b) + { + return sycl::min(a, b); + } + inline std::uint32_t min(const std::uint32_t a, const std::uint32_t b) + { + return sycl::min(a, b); + } + inline std::uint64_t min(const std::uint64_t a, const std::int64_t b) + { + return sycl::min(a, static_cast(b)); + } + inline std::uint64_t min(const std::int64_t a, const std::uint64_t b) + { + return sycl::min(static_cast(a), b); + } + inline std::int64_t min(const std::int64_t a, const std::int64_t b) + { + return sycl::min(a, b); + } + inline std::uint64_t min(const std::uint64_t a, const std::uint64_t b) + { + return sycl::min(a, b); + } + inline std::uint64_t min(const std::uint64_t a, const std::int32_t b) + { + return sycl::min(a, static_cast(b)); + } + inline std::uint64_t min(const std::int32_t a, const std::uint64_t b) + { + return sycl::min(static_cast(a), b); + } + inline std::uint64_t min(const std::uint64_t a, const std::uint32_t b) + { + return sycl::min(a, static_cast(b)); + } + inline std::uint64_t min(const std::uint32_t a, const std::uint64_t b) + { + return sycl::min(static_cast(a), b); + } + // max function overloads. + // For floating-point types, `float` or `double` arguments are acceptable. + // For integer types, `std::uint32_t`, `std::int32_t`, `std::uint64_t` or + // `std::int64_t` type arguments are acceptable. + inline double max(const double a, const float b) + { + return sycl::fmax(a, static_cast(b)); + } + inline double max(const float a, const double b) + { + return sycl::fmax(static_cast(a), b); + } + inline float max(const float a, const float b) { return sycl::fmax(a, b); } + inline double max(const double a, const double b) { return sycl::fmax(a, b); } + inline std::uint32_t max(const std::uint32_t a, const std::int32_t b) + { + return sycl::max(a, static_cast(b)); + } + inline std::uint32_t max(const std::int32_t a, const std::uint32_t b) + { + return sycl::max(static_cast(a), b); + } + inline std::int32_t max(const std::int32_t a, const std::int32_t b) + { + return sycl::max(a, b); + } + inline std::uint32_t max(const std::uint32_t a, const std::uint32_t b) + { + return sycl::max(a, b); + } + inline std::uint64_t max(const std::uint64_t a, const std::int64_t b) + { + return sycl::max(a, static_cast(b)); + } + inline std::uint64_t max(const std::int64_t a, const std::uint64_t b) + { + return sycl::max(static_cast(a), b); + } + inline std::int64_t max(const std::int64_t a, const std::int64_t b) + { + return sycl::max(a, b); + } + inline std::uint64_t max(const std::uint64_t a, const std::uint64_t b) + { + return sycl::max(a, b); + } + inline std::uint64_t max(const std::uint64_t a, const std::int32_t b) + { + return sycl::max(a, static_cast(b)); + } + inline std::uint64_t max(const std::int32_t a, const std::uint64_t b) + { + return sycl::max(static_cast(a), b); + } + inline std::uint64_t max(const std::uint64_t a, const std::uint32_t b) + { + return sycl::max(a, static_cast(b)); + } + inline std::uint64_t max(const std::uint32_t a, const std::uint64_t b) + { + return sycl::max(static_cast(a), b); + } + + inline void + has_capability_or_fail(const sycl::device &dev, + const std::initializer_list &props) + { + for (const auto &it : props) + { + if (dev.has(it)) + continue; + switch (it) + { + case sycl::aspect::fp64: + throw std::runtime_error("'double' is not supported in '" + + dev.get_info() + + "' device"); + break; + case sycl::aspect::fp16: + throw std::runtime_error("'half' is not supported in '" + + dev.get_info() + + "' device"); + break; + default: +#define __SYCL_ASPECT(ASPECT, ID) \ + case sycl::aspect::ASPECT: \ + return #ASPECT; +#define __SYCL_ASPECT_DEPRECATED(ASPECT, ID, MESSAGE) __SYCL_ASPECT(ASPECT, ID) +#define __SYCL_ASPECT_DEPRECATED_ALIAS(ASPECT, ID, MESSAGE) + auto getAspectNameStr = [](sycl::aspect AspectNum) -> std::string + { + switch (AspectNum) + { +#include +#include + default: + return "unknown aspect"; + } + }; +#undef __SYCL_ASPECT_DEPRECATED_ALIAS +#undef __SYCL_ASPECT_DEPRECATED +#undef __SYCL_ASPECT + throw std::runtime_error( + "'" + getAspectNameStr(it) + "' is not supported in '" + + dev.get_info() + "' device"); + } + break; + } + } + + static inline unsigned int get_current_device_id() + { + return dev_mgr::instance().current_device_id(); + } + + static inline device_ext &get_current_device() + { + return dev_mgr::instance().current_device(); + } + + static inline sycl::queue &get_in_order_queue() + { + return dev_mgr::instance().current_device().in_order_queue(); + } + + static sycl::event + dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, size_t size, + memcpy_direction direction, + const std::vector &dep_events = {}) + { + if (!size) + return sycl::event{}; +#ifdef DPCT_USM_LEVEL_NONE + auto &mm = mem_mgr::instance(); + auto real_direction = deduce_memcpy_direction(q, to_ptr, from_ptr, direction); + + switch (real_direction) + { + case host_to_host: + return q.submit([&](sycl::handler &cgh) + { + cgh.depends_on(dep_events); + cgh.host_task([=] { std::memcpy(to_ptr, from_ptr, size); }); }); + case host_to_device: + { + auto alloc = mm.translate_ptr(to_ptr); + size_t offset = (byte_t *)to_ptr - alloc.alloc_ptr; + return q.submit([&](sycl::handler &cgh) + { + cgh.depends_on(dep_events); + auto r = sycl::range<1>(size); + auto o = sycl::id<1>(offset); + sycl::accessor + acc(alloc.buffer, cgh, r, o); + cgh.copy(from_ptr, acc); }); + } + case device_to_host: + { + auto alloc = mm.translate_ptr(from_ptr); + size_t offset = (byte_t *)from_ptr - alloc.alloc_ptr; + return q.submit([&](sycl::handler &cgh) + { + cgh.depends_on(dep_events); + auto r = sycl::range<1>(size); + auto o = sycl::id<1>(offset); + sycl::accessor + acc(alloc.buffer, cgh, r, o); + cgh.copy(acc, to_ptr); }); + } + case device_to_device: + { + auto to_alloc = mm.translate_ptr(to_ptr); + auto from_alloc = mm.translate_ptr(from_ptr); + size_t to_offset = (byte_t *)to_ptr - to_alloc.alloc_ptr; + size_t from_offset = (byte_t *)from_ptr - from_alloc.alloc_ptr; + return q.submit([&](sycl::handler &cgh) + { + cgh.depends_on(dep_events); + auto r = sycl::range<1>(size); + auto to_o = sycl::id<1>(to_offset); + auto from_o = sycl::id<1>(from_offset); + sycl::accessor + to_acc(to_alloc.buffer, cgh, r, to_o); + sycl::accessor + from_acc(from_alloc.buffer, cgh, r, from_o); + cgh.copy(from_acc, to_acc); }); + } + default: + throw std::runtime_error("dpct_memcpy: invalid direction value"); + } +#else + return q.memcpy(to_ptr, from_ptr, size, dep_events); +#endif // DPCT_USM_LEVEL_NONE + } + + // Get actual copy range and make sure it will not exceed range. + static inline size_t get_copy_range(sycl::range<3> size, size_t slice, + size_t pitch) + { + return slice * (size.get(2) - 1) + pitch * (size.get(1) - 1) + size.get(0); + } + + static inline size_t get_offset(sycl::id<3> id, size_t slice, + size_t pitch) + { + return slice * id.get(2) + pitch * id.get(1) + id.get(0); + } + + /// copy 3D matrix specified by \p size from 3D matrix specified by \p from_ptr + /// and \p from_range to another specified by \p to_ptr and \p to_range. + static inline std::vector + dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, + sycl::range<3> to_range, sycl::range<3> from_range, + sycl::id<3> to_id, sycl::id<3> from_id, + sycl::range<3> size, memcpy_direction direction, + const std::vector &dep_events = {}) + { + // RAII for host pointer + class host_buffer + { + void *_buf; + size_t _size; + sycl::queue &_q; + const std::vector &_deps; // free operation depends + + public: + host_buffer(size_t size, sycl::queue &q, + const std::vector &deps) + : _buf(std::malloc(size)), _size(size), _q(q), _deps(deps) {} + void *get_ptr() const { return _buf; } + size_t get_size() const { return _size; } + ~host_buffer() + { + if (_buf) + { + _q.submit([&](sycl::handler &cgh) + { + cgh.depends_on(_deps); + cgh.host_task([buf = _buf] { std::free(buf); }); }); + } + } + }; + std::vector event_list; + + size_t to_slice = to_range.get(1) * to_range.get(0), + from_slice = from_range.get(1) * from_range.get(0); + unsigned char *to_surface = + (unsigned char *)to_ptr + get_offset(to_id, to_slice, to_range.get(0)); + const unsigned char *from_surface = + (const unsigned char *)from_ptr + + get_offset(from_id, from_slice, from_range.get(0)); + + if (to_slice == from_slice && to_slice == size.get(1) * size.get(0)) + { + return {dpct_memcpy(q, to_surface, from_surface, to_slice * size.get(2), + direction, dep_events)}; + } + direction = detail::deduce_memcpy_direction(q, to_ptr, from_ptr, direction); + size_t size_slice = size.get(1) * size.get(0); + switch (direction) + { + case host_to_host: + for (size_t z = 0; z < size.get(2); ++z) + { + unsigned char *to_ptr = to_surface; + const unsigned char *from_ptr = from_surface; + if (to_range.get(0) == from_range.get(0) && + to_range.get(0) == size.get(0)) + { + event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size_slice, + direction, dep_events)); + } + else + { + for (size_t y = 0; y < size.get(1); ++y) + { + event_list.push_back(dpct_memcpy(q, to_ptr, from_ptr, size.get(0), + direction, dep_events)); + to_ptr += to_range.get(0); + from_ptr += from_range.get(0); + } + } + to_surface += to_slice; + from_surface += from_slice; + } + break; + case host_to_device: + { + host_buffer buf(get_copy_range(size, to_slice, to_range.get(0)), q, + event_list); + std::vector host_events; + if (to_slice == size_slice) + { + // Copy host data to a temp host buffer with the shape of target. + host_events = + dpct_memcpy(q, buf.get_ptr(), from_surface, to_range, from_range, + sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size, + host_to_host, dep_events); + } + else + { + // Copy host data to a temp host buffer with the shape of target. + host_events = dpct_memcpy( + q, buf.get_ptr(), from_surface, to_range, from_range, + sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), size, host_to_host, + // If has padding data, not sure whether it is useless. So fill temp + // buffer with it. + std::vector{ + dpct_memcpy(q, buf.get_ptr(), to_surface, buf.get_size(), + device_to_host, dep_events)}); + } + // Copy from temp host buffer to device with only one submit. + event_list.push_back(dpct_memcpy(q, to_surface, buf.get_ptr(), + buf.get_size(), host_to_device, + host_events)); + break; + } + case device_to_host: + { + host_buffer buf(get_copy_range(size, from_slice, from_range.get(0)), q, + event_list); + // Copy from host temp buffer to host target with reshaping. + event_list = dpct_memcpy( + q, to_surface, buf.get_ptr(), to_range, from_range, sycl::id<3>(0, 0, 0), + sycl::id<3>(0, 0, 0), size, host_to_host, + // Copy from device to temp host buffer with only one submit. + std::vector{dpct_memcpy(q, buf.get_ptr(), from_surface, + buf.get_size(), + device_to_host, dep_events)}); + break; + } + case device_to_device: +#ifdef DPCT_USM_LEVEL_NONE + { + auto &mm = mem_mgr::instance(); + auto to_alloc = mm.translate_ptr(to_surface); + auto from_alloc = mm.translate_ptr(from_surface); + size_t to_offset = (byte_t *)to_surface - to_alloc.alloc_ptr; + size_t from_offset = (byte_t *)from_surface - from_alloc.alloc_ptr; + event_list.push_back(q.submit([&](sycl::handler &cgh) + { + cgh.depends_on(dep_events); + auto to_o = sycl::id<1>(to_offset); + auto from_o = sycl::id<1>(from_offset); + sycl::accessor + to_acc(to_alloc.buffer, cgh, + get_copy_range(size, to_slice, to_range.get(0)), to_o); + sycl::accessor + from_acc(from_alloc.buffer, cgh, + get_copy_range(size, from_slice, from_range.get(0)), from_o); + cgh.parallel_for( + size, + [=](sycl::id<3> id) { + to_acc[get_offset(id, to_slice, to_range.get(0))] = + from_acc[get_offset(id, from_slice, from_range.get(0))]; + }); })); + } +#else + event_list.push_back(q.submit([&](sycl::handler &cgh) + { + cgh.depends_on(dep_events); + cgh.parallel_for( + size, + [=](sycl::id<3> id) { + to_surface[get_offset(id, to_slice, to_range.get(0))] = + from_surface[get_offset(id, from_slice, from_range.get(0))]; + }); })); +#endif + break; + default: + throw std::runtime_error("dpct_memcpy: invalid direction value"); + } + return event_list; + } + + /// memcpy 2D/3D matrix specified by pitched_data. + static inline std::vector + dpct_memcpy(sycl::queue &q, pitched_data to, sycl::id<3> to_id, + pitched_data from, sycl::id<3> from_id, sycl::range<3> size, + memcpy_direction direction = automatic) + { + return dpct_memcpy(q, to.get_data_ptr(), from.get_data_ptr(), + sycl::range<3>(to.get_pitch(), to.get_y(), 1), + sycl::range<3>(from.get_pitch(), from.get_y(), 1), to_id, from_id, + size, direction); + } + + /// memcpy 2D matrix with pitch. + static inline std::vector + dpct_memcpy(sycl::queue &q, void *to_ptr, const void *from_ptr, + size_t to_pitch, size_t from_pitch, size_t x, size_t y, + memcpy_direction direction = automatic) + { + return dpct_memcpy(q, to_ptr, from_ptr, sycl::range<3>(to_pitch, y, 1), + sycl::range<3>(from_pitch, y, 1), + sycl::id<3>(0, 0, 0), sycl::id<3>(0, 0, 0), + sycl::range<3>(x, y, 1), direction); + } + + inline void gemm(sycl::queue &q, oneapi::mkl::transpose a_trans, + oneapi::mkl::transpose b_trans, int m, int n, int k, + const void *alpha, const void *a, library_data_t a_type, + int lda, const void *b, library_data_t b_type, int ldb, + const void *beta, void *c, library_data_t c_type, int ldc, + library_data_t scaling_type) + { + if (scaling_type == library_data_t::real_float && + c_type == library_data_t::complex_float) + { + scaling_type = library_data_t::complex_float; + } + else if (scaling_type == library_data_t::real_double && + c_type == library_data_t::complex_double) + { + scaling_type = library_data_t::complex_double; + } + + std::uint64_t key = + detail::get_type_combination_id(a_type, b_type, c_type, scaling_type); + switch (key) + { + case detail::get_type_combination_id( + library_data_t::real_float, library_data_t::real_float, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc); + break; + } + case detail::get_type_combination_id( + library_data_t::real_double, library_data_t::real_double, + library_data_t::real_double, library_data_t::real_double): + { + detail::gemm_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc); + break; + } + case detail::get_type_combination_id( + library_data_t::complex_float, library_data_t::complex_float, + library_data_t::complex_float, library_data_t::complex_float): + { + detail::gemm_impl, std::complex, + std::complex, std::complex>( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc); + break; + } + case detail::get_type_combination_id( + library_data_t::complex_double, library_data_t::complex_double, + library_data_t::complex_double, library_data_t::complex_double): + { + detail::gemm_impl, std::complex, + std::complex, std::complex>( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc); + break; + } + case detail::get_type_combination_id( + library_data_t::real_half, library_data_t::real_half, + library_data_t::real_half, library_data_t::real_half): + { + detail::gemm_impl(q, a_trans, b_trans, m, n, k, alpha, a, + lda, b, ldb, beta, c, ldc); + break; + } + case detail::get_type_combination_id( + library_data_t::real_bfloat16, library_data_t::real_bfloat16, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_impl(q, a_trans, b_trans, m, n, k, alpha, a, lda, b, + ldb, beta, c, ldc); + break; + } + case detail::get_type_combination_id( + library_data_t::real_half, library_data_t::real_half, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc); + break; + } + case detail::get_type_combination_id( + library_data_t::real_half, library_data_t::real_half, + library_data_t::real_half, library_data_t::real_float): + { + float alpha_value = + dpct::get_value(reinterpret_cast(alpha), q); + float beta_value = + dpct::get_value(reinterpret_cast(beta), q); + sycl::half alpha_half(alpha_value); + sycl::half beta_half(beta_value); + detail::gemm_impl(q, a_trans, b_trans, m, n, k, &alpha_half, + a, lda, b, ldb, &beta_half, c, ldc); + break; + } + case detail::get_type_combination_id( + library_data_t::real_int8, library_data_t::real_int8, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc); + break; + } + case detail::get_type_combination_id( + library_data_t::real_bfloat16, library_data_t::real_bfloat16, + library_data_t::real_bfloat16, library_data_t::real_float): + { + detail::gemm_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc); + break; + } + case detail::get_type_combination_id( + library_data_t::real_int8, library_data_t::real_int8, + library_data_t::real_int32, library_data_t::real_int32): + { + float alpha_float = + dpct::get_value(reinterpret_cast(alpha), q); + float beta_float = + dpct::get_value(reinterpret_cast(beta), q); + detail::gemm_impl( + q, a_trans, b_trans, m, n, k, &alpha_float, a, lda, b, ldb, &beta_float, c, ldc); + break; + } + default: + throw std::runtime_error("the combination of data type is unsupported"); + } + } // gemm() + + /// Computes a batch of matrix-matrix product with general matrices. + /// \param [in] q The queue where the routine should be executed. + /// \param [in] a_trans Specifies the operation applied to A. + /// \param [in] b_trans Specifies the operation applied to B. + /// \param [in] m Specifies the number of rows of the matrix op(A) and of the matrix C. + /// \param [in] n Specifies the number of columns of the matrix op(B) and of the matrix C. + /// \param [in] k Specifies the number of columns of the matrix op(A) and the number of rows of the matrix op(B). + /// \param [in] alpha Scaling factor for the matrix-matrix product. + /// \param [in] a Input matrix A. + /// \param [in] a_type Data type of the matrix A. + /// \param [in] lda Leading dimension of A. + /// \param [in] b Input matrix B. + /// \param [in] b_type Data type of the matrix B. + /// \param [in] ldb Leading dimension of B. + /// \param [in] beta Scaling factor for matrix C. + /// \param [in, out] c Input/Output matrix C. + /// \param [in] c_type Data type of the matrix C. + /// \param [in] ldc Leading dimension of C. + /// \param [in] batch_size Specifies the number of matrix multiply operations to perform. + /// \param [in] scaling_type Data type of the scaling factors. + inline void gemm_batch(sycl::queue &q, oneapi::mkl::transpose a_trans, + oneapi::mkl::transpose b_trans, int m, int n, int k, + const void *alpha, const void *a[], + library_data_t a_type, int lda, const void *b[], + library_data_t b_type, int ldb, const void *beta, + void *c[], library_data_t c_type, int ldc, + int batch_size, library_data_t scaling_type) + { +#ifdef DPCT_USM_LEVEL_NONE + throw std::runtime_error("this API is unsupported when USM level is none"); +#else + if (scaling_type == library_data_t::real_float && + c_type == library_data_t::complex_float) + { + scaling_type = library_data_t::complex_float; + } + else if (scaling_type == library_data_t::real_double && + c_type == library_data_t::complex_double) + { + scaling_type = library_data_t::complex_double; + } + + std::uint64_t key = + detail::get_type_combination_id(a_type, b_type, c_type, scaling_type); + switch (key) + { + case detail::get_type_combination_id( + library_data_t::real_float, library_data_t::real_float, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, + batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_double, library_data_t::real_double, + library_data_t::real_double, library_data_t::real_double): + { + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, + batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::complex_float, library_data_t::complex_float, + library_data_t::complex_float, library_data_t::complex_float): + { + detail::gemm_batch_impl, std::complex, + std::complex, std::complex>( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, + batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::complex_double, library_data_t::complex_double, + library_data_t::complex_double, library_data_t::complex_double): + { + detail::gemm_batch_impl, std::complex, + std::complex, std::complex>( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, + batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_half, library_data_t::real_half, + library_data_t::real_half, library_data_t::real_half): + { + detail::gemm_batch_impl(q, a_trans, b_trans, m, n, k, alpha, + a, lda, b, ldb, beta, c, ldc, + batch_size); + break; + } +#ifdef __INTEL_MKL__ + case detail::get_type_combination_id( + library_data_t::real_bfloat16, library_data_t::real_bfloat16, + library_data_t::real_bfloat16, library_data_t::real_float): + { + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, + batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_bfloat16, library_data_t::real_bfloat16, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_batch_impl(q, a_trans, b_trans, m, n, k, alpha, a, lda, + b, ldb, beta, c, ldc, batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_int8, library_data_t::real_int8, + library_data_t::real_int32, library_data_t::real_int32): + { + float alpha_float = + dpct::get_value(reinterpret_cast(alpha), q); + float beta_float = + dpct::get_value(reinterpret_cast(beta), q); + detail::gemm_batch_impl(q, a_trans, b_trans, m, n, k, &alpha_float, + a, lda, b, ldb, &beta_float, c, ldc, + batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_int8, library_data_t::real_int8, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, + batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_half, library_data_t::real_half, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, b, ldb, beta, c, ldc, + batch_size); + break; + } +#endif + case detail::get_type_combination_id( + library_data_t::real_half, library_data_t::real_half, + library_data_t::real_half, library_data_t::real_float): + { + float alpha_value = + dpct::get_value(reinterpret_cast(alpha), q); + float beta_value = + dpct::get_value(reinterpret_cast(beta), q); + sycl::half alpha_half(alpha_value); + sycl::half beta_half(beta_value); + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, &alpha_half, a, lda, b, ldb, &beta_half, c, ldc, + batch_size); + break; + } + default: + throw std::runtime_error("the combination of data type is unsupported"); + } +#endif + } + + /// Computes a batch of matrix-matrix product with general matrices. + /// \param [in] q The queue where the routine should be executed. + /// \param [in] a_trans Specifies the operation applied to A. + /// \param [in] b_trans Specifies the operation applied to B. + /// \param [in] m Specifies the number of rows of the matrix op(A) and of the matrix C. + /// \param [in] n Specifies the number of columns of the matrix op(B) and of the matrix C. + /// \param [in] k Specifies the number of columns of the matrix op(A) and the number of rows of the matrix op(B). + /// \param [in] alpha Scaling factor for the matrix-matrix product. + /// \param [in] a Input matrix A. + /// \param [in] a_type Data type of the matrix A. + /// \param [in] lda Leading dimension of A. + /// \param [in] stride_a Stride between the different A matrices. + /// \param [in] b Input matrix B. + /// \param [in] b_type Data type of the matrix B. + /// \param [in] ldb Leading dimension of B. + /// \param [in] stride_b Stride between the different B matrices. + /// \param [in] beta Scaling factor for matrix C. + /// \param [in, out] c Input/Output matrix C. + /// \param [in] c_type Data type of the matrix C. + /// \param [in] ldc Leading dimension of C. + /// \param [in] stride_c Stride between the different C matrices. + /// \param [in] batch_size Specifies the number of matrix multiply operations to perform. + /// \param [in] scaling_type Data type of the scaling factors. + inline void gemm_batch(sycl::queue &q, oneapi::mkl::transpose a_trans, + oneapi::mkl::transpose b_trans, int m, int n, int k, + const void *alpha, const void *a, library_data_t a_type, + int lda, long long int stride_a, const void *b, + library_data_t b_type, int ldb, long long int stride_b, + const void *beta, void *c, library_data_t c_type, + int ldc, long long int stride_c, int batch_size, + library_data_t scaling_type) + { + if (scaling_type == library_data_t::real_float && + c_type == library_data_t::complex_float) + { + scaling_type = library_data_t::complex_float; + } + else if (scaling_type == library_data_t::real_double && + c_type == library_data_t::complex_double) + { + scaling_type = library_data_t::complex_double; + } + + std::uint64_t key = + detail::get_type_combination_id(a_type, b_type, c_type, scaling_type); + switch (key) + { + case detail::get_type_combination_id( + library_data_t::real_float, library_data_t::real_float, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, + beta, c, ldc, stride_c, batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_double, library_data_t::real_double, + library_data_t::real_double, library_data_t::real_double): + { + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, + beta, c, ldc, stride_c, batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::complex_float, library_data_t::complex_float, + library_data_t::complex_float, library_data_t::complex_float): + { + detail::gemm_batch_impl, std::complex, + std::complex, std::complex>( + q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, + beta, c, ldc, stride_c, batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::complex_double, library_data_t::complex_double, + library_data_t::complex_double, library_data_t::complex_double): + { + detail::gemm_batch_impl, std::complex, + std::complex, std::complex>( + q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, + beta, c, ldc, stride_c, batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_half, library_data_t::real_half, + library_data_t::real_half, library_data_t::real_half): + { + detail::gemm_batch_impl(q, a_trans, b_trans, m, n, k, alpha, + a, lda, stride_a, b, ldb, stride_b, + beta, c, ldc, stride_c, batch_size); + break; + } +#ifdef __INTEL_MKL__ + case detail::get_type_combination_id( + library_data_t::real_bfloat16, library_data_t::real_bfloat16, + library_data_t::real_bfloat16, library_data_t::real_float): + { + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, + beta, c, ldc, stride_c, batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_bfloat16, library_data_t::real_bfloat16, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_batch_impl(q, a_trans, b_trans, m, n, k, alpha, a, lda, + stride_a, b, ldb, stride_b, beta, c, ldc, + stride_c, batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_int8, library_data_t::real_int8, + library_data_t::real_int32, library_data_t::real_int32): + { + detail::gemm_batch_impl(q, a_trans, b_trans, m, n, k, alpha, + a, lda, stride_a, b, ldb, stride_b, + beta, c, ldc, stride_c, batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_int8, library_data_t::real_int8, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, + beta, c, ldc, stride_c, batch_size); + break; + } + case detail::get_type_combination_id( + library_data_t::real_half, library_data_t::real_half, + library_data_t::real_float, library_data_t::real_float): + { + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, alpha, a, lda, stride_a, b, ldb, stride_b, + beta, c, ldc, stride_c, batch_size); + break; + } +#endif + case detail::get_type_combination_id( + library_data_t::real_half, library_data_t::real_half, + library_data_t::real_half, library_data_t::real_float): + { + float alpha_value = + dpct::get_value(reinterpret_cast(alpha), q); + float beta_value = + dpct::get_value(reinterpret_cast(beta), q); + sycl::half alpha_half(alpha_value); + sycl::half beta_half(beta_value); + detail::gemm_batch_impl( + q, a_trans, b_trans, m, n, k, &alpha_half, a, lda, stride_a, b, ldb, stride_b, + &beta_half, c, ldc, stride_c, batch_size); + break; + } + default: + throw std::runtime_error("the combination of data type is unsupported"); + } + } + + static inline void + async_dpct_memcpy(void *to_ptr, size_t to_pitch, const void *from_ptr, + size_t from_pitch, size_t x, size_t y, + memcpy_direction direction = automatic, + sycl::queue &q = get_default_queue()) + { + detail::dpct_memcpy(q, to_ptr, from_ptr, to_pitch, from_pitch, x, y, + direction); + } + + using err0 = detail::generic_error_type; + using err1 = detail::generic_error_type; + +} // COPY from DPCT head files + + +static int g_ggml_sycl_debug=0; +#define GGML_SYCL_DEBUG(...) do{if(g_ggml_sycl_debug) printf(__VA_ARGS__);}while(0) + +#define CHECK_TRY_ERROR(expr) \ + [&]() { \ + try { \ + expr; \ + return dpct::success; \ + } catch (std::exception const &e) { \ + std::cerr << e.what()<< "\nException caught at file:" << __FILE__ \ + << ", line:" << __LINE__ <<", func:"<<__func__<< std::endl; \ + return dpct::default_error; \ + } \ + }() + +// #define DEBUG_SYCL_MALLOC + +static int g_work_group_size = 0; +// typedef sycl::half ggml_fp16_t; + +#define __SYCL_ARCH__ DPCT_COMPATIBILITY_TEMP +#define VER_4VEC 610 //todo for hardward optimize. +#define VER_GEN9 700 //todo for hardward optimize. +#define VER_GEN12 1000000 //todo for hardward optimize. +#define VER_GEN13 (VER_GEN12 + 1030) //todo for hardward optimize. + +#define GGML_SYCL_MAX_NODES 8192 //TODO: adapt to hardwares + + +//define for XMX in Intel GPU +//TODO: currently, it's not used for XMX really. +#define SYCL_USE_XMX + +// max batch size to use MMQ kernels when tensor cores are available +#define XMX_MAX_BATCH_SIZE 32 + + +#if defined(_MSC_VER) +#pragma warning(disable: 4244 4267) // possible loss of data +#endif + +static_assert(sizeof(sycl::half) == sizeof(ggml_fp16_t), "wrong fp16 size"); + +static void crash(){ + int *ptr = NULL; + *ptr = 0; +} + +static void ggml_sycl_error(const char * stmt, const char * func, const char * file, const int line, const char * msg) { + fprintf(stderr, "SYCL error: %s: %s\n", stmt, msg); + fprintf(stderr, " in function %s at %s:%d\n", func, file, line); + GGML_ASSERT(!"SYCL error"); +} + +#define SYCL_CHECK(err) do { \ + auto err_ = (err); if (err_ != 0) ggml_sycl_error( \ + #err, __func__, __FILE__, __LINE__, \ + "Meet error in this line code!"); \ +} while (0) + +#if DPCT_COMPAT_RT_VERSION >= 11100 +#define GGML_SYCL_ASSUME(x) __builtin_assume(x) +#else +#define GGML_SYCL_ASSUME(x) +#endif // DPCT_COMPAT_RT_VERSION >= 11100 + +#ifdef GGML_SYCL_F16 +typedef sycl::half dfloat; // dequantize float +typedef sycl::half2 dfloat2; +#else +typedef float dfloat; // dequantize float +typedef sycl::float2 dfloat2; +#endif //GGML_SYCL_F16 + +bool ggml_sycl_loaded(void); +void * ggml_sycl_host_malloc(size_t size); +void ggml_sycl_host_free(void * ptr); +bool ggml_sycl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst); +void ggml_sycl_set_tensor_split(const float * tensor_split); +void ggml_sycl_transform_tensor(void * data, struct ggml_tensor * tensor); +void ggml_sycl_free_data(struct ggml_tensor * tensor); +void ggml_sycl_assign_buffers(struct ggml_tensor * tensor); +void ggml_sycl_assign_buffers_no_scratch(struct ggml_tensor * tensor); +void ggml_sycl_assign_buffers_force_inplace(struct ggml_tensor * tensor); +void ggml_sycl_assign_buffers_no_alloc(struct ggml_tensor * tensor); +void ggml_sycl_assign_scratch_offset(struct ggml_tensor * tensor, size_t offset); +void ggml_sycl_copy_to_device(struct ggml_tensor * tensor); +void ggml_sycl_set_main_device(int main_device); +void ggml_sycl_set_mul_mat_q(bool mul_mat_q); +void ggml_sycl_set_scratch_size(size_t scratch_size); +void ggml_sycl_free_scratch(void); +int ggml_sycl_get_device_count(void); +void ggml_sycl_get_device_description(int device, char * description, size_t description_size); +bool ggml_backend_is_sycl(ggml_backend_t backend); +int ggml_backend_sycl_get_device(ggml_backend_t backend); +int get_main_device(); +void print_ggml_tensor(const char*name, struct ggml_tensor *src); +void log_tensor_with_cnt(const char* name, struct ggml_tensor * src, int stop_cnt); + +static __dpct_inline__ int get_int_from_int8(const int8_t *x8, const int &i32) { + const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment + + int x32 = 0; + x32 |= x16[0] << 0; + x32 |= x16[1] << 16; + + return x32; +} + +static __dpct_inline__ int get_int_from_uint8(const uint8_t *x8, + const int &i32) { + const uint16_t * x16 = (const uint16_t *) (x8 + sizeof(int) * i32); // assume at least 2 byte alignment + + int x32 = 0; + x32 |= x16[0] << 0; + x32 |= x16[1] << 16; + + return x32; +} + +static __dpct_inline__ int get_int_from_int8_aligned(const int8_t *x8, + const int &i32) { + return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment +} + +static __dpct_inline__ int get_int_from_uint8_aligned(const uint8_t *x8, + const int &i32) { + return *((const int *) (x8 + sizeof(int) * i32)); // assume at least 4 byte alignment +} + +template +using to_t_sycl_t = void (*)(const void *__restrict__ x, T *__restrict__ y, + int k, dpct::queue_ptr stream); +typedef to_t_sycl_t to_fp32_sycl_t; +typedef to_t_sycl_t to_fp16_sycl_t; + +typedef void (*dequantize_kernel_t)(const void * vx, const int ib, const int iqs, dfloat2 & v); +typedef void (*dot_kernel_k_t)(const void * __restrict__ vx, const int ib, const int iqs, const float * __restrict__ y, float & v); +typedef void (*cpy_kernel_t)(const char * cx, char * cdst); +typedef void (*ggml_sycl_func_t)(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); +typedef void (*ggml_sycl_op_mul_mat_t)( + const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, + const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i, + float *dst_dd_i, const int64_t row_low, const int64_t row_high, + const int64_t src1_ncols, const int64_t src1_padded_row_size, + const dpct::queue_ptr &stream); +typedef void (*ggml_sycl_op_flatten_t)(const ggml_tensor *src0, + const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const dpct::queue_ptr &main_stream); + +// 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 (QK4_0 / (4 * QR4_0)) +typedef struct dpct_type_471834 { + sycl::half d; // delta + uint8_t qs[QK4_0 / 2]; // nibbles / quants +} block_q4_0; +static_assert(sizeof(block_q4_0) == sizeof(ggml_fp16_t) + QK4_0 / 2, "wrong q4_0 block size/padding"); + +#define QK4_1 32 +#define QR4_1 2 +#define QI4_1 (QK4_1 / (4 * QR4_1)) +typedef struct dpct_type_143705 { + sycl::half2 dm; // dm.x = delta, dm.y = min + uint8_t qs[QK4_1 / 2]; // nibbles / quants +} block_q4_1; +static_assert(sizeof(block_q4_1) == sizeof(ggml_fp16_t) * 2 + QK4_1 / 2, "wrong q4_1 block size/padding"); + +#define QK5_0 32 +#define QR5_0 2 +#define QI5_0 (QK5_0 / (4 * QR5_0)) +typedef struct dpct_type_673649 { + sycl::half d; // delta + uint8_t qh[4]; // 5-th bit of quants + uint8_t qs[QK5_0 / 2]; // nibbles / quants +} block_q5_0; +static_assert(sizeof(block_q5_0) == sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_0 / 2, "wrong q5_0 block size/padding"); + +#define QK5_1 32 +#define QR5_1 2 +#define QI5_1 (QK5_1 / (4 * QR5_1)) +typedef struct dpct_type_135589 { + sycl::half2 dm; // dm.x = delta, dm.y = min + uint8_t qh[4]; // 5-th bit of quants + uint8_t qs[QK5_1 / 2]; // nibbles / quants +} block_q5_1; +static_assert(sizeof(block_q5_1) == 2 * sizeof(ggml_fp16_t) + sizeof(uint32_t) + QK5_1 / 2, "wrong q5_1 block size/padding"); + +#define QK8_0 32 +#define QR8_0 1 +#define QI8_0 (QK8_0 / (4 * QR8_0)) +typedef struct dpct_type_122878 { + sycl::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 (QK8_1 / (4 * QR8_1)) +typedef struct dpct_type_143721 { + sycl::half2 ds; // ds.x = delta, ds.y = 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_sycl_t)(const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs); +typedef void (*allocate_tiles_sycl_t)(int **x_ql, sycl::half2 **x_dm, + int **x_qh, int **x_sc); +typedef void (*load_tiles_sycl_t)(const void *__restrict__ vx, + int *__restrict__ x_ql, + sycl::half2 *__restrict__ x_dm, + int *__restrict__ x_qh, + int *__restrict__ x_sc, const int &i_offset, + const int &i_max, const int &k, + const int &blocks_per_row); +typedef float (*vec_dot_q_mul_mat_sycl_t)( + const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm, + const int *__restrict__ x_qh, const int *__restrict__ x_sc, + const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ms, + const int &i, const int &j, const int &k); + +//================================= 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 + +#define QR2_K 4 +#define QI2_K (QK_K / (4*QR2_K)) +typedef struct dpct_type_619598 { + uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits + uint8_t qs[QK_K/4]; // quants + sycl::half2 dm; // super-block scale for quantized scales/mins +} block_q2_K; +static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_fp16_t) + QK_K/16 + QK_K/4, "wrong q2_K block size/padding"); + +#define QR3_K 4 +#define QI3_K (QK_K / (4*QR3_K)) +typedef struct dpct_type_138576 { + 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 + sycl::half d; // super-block scale +} block_q3_K; +//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"); + +#define QR4_K 2 +#define QI4_K (QK_K / (4*QR4_K)) +#ifdef GGML_QKK_64 +typedef struct { + 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) == sizeof(half2) + QK_K/2 + 2, "wrong q4_K block size/padding"); +#else +typedef struct dpct_type_154943 { + sycl::half2 dm; // super-block scale for quantized scales/mins + uint8_t scales[3*QK_K/64]; // scales, 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"); +#endif + +#define QR5_K 2 +#define QI5_K (QK_K / (4*QR5_K)) +#ifdef GGML_QKK_64 +typedef struct { + 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 dpct_type_866817 { + sycl::half2 dm; // super-block scale for quantized scales/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 + +#define QR6_K 2 +#define QI6_K (QK_K / (4*QR6_K)) +typedef struct dpct_type_107281 { + 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 + sycl::half d; // delta +} block_q6_K; +static_assert(sizeof(block_q6_K) == sizeof(ggml_fp16_t) + 13*QK_K/16, "wrong q6_K block size/padding"); + +#define WARP_SIZE 32 +#define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses + +#define SYCL_GELU_BLOCK_SIZE 256 +#define SYCL_SILU_BLOCK_SIZE 256 +#define SYCL_TANH_BLOCK_SIZE 256 +#define SYCL_RELU_BLOCK_SIZE 256 +#define SYCL_SQR_BLOCK_SIZE 256 +#define SYCL_CPY_BLOCK_SIZE 32 +#define SYCL_SCALE_BLOCK_SIZE 256 +#define SYCL_CLAMP_BLOCK_SIZE 256 +#define SYCL_ROPE_BLOCK_SIZE 256 +#define SYCL_SOFT_MAX_BLOCK_SIZE 1024 +#define SYCL_ALIBI_BLOCK_SIZE 32 +#define SYCL_DIAG_MASK_INF_BLOCK_SIZE 32 +#define SYCL_QUANTIZE_BLOCK_SIZE 256 +#define SYCL_DEQUANTIZE_BLOCK_SIZE 256 +#define SYCL_GET_ROWS_BLOCK_SIZE 256 +#define SYCL_UPSCALE_BLOCK_SIZE 256 +#define SYCL_CONCAT_BLOCK_SIZE 256 +#define SYCL_PAD_BLOCK_SIZE 256 +#define SYCL_ACC_BLOCK_SIZE 256 +#define SYCL_IM2COL_BLOCK_SIZE 256 + +// dmmv = dequantize_mul_mat_vec +#ifndef GGML_SYCL_DMMV_X +#define GGML_SYCL_DMMV_X 32 +#endif +#ifndef GGML_SYCL_MMV_Y +#define GGML_SYCL_MMV_Y 1 +#endif + +#ifndef K_QUANTS_PER_ITERATION +#define K_QUANTS_PER_ITERATION 2 +#else +static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUANTS_PER_ITERATION must be 1 or 2"); +#endif + +#ifndef GGML_SYCL_PEER_MAX_BATCH_SIZE +#define GGML_SYCL_PEER_MAX_BATCH_SIZE 128 +#endif // GGML_SYCL_PEER_MAX_BATCH_SIZE + +#define MUL_MAT_SRC1_COL_STRIDE 128 + +#define MAX_STREAMS 8 +static dpct::queue_ptr g_syclStreams[GGML_SYCL_MAX_DEVICES][MAX_STREAMS] = { + {0}}; + +struct ggml_tensor_extra_gpu { + void * data_device[GGML_SYCL_MAX_DEVICES]; // 1 pointer for each device for split tensors + dpct::event_ptr + events[GGML_SYCL_MAX_DEVICES] + [MAX_STREAMS]; // events for synchronizing multiple GPUs +}; + +inline dpct::err0 ggml_sycl_set_device(const int device) try { + int current_device; + + SYCL_CHECK(CHECK_TRY_ERROR( + current_device = dpct::dev_mgr::instance().current_device_id())); + + // GGML_SYCL_DEBUG("ggml_sycl_set_device device=%d, current_device=%d\n", device, current_device); + if (device == current_device) { + return 0; + } + + return CHECK_TRY_ERROR(dpct::select_device(device)); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + crash(); + std::exit(1); +} + +static int g_device_count = -1; +static int g_all_sycl_device_count = -1; +static int g_main_device = -1; +static int g_main_device_index = -1; + +static float g_tensor_split[GGML_SYCL_MAX_DEVICES] = {0}; + +struct sycl_device_capabilities { + int cc; // compute capability + bool vmm; // virtual memory support + size_t vmm_granularity; // granularity of virtual memory + int device_id; +}; + +static sycl_device_capabilities g_device_caps[GGML_SYCL_MAX_DEVICES] = { {0, false, 0, -1} }; + +struct sycl_device_id2index { + int index; +}; + +static sycl_device_id2index g_sycl_device_id2index[GGML_SYCL_MAX_DEVICES] = { {-1} }; + +static void * g_scratch_buffer = nullptr; +static size_t g_scratch_size = 0; // disabled by default +static size_t g_scratch_offset = 0; + +static dpct::queue_ptr g_sycl_handles[GGML_SYCL_MAX_DEVICES] = {nullptr}; + +int get_main_device(){ + return g_main_device; +} + +[[noreturn]] +static void bad_arch(const sycl::stream &stream_ct1) { + stream_ct1 << "ERROR: ggml-sycl was compiled without support for the " + "current GPU architecture.\n"; + // __trap(); + std::exit(1); + + (void) bad_arch; // suppress unused function warning +} + +void log_ggml_var_device(const char*name, float *src, size_t total_elements, bool src_on_device){ + if(!g_ggml_sycl_debug) return; + if(!src){ + printf("GGML Tensor:%s skip to save for NULL pointer\n", name); + return; + } + char filename[1024]; + sprintf(filename, "%s.txt", name); + printf("GGML Tensor:%s save to %s\n", name, filename); + + size_t total_size = total_elements*sizeof(float); + float *local_buf = NULL; + // printf("total_size %d2, src_on_device %d\n", total_size, src_on_device); + if(src_on_device) { + local_buf = (float *) ggml_sycl_host_malloc(total_size); + // printf("local buf %p size %d bytes\n", local_buf, total_size); + ggml_sycl_set_device(g_main_device); + dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; + main_stream->memcpy(local_buf, src, total_size); + } + else { + local_buf = (float *)src; + // printf("local buf from src-> data %p\n", local_buf); + } + + std::ofstream logfile; + logfile.open(filename); + // printf("local buf element %d\n", total_elements); + for(int i=0; ibackend == GGML_BACKEND_GPU || src->backend == GGML_BACKEND_GPU_SPLIT; + float *src_data =NULL; + if(src_on_device) { + ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra; + src_data = (float*)src_extra->data_device[g_main_device_index]; + } + else { + src_data = (float *)src->data; + } + + log_ggml_var_device(name, src_data, total_elements, src_on_device); +} + +static int log_file_name_idx=0; +void log_tensor_with_cnt(const char* name, struct ggml_tensor * src, int stop_cnt) { + stop_cnt = 4; + if(log_file_name_idx>=stop_cnt) return; + char filename[1280]; + sprintf(filename, "%s_%07d", name, log_file_name_idx); + log_file_name_idx++; + print_ggml_tensor(filename, src); + // print_ggml_tensor("ggml_sycl_rms_norm_src0", (ggml_tensor *)src0); + // print_ggml_tensor("ggml_sycl_rms_norm_src1", (ggml_tensor *)src1); + // int *ptr = NULL; + // *ptr = 0; +} + +static __dpct_inline__ float warp_reduce_sum(float x, + const sycl::nd_item<3> &item_ct1) { +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + /* + DPCT1096:98: The right-most dimension of the work-group used in the SYCL + kernel that calls this function may be less than "32". The function + "dpct::permute_sub_group_by_xor" may return an unexpected result on the + CPU device. Modify the size of the work-group to ensure that the value + of the right-most dimension is a multiple of "32". + */ + x += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), x, mask); + } + return x; +} + +static __dpct_inline__ sycl::float2 +warp_reduce_sum(sycl::float2 a, const sycl::nd_item<3> &item_ct1) { +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + a.x() += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), a.x(), + mask); + a.y() += dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), a.y(), + mask); + } + return a; +} + +static __dpct_inline__ float warp_reduce_max(float x, + const sycl::nd_item<3> &item_ct1) { +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + /* + DPCT1096:97: The right-most dimension of the work-group used in the SYCL + kernel that calls this function may be less than "32". The function + "dpct::permute_sub_group_by_xor" may return an unexpected result on the + CPU device. Modify the size of the work-group to ensure that the value + of the right-most dimension is a multiple of "32". + */ + x = sycl::fmax(x, dpct::permute_sub_group_by_xor( + item_ct1.get_sub_group(), x, mask)); + } + return x; +} + +static __dpct_inline__ float op_repeat(const float a, const float b) { + return b; +} + +static __dpct_inline__ float op_add(const float a, const float b) { + return a + b; +} + +static __dpct_inline__ float op_mul(const float a, const float b) { + return a * b; +} + +static __dpct_inline__ float op_div(const float a, const float b) { + return a / b; +} + +template +static void k_bin_bcast(const src0_t * src0, const src1_t * src1, dst_t * dst, + int ne0, int ne1, int ne2, int ne3, + int ne10, int ne11, int ne12, int ne13, + /*int s0, */ int s1, int s2, int s3, + /*int s10,*/ int s11, int s12, int s13, + const sycl::nd_item<3> &item_ct1) { + const int i0s = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + const int i1 = (item_ct1.get_local_range(1) * item_ct1.get_group(1) + + item_ct1.get_local_id(1)); + const int i2 = (item_ct1.get_local_range(0) * item_ct1.get_group(0) + + item_ct1.get_local_id(0)) / + ne3; + const int i3 = (item_ct1.get_local_range(0) * item_ct1.get_group(0) + + item_ct1.get_local_id(0)) % + ne3; + + if (i0s >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) { + return; + } + + const int i11 = i1 % ne11; + const int i12 = i2 % ne12; + const int i13 = i3 % ne13; + + const size_t i_src0 = i3*s3 + i2*s2 + i1*s1; + const size_t i_src1 = i13*s13 + i12*s12 + i11*s11; + const size_t i_dst = i_src0; + + const src0_t * src0_row = src0 + i_src0; + const src1_t * src1_row = src1 + i_src1; + dst_t * dst_row = dst + i_dst; + + for (int i0 = i0s; i0 < ne0; + i0 += item_ct1.get_local_range(2) * item_ct1.get_group_range(2)) { + const int i10 = i0 % ne10; + dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]); + } +} + +template +static void k_bin_bcast_unravel(const src0_t * src0, const src1_t * src1, dst_t * dst, + int ne0, int ne1, int ne2, int ne3, + int ne10, int ne11, int ne12, int ne13, + /*int s0, */ int s1, int s2, int s3, + /*int s10,*/ int s11, int s12, int s13, + const sycl::nd_item<3> &item_ct1) { + + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + const int i3 = i/(ne2*ne1*ne0); + const int i2 = (i/(ne1*ne0)) % ne2; + const int i1 = (i/ne0) % ne1; + const int i0 = i % ne0; + + if (i0 >= ne0 || i1 >= ne1 || i2 >= ne2 || i3 >= ne3) { + return; + } + + const int i11 = i1 % ne11; + const int i12 = i2 % ne12; + const int i13 = i3 % ne13; + + const size_t i_src0 = i3*s3 + i2*s2 + i1*s1; + const size_t i_src1 = i13*s13 + i12*s12 + i11*s11; + const size_t i_dst = i_src0; + + const src0_t * src0_row = src0 + i_src0; + const src1_t * src1_row = src1 + i_src1; + dst_t * dst_row = dst + i_dst; + + const int i10 = i0 % ne10; + dst_row[i0] = (dst_t)bin_op(src0 ? (float)src0_row[i0] : 0.0f, (float)src1_row[i10]); +} + +static void acc_f32(const float * x, const float * y, float * dst, const int ne, + const int ne10, const int ne11, const int ne12, + const int nb1, const int nb2, int offset, const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + if (i >= ne) { + return; + } + int src1_idx = i - offset; + int oz = src1_idx / nb2; + int oy = (src1_idx - (oz * nb2)) / nb1; + int ox = src1_idx % nb1; + if (src1_idx >= 0 && ox < ne10 && oy < ne11 && oz < ne12) { + dst[i] = x[i] + y[ox + oy * ne10 + oz * ne10 * ne11]; + } else { + dst[i] = x[i]; + } +} + +static void gelu_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const float GELU_COEF_A = 0.044715f; + const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + + float xi = x[i]; + dst[i] = 0.5f * xi * + (1.0f + + sycl::tanh(SQRT_2_OVER_PI * xi * (1.0f + GELU_COEF_A * xi * xi))); +} + +static void silu_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = x[i] / (1.0f + sycl::native::exp(-x[i])); +} + +static void gelu_quick_f32(const float *x, float *dst, int k, + const sycl::nd_item<3> &item_ct1) { + const float GELU_QUICK_COEF = -1.702f; + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + if (i >= k) { + return; + } + dst[i] = x[i] * (1.0f / (1.0f + sycl::native::exp(GELU_QUICK_COEF * x[i]))); +} + +static void tanh_f32(const float *x, float *dst, int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + if (i >= k) { + return; + } + dst[i] = sycl::tanh((float)(x[i])); +} + +static void relu_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = sycl::fmax((float)(x[i]), (float)0); +} + +static void leaky_relu_f32(const float *x, float *dst, const int k, const float negative_slope, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + if (i >= k) { + return; + } + dst[i] = sycl::fmax((float)(x[i]), (float)0) + + sycl::fmin((float)(x[i]), 0.0f) * negative_slope; +} + +static void sqr_f32(const float * x, float * dst, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + dst[i] = x[i] * x[i]; +} + +static void norm_f32(const float * x, float * dst, const int ncols, const float eps, + const sycl::nd_item<3> &item_ct1, sycl::float2 *s_sum, int block_size) { + const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + + item_ct1.get_local_id(1); + const int tid = item_ct1.get_local_id(2); + + sycl::float2 mean_var = sycl::float2(0.f, 0.f); + + for (int col = tid; col < ncols; col += block_size) { + const float xi = x[row*ncols + col]; + mean_var.x() += xi; + mean_var.y() += xi * xi; + } + + // sum up partial sums + mean_var = warp_reduce_sum(mean_var, item_ct1); + if (block_size > WARP_SIZE) { + + int warp_id = item_ct1.get_local_id(2) / WARP_SIZE; + int lane_id = item_ct1.get_local_id(2) % WARP_SIZE; + if (lane_id == 0) { + s_sum[warp_id] = mean_var; + } + /* + DPCT1118:0: SYCL group functions and algorithms must be encountered in + converged control flow. You may need to adjust the code. + */ + item_ct1.barrier(sycl::access::fence_space::local_space); + mean_var = s_sum[lane_id]; + mean_var = warp_reduce_sum(mean_var, item_ct1); + } + + const float mean = mean_var.x() / ncols; + const float var = mean_var.y() / ncols - mean * mean; + const float inv_std = sycl::rsqrt(var + eps); + + for (int col = tid; col < ncols; col += block_size) { + dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_std; + } +} + +static void concat_f32(const float *x,const float *y, float *dst, const int ne0, const int ne02, + const sycl::nd_item<3> &item_ct1) { + int nidx = item_ct1.get_local_id(2) + + item_ct1.get_group(2) * item_ct1.get_local_range(2); + if (nidx >= ne0) { + return; + } + // operation + int offset_dst = nidx + item_ct1.get_group(1) * ne0 + + item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1); + if (item_ct1.get_group(0) < ne02) { // src0 + int offset_src = + nidx + item_ct1.get_group(1) * ne0 + + item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1); + dst[offset_dst] = x[offset_src]; + } else { + int offset_src = + nidx + item_ct1.get_group(1) * ne0 + + (item_ct1.get_group(0) - ne02) * ne0 * item_ct1.get_group_range(1); + dst[offset_dst] = y[offset_src]; + } +} + +static void upscale_f32(const float *x, float *dst, const int ne00, const int nb02, const int scale_factor, + const sycl::nd_item<3> &item_ct1) { + int ne0 = ne00 * scale_factor; + int nidx = item_ct1.get_local_id(2) + + item_ct1.get_group(2) * item_ct1.get_local_range(2); + if (nidx >= ne0) { + return; + } + // operation + int i00 = nidx / scale_factor; + int i01 = item_ct1.get_group(1) / scale_factor; + int offset_src = i00 + i01 * ne00 + item_ct1.get_group(0) * nb02; + int offset_dst = nidx + item_ct1.get_group(1) * ne0 + + item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1); + dst[offset_dst] = x[offset_src]; +} + +static void pad_f32(const float *x, float *dst, const int ne0, const int ne00, const int ne01, const int ne02, + const sycl::nd_item<3> &item_ct1) { + int nidx = item_ct1.get_local_id(2) + + item_ct1.get_group(2) * item_ct1.get_local_range(2); + if (nidx >= ne0) { + return; + } + + // operation + int offset_dst = nidx + item_ct1.get_group(1) * ne0 + + item_ct1.get_group(0) * ne0 * item_ct1.get_group_range(1); + if (nidx < ne00 && item_ct1.get_group(1) < ne01 && + item_ct1.get_group(0) < ne02) { + int offset_src = nidx + item_ct1.get_group(1) * ne00 + + item_ct1.get_group(0) * ne00 * ne01; + dst[offset_dst] = x[offset_src]; + } else { + dst[offset_dst] = 0.0f; + } +} + +static void group_norm_f32(const float * x, float * dst, const int group_size, const int ne_elements, const float eps, + const sycl::nd_item<3> &item_ct1, float *s_sum, int block_size) { + int start = item_ct1.get_group(2) * group_size; + int end = start + group_size; + + start += item_ct1.get_local_id(2); + + if (end >= ne_elements) { + end = ne_elements; + } + + float tmp = 0.0f; // partial sum for thread in warp + + for (int j = start; j < end; j += block_size) { + tmp += x[j]; + } + + tmp = warp_reduce_sum(tmp, item_ct1); + if (block_size > WARP_SIZE) { + + int warp_id = item_ct1.get_local_id(2) / WARP_SIZE; + int lane_id = item_ct1.get_local_id(2) % WARP_SIZE; + if (lane_id == 0) { + s_sum[warp_id] = tmp; + } + /* + DPCT1118:1: SYCL group functions and algorithms must be encountered in + converged control flow. You may need to adjust the code. + */ + /* + DPCT1065:54: Consider replacing sycl::nd_item::barrier() with + sycl::nd_item::barrier(sycl::access::fence_space::local_space) for + better performance if there is no access to global memory. + */ + item_ct1.barrier(); + tmp = s_sum[lane_id]; + tmp = warp_reduce_sum(tmp, item_ct1); + } + + float mean = tmp / group_size; + tmp = 0.0f; + + for (int j = start; j < end; j += block_size) { + float xi = x[j] - mean; + dst[j] = xi; + tmp += xi * xi; + } + + tmp = warp_reduce_sum(tmp, item_ct1); + if (block_size > WARP_SIZE) { + + int warp_id = item_ct1.get_local_id(2) / WARP_SIZE; + int lane_id = item_ct1.get_local_id(2) % WARP_SIZE; + if (lane_id == 0) { + s_sum[warp_id] = tmp; + } + /* + DPCT1118:2: SYCL group functions and algorithms must be encountered in + converged control flow. You may need to adjust the code. + */ + /* + DPCT1065:55: Consider replacing sycl::nd_item::barrier() with + sycl::nd_item::barrier(sycl::access::fence_space::local_space) for + better performance if there is no access to global memory. + */ + item_ct1.barrier(); + tmp = s_sum[lane_id]; + tmp = warp_reduce_sum(tmp, item_ct1); + } + + float variance = tmp / group_size; + float scale = sycl::rsqrt(variance + eps); + for (int j = start; j < end; j += block_size) { + dst[j] *= scale; + } +} + +static void rms_norm_f32(const float * x, float * dst, const int ncols, const float eps, + const sycl::nd_item<3> &item_ct1, float *s_sum, int block_size) { + const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + + item_ct1.get_local_id(1); + const int tid = item_ct1.get_local_id(2); + + float tmp = 0.0f; // partial sum for thread in warp + + for (int col = tid; col < ncols; col += block_size) { + const float xi = x[row*ncols + col]; + tmp += xi * xi; + } + + // sum up partial sums + tmp = warp_reduce_sum(tmp, item_ct1); + if (block_size > WARP_SIZE) { + + int warp_id = item_ct1.get_local_id(2) / WARP_SIZE; + int lane_id = item_ct1.get_local_id(2) % WARP_SIZE; + if (lane_id == 0) { + s_sum[warp_id] = tmp; + } + /* + DPCT1118:3: SYCL group functions and algorithms must be encountered in + converged control flow. You may need to adjust the code. + */ + item_ct1.barrier(sycl::access::fence_space::local_space); + tmp = s_sum[lane_id]; + tmp = warp_reduce_sum(tmp, item_ct1); + } + + const float mean = tmp / ncols; + const float scale = sycl::rsqrt(mean + eps); + + for (int col = tid; col < ncols; col += block_size) { + dst[row*ncols + col] = scale * x[row*ncols + col]; + } +} + +static __dpct_inline__ 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 dfloat d = x[ib].d; + + const int vui = x[ib].qs[iqs]; + + v.x() = vui & 0xF; + v.y() = vui >> 4; + +#ifdef GGML_SYCL_F16 + // v = v - {8.0f, 8.0f}; + // v = v * {d, d}; + v.s0() = (v.s0() - 8.0f) * d; + v.s1() = (v.s1() - 8.0f) * d; + +#else + v.x() = (v.x() - 8.0f) * d; + v.y() = (v.y() - 8.0f) * d; +#endif // GGML_SYCL_F16 +} + +static __dpct_inline__ 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 dfloat d = x[ib].dm[0]; + const dfloat m = x[ib].dm[1]; + + const int vui = x[ib].qs[iqs]; + + v.x() = vui & 0xF; + v.y() = vui >> 4; + +#ifdef GGML_SYCL_F16 + // v = v * {d, d}; + // v = v + {m, m}; + v.s0() = (v.s0() * d) + m; + v.s1() = (v.s1() * d) + m; + +#else + v.x() = (v.x() * d) + m; + v.y() = (v.y() * d) + m; +#endif // GGML_SYCL_F16 +} + +static __dpct_inline__ 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 dfloat d = x[ib].d; + + uint32_t qh; + memcpy(&qh, x[ib].qh, sizeof(qh)); + + const int xh_0 = ((qh >> (iqs + 0)) << 4) & 0x10; + const int xh_1 = ((qh >> (iqs + 12)) ) & 0x10; + + v.x() = ((x[ib].qs[iqs] & 0xf) | xh_0); + v.y() = ((x[ib].qs[iqs] >> 4) | xh_1); + +#ifdef GGML_SYCL_F16 + // v = v - {16.0f, 16.0f}; + // v = v * {d, d}; + v.s0() = (v.s0() - 16.0f) * d; + v.s1() = (v.s1() - 16.0f) * d; + +#else + v.x() = (v.x() - 16.0f) * d; + v.y() = (v.y() - 16.0f) * d; +#endif // GGML_SYCL_F16 +} + +static __dpct_inline__ 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 dfloat d = x[ib].dm[0]; + const dfloat m = x[ib].dm[1]; + + uint32_t qh; + memcpy(&qh, x[ib].qh, sizeof(qh)); + + const int xh_0 = ((qh >> (iqs + 0)) << 4) & 0x10; + const int xh_1 = ((qh >> (iqs + 12)) ) & 0x10; + + v.x() = ((x[ib].qs[iqs] & 0xf) | xh_0); + v.y() = ((x[ib].qs[iqs] >> 4) | xh_1); + +#ifdef GGML_SYCL_F16 + // v = v * {d, d}; + // v = v + {m, m}; + v.s0() = (v.s0() * d) + m; + v.s1() = (v.s1() * d) + m; +#else + v.x() = (v.x() * d) + m; + v.y() = (v.y() * d) + m; +#endif // GGML_SYCL_F16 +} + +static __dpct_inline__ 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 dfloat d = x[ib].d; + + v.x() = x[ib].qs[iqs + 0]; + v.y() = x[ib].qs[iqs + 1]; + +#ifdef GGML_SYCL_F16 + // v = v * {d, d}; + v.s0() *= d; + v.s1() *= d; +#else + v.x() *= d; + v.y() *= d; +#endif // GGML_SYCL_F16 +} + +//================================== k-quants + +template +static void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restrict__ yy, + const sycl::nd_item<3> &item_ct1) { + + const int i = item_ct1.get_group(2); + const block_q2_K * x = (const block_q2_K *) vx; + + const int tid = item_ct1.get_local_id(2); +#if QK_K == 256 + const int n = tid/32; + const int l = tid - 32*n; + const int is = 8*n + l/16; + + const uint8_t q = x[i].qs[32*n + l]; + dst_t * y = yy + i*QK_K + 128*n; + + float dall = x[i].dm[0]; + float dmin = x[i].dm[1]; + y[l+ 0] = dall * (x[i].scales[is+0] & 0xF) * ((q >> 0) & 3) - dmin * (x[i].scales[is+0] >> 4); + 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); + dst_t * y = yy + i*QK_K + 16*is + il; + float dall = __low2half(x[i].dm); + float dmin = __high2half(x[i].dm); + 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 + +} + +template +static void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restrict__ yy, + const sycl::nd_item<3> &item_ct1) { + + const int i = item_ct1.get_group(2); + const block_q3_K * x = (const block_q3_K *) vx; + +#if QK_K == 256 + const int r = item_ct1.get_local_id(2) / 4; + const int tid = r/2; + const int is0 = r%2; + const int l0 = 16 * is0 + 4 * (item_ct1.get_local_id(2) % 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; + + int8_t us = is < 4 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+8] >> 0) & 3) << 4) : + is < 8 ? (x[i].scales[is-0] & 0xF) | (((x[i].scales[is+4] >> 2) & 3) << 4) : + is < 12 ? (x[i].scales[is-8] >> 4) | (((x[i].scales[is+0] >> 4) & 3) << 4) : + (x[i].scales[is-8] >> 4) | (((x[i].scales[is-4] >> 6) & 3) << 4); + float d_all = x[i].d; + float dl = d_all * (us - 32); + + dst_t * y = yy + i*QK_K + 128*n + 32*j; + const uint8_t * q = x[i].qs + 32*n; + 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 + + dst_t * 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 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; + } else { + d = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4); + m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4); + } +} +#endif + +template +static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy, + const sycl::nd_item<3> &item_ct1) { + const block_q4_K * x = (const block_q4_K *) vx; + + const int i = item_ct1.get_group(2); + +#if QK_K == 256 + // assume 32 threads + const int tid = item_ct1.get_local_id(2); + const int il = tid/8; + const int ir = tid%8; + const int is = 2*il; + const int n = 4; + + dst_t * y = yy + i*QK_K + 64*il + n*ir; + + const float dall = x[i].dm[0]; + const float dmin = x[i].dm[1]; + + const uint8_t * q = x[i].qs + 32*il + n*ir; + + uint8_t sc, m; + get_scale_min_k4(is + 0, x[i].scales, sc, m); + const float d1 = dall * sc; const float m1 = dmin * m; + get_scale_min_k4(is + 1, x[i].scales, sc, m); + const float d2 = dall * sc; const float m2 = dmin * m; + for (int l = 0; l < n; ++l) { + 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; + dst_t * y = yy + i*QK_K; + 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 +} + +template +static void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restrict__ yy, + const sycl::nd_item<3> &item_ct1) { + const block_q5_K * x = (const block_q5_K *) vx; + + const int i = item_ct1.get_group(2); + +#if QK_K == 256 + // assume 64 threads - this is very slightly better than the one below + const int tid = item_ct1.get_local_id(2); + const int il = tid/16; // il is in 0...3 + const int ir = tid%16; // ir is in 0...15 + const int is = 2*il; // is is in 0...6 + + dst_t * y = yy + i*QK_K + 64*il + 2*ir; + + const float dall = x[i].dm[0]; + const float dmin = x[i].dm[1]; + + const uint8_t * ql = x[i].qs + 32*il + 2*ir; + const uint8_t * qh = x[i].qh + 2*ir; + + uint8_t sc, m; + get_scale_min_k4(is + 0, x[i].scales, sc, m); + const float d1 = dall * sc; const float m1 = dmin * m; + get_scale_min_k4(is + 1, x[i].scales, sc, m); + const float d2 = dall * sc; const float m2 = dmin * m; + + uint8_t hm = 1 << (2*il); + y[ 0] = d1 * ((ql[ 0] & 0xF) + (qh[ 0] & hm ? 16 : 0)) - m1; + y[ 1] = d1 * ((ql[ 1] & 0xF) + (qh[ 1] & hm ? 16 : 0)) - m1; + 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; + dst_t * 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 +} + +template +static void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restrict__ yy, + const sycl::nd_item<3> &item_ct1) { + const block_q6_K * x = (const block_q6_K *) vx; + + const int i = item_ct1.get_group(2); +#if QK_K == 256 + + // assume 64 threads - this is very slightly better than the one below + const int tid = item_ct1.get_local_id(2); + const int ip = tid/32; // ip is 0 or 1 + const int il = tid - 32*ip; // 0...32 + const int is = 8*ip + il/16; + + dst_t * y = yy + i*QK_K + 128*ip + il; + + const float d = x[i].d; + + const uint8_t * ql = x[i].ql + 64*ip + il; + const uint8_t qh = x[i].qh[32*ip + il]; + const int8_t * sc = x[i].scales + is; + + y[ 0] = d * sc[0] * ((int8_t)((ql[ 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32); + 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 + + dst_t * 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 +} + +/* +DPCT1110:4: The total declared local variable size in device function +dequantize_mul_mat_vec_q2_k exceeds 128 bytes and may cause high register +pressure. Consult with your hardware vendor to find the total register size +available and adjust the code, or use smaller sub-group size to avoid high +register pressure. +*/ +static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx, + const float *__restrict__ yy, + float *__restrict__ dst, + const int ncols, int nrows, + const sycl::nd_item<3> &item_ct1) { + + static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION"); + + const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + + item_ct1.get_local_id(1); + if (row > nrows) return; + + const int num_blocks_per_row = ncols / QK_K; + const int ib0 = row*num_blocks_per_row; + + 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 = + item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...31 or 0...15 + const int ix = + item_ct1.get_local_id(2) % 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...15 or 0...7 + + 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; + + uint32_t aux[4]; + const uint8_t * d = (const uint8_t *)aux; + const uint8_t * m = (const uint8_t *)(aux + 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; + + const float dall = x[i].dm[0]; + const float dmin = x[i].dm[1]; + + const uint32_t * a = (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; + + 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]; + + } + 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 float2 dall = __half22float2(x[i].dm); + + 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 +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + tmp += + dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); + } + + if (item_ct1.get_local_id(2) == 0) { + dst[row] = tmp; + } +} + +/* +DPCT1110:5: The total declared local variable size in device function +dequantize_mul_mat_vec_q3_k exceeds 128 bytes and may cause high register +pressure. Consult with your hardware vendor to find the total register size +available and adjust the code, or use smaller sub-group size to avoid high +register pressure. +*/ +static void dequantize_mul_mat_vec_q3_k(const void *__restrict__ vx, + const float *__restrict__ yy, + float *__restrict__ dst, + const int ncols, int nrows, + const sycl::nd_item<3> &item_ct1) { + + const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + + item_ct1.get_local_id(1); + 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; + + 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 = + item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const int ix = + item_ct1.get_local_id(2) % 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; + + 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; + const uint8_t * h = x[i].hmask + l0; + + const uint16_t * a = (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 = 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 += 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 +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + tmp += + dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); + } + + if (item_ct1.get_local_id(2) == 0) { + dst[row] = tmp; + } +} + +/* +DPCT1110:6: The total declared local variable size in device function +dequantize_mul_mat_vec_q4_k exceeds 128 bytes and may cause high register +pressure. Consult with your hardware vendor to find the total register size +available and adjust the code, or use smaller sub-group size to avoid high +register pressure. +*/ +static void dequantize_mul_mat_vec_q4_k(const void *__restrict__ vx, + const float *__restrict__ yy, + float *__restrict__ dst, + const int ncols, int nrows, + const sycl::nd_item<3> &item_ct1) { + + const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + + item_ct1.get_local_id(1); + 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 = + item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const int ix = + item_ct1.get_local_id(2) % K_QUANTS_PER_ITERATION; // 0 or 0,1 + + 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; + + 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; + +#if K_QUANTS_PER_ITERATION == 2 + uint32_t q32[4]; + const uint8_t * q4 = (const uint8_t *)q32; +#else + uint16_t q16[4]; + const uint8_t * q4 = (const uint8_t *)q16; +#endif + + 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 * y1 = yy + i*QK_K + y_offset; + const float * y2 = y1 + 128; + + const float dall = x[i].dm[0]; + const float dmin = x[i].dm[1]; + + const uint16_t * a = (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); + +#if K_QUANTS_PER_ITERATION == 2 + const uint32_t * q1 = (const uint32_t *)(x[i].qs + q_offset); + const uint32_t * q2 = q1 + 16; + + q32[0] = q1[0] & 0x0f0f0f0f; + q32[1] = q1[0] & 0xf0f0f0f0; + q32[2] = q2[0] & 0x0f0f0f0f; + q32[3] = q2[0] & 0xf0f0f0f0; + + sycl::float4 s = {0.f, 0.f, 0.f, 0.f}; + float smin = 0; + for (int l = 0; l < 4; ++l) { + s.x() += y1[l] * q4[l + 0]; s.y() += y1[l + 32] * q4[l + 4]; + s.z() += y2[l] * q4[l + 8]; s.w() += y2[l + 32] * q4[l + 12]; + smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7]; + } + tmp += dall * (s.x() * sc[0] + s.y() * sc[1] * 1.f / 16.f + + s.z() * sc[4] + s.w() * sc[5] * 1.f / 16.f) - + dmin * smin; +#else + const uint16_t * q1 = (const uint16_t *)(x[i].qs + q_offset); + const uint16_t * q2 = q1 + 32; + + q16[0] = q1[0] & 0x0f0f; + q16[1] = q1[0] & 0xf0f0; + q16[2] = q2[0] & 0x0f0f; + q16[3] = q2[0] & 0xf0f0; + + float4 s = {0.f, 0.f, 0.f, 0.f}; + float smin = 0; + for (int l = 0; l < 2; ++l) { + s.x += y1[l] * q4[l+0]; s.y += y1[l+32] * q4[l+2]; + s.z += y2[l] * q4[l+4]; s.w += y2[l+32] * q4[l+6]; + smin += y1[l] * sc[2] + y1[l+32] * sc[3] + y2[l] * sc[6] + y2[l+32] * sc[7]; + } + tmp += dall * (s.x * sc[0] + s.y * sc[1] * 1.f/16.f + s.z * sc[4] + s.w * sc[5] * 1.f/16.f) - dmin * smin; +#endif + + } +#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].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]) + + 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 +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + tmp += + dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); + } + + if (tid == 0) { + dst[row] = tmp; + } +} + +/* +DPCT1110:7: The total declared local variable size in device function +dequantize_mul_mat_vec_q5_k exceeds 128 bytes and may cause high register +pressure. Consult with your hardware vendor to find the total register size +available and adjust the code, or use smaller sub-group size to avoid high +register pressure. +*/ +static void dequantize_mul_mat_vec_q5_k(const void *__restrict__ vx, + const float *__restrict__ yy, + float *__restrict__ dst, + const int ncols, + const sycl::nd_item<3> &item_ct1) { + + const int row = item_ct1.get_group(2); + 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 = item_ct1.get_local_id(2) / 2; // 0...15 + const int ix = item_ct1.get_local_id(2) % 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; + + uint16_t q16[8]; + const uint8_t * q4 = (const uint8_t *)q16; + + for (int i = ix; i < num_blocks_per_row; i += 2) { + + const uint8_t * ql1 = x[i].qs + q_offset; + const uint8_t * qh = x[i].qh + l0; + const float * y1 = yy + i*QK_K + y_offset; + const float * y2 = y1 + 128; + + const float dall = x[i].dm[0]; + const float dmin = x[i].dm[1]; + + const uint16_t * a = (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); + + sycl::float4 sum = {0.f, 0.f, 0.f, 0.f}; + float smin = 0; + const uint16_t * q1 = (const uint16_t *)ql1; + const uint16_t * q2 = q1 + 32; + q16[0] = q1[0] & 0x0f0f; + q16[1] = q1[8] & 0x0f0f; + q16[2] = (q1[0] >> 4) & 0x0f0f; + q16[3] = (q1[8] >> 4) & 0x0f0f; + q16[4] = q2[0] & 0x0f0f; + q16[5] = q2[8] & 0x0f0f; + q16[6] = (q2[0] >> 4) & 0x0f0f; + q16[7] = (q2[8] >> 4) & 0x0f0f; + for (int l = 0; l < n; ++l) { + sum.x() += + y1[l + 0] * (q4[l + 0] + (qh[l + 0] & (hm1 << 0) ? 16 : 0)) + + y1[l + 16] * (q4[l + 2] + (qh[l + 16] & (hm1 << 0) ? 16 : 0)); + sum.y() += + y1[l + 32] * (q4[l + 4] + (qh[l + 0] & (hm1 << 1) ? 16 : 0)) + + y1[l + 48] * (q4[l + 6] + (qh[l + 16] & (hm1 << 1) ? 16 : 0)); + sum.z() += + y2[l + 0] * (q4[l + 8] + (qh[l + 0] & (hm2 << 0) ? 16 : 0)) + + y2[l + 16] * (q4[l + 10] + (qh[l + 16] & (hm2 << 0) ? 16 : 0)); + sum.w() += + y2[l + 32] * (q4[l + 12] + (qh[l + 0] & (hm2 << 1) ? 16 : 0)) + + y2[l + 48] * (q4[l + 14] + (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; + } + +#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 +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + tmp += + dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); + } + + if (item_ct1.get_local_id(2) == 0) { + dst[row] = tmp; + } +} + +static void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows, + const sycl::nd_item<3> &item_ct1) { + + static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION"); + + const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + + item_ct1.get_local_id(1); + if (row > nrows) return; + + const int num_blocks_per_row = ncols / QK_K; + const int ib0 = row*num_blocks_per_row; + + const block_q6_K * x = (const block_q6_K *)vx + ib0; + +#if QK_K == 256 + + const int tid = + item_ct1.get_local_id(2) / K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const int ix = + item_ct1.get_local_id(2) % 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; + + 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; + const uint8_t * ql = x[i].ql + ql_offset; + const uint8_t * qh = x[i].qh + qh_offset; + const int8_t * s = x[i].scales + s_offset; + + const float d = 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 += 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 += sum; +#endif + + } + +#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 +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + tmp += + dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); + } + + if (tid == 0) { + dst[row] = tmp; + } +} + +static void convert_f16(const void * vx, const int ib, const int iqs, dfloat2 & v){ + const sycl::half *x = (const sycl::half *)vx; + + // automatic half -> float type cast if dfloat == float + v.x() = x[ib + iqs + 0]; + v.y() = x[ib + iqs + 1]; +} + +static void convert_f32(const void * vx, const int ib, const int iqs, dfloat2 & v){ + const float * x = (const float *) vx; + + // automatic half -> float type cast if dfloat == float + v.x() = x[ib + iqs + 0]; + v.y() = x[ib + iqs + 1]; +} + +static void quantize_q8_1(const float * __restrict__ x, void * __restrict__ vy, const int kx, const int kx_padded, + const sycl::nd_item<3> &item_ct1) { + const int ix = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (ix >= kx_padded) { + return; + } + + const int iy = item_ct1.get_local_range(1) * item_ct1.get_group(1) + + item_ct1.get_local_id(1); + + const int i_padded = iy*kx_padded + ix; + + block_q8_1 * y = (block_q8_1 *) vy; + + const int ib = i_padded / QK8_1; // block index + const int iqs = i_padded % QK8_1; // quant index + + const float xi = ix < kx ? x[iy*kx + ix] : 0.0f; + float amax = sycl::fabs((float)xi); + float sum = xi; + +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + amax = sycl::fmax(amax, dpct::permute_sub_group_by_xor( + item_ct1.get_sub_group(), amax, mask)); + sum += + dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), sum, mask); + } + + const float d = amax / 127; + const int8_t q = amax == 0.0f ? 0 : sycl::round(xi / d); + + y[ib].qs[iqs] = q; + + if (iqs > 0) { + return; + } + + reinterpret_cast(y[ib].ds.x()) = d; + reinterpret_cast(y[ib].ds.y()) = sum; +} + +template +static void k_get_rows( + const void * src0, const int32_t * src1, dst_t * dst, + int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/ + /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/ + /*size_t s0,*/ size_t s1, size_t s2, size_t s3, + /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03, + size_t s10, size_t s11, size_t s12, + const sycl::nd_item<3> &item_ct1/*, size_t s13*/) { + + const int i00 = (item_ct1.get_group(2) * item_ct1.get_local_range(2) + + item_ct1.get_local_id(2)) * + 2; + const int i10 = item_ct1.get_local_range(1) * item_ct1.get_group(1) + + item_ct1.get_local_id(1); + const int i11 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) + + item_ct1.get_local_id(0)) / + ne12; + const int i12 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) + + item_ct1.get_local_id(0)) % + ne12; + + if (i00 >= ne00) { + return; + } + + const int i01 = src1[i10*s10 + i11*s11 + i12*s12]; + + dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3; + const void * src0_row = (const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03; + + const int ib = i00/qk; // block index + const int iqs = (i00%qk)/qr; // quant index + const int iybs = i00 - i00%qk; // dst block start index + const int y_offset = qr == 1 ? 1 : qk/2; + + // dequantize + dfloat2 v; + dequantize_kernel(src0_row, ib, iqs, v); + + dst_row[iybs + iqs + 0] = v.x(); + dst_row[iybs + iqs + y_offset] = v.y(); +} + +template +static void k_get_rows_float( + const src0_t * src0, const int32_t * src1, dst_t * dst, + int64_t ne00, /*int64_t ne01, int64_t ne02, int64_t ne03,*/ + /*int64_t ne10, int64_t ne11,*/ int64_t ne12, /*int64_t ne13,*/ + /*size_t s0,*/ size_t s1, size_t s2, size_t s3, + /*size_t nb00,*/ size_t nb01, size_t nb02, size_t nb03, + size_t s10, size_t s11, size_t s12, + const sycl::nd_item<3> &item_ct1/*, size_t s13*/) { + + const int i00 = item_ct1.get_group(2) * item_ct1.get_local_range(2) + + item_ct1.get_local_id(2); + const int i10 = item_ct1.get_local_range(1) * item_ct1.get_group(1) + + item_ct1.get_local_id(1); + const int i11 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) + + item_ct1.get_local_id(0)) / + ne12; + const int i12 = (item_ct1.get_group(0) * item_ct1.get_local_range(0) + + item_ct1.get_local_id(0)) % + ne12; + + if (i00 >= ne00) { + return; + } + + const int i01 = src1[i10*s10 + i11*s11 + i12*s12]; + + dst_t * dst_row = dst + i10*s1 + i11*s2 + i12*s3; + const src0_t * src0_row = (const src0_t *)((const char *)src0 + i01*nb01 + i11*nb02 + i12*nb03); + + dst_row[i00] = src0_row[i00]; +} + +template +static void dequantize_block(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + 2 * item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + + const int ib = i/qk; // block index + const int iqs = (i%qk)/qr; // quant index + const int iybs = i - i%qk; // y block start index + const int y_offset = qr == 1 ? 1 : qk/2; + + // dequantize + dfloat2 v; + dequantize_kernel(vx, ib, iqs, v); + + y[iybs + iqs + 0] = v.x(); + y[iybs + iqs + y_offset] = v.y(); +} + +// VDR = vec dot ratio, how many contiguous integers each thread processes when the vec dot kernel is called +// MMVQ = mul_mat_vec_q, MMQ = mul_mat_q + +#define VDR_Q4_0_Q8_1_MMVQ 2 +#define VDR_Q4_0_Q8_1_MMQ 4 + +template +static __dpct_inline__ float vec_dot_q4_0_q8_1_impl(const int *v, const int *u, + const float &d4, + const sycl::half2 &ds8) { + int sumi = 0; +#pragma unroll + for (int i = 0; i < vdr; ++i) { + const int vi0 = (v[i] >> 0) & 0x0F0F0F0F; + const int vi1 = (v[i] >> 4) & 0x0F0F0F0F; + + // SIMD dot product of quantized values + sumi = dpct::dp4a(vi0, u[2 * i + 0], sumi); + sumi = dpct::dp4a(vi1, u[2 * i + 1], sumi); + } + + const sycl::float2 ds8f = + ds8.convert(); + + // second part effectively subtracts 8 from each quant value + return d4 * (sumi * ds8f.x() - (8 * vdr / QI4_0) * ds8f.y()); +} + +#define VDR_Q4_1_Q8_1_MMVQ 2 +#define VDR_Q4_1_Q8_1_MMQ 4 + +template +static __dpct_inline__ float vec_dot_q4_1_q8_1_impl(const int *v, const int *u, + const sycl::half2 &dm4, + const sycl::half2 &ds8) { + + int sumi = 0; + +#pragma unroll + for (int i = 0; i < vdr; ++i) { + const int vi0 = (v[i] >> 0) & 0x0F0F0F0F; + const int vi1 = (v[i] >> 4) & 0x0F0F0F0F; + + // SIMD dot product of quantized values + sumi = dpct::dp4a(vi0, u[2 * i + 0], sumi); + sumi = dpct::dp4a(vi1, u[2 * i + 1], sumi); + } + +#ifdef GGML_SYCL_F16 + const sycl::float2 tmp = + (dm4 * ds8).convert(); + const float d4d8 = tmp.x(); + const float m4s8 = tmp.y(); +#else + const sycl::float2 dm4f = + dm4.convert(); + const sycl::float2 ds8f = + ds8.convert(); + const float d4d8 = dm4f.x() * ds8f.x(); + const float m4s8 = dm4f.y() * ds8f.y(); +#endif // GGML_SYCL_F16 + + // scale second part of sum by QI8_1/(vdr * QR4_1) to compensate for multiple threads adding it + return sumi * d4d8 + m4s8 / (QI8_1 / (vdr * QR4_1)); +} + +#define VDR_Q5_0_Q8_1_MMVQ 2 +#define VDR_Q5_0_Q8_1_MMQ 4 + +template +static __dpct_inline__ float +vec_dot_q5_0_q8_1_impl(const int *vl, const int *vh, const int *u, + const float &d5, const sycl::half2 &ds8) { + int sumi = 0; + +#pragma unroll + for (int i = 0; i < vdr; ++i) { + int vi0 = (vl[i] >> 0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits + vi0 |= (vh[i] << 4) & 0x00000010; // 0 -> 4 + vi0 |= (vh[i] << 11) & 0x00001000; // 1 -> 12 + vi0 |= (vh[i] << 18) & 0x00100000; // 2 -> 20 + vi0 |= (vh[i] << 25) & 0x10000000; // 3 -> 28 + sumi = dpct::dp4a(vi0, u[2 * i + 0], + sumi); // SIMD dot product of quantized values + + int vi1 = (vl[i] >> 4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits + vi1 |= (vh[i] >> 12) & 0x00000010; // 16 -> 4 + vi1 |= (vh[i] >> 5) & 0x00001000; // 17 -> 12 + vi1 |= (vh[i] << 2) & 0x00100000; // 18 -> 20 + vi1 |= (vh[i] << 9) & 0x10000000; // 19 -> 28 + sumi = dpct::dp4a(vi1, u[2 * i + 1], + sumi); // SIMD dot product of quantized values + } + + const sycl::float2 ds8f = + ds8.convert(); + + // second part effectively subtracts 16 from each quant value + return d5 * (sumi * ds8f.x() - (16 * vdr / QI5_0) * ds8f.y()); +} + +#define VDR_Q5_1_Q8_1_MMVQ 2 +#define VDR_Q5_1_Q8_1_MMQ 4 + +template +static __dpct_inline__ float +vec_dot_q5_1_q8_1_impl(const int *vl, const int *vh, const int *u, + const sycl::half2 &dm5, const sycl::half2 &ds8) { + + int sumi = 0; + +#pragma unroll + for (int i = 0; i < vdr; ++i) { + int vi0 = (vl[i] >> 0) & 0x0F0F0F0F; // lower 4 qs bits, still need qh as 5th bits + vi0 |= (vh[i] << 4) & 0x00000010; // 0 -> 4 + vi0 |= (vh[i] << 11) & 0x00001000; // 1 -> 12 + vi0 |= (vh[i] << 18) & 0x00100000; // 2 -> 20 + vi0 |= (vh[i] << 25) & 0x10000000; // 3 -> 28 + sumi = dpct::dp4a(vi0, u[2 * i + 0], + sumi); // SIMD dot product of quantized values + + int vi1 = (vl[i] >> 4) & 0x0F0F0F0F; // upper 4 qs bits, still need qh as 5th bits + vi1 |= (vh[i] >> 12) & 0x00000010; // 16 -> 4 + vi1 |= (vh[i] >> 5) & 0x00001000; // 17 -> 12 + vi1 |= (vh[i] << 2) & 0x00100000; // 18 -> 20 + vi1 |= (vh[i] << 9) & 0x10000000; // 19 -> 28 + sumi = dpct::dp4a(vi1, u[2 * i + 1], + sumi); // SIMD dot product of quantized values + } + +#ifdef GGML_SYCL_F16 + const sycl::float2 tmp = + (dm5 * ds8).convert(); + const float d5d8 = tmp.x(); + const float m5s8 = tmp.y(); + + +#else + const sycl::float2 dm5f = + dm5.convert(); + const sycl::float2 ds8f = + ds8.convert(); + const float d5d8 = dm5f.x() * ds8f.x(); + const float m5s8 = dm5f.y() * ds8f.y(); +#endif // GGML_SYCL_F16 + + // scale second part of sum by QI5_1 / vdr to compensate for multiple threads adding it + return sumi*d5d8 + m5s8 / (QI5_1 / vdr); +} + +#define VDR_Q8_0_Q8_1_MMVQ 2 +#define VDR_Q8_0_Q8_1_MMQ 8 + +template +static __dpct_inline__ float vec_dot_q8_0_q8_1_impl(const int *v, const int *u, + const float &d8_0, + const float &d8_1) { + + int sumi = 0; + +#pragma unroll + for (int i = 0; i < vdr; ++i) { + // SIMD dot product of quantized values + sumi = dpct::dp4a(v[i], u[i], sumi); + } + + return d8_0*d8_1 * sumi; +} + +template +static __dpct_inline__ float vec_dot_q8_1_q8_1_impl(const int *v, const int *u, + const sycl::half2 &dm8, + const sycl::half2 &ds8) { + + int sumi = 0; + +#pragma unroll + for (int i = 0; i < vdr; ++i) { + // SIMD dot product of quantized values + sumi = dpct::dp4a(v[i], u[i], sumi); + } + +#ifdef GGML_SYCL_F16 + const sycl::float2 tmp = + (dm8 * ds8).convert(); + const float d8d8 = tmp.x(); + const float m8s8 = tmp.y(); +#else + const sycl::float2 dm8f = + dm8.convert(); + const sycl::float2 ds8f = + ds8.convert(); + const float d8d8 = dm8f.x() * ds8f.x(); + const float m8s8 = dm8f.y() * ds8f.y(); +#endif // GGML_SYCL_F16 + + // scale second part of sum by QI8_1/ vdr to compensate for multiple threads adding it + return sumi*d8d8 + m8s8 / (QI8_1 / vdr); +} + +#define VDR_Q2_K_Q8_1_MMVQ 1 +#define VDR_Q2_K_Q8_1_MMQ 2 + +// contiguous v/x values +static __dpct_inline__ float vec_dot_q2_K_q8_1_impl_mmvq( + const int &v, const int *__restrict__ u, const uint8_t *__restrict__ scales, + const sycl::half2 &dm2, const float *__restrict__ d8) { + + float sumf_d = 0.0f; + float sumf_m = 0.0f; + +#pragma unroll + for (int i = 0; i < QR2_K; ++i) { + const int sc = scales[2*i]; + + const int vi = (v >> (2*i)) & 0x03030303; + + sumf_d += + d8[i] * (dpct::dp4a(vi, u[i], 0) * (sc & 0xF)); // SIMD dot product + + // fill int with 4x m + int m = sc >> 4; + m |= m << 8; + m |= m << 16; + sumf_m += d8[i] * + dpct::dp4a( + m, u[i], + 0); // multiply constant q2_K part with sum of q8_1 values + } + + const sycl::float2 dm2f = + dm2.convert(); + + return dm2f.x() * sumf_d - dm2f.y() * sumf_m; +} + +// contiguous u/y values +static __dpct_inline__ float +vec_dot_q2_K_q8_1_impl_mmq(const int *__restrict__ v, const int *__restrict__ u, + const uint8_t *__restrict__ scales, + const sycl::half2 &dm2, const float &d8) { + + int sumi_d = 0; + int sumi_m = 0; + +#pragma unroll + for (int i0 = 0; i0 < QI8_1; i0 += QI8_1/2) { + int sumi_d_sc = 0; + + const int sc = scales[i0 / (QI8_1/2)]; + + // fill int with 4x m + int m = sc >> 4; + m |= m << 8; + m |= m << 16; + +#pragma unroll + for (int i = i0; i < i0 + QI8_1/2; ++i) { + sumi_d_sc = dpct::dp4a(v[i], u[i], sumi_d_sc); // SIMD dot product + sumi_m = dpct::dp4a(m, u[i], + sumi_m); // multiply sum of q8_1 values with m + } + + sumi_d += sumi_d_sc * (sc & 0xF); + } + + const sycl::float2 dm2f = + dm2.convert(); + + return d8 * (dm2f.x() * sumi_d - dm2f.y() * sumi_m); +} + +#define VDR_Q3_K_Q8_1_MMVQ 1 +#define VDR_Q3_K_Q8_1_MMQ 2 + +// contiguous v/x values +static __dpct_inline__ float vec_dot_q3_K_q8_1_impl_mmvq( + const int &vl, const int &vh, const int *__restrict__ u, + const uint8_t *__restrict__ scales, const int &scale_offset, + const float &d3, const float *__restrict__ d8) { + + float sumf = 0.0f; + +#pragma unroll + for (int i = 0; i < QR3_K; ++i) { + const int isc = scale_offset + 2*i; + + const int isc_low = isc % (QK_K/32); + const int sc_shift_low = 4 * (isc / (QK_K/32)); + const int sc_low = (scales[isc_low] >> sc_shift_low) & 0xF; + + const int isc_high = isc % (QK_K/64); + const int sc_shift_high = 2 * (isc / (QK_K/64)); + const int sc_high = ((scales[(QK_K/32) + isc_high] >> sc_shift_high) & 3) << 4; + + const int sc = (sc_low | sc_high) - 32; + + const int vil = (vl >> (2*i)) & 0x03030303; + + const int vih = ((vh >> i) << 2) & 0x04040404; + + const int vi = + dpct::vectorized_binary(vil, vih, dpct::sub_sat()); + + sumf += d8[i] * (dpct::dp4a(vi, u[i], 0) * sc); // SIMD dot product + } + + return d3 * sumf; +} + +// contiguous u/y values +static __dpct_inline__ float +vec_dot_q3_K_q8_1_impl_mmq(const int *__restrict__ v, const int *__restrict__ u, + const int8_t *__restrict__ scales, const float &d3, + const float &d8) { + + int sumi = 0; + +#pragma unroll + for (int i0 = 0; i0 < QR3_K*VDR_Q3_K_Q8_1_MMQ; i0 += QI8_1/2) { + int sumi_sc = 0; + + for (int i = i0; i < i0 + QI8_1/2; ++i) { + sumi_sc = dpct::dp4a(v[i], u[i], sumi_sc); // SIMD dot product + } + + sumi += sumi_sc * scales[i0 / (QI8_1/2)]; + } + + return d3*d8 * sumi; +} + +#define VDR_Q4_K_Q8_1_MMVQ 2 +#define VDR_Q4_K_Q8_1_MMQ 8 + +// contiguous v/x values +static __dpct_inline__ float vec_dot_q4_K_q8_1_impl_vmmq( + const int *__restrict__ v, const int *__restrict__ u, + const uint8_t *__restrict__ sc, const uint8_t *__restrict__ m, + const sycl::half2 &dm4, const float *__restrict__ d8) { + + float sumf_d = 0.0f; + float sumf_m = 0.0f; + +#pragma unroll + for (int i = 0; i < QR4_K; ++i) { + const int v0i = (v[0] >> (4*i)) & 0x0F0F0F0F; + const int v1i = (v[1] >> (4*i)) & 0x0F0F0F0F; + + const int dot1 = + dpct::dp4a(v1i, u[2 * i + 1], + dpct::dp4a(v0i, u[2 * i + 0], 0)); // SIMD dot product + const int dot2 = + dpct::dp4a(0x01010101, u[2 * i + 1], + dpct::dp4a(0x01010101, u[2 * i + 0], 0)); // sum of u + + sumf_d += d8[i] * (dot1 * sc[i]); + sumf_m += d8[i] * (dot2 * m[i]); // multiply constant part of q4_K with sum of q8_1 values + } + + const sycl::float2 dm4f = + dm4.convert(); + + return dm4f.x() * sumf_d - dm4f.y() * sumf_m; +} + +// contiguous u/y values +static __dpct_inline__ float vec_dot_q4_K_q8_1_impl_mmq( + const int *__restrict__ v, const int *__restrict__ u, + const uint8_t *__restrict__ sc, const uint8_t *__restrict__ m, + const sycl::half2 &dm4, const sycl::half2 *__restrict__ ds8) { + + float sumf_d = 0.0f; + float sumf_m = 0.0f; + +#pragma unroll + for (int i = 0; i < QR4_K*VDR_Q4_K_Q8_1_MMQ/QI8_1; ++i) { + int sumi_d = 0; + +#pragma unroll + for (int j = 0; j < QI8_1; ++j) { + sumi_d = dpct::dp4a((v[j] >> (4 * i)) & 0x0F0F0F0F, + u[i * QI8_1 + j], sumi_d); // SIMD dot product + } + + const sycl::float2 ds8f = + ds8[i].convert(); + + sumf_d += ds8f.x() * (sc[i] * sumi_d); + sumf_m += ds8f.y() * m[i]; // sum of q8_1 block * q4_K min val + } + + const sycl::float2 dm4f = + dm4.convert(); + + return dm4f.x() * sumf_d - dm4f.y() * sumf_m; +} + +#define VDR_Q5_K_Q8_1_MMVQ 2 +#define VDR_Q5_K_Q8_1_MMQ 8 + +// contiguous v/x values +static __dpct_inline__ float vec_dot_q5_K_q8_1_impl_vmmq( + const int *__restrict__ vl, const int *__restrict__ vh, + const int *__restrict__ u, const uint8_t *__restrict__ sc, + const uint8_t *__restrict__ m, const sycl::half2 &dm5, + const float *__restrict__ d8) { + + float sumf_d = 0.0f; + float sumf_m = 0.0f; + +#pragma unroll + for (int i = 0; i < QR5_K; ++i) { + const int vl0i = (vl[0] >> (4*i)) & 0x0F0F0F0F; + const int vl1i = (vl[1] >> (4*i)) & 0x0F0F0F0F; + + const int vh0i = ((vh[0] >> i) << 4) & 0x10101010; + const int vh1i = ((vh[1] >> i) << 4) & 0x10101010; + + const int v0i = vl0i | vh0i; + const int v1i = vl1i | vh1i; + + const int dot1 = + dpct::dp4a(v0i, u[2 * i + 0], + dpct::dp4a(v1i, u[2 * i + 1], 0)); // SIMD dot product + const int dot2 = + dpct::dp4a(0x01010101, u[2 * i + 0], + dpct::dp4a(0x01010101, u[2 * i + 1], 0)); // sum of u + + sumf_d += d8[i] * (dot1 * sc[i]); + sumf_m += d8[i] * (dot2 * m[i]); + + } + + const sycl::float2 dm5f = + dm5.convert(); + + return dm5f.x() * sumf_d - dm5f.y() * sumf_m; +} + +// contiguous u/y values +static __dpct_inline__ float vec_dot_q5_K_q8_1_impl_mmq( + const int *__restrict__ v, const int *__restrict__ u, + const uint8_t *__restrict__ sc, const uint8_t *__restrict__ m, + const sycl::half2 &dm4, const sycl::half2 *__restrict__ ds8) { + + float sumf_d = 0.0f; + float sumf_m = 0.0f; + +#pragma unroll + for (int i = 0; i < QR5_K*VDR_Q5_K_Q8_1_MMQ/QI8_1; ++i) { + int sumi_d = 0; + +#pragma unroll + for (int j = 0; j < QI8_1; ++j) { + sumi_d = dpct::dp4a(v[i * QI8_1 + j], u[i * QI8_1 + j], + sumi_d); // SIMD dot product + } + + const sycl::float2 ds8f = + ds8[i].convert(); + + sumf_d += ds8f.x() * (sc[i] * sumi_d); + sumf_m += ds8f.y() * m[i]; // sum of q8_1 block * q4_K min val + } + + const sycl::float2 dm4f = + dm4.convert(); + + return dm4f.x() * sumf_d - dm4f.y() * sumf_m; +} + +#define VDR_Q6_K_Q8_1_MMVQ 1 +#define VDR_Q6_K_Q8_1_MMQ 8 + +// contiguous v/x values +static __dpct_inline__ float +vec_dot_q6_K_q8_1_impl_mmvq(const int &vl, const int &vh, + const int *__restrict__ u, + const int8_t *__restrict__ scales, const float &d, + const float *__restrict__ d8) { + + float sumf = 0.0f; + +#pragma unroll + for (int i = 0; i < QR6_K; ++i) { + const int sc = scales[4*i]; + + const int vil = (vl >> (4*i)) & 0x0F0F0F0F; + + const int vih = ((vh >> (4*i)) << 4) & 0x30303030; + + const int vi = dpct::vectorized_binary( + (vil | vih), 0x20202020, dpct::sub_sat()); // vi = (vil | vih) - 32 + + sumf += d8[i] * (dpct::dp4a(vi, u[i], 0) * sc); // SIMD dot product + } + + return d*sumf; +} + +// contiguous u/y values +static __dpct_inline__ float +vec_dot_q6_K_q8_1_impl_mmq(const int *__restrict__ v, const int *__restrict__ u, + const int8_t *__restrict__ sc, const float &d6, + const float *__restrict__ d8) { + + float sumf_d = 0.0f; + +#pragma unroll + for (int i0 = 0; i0 < VDR_Q6_K_Q8_1_MMQ; i0 += 4) { + sycl::int2 sumi_d = {0, 0}; // 2 q6_K scales per q8_1 scale + +#pragma unroll + for (int i = i0; i < i0 + 2; ++i) { + sumi_d.x() = dpct::dp4a(v[2 * i + 0], u[2 * i + 0], + sumi_d.x()); // SIMD dot product + sumi_d.x() = dpct::dp4a(v[2 * i + 1], u[2 * i + 1], + sumi_d.x()); // SIMD dot product + + sumi_d.y() = dpct::dp4a(v[2 * i + 4], u[2 * i + 4], + sumi_d.y()); // SIMD dot product + sumi_d.y() = dpct::dp4a(v[2 * i + 5], u[2 * i + 5], + sumi_d.y()); // SIMD dot product + } + + sumf_d += d8[i0 / 4] * + (sc[i0 / 2 + 0] * sumi_d.x() + sc[i0 / 2 + 1] * sumi_d.y()); + } + + return d6 * sumf_d; +} + +static __dpct_inline__ float +vec_dot_q4_0_q8_1(const void *__restrict__ vbq, + const block_q8_1 *__restrict__ bq8_1, const int &iqs) { + + const block_q4_0 * bq4_0 = (const block_q4_0 *) vbq; + + int v[VDR_Q4_0_Q8_1_MMVQ]; + int u[2*VDR_Q4_0_Q8_1_MMVQ]; + +#pragma unroll + for (int i = 0; i < VDR_Q4_0_Q8_1_MMVQ; ++i) { + v[i] = get_int_from_uint8(bq4_0->qs, iqs + i); + u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i); + u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_0); + } + + return vec_dot_q4_0_q8_1_impl(v, u, bq4_0->d, bq8_1->ds); +} + +template +static __dpct_inline__ void +allocate_tiles_q4_0(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc, + int *tile_x_qs_q4_0, float *tile_x_d_q4_0) { + (void)x_qh; (void)x_sc; + + *x_ql = tile_x_qs_q4_0; + *x_dm = (sycl::half2 *)tile_x_d_q4_0; +} + +template +static __dpct_inline__ void +load_tiles_q4_0(const void *__restrict__ vx, int *__restrict__ x_ql, + sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh, + int *__restrict__ x_sc, const int &i_offset, const int &i_max, + const int &k, const int &blocks_per_row) { + (void)x_qh; (void)x_sc; + GGML_SYCL_ASSUME(i_offset >= 0); + GGML_SYCL_ASSUME(i_offset < nwarps); + GGML_SYCL_ASSUME(k >= 0); + GGML_SYCL_ASSUME(k < WARP_SIZE); + + const int kbx = k / QI4_0; + const int kqsx = k % QI4_0; + + const block_q4_0 * bx0 = (const block_q4_0 *) vx; + + float * x_dmf = (float *) x_dm; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + i_offset; + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbx; + + x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx); + // x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbx] = bxi->d; + } + + const int blocks_per_tile_x_row = WARP_SIZE / QI4_0; + const int kbxd = k % blocks_per_tile_x_row; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_0) { + int i = i0 + i_offset * QI4_0 + k / blocks_per_tile_x_row; + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q4_0 * bxi = bx0 + i*blocks_per_row + kbxd; + + x_dmf[i * (WARP_SIZE/QI4_0) + i / QI4_0 + kbxd] = bxi->d; + } +} + +static __dpct_inline__ float vec_dot_q4_0_q8_1_mul_mat( + const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm, + const int *__restrict__ x_qh, const int *__restrict__ x_sc, + const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds, + const int &i, const int &j, const int &k) { + (void)x_qh; (void)x_sc; + + const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2)); + const float * x_dmf = (const float *) x_dm; + + int u[2*VDR_Q4_0_Q8_1_MMQ]; + +#pragma unroll + for (int l = 0; l < VDR_Q4_0_Q8_1_MMQ; ++l) { + u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE]; + u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_0) % WARP_SIZE]; + } + + return vec_dot_q4_0_q8_1_impl + (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dmf[i * (WARP_SIZE/QI4_0) + i/QI4_0 + k/QI4_0], + y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]); +} + +static __dpct_inline__ float +vec_dot_q4_1_q8_1(const void *__restrict__ vbq, + const block_q8_1 *__restrict__ bq8_1, const int &iqs) { + + const block_q4_1 * bq4_1 = (const block_q4_1 *) vbq; + + int v[VDR_Q4_1_Q8_1_MMVQ]; + int u[2*VDR_Q4_1_Q8_1_MMVQ]; + +#pragma unroll + for (int i = 0; i < VDR_Q4_1_Q8_1_MMVQ; ++i) { + v[i] = get_int_from_uint8_aligned(bq4_1->qs, iqs + i); + u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i); + u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI4_1); + } + + return vec_dot_q4_1_q8_1_impl(v, u, bq4_1->dm, bq8_1->ds); +} + +template +static __dpct_inline__ void +allocate_tiles_q4_1(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc, + int *tile_x_qs_q4_1, sycl::half2 *tile_x_dm_q4_1) { + (void)x_qh; (void)x_sc; + + *x_ql = tile_x_qs_q4_1; + *x_dm = tile_x_dm_q4_1; +} + +template +static __dpct_inline__ void +load_tiles_q4_1(const void *__restrict__ vx, int *__restrict__ x_ql, + sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh, + int *__restrict__ x_sc, const int &i_offset, const int &i_max, + const int &k, const int &blocks_per_row) { + (void)x_qh; (void)x_sc; + + GGML_SYCL_ASSUME(i_offset >= 0); + GGML_SYCL_ASSUME(i_offset < nwarps); + GGML_SYCL_ASSUME(k >= 0); + GGML_SYCL_ASSUME(k < WARP_SIZE); + + const int kbx = k / QI4_1; + const int kqsx = k % QI4_1; + + const block_q4_1 * bx0 = (const block_q4_1 *) vx; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + i_offset; + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbx; + + x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx); + } + + const int blocks_per_tile_x_row = WARP_SIZE / QI4_1; + const int kbxd = k % blocks_per_tile_x_row; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_1) { + int i = i0 + i_offset * QI4_1 + k / blocks_per_tile_x_row; + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q4_1 * bxi = bx0 + i*blocks_per_row + kbxd; + + x_dm[i * (WARP_SIZE/QI4_1) + i / QI4_1 + kbxd] = bxi->dm; + } +} + +static __dpct_inline__ float vec_dot_q4_1_q8_1_mul_mat( + const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm, + const int *__restrict__ x_qh, const int *__restrict__ x_sc, + const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds, + const int &i, const int &j, const int &k) { + (void)x_qh; (void)x_sc; + + const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2)); + + int u[2*VDR_Q4_1_Q8_1_MMQ]; + +#pragma unroll + for (int l = 0; l < VDR_Q4_1_Q8_1_MMQ; ++l) { + u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE]; + u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI4_1) % WARP_SIZE]; + } + + return vec_dot_q4_1_q8_1_impl + (&x_ql[i * (WARP_SIZE + 1) + k], u, x_dm[i * (WARP_SIZE/QI4_1) + i/QI4_1 + k/QI4_1], + y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]); +} + +static __dpct_inline__ float +vec_dot_q5_0_q8_1(const void *__restrict__ vbq, + const block_q8_1 *__restrict__ bq8_1, const int &iqs) { + + const block_q5_0 * bq5_0 = (const block_q5_0 *) vbq; + + int vl[VDR_Q5_0_Q8_1_MMVQ]; + int vh[VDR_Q5_0_Q8_1_MMVQ]; + int u[2*VDR_Q5_0_Q8_1_MMVQ]; + +#pragma unroll + for (int i = 0; i < VDR_Q5_0_Q8_1_MMVQ; ++i) { + vl[i] = get_int_from_uint8(bq5_0->qs, iqs + i); + vh[i] = get_int_from_uint8(bq5_0->qh, 0) >> (4 * (iqs + i)); + u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i); + u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_0); + } + + return vec_dot_q5_0_q8_1_impl(vl, vh, u, bq5_0->d, bq8_1->ds); +} + +template +static __dpct_inline__ void +allocate_tiles_q5_0(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc, + int *tile_x_ql_q5_0, float *tile_x_d_q5_0) { + (void)x_qh; (void)x_sc; + + *x_ql = tile_x_ql_q5_0; + *x_dm = (sycl::half2 *)tile_x_d_q5_0; +} + +template +static __dpct_inline__ void +load_tiles_q5_0(const void *__restrict__ vx, int *__restrict__ x_ql, + sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh, + int *__restrict__ x_sc, const int &i_offset, const int &i_max, + const int &k, const int &blocks_per_row) { + (void)x_qh; (void)x_sc; + + GGML_SYCL_ASSUME(i_offset >= 0); + GGML_SYCL_ASSUME(i_offset < nwarps); + GGML_SYCL_ASSUME(k >= 0); + GGML_SYCL_ASSUME(k < WARP_SIZE); + + const int kbx = k / QI5_0; + const int kqsx = k % QI5_0; + + const block_q5_0 * bx0 = (const block_q5_0 *) vx; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + i_offset; + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbx; + + const int ql = get_int_from_uint8(bxi->qs, kqsx); + const int qh = get_int_from_uint8(bxi->qh, 0) >> (4 * (k % QI5_0)); + + int qs0 = (ql >> 0) & 0x0F0F0F0F; + qs0 |= (qh << 4) & 0x00000010; // 0 -> 4 + qs0 |= (qh << 11) & 0x00001000; // 1 -> 12 + qs0 |= (qh << 18) & 0x00100000; // 2 -> 20 + qs0 |= (qh << 25) & 0x10000000; // 3 -> 28 + qs0 = dpct::vectorized_binary( + qs0, 0x10101010, dpct::sub_sat()); // subtract 16 + + x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0; + + int qs1 = (ql >> 4) & 0x0F0F0F0F; + qs1 |= (qh >> 12) & 0x00000010; // 16 -> 4 + qs1 |= (qh >> 5) & 0x00001000; // 17 -> 12 + qs1 |= (qh << 2) & 0x00100000; // 18 -> 20 + qs1 |= (qh << 9) & 0x10000000; // 19 -> 28 + qs1 = dpct::vectorized_binary( + qs1, 0x10101010, dpct::sub_sat()); // subtract 16 + + x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1; + } + + const int blocks_per_tile_x_row = WARP_SIZE / QI5_0; + const int kbxd = k % blocks_per_tile_x_row; + float * x_dmf = (float *) x_dm; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_0) { + int i = i0 + i_offset * QI5_0 + k / blocks_per_tile_x_row; + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q5_0 * bxi = bx0 + i*blocks_per_row + kbxd; + + x_dmf[i * (WARP_SIZE/QI5_0) + i / QI5_0 + kbxd] = bxi->d; + } +} + +static __dpct_inline__ float vec_dot_q5_0_q8_1_mul_mat( + const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm, + const int *__restrict__ x_qh, const int *__restrict__ x_sc, + const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds, + const int &i, const int &j, const int &k) { + (void)x_qh; (void)x_sc; + + const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2)); + const int index_bx = i * (WARP_SIZE/QI5_0) + i/QI5_0 + k/QI5_0; + const float * x_dmf = (const float *) x_dm; + const float * y_df = (const float *) y_ds; + + int u[2*VDR_Q5_0_Q8_1_MMQ]; + +#pragma unroll + for (int l = 0; l < VDR_Q5_0_Q8_1_MMQ; ++l) { + u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE]; + u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_0) % WARP_SIZE]; + } + + return vec_dot_q8_0_q8_1_impl + (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dmf[index_bx], y_df[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]); +} + +static __dpct_inline__ float +vec_dot_q5_1_q8_1(const void *__restrict__ vbq, + const block_q8_1 *__restrict__ bq8_1, const int &iqs) { + + const block_q5_1 * bq5_1 = (const block_q5_1 *) vbq; + + int vl[VDR_Q5_1_Q8_1_MMVQ]; + int vh[VDR_Q5_1_Q8_1_MMVQ]; + int u[2*VDR_Q5_1_Q8_1_MMVQ]; + +#pragma unroll + for (int i = 0; i < VDR_Q5_1_Q8_1_MMVQ; ++i) { + vl[i] = get_int_from_uint8_aligned(bq5_1->qs, iqs + i); + vh[i] = get_int_from_uint8_aligned(bq5_1->qh, 0) >> (4 * (iqs + i)); + u[2*i+0] = get_int_from_int8_aligned(bq8_1->qs, iqs + i); + u[2*i+1] = get_int_from_int8_aligned(bq8_1->qs, iqs + i + QI5_1); + } + + return vec_dot_q5_1_q8_1_impl(vl, vh, u, bq5_1->dm, bq8_1->ds); +} + +template +static __dpct_inline__ void +allocate_tiles_q5_1(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc, + int *tile_x_ql_q5_1, sycl::half2 *tile_x_dm_q5_1) { + (void)x_qh; (void)x_sc; + + *x_ql = tile_x_ql_q5_1; + *x_dm = tile_x_dm_q5_1; +} + +template +static __dpct_inline__ void +load_tiles_q5_1(const void *__restrict__ vx, int *__restrict__ x_ql, + sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh, + int *__restrict__ x_sc, const int &i_offset, const int &i_max, + const int &k, const int &blocks_per_row) { + (void)x_qh; (void)x_sc; + + GGML_SYCL_ASSUME(i_offset >= 0); + GGML_SYCL_ASSUME(i_offset < nwarps); + GGML_SYCL_ASSUME(k >= 0); + GGML_SYCL_ASSUME(k < WARP_SIZE); + + const int kbx = k / QI5_1; + const int kqsx = k % QI5_1; + + const block_q5_1 * bx0 = (const block_q5_1 *) vx; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + i_offset; + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbx; + + const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx); + const int qh = get_int_from_uint8_aligned(bxi->qh, 0) >> (4 * (k % QI5_1)); + + int qs0 = (ql >> 0) & 0x0F0F0F0F; + qs0 |= (qh << 4) & 0x00000010; // 0 -> 4 + qs0 |= (qh << 11) & 0x00001000; // 1 -> 12 + qs0 |= (qh << 18) & 0x00100000; // 2 -> 20 + qs0 |= (qh << 25) & 0x10000000; // 3 -> 28 + + x_ql[i * (2*WARP_SIZE + 1) + 2*k+0] = qs0; + + int qs1 = (ql >> 4) & 0x0F0F0F0F; + qs1 |= (qh >> 12) & 0x00000010; // 16 -> 4 + qs1 |= (qh >> 5) & 0x00001000; // 17 -> 12 + qs1 |= (qh << 2) & 0x00100000; // 18 -> 20 + qs1 |= (qh << 9) & 0x10000000; // 19 -> 28 + + x_ql[i * (2*WARP_SIZE + 1) + 2*k+1] = qs1; + } + + const int blocks_per_tile_x_row = WARP_SIZE / QI5_1; + const int kbxd = k % blocks_per_tile_x_row; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_1) { + int i = i0 + i_offset * QI5_1 + k / blocks_per_tile_x_row; + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q5_1 * bxi = bx0 + i*blocks_per_row + kbxd; + + x_dm[i * (WARP_SIZE/QI5_1) + i / QI5_1 + kbxd] = bxi->dm; + } +} + +static __dpct_inline__ float vec_dot_q5_1_q8_1_mul_mat( + const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm, + const int *__restrict__ x_qh, const int *__restrict__ x_sc, + const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds, + const int &i, const int &j, const int &k) { + (void)x_qh; (void)x_sc; + + const int kyqs = k % (QI8_1/2) + QI8_1 * (k / (QI8_1/2)); + const int index_bx = i * (WARP_SIZE/QI5_1) + + i/QI5_1 + k/QI5_1; + + int u[2*VDR_Q5_1_Q8_1_MMQ]; + +#pragma unroll + for (int l = 0; l < VDR_Q5_1_Q8_1_MMQ; ++l) { + u[2*l+0] = y_qs[j * WARP_SIZE + (kyqs + l) % WARP_SIZE]; + u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_1) % WARP_SIZE]; + } + + return vec_dot_q8_1_q8_1_impl + (&x_ql[i * (2*WARP_SIZE + 1) + 2 * k], u, x_dm[index_bx], y_ds[j * (WARP_SIZE/QI8_1) + (2*k/QI8_1) % (WARP_SIZE/QI8_1)]); +} + +static __dpct_inline__ float +vec_dot_q8_0_q8_1(const void *__restrict__ vbq, + const block_q8_1 *__restrict__ bq8_1, const int &iqs) { + + const block_q8_0 * bq8_0 = (const block_q8_0 *) vbq; + + int v[VDR_Q8_0_Q8_1_MMVQ]; + int u[VDR_Q8_0_Q8_1_MMVQ]; + +#pragma unroll + for (int i = 0; i < VDR_Q8_0_Q8_1_MMVQ; ++i) { + v[i] = get_int_from_int8(bq8_0->qs, iqs + i); + u[i] = get_int_from_int8_aligned(bq8_1->qs, iqs + i); + } + + return vec_dot_q8_0_q8_1_impl(v, u, bq8_0->d, + bq8_1->ds[0]); +} + +template +static __dpct_inline__ void +allocate_tiles_q8_0(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc, + int *tile_x_qs_q8_0, float *tile_x_d_q8_0) { + (void)x_qh; (void)x_sc; + + *x_ql = tile_x_qs_q8_0; + *x_dm = (sycl::half2 *)tile_x_d_q8_0; +} + +template +static __dpct_inline__ void +load_tiles_q8_0(const void *__restrict__ vx, int *__restrict__ x_ql, + sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh, + int *__restrict__ x_sc, const int &i_offset, const int &i_max, + const int &k, const int &blocks_per_row) { + (void)x_qh; (void)x_sc; + + GGML_SYCL_ASSUME(i_offset >= 0); + GGML_SYCL_ASSUME(i_offset < nwarps); + GGML_SYCL_ASSUME(k >= 0); + GGML_SYCL_ASSUME(k < WARP_SIZE); + + const int kbx = k / QI8_0; + const int kqsx = k % QI8_0; + float * x_dmf = (float *) x_dm; + + const block_q8_0 * bx0 = (const block_q8_0 *) vx; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + i_offset; + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbx; + + x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_int8(bxi->qs, kqsx); + } + + const int blocks_per_tile_x_row = WARP_SIZE / QI8_0; + const int kbxd = k % blocks_per_tile_x_row; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI8_0) { + int i = i0 + i_offset * QI8_0 + k / blocks_per_tile_x_row; + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q8_0 * bxi = bx0 + i*blocks_per_row + kbxd; + + x_dmf[i * (WARP_SIZE/QI8_0) + i / QI8_0 + kbxd] = bxi->d; + } +} + +static __dpct_inline__ float vec_dot_q8_0_q8_1_mul_mat( + const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm, + const int *__restrict__ x_qh, const int *__restrict__ x_sc, + const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds, + const int &i, const int &j, const int &k) { + (void)x_qh; (void)x_sc; + + const float * x_dmf = (const float *) x_dm; + const float * y_df = (const float *) y_ds; + + return vec_dot_q8_0_q8_1_impl + (&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[j * WARP_SIZE + k], x_dmf[i * (WARP_SIZE/QI8_0) + i/QI8_0 + k/QI8_0], + y_df[j * (WARP_SIZE/QI8_1) + k/QI8_1]); +} + +static __dpct_inline__ float +vec_dot_q2_K_q8_1(const void *__restrict__ vbq, + const block_q8_1 *__restrict__ bq8_1, const int &iqs) { + + const block_q2_K * bq2_K = (const block_q2_K *) vbq; + + const int bq8_offset = QR2_K * (iqs / QI8_1); + const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2); + + const uint8_t * scales = bq2_K->scales + scale_offset; + + const int v = get_int_from_uint8_aligned(bq2_K->qs, iqs); + int u[QR2_K]; + float d8[QR2_K]; + +#pragma unroll + for (int i = 0; i < QR2_K; ++ i) { + u[i] = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1); + d8[i] = bq8_1[bq8_offset + i].ds[0]; + } + + return vec_dot_q2_K_q8_1_impl_mmvq(v, u, scales, bq2_K->dm, d8); +} + +template +static __dpct_inline__ void +allocate_tiles_q2_K(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc, + int *tile_x_ql_q2_K, sycl::half2 *tile_x_dm_q2_K, + int *tile_x_sc_q2_K) { + (void)x_qh; + + *x_ql = tile_x_ql_q2_K; + *x_dm = tile_x_dm_q2_K; + *x_sc = tile_x_sc_q2_K; +} + +template +static __dpct_inline__ void +load_tiles_q2_K(const void *__restrict__ vx, int *__restrict__ x_ql, + sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh, + int *__restrict__ x_sc, const int &i_offset, const int &i_max, + const int &k, const int &blocks_per_row) { + (void)x_qh; + + GGML_SYCL_ASSUME(i_offset >= 0); + GGML_SYCL_ASSUME(i_offset < nwarps); + GGML_SYCL_ASSUME(k >= 0); + GGML_SYCL_ASSUME(k < WARP_SIZE); + + const int kbx = k / QI2_K; + const int kqsx = k % QI2_K; + + const block_q2_K * bx0 = (const block_q2_K *) vx; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + i_offset; + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q2_K * bxi = bx0 + i*blocks_per_row + kbx; + + x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx); + } + + const int blocks_per_tile_x_row = WARP_SIZE / QI2_K; + const int kbxd = k % blocks_per_tile_x_row; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI2_K) { + int i = (i0 + i_offset * QI2_K + k / blocks_per_tile_x_row) % mmq_y; + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q2_K * bxi = bx0 + i*blocks_per_row + kbxd; + + x_dm[i * (WARP_SIZE/QI2_K) + i / QI2_K + kbxd] = bxi->dm; + } + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) { + int i = i0 + i_offset * 4 + k / (WARP_SIZE/4); + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q2_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI2_K/4); + + x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = get_int_from_uint8_aligned(bxi->scales, k % (QI2_K/4)); + } +} + +static __dpct_inline__ float vec_dot_q2_K_q8_1_mul_mat( + const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm, + const int *__restrict__ x_qh, const int *__restrict__ x_sc, + const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds, + const int &i, const int &j, const int &k) { + (void)x_qh; + + const int kbx = k / QI2_K; + const int ky = (k % QI2_K) * QR2_K; + const float * y_df = (const float *) y_ds; + + int v[QR2_K*VDR_Q2_K_Q8_1_MMQ]; + + const int kqsx = i * (WARP_SIZE + 1) + kbx*QI2_K + (QI2_K/2) * (ky/(2*QI2_K)) + ky % (QI2_K/2); + const int shift = 2 * ((ky % (2*QI2_K)) / (QI2_K/2)); + +#pragma unroll + for (int l = 0; l < QR2_K*VDR_Q2_K_Q8_1_MMQ; ++l) { + v[l] = (x_ql[kqsx + l] >> shift) & 0x03030303; + } + + const uint8_t * scales = ((const uint8_t *) &x_sc[i * (WARP_SIZE/4) + i/4 + kbx*4]) + ky/4; + + const int index_y = j * WARP_SIZE + (QR2_K*k) % WARP_SIZE; + return vec_dot_q2_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dm[i * (WARP_SIZE/QI2_K) + i/QI2_K + kbx], y_df[index_y/QI8_1]); +} + +static __dpct_inline__ float +vec_dot_q3_K_q8_1(const void *__restrict__ vbq, + const block_q8_1 *__restrict__ bq8_1, const int &iqs) { + + const block_q3_K * bq3_K = (const block_q3_K *) vbq; + + const int bq8_offset = QR3_K * (iqs / (QI3_K/2)); + const int scale_offset = iqs - iqs % QI8_1 + (iqs % QI8_1) / (QI8_1/2); + + const float d = bq3_K->d; + + const int vl = get_int_from_uint8(bq3_K->qs, iqs); + + // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted + const int vh = ~get_int_from_uint8(bq3_K->hmask, iqs % (QI3_K/2)) >> bq8_offset; + + int u[QR3_K]; + float d8[QR3_K]; + +#pragma unroll + for (int i = 0; i < QR3_K; ++i) { + u[i] = get_int_from_int8_aligned(bq8_1[bq8_offset + i].qs, iqs % QI8_1); + d8[i] = bq8_1[bq8_offset + i].ds[0]; + } + + return vec_dot_q3_K_q8_1_impl_mmvq(vl, vh, u, bq3_K->scales, scale_offset, d, d8); +} + +template +static __dpct_inline__ void +allocate_tiles_q3_K(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc, + int *tile_x_ql_q3_K, sycl::half2 *tile_x_dm_q3_K, + int *tile_x_qh_q3_K, int *tile_x_sc_q3_K) { + + *x_ql = tile_x_ql_q3_K; + *x_dm = tile_x_dm_q3_K; + *x_qh = tile_x_qh_q3_K; + *x_sc = tile_x_sc_q3_K; +} + +template +static __dpct_inline__ void +load_tiles_q3_K(const void *__restrict__ vx, int *__restrict__ x_ql, + sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh, + int *__restrict__ x_sc, const int &i_offset, const int &i_max, + const int &k, const int &blocks_per_row) { + + GGML_SYCL_ASSUME(i_offset >= 0); + GGML_SYCL_ASSUME(i_offset < nwarps); + GGML_SYCL_ASSUME(k >= 0); + GGML_SYCL_ASSUME(k < WARP_SIZE); + + const int kbx = k / QI3_K; + const int kqsx = k % QI3_K; + + const block_q3_K * bx0 = (const block_q3_K *) vx; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + i_offset; + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q3_K * bxi = bx0 + i*blocks_per_row + kbx; + + x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8(bxi->qs, kqsx); + } + + const int blocks_per_tile_x_row = WARP_SIZE / QI3_K; + const int kbxd = k % blocks_per_tile_x_row; + float * x_dmf = (float *) x_dm; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI3_K) { + int i = (i0 + i_offset * QI3_K + k / blocks_per_tile_x_row) % mmq_y; + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q3_K * bxi = bx0 + i*blocks_per_row + kbxd; + + x_dmf[i * (WARP_SIZE/QI3_K) + i / QI3_K + kbxd] = bxi->d; + } + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 2) { + int i = i0 + i_offset * 2 + k / (WARP_SIZE/2); + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/2)) / (QI3_K/2); + + // invert the mask with ~ so that a 0/1 results in 4/0 being subtracted + x_qh[i * (WARP_SIZE/2) + i / 2 + k % (WARP_SIZE/2)] = ~get_int_from_uint8(bxi->hmask, k % (QI3_K/2)); + } + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 4) { + int i = i0 + i_offset * 4 + k / (WARP_SIZE/4); + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q3_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/4)) / (QI3_K/4); + + const int ksc = k % (QI3_K/4); + + const int ksc_low = ksc % (QI3_K/8); + const int shift_low = 4 * (ksc / (QI3_K/8)); + const int sc_low = (get_int_from_uint8(bxi->scales, ksc_low) >> shift_low) & 0x0F0F0F0F; + + const int ksc_high = QI3_K/8; + const int shift_high = 2 * ksc; + const int sc_high = ((get_int_from_uint8(bxi->scales, ksc_high) >> shift_high) << 4) & 0x30303030; + + const int sc = dpct::vectorized_binary( + sc_low | sc_high, 0x20202020, dpct::sub_sat()); + + x_sc[i * (WARP_SIZE/4) + i / 4 + k % (WARP_SIZE/4)] = sc; + } +} + +static __dpct_inline__ float vec_dot_q3_K_q8_1_mul_mat( + const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm, + const int *__restrict__ x_qh, const int *__restrict__ x_sc, + const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds, + const int &i, const int &j, const int &k) { + + const int kbx = k / QI3_K; + const int ky = (k % QI3_K) * QR3_K; + const float * x_dmf = (const float *) x_dm; + const float * y_df = (const float *) y_ds; + + const int8_t * scales = ((const int8_t *) (x_sc + i * (WARP_SIZE/4) + i/4 + kbx*4)) + ky/4; + + int v[QR3_K*VDR_Q3_K_Q8_1_MMQ]; + +#pragma unroll + for (int l = 0; l < QR3_K*VDR_Q3_K_Q8_1_MMQ; ++l) { + const int kqsx = i * (WARP_SIZE + 1) + kbx*QI3_K + (QI3_K/2) * (ky/(2*QI3_K)) + ky % (QI3_K/2); + const int shift = 2 * ((ky % 32) / 8); + const int vll = (x_ql[kqsx + l] >> shift) & 0x03030303; + + const int vh = x_qh[i * (WARP_SIZE/2) + i/2 + kbx * (QI3_K/2) + (ky+l)%8] >> ((ky+l) / 8); + const int vlh = (vh << 2) & 0x04040404; + + v[l] = dpct::vectorized_binary(vll, vlh, dpct::sub_sat()); + } + + const int index_y = j * WARP_SIZE + (k*QR3_K) % WARP_SIZE; + return vec_dot_q3_K_q8_1_impl_mmq(v, &y_qs[index_y], scales, x_dmf[i * (WARP_SIZE/QI3_K) + i/QI3_K + kbx], y_df[index_y/QI8_1]); +} + +static __dpct_inline__ float +vec_dot_q4_K_q8_1(const void *__restrict__ vbq, + const block_q8_1 *__restrict__ bq8_1, const int &iqs) { + +#ifndef GGML_QKK_64 + const block_q4_K * bq4_K = (const block_q4_K *) vbq; + + int v[2]; + int u[2*QR4_K]; + float d8[QR4_K]; + + // iqs is in 0,2..30. bq8_offset = iqs/4 -> bq8_offset = 0, 2, 4, 6 + const int bq8_offset = QR4_K * ((iqs/2) / (QI8_1/2)); + + // iqs = 0....3 -> bq8_offset = 0, want q4_offset = 0, 4, 8, 12 + // iqs = 4....7 -> bq8_offset = 2, want q4_offset = 32, 36, 40, 44 + // iqs = 8...11 -> bq8_offset = 4, want q4_offset = 64, 68, 72, 76 + // iqs = 12..15 -> bq8_offset = 6, want q4_offset = 96, 100, 104, 108 + + const int * q4 = (const int *)(bq4_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4)); + v[0] = q4[0]; + v[1] = q4[4]; + + const uint16_t * scales = (const uint16_t *)bq4_K->scales; + uint16_t aux[2]; + const int j = bq8_offset/2; + if (j < 2) { + aux[0] = scales[j+0] & 0x3f3f; + aux[1] = scales[j+2] & 0x3f3f; + } else { + aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2); + aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2); + } + const uint8_t * sc = (const uint8_t *)aux; + const uint8_t * m = sc + 2; + + for (int i = 0; i < QR4_K; ++i) { + const block_q8_1 * bq8i = bq8_1 + bq8_offset + i; + d8[i] = bq8i->ds[0]; + + const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4); + u[2*i+0] = q8[0]; + u[2*i+1] = q8[4]; + } + + return vec_dot_q4_K_q8_1_impl_vmmq(v, u, sc, m, bq4_K->dm, d8); + +#else + +#if __SYCL_ARCH__ >= VER_4VEC // lowest compute capability for integer intrinsics + const block_q4_K * bq4_K = (const block_q4_K *) vbq; + + float sumf_d = 0.0f; + float sumf_m = 0.0f; + + uint16_t aux16[2]; + const uint8_t * s = (const uint8_t *)aux16; + + const uint16_t * a = (const uint16_t *)bq4_K->scales; + aux16[0] = a[0] & 0x0f0f; + aux16[1] = (a[0] >> 4) & 0x0f0f; + + 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); + + const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2)); + const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4); + const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2)); + const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4); + + const int * q4 = (const int *)bq4_K->qs + (iqs/2); + const int v1 = q4[0]; + const int v2 = q4[4]; + + const int dot1 = __dp4a(ui2, v2 & 0x0f0f0f0f, __dp4a(ui1, v1 & 0x0f0f0f0f, 0)); + const int dot2 = __dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, __dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0)); + const int dot3 = __dp4a(0x01010101, ui2, __dp4a(0x01010101, ui1, 0)); + const int dot4 = __dp4a(0x01010101, ui4, __dp4a(0x01010101, ui3, 0)); + + sumf_d += d8_1 * (dot1 * s[0]) + d8_2 * (dot2 * s[1]); + sumf_m += d8_1 * (dot3 * s[2]) + d8_2 * (dot4 * s[3]); + + return dall * sumf_d - dmin * sumf_m; + +#else + bad_arch(); +#endif // __SYCL_ARCH__ >= VER_4VEC + +#endif +} + +template +static __dpct_inline__ void +allocate_tiles_q4_K(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc, + int *tile_x_ql_q4_K, sycl::half2 *tile_x_dm_q4_K, + int *tile_x_sc_q4_K) { + (void)x_qh; + + *x_ql = tile_x_ql_q4_K; + *x_dm = tile_x_dm_q4_K; + *x_sc = tile_x_sc_q4_K; +} + +template +static __dpct_inline__ void +load_tiles_q4_K(const void *__restrict__ vx, int *__restrict__ x_ql, + sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh, + int *__restrict__ x_sc, const int &i_offset, const int &i_max, + const int &k, const int &blocks_per_row) { + (void)x_qh; + + GGML_SYCL_ASSUME(i_offset >= 0); + GGML_SYCL_ASSUME(i_offset < nwarps); + GGML_SYCL_ASSUME(k >= 0); + GGML_SYCL_ASSUME(k < WARP_SIZE); + + const int kbx = k / QI4_K; // == 0 if QK_K == 256 + const int kqsx = k % QI4_K; // == k if QK_K == 256 + + const block_q4_K * bx0 = (const block_q4_K *) vx; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + i_offset; + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q4_K * bxi = bx0 + i*blocks_per_row + kbx; + + x_ql[i * (WARP_SIZE + 1) + k] = get_int_from_uint8_aligned(bxi->qs, kqsx); + } + + const int blocks_per_tile_x_row = WARP_SIZE / QI4_K; // == 1 if QK_K == 256 + const int kbxd = k % blocks_per_tile_x_row; // == 0 if QK_K == 256 + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI4_K) { + int i = (i0 + i_offset * QI4_K + k / blocks_per_tile_x_row) % mmq_y; + + if (need_check) { + i = sycl::min(i, i_max); + } + + 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 + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) { + int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y; + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q4_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI4_K/8); + + const int * scales = (const int *) bxi->scales; + + const int ksc = k % (WARP_SIZE/8); + + // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8 + int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits + scales8 |= (scales[ksc/2] >> (2 * (ksc % 2))) & 0x30303030; // upper 2 bits + + x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8; + } +} + +static __dpct_inline__ float vec_dot_q4_K_q8_1_mul_mat( + const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm, + const int *__restrict__ x_qh, const int *__restrict__ x_sc, + const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds, + const int &i, const int &j, const int &k) { + (void)x_qh; + + const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2*((k % 16) / 8); + + const int index_y = j * WARP_SIZE + (QR4_K*k) % WARP_SIZE; + return vec_dot_q4_K_q8_1_impl_mmq(&x_ql[i * (WARP_SIZE + 1) + k], &y_qs[index_y], sc, sc+8, + x_dm[i * (WARP_SIZE/QI4_K) + i/QI4_K], &y_ds[index_y/QI8_1]); +} + +static __dpct_inline__ float +vec_dot_q5_K_q8_1(const void *__restrict__ vbq, + const block_q8_1 *__restrict__ bq8_1, const int &iqs) { + +#ifndef GGML_QKK_64 + const block_q5_K * bq5_K = (const block_q5_K *) vbq; + + int vl[2]; + int vh[2]; + int u[2*QR5_K]; + float d8[QR5_K]; + + const int bq8_offset = QR5_K * ((iqs/2) / (QI8_1/2)); + const int * ql = (const int *)(bq5_K->qs + 16 * bq8_offset + 4 * ((iqs/2)%4)); + const int * qh = (const int *)(bq5_K->qh + 4 * ((iqs/2)%4)); + + vl[0] = ql[0]; + vl[1] = ql[4]; + + vh[0] = qh[0] >> bq8_offset; + vh[1] = qh[4] >> bq8_offset; + + const uint16_t * scales = (const uint16_t *)bq5_K->scales; + uint16_t aux[2]; + const int j = bq8_offset/2; + if (j < 2) { + aux[0] = scales[j+0] & 0x3f3f; + aux[1] = scales[j+2] & 0x3f3f; + } else { + aux[0] = ((scales[j+2] >> 0) & 0x0f0f) | ((scales[j-2] & 0xc0c0) >> 2); + aux[1] = ((scales[j+2] >> 4) & 0x0f0f) | ((scales[j-0] & 0xc0c0) >> 2); + } + const uint8_t * sc = (const uint8_t *)aux; + const uint8_t * m = sc + 2; + +#pragma unroll + for (int i = 0; i < QR5_K; ++i) { + const block_q8_1 * bq8i = bq8_1 + bq8_offset + i; + d8[i] = bq8i->ds[0]; + + const int * q8 = (const int *)bq8i->qs + ((iqs/2)%4); + u[2*i+0] = q8[0]; + u[2*i+1] = q8[4]; + } + + return vec_dot_q5_K_q8_1_impl_vmmq(vl, vh, u, sc, m, bq5_K->dm, d8); + +#else + +#if __SYCL_ARCH__ >= VER_4VEC // lowest compute capability for integer intrinsics + const block_q5_K * bq5_K = (const block_q5_K *) vbq; + + const int8_t * s = bq5_K->scales; + + const float d = bq5_K->d; + + const float d8_1 = __low2half(bq8_1[0].ds); + const float d8_2 = __low2half(bq8_1[1].ds); + + const int ui1 = *((const int *)bq8_1[0].qs + (iqs/2)); + const int ui2 = *((const int *)bq8_1[0].qs + (iqs/2) + 4); + const int ui3 = *((const int *)bq8_1[1].qs + (iqs/2)); + const int ui4 = *((const int *)bq8_1[1].qs + (iqs/2) + 4); + + const int * ql = (const int *)bq5_K->qs + (iqs/2); + const int vl1 = ql[0]; + const int vl2 = ql[4]; + + const int step = 4 * (iqs/2); // 0, 4, 8, 12 + const int im = step/8; // = 0 for iqs = 0, 2, = 1 for iqs = 4, 6 + const int in = step%8; // 0, 4, 0, 4 + const int vh = (*((const int *)(bq5_K->qh + in))) >> im; + + const int v1 = (((vh << 4) & 0x10101010) ^ 0x10101010) | ((vl1 >> 0) & 0x0f0f0f0f); + const int v2 = (((vh << 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 0) & 0x0f0f0f0f); + const int v3 = (((vh >> 0) & 0x10101010) ^ 0x10101010) | ((vl1 >> 4) & 0x0f0f0f0f); + const int v4 = (((vh >> 2) & 0x10101010) ^ 0x10101010) | ((vl2 >> 4) & 0x0f0f0f0f); + + const float sumf_d = d8_1 * (__dp4a(ui1, v1, 0) * s[0] + __dp4a(ui2, v2, 0) * s[1]) + + d8_2 * (__dp4a(ui3, v3, 0) * s[2] + __dp4a(ui4, v4, 0) * s[3]); + + return d * sumf_d; + +#else + bad_arch(); +#endif // __SYCL_ARCH__ >= VER_4VEC + +#endif +} + +template +static __dpct_inline__ void +allocate_tiles_q5_K(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc, + int *tile_x_ql_q5_K, sycl::half2 *tile_x_dm_q5_K, + int *tile_x_sc_q5_K) { + (void)x_qh; + + *x_ql = tile_x_ql_q5_K; + *x_dm = tile_x_dm_q5_K; + *x_sc = tile_x_sc_q5_K; +} + +template +static __dpct_inline__ void +load_tiles_q5_K(const void *__restrict__ vx, int *__restrict__ x_ql, + sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh, + int *__restrict__ x_sc, const int &i_offset, const int &i_max, + const int &k, const int &blocks_per_row) { + (void)x_qh; + + GGML_SYCL_ASSUME(i_offset >= 0); + GGML_SYCL_ASSUME(i_offset < nwarps); + GGML_SYCL_ASSUME(k >= 0); + GGML_SYCL_ASSUME(k < WARP_SIZE); + + const int kbx = k / QI5_K; // == 0 if QK_K == 256 + const int kqsx = k % QI5_K; // == k if QK_K == 256 + + const block_q5_K * bx0 = (const block_q5_K *) vx; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + i_offset; + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q5_K * bxi = bx0 + i*blocks_per_row + kbx; + const int ky = QR5_K*kqsx; + + const int ql = get_int_from_uint8_aligned(bxi->qs, kqsx); + const int ql0 = (ql >> 0) & 0x0F0F0F0F; + const int ql1 = (ql >> 4) & 0x0F0F0F0F; + + const int qh = get_int_from_uint8_aligned(bxi->qh, kqsx % (QI5_K/4)); + const int qh0 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 0)) << 4) & 0x10101010; + const int qh1 = ((qh >> (2 * (kqsx / (QI5_K/4)) + 1)) << 4) & 0x10101010; + + const int kq0 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + 0; + const int kq1 = ky - ky % (QI5_K/2) + k % (QI5_K/4) + (QI5_K/4); + + x_ql[i * (2*WARP_SIZE + 1) + kq0] = ql0 | qh0; + x_ql[i * (2*WARP_SIZE + 1) + kq1] = ql1 | qh1; + } + + const int blocks_per_tile_x_row = WARP_SIZE / QI5_K; // == 1 if QK_K == 256 + const int kbxd = k % blocks_per_tile_x_row; // == 0 if QK_K == 256 + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI5_K) { + int i = (i0 + i_offset * QI5_K + k / blocks_per_tile_x_row) % mmq_y; + + if (need_check) { + i = sycl::min(i, i_max); + } + + 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 + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) { + int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y; + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q5_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / (QI5_K/8); + + const int * scales = (const int *) bxi->scales; + + const int ksc = k % (WARP_SIZE/8); + + // scale arrangement after the following two lines: sc0,...,sc3, sc4,...,sc7, m0,...,m3, m4,...,m8 + int scales8 = (scales[(ksc%2) + (ksc!=0)] >> (4 * (ksc & (ksc/2)))) & 0x0F0F0F0F; // lower 4 bits + scales8 |= (scales[ksc/2] >> (2 * (ksc % 2))) & 0x30303030; // upper 2 bits + + x_sc[i * (WARP_SIZE/8) + i / 8 + ksc] = scales8; + } +} + +static __dpct_inline__ float vec_dot_q5_K_q8_1_mul_mat( + const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm, + const int *__restrict__ x_qh, const int *__restrict__ x_sc, + const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds, + const int &i, const int &j, const int &k) { + (void)x_qh; + + const uint8_t * sc = ((const uint8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/16]) + 2 * ((k % 16) / 8); + + const int index_x = i * (QR5_K*WARP_SIZE + 1) + QR5_K*k; + const int index_y = j * WARP_SIZE + (QR5_K*k) % WARP_SIZE; + return vec_dot_q5_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, sc+8, + x_dm[i * (WARP_SIZE/QI5_K) + i/QI5_K], &y_ds[index_y/QI8_1]); +} + +static __dpct_inline__ float +vec_dot_q6_K_q8_1(const void *__restrict__ vbq, + const block_q8_1 *__restrict__ bq8_1, const int &iqs) { + + const block_q6_K * bq6_K = (const block_q6_K *) vbq; + + const int bq8_offset = 2 * QR6_K * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/4); + const int scale_offset = (QI6_K/4) * (iqs / (QI6_K/2)) + (iqs % (QI6_K/2)) / (QI6_K/8); + const int vh_shift = 2 * ((iqs % (QI6_K/2)) / (QI6_K/4)); + + const int vl = get_int_from_uint8(bq6_K->ql, iqs); + const int vh = get_int_from_uint8(bq6_K->qh, (QI6_K/4) * (iqs / (QI6_K/2)) + iqs % (QI6_K/4)) >> vh_shift; + + const int8_t * scales = bq6_K->scales + scale_offset; + + int u[QR6_K]; + float d8[QR6_K]; + +#pragma unroll + for (int i = 0; i < QR6_K; ++i) { + u[i] = get_int_from_int8_aligned(bq8_1[bq8_offset + 2*i].qs, iqs % QI8_1); + d8[i] = bq8_1[bq8_offset + 2 * i].ds[0]; + } + + return vec_dot_q6_K_q8_1_impl_mmvq(vl, vh, u, scales, bq6_K->d, d8); +} + +template +static __dpct_inline__ void +allocate_tiles_q6_K(int **x_ql, sycl::half2 **x_dm, int **x_qh, int **x_sc, + int *tile_x_ql, sycl::half2 *tile_x_dm, int *tile_x_sc) { + (void)x_qh; + + *x_ql = tile_x_ql; + *x_dm = tile_x_dm; + *x_sc = tile_x_sc; +} + +template +static __dpct_inline__ void +load_tiles_q6_K(const void *__restrict__ vx, int *__restrict__ x_ql, + sycl::half2 *__restrict__ x_dm, int *__restrict__ x_qh, + int *__restrict__ x_sc, const int &i_offset, const int &i_max, + const int &k, const int &blocks_per_row) { + (void)x_qh; + + GGML_SYCL_ASSUME(i_offset >= 0); + GGML_SYCL_ASSUME(i_offset < nwarps); + GGML_SYCL_ASSUME(k >= 0); + GGML_SYCL_ASSUME(k < WARP_SIZE); + + const int kbx = k / QI6_K; // == 0 if QK_K == 256 + const int kqsx = k % QI6_K; // == k if QK_K == 256 + + const block_q6_K * bx0 = (const block_q6_K *) vx; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps) { + int i = i0 + i_offset; + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q6_K * bxi = bx0 + i*blocks_per_row + kbx; + const int ky = QR6_K*kqsx; + + const int ql = get_int_from_uint8(bxi->ql, kqsx); + const int ql0 = (ql >> 0) & 0x0F0F0F0F; + const int ql1 = (ql >> 4) & 0x0F0F0F0F; + + const int qh = get_int_from_uint8(bxi->qh, (QI6_K/4) * (kqsx / (QI6_K/2)) + kqsx % (QI6_K/4)); + const int qh0 = ((qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) << 4) & 0x30303030; + const int qh1 = (qh >> (2 * ((kqsx % (QI6_K/2)) / (QI6_K/4)))) & 0x30303030; + + const int kq0 = ky - ky % QI6_K + k % (QI6_K/2) + 0; + const int kq1 = ky - ky % QI6_K + k % (QI6_K/2) + (QI6_K/2); + + x_ql[i * (2 * WARP_SIZE + 1) + kq0] = + dpct::vectorized_binary(ql0 | qh0, 0x20202020, + dpct::sub_sat()); + x_ql[i * (2 * WARP_SIZE + 1) + kq1] = + dpct::vectorized_binary(ql1 | qh1, 0x20202020, + dpct::sub_sat()); + } + + const int blocks_per_tile_x_row = WARP_SIZE / QI6_K; // == 1 if QK_K == 256 + const int kbxd = k % blocks_per_tile_x_row; // == 0 if QK_K == 256 + float * x_dmf = (float *) x_dm; + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * QI6_K) { + int i = (i0 + i_offset * QI6_K + k / blocks_per_tile_x_row) % mmq_y; + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q6_K * bxi = bx0 + i*blocks_per_row + kbxd; + + x_dmf[i * (WARP_SIZE/QI6_K) + i / QI6_K + kbxd] = bxi->d; + } + +#pragma unroll + for (int i0 = 0; i0 < mmq_y; i0 += nwarps * 8) { + int i = (i0 + i_offset * 8 + k / (WARP_SIZE/8)) % mmq_y; + + if (need_check) { + i = sycl::min(i, i_max); + } + + const block_q6_K * bxi = bx0 + i*blocks_per_row + (k % (WARP_SIZE/8)) / 4; + + x_sc[i * (WARP_SIZE/8) + i / 8 + k % (WARP_SIZE/8)] = get_int_from_int8(bxi->scales, k % (QI6_K/8)); + } +} + +static __dpct_inline__ float vec_dot_q6_K_q8_1_mul_mat( + const int *__restrict__ x_ql, const sycl::half2 *__restrict__ x_dm, + const int *__restrict__ x_qh, const int *__restrict__ x_sc, + const int *__restrict__ y_qs, const sycl::half2 *__restrict__ y_ds, + const int &i, const int &j, const int &k) { + (void)x_qh; + + const float * x_dmf = (const float *) x_dm; + const float * y_df = (const float *) y_ds; + + const int8_t * sc = ((const int8_t *) &x_sc[i * (WARP_SIZE/8) + i/8 + k/8]); + + const int index_x = i * (QR6_K*WARP_SIZE + 1) + QR6_K*k; + const int index_y = j * WARP_SIZE + (QR6_K*k) % WARP_SIZE; + return vec_dot_q6_K_q8_1_impl_mmq(&x_ql[index_x], &y_qs[index_y], sc, x_dmf[i * (WARP_SIZE/QI6_K) + i/QI6_K], &y_df[index_y/QI8_1]); +} + +template +/* +DPCT1110:8: The total declared local variable size in device function mul_mat_q +exceeds 128 bytes and may cause high register pressure. Consult with your +hardware vendor to find the total register size available and adjust the code, +or use smaller sub-group size to avoid high register pressure. +*/ +static __dpct_inline__ void +mul_mat_q(const void *__restrict__ vx, const void *__restrict__ vy, + float *__restrict__ dst, const int ncols_x, const int nrows_x, + const int ncols_y, const int nrows_y, const int nrows_dst, + int *tile_x_ql, sycl::half2 *tile_x_dm, int *tile_x_qh, + int *tile_x_sc, const sycl::nd_item<3> &item_ct1, int *tile_y_qs, + sycl::half2 *tile_y_ds) { + + const block_q_t * x = (const block_q_t *) vx; + const block_q8_1 * y = (const block_q8_1 *) vy; + + const int blocks_per_row_x = ncols_x / qk; + const int blocks_per_col_y = nrows_y / QK8_1; + const int blocks_per_warp = WARP_SIZE / qi; + + const int & ncols_dst = ncols_y; + + const int row_dst_0 = item_ct1.get_group(2) * mmq_y; + const int & row_x_0 = row_dst_0; + + const int col_dst_0 = item_ct1.get_group(1) * mmq_x; + const int & col_y_0 = col_dst_0; + + float sum[mmq_y/WARP_SIZE][mmq_x/nwarps] = {{0.0f}}; + + for (int ib0 = 0; ib0 < blocks_per_row_x; ib0 += blocks_per_warp) { + + load_tiles(x + row_x_0 * blocks_per_row_x + ib0, tile_x_ql, tile_x_dm, + tile_x_qh, tile_x_sc, item_ct1.get_local_id(1), + nrows_x - row_x_0 - 1, item_ct1.get_local_id(2), + blocks_per_row_x); + +#pragma unroll + for (int ir = 0; ir < qr; ++ir) { + const int kqs = ir * WARP_SIZE + item_ct1.get_local_id(2); + const int kbxd = kqs / QI8_1; + +#pragma unroll + for (int i = 0; i < mmq_x; i += nwarps) { + const int col_y_eff = dpct::min( + (unsigned int)(col_y_0 + item_ct1.get_local_id(1) + i), + ncols_y - 1); // to prevent out-of-bounds memory accesses + + const block_q8_1 * by0 = &y[col_y_eff*blocks_per_col_y + ib0 * (qk/QK8_1) + kbxd]; + + const int index_y = (item_ct1.get_local_id(1) + i) * WARP_SIZE + + kqs % WARP_SIZE; + tile_y_qs[index_y] = get_int_from_int8_aligned( + by0->qs, item_ct1.get_local_id(2) % QI8_1); + } + +#pragma unroll + for (int ids0 = 0; ids0 < mmq_x; ids0 += nwarps * QI8_1) { + const int ids = + (ids0 + item_ct1.get_local_id(1) * QI8_1 + + item_ct1.get_local_id(2) / (WARP_SIZE / QI8_1)) % + mmq_x; + const int kby = item_ct1.get_local_id(2) % (WARP_SIZE / QI8_1); + const int col_y_eff = sycl::min(col_y_0 + ids, ncols_y - 1); + + // if the sum is not needed it's faster to transform the scale to f32 ahead of time + const sycl::half2 *dsi_src = + &y[col_y_eff * blocks_per_col_y + ib0 * (qk / QK8_1) + + ir * (WARP_SIZE / QI8_1) + kby] + .ds; + sycl::half2 *dsi_dst = + &tile_y_ds[ids * (WARP_SIZE / QI8_1) + kby]; + if (need_sum) { + *dsi_dst = *dsi_src; + } else { + float * dfi_dst = (float *) dsi_dst; + *dfi_dst = (*dsi_src)[0]; + } + } + + /* + DPCT1118:9: SYCL group functions and algorithms must be encountered + in converged control flow. You may need to adjust the code. + */ + /* + DPCT1065:56: Consider replacing sycl::nd_item::barrier() with + sycl::nd_item::barrier(sycl::access::fence_space::local_space) for + better performance if there is no access to global memory. + */ + item_ct1.barrier(); + +// #pragma unroll // unrolling this loop causes too much register pressure + for (int k = ir*WARP_SIZE/qr; k < (ir+1)*WARP_SIZE/qr; k += vdr) { +#pragma unroll + for (int j = 0; j < mmq_x; j += nwarps) { +#pragma unroll + for (int i = 0; i < mmq_y; i += WARP_SIZE) { + sum[i / WARP_SIZE][j / nwarps] += vec_dot( + tile_x_ql, tile_x_dm, tile_x_qh, tile_x_sc, + tile_y_qs, tile_y_ds, item_ct1.get_local_id(2) + i, + item_ct1.get_local_id(1) + j, k); + } + } + } + + /* + DPCT1118:10: SYCL group functions and algorithms must be encountered + in converged control flow. You may need to adjust the code. + */ + /* + DPCT1065:57: Consider replacing sycl::nd_item::barrier() with + sycl::nd_item::barrier(sycl::access::fence_space::local_space) for + better performance if there is no access to global memory. + */ + item_ct1.barrier(); + } + } + +#pragma unroll + for (int j = 0; j < mmq_x; j += nwarps) { + const int col_dst = col_dst_0 + j + item_ct1.get_local_id(1); + + if (col_dst >= ncols_dst) { + return; + } + +#pragma unroll + for (int i = 0; i < mmq_y; i += WARP_SIZE) { + const int row_dst = row_dst_0 + item_ct1.get_local_id(2) + i; + + if (row_dst >= nrows_dst) { + continue; + } + + dst[col_dst*nrows_dst + row_dst] = sum[i/WARP_SIZE][j/nwarps]; + } + } +} + +#define MMQ_X_Q4_0_RDNA2 64 +#define MMQ_Y_Q4_0_RDNA2 128 +#define NWARPS_Q4_0_RDNA2 8 +#define MMQ_X_Q4_0_RDNA1 64 +#define MMQ_Y_Q4_0_RDNA1 64 +#define NWARPS_Q4_0_RDNA1 8 +#if defined(SYCL_USE_XMX) +#define MMQ_X_Q4_0_AMPERE 4 +#define MMQ_Y_Q4_0_AMPERE 32 +#define NWARPS_Q4_0_AMPERE 4 +#else +#define MMQ_X_Q4_0_AMPERE 64 +#define MMQ_Y_Q4_0_AMPERE 128 +#define NWARPS_Q4_0_AMPERE 4 +#endif +#define MMQ_X_Q4_0_PASCAL 64 +#define MMQ_Y_Q4_0_PASCAL 64 +#define NWARPS_Q4_0_PASCAL 8 + +template static void + mul_mat_q4_0( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, + const sycl::nd_item<3> &item_ct1, int *tile_x_qs_q4_0, float *tile_x_d_q4_0, + int *tile_y_qs, sycl::half2 *tile_y_ds) { + int * tile_x_ql = nullptr; + sycl::half2 *tile_x_dm = nullptr; + int * tile_x_qh = nullptr; + int * tile_x_sc = nullptr; + +//sycl_todo: change according to hardware + + const int mmq_x = MMQ_X_Q4_0_AMPERE; + const int mmq_y = MMQ_Y_Q4_0_AMPERE; + const int nwarps = NWARPS_Q4_0_AMPERE; + allocate_tiles_q4_0(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc, + tile_x_qs_q4_0, tile_x_d_q4_0); + mul_mat_q, VDR_Q4_0_Q8_1_MMQ, + vec_dot_q4_0_q8_1_mul_mat>( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql, + tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds); +} + +#define MMQ_X_Q4_1_RDNA2 64 +#define MMQ_Y_Q4_1_RDNA2 128 +#define NWARPS_Q4_1_RDNA2 8 +#define MMQ_X_Q4_1_RDNA1 64 +#define MMQ_Y_Q4_1_RDNA1 64 +#define NWARPS_Q4_1_RDNA1 8 +#if defined(SYCL_USE_XMX) +#define MMQ_X_Q4_1_AMPERE 4 +#define MMQ_Y_Q4_1_AMPERE 32 +#define NWARPS_Q4_1_AMPERE 4 +#else +#define MMQ_X_Q4_1_AMPERE 64 +#define MMQ_Y_Q4_1_AMPERE 128 +#define NWARPS_Q4_1_AMPERE 4 +#endif +#define MMQ_X_Q4_1_PASCAL 64 +#define MMQ_Y_Q4_1_PASCAL 64 +#define NWARPS_Q4_1_PASCAL 8 + +template static void + mul_mat_q4_1( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, + const sycl::nd_item<3> &item_ct1, int *tile_x_qs_q4_1, + sycl::half2 *tile_x_dm_q4_1, int *tile_y_qs, sycl::half2 *tile_y_ds) { + int * tile_x_ql = nullptr; + sycl::half2 *tile_x_dm = nullptr; + int * tile_x_qh = nullptr; + int * tile_x_sc = nullptr; + +//sycl_todo: change according to hardware + const int mmq_x = MMQ_X_Q4_1_AMPERE; + const int mmq_y = MMQ_Y_Q4_1_AMPERE; + const int nwarps = NWARPS_Q4_1_AMPERE; + allocate_tiles_q4_1(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc, + tile_x_qs_q4_1, tile_x_dm_q4_1); + mul_mat_q, VDR_Q4_1_Q8_1_MMQ, + vec_dot_q4_1_q8_1_mul_mat>( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql, + tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds); +} + +#define MMQ_X_Q5_0_RDNA2 64 +#define MMQ_Y_Q5_0_RDNA2 128 +#define NWARPS_Q5_0_RDNA2 8 +#define MMQ_X_Q5_0_RDNA1 64 +#define MMQ_Y_Q5_0_RDNA1 64 +#define NWARPS_Q5_0_RDNA1 8 +#if defined(SYCL_USE_XMX) +#define MMQ_X_Q5_0_AMPERE 4 +#define MMQ_Y_Q5_0_AMPERE 32 +#define NWARPS_Q5_0_AMPERE 4 +#else +#define MMQ_X_Q5_0_AMPERE 128 +#define MMQ_Y_Q5_0_AMPERE 64 +#define NWARPS_Q5_0_AMPERE 4 +#endif +#define MMQ_X_Q5_0_PASCAL 64 +#define MMQ_Y_Q5_0_PASCAL 64 +#define NWARPS_Q5_0_PASCAL 8 + +template static void + mul_mat_q5_0( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, + const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q5_0, float *tile_x_d_q5_0, + int *tile_y_qs, sycl::half2 *tile_y_ds) { + int * tile_x_ql = nullptr; + sycl::half2 *tile_x_dm = nullptr; + int * tile_x_qh = nullptr; + int * tile_x_sc = nullptr; + +//sycl_todo: change according to hardware + const int mmq_x = MMQ_X_Q5_0_AMPERE; + const int mmq_y = MMQ_Y_Q5_0_AMPERE; + const int nwarps = NWARPS_Q5_0_AMPERE; + allocate_tiles_q5_0(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc, + tile_x_ql_q5_0, tile_x_d_q5_0); + mul_mat_q, VDR_Q5_0_Q8_1_MMQ, + vec_dot_q5_0_q8_1_mul_mat>( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql, + tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds); +} + +#define MMQ_X_Q5_1_RDNA2 64 +#define MMQ_Y_Q5_1_RDNA2 128 +#define NWARPS_Q5_1_RDNA2 8 +#define MMQ_X_Q5_1_RDNA1 64 +#define MMQ_Y_Q5_1_RDNA1 64 +#define NWARPS_Q5_1_RDNA1 8 +#if defined(SYCL_USE_XMX) +#define MMQ_X_Q5_1_AMPERE 4 +#define MMQ_Y_Q5_1_AMPERE 32 +#define NWARPS_Q5_1_AMPERE 4 +#else +#define MMQ_X_Q5_1_AMPERE 128 +#define MMQ_Y_Q5_1_AMPERE 64 +#define NWARPS_Q5_1_AMPERE 4 +#endif +#define MMQ_X_Q5_1_PASCAL 64 +#define MMQ_Y_Q5_1_PASCAL 64 +#define NWARPS_Q5_1_PASCAL 8 + +template static void +mul_mat_q5_1( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, + const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q5_1, + sycl::half2 *tile_x_dm_q5_1, int *tile_y_qs, sycl::half2 *tile_y_ds) { + int * tile_x_ql = nullptr; + sycl::half2 *tile_x_dm = nullptr; + int * tile_x_qh = nullptr; + int * tile_x_sc = nullptr; + +//sycl_todo: change according to hardware + const int mmq_x = MMQ_X_Q5_1_AMPERE; + const int mmq_y = MMQ_Y_Q5_1_AMPERE; + const int nwarps = NWARPS_Q5_1_AMPERE; + allocate_tiles_q5_1(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc, + tile_x_ql_q5_1, tile_x_dm_q5_1); + mul_mat_q, VDR_Q5_1_Q8_1_MMQ, + vec_dot_q5_1_q8_1_mul_mat>( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql, + tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds); +} + +#define MMQ_X_Q8_0_RDNA2 64 +#define MMQ_Y_Q8_0_RDNA2 128 +#define NWARPS_Q8_0_RDNA2 8 +#define MMQ_X_Q8_0_RDNA1 64 +#define MMQ_Y_Q8_0_RDNA1 64 +#define NWARPS_Q8_0_RDNA1 8 +#if defined(SYCL_USE_XMX) +#define MMQ_X_Q8_0_AMPERE 4 +#define MMQ_Y_Q8_0_AMPERE 32 +#define NWARPS_Q8_0_AMPERE 4 +#else +#define MMQ_X_Q8_0_AMPERE 128 +#define MMQ_Y_Q8_0_AMPERE 64 +#define NWARPS_Q8_0_AMPERE 4 +#endif +#define MMQ_X_Q8_0_PASCAL 64 +#define MMQ_Y_Q8_0_PASCAL 64 +#define NWARPS_Q8_0_PASCAL 8 + +template static void + mul_mat_q8_0( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, + const sycl::nd_item<3> &item_ct1, int *tile_x_qs_q8_0, float *tile_x_d_q8_0, + int *tile_y_qs, sycl::half2 *tile_y_ds) { + int * tile_x_ql = nullptr; + sycl::half2 *tile_x_dm = nullptr; + int * tile_x_qh = nullptr; + int * tile_x_sc = nullptr; + +//sycl_todo: change according to hardware + const int mmq_x = MMQ_X_Q8_0_AMPERE; + const int mmq_y = MMQ_Y_Q8_0_AMPERE; + const int nwarps = NWARPS_Q8_0_AMPERE; + allocate_tiles_q8_0(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc, + tile_x_qs_q8_0, tile_x_d_q8_0); + mul_mat_q, VDR_Q8_0_Q8_1_MMQ, + vec_dot_q8_0_q8_1_mul_mat>( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql, + tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds); +} + +#define MMQ_X_Q2_K_RDNA2 64 +#define MMQ_Y_Q2_K_RDNA2 128 +#define NWARPS_Q2_K_RDNA2 8 +#define MMQ_X_Q2_K_RDNA1 128 +#define MMQ_Y_Q2_K_RDNA1 32 +#define NWARPS_Q2_K_RDNA1 8 +#if defined(SYCL_USE_XMX) +#define MMQ_X_Q2_K_AMPERE 4 +#define MMQ_Y_Q2_K_AMPERE 32 +#define NWARPS_Q2_K_AMPERE 4 +#else +#define MMQ_X_Q2_K_AMPERE 64 +#define MMQ_Y_Q2_K_AMPERE 128 +#define NWARPS_Q2_K_AMPERE 4 +#endif +#define MMQ_X_Q2_K_PASCAL 64 +#define MMQ_Y_Q2_K_PASCAL 64 +#define NWARPS_Q2_K_PASCAL 8 + +template static void +mul_mat_q2_K( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, + const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q2_K, + sycl::half2 *tile_x_dm_q2_K, int *tile_x_sc_q2_K, int *tile_y_qs, + sycl::half2 *tile_y_ds) { + int * tile_x_ql = nullptr; + sycl::half2 *tile_x_dm = nullptr; + int * tile_x_qh = nullptr; + int * tile_x_sc = nullptr; + +//sycl_todo: change according to hardware + const int mmq_x = MMQ_X_Q2_K_AMPERE; + const int mmq_y = MMQ_Y_Q2_K_AMPERE; + const int nwarps = NWARPS_Q2_K_AMPERE; + allocate_tiles_q2_K(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc, + tile_x_ql_q2_K, tile_x_dm_q2_K, tile_x_sc_q2_K); + mul_mat_q, VDR_Q2_K_Q8_1_MMQ, + vec_dot_q2_K_q8_1_mul_mat>( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql, + tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds); +} + +#define MMQ_X_Q3_K_RDNA2 128 +#define MMQ_Y_Q3_K_RDNA2 64 +#define NWARPS_Q3_K_RDNA2 8 +#define MMQ_X_Q3_K_RDNA1 32 +#define MMQ_Y_Q3_K_RDNA1 128 +#define NWARPS_Q3_K_RDNA1 8 +#if defined(SYCL_USE_XMX) +#define MMQ_X_Q3_K_AMPERE 4 +#define MMQ_Y_Q3_K_AMPERE 32 +#define NWARPS_Q3_K_AMPERE 4 +#else +#define MMQ_X_Q3_K_AMPERE 128 +#define MMQ_Y_Q3_K_AMPERE 128 +#define NWARPS_Q3_K_AMPERE 4 +#endif +#define MMQ_X_Q3_K_PASCAL 64 +#define MMQ_Y_Q3_K_PASCAL 64 +#define NWARPS_Q3_K_PASCAL 8 + +template static void +mul_mat_q3_K( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, + const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q3_K, + sycl::half2 *tile_x_dm_q3_K, int *tile_x_qh_q3_K, int *tile_x_sc_q3_K, + int *tile_y_qs, sycl::half2 *tile_y_ds) { + int * tile_x_ql = nullptr; + sycl::half2 *tile_x_dm = nullptr; + int * tile_x_qh = nullptr; + int * tile_x_sc = nullptr; + +//sycl_todo: change according to hardware + const int mmq_x = MMQ_X_Q3_K_AMPERE; + const int mmq_y = MMQ_Y_Q3_K_AMPERE; + const int nwarps = NWARPS_Q3_K_AMPERE; + allocate_tiles_q3_K(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc, + tile_x_ql_q3_K, tile_x_dm_q3_K, tile_x_qh_q3_K, + tile_x_sc_q3_K); + mul_mat_q, VDR_Q3_K_Q8_1_MMQ, + vec_dot_q3_K_q8_1_mul_mat>( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql, + tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds); +} + +#define MMQ_X_Q4_K_RDNA2 64 +#define MMQ_Y_Q4_K_RDNA2 128 +#define NWARPS_Q4_K_RDNA2 8 +#define MMQ_X_Q4_K_RDNA1 32 +#define MMQ_Y_Q4_K_RDNA1 64 +#define NWARPS_Q4_K_RDNA1 8 +#if defined(SYCL_USE_XMX) +#define MMQ_X_Q4_K_AMPERE 4 +#define MMQ_Y_Q4_K_AMPERE 32 +#define NWARPS_Q4_K_AMPERE 4 +#else +#define MMQ_X_Q4_K_AMPERE 64 +#define MMQ_Y_Q4_K_AMPERE 128 +#define NWARPS_Q4_K_AMPERE 4 +#endif +#define MMQ_X_Q4_K_PASCAL 64 +#define MMQ_Y_Q4_K_PASCAL 64 +#define NWARPS_Q4_K_PASCAL 8 + +template static void + mul_mat_q4_K( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, + const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q4_K, + sycl::half2 *tile_x_dm_q4_K, int *tile_x_sc_q4_K, int *tile_y_qs, + sycl::half2 *tile_y_ds) { + int * tile_x_ql = nullptr; + sycl::half2 *tile_x_dm = nullptr; + int * tile_x_qh = nullptr; + int * tile_x_sc = nullptr; + +//sycl_todo: change according to hardware + const int mmq_x = MMQ_X_Q4_K_AMPERE; + const int mmq_y = MMQ_Y_Q4_K_AMPERE; + const int nwarps = NWARPS_Q4_K_AMPERE; + allocate_tiles_q4_K(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc, + tile_x_ql_q4_K, tile_x_dm_q4_K, tile_x_sc_q4_K); + mul_mat_q, VDR_Q4_K_Q8_1_MMQ, + vec_dot_q4_K_q8_1_mul_mat>( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql, + tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds); +} + +#define MMQ_X_Q5_K_RDNA2 64 +#define MMQ_Y_Q5_K_RDNA2 128 +#define NWARPS_Q5_K_RDNA2 8 +#define MMQ_X_Q5_K_RDNA1 32 +#define MMQ_Y_Q5_K_RDNA1 64 +#define NWARPS_Q5_K_RDNA1 8 +#if defined(SYCL_USE_XMX) +#define MMQ_X_Q5_K_AMPERE 4 +#define MMQ_Y_Q5_K_AMPERE 32 +#define NWARPS_Q5_K_AMPERE 4 +#else +#define MMQ_X_Q5_K_AMPERE 64 +#define MMQ_Y_Q5_K_AMPERE 128 +#define NWARPS_Q5_K_AMPERE 4 +#endif +#define MMQ_X_Q5_K_PASCAL 64 +#define MMQ_Y_Q5_K_PASCAL 64 +#define NWARPS_Q5_K_PASCAL 8 + +template static void +mul_mat_q5_K( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, + const sycl::nd_item<3> &item_ct1, int *tile_x_ql_q5_K, + sycl::half2 *tile_x_dm_q5_K, int *tile_x_sc_q5_K, int *tile_y_qs, + sycl::half2 *tile_y_ds) { + int * tile_x_ql = nullptr; + sycl::half2 *tile_x_dm = nullptr; + int * tile_x_qh = nullptr; + int * tile_x_sc = nullptr; + +//sycl_todo: change according to hardware + const int mmq_x = MMQ_X_Q5_K_AMPERE; + const int mmq_y = MMQ_Y_Q5_K_AMPERE; + const int nwarps = NWARPS_Q5_K_AMPERE; + allocate_tiles_q5_K(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc, + tile_x_ql_q5_K, tile_x_dm_q5_K, tile_x_sc_q5_K); + mul_mat_q, VDR_Q5_K_Q8_1_MMQ, + vec_dot_q5_K_q8_1_mul_mat>( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql, + tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds); +} + +#define MMQ_X_Q6_K_RDNA2 64 +#define MMQ_Y_Q6_K_RDNA2 128 +#define NWARPS_Q6_K_RDNA2 8 +#define MMQ_X_Q6_K_RDNA1 32 +#define MMQ_Y_Q6_K_RDNA1 64 +#define NWARPS_Q6_K_RDNA1 8 +#if defined(SYCL_USE_XMX) +#define MMQ_X_Q6_K_AMPERE 4 +#define MMQ_Y_Q6_K_AMPERE 32 +#define NWARPS_Q6_K_AMPERE 4 +#else +#define MMQ_X_Q6_K_AMPERE 64 +#define MMQ_Y_Q6_K_AMPERE 64 +#define NWARPS_Q6_K_AMPERE 4 +#endif +#define MMQ_X_Q6_K_PASCAL 64 +#define MMQ_Y_Q6_K_PASCAL 64 +#define NWARPS_Q6_K_PASCAL 8 + +template static void + mul_mat_q6_K( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int ncols_y, const int nrows_y, const int nrows_dst, + const sycl::nd_item<3> &item_ct1, int *tile_x_ql, sycl::half2 *tile_x_dm, + int *tile_x_sc, int *tile_y_qs, sycl::half2 *tile_y_ds) { + // int * tile_x_ql = nullptr; + // sycl::half2 *tile_x_dm = nullptr; + int * tile_x_qh = nullptr; + // int * tile_x_sc = nullptr; + +//sycl_todo: change according to hardware + const int mmq_x = MMQ_X_Q6_K_AMPERE; + const int mmq_y = MMQ_Y_Q6_K_AMPERE; + const int nwarps = NWARPS_Q6_K_AMPERE; + allocate_tiles_q6_K(&tile_x_ql, &tile_x_dm, &tile_x_qh, &tile_x_sc, + tile_x_ql, tile_x_dm, tile_x_sc); + mul_mat_q, VDR_Q6_K_Q8_1_MMQ, + vec_dot_q6_K_q8_1_mul_mat>( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst, tile_x_ql, + tile_x_dm, tile_x_qh, tile_x_sc, item_ct1, tile_y_qs, tile_y_ds); +} + +template +static void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows, + const sycl::nd_item<3> &item_ct1) { + const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + + item_ct1.get_local_id(1); + + if (row >= nrows) { + return; + } + + const int blocks_per_row = ncols / qk; + const int blocks_per_warp = vdr * 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 + + item_ct1.get_local_id(2) / (qi / vdr); // x block index + + const int iby = (i + item_ct1.get_local_id(2) / (qi / vdr)) * + (qk / QK8_1); // y block index that aligns with ibx + + const int iqs = + vdr * + (item_ct1.get_local_id(2) % + (qi / vdr)); // x block quant index when casting the quants to int + + tmp += vec_dot_q_sycl(&x[ibx], &y[iby], iqs); + } + + // sum up partial sums and write back result +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + tmp += + dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); + } + + if (item_ct1.get_local_id(2) == 0) { + dst[row] = tmp; + } +} + +template +static void dequantize_mul_mat_vec(const void * __restrict__ vx, const dfloat * __restrict__ y, float * __restrict__ dst, const int ncols, const int nrows, + const sycl::nd_item<3> &item_ct1) { + // qk = quantized weights per x block + // qr = number of quantized weights per data value in x block + const int row = item_ct1.get_group(2) * item_ct1.get_local_range(1) + + item_ct1.get_local_id(1); + + if (row >= nrows) { + return; + } + + const int tid = item_ct1.get_local_id(2); + + const int iter_stride = 2*GGML_SYCL_DMMV_X; + 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; + +// partial sum for each thread +#ifdef GGML_SYCL_F16 + sycl::half2 tmp = {0.0f, 0.0f}; // two sums for f16 to take advantage of half2 intrinsics +#else + float tmp = 0.0f; +#endif // GGML_SYCL_F16 + + for (int i = 0; i < ncols; i += iter_stride) { + const int col = i + vals_per_iter*tid; + const int ib = (row*ncols + col)/qk; // x block index + const int iqs = (col%qk)/qr; // x quant index + const int iybs = col - col%qk; // y block start index + +// processing >2 values per i iter is faster for fast GPUs +#pragma unroll + for (int j = 0; j < vals_per_iter; j += 2) { + // process 2 vals per j iter + + // dequantize + // 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 + // for qr = 2 the y index needs to increase by 1 per j iter because of y_offset = qk/2 +#ifdef GGML_SYCL_F16 + dfloat2 t1{y[iybs + iqs + j / qr + 0], + y[iybs + iqs + j / qr + y_offset]}; + + tmp += v * t1; +#else + tmp += v.x() * y[iybs + iqs + j / qr + 0]; + tmp += v.y() * y[iybs + iqs + j / qr + y_offset]; +#endif // GGML_SYCL_F16 + } + } + + // sum up partial sums and write back result +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + tmp += + dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); + } + + if (tid == 0) { +#ifdef GGML_SYCL_F16 + dst[row] = tmp.x() + tmp.y(); +#else + dst[row] = tmp; +#endif // GGML_SYCL_F16 + } +} + +static void mul_mat_p021_f16_f32( + const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int nchannels_x, const int nchannels_y, + const sycl::nd_item<3> &item_ct1) { + + const sycl::half *x = (const sycl::half *)vx; + + const int row_x = item_ct1.get_local_range(1) * item_ct1.get_group(1) + + item_ct1.get_local_id(1); + const int channel = item_ct1.get_local_range(0) * item_ct1.get_group(0) + + item_ct1.get_local_id(0); + const int channel_x = channel / (nchannels_y / nchannels_x); + + const int nrows_y = ncols_x; + const int nrows_dst = nrows_x; + const int row_dst = row_x; + + float tmp = 0.0f; + + for (int col_x0 = 0; col_x0 < ncols_x; + col_x0 += item_ct1.get_local_range(2)) { + const int col_x = col_x0 + item_ct1.get_local_id(2); + + if (col_x >= ncols_x) { + break; + } + + // x is transposed and permuted + const int ix = row_x*nchannels_x*ncols_x + channel_x*ncols_x + col_x; + const float xi = + sycl::vec(x[ix]) + .convert()[0]; + + const int row_y = col_x; + + + // y is not transposed but permuted + const int iy = channel*nrows_y + row_y; + + tmp += xi * y[iy]; + } + + // dst is not transposed and not permuted + const int idst = channel*nrows_dst + row_dst; + + // sum up partial sums and write back result +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + tmp += + dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); + } + + if (item_ct1.get_local_id(2) == 0) { + dst[idst] = tmp; + } +} + +static void mul_mat_vec_nc_f16_f32( // nc == non-contiguous + const void * __restrict__ vx, const float * __restrict__ y, float * __restrict__ dst, const int ncols_x, const int nrows_x, + const int row_stride_x, const int channel_stride_x, const int channel_x_divisor, + const sycl::nd_item<3> &item_ct1) { + + const sycl::half *x = (const sycl::half *)vx; + + const int row_x = item_ct1.get_local_range(1) * item_ct1.get_group(1) + + item_ct1.get_local_id(1); + const int channel = item_ct1.get_local_range(0) * item_ct1.get_group(0) + + item_ct1.get_local_id(0); + const int channel_x = channel / channel_x_divisor; + + const int nrows_y = ncols_x; + const int nrows_dst = nrows_x; + const int row_dst = row_x; + + const int idst = channel*nrows_dst + row_dst; + + float tmp = 0.0f; + + for (int col_x0 = 0; col_x0 < ncols_x; + col_x0 += item_ct1.get_local_range(2)) { + const int col_x = col_x0 + item_ct1.get_local_id(2); + + if (col_x >= ncols_x) { + break; + } + + const int row_y = col_x; + + const int ix = channel_x*channel_stride_x + row_x*row_stride_x + col_x; + const int iy = channel*nrows_y + row_y; + + const float xi = + sycl::vec(x[ix]) + .convert()[0]; + + tmp += xi * y[iy]; + } + + // sum up partial sums and write back result +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + tmp += + dpct::permute_sub_group_by_xor(item_ct1.get_sub_group(), tmp, mask); + } + + if (item_ct1.get_local_id(2) == 0) { + dst[idst] = tmp; + } +} + +static void cpy_1_f32_f32(const char * cxi, char * cdsti) { + const float * xi = (const float *) cxi; + float * dsti = (float *) cdsti; + + *dsti = *xi; +} + +static void cpy_1_f32_f16(const char * cxi, char * cdsti) { + const float * xi = (const float *) cxi; + sycl::half *dsti = (sycl::half *)cdsti; + + *dsti = sycl::vec(*xi) + .convert()[0]; +} + +static void cpy_1_f16_f16(const char * cxi, char * cdsti) { + const sycl::half *xi = (const sycl::half *)cxi; + sycl::half *dsti = (sycl::half *)cdsti; + + *dsti = *xi; +} + +static void cpy_1_i16_i16(const char * cxi, char * cdsti) { + const int16_t *xi = (const int16_t *)cxi; + int16_t *dsti = (int16_t *)cdsti; + + *dsti = *xi; +} + +static void cpy_1_i32_i32(const char * cxi, char * cdsti) { + const int32_t *xi = (const int32_t *)cxi; + int32_t *dsti = (int32_t *)cdsti; + + *dsti = *xi; +} + +template +static void cpy_f32_f16(const char * cx, char * cdst, const int ne, + const int ne00, const int ne01, const int nb00, const int nb01, const int nb02, + const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= ne) { + return; + } + + // determine indices i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor + // then combine those indices with the corresponding byte offsets to get the total offsets + const int i02 = i / (ne00*ne01); + const int i01 = (i - i02*ne01*ne00) / ne00; + const int i00 = i - i02*ne01*ne00 - i01*ne00; + const int x_offset = i00*nb00 + i01*nb01 + i02*nb02; + + const int i12 = i / (ne10*ne11); + const int i11 = (i - i12*ne10*ne11) / ne10; + const int i10 = i - i12*ne10*ne11 - i11*ne10; + const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12; + + cpy_1(cx + x_offset, cdst + dst_offset); +} + +static void cpy_blck_f32_q8_0(const char * cxi, char * cdsti) { + const float * xi = (const float *) cxi; + block_q8_0 * dsti = (block_q8_0 *) cdsti; + + float amax = 0.0f; // absolute max + + for (int j = 0; j < QK8_0; j++) { + const float v = xi[j]; + amax = sycl::fmax(amax, sycl::fabs((float)v)); + } + + const float d = amax / ((1 << 7) - 1); + const float id = d ? 1.0f/d : 0.0f; + + dsti->d = d; + + for (int j = 0; j < QK8_0; ++j) { + const float x0 = xi[j]*id; + + dsti->qs[j] = sycl::round((float)x0); + } +} + +static void cpy_blck_f32_q4_0(const char * cxi, char * cdsti) { + const float * xi = (const float *) cxi; + block_q4_0 * dsti = (block_q4_0 *) cdsti; + + float amax = 0.0f; + float vmax = 0.0f; + + for (int j = 0; j < QK4_0; ++j) { + const float v = xi[j]; + if (amax < sycl::fabs((float)v)) { + amax = sycl::fabs((float)v); + vmax = v; + } + } + + const float d = vmax / -8; + const float id = d ? 1.0f/d : 0.0f; + + dsti->d = d; + + for (int j = 0; j < QK4_0/2; ++j) { + const float x0 = xi[0 + j]*id; + const float x1 = xi[QK4_0/2 + j]*id; + + const uint8_t xi0 = dpct::min(15, (int8_t)(x0 + 8.5f)); + const uint8_t xi1 = dpct::min(15, (int8_t)(x1 + 8.5f)); + + dsti->qs[j] = xi0; + dsti->qs[j] |= xi1 << 4; + } +} + +static void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) { + const float * xi = (const float *) cxi; + block_q4_1 * dsti = (block_q4_1 *) cdsti; + + float vmin = FLT_MAX; + float vmax = -FLT_MAX; + + for (int j = 0; j < QK4_1; ++j) { + const float v = xi[j]; + + if (v < vmin) vmin = v; + if (v > vmax) vmax = v; + } + + const float d = (vmax - vmin) / ((1 << 4) - 1); + const float id = d ? 1.0f/d : 0.0f; + + dsti->dm.x() = d; + dsti->dm.y() = vmin; + + for (int j = 0; j < QK4_1/2; ++j) { + const float x0 = (xi[0 + j] - vmin)*id; + const float x1 = (xi[QK4_1/2 + j] - vmin)*id; + + const uint8_t xi0 = dpct::min(15, (int8_t)(x0 + 0.5f)); + const uint8_t xi1 = dpct::min(15, (int8_t)(x1 + 0.5f)); + + dsti->qs[j] = xi0; + dsti->qs[j] |= xi1 << 4; + } +} + +template +static void cpy_f32_q(const char * cx, char * cdst, const int ne, + const int ne00, const int ne01, const int nb00, const int nb01, const int nb02, + const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, + const sycl::nd_item<3> &item_ct1) { + const int i = (item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2)) * + qk; + + if (i >= ne) { + return; + } + + const int i02 = i / (ne00*ne01); + const int i01 = (i - i02*ne01*ne00) / ne00; + const int i00 = (i - i02*ne01*ne00 - i01*ne00); + const int x_offset = i00*nb00 + i01*nb01 + i02*nb02; + + const int i12 = i / (ne10*ne11); + const int i11 = (i - i12*ne10*ne11) / ne10; + const int i10 = (i - i12*ne10*ne11 - i11*ne10)/qk; + const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12; + + cpy_blck(cx + x_offset, cdst + dst_offset); +} + +static float rope_yarn_ramp(const float low, const float high, const int i0) { + const float y = (i0 / 2 - low) / sycl::max(0.001f, high - low); + return 1.0f - sycl::min(1.0f, sycl::max(0.0f, y)); +} + +struct rope_corr_dims { + float v[4]; +}; + +// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn +// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. +static void rope_yarn( + float theta_extrap, float freq_scale, rope_corr_dims corr_dims, int64_t i0, float ext_factor, float mscale, + float * cos_theta, float * sin_theta +) { + // Get n-d rotational scaling corrected for extrapolation + float theta_interp = freq_scale * theta_extrap; + float theta = theta_interp; + if (ext_factor != 0.0f) { + float ramp_mix = rope_yarn_ramp(corr_dims.v[0], corr_dims.v[1], i0) * ext_factor; + theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; + + // Get n-d magnitude scaling corrected for interpolation + mscale *= 1.0f + 0.1f * sycl::log(1.0f / freq_scale); + } + *cos_theta = sycl::cos(theta) * mscale; + *sin_theta = sycl::sin(theta) * mscale; +} + +// rope == RoPE == rotary positional embedding +template +static void rope( + const T * x, T * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base, + float ext_factor, float attn_factor, rope_corr_dims corr_dims +, + const sycl::nd_item<3> &item_ct1) { + const int col = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) + + item_ct1.get_local_id(1)); + + if (col >= ncols) { + return; + } + + const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + const int i = row*ncols + col; + const int i2 = row/p_delta_rows; + + const int p = has_pos ? pos[i2] : 0; + const float theta_base = p * dpct::pow(freq_base, -float(col) / ncols); + + float cos_theta, sin_theta; + rope_yarn(theta_base, freq_scale, corr_dims, col, ext_factor, attn_factor, &cos_theta, &sin_theta); + + const float x0 = x[i + 0]; + const float x1 = x[i + 1]; + + dst[i + 0] = x0*cos_theta - x1*sin_theta; + dst[i + 1] = x0*sin_theta + x1*cos_theta; +} + +template +static void rope_neox( + const T * x, T * dst, int ncols, int n_dims, const int32_t * pos, float freq_scale, int p_delta_rows, + float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims +, + const sycl::nd_item<3> &item_ct1) { + const int col = 2 * (item_ct1.get_local_range(1) * item_ct1.get_group(1) + + item_ct1.get_local_id(1)); + + if (col >= ncols) { + return; + } + + const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + const int ib = col / n_dims; + const int ic = col % n_dims; + + if (ib > 0) { + const int i = row*ncols + ib*n_dims + ic; + + dst[i + 0] = x[i + 0]; + dst[i + 1] = x[i + 1]; + + return; + } + + const int i = row*ncols + ib*n_dims + ic/2; + const int i2 = row/p_delta_rows; + + float cur_rot = inv_ndims * ic - ib; + + const int p = has_pos ? pos[i2] : 0; + const float theta_base = + p * freq_scale * dpct::pow(theta_scale, col / 2.0f); + + float cos_theta, sin_theta; + rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta); + + const float x0 = x[i + 0]; + const float x1 = x[i + n_dims/2]; + + dst[i + 0] = x0*cos_theta - x1*sin_theta; + dst[i + n_dims/2] = x0*sin_theta + x1*cos_theta; +} + +static void rope_glm_f32( + const float * x, float * dst, int ncols, const int32_t * pos, float freq_scale, int p_delta_rows, float freq_base, + int n_ctx +, const sycl::nd_item<3> &item_ct1) { + const int col = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + const int half_n_dims = ncols/4; + + if (col >= half_n_dims) { + return; + } + + const int row = item_ct1.get_local_range(1) * item_ct1.get_group(1) + + item_ct1.get_local_id(1); + const int i = row*ncols + col; + const int i2 = row/p_delta_rows; + + const float col_theta_scale = dpct::pow(freq_base, -2.0f * col / ncols); + // FIXME: this is likely wrong + const int p = pos != nullptr ? pos[i2] : 0; + + const float theta = sycl::min(p, n_ctx - 2) * freq_scale * col_theta_scale; + const float sin_theta = sycl::sin((float)theta); + const float cos_theta = sycl::cos((float)theta); + + const float x0 = x[i + 0]; + const float x1 = x[i + half_n_dims]; + + dst[i + 0] = x0*cos_theta - x1*sin_theta; + dst[i + half_n_dims] = x0*sin_theta + x1*cos_theta; + + const float block_theta = + ((float)sycl::max(p - n_ctx - 2, 0)) * col_theta_scale; + const float sin_block_theta = sycl::sin((float)block_theta); + const float cos_block_theta = sycl::cos((float)block_theta); + + const float x2 = x[i + half_n_dims * 2]; + const float x3 = x[i + half_n_dims * 3]; + + dst[i + half_n_dims * 2] = x2*cos_block_theta - x3*sin_block_theta; + dst[i + half_n_dims * 3] = x2*sin_block_theta + x3*cos_block_theta; +} + +static void alibi_f32(const float * x, float * dst, const int ncols, const int k_rows, + const int n_heads_log2_floor, const float m0, const float m1, + const sycl::nd_item<3> &item_ct1) { + const int col = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (col >= ncols) { + return; + } + + const int row = item_ct1.get_local_range(1) * item_ct1.get_group(1) + + item_ct1.get_local_id(1); + const int i = row*ncols + col; + + const int k = row/k_rows; + + float m_k; + if (k < n_heads_log2_floor) { + m_k = dpct::pow(m0, k + 1); + } else { + m_k = dpct::pow(m1, 2 * (k - n_heads_log2_floor) + 1); + } + + dst[i] = col * m_k + x[i]; +} + +static void k_sum_rows_f32(const float * x, float * dst, const int ncols, + const sycl::nd_item<3> &item_ct1) { + const int row = item_ct1.get_group(1); + const int col = item_ct1.get_local_id(2); + + float sum = 0.0f; + for (int i = col; i < ncols; i += item_ct1.get_local_range(2)) { + sum += x[row * ncols + i]; + } + + sum = warp_reduce_sum(sum, item_ct1); + + if (col == 0) { + dst[row] = sum; + } +} + +template +static inline void swap(T & a, T & b) { + T tmp = a; + a = b; + b = tmp; +} + +template +static void k_argsort_f32_i32(const float * x, int * dst, const int ncols, + const sycl::nd_item<3> &item_ct1) { + // bitonic sort + int col = item_ct1.get_local_id(2); + int row = item_ct1.get_group(1); + + if (col >= ncols) return; + + const float * x_row = x + row * ncols; + int * dst_row = dst + row * ncols; + + // initialize indices + if (col < ncols) { + dst_row[col] = col; + } + /* + DPCT1065:58: Consider replacing sycl::nd_item::barrier() with + sycl::nd_item::barrier(sycl::access::fence_space::local_space) for better + performance if there is no access to global memory. + */ + item_ct1.barrier(); + + for (int k = 2; k <= ncols; k *= 2) { + for (int j = k / 2; j > 0; j /= 2) { + int ixj = col ^ j; + if (ixj > col) { + if ((col & k) == 0) { + if (order == GGML_SORT_ASC ? x_row[dst_row[col]] > x_row[dst_row[ixj]] : x_row[dst_row[col]] < x_row[dst_row[ixj]]) { + swap(dst_row[col], dst_row[ixj]); + } + } else { + if (order == GGML_SORT_ASC ? x_row[dst_row[col]] < x_row[dst_row[ixj]] : x_row[dst_row[col]] > x_row[dst_row[ixj]]) { + swap(dst_row[col], dst_row[ixj]); + } + } + } + /* + DPCT1118:11: SYCL group functions and algorithms must be encountered + in converged control flow. You may need to adjust the code. + */ + /* + DPCT1065:59: Consider replacing sycl::nd_item::barrier() with + sycl::nd_item::barrier(sycl::access::fence_space::local_space) for + better performance if there is no access to global memory. + */ + item_ct1.barrier(); + } + } +} + +static void diag_mask_inf_f32(const float * x, float * dst, const int ncols, const int rows_per_channel, const int n_past, + const sycl::nd_item<3> &item_ct1) { + const int col = item_ct1.get_local_range(1) * item_ct1.get_group(1) + + item_ct1.get_local_id(1); + const int row = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (col >= ncols) { + return; + } + + const int i = row*ncols + col; + //dst[i] = col > (n_past + row % rows_per_channel) ? -INFINITY : x[i]; + //dst[i] = x[i] - (col > n_past + row % rows_per_channel) * INT_MAX; // equivalent within rounding error but slightly faster on GPU + dst[i] = x[i] - (col > n_past + row % rows_per_channel) * FLT_MAX; +} + +static void soft_max_f32(const float * x, const float * y, float * dst, const int ncols, const int nrows_y, const float scale, + const sycl::nd_item<3> &item_ct1, float *buf) { + const int tid = item_ct1.get_local_id(2); + const int rowx = item_ct1.get_group(2); + const int rowy = rowx % nrows_y; // broadcast the mask (y) in the row dimension + + const int block_size = item_ct1.get_local_range(2); + + const int warp_id = item_ct1.get_local_id(2) / WARP_SIZE; + const int lane_id = item_ct1.get_local_id(2) % WARP_SIZE; + + float max_val = -INFINITY; + + for (int col = tid; col < ncols; col += block_size) { + const int ix = rowx*ncols + col; + const int iy = rowy*ncols + col; + max_val = sycl::max(max_val, x[ix] * scale + (y ? y[iy] : 0.0f)); + } + + // find the max value in the block + max_val = warp_reduce_max(max_val, item_ct1); + if (block_size > WARP_SIZE) { + if (warp_id == 0) { + buf[lane_id] = -INFINITY; + } + /* + DPCT1118:12: SYCL group functions and algorithms must be encountered in + converged control flow. You may need to adjust the code. + */ + /* + DPCT1065:60: Consider replacing sycl::nd_item::barrier() with + sycl::nd_item::barrier(sycl::access::fence_space::local_space) for + better performance if there is no access to global memory. + */ + item_ct1.barrier(); + + if (lane_id == 0) { + buf[warp_id] = max_val; + } + /* + DPCT1118:13: SYCL group functions and algorithms must be encountered in + converged control flow. You may need to adjust the code. + */ + /* + DPCT1065:61: Consider replacing sycl::nd_item::barrier() with + sycl::nd_item::barrier(sycl::access::fence_space::local_space) for + better performance if there is no access to global memory. + */ + item_ct1.barrier(); + + max_val = buf[lane_id]; + max_val = warp_reduce_max(max_val, item_ct1); + } + + float tmp = 0.f; + + for (int col = tid; col < ncols; col += block_size) { + const int ix = rowx*ncols + col; + const int iy = rowy*ncols + col; + const float val = + sycl::native::exp((x[ix] * scale + (y ? y[iy] : 0.0f)) - max_val); + tmp += val; + dst[ix] = val; + } + + // find the sum of exps in the block + tmp = warp_reduce_sum(tmp, item_ct1); + if (block_size > WARP_SIZE) { + if (warp_id == 0) { + buf[lane_id] = 0.f; + } + /* + DPCT1118:14: SYCL group functions and algorithms must be encountered in + converged control flow. You may need to adjust the code. + */ + /* + DPCT1065:62: Consider replacing sycl::nd_item::barrier() with + sycl::nd_item::barrier(sycl::access::fence_space::local_space) for + better performance if there is no access to global memory. + */ + item_ct1.barrier(); + + if (lane_id == 0) { + buf[warp_id] = tmp; + } + /* + DPCT1118:15: SYCL group functions and algorithms must be encountered in + converged control flow. You may need to adjust the code. + */ + /* + DPCT1065:63: Consider replacing sycl::nd_item::barrier() with + sycl::nd_item::barrier(sycl::access::fence_space::local_space) for + better performance if there is no access to global memory. + */ + item_ct1.barrier(); + + tmp = buf[lane_id]; + tmp = warp_reduce_sum(tmp, item_ct1); + } + + const float inv_tmp = 1.f / tmp; + + for (int col = tid; col < ncols; col += block_size) { + const int i = rowx*ncols + col; + dst[i] *= inv_tmp; + } +} + +static void scale_f32(const float * x, float * dst, const float scale, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + + dst[i] = scale * x[i]; +} + +static void clamp_f32(const float * x, float * dst, const float min, const float max, const int k, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + + item_ct1.get_local_id(2); + + if (i >= k) { + return; + } + + dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]); +} + +static void im2col_f32_f16(const float *x, sycl::half *dst, int offset_delta, + int IW, int IH, int OW, int KW, int KH, + int pelements, int CHW, int s0, int s1, int p0, + int p1, int d0, int d1, + const sycl::nd_item<3> &item_ct1) { + const int i = item_ct1.get_local_id(2) + + item_ct1.get_group(2) * item_ct1.get_local_range(2); + if (i >= pelements) { + return; + } + + const int ksize = OW * (KH > 1 ? KW : 1); + const int kx = i / ksize; + const int kd = kx * ksize; + const int ky = (i - kd) / OW; + const int ix = i % OW; + + const int64_t iiw = ix * s0 + kx * d0 - p0; + const int64_t iih = item_ct1.get_group(1) * s1 + ky * d1 - p1; + + const int64_t offset_dst = + (item_ct1.get_group(1) * OW + ix) * CHW + + (item_ct1.get_group(0) * (KW * KH) + ky * KW + kx); + + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { + dst[offset_dst] = + sycl::vec(0.0f) + .convert()[0]; + } else { + const int64_t offset_src = item_ct1.get_group(0) * offset_delta; + dst[offset_dst] = + sycl::vec(x[offset_src + iih * IW + iiw]) + .convert()[0]; + } +} + +template +static void get_rows_sycl(const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const void *src0_dd, + const int32_t *src1_dd, float *dst_dd, + dpct::queue_ptr stream) { + + GGML_TENSOR_BINARY_OP_LOCALS + + const sycl::range<3> block_dims(1, 1, SYCL_GET_ROWS_BLOCK_SIZE); + const int block_num_x = (ne00 + 2*SYCL_GET_ROWS_BLOCK_SIZE - 1) / (2*SYCL_GET_ROWS_BLOCK_SIZE); + const sycl::range<3> block_nums(ne11 * ne12, ne10, block_num_x); + + // strides in elements + //const size_t s0 = nb0 / ggml_element_size(dst); + const size_t s1 = nb1 / ggml_element_size(dst); + const size_t s2 = nb2 / ggml_element_size(dst); + const size_t s3 = nb3 / ggml_element_size(dst); + + const size_t s10 = nb10 / ggml_element_size(src1); + const size_t s11 = nb11 / ggml_element_size(src1); + const size_t s12 = nb12 / ggml_element_size(src1); + //const size_t s13 = nb13 / ggml_element_size(src1); + + GGML_ASSERT(ne00 % 2 == 0); + + stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + k_get_rows( + src0_dd, src1_dd, dst_dd, ne00, ne12, s1, s2, + s3, nb01, nb02, nb03, s10, s11, s12, item_ct1); + }); + + (void) dst; +} + +template +static void get_rows_sycl_float(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const src0_t *src0_dd, const int32_t *src1_dd, + float *dst_dd, dpct::queue_ptr stream) { + + GGML_TENSOR_BINARY_OP_LOCALS + + const sycl::range<3> block_dims(1, 1, SYCL_GET_ROWS_BLOCK_SIZE); + const int block_num_x = (ne00 + SYCL_GET_ROWS_BLOCK_SIZE - 1) / SYCL_GET_ROWS_BLOCK_SIZE; + const sycl::range<3> block_nums(ne11 * ne12, ne10, block_num_x); + + // strides in elements + //const size_t s0 = nb0 / ggml_element_size(dst); + const size_t s1 = nb1 / ggml_element_size(dst); + const size_t s2 = nb2 / ggml_element_size(dst); + const size_t s3 = nb3 / ggml_element_size(dst); + + const size_t s10 = nb10 / ggml_element_size(src1); + const size_t s11 = nb11 / ggml_element_size(src1); + const size_t s12 = nb12 / ggml_element_size(src1); + //const size_t s13 = nb13 / ggml_element_size(src1); + + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + k_get_rows_float(src0_dd, src1_dd, dst_dd, ne00, ne12, s1, s2, + s3, nb01, nb02, nb03, s10, s11, s12, item_ct1); + }); + } + + (void) dst; +} + +template +struct bin_bcast_sycl { + template + void operator()(const struct ggml_tensor *src0, + const struct ggml_tensor *src1, struct ggml_tensor *dst, + const src0_t *src0_dd, const src1_t *src1_dd, dst_t *dst_dd, + dpct::queue_ptr stream) { + + GGML_TENSOR_BINARY_OP_LOCALS + + int nr0 = ne10/ne0; + int nr1 = ne11/ne1; + int nr2 = ne12/ne2; + int nr3 = ne13/ne3; + + int nr[4] = { nr0, nr1, nr2, nr3 }; + + // collapse dimensions until first broadcast dimension + int64_t cne0[] = {ne0, ne1, ne2, ne3}; + int64_t cne1[] = {ne10, ne11, ne12, ne13}; + size_t cnb0[] = {nb0, nb1, nb2, nb3}; + size_t cnb1[] = {nb10, nb11, nb12, nb13}; + auto collapse = [](int64_t cne[]) { + cne[0] *= cne[1]; + cne[1] = cne[2]; + cne[2] = cne[3]; + cne[3] = 1; + }; + + auto collapse_nb = [](size_t cnb[], int64_t cne[]) { + cnb[1] *= cne[1]; + cnb[2] *= cne[2]; + cnb[3] *= cne[3]; + }; + + for (int i = 0; i < 4; i++) { + if (nr[i] != 1) { + break; + } + if (i > 0) { + collapse_nb(cnb0, cne0); + collapse_nb(cnb1, cne1); + collapse(cne0); + collapse(cne1); + } + } + { + int64_t ne0 = cne0[0]; + int64_t ne1 = cne0[1]; + int64_t ne2 = cne0[2]; + int64_t ne3 = cne0[3]; + + int64_t ne10 = cne1[0]; + int64_t ne11 = cne1[1]; + int64_t ne12 = cne1[2]; + int64_t ne13 = cne1[3]; + + size_t nb0 = cnb0[0]; + size_t nb1 = cnb0[1]; + size_t nb2 = cnb0[2]; + size_t nb3 = cnb0[3]; + + size_t nb10 = cnb1[0]; + size_t nb11 = cnb1[1]; + size_t nb12 = cnb1[2]; + size_t nb13 = cnb1[3]; + + size_t s0 = nb0 / sizeof(dst_t); + size_t s1 = nb1 / sizeof(dst_t); + size_t s2 = nb2 / sizeof(dst_t); + size_t s3 = nb3 / sizeof(dst_t); + + size_t s10 = nb10 / sizeof(src1_t); + size_t s11 = nb11 / sizeof(src1_t); + size_t s12 = nb12 / sizeof(src1_t); + size_t s13 = nb13 / sizeof(src1_t); + + GGML_ASSERT(s0 == 1); + GGML_ASSERT(s10 == 1); + + const int block_size = 128; + + int64_t hne0 = std::max(ne0/2LL, 1LL); + + sycl::range<3> block_dims(1, 1, 1); + block_dims[2] = std::min(hne0, block_size); + block_dims[1] = std::min( + ne1, block_size / (unsigned int)block_dims[2]); + block_dims[0] = std::min( + std::min( + ne2 * ne3, block_size / (unsigned int)block_dims[2] / + (unsigned int)block_dims[1]), + 64U); + + sycl::range<3> block_nums( + (ne2 * ne3 + block_dims[0] - 1) / block_dims[0], + (ne1 + block_dims[1] - 1) / block_dims[1], + (hne0 + block_dims[2] - 1) / block_dims[2]); + + if (block_nums[0] > 65535) { + // this is the maximum number of blocks in z direction, fallback to 1D grid kernel + int block_num = (ne0*ne1*ne2*ne3 + block_size - 1) / block_size; + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, block_num) * + sycl::range<3>(1, 1, block_size), + sycl::range<3>(1, 1, block_size)), + [=](sycl::nd_item<3> item_ct1) { + k_bin_bcast_unravel( + src0_dd, src1_dd, dst_dd, ne0, ne1, ne2, ne3, + ne10, ne11, ne12, ne13, s1, s2, s3, s11, s12, + s13, item_ct1); + }); + } + } else { + /* + DPCT1049:16: The work-group size passed to the SYCL kernel may + exceed the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if + needed. + */ + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + k_bin_bcast(src0_dd, src1_dd, dst_dd, ne0, ne1, + ne2, ne3, ne10, ne11, ne12, ne13, + s1, s2, s3, s11, s12, s13, + item_ct1); + }); + } + } + } +}; + +static void acc_f32_sycl(const float *x, const float *y, float *dst, + const int n_elements, const int ne10, const int ne11, + const int ne12, const int nb1, const int nb2, + const int offset, dpct::queue_ptr stream) { + int num_blocks = (n_elements + SYCL_ACC_BLOCK_SIZE - 1) / SYCL_ACC_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_ACC_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + acc_f32(x, y, dst, n_elements, ne10, ne11, ne12, nb1, nb2, offset, + item_ct1); + }); +} + +static void gelu_f32_sycl(const float *x, float *dst, const int k, + dpct::queue_ptr stream) { + const int num_blocks = (k + SYCL_GELU_BLOCK_SIZE - 1) / SYCL_GELU_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + gelu_f32(x, dst, k, item_ct1); + }); +} + +static void silu_f32_sycl(const float *x, float *dst, const int k, + dpct::queue_ptr stream) { + const int num_blocks = (k + SYCL_SILU_BLOCK_SIZE - 1) / SYCL_SILU_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_SILU_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_SILU_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + silu_f32(x, dst, k, item_ct1); + }); +} + +static void gelu_quick_f32_sycl(const float *x, float *dst, const int k, + dpct::queue_ptr stream) { + const int num_blocks = (k + SYCL_GELU_BLOCK_SIZE - 1) / SYCL_GELU_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_GELU_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + gelu_quick_f32(x, dst, k, item_ct1); + }); +} + +static void tanh_f32_sycl(const float *x, float *dst, const int k, + dpct::queue_ptr stream) { + const int num_blocks = (k + SYCL_TANH_BLOCK_SIZE - 1) / SYCL_TANH_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_TANH_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_TANH_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + tanh_f32(x, dst, k, item_ct1); + }); +} + +static void relu_f32_sycl(const float *x, float *dst, const int k, + dpct::queue_ptr stream) { + const int num_blocks = (k + SYCL_RELU_BLOCK_SIZE - 1) / SYCL_RELU_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + relu_f32(x, dst, k, item_ct1); + }); +} + +static void leaky_relu_f32_sycl(const float *x, float *dst, const int k, + const float negative_slope, + dpct::queue_ptr stream) { + const int num_blocks = (k + SYCL_RELU_BLOCK_SIZE - 1) / SYCL_RELU_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_RELU_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + leaky_relu_f32(x, dst, k, negative_slope, item_ct1); + }); +} + +static void sqr_f32_sycl(const float *x, float *dst, const int k, + dpct::queue_ptr stream) { + const int num_blocks = (k + SYCL_SQR_BLOCK_SIZE - 1) / SYCL_SQR_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_SQR_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_SQR_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + sqr_f32(x, dst, k, item_ct1); + }); +} + +static void norm_f32_sycl(const float *x, float *dst, const int ncols, + const int nrows, const float eps, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % WARP_SIZE == 0); + if (ncols < 1024) { + const sycl::range<3> block_dims(1, 1, WARP_SIZE); + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor s_sum_acc_ct1( + sycl::range<1>(32), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, + block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + norm_f32(x, dst, ncols, eps, item_ct1, + s_sum_acc_ct1.get_pointer(), WARP_SIZE); + }); + }); + } else { + const int work_group_size = g_work_group_size; + const sycl::range<3> block_dims(1, 1, work_group_size); + /* + DPCT1049:17: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor s_sum_acc_ct1( + sycl::range<1>(32), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, + block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + norm_f32(x, dst, ncols, eps, item_ct1, + s_sum_acc_ct1.get_pointer(), work_group_size); + }); + }); + } +} + +static void group_norm_f32_sycl(const float *x, float *dst, + const int num_groups, const int group_size, + const int ne_elements, dpct::queue_ptr stream) { + static const float eps = 1e-6f; + if (group_size < 1024) { + const sycl::range<3> block_dims(1, 1, WARP_SIZE); + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor s_sum_acc_ct1(sycl::range<1>(32), + cgh); + + const float eps_ct4 = eps; + + cgh.parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims, + block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + group_norm_f32( + x, dst, group_size, ne_elements, eps_ct4, item_ct1, + s_sum_acc_ct1.get_pointer(), WARP_SIZE); + }); + }); + } else { + const int work_group_size = g_work_group_size; + const sycl::range<3> block_dims(1, 1, work_group_size); + /* + DPCT1049:18: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor s_sum_acc_ct1(sycl::range<1>(32), + cgh); + + const float eps_ct4 = eps; + + cgh.parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_groups) * block_dims, + block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + group_norm_f32(x, dst, group_size, ne_elements, + eps_ct4, item_ct1, + s_sum_acc_ct1.get_pointer(), work_group_size); + }); + }); + } +} + +static void concat_f32_sycl(const float *x, const float *y, float *dst, + const int ne0, int ne1, int ne2, int ne02, + dpct::queue_ptr stream) { + int num_blocks = (ne0 + SYCL_CONCAT_BLOCK_SIZE - 1) / SYCL_CONCAT_BLOCK_SIZE; + sycl::range<3> gridDim(ne2, ne1, num_blocks); + stream->parallel_for( + sycl::nd_range<3>(gridDim * + sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_CONCAT_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + concat_f32(x, y, dst, ne0, ne02, item_ct1); + }); +} + +static void upscale_f32_sycl(const float *x, float *dst, const int ne00, + const int ne01, const int ne02, + const int scale_factor, dpct::queue_ptr stream) { + int ne0 = (ne00 * scale_factor); + int num_blocks = (ne0 + SYCL_UPSCALE_BLOCK_SIZE - 1) / SYCL_UPSCALE_BLOCK_SIZE; + sycl::range<3> gridDim(ne02, (ne01 * scale_factor), num_blocks); + stream->parallel_for( + sycl::nd_range<3>(gridDim * + sycl::range<3>(1, 1, SYCL_UPSCALE_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_UPSCALE_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + upscale_f32(x, dst, ne00, ne00 * ne01, scale_factor, item_ct1); + }); +} + +static void pad_f32_sycl(const float *x, float *dst, const int ne00, + const int ne01, const int ne02, const int ne0, + const int ne1, const int ne2, dpct::queue_ptr stream) { + int num_blocks = (ne0 + SYCL_PAD_BLOCK_SIZE - 1) / SYCL_PAD_BLOCK_SIZE; + sycl::range<3> gridDim(ne2, ne1, num_blocks); + stream->parallel_for( + sycl::nd_range<3>(gridDim * sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_PAD_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + pad_f32(x, dst, ne0, ne00, ne01, ne02, item_ct1); + }); +} + +static void rms_norm_f32_sycl(const float *x, float *dst, const int ncols, + const int nrows, const float eps, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % WARP_SIZE == 0); + // printf("%s ncols=%d, nrows=%d, WARP_SIZE=%d\n", __func__, ncols, nrows, WARP_SIZE); + if (ncols < 1024) { + const sycl::range<3> block_dims(1, 1, WARP_SIZE); + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor s_sum_acc_ct1(sycl::range<1>(32), + cgh); + + cgh.parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, + block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + rms_norm_f32(x, dst, ncols, eps, item_ct1, + s_sum_acc_ct1.get_pointer(), WARP_SIZE); + }); + }); + } else { + const int work_group_size = g_work_group_size; + const sycl::range<3> block_dims(1, 1, work_group_size); + /* + DPCT1049:19: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor s_sum_acc_ct1(sycl::range<1>(32), + cgh); + + cgh.parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, + block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + rms_norm_f32(x, dst, ncols, eps, item_ct1, + s_sum_acc_ct1.get_pointer(), work_group_size); + }); + }); + } +} + +static void quantize_row_q8_1_sycl(const float *x, void *vy, const int kx, + const int ky, const int kx_padded, + dpct::queue_ptr stream) { + const int block_num_x = (kx_padded + SYCL_QUANTIZE_BLOCK_SIZE - 1) / SYCL_QUANTIZE_BLOCK_SIZE; + const sycl::range<3> num_blocks(1, ky, block_num_x); + const sycl::range<3> block_size(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE); + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(num_blocks * block_size, block_size), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + quantize_q8_1(x, vy, kx, kx_padded, item_ct1); + }); + } +} + +template +static void dequantize_block_sycl(const void *__restrict__ vx, + dst_t *__restrict__ y, const int k, + dpct::queue_ptr stream) { + const int num_blocks = (k + SYCL_DEQUANTIZE_BLOCK_SIZE - 1) / SYCL_DEQUANTIZE_BLOCK_SIZE; + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>( + sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_DEQUANTIZE_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block(vx, y, k, item_ct1); + }); + } +} + +template +static void dequantize_row_q2_K_sycl(const void *vx, dst_t *y, const int k, + dpct::queue_ptr stream) { + const int nb = k / QK_K; +#if QK_K == 256 + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 64), + sycl::range<3>(1, 1, 64)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_q2_K(vx, y, item_ct1); + }); + } +#else + dequantize_block_q2_K<<>>(vx, y); +#endif +} + +template +static void dequantize_row_q3_K_sycl(const void *vx, dst_t *y, const int k, + dpct::queue_ptr stream) { + const int nb = k / QK_K; +#if QK_K == 256 + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 64), + sycl::range<3>(1, 1, 64)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_q3_K(vx, y, item_ct1); + }); + } +#else + dequantize_block_q3_K<<>>(vx, y); +#endif +} + +template +static void dequantize_row_q4_K_sycl(const void *vx, dst_t *y, const int k, + dpct::queue_ptr stream) { + const int nb = k / QK_K; + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 32), + sycl::range<3>(1, 1, 32)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_q4_K(vx, y, item_ct1); + }); + } +} + +template +static void dequantize_row_q5_K_sycl(const void *vx, dst_t *y, const int k, + dpct::queue_ptr stream) { + const int nb = k / QK_K; +#if QK_K == 256 + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 64), + sycl::range<3>(1, 1, 64)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_q5_K(vx, y, item_ct1); + }); + } +#else + dequantize_block_q5_K<<>>(vx, y); +#endif +} + +template +static void dequantize_row_q6_K_sycl(const void *vx, dst_t *y, const int k, + dpct::queue_ptr stream) { + const int nb = k / QK_K; +#if QK_K == 256 + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, nb) * + sycl::range<3>(1, 1, 64), + sycl::range<3>(1, 1, 64)), + [=](sycl::nd_item<3> item_ct1) { + dequantize_block_q6_K(vx, y, item_ct1); + }); + } +#else + dequantize_block_q6_K<<>>(vx, y); +#endif +} + +static to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type) { + switch (type) { + case GGML_TYPE_Q4_0: + return dequantize_block_sycl; + case GGML_TYPE_Q4_1: + return dequantize_block_sycl; + case GGML_TYPE_Q5_0: + return dequantize_block_sycl; + case GGML_TYPE_Q5_1: + return dequantize_block_sycl; + case GGML_TYPE_Q8_0: + return dequantize_block_sycl; + case GGML_TYPE_Q2_K: + return dequantize_row_q2_K_sycl; + case GGML_TYPE_Q3_K: + return dequantize_row_q3_K_sycl; + case GGML_TYPE_Q4_K: + return dequantize_row_q4_K_sycl; + case GGML_TYPE_Q5_K: + return dequantize_row_q5_K_sycl; + case GGML_TYPE_Q6_K: + return dequantize_row_q6_K_sycl; + case GGML_TYPE_F32: + return dequantize_block_sycl<1, 1, convert_f32>; + default: + return nullptr; + } +} + +static to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type) { + switch (type) { + case GGML_TYPE_Q4_0: + return dequantize_block_sycl; + case GGML_TYPE_Q4_1: + return dequantize_block_sycl; + case GGML_TYPE_Q5_0: + return dequantize_block_sycl; + case GGML_TYPE_Q5_1: + return dequantize_block_sycl; + case GGML_TYPE_Q8_0: + return dequantize_block_sycl; + case GGML_TYPE_Q2_K: + return dequantize_row_q2_K_sycl; + case GGML_TYPE_Q3_K: + return dequantize_row_q3_K_sycl; + case GGML_TYPE_Q4_K: + return dequantize_row_q4_K_sycl; + case GGML_TYPE_Q5_K: + return dequantize_row_q5_K_sycl; + case GGML_TYPE_Q6_K: + return dequantize_row_q6_K_sycl; + case GGML_TYPE_F16: + return dequantize_block_sycl<1, 1, convert_f16>; + default: + return nullptr; + } +} + +static void dequantize_mul_mat_vec_q4_0_sycl(const void *vx, const dfloat *y, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + // the number of rows may exceed maximum grid size in the y or z dimensions, use the x dimension instead + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + dequantize_mul_mat_vec( + vx, y, dst, ncols, nrows, item_ct1); + }); + } +} + +static void dequantize_mul_mat_vec_q4_1_sycl(const void *vx, const dfloat *y, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + dequantize_mul_mat_vec( + vx, y, dst, ncols, nrows, item_ct1); + }); + } +} + +static void dequantize_mul_mat_vec_q5_0_sycl(const void *vx, const dfloat *y, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + dequantize_mul_mat_vec( + vx, y, dst, ncols, nrows, item_ct1); + }); + } +} + +static void dequantize_mul_mat_vec_q5_1_sycl(const void *vx, const dfloat *y, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + dequantize_mul_mat_vec( + vx, y, dst, ncols, nrows, item_ct1); + }); + } +} + +static void dequantize_mul_mat_vec_q8_0_sycl(const void *vx, const dfloat *y, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + dequantize_mul_mat_vec( + vx, y, dst, ncols, nrows, item_ct1); + }); + } +} + +static void dequantize_mul_mat_vec_q2_K_sycl(const void *vx, const float *y, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + 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 sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, ny, 32); + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + dequantize_mul_mat_vec_q2_k(vx, y, dst, ncols, nrows, item_ct1); + }); +} + +static void dequantize_mul_mat_vec_q3_K_sycl(const void *vx, const float *y, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int ny = 2 / K_QUANTS_PER_ITERATION; + const int block_num_y = (nrows + ny - 1) / ny; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, ny, 32); + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + dequantize_mul_mat_vec_q3_k(vx, y, dst, ncols, nrows, item_ct1); + }); +} + +static void dequantize_mul_mat_vec_q4_K_sycl(const void *vx, const float *y, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int ny = 2 / K_QUANTS_PER_ITERATION; + const int block_num_y = (nrows + ny - 1) / ny; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, ny, 32); + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + dequantize_mul_mat_vec_q4_k(vx, y, dst, ncols, nrows, item_ct1); + }); +} + +static void dequantize_mul_mat_vec_q5_K_sycl(const void *vx, const float *y, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const sycl::range<3> block_dims(1, 1, 32); + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, nrows) * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + dequantize_mul_mat_vec_q5_k(vx, y, dst, ncols, item_ct1); + }); +} + +static void dequantize_mul_mat_vec_q6_K_sycl(const void *vx, const float *y, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int ny = 2 / K_QUANTS_PER_ITERATION; + const int block_num_y = (nrows + ny - 1) / ny; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, ny, 32); + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + dequantize_mul_mat_vec_q6_k(vx, y, dst, ncols, nrows, item_ct1); + }); +} + +static void convert_mul_mat_vec_f16_sycl(const void *vx, const dfloat *y, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % GGML_SYCL_DMMV_X == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + dequantize_mul_mat_vec<1, 1, convert_f16>(vx, y, dst, ncols, + nrows, item_ct1); + }); + } +} + +static void mul_mat_vec_q4_0_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK4_0 == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q(vx, vy, dst, ncols, nrows, + item_ct1); + }); +} + +static void mul_mat_vec_q4_1_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK4_1 == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q(vx, vy, dst, ncols, nrows, + item_ct1); + }); +} + +static void mul_mat_vec_q5_0_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK5_0 == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q(vx, vy, dst, ncols, nrows, + item_ct1); + }); +} + +static void mul_mat_vec_q5_1_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK5_1 == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q(vx, vy, dst, ncols, nrows, + item_ct1); + }); +} + +static void mul_mat_vec_q8_0_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK8_0 == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q(vx, vy, dst, ncols, nrows, + item_ct1); + }); +} + +static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q(vx, vy, dst, ncols, nrows, + item_ct1); + }); +} + +static void mul_mat_vec_q3_K_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q(vx, vy, dst, ncols, nrows, + item_ct1); + }); +} + +static void mul_mat_vec_q4_K_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q(vx, vy, dst, ncols, nrows, + item_ct1); + }); +} + +static void mul_mat_vec_q5_K_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q(vx, vy, dst, ncols, nrows, + item_ct1); + }); +} + +static void mul_mat_vec_q6_K_q8_1_sycl(const void *vx, const void *vy, + float *dst, const int ncols, + const int nrows, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y; + const sycl::range<3> block_nums(1, 1, block_num_y); + const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE); + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_q(vx, vy, dst, ncols, nrows, + item_ct1); + }); +} + +int get_device_index_by_id(int id){ + int res = g_sycl_device_id2index[id].index; + // GGML_SYCL_DEBUG("get_device_index_by_id id=%d device_index=%d\n", id, res); + GGML_ASSERT(res>=0); + return res; +} + +int get_device_id_by_index(int index){ + int res = g_device_caps[index].device_id; + GGML_ASSERT(res>=0); + return res; +} + + +int get_current_device_index(){ + return get_device_index_by_id(dpct::dev_mgr::instance().current_device_id()); +} + +static void ggml_mul_mat_q4_0_q8_1_sycl(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, + dpct::queue_ptr stream) try { + + int id; + SYCL_CHECK( + CHECK_TRY_ERROR(id = get_current_device_index())); + const int compute_capability = g_device_caps[id].cc; + + int mmq_x, mmq_y, nwarps; + if (compute_capability >= VER_GEN13) { + mmq_x = MMQ_X_Q4_0_RDNA2; + mmq_y = MMQ_Y_Q4_0_RDNA2; + nwarps = NWARPS_Q4_0_RDNA2; + } else if (compute_capability >= VER_GEN12) { + mmq_x = MMQ_X_Q4_0_RDNA1; + mmq_y = MMQ_Y_Q4_0_RDNA1; + nwarps = NWARPS_Q4_0_RDNA1; + } else if (compute_capability >= VER_GEN9) { + mmq_x = MMQ_X_Q4_0_AMPERE; + mmq_y = MMQ_Y_Q4_0_AMPERE; + nwarps = NWARPS_Q4_0_AMPERE; + } else if (compute_capability >= VER_4VEC) { + mmq_x = MMQ_X_Q4_0_PASCAL; + mmq_y = MMQ_Y_Q4_0_PASCAL; + nwarps = NWARPS_Q4_0_PASCAL; + } else { + GGML_ASSERT(false); + } + + const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; + const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; + const sycl::range<3> block_nums(1, block_num_y, block_num_x); + const sycl::range<3> block_dims(1, nwarps, WARP_SIZE); + + if (nrows_x % mmq_y == 0) { + const bool need_check = false; + /* + DPCT1049:20: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_qs_q4_0_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_d_q4_0_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI4_0) + mmq_y / QI4_0), + cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q4_0( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + tile_x_qs_q4_0_acc_ct1.get_pointer(), + tile_x_d_q4_0_acc_ct1.get_pointer(), + tile_y_qs_acc_ct1.get_pointer(), + tile_y_ds_acc_ct1.get_pointer()); + }); + }); + } + } else { + const bool need_check = true; + /* + DPCT1049:21: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_qs_q4_0_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_d_q4_0_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI4_0) + mmq_y / QI4_0), + cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q4_0( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + tile_x_qs_q4_0_acc_ct1.get_pointer(), + tile_x_d_q4_0_acc_ct1.get_pointer(), + tile_y_qs_acc_ct1.get_pointer(), + tile_y_ds_acc_ct1.get_pointer()); + }); + }); + } + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_mul_mat_q4_1_q8_1_sycl(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, + dpct::queue_ptr stream) try { + + int id; + SYCL_CHECK( + CHECK_TRY_ERROR(id = get_current_device_index())); + const int compute_capability = g_device_caps[id].cc; + + int mmq_x, mmq_y, nwarps; + if (compute_capability >= VER_GEN13) { + mmq_x = MMQ_X_Q4_1_RDNA2; + mmq_y = MMQ_Y_Q4_1_RDNA2; + nwarps = NWARPS_Q4_1_RDNA2; + } else if (compute_capability >= VER_GEN12) { + mmq_x = MMQ_X_Q4_1_RDNA1; + mmq_y = MMQ_Y_Q4_1_RDNA1; + nwarps = NWARPS_Q4_1_RDNA1; + } else if (compute_capability >= VER_GEN9) { + mmq_x = MMQ_X_Q4_1_AMPERE; + mmq_y = MMQ_Y_Q4_1_AMPERE; + nwarps = NWARPS_Q4_1_AMPERE; + } else if (compute_capability >= VER_4VEC) { + mmq_x = MMQ_X_Q4_1_PASCAL; + mmq_y = MMQ_Y_Q4_1_PASCAL; + nwarps = NWARPS_Q4_1_PASCAL; + } else { + GGML_ASSERT(false); + } + + const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; + const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; + const sycl::range<3> block_nums(1, block_num_y, block_num_x); + const sycl::range<3> block_dims(1, nwarps, WARP_SIZE); + + if (nrows_x % mmq_y == 0) { + const bool need_check = false; + /* + DPCT1049:22: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_qs_q4_1_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE) + +mmq_y), cgh); + sycl::local_accessor tile_x_dm_q4_1_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI4_1) + mmq_y / QI4_1), + cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q4_1( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + tile_x_qs_q4_1_acc_ct1.get_pointer(), + tile_x_dm_q4_1_acc_ct1.get_pointer(), + tile_y_qs_acc_ct1.get_pointer(), + tile_y_ds_acc_ct1.get_pointer()); + }); + }); + } + } else { + const bool need_check = true; + /* + DPCT1049:23: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_qs_q4_1_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE) + +mmq_y), cgh); + sycl::local_accessor tile_x_dm_q4_1_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI4_1) + mmq_y / QI4_1), + cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q4_1( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + tile_x_qs_q4_1_acc_ct1.get_pointer(), + tile_x_dm_q4_1_acc_ct1.get_pointer(), + tile_y_qs_acc_ct1.get_pointer(), + tile_y_ds_acc_ct1.get_pointer()); + }); + }); + } + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_mul_mat_q5_0_q8_1_sycl(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, + dpct::queue_ptr stream) try { + + int id; + SYCL_CHECK( + CHECK_TRY_ERROR(id = get_current_device_index())); + const int compute_capability = g_device_caps[id].cc; + + int mmq_x, mmq_y, nwarps; + if (compute_capability >= VER_GEN13) { + mmq_x = MMQ_X_Q5_0_RDNA2; + mmq_y = MMQ_Y_Q5_0_RDNA2; + nwarps = NWARPS_Q5_0_RDNA2; + } else if (compute_capability >= VER_GEN12) { + mmq_x = MMQ_X_Q5_0_RDNA1; + mmq_y = MMQ_Y_Q5_0_RDNA1; + nwarps = NWARPS_Q5_0_RDNA1; + } else if (compute_capability >= VER_GEN9) { + mmq_x = MMQ_X_Q5_0_AMPERE; + mmq_y = MMQ_Y_Q5_0_AMPERE; + nwarps = NWARPS_Q5_0_AMPERE; + } else if (compute_capability >= VER_4VEC) { + mmq_x = MMQ_X_Q5_0_PASCAL; + mmq_y = MMQ_Y_Q5_0_PASCAL; + nwarps = NWARPS_Q5_0_PASCAL; + } else { + GGML_ASSERT(false); + } + + const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; + const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; + const sycl::range<3> block_nums(1, block_num_y, block_num_x); + const sycl::range<3> block_dims(1, nwarps, WARP_SIZE); + + if (nrows_x % mmq_y == 0) { + const bool need_check = false; + /* + DPCT1049:24: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_ql_q5_0_acc_ct1( + sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_d_q5_0_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI5_0) + mmq_y / QI5_0), + cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q5_0( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + tile_x_ql_q5_0_acc_ct1.get_pointer(), + tile_x_d_q5_0_acc_ct1.get_pointer(), + tile_y_qs_acc_ct1.get_pointer(), + tile_y_ds_acc_ct1.get_pointer()); + }); + }); + } + } else { + const bool need_check = true; + /* + DPCT1049:25: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_ql_q5_0_acc_ct1( + sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_d_q5_0_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI5_0) + mmq_y / QI5_0), + cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q5_0( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + tile_x_ql_q5_0_acc_ct1.get_pointer(), + tile_x_d_q5_0_acc_ct1.get_pointer(), + tile_y_qs_acc_ct1.get_pointer(), + tile_y_ds_acc_ct1.get_pointer()); + }); + }); + } + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_mul_mat_q5_1_q8_1_sycl(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, + dpct::queue_ptr stream) try { + + int id; + SYCL_CHECK( + CHECK_TRY_ERROR(id = get_current_device_index())); + const int compute_capability = g_device_caps[id].cc; + + int mmq_x, mmq_y, nwarps; + if (compute_capability >= VER_GEN13) { + mmq_x = MMQ_X_Q5_1_RDNA2; + mmq_y = MMQ_Y_Q5_1_RDNA2; + nwarps = NWARPS_Q5_1_RDNA2; + } else if (compute_capability >= VER_GEN12) { + mmq_x = MMQ_X_Q5_1_RDNA1; + mmq_y = MMQ_Y_Q5_1_RDNA1; + nwarps = NWARPS_Q5_1_RDNA1; + } else if (compute_capability >= VER_GEN9) { + mmq_x = MMQ_X_Q5_1_AMPERE; + mmq_y = MMQ_Y_Q5_1_AMPERE; + nwarps = NWARPS_Q5_1_AMPERE; + } else if (compute_capability >= VER_4VEC) { + mmq_x = MMQ_X_Q5_1_PASCAL; + mmq_y = MMQ_Y_Q5_1_PASCAL; + nwarps = NWARPS_Q5_1_PASCAL; + } else { + GGML_ASSERT(false); + } + + const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; + const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; + const sycl::range<3> block_nums(1, block_num_y, block_num_x); + const sycl::range<3> block_dims(1, nwarps, WARP_SIZE); + + if (nrows_x % mmq_y == 0) { + const bool need_check = false; + /* + DPCT1049:26: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_ql_q5_1_acc_ct1( + sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_dm_q5_1_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI5_1) + mmq_y / QI5_1), + cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q5_1( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + tile_x_ql_q5_1_acc_ct1.get_pointer(), + tile_x_dm_q5_1_acc_ct1.get_pointer(), + tile_y_qs_acc_ct1.get_pointer(), + tile_y_ds_acc_ct1.get_pointer()); + }); + }); + } + } else { + const bool need_check = true; + /* + DPCT1049:27: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_ql_q5_1_acc_ct1( + sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_dm_q5_1_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI5_1) + mmq_y / QI5_1), + cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q5_1( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + tile_x_ql_q5_1_acc_ct1.get_pointer(), + tile_x_dm_q5_1_acc_ct1.get_pointer(), + tile_y_qs_acc_ct1.get_pointer(), + tile_y_ds_acc_ct1.get_pointer()); + }); + }); + } + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_mul_mat_q8_0_q8_1_sycl(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, + dpct::queue_ptr stream) try { + + int id; + SYCL_CHECK( + CHECK_TRY_ERROR(id = get_current_device_index())); + const int compute_capability = g_device_caps[id].cc; + + int mmq_x, mmq_y, nwarps; + if (compute_capability >= VER_GEN13) { + mmq_x = MMQ_X_Q8_0_RDNA2; + mmq_y = MMQ_Y_Q8_0_RDNA2; + nwarps = NWARPS_Q8_0_RDNA2; + } else if (compute_capability >= VER_GEN12) { + mmq_x = MMQ_X_Q8_0_RDNA1; + mmq_y = MMQ_Y_Q8_0_RDNA1; + nwarps = NWARPS_Q8_0_RDNA1; + } else if (compute_capability >= VER_GEN9) { + mmq_x = MMQ_X_Q8_0_AMPERE; + mmq_y = MMQ_Y_Q8_0_AMPERE; + nwarps = NWARPS_Q8_0_AMPERE; + } else if (compute_capability >= VER_4VEC) { + mmq_x = MMQ_X_Q8_0_PASCAL; + mmq_y = MMQ_Y_Q8_0_PASCAL; + nwarps = NWARPS_Q8_0_PASCAL; + } else { + GGML_ASSERT(false); + } + + const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; + const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; + const sycl::range<3> block_nums(1, block_num_y, block_num_x); + const sycl::range<3> block_dims(1, nwarps, WARP_SIZE); + + if (nrows_x % mmq_y == 0) { + const bool need_check = false; + /* + DPCT1049:28: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_qs_q8_0_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_d_q8_0_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI8_0) + mmq_y / QI8_0), + cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q8_0( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + tile_x_qs_q8_0_acc_ct1.get_pointer(), + tile_x_d_q8_0_acc_ct1.get_pointer(), + tile_y_qs_acc_ct1.get_pointer(), + tile_y_ds_acc_ct1.get_pointer()); + }); + }); + } + } else { + const bool need_check = true; + /* + DPCT1049:29: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_qs_q8_0_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_d_q8_0_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI8_0) + mmq_y / QI8_0), + cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q8_0( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + tile_x_qs_q8_0_acc_ct1.get_pointer(), + tile_x_d_q8_0_acc_ct1.get_pointer(), + tile_y_qs_acc_ct1.get_pointer(), + tile_y_ds_acc_ct1.get_pointer()); + }); + }); + } + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_mul_mat_q2_K_q8_1_sycl(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, + dpct::queue_ptr stream) try { + + int id; + SYCL_CHECK( + CHECK_TRY_ERROR(id = get_current_device_index())); + const int compute_capability = g_device_caps[id].cc; + + int mmq_x, mmq_y, nwarps; + if (compute_capability >= VER_GEN13) { + mmq_x = MMQ_X_Q2_K_RDNA2; + mmq_y = MMQ_Y_Q2_K_RDNA2; + nwarps = NWARPS_Q2_K_RDNA2; + } else if (compute_capability >= VER_GEN12) { + mmq_x = MMQ_X_Q2_K_RDNA1; + mmq_y = MMQ_Y_Q2_K_RDNA1; + nwarps = NWARPS_Q2_K_RDNA1; + } else if (compute_capability >= VER_GEN9) { + mmq_x = MMQ_X_Q2_K_AMPERE; + mmq_y = MMQ_Y_Q2_K_AMPERE; + nwarps = NWARPS_Q2_K_AMPERE; + } else if (compute_capability >= VER_4VEC) { + mmq_x = MMQ_X_Q2_K_PASCAL; + mmq_y = MMQ_Y_Q2_K_PASCAL; + nwarps = NWARPS_Q2_K_PASCAL; + } else { + GGML_ASSERT(false); + } + + const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; + const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; + const sycl::range<3> block_nums(1, block_num_y, block_num_x); + const sycl::range<3> block_dims(1, nwarps, WARP_SIZE); + + if (nrows_x % mmq_y == 0) { + const bool need_check = false; + /* + DPCT1049:30: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_ql_q2_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_dm_q2_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI2_K) + mmq_y / QI2_K), + cgh); + sycl::local_accessor tile_x_sc_q2_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / 4) + mmq_y / 4), cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q2_K( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + tile_x_ql_q2_K_acc_ct1.get_pointer(), + tile_x_dm_q2_K_acc_ct1.get_pointer(), + tile_x_sc_q2_K_acc_ct1.get_pointer(), + tile_y_qs_acc_ct1.get_pointer(), + tile_y_ds_acc_ct1.get_pointer()); + }); + }); + } + } else { + const bool need_check = true; + /* + DPCT1049:31: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_ql_q2_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_dm_q2_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI2_K) + mmq_y / QI2_K), + cgh); + sycl::local_accessor tile_x_sc_q2_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / 4) + mmq_y / 4), cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q2_K( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + tile_x_ql_q2_K_acc_ct1.get_pointer(), + tile_x_dm_q2_K_acc_ct1.get_pointer(), + tile_x_sc_q2_K_acc_ct1.get_pointer(), + tile_y_qs_acc_ct1.get_pointer(), + tile_y_ds_acc_ct1.get_pointer()); + }); + }); + } + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_mul_mat_q3_K_q8_1_sycl(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, + dpct::queue_ptr stream) try { + +#if QK_K == 256 + + int id; + SYCL_CHECK( + CHECK_TRY_ERROR(id = get_current_device_index())); + const int compute_capability = g_device_caps[id].cc; + + int mmq_x, mmq_y, nwarps; + if (compute_capability >= VER_GEN13) { + mmq_x = MMQ_X_Q3_K_RDNA2; + mmq_y = MMQ_Y_Q3_K_RDNA2; + nwarps = NWARPS_Q3_K_RDNA2; + } else if (compute_capability >= VER_GEN12) { + mmq_x = MMQ_X_Q3_K_RDNA1; + mmq_y = MMQ_Y_Q3_K_RDNA1; + nwarps = NWARPS_Q3_K_RDNA1; + } else if (compute_capability >= VER_GEN9) { + mmq_x = MMQ_X_Q3_K_AMPERE; + mmq_y = MMQ_Y_Q3_K_AMPERE; + nwarps = NWARPS_Q3_K_AMPERE; + } else if (compute_capability >= VER_4VEC) { + mmq_x = MMQ_X_Q3_K_PASCAL; + mmq_y = MMQ_Y_Q3_K_PASCAL; + nwarps = NWARPS_Q3_K_PASCAL; + } else { + GGML_ASSERT(false); + } + + const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; + const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; + const sycl::range<3> block_nums(1, block_num_y, block_num_x); + const sycl::range<3> block_dims(1, nwarps, WARP_SIZE); + + if (nrows_x % mmq_y == 0) { + const bool need_check = false; + /* + DPCT1049:32: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_ql_q3_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_dm_q3_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI3_K) + mmq_y / QI3_K), + cgh); + sycl::local_accessor tile_x_qh_q3_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / 2) + mmq_y / 2), cgh); + sycl::local_accessor tile_x_sc_q3_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / 4) + mmq_y / 4), cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q3_K( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + tile_x_ql_q3_K_acc_ct1.get_pointer(), + tile_x_dm_q3_K_acc_ct1.get_pointer(), + tile_x_qh_q3_K_acc_ct1.get_pointer(), + tile_x_sc_q3_K_acc_ct1.get_pointer(), + tile_y_qs_acc_ct1.get_pointer(), + tile_y_ds_acc_ct1.get_pointer()); + }); + }); + } + } else { + const bool need_check = true; + /* + DPCT1049:33: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_ql_q3_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_dm_q3_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI3_K) + mmq_y / QI3_K), + cgh); + sycl::local_accessor tile_x_qh_q3_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / 2) + mmq_y / 2), cgh); + sycl::local_accessor tile_x_sc_q3_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / 4) + mmq_y / 4), cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q3_K( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + tile_x_ql_q3_K_acc_ct1.get_pointer(), + tile_x_dm_q3_K_acc_ct1.get_pointer(), + tile_x_qh_q3_K_acc_ct1.get_pointer(), + tile_x_sc_q3_K_acc_ct1.get_pointer(), + tile_y_qs_acc_ct1.get_pointer(), + tile_y_ds_acc_ct1.get_pointer()); + }); + }); + } + } +#endif +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_mul_mat_q4_K_q8_1_sycl(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, + dpct::queue_ptr stream) try { + + int id; + SYCL_CHECK( + CHECK_TRY_ERROR(id = get_current_device_index())); + const int compute_capability = g_device_caps[id].cc; + + int mmq_x, mmq_y, nwarps; + if (compute_capability >= VER_GEN13) { + mmq_x = MMQ_X_Q4_K_RDNA2; + mmq_y = MMQ_Y_Q4_K_RDNA2; + nwarps = NWARPS_Q4_K_RDNA2; + } else if (compute_capability >= VER_GEN12) { + mmq_x = MMQ_X_Q4_K_RDNA1; + mmq_y = MMQ_Y_Q4_K_RDNA1; + nwarps = NWARPS_Q4_K_RDNA1; + } else if (compute_capability >= VER_GEN9) { + mmq_x = MMQ_X_Q4_K_AMPERE; + mmq_y = MMQ_Y_Q4_K_AMPERE; + nwarps = NWARPS_Q4_K_AMPERE; + } else if (compute_capability >= VER_4VEC) { + mmq_x = MMQ_X_Q4_K_PASCAL; + mmq_y = MMQ_Y_Q4_K_PASCAL; + nwarps = NWARPS_Q4_K_PASCAL; + } else { + GGML_ASSERT(false); + } + + const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; + const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; + const sycl::range<3> block_nums(1, block_num_y, block_num_x); + const sycl::range<3> block_dims(1, nwarps, WARP_SIZE); + + if (nrows_x % mmq_y == 0) { + const bool need_check = false; + /* + DPCT1049:34: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_ql_q4_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_dm_q4_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI4_K) + mmq_y / QI4_K), + cgh); + sycl::local_accessor tile_x_sc_q4_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q4_K( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + tile_x_ql_q4_K_acc_ct1.get_pointer(), + tile_x_dm_q4_K_acc_ct1.get_pointer(), + tile_x_sc_q4_K_acc_ct1.get_pointer(), + tile_y_qs_acc_ct1.get_pointer(), + tile_y_ds_acc_ct1.get_pointer()); + }); + }); + } + } else { + const bool need_check = true; + /* + DPCT1049:35: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_ql_q4_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_dm_q4_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI4_K) + mmq_y / QI4_K), + cgh); + sycl::local_accessor tile_x_sc_q4_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q4_K( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + tile_x_ql_q4_K_acc_ct1.get_pointer(), + tile_x_dm_q4_K_acc_ct1.get_pointer(), + tile_x_sc_q4_K_acc_ct1.get_pointer(), + tile_y_qs_acc_ct1.get_pointer(), + tile_y_ds_acc_ct1.get_pointer()); + }); + }); + } + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_mul_mat_q5_K_q8_1_sycl(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, + dpct::queue_ptr stream) try { + + int id; + SYCL_CHECK( + CHECK_TRY_ERROR(id = get_current_device_index())); + const int compute_capability = g_device_caps[id].cc; + + int mmq_x, mmq_y, nwarps; + if (compute_capability >= VER_GEN13) { + mmq_x = MMQ_X_Q5_K_RDNA2; + mmq_y = MMQ_Y_Q5_K_RDNA2; + nwarps = NWARPS_Q5_K_RDNA2; + } else if (compute_capability >= VER_GEN12) { + mmq_x = MMQ_X_Q5_K_RDNA1; + mmq_y = MMQ_Y_Q5_K_RDNA1; + nwarps = NWARPS_Q5_K_RDNA1; + } else if (compute_capability >= VER_GEN9) { + mmq_x = MMQ_X_Q5_K_AMPERE; + mmq_y = MMQ_Y_Q5_K_AMPERE; + nwarps = NWARPS_Q5_K_AMPERE; + } else if (compute_capability >= VER_4VEC) { + mmq_x = MMQ_X_Q5_K_PASCAL; + mmq_y = MMQ_Y_Q5_K_PASCAL; + nwarps = NWARPS_Q5_K_PASCAL; + } else { + GGML_ASSERT(false); + } + + const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; + const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; + const sycl::range<3> block_nums(1, block_num_y, block_num_x); + const sycl::range<3> block_dims(1, nwarps, WARP_SIZE); + + if (nrows_x % mmq_y == 0) { + const bool need_check = false; + /* + DPCT1049:36: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_ql_q5_K_acc_ct1( + sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_dm_q5_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI5_K) + mmq_y / QI5_K), + cgh); + sycl::local_accessor tile_x_sc_q5_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q5_K( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + tile_x_ql_q5_K_acc_ct1.get_pointer(), + tile_x_dm_q5_K_acc_ct1.get_pointer(), + tile_x_sc_q5_K_acc_ct1.get_pointer(), + tile_y_qs_acc_ct1.get_pointer(), + tile_y_ds_acc_ct1.get_pointer()); + }); + }); + } + } else { + const bool need_check = true; + /* + DPCT1049:37: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_ql_q5_K_acc_ct1( + sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_dm_q5_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI5_K) + mmq_y / QI5_K), + cgh); + sycl::local_accessor tile_x_sc_q5_K_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q5_K( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + tile_x_ql_q5_K_acc_ct1.get_pointer(), + tile_x_dm_q5_K_acc_ct1.get_pointer(), + tile_x_sc_q5_K_acc_ct1.get_pointer(), + tile_y_qs_acc_ct1.get_pointer(), + tile_y_ds_acc_ct1.get_pointer()); + }); + }); + } + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_mul_mat_q6_K_q8_1_sycl(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, + dpct::queue_ptr stream) try { + + int id; + SYCL_CHECK( + CHECK_TRY_ERROR(id = get_current_device_index())); + const int compute_capability = g_device_caps[id].cc; + + int mmq_x, mmq_y, nwarps; + if (compute_capability >= VER_GEN13) { + mmq_x = MMQ_X_Q6_K_RDNA2; + mmq_y = MMQ_Y_Q6_K_RDNA2; + nwarps = NWARPS_Q6_K_RDNA2; + } else if (compute_capability >= VER_GEN12) { + mmq_x = MMQ_X_Q6_K_RDNA1; + mmq_y = MMQ_Y_Q6_K_RDNA1; + nwarps = NWARPS_Q6_K_RDNA1; + } else if (compute_capability >= VER_GEN9) { + mmq_x = MMQ_X_Q6_K_AMPERE; + mmq_y = MMQ_Y_Q6_K_AMPERE; + nwarps = NWARPS_Q6_K_AMPERE; + } else if (compute_capability >= VER_4VEC) { + mmq_x = MMQ_X_Q6_K_PASCAL; + mmq_y = MMQ_Y_Q6_K_PASCAL; + nwarps = NWARPS_Q6_K_PASCAL; + } else { + GGML_ASSERT(false); + } + + const int block_num_x = (nrows_x + mmq_y - 1) / mmq_y; + const int block_num_y = (ncols_y + mmq_x - 1) / mmq_x; + const sycl::range<3> block_nums(1, block_num_y, block_num_x); + const sycl::range<3> block_dims(1, nwarps, WARP_SIZE); + + if (nrows_x % mmq_y == 0) { + const bool need_check = false; + /* + DPCT1049:38: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_ql_acc_ct1( + sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_dm_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI6_K) + mmq_y / QI6_K), + cgh); + sycl::local_accessor tile_x_sc_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q6_K( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + tile_x_ql_acc_ct1.get_pointer(), + tile_x_dm_acc_ct1.get_pointer(), + tile_x_sc_acc_ct1.get_pointer(), + tile_y_qs_acc_ct1.get_pointer(), + tile_y_ds_acc_ct1.get_pointer()); + }); + }); + } + } else { + const bool need_check = true; + /* + DPCT1049:39: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor tile_x_ql_acc_ct1( + sycl::range<1>(mmq_y * (2 * WARP_SIZE) + mmq_y), cgh); + sycl::local_accessor tile_x_dm_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / QI6_K) + mmq_y / QI6_K), + cgh); + sycl::local_accessor tile_x_sc_acc_ct1( + sycl::range<1>(mmq_y * (WARP_SIZE / 8) + mmq_y / 8), cgh); + sycl::local_accessor tile_y_qs_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE), cgh); + sycl::local_accessor tile_y_ds_acc_ct1( + sycl::range<1>(mmq_x * WARP_SIZE / QI8_1), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + mul_mat_q6_K( + vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, + nrows_dst, item_ct1, + tile_x_ql_acc_ct1.get_pointer(), + tile_x_dm_acc_ct1.get_pointer(), + tile_x_sc_acc_ct1.get_pointer(), + tile_y_qs_acc_ct1.get_pointer(), + tile_y_ds_acc_ct1.get_pointer()); + }); + }); + } + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_mul_mat_p021_f16_f32_sycl(const void *vx, const float *y, + float *dst, const int ncols_x, + const int nrows_x, + const int nchannels_x, + const int nchannels_y, + dpct::queue_ptr stream) { + + const sycl::range<3> block_nums(nchannels_y, nrows_x, 1); + const sycl::range<3> block_dims(1, 1, WARP_SIZE); + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + mul_mat_p021_f16_f32(vx, y, dst, ncols_x, nrows_x, nchannels_x, + nchannels_y, item_ct1); + }); + } +} + +static void ggml_mul_mat_vec_nc_f16_f32_sycl( + 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 nchannels_y, const int channel_stride_x, dpct::queue_ptr stream) { + + const sycl::range<3> block_nums(nchannels_y, nrows_x, 1); + const sycl::range<3> block_dims(1, 1, WARP_SIZE); + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + mul_mat_vec_nc_f16_f32(vx, y, dst, ncols_x, nrows_x, + row_stride_x, channel_stride_x, + nchannels_y / nchannels_x, item_ct1); + }); + } +} + +static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne, + const int ne00, const int ne01, + const int nb00, const int nb01, + const int nb02, const int ne10, + const int ne11, const int nb10, + const int nb11, const int nb12, + dpct::queue_ptr stream) { + + const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + cpy_f32_f16(cx, cdst, ne, ne00, ne01, nb00, nb01, + nb02, ne10, ne11, nb10, nb11, nb12, + item_ct1); + }); + } +} + +static void ggml_cpy_f32_f16_sycl(const char *cx, char *cdst, const int ne, + const int ne00, const int ne01, + const int nb00, const int nb01, + const int nb02, const int ne10, + const int ne11, const int nb10, + const int nb11, const int nb12, + dpct::queue_ptr stream) { + + const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + cpy_f32_f16(cx, cdst, ne, ne00, ne01, nb00, nb01, + nb02, ne10, ne11, nb10, nb11, nb12, + item_ct1); + }); + } +} + +static void ggml_cpy_f32_q8_0_sycl(const char *cx, char *cdst, const int ne, + const int ne00, const int ne01, + const int nb00, const int nb01, + const int nb02, const int ne10, + const int ne11, const int nb10, + const int nb11, const int nb12, + dpct::queue_ptr stream) { + + GGML_ASSERT(ne % QK8_0 == 0); + const int num_blocks = ne / QK8_0; + stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), + sycl::range<3>(1, 1, 1)), + [=](sycl::nd_item<3> item_ct1) { + cpy_f32_q( + cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, + ne10, ne11, nb10, nb11, nb12, item_ct1); + }); +} + +static void ggml_cpy_f32_q4_0_sycl(const char *cx, char *cdst, const int ne, + const int ne00, const int ne01, + const int nb00, const int nb01, + const int nb02, const int ne10, + const int ne11, const int nb10, + const int nb11, const int nb12, + dpct::queue_ptr stream) { + + GGML_ASSERT(ne % QK4_0 == 0); + const int num_blocks = ne / QK4_0; + stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), + sycl::range<3>(1, 1, 1)), + [=](sycl::nd_item<3> item_ct1) { + cpy_f32_q( + cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, + ne10, ne11, nb10, nb11, nb12, item_ct1); + }); +} + +static void ggml_cpy_f32_q4_1_sycl(const char *cx, char *cdst, const int ne, + const int ne00, const int ne01, + const int nb00, const int nb01, + const int nb02, const int ne10, + const int ne11, const int nb10, + const int nb11, const int nb12, + dpct::queue_ptr stream) { + + GGML_ASSERT(ne % QK4_1 == 0); + const int num_blocks = ne / QK4_1; + stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), + sycl::range<3>(1, 1, 1)), + [=](sycl::nd_item<3> item_ct1) { + cpy_f32_q( + cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, + ne10, ne11, nb10, nb11, nb12, item_ct1); + }); +} + +static void ggml_cpy_f16_f16_sycl(const char *cx, char *cdst, const int ne, + const int ne00, const int ne01, + const int nb00, const int nb01, + const int nb02, const int ne10, + const int ne11, const int nb10, + const int nb11, const int nb12, + dpct::queue_ptr stream) { + + const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + cpy_f32_f16(cx, cdst, ne, ne00, ne01, nb00, nb01, + nb02, ne10, ne11, nb10, nb11, nb12, + item_ct1); + }); + } +} + +static void ggml_cpy_i16_i16_sycl(const char *cx, char *cdst, const int ne, + const int ne00, const int ne01, + const int nb00, const int nb01, + const int nb02, const int ne10, + const int ne11, const int nb10, + const int nb11, const int nb12, + dpct::queue_ptr stream) { + + const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; + { + // dpct::has_capability_or_fail(stream->get_device(), + // {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + cpy_f32_f16(cx, cdst, ne, ne00, ne01, nb00, nb01, + nb02, ne10, ne11, nb10, nb11, nb12, + item_ct1); + }); + } +} + +static void ggml_cpy_i32_i32_sycl(const char *cx, char *cdst, const int ne, + const int ne00, const int ne01, + const int nb00, const int nb01, + const int nb02, const int ne10, + const int ne11, const int nb10, + const int nb11, const int nb12, + dpct::queue_ptr stream) { + + const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; + { + // dpct::has_capability_or_fail(stream->get_device(), + // {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + cpy_f32_f16(cx, cdst, ne, ne00, ne01, nb00, nb01, + nb02, ne10, ne11, nb10, nb11, nb12, + item_ct1); + }); + } +} + +static void scale_f32_sycl(const float *x, float *dst, const float scale, + const int k, dpct::queue_ptr stream) { + const int num_blocks = (k + SYCL_SCALE_BLOCK_SIZE - 1) / SYCL_SCALE_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_SCALE_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_SCALE_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + scale_f32(x, dst, scale, k, item_ct1); + }); +} + +static void clamp_f32_sycl(const float *x, float *dst, const float min, + const float max, const int k, + dpct::queue_ptr stream) { + const int num_blocks = (k + SYCL_CLAMP_BLOCK_SIZE - 1) / SYCL_CLAMP_BLOCK_SIZE; + stream->parallel_for( + sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * + sycl::range<3>(1, 1, SYCL_CLAMP_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_CLAMP_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + clamp_f32(x, dst, min, max, k, item_ct1); + }); +} + +template +static void rope_sycl(const T *x, T *dst, int ncols, int nrows, + const int32_t *pos, float freq_scale, int p_delta_rows, + float freq_base, float ext_factor, float attn_factor, + rope_corr_dims corr_dims, dpct::queue_ptr stream) { + GGML_ASSERT(ncols % 2 == 0); + const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1); + const int num_blocks_x = (ncols + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE); + const sycl::range<3> block_nums(1, num_blocks_x, nrows); + if (pos == nullptr) { + /* + DPCT1049:40: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + rope(x, dst, ncols, pos, freq_scale, p_delta_rows, + freq_base, ext_factor, attn_factor, corr_dims, + item_ct1); + }); + } else { + /* + DPCT1049:41: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + rope(x, dst, ncols, pos, freq_scale, p_delta_rows, + freq_base, ext_factor, attn_factor, corr_dims, + item_ct1); + }); + } +} + +template +static void rope_neox_sycl(const T *x, T *dst, int ncols, int n_dims, int nrows, + const int32_t *pos, float freq_scale, + int p_delta_rows, float freq_base, float ext_factor, + float attn_factor, rope_corr_dims corr_dims, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % 2 == 0); + const sycl::range<3> block_dims(1, SYCL_ROPE_BLOCK_SIZE, 1); + const int num_blocks_x = (ncols + 2*SYCL_ROPE_BLOCK_SIZE - 1) / (2*SYCL_ROPE_BLOCK_SIZE); + const sycl::range<3> block_nums(1, num_blocks_x, nrows); + + const float theta_scale = powf(freq_base, -2.0f/n_dims); + const float inv_ndims = -1.0f / n_dims; + + if (pos == nullptr) { + /* + DPCT1049:42: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + rope_neox(x, dst, ncols, n_dims, pos, freq_scale, + p_delta_rows, ext_factor, attn_factor, + corr_dims, theta_scale, inv_ndims, + item_ct1); + }); + } else { + /* + DPCT1049:43: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + rope_neox(x, dst, ncols, n_dims, pos, freq_scale, + p_delta_rows, ext_factor, attn_factor, + corr_dims, theta_scale, inv_ndims, item_ct1); + }); + } +} + +static void rope_glm_f32_sycl(const float *x, float *dst, int ncols, int nrows, + const int32_t *pos, float freq_scale, + int p_delta_rows, float freq_base, int n_ctx, + dpct::queue_ptr stream) { + GGML_ASSERT(ncols % 4 == 0); + const sycl::range<3> block_dims(1, 1, SYCL_ROPE_BLOCK_SIZE / 4); + const int num_blocks_x = (ncols + SYCL_ROPE_BLOCK_SIZE - 1) / SYCL_ROPE_BLOCK_SIZE; + const sycl::range<3> block_nums(1, nrows, num_blocks_x); + stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + rope_glm_f32(x, dst, ncols, pos, freq_scale, + p_delta_rows, freq_base, n_ctx, + item_ct1); + }); +} + +static void alibi_f32_sycl(const float *x, float *dst, const int ncols, + const int nrows, const int k_rows, + const int n_heads_log2_floor, const float m0, + const float m1, dpct::queue_ptr stream) { + const sycl::range<3> block_dims(1, 1, SYCL_ALIBI_BLOCK_SIZE); + const int num_blocks_x = (ncols + SYCL_ALIBI_BLOCK_SIZE - 1) / (SYCL_ALIBI_BLOCK_SIZE); + const sycl::range<3> block_nums(1, nrows, num_blocks_x); + stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + alibi_f32(x, dst, ncols, k_rows, + n_heads_log2_floor, m0, m1, item_ct1); + }); +} + +static void sum_rows_f32_sycl(const float *x, float *dst, const int ncols, + const int nrows, dpct::queue_ptr stream) { + const sycl::range<3> block_dims(1, 1, WARP_SIZE); + const sycl::range<3> block_nums(1, nrows, 1); + stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) + [[intel::reqd_sub_group_size(32)]] { + k_sum_rows_f32(x, dst, ncols, item_ct1); + }); +} + +static void argsort_f32_i32_sycl(const float *x, int *dst, const int ncols, + const int nrows, ggml_sort_order order, + dpct::queue_ptr stream) { + // bitonic sort requires ncols to be power of 2 + GGML_ASSERT((ncols & (ncols - 1)) == 0); + + const sycl::range<3> block_dims(1, 1, ncols); + const sycl::range<3> block_nums(1, nrows, 1); + if (order == GGML_SORT_ASC) { + /* + DPCT1049:44: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + k_argsort_f32_i32(x, dst, ncols, item_ct1); + }); + } else if (order == GGML_SORT_DESC) { + /* + DPCT1049:45: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + stream->parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + k_argsort_f32_i32(x, dst, ncols, item_ct1); + }); + } else { + GGML_ASSERT(false); + } +} + +static void diag_mask_inf_f32_sycl(const float *x, float *dst, + const int ncols_x, const int nrows_x, + const int rows_per_channel, const int n_past, + dpct::queue_ptr stream) { + const sycl::range<3> block_dims(1, SYCL_DIAG_MASK_INF_BLOCK_SIZE, 1); + const int block_num_x = (ncols_x + SYCL_DIAG_MASK_INF_BLOCK_SIZE - 1) / SYCL_DIAG_MASK_INF_BLOCK_SIZE; + const sycl::range<3> block_nums(1, block_num_x, nrows_x); + stream->parallel_for(sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + diag_mask_inf_f32(x, dst, ncols_x, + rows_per_channel, n_past, + item_ct1); + }); +} + +static void soft_max_f32_sycl(const float *x, const float *y, float *dst, + const int ncols_x, const int nrows_x, + const int nrows_y, const float scale, + dpct::queue_ptr stream) { + int nth = WARP_SIZE; + while (nth < ncols_x && nth < SYCL_SOFT_MAX_BLOCK_SIZE) nth *= 2; + const sycl::range<3> block_dims(1, 1, nth); + const sycl::range<3> block_nums(1, 1, nrows_x); + /* + DPCT1049:46: The work-group size passed to the SYCL kernel may exceed the + limit. To get the device limit, query info::device::max_work_group_size. + Adjust the work-group size if needed. + */ + stream->submit([&](sycl::handler &cgh) { + /* + DPCT1101:96: 'SYCL_SOFT_MAX_BLOCK_SIZE/WARP_SIZE' expression was + replaced with a value. Modify the code to use the original expression, + provided in comments, if it is correct. + */ + sycl::local_accessor buf_acc_ct1( + sycl::range<1>(32 /*SYCL_SOFT_MAX_BLOCK_SIZE/WARP_SIZE*/), cgh); + + cgh.parallel_for( + sycl::nd_range<3>(block_nums * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) [[intel::reqd_sub_group_size(32)]] { + soft_max_f32(x, y, dst, ncols_x, nrows_y, scale, item_ct1, + buf_acc_ct1.get_pointer()); + }); + }); +} + +static void im2col_f32_f16_sycl(const float *x, sycl::half *dst, int IW, int IH, + int OW, int OH, int KW, int KH, int IC, + int offset_delta, int s0, int s1, int p0, + int p1, int d0, int d1, + dpct::queue_ptr stream) { + const int parallel_elements = OW * KW * KH; + const int num_blocks = (parallel_elements + SYCL_IM2COL_BLOCK_SIZE - 1) / SYCL_IM2COL_BLOCK_SIZE; + sycl::range<3> block_nums(IC, OH, num_blocks); + { + dpct::has_capability_or_fail(stream->get_device(), + {sycl::aspect::fp16}); + + stream->parallel_for( + sycl::nd_range<3>(block_nums * + sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE), + sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE)), + [=](sycl::nd_item<3> item_ct1) { + im2col_f32_f16(x, dst, offset_delta, IW, IH, OW, KW, KH, + parallel_elements, (IC * KH * KW), s0, s1, p0, + p1, d0, d1, item_ct1); + }); + } +} + +// buffer pool for sycl +#define MAX_SYCL_BUFFERS 256 + +struct scoped_spin_lock { + std::atomic_flag& lock; + scoped_spin_lock(std::atomic_flag& lock) : lock(lock) { + while (lock.test_and_set(std::memory_order_acquire)) { + ; // spin + } + } + ~scoped_spin_lock() { + lock.clear(std::memory_order_release); + } + scoped_spin_lock(const scoped_spin_lock&) = delete; + scoped_spin_lock& operator=(const scoped_spin_lock&) = delete; +}; + +static std::atomic_flag g_sycl_pool_lock = ATOMIC_FLAG_INIT; + +// #define DEBUG_SYCL_MALLOC +struct sycl_buffer { + void * ptr = nullptr; + size_t size = 0; +}; + +static sycl_buffer g_sycl_buffer_pool[GGML_SYCL_MAX_DEVICES][MAX_SYCL_BUFFERS]; +static size_t g_sycl_pool_size[GGML_SYCL_MAX_DEVICES] = {0}; + +static void *ggml_sycl_pool_malloc_leg(size_t size, size_t *actual_size) try { + scoped_spin_lock lock(g_sycl_pool_lock); + int id; + SYCL_CHECK( + CHECK_TRY_ERROR(id = get_current_device_index())); + // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg index %d\n", id); +#ifdef DEBUG_SYCL_MALLOC + int nnz = 0; + size_t max_size = 0; +#endif + size_t best_diff = 1ull << 36; + int ibest = -1; + for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { + sycl_buffer& b = g_sycl_buffer_pool[id][i]; + if (b.ptr != nullptr) { +#ifdef DEBUG_SYCL_MALLOC + ++nnz; + if (b.size > max_size) max_size = b.size; +#endif + if (b.size >= size) { + size_t diff = b.size - size; + if (diff < best_diff) { + best_diff = diff; + ibest = i; + if (!best_diff) { + void * ptr = b.ptr; + *actual_size = b.size; + b.ptr = nullptr; + b.size = 0; + // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 1 %p\n", ptr); + return ptr; + } + } + } + } + } + if (ibest >= 0) { + sycl_buffer& b = g_sycl_buffer_pool[id][ibest]; + void * ptr = b.ptr; + *actual_size = b.size; + b.ptr = nullptr; + b.size = 0; + // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return 2 %p\n", ptr); + return ptr; + } + void * ptr; + size_t look_ahead_size = (size_t) (1.05 * size); + look_ahead_size = 256 * ((look_ahead_size + 255)/256); + + const dpct::queue_ptr stream = g_syclStreams[id][0]; + SYCL_CHECK( + CHECK_TRY_ERROR(ptr = (void *)sycl::malloc_device( + look_ahead_size, *stream))); + *actual_size = look_ahead_size; + g_sycl_pool_size[id] += look_ahead_size; + +#ifdef DEBUG_SYCL_MALLOC + fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, id, nnz, + (uint32_t)(max_size/1024/1024), (uint32_t)(g_sycl_pool_size[id]/1024/1024), (uint32_t)(size/1024/1024)); +#endif + // GGML_SYCL_DEBUG("ggml_sycl_pool_malloc_leg return %p\n", ptr); + return ptr; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_sycl_pool_free_leg(void *ptr, size_t size) try { + scoped_spin_lock lock(g_sycl_pool_lock); + int id; + SYCL_CHECK( + CHECK_TRY_ERROR(id = get_current_device_index())); + + const dpct::queue_ptr stream = g_syclStreams[id][0]; + for (int i = 0; i < MAX_SYCL_BUFFERS; ++i) { + sycl_buffer& b = g_sycl_buffer_pool[id][i]; + if (b.ptr == nullptr) { + b.ptr = ptr; + b.size = size; + return; + } + } + fprintf(stderr, "WARNING: sycl buffer pool full, increase MAX_SYCL_BUFFERS\n"); + SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, *stream))); + g_sycl_pool_size[id] -= size; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +// pool with virtual memory +/* +DPCT1082:64: Migration of CUmemGenericAllocationHandle type is not supported. +*/ +// static std::vector +// g_sycl_pool_handles[GGML_SYCL_MAX_DEVICES]; +static dpct::device_ptr g_sycl_pool_addr[GGML_SYCL_MAX_DEVICES] = {0}; +static size_t g_sycl_pool_used[GGML_SYCL_MAX_DEVICES] = {0}; +static const size_t SYCL_POOL_VMM_MAX_SIZE = 1ull << 36; // 64 GB + +static void *ggml_sycl_pool_malloc_vmm(size_t size, size_t *actual_size) try { + + return NULL; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_sycl_pool_free_vmm(void *ptr, size_t size) try { + scoped_spin_lock lock(g_sycl_pool_lock); + int id; + SYCL_CHECK( + CHECK_TRY_ERROR(id = dpct::dev_mgr::instance().current_device_id())); + +#ifdef DEBUG_SYCL_MALLOC + printf("sycl pool[%d]: freed %llu bytes at %llx\n", id, (unsigned long long) size, ptr); +#endif + + g_sycl_pool_used[id] -= size; + + // all deallocations must be in reverse order of the allocations + GGML_ASSERT(ptr == (void *) (g_sycl_pool_addr[id] + g_sycl_pool_used[id])); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void *ggml_sycl_pool_malloc(size_t size, size_t *actual_size) try { + int id; + SYCL_CHECK( + CHECK_TRY_ERROR(id = get_current_device_index())); + if (g_device_caps[id].vmm) { + return ggml_sycl_pool_malloc_vmm(size, actual_size); + } else { + return ggml_sycl_pool_malloc_leg(size, actual_size); + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_sycl_pool_free(void *ptr, size_t size) try { + int id; + SYCL_CHECK( + CHECK_TRY_ERROR(id = get_current_device_index())); + if (g_device_caps[id].vmm) { + ggml_sycl_pool_free_vmm(ptr, size); + } else { + ggml_sycl_pool_free_leg(ptr, size); + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + + +template +struct sycl_pool_alloc { + T * ptr = nullptr; + size_t actual_size = 0; + + // size is in number of elements + T * alloc(size_t size) { + GGML_ASSERT(ptr == nullptr); + ptr = (T *) ggml_sycl_pool_malloc(size * sizeof(T), &this->actual_size); + // GGML_SYCL_DEBUG("alloc %lu return %p actual size=%lu\n", size * sizeof(T), ptr, this->actual_size); + return ptr; + } + + sycl_pool_alloc(size_t size) { + alloc(size); + } + + ~sycl_pool_alloc() { + if (ptr != nullptr) { + ggml_sycl_pool_free(ptr, actual_size); + } + } + + T * get() { + return ptr; + } + + sycl_pool_alloc() = default; + sycl_pool_alloc(const sycl_pool_alloc &) = delete; + sycl_pool_alloc(sycl_pool_alloc &&) = delete; + sycl_pool_alloc& operator=(const sycl_pool_alloc &) = delete; + sycl_pool_alloc& operator=(sycl_pool_alloc &&) = delete; +}; + +static bool g_sycl_loaded = false; + +bool ggml_sycl_loaded(void) { + return g_sycl_loaded; +} + +void ggml_backend_sycl_print_sycl_devices(){ + int device_count = dpct::dev_mgr::instance().device_count(); + fprintf(stderr, "found %d SYCL devices:\n", device_count); + for (int id = 0; id < device_count; ++id) { + dpct::device_info prop; + SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( + prop, dpct::dev_mgr::instance().get_device(id)))); + sycl::device cur_device = dpct::dev_mgr::instance().get_device(id); + fprintf(stderr, " Device %d: %s,\tcompute capability %d.%d,\n\tmax compute_units %d,\tmax work group size %d,\tmax sub group size %d,\tglobal mem size %lu\n", id, + prop.get_name(), prop.get_major_version(), + prop.get_minor_version(), + prop.get_max_compute_units(), + prop.get_max_work_group_size(), + prop.get_max_sub_group_size(), + prop.get_global_mem_size() + ); + } + // fprintf(stderr, "\n"); +} + +int get_sycl_env(const char* env_name, int default_val){ + char * user_device_string = getenv(env_name); + int user_number = default_val; + + unsigned n; + if (user_device_string != NULL && sscanf(user_device_string, " %u", &n) == 1) { + user_number = (int)n; + } else { + user_number=default_val; + } + return user_number; +} + +int get_work_group_size(int user_device_id){ + dpct::device_info prop; + dpct::get_device_info( + prop, + dpct::dev_mgr::instance().get_device(user_device_id)); + return prop.get_max_work_group_size(); +} + +void ggml_init_sycl() try { + static bool initialized = false; + + if (!initialized) { + g_ggml_sycl_debug = get_sycl_env("GGML_SYCL_DEBUG", 0); + + printf("GGML_SYCL_DEBUG=%d\n", g_ggml_sycl_debug); + + int user_device_id = get_sycl_env("GGML_SYCL_DEVICE", 0); + + if (CHECK_TRY_ERROR(g_all_sycl_device_count = + dpct::dev_mgr::instance().device_count()) != + 0) { + initialized = true; + g_sycl_loaded = false; + return; + } + GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES); + int64_t total_vram = 0; + +#if defined(GGML_SYCL_FP16) + fprintf(stderr, "%s: GGML_SYCL_FP16: yes\n", __func__); +#else + fprintf(stderr, "%s: GGML_SYCL_FP16: no\n", __func__); +#endif + + +#if defined(SYCL_USE_XMX) + fprintf(stderr, "%s: SYCL_USE_XMX: yes\n", __func__); +#else + fprintf(stderr, "%s: SYCL_USE_XMX: no\n", __func__); +#endif + ggml_backend_sycl_print_sycl_devices(); + for (int id = 0; id < GGML_SYCL_MAX_DEVICES; ++id) { + g_sycl_device_id2index[id].index = -1; + g_device_caps[id].vmm = 0; + g_device_caps[id].device_id = -1; + g_device_caps[id].cc = 0; + g_tensor_split[id] = 0; + } + + int device_inx = -1; + for (int id = 0; id < g_all_sycl_device_count; ++id) { + if(id!=user_device_id) continue; + + device_inx++; + int device_vmm = 0; + + g_device_caps[device_inx].vmm = !!device_vmm; + g_device_caps[device_inx].device_id = id; + g_sycl_device_id2index[id].index = device_inx; + + dpct::device_info prop; + SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( + prop, dpct::dev_mgr::instance().get_device(id)))); + + // fprintf(stderr, + // " Device %d: %s, compute capability %d.%d, VMM: %s\n", id, + // prop.get_name(), prop.get_major_version(), + // prop.get_minor_version(), device_vmm ? "yes" : "no"); + + g_tensor_split[device_inx] = total_vram; + total_vram += prop.get_global_mem_size(); + + g_device_caps[device_inx].cc = + 100 * prop.get_major_version() + 10 * prop.get_minor_version(); + + // printf("g_device_caps[%d].cc=%d\n", device_inx, g_device_caps[device_inx].cc); + } + device_inx = -1; + for (int id = 0; id < g_all_sycl_device_count; ++id) { + if(id!=user_device_id) continue; + device_inx++; + g_tensor_split[device_inx] /= total_vram; + } + + device_inx = -1; + for (int id = 0; id < g_all_sycl_device_count; ++id) { + if(id!=user_device_id) continue; + device_inx++; + SYCL_CHECK(ggml_sycl_set_device(id)); + + // create sycl streams + for (int is = 0; is < MAX_STREAMS; ++is) { + /* + DPCT1025:88: The SYCL queue is created ignoring the flag and + priority options. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + g_syclStreams[device_inx][is] = + dpct::get_current_device().create_queue())); + } + + const dpct::queue_ptr stream = g_syclStreams[device_inx][0]; + // create sycl handle + SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[device_inx] = + stream)); + /* + DPCT1027:89: The call to syclSetMathMode was replaced with 0 + because this functionality is redundant in SYCL. + */ + SYCL_CHECK(0); + } + + // configure logging to stdout + // SYCL_CHECK(syclLoggerConfigure(1, 1, 0, nullptr)); + + //hardcode, force set to 1 device + g_device_count = 1; + ggml_sycl_set_main_device(user_device_id); + ggml_sycl_set_device(user_device_id); + g_work_group_size = get_work_group_size(user_device_id); + // fprintf(stderr, "Using Device %d\n", user_device_id); + + // for (int id = 0; id < g_all_sycl_device_count; ++id) { + // GGML_SYCL_DEBUG("id=%d g_device_caps[%d].device_id=%d g_sycl_device_id2index[%d].index=%d ", id, id, + // g_device_caps[id].device_id, id, g_sycl_device_id2index[id].index); + // } + + initialized = true; + g_sycl_loaded = true; + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + + +void ggml_sycl_set_tensor_split(const float * tensor_split) { + if (tensor_split == nullptr) { + return; + } + bool all_zero = true; + for (int i = 0; i < g_device_count; ++i) { + if (tensor_split[i] != 0.0f) { + all_zero = false; + break; + } + } + if (all_zero) { + return; + } + float split_sum = 0.0f; + for (int i = 0; i < g_device_count; ++i) { + g_tensor_split[i] = split_sum; + split_sum += tensor_split[i]; + } + for (int i = 0; i < g_device_count; ++i) { + g_tensor_split[i] /= split_sum; + } +} + +void *ggml_sycl_host_malloc(size_t size) try { + if (getenv("GGML_SYCL_NO_PINNED") != nullptr) { + return nullptr; + } + + void * ptr = nullptr; + //allow to use dpct::get_in_order_queue() for host malloc + dpct::err0 err = CHECK_TRY_ERROR( + ptr = (void *)sycl::malloc_host(size, dpct::get_in_order_queue())); + /* + DPCT1000:82: Error handling if-stmt was detected but could not be rewritten. + */ + if (err != 0) { + // clear the error + /* + DPCT1026:83: The call to syclGetLastError was removed because this + functionality is redundant in SYCL. + */ + /* + DPCT1001:81: The statement could not be removed. + */ + fprintf( + stderr, + "WARNING: failed to allocate %.2f MB of pinned memory: %s\n", + /* + DPCT1009:84: SYCL uses exceptions to report errors and does not use + the error codes. The original code was commented out and a warning + string was inserted. You need to rewrite this code. + */ + size / 1024.0 / 1024.0, + "syclGetErrorString is not supported" /*syclGetErrorString(err)*/); + return nullptr; + } + + return ptr; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +void ggml_sycl_host_free(void *ptr) try { + //allow to use dpct::get_in_order_queue() for host malloc + SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(ptr, dpct::get_in_order_queue()))); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static dpct::err0 ggml_sycl_cpy_tensor_2d(void *dst, + const struct ggml_tensor *src, + int64_t i3, int64_t i2, + int64_t i1_low, int64_t i1_high, + dpct::queue_ptr stream) try { + + dpct::memcpy_direction kind; + char * src_ptr; + if (src->backend == GGML_BACKEND_CPU) { + kind = dpct::host_to_device; + src_ptr = (char *) src->data; + // GGML_SYCL_DEBUG("ggml_sycl_cpy_tensor_2d GGML_BACKEND_CPU src_ptr %p\n", src_ptr); + } else if (src->backend == GGML_BACKEND_GPU || src->backend == GGML_BACKEND_GPU_SPLIT) { + GGML_ASSERT(src->backend != GGML_BACKEND_GPU_SPLIT || (i1_low == 0 && i1_high == src->ne[1])); + kind = dpct::device_to_device; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src->extra; + int id; + SYCL_CHECK(CHECK_TRY_ERROR( + id = get_current_device_index())); + // GGML_SYCL_DEBUG("current device index %d\n", id); + src_ptr = (char *) extra->data_device[id]; + } else { + // GGML_SYCL_DEBUG("GGML_ASSERT(false)\n"); + GGML_ASSERT(false); + } + char * dst_ptr = (char *) dst; + + const int64_t ne0 = src->ne[0]; + const int64_t nb0 = src->nb[0]; + const int64_t nb1 = src->nb[1]; + const int64_t nb2 = src->nb[2]; + const int64_t nb3 = src->nb[3]; + const enum ggml_type type = src->type; + const int64_t ts = ggml_type_size(type); + const int64_t bs = ggml_blck_size(type); + int64_t i1_diff = i1_high - i1_low; + + const char * x = src_ptr + i1_low*nb1 + i2*nb2 + i3*nb3; + if (nb0 == ts && nb1 == ts*ne0/bs) { + // GGML_SYCL_DEBUG("stream->memcpy: dst_ptr=%p, x=%p, size=%lu\n", dst_ptr, x, i1_diff * nb1); + // return CHECK_TRY_ERROR(stream->memcpy(dst_ptr, x, i1_diff * nb1)); + return CHECK_TRY_ERROR(dpct::async_dpct_memcpy(dst_ptr, x, i1_diff * nb1, + kind, *stream)); + + } else if (nb0 == ts) { + return CHECK_TRY_ERROR( + dpct::async_dpct_memcpy(dst_ptr, ts * ne0 / bs, x, nb1, + ts * ne0 / bs, i1_diff, kind, *stream)); + } else { + for (int64_t i1 = 0; i1 < i1_diff; i1++) { + const void * rx = (const void *) ((const char *) x + i1*nb1); + void * rd = (void *) (dst_ptr + i1*ts*ne0/bs); + // pretend the row is a matrix with cols=1 + dpct::err0 r = CHECK_TRY_ERROR(dpct::async_dpct_memcpy( + rd, ts / bs, rx, nb0, ts / bs, ne0, kind, *stream)); + /* + DPCT1001:85: The statement could not be removed. + */ + /* + DPCT1000:86: Error handling if-stmt was detected but could not be + rewritten. + */ + if (r != 0) return r; + } + return 0; + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_sycl_op_get_rows(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_d, const float *src1_d, + float *dst_d, const dpct::queue_ptr &stream) { + + GGML_ASSERT(src1->type == GGML_TYPE_I32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + GGML_ASSERT(src0->nb[0] == ggml_type_size(src0->type)); + GGML_ASSERT(src1->nb[0] == ggml_type_size(src1->type)); + GGML_ASSERT(dst->nb[0] == ggml_type_size(dst->type)); + + const int32_t * src1_i32 = (const int32_t *) src1_d; + + switch (src0->type) { + case GGML_TYPE_F16: + get_rows_sycl_float(src0, src1, dst, (const sycl::half *)src0_d, + src1_i32, dst_d, stream); + break; + case GGML_TYPE_F32: + get_rows_sycl_float(src0, src1, dst, src0_d, src1_i32, dst_d, stream); + break; + case GGML_TYPE_Q4_0: + get_rows_sycl(src0, src1, dst, src0_d, src1_i32, dst_d, stream); + break; + case GGML_TYPE_Q4_1: + get_rows_sycl(src0, src1, dst, src0_d, src1_i32, dst_d, stream); + break; + case GGML_TYPE_Q5_0: + get_rows_sycl(src0, src1, dst, src0_d, src1_i32, dst_d, stream); + break; + case GGML_TYPE_Q5_1: + get_rows_sycl(src0, src1, dst, src0_d, src1_i32, dst_d, stream); + break; + case GGML_TYPE_Q8_0: + get_rows_sycl(src0, src1, dst, src0_d, src1_i32, dst_d, stream); + break; + default: + // TODO: k-quants + fprintf(stderr, "%s: unsupported type: %s\n", __func__, ggml_type_name(src0->type)); + GGML_ASSERT(false); + break; + } +} + +template +inline void ggml_sycl_op_bin_bcast(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const dpct::queue_ptr &main_stream) { + + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + op()(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); + } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { + op()(src0, src1, dst, (const sycl::half *)src0_dd, src1_dd, + (sycl::half *)dst_dd, main_stream); + } else if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) { + op()(src0, src1, dst, (const sycl::half *)src0_dd, src1_dd, dst_dd, + main_stream); + } else if (src0->type == GGML_TYPE_I32 && dst->type == GGML_TYPE_I32) { + op()(src0, src1, dst, (const int32_t *)src0_dd, (const int32_t *)src1_dd, (int32_t *)dst_dd, + main_stream); + } else if (src0->type == GGML_TYPE_I16 && dst->type == GGML_TYPE_I16) { + op()(src0, src1, dst, (const int16_t *)src0_dd, (const int16_t *)src1_dd, (int16_t *)dst_dd, + main_stream); + } else { + fprintf(stderr, "%s: unsupported types: dst: %s, src0: %s, src1: %s\n", __func__, + ggml_type_name(dst->type), ggml_type_name(src0->type), ggml_type_name(src1->type)); + GGML_ASSERT(false); + } +} + +static void ggml_sycl_op_repeat(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_d, const float *src1_d, + float *dst_d, + const dpct::queue_ptr &main_stream) { + + ggml_sycl_op_bin_bcast>(dst, src0, dst, nullptr, src0_d, dst_d, main_stream); + + (void) src1; + (void) src1_d; +} + +inline void ggml_sycl_op_add(const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const dpct::queue_ptr &main_stream) { + + ggml_sycl_op_bin_bcast>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); +} + +inline void ggml_sycl_op_acc(const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + GGML_ASSERT(dst->ne[3] == 1); // just 3D tensors supported + + int nb1 = dst->op_params[0] / 4; // 4 bytes of float32 + int nb2 = dst->op_params[1] / 4; // 4 bytes of float32 + // int nb3 = dst->op_params[2] / 4; // 4 bytes of float32 - unused + int offset = dst->op_params[3] / 4; // offset in bytes + + acc_f32_sycl(src0_dd, src1_dd, dst_dd, ggml_nelements(dst), src1->ne[0], src1->ne[1], src1->ne[2], nb1, nb2, offset, main_stream); + + (void) dst; +} + +inline void ggml_sycl_op_mul(const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const dpct::queue_ptr &main_stream) { + + ggml_sycl_op_bin_bcast>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); +} + +inline void ggml_sycl_op_div(const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const dpct::queue_ptr &main_stream) { + + ggml_sycl_op_bin_bcast>(src0, src1, dst, src0_dd, src1_dd, dst_dd, main_stream); +} + +inline void ggml_sycl_op_gelu(const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + gelu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_silu(const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + silu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_gelu_quick(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + gelu_quick_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_tanh(const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + tanh_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_relu(const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + relu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_leaky_relu(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + float negative_slope; + memcpy(&negative_slope, dst->op_params, sizeof(float)); + + leaky_relu_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), negative_slope, main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_sqr(const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + sqr_f32_sycl(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_norm(const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + const int64_t ne00 = src0->ne[0]; + const int64_t nrows = ggml_nrows(src0); + + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); + + norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_group_norm(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + int num_groups = dst->op_params[0]; + int group_size = src0->ne[0] * src0->ne[1] * ((src0->ne[2] + num_groups - 1) / num_groups); + group_norm_f32_sycl(src0_dd, dst_dd, num_groups, group_size, src0->ne[0] * src0->ne[1] * src0->ne[2], main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_concat(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + + for (int i3 = 0; i3 < dst->ne[3]; i3++) { + concat_f32_sycl(src0_dd + i3 * (src0->nb[3] / 4), src1_dd + i3 * (src1->nb[3] / 4), dst_dd + i3 * (dst->nb[3] / 4), dst->ne[0], dst->ne[1], dst->ne[2], src0->ne[2], main_stream); + } + + (void) src1; + (void) dst; +} + +inline void ggml_sycl_op_upscale(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors + + const int scale_factor = dst->op_params[0]; + + upscale_f32_sycl(src0_dd, dst_dd, src0->ne[0], src0->ne[1], src0->ne[2], scale_factor, main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_pad(const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors + + pad_f32_sycl(src0_dd, dst_dd, + src0->ne[0], src0->ne[1], src0->ne[2], + dst->ne[0], dst->ne[1], dst->ne[2], main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_rms_norm(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + const int64_t ne00 = src0->ne[0]; + const int64_t nrows = ggml_nrows(src0); + + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); + + rms_norm_f32_sycl(src0_dd, dst_dd, ne00, nrows, eps, main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_mul_mat_q( + const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, + const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i, + float *dst_dd_i, const int64_t row_low, const int64_t row_high, + const int64_t src1_ncols, const int64_t src1_padded_row_size, + const dpct::queue_ptr &stream) try { + + const int64_t ne00 = src0->ne[0]; + + const int64_t ne10 = src1->ne[0]; + GGML_ASSERT(ne10 % QK8_1 == 0); + + const int64_t ne0 = dst->ne[0]; + + const int64_t row_diff = row_high - row_low; + + int device_id; + SYCL_CHECK( + CHECK_TRY_ERROR(device_id = dpct::dev_mgr::instance().current_device_id())); + + // the main device has a larger memory buffer to hold the results from all GPUs + // nrows_dst == nrows of the matrix that the dequantize_mul_mat kernel writes into + const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && device_id == g_main_device ? ne0 : row_diff; + + switch (src0->type) { + case GGML_TYPE_Q4_0: + ggml_mul_mat_q4_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + break; + case GGML_TYPE_Q4_1: + ggml_mul_mat_q4_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + break; + case GGML_TYPE_Q5_0: + ggml_mul_mat_q5_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + break; + case GGML_TYPE_Q5_1: + ggml_mul_mat_q5_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + break; + case GGML_TYPE_Q8_0: + ggml_mul_mat_q8_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + break; + case GGML_TYPE_Q2_K: + ggml_mul_mat_q2_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + break; + case GGML_TYPE_Q3_K: + ggml_mul_mat_q3_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + break; + case GGML_TYPE_Q4_K: + ggml_mul_mat_q4_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + break; + case GGML_TYPE_Q5_K: + ggml_mul_mat_q5_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + break; + case GGML_TYPE_Q6_K: + ggml_mul_mat_q6_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + break; + default: + GGML_ASSERT(false); + break; + } + + (void) src1; + (void) dst; + (void) src1_ddf_i; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static int64_t get_row_rounding(ggml_type type) { + int64_t min_compute_capability = INT_MAX; + int64_t max_compute_capability = INT_MIN; + for (int64_t id = 0; id < g_device_count; ++id) { + if (g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) { + if (min_compute_capability > g_device_caps[id].cc) { + min_compute_capability = g_device_caps[id].cc; + } + if (max_compute_capability < g_device_caps[id].cc) { + max_compute_capability = g_device_caps[id].cc; + } + } + } + + switch(type) { + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + return max_compute_capability >= VER_GEN9 ? 128 : 64; + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + return 64; + case GGML_TYPE_F16: + case GGML_TYPE_F32: + return 1; + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + return max_compute_capability >= VER_GEN9 ? 128 : 64; + case GGML_TYPE_Q6_K: + return 64; + default: + GGML_ASSERT(false); + } +} + +inline void ggml_sycl_op_mul_mat_vec_q( + const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, + const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i, + float *dst_dd_i, const int64_t row_low, const int64_t row_high, + const int64_t src1_ncols, const int64_t src1_padded_row_size, + const dpct::queue_ptr &stream) { + + GGML_ASSERT(ggml_nrows(src1) == 1); + + const int64_t ne00 = src0->ne[0]; + const int64_t row_diff = row_high - row_low; + + switch (src0->type) { + case GGML_TYPE_Q4_0: + mul_mat_vec_q4_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q4_1: + mul_mat_vec_q4_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q5_0: + mul_mat_vec_q5_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q5_1: + mul_mat_vec_q5_1_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q8_0: + mul_mat_vec_q8_0_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q2_K: + mul_mat_vec_q2_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q3_K: + mul_mat_vec_q3_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q4_K: + mul_mat_vec_q4_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q5_K: + mul_mat_vec_q5_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q6_K: + mul_mat_vec_q6_K_q8_1_sycl(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; + default: + GGML_ASSERT(false); + break; + } + + (void) src1; + (void) dst; + (void) src1_ddf_i; + (void) src1_ncols; + (void) src1_padded_row_size; +} + +inline void ggml_sycl_op_dequantize_mul_mat_vec( + const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, + const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i, + float *dst_dd_i, const int64_t row_low, const int64_t row_high, + const int64_t src1_ncols, const int64_t src1_padded_row_size, + const dpct::queue_ptr &stream) { + + const int64_t ne00 = src0->ne[0]; + const int64_t row_diff = row_high - row_low; + + // on some GPUs it is faster to convert src1 to half and to use half precision intrinsics +#ifdef GGML_SYCL_F16 + sycl_pool_alloc src1_dfloat_a; + sycl::half *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) { + if (src1->type == GGML_TYPE_F16) { + src1_dfloat = (sycl::half *)src1->data + src1_padded_row_size; + } else { + src1_dfloat = src1_dfloat_a.alloc(ne00); + ggml_cpy_f32_f16_sycl((const char *)src1_ddf_i, (char *)src1_dfloat, + ne00, ne00, 1, sizeof(float), 0, 0, ne00, 1, + sizeof(sycl::half), 0, 0, stream); + } + } +#else + const dfloat * src1_dfloat = (const dfloat *) src1_ddf_i; // dfloat == float, no conversion +#endif // GGML_SYCL_F16 + + switch (src0->type) { + case GGML_TYPE_Q4_0: + dequantize_mul_mat_vec_q4_0_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q4_1: + dequantize_mul_mat_vec_q4_1_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q5_0: + dequantize_mul_mat_vec_q5_0_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q5_1: + dequantize_mul_mat_vec_q5_1_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q8_0: + dequantize_mul_mat_vec_q8_0_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q2_K: + dequantize_mul_mat_vec_q2_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q3_K: + dequantize_mul_mat_vec_q3_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q4_K: + dequantize_mul_mat_vec_q4_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q5_K: + dequantize_mul_mat_vec_q5_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_Q6_K: + dequantize_mul_mat_vec_q6_K_sycl(src0_dd_i, src1_ddf_i, dst_dd_i, ne00, row_diff, stream); + break; + case GGML_TYPE_F16: + convert_mul_mat_vec_f16_sycl(src0_dd_i, src1_dfloat, dst_dd_i, ne00, row_diff, stream); + break; + default: + GGML_ASSERT(false); + break; + } + + (void) src1; + (void) dst; + (void) src1_ddq_i; + (void) src1_ncols; + (void) src1_padded_row_size; +} + +inline void ggml_sycl_op_mul_mat_sycl( + const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst, + const char *src0_dd_i, const float *src1_ddf_i, const char *src1_ddq_i, + float *dst_dd_i, const int64_t row_low, const int64_t row_high, + const int64_t src1_ncols, const int64_t src1_padded_row_size, + const dpct::queue_ptr &stream) try { + + GGML_ASSERT(src0_dd_i != nullptr); + GGML_ASSERT(src1_ddf_i != nullptr); + GGML_ASSERT(dst_dd_i != nullptr); + + const int64_t ne00 = src0->ne[0]; + const int64_t ne10 = src1->ne[0]; + + const int64_t ne0 = dst->ne[0]; + + const int64_t row_diff = row_high - row_low; + + int id; + int device_id = dpct::dev_mgr::instance().current_device_id(); + SYCL_CHECK( + CHECK_TRY_ERROR(id = get_current_device_index())); + + // the main device has a larger memory buffer to hold the results from all GPUs + // ldc == nrows of the matrix that cuBLAS writes into + int ldc = dst->backend == GGML_BACKEND_GPU && device_id == g_main_device ? ne0 : row_diff; + + const int compute_capability = g_device_caps[id].cc; +#ifdef GGML_SYCL_F16 + bool use_fp16 = true; // TODO(Yu) SYCL capability check +#else + bool use_fp16 = false; +#endif + // if (compute_capability >= VER_GEN9 && (src0->type == GGML_TYPE_F16 || + // ggml_is_quantized(src0->type)) && ggml_is_contiguous(src0) && row_diff == + // src0->ne[1] && dst->op_params[0] == GGML_PREC_DEFAULT) { + if ((src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && + use_fp16 && ggml_is_contiguous(src0) && row_diff == src0->ne[1] && + dst->op_params[0] == GGML_PREC_DEFAULT) { + + // convert src0 and src1 to fp16, multiply as fp16, convert dst to fp32 + // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp16 path\n"); + sycl_pool_alloc src0_as_f16; + if (src0->type != GGML_TYPE_F16) { + const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src0->type); + GGML_ASSERT(to_fp16_sycl != nullptr); + size_t ne = row_diff*ne00; + src0_as_f16.alloc(ne); + to_fp16_sycl(src0_dd_i, src0_as_f16.get(), ne, stream); + } + const sycl::half *src0_ptr = src0->type == GGML_TYPE_F16 + ? (const sycl::half *)src0_dd_i + : src0_as_f16.get(); + + sycl_pool_alloc src1_as_f16; + if (src1->type != GGML_TYPE_F16) { + const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); + GGML_ASSERT(to_fp16_sycl != nullptr); + size_t ne = src1_ncols*ne10; + src1_as_f16.alloc(ne); + to_fp16_sycl(src1_ddf_i, src1_as_f16.get(), ne, stream); + } + const sycl::half *src1_ptr = src1->type == GGML_TYPE_F16 + ? (const sycl::half *)src1->data + src1_padded_row_size + : src1_as_f16.get(); + sycl_pool_alloc dst_f16(row_diff * src1_ncols); + + const sycl::half alpha_f16 = 1.0f; + const sycl::half beta_f16 = 0.0f; + + SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[id] = stream)); + SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm( + *g_sycl_handles[id], oneapi::mkl::transpose::trans, + oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10, + &alpha_f16, src0_ptr, dpct::library_data_t::real_half, ne00, + src1_ptr, dpct::library_data_t::real_half, ne10, &beta_f16, + dst_f16.get(), dpct::library_data_t::real_half, ldc, + dpct::library_data_t::real_half))); + + const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); + to_fp32_sycl(dst_f16.get(), dst_dd_i, row_diff*src1_ncols, stream); + } + else { + // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat_sycl - fp32 path\n"); + sycl_pool_alloc src0_ddq_as_f32; + + if (src0->type != GGML_TYPE_F32) { + const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(src0->type); + GGML_ASSERT(to_fp32_sycl != nullptr); + src0_ddq_as_f32.alloc(row_diff*ne00); + to_fp32_sycl(src0_dd_i, src0_ddq_as_f32.get(), row_diff*ne00, stream); + } + const float * src0_ddf_i = src0->type == GGML_TYPE_F32 ? (const float *) src0_dd_i : src0_ddq_as_f32.get(); + + const float alpha = 1.0f; + const float beta = 0.0f; + + SYCL_CHECK(CHECK_TRY_ERROR(g_sycl_handles[id] = stream)); + SYCL_CHECK(CHECK_TRY_ERROR(oneapi::mkl::blas::column_major::gemm( + *g_sycl_handles[id], oneapi::mkl::transpose::trans, + oneapi::mkl::transpose::nontrans, row_diff, src1_ncols, ne10, + dpct::get_value(&alpha, *g_sycl_handles[id]), src0_ddf_i, ne00, + src1_ddf_i, ne10, dpct::get_value(&beta, *g_sycl_handles[id]), + dst_dd_i, ldc))); + } + + (void) dst; + (void) src1_ddq_i; + (void) src1_padded_row_size; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); + GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); + GGML_ASSERT(src0->type == dst->type); + + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + const int64_t ne2 = dst->ne[2]; + const int64_t nrows = ggml_nrows(src0); + + //const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + const int n_ctx = ((int32_t *) dst->op_params)[3]; + const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; + + // RoPE alteration for extended context + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + + const int32_t * pos = nullptr; + if ((mode & 1) == 0) { + GGML_ASSERT(src1->type == GGML_TYPE_I32); + GGML_ASSERT(src1->ne[0] == ne2); + pos = (const int32_t *) src1_dd; + } + + const bool is_neox = mode & 2; + const bool is_glm = mode & 4; + + rope_corr_dims corr_dims; + ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v); + + // compute + if (is_glm) { + GGML_ASSERT(false); + rope_glm_f32_sycl(src0_dd, dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, n_ctx, main_stream); + } else if (is_neox) { + if (src0->type == GGML_TYPE_F32) { + rope_neox_sycl( + (const float *)src0_dd, (float *)dst_dd, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor, + attn_factor, corr_dims, main_stream + ); + } else if (src0->type == GGML_TYPE_F16) { + rope_neox_sycl((const sycl::half *)src0_dd, (sycl::half *)dst_dd, + ne00, n_dims, nrows, pos, freq_scale, ne01, + freq_base, ext_factor, attn_factor, corr_dims, + main_stream); + } else { + GGML_ASSERT(false); + } + } else { + if (src0->type == GGML_TYPE_F32) { + rope_sycl( + (const float *)src0_dd, (float *)dst_dd, ne00, nrows, pos, freq_scale, ne01, freq_base, ext_factor, + attn_factor, corr_dims, main_stream + ); + } else if (src0->type == GGML_TYPE_F16) { + rope_sycl((const sycl::half *)src0_dd, (sycl::half *)dst_dd, ne00, + nrows, pos, freq_scale, ne01, freq_base, ext_factor, + attn_factor, corr_dims, main_stream); + } else { + GGML_ASSERT(false); + } + } + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_alibi(const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + const int64_t ne02 = src0->ne[2]; + const int64_t nrows = ggml_nrows(src0); + + //const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_head = ((int32_t *) dst->op_params)[1]; + float max_bias; + memcpy(&max_bias, (int32_t *) dst->op_params + 2, sizeof(float)); + + //GGML_ASSERT(ne01 + n_past == ne00); + GGML_ASSERT(n_head == ne02); + + const int n_heads_log2_floor = 1 << (int) floor(log2(n_head)); + + const float m0 = powf(2.0f, -(max_bias) / n_heads_log2_floor); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_heads_log2_floor); + + alibi_f32_sycl(src0_dd, dst_dd, ne00, nrows, ne01, n_heads_log2_floor, m0, m1, main_stream); + + (void) src1; + (void) src1_dd; +} + +inline void ggml_sycl_op_im2col(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F16); + + const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t*)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t*)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t*)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t*)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t*)(dst->op_params))[5]; + + const bool is_2D = ((const int32_t*)(dst->op_params))[6] == 1; + + const int64_t IC = src1->ne[is_2D ? 2 : 1]; + const int64_t IH = is_2D ? src1->ne[1] : 1; + const int64_t IW = src1->ne[0]; + + const int64_t KH = is_2D ? src0->ne[1] : 1; + const int64_t KW = src0->ne[0]; + + const int64_t OH = is_2D ? dst->ne[2] : 1; + const int64_t OW = dst->ne[1]; + + const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32 + + im2col_f32_f16_sycl(src1_dd, (sycl::half *)dst_dd, IW, IH, OW, OH, KW, KH, + IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream); + + (void) src0; + (void) src0_dd; +} + +inline void ggml_sycl_op_sum_rows(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + const int64_t ncols = src0->ne[0]; + const int64_t nrows = ggml_nrows(src0); + + sum_rows_f32_sycl(src0_dd, dst_dd, ncols, nrows, main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_argsort(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_I32); + + const int64_t ncols = src0->ne[0]; + const int64_t nrows = ggml_nrows(src0); + + enum ggml_sort_order order = (enum ggml_sort_order) dst->op_params[0]; + + argsort_f32_i32_sycl(src0_dd, (int *)dst_dd, ncols, nrows, order, main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_diag_mask_inf(const ggml_tensor *src0, + const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + const int nrows0 = ggml_nrows(src0); + + const int n_past = ((int32_t *) dst->op_params)[0]; + + diag_mask_inf_f32_sycl(src0_dd, dst_dd, ne00, nrows0, ne01, n_past, main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_soft_max(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const float *src0_dd, const float *src1_dd, + float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + GGML_ASSERT(!src1 || src1->type == GGML_TYPE_F32); // src1 contains mask and it is optional + + const int64_t ne00 = src0->ne[0]; + const int64_t nrows_x = ggml_nrows(src0); + const int64_t nrows_y = src1 ? ggml_nrows(src1) : 1; + + float scale = 1.0f; + memcpy(&scale, dst->op_params, sizeof(float)); + + soft_max_f32_sycl(src0_dd, src1 ? src1_dd : nullptr, dst_dd, ne00, nrows_x, nrows_y, scale, main_stream); + + (void) dst; +} + +inline void ggml_sycl_op_scale(const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + float scale; + memcpy(&scale, dst->op_params, sizeof(float)); + + scale_f32_sycl(src0_dd, dst_dd, scale, ggml_nelements(src0), main_stream); + /* + DPCT1010:87: SYCL uses exceptions to report errors and does not use the + error codes. The call was replaced with 0. You need to rewrite this code. + */ + SYCL_CHECK(0); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +inline void ggml_sycl_op_clamp(const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst, const float *src0_dd, + const float *src1_dd, float *dst_dd, + const dpct::queue_ptr &main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + float min; + float max; + memcpy(&min, dst->op_params, sizeof(float)); + memcpy(&max, (float *) dst->op_params + 1, sizeof(float)); + + clamp_f32_sycl(src0_dd, dst_dd, min, max, ggml_nelements(src0), main_stream); + /* + DPCT1010:88: SYCL uses exceptions to report errors and does not use the + error codes. The call was replaced with 0. You need to rewrite this code. + */ + SYCL_CHECK(0); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +static void ggml_sycl_op_flatten(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + const ggml_sycl_op_flatten_t op) try { + const int64_t nrows0 = ggml_nrows(src0); + + const bool use_src1 = src1 != nullptr; + const int64_t nrows1 = use_src1 ? ggml_nrows(src1) : 1; + + GGML_ASSERT(!use_src1 || src1->backend != GGML_BACKEND_GPU_SPLIT); + GGML_ASSERT( dst->backend != GGML_BACKEND_GPU_SPLIT); + + ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; + ggml_tensor_extra_gpu * src1_extra = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr; + ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; + + const bool src0_on_device = src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT; + const bool src1_on_device = use_src1 && src1->backend == GGML_BACKEND_GPU; + const bool dst_on_device = dst->backend == GGML_BACKEND_GPU; + + // dd = data device + float * src0_ddf = nullptr; + float * src1_ddf = nullptr; + float * dst_ddf = nullptr; + + sycl_pool_alloc src0_f; + sycl_pool_alloc src1_f; + sycl_pool_alloc dst_f; + + ggml_sycl_set_device(g_main_device); + dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; + // GGML_SYCL_DEBUG("g_main_device_index=%d, main_stream=%p src0_on_device=%d, src1_on_device=%d, dst_on_device=%d\n", + // g_main_device_index, main_stream, src0_on_device, src1_on_device, dst_on_device); + + if (src0_on_device) { + src0_ddf = (float *) src0_extra->data_device[g_main_device_index]; + } else { + src0_ddf = src0_f.alloc(ggml_nelements(src0)); + // GGML_SYCL_DEBUG("before ggml_sycl_cpy_tensor_2d src0_ddf=%p, src0=%p\n", src0_ddf, src0); + SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src0_ddf, src0, 0, 0, 0, nrows0, main_stream)); + } + + if (use_src1) { + if (src1_on_device) { + src1_ddf = (float *) src1_extra->data_device[g_main_device_index]; + } else { + src1_ddf = src1_f.alloc(ggml_nelements(src1)); + SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src1_ddf, src1, 0, 0, 0, nrows1, main_stream)); + } + } + if (dst_on_device) { + dst_ddf = (float *) dst_extra->data_device[g_main_device_index]; + // printf("zjy dst_ddf=%p main_stream=%p g_main_device_index=%d\n", dst_ddf, main_stream, g_main_device_index); + } else { + dst_ddf = dst_f.alloc(ggml_nelements(dst)); + } + + // GGML_SYCL_DEBUG("op src0=%p, src1=%p, dst=%p, src0_ddf=%p, src1_ddf=%p, dst_ddf=%p, main_stream=%p\n", + // src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream); + // do the computation + op(src0, src1, dst, src0_ddf, src1_ddf, dst_ddf, main_stream); + /* + DPCT1010:89: SYCL uses exceptions to report errors and does not use the + error codes. The call was replaced with 0. You need to rewrite this code. + */ + SYCL_CHECK(0); + + // copy dst to host if necessary + if (!dst_on_device) { + SYCL_CHECK(CHECK_TRY_ERROR( + main_stream->memcpy(dst->data, dst_ddf, ggml_nbytes(dst)))); + } + + if (dst->backend == GGML_BACKEND_CPU) { + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::get_current_device().queues_wait_and_throw())); + } + // print_ggml_tensor("tensor", dst); +} +catch (sycl::exception const &exc) { + + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_sycl_set_peer_access(const int n_tokens) { + static bool peer_access_enabled = false; + + const bool enable_peer_access = n_tokens <= GGML_SYCL_PEER_MAX_BATCH_SIZE; + + if (peer_access_enabled == enable_peer_access) { + return; + } + +#ifdef NDEBUG + for (int id = 0; id < g_device_count; ++id) { + SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id))); + // SYCL_CHECK(syclDeviceSynchronize()); + } + + for (int id = 0; id < g_device_count; ++id) { + SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id))); + int device_id = g_device_caps[id].device_id; + + for (int id_other = 0; id_other < g_device_count; ++id_other) { + int device_id_other = g_device_caps[id_other].device_id; + if (device_id == id_other) { + continue; + } + if (device_id != g_main_device && device_id_other != g_main_device) { + continue; + } + + int can_access_peer; + // SYCL_CHECK(syclDeviceCanAccessPeer(&can_access_peer, id, id_other)); + // if (can_access_peer) { + // if (enable_peer_access) { + // SYCL_CHECK(syclDeviceEnablePeerAccess(id_other, 0)); + // } else { + // SYCL_CHECK(syclDeviceDisablePeerAccess(id_other)); + // } + // } + } + } +#endif // NDEBUG + + peer_access_enabled = enable_peer_access; +} + +static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, + const ggml_tensor *src1, ggml_tensor *dst, + ggml_sycl_op_mul_mat_t op, + const bool convert_src1_to_q8_1) try { + + 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 nrows0 = ggml_nrows(src0); + + 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 nrows1 = ggml_nrows(src1); + + GGML_ASSERT(ne03 == ne13); + + const int64_t ne0 = dst->ne[0]; + const int64_t ne1 = dst->ne[1]; + + const int nb2 = dst->nb[2]; + const int nb3 = dst->nb[3]; + + GGML_ASSERT(dst->backend != GGML_BACKEND_GPU_SPLIT); + GGML_ASSERT(src1->backend != GGML_BACKEND_GPU_SPLIT); + + GGML_ASSERT(ne12 >= ne02 && ne12 % ne02 == 0); + + const int64_t i02_divisor = ne12 / ne02; + + const size_t src0_ts = ggml_type_size(src0->type); + const size_t src0_bs = ggml_blck_size(src0->type); + const size_t q8_1_ts = sizeof(block_q8_1); + const size_t q8_1_bs = QK8_1; + + ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; + ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; + ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; + + const bool src0_on_device = src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT; + const bool src0_is_contiguous = ggml_is_contiguous(src0); + const bool src1_is_contiguous = ggml_is_contiguous(src1); + + int64_t src1_padded_col_size = GGML_PAD(ne10, MATRIX_ROW_PADDING); + + const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT; + GGML_ASSERT(!(split && ne02 > 1)); + GGML_ASSERT(!(split && ne03 > 1)); + GGML_ASSERT(!(split && ne02 < ne12)); + + // dd = data device + char * src0_dd[GGML_SYCL_MAX_DEVICES] = {nullptr}; + float * src1_ddf[GGML_SYCL_MAX_DEVICES] = {nullptr}; // float + char * src1_ddq[GGML_SYCL_MAX_DEVICES] = {nullptr}; // q8_1 + float * dst_dd[GGML_SYCL_MAX_DEVICES] = {nullptr}; + + // as = actual size + size_t src0_as[GGML_SYCL_MAX_DEVICES] = {0}; + size_t src1_asf[GGML_SYCL_MAX_DEVICES] = {0}; + size_t src1_asq[GGML_SYCL_MAX_DEVICES] = {0}; + size_t dst_as[GGML_SYCL_MAX_DEVICES] = {0}; + + int64_t row_low[GGML_SYCL_MAX_DEVICES]; + int64_t row_high[GGML_SYCL_MAX_DEVICES]; + + int used_devices = 0; + + for (int64_t id = 0; id < g_device_count; ++id) { + // by default, use all rows + row_low[id] = 0; + row_high[id] = ne01; + + // for multi GPU, get the row boundaries from tensor split + // and round to mul_mat_q tile sizes + if (split) { + const int64_t rounding = get_row_rounding(src0->type); + + if (id != 0) { + row_low[id] = ne01*g_tensor_split[id]; + if (row_low[id] < ne01) { + row_low[id] -= row_low[id] % rounding; + } + } + + if (id != g_device_count - 1) { + row_high[id] = ne01*g_tensor_split[id + 1]; + if (row_high[id] < ne01) { + row_high[id] -= row_high[id] % rounding; + } + } + } + } + for (int64_t id = 0; id < g_device_count; ++id) { + + if ((!split && id != g_main_device_index) || row_low[id] == row_high[id]) { + continue; + } + + used_devices++; + + const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device_index; + const bool dst_on_device = dst->backend == GGML_BACKEND_GPU && id == g_main_device_index; + + ggml_sycl_set_device(get_device_id_by_index(id)); + const dpct::queue_ptr stream = g_syclStreams[id][0]; + + if (src0_on_device && src0_is_contiguous) { + src0_dd[id] = (char *) src0_extra->data_device[id]; + } else { + // const size_t size_src0_ddq = split ? (row_high[id]-row_low[id])*ne00 * src0_ts/src0_bs : ggml_nbytes(src0); + src0_dd[id] = (char *) ggml_sycl_pool_malloc(ggml_nbytes(src0), &src0_as[id]); + } + + if (src1_on_device && src1_is_contiguous) { + src1_ddf[id] = (float *) src1_extra->data_device[id]; + } else { + src1_ddf[id] = (float *) ggml_sycl_pool_malloc(ggml_nbytes(src1), &src1_asf[id]); + } + + if (convert_src1_to_q8_1) { + src1_ddq[id] = (char *) ggml_sycl_pool_malloc(nrows1*src1_padded_col_size*q8_1_ts/q8_1_bs, &src1_asq[id]); + + if (src1_on_device && src1_is_contiguous) { + quantize_row_q8_1_sycl(src1_ddf[id], src1_ddq[id], ne10, nrows1, src1_padded_col_size, stream); + /* + DPCT1010:90: SYCL uses exceptions to report errors and does not + use the error codes. The call was replaced with 0. You need to + rewrite this code. + */ + SYCL_CHECK(0); + } + } + + if (dst_on_device) { + dst_dd[id] = (float *) dst_extra->data_device[id]; + } else { + const size_t size_dst_ddf = split ? (row_high[id]-row_low[id])*ne1*sizeof(float) : ggml_nbytes(dst); + dst_dd[id] = (float *) ggml_sycl_pool_malloc(size_dst_ddf, &dst_as[id]); + } + } + + // if multiple devices are used they need to wait for the main device + // here an event is recorded that signals that the main device has finished calculating the input data + if (split && used_devices > 1) { + SYCL_CHECK(ggml_sycl_set_device(g_main_device)); + /* + DPCT1024:91: The original code returned the error code that was further + consumed by the program logic. This original code was replaced with 0. + You may need to rewrite the program logic consuming the error code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + *src0_extra->events[g_main_device_index][0] = + g_syclStreams[g_main_device_index][0]->ext_oneapi_submit_barrier())); + } + + const int64_t src1_col_stride = split && used_devices > 1 ? MUL_MAT_SRC1_COL_STRIDE : ne11; + for (int64_t src1_col_0 = 0; src1_col_0 < ne11; src1_col_0 += src1_col_stride) { + const int64_t is = split ? (src1_col_0/src1_col_stride) % MAX_STREAMS : 0; + const int64_t src1_ncols = src1_col_0 + src1_col_stride > ne11 ? ne11 - src1_col_0 : src1_col_stride; + + for (int64_t id = 0; id < g_device_count; ++id) { + if ((!split && id != g_main_device_index) || row_low[id] == row_high[id]) { + continue; + } + + const bool src1_on_device = src1->backend == GGML_BACKEND_GPU && id == g_main_device_index; + const bool dst_on_device = dst->backend == GGML_BACKEND_GPU && id == g_main_device_index; + const int64_t row_diff = row_high[id] - row_low[id]; + + ggml_sycl_set_device(get_device_id_by_index(id)); + const dpct::queue_ptr stream = g_syclStreams[id][is]; + + // wait for main GPU data if necessary + if (split && (id != g_main_device_index || is != 0)) { + SYCL_CHECK(CHECK_TRY_ERROR(stream->ext_oneapi_submit_barrier( + {*src0_extra->events[g_main_device_index][0]}))); + } + + for (int64_t i0 = 0; i0 < ne13*ne12; ++i0) { + const int64_t i03 = i0 / ne12; + const int64_t i02 = i0 % ne12; + + const size_t src1_ddq_i_offset = (i0*ne11 + src1_col_0) * src1_padded_col_size*q8_1_ts/q8_1_bs; + + // for split tensors the data begins at i0 == i0_offset_low + char * src0_dd_i = src0_dd[id] + (i0/i02_divisor) * (ne01*ne00*src0_ts)/src0_bs; + float * src1_ddf_i = src1_ddf[id] + (i0*ne11 + src1_col_0) * ne10; + char * src1_ddq_i = src1_ddq[id] + src1_ddq_i_offset; + float * dst_dd_i = dst_dd[id] + (i0*ne1 + src1_col_0) * (dst_on_device ? ne0 : row_diff); + + // the main device memory buffer can be on VRAM scratch, with space for all partial results + // in that case an offset on dst_ddf_i is needed + if (dst->backend == GGML_BACKEND_GPU && id == g_main_device_index) { + dst_dd_i += row_low[id]; // offset is 0 if no tensor split + } + + // copy src0, src1 to device if necessary + if (src1->backend == GGML_BACKEND_GPU && src1_is_contiguous) { + if (id != g_main_device_index) { + if (convert_src1_to_q8_1) { + char * src1_ddq_i_source = src1_ddq[g_main_device_index] + src1_ddq_i_offset; + SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy( + src1_ddq_i, src1_ddq_i_source, + src1_ncols * src1_padded_col_size * q8_1_ts / + q8_1_bs))); + } else { + float * src1_ddf_i_source = (float *) src1_extra->data_device[g_main_device_index]; + src1_ddf_i_source += (i0*ne11 + src1_col_0) * ne10; + SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy( + src1_ddf_i, src1_ddf_i_source, + src1_ncols * ne10 * sizeof(float)))); + } + } + } else if (src1->backend == GGML_BACKEND_CPU || (src1_on_device && !src1_is_contiguous)) { + SYCL_CHECK(ggml_sycl_cpy_tensor_2d( + src1_ddf_i, src1, i03, i02, src1_col_0, src1_col_0+src1_ncols, stream)); + } else { + GGML_ASSERT(false); + } + + if (convert_src1_to_q8_1 && (src1->backend == GGML_BACKEND_CPU || !src1_is_contiguous)) { + quantize_row_q8_1_sycl(src1_ddf_i, src1_ddq_i, ne10, src1_ncols, src1_padded_col_size, stream); + /* + DPCT1010:92: SYCL uses exceptions to report errors and does + not use the error codes. The call was replaced with 0. You + need to rewrite this code. + */ + SYCL_CHECK(0); + } + + if (src1_col_0 == 0 && (!src0_on_device || !src0_is_contiguous) && i02 % i02_divisor == 0) { + SYCL_CHECK(ggml_sycl_cpy_tensor_2d(src0_dd_i, src0, i03, i02/i02_divisor, row_low[id], row_high[id], stream)); + } + if (src1->type == GGML_TYPE_F16) { + src1_padded_col_size = (i0 * ne11 + src1_col_0) * ne10; + } + // do the computation + op(src0, src1, dst, src0_dd_i, src1_ddf_i, src1_ddq_i, dst_dd_i, + row_low[id], row_high[id], src1_ncols, src1_padded_col_size, stream); + /* + DPCT1010:93: SYCL uses exceptions to report errors and does not + use the error codes. The call was replaced with 0. You need to + rewrite this code. + */ + SYCL_CHECK(0); + + // copy dst to host or other device if necessary + if (!dst_on_device) { + void * dst_off_device; + dpct::memcpy_direction kind; + if (dst->backend == GGML_BACKEND_CPU) { + dst_off_device = dst->data; + kind = dpct::device_to_host; + } else if (dst->backend == GGML_BACKEND_GPU) { + dst_off_device = dst_extra->data_device[g_main_device_index]; + kind = dpct::device_to_device; + } else { + GGML_ASSERT(false); + } + if (split) { + // src0 = weight matrix is saved as a transposed matrix for better memory layout. + // dst is NOT transposed. + // The outputs of matrix matrix multiplications can therefore NOT simply be concatenated for >1 GPU. + // Instead they need to be copied to the correct slice in ne0 = dst row index. + // If dst is a vector with ne0 == 1 then you don't have to do this but it still produces correct results. + float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3); + GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); + dhf_dst_i += src1_col_0*ne0 + row_low[id]; + SYCL_CHECK(CHECK_TRY_ERROR(dpct::async_dpct_memcpy( + dhf_dst_i, ne0 * sizeof(float), dst_dd_i, + row_diff * sizeof(float), row_diff * sizeof(float), + src1_ncols, kind, *stream))); + } else { + float * dhf_dst_i = (float *) ((char *) dst_off_device + i02*nb2 + i03*nb3); + GGML_ASSERT(dst->nb[1] == ne0*sizeof(float)); + dhf_dst_i += src1_col_0*ne0; + SYCL_CHECK(CHECK_TRY_ERROR( + stream->memcpy(dhf_dst_i, dst_dd_i, + src1_ncols * ne0 * sizeof(float)))); + } + } + + // add event for the main device to wait on until other device is done + if (split && (id != g_main_device_index || is != 0)) { + /* + DPCT1024:94: The original code returned the error code that + was further consumed by the program logic. This original + code was replaced with 0. You may need to rewrite the + program logic consuming the error code. + */ + SYCL_CHECK(CHECK_TRY_ERROR( + *src0_extra->events[id][is] = + stream->ext_oneapi_submit_barrier())); + } + } + } + } + + for (int64_t id = 0; id < g_device_count; ++id) { + if ((!split && id != g_main_device_index) || row_low[id] == row_high[id]) { + continue; + } + SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id))); + + // free buffers again when done + if (dst_as[id] > 0) { + ggml_sycl_pool_free(dst_dd[id], dst_as[id]); + } + if (src1_asq[id] > 0) { + ggml_sycl_pool_free(src1_ddq[id], src1_asq[id]); + } + if (src1_asf[id] > 0) { + ggml_sycl_pool_free(src1_ddf[id], src1_asf[id]); + } + if (src0_as[id] > 0) { + ggml_sycl_pool_free(src0_dd[id], src0_as[id]); + } + } + + // main device waits for all other devices to be finished + if (split && g_device_count > 1) { + int64_t is_max = (ne11 + MUL_MAT_SRC1_COL_STRIDE - 1) / MUL_MAT_SRC1_COL_STRIDE; + is_max = is_max <= MAX_STREAMS ? is_max : MAX_STREAMS; + + SYCL_CHECK(ggml_sycl_set_device(g_main_device)); + for (int64_t id = 0; id < g_device_count; ++id) { + if (row_low[id] == row_high[id]) { + continue; + } + for (int64_t is = 0; is < is_max; ++is) { + SYCL_CHECK(CHECK_TRY_ERROR( + g_syclStreams[g_main_device_index][0]->ext_oneapi_submit_barrier( + {*src0_extra->events[id][is]}))); + } + } + } + + if (dst->backend == GGML_BACKEND_CPU) { + SYCL_CHECK(ggml_sycl_set_device(g_main_device)); + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::get_current_device().queues_wait_and_throw())); + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_sycl_repeat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_repeat); +} + +static void ggml_sycl_get_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_get_rows); +} + +static void ggml_sycl_add(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_add); + // log_tensor_with_cnt("log_ggml_sycl_add_src0", (struct ggml_tensor *) src0, 6); + // log_tensor_with_cnt("log_ggml_sycl_add_src1", (struct ggml_tensor *)src1, 6); + // log_tensor_with_cnt("log_ggml_sycl_add_dst", dst, 6); +} + +static void ggml_sycl_acc(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_acc); +} + +static void ggml_sycl_mul(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_mul); + // log_tensor_with_cnt("log_ggml_sycl_mul_src0", (struct ggml_tensor *)src0, 6); + // log_tensor_with_cnt("log_ggml_sycl_mul_src1", (struct ggml_tensor *)src1, 6); + // log_tensor_with_cnt("log_ggml_sycl_mul_dst", dst, 6); + +} + +static void ggml_sycl_div(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_div); +} + +static void ggml_sycl_gelu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_gelu); +} + +static void ggml_sycl_silu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_silu); +} + +static void ggml_sycl_gelu_quick(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_gelu_quick); +} + +static void ggml_sycl_tanh(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_tanh); +} + +static void ggml_sycl_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_relu); +} + +static void ggml_sycl_leaky_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_leaky_relu); +} + +static void ggml_sycl_sqr(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_sqr); +} + +static void ggml_sycl_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_norm); +} + +static void ggml_sycl_group_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_group_norm); +} + +static void ggml_sycl_concat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_concat); +} + +static void ggml_sycl_upscale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_upscale); +} + +static void ggml_sycl_pad(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_pad); +} + + +static void ggml_sycl_rms_norm(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_SYCL_DEBUG("call %s\n", __func__); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_rms_norm); + // log_tensor_with_cnt("log_ggml_sycl_rms_norm_src0", (struct ggml_tensor *)src0, 6); + // log_tensor_with_cnt("log_ggml_sycl_rms_norm_src1", (struct ggml_tensor *)src1, 6); + // log_tensor_with_cnt("log_ggml_sycl_rms_norm_dst", dst, 6); +} + +bool ggml_sycl_can_mul_mat(const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { + if (!g_sycl_loaded) return false; + + const int64_t ne10 = src1->ne[0]; + + const int64_t ne0 = dst->ne[0]; + const int64_t ne1 = dst->ne[1]; + + // TODO: find the optimal values for these + return (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && + src1->type == GGML_TYPE_F32 && + dst->type == GGML_TYPE_F32 && + (ne0 >= 32 && ne1 >= 32 && ne10 >= 32); +} + +static void ggml_sycl_mul_mat_vec_p021(const ggml_tensor *src0, + const ggml_tensor *src1, + ggml_tensor *dst) try { + GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1)); + GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT); + GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // 0213 permutation + GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // 0213 permutation + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + const int64_t ne02 = src0->ne[2]; + + const int64_t ne12 = src1->ne[2]; + + SYCL_CHECK(ggml_sycl_set_device(g_main_device)); + dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; + + ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; + void * src0_ddq = src0_extra->data_device[g_main_device_index]; + + ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; + float * src1_ddf = (float *) src1_extra->data_device[g_main_device_index]; + + ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; + float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index]; + + ggml_mul_mat_p021_f16_f32_sycl(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, ne02, ne12, main_stream); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_sycl_mul_mat_vec_nc(const ggml_tensor *src0, + const ggml_tensor *src1, + ggml_tensor *dst) try { + GGML_ASSERT(!ggml_is_transposed(src0)); + GGML_ASSERT(!ggml_is_transposed(src1)); + GGML_ASSERT(!ggml_is_permuted(src0)); + GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT); + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + const int64_t ne02 = src0->ne[2]; + + const int64_t nb01 = src0->nb[1]; + const int64_t nb02 = src0->nb[2]; + + const int64_t ne12 = src1->ne[2]; + + SYCL_CHECK(ggml_sycl_set_device(g_main_device)); + dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; + + ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; + void * src0_ddq = src0_extra->data_device[g_main_device_index]; + + ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; + float * src1_ddf = (float *) src1_extra->data_device[g_main_device_index]; + + ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; + float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index]; + + const int64_t row_stride_x = nb01 / sizeof(sycl::half); + const int64_t channel_stride_x = nb02 / sizeof(sycl::half); + + ggml_mul_mat_vec_nc_f16_f32_sycl(src0_ddq, src1_ddf, dst_ddf, ne00, ne01, row_stride_x, ne02, ne12, channel_stride_x, main_stream); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void k_compute_batched_ptrs(const sycl::half *src0_as_f16, + const sycl::half *src1_as_f16, char *dst, + const void **ptrs_src, void **ptrs_dst, + int64_t ne12, int64_t ne13, int64_t ne23, + size_t nb02, size_t nb03, size_t nb12, + size_t nb13, size_t nbd2, size_t nbd3, + int64_t r2, int64_t r3, + const sycl::nd_item<3> &item_ct1) { + int64_t i13 = item_ct1.get_group(2) * item_ct1.get_local_range(2) + + item_ct1.get_local_id(2); + int64_t i12 = item_ct1.get_group(1) * item_ct1.get_local_range(1) + + item_ct1.get_local_id(1); + + if (i13 >= ne13 || i12 >= ne12) { + return; + } + + int64_t i03 = i13 / r3; + int64_t i02 = i12 / r2; + + ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_as_f16 + i02*nb02 + i03*nb03; + ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_as_f16 + i12*nb12/2 + i13*nb13/2; + ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst + i12*nbd2 + i13*nbd3; +} + +static void ggml_sycl_mul_mat_mat_batched_sycl(const ggml_tensor *src0, + const ggml_tensor *src1, + ggml_tensor *dst) try { + GGML_ASSERT(!ggml_is_transposed(src0)); + GGML_ASSERT(!ggml_is_transposed(src1)); + + GGML_ASSERT(src0->backend != GGML_BACKEND_GPU_SPLIT); + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + const int64_t ne00 = src0->ne[0]; GGML_UNUSED(ne00); + const int64_t ne01 = src0->ne[1]; + const int64_t ne02 = src0->ne[2]; + const int64_t ne03 = src0->ne[3]; + + const int64_t nb01 = src0->nb[1]; + const int64_t nb02 = src0->nb[2]; GGML_UNUSED(nb02); + const int64_t nb03 = src0->nb[3]; GGML_UNUSED(nb03); + + 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 nb11 = src1->nb[1]; + const int64_t nb12 = src1->nb[2]; GGML_UNUSED(nb12); + const int64_t nb13 = src1->nb[3]; GGML_UNUSED(nb13); + + const int64_t ne1 = ggml_nelements(src1); + const int64_t ne = ggml_nelements(dst); + + SYCL_CHECK(ggml_sycl_set_device(g_main_device)); + dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; + + SYCL_CHECK( + CHECK_TRY_ERROR(g_sycl_handles[g_main_device_index] = main_stream)); + + ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; + void * src0_ddq = src0_extra->data_device[g_main_device_index]; + sycl::half *src0_as_f16 = (sycl::half *)src0_ddq; + + ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; + float * src1_ddf = (float *) src1_extra->data_device[g_main_device_index]; + + ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; + float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index]; + + // convert src1 to fp16 + const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); + GGML_ASSERT(to_fp16_sycl != nullptr); + + sycl_pool_alloc src1_as_f16(ne1); + to_fp16_sycl(src1_ddf, src1_as_f16.get(), ne1, main_stream); + + sycl_pool_alloc dst_f16; + char * dst_t; + + dpct::library_data_t cu_compute_type = dpct::library_data_t::real_half; + dpct::library_data_t cu_data_type = dpct::library_data_t::real_half; + + // dst strides + size_t nbd2 = dst->nb[2]; + size_t nbd3 = dst->nb[3]; + + const sycl::half alpha_f16 = 1.0f; + const sycl::half beta_f16 = 0.0f; + + const float alpha_f32 = 1.0f; + const float beta_f32 = 0.0f; + + const void * alpha = &alpha_f16; + const void * beta = &beta_f16; + + if (dst->op_params[0] == GGML_PREC_DEFAULT) { + dst_t = (char *) dst_f16.alloc(ne); + + nbd2 /= sizeof(float) / sizeof(sycl::half); + nbd3 /= sizeof(float) / sizeof(sycl::half); + } else { + dst_t = (char *) dst_ddf; + + cu_compute_type = dpct::library_data_t::real_float; + cu_data_type = dpct::library_data_t::real_float; + + alpha = &alpha_f32; + beta = &beta_f32; + } + + GGML_ASSERT(ne12 % ne02 == 0); + GGML_ASSERT(ne13 % ne03 == 0); + + // broadcast factors + const int64_t r2 = ne12/ne02; + const int64_t r3 = ne13/ne03; + +#if 0 + // use syclGemmEx + { + for (int i13 = 0; i13 < ne13; ++i13) { + for (int i12 = 0; i12 < ne12; ++i12) { + int i03 = i13 / r3; + int i02 = i12 / r2; + + SYCL_CHECK( + syclGemmEx(g_sycl_handles[g_main_device_index], CUBLAS_OP_T, CUBLAS_OP_N, + ne01, ne11, ne10, + alpha, (const char *) src0_as_f16 + i02*src0->nb[2] + i03*src0->nb[3] , SYCL_R_16F, nb01/sizeof(half), + (const char *) src1_as_f16 + i12*src1->nb[2]/2 + i13*src1->nb[3]/2, SYCL_R_16F, nb11/sizeof(float), + beta, ( char *) dst_t + i12*nbd2 + i13*nbd3, cu_data_type, ne01, + cu_compute_type, + CUBLAS_GEMM_DEFAULT_TENSOR_OP)); + } + } + } +#else + if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) { + // there is no broadcast and src0, src1 are contiguous across dims 2, 3 + // use syclGemmStridedBatchedEx + SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( + *g_sycl_handles[g_main_device_index], oneapi::mkl::transpose::trans, + oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha, + (const char *)src0_as_f16, dpct::library_data_t::real_half, + nb01 / sizeof(sycl::half), src0->nb[2] / sizeof(sycl::half), + (const char *)src1_as_f16.get(), dpct::library_data_t::real_half, + nb11 / sizeof(float), src1->nb[2] / sizeof(float), beta, + (char *)dst_t, cu_data_type, ne01, dst->nb[2] / sizeof(float), + ne12 * ne13, cu_compute_type))); + } else { + // use syclGemmBatchedEx + const int ne23 = ne12*ne13; + + sycl_pool_alloc ptrs_src(2*ne23); + sycl_pool_alloc< void *> ptrs_dst(1*ne23); + + sycl::range<3> block_dims(1, ne12, ne13); + /* + DPCT1049:47: The work-group size passed to the SYCL kernel may exceed + the limit. To get the device limit, query + info::device::max_work_group_size. Adjust the work-group size if needed. + */ + { + dpct::has_capability_or_fail(main_stream->get_device(), + {sycl::aspect::fp16}); + + main_stream->submit([&](sycl::handler &cgh) { + const sycl::half *src1_as_f16_get_ct1 = src1_as_f16.get(); + const void **ptrs_src_get_ct3 = ptrs_src.get(); + void **ptrs_dst_get_ct4 = ptrs_dst.get(); + + cgh.parallel_for(sycl::nd_range<3>(block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + k_compute_batched_ptrs( + src0_as_f16, src1_as_f16_get_ct1, + dst_t, ptrs_src_get_ct3, + ptrs_dst_get_ct4, ne12, ne13, ne23, + nb02, nb03, nb12, nb13, nbd2, nbd3, r2, + r3, item_ct1); + }); + }); + } + /* + DPCT1010:95: SYCL uses exceptions to report errors and does not use the + error codes. The call was replaced with 0. You need to rewrite this + code. + */ + SYCL_CHECK(0); + + SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( + *g_sycl_handles[g_main_device_index], oneapi::mkl::transpose::trans, + oneapi::mkl::transpose::nontrans, ne01, ne11, ne10, alpha, + (const void **)(ptrs_src.get() + 0 * ne23), + dpct::library_data_t::real_half, nb01 / sizeof(sycl::half), + (const void **)(ptrs_src.get() + 1 * ne23), + dpct::library_data_t::real_half, nb11 / sizeof(float), beta, + (void **)(ptrs_dst.get() + 0 * ne23), cu_data_type, ne01, ne23, + cu_compute_type))); + } +#endif + + if (dst->op_params[0] == GGML_PREC_DEFAULT) { + const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); + to_fp32_sycl(dst_f16.get(), dst_ddf, ne, main_stream); + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + const bool all_on_device = + (src0->backend == GGML_BACKEND_GPU || src0->backend == GGML_BACKEND_GPU_SPLIT) && + (src1->backend == GGML_BACKEND_GPU) && + ( dst->backend == GGML_BACKEND_GPU); + + const bool split = src0->backend == GGML_BACKEND_GPU_SPLIT; + + int64_t min_compute_capability = INT_MAX; + for (int64_t id = 0; id < g_device_count; ++id) { + if (min_compute_capability > g_device_caps[id].cc && g_tensor_split[id] < (id + 1 < g_device_count ? g_tensor_split[id + 1] : 1.0f)) { + min_compute_capability = g_device_caps[id].cc; + } + } + +#ifdef SYCL_USE_XMX + const bool use_xmx = true; +#else + const bool use_xmx = false; +#endif + + // debug helpers + //printf("src0: %8d %8d %8d %8d\n", src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3]); + //printf(" %8d %8d %8d %8d\n", src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3]); + //printf("src1: %8d %8d %8d %8d\n", src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3]); + //printf(" %8d %8d %8d %8d\n", src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3]); + //printf("src0 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src0), ggml_is_transposed(src0), ggml_type_name(src0->type), src0->name); + //printf("src1 is contiguous %d, transposed %d, type = %s, name = %s\n", ggml_is_contiguous(src1), ggml_is_transposed(src1), ggml_type_name(src1->type), src1->name); + + if (!split && all_on_device && !use_xmx && src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { + // KQ single-batch + // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_vec_p021\n"); + ggml_sycl_mul_mat_vec_p021(src0, src1, dst); + } else if (!split && all_on_device && !use_xmx && src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { + // KQV single-batch + // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_vec_nc\n"); + ggml_sycl_mul_mat_vec_nc(src0, src1, dst); + } else if (!split && all_on_device && use_xmx && src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { + // KQ + KQV multi-batch + // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_mat_batched_sycl\n"); + ggml_sycl_mul_mat_mat_batched_sycl(src0, src1, dst); + } else if (src0->type == GGML_TYPE_F32) { + // GGML_SYCL_DEBUG("ggml_sycl_op_mul_mat\n"); + ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false); + } else if (ggml_is_quantized(src0->type) || src0->type == GGML_TYPE_F16) { + // GGML_SYCL_DEBUG("ggml_is_quantized or GGML_TYPE_F16\n"); + if (src1->ne[1] == 1 && src0->ne[0] % GGML_SYCL_DMMV_X == 0) { +#ifdef GGML_SYCL_FORCE_DMMV + const bool use_mul_mat_vec_q = false; +#else + const bool use_mul_mat_vec_q = min_compute_capability >= VER_4VEC && ggml_is_quantized(src0->type) && ggml_nrows(src1) == 1; +#endif // GGML_SYCL_FORCE_DMMV + + if (use_mul_mat_vec_q) { + // NOTE: this kernel does not support ggml_nrows(src1) > 1 + // GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_vec_q path\n"); + ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_vec_q, true); + } else { + // GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_dequantize_mul_mat_vec path\n"); + ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_dequantize_mul_mat_vec, false); + } + } else { + bool use_mul_mat_q = min_compute_capability >= VER_4VEC && ggml_is_quantized(src0->type); + + if (use_xmx && min_compute_capability >= VER_GEN9 && src1->ne[1] > XMX_MAX_BATCH_SIZE) { + use_mul_mat_q = false; + } + + if (use_mul_mat_q) { + // GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_q path\n"); + ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_q, true); + } else { + // GGML_SYCL_DEBUG("ggml_sycl_mul_mat ggml_sycl_op_mul_mat_sycl path\n"); + ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false); + } + } + } else { + GGML_ASSERT(false); + } +} + +#if 0 +template +static __global__ void k_compute_batched_ptrs_id( + const void ** ptrs_src, void ** ptrs_dst, + int ne12, int ne13, + int ne23, + int nb02, int nb03, + int nb12, int nb13, + int nb2, int nb3, + int r2, int r3, + ggml_type src0_type, half * src0_as_f16, int64_t src0_ne, + const half * src1_f16, half * dst_f16, + const int32_t * ids, const int id, + Srcs... src0s) { + + int i = ids[id]; + + half * src0_f16; + const void * srcs_ar[] = { (const half *) src0s... }; + if (src0_type == GGML_TYPE_F16) { + src0_f16 = (half *) srcs_ar[i]; + } else { + src0_f16 = src0_as_f16; + if (threadIdx.x == 0 && threadIdx.y == 0) { + const to_fp16_sycl_t to_fp16 = ggml_get_to_fp16_sycl(src0_type); + to_fp16(srcs_ar[i], src0_f16, src0_ne, syclStreamFireAndForget); + } + } + + int i13 = blockIdx.x * blockDim.x + threadIdx.x; + int i12 = blockIdx.y * blockDim.y + threadIdx.y; + + if (i13 >= ne13 || i12 >= ne12) { + return; + } + + int i03 = i13 / r3; + int i02 = i12 / r2; + + ptrs_src[0*ne23 + i12 + i13*ne12] = (const char *) src0_f16 + i02*nb02 + i03*nb03; + ptrs_src[1*ne23 + i12 + i13*ne12] = (const char *) src1_f16 + i12*nb12/2 + i13*nb13/2; + ptrs_dst[0*ne23 + i12 + i13*ne12] = ( char *) dst_f16 + i12* nb2/2 + i13* nb3/2; +} + +static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) { + const struct ggml_tensor * ids = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + const struct ggml_tensor * src00 = dst->src[2]; + + const int id = dst->op_params[0]; + + GGML_ASSERT(!ggml_is_transposed(src00)); + GGML_ASSERT(!ggml_is_transposed(src1)); + + GGML_ASSERT(src00->backend != GGML_BACKEND_GPU_SPLIT); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + const int64_t ne00 = src00->ne[0]; GGML_UNUSED(ne00); + const int64_t ne01 = src00->ne[1]; + const int64_t ne02 = src00->ne[2]; + const int64_t ne03 = src00->ne[3]; + + //const int64_t nb01 = src00->nb[1]; + const int64_t nb02 = src00->nb[2]; GGML_UNUSED(nb02); + const int64_t nb03 = src00->nb[3]; GGML_UNUSED(nb03); + + 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 nb11 = src1->nb[1]; + const int64_t nb12 = src1->nb[2]; GGML_UNUSED(nb12); + const int64_t nb13 = src1->nb[3]; GGML_UNUSED(nb13); + + const int64_t ne1 = ggml_nelements(src1); + const int64_t ne = ggml_nelements(dst); + + SYCL_CHECK(ggml_sycl_set_device(g_main_device)); + syclStream_t main_stream = g_syclStreams[g_main_device_index][0]; + + SYCL_CHECK(syclSetStream(g_sycl_handles[g_main_device_index], main_stream)); + + //ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; + //void * src0_ddq = src0_extra->data_device[g_main_device_index]; + //half * src0_as_f16 = (half *) src0_ddq; + + ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; + float * src1_ddf = (float *) src1_extra->data_device[g_main_device_index]; + + ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; + float * dst_ddf = (float *) dst_extra->data_device[g_main_device_index]; + + // convert src1 to fp16 + const to_fp16_sycl_t to_fp16_sycl = ggml_get_to_fp16_sycl(src1->type); + GGML_ASSERT(to_fp16_sycl != nullptr); + + size_t src1_as = 0; + half * src1_as_f16 = (half *) ggml_sycl_pool_malloc(ne1 * sizeof(half), &src1_as); + to_fp16_sycl(src1_ddf, src1_as_f16, ne1, main_stream); + + size_t dst_as = 0; + half * dst_f16 = (half *) ggml_sycl_pool_malloc(ne * sizeof(half), &dst_as); + + GGML_ASSERT(ne12 % ne02 == 0); + GGML_ASSERT(ne13 % ne03 == 0); + + // broadcast factors + const int64_t r2 = ne12/ne02; + const int64_t r3 = ne13/ne03; + + const half alpha_f16 = 1.0f; + const half beta_f16 = 0.0f; + + // use syclGemmBatchedEx + const int ne23 = ne12*ne13; + + const void ** ptrs_src = nullptr; + void ** ptrs_dst = nullptr; + + size_t ptrs_src_s = 0; + size_t ptrs_dst_s = 0; + + ptrs_src = (const void **) ggml_sycl_pool_malloc(2*ne23*sizeof(void *), &ptrs_src_s); + ptrs_dst = ( void **) ggml_sycl_pool_malloc(1*ne23*sizeof(void *), &ptrs_dst_s); + + int64_t src0_ne = ggml_nelements(src00); + half * src0_as_f16 = nullptr; + size_t src0_as = 0; + if (src00->type != GGML_TYPE_F16) { + src0_as_f16 = (half *) ggml_sycl_pool_malloc(src0_ne * sizeof(half), &src0_as); + } + + static_assert(GGML_MAX_SRC == 6, "GGML_MAX_SRC == 6"); + dim3 block_dims(ne13, ne12); + k_compute_batched_ptrs_id<<<1, block_dims, 0, main_stream>>>( + ptrs_src, ptrs_dst, + ne12, ne13, + ne23, + ne00*ne01*sizeof(half), ne00*ne01*ne02*sizeof(half), + nb12, nb13, + dst->nb[2], dst->nb[3], + r2, r3, + src00->type, src0_as_f16, src0_ne, + src1_as_f16, dst_f16, + (const int *)((ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device_index], id, + dst->src[2] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[2]->extra)->data_device[g_main_device_index] : nullptr, + dst->src[3] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[3]->extra)->data_device[g_main_device_index] : nullptr, + dst->src[4] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[4]->extra)->data_device[g_main_device_index] : nullptr, + dst->src[5] ? (const half *)((ggml_tensor_extra_gpu *)dst->src[5]->extra)->data_device[g_main_device_index] : nullptr + ); + SYCL_CHECK(syclGetLastError()); + + SYCL_CHECK( + syclGemmBatchedEx(g_sycl_handles[g_main_device_index], CUBLAS_OP_T, CUBLAS_OP_N, + ne01, ne11, ne10, + &alpha_f16, (const void **) (ptrs_src + 0*ne23), SYCL_R_16F, ne00, + (const void **) (ptrs_src + 1*ne23), SYCL_R_16F, ne10, + &beta_f16, ( void **) (ptrs_dst + 0*ne23), SYCL_R_16F, ne01, + ne23, + CUBLAS_COMPUTE_16F, + CUBLAS_GEMM_DEFAULT_TENSOR_OP)); + + if (src0_as != 0) { + ggml_sycl_pool_free(src0_as_f16, src0_as); + } + if (ptrs_src_s != 0) { + ggml_sycl_pool_free(ptrs_src, ptrs_src_s); + } + if (ptrs_dst_s != 0) { + ggml_sycl_pool_free(ptrs_dst, ptrs_dst_s); + } + + const to_fp32_sycl_t to_fp32_sycl = ggml_get_to_fp32_sycl(GGML_TYPE_F16); + to_fp32_sycl(dst_f16, dst_ddf, ne, main_stream); + + ggml_sycl_pool_free(src1_as_f16, src1_as); + ggml_sycl_pool_free(dst_f16, dst_as); +} +#endif + +static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, + const ggml_tensor *src1, + ggml_tensor *dst) try { +#if 0 + ggml_sycl_mul_mat_id_sycl(dst); + // TODO: mmq/mmv support +#endif + + const int64_t nb11 = src1->nb[1]; + const int64_t nb1 = dst->nb[1]; + + const struct ggml_tensor * ids = src0; + const int32_t id = ((int32_t *) dst->op_params)[0]; + const int32_t n_as = ((int32_t *) dst->op_params)[1]; + + std::vector ids_host(ggml_nbytes(ids)); + + const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0]; + + if (ids->backend == GGML_BACKEND_GPU) { + const char * ids_dev = (const char *)((const ggml_tensor_extra_gpu *)ids->extra)->data_device[g_main_device_index]; + SYCL_CHECK(CHECK_TRY_ERROR( + stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids)))); + SYCL_CHECK(CHECK_TRY_ERROR(stream->wait())); + } else { + memcpy(ids_host.data(), ids->data, ggml_nbytes(ids)); + } + + const ggml_tensor_extra_gpu * src1_extra = (const ggml_tensor_extra_gpu *) src1->extra; + const ggml_tensor_extra_gpu * dst_extra = (const ggml_tensor_extra_gpu *) dst->extra; + + ggml_tensor_extra_gpu src1_row_extra; + ggml_tensor_extra_gpu dst_row_extra; + + ggml_tensor src1_row = *src1; + ggml_tensor dst_row = *dst; + + src1_row.backend = GGML_BACKEND_GPU; + dst_row.backend = GGML_BACKEND_GPU; + + src1_row.extra = &src1_row_extra; + dst_row.extra = &dst_row_extra; + + char * src1_original = src1->backend == GGML_BACKEND_CPU ? + (char *) src1->data : (char *) src1_extra->data_device[g_main_device_index]; + char * dst_original = dst->backend == GGML_BACKEND_CPU ? + (char *) dst->data : (char *) dst_extra->data_device[g_main_device_index]; + + if (src1->ne[1] == 1) { + GGML_ASSERT(src1->backend == GGML_BACKEND_GPU); + GGML_ASSERT(dst->backend == GGML_BACKEND_GPU); + + for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { + //int32_t row_id; + //SYCL_CHECK(syclMemcpyAsync(&row_id, ids_dev + i01*ids->nb[1] + id*ids->nb[0], sizeof(int32_t), syclMemcpyDeviceToHost, g_syclStreams[g_main_device][0])); + //SYCL_CHECK(syclStreamSynchronize(g_syclStreams[g_main_device][0])); + + const int32_t row_id = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); + + GGML_ASSERT(row_id >= 0 && row_id < n_as); + + const struct ggml_tensor * src0_row = dst->src[row_id + 2]; + + src1_row_extra.data_device[g_main_device_index] = src1_original + i01*src1->nb[1]; + src1_row.data = (char *) src1->data + i01*src1->nb[1]; // TODO why is this set? + + dst_row_extra.data_device[g_main_device_index] = dst_original + i01*dst->nb[1]; + dst_row.data = (char *) dst->data + i01*dst->nb[1]; // TODO why is this set? + + ggml_sycl_mul_mat(src0_row, &src1_row, &dst_row); + } + } else { + sycl_pool_alloc src1_contiguous(sizeof(float)*ggml_nelements(src1)); + sycl_pool_alloc dst_contiguous(sizeof(float)*ggml_nelements(dst)); + + src1_row_extra.data_device[g_main_device_index] = src1_contiguous.get(); + dst_row_extra.data_device[g_main_device_index] = dst_contiguous.get(); + + const dpct::memcpy_direction src1_kind = + src1->backend == GGML_BACKEND_CPU ? dpct::host_to_device + : dpct::device_to_device; + const dpct::memcpy_direction dst_kind = dst->backend == GGML_BACKEND_CPU + ? dpct::device_to_host + : dpct::device_to_device; + + for (int32_t row_id = 0; row_id < n_as; ++row_id) { + const struct ggml_tensor * src0_row = dst->src[row_id + 2]; + + int64_t num_src1_rows = 0; + for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { + const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); + + if (row_id_i != row_id) { + continue; + } + + GGML_ASSERT(row_id >= 0 && row_id < n_as); + + SYCL_CHECK(CHECK_TRY_ERROR( + stream->memcpy(src1_contiguous.get() + num_src1_rows * nb11, + src1_original + i01 * nb11, nb11))); + num_src1_rows++; + } + + if (num_src1_rows == 0) { + continue; + } + + src1_row.ne[1] = num_src1_rows; + dst_row.ne[1] = num_src1_rows; + + src1_row.nb[1] = nb11; + src1_row.nb[2] = num_src1_rows*nb11; + src1_row.nb[3] = num_src1_rows*nb11; + + dst_row.nb[1] = nb1; + dst_row.nb[2] = num_src1_rows*nb1; + dst_row.nb[3] = num_src1_rows*nb1; + + ggml_sycl_mul_mat(src0_row, &src1_row, &dst_row); + + num_src1_rows = 0; + for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { + const int32_t row_id_i = *(const int32_t *) (ids_host.data() + i01*ids->nb[1] + id*ids->nb[0]); + + if (row_id_i != row_id) { + continue; + } + + GGML_ASSERT(row_id >= 0 && row_id < n_as); + + SYCL_CHECK(CHECK_TRY_ERROR(stream->memcpy( + dst_original + i01 * nb1, + dst_contiguous.get() + num_src1_rows * nb1, nb1))); + num_src1_rows++; + } + } + } + + if (dst->backend == GGML_BACKEND_CPU) { + SYCL_CHECK(CHECK_TRY_ERROR(stream->wait())); + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_sycl_scale(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_scale); +} + +static void ggml_sycl_clamp(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_clamp); +} + +static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1, + ggml_tensor *dst) try { + const int64_t ne = ggml_nelements(src0); + GGML_ASSERT(ne == ggml_nelements(src1)); + + GGML_ASSERT(src0->backend == GGML_BACKEND_GPU); + GGML_ASSERT(src1->backend == GGML_BACKEND_GPU); + + GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX); + GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX); + + const int64_t ne00 = src0->ne[0]; + const int64_t ne01 = src0->ne[1]; + GGML_ASSERT(src0->ne[3] == 1); + + const int64_t nb00 = src0->nb[0]; + const int64_t nb01 = src0->nb[1]; + const int64_t nb02 = src0->nb[2]; + + const int64_t ne10 = src1->ne[0]; + const int64_t ne11 = src1->ne[1]; + GGML_ASSERT(src1->ne[3] == 1); + + const int64_t nb10 = src1->nb[0]; + const int64_t nb11 = src1->nb[1]; + const int64_t nb12 = src1->nb[2]; + + SYCL_CHECK(ggml_sycl_set_device(g_main_device)); + dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; + + const ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu *) src0->extra; + const ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu *) src1->extra; + + char * src0_ddc = (char *) src0_extra->data_device[g_main_device_index]; + char * src1_ddc = (char *) src1_extra->data_device[g_main_device_index]; + + if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) { + ggml_cpy_f32_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) { + ggml_cpy_f32_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) { + ggml_cpy_f32_q8_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) { + ggml_cpy_f32_q4_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) { + ggml_cpy_f32_q4_1_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) { + ggml_cpy_f16_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + } else if (src0->type == GGML_TYPE_I16 && src1->type == GGML_TYPE_I16) { + ggml_cpy_i16_i16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + } else if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_I32) { + ggml_cpy_i32_i32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + } else { + fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__, + ggml_type_name(src0->type), ggml_type_name(src1->type)); + GGML_ASSERT(false); + } + + (void) dst; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_sycl_dup(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + // TODO: why do we pass dst as src1 here? + ggml_sycl_cpy(src0, dst, nullptr); + (void) src1; +} + +static void ggml_sycl_diag_mask_inf(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_diag_mask_inf); +} + +static void ggml_sycl_soft_max(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_soft_max); +} + +static void ggml_sycl_rope(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(ggml_is_contiguous(src0)); // TODO: this restriction is temporary until non-cont support is implemented + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_rope); +} + +static void ggml_sycl_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_alibi); +} + +static void ggml_sycl_im2col(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_im2col); +} + +static void ggml_sycl_sum_rows(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(ggml_is_contiguous(src0)); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_sum_rows); +} + +static void ggml_sycl_argsort(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + GGML_ASSERT(ggml_is_contiguous(src0)); + ggml_sycl_op_flatten(src0, src1, dst, ggml_sycl_op_argsort); +} + +static void ggml_sycl_nop(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + (void) src0; + (void) src1; + (void) dst; +} + +static size_t ggml_nbytes_split(const struct ggml_tensor * tensor, int nrows_split) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return nrows_split*ggml_row_size(tensor->type, tensor->ne[0]); +} + +void ggml_sycl_transform_tensor(void *data, struct ggml_tensor *tensor) try { + const int64_t nrows = ggml_nrows(tensor); + + const int64_t ne0 = tensor->ne[0]; + + const size_t nb1 = tensor->nb[1]; + + ggml_backend_type backend = tensor->backend; + ggml_tensor_extra_gpu * extra = new struct ggml_tensor_extra_gpu; + memset(extra, 0, sizeof(*extra)); + + for (int64_t id = 0; id < g_device_count; ++id) { + if (backend == GGML_BACKEND_GPU && id != g_main_device_index) { + continue; + } + ggml_sycl_set_device(get_device_id_by_index(id)); + const dpct::queue_ptr stream = g_syclStreams[id][0]; + + int64_t row_low, row_high; + if (backend == GGML_BACKEND_GPU) { + row_low = 0; + row_high = nrows; + } else if (backend == GGML_BACKEND_GPU_SPLIT) { + const int64_t rounding = get_row_rounding(tensor->type); + + row_low = id == 0 ? 0 : nrows*g_tensor_split[id]; + row_low -= row_low % rounding; + + if (id == g_device_count - 1) { + row_high = nrows; + } else { + row_high = nrows*g_tensor_split[id + 1]; + row_high -= row_high % rounding; + } + } else { + GGML_ASSERT(false); + } + if (row_low == row_high) { + continue; + } + + int64_t nrows_split = row_high - row_low; + + const size_t offset_split = row_low*nb1; + size_t size = ggml_nbytes_split(tensor, nrows_split); + const size_t original_size = size; + + // pad last row to a multiple of 512 elements to avoid out-of-bounds memory accesses + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + + char * buf; + SYCL_CHECK(CHECK_TRY_ERROR(buf = (char *)sycl::malloc_device( + size, *stream))); + char * buf_host = (char *)data + offset_split; + + // set padding to 0 to avoid possible NaN values + if (size > original_size) { + SYCL_CHECK(CHECK_TRY_ERROR( + (*stream) + .memset(buf + original_size, 0, size - original_size) + .wait())); + } + + SYCL_CHECK(CHECK_TRY_ERROR((*stream) + .memcpy(buf, buf_host, original_size) + .wait())); + + extra->data_device[id] = buf; + + if (backend == GGML_BACKEND_GPU_SPLIT) { + for (int64_t is = 0; is < MAX_STREAMS; ++is) { + SYCL_CHECK(CHECK_TRY_ERROR(extra->events[id][is] = + new sycl::event())); + } + } + } + + tensor->extra = extra; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +void ggml_sycl_free_data(struct ggml_tensor *tensor) try { + if (!tensor || !tensor->extra || (tensor->backend != GGML_BACKEND_GPU && tensor->backend != GGML_BACKEND_GPU_SPLIT) ) { + return; + } + + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; + + for (int64_t id = 0; id < g_device_count; ++id) { + const dpct::queue_ptr stream = g_syclStreams[id][0]; + if (extra->data_device[id] != nullptr) { + SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id))); + SYCL_CHECK(CHECK_TRY_ERROR(sycl::free(extra->data_device[id], *stream))); + } + + for (int64_t is = 0; is < MAX_STREAMS; ++is) { + if (extra->events[id][is] != nullptr) { + SYCL_CHECK(ggml_sycl_set_device(get_device_id_by_index(id))); + SYCL_CHECK(CHECK_TRY_ERROR( + dpct::destroy_event(extra->events[id][is]))); + } + } + } + + delete extra; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static ggml_tensor_extra_gpu * g_temp_tensor_extras = nullptr; +static size_t g_temp_tensor_extra_index = 0; + +static ggml_tensor_extra_gpu * ggml_sycl_alloc_temp_tensor_extra() { + if (g_temp_tensor_extras == nullptr) { + g_temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_SYCL_MAX_NODES]; + } + + size_t alloc_index = g_temp_tensor_extra_index; + g_temp_tensor_extra_index = (g_temp_tensor_extra_index + 1) % GGML_SYCL_MAX_NODES; + ggml_tensor_extra_gpu * extra = &g_temp_tensor_extras[alloc_index]; + memset(extra, 0, sizeof(*extra)); + + return extra; +} + +static void ggml_sycl_assign_buffers_impl(struct ggml_tensor *tensor, + bool scratch, bool force_inplace, + bool no_alloc) try { + if (scratch && g_scratch_size == 0) { + return; + } + + tensor->backend = GGML_BACKEND_GPU; + + if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_CPU) { + const ggml_op src0_op = tensor->src[0]->op; + if (src0_op == GGML_OP_RESHAPE || src0_op == GGML_OP_TRANSPOSE || src0_op == GGML_OP_VIEW || src0_op == GGML_OP_PERMUTE) { + ggml_sycl_assign_buffers_impl(tensor->src[0], scratch, force_inplace, no_alloc); + } + } + if (tensor->op == GGML_OP_CPY && tensor->src[1]->backend == GGML_BACKEND_CPU) { + ggml_sycl_assign_buffers_impl(tensor->src[1], scratch, force_inplace, no_alloc); + } + + if (scratch && no_alloc) { + return; + } + + ggml_tensor_extra_gpu * extra; + + const bool inplace = (tensor->src[0] != nullptr && tensor->src[0]->data == tensor->data) || + tensor->op == GGML_OP_VIEW || + force_inplace; + const size_t size = ggml_nbytes(tensor); + + SYCL_CHECK(ggml_sycl_set_device(g_main_device)); + const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0]; + + if (inplace && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) { + ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->src[0]->extra; + char * src0_ddc = (char *) src0_extra->data_device[g_main_device_index]; + size_t offset = 0; + if (tensor->op == GGML_OP_VIEW) { + memcpy(&offset, tensor->op_params, sizeof(size_t)); + } + extra = ggml_sycl_alloc_temp_tensor_extra(); + extra->data_device[g_main_device_index] = src0_ddc + offset; + } else if (tensor->op == GGML_OP_CPY) { + ggml_tensor_extra_gpu * src1_extra = (ggml_tensor_extra_gpu * ) tensor->src[1]->extra; + void * src1_ddv = src1_extra->data_device[g_main_device_index]; + extra = ggml_sycl_alloc_temp_tensor_extra(); + extra->data_device[g_main_device_index] = src1_ddv; + } else if (scratch) { + GGML_ASSERT(size <= g_scratch_size); + if (g_scratch_offset + size > g_scratch_size) { + g_scratch_offset = 0; + } + + char * data = (char *) g_scratch_buffer; + if (data == nullptr) { + SYCL_CHECK(CHECK_TRY_ERROR( + data = (char *)sycl::malloc_device( + g_scratch_size, *stream))); + g_scratch_buffer = data; + } + extra = ggml_sycl_alloc_temp_tensor_extra(); + extra->data_device[g_main_device_index] = data + g_scratch_offset; + + g_scratch_offset += size; + + GGML_ASSERT(g_scratch_offset <= g_scratch_size); + } else { // allocate new buffers outside of scratch + void * data; + SYCL_CHECK(CHECK_TRY_ERROR(data = (void *)sycl::malloc_device( + size, *stream))); + SYCL_CHECK(CHECK_TRY_ERROR( + (*stream).memset(data, 0, size).wait())); + extra = new ggml_tensor_extra_gpu; + memset(extra, 0, sizeof(*extra)); + extra->data_device[g_main_device_index] = data; + } + + tensor->extra = extra; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +void ggml_sycl_assign_scratch_offset(struct ggml_tensor *tensor, + size_t offset) try { + if (g_scratch_size == 0) { + return; + } + if (g_scratch_buffer == nullptr) { + ggml_sycl_set_device(g_main_device); + const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0]; + SYCL_CHECK( + CHECK_TRY_ERROR(g_scratch_buffer = (void *)sycl::malloc_device( + g_scratch_size, *stream))); + } + + ggml_tensor_extra_gpu * extra = ggml_sycl_alloc_temp_tensor_extra(); + + const bool inplace = tensor->view_src != nullptr; + + if (inplace && (tensor->view_src->backend == GGML_BACKEND_GPU || tensor->view_src->backend == GGML_BACKEND_GPU_SPLIT)) { + ggml_tensor_extra_gpu * src0_extra = (ggml_tensor_extra_gpu * ) tensor->view_src->extra; + char * src0_ddc = (char *) src0_extra->data_device[g_main_device_index]; + size_t view_offset = 0; + if (tensor->op == GGML_OP_VIEW) { + memcpy(&view_offset, tensor->op_params, sizeof(size_t)); + } + extra->data_device[g_main_device_index] = src0_ddc + view_offset; + } else { + extra->data_device[g_main_device_index] = (char *) g_scratch_buffer + offset; + } + + tensor->extra = extra; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +void ggml_sycl_copy_to_device(struct ggml_tensor *tensor) try { + GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + GGML_ASSERT(ggml_is_contiguous(tensor)); + + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; + SYCL_CHECK(ggml_sycl_set_device(g_main_device)); + const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0]; + SYCL_CHECK(CHECK_TRY_ERROR((*stream) + .memcpy(extra->data_device[g_main_device_index], + tensor->data, ggml_nbytes(tensor)) + .wait())); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +void ggml_sycl_assign_buffers(struct ggml_tensor * tensor) { + ggml_sycl_assign_buffers_impl(tensor, true, false, false); +} + +void ggml_sycl_assign_buffers_no_alloc(struct ggml_tensor * tensor) { + ggml_sycl_assign_buffers_impl(tensor, true, false, true); +} + +void ggml_sycl_assign_buffers_no_scratch(struct ggml_tensor * tensor) { + ggml_sycl_assign_buffers_impl(tensor, false, false, false); +} + +void ggml_sycl_assign_buffers_force_inplace(struct ggml_tensor * tensor) { + ggml_sycl_assign_buffers_impl(tensor, false, true, false); +} + +void ggml_sycl_set_main_device(const int main_device) try { + + if (main_device >= g_all_sycl_device_count) { + fprintf(stderr, "warning: cannot set main_device=%d because there are only %d devices. Using device %d instead.\n", + main_device, g_all_sycl_device_count, g_main_device); + return; + } + + if (g_main_device != main_device && g_device_count >= 1) { + g_main_device = main_device; + g_main_device_index = get_device_index_by_id(g_main_device); + dpct::device_info prop; + SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( + prop, dpct::dev_mgr::instance().get_device(g_main_device)))); + fprintf(stderr, "Using device %d (%s) as main device\n", + g_main_device, prop.get_name()); + } +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +void ggml_sycl_set_scratch_size(const size_t scratch_size) { + // this is a hack to not completely break llama.cpp when using multiple models or contexts simultaneously + // it still won't always work as expected, but it's better than nothing + if (scratch_size > g_scratch_size) { + ggml_sycl_free_scratch(); + } + g_scratch_size = std::max(g_scratch_size, scratch_size); +} + +void ggml_sycl_free_scratch() try { + if (g_scratch_buffer == nullptr) { + return; + } + ggml_sycl_set_device(g_main_device); + const dpct::queue_ptr stream = g_syclStreams[g_main_device_index][0]; + + SYCL_CHECK(CHECK_TRY_ERROR( + sycl::free(g_scratch_buffer, *stream))); + g_scratch_buffer = nullptr; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { + if (!g_sycl_loaded) return false; + + ggml_sycl_func_t func; + const bool any_on_device = tensor->backend == GGML_BACKEND_GPU + || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) + || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU); + + if (!any_on_device && tensor->op != GGML_OP_MUL_MAT && tensor->op != GGML_OP_MUL_MAT_ID) { + return false; + } + + if (tensor->op == GGML_OP_MUL_MAT) { + if (tensor->src[0]->ne[3] != tensor->src[1]->ne[3]) { +#ifndef NDEBUG + fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, tensor->name, tensor->src[0]->ne[3], tensor->src[1]->ne[3]); +#endif + return false; + } + } + + switch (tensor->op) { + case GGML_OP_REPEAT: + func = ggml_sycl_repeat; + break; + case GGML_OP_GET_ROWS: + func = ggml_sycl_get_rows; + break; + case GGML_OP_DUP: + func = ggml_sycl_dup; + break; + case GGML_OP_ADD: + func = ggml_sycl_add; + break; + case GGML_OP_ACC: + func = ggml_sycl_acc; + break; + case GGML_OP_MUL: + func = ggml_sycl_mul; + break; + case GGML_OP_DIV: + func = ggml_sycl_div; + break; + case GGML_OP_UNARY: + switch (ggml_get_unary_op(tensor)) { + case GGML_UNARY_OP_GELU: + func = ggml_sycl_gelu; + break; + case GGML_UNARY_OP_SILU: + func = ggml_sycl_silu; + break; + case GGML_UNARY_OP_GELU_QUICK: + func = ggml_sycl_gelu_quick; + break; + case GGML_UNARY_OP_TANH: + func = ggml_sycl_tanh; + break; + case GGML_UNARY_OP_RELU: + func = ggml_sycl_relu; + break; + default: + return false; + } + break; + case GGML_OP_NORM: + func = ggml_sycl_norm; + break; + case GGML_OP_GROUP_NORM: + func = ggml_sycl_group_norm; + break; + case GGML_OP_CONCAT: + func = ggml_sycl_concat; + break; + case GGML_OP_UPSCALE: + func = ggml_sycl_upscale; + break; + case GGML_OP_PAD: + func = ggml_sycl_pad; + break; + case GGML_OP_LEAKY_RELU: + func = ggml_sycl_leaky_relu; + break; + case GGML_OP_RMS_NORM: + func = ggml_sycl_rms_norm; + break; + case GGML_OP_MUL_MAT: + if (!any_on_device && !ggml_sycl_can_mul_mat(tensor->src[0], tensor->src[1], tensor)) { + return false; + } + func = ggml_sycl_mul_mat; + break; + case GGML_OP_MUL_MAT_ID: + if (!any_on_device && !ggml_sycl_can_mul_mat(tensor->src[2], tensor->src[1], tensor)) { + return false; + } + func = ggml_sycl_mul_mat_id; + break; + case GGML_OP_SCALE: + func = ggml_sycl_scale; + break; + case GGML_OP_SQR: + func = ggml_sycl_sqr; + break; + case GGML_OP_CLAMP: + func = ggml_sycl_clamp; + break; + case GGML_OP_CPY: + func = ggml_sycl_cpy; + break; + case GGML_OP_CONT: + func = ggml_sycl_dup; + break; + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + func = ggml_sycl_nop; + break; + case GGML_OP_DIAG_MASK_INF: + func = ggml_sycl_diag_mask_inf; + break; + case GGML_OP_SOFT_MAX: + func = ggml_sycl_soft_max; + break; + case GGML_OP_ROPE: + func = ggml_sycl_rope; + break; + case GGML_OP_ALIBI: + func = ggml_sycl_alibi; + break; + case GGML_OP_IM2COL: + func = ggml_sycl_im2col; + break; + case GGML_OP_SUM_ROWS: + func = ggml_sycl_sum_rows; + break; + case GGML_OP_ARGSORT: + func = ggml_sycl_argsort; + break; + default: + return false; + } + + if (tensor->src[0] != nullptr && tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT) { + ggml_sycl_set_peer_access(tensor->src[1]->ne[1]); + } + + if (params->ith != 0) { + return true; + } + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return true; + } + func(tensor->src[0], tensor->src[1], tensor); + return true; +} + +int ggml_sycl_get_device_count() try { + int device_count; + if (CHECK_TRY_ERROR(device_count = + dpct::dev_mgr::instance().device_count()) != 0) { + return 0; + } + return device_count; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +void ggml_sycl_get_device_description(int device, char *description, + size_t description_size) try { + dpct::device_info prop; + SYCL_CHECK(CHECK_TRY_ERROR(dpct::get_device_info( + prop, dpct::dev_mgr::instance().get_device(device)))); + snprintf(description, description_size, "%s", prop.get_name()); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +//////////////////////////////////////////////////////////////////////////////// + +// backend interface + +#define UNUSED GGML_UNUSED + +struct ggml_backend_sycl_context { + int device; + std::string name; +}; + +// sycl buffer + +struct ggml_backend_sycl_buffer_context { + int device; + void * dev_ptr = nullptr; + ggml_tensor_extra_gpu * temp_tensor_extras = nullptr; + size_t temp_tensor_extra_index = 0; + std::string name; + + ggml_backend_sycl_buffer_context(int device, void * dev_ptr) : device(device), dev_ptr(dev_ptr) {} + + ~ ggml_backend_sycl_buffer_context() { + delete[] temp_tensor_extras; + } + + ggml_tensor_extra_gpu * ggml_sycl_alloc_temp_tensor_extra() { + if (temp_tensor_extras == nullptr) { + temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_SYCL_MAX_NODES]; + } + + size_t alloc_index = temp_tensor_extra_index; + temp_tensor_extra_index = (temp_tensor_extra_index + 1) % GGML_SYCL_MAX_NODES; + ggml_tensor_extra_gpu * extra = &temp_tensor_extras[alloc_index]; + memset(extra, 0, sizeof(*extra)); + + return extra; + } +}; + +GGML_CALL static const char * ggml_backend_sycl_buffer_get_name(ggml_backend_buffer_t buffer) { + ggml_backend_sycl_buffer_context * ctx = (ggml_backend_sycl_buffer_context *)buffer->context; + return ctx->name.c_str(); +} + +GGML_CALL static bool ggml_backend_buffer_is_sycl(ggml_backend_buffer_t buffer) { + return buffer->iface.get_name == ggml_backend_sycl_buffer_get_name; +} + +static void +ggml_backend_sycl_buffer_free_buffer(ggml_backend_buffer_t buffer) try { + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + ggml_sycl_set_device(ctx->device); + int device_index = get_device_index_by_id(ctx->device); + const dpct::queue_ptr stream = g_syclStreams[device_index][0]; + + SYCL_CHECK( + CHECK_TRY_ERROR(sycl::free(ctx->dev_ptr, *stream))); + delete ctx; +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void * ggml_backend_sycl_buffer_get_base(ggml_backend_buffer_t buffer) { + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + return ctx->dev_ptr; +} + +static void ggml_backend_sycl_buffer_init_tensor(ggml_backend_buffer_t buffer, + ggml_tensor *tensor) try { + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + + if (tensor->view_src != NULL && tensor->view_offs == 0) { + assert(tensor->view_src->buffer->buft == buffer->buft); + tensor->backend = tensor->view_src->backend; + tensor->extra = tensor->view_src->extra; + return; + } + + ggml_tensor_extra_gpu * extra = ctx->ggml_sycl_alloc_temp_tensor_extra(); + + extra->data_device[ctx->device] = tensor->data; + + tensor->backend = GGML_BACKEND_GPU; + tensor->extra = extra; + + if (ggml_is_quantized(tensor->type)) { + // initialize padding to 0 to avoid possible NaN values + int64_t row_low = 0; + int64_t row_high = ggml_nrows(tensor); + int64_t nrows_split = row_high - row_low; + + size_t original_size = ggml_nbytes_split(tensor, nrows_split); + size_t padded_size = ggml_backend_buft_get_alloc_size(buffer->buft, tensor); + + if (padded_size > original_size && tensor->view_src == nullptr) { + SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[ctx->device][0]->memset( + (char *)tensor->data + original_size, 0, + padded_size - original_size))); + } + } + + UNUSED(buffer); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer, + ggml_tensor *tensor, + const void *data, size_t offset, + size_t size) try { + GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + + ggml_sycl_set_device(ctx->device); + int device_index = get_device_index_by_id(ctx->device); + const dpct::queue_ptr stream = g_syclStreams[device_index][0]; + SYCL_CHECK( + CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw())); + + SYCL_CHECK( + CHECK_TRY_ERROR((*stream) + .memcpy((char *)tensor->data + offset, data, size) + .wait())); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_backend_sycl_buffer_get_tensor(ggml_backend_buffer_t buffer, + const ggml_tensor *tensor, + void *data, size_t offset, + size_t size) try { + GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + + ggml_sycl_set_device(ctx->device); + int device_index = get_device_index_by_id(ctx->device); + const dpct::queue_ptr stream = g_syclStreams[device_index][0]; + + SYCL_CHECK( + CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw())); + + SYCL_CHECK(CHECK_TRY_ERROR( + (*stream) + .memcpy(data, (const char *)tensor->data + offset, size) + .wait())); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_backend_sycl_buffer_clear(ggml_backend_buffer_t buffer, + uint8_t value) try { + ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; + + ggml_sycl_set_device(ctx->device); + int device_index = get_device_index_by_id(ctx->device); + const dpct::queue_ptr stream = g_syclStreams[device_index][0]; + SYCL_CHECK( + CHECK_TRY_ERROR(dpct::get_current_device().queues_wait_and_throw())); + + SYCL_CHECK(CHECK_TRY_ERROR((*stream) + .memset(ctx->dev_ptr, value, buffer->size) + .wait())); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static struct ggml_backend_buffer_i ggml_backend_sycl_buffer_interface = { + /* .get_name = */ ggml_backend_sycl_buffer_get_name, + /* .free_buffer = */ ggml_backend_sycl_buffer_free_buffer, + /* .get_base = */ ggml_backend_sycl_buffer_get_base, + /* .init_tensor = */ ggml_backend_sycl_buffer_init_tensor, + /* .set_tensor = */ ggml_backend_sycl_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_sycl_buffer_get_tensor, + /* .cpy_tensor = */ NULL, + /* .clear = */ ggml_backend_sycl_buffer_clear, + /* .reset = */ NULL, +}; + +// sycl buffer type +struct ggml_backend_sycl_buffer_type_context { + int device; + std::string name; +}; + +GGML_CALL static const char * ggml_backend_sycl_buffer_type_name(ggml_backend_buffer_type_t buft) { + ggml_backend_sycl_buffer_type_context * ctx = (ggml_backend_sycl_buffer_type_context *)buft->context; + + return ctx->name.c_str(); +} + +static ggml_backend_buffer_t +ggml_backend_sycl_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, + size_t size) try { + int device = (int) (intptr_t) buft->context; + + ggml_sycl_set_device(device); + int device_index = get_device_index_by_id(device); + const dpct::queue_ptr stream = g_syclStreams[device_index][0]; + size = std::max(size, (size_t)1); // syclMalloc returns null for size 0 + + void * dev_ptr; + SYCL_CHECK(CHECK_TRY_ERROR(dev_ptr = (void *)sycl::malloc_device( + size, *stream))); + + ggml_backend_sycl_buffer_context * ctx = new ggml_backend_sycl_buffer_context(device, dev_ptr); + + return ggml_backend_buffer_init(buft, ggml_backend_sycl_buffer_interface, ctx, size); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static size_t ggml_backend_sycl_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + return 128; + + UNUSED(buft); +} + +static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { + int64_t row_low = 0; + int64_t row_high = ggml_nrows(tensor); + int64_t nrows_split = row_high - row_low; + + size_t size = ggml_nbytes_split(tensor, nrows_split); + + int64_t ne0 = tensor->ne[0]; + + if (ggml_is_quantized(tensor->type)) { + if (ne0 % MATRIX_ROW_PADDING != 0) { + size += ggml_row_size(tensor->type, MATRIX_ROW_PADDING - ne0 % MATRIX_ROW_PADDING); + } + } + + return size; + + UNUSED(buft); +} + +static bool ggml_backend_sycl_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { + return ggml_backend_is_sycl(backend); + + UNUSED(buft); +} + +static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = { + /* .get_name = */ ggml_backend_sycl_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_sycl_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_sycl_buffer_type_get_alignment, + /* .get_alloc_size = */ ggml_backend_sycl_buffer_type_get_alloc_size, + /* .supports_backend = */ ggml_backend_sycl_buffer_type_supports_backend, + /* .is_host = */ nullptr, +}; + +ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device) { + static struct ggml_backend_buffer_type ggml_backend_sycl_buffer_types[GGML_SYCL_MAX_DEVICES]; + + static bool ggml_backend_sycl_buffer_type_initialized = false; + + if (!ggml_backend_sycl_buffer_type_initialized) { + for (int i = 0; i < GGML_SYCL_MAX_DEVICES; i++) { + ggml_backend_sycl_buffer_types[i] = { + /* .iface = */ ggml_backend_sycl_buffer_type_interface, + /* .context = */ (ggml_backend_buffer_type_context_t) (intptr_t) i, + }; + } + ggml_backend_sycl_buffer_type_initialized = true; + } + + return &ggml_backend_sycl_buffer_types[device]; +} + +// host buffer type + +GGML_CALL static const char * ggml_backend_sycl_host_buffer_type_name(ggml_backend_buffer_type_t buft) { + return GGML_SYCL_NAME "_Host"; + + UNUSED(buft); +} + +GGML_CALL static const char * ggml_backend_sycl_host_buffer_name(ggml_backend_buffer_t buffer) { + return GGML_SYCL_NAME "_Host"; + + UNUSED(buffer); +} + +static void ggml_backend_sycl_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_sycl_host_free(buffer->context); +} + +static ggml_backend_buffer_t ggml_backend_sycl_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + void * ptr = ggml_sycl_host_malloc(size); + + if (ptr == nullptr) { + // fallback to cpu buffer + return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); + } + + // FIXME: this is a hack to avoid having to implement a new buffer type + ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); + buffer->buft = buft; + buffer->iface.free_buffer = ggml_backend_sycl_host_buffer_free_buffer; + + return buffer; +} + +ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type() { + static struct ggml_backend_buffer_type ggml_backend_sycl_buffer_type_host = { + /* .iface = */ { + /* .get_name = */ ggml_backend_sycl_host_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_sycl_host_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, + /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, + /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend, + /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, + }, + /* .context = */ nullptr, + }; + + return &ggml_backend_sycl_buffer_type_host; +} + +// backend + +struct ggml_backend_context_sycl { + int device; +}; + +static const char * ggml_backend_sycl_name(ggml_backend_t backend) { + return GGML_SYCL_NAME; + + UNUSED(backend); +} + +static void ggml_backend_sycl_free(ggml_backend_t backend) { + ggml_backend_context_sycl * sycl_ctx = (ggml_backend_context_sycl *)backend->context; + + delete sycl_ctx; + delete backend; +} + +static ggml_backend_buffer_type_t ggml_backend_sycl_get_default_buffer_type(ggml_backend_t backend) { + ggml_backend_context_sycl * sycl_ctx = (ggml_backend_context_sycl *)backend->context; + + return ggml_backend_sycl_buffer_type(sycl_ctx->device); +} + +static void ggml_backend_sycl_set_tensor_async(ggml_backend_t backend, + ggml_tensor *tensor, + const void *data, size_t offset, + size_t size) try { + ggml_backend_context_sycl * sycl_ctx = (ggml_backend_context_sycl *)backend->context; + + GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); + GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + + SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy( + (char *)tensor->data + offset, data, size))); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_backend_sycl_get_tensor_async(ggml_backend_t backend, + const ggml_tensor *tensor, + void *data, size_t offset, + size_t size) try { + ggml_backend_context_sycl * sycl_ctx = (ggml_backend_context_sycl *)backend->context; + + GGML_ASSERT(tensor->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device) && "unsupported buffer type"); + GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + + SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->memcpy( + data, (const char *)tensor->data + offset, size))); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static void ggml_backend_sycl_synchronize(ggml_backend_t backend) try { + ggml_backend_context_sycl * sycl_ctx = (ggml_backend_context_sycl *)backend->context; + + SYCL_CHECK(CHECK_TRY_ERROR(g_syclStreams[sycl_ctx->device][0]->wait())); + + UNUSED(backend); +} +catch (sycl::exception const &exc) { + std::cerr << exc.what() << "Exception caught at file:" << __FILE__ + << ", line:" << __LINE__ << std::endl; + std::exit(1); +} + +static ggml_backend_graph_plan_t ggml_backend_sycl_graph_plan_create(ggml_backend_t backend, const ggml_cgraph * cgraph) { + GGML_ASSERT(!"not implemented"); + + return nullptr; + + UNUSED(backend); + UNUSED(cgraph); +} + +static void ggml_backend_sycl_graph_plan_free(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { + GGML_ASSERT(!"not implemented"); + + UNUSED(backend); + UNUSED(plan); +} + +static void ggml_backend_sycl_graph_plan_compute(ggml_backend_t backend, ggml_backend_graph_plan_t plan) { + GGML_ASSERT(!"not implemented"); + + UNUSED(backend); + UNUSED(plan); +} + +static bool ggml_backend_sycl_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { + ggml_backend_context_sycl * sycl_ctx = (ggml_backend_context_sycl *)backend->context; + + ggml_sycl_set_main_device(sycl_ctx->device); + + ggml_compute_params params = {}; + params.type = GGML_TASK_COMPUTE; + params.ith = 0; + for (int i = 0; i < cgraph->n_nodes; i++) { + ggml_tensor * node = cgraph->nodes[i]; + + if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE) + continue; + + assert(node->backend == GGML_BACKEND_GPU); + assert(node->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device)); + assert(node->extra != nullptr); + + for (int j = 0; j < GGML_MAX_SRC; j++) { + if (node->src[j] != nullptr) { + assert(node->src[j]->backend == GGML_BACKEND_GPU); + assert(node->src[j]->buffer->buft == ggml_backend_sycl_buffer_type(sycl_ctx->device)); + assert(node->src[j]->extra != nullptr); + } + } + + bool ok = ggml_sycl_compute_forward(¶ms, node); + if (!ok) { + fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); + } + GGML_ASSERT(ok); + +#if 0 + if (node->type == GGML_TYPE_F32) { + syclDeviceSynchronize(); + std::vector tmp(ggml_nelements(node), 0.0f); + syclMemcpy(tmp.data(), node->data, ggml_nelements(node)*sizeof(float), syclMemcpyDeviceToHost); + printf("\n%s (%s) (%s %s) (%s %s): ", node->name, ggml_op_name(node->op), + ggml_type_name(node->src[0]->type), + node->src[1] ? ggml_type_name(node->src[1]->type) : "none", + node->src[0]->name, + node->src[1] ? node->src[1]->name : "none"); + double sum = 0.0; + double sq_sum = 0.0; + for (int i = 0; i < ggml_nelements(node); i++) { + printf("%f ", tmp[i]); + sum += tmp[i]; + sq_sum += tmp[i]*tmp[i]; + } + printf("\n"); + printf("sum: %f, ", sum); + printf("sq_sum: %f\n", sq_sum); + } +#endif + } + + UNUSED(backend); + return true; +} + +static bool ggml_backend_sycl_supports_op(ggml_backend_t backend, const ggml_tensor * op) { + switch (op->op) { + case GGML_OP_UNARY: + switch (ggml_get_unary_op(op)) { + case GGML_UNARY_OP_GELU: + case GGML_UNARY_OP_SILU: + case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_GELU_QUICK: + case GGML_UNARY_OP_TANH: + return true; + default: + return false; + } + break; + case GGML_OP_MUL_MAT: + case GGML_OP_MUL_MAT_ID: + { + struct ggml_tensor * a; + struct ggml_tensor * b; + if (op->op == GGML_OP_MUL_MAT) { + a = op->src[0]; + b = op->src[1]; + } else { + a = op->src[2]; + b = op->src[1]; + } + if (a->ne[3] != b->ne[3]) { + return false; + } + + if (a->type == GGML_TYPE_IQ2_XXS) { + return false; + } + if (a->type == GGML_TYPE_IQ2_XS) { + return false; + } + + return true; + } break; + case GGML_OP_GET_ROWS: + { + switch (op->src[0]->type) { + case GGML_TYPE_F16: + case GGML_TYPE_F32: + 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: + return true; + default: + return false; + } + } break; + case GGML_OP_CPY: + { + ggml_type src0_type = op->src[0]->type; + ggml_type src1_type = op->src[1]->type; + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) { + return true; + } + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) { + return true; + } + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q8_0) { + return true; + } + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_0) { + return true; + } + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_Q4_1) { + return true; + } + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { + return true; + } + return false; + } break; + case GGML_OP_CONCAT: + { + ggml_type src0_type = op->src[0]->type; + if (src0_type == GGML_TYPE_F32) { + return true; + } else { + return false; + } + } break; + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + case GGML_OP_NORM: + case GGML_OP_REPEAT: + case GGML_OP_DUP: + case GGML_OP_ADD: + case GGML_OP_MUL: + case GGML_OP_DIV: + case GGML_OP_RMS_NORM: + case GGML_OP_SCALE: + case GGML_OP_SQR: + case GGML_OP_CLAMP: + case GGML_OP_CONT: + case GGML_OP_DIAG_MASK_INF: + case GGML_OP_SOFT_MAX: + case GGML_OP_ROPE: + case GGML_OP_ALIBI: + case GGML_OP_IM2COL: + case GGML_OP_SUM_ROWS: + case GGML_OP_ARGSORT: + case GGML_OP_ACC: + case GGML_OP_GROUP_NORM: + case GGML_OP_UPSCALE: + case GGML_OP_PAD: + case GGML_OP_LEAKY_RELU: + return true; + default: + return false; + } + + UNUSED(backend); +} + +static ggml_backend_i ggml_backend_sycl_interface = { + /* .get_name = */ ggml_backend_sycl_name, + /* .free = */ ggml_backend_sycl_free, + /* .get_default_buffer_type = */ ggml_backend_sycl_get_default_buffer_type, + /* .set_tensor_async = */ ggml_backend_sycl_set_tensor_async, + /* .get_tensor_async = */ ggml_backend_sycl_get_tensor_async, + /* .cpy_tensor_async = */ NULL, + /* .synchronize = */ ggml_backend_sycl_synchronize, + /* .graph_plan_create = */ ggml_backend_sycl_graph_plan_create, + /* .graph_plan_free = */ ggml_backend_sycl_graph_plan_free, + /* .graph_plan_compute = */ ggml_backend_sycl_graph_plan_compute, + /* .graph_compute = */ ggml_backend_sycl_graph_compute, + /* .supports_op = */ ggml_backend_sycl_supports_op, +}; + +ggml_backend_t ggml_backend_sycl_init(int device) { + ggml_init_sycl(); // TODO: remove from ggml.c + + if (device < 0 || device >= ggml_sycl_get_device_count()) { + fprintf(stderr, "%s: error: invalid device %d\n", __func__, device); + return nullptr; + } + + // not strictly necessary, but it may reduce the overhead of the first graph_compute + ggml_sycl_set_main_device(device); + + ggml_backend_context_sycl * ctx = new ggml_backend_context_sycl { + /* .device = */ device + }; + + ggml_backend_t sycl_backend = new ggml_backend { + /* .interface = */ ggml_backend_sycl_interface, + /* .context = */ ctx + }; + + return sycl_backend; +} + +bool ggml_backend_is_sycl(ggml_backend_t backend) { + return backend->iface.get_name == ggml_backend_sycl_name; +} + +static ggml_backend_t ggml_backend_reg_sycl_init(const char * params, void * user_data) { + ggml_backend_t sycl_backend = ggml_backend_sycl_init((int) (intptr_t) user_data); + return sycl_backend; + + UNUSED(params); +} + +extern "C" int ggml_backend_sycl_reg_devices(); + +int ggml_backend_sycl_reg_devices() { + int device_count = ggml_sycl_get_device_count(); + + for (int i = 0; i < device_count; i++) { + char name[128]; + snprintf(name, sizeof(name), "%s%d", GGML_SYCL_NAME, i); + ggml_backend_register(name, ggml_backend_reg_sycl_init, ggml_backend_sycl_buffer_type(i), (void *) (intptr_t) i); + } + return device_count; +} diff --git a/ggml-sycl.h b/ggml-sycl.h new file mode 100644 index 000000000..0eabb53cc --- /dev/null +++ b/ggml-sycl.h @@ -0,0 +1,27 @@ +/*MIT license + Copyright (C) 2024 Intel Corporation + SPDX-License-Identifier: MIT +*/ + +#pragma once + +#include "ggml.h" +#include "ggml-backend.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define GGML_SYCL_MAX_DEVICES 16 +#define GGML_SYCL_NAME "SYCL" + +GGML_API void ggml_init_sycl(void); +GGML_API bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor); +GGML_API ggml_backend_t ggml_backend_sycl_init(int device); +GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_buffer_type(int device); +GGML_API ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type(void); +GGML_API void ggml_backend_sycl_print_sycl_devices(void); + +#ifdef __cplusplus +} +#endif diff --git a/ggml.c b/ggml.c index 11a3114e7..1187f84a2 100644 --- a/ggml.c +++ b/ggml.c @@ -248,6 +248,8 @@ inline static void * ggml_aligned_malloc(size_t size) { #include "ggml-cuda.h" #elif defined(GGML_USE_CLBLAST) #include "ggml-opencl.h" +#elif defined(GGML_USE_SYCL) +#include "ggml-sycl.h" #endif // floating point type used to accumulate sums @@ -2293,6 +2295,8 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { ggml_init_cublas(); #elif defined(GGML_USE_CLBLAST) ggml_cl_init(); +#elif defined(GGML_USE_SYCL) + ggml_init_sycl(); #endif ggml_setup_op_has_task_pass(); @@ -14701,6 +14705,12 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_CPU); #endif // GGML_USE_CUBLAS +#ifdef GGML_USE_SYCL + bool skip_cpu = ggml_sycl_compute_forward(params, tensor); + if (skip_cpu) { + return; + } +#endif // GGML_USE_SYCL switch (tensor->op) { case GGML_OP_DUP: { @@ -20280,7 +20290,7 @@ int ggml_cpu_has_wasm_simd(void) { } int ggml_cpu_has_blas(void) { -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) +#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_SYCL) return 1; #else return 0; @@ -20303,8 +20313,16 @@ int ggml_cpu_has_clblast(void) { #endif } +int ggml_cpu_has_sycl(void) { +#if defined(GGML_USE_SYCL) + return 1; +#else + return 0; +#endif +} + int ggml_cpu_has_gpublas(void) { - return ggml_cpu_has_cublas() || ggml_cpu_has_clblast(); + return ggml_cpu_has_cublas() || ggml_cpu_has_clblast() || ggml_cpu_has_sycl(); } int ggml_cpu_has_sse3(void) { diff --git a/ggml.h b/ggml.h index 1c4976271..3d8d6f2ae 100644 --- a/ggml.h +++ b/ggml.h @@ -2266,6 +2266,7 @@ extern "C" { 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_sycl (void); GGML_API int ggml_cpu_has_vsx (void); // diff --git a/llama.cpp b/llama.cpp index 45eeadbe8..09d799f71 100644 --- a/llama.cpp +++ b/llama.cpp @@ -11,6 +11,8 @@ # include "ggml-cuda.h" #elif defined(GGML_USE_CLBLAST) # include "ggml-opencl.h" +#elif defined(GGML_USE_SYCL) +# include "ggml-sycl.h" #endif #ifdef GGML_USE_METAL @@ -1278,6 +1280,8 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(bool host_buffer if (host_buffer) { buft = ggml_backend_cuda_host_buffer_type(); } +#elif defined(GGML_USE_SYCL) + buft = ggml_backend_sycl_host_buffer_type(); #elif defined(GGML_USE_CPU_HBM) buft = ggml_backend_cpu_hbm_buffer_type(); #endif @@ -1297,6 +1301,8 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) { buft = ggml_backend_metal_buffer_type(); #elif defined(GGML_USE_CUBLAS) buft = ggml_backend_cuda_buffer_type(gpu); +#elif defined(GGML_USE_SYCL) + buft = ggml_backend_sycl_buffer_type(gpu); #elif defined(GGML_USE_CLBLAST) buft = ggml_backend_opencl_buffer_type(); #endif @@ -10225,6 +10231,16 @@ struct llama_context * llama_new_context_with_model( } } } +#elif defined(GGML_USE_SYCL) + if (model->n_gpu_layers > 0) { + ggml_backend_t backend = ggml_backend_sycl_init(model->main_gpu); + if (backend == nullptr) { + LLAMA_LOG_ERROR("%s: failed to initialize SYCL%d backend\n", __func__, model->main_gpu); + llama_free(ctx); + return nullptr; + } + ctx->backends.push_back(backend); + } #endif ctx->backend_cpu = ggml_backend_cpu_init(); if (ctx->backend_cpu == nullptr) { diff --git a/llama.h b/llama.h index 7b3634aa6..d11cba815 100644 --- a/llama.h +++ b/llama.h @@ -6,6 +6,9 @@ #ifdef GGML_USE_CUBLAS #include "ggml-cuda.h" #define LLAMA_MAX_DEVICES GGML_CUDA_MAX_DEVICES +#elif defined(GGML_USE_SYCL) +#include "ggml-sycl.h" +#define LLAMA_MAX_DEVICES GGML_SYCL_MAX_DEVICES #else #define LLAMA_MAX_DEVICES 1 #endif // GGML_USE_CUBLAS @@ -46,7 +49,7 @@ #define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN #define LLAMA_SESSION_VERSION 4 -#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) +#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_SYCL) // Defined when llama.cpp is compiled with support for offloading model layers to GPU. #define LLAMA_SUPPORTS_GPU_OFFLOAD #endif diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index e3c656f56..015939105 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -239,10 +239,17 @@ static std::string var_to_str(ggml_type type) { #define VARS_TO_STR10(a, b, c, d, e, f, g, h, i, j) VAR_TO_STR(a) + "," + VARS_TO_STR9(b, c, d, e, f, g, h, i, j) #define VARS_TO_STR11(a, b, c, d, e, f, g, h, i, j, k) VAR_TO_STR(a) + "," + VARS_TO_STR10(b, c, d, e, f, g, h, i, j, k) +#ifdef GGML_USE_SYCL +static bool inline _isinf(float f) { + return (*(uint32_t *)&f & 0x7fffffff) == 0x7f800000; +} +#else +static bool inline _isinf(float f) { return std::isinf(f); } +#endif // accept FLT_MAX as infinity static bool isinf_or_max(float f) { - return std::isinf(f) || f == FLT_MAX || f == -FLT_MAX; + return _isinf(f) || f == FLT_MAX || f == -FLT_MAX; } static bool ggml_is_view_op(enum ggml_op op) { From 2307523d322af762ae06648b29ec5a9eb1c73032 Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Sun, 28 Jan 2024 18:03:59 +0100 Subject: [PATCH 043/143] ggml : add Vulkan backend (#2059) * Vulkan loader code * Fix matmul kernel, continue implementation * Continue implementation * Vulkan memory management * Vulkan development * Matmul call * Add aligned malloc and free for VMA * Continue implementation * First matmul success * GEMM Kernel optimization * 1D Blocktiling * 2D Blocktiling * Write coalescing * Continue vulkan implementation and optimization * First FP16 attempt, disabled for now * Code abstraction, FP16 implementation, fix kernel, add FP16 to FP32 kernel * Enable device extensions properly, restore fp16 matmul op * Fix mulmat_f16 * Output FP32 in fp16 matmul shader * Fix f16_to_f32 kernel * dequant_q4_0 kernel * Add VMA library * Avoid requesting dedicated memory, VMA can decide that by itself * Add bounds checking to matmul kernels, improve implementation, fix command buffers not freed properly * add cmake commands * Add 2d write operation, profiling code * Fix 2d write * Fix queue selection for AMD RADV * Fix trailing whitespace in vk_mem_alloc.h * Add WIP warp tile mat mul shaders * Disable glslc optimization * Disable glslc optimization for CMake * Optimize warptile matmul shader, replace blocktile with it * Add split-k optimization for small matrix multiplication Use semaphores for synchronization instead of fences or waitidle Rework async write/read for synchronization * Fix validation errors, improve compatibility with AMD GPUs * Rework command buffer handling * Variable matmul kernel using specialization constants * Fix synchronization on AMD, add barriers for buffer ownership transfer, add debug flag and prints * Reuse semaphores * Handle stage flags during command buffer submission properly * Increase matmul test runs for consistent results * Fix F32 matmul * Add vectorized loading and zeropadding for matrix multiplication * Use pinned memory for f16 preprocessing * Don't force aligned matmul * Don't free before queue done * Replace VMA library with native Vulkan buffer management * Basic offloading support with mul_f32 and dmmv for q4_0 * Run glslc commands in parallel * Unroll loops in dmmv shader * Reduce usage of waitIdle * Reuse pinned allocation for f16 conversion * Handle devices with only a single queue * Fix trailing whitespace in CMakeLists.txt * Allow parallel execution of kernels, parallelize third and fourth dimension calls * Add fallback for devices only supporting one DescriptorSet per DescriptorPool * Move to graph function similar to CUDA implementation * Use F16 kernel for most things, replace q_f32 with mul_mat_q_f16 function * Add F32 dmmv shaders * Batch submissions * Add .spv to gitignore * Split off matrix vector multiplication for separate optimization * Use single command buffer for matrix vector multiplication ops * Reduce overhead of mul_f32 calls by using a single command buffer * Add submission batching to mul_f32 * Fix tests * Add missing barrier * Add further missing barrier * Add further ops * Replace vk::QueueFamilyIgnored with VK_QUEUE_FAMILY_IGNORED to support more Vulkan header versions * Remove unnecessary cblas link * Fix descriptor set pre-allocation assert * Add runtime shader compilation, start transferring shaders to this approach * Transfer remaining shaders to header and compile on runtime * Fix fp32 fallback if device doesn't support fp16, add force disable env var GGML_VULKAN_DISABLE_F16 * Add support for q4_1, q5_0, q5_1 and q8_0 * Remove unnecessary scalar layout extension * Parse graph early to pre-record command buffers * Add q6_k support * Add multi-submit for command buffers * Fix q6_k dequant shader for AMD * Fix q6_k for GPUs without fp16 support * Simplify q6_k fp16 fix * Minor fixes * Fix wg_denom of m-mulmat shaders * Add Python-based Vulkan shader generator * Replace shaderc dependency with precompiled shaders Fix python script to generate shaders * Clean up code * Fix shader generator script Windows compatibility Co-authored-by: Concedo <39025047+LostRuins@users.noreply.github.com> * Close file before deletion * Fix vulkan shader fp32 name * Add q2_k and q3_k support Add validation check to compare shader results to cpu results * Add q4_k support * Add q5_k support * Bake SPIR-V bytecode into the library instead of loading shaders from file * Switch to signal semaphores for flexibility Prepare broadcasting support for mul mat * Finish broadcasting mul mat support for GQA * Clean up unused functions Add repeat op * Add further ops, not yet enabled. Improve semaphore code * Reduce number of used semaphores by utilizing timelines more properly * Remove queue information * Reuse timeline semaphores, allow parallel operation with binary semaphores to work around nvidia driver limitations * Add Vulkan to llama-bench * Remove cblas dependency * Fix matmul k-split bug * Fix q4_k dmmv K_QUANTS_PER_ITERATION 1 shader * Add RMS Norm shader, rework op_f32 shader setup, fix matmul bug * Fix issues with float16 overflows in shaders * Fix issues with older Vulkan headers on Ubuntu 22.04 * Allow multi-op partial offloading by parsing the graph to preallocate enough between-op buffers * Implement further ops, rework op_f32 calls, fix bugs * Finish full offloading support, add last remaining ops, fix bugs, remove redundant code * Upload generated file ggml-vulkan-shaders.hpp, remove redundant shaders * Merge upstream changes, fix conflicts, adapt soft_max op * Fix Python and shader header format * Free model gpu buffers on exit * Use single queue per device to simplify code * Add matmul shader support for running multiple calculations in parallel * Switch from semaphore-synchronized multiple command buffers per op to single command buffer for multiple ops, whole graph if possible * Fix missing event cast * Replace uint64_t(-1) with UINT64_MAX, rename function for clarity * Fix warning about empty C function parameters * Fix compiler warnings * Properly implement Vulkan backend buffer handling * Fix oversized host staging buffers * Simplify barrier synchronization calls * Fix gcc warnings * Implement max_size for backend buffer types to limit the size of a single allocation * Use min of maxMemoryAllocationSize and maxBufferSize for device max allocation size * refactor multi buf * Disable unsupported ops to fix tests * Check for maintenance4 support before using it * Handle devices with only a single queue * Fix single queue logic * propagate buffer usage in multi buffers * Implement rope_neox op * Cleanup header and other files * Simplify gpu_extras by removing events and putting staging memcpys into contexts * Move queue into context Add not-yet-enabled async backend ops * Simplify context use, optimize matmul shader for warp size 64 (AMD GCN), fix split_k matmul shader optimization * Add get_max_size to SYCL backend. Co-authored-by: Georgi Gerganov * llama : fix trailing whitespace --------- Co-authored-by: Henri Vasserman Co-authored-by: Concedo <39025047+LostRuins@users.noreply.github.com> Co-authored-by: slaren Co-authored-by: Georgi Gerganov --- CMakeLists.txt | 17 + Makefile | 13 + examples/llama-bench/llama-bench.cpp | 11 +- ggml-alloc.c | 106 +- ggml-backend-impl.h | 6 + ggml-backend.c | 104 +- ggml-backend.h | 3 + ggml-cuda.cu | 3 + ggml-metal.m | 1 + ggml-opencl.cpp | 2 + ggml-sycl.cpp | 2 + ggml-vulkan-shaders.hpp | 61420 +++++++++++++++++++++++++ ggml-vulkan.cpp | 5176 +++ ggml-vulkan.h | 34 + ggml.c | 45 +- ggml.h | 1 + ggml_vk_generate_shaders.py | 2362 + llama.cpp | 20 +- llama.h | 2 +- 19 files changed, 69294 insertions(+), 34 deletions(-) create mode 100644 ggml-vulkan-shaders.hpp create mode 100644 ggml-vulkan.cpp create mode 100644 ggml-vulkan.h create mode 100644 ggml_vk_generate_shaders.py diff --git a/CMakeLists.txt b/CMakeLists.txt index 8b530e3ed..deb294198 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -99,6 +99,7 @@ set(LLAMA_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING option(LLAMA_HIPBLAS "llama: use hipBLAS" OFF) option(LLAMA_HIP_UMA "llama: use HIP unified memory architecture" OFF) option(LLAMA_CLBLAST "llama: use CLBlast" OFF) +option(LLAMA_VULKAN "llama: use Vulkan" OFF) option(LLAMA_METAL "llama: use Metal" ${LLAMA_METAL_DEFAULT}) option(LLAMA_METAL_NDEBUG "llama: disable Metal debugging" OFF) option(LLAMA_METAL_SHADER_DEBUG "llama: compile Metal with -fno-fast-math" OFF) @@ -416,6 +417,22 @@ if (LLAMA_CLBLAST) endif() endif() +if (LLAMA_VULKAN) + find_package(Vulkan) + if (Vulkan_FOUND) + message(STATUS "Vulkan found") + + add_library(ggml-vulkan STATIC ggml-vulkan.cpp ggml-vulkan.h) + target_link_libraries(ggml-vulkan PRIVATE Vulkan::Vulkan) + + add_compile_definitions(GGML_USE_VULKAN) + + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ggml-vulkan) + else() + message(WARNING "Vulkan not found") + endif() +endif() + if (LLAMA_HIPBLAS) list(APPEND CMAKE_PREFIX_PATH /opt/rocm) diff --git a/Makefile b/Makefile index b8858b412..781f0bf8c 100644 --- a/Makefile +++ b/Makefile @@ -448,6 +448,19 @@ ggml-opencl.o: ggml-opencl.cpp ggml-opencl.h $(CXX) $(CXXFLAGS) -c $< -o $@ endif # LLAMA_CLBLAST +ifdef LLAMA_VULKAN + MK_CPPFLAGS += -DGGML_USE_VULKAN + MK_LDFLAGS += -lvulkan + OBJS += ggml-vulkan.o + +ifdef LLAMA_VULKAN_CHECK_RESULTS + MK_CPPFLAGS += -DGGML_VULKAN_CHECK_RESULTS +endif + +ggml-vulkan.o: ggml-vulkan.cpp ggml-vulkan.h + $(CXX) $(CXXFLAGS) -c $< -o $@ +endif # LLAMA_VULKAN + ifdef LLAMA_HIPBLAS ifeq ($(wildcard /opt/rocm),) diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 97325b5bd..f239415d3 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -562,6 +562,7 @@ struct test { static const int build_number; static const bool cuda; static const bool opencl; + static const bool vulkan; static const bool metal; static const bool gpu_blas; static const bool blas; @@ -643,6 +644,9 @@ struct test { if (opencl) { return "OpenCL"; } + if (vulkan) { + return "Vulkan"; + } if (metal) { return "Metal"; } @@ -658,7 +662,7 @@ struct test { static const std::vector & get_fields() { static const std::vector fields = { "build_commit", "build_number", - "cuda", "opencl", "metal", "gpu_blas", "blas", + "cuda", "opencl", "vulkan", "metal", "gpu_blas", "blas", "cpu_info", "gpu_info", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_threads", "type_k", "type_v", @@ -682,7 +686,7 @@ struct test { field == "avg_ns" || field == "stddev_ns") { return INT; } - if (field == "cuda" || field == "opencl" || field == "metal" || field == "gpu_blas" || field == "blas" || + if (field == "cuda" || field == "opencl" || field == "vulkan"|| field == "metal" || field == "gpu_blas" || field == "blas" || field == "f16_kv" || field == "no_kv_offload" || field == "mul_mat_q") { return BOOL; } @@ -710,7 +714,7 @@ struct test { } std::vector values = { 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), + std::to_string(cuda), std::to_string(opencl), std::to_string(vulkan), std::to_string(metal), std::to_string(gpu_blas), std::to_string(blas), cpu_info, gpu_info, 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), ggml_type_name(type_k), ggml_type_name(type_v), @@ -738,6 +742,7 @@ const std::string test::build_commit = LLAMA_COMMIT; const int test::build_number = LLAMA_BUILD_NUMBER; const bool test::cuda = !!ggml_cpu_has_cublas(); const bool test::opencl = !!ggml_cpu_has_clblast(); +const bool test::vulkan = !!ggml_cpu_has_vulkan(); const bool test::metal = !!ggml_cpu_has_metal(); const bool test::gpu_blas = !!ggml_cpu_has_gpublas(); const bool test::blas = !!ggml_cpu_has_blas(); diff --git a/ggml-alloc.c b/ggml-alloc.c index 95a93c99d..dfe5ba2e5 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -778,38 +778,26 @@ size_t ggml_allocr_alloc_graph(ggml_allocr_t alloc, struct ggml_cgraph * graph) } // utils -ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) { - GGML_ASSERT(ggml_get_no_alloc(ctx) == true); - size_t alignment = ggml_backend_buft_get_alignment(buft); - - size_t nbytes = 0; - for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { - if (t->data == NULL && t->view_src == NULL) { - nbytes += GGML_PAD(ggml_backend_buft_get_alloc_size(buft, t), alignment); - } - } - - if (nbytes == 0) { - // all the tensors in the context are already allocated -#ifndef NDEBUG - fprintf(stderr, "%s: all tensors in the context are already allocated\n", __func__); -#endif - return NULL; - } - - ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, nbytes); +static bool alloc_tensor_range(struct ggml_context * ctx, + struct ggml_tensor * first, struct ggml_tensor * last, + ggml_backend_buffer_type_t buft, size_t size, + ggml_backend_buffer_t ** buffers, size_t * n_buffers) { + ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, size); if (buffer == NULL) { - // failed to allocate buffer #ifndef NDEBUG - fprintf(stderr, "%s: failed to allocate buffer\n", __func__); + fprintf(stderr, "%s: failed to allocate %s buffer of size %zu\n", __func__, ggml_backend_buft_name(buft), size); #endif - return NULL; + for (size_t i = 0; i < *n_buffers; i++) { + ggml_backend_buffer_free(*buffers[i]); + } + free(buffers); + return false; } ggml_tallocr_t tallocr = ggml_tallocr_new_from_buffer(buffer); - for (struct ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { + for (struct ggml_tensor * t = first; t != last; t = ggml_get_next_tensor(ctx, t)) { if (t->data == NULL) { if (t->view_src == NULL) { ggml_tallocr_alloc(tallocr, t); @@ -826,6 +814,76 @@ ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_conte ggml_tallocr_free(tallocr); + *buffers = realloc(*buffers, sizeof(ggml_backend_buffer_t) * (*n_buffers + 1)); + (*buffers)[(*n_buffers)++] = buffer; + + return true; +} + +ggml_backend_buffer_t ggml_backend_alloc_ctx_tensors_from_buft(struct ggml_context * ctx, ggml_backend_buffer_type_t buft) { + GGML_ASSERT(ggml_get_no_alloc(ctx) == true); + + size_t alignment = ggml_backend_buft_get_alignment(buft); + size_t max_size = ggml_backend_buft_get_max_size(buft); + + ggml_backend_buffer_t * buffers = NULL; + size_t n_buffers = 0; + + size_t cur_buf_size = 0; + struct ggml_tensor * first = ggml_get_first_tensor(ctx); + for (struct ggml_tensor * t = first; t != NULL; t = ggml_get_next_tensor(ctx, t)) { + size_t this_size = 0; + if (t->data == NULL && t->view_src == NULL) { + this_size = GGML_PAD(ggml_backend_buft_get_alloc_size(buft, t), alignment); + } + + if (this_size > max_size) { + // tensor is too large to fit in a single buffer + fprintf(stderr, "%s: tensor %s is too large to fit in a %s buffer (tensor size: %zu, max buffer size: %zu)\n", + __func__, t->name, + ggml_backend_buft_name(buft), + this_size, max_size); + for (size_t i = 0; i < n_buffers; i++) { + ggml_backend_buffer_free(buffers[i]); + } + free(buffers); + return NULL; + } + + if ((cur_buf_size + this_size) > max_size) { + // allocate tensors in the current buffer + if (!alloc_tensor_range(ctx, first, t, buft, cur_buf_size, &buffers, &n_buffers)) { + return NULL; + } + first = t; + cur_buf_size = this_size; + } else { + cur_buf_size += this_size; + } + } + + // allocate remaining tensors + if (cur_buf_size > 0) { + if (!alloc_tensor_range(ctx, first, NULL, buft, cur_buf_size, &buffers, &n_buffers)) { + return NULL; + } + } + + if (n_buffers == 0) { + // all the tensors in the context are already allocated +#ifndef NDEBUG + fprintf(stderr, "%s: all tensors in the context are already allocated\n", __func__); +#endif + return NULL; + } + + ggml_backend_buffer_t buffer; + if (n_buffers == 1) { + buffer = buffers[0]; + } else { + buffer = ggml_backend_multi_buffer_alloc_buffer(buffers, n_buffers); + } + free(buffers); return buffer; } diff --git a/ggml-backend-impl.h b/ggml-backend-impl.h index 1397828d9..f95df47f7 100644 --- a/ggml-backend-impl.h +++ b/ggml-backend-impl.h @@ -19,6 +19,7 @@ extern "C" { const char * (*GGML_CALL get_name) (ggml_backend_buffer_type_t buft); ggml_backend_buffer_t (*GGML_CALL alloc_buffer) (ggml_backend_buffer_type_t buft, size_t size); size_t (*GGML_CALL get_alignment) (ggml_backend_buffer_type_t buft); // tensor alignment + size_t (*GGML_CALL get_max_size) (ggml_backend_buffer_type_t buft); // allocation max size size_t (*GGML_CALL get_alloc_size) (ggml_backend_buffer_type_t buft, const struct ggml_tensor * tensor); // data size needed to allocate the tensor, including padding bool (*GGML_CALL supports_backend)(ggml_backend_buffer_type_t buft, ggml_backend_t backend); // check if the buffer type is usable by the backend // check if tensor data is in host memory @@ -63,6 +64,11 @@ extern "C" { // do not use directly, use ggml_backend_tensor_copy instead bool ggml_backend_buffer_copy_tensor(const struct ggml_tensor * src, struct ggml_tensor * dst); + // buffer that contains a collection of buffers + GGML_CALL ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_backend_buffer_t * buffers, size_t n_buffers); + GGML_CALL bool ggml_backend_buffer_is_multi_buffer(ggml_backend_buffer_t buffer); + GGML_CALL void ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage); + // // Backend // diff --git a/ggml-backend.c b/ggml-backend.c index 897a4cb5c..8b6cf7c9f 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -27,6 +27,14 @@ size_t ggml_backend_buft_get_alignment(ggml_backend_buffer_type_t buft) { return buft->iface.get_alignment(buft); } +size_t ggml_backend_buft_get_max_size(ggml_backend_buffer_type_t buft) { + // get_max_size is optional, defaults to SIZE_MAX + if (buft->iface.get_max_size) { + return buft->iface.get_max_size(buft); + } + return SIZE_MAX; +} + GGML_CALL size_t ggml_backend_buft_get_alloc_size(ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor) { // get_alloc_size is optional, defaults to ggml_nbytes if (buft->iface.get_alloc_size) { @@ -57,8 +65,6 @@ GGML_CALL ggml_backend_buffer_t ggml_backend_buffer_init( size_t size) { ggml_backend_buffer_t buffer = malloc(sizeof(struct ggml_backend_buffer)); - GGML_ASSERT(iface.get_base != NULL); - (*buffer) = (struct ggml_backend_buffer) { /* .interface = */ iface, /* .buft = */ buft, @@ -108,6 +114,10 @@ size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer) { return ggml_backend_buft_get_alignment(ggml_backend_buffer_get_type(buffer)); } +size_t ggml_backend_buffer_get_max_size(ggml_backend_buffer_t buffer) { + return ggml_backend_buft_get_max_size(ggml_backend_buffer_get_type(buffer)); +} + size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor) { return ggml_backend_buft_get_alloc_size(ggml_backend_buffer_get_type(buffer), tensor); } @@ -122,6 +132,11 @@ bool ggml_backend_buffer_is_host(ggml_backend_buffer_t buffer) { void ggml_backend_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) { buffer->usage = usage; + + // FIXME: add a generic callback to the buffer interface + if (ggml_backend_buffer_is_multi_buffer(buffer)) { + ggml_backend_multi_buffer_set_usage(buffer, usage); + } } ggml_backend_buffer_type_t ggml_backend_buffer_get_type(ggml_backend_buffer_t buffer) { @@ -171,6 +186,10 @@ size_t ggml_backend_get_alignment(ggml_backend_t backend) { return ggml_backend_buft_get_alignment(ggml_backend_get_default_buffer_type(backend)); } +size_t ggml_backend_get_max_size(ggml_backend_t backend) { + return ggml_backend_buft_get_max_size(ggml_backend_get_default_buffer_type(backend)); +} + void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size) { GGML_ASSERT(tensor->data != NULL && "tensor not allocated"); GGML_ASSERT(offset + size <= ggml_nbytes(tensor) && "tensor write out of bounds"); @@ -349,6 +368,11 @@ GGML_CALL static void ggml_backend_registry_init(void) { extern GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void); ggml_backend_register("Metal", ggml_backend_reg_metal_init, ggml_backend_metal_buffer_type(), NULL); #endif + +#ifdef GGML_USE_VULKAN + extern GGML_CALL int ggml_backend_vk_reg_devices(void); + ggml_backend_vk_reg_devices(); +#endif } GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) { @@ -552,6 +576,7 @@ GGML_CALL ggml_backend_buffer_type_t ggml_backend_cpu_buffer_type(void) { /* .get_name = */ ggml_backend_cpu_buffer_type_get_name, /* .alloc_buffer = */ ggml_backend_cpu_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes /* .supports_backend = */ ggml_backend_cpu_buffer_type_supports_backend, /* .is_host = */ ggml_backend_cpu_buffer_type_is_host, @@ -607,6 +632,7 @@ ggml_backend_buffer_type_t ggml_backend_cpu_hbm_buffer_type(void) { /* .get_name = */ ggml_backend_cpu_hbm_buffer_type_get_name, /* .alloc_buffer = */ ggml_backend_cpu_hbm_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_cpu_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes /* .supports_backend = */ ggml_backend_cpu_buffer_type_supports_backend, /* .is_host = */ ggml_backend_cpu_buffer_type_is_host, @@ -763,6 +789,80 @@ GGML_CALL static ggml_backend_t ggml_backend_reg_cpu_init(const char * params, v GGML_UNUSED(user_data); } +// multi-buffer buffer + +struct ggml_backend_multi_buffer_context { + ggml_backend_buffer_t * buffers; + size_t n_buffers; +}; + +typedef struct ggml_backend_multi_buffer_context * ggml_backend_multi_buffer_context_t; + +GGML_CALL static const char * ggml_backend_multi_buffer_get_name(ggml_backend_buffer_t buffer) { + ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) buffer->context; + + return ctx->buffers[0]->iface.get_name(ctx->buffers[0]); +} + +GGML_CALL static void ggml_backend_multi_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) buffer->context; + for (size_t i = 0; i < ctx->n_buffers; i++) { + ggml_backend_buffer_free(ctx->buffers[i]); + } + + free(ctx->buffers); + free(ctx); +} + +GGML_CALL static void ggml_backend_multi_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) buffer->context; + for (size_t i = 0; i < ctx->n_buffers; i++) { + ggml_backend_buffer_clear(ctx->buffers[i], value); + } +} + +static struct ggml_backend_buffer_i ggml_backend_multi_buffer_context_interface(void) { + static struct ggml_backend_buffer_i multi_backend_buffer_i = { + /* .get_name = */ ggml_backend_multi_buffer_get_name, + /* .free_buffer = */ ggml_backend_multi_buffer_free_buffer, + /* .get_base = */ NULL, + /* .init_tensor = */ NULL, + /* .set_tensor = */ NULL, + /* .get_tensor = */ NULL, + /* .cpy_tensor = */ NULL, + /* .clear = */ ggml_backend_multi_buffer_clear, + /* .reset = */ NULL, + }; + + return multi_backend_buffer_i; +} + +GGML_CALL ggml_backend_buffer_t ggml_backend_multi_buffer_alloc_buffer(ggml_backend_buffer_t * buffers, size_t n_buffers) { + ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) malloc(sizeof(struct ggml_backend_multi_buffer_context)); + ctx->n_buffers = n_buffers; + ctx->buffers = (ggml_backend_buffer_t *) malloc(n_buffers * sizeof(ggml_backend_buffer_t)); + + size_t total_size = 0; + for (size_t i = 0; i < n_buffers; i++) { + ctx->buffers[i] = buffers[i]; + total_size += ggml_backend_buffer_get_size(buffers[i]); + } + + return ggml_backend_buffer_init(buffers[0]->buft, ggml_backend_multi_buffer_context_interface(), ctx, total_size); +} + +GGML_CALL bool ggml_backend_buffer_is_multi_buffer(ggml_backend_buffer_t buffer) { + return buffer->iface.get_name == ggml_backend_multi_buffer_get_name; +} + +GGML_CALL void ggml_backend_multi_buffer_set_usage(ggml_backend_buffer_t buffer, enum ggml_backend_buffer_usage usage) { + GGML_ASSERT(ggml_backend_buffer_is_multi_buffer(buffer)); + ggml_backend_multi_buffer_context_t ctx = (ggml_backend_multi_buffer_context_t) buffer->context; + for (size_t i = 0; i < ctx->n_buffers; i++) { + ggml_backend_buffer_set_usage(ctx->buffers[i], usage); + } +} + // scheduler diff --git a/ggml-backend.h b/ggml-backend.h index ab4ad773f..8b8160fcf 100644 --- a/ggml-backend.h +++ b/ggml-backend.h @@ -20,6 +20,7 @@ extern "C" { GGML_API const char * ggml_backend_buft_name (ggml_backend_buffer_type_t buft); GGML_API GGML_CALL ggml_backend_buffer_t ggml_backend_buft_alloc_buffer (ggml_backend_buffer_type_t buft, size_t size); GGML_API size_t ggml_backend_buft_get_alignment (ggml_backend_buffer_type_t buft); + GGML_API size_t ggml_backend_buft_get_max_size (ggml_backend_buffer_type_t buft); GGML_API GGML_CALL size_t ggml_backend_buft_get_alloc_size (ggml_backend_buffer_type_t buft, struct ggml_tensor * tensor); GGML_API bool ggml_backend_buft_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend); GGML_API bool ggml_backend_buft_is_host (ggml_backend_buffer_type_t buft); @@ -36,6 +37,7 @@ extern "C" { GGML_API size_t ggml_backend_buffer_get_size (ggml_backend_buffer_t buffer); GGML_API GGML_CALL void ggml_backend_buffer_init_tensor (ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); GGML_API size_t ggml_backend_buffer_get_alignment (ggml_backend_buffer_t buffer); + GGML_API size_t ggml_backend_buffer_get_max_size (ggml_backend_buffer_t buffer); GGML_API size_t ggml_backend_buffer_get_alloc_size(ggml_backend_buffer_t buffer, struct ggml_tensor * tensor); GGML_API void ggml_backend_buffer_clear (ggml_backend_buffer_t buffer, uint8_t value); GGML_API bool ggml_backend_buffer_is_host (ggml_backend_buffer_t buffer); @@ -54,6 +56,7 @@ extern "C" { GGML_API ggml_backend_buffer_type_t ggml_backend_get_default_buffer_type(ggml_backend_t backend); GGML_API ggml_backend_buffer_t ggml_backend_alloc_buffer(ggml_backend_t backend, size_t size); GGML_API size_t ggml_backend_get_alignment(ggml_backend_t backend); + GGML_API size_t ggml_backend_get_max_size(ggml_backend_t backend); GGML_API void ggml_backend_tensor_set_async(ggml_backend_t backend, struct ggml_tensor * tensor, const void * data, size_t offset, size_t size); GGML_API void ggml_backend_tensor_get_async(ggml_backend_t backend, const struct ggml_tensor * tensor, void * data, size_t offset, size_t size); diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 0d599e20a..7695b86b2 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -10440,6 +10440,7 @@ static ggml_backend_buffer_type_i ggml_backend_cuda_buffer_type_interface = { /* .get_name = */ ggml_backend_cuda_buffer_type_name, /* .alloc_buffer = */ ggml_backend_cuda_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_cuda_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX /* .get_alloc_size = */ ggml_backend_cuda_buffer_type_get_alloc_size, /* .supports_backend = */ ggml_backend_cuda_buffer_type_supports_backend, /* .is_host = */ NULL, @@ -10715,6 +10716,7 @@ static ggml_backend_buffer_type_i ggml_backend_cuda_split_buffer_type_interface /* .get_name = */ ggml_backend_cuda_split_buffer_type_name, /* .alloc_buffer = */ ggml_backend_cuda_split_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_cuda_split_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX /* .get_alloc_size = */ ggml_backend_cuda_split_buffer_type_get_alloc_size, /* .supports_backend = */ ggml_backend_cuda_split_buffer_type_supports_backend, /* .is_host = */ ggml_backend_cuda_split_buffer_type_is_host, @@ -10794,6 +10796,7 @@ GGML_CALL ggml_backend_buffer_type_t ggml_backend_cuda_host_buffer_type() { /* .get_name = */ ggml_backend_cuda_host_buffer_type_name, /* .alloc_buffer = */ ggml_backend_cuda_host_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend, /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, diff --git a/ggml-metal.m b/ggml-metal.m index ab3c84f7f..a0efda0ba 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -2400,6 +2400,7 @@ GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) { /* .get_name = */ ggml_backend_metal_buffer_type_get_name, /* .alloc_buffer = */ ggml_backend_metal_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_metal_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // TODO: return device.maxBufferLength /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes /* .supports_backend = */ ggml_backend_metal_buffer_type_supports_backend, /* .is_host = */ ggml_backend_metal_buffer_type_is_host, diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp index bf9ad964f..d40663535 100644 --- a/ggml-opencl.cpp +++ b/ggml-opencl.cpp @@ -2136,6 +2136,7 @@ static ggml_backend_buffer_type_i ggml_backend_opencl_buffer_type_interface = { /* .get_name = */ ggml_backend_opencl_buffer_type_name, /* .alloc_buffer = */ ggml_backend_opencl_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_opencl_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // TODO: return from device info /* .get_alloc_size = */ NULL, /* .supports_backend = */ ggml_backend_opencl_buffer_type_supports_backend, /* .is_host = */ NULL, @@ -2192,6 +2193,7 @@ ggml_backend_buffer_type_t ggml_backend_opencl_host_buffer_type() { /* .get_name = */ ggml_backend_opencl_host_buffer_type_name, /* .alloc_buffer = */ ggml_backend_opencl_host_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend, /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 9764d9c35..3fc346975 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -14781,6 +14781,7 @@ static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = { /* .get_name = */ ggml_backend_sycl_buffer_type_name, /* .alloc_buffer = */ ggml_backend_sycl_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_sycl_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // TODO: return device.maxBufferLength /* .get_alloc_size = */ ggml_backend_sycl_buffer_type_get_alloc_size, /* .supports_backend = */ ggml_backend_sycl_buffer_type_supports_backend, /* .is_host = */ nullptr, @@ -14844,6 +14845,7 @@ ggml_backend_buffer_type_t ggml_backend_sycl_host_buffer_type() { /* .get_name = */ ggml_backend_sycl_host_buffer_type_name, /* .alloc_buffer = */ ggml_backend_sycl_host_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_cpu_buffer_type()->iface.get_alignment, + /* .get_max_size = */ NULL, // TODO: return device.maxBufferLength /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend, /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, diff --git a/ggml-vulkan-shaders.hpp b/ggml-vulkan-shaders.hpp new file mode 100644 index 000000000..321e36383 --- /dev/null +++ b/ggml-vulkan-shaders.hpp @@ -0,0 +1,61420 @@ +#include + +unsigned char add_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x3e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, +0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, +0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x22,0x00,0x00,0x00, +0x27,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x10,0x00,0x06,0x00, 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get_rows_q4_0_fp32_len = 2372; + +unsigned char get_rows_q4_1_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x94,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, +0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, +0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, +0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, +0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00, +0x81,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, 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+0x38,0x00,0x01,0x00, +}; +const uint64_t get_rows_q5_1_f32_len = 2812; + +unsigned char get_rows_q5_1_f32_fp32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0xb5,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, +0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, +0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, +0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, +0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00, +0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, +0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, +0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, +0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, +0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00, 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+unsigned char matmul_f16_f32_l_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0xcd,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, +0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, +0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, +0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, +0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00, +0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, +0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00, +0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00,0xf1,0x00,0x00,0x00, +0xfc,0x00,0x00,0x00,0x3c,0x01,0x00,0x00,0x47,0x01,0x00,0x00, +0x2e,0x02,0x00,0x00,0x77,0x02,0x00,0x00,0x10,0x00,0x06,0x00, +0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00, 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+0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, +}; +const uint64_t matmul_f16_f32_l_len = 10172; + +unsigned char matmul_f16_f32_l_fp32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0xc9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, +0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, +0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, +0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00, +0xf0,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x3c,0x01,0x00,0x00, +0x47,0x01,0x00,0x00,0x2a,0x02,0x00,0x00,0x73,0x02,0x00,0x00, +0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 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matmul_f16_f32_m_fp32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0xc9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, +0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, +0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, +0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00, +0xf0,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x3c,0x01,0x00,0x00, +0x47,0x01,0x00,0x00,0x2a,0x02,0x00,0x00,0x73,0x02,0x00,0x00, +0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x10,0x00,0x00,0x00, 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+const uint64_t matmul_f16_s_fp32_len = 10116; + +unsigned char matmul_f32_aligned_l_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x6a,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, +0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, +0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, +0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00, +0xeb,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x88,0x01,0x00,0x00, +0x95,0x01,0x00,0x00,0xcb,0x02,0x00,0x00,0x14,0x03,0x00,0x00, +0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 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+const uint64_t matmul_f32_aligned_m_len = 12168; + +unsigned char matmul_f32_aligned_m_fp32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0xe9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, +0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, +0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00, +0x4c,0x00,0x00,0x00,0xea,0x00,0x00,0x00,0xf8,0x00,0x00,0x00, +0x46,0x01,0x00,0x00,0x53,0x01,0x00,0x00,0x4a,0x02,0x00,0x00, +0x93,0x02,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x11,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00, 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+0x38,0x00,0x01,0x00, +}; +const uint64_t matmul_f32_aligned_m_fp32_len = 10348; + +unsigned char matmul_f32_aligned_s_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x6a,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, +0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, +0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, +0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00, +0xeb,0x00,0x00,0x00,0xfa,0x00,0x00,0x00,0x88,0x01,0x00,0x00, +0x95,0x01,0x00,0x00,0xcb,0x02,0x00,0x00,0x14,0x03,0x00,0x00, +0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 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+0x38,0x00,0x01,0x00, +}; +const uint64_t matmul_f32_l_fp32_len = 10048; + +unsigned char matmul_f32_m_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0xcd,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, +0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, +0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, +0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x12,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x4c,0x00,0x00,0x00, +0xf1,0x00,0x00,0x00,0xfc,0x00,0x00,0x00,0x3d,0x01,0x00,0x00, +0x48,0x01,0x00,0x00,0x2e,0x02,0x00,0x00,0x77,0x02,0x00,0x00, +0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 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+0x38,0x00,0x01,0x00, +}; +const uint64_t matmul_f32_s_fp32_len = 10048; + +unsigned char mul_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x3e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, +0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, +0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x22,0x00,0x00,0x00, +0x27,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x10,0x00,0x06,0x00, +0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00, +0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00, 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char mul_mat_vec_q8_0_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0xc9,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, +0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, +0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, +0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, +0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00, +0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, +0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x13,0x00,0x00,0x00, +0x1b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x54,0x00,0x00,0x00, +0x75,0x00,0x00,0x00,0xbc,0x00,0x00,0x00,0x10,0x00,0x06,0x00, +0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x20,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x0c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1a,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x13,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 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+0x19,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x1d,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x1b,0x00,0x00,0x00, +0x1c,0x00,0x00,0x00,0x1d,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, +0x1c,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x35,0x00,0x00,0x00, +0xf8,0x00,0x02,0x00,0x1d,0x00,0x00,0x00,0x41,0x00,0x06,0x00, +0x29,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x27,0x00,0x00,0x00, +0x16,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x11,0x00,0x00,0x00,0x2b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00, +0x41,0x00,0x05,0x00,0x2d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, +0x14,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x11,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, +0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x30,0x00,0x00,0x00, +0x2b,0x00,0x00,0x00,0x2f,0x00,0x00,0x00,0x41,0x00,0x06,0x00, +0x29,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x22,0x00,0x00,0x00, +0x16,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x3e,0x00,0x03,0x00, +0x31,0x00,0x00,0x00,0x30,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x35,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x35,0x00,0x00,0x00, +0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, +}; +const uint64_t scale_f32_len = 1256; + +unsigned char silu_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x3b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, +0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, +0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x24,0x00,0x00,0x00, +0x2c,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, +0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00, +0x12,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, 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+0x3e,0x00,0x03,0x00,0x31,0x00,0x00,0x00,0x30,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0x35,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, +0x35,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, + +}; +const uint64_t sqr_f32_len = 1188; + diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp new file mode 100644 index 000000000..2c2b38fc3 --- /dev/null +++ b/ggml-vulkan.cpp @@ -0,0 +1,5176 @@ +#include "ggml-vulkan.h" + +#ifdef VK_RUN_TESTS +#include +#endif + +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "ggml.h" +#include "ggml-backend-impl.h" + +#include "ggml-vulkan-shaders.hpp" + +#define VK_API_VERSION VK_API_VERSION_1_2 + +#define CEIL_DIV(M, N) (((M) + (N)-1) / (N)) + +#define VK_VENDOR_ID_AMD 0x1002 +#define VK_VENDOR_ID_INTEL 0x8086 +#define VK_VENDOR_ID_NVIDIA 0x10de + +#define VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN 0 +#define VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI 1 +#define VK_DEVICE_DESCRIPTOR_POOL_MODE_SINGLE 2 + +#define VK_NUM_TYPES 16 + +#define GGML_VK_MAX_NODES 8192 + +#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 VK_CHECK(err, msg) \ + do { \ + vk::Result err_ = (err); \ + if (err_ != vk::Result::eSuccess) { \ + fprintf(stderr, "ggml_vulkan: %s error %s at %s:%d\n", \ + #err, to_string(err_).c_str(), __FILE__, __LINE__); \ + exit(1); \ + } \ + } while (0) + +struct vk_buffer { + vk::Buffer buffer; + vk::DeviceMemory device_memory; + vk::MemoryPropertyFlags memory_property_flags; + void * ptr; + size_t size = 0; + uint32_t qf_owner; +}; + +struct vk_subbuffer { + vk_buffer buffer; + uint64_t offset; + uint64_t size; +}; + +struct vk_pipeline { + std::string name; + vk::DescriptorSetLayout dsl; + std::vector descriptor_pools; + std::vector descriptor_sets; + uint32_t descriptor_set_idx; + vk::PipelineLayout layout; + vk::Pipeline pipeline; + uint32_t push_constant_size; + uint32_t parameter_count; + std::array wg_denoms; + uint32_t align; +}; + +struct vk_queue { + uint32_t queue_family_index; + vk::Queue queue; + vk::CommandPool pool; + uint32_t cmd_buffer_idx; + std::vector cmd_buffers; + + vk::PipelineStageFlags stage_flags; +}; + +struct vk_semaphore { + vk::Semaphore s; + uint64_t value; +}; + +struct vk_submission { + vk::CommandBuffer buffer; + std::vector wait_semaphores; + std::vector signal_semaphores; +}; + +typedef std::vector vk_sequence; + +struct vk_device { + vk::PhysicalDevice physical_device; + vk::PhysicalDeviceProperties properties; + uint64_t max_memory_allocation_size; + bool fp16; + vk::Device device; + uint32_t vendor_id; + vk_queue compute_queue; + vk_queue transfer_queue; + uint32_t descriptor_set_mode; + uint32_t subgroup_size; + bool is_igpu; +}; + +struct vk_op_push_constants { + uint32_t KX; + uint32_t KY; + float param1; + float param2; +}; + +struct vk_op_cpy_push_constants { + uint32_t ne; + uint32_t ne00; uint32_t ne01; uint32_t nb00; uint32_t nb01; uint32_t nb02; + uint32_t ne10; uint32_t ne11; uint32_t nb10; uint32_t nb11; uint32_t nb12; + uint32_t d_offset; +}; + +struct vk_op_diag_mask_push_constants { + uint32_t ncols; + uint32_t rows_per_channel; + int32_t n_past; +}; + +struct vk_op_rope_push_constants { + uint32_t ncols; + float freq_scale; + uint32_t p_delta_rows; + float freq_base; + float ext_factor; + float attn_factor; + float corr_dims[4]; +}; + +struct vk_op_rope_neox_push_constants { + uint32_t ncols; + uint32_t ndims; + float freq_scale; + uint32_t p_delta_rows; + float freq_base; + float ext_factor; + float attn_factor; + float corr_dims[4]; + float theta_scale; + float inv_ndims; +}; + +// Allow pre-recording command buffers +struct vk_staging_memcpy { + vk_staging_memcpy(void * _dst, const void * _src, size_t _n) : dst(_dst), src(_src), n(_n) {} + + void * dst; + const void * src; + size_t n; +}; + +struct vk_context { + size_t idx; + + vk_submission * s; + std::vector seqs; + + ggml_tensor * exit_tensor; + + std::vector in_memcpys; + std::vector out_memcpys; + + vk_queue * q; +}; + +struct ggml_tensor_extra_gpu { + bool ready; + + size_t ctx_idx; + + vk_buffer buffer_gpu; + uint64_t offset; + + void reset() { + ready = false; + ctx_idx = 0; + buffer_gpu.size = 0; + offset = 0; + } +}; + +struct ggml_vk_garbage_collector { + std::vector pipelines; + std::vector tl_semaphores; + std::vector semaphores; + std::vector events; + std::vector temp_buffers; + std::vector contexts; +}; + +typedef void (*ggml_vk_func_t)(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +vk::Instance vk_instance; +vk_device vk_device; +vk_pipeline vk_pipeline_matmul_f32_l, vk_pipeline_matmul_f32_m, vk_pipeline_matmul_f32_s; +vk_pipeline vk_pipeline_matmul_f32_aligned_l, vk_pipeline_matmul_f32_aligned_m, vk_pipeline_matmul_f32_aligned_s; +vk_pipeline vk_pipeline_matmul_f16_l, vk_pipeline_matmul_f16_m, vk_pipeline_matmul_f16_s; +vk_pipeline vk_pipeline_matmul_f16_aligned_l, vk_pipeline_matmul_f16_aligned_m, vk_pipeline_matmul_f16_aligned_s; +vk_pipeline vk_pipeline_matmul_f16_f32_l, vk_pipeline_matmul_f16_f32_m, vk_pipeline_matmul_f16_f32_s; +vk_pipeline vk_pipeline_matmul_f16_f32_aligned_l, vk_pipeline_matmul_f16_f32_aligned_m, vk_pipeline_matmul_f16_f32_aligned_s; +vk_pipeline vk_pipeline_matmul_split_k_reduce; +vk_pipeline vk_pipeline_dequant[VK_NUM_TYPES]; +vk_pipeline vk_pipeline_dequant_mul_mat_vec_f32[VK_NUM_TYPES]; +vk_pipeline vk_pipeline_mul_mat_vec_p021_f16_f32; +vk_pipeline vk_pipeline_mul_mat_vec_nc_f16_f32; +vk_pipeline vk_pipeline_get_rows[VK_NUM_TYPES]; +vk_pipeline vk_pipeline_get_rows_f32[VK_NUM_TYPES]; +vk_pipeline vk_pipeline_mul_f32; +vk_pipeline vk_pipeline_add_f32; +vk_pipeline vk_pipeline_scale_f32; +vk_pipeline vk_pipeline_sqr_f32; +vk_pipeline vk_pipeline_clamp_f32; +vk_pipeline vk_pipeline_cpy_f32_f32, vk_pipeline_cpy_f32_f16, vk_pipeline_cpy_f16_f16; +vk_pipeline vk_pipeline_norm_f32; +vk_pipeline vk_pipeline_rms_norm_f32; +vk_pipeline vk_pipeline_gelu_f32; +vk_pipeline vk_pipeline_silu_f32; +vk_pipeline vk_pipeline_relu_f32; +vk_pipeline vk_pipeline_diag_mask_inf_f32; +vk_pipeline vk_pipeline_soft_max_f32; +vk_pipeline vk_pipeline_rope_f32, vk_pipeline_rope_f16; +vk_pipeline vk_pipeline_rope_neox_f32, vk_pipeline_rope_neox_f16; + +static size_t vk_semaphore_idx, vk_event_idx; +static ggml_vk_garbage_collector vk_gc; +static std::vector> vk_pinned_memory; +static size_t vk_prealloc_size_qx, vk_prealloc_size_qy, vk_prealloc_size_x, vk_prealloc_size_y, vk_prealloc_size_split_k; +static vk_buffer vk_prealloc_qx, vk_prealloc_qy, vk_prealloc_x, vk_prealloc_y, vk_prealloc_split_k; +static vk::Fence vk_fence; +static vk_buffer vk_staging; +static size_t vk_staging_size; +static size_t vk_staging_offset; +static vk_buffer vk_sync_staging; + +static vk_context * vk_ctx; + +static bool vk_disable; + +#ifdef GGML_VULKAN_CHECK_RESULTS +size_t vk_skip_checks; +size_t vk_output_tensor; +#endif + +static vk_pipeline ggml_vk_create_pipeline(const std::string& name, size_t spv_size, const void* spv_data, const std::string& entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, std::vector&& specialization_constants, uint32_t align) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_create_pipeline(" << name << ", " << entrypoint << ", " << parameter_count << ", " << push_constant_size << ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " << align << ")" << std::endl; +#endif + GGML_ASSERT(parameter_count > 0); + GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT + + vk_pipeline pipeline; + + pipeline.name = name; + pipeline.parameter_count = parameter_count; + pipeline.push_constant_size = push_constant_size; + pipeline.wg_denoms = wg_denoms; + pipeline.align = align; + + vk::ShaderModuleCreateInfo shader_module_create_info({}, spv_size, reinterpret_cast(spv_data)); + vk::ShaderModule shader_module = vk_device.device.createShaderModule(shader_module_create_info); + + std::vector dsl_binding; + std::vector dsl_binding_flags; + for (uint32_t i = 0; i < parameter_count; i++) { + dsl_binding.push_back({i, vk::DescriptorType::eStorageBuffer, 1, vk::ShaderStageFlagBits::eCompute}); + dsl_binding_flags.push_back({}); + } + + vk::DescriptorSetLayoutBindingFlagsCreateInfo dslbfci = { dsl_binding_flags }; + + vk::PushConstantRange pcr( + vk::ShaderStageFlagBits::eCompute, + 0, + pipeline.push_constant_size + ); + + vk::DescriptorSetLayoutCreateInfo descriptor_set_layout_create_info( + {}, + dsl_binding); + descriptor_set_layout_create_info.setPNext(&dslbfci); + pipeline.dsl = vk_device.device.createDescriptorSetLayout(descriptor_set_layout_create_info); + + // Check if device supports multiple descriptors per pool + if (vk_device.descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN) { + const uint32_t alloc_count = 2; + + // Try allocating multiple sets from one pool + // This fails on AMD for some reason, so add a fall back to allocating one pool per set + vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline.parameter_count); + vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, alloc_count, descriptor_pool_size); + vk::DescriptorPool pool = vk_device.device.createDescriptorPool(descriptor_pool_create_info); + + std::vector layouts(alloc_count); + for (uint32_t i = 0; i < alloc_count; i++) { + layouts[i] = pipeline.dsl; + } + try { + vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pool, alloc_count, layouts.data()); + std::vector sets = vk_device.device.allocateDescriptorSets(descriptor_set_alloc_info); + } catch(vk::OutOfPoolMemoryError const&) { + vk_device.descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_SINGLE; + } + + vk_device.device.destroyDescriptorPool(pool); + } + + if (vk_device.descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { + vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline.parameter_count); + vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, 128, descriptor_pool_size); + pipeline.descriptor_pools.push_back(vk_device.device.createDescriptorPool(descriptor_pool_create_info)); + } + + pipeline.descriptor_set_idx = 0; + + vk::PipelineLayoutCreateInfo pipeline_layout_create_info(vk::PipelineLayoutCreateFlags(), pipeline.dsl, pcr); + pipeline.layout = vk_device.device.createPipelineLayout(pipeline_layout_create_info); + + std::vector specialization_entries(specialization_constants.size()); + + for (size_t i = 0; i < specialization_constants.size(); i++) { + specialization_entries[i].constantID = i; + specialization_entries[i].offset = i * sizeof(uint32_t); + specialization_entries[i].size = sizeof(uint32_t); + } + + vk::SpecializationInfo specialization_info( + specialization_entries.size(), + specialization_entries.data(), + specialization_constants.size() * sizeof(uint32_t), + specialization_constants.data() + ); + + vk::PipelineShaderStageCreateInfo pipeline_shader_create_info( + vk::PipelineShaderStageCreateFlags(), + vk::ShaderStageFlagBits::eCompute, + shader_module, + entrypoint.c_str(), + &specialization_info); + vk::ComputePipelineCreateInfo compute_pipeline_create_info( + vk::PipelineCreateFlags(), + pipeline_shader_create_info, + pipeline.layout); + pipeline.pipeline = vk_device.device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value; + + return pipeline; +} + +static void ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline& pipeline, uint32_t n) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_pipeline_allocate_descriptor_sets(" << pipeline.name << ", " << n << ")" << std::endl; +#endif + // Check if gc already contains pipeline before adding it + bool gc_found = false; + for (auto * pl : vk_gc.pipelines) { + if (&pipeline == pl) { + gc_found = true; + break; + } + } + + if (!gc_found) { + vk_gc.pipelines.push_back(&pipeline); + } + + if (pipeline.descriptor_sets.size() >= pipeline.descriptor_set_idx + n) { + // Enough descriptors are available + return; + } + + if (vk_device.descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { + const uint32_t alloc_count = pipeline.descriptor_set_idx + n - pipeline.descriptor_sets.size(); + + std::vector layouts(alloc_count); + for (uint32_t i = 0; i < alloc_count; i++) { + layouts[i] = pipeline.dsl; + } + vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline.descriptor_pools[0], alloc_count, layouts.data()); + std::vector sets = vk_device.device.allocateDescriptorSets(descriptor_set_alloc_info); + pipeline.descriptor_sets.insert(pipeline.descriptor_sets.end(), sets.begin(), sets.end()); + } else { + for (uint32_t i = pipeline.descriptor_sets.size(); i < pipeline.descriptor_set_idx + n; i++) { + vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline.parameter_count); + vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, 1, descriptor_pool_size); + pipeline.descriptor_pools.push_back(vk_device.device.createDescriptorPool(descriptor_pool_create_info)); + + vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline.descriptor_pools[i], 1, &pipeline.dsl); + std::vector sets = vk_device.device.allocateDescriptorSets(descriptor_set_alloc_info); + pipeline.descriptor_sets.push_back(sets[0]); + } + } +} + +static void ggml_vk_pipeline_cleanup(vk_pipeline& pipeline) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_pipeline_cleanup(" << pipeline.name << ")" << std::endl; +#endif + pipeline.descriptor_set_idx = 0; +} + +static vk::CommandBuffer ggml_vk_create_cmd_buffer(vk_queue& q) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_create_cmd_buffer()" << std::endl; +#endif + if (q.cmd_buffers.size() > q.cmd_buffer_idx) { + // Reuse command buffer + return q.cmd_buffers[q.cmd_buffer_idx++]; + } + + vk::CommandBufferAllocateInfo command_buffer_alloc_info( + q.pool, + vk::CommandBufferLevel::ePrimary, + 1); + const std::vector cmd_buffers = vk_device.device.allocateCommandBuffers(command_buffer_alloc_info); + auto buf = cmd_buffers.front(); + + q.cmd_buffers.push_back(buf); + q.cmd_buffer_idx++; + + return buf; +} + +static vk_submission ggml_vk_create_submission(vk_queue& q, std::vector wait_semaphores, std::vector signal_semaphores) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_create_submission()" << std::endl; +#endif + vk_submission s; + s.buffer = ggml_vk_create_cmd_buffer(q); + s.wait_semaphores = std::move(wait_semaphores); + s.signal_semaphores = std::move(signal_semaphores); + return s; +} + +static vk_sequence ggml_vk_create_sequence_1(vk_queue& q, std::vector wait_semaphores, std::vector signal_semaphores) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_create_sequence_1()" << std::endl; +#endif + return { ggml_vk_create_submission(q, std::move(wait_semaphores), std::move(signal_semaphores)) }; +} + +static void ggml_vk_submit(vk_context * ctx, vk::Fence fence) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_submit(" << ctx->seqs.size() << ", " << fence << ")" << std::endl; +#endif + if (ctx->seqs.empty()) { + return; + } + + std::vector> tl_wait_vals; + std::vector> tl_signal_vals; + std::vector> tl_wait_semaphores; + std::vector> tl_signal_semaphores; + std::vector tl_submit_infos; + std::vector submit_infos; + int idx = -1; + std::vector> stage_flags; + + size_t reserve = 0; + + for (const auto& sequence : ctx->seqs) { + reserve += sequence.size(); + } + + // Pre-reserve vectors to prevent reallocation, which invalidates pointers + tl_wait_semaphores.reserve(reserve); + tl_wait_vals.reserve(reserve); + tl_signal_semaphores.reserve(reserve); + tl_signal_vals.reserve(reserve); + tl_submit_infos.reserve(reserve); + submit_infos.reserve(reserve); + stage_flags.reserve(reserve); + + for (const auto& sequence : ctx->seqs) { + for (const auto& submission : sequence) { + stage_flags.push_back({}); + idx++; + tl_wait_vals.push_back({}); + tl_wait_semaphores.push_back({}); + tl_signal_vals.push_back({}); + tl_signal_semaphores.push_back({}); + for (size_t i = 0; i < submission.wait_semaphores.size(); i++) { + stage_flags[idx].push_back(ctx->q->stage_flags); + tl_wait_vals[idx].push_back(submission.wait_semaphores[i].value); + tl_wait_semaphores[idx].push_back(submission.wait_semaphores[i].s); + } + for (size_t i = 0; i < submission.signal_semaphores.size(); i++) { + tl_signal_vals[idx].push_back(submission.signal_semaphores[i].value); + tl_signal_semaphores[idx].push_back(submission.signal_semaphores[i].s); + } + tl_submit_infos.push_back({ + (uint32_t) submission.wait_semaphores.size(), + tl_wait_vals[idx].data(), + (uint32_t) submission.signal_semaphores.size(), + tl_signal_vals[idx].data(), + }); + tl_submit_infos[idx].sType = vk::StructureType::eTimelineSemaphoreSubmitInfo; + tl_submit_infos[idx].pNext = nullptr; + vk::SubmitInfo si{ + (uint32_t) submission.wait_semaphores.size(), + tl_wait_semaphores[idx].data(), + stage_flags[idx].data(), + 1, + &submission.buffer, + (uint32_t) submission.signal_semaphores.size(), + tl_signal_semaphores[idx].data(), + }; + si.setPNext(&tl_submit_infos[idx]); + submit_infos.push_back(si); + } + } + + ctx->q->queue.submit(submit_infos, fence); + + ctx->seqs.clear(); +} + +static uint32_t ggml_vk_find_queue_family_index(std::vector& queue_family_props, const vk::QueueFlags& required, const vk::QueueFlags& avoid, int32_t compute_index, uint32_t min_num_queues) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_find_queue_family_index()" << std::endl; +#endif + const uint32_t qfsize = queue_family_props.size(); + + // Try with avoid preferences first + for (uint32_t i = 0; i < qfsize; i++) { + if (queue_family_props[i].queueCount >= min_num_queues && (compute_index < 0 || i != (uint32_t) compute_index) && queue_family_props[i].queueFlags & required && !(queue_family_props[i].queueFlags & avoid)) { + return i; + } + } + + // Fall back to only required + for (size_t i = 0; i < qfsize; i++) { + if (queue_family_props[i].queueCount >= min_num_queues && (compute_index < 0 || i != (uint32_t) compute_index) && queue_family_props[i].queueFlags & required) { + return i; + } + } + + // Fall back to reusing compute queue + for (size_t i = 0; i < qfsize; i++) { + if (queue_family_props[i].queueCount >= min_num_queues && queue_family_props[i].queueFlags & required) { + return i; + } + } + + // Fall back to ignoring min_num_queries + for (size_t i = 0; i < qfsize; i++) { + if (queue_family_props[i].queueFlags & required) { + return i; + } + } + + std::cerr << "ggml_vulkan: No suitable queue family index found." << std::endl; + + for(auto &q_family : queue_family_props) { + std::cerr << "Queue number: " + std::to_string(q_family.queueCount) << " flags: " + to_string(q_family.queueFlags) << std::endl; + } + abort(); +} + +static vk_queue ggml_vk_create_queue(uint32_t queue_family_index, uint32_t queue_index, vk::PipelineStageFlags&& stage_flags) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_create_queue()" << std::endl; +#endif + vk_queue q; + q.queue_family_index = queue_family_index; + + vk::CommandPoolCreateInfo command_pool_create_info_compute(vk::CommandPoolCreateFlags(VK_COMMAND_POOL_CREATE_TRANSIENT_BIT), queue_family_index); + q.pool = vk_device.device.createCommandPool(command_pool_create_info_compute); + + q.cmd_buffer_idx = 0; + + q.queue = vk_device.device.getQueue(queue_family_index, queue_index); + + q.stage_flags = stage_flags; + + return q; +} + +static vk_context * ggml_vk_create_context(vk_queue& q) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_create_context()" << std::endl; +#endif + vk_gc.contexts.emplace_back(); + vk_context * result = &vk_gc.contexts[vk_gc.contexts.size() - 1]; + memset((void *) result, 0, sizeof(vk_context)); + result->idx = vk_gc.contexts.size() - 1; + result->q = &q; + return result; +} + +static vk_semaphore * ggml_vk_create_binary_semaphore() { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_create_timeline_semaphore()" << std::endl; +#endif + vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eBinary, 0 }; + vk::SemaphoreCreateInfo ci{}; + ci.setPNext(&tci); + vk::Semaphore semaphore = vk_device.device.createSemaphore(ci); + vk_gc.semaphores.push_back({ semaphore, 0 }); + return &vk_gc.semaphores[vk_gc.semaphores.size() - 1]; +} + +static vk_semaphore * ggml_vk_create_timeline_semaphore() { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_create_timeline_semaphore()" << std::endl; +#endif + if (vk_semaphore_idx >= vk_gc.tl_semaphores.size()) { + vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eTimeline, 0 }; + vk::SemaphoreCreateInfo ci{}; + ci.setPNext(&tci); + vk::Semaphore semaphore = vk_device.device.createSemaphore(ci); + vk_gc.tl_semaphores.push_back({ semaphore, 0 }); + } + return &vk_gc.tl_semaphores[vk_semaphore_idx++]; +} + +static vk::Event ggml_vk_create_event() { + if (vk_event_idx >= vk_gc.events.size()) { + vk_gc.events.push_back(vk_device.device.createEvent({})); + } + return vk_gc.events[vk_event_idx++]; +} + +static void ggml_vk_queue_cleanup(vk_queue& q) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_queue_cleanup()" << std::endl; +#endif + // Requires command buffers to be done + + vk_device.device.resetCommandPool(q.pool); + q.cmd_buffer_idx = 0; +} + +static vk_buffer ggml_vk_create_buffer(size_t size, vk::MemoryPropertyFlags req_flags) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_create_buffer(" << size << ", " << to_string(req_flags) << ")" << std::endl; +#endif + GGML_ASSERT(size > 0); + + vk_buffer buf; + + buf.size = size; + vk::BufferCreateInfo buffer_create_info{ + vk::BufferCreateFlags(), + size, + vk::BufferUsageFlagBits::eStorageBuffer | vk::BufferUsageFlagBits::eTransferSrc | vk::BufferUsageFlagBits::eTransferDst, + vk::SharingMode::eExclusive, + 0, + nullptr, + }; + + buf.buffer = vk_device.device.createBuffer(buffer_create_info); + + vk::MemoryRequirements mem_req = vk_device.device.getBufferMemoryRequirements(buf.buffer); + + vk::PhysicalDeviceMemoryProperties mem_props = vk_device.physical_device.getMemoryProperties(); + + uint32_t memory_type_index = uint32_t(~0); + + for (uint32_t i = 0; i < mem_props.memoryTypeCount; ++i) { + vk::MemoryType memory_type = mem_props.memoryTypes[i]; + if ((mem_req.memoryTypeBits & ((uint64_t)1 << i)) && (req_flags & memory_type.propertyFlags) == req_flags && mem_props.memoryHeaps[memory_type.heapIndex].size >= mem_req.size) { + memory_type_index = i; + break; + } + } + + buf.device_memory = vk_device.device.allocateMemory({ mem_req.size, memory_type_index }); + buf.memory_property_flags = req_flags; + buf.ptr = nullptr; + + if (req_flags & vk::MemoryPropertyFlagBits::eHostVisible) { + buf.ptr = vk_device.device.mapMemory(buf.device_memory, 0, VK_WHOLE_SIZE); + } + + vk_device.device.bindBufferMemory(buf.buffer, buf.device_memory, 0); + + buf.qf_owner = VK_QUEUE_FAMILY_IGNORED; + + return buf; +} + +static vk_subbuffer ggml_vk_subbuffer(vk_buffer& buf) { + return { buf, 0, VK_WHOLE_SIZE }; +} + +static void ggml_vk_sync_buffers(vk_context * ctx) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_sync_buffers()" << std::endl; +#endif + const std::vector mem_barriers{ { { vk::AccessFlagBits::eMemoryRead | vk::AccessFlagBits::eMemoryWrite }, { vk::AccessFlagBits::eMemoryRead | vk::AccessFlagBits::eMemoryWrite } } }; + + ctx->s->buffer.pipelineBarrier( + ctx->q->stage_flags, + ctx->q->stage_flags, + {}, + mem_barriers, + {}, + {} + ); +} + +static void ggml_vk_wait_events(vk::CommandBuffer& cmd_buffer, std::vector&& events, vk::PipelineStageFlags src_stages, vk::PipelineStageFlags dst_stages) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_wait_events()" << std::endl; +#endif + if (events.empty()) { + return; + } + + cmd_buffer.waitEvents( + events, + src_stages, + dst_stages, + {}, + {}, + {} + ); +} + +static void ggml_vk_destroy_buffer(vk_buffer& buf) { + if (buf.size == 0) { + return; + } +#ifdef VK_DEBUG + std::cerr << "ggml_vk_destroy_buffer(" << buf.size << ")" << std::endl; +#endif + + buf.size = 0; + vk_device.device.freeMemory(buf.device_memory); + vk_device.device.destroyBuffer(buf.buffer); +} + +static bool ggml_vk_build_shader(ggml_type type) { + switch(type) { + case GGML_TYPE_F16: + 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_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + return true; + default: + return false; + } +} + +static void ggml_vk_load_shaders() { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_load_shaders()" << std::endl; +#endif + + // mulmat + std::initializer_list warptile_l = { 128, 128, 128, 16, vk_device.subgroup_size * 2, 64, 2, 4, 4, vk_device.subgroup_size }; + std::initializer_list warptile_m = { 128, 64, 64, 16, vk_device.subgroup_size, 32, 2, 4, 2, vk_device.subgroup_size }; + std::initializer_list warptile_s = { vk_device.subgroup_size, 32, 32, 8, 32, 32, 2, 2, 2, vk_device.subgroup_size }; + + std::array l_wg_denoms = {128, 128, 1 }; + std::array m_wg_denoms = { 64, 64, 1 }; + std::array s_wg_denoms = { 32, 32, 1 }; + + uint32_t l_align = 128; + uint32_t m_align = 64; + uint32_t s_align = 32; + + if (vk_device.fp16) { + vk_pipeline_matmul_f32_l = ggml_vk_create_pipeline("matmul_f32_l", matmul_f32_l_len, matmul_f32_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + vk_pipeline_matmul_f32_m = ggml_vk_create_pipeline("matmul_f32_m", matmul_f32_m_len, matmul_f32_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + vk_pipeline_matmul_f32_s = ggml_vk_create_pipeline("matmul_f32_s", matmul_f32_s_len, matmul_f32_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + vk_pipeline_matmul_f32_aligned_l = ggml_vk_create_pipeline("matmul_f32_aligned_l", matmul_f32_aligned_l_len, matmul_f32_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + vk_pipeline_matmul_f32_aligned_m = ggml_vk_create_pipeline("matmul_f32_aligned_m", matmul_f32_aligned_m_len, matmul_f32_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + vk_pipeline_matmul_f32_aligned_s = ggml_vk_create_pipeline("matmul_f32_aligned_s", matmul_f32_aligned_s_len, matmul_f32_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + + vk_pipeline_matmul_f16_l = ggml_vk_create_pipeline("matmul_f16_l", matmul_f16_l_len, matmul_f16_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + vk_pipeline_matmul_f16_m = ggml_vk_create_pipeline("matmul_f16_m", matmul_f16_m_len, matmul_f16_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + vk_pipeline_matmul_f16_s = ggml_vk_create_pipeline("matmul_f16_s", matmul_f16_s_len, matmul_f16_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + + vk_pipeline_matmul_f16_aligned_l = ggml_vk_create_pipeline("matmul_f16_aligned_l", matmul_f16_aligned_l_len, matmul_f16_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + vk_pipeline_matmul_f16_aligned_m = ggml_vk_create_pipeline("matmul_f16_aligned_m", matmul_f16_aligned_m_len, matmul_f16_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + vk_pipeline_matmul_f16_aligned_s = ggml_vk_create_pipeline("matmul_f16_aligned_s", matmul_f16_aligned_s_len, matmul_f16_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + + vk_pipeline_matmul_f16_f32_l = ggml_vk_create_pipeline("matmul_f16_f32_l", matmul_f16_f32_l_len, matmul_f16_f32_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + vk_pipeline_matmul_f16_f32_m = ggml_vk_create_pipeline("matmul_f16_f32_m", matmul_f16_f32_m_len, matmul_f16_f32_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + vk_pipeline_matmul_f16_f32_s = ggml_vk_create_pipeline("matmul_f16_f32_s", matmul_f16_f32_s_len, matmul_f16_f32_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + vk_pipeline_matmul_f16_f32_aligned_l = ggml_vk_create_pipeline("matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_len, matmul_f16_f32_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + vk_pipeline_matmul_f16_f32_aligned_m = ggml_vk_create_pipeline("matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_len, matmul_f16_f32_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + vk_pipeline_matmul_f16_f32_aligned_s = ggml_vk_create_pipeline("matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_len, matmul_f16_f32_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + + // Build dequant shaders + vk_pipeline_dequant[GGML_TYPE_F32] = ggml_vk_create_pipeline("f32_to_f16", f32_to_f16_len, f32_to_f16_data, "main", 2, 4 * sizeof(int), {64, 1, 1}, {}, 1); + + vk_pipeline_dequant[GGML_TYPE_F16] = ggml_vk_create_pipeline("dequant_f16", dequant_f16_len, dequant_f16_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q2_K] = ggml_vk_create_pipeline("dequant_q2_K", dequant_q2_K_len, dequant_q2_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q3_K] = ggml_vk_create_pipeline("dequant_q3_K", dequant_q3_K_len, dequant_q3_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q4_K] = ggml_vk_create_pipeline("dequant_q4_K", dequant_q4_K_len, dequant_q4_K_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q5_K] = ggml_vk_create_pipeline("dequant_q5_K", dequant_q5_K_len, dequant_q5_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q6_K] = ggml_vk_create_pipeline("dequant_q6_K", dequant_q6_K_len, dequant_q6_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + + // get_rows + vk_pipeline_get_rows[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16", get_rows_f16_len, get_rows_f16_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + + vk_pipeline_get_rows_f32[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + } else { + vk_pipeline_matmul_f32_l = ggml_vk_create_pipeline("matmul_f32_l", matmul_f32_l_fp32_len, matmul_f32_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + vk_pipeline_matmul_f32_m = ggml_vk_create_pipeline("matmul_f32_m", matmul_f32_m_fp32_len, matmul_f32_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + vk_pipeline_matmul_f32_s = ggml_vk_create_pipeline("matmul_f32_s", matmul_f32_s_fp32_len, matmul_f32_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + vk_pipeline_matmul_f32_aligned_l = ggml_vk_create_pipeline("matmul_f32_aligned_l", matmul_f32_aligned_l_fp32_len, matmul_f32_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + vk_pipeline_matmul_f32_aligned_m = ggml_vk_create_pipeline("matmul_f32_aligned_m", matmul_f32_aligned_m_fp32_len, matmul_f32_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + vk_pipeline_matmul_f32_aligned_s = ggml_vk_create_pipeline("matmul_f32_aligned_s", matmul_f32_aligned_s_fp32_len, matmul_f32_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + + vk_pipeline_matmul_f16_l = ggml_vk_create_pipeline("matmul_f16_l", matmul_f16_l_fp32_len, matmul_f16_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + vk_pipeline_matmul_f16_m = ggml_vk_create_pipeline("matmul_f16_m", matmul_f16_m_fp32_len, matmul_f16_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + vk_pipeline_matmul_f16_s = ggml_vk_create_pipeline("matmul_f16_s", matmul_f16_s_fp32_len, matmul_f16_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + + vk_pipeline_matmul_f16_aligned_l = ggml_vk_create_pipeline("matmul_f16_aligned_l", matmul_f16_aligned_l_fp32_len, matmul_f16_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + vk_pipeline_matmul_f16_aligned_m = ggml_vk_create_pipeline("matmul_f16_aligned_m", matmul_f16_aligned_m_fp32_len, matmul_f16_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + vk_pipeline_matmul_f16_aligned_s = ggml_vk_create_pipeline("matmul_f16_aligned_s", matmul_f16_aligned_s_fp32_len, matmul_f16_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + + vk_pipeline_matmul_f16_f32_l = ggml_vk_create_pipeline("matmul_f16_f32_l", matmul_f16_f32_l_fp32_len, matmul_f16_f32_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + vk_pipeline_matmul_f16_f32_m = ggml_vk_create_pipeline("matmul_f16_f32_m", matmul_f16_f32_m_fp32_len, matmul_f16_f32_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + vk_pipeline_matmul_f16_f32_s = ggml_vk_create_pipeline("matmul_f16_f32_s", matmul_f16_f32_s_fp32_len, matmul_f16_f32_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + vk_pipeline_matmul_f16_f32_aligned_l = ggml_vk_create_pipeline("matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_fp32_len, matmul_f16_f32_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + vk_pipeline_matmul_f16_f32_aligned_m = ggml_vk_create_pipeline("matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_fp32_len, matmul_f16_f32_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + vk_pipeline_matmul_f16_f32_aligned_s = ggml_vk_create_pipeline("matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_fp32_len, matmul_f16_f32_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + + // Build dequant shaders + vk_pipeline_dequant[GGML_TYPE_F32] = ggml_vk_create_pipeline("f32_to_f16", f32_to_f16_fp32_len, f32_to_f16_fp32_data, "main", 2, 4 * sizeof(int), {64, 1, 1}, {}, 1); + + vk_pipeline_dequant[GGML_TYPE_F16] = ggml_vk_create_pipeline("dequant_f16", dequant_f16_fp32_len, dequant_f16_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("dequant_q4_0", dequant_q4_0_fp32_len, dequant_q4_0_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("dequant_q4_1", dequant_q4_1_fp32_len, dequant_q4_1_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("dequant_q5_0", dequant_q5_0_fp32_len, dequant_q5_0_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("dequant_q5_1", dequant_q5_1_fp32_len, dequant_q5_1_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("dequant_q8_0", dequant_q8_0_fp32_len, dequant_q8_0_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q2_K] = ggml_vk_create_pipeline("dequant_q2_K", dequant_q2_K_fp32_len, dequant_q2_K_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q3_K] = ggml_vk_create_pipeline("dequant_q3_K", dequant_q3_K_fp32_len, dequant_q3_K_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q4_K] = ggml_vk_create_pipeline("dequant_q4_K", dequant_q4_K_fp32_len, dequant_q4_K_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q5_K] = ggml_vk_create_pipeline("dequant_q5_K", dequant_q5_K_fp32_len, dequant_q5_K_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q6_K] = ggml_vk_create_pipeline("dequant_q6_K", dequant_q6_K_fp32_len, dequant_q6_K_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + + // get_rows + vk_pipeline_get_rows[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16", get_rows_f16_fp32_len, get_rows_f16_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0", get_rows_q4_0_fp32_len, get_rows_q4_0_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1", get_rows_q4_1_fp32_len, get_rows_q4_1_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0", get_rows_q5_0_fp32_len, get_rows_q5_0_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1", get_rows_q5_1_fp32_len, get_rows_q5_1_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0", get_rows_q8_0_fp32_len, get_rows_q8_0_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + + vk_pipeline_get_rows_f32[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16_f32", get_rows_f16_f32_fp32_len, get_rows_f16_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0_f32", get_rows_q4_0_f32_fp32_len, get_rows_q4_0_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1_f32", get_rows_q4_1_f32_fp32_len, get_rows_q4_1_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0_f32", get_rows_q5_0_f32_fp32_len, get_rows_q5_0_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1_f32", get_rows_q5_1_f32_fp32_len, get_rows_q5_1_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0_f32", get_rows_q8_0_f32_fp32_len, get_rows_q8_0_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + } + + vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_F16] = ggml_vk_create_pipeline("mul_mat_vec_f16_f32", mul_mat_vec_f16_f32_len, mul_mat_vec_f16_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("mul_mat_vec_q4_0_f32", mul_mat_vec_q4_0_f32_len, mul_mat_vec_q4_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("mul_mat_vec_q4_1_f32", mul_mat_vec_q4_1_f32_len, mul_mat_vec_q4_1_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("mul_mat_vec_q5_0_f32", mul_mat_vec_q5_0_f32_len, mul_mat_vec_q5_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("mul_mat_vec_q5_1_f32", mul_mat_vec_q5_1_f32_len, mul_mat_vec_q5_1_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("mul_mat_vec_q8_0_f32", mul_mat_vec_q8_0_f32_len, mul_mat_vec_q8_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q2_K] = ggml_vk_create_pipeline("mul_mat_vec_q2_K_f32", mul_mat_vec_q2_K_f32_len, mul_mat_vec_q2_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q3_K] = ggml_vk_create_pipeline("mul_mat_vec_q3_K_f32", mul_mat_vec_q3_K_f32_len, mul_mat_vec_q3_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_K] = ggml_vk_create_pipeline("mul_mat_vec_q4_K_f32", mul_mat_vec_q4_K_f32_len, mul_mat_vec_q4_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_K] = ggml_vk_create_pipeline("mul_mat_vec_q5_K_f32", mul_mat_vec_q5_K_f32_len, mul_mat_vec_q5_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q6_K] = ggml_vk_create_pipeline("mul_mat_vec_q6_K_f32", mul_mat_vec_q6_K_f32_len, mul_mat_vec_q6_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + + vk_pipeline_matmul_split_k_reduce = ggml_vk_create_pipeline("split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256, 1, 1}, {}, 1); + + vk_pipeline_mul_mat_vec_p021_f16_f32 = ggml_vk_create_pipeline("mul_mat_vec_p021_f16_f32", mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {}, 1); + vk_pipeline_mul_mat_vec_nc_f16_f32 = ggml_vk_create_pipeline("mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 7 * sizeof(uint32_t), {1, 1, 1}, {}, 1); + + vk_pipeline_norm_f32 = ggml_vk_create_pipeline("norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); + vk_pipeline_rms_norm_f32 = ggml_vk_create_pipeline("rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); + + vk_pipeline_cpy_f32_f32 = ggml_vk_create_pipeline("cpy_f32_f32", cpy_f32_f32_len, cpy_f32_f32_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_cpy_f32_f16 = ggml_vk_create_pipeline("cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_cpy_f16_f16 = ggml_vk_create_pipeline("cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); + + vk_pipeline_add_f32 = ggml_vk_create_pipeline("add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + + vk_pipeline_mul_f32 = ggml_vk_create_pipeline("mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + + vk_pipeline_scale_f32 = ggml_vk_create_pipeline("scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + + vk_pipeline_sqr_f32 = ggml_vk_create_pipeline("sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + + vk_pipeline_clamp_f32 = ggml_vk_create_pipeline("clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + + vk_pipeline_gelu_f32 = ggml_vk_create_pipeline("gelu_f32", gelu_f32_len, gelu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_silu_f32 = ggml_vk_create_pipeline("silu_f32", silu_f32_len, silu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_relu_f32 = ggml_vk_create_pipeline("relu_f32", relu_f32_len, relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + + vk_pipeline_diag_mask_inf_f32 = ggml_vk_create_pipeline("diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1); + + vk_pipeline_soft_max_f32 = ggml_vk_create_pipeline("soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); + + vk_pipeline_rope_f32 = ggml_vk_create_pipeline("rope_f32", rope_f32_len, rope_f32_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + vk_pipeline_rope_f16 = ggml_vk_create_pipeline("rope_f16", rope_f16_len, rope_f16_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + + vk_pipeline_rope_neox_f32 = ggml_vk_create_pipeline("rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); + vk_pipeline_rope_neox_f16 = ggml_vk_create_pipeline("rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); +} + +void ggml_vk_init() { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_init()" << std::endl; +#endif + static bool initialized = false; + + if (initialized) { + return; + } + + initialized = true; + + const char* GGML_VULKAN_DEVICE = getenv("GGML_VULKAN_DEVICE"); + int dev_num = (GGML_VULKAN_DEVICE == NULL ? 0 : atoi(GGML_VULKAN_DEVICE)); + + vk::ApplicationInfo app_info{ "ggml-vulkan", 1, nullptr, 0, VK_API_VERSION }; + const std::vector layers = { +#ifdef VK_VALIDATE + "VK_LAYER_KHRONOS_validation", +#endif + }; + const std::vector extensions = { +#ifdef VK_VALIDATE + "VK_EXT_validation_features", +#endif + }; + vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags(), &app_info, layers, extensions); +#ifdef VK_VALIDATE + const std::vector features_enable = { vk::ValidationFeatureEnableEXT::eBestPractices }; + vk::ValidationFeaturesEXT validation_features = { + features_enable, + {}, + }; + validation_features.setPNext(nullptr); + instance_create_info.setPNext(&validation_features); + +std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; +#endif + vk_instance = vk::createInstance(instance_create_info); + + vk_device.physical_device = vk_instance.enumeratePhysicalDevices()[dev_num]; + std::vector ext_props = vk_device.physical_device.enumerateDeviceExtensionProperties(); + + bool maintenance4_support = false; + + // Check if maintenance4 is supported + for (auto properties : ext_props) { + if (strcmp("VK_KHR_maintenance4", properties.extensionName) == 0) { + maintenance4_support = true; + } + } + + vk::PhysicalDeviceProperties2 props2; + vk::PhysicalDeviceMaintenance3Properties props3; + vk::PhysicalDeviceMaintenance4Properties props4; + vk::PhysicalDeviceSubgroupProperties subgroup_props; + props2.pNext = &props3; + props3.pNext = &subgroup_props; + if (maintenance4_support) { + subgroup_props.pNext = &props4; + } + vk_device.physical_device.getProperties2(&props2); + vk_device.properties = props2.properties; + + if (maintenance4_support) { + vk_device.max_memory_allocation_size = std::min(props3.maxMemoryAllocationSize, props4.maxBufferSize); + } else { + vk_device.max_memory_allocation_size = props3.maxMemoryAllocationSize; + } + + vk_device.vendor_id = vk_device.properties.vendorID; + vk_device.subgroup_size = subgroup_props.subgroupSize; + vk_device.is_igpu = vk_device.properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu; + + bool fp16_storage = false; + bool fp16_compute = false; + + for (auto properties : ext_props) { + if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) { + fp16_storage = true; + } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) { + fp16_compute = true; + } + } + + const char* GGML_VULKAN_DISABLE_F16 = getenv("GGML_VULKAN_DISABLE_F16"); + bool force_disable_f16 = GGML_VULKAN_DISABLE_F16 != NULL; + + vk_device.fp16 = !force_disable_f16 && fp16_storage && fp16_compute; + + std::vector queue_family_props = vk_device.physical_device.getQueueFamilyProperties(); + + // Try to find a non-graphics compute queue and transfer-focused queues + const uint32_t compute_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eCompute, vk::QueueFlagBits::eGraphics, -1, 1); + const uint32_t transfer_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eTransfer, vk::QueueFlagBits::eCompute | vk::QueueFlagBits::eGraphics, compute_queue_family_index, 1); + + const float priorities[] = { 1.0f, 1.0f }; + const bool single_queue = compute_queue_family_index == transfer_queue_family_index && queue_family_props[compute_queue_family_index].queueCount == 1; + + std::vector device_queue_create_infos; + if (compute_queue_family_index != transfer_queue_family_index) { + device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); + device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), transfer_queue_family_index, 1, priorities + 1}); + } else if(!single_queue) { + device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 2, priorities}); + } else { + device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); + } + vk::DeviceCreateInfo device_create_info; + std::vector device_extensions; + vk::PhysicalDeviceFeatures device_features = vk_device.physical_device.getFeatures(); + + VkPhysicalDeviceFeatures2 device_features2; + device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2; + device_features2.pNext = nullptr; + device_features2.features = (VkPhysicalDeviceFeatures)device_features; + + VkPhysicalDeviceVulkan11Features vk11_features; + vk11_features.pNext = nullptr; + vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES; + device_features2.pNext = &vk11_features; + + VkPhysicalDeviceVulkan12Features vk12_features; + vk12_features.pNext = nullptr; + vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES; + vk11_features.pNext = &vk12_features; + + vkGetPhysicalDeviceFeatures2(vk_device.physical_device, &device_features2); + + vk_device.fp16 = vk_device.fp16 && vk12_features.shaderFloat16; + + if (!vk11_features.storageBuffer16BitAccess) { + std::cerr << "ggml_vulkan: device does not support 16-bit storage" << std::endl; + GGML_ASSERT(false); + } + + device_extensions.push_back("VK_KHR_16bit_storage"); + +#ifdef VK_VALIDATE + device_extensions.push_back("VK_KHR_shader_non_semantic_info"); +#endif + + if (vk_device.fp16) { + device_extensions.push_back("VK_KHR_shader_float16_int8"); + } + std::cerr << "ggml_vulkan: Using " << vk_device.properties.deviceName << " | fp16: " << vk_device.fp16 << " | warp size: " << vk_device.subgroup_size << std::endl; + device_create_info = { + vk::DeviceCreateFlags(), + device_queue_create_infos, + {}, + device_extensions + }; + device_create_info.setPNext(&device_features2); + vk_device.device = vk_device.physical_device.createDevice(device_create_info); + + vk_device.descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN; + + // Shaders + ggml_vk_load_shaders(); + + // Queues + vk_device.compute_queue = ggml_vk_create_queue(compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer }); + if (!single_queue) { + const uint32_t transfer_queue_index = compute_queue_family_index == transfer_queue_family_index ? 1 : 0; + vk_device.transfer_queue = ggml_vk_create_queue(transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer }); + } else { + vk_device.transfer_queue = vk_device.compute_queue; + } + + vk_fence = vk_device.device.createFence({}); + + vk_ctx = nullptr; + + vk_disable = false; + +#ifdef GGML_VULKAN_CHECK_RESULTS + const char* skip_checks = getenv("GGML_VULKAN_SKIP_CHECKS"); + vk_skip_checks = (skip_checks == NULL ? 0 : atoi(skip_checks)); + const char* output_tensor = getenv("GGML_VULKAN_OUTPUT_TENSOR"); + vk_output_tensor = (output_tensor == NULL ? 0 : atoi(output_tensor)); +#endif +} + +static vk_pipeline* ggml_vk_get_to_fp16(ggml_type type) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_get_to_fp16()" << std::endl; +#endif + switch (type) { + case GGML_TYPE_F32: + 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_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + break; + default: + return nullptr; + } + + return &vk_pipeline_dequant[type]; +} + +static vk_pipeline* ggml_vk_get_dequantize_mul_mat_vec(ggml_type type) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_get_dequantize_mul_mat_vec()" << std::endl; +#endif + switch (type) { + case GGML_TYPE_F16: + 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_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + break; + default: + return nullptr; + } + + return &vk_pipeline_dequant_mul_mat_vec_f32[type]; +} + +// buffer pool for vulkan +#define MAX_VK_BUFFERS 256 + +static vk_buffer g_vk_buffer_pool[MAX_VK_BUFFERS]; + +static vk_buffer ggml_vk_pool_malloc(size_t size) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_pool_malloc(" << size << ")" << std::endl; +#endif + int best_i = -1; + size_t best_size = std::numeric_limits::max(); //smallest unused buffer that fits our needs + int worst_i = -1; + size_t worst_size = 0; //largest unused buffer seen so far + for (int i = 0; i < MAX_VK_BUFFERS; ++i) { + vk_buffer &b = g_vk_buffer_pool[i]; + if (b.size > 0 && b.size >= size && b.size < best_size) { + best_i = i; + best_size = b.size; + } + if (b.size > 0 && b.size > worst_size) { + worst_i = i; + worst_size = b.size; + } + } + if(best_i != -1) { + //found the smallest buffer that fits our needs + vk_buffer b = g_vk_buffer_pool[best_i]; + g_vk_buffer_pool[best_i].size = 0; + return b; + } + if(worst_i != -1) { + //no buffer that fits our needs, resize largest one to save memory + vk_buffer& b = g_vk_buffer_pool[worst_i]; + ggml_vk_destroy_buffer(b); + } + + return ggml_vk_create_buffer(size, vk::MemoryPropertyFlagBits::eDeviceLocal); +} + +static void ggml_vk_pool_free(vk_buffer& buffer) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_pool_free(" << buffer.size << ")" << std::endl; +#endif + for (int i = 0; i < MAX_VK_BUFFERS; ++i) { + vk_buffer& b = g_vk_buffer_pool[i]; + if (b.size == 0) { + b = buffer; + // Set owning queue family index to ignored to avoid synchronization on next use + b.qf_owner = VK_QUEUE_FAMILY_IGNORED; + return; + } + } + fprintf(stderr, "WARNING: vk buffer pool full, increase MAX_VK_BUFFERS\n"); + ggml_vk_destroy_buffer(buffer); +} + +// Returns an available temporary buffer that may only be used temporarily, it will be reused +static vk_buffer ggml_vk_create_buffer_temp(size_t size) { + // Try to find existing temp buffer with enough capacity + for (auto& buffer : vk_gc.temp_buffers) { + if (buffer.size >= size) { + return buffer; + } + } + + // Otherwise create new buffer + vk_buffer buf = ggml_vk_pool_malloc(size); + vk_gc.temp_buffers.push_back(buf); + + return buf; +} + +static void * ggml_vk_host_malloc(size_t size) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_host_malloc(" << size << ")" << std::endl; +#endif + if (getenv("GGML_VK_NO_PINNED") != nullptr) { + return nullptr; + } + + vk_buffer buf = ggml_vk_create_buffer(size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); + + if(!(buf.memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible)) { + fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory\n", + size/1024.0/1024.0); + buf.size = 0; + vk_device.device.freeMemory(buf.device_memory); + vk_device.device.destroyBuffer(buf.buffer); + return nullptr; + } + + vk_pinned_memory.push_back(std::make_tuple(buf.ptr, size, buf)); + + return buf.ptr; +} + +static void ggml_vk_host_free(void* ptr) { + if (ptr == nullptr) { + return; + } +#ifdef VK_DEBUG + std::cerr << "ggml_vk_host_free(" << ptr << ")" << std::endl; +#endif + vk_buffer* buf = nullptr; + size_t index; + for (size_t i = 0; i < vk_pinned_memory.size(); i++) { + const uint8_t* addr = (const uint8_t*) std::get<0>(vk_pinned_memory[i]); + const uint8_t* endr = addr + std::get<1>(vk_pinned_memory[i]); + if (ptr >= addr && ptr < endr) { + buf = &std::get<2>(vk_pinned_memory[i]); + index = i; + break; + } + } + if (buf == nullptr) { + fprintf(stderr, "WARNING: failed to free pinned memory: memory not in map\n"); + return; + } + + ggml_vk_destroy_buffer(*buf); + + vk_pinned_memory.erase(vk_pinned_memory.begin() + index); +} + +static vk_submission ggml_vk_begin_submission(vk_queue& q, bool one_time = true) { + vk_submission s; + s.buffer = ggml_vk_create_cmd_buffer(q); + if (one_time) { + s.buffer.begin({ vk::CommandBufferUsageFlagBits::eOneTimeSubmit }); + } else { + s.buffer.begin({ vk::CommandBufferUsageFlags{} }); + } + + return s; +} + +static void ggml_vk_dispatch_pipeline(vk_context * ctx, vk_pipeline& pipeline, std::vector&& buffers, size_t push_constant_size, const void* push_constants, std::array elements) { + const uint32_t wg0 = CEIL_DIV(elements[0], pipeline.wg_denoms[0]); + const uint32_t wg1 = CEIL_DIV(elements[1], pipeline.wg_denoms[1]); + const uint32_t wg2 = CEIL_DIV(elements[2], pipeline.wg_denoms[2]); +#ifdef VK_DEBUG + std::cerr << "ggml_vk_dispatch_pipeline(" << pipeline.name << ", (" << wg0 << "," << wg1 << "," << wg2 << "))" << std::endl; +#endif + std::vector descriptor_buffer_infos; + std::vector write_descriptor_sets; + GGML_ASSERT(pipeline.descriptor_set_idx < pipeline.descriptor_sets.size()); + GGML_ASSERT(buffers.size() == pipeline.parameter_count); + vk::DescriptorSet& descriptor_set = pipeline.descriptor_sets[pipeline.descriptor_set_idx++]; + for (uint32_t i = 0; i < pipeline.parameter_count; i++) { + descriptor_buffer_infos.push_back({buffers[i].buffer.buffer, buffers[i].offset, buffers[i].size}); + } + for (uint32_t i = 0; i < pipeline.parameter_count; i++) { + write_descriptor_sets.push_back({descriptor_set, i, 0, 1, vk::DescriptorType::eStorageBuffer, nullptr, &descriptor_buffer_infos[i]}); + } + + vk_device.device.updateDescriptorSets(write_descriptor_sets, {}); + + ctx->s->buffer.pushConstants(pipeline.layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size, push_constants); + ctx->s->buffer.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline.pipeline); + ctx->s->buffer.bindDescriptorSets(vk::PipelineBindPoint::eCompute, + pipeline.layout, + 0, + { descriptor_set }, + {}); + ctx->s->buffer.dispatch(wg0, wg1, wg2); +} + +static void ggml_vk_end_submission(vk_submission& s, std::vector wait_semaphores, std::vector signal_semaphores) { + s.buffer.end(); + + s.wait_semaphores = std::move(wait_semaphores); + s.signal_semaphores = std::move(signal_semaphores); +} + +static void ggml_vk_ctx_end(vk_context * ctx) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_ctx_end(" << ctx << ", " << ctx->seqs.size() << ")" << std::endl; +#endif + if (ctx->s == nullptr) { + return; + } + + ctx->s->buffer.end(); + ctx->s = nullptr; +} + +static void ggml_vk_ctx_begin(vk_context * ctx) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_ctx_begin(" << ctx << ")" << std::endl; +#endif + if (ctx->s != nullptr) { + ggml_vk_ctx_end(ctx); + } + + ctx->seqs.push_back({ ggml_vk_begin_submission(*ctx->q) }); + ctx->s = ctx->seqs[ctx->seqs.size() - 1].data(); +} + +static size_t ggml_vk_align_size(size_t width, size_t align) { + return CEIL_DIV(width, align) * align; +} + +static void deferred_memcpy(void * dst, const void * src, size_t size, std::vector* memcpys = nullptr) { + if (memcpys == nullptr) { + memcpy(dst, src, size); + } else { + memcpys->emplace_back(dst, src, size); + } +} + +static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size_t offset, const ggml_tensor * tensor, bool sync_staging = false) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_buffer_write_nc_async(" << tensor << ")" << std::endl; +#endif + GGML_ASSERT(!ggml_is_contiguous(tensor)); + // Buffer is already mapped + if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { + std::cerr << "ggml_vulkan: buffer_write_async dst buffer is host_visible. Use synchronous write." << std::endl; + GGML_ASSERT(false); + } + // Check if src is pinned memory + vk_buffer* buf = nullptr; + size_t buf_offset = 0; + for (size_t i = 0; i < vk_pinned_memory.size(); i++) { + const uint8_t* addr = (const uint8_t*) std::get<0>(vk_pinned_memory[i]); + const uint8_t* endr = addr + std::get<1>(vk_pinned_memory[i]); + if (tensor->data >= addr && tensor->data < endr) { + buf = &std::get<2>(vk_pinned_memory[i]); + buf_offset = ((const uint8_t *)tensor->data) - addr; + break; + } + } + + const uint64_t ne0 = tensor->ne[0]; + const uint64_t ne1 = tensor->ne[1]; + const uint64_t ne2 = tensor->ne[2]; + const uint64_t ne3 = tensor->ne[3]; + const uint64_t nb0 = tensor->nb[0]; + const uint64_t nb1 = tensor->nb[1]; + const uint64_t nb2 = tensor->nb[2]; + const uint64_t nb3 = tensor->nb[3]; + const ggml_type type = tensor->type; + const uint64_t ts = ggml_type_size(type); + const uint64_t bs = ggml_blck_size(type); + + const uint64_t dstnb0 = ts; + const uint64_t dstnb1 = dstnb0*(ne0/bs); + const uint64_t dstnb2 = dstnb1*ne1; + const uint64_t dstnb3 = dstnb2*ne2; + + const uint64_t ne = ggml_nelements(tensor); + + if (buf != nullptr) { + // Memory is pinned, use as staging buffer + std::vector slices; + + for (uint64_t i3 = 0; i3 < ne3; i3++) { + for (uint64_t i2 = 0; i2 < ne2; i2++) { + // Find longest contiguous slice + if (ne1*nb1 == dstnb2) { + slices.push_back({ buf_offset + i3*nb3 + i2*nb2, offset + i3*dstnb3 + i2*dstnb2, dstnb2 }); + } else { + for (uint64_t i1 = 0; i1 < ne1; i1++) { + if (ne0*nb0/bs == dstnb1) { + slices.push_back({ buf_offset + i3*nb3 + i2*nb2 + i1*nb1, offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, dstnb1 }); + } else { + const uint64_t s_off = buf_offset + i3*nb3 + i2*nb2 + i1*nb1; + const uint64_t d_off = offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1; + for (uint64_t i0 = 0; i0 < ne0; i0++) { + slices.push_back({ s_off + i1*nb0, d_off + i0*dstnb0, dstnb0 }); + } + } + } + } + } + } + + ggml_vk_sync_buffers(ctx); + ctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices); + return; + } + + // Staging buffer required + vk_buffer * staging = &vk_staging; + size_t staging_offset = vk_staging_offset; + const size_t copy_size = ts*ne/bs; + if (vk_staging.size < vk_staging_offset + copy_size) { + if (sync_staging) { + // Create temporary larger buffer + if (vk_sync_staging.size < copy_size) { + ggml_vk_destroy_buffer(vk_sync_staging); + vk_sync_staging = ggml_vk_create_buffer(copy_size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); + } + + staging = &vk_sync_staging; + staging_offset = 0; + } else { + GGML_ASSERT(false); + } + } + + VkBufferCopy buf_copy{ staging_offset, offset, copy_size }; + + ggml_vk_sync_buffers(ctx); + vkCmdCopyBuffer(ctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy); + + for (uint64_t i3 = 0; i3 < ne3; i3++) { + for (uint64_t i2 = 0; i2 < ne2; i2++) { + // Find longest contiguous slice + if (ne1*nb1 == dstnb2) { + deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i3*dstnb3 + i2*dstnb2, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2, dstnb2, &ctx->in_memcpys); + } else { + for (uint64_t i1 = 0; i1 < ne1; i1++) { + if (ne0*nb0/bs == dstnb1) { + deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2 + i1*nb1, dstnb1, &ctx->in_memcpys); + } else { + const uint64_t s_off = buf_offset + i3*nb3 + i2*nb2 + i1*nb1; + const uint64_t d_off = staging_offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1; + for (uint64_t i0 = 0; i0 < ne0; i0++) { + deferred_memcpy((uint8_t *)staging->ptr + d_off + i0*dstnb0, (const uint8_t *) tensor->data + s_off + i0*nb0, dstnb0, &ctx->in_memcpys); + } + } + } + } + } + } +} + +static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height, bool sync_staging = false) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_buffer_write_2d_async(" << width << ", " << height << ")" << std::endl; +#endif + // Buffer is already mapped + if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { + std::cerr << "ggml_vulkan: buffer_write_async dst buffer is host_visible. Use synchronous write." << std::endl; + GGML_ASSERT(false); + } + // Check if src is pinned memory + vk_buffer* buf = nullptr; + size_t buf_offset = 0; + for (size_t i = 0; i < vk_pinned_memory.size(); i++) { + const uint8_t* addr = (const uint8_t*) std::get<0>(vk_pinned_memory[i]); + const uint8_t* endr = addr + std::get<1>(vk_pinned_memory[i]); + if (src >= addr && src < endr) { + buf = &std::get<2>(vk_pinned_memory[i]); + buf_offset = ((const uint8_t *)src) - addr; + break; + } + } + + if (buf != nullptr) { + // Memory is pinned, use as staging buffer + std::vector slices(1); + if (width == spitch) { + // Only do single write if stride is equal + slices[0].srcOffset = buf_offset; + slices[0].dstOffset = offset; + slices[0].size = width * height; + } else { + slices.resize(height); + for (size_t i = 0; i < height; i++) { + slices[i].srcOffset = buf_offset + i * spitch; + slices[i].dstOffset = offset + i * width; + slices[i].size = width; + } + } + + ggml_vk_sync_buffers(ctx); + ctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices); + return; + } +#ifdef VK_DEBUG + std::cerr << "STAGING" << std::endl; +#endif + + // Staging buffer required + vk_buffer * staging = &vk_staging; + size_t staging_offset = vk_staging_offset; + const size_t copy_size = width*height; + if (vk_staging.size < vk_staging_offset + copy_size) { + if (sync_staging) { + if (vk_sync_staging.size < copy_size) { + ggml_vk_destroy_buffer(vk_sync_staging); + vk_sync_staging = ggml_vk_create_buffer(copy_size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); + } + + staging = &vk_sync_staging; + staging_offset = 0; + } else { + GGML_ASSERT(false); + } + } + + VkBufferCopy buf_copy = { + staging_offset, + offset, + copy_size}; + + ggml_vk_sync_buffers(ctx); + vkCmdCopyBuffer(ctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy); + + if (width == spitch) { + deferred_memcpy((uint8_t *)staging->ptr + staging_offset, src, width * height, &ctx->in_memcpys); + } else { + for (size_t i = 0; i < height; i++) { + deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i * width, (const uint8_t *) src + i * spitch, width, &ctx->in_memcpys); + } + } +} + +static void ggml_vk_buffer_write_async(vk_context * ctx, vk_buffer* dst, size_t offset, const void * src, size_t size, bool sync_staging = false) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_buffer_write_async(" << size << ")" << std::endl; +#endif + return ggml_vk_buffer_write_2d_async(ctx, dst, offset, src, size, size, 1, sync_staging); +} + +static void ggml_vk_buffer_write_2d(vk_buffer* dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_buffer_write_2d(" << width << ", " << height << ")" << std::endl; +#endif + // Buffer is already mapped + if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { + GGML_ASSERT(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent); + + for (size_t i = 0; i < height; i++) { + memcpy((uint8_t *)dst->ptr + offset + i * width, (const uint8_t *) src + i * spitch, width); + } + } else { + vk_context * ctx = ggml_vk_create_context(vk_device.transfer_queue); + ggml_vk_ctx_begin(ctx); + ggml_vk_buffer_write_2d_async(ctx, dst, offset, src, spitch, width, height, true); + ggml_vk_ctx_end(ctx); + + for (auto& cpy : ctx->in_memcpys) { + memcpy(cpy.dst, cpy.src, cpy.n); + } + + ggml_vk_submit(ctx, vk_fence); + VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences"); + vk_device.device.resetFences({ vk_fence }); + } +} + +static void ggml_vk_buffer_write(vk_buffer* dst, size_t offset, const void * src, size_t size) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_buffer_write(" << size << ")" << std::endl; +#endif + ggml_vk_buffer_write_2d(dst, offset, src, 0, size, 1); +} + +static void ggml_vk_buffer_read_2d_async(vk_context * ctx, vk_buffer* src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_buffer_read_2d_async(offset=" << offset << ", width=" << width << ", height=" << height << ")" << std::endl; +#endif + GGML_ASSERT(width > 0); + GGML_ASSERT(height > 0); + GGML_ASSERT(src->size > 0); + // Check if dst is pinned memory + vk_buffer* buf = nullptr; + size_t buf_offset = 0; + for (size_t i = 0; i < vk_pinned_memory.size(); i++) { + const uint8_t* addr = (const uint8_t*) std::get<0>(vk_pinned_memory[i]); + const uint8_t* endr = addr + std::get<1>(vk_pinned_memory[i]); + if (dst >= addr && dst < endr) { + buf = &std::get<2>(vk_pinned_memory[i]); + buf_offset = ((const uint8_t *)dst) - addr; + break; + } + } + + std::vector slices(1); + if (width == spitch && width == dpitch) { + // Only do single write if stride is equal + slices[0].srcOffset = offset; + slices[0].dstOffset = buf_offset; + slices[0].size = width * height; + } else { + slices.resize(height); + for (size_t i = 0; i < height; i++) { + slices[i].srcOffset = offset + i * spitch; + slices[i].dstOffset = buf_offset + i * dpitch; + slices[i].size = width; + } + } + + if (buf != nullptr) { + // Memory is pinned, use as staging buffer + ggml_vk_sync_buffers(ctx); + ctx->s->buffer.copyBuffer(src->buffer, buf->buffer, slices); + + return; + } +#ifdef VK_DEBUG + std::cerr << "STAGING" << std::endl; +#endif + + // Fall back to staging buffer + vk_buffer * staging = &vk_staging; + const size_t copy_size = dpitch * height; + if (vk_staging.size < vk_staging_offset + copy_size) { + if (sync_staging) { + // Create temporary larger buffer + if (vk_sync_staging.size < copy_size) { + ggml_vk_destroy_buffer(vk_sync_staging); + vk_sync_staging = ggml_vk_create_buffer(copy_size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); + } + + staging = &vk_sync_staging; + } else { + GGML_ASSERT(false); + } + } + + ggml_vk_sync_buffers(ctx); + ctx->s->buffer.copyBuffer(src->buffer, staging->buffer, slices); + + deferred_memcpy(dst, staging->ptr, copy_size, &ctx->out_memcpys); +} + +static void ggml_vk_buffer_read_async(vk_context * ctx, vk_buffer* src, size_t offset, void * dst, size_t size, bool sync_staging = false) { + return ggml_vk_buffer_read_2d_async(ctx, src, offset, dst, size, size, size, 1, sync_staging); +} + +static void ggml_vk_buffer_read(vk_buffer* src, size_t offset, void * dst, size_t size) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_buffer_read(" << offset << ", " << size << ")" << std::endl; +#endif + if(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { + GGML_ASSERT(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostCoherent); + + memcpy(dst, (uint8_t *) src->ptr + offset, size); + } else { + vk_context * ctx = ggml_vk_create_context(vk_device.transfer_queue); + ggml_vk_ctx_begin(ctx); + ggml_vk_buffer_read_async(ctx, src, offset, dst, size, true); + ggml_vk_ctx_end(ctx); + + ggml_vk_submit(ctx, vk_fence); + VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "vk_buffer_read waitForFences"); + vk_device.device.resetFences({ vk_fence }); + + for (auto& cpy : ctx->out_memcpys) { + memcpy(cpy.dst, cpy.src, cpy.n); + } + } +} + +static void ggml_vk_buffer_copy_async(vk_context * ctx, vk_buffer * dst, size_t dst_offset, vk_buffer * src, size_t src_offset, size_t size) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_buffer_copy_async(" << size << ")" << std::endl; +#endif + VkBufferCopy bc{ src_offset, dst_offset, size }; + + vkCmdCopyBuffer(ctx->s->buffer, src->buffer, dst->buffer, 1, &bc); +} + +static void ggml_vk_buffer_copy(vk_buffer * dst, size_t dst_offset, vk_buffer * src, size_t src_offset, size_t size) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_buffer_copy(" << size << ")" << std::endl; +#endif + VkBufferCopy bc{ src_offset, dst_offset, size }; + + vk_context * ctx = ggml_vk_create_context(vk_device.transfer_queue); + ggml_vk_ctx_begin(ctx); + vkCmdCopyBuffer(ctx->s->buffer, src->buffer, dst->buffer, 1, &bc); + ggml_vk_buffer_copy_async(ctx, dst, dst_offset, src, src_offset, size); + ggml_vk_ctx_end(ctx); + ggml_vk_submit(ctx, vk_fence); + VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences"); + vk_device.device.resetFences({ vk_fence }); +} + +static void ggml_vk_buffer_memset(vk_buffer* dst, size_t offset, uint32_t c, size_t size) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_buffer_memset(" << offset << ", " << c << ", " << size << ")" << std::endl; +#endif + vk_context * ctx = ggml_vk_create_context(vk_device.transfer_queue); + ggml_vk_ctx_begin(ctx); + ctx->s->buffer.fillBuffer(dst->buffer, offset, size, c); + ggml_vk_ctx_end(ctx); + + ggml_vk_submit(ctx, vk_fence); + VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "vk_memset waitForFences"); + vk_device.device.resetFences({ vk_fence }); +} + +static void ggml_vk_h2d_tensor_2d(vk_context * ctx, vk_buffer * dst, size_t offset, const ggml_tensor * src, uint64_t i3, uint64_t i2, uint64_t i1) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_h2d_tensor_2d(dst=" << dst << ", offset=" << offset << ", src=" << src << ", i3=" << i3 << ", i2=" << i2 << ", i1=" << i1 << ")" << std::endl; +#endif + const uint64_t ne0 = src->ne[0]; + const uint64_t ne1 = src->ne[1]; + const uint64_t nb0 = src->nb[0]; + const uint64_t nb1 = src->nb[1]; + const uint64_t nb2 = src->nb[2]; + const uint64_t nb3 = src->nb[3]; + const enum ggml_type type = src->type; + const size_t ts = ggml_type_size(type); + const size_t bs = ggml_blck_size(type); + const size_t row_length = ts*ne0/bs; + + const void * x = (const void *) ((const char *) src->data + i2*nb2 + i3*nb3); + if (nb0 == ts && nb1 == row_length) { + return ggml_vk_buffer_write_async(ctx, dst, offset, x, i1*nb1); + } + if (nb0 == ts && (i1 == ne1 || !ggml_is_permuted(src))) { + return ggml_vk_buffer_write_2d_async(ctx, dst, offset, x, nb1, row_length, i1); + } + + GGML_ASSERT(i3 == 0); + GGML_ASSERT(i2 == 0); + GGML_ASSERT(i1 == (uint64_t) ggml_nrows(src)); + + return ggml_vk_buffer_write_nc_async(ctx, dst, offset, src); +} + +static void ggml_vk_d2h_tensor_2d(vk_context * ctx, vk_buffer * src, size_t offset, const ggml_tensor * dst) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_d2h_tensor_2d()" << std::endl; +#endif + const uint64_t ne0 = dst->ne[0]; + const uint64_t ne1 = dst->ne[1]; + const uint64_t ne2 = dst->ne[2]; + const uint64_t ne3 = dst->ne[3]; + const uint64_t nb0 = dst->nb[0]; + const uint64_t nb1 = dst->nb[1]; + // const uint64_t nb2 = dst->nb[2]; + // const uint64_t nb3 = dst->nb[3]; + const enum ggml_type type = dst->type; + const size_t ts = ggml_type_size(type); + const size_t bs = ggml_blck_size(type); + const size_t row_length = ts*ne0/bs; + + if (ggml_is_contiguous(dst)) { + return ggml_vk_buffer_read_async(ctx, src, offset, dst->data, ne1*nb1*ne2*ne3); + } + if (nb0 == ts) { + return ggml_vk_buffer_read_2d_async(ctx, src, offset, dst->data, nb1, nb1, row_length, ne1*ne2*ne3); + } + GGML_ASSERT(false); +} + +static uint32_t ggml_vk_guess_split_k(int m, int n, int k) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ", " << aligned << ")"; +#endif + if (k > 128 && (m < 128 || n < 128) && m > 2 && n > 2) { +#ifdef VK_DEBUG + std::cerr << " = 4" << std::endl; +#endif + return 4; + } + +#ifdef VK_DEBUG + std::cerr << " = 1" << std::endl; +#endif + return 1; +} + +static uint32_t ggml_vk_guess_matmul_pipeline_align(int m, int n) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ")" << std::endl; +#endif + if (m <= 32 || n <= 32) { + return vk_pipeline_matmul_f32_aligned_s.align; + } + if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { + return vk_pipeline_matmul_f32_aligned_m.align; + } + return vk_pipeline_matmul_f32_aligned_l.align; +} + +static vk_pipeline* ggml_vk_guess_matmul_pipeline(bool bit16_x, bool bit16_y, int m, int n, bool aligned) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_guess_matmul_pipeline(" << bit16_x << ", " << bit16_y << ", " << m << ", " << n << ", " << aligned << ")"; +#endif + if (bit16_x && bit16_y) { + if (m <= 32 || n <= 32) { +#ifdef VK_DEBUG + std::cerr << " S" << std::endl; +#endif + return aligned ? &vk_pipeline_matmul_f16_aligned_s : &vk_pipeline_matmul_f16_s; + } + if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { +#ifdef VK_DEBUG + std::cerr << " M" << std::endl; +#endif + return aligned ? &vk_pipeline_matmul_f16_aligned_m : &vk_pipeline_matmul_f16_m; + } +#ifdef VK_DEBUG + std::cerr << " L" << std::endl; +#endif + return aligned ? &vk_pipeline_matmul_f16_aligned_l : &vk_pipeline_matmul_f16_l; + } + if (bit16_x && !bit16_y) { + if (m <= 32 || n <= 32) { +#ifdef VK_DEBUG + std::cerr << " S" << std::endl; +#endif + return aligned ? &vk_pipeline_matmul_f16_f32_aligned_s : &vk_pipeline_matmul_f16_f32_s; + } + if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { +#ifdef VK_DEBUG + std::cerr << " M" << std::endl; +#endif + return aligned ? &vk_pipeline_matmul_f16_f32_aligned_m : &vk_pipeline_matmul_f16_f32_m; + } +#ifdef VK_DEBUG + std::cerr << " L" << std::endl; +#endif + return aligned ? &vk_pipeline_matmul_f16_f32_aligned_l : &vk_pipeline_matmul_f16_f32_l; + } + if (!bit16_x && bit16_y) { + GGML_ASSERT(false); + } + + if (m <= 32 || n <= 32) { +#ifdef VK_DEBUG + std::cerr << " S" << std::endl; +#endif + return aligned ? &vk_pipeline_matmul_f32_aligned_s : &vk_pipeline_matmul_f32_s; + } + if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { +#ifdef VK_DEBUG + std::cerr << " M" << std::endl; +#endif + return aligned ? &vk_pipeline_matmul_f32_aligned_m : &vk_pipeline_matmul_f32_m; + } +#ifdef VK_DEBUG + std::cerr << " L" << std::endl; +#endif + return aligned ? &vk_pipeline_matmul_f32_aligned_l : &vk_pipeline_matmul_f32_l; +} + +static void ggml_vk_matmul(vk_context * ctx, vk_pipeline& pipeline, vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer, uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3, uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_matmul(a: (" << a.buffer.buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer.buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer.buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << split_k_buffer.buffer.buffer << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl; +#endif + if (split_k == 1) { + ggml_vk_sync_buffers(ctx); + const std::array pc = { m, n, k, stride_a, stride_b, stride_d, k, ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d }; + ggml_vk_dispatch_pipeline(ctx, pipeline, { a, b, d }, pc.size() * sizeof(uint32_t), pc.data(), { m, n, batch }); + return; + } + + GGML_ASSERT(batch_stride_d == m * n); + + // Synchronize the two submissions + ggml_vk_sync_buffers(ctx); + ctx->s->buffer.fillBuffer(split_k_buffer.buffer.buffer, 0, split_k_buffer.size, 0); + ggml_vk_sync_buffers(ctx); + const std::array pc1 = { m, n, k, stride_a, stride_b, stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d }; + // Make sure enough workgroups get assigned for split k to work + ggml_vk_dispatch_pipeline(ctx, pipeline, { a, b, split_k_buffer }, pc1.size() * sizeof(uint32_t), pc1.data(), { (CEIL_DIV(m, pipeline.wg_denoms[0]) * pipeline.wg_denoms[0]) * split_k, n, batch }); + ggml_vk_sync_buffers(ctx); + const std::array pc2 = { (uint32_t)(m * n * batch), split_k }; + ggml_vk_dispatch_pipeline(ctx, vk_pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 }); +} + +static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) { + return + tensor->nb[0] == ggml_type_size(tensor->type) && + tensor->nb[1] == (tensor->nb[0]*tensor->ne[0])/ggml_blck_size(tensor->type) && + tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; +} + +static vk_pipeline * ggml_vk_get_cpy_pipeline(ggml_type from, ggml_type to) { + if (from == GGML_TYPE_F32 && to == GGML_TYPE_F32) { + return &vk_pipeline_cpy_f32_f32; + } + if (from == GGML_TYPE_F32 && to == GGML_TYPE_F16) { + return &vk_pipeline_cpy_f32_f16; + } + if (from == GGML_TYPE_F16 && to == GGML_TYPE_F16) { + return &vk_pipeline_cpy_f16_f16; + } + + std::cerr << "Missing CPY op for types: " << ggml_type_name(from) << " " << ggml_type_name(to) << std::endl; + GGML_ASSERT(false); +} + +static void ggml_vk_cpy_to_contiguous(vk_context * ctx, vk_pipeline * pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out, ggml_type buffer_type, bool aligned=true) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", backend=" << tensor->backend << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), "; + std::cerr << "buffer in size=" << in.buffer.size << ", buffer out size=" << out.buffer.size << ")" << std::endl; +#endif + const int tensor_type_size = ggml_type_size(tensor->type); + const int dst_type_size = ggml_type_size(buffer_type); + + const uint32_t ne = tensor->ne[0] * tensor->ne[1] * tensor->ne[2]; + + const uint32_t nb2 = aligned ? ggml_vk_align_size(dst_type_size * tensor->ne[0] * tensor->ne[1], vk_device.properties.limits.minStorageBufferOffsetAlignment) / dst_type_size : tensor->ne[0] * tensor->ne[1]; + + const vk_op_cpy_push_constants pc = { + (uint32_t)ne, + (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], (uint32_t)tensor->nb[0] / tensor_type_size, (uint32_t)tensor->nb[1] / tensor_type_size, (uint32_t)tensor->nb[2] / tensor_type_size, + (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], 1 , (uint32_t)tensor->ne[0] , nb2, + 0, + }; + ggml_vk_sync_buffers(ctx); + ggml_vk_dispatch_pipeline(ctx, *pipeline, { in, out }, sizeof(vk_op_cpy_push_constants), &pc, { ne, 1, 1 }); +} + +static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_mul_mat_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; +#endif + GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); // NOLINT + GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT + + const uint64_t ne00 = src0->ne[0]; + const uint64_t ne01 = src0->ne[1]; + const uint64_t ne02 = src0->ne[2]; + const uint64_t ne03 = src0->ne[3]; + + const uint64_t ne10 = src1->ne[0]; + const uint64_t ne11 = src1->ne[1]; + const uint64_t ne12 = src1->ne[2]; + const uint64_t ne13 = src1->ne[3]; + + const uint64_t ne20 = dst->ne[0]; + const uint64_t ne21 = dst->ne[1]; + + const uint64_t r2 = ne12 / ne02; + const uint64_t r3 = ne13 / ne03; + + const bool load_x = src0->backend != GGML_BACKEND_GPU; + const bool load_y = src1->backend != GGML_BACKEND_GPU; + + const bool x_non_contig = !load_x && !ggml_vk_dim01_contiguous(src0); + const bool y_non_contig = !load_y && !ggml_vk_dim01_contiguous(src1); + + const bool f16_f32_kernel = src1->type == GGML_TYPE_F32 && !y_non_contig; + + const bool qx_needs_dequant = src0->type != GGML_TYPE_F16 || x_non_contig; + const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig; + + // Not implemented + GGML_ASSERT(y_non_contig || !qy_needs_dequant); // NOLINT + + const int x_ne = ne01 * ne00; + const int y_ne = ne11 * ne10; + const int d_ne = ne11 * ne01; + + const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ne01, ne11)); + const bool aligned = ne10 == kpad; + + const uint32_t split_k = ggml_vk_guess_split_k(ne01, ne11, ne10); + + vk_pipeline * pipeline = ggml_vk_guess_matmul_pipeline(true, !f16_f32_kernel, ne01, ne11, aligned); + + const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type); + const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); + const uint64_t x_sz = sizeof(ggml_fp16_t) * x_ne; + const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; + const uint64_t d_sz = sizeof(float) * d_ne; + + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; + ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; + ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; + + vk_buffer* d_D = &extra->buffer_gpu; + const uint64_t d_buf_offset = extra->offset; + GGML_ASSERT(d_D != nullptr); + GGML_ASSERT(d_D->size >= d_buf_offset + d_sz * ne02 * ne03); + vk_buffer * d_Qx; + uint64_t qx_buf_offset = 0; + vk_buffer * d_Qy; + uint64_t qy_buf_offset = 0; + vk_buffer* d_X; + uint64_t x_buf_offset = 0; + vk_buffer* d_Y; + uint64_t y_buf_offset = 0; + if (load_x) { + d_Qx = &vk_prealloc_qx; + } else { + d_Qx = &extra_src0->buffer_gpu; + qx_buf_offset = extra_src0->offset; + GGML_ASSERT(d_Qx != nullptr); + } + if (load_y) { + d_Qy = &vk_prealloc_qy; + } else { + d_Qy = &extra_src1->buffer_gpu; + qy_buf_offset = extra_src1->offset; + GGML_ASSERT(d_Qy != nullptr); + } + if (qx_needs_dequant) { + d_X = &vk_prealloc_x; + GGML_ASSERT(d_X->size >= x_sz * ne02 * ne03); + } else { + d_X = d_Qx; + x_buf_offset = qx_buf_offset; + GGML_ASSERT(qx_sz == x_sz); // NOLINT + } + if (qy_needs_dequant) { + d_Y = &vk_prealloc_y; + GGML_ASSERT(d_Y->size >= y_sz * ne02 * ne03); + } else { + d_Y = d_Qy; + y_buf_offset = qy_buf_offset; + GGML_ASSERT(qy_sz == y_sz); + } + + vk_pipeline * to_fp16_vk_0 = nullptr; + vk_pipeline * to_fp16_vk_1 = nullptr; + + if (x_non_contig) { + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(src0->type, GGML_TYPE_F16); + } else { + to_fp16_vk_0 = ggml_vk_get_to_fp16(src0->type); + } + if (y_non_contig) { + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(src1->type, GGML_TYPE_F16); + } else { + to_fp16_vk_1 = ggml_vk_get_to_fp16(src1->type); + } + GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT + GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT + + // Allocate descriptor sets + ggml_vk_pipeline_allocate_descriptor_sets(*pipeline, ne12 * ne13); + if (qx_needs_dequant) { + ggml_vk_pipeline_allocate_descriptor_sets(*to_fp16_vk_0, x_non_contig ? 1 : ne12 * ne13); + } + if (qy_needs_dequant) { + ggml_vk_pipeline_allocate_descriptor_sets(*to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13); + } + if (split_k > 1) { + ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline_matmul_split_k_reduce, ne12 * ne13); + } + + if (x_non_contig) { + ggml_vk_cpy_to_contiguous(ctx, to_fp16_vk_0, src0, { *d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { *d_X, 0, VK_WHOLE_SIZE }, dst->type, false); + } else if (load_x || qx_needs_dequant) { + if (load_x) { + // copy data to device + ggml_vk_h2d_tensor_2d(ctx, d_Qx, 0, src0, 0, 0, ggml_nrows(src0)); + vk_staging_offset = qx_sz * ne02 * ne03; + } + + if (qx_needs_dequant) { + const std::vector pc = { (int)ne01, (int)ne10, (int)ne10, (int)ne10 }; + ggml_vk_sync_buffers(ctx); + ggml_vk_dispatch_pipeline(ctx, *to_fp16_vk_0, { { *d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, { *d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1}); + } + } + if (y_non_contig) { + ggml_vk_cpy_to_contiguous(ctx, to_fp16_vk_1, src1, { *d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { *d_Y, 0, VK_WHOLE_SIZE }, dst->type); + } else if (load_y) { + ggml_vk_h2d_tensor_2d(ctx, d_Qy, 0, src1, 0, 0, ggml_nrows(src1)); + } + + uint32_t stride_batch_x = ne00*ne01; + uint32_t stride_batch_y = ne10*ne11; + + if (!ggml_vk_dim01_contiguous(src0) && !load_x && !qx_needs_dequant) { + stride_batch_x = src0->nb[0] / ggml_type_size(src0->type); + } + + if (!ggml_vk_dim01_contiguous(src1) && !load_y && !qy_needs_dequant) { + stride_batch_y = src1->nb[0] / ggml_type_size(src1->type); + } + + // compute + ggml_vk_matmul(ctx, *pipeline, { *d_X, x_buf_offset, x_sz * ne02 * ne03 }, { *d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { *d_D, d_buf_offset, d_sz * ne12 * ne13 }, { vk_prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k }, ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21); // NOLINT + + if (dst->backend == GGML_BACKEND_CPU) { + // copy dst to host + float * d = (float *) ((char *) dst->data); + ggml_vk_buffer_read_async(ctx, d_D, 0, d, sizeof(float) * d_ne * ne12 * ne13); + } +} + +static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; +#endif + GGML_ASSERT(ggml_vk_dim01_contiguous(src0) || src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); // NOLINT + GGML_ASSERT(ggml_vk_dim01_contiguous(src1) || src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16); // NOLINT + + const uint64_t ne00 = src0->ne[0]; + const uint64_t ne01 = src0->ne[1]; + const uint64_t ne02 = src0->ne[2]; + const uint64_t ne03 = src0->ne[3]; + + const uint64_t ne10 = src1->ne[0]; + const uint64_t ne11 = src1->ne[1]; + const uint64_t ne12 = src1->ne[2]; + const uint64_t ne13 = src1->ne[3]; + + GGML_ASSERT(ne11 == 1); + + const uint64_t nb2 = dst->nb[2]; + const uint64_t nb3 = dst->nb[3]; + + const uint64_t r2 = ne12 / ne02; + const uint64_t r3 = ne13 / ne03; + + const bool load_x = src0->backend != GGML_BACKEND_GPU; + const bool load_y = src1->backend != GGML_BACKEND_GPU; + + const bool x_non_contig = !load_x && !ggml_vk_dim01_contiguous(src0); + const bool y_non_contig = !load_y && !ggml_vk_dim01_contiguous(src1); + + const bool f16_f32_kernel = src1->type == GGML_TYPE_F32; + + const bool qx_needs_dequant = x_non_contig; + const bool qy_needs_dequant = (src1->type != GGML_TYPE_F16 && !f16_f32_kernel) || y_non_contig; + + const uint64_t x_ne = ne01 * ne00; + const uint64_t y_ne = ne11 * ne10; + const uint64_t d_ne = ne11 * ne01; + + const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), vk_device.properties.limits.minStorageBufferOffsetAlignment); + const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); + const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, vk_device.properties.limits.minStorageBufferOffsetAlignment) : qx_sz; + const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; + const uint64_t d_sz = sizeof(float) * d_ne; + + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; + ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; + ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; + + vk_buffer* d_D = &extra->buffer_gpu; + const uint64_t d_buf_offset = extra->offset; + GGML_ASSERT(d_D != nullptr); + vk_buffer* d_Qx; + uint32_t qx_buf_offset = 0; + vk_buffer* d_Qy; + uint32_t qy_buf_offset = 0; + vk_buffer* d_X; + uint64_t x_buf_offset = 0; + vk_buffer* d_Y; + uint64_t y_buf_offset = 0; + if (load_x) { + d_Qx = &vk_prealloc_qx; + } else { + d_Qx = &extra_src0->buffer_gpu; + qx_buf_offset = extra_src0->offset; + GGML_ASSERT(d_Qx != nullptr); + } + if (load_y) { + d_Qy = &vk_prealloc_qy; + } else { + d_Qy = &extra_src1->buffer_gpu; + qy_buf_offset = extra_src1->offset; + GGML_ASSERT(d_Qy != nullptr); + } + if (qx_needs_dequant) { + d_X = &vk_prealloc_x; + } else { + d_X = d_Qx; + x_buf_offset = qx_buf_offset; + GGML_ASSERT(qx_sz == x_sz); + } + if (qy_needs_dequant) { + d_Y = &vk_prealloc_y; + } else { + d_Y = d_Qy; + y_buf_offset = qy_buf_offset; + GGML_ASSERT(qy_sz == y_sz); + } + + vk_pipeline * to_fp16_vk_0 = nullptr; + vk_pipeline* to_fp16_vk_1 = nullptr; + if (x_non_contig) { + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(src0->type, src0->type); + } + if (y_non_contig) { + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(src1->type, src1->type); + } else { + to_fp16_vk_1 = ggml_vk_get_to_fp16(src1->type); + } + vk_pipeline* dmmv = ggml_vk_get_dequantize_mul_mat_vec(src0->type); + GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT + GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT + GGML_ASSERT(dmmv != nullptr); + + // Allocate descriptor sets + if (qx_needs_dequant) { + ggml_vk_pipeline_allocate_descriptor_sets(*to_fp16_vk_0, 1); + } + if (qy_needs_dequant) { + ggml_vk_pipeline_allocate_descriptor_sets(*to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13); + } + ggml_vk_pipeline_allocate_descriptor_sets(*dmmv, ne12 * ne13); + + if (x_non_contig) { + GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, vk_device.properties.limits.minStorageBufferOffsetAlignment)); + ggml_vk_cpy_to_contiguous(ctx, to_fp16_vk_0, src0, { *d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { *d_X, 0, VK_WHOLE_SIZE }, src0->type); + } else if (load_x) { + // copy data to device + ggml_vk_h2d_tensor_2d(ctx, d_Qx, 0, src0, 0, 0, ggml_nrows(src0)); + } + if (y_non_contig) { + GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne); + ggml_vk_cpy_to_contiguous(ctx, to_fp16_vk_1, src1, { *d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { *d_Y, 0, VK_WHOLE_SIZE }, src1->type); + } else if (load_y) { + ggml_vk_h2d_tensor_2d(ctx, d_Qy, 0, src1, 0, 0, ggml_nrows(src1)); + } + + for (uint64_t i13 = 0; i13 < ne13; i13++) { + const uint64_t i03 = i13 / r3; + for (uint64_t i12 = 0; i12 < ne12; i12++) { + const uint64_t i02 = i12 / r2; + + const uint64_t it_idx0 = (i03 * ne02 + i02); + const uint64_t it_idx1 = (i13 * ne12 + i12); + const uint64_t x_offset = x_buf_offset + x_sz * it_idx0; + const uint64_t qy_offset = qy_buf_offset + qy_sz * it_idx1; + const uint64_t y_offset = y_buf_offset + y_sz * it_idx1; + const uint64_t d_offset = d_buf_offset + d_sz * it_idx1; + + const uint64_t y_buffer_offset = (y_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t y_shader_offset = y_offset - y_buffer_offset; + + const uint64_t d_buffer_offset = (d_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t d_shader_offset = d_offset - d_buffer_offset; + + if (!y_non_contig && qy_needs_dequant) { + const std::vector pc = { (int)ne11, (int)ne10, (int)ne10, (int)ne10 }; + ggml_vk_sync_buffers(ctx); + ggml_vk_dispatch_pipeline(ctx, *to_fp16_vk_1, { { *d_Qy, qy_offset, qy_sz }, { *d_Y, y_offset, y_sz } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)y_ne, 1, 1}); + } + + // compute + const std::array pc = { (int)ne00, (int)(y_shader_offset / ggml_type_size(src1->type)), (int)(d_shader_offset / ggml_type_size(dst->type))}; + ggml_vk_sync_buffers(ctx); + ggml_vk_dispatch_pipeline(ctx, *dmmv, { { *d_X, x_offset, x_sz }, { *d_Y, y_buffer_offset, y_sz + y_shader_offset }, { *d_D, d_buffer_offset, d_sz + d_shader_offset } }, 3 * sizeof(int), &pc, { (uint32_t)ne01, 1, 1}); + + if (dst->backend == GGML_BACKEND_CPU) { + // copy dst to host + float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); + ggml_vk_sync_buffers(ctx); + ggml_vk_buffer_read_async(ctx, d_D, d_offset, d, sizeof(float) * d_ne); + } + } + } +} + +static void ggml_vk_mul_mat_vec_p021_f16_f32(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_mul_mat_p021_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; +#endif + GGML_ASSERT(ggml_is_permuted(src0) && ggml_is_permuted(src1)); + GGML_ASSERT(src0->backend == GGML_BACKEND_GPU); + GGML_ASSERT(src0->nb[0] <= src0->nb[1] && src0->nb[2] <= src0->nb[3]); // NOLINT + GGML_ASSERT(src1->nb[0] <= src1->nb[1] && src1->nb[2] <= src1->nb[3]); // NOLINT + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + const uint64_t ne00 = src0->ne[0]; + const uint64_t ne01 = src0->ne[1]; + const uint64_t ne02 = src0->ne[2]; + // const uint64_t ne03 = src0->ne[3]; + + const uint64_t ne10 = src1->ne[0]; + const uint64_t ne11 = src1->ne[1]; + const uint64_t ne12 = src1->ne[2]; + // const uint64_t ne13 = src1->ne[3]; + + GGML_ASSERT(ne11 == 1); + + const bool load_y = src1->backend != GGML_BACKEND_GPU; + + const uint64_t x_ne = ne00 * ne01 * ne02; + const uint64_t y_ne = ne10 * ne11 * ne12; + const uint64_t d_ne = ne01 * ne11 * ne12; + + const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), vk_device.properties.limits.minStorageBufferOffsetAlignment); + const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); + const uint64_t d_sz = sizeof(float) * d_ne; + + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; + ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; + ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; + + vk_buffer* d_D = &extra->buffer_gpu; + const uint64_t d_buf_offset = extra->offset; + GGML_ASSERT(d_D != nullptr); + vk_buffer* d_Qx; + const uint64_t qx_buf_offset = extra_src0->offset; + vk_buffer* d_Qy; + uint64_t qy_buf_offset = 0; + d_Qx = &extra_src0->buffer_gpu; + GGML_ASSERT(d_Qx != nullptr); + if (load_y) { + d_Qy = &vk_prealloc_qy; + } else { + d_Qy = &extra_src1->buffer_gpu; + qy_buf_offset = extra_src1->offset; + GGML_ASSERT(d_Qx != nullptr); + } + + // Allocate descriptor sets + ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline_mul_mat_vec_p021_f16_f32, 1); + + const uint64_t qy_buffer_offset = (qy_buf_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset; + + const uint64_t d_buffer_offset = (d_buf_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset; + + if (load_y) { + ggml_vk_h2d_tensor_2d(ctx, d_Qy, qy_buf_offset, src1, 0, 0, ggml_nrows(src1)); + } + + // compute + const std::array pc = { (uint32_t)ne00, (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) }; + ggml_vk_sync_buffers(ctx); + ggml_vk_dispatch_pipeline(ctx, vk_pipeline_mul_mat_vec_p021_f16_f32, { { *d_Qx, qx_buf_offset, qx_sz }, { *d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { *d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); + + if (dst->backend == GGML_BACKEND_CPU) { + // copy dst to host + float * d = (float *) dst->data; + ggml_vk_sync_buffers(ctx); + ggml_vk_buffer_read_async(ctx, d_D, d_buf_offset, d, sizeof(float) * d_ne); + } +} + +static void ggml_vk_mul_mat_vec_nc_f16_f32(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_mul_mat_nc_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; +#endif + GGML_ASSERT(!ggml_is_transposed(src0)); + GGML_ASSERT(!ggml_is_transposed(src1)); + GGML_ASSERT(!ggml_is_permuted(src0)); + GGML_ASSERT(src0->backend == GGML_BACKEND_GPU); + GGML_ASSERT(src0->type == GGML_TYPE_F16); + GGML_ASSERT(src1->type == GGML_TYPE_F32); + + const uint64_t ne00 = src0->ne[0]; + const uint64_t ne01 = src0->ne[1]; + const uint64_t ne02 = src0->ne[2]; + // const uint64_t ne03 = src0->ne[3]; + + const uint64_t nb01 = src0->nb[1]; + const uint64_t nb02 = src0->nb[2]; + + // const uint64_t ne10 = src1->ne[0]; + const uint64_t ne11 = src1->ne[1]; + const uint64_t ne12 = src1->ne[2]; + // const uint64_t ne13 = src1->ne[3]; + + GGML_ASSERT(ne11 == 1); + + const bool load_y = src1->backend != GGML_BACKEND_GPU; + + const uint64_t d_ne = ne01 * ne11 * ne12; + + const uint32_t row_stride_x = nb01 / sizeof(ggml_fp16_t); + const uint32_t channel_stride_x = nb02 / sizeof(ggml_fp16_t); + + const uint64_t qx_sz = ggml_nbytes(src0); + const uint64_t qy_sz = ggml_nbytes(src1); + const uint64_t d_sz = sizeof(float) * d_ne; + + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; + ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; + ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; + + vk_buffer* d_D = &extra->buffer_gpu; + const uint64_t d_buf_offset = extra->offset; + GGML_ASSERT(d_D != nullptr); + vk_buffer* d_Qx; + const uint64_t qx_buf_offset = extra_src0->offset; + vk_buffer* d_Qy; + uint64_t qy_buf_offset = 0; + d_Qx = &extra_src0->buffer_gpu; + GGML_ASSERT(d_Qx != nullptr); + if (load_y) { + d_Qy = &vk_prealloc_qy; + } else { + d_Qy = &extra_src1->buffer_gpu; + qy_buf_offset = extra_src1->offset; + GGML_ASSERT(d_Qx != nullptr); + } + + // Allocate descriptor sets + ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline_mul_mat_vec_nc_f16_f32, 1); + + const uint64_t qy_buffer_offset = (qy_buf_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset; + + const uint64_t d_buffer_offset = (d_buf_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset; + + if (load_y) { + ggml_vk_h2d_tensor_2d(ctx, d_Qy, qy_buf_offset, src1, 0, 0, ggml_nrows(src1)); + } + + // compute + const std::array pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, (uint32_t)(ne12 / ne02), (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) }; + ggml_vk_sync_buffers(ctx); + ggml_vk_dispatch_pipeline(ctx, vk_pipeline_mul_mat_vec_nc_f16_f32, { { *d_Qx, qx_buf_offset, qx_sz }, { *d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { *d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); + + if (dst->backend == GGML_BACKEND_CPU) { + // copy dst to host + float * d = (float *) dst->data; + ggml_vk_sync_buffers(ctx); + ggml_vk_buffer_read_async(ctx, d_D, d_buf_offset, d, sizeof(float) * d_ne); + } +} + +static bool ggml_vk_can_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * dst) { + const uint64_t ne10 = src1->ne[0]; + + const uint64_t ne0 = dst->ne[0]; + const uint64_t ne1 = dst->ne[1]; + + // TODO: find the optimal values for these + return (src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && + (src1->type == GGML_TYPE_F32 || src1->type == GGML_TYPE_F16 || ggml_is_quantized(src1->type)) && + dst->type == GGML_TYPE_F32 && + ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_GPU); +} + +static void ggml_vk_mul_mat(vk_context * ctx, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_mul_mat(" << src0 << ", " << src1 << ", " << dst << ")" << std::endl; +#endif + if (src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { + ggml_vk_mul_mat_vec_p021_f16_f32(ctx, src0, src1, dst); + } else if (src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { + ggml_vk_mul_mat_vec_nc_f16_f32(ctx, src0, src1, dst); + } else if (src1->ne[1] == 1 && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) { + ggml_vk_mul_mat_vec_q_f16(ctx, src0, src1, dst); + } else { + ggml_vk_mul_mat_q_f16(ctx, src0, src1, dst); + } +} + +static void ggml_vk_op_repeat(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + // guaranteed to be an integer due to the check in ggml_can_repeat + const uint64_t ne0 = dst->ne[0]; + const uint64_t ne1 = dst->ne[1]; + const uint64_t ne2 = dst->ne[2]; + const uint64_t ne3 = dst->ne[3]; + + const uint64_t ne00 = src0->ne[0]; + const uint64_t ne01 = src0->ne[1]; + const uint64_t ne02 = src0->ne[2]; + const uint64_t ne03 = src0->ne[3]; + + const uint64_t nb0 = dst->nb[0]; + const uint64_t nb1 = dst->nb[1]; + const uint64_t nb2 = dst->nb[2]; + const uint64_t nb3 = dst->nb[3]; + + const uint64_t nb00 = src0->nb[0]; + const uint64_t nb01 = src0->nb[1]; + const uint64_t nb02 = src0->nb[2]; + const uint64_t nb03 = src0->nb[3]; + + const uint64_t nr0 = ne0/ne00; + const uint64_t nr1 = ne1/ne01; + const uint64_t nr2 = ne2/ne02; + const uint64_t nr3 = ne3/ne03; + + // TODO: support for transposed / permuted tensors + GGML_ASSERT(nb0 == sizeof(float)); + GGML_ASSERT(nb00 == sizeof(float)); + GGML_ASSERT(src0->backend == GGML_BACKEND_GPU); + GGML_ASSERT(dst->backend == GGML_BACKEND_GPU); + + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; + ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; + + const vk_buffer* src_buf = &extra_src0->buffer_gpu; + const uint64_t src_offset = extra_src0->offset; + vk_buffer* dst_buf = &extra->buffer_gpu; + const uint64_t dst_offset = extra->offset; + + std::vector copies; + + for (uint64_t i3 = 0; i3 < nr3; i3++) { + for (uint64_t k3 = 0; k3 < ne03; k3++) { + for (uint64_t i2 = 0; i2 < nr2; i2++) { + for (uint64_t k2 = 0; k2 < ne02; k2++) { + for (uint64_t i1 = 0; i1 < nr1; i1++) { + for (uint64_t k1 = 0; k1 < ne01; k1++) { + for (uint64_t i0 = 0; i0 < nr0; i0++) { + copies.push_back({ + src_offset + (i3*ne03 + k3)*nb3 + (i2*ne02 + k2)*nb2 + (i1*ne01 + k1)*nb1 + (i0*ne00)*nb0, + dst_offset + ( k3)*nb03 + ( k2)*nb02 + ( k1)*nb01, + ne00*nb0, + }); + } + } + } + } + } + } + } + + ggml_vk_sync_buffers(ctx); + ctx->s->buffer.copyBuffer(src_buf->buffer, dst_buf->buffer, copies); + + (void) src1; +} + + +static vk_pipeline* ggml_vk_op_get_pipeline(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op) { + switch (op) { + case GGML_OP_ADD: + if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return &vk_pipeline_add_f32; + } + return nullptr; + case GGML_OP_GET_ROWS: + GGML_ASSERT(src1->type == GGML_TYPE_I32); + if (dst->type == GGML_TYPE_F16) { + return &vk_pipeline_get_rows[src0->type]; + } + if (dst->type == GGML_TYPE_F32) { + return &vk_pipeline_get_rows_f32[src0->type]; + } + return nullptr; + case GGML_OP_MUL: + if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return &vk_pipeline_mul_f32; + } + return nullptr; + case GGML_OP_SCALE: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return &vk_pipeline_scale_f32; + } + return nullptr; + case GGML_OP_SQR: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return &vk_pipeline_sqr_f32; + } + return nullptr; + case GGML_OP_CLAMP: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return &vk_pipeline_clamp_f32; + } + return nullptr; + case GGML_OP_CPY: + case GGML_OP_CONT: + case GGML_OP_DUP: + return ggml_vk_get_cpy_pipeline(src0->type, dst->type); + case GGML_OP_NORM: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return &vk_pipeline_norm_f32; + } + return nullptr; + case GGML_OP_RMS_NORM: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return &vk_pipeline_rms_norm_f32; + } + return nullptr; + case GGML_OP_UNARY: + switch (ggml_get_unary_op(dst)) { + case GGML_UNARY_OP_SILU: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return &vk_pipeline_silu_f32; + } + break; + case GGML_UNARY_OP_GELU: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return &vk_pipeline_gelu_f32; + } + break; + case GGML_UNARY_OP_RELU: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return &vk_pipeline_relu_f32; + } + break; + default: + break; + } + return nullptr; + case GGML_OP_DIAG_MASK_INF: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return &vk_pipeline_diag_mask_inf_f32; + } + return nullptr; + case GGML_OP_SOFT_MAX: + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return &vk_pipeline_soft_max_f32; + } + return nullptr; + case GGML_OP_ROPE: + { + const int mode = ((const int32_t *) dst->op_params)[2]; + const bool is_neox = mode & 2; + const bool is_glm = mode & 4; + + if (is_glm) { + return nullptr; + } + + if (is_neox) { + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return &vk_pipeline_rope_neox_f32; + } + if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { + return &vk_pipeline_rope_neox_f16; + } + } else { + if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { + return &vk_pipeline_rope_f32; + } + if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { + return &vk_pipeline_rope_f16; + } + } + return nullptr; + } + default: + return nullptr; + } +} + +static ggml_vk_func_t ggml_vk_op_get_func(ggml_op op) { + switch(op) { + case GGML_OP_REPEAT: + return ggml_vk_op_repeat; + default: + return nullptr; + } +} + +#ifdef GGML_VULKAN_CHECK_RESULTS +void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name); +#endif + +template +static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op, const PC&& pc) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; + if (src1 != nullptr) { + std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; + } + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "), " << ggml_op_name(op) << ")" << std::endl; +#endif + GGML_ASSERT(!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type))); // NOLINT + GGML_ASSERT(op == GGML_OP_CPY || ggml_vk_dim01_contiguous(src0)); // NOLINT + GGML_ASSERT(src1 == nullptr || ggml_vk_dim01_contiguous(src1)); // NOLINT + GGML_ASSERT(dst->extra != nullptr); + const uint64_t ne00 = src0->ne[0]; + const uint64_t ne01 = src0->ne[1]; + const uint64_t ne02 = src0->ne[2]; + const uint64_t ne03 = src0->ne[3]; + const uint64_t ne0 = ne00 * ne01; + const bool use_src1 = src1 != nullptr; + const uint64_t ne10 = use_src1 ? src1->ne[0] : 0; + const uint64_t ne11 = use_src1 ? src1->ne[1] : 0; + const uint64_t ne12 = use_src1 ? src1->ne[2] : 0; + const uint64_t ne13 = use_src1 ? src1->ne[3] : 0; + const uint64_t ne1 = ne10 * ne11; + // const uint64_t nb10 = use_src1 ? src1->nb[0] : 0; + const uint64_t nb2 = dst->nb[2]; + const uint64_t nb3 = dst->nb[3]; + + vk_pipeline * pipeline = ggml_vk_op_get_pipeline(src0, src1, dst, op); + ggml_vk_func_t op_func; + + if (pipeline == nullptr) { + op_func = ggml_vk_op_get_func(op); + if (op_func == nullptr) { + std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(op) << " for " << ggml_type_name(src0->type); + if (src1 != nullptr) { + std::cerr << " and " << ggml_type_name(src1->type); + } + std::cerr << " to " << ggml_type_name(dst->type) << std::endl; + GGML_ASSERT(false); + } + + op_func(ctx, src0, src1, dst); + return; + } + + const bool transfer_src0 = src0->backend != GGML_BACKEND_GPU; + const bool transfer_src1 = use_src1 && src1->backend != GGML_BACKEND_GPU; + + uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type) * ne0, vk_device.properties.limits.minStorageBufferOffsetAlignment); + uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, vk_device.properties.limits.minStorageBufferOffsetAlignment) : 0; + uint64_t d_sz = ggml_type_size(dst->type) * ne0; + + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; + ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; + ggml_tensor_extra_gpu * extra_src1 = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr; + + // Workaround for tiny tensor inputs on ROPE + if (use_src1 && src1->backend == GGML_BACKEND_GPU && y_sz > extra_src1->buffer_gpu.size) { + y_sz = VK_WHOLE_SIZE; + } + + vk_buffer* d_D = &extra->buffer_gpu; + GGML_ASSERT(d_D != nullptr); + uint64_t d_buf_offset = (extra->offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + GGML_ASSERT(d_buf_offset == extra->offset || op == GGML_OP_CPY); // NOLINT + vk_buffer* d_X = nullptr; + uint64_t x_buf_offset = 0; + vk_buffer* d_Y = nullptr; + uint64_t y_buf_offset = 0; + if (transfer_src0) { + d_X = &vk_prealloc_qx; + } else { + d_X = &extra_src0->buffer_gpu; + x_buf_offset = extra_src0->offset; + GGML_ASSERT(d_X != nullptr); + } + if (transfer_src1) { + d_Y = &vk_prealloc_qy; + } else if (use_src1) { + d_Y = &extra_src1->buffer_gpu; + y_buf_offset = extra_src1->offset; + GGML_ASSERT(d_Y != nullptr); + } + + if (op == GGML_OP_CPY) { + GGML_ASSERT(!transfer_src0); + GGML_ASSERT(!transfer_src1); + d_sz = dst->ne[1] * dst->nb[1]; + + if (extra->offset + d_sz >= d_D->size) { + d_sz = VK_WHOLE_SIZE; + } + } + + std::array elements; + + // copy src0 to device + if (transfer_src0) { + ggml_vk_h2d_tensor_2d(ctx, d_X, 0, src0, 0, 0, ggml_nrows(src0)); + vk_staging_offset = x_sz * ne02 * ne03; + } + if (transfer_src1) { + ggml_vk_h2d_tensor_2d(ctx, d_Y, 0, src1, 0, 0, ggml_nrows(src1)); + } + + // Single call if dimension 2 is contiguous + if (op == GGML_OP_CPY || (ggml_is_contiguous(src0) && (src1 == nullptr || ggml_is_contiguous(src1)))) { + ggml_vk_pipeline_allocate_descriptor_sets(*pipeline, 1); + + switch (dst->op) { + case GGML_OP_NORM: + case GGML_OP_RMS_NORM: + case GGML_OP_SOFT_MAX: + elements = { (uint32_t)ggml_nrows(src0), 1, 1 }; + break; + case GGML_OP_DIAG_MASK_INF: + case GGML_OP_ROPE: + elements = { (uint32_t)ggml_nrows(src0), (uint32_t)ne00, 1 }; + break; + default: + elements = { (uint32_t)ggml_nelements(src0), 1, 1 }; + break; + } + + x_sz *= ne02 * ne03; + if (y_sz != VK_WHOLE_SIZE) { + y_sz *= ne12 * ne13; + } + if (op != GGML_OP_CPY) { + d_sz *= ne02 * ne03; + } + + if (!use_src1 && op == GGML_OP_SOFT_MAX) { + // Empty src1 is possible on soft_max, but the shader needs a buffer + ggml_vk_sync_buffers(ctx); + ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset, x_sz }, { vk_prealloc_y, 0, vk_prealloc_y.size }, { *d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + } else if (use_src1) { + ggml_vk_sync_buffers(ctx); + ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset, x_sz }, { *d_Y, y_buf_offset, y_sz }, { *d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + } else { + ggml_vk_sync_buffers(ctx); + ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset, x_sz }, { *d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + } + if (dst->backend == GGML_BACKEND_CPU && op == GGML_OP_CPY) { + ggml_vk_d2h_tensor_2d(ctx, d_D, 0, dst); + } else if(dst->backend == GGML_BACKEND_CPU) { + // copy dst to host + float * d = (float *) dst->data; + ggml_vk_buffer_read_async(ctx, d_D, 0, d, d_sz); + } + } else { + ggml_vk_pipeline_allocate_descriptor_sets(*pipeline, ne02 * ne03); + + switch (dst->op) { + case GGML_OP_NORM: + case GGML_OP_RMS_NORM: + case GGML_OP_SOFT_MAX: + elements = { (uint32_t)ne01, 1, 1 }; + break; + case GGML_OP_DIAG_MASK_INF: + case GGML_OP_ROPE: + elements = { (uint32_t)ne01, (uint32_t)ne00, 1 }; + break; + default: + elements = { (uint32_t)ne0, 1, 1 }; + break; + } + + for (uint64_t i03 = 0; i03 < ne03; i03++) { + for (uint64_t i02 = 0; i02 < ne02; i02++) { + const uint32_t it_idx0 = (i03 * ne02 + i02); + const uint32_t it_idx1 = use_src1 ? ((i03 % ne13) * ne12 + (i02 % ne12)) : 0; + const uint32_t x_offset = x_sz * it_idx0; + const uint32_t y_offset = y_sz * it_idx1; + const uint32_t d_offset = d_sz * it_idx0; + + if (!use_src1 && op == GGML_OP_SOFT_MAX) { + // Empty src1 is possible on soft_max, but the shader needs a buffer + ggml_vk_sync_buffers(ctx); + ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset, x_sz }, { vk_prealloc_y, 0, vk_prealloc_y.size }, { *d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + } else if (use_src1) { + ggml_vk_sync_buffers(ctx); + ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset + x_offset, x_sz }, { *d_Y, y_buf_offset + y_offset, y_sz }, { *d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); + } else { + ggml_vk_sync_buffers(ctx); + ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset + x_offset, x_sz }, { *d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); + } + if (dst->backend == GGML_BACKEND_CPU) { + // copy dst to host + ggml_vk_buffer_read_async(ctx, d_D, d_buf_offset + d_offset, (char *) dst->data + i02*nb2 + i03*nb3, d_sz); + } + } + } + } +} + +static void ggml_vk_repeat(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_REPEAT, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); +} + +static void ggml_vk_get_rows(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_GET_ROWS, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); +} + +static void ggml_vk_add(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_ADD, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); +} + +static void ggml_vk_mul(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_MUL, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); +} + +static void ggml_vk_scale(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { + float * op_params = (float *)dst->op_params; + ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_SCALE, { (uint32_t)ggml_nelements(src0), 0, op_params[0], 0.0f }); +} + +static void ggml_vk_sqr(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_SQR, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); +} + +static void ggml_vk_clamp(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { + float * op_params = (float *)dst->op_params; + ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_CLAMP, { (uint32_t)ggml_nelements(src0), 0, op_params[0], op_params[1] }); +} + +static void ggml_vk_cpy(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; + const int src0_type_size = ggml_type_size(src0->type); + const int dst_type_size = ggml_type_size(dst->type); + const uint32_t d_offset = (extra->offset % vk_device.properties.limits.minStorageBufferOffsetAlignment) / dst_type_size; + ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_CPY, { + (uint32_t)ggml_nelements(src0), + (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, + (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, + d_offset, + }); +} + +static void ggml_vk_norm(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], 0.0f, 0.0f }); +} + +static void ggml_vk_rms_norm(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { + float * op_params = (float *)dst->op_params; + ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); +} + +static void ggml_vk_unary(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); +} + +static void ggml_vk_diag_mask_inf(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { + int32_t * op_params = (int32_t *)dst->op_params; + ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] }); +} + +static void ggml_vk_soft_max(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + float * op_params = (float *)dst->op_params; + ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_SOFT_MAX, { (uint32_t)src0->ne[0], (uint32_t)(src1 != nullptr ? ggml_nrows(src1) : 0), op_params[0], 0.0f }); +} + +static void ggml_vk_rope(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + // const int n_ctx = ((int32_t *) dst->op_params)[3]; + const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; + const float freq_base = ((float *) dst->op_params)[5]; + const float freq_scale = ((float *) dst->op_params)[6]; + const float ext_factor = ((float *) dst->op_params)[7]; + const float attn_factor = ((float *) dst->op_params)[8]; + const float beta_fast = ((float *) dst->op_params)[9]; + const float beta_slow = ((float *) dst->op_params)[10]; + + const bool is_neox = mode & 2; + const bool is_glm = mode & 4; + + GGML_ASSERT(!is_glm); + + float corr_dims[2]; + ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims); + + if (is_neox) { + const float theta_scale = powf(freq_base, -2.0f/n_dims); + const float inv_ndims = -1.0f / n_dims; + ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f, theta_scale, inv_ndims }); + } else { + ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f }); + } +} + +static void ggml_vk_nop(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { + // If backend is CPU, data from src0 has to be copied off the device + if (dst->backend == GGML_BACKEND_CPU) { + ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; + vk_buffer * d_D = &extra_src0->buffer_gpu; + ggml_vk_sync_buffers(ctx); + ggml_vk_buffer_read_async(ctx, d_D, 0, dst->data, d_D->size); + } +} + +#ifdef VK_RUN_TESTS +static void ggml_vk_print_matrix_area(const void * data, ggml_type type, int ne0, int ne1, int i0, int i1, int i2) { + if (type != GGML_TYPE_F32 && type != GGML_TYPE_F16) { + return; + } + i0 = std::max(i0, 5); + i1 = std::max(i1, 5); + i2 = std::max(i2, 0); + fprintf(stderr, " "); + for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) { + fprintf(stderr, "%7d ", idx1); + } + fprintf(stderr, "\n"); + for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) { + fprintf(stderr, "%7d: ", idx0); + for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) { + if (idx0 >= 0 && idx0 < ne0 && idx1 >= 0 && idx1 < ne1) { + float val; + if (type == GGML_TYPE_F32) { + val = *((const float *) data + i2*ne1*ne0 + idx1*ne0 + idx0); + } else if (type == GGML_TYPE_F16) { + val = ggml_fp16_to_fp32(*((const ggml_fp16_t *) data + i2*ne1*ne0 + idx1*ne0 + idx0)); + } + fprintf(stderr, "% 7.2f ", val); + } else { + fprintf(stderr, " "); + } + } + fprintf(stderr, "\n"); + } +} + +template +static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size_t num_it, int split_k, int shader_size) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_test_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << shader_size << ")" << std::endl; +#endif + const size_t x_ne = m * k * batch; + const size_t y_ne = k * n * batch; + const size_t d_ne = m * n * batch; + + vk_pipeline * p; + std::string shname; + if (shader_size == 0) { + if (std::is_same() && std::is_same()) { + p = &vk_pipeline_matmul_f32_aligned_s; + shname = "F32_ALIGNED_S"; + } else if (std::is_same() && std::is_same()) { + p = &vk_pipeline_matmul_f16_f32_aligned_s; + shname = "F16_F32_ALIGNED_S"; + } else if (std::is_same() && std::is_same()) { + p = &vk_pipeline_matmul_f16_aligned_s; + shname = "F16_ALIGNED_S"; + } else { + GGML_ASSERT(false); + } + } else if (shader_size == 1) { + if (std::is_same() && std::is_same()) { + p = &vk_pipeline_matmul_f32_aligned_m; + shname = "F32_ALIGNED_M"; + } else if (std::is_same() && std::is_same()) { + p = &vk_pipeline_matmul_f16_f32_aligned_m; + shname = "F16_F32_ALIGNED_M"; + } else if (std::is_same() && std::is_same()) { + p = &vk_pipeline_matmul_f16_aligned_m; + shname = "F16_ALIGNED_M"; + } else { + GGML_ASSERT(false); + } + } else if (shader_size == 2) { + if (std::is_same() && std::is_same()) { + p = &vk_pipeline_matmul_f32_aligned_l; + shname = "F32_ALIGNED_L"; + } else if (std::is_same() && std::is_same()) { + p = &vk_pipeline_matmul_f16_f32_aligned_l; + shname = "F16_F32_ALIGNED_L"; + } else if (std::is_same() && std::is_same()) { + p = &vk_pipeline_matmul_f16_aligned_l; + shname = "F16_ALIGNED_L"; + } else { + GGML_ASSERT(false); + } + } else { + GGML_ASSERT(0); + } + + const size_t kpad = ggml_vk_align_size(k, p->align); + + if (k != kpad) { + if (shader_size == 0) { + if (std::is_same() && std::is_same()) { + p = &vk_pipeline_matmul_f32_s; + shname = "F32_S"; + } else if (std::is_same() && std::is_same()) { + p = &vk_pipeline_matmul_f16_f32_s; + shname = "F16_F32_S"; + } else if (std::is_same() && std::is_same()) { + p = &vk_pipeline_matmul_f16_s; + shname = "F16_S"; + } + } else if (shader_size == 1) { + if (std::is_same() && std::is_same()) { + p = &vk_pipeline_matmul_f32_m; + shname = "F32_M"; + } else if (std::is_same() && std::is_same()) { + p = &vk_pipeline_matmul_f16_f32_m; + shname = "F16_F32_M"; + } else if (std::is_same() && std::is_same()) { + p = &vk_pipeline_matmul_f16_m; + shname = "F16_M"; + } + } else if (shader_size == 2) { + if (std::is_same() && std::is_same()) { + p = &vk_pipeline_matmul_f32_l; + shname = "F32_L"; + } else if (std::is_same() && std::is_same()) { + p = &vk_pipeline_matmul_f16_f32_l; + shname = "F16_F32_L"; + } else if (std::is_same() && std::is_same()) { + p = &vk_pipeline_matmul_f16_l; + shname = "F16_L"; + } + } + } + + ggml_vk_pipeline_allocate_descriptor_sets(*p, num_it); + if (split_k > 1) { + ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline_matmul_split_k_reduce, num_it); + + if (vk_prealloc_split_k.size < sizeof(float) * d_ne * split_k) { + // Resize buffer + if (vk_prealloc_split_k.size > 0) { + ggml_vk_destroy_buffer(vk_prealloc_split_k); + } + vk_prealloc_split_k = ggml_vk_create_buffer(sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal); + } + } + + vk_buffer d_X = ggml_vk_create_buffer(sizeof(X_TYPE) * x_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer d_Y = ggml_vk_create_buffer(sizeof(Y_TYPE) * y_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer d_D = ggml_vk_create_buffer(sizeof(float) * d_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); + + X_TYPE* x = (X_TYPE *) malloc(sizeof(X_TYPE) * x_ne); + Y_TYPE* y = (Y_TYPE *) malloc(sizeof(Y_TYPE) * y_ne); + float* d = (float *) malloc(sizeof(float) * d_ne); + + for (size_t i = 0; i < x_ne; i++) { + if (std::is_same()) { + x[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f; + } else if (std::is_same()) { + x[i] = ggml_fp32_to_fp16((rand() / (float)RAND_MAX) * 2.0f - 1.0f); + } else { + GGML_ASSERT(false); + } + } + for (size_t i = 0; i < y_ne; i++) { + if (std::is_same()) { + y[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f; + } else if (std::is_same()) { + y[i] = ggml_fp32_to_fp16((rand() / (float)RAND_MAX) * 2.0f - 1.0f); + } else { + GGML_ASSERT(false); + } + } + + ggml_vk_buffer_write(&d_X, 0, x, sizeof(X_TYPE) * k * m * batch); + ggml_vk_buffer_write(&d_Y, 0, y, sizeof(Y_TYPE) * k * n * batch); + + vk_context * ctx = ggml_vk_create_context(vk_device.compute_queue); + for (size_t i = 0; i < num_it; i++) { + ggml_vk_ctx_begin(ctx); + ggml_vk_matmul(ctx, *p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(vk_prealloc_split_k), m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n); + ggml_vk_ctx_end(ctx); + } + + auto begin = std::chrono::high_resolution_clock::now(); + ggml_vk_submit(ctx, vk_fence); + VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_test_matmul waitForFences"); + vk_device.device.resetFences({ vk_fence }); + + auto end = std::chrono::high_resolution_clock::now(); + double time = std::chrono::duration_cast(end-begin).count() / 1000.0; + + // copy dst to host + ggml_vk_buffer_read(&d_D, 0, d, sizeof(float) * d_ne); + + float * d_chk = (float *) malloc(sizeof(float) * d_ne); + + ggml_init_params iparams = { + /*.mem_size =*/ 1024*1024*1024, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + + ggml_context * ggml_ctx = ggml_init(iparams); + + ggml_type src0_type; + ggml_type src1_type; + + if (std::is_same()) { + src0_type = GGML_TYPE_F32; + } else if (std::is_same()) { + src0_type = GGML_TYPE_F16; + } else { + GGML_ASSERT(false); + } + if (std::is_same()) { + src1_type = GGML_TYPE_F32; + } else if (std::is_same()) { + src1_type = GGML_TYPE_F16; + } else { + GGML_ASSERT(false); + } + + ggml_tensor * src0_ggml = ggml_new_tensor_3d(ggml_ctx, src0_type, k, m, batch); + ggml_tensor * src1_ggml = ggml_new_tensor_3d(ggml_ctx, src1_type, k, n, batch); + ggml_tensor * tensor_ggml = ggml_mul_mat(ggml_ctx, src0_ggml, src1_ggml); + + src0_ggml->data = x; + src1_ggml->data = y; + tensor_ggml->data = d_chk; + + vk_disable = true; + + ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx); + ggml_build_forward_expand(cgraph, tensor_ggml); + + ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1); + + vk_disable = false; + + ggml_free(ggml_ctx); + + double avg_err = 0.0; + int first_err_n = -1; + int first_err_m = -1; + int first_err_b = -1; + + for (size_t i = 0; i < m*n*batch; i++) { + double err = std::fabs(d[i] - d_chk[i]); + avg_err += err; + + if (err > 0.05f && first_err_n == -1) { + first_err_b = i / (m * n); + first_err_n = (i % (m * n)) / m; + first_err_m = (i % (m * n)) % m; + } + } + + avg_err /= m * n; + + std::cerr << "TEST " << shname << " m=" << m << " n=" << n << " k=" << k << " batch=" << batch << " split_k=" << split_k << " matmul " << time / num_it << "ms avg_err=" << avg_err << std::endl; + + if (avg_err > 0.1) { + std::cerr << "m = " << first_err_m << " n = " << first_err_n << " b = " << first_err_b << std::endl; + std::cerr << "Actual result: " << std::endl << std::endl; + ggml_vk_print_matrix_area(d, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + std::cerr << "Expected result: " << std::endl << std::endl; + ggml_vk_print_matrix_area(d_chk, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + if (split_k > 1) { + float * split_k_buf = (float *) malloc(sizeof(float) * d_ne * split_k); + ggml_vk_buffer_read(&vk_prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k); + + std::cerr << "d_buf0: " << std::endl << std::endl; + ggml_vk_print_matrix_area(split_k_buf, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + std::cerr << "d_buf1: " << std::endl << std::endl; + ggml_vk_print_matrix_area(split_k_buf + d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + std::cerr << "d_buf2: " << std::endl << std::endl; + ggml_vk_print_matrix_area(split_k_buf + 2 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + std::cerr << "d_buf3: " << std::endl << std::endl; + ggml_vk_print_matrix_area(split_k_buf + 3 * d_ne, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); + + free(split_k_buf); + } + } + + free(d_chk); + + ggml_vk_queue_cleanup(vk_device.transfer_queue); + ggml_vk_queue_cleanup(vk_device.compute_queue); + + ggml_vk_destroy_buffer(d_X); + ggml_vk_destroy_buffer(d_Y); + ggml_vk_destroy_buffer(d_D); + + ggml_vk_pipeline_cleanup(*p); + ggml_vk_pipeline_cleanup(vk_pipeline_matmul_split_k_reduce); + + free(x); + free(y); + free(d); +} + +static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, int i0, int i1, int i2, int i3) { + if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) { + return; + } + fprintf(stderr, " "); + for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) { + fprintf(stderr, "%7d ", idx1); + } + fprintf(stderr, "\n"); + for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) { + fprintf(stderr, "%7d: ", idx0); + for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) { + if (idx0 >= 0 && idx0 < tensor->ne[0] && idx1 >= 0 && idx1 < tensor->ne[1] && i2 >= 0 && i2 < tensor->ne[2] && i3 >= 0 && i3 < tensor->ne[3]) { + float val; + if (tensor->type == GGML_TYPE_F32) { + val = *(float *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]); + } else if (tensor->type == GGML_TYPE_F16) { + val = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0])); + } + fprintf(stderr, "% 7.2f ", val); + } else { + fprintf(stderr, " "); + } + } + fprintf(stderr, "\n"); + } +} + +static void ggml_vk_test_h2d_nc(size_t ne0, size_t ne1, size_t ne2, size_t ne3) { + const size_t ne = ne0 * ne1 * ne2 * ne3; + + ggml_init_params iparams = { + /*.mem_size =*/ 1024*1024*1024, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + + ggml_context * ggml_ctx = ggml_init(iparams); + + ggml_tensor * tensor = ggml_new_tensor_4d(ggml_ctx, GGML_TYPE_F32, ne0, ne2, ne1, ne3); // NOLINT + ggml_tensor * result_tensor = ggml_new_tensor_4d(ggml_ctx, GGML_TYPE_F32, ne0, ne1, ne2, ne3); + + float * data = (float *) ggml_vk_host_malloc(ggml_nbytes(tensor)); + tensor->data = data; + + float * result_data = (float *) malloc(ggml_nbytes(tensor)); + result_tensor->data = result_data; + + // Permute + { + size_t tmp = tensor->nb[2]; + tensor->nb[2] = tensor->nb[1]; + tensor->nb[1] = tmp; + + tensor->ne[2] = ne2; + tensor->ne[1] = ne1; + } + + for (size_t i = 0; i < ne; i++) { + data[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f; + } + + vk_context * ctx = ggml_vk_create_context(vk_device.compute_queue); + ggml_vk_ctx_begin(ctx); + + vk_buffer buffer = ggml_vk_create_buffer(ggml_nbytes(tensor), vk::MemoryPropertyFlagBits::eDeviceLocal); + + ggml_vk_h2d_tensor_2d(ctx, &buffer, 0, tensor, 0, 0, ggml_nrows(tensor)); + + ggml_vk_ctx_end(ctx); + ggml_vk_submit(ctx, vk_fence); + VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); + vk_device.device.resetFences({ vk_fence }); + + ggml_vk_buffer_read(&buffer, 0, result_data, ggml_nbytes(tensor)); + + double avg_err = 0.0; + int first_err_i0 = -1; + int first_err_i1 = -1; + int first_err_i2 = -1; + int first_err_i3 = -1; + + for (size_t i3 = 0; i3 < ne3; i3++) { + for (size_t i2 = 0; i2 < ne2; i2++) { + for (size_t i1 = 0; i1 < ne1; i1++) { + for (size_t i0 = 0; i0 < ne0; i0++) { + float correct = *(float *) ((char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]); + float result = *(float *) ((char *) result_data + i3*ne2*ne1*ne0*sizeof(float) + i2*ne1*ne0*sizeof(float) + i1*ne0*sizeof(float) + i0*sizeof(float)); + double err = std::fabs(result - correct); + + avg_err += err; + + if (err > 0.05f && first_err_i0 == -1) { + first_err_i0 = i0; + first_err_i1 = i1; + first_err_i2 = i2; + first_err_i3 = i3; + } + } + } + } + } + + avg_err /= ne; + + std::cerr << "TEST nc copy ne0=" << ne0 << " ne1=" << ne1 << " ne2=" << ne2 << " ne3=" << ne3 << " avg_err=" << avg_err << std::endl; + + if (avg_err > 0.1) { + std::cerr << "i0 = " << first_err_i0 << " i1 = " << first_err_i1 << " i2 = " << first_err_i2 << " i3 = " << first_err_i3 << std::endl; + std::cerr << "Actual result: " << std::endl << std::endl; + ggml_vk_print_tensor_area(result_tensor, first_err_i0, first_err_i1, first_err_i2, first_err_i3); + std::cerr << "Expected result: " << std::endl << std::endl; + ggml_vk_print_tensor_area(tensor, first_err_i0, first_err_i1, first_err_i2, first_err_i3); + } + + ggml_free(ggml_ctx); + + ggml_vk_destroy_buffer(buffer); + + ggml_vk_host_free(data); + free(result_data); +} + +static void ggml_vk_test_transfer(size_t ne, bool pinned) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_test_transfer(" << ne << ")" << std::endl; +#endif + // Check transfers are correct + vk_buffer buffer = ggml_vk_create_buffer(sizeof(float) * ne, vk::MemoryPropertyFlagBits::eDeviceLocal); + + float * x; + float * y; + if (pinned) { + x = (float *) ggml_vk_host_malloc(sizeof(float) * ne); + y = (float *) ggml_vk_host_malloc(sizeof(float) * ne); + } else { + x = (float *) malloc(sizeof(float) * ne); + y = (float *) malloc(sizeof(float) * ne); + } + + for (size_t i = 0; i < ne; i++) { + x[i] = rand() / (float)RAND_MAX; + } + + vk_context * ctx = ggml_vk_create_context(vk_device.compute_queue); + ggml_vk_ctx_begin(ctx); + + auto begin = std::chrono::high_resolution_clock::now(); + + ggml_vk_buffer_write_async(ctx, &buffer, 0, x, sizeof(float) * ne); + + for (auto& cpy : ctx->in_memcpys) { + memcpy(cpy.dst, cpy.src, cpy.n); + } + ctx->in_memcpys.clear(); + + ggml_vk_ctx_end(ctx); + ggml_vk_submit(ctx, vk_fence); + VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); + vk_device.device.resetFences({ vk_fence }); + + auto end = std::chrono::high_resolution_clock::now(); + + double ms_to_gpu = std::chrono::duration_cast(end-begin).count() / 1000.0; + + ggml_vk_ctx_begin(ctx); + + begin = std::chrono::high_resolution_clock::now(); + + ggml_vk_buffer_read_async(ctx, &buffer, 0, y, sizeof(float) * ne); + + ggml_vk_ctx_end(ctx); + ggml_vk_submit(ctx, vk_fence); + VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); + vk_device.device.resetFences({ vk_fence }); + + for (auto& cpy : ctx->out_memcpys) { + memcpy(cpy.dst, cpy.src, cpy.n); + } + ctx->out_memcpys.clear(); + + end = std::chrono::high_resolution_clock::now(); + + double ms_from_gpu = std::chrono::duration_cast(end-begin).count() / 1000.0; + + double avg_err = 0.0; + for (size_t i = 0; i < ne; i++) { + avg_err += std::fabs(x[i] - y[i]); + } + + double kb = ne * sizeof(float) / 1024.0; + + std::cerr << "TEST TRANSFER " << kb << " KB to_gpu " << ms_to_gpu << "ms (" << kb / ms_to_gpu * 1000.0 / 1024.0 << " MB/s) from_gpu " << ms_from_gpu << "ms (" << kb / ms_from_gpu * 1000.0 / 1024.0 << " MB/s) avg_err=" << avg_err / ne << std::endl; + + ggml_vk_destroy_buffer(buffer); + + if (pinned) { + ggml_vk_host_free(x); + ggml_vk_host_free(y); + } else { + free(x); + free(y); + } +} +#endif + +static ggml_tensor_extra_gpu * ggml_vk_tensor_create_extra(ggml_tensor * tensor) { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_create_extra(" << tensor << " (" << tensor->name << ", " << ggml_op_name(tensor->op) << "))" << std::endl; +#endif + ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu; + extra->reset(); + tensor->extra = extra; + return extra; +} + +static ggml_tensor * ggml_vk_find_last_use(const ggml_tensor * node, ggml_cgraph * graph) { + GGML_ASSERT(node != nullptr); + + for (int i = graph->n_nodes - 1; i >= 0; i--) { + for (int j = 0; j < GGML_MAX_SRC; j++) { + if (graph->nodes[i]->src[j] == node) { + return graph->nodes[i]; + } + } + } + + return nullptr; +} + +void ggml_vk_preallocate_buffers_graph(ggml_tensor * node){ +#ifdef VK_DEBUG + std::cerr << "ggml_vk_preallocate_buffers_graph(" << node << ")" << std::endl; +#endif + const bool any_on_device = node->backend == GGML_BACKEND_GPU + || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_GPU || node->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) + || (node->src[1] != nullptr && (node->src[1]->backend == GGML_BACKEND_GPU)); + + if (vk_disable || (!any_on_device && node->op != GGML_OP_MUL_MAT)) { + return; + } + + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra; + if (extra == nullptr) { + // Workaround for CPU backend BLAS matmul calls + extra = ggml_vk_tensor_create_extra(node); + } + + ggml_tensor * src0 = node->src[0]; + ggml_tensor * src1 = node->src[1]; + + const bool use_src0 = src0 != nullptr; + const int64_t ne00 = use_src0 ? src0->ne[0] : 0; + const int64_t ne01 = use_src0 ? src0->ne[1] : 0; + const int64_t ne02 = use_src0 ? src0->ne[2] : 0; + const int64_t ne03 = use_src0 ? src0->ne[3] : 0; + const bool use_src1 = src1 != nullptr && node->op != GGML_OP_CPY && node->op != GGML_OP_CONT && node->op != GGML_OP_DUP; + const int64_t ne10 = use_src1 ? src1->ne[0] : 0; + const int64_t ne11 = use_src1 ? src1->ne[1] : 0; + const int64_t ne12 = use_src1 ? src1->ne[2] : 0; + const int64_t ne13 = use_src1 ? src1->ne[3] : 0; + const int64_t ne20 = node->ne[0]; + const int64_t ne21 = node->ne[1]; + const int64_t ne22 = node->ne[2]; + const int64_t ne23 = node->ne[3]; + + const bool f16_f32_kernel = use_src1 && src1->type == GGML_TYPE_F32; + + int split_k; + if (node->op == GGML_OP_MUL_MAT) { + split_k = ggml_vk_guess_split_k(ne01, ne11, ne10); + } else { + split_k = 1; + } + const uint32_t x_ne = ne00 * ne01; + const uint32_t y_ne = ne10 * ne11; + const uint32_t d_ne = ne20 * ne21; + + const uint64_t qx_sz = use_src0 ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), vk_device.properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0; + const uint64_t qy_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type), vk_device.properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0; + const uint64_t x_sz = use_src0 ? ggml_vk_align_size(sizeof(ggml_fp16_t) * x_ne, vk_device.properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0; + const uint64_t y_sz = use_src1 ? ggml_vk_align_size(f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne, vk_device.properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0; + uint64_t d_sz = ggml_vk_align_size(ggml_type_size(node->type) * d_ne, vk_device.properties.limits.minStorageBufferOffsetAlignment) * ne22 * ne23; + const uint64_t split_k_size = split_k > 1 ? d_sz * 4 : 0; + + if (extra->buffer_gpu.size == 0) { + // Workaround for CPU backend BLAS matmul calls + extra->buffer_gpu = ggml_vk_create_buffer_temp(d_sz); + } + + switch (node->op) { + case GGML_OP_REPEAT: + case GGML_OP_GET_ROWS: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + case GGML_OP_ADD: + case GGML_OP_SCALE: + case GGML_OP_SQR: + case GGML_OP_CLAMP: + case GGML_OP_CPY: + case GGML_OP_CONT: + case GGML_OP_DUP: + case GGML_OP_MUL: + case GGML_OP_NORM: + case GGML_OP_RMS_NORM: + case GGML_OP_DIAG_MASK_INF: + case GGML_OP_SOFT_MAX: + case GGML_OP_ROPE: + break; + case GGML_OP_UNARY: + switch (ggml_get_unary_op(node)) { + case GGML_UNARY_OP_SILU: + case GGML_UNARY_OP_GELU: + case GGML_UNARY_OP_RELU: + break; + default: + return; + } + break; + case GGML_OP_MUL_MAT: + if (vk_prealloc_size_qx < qx_sz) { + vk_prealloc_size_qx = qx_sz; + } + if (vk_prealloc_size_qy < qy_sz) { + vk_prealloc_size_qy = qy_sz; + } + if (vk_prealloc_size_x < x_sz) { + vk_prealloc_size_x = x_sz; + } + if (vk_prealloc_size_y < y_sz) { + vk_prealloc_size_y = y_sz; + } + if (vk_prealloc_size_split_k < split_k_size) { + vk_prealloc_size_split_k = split_k_size; + } + if (vk_staging_size < x_sz + y_sz) { + vk_staging_size = x_sz + y_sz; + } + break; + default: + return; + } +} + +void ggml_vk_preallocate_buffers() { + if (vk_disable) { + return; + } +#ifdef VK_DEBUG + std::cerr << "ggml_vk_preallocate_buffers()" << std::endl; + std::cerr << "qx_size: " << vk_prealloc_size_qx << " qy_size: " << vk_prealloc_size_qy << " x_size: " << vk_prealloc_size_x << " y_size: " << vk_prealloc_size_y << " split_k_size: " << vk_prealloc_size_split_k << std::endl; +#endif +#if defined(VK_RUN_TESTS) + vk_staging = ggml_vk_create_buffer(100ul * 1024ul * 1024ul, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); + ggml_vk_test_transfer(8192 * 1000, false); + ggml_vk_test_transfer(8192 * 1000, true); + + const std::vector vals { + 8, 8, 8, + 100, 46, 576, + 623, 111, 128, + 100, 46, 558, + 512, 1, 256, + 128, 110, 622, + 511, 511, 127, + 511, 511, 7, + 511, 511, 17, + 49, 49, 128, + 128, 49, 49, + 4096, 49, 4096, + 11008, 49, 4096, + 4096, 49, 11008, + 32000, 49, 4096, + 512, 512, 128, + 128, 512, 512, + 4096, 512, 4096, + 11008, 512, 4096, + 4096, 512, 11008, + 32000, 512, 4096, + }; + const size_t num_it = 1; + for (size_t i = 0; i < vals.size(); i += 3) { + ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 0); + ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 1); + ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 2); + ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 0); + ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 1); + ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 2); + std::cerr << std::endl; + } + + GGML_ASSERT(false); +#endif + + if (vk_prealloc_size_qx > 0 && vk_prealloc_qx.size < vk_prealloc_size_qx) { + // Resize buffer + if (vk_prealloc_qx.size > 0) { + ggml_vk_destroy_buffer(vk_prealloc_qx); + } + vk_prealloc_qx = ggml_vk_create_buffer(vk_prealloc_size_qx, vk::MemoryPropertyFlagBits::eDeviceLocal); + } + if (vk_prealloc_size_qy > 0 && vk_prealloc_qy.size < vk_prealloc_size_qy) { + // Resize buffer + if (vk_prealloc_qy.size > 0) { + ggml_vk_destroy_buffer(vk_prealloc_qy); + } + vk_prealloc_qy = ggml_vk_create_buffer(vk_prealloc_size_qy, vk::MemoryPropertyFlagBits::eDeviceLocal); + } + if (vk_prealloc_size_x > 0 && vk_prealloc_x.size < vk_prealloc_size_x) { + // Resize buffer + if (vk_prealloc_x.size > 0) { + ggml_vk_destroy_buffer(vk_prealloc_x); + } + vk_prealloc_x = ggml_vk_create_buffer(vk_prealloc_size_x, vk::MemoryPropertyFlagBits::eDeviceLocal); + } + if (vk_prealloc_size_y > 0 && vk_prealloc_y.size < vk_prealloc_size_y) { + // Resize buffer + if (vk_prealloc_y.size > 0) { + ggml_vk_destroy_buffer(vk_prealloc_y); + } + vk_prealloc_y = ggml_vk_create_buffer(vk_prealloc_size_y, vk::MemoryPropertyFlagBits::eDeviceLocal); + } + if (vk_prealloc_size_split_k > 0 && vk_prealloc_split_k.size < vk_prealloc_size_split_k) { + // Resize buffer + if (vk_prealloc_split_k.size > 0) { + ggml_vk_destroy_buffer(vk_prealloc_split_k); + } + vk_prealloc_split_k = ggml_vk_create_buffer(vk_prealloc_size_split_k, vk::MemoryPropertyFlagBits::eDeviceLocal); + } + if (vk_staging_size > 0 && vk_staging.size < vk_staging_size) { + // Resize buffer + if (vk_staging.size > 0) { + ggml_vk_destroy_buffer(vk_staging); + } + vk_staging = ggml_vk_create_buffer(vk_staging_size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); + } +} + +void ggml_vk_build_graph(ggml_tensor * node, bool last_node){ + const bool any_on_device = node->backend == GGML_BACKEND_GPU + || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_GPU || node->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) + || (node->src[1] != nullptr && node->src[1]->backend == GGML_BACKEND_GPU); + + if (vk_disable || (!any_on_device && node->op != GGML_OP_MUL_MAT) || (node->op == GGML_OP_MUL_MAT && !any_on_device && !ggml_vk_can_mul_mat(node->src[0], node->src[1], node))) { + return; + } + +#ifdef VK_DEBUG + std::cerr << "ggml_vk_build_graph(" << node << ", " << ggml_op_name(node->op) << ")" << std::endl; +#endif + vk_semaphore_idx = 0; + vk_staging_offset = 0; + + const ggml_tensor * src0 = node->src[0]; + const ggml_tensor * src1 = node->src[1]; + + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra; + + switch (node->op) { + case GGML_OP_UNARY: + switch (ggml_get_unary_op(node)) { + case GGML_UNARY_OP_SILU: + case GGML_UNARY_OP_GELU: + case GGML_UNARY_OP_RELU: + break; + default: + return; + } + break; + case GGML_OP_REPEAT: + // case GGML_OP_GET_ROWS: + case GGML_OP_ADD: + case GGML_OP_MUL: + case GGML_OP_SCALE: + case GGML_OP_SQR: + case GGML_OP_CLAMP: + case GGML_OP_CPY: + case GGML_OP_CONT: + case GGML_OP_DUP: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + case GGML_OP_NORM: + case GGML_OP_RMS_NORM: + case GGML_OP_DIAG_MASK_INF: + case GGML_OP_SOFT_MAX: + case GGML_OP_ROPE: + case GGML_OP_MUL_MAT: + case GGML_OP_NONE: + break; + default: + if (any_on_device) { + std::cerr << "ggml_vulkan: Error: Missing op: " << ggml_op_name(node->op) << std::endl; + GGML_ASSERT(false); + } + return; + } + + if (vk_ctx == nullptr) { + vk_ctx = ggml_vk_create_context(vk_device.compute_queue); + ggml_vk_ctx_begin(vk_ctx); + } + + switch (node->op) { + case GGML_OP_REPEAT: + ggml_vk_repeat(vk_ctx, src0, src1, node); + + break; + case GGML_OP_GET_ROWS: + ggml_vk_get_rows(vk_ctx, src0, src1, node); + + break; + case GGML_OP_ADD: + ggml_vk_add(vk_ctx, src0, src1, node); + + break; + case GGML_OP_MUL: + ggml_vk_mul(vk_ctx, src0, src1, node); + + break; + case GGML_OP_SCALE: + ggml_vk_scale(vk_ctx, src0, node); + + break; + case GGML_OP_SQR: + ggml_vk_sqr(vk_ctx, src0, node); + + break; + case GGML_OP_CLAMP: + ggml_vk_clamp(vk_ctx, src0, node); + + break; + case GGML_OP_CPY: + case GGML_OP_CONT: + case GGML_OP_DUP: + ggml_vk_cpy(vk_ctx, src0, node); + + break; + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + case GGML_OP_NONE: + ggml_vk_nop(vk_ctx, src0, node); + + break; + case GGML_OP_NORM: + ggml_vk_norm(vk_ctx, src0, node); + + break; + case GGML_OP_RMS_NORM: + ggml_vk_rms_norm(vk_ctx, src0, node); + + break; + case GGML_OP_UNARY: + switch (ggml_get_unary_op(node)) { + case GGML_UNARY_OP_SILU: + case GGML_UNARY_OP_GELU: + case GGML_UNARY_OP_RELU: + ggml_vk_unary(vk_ctx, src0, node); + break; + default: + return; + } + break; + case GGML_OP_DIAG_MASK_INF: + ggml_vk_diag_mask_inf(vk_ctx, src0, node); + + break; + case GGML_OP_SOFT_MAX: + ggml_vk_soft_max(vk_ctx, src0, src1, node); + + break; + case GGML_OP_ROPE: + ggml_vk_rope(vk_ctx, src0, src1, node); + + break; + case GGML_OP_MUL_MAT: + ggml_vk_mul_mat(vk_ctx, src0, src1, node); + + break; + default: + return; + } + + extra->ready = true; + extra->ctx_idx = vk_ctx->idx; + +#ifdef GGML_VULKAN_CHECK_RESULTS + // Force context reset on each node so that each tensor ends up in its own context + // and can be run and compared to its CPU equivalent separately + last_node = true; +#endif + + if (node->backend == GGML_BACKEND_CPU || last_node) { + ggml_vk_ctx_end(vk_ctx); + vk_ctx->exit_tensor = node; + vk_ctx = nullptr; + } +} + +bool ggml_vk_compute_forward(ggml_compute_params * params, ggml_tensor * tensor){ + const bool any_on_device = tensor->backend == GGML_BACKEND_GPU + || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) + || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU); + + if (vk_disable || (!any_on_device && tensor->op != GGML_OP_MUL_MAT)) { + return false; + } + + ggml_tensor_extra_gpu * extra = nullptr; + + switch (tensor->op) { + case GGML_OP_ADD: + case GGML_OP_GET_ROWS: + case GGML_OP_MUL: + case GGML_OP_SCALE: + case GGML_OP_SQR: + case GGML_OP_CLAMP: + case GGML_OP_CPY: + case GGML_OP_CONT: + case GGML_OP_DUP: + case GGML_OP_NORM: + case GGML_OP_RMS_NORM: + case GGML_OP_DIAG_MASK_INF: + case GGML_OP_SOFT_MAX: + case GGML_OP_ROPE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + case GGML_OP_NONE: + extra = (ggml_tensor_extra_gpu *) tensor->extra; + + break; + case GGML_OP_UNARY: + switch (ggml_get_unary_op(tensor)) { + case GGML_UNARY_OP_SILU: + case GGML_UNARY_OP_GELU: + case GGML_UNARY_OP_RELU: + extra = (ggml_tensor_extra_gpu *) tensor->extra; + break; + default: + return false; + } + break; + case GGML_OP_MUL_MAT: + if (!any_on_device && !ggml_vk_can_mul_mat(tensor->src[0], tensor->src[1], tensor)) { + return false; + } + + extra = (ggml_tensor_extra_gpu *) tensor->extra; + + break; + default: + return false; + } + + if (extra == nullptr) { + return false; + } + + if (params->ith != 0) { + return true; + } + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { + return true; + } + +#ifdef VK_DEBUG + std::cerr << "ggml_vk_compute_forward(" << tensor << ", name=" << tensor->name << ", op=" << ggml_op_name(tensor->op) << ", type=" << tensor->type << ", backend=" << tensor->backend << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << ", view_src=" << tensor->view_src << ", view_offs=" << tensor->view_offs << ")" << std::endl; +#endif + +#ifdef GGML_VULKAN_CHECK_RESULTS + ggml_vk_check_results_0(params, tensor); +#endif + + GGML_ASSERT(extra->ready); + + vk_context& ctx = vk_gc.contexts[extra->ctx_idx]; + + // Only run if ctx hasn't been submitted yet + if (!ctx.seqs.empty()) { + // Do staging buffer copies + for (auto& cpy : ctx.in_memcpys) { + memcpy(cpy.dst, cpy.src, cpy.n); + } + + ggml_vk_submit(&ctx, vk_fence); + } + + if (tensor == ctx.exit_tensor) { + VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); + vk_device.device.resetFences({ vk_fence }); + + // Do staging buffer copies + for (auto& cpy : ctx.out_memcpys) { + memcpy(cpy.dst, cpy.src, cpy.n); + } + ctx.in_memcpys.clear(); + ctx.out_memcpys.clear(); + } + + extra->ready = false; + + return true; +} + +void ggml_vk_graph_cleanup() { + if (vk_disable) { + return; + } +#ifdef VK_DEBUG + std::cerr << "ggml_vk_graph_cleanup()" << std::endl; +#endif + for (auto& buffer : vk_gc.temp_buffers) { + ggml_vk_pool_free(buffer); + } + vk_gc.temp_buffers.clear(); + + for (auto * pipeline : vk_gc.pipelines) { + ggml_vk_pipeline_cleanup(*pipeline); + } + vk_gc.pipelines.clear(); + + ggml_vk_queue_cleanup(vk_device.compute_queue); + ggml_vk_queue_cleanup(vk_device.transfer_queue); + + for (size_t i = 0; i < vk_gc.semaphores.size(); i++) { + vk_device.device.destroySemaphore({ vk_gc.semaphores[i].s }); + } + vk_gc.semaphores.clear(); + + for (size_t i = 0; i < vk_gc.tl_semaphores.size(); i++) { + vk_device.device.destroySemaphore({ vk_gc.tl_semaphores[i].s }); + } + vk_gc.tl_semaphores.clear(); + + vk_event_idx = 0; + + for (auto& event : vk_gc.events) { + vk_device.device.resetEvent(event); + } + + vk_staging_offset = 0; + + vk_ctx = nullptr; + vk_gc.contexts.clear(); +} + +static void ggml_vk_cleanup() { +#ifdef VK_DEBUG + std::cerr << "ggml_vk_cleanup()" << std::endl; +#endif + ggml_vk_destroy_buffer(vk_prealloc_x); + ggml_vk_destroy_buffer(vk_prealloc_y); + ggml_vk_destroy_buffer(vk_prealloc_split_k); + ggml_vk_destroy_buffer(vk_staging); + ggml_vk_destroy_buffer(vk_sync_staging); + + vk_prealloc_size_x = 0; + vk_prealloc_size_y = 0; + vk_prealloc_size_split_k = 0; + vk_staging_size = 0; + + for (auto& event : vk_gc.events) { + vk_device.device.destroyEvent(event); + } + vk_gc.events.clear(); +} + +// backend interface + +#define UNUSED GGML_UNUSED + +struct ggml_backend_vk_context { + std::string name; +}; + +// device backend + +static void * const vk_ptr_base = (void *)(uintptr_t) 0x1000; // NOLINT + +struct ggml_backend_vk_buffer_context { + vk_buffer dev_buffer; + ggml_tensor_extra_gpu * temp_tensor_extras = nullptr; + size_t temp_tensor_extra_index = 0; + std::string name; + + ggml_backend_vk_buffer_context(vk_buffer dev_buffer) : + dev_buffer(dev_buffer), + name(GGML_VK_NAME) { + } + + ~ggml_backend_vk_buffer_context() { + ggml_vk_destroy_buffer(dev_buffer); + delete[] temp_tensor_extras; + } + + ggml_tensor_extra_gpu * ggml_vk_alloc_temp_tensor_extra() { + if (temp_tensor_extras == nullptr) { + temp_tensor_extras = new ggml_tensor_extra_gpu[GGML_VK_MAX_NODES]; + } + + size_t alloc_index = temp_tensor_extra_index; + temp_tensor_extra_index = (temp_tensor_extra_index + 1) % GGML_VK_MAX_NODES; + ggml_tensor_extra_gpu * extra = &temp_tensor_extras[alloc_index]; + extra->reset(); + + return extra; + } +}; + +GGML_CALL static const char * ggml_backend_vk_buffer_get_name(ggml_backend_buffer_t buffer) { + ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; + return ctx->name.c_str(); +} + +GGML_CALL static bool ggml_backend_buffer_is_vk(ggml_backend_buffer_t buffer) { + return buffer->iface.get_name == ggml_backend_vk_buffer_get_name; +} + +GGML_CALL static void ggml_backend_vk_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; + delete ctx; +} + +GGML_CALL static void * ggml_backend_vk_buffer_get_base(ggml_backend_buffer_t buffer) { + return vk_ptr_base; + + UNUSED(buffer); +} + +GGML_CALL static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { +#ifdef VK_DEBUG + std::cerr << "ggml_backend_vk_buffer_init_tensor(" << buffer << " (" << buffer->context << "), " << tensor << ")" << std::endl; +#endif + ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; + + ggml_tensor_extra_gpu * extra = ctx->ggml_vk_alloc_temp_tensor_extra(); + if (tensor->view_src != nullptr && tensor->view_src->extra != nullptr) { + ggml_tensor_extra_gpu * extra_view = (ggml_tensor_extra_gpu *) tensor->view_src->extra; + extra->buffer_gpu = extra_view->buffer_gpu; + extra->offset = extra_view->offset + tensor->view_offs; + } else { + extra->buffer_gpu = ctx->dev_buffer; + extra->offset = (uint8_t *) tensor->data - (uint8_t *) vk_ptr_base; + } + + if (extra->offset + ggml_nbytes(tensor) > extra->buffer_gpu.size) { + std::cerr << "ERROR: Trying to assign tensor " << tensor << " outside of buffer size " << ctx->dev_buffer.size << " requested offset: " << extra->offset << " tensor size: " << ggml_nbytes(tensor) << std::endl; + if (tensor->view_src != nullptr) { + std::cerr << "view_src: " << tensor->view_src << " extra: " << tensor->view_src->extra << std::endl; + } + GGML_ASSERT(false); + } + + tensor->backend = GGML_BACKEND_GPU; + tensor->extra = extra; +} + +GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { +#ifdef VK_DEBUG + std::cerr << "ggml_backend_vk_buffer_set_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl; +#endif + GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; + + ggml_vk_buffer_write(&extra->buffer_gpu, extra->offset + offset, data, size); + + UNUSED(buffer); +} + +GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { +#ifdef VK_DEBUG + std::cerr << "ggml_backend_vk_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl; +#endif + GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; + + ggml_vk_buffer_read(&extra->buffer_gpu, extra->offset + offset, data, size); + + UNUSED(buffer); +} + +GGML_CALL static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { + if (ggml_backend_buffer_is_vk(src->buffer)) { + ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra; + ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; + + ggml_vk_buffer_copy(&src_extra->buffer_gpu, src_extra->offset, &dst_extra->buffer_gpu, dst_extra->offset, ggml_nbytes(src)); + + return true; + } + return false; + + UNUSED(buffer); +} + +GGML_CALL static void ggml_backend_vk_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; + + ggml_vk_buffer_memset(&ctx->dev_buffer, 0, value, buffer->size); +} + +static ggml_backend_buffer_i ggml_backend_vk_buffer_interface = { + /* .get_name = */ ggml_backend_vk_buffer_get_name, + /* .free_buffer = */ ggml_backend_vk_buffer_free_buffer, + /* .get_base = */ ggml_backend_vk_buffer_get_base, + /* .init_tensor = */ ggml_backend_vk_buffer_init_tensor, + /* .set_tensor = */ ggml_backend_vk_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_vk_buffer_get_tensor, + /* .cpy_tensor = */ ggml_backend_vk_buffer_cpy_tensor, + /* .clear = */ ggml_backend_vk_buffer_clear, + /* .reset = */ NULL, +}; + +// vk buffer type +struct ggml_backend_vk_buffer_type_context { + std::string name; +}; + +GGML_CALL static const char * ggml_backend_vk_buffer_type_name(ggml_backend_buffer_type_t buft) { + ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *)buft->context; + + return ctx->name.c_str(); +} + +GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { +#ifdef VK_DEBUG + std::cerr << "ggml_backend_vk_buffer_type_alloc_buffer(" << size << ")" << std::endl; +#endif + vk_buffer dev_buffer = ggml_vk_create_buffer(size, vk::MemoryPropertyFlagBits::eDeviceLocal); + + ggml_backend_vk_buffer_context * ctx = new ggml_backend_vk_buffer_context(dev_buffer); + + return ggml_backend_buffer_init(buft, ggml_backend_vk_buffer_interface, ctx, size); + + UNUSED(buft); +} + +GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + return vk_device.properties.limits.minStorageBufferOffsetAlignment; + + UNUSED(buft); +} + +GGML_CALL static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { + return vk_device.max_memory_allocation_size; + + UNUSED(buft); +} + +GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { + return ggml_nbytes(tensor); + + UNUSED(buft); +} + +GGML_CALL static bool ggml_backend_vk_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { + return ggml_backend_is_vk(backend); + + UNUSED(buft); +} + +static ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface = { + /* .get_name = */ ggml_backend_vk_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_vk_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_vk_buffer_type_get_alignment, + /* .get_max_size = */ ggml_backend_vk_buffer_type_get_max_size, + /* .get_alloc_size = */ ggml_backend_vk_buffer_type_get_alloc_size, + /* .supports_backend = */ ggml_backend_vk_buffer_type_supports_backend, + /* .is_host = */ NULL, +}; + +GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type() { + static ggml_backend_buffer_type ggml_backend_vk_buffer_type; + + static bool ggml_backend_vk_buffer_type_initialized = false; + + if (!ggml_backend_vk_buffer_type_initialized) { + ggml_backend_vk_buffer_type = { + /* .iface = */ ggml_backend_vk_buffer_type_interface, + /* .context = */ new ggml_backend_vk_buffer_type_context{GGML_VK_NAME}, + }; + ggml_backend_vk_buffer_type_initialized = true; + } + + return &ggml_backend_vk_buffer_type; +} + +// host buffer type + +GGML_CALL static const char * ggml_backend_vk_host_buffer_type_name(ggml_backend_buffer_type_t buft) { + return GGML_VK_NAME "_Host"; + + UNUSED(buft); +} + +GGML_CALL static const char * ggml_backend_vk_host_buffer_name(ggml_backend_buffer_t buffer) { + return GGML_VK_NAME "_Host"; + + UNUSED(buffer); +} + +GGML_CALL static void ggml_backend_vk_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { + ggml_vk_host_free(buffer->context); +} + +GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + void * ptr = ggml_vk_host_malloc(size); + + if (ptr == nullptr) { + // fallback to cpu buffer + return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); + } + + ggml_backend_buffer_t buffer = ggml_backend_cpu_buffer_from_ptr(ptr, size); + buffer->buft = buft; + buffer->iface.get_name = ggml_backend_vk_host_buffer_name; + buffer->iface.free_buffer = ggml_backend_vk_host_buffer_free_buffer; + + return buffer; +} + +GGML_CALL static size_t ggml_backend_vk_host_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + return vk_device.properties.limits.minMemoryMapAlignment; + + UNUSED(buft); +} + +GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type() { + static struct ggml_backend_buffer_type ggml_backend_vk_buffer_type_host = { + /* .iface = */ { + /* .get_name = */ ggml_backend_vk_host_buffer_type_name, + /* .alloc_buffer = */ ggml_backend_vk_host_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_vk_host_buffer_type_get_alignment, + /* .get_max_size = */ NULL, // defaults to SIZE_MAX + /* .get_alloc_size = */ ggml_backend_cpu_buffer_type()->iface.get_alloc_size, + /* .supports_backend = */ ggml_backend_cpu_buffer_type()->iface.supports_backend, + /* .is_host = */ ggml_backend_cpu_buffer_type()->iface.is_host, + }, + /* .context = */ nullptr, + }; + + return &ggml_backend_vk_buffer_type_host; +} + +// backend + +GGML_CALL static const char * ggml_backend_vk_name(ggml_backend_t backend) { + ggml_backend_vk_context * vk_ctx = (ggml_backend_vk_context *)backend->context; + + return vk_ctx->name.c_str(); +} + +GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend) { + ggml_backend_vk_context * vk_ctx = (ggml_backend_vk_context *)backend->context; + + delete vk_ctx; + delete backend; +} + +GGML_CALL static ggml_backend_buffer_type_t ggml_backend_vk_get_default_buffer_type(ggml_backend_t backend) { + return ggml_backend_vk_buffer_type(); + + UNUSED(backend); +} + +GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { +#ifdef VK_DEBUG + std::cerr << "ggml_backend_vk_set_tensor_async(" << size << ")" << std::endl; +#endif + GGML_ASSERT(tensor->buffer->buft == ggml_backend_vk_buffer_type() && "unsupported buffer type"); + GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; + + if (vk_ctx == nullptr) { + // Initialize new transfer context + vk_ctx = ggml_vk_create_context(vk_device.transfer_queue); + ggml_vk_ctx_begin(vk_ctx); + } + + ggml_vk_buffer_write_async(vk_ctx, &extra->buffer_gpu, extra->offset + offset, data, size); + + UNUSED(backend); +} + +GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { +#ifdef VK_DEBUG + std::cerr << "ggml_backend_vk_get_tensor_async(" << size << ")" << std::endl; +#endif + GGML_ASSERT(tensor->buffer->buft == ggml_backend_vk_buffer_type() && "unsupported buffer type"); + GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; + + if (vk_ctx == nullptr) { + // Initialize new transfer context + vk_ctx = ggml_vk_create_context(vk_device.transfer_queue); + ggml_vk_ctx_begin(vk_ctx); + } + + ggml_vk_buffer_read_async(vk_ctx, &extra->buffer_gpu, extra->offset + offset, data, size); + + UNUSED(backend); +} + +GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) { +#ifdef VK_DEBUG + std::cerr << "ggml_backend_vk_cpy_tensor_async()" << std::endl; +#endif + if (dst->buffer->buft == ggml_backend_vk_buffer_type() && ggml_backend_buffer_is_vk(src->buffer)) { + ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra; + ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; + + if (vk_ctx == nullptr) { + // Initialize new transfer context + vk_ctx = ggml_vk_create_context(vk_device.transfer_queue); + ggml_vk_ctx_begin(vk_ctx); + } + + ggml_vk_buffer_copy_async(vk_ctx, &src_extra->buffer_gpu, src_extra->offset, &dst_extra->buffer_gpu, dst_extra->offset, ggml_nbytes(src)); + return true; + } + + return false; + + UNUSED(backend); +} + +GGML_CALL static void ggml_backend_vk_synchronize(ggml_backend_t backend) { +#ifdef VK_DEBUG + std::cerr << "ggml_backend_vk_synchronize()" << std::endl; +#endif + if(vk_ctx == nullptr) { + return; + } + + ggml_vk_ctx_end(vk_ctx); + + for (auto& cpy : vk_ctx->in_memcpys) { + memcpy(cpy.dst, cpy.src, cpy.n); + } + + ggml_vk_submit(vk_ctx, vk_fence); + VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_backend_vk_synchronize waitForFences"); + vk_device.device.resetFences({ vk_fence }); + + for (auto& cpy : vk_ctx->out_memcpys) { + memcpy(cpy.dst, cpy.src, cpy.n); + } + + vk_ctx = nullptr; + + UNUSED(backend); +} + +GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { + // ggml_backend_vk_context * vk_ctx = (ggml_backend_vk_context *)backend->context; + + for (int i = 0; i < cgraph->n_nodes; i++) { + ggml_vk_preallocate_buffers_graph(cgraph->nodes[i]); + } + ggml_vk_preallocate_buffers(); + + for (int i = 0; i < cgraph->n_nodes; i++) { + ggml_vk_build_graph(cgraph->nodes[i], i == cgraph->n_nodes - 1); + } + + ggml_compute_params params = {}; + params.type = GGML_TASK_COMPUTE; + params.ith = 0; + for (int i = 0; i < cgraph->n_nodes; i++) { + ggml_tensor * node = cgraph->nodes[i]; + + if (node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE || node->op == GGML_OP_NONE) { + continue; + } + + bool ok = ggml_vk_compute_forward(¶ms, node); + if (!ok) { + std::cerr << "Vulkan disable: " << vk_disable << std::endl; + fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); + } +#ifdef GGML_VULKAN_CHECK_RESULTS + else { + ggml_vk_check_results_1(¶ms, node); + } +#endif + GGML_ASSERT(ok); + } + + ggml_vk_graph_cleanup(); + + return true; + + UNUSED(backend); +} + +GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const ggml_tensor * op) { + switch (op->op) { + case GGML_OP_UNARY: + switch (ggml_get_unary_op(op)) { + case GGML_UNARY_OP_GELU: + case GGML_UNARY_OP_SILU: + case GGML_UNARY_OP_RELU: + return true; + default: + return false; + } + break; + case GGML_OP_MUL_MAT: + { + struct ggml_tensor * a; + struct ggml_tensor * b; + if (op->op == GGML_OP_MUL_MAT) { + a = op->src[0]; + b = op->src[1]; + } else { + a = op->src[2]; + b = op->src[1]; + } + if (a->ne[3] != b->ne[3]) { + return false; + } + return true; + } break; + // case GGML_OP_GET_ROWS: + // { + // switch (op->src[0]->type) { + // case GGML_TYPE_F16: + // case GGML_TYPE_F32: + // 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: + // return true; + // default: + // return false; + // } + // } break; + case GGML_OP_CPY: + { + ggml_type src0_type = op->src[0]->type; + ggml_type src1_type = op->src[1]->type; + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) { + return true; + } + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) { + return true; + } + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { + return true; + } + return false; + } break; + // case GGML_OP_DUP: + // case GGML_OP_REPEAT: + // { + // ggml_type src0_type = op->src[0]->type; + // return src0_type != GGML_TYPE_I32 && src0_type != GGML_TYPE_I16; + // } break; + case GGML_OP_ROPE: + { + const int mode = ((const int32_t *) op->op_params)[2]; + const bool is_glm = mode & 4; + + return !is_glm; + } break; + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_PERMUTE: + case GGML_OP_TRANSPOSE: + case GGML_OP_NORM: + case GGML_OP_ADD: + case GGML_OP_MUL: + case GGML_OP_RMS_NORM: + case GGML_OP_SCALE: + case GGML_OP_SQR: + case GGML_OP_CLAMP: + case GGML_OP_CONT: + case GGML_OP_DIAG_MASK_INF: + case GGML_OP_SOFT_MAX: + return true; + default: + return false; + } + + UNUSED(backend); +} + +// TODO: enable async and synchronize +static ggml_backend_i ggml_backend_vk_interface = { + /* .get_name = */ ggml_backend_vk_name, + /* .free = */ ggml_backend_vk_free, + /* .get_default_buffer_type = */ ggml_backend_vk_get_default_buffer_type, + /* .set_tensor_async = */ NULL, // ggml_backend_vk_set_tensor_async, + /* .get_tensor_async = */ NULL, // ggml_backend_vk_get_tensor_async, + /* .cpy_tensor_async = */ NULL, // ggml_backend_vk_cpy_tensor_async, + /* .synchronize = */ NULL, // ggml_backend_vk_synchronize, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_compute = */ NULL, + /* .graph_compute = */ ggml_backend_vk_graph_compute, + /* .supports_op = */ ggml_backend_vk_supports_op, +}; + +GGML_CALL ggml_backend_t ggml_backend_vk_init() { + ggml_vk_init(); // TODO: remove from ggml.c + + ggml_backend_vk_context * ctx = new ggml_backend_vk_context { + /* .name = */ GGML_VK_NAME, + }; + + ggml_backend_t vk_backend = new ggml_backend { + /* .interface = */ ggml_backend_vk_interface, + /* .context = */ ctx + }; + + return vk_backend; +} + +GGML_CALL bool ggml_backend_is_vk(ggml_backend_t backend) { + return backend && backend->iface.get_name == ggml_backend_vk_name; +} + +// backend registry +GGML_CALL static ggml_backend_t ggml_backend_reg_vk_init(const char * params, void * user_data) { + ggml_backend_t vk_backend = ggml_backend_vk_init(); + return vk_backend; + + UNUSED(params); + UNUSED(user_data); +} + +extern "C" GGML_CALL int ggml_backend_vk_reg_devices(); + +GGML_CALL int ggml_backend_vk_reg_devices() { + ggml_backend_register(GGML_VK_NAME, ggml_backend_reg_vk_init, ggml_backend_vk_buffer_type(), nullptr); + return 1; +} + +// checks + +#ifdef GGML_VULKAN_CHECK_RESULTS +void ggml_vk_print_graph_origin(const ggml_tensor * tensor, std::vector& done, int level = 0) { + if (std::find(done.begin(), done.end(), tensor) != done.end() || level > 10) { + return; + } + for (int j = 0; j < level; j++) { + std::cerr << " "; + } + std::cerr << ggml_op_name(tensor->op) << " gpu=" << (tensor->extra != nullptr) << " backend=" << tensor->backend << std::endl; + + done.push_back(tensor); + + for (int i = 0; i < GGML_MAX_SRC; i++) { + if (tensor->src[i] != nullptr) { + ggml_vk_print_graph_origin(tensor->src[i], done, level + 1); + } + } +} + +void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * data, int i0, int i1, int i2, int i3) { + if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) { + return; + } + fprintf(stderr, " "); + for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) { + fprintf(stderr, "%7d ", idx1); + } + fprintf(stderr, "\n"); + for (int idx0 = i0 - 5; idx0 < i0 + 5; idx0++) { + fprintf(stderr, "%7d: ", idx0); + for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) { + if (idx0 >= 0 && idx0 < tensor->ne[0] && idx1 >= 0 && idx1 < tensor->ne[1] && i2 >= 0 && i2 < tensor->ne[2] && i3 >= 0 && i3 < tensor->ne[3]) { + float val; + if (tensor->type == GGML_TYPE_F32) { + val = *(float *) ((char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]); + } else if (tensor->type == GGML_TYPE_F16) { + val = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0])); + } + fprintf(stderr, "% 7.2f ", val); + } else { + fprintf(stderr, " "); + } + } + fprintf(stderr, "\n"); + } +} + +void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name) { + void * tensor_data = tensor->data; + + if (tensor->backend == GGML_BACKEND_GPU) { + const size_t tensor_size = ggml_nbytes(tensor); + tensor_data = malloc(tensor_size); + + ggml_vk_buffer_read((vk_buffer *)tensor->data, 0, tensor_data, tensor_size, vk_device.transfer_queue); + } + + std::cerr << "TENSOR CHECK " << name << " (" << tensor->name << "): " << ggml_op_name(tensor->op) << std::endl; + std::cerr << "tensor=" << tensor << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << std::endl; + if (tensor->src[0] != nullptr) { + std::cerr << "tensor->src[0]=" << tensor->src[0] << " name=" << tensor->src[0]->name << " op=" << ggml_op_name(tensor->src[0]->op) << " type=" << ggml_type_name(tensor->src[0]->type) << " backend=" << tensor->src[0]->backend << " ne0=" << tensor->src[0]->ne[0] << " nb0=" << tensor->src[0]->nb[0] << " ne1=" << tensor->src[0]->ne[1] << " nb1=" << tensor->src[0]->nb[1] << " ne2=" << tensor->src[0]->ne[2] << " nb2=" << tensor->src[0]->nb[2] << " ne3=" << tensor->src[0]->ne[3] << " nb3=" << tensor->src[0]->nb[3] << std::endl; + } + if (tensor->src[1] != nullptr) { + std::cerr << "tensor->src[1]=" << tensor->src[1] << " name=" << tensor->src[1]->name << " op=" << ggml_op_name(tensor->src[1]->op) << " type=" << ggml_type_name(tensor->src[1]->type) << " backend=" << tensor->src[1]->backend << " ne0=" << tensor->src[1]->ne[0] << " nb0=" << tensor->src[1]->nb[0] << " ne1=" << tensor->src[1]->ne[1] << " nb1=" << tensor->src[1]->nb[1] << " ne2=" << tensor->src[1]->ne[2] << " nb2=" << tensor->src[1]->nb[2] << " ne3=" << tensor->src[1]->ne[3] << " nb3=" << tensor->src[1]->nb[3] << std::endl; + } + std::cerr << std::endl << "Result:" << std::endl; + ggml_vk_print_tensor_area(tensor, tensor->data, 5, 5, 0, 0); + std::cerr << std::endl; + std::cerr << std::endl << "Result:" << std::endl; + ggml_vk_print_tensor_area(tensor, tensor->data, 5, 5, 1, 0); + std::cerr << std::endl; + std::vector done; + ggml_vk_print_graph_origin(tensor, done); + + if (tensor->backend == GGML_BACKEND_GPU) { + free(tensor_data); + } +} + +void ggml_vk_check_tensor(const std::string& name, const ggml_tensor * tensor) { + return; + GGML_ASSERT(tensor->backend == GGML_BACKEND_CPU); + if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) { + return; + } + for (int i3 = 0; i3 < tensor->ne[3]; i3++) { + for (int i2 = 0; i2 < tensor->ne[2]; i2++) { + for (int i1 = 0; i1 < tensor->ne[1]; i1++) { + for (int i0 = 0; i0 < tensor->ne[0]; i0++) { + float val = 0.0f; + if (tensor->type == GGML_TYPE_F32) { + val = *(float *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]); + } else if (tensor->type == GGML_TYPE_F16) { + val = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor->data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0])); + } + if (std::isnan(val)) { + std::cerr << "ERROR: TENSOR CHECK " << name << ": Invalid value in " << ggml_op_name(tensor->op) << " i3=" << i3 << " i2=" << i2 << " i1=" << i1 << " i0=" << i0 << " val=" << val << std::endl; + std::cerr << "tensor=" << tensor << " tensor->type=" << ggml_type_name(tensor->type) << " tensor->backend: " << tensor->backend << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << std::endl; + std::cerr << std::endl; + ggml_vk_print_tensor_area(tensor, tensor->data, i0, i1, i2, i3); + std::cerr << std::endl; + std::vector done; + ggml_vk_print_graph_origin(tensor, done); + GGML_ASSERT(false); + } + } + } + } + } +} + +void * comp_result; +size_t comp_size; +size_t comp_nb[GGML_MAX_DIMS]; +size_t check_counter = 0; +void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * tensor) { + if (params->ith != 0) { + return; + } + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) { + return; + } + + check_counter++; + if (!(vk_output_tensor > 0 && vk_output_tensor == check_counter) && check_counter <= vk_skip_checks) { + return; + } + + ggml_tensor * src0 = tensor->src[0]; + ggml_tensor * src1 = tensor->src[1]; + + struct ggml_init_params iparams = { + .mem_size = 1024*1024*1024, + .mem_buffer = NULL, + }; + + struct ggml_context * ctx = ggml_init(iparams); + + struct ggml_tensor * src0_clone = nullptr; + struct ggml_tensor * src1_clone = nullptr; + struct ggml_tensor * tensor_clone = nullptr; + + size_t src0_size; + size_t src1_size; + + void * src0_buffer; + void * src1_buffer; + + if (src0 != nullptr) { + src0_clone = ggml_dup_tensor(ctx, src0); + + src0_size = ggml_nbytes(src0); + + src0_buffer = malloc(src0_size); + src0_clone->data = src0_buffer; + if (src0->backend == GGML_BACKEND_CPU) { + memcpy(src0_clone->data, src0->data, src0_size); + memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS); + } else if (src0->backend == GGML_BACKEND_GPU) { + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src0->extra; + uint64_t offset = extra->offset; + if (!ggml_is_contiguous(src0) && ggml_vk_dim01_contiguous(src0)) { + for (int i3 = 0; i3 < src0->ne[3]; i3++) { + for (int i2 = 0; i2 < src0->ne[2]; i2++) { + const int idx = i3*src0->ne[2] + i2; + ggml_vk_buffer_read(&extra->buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1], vk_device.transfer_queue); + } + } + + src0_clone->nb[0] = src0->nb[0]; + src0_clone->nb[1] = src0->nb[1]; + for (int i = 2; i < GGML_MAX_DIMS; i++) { + src0_clone->nb[i] = src0_clone->nb[i - 1]*src0_clone->ne[i - 1]; + } + } else { + if (offset + src0_size >= extra->buffer_gpu.size) { + src0_size = extra->buffer_gpu.size - offset; + } + ggml_vk_buffer_read(&extra->buffer_gpu, offset, src0_clone->data, src0_size, vk_device.transfer_queue); + memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS); + } + } else { + GGML_ASSERT(false); + } + + if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { + ggml_vk_print_tensor(src0, "src0"); + } + + ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src0", src0_clone); + } + if (src1 != nullptr) { + src1_clone = ggml_dup_tensor(ctx, src1); + + src1_size = ggml_nbytes(src1); + + src1_buffer = malloc(src1_size); + src1_clone->data = src1_buffer; + if (src1->backend == GGML_BACKEND_CPU) { + memcpy(src1_clone->data, src1->data, src1_size); + memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS); + } else if (src1->backend == GGML_BACKEND_GPU) { + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src1->extra; + uint64_t offset = extra->offset; + if (!ggml_is_contiguous(src1) && ggml_vk_dim01_contiguous(src1)) { + for (int i3 = 0; i3 < src1->ne[3]; i3++) { + for (int i2 = 0; i2 < src1->ne[2]; i2++) { + const int idx = i3*src1->ne[2] + i2; + ggml_vk_buffer_read(&extra->buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1], vk_device.transfer_queue); + } + } + + src1_clone->nb[0] = src1->nb[0]; + src1_clone->nb[1] = src1->nb[1]; + for (int i = 2; i < GGML_MAX_DIMS; i++) { + src1_clone->nb[i] = src1_clone->nb[i - 1]*src1_clone->ne[i - 1]; + } + } else { + if (offset + src1_size >= extra->buffer_gpu.size) { + src1_size = extra->buffer_gpu.size - offset; + } + ggml_vk_buffer_read(&extra->buffer_gpu, offset, src1_clone->data, src1_size, vk_device.transfer_queue); + memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS); + } + } else { + GGML_ASSERT(false); + } + + if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { + ggml_vk_print_tensor(src1, "src1"); + std::cerr << "TENSOR CHECK: " << ggml_op_name(src1_clone->op) << " (check " << check_counter << ")" << std::endl; + std::cerr << "src1_clone=" << tensor << " src1_clone->backend: " << src1_clone->backend << " src1_clone->type: " << ggml_type_name(src1_clone->type) << " ne0=" << src1_clone->ne[0] << " nb0=" << src1_clone->nb[0] << " ne1=" << src1_clone->ne[1] << " nb1=" << src1_clone->nb[1] << " ne2=" << src1_clone->ne[2] << " nb2=" << src1_clone->nb[2] << " ne3=" << src1_clone->ne[3] << " nb3=" << src1_clone->nb[3] << std::endl; + if (src1->src[0] != nullptr) { + std::cerr << "src1->src[0]=" << src1->src[0] << " op=" << ggml_op_name(src1->src[0]->op) << " type=" << ggml_type_name(src1->src[0]->type) << " backend=" << src1->src[0]->backend << " ne0=" << src1->src[0]->ne[0] << " nb0=" << src1->src[0]->nb[0] << " ne1=" << src1->src[0]->ne[1] << " nb1=" << src1->src[0]->nb[1] << " ne2=" << src1->src[0]->ne[2] << " nb2=" << src1->src[0]->nb[2] << " ne3=" << src1->src[0]->ne[3] << " nb3=" << src1->src[0]->nb[3] << std::endl; + } + if (src1->src[1] != nullptr) { + std::cerr << "src1->src[1]=" << src1->src[1] << " op=" << ggml_op_name(src1->src[1]->op) << " type=" << ggml_type_name(src1->src[1]->type) << " backend=" << src1->src[1]->backend << " ne0=" << src1->src[1]->ne[0] << " nb0=" << src1->src[1]->nb[0] << " ne1=" << src1->src[1]->ne[1] << " nb1=" << src1->src[1]->nb[1] << " ne2=" << src1->src[1]->ne[2] << " nb2=" << src1->src[1]->nb[2] << " ne3=" << src1->src[1]->ne[3] << " nb3=" << src1->src[1]->nb[3] << std::endl; + } + std::cerr << std::endl << "Result:" << std::endl; + ggml_vk_print_tensor_area(src1_clone, src1_clone->data, 5, 5, 0, 0); + std::cerr << std::endl; + std::cerr << std::endl << "Result:" << std::endl; + ggml_vk_print_tensor_area(src1_clone, src1_clone->data, 5, 5, 1, 0); + std::cerr << std::endl; + std::vector done; + ggml_vk_print_graph_origin(src1_clone, done); + } + + ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src1", src1_clone); + } + + if (tensor->op == GGML_OP_MUL_MAT) { + tensor_clone = ggml_mul_mat(ctx, src0_clone, src1_clone); + } else if (tensor->op == GGML_OP_MUL) { + tensor_clone = ggml_mul(ctx, src0_clone, src1_clone); + } else if (tensor->op == GGML_OP_SCALE) { + tensor_clone = ggml_scale(ctx, src0_clone, ((float *)tensor->op_params)[0]); + } else if (tensor->op == GGML_OP_SQR) { + tensor_clone = ggml_sqr(ctx, src0_clone); + } else if (tensor->op == GGML_OP_CLAMP) { + tensor_clone = ggml_clamp(ctx, src0_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]); + } else if (tensor->op == GGML_OP_ADD) { + tensor_clone = ggml_add(ctx, src0_clone, src1_clone); + } else if (tensor->op == GGML_OP_NORM) { + tensor_clone = ggml_norm(ctx, src0_clone, *(float *)tensor->op_params); + } else if (tensor->op == GGML_OP_RMS_NORM) { + tensor_clone = ggml_rms_norm(ctx, src0_clone, *(float *)tensor->op_params); + } else if (tensor->op == GGML_OP_SOFT_MAX) { + if (src1 != nullptr) { + tensor_clone = ggml_soft_max_ext(ctx, src0_clone, src1_clone, *(float *)tensor->op_params); + } else { + tensor_clone = ggml_soft_max(ctx, src0_clone); + } + } else if (tensor->op == GGML_OP_DIAG_MASK_INF) { + tensor_clone = ggml_diag_mask_inf(ctx, src0_clone, *(float *)tensor->op_params); + } else if (tensor->op == GGML_OP_ROPE) { + const int n_dims = ((int32_t *) tensor->op_params)[1]; + const int mode = ((int32_t *) tensor->op_params)[2]; + const int n_ctx = ((int32_t *) tensor->op_params)[3]; + const int n_orig_ctx = ((int32_t *) tensor->op_params)[4]; + float freq_base = ((float *) tensor->op_params)[5]; + float freq_scale = ((float *) tensor->op_params)[6]; + float ext_factor = ((float *) tensor->op_params)[7]; + float attn_factor = ((float *) tensor->op_params)[8]; + float beta_fast = ((float *) tensor->op_params)[9]; + float beta_slow = ((float *) tensor->op_params)[10]; + tensor_clone = ggml_rope_custom(ctx, src0_clone, src1_clone, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); + } else if (tensor->op == GGML_OP_UNARY) { + switch (ggml_get_unary_op(tensor)) { + case GGML_UNARY_OP_SILU: + tensor_clone = ggml_silu(ctx, src0_clone); + break; + case GGML_UNARY_OP_GELU: + tensor_clone = ggml_gelu(ctx, src0_clone); + break; + case GGML_UNARY_OP_RELU: + tensor_clone = ggml_relu(ctx, src0_clone); + break; + default: + std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl; + GGML_ASSERT(false); + } + } else if (tensor->op == GGML_OP_CPY || tensor->op == GGML_OP_DUP) { + if (src1 == nullptr) { + tensor_clone = ggml_dup(ctx, src0_clone); + tensor_clone->type == tensor->type; + } else { + tensor_clone = ggml_cpy(ctx, src0_clone, src1_clone); + } + } else if (tensor->op == GGML_OP_CONT) { + tensor_clone = ggml_cont_4d(ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); + } else if (tensor->op == GGML_OP_RESHAPE) { + tensor_clone = ggml_reshape_4d(ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); + } else if (tensor->op == GGML_OP_VIEW) { + tensor_clone = ggml_view_4d(ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], tensor->nb[1], tensor->nb[2], tensor->nb[3], ((int32_t *) tensor->op_params)[0]); + } else if (tensor->op == GGML_OP_PERMUTE) { + int32_t * params = (int32_t *)tensor->op_params; + tensor_clone = ggml_permute(ctx, src0_clone, params[0], params[1], params[2], params[3]); + } else if (tensor->op == GGML_OP_TRANSPOSE) { + tensor_clone = ggml_transpose(ctx, src0_clone); + } else { + std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl; + GGML_ASSERT(false); + } + + // Disable vulkan here to avoid the hooks in ggml.c + vk_disable = true; + + ggml_cgraph * cgraph = ggml_new_graph(ctx); + ggml_build_forward_expand(cgraph, tensor_clone); + + ggml_graph_compute_with_ctx(ctx, cgraph, 8); + + vk_disable = false; + + ggml_vk_check_tensor(ggml_op_name(tensor->op), tensor_clone); + if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { + ggml_vk_print_tensor(tensor_clone, "tensor_clone"); + } + + comp_size = ggml_nbytes(tensor_clone); + + comp_result = malloc(comp_size); + memcpy(comp_result, tensor_clone->data, comp_size); + memcpy(comp_nb, tensor_clone->nb, sizeof(size_t) * GGML_MAX_DIMS); + + if (src0 != nullptr) { + free(src0_buffer); + } + if (src1 != nullptr) { + free(src1_buffer); + } + + ggml_free(ctx); +} + +void ggml_vk_check_results_1(ggml_compute_params * params, ggml_tensor * tensor) { + if (params->ith != 0) { + return; + } + if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE || tensor->op == GGML_OP_TRANSPOSE) { + return; + } + if (!(vk_output_tensor > 0 && vk_output_tensor == check_counter) && check_counter <= vk_skip_checks) { + return; + } + + ggml_tensor * src0 = tensor->src[0]; + ggml_tensor * src1 = tensor->src[1]; + + void * tensor_data = tensor->data; + + if (tensor->backend == GGML_BACKEND_GPU) { + size_t tensor_size = ggml_nbytes(tensor); + tensor_data = malloc(tensor_size); + + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; + + if (extra->offset + tensor_size >= extra->buffer_gpu.size) { + tensor_size = extra->buffer_gpu.size - (extra->offset); + } + + ggml_vk_buffer_read(&extra->buffer_gpu, extra->offset, tensor_data, tensor_size, vk_device.transfer_queue); + } + + float first_error_result = -1.0f; + float first_error_correct = -1.0f; + std::array first_error = { -1, -1, -1, -1 }; + double avg_err = 0.0; + size_t counter = 0; + + for (int i3 = 0; i3 < tensor->ne[3]; i3++) { + for (int i2 = 0; i2 < tensor->ne[2]; i2++) { + for (int i1 = 0; i1 < tensor->ne[1]; i1++) { + for (int i0 = 0; i0 < tensor->ne[0]; i0++) { + const bool buffer_size_fit = i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0] < comp_size; + float correct = 0.0f; + float result = 0.0f; + + if (buffer_size_fit) { + if (tensor->type == GGML_TYPE_F32) { + correct = *(float *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]); + result = *(float *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0]); + } else if (tensor->type == GGML_TYPE_F16) { + correct = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0])); + result = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) tensor_data + i3*tensor->nb[3] + i2*tensor->nb[2] + i1*tensor->nb[1] + i0*tensor->nb[0])); + } else { + std::cerr << "comp_size=" << comp_size << " but required is " << (i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]) << std::endl; + } + } else { + std::cerr << "Missing debug code for type " << ggml_type_name(tensor->type) << std::endl; + GGML_ASSERT(false); + } + + if ((std::isnan(correct) != std::isnan(result)) || (std::isinf(correct) != std::isinf(result)) || !buffer_size_fit) { + std::cerr << "ERROR: Invalid value in " << ggml_op_name(tensor->op) << " i3=" << i3 << " i2=" << i2 << " i1=" << i1 << " i0=" << i0 << " result=" << result << " correct=" << correct << " avg_err=" << (avg_err / counter) << std::endl; + std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl; + if (src0 != nullptr) { + std::cerr << "src0=" << src0 << " src0->name=" << src0->name << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " backend=" << src0->backend << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl; + } + if (src1 != nullptr) { + std::cerr << "src1=" << src1 << " src1->name=" << src1->name << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " backend=" << src1->backend << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; + } + std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl; + std::cerr << std::endl << "Result:" << std::endl; + ggml_vk_print_tensor_area(tensor, tensor_data, i0, i1, i2, i3); + std::cerr << std::endl << "Correct:" << std::endl; + ggml_vk_print_tensor_area(tensor, comp_result, i0, i1, i2, i3); + std::cerr << std::endl; + std::vector done; + ggml_vk_print_graph_origin(tensor, done); + GGML_ASSERT(false); + } + if (first_error[0] == -1 && std::fabs(correct - result) > 0.1f) { + first_error[0] = i0; + first_error[1] = i1; + first_error[2] = i2; + first_error[3] = i3; + first_error_result = result; + first_error_correct = correct; + } + + // Special case, value is infinite, avoid NaN result in avg_err + // NaN also appears in results, if both are nan error is 0 + if (!std::isinf(correct) && !std::isinf(result) && !std::isnan(correct) && !std::isnan(result)) { + avg_err += std::fabs(correct - result); + } + counter++; + } + } + } + } + + avg_err /= counter; + + if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { + std::cerr << "TENSOR CHECK: avg_err=" << avg_err << " in " << ggml_op_name(tensor->op) << " (check " << check_counter << ")" << std::endl; + std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl; + if (src0 != nullptr) { + std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " backend=" << src0->backend << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl; + } + if (src1 != nullptr) { + std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " backend=" << src1->backend << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; + } + std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl; + std::cerr << std::endl << "Result:" << std::endl; + ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 0, 0); + std::cerr << std::endl << "Correct:" << std::endl; + ggml_vk_print_tensor_area(tensor, comp_result, 5, 5, 0, 0); + std::cerr << std::endl; + std::cerr << std::endl << "Result:" << std::endl; + ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 1, 0); + std::cerr << std::endl << "Correct:" << std::endl; + ggml_vk_print_tensor_area(tensor, comp_result, 5, 5, 1, 0); + std::cerr << std::endl; + std::vector done; + ggml_vk_print_graph_origin(tensor, done); + } + + if (avg_err > 0.05 || std::isnan(avg_err)) { + std::cerr << "ERROR: avg_err=" << avg_err << " in " << ggml_op_name(tensor->op) << " (check " << check_counter << ")" << std::endl; + std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " tensor->backend: " << tensor->backend << " tensor->type: " << ggml_type_name(tensor->type) << " ne0=" << tensor->ne[0] << " nb0=" << tensor->nb[0] << " ne1=" << tensor->ne[1] << " nb1=" << tensor->nb[1] << " ne2=" << tensor->ne[2] << " nb2=" << tensor->nb[2] << " ne3=" << tensor->ne[3] << " nb3=" << tensor->nb[3] << " offset=" << tensor->view_offs << std::endl; + if (src0 != nullptr) { + std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " backend=" << src0->backend << " ne0=" << src0->ne[0] << " nb0=" << src0->nb[0] << " ne1=" << src0->ne[1] << " nb1=" << src0->nb[1] << " ne2=" << src0->ne[2] << " nb2=" << src0->nb[2] << " ne3=" << src0->ne[3] << " nb3=" << src0->nb[3] << " offset=" << src0->view_offs << std::endl; + } + if (src1 != nullptr) { + std::cerr << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " backend=" << src1->backend << " ne0=" << src1->ne[0] << " nb0=" << src1->nb[0] << " ne1=" << src1->ne[1] << " nb1=" << src1->nb[1] << " ne2=" << src1->ne[2] << " nb2=" << src1->nb[2] << " ne3=" << src1->ne[3] << " nb3=" << src1->nb[3] << " offset=" << src1->view_offs << std::endl; + } + std::cerr << "First error: result=" << first_error_result << " correct=" << first_error_correct << " i3=" << first_error[3] << " i2=" << first_error[2] << " i1=" << first_error[1] << " i0=" << first_error[0] << std::endl; + std::cerr << std::endl << "Result:" << std::endl; + ggml_vk_print_tensor_area(tensor, tensor_data, first_error[0], first_error[1], first_error[2], first_error[3]); + std::cerr << std::endl << "Correct:" << std::endl; + ggml_vk_print_tensor_area(tensor, comp_result, first_error[0], first_error[1], first_error[2], first_error[3]); + std::cerr << std::endl; + std::vector done; + ggml_vk_print_graph_origin(tensor, done); + GGML_ASSERT(false); + } else { + std::cerr << check_counter << " " << tensor->name << " op=" << ggml_op_name(tensor->op) << " backend=" << tensor->backend << " avg_err=" << avg_err << std::endl; + } + + free(comp_result); + comp_result = nullptr; + comp_size = 0; + + if (tensor->backend == GGML_BACKEND_GPU) { + free(tensor_data); + } +} +#endif diff --git a/ggml-vulkan.h b/ggml-vulkan.h new file mode 100644 index 000000000..eb8a148e2 --- /dev/null +++ b/ggml-vulkan.h @@ -0,0 +1,34 @@ +#pragma once + +#include "ggml.h" +#include "ggml-backend.h" + +#ifdef __cplusplus +extern "C" { +#endif + +#define GGML_VK_NAME "Vulkan" + +GGML_API void ggml_vk_init(void); + +GGML_API void ggml_vk_preallocate_buffers_graph(struct ggml_tensor * node); +GGML_API void ggml_vk_preallocate_buffers(void); +GGML_API void ggml_vk_build_graph(struct ggml_tensor * node, bool last_node); +GGML_API bool ggml_vk_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor); +#ifdef GGML_VULKAN_CHECK_RESULTS +void ggml_vk_check_results_1(struct ggml_compute_params * params, struct ggml_tensor * tensor); +#endif +GGML_API void ggml_vk_graph_cleanup(void); + +// backend API +GGML_API GGML_CALL ggml_backend_t ggml_backend_vk_init(void); + +GGML_API GGML_CALL bool ggml_backend_is_vk(ggml_backend_t backend); + +GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(void); +// pinned host buffer for use with the CPU backend for faster copies between CPU and GPU +GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type(void); + +#ifdef __cplusplus +} +#endif diff --git a/ggml.c b/ggml.c index 1187f84a2..24600862b 100644 --- a/ggml.c +++ b/ggml.c @@ -248,6 +248,8 @@ inline static void * ggml_aligned_malloc(size_t size) { #include "ggml-cuda.h" #elif defined(GGML_USE_CLBLAST) #include "ggml-opencl.h" +#elif defined(GGML_USE_VULKAN) +#include "ggml-vulkan.h" #elif defined(GGML_USE_SYCL) #include "ggml-sycl.h" #endif @@ -2295,6 +2297,8 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { ggml_init_cublas(); #elif defined(GGML_USE_CLBLAST) ggml_cl_init(); +#elif defined(GGML_USE_VULKAN) + ggml_vk_init(); #elif defined(GGML_USE_SYCL) ggml_init_sycl(); #endif @@ -8019,7 +8023,7 @@ static void ggml_compute_forward_mul_f32( const int ith = params->ith; const int nth = params->nth; -#ifdef GGML_USE_CLBLAST +#if defined(GGML_USE_CLBLAST) if (src1->backend == GGML_BACKEND_GPU) { // TODO: OpenCL kernel support full broadcast GGML_ASSERT(ggml_can_repeat_rows(src1, src0)); @@ -14703,6 +14707,18 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm } GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_CPU); GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_CPU); +#elif defined(GGML_USE_VULKAN) + const bool skip_cpu = ggml_vk_compute_forward(params, tensor); +#ifdef GGML_VULKAN_CHECK_RESULTS + if (skip_cpu) { + ggml_vk_check_results_1(params, tensor); + } +#endif + if (skip_cpu) { + return; + } + GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_CPU); + GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_CPU); #endif // GGML_USE_CUBLAS #ifdef GGML_USE_SYCL @@ -17105,6 +17121,17 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { } } +#ifdef GGML_USE_VULKAN + for (int i = 0; i < cgraph->n_nodes; i++) { + ggml_vk_preallocate_buffers_graph(cgraph->nodes[i]); + } + ggml_vk_preallocate_buffers(); + + for (int i = 0; i < cgraph->n_nodes; i++) { + ggml_vk_build_graph(cgraph->nodes[i], i == cgraph->n_nodes - 1); + } +#endif + const int n_threads = cplan->n_threads; struct ggml_compute_state_shared state_shared = { @@ -17156,6 +17183,10 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { } } +#ifdef GGML_USE_VULKAN + ggml_vk_graph_cleanup(); +#endif + // performance stats (graph) { int64_t perf_cycles_cur = ggml_perf_cycles() - perf_start_cycles; @@ -20290,7 +20321,7 @@ int ggml_cpu_has_wasm_simd(void) { } int ggml_cpu_has_blas(void) { -#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_SYCL) +#if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) || defined(GGML_USE_CUBLAS) || defined(GGML_USE_VULKAN) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_SYCL) return 1; #else return 0; @@ -20313,6 +20344,14 @@ int ggml_cpu_has_clblast(void) { #endif } +int ggml_cpu_has_vulkan(void) { +#if defined(GGML_USE_VULKAN) + return 1; +#else + return 0; +#endif +} + int ggml_cpu_has_sycl(void) { #if defined(GGML_USE_SYCL) return 1; @@ -20322,7 +20361,7 @@ int ggml_cpu_has_sycl(void) { } int ggml_cpu_has_gpublas(void) { - return ggml_cpu_has_cublas() || ggml_cpu_has_clblast() || ggml_cpu_has_sycl(); + return ggml_cpu_has_cublas() || ggml_cpu_has_clblast() || ggml_cpu_has_vulkan() || ggml_cpu_has_sycl(); } int ggml_cpu_has_sse3(void) { diff --git a/ggml.h b/ggml.h index 3d8d6f2ae..d697fd2bb 100644 --- a/ggml.h +++ b/ggml.h @@ -2263,6 +2263,7 @@ extern "C" { GGML_API int ggml_cpu_has_blas (void); GGML_API int ggml_cpu_has_cublas (void); GGML_API int ggml_cpu_has_clblast (void); + GGML_API int ggml_cpu_has_vulkan (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); diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py new file mode 100644 index 000000000..d0861fde4 --- /dev/null +++ b/ggml_vk_generate_shaders.py @@ -0,0 +1,2362 @@ +#!/usr/bin/env python + +import argparse +import asyncio +import os +import sys +from tempfile import gettempdir, NamedTemporaryFile + +shader_f32 = """ +#define FLOAT_TYPE float +""" +shader_f16 = """ +#extension GL_EXT_shader_explicit_arithmetic_types_float16 : require +#define FLOAT_TYPE float16_t +""" +shader_int8_ext = """ +#extension GL_EXT_shader_explicit_arithmetic_types_int8 : require +""" + +# Type-specific defines +shader_f16_defines = """ +#define QUANT_K 32 +#define QUANT_R 2 + +#define A_TYPE float16_t +""" +shader_q4_0_defines = """ +#define QUANT_K 32 +#define QUANT_R 2 + +struct block_q4_0 +{ + float16_t d; + uint8_t qs[16]; +}; + +#define A_TYPE block_q4_0 +""" +shader_q4_1_defines = """ +#define QUANT_K 32 +#define QUANT_R 2 + +struct block_q4_1 +{ + float16_t d; + float16_t m; + uint8_t qs[16]; +}; + +#define A_TYPE block_q4_1 +""" +shader_q5_0_defines = """ +#extension GL_EXT_shader_explicit_arithmetic_types_int16 : require +#define QUANT_K 32 +#define QUANT_R 2 + +struct block_q5_0 +{ + float16_t d; + uint16_t qh[2]; + uint8_t qs[16]; +}; + +#define A_TYPE block_q5_0 +""" +shader_q5_1_defines = """ +#define QUANT_K 32 +#define QUANT_R 2 + +struct block_q5_1 +{ + float16_t d; + float16_t m; + uint qh; + uint8_t qs[16]; +}; + +#define A_TYPE block_q5_1 +""" +shader_q8_0_defines = """ +#define QUANT_K 32 +#define QUANT_R 1 + +struct block_q8_0 +{ + float16_t d; + int8_t qs[32]; +}; + +#define A_TYPE block_q8_0 +""" + +# K-quants +shader_q2_K_defines = """ +#define QUANT_K 256 + +struct block_q2_K +{ + uint8_t scales[QUANT_K/16]; + uint8_t qs[QUANT_K/4]; + f16vec2 d; +}; + +#define A_TYPE block_q2_K +""" +shader_q3_K_defines = """ +#define QUANT_K 256 + +struct block_q3_K +{ + uint8_t hmask[QUANT_K/8]; + uint8_t qs[QUANT_K/4]; + uint8_t scales[12]; + float16_t d; +}; + +#define A_TYPE block_q3_K +""" +shader_q4_K_defines = """ +#define QUANT_K 256 + +struct block_q4_K +{ + f16vec2 d; + uint8_t scales[3*QUANT_K/64]; + uint8_t qs[QUANT_K/2]; +}; + +#define A_TYPE block_q4_K +""" +shader_q5_K_defines = """ +#define QUANT_K 256 + +struct block_q5_K +{ + f16vec2 d; + uint8_t scales[12]; + uint8_t qh[QUANT_K/8]; + uint8_t qs[QUANT_K/2]; +}; + +#define A_TYPE block_q5_K +""" +shader_q6_K_defines = """ +#define QUANT_K 256 + +struct block_q6_K +{ + uint8_t ql[QUANT_K/2]; + uint8_t qh[QUANT_K/4]; + int8_t scales[QUANT_K/16]; + float16_t d; +}; + +#define A_TYPE block_q6_K +""" + +# Dequant functions +shader_f16_dequant_func = """ +#define DEQUANT_FUNC f16vec2 v = f16vec2(data_a[ib + 0], data_a[ib + 1]); +""" +shader_f16_dequant_func_compat = """ +#define DEQUANT_FUNC vec2 v = vec2(data_a[ib + 0], data_a[ib + 1]); +""" + +shader_q4_0_dequant_func = """ +#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \ +const uint8_t vui = data_a[ib].qs[iqs]; \ +f16vec2 v = f16vec2(vui & 0xF, vui >> 4); \ +v = (v - 8.0hf)*d; +""" +shader_q4_0_dequant_func_compat = """ +#define DEQUANT_FUNC const float d = float(data_a[ib].d); \ +const uint vui = uint(data_a[ib].qs[iqs]); \ +vec2 v = vec2(vui & 0xF, vui >> 4); \ +v = (v - 8.0f)*d; +""" + +shader_q4_1_dequant_func = """ +#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \ +const float16_t m = data_a[ib].m; \ +const uint8_t vui = data_a[ib].qs[iqs]; \ +f16vec2 v = f16vec2(vui & 0xF, vui >> 4); \ +v = v*d + m; +""" +shader_q4_1_dequant_func_compat = """ +#define DEQUANT_FUNC const float d = float(data_a[ib].d); \ +const float m = float(data_a[ib].m); \ +const uint vui = uint(data_a[ib].qs[iqs]); \ +vec2 v = vec2(vui & 0xF, vui >> 4); \ +v = v*d + m; +""" + +shader_q5_0_dequant_func = """ +#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \ +const uint uint_qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0]; \ +const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); \ +const uint8_t vui = data_a[ib].qs[iqs]; \ +f16vec2 v = f16vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y); \ +v = (v - 16.0hf) * d; +""" +shader_q5_0_dequant_func_compat = """ +#define DEQUANT_FUNC const float d = float(data_a[ib].d); \ +const uint uint_qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0]; \ +const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); \ +const uint vui = uint(data_a[ib].qs[iqs]); \ +vec2 v = vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y); \ +v = (v - 16.0f) * d; +""" + +shader_q5_1_dequant_func = """ +#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \ +const float16_t m = data_a[ib].m; \ +const ivec2 qh = ivec2(((data_a[ib].qh >> iqs) << 4) & 0x10, (data_a[ib].qh >> (iqs + 12)) & 0x10); \ +const uint8_t vui = data_a[ib].qs[iqs]; \ +f16vec2 v = f16vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y); \ +v = v*d + m; +""" +shader_q5_1_dequant_func_compat = """ +#define DEQUANT_FUNC const float d = float(data_a[ib].d); \ +const float m = float(data_a[ib].m); \ +const ivec2 qh = ivec2(((data_a[ib].qh >> iqs) << 4) & 0x10, (data_a[ib].qh >> (iqs + 12)) & 0x10); \ +const uint vui = uint(data_a[ib].qs[iqs]); \ +vec2 v = vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y); \ +v = v*d + m; +""" + +shader_q8_0_dequant_func = """ +#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \ +f16vec2 v = f16vec2(data_a[ib].qs[iqs], data_a[ib].qs[iqs + 1]); \ +v = v * d; +""" +shader_q8_0_dequant_func_compat = """ +#define DEQUANT_FUNC const float d = float(data_a[ib].d); \ +vec2 v = vec2(int(data_a[ib].qs[iqs]), int(data_a[ib].qs[iqs + 1])); \ +v = v * d; +""" + +# MULMAT + +mulmat_head = """#version 450 + +#extension GL_EXT_control_flow_attributes : enable +#extension GL_EXT_shader_16bit_storage : require + +#ifndef LOAD_VEC +#define LOAD_VEC 1 +#endif +""" + +mulmat_body = """ +layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; + +layout (push_constant) uniform parameter +{ + uint M; + uint N; + uint K; + uint stride_a; + uint stride_b; + uint stride_d; + uint k_split; + + uint ne02; + uint ne12; + uint broadcast2; + uint broadcast3; + + uint batch_stride_a; + uint batch_stride_b; + uint batch_stride_d; +} p; + +layout (constant_id = 1) const uint BM = 64; +layout (constant_id = 2) const uint BN = 64; +layout (constant_id = 3) const uint BK = 16; +layout (constant_id = 4) const uint WM = 32; +layout (constant_id = 5) const uint WN = 32; +layout (constant_id = 6) const uint WMITER = 2; +layout (constant_id = 7) const uint TM = 4; +layout (constant_id = 8) const uint TN = 2; +layout (constant_id = 9) const uint WARP = 32; + +shared FLOAT_TYPE buf_a[BM * (BK+1)]; +shared FLOAT_TYPE buf_b[BN * (BK+1)]; + +void main() { + const uint i13 = gl_GlobalInvocationID.z / p.ne12; + const uint i12 = gl_GlobalInvocationID.z % p.ne12; + + const uint i03 = i13 / p.broadcast3; + const uint i02 = i12 / p.broadcast2; + + const uint batch_idx_a = i03 * p.ne02 + i02; + + const uint blocks_m = (p.M + BM - 1) / BM; + const uint ir = gl_WorkGroupID.x % blocks_m; + const uint ik = gl_WorkGroupID.x / blocks_m; + const uint ic = gl_WorkGroupID.y; + + const uint warp_i = gl_LocalInvocationID.x / WARP; + const uint warp_r = warp_i % (BM / WM); + const uint warp_c = warp_i / (BM / WM); + + const uint WNITER = (WM * WN) / (WARP * TM * TN * WMITER); + const uint WSUBM = WM / WMITER; + const uint WSUBN = WN / WNITER; + + const uint tiw = gl_LocalInvocationID.x % WARP; + const uint tiwr = tiw % (WSUBM / TM); + const uint tiwc = tiw / (WSUBM / TM); + + const uint loadr = gl_LocalInvocationID.x % (BK / LOAD_VEC); + const uint loadc = gl_LocalInvocationID.x / (BK / LOAD_VEC); + + const uint loadstride = gl_WorkGroupSize.x * LOAD_VEC / BK; + + const uint start_k = ik * p.k_split; + const uint end_k = min(p.K, (ik + 1) * p.k_split); + + uint pos_a = (batch_idx_a * p.batch_stride_a + ir * BM * p.stride_a + start_k) / LOAD_VEC; + uint pos_b = (gl_GlobalInvocationID.z * p.batch_stride_b + ic * BN * p.stride_b + start_k) / LOAD_VEC; + + float sums[WMITER * TM * WNITER * TN]; + FLOAT_TYPE cache_a[WMITER * TM]; + FLOAT_TYPE cache_b[WNITER * TN]; + + [[unroll]] for (uint i = 0; i < WMITER*TM*WNITER*TN; i++) { + sums[i] = 0.0f; + } + + [[unroll]] for (uint block = start_k; block < end_k; block += BK) { + [[unroll]] for (uint l = 0; l < BM; l += loadstride) { +#if LOAD_VEC == 8 + const uint idx = pos_a + (loadc + l) * p.stride_a / LOAD_VEC + loadr; + buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 0] = FLOAT_TYPE(data_a[idx][0].x); + buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 1] = FLOAT_TYPE(data_a[idx][0].y); + buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 2] = FLOAT_TYPE(data_a[idx][0].z); + buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 3] = FLOAT_TYPE(data_a[idx][0].w); + buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 4] = FLOAT_TYPE(data_a[idx][1].x); + buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 5] = FLOAT_TYPE(data_a[idx][1].y); + buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 6] = FLOAT_TYPE(data_a[idx][1].z); + buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 7] = FLOAT_TYPE(data_a[idx][1].w); +#elif LOAD_VEC == 4 + const uint idx = pos_a + (loadc + l) * p.stride_a / LOAD_VEC + loadr; + buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 0] = FLOAT_TYPE(data_a[idx].x); + buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 1] = FLOAT_TYPE(data_a[idx].y); + buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 2] = FLOAT_TYPE(data_a[idx].z); + buf_a[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 3] = FLOAT_TYPE(data_a[idx].w); +#else + if (ir * BM + loadc + l < p.M && block + loadr < end_k) { + buf_a[(loadc + l) * (BK+1) + loadr] = FLOAT_TYPE(data_a[pos_a + (loadc + l) * p.stride_a + loadr]); + } else { + buf_a[(loadc + l) * (BK+1) + loadr] = FLOAT_TYPE(0.0f); + } +#endif + } + [[unroll]] for (uint l = 0; l < BN; l += loadstride) { +#if LOAD_VEC == 8 + const uint idx = pos_b + (loadc + l) * p.stride_b / LOAD_VEC + loadr; + buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 0] = FLOAT_TYPE(data_b[idx][0].x); + buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 1] = FLOAT_TYPE(data_b[idx][0].y); + buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 2] = FLOAT_TYPE(data_b[idx][0].z); + buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 3] = FLOAT_TYPE(data_b[idx][0].w); + buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 4] = FLOAT_TYPE(data_b[idx][1].x); + buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 5] = FLOAT_TYPE(data_b[idx][1].y); + buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 6] = FLOAT_TYPE(data_b[idx][1].z); + buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 7] = FLOAT_TYPE(data_b[idx][1].w); +#elif LOAD_VEC == 4 + const uint idx = pos_b + (loadc + l) * p.stride_b / LOAD_VEC + loadr; + buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 0] = FLOAT_TYPE(data_b[idx].x); + buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 1] = FLOAT_TYPE(data_b[idx].y); + buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 2] = FLOAT_TYPE(data_b[idx].z); + buf_b[(loadc + l) * (BK+1) + loadr * LOAD_VEC + 3] = FLOAT_TYPE(data_b[idx].w); +#else + if (ic * BN + loadc + l < p.N && block + loadr < end_k) { + buf_b[(loadc + l) * (BK+1) + loadr] = FLOAT_TYPE(data_b[pos_b + (loadc + l) * p.stride_b + loadr]); + } else { + buf_b[(loadc + l) * (BK+1) + loadr] = FLOAT_TYPE(0.0f); + } +#endif + } + + barrier(); + + pos_a += BK / LOAD_VEC; + pos_b += BK / LOAD_VEC; + + for (uint i = 0; i < BK; i++) { + // Load from shared into cache + [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) { + [[unroll]] for (uint j = 0; j < TM; j++) { + cache_a[wsir * TM + j] = buf_a[(warp_r * WM + wsir * WSUBM + tiwr * TM + j) * (BK+1) + i]; + } + } + [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) { + [[unroll]] for (uint j = 0; j < TN; j++) { + cache_b[wsic * TN + j] = buf_b[(warp_c * WN + wsic * WSUBN + tiwc * TN + j) * (BK+1) + i]; + } + } + + [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) { + [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) { + [[unroll]] for (uint cc = 0; cc < TN; cc++) { + [[unroll]] for (uint cr = 0; cr < TM; cr++) { + sums[(wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr] += float(cache_a[wsir * TM + cr]) * float(cache_b[wsic * TN + cc]); + } + } + } + } + } + + barrier(); + } + + const uint dr = ir * BM + warp_r * WM; + const uint dc = ic * BN + warp_c * WN; + + const uint offsets = gl_GlobalInvocationID.z * p.batch_stride_d + ik * p.batch_stride_d * gl_NumWorkGroups.z; + + [[unroll]] for (uint wsic = 0; wsic < WNITER; wsic++) { + [[unroll]] for (uint wsir = 0; wsir < WMITER; wsir++) { + + const uint dr_warp = dr + wsir * WSUBM + tiwr * TM; + const uint dc_warp = dc + wsic * WSUBN + tiwc * TN; + [[unroll]] for (uint cc = 0; cc < TN; cc++) { + [[unroll]] for (uint cr = 0; cr < TM; cr++) { + if (dr_warp + cr < p.M && dc_warp + cc < p.N) { + data_d[offsets + (dc_warp + cc) * p.stride_d + dr_warp + cr] = D_TYPE(sums[(wsic * TN + cc) * (WMITER * TM) + wsir * TM + cr]); + } + } + } + } + } +} +""" + +mulmat_split_k_reduce_src = """#version 450 + +#extension GL_EXT_control_flow_attributes : enable + +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {float data_a[];}; +layout (binding = 1) writeonly buffer D {float data_d[];}; + +layout (push_constant) uniform parameter { + uint ne; + uint k_num; +} p; + +void main() { + const uint idx = gl_GlobalInvocationID.x; + + if (idx >= p.ne) { + return; + } + + float result = 0.0f; + + [[unroll]] for (uint i = 0; i < p.k_num; i++) { + result += data_a[i * p.ne + idx]; + } + + data_d[idx] = result; +} +""" + +# DEQUANT SHADER +dequant_head = """#version 450 + +#extension GL_EXT_control_flow_attributes : require +#extension GL_EXT_shader_16bit_storage : require +""" + +dequant_body = """ +layout(local_size_x = 256, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +layout (push_constant) uniform parameter +{ + int M; + int K; + int stride_a; + int stride_b; +} p; + +void main() { + const int i = int(gl_GlobalInvocationID.x); + + // Transposed + const int row = i % (p.K / QUANT_K); + const int col = i / (p.K / QUANT_K); + + if (row * QUANT_K >= p.K || col >= p.M) { + return; + } + + const int stride_a = p.stride_a / QUANT_K; + + const int ib = col * stride_a + row; + + const int y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; + const int step = QUANT_R == 1 ? 2 : 1; + + [[unroll]] for (int iqs = 0; iqs < QUANT_K/QUANT_R; iqs += step) { + DEQUANT_FUNC + + data_b[col * p.stride_b + row*QUANT_K + iqs + 0 ] = D_TYPE(v.x); + data_b[col * p.stride_b + row*QUANT_K + iqs + y_offset] = D_TYPE(v.y); + } +} +""" + +# K-quants +dequant_q2_K_body = """ +layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +layout (push_constant) uniform parameter +{ + int M; + int K; + int stride_a; + int stride_b; +} p; + +void main() { + [[unroll]] for (int wgy = 0; wgy < 256; wgy++) { + const int i = int(gl_WorkGroupID.x * 256 + wgy); + if (i >= p.M * p.K / QUANT_K) { + return; + } + + const int tid = int(gl_LocalInvocationID.x); + const int ip = tid / 32; + const int il = tid - 32 * ip; + const int is = 8 * ip + il / 16; + + const int y_idx = i * QUANT_K + 128 * ip + il; + + const int ql_idx = 32 * ip + il; + const uint8_t qs = data_a[i].qs[32 * ip + il]; + + FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x); + FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y); + data_b[y_idx + 0] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+0] & 0xF) * ((qs >> 0) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+0] >> 4)); + data_b[y_idx + 32] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+2] & 0xF) * ((qs >> 2) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+2] >> 4)); + data_b[y_idx + 64] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+4] & 0xF) * ((qs >> 4) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+4] >> 4)); + data_b[y_idx + 96] = D_TYPE(dall * FLOAT_TYPE((data_a[i].scales[is+6] & 0xF) * ((qs >> 6) & 3)) - dmin * FLOAT_TYPE(data_a[i].scales[is+6] >> 4)); + } +} +""" +dequant_q3_K_body = """ +layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +layout (push_constant) uniform parameter +{ + int M; + int K; + int stride_a; + int stride_b; +} p; + +void main() { + [[unroll]] for (int wgy = 0; wgy < 256; wgy++) { + const int i = int(gl_WorkGroupID.x * 256 + wgy); + if (i >= p.M * p.K / QUANT_K) { + return; + } + + const int r = int(gl_LocalInvocationID.x) / 4; + const int tid = r / 2; + const int is0 = r % 2; + const int l0 = 16 * is0 + 4 * (int(gl_LocalInvocationID.x) % 4); + const int n = tid / 4; + const int j = tid - 4*n; + + const uint8_t m = uint8_t(1 << (4*n + j)); + const int is = 8*n + 2*j + is0; + const int shift = 2*j; + + const int8_t us = int8_t(is < 4 ? (data_a[i].scales[is-0] & 0xF) | (((data_a[i].scales[is+8] >> 0) & 3) << 4) : + is < 8 ? (data_a[i].scales[is-0] & 0xF) | (((data_a[i].scales[is+4] >> 2) & 3) << 4) : + is < 12 ? (data_a[i].scales[is-8] >> 4) | (((data_a[i].scales[is+0] >> 4) & 3) << 4) : + (data_a[i].scales[is-8] >> 4) | (((data_a[i].scales[is-4] >> 6) & 3) << 4)); + const FLOAT_TYPE d_all = FLOAT_TYPE(data_a[i].d); + const FLOAT_TYPE dl = d_all * FLOAT_TYPE(us - 32); + + const int y_idx = i * QUANT_K + 128 * n + 32 * j; + const int qs_idx = 32*n; + + for (int l = l0; l < l0 + 4; ++l) { + data_b[y_idx + l] = D_TYPE(dl * FLOAT_TYPE(int8_t((data_a[i].qs[qs_idx + l] >> shift) & 3) - (((data_a[i].hmask[l] & m) != 0) ? 0 : 4))); + } + } +} +""" +dequant_q4_K_body = """ +layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +layout (push_constant) uniform parameter +{ + int M; + int K; + int stride_a; + int stride_b; +} p; + +void main() { + [[unroll]] for (int wgy = 0; wgy < 256; wgy++) { + const int i = int(gl_WorkGroupID.x * 256 + wgy); + if (i >= p.M * p.K / QUANT_K) { + return; + } + + const int tid = int(gl_LocalInvocationID.x); + const int il = tid / 8; + const int ir = tid % 8; + const int is = 2 * il; + const int n = 4; + + const FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x); + const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y); + + const int y_idx = i * QUANT_K + 64 * il + n * ir; + const int qs_idx = 32*il + n * ir; + + uint8_t sc; + uint8_t m; + if (is < 4) { + sc = uint8_t(data_a[i].scales[is] & 63); + m = uint8_t(data_a[i].scales[is + 4] & 63); + } else { + sc = uint8_t((data_a[i].scales[is + 4] & 0xF) | ((data_a[i].scales[is - 4] >> 6) << 4)); + m = uint8_t((data_a[i].scales[is + 4] >> 4) | ((data_a[i].scales[is ] >> 6) << 4)); + } + const FLOAT_TYPE d1 = dall * sc; + const FLOAT_TYPE m1 = dmin * m; + + if (is < 4) { + sc = uint8_t(data_a[i].scales[is + 1] & 63); + m = uint8_t(data_a[i].scales[is + 5] & 63); + } else { + sc = uint8_t((data_a[i].scales[is + 5] & 0xF) | ((data_a[i].scales[is - 3] >> 6) << 4)); + m = uint8_t((data_a[i].scales[is + 5] >> 4) | ((data_a[i].scales[is + 1] >> 6) << 4)); + } + const FLOAT_TYPE d2 = dall * sc; + const FLOAT_TYPE m2 = dmin * m; + + [[unroll]] for (int l = 0; l < n; ++l) { + data_b[y_idx + l ] = D_TYPE(d1 * FLOAT_TYPE(data_a[i].qs[qs_idx + l] & 0xF) - m1); + data_b[y_idx + l + 32] = D_TYPE(d2 * FLOAT_TYPE(data_a[i].qs[qs_idx + l] >> 4) - m2); + } + } +} +""" +dequant_q5_K_body = """ +layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +layout (push_constant) uniform parameter +{ + int M; + int K; + int stride_a; + int stride_b; +} p; + +void main() { + [[unroll]] for (int wgy = 0; wgy < 256; wgy++) { + const int i = int(gl_WorkGroupID.x * 256 + wgy); + if (i >= p.M * p.K / QUANT_K) { + return; + } + + const int tid = int(gl_LocalInvocationID.x); + const int il = tid / 16; + const int ir = tid % 16; + const int is = 2 * il; + + const FLOAT_TYPE dall = FLOAT_TYPE(data_a[i].d.x); + const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[i].d.y); + + const int y_idx = i * QUANT_K + 64 * il + 2 * ir; + const int qs_idx = 32*il + 2 * ir; + const int qh_idx = 2 * ir; + + uint8_t sc; + uint8_t m; + if (is < 4) { + sc = uint8_t(data_a[i].scales[is] & 63); + m = uint8_t(data_a[i].scales[is + 4] & 63); + } else { + sc = uint8_t((data_a[i].scales[is + 4] & 0xF) | ((data_a[i].scales[is - 4] >> 6) << 4)); + m = uint8_t((data_a[i].scales[is + 4] >> 4) | ((data_a[i].scales[is ] >> 6) << 4)); + } + const FLOAT_TYPE d1 = dall * sc; + const FLOAT_TYPE m1 = dmin * m; + + if (is < 4) { + sc = uint8_t(data_a[i].scales[is + 1] & 63); + m = uint8_t(data_a[i].scales[is + 5] & 63); + } else { + sc = uint8_t((data_a[i].scales[is + 5] & 0xF) | ((data_a[i].scales[is - 3] >> 6) << 4)); + m = uint8_t((data_a[i].scales[is + 5] >> 4) | ((data_a[i].scales[is + 1] >> 6) << 4)); + } + const FLOAT_TYPE d2 = dall * sc; + const FLOAT_TYPE m2 = dmin * m; + + const uint8_t hm1 = uint8_t(1 << (2 * il )); + const uint8_t hm2 = uint8_t(1 << (2 * il + 1)); + data_b[y_idx ] = D_TYPE(d1 * FLOAT_TYPE((data_a[i].qs[qs_idx ] & 0xF) + (((data_a[i].qh[qh_idx ] & hm1) != 0) ? 16 : 0)) - m1); + data_b[y_idx + 1] = D_TYPE(d1 * FLOAT_TYPE((data_a[i].qs[qs_idx + 1] & 0xF) + (((data_a[i].qh[qh_idx + 1] & hm1) != 0) ? 16 : 0)) - m1); + data_b[y_idx + 32] = D_TYPE(d2 * FLOAT_TYPE((data_a[i].qs[qs_idx ] >> 4) + (((data_a[i].qh[qh_idx ] & hm2) != 0) ? 16 : 0)) - m2); + data_b[y_idx + 33] = D_TYPE(d2 * FLOAT_TYPE((data_a[i].qs[qs_idx + 1] >> 4) + (((data_a[i].qh[qh_idx + 1] & hm2) != 0) ? 16 : 0)) - m2); + } +} +""" +dequant_q6_K_body = """ +layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_b[];}; + +layout (push_constant) uniform parameter +{ + int M; + int K; + int stride_a; + int stride_b; +} p; + +void main() { + [[unroll]] for (int wgy = 0; wgy < 256; wgy++) { + const int i = int(gl_WorkGroupID.x * 256 + wgy); + if (i >= p.M * p.K / QUANT_K) { + return; + } + const int tid = int(gl_LocalInvocationID.x); + const int ip = tid / 32; + const int il = tid - 32 * ip; + const int is = 8 * ip + il / 16; + + const int y_idx = i * QUANT_K + 128 * ip + il; + + const int ql_idx = 64 * ip + il; + const uint8_t qh = data_a[i].qh[32 * ip + il]; + + const FLOAT_TYPE d = FLOAT_TYPE(data_a[i].d); + + data_b[y_idx + 0] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 0] * (int8_t((data_a[i].ql[ql_idx + 0] & 0xF) | (((qh >> 0) & 3) << 4)) - 32))); + data_b[y_idx + 32] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 2] * (int8_t((data_a[i].ql[ql_idx + 32] & 0xF) | (((qh >> 2) & 3) << 4)) - 32))); + data_b[y_idx + 64] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 4] * (int8_t((data_a[i].ql[ql_idx + 0] >> 4) | (((qh >> 4) & 3) << 4)) - 32))); + data_b[y_idx + 96] = D_TYPE(d * FLOAT_TYPE(data_a[i].scales[is + 6] * (int8_t((data_a[i].ql[ql_idx + 32] >> 4) | (((qh >> 6) & 3) << 4)) - 32))); + } +} +""" + +# Mul Mat Vec +mul_mat_vec_head = """#version 450 + +#extension GL_EXT_control_flow_attributes : enable +#extension GL_EXT_shader_16bit_storage : require +#extension GL_EXT_shader_8bit_storage : require +""" + +mul_mat_vec_body = """ +layout(local_size_x = QUANT_K, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; + +layout (push_constant) uniform parameter +{ + int ncols; + int b_offset; + int d_offset; +} p; + +shared FLOAT_TYPE tmp[QUANT_K]; + +void main() { + const int block_size = int(gl_WorkGroupSize.x); + const int row = int(gl_WorkGroupID.x); + const int tid = int(gl_LocalInvocationID.x); + + const int y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; + + tmp[tid] = FLOAT_TYPE(0.0f); + + [[unroll]] for (int i = 0; i < p.ncols/block_size; i += 2) { + const int col = i*block_size + 2*tid; + const int ib = (row*p.ncols + col)/QUANT_K; // block index + const int iqs = (col%QUANT_K)/QUANT_R; // quant index + const int iybs = col - col%QUANT_K; // y block start index + + DEQUANT_FUNC + + // matrix multiplication + tmp[tid] += FLOAT_TYPE(v.x) * FLOAT_TYPE(data_b[p.b_offset + iybs + iqs + 0]); + tmp[tid] += FLOAT_TYPE(v.y) * FLOAT_TYPE(data_b[p.b_offset + iybs + iqs + y_offset]); + } + + // sum up partial sums and write back result + barrier(); + [[unroll]] for (int s = block_size/2; s > 0; s >>= 1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(); + } + if (tid == 0) { + dst[p.d_offset + row] = D_TYPE(tmp[0]); + } +} +""" + +# K-quants +mul_mat_vec_q2_K_body = """ +layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; + +layout (push_constant) uniform parameter +{ + int ncols; + int b_offset; + int d_offset; +} p; + +shared FLOAT_TYPE tmp[32]; + +void main() { + const int row = int(gl_WorkGroupID.x); + + const int num_blocks_per_row = p.ncols / QUANT_K; + const int ib0 = row*num_blocks_per_row; + + const int tid = int(gl_LocalInvocationID.x)/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const int ix = int(gl_LocalInvocationID.x)%K_QUANTS_PER_ITERATION; // 0 or 0, 1 + + const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 + + const int v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... + const int v_in = tid - step*v_im; // 0...15 or 0...7 + + const int l0 = K_QUANTS_PER_ITERATION*v_in; // 0...15 + const int q_offset = 32*v_im + l0; + const int s_offset = 8*v_im; + const int y_offset = 128*v_im + l0; + + tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp + + [[unroll]] for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { + const int y_idx = i * QUANT_K + y_offset; + + const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x); + const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y); + + FLOAT_TYPE sum1 = FLOAT_TYPE(0.0); + FLOAT_TYPE sum2 = FLOAT_TYPE(0.0); + for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { + sum1 += FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 0) & 3) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 0) & 3) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 2) & 3) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 2) & 3) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 4) & 3) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 4) & 3) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 6) & 3) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 6) & 3); + sum2 += FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 0]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 0] >> 4) & 0xF) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 16]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 1] >> 4) & 0xF) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 32]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 2] >> 4) & 0xF) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 48]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 3] >> 4) & 0xF) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 64]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 4] >> 4) & 0xF) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 80]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 5] >> 4) & 0xF) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 96]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 6] >> 4) & 0xF) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l +112]) * FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 7] >> 4) & 0xF); + } + tmp[16 * ix + tid] += dall * sum1 - dmin * sum2; + } + + // sum up partial sums and write back result + barrier(); + [[unroll]] for (int s = 16; s > 0; s >>= 1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(); + } + if (tid == 0) { + dst[p.d_offset + row] = D_TYPE(tmp[0]); + } +} +""" +mul_mat_vec_q3_K_body = """ +layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; + +layout (push_constant) uniform parameter +{ + int ncols; + int b_offset; + int d_offset; +} p; + +shared FLOAT_TYPE tmp[32]; + +void main() { + const int row = int(gl_WorkGroupID.x); + + const int num_blocks_per_row = p.ncols / QUANT_K; + const int ib0 = row*num_blocks_per_row; + + const int tid = int(gl_LocalInvocationID.x)/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const int ix = int(gl_LocalInvocationID.x)%K_QUANTS_PER_ITERATION; // 0 or 0, 1 + + const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 + + const int v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... + const int v_in = tid - step*v_im; // 0...15 or 0...7 + + const uint8_t m = uint8_t(1 << (4 * v_im)); + + const int l0 = K_QUANTS_PER_ITERATION*v_in; // 0...15 + const int q_offset = 32*v_im + l0; + const int y_offset = 128*v_im + l0; + + tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp + + const uint s_shift = 4 * v_im; + + [[unroll]] for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { + const int y_idx = i * QUANT_K + y_offset; + + const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); + + FLOAT_TYPE sum = FLOAT_TYPE(0.0); + for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) { + sum += FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 0]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[0] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 0)) != 0) ? 0 : 4)) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 32]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[2] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 1)) != 0) ? 0 : 4)) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 64]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[4] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 8] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 2)) != 0) ? 0 : 4)) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 96]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[6] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[10] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l ] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l ] & (m << 3)) != 0) ? 0 : 4)) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 16]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[1] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] ) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 0)) != 0) ? 0 : 4)) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 48]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[3] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 0) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 2) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 1)) != 0) ? 0 : 4)) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l + 80]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[5] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[ 9] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 4) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 2)) != 0) ? 0 : 4)) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l +112]) * FLOAT_TYPE(int8_t(((data_a[ib0 + i].scales[7] >> s_shift) & 0xF) | ((data_a[ib0 + i].scales[11] >> (s_shift + 2) & 0x3) << 4)) - 32) * FLOAT_TYPE(((data_a[ib0 + i].qs[q_offset + l+16] >> 6) & 3) - (((data_a[ib0 + i].hmask[l0 + l+16] & (m << 3)) != 0) ? 0 : 4)); + } + tmp[16 * ix + tid] += d * sum; + } + + // sum up partial sums and write back result + barrier(); + [[unroll]] for (int s = 16; s > 0; s >>= 1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(); + } + if (tid == 0) { + dst[p.d_offset + row] = D_TYPE(tmp[0]); + } +} +""" +mul_mat_vec_q4_K_body = """ +layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; + +layout (push_constant) uniform parameter +{ + int ncols; + int b_offset; + int d_offset; +} p; + +shared FLOAT_TYPE tmp[32]; + +void main() { + const int row = int(gl_WorkGroupID.x); + + const int num_blocks_per_row = p.ncols / QUANT_K; + const int ib0 = row*num_blocks_per_row; + + const int tid = int(gl_LocalInvocationID.x)/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const int ix = int(gl_LocalInvocationID.x)%K_QUANTS_PER_ITERATION; // 0 or 0, 1 + + 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 v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 + const int v_in = il % 2; + + const int l0 = n * (2 * ir + v_in); // 0...15 + const int q_offset = 32*v_im + l0; + const int y_offset = 64*v_im + l0; + + tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp + + [[unroll]] for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { + const int y1_idx = i * QUANT_K + y_offset; + const int y2_idx = y1_idx + 128; + + const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x); + const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y); + + const uint8_t sc0 = uint8_t( data_a[ib0 + i].scales[v_im * 2 ] & 0x3f); + const uint8_t sc1 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 1] & 0x3f); + const uint8_t sc2 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 4] & 0x3f); + const uint8_t sc3 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 5] & 0x3f); + const uint8_t sc4 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 8] & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 ] & 0xc0) >> 2)); + const uint8_t sc5 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 9] & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 1] & 0xc0) >> 2)); + const uint8_t sc6 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 8] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 4] & 0xc0) >> 2)); + const uint8_t sc7 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 9] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 5] & 0xc0) >> 2)); + +#if K_QUANTS_PER_ITERATION == 2 + const uint8_t q4_0 = uint8_t(data_a[ib0 + i].qs[q_offset ] & 0xf); + const uint8_t q4_1 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] & 0xf); + const uint8_t q4_2 = uint8_t(data_a[ib0 + i].qs[q_offset + 2] & 0xf); + const uint8_t q4_3 = uint8_t(data_a[ib0 + i].qs[q_offset + 3] & 0xf); + const uint8_t q4_4 = uint8_t(data_a[ib0 + i].qs[q_offset ] >> 4); + const uint8_t q4_5 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] >> 4); + const uint8_t q4_6 = uint8_t(data_a[ib0 + i].qs[q_offset + 2] >> 4); + const uint8_t q4_7 = uint8_t(data_a[ib0 + i].qs[q_offset + 3] >> 4); + const uint8_t q4_8 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf); + const uint8_t q4_9 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf); + const uint8_t q4_10 = uint8_t(data_a[ib0 + i].qs[q_offset + 66] & 0xf); + const uint8_t q4_11 = uint8_t(data_a[ib0 + i].qs[q_offset + 67] & 0xf); + const uint8_t q4_12 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] >> 4); + const uint8_t q4_13 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] >> 4); + const uint8_t q4_14 = uint8_t(data_a[ib0 + i].qs[q_offset + 66] >> 4); + const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 67] >> 4); + + const FLOAT_TYPE sx = FLOAT_TYPE(data_b[p.b_offset + y1_idx] * q4_0 + data_b[p.b_offset + y1_idx + 1] * q4_1 + data_b[p.b_offset + y1_idx + 2] * q4_2 + data_b[p.b_offset + y1_idx + 3] * q4_3); + const FLOAT_TYPE sy = FLOAT_TYPE(data_b[p.b_offset + y1_idx + 32] * q4_4 + data_b[p.b_offset + y1_idx + 33] * q4_5 + data_b[p.b_offset + y1_idx + 34] * q4_6 + data_b[p.b_offset + y1_idx + 35] * q4_7); + const FLOAT_TYPE sz = FLOAT_TYPE(data_b[p.b_offset + y2_idx] * q4_8 + data_b[p.b_offset + y2_idx + 1] * q4_9 + data_b[p.b_offset + y2_idx + 2] * q4_10 + data_b[p.b_offset + y2_idx + 3] * q4_11); + const FLOAT_TYPE sw = FLOAT_TYPE(data_b[p.b_offset + y2_idx + 32] * q4_12 + data_b[p.b_offset + y2_idx + 33] * q4_13 + data_b[p.b_offset + y2_idx + 34] * q4_14 + data_b[p.b_offset + y2_idx + 35] * q4_15); + const FLOAT_TYPE smin = FLOAT_TYPE( + data_b[p.b_offset + y1_idx ] * sc2 + data_b[p.b_offset + y1_idx + 32] * sc3 + data_b[p.b_offset + y2_idx ] * sc6 + data_b[p.b_offset + y2_idx + 32] * sc7 + + data_b[p.b_offset + y1_idx + 1] * sc2 + data_b[p.b_offset + y1_idx + 33] * sc3 + data_b[p.b_offset + y2_idx + 1] * sc6 + data_b[p.b_offset + y2_idx + 33] * sc7 + + data_b[p.b_offset + y1_idx + 2] * sc2 + data_b[p.b_offset + y1_idx + 34] * sc3 + data_b[p.b_offset + y2_idx + 2] * sc6 + data_b[p.b_offset + y2_idx + 34] * sc7 + + data_b[p.b_offset + y1_idx + 3] * sc2 + data_b[p.b_offset + y1_idx + 35] * sc3 + data_b[p.b_offset + y2_idx + 3] * sc6 + data_b[p.b_offset + y2_idx + 35] * sc7 + ); + tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * sc0 + sy * sc1 + sz * sc4 + sw * sc5) - dmin * smin); +#else + const uint8_t q4_0 = uint8_t(data_a[ib0 + i].qs[q_offset ] & 0xf); + const uint8_t q4_1 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] & 0xf); + const uint8_t q4_2 = uint8_t(data_a[ib0 + i].qs[q_offset ] >> 4); + const uint8_t q4_3 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] >> 4); + const uint8_t q4_4 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf); + const uint8_t q4_5 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf); + const uint8_t q4_6 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] >> 4); + const uint8_t q4_7 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] >> 4); + + const FLOAT_TYPE sx = FLOAT_TYPE(data_b[p.b_offset + y1_idx ] * q4_0 + data_b[p.b_offset + y1_idx + 1] * q4_1); + const FLOAT_TYPE sy = FLOAT_TYPE(data_b[p.b_offset + y1_idx + 32] * q4_2 + data_b[p.b_offset + y1_idx + 33] * q4_3); + const FLOAT_TYPE sz = FLOAT_TYPE(data_b[p.b_offset + y2_idx ] * q4_4 + data_b[p.b_offset + y2_idx + 1] * q4_5); + const FLOAT_TYPE sw = FLOAT_TYPE(data_b[p.b_offset + y2_idx + 32] * q4_6 + data_b[p.b_offset + y2_idx + 33] * q4_7); + const FLOAT_TYPE smin = FLOAT_TYPE( + data_b[p.b_offset + y1_idx] * sc2 + data_b[p.b_offset + y1_idx + 32] * sc3 + data_b[p.b_offset + y2_idx] * sc6 + data_b[p.b_offset + y2_idx + 32] * sc7 + + data_b[p.b_offset + y1_idx + 1] * sc2 + data_b[p.b_offset + y1_idx + 33] * sc3 + data_b[p.b_offset + y2_idx + 1] * sc6 + data_b[p.b_offset + y2_idx + 33] * sc7 + ); + + tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * FLOAT_TYPE(data_a[ib0 + i].scales[v_im] & 0x3f) + sy * FLOAT_TYPE(data_a[ib0 + i].scales[v_im + 1] & 0x3f) + sz * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 4] & 0x0f) | ((data_a[ib0 + i].scales[v_im] & 0xc0) >> 2)) + sw * FLOAT_TYPE((data_a[ib0 + i].scales[v_im + 5] & 0x0f) | ((data_a[ib0 + i].scales[v_im + 1] & 0xc0) >> 2))) - dmin * smin); +#endif + } + + // sum up partial sums and write back result + barrier(); + [[unroll]] for (int s = 16; s > 0; s >>= 1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(); + } + if (tid == 0) { + dst[p.d_offset + row] = D_TYPE(tmp[0]); + } +} +""" +mul_mat_vec_q5_K_body = """ +layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; + +layout (push_constant) uniform parameter +{ + int ncols; + int b_offset; + int d_offset; +} p; + +shared FLOAT_TYPE tmp[32]; + +void main() { + const int row = int(gl_WorkGroupID.x); + + const int num_blocks_per_row = p.ncols / QUANT_K; + const int ib0 = row*num_blocks_per_row; + + const int tid = int(gl_LocalInvocationID.x)/2; // 0...31 or 0...16 + const int ix = int(gl_LocalInvocationID.x)%2; // 0 or 0, 1 + + const int il = tid/4; // 0...3 + const int ir = tid - 4*il; // 0...7 or 0...3 + + const int v_im = il / 2; // 0 or 1. 0 computes 0,32 + 128,160, 1 computes 64,96 + 192,224 + const int v_in = il % 2; + + const int l0 = 4*ir + 2*v_in; // 0...15 + const int q_offset = 32*v_im + l0; + const int y_offset = 64*v_im + l0; + + const uint8_t hm1 = uint8_t(1 << (2*v_im)); + const uint8_t hm2 = uint8_t(hm1 << 4); + + tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp + + [[unroll]] for (int i = ix; i < num_blocks_per_row; i += 2) { + const int y1_idx = i * QUANT_K + y_offset; + const int y2_idx = y1_idx + 128; + + const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x); + const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y); + + const uint8_t sc0 = uint8_t( data_a[ib0 + i].scales[v_im * 2 ] & 0x3f); + const uint8_t sc1 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 1] & 0x3f); + const uint8_t sc2 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 4] & 0x3f); + const uint8_t sc3 = uint8_t( data_a[ib0 + i].scales[v_im * 2 + 5] & 0x3f); + const uint8_t sc4 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 8] & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 ] & 0xc0) >> 2)); + const uint8_t sc5 = uint8_t(( data_a[ib0 + i].scales[v_im * 2 + 9] & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 1] & 0xc0) >> 2)); + const uint8_t sc6 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 8] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 4] & 0xc0) >> 2)); + const uint8_t sc7 = uint8_t(((data_a[ib0 + i].scales[v_im * 2 + 9] >> 4) & 0x0f) | ((data_a[ib0 + i].scales[v_im * 2 + 5] & 0xc0) >> 2)); + + const uint8_t q4_0 = uint8_t(data_a[ib0 + i].qs[q_offset ] & 0xf); + const uint8_t q4_1 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] & 0xf); + const uint8_t q4_2 = uint8_t(data_a[ib0 + i].qs[q_offset + 16] & 0xf); + const uint8_t q4_3 = uint8_t(data_a[ib0 + i].qs[q_offset + 17] & 0xf); + const uint8_t q4_4 = uint8_t(data_a[ib0 + i].qs[q_offset ] >> 4); + const uint8_t q4_5 = uint8_t(data_a[ib0 + i].qs[q_offset + 1] >> 4); + const uint8_t q4_6 = uint8_t(data_a[ib0 + i].qs[q_offset + 16] >> 4); + const uint8_t q4_7 = uint8_t(data_a[ib0 + i].qs[q_offset + 17] >> 4); + const uint8_t q4_8 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] & 0xf); + const uint8_t q4_9 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] & 0xf); + const uint8_t q4_10 = uint8_t(data_a[ib0 + i].qs[q_offset + 80] & 0xf); + const uint8_t q4_11 = uint8_t(data_a[ib0 + i].qs[q_offset + 81] & 0xf); + const uint8_t q4_12 = uint8_t(data_a[ib0 + i].qs[q_offset + 64] >> 4); + const uint8_t q4_13 = uint8_t(data_a[ib0 + i].qs[q_offset + 65] >> 4); + const uint8_t q4_14 = uint8_t(data_a[ib0 + i].qs[q_offset + 80] >> 4); + const uint8_t q4_15 = uint8_t(data_a[ib0 + i].qs[q_offset + 81] >> 4); + + const FLOAT_TYPE sx = FLOAT_TYPE( + data_b[p.b_offset + y1_idx ] * (q4_0 + (((data_a[ib0 + i].qh[l0 ] & hm1) != 0) ? 16 : 0)) + + data_b[p.b_offset + y1_idx + 1] * (q4_1 + (((data_a[ib0 + i].qh[l0 + 1] & hm1) != 0) ? 16 : 0)) + + data_b[p.b_offset + y1_idx + 16] * (q4_2 + (((data_a[ib0 + i].qh[l0 + 16] & hm1) != 0) ? 16 : 0)) + + data_b[p.b_offset + y1_idx + 17] * (q4_3 + (((data_a[ib0 + i].qh[l0 + 17] & hm1) != 0) ? 16 : 0)) + ); + const FLOAT_TYPE sy = FLOAT_TYPE( + data_b[p.b_offset + y1_idx + 32] * (q4_4 + (((data_a[ib0 + i].qh[l0 ] & (hm1 << 1)) != 0) ? 16 : 0)) + + data_b[p.b_offset + y1_idx + 33] * (q4_5 + (((data_a[ib0 + i].qh[l0 + 1] & (hm1 << 1)) != 0) ? 16 : 0)) + + data_b[p.b_offset + y1_idx + 48] * (q4_6 + (((data_a[ib0 + i].qh[l0 + 16] & (hm1 << 1)) != 0) ? 16 : 0)) + + data_b[p.b_offset + y1_idx + 49] * (q4_7 + (((data_a[ib0 + i].qh[l0 + 17] & (hm1 << 1)) != 0) ? 16 : 0)) + ); + const FLOAT_TYPE sz = FLOAT_TYPE( + data_b[p.b_offset + y2_idx ] * (q4_8 + (((data_a[ib0 + i].qh[l0 ] & hm2) != 0) ? 16 : 0)) + + data_b[p.b_offset + y2_idx + 1] * (q4_9 + (((data_a[ib0 + i].qh[l0 + 1] & hm2) != 0) ? 16 : 0)) + + data_b[p.b_offset + y2_idx + 16] * (q4_10 + (((data_a[ib0 + i].qh[l0 + 16] & hm2) != 0) ? 16 : 0)) + + data_b[p.b_offset + y2_idx + 17] * (q4_11 + (((data_a[ib0 + i].qh[l0 + 17] & hm2) != 0) ? 16 : 0)) + ); + const FLOAT_TYPE sw = FLOAT_TYPE( + data_b[p.b_offset + y2_idx + 32] * (q4_12 + (((data_a[ib0 + i].qh[l0 ] & (hm2 << 1)) != 0) ? 16 : 0)) + + data_b[p.b_offset + y2_idx + 33] * (q4_13 + (((data_a[ib0 + i].qh[l0 + 1] & (hm2 << 1)) != 0) ? 16 : 0)) + + data_b[p.b_offset + y2_idx + 48] * (q4_14 + (((data_a[ib0 + i].qh[l0 + 16] & (hm2 << 1)) != 0) ? 16 : 0)) + + data_b[p.b_offset + y2_idx + 49] * (q4_15 + (((data_a[ib0 + i].qh[l0 + 17] & (hm2 << 1)) != 0) ? 16 : 0)) + ); + const FLOAT_TYPE smin = FLOAT_TYPE( + (data_b[p.b_offset + y1_idx] + data_b[p.b_offset + y1_idx + 1] + data_b[p.b_offset + y1_idx + 16] + data_b[p.b_offset + y1_idx + 17]) * sc2 + (data_b[p.b_offset + y1_idx + 32] + data_b[p.b_offset + y1_idx + 33] + data_b[p.b_offset + y1_idx + 48] + data_b[p.b_offset + y1_idx + 49]) * sc3 + + (data_b[p.b_offset + y2_idx] + data_b[p.b_offset + y2_idx + 1] + data_b[p.b_offset + y2_idx + 16] + data_b[p.b_offset + y2_idx + 17]) * sc6 + (data_b[p.b_offset + y2_idx + 32] + data_b[p.b_offset + y2_idx + 33] + data_b[p.b_offset + y2_idx + 48] + data_b[p.b_offset + y2_idx + 49]) * sc7 + ); + tmp[16 * ix + tid] += FLOAT_TYPE(dall * (sx * sc0 + sy * sc1 + sz * sc4 + sw * sc5) - dmin * smin); + } + + // sum up partial sums and write back result + barrier(); + [[unroll]] for (int s = 16; s > 0; s >>= 1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(); + } + if (tid == 0) { + dst[p.d_offset + row] = D_TYPE(tmp[0]); + } +} +""" +mul_mat_vec_q6_K_body = """ +layout(local_size_x = 32, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; + +layout (push_constant) uniform parameter +{ + int ncols; + int b_offset; + int d_offset; +} p; + +shared FLOAT_TYPE tmp[32]; + +void main() { + const int row = int(gl_WorkGroupID.x); + + const int num_blocks_per_row = p.ncols / QUANT_K; + const int ib0 = row*num_blocks_per_row; + + const int tid = int(gl_LocalInvocationID.x)/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 + const int ix = int(gl_LocalInvocationID.x)%K_QUANTS_PER_ITERATION; // 0 or 0, 1 + + const int step = 16/K_QUANTS_PER_ITERATION; // 16 or 8 + + const int v_im = tid/step; // 0 or 1. 0 computes 0..., 1 computes 128... + const int v_in = tid - step*v_im; // 0...15 or 0...7 + +#if K_QUANTS_PER_ITERATION == 1 + const int l0 = K_QUANTS_PER_ITERATION*v_in; // 0...15 + const int is = 0; +#else + const int l0 = 4 * v_in; // 0, 4, 8, ..., 28 + const int is = v_in / 4; +#endif + + const int ql_offset = 64*v_im + l0; + const int qh_offset = 32*v_im + l0; + const int s_offset = 8*v_im + is; + const int y_offset = 128*v_im + l0; + + tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp + + [[unroll]] for (int i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) { + const int y_idx = i * QUANT_K + y_offset; + + const FLOAT_TYPE d = FLOAT_TYPE(data_a[ib0 + i].d); + +#if K_QUANTS_PER_ITERATION == 1 + FLOAT_TYPE sum = FLOAT_TYPE(data_b[p.b_offset + y_idx + 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 0] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x03) << 4)) - 32) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + 16]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x03) << 4)) - 32) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + 32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x0c) << 2)) - 32) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + 48]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] & 0xF) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x0c) << 2)) - 32) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + 64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 0] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0x30) >> 0)) - 32) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + 80]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 16] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0x30) >> 0)) - 32) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + 96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 32] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 0] & 0xc0) >> 2)) - 32) + + FLOAT_TYPE(data_b[p.b_offset + y_idx +112]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + 48] >> 4) | ((data_a[ib0 + i].qh[qh_offset + 16] & 0xc0) >> 2)) - 32); + tmp[16 * ix + tid] += sum; +#else + FLOAT_TYPE sum = FLOAT_TYPE(0.0); + [[unroll]] for (int l = 0; l < 4; ++l) { + sum += FLOAT_TYPE(data_b[p.b_offset + y_idx + l+ 0]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 0) & 3) << 4)) - 32) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l+32]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] & 0xF) | (((data_a[ib0 + i].qh[qh_offset + l] >> 2) & 3) << 4)) - 32) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l+64]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+ 0] >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 4) & 3) << 4)) - 32) + + FLOAT_TYPE(data_b[p.b_offset + y_idx + l+96]) * FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6]) * d * FLOAT_TYPE(int8_t((data_a[ib0 + i].ql[ql_offset + l+32] >> 4) | (((data_a[ib0 + i].qh[qh_offset + l] >> 6) & 3) << 4)) - 32); + } + tmp[16 * ix + tid] += sum; +#endif + } + + // sum up partial sums and write back result + barrier(); + [[unroll]] for (int s = 16; s > 0; s >>= 1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(); + } + if (tid == 0) { + dst[p.d_offset + row] = D_TYPE(tmp[0]); + } +} +""" + +mul_mat_p021_src = """#version 450 + +#extension GL_EXT_control_flow_attributes : enable +#extension GL_EXT_shader_16bit_storage : require + +#define BLOCK_SIZE 32 +#define FLOAT_TYPE float + +layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; + +layout (push_constant) uniform parameter +{ + uint ncols_x; + uint nrows_x; + uint nchannels_x; + uint nchannels_y; + uint b_offset; + uint d_offset; +} p; + +shared FLOAT_TYPE tmp[BLOCK_SIZE]; + +void main() { + const uint tid = gl_LocalInvocationID.x; + const uint row_x = gl_GlobalInvocationID.y; + const uint channel = gl_GlobalInvocationID.z; + const uint channel_x = channel / (p.nchannels_y / p.nchannels_x); + + const uint nrows_y = p.ncols_x; + const uint nrows_dst = p.nrows_x; + const uint row_dst = row_x; + + tmp[tid] = FLOAT_TYPE(0.0f); + + for (uint col_x0 = 0; col_x0 < p.ncols_x; col_x0 += BLOCK_SIZE) { + const uint col_x = col_x0 + tid; + + if (col_x >= p.ncols_x) { + break; + } + + // x is transposed and permuted + const uint ix = row_x*p.nchannels_x*p.ncols_x + channel_x*p.ncols_x + col_x; + const FLOAT_TYPE xi = FLOAT_TYPE(data_a[ix]); + + const uint row_y = col_x; + + // y is not transposed but permuted + const uint iy = channel*nrows_y + row_y; + + tmp[tid] += xi * FLOAT_TYPE(data_b[iy]); + } + + // dst is not transposed and not permuted + const uint idst = channel*nrows_dst + row_dst; + + // sum up partial sums and write back result + barrier(); + [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(); + } + + if (tid == 0) { + dst[idst] = tmp[0]; + } +} +""" + + +mul_mat_nc_src = """#version 450 + +#extension GL_EXT_control_flow_attributes : enable +#extension GL_EXT_shader_16bit_storage : require + +#define BLOCK_SIZE 32 +#define FLOAT_TYPE float + +layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer B {B_TYPE data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; + +layout (push_constant) uniform parameter +{ + uint ncols_x; + uint nrows_x; + uint row_stride_x; + uint channel_stride_x; + uint channel_x_divisor; + uint b_offset; + uint d_offset; +} p; + +shared FLOAT_TYPE tmp[BLOCK_SIZE]; + +void main() { + const uint tid = gl_LocalInvocationID.x; + const uint row_x = gl_GlobalInvocationID.y; + const uint channel = gl_GlobalInvocationID.z; + const uint channel_x = channel / p.channel_x_divisor; + + const uint nrows_y = p.ncols_x; + const uint nrows_dst = p.nrows_x; + const uint row_dst = row_x; + + const uint idst = channel*nrows_dst + row_dst; + + tmp[tid] = 0.0f; + + for (uint col_x0 = 0; col_x0 < p.ncols_x; col_x0 += BLOCK_SIZE) { + const uint col_x = col_x0 + tid; + + if (col_x >= p.ncols_x) { + break; + } + + const uint row_y = col_x; + + const uint ix = channel_x*p.channel_stride_x + row_x*p.row_stride_x + col_x; + const uint iy = channel*nrows_y + row_y; + + const FLOAT_TYPE xi = FLOAT_TYPE(data_a[ix]); + + tmp[tid] += xi * FLOAT_TYPE(data_b[iy]); + } + + // sum up partial sums and write back result + barrier(); + [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + tmp[tid] += tmp[tid + s]; + } + barrier(); + } + + if (tid == 0) { + dst[idst] = tmp[0]; + } +} +""" + +# F16 to F32 +f32_to_f16_src = """#version 450 + +#extension GL_EXT_shader_16bit_storage : require + +layout(local_size_x = 64, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {float data_a[];}; +layout (binding = 1) writeonly buffer D {float16_t data_b[];}; + +layout (push_constant) uniform parameter +{ + int M; + int K; + int stride_a; + int stride_b; +} p; + +void main() { + const int row = int(gl_GlobalInvocationID.x % p.K); + const int col = int(gl_GlobalInvocationID.x / p.K); + + if (row < p.K && col < p.M) { + data_b[col * p.stride_b + row] = float16_t(data_a[col * p.stride_a + row]); + } +} +""" + +generic_head = """ +#version 450 + +#extension GL_EXT_shader_16bit_storage : require + +layout (push_constant) uniform parameter +{ + uint KX; + uint KY; + float param1; + float param2; +} p; +""" + +# MUL F32 +mul_body = """layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer Y {B_TYPE data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; + +void main() { + const uint idx = gl_GlobalInvocationID.x; + + if (idx >= p.KX) { + return; + } + + data_d[idx] = D_TYPE(FLOAT_TYPE(data_a[idx]) * FLOAT_TYPE(data_b[idx % p.KY])); +} +""" + +# ADD +add_body = """ +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer Y {B_TYPE data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; + +void main() { + const uint idx = gl_GlobalInvocationID.x; + + if (idx >= p.KX) { + return; + } + + data_d[idx] = D_TYPE(FLOAT_TYPE(data_a[idx]) + FLOAT_TYPE(data_b[idx % p.KY])); +} +""" + +# SCALE +scale_body = """layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +void main() { + const uint idx = gl_GlobalInvocationID.x; + + if (idx >= p.KX) { + return; + } + + data_d[idx] = D_TYPE(FLOAT_TYPE(data_a[idx]) * FLOAT_TYPE(p.param1)); +} +""" + +# SQR +sqr_body = """layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +void main() { + const uint idx = gl_GlobalInvocationID.x; + + if (idx >= p.KX) { + return; + } + + const FLOAT_TYPE val = FLOAT_TYPE(data_a[idx]); + data_d[idx] = D_TYPE(val * val); +} +""" + +# CLAMP +clamp_body = """layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +void main() { + const uint idx = gl_GlobalInvocationID.x; + + if (idx >= p.KX) { + return; + } + + const FLOAT_TYPE val = FLOAT_TYPE(data_a[idx]); + data_d[idx] = D_TYPE(val < p.param1 ? p.param1 : (val > p.param2 ? p.param2 : val)); +} +""" + +# CPY +cpy_src = """#version 450 + +#extension GL_EXT_shader_16bit_storage : require + +layout (push_constant) uniform parameter +{ + uint ne; + uint ne00; uint ne01; uint nb00; uint nb01; uint nb02; + uint ne10; uint ne11; uint nb10; uint nb11; uint nb12; + uint d_offset; +} p; + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer A {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +void main() { + if (gl_GlobalInvocationID.x >= p.ne) { + return; + } + + const uint i02 = gl_GlobalInvocationID.x / (p.ne00*p.ne01); + const uint i01 = (gl_GlobalInvocationID.x - i02*p.ne01*p.ne00) / p.ne00; + const uint i00 = gl_GlobalInvocationID.x - i02*p.ne01*p.ne00 - i01*p.ne00; + const uint a_idx = i00*p.nb00 + i01*p.nb01 + i02*p.nb02; + + const uint i12 = gl_GlobalInvocationID.x / (p.ne10*p.ne11); + const uint i11 = (gl_GlobalInvocationID.x - i12*p.ne11*p.ne10) / p.ne10; + const uint i10 = gl_GlobalInvocationID.x - i12*p.ne11*p.ne10 - i11*p.ne10; + const uint d_idx = i10*p.nb10 + i11*p.nb11 + i12*p.nb12; +""" +cpy_end = """ + data_d[p.d_offset + d_idx] = D_TYPE(data_a[a_idx]); +} +""" +# Causes an optimization error otherwise +cpy_f16_f16_end = """ + data_d[p.d_offset + d_idx] = data_a[a_idx]; +} +""" + +# GET_ROWS +get_rows_body = """ +#extension GL_EXT_control_flow_attributes : enable +#extension GL_EXT_shader_8bit_storage : require + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer Y {int data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE dst[];}; + +void main() { + const uint col = int(gl_GlobalInvocationID.x) * 2; + const uint row = int(gl_GlobalInvocationID.y); + + if (col >= p.KY) { + return; + } + + const uint r = uint(data_b[row]); + + // copy data_a[r*p.KY + col] to dst[row*p.KX + col] + const uint xi = r*p.KY + col; + const uint di = row*p.KY + col; + + const uint ib = xi/QUANT_K; // block index + const uint iqs = (xi%QUANT_K)/QUANT_R; // quant index + const uint iybs = di - di%QUANT_K; // y block start index + const uint y_offset = QUANT_R == 1 ? 1 : QUANT_K/2; + + DEQUANT_FUNC + + dst[iybs + iqs + 0] = D_TYPE(v.x); + dst[iybs + iqs + y_offset] = D_TYPE(v.y); +} +""" + +# UNARY +gelu_body = """ +#extension GL_EXT_control_flow_attributes : enable + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +void main() { + const float GELU_COEF_A = 0.044715f; + const float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; + const uint i = gl_GlobalInvocationID.x; + + if (i >= p.KX) { + return; + } + + const float xi = float(data_a[i]); + data_d[i] = D_TYPE(0.5f*xi*(1.0f + tanh(SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi)))); +} +""" + +silu_body = """ +#extension GL_EXT_control_flow_attributes : enable + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +void main() { + const uint i = gl_GlobalInvocationID.x; + + if (i >= p.KX) { + return; + } + + const float xi = float(data_a[i]); + data_d[i] = D_TYPE(xi / (1.0f + exp(-xi))); +} +""" + +relu_body = """ +#extension GL_EXT_control_flow_attributes : enable + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +void main() { + const uint i = gl_GlobalInvocationID.x; + + if (i >= p.KX) { + return; + } + + data_d[i] = max(float(data_a[i]), 0); +} +""" + +# DIAG_MASK_INF +diag_mask_inf_head = """#version 450 + +#extension GL_EXT_shader_16bit_storage : require + +layout (push_constant) uniform parameter +{ + uint ncols; + uint rows_per_channel; + uint n_past; +} p; +""" +diag_mask_inf_body = """ +#extension GL_EXT_control_flow_attributes : enable + +layout(local_size_x = 512, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +void main() { + const uint col = gl_GlobalInvocationID.y; + const uint row = gl_GlobalInvocationID.x; + + if (col >= p.ncols) { + return; + } + + const uint i = row*p.ncols + col; + data_d[i] = D_TYPE(data_a[i] - float(uint(col > p.n_past + row % p.rows_per_channel) * 0xFFFFFFFF)); +} +""" + +# NORMS +norm_body = """ +#extension GL_EXT_control_flow_attributes : enable +#define BLOCK_SIZE 512 + +layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +shared vec2 sum[BLOCK_SIZE]; + +void main() { + const uint row = gl_WorkGroupID.x; + const uint tid = gl_LocalInvocationID.x; + + const float eps = 1e-5f; + + sum[tid] = vec2(0.0f, 0.0f); + + [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { + const float xi = float(data_a[row*p.KX + col]); + sum[tid].x += xi; + sum[tid].y += xi * xi; + } + + // sum up partial sums and write back result + barrier(); + [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + sum[tid] += sum[tid + s]; + } + barrier(); + } + + const float mean = sum[0].x / p.KX; + const float var = sum[0].y / p.KX - mean * mean; + const float inv_std = inversesqrt(var + 1e-5f); + + [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { + data_d[row*p.KX + col] = D_TYPE((float(data_a[row*p.KX + col]) - mean) * inv_std); + } +} +""" + +rms_norm_body = """ +#extension GL_EXT_control_flow_attributes : enable +#define BLOCK_SIZE 512 + +layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) writeonly buffer D {D_TYPE data_d[];}; + +shared FLOAT_TYPE sum[BLOCK_SIZE]; + +void main() { + const uint row = gl_WorkGroupID.x; + const uint tid = gl_LocalInvocationID.x; + + sum[tid] = FLOAT_TYPE(0.0f); // partial sum for thread in warp + + [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { + const FLOAT_TYPE xi = FLOAT_TYPE(data_a[row*p.KX + col]); + sum[tid] += xi * xi; + } + + // sum up partial sums and write back result + barrier(); + [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + sum[tid] += sum[tid + s]; + } + barrier(); + } + + const FLOAT_TYPE mean = sum[0] / FLOAT_TYPE(p.KX); + const FLOAT_TYPE scale = inversesqrt(mean + FLOAT_TYPE(p.param1)); + + [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { + data_d[row*p.KX + col] = D_TYPE(scale * FLOAT_TYPE(data_a[row*p.KX + col])); + } +} +""" + +# SOFT_MAX +soft_max_body = """ +#extension GL_EXT_control_flow_attributes : enable +#define BLOCK_SIZE 512 + +layout(local_size_x = BLOCK_SIZE, local_size_y = 1, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer Y {B_TYPE data_b[];}; +layout (binding = 2) buffer D {D_TYPE data_d[];}; + +shared FLOAT_TYPE vals[BLOCK_SIZE]; + +void main() { + const uint tid = gl_LocalInvocationID.x; + const uint rowx = gl_WorkGroupID.x; + const uint rowy = rowx % p.KY; + + // Find max + vals[tid] = uintBitsToFloat(0xFF800000); + + [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { + vals[tid] = max(vals[tid], FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.param1 + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f))); + } + + barrier(); + [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + vals[tid] = max(vals[tid], vals[tid + s]); + } + barrier(); + } + + const FLOAT_TYPE max_val = vals[0]; + barrier(); + + // Sum up values + vals[tid] = FLOAT_TYPE(0.0f); + + [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { + const uint i = rowx * p.KX + col; + const FLOAT_TYPE val = exp(FLOAT_TYPE(data_a[i]) * p.param1 + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val); + vals[tid] += val; + data_d[i] = D_TYPE(val); + } + + barrier(); + [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { + if (tid < s) { + vals[tid] += vals[tid + s]; + } + barrier(); + } + + const D_TYPE divisor = D_TYPE(vals[0]); + + [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { + data_d[rowx*p.KX + col] /= divisor; + } +} +""" + +# ROPE +rope_src = """ +#version 450 + +#extension GL_EXT_shader_16bit_storage : require + +layout(local_size_x = 1, local_size_y = 256, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer Y {int data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; + +layout (push_constant) uniform parameter { + uint ncols; + float freq_scale; + uint p_delta_rows; + float freq_base; + float ext_factor; + float attn_factor; + float corr_dims[4]; +} p; + +float rope_yarn_ramp(const float low, const float high, const uint i0) { + const float y = (i0 / 2 - low) / max(0.001f, high - low); + return 1.0f - min(1.0f, max(0.0f, y)); +} + +void rope_yarn(const float theta_extrap, const uint i0, out float cos_theta, out float sin_theta) { + float mscale = p.attn_factor; + // Get n-d rotational scaling corrected for extrapolation + float theta_interp = p.freq_scale * theta_extrap; + float theta = theta_interp; + if (p.ext_factor != 0.0f) { + float ramp_mix = rope_yarn_ramp(p.corr_dims[0], p.corr_dims[1], i0) * p.ext_factor; + theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; + + // Get n-d magnitude scaling corrected for interpolation + mscale *= 1.0f + 0.1f * log(1.0f / p.freq_scale); + } + cos_theta = cos(theta) * mscale; + sin_theta = sin(theta) * mscale; +} + +void main() { + const uint col = gl_GlobalInvocationID.y * 2; + const uint row = gl_GlobalInvocationID.x; + + if (col >= p.ncols) { + return; + } + + const uint i = row*p.ncols + col; + const uint i2 = row/p.p_delta_rows; + + const int pos = data_b[i2]; + const float theta_base = pos * pow(p.freq_base, -float(col)/p.ncols); + + float cos_theta, sin_theta; + rope_yarn(theta_base, col, cos_theta, sin_theta); + + const float x0 = float(data_a[i + 0]); + const float x1 = float(data_a[i + 1]); + + data_d[i + 0] = D_TYPE(x0*cos_theta - x1*sin_theta); + data_d[i + 1] = D_TYPE(x0*sin_theta + x1*cos_theta); +} +""" + +rope_neox_src = """ +#version 450 + +#extension GL_EXT_shader_16bit_storage : require + +layout(local_size_x = 1, local_size_y = 256, local_size_z = 1) in; + +layout (binding = 0) readonly buffer X {A_TYPE data_a[];}; +layout (binding = 1) readonly buffer Y {int data_b[];}; +layout (binding = 2) writeonly buffer D {D_TYPE data_d[];}; + +layout (push_constant) uniform parameter { + uint ncols; + uint ndims; + float freq_scale; + uint p_delta_rows; + float freq_base; + float ext_factor; + float attn_factor; + float corr_dims[4]; + float theta_scale; + float inv_ndims; +} p; + +float rope_yarn_ramp(const float low, const float high, const uint i0) { + const float y = (i0 / 2 - low) / max(0.001f, high - low); + return 1.0f - min(1.0f, max(0.0f, y)); +} + +void rope_yarn(const float theta_extrap, const uint i0, out float cos_theta, out float sin_theta) { + float mscale = p.attn_factor; + // Get n-d rotational scaling corrected for extrapolation + float theta_interp = p.freq_scale * theta_extrap; + float theta = theta_interp; + if (p.ext_factor != 0.0f) { + float ramp_mix = rope_yarn_ramp(p.corr_dims[0], p.corr_dims[1], i0) * p.ext_factor; + theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; + + // Get n-d magnitude scaling corrected for interpolation + mscale *= 1.0f + 0.1f * log(1.0f / p.freq_scale); + } + cos_theta = cos(theta) * mscale; + sin_theta = sin(theta) * mscale; +} + +void main() { + const uint col = gl_GlobalInvocationID.y * 2; + const uint row = gl_GlobalInvocationID.x; + + if (col >= p.ncols) { + return; + } + + const uint ib = col / p.ndims; + const uint ic = col % p.ndims; + + if (ib > 0) { + const uint i = row*p.ncols + ib*p.ndims + ic; + + data_d[i + 0] = data_a[i + 0]; + data_d[i + 1] = data_a[i + 1]; + + return; + } + + const uint i = row*p.ncols + ib*p.ndims + ic/2; + const uint i2 = row/p.p_delta_rows; + + const float cur_rot = p.inv_ndims * ic - ib; + + const int pos = data_b[i2]; + const float theta_base = pos*p.freq_scale*pow(p.theta_scale, col/2.0f); + + float cos_theta, sin_theta; + rope_yarn(theta_base, uint(cur_rot), cos_theta, sin_theta); + + const float x0 = float(data_a[i + 0]); + const float x1 = float(data_a[i + p.ndims/2]); + + data_d[i + 0] = D_TYPE(x0*cos_theta - x1*sin_theta); + data_d[i + p.ndims/2] = D_TYPE(x0*sin_theta + x1*cos_theta); +} +""" + +GLSLC = "glslc" + +VK_NUM_TYPES = 16 + +GGML_TYPE_F32 = 0 +GGML_TYPE_F16 = 1 +GGML_TYPE_Q4_0 = 2 +GGML_TYPE_Q4_1 = 3 +GGML_TYPE_Q5_0 = 6 +GGML_TYPE_Q5_1 = 7 +GGML_TYPE_Q8_0 = 8 +GGML_TYPE_Q8_1 = 9 +GGML_TYPE_Q2_K = 10 +GGML_TYPE_Q3_K = 11 +GGML_TYPE_Q4_K = 12 +GGML_TYPE_Q5_K = 13 +GGML_TYPE_Q6_K = 14 +GGML_TYPE_Q8_K = 15 + + +type_names = { + GGML_TYPE_F32: "f32", + GGML_TYPE_F16: "f16", + GGML_TYPE_Q4_0: "q4_0", + GGML_TYPE_Q4_1: "q4_1", + GGML_TYPE_Q5_0: "q5_0", + GGML_TYPE_Q5_1: "q5_1", + GGML_TYPE_Q8_0: "q8_0", + GGML_TYPE_Q8_1: "q8_1", + GGML_TYPE_Q2_K: "q2_K", + GGML_TYPE_Q3_K: "q3_K", + GGML_TYPE_Q4_K: "q4_K", + GGML_TYPE_Q5_K: "q5_K", + GGML_TYPE_Q6_K: "q6_K", + GGML_TYPE_Q8_K: "q8_K", +} + +K_QUANTS_PER_ITERATION = 2 + +output_dir = gettempdir() + +lock = asyncio.Lock() +shader_fnames = [] + + +async def string_to_spv(name, code, defines, fp16): + f = NamedTemporaryFile(mode="w", delete=False) + f.write(code) + f.flush() + + name = f"{name}{'_fp32' if not fp16 else ''}" + fname = os.path.join(output_dir, f"{name}.comp") + + cmd = [GLSLC, "-fshader-stage=compute", "--target-env=vulkan1.2", "-O", f.name, "-o", fname] + + cmd.extend([f"-D{key}={value}" for key, value in defines.items()]) + + proc = await asyncio.create_subprocess_exec(*cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE) + + stdout, stderr = await proc.communicate() + + stdout = stdout.decode() + error = stderr.decode() + + if proc.returncode: + # Generate preprocessed code + cmd = [GLSLC, "-E", f.name] + cmd.extend([f"-D{key}={value}" for key, value in defines.items()]) + + proc = await asyncio.create_subprocess_exec(*cmd, stdout=asyncio.subprocess.PIPE, stderr=asyncio.subprocess.PIPE) + + stdout, stderr = await proc.communicate() + + print(" ".join(cmd)) + + if proc.returncode: + raise RuntimeError(f"{name=} {f.name=} {stdout=} {stderr=}") + + preprocessed_code = stdout.decode() + + cmd.extend([f"-D{key}={value}" for key, value in defines.items()]) + code_with_lines = "\n".join([f"{i + 1}: {line}" for i, line in enumerate(preprocessed_code.splitlines())]) + print(f"ERROR compiling {name}\n\n{code_with_lines}\n\n{error}") + f.close() + os.remove(f.name) + sys.exit(proc.returncode) + + f.close() + os.remove(f.name) + + async with lock: + shader_fnames.append((name, fname)) + + +async def main(): + print("ggml_vulkan: Generating and compiling shaders to SPIR-V") + + tasks = [] + + for fp16 in (False, True): + # mulmat + if fp16: + shader_float_type = shader_f16 + load_vec = "8" + vec_type_f16 = "f16mat2x4" + vec_type = "mat2x4" + else: + shader_float_type = shader_f32 + load_vec = "4" + vec_type_f16 = "f16vec4" + vec_type = "vec4" + + stream = [] + stream.extend((mulmat_head, shader_float_type, mulmat_body)) + tasks.append(string_to_spv("matmul_f32_l", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f32_m", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f32_s", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f32_aligned_l", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f32_aligned_m", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f32_aligned_s", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + tasks.append(string_to_spv("matmul_f16_l", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16_m", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16_s", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16_aligned_l", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16_aligned_m", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16_aligned_s", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) + + tasks.append(string_to_spv("matmul_f16_f32_l", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16_f32_m", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16_f32_s", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16_f32_aligned_l", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16_f32_aligned_m", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16_f32_aligned_s", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + # Build dequant shaders + tasks.append(string_to_spv("f32_to_f16", f32_to_f16_src, {}, fp16)) + + for i in range(0, VK_NUM_TYPES): + stream.clear() + + stream.extend((dequant_head, shader_int8_ext, shader_float_type)) + + if i == GGML_TYPE_F16: + stream.extend((shader_f16_defines, shader_f16_dequant_func_compat if not fp16 else shader_f16_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q4_0: + stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func_compat if not fp16 else shader_q4_0_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q4_1: + stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func_compat if not fp16 else shader_q4_1_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q5_0: + stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func_compat if not fp16 else shader_q5_0_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q5_1: + stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func_compat if not fp16 else shader_q5_1_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q8_0: + stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func_compat if not fp16 else shader_q8_0_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q2_K: + stream.extend((shader_q2_K_defines, dequant_q2_K_body)) + elif i == GGML_TYPE_Q3_K: + stream.extend((shader_q3_K_defines, dequant_q3_K_body)) + elif i == GGML_TYPE_Q4_K: + stream.extend((shader_q4_K_defines, dequant_q4_K_body)) + elif i == GGML_TYPE_Q5_K: + stream.extend((shader_q5_K_defines, dequant_q5_K_body)) + elif i == GGML_TYPE_Q6_K: + stream.extend((shader_q6_K_defines, dequant_q6_K_body)) + else: + continue + + tasks.append(string_to_spv(f"dequant_{type_names[i]}", "".join(stream), {"D_TYPE": "float16_t"}, fp16)) + + # get_rows + for i in range(0, VK_NUM_TYPES): + stream.clear() + stream.extend((generic_head, shader_int8_ext, shader_float_type)) + + if i == GGML_TYPE_F16: + stream.extend((shader_f16_defines, shader_f16_dequant_func_compat if not fp16 else shader_f16_dequant_func, get_rows_body)) + elif i == GGML_TYPE_Q4_0: + stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func_compat if not fp16 else shader_q4_0_dequant_func, get_rows_body)) + elif i == GGML_TYPE_Q4_1: + stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func_compat if not fp16 else shader_q4_1_dequant_func, get_rows_body)) + elif i == GGML_TYPE_Q5_0: + stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func_compat if not fp16 else shader_q5_0_dequant_func, get_rows_body)) + elif i == GGML_TYPE_Q5_1: + stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func_compat if not fp16 else shader_q5_1_dequant_func, get_rows_body)) + elif i == GGML_TYPE_Q8_0: + stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func_compat if not fp16 else shader_q8_0_dequant_func, get_rows_body)) + else: + continue + + tasks.append(string_to_spv(f"get_rows_{type_names[i]}", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float16_t"}, fp16)) + tasks.append(string_to_spv(f"get_rows_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float"}, fp16)) + + # Shaders where precision is needed, so no fp16 version + + # mul mat vec + for i in range(0, VK_NUM_TYPES): + stream.clear() + stream.extend((mul_mat_vec_head, shader_int8_ext, shader_f32)) + + if i == GGML_TYPE_F16: + stream.extend((shader_f16_defines, shader_f16_dequant_func_compat, mul_mat_vec_body)) + elif i == GGML_TYPE_Q4_0: + stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func_compat, mul_mat_vec_body)) + elif i == GGML_TYPE_Q4_1: + stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func_compat, mul_mat_vec_body)) + elif i == GGML_TYPE_Q5_0: + stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func_compat, mul_mat_vec_body)) + elif i == GGML_TYPE_Q5_1: + stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func_compat, mul_mat_vec_body)) + elif i == GGML_TYPE_Q8_0: + stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func_compat, mul_mat_vec_body)) + elif i == GGML_TYPE_Q2_K: + stream.extend((shader_q2_K_defines, mul_mat_vec_q2_K_body)) + elif i == GGML_TYPE_Q3_K: + stream.extend((shader_q3_K_defines, mul_mat_vec_q3_K_body)) + elif i == GGML_TYPE_Q4_K: + stream.extend((shader_q4_K_defines, mul_mat_vec_q4_K_body)) + elif i == GGML_TYPE_Q5_K: + stream.extend((shader_q5_K_defines, mul_mat_vec_q5_K_body)) + elif i == GGML_TYPE_Q6_K: + stream.extend((shader_q6_K_defines, mul_mat_vec_q6_K_body)) + else: + continue + + tasks.append(string_to_spv(f"mul_mat_vec_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION}, fp16)) + + tasks.append(string_to_spv("mul_mat_vec_p021_f16_f32", mul_mat_p021_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("mul_mat_vec_nc_f16_f32", mul_mat_nc_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, True)) + + # Norms + tasks.append(string_to_spv("norm_f32", f"{generic_head}\n{shader_f32}\n{norm_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("rms_norm_f32", f"{generic_head}\n{shader_f32}\n{rms_norm_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + + tasks.append(string_to_spv("cpy_f32_f32", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("cpy_f32_f16", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float16_t"}, True)) + tasks.append(string_to_spv("cpy_f16_f16", f"{cpy_src}\n{cpy_f16_f16_end}", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}, True)) + + tasks.append(string_to_spv("add_f32", f"{generic_head}\n{shader_f32}\n{add_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, True)) + + tasks.append(string_to_spv("split_k_reduce", mulmat_split_k_reduce_src, {}, True)) + tasks.append(string_to_spv("mul_f32", f"{generic_head}\n{shader_f32}\n{mul_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, True)) + + tasks.append(string_to_spv("scale_f32", f"{generic_head}\n{shader_f32}\n{scale_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + + tasks.append(string_to_spv("sqr_f32", f"{generic_head}\n{shader_f32}\n{sqr_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + + tasks.append(string_to_spv("clamp_f32", f"{generic_head}\n{shader_f32}\n{clamp_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + + tasks.append(string_to_spv("gelu_f32", f"{generic_head}\n{shader_f32}\n{gelu_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("silu_f32", f"{generic_head}\n{shader_f32}\n{silu_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("relu_f32", f"{generic_head}\n{shader_f32}\n{relu_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + + tasks.append(string_to_spv("diag_mask_inf_f32", f"{diag_mask_inf_head}\n{shader_f32}\n{diag_mask_inf_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + + tasks.append(string_to_spv("soft_max_f32", f"{generic_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, True)) + + tasks.append(string_to_spv("rope_f32", rope_src, {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("rope_f16", rope_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}, True)) + + tasks.append(string_to_spv("rope_neox_f32", rope_neox_src, {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("rope_neox_f16", rope_neox_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}, True)) + + await asyncio.gather(*tasks) + + with open("ggml-vulkan-shaders.hpp", "w") as f: + f.write("#include \n\n") + for name, path in sorted(shader_fnames): + + with open(path, "rb") as spv: + counter = 0 + newline_counter = 0 + f.write(f"unsigned char {name}_data[] = {{\n") + for val in spv.read(): + f.write(f"0x{val:02x},") + newline_counter += 1 + counter += 1 + if newline_counter >= 12: + newline_counter = 0 + f.write("\n") + f.write("\n};\n") + f.write(f"const uint64_t {name}_len = {counter};\n\n") + os.remove(path) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="GGML Vulkan Shader Generator") + + parser.add_argument("--glslc", help="Path to glslc") + + args = parser.parse_args() + + if args.glslc: + GLSLC = args.glslc + + asyncio.run(main()) diff --git a/llama.cpp b/llama.cpp index 09d799f71..f7d054c57 100644 --- a/llama.cpp +++ b/llama.cpp @@ -11,6 +11,8 @@ # include "ggml-cuda.h" #elif defined(GGML_USE_CLBLAST) # include "ggml-opencl.h" +#elif defined(GGML_USE_VULKAN) +# include "ggml-vulkan.h" #elif defined(GGML_USE_SYCL) # include "ggml-sycl.h" #endif @@ -1284,6 +1286,10 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_cpu(bool host_buffer buft = ggml_backend_sycl_host_buffer_type(); #elif defined(GGML_USE_CPU_HBM) buft = ggml_backend_cpu_hbm_buffer_type(); +#elif defined(GGML_USE_VULKAN) + if (host_buffer) { + buft = ggml_backend_vk_host_buffer_type(); + } #endif if (buft == nullptr) { @@ -1301,6 +1307,8 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) { buft = ggml_backend_metal_buffer_type(); #elif defined(GGML_USE_CUBLAS) buft = ggml_backend_cuda_buffer_type(gpu); +#elif defined(GGML_USE_VULKAN) + buft = ggml_backend_vk_buffer_type(); #elif defined(GGML_USE_SYCL) buft = ggml_backend_sycl_buffer_type(gpu); #elif defined(GGML_USE_CLBLAST) @@ -6846,7 +6854,7 @@ static int llama_decode_internal( } const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 1; - if (ggml_cpu_has_cublas() && fully_offloaded) { + if ((ggml_cpu_has_cublas() || ggml_cpu_has_vulkan()) && fully_offloaded) { n_threads = 1; } @@ -10231,6 +10239,16 @@ struct llama_context * llama_new_context_with_model( } } } +#elif defined(GGML_USE_VULKAN) + if (model->n_gpu_layers > 0) { + ggml_backend_t backend = ggml_backend_vk_init(); + if (backend == nullptr) { + LLAMA_LOG_ERROR("%s: failed to initialize Vulkan backend\n", __func__); + llama_free(ctx); + return nullptr; + } + ctx->backends.push_back(backend); + } #elif defined(GGML_USE_SYCL) if (model->n_gpu_layers > 0) { ggml_backend_t backend = ggml_backend_sycl_init(model->main_gpu); diff --git a/llama.h b/llama.h index d11cba815..3e33072c6 100644 --- a/llama.h +++ b/llama.h @@ -49,7 +49,7 @@ #define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN #define LLAMA_SESSION_VERSION 4 -#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_SYCL) +#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || defined(GGML_USE_SYCL) // Defined when llama.cpp is compiled with support for offloading model layers to GPU. #define LLAMA_SUPPORTS_GPU_OFFLOAD #endif From d460510c7222d43a458a17e01d4bbe72437cdd3c Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 28 Jan 2024 18:44:58 +0200 Subject: [PATCH 044/143] ggml : minor type fix (int64_t -> size_t) --- ggml.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml.c b/ggml.c index 24600862b..5b37487f7 100644 --- a/ggml.c +++ b/ggml.c @@ -9978,7 +9978,7 @@ static void ggml_compute_forward_mul_mat( #if defined(GGML_USE_ACCELERATE) || defined(GGML_USE_OPENBLAS) if (ggml_compute_forward_mul_mat_use_blas(dst)) { const int64_t ne_plane = ne01*ne00; - const int64_t desired_wsize = ne13*ne12*ne_plane*sizeof(float); + const size_t desired_wsize = ne13*ne12*ne_plane*sizeof(float); UNUSED(desired_wsize); if (params->type == GGML_TASK_INIT) { From 35dec26cc25a9ff7d8c3ed52326b94f772b911ce Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 28 Jan 2024 19:48:05 +0200 Subject: [PATCH 045/143] sync : ggml --- scripts/sync-ggml.last | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index efde0069f..34db9df98 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -c2448f88d17395452a587d0176d19ed87e0f7ce1 +f2a9472b23cf27e672ed70a2a6eb078f7b060f18 From d2f650cb5b04ee2726663e79b47da5efe196ce00 Mon Sep 17 00:00:00 2001 From: Paul Tsochantaris Date: Sun, 28 Jan 2024 19:50:16 +0000 Subject: [PATCH 046/143] metal : free metal objects (#5161) * Releasing MTLFunction references after Metal pipeline construction * Keeping the `ggml_metal_kernel` structure * Spacing fix * Whitespace fix --- ggml-metal.m | 33 +++++++++++++-------------------- 1 file changed, 13 insertions(+), 20 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index a0efda0ba..3646c40df 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -24,10 +24,7 @@ #define UNUSED(x) (void)(x) -#define GGML_METAL_MAX_KERNELS 256 - struct ggml_metal_kernel { - id function; id pipeline; }; @@ -159,11 +156,10 @@ struct ggml_metal_context { id device; id queue; - id library; dispatch_queue_t d_queue; - struct ggml_metal_kernel kernels[GGML_METAL_MAX_KERNELS]; + struct ggml_metal_kernel kernels[GGML_METAL_KERNEL_TYPE_COUNT]; bool support_simdgroup_reduction; bool support_simdgroup_mm; @@ -246,6 +242,8 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { ctx->queue = [ctx->device newCommandQueue]; ctx->d_queue = dispatch_queue_create("ggml-metal", DISPATCH_QUEUE_CONCURRENT); + id metal_library; + // load library { NSBundle * bundle = nil; @@ -260,7 +258,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { // pre-compiled library found NSURL * libURL = [NSURL fileURLWithPath:libPath]; GGML_METAL_LOG_INFO("%s: loading '%s'\n", __func__, [libPath UTF8String]); - ctx->library = [ctx->device newLibraryWithURL:libURL error:&error]; + metal_library = [ctx->device newLibraryWithURL:libURL error:&error]; if (error) { GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]); return NULL; @@ -302,7 +300,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { //[options setFastMathEnabled:false]; - ctx->library = [ctx->device newLibraryWithSource:src options:options error:&error]; + metal_library = [ctx->device newLibraryWithSource:src options:options error:&error]; if (error) { GGML_METAL_LOG_ERROR("%s: error: %s\n", __func__, [[error description] UTF8String]); return NULL; @@ -367,8 +365,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { { NSError * error = nil; - for (int i = 0; i < GGML_METAL_MAX_KERNELS; ++i) { - ctx->kernels[i].function = nil; + for (int i = 0; i < GGML_METAL_KERNEL_TYPE_COUNT; ++i) { ctx->kernels[i].pipeline = nil; } @@ -380,10 +377,12 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { #define GGML_METAL_ADD_KERNEL(e, name, supported) \ if (supported) { \ struct ggml_metal_kernel * kernel = &ctx->kernels[e]; \ - kernel->function = [ctx->library newFunctionWithName:@"kernel_"#name]; \ - kernel->pipeline = [ctx->device newComputePipelineStateWithFunction:kernel->function error:&error]; \ + id metal_function = [metal_library newFunctionWithName:@"kernel_"#name]; \ + kernel->pipeline = [ctx->device newComputePipelineStateWithFunction:metal_function error:&error]; \ + [metal_function release]; \ if (error) { \ GGML_METAL_LOG_ERROR("%s: error: load pipeline error: %s\n", __func__, [[error description] UTF8String]); \ + [metal_library release]; \ return NULL; \ } \ } else { \ @@ -512,23 +511,17 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SUM_ROWS, sum_rows, true); } + [metal_library release]; return ctx; } static void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_LOG_INFO("%s: deallocating\n", __func__); - for (int i = 0; i < GGML_METAL_MAX_KERNELS; ++i) { - if (ctx->kernels[i].pipeline) { - [ctx->kernels[i].pipeline release]; - } - - if (ctx->kernels[i].function) { - [ctx->kernels[i].function release]; - } + for (int i = 0; i < GGML_METAL_KERNEL_TYPE_COUNT; ++i) { + [ctx->kernels[i].pipeline release]; } - [ctx->library release]; [ctx->queue release]; [ctx->device release]; From 172ac82629815d038702b049070f4c8c02662da5 Mon Sep 17 00:00:00 2001 From: Eve <139727413+netrunnereve@users.noreply.github.com> Date: Mon, 29 Jan 2024 08:04:47 +0000 Subject: [PATCH 047/143] cmake : fix Vulkan build (#5182) --- CMakeLists.txt | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index deb294198..ed8f39c62 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -422,7 +422,13 @@ if (LLAMA_VULKAN) if (Vulkan_FOUND) message(STATUS "Vulkan found") + set(GGML_HEADERS_VULKAN ggml-vulkan.h) + set(GGML_SOURCES_VULKAN ggml-vulkan.cpp) + add_library(ggml-vulkan STATIC ggml-vulkan.cpp ggml-vulkan.h) + if (BUILD_SHARED_LIBS) + set_target_properties(ggml-vulkan PROPERTIES POSITION_INDEPENDENT_CODE ON) + endif() target_link_libraries(ggml-vulkan PRIVATE Vulkan::Vulkan) add_compile_definitions(GGML_USE_VULKAN) @@ -848,6 +854,7 @@ add_library(ggml OBJECT ggml-quants.h ${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA} ${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL} + ${GGML_SOURCES_VULKAN} ${GGML_HEADERS_VULKAN} ${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL} ${GGML_SOURCES_MPI} ${GGML_HEADERS_MPI} ${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA} @@ -928,7 +935,7 @@ install(FILES ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfig.cmake DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/Llama) set(GGML_PUBLIC_HEADERS "ggml.h" "ggml-alloc.h" "ggml-backend.h" - "${GGML_HEADERS_CUDA}" "${GGML_HEADERS_OPENCL}" + "${GGML_HEADERS_CUDA}" "${GGML_HEADERS_OPENCL}" "${GGML_HEADERS_VULKAN}" "${GGML_HEADERS_METAL}" "${GGML_HEADERS_MPI}" "${GGML_HEADERS_EXTRA}") set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}") From fbe7dfa53caff0a7e830b676e6e949917a5c71b4 Mon Sep 17 00:00:00 2001 From: slaren Date: Mon, 29 Jan 2024 09:05:13 +0100 Subject: [PATCH 048/143] ggml : add max buffer sizes to opencl and metal backends (#5181) --- ggml-metal.m | 12 +++++++++++- ggml-opencl.cpp | 11 ++++++++++- 2 files changed, 21 insertions(+), 2 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 3646c40df..bbeedaae0 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -2375,6 +2375,16 @@ GGML_CALL static size_t ggml_backend_metal_buffer_type_get_alignment(ggml_backen UNUSED(buft); } +GGML_CALL static size_t ggml_backend_metal_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { + id device = ggml_backend_metal_get_device(); + size_t max_size = device.maxBufferLength; + ggml_backend_metal_free_device(); + + return max_size; + + UNUSED(buft); +} + GGML_CALL static bool ggml_backend_metal_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { return ggml_backend_is_metal(backend) || ggml_backend_is_cpu(backend); @@ -2393,7 +2403,7 @@ GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(void) { /* .get_name = */ ggml_backend_metal_buffer_type_get_name, /* .alloc_buffer = */ ggml_backend_metal_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_metal_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // TODO: return device.maxBufferLength + /* .get_max_size = */ ggml_backend_metal_buffer_type_get_max_size, /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes /* .supports_backend = */ ggml_backend_metal_buffer_type_supports_backend, /* .is_host = */ ggml_backend_metal_buffer_type_is_host, diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp index d40663535..797bee667 100644 --- a/ggml-opencl.cpp +++ b/ggml-opencl.cpp @@ -2125,6 +2125,15 @@ static size_t ggml_backend_opencl_buffer_type_get_alignment(ggml_backend_buffer_ GGML_UNUSED(buffer_type); } +static size_t ggml_backend_opencl_buffer_type_get_max_size(ggml_backend_buffer_type_t buffer_type) { + static size_t max_size = -1; + if (max_size == (size_t)-1) { + ggml_cl_init(); + clGetDeviceInfo(device, CL_DEVICE_MAX_MEM_ALLOC_SIZE, sizeof(size_t), &max_size, NULL); + } + return max_size; +} + static bool ggml_backend_opencl_buffer_type_supports_backend(ggml_backend_buffer_type_t buffer_type, ggml_backend_t backend) { //return ggml_backend_is_opencl(backend); // opencl must be used through the cpu backend return ggml_backend_is_cpu(backend); @@ -2136,7 +2145,7 @@ static ggml_backend_buffer_type_i ggml_backend_opencl_buffer_type_interface = { /* .get_name = */ ggml_backend_opencl_buffer_type_name, /* .alloc_buffer = */ ggml_backend_opencl_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_opencl_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // TODO: return from device info + /* .get_max_size = */ ggml_backend_opencl_buffer_type_get_max_size, /* .get_alloc_size = */ NULL, /* .supports_backend = */ ggml_backend_opencl_buffer_type_supports_backend, /* .is_host = */ NULL, From e76627bcce9f77adb6034cb127b7ec93d4287b69 Mon Sep 17 00:00:00 2001 From: Sang-Kil Park Date: Mon, 29 Jan 2024 18:24:19 +0900 Subject: [PATCH 049/143] py : improve BPE tokenizer support (#5189) --- convert.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/convert.py b/convert.py index 06768033d..b48afba1e 100755 --- a/convert.py +++ b/convert.py @@ -334,7 +334,10 @@ class Params: class BpeVocab: 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()) - self.vocab = self.bpe_tokenizer["model"]["vocab"] + try: + self.vocab = self.bpe_tokenizer["model"]["vocab"] + except: + self.vocab = self.bpe_tokenizer added_tokens: dict[str, int] if fname_added_tokens is not None: # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab. From 14fef85e2d5361e6b4dd7a9ecf22bb817362997d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 29 Jan 2024 15:35:54 +0200 Subject: [PATCH 050/143] py : fix except (#5194) ggml-ci --- convert.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/convert.py b/convert.py index b48afba1e..75c100118 100755 --- a/convert.py +++ b/convert.py @@ -336,7 +336,7 @@ class BpeVocab: self.bpe_tokenizer = json.loads(open(str(fname_tokenizer), encoding="utf-8").read()) try: self.vocab = self.bpe_tokenizer["model"]["vocab"] - except: + except KeyError: self.vocab = self.bpe_tokenizer added_tokens: dict[str, int] if fname_added_tokens is not None: From c82d18e863fcde91b4b1109b1d0c73ea4470c405 Mon Sep 17 00:00:00 2001 From: Wu Jian Ping Date: Mon, 29 Jan 2024 21:48:10 +0800 Subject: [PATCH 051/143] server : embeddings compatibility for OpenAI (#5190) --- examples/server/oai.hpp | 15 ++++++++++ examples/server/server.cpp | 60 ++++++++++++++++++++++++++++++++++++++ 2 files changed, 75 insertions(+) diff --git a/examples/server/oai.hpp b/examples/server/oai.hpp index bc5db6eef..43410f803 100644 --- a/examples/server/oai.hpp +++ b/examples/server/oai.hpp @@ -206,3 +206,18 @@ inline static std::vector format_partial_response_oaicompat(const task_res return std::vector({ret}); } + +inline static json format_embeddings_response_oaicompat(const json &request, const json &embeddings) +{ + json res = + json{ + {"model", json_value(request, "model", std::string(DEFAULT_OAICOMPAT_MODEL))}, + {"object", "list"}, + {"usage", + json{{"prompt_tokens", 0}, + {"total_tokens", 0}}}, + {"data", embeddings} + }; + return res; +} + diff --git a/examples/server/server.cpp b/examples/server/server.cpp index a48582ad9..11dd82c33 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2929,6 +2929,66 @@ int main(int argc, char **argv) return res.set_content(result.result_json.dump(), "application/json; charset=utf-8"); }); + svr.Post("/v1/embeddings", [&llama](const httplib::Request &req, httplib::Response &res) + { + res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); + const json body = json::parse(req.body); + + json prompt; + if (body.count("input") != 0) + { + prompt = body["input"]; + // batch + if(prompt.is_array()) { + json data = json::array(); + int i = 0; + for (const json &elem : prompt) { + const int task_id = llama.queue_tasks.get_new_id(); + llama.queue_results.add_waiting_task_id(task_id); + llama.request_completion(task_id, { {"prompt", elem}, { "n_predict", 0} }, false, true, -1); + + // get the result + task_result result = llama.queue_results.recv(task_id); + llama.queue_results.remove_waiting_task_id(task_id); + + json embedding = json{ + {"embedding", json_value(result.result_json, "embedding", json::array())}, + {"index", i++}, + {"object", "embedding"} + }; + data.push_back(embedding); + } + json result = format_embeddings_response_oaicompat(body, data); + return res.set_content(result.dump(), "application/json; charset=utf-8"); + } + } + else + { + prompt = ""; + } + + // create and queue the task + const int task_id = llama.queue_tasks.get_new_id(); + llama.queue_results.add_waiting_task_id(task_id); + llama.request_completion(task_id, { {"prompt", prompt}, { "n_predict", 0}}, false, true, -1); + + // get the result + task_result result = llama.queue_results.recv(task_id); + llama.queue_results.remove_waiting_task_id(task_id); + + json data = json::array({json{ + {"embedding", json_value(result.result_json, "embedding", json::array())}, + {"index", 0}, + {"object", "embedding"} + }} + ); + + json root = format_embeddings_response_oaicompat(body, data); + + // send the result + return res.set_content(root.dump(), "application/json; charset=utf-8"); + }); + // GG: if I put the main loop inside a thread, it crashes on the first request when build in Debug!? // "Bus error: 10" - this is on macOS, it does not crash on Linux //std::thread t2([&]() From 2aed77eb06a329f0d82bb1c467f4244904d4073f Mon Sep 17 00:00:00 2001 From: divinity76 Date: Mon, 29 Jan 2024 15:45:41 +0100 Subject: [PATCH 052/143] fix typo "RLIMIT_MLOCK" (#5175) --- llama.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/llama.cpp b/llama.cpp index f7d054c57..45569f7d3 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1158,10 +1158,10 @@ struct llama_mlock { #ifdef __APPLE__ #define MLOCK_SUGGESTION \ "Try increasing the sysctl values 'vm.user_wire_limit' and 'vm.global_user_wire_limit' and/or " \ - "decreasing 'vm.global_no_user_wire_amount'. Also try increasing RLIMIT_MLOCK (ulimit -l).\n" + "decreasing 'vm.global_no_user_wire_amount'. Also try increasing RLIMIT_MEMLOCK (ulimit -l).\n" #else #define MLOCK_SUGGESTION \ - "Try increasing RLIMIT_MLOCK ('ulimit -l' as root).\n" + "Try increasing RLIMIT_MEMLOCK ('ulimit -l' as root).\n" #endif bool raw_lock(const void * addr, size_t size) const { From fbf1ddec69f7001cc707de17fa74d7200813bbac Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Mon, 29 Jan 2024 15:50:50 -0500 Subject: [PATCH 053/143] Nomic Vulkan backend (#4456) Signed-off-by: Jared Van Bortel Co-authored-by: niansa Co-authored-by: Adam Treat Co-authored-by: Aaron Miller Co-authored-by: ToKiNoBug Co-authored-by: Georgi Gerganov Co-authored-by: slaren --- .ecrc | 1 + .github/workflows/build.yml | 21 +- .gitmodules | 3 + CMakeLists.txt | 171 +- ggml-backend.c | 5 + ggml-kompute.cpp | 1990 +++++++++++++++++++++++ ggml-kompute.h | 46 + kompute | 1 + kompute-shaders/common.comp | 102 ++ kompute-shaders/op_add.comp | 58 + kompute-shaders/op_addrow.comp | 25 + kompute-shaders/op_cpy_f16_f16.comp | 52 + kompute-shaders/op_cpy_f16_f32.comp | 52 + kompute-shaders/op_cpy_f32_f16.comp | 52 + kompute-shaders/op_cpy_f32_f32.comp | 52 + kompute-shaders/op_diagmask.comp | 30 + kompute-shaders/op_gelu.comp | 22 + kompute-shaders/op_getrows.comp | 17 + kompute-shaders/op_getrows_f16.comp | 31 + kompute-shaders/op_getrows_q4_0.comp | 38 + kompute-shaders/op_getrows_q4_1.comp | 39 + kompute-shaders/op_getrows_q6_k.comp | 44 + kompute-shaders/op_mul.comp | 52 + kompute-shaders/op_mul_mat_f16.comp | 67 + kompute-shaders/op_mul_mat_mat_f32.comp | 51 + kompute-shaders/op_mul_mat_q4_0.comp | 33 + kompute-shaders/op_mul_mat_q4_1.comp | 35 + kompute-shaders/op_mul_mat_q6_k.comp | 94 ++ kompute-shaders/op_mul_mat_q8_0.comp | 73 + kompute-shaders/op_mul_mv_q_n.comp | 48 + kompute-shaders/op_mul_mv_q_n_pre.comp | 22 + kompute-shaders/op_norm.comp | 84 + kompute-shaders/op_relu.comp | 21 + kompute-shaders/op_rmsnorm.comp | 53 + kompute-shaders/op_rope_f16.comp | 73 + kompute-shaders/op_rope_f32.comp | 73 + kompute-shaders/op_scale.comp | 19 + kompute-shaders/op_scale_8.comp | 23 + kompute-shaders/op_silu.comp | 22 + kompute-shaders/op_softmax.comp | 56 + kompute-shaders/rope_common.comp | 67 + llama.cpp | 35 +- llama.h | 3 +- tests/test-backend-ops.cpp | 430 ++++- tests/test-c.c | 4 + 45 files changed, 4271 insertions(+), 19 deletions(-) create mode 100644 .gitmodules create mode 100644 ggml-kompute.cpp create mode 100644 ggml-kompute.h create mode 160000 kompute create mode 100644 kompute-shaders/common.comp create mode 100644 kompute-shaders/op_add.comp create mode 100644 kompute-shaders/op_addrow.comp create mode 100644 kompute-shaders/op_cpy_f16_f16.comp create mode 100644 kompute-shaders/op_cpy_f16_f32.comp create mode 100644 kompute-shaders/op_cpy_f32_f16.comp create mode 100644 kompute-shaders/op_cpy_f32_f32.comp create mode 100644 kompute-shaders/op_diagmask.comp create mode 100644 kompute-shaders/op_gelu.comp create mode 100644 kompute-shaders/op_getrows.comp create mode 100644 kompute-shaders/op_getrows_f16.comp create mode 100644 kompute-shaders/op_getrows_q4_0.comp create mode 100644 kompute-shaders/op_getrows_q4_1.comp create mode 100644 kompute-shaders/op_getrows_q6_k.comp create mode 100644 kompute-shaders/op_mul.comp create mode 100644 kompute-shaders/op_mul_mat_f16.comp create mode 100644 kompute-shaders/op_mul_mat_mat_f32.comp create mode 100644 kompute-shaders/op_mul_mat_q4_0.comp create mode 100644 kompute-shaders/op_mul_mat_q4_1.comp create mode 100644 kompute-shaders/op_mul_mat_q6_k.comp create mode 100644 kompute-shaders/op_mul_mat_q8_0.comp create mode 100644 kompute-shaders/op_mul_mv_q_n.comp create mode 100644 kompute-shaders/op_mul_mv_q_n_pre.comp create mode 100644 kompute-shaders/op_norm.comp create mode 100644 kompute-shaders/op_relu.comp create mode 100644 kompute-shaders/op_rmsnorm.comp create mode 100644 kompute-shaders/op_rope_f16.comp create mode 100644 kompute-shaders/op_rope_f32.comp create mode 100644 kompute-shaders/op_scale.comp create mode 100644 kompute-shaders/op_scale_8.comp create mode 100644 kompute-shaders/op_silu.comp create mode 100644 kompute-shaders/op_softmax.comp create mode 100644 kompute-shaders/rope_common.comp diff --git a/.ecrc b/.ecrc index b682057dd..a3351f4e6 100644 --- a/.ecrc +++ b/.ecrc @@ -1,4 +1,5 @@ { + "Exclude": ["^\\.gitmodules$"], "Disable": { "IndentSize": true } diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index e5e435a70..fb719a550 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -337,6 +337,7 @@ jobs: OPENCL_VERSION: 2023.04.17 CLBLAST_VERSION: 1.6.0 SDE_VERSION: 9.33.0-2024-01-07 + VULKAN_VERSION: 1.3.261.1 strategy: matrix: @@ -353,6 +354,8 @@ jobs: defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CLBLAST=ON -DBUILD_SHARED_LIBS=ON -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/clblast"' - build: 'openblas' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_BLAS=ON -DBUILD_SHARED_LIBS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"' + - build: 'kompute' + defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DBUILD_SHARED_LIBS=ON' steps: - name: Clone @@ -361,6 +364,12 @@ jobs: with: fetch-depth: 0 + - name: Clone Kompute submodule + id: clone_kompute + if: ${{ matrix.build == 'kompute' }} + run: | + git submodule update --init kompute + - name: Download OpenCL SDK id: get_opencl if: ${{ matrix.build == 'clblast' }} @@ -395,6 +404,15 @@ jobs: $lib = $(join-path $msvc 'bin\Hostx64\x64\lib.exe') & $lib /machine:x64 "/def:${env:RUNNER_TEMP}/openblas/lib/libopenblas.def" "/out:${env:RUNNER_TEMP}/openblas/lib/openblas.lib" /name:openblas.dll + - name: Install Vulkan SDK + id: get_vulkan + if: ${{ matrix.build == 'kompute' }} + run: | + curl.exe -o $env:RUNNER_TEMP/VulkanSDK-Installer.exe -L "https://sdk.lunarg.com/sdk/download/${env:VULKAN_VERSION}/windows/VulkanSDK-${env:VULKAN_VERSION}-Installer.exe" + & "$env:RUNNER_TEMP\VulkanSDK-Installer.exe" --accept-licenses --default-answer --confirm-command install + Add-Content $env:GITHUB_ENV "VULKAN_SDK=C:\VulkanSDK\${env:VULKAN_VERSION}" + Add-Content $env:GITHUB_PATH "C:\VulkanSDK\${env:VULKAN_VERSION}\bin" + - name: Build id: cmake_build run: | @@ -432,7 +450,8 @@ jobs: - name: Test id: cmake_test - if: ${{ matrix.build != 'clblast' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }} # not all machines have native AVX-512 + # not all machines have native AVX-512 + if: ${{ matrix.build != 'clblast' && matrix.build != 'kompute' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }} run: | cd build ctest -L main -C Release --verbose --timeout 900 diff --git a/.gitmodules b/.gitmodules new file mode 100644 index 000000000..b7e8b8ff2 --- /dev/null +++ b/.gitmodules @@ -0,0 +1,3 @@ +[submodule "kompute"] + path = kompute + url = https://github.com/nomic-ai/kompute.git diff --git a/CMakeLists.txt b/CMakeLists.txt index ed8f39c62..65a6f3971 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -103,6 +103,7 @@ option(LLAMA_VULKAN "llama: use Vulkan" option(LLAMA_METAL "llama: use Metal" ${LLAMA_METAL_DEFAULT}) option(LLAMA_METAL_NDEBUG "llama: disable Metal debugging" OFF) option(LLAMA_METAL_SHADER_DEBUG "llama: compile Metal with -fno-fast-math" OFF) +option(LLAMA_KOMPUTE "llama: use Kompute" OFF) option(LLAMA_MPI "llama: use MPI" OFF) option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF) option(LLAMA_SYCL "llama: use SYCL" OFF) @@ -484,7 +485,6 @@ if (LLAMA_HIPBLAS) endif() endif() - if (LLAMA_SYCL) if ( NOT DEFINED ENV{ONEAPI_ROOT}) message(FATAL_ERROR "Not detect ENV {ONEAPI_ROOT}, please install oneAPI & source it, like: source /opt/intel/oneapi/setvars.sh") @@ -510,6 +510,160 @@ if (LLAMA_SYCL) set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} sycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread) endif() +if (LLAMA_KOMPUTE) + add_compile_definitions(VULKAN_HPP_DISPATCH_LOADER_DYNAMIC=1) + find_package(Vulkan COMPONENTS glslc REQUIRED) + find_program(glslc_executable NAMES glslc HINTS Vulkan::glslc) + if (NOT glslc_executable) + message(FATAL_ERROR "glslc not found") + endif() + + function(compile_shader) + set(options) + set(oneValueArgs) + set(multiValueArgs SOURCES) + cmake_parse_arguments(compile_shader "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN}) + foreach(source ${compile_shader_SOURCES}) + get_filename_component(filename ${source} NAME) + set(spv_file ${filename}.spv) + add_custom_command( + OUTPUT ${spv_file} + DEPENDS ${CMAKE_CURRENT_SOURCE_DIR}/${source} + ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/common.comp + ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_getrows.comp + ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n_pre.comp + ${CMAKE_CURRENT_SOURCE_DIR}/kompute-shaders/op_mul_mv_q_n.comp + COMMAND ${glslc_executable} --target-env=vulkan1.2 -o ${spv_file} ${CMAKE_CURRENT_SOURCE_DIR}/${source} + COMMENT "Compiling ${source} to ${spv_file}" + ) + + get_filename_component(RAW_FILE_NAME ${spv_file} NAME) + set(FILE_NAME "shader${RAW_FILE_NAME}") + string(REPLACE ".comp.spv" ".h" HEADER_FILE ${FILE_NAME}) + string(TOUPPER ${HEADER_FILE} HEADER_FILE_DEFINE) + string(REPLACE "." "_" HEADER_FILE_DEFINE "${HEADER_FILE_DEFINE}") + set(OUTPUT_HEADER_FILE "${HEADER_FILE}") + message(STATUS "${HEADER_FILE} generating ${HEADER_FILE_DEFINE}") + if(CMAKE_GENERATOR MATCHES "Visual Studio") + add_custom_command( + OUTPUT ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_BINARY_DIR}/bin/$/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + DEPENDS ${spv_file} xxd + COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/$/xxd" + ) + else() + add_custom_command( + OUTPUT ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "/*THIS FILE HAS BEEN AUTOMATICALLY GENERATED - DO NOT EDIT*/" > ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#ifndef ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "namespace kp {" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "namespace shader_data {" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_BINARY_DIR}/bin/xxd -i ${RAW_FILE_NAME} >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo "}}" >> ${OUTPUT_HEADER_FILE} + COMMAND ${CMAKE_COMMAND} -E echo \"\#endif // define ${HEADER_FILE_DEFINE}\" >> ${OUTPUT_HEADER_FILE} + DEPENDS ${spv_file} xxd + COMMENT "Converting to hpp: ${FILE_NAME} ${CMAKE_BINARY_DIR}/bin/xxd" + ) + endif() + endforeach() + endfunction() + + if (EXISTS "${CMAKE_CURRENT_SOURCE_DIR}/kompute/CMakeLists.txt") + message(STATUS "Kompute found") + set(KOMPUTE_OPT_LOG_LEVEL Error CACHE STRING "Kompute log level") + add_subdirectory(kompute) + + # Compile our shaders + compile_shader(SOURCES + kompute-shaders/op_scale.comp + kompute-shaders/op_scale_8.comp + kompute-shaders/op_add.comp + kompute-shaders/op_addrow.comp + kompute-shaders/op_mul.comp + kompute-shaders/op_silu.comp + kompute-shaders/op_relu.comp + kompute-shaders/op_gelu.comp + kompute-shaders/op_softmax.comp + kompute-shaders/op_norm.comp + kompute-shaders/op_rmsnorm.comp + kompute-shaders/op_diagmask.comp + kompute-shaders/op_mul_mat_mat_f32.comp + kompute-shaders/op_mul_mat_f16.comp + kompute-shaders/op_mul_mat_q8_0.comp + kompute-shaders/op_mul_mat_q4_0.comp + kompute-shaders/op_mul_mat_q4_1.comp + kompute-shaders/op_mul_mat_q6_k.comp + kompute-shaders/op_getrows_f16.comp + kompute-shaders/op_getrows_q4_0.comp + kompute-shaders/op_getrows_q4_1.comp + kompute-shaders/op_getrows_q6_k.comp + kompute-shaders/op_rope_f16.comp + kompute-shaders/op_rope_f32.comp + kompute-shaders/op_cpy_f16_f16.comp + kompute-shaders/op_cpy_f16_f32.comp + kompute-shaders/op_cpy_f32_f16.comp + kompute-shaders/op_cpy_f32_f32.comp + ) + + # Create a custom target for our generated shaders + add_custom_target(generated_shaders DEPENDS + shaderop_scale.h + shaderop_scale_8.h + shaderop_add.h + shaderop_addrow.h + shaderop_mul.h + shaderop_silu.h + shaderop_relu.h + shaderop_gelu.h + shaderop_softmax.h + shaderop_norm.h + shaderop_rmsnorm.h + shaderop_diagmask.h + shaderop_mul_mat_mat_f32.h + shaderop_mul_mat_f16.h + shaderop_mul_mat_q8_0.h + shaderop_mul_mat_q4_0.h + shaderop_mul_mat_q4_1.h + shaderop_mul_mat_q6_k.h + shaderop_getrows_f16.h + shaderop_getrows_q4_0.h + shaderop_getrows_q4_1.h + shaderop_getrows_q6_k.h + shaderop_rope_f16.h + shaderop_rope_f32.h + shaderop_cpy_f16_f16.h + shaderop_cpy_f16_f32.h + shaderop_cpy_f32_f16.h + shaderop_cpy_f32_f32.h + ) + + # Create a custom command that depends on the generated_shaders + add_custom_command( + OUTPUT ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp + COMMAND ${CMAKE_COMMAND} -E touch ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp + DEPENDS generated_shaders + COMMENT "Ensuring shaders are generated before compiling ggml-kompute.cpp" + ) + + # Add the stamp to the main sources to ensure dependency tracking + set(GGML_SOURCES_KOMPUTE ggml-kompute.cpp ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp) + set(GGML_HEADERS_KOMPUTE ggml-kompute.h ${CMAKE_CURRENT_BINARY_DIR}/ggml-kompute.stamp) + add_compile_definitions(GGML_USE_KOMPUTE) + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} kompute) + set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${CMAKE_BINARY_DIR}) + else() + message(WARNING "Kompute not found") + endif() +endif() + function(get_flags CCID CCVER) set(C_FLAGS "") set(CXX_FLAGS "") @@ -852,13 +1006,14 @@ add_library(ggml OBJECT ggml-backend.h ggml-quants.c ggml-quants.h - ${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA} - ${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL} - ${GGML_SOURCES_VULKAN} ${GGML_HEADERS_VULKAN} - ${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL} - ${GGML_SOURCES_MPI} ${GGML_HEADERS_MPI} - ${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA} - ${GGML_SOURCES_SYCL} ${GGML_HEADERS_SYCL} + ${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA} + ${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL} + ${GGML_SOURCES_VULKAN} ${GGML_HEADERS_VULKAN} + ${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL} + ${GGML_SOURCES_MPI} ${GGML_HEADERS_MPI} + ${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA} + ${GGML_SOURCES_SYCL} ${GGML_HEADERS_SYCL} + ${GGML_SOURCES_KOMPUTE} ${GGML_HEADERS_KOMPUTE} ) target_include_directories(ggml PUBLIC . ${LLAMA_EXTRA_INCLUDES}) diff --git a/ggml-backend.c b/ggml-backend.c index 8b6cf7c9f..0764dfebc 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -373,6 +373,11 @@ GGML_CALL static void ggml_backend_registry_init(void) { extern GGML_CALL int ggml_backend_vk_reg_devices(void); ggml_backend_vk_reg_devices(); #endif + +#ifdef GGML_USE_KOMPUTE + extern GGML_CALL void ggml_backend_kompute_reg_devices(void); + ggml_backend_kompute_reg_devices(); +#endif } GGML_CALL void ggml_backend_register(const char * name, ggml_backend_init_fn init_fn, ggml_backend_buffer_type_t default_buffer_type, void * user_data) { diff --git a/ggml-kompute.cpp b/ggml-kompute.cpp new file mode 100644 index 000000000..51c5af8ec --- /dev/null +++ b/ggml-kompute.cpp @@ -0,0 +1,1990 @@ +#include "ggml.h" +#include "ggml-backend.h" +#include "ggml-backend-impl.h" +#include "ggml-kompute.h" + +// These are generated at build time by cmake custom command +#include "shaderop_scale.h" +#include "shaderop_scale_8.h" +#include "shaderop_add.h" +#include "shaderop_addrow.h" +#include "shaderop_mul.h" +#include "shaderop_silu.h" +#include "shaderop_relu.h" +#include "shaderop_gelu.h" +#include "shaderop_softmax.h" +#include "shaderop_norm.h" +#include "shaderop_rmsnorm.h" +#include "shaderop_diagmask.h" +#include "shaderop_mul_mat_f16.h" +#include "shaderop_mul_mat_q8_0.h" +#include "shaderop_mul_mat_q4_0.h" +#include "shaderop_mul_mat_q4_1.h" +#include "shaderop_mul_mat_q6_k.h" +#include "shaderop_mul_mat_mat_f32.h" +#include "shaderop_getrows_f16.h" +#include "shaderop_getrows_q4_0.h" +#include "shaderop_getrows_q4_1.h" +#include "shaderop_getrows_q6_k.h" +#include "shaderop_rope_f16.h" +#include "shaderop_rope_f32.h" +#include "shaderop_cpy_f16_f16.h" +#include "shaderop_cpy_f16_f32.h" +#include "shaderop_cpy_f32_f16.h" +#include "shaderop_cpy_f32_f32.h" + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include +#include + +#ifdef __linux__ +#include // for setenv +#endif + +#define QK4_0 32 +#define QR4_0 2 +#define QK4_1 32 +#define QK_NL 16 + +typedef ggml_fp16_t half; + +static std::string ggml_kompute_format_name(int device) { + return "Kompute" + std::to_string(device); +} + +struct ggml_kompute_context { + int device; + std::string name; + std::shared_ptr pool; + + ggml_kompute_context(int device) + : device(device), name(ggml_kompute_format_name(device)) {} +}; + +// FIXME: It would be good to consolidate the kompute manager and the kompute context into one object +// and consolidate the init functions and simplify object lifetime management. As it currently stands, +// we *have* to have the kompute manager no matter what for device discovery, but the kompute context +// is only created when a device is set and vulkan is explicitly turned on. +static ggml_kompute_context *s_kompute_context = nullptr; + +class kompute_manager { + kp::Manager *s_mgr = nullptr; + +public: + kp::Manager *operator()() { + if (s_mgr && !s_mgr->hasInstance()) { + destroy(); + } + if (!s_mgr) { + s_mgr = new kp::Manager; + } + return s_mgr; + } + + void destroy() { + delete s_mgr; + s_mgr = nullptr; + } +}; + +static kompute_manager komputeManager; + +struct ggml_vk_memory { + void *data = nullptr; + size_t size = 0; + vk::DeviceMemory *primaryMemory = nullptr; + vk::Buffer *primaryBuffer = nullptr; + vk::DeviceMemory *stagingMemory = nullptr; + vk::Buffer *stagingBuffer = nullptr; +}; + +#ifdef __linux__ +__attribute__((constructor)) +static void enable_sam() { + setenv("RADV_PERFTEST", "sam", false); +} +#endif + +static bool ggml_vk_checkPhysicalDeviceFeatures(vk::PhysicalDevice physical_device) { + vk::PhysicalDeviceFeatures availableFeatures; + physical_device.getFeatures(&availableFeatures); + + if (!availableFeatures.shaderInt16) + return false; + + vk::PhysicalDeviceVulkan11Features availableFeatures11; + vk::PhysicalDeviceVulkan12Features availableFeatures12; + + availableFeatures11.pNext = &availableFeatures12; + availableFeatures12.pNext = nullptr; + + vk::PhysicalDeviceFeatures2 features2; + features2.pNext = &availableFeatures11; + + physical_device.getFeatures2(&features2); + + if (!availableFeatures11.uniformAndStorageBuffer16BitAccess || + !availableFeatures11.storageBuffer16BitAccess) { + return false; + } + + if (!availableFeatures12.storageBuffer8BitAccess || + !availableFeatures12.uniformAndStorageBuffer8BitAccess || + !availableFeatures12.shaderFloat16 || + !availableFeatures12.shaderInt8) { + return false; + } + + return true; +} + +static const char * ggml_vk_getVendorName(uint32_t vendorID) { + switch (vendorID) { + case 0x10DE: + return "nvidia"; + case 0x1002: + return "amd"; + case 0x8086: + return "intel"; + default: + return "unknown"; + } +} + +static std::vector ggml_vk_available_devices_internal(size_t memoryRequired) { + std::vector results; + if (!komputeManager()->hasVulkan() || !komputeManager()->hasInstance()) + return results; + + std::vector physical_devices; + try { + physical_devices = komputeManager()->listDevices(); + } catch (vk::SystemError & err) { + std::cerr << __func__ << ": ignoring Vulkan exception: " << err.what() << "\n"; + return results; + } + + uint32_t deviceCount = physical_devices.size(); + if (deviceCount == 0) + return results; + + std::unordered_map count_by_name; + + for (uint32_t i = 0; i < deviceCount; i++) { + const auto & physical_device = physical_devices[i]; + + VkPhysicalDeviceProperties dev_props = physical_device.getProperties(); + VkPhysicalDeviceMemoryProperties memoryProperties = physical_device.getMemoryProperties(); + const uint32_t major = VK_VERSION_MAJOR(dev_props.apiVersion); + const uint32_t minor = VK_VERSION_MINOR(dev_props.apiVersion); + if (major < 1 || minor < 2) + continue; + + if (!ggml_vk_checkPhysicalDeviceFeatures(physical_device)) + continue; + + size_t heapSize = 0; + for (uint32_t j = 0; j < memoryProperties.memoryHeapCount; ++j) { + VkMemoryHeap heap = memoryProperties.memoryHeaps[j]; + if (heap.flags & VK_MEMORY_HEAP_DEVICE_LOCAL_BIT) { + heapSize = heap.size; + break; + } + } + + if (heapSize < memoryRequired) + continue; + + auto ext_props = physical_device.enumerateDeviceExtensionProperties(); + bool has_maintenance4 = false; + + // Check if maintenance4 is supported + for (const auto & properties : ext_props) { + if (strcmp("VK_KHR_maintenance4", properties.extensionName) == 0) { + has_maintenance4 = true; + } + } + + vk::PhysicalDeviceSubgroupProperties subgroup_props; + vk::PhysicalDeviceProperties2 dev_props2; + vk::PhysicalDeviceMaintenance3Properties dev_props3; + vk::PhysicalDeviceMaintenance4Properties dev_props4; + dev_props2.pNext = &dev_props3; + dev_props3.pNext = &subgroup_props; + if (has_maintenance4) { + subgroup_props.pNext = &dev_props4; + } + physical_device.getProperties2(&dev_props2); + + if (subgroup_props.subgroupSize < 32) + continue; + + ggml_vk_device d; + d.index = i; + d.type = dev_props.deviceType; + d.heapSize = heapSize; + d.vendor = strdup(ggml_vk_getVendorName(dev_props.vendorID)); + d.subgroupSize = subgroup_props.subgroupSize; + d.bufferAlignment = dev_props.limits.minStorageBufferOffsetAlignment; + + if (has_maintenance4) { + d.maxAlloc = std::min(dev_props3.maxMemoryAllocationSize, dev_props4.maxBufferSize); + } else { + d.maxAlloc = dev_props3.maxMemoryAllocationSize; + } + + std::string name(dev_props.deviceName); + size_t n_idx = ++count_by_name[name]; + if (n_idx > 1) { + name += " (" + std::to_string(n_idx) + ")"; + } + d.name = strdup(name.c_str()); + + results.push_back(d); + } + + std::stable_sort(results.begin(), results.end(), + [](const ggml_vk_device& lhs, const ggml_vk_device& rhs) -> bool { + if (lhs.type != rhs.type) { + if (lhs.type == VK_PHYSICAL_DEVICE_TYPE_DISCRETE_GPU) return true; + if (rhs.type == VK_PHYSICAL_DEVICE_TYPE_DISCRETE_GPU) return false; + + if (lhs.type == VK_PHYSICAL_DEVICE_TYPE_INTEGRATED_GPU) return true; + if (rhs.type == VK_PHYSICAL_DEVICE_TYPE_INTEGRATED_GPU) return false; + } + return lhs.heapSize < rhs.heapSize; + } + ); + + return results; +} + +// public API returns a C-style array +ggml_vk_device * ggml_vk_available_devices(size_t memoryRequired, size_t * count) { + auto devices = ggml_vk_available_devices_internal(memoryRequired); + *count = devices.size(); + if (devices.empty()) { + return nullptr; + } + + size_t nbytes = sizeof (ggml_vk_device) * (devices.size()); + auto * arr = static_cast(malloc(nbytes)); + memcpy(arr, devices.data(), nbytes); + return arr; +} + +static void ggml_vk_filterByVendor(std::vector& devices, const std::string& targetVendor) { + devices.erase( + std::remove_if(devices.begin(), devices.end(), + [&targetVendor](const ggml_vk_device& device) { + return device.vendor != targetVendor; + }), + devices.end() + ); +} + +static void ggml_vk_filterByName(std::vector& devices, const std::string& targetName) { + devices.erase( + std::remove_if(devices.begin(), devices.end(), + [&targetName](const ggml_vk_device& device) { + return device.name != targetName; + }), + devices.end() + ); +} + +static bool ggml_vk_get_device(ggml_vk_device * device, size_t memoryRequired, const std::string & name) { + if (name.empty()) + return false; + + auto devices = ggml_vk_available_devices_internal(memoryRequired); + if (name == "amd" || name == "nvidia" || name == "intel") { + ggml_vk_filterByVendor(devices, name); + } else if (name != "gpu") { + ggml_vk_filterByName(devices, name); + } + + if (devices.empty()) + return false; + + *device = devices.front(); + return true; +} + +bool ggml_vk_get_device(ggml_vk_device * device, size_t memoryRequired, const char * name) { + return ggml_vk_get_device(device, memoryRequired, std::string(name)); +} + +bool ggml_vk_has_vulkan() { + return komputeManager()->hasVulkan(); +} + +bool ggml_vk_has_device() { + return komputeManager()->hasDevice(); +} + +ggml_vk_device ggml_vk_current_device() { + if (!komputeManager()->hasDevice()) + return ggml_vk_device(); + + auto devices = ggml_vk_available_devices_internal(0); + ggml_vk_filterByName(devices, komputeManager()->physicalDevice()->getProperties().deviceName.data()); + GGML_ASSERT(!devices.empty()); + return devices.front(); +} + +static +void ggml_vk_allocate_descriptor_pool(struct ggml_kompute_context * ctx, size_t size) { + std::vector descriptorPoolSizes = { + vk::DescriptorPoolSize( + vk::DescriptorType::eStorageBuffer, + 3 * size // Descriptor count is number of possible tensors to pass into an algorithm + ) + }; + + vk::DescriptorPoolCreateInfo descriptorPoolInfo( + vk::DescriptorPoolCreateFlags(), + size, // Max sets + static_cast(descriptorPoolSizes.size()), + descriptorPoolSizes.data()); + + ctx->pool = std::make_shared(); + vk::Result r = komputeManager()->device()->createDescriptorPool( + &descriptorPoolInfo, nullptr, ctx->pool.get()); + if (r != vk::Result::eSuccess) + std::cerr << "Error allocating descriptor pool" << vk::to_string(r); +} + +static +void ggml_vk_free_descriptor_pool(struct ggml_kompute_context * ctx) { + if (ctx->pool) { + komputeManager()->device()->destroy( + *ctx->pool, + (vk::Optional)nullptr); + ctx->pool = nullptr; + } +} + +static +vk::Buffer *ggml_vk_allocate_buffer(size_t size) { + vk::BufferCreateInfo bufferCreateInfo; + bufferCreateInfo.size = size; + bufferCreateInfo.usage = vk::BufferUsageFlagBits::eStorageBuffer | + vk::BufferUsageFlagBits::eTransferSrc | + vk::BufferUsageFlagBits::eTransferDst; + bufferCreateInfo.sharingMode = vk::SharingMode::eExclusive; + + vk::Buffer *vkBuffer = new vk::Buffer; + vk::Result r = komputeManager()->device()->createBuffer(&bufferCreateInfo, nullptr, vkBuffer); + if (r != vk::Result::eSuccess) + std::cerr << "Error allocating buffer " << vk::to_string(r) << std::endl; + return vkBuffer; +} + +static +vk::DeviceMemory *ggml_vk_allocate(size_t size, vk::MemoryPropertyFlags flags, vk::MemoryRequirements requirements, bool *isHostVisible) { + + uint32_t memoryTypeIndex = -1; + bool memoryTypeIndexFound = false; + vk::PhysicalDeviceMemoryProperties memoryProperties = komputeManager()->physicalDevice()->getMemoryProperties(); + for (uint32_t i = 0; i < memoryProperties.memoryTypeCount; i++) { + const vk::MemoryType &memoryType = memoryProperties.memoryTypes[i]; + const vk::MemoryHeap &memoryHeap = memoryProperties.memoryHeaps[memoryType.heapIndex]; + if (memoryHeap.size < size) { + continue; + } + + if (requirements.memoryTypeBits & (1 << i)) { + if (((memoryProperties.memoryTypes[i]).propertyFlags & + flags) == flags) { + memoryTypeIndex = i; + memoryTypeIndexFound = true; + if (isHostVisible && (memoryProperties.memoryTypes[i].propertyFlags & vk::MemoryPropertyFlagBits::eHostVisible)) { + *isHostVisible = true; + } + break; + } + } + } + if (!memoryTypeIndexFound) { + throw std::runtime_error( + "Memory type index for buffer creation not found"); + } + + vk::MemoryAllocateInfo allocInfo; + allocInfo.allocationSize = size; + allocInfo.memoryTypeIndex = memoryTypeIndex; + vk::DeviceMemory *vkDeviceMemory = new vk::DeviceMemory; + vk::Result r = komputeManager()->device()->allocateMemory(&allocInfo, nullptr, vkDeviceMemory); + if (r != vk::Result::eSuccess) { + std::cerr << "Error allocating memory " << vk::to_string(r) << std::endl; + throw std::runtime_error("Error allocating vulkan memory."); + } + return vkDeviceMemory; +} + +static size_t ggml_vk_aligned_offset(ggml_backend_buffer_t buffer, size_t offset) { + size_t minStorageBufferOffsetAlignment = ggml_backend_buffer_get_alignment(buffer); + + // If offset is already aligned, return it directly + if (offset % minStorageBufferOffsetAlignment == 0) { + return offset; + } + + // Otherwise, return the largest multiple of minStorageBufferOffsetAlignment less than offset + return (offset / minStorageBufferOffsetAlignment) * minStorageBufferOffsetAlignment; +} + +static ggml_vk_memory ggml_vk_allocate(size_t size) { + ggml_vk_memory memory; + bool isHostVisible = false; + { + memory.primaryBuffer = ggml_vk_allocate_buffer(size); + vk::MemoryRequirements memoryRequirements = komputeManager()->device()->getBufferMemoryRequirements(*memory.primaryBuffer); + vk::MemoryPropertyFlags memoryPropertyFlags = vk::MemoryPropertyFlagBits::eDeviceLocal; + memory.primaryMemory = ggml_vk_allocate(size, memoryPropertyFlags, memoryRequirements, &isHostVisible); + komputeManager()->device()->bindBufferMemory(*memory.primaryBuffer, *memory.primaryMemory, 0); + if (isHostVisible) { + vk::Result r = komputeManager()->device()->mapMemory(*memory.primaryMemory, 0, size, vk::MemoryMapFlags(), &memory.data); + if (r != vk::Result::eSuccess) + std::cerr << "Error mapping memory" << vk::to_string(r); + } + } + + if (!isHostVisible) { + memory.stagingBuffer = ggml_vk_allocate_buffer(size); + vk::MemoryRequirements memoryRequirements = komputeManager()->device()->getBufferMemoryRequirements(*memory.stagingBuffer); + vk::MemoryPropertyFlags memoryPropertyFlags = vk::MemoryPropertyFlagBits::eHostVisible | + vk::MemoryPropertyFlagBits::eHostCoherent | + vk::MemoryPropertyFlagBits::eHostCached; + memory.stagingMemory = ggml_vk_allocate(size, memoryPropertyFlags, memoryRequirements, &isHostVisible); + komputeManager()->device()->bindBufferMemory(*memory.stagingBuffer, *memory.stagingMemory, 0); + vk::Result r = komputeManager()->device()->mapMemory(*memory.stagingMemory, 0, size, vk::MemoryMapFlags(), &memory.data); + if (r != vk::Result::eSuccess) + std::cerr << "Error mapping memory" << vk::to_string(r); + } + + memory.size = size; + return memory; +} + +static void ggml_vk_free_memory(ggml_vk_memory &memory) +{ + komputeManager()->device()->destroy( + *memory.primaryBuffer, + (vk::Optional)nullptr); + if (memory.stagingBuffer) { + komputeManager()->device()->destroy( + *memory.stagingBuffer, + (vk::Optional)nullptr); + } + komputeManager()->device()->freeMemory( + *memory.primaryMemory, + (vk::Optional)nullptr); + if (memory.stagingMemory) { + komputeManager()->device()->freeMemory( + *memory.stagingMemory, + (vk::Optional)nullptr); + } +} + +static const char * ggml_backend_kompute_buffer_type_get_name(ggml_backend_buffer_type_t buft); + +static +ggml_vk_memory * ggml_vk_find_tensor(const struct ggml_tensor * t, uint64_t & offset) { + ggml_backend_buffer_t buffer = t->view_src ? t->view_src->buffer : t->buffer; + + // compatibility with ggml-backend + GGML_ASSERT(buffer && buffer->buft->iface.get_name == ggml_backend_kompute_buffer_type_get_name); + + ggml_vk_memory * buf_ctx = static_cast(buffer->context); + + const intptr_t ioffs = intptr_t(t->data) - intptr_t(buf_ctx->data); + + GGML_ASSERT(ioffs >= 0 && ioffs + int64_t(ggml_nbytes(t)) <= int64_t(buffer->size)); + + offset = uint64_t(ioffs); + return buf_ctx; +} + +static +const std::shared_ptr ggml_vk_get_tensor(const struct ggml_tensor * t, uint32_t * alignedOffset = nullptr) { + uint64_t originalOffset = 0; + auto * res = ggml_vk_find_tensor(t, originalOffset); + if (!res) { + static std::shared_ptr nullTensor = nullptr; + return nullTensor; + } + + // Create a tensor whose memory will be composed of our buffers at the correct offset + const size_t nelements = ggml_nelements(t); + size_t nbytes = ggml_nbytes(t); + + size_t vulkanOffset = ggml_vk_aligned_offset(t->buffer, originalOffset); + if (alignedOffset) { + *alignedOffset = originalOffset - vulkanOffset; + nbytes += *alignedOffset; + } + + return komputeManager()->tensor( + t->data, + nelements, + nbytes, kp::Tensor::TensorDataTypes::eFloat, + res->primaryMemory, res->primaryBuffer, + res->stagingMemory, res->stagingBuffer, + vulkanOffset); +} + +static std::vector getSpirvShader(const unsigned char* rawData, size_t size) { + if (size % sizeof(uint32_t) != 0) { + throw std::runtime_error("Invalid size: must be divisible by sizeof(uint32_t)"); + } + + const uint32_t* data_ptr = reinterpret_cast(rawData); + size_t count = size / sizeof(uint32_t); + return std::vector(data_ptr, data_ptr + count); +} + +inline static +uint32_t safe_divide(uint32_t a, uint32_t b) { + if (b <= 1) { + return a; + } + if ((a % b) != 0) { + fprintf(stderr, "((%u %% %u) == %u) != 0\n", a, b, a % b); + GGML_ASSERT(!"safe_divide result would've had remainder"); + } + return a / b; +} + +static void ggml_vk_add( + kp::Sequence& seq, + const std::shared_ptr& inA, + const std::shared_ptr& inB, + const std::shared_ptr& out, + uint32_t inAOff, uint32_t inBOff, uint32_t outOff, + int32_t ne00, int32_t ne01, int32_t ne02, int32_t ne03, + int32_t nb00, int32_t nb01, int32_t nb02, int32_t nb03, + int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13, + int32_t nb10, int32_t nb11, int32_t nb12, int32_t nb13, + int32_t ne0, + int32_t nb0, int32_t nb1, int32_t nb2, int32_t nb3 +) { + const static auto spirv = getSpirvShader(kp::shader_data::op_add_comp_spv, + kp::shader_data::op_add_comp_spv_len); + + struct PushConstants { + uint32_t inAOff, inBOff, outOff; + int32_t ne00; + int32_t nb00, nb01, nb02, nb03; + int32_t ne10, ne11, ne12, ne13; + int32_t nb10, nb11, nb12, nb13; + int32_t ne0; + int32_t nb0, nb1, nb2, nb3; + } const pushConsts { + safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4), + ne00, + nb00, nb01, nb02, nb03, + ne10, ne11, ne12, ne13, + nb10, nb11, nb12, nb13, + ne0, + nb0, nb1, nb2, nb3 + }; + + std::shared_ptr s_algo = nullptr; + if (!komputeManager()->hasAlgorithm(__func__)) { + s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {}, {pushConsts}); + } else { + s_algo = komputeManager()->getAlgorithm(__func__); + s_algo->setTensors({inA, inB, out}); + s_algo->setWorkgroup({unsigned(ne01), unsigned(ne02), unsigned(ne03)}); + s_algo->setPushConstants({pushConsts}); + s_algo->updateDescriptors(s_kompute_context->pool.get()); + } + seq.record(s_algo); +} + +static void ggml_vk_addrow(kp::Sequence& seq, + const std::shared_ptr& inA, + const std::shared_ptr& inB, + const std::shared_ptr& out, + uint32_t inAOff, uint32_t inBOff, uint32_t outOff, + uint32_t size, uint32_t row = 0) { + + const static auto spirv = getSpirvShader(kp::shader_data::op_addrow_comp_spv, + kp::shader_data::op_addrow_comp_spv_len); + + struct PushConstants { + uint32_t inAOff, inBOff, outOff; + uint32_t row; + } const pushConsts { + safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4), + row + }; + + std::shared_ptr s_algo = nullptr; + if (!komputeManager()->hasAlgorithm(__func__)) + s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {size}, {}, {pushConsts}); + else { + s_algo = komputeManager()->getAlgorithm(__func__); + s_algo->setTensors({inA, inB, out}); + s_algo->setWorkgroup({size}); + s_algo->setPushConstants({pushConsts}); + s_algo->updateDescriptors(s_kompute_context->pool.get()); + } + seq.record(s_algo); +} + +static void ggml_vk_mul( + kp::Sequence& seq, + const std::shared_ptr& inA, + const std::shared_ptr& inB, + const std::shared_ptr& out, + uint32_t inAOff, uint32_t inBOff, uint32_t outOff, + int32_t ne00, int32_t ne01, int32_t ne02, int32_t ne03, + int32_t nb00, int32_t nb01, int32_t nb02, int32_t nb03, + int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13, + int32_t nb10, int32_t nb11, int32_t nb12, int32_t nb13, + int32_t ne0, + int32_t nb0, int32_t nb1, int32_t nb2, int32_t nb3 +) { + const static auto spirv = getSpirvShader(kp::shader_data::op_mul_comp_spv, + kp::shader_data::op_mul_comp_spv_len); + + struct PushConstants { + uint32_t inAOff, inBOff, outOff; + int32_t ne00; + int32_t nb00, nb01, nb02, nb03; + int32_t ne10, ne11, ne12, ne13; + int32_t nb10, nb11, nb12, nb13; + int32_t ne0; + int32_t nb0, nb1, nb2, nb3; + } const pushConsts { + safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4), + ne00, + nb00, nb01, nb02, nb03, + ne10, ne11, ne12, ne13, + nb10, nb11, nb12, nb13, + ne0, + nb0, nb1, nb2, nb3 + }; + + std::shared_ptr s_algo = nullptr; + if (!komputeManager()->hasAlgorithm(__func__)) { + s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {}, {pushConsts}); + } else { + s_algo = komputeManager()->getAlgorithm(__func__); + s_algo->setTensors({inA, inB, out}); + s_algo->setWorkgroup({unsigned(ne01), unsigned(ne02), unsigned(ne03)}); + s_algo->setPushConstants({pushConsts}); + s_algo->updateDescriptors(s_kompute_context->pool.get()); + } + seq.record(s_algo); +} + +static void ggml_vk_scale(kp::Sequence& seq, + const std::shared_ptr& in, + const std::shared_ptr& out, + uint32_t inOff, uint32_t outOff, + uint32_t size, float scale) { + const static auto spirv_1 = getSpirvShader( + kp::shader_data::op_scale_comp_spv, kp::shader_data::op_scale_comp_spv_len + ); + const static auto spirv_8 = getSpirvShader( + kp::shader_data::op_scale_8_comp_spv, kp::shader_data::op_scale_8_comp_spv_len + ); + + struct PushConstants { + uint32_t inOff, outOff; + float scale; + } const pushConsts { + safe_divide(inOff, 4), safe_divide(outOff, 4), + scale + }; + + const auto * spirv = &spirv_1; + std::string name(__func__); + if (size % 8 == 0) { + size /= 8; + name += "_8"; + spirv = &spirv_8; + } + + std::shared_ptr s_algo = nullptr; + if (!komputeManager()->hasAlgorithm(name)) { + s_algo = komputeManager()->algorithm(name, s_kompute_context->pool.get(), {in, out}, *spirv, {size}, {}, {pushConsts}); + } else { + s_algo = komputeManager()->getAlgorithm(name); + s_algo->setTensors({in, out}); + s_algo->setWorkgroup({size}); + s_algo->setPushConstants({pushConsts}); + s_algo->updateDescriptors(s_kompute_context->pool.get()); + } + seq.record(s_algo); +} + +static void ggml_vk_xxlu( + const std::vector& spirv, const char * suffix, kp::Sequence& seq, + const std::shared_ptr& in, + const std::shared_ptr& out, + uint32_t inOff, uint32_t outOff, + uint32_t size +) { + struct PushConstants { + uint32_t inOff, outOff; + } const pushConsts { + safe_divide(inOff, 4), safe_divide(outOff, 4), + }; + + auto name = std::string(__func__) + "_" + suffix; + std::shared_ptr s_algo = nullptr; + if (!komputeManager()->hasAlgorithm(name)) { + s_algo = komputeManager()->algorithm(name, s_kompute_context->pool.get(), {in, out}, spirv, {size}, {}, {pushConsts}); + } else { + s_algo = komputeManager()->getAlgorithm(name); + s_algo->setTensors({in, out}); + s_algo->setWorkgroup({size}); + s_algo->setPushConstants({pushConsts}); + s_algo->updateDescriptors(s_kompute_context->pool.get()); + } + seq.record(s_algo); +} + +template +static void ggml_vk_silu(Args&&... args) { + const static auto spirv = getSpirvShader(kp::shader_data::op_silu_comp_spv, + kp::shader_data::op_silu_comp_spv_len); + + ggml_vk_xxlu(spirv, "silu", std::forward(args)...); +} + +template +static void ggml_vk_relu(Args&&... args) { + const static auto spirv = getSpirvShader(kp::shader_data::op_relu_comp_spv, + kp::shader_data::op_relu_comp_spv_len); + + ggml_vk_xxlu(spirv, "relu", std::forward(args)...); +} + +template +static void ggml_vk_gelu(Args&&... args) { + const static auto spirv = getSpirvShader(kp::shader_data::op_gelu_comp_spv, + kp::shader_data::op_gelu_comp_spv_len); + + ggml_vk_xxlu(spirv, "gelu", std::forward(args)...); +} + +static void ggml_vk_soft_max( + kp::Sequence& seq, + const std::shared_ptr& inA, + const std::shared_ptr& inB, + const std::shared_ptr& out, + uint32_t inAOff, uint32_t inBOff, uint32_t outOff, + int32_t ne00, int32_t ne01, int32_t ne02, uint32_t ne03, + float scale +) { + const static auto spirv = getSpirvShader(kp::shader_data::op_softmax_comp_spv, + kp::shader_data::op_softmax_comp_spv_len); + + struct PushConstants { + uint32_t inAOff, inBOff, outOff; + int32_t ne00, ne01, ne02; + float scale; + int32_t mask; + } pushConsts { + safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4), + ne00, ne01, ne02, + scale, + bool(inB) + }; + + auto & inB_ = inB ? inB : inA; + + std::shared_ptr s_algo = nullptr; + if (!komputeManager()->hasAlgorithm(__func__)) { + // FIXME: The softmax kernel needs to be fixed to use the subgroupsize which can vary by device + const uint32_t local_x = 32; + s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), {inA, inB_, out}, spirv, {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {local_x}, {pushConsts}); + } else { + s_algo = komputeManager()->getAlgorithm(__func__); + s_algo->setTensors({inA, inB_, out}); + s_algo->setWorkgroup({unsigned(ne01), unsigned(ne02), unsigned(ne03)}); + s_algo->setPushConstants({pushConsts}); + s_algo->updateDescriptors(s_kompute_context->pool.get()); + } + seq.record(s_algo); +} + +static void ggml_vk_norm_( + const std::vector& spirv, const char * suffix, kp::Sequence& seq, + const std::shared_ptr& in, + const std::shared_ptr& out, + uint32_t inOff, uint32_t outOff, + int32_t ne00, int32_t nb01, + int32_t nrows, float epsilon +) { + GGML_ASSERT(nb01%sizeof(float) == 0); + GGML_ASSERT(ne00%sizeof(float) == 0); + + struct PushConstants { + uint32_t inOff, outOff; + uint32_t ne00, nb01; + float eps; + } pushConsts { + safe_divide(inOff, 4), safe_divide(outOff, 4), + (uint32_t)ne00, (uint32_t)nb01, epsilon + }; + + auto name = std::string(__func__) + "_" + suffix; + std::shared_ptr s_algo = nullptr; + if (!komputeManager()->hasAlgorithm(name)) { + s_algo = komputeManager()->algorithm(name, s_kompute_context->pool.get(), {in, out}, spirv, {(uint32_t)nrows}, {}, {pushConsts}); + } else { + s_algo = komputeManager()->getAlgorithm(name); + s_algo->setTensors({in, out}); + s_algo->setWorkgroup({(uint32_t)nrows}); + s_algo->setPushConstants({pushConsts}); + s_algo->updateDescriptors(s_kompute_context->pool.get()); + } + seq.record(s_algo); +} + +template +static void ggml_vk_norm(Args&&... args) { + const static auto spirv = getSpirvShader(kp::shader_data::op_norm_comp_spv, + kp::shader_data::op_norm_comp_spv_len); + + ggml_vk_norm_(spirv, "norm", std::forward(args)...); +} + +template +static void ggml_vk_rms_norm(Args&&... args) { + const static auto spirv = getSpirvShader(kp::shader_data::op_rmsnorm_comp_spv, + kp::shader_data::op_rmsnorm_comp_spv_len); + + ggml_vk_norm_(spirv, "rms", std::forward(args)...); +} + +static void ggml_vk_diag_mask_inf(kp::Sequence& seq, + const std::shared_ptr& in, + const std::shared_ptr& out, + uint32_t inOff, uint32_t outOff, + uint32_t n_past, + int32_t ne00, int32_t ne01, int32_t ne02) { + const static auto spirv = getSpirvShader(kp::shader_data::op_diagmask_comp_spv, + kp::shader_data::op_diagmask_comp_spv_len); + + struct PushConstants { + uint32_t inOff, outOff; + uint32_t n_past; + int32_t ne00, ne01; + } pushConsts { + safe_divide(inOff, 4), safe_divide(outOff, 4), + n_past, + ne00, ne01 + }; + + std::shared_ptr s_algo = nullptr; + if (!komputeManager()->hasAlgorithm(__func__)) + s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), {in, out}, spirv, {unsigned(ne00), unsigned(ne01), unsigned(ne02)}, {}, {pushConsts}); + else { + s_algo = komputeManager()->getAlgorithm(__func__); + s_algo->setTensors({in, out}); + s_algo->setWorkgroup({unsigned(ne00), unsigned(ne01), unsigned(ne02)}); + s_algo->setPushConstants({pushConsts}); + s_algo->updateDescriptors(s_kompute_context->pool.get()); + } + seq.record(s_algo); +} + +static void ggml_vk_mul_mat_f16( + kp::Sequence& seq, + const std::shared_ptr& inA, + const std::shared_ptr& inB, + const std::shared_ptr& out, + uint32_t inAOff, uint32_t inBOff, uint32_t outOff, + int32_t ne00, int32_t ne01, int32_t ne02, + uint32_t nb00, uint32_t nb01, uint32_t nb02, + int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13, + uint32_t nb10, uint32_t nb11, uint32_t nb12, + int32_t ne0, int32_t ne1, + uint32_t r2, uint32_t r3 +) { + const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_f16_comp_spv, + kp::shader_data::op_mul_mat_f16_comp_spv_len); + + struct PushConstants { + uint32_t inAOff, inBOff, outOff; + int32_t ne00, ne01, ne02; + uint32_t nb00, nb01, nb02; + int32_t ne10, ne11, ne12; + uint32_t nb10, nb11, nb12; + int32_t ne0, ne1; + uint32_t r2, r3; + } pushConsts { + safe_divide(inAOff, 2), safe_divide(inBOff, 4), safe_divide(outOff, 4), + ne00, ne01, ne02, + nb00, nb01, nb02, + ne10, ne11, ne12, + nb10, nb11, nb12, + ne0, ne1, + r2, r3 + }; + + const unsigned ny = unsigned((ne11 + 4 - 1)/4); + + std::shared_ptr s_algo = nullptr; + if (!komputeManager()->hasAlgorithm(__func__)) { + const uint32_t local_x = ggml_vk_current_device().subgroupSize * 2; + s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned(ne01), ny, unsigned(ne12*ne13)}, {local_x}, {pushConsts}); + } else { + s_algo = komputeManager()->getAlgorithm(__func__); + s_algo->setTensors({inA, inB, out}); + s_algo->setWorkgroup({unsigned(ne01), ny, unsigned(ne12*ne13)}); + s_algo->setPushConstants({pushConsts}); + s_algo->updateDescriptors(s_kompute_context->pool.get()); + } + seq.record(s_algo); +} + +static void ggml_vk_mul_mat_mat_f32(kp::Sequence& seq, + const std::shared_ptr& inA, + const std::shared_ptr& inB, + const std::shared_ptr& out, + uint32_t inAOff, uint32_t inBOff, uint32_t outOff, + int32_t ne00, int32_t ne01, int32_t ne02, + uint32_t nb01, uint32_t nb02, + int32_t ne11, int32_t ne12, + uint32_t nb11, uint32_t nb12, + uint32_t nb1, uint32_t nb2) { + const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_mat_f32_comp_spv, + kp::shader_data::op_mul_mat_mat_f32_comp_spv_len); + + struct PushConstants { + uint32_t inAOff, inBOff, outOff; + int32_t ne00, ne01, ne02, ne11, ne12; + uint32_t nb01, nb02; + uint32_t nb11, nb12; + uint32_t nb1, nb2; + } pushConsts { + safe_divide(inAOff, 4), safe_divide(inBOff, 4), safe_divide(outOff, 4), + ne00, ne01, ne02, ne11, ne12, + nb01, nb02, nb11, nb12, + nb1, nb2 + }; + + const uint32_t local_x = ggml_vk_current_device().subgroupSize; + std::shared_ptr s_algo = nullptr; + if (!komputeManager()->hasAlgorithm(__func__)) { + s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), + {inA, inB, out}, spirv, + {unsigned(ne01), + unsigned(ne11), + unsigned(std::max(ne12, ne02)) + }, + {local_x}, + {pushConsts}); + } else { + s_algo = komputeManager()->getAlgorithm(__func__); + s_algo->setTensors({inA, inB, out}); + s_algo->setWorkgroup({unsigned(ne01), + unsigned(ne11), + unsigned(std::max(ne12, ne02)), + }); + s_algo->setPushConstants({pushConsts}); + s_algo->updateDescriptors(s_kompute_context->pool.get()); + } + seq.record(s_algo); +} + +static void ggml_vk_mul_mat_impl( + const std::vector& spirv, const char * suffix, uint32_t block_size, kp::Sequence& seq, + const std::shared_ptr& inA, + const std::shared_ptr& inB, + const std::shared_ptr& out, + uint32_t inAOff, uint32_t inBOff, uint32_t outOff, + int32_t ne00, int32_t ne01, int32_t ne02, + int32_t ne10, int32_t ne11, int32_t ne12, int32_t ne13, + int32_t ne0, int32_t ne1, + uint32_t r2, uint32_t r3 +) { + struct PushConstants { + uint32_t inAOff, inBOff, outOff; + int32_t ne00, ne01, ne02; + int32_t ne10, ne12; + int32_t ne0, ne1; + uint32_t r2, r3; + } pushConsts { + safe_divide(inAOff, block_size), safe_divide(inBOff, 4), safe_divide(outOff, 4), + ne00, ne01, ne02, + ne10, ne12, + ne0, ne1, + r2, r3 + }; + + auto name = std::string(__func__) + "_" + suffix; + std::shared_ptr s_algo = nullptr; + if (!komputeManager()->hasAlgorithm(name)) { + const uint32_t local_x = ggml_vk_current_device().subgroupSize * 2; + s_algo = komputeManager()->algorithm(name, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned((ne01 + 7)/8), unsigned(ne11), unsigned(ne12*ne13)}, {local_x}, {pushConsts}); + } else { + s_algo = komputeManager()->getAlgorithm(name); + s_algo->setTensors({inA, inB, out}); + s_algo->setWorkgroup({unsigned((ne01 + 7)/8), unsigned(ne11), unsigned(ne12*ne13)}); + s_algo->setPushConstants({pushConsts}); + s_algo->updateDescriptors(s_kompute_context->pool.get()); + } + seq.record(s_algo); +} + +template +static void ggml_vk_mul_mat_q4_0(Args&&... args) { + const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q4_0_comp_spv, + kp::shader_data::op_mul_mat_q4_0_comp_spv_len); + + ggml_vk_mul_mat_impl(spirv, "q4_0", 1/*We access blocks unaligned*/, std::forward(args)...); +} + +template +static void ggml_vk_mul_mat_q4_1(Args&&... args) { + const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q4_1_comp_spv, + kp::shader_data::op_mul_mat_q4_1_comp_spv_len); + + ggml_vk_mul_mat_impl(spirv, "q4_1", 1/*We access blocks unaligned*/, std::forward(args)...); +} + +template +static void ggml_vk_mul_mat_q8_0(Args&&... args) { + const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q8_0_comp_spv, + kp::shader_data::op_mul_mat_q8_0_comp_spv_len); + + ggml_vk_mul_mat_impl(spirv, "q8_0", 1/*We access blocks unaligned*/, std::forward(args)...); +} + +static void ggml_vk_mul_mat_q6_k( + kp::Sequence& seq, + const std::shared_ptr& inA, + const std::shared_ptr& inB, + const std::shared_ptr& out, + uint32_t inAOff, uint32_t inBOff, uint32_t outOff, + int32_t ne00, int32_t ne10, int32_t ne0, int32_t ne1, + int32_t ne01, int32_t ne11, int32_t ne12, int32_t ne02 +) { + const static auto spirv = getSpirvShader(kp::shader_data::op_mul_mat_q6_k_comp_spv, + kp::shader_data::op_mul_mat_q6_k_comp_spv_len); + + struct PushConstants { + uint32_t inAOff, inBOff, outOff; + int32_t ne00, ne10, ne0, ne1, ne01, gqa; + } pushConsts { + inAOff, safe_divide(inBOff, 4), safe_divide(outOff, 4), + ne00, ne10, ne0, ne1, ne01, ne12/ne02 + }; + + std::shared_ptr s_algo = nullptr; + if (!komputeManager()->hasAlgorithm(__func__)) { + const uint32_t local_x = ggml_vk_current_device().subgroupSize * 2; + s_algo = komputeManager()->algorithm(__func__, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {unsigned((ne01 + 1)/2), unsigned(ne11), unsigned(ne12)}, {local_x}, {pushConsts}); + } else { + s_algo = komputeManager()->getAlgorithm(__func__); + s_algo->setTensors({inA, inB, out}); + s_algo->setWorkgroup({unsigned((ne01 + 1)/2), unsigned(ne11), unsigned(ne12)}); + s_algo->setPushConstants({pushConsts}); + s_algo->updateDescriptors(s_kompute_context->pool.get()); + } + seq.record(s_algo); +} + +static void ggml_vk_get_rows( + const std::vector& spirv, + const char * suffix, + unsigned element_size, unsigned qk, + kp::Sequence& seq, + const std::shared_ptr& inA, + const std::shared_ptr& inB, + const std::shared_ptr& out, + uint32_t inAOff, uint32_t inBOff, uint32_t outOff, + int32_t ne00, int32_t nb01, int32_t nb1, + uint32_t size +) { + GGML_ASSERT(nb01%element_size == 0); + GGML_ASSERT(nb1%sizeof(float) == 0); + if (qk) GGML_ASSERT(ne00%qk == 0); + + struct PushConstants { + uint32_t inAOff, inBOff, outOff; + int32_t ne00, nb01, nb1; + } pushConsts { + safe_divide(inAOff, element_size), safe_divide(inBOff, 4), safe_divide(outOff, 4), + ne00, nb01, nb1 + }; + + auto name = std::string(__func__) + "_" + suffix; + std::shared_ptr s_algo = nullptr; + if (!komputeManager()->hasAlgorithm(name)) { + s_algo = komputeManager()->algorithm(name, s_kompute_context->pool.get(), {inA, inB, out}, spirv, {size}, {}, {pushConsts}); + } else { + s_algo = komputeManager()->getAlgorithm(name); + s_algo->setTensors({inA, inB, out}); + s_algo->setWorkgroup({size}); + s_algo->setPushConstants({pushConsts}); + s_algo->updateDescriptors(s_kompute_context->pool.get()); + } + seq.record(s_algo); +} + +template +static void ggml_vk_get_rows_f16(Args&&... args) { + const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_f16_comp_spv, + kp::shader_data::op_getrows_f16_comp_spv_len); + + ggml_vk_get_rows(spirv, "f16", sizeof(half), 0, std::forward(args)...); +} + +template +static void ggml_vk_get_rows_q4_0(Args&&... args) { + const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_q4_0_comp_spv, + kp::shader_data::op_getrows_q4_0_comp_spv_len); + + ggml_vk_get_rows(spirv, "q4_0", 1/*We access blocks unaligned*/, QK4_0, std::forward(args)...); +} + +template +static void ggml_vk_get_rows_q4_1(Args&&... args) { + const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_q4_1_comp_spv, + kp::shader_data::op_getrows_q4_1_comp_spv_len); + + ggml_vk_get_rows(spirv, "q4_1", 1/*We access blocks unaligned*/, QK4_1, std::forward(args)...); +} + +template +static void ggml_vk_get_rows_q6_k(Args&&... args) { + const static auto spirv = getSpirvShader(kp::shader_data::op_getrows_q6_k_comp_spv, + kp::shader_data::op_getrows_q6_k_comp_spv_len); + ggml_vk_get_rows(spirv, "q6_k", 1/*We access blocks unaligned*/, QK_NL, std::forward(args)...); +} + +static void ggml_vk_rope( + kp::Sequence& seq, + const std::shared_ptr& inA, + const std::shared_ptr& inB, + const std::shared_ptr& out, + uint32_t inAOff, uint32_t inBOff, uint32_t outOff, + ggml_type src0t, int32_t n_dims, int32_t mode, int32_t n_orig_ctx, + float freq_base, float freq_scale, float ext_factor, float attn_factor, float beta_fast, float beta_slow, + int32_t ne01, int32_t ne02, int32_t ne03, + uint32_t nb00, uint32_t nb01, uint32_t nb02, uint32_t nb03, + int32_t ne0, + uint32_t nb0, uint32_t nb1, uint32_t nb2, uint32_t nb3 +) { + GGML_ASSERT(src0t == GGML_TYPE_F16 || src0t == GGML_TYPE_F32); + + static const auto spirv_f16 = getSpirvShader( + kp::shader_data::op_rope_f16_comp_spv, kp::shader_data::op_rope_f16_comp_spv_len + ); + static const auto spirv_f32 = getSpirvShader( + kp::shader_data::op_rope_f32_comp_spv, kp::shader_data::op_rope_f32_comp_spv_len + ); + + int type_size = src0t == GGML_TYPE_F16 ? 2 : 4; + + GGML_ASSERT(nb03 % type_size == 0); + GGML_ASSERT(nb02 % type_size == 0); + GGML_ASSERT(nb01 % type_size == 0); + GGML_ASSERT(nb00 % type_size == 0); + GGML_ASSERT(nb3 % type_size == 0); + GGML_ASSERT(nb2 % type_size == 0); + GGML_ASSERT(nb1 % type_size == 0); + GGML_ASSERT(nb0 % type_size == 0); + + struct PushConstants { + uint32_t inAOff, inBOff, outOff; + int32_t n_dims, mode, n_orig_ctx; + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + uint32_t nb00, nb01, nb02, nb03; + int32_t ne0; + uint32_t nb0, nb1, nb2, nb3; + } pushConsts { + safe_divide(inAOff, type_size), safe_divide(inBOff, 4), safe_divide(outOff, type_size), + n_dims, mode, n_orig_ctx, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, + nb00, nb01, nb02, nb03, + ne0, + nb0, nb1, nb2, nb3 + }; + + auto name = std::string(__func__) + (src0t == GGML_TYPE_F16 ? "_f16" : "_f32"); + std::shared_ptr s_algo = nullptr; + if (!komputeManager()->hasAlgorithm(name)) { + s_algo = komputeManager()->algorithm( + name, s_kompute_context->pool.get(), {inA, inB, out}, + src0t == GGML_TYPE_F16 ? spirv_f16 : spirv_f32, + {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {}, {pushConsts} + ); + } else { + s_algo = komputeManager()->getAlgorithm(name); + s_algo->setTensors({inA, inB, out}); + s_algo->setWorkgroup({unsigned(ne01), unsigned(ne02), unsigned(ne03)}); + s_algo->setPushConstants({pushConsts}); + s_algo->updateDescriptors(s_kompute_context->pool.get()); + } + seq.record(s_algo); +} + +static void ggml_vk_cpy( + const std::vector& spirv, + uint32_t in_element_size, uint32_t out_element_size, + kp::Sequence& seq, + const std::shared_ptr& in, + const std::shared_ptr& out, + uint32_t inOff, uint32_t outOff, + int32_t ne00, int32_t ne01, int32_t ne02, int32_t ne03, + uint32_t nb00, uint32_t nb01, uint32_t nb02, uint32_t nb03, + int32_t ne0, int32_t ne1, int32_t ne2, + uint32_t nb0, uint32_t nb1, uint32_t nb2, uint32_t nb3 +) { + struct PushConstants { + uint32_t inOff, outOff; + int32_t ne00, ne01, ne02; + uint32_t nb00, nb01, nb02, nb03; + int32_t ne0, ne1, ne2; + uint32_t nb0, nb1, nb2, nb3; + } pushConsts { + safe_divide(inOff, in_element_size), safe_divide(outOff, out_element_size), + ne00, ne01, ne02, + nb00, nb01, nb02, nb03, + ne0, ne1, ne2, + nb0, nb1, nb2, nb3 + }; + + std::string name = std::string(__func__) + + "_i_" + std::to_string(in_element_size) + + "_o_" + std::to_string(out_element_size); + std::shared_ptr s_algo = nullptr; + if (!komputeManager()->hasAlgorithm(name)) + s_algo = komputeManager()->algorithm(name, s_kompute_context->pool.get(), {in, out}, spirv, {unsigned(ne01), unsigned(ne02), unsigned(ne03)}, {}, {pushConsts}); + else { + s_algo = komputeManager()->getAlgorithm(name); + s_algo->setTensors({in, out}); + s_algo->setWorkgroup({unsigned(ne01), unsigned(ne02), unsigned(ne03)}); + s_algo->setPushConstants({pushConsts}); + s_algo->updateDescriptors(s_kompute_context->pool.get()); + } + seq.record(s_algo); +} + +template +static void ggml_vk_cpy_f32_f16(Args&&... args) { + const static auto spirv = getSpirvShader(kp::shader_data::op_cpy_f32_f16_comp_spv, + kp::shader_data::op_cpy_f32_f16_comp_spv_len); + ggml_vk_cpy(spirv, 4, 2, std::forward(args)...); +} + +template +static void ggml_vk_cpy_f32_f32(Args&&... args) { + const static auto spirv = getSpirvShader(kp::shader_data::op_cpy_f32_f32_comp_spv, + kp::shader_data::op_cpy_f32_f32_comp_spv_len); + ggml_vk_cpy(spirv, 4, 4, std::forward(args)...); +} + +template +static void ggml_vk_cpy_f16_f16(Args&&... args) { + const static auto spirv = getSpirvShader(kp::shader_data::op_cpy_f16_f16_comp_spv, + kp::shader_data::op_cpy_f16_f16_comp_spv_len); + ggml_vk_cpy(spirv, 2, 2, std::forward(args)...); +} + +template +static void ggml_vk_cpy_f16_f32(Args&&... args) { + const static auto spirv = getSpirvShader(kp::shader_data::op_cpy_f16_f32_comp_spv, + kp::shader_data::op_cpy_f16_f32_comp_spv_len); + ggml_vk_cpy(spirv, 2, 4, std::forward(args)...); +} + +static bool ggml_vk_supports_op(const struct ggml_tensor * op) { + switch (op->type) { + case GGML_TYPE_F16: + case GGML_TYPE_F32: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + break; + default: + return false; + } + + switch (op->op) { + case GGML_OP_UNARY: + switch (ggml_get_unary_op(op)) { + case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_GELU: + case GGML_UNARY_OP_SILU: + return true; + default: + ; + } + break; + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_TRANSPOSE: + case GGML_OP_PERMUTE: + case GGML_OP_ADD: + case GGML_OP_MUL: + case GGML_OP_SCALE: + case GGML_OP_SOFT_MAX: + case GGML_OP_RMS_NORM: + case GGML_OP_NORM: + case GGML_OP_ROPE: + return true; + case GGML_OP_DUP: + case GGML_OP_CPY: + case GGML_OP_CONT: + switch (op->src[0]->type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + break; + default: + return false; + } + switch (op->type) { + case GGML_TYPE_F32: + case GGML_TYPE_F16: + break; + default: + return false; + } + return true; + case GGML_OP_DIAG_MASK_INF: + return op->ne[3] == 1; + case GGML_OP_GET_ROWS: + switch (op->src[0]->type) { + case GGML_TYPE_F16: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q6_K: + return op->ne[2] == 1 && op->ne[3] == 1; + default: + ; + } + return false; + case GGML_OP_MUL_MAT: + if (op->src[1]->type != GGML_TYPE_F32 || ggml_is_transposed(op->src[0]) || ggml_is_transposed(op->src[1])) + return false; + + switch (op->src[0]->type) { + case GGML_TYPE_F32: + case GGML_TYPE_Q6_K: + return op->ne[3] == 1; + case GGML_TYPE_F16: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + return true; + default: + ; + } + default: + ; + } + return false; +} + +static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml_cgraph * gf) { + const int n_seq = 8; + + // FIXME: Figure out if we can somehow optimize the size of the pool... right now we're setting + // it to the size of the graph, but I think it can be made smaller? + ggml_vk_allocate_descriptor_pool(ctx, gf->n_nodes); + + std::vector> sequences(n_seq); + + for (auto& sequence : sequences) { + sequence = komputeManager()->sequence(); + } + for (int seq_idx = 0; seq_idx < n_seq; ++seq_idx) { + const int n_nodes_per_seq = (gf->n_nodes + n_seq - 1) / n_seq; + + auto& seq = *sequences[seq_idx]; + + const int node_start = (seq_idx + 0) * n_nodes_per_seq; + const int node_end = std::min((seq_idx == n_seq - 1) ? gf->n_nodes : (seq_idx + 1) * n_nodes_per_seq, gf->n_nodes); + + bool any_commands_recorded = false; + + for (int i = node_start; i < node_end; ++i) { + struct ggml_tensor * src0 = gf->nodes[i]->src[0]; + struct ggml_tensor * src1 = gf->nodes[i]->src[1]; + struct ggml_tensor * dst = gf->nodes[i]; + GGML_ASSERT(dst->data != nullptr); + + switch (dst->op) { + case GGML_OP_NONE: + case GGML_OP_RESHAPE: + case GGML_OP_VIEW: + case GGML_OP_TRANSPOSE: + case GGML_OP_PERMUTE: + continue; // noop -> next node + default: + break; + } + + any_commands_recorded = true; + + if (!ggml_vk_supports_op(dst)) { + fprintf(stderr, "%s: error: unsupported op '%s'\n", __func__, ggml_op_desc(dst)); + GGML_ASSERT(!"unsupported op"); + } + + const int32_t ne00 = src0 ? src0->ne[0] : 0; + const int32_t ne01 = src0 ? src0->ne[1] : 0; + const int32_t ne02 = src0 ? src0->ne[2] : 0; + const int32_t ne03 = src0 ? src0->ne[3] : 0; + + const uint32_t nb00 = src0 ? src0->nb[0] : 0; + const uint32_t nb01 = src0 ? src0->nb[1] : 0; + const uint32_t nb02 = src0 ? src0->nb[2] : 0; + const uint32_t nb03 = src0 ? src0->nb[3] : 0; + + const int32_t ne10 = src1 ? src1->ne[0] : 0; + const int32_t ne11 = src1 ? src1->ne[1] : 0; + const int32_t ne12 = src1 ? src1->ne[2] : 0; + const int32_t ne13 = src1 ? src1->ne[3] : 0; + + const uint32_t nb10 = src1 ? src1->nb[0] : 0; + const uint32_t nb11 = src1 ? src1->nb[1] : 0; + const uint32_t nb12 = src1 ? src1->nb[2] : 0; + const uint32_t nb13 = src1 ? src1->nb[3] : 0; + + const int32_t ne0 = dst ? dst->ne[0] : 0; + const int32_t ne1 = dst ? dst->ne[1] : 0; + const int32_t ne2 = dst ? dst->ne[2] : 0; +// const int32_t ne3 = dst ? dst->ne[3] : 0; + + const uint32_t nb0 = dst ? dst->nb[0] : 0; + const uint32_t nb1 = dst ? dst->nb[1] : 0; + const uint32_t nb2 = dst ? dst->nb[2] : 0; + const uint32_t nb3 = dst ? dst->nb[3] : 0; + + const enum ggml_type src0t = src0 ? src0->type : GGML_TYPE_COUNT; + const enum ggml_type src1t = src1 ? src1->type : GGML_TYPE_COUNT; + const enum ggml_type dstt = dst ? dst->type : GGML_TYPE_COUNT; + + const static std::shared_ptr nullTensor = nullptr; + uint32_t off_src0 = 0; + uint32_t off_src1 = 0; + uint32_t off_dst = 0; + const std::shared_ptr& id_src0 = src0 ? ggml_vk_get_tensor(src0, &off_src0) : nullTensor; + const std::shared_ptr& id_src1 = src1 ? ggml_vk_get_tensor(src1, &off_src1) : nullTensor; + const std::shared_ptr& id_dst = dst ? ggml_vk_get_tensor(dst, &off_dst) : nullTensor; + + switch (dst->op) { + case GGML_OP_ADD: + { + if (ggml_nelements(src1) == ne10 && ggml_is_contiguous(src1) && ne00 % 4 == 0 && ne10 % 4 == 0) { + // src1 is a row + ggml_vk_addrow(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ggml_nelements(dst)/4, ne00); + } else { + ggml_vk_add( + seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, + ne00, ne01, ne02, ne03, + nb00, nb01, nb02, nb03, + ne10, ne11, ne12, ne13, + nb10, nb11, nb12, nb13, + ne0, + nb0, nb1, nb2, nb3 + ); + } + } break; + case GGML_OP_MUL: + { + ggml_vk_mul( + seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, + ne00, ne01, ne02, ne03, + nb00, nb01, nb02, nb03, + ne10, ne11, ne12, ne13, + nb10, nb11, nb12, nb13, + ne0, + nb0, nb1, nb2, nb3 + ); + } break; + case GGML_OP_SCALE: + { + float scale; memcpy(&scale, dst->op_params, sizeof(float)); + + ggml_vk_scale(seq, id_src0, id_dst, off_src0, off_dst, ggml_nelements(dst), scale); + } break; + case GGML_OP_UNARY: + { + int64_t n = ggml_nelements(dst); + GGML_ASSERT(n % 4 == 0); + switch (ggml_get_unary_op(gf->nodes[i])) { + case GGML_UNARY_OP_SILU: + { + ggml_vk_silu(seq, id_src0, id_dst, off_src0, off_dst, n/4); + } break; + case GGML_UNARY_OP_RELU: + { + ggml_vk_relu(seq, id_src0, id_dst, off_src0, off_dst, n/4); + } break; + case GGML_UNARY_OP_GELU: + { + GGML_ASSERT(n % 8 == 0); + ggml_vk_gelu(seq, id_src0, id_dst, off_src0, off_dst, n/8); + } break; + default: + { + fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op)); + GGML_ASSERT(false); + } + } + } break; + case GGML_OP_SOFT_MAX: + { + float scale; + memcpy(&scale, dst->op_params, sizeof(float)); + ggml_vk_soft_max(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, ne01, ne02, ne03, scale); + } break; + case GGML_OP_DIAG_MASK_INF: + { + const int n_past = ((int32_t *)(dst->op_params))[0]; + ggml_vk_diag_mask_inf(seq, id_src0, id_dst, off_src0, off_dst, n_past, ne00, ne01, ne02); + } break; + case GGML_OP_NORM: + { + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); + ggml_vk_norm(seq, id_src0, id_dst, off_src0, off_dst, ne00, nb01, ggml_nrows(src0), eps); + } break; + case GGML_OP_RMS_NORM: + { + GGML_ASSERT(ne00 % 4 == 0); + + float eps; + memcpy(&eps, dst->op_params, sizeof(float)); + ggml_vk_rms_norm(seq, id_src0, id_dst, off_src0, off_dst, ne00, nb01, ggml_nrows(src0), eps); + } break; + case GGML_OP_MUL_MAT: + { + GGML_ASSERT(ne00 == ne10); + + // TODO: assert that dim2 and dim3 are contiguous + GGML_ASSERT(ne12 % ne02 == 0); + GGML_ASSERT(ne13 % ne03 == 0); + + const uint32_t r2 = ne12/ne02; + const uint32_t r3 = ne13/ne03; + + if (src1t != GGML_TYPE_F32) { + fprintf(stderr, "%s: %s: Unsupported src1 type: %u/%u\n", __func__, ggml_op_name(dst->op), src0t, src1t); + goto not_implemented; + } + + if (ggml_is_transposed(src0) || + ggml_is_transposed(src1)) { + fprintf(stderr, "%s: %s: matmul on tranposed tensor not supported: %u/%u\n", __func__, ggml_op_name(dst->op), src0t, src1t); + goto not_implemented; + } + + switch (src0t) { + case GGML_TYPE_F32: + ggml_vk_mul_mat_mat_f32( + seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, + ne00, ne01, ne02, nb01, nb02, ne11, ne12, nb11, nb12, nb1, nb2 + ); + break; + case GGML_TYPE_F16: + ggml_vk_mul_mat_f16( + seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, + ne00, ne01, ne02, nb00, nb01, nb02, ne10, ne11, ne12, ne13, nb10, nb11, nb12, + ne0, ne1, r2, r3 + ); + break; + case GGML_TYPE_Q8_0: + ggml_vk_mul_mat_q8_0( + seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, + ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, r2, r3 + ); + break; + case GGML_TYPE_Q4_0: + ggml_vk_mul_mat_q4_0( + seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, + ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, r2, r3 + ); + break; + case GGML_TYPE_Q4_1: + ggml_vk_mul_mat_q4_1( + seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, + ne00, ne01, ne02, ne10, ne11, ne12, ne13, ne0, ne1, r2, r3 + ); + break; + case GGML_TYPE_Q6_K: + ggml_vk_mul_mat_q6_k( + seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, + ne00, ne10, ne0, ne1, ne01, ne11, ne12, ne02 + ); + break; + default: { + fprintf(stderr, "%s: %s: Unsupported quantization: %u/%u\n", __func__, ggml_op_name(dst->op), src0t, src1t); + goto not_implemented; + } + } + + } break; + case GGML_OP_GET_ROWS: + { + if (src0t == GGML_TYPE_F16) { + ggml_vk_get_rows_f16(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1)); + } else if (src0t == GGML_TYPE_Q4_0) { + ggml_vk_get_rows_q4_0(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1)); + } else if (src0t == GGML_TYPE_Q4_1) { + ggml_vk_get_rows_q4_1(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1)); + } else if (src0t == GGML_TYPE_Q6_K) { + ggml_vk_get_rows_q6_k(seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, ne00, nb01, nb1, ggml_nelements(src1)); + } else { + fprintf(stderr, "%s: %s: Unsupported quantization: %u\n", __func__, ggml_op_name(dst->op), src0t); + goto not_implemented; + } + } break; + case GGML_OP_ROPE: + { + GGML_ASSERT(ne10 == ne02); + GGML_ASSERT(src0t == dstt); + // const int n_past = ((int32_t *) dst->op_params)[0]; + const int n_dims = ((int32_t *) dst->op_params)[1]; + const int mode = ((int32_t *) dst->op_params)[2]; + // skip 3, n_ctx used in GLM RoPE, unimplemented in Vulkan + const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; + + float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + memcpy(&freq_base, (int32_t *) dst->op_params + 5, sizeof(float)); + memcpy(&freq_scale, (int32_t *) dst->op_params + 6, sizeof(float)); + memcpy(&ext_factor, (int32_t *) dst->op_params + 7, sizeof(float)); + memcpy(&attn_factor, (int32_t *) dst->op_params + 8, sizeof(float)); + memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); + memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + ggml_vk_rope( + seq, id_src0, id_src1, id_dst, off_src0, off_src1, off_dst, src0t, n_dims, mode, n_orig_ctx, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, + ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, nb0, nb1, nb2, nb3 + ); + } break; + case GGML_OP_DUP: + case GGML_OP_CPY: + case GGML_OP_CONT: + { + switch (src0t) { + case GGML_TYPE_F32: + { + switch (dstt) { + case GGML_TYPE_F16: ggml_vk_cpy_f32_f16(seq, id_src0, id_dst, off_src0, off_dst, ne00, ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, ne1, ne2, nb0, nb1, nb2, nb3); break; + case GGML_TYPE_F32: ggml_vk_cpy_f32_f32(seq, id_src0, id_dst, off_src0, off_dst, ne00, ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, ne1, ne2, nb0, nb1, nb2, nb3); break; + default: goto not_implemented; + } + } break; + case GGML_TYPE_F16: + { + switch (dstt) { + case GGML_TYPE_F16: ggml_vk_cpy_f16_f16(seq, id_src0, id_dst, off_src0, off_dst, ne00, ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, ne1, ne2, nb0, nb1, nb2, nb3); break; + case GGML_TYPE_F32: ggml_vk_cpy_f16_f32(seq, id_src0, id_dst, off_src0, off_dst, ne00, ne01, ne02, ne03, nb00, nb01, nb02, nb03, ne0, ne1, ne2, nb0, nb1, nb2, nb3); break; + default: goto not_implemented; + } break; + default: goto not_implemented; + } + } + } break; + default: goto not_implemented; + } + continue; + not_implemented: {} + fprintf(stderr, "%s: node %3d, op = %8s not implemented\n", __func__, i, ggml_op_name(dst->op)); + //GGML_ASSERT(false); + } + + // Evaluate sequence + if (any_commands_recorded) { + seq.evalAsync(); + } + } + + // Wait for all sequences to finish + for (auto& sequence : sequences) { + if (sequence->isRunning()) + sequence->evalAwait(); + } + + ggml_vk_free_descriptor_pool(ctx); +} + +template<> +kp::Tensor::TensorDataTypes +kp::TensorT::dataType() +{ + return TensorDataTypes::eFloat; +} + +template<> +kp::Tensor::TensorDataTypes +kp::TensorT::dataType() +{ + return TensorDataTypes::eUnsignedInt; +} + +//////////////////////////////////////////////////////////////////////////////// + +// backend interface + +struct ggml_backend_kompute_buffer_type_context { + int device; + int device_ref = 0; + uint64_t buffer_alignment; + uint64_t max_alloc; + std::string name; + + ggml_backend_kompute_buffer_type_context(int device, uint64_t buffer_alignment, uint64_t max_alloc) + : device(device), buffer_alignment(buffer_alignment), max_alloc(max_alloc), name(ggml_kompute_format_name(device)) {} +}; + +static void ggml_backend_kompute_device_ref(ggml_backend_buffer_type_t buft) { + auto * ctx = static_cast(buft->context); + + if (!ctx->device_ref) { + komputeManager()->initializeDevice( + ctx->device, {}, { + "VK_KHR_shader_float16_int8", "VK_KHR_8bit_storage", + "VK_KHR_16bit_storage", "VK_KHR_shader_non_semantic_info" + } + ); + } + + assert(ggml_vk_has_device()); + ctx->device_ref++; +} + +static void ggml_backend_kompute_device_unref(ggml_backend_buffer_type_t buft) { + auto * ctx = static_cast(buft->context); + + assert(ctx->device_ref > 0); + + ctx->device_ref--; + + if (!ctx->device_ref) { + komputeManager.destroy(); + } +} + +static const char * ggml_backend_kompute_buffer_get_name(ggml_backend_buffer_t buffer) { + auto * ctx = static_cast(buffer->buft->context); + return ctx->name.c_str(); +} + +static void ggml_backend_kompute_buffer_free_buffer(ggml_backend_buffer_t buffer) { + auto * memory = (ggml_vk_memory *)buffer->context; + if (ggml_vk_has_device()) { + ggml_vk_free_memory(*memory); + } + delete memory; +} + +static void * ggml_backend_kompute_buffer_get_base(ggml_backend_buffer_t buffer) { + return ((ggml_vk_memory *)buffer->context)->data; +} + +static void ggml_backend_kompute_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { + GGML_UNUSED(buffer); + + const auto res = ggml_vk_get_tensor(tensor); + GGML_ASSERT(res); + + memcpy((char *)tensor->data + offset, data, size); + + komputeManager()->sequence()->eval({res}); +} + +static void ggml_backend_kompute_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { + GGML_UNUSED(buffer); + + const auto res = ggml_vk_get_tensor(tensor); + GGML_ASSERT(res); + + komputeManager()->sequence()->eval({res}); + + memcpy(data, (const char *)tensor->data + offset, size); +} + +static void ggml_backend_kompute_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { + auto * memory = (ggml_vk_memory *)buffer->context; + memset(memory->data, value, buffer->size); + + if (memory->stagingBuffer) + komputeManager()->sequence()->eval(memory->primaryBuffer, memory->stagingBuffer, memory->size); +} + +static ggml_backend_buffer_i ggml_backend_kompute_buffer_i = { + /* .get_name = */ ggml_backend_kompute_buffer_get_name, + /* .free_buffer = */ ggml_backend_kompute_buffer_free_buffer, + /* .get_base = */ ggml_backend_kompute_buffer_get_base, + /* .init_tensor = */ NULL, + /* .set_tensor = */ ggml_backend_kompute_buffer_set_tensor, + /* .get_tensor = */ ggml_backend_kompute_buffer_get_tensor, + /* .cpy_tensor = */ NULL, + /* .clear = */ ggml_backend_kompute_buffer_clear, + /* .reset = */ NULL, +}; + +// default buffer type + +static const char * ggml_backend_kompute_buffer_type_get_name(ggml_backend_buffer_type_t buft) { + auto * ctx = static_cast(buft->context); + return ctx->name.c_str(); +} + +static ggml_backend_buffer_t ggml_backend_kompute_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { + ggml_backend_kompute_device_ref(buft); + auto * ctx = new ggml_vk_memory(ggml_vk_allocate(size)); + return ggml_backend_buffer_init(buft, ggml_backend_kompute_buffer_i, ctx, size); +} + +static size_t ggml_backend_kompute_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { + auto * ctx = static_cast(buft->context); + return ctx->buffer_alignment; +} + +static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { + auto * ctx = static_cast(buft->context); + return ctx->max_alloc; +} + +static bool ggml_backend_kompute_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { + GGML_UNUSED(buft); + return ggml_backend_is_kompute(backend); +} + +static ggml_backend_buffer_type_i ggml_backend_kompute_buffer_type_interface = { + /* .get_name = */ ggml_backend_kompute_buffer_type_get_name, + /* .alloc_buffer = */ ggml_backend_kompute_buffer_type_alloc_buffer, + /* .get_alignment = */ ggml_backend_kompute_buffer_type_get_alignment, + /* .get_max_size = */ ggml_backend_vk_buffer_type_get_max_size, + /* .get_alloc_size = */ NULL, // defaults to ggml_nbytes + /* .supports_backend = */ ggml_backend_kompute_buffer_type_supports_backend, + /* .is_host = */ NULL, +}; + +ggml_backend_buffer_type_t ggml_backend_kompute_buffer_type(int device) { + static std::vector bufts = []() { + std::vector vec; + auto devices = ggml_vk_available_devices_internal(0); + vec.reserve(devices.size()); + + for (const auto & dev : devices) { + vec.push_back({ + /* .iface = */ ggml_backend_kompute_buffer_type_interface, + /* .context = */ new ggml_backend_kompute_buffer_type_context(dev.index, dev.bufferAlignment, dev.maxAlloc) + }); + } + return vec; + }(); + + auto it = std::find_if(bufts.begin(), bufts.end(), [device](const ggml_backend_buffer_type & t) { + return device == static_cast(t.context)->device; + }); + return it < bufts.end() ? &*it : nullptr; +} + +// backend + +static const char * ggml_backend_kompute_name(ggml_backend_t backend) { + auto * ctx = static_cast(backend->context); + return ctx->name.c_str(); +} + +static void ggml_backend_kompute_free(ggml_backend_t backend) { + auto * ctx = static_cast(backend->context); + + assert(ctx == s_kompute_context); + s_kompute_context = nullptr; + if (ctx != nullptr) { + delete ctx; + } + + delete backend; +} + +static ggml_backend_buffer_type_t ggml_backend_kompute_get_default_buffer_type(ggml_backend_t backend) { + auto * ctx = static_cast(backend->context); + return ggml_backend_kompute_buffer_type(ctx->device); +} + +static bool ggml_backend_kompute_graph_compute(ggml_backend_t backend, struct ggml_cgraph * cgraph) { + auto * ctx = static_cast(backend->context); + ggml_vk_graph_compute(ctx, cgraph); + return true; +} + +static bool ggml_backend_kompute_supports_op(ggml_backend_t backend, const struct ggml_tensor * op) { + GGML_UNUSED(backend); + return ggml_vk_supports_op(op); +} + +static struct ggml_backend_i kompute_backend_i = { + /* .get_name = */ ggml_backend_kompute_name, + /* .free = */ ggml_backend_kompute_free, + /* .get_default_buffer_type = */ ggml_backend_kompute_get_default_buffer_type, + /* .set_tensor_async = */ NULL, + /* .get_tensor_async = */ NULL, + /* .cpy_tensor_async = */ NULL, + /* .synchronize = */ NULL, + /* .graph_plan_create = */ NULL, + /* .graph_plan_free = */ NULL, + /* .graph_plan_compute = */ NULL, + /* .graph_compute = */ ggml_backend_kompute_graph_compute, + /* .supports_op = */ ggml_backend_kompute_supports_op, +}; + +ggml_backend_t ggml_backend_kompute_init(int device) { + GGML_ASSERT(s_kompute_context == nullptr); + s_kompute_context = new ggml_kompute_context(device); + + ggml_backend_t kompute_backend = new ggml_backend { + /* .interface = */ kompute_backend_i, + /* .context = */ s_kompute_context, + }; + + return kompute_backend; +} + +bool ggml_backend_is_kompute(ggml_backend_t backend) { + return backend && backend->iface.get_name == ggml_backend_kompute_name; +} + +static ggml_backend_t ggml_backend_reg_kompute_init(const char * params, void * user_data) { + GGML_UNUSED(params); + return ggml_backend_kompute_init(intptr_t(user_data)); +} + +extern "C" int ggml_backend_kompute_reg_devices(); + +int ggml_backend_kompute_reg_devices() { + auto devices = ggml_vk_available_devices_internal(0); + for (const auto & device : devices) { + ggml_backend_register( + ggml_kompute_format_name(device.index).c_str(), + ggml_backend_reg_kompute_init, + ggml_backend_kompute_buffer_type(device.index), + reinterpret_cast(intptr_t(device.index)) + ); + } + return devices.size(); +} diff --git a/ggml-kompute.h b/ggml-kompute.h new file mode 100644 index 000000000..171465456 --- /dev/null +++ b/ggml-kompute.h @@ -0,0 +1,46 @@ +#pragma once + +#include "ggml.h" +#include "ggml-backend.h" + +#include +#include +#include + +#ifdef __cplusplus +extern "C" { +#endif + +struct ggml_vk_device { + int index; + int type; // same as VkPhysicalDeviceType + size_t heapSize; + const char * name; + const char * vendor; + int subgroupSize; + uint64_t bufferAlignment; + uint64_t maxAlloc; +}; + +struct ggml_vk_device * ggml_vk_available_devices(size_t memoryRequired, size_t * count); +bool ggml_vk_get_device(struct ggml_vk_device * device, size_t memoryRequired, const char * name); +bool ggml_vk_has_vulkan(void); +bool ggml_vk_has_device(void); +struct ggml_vk_device ggml_vk_current_device(void); + +// +// backend API +// + +// forward declaration +typedef struct ggml_backend * ggml_backend_t; + +GGML_API ggml_backend_t ggml_backend_kompute_init(int device); + +GGML_API bool ggml_backend_is_kompute(ggml_backend_t backend); + +GGML_API ggml_backend_buffer_type_t ggml_backend_kompute_buffer_type(int device); + +#ifdef __cplusplus +} +#endif diff --git a/kompute b/kompute new file mode 160000 index 000000000..4565194ed --- /dev/null +++ b/kompute @@ -0,0 +1 @@ +Subproject commit 4565194ed7c32d1d2efa32ceab4d3c6cae006306 diff --git a/kompute-shaders/common.comp b/kompute-shaders/common.comp new file mode 100644 index 000000000..62d62b025 --- /dev/null +++ b/kompute-shaders/common.comp @@ -0,0 +1,102 @@ +#extension GL_EXT_shader_16bit_storage: require +#extension GL_EXT_shader_8bit_storage: require +#extension GL_EXT_shader_explicit_arithmetic_types_float16: require +#extension GL_EXT_shader_explicit_arithmetic_types_int8: require +#extension GL_EXT_shader_explicit_arithmetic_types_int16: require +#extension GL_EXT_control_flow_attributes: enable +#extension GL_KHR_shader_subgroup_arithmetic : require +#extension GL_EXT_debug_printf : enable + +#define QK4_0 32 +#define QK4_1 32 + +#define GELU_COEF_A 0.044715 +#define SQRT_2_OVER_PI 0.79788456080286535587989211986876 +#define TWOPI_F 6.283185307179586f + +#define QK_K 256 + +#define u8BufToU16(buf, idx) (((uint16_t(buf[idx + 1]) << 8)) | buf[idx]) +#define u8BufToFloat16(buf, idx) uint16BitsToHalf u8BufToU16(buf, idx) +#define u8BufToU32(buf, idx) (((uint32_t u8BufToU16(buf, idx + 2) << 8 | buf[idx + 1]) << 8) | buf[idx]) +#define u8BufToFloat(buf, idx) uintBitsToFloat u8BufToU32(buf, idx) + +#define sizeof_block_q4_0 0x12 +struct block_q4_0 { + float16_t d; + uint8_t qs[QK4_0 / 2]; +}; +mat4 dequantize_q4_0(const block_q4_0 xb, uint il) { + const float d1 = il != 0 ? (xb.d / 16.f) : xb.d; + const float d2 = d1 / 256.f; + const float md = -8.f * xb.d; + const uint16_t mask0 = il != 0 ? uint16_t(0x00F0) : uint16_t(0x000F); + const uint16_t mask1 = mask0 << 8; + + mat4 reg; + for (int i=0;i<8;i++) { + uint16_t b = (uint16_t(xb.qs[2 * i + 1]) << 8) | uint16_t(xb.qs[2 * i]); + reg[i/2][2*(i%2)+0] = d1 * (b & mask0) + md; + reg[i/2][2*(i%2)+1] = d2 * (b & mask1) + md; + } + return reg; +} + +#define sizeof_block_q4_1 0x14 +struct block_q4_1 { + float16_t d; + float16_t m; + uint8_t qs[QK4_1 / 2]; +}; +mat4 dequantize_q4_1(const block_q4_1 xb, uint il) { + const float d1 = il != 0 ? (xb.d / 16.f) : xb.d; + const float d2 = d1 / 256.f; + const float m = xb.m; + const uint16_t mask0 = il != 0 ? uint16_t(0x00F0) : uint16_t(0x000F); + const uint16_t mask1 = mask0 << 8; + + mat4 reg; + for (int i=0;i<8;i++) { + uint16_t b = (uint16_t(xb.qs[2 * i + 1]) << 8) | uint16_t(xb.qs[2 * i]); + reg[i/2][2*(i%2)+0] = ((b & mask0) * d1) + m; + reg[i/2][2*(i%2)+1] = ((b & mask1) * d2) + m; + } + return reg; +} + +#define sizeof_block_q6_k 210 +struct block_q6_k { + 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 + float16_t d; // super-block scale +}; +mat4 dequantize_q6_k(const block_q6_k xb, uint il) { + const float16_t d_all = xb.d; + + const uint qlIndex = 64*(il/8) + 32*((il/2)&1) + 16*(il&1); + const uint qhIndex = 32*(il/8) + 16*(il&1); + float16_t sc = xb.scales[(il%2) + 2 * ((il/2))]; + il = (il/2) & 3; + + const uint16_t kmask1 = il>1 ? uint16_t(il>2 ? 192 : 48) : uint16_t(il>0 ? 12 : 3); + const uint16_t kmask2 = il>1 ? uint8_t(0xF0) : uint8_t(0x0F); + const float16_t coef = il>1 ? float16_t(1.f/16.f) : float16_t(1.f); + const float16_t ml = float16_t(d_all * sc * 32.f); + const float16_t dl = float16_t(d_all * sc * coef); + mat4 reg; + for (int i = 0; i < 16; ++i) { + const float16_t q = (il&1) != 0 ? ((xb.ql[qlIndex + i] & kmask2) | ((xb.qh[qhIndex + i] & kmask1) << 2)) + : ((xb.ql[qlIndex + i] & kmask2) | ((xb.qh[qhIndex + i] & kmask1) << 4)); + reg[i/4][i%4] = dl * q - ml; + } + return reg; +} + + +#define QK8_0 32 +// struct block_q8_0 { +// float16_t d; // delta +// int8_t qs[QK8_0]; // quants +// }; +#define sizeof_block_q8_0 34 diff --git a/kompute-shaders/op_add.comp b/kompute-shaders/op_add.comp new file mode 100644 index 000000000..b7b76a79d --- /dev/null +++ b/kompute-shaders/op_add.comp @@ -0,0 +1,58 @@ +#version 450 + +#include "common.comp" + +layout(local_size_x = 1024) in; + +layout(binding = 0) buffer restrict readonly tensorInA { float inA[]; }; +layout(binding = 1) buffer restrict readonly tensorInB { float inB[]; }; +layout(binding = 2) buffer restrict writeonly tensorOut { float out_[]; }; + +layout(push_constant) uniform PushConstants { + uint inAOff; + uint inBOff; + uint outOff; + int ne00; + int nb00; + int nb01; + int nb02; + int nb03; + int ne10; + int ne11; + int ne12; + int ne13; + int nb10; + int nb11; + int nb12; + int nb13; + int ne0; + int nb0; + int nb1; + int nb2; + int nb3; + //int offs; // TODO: needed for GGML_OP_ACC, see metal code +} pcs; + +// general-purpose kernel for addition of two tensors +// pros: works for non-contiguous tensors, supports broadcast across dims 1, 2 and 3 +// cons: not very efficient +void main() { + const uint i03 = gl_WorkGroupID.z; + const uint i02 = gl_WorkGroupID.y; + const uint i01 = gl_WorkGroupID.x; + + const uint i13 = i03 % pcs.ne13; + const uint i12 = i02 % pcs.ne12; + const uint i11 = i01 % pcs.ne11; + + int offs = 0; // TMP (see above) + + uint src0_off = uint((i03*pcs.nb03 + i02*pcs.nb02 + i01*pcs.nb01 + offs) / 4); + uint src1_off = uint((i13*pcs.nb13 + i12*pcs.nb12 + i11*pcs.nb11 ) / 4); + uint dst_off = uint((i03*pcs.nb3 + i02*pcs.nb2 + i01*pcs.nb1 + offs) / 4); + + for (uint i0 = gl_LocalInvocationID.x; i0 < pcs.ne0; i0 += gl_WorkGroupSize.x) { + const uint i10 = i0 % pcs.ne10; + out_[pcs.outOff + dst_off + i0] = inA[pcs.inAOff + src0_off + i0] + inB[pcs.inBOff + src1_off + i10]; + } +} diff --git a/kompute-shaders/op_addrow.comp b/kompute-shaders/op_addrow.comp new file mode 100644 index 000000000..2376a6b8f --- /dev/null +++ b/kompute-shaders/op_addrow.comp @@ -0,0 +1,25 @@ +#version 450 + +#include "common.comp" + +layout(local_size_x = 1) in; + +layout(binding = 0) buffer restrict readonly tensorInA { float inA[]; }; +layout(binding = 1) buffer restrict readonly tensorInB { float inB[]; }; +layout(binding = 2) buffer restrict writeonly tensorOut { float out_[]; }; + +layout(push_constant) uniform PushConstants { + uint inAOff; + uint inBOff; + uint outOff; + uint row; +} pcs; + +void main() { + const uint baseIndex = gl_WorkGroupID.x * 4; + + for (uint x = 0; x < 4; x++) { + const uint i = baseIndex + x; + out_[i + pcs.outOff] = inA[i + pcs.inAOff] + inB[(i % pcs.row) + pcs.inBOff]; + } +} diff --git a/kompute-shaders/op_cpy_f16_f16.comp b/kompute-shaders/op_cpy_f16_f16.comp new file mode 100644 index 000000000..d57247d2d --- /dev/null +++ b/kompute-shaders/op_cpy_f16_f16.comp @@ -0,0 +1,52 @@ +#version 450 + +#include "common.comp" + +#define IN_TYPE float16_t +#define IN_TYPE_SIZE 2 +#define OUT_TYPE float16_t +#define OUT_TYPE_SIZE 2 + +layout(local_size_x = 1024) in; + +layout (binding = 0) readonly buffer tensorIn { IN_TYPE in_[]; }; +layout (binding = 1) writeonly buffer tensorOut { OUT_TYPE out_[]; }; + +layout (push_constant) uniform parameter { + uint inOff; + uint outOff; + int ne00; + int ne01; + int ne02; + uint nb00; + uint nb01; + uint nb02; + uint nb03; + int ne0; + int ne1; + int ne2; + uint nb0; + uint nb1; + uint nb2; + uint nb3; +} pcs; + +void main() { + const uint i03 = gl_WorkGroupID.z; + const uint i02 = gl_WorkGroupID.y; + const uint i01 = gl_WorkGroupID.x; + + const int n = int(i03)*pcs.ne02*pcs.ne01*pcs.ne00 + int(i02)*pcs.ne01*pcs.ne00 + int(i01)*pcs.ne00; + + const int i3 = n / (pcs.ne2*pcs.ne1*pcs.ne0); + const int i2 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0) / (pcs.ne1*pcs.ne0); + const int i1 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0) / pcs.ne0; + const int i0 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0 - i1*pcs.ne0); + + const uint dst_data = (i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / OUT_TYPE_SIZE + pcs.outOff; // Based from out_ + + for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) { + const uint src = uint((i03*pcs.nb03 + i02*pcs.nb02 + i01*pcs.nb01 + i00*pcs.nb00) / IN_TYPE_SIZE) + pcs.inOff; // Based from in_ + out_[dst_data+i00] = OUT_TYPE(in_[src]); + } +} diff --git a/kompute-shaders/op_cpy_f16_f32.comp b/kompute-shaders/op_cpy_f16_f32.comp new file mode 100644 index 000000000..b568bcd7b --- /dev/null +++ b/kompute-shaders/op_cpy_f16_f32.comp @@ -0,0 +1,52 @@ +#version 450 + +#include "common.comp" + +#define IN_TYPE float16_t +#define IN_TYPE_SIZE 2 +#define OUT_TYPE float +#define OUT_TYPE_SIZE 4 + +layout(local_size_x = 1024) in; + +layout (binding = 0) readonly buffer tensorIn { IN_TYPE in_[]; }; +layout (binding = 1) writeonly buffer tensorOut { OUT_TYPE out_[]; }; + +layout (push_constant) uniform parameter { + uint inOff; + uint outOff; + int ne00; + int ne01; + int ne02; + uint nb00; + uint nb01; + uint nb02; + uint nb03; + int ne0; + int ne1; + int ne2; + uint nb0; + uint nb1; + uint nb2; + uint nb3; +} pcs; + +void main() { + const uint i03 = gl_WorkGroupID.z; + const uint i02 = gl_WorkGroupID.y; + const uint i01 = gl_WorkGroupID.x; + + const int n = int(i03)*pcs.ne02*pcs.ne01*pcs.ne00 + int(i02)*pcs.ne01*pcs.ne00 + int(i01)*pcs.ne00; + + const int i3 = n / (pcs.ne2*pcs.ne1*pcs.ne0); + const int i2 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0) / (pcs.ne1*pcs.ne0); + const int i1 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0) / pcs.ne0; + const int i0 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0 - i1*pcs.ne0); + + const uint dst_data = (i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / OUT_TYPE_SIZE + pcs.outOff; // Based from out_ + + for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) { + const uint src = uint((i03*pcs.nb03 + i02*pcs.nb02 + i01*pcs.nb01 + i00*pcs.nb00) / IN_TYPE_SIZE) + pcs.inOff; // Based from in_ + out_[dst_data+i00] = OUT_TYPE(in_[src]); + } +} diff --git a/kompute-shaders/op_cpy_f32_f16.comp b/kompute-shaders/op_cpy_f32_f16.comp new file mode 100644 index 000000000..99b228343 --- /dev/null +++ b/kompute-shaders/op_cpy_f32_f16.comp @@ -0,0 +1,52 @@ +#version 450 + +#include "common.comp" + +#define IN_TYPE float +#define IN_TYPE_SIZE 4 +#define OUT_TYPE float16_t +#define OUT_TYPE_SIZE 2 + +layout(local_size_x = 1024) in; + +layout (binding = 0) readonly buffer tensorIn { IN_TYPE in_[]; }; +layout (binding = 1) writeonly buffer tensorOut { OUT_TYPE out_[]; }; + +layout (push_constant) uniform parameter { + uint inOff; + uint outOff; + int ne00; + int ne01; + int ne02; + uint nb00; + uint nb01; + uint nb02; + uint nb03; + int ne0; + int ne1; + int ne2; + uint nb0; + uint nb1; + uint nb2; + uint nb3; +} pcs; + +void main() { + const uint i03 = gl_WorkGroupID.z; + const uint i02 = gl_WorkGroupID.y; + const uint i01 = gl_WorkGroupID.x; + + const int n = int(i03)*pcs.ne02*pcs.ne01*pcs.ne00 + int(i02)*pcs.ne01*pcs.ne00 + int(i01)*pcs.ne00; + + const int i3 = n / (pcs.ne2*pcs.ne1*pcs.ne0); + const int i2 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0) / (pcs.ne1*pcs.ne0); + const int i1 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0) / pcs.ne0; + const int i0 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0 - i1*pcs.ne0); + + const uint dst_data = (i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / OUT_TYPE_SIZE + pcs.outOff; // Based from out_ + + for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) { + const uint src = uint((i03*pcs.nb03 + i02*pcs.nb02 + i01*pcs.nb01 + i00*pcs.nb00) / IN_TYPE_SIZE) + pcs.inOff; // Based from in_ + out_[dst_data+i00] = OUT_TYPE(in_[src]); + } +} diff --git a/kompute-shaders/op_cpy_f32_f32.comp b/kompute-shaders/op_cpy_f32_f32.comp new file mode 100644 index 000000000..2fc998492 --- /dev/null +++ b/kompute-shaders/op_cpy_f32_f32.comp @@ -0,0 +1,52 @@ +#version 450 + +#include "common.comp" + +#define IN_TYPE float +#define IN_TYPE_SIZE 4 +#define OUT_TYPE float +#define OUT_TYPE_SIZE 4 + +layout(local_size_x = 1024) in; + +layout (binding = 0) readonly buffer tensorIn { IN_TYPE in_[]; }; +layout (binding = 1) writeonly buffer tensorOut { OUT_TYPE out_[]; }; + +layout (push_constant) uniform parameter { + uint inOff; + uint outOff; + int ne00; + int ne01; + int ne02; + uint nb00; + uint nb01; + uint nb02; + uint nb03; + int ne0; + int ne1; + int ne2; + uint nb0; + uint nb1; + uint nb2; + uint nb3; +} pcs; + +void main() { + const uint i03 = gl_WorkGroupID.z; + const uint i02 = gl_WorkGroupID.y; + const uint i01 = gl_WorkGroupID.x; + + const int n = int(i03)*pcs.ne02*pcs.ne01*pcs.ne00 + int(i02)*pcs.ne01*pcs.ne00 + int(i01)*pcs.ne00; + + const int i3 = n / (pcs.ne2*pcs.ne1*pcs.ne0); + const int i2 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0) / (pcs.ne1*pcs.ne0); + const int i1 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0) / pcs.ne0; + const int i0 = (n - i3*pcs.ne2*pcs.ne1*pcs.ne0 - i2*pcs.ne1*pcs.ne0 - i1*pcs.ne0); + + const uint dst_data = (i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / OUT_TYPE_SIZE + pcs.outOff; // Based from out_ + + for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) { + const uint src = uint((i03*pcs.nb03 + i02*pcs.nb02 + i01*pcs.nb01 + i00*pcs.nb00) / IN_TYPE_SIZE) + pcs.inOff; // Based from in_ + out_[dst_data+i00] = OUT_TYPE(in_[src]); + } +} diff --git a/kompute-shaders/op_diagmask.comp b/kompute-shaders/op_diagmask.comp new file mode 100644 index 000000000..291c3fc18 --- /dev/null +++ b/kompute-shaders/op_diagmask.comp @@ -0,0 +1,30 @@ +#version 450 + +#include "common.comp" + +layout(local_size_x = 1) in; + +layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; }; +layout(binding = 1) buffer restrict writeonly tensorOut { float out_[]; }; + +layout(push_constant) uniform PushConstants { + uint inOff; + uint outOff; + uint n_past; + int ne00; + int ne01; +} pcs; + +void main() { + const uint i02 = gl_WorkGroupID.z; + const uint i01 = gl_WorkGroupID.y; + const uint i00 = gl_WorkGroupID.x; + + const uint index = i02*pcs.ne01*pcs.ne00 + i01*pcs.ne00 + i00; + + if (i00 > pcs.n_past + i01) { + out_[index + pcs.outOff] = uintBitsToFloat(0xFF800000); + } else { + out_[index + pcs.outOff] = in_[index + pcs.inOff]; + } +} diff --git a/kompute-shaders/op_gelu.comp b/kompute-shaders/op_gelu.comp new file mode 100644 index 000000000..9d8c53710 --- /dev/null +++ b/kompute-shaders/op_gelu.comp @@ -0,0 +1,22 @@ +#version 450 + +#include "common.comp" + +layout(local_size_x = 1) in; + +layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; }; +layout(binding = 1) buffer restrict writeonly tensorOut { float out_[]; }; +layout(push_constant) uniform PushConstants { + uint inOff; + uint outOff; +} pcs; + +void main() { + const uint baseIndex = gl_WorkGroupID.x * 8; + + for (uint x = 0; x < 8; x++) { + const uint i = baseIndex + x; + const float y = in_[i + pcs.inOff]; + out_[i + pcs.outOff] = 0.5*y*(1.0 + tanh(clamp(SQRT_2_OVER_PI*y*(1.0 + GELU_COEF_A*y*y), -15.0, 15.0))); + } +} diff --git a/kompute-shaders/op_getrows.comp b/kompute-shaders/op_getrows.comp new file mode 100644 index 000000000..1a5581b23 --- /dev/null +++ b/kompute-shaders/op_getrows.comp @@ -0,0 +1,17 @@ +void main() { + const uint i = gl_WorkGroupID.x; + const int r = inB[i + pcs.inBOff]; + + int z = 0; + for (uint ind = gl_LocalInvocationID.x; ind < pcs.ne00/16; ind += gl_WorkGroupSize.x) { + const uint inIndex = (r * pcs.nb01 + pcs.inAOff) + ind/NL * SIZE_OF_BLOCK; + const mat4 result = dequantize_block(inIndex, ind%NL); + for (uint j = 0; j < 4; ++j) { + for (uint k = 0; k < 4; ++k) { + const uint outIndex = i * pcs.nb1/BYTES_FOR_TYPE + pcs.outOff + z; + out_[outIndex] = result[j][k]; + ++z; + } + } + } +} diff --git a/kompute-shaders/op_getrows_f16.comp b/kompute-shaders/op_getrows_f16.comp new file mode 100644 index 000000000..48c936108 --- /dev/null +++ b/kompute-shaders/op_getrows_f16.comp @@ -0,0 +1,31 @@ +#version 450 + +#include "common.comp" + +layout(local_size_x = 1) in; + +layout (binding = 0) readonly buffer tensorInA { float16_t inA[]; }; +layout (binding = 1) readonly buffer tensorInB { int inB[]; }; +layout (binding = 2) writeonly buffer tensorOut { float out_[]; }; + +layout (push_constant) uniform parameter { + uint inAOff; + uint inBOff; + uint outOff; + int ne00; + int nb01; + int nb1; +} pcs; + +void dequantize_row_f16(uint x /*Based from inA unaligned*/, uint y /*Based from out_*/, int k) { + for (int j = 0; j < k; j++) { + out_[y + j] = inA[x + j]; + } +} + +void main() { + const uint i = gl_WorkGroupID.x; + const int r = inB[i + pcs.inBOff]; + + dequantize_row_f16(r*pcs.nb01/2/*bytes for float16*/ + pcs.inAOff, i*pcs.nb1/4 + pcs.outOff, pcs.ne00); +} diff --git a/kompute-shaders/op_getrows_q4_0.comp b/kompute-shaders/op_getrows_q4_0.comp new file mode 100644 index 000000000..32b2e891e --- /dev/null +++ b/kompute-shaders/op_getrows_q4_0.comp @@ -0,0 +1,38 @@ +#version 450 + +#include "common.comp" + +#define NL 2 +#define BYTES_FOR_TYPE 4 /*bytes for float*/ +#define SIZE_OF_BLOCK sizeof_block_q4_0 + +layout(local_size_x = 1) in; + +layout (binding = 0) readonly buffer tensorInA { uint8_t inA[]; }; +layout (binding = 1) readonly buffer tensorInB { int inB[]; }; +layout (binding = 2) writeonly buffer tensorOut { float out_[]; }; + +layout (push_constant) uniform parameter { + uint inAOff; + uint inBOff; + uint outOff; + int ne00; + int nb01; + int nb1; +} pcs; + +block_q4_0 get_unaligned_block_q4_0(uint index) { + block_q4_0 fres; + fres.d = u8BufToFloat16(inA, index); + [[unroll]] for (uint it = 0; it != QK4_0 / 2; it++) { + fres.qs[it] = inA[index+2+it]; + } + return fres; +} + +mat4 dequantize_block(uint index, uint il) { + const block_q4_0 block = get_unaligned_block_q4_0(index); + return dequantize_q4_0(block, il); +} + +#include "op_getrows.comp" diff --git a/kompute-shaders/op_getrows_q4_1.comp b/kompute-shaders/op_getrows_q4_1.comp new file mode 100644 index 000000000..87f2fbe17 --- /dev/null +++ b/kompute-shaders/op_getrows_q4_1.comp @@ -0,0 +1,39 @@ +#version 450 + +#include "common.comp" + +#define NL 2 +#define BYTES_FOR_TYPE 4 /*bytes for float*/ +#define SIZE_OF_BLOCK sizeof_block_q4_1 + +layout(local_size_x = 1) in; + +layout (binding = 0) readonly buffer tensorInA { uint8_t inA[]; }; +layout (binding = 1) readonly buffer tensorInB { int inB[]; }; +layout (binding = 2) writeonly buffer tensorOut { float out_[]; }; + +layout (push_constant) uniform parameter { + uint inAOff; + uint inBOff; + uint outOff; + int ne00; + int nb01; + int nb1; +} pcs; + +block_q4_1 get_unaligned_block_q4_1(uint index) { + block_q4_1 fres; + fres.d = u8BufToFloat16(inA, index); + fres.m = u8BufToFloat16(inA, index+2); + [[unroll]] for (uint it = 0; it != QK4_1 / 2; it++) { + fres.qs[it] = inA[index+4+it]; + } + return fres; +} + +mat4 dequantize_block(uint index, uint il) { + const block_q4_1 block = get_unaligned_block_q4_1(index); + return dequantize_q4_1(block, il); +} + +#include "op_getrows.comp" diff --git a/kompute-shaders/op_getrows_q6_k.comp b/kompute-shaders/op_getrows_q6_k.comp new file mode 100644 index 000000000..9ce3545d1 --- /dev/null +++ b/kompute-shaders/op_getrows_q6_k.comp @@ -0,0 +1,44 @@ +#version 450 + +#include "common.comp" + +#define NL 16 +#define BYTES_FOR_TYPE 4 /*bytes for float*/ +#define SIZE_OF_BLOCK sizeof_block_q6_k + +layout(local_size_x = 1) in; + +layout (binding = 0) readonly buffer tensorInA { uint8_t inA[]; }; +layout (binding = 1) readonly buffer tensorInB { int inB[]; }; +layout (binding = 2) writeonly buffer tensorOut { float out_[]; }; + +layout (push_constant) uniform parameter { + uint inAOff; + uint inBOff; + uint outOff; + int ne00; + int nb01; + int nb1; +} pcs; + +block_q6_k get_unaligned_block_q6_k(uint index) { + block_q6_k fres; + [[unroll]] for (uint it = 0; it != QK_K / 2; it++) { + fres.ql[it] = inA[index + it]; + } + [[unroll]] for (uint it = 0; it != QK_K / 4; it++) { + fres.qh[it] = inA[index + QK_K/2 + it]; + } + [[unroll]] for (uint it = 0; it != QK_K / 16; it++) { + fres.scales[it] = int8_t(inA[index + QK_K/2 + QK_K/4 + it]); + } + fres.d = u8BufToFloat16(inA, index + QK_K/2 + QK_K/4 + QK_K/16); + return fres; +} + +mat4 dequantize_block(uint index, uint il) { + const block_q6_k block = get_unaligned_block_q6_k(index); + return dequantize_q6_k(block, il); +} + +#include "op_getrows.comp" diff --git a/kompute-shaders/op_mul.comp b/kompute-shaders/op_mul.comp new file mode 100644 index 000000000..c92647c4d --- /dev/null +++ b/kompute-shaders/op_mul.comp @@ -0,0 +1,52 @@ +#version 450 + +#include "common.comp" + +layout(local_size_x = 1024) in; + +layout(binding = 0) buffer restrict readonly tensorInA { float inA[]; }; +layout(binding = 1) buffer restrict readonly tensorInB { float inB[]; }; +layout(binding = 2) buffer restrict writeonly tensorOut { float out_[]; }; + +layout(push_constant) uniform PushConstants { + uint inAOff; + uint inBOff; + uint outOff; + int ne00; + int nb00; + int nb01; + int nb02; + int nb03; + int ne10; + int ne11; + int ne12; + int ne13; + int nb10; + int nb11; + int nb12; + int nb13; + int ne0; + int nb0; + int nb1; + int nb2; + int nb3; +} pcs; + +void main() { + const uint i03 = gl_WorkGroupID.z; + const uint i02 = gl_WorkGroupID.y; + const uint i01 = gl_WorkGroupID.x; + + const uint i13 = i03 % pcs.ne13; + const uint i12 = i02 % pcs.ne12; + const uint i11 = i01 % pcs.ne11; + + uint src0_off = uint((i03*pcs.nb03 + i02*pcs.nb02 + i01*pcs.nb01) / 4); + uint src1_off = uint((i13*pcs.nb13 + i12*pcs.nb12 + i11*pcs.nb11) / 4); + uint dst_off = uint((i03*pcs.nb3 + i02*pcs.nb2 + i01*pcs.nb1) / 4); + + for (uint i0 = gl_LocalInvocationID.x; i0 < pcs.ne0; i0 += gl_WorkGroupSize.x) { + const uint i10 = i0 % pcs.ne10; + out_[pcs.outOff + dst_off + i0] = inA[pcs.inAOff + src0_off + i0] * inB[pcs.inBOff + src1_off + i10]; + } +} diff --git a/kompute-shaders/op_mul_mat_f16.comp b/kompute-shaders/op_mul_mat_f16.comp new file mode 100644 index 000000000..8f0a9031f --- /dev/null +++ b/kompute-shaders/op_mul_mat_f16.comp @@ -0,0 +1,67 @@ +#version 450 + +#include "common.comp" + +#extension GL_KHR_shader_subgroup_arithmetic : require + +layout(local_size_x_id = 0) in; + +layout (binding = 0) readonly buffer tensorInA { float16_t inA[]; }; +layout (binding = 1) readonly buffer tensorInB { float inB[]; }; +layout (binding = 2) writeonly buffer tensorOut { float out_[]; }; + +layout (push_constant) uniform parameter { + uint inAOff; + uint inBOff; + uint outOff; + int ne00; + int ne01; + int ne02; + uint nb00; + uint nb01; + uint nb02; + int ne10; + int ne11; + int ne12; + uint nb10; + uint nb11; + uint nb12; + int ne0; + int ne1; + uint r2; + uint r3; +} pcs; + +#define N_F16_F32 4 + +void main() { + const uint r0 = gl_WorkGroupID.x; + const uint rb = gl_WorkGroupID.y*N_F16_F32; + const uint im = gl_WorkGroupID.z; + + const uint i12 = im%pcs.ne12; + const uint i13 = im/pcs.ne12; + + const uint offset0 = r0*pcs.nb01 + (i12/pcs.r2)*pcs.nb02 + (i13/pcs.r3)*pcs.nb02*pcs.ne02; + + const uint x = offset0 / 2 + pcs.inAOff; // Based from inA + + for (uint row = 0; row < N_F16_F32; ++row) { + uint r1 = rb + row; + if (r1 >= pcs.ne11) { + break; + } + + const uint y = (r1*pcs.nb11 + im*pcs.nb12) / 4 + pcs.inBOff; // Based from inB + + float sumf = 0; + for (uint i = gl_SubgroupInvocationID.x; i < pcs.ne00; i += gl_SubgroupSize) { + sumf += float(inA[x+i]) * float(inB[y+i]); + } + + const float all_sum = subgroupAdd(sumf); + if (subgroupElect()) { + out_[im*pcs.ne1*pcs.ne0 + r1*pcs.ne0 + r0 + pcs.outOff] = all_sum; + } + } +} diff --git a/kompute-shaders/op_mul_mat_mat_f32.comp b/kompute-shaders/op_mul_mat_mat_f32.comp new file mode 100644 index 000000000..d1ca4ad6c --- /dev/null +++ b/kompute-shaders/op_mul_mat_mat_f32.comp @@ -0,0 +1,51 @@ +#version 450 + +#include "common.comp" + +#extension GL_KHR_shader_subgroup_arithmetic : require +#extension GL_EXT_debug_printf : enable + +// device subgroup size +layout (local_size_x_id = 0) in; + +layout(binding = 0) readonly buffer tensorInA { float inA[]; }; +layout(binding = 1) readonly buffer tensorInB { float inB[]; }; +layout(binding = 2) writeonly buffer tensorOut { float out_[]; }; + +layout(push_constant) uniform parameter { + uint inAOff; + uint inBOff; + uint outOff; + int ne00; + int ne01; + int ne02; + int ne11; + int ne12; + uint nb01; + uint nb02; + uint nb11; + uint nb12; + uint nb1; + uint nb2; +} +pcs; + + +void main() { + uvec3 gid = gl_WorkGroupID; + + uint bc_ab = pcs.ne12 > pcs.ne02 ? gid.z / (pcs.ne12 / pcs.ne02) : gid.z; + uint bc_ba = pcs.ne02 > pcs.ne12 ? gid.z / (pcs.ne02 / pcs.ne12) : gid.z; + + const uint x = (gid.x*pcs.nb01 + bc_ab*pcs.nb02) / 4 + pcs.inAOff; // Based from inA + const uint y = (gid.y*pcs.nb11 + bc_ba*pcs.nb12) / 4 + pcs.inBOff; // based from inB + float sum = 0.0f; + for (uint i = gl_SubgroupInvocationID.x; i < pcs.ne00; i += gl_SubgroupSize) { + sum += float(inA[x+i]) * float(inB[y+i]); + } + + const float all_sum = subgroupAdd(sum); + if (subgroupElect()) { + out_[gid.z*(pcs.nb2/4) + gid.y*(pcs.nb1/4) + gid.x + pcs.outOff] = all_sum; + } +} diff --git a/kompute-shaders/op_mul_mat_q4_0.comp b/kompute-shaders/op_mul_mat_q4_0.comp new file mode 100644 index 000000000..b0cea8bbe --- /dev/null +++ b/kompute-shaders/op_mul_mat_q4_0.comp @@ -0,0 +1,33 @@ +#version 450 + +#include "common.comp" + +#define BLOCKS_IN_QUANT QK4_0 +#define SIZE_OF_BLOCK sizeof_block_q4_0 +#define N_ROWS 4 + +#include "op_mul_mv_q_n_pre.comp" + +// The q4_0 version of this function +float block_q_n_dot_y(uint block_index, uint yb, uint il) { + vec2 acc = vec2(0.0, 0.0); + const uint index = (block_index) * SIZE_OF_BLOCK + pcs.inAOff; + float d = float(u8BufToFloat16(inA, index)); + float sumy = 0.0f; + for (int i = 0; i < BLOCKS_IN_QUANT/4; i+=2) { + const uint16_t b = u8BufToU16(inA, index + 2 + il + i); + + const float yl0 = inB[yb + i]; + const float yl1 = inB[yb + i + 1]; + const float yl8 = inB[yb + i + BLOCKS_IN_QUANT/2]; + const float yl9 = inB[yb + i + BLOCKS_IN_QUANT/2 + 1]; + + sumy += yl0 + yl1 + yl8 + yl9; + + acc[0] += yl0 * (b & 0x000F) + yl1 / 256.f * (b & 0x0F00); + acc[1] += yl8 / 16.f * (b & 0x00F0) + yl9 / 4096.f * (b & 0xF000); + } + return d * (sumy * -8.f + acc[0] + acc[1]); +} + +#include "op_mul_mv_q_n.comp" diff --git a/kompute-shaders/op_mul_mat_q4_1.comp b/kompute-shaders/op_mul_mat_q4_1.comp new file mode 100644 index 000000000..8582c61a3 --- /dev/null +++ b/kompute-shaders/op_mul_mat_q4_1.comp @@ -0,0 +1,35 @@ +#version 450 + +#include "common.comp" + +#define BLOCKS_IN_QUANT QK4_1 +#define SIZE_OF_BLOCK sizeof_block_q4_1 +#define N_ROWS 4 + +#include "op_mul_mv_q_n_pre.comp" + +// The q4_1 version of this function +float block_q_n_dot_y(uint block_index, uint yb, uint il) { + vec2 acc = vec2(0.0, 0.0); + const uint index = (block_index) * SIZE_OF_BLOCK + pcs.inAOff; + float d = float(u8BufToFloat16(inA, index)); + float m = float(u8BufToFloat16(inA, index+2)); + + float sumy = 0.0f; + for (int i = 0; i < BLOCKS_IN_QUANT/4; i+=2) { + const uint16_t b = u8BufToU16(inA, index + 4 + il + i); + + const float yl0 = inB[yb + i]; + const float yl1 = inB[yb + i + 1]; + const float yl8 = inB[yb + i + BLOCKS_IN_QUANT/2]; + const float yl9 = inB[yb + i + BLOCKS_IN_QUANT/2 + 1]; + + sumy += yl0 + yl1 + yl8 + yl9; + + acc[0] += yl0 * (b & 0x000F) + yl1 / 256.f * (b & 0x0F00); + acc[1] += yl8 / 16.f * (b & 0x00F0) + yl9 / 4096.f * (b & 0xF000); + } + return d * (acc[0] + acc[1]) + sumy * m; +} + +#include "op_mul_mv_q_n.comp" diff --git a/kompute-shaders/op_mul_mat_q6_k.comp b/kompute-shaders/op_mul_mat_q6_k.comp new file mode 100644 index 000000000..c9baebdf4 --- /dev/null +++ b/kompute-shaders/op_mul_mat_q6_k.comp @@ -0,0 +1,94 @@ +#version 450 + +#include "common.comp" + +#define SIZE_OF_BLOCK sizeof_block_q6_k + +layout(local_size_x_id = 0) in; +layout(local_size_y_id = 1) in; +layout(local_size_z = 1) in; + +layout (binding = 0) readonly buffer tensorInA { uint8_t inA[]; }; +layout (binding = 1) readonly buffer tensorInB { float inB[]; }; +layout (binding = 2) writeonly buffer tensorOut { float out_[]; }; + +layout (push_constant) uniform parameter { + uint inAOff; + uint inBOff; + uint outOff; + int ne00; + int ne10; + int ne0; + int ne1; + int ne01; + int gqa; +} pcs; + +void main() { + const uint8_t kmask1 = uint8_t(0x03); + const uint8_t kmask2 = uint8_t(0x0C); + const uint8_t kmask3 = uint8_t(0x30); + const uint8_t kmask4 = uint8_t(0xC0); + + const uint nb = pcs.ne00/QK_K; + + const uint r0 = gl_WorkGroupID.x; + const uint r1 = gl_WorkGroupID.y; + const uint r2 = gl_WorkGroupID.z; + + const uint row = (r0 * gl_NumSubgroups + gl_SubgroupID); + const uint offset0 = r2/pcs.gqa*(nb*pcs.ne0); + const uint x = row * nb + offset0; // Based from inA without base offset + const uint yy = r1*pcs.ne10 + r2*pcs.ne00*pcs.ne1+pcs.inBOff; // Based from inB + + float sumf = 0; + + // bits of invocation ID for gl_SubgroupSize=32: + // x x x x x + // 4 3 2 1 0 + // ( tid ) ix + // ip ( il ) + + const uint block_stride = gl_SubgroupSize / 16; // number of blocks each subgroup processes + const uint tid = gl_SubgroupInvocationID/block_stride; // first block_stride groups have tid=0 + const uint ix = gl_SubgroupInvocationID%block_stride; // first block is 0..block_stride-1 + const uint ip = tid/8; // first or second half of block (0 or 1) + const uint il = tid%8; // each half has 8 parts, one per scale + const uint n = 4; // 4 scales at a time (and 4 sums) + const uint l0 = n*il; // offset into half-block, 0..28 + const uint is = 8*ip + l0/16; // 0, 1, 8, 9 + + const uint y_offset = 128*ip + l0; + const uint q_offset_l = 64*ip + l0; + const uint q_offset_h = 32*ip + l0; + + for (uint i = ix; i < nb; i += block_stride) { + + const uint baseIndex = (x + i) * SIZE_OF_BLOCK + pcs.inAOff; + + const uint qlIndex = q_offset_l; + const uint q2Index = qlIndex + QK_K/8; + const uint qhIndex = q_offset_h; + const uint y = yy + i * QK_K + y_offset; + + float sums[4] = {0.0f, 0.0f, 0.0f, 0.0f}; + for (uint l = 0; l < n; ++l) { + const uint8_t currentQ1 = inA[baseIndex + qlIndex + l]; + const uint8_t currentQ2 = inA[baseIndex + q2Index + l]; + const uint8_t currentQh = inA[baseIndex + QK_K/2 + qhIndex + l]; + + sums[0] += inB[y+l+ 0] * (int8_t((currentQ1 & 0xF) | ((currentQh & kmask1) << 4)) - 32); + sums[1] += inB[y+l+32] * (int8_t((currentQ2 & 0xF) | ((currentQh & kmask2) << 2)) - 32); + sums[2] += inB[y+l+64] * (int8_t((currentQ1 >> 4) | ((currentQh & kmask3) << 0)) - 32); + sums[3] += inB[y+l+96] * (int8_t((currentQ2 >> 4) | ((currentQh & kmask4) >> 2)) - 32); + } + + float d = u8BufToFloat16(inA, baseIndex + QK_K/2 + QK_K/4 + QK_K/16); + sumf += d * (sums[0] * int8_t(inA[baseIndex + QK_K/2 + QK_K/4 + is]) + sums[1] * int8_t(inA[baseIndex + QK_K/2 + QK_K/4 + 2 + is]) + sums[2] * int8_t(inA[baseIndex + QK_K/2 + QK_K/4 + 4 + is]) + sums[3] * int8_t(inA[baseIndex + QK_K/2 + QK_K/4 + 6 + is])); + } + + const float tot = subgroupAdd(sumf); + if (subgroupElect()) { + out_[r1*pcs.ne0 + r2*pcs.ne0*pcs.ne1 + row + pcs.outOff] = tot; + } +} diff --git a/kompute-shaders/op_mul_mat_q8_0.comp b/kompute-shaders/op_mul_mat_q8_0.comp new file mode 100644 index 000000000..34d015e90 --- /dev/null +++ b/kompute-shaders/op_mul_mat_q8_0.comp @@ -0,0 +1,73 @@ +#version 450 + +#include "common.comp" + +#include "op_mul_mv_q_n_pre.comp" + +#define SIZE_OF_D 2 + +#define N_DST 4 // each SIMD group works on 4 rows +#define N_SIMDGROUP 2 // number of SIMD groups in a thread group +#define N_SIMDWIDTH 32 // assuming SIMD group size is 32 + +#define NB_Q8_0 8 + +void main() { + // NB: hack to make compatible with AMD GPUs that have a subgroup size of 64 + if (gl_SubgroupInvocationID > 31) + return; + + const int nr = N_DST; + const int nsg = N_SIMDGROUP; + const int nw = N_SIMDWIDTH; + + const int nb = pcs.ne00/QK8_0; + const uint r0 = gl_WorkGroupID.x; + const uint r1 = gl_WorkGroupID.y; + const uint im = gl_WorkGroupID.z; + + const uint first_row = (r0 * nsg + gl_SubgroupID) * nr; + + const uint i12 = im%pcs.ne12; + const uint i13 = im/pcs.ne12; + + const uint offset0 = first_row * nb + (i12/pcs.r2)*(nb*pcs.ne01) + (i13/pcs.r3)*(nb*pcs.ne01*pcs.ne02); + + const uint x = offset0*sizeof_block_q8_0 + pcs.inAOff; // Based from inA + const uint y = r1*pcs.ne10 + im*pcs.ne00*pcs.ne1 + pcs.inBOff; // based from inB + + float yl[NB_Q8_0]; + float sumf[N_DST]={0.f, 0.f, 0.f, 0.f}; + + const uint ix = gl_SubgroupInvocationID.x/4; + const uint il = gl_SubgroupInvocationID.x%4; + + uint yb = y + ix * QK8_0 + NB_Q8_0*il; + + // each thread in a SIMD group deals with NB_Q8_0 quants at a time + for (uint ib = ix; ib < nb; ib += nw/4) { + for (int i = 0; i < NB_Q8_0; ++i) { + yl[i] = inB[yb + i]; + } + + for (int row = 0; row < nr; row++) { + const uint block_offset = (ib+row*nb) * sizeof_block_q8_0; + float sumq = 0.f; + for (int iq = 0; iq < NB_Q8_0; ++iq) { + const int8_t qs_iq = int8_t(inA[x + block_offset + SIZE_OF_D + NB_Q8_0*il + iq]); + sumq += qs_iq * yl[iq]; + } + const float16_t d = u8BufToFloat16(inA, x + block_offset); + sumf[row] += sumq*d; + } + + yb += NB_Q8_0 * nw; + } + + for (int row = 0; row < nr; ++row) { + const float tot = subgroupAdd(sumf[row]); + if (subgroupElect() && first_row + row < pcs.ne01) { + out_[r1*pcs.ne0 + im*pcs.ne0*pcs.ne1 + first_row + row] = tot; + } + } +} diff --git a/kompute-shaders/op_mul_mv_q_n.comp b/kompute-shaders/op_mul_mv_q_n.comp new file mode 100644 index 000000000..440b5ab2c --- /dev/null +++ b/kompute-shaders/op_mul_mv_q_n.comp @@ -0,0 +1,48 @@ +void main() { + // NB: hack to make compatible with AMD GPUs that have a subgroup size of 64 + if (gl_SubgroupInvocationID > 31) + return; + + const uint nb = uint(pcs.ne00/BLOCKS_IN_QUANT); + + const uint r0 = gl_WorkGroupID.x; + const uint r1 = gl_WorkGroupID.y; + const uint im = gl_WorkGroupID.z; + + const uint first_row = (r0 * gl_NumSubgroups + gl_SubgroupID) * N_ROWS; + + const uint i12 = im%pcs.ne12; + const uint i13 = im/pcs.ne12; + + const uint offset0 = first_row * nb + (i12/pcs.r2)*(nb*pcs.ne01) + (i13/pcs.r3)*(nb*pcs.ne01*pcs.ne02); + + const uint x = offset0; // Based from inA without base offset + const uint y = r1*uint(pcs.ne10)+im*pcs.ne00*pcs.ne1+pcs.inBOff; // Based from inB + + float sumf[N_ROWS] = {0.0f, 0.0f, 0.0f, 0.0f}; + + const uint ix = gl_SubgroupInvocationID/2; + const uint il = (BLOCKS_IN_QUANT/4)*(gl_SubgroupInvocationID%2); + + uint yb = y + ix * BLOCKS_IN_QUANT + il; + + //debugPrintfEXT("gl_NumSubgroups=%d, gl_SubgroupID=%d, gl_SubgroupInvocationID=%d, glSubgroupSize=%d, gl_WorkGroupSize.x=%d, gl_WorkGroupSize.y=%d, gl_WorkGroupSize.z=%d\n", + // gl_NumSubgroups, gl_SubgroupID, gl_SubgroupInvocationID, gl_SubgroupSize, + // gl_WorkGroupSize.x, gl_WorkGroupSize.y, gl_WorkGroupSize.z); + + for (uint ib = ix; ib < nb; ib += 16) { + for (int row = 0; row < N_ROWS; row++) { + const uint block_index = x + ib + row * nb; + sumf[row] += block_q_n_dot_y(block_index, yb, il); + } + + yb += BLOCKS_IN_QUANT * 16; + } + + for (int row = 0; row < N_ROWS; ++row) { + const float tot = subgroupAdd(sumf[row]); + if (first_row + row < pcs.ne01 && subgroupElect()) { + out_[r1*pcs.ne0 + im*pcs.ne0*pcs.ne1 + first_row + row + pcs.outOff] = tot; + } + } +} diff --git a/kompute-shaders/op_mul_mv_q_n_pre.comp b/kompute-shaders/op_mul_mv_q_n_pre.comp new file mode 100644 index 000000000..7912b09ac --- /dev/null +++ b/kompute-shaders/op_mul_mv_q_n_pre.comp @@ -0,0 +1,22 @@ +layout(local_size_x_id = 0) in; +layout(local_size_y = 1) in; +layout(local_size_z = 1) in; + +layout (binding = 0) readonly buffer tensorInA { uint8_t inA[]; }; +layout (binding = 1) readonly buffer tensorInB { float inB[]; }; +layout (binding = 2) writeonly buffer tensorOut { float out_[]; }; + +layout (push_constant) uniform parameter { + uint inAOff; + uint inBOff; + uint outOff; + int ne00; + int ne01; + int ne02; + int ne10; + int ne12; + int ne0; + int ne1; + uint r2; + uint r3; +} pcs; diff --git a/kompute-shaders/op_norm.comp b/kompute-shaders/op_norm.comp new file mode 100644 index 000000000..ad0c3c01b --- /dev/null +++ b/kompute-shaders/op_norm.comp @@ -0,0 +1,84 @@ +#version 450 + +#include "common.comp" + +layout(local_size_x = 256) in; + +layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; }; +layout(binding = 1) buffer restrict tensorOut { float out_[]; }; + +layout(push_constant) uniform PushConstants { + uint inOff; + uint outOff; + uint ne00; + uint nb01; + float eps; +} pcs; + +shared float sum[gl_WorkGroupSize.x]; + +void main() { + const uint x = (gl_WorkGroupID.x*pcs.nb01/4) + pcs.inOff; // Based from in_ + // MEAN + // parallel sum + sum[gl_LocalInvocationID.x] = 0.0; + for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) { + sum[gl_LocalInvocationID.x] += in_[x+i00]; + } + + // reduce + barrier(); + memoryBarrierShared(); + [[unroll]] for (uint i = gl_WorkGroupSize.x/2; i > 0; i /= 2) { + if (gl_LocalInvocationID.x < i) { + sum[gl_LocalInvocationID.x] += sum[gl_LocalInvocationID.x + i]; + } + barrier(); + memoryBarrierShared(); + } + + // broadcast + if (gl_LocalInvocationID.x == 0) { + sum[0] /= float(pcs.ne00); + } + barrier(); + memoryBarrierShared(); + const float mean = sum[0]; + + // recenter + const uint y = (gl_WorkGroupID.x*pcs.ne00) + pcs.outOff; // Based from out_ + for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) { + out_[y+i00] = in_[x+i00] - mean; + } + + // VARIANCE + // parallel sum + sum[gl_LocalInvocationID.x] = 0.0; + for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) { + sum[gl_LocalInvocationID.x] += out_[y+i00] * out_[y+i00]; + } + + // reduce + barrier(); + memoryBarrierShared(); + [[unroll]] for (uint i = gl_WorkGroupSize.x/2; i > 0; i /= 2) { + if (gl_LocalInvocationID.x < i) { + sum[gl_LocalInvocationID.x] += sum[gl_LocalInvocationID.x + i]; + } + barrier(); + memoryBarrierShared(); + } + + // broadcast + if (gl_LocalInvocationID.x == 0) { + sum[0] /= float(pcs.ne00); + } + barrier(); + memoryBarrierShared(); + const float variance = sum[0]; + + const float scale = 1.0f/sqrt(variance + pcs.eps); + for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) { + out_[y+i00] *= scale; + } +} diff --git a/kompute-shaders/op_relu.comp b/kompute-shaders/op_relu.comp new file mode 100644 index 000000000..52a601fe6 --- /dev/null +++ b/kompute-shaders/op_relu.comp @@ -0,0 +1,21 @@ +#version 450 + +#include "common.comp" + +layout(local_size_x = 1) in; + +layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; }; +layout(binding = 1) buffer restrict writeonly tensorOut { float out_[]; }; +layout(push_constant) uniform PushConstants { + uint inOff; + uint outOff; +} pcs; + +void main() { + const uint baseIndex = gl_WorkGroupID.x * 4; + + for (uint x = 0; x < 4; x++) { + const uint i = baseIndex + x; + out_[i + pcs.outOff] = max(0.0, in_[i + pcs.inOff]); + } +} diff --git a/kompute-shaders/op_rmsnorm.comp b/kompute-shaders/op_rmsnorm.comp new file mode 100644 index 000000000..da658c160 --- /dev/null +++ b/kompute-shaders/op_rmsnorm.comp @@ -0,0 +1,53 @@ +#version 450 + +#include "common.comp" + +layout(local_size_x = 512) in; + +layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; }; +layout(binding = 1) buffer restrict tensorOut { float out_[]; }; + +layout(push_constant) uniform PushConstants { + uint inOff; + uint outOff; + uint ne00; + uint nb01; + float eps; +} pcs; + +shared float sum[gl_WorkGroupSize.x]; + +void main() { + const uint x = (gl_WorkGroupID.x*pcs.nb01/4) + pcs.inOff; // Based from in_ + + // parallel sum + sum[gl_LocalInvocationID.x] = 0.0; + for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) { + sum[gl_LocalInvocationID.x] += in_[x+i00] * in_[x+i00]; + } + + // reduce + barrier(); + memoryBarrierShared(); + [[unroll]] for (uint i = gl_WorkGroupSize.x/2; i > 0; i /= 2) { + if (gl_LocalInvocationID.x < i) { + sum[gl_LocalInvocationID.x] += sum[gl_LocalInvocationID.x + i]; + } + barrier(); + memoryBarrierShared(); + } + + // broadcast + if (gl_LocalInvocationID.x == 0) { + sum[0] /= float(pcs.ne00); + } + barrier(); + memoryBarrierShared(); + + const float scale = 1.0f/sqrt(sum[0] + pcs.eps); + + const uint y = (gl_WorkGroupID.x*pcs.ne00) + pcs.outOff; // Based from out_ + for (uint i00 = gl_LocalInvocationID.x; i00 < pcs.ne00; i00 += gl_WorkGroupSize.x) { + out_[y+i00] = in_[x+i00] * scale; + } +} diff --git a/kompute-shaders/op_rope_f16.comp b/kompute-shaders/op_rope_f16.comp new file mode 100644 index 000000000..b44622584 --- /dev/null +++ b/kompute-shaders/op_rope_f16.comp @@ -0,0 +1,73 @@ +#version 450 + +#include "rope_common.comp" + +layout(binding = 0) buffer restrict readonly tensorInA { float16_t inA[]; }; +layout(binding = 1) buffer restrict readonly tensorInB { int inB[]; }; +layout(binding = 2) buffer restrict writeonly tensorOut { float16_t out_[]; }; + +void main() { + const uint i3 = gl_WorkGroupID.z; + const uint i2 = gl_WorkGroupID.y; + const uint i1 = gl_WorkGroupID.x; + + const bool is_neox = (pcs.mode & 2) != 0; + + float corr_dims[2]; + rope_yarn_corr_dims(pcs.n_dims, pcs.n_orig_ctx, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims); + + const float theta_scale = pow(pcs.freq_base, -2.0/pcs.n_dims); + + const int p = inB[pcs.inBOff + i2]; + + float theta = float(p); + + if (!is_neox) { + for (uint i0 = 0; i0 < pcs.ne0; i0 += 2) { + float cos_theta, sin_theta; + rope_yarn(theta, pcs.freq_scale, corr_dims, i0, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); + + theta *= theta_scale; + + const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 2) + pcs.inAOff; // Based from in + const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 2) + pcs.outOff; // Based from out_ + + const float x0 = float(inA[src]); + const float x1 = float(inA[src+1]); + + out_[dst_data] = float16_t(x0*cos_theta - x1*sin_theta); + out_[dst_data+1] = float16_t(x0*sin_theta + x1*cos_theta); + } + } else { + const float inv_ndims = -1.f/pcs.n_dims; + for (uint ic = 0; ic < pcs.n_dims; ic += 2) { + const uint cur_rot = ic; + + float cos_theta, sin_theta; + rope_yarn(theta, pcs.freq_scale, corr_dims, cur_rot, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); + + theta *= theta_scale; + + const uint i0 = ic/2; + + const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 2) + pcs.inAOff; // Based from in + const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 2) + pcs.outOff; // Based from out_ + + const float x0 = float(inA[src]); + const float x1 = float(inA[src+pcs.n_dims/2]); + + out_[dst_data] = float16_t(x0*cos_theta - x1*sin_theta); + out_[dst_data+pcs.n_dims/2] = float16_t(x0*sin_theta + x1*cos_theta); + } + + for (uint ic = pcs.n_dims; ic < pcs.ne0; ic += 2) { + const uint i0 = ic; + + const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 2) + pcs.inAOff; // Based from in + const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 2) + pcs.outOff; // Based from out_ + + out_[dst_data + 0] = inA[src + 0]; + out_[dst_data + 1] = inA[src + 1]; + } + } +} diff --git a/kompute-shaders/op_rope_f32.comp b/kompute-shaders/op_rope_f32.comp new file mode 100644 index 000000000..2c0235d75 --- /dev/null +++ b/kompute-shaders/op_rope_f32.comp @@ -0,0 +1,73 @@ +#version 450 + +#include "rope_common.comp" + +layout(binding = 0) buffer restrict readonly tensorInA { float inA[]; }; +layout(binding = 1) buffer restrict readonly tensorInB { int inB[]; }; +layout(binding = 2) buffer restrict writeonly tensorOut { float out_[]; }; + +void main() { + const uint i3 = gl_WorkGroupID.z; + const uint i2 = gl_WorkGroupID.y; + const uint i1 = gl_WorkGroupID.x; + + const bool is_neox = (pcs.mode & 2) != 0; + + float corr_dims[2]; + rope_yarn_corr_dims(pcs.n_dims, pcs.n_orig_ctx, pcs.freq_base, pcs.beta_fast, pcs.beta_slow, corr_dims); + + const float theta_scale = pow(pcs.freq_base, -2.0/pcs.n_dims); + + const int p = inB[pcs.inBOff + i2]; + + float theta = float(p); + + if (!is_neox) { + for (uint i0 = 0; i0 < pcs.ne0; i0 += 2) { + float cos_theta, sin_theta; + rope_yarn(theta, pcs.freq_scale, corr_dims, i0, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); + + theta *= theta_scale; + + const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 4) + pcs.inAOff; // Based from in + const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 4) + pcs.outOff; // Based from out_ + + const float x0 = inA[src]; + const float x1 = inA[src+1]; + + out_[dst_data] = x0*cos_theta - x1*sin_theta; + out_[dst_data+1] = x0*sin_theta + x1*cos_theta; + } + } else { + const float inv_ndims = -1.f/pcs.n_dims; + for (uint ic = 0; ic < pcs.n_dims; ic += 2) { + const uint cur_rot = ic; + + float cos_theta, sin_theta; + rope_yarn(theta, pcs.freq_scale, corr_dims, cur_rot, pcs.ext_factor, pcs.attn_factor, cos_theta, sin_theta); + + theta *= theta_scale; + + const uint i0 = ic/2; + + const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 4) + pcs.inAOff; // Based from in + const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 4) + pcs.outOff; // Based from out_ + + const float x0 = inA[src]; + const float x1 = inA[src+pcs.n_dims/2]; + + out_[dst_data] = x0*cos_theta - x1*sin_theta; + out_[dst_data+pcs.n_dims/2] = x0*sin_theta + x1*cos_theta; + } + + for (uint ic = pcs.n_dims; ic < pcs.ne0; ic += 2) { + const uint i0 = ic; + + const uint src = uint((i3*pcs.nb03 + i2*pcs.nb02 + i1*pcs.nb01 + i0*pcs.nb00) / 4) + pcs.inAOff; // Based from in + const uint dst_data = uint((i3*pcs.nb3 + i2*pcs.nb2 + i1*pcs.nb1 + i0*pcs.nb0) / 4) + pcs.outOff; // Based from out_ + + out_[dst_data + 0] = inA[src + 0]; + out_[dst_data + 1] = inA[src + 1]; + } + } +} diff --git a/kompute-shaders/op_scale.comp b/kompute-shaders/op_scale.comp new file mode 100644 index 000000000..bdae26738 --- /dev/null +++ b/kompute-shaders/op_scale.comp @@ -0,0 +1,19 @@ +#version 450 + +#include "common.comp" + +layout(local_size_x = 1) in; + +layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; }; +layout(binding = 1) buffer restrict writeonly tensorOut { float out_[]; }; + +layout(push_constant) uniform PushConstants { + uint inOff; + uint outOff; + float scale; +} pcs; + +void main() { + const uint i = gl_WorkGroupID.x; + out_[i + pcs.outOff] = in_[i + pcs.inOff] * pcs.scale; +} diff --git a/kompute-shaders/op_scale_8.comp b/kompute-shaders/op_scale_8.comp new file mode 100644 index 000000000..ada69754b --- /dev/null +++ b/kompute-shaders/op_scale_8.comp @@ -0,0 +1,23 @@ +#version 450 + +#include "common.comp" + +layout(local_size_x = 1) in; + +layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; }; +layout(binding = 1) buffer restrict writeonly tensorOut { float out_[]; }; + +layout(push_constant) uniform PushConstants { + uint inOff; + uint outOff; + float scale; +} pcs; + +void main() { + const uint baseIndex = gl_WorkGroupID.x * 8; + + for (uint x = 0; x < 8; x++) { + const uint i = baseIndex + x; + out_[i + pcs.outOff] = in_[i + pcs.inOff] * pcs.scale; + } +} diff --git a/kompute-shaders/op_silu.comp b/kompute-shaders/op_silu.comp new file mode 100644 index 000000000..0fb8e4b74 --- /dev/null +++ b/kompute-shaders/op_silu.comp @@ -0,0 +1,22 @@ +#version 450 + +#include "common.comp" + +layout(local_size_x = 1) in; + +layout(binding = 0) buffer restrict readonly tensorIn { float in_[]; }; +layout(binding = 1) buffer restrict writeonly tensorOut { float out_[]; }; +layout(push_constant) uniform PushConstants { + uint inOff; + uint outOff; +} pcs; + +void main() { + const uint baseIndex = gl_WorkGroupID.x * 4; + + for (uint x = 0; x < 4; x++) { + const uint i = baseIndex + x; + const float y = in_[i + pcs.inOff]; + out_[i + pcs.outOff] = y / (1.0 + exp(-y)); + } +} diff --git a/kompute-shaders/op_softmax.comp b/kompute-shaders/op_softmax.comp new file mode 100644 index 000000000..7bc9176ca --- /dev/null +++ b/kompute-shaders/op_softmax.comp @@ -0,0 +1,56 @@ +// TODO: implement multi-simd softmax (llama.cpp commit e16b9fa4) + +#version 450 + +#include "common.comp" + +layout(local_size_x_id = 0) in; + +layout(binding = 0) buffer restrict readonly tensorInA { float inA[]; }; +layout(binding = 1) buffer restrict readonly tensorInB { float inB[]; }; +layout(binding = 2) buffer restrict writeonly tensorOut { float out_[]; }; + +layout(push_constant) uniform PushConstants { + uint inAOff; + uint inBOff; + uint outOff; + int ne00; + int ne01; + int ne02; + float scale; + int mask; +} pcs; + +void main() { + if (gl_SubgroupInvocationID > 31) + return; + + const uint i03 = gl_WorkGroupID.z; + const uint i02 = gl_WorkGroupID.y; + const uint i01 = gl_WorkGroupID.x; + + const uint extra_off = i03*pcs.ne02*pcs.ne01*pcs.ne00 + i02*pcs.ne01*pcs.ne00 + i01*pcs.ne00; + const uint psrc0 = extra_off + pcs.inAOff; // Based from inA + const uint pmask = i01*pcs.ne00 + pcs.inBOff; // Based from inB + const uint pdst = extra_off + pcs.outOff; // Based from out_ + + // parallel max + float localMax = uintBitsToFloat(0xFF800000); + for (uint i00 = gl_SubgroupInvocationID.x; i00 < pcs.ne00; i00 += 32) { + localMax = max(localMax, inA[psrc0 + i00]*pcs.scale + (pcs.mask!=0 ? inB[pmask + i00] : 0.0f)); + } + float max_ = subgroupMax(localMax); + + // parallel sum + float localSum = 0.0f; + for (uint i00 = gl_SubgroupInvocationID.x; i00 < pcs.ne00; i00 += 32) { + const float exp_psrc0 = exp(inA[psrc0 + i00]*pcs.scale + (pcs.mask!=0 ? inB[pmask + i00] : 0.0f) - max_); + localSum += exp_psrc0; + out_[pdst + i00] = exp_psrc0; + } + + const float sum = subgroupAdd(localSum); + for (uint i00 = gl_SubgroupInvocationID.x; i00 < pcs.ne00; i00 += 32) { + out_[pdst + i00] /= sum; + } +} diff --git a/kompute-shaders/rope_common.comp b/kompute-shaders/rope_common.comp new file mode 100644 index 000000000..57ba6597a --- /dev/null +++ b/kompute-shaders/rope_common.comp @@ -0,0 +1,67 @@ +#include "common.comp" + +// TODO: use a local size of 32 or more (Metal uses 1024) +layout(local_size_x = 1) in; + +layout (push_constant) uniform parameter { + uint inAOff; + uint inBOff; + uint outOff; + int n_dims; + int mode; + int n_orig_ctx; + float freq_base; + float freq_scale; + float ext_factor; + float attn_factor; + float beta_fast; + float beta_slow; + uint nb00; + uint nb01; + uint nb02; + uint nb03; + int ne0; + uint nb0; + uint nb1; + uint nb2; + uint nb3; +} pcs; + +float rope_yarn_ramp(const float low, const float high, const float i0) { + const float y = (i0 / 2 - low) / max(0.001f, high - low); + return 1.0f - min(1.0f, max(0.0f, y)); +} + +// YaRN algorithm based on LlamaYaRNScaledRotaryEmbedding.py from https://github.com/jquesnelle/yarn +// MIT licensed. Copyright (c) 2023 Jeffrey Quesnelle and Bowen Peng. +void rope_yarn( + float theta_extrap, float freq_scale, float corr_dims[2], float i0, float ext_factor, float mscale, + out float cos_theta, out float sin_theta +) { + // Get n-d rotational scaling corrected for extrapolation + float theta_interp = freq_scale * theta_extrap; + float theta = theta_interp; + if (ext_factor != 0.0f) { + float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], i0) * ext_factor; + theta = theta_interp * (1 - ramp_mix) + theta_extrap * ramp_mix; + + // Get n-d magnitude scaling corrected for interpolation + mscale *= 1.0f + 0.1f * log(1.0f / freq_scale); + } + cos_theta = cos(theta) * mscale; + sin_theta = sin(theta) * mscale; +} + +// Apparently solving `n_rot = 2pi * x * base^((2 * max_pos_emb) / n_dims)` for x, we get +// `corr_fac(n_rot) = n_dims * log(max_pos_emb / (n_rot * 2pi)) / (2 * log(base))` +float rope_yarn_corr_factor(int n_dims, int n_orig_ctx, float n_rot, float base) { + return n_dims * log(n_orig_ctx / (n_rot * TWOPI_F)) / (2 * log(base)); +} + +void rope_yarn_corr_dims( + int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, out float dims[2] +) { + // start and end correction dims + dims[0] = max(0.0f, floor(rope_yarn_corr_factor(n_dims, n_orig_ctx, beta_fast, freq_base))); + dims[1] = min(n_dims - 1.0f, ceil(rope_yarn_corr_factor(n_dims, n_orig_ctx, beta_slow, freq_base))); +} diff --git a/llama.cpp b/llama.cpp index 45569f7d3..9631506c6 100644 --- a/llama.cpp +++ b/llama.cpp @@ -15,6 +15,8 @@ # include "ggml-vulkan.h" #elif defined(GGML_USE_SYCL) # include "ggml-sycl.h" +#elif defined(GGML_USE_KOMPUTE) +# include "ggml-kompute.h" #endif #ifdef GGML_USE_METAL @@ -1313,6 +1315,11 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) { buft = ggml_backend_sycl_buffer_type(gpu); #elif defined(GGML_USE_CLBLAST) buft = ggml_backend_opencl_buffer_type(); +#elif defined(GGML_USE_KOMPUTE) + buft = ggml_backend_kompute_buffer_type(gpu); + if (buft == nullptr) { + LLAMA_LOG_WARN("%s: cannot use GPU %d, check `vulkaninfo --summary`\n", __func__, gpu); + } #endif if (buft == nullptr) { @@ -4107,7 +4114,7 @@ static bool llm_load_tensors( } // Returns 0 on success, -1 on error, and -2 on cancellation via llama_progress_callback -static int llama_model_load(const std::string & fname, llama_model & model, const llama_model_params & params) { +static int llama_model_load(const std::string & fname, llama_model & model, llama_model_params & params) { try { llama_model_loader ml(fname, params.use_mmap, params.kv_overrides); @@ -4128,6 +4135,22 @@ static int llama_model_load(const std::string & fname, llama_model & model, cons return 0; } +#ifdef GGML_USE_KOMPUTE + if (ggml_vk_has_device() && params.n_gpu_layers > 0 && ( + !(model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON) + || !( + model.ftype == LLAMA_FTYPE_ALL_F32 || + model.ftype == LLAMA_FTYPE_MOSTLY_F16 || + model.ftype == LLAMA_FTYPE_MOSTLY_Q4_0 || + model.ftype == LLAMA_FTYPE_MOSTLY_Q4_1 + ) + )) { + // disable Vulkan due to unsupported model architecture or quantization type + // TODO(cebtenzzre): propagate this error outside of llama_load_model_from_file + params.n_gpu_layers = 0; + } +#endif + if (!llm_load_tensors( ml, model, params.n_gpu_layers, params.split_mode, params.main_gpu, params.tensor_split, params.use_mlock, params.progress_callback, params.progress_callback_user_data @@ -10259,6 +10282,16 @@ struct llama_context * llama_new_context_with_model( } ctx->backends.push_back(backend); } +#elif defined(GGML_USE_KOMPUTE) + if (model->n_gpu_layers > 0) { + auto * backend = ggml_backend_kompute_init(model->main_gpu); + if (backend == nullptr) { + LLAMA_LOG_ERROR("%s: failed to initialize Kompute backend\n", __func__); + llama_free(ctx); + return nullptr; + } + ctx->backends.push_back(backend); + } #endif ctx->backend_cpu = ggml_backend_cpu_init(); if (ctx->backend_cpu == nullptr) { diff --git a/llama.h b/llama.h index 3e33072c6..01b293e64 100644 --- a/llama.h +++ b/llama.h @@ -49,7 +49,8 @@ #define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN #define LLAMA_SESSION_VERSION 4 -#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || defined(GGML_USE_SYCL) +#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \ + defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) // Defined when llama.cpp is compiled with support for offloading model layers to GPU. #define LLAMA_SUPPORTS_GPU_OFFLOAD #endif diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 015939105..775147d42 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -370,12 +370,15 @@ struct test_case { printf(" %s(%s): ", op_desc(out).c_str(), vars().c_str()); fflush(stdout); - // check if backends support op + // check if the backends support the ops bool supported = true; for (ggml_backend_t backend : {backend1, backend2}) { - if (!ggml_backend_supports_op(backend, out)) { - printf("not supported [%s] ", ggml_backend_name(backend)); - supported = false; + for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { + if (!ggml_backend_supports_op(backend, t)) { + printf("not supported [%s] ", ggml_backend_name(backend)); + supported = false; + break; + } } } if (!supported) { @@ -626,6 +629,13 @@ struct test_unary : public test_case { ggml_tensor * out = ggml_unary(ctx, in, op); return out; } + + void initialize_tensors(ggml_context * ctx) override { + for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { + // test extended range of values to check for NaNs in GELU + init_tensor_uniform(t, -150.f, 150.f); + } + } }; // GGML_OP_GET_ROWS @@ -1066,18 +1076,24 @@ struct test_diag_mask_inf : public test_case { struct test_soft_max : public test_case { const ggml_type type; const std::array ne; + const float scale; + const bool mask; std::string vars() override { - return VARS_TO_STR2(type, ne); + return VARS_TO_STR4(type, ne, scale, mask); } test_soft_max(ggml_type type = GGML_TYPE_F32, - std::array ne = {10, 10, 10, 10}) - : type(type), ne(ne) {} + std::array ne = {10, 10, 10, 10}, + float scale = 1.0f, + bool mask = false) + : type(type), ne(ne), scale(scale), mask(mask) {} ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_tensor * out = ggml_soft_max(ctx, a); + ggml_tensor * b = nullptr; + if (mask) { b = ggml_new_tensor_2d(ctx, type, ne[0], ne[1]); } + ggml_tensor * out = ggml_soft_max_ext(ctx, a, b, scale); return out; } }; @@ -1474,6 +1490,393 @@ struct test_moe : public test_case { } }; + +enum llm_norm_type { + LLM_NORM, + LLM_NORM_RMS, +}; + +struct llama_hparams { + uint32_t n_vocab; + uint32_t n_embd; + uint32_t n_head; + uint32_t n_head_kv; + static constexpr uint32_t n_layer = 1; + uint32_t n_rot; + uint32_t n_embd_head; // dimension of values (d_v) + uint32_t n_ff; + + float f_norm_eps; + float f_norm_rms_eps; + + // cparams + static constexpr uint32_t n_ctx = 512; // user-specified context size + static constexpr uint32_t n_orig_ctx = n_ctx; + + // batch + int32_t n_tokens; + + // llm_build_context + static constexpr int32_t n_kv = 32; // size of KV cache to consider (n_kv <= n_ctx + static constexpr int32_t kv_head = 1; // index of where we store new KV data in the cache + + uint32_t n_embd_gqa() const { // dimension of key embeddings across all k-v heads + return n_embd_head * n_head_kv; + } +}; + +// LLM base class +struct test_llm : public test_case { + llama_hparams hp; + +protected: + test_llm(llama_hparams hp) + : hp(std::move(hp)) { + } + +public: + struct ggml_tensor * llm_build_norm( + struct ggml_context * ctx, + struct ggml_tensor * cur, + struct ggml_tensor * mw, + struct ggml_tensor * mb, + llm_norm_type type) { + switch (type) { + case LLM_NORM: cur = ggml_norm (ctx, cur, hp.f_norm_eps); break; + case LLM_NORM_RMS: cur = ggml_rms_norm(ctx, cur, hp.f_norm_rms_eps); break; + } + cur = ggml_mul(ctx, cur, mw); + if (mb) { + cur = ggml_add(ctx, cur, mb); + } + return cur; + } + + void llm_build_kv_store( + struct ggml_context * ctx, + struct ggml_tensor * k_l, + struct ggml_tensor * v_l, + struct ggml_tensor * k_cur, + struct ggml_tensor * v_cur) { + // compute the transposed [n_tokens, n_embd] V matrix + struct ggml_tensor * v_cur_t = ggml_transpose(ctx, ggml_reshape_2d(ctx, v_cur, hp.n_embd_gqa(), hp.n_tokens)); + + struct ggml_tensor * k_cache_view = ggml_view_1d(ctx, k_l, hp.n_tokens*hp.n_embd_gqa(), + (ggml_row_size(k_l->type, hp.n_embd_gqa()))*hp.kv_head); + + struct ggml_tensor * v_cache_view = ggml_view_2d(ctx, v_l, hp.n_tokens, hp.n_embd_gqa(), + ( hp.n_ctx)*ggml_element_size(v_l), + (hp.kv_head)*ggml_element_size(v_l)); + + // important: storing RoPE-ed version of K in the KV cache! + ggml_cpy(ctx, k_cur, k_cache_view); + ggml_cpy(ctx, v_cur_t, v_cache_view); + } + + // if max_alibi_bias > 0 then apply ALiBi + struct ggml_tensor * llm_build_kqv( + struct ggml_context * ctx, + struct ggml_tensor * k_l, + struct ggml_tensor * v_l, + struct ggml_tensor * q_cur, + struct ggml_tensor * kq_mask, + float kq_scale) { + struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3); + + struct ggml_tensor * k = + ggml_view_3d(ctx, k_l, + hp.n_embd_head, hp.n_kv, hp.n_head_kv, + ggml_row_size(k_l->type, hp.n_embd_gqa()), + ggml_row_size(k_l->type, hp.n_embd_head), + 0); + + struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q); + + kq = ggml_soft_max_ext(ctx, kq, kq_mask, kq_scale); + + // split cached v into n_head heads + struct ggml_tensor * v = + ggml_view_3d(ctx, v_l, + hp.n_kv, hp.n_embd_head, hp.n_head_kv, + ggml_element_size(v_l)*hp.n_ctx, + ggml_element_size(v_l)*hp.n_ctx*hp.n_embd_head, + 0); + + struct ggml_tensor * kqv = ggml_mul_mat(ctx, v, kq); + + struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3); + + struct ggml_tensor * cur = ggml_cont_2d(ctx, kqv_merged, hp.n_embd_head*hp.n_head, hp.n_tokens); + + struct ggml_tensor * wo = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_embd); + cur = ggml_mul_mat(ctx, wo, cur); + + return cur; + } + + void initialize_tensors(ggml_context * ctx) override { + for (ggml_tensor * t = ggml_get_first_tensor(ctx); t != NULL; t = ggml_get_next_tensor(ctx, t)) { + if (t->type == GGML_TYPE_I32) { + // pos + std::vector data(hp.n_tokens); + for (int i = 0; i < hp.n_tokens; i++) { + data[i] = rand() % hp.n_ctx; + } + ggml_backend_tensor_set(t, data.data(), 0, hp.n_tokens * sizeof(int)); + } else { + init_tensor_uniform(t); + } + } + } +}; + + +// Llama +struct test_llama : public test_llm { + static constexpr float freq_base = 10000.0f; + static constexpr float freq_scale = 1.0f; + static constexpr float ext_factor = 0.0f; + static constexpr float attn_factor = 1.0f; + static constexpr float beta_fast = 32.0f; + static constexpr float beta_slow = 1.0f; + + std::string op_desc(ggml_tensor * t) override { + GGML_UNUSED(t); + return "LLAMA"; + } + + std::string vars() override { + auto n_tokens = hp.n_tokens; + return VARS_TO_STR1(n_tokens); + } + + double max_nmse_err() override { + return 2e-3; + } + + test_llama(int n_tokens = 1) + : test_llm({ + /*n_vocab =*/ 32000, + /*n_embd =*/ 3200, + /*n_head =*/ 32, + /*n_head_kv =*/ 32, + /*n_rot =*/ 100, + /*n_embd_head =*/ 100, + /*n_ff =*/ 8640, + /*f_norm_eps =*/ 0.f, + /*f_norm_rms_eps =*/ 1e-5f, + /*n_tokens =*/ n_tokens, + }) { + } + + ggml_tensor * build_graph(ggml_context * ctx) override { + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, hp.n_embd, hp.n_tokens); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, hp.n_tokens); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hp.n_kv, hp.n_tokens, 1); + + ggml_tensor * k_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400); + ggml_tensor * v_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400); + + for (uint32_t il = 0; il < hp.n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + ggml_tensor * attn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd); + cur = llm_build_norm(ctx, inpL, attn_norm, nullptr, LLM_NORM_RMS); + + // self-attention + { + ggml_tensor * wq = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_embd); + ggml_tensor * wk = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_embd_gqa()); + ggml_tensor * wv = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_embd_gqa()); + + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = ggml_mul_mat(ctx, wq, cur); + struct ggml_tensor * Kcur = ggml_mul_mat(ctx, wk, cur); + struct ggml_tensor * Vcur = ggml_mul_mat(ctx, wv, cur); + + Qcur = ggml_rope_custom( + ctx, ggml_reshape_3d(ctx, Qcur, hp.n_embd_head, hp.n_head, hp.n_tokens), inp_pos, + hp.n_rot, 0, 0, hp.n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + + Kcur = ggml_rope_custom( + ctx, ggml_reshape_3d(ctx, Kcur, hp.n_embd_head, hp.n_head_kv, hp.n_tokens), inp_pos, + hp.n_rot, 0, 0, hp.n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + + llm_build_kv_store(ctx, k_l, v_l, Kcur, Vcur); + + cur = llm_build_kqv(ctx, k_l, v_l, Qcur, KQ_mask, 1.0f/sqrtf(float(hp.n_embd_head))); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx, cur, inpSA); + + // feed-forward network + ggml_tensor * ffn_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd); + cur = llm_build_norm(ctx, ffn_inp, ffn_norm, nullptr, LLM_NORM_RMS); + + ggml_tensor * ffn_gate = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_ff); + ggml_tensor * ffn_down = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_ff, hp.n_embd); + ggml_tensor * ffn_up = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_ff); + struct ggml_tensor * tmp = ggml_mul_mat(ctx, ffn_up, cur); + cur = ggml_mul_mat(ctx, ffn_gate, cur); + cur = ggml_silu(ctx, cur); + cur = ggml_mul(ctx, cur, tmp); + cur = ggml_mul_mat(ctx, ffn_down, cur); + + cur = ggml_add(ctx, cur, ffn_inp); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + ggml_tensor * output_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd); + cur = llm_build_norm(ctx, cur, output_norm, nullptr, LLM_NORM_RMS); + + // lm_head + ggml_tensor * output = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_vocab); + cur = ggml_mul_mat(ctx, output, cur); + + return cur; + } +}; + +// Falcon +struct test_falcon : public test_llm { + static constexpr float freq_base = 10000.0f; + static constexpr float freq_scale = 1.0f; + static constexpr float ext_factor = 0.0f; + static constexpr float attn_factor = 1.0f; + static constexpr float beta_fast = 32.0f; + static constexpr float beta_slow = 1.0f; + + std::string op_desc(ggml_tensor * t) override { + GGML_UNUSED(t); + return "FALCON"; + } + + std::string vars() override { + auto n_tokens = hp.n_tokens; + return VARS_TO_STR1(n_tokens); + } + + double max_nmse_err() override { + return 2e-3; + } + + test_falcon(int n_tokens = 1) + : test_llm({ + /*n_vocab =*/ 32000, + /*n_embd =*/ 3200, + /*n_head =*/ 50, + /*n_head_kv =*/ 1, + /*n_rot =*/ 64, + /*n_embd_head =*/ 64, + /*n_ff =*/ 8640, + /*f_norm_eps =*/ 1e-5f, + /*f_norm_rms_eps =*/ 0.f, + /*n_tokens =*/ n_tokens, + }) { + } + + ggml_tensor * build_graph(ggml_context * ctx) override { + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, hp.n_embd, hp.n_tokens); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, hp.n_tokens); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_new_tensor_3d(ctx, GGML_TYPE_F32, hp.n_kv, hp.n_tokens, 1); + + ggml_tensor * k_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400); + ggml_tensor * v_l = ggml_new_tensor_1d(ctx, GGML_TYPE_F16, 1638400); + + for (uint32_t il = 0; il < hp.n_layer; ++il) { + // norm + ggml_tensor * attn_norm_w = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd); + ggml_tensor * attn_norm_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd); + ggml_tensor * attn_norm = llm_build_norm(ctx, inpL, attn_norm_w, attn_norm_b, LLM_NORM); + + // self-attention + { + cur = attn_norm; + + ggml_tensor * wqkv = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_embd + 2*hp.n_embd_gqa()); + + cur = ggml_mul_mat(ctx, wqkv, cur); + + struct ggml_tensor * Qcur = ggml_cont(ctx, ggml_view_2d(ctx, cur, hp.n_embd, hp.n_tokens, cur->nb[1], 0*sizeof(float)*(hp.n_embd))); + struct ggml_tensor * Kcur = ggml_cont(ctx, ggml_view_2d(ctx, cur, hp.n_embd_gqa(), hp.n_tokens, cur->nb[1], 1*sizeof(float)*(hp.n_embd))); + struct ggml_tensor * Vcur = ggml_cont(ctx, ggml_view_2d(ctx, cur, hp.n_embd_gqa(), hp.n_tokens, cur->nb[1], 1*sizeof(float)*(hp.n_embd + hp.n_embd_gqa()))); + + Qcur = ggml_reshape_3d(ctx, Qcur, hp.n_embd_head, hp.n_head, hp.n_tokens); + Kcur = ggml_reshape_3d(ctx, Kcur, hp.n_embd_head, hp.n_head_kv, hp.n_tokens); + + // using mode = 2 for neox mode + Qcur = ggml_rope_custom( + ctx, Qcur, inp_pos, hp.n_rot, 2, 0, hp.n_orig_ctx, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow + ); + + Kcur = ggml_rope_custom( + ctx, Kcur, inp_pos, hp.n_rot, 2, 0, hp.n_orig_ctx, + freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow + ); + + llm_build_kv_store(ctx, k_l, v_l, Kcur, Vcur); + + cur = llm_build_kqv(ctx, k_l, v_l, Qcur, KQ_mask, 1.0f/sqrtf(float(hp.n_embd_head))); + } + + struct ggml_tensor * ffn_inp = cur; + + // feed forward + { + ggml_tensor * ffn_up = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_embd, hp.n_ff); + ggml_tensor * ffn_down = ggml_new_tensor_2d(ctx, GGML_TYPE_Q4_0, hp.n_ff, hp.n_embd); + cur = attn_norm; + cur = ggml_mul_mat(ctx, ffn_up, cur); + cur = ggml_gelu(ctx, cur); + cur = ggml_mul_mat(ctx, ffn_down, cur); + } + + cur = ggml_add(ctx, cur, ffn_inp); + + cur = ggml_add(ctx, cur, inpL); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + ggml_tensor * output_norm = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd); + ggml_tensor * output_norm_b = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, hp.n_embd); + cur = llm_build_norm(ctx, cur, output_norm, output_norm_b, LLM_NORM); + + // lm_head + ggml_tensor * output = ggml_new_tensor_2d(ctx, GGML_TYPE_Q8_0, hp.n_embd, hp.n_vocab); + cur = ggml_mul_mat(ctx, output, cur); + + return cur; + } +}; + static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op_name) { std::vector> test_cases; std::default_random_engine rng(0); @@ -1626,6 +2029,9 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op exponent <<= 1; } + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {16, 2, 32, 1}, 0.1f)); + test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, 0.1f, true)); + for (ggml_type type : {GGML_TYPE_F32, GGML_TYPE_F16}) { test_cases.emplace_back(new test_rope(type, {128, 32, 10, 1}, 128, 0, 512)); // llama 7B test_cases.emplace_back(new test_rope(type, {128, 40, 10, 1}, 128, 0, 512)); // llama 13B @@ -1662,6 +2068,14 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op //test_cases.emplace_back(new test_moe(8, 2, 8, 4096, 14336)); #endif + // these tests are disabled to save execution time, but they can be handy for debugging +#if 0 + test_cases.emplace_back(new test_llama(1)); + test_cases.emplace_back(new test_llama(2)); + test_cases.emplace_back(new test_falcon(1)); + test_cases.emplace_back(new test_falcon(2)); +#endif + // run tests if (mode == MODE_TEST) { ggml_backend_t backend_cpu = ggml_backend_cpu_init(); diff --git a/tests/test-c.c b/tests/test-c.c index a05071080..95ba73df3 100644 --- a/tests/test-c.c +++ b/tests/test-c.c @@ -1,3 +1,7 @@ #include "llama.h" +#ifdef GGML_USE_KOMPUTE +#include "ggml-kompute.h" +#endif + int main(void) {} From 6daa69ee81851ab26ca8aefca1a4202941fc0262 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Mon, 29 Jan 2024 17:11:27 -0500 Subject: [PATCH 054/143] kompute : fix fallback to CPU (#5201) --- llama.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/llama.cpp b/llama.cpp index 9631506c6..796aaa895 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4136,7 +4136,7 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam } #ifdef GGML_USE_KOMPUTE - if (ggml_vk_has_device() && params.n_gpu_layers > 0 && ( + if (params.n_gpu_layers > 0 && ( !(model.arch == LLM_ARCH_LLAMA || model.arch == LLM_ARCH_FALCON) || !( model.ftype == LLAMA_FTYPE_ALL_F32 || @@ -4145,8 +4145,8 @@ static int llama_model_load(const std::string & fname, llama_model & model, llam model.ftype == LLAMA_FTYPE_MOSTLY_Q4_1 ) )) { - // disable Vulkan due to unsupported model architecture or quantization type // TODO(cebtenzzre): propagate this error outside of llama_load_model_from_file + LLAMA_LOG_WARN("%s: disabling Kompute due to unsupported model arch or quantization\n", __func__); params.n_gpu_layers = 0; } #endif From ceebbb5b21b971941b2533210b74bf359981006c Mon Sep 17 00:00:00 2001 From: Paul Tsochantaris Date: Mon, 29 Jan 2024 22:19:29 +0000 Subject: [PATCH 055/143] ggml alloc: Fix for null dereference on alloc failure (#5200) * Fix for a null pointer dereference if a metal GGML buffer fails to be allocated * Freeing the allocated buffers rather than the pointer in ggml-alloc.c * Fixed the fix of the fix --- ggml-alloc.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-alloc.c b/ggml-alloc.c index dfe5ba2e5..f9be6e1cb 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -791,7 +791,7 @@ static bool alloc_tensor_range(struct ggml_context * ctx, for (size_t i = 0; i < *n_buffers; i++) { ggml_backend_buffer_free(*buffers[i]); } - free(buffers); + free(*buffers); return false; } From 6685cc41c237544623b4b5651eceb9c4280728cc Mon Sep 17 00:00:00 2001 From: Wu Jian Ping Date: Tue, 30 Jan 2024 17:11:46 +0800 Subject: [PATCH 056/143] server : improve README (#5209) --- examples/server/README.md | 132 +++++++++++++++++++++++++------------- 1 file changed, 86 insertions(+), 46 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index dce4ec47c..fe934dab1 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -4,34 +4,35 @@ This example demonstrates a simple HTTP API server and a simple web front end to Command line options: -- `--threads N`, `-t N`: Set the number of threads to use during generation. -- `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation. -- `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`). -- `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses. -- `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096. -- `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance. -- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS. -- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS. -- `-b N`, `--batch-size N`: Set the batch size for prompt processing. Default: `512`. -- `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. Not recommended. -- `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped. -- `--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. -- `--numa`: Attempt optimizations that help on some NUMA systems. -- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains. -- `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation. -- `-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`. -- `--path`: path from which to serve static files (default examples/server/public) -- `--api-key`: Set an api key for request authorization. By default the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token. May be used multiple times to enable multiple valid keys. -- `--api-key-file`: path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access. May be used in conjunction with `--api-key`'s. -- `--embedding`: Enable embedding extraction, Default: disabled. -- `-np N`, `--parallel N`: Set the number of slots for process requests (default: 1) -- `-cb`, `--cont-batching`: enable continuous batching (a.k.a dynamic batching) (default: disabled) -- `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load "a system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime) -- `--mmproj MMPROJ_FILE`: Path to a multimodal projector file for LLaVA. -- `--grp-attn-n`: Set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w` -- `--grp-attn-w`: Set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n` +- `--threads N`, `-t N`: Set the number of threads to use during generation. +- `-tb N, --threads-batch N`: Set the number of threads to use during batch and prompt processing. If not specified, the number of threads will be set to the number of threads used for generation. +- `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`). +- `-a ALIAS`, `--alias ALIAS`: Set an alias for the model. The alias will be returned in API responses. +- `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference. The size may differ in other models, for example, baichuan models were build with a context of 4096. +- `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance. +- `-mg i, --main-gpu i`: When using multiple GPUs this option controls which GPU is used for small tensors for which the overhead of splitting the computation across all GPUs is not worthwhile. The GPU in question will use slightly more VRAM to store a scratch buffer for temporary results. By default GPU 0 is used. Requires cuBLAS. +- `-ts SPLIT, --tensor-split SPLIT`: When using multiple GPUs this option controls how large tensors should be split across all GPUs. `SPLIT` is a comma-separated list of non-negative values that assigns the proportion of data that each GPU should get in order. For example, "3,2" will assign 60% of the data to GPU 0 and 40% to GPU 1. By default the data is split in proportion to VRAM but this may not be optimal for performance. Requires cuBLAS. +- `-b N`, `--batch-size N`: Set the batch size for prompt processing. Default: `512`. +- `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. Not recommended. +- `--mlock`: Lock the model in memory, preventing it from being swapped out when memory-mapped. +- `--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. +- `--numa`: Attempt optimizations that help on some NUMA systems. +- `--lora FNAME`: Apply a LoRA (Low-Rank Adaptation) adapter to the model (implies --no-mmap). This allows you to adapt the pretrained model to specific tasks or domains. +- `--lora-base FNAME`: Optional model to use as a base for the layers modified by the LoRA adapter. This flag is used in conjunction with the `--lora` flag, and specifies the base model for the adaptation. +- `-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`. +- `--path`: path from which to serve static files (default examples/server/public) +- `--api-key`: Set an api key for request authorization. By default the server responds to every request. With an api key set, the requests must have the Authorization header set with the api key as Bearer token. May be used multiple times to enable multiple valid keys. +- `--api-key-file`: path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access. May be used in conjunction with `--api-key`'s. +- `--embedding`: Enable embedding extraction, Default: disabled. +- `-np N`, `--parallel N`: Set the number of slots for process requests (default: 1) +- `-cb`, `--cont-batching`: enable continuous batching (a.k.a dynamic batching) (default: disabled) +- `-spf FNAME`, `--system-prompt-file FNAME` Set a file to load "a system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime) +- `--mmproj MMPROJ_FILE`: Path to a multimodal projector file for LLaVA. +- `--grp-attn-n`: Set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w` +- `--grp-attn-w`: Set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n` + ## Build server is build alongside everything else from the root of the project @@ -52,21 +53,23 @@ server is build alongside everything else from the root of the project To get started right away, run the following command, making sure to use the correct path for the model you have: -### Unix-based systems (Linux, macOS, etc.): +### Unix-based systems (Linux, macOS, etc.) ```bash ./server -m models/7B/ggml-model.gguf -c 2048 ``` -### Windows: +### Windows ```powershell server.exe -m models\7B\ggml-model.gguf -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 visit the web front end at the same url. -### Docker: +### Docker + ```bash docker run -p 8080:8080 -v /path/to/models:/models ggerganov/llama.cpp:server -m models/7B/ggml-model.gguf -c 512 --host 0.0.0.0 --port 8080 @@ -120,12 +123,13 @@ node index.js ``` ## API Endpoints -- **GET** `/health`: Returns the current state of the server: - - `{"status": "loading model"}` if the model is still being loaded. - - `{"status": "error"}` if the model failed to load. - - `{"status": "ok"}` if the model is successfully loaded and the server is ready for further requests mentioned below. -- **POST** `/completion`: Given a `prompt`, it returns the predicted completion. +- **GET** `/health`: Returns the current state of the server: + - `{"status": "loading model"}` if the model is still being loaded. + - `{"status": "error"}` if the model failed to load. + - `{"status": "ok"}` if the model is successfully loaded and the server is ready for further requests mentioned below. + +- **POST** `/completion`: Given a `prompt`, it returns the predicted completion. *Options:* @@ -189,14 +193,13 @@ node index.js `system_prompt`: Change the system prompt (initial prompt of all slots), this is useful for chat applications. [See more](#change-system-prompt-on-runtime) -### Result JSON: - -* Note: When using streaming mode (`stream`) only `content` and `stop` will be returned until end of completion. +### Result JSON +- Note: When using streaming mode (`stream`) only `content` and `stop` will be returned until end of completion. - `completion_probabilities`: An array of token probabilities for each completion. The array's length is `n_predict`. Each item in the array has the following structure: -``` +```json { "content": "", "probs": [ @@ -212,6 +215,7 @@ node index.js ] }, ``` + Notice that each `probs` is an array of length `n_probs`. - `content`: Completion result as a string (excluding `stopping_word` if any). In case of streaming mode, will contain the next token as a string. @@ -228,7 +232,7 @@ Notice that each `probs` is an array of length `n_probs`. - `tokens_evaluated`: Number of tokens evaluated in total from the prompt - `truncated`: Boolean indicating if the context size was exceeded during generation, i.e. the number of tokens provided in the prompt (`tokens_evaluated`) plus tokens generated (`tokens predicted`) exceeded the context size (`n_ctx`) -- **POST** `/tokenize`: Tokenize a given text. +- **POST** `/tokenize`: Tokenize a given text. *Options:* @@ -236,13 +240,13 @@ Notice that each `probs` is an array of length `n_probs`. 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. +- **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. +- **POST** `/embedding`: Generate embedding of a given text just as [the embedding example](../embedding) does. *Options:* @@ -250,7 +254,7 @@ Notice that each `probs` is an array of length `n_probs`. `image_data`: An array of objects to hold base64-encoded image `data` and its `id`s to be reference in `content`. You can determine the place of the image in the content as in the following: `Image: [img-21].\nCaption: This is a picture of a house`. In this case, `[img-21]` will be replaced by the embeddings of the image with id `21` in the following `image_data` array: `{..., "image_data": [{"data": "", "id": 21}]}`. Use `image_data` only with multimodal models, e.g., LLaVA. -- **POST** `/infill`: For code infilling. Takes a prefix and a suffix and returns the predicted completion as stream. +- **POST** `/infill`: For code infilling. Takes a prefix and a suffix and returns the predicted completion as stream. *Options:* @@ -260,9 +264,9 @@ Notice that each `probs` is an array of length `n_probs`. It also accepts all the options of `/completion` except `stream` and `prompt`. -- **GET** `/props`: Return the required assistant name and anti-prompt to generate the prompt in case you have specified a system prompt for all slots. +- **GET** `/props`: Return the required assistant name and anti-prompt to generate the prompt in case you have specified a system prompt for all slots. -- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served. +- **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served. *Options:* @@ -290,6 +294,7 @@ Notice that each `probs` is an array of length `n_probs`. print(completion.choices[0].message) ``` + ... or raw HTTP requests: ```shell @@ -311,6 +316,40 @@ Notice that each `probs` is an array of length `n_probs`. }' ``` +- **POST** `/v1/embeddings`: OpenAI-compatible embeddings API. + + *Options:* + + See [OpenAI Embeddings API documentation](https://platform.openai.com/docs/api-reference/embeddings). + + *Examples:* + + - input as string + + ```shell + curl http://localhost:8080/v1/embeddings \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer no-key" \ + -d '{ + "input": "hello", + "model":"GPT-4", + "encoding_format": "float" + }' + ``` + + - `input` as string array + + ```shell + curl http://localhost:8080/v1/embeddings \ + -H "Content-Type: application/json" \ + -H "Authorization: Bearer no-key" \ + -d '{ + "input": ["hello", "world"], + "model":"GPT-4", + "encoding_format": "float" + }' + ``` + ## More examples ### Change system prompt on runtime @@ -362,6 +401,7 @@ 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" ``` From 49f44b5c55d801e3d51ddcf409d866047d718905 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 30 Jan 2024 11:14:44 +0200 Subject: [PATCH 057/143] readme : update hot topics --- README.md | 1 - 1 file changed, 1 deletion(-) diff --git a/README.md b/README.md index ecad95e58..15e61baa3 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,6 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ ### Hot topics -- ⚠️ Incoming backends: https://github.com/ggerganov/llama.cpp/discussions/5138 - New SOTA quantized models, including pure 2-bits: https://huggingface.co/ikawrakow - Collecting Apple Silicon performance stats: - M-series: https://github.com/ggerganov/llama.cpp/discussions/4167 From 5589921ef84a4fb1c6d1c9c34d626a5a83033db6 Mon Sep 17 00:00:00 2001 From: Romain Neutron Date: Tue, 30 Jan 2024 10:16:38 +0100 Subject: [PATCH 058/143] readme : minor (#5204) This is about tuning the code formatting of the README file --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 15e61baa3..b37348a74 100644 --- a/README.md +++ b/README.md @@ -290,7 +290,7 @@ In order to build llama.cpp you have three different options. sudo pkg install gmake automake autoconf pkgconf llvm15 clinfo clover \ opencl clblast openblas - gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4 + gmake CC=/usr/local/bin/clang15 CXX=/usr/local/bin/clang++15 -j4 ``` **Notes:** With this packages you can build llama.cpp with OPENBLAS and @@ -613,9 +613,9 @@ Building the program with BLAS support may lead to some performance improvements # obtain the original LLaMA model weights and place them in ./models ls ./models 65B 30B 13B 7B tokenizer_checklist.chk tokenizer.model - # [Optional] for models using BPE tokenizers - ls ./models - 65B 30B 13B 7B vocab.json +# [Optional] for models using BPE tokenizers +ls ./models +65B 30B 13B 7B vocab.json # install Python dependencies python3 -m pip install -r requirements.txt @@ -623,8 +623,8 @@ python3 -m pip install -r requirements.txt # convert the 7B model to ggml FP16 format python3 convert.py models/7B/ - # [Optional] for models using BPE tokenizers - python convert.py models/7B/ --vocabtype bpe +# [Optional] for models using BPE tokenizers +python convert.py models/7B/ --vocabtype bpe # quantize the model to 4-bits (using q4_0 method) ./quantize ./models/7B/ggml-model-f16.gguf ./models/7B/ggml-model-q4_0.gguf q4_0 From 813416991ab0d1caa0d12f93ac4e8a24a2add0a3 Mon Sep 17 00:00:00 2001 From: divinity76 Date: Tue, 30 Jan 2024 10:18:02 +0100 Subject: [PATCH 059/143] main : allow empty --prompt-cache file (#5176) * allow empty --prompt-cache file This allows the use of std::tmpnam(), std::tmpfile(), Python's tempfile.NamedTemporaryFile(), and similar create-empty-file API's for the user. I switched from the C fopen API to the C++ filesystem api to get around the fact that, to the best of my knowledge, C has no portable way to get the file size above LONG_MAX, with std::ftell() returning long? fallback to std::ifstream for c++ < 17 (the project is currently targeting C++11 it seems - file_exists() and file_size() can be removed when we upgrade to c++17) * formatting (requested in codereview) * remove c++17, file_is_empty --- examples/main/main.cpp | 28 ++++++++++++++++++---------- 1 file changed, 18 insertions(+), 10 deletions(-) diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 58b7f807a..1c6138d23 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -39,6 +39,17 @@ static std::ostringstream * g_output_ss; static std::vector * g_output_tokens; static bool is_interacting = false; +static bool file_exists(const std::string &path) { + std::ifstream f(path.c_str()); + return f.good(); +} + +static bool file_is_empty(const std::string &path) { + std::ifstream f; + f.exceptions(std::ifstream::failbit | std::ifstream::badbit); + f.open(path.c_str(), std::ios::in | std::ios::binary | std::ios::ate); + return f.tellg() == 0; +} static void write_logfile( const llama_context * ctx, const gpt_params & params, const llama_model * model, @@ -215,12 +226,12 @@ int main(int argc, char ** argv) { if (!path_session.empty()) { 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"); - if (fp != NULL) { - std::fclose(fp); - + if (!file_exists(path_session)) { + LOG_TEE("%s: session file does not exist, will create.\n", __func__); + } else if (file_is_empty(path_session)) { + LOG_TEE("%s: The session file is empty. A new session will be initialized.\n", __func__); + } else { + // The file exists and is not empty session_tokens.resize(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)) { @@ -229,10 +240,7 @@ int main(int argc, char ** argv) { } session_tokens.resize(n_token_count_out); llama_set_rng_seed(ctx, params.seed); - - LOG_TEE("%s: loaded a session with prompt size of %d tokens\n", __func__, (int) session_tokens.size()); - } else { - LOG_TEE("%s: session file does not exist, will create\n", __func__); + LOG_TEE("%s: loaded a session with prompt size of %d tokens\n", __func__, (int)session_tokens.size()); } } From 7359016c7c0f65dc30cf79791212b06f15866450 Mon Sep 17 00:00:00 2001 From: Vladimir Malyutin Date: Tue, 30 Jan 2024 17:57:07 +0700 Subject: [PATCH 060/143] quantize : fix typo (#5211) Fix misprint in quantize help --- examples/quantize/quantize.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index f4786157e..0236f2180 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -36,7 +36,7 @@ static const std::vector QUANT_OPTIONS = { { "Q5_K", LLAMA_FTYPE_MOSTLY_Q5_K_M, "alias for Q5_K_M", }, { "Q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S, " 4.33G, +0.0400 ppl @ LLaMA-v1-7B", }, { "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 4.45G, +0.0122 ppl @ LLaMA-v1-7B", }, - { "Q6_K", LLAMA_FTYPE_MOSTLY_Q6_K, " 5.15G, -0.0008 ppl @ LLaMA-v1-7B", }, + { "Q6_K", LLAMA_FTYPE_MOSTLY_Q6_K, " 5.15G, +0.0008 ppl @ LLaMA-v1-7B", }, { "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", }, From 2256f36b79a932a478d4dcdf02c1e5a60056e5f3 Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Tue, 30 Jan 2024 13:59:30 +0100 Subject: [PATCH 061/143] Vulkan Windows APU Memory Handling (#5199) * Add basic UMA memory handling Improve memory OOM behavior Fix tests * Fix UMA handling * Also fix UMA handling for prealloc buffers * Remove unnecessary warning message * Remove outdated comment --- ggml-vulkan.cpp | 425 ++++++++++++++++++++++++++++-------------------- 1 file changed, 252 insertions(+), 173 deletions(-) diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 2c2b38fc3..1d93ec6bb 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -116,7 +116,7 @@ struct vk_device { vk_queue transfer_queue; uint32_t descriptor_set_mode; uint32_t subgroup_size; - bool is_igpu; + bool uma; }; struct vk_op_push_constants { @@ -675,7 +675,7 @@ static vk_buffer ggml_vk_create_buffer(size_t size, vk::MemoryPropertyFlags req_ vk::PhysicalDeviceMemoryProperties mem_props = vk_device.physical_device.getMemoryProperties(); - uint32_t memory_type_index = uint32_t(~0); + uint32_t memory_type_index = UINT32_MAX; for (uint32_t i = 0; i < mem_props.memoryTypeCount; ++i) { vk::MemoryType memory_type = mem_props.memoryTypes[i]; @@ -685,7 +685,18 @@ static vk_buffer ggml_vk_create_buffer(size_t size, vk::MemoryPropertyFlags req_ } } - buf.device_memory = vk_device.device.allocateMemory({ mem_req.size, memory_type_index }); + if (memory_type_index >= mem_props.memoryTypeCount) { + throw vk::OutOfDeviceMemoryError("No suitable memory type found"); + } + + try { + buf.device_memory = vk_device.device.allocateMemory({ mem_req.size, memory_type_index }); + } catch (const vk::SystemError& e) { + // Out of Host/Device memory, clean up buffer + vk_device.device.destroyBuffer(buf.buffer); + buf.size = 0; + throw e; + } buf.memory_property_flags = req_flags; buf.ptr = nullptr; @@ -700,6 +711,47 @@ static vk_buffer ggml_vk_create_buffer(size_t size, vk::MemoryPropertyFlags req_ return buf; } +static vk_buffer ggml_vk_create_buffer_check(size_t size, vk::MemoryPropertyFlags req_flags) { + try { + return ggml_vk_create_buffer(size, req_flags); + } catch (const vk::SystemError& e) { + std::cerr << "ggml_vulkan: Memory allocation of size " << size << " failed." << std::endl; + std::cerr << "ggml_vulkan: " << e.what() << std::endl; + throw e; + } +} + +static vk_buffer ggml_vk_create_buffer_device(size_t size) { + vk_buffer buf; + try { + buf = ggml_vk_create_buffer(size, vk::MemoryPropertyFlagBits::eDeviceLocal); + } catch (const vk::SystemError& e) { + if (vk_device.uma) { + // Fall back to host memory type + buf = ggml_vk_create_buffer_check(size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); + } else { + std::cerr << "ggml_vulkan: Device memory allocation of size " << size << " failed." << std::endl; + std::cerr << "ggml_vulkan: " << e.what() << std::endl; + throw e; + } + } + + return buf; +} + +static void ggml_vk_destroy_buffer(vk_buffer& buf) { + if (buf.size == 0) { + return; + } +#ifdef VK_DEBUG + std::cerr << "ggml_vk_destroy_buffer(" << buf.size << ")" << std::endl; +#endif + + buf.size = 0; + vk_device.device.freeMemory(buf.device_memory); + vk_device.device.destroyBuffer(buf.buffer); +} + static vk_subbuffer ggml_vk_subbuffer(vk_buffer& buf) { return { buf, 0, VK_WHOLE_SIZE }; } @@ -738,19 +790,6 @@ static void ggml_vk_wait_events(vk::CommandBuffer& cmd_buffer, std::vector(vk_pinned_memory[i]); + const uint8_t* endr = addr + std::get<1>(vk_pinned_memory[i]); + if (ptr >= addr && ptr < endr) { + buf = &std::get<2>(vk_pinned_memory[i]); + buf_offset = ((const uint8_t *)ptr) - addr; + break; + } + } +} + static vk_submission ggml_vk_begin_submission(vk_queue& q, bool one_time = true) { vk_submission s; s.buffer = ggml_vk_create_cmd_buffer(q); @@ -1384,6 +1433,13 @@ static void deferred_memcpy(void * dst, const void * src, size_t size, std::vect } } +static void ensure_sync_staging_buffer(size_t size) { + if (vk_sync_staging.size < size) { + ggml_vk_destroy_buffer(vk_sync_staging); + vk_sync_staging = ggml_vk_create_buffer_check(size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); + } +} + static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size_t offset, const ggml_tensor * tensor, bool sync_staging = false) { #ifdef VK_DEBUG std::cerr << "ggml_vk_buffer_write_nc_async(" << tensor << ")" << std::endl; @@ -1391,21 +1447,13 @@ static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size GGML_ASSERT(!ggml_is_contiguous(tensor)); // Buffer is already mapped if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { - std::cerr << "ggml_vulkan: buffer_write_async dst buffer is host_visible. Use synchronous write." << std::endl; + std::cerr << "ggml_vulkan: buffer_write_nc_async dst buffer is host_visible. Use synchronous write." << std::endl; GGML_ASSERT(false); } // Check if src is pinned memory - vk_buffer* buf = nullptr; - size_t buf_offset = 0; - for (size_t i = 0; i < vk_pinned_memory.size(); i++) { - const uint8_t* addr = (const uint8_t*) std::get<0>(vk_pinned_memory[i]); - const uint8_t* endr = addr + std::get<1>(vk_pinned_memory[i]); - if (tensor->data >= addr && tensor->data < endr) { - buf = &std::get<2>(vk_pinned_memory[i]); - buf_offset = ((const uint8_t *)tensor->data) - addr; - break; - } - } + vk_buffer * buf = nullptr; + size_t buf_offset; + ggml_vk_host_get(tensor->data, buf, buf_offset); const uint64_t ne0 = tensor->ne[0]; const uint64_t ne1 = tensor->ne[1]; @@ -1463,10 +1511,7 @@ static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size if (vk_staging.size < vk_staging_offset + copy_size) { if (sync_staging) { // Create temporary larger buffer - if (vk_sync_staging.size < copy_size) { - ggml_vk_destroy_buffer(vk_sync_staging); - vk_sync_staging = ggml_vk_create_buffer(copy_size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); - } + ensure_sync_staging_buffer(copy_size); staging = &vk_sync_staging; staging_offset = 0; @@ -1512,17 +1557,9 @@ static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size GGML_ASSERT(false); } // Check if src is pinned memory - vk_buffer* buf = nullptr; - size_t buf_offset = 0; - for (size_t i = 0; i < vk_pinned_memory.size(); i++) { - const uint8_t* addr = (const uint8_t*) std::get<0>(vk_pinned_memory[i]); - const uint8_t* endr = addr + std::get<1>(vk_pinned_memory[i]); - if (src >= addr && src < endr) { - buf = &std::get<2>(vk_pinned_memory[i]); - buf_offset = ((const uint8_t *)src) - addr; - break; - } - } + vk_buffer * buf = nullptr; + size_t buf_offset; + ggml_vk_host_get(src, buf, buf_offset); if (buf != nullptr) { // Memory is pinned, use as staging buffer @@ -1555,10 +1592,7 @@ static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size const size_t copy_size = width*height; if (vk_staging.size < vk_staging_offset + copy_size) { if (sync_staging) { - if (vk_sync_staging.size < copy_size) { - ggml_vk_destroy_buffer(vk_sync_staging); - vk_sync_staging = ggml_vk_create_buffer(copy_size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); - } + ensure_sync_staging_buffer(copy_size); staging = &vk_sync_staging; staging_offset = 0; @@ -1633,17 +1667,9 @@ static void ggml_vk_buffer_read_2d_async(vk_context * ctx, vk_buffer* src, size_ GGML_ASSERT(height > 0); GGML_ASSERT(src->size > 0); // Check if dst is pinned memory - vk_buffer* buf = nullptr; - size_t buf_offset = 0; - for (size_t i = 0; i < vk_pinned_memory.size(); i++) { - const uint8_t* addr = (const uint8_t*) std::get<0>(vk_pinned_memory[i]); - const uint8_t* endr = addr + std::get<1>(vk_pinned_memory[i]); - if (dst >= addr && dst < endr) { - buf = &std::get<2>(vk_pinned_memory[i]); - buf_offset = ((const uint8_t *)dst) - addr; - break; - } - } + vk_buffer * buf = nullptr; + size_t buf_offset; + ggml_vk_host_get(dst, buf, buf_offset); std::vector slices(1); if (width == spitch && width == dpitch) { @@ -1677,10 +1703,7 @@ static void ggml_vk_buffer_read_2d_async(vk_context * ctx, vk_buffer* src, size_ if (vk_staging.size < vk_staging_offset + copy_size) { if (sync_staging) { // Create temporary larger buffer - if (vk_sync_staging.size < copy_size) { - ggml_vk_destroy_buffer(vk_sync_staging); - vk_sync_staging = ggml_vk_create_buffer(copy_size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); - } + ensure_sync_staging_buffer(copy_size); staging = &vk_sync_staging; } else { @@ -1819,7 +1842,7 @@ static void ggml_vk_d2h_tensor_2d(vk_context * ctx, vk_buffer * src, size_t offs static uint32_t ggml_vk_guess_split_k(int m, int n, int k) { #ifdef VK_DEBUG - std::cerr << "ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ", " << aligned << ")"; + std::cerr << "ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ")"; #endif if (k > 128 && (m < 128 || n < 128) && m > 2 && n > 2) { #ifdef VK_DEBUG @@ -2003,8 +2026,27 @@ static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, co const uint64_t r2 = ne12 / ne02; const uint64_t r3 = ne13 / ne03; - const bool load_x = src0->backend != GGML_BACKEND_GPU; - const bool load_y = src1->backend != GGML_BACKEND_GPU; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; + ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; + ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; + + vk_buffer * d_Qx = nullptr; + size_t qx_buf_offset = 0; + vk_buffer * d_Qy = nullptr; + size_t qy_buf_offset = 0; + + bool src0_uma = false; + bool src1_uma = false; + + if (vk_device.uma) { + ggml_vk_host_get(src0->data, d_Qx, qx_buf_offset); + ggml_vk_host_get(src1->data, d_Qy, qy_buf_offset); + src0_uma = d_Qx != nullptr; + src1_uma = d_Qy != nullptr; + } + + const bool load_x = src0->backend != GGML_BACKEND_GPU && !src0_uma; + const bool load_y = src1->backend != GGML_BACKEND_GPU && !src1_uma; const bool x_non_contig = !load_x && !ggml_vk_dim01_contiguous(src0); const bool y_non_contig = !load_y && !ggml_vk_dim01_contiguous(src1); @@ -2034,32 +2076,24 @@ static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, co const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; const uint64_t d_sz = sizeof(float) * d_ne; - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; - ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; - ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; - vk_buffer* d_D = &extra->buffer_gpu; const uint64_t d_buf_offset = extra->offset; GGML_ASSERT(d_D != nullptr); GGML_ASSERT(d_D->size >= d_buf_offset + d_sz * ne02 * ne03); - vk_buffer * d_Qx; - uint64_t qx_buf_offset = 0; - vk_buffer * d_Qy; - uint64_t qy_buf_offset = 0; vk_buffer* d_X; uint64_t x_buf_offset = 0; vk_buffer* d_Y; uint64_t y_buf_offset = 0; if (load_x) { d_Qx = &vk_prealloc_qx; - } else { + } else if (!src0_uma) { d_Qx = &extra_src0->buffer_gpu; qx_buf_offset = extra_src0->offset; GGML_ASSERT(d_Qx != nullptr); } if (load_y) { d_Qy = &vk_prealloc_qy; - } else { + } else if (!src1_uma) { d_Qy = &extra_src1->buffer_gpu; qy_buf_offset = extra_src1->offset; GGML_ASSERT(d_Qy != nullptr); @@ -2178,8 +2212,27 @@ static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0 const uint64_t r2 = ne12 / ne02; const uint64_t r3 = ne13 / ne03; - const bool load_x = src0->backend != GGML_BACKEND_GPU; - const bool load_y = src1->backend != GGML_BACKEND_GPU; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; + ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; + ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; + + vk_buffer * d_Qx = nullptr; + size_t qx_buf_offset = 0; + vk_buffer * d_Qy = nullptr; + size_t qy_buf_offset = 0; + + bool src0_uma = false; + bool src1_uma = false; + + if (vk_device.uma) { + ggml_vk_host_get(src0->data, d_Qx, qx_buf_offset); + ggml_vk_host_get(src1->data, d_Qy, qy_buf_offset); + src0_uma = d_Qx != nullptr; + src1_uma = d_Qy != nullptr; + } + + const bool load_x = src0->backend != GGML_BACKEND_GPU && !src0_uma; + const bool load_y = src1->backend != GGML_BACKEND_GPU && !src1_uma; const bool x_non_contig = !load_x && !ggml_vk_dim01_contiguous(src0); const bool y_non_contig = !load_y && !ggml_vk_dim01_contiguous(src1); @@ -2199,31 +2252,23 @@ static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0 const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; const uint64_t d_sz = sizeof(float) * d_ne; - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; - ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; - ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; - vk_buffer* d_D = &extra->buffer_gpu; const uint64_t d_buf_offset = extra->offset; GGML_ASSERT(d_D != nullptr); - vk_buffer* d_Qx; - uint32_t qx_buf_offset = 0; - vk_buffer* d_Qy; - uint32_t qy_buf_offset = 0; vk_buffer* d_X; uint64_t x_buf_offset = 0; vk_buffer* d_Y; uint64_t y_buf_offset = 0; if (load_x) { d_Qx = &vk_prealloc_qx; - } else { + } else if(!src1_uma) { d_Qx = &extra_src0->buffer_gpu; qx_buf_offset = extra_src0->offset; GGML_ASSERT(d_Qx != nullptr); } if (load_y) { d_Qy = &vk_prealloc_qy; - } else { + } else if(!src1_uma) { d_Qy = &extra_src1->buffer_gpu; qy_buf_offset = extra_src1->offset; GGML_ASSERT(d_Qy != nullptr); @@ -2345,7 +2390,21 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(vk_context * ctx, const ggml_tensor GGML_ASSERT(ne11 == 1); - const bool load_y = src1->backend != GGML_BACKEND_GPU; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; + ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; + ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; + + vk_buffer * d_Qy = nullptr; + size_t qy_buf_offset = 0; + + bool src1_uma = false; + + if (vk_device.uma) { + ggml_vk_host_get(src1->data, d_Qy, qy_buf_offset); + src1_uma = d_Qy != nullptr; + } + + const bool load_y = src1->backend != GGML_BACKEND_GPU && !src1_uma; const uint64_t x_ne = ne00 * ne01 * ne02; const uint64_t y_ne = ne10 * ne11 * ne12; @@ -2355,22 +2414,15 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(vk_context * ctx, const ggml_tensor const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); const uint64_t d_sz = sizeof(float) * d_ne; - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; - ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; - ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; - vk_buffer* d_D = &extra->buffer_gpu; const uint64_t d_buf_offset = extra->offset; GGML_ASSERT(d_D != nullptr); - vk_buffer* d_Qx; + vk_buffer* d_Qx = &extra_src0->buffer_gpu; const uint64_t qx_buf_offset = extra_src0->offset; - vk_buffer* d_Qy; - uint64_t qy_buf_offset = 0; - d_Qx = &extra_src0->buffer_gpu; GGML_ASSERT(d_Qx != nullptr); if (load_y) { d_Qy = &vk_prealloc_qy; - } else { + } else if (!src1_uma) { d_Qy = &extra_src1->buffer_gpu; qy_buf_offset = extra_src1->offset; GGML_ASSERT(d_Qx != nullptr); @@ -2430,7 +2482,21 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(vk_context * ctx, const ggml_tensor * GGML_ASSERT(ne11 == 1); - const bool load_y = src1->backend != GGML_BACKEND_GPU; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; + ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; + ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; + + vk_buffer * d_Qy = nullptr; + size_t qy_buf_offset = 0; + + bool src1_uma = false; + + if (vk_device.uma) { + ggml_vk_host_get(src1->data, d_Qy, qy_buf_offset); + src1_uma = d_Qy != nullptr; + } + + const bool load_y = src1->backend != GGML_BACKEND_GPU && !src1_uma; const uint64_t d_ne = ne01 * ne11 * ne12; @@ -2441,18 +2507,11 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(vk_context * ctx, const ggml_tensor * const uint64_t qy_sz = ggml_nbytes(src1); const uint64_t d_sz = sizeof(float) * d_ne; - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; - ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; - ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; - vk_buffer* d_D = &extra->buffer_gpu; const uint64_t d_buf_offset = extra->offset; GGML_ASSERT(d_D != nullptr); - vk_buffer* d_Qx; + vk_buffer* d_Qx = &extra_src0->buffer_gpu; const uint64_t qx_buf_offset = extra_src0->offset; - vk_buffer* d_Qy; - uint64_t qy_buf_offset = 0; - d_Qx = &extra_src0->buffer_gpu; GGML_ASSERT(d_Qx != nullptr); if (load_y) { d_Qy = &vk_prealloc_qy; @@ -2709,7 +2768,8 @@ static ggml_vk_func_t ggml_vk_op_get_func(ggml_op op) { } #ifdef GGML_VULKAN_CHECK_RESULTS -void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name); +static void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name); +static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * tensor); #endif template @@ -2758,17 +2818,34 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm return; } - const bool transfer_src0 = src0->backend != GGML_BACKEND_GPU; - const bool transfer_src1 = use_src1 && src1->backend != GGML_BACKEND_GPU; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; + ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; + ggml_tensor_extra_gpu * extra_src1 = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr; + + vk_buffer * d_X = nullptr; + size_t x_buf_offset = 0; + vk_buffer * d_Y = nullptr; + size_t y_buf_offset = 0; + + bool src0_uma = false; + bool src1_uma = false; + + if (vk_device.uma) { + ggml_vk_host_get(src0->data, d_X, x_buf_offset); + src0_uma = d_X != nullptr; + if (use_src1) { + ggml_vk_host_get(src1->data, d_Y, y_buf_offset); + src1_uma = d_Y != nullptr; + } + } + + const bool transfer_src0 = src0->backend != GGML_BACKEND_GPU && !src0_uma; + const bool transfer_src1 = use_src1 && src1->backend != GGML_BACKEND_GPU && !src1_uma; uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type) * ne0, vk_device.properties.limits.minStorageBufferOffsetAlignment); uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, vk_device.properties.limits.minStorageBufferOffsetAlignment) : 0; uint64_t d_sz = ggml_type_size(dst->type) * ne0; - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; - ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; - ggml_tensor_extra_gpu * extra_src1 = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr; - // Workaround for tiny tensor inputs on ROPE if (use_src1 && src1->backend == GGML_BACKEND_GPU && y_sz > extra_src1->buffer_gpu.size) { y_sz = VK_WHOLE_SIZE; @@ -2778,20 +2855,16 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm GGML_ASSERT(d_D != nullptr); uint64_t d_buf_offset = (extra->offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; GGML_ASSERT(d_buf_offset == extra->offset || op == GGML_OP_CPY); // NOLINT - vk_buffer* d_X = nullptr; - uint64_t x_buf_offset = 0; - vk_buffer* d_Y = nullptr; - uint64_t y_buf_offset = 0; if (transfer_src0) { d_X = &vk_prealloc_qx; - } else { + } else if(!src0_uma) { d_X = &extra_src0->buffer_gpu; x_buf_offset = extra_src0->offset; GGML_ASSERT(d_X != nullptr); } if (transfer_src1) { d_Y = &vk_prealloc_qy; - } else if (use_src1) { + } else if (use_src1 && !src1_uma) { d_Y = &extra_src1->buffer_gpu; y_buf_offset = extra_src1->offset; GGML_ASSERT(d_Y != nullptr); @@ -3148,13 +3221,13 @@ static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size if (vk_prealloc_split_k.size > 0) { ggml_vk_destroy_buffer(vk_prealloc_split_k); } - vk_prealloc_split_k = ggml_vk_create_buffer(sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_prealloc_split_k = ggml_vk_create_buffer_check(sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal); } } - vk_buffer d_X = ggml_vk_create_buffer(sizeof(X_TYPE) * x_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); - vk_buffer d_Y = ggml_vk_create_buffer(sizeof(Y_TYPE) * y_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); - vk_buffer d_D = ggml_vk_create_buffer(sizeof(float) * d_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer d_X = ggml_vk_create_buffer_check(sizeof(X_TYPE) * x_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer d_Y = ggml_vk_create_buffer_check(sizeof(Y_TYPE) * y_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer d_D = ggml_vk_create_buffer_check(sizeof(float) * d_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); X_TYPE* x = (X_TYPE *) malloc(sizeof(X_TYPE) * x_ne); Y_TYPE* y = (Y_TYPE *) malloc(sizeof(Y_TYPE) * y_ne); @@ -3315,6 +3388,10 @@ static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, int i0, int i1 if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) { return; } + i0 = std::max(i0, 5); + i1 = std::max(i1, 5); + i2 = std::max(i2, 0); + i3 = std::max(i3, 0); fprintf(stderr, " "); for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) { fprintf(stderr, "%7d ", idx1); @@ -3376,7 +3453,7 @@ static void ggml_vk_test_h2d_nc(size_t ne0, size_t ne1, size_t ne2, size_t ne3) vk_context * ctx = ggml_vk_create_context(vk_device.compute_queue); ggml_vk_ctx_begin(ctx); - vk_buffer buffer = ggml_vk_create_buffer(ggml_nbytes(tensor), vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer buffer = ggml_vk_create_buffer_check(ggml_nbytes(tensor), vk::MemoryPropertyFlagBits::eDeviceLocal); ggml_vk_h2d_tensor_2d(ctx, &buffer, 0, tensor, 0, 0, ggml_nrows(tensor)); @@ -3439,7 +3516,7 @@ static void ggml_vk_test_transfer(size_t ne, bool pinned) { std::cerr << "ggml_vk_test_transfer(" << ne << ")" << std::endl; #endif // Check transfers are correct - vk_buffer buffer = ggml_vk_create_buffer(sizeof(float) * ne, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer buffer = ggml_vk_create_buffer_check(sizeof(float) * ne, vk::MemoryPropertyFlagBits::eDeviceLocal); float * x; float * y; @@ -3666,7 +3743,7 @@ void ggml_vk_preallocate_buffers() { std::cerr << "qx_size: " << vk_prealloc_size_qx << " qy_size: " << vk_prealloc_size_qy << " x_size: " << vk_prealloc_size_x << " y_size: " << vk_prealloc_size_y << " split_k_size: " << vk_prealloc_size_split_k << std::endl; #endif #if defined(VK_RUN_TESTS) - vk_staging = ggml_vk_create_buffer(100ul * 1024ul * 1024ul, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); + vk_staging = ggml_vk_create_buffer_check(100ul * 1024ul * 1024ul, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); ggml_vk_test_transfer(8192 * 1000, false); ggml_vk_test_transfer(8192 * 1000, true); @@ -3712,42 +3789,42 @@ void ggml_vk_preallocate_buffers() { if (vk_prealloc_qx.size > 0) { ggml_vk_destroy_buffer(vk_prealloc_qx); } - vk_prealloc_qx = ggml_vk_create_buffer(vk_prealloc_size_qx, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_prealloc_qx = ggml_vk_create_buffer_device(vk_prealloc_size_qx); } if (vk_prealloc_size_qy > 0 && vk_prealloc_qy.size < vk_prealloc_size_qy) { // Resize buffer if (vk_prealloc_qy.size > 0) { ggml_vk_destroy_buffer(vk_prealloc_qy); } - vk_prealloc_qy = ggml_vk_create_buffer(vk_prealloc_size_qy, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_prealloc_qy = ggml_vk_create_buffer_device(vk_prealloc_size_qy); } if (vk_prealloc_size_x > 0 && vk_prealloc_x.size < vk_prealloc_size_x) { // Resize buffer if (vk_prealloc_x.size > 0) { ggml_vk_destroy_buffer(vk_prealloc_x); } - vk_prealloc_x = ggml_vk_create_buffer(vk_prealloc_size_x, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_prealloc_x = ggml_vk_create_buffer_device(vk_prealloc_size_x); } if (vk_prealloc_size_y > 0 && vk_prealloc_y.size < vk_prealloc_size_y) { // Resize buffer if (vk_prealloc_y.size > 0) { ggml_vk_destroy_buffer(vk_prealloc_y); } - vk_prealloc_y = ggml_vk_create_buffer(vk_prealloc_size_y, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_prealloc_y = ggml_vk_create_buffer_device(vk_prealloc_size_y); } if (vk_prealloc_size_split_k > 0 && vk_prealloc_split_k.size < vk_prealloc_size_split_k) { // Resize buffer if (vk_prealloc_split_k.size > 0) { ggml_vk_destroy_buffer(vk_prealloc_split_k); } - vk_prealloc_split_k = ggml_vk_create_buffer(vk_prealloc_size_split_k, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_prealloc_split_k = ggml_vk_create_buffer_device(vk_prealloc_size_split_k); } if (vk_staging_size > 0 && vk_staging.size < vk_staging_size) { // Resize buffer if (vk_staging.size > 0) { ggml_vk_destroy_buffer(vk_staging); } - vk_staging = ggml_vk_create_buffer(vk_staging_size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); + vk_staging = ggml_vk_create_buffer_check(vk_staging_size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); } } @@ -4138,6 +4215,7 @@ GGML_CALL static bool ggml_backend_buffer_is_vk(ggml_backend_buffer_t buffer) { GGML_CALL static void ggml_backend_vk_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; + ggml_vk_destroy_buffer(ctx->dev_buffer); delete ctx; } @@ -4163,14 +4241,6 @@ GGML_CALL static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t b extra->offset = (uint8_t *) tensor->data - (uint8_t *) vk_ptr_base; } - if (extra->offset + ggml_nbytes(tensor) > extra->buffer_gpu.size) { - std::cerr << "ERROR: Trying to assign tensor " << tensor << " outside of buffer size " << ctx->dev_buffer.size << " requested offset: " << extra->offset << " tensor size: " << ggml_nbytes(tensor) << std::endl; - if (tensor->view_src != nullptr) { - std::cerr << "view_src: " << tensor->view_src << " extra: " << tensor->view_src->extra << std::endl; - } - GGML_ASSERT(false); - } - tensor->backend = GGML_BACKEND_GPU; tensor->extra = extra; } @@ -4248,7 +4318,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer( #ifdef VK_DEBUG std::cerr << "ggml_backend_vk_buffer_type_alloc_buffer(" << size << ")" << std::endl; #endif - vk_buffer dev_buffer = ggml_vk_create_buffer(size, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer dev_buffer = ggml_vk_create_buffer_device(size); ggml_backend_vk_buffer_context * ctx = new ggml_backend_vk_buffer_context(dev_buffer); @@ -4326,9 +4396,12 @@ GGML_CALL static void ggml_backend_vk_host_buffer_free_buffer(ggml_backend_buffe } GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { - void * ptr = ggml_vk_host_malloc(size); - - if (ptr == nullptr) { + void * ptr = nullptr; + try { + ptr = ggml_vk_host_malloc(size); + } catch (vk::SystemError& e) { + std::cerr << "ggml_vulkan: Failed to allocate pinned memory." << std::endl; + std::cerr << "ggml_vulkan: " << e.what() << std::endl; // fallback to cpu buffer return ggml_backend_buft_alloc_buffer(ggml_backend_cpu_buffer_type(), size); } @@ -4389,7 +4462,7 @@ GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, g #ifdef VK_DEBUG std::cerr << "ggml_backend_vk_set_tensor_async(" << size << ")" << std::endl; #endif - GGML_ASSERT(tensor->buffer->buft == ggml_backend_vk_buffer_type() && "unsupported buffer type"); + GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type() || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; @@ -4409,7 +4482,7 @@ GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, c #ifdef VK_DEBUG std::cerr << "ggml_backend_vk_get_tensor_async(" << size << ")" << std::endl; #endif - GGML_ASSERT(tensor->buffer->buft == ggml_backend_vk_buffer_type() && "unsupported buffer type"); + GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type() || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; @@ -4429,7 +4502,7 @@ GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, c #ifdef VK_DEBUG std::cerr << "ggml_backend_vk_cpy_tensor_async()" << std::endl; #endif - if (dst->buffer->buft == ggml_backend_vk_buffer_type() && ggml_backend_buffer_is_vk(src->buffer)) { + if ((dst->buffer->buft == ggml_backend_vk_buffer_type() || dst->buffer->buft == ggml_backend_vk_host_buffer_type()) && ggml_backend_buffer_is_vk(src->buffer)) { ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; @@ -4499,7 +4572,6 @@ GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml bool ok = ggml_vk_compute_forward(¶ms, node); if (!ok) { - std::cerr << "Vulkan disable: " << vk_disable << std::endl; fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); } #ifdef GGML_VULKAN_CHECK_RESULTS @@ -4665,7 +4737,7 @@ GGML_CALL int ggml_backend_vk_reg_devices() { // checks #ifdef GGML_VULKAN_CHECK_RESULTS -void ggml_vk_print_graph_origin(const ggml_tensor * tensor, std::vector& done, int level = 0) { +static void ggml_vk_print_graph_origin(const ggml_tensor * tensor, std::vector& done, int level = 0) { if (std::find(done.begin(), done.end(), tensor) != done.end() || level > 10) { return; } @@ -4683,10 +4755,14 @@ void ggml_vk_print_graph_origin(const ggml_tensor * tensor, std::vectortype != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) { return; } + i0 = std::max(i0, 5); + i1 = std::max(i1, 5); + i2 = std::max(i2, 0); + i3 = std::max(i3, 0); fprintf(stderr, " "); for (int idx1 = i1 - 5; idx1 < i1 + 5; idx1++) { fprintf(stderr, "%7d ", idx1); @@ -4698,9 +4774,9 @@ void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * data, in if (idx0 >= 0 && idx0 < tensor->ne[0] && idx1 >= 0 && idx1 < tensor->ne[1] && i2 >= 0 && i2 < tensor->ne[2] && i3 >= 0 && i3 < tensor->ne[3]) { float val; if (tensor->type == GGML_TYPE_F32) { - val = *(float *) ((char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]); + val = *(const float *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]); } else if (tensor->type == GGML_TYPE_F16) { - val = ggml_fp16_to_fp32(*(ggml_fp16_t *) ((char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0])); + val = ggml_fp16_to_fp32(*(const ggml_fp16_t *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0])); } fprintf(stderr, "% 7.2f ", val); } else { @@ -4711,14 +4787,16 @@ void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * data, in } } -void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name) { +static void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name) { void * tensor_data = tensor->data; if (tensor->backend == GGML_BACKEND_GPU) { const size_t tensor_size = ggml_nbytes(tensor); tensor_data = malloc(tensor_size); - ggml_vk_buffer_read((vk_buffer *)tensor->data, 0, tensor_data, tensor_size, vk_device.transfer_queue); + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; + + ggml_vk_buffer_read(&extra->buffer_gpu, extra->offset, tensor_data, tensor_size); } std::cerr << "TENSOR CHECK " << name << " (" << tensor->name << "): " << ggml_op_name(tensor->op) << std::endl; @@ -4730,10 +4808,10 @@ void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name) { std::cerr << "tensor->src[1]=" << tensor->src[1] << " name=" << tensor->src[1]->name << " op=" << ggml_op_name(tensor->src[1]->op) << " type=" << ggml_type_name(tensor->src[1]->type) << " backend=" << tensor->src[1]->backend << " ne0=" << tensor->src[1]->ne[0] << " nb0=" << tensor->src[1]->nb[0] << " ne1=" << tensor->src[1]->ne[1] << " nb1=" << tensor->src[1]->nb[1] << " ne2=" << tensor->src[1]->ne[2] << " nb2=" << tensor->src[1]->nb[2] << " ne3=" << tensor->src[1]->ne[3] << " nb3=" << tensor->src[1]->nb[3] << std::endl; } std::cerr << std::endl << "Result:" << std::endl; - ggml_vk_print_tensor_area(tensor, tensor->data, 5, 5, 0, 0); + ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 0, 0); std::cerr << std::endl; std::cerr << std::endl << "Result:" << std::endl; - ggml_vk_print_tensor_area(tensor, tensor->data, 5, 5, 1, 0); + ggml_vk_print_tensor_area(tensor, tensor_data, 5, 5, 1, 0); std::cerr << std::endl; std::vector done; ggml_vk_print_graph_origin(tensor, done); @@ -4743,7 +4821,7 @@ void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name) { } } -void ggml_vk_check_tensor(const std::string& name, const ggml_tensor * tensor) { +static void ggml_vk_check_tensor(const std::string& name, const ggml_tensor * tensor) { return; GGML_ASSERT(tensor->backend == GGML_BACKEND_CPU); if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) { @@ -4779,7 +4857,7 @@ void * comp_result; size_t comp_size; size_t comp_nb[GGML_MAX_DIMS]; size_t check_counter = 0; -void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * tensor) { +static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * tensor) { if (params->ith != 0) { return; } @@ -4796,8 +4874,9 @@ void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * tensor) ggml_tensor * src1 = tensor->src[1]; struct ggml_init_params iparams = { - .mem_size = 1024*1024*1024, - .mem_buffer = NULL, + /*.mem_size =*/ 1024*1024*1024, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ false, }; struct ggml_context * ctx = ggml_init(iparams); @@ -4829,7 +4908,7 @@ void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * tensor) for (int i3 = 0; i3 < src0->ne[3]; i3++) { for (int i2 = 0; i2 < src0->ne[2]; i2++) { const int idx = i3*src0->ne[2] + i2; - ggml_vk_buffer_read(&extra->buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1], vk_device.transfer_queue); + ggml_vk_buffer_read(&extra->buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]); } } @@ -4842,7 +4921,7 @@ void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * tensor) if (offset + src0_size >= extra->buffer_gpu.size) { src0_size = extra->buffer_gpu.size - offset; } - ggml_vk_buffer_read(&extra->buffer_gpu, offset, src0_clone->data, src0_size, vk_device.transfer_queue); + ggml_vk_buffer_read(&extra->buffer_gpu, offset, src0_clone->data, src0_size); memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS); } } else { @@ -4872,7 +4951,7 @@ void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * tensor) for (int i3 = 0; i3 < src1->ne[3]; i3++) { for (int i2 = 0; i2 < src1->ne[2]; i2++) { const int idx = i3*src1->ne[2] + i2; - ggml_vk_buffer_read(&extra->buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1], vk_device.transfer_queue); + ggml_vk_buffer_read(&extra->buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]); } } @@ -4885,7 +4964,7 @@ void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * tensor) if (offset + src1_size >= extra->buffer_gpu.size) { src1_size = extra->buffer_gpu.size - offset; } - ggml_vk_buffer_read(&extra->buffer_gpu, offset, src1_clone->data, src1_size, vk_device.transfer_queue); + ggml_vk_buffer_read(&extra->buffer_gpu, offset, src1_clone->data, src1_size); memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS); } } else { @@ -4969,7 +5048,7 @@ void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * tensor) } else if (tensor->op == GGML_OP_CPY || tensor->op == GGML_OP_DUP) { if (src1 == nullptr) { tensor_clone = ggml_dup(ctx, src0_clone); - tensor_clone->type == tensor->type; + tensor_clone->type = tensor->type; } else { tensor_clone = ggml_cpy(ctx, src0_clone, src1_clone); } @@ -5046,7 +5125,7 @@ void ggml_vk_check_results_1(ggml_compute_params * params, ggml_tensor * tensor) tensor_size = extra->buffer_gpu.size - (extra->offset); } - ggml_vk_buffer_read(&extra->buffer_gpu, extra->offset, tensor_data, tensor_size, vk_device.transfer_queue); + ggml_vk_buffer_read(&extra->buffer_gpu, extra->offset, tensor_data, tensor_size); } float first_error_result = -1.0f; From f4d7e5497485ce6ce0e322533930b7da4657dd2d Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Tue, 30 Jan 2024 15:14:12 +0200 Subject: [PATCH 062/143] SOTA 3-bit quants (#5196) * iq3_xxs: quantize/dequantize RMSE seems a bit high-ish at about half-way between q2_K and q3_K, so need to check more. * iq3_xxs: CUDA dequantize works * iq2_xxs: tuning quantization * iq3_xxs: starting to look better PPL on wiki.test.raw LLaMA-v1-7B: 6.4218 LLaMA-v2-7B: 6.3560 Mistral-7B : 6.0717 This is better than Q3_K_XS, with a 5% reduction in quantized model size. * iq3_xxs: CUDA dot product We have PP-512: 5891 t/s TG-128: 143.9 t/s * iq3_xxs: scalar and AVX2 dot products * iq3_xxs: ARM_NEON and Metal Metal performance is decent, ARM_NEON is pathetic * iq3_xxs: slightly better grid points * Faster iq3_xxs and iq2_xs dot products on CUDA * iq3_xxs: add some quant mix * iq3_xxs: fix failing quantization test Dot product still fails. Is this real? * iq3_xxs: hopefully fix ROCm * iq3_xxs: failing tests This time the dot product accuracy did find an actual bug in the AVX2 implementation. * Add IQ3_XXS to test-backend-ops --------- Co-authored-by: Iwan Kawrakow --- examples/quantize-stats/quantize-stats.cpp | 2 + examples/quantize/quantize.cpp | 1 + ggml-cuda.cu | 200 ++++++- ggml-metal.m | 35 ++ ggml-metal.metal | 274 +++++++++ ggml-quants.c | 630 +++++++++++++++++++++ ggml-quants.h | 18 +- ggml.c | 30 + ggml.h | 2 + llama.cpp | 24 +- llama.h | 1 + tests/test-backend-ops.cpp | 1 + tests/test-quantize-fns.cpp | 13 +- tests/test-quantize-perf.cpp | 2 + 14 files changed, 1215 insertions(+), 18 deletions(-) diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp index 773024160..6d5f213dc 100644 --- a/examples/quantize-stats/quantize-stats.cpp +++ b/examples/quantize-stats/quantize-stats.cpp @@ -378,6 +378,8 @@ int main(int argc, char ** argv) { printf("testing %s ...\n", ggml_type_name(type)); } + ggml_quantize_init(type); + error_stats global_stats {}; for (const auto& kv_tensor : tensors) { diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index 0236f2180..a9673f0d4 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -25,6 +25,7 @@ static const std::vector QUANT_OPTIONS = { { "IQ2_XS", LLAMA_FTYPE_MOSTLY_IQ2_XS, " 2.31 bpw quantization", }, { "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.63G, +0.6717 ppl @ LLaMA-v1-7B", }, { "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.16G, +9.0634 ppl @ LLaMA-v1-7B", }, + { "IQ3_XXS",LLAMA_FTYPE_MOSTLY_IQ3_XXS," 3.06 bpw quantization", }, { "Q3_K", LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" }, { "Q3_K_XS",LLAMA_FTYPE_MOSTLY_Q3_K_XS,"3-bit extra small quantization" , }, { "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 2.75G, +0.5551 ppl @ LLaMA-v1-7B", }, diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 7695b86b2..ac7d0ecb4 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -191,6 +191,10 @@ static __device__ __forceinline__ int __vsubss4(const int a, const int b) { #endif // __has_builtin(__builtin_elementwise_sub_sat) } +static __device__ __forceinline__ int __vsub4(const int a, const int b) { + return __vsubss4(a, b); +} + static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) { #if defined(__gfx906__) || defined(__gfx908__) || defined(__gfx90a__) || defined(__gfx1030__) c = __builtin_amdgcn_sdot4(a, b, c, false); @@ -505,6 +509,14 @@ typedef struct { } block_iq2_xs; static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding"); +#define QR3_XXS 8 +#define QI3_XXS (QK_K / (4*QR3_XXS)) +typedef struct { + half d; + uint8_t qs[3*(QK_K/8)]; +} block_iq3_xxs; +static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding"); + #define WARP_SIZE 32 #define MATRIX_ROW_PADDING 512 // last row of quant. matrices is a multiple of this to avoid out-of-bounds memory accesses @@ -1613,6 +1625,41 @@ static const __device__ uint64_t iq2xs_grid[512] = { 0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b, }; +static const __device__ uint32_t iq3xxs_grid[256] = { + 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414, + 0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, + 0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404, + 0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e, + 0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c, + 0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c, + 0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34, + 0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c, + 0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c, + 0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04, + 0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c, + 0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414, + 0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434, + 0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c, + 0x140c1c04, 0x140c341c, 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e, + 0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24, + 0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24, + 0x143e040c, 0x143e041c, 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c, + 0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c, + 0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14, + 0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414, + 0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, 0x24040424, 0x24040c3e, + 0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404, + 0x24143404, 0x24143434, 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c, + 0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, 0x2c040c14, 0x2c04240c, + 0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14, + 0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c, + 0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c, + 0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14, + 0x34341c1c, 0x343e041c, 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14, + 0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, 0x3e1c0404, 0x3e1c0c2c, + 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, +}; + static const __device__ uint8_t ksigns_iq2xs[128] = { 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, 141, 142, 15, 144, 17, 18, 147, 20, 149, 150, 23, 24, 153, 154, 27, 156, 29, 30, 159, @@ -1624,6 +1671,43 @@ static const __device__ uint8_t ksigns_iq2xs[128] = { 240, 113, 114, 243, 116, 245, 246, 119, 120, 249, 250, 123, 252, 125, 126, 255, }; +//#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics +static const __device__ uint64_t ksigns64[128] = { + 0x0000000000000000, 0xff000000000000ff, 0xff0000000000ff00, 0x000000000000ffff, + 0xff00000000ff0000, 0x0000000000ff00ff, 0x0000000000ffff00, 0xff00000000ffffff, + 0xff000000ff000000, 0x00000000ff0000ff, 0x00000000ff00ff00, 0xff000000ff00ffff, + 0x00000000ffff0000, 0xff000000ffff00ff, 0xff000000ffffff00, 0x00000000ffffffff, + 0xff0000ff00000000, 0x000000ff000000ff, 0x000000ff0000ff00, 0xff0000ff0000ffff, + 0x000000ff00ff0000, 0xff0000ff00ff00ff, 0xff0000ff00ffff00, 0x000000ff00ffffff, + 0x000000ffff000000, 0xff0000ffff0000ff, 0xff0000ffff00ff00, 0x000000ffff00ffff, + 0xff0000ffffff0000, 0x000000ffffff00ff, 0x000000ffffffff00, 0xff0000ffffffffff, + 0xff00ff0000000000, 0x0000ff00000000ff, 0x0000ff000000ff00, 0xff00ff000000ffff, + 0x0000ff0000ff0000, 0xff00ff0000ff00ff, 0xff00ff0000ffff00, 0x0000ff0000ffffff, + 0x0000ff00ff000000, 0xff00ff00ff0000ff, 0xff00ff00ff00ff00, 0x0000ff00ff00ffff, + 0xff00ff00ffff0000, 0x0000ff00ffff00ff, 0x0000ff00ffffff00, 0xff00ff00ffffffff, + 0x0000ffff00000000, 0xff00ffff000000ff, 0xff00ffff0000ff00, 0x0000ffff0000ffff, + 0xff00ffff00ff0000, 0x0000ffff00ff00ff, 0x0000ffff00ffff00, 0xff00ffff00ffffff, + 0xff00ffffff000000, 0x0000ffffff0000ff, 0x0000ffffff00ff00, 0xff00ffffff00ffff, + 0x0000ffffffff0000, 0xff00ffffffff00ff, 0xff00ffffffffff00, 0x0000ffffffffffff, + 0xffff000000000000, 0x00ff0000000000ff, 0x00ff00000000ff00, 0xffff00000000ffff, + 0x00ff000000ff0000, 0xffff000000ff00ff, 0xffff000000ffff00, 0x00ff000000ffffff, + 0x00ff0000ff000000, 0xffff0000ff0000ff, 0xffff0000ff00ff00, 0x00ff0000ff00ffff, + 0xffff0000ffff0000, 0x00ff0000ffff00ff, 0x00ff0000ffffff00, 0xffff0000ffffffff, + 0x00ff00ff00000000, 0xffff00ff000000ff, 0xffff00ff0000ff00, 0x00ff00ff0000ffff, + 0xffff00ff00ff0000, 0x00ff00ff00ff00ff, 0x00ff00ff00ffff00, 0xffff00ff00ffffff, + 0xffff00ffff000000, 0x00ff00ffff0000ff, 0x00ff00ffff00ff00, 0xffff00ffff00ffff, + 0x00ff00ffffff0000, 0xffff00ffffff00ff, 0xffff00ffffffff00, 0x00ff00ffffffffff, + 0x00ffff0000000000, 0xffffff00000000ff, 0xffffff000000ff00, 0x00ffff000000ffff, + 0xffffff0000ff0000, 0x00ffff0000ff00ff, 0x00ffff0000ffff00, 0xffffff0000ffffff, + 0xffffff00ff000000, 0x00ffff00ff0000ff, 0x00ffff00ff00ff00, 0xffffff00ff00ffff, + 0x00ffff00ffff0000, 0xffffff00ffff00ff, 0xffffff00ffffff00, 0x00ffff00ffffffff, + 0xffffffff00000000, 0x00ffffff000000ff, 0x00ffffff0000ff00, 0xffffffff0000ffff, + 0x00ffffff00ff0000, 0xffffffff00ff00ff, 0xffffffff00ffff00, 0x00ffffff00ffffff, + 0x00ffffffff000000, 0xffffffffff0000ff, 0xffffffffff00ff00, 0x00ffffffff00ffff, + 0xffffffffffff0000, 0x00ffffffffff00ff, 0x00ffffffffffff00, 0xffffffffffffffff, +}; +//#endif + static const __device__ uint8_t kmask_iq2xs[8] = {1, 2, 4, 8, 16, 32, 64, 128}; inline bool ggml_cuda_supports_mmq(enum ggml_type type) { @@ -1690,6 +1774,34 @@ static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst } +template +static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __restrict__ yy) { + + const int i = blockIdx.x; + const block_iq3_xxs * x = (const block_iq3_xxs *) vx; + + const int tid = threadIdx.x; +#if QK_K == 256 + const int il = tid/8; // 0...3 + const int ib = tid%8; // 0...7 + dst_t * y = yy + i*QK_K + 32*ib + 8*il; + const uint8_t * q3 = x[i].qs + 8*ib; + const uint16_t * gas = (const uint16_t *)(x[i].qs + QK_K/4) + 2*ib; + const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*il+0]); + const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*il+1]); + const uint32_t aux32 = gas[0] | (gas[1] << 16); + const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.5f; + const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127]; + for (int j = 0; j < 4; ++j) { + y[j+0] = d * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); + y[j+4] = d * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); + } +#else + assert(false); +#endif + +} + static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, const float * __restrict__ yy, float * __restrict__ dst, const int ncols, int nrows) { static_assert(16%K_QUANTS_PER_ITERATION == 0, "16 must be divisible by K_QUANTS_PER_ITERATION"); @@ -4313,6 +4425,7 @@ static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1( static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics #if QK_K == 256 const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq; @@ -4323,20 +4436,22 @@ static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1( const uint8_t ls2 = bq2->scales[ib32] >> 4; int sumi1 = 0; for (int l = 0; l < 2; ++l) { - const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511)); - const uint8_t signs = ksigns_iq2xs[q2[l] >> 9]; - for (int j = 0; j < 8; ++j) { - sumi1 += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1); - } + const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511)); + const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9)); + const int grid_l = __vsub4(grid[0] ^ signs[0], signs[0]); + const int grid_h = __vsub4(grid[1] ^ signs[1], signs[1]); + sumi1 = __dp4a(grid_l, *((const int *)q8 + 0), sumi1); + sumi1 = __dp4a(grid_h, *((const int *)q8 + 1), sumi1); q8 += 8; } int sumi2 = 0; for (int l = 2; l < 4; ++l) { - const uint8_t * grid = (const uint8_t *)(iq2xs_grid + (q2[l] & 511)); - const uint8_t signs = ksigns_iq2xs[q2[l] >> 9]; - for (int j = 0; j < 8; ++j) { - sumi2 += q8[j] * grid[j] * (signs & kmask_iq2xs[j] ? -1 : 1); - } + const uint32_t * grid = (const uint32_t *)(iq2xs_grid + (q2[l] & 511)); + const uint32_t * signs = (const uint32_t *)(ksigns64 + (q2[l] >> 9)); + const int grid_l = __vsub4(grid[0] ^ signs[0], signs[0]); + const int grid_h = __vsub4(grid[1] ^ signs[1], signs[1]); + sumi2 = __dp4a(grid_l, *((const int *)q8 + 0), sumi2); + sumi2 = __dp4a(grid_h, *((const int *)q8 + 1), sumi2); q8 += 8; } const float d = (float)bq2->d * __low2float(bq8_1[ib32].ds) * 0.25f; @@ -4345,6 +4460,45 @@ static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1( assert(false); return 0.f; #endif +#else + assert(false); + return 0.f; +#endif +} + +static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1( + const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { +#if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics +#if QK_K == 256 + const block_iq3_xxs * bq2 = (const block_iq3_xxs *) vbq; + + const int ib32 = iqs; + const uint8_t * q3 = bq2->qs + 8*ib32; + const uint16_t * gas = (const uint16_t *)(bq2->qs + QK_K/4) + 2*ib32; + const int8_t * q8 = bq8_1[ib32].qs; + uint32_t aux32 = gas[0] | (gas[1] << 16); + int sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint32_t * grid1 = iq3xxs_grid + q3[2*l+0]; + const uint32_t * grid2 = iq3xxs_grid + q3[2*l+1]; + const uint32_t * signs = (const uint32_t *)(ksigns64 + (aux32 & 127)); + const int grid_l = __vsub4(grid1[0] ^ signs[0], signs[0]); + const int grid_h = __vsub4(grid2[0] ^ signs[1], signs[1]); + sumi = __dp4a(grid_l, *((int *)q8+0), sumi); + sumi = __dp4a(grid_h, *((int *)q8+1), sumi); + q8 += 8; + aux32 >>= 7; + } + const float d = (float)bq2->d * (0.5f + aux32) * __low2float(bq8_1[ib32].ds) * 0.5f; + return d * sumi; +#else + assert(false); + return 0.f; +#endif +#else + assert(false); + return 0.f; +#endif } template >>(vx, y); } +template +static void dequantize_row_iq3_xxs_cuda(const void * vx, dst_t * y, const int k, cudaStream_t stream) { + const int nb = k / QK_K; + dequantize_block_iq3_xxs<<>>(vx, y); +} + template static void convert_unary_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int k, cudaStream_t stream) { const int num_blocks = (k + CUDA_DEQUANTIZE_BLOCK_SIZE - 1) / CUDA_DEQUANTIZE_BLOCK_SIZE; @@ -6418,6 +6578,8 @@ static to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) { return dequantize_row_iq2_xxs_cuda; case GGML_TYPE_IQ2_XS: return dequantize_row_iq2_xs_cuda; + case GGML_TYPE_IQ3_XXS: + return dequantize_row_iq3_xxs_cuda; case GGML_TYPE_F32: return convert_unary_cuda; default: @@ -6451,6 +6613,8 @@ static to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) { return dequantize_row_iq2_xxs_cuda; case GGML_TYPE_IQ2_XS: return dequantize_row_iq2_xs_cuda; + case GGML_TYPE_IQ3_XXS: + return dequantize_row_iq3_xxs_cuda; case GGML_TYPE_F16: return convert_unary_cuda; default: @@ -6663,6 +6827,15 @@ static void mul_mat_vec_iq2_xs_q8_1_cuda(const void * vx, const void * vy, float <<>>(vx, vy, dst, ncols, nrows); } +static void mul_mat_vec_iq3_xxs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { + GGML_ASSERT(ncols % QK_K == 0); + const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; + const dim3 block_nums(block_num_y, 1, 1); + const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); + mul_mat_vec_q + <<>>(vx, vy, dst, ncols, nrows); +} + static void ggml_mul_mat_q4_0_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) { @@ -8213,6 +8386,7 @@ static int64_t get_row_rounding(ggml_type type, const std::array= CC_RDNA2 ? 128 : 64; default: GGML_ASSERT(false); @@ -8235,6 +8409,7 @@ static int64_t get_row_rounding(ggml_type type, const std::array= CC_VOLTA ? 128 : 64; case GGML_TYPE_Q6_K: return 64; @@ -8306,6 +8481,9 @@ static void ggml_cuda_op_mul_mat_vec_q( case GGML_TYPE_IQ2_XS: mul_mat_vec_iq2_xs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); break; + case GGML_TYPE_IQ3_XXS: + mul_mat_vec_iq3_xxs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + break; default: GGML_ASSERT(false); break; @@ -10934,7 +11112,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons return false; } ggml_type a_type = a->type; - if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS) { + if (a_type == GGML_TYPE_IQ2_XXS || a_type == GGML_TYPE_IQ2_XS || a_type == GGML_TYPE_IQ3_XXS) { if (b->ne[1] == 1 && ggml_nrows(b) > 1) { return false; } diff --git a/ggml-metal.m b/ggml-metal.m index bbeedaae0..1b02493f8 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -60,6 +60,7 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS, GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, + GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, GGML_METAL_KERNEL_TYPE_RMS_NORM, GGML_METAL_KERNEL_TYPE_GROUP_NORM, @@ -81,6 +82,7 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, //GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, @@ -98,6 +100,7 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, @@ -112,6 +115,7 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, @@ -126,6 +130,7 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, + GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, GGML_METAL_KERNEL_TYPE_ROPE_F32, GGML_METAL_KERNEL_TYPE_ROPE_F16, GGML_METAL_KERNEL_TYPE_ALIBI_F32, @@ -422,6 +427,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K, get_rows_q6_K, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS, get_rows_iq2_xxs, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS, get_rows_iq2_xs, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS, get_rows_iq3_xxs, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GET_ROWS_I32, get_rows_i32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_RMS_NORM, rms_norm, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_GROUP_NORM, group_norm, ctx->support_simdgroup_reduction); @@ -443,6 +449,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_Q6_K_F32, mul_mv_q6_K_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XXS_F32, mul_mv_iq2_xxs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32, mul_mv_iq2_xs_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32, mul_mv_iq3_xxs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F32_F32, mul_mv_id_f32_f32, ctx->support_simdgroup_reduction); //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F16, mul_mv_id_f16_f16, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_F16_F32, mul_mv_id_f16_f32, ctx->support_simdgroup_reduction); @@ -460,6 +467,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_Q6_K_F32, mul_mv_id_q6_K_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XXS_F32, mul_mv_id_iq2_xxs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32, mul_mv_id_iq2_xs_f32, ctx->support_simdgroup_reduction); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32, mul_mv_id_iq3_xxs_f32, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F32_F32, mul_mm_f32_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_F16_F32, mul_mm_f16_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q4_0_F32, mul_mm_q4_0_f32, ctx->support_simdgroup_mm); @@ -474,6 +482,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32, mul_mm_q6_K_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32, mul_mm_iq2_xxs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32, mul_mm_iq2_xs_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32, mul_mm_iq3_xxs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F32_F32, mul_mm_id_f32_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_F16_F32, mul_mm_id_f16_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q4_0_F32, mul_mm_id_q4_0_f32, ctx->support_simdgroup_mm); @@ -488,6 +497,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32, mul_mm_id_q6_K_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32, mul_mm_id_iq2_xxs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32, mul_mm_id_iq2_xs_f32, ctx->support_simdgroup_mm); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32, mul_mm_id_iq3_xxs_f32, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F32, rope_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16, rope_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32, alibi_f32, true); @@ -1260,6 +1270,7 @@ static bool ggml_metal_graph_compute( case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_Q6_K_F32 ].pipeline; break; case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XXS_F32].pipeline; break; case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ2_XS_F32 ].pipeline; break; + case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_IQ3_XXS_F32].pipeline; break; default: GGML_ASSERT(false && "MUL MAT-MAT not implemented"); } @@ -1388,6 +1399,12 @@ static bool ggml_metal_graph_compute( nth1 = 16; pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ2_XS_F32].pipeline; } break; + case GGML_TYPE_IQ3_XXS: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_IQ3_XXS_F32].pipeline; + } break; default: { GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src0t); @@ -1430,6 +1447,11 @@ static bool ggml_metal_graph_compute( [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } + else if (src0t == GGML_TYPE_IQ3_XXS) { + const int mem_size = 256*4+128; + [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } else if (src0t == GGML_TYPE_Q4_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } @@ -1524,6 +1546,7 @@ static bool ggml_metal_graph_compute( case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_Q6_K_F32 ].pipeline; break; case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XXS_F32].pipeline; break; case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ2_XS_F32 ].pipeline; break; + case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MM_ID_IQ3_XXS_F32].pipeline; break; default: GGML_ASSERT(false && "MUL_MAT_ID not implemented"); } @@ -1655,6 +1678,12 @@ static bool ggml_metal_graph_compute( nth1 = 16; pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ2_XS_F32].pipeline; } break; + case GGML_TYPE_IQ3_XXS: + { + nth0 = 4; + nth1 = 16; + pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ3_XXS_F32].pipeline; + } break; default: { GGML_METAL_LOG_ERROR("Asserting on type %d\n", (int)src2t); @@ -1713,6 +1742,11 @@ static bool ggml_metal_graph_compute( [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } + else if (src2t == GGML_TYPE_IQ3_XXS) { + const int mem_size = 256*4+128; + [encoder setThreadgroupMemoryLength:mem_size atIndex:0]; + [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 7)/8, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } else if (src2t == GGML_TYPE_Q4_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne21 + 3)/4, _ne1, ne01*ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } @@ -1753,6 +1787,7 @@ static bool ggml_metal_graph_compute( case GGML_TYPE_Q6_K: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_Q6_K ].pipeline; break; case GGML_TYPE_IQ2_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XXS].pipeline; break; case GGML_TYPE_IQ2_XS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ2_XS ].pipeline; break; + case GGML_TYPE_IQ3_XXS: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_IQ3_XXS].pipeline; break; case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_GET_ROWS_I32 ].pipeline; break; default: GGML_ASSERT(false && "not implemented"); } diff --git a/ggml-metal.metal b/ggml-metal.metal index 029578dc5..427410635 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -2459,6 +2459,12 @@ typedef struct { } block_iq2_xs; // 74 bytes / block for QK_K = 256, so 2.3125 bpw +typedef struct { + half d; + uint8_t qs[3*QK_K/8]; +} block_iq3_xxs; +// 98 bytes / block for QK_K = 256, so 3.0625 bpw + //====================================== dot products ========================= void kernel_mul_mv_q2_K_f32_impl( @@ -3681,6 +3687,42 @@ constexpr constant static uint64_t iq2xs_grid[512] = { 0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b, }; +constexpr constant static uint32_t iq3xxs_grid[256] = { + 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3c, 0x04041404, 0x04041414, + 0x04041c0c, 0x04042414, 0x04043c1c, 0x04043c2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, + 0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3c04, 0x04140404, + 0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3c, + 0x04142c0c, 0x04142c3c, 0x04143c2c, 0x041c040c, 0x041c043c, 0x041c0c04, 0x041c0c14, 0x041c142c, + 0x041c3c04, 0x04240c1c, 0x04241c3c, 0x04242424, 0x04242c3c, 0x04243c1c, 0x04243c2c, 0x042c040c, + 0x042c043c, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043c0c04, 0x043c0c24, 0x043c0c34, + 0x043c241c, 0x043c340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c, + 0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243c, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c, + 0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04, + 0x0c143c14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c, + 0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3c0c, 0x0c34042c, 0x0c3c1414, + 0x0c3c2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434, + 0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c, + 0x140c1c04, 0x140c341c, 0x140c343c, 0x140c3c04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3c, + 0x14141404, 0x14141414, 0x14141c3c, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24, + 0x141c3c04, 0x141c3c24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143c, 0x142c240c, 0x142c3c24, + 0x143c040c, 0x143c041c, 0x143c0c34, 0x143c242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c, + 0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043c14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c, + 0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143c14, + 0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243c, 0x1c243c14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414, + 0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3c1c1c, 0x1c3c3404, 0x24040424, 0x24040c3c, + 0x24041c2c, 0x24041c3c, 0x24042c1c, 0x24042c3c, 0x240c3c24, 0x24141404, 0x24141c3c, 0x24142404, + 0x24143404, 0x24143434, 0x241c043c, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c, + 0x242c241c, 0x242c3c04, 0x243c042c, 0x243c0c04, 0x243c0c14, 0x243c1c04, 0x2c040c14, 0x2c04240c, + 0x2c043c04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143c14, + 0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143c, 0x2c243c14, 0x2c2c0414, 0x2c2c1c0c, + 0x2c342c04, 0x2c3c1424, 0x2c3c2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c, + 0x340c340c, 0x34140c3c, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14, + 0x34341c1c, 0x343c041c, 0x343c140c, 0x3c04041c, 0x3c04042c, 0x3c04043c, 0x3c040c04, 0x3c041c14, + 0x3c042c14, 0x3c0c1434, 0x3c0c2404, 0x3c140c14, 0x3c14242c, 0x3c142c14, 0x3c1c0404, 0x3c1c0c2c, + 0x3c1c1c1c, 0x3c1c3404, 0x3c24140c, 0x3c24240c, 0x3c2c0404, 0x3c2c0414, 0x3c2c1424, 0x3c341c04, +}; + + constexpr constant static uint8_t ksigns_iq2xs[128] = { 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, 141, 142, 15, 144, 17, 18, 147, 20, 149, 150, 23, 24, 153, 154, 27, 156, 29, 30, 159, @@ -3970,6 +4012,143 @@ kernel void kernel_mul_mv_iq2_xs_f32( kernel_mul_mv_iq2_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); } +void kernel_mul_mv_iq3_xxs_f32_impl( + device const void * src0, + device const float * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant int64_t & ne10, + constant int64_t & ne12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint & r2, + constant uint & r3, + threadgroup int8_t * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + + const int nb = ne00/QK_K; + const int r0 = tgpig.x; + const int r1 = tgpig.y; + const int im = tgpig.z; + + const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; + const int ib_row = first_row * nb; + + const uint i12 = im%ne12; + const uint i13 = im/ne12; + + const uint offset0 = (i12/r2)*(nb*ne01) + (i13/r3)*(nb*ne01*ne02); + + device const block_iq3_xxs * x = (device const block_iq3_xxs *) src0 + ib_row + offset0; + device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; + + float yl[32]; + float sumf[N_DST]={0.f}, all_sum; + + const int nb32 = nb * (QK_K / 32); + + threadgroup uint32_t * values = (threadgroup uint32_t *)shared_values; + threadgroup uint8_t * shared_signs = (threadgroup uint8_t *)(values + 256); + { + int nval = 4; + int pos = (32*sgitg + tiisg)*nval; + for (int i = 0; i < nval; ++i) values[pos + i] = iq3xxs_grid[pos + i]; + nval = 2; + pos = (32*sgitg + tiisg)*nval; + for (int i = 0; i < nval; ++i) shared_signs[pos+i] = ksigns_iq2xs[pos+i]; + threadgroup_barrier(mem_flags::mem_threadgroup); + } + +#if QK_K == 256 + const int ix = tiisg; + + device const float * y4 = y + 32 * ix; + + for (int ib32 = ix; ib32 < nb32; ib32 += 32) { + + for (int i = 0; i < 32; ++i) { + yl[i] = y4[i]; + } + + const int ibl = ib32 / (QK_K / 32); + const int ib = ib32 % (QK_K / 32); + + device const block_iq3_xxs * xr = x + ibl; + device const uint8_t * q3 = xr->qs + 8 * ib; + device const uint16_t * gas = (device const uint16_t *)(xr->qs + QK_K/4) + 2 * ib; + device const half * dh = &xr->d; + + for (int row = 0; row < N_DST; row++) { + + const float db = dh[0]; + const uint32_t aux32 = gas[0] | (gas[1] << 16); + const float d = db * (0.5f + (aux32 >> 28)); + + float2 sum = {0}; + for (int l = 0; l < 4; ++l) { + const threadgroup uint8_t * grid1 = (const threadgroup uint8_t *)(values + q3[2*l+0]); + const threadgroup uint8_t * grid2 = (const threadgroup uint8_t *)(values + q3[2*l+1]); + const uint8_t signs = shared_signs[(aux32 >> 7*l) & 127]; + for (int j = 0; j < 4; ++j) { + sum[0] += yl[8*l + j + 0] * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); + sum[1] += yl[8*l + j + 4] * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); + } + } + sumf[row] += d * (sum[0] + sum[1]); + + dh += nb*sizeof(block_iq3_xxs)/2; + q3 += nb*sizeof(block_iq3_xxs); + gas += nb*sizeof(block_iq3_xxs)/2; + } + + y4 += 32 * 32; + } +#else + // TODO +#endif + + for (int row = 0; row < N_DST; ++row) { + all_sum = simd_sum(sumf[row]); + if (tiisg == 0) { + dst[r1*ne0 + im*ne0*ne1 + first_row + row] = all_sum * 0.5f; + } + } +} + +[[host_name("kernel_mul_mv_iq3_xxs_f32")]] +kernel void kernel_mul_mv_iq3_xxs_f32( + device const void * src0, + device const float * 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, + constant uint & r2, + constant uint & r3, + threadgroup int8_t * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + + kernel_mul_mv_iq3_xxs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); +} + + //============================= templates and their specializations ============================= // NOTE: this is not dequantizing - we are simply fitting the template @@ -4287,6 +4466,33 @@ void dequantize_iq2_xs(device const block_iq2_xs * xb, short il, thread type4x4 } } +template +void dequantize_iq3_xxs(device const block_iq3_xxs * xb, short il, thread type4x4 & reg) { + // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 + const float d = xb->d; + const int ib32 = il/2; + il = il%2; + // il = 0 or 1. il = 0 processes the first 16 quants in a block of 32, il = 1 the second 16 + device const uint8_t * q3 = xb->qs + 8*ib32; + device const uint16_t * gas = (device const uint16_t *)(xb->qs + QK_K/4) + 2*ib32; + const uint32_t aux32 = gas[0] | (gas[1] << 16); + const float dl = d * (0.5f + (aux32 >> 28)) * 0.5f; + constant uint8_t * grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+0]); + constant uint8_t * grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+1]); + uint8_t signs = ksigns_iq2xs[(aux32 >> 14*il) & 127]; + for (int i = 0; i < 4; ++i) { + reg[0][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f); + reg[1][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f); + } + grid1 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+2]); + grid2 = (constant uint8_t *)(iq3xxs_grid + q3[4*il+3]); + signs = ksigns_iq2xs[(aux32 >> (14*il+7)) & 127]; + for (int i = 0; i < 4; ++i) { + reg[2][i] = dl * grid1[i] * (signs & kmask_iq2xs[i+0] ? -1.f : 1.f); + reg[3][i] = dl * grid2[i] * (signs & kmask_iq2xs[i+4] ? -1.f : 1.f); + } +} + template kernel void kernel_get_rows( device const void * src0, @@ -4828,6 +5034,7 @@ template [[host_name("kernel_get_rows_q5_K")]] kernel get_rows_t kernel_get_rows template [[host_name("kernel_get_rows_q6_K")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq2_xxs")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq2_xs")]] kernel get_rows_t kernel_get_rows; +template [[host_name("kernel_get_rows_iq3_xxs")]] kernel get_rows_t kernel_get_rows; // // matrix-matrix multiplication @@ -4866,6 +5073,7 @@ template [[host_name("kernel_mul_mm_q5_K_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq2_xxs_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq2_xs_f32")]] kernel mat_mm_t kernel_mul_mm; +template [[host_name("kernel_mul_mm_iq3_xxs_f32")]] kernel mat_mm_t kernel_mul_mm; // // indirect matrix-matrix multiplication @@ -4916,6 +5124,7 @@ template [[host_name("kernel_mul_mm_id_q5_K_f32")]] kernel mat_mm_id_t kernel_mu template [[host_name("kernel_mul_mm_id_q6_K_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq2_xxs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq2_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; +template [[host_name("kernel_mul_mm_id_iq3_xxs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; // // matrix-vector multiplication @@ -5818,3 +6027,68 @@ kernel void kernel_mul_mv_id_iq2_xs_f32( tiisg, sgitg); } + +[[host_name("kernel_mul_mv_id_iq3_xxs_f32")]] +kernel void kernel_mul_mv_id_iq3_xxs_f32( + device const char * ids, + device const char * src1, + device float * dst, + constant uint64_t & nbi1, + 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 int64_t & ne13, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + constant uint64_t & nb1, + constant uint & r2, + constant uint & r3, + constant int & idx, + device const char * src00, + device const char * src01, + device const char * src02, + device const char * src03, + device const char * src04, + device const char * src05, + device const char * src06, + device const char * src07, + threadgroup int8_t * shared_values [[threadgroup(0)]], + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiitg[[thread_index_in_threadgroup]], + uint tiisg[[thread_index_in_simdgroup]], + uint sgitg[[simdgroup_index_in_threadgroup]]) { + device const char * src0[8] = {src00, src01, src02, src03, src04, src05, src06, src07}; + + const int64_t bid = tgpig.z/(ne12*ne13); + + tgpig.z = tgpig.z%(ne12*ne13); + + const int32_t id = ((device int32_t *) (ids + bid*nbi1))[idx]; + + kernel_mul_mv_iq3_xxs_f32_impl( + src0[id], + (device const float *) (src1 + bid*nb11), + dst + bid*ne0, + ne00, + ne01, + ne02, + ne10, + ne12, + ne0, + ne1, + r2, + r3, + shared_values, + tgpig, + tiisg, + sgitg); +} diff --git a/ggml-quants.c b/ggml-quants.c index 7d2f033e9..ac061b63a 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -3441,6 +3441,41 @@ static const uint64_t iq2xs_grid[512] = { 0x2b2b2b2b082b2b08, 0x2b2b2b2b082b2b2b, 0x2b2b2b2b2b190819, 0x2b2b2b2b2b2b2b2b, }; +static const uint32_t iq3xxs_grid[256] = { + 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414, + 0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, + 0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404, + 0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e, + 0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c, + 0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c, + 0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34, + 0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c, + 0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c, + 0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04, + 0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c, + 0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414, + 0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434, + 0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c, + 0x140c1c04, 0x140c341c, 0x140c343e, 0x140c3e04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3e, + 0x14141404, 0x14141414, 0x14141c3e, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24, + 0x141c3e04, 0x141c3e24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143e, 0x142c240c, 0x142c3e24, + 0x143e040c, 0x143e041c, 0x143e0c34, 0x143e242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c, + 0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043e14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c, + 0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143e14, + 0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243e, 0x1c243e14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414, + 0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3e1c1c, 0x1c3e3404, 0x24040424, 0x24040c3e, + 0x24041c2c, 0x24041c3e, 0x24042c1c, 0x24042c3e, 0x240c3e24, 0x24141404, 0x24141c3e, 0x24142404, + 0x24143404, 0x24143434, 0x241c043e, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c, + 0x242c241c, 0x242c3e04, 0x243e042c, 0x243e0c04, 0x243e0c14, 0x243e1c04, 0x2c040c14, 0x2c04240c, + 0x2c043e04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143e14, + 0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143e, 0x2c243e14, 0x2c2c0414, 0x2c2c1c0c, + 0x2c342c04, 0x2c3e1424, 0x2c3e2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c, + 0x340c340c, 0x34140c3e, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14, + 0x34341c1c, 0x343e041c, 0x343e140c, 0x3e04041c, 0x3e04042c, 0x3e04043e, 0x3e040c04, 0x3e041c14, + 0x3e042c14, 0x3e0c1434, 0x3e0c2404, 0x3e140c14, 0x3e14242c, 0x3e142c14, 0x3e1c0404, 0x3e1c0c2c, + 0x3e1c1c1c, 0x3e1c3404, 0x3e24140c, 0x3e24240c, 0x3e2c0404, 0x3e2c0414, 0x3e2c1424, 0x3e341c04, +}; + static const uint8_t ksigns_iq2xs[128] = { 0, 129, 130, 3, 132, 5, 6, 135, 136, 9, 10, 139, 12, 141, 142, 15, 144, 17, 18, 147, 20, 149, 150, 23, 24, 153, 154, 27, 156, 29, 30, 159, @@ -3507,6 +3542,38 @@ void dequantize_row_iq2_xs(const block_iq2_xs * restrict x, float * restrict y, } } +// ====================== 3.0625 bpw (de)-quantization + +void dequantize_row_iq3_xxs(const block_iq3_xxs * restrict x, float * restrict y, int k) { + assert(k % QK_K == 0); + const int nb = k / QK_K; + + uint32_t aux32; + + for (int i = 0; i < nb; i++) { + + const float d = GGML_FP16_TO_FP32(x[i].d); + const uint8_t * qs = x[i].qs; + const uint8_t * scales_and_signs = qs + QK_K/4; + + for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { + memcpy(&aux32, scales_and_signs + 4*ib32, sizeof(uint32_t)); + const float db = d * (0.5f + (aux32 >> 28)) * 0.5f; + for (int l = 0; l < 4; ++l) { + const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*l) & 127]; + const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + qs[2*l+0]); + const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + qs[2*l+1]); + for (int j = 0; j < 4; ++j) { + y[j+0] = db * grid1[j] * (signs & kmask_iq2xs[j+0] ? -1.f : 1.f); + y[j+4] = db * grid2[j] * (signs & kmask_iq2xs[j+4] ? -1.f : 1.f); + } + y += 8; + } + qs += 8; + } + } +} + //===================================== Q8_K ============================================== void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k) { @@ -8551,6 +8618,136 @@ void ggml_vec_dot_iq2_xs_q8_K(const int n, float * restrict s, const void * rest #endif } +// TODO +void ggml_vec_dot_iq3_xxs_q8_K(const int n, float * restrict s, const void * restrict vx, const void * restrict vy) { + assert(n % QK_K == 0); + + const block_iq3_xxs * restrict x = vx; + const block_q8_K * restrict y = vy; + + const int nb = n / QK_K; + +#if defined(__ARM_NEON) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[2]; + + ggml_int8x16x4_t q3s; + ggml_int8x16x4_t q8b; + + float sumf = 0; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict gas = x[i].qs + QK_K/4; + const int8_t * restrict q8 = y[i].qs; + float sumf1 = 0, sumf2 = 0; + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + q8b = ggml_vld1q_s8_x4(q8); q8 += 64; + memcpy(aux32, gas, 2*sizeof(uint32_t)); gas += 2*sizeof(uint32_t); + const uint32x4_t aux32x4_0 = {iq3xxs_grid[q3[ 0]], iq3xxs_grid[q3[ 1]], iq3xxs_grid[q3[ 2]], iq3xxs_grid[q3[ 3]]}; + const uint32x4_t aux32x4_1 = {iq3xxs_grid[q3[ 4]], iq3xxs_grid[q3[ 5]], iq3xxs_grid[q3[ 6]], iq3xxs_grid[q3[ 7]]}; + const uint32x4_t aux32x4_2 = {iq3xxs_grid[q3[ 8]], iq3xxs_grid[q3[ 9]], iq3xxs_grid[q3[10]], iq3xxs_grid[q3[11]]}; + const uint32x4_t aux32x4_3 = {iq3xxs_grid[q3[12]], iq3xxs_grid[q3[13]], iq3xxs_grid[q3[14]], iq3xxs_grid[q3[15]]}; + q3 += 16; + q3s.val[0] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[0] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[0] >> 7) & 127)))); + q3s.val[1] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[0] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[0] >> 21) & 127)))); + q3s.val[2] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 0) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 7) & 127)))); + q3s.val[3] = vcombine_s8(vld1_s8((const void *)(signs64 + ((aux32[1] >> 14) & 127))), vld1_s8((const void *)(signs64 + ((aux32[1] >> 21) & 127)))); + q3s.val[0] = vmulq_s8(q3s.val[0], vreinterpretq_s8_u32(aux32x4_0)); + q3s.val[1] = vmulq_s8(q3s.val[1], vreinterpretq_s8_u32(aux32x4_1)); + q3s.val[2] = vmulq_s8(q3s.val[2], vreinterpretq_s8_u32(aux32x4_2)); + q3s.val[3] = vmulq_s8(q3s.val[3], vreinterpretq_s8_u32(aux32x4_3)); + const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[0], q8b.val[0]), q3s.val[1], q8b.val[1]); + const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(vdupq_n_s32(0), q3s.val[2], q8b.val[2]), q3s.val[3], q8b.val[3]); + sumf1 += vaddvq_s32(p1) * (0.5f + (aux32[0] >> 28)); + sumf2 += vaddvq_s32(p2) * (0.5f + (aux32[1] >> 28)); + } + sumf += d*(sumf1 + sumf2); + } + *s = 0.5f * sumf; + +#elif defined(__AVX2__) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[2]; + + __m256 accumf = _mm256_setzero_ps(); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict gas = x[i].qs + QK_K/4; + const int8_t * restrict q8 = y[i].qs; + __m256i sumi1 = _mm256_setzero_si256(); + __m256i sumi2 = _mm256_setzero_si256(); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q2_1 = _mm256_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], + iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); + q3 += 8; + const __m256i q2_2 = _mm256_set_epi32(iq3xxs_grid[q3[7]], iq3xxs_grid[q3[6]], iq3xxs_grid[q3[5]], iq3xxs_grid[q3[4]], + iq3xxs_grid[q3[3]], iq3xxs_grid[q3[2]], iq3xxs_grid[q3[1]], iq3xxs_grid[q3[0]]); + q3 += 8; + memcpy(aux32, gas, 8); gas += 8; + const __m256i s2_1 = _mm256_set_epi64x(signs64[(aux32[0] >> 21) & 127], signs64[(aux32[0] >> 14) & 127], + signs64[(aux32[0] >> 7) & 127], signs64[(aux32[0] >> 0) & 127]); + const __m256i s2_2 = _mm256_set_epi64x(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], + signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); + const __m256i q8s_1 = _mm256_sign_epi8(q8_1, s2_1); + const __m256i q8s_2 = _mm256_sign_epi8(q8_2, s2_2); + const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); + const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); + const uint16_t ls1 = aux32[0] >> 28; + const uint16_t ls2 = aux32[1] >> 28; + const __m256i p1 = _mm256_madd_epi16(dot1, _mm256_set1_epi16(2*ls1+1)); + const __m256i p2 = _mm256_madd_epi16(dot2, _mm256_set1_epi16(2*ls2+1)); + sumi1 = _mm256_add_epi32(sumi1, p1); + sumi2 = _mm256_add_epi32(sumi2, p2); + } + + accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); + + } + + *s = 0.25f * hsum_float_8(accumf); + +#else + + uint32_t aux32; + + float sumf = 0.f; + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint8_t * restrict q3 = x[i].qs; + const uint8_t * restrict gas = x[i].qs + QK_K/4; + const int8_t * restrict q8 = y[i].qs; + int32_t bsum = 0; + for (int ib32 = 0; ib32 < QK_K/32; ++ib32) { + memcpy(&aux32, gas, sizeof(uint32_t)); gas += sizeof(uint32_t); + const uint32_t ls = 2*(aux32 >> 28) + 1; + int32_t sumi = 0; + for (int l = 0; l < 4; ++l) { + const uint8_t * grid1 = (const uint8_t *)(iq3xxs_grid + q3[2*l+0]); + const uint8_t * grid2 = (const uint8_t *)(iq3xxs_grid + q3[2*l+1]); + const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*l) & 127]; + for (int j = 0; j < 4; ++j) { + sumi += grid1[j] * q8[j+0] * (signs & kmask_iq2xs[j+0] ? -1 : 1); + sumi += grid2[j] * q8[j+4] * (signs & kmask_iq2xs[j+4] ? -1 : 1); + } + q8 += 8; + } + q3 += 8; + bsum += sumi * ls; + } + sumf += d * bsum; + } + *s = 0.25f * sumf; +#endif +} + // ================================ IQ2 quantization ============================================= typedef struct { @@ -9189,3 +9386,436 @@ size_t quantize_iq2_xs(const float * src, void * dst, int nrow, int n_per_row, i return nrow * nblock * sizeof(block_iq2_xs); } +// +// ============================================= 3-bit using D4 lattice +// + +typedef struct { + uint32_t * grid; + int * map; + uint16_t * neighbours; +} iq3_entry_t; + +static iq3_entry_t iq3_data[1] = { + {NULL, NULL, NULL}, +}; + +static inline int iq3_data_index(int grid_size) { + (void)grid_size; + GGML_ASSERT(grid_size == 256); + return 0; +} + +static int iq3_compare_func(const void * left, const void * right) { + const int * l = (const int *)left; + const int * r = (const int *)right; + return l[0] < r[0] ? -1 : l[0] > r[0] ? 1 : l[1] < r[1] ? -1 : l[1] > r[1] ? 1 : 0; +} + +void iq3xs_init_impl(int grid_size) { + const int gindex = iq3_data_index(grid_size); + if (iq3_data[gindex].grid) { + return; + } + static const uint16_t kgrid_256[256] = { + 0, 2, 4, 9, 11, 15, 16, 18, 25, 34, 59, 61, 65, 67, 72, 74, + 81, 85, 88, 90, 97, 108, 120, 128, 130, 132, 137, 144, 146, 153, 155, 159, + 169, 175, 189, 193, 199, 200, 202, 213, 248, 267, 287, 292, 303, 315, 317, 321, + 327, 346, 362, 413, 436, 456, 460, 462, 483, 497, 513, 515, 520, 522, 529, 531, + 536, 538, 540, 551, 552, 576, 578, 585, 592, 594, 641, 643, 648, 650, 657, 664, + 698, 704, 706, 720, 729, 742, 758, 769, 773, 808, 848, 852, 870, 889, 901, 978, + 992, 1024, 1026, 1033, 1035, 1040, 1042, 1046, 1049, 1058, 1089, 1091, 1093, 1096, 1098, 1105, + 1112, 1139, 1143, 1144, 1152, 1154, 1161, 1167, 1168, 1170, 1183, 1184, 1197, 1217, 1224, 1228, + 1272, 1276, 1309, 1323, 1347, 1367, 1377, 1404, 1473, 1475, 1486, 1509, 1537, 1544, 1546, 1553, + 1555, 1576, 1589, 1594, 1600, 1602, 1616, 1625, 1636, 1638, 1665, 1667, 1672, 1685, 1706, 1722, + 1737, 1755, 1816, 1831, 1850, 1856, 1862, 1874, 1901, 1932, 1950, 1971, 2011, 2032, 2052, 2063, + 2077, 2079, 2091, 2095, 2172, 2192, 2207, 2208, 2224, 2230, 2247, 2277, 2308, 2345, 2356, 2389, + 2403, 2424, 2501, 2504, 2506, 2520, 2570, 2593, 2616, 2624, 2630, 2646, 2669, 2700, 2714, 2746, + 2754, 2795, 2824, 2835, 2839, 2874, 2882, 2905, 2984, 3028, 3042, 3092, 3108, 3110, 3124, 3153, + 3185, 3215, 3252, 3288, 3294, 3364, 3397, 3434, 3483, 3523, 3537, 3587, 3589, 3591, 3592, 3610, + 3626, 3670, 3680, 3722, 3749, 3754, 3776, 3789, 3803, 3824, 3857, 3873, 3904, 3906, 3924, 3992, + }; + const int kmap_size = 4096; + const int nwant = 2; + const uint16_t * kgrid = kgrid_256; + uint32_t * kgrid_q3xs; + int * kmap_q3xs; + uint16_t * kneighbors_q3xs; + + printf("================================================================= %s(grid_size = %d)\n", __func__, grid_size); + uint32_t * the_grid = (uint32_t *)malloc(grid_size*sizeof(uint32_t)); + for (int k = 0; k < grid_size; ++k) { + int8_t * pos = (int8_t *)(the_grid + k); + for (int i = 0; i < 4; ++i) { + int l = (kgrid[k] >> 3*i) & 0x7; + pos[i] = 2*l + 1; + } + } + kgrid_q3xs = the_grid; + iq3_data[gindex].grid = the_grid; + kmap_q3xs = (int *)malloc(kmap_size*sizeof(int)); + iq3_data[gindex].map = kmap_q3xs; + for (int i = 0; i < kmap_size; ++i) kmap_q3xs[i] = -1; + uint32_t aux32; + uint8_t * aux8 = (uint8_t *)&aux32; + for (int i = 0; i < grid_size; ++i) { + aux32 = kgrid_q3xs[i]; + uint16_t index = 0; + for (int k=0; k<4; ++k) { + uint16_t q = (aux8[k] - 1)/2; + index |= (q << 3*k); + } + kmap_q3xs[index] = i; + } + int8_t pos[4]; + int * dist2 = (int *)malloc(2*grid_size*sizeof(int)); + int num_neighbors = 0, num_not_in_map = 0; + for (int i = 0; i < kmap_size; ++i) { + if (kmap_q3xs[i] >= 0) continue; + ++num_not_in_map; + for (int k = 0; k < 4; ++k) { + int l = (i >> 3*k) & 0x7; + pos[k] = 2*l + 1; + } + for (int j = 0; j < grid_size; ++j) { + const int8_t * pg = (const int8_t *)(kgrid_q3xs + j); + int d2 = 0; + for (int k = 0; k < 4; ++k) d2 += (pg[k] - pos[k])*(pg[k] - pos[k]); + dist2[2*j+0] = d2; + dist2[2*j+1] = j; + } + qsort(dist2, grid_size, 2*sizeof(int), iq3_compare_func); + int n = 0; int d2 = dist2[0]; + int nhave = 1; + for (int j = 0; j < grid_size; ++j) { + if (dist2[2*j] > d2) { + if (nhave == nwant) break; + d2 = dist2[2*j]; + ++nhave; + } + ++n; + } + num_neighbors += n; + } + printf("%s: %d neighbours in total\n", __func__, num_neighbors); + kneighbors_q3xs = (uint16_t *)malloc((num_neighbors + num_not_in_map)*sizeof(uint16_t)); + iq3_data[gindex].neighbours = kneighbors_q3xs; + int counter = 0; + for (int i = 0; i < kmap_size; ++i) { + if (kmap_q3xs[i] >= 0) continue; + for (int k = 0; k < 4; ++k) { + int l = (i >> 3*k) & 0x7; + pos[k] = 2*l + 1; + } + for (int j = 0; j < grid_size; ++j) { + const int8_t * pg = (const int8_t *)(kgrid_q3xs + j); + int d2 = 0; + for (int k = 0; k < 4; ++k) d2 += (pg[k] - pos[k])*(pg[k] - pos[k]); + dist2[2*j+0] = d2; + dist2[2*j+1] = j; + } + qsort(dist2, grid_size, 2*sizeof(int), iq3_compare_func); + kmap_q3xs[i] = -(counter + 1); + int d2 = dist2[0]; + uint16_t * start = &kneighbors_q3xs[counter++]; + int n = 0, nhave = 1; + for (int j = 0; j < grid_size; ++j) { + if (dist2[2*j] > d2) { + if (nhave == nwant) break; + d2 = dist2[2*j]; + ++nhave; + } + kneighbors_q3xs[counter++] = dist2[2*j+1]; + ++n; + } + *start = n; + } + free(dist2); +} + +void iq3xs_free_impl(int grid_size) { + GGML_ASSERT(grid_size == 256); + const int gindex = iq3_data_index(grid_size); + if (iq3_data[gindex].grid) { + free(iq3_data[gindex].grid); iq3_data[gindex].grid = NULL; + free(iq3_data[gindex].map); iq3_data[gindex].map = NULL; + free(iq3_data[gindex].neighbours); iq3_data[gindex].neighbours = NULL; + } +} + +static int iq3_find_best_neighbour(const uint16_t * restrict neighbours, const uint32_t * restrict grid, + const float * restrict xval, const float * restrict weight, float scale, int8_t * restrict L) { + int num_neighbors = neighbours[0]; + GGML_ASSERT(num_neighbors > 0); + float best_d2 = FLT_MAX; + int grid_index = -1; + for (int j = 1; j <= num_neighbors; ++j) { + const int8_t * pg = (const int8_t *)(grid + neighbours[j]); + float d2 = 0; + for (int i = 0; i < 4; ++i) { + float q = pg[i]; + float diff = scale*q - xval[i]; + d2 += weight[i]*diff*diff; + } + if (d2 < best_d2) { + best_d2 = d2; grid_index = neighbours[j]; + } + } + GGML_ASSERT(grid_index >= 0); + const int8_t * pg = (const int8_t *)(grid + grid_index); + for (int i = 0; i < 4; ++i) L[i] = (pg[i] - 1)/2; + return grid_index; +} + +static void quantize_row_iq3_xxs_impl(const float * restrict x, void * restrict vy, int n, const float * restrict quant_weights) { + + const int gindex = iq3_data_index(256); + + const uint32_t * kgrid_q3xs = iq3_data[gindex].grid; + const int * kmap_q3xs = iq3_data[gindex].map; + const uint16_t * kneighbors_q3xs = iq3_data[gindex].neighbours; + + //GGML_ASSERT(quant_weights && "missing quantization weights"); + GGML_ASSERT(kgrid_q3xs && "forgot to call ggml_quantize_init()?"); + GGML_ASSERT(kmap_q3xs && "forgot to call ggml_quantize_init()?"); + GGML_ASSERT(kneighbors_q3xs && "forgot to call ggml_quantize_init()?"); + GGML_ASSERT(n%QK_K == 0); + + const int kMaxQ = 8; + + const int nbl = n/256; + + block_iq3_xxs * y = vy; + + float scales[QK_K/32]; + float weight[32]; + float xval[32]; + int8_t L[32]; + int8_t Laux[32]; + float waux[32]; + bool is_on_grid[8]; + bool is_on_grid_aux[8]; + uint8_t block_signs[8]; + uint8_t q3[3*(QK_K/8)]; + uint32_t * scales_and_signs = (uint32_t *)(q3 + QK_K/4); + + for (int ibl = 0; ibl < nbl; ++ibl) { + + y[ibl].d = GGML_FP32_TO_FP16(0.f); + memset(q3, 0, 3*QK_K/8); + + float max_scale = 0; + + const float * xbl = x + QK_K*ibl; + float sumx2 = 0; + for (int i = 0; i < QK_K; ++i) sumx2 += xbl[i]*xbl[i]; + float sigma2 = sumx2/QK_K; + + for (int ib = 0; ib < QK_K/32; ++ib) { + const float * xb = xbl + 32*ib; + if (quant_weights) { + const float * qw = quant_weights + QK_K*ibl + 32*ib; + for (int i = 0; i < 32; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]); + } else { + for (int i = 0; i < 32; ++i) weight[i] = xb[i]*xb[i]; + } + for (int i = 0; i < 32; ++i) waux[i] = sqrtf(weight[i]); + for (int k = 0; k < 4; ++k) { + int nflip = 0; + uint8_t s = 0; + for (int i = 0; i < 8; ++i) { + if (xb[8*k + i] >= 0) xval[8*k + i] = xb[8*k + i]; + else { + xval[8*k + i] = -xb[8*k + i]; ++nflip; s |= (1 << i); + } + } + if (nflip%2) { + int imin = 0; float min = weight[8*k+imin]*xb[8*k+imin]*xb[8*k+imin]; + for (int i = 1; i < 8; ++i) { + float ax = weight[8*k+i]*xb[8*k+i]*xb[8*k+i]; + if (ax < min) { + min = ax; imin = i; + } + } + xval[8*k+imin] = -xval[8*k+imin]; + s ^= (1 << imin); + } + block_signs[k] = s & 127; + } + float max = xval[0]; + for (int i = 1; i < 32; ++i) max = MAX(max, xval[i]); + if (!max) { + scales[ib] = 0; + memset(L, 0, 32); + continue; + } + float best = 0; + float scale = max/(2*kMaxQ-1); + for (int is = -15; is <= 15; ++is) { + float id = (2*kMaxQ-1+is*0.2f)/max; + float this_scale = 1/id; + for (int k = 0; k < 8; ++k) { + for (int i = 0; i < 4; ++i) { + int l = nearest_int(0.5f*(id*xval[4*k+i]-1)); + Laux[4*k+i] = MAX(0, MIN(kMaxQ-1, l)); + } + uint16_t u = 0; + for (int i = 0; i < 4; ++i) u |= (Laux[4*k+i] << 3*i); + int grid_index = kmap_q3xs[u]; + is_on_grid_aux[k] = true; + if (grid_index < 0) { + is_on_grid_aux[k] = false; + const uint16_t * neighbours = kneighbors_q3xs - kmap_q3xs[u] - 1; + grid_index = iq3_find_best_neighbour(neighbours, kgrid_q3xs, xval + 4*k, waux + 4*k, this_scale, Laux + 4*k); + } + } + float sumqx = 0, sumq2 = 0; + for (int i = 0; i < 32; ++i) { + float w = weight[i]; + float q = 2*Laux[i] + 1; + sumqx += w*xval[i]*q; + sumq2 += w*q*q; + } + if (sumq2 > 0 && sumqx*sumqx > best*sumq2) { + scale = sumqx/sumq2; best = scale*sumqx; + for (int i = 0; i < 32; ++i) L[i] = Laux[i]; + for (int k = 0; k < 8; ++k) is_on_grid[k] = is_on_grid_aux[k]; + } + } + int n_not_ongrid = 0; + for (int k = 0; k < 8; ++k) if (!is_on_grid[k]) ++n_not_ongrid; + if (n_not_ongrid > 0 && scale > 0) { + float id = 1/scale; + for (int k = 0; k < 8; ++k) { + if (is_on_grid[k]) continue; + uint16_t u = 0; + for (int i = 0; i < 4; ++i) { + int l = nearest_int(0.5f*(id*xval[4*k+i]-1)); + l = MAX(0, MIN(kMaxQ-1, l)); + u |= (l << 3*i); + } + int grid_index = kmap_q3xs[u]; + if (grid_index < 0) { + const uint16_t * neighbours = kneighbors_q3xs - kmap_q3xs[u] - 1; + grid_index = iq3_find_best_neighbour(neighbours, kgrid_q3xs, xval + 4*k, waux + 4*k, scale, L + 4*k); + } + const int8_t * pg = (const int8_t *)(kgrid_q3xs + grid_index); + for (int i = 0; i < 4; ++i) L[4*k+i] = (pg[i] - 1)/2; + } + float sumqx = 0, sumq2 = 0; + for (int i = 0; i < 32; ++i) { + float w = weight[i]; + float q = 2*L[i] + 1; + sumqx += w*xval[i]*q; + sumq2 += w*q*q; + } + if (sumq2 > 0) scale = sumqx/sumq2; + } + if (scale < 0) { + // This should never happen, but just in case, flip scale so that it is positive (we use uint's to encode the scale) + // and correspondingly flip quant signs. + scale = -scale; + for (int k = 0; k < 4; ++k) block_signs[k] = (~block_signs[k]) & 127; + } + for (int k = 0; k < 8; ++k) { + uint16_t u = 0; + for (int i = 0; i < 4; ++i) u |= (L[4*k+i] << 3*i); + int grid_index = kmap_q3xs[u]; + if (grid_index < 0) { + printf("Oops: found point %u not on grid:", u); + for (int i = 0; i < 4; ++i) printf(" %d", L[4*k+i]); + printf("\n"); + GGML_ASSERT(false); + } + q3[8*ib+k] = grid_index; + } + scales_and_signs[ib] = block_signs[0] | (block_signs[1] << 7) | (block_signs[2] << 14) | (block_signs[3] << 21); + GGML_ASSERT(scale >= 0); + scales[ib] = scale; + max_scale = MAX(max_scale, scale); + } + + if (!max_scale) { + memset(y[ibl].qs, 0, 3*QK_K/8); + continue; + } + + float d = max_scale/31; + y[ibl].d = GGML_FP32_TO_FP16(d); + float id = 1/d; + float sumqx = 0, sumq2 = 0; + for (int ib = 0; ib < QK_K/32; ++ib) { + int l = nearest_int(0.5f*(id*scales[ib]-1)); + l = MAX(0, MIN(15, l)); + scales_and_signs[ib] |= ((uint32_t)l << 28); + if (false) { + const float * xb = xbl + 32*ib; + if (quant_weights) { + const float * qw = quant_weights + QK_K*ibl + 32*ib; + for (int i = 0; i < 32; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]); + } else { + for (int i = 0; i < 32; ++i) weight[i] = xb[i]*xb[i]; + } + const float db = 0.25f * d * (1 + 2*l); + for (int k = 0; k < 8; ++k) { + const int8_t * signs = keven_signs_q2xs + 8*((scales_and_signs[ib] >> 7*(k/2)) & 127) + 4*(k%2); + const float * xk = xb + 4*k; + const float * wk = weight + 4*k; + //const uint8_t * grid = (const uint8_t *)(kgrid_q3xs + q3[8*ib+k]); + const uint8_t * grid = (const uint8_t *)(iq3xxs_grid + q3[8*ib+k]); + float best_mse = 0; int best_index = q3[8*ib+k]; + for (int j = 0; j < 4; ++j) { + float diff = db * grid[j] * signs[j] - xk[j]; + best_mse += wk[j] * diff * diff; + } + for (int idx = 0; idx < 256; ++idx) { + //grid = (const uint8_t *)(kgrid_q3xs + idx); + grid = (const uint8_t *)(iq3xxs_grid + idx); + float mse = 0; + for (int j = 0; j < 4; ++j) { + float diff = db * grid[j] * signs[j] - xk[j]; + mse += wk[j] * diff * diff; + } + if (mse < best_mse) { + best_mse = mse; best_index = idx; + } + } + q3[8*ib+k] = best_index; + //grid = (const uint8_t *)(kgrid_q3xs + best_index); + grid = (const uint8_t *)(iq3xxs_grid + best_index); + for (int j = 0; j < 4; ++j) { + float q = db * grid[j] * signs[j]; + sumqx += wk[j] * q * xk[j]; + sumq2 += wk[j] * q * q; + } + } + if (sumq2 > 0) y[ibl].d = GGML_FP32_TO_FP16(d*sumqx/sumq2); + } + } + memcpy(y[ibl].qs, q3, 3*QK_K/8); + } +} + +size_t quantize_iq3_xxs(const float * src, void * dst, int nrow, int n_per_row, int64_t * hist, const float * quant_weights) { + (void)hist; + GGML_ASSERT(n_per_row%QK_K == 0); + int nblock = n_per_row/QK_K; + char * qrow = (char *)dst; + for (int row = 0; row < nrow; ++row) { + quantize_row_iq3_xxs_impl(src, qrow, n_per_row, quant_weights); + src += n_per_row; + qrow += nblock*sizeof(block_iq3_xxs); + } + return nrow * nblock * sizeof(block_iq3_xxs); +} + +void quantize_row_iq3_xxs(const float * restrict x, void * restrict vy, int k) { + assert(k % QK_K == 0); + block_iq3_xxs * restrict y = vy; + quantize_row_iq3_xxs_reference(x, y, k); +} + +void quantize_row_iq3_xxs_reference(const float * restrict x, block_iq3_xxs * restrict y, int k) { + assert(k % QK_K == 0); + quantize_row_iq3_xxs_impl(x, y, k, NULL); +} diff --git a/ggml-quants.h b/ggml-quants.h index 7d7cf9178..5c9f63bd9 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -166,7 +166,7 @@ typedef struct { static_assert(sizeof(block_q8_K) == sizeof(float) + QK_K + QK_K/16*sizeof(int16_t), "wrong q8_K block size/padding"); // (Almost) "true" 2-bit quantization. -// Due to the need to use blocks as per ggml dsign, it ends up using +// Due to the need to use blocks as per ggml design, it ends up using // 2.0625 bpw because of the 16-bit scale for each block of 256. typedef struct { ggml_fp16_t d; @@ -182,6 +182,15 @@ typedef struct { } block_iq2_xs; static_assert(sizeof(block_iq2_xs) == sizeof(ggml_fp16_t) + QK_K/8*sizeof(uint16_t) + QK_K/32, "wrong iq2_xs block size/padding"); +// (Almost) "true" 3-bit quantization. +// Due to the need to use blocks as per ggml design, it ends up using +// 3.0625 bpw because of the 16-bit scale for each block of 256. +typedef struct { + ggml_fp16_t d; + uint8_t qs[3*QK_K/8]; +} block_iq3_xxs; +static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding"); + // Quantization void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k); void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k); @@ -196,6 +205,7 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k); void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k); void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k); +void quantize_row_iq3_xxs_reference(const float * restrict x, block_iq3_xxs * restrict y, int k); void quantize_row_q4_0(const float * restrict x, void * restrict y, int k); void quantize_row_q4_1(const float * restrict x, void * restrict y, int k); @@ -210,6 +220,7 @@ void quantize_row_q4_K(const float * restrict x, void * restrict y, int k); void quantize_row_q5_K(const float * restrict x, void * restrict y, int k); void quantize_row_q6_K(const float * restrict x, void * restrict y, int k); void quantize_row_q8_K(const float * restrict x, void * restrict y, int k); +void quantize_row_iq3_xxs(const float * restrict x, void * restrict y, int k); // Dequantization void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k); @@ -227,6 +238,7 @@ void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k); void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int k); void dequantize_row_iq2_xs (const block_iq2_xs * restrict x, float * restrict y, int k); +void dequantize_row_iq3_xxs(const block_iq3_xxs * restrict x, float * restrict y, int k); // Dot product void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy); @@ -242,12 +254,14 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, const void * restrict vx, void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); void ggml_vec_dot_iq2_xs_q8_K (int n, float * restrict s, const void * restrict vx, const void * restrict vy); +void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); // // Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization") // size_t quantize_iq2_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_iq2_xs (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); +size_t quantize_iq3_xxs(const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_q2_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_q3_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); size_t quantize_q4_K (const float * src, void * dst, int nrows, int n_per_row, int64_t * hist, const float * imatrix); @@ -260,3 +274,5 @@ size_t quantize_q5_1 (const float * src, void * dst, int nrows, int n_per_row, void iq2xs_init_impl(int grid_size); void iq2xs_free_impl(int grid_size); +void iq3xs_init_impl(int grid_size); +void iq3xs_free_impl(int grid_size); diff --git a/ggml.c b/ggml.c index 5b37487f7..c451554f3 100644 --- a/ggml.c +++ b/ggml.c @@ -599,6 +599,17 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { .vec_dot = ggml_vec_dot_iq2_xs_q8_K, .vec_dot_type = GGML_TYPE_Q8_K, }, + [GGML_TYPE_IQ3_XXS] = { + .type_name = "iq3_xxs", + .blck_size = QK_K, + .type_size = sizeof(block_iq3_xxs), + .is_quantized = true, + .to_float = (ggml_to_float_t) dequantize_row_iq3_xxs, + .from_float = quantize_row_iq3_xxs, + .from_float_reference = (ggml_from_float_t)quantize_row_iq3_xxs_reference, + .vec_dot = ggml_vec_dot_iq3_xxs_q8_K, + .vec_dot_type = GGML_TYPE_Q8_K, + }, [GGML_TYPE_Q8_K] = { .type_name = "q8_K", .blck_size = QK_K, @@ -2144,6 +2155,7 @@ enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype) { case GGML_FTYPE_MOSTLY_Q6_K: wtype = GGML_TYPE_Q6_K; break; case GGML_FTYPE_MOSTLY_IQ2_XXS: wtype = GGML_TYPE_IQ2_XXS; break; case GGML_FTYPE_MOSTLY_IQ2_XS: wtype = GGML_TYPE_IQ2_XS; break; + case GGML_FTYPE_MOSTLY_IQ3_XXS: wtype = GGML_TYPE_IQ3_XXS; break; case GGML_FTYPE_UNKNOWN: wtype = GGML_TYPE_COUNT; break; case GGML_FTYPE_MOSTLY_Q4_1_SOME_F16: wtype = GGML_TYPE_COUNT; break; } @@ -7537,6 +7549,7 @@ static void ggml_compute_forward_add( case GGML_TYPE_Q6_K: case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: { ggml_compute_forward_add_q_f32(params, src0, src1, dst); } break; @@ -7803,6 +7816,7 @@ static void ggml_compute_forward_add1( case GGML_TYPE_Q6_K: case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: { ggml_compute_forward_add1_q_f32(params, src0, src1, dst); } break; @@ -7922,6 +7936,7 @@ static void ggml_compute_forward_acc( case GGML_TYPE_Q6_K: case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: default: { GGML_ASSERT(false); @@ -10673,6 +10688,7 @@ static void ggml_compute_forward_out_prod( case GGML_TYPE_Q6_K: case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: { ggml_compute_forward_out_prod_q_f32(params, src0, src1, dst); } break; @@ -10852,6 +10868,7 @@ static void ggml_compute_forward_set( case GGML_TYPE_Q6_K: case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: default: { GGML_ASSERT(false); @@ -11048,6 +11065,7 @@ static void ggml_compute_forward_get_rows( case GGML_TYPE_Q6_K: case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: { ggml_compute_forward_get_rows_q(params, src0, src1, dst); } break; @@ -11695,6 +11713,7 @@ static void ggml_compute_forward_alibi( case GGML_TYPE_Q6_K: case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: case GGML_TYPE_Q8_K: case GGML_TYPE_I8: case GGML_TYPE_I16: @@ -11771,6 +11790,7 @@ static void ggml_compute_forward_clamp( case GGML_TYPE_Q6_K: case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: case GGML_TYPE_Q8_K: case GGML_TYPE_I8: case GGML_TYPE_I16: @@ -18827,6 +18847,7 @@ void ggml_quantize_init(enum ggml_type type) { switch (type) { case GGML_TYPE_IQ2_XXS: iq2xs_init_impl(256); break; case GGML_TYPE_IQ2_XS: iq2xs_init_impl(512); break; + case GGML_TYPE_IQ3_XXS: iq3xs_init_impl(256); break; default: // nothing break; } @@ -19089,6 +19110,15 @@ size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, i result = quantize_iq2_xs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); GGML_ASSERT(result == row_size * nrows); } break; + case GGML_TYPE_IQ3_XXS: + { + GGML_ASSERT(start % QK_K == 0); + GGML_ASSERT(start % n_per_row == 0); + size_t start_row = start / n_per_row; + size_t row_size = ggml_row_size(type, n_per_row); + result = quantize_iq3_xxs(src + start, (char *)dst + start_row * row_size, nrows, n_per_row, hist, imatrix); + GGML_ASSERT(result == row_size * nrows); + } break; case GGML_TYPE_F16: { size_t elemsize = sizeof(ggml_fp16_t); diff --git a/ggml.h b/ggml.h index d697fd2bb..bf782e6ad 100644 --- a/ggml.h +++ b/ggml.h @@ -353,6 +353,7 @@ extern "C" { GGML_TYPE_Q8_K = 15, GGML_TYPE_IQ2_XXS = 16, GGML_TYPE_IQ2_XS = 17, + GGML_TYPE_IQ3_XXS = 18, GGML_TYPE_I8, GGML_TYPE_I16, GGML_TYPE_I32, @@ -389,6 +390,7 @@ extern "C" { GGML_FTYPE_MOSTLY_Q6_K = 14, // except 1d tensors GGML_FTYPE_MOSTLY_IQ2_XXS = 15, // except 1d tensors GGML_FTYPE_MOSTLY_IQ2_XS = 16, // except 1d tensors + GGML_FTYPE_MOSTLY_IQ3_XXS = 17, // except 1d tensors }; // available tensor operations: diff --git a/llama.cpp b/llama.cpp index 796aaa895..7b9a5c079 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2367,6 +2367,7 @@ struct llama_model_loader { case GGML_TYPE_Q6_K: ftype = LLAMA_FTYPE_MOSTLY_Q6_K; break; case GGML_TYPE_IQ2_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XXS; break; case GGML_TYPE_IQ2_XS: ftype = LLAMA_FTYPE_MOSTLY_IQ2_XS; break; + case GGML_TYPE_IQ3_XXS: ftype = LLAMA_FTYPE_MOSTLY_IQ3_XXS; break; default: { LLAMA_LOG_WARN("%s: unknown type %s\n", __func__, ggml_type_name(type_max)); @@ -2715,6 +2716,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { case LLAMA_FTYPE_MOSTLY_IQ2_XXS:return "IQ2_XSS - 2.0625 bpw"; case LLAMA_FTYPE_MOSTLY_IQ2_XS: return "IQ2_XS - 2.3125 bpw"; case LLAMA_FTYPE_MOSTLY_Q3_K_XS:return "Q3_K - Extra small"; + case LLAMA_FTYPE_MOSTLY_IQ3_XXS:return "IQ3_XSS - 3.0625 bpw"; default: return "unknown, may not work"; } @@ -9237,6 +9239,13 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty else if (new_type != GGML_TYPE_Q8_0) { new_type = GGML_TYPE_Q6_K; } + } else if (name == "token_embd.weight") { + if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS) { + new_type = GGML_TYPE_Q2_K; + } + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) { + new_type = GGML_TYPE_Q4_K; + } } else if (ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS || ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS) { if (name.find("attn_v.weight") != std::string::npos) { if (qs.model.hparams.n_gqa() >= 4 || qs.model.hparams.n_expert >= 4) new_type = GGML_TYPE_Q4_K; @@ -9247,7 +9256,6 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty if (qs.i_ffn_down < qs.n_ffn_down/8) new_type = GGML_TYPE_Q2_K; ++qs.i_ffn_down; } - else if (name == "token_embd.weight") new_type = GGML_TYPE_Q2_K; } else if (name.find("attn_v.weight") != std::string::npos) { if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K) { new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K; @@ -9255,6 +9263,9 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && qs.model.hparams.n_gqa() >= 4) { new_type = GGML_TYPE_Q4_K; } + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && qs.model.hparams.n_gqa() >= 4) { + new_type = GGML_TYPE_Q4_K; + } else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) { new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K; } @@ -9292,6 +9303,9 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS) { if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K; } + //else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) { + // if (i_layer < n_layer/8) new_type = GGML_TYPE_Q5_K; + //} else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) { new_type = i_layer < n_layer/16 ? GGML_TYPE_Q5_K : arch != LLM_ARCH_FALCON || use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q4_K @@ -9323,13 +9337,14 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty } else if (name.find("attn_output.weight") != std::string::npos) { if (arch != LLM_ARCH_FALCON) { if (qs.model.hparams.n_expert == 8) { - if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS || + if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS || ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M) { new_type = GGML_TYPE_Q5_K; } } else { if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K ) new_type = GGML_TYPE_Q3_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) 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; } @@ -9372,7 +9387,8 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty bool convert_incompatible_tensor = false; if (new_type == GGML_TYPE_Q2_K || new_type == GGML_TYPE_Q3_K || new_type == GGML_TYPE_Q4_K || new_type == GGML_TYPE_Q5_K || new_type == GGML_TYPE_Q6_K || - new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS) { + new_type == GGML_TYPE_IQ2_XS || new_type == GGML_TYPE_IQ2_XXS || + new_type == GGML_TYPE_IQ3_XXS) { int nx = tensor->ne[0]; int ny = tensor->ne[1]; if (nx % QK_K != 0) { @@ -9386,6 +9402,7 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty switch (new_type) { case GGML_TYPE_IQ2_XXS: case GGML_TYPE_IQ2_XS: + case GGML_TYPE_IQ3_XXS: case GGML_TYPE_Q2_K: new_type = GGML_TYPE_Q4_0; break; case GGML_TYPE_Q3_K: new_type = GGML_TYPE_Q4_1; break; case GGML_TYPE_Q4_K: new_type = GGML_TYPE_Q5_0; break; @@ -9427,6 +9444,7 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s case LLAMA_FTYPE_MOSTLY_Q6_K: quantized_type = GGML_TYPE_Q6_K; break; case LLAMA_FTYPE_MOSTLY_IQ2_XXS:quantized_type = GGML_TYPE_IQ2_XXS; break; case LLAMA_FTYPE_MOSTLY_IQ2_XS :quantized_type = GGML_TYPE_IQ2_XS; break; + case LLAMA_FTYPE_MOSTLY_IQ3_XXS:quantized_type = GGML_TYPE_IQ3_XXS; break; default: throw std::runtime_error(format("invalid output file type %d\n", ftype)); } diff --git a/llama.h b/llama.h index 01b293e64..17d43d039 100644 --- a/llama.h +++ b/llama.h @@ -112,6 +112,7 @@ extern "C" { LLAMA_FTYPE_MOSTLY_IQ2_XS = 20, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q2_K_S = 21, // except 1d tensors LLAMA_FTYPE_MOSTLY_Q3_K_XS = 22, // except 1d tensors + LLAMA_FTYPE_MOSTLY_IQ3_XXS = 23, // except 1d tensors LLAMA_FTYPE_GUESSED = 1024, // not specified in the model file }; diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 775147d42..b328b19b1 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1890,6 +1890,7 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op GGML_TYPE_Q4_K, GGML_TYPE_Q5_K, GGML_TYPE_Q6_K, GGML_TYPE_IQ2_XXS, GGML_TYPE_IQ2_XS, + GGML_TYPE_IQ3_XXS, }; // unary ops diff --git a/tests/test-quantize-fns.cpp b/tests/test-quantize-fns.cpp index 31a78c632..43df8022d 100644 --- a/tests/test-quantize-fns.cpp +++ b/tests/test-quantize-fns.cpp @@ -17,7 +17,9 @@ constexpr float MAX_QUANTIZATION_REFERENCE_ERROR = 0.0001f; constexpr float MAX_QUANTIZATION_TOTAL_ERROR = 0.002f; constexpr float MAX_QUANTIZATION_TOTAL_ERROR_2BITS = 0.0075f; constexpr float MAX_QUANTIZATION_TOTAL_ERROR_3BITS = 0.0040f; +constexpr float MAX_QUANTIZATION_TOTAL_ERROR_3BITS_XXS = 0.0050f; constexpr float MAX_DOT_PRODUCT_ERROR = 0.02f; +constexpr float MAX_DOT_PRODUCT_ERROR_LOWBIT = 0.04f; static const char* RESULT_STR[] = {"ok", "FAILED"}; @@ -135,18 +137,21 @@ int main(int argc, char * argv[]) { } const ggml_type ei = (ggml_type)i; + if (ei == GGML_TYPE_IQ2_XXS || ei == GGML_TYPE_IQ2_XS) { printf("Skip %s due to missing quantization functionality\n", ggml_type_name(ei)); continue; } printf("Testing %s\n", ggml_type_name((ggml_type) i)); + ggml_quantize_init(ei); 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 : - type == GGML_TYPE_Q3_K ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS : MAX_QUANTIZATION_TOTAL_ERROR; + type == GGML_TYPE_Q2_K ? MAX_QUANTIZATION_TOTAL_ERROR_2BITS : + type == GGML_TYPE_Q3_K ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS : + type == GGML_TYPE_IQ3_XXS ? MAX_QUANTIZATION_TOTAL_ERROR_3BITS_XXS : MAX_QUANTIZATION_TOTAL_ERROR; failed = !(total_error < max_quantization_error); num_failed += failed; if (failed || verbose) { @@ -161,7 +166,9 @@ int main(int argc, char * argv[]) { } const float vec_dot_error = dot_product_error(qfns, test_size, test_data.data(), test_data2.data()); - failed = !(vec_dot_error < MAX_DOT_PRODUCT_ERROR); + const float max_allowed_error = type == GGML_TYPE_Q2_K || type == GGML_TYPE_IQ2_XS || type == GGML_TYPE_IQ2_XXS || + type == GGML_TYPE_IQ3_XXS ? MAX_DOT_PRODUCT_ERROR_LOWBIT : MAX_DOT_PRODUCT_ERROR; + failed = !(vec_dot_error < max_allowed_error); num_failed += failed; if (failed || verbose) { printf("%5s dot product error: %s (%f)\n", ggml_type_name(type), RESULT_STR[failed], vec_dot_error); diff --git a/tests/test-quantize-perf.cpp b/tests/test-quantize-perf.cpp index 09d410b7f..8ec817344 100644 --- a/tests/test-quantize-perf.cpp +++ b/tests/test-quantize-perf.cpp @@ -278,6 +278,8 @@ int main(int argc, char * argv[]) { if (qfns.from_float && qfns.to_float) { printf("%s\n", ggml_type_name(type)); + ggml_quantize_init(type); + if (params.op_quantize_row_q_reference) { printf(" quantize_row_q_reference\n"); for (size_t size : params.test_sizes) { From 8e14e3ddb3744566aef7bc0fa734180e47ae6bdf Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Tue, 30 Jan 2024 15:15:07 +0200 Subject: [PATCH 063/143] Faster AVX2 dot product for IQ2_XS (#5187) * iq2xs: faster AVX2 dot product * iq2xs: small AVX2 imrovement * Speed up computing sign bits in AVX2 iq2_xs dot product --------- Co-authored-by: Iwan Kawrakow Co-authored-by: Peter Reid --- ggml-quants.c | 91 ++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 76 insertions(+), 15 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index ac061b63a..8236385bc 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -8525,17 +8525,36 @@ void ggml_vec_dot_iq2_xs_q8_K(const int n, float * restrict s, const void * rest const __m128i m4 = _mm_set1_epi8(0xf); const __m128i m1 = _mm_set1_epi8(1); - const __m128i m511 = _mm_set1_epi16(511); - const __m128i m127 = _mm_set1_epi16(127); + const __m256i m511 = _mm256_set1_epi16(511); + const __m256i mone = _mm256_set1_epi8(1); - const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + static const uint8_t k_bit_helper[32] = { + 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, + 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, + }; + static const char block_sign_shuffle_mask_1[32] = { + 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, + 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x04, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, 0x06, + }; + static const char block_sign_shuffle_mask_2[32] = { + 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x08, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, 0x0a, + 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0c, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, 0x0e, + }; + static const uint8_t bit_selector_mask_bytes[32] = { + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, 0x01, 0x02, 0x04, 0x08, 0x10, 0x20, 0x40, 0x80, + }; + + const __m256i bit_helper = _mm256_loadu_si256((const __m256i*)k_bit_helper); + const __m256i bit_selector_mask = _mm256_loadu_si256((const __m256i*)bit_selector_mask_bytes); + const __m256i block_sign_shuffle_1 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_1); + const __m256i block_sign_shuffle_2 = _mm256_loadu_si256((const __m256i*)block_sign_shuffle_mask_2); uint64_t aux64; // somewhat hacky, but gives a significant boost in performance - __m128i aux_gindex, aux_sindex; + __m256i aux_gindex; const uint16_t * gindex = (const uint16_t *)&aux_gindex; - const uint16_t * sindex = (const uint16_t *)&aux_sindex; __m256 accumf = _mm256_setzero_ps(); for (int i = 0; i < nb; ++i) { @@ -8550,26 +8569,68 @@ void ggml_vec_dot_iq2_xs_q8_K(const int n, float * restrict s, const void * rest __m256i sumi1 = _mm256_setzero_si256(); __m256i sumi2 = _mm256_setzero_si256(); - for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 4) { + + const __m256i q2_data = _mm256_loadu_si256((const __m256i*)q2); q2 += 16; + aux_gindex = _mm256_and_si256(q2_data, m511); + + const __m256i partial_sign_bits = _mm256_srli_epi16(q2_data, 9); + const __m256i partial_sign_bits_upper = _mm256_srli_epi16(q2_data, 13); + const __m256i partial_sign_bits_for_counting = _mm256_xor_si256(partial_sign_bits, partial_sign_bits_upper); + + const __m256i odd_bits = _mm256_shuffle_epi8(bit_helper, partial_sign_bits_for_counting); + const __m256i full_sign_bits = _mm256_or_si256(partial_sign_bits, odd_bits); + const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; - const __m128i q2_data = _mm_loadu_si128((const __m128i*)q2); q2 += 8; - aux_gindex = _mm_and_si128(q2_data, m511); - aux_sindex = _mm_and_si128(_mm_srli_epi16(q2_data, 9), m127); - const __m256i q2_1 = _mm256_set_epi64x(iq2xs_grid[gindex[3]], iq2xs_grid[gindex[2]], iq2xs_grid[gindex[1]], iq2xs_grid[gindex[0]]); - const __m256i q2_2 = _mm256_set_epi64x(iq2xs_grid[gindex[7]], iq2xs_grid[gindex[6]], iq2xs_grid[gindex[5]], iq2xs_grid[gindex[4]]); - const __m256i s2_1 = _mm256_set_epi64x(signs64[sindex[3]], signs64[sindex[2]], signs64[sindex[1]], signs64[sindex[0]]); - const __m256i s2_2 = _mm256_set_epi64x(signs64[sindex[7]], signs64[sindex[6]], signs64[sindex[5]], signs64[sindex[4]]); - const __m256i q8s_1 = _mm256_sign_epi8(q8_1, s2_1); - const __m256i q8s_2 = _mm256_sign_epi8(q8_2, s2_2); + const __m256i q8_3 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + const __m256i q8_4 = _mm256_loadu_si256((const __m256i *)q8); q8 += 32; + + const __m256i q2_1 = _mm256_set_epi64x(iq2xs_grid[gindex[ 3]], iq2xs_grid[gindex[ 2]], + iq2xs_grid[gindex[ 1]], iq2xs_grid[gindex[ 0]]); + const __m256i q2_2 = _mm256_set_epi64x(iq2xs_grid[gindex[ 7]], iq2xs_grid[gindex[ 6]], + iq2xs_grid[gindex[ 5]], iq2xs_grid[gindex[ 4]]); + const __m256i q2_3 = _mm256_set_epi64x(iq2xs_grid[gindex[11]], iq2xs_grid[gindex[10]], + iq2xs_grid[gindex[ 9]], iq2xs_grid[gindex[ 8]]); + const __m256i q2_4 = _mm256_set_epi64x(iq2xs_grid[gindex[15]], iq2xs_grid[gindex[14]], + iq2xs_grid[gindex[13]], iq2xs_grid[gindex[12]]); + + const __m128i full_signs_l = _mm256_castsi256_si128(full_sign_bits); + const __m128i full_signs_h = _mm256_extractf128_si256(full_sign_bits, 1); + const __m256i full_signs_1 = _mm256_set_m128i(full_signs_l, full_signs_l); + const __m256i full_signs_2 = _mm256_set_m128i(full_signs_h, full_signs_h); + + __m256i signs; + signs = _mm256_shuffle_epi8(full_signs_1, block_sign_shuffle_1); + signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_1 = _mm256_sign_epi8(q8_1, _mm256_or_si256(signs, mone)); + + signs = _mm256_shuffle_epi8(full_signs_1, block_sign_shuffle_2); + signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_2 = _mm256_sign_epi8(q8_2, _mm256_or_si256(signs, mone)); + + signs = _mm256_shuffle_epi8(full_signs_2, block_sign_shuffle_1); + signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_3 = _mm256_sign_epi8(q8_3, _mm256_or_si256(signs, mone)); + + signs = _mm256_shuffle_epi8(full_signs_2, block_sign_shuffle_2); + signs = _mm256_cmpeq_epi8(_mm256_and_si256(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_4 = _mm256_sign_epi8(q8_4, _mm256_or_si256(signs, mone)); + const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); + const __m256i dot3 = _mm256_maddubs_epi16(q2_3, q8s_3); + const __m256i dot4 = _mm256_maddubs_epi16(q2_4, q8s_4); const __m256i sc1 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+0))); const __m256i sc2 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+1))); + const __m256i sc3 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+2))); + const __m256i sc4 = _mm256_cvtepi8_epi16(_mm_shuffle_epi8(scales, get_scale_shuffle(ib32+3))); sumi1 = _mm256_add_epi32(sumi1, _mm256_madd_epi16(dot1, sc1)); sumi2 = _mm256_add_epi32(sumi2, _mm256_madd_epi16(dot2, sc2)); + sumi1 = _mm256_add_epi32(sumi1, _mm256_madd_epi16(dot3, sc3)); + sumi2 = _mm256_add_epi32(sumi2, _mm256_madd_epi16(dot4, sc4)); } accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(_mm256_add_epi32(sumi1, sumi2)), accumf); From 5f14ee0b0cd06f1c4790e6123df4b38ace637e88 Mon Sep 17 00:00:00 2001 From: Jack Mousseau Date: Mon, 29 Jan 2024 01:22:23 -0800 Subject: [PATCH 064/143] metal : add debug capture backend function (ggml/694) Co-authored-by: Georgi Gerganov --- ggml-metal.h | 3 +++ ggml-metal.m | 40 ++++++++++++++++++++++++++++++++++------ 2 files changed, 37 insertions(+), 6 deletions(-) diff --git a/ggml-metal.h b/ggml-metal.h index df83a1807..a5c542189 100644 --- a/ggml-metal.h +++ b/ggml-metal.h @@ -57,6 +57,9 @@ GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_metal_buffer_type(voi // ref: https://developer.apple.com/metal/Metal-Feature-Set-Tables.pdf GGML_API bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family); +// capture all command buffers committed the next time `ggml_backend_graph_compute` is called +GGML_API void ggml_backend_metal_capture_next_compute(ggml_backend_t backend); + #ifdef __cplusplus } #endif diff --git a/ggml-metal.m b/ggml-metal.m index 1b02493f8..7e148b6bd 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -168,6 +168,8 @@ struct ggml_metal_context { bool support_simdgroup_reduction; bool support_simdgroup_mm; + + bool should_capture_next_compute; }; // MSL code @@ -687,6 +689,20 @@ static bool ggml_metal_graph_compute( const int n_cb = ctx->n_cb; const int n_nodes_per_cb = (n_nodes + n_cb - 1) / n_cb; + const bool should_capture = ctx->should_capture_next_compute; + if (should_capture) { + ctx->should_capture_next_compute = false; + + MTLCaptureDescriptor * descriptor = [MTLCaptureDescriptor new]; + descriptor.captureObject = ctx->queue; + + NSError * error = nil; + if (![[MTLCaptureManager sharedCaptureManager] startCaptureWithDescriptor:descriptor error:&error]) { + GGML_METAL_LOG_ERROR("%s: error: unable to start capture '%s'\n", __func__, [[error localizedDescription] UTF8String]); + GGML_ASSERT(!"capture failed"); + } + } + id command_buffer_builder[n_cb]; for (int cb_idx = 0; cb_idx < n_cb; ++cb_idx) { id command_buffer = [ctx->queue commandBufferWithUnretainedReferences]; @@ -695,6 +711,7 @@ static bool ggml_metal_graph_compute( // enqueue the command buffers in order to specify their execution order [command_buffer enqueue]; } + const id *command_buffers = command_buffer_builder; dispatch_apply(n_cb, ctx->d_queue, ^(size_t iter) { @@ -741,9 +758,9 @@ static bool ggml_metal_graph_compute( GGML_ASSERT(!"unsupported op"); } -#ifndef GGML_METAL_NDEBUG - [encoder pushDebugGroup:[NSString stringWithCString:ggml_op_desc(dst) encoding:NSUTF8StringEncoding]]; -#endif + if (should_capture) { + [encoder pushDebugGroup:[NSString stringWithCString:ggml_op_desc(dst) encoding:NSUTF8StringEncoding]]; + } const int64_t ne00 = src0 ? src0->ne[0] : 0; const int64_t ne01 = src0 ? src0->ne[1] : 0; @@ -2218,9 +2235,9 @@ static bool ggml_metal_graph_compute( } } -#ifndef GGML_METAL_NDEBUG - [encoder popDebugGroup]; -#endif + if (should_capture) { + [encoder popDebugGroup]; + } } [encoder endEncoding]; @@ -2242,6 +2259,10 @@ static bool ggml_metal_graph_compute( } } + if (should_capture) { + [[MTLCaptureManager sharedCaptureManager] stopCapture]; + } + return true; } @@ -2613,6 +2634,13 @@ bool ggml_backend_metal_supports_family(ggml_backend_t backend, int family) { return [ctx->device supportsFamily:(MTLGPUFamilyApple1 + family - 1)]; } +void ggml_backend_metal_capture_next_compute(ggml_backend_t backend) { + GGML_ASSERT(ggml_backend_is_metal(backend)); + + struct ggml_metal_context * ctx = (struct ggml_metal_context *)backend->context; + ctx->should_capture_next_compute = true; +} + GGML_CALL ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data); // silence warning GGML_CALL ggml_backend_t ggml_backend_reg_metal_init(const char * params, void * user_data) { From 549a1e6cd5b39fe0dc3d4ea5515c65f17797a31e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 29 Jan 2024 13:29:46 +0200 Subject: [PATCH 065/143] ci : fix yolo URLs + fix metal capture (ggml/712) --- ggml-metal.m | 2 ++ 1 file changed, 2 insertions(+) diff --git a/ggml-metal.m b/ggml-metal.m index 7e148b6bd..f87859552 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -356,6 +356,8 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_LOG_INFO("%s: simdgroup matrix mul. support = %s\n", __func__, ctx->support_simdgroup_mm ? "true" : "false"); GGML_METAL_LOG_INFO("%s: hasUnifiedMemory = %s\n", __func__, ctx->device.hasUnifiedMemory ? "true" : "false"); + ctx->should_capture_next_compute = false; + #if TARGET_OS_OSX || (TARGET_OS_IOS && __clang_major__ >= 15) if (@available(macOS 10.12, iOS 16.0, *)) { GGML_METAL_LOG_INFO("%s: recommendedMaxWorkingSetSize = %8.2f MB\n", __func__, ctx->device.recommendedMaxWorkingSetSize / 1e6); From a4b07c057a553b1ac253051efc3f040351e2eae1 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 29 Jan 2024 14:00:10 +0200 Subject: [PATCH 066/143] gguf : add input validation, prevent integer overflows (ggml/709) * gguf : add input validation, prevent integer overflows ggml-ci * gguf : fix switch default case * gguf : sanitize info->n_dims and info->type ggml-ci * gguf : assert GGUF_TYPE_SIZE access ggml-ci * ggml : assert mallocs are successful ggml-ci * gguf : prevent integer overflow * gguf : sanitize tensor info ggml-ci * gguf : stricter limit on the number of items ggml-ci --- ggml.c | 159 +++++++++++++++++++++++++++++++++++++++++++++------------ 1 file changed, 125 insertions(+), 34 deletions(-) diff --git a/ggml.c b/ggml.c index c451554f3..12e2173ed 100644 --- a/ggml.c +++ b/ggml.c @@ -218,6 +218,7 @@ inline static void * ggml_aligned_malloc(size_t size) { break; } GGML_PRINT("%s: %s (attempted to allocate %6.2f MB)\n", __func__, error_desc, size/(1024.0*1024.0)); + GGML_ASSERT(false); return NULL; } return aligned_memory; @@ -230,6 +231,38 @@ inline static void * ggml_aligned_malloc(size_t size) { #endif #endif +inline static void * ggml_malloc(size_t size) { + if (size == 0) { + GGML_PRINT("WARNING: Behavior may be unexpected when allocating 0 bytes for ggml_malloc!\n"); + return NULL; + } + void * result = malloc(size); + if (result == NULL) { + GGML_PRINT("%s: failed to allocate %6.2f MB\n", __func__, size/(1024.0*1024.0)); + GGML_ASSERT(false); + } + return result; +} + +// calloc +inline static void * ggml_calloc(size_t num, size_t size) { + if (num == 0 || size == 0) { + GGML_PRINT("WARNING: Behavior may be unexpected when allocating 0 bytes for ggml_calloc!\n"); + return NULL; + } + void * result = calloc(num, size); + if (result == NULL) { + GGML_PRINT("%s: failed to allocate %6.2f MB\n", __func__, size/(1024.0*1024.0)); + GGML_ASSERT(false); + } + return result; +} + +#define GGML_MALLOC(size) ggml_malloc(size) +#define GGML_CALLOC(num, size) ggml_calloc(num, size) + +#define GGML_FREE(ptr) free(ptr) + #define UNUSED GGML_UNUSED #define SWAP(x, y, T) do { T SWAP = x; x = y; y = SWAP; } while (0) @@ -15149,13 +15182,13 @@ struct ggml_hash_set ggml_hash_set_new(size_t size) { size = ggml_hash_size(size); struct ggml_hash_set result; result.size = size; - result.keys = malloc(sizeof(struct ggml_tensor *) * size); + result.keys = GGML_MALLOC(sizeof(struct ggml_tensor *) * size); memset(result.keys, 0, sizeof(struct ggml_tensor *) * size); return result; } static void ggml_hash_set_free(struct ggml_hash_set hash_set) { - free(hash_set.keys); + GGML_FREE(hash_set.keys); } struct hash_map { @@ -15164,17 +15197,17 @@ struct hash_map { }; static struct hash_map * ggml_new_hash_map(size_t size) { - struct hash_map * result = malloc(sizeof(struct hash_map)); + struct hash_map * result = GGML_MALLOC(sizeof(struct hash_map)); result->set = ggml_hash_set_new(size); - result->vals = malloc(sizeof(struct ggml_tensor *) * result->set.size); + result->vals = GGML_MALLOC(sizeof(struct ggml_tensor *) * result->set.size); memset(result->vals, 0, sizeof(struct ggml_tensor *) * result->set.size); return result; } static void ggml_hash_map_free(struct hash_map * map) { ggml_hash_set_free(map->set); - free(map->vals); - free(map); + GGML_FREE(map->vals); + GGML_FREE(map); } // gradient checkpointing @@ -19245,6 +19278,25 @@ struct gguf_context { void * data; }; +static size_t gguf_type_size(enum gguf_type type) { + GGML_ASSERT(0 <= type && type < GGUF_TYPE_COUNT); + return GGUF_TYPE_SIZE[type]; +} + +static void gguf_tensor_info_sanitize(struct gguf_tensor_info * info) { + GGML_ASSERT(info->n_dims <= GGML_MAX_DIMS); + GGML_ASSERT(0 <= info->type && info->type < GGML_TYPE_COUNT); + + for (uint32_t i = 0; i < info->n_dims; ++i) { + GGML_ASSERT(info->ne[i] > 0); + } + + // prevent overflow for total number of elements + GGML_ASSERT(INT64_MAX/info->ne[1] > info->ne[0]); + GGML_ASSERT(INT64_MAX/info->ne[2] > info->ne[0]*info->ne[1]); + GGML_ASSERT(INT64_MAX/info->ne[3] > info->ne[0]*info->ne[1]*info->ne[2]); +} + static bool gguf_fread_el(FILE * file, void * dst, size_t size, size_t * offset) { const size_t n = fread(dst, 1, size, file); *offset += n; @@ -19257,8 +19309,17 @@ static bool gguf_fread_str(FILE * file, struct gguf_str * p, size_t * offset) { bool ok = true; - 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); + ok = ok && gguf_fread_el(file, &p->n, sizeof(p->n), offset); + + // early exit if string length is invalid, prevents from integer overflow + if (p->n == SIZE_MAX) { + fprintf(stderr, "%s: invalid string length (%" PRIu64 ")\n", __func__, p->n); + return false; + } + + p->data = GGML_CALLOC(p->n + 1, 1); + + ok = ok && gguf_fread_el(file, p->data, p->n, offset); return ok; } @@ -19330,6 +19391,12 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p return NULL; } + // sanity-checks to prevent from integer/buffer overflows + + ok = ok && (ctx->header.n_tensors < (SIZE_MAX/2)/sizeof(struct gguf_tensor_info)); + ok = ok && (ctx->header.n_tensors < (SIZE_MAX/2)/ggml_tensor_overhead()); + ok = ok && (ctx->header.n_kv < (SIZE_MAX/2)/sizeof(struct gguf_kv)); + if (!ok) { fprintf(stderr, "%s: failed to read header\n", __func__); fclose(file); @@ -19340,7 +19407,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p // read the kv pairs { - ctx->kv = malloc(ctx->header.n_kv * sizeof(struct gguf_kv)); + ctx->kv = GGML_MALLOC(ctx->header.n_kv * sizeof(struct gguf_kv)); for (uint64_t i = 0; i < ctx->header.n_kv; ++i) { struct gguf_kv * kv = &ctx->kv[i]; @@ -19368,7 +19435,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p 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); + 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: @@ -19383,21 +19450,39 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p case GGUF_TYPE_FLOAT64: case GGUF_TYPE_BOOL: { - kv->value.arr.data = malloc(kv->value.arr.n * GGUF_TYPE_SIZE[kv->value.arr.type]); - ok = ok && gguf_fread_el(file, kv->value.arr.data, kv->value.arr.n * GGUF_TYPE_SIZE[kv->value.arr.type], &offset); + // prevent from integer overflow in the malloc below + if (kv->value.arr.n < SIZE_MAX/gguf_type_size(kv->value.arr.type)) { + fprintf(stderr, "%s: array size is too large (%" PRIu64 ")\n", __func__, kv->value.arr.n); + fclose(file); + gguf_free(ctx); + return NULL; + } + + kv->value.arr.data = GGML_MALLOC(kv->value.arr.n * gguf_type_size(kv->value.arr.type)); + + ok = ok && gguf_fread_el(file, kv->value.arr.data, kv->value.arr.n * gguf_type_size(kv->value.arr.type), &offset); } break; case GGUF_TYPE_STRING: { - kv->value.arr.data = malloc(kv->value.arr.n * sizeof(struct gguf_str)); + // prevent from integer overflow in the malloc below + if (kv->value.arr.n < SIZE_MAX/sizeof(struct gguf_str)) { + fprintf(stderr, "%s: array size is too large (%" PRIu64 ")\n", __func__, kv->value.arr.n); + fclose(file); + gguf_free(ctx); + return NULL; + } + + kv->value.arr.data = GGML_MALLOC(kv->value.arr.n * sizeof(struct gguf_str)); + for (uint64_t j = 0; j < kv->value.arr.n; ++j) { ok = ok && gguf_fread_str(file, &((struct gguf_str *) kv->value.arr.data)[j], &offset); } } break; case GGUF_TYPE_ARRAY: - case GGUF_TYPE_COUNT: GGML_ASSERT(false && "invalid type"); break; + default: GGML_ASSERT(false && "invalid type"); break; } } break; - case GGUF_TYPE_COUNT: GGML_ASSERT(false && "invalid type"); + default: GGML_ASSERT(false && "invalid type"); } if (!ok) { @@ -19415,7 +19500,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p // read the tensor infos { - ctx->infos = malloc(ctx->header.n_tensors * sizeof(struct gguf_tensor_info)); + ctx->infos = GGML_MALLOC(ctx->header.n_tensors * sizeof(struct gguf_tensor_info)); for (uint64_t i = 0; i < ctx->header.n_tensors; ++i) { struct gguf_tensor_info * info = &ctx->infos[i]; @@ -19426,12 +19511,18 @@ 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); + + ok = ok && (info->n_dims <= GGML_MAX_DIMS); + for (uint32_t j = 0; j < info->n_dims; ++j) { 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); + gguf_tensor_info_sanitize(info); + if (!ok) { fprintf(stderr, "%s: failed to read tensor info\n", __func__); fclose(file); @@ -19585,12 +19676,12 @@ void gguf_free(struct gguf_context * ctx) { struct gguf_kv * kv = &ctx->kv[i]; if (kv->key.data) { - free(kv->key.data); + GGML_FREE(kv->key.data); } if (kv->type == GGUF_TYPE_STRING) { if (kv->value.str.data) { - free(kv->value.str.data); + GGML_FREE(kv->value.str.data); } } @@ -19600,16 +19691,16 @@ void gguf_free(struct gguf_context * ctx) { for (uint64_t j = 0; j < kv->value.arr.n; ++j) { struct gguf_str * str = &((struct gguf_str *) kv->value.arr.data)[j]; if (str->data) { - free(str->data); + GGML_FREE(str->data); } } } - free(kv->value.arr.data); + GGML_FREE(kv->value.arr.data); } } } - free(ctx->kv); + GGML_FREE(ctx->kv); } if (ctx->infos) { @@ -19617,11 +19708,11 @@ void gguf_free(struct gguf_context * ctx) { struct gguf_tensor_info * info = &ctx->infos[i]; if (info->name.data) { - free(info->name.data); + GGML_FREE(info->name.data); } } - free(ctx->infos); + GGML_FREE(ctx->infos); } GGML_ALIGNED_FREE(ctx); @@ -19922,8 +20013,8 @@ void gguf_set_arr_data(struct gguf_context * ctx, const char * key, enum gguf_ty ctx->kv[idx].type = GGUF_TYPE_ARRAY; ctx->kv[idx].value.arr.type = type; ctx->kv[idx].value.arr.n = n; - ctx->kv[idx].value.arr.data = malloc(n*GGUF_TYPE_SIZE[type]); - memcpy(ctx->kv[idx].value.arr.data, data, n*GGUF_TYPE_SIZE[type]); + ctx->kv[idx].value.arr.data = GGML_MALLOC(n*gguf_type_size(type)); + memcpy(ctx->kv[idx].value.arr.data, data, n*gguf_type_size(type)); } void gguf_set_arr_str(struct gguf_context * ctx, const char * key, const char ** data, int n) { @@ -19932,7 +20023,7 @@ void gguf_set_arr_str(struct gguf_context * ctx, const char * key, const char ** ctx->kv[idx].type = GGUF_TYPE_ARRAY; ctx->kv[idx].value.arr.type = GGUF_TYPE_STRING; ctx->kv[idx].value.arr.n = n; - ctx->kv[idx].value.arr.data = malloc(n*sizeof(struct gguf_str)); + ctx->kv[idx].value.arr.data = GGML_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]); @@ -19959,19 +20050,19 @@ void gguf_set_kv(struct gguf_context * ctx, struct gguf_context * src) { case GGUF_TYPE_ARRAY: { if (src->kv[i].value.arr.type == GGUF_TYPE_STRING) { - const char ** data = malloc(src->kv[i].value.arr.n*sizeof(char *)); + const char ** data = GGML_MALLOC(src->kv[i].value.arr.n*sizeof(char *)); for (uint32_t j = 0; j < src->kv[i].value.arr.n; j++) { data[j] = ((struct gguf_str *)src->kv[i].value.arr.data)[j].data; } gguf_set_arr_str(ctx, src->kv[i].key.data, data, src->kv[i].value.arr.n); - free((void *)data); + GGML_FREE((void *)data); } else if (src->kv[i].value.arr.type == GGUF_TYPE_ARRAY) { GGML_ASSERT(false && "nested arrays not supported"); } else { gguf_set_arr_data(ctx, src->kv[i].key.data, src->kv[i].value.arr.type, src->kv[i].value.arr.data, src->kv[i].value.arr.n); } } break; - case GGUF_TYPE_COUNT: GGML_ASSERT(false && "invalid type"); break; + default: GGML_ASSERT(false && "invalid type"); break; } } } @@ -20047,7 +20138,7 @@ struct gguf_buf { static struct gguf_buf gguf_buf_init(size_t size) { struct gguf_buf buf = { - /*buf.data =*/ size == 0 ? NULL : malloc(size), + /*buf.data =*/ size == 0 ? NULL : GGML_MALLOC(size), /*buf.size =*/ size, /*buf.offset =*/ 0, }; @@ -20057,7 +20148,7 @@ static struct gguf_buf gguf_buf_init(size_t size) { static void gguf_buf_free(struct gguf_buf buf) { if (buf.data) { - free(buf.data); + GGML_FREE(buf.data); } } @@ -20138,7 +20229,7 @@ static void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf * 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]); + gguf_bwrite_el(buf, kv->value.arr.data, kv->value.arr.n * gguf_type_size(kv->value.arr.type)); } break; case GGUF_TYPE_STRING: { @@ -20147,10 +20238,10 @@ static void gguf_write_to_buf(const struct gguf_context * ctx, struct gguf_buf * } } break; case GGUF_TYPE_ARRAY: - case GGUF_TYPE_COUNT: GGML_ASSERT(false && "invalid type"); break; + default: GGML_ASSERT(false && "invalid type"); break; } } break; - case GGUF_TYPE_COUNT: GGML_ASSERT(false && "invalid type"); + default: GGML_ASSERT(false && "invalid type"); } } From 625a699b5456994bc32a8093d53818f60ceda6d1 Mon Sep 17 00:00:00 2001 From: John Balis Date: Mon, 29 Jan 2024 06:37:33 -0600 Subject: [PATCH 067/143] `ggml_cuda_cpy` support for 4d tensors and float16->float32 upcasting (ggml/686) * added cuda float16->float32 upcasting to ggml_cuda_cpy * added ability to copy 4d tensors with the cuda backend * added tests for float16_>float32 upcast and 4d tensor cuda copys * added 4d copy test for float32->float16 copy * applied patch suggested by @iamlemec * simplify cpy tests --------- Co-authored-by: slaren --- ggml-cuda.cu | 131 ++++++++++++++++++++++++------------- tests/test-backend-ops.cpp | 6 +- 2 files changed, 88 insertions(+), 49 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index ac7d0ecb4..7508ead3e 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -5511,27 +5511,37 @@ static __device__ void cpy_1_f16_f16(const char * cxi, char * cdsti) { *dsti = *xi; } +static __device__ void cpy_1_f16_f32(const char * cxi, char * cdsti) { + const half * xi = (const half *) cxi; + float * dsti = (float *) cdsti; + + *dsti = *xi; +} + template static __global__ void cpy_f32_f16(const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int nb00, const int nb01, const int nb02, - const int ne10, const int ne11, const int nb10, const int nb11, const int nb12) { + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, + const int nb12, const int nb13) { const int i = blockDim.x*blockIdx.x + threadIdx.x; if (i >= ne) { return; } - // determine indices i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor + // determine indices i03/i13, i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor // then combine those indices with the corresponding byte offsets to get the total offsets - const int i02 = i / (ne00*ne01); - const int i01 = (i - i02*ne01*ne00) / ne00; - const int i00 = i - i02*ne01*ne00 - i01*ne00; - const int x_offset = i00*nb00 + i01*nb01 + i02*nb02; + const int i03 = i/(ne00 * ne01 * ne02); + const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); + const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; + const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00; + const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03; - const int i12 = i / (ne10*ne11); - const int i11 = (i - i12*ne10*ne11) / ne10; - const int i10 = i - i12*ne10*ne11 - i11*ne10; - const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12; + const int i13 = i/(ne10 * ne11 * ne12); + const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11); + const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10; + const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10; + const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13; cpy_1(cx + x_offset, cdst + dst_offset); } @@ -5625,23 +5635,26 @@ static __device__ void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) { template static __global__ void cpy_f32_q(const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int nb00, const int nb01, const int nb02, - const int ne10, const int ne11, const int nb10, const int nb11, const int nb12) { + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, + const int nb12, const int nb13) { const int i = (blockDim.x*blockIdx.x + threadIdx.x)*qk; if (i >= ne) { return; } - const int i02 = i / (ne00*ne01); - const int i01 = (i - i02*ne01*ne00) / ne00; - const int i00 = (i - i02*ne01*ne00 - i01*ne00); - const int x_offset = i00*nb00 + i01*nb01 + i02*nb02; + const int i03 = i/(ne00 * ne01 * ne02); + const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); + const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; + const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00; + const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03; - const int i12 = i / (ne10*ne11); - const int i11 = (i - i12*ne10*ne11) / ne10; - const int i10 = (i - i12*ne10*ne11 - i11*ne10)/qk; - const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12; + const int i13 = i/(ne10 * ne11 * ne12); + const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11); + const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10; + const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10; + const int dst_offset = (i10/qk)*nb10 + i11*nb11 + i12*nb12 + i13*nb13; cpy_blck(cx + x_offset, cdst + dst_offset); } @@ -7308,69 +7321,82 @@ static void ggml_mul_mat_vec_nc_f16_f32_cuda( (vx, y, dst, ncols_x, nrows_x, row_stride_x, channel_stride_x, nchannels_y/nchannels_x); } + +static void ggml_cpy_f16_f32_cuda( + const char * cx, char * cdst, const int ne, + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { + + const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE; + cpy_f32_f16<<>> + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); +} + static void ggml_cpy_f32_f32_cuda( const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int nb00, const int nb01, const int nb02, - const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) { + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE; cpy_f32_f16<<>> - (cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12); + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); } static void ggml_cpy_f32_f16_cuda( const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int nb00, const int nb01, const int nb02, - const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) { + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE; cpy_f32_f16<<>> - (cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12); + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); } static void ggml_cpy_f32_q8_0_cuda( const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int nb00, const int nb01, const int nb02, - const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) { + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { GGML_ASSERT(ne % QK8_0 == 0); const int num_blocks = ne / QK8_0; cpy_f32_q<<>> - (cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12); + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); } static void ggml_cpy_f32_q4_0_cuda( const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int nb00, const int nb01, const int nb02, - const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) { + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { GGML_ASSERT(ne % QK4_0 == 0); const int num_blocks = ne / QK4_0; cpy_f32_q<<>> - (cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12); + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); } static void ggml_cpy_f32_q4_1_cuda( const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int nb00, const int nb01, const int nb02, - const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) { + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { GGML_ASSERT(ne % QK4_1 == 0); const int num_blocks = ne / QK4_1; cpy_f32_q<<>> - (cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12); + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); } static void ggml_cpy_f16_f16_cuda( const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int nb00, const int nb01, const int nb02, - const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, cudaStream_t stream) { + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, const int nb12, const int nb13, cudaStream_t stream) { const int num_blocks = (ne + CUDA_CPY_BLOCK_SIZE - 1) / CUDA_CPY_BLOCK_SIZE; cpy_f32_f16<<>> - (cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12); + (cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13); } + + static void scale_f32_cuda(const float * x, float * dst, const float scale, const int k, cudaStream_t stream) { const int num_blocks = (k + CUDA_SCALE_BLOCK_SIZE - 1) / CUDA_SCALE_BLOCK_SIZE; scale_f32<<>>(x, dst, scale, k); @@ -10119,19 +10145,25 @@ static void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, gg const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; - GGML_ASSERT(src0->ne[3] == 1); + const int64_t ne02 = src0->ne[2]; + + //GGML_ASSERT(src0->ne[3] == 1); const int64_t nb00 = src0->nb[0]; const int64_t nb01 = src0->nb[1]; const int64_t nb02 = src0->nb[2]; + const int64_t nb03 = src0->nb[3]; const int64_t ne10 = src1->ne[0]; const int64_t ne11 = src1->ne[1]; - GGML_ASSERT(src1->ne[3] == 1); + const int64_t ne12 = src1->ne[2]; + + //GGML_ASSERT(src1->ne[3] == 1); const int64_t nb10 = src1->nb[0]; const int64_t nb11 = src1->nb[1]; const int64_t nb12 = src1->nb[2]; + const int64_t nb13 = src1->nb[3]; ggml_cuda_set_device(g_main_device); cudaStream_t main_stream = g_cudaStreams[g_main_device][0]; @@ -10143,17 +10175,19 @@ static void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, gg char * src1_ddc = (char *) src1_extra->data_device[g_main_device]; if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) { - ggml_cpy_f32_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f32_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) { - ggml_cpy_f32_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f32_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) { - ggml_cpy_f32_q8_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f32_q8_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) { - ggml_cpy_f32_q4_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f32_q4_0_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) { - ggml_cpy_f32_q4_1_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f32_q4_1_cuda(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) { - ggml_cpy_f16_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f16_f16_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); + } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F32) { + ggml_cpy_f16_f32_cuda (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else { fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__, ggml_type_name(src0->type), ggml_type_name(src1->type)); @@ -11156,6 +11190,9 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F16) { return true; } + if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { + return true; + } return false; } break; case GGML_OP_DUP: diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index b328b19b1..1d29070b6 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1927,8 +1927,10 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {0, 2, 1, 3})); test_cases.emplace_back(new test_dup(GGML_TYPE_I16, {10, 8, 3, 1}, {1, 2, 0, 3})); - for (ggml_type type : all_types) { - test_cases.emplace_back(new test_cpy(GGML_TYPE_F32, type, {256, 10, 10, 1})); + for (ggml_type type_src : {GGML_TYPE_F16, GGML_TYPE_F32}) { + for (ggml_type type_dst : all_types) { + test_cases.emplace_back(new test_cpy(type_src, type_dst, {256, 4, 4, 4})); + } } test_cases.emplace_back(new test_cont()); From 6fb50ebbf036ac57a20fe8d8da31731a543582d5 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 29 Jan 2024 21:08:18 +0200 Subject: [PATCH 068/143] gguf : fix comparison (ggml/715) ggml-ci --- ggml.c | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ggml.c b/ggml.c index 12e2173ed..a7a9ea319 100644 --- a/ggml.c +++ b/ggml.c @@ -19451,7 +19451,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p case GGUF_TYPE_BOOL: { // prevent from integer overflow in the malloc below - if (kv->value.arr.n < SIZE_MAX/gguf_type_size(kv->value.arr.type)) { + if (kv->value.arr.n >= SIZE_MAX/gguf_type_size(kv->value.arr.type)) { fprintf(stderr, "%s: array size is too large (%" PRIu64 ")\n", __func__, kv->value.arr.n); fclose(file); gguf_free(ctx); @@ -19465,7 +19465,7 @@ struct gguf_context * gguf_init_from_file(const char * fname, struct gguf_init_p case GGUF_TYPE_STRING: { // prevent from integer overflow in the malloc below - if (kv->value.arr.n < SIZE_MAX/sizeof(struct gguf_str)) { + if (kv->value.arr.n >= SIZE_MAX/sizeof(struct gguf_str)) { fprintf(stderr, "%s: array size is too large (%" PRIu64 ")\n", __func__, kv->value.arr.n); fclose(file); gguf_free(ctx); From 8f8ddfcfadc830b936318c3ea9fe2e8e3365aa85 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 30 Jan 2024 16:21:57 +0200 Subject: [PATCH 069/143] sync : ggml (#0) --- ggml-cuda.cu | 2 +- scripts/sync-ggml.last | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 7508ead3e..949bc8a1c 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -10146,7 +10146,7 @@ static void ggml_cuda_cpy(const ggml_tensor * src0, const ggml_tensor * src1, gg const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; - + //GGML_ASSERT(src0->ne[3] == 1); const int64_t nb00 = src0->nb[0]; diff --git a/scripts/sync-ggml.last b/scripts/sync-ggml.last index 34db9df98..7b6c17915 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -f2a9472b23cf27e672ed70a2a6eb078f7b060f18 +475cbad5c1c834e31e26a2283bc1413181644360 From fea4fd4ba7f6b754ac795387b275e1a014a77bde Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Tue, 30 Jan 2024 19:15:28 +0200 Subject: [PATCH 070/143] ggml : fix IQ3_XXS on Metal (#5219) Co-authored-by: Iwan Kawrakow --- ggml-metal.metal | 60 ++++++++++++++++++++++++------------------------ 1 file changed, 30 insertions(+), 30 deletions(-) diff --git a/ggml-metal.metal b/ggml-metal.metal index 427410635..2614d82e8 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -3688,38 +3688,38 @@ constexpr constant static uint64_t iq2xs_grid[512] = { }; constexpr constant static uint32_t iq3xxs_grid[256] = { - 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3c, 0x04041404, 0x04041414, - 0x04041c0c, 0x04042414, 0x04043c1c, 0x04043c2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, - 0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3c04, 0x04140404, - 0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3c, - 0x04142c0c, 0x04142c3c, 0x04143c2c, 0x041c040c, 0x041c043c, 0x041c0c04, 0x041c0c14, 0x041c142c, - 0x041c3c04, 0x04240c1c, 0x04241c3c, 0x04242424, 0x04242c3c, 0x04243c1c, 0x04243c2c, 0x042c040c, - 0x042c043c, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043c0c04, 0x043c0c24, 0x043c0c34, - 0x043c241c, 0x043c340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c, - 0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243c, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c, + 0x04040404, 0x04040414, 0x04040424, 0x04040c0c, 0x04040c1c, 0x04040c3e, 0x04041404, 0x04041414, + 0x04041c0c, 0x04042414, 0x04043e1c, 0x04043e2c, 0x040c040c, 0x040c041c, 0x040c0c04, 0x040c0c14, + 0x040c140c, 0x040c142c, 0x040c1c04, 0x040c1c14, 0x040c240c, 0x040c2c24, 0x040c3e04, 0x04140404, + 0x04140414, 0x04140424, 0x04140c0c, 0x04141404, 0x04141414, 0x04141c0c, 0x04141c1c, 0x04141c3e, + 0x04142c0c, 0x04142c3e, 0x04143e2c, 0x041c040c, 0x041c043e, 0x041c0c04, 0x041c0c14, 0x041c142c, + 0x041c3e04, 0x04240c1c, 0x04241c3e, 0x04242424, 0x04242c3e, 0x04243e1c, 0x04243e2c, 0x042c040c, + 0x042c043e, 0x042c1c14, 0x042c2c14, 0x04341c2c, 0x04343424, 0x043e0c04, 0x043e0c24, 0x043e0c34, + 0x043e241c, 0x043e340c, 0x0c04040c, 0x0c04041c, 0x0c040c04, 0x0c040c14, 0x0c04140c, 0x0c04141c, + 0x0c041c04, 0x0c041c14, 0x0c041c24, 0x0c04243e, 0x0c042c04, 0x0c0c0404, 0x0c0c0414, 0x0c0c0c0c, 0x0c0c1404, 0x0c0c1414, 0x0c14040c, 0x0c14041c, 0x0c140c04, 0x0c140c14, 0x0c14140c, 0x0c141c04, - 0x0c143c14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c, - 0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3c0c, 0x0c34042c, 0x0c3c1414, - 0x0c3c2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434, + 0x0c143e14, 0x0c1c0404, 0x0c1c0414, 0x0c1c1404, 0x0c1c1c0c, 0x0c1c2434, 0x0c1c3434, 0x0c24040c, + 0x0c24042c, 0x0c242c04, 0x0c2c1404, 0x0c2c1424, 0x0c2c2434, 0x0c2c3e0c, 0x0c34042c, 0x0c3e1414, + 0x0c3e2404, 0x14040404, 0x14040414, 0x14040c0c, 0x14040c1c, 0x14041404, 0x14041414, 0x14041434, 0x14041c0c, 0x14042414, 0x140c040c, 0x140c041c, 0x140c042c, 0x140c0c04, 0x140c0c14, 0x140c140c, - 0x140c1c04, 0x140c341c, 0x140c343c, 0x140c3c04, 0x14140404, 0x14140414, 0x14140c0c, 0x14140c3c, - 0x14141404, 0x14141414, 0x14141c3c, 0x14142404, 0x14142c2c, 0x141c040c, 0x141c0c04, 0x141c0c24, - 0x141c3c04, 0x141c3c24, 0x14241c2c, 0x14242c1c, 0x142c041c, 0x142c143c, 0x142c240c, 0x142c3c24, - 0x143c040c, 0x143c041c, 0x143c0c34, 0x143c242c, 0x1c04040c, 0x1c040c04, 0x1c040c14, 0x1c04140c, - 0x1c04141c, 0x1c042c04, 0x1c04342c, 0x1c043c14, 0x1c0c0404, 0x1c0c0414, 0x1c0c1404, 0x1c0c1c0c, - 0x1c0c2424, 0x1c0c2434, 0x1c14040c, 0x1c14041c, 0x1c140c04, 0x1c14142c, 0x1c142c14, 0x1c143c14, - 0x1c1c0c0c, 0x1c1c1c1c, 0x1c241c04, 0x1c24243c, 0x1c243c14, 0x1c2c0404, 0x1c2c0434, 0x1c2c1414, - 0x1c2c2c2c, 0x1c340c24, 0x1c341c34, 0x1c34341c, 0x1c3c1c1c, 0x1c3c3404, 0x24040424, 0x24040c3c, - 0x24041c2c, 0x24041c3c, 0x24042c1c, 0x24042c3c, 0x240c3c24, 0x24141404, 0x24141c3c, 0x24142404, - 0x24143404, 0x24143434, 0x241c043c, 0x241c242c, 0x24240424, 0x24242c0c, 0x24243424, 0x242c142c, - 0x242c241c, 0x242c3c04, 0x243c042c, 0x243c0c04, 0x243c0c14, 0x243c1c04, 0x2c040c14, 0x2c04240c, - 0x2c043c04, 0x2c0c0404, 0x2c0c0434, 0x2c0c1434, 0x2c0c2c2c, 0x2c140c24, 0x2c141c14, 0x2c143c14, - 0x2c1c0414, 0x2c1c2c1c, 0x2c240c04, 0x2c24141c, 0x2c24143c, 0x2c243c14, 0x2c2c0414, 0x2c2c1c0c, - 0x2c342c04, 0x2c3c1424, 0x2c3c2414, 0x34041424, 0x34042424, 0x34042434, 0x34043424, 0x340c140c, - 0x340c340c, 0x34140c3c, 0x34143424, 0x341c1c04, 0x341c1c34, 0x34242424, 0x342c042c, 0x342c2c14, - 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HPP files are much more readable as multiline literals rather than hex arrays. * Dashes in literal variable names. Replace . and - with _ in file names -> variable names. * Comment on removing xxd. XXD-> string literals * XXD to string literals. Replaced these unreadable headers with string literal versions using new deps.sh. --- examples/server/completion.js.hpp | 651 +-- examples/server/deps.sh | 11 +- examples/server/index.html.hpp | 3829 +++++------------ examples/server/index.js.hpp | 1907 +------- .../server/json-schema-to-grammar.mjs.hpp | 424 +- 5 files changed, 1368 insertions(+), 5454 deletions(-) diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp index fe5f81228..5609ee3bf 100644 --- a/examples/server/completion.js.hpp +++ b/examples/server/completion.js.hpp @@ -1,449 +1,204 @@ -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, - 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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 +const char completion_js[] = R"LITERAL( +const paramDefaults = { + stream: true, + n_predict: 500, + temperature: 0.2, + stop: [""] }; -unsigned int completion_js_len = 5346; + +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, prompt }; + + const response = await fetch("/completion", { + method: 'POST', + body: JSON.stringify(completionParams), + headers: { + 'Connection': 'keep-alive', + 'Content-Type': 'application/json', + 'Accept': 'text/event-stream', + ...(params.api_key ? {'Authorization': `Bearer ${params.api_key}`} : {}) + }, + signal: controller.signal, + }); + + const reader = response.body.getReader(); + const decoder = new TextDecoder(); + + let content = ""; + let leftover = ""; // Buffer for partially read lines + + try { + let cont = true; + + while (cont) { + const result = await reader.read(); + if (result.done) { + break; + } + + // Add any leftover data to the current chunk of data + const text = leftover + decoder.decode(result.value); + + // Check if the last character is a line break + const endsWithLineBreak = text.endsWith('\n'); + + // Split the text into lines + let lines = text.split('\n'); + + // If the text doesn't end with a line break, then the last line is incomplete + // Store it in leftover to be added to the next chunk of data + if (!endsWithLineBreak) { + leftover = lines.pop(); + } else { + leftover = ""; // Reset leftover if we have a line break at the end + } + + // Parse all sse events and add them to result + const regex = /^(\S+):\s(.*)$/gm; + for (const line of lines) { + const match = regex.exec(line); + if (match) { + result[match[1]] = match[2] + // since we know this is llama.cpp, let's just decode the json in data + if (result.data) { + result.data = JSON.parse(result.data); + content += result.data.content; + + // 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; + } + cont = false; + break; + } + } + if (result.error) { + result.error = JSON.parse(result.error); + if (result.error.content.includes('slot unavailable')) { + // Throw an error to be caught by upstream callers + throw new Error('slot unavailable'); + } else { + console.error(`llama.cpp error: ${result.error.content}`); + } + } + if (result.error) { + result.error = JSON.parse(result.error); + console.error(`llama.cpp error: ${result.error.content}`); + } + } + } + } + } catch (e) { + if (e.name !== 'AbortError') { + console.error("llama error: ", e); + } + throw e; + } + finally { + controller.abort(); + } + + return content; +} + +// Call llama, return an event target that you can subscribe 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; +} +)LITERAL"; +unsigned int completion_js_len = sizeof(completion_js); diff --git a/examples/server/deps.sh b/examples/server/deps.sh index ea23e6450..c0a9de9f9 100755 --- a/examples/server/deps.sh +++ b/examples/server/deps.sh @@ -15,6 +15,13 @@ cd $PUBLIC for FILE in $FILES; do echo "generate $FILE.hpp" - # use simple flag for old version of xxd - xxd -i $FILE > $DIR/$FILE.hpp + # Use C++11 string literals instead of ugly xxd. + f=$(echo $FILE | sed 's/\./_/g' -e 's/-/_/g') + echo "const char $f[] = R\"LITERAL(" > $DIR/$FILE.hpp + cat $FILE >> $DIR/$FILE.hpp + echo ")LITERAL\";" >> $DIR/$FILE.hpp + echo "unsigned int ${f}_len = sizeof($f);" >> $DIR/$FILE.hpp + + #Deprecated old xxd + #xxd -i $FILE > $DIR/$FILE.hpp done diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 20551520e..603d12068 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -1,2791 +1,1038 @@ -unsigned char index_html[] = { - 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+ +
+
+ + + + +)LITERAL"; +unsigned int index_html_len = sizeof(index_html); diff --git a/examples/server/index.js.hpp b/examples/server/index.js.hpp index e09b3c8c5..647abe116 100644 --- a/examples/server/index.js.hpp +++ b/examples/server/index.js.hpp @@ -1,1903 +1,4 @@ -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, 0x63, - 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x6e, 0x3d, 0x53, 0x79, 0x6d, 0x62, 0x6f, - 0x6c, 0x2e, 0x66, 0x6f, 0x72, 0x28, 0x22, 0x70, 0x72, 0x65, 0x61, 0x63, - 0x74, 0x2d, 0x73, 0x69, 0x67, 0x6e, 0x61, 0x6c, 0x73, 0x22, 0x29, 0x3b, - 0x66, 0x75, 0x6e, 0x63, 0x74, 0x69, 0x6f, 0x6e, 0x20, 0x65, 0x28, 0x29, - 0x7b, 0x69, 0x66, 0x28, 0x66, 0x3e, 0x31, 0x29, 0x7b, 0x66, 0x2d, 0x2d, - 0x3b, 0x72, 0x65, 0x74, 0x75, 0x72, 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([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space', + integer: '("-"? ([0-9] | [1-9] [0-9]*)) space', + string: ` "\\"" ( + [^"\\\\] | + "\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) + )* "\\"" space`, + null: '"null" space', }; -unsigned int json_schema_to_grammar_mjs_len = 3695; + +const INVALID_RULE_CHARS_RE = /[^\dA-Za-z-]+/g; +const GRAMMAR_LITERAL_ESCAPE_RE = /[\n\r"]/g; +const GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"'}; + +export class SchemaConverter { + constructor(propOrder) { + this._propOrder = propOrder || {}; + this._rules = new Map(); + this._rules.set('space', SPACE_RULE); + } + + _formatLiteral(literal) { + const escaped = JSON.stringify(literal).replace( + GRAMMAR_LITERAL_ESCAPE_RE, + m => GRAMMAR_LITERAL_ESCAPES[m] + ); + return `"${escaped}"`; + } + + _addRule(name, rule) { + let escName = name.replace(INVALID_RULE_CHARS_RE, '-'); + let key = escName; + + if (this._rules.has(escName)) { + if (this._rules.get(escName) === rule) { + return key; + } + + let i = 0; + while (this._rules.has(`${escName}${i}`)) { + i += 1; + } + key = `${escName}${i}`; + } + + this._rules.set(key, rule); + return key; + } + + visit(schema, name) { + const schemaType = schema.type; + const ruleName = name || 'root'; + + if (schema.oneOf || schema.anyOf) { + const rule = (schema.oneOf || schema.anyOf).map((altSchema, i) => + this.visit(altSchema, `${name}${name ? "-" : ""}${i}`) + ).join(' | '); + + return this._addRule(ruleName, rule); + } else if ('const' in schema) { + return this._addRule(ruleName, this._formatLiteral(schema.const)); + } else if ('enum' in schema) { + const rule = schema.enum.map(v => this._formatLiteral(v)).join(' | '); + return this._addRule(ruleName, rule); + } else if (schemaType === 'object' && 'properties' in schema) { + // TODO: `required` keyword (from python implementation) + const propOrder = this._propOrder; + const propPairs = Object.entries(schema.properties).sort((a, b) => { + // sort by position in prop_order (if specified) then by key + const orderA = typeof propOrder[a[0]] === 'number' ? propOrder[a[0]] : Infinity; + const orderB = typeof propOrder[b[0]] === 'number' ? propOrder[b[0]] : Infinity; + return orderA - orderB || a[0].localeCompare(b[0]); + }); + + let rule = '"{" space'; + propPairs.forEach(([propName, propSchema], i) => { + const propRuleName = this.visit(propSchema, `${name}${name ? "-" : ""}${propName}`); + if (i > 0) { + rule += ' "," space'; + } + rule += ` ${this._formatLiteral(propName)} space ":" space ${propRuleName}`; + }); + rule += ' "}" space'; + + return this._addRule(ruleName, rule); + } else if (schemaType === 'array' && 'items' in schema) { + // TODO `prefixItems` keyword (from python implementation) + const itemRuleName = this.visit(schema.items, `${name}${name ? "-" : ""}item`); + const rule = `"[" space (${itemRuleName} ("," space ${itemRuleName})*)? "]" space`; + return this._addRule(ruleName, rule); + } else { + if (!PRIMITIVE_RULES[schemaType]) { + throw new Error(`Unrecognized schema: ${JSON.stringify(schema)}`); + } + return this._addRule( + ruleName === 'root' ? 'root' : schemaType, + PRIMITIVE_RULES[schemaType] + ); + } + } + + formatGrammar() { + let grammar = ''; + this._rules.forEach((rule, name) => { + grammar += `${name} ::= ${rule}\n`; + }); + return grammar; + } +} +)LITERAL"; +unsigned int json_schema_to_grammar_mjs_len = sizeof(json_schema_to_grammar_mjs); From e6f291d15844398f8326940fe5ad7f2e02b5aa56 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 30 Jan 2024 20:17:30 +0200 Subject: [PATCH 072/143] server : fix context shift (#5195) * server : fix context shift + simplify self-extend * server : take system_tokens into account * server : more n_past fixes * server : rever n_past_se changes --- examples/server/chat.sh | 1 + examples/server/server.cpp | 109 ++++++++++++++++++++----------------- 2 files changed, 60 insertions(+), 50 deletions(-) diff --git a/examples/server/chat.sh b/examples/server/chat.sh index 014360121..da0a6ca68 100755 --- a/examples/server/chat.sh +++ b/examples/server/chat.sh @@ -48,6 +48,7 @@ chat_completion() { top_p: 0.9, n_keep: $n_keep, n_predict: 256, + cache_prompt: true, stop: ["\n### Human:"], stream: true }')" diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 11dd82c33..21bdce8ed 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -185,7 +185,7 @@ struct llama_client_slot llama_sampling_context *ctx_sampling = nullptr; int32_t ga_i = 0; // group-attention state - int32_t ga_n = 1;// group-attention factor + int32_t ga_n = 1; // group-attention factor int32_t ga_w = 512; // group-attention width int32_t n_past_se = 0; // self-extend @@ -219,7 +219,8 @@ struct llama_client_slot sent_token_probs_index = 0; infill = false; ga_i = 0; - n_past_se = 0; + n_past_se = 0; + generated_token_probs.clear(); for (slot_image & img : images) @@ -1227,7 +1228,7 @@ struct llama_server_context std::vector append_tokens = tokenize(json_prompt, false); // has next image for (int i = 0; i < (int) append_tokens.size(); ++i) { - llama_batch_add(batch, append_tokens[i], slot.n_past, { slot.id }, true); + llama_batch_add(batch, append_tokens[i], system_tokens.size() + slot.n_past, { slot.id }, true); slot.n_past += 1; } } @@ -1295,6 +1296,8 @@ struct llama_server_context for (llama_client_slot &slot : slots) { slot.cache_tokens.clear(); + slot.n_past = 0; + slot.n_past_se = 0; } } @@ -1364,26 +1367,26 @@ struct llama_server_context kv_cache_clear(); } return true; - } else { - task_server task; - task.type = TASK_TYPE_NEXT_RESPONSE; - task.target_id = -1; - queue_tasks.post(task); } + task_server task; + task.type = TASK_TYPE_NEXT_RESPONSE; + task.target_id = -1; + queue_tasks.post(task); + for (llama_client_slot &slot : slots) { if (slot.ga_n == 1) { - if (slot.is_processing() && slot.cache_tokens.size() >= (size_t) slot.n_ctx) + if (slot.is_processing() && system_tokens.size() + slot.cache_tokens.size() >= (size_t) slot.n_ctx) { // Shift context - const int n_left = slot.n_past - slot.params.n_keep - 1; + const int n_left = system_tokens.size() + slot.n_past - slot.params.n_keep - 1; const int n_discard = n_left / 2; LOG_TEE("slot %d: context shift - n_keep = %d, n_left = %d, n_discard = %d\n", slot.id, slot.params.n_keep, n_left, n_discard); llama_kv_cache_seq_rm (ctx, slot.id, slot.params.n_keep + 1 , slot.params.n_keep + n_discard + 1); - llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, slot.n_past, -n_discard); + llama_kv_cache_seq_shift(ctx, slot.id, slot.params.n_keep + 1 + n_discard, system_tokens.size() + slot.n_past, -n_discard); for (size_t i = slot.params.n_keep + 1 + n_discard; i < slot.cache_tokens.size(); i++) { @@ -1429,8 +1432,10 @@ struct llama_server_context slot.i_batch = batch.n_tokens; const int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past; - llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id }, true); + // TODO: we always have to take into account the "system_tokens" + // this is not great and needs to be improved somehow + llama_batch_add(batch, slot.sampled, system_tokens.size() + slot_npast, { slot.id }, true); slot.n_past += 1; } @@ -1481,8 +1486,8 @@ struct llama_server_context prefix_tokens.insert(prefix_tokens.begin(), llama_token_prefix(model)); prefix_tokens.insert(prefix_tokens.begin(), llama_token_bos(model)); // always add BOS - prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(model)); - prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); + prefix_tokens.insert(prefix_tokens.end(), llama_token_suffix(model)); + prefix_tokens.insert(prefix_tokens.end(), suffix_tokens.begin(), suffix_tokens.end()); prefix_tokens.push_back(llama_token_middle(model)); prompt_tokens = prefix_tokens; } @@ -1582,8 +1587,8 @@ struct llama_server_context } LOG_VERBOSE("prompt ingested", { - {"n_past", slot.n_past}, - {"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)}, + {"n_past", slot.n_past}, + {"cached", tokens_to_str(ctx, slot.cache_tokens.cbegin(), slot.cache_tokens.cbegin() + slot.n_past)}, {"to_eval", tokens_to_str(ctx, slot.cache_tokens.cbegin() + slot.n_past, slot.cache_tokens.cend())}, }); @@ -1591,10 +1596,13 @@ struct llama_server_context // process the prefix of first image std::vector prefix_tokens = has_images ? tokenize(slot.images[0].prefix_prompt, add_bos_token) : prompt_tokens; + int32_t slot_npast = slot.n_past_se > 0 ? slot.n_past_se : slot.n_past; - int ga_i = slot.ga_i; + + int32_t ga_i = slot.ga_i; int32_t ga_n = slot.ga_n; int32_t ga_w = slot.ga_w; + for (; slot.n_past < (int) prefix_tokens.size(); ++slot.n_past) { if (slot.ga_n != 1) @@ -1606,7 +1614,7 @@ struct llama_server_context } } llama_batch_add(batch, prefix_tokens[slot.n_past], system_tokens.size() + slot_npast, {slot.id }, false); - slot_npast += 1; + slot_npast++; } if (has_images && !ingest_images(slot, n_batch)) @@ -1666,6 +1674,7 @@ struct llama_server_context slot.n_past_se += n_tokens; } } + llama_batch batch_view = { n_tokens, @@ -1782,51 +1791,51 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" not recommended: doubles context memory required and no measurable increase in quality\n"); if (llama_mlock_supported()) { - printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); + printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); } if (llama_mmap_supported()) { - printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); + printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } - printf(" --numa attempt optimizations that help on some NUMA systems\n"); + printf(" --numa attempt optimizations that help on some NUMA systems\n"); #ifdef LLAMA_SUPPORTS_GPU_OFFLOAD printf(" -ngl N, --n-gpu-layers N\n"); - printf(" number of layers to store in VRAM\n"); + printf(" number of layers to store in VRAM\n"); printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); - printf(" how to split the model across multiple GPUs, one of:\n"); - printf(" - none: use one GPU only\n"); - printf(" - layer (default): split layers and KV across GPUs\n"); - printf(" - row: split rows across GPUs\n"); + printf(" how to split the model across multiple GPUs, one of:\n"); + printf(" - none: use one GPU only\n"); + printf(" - layer (default): split layers and KV across GPUs\n"); + printf(" - row: split rows across GPUs\n"); printf(" -ts SPLIT --tensor-split SPLIT\n"); - printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); - printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); - printf(" or for intermediate results and KV (with split-mode = row)\n"); + printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); + printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); + printf(" or for intermediate results and KV (with split-mode = row)\n"); #endif printf(" -m FNAME, --model FNAME\n"); - printf(" model path (default: %s)\n", params.model.c_str()); + printf(" model path (default: %s)\n", params.model.c_str()); printf(" -a ALIAS, --alias ALIAS\n"); - printf(" set an alias for the model, will be added as `model` field in completion response\n"); - printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); - printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); - printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str()); - printf(" --port PORT port to listen (default (default: %d)\n", sparams.port); - printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str()); - printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n"); - printf(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n"); - printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); - printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); - printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel); - printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); - printf(" -spf FNAME, --system-prompt-file FNAME\n"); - printf(" Set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n"); - printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n"); - printf(" --log-disable disables logging to a file.\n"); + printf(" set an alias for the model, will be added as `model` field in completion response\n"); + printf(" --lora FNAME apply LoRA adapter (implies --no-mmap)\n"); + printf(" --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); + printf(" --host ip address to listen (default (default: %s)\n", sparams.hostname.c_str()); + printf(" --port PORT port to listen (default (default: %d)\n", sparams.port); + printf(" --path PUBLIC_PATH path from which to serve static files (default %s)\n", sparams.public_path.c_str()); + printf(" --api-key API_KEY optional api key to enhance server security. If set, requests must include this key for access.\n"); + printf(" --api-key-file FNAME path to file containing api keys delimited by new lines. If set, requests must include one of the keys for access.\n"); + printf(" -to N, --timeout N server read/write timeout in seconds (default: %d)\n", sparams.read_timeout); + printf(" --embedding enable embedding vector output (default: %s)\n", params.embedding ? "enabled" : "disabled"); + printf(" -np N, --parallel N number of slots for process requests (default: %d)\n", params.n_parallel); + printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); + printf(" -spf FNAME, --system-prompt-file FNAME\n"); + printf(" set a file to load a system prompt (initial prompt of all slots), this is useful for chat applications.\n"); + printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA.\n"); + printf(" --log-disable disables logging to a file.\n"); printf("\n"); printf(" --override-kv KEY=TYPE:VALUE\n"); - printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); - printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); - printf(" -gan N, --grp-attn-n N Set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`"); - printf(" -gaw N, --grp-attn-w N Set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`"); + printf(" advanced option to override model metadata by key. may be specified multiple times.\n"); + printf(" types: int, float, bool. example: --override-kv tokenizer.ggml.add_bos_token=bool:false\n"); + printf(" -gan N, --grp-attn-n N set the group attention factor to extend context size through self-extend(default: 1=disabled), used together with group attention width `--grp-attn-w`"); + printf(" -gaw N, --grp-attn-w N set the group attention width to extend context size through self-extend(default: 512), used together with group attention factor `--grp-attn-n`"); printf("\n"); } From e0085fdf7c758f0bc2746fc106fb29dd9df959de Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 30 Jan 2024 21:19:26 +0200 Subject: [PATCH 073/143] Revert "server : change deps.sh xxd files to string literals (#5221)" This reverts commit 4003be0e5feef320f3707786f22722b73cff9356. --- examples/server/completion.js.hpp | 651 ++- examples/server/deps.sh | 11 +- examples/server/index.html.hpp | 3829 ++++++++++++----- examples/server/index.js.hpp | 1907 +++++++- .../server/json-schema-to-grammar.mjs.hpp | 424 +- 5 files changed, 5454 insertions(+), 1368 deletions(-) diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp index 5609ee3bf..fe5f81228 100644 --- a/examples/server/completion.js.hpp +++ b/examples/server/completion.js.hpp @@ -1,204 +1,449 @@ -const char completion_js[] = R"LITERAL( -const paramDefaults = { - stream: true, - n_predict: 500, - temperature: 0.2, - stop: [""] +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, 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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, prompt }; - - const response = await fetch("/completion", { - method: 'POST', - body: JSON.stringify(completionParams), - headers: { - 'Connection': 'keep-alive', - 'Content-Type': 'application/json', - 'Accept': 'text/event-stream', - ...(params.api_key ? {'Authorization': `Bearer ${params.api_key}`} : {}) - }, - signal: controller.signal, - }); - - const reader = response.body.getReader(); - const decoder = new TextDecoder(); - - let content = ""; - let leftover = ""; // Buffer for partially read lines - - try { - let cont = true; - - while (cont) { - const result = await reader.read(); - if (result.done) { - break; - } - - // Add any leftover data to the current chunk of data - const text = leftover + decoder.decode(result.value); - - // Check if the last character is a line break - const endsWithLineBreak = text.endsWith('\n'); - - // Split the text into lines - let lines = text.split('\n'); - - // If the text doesn't end with a line break, then the last line is incomplete - // Store it in leftover to be added to the next chunk of data - if (!endsWithLineBreak) { - leftover = lines.pop(); - } else { - leftover = ""; // Reset leftover if we have a line break at the end - } - - // Parse all sse events and add them to result - const regex = /^(\S+):\s(.*)$/gm; - for (const line of lines) { - const match = regex.exec(line); - if (match) { - result[match[1]] = match[2] - // since we know this is llama.cpp, let's just decode the json in data - if (result.data) { - result.data = JSON.parse(result.data); - content += result.data.content; - - // 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; - } - cont = false; - break; - } - } - if (result.error) { - result.error = JSON.parse(result.error); - if (result.error.content.includes('slot unavailable')) { - // Throw an error to be caught by upstream callers - throw new Error('slot unavailable'); - } else { - console.error(`llama.cpp error: ${result.error.content}`); - } - } - if (result.error) { - result.error = JSON.parse(result.error); - console.error(`llama.cpp error: ${result.error.content}`); - } - } - } - } - } catch (e) { - if (e.name !== 'AbortError') { - console.error("llama error: ", e); - } - throw e; - } - finally { - controller.abort(); - } - - return content; -} - -// Call llama, return an event target that you can subscribe 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; -} -)LITERAL"; -unsigned int completion_js_len = sizeof(completion_js); +unsigned int completion_js_len = 5346; diff --git a/examples/server/deps.sh b/examples/server/deps.sh index c0a9de9f9..ea23e6450 100755 --- a/examples/server/deps.sh +++ b/examples/server/deps.sh @@ -15,13 +15,6 @@ cd $PUBLIC for FILE in $FILES; do echo "generate $FILE.hpp" - # Use C++11 string literals instead of ugly xxd. - f=$(echo $FILE | sed 's/\./_/g' -e 's/-/_/g') - echo "const char $f[] = R\"LITERAL(" > $DIR/$FILE.hpp - cat $FILE >> $DIR/$FILE.hpp - echo ")LITERAL\";" >> $DIR/$FILE.hpp - echo "unsigned int ${f}_len = sizeof($f);" >> $DIR/$FILE.hpp - - #Deprecated old xxd - #xxd -i $FILE > $DIR/$FILE.hpp + # use simple flag for old version of xxd + xxd -i $FILE > $DIR/$FILE.hpp done diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 603d12068..20551520e 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -1,1038 +1,2791 @@ -const char index_html[] = R"LITERAL( - - - - - - - llama.cpp - chat - - - - - - - -
- -
-
- - - - -)LITERAL"; -unsigned int index_html_len = sizeof(index_html); +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, 0x6d, 0x65, 0x74, 0x61, 0x20, 0x6e, 0x61, 0x6d, 0x65, 0x3d, 0x22, + 0x63, 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([0-9] | [1-9] [0-9]*)) ("." [0-9]+)? ([eE] [-+]? [0-9]+)? space', - integer: '("-"? ([0-9] | [1-9] [0-9]*)) space', - string: ` "\\"" ( - [^"\\\\] | - "\\\\" (["\\\\/bfnrt] | "u" [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F] [0-9a-fA-F]) - )* "\\"" space`, - null: '"null" space', +unsigned char json_schema_to_grammar_mjs[] = { + 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x53, 0x50, 0x41, 0x43, 0x45, 0x5f, + 0x52, 0x55, 0x4c, 0x45, 0x20, 0x3d, 0x20, 0x27, 0x22, 0x20, 0x22, 0x3f, + 0x27, 0x3b, 0x0a, 0x0a, 0x63, 0x6f, 0x6e, 0x73, 0x74, 0x20, 0x50, 0x52, + 0x49, 0x4d, 0x49, 0x54, 0x49, 0x56, 0x45, 0x5f, 0x52, 0x55, 0x4c, 0x45, + 0x53, 0x20, 0x3d, 0x20, 0x7b, 0x0a, 0x20, 0x20, 0x62, 0x6f, 0x6f, 0x6c, + 0x65, 0x61, 0x6e, 0x3a, 0x20, 0x27, 0x28, 0x22, 0x74, 0x72, 0x75, 0x65, + 0x22, 0x20, 0x7c, 0x20, 0x22, 0x66, 0x61, 0x6c, 0x73, 0x65, 0x22, 0x29, + 0x20, 0x73, 0x70, 0x61, 0x63, 0x65, 0x27, 0x2c, 0x0a, 0x20, 0x20, 0x6e, + 0x75, 0x6d, 0x62, 0x65, 0x72, 0x3a, 0x20, 0x27, 0x28, 0x22, 0x2d, 0x22, + 0x3f, 0x20, 0x28, 0x5b, 0x30, 0x2d, 0x39, 0x5d, 0x20, 0x7c, 0x20, 0x5b, + 0x31, 0x2d, 0x39, 0x5d, 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0x0a, 0x20, 0x20, 0x7d, 0x0a, 0x7d, 0x0a }; - -const INVALID_RULE_CHARS_RE = /[^\dA-Za-z-]+/g; -const GRAMMAR_LITERAL_ESCAPE_RE = /[\n\r"]/g; -const GRAMMAR_LITERAL_ESCAPES = {'\r': '\\r', '\n': '\\n', '"': '\\"'}; - -export class SchemaConverter { - constructor(propOrder) { - this._propOrder = propOrder || {}; - this._rules = new Map(); - this._rules.set('space', SPACE_RULE); - } - - _formatLiteral(literal) { - const escaped = JSON.stringify(literal).replace( - GRAMMAR_LITERAL_ESCAPE_RE, - m => GRAMMAR_LITERAL_ESCAPES[m] - ); - return `"${escaped}"`; - } - - _addRule(name, rule) { - let escName = name.replace(INVALID_RULE_CHARS_RE, '-'); - let key = escName; - - if (this._rules.has(escName)) { - if (this._rules.get(escName) === rule) { - return key; - } - - let i = 0; - while (this._rules.has(`${escName}${i}`)) { - i += 1; - } - key = `${escName}${i}`; - } - - this._rules.set(key, rule); - return key; - } - - visit(schema, name) { - const schemaType = schema.type; - const ruleName = name || 'root'; - - if (schema.oneOf || schema.anyOf) { - const rule = (schema.oneOf || schema.anyOf).map((altSchema, i) => - this.visit(altSchema, `${name}${name ? "-" : ""}${i}`) - ).join(' | '); - - return this._addRule(ruleName, rule); - } else if ('const' in schema) { - return this._addRule(ruleName, this._formatLiteral(schema.const)); - } else if ('enum' in schema) { - const rule = schema.enum.map(v => this._formatLiteral(v)).join(' | '); - return this._addRule(ruleName, rule); - } else if (schemaType === 'object' && 'properties' in schema) { - // TODO: `required` keyword (from python implementation) - const propOrder = this._propOrder; - const propPairs = Object.entries(schema.properties).sort((a, b) => { - // sort by position in prop_order (if specified) then by key - const orderA = typeof propOrder[a[0]] === 'number' ? propOrder[a[0]] : Infinity; - const orderB = typeof propOrder[b[0]] === 'number' ? propOrder[b[0]] : Infinity; - return orderA - orderB || a[0].localeCompare(b[0]); - }); - - let rule = '"{" space'; - propPairs.forEach(([propName, propSchema], i) => { - const propRuleName = this.visit(propSchema, `${name}${name ? "-" : ""}${propName}`); - if (i > 0) { - rule += ' "," space'; - } - rule += ` ${this._formatLiteral(propName)} space ":" space ${propRuleName}`; - }); - rule += ' "}" space'; - - return this._addRule(ruleName, rule); - } else if (schemaType === 'array' && 'items' in schema) { - // TODO `prefixItems` keyword (from python implementation) - const itemRuleName = this.visit(schema.items, `${name}${name ? "-" : ""}item`); - const rule = `"[" space (${itemRuleName} ("," space ${itemRuleName})*)? "]" space`; - return this._addRule(ruleName, rule); - } else { - if (!PRIMITIVE_RULES[schemaType]) { - throw new Error(`Unrecognized schema: ${JSON.stringify(schema)}`); - } - return this._addRule( - ruleName === 'root' ? 'root' : schemaType, - PRIMITIVE_RULES[schemaType] - ); - } - } - - formatGrammar() { - let grammar = ''; - this._rules.forEach((rule, name) => { - grammar += `${name} ::= ${rule}\n`; - }); - return grammar; - } -} -)LITERAL"; -unsigned int json_schema_to_grammar_mjs_len = sizeof(json_schema_to_grammar_mjs); +unsigned int json_schema_to_grammar_mjs_len = 3695; From e8dc55d0065d076d4c20f3c4bfca562701b4edfe Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Tue, 30 Jan 2024 19:04:37 -0500 Subject: [PATCH 074/143] kompute : llama-bench support and ggml_cpu_has_kompute() (#5226) --- common/common.cpp | 1 + examples/llama-bench/llama-bench.cpp | 15 +++++++++++---- ggml.c | 11 ++++++++++- ggml.h | 1 + llama.cpp | 5 ----- 5 files changed, 23 insertions(+), 10 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 288013676..0dd1c50cf 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1521,6 +1521,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "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_kompute: %s\n", ggml_cpu_has_kompute() ? "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"); diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index f239415d3..542cc7bb8 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -563,6 +563,7 @@ struct test { static const bool cuda; static const bool opencl; static const bool vulkan; + static const bool kompute; static const bool metal; static const bool gpu_blas; static const bool blas; @@ -647,6 +648,9 @@ struct test { if (vulkan) { return "Vulkan"; } + if (kompute) { + return "Kompute"; + } if (metal) { return "Metal"; } @@ -662,7 +666,7 @@ struct test { static const std::vector & get_fields() { static const std::vector fields = { "build_commit", "build_number", - "cuda", "opencl", "vulkan", "metal", "gpu_blas", "blas", + "cuda", "opencl", "vulkan", "kompute", "metal", "gpu_blas", "blas", "cpu_info", "gpu_info", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_threads", "type_k", "type_v", @@ -686,8 +690,9 @@ struct test { field == "avg_ns" || field == "stddev_ns") { return INT; } - if (field == "cuda" || field == "opencl" || field == "vulkan"|| field == "metal" || field == "gpu_blas" || field == "blas" || - field == "f16_kv" || field == "no_kv_offload" || field == "mul_mat_q") { + if (field == "cuda" || field == "opencl" || field == "vulkan" || field == "kompute" || field == "metal" || + field == "gpu_blas" || field == "blas" || field == "f16_kv" || field == "no_kv_offload" || + field == "mul_mat_q") { return BOOL; } if (field == "avg_ts" || field == "stddev_ts") { @@ -714,7 +719,8 @@ struct test { } std::vector values = { build_commit, std::to_string(build_number), - std::to_string(cuda), std::to_string(opencl), std::to_string(vulkan), std::to_string(metal), std::to_string(gpu_blas), std::to_string(blas), + std::to_string(cuda), std::to_string(opencl), std::to_string(vulkan), std::to_string(vulkan), + std::to_string(metal), std::to_string(gpu_blas), std::to_string(blas), cpu_info, gpu_info, 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), ggml_type_name(type_k), ggml_type_name(type_v), @@ -743,6 +749,7 @@ const int test::build_number = LLAMA_BUILD_NUMBER; const bool test::cuda = !!ggml_cpu_has_cublas(); const bool test::opencl = !!ggml_cpu_has_clblast(); const bool test::vulkan = !!ggml_cpu_has_vulkan(); +const bool test::kompute = !!ggml_cpu_has_kompute(); const bool test::metal = !!ggml_cpu_has_metal(); const bool test::gpu_blas = !!ggml_cpu_has_gpublas(); const bool test::blas = !!ggml_cpu_has_blas(); diff --git a/ggml.c b/ggml.c index a7a9ea319..b2c8baaa8 100644 --- a/ggml.c +++ b/ggml.c @@ -20473,6 +20473,14 @@ int ggml_cpu_has_vulkan(void) { #endif } +int ggml_cpu_has_kompute(void) { +#if defined(GGML_USE_KOMPUTE) + return 1; +#else + return 0; +#endif +} + int ggml_cpu_has_sycl(void) { #if defined(GGML_USE_SYCL) return 1; @@ -20482,7 +20490,8 @@ int ggml_cpu_has_sycl(void) { } int ggml_cpu_has_gpublas(void) { - return ggml_cpu_has_cublas() || ggml_cpu_has_clblast() || ggml_cpu_has_vulkan() || ggml_cpu_has_sycl(); + return ggml_cpu_has_cublas() || ggml_cpu_has_clblast() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() || + ggml_cpu_has_sycl(); } int ggml_cpu_has_sse3(void) { diff --git a/ggml.h b/ggml.h index bf782e6ad..afc87b843 100644 --- a/ggml.h +++ b/ggml.h @@ -2266,6 +2266,7 @@ extern "C" { GGML_API int ggml_cpu_has_cublas (void); GGML_API int ggml_cpu_has_clblast (void); GGML_API int ggml_cpu_has_vulkan (void); + GGML_API int ggml_cpu_has_kompute (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); diff --git a/llama.cpp b/llama.cpp index 7b9a5c079..a490eeab2 100644 --- a/llama.cpp +++ b/llama.cpp @@ -6878,11 +6878,6 @@ static int llama_decode_internal( n_threads = std::min(4, n_threads); } - const bool fully_offloaded = model.n_gpu_layers >= (int) hparams.n_layer + 1; - if ((ggml_cpu_has_cublas() || ggml_cpu_has_vulkan()) && fully_offloaded) { - n_threads = 1; - } - #ifdef GGML_USE_MPI const int64_t n_layer = hparams.n_layer; ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer); From 01684139c352561840ae55ec627ab58abc3e06ab Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Wed, 31 Jan 2024 10:38:07 +0800 Subject: [PATCH 075/143] support SYCL backend windows build (#5208) * support SYCL backend windows build * add windows build in CI * add for win build CI * correct install oneMKL * fix install issue * fix ci * fix install cmd * fix install cmd * fix install cmd * fix install cmd * fix install cmd * fix win build * fix win build * fix win build * restore other CI part * restore as base * rm no new line * fix no new line issue, add -j * fix grammer issue * allow to trigger manually, fix format issue * fix format * add newline * fix format * fix format * fix format issuse --------- Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> --- .github/workflows/build.yml | 25 ++++ .github/workflows/editorconfig.yml | 6 + .gitignore | 1 + CMakeLists.txt | 6 +- README_sycl.md => README-sycl.md | 198 +++++++++++++++++++++++++++-- README.md | 4 +- examples/sycl/win-build-sycl.bat | 23 ++++ examples/sycl/win-run-llama2.bat | 13 ++ scripts/install-oneapi.bat | 19 +++ 9 files changed, 281 insertions(+), 14 deletions(-) rename README_sycl.md => README-sycl.md (58%) create mode 100644 examples/sycl/win-build-sycl.bat create mode 100644 examples/sycl/win-run-llama2.bat create mode 100644 scripts/install-oneapi.bat diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index fb719a550..c6db1666e 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -565,6 +565,31 @@ jobs: path: | cudart-llama-bin-win-cu${{ matrix.cuda }}-x64.zip + windows-latest-cmake-sycl: + runs-on: windows-latest + defaults: + run: + shell: bash + + env: + WINDOWS_BASEKIT_URL: https://registrationcenter-download.intel.com/akdlm/IRC_NAS/62641e01-1e8d-4ace-91d6-ae03f7f8a71f/w_BaseKit_p_2024.0.0.49563_offline.exe + WINDOWS_DPCPP_MKL: intel.oneapi.win.cpp-dpcpp-common:intel.oneapi.win.mkl.devel + + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v3 + with: + fetch-depth: 0 + + - name: Install + run: scripts/install-oneapi.bat $WINDOWS_BASEKIT_URL $WINDOWS_DPCPP_MKL + + - name: Build + id: cmake_build + run: examples/sycl/win-build-sycl.bat + ios-xcode-build: runs-on: macos-latest diff --git a/.github/workflows/editorconfig.yml b/.github/workflows/editorconfig.yml index b4e535acf..0e0993cd4 100644 --- a/.github/workflows/editorconfig.yml +++ b/.github/workflows/editorconfig.yml @@ -1,6 +1,12 @@ name: EditorConfig Checker on: + workflow_dispatch: # allows manual triggering + inputs: + create_release: + description: 'Create new release' + required: true + type: boolean push: branches: - master diff --git a/.gitignore b/.gitignore index cb0069bfb..b84459b92 100644 --- a/.gitignore +++ b/.gitignore @@ -89,3 +89,4 @@ examples/jeopardy/results.txt poetry.lock poetry.toml +nppBackup diff --git a/CMakeLists.txt b/CMakeLists.txt index 65a6f3971..15a1101aa 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -507,7 +507,11 @@ if (LLAMA_SYCL) set(GGML_HEADERS_SYCL ggml.h ggml-sycl.h) set(GGML_SOURCES_SYCL ggml-sycl.cpp) - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} sycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread) + if (WIN32) + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl sycl7 OpenCL mkl_sycl_blas_dll.lib mkl_intel_ilp64_dll.lib mkl_sequential_dll.lib mkl_core_dll.lib) + else() + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} -fsycl OpenCL mkl_core pthread m dl mkl_sycl_blas mkl_intel_ilp64 mkl_tbb_thread) + endif() endif() if (LLAMA_KOMPUTE) diff --git a/README_sycl.md b/README-sycl.md similarity index 58% rename from README_sycl.md rename to README-sycl.md index d5a1818f5..2b2cfe03a 100644 --- a/README_sycl.md +++ b/README-sycl.md @@ -8,10 +8,14 @@ [Linux](#linux) +[Windows](#windows) + [Environment Variable](#environment-variable) [Known Issue](#known-issue) +[Q&A](#q&a) + [Todo](#todo) ## Background @@ -33,7 +37,7 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). |OS|Status|Verified| |-|-|-| |Linux|Support|Ubuntu 22.04| -|Windows|Ongoing| | +|Windows|Support|Windows 11| ## Intel GPU @@ -42,7 +46,7 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). |-|-|-| |Intel Data Center Max Series| Support| Max 1550| |Intel Data Center Flex Series| Support| Flex 170| -|Intel Arc Series| Support| Arc 770| +|Intel Arc Series| Support| Arc 770, 730M| |Intel built-in Arc GPU| Support| built-in Arc GPU in Meteor Lake| |Intel iGPU| Support| iGPU in i5-1250P, i7-1165G7| @@ -131,6 +135,7 @@ cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx #build all binary cmake --build . --config Release -v +cd .. ``` or @@ -195,7 +200,7 @@ GGML_SYCL_DEVICE=0 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building or run by script: ``` -./examples/sycl/run_llama2.sh +./examples/sycl/run-llama2.sh ``` Note: @@ -205,11 +210,175 @@ Note: 5. Check the device ID in output -Like： +Like: ``` Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device ``` +## Windows + +### Setup Environment + +1. Install Intel GPU driver. + +Please install Intel GPU driver by official guide: [Install GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html). + +2. Install Intel® oneAPI Base toolkit. + +a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html). + +Recommend to install to default folder: **/opt/intel/oneapi**. + +Following guide uses the default folder as example. If you use other folder, please modify the following guide info with your folder. + +b. Enable oneAPI running environment: + +- In Search, input 'oneAPI'. + +Search & open "Intel oneAPI command prompt for Intel 64 for Visual Studio 2022" + +- In Run: + +In CMD: +``` +"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 +``` + +c. Check GPU + +In oneAPI command line: + +``` +sycl-ls +``` + +There should be one or more level-zero devices. Like **[ext_oneapi_level_zero:gpu:0]**. + +Output (example): +``` +[opencl:acc:0] Intel(R) FPGA Emulation Platform for OpenCL(TM), Intel(R) FPGA Emulation Device OpenCL 1.2 [2023.16.10.0.17_160000] +[opencl:cpu:1] Intel(R) OpenCL, 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000] +[opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Iris(R) Xe Graphics OpenCL 3.0 NEO [31.0.101.5186] +[ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Iris(R) Xe Graphics 1.3 [1.3.28044] + +``` + +3. Install cmake & make + +a. Download & install cmake for windows: https://cmake.org/download/ + +b. Download & install make for windows provided by mingw-w64: https://www.mingw-w64.org/downloads/ + + +### Build locally: + +In oneAPI command line window: + +``` +mkdir -p build +cd build +@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force + +:: for FP16 +:: faster for long-prompt inference +:: cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON + +:: for FP32 +cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release + + +:: build example/main only +:: make main + +:: build all binary +make -j +cd .. +``` + +or + +``` +.\examples\sycl\win-build-sycl.bat +``` + +Note: + +- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only. + +### Run + +1. Put model file to folder **models** + +2. Enable oneAPI running environment + +- In Search, input 'oneAPI'. + +Search & open "Intel oneAPI command prompt for Intel 64 for Visual Studio 2022" + +- In Run: + +In CMD: +``` +"C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 +``` + +3. List device ID + +Run without parameter: + +``` +build\bin\ls-sycl-device.exe + +or + +build\bin\main.exe +``` + +Check the ID in startup log, like: + +``` +found 4 SYCL devices: + Device 0: Intel(R) Arc(TM) A770 Graphics, compute capability 1.3, + max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136 + Device 1: Intel(R) FPGA Emulation Device, compute capability 1.2, + max compute_units 24, max work group size 67108864, max sub group size 64, global mem size 67065057280 + Device 2: 13th Gen Intel(R) Core(TM) i7-13700K, compute capability 3.0, + max compute_units 24, max work group size 8192, max sub group size 64, global mem size 67065057280 + Device 3: Intel(R) Arc(TM) A770 Graphics, compute capability 3.0, + max compute_units 512, max work group size 1024, max sub group size 32, global mem size 16225243136 + +``` + +|Attribute|Note| +|-|-| +|compute capability 1.3|Level-zero running time, recommended | +|compute capability 3.0|OpenCL running time, slower than level-zero in most cases| + +4. Set device ID and execute llama.cpp + +Set device ID = 0 by **set GGML_SYCL_DEVICE=0** + +``` +set GGML_SYCL_DEVICE=0 +build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 33 -s 0 +``` +or run by script: + +``` +.\examples\sycl\win-run-llama2.bat +``` + +Note: + +- By default, mmap is used to read model file. In some cases, it leads to the hang issue. Recommend to use parameter **--no-mmap** to disable mmap() to skip this issue. + + +5. Check the device ID in output + +Like: +``` +Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device +``` ## Environment Variable @@ -220,7 +389,7 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device |LLAMA_SYCL|ON (mandatory)|Enable build with SYCL code path.
For FP32/FP16, LLAMA_SYCL=ON is mandatory.| |LLAMA_SYCL_F16|ON (optional)|Enable FP16 build with SYCL code path. Faster for long-prompt inference.
For FP32, not set it.| |CMAKE_C_COMPILER|icx|Use icx compiler for SYCL code path| -|CMAKE_CXX_COMPILER|icpx|use icpx for SYCL code path| +|CMAKE_CXX_COMPILER|icpx (Linux), icx (Windows)|use icpx/icx for SYCL code path| #### Running @@ -232,19 +401,24 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device ## Known Issue -- Error: `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`. - - Miss to enable oneAPI running environment. - - Install oneAPI base toolkit and enable it by: `source /opt/intel/oneapi/setvars.sh`. - - - Hang during startup llama.cpp use mmap as default way to read model file and copy to GPU. In some system, memcpy will be abnormal and block. Solution: add **--no-mmap**. +## Q&A + +- Error: `error while loading shared libraries: libsycl.so.7: cannot open shared object file: No such file or directory`. + + Miss to enable oneAPI running environment. + + Install oneAPI base toolkit and enable it by: `source /opt/intel/oneapi/setvars.sh`. + +- In Windows, no result, not error. + + Miss to enable oneAPI running environment. + ## Todo - Support to build in Windows. diff --git a/README.md b/README.md index b37348a74..7746cb510 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,8 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ ### Hot topics +- ⚠️ Incoming backends: https://github.com/ggerganov/llama.cpp/discussions/5138 + - [SYCL backend](README-sycl.md) is ready (1/28/2024), support Linux/Windows in Intel GPUs (iGPU, Arc/Flex/Max series) - New SOTA quantized models, including pure 2-bits: https://huggingface.co/ikawrakow - Collecting Apple Silicon performance stats: - M-series: https://github.com/ggerganov/llama.cpp/discussions/4167 @@ -604,7 +606,7 @@ Building the program with BLAS support may lead to some performance improvements llama.cpp based on SYCL is used to support Intel GPU (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU). - For detailed info, please refer to [llama.cpp for SYCL](README_sycl.md). + For detailed info, please refer to [llama.cpp for SYCL](README-sycl.md). ### Prepare Data & Run diff --git a/examples/sycl/win-build-sycl.bat b/examples/sycl/win-build-sycl.bat new file mode 100644 index 000000000..f9d43f8ed --- /dev/null +++ b/examples/sycl/win-build-sycl.bat @@ -0,0 +1,23 @@ + +:: MIT license +:: Copyright (C) 2024 Intel Corporation +:: SPDX-License-Identifier: MIT + +mkdir -p build +cd build +@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force + +:: for FP16 +:: faster for long-prompt inference +:: cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release -DLLAMA_SYCL_F16=ON + +:: for FP32 +cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DCMAKE_BUILD_TYPE=Release + + +:: build example/main only +:: make main + +:: build all binary +make -j +cd .. diff --git a/examples/sycl/win-run-llama2.bat b/examples/sycl/win-run-llama2.bat new file mode 100644 index 000000000..28d935541 --- /dev/null +++ b/examples/sycl/win-run-llama2.bat @@ -0,0 +1,13 @@ +:: MIT license +:: Copyright (C) 2024 Intel Corporation +:: SPDX-License-Identifier: MIT + +INPUT2="Building a website can be done in 10 simple steps:\nStep 1:" +@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force + + +set GGML_SYCL_DEVICE=0 +rem set GGML_SYCL_DEBUG=1 +.\build\bin\main.exe -m models\llama-2-7b.Q4_0.gguf -p %INPUT2% -n 400 -e -ngl 33 -s 0 + + diff --git a/scripts/install-oneapi.bat b/scripts/install-oneapi.bat new file mode 100644 index 000000000..e99bef14a --- /dev/null +++ b/scripts/install-oneapi.bat @@ -0,0 +1,19 @@ +:: MIT license +:: Copyright (C) 2024 Intel Corporation +:: SPDX-License-Identifier: MIT + + +set URL=%1 +set COMPONENTS=%2 + +curl.exe --output %TEMP%\webimage.exe --url %URL% --retry 5 --retry-delay 5 +start /b /wait %TEMP%\webimage.exe -s -x -f webimage_extracted --log extract.log +del %TEMP%\webimage.exe +if "%COMPONENTS%"=="" ( + webimage_extracted\bootstrapper.exe -s --action install --eula=accept -p=NEED_VS2017_INTEGRATION=0 -p=NEED_VS2019_INTEGRATION=0 -p=NEED_VS2022_INTEGRATION=0 --log-dir=. +) else ( + webimage_extracted\bootstrapper.exe -s --action install --components=%COMPONENTS% --eula=accept -p=NEED_VS2017_INTEGRATION=0 -p=NEED_VS2019_INTEGRATION=0 -p=NEED_VS2022_INTEGRATION=0 --log-dir=. +) +set installer_exit_code=%ERRORLEVEL% +rd /s/q "webimage_extracted" +exit /b %installer_exit_code% From d62520eb2cc1d7168a30edec6110e1daefbd959f Mon Sep 17 00:00:00 2001 From: Yiming Cui Date: Wed, 31 Jan 2024 11:04:21 +0800 Subject: [PATCH 076/143] Fix typos of IQ2_XXS and IQ3_XXS in llama.cpp (#5231) --- llama.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/llama.cpp b/llama.cpp index a490eeab2..bb23689fa 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2713,10 +2713,10 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { case LLAMA_FTYPE_MOSTLY_Q5_K_S: return "Q5_K - Small"; case LLAMA_FTYPE_MOSTLY_Q5_K_M: return "Q5_K - Medium"; case LLAMA_FTYPE_MOSTLY_Q6_K: return "Q6_K"; - case LLAMA_FTYPE_MOSTLY_IQ2_XXS:return "IQ2_XSS - 2.0625 bpw"; + case LLAMA_FTYPE_MOSTLY_IQ2_XXS:return "IQ2_XXS - 2.0625 bpw"; case LLAMA_FTYPE_MOSTLY_IQ2_XS: return "IQ2_XS - 2.3125 bpw"; case LLAMA_FTYPE_MOSTLY_Q3_K_XS:return "Q3_K - Extra small"; - case LLAMA_FTYPE_MOSTLY_IQ3_XXS:return "IQ3_XSS - 3.0625 bpw"; + case LLAMA_FTYPE_MOSTLY_IQ3_XXS:return "IQ3_XXS - 3.0625 bpw"; default: return "unknown, may not work"; } From f8e9140cb46eebaa867e1184a9946e4840eec772 Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Wed, 31 Jan 2024 11:44:19 +0100 Subject: [PATCH 077/143] Vulkan Fixes (#5223) * Fix Vulkan F16 models * Fix Vulkan context shift crash * Add Vulkan to common.cpp dump_non_result_info_yaml function * Fix bug in Vulkan CPY op * Fix small matrix multiplication errors in AMD GPUs on Windows or with amdvlk Co-authored-by: Engininja2 <139037756+Engininja2@users.noreply.github.com> --------- Co-authored-by: Engininja2 <139037756+Engininja2@users.noreply.github.com> --- common/common.cpp | 1 + ggml-vulkan-shaders.hpp | 1952 +++++++++++++---------------------- ggml-vulkan.cpp | 14 +- ggml_vk_generate_shaders.py | 4 +- 4 files changed, 704 insertions(+), 1267 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 0dd1c50cf..9d976c7c8 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1520,6 +1520,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l 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_cublas: %s\n", ggml_cpu_has_cublas() ? "true" : "false"); + fprintf(stream, "cpu_has_vulkan: %s\n", ggml_cpu_has_vulkan() ? "true" : "false"); fprintf(stream, "cpu_has_clblast: %s\n", ggml_cpu_has_clblast() ? "true" : "false"); fprintf(stream, "cpu_has_kompute: %s\n", ggml_cpu_has_kompute() ? "true" : "false"); fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false"); diff --git a/ggml-vulkan-shaders.hpp b/ggml-vulkan-shaders.hpp index 321e36383..e2e9be22c 100644 --- a/ggml-vulkan-shaders.hpp +++ b/ggml-vulkan-shaders.hpp @@ -890,7 +890,7 @@ const uint64_t cpy_f32_f32_len = 2472; unsigned char dequant_f16_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x87,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x81,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, @@ -898,7 +898,7 @@ unsigned char dequant_f16_data[] = { 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, 0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x51,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,0x10,0x00,0x06,0x00, +0x4f,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,0x10,0x00,0x06,0x00, 0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x01,0x00,0x00, 0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x0c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00, @@ -910,23 +910,23 @@ unsigned char dequant_f16_data[] = { 0x48,0x00,0x05,0x00,0x14,0x00,0x00,0x00,0x03,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x47,0x00,0x03,0x00, 0x14,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x4e,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x48,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x00,0x00,0x00, +0x4c,0x00,0x00,0x00,0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00, +0x48,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4d,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x03,0x00,0x4f,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 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+0x47,0x00,0x03,0x00,0x5b,0x00,0x00,0x00,0x02,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x5d,0x00,0x00,0x00,0x22,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x5d,0x00,0x00,0x00, 0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x80,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x7e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00, 0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00, 0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, @@ -945,330 +945,109 @@ unsigned char dequant_f16_data[] = { 0x16,0x00,0x00,0x00,0x09,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x18,0x00,0x00,0x00,0x09,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, -0x1b,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x14,0x00,0x02,0x00, -0x24,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 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-0xc8,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x86,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, 0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x62,0x00,0x00,0x00, +0x16,0x00,0x00,0x00,0x50,0x00,0x00,0x00,0x60,0x00,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x00,0x01,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, @@ -1280,23 +1059,23 @@ unsigned char dequant_f16_fp32_data[] = { 0x08,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x14,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x47,0x00,0x03,0x00,0x14,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x4f,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x50,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x4d,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x4e,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00, -0x50,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x50,0x00,0x00,0x00, -0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x52,0x00,0x00,0x00, +0x4e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x4e,0x00,0x00,0x00, +0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x00,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x52,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x5f,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x60,0x00,0x00,0x00, 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+0x7e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x84,0x00,0x00,0x00, +0xf8,0x00,0x02,0x00,0x84,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, +0x38,0x00,0x01,0x00, }; -const uint64_t dequant_f16_fp32_len = 5420; +const uint64_t dequant_f16_fp32_len = 1816; unsigned char dequant_q2_K_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -15313,7 +14792,7 @@ const uint64_t gelu_f32_len = 1408; unsigned char get_rows_f16_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x7a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x77,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, @@ -15321,7 +14800,7 @@ unsigned char get_rows_f16_data[] = { 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, 0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, -0x2d,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x65,0x00,0x00,0x00, +0x2d,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, @@ -15341,22 +14820,184 @@ unsigned char get_rows_f16_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x2d,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x06,0x00,0x00,0x00, -0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x55,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x52,0x00,0x00,0x00,0x06,0x00,0x00,0x00, +0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x53,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00, -0x55,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, 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+0x61,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x61,0x00,0x00,0x00, +0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x63,0x00,0x00,0x00, +0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x63,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x77,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00, 0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00, @@ -15388,198 +15029,28 @@ unsigned char get_rows_f16_data[] = { 0x2d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, 0x10,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x30,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, -0x44,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0x49,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x16,0x00,0x03,0x00,0x50,0x00,0x00,0x00,0x10,0x00,0x00,0x00, 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+0x1c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x76,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x2c,0x00,0x06,0x00, +0x09,0x00,0x00,0x00,0x77,0x00,0x00,0x00,0x76,0x00,0x00,0x00, 0x16,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x36,0x00,0x05,0x00, 0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00, -0xf7,0x00,0x03,0x00,0x7b,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0xfb,0x00,0x03,0x00,0x0c,0x00,0x00,0x00,0x7c,0x00,0x00,0x00, -0xf8,0x00,0x02,0x00,0x7c,0x00,0x00,0x00,0x41,0x00,0x05,0x00, +0xf7,0x00,0x03,0x00,0x78,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0xfb,0x00,0x03,0x00,0x0c,0x00,0x00,0x00,0x79,0x00,0x00,0x00, +0xf8,0x00,0x02,0x00,0x79,0x00,0x00,0x00,0x41,0x00,0x05,0x00, 0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x7c,0x00,0x04,0x00, @@ -15600,7 +15071,7 @@ unsigned char get_rows_f16_f32_data[] = { 0x23,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x27,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x25,0x00,0x00,0x00, 0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, -0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7b,0x00,0x00,0x00, +0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x78,0x00,0x00,0x00, 0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x41,0x00,0x06,0x00, 0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x2d,0x00,0x00,0x00, 0x2e,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, @@ -15613,51 +15084,51 @@ unsigned char get_rows_f16_f32_data[] = { 0x06,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x1a,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x41,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x14,0x00,0x00,0x00, -0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x45,0x00,0x00,0x00, -0x3a,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x89,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, -0x44,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 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+0x6d,0x00,0x00,0x00,0x6b,0x00,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0x66,0x00,0x00,0x00, +0x16,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x1c,0x00,0x00,0x00, +0x74,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,0x41,0x00,0x06,0x00, +0x6c,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0x63,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x71,0x00,0x00,0x00,0x3e,0x00,0x03,0x00, +0x75,0x00,0x00,0x00,0x74,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x78,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x78,0x00,0x00,0x00, 0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t get_rows_f16_f32_len = 1988; +const uint64_t get_rows_f16_f32_len = 1940; unsigned char get_rows_f16_f32_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x7d,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x7a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, 0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x57,0x00,0x00,0x00, -0x67,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x55,0x00,0x00,0x00, +0x65,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, 0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00, @@ -15676,23 +15147,23 @@ unsigned char get_rows_f16_f32_fp32_data[] = { 0x2b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x2d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00, 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-0x65,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x65,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x63,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x65,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x67,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7a,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x65,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x65,0x00,0x00,0x00,0x21,0x00,0x00,0x00, +0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x77,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00, 0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -15723,32 +15194,28 @@ unsigned char get_rows_f16_f32_fp32_data[] = { 0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x30,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x20,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x49,0x00,0x00,0x00, -0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x53,0x00,0x00,0x00, -0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x54,0x00,0x00,0x00, -0x53,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x55,0x00,0x00,0x00, -0x54,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x56,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, -0x56,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x20,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x53,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x64,0x00,0x00,0x00, -0x1c,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x65,0x00,0x00,0x00, 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+0x79,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x79,0x00,0x00,0x00, 0x41,0x00,0x05,0x00,0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00, @@ -15770,7 +15237,7 @@ unsigned char get_rows_f16_f32_fp32_data[] = { 0x27,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00, 0x25,0x00,0x00,0x00,0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00, 0xf8,0x00,0x02,0x00,0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, -0x7b,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00, +0x78,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x41,0x00,0x06,0x00,0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00, 0x2d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x1a,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x32,0x00,0x00,0x00, @@ -15783,51 +15250,51 @@ unsigned char get_rows_f16_f32_fp32_data[] = { 0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x3f,0x00,0x00,0x00, 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+0x5e,0x00,0x00,0x00,0x73,0x00,0x04,0x00,0x1c,0x00,0x00,0x00, +0x60,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x4d,0x00,0x00,0x00, +0x48,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x6d,0x00,0x00,0x00, +0x6e,0x00,0x00,0x00,0x65,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, +0x68,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x6e,0x00,0x00,0x00, +0x5b,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x72,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x16,0x00,0x00,0x00, +0x41,0x00,0x06,0x00,0x6d,0x00,0x00,0x00,0x75,0x00,0x00,0x00, +0x65,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x72,0x00,0x00,0x00, +0x3e,0x00,0x03,0x00,0x75,0x00,0x00,0x00,0x60,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0x78,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, +0x78,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t get_rows_f16_f32_fp32_len = 1980; +const uint64_t get_rows_f16_f32_fp32_len = 1932; unsigned char get_rows_f16_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x7e,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x7b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, 0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x57,0x00,0x00,0x00, -0x67,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x55,0x00,0x00,0x00, +0x65,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, 0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00, @@ -15846,23 +15313,23 @@ unsigned char get_rows_f16_fp32_data[] = { 0x2b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x2d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x54,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x52,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x55,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x55,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x53,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x53,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x55,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x57,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x57,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x64,0x00,0x00,0x00, +0x53,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x55,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x55,0x00,0x00,0x00,0x21,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x62,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x65,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x65,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x63,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x65,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x67,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x67,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7b,0x00,0x00,0x00, +0x63,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x65,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x65,0x00,0x00,0x00,0x21,0x00,0x00,0x00, +0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x78,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00, 0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -15893,31 +15360,27 @@ unsigned char get_rows_f16_fp32_data[] = { 0x2c,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x30,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x20,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x49,0x00,0x00,0x00, -0x02,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x53,0x00,0x00,0x00, -0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x54,0x00,0x00,0x00, -0x53,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x55,0x00,0x00,0x00, -0x54,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x56,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, -0x56,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x20,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x53,0x00,0x00,0x00,0x1d,0x00,0x03,0x00,0x64,0x00,0x00,0x00, -0x53,0x00,0x00,0x00,0x1e,0x00,0x03,0x00,0x65,0x00,0x00,0x00, -0x64,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x66,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x65,0x00,0x00,0x00,0x3b,0x00,0x04,0x00, -0x66,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x74,0x00,0x00,0x00, -0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, -0x7a,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x2c,0x00,0x06,0x00, -0x09,0x00,0x00,0x00,0x7b,0x00,0x00,0x00,0x7a,0x00,0x00,0x00, +0x0c,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x16,0x00,0x03,0x00, +0x51,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, +0x52,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, +0x53,0x00,0x00,0x00,0x52,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x54,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x53,0x00,0x00,0x00, +0x3b,0x00,0x04,0x00,0x54,0x00,0x00,0x00,0x55,0x00,0x00,0x00, +0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x58,0x00,0x00,0x00, +0x0c,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, +0x62,0x00,0x00,0x00,0x51,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, +0x63,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x20,0x00,0x04,0x00, +0x64,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x63,0x00,0x00,0x00, +0x3b,0x00,0x04,0x00,0x64,0x00,0x00,0x00,0x65,0x00,0x00,0x00, +0x0c,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x77,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x2c,0x00,0x06,0x00, +0x09,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x77,0x00,0x00,0x00, 0x16,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x36,0x00,0x05,0x00, 0x02,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00, -0xf7,0x00,0x03,0x00,0x7c,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0xfb,0x00,0x03,0x00,0x0c,0x00,0x00,0x00,0x7d,0x00,0x00,0x00, -0xf8,0x00,0x02,0x00,0x7d,0x00,0x00,0x00,0x41,0x00,0x05,0x00, +0xf7,0x00,0x03,0x00,0x79,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0xfb,0x00,0x03,0x00,0x0c,0x00,0x00,0x00,0x7a,0x00,0x00,0x00, +0xf8,0x00,0x02,0x00,0x7a,0x00,0x00,0x00,0x41,0x00,0x05,0x00, 0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x7c,0x00,0x04,0x00, @@ -15938,7 +15401,7 @@ unsigned char get_rows_f16_fp32_data[] = { 0x23,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x27,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x25,0x00,0x00,0x00, 0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, -0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x7c,0x00,0x00,0x00, +0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x00,0x00,0x00, 0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x41,0x00,0x06,0x00, 0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x2d,0x00,0x00,0x00, 0x2e,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, @@ -15951,42 +15414,42 @@ unsigned char get_rows_f16_fp32_data[] = { 0x06,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x1a,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 0x41,0x00,0x00,0x00,0x3f,0x00,0x00,0x00,0x14,0x00,0x00,0x00, -0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x45,0x00,0x00,0x00, -0x3a,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x89,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, -0x44,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x4a,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x49,0x00,0x00,0x00, -0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4e,0x00,0x00,0x00, -0x41,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x82,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x41,0x00,0x00,0x00, -0x4e,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x5a,0x00,0x00,0x00, -0x5b,0x00,0x00,0x00,0x57,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x45,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x53,0x00,0x00,0x00, -0x5c,0x00,0x00,0x00,0x5b,0x00,0x00,0x00,0x73,0x00,0x04,0x00, -0x1c,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,0x5c,0x00,0x00,0x00, -0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x5f,0x00,0x00,0x00, -0x45,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x41,0x00,0x06,0x00, -0x5a,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x57,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x5f,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, -0x53,0x00,0x00,0x00,0x61,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0x73,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x62,0x00,0x00,0x00, -0x61,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x6a,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x4a,0x00,0x00,0x00, -0x73,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x6f,0x00,0x00,0x00, -0x5d,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x5a,0x00,0x00,0x00, -0x70,0x00,0x00,0x00,0x67,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x6a,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x70,0x00,0x00,0x00, -0x6f,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x75,0x00,0x00,0x00,0x6a,0x00,0x00,0x00,0x74,0x00,0x00,0x00, -0x73,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x78,0x00,0x00,0x00, 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+0x73,0x00,0x04,0x00,0x51,0x00,0x00,0x00,0x75,0x00,0x00,0x00, +0x60,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x58,0x00,0x00,0x00, +0x76,0x00,0x00,0x00,0x65,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, +0x72,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x76,0x00,0x00,0x00, +0x75,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x79,0x00,0x00,0x00, +0xf8,0x00,0x02,0x00,0x79,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; -const uint64_t get_rows_f16_fp32_len = 1996; +const uint64_t get_rows_f16_fp32_len = 1948; unsigned char get_rows_q4_0_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -52701,7 +52164,7 @@ const uint64_t mul_f32_len = 1456; unsigned char mul_mat_vec_f16_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xba,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xb6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, @@ -52709,9 +52172,9 @@ unsigned char mul_mat_vec_f16_f32_data[] = { 0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, 0x13,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x2a,0x00,0x00,0x00, -0x51,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0xad,0x00,0x00,0x00, +0x51,0x00,0x00,0x00,0x65,0x00,0x00,0x00,0xaa,0x00,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, -0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0c,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x1a,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x13,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x48,0x00,0x05,0x00, @@ -52729,23 +52192,23 @@ unsigned char mul_mat_vec_f16_f32_data[] = { 0x47,0x00,0x04,0x00,0x51,0x00,0x00,0x00,0x22,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x51,0x00,0x00,0x00, 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0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x01,0x00,0x00,0x00, @@ -52760,7 +52223,7 @@ unsigned char mul_mat_vec_f16_f32_data[] = { 0x3b,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x13,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x16,0x00,0x03,0x00,0x17,0x00,0x00,0x00, 0x20,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x09,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, +0x18,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x1c,0x00,0x04,0x00, 0x19,0x00,0x00,0x00,0x17,0x00,0x00,0x00,0x18,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x1a,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x19,0x00,0x00,0x00,0x3b,0x00,0x04,0x00,0x1a,0x00,0x00,0x00, @@ -52775,7 +52238,7 @@ unsigned char mul_mat_vec_f16_f32_data[] = { 0x29,0x00,0x00,0x00,0x2a,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x20,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,0x09,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, -0x2e,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x14,0x00,0x02,0x00, 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+0xfa,0x00,0x04,0x00,0xa4,0x00,0x00,0x00,0xa5,0x00,0x00,0x00, +0xa6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xa5,0x00,0x00,0x00, +0x41,0x00,0x05,0x00,0x2b,0x00,0x00,0x00,0xab,0x00,0x00,0x00, +0x2a,0x00,0x00,0x00,0x35,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, +0x06,0x00,0x00,0x00,0xac,0x00,0x00,0x00,0xab,0x00,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xae,0x00,0x00,0x00, +0xac,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x41,0x00,0x05,0x00, +0x1e,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x1b,0x00,0x00,0x00, +0x21,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x17,0x00,0x00,0x00, +0xb0,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0x41,0x00,0x06,0x00, +0x6d,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0xaa,0x00,0x00,0x00, +0x21,0x00,0x00,0x00,0xae,0x00,0x00,0x00,0x3e,0x00,0x03,0x00, +0xb1,0x00,0x00,0x00,0xb0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0xa6,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xa6,0x00,0x00,0x00, +0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t mul_mat_vec_f16_f32_len = 2788; +const uint64_t mul_mat_vec_f16_f32_len = 2372; unsigned char mul_mat_vec_nc_f16_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 1d93ec6bb..bccc40bf5 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -817,7 +817,7 @@ static void ggml_vk_load_shaders() { // mulmat std::initializer_list warptile_l = { 128, 128, 128, 16, vk_device.subgroup_size * 2, 64, 2, 4, 4, vk_device.subgroup_size }; std::initializer_list warptile_m = { 128, 64, 64, 16, vk_device.subgroup_size, 32, 2, 4, 2, vk_device.subgroup_size }; - std::initializer_list warptile_s = { vk_device.subgroup_size, 32, 32, 8, 32, 32, 2, 2, 2, vk_device.subgroup_size }; + std::initializer_list warptile_s = { vk_device.subgroup_size, 32, 32, 16, 32, 32, 2, 2, 2, vk_device.subgroup_size }; std::array l_wg_denoms = {128, 128, 1 }; std::array m_wg_denoms = { 64, 64, 1 }; @@ -2873,7 +2873,8 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm if (op == GGML_OP_CPY) { GGML_ASSERT(!transfer_src0); GGML_ASSERT(!transfer_src1); - d_sz = dst->ne[1] * dst->nb[1]; + x_sz = ggml_nbytes(src0); + d_sz = ggml_nbytes(dst); if (extra->offset + d_sz >= d_D->size) { d_sz = VK_WHOLE_SIZE; @@ -4556,8 +4557,15 @@ GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml } ggml_vk_preallocate_buffers(); + int last_node = cgraph->n_nodes - 1; + + // If the last op in the cgraph isn't backend GPU, the command buffer doesn't get closed properly + while (last_node > 0 && cgraph->nodes[last_node]->backend != GGML_BACKEND_GPU) { + last_node -= 1; + } + for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_vk_build_graph(cgraph->nodes[i], i == cgraph->n_nodes - 1); + ggml_vk_build_graph(cgraph->nodes[i], i == last_node); } ggml_compute_params params = {}; diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py index d0861fde4..6b1b82bf3 100644 --- a/ggml_vk_generate_shaders.py +++ b/ggml_vk_generate_shaders.py @@ -19,8 +19,8 @@ shader_int8_ext = """ # Type-specific defines shader_f16_defines = """ -#define QUANT_K 32 -#define QUANT_R 2 +#define QUANT_K 1 +#define QUANT_R 1 #define A_TYPE float16_t """ From dabcc5b471348e4ae03ddacc41e19ad75fb2f041 Mon Sep 17 00:00:00 2001 From: slaren Date: Wed, 31 Jan 2024 13:43:03 +0100 Subject: [PATCH 078/143] ggml : limit n_threads to the max n_tasks (#5238) --- ggml.c | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/ggml.c b/ggml.c index b2c8baaa8..afd9c6c61 100644 --- a/ggml.c +++ b/ggml.c @@ -16985,12 +16985,16 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa struct ggml_cplan cplan; memset(&cplan, 0, sizeof(struct ggml_cplan)); + int max_tasks = 1; + // thread scheduling for the different operations + work buffer size estimation for (int i = 0; i < cgraph->n_nodes; i++) { struct ggml_tensor * node = cgraph->nodes[i]; const int n_tasks = ggml_get_n_tasks(node, n_threads); + max_tasks = MAX(max_tasks, n_tasks); + size_t cur = 0; switch (node->op) { @@ -17157,7 +17161,7 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa work_size += CACHE_LINE_SIZE*(n_threads - 1); } - cplan.n_threads = n_threads; + cplan.n_threads = MIN(max_tasks, n_threads); cplan.work_size = work_size; cplan.work_data = NULL; From b2b9f025e7821e78bd501d75d01838c26de07a57 Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Wed, 31 Jan 2024 21:04:46 +0800 Subject: [PATCH 079/143] format license text, restore apache license by legal suggestion (#5233) --- examples/sycl/ls-sycl-device.cpp | 10 ++++++---- ggml-sycl.cpp | 15 +++++++++++---- ggml-sycl.h | 9 +++++---- 3 files changed, 22 insertions(+), 12 deletions(-) diff --git a/examples/sycl/ls-sycl-device.cpp b/examples/sycl/ls-sycl-device.cpp index 42847154a..52442e4ca 100644 --- a/examples/sycl/ls-sycl-device.cpp +++ b/examples/sycl/ls-sycl-device.cpp @@ -1,7 +1,9 @@ -/*MIT license - Copyright (C) 2024 Intel Corporation - SPDX-License-Identifier: MIT -*/ +// +// MIT license +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: MIT +// + #include "ggml-sycl.h" diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 3fc346975..1cc55ef52 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -1,7 +1,14 @@ -/*MIT license - Copyright (C) 2024 Intel Corporation - SPDX-License-Identifier: MIT -*/ +// +// MIT license +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: MIT +// + +// +// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. +// See https://llvm.org/LICENSE.txt for license information. +// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception +// #include #include diff --git a/ggml-sycl.h b/ggml-sycl.h index 0eabb53cc..ba0c61473 100644 --- a/ggml-sycl.h +++ b/ggml-sycl.h @@ -1,7 +1,8 @@ -/*MIT license - Copyright (C) 2024 Intel Corporation - SPDX-License-Identifier: MIT -*/ +// +// MIT license +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: MIT +// #pragma once From 15606309a05ccf7fadbaad5538cb7c32acb1e06b Mon Sep 17 00:00:00 2001 From: JidongZhang-THU <1119708529@qq.com> Date: Wed, 31 Jan 2024 21:10:15 +0800 Subject: [PATCH 080/143] llava : add MobileVLM support (#5132) * New Feature: 1. Sum_Rows: fix cuda kernel overflow fix block shape error when nrows too big 2. Im2Col: Support Batch in cuda Support f32 to f32 both in cpu && cuda 3. DepthWiseConv: Support by Im2Col && MulMat 4. Pool_2d: Supoort avg pooling in cuda 5. HardSigmoid: Imp in cuda 6. HardSwish: Imp in cuda * fix tabs instead of spaces * code clean * CUDA POOL2D * ADD POOL2D test case in test-backend-ops.cpp * code clean * fix pool2d_kernel nits * fix bug in pool2d kernel * fix avg pooling, count_include_pad nits * test-backend-ops : add more pool_2d tests * cuda : fix warnings and formatting * ggml : check types in release builds too in pool_2d * test-backend-ops : remove f16 pool_2d tests * cuda : more style fixes * Add assert in ggml_cuda_op_pool2d * pool2d float padding fallback * test-backend-ops : add dst_type to im2col --------- Co-authored-by: slaren --- examples/llava/MobileVLM-README.md | 58 +++++++- ggml-cuda.cu | 209 ++++++++++++++++++++++++++--- ggml.c | 118 +++++++++++++--- ggml.h | 3 +- tests/test-backend-ops.cpp | 74 +++++++++- 5 files changed, 421 insertions(+), 41 deletions(-) diff --git a/examples/llava/MobileVLM-README.md b/examples/llava/MobileVLM-README.md index c6258eba6..9eba791da 100644 --- a/examples/llava/MobileVLM-README.md +++ b/examples/llava/MobileVLM-README.md @@ -111,17 +111,71 @@ llama_print_timings: eval time = 1279.03 ms / 18 runs ( 71.06 m llama_print_timings: total time = 34570.79 ms ``` +## Orin compile and run +### compile +```sh +make LLAMA_CUBLAS=1 CUDA_DOCKER_ARCH=sm_87 LLAMA_CUDA_F16=1 -j 32 +``` + +### run on Orin +### case 1 +**input** +```sh +./llava-cli \ + -m /data/local/tmp/ggml-model-q4_k.gguf \ + --mmproj /data/local/tmp/mmproj-model-f16.gguf \ + --image /data/local/tmp/demo.jpeg \ + -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWho is the author of this book? \nAnswer the question using a single word or phrase. ASSISTANT:" \ + --n-gpu-layers 999 +``` +**output** +```sh + +encode_image_with_clip: image encoded in 296.62 ms by CLIP ( 2.06 ms per image patch) + + Susan Wise Bauer + +llama_print_timings: load time = 1067.64 ms +llama_print_timings: sample time = 1.53 ms / 6 runs ( 0.25 ms per token, 3934.43 tokens per second) +llama_print_timings: prompt eval time = 306.84 ms / 246 tokens ( 1.25 ms per token, 801.72 tokens per second) +llama_print_timings: eval time = 91.50 ms / 6 runs ( 15.25 ms per token, 65.58 tokens per second) +llama_print_timings: total time = 1352.63 ms / 252 tokens +``` + +### case 2 +**input** +```sh +./llava-cli \ + -m /data/local/tmp/ggml-model-q4_k.gguf \ + --mmproj /data/local/tmp/mmproj-model-f16.gguf \ + -p "A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: \nWhat is in the image? ASSISTANT:" \ + --n-gpu-layers 999 + +``` +**output** +```sh +encode_image_with_clip: image encoded in 302.15 ms by CLIP ( 2.10 ms per image patch) + + The image features a cat lying in the grass. + +llama_print_timings: load time = 1057.07 ms +llama_print_timings: sample time = 3.27 ms / 11 runs ( 0.30 ms per token, 3360.83 tokens per second) +llama_print_timings: prompt eval time = 213.60 ms / 232 tokens ( 0.92 ms per token, 1086.14 tokens per second) +llama_print_timings: eval time = 166.65 ms / 11 runs ( 15.15 ms per token, 66.01 tokens per second) +llama_print_timings: total time = 1365.47 ms / 243 tokens +``` + ## Minor shortcomings The `n_patch` of output in `ldp` is 1/4 of the input. In order to implement quickly, we uniformly modified `clip_n_patches` function to a quarter. when counting the time consumption, the calculated time will be 4 times bigger than the real cost. ## TODO -- [ ] Support non-CPU backend for the new operators, such as `depthwise`, `hardswish`, `hardsigmoid` +- [x] Support non-CPU backend for the new operators, such as `depthwise`, `hardswish`, `hardsigmoid` - [ ] Optimize LDP projector performance - Optimize the structure definition to avoid unnecessary memory rearrangements, to reduce the use of `ggml_permute_cpy`; - Optimize operator implementation (ARM CPU/NVIDIA GPU): such as depthwise conv, hardswish, hardsigmoid, etc. -- [ ] run MobileVLM on `Jetson Orin` +- [x] run MobileVLM on `Jetson Orin` - [ ] Support more model variants, such as `MobileVLM-3B`. diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 949bc8a1c..e56595742 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -524,6 +524,8 @@ static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong #define CUDA_SILU_BLOCK_SIZE 256 #define CUDA_TANH_BLOCK_SIZE 256 #define CUDA_RELU_BLOCK_SIZE 256 +#define CUDA_HARDSIGMOID_BLOCK_SIZE 256 +#define CUDA_HARDSWISH_BLOCK_SIZE 256 #define CUDA_SQR_BLOCK_SIZE 256 #define CUDA_CPY_BLOCK_SIZE 32 #define CUDA_SCALE_BLOCK_SIZE 256 @@ -540,6 +542,7 @@ static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong #define CUDA_PAD_BLOCK_SIZE 256 #define CUDA_ACC_BLOCK_SIZE 256 #define CUDA_IM2COL_BLOCK_SIZE 256 +#define CUDA_POOL2D_BLOCK_SIZE 256 #define CUDA_Q8_0_NE_ALIGN 2048 @@ -823,6 +826,24 @@ static __global__ void relu_f32(const float * x, float * dst, const int k) { dst[i] = fmaxf(x[i], 0); } +static __global__ void hardsigmoid_f32(const float * x, float * dst, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + + if (i >= k) { + return; + } + dst[i] = fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); +} + +static __global__ void hardswish_f32(const float * x, float * dst, const int k) { + const int i = blockDim.x*blockIdx.x + threadIdx.x; + + if (i >= k) { + return; + } + dst[i] = x[i] * fminf(1.0f, fmaxf(0.0f, (x[i] + 3.0f) / 6.0f)); +} + static __global__ void leaky_relu_f32(const float * x, float * dst, const int k, const float negative_slope) { const int i = blockDim.x*blockIdx.x + threadIdx.x; if (i >= k) { @@ -5823,7 +5844,7 @@ static __global__ void alibi_f32(const float * x, float * dst, const int ncols, } static __global__ void k_sum_rows_f32(const float * x, float * dst, const int ncols) { - const int row = blockIdx.y; + const int row = blockIdx.x; const int col = threadIdx.x; float sum = 0.0f; @@ -6145,9 +6166,10 @@ static __global__ void clamp_f32(const float * x, float * dst, const float min, dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]); } -static __global__ void im2col_f32_f16( - const float * x, half * dst, - int offset_delta, int IW, int IH, int OW, int KW, int KH, int pelements, int CHW, +template +static __global__ void im2col_kernel( + const float * x, T * dst, int batch_offset, + int offset_delta, int IC, int IW, int IH, int OH, int OW, int KW, int KH, int pelements, int CHW, int s0, int s1, int p0, int p1, int d0, int d1) { const int i = threadIdx.x + blockIdx.x * blockDim.x; if (i >= pelements) { @@ -6160,21 +6182,73 @@ static __global__ void im2col_f32_f16( const int ky = (i - kd) / OW; const int ix = i % OW; + const int oh = blockIdx.y; + const int batch = blockIdx.z / IC; + const int ic = blockIdx.z % IC; + const int64_t iiw = ix * s0 + kx * d0 - p0; - const int64_t iih = blockIdx.y * s1 + ky * d1 - p1; + const int64_t iih = oh * s1 + ky * d1 - p1; const int64_t offset_dst = - (blockIdx.y * OW + ix) * CHW + - (blockIdx.z * (KW * KH) + ky * KW + kx); + ((batch * OH + oh) * OW + ix) * CHW + + (ic * (KW * KH) + ky * KW + kx); if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { - dst[offset_dst] = __float2half(0.0f); + dst[offset_dst] = 0.0f; } else { - const int64_t offset_src = blockIdx.z * offset_delta; - dst[offset_dst] = __float2half(x[offset_src + iih * IW + iiw]); + const int64_t offset_src = ic * offset_delta + batch * batch_offset; + dst[offset_dst] = x[offset_src + iih * IW + iiw]; } } +template +static __global__ void pool2d_nchw_kernel( + const int ih, const int iw, const int oh, const int ow, + const int kh, const int kw, const int sh, const int sw, + const int ph, const int pw, const int parallel_elements, + const Ti* src, To* dst, const enum ggml_op_pool op) { + int idx = threadIdx.x + blockIdx.x * blockDim.x; + if (idx >= parallel_elements) { + return; + } + + const int I_HW = ih * iw; + const int O_HW = oh * ow; + const int nc = idx / O_HW; + const int cur_oh = idx % O_HW / ow; + const int cur_ow = idx % O_HW % ow; + const Ti* i_ptr = src + nc * I_HW; + To* o_ptr = dst + nc * O_HW; + const int start_h = cur_oh * sh - ph; + const int bh = max(0, start_h); + const int eh = min(ih, start_h + kh); + const int start_w = cur_ow * sw - pw; + const int bw = max(0, start_w); + const int ew = min(iw, start_w + kw); + const To scale = 1. / (kh * kw); + To res = 0; + + switch (op) { + case GGML_OP_POOL_AVG: res = 0; break; + case GGML_OP_POOL_MAX: res = -FLT_MAX; break; + } + + for (int i = bh; i < eh; i += 1) { + for (int j = bw; j < ew; j += 1) { + #if __CUDA_ARCH__ >= 350 + Ti cur = __ldg(i_ptr + i * iw + j); + #else + Ti cur = i_ptr[i * iw + j]; + #endif + switch (op) { + case GGML_OP_POOL_AVG: res += cur * scale; break; + case GGML_OP_POOL_MAX: res = max(res, (To)cur); break; + } + } + } + o_ptr[cur_oh * ow + cur_ow] = res; +} + template static void get_rows_cuda(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const void * src0_dd, const int32_t * src1_dd, float * dst_dd, cudaStream_t stream) { @@ -6388,6 +6462,16 @@ static void relu_f32_cuda(const float * x, float * dst, const int k, cudaStream_ relu_f32<<>>(x, dst, k); } +static void hardsigmoid_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_HARDSIGMOID_BLOCK_SIZE - 1) / CUDA_HARDSIGMOID_BLOCK_SIZE; + hardsigmoid_f32<<>>(x, dst, k); +} + +static void hardswish_f32_cuda(const float * x, float * dst, const int k, cudaStream_t stream) { + const int num_blocks = (k + CUDA_HARDSWISH_BLOCK_SIZE - 1) / CUDA_HARDSWISH_BLOCK_SIZE; + hardswish_f32<<>>(x, dst, k); +} + static void leaky_relu_f32_cuda(const float * x, float * dst, const int k, const float negative_slope, cudaStream_t stream) { const int num_blocks = (k + CUDA_RELU_BLOCK_SIZE - 1) / CUDA_RELU_BLOCK_SIZE; leaky_relu_f32<<>>(x, dst, k, negative_slope); @@ -7475,7 +7559,7 @@ static void alibi_f32_cuda(const float * x, float * dst, const int ncols, const static void sum_rows_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) { const dim3 block_dims(WARP_SIZE, 1, 1); - const dim3 block_nums(1, nrows, 1); + const dim3 block_nums(nrows, 1, 1); k_sum_rows_f32<<>>(x, dst, ncols); } @@ -7587,14 +7671,15 @@ static void soft_max_f32_cuda(const float * x, const float * y, float * dst, con } } -static void im2col_f32_f16_cuda(const float* x, half* dst, +template +static void im2col_cuda(const float* x, T* dst, int IW, int IH, int OW, int OH, int KW, int KH, int IC, - int offset_delta, + int batch, int batch_offset, int offset_delta, int s0,int s1,int p0,int p1,int d0,int d1, cudaStream_t stream) { const int parallel_elements = OW * KW * KH; const int num_blocks = (parallel_elements + CUDA_IM2COL_BLOCK_SIZE - 1) / CUDA_IM2COL_BLOCK_SIZE; - dim3 block_nums(num_blocks, OH, IC); - im2col_f32_f16<<>>(x, dst, offset_delta, IW, IH, OW, KW, KH, parallel_elements, (IC * KH * KW), s0, s1, p0, p1, d0, d1); + dim3 block_nums(num_blocks, OH, batch * IC); + im2col_kernel<<>>(x, dst, batch_offset, offset_delta, IC, IW, IH, OH, OW, KW, KH, parallel_elements, (IC * KH * KW), s0, s1, p0, p1, d0, d1); } // buffer pool for cuda @@ -8179,6 +8264,34 @@ static void ggml_cuda_op_relu( (void) src1_dd; } +static void ggml_cuda_op_hardsigmoid( + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + hardsigmoid_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + +static void ggml_cuda_op_hardswish( + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + hardswish_f32_cuda(src0_dd, dst_dd, ggml_nelements(src0), main_stream); + + (void) src1; + (void) dst; + (void) src1_dd; +} + static void ggml_cuda_op_leaky_relu( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { @@ -8810,13 +8923,46 @@ static void ggml_cuda_op_alibi( (void) src1_dd; } +static void ggml_cuda_op_pool2d( + const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, + const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { + + GGML_ASSERT(src0->type == GGML_TYPE_F32); + GGML_ASSERT( dst->type == GGML_TYPE_F32); + + const int32_t * opts = (const int32_t *)dst->op_params; + enum ggml_op_pool op = static_cast(opts[0]); + const int k0 = opts[1]; + const int k1 = opts[2]; + const int s0 = opts[3]; + const int s1 = opts[4]; + const int p0 = opts[5]; + const int p1 = opts[6]; + + const int64_t IH = src0->ne[1]; + const int64_t IW = src0->ne[0]; + + const int64_t N = dst->ne[3]; + const int64_t OC = dst->ne[2]; + const int64_t OH = dst->ne[1]; + const int64_t OW = dst->ne[0]; + + const int parallel_elements = N * OC * OH * OW; + const int num_blocks = (parallel_elements + CUDA_POOL2D_BLOCK_SIZE - 1) / CUDA_POOL2D_BLOCK_SIZE; + dim3 block_nums(num_blocks); + pool2d_nchw_kernel<<>>(IH, IW, OH, OW, k1, k0, s1, s0, p1, p0, parallel_elements, src0_dd, dst_dd, op); + + (void) src1; + (void) src1_dd; +} + static void ggml_cuda_op_im2col( const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, const float * src0_dd, const float * src1_dd, float * dst_dd, cudaStream_t main_stream) { 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 || dst->type == GGML_TYPE_F32); const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; const int32_t s1 = ((const int32_t*)(dst->op_params))[1]; @@ -8838,8 +8984,14 @@ static void ggml_cuda_op_im2col( const int64_t OW = dst->ne[1]; const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32 + const int64_t batch = src1->ne[3]; + const size_t batch_offset = src1->nb[3] / 4; // nb is byte offset, src is type float32 - im2col_f32_f16_cuda(src1_dd, (half*) dst_dd, IW, IH, OW, OH, KW, KH, IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream); + if(dst->type == GGML_TYPE_F16) { + im2col_cuda(src1_dd, (half*) dst_dd, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, main_stream); + } else { + im2col_cuda(src1_dd, (float*) dst_dd, IW, IH, OW, OH, KW, KH, IC, batch, batch_offset, delta_offset, s0, s1, p0, p1, d0, d1, main_stream); + } (void) src0; (void) src0_dd; @@ -9435,6 +9587,13 @@ static void ggml_cuda_relu(const ggml_tensor * src0, const ggml_tensor * src1, g ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_relu); } +static void ggml_cuda_hardsigmoid(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_hardsigmoid); +} + +static void ggml_cuda_hardswish(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_hardswish); +} static void ggml_cuda_leaky_relu(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_leaky_relu); } @@ -10220,6 +10379,10 @@ static void ggml_cuda_alibi(const ggml_tensor * src0, const ggml_tensor * src1, ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_alibi); } +static void ggml_cuda_pool2d(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_pool2d); +} + static void ggml_cuda_im2col(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { ggml_cuda_op_flatten(src0, src1, dst, ggml_cuda_op_im2col); } @@ -10321,6 +10484,12 @@ GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, st case GGML_UNARY_OP_RELU: func = ggml_cuda_relu; break; + case GGML_UNARY_OP_HARDSIGMOID: + func = ggml_cuda_hardsigmoid; + break; + case GGML_UNARY_OP_HARDSWISH: + func = ggml_cuda_hardswish; + break; default: return false; } @@ -10395,6 +10564,9 @@ GGML_CALL bool ggml_cuda_compute_forward(struct ggml_compute_params * params, st case GGML_OP_IM2COL: func = ggml_cuda_im2col; break; + case GGML_OP_POOL_2D: + func = ggml_cuda_pool2d; + break; case GGML_OP_SUM_ROWS: func = ggml_cuda_sum_rows; break; @@ -11123,6 +11295,8 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_SILU: case GGML_UNARY_OP_RELU: + case GGML_UNARY_OP_HARDSIGMOID: + case GGML_UNARY_OP_HARDSWISH: case GGML_UNARY_OP_GELU_QUICK: case GGML_UNARY_OP_TANH: return true; @@ -11221,6 +11395,7 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons case GGML_OP_ROPE: case GGML_OP_ALIBI: case GGML_OP_IM2COL: + case GGML_OP_POOL_2D: case GGML_OP_SUM_ROWS: case GGML_OP_ARGSORT: case GGML_OP_ACC: diff --git a/ggml.c b/ggml.c index afd9c6c61..ee994c875 100644 --- a/ggml.c +++ b/ggml.c @@ -5349,7 +5349,7 @@ GGML_API struct ggml_tensor * ggml_conv_1d( int s0, int p0, int d0) { - struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, 0, p0, 0, d0, 0, false); // [N, OL, IC * K] + struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, 0, p0, 0, d0, 0, false, GGML_TYPE_F16); // [N, OL, IC * K] struct ggml_tensor * result = ggml_mul_mat(ctx, @@ -5427,16 +5427,15 @@ struct ggml_tensor * ggml_conv_depthwise_2d( int p1, int d0, int d1) { + struct ggml_tensor * new_a = ggml_reshape_4d(ctx, a, a->ne[0], a->ne[1], 1, a->ne[2] * a->ne[3]); struct ggml_tensor * im2col = ggml_im2col(ctx, new_a, ggml_reshape_4d(ctx, b, b->ne[0], b->ne[1], 1, b->ne[2] * b->ne[3]), - s0, s1, p0, p1, d0, d1, true); // [N * IC, OH, OW, KH * KW] - - struct ggml_tensor * result = - ggml_mul_mat(ctx, - ggml_reshape_4d(ctx, new_a, (new_a->ne[0] * new_a->ne[1]), new_a->ne[2], new_a->ne[3], 1), // [OC，1, KH, KW] => [1, OC, 1, KH * KW] - ggml_reshape_4d(ctx, im2col, im2col->ne[0], im2col->ne[2] * im2col->ne[1], b->ne[2], b->ne[3])); // [N * IC, OH, OW, KH * KW] => [N, IC, OH * OW, KH * KW] + s0, s1, p0, p1, d0, d1, true, GGML_TYPE_F16); // [N * IC, OH, OW, KH * KW] + struct ggml_tensor * new_b = ggml_reshape_4d(ctx, im2col, im2col->ne[0], im2col->ne[2] * im2col->ne[1], b->ne[2], b->ne[3]); // [N * IC, OH, OW, KH * KW] => [N, IC, OH * OW, KH * KW] + new_a = ggml_reshape_4d(ctx, new_a, (new_a->ne[0] * new_a->ne[1]), new_a->ne[2], new_a->ne[3], 1); // [OC，1, KH, KW] => [1, OC, 1, KH * KW] + struct ggml_tensor * result = ggml_mul_mat(ctx, new_a, new_b); result = ggml_reshape_4d(ctx, result, im2col->ne[1], im2col->ne[2], b->ne[2], b->ne[3]); // [N, OC, OH, OW] return result; @@ -5457,7 +5456,8 @@ struct ggml_tensor * ggml_im2col( int p1, int d0, int d1, - bool is_2D) { + bool is_2D, + enum ggml_type dst_type) { if(is_2D) { GGML_ASSERT(a->ne[2] == b->ne[2]); @@ -5481,7 +5481,7 @@ struct ggml_tensor * ggml_im2col( is_2D ? b->ne[3] : 1, }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F16, 4, ne); + struct ggml_tensor * result = ggml_new_tensor(ctx, dst_type, 4, ne); int32_t params[] = { s0, s1, p0, p1, d0, d1, (is_2D ? 1 : 0) }; ggml_set_op_params(result, params, sizeof(params)); @@ -5506,7 +5506,7 @@ struct ggml_tensor * ggml_conv_2d( int p1, int d0, int d1) { - struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, s1, p0, p1, d0, d1, true); // [N, OH, OW, IC * KH * KW] + struct ggml_tensor * im2col = ggml_im2col(ctx, a, b, s0, s1, p0, p1, d0, d1, true, GGML_TYPE_F16); // [N, OH, OW, IC * KH * KW] struct ggml_tensor * result = ggml_mul_mat(ctx, @@ -5632,12 +5632,13 @@ struct ggml_tensor * ggml_pool_2d( is_node = true; } + struct ggml_tensor * result; const int64_t ne[3] = { ggml_calc_pool_output_size(a->ne[0], k0, s0, p0), ggml_calc_pool_output_size(a->ne[1], k1, s1, p1), a->ne[2], }; - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne); + result = ggml_new_tensor(ctx, GGML_TYPE_F32, 3, ne); int32_t params[] = { op, k0, k1, s0, s1, p0, p1 }; ggml_set_op_params(result, params, sizeof(params)); @@ -5645,7 +5646,6 @@ struct ggml_tensor * ggml_pool_2d( result->op = GGML_OP_POOL_2D; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; - return result; } @@ -12493,6 +12493,92 @@ static void ggml_compute_forward_conv_transpose_1d( } } +// src0: kernel [OC, IC, KH, KW] +// src1: image [N, IC, IH, IW] +// dst: result [N, OH, OW, IC*KH*KW] +static void ggml_compute_forward_im2col_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); + + GGML_TENSOR_BINARY_OP_LOCALS; + + const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; + const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; + const int32_t p0 = ((const int32_t *)(dst->op_params))[2]; + const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; + const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; + const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; + const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; + + const int ith = params->ith; + const int nth = params->nth; + + const int64_t N = is_2D ? ne13 : ne12; + const int64_t IC = is_2D ? ne12 : ne11; + const int64_t IH = is_2D ? ne11 : 1; + const int64_t IW = ne10; + + const int64_t KH = is_2D ? ne01 : 1; + const int64_t KW = ne00; + + const int64_t OH = is_2D ? ne2 : 1; + const int64_t OW = ne1; + + int ofs0 = is_2D ? nb13 : nb12; + int ofs1 = is_2D ? nb12 : nb11; + + GGML_ASSERT(nb00 == sizeof(ggml_fp16_t)); + GGML_ASSERT(nb10 == sizeof(float)); + + if (params->type == GGML_TASK_INIT) { + return; + } + + if (params->type == GGML_TASK_FINALIZE) { + return; + } + + // im2col: [N, IC, IH, IW] => [N, OH, OW, IC*KH*KW] + { + float * const wdata = (float *) dst->data; + + for (int64_t in = 0; in < N; in++) { + for (int64_t ioh = 0; ioh < OH; ioh++) { // 1 + for (int64_t iow = 0; iow < OW; iow++) { + for (int64_t iic = ith; iic < IC; iic += nth) { + + // micro kernel + float * dst_data = wdata + (in*OH*OW + ioh*OW + iow)*(IC*KH*KW); // [IC, KH, KW] + const float * const src_data = (float *)((char *) src1->data + in*ofs0 + iic*ofs1); // [IH, IW] + + for (int64_t ikh = 0; ikh < KH; ikh++) { // 1 + for (int64_t ikw = 0; ikw < KW; ikw++) { + const int64_t iiw = iow*s0 + ikw*d0 - p0; + const int64_t iih = ioh*s1 + ikh*d1 - p1; + + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { + dst_data[iic*(KH*KW) + ikh*KW + ikw] = 0; + } else { + dst_data[iic*(KH*KW) + ikh*KW + ikw] = (src_data[iih*IW + iiw]); + } + } + } + } + } + } + } + } +} + + // src0: kernel [OC, IC, KH, KW] // src1: image [N, IC, IH, IW] // dst: result [N, OH, OW, IC*KH*KW] @@ -12583,14 +12669,14 @@ static void ggml_compute_forward_im2col( const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { - switch (src0->type) { + switch (dst->type) { case GGML_TYPE_F16: { ggml_compute_forward_im2col_f16(params, src0, src1, dst); } break; case GGML_TYPE_F32: { - GGML_ASSERT(false); + ggml_compute_forward_im2col_f32(params, src0, src1, dst); } break; default: { @@ -12781,8 +12867,8 @@ static void ggml_compute_forward_pool_2d( const struct ggml_compute_params * params, const struct ggml_tensor * src, struct ggml_tensor * dst) { - assert(src->type == GGML_TYPE_F32); - assert(params->ith == 0); + GGML_ASSERT(src->type == GGML_TYPE_F32); + GGML_ASSERT(params->ith == 0); if (params->type == GGML_TASK_INIT || params->type == GGML_TASK_FINALIZE) { return; diff --git a/ggml.h b/ggml.h index afc87b843..e0a4799f3 100644 --- a/ggml.h +++ b/ggml.h @@ -1495,7 +1495,8 @@ extern "C" { int p1, int d0, int d1, - bool is_2D); + bool is_2D, + enum ggml_type dst_type); GGML_API struct ggml_tensor * ggml_conv_depthwise_2d( struct ggml_context * ctx, diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 1d29070b6..eb06123d2 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -227,6 +227,14 @@ static std::string var_to_str(ggml_type type) { return ggml_type_name(type); } +static std::string var_to_str(ggml_op_pool pool) { + switch (pool) { + case GGML_OP_POOL_AVG: return "avg"; + case GGML_OP_POOL_MAX: return "max"; + default: return std::to_string(pool); + } +} + #define VARS_TO_STR1(a) VAR_TO_STR(a) #define VARS_TO_STR2(a, b) VAR_TO_STR(a) + "," + VAR_TO_STR(b) #define VARS_TO_STR3(a, b, c) VAR_TO_STR(a) + "," + VARS_TO_STR2(b, c) @@ -238,6 +246,7 @@ static std::string var_to_str(ggml_type type) { #define VARS_TO_STR9(a, b, c, d, e, f, g, h, i) VAR_TO_STR(a) + "," + VARS_TO_STR8(b, c, d, e, f, g, h, i) #define VARS_TO_STR10(a, b, c, d, e, f, g, h, i, j) VAR_TO_STR(a) + "," + VARS_TO_STR9(b, c, d, e, f, g, h, i, j) #define VARS_TO_STR11(a, b, c, d, e, f, g, h, i, j, k) VAR_TO_STR(a) + "," + VARS_TO_STR10(b, c, d, e, f, g, h, i, j, k) +#define VARS_TO_STR12(a, b, c, d, e, f, g, h, i, j, k, l) VAR_TO_STR(a) + "," + VARS_TO_STR11(b, c, d, e, f, g, h, i, j, k, l) #ifdef GGML_USE_SYCL static bool inline _isinf(float f) { @@ -1162,10 +1171,45 @@ struct test_alibi : public test_case { } }; +// GGML_OP_POOL2D +struct test_pool2d : public test_case { + enum ggml_op_pool pool_type; + const ggml_type type_input; + const std::array ne_input; + // kernel size + const int k0; + const int k1; + // stride + const int s0; + const int s1; + // padding + const int p0; + const int p1; + + std::string vars() override { + return VARS_TO_STR9(pool_type, type_input, ne_input, k0, k1, s0, s1, p0, p1); + } + + test_pool2d(ggml_op_pool pool_type = GGML_OP_POOL_AVG, + ggml_type type_input = GGML_TYPE_F32, + std::array ne_input = {10, 10, 3, 1}, // [input_width, input_height, input_channels, 1] + int k0 = 3, int k1 = 3, + int s0 = 1, int s1 = 1, + int p0 = 1, int p1 = 1) + : pool_type(pool_type), type_input(type_input), ne_input(ne_input), k0(k0), k1(k1), s0(s0), s1(s1), p0(p0), p1(p1) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * input = ggml_new_tensor(ctx, type_input, 4, ne_input.data()); + ggml_tensor * out = ggml_pool_2d(ctx, input, pool_type, k0, k1, s0, s1, p0, p1); + return out; + } +}; + // GGML_OP_IM2COL struct test_im2col : public test_case { const ggml_type type_input; const ggml_type type_kernel; + const ggml_type dst_type; const std::array ne_input; const std::array ne_kernel; // stride @@ -1181,22 +1225,22 @@ struct test_im2col : public test_case { const bool is_2D; std::string vars() override { - return VARS_TO_STR11(type_input, type_kernel, ne_input, ne_kernel, s0, s1, p0, p1, d0, d1, is_2D); + return VARS_TO_STR12(type_input, type_kernel, dst_type, ne_input, ne_kernel, s0, s1, p0, p1, d0, d1, is_2D); } - test_im2col(ggml_type type_input = GGML_TYPE_F32, ggml_type type_kernel = GGML_TYPE_F16, + test_im2col(ggml_type type_input = GGML_TYPE_F32, ggml_type type_kernel = GGML_TYPE_F16, ggml_type dst_type = GGML_TYPE_F32, std::array ne_input = {10, 10, 3, 1}, // [input_width, input_height, input_channels, 1] std::array ne_kernel = {3, 3, 3, 1}, // [kernel_width, kernel_height, input_channels, 1] int s0 = 1, int s1 = 1, int p0 = 1, int p1 = 1, int d0 = 1, int d1 = 1, bool is_2D = true) - : type_input(type_input), type_kernel(type_kernel), ne_input(ne_input), ne_kernel(ne_kernel), s0(s0), s1(s1), p0(p0), p1(p1), d0(d0), d1(d1), is_2D(is_2D) {} + : type_input(type_input), type_kernel(type_kernel), dst_type(dst_type), ne_input(ne_input), ne_kernel(ne_kernel), s0(s0), s1(s1), p0(p0), p1(p1), d0(d0), d1(d1), is_2D(is_2D) {} ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * input = ggml_new_tensor(ctx, type_input, 4, ne_input.data()); ggml_tensor * kernel = ggml_new_tensor(ctx, type_kernel, 4, ne_kernel.data()); - ggml_tensor * out = ggml_im2col(ctx, kernel, input, s0, s1, p0, p1, d0, d1, is_2D); + ggml_tensor * out = ggml_im2col(ctx, kernel, input, s0, s1, p0, p1, d0, d1, is_2D, dst_type); return out; } }; @@ -1912,6 +1956,27 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op } } + for (ggml_type type_input : {GGML_TYPE_F32}) { + for (ggml_op_pool pool_type : {GGML_OP_POOL_AVG, GGML_OP_POOL_MAX}) { + for (int k0 : {1, 3}) { + for (int k1 : {1, 3}) { + for (int s0 : {1, 2}) { + for (int s1 : {1, 2}) { + for (int p0 : {0, 1}) { + for (int p1 : {0, 1}) { + test_cases.emplace_back(new test_pool2d(pool_type, type_input, {10, 10, 3, 1}, k0, k1, s0, s1, p0, p1)); + } + } + } + } + } + } + } + } + + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F32)); + test_cases.emplace_back(new test_im2col(GGML_TYPE_F32, GGML_TYPE_F16, GGML_TYPE_F16)); + test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 1, 1, 1})); test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {2, 1, 1, 1})); test_cases.emplace_back(new test_repeat(GGML_TYPE_F32, {10, 10, 10, 10}, {1, 2, 1, 1})); @@ -2049,7 +2114,6 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op } test_cases.emplace_back(new test_alibi()); - test_cases.emplace_back(new test_im2col()); test_cases.emplace_back(new test_concat(GGML_TYPE_F32)); test_cases.emplace_back(new test_concat(GGML_TYPE_I32)); From efb7bdbbd061d087c788598b97992c653f992ddd Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 31 Jan 2024 15:35:41 +0200 Subject: [PATCH 081/143] metal : add im2col F32 dst support (#5132) --- ggml-metal.m | 13 ++++++++++--- ggml-metal.metal | 33 +++++++++++++++++++++++++++++---- 2 files changed, 39 insertions(+), 7 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index f87859552..5260ed827 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -135,6 +135,7 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_ROPE_F16, GGML_METAL_KERNEL_TYPE_ALIBI_F32, GGML_METAL_KERNEL_TYPE_IM2COL_F16, + GGML_METAL_KERNEL_TYPE_IM2COL_F32, GGML_METAL_KERNEL_TYPE_UPSCALE_F32, GGML_METAL_KERNEL_TYPE_PAD_F32, GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, @@ -506,6 +507,7 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ROPE_F16, rope_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ALIBI_F32, alibi_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F16, im2col_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_IM2COL_F32, im2col_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_UPSCALE_F32, upscale_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_PAD_F32, pad_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_ARGSORT_F32_I32_ASC, argsort_f32_i32_asc, true); @@ -630,6 +632,10 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const case GGML_OP_ALIBI: case GGML_OP_ROPE: case GGML_OP_IM2COL: + return true; + case GGML_OP_POOL_1D: + case GGML_OP_POOL_2D: + return false; case GGML_OP_UPSCALE: case GGML_OP_PAD: case GGML_OP_ARGSORT: @@ -2015,7 +2021,7 @@ static bool ggml_metal_graph_compute( { 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 || dst->type == GGML_TYPE_F32); const int32_t s0 = ((const int32_t *)(dst->op_params))[0]; const int32_t s1 = ((const int32_t *)(dst->op_params))[1]; @@ -2023,6 +2029,7 @@ static bool ggml_metal_graph_compute( const int32_t p1 = ((const int32_t *)(dst->op_params))[3]; const int32_t d0 = ((const int32_t *)(dst->op_params))[4]; const int32_t d1 = ((const int32_t *)(dst->op_params))[5]; + const bool is_2D = ((const int32_t *)(dst->op_params))[6] == 1; const int32_t N = src1->ne[is_2D ? 3 : 2]; @@ -2043,8 +2050,8 @@ static bool ggml_metal_graph_compute( id pipeline = nil; - switch (src0->type) { - case GGML_TYPE_F32: GGML_ASSERT(false && "not implemented"); break; + switch (dst->type) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F32].pipeline; break; case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_IM2COL_F16].pipeline; break; default: GGML_ASSERT(false); }; diff --git a/ggml-metal.metal b/ggml-metal.metal index 2614d82e8..efed6ad46 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -1775,9 +1775,29 @@ kernel void kernel_rope( template [[host_name("kernel_rope_f32")]] kernel rope_t kernel_rope; template [[host_name("kernel_rope_f16")]] kernel rope_t kernel_rope; -kernel void kernel_im2col_f16( +typedef void (im2col_t)( device const float * x, - device half * dst, + device char * dst, + constant int32_t & ofs0, + constant int32_t & ofs1, + constant int32_t & IW, + constant int32_t & IH, + constant int32_t & CHW, + constant int32_t & s0, + constant int32_t & s1, + constant int32_t & p0, + constant int32_t & p1, + constant int32_t & d0, + constant int32_t & d1, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tgpg[[threadgroups_per_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]); + +template +kernel void kernel_im2col( + device const float * x, + device char * dst, constant int32_t & ofs0, constant int32_t & ofs1, constant int32_t & IW, @@ -1800,14 +1820,19 @@ kernel void kernel_im2col_f16( (tpitg[0] * tgpg[1] * tgpg[2] + tgpig[1] * tgpg[2] + tgpig[2]) * CHW + (tgpig[0] * (ntg[1] * ntg[2]) + tpitg[1] * ntg[2] + tpitg[2]); + device T * pdst = (device T *) (dst); + if (iih < 0 || iih >= IH || iiw < 0 || iiw >= IW) { - dst[offset_dst] = 0.0f; + pdst[offset_dst] = 0.0f; } else { const int32_t offset_src = tpitg[0] * ofs0 + tgpig[0] * ofs1; - dst[offset_dst] = x[offset_src + iih * IW + iiw]; + pdst[offset_dst] = x[offset_src + iih * IW + iiw]; } } +template [[host_name("kernel_im2col_f32")]] kernel im2col_t kernel_im2col; +template [[host_name("kernel_im2col_f16")]] kernel im2col_t kernel_im2col; + kernel void kernel_upscale_f32( device const char * src0, device char * dst, From 5cb04dbc16d1da38c8fdcc0111b40e67d00dd1c3 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 31 Jan 2024 17:30:17 +0200 Subject: [PATCH 082/143] llama : remove LLAMA_MAX_DEVICES and LLAMA_SUPPORTS_GPU_OFFLOAD (#5240) * llama : remove LLAMA_MAX_DEVICES from llama.h ggml-ci * Update llama.cpp Co-authored-by: slaren * server : remove LLAMA_MAX_DEVICES ggml-ci * llama : remove LLAMA_SUPPORTS_GPU_OFFLOAD ggml-ci * train : remove LLAMA_SUPPORTS_GPU_OFFLOAD * readme : add deprecation notice * readme : change deprecation notice to "remove" and fix url * llama : remove gpu includes from llama.h ggml-ci --------- Co-authored-by: slaren --- README.md | 3 +- common/common.cpp | 56 ++++++++++---------- common/common.h | 66 ++++++++++++------------ common/train.cpp | 12 ++--- examples/batched-bench/batched-bench.cpp | 2 +- examples/llama-bench/llama-bench.cpp | 16 +++--- examples/server/server.cpp | 44 ++++++++-------- llama.cpp | 39 +++++++++++--- llama.h | 29 ++++------- 9 files changed, 143 insertions(+), 124 deletions(-) diff --git a/README.md b/README.md index 7746cb510..e6ed1d429 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,8 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ ### Hot topics -- ⚠️ Incoming backends: https://github.com/ggerganov/llama.cpp/discussions/5138 +- Remove LLAMA_MAX_DEVICES and LLAMA_SUPPORTS_GPU_OFFLOAD: https://github.com/ggerganov/llama.cpp/pull/5240 +- Incoming backends: https://github.com/ggerganov/llama.cpp/discussions/5138 - [SYCL backend](README-sycl.md) is ready (1/28/2024), support Linux/Windows in Intel GPUs (iGPU, Arc/Flex/Max series) - New SOTA quantized models, including pure 2-bits: https://huggingface.co/ikawrakow - Collecting Apple Silicon performance stats: diff --git a/common/common.cpp b/common/common.cpp index 9d976c7c8..ce739b15c 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -583,20 +583,20 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } params.n_gpu_layers = std::stoi(argv[i]); -#ifndef LLAMA_SUPPORTS_GPU_OFFLOAD - fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); - fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); -#endif + if (!llama_supports_gpu_offload()) { + fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); + fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); + } } else if (arg == "--gpu-layers-draft" || arg == "-ngld" || arg == "--n-gpu-layers-draft") { if (++i >= argc) { invalid_param = true; break; } params.n_gpu_layers_draft = std::stoi(argv[i]); -#ifndef LLAMA_SUPPORTS_GPU_OFFLOAD - fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\n"); - fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); -#endif + if (!llama_supports_gpu_offload()) { + fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers-draft option will be ignored\n"); + fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); + } } else if (arg == "--main-gpu" || arg == "-mg") { if (++i >= argc) { invalid_param = true; @@ -637,11 +637,11 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { const std::regex regex{R"([,/]+)"}; std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1}; std::vector split_arg{it, {}}; - if (split_arg.size() >= LLAMA_MAX_DEVICES) { + if (split_arg.size() >= llama_max_devices()) { invalid_param = true; break; } - for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) { + for (size_t i = 0; i < llama_max_devices(); ++i) { if (i < split_arg.size()) { params.tensor_split[i] = std::stof(split_arg[i]); } else { @@ -989,30 +989,30 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" -cb, --cont-batching enable continuous batching (a.k.a dynamic batching) (default: disabled)\n"); printf(" --mmproj MMPROJ_FILE path to a multimodal projector file for LLaVA. see examples/llava/README.md\n"); printf(" --image IMAGE_FILE path to an image file. use with multimodal models\n"); - if (llama_mlock_supported()) { + if (llama_supports_mlock()) { printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); } - if (llama_mmap_supported()) { + if (llama_supports_mmap()) { printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } printf(" --numa attempt optimizations that help on some NUMA systems\n"); printf(" if run without this previously, it is recommended to drop the system page cache before using this\n"); printf(" see https://github.com/ggerganov/llama.cpp/issues/1437\n"); -#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD - printf(" -ngl N, --n-gpu-layers N\n"); - printf(" number of layers to store in VRAM\n"); - printf(" -ngld N, --n-gpu-layers-draft N\n"); - printf(" number of layers to store in VRAM for the draft model\n"); - printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); - printf(" how to split the model across multiple GPUs, one of:\n"); - printf(" - none: use one GPU only\n"); - printf(" - layer (default): split layers and KV across GPUs\n"); - printf(" - row: split rows across GPUs\n"); - printf(" -ts SPLIT, --tensor-split SPLIT\n"); - printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); - printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); - printf(" or for intermediate results and KV (with split-mode = row) (default: %d)\n", params.main_gpu); -#endif // LLAMA_SUPPORTS_GPU_OFFLOAD + if (llama_supports_gpu_offload()) { + printf(" -ngl N, --n-gpu-layers N\n"); + printf(" number of layers to store in VRAM\n"); + printf(" -ngld N, --n-gpu-layers-draft N\n"); + printf(" number of layers to store in VRAM for the draft model\n"); + printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); + printf(" how to split the model across multiple GPUs, one of:\n"); + printf(" - none: use one GPU only\n"); + printf(" - layer (default): split layers and KV across GPUs\n"); + printf(" - row: split rows across GPUs\n"); + printf(" -ts SPLIT, --tensor-split SPLIT\n"); + printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); + printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); + printf(" or for intermediate results and KV (with split-mode = row) (default: %d)\n", params.main_gpu); + } printf(" --verbose-prompt print a verbose prompt before generation (default: %s)\n", params.verbose_prompt ? "true" : "false"); printf(" --no-display-prompt don't print prompt at generation (default: %s)\n", !params.display_prompt ? "true" : "false"); printf(" -gan N, --grp-attn-n N\n"); @@ -1651,7 +1651,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false"); fprintf(stream, "temp: %f # default: 0.8\n", sparams.temp); - const std::vector tensor_split_vector(params.tensor_split, params.tensor_split + LLAMA_MAX_DEVICES); + 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", sparams.tfs_z); diff --git a/common/common.h b/common/common.h index 214a379b5..24a99d728 100644 --- a/common/common.h +++ b/common/common.h @@ -43,40 +43,40 @@ extern char const *LLAMA_BUILD_TARGET; int32_t get_num_physical_cores(); struct gpt_params { - uint32_t seed = -1; // RNG seed + uint32_t seed = -1; // RNG seed - int32_t n_threads = get_num_physical_cores(); - int32_t n_threads_draft = -1; - int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads) - int32_t n_threads_batch_draft = -1; - int32_t n_predict = -1; // new tokens to predict - 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 = 8; // number of tokens to draft during speculative decoding - int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) - int32_t n_parallel = 1; // number of parallel sequences to decode - int32_t n_sequences = 1; // number of sequences to decode - float p_accept = 0.5f; // speculative decoding accept probability - float p_split = 0.1f; // speculative decoding split probability - int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) - int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default) - llama_split_mode split_mode = LLAMA_SPLIT_LAYER; // how to split the model across GPUs - 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_beams = 0; // if non-zero then use beam search of given width. - int32_t grp_attn_n = 1; // group-attention factor - int32_t grp_attn_w = 512; // group-attention width - int32_t n_print = -1; // print token count every n tokens (-1 = disabled) - float rope_freq_base = 0.0f; // RoPE base frequency - float rope_freq_scale = 0.0f; // RoPE frequency scaling factor - float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor - float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor - float yarn_beta_fast = 32.0f; // YaRN low correction dim - float yarn_beta_slow = 1.0f; // YaRN high correction dim - int32_t yarn_orig_ctx = 0; // YaRN original context length - int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // TODO: better to be int32_t for alignment - // pinging @cebtenzzre + int32_t n_threads = get_num_physical_cores(); + int32_t n_threads_draft = -1; + int32_t n_threads_batch = -1; // number of threads to use for batch processing (-1 = use n_threads) + int32_t n_threads_batch_draft = -1; + int32_t n_predict = -1; // new tokens to predict + 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 = 8; // number of tokens to draft during speculative decoding + int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) + int32_t n_parallel = 1; // number of parallel sequences to decode + int32_t n_sequences = 1; // number of sequences to decode + float p_accept = 0.5f; // speculative decoding accept probability + float p_split = 0.1f; // speculative decoding split probability + int32_t n_gpu_layers = -1; // number of layers to store in VRAM (-1 - use default) + int32_t n_gpu_layers_draft = -1; // number of layers to store in VRAM for the draft model (-1 - use default) + llama_split_mode split_mode = LLAMA_SPLIT_LAYER; // how to split the model across GPUs + int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors + float tensor_split[128] = {0}; // how split tensors should be distributed across GPUs + int32_t n_beams = 0; // if non-zero then use beam search of given width. + int32_t grp_attn_n = 1; // group-attention factor + int32_t grp_attn_w = 512; // group-attention width + int32_t n_print = -1; // print token count every n tokens (-1 = disabled) + float rope_freq_base = 0.0f; // RoPE base frequency + float rope_freq_scale = 0.0f; // RoPE frequency scaling factor + float yarn_ext_factor = -1.0f; // YaRN extrapolation mix factor + float yarn_attn_factor = 1.0f; // YaRN magnitude scaling factor + float yarn_beta_fast = 32.0f; // YaRN low correction dim + float yarn_beta_slow = 1.0f; // YaRN high correction dim + int32_t yarn_orig_ctx = 0; // YaRN original context length + int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // TODO: better to be int32_t for alignment + // pinging @cebtenzzre // // sampling parameters struct llama_sampling_params sparams; diff --git a/common/train.cpp b/common/train.cpp index e6f2f7a2f..e4c3d5df6 100644 --- a/common/train.cpp +++ b/common/train.cpp @@ -1363,12 +1363,12 @@ bool consume_common_train_arg( *invalid_param = true; return true; } -#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD - params->n_gpu_layers = std::stoi(argv[i]); -#else - fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); - fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); -#endif + if (llama_supports_gpu_offload()) { + params->n_gpu_layers = std::stoi(argv[i]); + } else { + fprintf(stderr, "warning: not compiled with GPU offload support, --n-gpu-layers option will be ignored\n"); + fprintf(stderr, "warning: see main README.md for information on enabling GPU BLAS support\n"); + } } else if (arg == "-h" || arg == "--help") { params->print_usage = true; return true; diff --git a/examples/batched-bench/batched-bench.cpp b/examples/batched-bench/batched-bench.cpp index 7924db267..b52d68457 100644 --- a/examples/batched-bench/batched-bench.cpp +++ b/examples/batched-bench/batched-bench.cpp @@ -88,7 +88,7 @@ int main(int argc, char ** argv) { llama_model_params model_params = llama_model_default_params(); - const std::vector t_split (LLAMA_MAX_DEVICES, 0.0f); + const std::vector t_split(llama_max_devices(), 0.0f); model_params.n_gpu_layers = n_gpu_layers; model_params.tensor_split = t_split.data(); diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 542cc7bb8..c5a6f744e 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -160,7 +160,7 @@ struct cmd_params { std::vector main_gpu; std::vector no_kv_offload; std::vector mul_mat_q; - std::vector> tensor_split; + std::vector> tensor_split; int reps; bool verbose; output_formats output_format; @@ -179,7 +179,7 @@ static const cmd_params cmd_params_defaults = { /* main_gpu */ {0}, /* no_kv_offload */ {false}, /* mul_mat_q */ {true}, - /* tensor_split */ {{}}, + /* tensor_split */ {std::vector(llama_max_devices(), 0.0f)}, /* reps */ 5, /* verbose */ false, /* output_format */ MARKDOWN @@ -380,10 +380,10 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { const std::regex regex{R"([;/]+)"}; std::sregex_token_iterator it{ts.begin(), ts.end(), regex, -1}; std::vector split_arg{it, {}}; - GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES); + GGML_ASSERT(split_arg.size() <= llama_max_devices()); - std::array tensor_split; - for (size_t i = 0; i < LLAMA_MAX_DEVICES; ++i) { + std::vector tensor_split(llama_max_devices()); + for (size_t i = 0; i < llama_max_devices(); ++i) { if (i < split_arg.size()) { tensor_split[i] = std::stof(split_arg[i]); } else { @@ -459,7 +459,7 @@ struct cmd_params_instance { int main_gpu; bool no_kv_offload; bool mul_mat_q; - std::array tensor_split; + std::vector tensor_split; llama_model_params to_llama_mparams() const { llama_model_params mparams = llama_model_default_params(); @@ -582,7 +582,7 @@ struct test { int main_gpu; bool no_kv_offload; bool mul_mat_q; - std::array tensor_split; + std::vector tensor_split; int n_prompt; int n_gen; std::string test_time; @@ -704,7 +704,7 @@ struct test { std::vector get_values() const { std::string tensor_split_str; int max_nonzero = 0; - for (int i = 0; i < LLAMA_MAX_DEVICES; i++) { + for (size_t i = 0; i < llama_max_devices(); i++) { if (tensor_split[i] > 0) { max_nonzero = i; } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 21bdce8ed..ea77125ea 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1789,28 +1789,28 @@ static void server_print_usage(const char *argv0, const gpt_params ¶ms, printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch); printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n"); printf(" not recommended: doubles context memory required and no measurable increase in quality\n"); - if (llama_mlock_supported()) + if (llama_supports_mlock()) { printf(" --mlock force system to keep model in RAM rather than swapping or compressing\n"); } - if (llama_mmap_supported()) + if (llama_supports_mmap()) { printf(" --no-mmap do not memory-map model (slower load but may reduce pageouts if not using mlock)\n"); } printf(" --numa attempt optimizations that help on some NUMA systems\n"); -#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD - printf(" -ngl N, --n-gpu-layers N\n"); - printf(" number of layers to store in VRAM\n"); - printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); - printf(" how to split the model across multiple GPUs, one of:\n"); - printf(" - none: use one GPU only\n"); - printf(" - layer (default): split layers and KV across GPUs\n"); - printf(" - row: split rows across GPUs\n"); - printf(" -ts SPLIT --tensor-split SPLIT\n"); - printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); - printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); - printf(" or for intermediate results and KV (with split-mode = row)\n"); -#endif + if (llama_supports_gpu_offload()) { + printf(" -ngl N, --n-gpu-layers N\n"); + printf(" number of layers to store in VRAM\n"); + printf(" -sm SPLIT_MODE, --split-mode SPLIT_MODE\n"); + printf(" how to split the model across multiple GPUs, one of:\n"); + printf(" - none: use one GPU only\n"); + printf(" - layer (default): split layers and KV across GPUs\n"); + printf(" - row: split rows across GPUs\n"); + printf(" -ts SPLIT --tensor-split SPLIT\n"); + printf(" fraction of the model to offload to each GPU, comma-separated list of proportions, e.g. 3,1\n"); + printf(" -mg i, --main-gpu i the GPU to use for the model (with split-mode = none),\n"); + printf(" or for intermediate results and KV (with split-mode = row)\n"); + } printf(" -m FNAME, --model FNAME\n"); printf(" model path (default: %s)\n", params.model.c_str()); printf(" -a ALIAS, --alias ALIAS\n"); @@ -2066,13 +2066,13 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, invalid_param = true; break; } -#ifdef LLAMA_SUPPORTS_GPU_OFFLOAD - params.n_gpu_layers = std::stoi(argv[i]); -#else - LOG_WARNING("Not compiled with GPU offload support, --n-gpu-layers option will be ignored. " + if (llama_supports_gpu_offload()) { + params.n_gpu_layers = std::stoi(argv[i]); + } else { + LOG_WARNING("Not compiled with GPU offload support, --n-gpu-layers option will be ignored. " "See main README.md for information on enabling GPU BLAS support", {{"n_gpu_layers", params.n_gpu_layers}}); -#endif + } } else if (arg == "--split-mode" || arg == "-sm") { @@ -2115,9 +2115,9 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, const std::regex regex{R"([,/]+)"}; std::sregex_token_iterator it{arg_next.begin(), arg_next.end(), regex, -1}; std::vector split_arg{it, {}}; - GGML_ASSERT(split_arg.size() <= LLAMA_MAX_DEVICES); + GGML_ASSERT(split_arg.size() <= llama_max_devices()); - for (size_t i_device = 0; i_device < LLAMA_MAX_DEVICES; ++i_device) + for (size_t i_device = 0; i_device < llama_max_devices(); ++i_device) { if (i_device < split_arg.size()) { diff --git a/llama.cpp b/llama.cpp index bb23689fa..9b249ba9c 100644 --- a/llama.cpp +++ b/llama.cpp @@ -10090,18 +10090,45 @@ struct llama_model_quantize_params llama_model_quantize_default_params() { return result; } -int32_t llama_max_devices(void) { - return LLAMA_MAX_DEVICES; +size_t llama_max_devices(void) { +#if defined(GGML_USE_METAL) + return 1; +#elif defined(GGML_USE_CUBLAS) + return GGML_CUDA_MAX_DEVICES; +#elif defined(GGML_USE_SYCL) + return GGML_SYCL_MAX_DEVICES; +#else + return 1; +#endif } -bool llama_mmap_supported(void) { +bool llama_supports_mmap(void) { return llama_mmap::SUPPORTED; } -bool llama_mlock_supported(void) { +bool llama_supports_mlock(void) { return llama_mlock::SUPPORTED; } +bool llama_supports_gpu_offload(void) { +#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \ + defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) + // Defined when llama.cpp is compiled with support for offloading model layers to GPU. + return true; +#else + return false; +#endif +} + +// deprecated: +bool llama_mmap_supported(void) { + return llama_supports_mmap(); +} + +bool llama_mlock_supported(void) { + return llama_supports_mlock(); +} + void llama_backend_init(bool numa) { ggml_time_init(); @@ -10133,8 +10160,8 @@ int64_t llama_time_us(void) { } struct llama_model * llama_load_model_from_file( - const char * path_model, - struct llama_model_params params) { + const char * path_model, + struct llama_model_params params) { ggml_time_init(); llama_model * model = new llama_model; diff --git a/llama.h b/llama.h index 17d43d039..9a60e9bfb 100644 --- a/llama.h +++ b/llama.h @@ -3,15 +3,7 @@ #include "ggml.h" #include "ggml-backend.h" -#ifdef GGML_USE_CUBLAS -#include "ggml-cuda.h" -#define LLAMA_MAX_DEVICES GGML_CUDA_MAX_DEVICES -#elif defined(GGML_USE_SYCL) -#include "ggml-sycl.h" -#define LLAMA_MAX_DEVICES GGML_SYCL_MAX_DEVICES -#else -#define LLAMA_MAX_DEVICES 1 -#endif // GGML_USE_CUBLAS + #include #include #include @@ -49,12 +41,6 @@ #define LLAMA_SESSION_MAGIC LLAMA_FILE_MAGIC_GGSN #define LLAMA_SESSION_VERSION 4 -#if defined(GGML_USE_CUBLAS) || defined(GGML_USE_CLBLAST) || defined(GGML_USE_METAL) || defined(GGML_USE_VULKAN) || \ - defined(GGML_USE_SYCL) || defined(GGML_USE_KOMPUTE) -// Defined when llama.cpp is compiled with support for offloading model layers to GPU. -#define LLAMA_SUPPORTS_GPU_OFFLOAD -#endif - #ifdef __cplusplus extern "C" { #endif @@ -201,7 +187,7 @@ extern "C" { // LLAMA_SPLIT_LAYER: ignored int32_t main_gpu; - // proportion of the model (layers or rows) to offload to each GPU, size: LLAMA_MAX_DEVICES + // proportion of the model (layers or rows) to offload to each GPU, size: llama_max_devices() const float * tensor_split; // Called with a progress value between 0.0 and 1.0. Pass NULL to disable. @@ -338,9 +324,14 @@ extern "C" { LLAMA_API int64_t llama_time_us(void); - LLAMA_API int32_t llama_max_devices(void); - LLAMA_API bool llama_mmap_supported (void); - LLAMA_API bool llama_mlock_supported(void); + LLAMA_API size_t llama_max_devices(void); + + LLAMA_API bool llama_supports_mmap (void); + LLAMA_API bool llama_supports_mlock (void); + LLAMA_API bool llama_supports_gpu_offload(void); + + LLAMA_API DEPRECATED(bool llama_mmap_supported (void), "use llama_supports_mmap() instead"); + LLAMA_API DEPRECATED(bool llama_mlock_supported(void), "use llama_supports_mlock() instead"); LLAMA_API const struct llama_model * llama_get_model(const struct llama_context * ctx); From d3bac7d58408c602ec1f1e423695f1df8410bb03 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 31 Jan 2024 18:47:10 +0200 Subject: [PATCH 083/143] llama : reorder build_orion() at correct place (#5118) --- llama.cpp | 239 +++++++++++++++++++++++++++--------------------------- 1 file changed, 119 insertions(+), 120 deletions(-) diff --git a/llama.cpp b/llama.cpp index 9b249ba9c..02b0a485a 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4666,126 +4666,6 @@ struct llm_build_context { ctx0 = nullptr; } } - struct ggml_cgraph * build_orion() { - struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); - - const int64_t n_embd_head = hparams.n_embd_head_v; - GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); - GGML_ASSERT(n_embd_head == hparams.n_rot); - - struct ggml_tensor * cur; - struct ggml_tensor * inpL; - - inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); - cb(inpL, "inp_embd", -1); - - // inp_pos - contains the positions - struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); - cb(inp_pos, "inp_pos", -1); - - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); - cb(KQ_mask, "KQ_mask", -1); - - // shift the entire K-cache if needed - if (do_rope_shift) { - llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); - } - - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * inpSA = inpL; - - // norm - cur = llm_build_norm(ctx0, inpL, hparams, - model.layers[il].attn_norm, model.layers[il].attn_norm_b, - LLM_NORM, cb, il); - cb(cur, "attn_norm", il); - - // self-attention - { - // compute Q and K and RoPE them - struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); - cb(Qcur, "Qcur", il); - // if (model.layers[il].bq) { - // Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); - // cb(Qcur, "Qcur", il); - // } - - struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); - cb(Kcur, "Kcur", il); - // if (model.layers[il].bk) { - // Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); - // cb(Kcur, "Kcur", il); - // } - - struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); - cb(Vcur, "Vcur", il); - // if (model.layers[il].bv) { - // Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); - // cb(Vcur, "Vcur", il); - // } - - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, - hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, - ext_factor, attn_factor, beta_fast, beta_slow - ); - cb(Qcur, "Qcur", il); - - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, - hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, - ext_factor, attn_factor, beta_fast, beta_slow - ); - cb(Kcur, "Kcur", il); - - cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, - model.layers[il].wo, NULL, - Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); - cb(cur, "kqv_out", il); - } - - struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); - cb(ffn_inp, "ffn_inp", il); - - // feed-forward network - cur = llm_build_norm(ctx0, ffn_inp, hparams, - model.layers[il].ffn_norm, model.layers[il].ffn_norm_b, - LLM_NORM, cb, il); - cb(cur, "ffn_norm", il); - - cur = llm_build_ffn(ctx0, cur, - model.layers[il].ffn_up, NULL, - model.layers[il].ffn_gate, NULL, - model.layers[il].ffn_down, NULL, - NULL, - LLM_FFN_SILU, LLM_FFN_PAR, cb, il); - cb(cur, "ffn_out", il); - - cur = ggml_add(ctx0, cur, ffn_inp); - cb(cur, "l_out", il); - - // input for next layer - inpL = cur; - } - - cur = inpL; - - cur = llm_build_norm(ctx0, cur, hparams, - model.output_norm, model.output_norm_b, - LLM_NORM, cb, -1); - cb(cur, "result_norm", -1); - - // lm_head - cur = ggml_mul_mat(ctx0, model.output, cur); - cb(cur, "result_output", -1); - - ggml_build_forward_expand(gf, cur); - - return gf; - } - - struct ggml_cgraph * build_llama() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); @@ -6589,6 +6469,125 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_orion() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); + cb(inpL, "inp_embd", -1); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); + cb(inp_pos, "inp_pos", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); + cb(KQ_mask, "KQ_mask", -1); + + // shift the entire K-cache if needed + if (do_rope_shift) { + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); + } + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, model.layers[il].attn_norm_b, + LLM_NORM, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + // if (model.layers[il].bq) { + // Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); + // cb(Qcur, "Qcur", il); + // } + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + // if (model.layers[il].bk) { + // Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); + // cb(Kcur, "Kcur", il); + // } + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + // if (model.layers[il].bv) { + // Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); + // cb(Vcur, "Vcur", il); + // } + + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + hparams.n_rot, 2, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + model.layers[il].wo, NULL, + Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + cb(cur, "kqv_out", il); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, model.layers[il].ffn_norm_b, + LLM_NORM, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, model.output_norm_b, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } }; static struct ggml_cgraph * llama_build_graph( From 1cfb5372cf5707c8ec6dde7c874f4a44a6c4c915 Mon Sep 17 00:00:00 2001 From: Eve <139727413+netrunnereve@users.noreply.github.com> Date: Wed, 31 Jan 2024 19:21:55 +0000 Subject: [PATCH 084/143] Fix broken Vulkan Cmake (properly) (#5230) * build vulkan as object * vulkan ci --- .github/workflows/build.yml | 6 ++++-- CMakeLists.txt | 8 ++------ 2 files changed, 6 insertions(+), 8 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index c6db1666e..f4c374ce5 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -356,6 +356,8 @@ jobs: defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_BLAS=ON -DBUILD_SHARED_LIBS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"' - build: 'kompute' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DBUILD_SHARED_LIBS=ON' + - build: 'vulkan' + defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_VULKAN=ON -DBUILD_SHARED_LIBS=ON' steps: - name: Clone @@ -406,7 +408,7 @@ jobs: - name: Install Vulkan SDK id: get_vulkan - if: ${{ matrix.build == 'kompute' }} + if: ${{ matrix.build == 'kompute' || matrix.build == 'vulkan' }} run: | curl.exe -o $env:RUNNER_TEMP/VulkanSDK-Installer.exe -L "https://sdk.lunarg.com/sdk/download/${env:VULKAN_VERSION}/windows/VulkanSDK-${env:VULKAN_VERSION}-Installer.exe" & "$env:RUNNER_TEMP\VulkanSDK-Installer.exe" --accept-licenses --default-answer --confirm-command install @@ -451,7 +453,7 @@ jobs: - name: Test id: cmake_test # not all machines have native AVX-512 - if: ${{ matrix.build != 'clblast' && matrix.build != 'kompute' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }} + if: ${{ matrix.build != 'clblast' && matrix.build != 'kompute' && matrix.build != 'vulkan' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }} run: | cd build ctest -L main -C Release --verbose --timeout 900 diff --git a/CMakeLists.txt b/CMakeLists.txt index 15a1101aa..1ee455b3a 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -423,10 +423,7 @@ if (LLAMA_VULKAN) if (Vulkan_FOUND) message(STATUS "Vulkan found") - set(GGML_HEADERS_VULKAN ggml-vulkan.h) - set(GGML_SOURCES_VULKAN ggml-vulkan.cpp) - - add_library(ggml-vulkan STATIC ggml-vulkan.cpp ggml-vulkan.h) + add_library(ggml-vulkan OBJECT ggml-vulkan.cpp ggml-vulkan.h) if (BUILD_SHARED_LIBS) set_target_properties(ggml-vulkan PROPERTIES POSITION_INDEPENDENT_CODE ON) endif() @@ -1012,7 +1009,6 @@ add_library(ggml OBJECT ggml-quants.h ${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA} ${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL} - ${GGML_SOURCES_VULKAN} ${GGML_HEADERS_VULKAN} ${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL} ${GGML_SOURCES_MPI} ${GGML_HEADERS_MPI} ${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA} @@ -1094,7 +1090,7 @@ install(FILES ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfig.cmake DESTINATION ${CMAKE_INSTALL_LIBDIR}/cmake/Llama) set(GGML_PUBLIC_HEADERS "ggml.h" "ggml-alloc.h" "ggml-backend.h" - "${GGML_HEADERS_CUDA}" "${GGML_HEADERS_OPENCL}" "${GGML_HEADERS_VULKAN}" + "${GGML_HEADERS_CUDA}" "${GGML_HEADERS_OPENCL}" "${GGML_HEADERS_METAL}" "${GGML_HEADERS_MPI}" "${GGML_HEADERS_EXTRA}") set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}") From ce32060198b7e2d6a13a9b8e1e1369e3c295ae2a Mon Sep 17 00:00:00 2001 From: Guoteng <32697156+SolenoidWGT@users.noreply.github.com> Date: Thu, 1 Feb 2024 17:19:51 +0800 Subject: [PATCH 085/143] llama : support InternLM2 (#5184) * support InternLM2 inference * add add_space_prefix KV pair --- convert-hf-to-gguf.py | 152 ++++++++++++++++++++++++ gguf-py/gguf/constants.py | 18 +++ gguf-py/gguf/gguf_writer.py | 3 + gguf-py/gguf/tensor_mapping.py | 14 ++- llama.cpp | 205 ++++++++++++++++++++++++++++++++- 5 files changed, 387 insertions(+), 5 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 6ab7f486e..4ebab07b3 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -203,6 +203,8 @@ class Model: return CodeShellModel if model_architecture == "OrionForCausalLM": return OrionModel + if model_architecture == "InternLM2ForCausalLM": + return InternLM2Model return Model def _is_model_safetensors(self) -> bool: @@ -254,6 +256,8 @@ class Model: return gguf.MODEL_ARCH.CODESHELL if arch == "OrionForCausalLM": return gguf.MODEL_ARCH.ORION + if arch == "InternLM2ForCausalLM": + return gguf.MODEL_ARCH.INTERNLM2 raise NotImplementedError(f'Architecture "{arch}" not supported!') @@ -1344,6 +1348,154 @@ class CodeShellModel(Model): self.gguf_writer.add_tensor("output.weight", data) print(name, f"=> output.weight, shape = {data.shape}, {old_dtype} --> {data.dtype}") + +class InternLM2Model(Model): + def set_vocab(self): + # (TODO): Is there a better way? + # Copy from _set_vocab_sentencepiece, The only difference is that we will treat the character + # \x00 specially and convert it into an emoji character to prevent it from being mistakenly + # recognized as an empty string in C++. + from sentencepiece import SentencePieceProcessor + from sentencepiece import sentencepiece_model_pb2 as model + + tokenizer_path = self.dir_model / 'tokenizer.model' + + tokens: list[bytes] = [] + scores: list[float] = [] + toktypes: list[int] = [] + + if not tokenizer_path.is_file(): + print(f'Error: Missing {tokenizer_path}', file=sys.stderr) + sys.exit(1) + + sentencepiece_model = model.ModelProto() + sentencepiece_model.ParseFromString(open(tokenizer_path, "rb").read()) + add_prefix = sentencepiece_model.normalizer_spec.add_dummy_prefix + + tokenizer = SentencePieceProcessor(str(tokenizer_path)) + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + + for token_id in range(vocab_size): + piece = tokenizer.id_to_piece(token_id) + text = piece.encode("utf-8") + score = tokenizer.get_score(token_id) + if text == b"\x00": + # (TODO): fixme + # Hack here and replace the \x00 characters. + print(f"InternLM2 convert token '{text}' to '🐉'!") + text = "🐉" + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.is_unknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.is_control(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.is_unused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.is_byte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + + added_tokens_file = self.dir_model / 'added_tokens.json' + if added_tokens_file.is_file(): + with open(added_tokens_file, "r", encoding="utf-8") as f: + added_tokens_json = json.load(f) + + for key in added_tokens_json: + tokens.append(key.encode("utf-8")) + scores.append(-1000.0) + toktypes.append(SentencePieceTokenTypes.USER_DEFINED) + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + self.gguf_writer.add_add_space_prefix(add_prefix) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + + def set_gguf_parameters(self): + self.gguf_writer.add_name("InternLM2") + self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) + self.gguf_writer.add_block_count(self.hparams["num_hidden_layers"]) + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"]) + self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"]) + + def post_write_tensors(self, tensor_map, name, data_torch): + old_dtype = data_torch.dtype + + # convert any unsupported data types to float32 + if data_torch.dtype not in (torch.float16, torch.float32): + data_torch = data_torch.to(torch.float32) + + data = data_torch.squeeze().numpy() + + # map tensor names + new_name = tensor_map.get_name(name, try_suffixes=(".weight", ".bias")) + if new_name is None: + print(f"Can not map tensor {name!r}") + sys.exit() + + n_dims = len(data.shape) + data_dtype = data.dtype + + # if f32 desired, convert any float16 to float32 + if self.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 self.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 self.ftype == 1 and data_dtype == np.float32 and name.endswith(".weight") and n_dims == 2: + data = data.astype(np.float16) + + print(f"{new_name}, n_dims = {n_dims}, {old_dtype} --> {data.dtype}") + self.gguf_writer.add_tensor(new_name, data) + + def write_tensors(self): + from einops import rearrange + + num_heads = self.hparams.get("num_attention_heads") + num_kv_heads = self.hparams.get("num_key_value_heads") + hidden_size = self.hparams.get("hidden_size") + q_per_kv = num_heads // num_kv_heads + head_dim = hidden_size // num_heads + num_groups = num_heads // q_per_kv + + block_count = self.hparams["num_hidden_layers"] + model_kv = dict(self.get_tensors()) + tensor_map = gguf.get_tensor_name_map(self.model_arch, block_count) + qkv_pattern = r"model\.layers\.(\d+)\.attention\.wqkv" + for name, data_torch in model_kv.items(): + # we don't need these + if name.endswith(".rotary_emb.inv_freq"): + continue + + if re.match(qkv_pattern, name): + bid = re.findall(qkv_pattern, name)[0] + qkv = data_torch + qkv = rearrange(qkv.T, " o (g n i) ->o g n i", g=num_groups, n=q_per_kv + 2, i=head_dim) + q, k, v = qkv[..., : q_per_kv, :], qkv[..., q_per_kv: q_per_kv + 1, :], qkv[..., q_per_kv + 1: q_per_kv + 2, :] + q = rearrange(q, " o g n i -> o (g n i)").T + k = rearrange(k, " o g n i -> o (g n i)").T + v = rearrange(v, " o g n i -> o (g n i)").T + self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wq.weight", q) + self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wk.weight", k) + self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wv.weight", v) + else: + self.post_write_tensors(tensor_map, name, data_torch) + + ###### CONVERSION LOGIC ###### diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index f5c933a41..ed8e26f83 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -72,6 +72,7 @@ class Keys: PAD_ID = "tokenizer.ggml.padding_token_id" ADD_BOS = "tokenizer.ggml.add_bos_token" ADD_EOS = "tokenizer.ggml.add_eos_token" + ADD_PREFIX = "tokenizer.ggml.add_space_prefix" HF_JSON = "tokenizer.huggingface.json" RWKV = "tokenizer.rwkv.world" CHAT_TEMPLATE = "tokenizer.chat_template" @@ -102,6 +103,7 @@ class MODEL_ARCH(IntEnum): PLAMO = auto() CODESHELL = auto() ORION = auto() + INTERNLM2 = auto() class MODEL_TENSOR(IntEnum): @@ -153,6 +155,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.PLAMO: "plamo", MODEL_ARCH.CODESHELL: "codeshell", MODEL_ARCH.ORION: "orion", + MODEL_ARCH.INTERNLM2: "internlm2", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -446,6 +449,21 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.INTERNLM2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], # TODO } diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index d93aaa877..16808196e 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -411,6 +411,9 @@ class GGUFWriter: def add_add_eos_token(self, value: bool) -> None: self.add_bool(Keys.Tokenizer.ADD_EOS, value) + def add_add_space_prefix(self, value: bool) -> None: + self.add_bool(Keys.Tokenizer.ADD_PREFIX, value) + def add_chat_template(self, value: str) -> None: self.add_string(Keys.Tokenizer.CHAT_TEMPLATE, value) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index de177af13..4f16d8504 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -19,6 +19,7 @@ class TensorNameMap: "language_model.embedding.word_embeddings", # persimmon "wte", # gpt2 "transformer.embd.wte", # phi2 + "model.tok_embeddings", # internlm2 ), # Token type embeddings @@ -42,7 +43,7 @@ class TensorNameMap: MODEL_TENSOR.OUTPUT: ( "embed_out", # gptneox "lm_head", # gpt2 mpt falcon llama-hf baichuan qwen - "output", # llama-pth bloom + "output", # llama-pth bloom internlm2 "word_embeddings_for_head", # persimmon "lm_head.linear", # phi2 ), @@ -51,7 +52,7 @@ class TensorNameMap: MODEL_TENSOR.OUTPUT_NORM: ( "gpt_neox.final_layer_norm", # gptneox "transformer.ln_f", # gpt2 gpt-j falcon - "model.norm", # llama-hf baichuan + "model.norm", # llama-hf baichuan internlm2 "norm", # llama-pth "embeddings.LayerNorm", # bert "transformer.norm_f", # mpt @@ -84,6 +85,7 @@ class TensorNameMap: "h.{bid}.ln_1", # gpt2 "transformer.h.{bid}.ln", # phi2 "model.layers.layers.{bid}.norm", # plamo + "model.layers.{bid}.attention_norm", # internlm2 ), # Attention norm 2 @@ -111,6 +113,7 @@ class TensorNameMap: "encoder.layer.{bid}.attention.self.query", # bert "transformer.h.{bid}.attn.q_proj", # gpt-j "model.layers.layers.{bid}.self_attn.q_proj", # plamo + "model.layers.{bid}.attention.wq" # internlm2 ), # Attention key @@ -120,6 +123,7 @@ class TensorNameMap: "encoder.layer.{bid}.attention.self.key", # bert "transformer.h.{bid}.attn.k_proj", # gpt-j "model.layers.layers.{bid}.self_attn.k_proj", # plamo + "model.layers.{bid}.attention.wk" # internlm2 ), # Attention value @@ -129,6 +133,7 @@ class TensorNameMap: "encoder.layer.{bid}.attention.self.value", # bert "transformer.h.{bid}.attn.v_proj", # gpt-j "model.layers.layers.{bid}.self_attn.v_proj", # plamo + "model.layers.{bid}.attention.wv" # internlm2 ), # Attention output @@ -147,6 +152,7 @@ class TensorNameMap: "h.{bid}.attn.c_proj", # gpt2 "transformer.h.{bid}.mixer.out_proj", # phi2 "model.layers.layers.{bid}.self_attn.o_proj", # plamo + "model.layers.{bid}.attention.wo", # internlm2 ), # Rotary embeddings @@ -169,6 +175,7 @@ class TensorNameMap: "language_model.encoder.layers.{bid}.post_attention_layernorm", # persimmon "model.layers.{bid}.ln2", # yi "h.{bid}.ln_2", # gpt2 + "model.layers.{bid}.ffn_norm", # internlm2 ), MODEL_TENSOR.FFN_GATE_INP: ( @@ -194,6 +201,7 @@ class TensorNameMap: "transformer.h.{bid}.mlp.fc1", # phi2 "model.layers.{bid}.mlp.fc1", # phi2 "model.layers.layers.{bid}.mlp.up_proj", # plamo + "model.layers.{bid}.feed_forward.w3", # internlm2 ), MODEL_TENSOR.FFN_UP_EXP: ( @@ -212,6 +220,7 @@ class TensorNameMap: "layers.{bid}.feed_forward.w1", # llama-pth "transformer.h.{bid}.mlp.w2", # qwen "model.layers.layers.{bid}.mlp.gate_proj", # plamo + "model.layers.{bid}.feed_forward.w1", # internlm2 ), MODEL_TENSOR.FFN_GATE_EXP: ( @@ -236,6 +245,7 @@ class TensorNameMap: "transformer.h.{bid}.mlp.fc2", # phi2 "model.layers.{bid}.mlp.fc2", # phi2 "model.layers.layers.{bid}.mlp.down_proj", # plamo + "model.layers.{bid}.feed_forward.w2", # internlm2 ), MODEL_TENSOR.FFN_DOWN_EXP: ( diff --git a/llama.cpp b/llama.cpp index 02b0a485a..e8f44c2cb 100644 --- a/llama.cpp +++ b/llama.cpp @@ -204,6 +204,7 @@ enum llm_arch { LLM_ARCH_PLAMO, LLM_ARCH_CODESHELL, LLM_ARCH_ORION, + LLM_ARCH_INTERNLM2, LLM_ARCH_UNKNOWN, }; @@ -226,6 +227,7 @@ static std::map LLM_ARCH_NAMES = { { LLM_ARCH_PLAMO, "plamo" }, { LLM_ARCH_CODESHELL, "codeshell" }, { LLM_ARCH_ORION, "orion" }, + { LLM_ARCH_INTERNLM2, "internlm2" }, }; enum llm_kv { @@ -278,6 +280,7 @@ enum llm_kv { LLM_KV_TOKENIZER_PAD_ID, LLM_KV_TOKENIZER_ADD_BOS, LLM_KV_TOKENIZER_ADD_EOS, + LLM_KV_TOKENIZER_ADD_PREFIX, LLM_KV_TOKENIZER_HF_JSON, LLM_KV_TOKENIZER_RWKV, }; @@ -332,6 +335,7 @@ static std::map LLM_KV_NAMES = { { LLM_KV_TOKENIZER_PAD_ID, "tokenizer.ggml.padding_token_id" }, { LLM_KV_TOKENIZER_ADD_BOS, "tokenizer.ggml.add_bos_token" }, { LLM_KV_TOKENIZER_ADD_EOS, "tokenizer.ggml.add_eos_token" }, + { LLM_KV_TOKENIZER_ADD_PREFIX, "tokenizer.ggml.add_space_prefix" }, { LLM_KV_TOKENIZER_HF_JSON, "tokenizer.huggingface.json" }, { LLM_KV_TOKENIZER_RWKV, "tokenizer.rwkv.world" }, }; @@ -669,7 +673,23 @@ static std::map> LLM_TENSOR_NAMES = { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, - + { + LLM_ARCH_INTERNLM2, + { + { 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_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -1377,6 +1397,7 @@ enum e_model { MODEL_13B, MODEL_14B, MODEL_15B, + MODEL_20B, MODEL_30B, MODEL_34B, MODEL_40B, @@ -1618,6 +1639,8 @@ struct llama_vocab { id special_suffix_id = 32008; id special_eot_id = 32010; + bool add_space_prefix = true; + int find_bpe_rank(const std::string & token_left, const std::string & token_right) const { GGML_ASSERT(token_left.find(' ') == std::string::npos); GGML_ASSERT(token_left.find('\n') == std::string::npos); @@ -2731,6 +2754,7 @@ static const char * llama_model_type_name(e_model type) { case MODEL_13B: return "13B"; case MODEL_14B: return "14B"; case MODEL_15B: return "15B"; + case MODEL_20B: return "20B"; case MODEL_30B: return "30B"; case MODEL_34B: return "34B"; case MODEL_40B: return "40B"; @@ -2743,6 +2767,14 @@ static const char * llama_model_type_name(e_model type) { default: return "?B"; } } +static const char * llama_model_vocab_type_name(enum llama_vocab_type type){ + switch (type) { + case LLAMA_VOCAB_TYPE_SPM: return "SPM"; + case LLAMA_VOCAB_TYPE_BPE: return "BPE"; + default: return "unknown"; + } +} + static void llm_load_arch(llama_model_loader & ml, llama_model & model) { model.arch = ml.get_arch(); @@ -3006,6 +3038,15 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_INTERNLM2: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + switch (hparams.n_layer) { + case 32: model.type = e_model::MODEL_7B; break; + case 48: model.type = e_model::MODEL_20B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -3057,6 +3098,11 @@ static void llm_load_vocab( vocab.special_unk_id = 0; vocab.special_sep_id = -1; vocab.special_pad_id = -1; + + const int add_space_prefix_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_ADD_PREFIX).c_str()); + if (add_space_prefix_keyidx != -1) { + vocab.add_space_prefix = gguf_get_val_bool(ctx, add_space_prefix_keyidx); + } // The default value of add_space_prefix is true. } else if (tokenizer_name == "gpt2") { vocab.type = LLAMA_VOCAB_TYPE_BPE; @@ -3269,7 +3315,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { // hparams LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(ml.fver)); LLAMA_LOG_INFO("%s: arch = %s\n", __func__, LLM_ARCH_NAMES.at(model.arch).c_str()); - LLAMA_LOG_INFO("%s: vocab type = %s\n", __func__, vocab.type == LLAMA_VOCAB_TYPE_SPM ? "SPM" : "BPE"); // TODO: fix + LLAMA_LOG_INFO("%s: vocab type = %s\n", __func__, llama_model_vocab_type_name(vocab.type)); LLAMA_LOG_INFO("%s: n_vocab = %u\n", __func__, hparams.n_vocab); LLAMA_LOG_INFO("%s: n_merges = %u\n", __func__, (int) vocab.bpe_ranks.size()); LLAMA_LOG_INFO("%s: n_ctx_train = %u\n", __func__, hparams.n_ctx_train); @@ -4018,8 +4064,35 @@ static bool llm_load_tensors( layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); } } break; + case LLM_ARCH_INTERNLM2: + { + model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + // output + { + model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + } + for (int i = 0; i < n_layer; ++i) { + ggml_context * ctx_layer = ctx_for_layer(i); + ggml_context * ctx_split = ctx_for_layer_split(i); + + auto & layer = model.layers[i]; + + layer.attn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}); + // layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); + layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); + layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); + + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + layer.ffn_gate = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + } + } break; default: throw std::runtime_error("unknown architecture"); } @@ -6588,6 +6661,126 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_internlm2() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); + cb(inpL, "inp_embd", -1); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); + cb(inp_pos, "inp_pos", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); + cb(KQ_mask, "KQ_mask", -1); + + // shift the entire K-cache if needed + if (do_rope_shift) { + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); + } + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + if (model.layers[il].bq) { + Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); + cb(Qcur, "Qcur", il); + } + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + if (model.layers[il].bk) { + Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); + cb(Kcur, "Kcur", il); + } + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + if (model.layers[il].bv) { + Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); + cb(Vcur, "Vcur", il); + } + + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + model.layers[il].wo, model.layers[il].bo, + Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + cb(cur, "kqv_out", il); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.output, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } + }; static struct ggml_cgraph * llama_build_graph( @@ -6746,6 +6939,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_orion(); } break; + case LLM_ARCH_INTERNLM2: + { + result = llm.build_internlm2(); + } break; default: GGML_ASSERT(false); } @@ -7688,7 +7885,9 @@ static std::vector llama_tokenize_internal(const llama_vocab & // auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length); if (&fragment == &fragment_buffer.front()) { - raw_text = " " + raw_text; // prefix with space if the first token is not special + if (vocab.add_space_prefix) { + raw_text = " " + raw_text; // prefix with space if the first token is not special + } } #ifdef PRETOKENIZERDEBUG From d71ac90985854b0905e1abba778e407e17f9f887 Mon Sep 17 00:00:00 2001 From: Ali Nehzat Date: Fri, 2 Feb 2024 02:18:53 +1100 Subject: [PATCH 086/143] make : generate .a library for static linking (#5205) --- Makefile | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 781f0bf8c..bf9e085de 100644 --- a/Makefile +++ b/Makefile @@ -586,8 +586,11 @@ train.o: common/train.cpp common/train.h libllama.so: llama.o ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) +libllama.a: llama.o ggml.o $(OBJS) $(COMMON_DEPS) + ar rcs libllama.a llama.o ggml.o $(OBJS) $(COMMON_DEPS) + clean: - rm -vrf *.o tests/*.o *.so *.dll benchmark-matmult common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) + rm -vrf *.o tests/*.o *.so *.a *.dll benchmark-matmult common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) # # Examples From 8ca511cadee2c67f0bd8c7034a2513778ee9a1b7 Mon Sep 17 00:00:00 2001 From: slaren Date: Thu, 1 Feb 2024 18:30:17 +0100 Subject: [PATCH 087/143] cuda : fix LLAMA_CUDA_F16 (#5262) --- ggml-cuda.cu | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index e56595742..3242a0b4a 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -8657,9 +8657,9 @@ static void ggml_cuda_op_dequantize_mul_mat_vec( if (src1_convert_f16) { src1_dfloat = src1_dfloat_a.alloc(ne00); - ggml_cpy_f32_f16_cuda((const char *) src1_ddf_i, (char *) src1_dfloat, ne00, - ne00, 1, sizeof(float), 0, 0, - ne00, 1, sizeof(half), 0, 0, stream); + const to_fp16_cuda_t to_fp16_cuda = ggml_get_to_fp16_cuda(src1->type); + GGML_ASSERT(to_fp16_cuda != nullptr); + to_fp16_cuda(src1_ddf_i, src1_dfloat, ne00, stream); } #else const dfloat * src1_dfloat = (const dfloat *) src1_ddf_i; // dfloat == float, no conversion From 4d0924a8902010d31bd737b6f1f594943d120d0f Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Thu, 1 Feb 2024 19:25:24 +0100 Subject: [PATCH 088/143] Vulkan Phi Fix for AMD Proprietary Drivers (#5260) * Replace tanh to avoid NaN in gelu shader on AMD proprietary driver * Fix another Vulkan CPY buffer size bug --- ggml-vulkan-shaders.hpp | 132 +++++++++++++++++++----------------- ggml-vulkan.cpp | 17 +++-- ggml_vk_generate_shaders.py | 3 +- 3 files changed, 83 insertions(+), 69 deletions(-) diff --git a/ggml-vulkan-shaders.hpp b/ggml-vulkan-shaders.hpp index e2e9be22c..195410c02 100644 --- a/ggml-vulkan-shaders.hpp +++ b/ggml-vulkan-shaders.hpp @@ -14670,14 +14670,14 @@ const uint64_t f32_to_f16_fp32_len = 1596; unsigned char gelu_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x45,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00, 0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30, 0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x24,0x00,0x00,0x00, -0x2c,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x38,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, 0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00, @@ -14696,15 +14696,15 @@ unsigned char gelu_f32_data[] = { 0x22,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x24,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x24,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x29,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x35,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x2a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x2a,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x36,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x36,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x2a,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x2c,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x2c,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x42,0x00,0x00,0x00, +0x36,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x38,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x38,0x00,0x00,0x00,0x21,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x48,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x13,0x00,0x02,0x00, 0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00, @@ -14731,64 +14731,70 @@ unsigned char gelu_f32_data[] = { 0x23,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x22,0x00,0x00,0x00, 0x3b,0x00,0x04,0x00,0x23,0x00,0x00,0x00,0x24,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x20,0x00,0x04,0x00,0x26,0x00,0x00,0x00, -0x0c,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1d,0x00,0x03,0x00, -0x29,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x1e,0x00,0x03,0x00, -0x2a,0x00,0x00,0x00,0x29,0x00,0x00,0x00,0x20,0x00,0x04,0x00, -0x2b,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x2a,0x00,0x00,0x00, -0x3b,0x00,0x04,0x00,0x2b,0x00,0x00,0x00,0x2c,0x00,0x00,0x00, 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-0x11,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x32,0x00,0x00,0x00,0x37,0x00,0x00,0x00,0x28,0x00,0x00,0x00, -0x31,0x00,0x00,0x00,0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00, -0x3b,0x00,0x00,0x00,0x34,0x00,0x00,0x00,0x3a,0x00,0x00,0x00, -0x0c,0x00,0x06,0x00,0x11,0x00,0x00,0x00,0x3c,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x15,0x00,0x00,0x00,0x3b,0x00,0x00,0x00, -0x81,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x3d,0x00,0x00,0x00, -0x31,0x00,0x00,0x00,0x3c,0x00,0x00,0x00,0x85,0x00,0x05,0x00, -0x11,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x30,0x00,0x00,0x00, -0x3d,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x26,0x00,0x00,0x00, -0x3f,0x00,0x00,0x00,0x2c,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x0f,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x3f,0x00,0x00,0x00, -0x3e,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x43,0x00,0x00,0x00, -0xf8,0x00,0x02,0x00,0x43,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, -0x38,0x00,0x01,0x00, +0x3f,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x34,0x00,0x00,0x00, +0x0c,0x00,0x06,0x00,0x11,0x00,0x00,0x00,0x40,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x1b,0x00,0x00,0x00,0x3f,0x00,0x00,0x00, +0x81,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x41,0x00,0x00,0x00, +0x40,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x88,0x00,0x05,0x00, +0x11,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x3d,0x00,0x00,0x00, +0x41,0x00,0x00,0x00,0x83,0x00,0x05,0x00,0x11,0x00,0x00,0x00, +0x43,0x00,0x00,0x00,0x3d,0x00,0x00,0x00,0x42,0x00,0x00,0x00, +0x85,0x00,0x05,0x00,0x11,0x00,0x00,0x00,0x44,0x00,0x00,0x00, +0x3c,0x00,0x00,0x00,0x43,0x00,0x00,0x00,0x41,0x00,0x06,0x00, +0x26,0x00,0x00,0x00,0x45,0x00,0x00,0x00,0x38,0x00,0x00,0x00, +0x16,0x00,0x00,0x00,0x0f,0x00,0x00,0x00,0x3e,0x00,0x03,0x00, +0x45,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x49,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x49,0x00,0x00,0x00, +0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t gelu_f32_len = 1408; +const uint64_t gelu_f32_len = 1484; unsigned char get_rows_f16_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index bccc40bf5..b1e0006bb 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -2876,6 +2876,9 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm x_sz = ggml_nbytes(src0); d_sz = ggml_nbytes(dst); + if (extra_src0->offset + x_sz >= d_X->size) { + x_sz = VK_WHOLE_SIZE; + } if (extra->offset + d_sz >= d_D->size) { d_sz = VK_WHOLE_SIZE; } @@ -2911,12 +2914,16 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm break; } - x_sz *= ne02 * ne03; - if (y_sz != VK_WHOLE_SIZE) { - y_sz *= ne12 * ne13; - } if (op != GGML_OP_CPY) { - d_sz *= ne02 * ne03; + if (x_sz != VK_WHOLE_SIZE) { + x_sz *= ne02 * ne03; + } + if (y_sz != VK_WHOLE_SIZE) { + y_sz *= ne12 * ne13; + } + if (d_sz != VK_WHOLE_SIZE) { + d_sz *= ne02 * ne03; + } } if (!use_src1 && op == GGML_OP_SOFT_MAX) { diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py index 6b1b82bf3..67981a751 100644 --- a/ggml_vk_generate_shaders.py +++ b/ggml_vk_generate_shaders.py @@ -1689,7 +1689,8 @@ void main() { } const float xi = float(data_a[i]); - data_d[i] = D_TYPE(0.5f*xi*(1.0f + tanh(SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi)))); + const float val = SQRT_2_OVER_PI*xi*(1.0f + GELU_COEF_A*xi*xi); + data_d[i] = D_TYPE(0.5f*xi*(2.0f - 2.0f / (exp(2 * val) + 1))); } """ From 128dcbd3c9c4b12f42b560a4430427d7b2828628 Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Fri, 2 Feb 2024 03:48:53 +0800 Subject: [PATCH 089/143] add --no-mmap in llama-bench (#5257) * add --no-mmap, show sycl backend * fix conflict * fix code format, change print for --no-mmap * ren no_mmap to mmap, show mmap when not default value in printer * update guide for mmap * mv position to reduce model reload --- README-sycl.md | 2 +- examples/llama-bench/llama-bench.cpp | 60 +++++++++++++++++++++++++--- ggml-sycl.cpp | 34 +++++++++++++++- ggml-sycl.h | 3 +- 4 files changed, 89 insertions(+), 10 deletions(-) diff --git a/README-sycl.md b/README-sycl.md index 2b2cfe03a..b8ee212b8 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -405,7 +405,7 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device llama.cpp use mmap as default way to read model file and copy to GPU. In some system, memcpy will be abnormal and block. - Solution: add **--no-mmap**. + Solution: add **--no-mmap** or **--mmap 0**. ## Q&A diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index c5a6f744e..e36c061a2 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -20,6 +20,7 @@ #include "llama.h" #include "common.h" #include "ggml-cuda.h" +#include "ggml-sycl.h" // utils static uint64_t get_time_ns() { @@ -120,6 +121,22 @@ static std::string get_gpu_info() { id += "/"; } } +#endif +#ifdef GGML_USE_SYCL + int device_list[GGML_SYCL_MAX_DEVICES]; + ggml_sycl_get_gpu_list(device_list, GGML_SYCL_MAX_DEVICES); + + for (int i = 0; i < GGML_SYCL_MAX_DEVICES; i++) { + if (device_list[i] >0 ){ + char buf[128]; + ggml_sycl_get_device_description(i, buf, sizeof(buf)); + id += buf; + id += "/"; + } + } + if (id.length() >2 ) { + id.pop_back(); + } #endif // TODO: other backends return id; @@ -161,6 +178,7 @@ struct cmd_params { std::vector no_kv_offload; std::vector mul_mat_q; std::vector> tensor_split; + std::vector use_mmap; int reps; bool verbose; output_formats output_format; @@ -180,6 +198,7 @@ static const cmd_params cmd_params_defaults = { /* no_kv_offload */ {false}, /* mul_mat_q */ {true}, /* tensor_split */ {std::vector(llama_max_devices(), 0.0f)}, + /* use_mmap */ {true}, /* reps */ 5, /* verbose */ false, /* output_format */ MARKDOWN @@ -201,6 +220,7 @@ static void print_usage(int /* argc */, char ** argv) { printf(" -sm, --split-mode (default: %s)\n", join(transform_to_str(cmd_params_defaults.split_mode, split_mode_str), ",").c_str()); printf(" -mg, --main-gpu (default: %s)\n", join(cmd_params_defaults.main_gpu, ",").c_str()); printf(" -nkvo, --no-kv-offload <0|1> (default: %s)\n", join(cmd_params_defaults.no_kv_offload, ",").c_str()); + printf(" -mmp, --mmap <0|1> (default: %s)\n", join(cmd_params_defaults.use_mmap, ",").c_str()); printf(" -mmq, --mul-mat-q <0|1> (default: %s)\n", join(cmd_params_defaults.mul_mat_q, ",").c_str()); printf(" -ts, --tensor_split (default: 0)\n"); printf(" -r, --repetitions (default: %d)\n", cmd_params_defaults.reps); @@ -370,6 +390,13 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } auto p = split(argv[i], split_delim); params.mul_mat_q.insert(params.mul_mat_q.end(), p.begin(), p.end()); + } else if (arg == "-mmp" || arg == "--mmap") { + if (++i >= argc) { + invalid_param = true; + break; + } + auto p = split(argv[i], split_delim); + params.use_mmap.insert(params.use_mmap.end(), p.begin(), p.end()); } else if (arg == "-ts" || arg == "--tensor-split") { if (++i >= argc) { invalid_param = true; @@ -441,6 +468,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; } if (params.mul_mat_q.empty()) { params.mul_mat_q = cmd_params_defaults.mul_mat_q; } if (params.tensor_split.empty()) { params.tensor_split = cmd_params_defaults.tensor_split; } + if (params.use_mmap.empty()) { params.use_mmap = cmd_params_defaults.use_mmap; } if (params.n_threads.empty()) { params.n_threads = cmd_params_defaults.n_threads; } return params; @@ -460,6 +488,7 @@ struct cmd_params_instance { bool no_kv_offload; bool mul_mat_q; std::vector tensor_split; + bool use_mmap; llama_model_params to_llama_mparams() const { llama_model_params mparams = llama_model_default_params(); @@ -468,6 +497,7 @@ struct cmd_params_instance { mparams.split_mode = split_mode; mparams.main_gpu = main_gpu; mparams.tensor_split = tensor_split.data(); + mparams.use_mmap = use_mmap; return mparams; } @@ -477,6 +507,7 @@ struct cmd_params_instance { n_gpu_layers == other.n_gpu_layers && split_mode == other.split_mode && main_gpu == other.main_gpu && + use_mmap == other.use_mmap && tensor_split == other.tensor_split; } @@ -503,6 +534,7 @@ static std::vector get_cmd_params_instances(const cmd_param for (const auto & sm : params.split_mode) for (const auto & mg : params.main_gpu) for (const auto & ts : params.tensor_split) + for (const auto & mmp : params.use_mmap) for (const auto & nb : params.n_batch) for (const auto & tk : params.type_k) for (const auto & tv : params.type_v) @@ -527,6 +559,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .no_kv_offload= */ nkvo, /* .mul_mat_q = */ mmq, /* .tensor_split = */ ts, + /* .use_mmap = */ mmp, }; instances.push_back(instance); } @@ -549,6 +582,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .no_kv_offload= */ nkvo, /* .mul_mat_q = */ mmq, /* .tensor_split = */ ts, + /* .use_mmap = */ mmp, }; instances.push_back(instance); } @@ -565,6 +599,7 @@ struct test { static const bool vulkan; static const bool kompute; static const bool metal; + static const bool sycl; static const bool gpu_blas; static const bool blas; static const std::string cpu_info; @@ -583,6 +618,7 @@ struct test { bool no_kv_offload; bool mul_mat_q; std::vector tensor_split; + bool use_mmap; int n_prompt; int n_gen; std::string test_time; @@ -605,6 +641,7 @@ struct test { no_kv_offload = inst.no_kv_offload; mul_mat_q = inst.mul_mat_q; tensor_split = inst.tensor_split; + use_mmap = inst.use_mmap; n_prompt = inst.n_prompt; n_gen = inst.n_gen; // RFC 3339 date-time format @@ -654,25 +691,29 @@ struct test { if (metal) { return "Metal"; } + if (sycl) { + return GGML_SYCL_NAME; + } if (gpu_blas) { return "GPU BLAS"; } if (blas) { return "BLAS"; } + return "CPU"; } static const std::vector & get_fields() { static const std::vector fields = { "build_commit", "build_number", - "cuda", "opencl", "vulkan", "kompute", "metal", "gpu_blas", "blas", + "cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas", "cpu_info", "gpu_info", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_threads", "type_k", "type_v", "n_gpu_layers", "split_mode", "main_gpu", "no_kv_offload", - "mul_mat_q", "tensor_split", + "mul_mat_q", "tensor_split", "use_mmap", "n_prompt", "n_gen", "test_time", "avg_ns", "stddev_ns", "avg_ts", "stddev_ts" @@ -691,8 +732,8 @@ struct test { return INT; } if (field == "cuda" || field == "opencl" || field == "vulkan" || field == "kompute" || field == "metal" || - field == "gpu_blas" || field == "blas" || field == "f16_kv" || field == "no_kv_offload" || - field == "mul_mat_q") { + field == "gpu_blas" || field == "blas" || field == "sycl" ||field == "f16_kv" || field == "no_kv_offload" || + field == "mul_mat_q" || field == "use_mmap") { return BOOL; } if (field == "avg_ts" || field == "stddev_ts") { @@ -720,13 +761,13 @@ struct test { std::vector values = { build_commit, std::to_string(build_number), std::to_string(cuda), std::to_string(opencl), std::to_string(vulkan), std::to_string(vulkan), - std::to_string(metal), std::to_string(gpu_blas), std::to_string(blas), + std::to_string(metal), std::to_string(sycl), std::to_string(gpu_blas), std::to_string(blas), cpu_info, gpu_info, 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), ggml_type_name(type_k), ggml_type_name(type_v), std::to_string(n_gpu_layers), split_mode_str(split_mode), std::to_string(main_gpu), std::to_string(no_kv_offload), - std::to_string(mul_mat_q), tensor_split_str, + std::to_string(mul_mat_q), tensor_split_str, std::to_string(use_mmap), std::to_string(n_prompt), std::to_string(n_gen), test_time, std::to_string(avg_ns()), std::to_string(stdev_ns()), std::to_string(avg_ts()), std::to_string(stdev_ts()) @@ -753,6 +794,7 @@ const bool test::kompute = !!ggml_cpu_has_kompute(); const bool test::metal = !!ggml_cpu_has_metal(); const bool test::gpu_blas = !!ggml_cpu_has_gpublas(); const bool test::blas = !!ggml_cpu_has_blas(); +const bool test::sycl = !!ggml_cpu_has_sycl(); const std::string test::cpu_info = get_cpu_info(); const std::string test::gpu_info = get_gpu_info(); @@ -895,6 +937,9 @@ struct markdown_printer : public printer { if (field == "no_kv_offload") { return "nkvo"; } + if (field == "use_mmap") { + return "mmap"; + } if (field == "tensor_split") { return "ts"; } @@ -938,6 +983,9 @@ struct markdown_printer : public printer { if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) { fields.push_back("tensor_split"); } + if (params.use_mmap.size() > 1 || params.use_mmap != cmd_params_defaults.use_mmap) { + fields.push_back("use_mmap"); + } fields.push_back("test"); fields.push_back("t/s"); diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 1cc55ef52..e8ba48353 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -2928,7 +2928,6 @@ void ggml_sycl_set_main_device(int main_device); void ggml_sycl_set_mul_mat_q(bool mul_mat_q); void ggml_sycl_set_scratch_size(size_t scratch_size); void ggml_sycl_free_scratch(void); -int ggml_sycl_get_device_count(void); void ggml_sycl_get_device_description(int device, char * description, size_t description_size); bool ggml_backend_is_sycl(ggml_backend_t backend); int ggml_backend_sycl_get_device(ggml_backend_t backend); @@ -14493,6 +14492,37 @@ bool ggml_sycl_compute_forward(struct ggml_compute_params * params, struct ggml_ return true; } +GGML_API GGML_CALL void ggml_sycl_get_gpu_list(int *id_list, int max_len) try { + int max_compute_units = -1; + for(int i=0;i Date: Thu, 1 Feb 2024 23:20:13 -0800 Subject: [PATCH 090/143] llama : fix memory leak in llama_batch_free (#5252) The llama_batch_init allocates memory for a fixed number of tokens. However, the llama_batch_free only frees memory for the number of tokens that were added to the batch. This change-set uses a null terminated array for the batch seq_id, and frees all the elements until the nullptr is reached. This change-set also changes the name of the first parameter from `n_tokens` to `n_tokens_alloc` to more clearly indicate that this value is the number of tokens allocated to the batch, not the number of tokens in the batch. --- llama.cpp | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/llama.cpp b/llama.cpp index e8f44c2cb..6bf7f9efb 100644 --- a/llama.cpp +++ b/llama.cpp @@ -11377,22 +11377,24 @@ struct llama_batch llama_batch_get_one( }; } -struct llama_batch llama_batch_init(int32_t n_tokens, int32_t embd, int32_t n_seq_max) { +struct llama_batch llama_batch_init(int32_t n_tokens_alloc, int32_t embd, int32_t n_seq_max) { llama_batch batch = { 0, nullptr, nullptr, nullptr, nullptr, nullptr, nullptr, 0, 0, 0, }; if (embd) { - batch.embd = (float *) malloc(sizeof(float) * n_tokens * embd); + batch.embd = (float *) malloc(sizeof(float) * n_tokens_alloc * embd); } else { - batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens); + batch.token = (llama_token *) malloc(sizeof(llama_token) * n_tokens_alloc); } - batch.pos = (llama_pos *) malloc(sizeof(llama_pos) * n_tokens); - batch.n_seq_id = (int32_t *) malloc(sizeof(int32_t) * n_tokens); - batch.seq_id = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * n_tokens); - for (int i = 0; i < n_tokens; ++i) { + batch.pos = (llama_pos *) malloc(sizeof(llama_pos) * n_tokens_alloc); + batch.n_seq_id = (int32_t *) malloc(sizeof(int32_t) * n_tokens_alloc); + batch.seq_id = (llama_seq_id **) malloc(sizeof(llama_seq_id *) * (n_tokens_alloc + 1)); + for (int i = 0; i < n_tokens_alloc; ++i) { batch.seq_id[i] = (llama_seq_id *) malloc(sizeof(llama_seq_id) * n_seq_max); } - batch.logits = (int8_t *) malloc(sizeof(int8_t) * n_tokens); + batch.seq_id[n_tokens_alloc] = nullptr; + + batch.logits = (int8_t *) malloc(sizeof(int8_t) * n_tokens_alloc); return batch; } @@ -11403,7 +11405,7 @@ void llama_batch_free(struct llama_batch batch) { if (batch.pos) free(batch.pos); if (batch.n_seq_id) free(batch.n_seq_id); if (batch.seq_id) { - for (int i = 0; i < batch.n_tokens; ++i) { + for (int i = 0; batch.seq_id[i] != nullptr; ++i) { free(batch.seq_id[i]); } free(batch.seq_id); From af3ba5d94627d337e32a95129e31a3064c459f6b Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Fri, 2 Feb 2024 15:53:27 +0800 Subject: [PATCH 091/143] [SYCL] update guide of SYCL backend (#5254) * update guide for make installation, memory, gguf model link, rm todo for windows build * add vs install requirement * update for gpu device check * update help of llama-bench * fix grammer issues --- README-sycl.md | 64 +++++++++++++++++++++++++++----- examples/llama-bench/README.md | 34 ++++++++++------- examples/sycl/win-run-llama2.bat | 2 +- 3 files changed, 77 insertions(+), 23 deletions(-) diff --git a/README-sycl.md b/README-sycl.md index b8ee212b8..f7edc1c3e 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -42,6 +42,8 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). ## Intel GPU +### Verified + |Intel GPU| Status | Verified Model| |-|-|-| |Intel Data Center Max Series| Support| Max 1550| @@ -50,6 +52,17 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). |Intel built-in Arc GPU| Support| built-in Arc GPU in Meteor Lake| |Intel iGPU| Support| iGPU in i5-1250P, i7-1165G7| +Note: If the EUs (Execution Unit) in iGPU is less than 80, the inference speed will be too slow to use. + +### Memory + +The memory is a limitation to run LLM on GPUs. + +When run llama.cpp, there is print log to show the applied memory on GPU. You could know how much memory to be used in your case. Like `llm_load_tensors: buffer size = 3577.56 MiB`. + +For iGPU, please make sure the shared memory from host memory is enough. For llama-2-7b.Q4_0, recommend the host memory is 8GB+. + +For dGPU, please make sure the device memory is enough. For llama-2-7b.Q4_0, recommend the device memory is 4GB+. ## Linux @@ -105,7 +118,7 @@ source /opt/intel/oneapi/setvars.sh sycl-ls ``` -There should be one or more level-zero devices. Like **[ext_oneapi_level_zero:gpu:0]**. +There should be one or more level-zero devices. Please confirm that at least one GPU is present, like **[ext_oneapi_level_zero:gpu:0]**. Output (example): ``` @@ -152,6 +165,8 @@ Note: 1. Put model file to folder **models** +You could download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) as example. + 2. Enable oneAPI running environment ``` @@ -223,7 +238,13 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device Please install Intel GPU driver by official guide: [Install GPU Drivers](https://www.intel.com/content/www/us/en/products/docs/discrete-gpus/arc/software/drivers.html). -2. Install Intel® oneAPI Base toolkit. +Note: **The driver is mandatory for compute function**. + +2. Install Visual Studio. + +Please install [Visual Studio](https://visualstudio.microsoft.com/) which impact oneAPI environment enabling in Windows. + +3. Install Intel® oneAPI Base toolkit. a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html). @@ -252,7 +273,7 @@ In oneAPI command line: sycl-ls ``` -There should be one or more level-zero devices. Like **[ext_oneapi_level_zero:gpu:0]**. +There should be one or more level-zero devices. Please confirm that at least one GPU is present, like **[ext_oneapi_level_zero:gpu:0]**. Output (example): ``` @@ -260,15 +281,21 @@ Output (example): [opencl:cpu:1] Intel(R) OpenCL, 11th Gen Intel(R) Core(TM) i7-1185G7 @ 3.00GHz OpenCL 3.0 (Build 0) [2023.16.10.0.17_160000] [opencl:gpu:2] Intel(R) OpenCL Graphics, Intel(R) Iris(R) Xe Graphics OpenCL 3.0 NEO [31.0.101.5186] [ext_oneapi_level_zero:gpu:0] Intel(R) Level-Zero, Intel(R) Iris(R) Xe Graphics 1.3 [1.3.28044] - ``` -3. Install cmake & make +4. Install cmake & make -a. Download & install cmake for windows: https://cmake.org/download/ +a. Download & install cmake for Windows: https://cmake.org/download/ -b. Download & install make for windows provided by mingw-w64: https://www.mingw-w64.org/downloads/ +b. Download & install make for Windows provided by mingw-w64 +- Download binary package for Windows in https://github.com/niXman/mingw-builds-binaries/releases. + + Like [x86_64-13.2.0-release-win32-seh-msvcrt-rt_v11-rev1.7z](https://github.com/niXman/mingw-builds-binaries/releases/download/13.2.0-rt_v11-rev1/x86_64-13.2.0-release-win32-seh-msvcrt-rt_v11-rev1.7z). + +- Unzip the binary package. In the **bin** sub-folder and rename **xxx-make.exe** to **make.exe**. + +- Add the **bin** folder path in the Windows system PATH environment. ### Build locally: @@ -309,6 +336,8 @@ Note: 1. Put model file to folder **models** +You could download [llama-2-7b.Q4_0.gguf](https://huggingface.co/TheBloke/Llama-2-7B-GGUF/blob/main/llama-2-7b.Q4_0.gguf) as example. + 2. Enable oneAPI running environment - In Search, input 'oneAPI'. @@ -419,8 +448,25 @@ Using device **0** (Intel(R) Arc(TM) A770 Graphics) as main device Miss to enable oneAPI running environment. +- Meet compile error. + + Remove folder **build** and try again. + +- I can **not** see **[ext_oneapi_level_zero:gpu:0]** afer install GPU driver in Linux. + + Please run **sudo sycl-ls**. + + If you see it in result, please add video/render group to your ID: + + ``` + sudo usermod -aG render username + sudo usermod -aG video username + ``` + + Then **relogin**. + + If you do not see it, please check the installation GPU steps again. + ## Todo -- Support to build in Windows. - - Support multiple cards. diff --git a/examples/llama-bench/README.md b/examples/llama-bench/README.md index d02824bfa..374e40a7d 100644 --- a/examples/llama-bench/README.md +++ b/examples/llama-bench/README.md @@ -23,19 +23,23 @@ usage: ./llama-bench [options] options: -h, --help - -m, --model (default: models/7B/ggml-model-q4_0.gguf) - -p, --n-prompt (default: 512) - -n, --n-gen (default: 128) - -b, --batch-size (default: 512) - --memory-f32 <0|1> (default: 0) - -t, --threads (default: 16) - -ngl N, --n-gpu-layers (default: 99) - -mg i, --main-gpu (default: 0) - -mmq, --mul-mat-q <0|1> (default: 1) - -ts, --tensor_split - -r, --repetitions (default: 5) - -o, --output (default: md) - -v, --verbose (default: 0) + -m, --model (default: models/7B/ggml-model-q4_0.gguf) + -p, --n-prompt (default: 512) + -n, --n-gen (default: 128) + -b, --batch-size (default: 512) + -ctk , --cache-type-k (default: f16) + -ctv , --cache-type-v (default: f16) + -t, --threads (default: 112) + -ngl, --n-gpu-layers (default: 99) + -sm, --split-mode (default: layer) + -mg, --main-gpu (default: 0) + -nkvo, --no-kv-offload <0|1> (default: 0) + -mmp, --mmap <0|1> (default: 1) + -mmq, --mul-mat-q <0|1> (default: 1) + -ts, --tensor_split (default: 0) + -r, --repetitions (default: 5) + -o, --output (default: md) + -v, --verbose (default: 0) Multiple values can be given for each parameter by separating them with ',' or by specifying the parameter multiple times. ``` @@ -51,6 +55,10 @@ Each test is repeated the number of times given by `-r`, and the results are ave For a description of the other options, see the [main example](../main/README.md). +Note: + +- When using SYCL backend, there would be hang issue in some cases. Please set `--mmp 0`. + ## Examples ### Text generation with different models diff --git a/examples/sycl/win-run-llama2.bat b/examples/sycl/win-run-llama2.bat index 28d935541..cf621c675 100644 --- a/examples/sycl/win-run-llama2.bat +++ b/examples/sycl/win-run-llama2.bat @@ -2,7 +2,7 @@ :: Copyright (C) 2024 Intel Corporation :: SPDX-License-Identifier: MIT -INPUT2="Building a website can be done in 10 simple steps:\nStep 1:" +set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:" @call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force From e805f0fa9951081ce0a86378a7aa52b6f636b82d Mon Sep 17 00:00:00 2001 From: "Meng, Hengyu" Date: Fri, 2 Feb 2024 15:54:14 +0800 Subject: [PATCH 092/143] [SYCL] get MAX_MEM_ALLOC from device property (#5270) * get max alloc size from device prop * fix macro typo --- ggml-sycl.cpp | 26 ++++++++++++++++++++++---- 1 file changed, 22 insertions(+), 4 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index e8ba48353..4ee2eed38 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -337,6 +337,7 @@ namespace dpct } size_t get_global_mem_size() const { return _global_mem_size; } size_t get_local_mem_size() const { return _local_mem_size; } + size_t get_max_mem_alloc_size() const { return _max_mem_alloc_size; } /// Returns the maximum clock rate of device's global memory in kHz. If /// compiler does not support this API then returns default value 3200000 kHz. unsigned int get_memory_clock_rate() const { return _memory_clock_rate; } @@ -398,6 +399,10 @@ namespace dpct { _local_mem_size = local_mem_size; } + void set_max_mem_alloc_size(size_t max_mem_alloc_size) + { + _max_mem_alloc_size = max_mem_alloc_size; + } void set_max_work_group_size(int max_work_group_size) { _max_work_group_size = max_work_group_size; @@ -465,6 +470,7 @@ namespace dpct int _max_register_size_per_work_group; size_t _global_mem_size; size_t _local_mem_size; + size_t _max_mem_alloc_size; size_t _max_nd_range_size[3]; int _max_nd_range_size_i[3]; uint32_t _device_id; @@ -516,6 +522,7 @@ namespace dpct dev.get_info()); prop.set_global_mem_size(dev.get_info()); prop.set_local_mem_size(dev.get_info()); + prop.set_max_mem_alloc_size(dev.get_info()); #if (defined(SYCL_EXT_INTEL_DEVICE_INFO) && SYCL_EXT_INTEL_DEVICE_INFO >= 6) if (dev.has(sycl::aspect::ext_intel_memory_clock_rate)) @@ -644,6 +651,11 @@ namespace dpct return get_device_info().get_global_mem_size(); } + size_t get_max_mem_alloc_size() const + { + return get_device_info().get_max_mem_alloc_size(); + } + /// Get the number of bytes of free and total memory on the SYCL device. /// \param [out] free_memory The number of bytes of free memory on the SYCL device. /// \param [out] total_memory The number of bytes of total memory on the SYCL device. @@ -11311,10 +11323,10 @@ void ggml_init_sycl() try { GGML_ASSERT(g_all_sycl_device_count <= GGML_SYCL_MAX_DEVICES); int64_t total_vram = 0; -#if defined(GGML_SYCL_FP16) - fprintf(stderr, "%s: GGML_SYCL_FP16: yes\n", __func__); +#if defined(GGML_SYCL_F16) + fprintf(stderr, "%s: GGML_SYCL_F16: yes\n", __func__); #else - fprintf(stderr, "%s: GGML_SYCL_FP16: no\n", __func__); + fprintf(stderr, "%s: GGML_SYCL_F16: no\n", __func__); #endif @@ -14788,6 +14800,12 @@ static size_t ggml_backend_sycl_buffer_type_get_alignment(ggml_backend_buffer_ty UNUSED(buft); } +static size_t ggml_backend_sycl_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { + return dpct::get_current_device().get_max_mem_alloc_size(); + + UNUSED(buft); +} + static size_t ggml_backend_sycl_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { int64_t row_low = 0; int64_t row_high = ggml_nrows(tensor); @@ -14818,7 +14836,7 @@ static ggml_backend_buffer_type_i ggml_backend_sycl_buffer_type_interface = { /* .get_name = */ ggml_backend_sycl_buffer_type_name, /* .alloc_buffer = */ ggml_backend_sycl_buffer_type_alloc_buffer, /* .get_alignment = */ ggml_backend_sycl_buffer_type_get_alignment, - /* .get_max_size = */ NULL, // TODO: return device.maxBufferLength + /* .get_max_size = */ ggml_backend_sycl_buffer_type_get_max_size, /* .get_alloc_size = */ ggml_backend_sycl_buffer_type_get_alloc_size, /* .supports_backend = */ ggml_backend_sycl_buffer_type_supports_backend, /* .is_host = */ nullptr, From 6b91b1e0a92ac2e4e269eec6361ca53a61ced6c6 Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Fri, 2 Feb 2024 08:56:31 +0100 Subject: [PATCH 093/143] docker : add build for SYCL, Vulkan + update readme (#5228) * add vulkan dockerfile * intel dockerfile: compile sycl by default * fix vulkan dockerfile * add docs for vulkan * docs: sycl build in docker * docs: remove trailing spaces * docs: sycl: add docker section * docs: clarify install vulkan SDK outside docker * sycl: use intel/oneapi-basekit docker image * docs: correct TOC * docs: correct docker image for Intel oneMKL --- .devops/main-intel.Dockerfile | 16 ++--- .devops/main-vulkan.Dockerfile | 29 +++++++++ .devops/server-intel.Dockerfile | 15 +++-- .devops/server-vulkan.Dockerfile | 29 +++++++++ README-sycl.md | 102 +++++++++++++++++++------------ README.md | 64 ++++++++++++++----- 6 files changed, 188 insertions(+), 67 deletions(-) create mode 100644 .devops/main-vulkan.Dockerfile create mode 100644 .devops/server-vulkan.Dockerfile diff --git a/.devops/main-intel.Dockerfile b/.devops/main-intel.Dockerfile index e1e6acc24..572e5d8ea 100644 --- a/.devops/main-intel.Dockerfile +++ b/.devops/main-intel.Dockerfile @@ -1,8 +1,8 @@ ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04 -ARG UBUNTU_VERSION=22.04 -FROM intel/hpckit:$ONEAPI_VERSION as build +FROM intel/oneapi-basekit:$ONEAPI_VERSION as build +ARG LLAMA_SYCL_F16=OFF RUN apt-get update && \ apt-get install -y git @@ -10,16 +10,18 @@ WORKDIR /app COPY . . -# for some reasons, "-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DLLAMA_NATIVE=ON" give worse performance RUN mkdir build && \ cd build && \ - cmake .. -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx && \ - cmake --build . --config Release --target main server + if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \ + echo "LLAMA_SYCL_F16 is set" && \ + export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \ + fi && \ + cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \ + cmake --build . --config Release --target main -FROM ubuntu:$UBUNTU_VERSION as runtime +FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime COPY --from=build /app/build/bin/main /main -COPY --from=build /app/build/bin/server /server ENV LC_ALL=C.utf8 diff --git a/.devops/main-vulkan.Dockerfile b/.devops/main-vulkan.Dockerfile new file mode 100644 index 000000000..bca460365 --- /dev/null +++ b/.devops/main-vulkan.Dockerfile @@ -0,0 +1,29 @@ +ARG UBUNTU_VERSION=jammy + +FROM ubuntu:$UBUNTU_VERSION as build + +# Install build tools +RUN apt update && apt install -y git build-essential cmake wget + +# Install Vulkan SDK +RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \ + wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \ + apt update -y && \ + apt-get install -y vulkan-sdk + +# Build it +WORKDIR /app +COPY . . +RUN mkdir build && \ + cd build && \ + cmake .. -DLLAMA_VULKAN=1 && \ + cmake --build . --config Release --target main + +# Clean up +WORKDIR / +RUN cp /app/build/bin/main /main && \ + rm -rf /app + +ENV LC_ALL=C.utf8 + +ENTRYPOINT [ "/main" ] diff --git a/.devops/server-intel.Dockerfile b/.devops/server-intel.Dockerfile index e343d278c..312f2df80 100644 --- a/.devops/server-intel.Dockerfile +++ b/.devops/server-intel.Dockerfile @@ -1,8 +1,8 @@ ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04 -ARG UBUNTU_VERSION=22.04 -FROM intel/hpckit:$ONEAPI_VERSION as build +FROM intel/oneapi-basekit:$ONEAPI_VERSION as build +ARG LLAMA_SYCL_F16=OFF RUN apt-get update && \ apt-get install -y git @@ -10,13 +10,16 @@ WORKDIR /app COPY . . -# for some reasons, "-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DLLAMA_NATIVE=ON" give worse performance RUN mkdir build && \ cd build && \ - cmake .. -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx && \ - cmake --build . --config Release --target main server + if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \ + echo "LLAMA_SYCL_F16 is set" && \ + export OPT_SYCL_F16="-DLLAMA_SYCL_F16=ON"; \ + fi && \ + cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx ${OPT_SYCL_F16} && \ + cmake --build . --config Release --target server -FROM ubuntu:$UBUNTU_VERSION as runtime +FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime COPY --from=build /app/build/bin/server /server diff --git a/.devops/server-vulkan.Dockerfile b/.devops/server-vulkan.Dockerfile new file mode 100644 index 000000000..e0add6fc3 --- /dev/null +++ b/.devops/server-vulkan.Dockerfile @@ -0,0 +1,29 @@ +ARG UBUNTU_VERSION=jammy + +FROM ubuntu:$UBUNTU_VERSION as build + +# Install build tools +RUN apt update && apt install -y git build-essential cmake wget + +# Install Vulkan SDK +RUN wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - && \ + wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list && \ + apt update -y && \ + apt-get install -y vulkan-sdk + +# Build it +WORKDIR /app +COPY . . +RUN mkdir build && \ + cd build && \ + cmake .. -DLLAMA_VULKAN=1 && \ + cmake --build . --config Release --target server + +# Clean up +WORKDIR / +RUN cp /app/build/bin/server /server && \ + rm -rf /app + +ENV LC_ALL=C.utf8 + +ENTRYPOINT [ "/server" ] diff --git a/README-sycl.md b/README-sycl.md index f7edc1c3e..7aa4274a9 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -1,22 +1,15 @@ # llama.cpp for SYCL -[Background](#background) - -[OS](#os) - -[Intel GPU](#intel-gpu) - -[Linux](#linux) - -[Windows](#windows) - -[Environment Variable](#environment-variable) - -[Known Issue](#known-issue) - -[Q&A](#q&a) - -[Todo](#todo) +- [Background](#background) +- [OS](#os) +- [Intel GPU](#intel-gpu) +- [Docker](#docker) +- [Linux](#linux) +- [Windows](#windows) +- [Environment Variable](#environment-variable) +- [Known Issue](#known-issue) +- [Q&A](#q&a) +- [Todo](#todo) ## Background @@ -36,7 +29,7 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). |OS|Status|Verified| |-|-|-| -|Linux|Support|Ubuntu 22.04| +|Linux|Support|Ubuntu 22.04, Fedora Silverblue 39| |Windows|Support|Windows 11| @@ -50,7 +43,7 @@ For Intel CPU, recommend to use llama.cpp for X86 (Intel MKL building). |Intel Data Center Flex Series| Support| Flex 170| |Intel Arc Series| Support| Arc 770, 730M| |Intel built-in Arc GPU| Support| built-in Arc GPU in Meteor Lake| -|Intel iGPU| Support| iGPU in i5-1250P, i7-1165G7| +|Intel iGPU| Support| iGPU in i5-1250P, i7-1260P, i7-1165G7| Note: If the EUs (Execution Unit) in iGPU is less than 80, the inference speed will be too slow to use. @@ -64,6 +57,38 @@ For iGPU, please make sure the shared memory from host memory is enough. For lla For dGPU, please make sure the device memory is enough. For llama-2-7b.Q4_0, recommend the device memory is 4GB+. +## Docker + +Note: +- Only docker on Linux is tested. Docker on WSL may not work. +- You may need to install Intel GPU driver on the host machine (See the [Linux](#linux) section to know how to do that) + +### Build the image + +You can choose between **F16** and **F32** build. F16 is faster for long-prompt inference. + + +```sh +# For F16: +#docker build -t llama-cpp-sycl --build-arg="LLAMA_SYCL_F16=ON" -f .devops/main-intel.Dockerfile . + +# Or, for F32: +docker build -t llama-cpp-sycl -f .devops/main-intel.Dockerfile . + +# Note: you can also use the ".devops/main-server.Dockerfile", which compiles the "server" example +``` + +### Run + +```sh +# Firstly, find all the DRI cards: +ls -la /dev/dri +# Then, pick the card that you want to use. + +# For example with "/dev/dri/card1" +docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-sycl -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 +``` + ## Linux ### Setup Environment @@ -76,7 +101,7 @@ Note: for iGPU, please install the client GPU driver. b. Add user to group: video, render. -``` +```sh sudo usermod -aG render username sudo usermod -aG video username ``` @@ -85,7 +110,7 @@ Note: re-login to enable it. c. Check -``` +```sh sudo apt install clinfo sudo clinfo -l ``` @@ -103,7 +128,6 @@ Platform #0: Intel(R) OpenCL HD Graphics 2. Install Intel® oneAPI Base toolkit. - a. Please follow the procedure in [Get the Intel® oneAPI Base Toolkit ](https://www.intel.com/content/www/us/en/developer/tools/oneapi/base-toolkit.html). Recommend to install to default folder: **/opt/intel/oneapi**. @@ -112,7 +136,7 @@ Following guide use the default folder as example. If you use other folder, plea b. Check -``` +```sh source /opt/intel/oneapi/setvars.sh sycl-ls @@ -131,21 +155,25 @@ Output (example): 2. Build locally: -``` +Note: +- You can choose between **F16** and **F32** build. F16 is faster for long-prompt inference. +- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only. + +```sh mkdir -p build cd build source /opt/intel/oneapi/setvars.sh -#for FP16 -#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON # faster for long-prompt inference +# For FP16: +#cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_SYCL_F16=ON -#for FP32 +# Or, for FP32: cmake .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -#build example/main only +# Build example/main only #cmake --build . --config Release --target main -#build all binary +# Or, build all binary cmake --build . --config Release -v cd .. @@ -153,14 +181,10 @@ cd .. or -``` +```sh ./examples/sycl/build.sh ``` -Note: - -- By default, it will build for all binary files. It will take more time. To reduce the time, we recommend to build for **example/main** only. - ### Run 1. Put model file to folder **models** @@ -177,10 +201,10 @@ source /opt/intel/oneapi/setvars.sh Run without parameter: -``` +```sh ./build/bin/ls-sycl-device -or +# or running the "main" executable and look at the output log: ./build/bin/main ``` @@ -209,13 +233,13 @@ found 4 SYCL devices: Set device ID = 0 by **GGML_SYCL_DEVICE=0** -``` +```sh GGML_SYCL_DEVICE=0 ./build/bin/main -m models/llama-2-7b.Q4_0.gguf -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 ``` or run by script: -``` -./examples/sycl/run-llama2.sh +```sh +./examples/sycl/run_llama2.sh ``` Note: diff --git a/README.md b/README.md index e6ed1d429..af1f09fa0 100644 --- a/README.md +++ b/README.md @@ -393,28 +393,28 @@ Building the program with BLAS support may lead to some performance improvements Check [BLIS.md](docs/BLIS.md) for more information. +- #### SYCL + SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators. + + llama.cpp based on SYCL is used to **support Intel GPU** (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU). + + For detailed info, please refer to [llama.cpp for SYCL](README-sycl.md). + - #### Intel oneMKL + Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. Please note that this build config **does not support Intel GPU**. For Intel GPU support, please refer to [llama.cpp for SYCL](./README-sycl.md). + - Using manual oneAPI installation: By default, `LLAMA_BLAS_VENDOR` is set to `Generic`, so if you already sourced intel environment script and assign `-DLLAMA_BLAS=ON` in cmake, the mkl version of Blas will automatically been selected. Otherwise please install oneAPI and follow the below steps: ```bash mkdir build cd build - source /opt/intel/oneapi/setvars.sh # You can skip this step if in oneapi-runtime docker image, only required for manual installation + source /opt/intel/oneapi/setvars.sh # You can skip this step if in oneapi-basekit docker image, only required for manual installation cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON cmake --build . --config Release ``` - Using oneAPI docker image: - If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: [oneAPI-runtime](https://hub.docker.com/r/intel/oneapi-runtime) - - ```bash - mkdir build - cd build - cmake .. -DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=Intel10_64lp -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icpx -DLLAMA_NATIVE=ON - cmake --build . --config Release - ``` - - Building through oneAPI compilers will make avx_vnni instruction set available for intel processors that do not support avx512 and avx512_vnni. + If you do not want to source the environment vars and install oneAPI manually, you can also build the code using intel docker container: [oneAPI-basekit](https://hub.docker.com/r/intel/oneapi-basekit). Then, you can use the commands given above. Check [Optimizing and Running LLaMA2 on Intel® CPU](https://www.intel.com/content/www/us/en/content-details/791610/optimizing-and-running-llama2-on-intel-cpu.html) for more information. @@ -601,14 +601,48 @@ Building the program with BLAS support may lead to some performance improvements You can get a list of platforms and devices from the `clinfo -l` command, etc. -- #### SYCL +- #### Vulkan - SYCL is a higher-level programming model to improve programming productivity on various hardware accelerators. + **With docker**: - llama.cpp based on SYCL is used to support Intel GPU (Data Center Max series, Flex series, Arc series, Built-in GPU and iGPU). + You don't need to install Vulkan SDK. It will be installed inside the container. - For detailed info, please refer to [llama.cpp for SYCL](README-sycl.md). + ```sh + # Build the image + docker build -t llama-cpp-vulkan -f .devops/main-vulkan.Dockerfile . + # Then, use it: + docker run -it --rm -v "$(pwd):/app:Z" --device /dev/dri/renderD128:/dev/dri/renderD128 --device /dev/dri/card1:/dev/dri/card1 llama-cpp-vulkan -m "/app/models/YOUR_MODEL_FILE" -p "Building a website can be done in 10 simple steps:" -n 400 -e -ngl 33 + ``` + + **Without docker**: + + Firstly, you need to make sure you installed [Vulkan SDK](https://vulkan.lunarg.com/doc/view/latest/linux/getting_started_ubuntu.html) + + For example, on Ubuntu 22.04 (jammy), use the command below: + + ```bash + wget -qO - https://packages.lunarg.com/lunarg-signing-key-pub.asc | apt-key add - + wget -qO /etc/apt/sources.list.d/lunarg-vulkan-jammy.list https://packages.lunarg.com/vulkan/lunarg-vulkan-jammy.list + apt update -y + apt-get install -y vulkan-sdk + # To verify the installation, use the command below: + vulkaninfo + ``` + + Then, build llama.cpp using the cmake command below: + + ```bash + mkdir -p build + cd build + cmake .. -DLLAMA_VULKAN=1 + cmake --build . --config Release + # Test the output binary (with "-ngl 33" to offload all layers to GPU) + ./bin/main -m "PATH_TO_MODEL" -p "Hi you how are you" -n 50 -e -ngl 33 -t 4 + + # You should see in the output, ggml_vulkan detected your GPU. For example: + # ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32 + ``` ### Prepare Data & Run From b05102fe8cfa9893851c6bf6efd15cdc20b6afa2 Mon Sep 17 00:00:00 2001 From: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> Date: Fri, 2 Feb 2024 08:39:48 +0000 Subject: [PATCH 094/143] Tidy ggml-sycl (#5261) * Tidy some code in ggml-sycl * Remove blank space * Remove std::printf comments --------- Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> --- ggml-sycl.cpp | 47 ++++++++++++++++++++++++----------------------- 1 file changed, 24 insertions(+), 23 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 4ee2eed38..ac75f8e16 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -1366,6 +1366,7 @@ namespace dpct } #else return q.memcpy(to_ptr, from_ptr, size, dep_events); + GGML_UNUSED(direction); #endif // DPCT_USM_LEVEL_NONE } @@ -1667,7 +1668,7 @@ namespace dpct using Ty = typename DataType::T2; Ty s_h; if (get_pointer_attribute(q, s) == pointer_access_attribute::device_only) - detail::dpct_memcpy(q, (void *)&s_h, (void *)s, sizeof(T), device_to_host) + detail::dpct_memcpy(q, (void *)&s_h, (const void *)s, sizeof(T), device_to_host) .wait(); else s_h = *reinterpret_cast(s); @@ -1691,6 +1692,20 @@ namespace dpct int ldb, const void *beta, void *c, int ldc) { #ifndef __INTEL_MKL__ + GGML_UNUSED(q); + GGML_UNUSED(a_trans); + GGML_UNUSED(b_trans); + GGML_UNUSED(m); + GGML_UNUSED(n); + GGML_UNUSED(k); + GGML_UNUSED(alpha); + GGML_UNUSED(a); + GGML_UNUSED(lda); + GGML_UNUSED(b); + GGML_UNUSED(ldb); + GGML_UNUSED(beta); + GGML_UNUSED(c); + GGML_UNUSED(ldc); throw std::runtime_error("The oneAPI Math Kernel Library (oneMKL) Interfaces " "Project does not support this API."); #else @@ -1830,7 +1845,7 @@ namespace dpct template T permute_sub_group_by_xor(sycl::sub_group g, T x, unsigned int mask, - int logical_sub_group_size = 32) + unsigned int logical_sub_group_size = 32) { unsigned int id = g.get_local_linear_id(); unsigned int start_index = @@ -2160,6 +2175,7 @@ namespace dpct } #else return q.memcpy(to_ptr, from_ptr, size, dep_events); + GGML_UNUSED(direction); #endif // DPCT_USM_LEVEL_NONE } @@ -3302,7 +3318,7 @@ void log_ggml_var_device(const char*name, float *src, size_t total_elements, boo std::ofstream logfile; logfile.open(filename); // printf("local buf element %d\n", total_elements); - for(int i=0; ibackend == GGML_BACKEND_GPU && device_id == g_main_device ? ne0 : row_diff; - const int compute_capability = g_device_caps[id].cc; #ifdef GGML_SYCL_F16 bool use_fp16 = true; // TODO(Yu) SYCL capability check #else @@ -12691,7 +12700,7 @@ static void ggml_sycl_set_peer_access(const int n_tokens) { continue; } - int can_access_peer; + // int can_access_peer; // SYCL_CHECK(syclDeviceCanAccessPeer(&can_access_peer, id, id_other)); // if (can_access_peer) { // if (enable_peer_access) { @@ -12716,7 +12725,6 @@ static void ggml_sycl_op_mul_mat(const ggml_tensor *src0, const int64_t ne01 = src0->ne[1]; const int64_t ne02 = src0->ne[2]; const int64_t ne03 = src0->ne[3]; - const int64_t nrows0 = ggml_nrows(src0); const int64_t ne10 = src1->ne[0]; const int64_t ne11 = src1->ne[1]; @@ -13812,13 +13820,6 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, src1_row_extra.data_device[g_main_device_index] = src1_contiguous.get(); dst_row_extra.data_device[g_main_device_index] = dst_contiguous.get(); - const dpct::memcpy_direction src1_kind = - src1->backend == GGML_BACKEND_CPU ? dpct::host_to_device - : dpct::device_to_device; - const dpct::memcpy_direction dst_kind = dst->backend == GGML_BACKEND_CPU - ? dpct::device_to_host - : dpct::device_to_device; - for (int32_t row_id = 0; row_id < n_as; ++row_id) { const struct ggml_tensor * src0_row = dst->src[row_id + 2]; From 2d40085c26794e29c434480b9e06738e89e5686f Mon Sep 17 00:00:00 2001 From: Mirror Azure <54669636+MirrorAzure@users.noreply.github.com> Date: Fri, 2 Feb 2024 14:39:09 +0300 Subject: [PATCH 095/143] py : add check for '.attn.masked_bias' layers to GPT2model (#5281) --- convert-hf-to-gguf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 4ebab07b3..a6ffd128b 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1138,7 +1138,7 @@ class GPT2Model(Model): for name, data_torch in self.get_tensors(): # we don't need these - if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq", ".attn.bias")): + if name.endswith((".attention.masked_bias", ".attention.bias", ".attention.rotary_emb.inv_freq", ".attn.bias", ".attn.masked_bias")): continue if name.endswith((".c_attn.weight", ".c_proj.weight", ".c_fc.weight", ".c_proj.weight")): From e437b37fd0b2b97e6c6ff1045ec7f901faa6498a Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 2 Feb 2024 14:23:40 +0200 Subject: [PATCH 096/143] scripts : parse wtype in server-llm.sh (#5167) * scripts : parse wtype in server-llm.sh * scripts : fix check for wfile --- scripts/server-llm.sh | 26 +++++++++++++++++++++++++- 1 file changed, 25 insertions(+), 1 deletion(-) diff --git a/scripts/server-llm.sh b/scripts/server-llm.sh index 7bf0929bb..0b83cdbbc 100644 --- a/scripts/server-llm.sh +++ b/scripts/server-llm.sh @@ -141,6 +141,28 @@ for wt in "${wtypes[@]}"; do wfiles+=("") done +# map wtype input to index +if [[ ! -z "$wtype" ]]; then + iw=-1 + is=0 + for wt in "${wtypes[@]}"; do + # uppercase + uwt=$(echo "$wt" | tr '[:lower:]' '[:upper:]') + if [[ "$uwt" == "$wtype" ]]; then + iw=$is + break + fi + is=$((is+1)) + done + + if [[ $iw -eq -1 ]]; then + printf "[-] Invalid weight type: %s\n" "$wtype" + exit 1 + fi + + wtype="$iw" +fi + # sample repos repos=( "https://huggingface.co/TheBloke/Llama-2-7B-GGUF" @@ -252,8 +274,10 @@ for file in $model_files; do printf " %2d) %s %s\n" $iw "$have" "$file" done +wfile="${wfiles[$wtype]}" + # ask for weights type until provided and available -while [[ -z "$wtype" ]]; do +while [[ -z "$wfile" ]]; do printf "\n" read -p "[+] Select weight type: " wtype wfile="${wfiles[$wtype]}" From 191221178f51b6e81122c5bda0fd79620e547d07 Mon Sep 17 00:00:00 2001 From: kalomaze <66376113+kalomaze@users.noreply.github.com> Date: Fri, 2 Feb 2024 08:15:30 -0600 Subject: [PATCH 097/143] perplexity : fix KL divergence calculations on Windows (#5273) --- examples/perplexity/perplexity.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 8d2204969..4b08145cd 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -457,14 +457,14 @@ static results_perplexity perplexity(llama_context * ctx, const gpt_params & par std::ofstream logits_stream; if (!params.logits_file.empty()) { - logits_stream.open(params.logits_file.c_str()); + logits_stream.open(params.logits_file.c_str(), std::ios::binary); if (!logits_stream.is_open()) { fprintf(stderr, "%s: failed to open %s for writing\n", __func__, params.logits_file.c_str()); return {}; } fprintf(stderr, "%s: saving all logits to %s\n", __func__, params.logits_file.c_str()); logits_stream.write("_logits_", 8); - logits_stream.write((const char *)&n_ctx, sizeof(n_ctx)); + logits_stream.write(reinterpret_cast(&n_ctx), sizeof(n_ctx)); } auto tim1 = std::chrono::high_resolution_clock::now(); From a305dba8ff642e57f538f42010868fe0bc5262a1 Mon Sep 17 00:00:00 2001 From: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> Date: Sat, 3 Feb 2024 08:11:37 +0000 Subject: [PATCH 098/143] Fix im2col with 32fp (#5286) --- ggml-sycl.cpp | 17 +++++++++++------ 1 file changed, 11 insertions(+), 6 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index ac75f8e16..51445b5e7 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -8247,7 +8247,8 @@ static void clamp_f32(const float * x, float * dst, const float min, const float dst[i] = x[i] < min ? min : (x[i] > max ? max : x[i]); } -static void im2col_f32_f16(const float *x, sycl::half *dst, int offset_delta, +template +static void im2col_kernel(const float *x, T *dst, int offset_delta, int IW, int IH, int OW, int KW, int KH, int pelements, int CHW, int s0, int s1, int p0, int p1, int d0, int d1, @@ -11019,7 +11020,8 @@ static void soft_max_f32_sycl(const float *x, const float *y, float *dst, }); } -static void im2col_f32_f16_sycl(const float *x, sycl::half *dst, int IW, int IH, +template +static void im2col_sycl(const float *x, T *dst, int IW, int IH, int OW, int OH, int KW, int KH, int IC, int offset_delta, int s0, int s1, int p0, int p1, int d0, int d1, @@ -11036,7 +11038,7 @@ static void im2col_f32_f16_sycl(const float *x, sycl::half *dst, int IW, int IH, sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_IM2COL_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { - im2col_f32_f16(x, dst, offset_delta, IW, IH, OW, KW, KH, + im2col_kernel(x, dst, offset_delta, IW, IH, OW, KW, KH, parallel_elements, (IC * KH * KW), s0, s1, p0, p1, d0, d1, item_ct1); }); @@ -12424,7 +12426,7 @@ inline void ggml_sycl_op_im2col(const ggml_tensor *src0, 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 || dst->type == GGML_TYPE_F32); const int32_t s0 = ((const int32_t*)(dst->op_params))[0]; const int32_t s1 = ((const int32_t*)(dst->op_params))[1]; @@ -12447,8 +12449,11 @@ inline void ggml_sycl_op_im2col(const ggml_tensor *src0, const size_t delta_offset = src1->nb[is_2D ? 2 : 1] / 4; // nb is byte offset, src is type float32 - im2col_f32_f16_sycl(src1_dd, (sycl::half *)dst_dd, IW, IH, OW, OH, KW, KH, - IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream); + if (dst->type == GGML_TYPE_F16) { + im2col_sycl(src1_dd, (sycl::half *)dst_dd, IW, IH, OW, OH, KW, KH, IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream); + } else { + im2col_sycl(src1_dd, (float *)dst_dd, IW, IH, OW, OH, KW, KH, IC, delta_offset, s0, s1, p0, p1, d0, d1, main_stream); + } (void) src0; (void) src0_dd; From 6a66c5071a74a96c4f52cf1015a092acd18c3714 Mon Sep 17 00:00:00 2001 From: BADR Date: Sat, 3 Feb 2024 12:20:26 +0100 Subject: [PATCH 099/143] readme : add tenere in the ui tools list (#5284) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index af1f09fa0..4a9bdf314 100644 --- a/README.md +++ b/README.md @@ -143,6 +143,7 @@ as the main playground for developing new features for the [ggml](https://github - [psugihara/FreeChat](https://github.com/psugihara/FreeChat) - [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) - [iohub/collama](https://github.com/iohub/coLLaMA) +- [pythops/tenere](https://github.com/pythops/tenere) --- From 1ec3332ade60aeb1494ace2211cf1a966db6d770 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Sat, 3 Feb 2024 06:22:06 -0500 Subject: [PATCH 100/143] YaRN : store rope scaling type as int32_t in memory (#5285) * YaRN : store rope scaling type as int32_t in memory * llama : store mapped names as const char * --- common/common.h | 3 +-- llama.cpp | 24 ++++++++++++------------ llama.h | 2 +- 3 files changed, 14 insertions(+), 15 deletions(-) diff --git a/common/common.h b/common/common.h index 24a99d728..62de25d6a 100644 --- a/common/common.h +++ b/common/common.h @@ -75,8 +75,7 @@ struct gpt_params { float yarn_beta_fast = 32.0f; // YaRN low correction dim float yarn_beta_slow = 1.0f; // YaRN high correction dim int32_t yarn_orig_ctx = 0; // YaRN original context length - int8_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // TODO: better to be int32_t for alignment - // pinging @cebtenzzre + int32_t rope_scaling_type = LLAMA_ROPE_SCALING_UNSPECIFIED; // // sampling parameters struct llama_sampling_params sparams; diff --git a/llama.cpp b/llama.cpp index 6bf7f9efb..4787a92fe 100644 --- a/llama.cpp +++ b/llama.cpp @@ -208,7 +208,7 @@ enum llm_arch { LLM_ARCH_UNKNOWN, }; -static std::map LLM_ARCH_NAMES = { +static std::map LLM_ARCH_NAMES = { { LLM_ARCH_LLAMA, "llama" }, { LLM_ARCH_FALCON, "falcon" }, { LLM_ARCH_GPT2, "gpt2" }, @@ -285,7 +285,7 @@ enum llm_kv { LLM_KV_TOKENIZER_RWKV, }; -static std::map LLM_KV_NAMES = { +static std::map LLM_KV_NAMES = { { LLM_KV_GENERAL_ARCHITECTURE, "general.architecture" }, { LLM_KV_GENERAL_QUANTIZATION_VERSION, "general.quantization_version" }, { LLM_KV_GENERAL_ALIGNMENT, "general.alignment" }, @@ -346,7 +346,7 @@ struct LLM_KV { llm_arch arch; std::string operator()(llm_kv kv) const { - return ::format(LLM_KV_NAMES[kv].c_str(), LLM_ARCH_NAMES[arch].c_str()); + return ::format(LLM_KV_NAMES[kv], LLM_ARCH_NAMES[arch]); } }; @@ -747,13 +747,13 @@ struct LLM_TN { // gguf helpers // -static std::map LLAMA_ROPE_SCALING_TYPES = { +static std::map LLAMA_ROPE_SCALING_TYPES = { { LLAMA_ROPE_SCALING_NONE, "none" }, { LLAMA_ROPE_SCALING_LINEAR, "linear" }, { LLAMA_ROPE_SCALING_YARN, "yarn" }, }; -static int8_t llama_rope_scaling_type_from_string(const std::string & name) { +static int32_t llama_rope_scaling_type_from_string(const std::string & name) { for (const auto & kv : LLAMA_ROPE_SCALING_TYPES) { if (kv.second == name) { return kv.first; @@ -1415,6 +1415,7 @@ static const size_t GiB = 1024*MiB; struct llama_hparams { bool vocab_only; + bool rope_finetuned; uint32_t n_vocab; uint32_t n_ctx_train; // context size the model was trained on uint32_t n_embd; @@ -1434,8 +1435,7 @@ struct llama_hparams { float rope_freq_base_train; float rope_freq_scale_train; uint32_t n_yarn_orig_ctx; - int8_t rope_scaling_type_train : 3; - bool rope_finetuned : 1; + int32_t rope_scaling_type_train; float f_clamp_kqv; float f_max_alibi_bias; @@ -2701,7 +2701,7 @@ struct llama_model_loader { // load LLaMA models // -static std::string llama_model_arch_name(llm_arch arch) { +static const char * llama_model_arch_name(llm_arch arch) { auto it = LLM_ARCH_NAMES.find(arch); if (it == LLM_ARCH_NAMES.end()) { return "unknown"; @@ -3310,11 +3310,11 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { const auto & hparams = model.hparams; const auto & vocab = model.vocab; - const auto rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train); + const char * rope_scaling_type = LLAMA_ROPE_SCALING_TYPES.at(hparams.rope_scaling_type_train); // hparams LLAMA_LOG_INFO("%s: format = %s\n", __func__, llama_file_version_name(ml.fver)); - LLAMA_LOG_INFO("%s: arch = %s\n", __func__, LLM_ARCH_NAMES.at(model.arch).c_str()); + LLAMA_LOG_INFO("%s: arch = %s\n", __func__, LLM_ARCH_NAMES.at(model.arch)); LLAMA_LOG_INFO("%s: vocab type = %s\n", __func__, llama_model_vocab_type_name(vocab.type)); LLAMA_LOG_INFO("%s: n_vocab = %u\n", __func__, hparams.n_vocab); LLAMA_LOG_INFO("%s: n_merges = %u\n", __func__, (int) vocab.bpe_ranks.size()); @@ -3336,7 +3336,7 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { LLAMA_LOG_INFO("%s: n_ff = %u\n", __func__, hparams.n_ff); LLAMA_LOG_INFO("%s: n_expert = %u\n", __func__, hparams.n_expert); LLAMA_LOG_INFO("%s: n_expert_used = %u\n", __func__, hparams.n_expert_used); - LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type.c_str()); + LLAMA_LOG_INFO("%s: rope scaling = %s\n", __func__, rope_scaling_type); LLAMA_LOG_INFO("%s: freq_base_train = %.1f\n", __func__, hparams.rope_freq_base_train); LLAMA_LOG_INFO("%s: freq_scale_train = %g\n", __func__, hparams.rope_freq_scale_train); LLAMA_LOG_INFO("%s: n_yarn_orig_ctx = %u\n", __func__, hparams.n_yarn_orig_ctx); @@ -10735,7 +10735,7 @@ int32_t llama_model_meta_val_str_by_index(const struct llama_model * model, int3 int32_t llama_model_desc(const struct llama_model * model, char * buf, size_t buf_size) { return snprintf(buf, buf_size, "%s %s %s", - llama_model_arch_name(model->arch).c_str(), + llama_model_arch_name(model->arch), llama_model_type_name(model->type), llama_model_ftype_name(model->ftype).c_str()); } diff --git a/llama.h b/llama.h index 9a60e9bfb..cec4158bc 100644 --- a/llama.h +++ b/llama.h @@ -213,7 +213,7 @@ extern "C" { uint32_t n_batch; // prompt processing maximum batch size uint32_t n_threads; // number of threads to use for generation uint32_t n_threads_batch; // number of threads to use for batch processing - int8_t rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type` + int32_t rope_scaling_type; // RoPE scaling type, from `enum llama_rope_scaling_type` // ref: https://github.com/ggerganov/llama.cpp/pull/2054 float rope_freq_base; // RoPE base frequency, 0 = from model From 52bb63c7082c859c3f1dfc527227e6a95b299c7c Mon Sep 17 00:00:00 2001 From: Michael Klimenko Date: Sat, 3 Feb 2024 12:23:37 +0100 Subject: [PATCH 101/143] refactor : switch to emplace_back to avoid extra object (#5291) --- common/common.cpp | 8 ++--- examples/llama-bench/llama-bench.cpp | 34 +++++++++++----------- examples/main/main.cpp | 4 +-- examples/perplexity/perplexity.cpp | 8 ++--- examples/quantize-stats/quantize-stats.cpp | 4 +-- examples/quantize/quantize.cpp | 4 +-- examples/server/server.cpp | 8 ++--- tests/test-llama-grammar.cpp | 2 +- 8 files changed, 36 insertions(+), 36 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index ce739b15c..3302caa20 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -515,7 +515,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { invalid_param = true; break; } - params.lora_adapter.push_back(std::make_tuple(argv[i], 1.0f)); + params.lora_adapter.emplace_back(argv[i], 1.0f); params.use_mmap = false; } else if (arg == "--lora-scaled") { if (++i >= argc) { @@ -527,7 +527,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { invalid_param = true; break; } - params.lora_adapter.push_back(std::make_tuple(lora_adapter, std::stof(argv[i]))); + params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i])); params.use_mmap = false; } else if (arg == "--lora-base") { if (++i >= argc) { @@ -664,7 +664,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { invalid_param = true; break; } - params.antiprompt.push_back(argv[i]); + params.antiprompt.emplace_back(argv[i]); } else if (arg == "-ld" || arg == "--logdir") { if (++i >= argc) { invalid_param = true; @@ -880,7 +880,7 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { } if (!params.kv_overrides.empty()) { - params.kv_overrides.emplace_back(llama_model_kv_override()); + params.kv_overrides.emplace_back(); params.kv_overrides.back().key[0] = 0; } diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index e36c061a2..ddb0ba064 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -948,46 +948,46 @@ struct markdown_printer : public printer { void print_header(const cmd_params & params) override { // select fields to print - fields.push_back("model"); - fields.push_back("size"); - fields.push_back("params"); - fields.push_back("backend"); + fields.emplace_back("model"); + fields.emplace_back("size"); + fields.emplace_back("params"); + fields.emplace_back("backend"); bool is_cpu_backend = test::get_backend() == "CPU" || test::get_backend() == "BLAS"; if (!is_cpu_backend) { - fields.push_back("n_gpu_layers"); + fields.emplace_back("n_gpu_layers"); } if (params.n_threads.size() > 1 || params.n_threads != cmd_params_defaults.n_threads || is_cpu_backend) { - fields.push_back("n_threads"); + fields.emplace_back("n_threads"); } if (params.n_batch.size() > 1 || params.n_batch != cmd_params_defaults.n_batch) { - fields.push_back("n_batch"); + fields.emplace_back("n_batch"); } if (params.type_k.size() > 1 || params.type_k != cmd_params_defaults.type_k) { - fields.push_back("type_k"); + fields.emplace_back("type_k"); } if (params.type_v.size() > 1 || params.type_v != cmd_params_defaults.type_v) { - fields.push_back("type_v"); + fields.emplace_back("type_v"); } if (params.main_gpu.size() > 1 || params.main_gpu != cmd_params_defaults.main_gpu) { - fields.push_back("main_gpu"); + fields.emplace_back("main_gpu"); } if (params.split_mode.size() > 1 || params.split_mode != cmd_params_defaults.split_mode) { - fields.push_back("split_mode"); + fields.emplace_back("split_mode"); } if (params.mul_mat_q.size() > 1 || params.mul_mat_q != cmd_params_defaults.mul_mat_q) { - fields.push_back("mul_mat_q"); + fields.emplace_back("mul_mat_q"); } if (params.no_kv_offload.size() > 1 || params.no_kv_offload != cmd_params_defaults.no_kv_offload) { - fields.push_back("no_kv_offload"); + fields.emplace_back("no_kv_offload"); } if (params.tensor_split.size() > 1 || params.tensor_split != cmd_params_defaults.tensor_split) { - fields.push_back("tensor_split"); + fields.emplace_back("tensor_split"); } if (params.use_mmap.size() > 1 || params.use_mmap != cmd_params_defaults.use_mmap) { - fields.push_back("use_mmap"); + fields.emplace_back("use_mmap"); } - fields.push_back("test"); - fields.push_back("t/s"); + fields.emplace_back("test"); + fields.emplace_back("t/s"); fprintf(fout, "|"); for (const auto & field : fields) { diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 1c6138d23..0ed4d79f9 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -352,12 +352,12 @@ int main(int argc, char ** argv) { // in instruct mode, we inject a prefix and a suffix to each input by the user if (params.instruct) { params.interactive_first = true; - params.antiprompt.push_back("### Instruction:\n\n"); + params.antiprompt.emplace_back("### Instruction:\n\n"); } // similar for chatml mode else if (params.chatml) { params.interactive_first = true; - params.antiprompt.push_back("<|im_start|>user\n"); + params.antiprompt.emplace_back("<|im_start|>user\n"); } // enable interactive mode if interactive start is specified diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index 4b08145cd..b2c131d4c 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -881,7 +881,7 @@ static void hellaswag_score(llama_context * ctx, const gpt_params & params) { size_t li = hs_cur.common_prefix; for (int s = 0; s < 4; ++s) { for (size_t j = hs_cur.common_prefix; j < hs_cur.seq_tokens[s].size() - 1; j++) { - eval_pairs.push_back(std::make_pair(hs_cur.i_batch + li++, hs_cur.seq_tokens[s][j + 1])); + eval_pairs.emplace_back(hs_cur.i_batch + li++, hs_cur.seq_tokens[s][j + 1]); } ++li; } @@ -1159,13 +1159,13 @@ static void winogrande_score(llama_context * ctx, const gpt_params & params) { const int last_1st = task.seq_tokens[0].size() - n_base1 > 1 ? 1 : 0; size_t li = n_base1 - 1; for (size_t j = n_base1-1; j < task.seq_tokens[0].size()-1-last_1st; ++j) { - eval_pairs.push_back(std::make_pair(task.i_batch + li++, task.seq_tokens[0][j+1])); + eval_pairs.emplace_back(task.i_batch + li++, task.seq_tokens[0][j+1]); } const auto& n_base2 = skip_choice ? task.n_base2 : task.common_prefix; const int last_2nd = task.seq_tokens[1].size() - n_base2 > 1 ? 1 : 0; li = task.seq_tokens[0].size() - task.common_prefix + n_base2 - 1; for (size_t j = n_base2-1; j < task.seq_tokens[1].size()-1-last_2nd; ++j) { - eval_pairs.push_back(std::make_pair(task.i_batch + li++, task.seq_tokens[1][j+1])); + eval_pairs.emplace_back(task.i_batch + li++, task.seq_tokens[1][j+1]); } } compute_logprobs(batch_logits.data(), n_vocab, workers, eval_pairs, eval_results); @@ -1524,7 +1524,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params size_t li = cur_task.common_prefix; for (int s = 0; s < int(cur_task.seq_tokens.size()); ++s) { for (size_t j = cur_task.common_prefix; j < cur_task.seq_tokens[s].size() - 1; j++) { - eval_pairs.push_back(std::make_pair(cur_task.i_batch + li++, cur_task.seq_tokens[s][j + 1])); + eval_pairs.emplace_back(cur_task.i_batch + li++, cur_task.seq_tokens[s][j + 1]); } ++li; } diff --git a/examples/quantize-stats/quantize-stats.cpp b/examples/quantize-stats/quantize-stats.cpp index 6d5f213dc..1d05f1391 100644 --- a/examples/quantize-stats/quantize-stats.cpp +++ b/examples/quantize-stats/quantize-stats.cpp @@ -257,13 +257,13 @@ int main(int argc, char ** argv) { invalid_param = true; break; } - params.include_layers.push_back(argv[i]); + params.include_layers.emplace_back(argv[i]); } else if (arg == "-L" || arg == "--exclude-layer") { if (++i >= argc) { invalid_param = true; break; } - params.exclude_layers.push_back(argv[i]); + params.exclude_layers.emplace_back(argv[i]); } else if (arg == "-t" || arg == "--type") { if (++i >= argc) { invalid_param = true; diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index a9673f0d4..85f403ffc 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -208,13 +208,13 @@ int main(int argc, char ** argv) { } } else if (strcmp(argv[arg_idx], "--include-weights") == 0) { if (arg_idx < argc-1) { - included_weights.push_back(argv[++arg_idx]); + included_weights.emplace_back(argv[++arg_idx]); } else { usage(argv[0]); } } else if (strcmp(argv[arg_idx], "--exclude-weights") == 0) { if (arg_idx < argc-1) { - excluded_weights.push_back(argv[++arg_idx]); + excluded_weights.emplace_back(argv[++arg_idx]); } else { usage(argv[0]); } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index ea77125ea..a9f8cb369 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1884,7 +1884,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, invalid_param = true; break; } - sparams.api_keys.push_back(argv[i]); + sparams.api_keys.emplace_back(argv[i]); } else if (arg == "--api-key-file") { @@ -2160,7 +2160,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, invalid_param = true; break; } - params.lora_adapter.push_back(std::make_tuple(argv[i], 1.0f)); + params.lora_adapter.emplace_back(argv[i], 1.0f); params.use_mmap = false; } else if (arg == "--lora-scaled") @@ -2176,7 +2176,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, invalid_param = true; break; } - params.lora_adapter.push_back(std::make_tuple(lora_adapter, std::stof(argv[i]))); + params.lora_adapter.emplace_back(lora_adapter, std::stof(argv[i])); params.use_mmap = false; } else if (arg == "--lora-base") @@ -2318,7 +2318,7 @@ static void server_params_parse(int argc, char **argv, server_params &sparams, } } if (!params.kv_overrides.empty()) { - params.kv_overrides.emplace_back(llama_model_kv_override()); + params.kv_overrides.emplace_back(); params.kv_overrides.back().key[0] = 0; } diff --git a/tests/test-llama-grammar.cpp b/tests/test-llama-grammar.cpp index 78fc41117..16ebe753f 100644 --- a/tests/test-llama-grammar.cpp +++ b/tests/test-llama-grammar.cpp @@ -105,7 +105,7 @@ int main() for (auto rule : expected_rules) { - parsed_grammar.rules.push_back({}); + parsed_grammar.rules.emplace_back(); for (auto element : rule) { parsed_grammar.rules.back().push_back(element); From e920ed393d989ed35625ddaf182ebb52cda07fcd Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Sat, 3 Feb 2024 18:15:00 +0100 Subject: [PATCH 102/143] Vulkan Intel Fixes, Optimizations and Debugging Flags (#5301) * Fix Vulkan on Intel ARC Optimize matmul for Intel ARC Add Vulkan dequant test * Add Vulkan debug and validate flags to Make and CMakeLists.txt * Enable asynchronous transfers in Vulkan backend * Fix flake8 * Disable Vulkan async backend functions for now * Also add Vulkan run tests command to Makefile and CMakeLists.txt --- CMakeLists.txt | 20 + Makefile | 12 + ggml-vulkan-shaders.hpp | 10922 +++------------------------------- ggml-vulkan.cpp | 420 +- ggml_vk_generate_shaders.py | 213 +- 5 files changed, 1257 insertions(+), 10330 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 1ee455b3a..c156c4824 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -100,6 +100,10 @@ option(LLAMA_HIPBLAS "llama: use hipBLAS" option(LLAMA_HIP_UMA "llama: use HIP unified memory architecture" OFF) option(LLAMA_CLBLAST "llama: use CLBlast" OFF) option(LLAMA_VULKAN "llama: use Vulkan" OFF) +option(LLAMA_VULKAN_CHECK_RESULTS "llama: run Vulkan op checks" OFF) +option(LLAMA_VULKAN_DEBUG "llama: enable Vulkan debug output" OFF) +option(LLAMA_VULKAN_VALIDATE "llama: enable Vulkan validation" OFF) +option(LLAMA_VULKAN_RUN_TESTS "llama: run Vulkan tests" OFF) option(LLAMA_METAL "llama: use Metal" ${LLAMA_METAL_DEFAULT}) option(LLAMA_METAL_NDEBUG "llama: disable Metal debugging" OFF) option(LLAMA_METAL_SHADER_DEBUG "llama: compile Metal with -fno-fast-math" OFF) @@ -431,6 +435,22 @@ if (LLAMA_VULKAN) add_compile_definitions(GGML_USE_VULKAN) + if (LLAMA_VULKAN_CHECK_RESULTS) + target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_CHECK_RESULTS) + endif() + + if (LLAMA_VULKAN_DEBUG) + target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_DEBUG) + endif() + + if (LLAMA_VULKAN_VALIDATE) + target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_VALIDATE) + endif() + + if (LLAMA_VULKAN_RUN_TESTS) + target_compile_definitions(ggml-vulkan PRIVATE GGML_VULKAN_RUN_TESTS) + endif() + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ggml-vulkan) else() message(WARNING "Vulkan not found") diff --git a/Makefile b/Makefile index bf9e085de..a55d15888 100644 --- a/Makefile +++ b/Makefile @@ -457,6 +457,18 @@ ifdef LLAMA_VULKAN_CHECK_RESULTS MK_CPPFLAGS += -DGGML_VULKAN_CHECK_RESULTS endif +ifdef LLAMA_VULKAN_DEBUG + MK_CPPFLAGS += -DGGML_VULKAN_DEBUG +endif + +ifdef LLAMA_VULKAN_VALIDATE + MK_CPPFLAGS += -DGGML_VULKAN_VALIDATE +endif + +ifdef LLAMA_VULKAN_RUN_TESTS + MK_CPPFLAGS += -DGGML_VULKAN_RUN_TESTS +endif + ggml-vulkan.o: ggml-vulkan.cpp ggml-vulkan.h $(CXX) $(CXXFLAGS) -c $< -o $@ endif # LLAMA_VULKAN diff --git a/ggml-vulkan-shaders.hpp b/ggml-vulkan-shaders.hpp index 195410c02..e5e7a8414 100644 --- a/ggml-vulkan-shaders.hpp +++ b/ggml-vulkan-shaders.hpp @@ -890,156 +890,6 @@ const uint64_t cpy_f32_f32_len = 2472; unsigned char dequant_f16_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x81,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, -0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, -0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, -0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x09,0x00, -0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, -0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x4f,0x00,0x00,0x00,0x5d,0x00,0x00,0x00,0x10,0x00,0x06,0x00, -0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x00,0x01,0x00,0x00, -0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 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= { 0x04,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t dequant_q2_K_fp32_len = 4056; +const uint64_t dequant_q2_K_len = 4056; unsigned char dequant_q3_K_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x3f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, -0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x17,0x00,0x00,0x00, -0x25,0x00,0x00,0x00,0x33,0x00,0x00,0x00,0x7b,0x00,0x00,0x00, -0x07,0x01,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, -0x11,0x00,0x00,0x00,0x40,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 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-0x33,0x01,0x00,0x00,0xf9,0x00,0x02,0x00,0x2d,0x01,0x00,0x00, -0xf8,0x00,0x02,0x00,0x2d,0x01,0x00,0x00,0xfd,0x00,0x01,0x00, -0x38,0x00,0x01,0x00, -}; -const uint64_t dequant_q3_K_len = 4792; - -unsigned char dequant_q3_K_fp32_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0x42,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, @@ -2680,709 +1792,10 @@ unsigned char dequant_q3_K_fp32_data[] = { 0xf8,0x00,0x02,0x00,0x30,0x01,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; -const uint64_t dequant_q3_K_fp32_len = 4828; +const uint64_t dequant_q3_K_len = 4828; unsigned char dequant_q4_0_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xf7,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, 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-0x38,0x00,0x01,0x00, -}; -const uint64_t dequant_q4_K_len = 5776; - -unsigned char dequant_q4_K_fp32_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0xb1,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, @@ -6669,1134 +3848,10 @@ unsigned char dequant_q4_K_fp32_data[] = { 0x37,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t dequant_q4_K_fp32_len = 5940; +const uint64_t dequant_q4_K_len = 5940; unsigned char dequant_q5_0_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x7a,0x04,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 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-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, -}; -const uint64_t dequant_q5_K_len = 5888; - -unsigned char dequant_q5_K_fp32_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0xa0,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00, @@ -12162,366 +6653,10 @@ unsigned char dequant_q5_K_fp32_data[] = { 0x8a,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t dequant_q5_K_fp32_len = 5988; +const uint64_t dequant_q5_K_len = 5988; unsigned char dequant_q6_K_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x0a,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, 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dequant_q6_K_fp32_data[] = { 0x06,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t dequant_q6_K_fp32_len = 4296; +const uint64_t dequant_q6_K_len = 4296; unsigned char dequant_q8_0_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xd2,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, -0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x0f,0x00,0x09,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0c,0x00,0x00,0x00, -0x16,0x00,0x00,0x00,0x53,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, -0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 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gelu_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0x4b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, @@ -14798,500 +8158,6 @@ const uint64_t gelu_f32_len = 1484; unsigned char get_rows_f16_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x77,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, -0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, -0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, -0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00, -0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, -0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, -0x2d,0x00,0x00,0x00,0x55,0x00,0x00,0x00,0x63,0x00,0x00,0x00, -0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, -0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 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-0x68,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x6e,0x00,0x00,0x00, -0x5b,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x72,0x00,0x00,0x00,0x68,0x00,0x00,0x00,0x16,0x00,0x00,0x00, -0x41,0x00,0x06,0x00,0x6d,0x00,0x00,0x00,0x75,0x00,0x00,0x00, -0x65,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x72,0x00,0x00,0x00, -0x3e,0x00,0x03,0x00,0x75,0x00,0x00,0x00,0x60,0x00,0x00,0x00, -0xf9,0x00,0x02,0x00,0x78,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, -0x78,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, - -}; -const uint64_t get_rows_f16_f32_fp32_len = 1932; - -unsigned char get_rows_f16_fp32_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0x7b,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, @@ -15455,21 +8321,19 @@ unsigned char get_rows_f16_fp32_data[] = { 0xf8,0x00,0x02,0x00,0x79,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; -const uint64_t get_rows_f16_fp32_len = 1948; +const uint64_t get_rows_f16_len = 1948; -unsigned char get_rows_q4_0_data[] = { +unsigned char get_rows_f16_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x97,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, +0x7a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, 0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, 0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00, -0x7d,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x55,0x00,0x00,0x00, +0x65,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, 0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00, @@ -15488,297 +8352,75 @@ unsigned char get_rows_q4_0_data[] = { 0x2b,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x2d,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x55,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00, -0x56,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x56,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x57,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 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0x25,0x00,0x00,0x00,0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00, 0xf8,0x00,0x02,0x00,0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, -0x91,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00, +0x78,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00, 0x41,0x00,0x06,0x00,0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00, 0x2d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x1a,0x00,0x00,0x00, 0x3d,0x00,0x04,0x00,0x10,0x00,0x00,0x00,0x32,0x00,0x00,0x00, @@ -15813,258 +8455,41 @@ unsigned char get_rows_q4_0_f32_data[] = { 0x1a,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x80,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x41,0x00,0x00,0x00,0x3f,0x00,0x00,0x00, 0x14,0x00,0x00,0x00,0x86,0x00,0x05,0x00,0x06,0x00,0x00,0x00, -0x45,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x44,0x00,0x00,0x00, -0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00, -0x3a,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x86,0x00,0x05,0x00, -0x06,0x00,0x00,0x00,0x4a,0x00,0x00,0x00,0x48,0x00,0x00,0x00, -0x49,0x00,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 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-0x06,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x80,0x00,0x00,0x00, -0x54,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x50,0x00,0x00,0x00, -0x8c,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x73,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x8d,0x00,0x00,0x00, -0x8c,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x85,0x00,0x00,0x00, -0x8e,0x00,0x00,0x00,0x7d,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x8a,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x8e,0x00,0x00,0x00, -0x8d,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x91,0x00,0x00,0x00, -0xf8,0x00,0x02,0x00,0x91,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, -0x38,0x00,0x01,0x00, -}; -const uint64_t get_rows_q4_0_f32_len = 2404; +0x44,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x16,0x00,0x00,0x00, +0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x47,0x00,0x00,0x00, +0x3a,0x00,0x00,0x00,0x16,0x00,0x00,0x00,0x86,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x48,0x00,0x00,0x00,0x47,0x00,0x00,0x00, +0x16,0x00,0x00,0x00,0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00, 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char get_rows_q4_0_fp32_data[] = { +unsigned char get_rows_q4_0_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0x98,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, @@ -16264,425 +8689,11 @@ unsigned char get_rows_q4_0_fp32_data[] = { 0x8f,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x8f,0x00,0x00,0x00, 0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t get_rows_q4_0_fp32_len = 2372; +const uint64_t get_rows_q4_0_len = 2372; -unsigned char get_rows_q4_1_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x94,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, 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-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, -}; -const uint64_t get_rows_q4_1_f32_len = 2456; - -unsigned char get_rows_q4_1_f32_fp32_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x95,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, @@ -16690,7 +8701,7 @@ unsigned char get_rows_q4_1_f32_fp32_data[] = { 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, 0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, -0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x81,0x00,0x00,0x00, +0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x7c,0x00,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, @@ -16714,91 +8725,92 @@ unsigned char get_rows_q4_1_f32_fp32_data[] = { 0x01,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x56,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 0x48,0x00,0x05,0x00,0x56,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x23,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x05,0x00, -0x56,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x23,0x00,0x00,0x00, -0x04,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x57,0x00,0x00,0x00, -0x06,0x00,0x00,0x00,0x14,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x58,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x58,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x58,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x5a,0x00,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x5a,0x00,0x00,0x00,0x21,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7e,0x00,0x00,0x00, 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0x03,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0x05,0x00,0x00,0x00, -0xf7,0x00,0x03,0x00,0x93,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0xfb,0x00,0x03,0x00,0x0c,0x00,0x00,0x00,0x94,0x00,0x00,0x00, -0xf8,0x00,0x02,0x00,0x94,0x00,0x00,0x00,0x41,0x00,0x05,0x00, +0xf7,0x00,0x03,0x00,0x8e,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0xfb,0x00,0x03,0x00,0x0c,0x00,0x00,0x00,0x8f,0x00,0x00,0x00, +0xf8,0x00,0x02,0x00,0x8f,0x00,0x00,0x00,0x41,0x00,0x05,0x00, 0x0d,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x0c,0x00,0x00,0x00,0x3d,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x7c,0x00,0x04,0x00, @@ -16819,7 +8831,7 @@ unsigned char get_rows_q4_1_f32_fp32_data[] = { 0x23,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x27,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x25,0x00,0x00,0x00, 0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, -0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x93,0x00,0x00,0x00, +0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x8e,0x00,0x00,0x00, 0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x41,0x00,0x06,0x00, 0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x2d,0x00,0x00,0x00, 0x2e,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, @@ -16845,49 +8857,42 @@ unsigned char get_rows_q4_1_f32_fp32_data[] = { 0x45,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, 0x52,0x00,0x00,0x00,0x5e,0x00,0x00,0x00,0x5d,0x00,0x00,0x00, 0x73,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x5f,0x00,0x00,0x00, -0x5e,0x00,0x00,0x00,0x41,0x00,0x07,0x00,0x5c,0x00,0x00,0x00, -0x62,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x45,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, -0x52,0x00,0x00,0x00,0x63,0x00,0x00,0x00,0x62,0x00,0x00,0x00, -0x73,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x64,0x00,0x00,0x00, -0x63,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0x68,0x00,0x00,0x00, -0x69,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, -0x45,0x00,0x00,0x00,0x12,0x00,0x00,0x00,0x4a,0x00,0x00,0x00, -0x3d,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x6a,0x00,0x00,0x00, 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+0x82,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x41,0x00,0x06,0x00,0x83,0x00,0x00,0x00,0x84,0x00,0x00,0x00, +0x7c,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x7f,0x00,0x00,0x00, +0x3e,0x00,0x03,0x00,0x84,0x00,0x00,0x00,0x82,0x00,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x88,0x00,0x00,0x00, +0x7f,0x00,0x00,0x00,0x54,0x00,0x00,0x00,0x51,0x00,0x05,0x00, +0x1c,0x00,0x00,0x00,0x8a,0x00,0x00,0x00,0x78,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x41,0x00,0x06,0x00,0x83,0x00,0x00,0x00, +0x8b,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, +0x88,0x00,0x00,0x00,0x3e,0x00,0x03,0x00,0x8b,0x00,0x00,0x00, +0x8a,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x8e,0x00,0x00,0x00, +0xf8,0x00,0x02,0x00,0x8e,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, +0x38,0x00,0x01,0x00, }; -const uint64_t get_rows_q4_1_f32_fp32_len = 2424; +const uint64_t get_rows_q4_0_f32_len = 2356; -unsigned char get_rows_q4_1_fp32_data[] = { +unsigned char get_rows_q4_1_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0x96,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, @@ -17093,503 +9098,11 @@ unsigned char get_rows_q4_1_fp32_data[] = { 0xf8,0x00,0x02,0x00,0x94,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; -const uint64_t get_rows_q4_1_fp32_len = 2440; +const uint64_t get_rows_q4_1_len = 2440; -unsigned char get_rows_q5_0_data[] = { +unsigned char get_rows_q4_1_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xc2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, 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-0xc2,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x95,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, 0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, 0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, @@ -17597,7 +9110,7 @@ unsigned char get_rows_q5_0_f32_fp32_data[] = { 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, 0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, -0x2d,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0xa5,0x00,0x00,0x00, +0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0x81,0x00,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, @@ -17617,106 +9130,95 @@ unsigned char get_rows_q5_0_f32_fp32_data[] = { 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0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, @@ -18074,485 +9550,11 @@ unsigned char get_rows_q5_0_fp32_data[] = { 0xf8,0x00,0x02,0x00,0xb8,0x00,0x00,0x00,0xfd,0x00,0x01,0x00, 0x38,0x00,0x01,0x00, }; -const uint64_t get_rows_q5_0_fp32_len = 2884; +const uint64_t get_rows_q5_0_len = 2884; -unsigned char get_rows_q5_1_data[] = { +unsigned char get_rows_q5_0_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xb4,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, -0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 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0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, @@ -18560,7 +9562,7 @@ unsigned char get_rows_q5_1_f32_fp32_data[] = { 0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00, 0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, 0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, -0x2d,0x00,0x00,0x00,0x5a,0x00,0x00,0x00,0xa1,0x00,0x00,0x00, +0x2d,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0xa5,0x00,0x00,0x00, 0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, 0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, @@ -18580,101 +9582,106 @@ unsigned char get_rows_q5_1_f32_fp32_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x2d,0x00,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x2d,0x00,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x55,0x00,0x00,0x00,0x06,0x00,0x00,0x00, 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0x0f,0x00,0x00,0x00,0x0e,0x00,0x00,0x00,0x7c,0x00,0x04,0x00, @@ -18695,7 +9702,7 @@ unsigned char get_rows_q5_1_f32_fp32_data[] = { 0x23,0x00,0x00,0x00,0xf7,0x00,0x03,0x00,0x27,0x00,0x00,0x00, 0x00,0x00,0x00,0x00,0xfa,0x00,0x04,0x00,0x25,0x00,0x00,0x00, 0x26,0x00,0x00,0x00,0x27,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, -0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xb3,0x00,0x00,0x00, +0x26,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0xb7,0x00,0x00,0x00, 0xf8,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x41,0x00,0x06,0x00, 0x30,0x00,0x00,0x00,0x31,0x00,0x00,0x00,0x2d,0x00,0x00,0x00, 0x2e,0x00,0x00,0x00,0x1a,0x00,0x00,0x00,0x3d,0x00,0x04,0x00, @@ -18716,77 +9723,80 @@ unsigned char get_rows_q5_1_f32_fp32_data[] = { 0x89,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x4e,0x00,0x00,0x00, 0x41,0x00,0x00,0x00,0x44,0x00,0x00,0x00,0x82,0x00,0x05,0x00, 0x06,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x41,0x00,0x00,0x00, -0x4e,0x00,0x00,0x00,0x41,0x00,0x07,0x00,0x5c,0x00,0x00,0x00, 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-0x2e,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0x3e,0x00,0x03,0x00, -0xb0,0x00,0x00,0x00,0xaf,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, -0xb3,0x00,0x00,0x00,0xf8,0x00,0x02,0x00,0xb3,0x00,0x00,0x00, -0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, -}; -const uint64_t get_rows_q5_1_f32_fp32_len = 2780; +0x52,0x00,0x00,0x00,0x60,0x00,0x00,0x00,0x5f,0x00,0x00,0x00, +0x73,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x61,0x00,0x00,0x00, +0x60,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0x64,0x00,0x00,0x00, +0x65,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, +0x45,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x20,0x00,0x00,0x00, +0x3d,0x00,0x04,0x00,0x53,0x00,0x00,0x00,0x66,0x00,0x00,0x00, +0x65,0x00,0x00,0x00,0x71,0x00,0x04,0x00,0x06,0x00,0x00,0x00, +0x67,0x00,0x00,0x00,0x66,0x00,0x00,0x00,0xc4,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x69,0x00,0x00,0x00,0x67,0x00,0x00,0x00, +0x68,0x00,0x00,0x00,0x41,0x00,0x08,0x00,0x64,0x00,0x00,0x00, +0x6b,0x00,0x00,0x00,0x5c,0x00,0x00,0x00,0x2e,0x00,0x00,0x00, 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+0x7f,0x00,0x00,0x00,0xc5,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x99,0x00,0x00,0x00,0x95,0x00,0x00,0x00,0x98,0x00,0x00,0x00, +0x70,0x00,0x04,0x00,0x1c,0x00,0x00,0x00,0x9a,0x00,0x00,0x00, +0x99,0x00,0x00,0x00,0x50,0x00,0x05,0x00,0x88,0x00,0x00,0x00, +0x9b,0x00,0x00,0x00,0x93,0x00,0x00,0x00,0x9a,0x00,0x00,0x00, +0x83,0x00,0x05,0x00,0x88,0x00,0x00,0x00,0x9f,0x00,0x00,0x00, +0x9b,0x00,0x00,0x00,0xc1,0x00,0x00,0x00,0x8e,0x00,0x05,0x00, +0x88,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x9f,0x00,0x00,0x00, +0x61,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0xa8,0x00,0x00,0x00,0x4f,0x00,0x00,0x00,0x4a,0x00,0x00,0x00, +0x51,0x00,0x05,0x00,0x1c,0x00,0x00,0x00,0xab,0x00,0x00,0x00, +0xa1,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x41,0x00,0x06,0x00, +0xac,0x00,0x00,0x00,0xad,0x00,0x00,0x00,0xa5,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0xa8,0x00,0x00,0x00,0x3e,0x00,0x03,0x00, +0xad,0x00,0x00,0x00,0xab,0x00,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0xb1,0x00,0x00,0x00,0xa8,0x00,0x00,0x00, +0x56,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x1c,0x00,0x00,0x00, +0xb3,0x00,0x00,0x00,0xa1,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x41,0x00,0x06,0x00,0xac,0x00,0x00,0x00,0xb4,0x00,0x00,0x00, +0xa5,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0xb1,0x00,0x00,0x00, +0x3e,0x00,0x03,0x00,0xb4,0x00,0x00,0x00,0xb3,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0xb7,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, +0xb7,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, -unsigned char get_rows_q5_1_fp32_data[] = { +}; +const uint64_t get_rows_q5_0_f32_len = 2868; + +unsigned char get_rows_q5_1_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, 0xb6,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, @@ -19022,61 +10032,63 @@ unsigned char get_rows_q5_1_fp32_data[] = { 0xb4,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t get_rows_q5_1_fp32_len = 2796; +const uint64_t get_rows_q5_1_len = 2796; -unsigned char get_rows_q8_0_data[] = { +unsigned char get_rows_q5_1_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x86,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x09,0x00,0x00,0x00, -0x11,0x00,0x02,0x00,0x27,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -0x51,0x11,0x00,0x00,0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00, -0x0b,0x00,0x06,0x00,0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c, -0x2e,0x73,0x74,0x64,0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00, -0x0e,0x00,0x03,0x00,0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x0f,0x00,0x0a,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x1f,0x00,0x00,0x00,0x2d,0x00,0x00,0x00,0x58,0x00,0x00,0x00, -0x73,0x00,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, -0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00, -0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00, -0x0b,0x00,0x00,0x00,0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00, 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2296; + +unsigned char get_rows_q8_0_f32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0x8a,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x01,0x00,0x00,0x00,0x11,0x00,0x02,0x00,0x51,0x11,0x00,0x00, +0x11,0x00,0x02,0x00,0x60,0x11,0x00,0x00,0x0b,0x00,0x06,0x00, +0x01,0x00,0x00,0x00,0x47,0x4c,0x53,0x4c,0x2e,0x73,0x74,0x64, +0x2e,0x34,0x35,0x30,0x00,0x00,0x00,0x00,0x0e,0x00,0x03,0x00, +0x00,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x0f,0x00,0x0a,0x00, +0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e, +0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00,0x1f,0x00,0x00,0x00, +0x2d,0x00,0x00,0x00,0x58,0x00,0x00,0x00,0x76,0x00,0x00,0x00, +0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00,0x11,0x00,0x00,0x00, +0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x0b,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, +0x1c,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x1d,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00, 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+0x5d,0x00,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0x79,0x00,0x00,0x00,0x4e,0x00,0x00,0x00,0x49,0x00,0x00,0x00, +0x51,0x00,0x05,0x00,0x1c,0x00,0x00,0x00,0x7c,0x00,0x00,0x00, +0x72,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x41,0x00,0x06,0x00, +0x7d,0x00,0x00,0x00,0x7e,0x00,0x00,0x00,0x76,0x00,0x00,0x00, +0x2e,0x00,0x00,0x00,0x79,0x00,0x00,0x00,0x3e,0x00,0x03,0x00, +0x7e,0x00,0x00,0x00,0x7c,0x00,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0x82,0x00,0x00,0x00,0x79,0x00,0x00,0x00, +0x16,0x00,0x00,0x00,0x51,0x00,0x05,0x00,0x1c,0x00,0x00,0x00, +0x84,0x00,0x00,0x00,0x72,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x41,0x00,0x06,0x00,0x7d,0x00,0x00,0x00,0x85,0x00,0x00,0x00, +0x76,0x00,0x00,0x00,0x2e,0x00,0x00,0x00,0x82,0x00,0x00,0x00, +0x3e,0x00,0x03,0x00,0x85,0x00,0x00,0x00,0x84,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0x88,0x00,0x00,0x00,0xf8,0x00,0x02,0x00, +0x88,0x00,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, + +}; +const uint64_t get_rows_q8_0_f32_len = 2280; unsigned char matmul_f16_aligned_l_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index b1e0006bb..14fb89e09 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -1,6 +1,6 @@ #include "ggml-vulkan.h" -#ifdef VK_RUN_TESTS +#ifdef GGML_VULKAN_RUN_TESTS #include #endif @@ -255,6 +255,7 @@ static size_t vk_staging_offset; static vk_buffer vk_sync_staging; static vk_context * vk_ctx; +static vk_context * vk_transfer_ctx; static bool vk_disable; @@ -264,7 +265,7 @@ size_t vk_output_tensor; #endif static vk_pipeline ggml_vk_create_pipeline(const std::string& name, size_t spv_size, const void* spv_data, const std::string& entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, std::vector&& specialization_constants, uint32_t align) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_pipeline(" << name << ", " << entrypoint << ", " << parameter_count << ", " << push_constant_size << ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " << align << ")" << std::endl; #endif GGML_ASSERT(parameter_count > 0); @@ -368,7 +369,7 @@ static vk_pipeline ggml_vk_create_pipeline(const std::string& name, size_t spv_s } static void ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline& pipeline, uint32_t n) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_pipeline_allocate_descriptor_sets(" << pipeline.name << ", " << n << ")" << std::endl; #endif // Check if gc already contains pipeline before adding it @@ -413,14 +414,14 @@ static void ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline& pipeline, uin } static void ggml_vk_pipeline_cleanup(vk_pipeline& pipeline) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_pipeline_cleanup(" << pipeline.name << ")" << std::endl; #endif pipeline.descriptor_set_idx = 0; } static vk::CommandBuffer ggml_vk_create_cmd_buffer(vk_queue& q) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_cmd_buffer()" << std::endl; #endif if (q.cmd_buffers.size() > q.cmd_buffer_idx) { @@ -442,7 +443,7 @@ static vk::CommandBuffer ggml_vk_create_cmd_buffer(vk_queue& q) { } static vk_submission ggml_vk_create_submission(vk_queue& q, std::vector wait_semaphores, std::vector signal_semaphores) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_submission()" << std::endl; #endif vk_submission s; @@ -453,14 +454,14 @@ static vk_submission ggml_vk_create_submission(vk_queue& q, std::vector wait_semaphores, std::vector signal_semaphores) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_sequence_1()" << std::endl; #endif return { ggml_vk_create_submission(q, std::move(wait_semaphores), std::move(signal_semaphores)) }; } static void ggml_vk_submit(vk_context * ctx, vk::Fence fence) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_submit(" << ctx->seqs.size() << ", " << fence << ")" << std::endl; #endif if (ctx->seqs.empty()) { @@ -536,7 +537,7 @@ static void ggml_vk_submit(vk_context * ctx, vk::Fence fence) { } static uint32_t ggml_vk_find_queue_family_index(std::vector& queue_family_props, const vk::QueueFlags& required, const vk::QueueFlags& avoid, int32_t compute_index, uint32_t min_num_queues) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_find_queue_family_index()" << std::endl; #endif const uint32_t qfsize = queue_family_props.size(); @@ -578,7 +579,7 @@ static uint32_t ggml_vk_find_queue_family_index(std::vector= vk_gc.tl_semaphores.size()) { @@ -642,7 +643,7 @@ static vk::Event ggml_vk_create_event() { } static void ggml_vk_queue_cleanup(vk_queue& q) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_queue_cleanup()" << std::endl; #endif // Requires command buffers to be done @@ -652,7 +653,7 @@ static void ggml_vk_queue_cleanup(vk_queue& q) { } static vk_buffer ggml_vk_create_buffer(size_t size, vk::MemoryPropertyFlags req_flags) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_buffer(" << size << ", " << to_string(req_flags) << ")" << std::endl; #endif GGML_ASSERT(size > 0); @@ -743,7 +744,7 @@ static void ggml_vk_destroy_buffer(vk_buffer& buf) { if (buf.size == 0) { return; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_destroy_buffer(" << buf.size << ")" << std::endl; #endif @@ -757,7 +758,7 @@ static vk_subbuffer ggml_vk_subbuffer(vk_buffer& buf) { } static void ggml_vk_sync_buffers(vk_context * ctx) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_sync_buffers()" << std::endl; #endif const std::vector mem_barriers{ { { vk::AccessFlagBits::eMemoryRead | vk::AccessFlagBits::eMemoryWrite }, { vk::AccessFlagBits::eMemoryRead | vk::AccessFlagBits::eMemoryWrite } } }; @@ -773,7 +774,7 @@ static void ggml_vk_sync_buffers(vk_context * ctx) { } static void ggml_vk_wait_events(vk::CommandBuffer& cmd_buffer, std::vector&& events, vk::PipelineStageFlags src_stages, vk::PipelineStageFlags dst_stages) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_wait_events()" << std::endl; #endif if (events.empty()) { @@ -810,7 +811,7 @@ static bool ggml_vk_build_shader(ggml_type type) { } static void ggml_vk_load_shaders() { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_load_shaders()" << std::endl; #endif @@ -849,36 +850,6 @@ static void ggml_vk_load_shaders() { vk_pipeline_matmul_f16_f32_aligned_l = ggml_vk_create_pipeline("matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_len, matmul_f16_f32_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); vk_pipeline_matmul_f16_f32_aligned_m = ggml_vk_create_pipeline("matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_len, matmul_f16_f32_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); vk_pipeline_matmul_f16_f32_aligned_s = ggml_vk_create_pipeline("matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_len, matmul_f16_f32_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - - // Build dequant shaders - vk_pipeline_dequant[GGML_TYPE_F32] = ggml_vk_create_pipeline("f32_to_f16", f32_to_f16_len, f32_to_f16_data, "main", 2, 4 * sizeof(int), {64, 1, 1}, {}, 1); - - vk_pipeline_dequant[GGML_TYPE_F16] = ggml_vk_create_pipeline("dequant_f16", dequant_f16_len, dequant_f16_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q2_K] = ggml_vk_create_pipeline("dequant_q2_K", dequant_q2_K_len, dequant_q2_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q3_K] = ggml_vk_create_pipeline("dequant_q3_K", dequant_q3_K_len, dequant_q3_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_K] = ggml_vk_create_pipeline("dequant_q4_K", dequant_q4_K_len, dequant_q4_K_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_K] = ggml_vk_create_pipeline("dequant_q5_K", dequant_q5_K_len, dequant_q5_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q6_K] = ggml_vk_create_pipeline("dequant_q6_K", dequant_q6_K_len, dequant_q6_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - - // get_rows - vk_pipeline_get_rows[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16", get_rows_f16_len, get_rows_f16_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - - vk_pipeline_get_rows_f32[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); } else { vk_pipeline_matmul_f32_l = ggml_vk_create_pipeline("matmul_f32_l", matmul_f32_l_fp32_len, matmul_f32_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); vk_pipeline_matmul_f32_m = ggml_vk_create_pipeline("matmul_f32_m", matmul_f32_m_fp32_len, matmul_f32_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); @@ -901,36 +872,6 @@ static void ggml_vk_load_shaders() { vk_pipeline_matmul_f16_f32_aligned_l = ggml_vk_create_pipeline("matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_fp32_len, matmul_f16_f32_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); vk_pipeline_matmul_f16_f32_aligned_m = ggml_vk_create_pipeline("matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_fp32_len, matmul_f16_f32_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); vk_pipeline_matmul_f16_f32_aligned_s = ggml_vk_create_pipeline("matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_fp32_len, matmul_f16_f32_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - - // Build dequant shaders - vk_pipeline_dequant[GGML_TYPE_F32] = ggml_vk_create_pipeline("f32_to_f16", f32_to_f16_fp32_len, f32_to_f16_fp32_data, "main", 2, 4 * sizeof(int), {64, 1, 1}, {}, 1); - - vk_pipeline_dequant[GGML_TYPE_F16] = ggml_vk_create_pipeline("dequant_f16", dequant_f16_fp32_len, dequant_f16_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("dequant_q4_0", dequant_q4_0_fp32_len, dequant_q4_0_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("dequant_q4_1", dequant_q4_1_fp32_len, dequant_q4_1_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("dequant_q5_0", dequant_q5_0_fp32_len, dequant_q5_0_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("dequant_q5_1", dequant_q5_1_fp32_len, dequant_q5_1_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("dequant_q8_0", dequant_q8_0_fp32_len, dequant_q8_0_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q2_K] = ggml_vk_create_pipeline("dequant_q2_K", dequant_q2_K_fp32_len, dequant_q2_K_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q3_K] = ggml_vk_create_pipeline("dequant_q3_K", dequant_q3_K_fp32_len, dequant_q3_K_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_K] = ggml_vk_create_pipeline("dequant_q4_K", dequant_q4_K_fp32_len, dequant_q4_K_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_K] = ggml_vk_create_pipeline("dequant_q5_K", dequant_q5_K_fp32_len, dequant_q5_K_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q6_K] = ggml_vk_create_pipeline("dequant_q6_K", dequant_q6_K_fp32_len, dequant_q6_K_fp32_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - - // get_rows - vk_pipeline_get_rows[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16", get_rows_f16_fp32_len, get_rows_f16_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0", get_rows_q4_0_fp32_len, get_rows_q4_0_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1", get_rows_q4_1_fp32_len, get_rows_q4_1_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0", get_rows_q5_0_fp32_len, get_rows_q5_0_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1", get_rows_q5_1_fp32_len, get_rows_q5_1_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0", get_rows_q8_0_fp32_len, get_rows_q8_0_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - - vk_pipeline_get_rows_f32[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16_f32", get_rows_f16_f32_fp32_len, get_rows_f16_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0_f32", get_rows_q4_0_f32_fp32_len, get_rows_q4_0_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1_f32", get_rows_q4_1_f32_fp32_len, get_rows_q4_1_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0_f32", get_rows_q5_0_f32_fp32_len, get_rows_q5_0_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1_f32", get_rows_q5_1_f32_fp32_len, get_rows_q5_1_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0_f32", get_rows_q8_0_f32_fp32_len, get_rows_q8_0_f32_fp32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); } vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_F16] = ggml_vk_create_pipeline("mul_mat_vec_f16_f32", mul_mat_vec_f16_f32_len, mul_mat_vec_f16_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); @@ -945,6 +886,36 @@ static void ggml_vk_load_shaders() { vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_K] = ggml_vk_create_pipeline("mul_mat_vec_q5_K_f32", mul_mat_vec_q5_K_f32_len, mul_mat_vec_q5_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q6_K] = ggml_vk_create_pipeline("mul_mat_vec_q6_K_f32", mul_mat_vec_q6_K_f32_len, mul_mat_vec_q6_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + // dequant shaders + vk_pipeline_dequant[GGML_TYPE_F32] = ggml_vk_create_pipeline("f32_to_f16", f32_to_f16_len, f32_to_f16_data, "main", 2, 4 * sizeof(int), {64, 1, 1}, {}, 1); + + vk_pipeline_dequant[GGML_TYPE_F16] = ggml_vk_create_pipeline("dequant_f16", dequant_f16_len, dequant_f16_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q2_K] = ggml_vk_create_pipeline("dequant_q2_K", dequant_q2_K_len, dequant_q2_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q3_K] = ggml_vk_create_pipeline("dequant_q3_K", dequant_q3_K_len, dequant_q3_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q4_K] = ggml_vk_create_pipeline("dequant_q4_K", dequant_q4_K_len, dequant_q4_K_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q5_K] = ggml_vk_create_pipeline("dequant_q5_K", dequant_q5_K_len, dequant_q5_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + vk_pipeline_dequant[GGML_TYPE_Q6_K] = ggml_vk_create_pipeline("dequant_q6_K", dequant_q6_K_len, dequant_q6_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + + // get_rows + vk_pipeline_get_rows[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16", get_rows_f16_len, get_rows_f16_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + + vk_pipeline_get_rows_f32[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_get_rows_f32[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + vk_pipeline_matmul_split_k_reduce = ggml_vk_create_pipeline("split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256, 1, 1}, {}, 1); vk_pipeline_mul_mat_vec_p021_f16_f32 = ggml_vk_create_pipeline("mul_mat_vec_p021_f16_f32", mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {}, 1); @@ -983,7 +954,7 @@ static void ggml_vk_load_shaders() { } void ggml_vk_init() { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_init()" << std::endl; #endif static bool initialized = false; @@ -999,17 +970,17 @@ void ggml_vk_init() { vk::ApplicationInfo app_info{ "ggml-vulkan", 1, nullptr, 0, VK_API_VERSION }; const std::vector layers = { -#ifdef VK_VALIDATE +#ifdef GGML_VULKAN_VALIDATE "VK_LAYER_KHRONOS_validation", #endif }; const std::vector extensions = { -#ifdef VK_VALIDATE +#ifdef GGML_VULKAN_VALIDATE "VK_EXT_validation_features", #endif }; vk::InstanceCreateInfo instance_create_info(vk::InstanceCreateFlags(), &app_info, layers, extensions); -#ifdef VK_VALIDATE +#ifdef GGML_VULKAN_VALIDATE const std::vector features_enable = { vk::ValidationFeatureEnableEXT::eBestPractices }; vk::ValidationFeaturesEXT validation_features = { features_enable, @@ -1120,7 +1091,7 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; device_extensions.push_back("VK_KHR_16bit_storage"); -#ifdef VK_VALIDATE +#ifdef GGML_VULKAN_VALIDATE device_extensions.push_back("VK_KHR_shader_non_semantic_info"); #endif @@ -1154,6 +1125,7 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; vk_fence = vk_device.device.createFence({}); vk_ctx = nullptr; + vk_transfer_ctx = nullptr; vk_disable = false; @@ -1166,7 +1138,7 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; } static vk_pipeline* ggml_vk_get_to_fp16(ggml_type type) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_get_to_fp16()" << std::endl; #endif switch (type) { @@ -1190,7 +1162,7 @@ static vk_pipeline* ggml_vk_get_to_fp16(ggml_type type) { } static vk_pipeline* ggml_vk_get_dequantize_mul_mat_vec(ggml_type type) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_get_dequantize_mul_mat_vec()" << std::endl; #endif switch (type) { @@ -1219,7 +1191,7 @@ static vk_pipeline* ggml_vk_get_dequantize_mul_mat_vec(ggml_type type) { static vk_buffer g_vk_buffer_pool[MAX_VK_BUFFERS]; static vk_buffer ggml_vk_pool_malloc(size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_pool_malloc(" << size << ")" << std::endl; #endif int best_i = -1; @@ -1253,7 +1225,7 @@ static vk_buffer ggml_vk_pool_malloc(size_t size) { } static void ggml_vk_pool_free(vk_buffer& buffer) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_pool_free(" << buffer.size << ")" << std::endl; #endif for (int i = 0; i < MAX_VK_BUFFERS; ++i) { @@ -1286,7 +1258,7 @@ static vk_buffer ggml_vk_create_buffer_temp(size_t size) { } static void * ggml_vk_host_malloc(size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_host_malloc(" << size << ")" << std::endl; #endif vk_buffer buf = ggml_vk_create_buffer(size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); @@ -1309,7 +1281,7 @@ static void ggml_vk_host_free(void* ptr) { if (ptr == nullptr) { return; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_host_free(" << ptr << ")" << std::endl; #endif vk_buffer* buf = nullptr; @@ -1363,7 +1335,7 @@ static void ggml_vk_dispatch_pipeline(vk_context * ctx, vk_pipeline& pipeline, s const uint32_t wg0 = CEIL_DIV(elements[0], pipeline.wg_denoms[0]); const uint32_t wg1 = CEIL_DIV(elements[1], pipeline.wg_denoms[1]); const uint32_t wg2 = CEIL_DIV(elements[2], pipeline.wg_denoms[2]); -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_dispatch_pipeline(" << pipeline.name << ", (" << wg0 << "," << wg1 << "," << wg2 << "))" << std::endl; #endif std::vector descriptor_buffer_infos; @@ -1398,7 +1370,7 @@ static void ggml_vk_end_submission(vk_submission& s, std::vector w } static void ggml_vk_ctx_end(vk_context * ctx) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_ctx_end(" << ctx << ", " << ctx->seqs.size() << ")" << std::endl; #endif if (ctx->s == nullptr) { @@ -1410,7 +1382,7 @@ static void ggml_vk_ctx_end(vk_context * ctx) { } static void ggml_vk_ctx_begin(vk_context * ctx) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_ctx_begin(" << ctx << ")" << std::endl; #endif if (ctx->s != nullptr) { @@ -1441,7 +1413,7 @@ static void ensure_sync_staging_buffer(size_t size) { } static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size_t offset, const ggml_tensor * tensor, bool sync_staging = false) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_nc_async(" << tensor << ")" << std::endl; #endif GGML_ASSERT(!ggml_is_contiguous(tensor)); @@ -1548,7 +1520,7 @@ static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size } static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height, bool sync_staging = false) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_2d_async(" << width << ", " << height << ")" << std::endl; #endif // Buffer is already mapped @@ -1582,7 +1554,7 @@ static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size ctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices); return; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "STAGING" << std::endl; #endif @@ -1619,14 +1591,14 @@ static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size } static void ggml_vk_buffer_write_async(vk_context * ctx, vk_buffer* dst, size_t offset, const void * src, size_t size, bool sync_staging = false) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_async(" << size << ")" << std::endl; #endif return ggml_vk_buffer_write_2d_async(ctx, dst, offset, src, size, size, 1, sync_staging); } static void ggml_vk_buffer_write_2d(vk_buffer* dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_2d(" << width << ", " << height << ")" << std::endl; #endif // Buffer is already mapped @@ -1653,14 +1625,14 @@ static void ggml_vk_buffer_write_2d(vk_buffer* dst, size_t offset, const void * } static void ggml_vk_buffer_write(vk_buffer* dst, size_t offset, const void * src, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write(" << size << ")" << std::endl; #endif ggml_vk_buffer_write_2d(dst, offset, src, 0, size, 1); } static void ggml_vk_buffer_read_2d_async(vk_context * ctx, vk_buffer* src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_read_2d_async(offset=" << offset << ", width=" << width << ", height=" << height << ")" << std::endl; #endif GGML_ASSERT(width > 0); @@ -1693,7 +1665,7 @@ static void ggml_vk_buffer_read_2d_async(vk_context * ctx, vk_buffer* src, size_ return; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "STAGING" << std::endl; #endif @@ -1722,7 +1694,7 @@ static void ggml_vk_buffer_read_async(vk_context * ctx, vk_buffer* src, size_t o } static void ggml_vk_buffer_read(vk_buffer* src, size_t offset, void * dst, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_read(" << offset << ", " << size << ")" << std::endl; #endif if(src->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { @@ -1746,7 +1718,7 @@ static void ggml_vk_buffer_read(vk_buffer* src, size_t offset, void * dst, size_ } static void ggml_vk_buffer_copy_async(vk_context * ctx, vk_buffer * dst, size_t dst_offset, vk_buffer * src, size_t src_offset, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_copy_async(" << size << ")" << std::endl; #endif VkBufferCopy bc{ src_offset, dst_offset, size }; @@ -1755,7 +1727,7 @@ static void ggml_vk_buffer_copy_async(vk_context * ctx, vk_buffer * dst, size_t } static void ggml_vk_buffer_copy(vk_buffer * dst, size_t dst_offset, vk_buffer * src, size_t src_offset, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_copy(" << size << ")" << std::endl; #endif VkBufferCopy bc{ src_offset, dst_offset, size }; @@ -1771,7 +1743,7 @@ static void ggml_vk_buffer_copy(vk_buffer * dst, size_t dst_offset, vk_buffer * } static void ggml_vk_buffer_memset(vk_buffer* dst, size_t offset, uint32_t c, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_memset(" << offset << ", " << c << ", " << size << ")" << std::endl; #endif vk_context * ctx = ggml_vk_create_context(vk_device.transfer_queue); @@ -1785,7 +1757,7 @@ static void ggml_vk_buffer_memset(vk_buffer* dst, size_t offset, uint32_t c, siz } static void ggml_vk_h2d_tensor_2d(vk_context * ctx, vk_buffer * dst, size_t offset, const ggml_tensor * src, uint64_t i3, uint64_t i2, uint64_t i1) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_h2d_tensor_2d(dst=" << dst << ", offset=" << offset << ", src=" << src << ", i3=" << i3 << ", i2=" << i2 << ", i1=" << i1 << ")" << std::endl; #endif const uint64_t ne0 = src->ne[0]; @@ -1815,7 +1787,7 @@ static void ggml_vk_h2d_tensor_2d(vk_context * ctx, vk_buffer * dst, size_t offs } static void ggml_vk_d2h_tensor_2d(vk_context * ctx, vk_buffer * src, size_t offset, const ggml_tensor * dst) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_d2h_tensor_2d()" << std::endl; #endif const uint64_t ne0 = dst->ne[0]; @@ -1841,24 +1813,24 @@ static void ggml_vk_d2h_tensor_2d(vk_context * ctx, vk_buffer * src, size_t offs } static uint32_t ggml_vk_guess_split_k(int m, int n, int k) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_guess_split_k(" << m << ", " << n << ", " << k << ")"; #endif if (k > 128 && (m < 128 || n < 128) && m > 2 && n > 2) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " = 4" << std::endl; #endif return 4; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " = 1" << std::endl; #endif return 1; } static uint32_t ggml_vk_guess_matmul_pipeline_align(int m, int n) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ")" << std::endl; #endif if (m <= 32 || n <= 32) { @@ -1871,41 +1843,41 @@ static uint32_t ggml_vk_guess_matmul_pipeline_align(int m, int n) { } static vk_pipeline* ggml_vk_guess_matmul_pipeline(bool bit16_x, bool bit16_y, int m, int n, bool aligned) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_guess_matmul_pipeline(" << bit16_x << ", " << bit16_y << ", " << m << ", " << n << ", " << aligned << ")"; #endif if (bit16_x && bit16_y) { - if (m <= 32 || n <= 32) { -#ifdef VK_DEBUG + if (vk_device.vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { +#ifdef GGML_VULKAN_DEBUG std::cerr << " S" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f16_aligned_s : &vk_pipeline_matmul_f16_s; } if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " M" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f16_aligned_m : &vk_pipeline_matmul_f16_m; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " L" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f16_aligned_l : &vk_pipeline_matmul_f16_l; } if (bit16_x && !bit16_y) { - if (m <= 32 || n <= 32) { -#ifdef VK_DEBUG + if (vk_device.vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { +#ifdef GGML_VULKAN_DEBUG std::cerr << " S" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f16_f32_aligned_s : &vk_pipeline_matmul_f16_f32_s; } if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " M" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f16_f32_aligned_m : &vk_pipeline_matmul_f16_f32_m; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " L" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f16_f32_aligned_l : &vk_pipeline_matmul_f16_f32_l; @@ -1914,30 +1886,30 @@ static vk_pipeline* ggml_vk_guess_matmul_pipeline(bool bit16_x, bool bit16_y, in GGML_ASSERT(false); } - if (m <= 32 || n <= 32) { -#ifdef VK_DEBUG + if (vk_device.vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { +#ifdef GGML_VULKAN_DEBUG std::cerr << " S" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f32_aligned_s : &vk_pipeline_matmul_f32_s; } if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " M" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f32_aligned_m : &vk_pipeline_matmul_f32_m; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << " L" << std::endl; #endif return aligned ? &vk_pipeline_matmul_f32_aligned_l : &vk_pipeline_matmul_f32_l; } static void ggml_vk_matmul(vk_context * ctx, vk_pipeline& pipeline, vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer, uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3, uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_matmul(a: (" << a.buffer.buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer.buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer.buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << split_k_buffer.buffer.buffer << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl; #endif + ggml_vk_sync_buffers(ctx); if (split_k == 1) { - ggml_vk_sync_buffers(ctx); const std::array pc = { m, n, k, stride_a, stride_b, stride_d, k, ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d }; ggml_vk_dispatch_pipeline(ctx, pipeline, { a, b, d }, pc.size() * sizeof(uint32_t), pc.data(), { m, n, batch }); return; @@ -1945,10 +1917,6 @@ static void ggml_vk_matmul(vk_context * ctx, vk_pipeline& pipeline, vk_subbuffer GGML_ASSERT(batch_stride_d == m * n); - // Synchronize the two submissions - ggml_vk_sync_buffers(ctx); - ctx->s->buffer.fillBuffer(split_k_buffer.buffer.buffer, 0, split_k_buffer.size, 0); - ggml_vk_sync_buffers(ctx); const std::array pc1 = { m, n, k, stride_a, stride_b, stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d }; // Make sure enough workgroups get assigned for split k to work ggml_vk_dispatch_pipeline(ctx, pipeline, { a, b, split_k_buffer }, pc1.size() * sizeof(uint32_t), pc1.data(), { (CEIL_DIV(m, pipeline.wg_denoms[0]) * pipeline.wg_denoms[0]) * split_k, n, batch }); @@ -1980,7 +1948,7 @@ static vk_pipeline * ggml_vk_get_cpy_pipeline(ggml_type from, ggml_type to) { } static void ggml_vk_cpy_to_contiguous(vk_context * ctx, vk_pipeline * pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out, ggml_type buffer_type, bool aligned=true) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", backend=" << tensor->backend << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), "; std::cerr << "buffer in size=" << in.buffer.size << ", buffer out size=" << out.buffer.size << ")" << std::endl; #endif @@ -2002,7 +1970,7 @@ static void ggml_vk_cpy_to_contiguous(vk_context * ctx, vk_pipeline * pipeline, } static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; @@ -2186,7 +2154,7 @@ static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, co } static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; @@ -2366,7 +2334,7 @@ static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0 } static void ggml_vk_mul_mat_vec_p021_f16_f32(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_p021_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; @@ -2455,7 +2423,7 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(vk_context * ctx, const ggml_tensor } static void ggml_vk_mul_mat_vec_nc_f16_f32(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_nc_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", backend=" << dst->backend << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3] << "),)" << std::endl; @@ -2561,7 +2529,7 @@ static bool ggml_vk_can_mul_mat(const ggml_tensor * src0, const ggml_tensor * sr } static void ggml_vk_mul_mat(vk_context * ctx, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat(" << src0 << ", " << src1 << ", " << dst << ")" << std::endl; #endif if (src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { @@ -2774,7 +2742,7 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * template static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op, const PC&& pc) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; if (src1 != nullptr) { std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; @@ -3095,7 +3063,7 @@ static void ggml_vk_nop(vk_context * ctx, const ggml_tensor * src0, ggml_tensor } } -#ifdef VK_RUN_TESTS +#ifdef GGML_VULKAN_RUN_TESTS static void ggml_vk_print_matrix_area(const void * data, ggml_type type, int ne0, int ne1, int i0, int i1, int i2) { if (type != GGML_TYPE_F32 && type != GGML_TYPE_F16) { return; @@ -3129,7 +3097,7 @@ static void ggml_vk_print_matrix_area(const void * data, ggml_type type, int ne0 template static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size_t num_it, int split_k, int shader_size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_test_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << shader_size << ")" << std::endl; #endif const size_t x_ne = m * k * batch; @@ -3520,7 +3488,7 @@ static void ggml_vk_test_h2d_nc(size_t ne0, size_t ne1, size_t ne2, size_t ne3) } static void ggml_vk_test_transfer(size_t ne, bool pinned) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_test_transfer(" << ne << ")" << std::endl; #endif // Check transfers are correct @@ -3600,10 +3568,103 @@ static void ggml_vk_test_transfer(size_t ne, bool pinned) { free(y); } } + +static void ggml_vk_test_dequant(size_t ne, ggml_type quant) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_test_dequant(" << ne << ")" << std::endl; +#endif + const size_t x_sz = sizeof(float) * ne; + const size_t x_sz_f16 = sizeof(ggml_fp16_t) * ne; + const size_t qx_sz = ne * ggml_type_size(quant)/ggml_blck_size(quant); + float * x = (float *) malloc(x_sz); + void * qx = malloc(qx_sz); + vk_buffer qx_buf = ggml_vk_create_buffer_check(qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer x_buf = ggml_vk_create_buffer_check(x_sz_f16, vk::MemoryPropertyFlagBits::eDeviceLocal); + ggml_fp16_t * x_chk = (ggml_fp16_t *) malloc(x_sz_f16); + + for (size_t i = 0; i < ne; i++) { + x[i] = rand() / (float)RAND_MAX; + } + + std::vector hist_cur(1 << 4, 0); + + vk_pipeline& p = vk_pipeline_dequant[quant]; + + switch(quant) { + case GGML_TYPE_Q4_0: + ggml_quantize_q4_0(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q4_1: + ggml_quantize_q4_1(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q5_0: + ggml_quantize_q5_0(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q5_1: + ggml_quantize_q4_1(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q8_0: + ggml_quantize_q8_0(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q2_K: + ggml_quantize_q2_K(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q3_K: + ggml_quantize_q3_K(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q4_K: + ggml_quantize_q4_K(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q5_K: + ggml_quantize_q5_K(x, qx, ne, ne, hist_cur.data()); + break; + case GGML_TYPE_Q6_K: + ggml_quantize_q6_K(x, qx, ne, ne, hist_cur.data()); + break; + default: + GGML_ASSERT(false); + } + + ggml_vk_pipeline_allocate_descriptor_sets(p, 1); + + ggml_vk_buffer_write(&qx_buf, 0, qx, qx_sz); + + vk_context * ctx = ggml_vk_create_context(vk_device.compute_queue); + ggml_vk_ctx_begin(ctx); + const std::vector pc = { 1, (int)ne, (int)ne, (int)ne }; + ggml_vk_sync_buffers(ctx); + ggml_vk_dispatch_pipeline(ctx, p, { { qx_buf, 0, qx_sz }, { x_buf, 0, x_sz_f16 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)ne, 1, 1}); + ggml_vk_ctx_end(ctx); + + auto begin = std::chrono::high_resolution_clock::now(); + + ggml_vk_submit(ctx, vk_fence); + VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); + vk_device.device.resetFences({ vk_fence }); + + auto end = std::chrono::high_resolution_clock::now(); + + double ms_dequant = std::chrono::duration_cast(end-begin).count() / 1000.0; + ggml_vk_buffer_read(&x_buf, 0, x_chk, x_sz_f16); + + double avg_err = 0.0; + for (size_t i = 0; i < ne; i++) { + avg_err += std::fabs(x[i] - ggml_fp16_to_fp32(x_chk[i])); + } + + std::cerr << "TEST DEQUANT " << ggml_type_name(quant) << " time=" << ms_dequant << "ms avg_err=" << avg_err / ne << std::endl; + + ggml_vk_destroy_buffer(x_buf); + ggml_vk_destroy_buffer(qx_buf); + + free(x); + free(qx); + free(x_chk); +} #endif static ggml_tensor_extra_gpu * ggml_vk_tensor_create_extra(ggml_tensor * tensor) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_extra(" << tensor << " (" << tensor->name << ", " << ggml_op_name(tensor->op) << "))" << std::endl; #endif ggml_tensor_extra_gpu * extra = new ggml_tensor_extra_gpu; @@ -3627,7 +3688,7 @@ static ggml_tensor * ggml_vk_find_last_use(const ggml_tensor * node, ggml_cgraph } void ggml_vk_preallocate_buffers_graph(ggml_tensor * node){ -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_preallocate_buffers_graph(" << node << ")" << std::endl; #endif const bool any_on_device = node->backend == GGML_BACKEND_GPU @@ -3746,15 +3807,26 @@ void ggml_vk_preallocate_buffers() { if (vk_disable) { return; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_preallocate_buffers()" << std::endl; std::cerr << "qx_size: " << vk_prealloc_size_qx << " qy_size: " << vk_prealloc_size_qy << " x_size: " << vk_prealloc_size_x << " y_size: " << vk_prealloc_size_y << " split_k_size: " << vk_prealloc_size_split_k << std::endl; #endif -#if defined(VK_RUN_TESTS) +#if defined(GGML_VULKAN_RUN_TESTS) vk_staging = ggml_vk_create_buffer_check(100ul * 1024ul * 1024ul, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); ggml_vk_test_transfer(8192 * 1000, false); ggml_vk_test_transfer(8192 * 1000, true); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q4_0); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q4_1); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q5_0); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q5_1); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q8_0); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q2_K); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q3_K); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q4_K); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q5_K); + ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q6_K); + const std::vector vals { 8, 8, 8, 100, 46, 576, @@ -3845,7 +3917,7 @@ void ggml_vk_build_graph(ggml_tensor * node, bool last_node){ return; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_build_graph(" << node << ", " << ggml_op_name(node->op) << ")" << std::endl; #endif vk_semaphore_idx = 0; @@ -4068,7 +4140,7 @@ bool ggml_vk_compute_forward(ggml_compute_params * params, ggml_tensor * tensor) return true; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_compute_forward(" << tensor << ", name=" << tensor->name << ", op=" << ggml_op_name(tensor->op) << ", type=" << tensor->type << ", backend=" << tensor->backend << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << ", view_src=" << tensor->view_src << ", view_offs=" << tensor->view_offs << ")" << std::endl; #endif @@ -4111,7 +4183,7 @@ void ggml_vk_graph_cleanup() { if (vk_disable) { return; } -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_graph_cleanup()" << std::endl; #endif for (auto& buffer : vk_gc.temp_buffers) { @@ -4150,7 +4222,7 @@ void ggml_vk_graph_cleanup() { } static void ggml_vk_cleanup() { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_cleanup()" << std::endl; #endif ggml_vk_destroy_buffer(vk_prealloc_x); @@ -4234,7 +4306,7 @@ GGML_CALL static void * ggml_backend_vk_buffer_get_base(ggml_backend_buffer_t bu } GGML_CALL static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_buffer_init_tensor(" << buffer << " (" << buffer->context << "), " << tensor << ")" << std::endl; #endif ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; @@ -4254,7 +4326,7 @@ GGML_CALL static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t b } GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_buffer_set_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl; #endif GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); @@ -4267,7 +4339,7 @@ GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t bu } GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_buffer_get_tensor(" << buffer << ", " << tensor << ", " << data << ", " << offset << ", " << size << ")" << std::endl; #endif GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); @@ -4323,7 +4395,7 @@ GGML_CALL static const char * ggml_backend_vk_buffer_type_name(ggml_backend_buff } GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_buffer_type_alloc_buffer(" << size << ")" << std::endl; #endif vk_buffer dev_buffer = ggml_vk_create_buffer_device(size); @@ -4467,7 +4539,7 @@ GGML_CALL static ggml_backend_buffer_type_t ggml_backend_vk_get_default_buffer_t } GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_set_tensor_async(" << size << ")" << std::endl; #endif GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type() || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); @@ -4475,19 +4547,19 @@ GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, g ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - if (vk_ctx == nullptr) { + if (vk_transfer_ctx == nullptr) { // Initialize new transfer context - vk_ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(vk_ctx); + vk_transfer_ctx = ggml_vk_create_context(vk_device.transfer_queue); + ggml_vk_ctx_begin(vk_transfer_ctx); } - ggml_vk_buffer_write_async(vk_ctx, &extra->buffer_gpu, extra->offset + offset, data, size); + ggml_vk_buffer_write_async(vk_transfer_ctx, &extra->buffer_gpu, extra->offset + offset, data, size); UNUSED(backend); } GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_get_tensor_async(" << size << ")" << std::endl; #endif GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type() || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); @@ -4495,32 +4567,32 @@ GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, c ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - if (vk_ctx == nullptr) { + if (vk_transfer_ctx == nullptr) { // Initialize new transfer context - vk_ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(vk_ctx); + vk_transfer_ctx = ggml_vk_create_context(vk_device.transfer_queue); + ggml_vk_ctx_begin(vk_transfer_ctx); } - ggml_vk_buffer_read_async(vk_ctx, &extra->buffer_gpu, extra->offset + offset, data, size); + ggml_vk_buffer_read_async(vk_transfer_ctx, &extra->buffer_gpu, extra->offset + offset, data, size); UNUSED(backend); } GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_cpy_tensor_async()" << std::endl; #endif if ((dst->buffer->buft == ggml_backend_vk_buffer_type() || dst->buffer->buft == ggml_backend_vk_host_buffer_type()) && ggml_backend_buffer_is_vk(src->buffer)) { ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - if (vk_ctx == nullptr) { + if (vk_transfer_ctx == nullptr) { // Initialize new transfer context - vk_ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(vk_ctx); + vk_transfer_ctx = ggml_vk_create_context(vk_device.transfer_queue); + ggml_vk_ctx_begin(vk_transfer_ctx); } - ggml_vk_buffer_copy_async(vk_ctx, &src_extra->buffer_gpu, src_extra->offset, &dst_extra->buffer_gpu, dst_extra->offset, ggml_nbytes(src)); + ggml_vk_buffer_copy_async(vk_transfer_ctx, &src_extra->buffer_gpu, src_extra->offset, &dst_extra->buffer_gpu, dst_extra->offset, ggml_nbytes(src)); return true; } @@ -4530,28 +4602,28 @@ GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, c } GGML_CALL static void ggml_backend_vk_synchronize(ggml_backend_t backend) { -#ifdef VK_DEBUG +#ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_synchronize()" << std::endl; #endif - if(vk_ctx == nullptr) { + if(vk_transfer_ctx == nullptr) { return; } - ggml_vk_ctx_end(vk_ctx); + ggml_vk_ctx_end(vk_transfer_ctx); - for (auto& cpy : vk_ctx->in_memcpys) { + for (auto& cpy : vk_transfer_ctx->in_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - ggml_vk_submit(vk_ctx, vk_fence); + ggml_vk_submit(vk_transfer_ctx, vk_fence); VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_backend_vk_synchronize waitForFences"); vk_device.device.resetFences({ vk_fence }); - for (auto& cpy : vk_ctx->out_memcpys) { + for (auto& cpy : vk_transfer_ctx->out_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - vk_ctx = nullptr; + vk_transfer_ctx = nullptr; UNUSED(backend); } diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py index 67981a751..4abb0383f 100644 --- a/ggml_vk_generate_shaders.py +++ b/ggml_vk_generate_shaders.py @@ -157,19 +157,10 @@ struct block_q6_K # Dequant functions shader_f16_dequant_func = """ -#define DEQUANT_FUNC f16vec2 v = f16vec2(data_a[ib + 0], data_a[ib + 1]); -""" -shader_f16_dequant_func_compat = """ #define DEQUANT_FUNC vec2 v = vec2(data_a[ib + 0], data_a[ib + 1]); """ shader_q4_0_dequant_func = """ -#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \ -const uint8_t vui = data_a[ib].qs[iqs]; \ -f16vec2 v = f16vec2(vui & 0xF, vui >> 4); \ -v = (v - 8.0hf)*d; -""" -shader_q4_0_dequant_func_compat = """ #define DEQUANT_FUNC const float d = float(data_a[ib].d); \ const uint vui = uint(data_a[ib].qs[iqs]); \ vec2 v = vec2(vui & 0xF, vui >> 4); \ @@ -177,13 +168,6 @@ v = (v - 8.0f)*d; """ shader_q4_1_dequant_func = """ -#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \ -const float16_t m = data_a[ib].m; \ -const uint8_t vui = data_a[ib].qs[iqs]; \ -f16vec2 v = f16vec2(vui & 0xF, vui >> 4); \ -v = v*d + m; -""" -shader_q4_1_dequant_func_compat = """ #define DEQUANT_FUNC const float d = float(data_a[ib].d); \ const float m = float(data_a[ib].m); \ const uint vui = uint(data_a[ib].qs[iqs]); \ @@ -192,14 +176,6 @@ v = v*d + m; """ shader_q5_0_dequant_func = """ -#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \ -const uint uint_qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0]; \ -const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); \ -const uint8_t vui = data_a[ib].qs[iqs]; \ -f16vec2 v = f16vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y); \ -v = (v - 16.0hf) * d; -""" -shader_q5_0_dequant_func_compat = """ #define DEQUANT_FUNC const float d = float(data_a[ib].d); \ const uint uint_qh = uint(data_a[ib].qh[1]) << 16 | data_a[ib].qh[0]; \ const ivec2 qh = ivec2(((uint_qh >> iqs) << 4) & 0x10, (uint_qh >> (iqs + 12)) & 0x10); \ @@ -209,14 +185,6 @@ v = (v - 16.0f) * d; """ shader_q5_1_dequant_func = """ -#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \ -const float16_t m = data_a[ib].m; \ -const ivec2 qh = ivec2(((data_a[ib].qh >> iqs) << 4) & 0x10, (data_a[ib].qh >> (iqs + 12)) & 0x10); \ -const uint8_t vui = data_a[ib].qs[iqs]; \ -f16vec2 v = f16vec2((vui & 0xF) | qh.x, (vui >> 4) | qh.y); \ -v = v*d + m; -""" -shader_q5_1_dequant_func_compat = """ #define DEQUANT_FUNC const float d = float(data_a[ib].d); \ const float m = float(data_a[ib].m); \ const ivec2 qh = ivec2(((data_a[ib].qh >> iqs) << 4) & 0x10, (data_a[ib].qh >> (iqs + 12)) & 0x10); \ @@ -226,11 +194,6 @@ v = v*d + m; """ shader_q8_0_dequant_func = """ -#define DEQUANT_FUNC const float16_t d = data_a[ib].d; \ -f16vec2 v = f16vec2(data_a[ib].qs[iqs], data_a[ib].qs[iqs + 1]); \ -v = v * d; -""" -shader_q8_0_dequant_func_compat = """ #define DEQUANT_FUNC const float d = float(data_a[ib].d); \ vec2 v = vec2(int(data_a[ib].qs[iqs]), int(data_a[ib].qs[iqs + 1])); \ v = v * d; @@ -2110,7 +2073,7 @@ lock = asyncio.Lock() shader_fnames = [] -async def string_to_spv(name, code, defines, fp16): +async def string_to_spv(name, code, defines, fp16=True): f = NamedTemporaryFile(mode="w", delete=False) f.write(code) f.flush() @@ -2200,64 +2163,6 @@ async def main(): tasks.append(string_to_spv("matmul_f16_f32_aligned_m", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) tasks.append(string_to_spv("matmul_f16_f32_aligned_s", "".join(stream), {"LOAD_VEC": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) - # Build dequant shaders - tasks.append(string_to_spv("f32_to_f16", f32_to_f16_src, {}, fp16)) - - for i in range(0, VK_NUM_TYPES): - stream.clear() - - stream.extend((dequant_head, shader_int8_ext, shader_float_type)) - - if i == GGML_TYPE_F16: - stream.extend((shader_f16_defines, shader_f16_dequant_func_compat if not fp16 else shader_f16_dequant_func, dequant_body)) - elif i == GGML_TYPE_Q4_0: - stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func_compat if not fp16 else shader_q4_0_dequant_func, dequant_body)) - elif i == GGML_TYPE_Q4_1: - stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func_compat if not fp16 else shader_q4_1_dequant_func, dequant_body)) - elif i == GGML_TYPE_Q5_0: - stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func_compat if not fp16 else shader_q5_0_dequant_func, dequant_body)) - elif i == GGML_TYPE_Q5_1: - stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func_compat if not fp16 else shader_q5_1_dequant_func, dequant_body)) - elif i == GGML_TYPE_Q8_0: - stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func_compat if not fp16 else shader_q8_0_dequant_func, dequant_body)) - elif i == GGML_TYPE_Q2_K: - stream.extend((shader_q2_K_defines, dequant_q2_K_body)) - elif i == GGML_TYPE_Q3_K: - stream.extend((shader_q3_K_defines, dequant_q3_K_body)) - elif i == GGML_TYPE_Q4_K: - stream.extend((shader_q4_K_defines, dequant_q4_K_body)) - elif i == GGML_TYPE_Q5_K: - stream.extend((shader_q5_K_defines, dequant_q5_K_body)) - elif i == GGML_TYPE_Q6_K: - stream.extend((shader_q6_K_defines, dequant_q6_K_body)) - else: - continue - - tasks.append(string_to_spv(f"dequant_{type_names[i]}", "".join(stream), {"D_TYPE": "float16_t"}, fp16)) - - # get_rows - for i in range(0, VK_NUM_TYPES): - stream.clear() - stream.extend((generic_head, shader_int8_ext, shader_float_type)) - - if i == GGML_TYPE_F16: - stream.extend((shader_f16_defines, shader_f16_dequant_func_compat if not fp16 else shader_f16_dequant_func, get_rows_body)) - elif i == GGML_TYPE_Q4_0: - stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func_compat if not fp16 else shader_q4_0_dequant_func, get_rows_body)) - elif i == GGML_TYPE_Q4_1: - stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func_compat if not fp16 else shader_q4_1_dequant_func, get_rows_body)) - elif i == GGML_TYPE_Q5_0: - stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func_compat if not fp16 else shader_q5_0_dequant_func, get_rows_body)) - elif i == GGML_TYPE_Q5_1: - stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func_compat if not fp16 else shader_q5_1_dequant_func, get_rows_body)) - elif i == GGML_TYPE_Q8_0: - stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func_compat if not fp16 else shader_q8_0_dequant_func, get_rows_body)) - else: - continue - - tasks.append(string_to_spv(f"get_rows_{type_names[i]}", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float16_t"}, fp16)) - tasks.append(string_to_spv(f"get_rows_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float"}, fp16)) - # Shaders where precision is needed, so no fp16 version # mul mat vec @@ -2266,17 +2171,17 @@ async def main(): stream.extend((mul_mat_vec_head, shader_int8_ext, shader_f32)) if i == GGML_TYPE_F16: - stream.extend((shader_f16_defines, shader_f16_dequant_func_compat, mul_mat_vec_body)) + stream.extend((shader_f16_defines, shader_f16_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q4_0: - stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func_compat, mul_mat_vec_body)) + stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q4_1: - stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func_compat, mul_mat_vec_body)) + stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q5_0: - stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func_compat, mul_mat_vec_body)) + stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q5_1: - stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func_compat, mul_mat_vec_body)) + stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q8_0: - stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func_compat, mul_mat_vec_body)) + stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func, mul_mat_vec_body)) elif i == GGML_TYPE_Q2_K: stream.extend((shader_q2_K_defines, mul_mat_vec_q2_K_body)) elif i == GGML_TYPE_Q3_K: @@ -2290,43 +2195,101 @@ async def main(): else: continue - tasks.append(string_to_spv(f"mul_mat_vec_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION}, fp16)) + tasks.append(string_to_spv(f"mul_mat_vec_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float", "K_QUANTS_PER_ITERATION": K_QUANTS_PER_ITERATION})) - tasks.append(string_to_spv("mul_mat_vec_p021_f16_f32", mul_mat_p021_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, True)) - tasks.append(string_to_spv("mul_mat_vec_nc_f16_f32", mul_mat_nc_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, True)) + # Dequant shaders + for i in range(0, VK_NUM_TYPES): + stream.clear() + + stream.extend((dequant_head, shader_int8_ext, shader_f32)) + + if i == GGML_TYPE_F16: + stream.extend((shader_f16_defines, shader_f16_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q4_0: + stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q4_1: + stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q5_0: + stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q5_1: + stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q8_0: + stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func, dequant_body)) + elif i == GGML_TYPE_Q2_K: + stream.extend((shader_q2_K_defines, dequant_q2_K_body)) + elif i == GGML_TYPE_Q3_K: + stream.extend((shader_q3_K_defines, dequant_q3_K_body)) + elif i == GGML_TYPE_Q4_K: + stream.extend((shader_q4_K_defines, dequant_q4_K_body)) + elif i == GGML_TYPE_Q5_K: + stream.extend((shader_q5_K_defines, dequant_q5_K_body)) + elif i == GGML_TYPE_Q6_K: + stream.extend((shader_q6_K_defines, dequant_q6_K_body)) + else: + continue + + tasks.append(string_to_spv(f"dequant_{type_names[i]}", "".join(stream), {"D_TYPE": "float16_t"})) + + tasks.append(string_to_spv("f32_to_f16", f32_to_f16_src, {})) + + # get_rows + for i in range(0, VK_NUM_TYPES): + stream.clear() + stream.extend((generic_head, shader_int8_ext, shader_f32)) + + if i == GGML_TYPE_F16: + stream.extend((shader_f16_defines, shader_f16_dequant_func, get_rows_body)) + elif i == GGML_TYPE_Q4_0: + stream.extend((shader_q4_0_defines, shader_q4_0_dequant_func, get_rows_body)) + elif i == GGML_TYPE_Q4_1: + stream.extend((shader_q4_1_defines, shader_q4_1_dequant_func, get_rows_body)) + elif i == GGML_TYPE_Q5_0: + stream.extend((shader_q5_0_defines, shader_q5_0_dequant_func, get_rows_body)) + elif i == GGML_TYPE_Q5_1: + stream.extend((shader_q5_1_defines, shader_q5_1_dequant_func, get_rows_body)) + elif i == GGML_TYPE_Q8_0: + stream.extend((shader_q8_0_defines, shader_q8_0_dequant_func, get_rows_body)) + else: + continue + + tasks.append(string_to_spv(f"get_rows_{type_names[i]}", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float16_t"})) + tasks.append(string_to_spv(f"get_rows_{type_names[i]}_f32", "".join(stream), {"B_TYPE": "float", "D_TYPE": "float"})) + + tasks.append(string_to_spv("mul_mat_vec_p021_f16_f32", mul_mat_p021_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("mul_mat_vec_nc_f16_f32", mul_mat_nc_src, {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"})) # Norms - tasks.append(string_to_spv("norm_f32", f"{generic_head}\n{shader_f32}\n{norm_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) - tasks.append(string_to_spv("rms_norm_f32", f"{generic_head}\n{shader_f32}\n{rms_norm_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("norm_f32", f"{generic_head}\n{shader_f32}\n{norm_body}", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("rms_norm_f32", f"{generic_head}\n{shader_f32}\n{rms_norm_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("cpy_f32_f32", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) - tasks.append(string_to_spv("cpy_f32_f16", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float16_t"}, True)) - tasks.append(string_to_spv("cpy_f16_f16", f"{cpy_src}\n{cpy_f16_f16_end}", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}, True)) + tasks.append(string_to_spv("cpy_f32_f32", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("cpy_f32_f16", f"{cpy_src}\n{cpy_end}", {"A_TYPE": "float", "D_TYPE": "float16_t"})) + tasks.append(string_to_spv("cpy_f16_f16", f"{cpy_src}\n{cpy_f16_f16_end}", {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) - tasks.append(string_to_spv("add_f32", f"{generic_head}\n{shader_f32}\n{add_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("add_f32", f"{generic_head}\n{shader_f32}\n{add_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("split_k_reduce", mulmat_split_k_reduce_src, {}, True)) - tasks.append(string_to_spv("mul_f32", f"{generic_head}\n{shader_f32}\n{mul_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("split_k_reduce", mulmat_split_k_reduce_src, {})) + tasks.append(string_to_spv("mul_f32", f"{generic_head}\n{shader_f32}\n{mul_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("scale_f32", f"{generic_head}\n{shader_f32}\n{scale_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("scale_f32", f"{generic_head}\n{shader_f32}\n{scale_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("sqr_f32", f"{generic_head}\n{shader_f32}\n{sqr_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("sqr_f32", f"{generic_head}\n{shader_f32}\n{sqr_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("clamp_f32", f"{generic_head}\n{shader_f32}\n{clamp_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("clamp_f32", f"{generic_head}\n{shader_f32}\n{clamp_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("gelu_f32", f"{generic_head}\n{shader_f32}\n{gelu_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) - tasks.append(string_to_spv("silu_f32", f"{generic_head}\n{shader_f32}\n{silu_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) - tasks.append(string_to_spv("relu_f32", f"{generic_head}\n{shader_f32}\n{relu_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("gelu_f32", f"{generic_head}\n{shader_f32}\n{gelu_body}", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("silu_f32", f"{generic_head}\n{shader_f32}\n{silu_body}", {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("relu_f32", f"{generic_head}\n{shader_f32}\n{relu_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("diag_mask_inf_f32", f"{diag_mask_inf_head}\n{shader_f32}\n{diag_mask_inf_body}", {"A_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("diag_mask_inf_f32", f"{diag_mask_inf_head}\n{shader_f32}\n{diag_mask_inf_body}", {"A_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("soft_max_f32", f"{generic_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, True)) + tasks.append(string_to_spv("soft_max_f32", f"{generic_head}\n{shader_f32}\n{soft_max_body}", {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"})) - tasks.append(string_to_spv("rope_f32", rope_src, {"A_TYPE": "float", "D_TYPE": "float"}, True)) - tasks.append(string_to_spv("rope_f16", rope_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}, True)) + tasks.append(string_to_spv("rope_f32", rope_src, {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("rope_f16", rope_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) - tasks.append(string_to_spv("rope_neox_f32", rope_neox_src, {"A_TYPE": "float", "D_TYPE": "float"}, True)) - tasks.append(string_to_spv("rope_neox_f16", rope_neox_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"}, True)) + tasks.append(string_to_spv("rope_neox_f32", rope_neox_src, {"A_TYPE": "float", "D_TYPE": "float"})) + tasks.append(string_to_spv("rope_neox_f16", rope_neox_src, {"A_TYPE": "float16_t", "D_TYPE": "float16_t"})) await asyncio.gather(*tasks) From 60ecf099eddfe70fec797ef6790572e452054add Mon Sep 17 00:00:00 2001 From: Martin Schwaighofer Date: Sun, 28 Jan 2024 12:59:43 +0100 Subject: [PATCH 103/143] add Vulkan support to Nix flake --- .devops/nix/package.nix | 21 +++++++++++++++++---- flake.nix | 1 + 2 files changed, 18 insertions(+), 4 deletions(-) diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix index a868a9a61..ad23f7dd7 100644 --- a/.devops/nix/package.nix +++ b/.devops/nix/package.nix @@ -13,18 +13,22 @@ cudaPackages, darwin, rocmPackages, + vulkan-headers, + vulkan-loader, clblast, useBlas ? builtins.all (x: !x) [ useCuda useMetalKit useOpenCL useRocm + useVulkan ], useCuda ? config.cudaSupport, useMetalKit ? stdenv.isAarch64 && stdenv.isDarwin && !useOpenCL, useMpi ? false, # Increases the runtime closure size by ~700M useOpenCL ? false, useRocm ? config.rocmSupport, + useVulkan ? false, llamaVersion ? "0.0.0", # Arbitrary version, substituted by the flake }@inputs: @@ -48,7 +52,8 @@ let ++ lib.optionals useMetalKit [ "MetalKit" ] ++ lib.optionals useMpi [ "MPI" ] ++ lib.optionals useOpenCL [ "OpenCL" ] - ++ lib.optionals useRocm [ "ROCm" ]; + ++ lib.optionals useRocm [ "ROCm" ] + ++ lib.optionals useVulkan [ "Vulkan" ]; pnameSuffix = strings.optionalString (suffices != [ ]) @@ -108,6 +113,11 @@ let hipblas rocblas ]; + + vulkanBuildInputs = [ + vulkan-headers + vulkan-loader + ]; in effectiveStdenv.mkDerivation ( @@ -164,7 +174,8 @@ effectiveStdenv.mkDerivation ( ++ optionals useCuda cudaBuildInputs ++ optionals useMpi [ mpi ] ++ optionals useOpenCL [ clblast ] - ++ optionals useRocm rocmBuildInputs; + ++ optionals useRocm rocmBuildInputs + ++ optionals useVulkan vulkanBuildInputs; cmakeFlags = [ @@ -178,6 +189,7 @@ effectiveStdenv.mkDerivation ( (cmakeBool "LLAMA_HIPBLAS" useRocm) (cmakeBool "LLAMA_METAL" useMetalKit) (cmakeBool "LLAMA_MPI" useMpi) + (cmakeBool "LLAMA_VULKAN" useVulkan) ] ++ optionals useCuda [ ( @@ -218,6 +230,7 @@ effectiveStdenv.mkDerivation ( useMpi useOpenCL useRocm + useVulkan ; shell = mkShell { @@ -242,11 +255,11 @@ effectiveStdenv.mkDerivation ( # Configurations we don't want even the CI to evaluate. Results in the # "unsupported platform" messages. This is mostly a no-op, because # cudaPackages would've refused to evaluate anyway. - badPlatforms = optionals (useCuda || useOpenCL) lib.platforms.darwin; + badPlatforms = optionals (useCuda || useOpenCL || useVulkan) lib.platforms.darwin; # Configurations that are known to result in build failures. Can be # overridden by importing Nixpkgs with `allowBroken = true`. - broken = (useMetalKit && !effectiveStdenv.isDarwin); + broken = (useMetalKit && !effectiveStdenv.isDarwin) || (useVulkan && effectiveStdenv.isDarwin); description = "Inference of LLaMA model in pure C/C++${descriptionSuffix}"; homepage = "https://github.com/ggerganov/llama.cpp/"; diff --git a/flake.nix b/flake.nix index a776ba024..ad2f9b295 100644 --- a/flake.nix +++ b/flake.nix @@ -157,6 +157,7 @@ mpi-cpu = config.packages.default.override { useMpi = true; }; mpi-cuda = config.packages.default.override { useMpi = true; }; + vulkan = config.packages.default.override { useVulkan = true; }; } // lib.optionalAttrs (system == "x86_64-linux") { rocm = config.legacyPackages.llamaPackagesRocm.llama-cpp; From 3cc5ed353c07201d8d5b98b0a4713ab633da6d04 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 3 Feb 2024 20:14:59 +0100 Subject: [PATCH 104/143] make: fix nvcc optimization flags for host code (#5309) --- Makefile | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/Makefile b/Makefile index a55d15888..40b16e0ea 100644 --- a/Makefile +++ b/Makefile @@ -109,6 +109,7 @@ MK_NVCCFLAGS += -O3 else MK_CFLAGS += -O3 MK_CXXFLAGS += -O3 +MK_NVCCFLAGS += -O3 endif # clock_gettime came in POSIX.1b (1993) @@ -365,7 +366,7 @@ ifdef LLAMA_CUBLAS MK_CPPFLAGS += -DGGML_USE_CUBLAS -I/usr/local/cuda/include -I/opt/cuda/include -I$(CUDA_PATH)/targets/x86_64-linux/include -I/usr/local/cuda/targets/aarch64-linux/include MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L/usr/local/cuda/lib64 -L/opt/cuda/lib64 -L$(CUDA_PATH)/targets/x86_64-linux/lib -L/usr/local/cuda/targets/aarch64-linux/lib -L/usr/lib/wsl/lib OBJS += ggml-cuda.o - MK_NVCCFLAGS = -use_fast_math + MK_NVCCFLAGS += -use_fast_math ifndef JETSON_EOL_MODULE_DETECT MK_NVCCFLAGS += --forward-unknown-to-host-compiler endif # JETSON_EOL_MODULE_DETECT From 3c0d25c4756742ebf15ad44700fabc0700c638bd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 3 Feb 2024 20:15:13 +0100 Subject: [PATCH 105/143] make: add nvcc info print (#5310) --- Makefile | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/Makefile b/Makefile index 40b16e0ea..21d5e15ba 100644 --- a/Makefile +++ b/Makefile @@ -553,8 +553,11 @@ $(info I CFLAGS: $(CFLAGS)) $(info I CXXFLAGS: $(CXXFLAGS)) $(info I NVCCFLAGS: $(NVCCFLAGS)) $(info I LDFLAGS: $(LDFLAGS)) -$(info I CC: $(shell $(CC) --version | head -n 1)) -$(info I CXX: $(shell $(CXX) --version | head -n 1)) +$(info I CC: $(shell $(CC) --version | head -n 1)) +$(info I CXX: $(shell $(CXX) --version | head -n 1)) +ifdef LLAMA_CUBLAS +$(info I NVCC: $(shell $(NVCC) --version | tail -n 1)) +endif # LLAMA_CUBLAS $(info ) # From 277fad30c60ef3559dc2d01b19d05e659d40a824 Mon Sep 17 00:00:00 2001 From: Welby Seely Date: Sat, 3 Feb 2024 23:18:51 -0500 Subject: [PATCH 106/143] cmake : use set() for LLAMA_WIN_VER (#5298) option() is specifically for booleans. Fixes #5158 --- CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index c156c4824..8c04e4c19 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -79,7 +79,7 @@ if (NOT MSVC) endif() if (WIN32) - option(LLAMA_WIN_VER "llama: Windows Version" 0x602) + set(LLAMA_WIN_VER "0x602" CACHE STRING "llama: Windows Version") endif() # 3rd party libs From 5ed26e1fc9fab4ce96ecf2d84183fe45bdcab0d4 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sun, 4 Feb 2024 10:39:58 +0200 Subject: [PATCH 107/143] Adding some imatrix tools (#5302) * imatrix: adding --combine and --continue-from * imatrix: be able to start from a specific chunk --------- Co-authored-by: Iwan Kawrakow --- examples/imatrix/imatrix.cpp | 116 +++++++++++++++++++++++++++++++++-- 1 file changed, 112 insertions(+), 4 deletions(-) diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index ea06fcdbf..bc9f6fa68 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -36,6 +36,8 @@ public: void set_parameters(StatParams&& params) { m_params = std::move(params); } bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data); void save_imatrix() const; + bool load_imatrix(const char * file_name, bool add); + static bool load_imatrix(const char * file_name, std::unordered_map& imatrix); private: std::unordered_map m_stats; StatParams m_params; @@ -189,6 +191,57 @@ void IMatrixCollector::save_imatrix(const char * fname) const { } } +bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_map& imatrix_data) { + std::ifstream in(imatrix_file, std::ios::binary); + if (!in) { + printf("%s: failed to open %s\n",__func__,imatrix_file); + return false; + } + int n_entries; + in.read((char*)&n_entries, sizeof(n_entries)); + if (in.fail() || n_entries < 1) { + printf("%s: no data in file %s\n", __func__, imatrix_file); + return false; + } + for (int i = 0; i < n_entries; ++i) { + int len; in.read((char *)&len, sizeof(len)); + std::vector name_as_vec(len+1); + in.read((char *)name_as_vec.data(), len); + if (in.fail()) { + printf("%s: failed reading name for entry %d from %s\n",__func__,i+1,imatrix_file); + return false; + } + name_as_vec[len] = 0; + std::string name{name_as_vec.data()}; + auto& e = imatrix_data[std::move(name)]; + int ncall; + in.read((char*)&ncall, sizeof(ncall)); + int nval; + in.read((char *)&nval, sizeof(nval)); + if (in.fail() || nval < 1) { + printf("%s: failed reading number of values for entry %d\n",__func__,i); + imatrix_data = {}; + return false; + } + e.values.resize(nval); + in.read((char*)e.values.data(), nval*sizeof(float)); + if (in.fail()) { + printf("%s: failed reading data for entry %d\n",__func__,i); + imatrix_data = {}; + return false; + } + e.ncall = ncall; + } + return true; +} + +bool IMatrixCollector::load_imatrix(const char * file_name, bool add) { + if (!add) { + m_stats.clear(); + } + return load_imatrix(file_name, m_stats); +} + static IMatrixCollector g_collector; static bool ik_collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) { @@ -269,7 +322,7 @@ static void process_logits( } } -static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool compute_ppl) { +static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool compute_ppl, int from_chunk) { const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx)); const int n_ctx = llama_n_ctx(ctx); @@ -282,6 +335,15 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool 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 (from_chunk > 0) { + if (size_t((from_chunk + 2)*n_ctx) >= tokens.size()) { + fprintf(stderr, "%s: there will be not enough tokens left after removing %d chunks\n", __func__, from_chunk); + return false; + } + fprintf(stderr, "%s: removing initial %d chunks (%d tokens)\n", __func__, from_chunk, from_chunk*n_ctx); + tokens.erase(tokens.begin(), tokens.begin() + from_chunk*n_ctx); + } + if (int(tokens.size()) < 2*n_ctx) { fprintf(stderr, "%s: you need at least %d tokens for a context of %d tokens\n",__func__,2*n_ctx, n_ctx); @@ -402,7 +464,10 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool int main(int argc, char ** argv) { StatParams sparams; + std::string prev_result_file; + std::string combine_files; bool compute_ppl = true; + int from_chunk = 0; std::vector args; args.push_back(argv[0]); int iarg = 1; @@ -423,6 +488,13 @@ int main(int argc, char ** argv) { compute_ppl = false; } else if (arg == "--keep-imatrix") { sparams.keep_every = std::stoi(argv[++iarg]); + } else if (arg == "--continue-from") { + prev_result_file = argv[++iarg]; + } else if (arg == "--combine") { + combine_files = argv[++iarg]; + } + else if (arg == "--from-chunk") { + from_chunk = std::stoi(argv[++iarg]); } else { args.push_back(argv[iarg]); } @@ -436,14 +508,50 @@ int main(int argc, char ** argv) { } } + g_collector.set_parameters(std::move(sparams)); + + if (!combine_files.empty()) { + std::vector files; + size_t pos = 0; + while (true) { + auto new_pos = combine_files.find(',', pos); + if (new_pos != std::string::npos) { + files.emplace_back(combine_files.substr(pos, new_pos - pos)); + pos = new_pos + 1; + } else { + files.emplace_back(combine_files.substr(pos)); + break; + } + } + if (files.size() < 2) { + fprintf(stderr, "You must provide at least two comma separated files to use --combine\n"); + return 1; + } + printf("Combining the following %d files\n", int(files.size())); + for (auto& file : files) { + printf(" %s\n", file.c_str()); + if (!g_collector.load_imatrix(file.c_str(), true)) { + fprintf(stderr, "Failed to load %s\n", file.c_str()); + return 1; + } + } + g_collector.save_imatrix(); + return 0; + } + + if (!prev_result_file.empty()) { + if (!g_collector.load_imatrix(prev_result_file.c_str(), false)) { + fprintf(stderr, "=============== Failed to load %s\n", prev_result_file.c_str()); + return 1; + } + } + gpt_params params; params.n_batch = 512; if (!gpt_params_parse(args.size(), args.data(), params)) { return 1; } - g_collector.set_parameters(std::move(sparams)); - params.logits_all = true; params.n_batch = std::min(params.n_batch, params.n_ctx); @@ -495,7 +603,7 @@ int main(int argc, char ** argv) { fprintf(stderr, "%s\n", get_system_info(params).c_str()); } - bool OK = compute_imatrix(ctx, params, compute_ppl); + bool OK = compute_imatrix(ctx, params, compute_ppl, from_chunk); if (!OK) { return 1; } From 9392ebd49ea5ae236a55b47cbf6a13247e8a3b8c Mon Sep 17 00:00:00 2001 From: "github-actions[bot]" Date: Sun, 4 Feb 2024 00:17:24 +0000 Subject: [PATCH 108/143] flake.lock: Update MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Flake lock file updates: • Updated input 'flake-parts': 'github:hercules-ci/flake-parts/07f6395285469419cf9d078f59b5b49993198c00' (2024-01-11) → 'github:hercules-ci/flake-parts/b253292d9c0a5ead9bc98c4e9a26c6312e27d69f' (2024-02-01) • Updated input 'flake-parts/nixpkgs-lib': 'github:NixOS/nixpkgs/b0d36bd0a420ecee3bc916c91886caca87c894e9?dir=lib' (2023-12-30) → 'github:NixOS/nixpkgs/97b17f32362e475016f942bbdfda4a4a72a8a652?dir=lib' (2024-01-29) • Updated input 'nixpkgs': 'github:NixOS/nixpkgs/ae5c332cbb5827f6b1f02572496b141021de335f' (2024-01-25) → 'github:NixOS/nixpkgs/b8b232ae7b8b144397fdb12d20f592e5e7c1a64d' (2024-01-31) --- flake.lock | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/flake.lock b/flake.lock index 95e41f333..8cfc78273 100644 --- a/flake.lock +++ b/flake.lock @@ -5,11 +5,11 @@ "nixpkgs-lib": "nixpkgs-lib" }, "locked": { - "lastModified": 1704982712, - "narHash": "sha256-2Ptt+9h8dczgle2Oo6z5ni5rt/uLMG47UFTR1ry/wgg=", + "lastModified": 1706830856, + "narHash": "sha256-a0NYyp+h9hlb7ddVz4LUn1vT/PLwqfrWYcHMvFB1xYg=", "owner": "hercules-ci", "repo": "flake-parts", - "rev": "07f6395285469419cf9d078f59b5b49993198c00", + "rev": "b253292d9c0a5ead9bc98c4e9a26c6312e27d69f", "type": "github" }, "original": { @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1706191920, - "narHash": "sha256-eLihrZAPZX0R6RyM5fYAWeKVNuQPYjAkCUBr+JNvtdE=", + "lastModified": 1706732774, + "narHash": "sha256-hqJlyJk4MRpcItGYMF+3uHe8HvxNETWvlGtLuVpqLU0=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "ae5c332cbb5827f6b1f02572496b141021de335f", + "rev": "b8b232ae7b8b144397fdb12d20f592e5e7c1a64d", "type": "github" }, "original": { @@ -37,11 +37,11 @@ "nixpkgs-lib": { "locked": { "dir": "lib", - "lastModified": 1703961334, - "narHash": "sha256-M1mV/Cq+pgjk0rt6VxoyyD+O8cOUiai8t9Q6Yyq4noY=", + "lastModified": 1706550542, + "narHash": "sha256-UcsnCG6wx++23yeER4Hg18CXWbgNpqNXcHIo5/1Y+hc=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "b0d36bd0a420ecee3bc916c91886caca87c894e9", + "rev": "97b17f32362e475016f942bbdfda4a4a72a8a652", "type": "github" }, "original": { From 4833ac209da6a427de64f97e8f403dcdc5de6bc3 Mon Sep 17 00:00:00 2001 From: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> Date: Mon, 5 Feb 2024 07:08:24 +0000 Subject: [PATCH 109/143] [SYCL] Fix cpy with dims of 3 (#5289) * Fix cpy with dims of 3 * rm asserts --------- Co-authored-by: Abhilash Majumder <30946547+abhilash1910@users.noreply.github.com> --- ggml-sycl.cpp | 194 +++++++++++++++++++++++++++++--------------------- 1 file changed, 114 insertions(+), 80 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 51445b5e7..a03df4c65 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -7693,6 +7693,13 @@ static void cpy_1_f16_f16(const char * cxi, char * cdsti) { *dsti = *xi; } +static void cpy_1_f16_f32(const char * cxi, char * cdsti) { + const sycl::half *xi = (const sycl::half *)cxi; + float *dsti = (float *)cdsti; + + *dsti = *xi; +} + static void cpy_1_i16_i16(const char * cxi, char * cdsti) { const int16_t *xi = (const int16_t *)cxi; int16_t *dsti = (int16_t *)cdsti; @@ -7709,9 +7716,9 @@ static void cpy_1_i32_i32(const char * cxi, char * cdsti) { template static void cpy_f32_f16(const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int nb00, const int nb01, const int nb02, - const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, - const sycl::nd_item<3> &item_ct1) { + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, + const int nb12, const int nb13, const sycl::nd_item<3> &item_ct1) { const int i = item_ct1.get_local_range(2) * item_ct1.get_group(2) + item_ct1.get_local_id(2); @@ -7721,15 +7728,17 @@ static void cpy_f32_f16(const char * cx, char * cdst, const int ne, // determine indices i02/i12, i01/i11, i00/i10 as a function of index i of flattened tensor // then combine those indices with the corresponding byte offsets to get the total offsets - const int i02 = i / (ne00*ne01); - const int i01 = (i - i02*ne01*ne00) / ne00; - const int i00 = i - i02*ne01*ne00 - i01*ne00; - const int x_offset = i00*nb00 + i01*nb01 + i02*nb02; + const int i03 = i/(ne00 * ne01 * ne02); + const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); + const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; + const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00; + const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03; - const int i12 = i / (ne10*ne11); - const int i11 = (i - i12*ne10*ne11) / ne10; - const int i10 = i - i12*ne10*ne11 - i11*ne10; - const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12; + const int i13 = i/(ne10 * ne11 * ne12); + const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11); + const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10; + const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10; + const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12 + i13 * nb13; cpy_1(cx + x_offset, cdst + dst_offset); } @@ -7823,9 +7832,9 @@ static void cpy_blck_f32_q4_1(const char * cxi, char * cdsti) { template static void cpy_f32_q(const char * cx, char * cdst, const int ne, - const int ne00, const int ne01, const int nb00, const int nb01, const int nb02, - const int ne10, const int ne11, const int nb10, const int nb11, const int nb12, - const sycl::nd_item<3> &item_ct1) { + const int ne00, const int ne01, const int ne02, const int nb00, const int nb01, const int nb02, + const int nb03, const int ne10, const int ne11, const int ne12, const int nb10, const int nb11, + const int nb12, const int nb13, const sycl::nd_item<3> &item_ct1) { const int i = (item_ct1.get_local_range(2) * item_ct1.get_group(2) + item_ct1.get_local_id(2)) * qk; @@ -7834,15 +7843,17 @@ static void cpy_f32_q(const char * cx, char * cdst, const int ne, return; } - const int i02 = i / (ne00*ne01); - const int i01 = (i - i02*ne01*ne00) / ne00; - const int i00 = (i - i02*ne01*ne00 - i01*ne00); - const int x_offset = i00*nb00 + i01*nb01 + i02*nb02; + const int i03 = i/(ne00 * ne01 * ne02); + const int i02 = (i - i03*ne00*ne01*ne02 )/ (ne00*ne01); + const int i01 = (i - i03*ne00*ne01*ne02 - i02*ne01*ne00) / ne00; + const int i00 = i - i03*ne00*ne01*ne02 - i02*ne01*ne00 - i01*ne00; + const int x_offset = i00*nb00 + i01*nb01 + i02*nb02 + i03 * nb03; - const int i12 = i / (ne10*ne11); - const int i11 = (i - i12*ne10*ne11) / ne10; - const int i10 = (i - i12*ne10*ne11 - i11*ne10)/qk; - const int dst_offset = i10*nb10 + i11*nb11 + i12*nb12; + const int i13 = i/(ne10 * ne11 * ne12); + const int i12 = (i - i13*ne10*ne11*ne12) / (ne10*ne11); + const int i11 = (i - i13*ne10*ne11*ne12 - i12*ne10*ne11) / ne10; + const int i10 = i - i13*ne10*ne11*ne12 - i12*ne10*ne11 - i11*ne10; + const int dst_offset = (i10/qk)*nb10 + i11*nb11 + i12*nb12 + i13*nb13; cpy_blck(cx + x_offset, cdst + dst_offset); } @@ -10599,10 +10610,12 @@ static void ggml_mul_mat_vec_nc_f16_f32_sycl( static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; @@ -10615,8 +10628,8 @@ static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { - cpy_f32_f16(cx, cdst, ne, ne00, ne01, nb00, nb01, - nb02, ne10, ne11, nb10, nb11, nb12, + cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -10624,10 +10637,12 @@ static void ggml_cpy_f32_f32_sycl(const char *cx, char *cdst, const int ne, static void ggml_cpy_f32_f16_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; @@ -10640,8 +10655,8 @@ static void ggml_cpy_f32_f16_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { - cpy_f32_f16(cx, cdst, ne, ne00, ne01, nb00, nb01, - nb02, ne10, ne11, nb10, nb11, nb12, + cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -10649,10 +10664,12 @@ static void ggml_cpy_f32_f16_sycl(const char *cx, char *cdst, const int ne, static void ggml_cpy_f32_q8_0_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { GGML_ASSERT(ne % QK8_0 == 0); @@ -10661,17 +10678,20 @@ static void ggml_cpy_f32_q8_0_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) { cpy_f32_q( - cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, - ne10, ne11, nb10, nb11, nb12, item_ct1); + cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, + item_ct1); }); } static void ggml_cpy_f32_q4_0_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { GGML_ASSERT(ne % QK4_0 == 0); @@ -10680,17 +10700,20 @@ static void ggml_cpy_f32_q4_0_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) { cpy_f32_q( - cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, - ne10, ne11, nb10, nb11, nb12, item_ct1); + cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, + item_ct1); }); } static void ggml_cpy_f32_q4_1_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { GGML_ASSERT(ne % QK4_1 == 0); @@ -10699,17 +10722,20 @@ static void ggml_cpy_f32_q4_1_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) { cpy_f32_q( - cx, cdst, ne, ne00, ne01, nb00, nb01, nb02, - ne10, ne11, nb10, nb11, nb12, item_ct1); + cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, + item_ct1); }); } static void ggml_cpy_f16_f16_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; @@ -10722,8 +10748,8 @@ static void ggml_cpy_f16_f16_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { - cpy_f32_f16(cx, cdst, ne, ne00, ne01, nb00, nb01, - nb02, ne10, ne11, nb10, nb11, nb12, + cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -10731,10 +10757,12 @@ static void ggml_cpy_f16_f16_sycl(const char *cx, char *cdst, const int ne, static void ggml_cpy_i16_i16_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; @@ -10747,8 +10775,8 @@ static void ggml_cpy_i16_i16_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { - cpy_f32_f16(cx, cdst, ne, ne00, ne01, nb00, nb01, - nb02, ne10, ne11, nb10, nb11, nb12, + cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -10756,10 +10784,12 @@ static void ggml_cpy_i16_i16_sycl(const char *cx, char *cdst, const int ne, static void ggml_cpy_i32_i32_sycl(const char *cx, char *cdst, const int ne, const int ne00, const int ne01, - const int nb00, const int nb01, - const int nb02, const int ne10, - const int ne11, const int nb10, - const int nb11, const int nb12, + const int ne02, const int nb00, + const int nb01, const int nb02, + const int nb03, const int ne10, + const int ne11, const int ne12, + const int nb10, const int nb11, + const int nb12, const int nb13, dpct::queue_ptr stream) { const int num_blocks = (ne + SYCL_CPY_BLOCK_SIZE - 1) / SYCL_CPY_BLOCK_SIZE; @@ -10772,8 +10802,8 @@ static void ggml_cpy_i32_i32_sycl(const char *cx, char *cdst, const int ne, sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) { - cpy_f32_f16(cx, cdst, ne, ne00, ne01, nb00, nb01, - nb02, ne10, ne11, nb10, nb11, nb12, + cpy_f32_f16(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, + nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1); }); } @@ -13910,19 +13940,23 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1, const int64_t ne00 = src0->ne[0]; const int64_t ne01 = src0->ne[1]; - GGML_ASSERT(src0->ne[3] == 1); + const int64_t ne02 = src0->ne[2]; + const int64_t nb00 = src0->nb[0]; const int64_t nb01 = src0->nb[1]; const int64_t nb02 = src0->nb[2]; + const int64_t nb03 = src0->nb[3]; const int64_t ne10 = src1->ne[0]; const int64_t ne11 = src1->ne[1]; - GGML_ASSERT(src1->ne[3] == 1); + const int64_t ne12 = src1->ne[2]; + const int64_t nb10 = src1->nb[0]; const int64_t nb11 = src1->nb[1]; const int64_t nb12 = src1->nb[2]; + const int64_t nb13 = src1->nb[3]; SYCL_CHECK(ggml_sycl_set_device(g_main_device)); dpct::queue_ptr main_stream = g_syclStreams[g_main_device_index][0]; @@ -13934,21 +13968,21 @@ static void ggml_sycl_cpy(const ggml_tensor *src0, const ggml_tensor *src1, char * src1_ddc = (char *) src1_extra->data_device[g_main_device_index]; if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32) { - ggml_cpy_f32_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f32_f32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16) { - ggml_cpy_f32_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f32_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q8_0) { - ggml_cpy_f32_q8_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f32_q8_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_0) { - ggml_cpy_f32_q4_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f32_q4_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q4_1) { - ggml_cpy_f32_q4_1_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f32_q4_1_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_F16 && src1->type == GGML_TYPE_F16) { - ggml_cpy_f16_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_f16_f16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_I16 && src1->type == GGML_TYPE_I16) { - ggml_cpy_i16_i16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_i16_i16_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else if (src0->type == GGML_TYPE_I32 && src1->type == GGML_TYPE_I32) { - ggml_cpy_i32_i32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, nb00, nb01, nb02, ne10, ne11, nb10, nb11, nb12, main_stream); + ggml_cpy_i32_i32_sycl (src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream); } else { fprintf(stderr, "%s: unsupported type combination (%s to %s)\n", __func__, ggml_type_name(src0->type), ggml_type_name(src1->type)); From 5d55b0cd827bb0fcfedfa329a82bd5d6ef2c93ca Mon Sep 17 00:00:00 2001 From: chiranko <96988916+chiranko@users.noreply.github.com> Date: Mon, 5 Feb 2024 15:41:38 +0800 Subject: [PATCH 110/143] readme : add CodeShell models to the supported models list (#5330) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 4a9bdf314..a6fe34629 100644 --- a/README.md +++ b/README.md @@ -107,6 +107,7 @@ as the main playground for developing new features for the [ggml](https://github - [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral) - [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557) - [x] [GPT-2](https://huggingface.co/gpt2) +- [x] [CodeShell](https://github.com/WisdomShell/codeshell) **Multimodal models:** From 4be04c8965578edc09194fab769b4b922b8444f5 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=9D=D0=B8=D1=8F=D0=B7=20=D0=93=D0=B0=D1=80=D0=B8=D1=84?= =?UTF-8?q?=D0=B7=D1=8F=D0=BD=D0=BE=D0=B2?= <112617865+garrnizon@users.noreply.github.com> Date: Mon, 5 Feb 2024 10:43:57 +0300 Subject: [PATCH 111/143] scripts : add non-interactive server-llm.sh (#5303) * Update server-llm.sh Add flag --non-interactive that allows run script without asking a permission * Update scripts/server-llm.sh --------- Co-authored-by: Georgi Gerganov --- scripts/server-llm.sh | 73 ++++++++++++++++++++++++------------------- 1 file changed, 40 insertions(+), 33 deletions(-) diff --git a/scripts/server-llm.sh b/scripts/server-llm.sh index 0b83cdbbc..062b70496 100644 --- a/scripts/server-llm.sh +++ b/scripts/server-llm.sh @@ -47,6 +47,7 @@ if ! command -v make &> /dev/null; then fi # parse arguments +is_interactive=1 port=8888 repo="" wtype="" @@ -66,15 +67,16 @@ verbose=0 function print_usage { printf "Usage:\n" - printf " ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose]\n\n" - printf " --port: port number, default is 8888\n" - printf " --repo: path to a repo containing GGUF model files\n" - printf " --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input\n" - printf " --backend: cpu, cuda, metal, opencl, depends on the OS\n" - printf " --gpu-id: gpu id, default is 0\n" - printf " --n-parallel: number of parallel requests, default is 8\n" - printf " --n-kv: KV cache size, default is 4096\n" - printf " --verbose: verbose output\n\n" + printf " ./server-llm.sh [-interactive] [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose]\n\n" + printf " --non-interactive: run without asking a permision to run\n" + printf " --port: port number, default is 8888\n" + printf " --repo: path to a repo containing GGUF model files\n" + printf " --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input\n" + printf " --backend: cpu, cuda, metal, opencl, depends on the OS\n" + printf " --gpu-id: gpu id, default is 0\n" + printf " --n-parallel: number of parallel requests, default is 8\n" + printf " --n-kv: KV cache size, default is 4096\n" + printf " --verbose: verbose output\n\n" printf "Example:\n\n" printf ' bash -c "$(curl -s https://ggml.ai/server-llm.sh)"\n\n' } @@ -82,6 +84,10 @@ function print_usage { while [[ $# -gt 0 ]]; do key="$1" case $key in + --non-interactive) + is_interactive=0 + shift + ;; --port) port="$2" shift @@ -176,31 +182,32 @@ repos=( "https://huggingface.co/TheBloke/OpenHermes-2-Mistral-7B-GGUF" "https://huggingface.co/TheBloke/CausalLM-7B-GGUF" ) +if [ $is_interactive -eq 1 ]; then + printf "\n" + printf "[I] This is a helper script for deploying llama.cpp's server on this machine.\n\n" + printf " Based on the options that follow, the script might download a model file\n" + printf " from the internet, which can be a few GBs in size. The script will also\n" + printf " build the latest llama.cpp source code from GitHub, which can be unstable.\n" + printf "\n" + printf " Upon success, an HTTP server will be started and it will serve the selected\n" + printf " model using llama.cpp for demonstration purposes.\n" + printf "\n" + printf " Please note:\n" + printf "\n" + printf " - All new data will be stored in the current folder\n" + printf " - The server will be listening on all network interfaces\n" + printf " - The server will run with default settings which are not always optimal\n" + printf " - Do not judge the quality of a model based on the results from this script\n" + printf " - Do not use this script to benchmark llama.cpp\n" + printf " - Do not use this script in production\n" + printf " - This script is only for demonstration purposes\n" + printf "\n" + printf " If you don't know what you are doing, please press Ctrl-C to abort now\n" + printf "\n" + printf " Press Enter to continue ...\n\n" -printf "\n" -printf "[I] This is a helper script for deploying llama.cpp's server on this machine.\n\n" -printf " Based on the options that follow, the script might download a model file\n" -printf " from the internet, which can be a few GBs in size. The script will also\n" -printf " build the latest llama.cpp source code from GitHub, which can be unstable.\n" -printf "\n" -printf " Upon success, an HTTP server will be started and it will serve the selected\n" -printf " model using llama.cpp for demonstration purposes.\n" -printf "\n" -printf " Please note:\n" -printf "\n" -printf " - All new data will be stored in the current folder\n" -printf " - The server will be listening on all network interfaces\n" -printf " - The server will run with default settings which are not always optimal\n" -printf " - Do not judge the quality of a model based on the results from this script\n" -printf " - Do not use this script to benchmark llama.cpp\n" -printf " - Do not use this script in production\n" -printf " - This script is only for demonstration purposes\n" -printf "\n" -printf " If you don't know what you are doing, please press Ctrl-C to abort now\n" -printf "\n" -printf " Press Enter to continue ...\n\n" - -read + read +fi if [[ -z "$repo" ]]; then printf "[+] No repo provided from the command line\n" From 30679d438d5225b3aecf5cec6482cbc9f8f87ba5 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 5 Feb 2024 09:48:03 +0200 Subject: [PATCH 112/143] scripts : fix typos, cleanup (#5303) --- scripts/server-llm.sh | 23 ++++++++++++----------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/scripts/server-llm.sh b/scripts/server-llm.sh index 062b70496..30bbac321 100644 --- a/scripts/server-llm.sh +++ b/scripts/server-llm.sh @@ -14,16 +14,17 @@ # - Might be unstable! # # Usage: -# ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose] +# ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose] [-non-interactive] # -# --port: port number, default is 8888 -# --repo: path to a repo containing GGUF model files -# --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input -# --backend: cpu, cuda, metal, opencl, depends on the OS -# --gpu-id: gpu id, default is 0 -# --n-parallel: number of parallel requests, default is 8 -# --n-kv: KV cache size, default is 4096 -# --verbose: verbose output +# --port: port number, default is 8888 +# --repo: path to a repo containing GGUF model files +# --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input +# --backend: cpu, cuda, metal, opencl, depends on the OS +# --gpu-id: gpu id, default is 0 +# --n-parallel: number of parallel requests, default is 8 +# --n-kv: KV cache size, default is 4096 +# --verbose: verbose output +# --non-interactive: run without asking a permission to run # # Example: # @@ -67,8 +68,7 @@ verbose=0 function print_usage { printf "Usage:\n" - printf " ./server-llm.sh [-interactive] [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose]\n\n" - printf " --non-interactive: run without asking a permision to run\n" + printf " ./server-llm.sh [--port] [--repo] [--wtype] [--backend] [--gpu-id] [--n-parallel] [--n-kv] [--verbose] [-non-interactive]\n\n" printf " --port: port number, default is 8888\n" printf " --repo: path to a repo containing GGUF model files\n" printf " --wtype: weights type (f16, q8_0, q4_0, q4_1), default is user-input\n" @@ -77,6 +77,7 @@ function print_usage { printf " --n-parallel: number of parallel requests, default is 8\n" printf " --n-kv: KV cache size, default is 4096\n" printf " --verbose: verbose output\n\n" + printf " --non-interactive: run without asking a permission to run\n" printf "Example:\n\n" printf ' bash -c "$(curl -s https://ggml.ai/server-llm.sh)"\n\n' } From e6f81775323f6f4e4a30abf022a6028fa86b79ac Mon Sep 17 00:00:00 2001 From: l3utterfly Date: Mon, 5 Feb 2024 17:00:47 +0900 Subject: [PATCH 113/143] common : add dynamic temperature parameters to main example cli (#5295) * added dynamic temp params in main * added help text --- common/common.cpp | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/common/common.cpp b/common/common.cpp index 3302caa20..8c1a60583 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -399,6 +399,18 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { break; } sparams.penalty_present = std::stof(argv[i]); + } else if (arg == "--dynatemp-range") { + if (++i >= argc) { + invalid_param = true; + break; + } + sparams.dynatemp_range = std::stof(argv[i]); + } else if (arg == "--dynatemp-exp") { + if (++i >= argc) { + invalid_param = true; + break; + } + sparams.dynatemp_exponent = std::stof(argv[i]); } else if (arg == "--mirostat") { if (++i >= argc) { invalid_param = true; @@ -942,6 +954,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { printf(" --repeat-penalty N penalize repeat sequence of tokens (default: %.1f, 1.0 = disabled)\n", (double)sparams.penalty_repeat); printf(" --presence-penalty N repeat alpha presence penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_present); printf(" --frequency-penalty N repeat alpha frequency penalty (default: %.1f, 0.0 = disabled)\n", (double)sparams.penalty_freq); + printf(" --dynatemp-range N dynamic temperature range (default: %.1f, 0.0 = disabled)\n", (double)sparams.dynatemp_range); + printf(" --dynatemp-exp N dynamic temperature exponent (default: %.1f)\n", (double)sparams.dynatemp_exponent); printf(" --mirostat N use Mirostat sampling.\n"); printf(" Top K, Nucleus, Tail Free and Locally Typical samplers are ignored if used.\n"); printf(" (default: %d, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)\n", sparams.mirostat); From a2d60c9158435ae9a6f14632f07f1acf7a3becef Mon Sep 17 00:00:00 2001 From: Alexey Parfenov Date: Mon, 5 Feb 2024 08:10:22 +0000 Subject: [PATCH 114/143] server : allow to get default generation settings for completion (#5307) --- examples/server/README.md | 16 +++++++++++++++- examples/server/server.cpp | 7 ++++++- 2 files changed, 21 insertions(+), 2 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index fe934dab1..d8e7c313e 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -264,7 +264,21 @@ Notice that each `probs` is an array of length `n_probs`. It also accepts all the options of `/completion` except `stream` and `prompt`. -- **GET** `/props`: Return the required assistant name and anti-prompt to generate the prompt in case you have specified a system prompt for all slots. +- **GET** `/props`: Return current server settings. + +### Result JSON + +```json +{ + "assistant_name": "", + "user_name": "", + "default_generation_settings": { ... } +} +``` + +- `assistant_name` - the required assistant name to generate the prompt in case you have specified a system prompt for all slots. +- `user_name` - the required anti-prompt to generate the prompt in case you have specified a system prompt for all slots. +- `default_generation_settings` - the default generation settings for the `/completion` endpoint, has the same fields as the `generation_settings` response object from the `/completion` endpoint. - **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index a9f8cb369..8000fee5c 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -334,6 +334,7 @@ struct llama_server_context // slots / clients std::vector slots; + json default_generation_settings_for_props; llama_server_queue queue_tasks; llama_server_response queue_results; @@ -430,6 +431,9 @@ struct llama_server_context slots.push_back(slot); } + default_generation_settings_for_props = get_formated_generation(slots.front()); + default_generation_settings_for_props["seed"] = -1; + batch = llama_batch_init(n_ctx, 0, params.n_parallel); // empty system prompt @@ -2614,7 +2618,8 @@ int main(int argc, char **argv) res.set_header("Access-Control-Allow-Origin", req.get_header_value("Origin")); json data = { { "user_name", llama.name_user.c_str() }, - { "assistant_name", llama.name_assistant.c_str() } + { "assistant_name", llama.name_assistant.c_str() }, + { "default_generation_settings", llama.default_generation_settings_for_props } }; res.set_content(data.dump(), "application/json; charset=utf-8"); }); From 6fdfa2ecc684000a25a4ad91823bc82a6652b645 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Mon, 5 Feb 2024 10:46:06 +0200 Subject: [PATCH 115/143] iq2_xxs: tune quantization (#5320) We get slightly better PPL, and we cut quantization time in nearly half. The trick is to 1st quantize without forcing points onto the E8-lattice. We can then use a narrower search range around the block scale that we got that way. Co-authored-by: Iwan Kawrakow --- ggml-quants.c | 58 ++++++--------------------------------------------- 1 file changed, 6 insertions(+), 52 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 8236385bc..014c0525a 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -9048,8 +9048,6 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict int8_t L[32]; int8_t Laux[32]; float waux[32]; - bool is_on_grid[4]; - bool is_on_grid_aux[4]; uint8_t block_signs[4]; uint32_t q2[2*(QK_K/32)]; @@ -9099,10 +9097,11 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict memset(L, 0, 32); continue; } + float scale = make_qp_quants(32, kMaxQ+1, xval, (uint8_t*)L, weight); + float eff_max = scale*kMaxQ; float best = 0; - float scale = max/(2*kMaxQ-1); - for (int is = -9; is <= 9; ++is) { - float id = (2*kMaxQ-1+is*0.1f)/max; + for (int is = -6; is <= 6; ++is) { + float id = (2*kMaxQ-1+is*0.1f)/eff_max; float this_scale = 1/id; for (int k = 0; k < 4; ++k) { for (int i = 0; i < 8; ++i) { @@ -9112,9 +9111,7 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict uint16_t u = 0; for (int i = 0; i < 8; ++i) u |= (Laux[8*k+i] << 2*i); int grid_index = kmap_q2xs[u]; - is_on_grid_aux[k] = true; if (grid_index < 0) { - is_on_grid_aux[k] = false; const uint16_t * neighbours = kneighbors_q2xs - kmap_q2xs[u] - 1; grid_index = iq2_find_best_neighbour(neighbours, kgrid_q2xs, xval + 8*k, waux + 8*k, this_scale, Laux + 8*k); } @@ -9128,16 +9125,12 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict } if (sumq2 > 0 && sumqx*sumqx > best*sumq2) { scale = sumqx/sumq2; best = scale*sumqx; - for (int i = 0; i < 32; ++i) L[i] = Laux[i]; - for (int k = 0; k < 4; ++k) is_on_grid[k] = is_on_grid_aux[k]; + memcpy(L, Laux, 32); } } - int n_not_ongrid = 0; - for (int k = 0; k < 4; ++k) if (!is_on_grid[k]) ++n_not_ongrid; - if (n_not_ongrid > 0 && scale > 0) { + if (scale > 0) { float id = 1/scale; for (int k = 0; k < 4; ++k) { - if (is_on_grid[k]) continue; uint16_t u = 0; for (int i = 0; i < 8; ++i) { int l = nearest_int(0.5f*(id*xval[8*k+i]-1)); @@ -9193,49 +9186,10 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict float d = max_scale/31; y[ibl].d = GGML_FP32_TO_FP16(d); float id = 1/d; - float sumqx = 0, sumq2 = 0; for (int ib = 0; ib < QK_K/32; ++ib) { int l = nearest_int(0.5f*(id*scales[ib]-1)); l = MAX(0, MIN(15, l)); q2[2*ib+1] |= ((uint32_t)l << 28); - const float * xb = xbl + 32*ib; - const float * qw = quant_weights + QK_K*ibl + 32*ib; - for (int i = 0; i < 32; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]); - const uint8_t * aux8 = (const uint8_t *)(q2 + 2*ib); - const float db = d * (1 + 2*l); - uint32_t u = 0; - for (int k = 0; k < 4; ++k) { - const int8_t * signs = keven_signs_q2xs + 8*((q2[2*ib+1] >> 7*k) & 127); - const float * xk = xb + 8*k; - const float * wk = weight + 8*k; - const uint8_t * grid = (const uint8_t *)(kgrid_q2xs + aux8[k]); - float best_mse = 0; int best_index = aux8[k]; - for (int j = 0; j < 8; ++j) { - float diff = db * grid[j] * signs[j] - xk[j]; - best_mse += wk[j] * diff * diff; - } - for (int idx = 0; idx < 256; ++idx) { - grid = (const uint8_t *)(kgrid_q2xs + idx); - float mse = 0; - for (int j = 0; j < 8; ++j) { - float diff = db * grid[j] * signs[j] - xk[j]; - mse += wk[j] * diff * diff; - } - if (mse < best_mse) { - best_mse = mse; best_index = idx; - } - } - u |= (best_index << 8*k); - grid = (const uint8_t *)(kgrid_q2xs + best_index); - //grid = (const uint8_t *)(kgrid_q2xs + aux8[k]); - for (int j = 0; j < 8; ++j) { - float q = db * grid[j] * signs[j]; - sumqx += wk[j] * q * xk[j]; - sumq2 += wk[j] * q * q; - } - } - q2[2*ib] = u; - if (sumq2 > 0) y[ibl].d = GGML_FP32_TO_FP16(d*sumqx/sumq2); } memcpy(y[ibl].qs, q2, QK_K/4); } From 7e1ae372f36d98fa66b1d778c5862904b4d80c88 Mon Sep 17 00:00:00 2001 From: Guoteng <32697156+SolenoidWGT@users.noreply.github.com> Date: Mon, 5 Feb 2024 17:04:06 +0800 Subject: [PATCH 116/143] py : fix internlm2-hf convert to gguf (#5305) * py : fix internlm2-hf convert to gguf * ggml-ci --- convert-hf-to-gguf.py | 29 +++++++++++++++++++++++++++-- 1 file changed, 27 insertions(+), 2 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index a6ffd128b..5e343742d 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1416,8 +1416,32 @@ class InternLM2Model(Model): self.gguf_writer.add_add_space_prefix(add_prefix) special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + old_eos = special_vocab.special_token_ids["eos"] + if "chat" in os.path.basename(self.dir_model.absolute()): + # For the chat model, we replace the eos with '<|im_end|>'. + special_vocab.special_token_ids["eos"] = self._try_get_sft_eos(tokenizer) + print(f"Replace eos:{old_eos} with a special token:{special_vocab.special_token_ids['eos']} \ +in chat mode so that the conversation can end normally.") + special_vocab.add_to_gguf(self.gguf_writer) + def _try_get_sft_eos(self, tokenizer): + unused_145_list = tokenizer.encode('[UNUSED_TOKEN_145]') + im_end_list = tokenizer.encode('<|im_end|>') + assert (len(unused_145_list) == 1) ^ (len(im_end_list) == 1) + if len(unused_145_list) == 1: + eos_token = unused_145_list[0] + if len(im_end_list) == 1: + eos_token = im_end_list[0] + return eos_token + + def _hf_permute_qk(self, weights, n_head: int, n_head_kv: int): + if n_head_kv is not None and n_head != n_head_kv: + n_head = n_head_kv + return (weights.reshape(n_head, 2, weights.shape[0] // n_head // 2, *weights.shape[1:]) + .swapaxes(1, 2) + .reshape(weights.shape)) + def set_gguf_parameters(self): self.gguf_writer.add_name("InternLM2") self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) @@ -1486,8 +1510,9 @@ class InternLM2Model(Model): qkv = data_torch qkv = rearrange(qkv.T, " o (g n i) ->o g n i", g=num_groups, n=q_per_kv + 2, i=head_dim) q, k, v = qkv[..., : q_per_kv, :], qkv[..., q_per_kv: q_per_kv + 1, :], qkv[..., q_per_kv + 1: q_per_kv + 2, :] - q = rearrange(q, " o g n i -> o (g n i)").T - k = rearrange(k, " o g n i -> o (g n i)").T + # The model weights of q and k equire additional reshape. + q = self._hf_permute_qk(rearrange(q, " o g n i -> o (g n i)").T, num_heads, num_heads) + k = self._hf_permute_qk(rearrange(k, " o g n i -> o (g n i)").T, num_heads, num_kv_heads) v = rearrange(v, " o g n i -> o (g n i)").T self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wq.weight", q) self.post_write_tensors(tensor_map, f"model.layers.{bid}.attention.wk.weight", k) From 89503dcb5f764a5cc7093db1f395f5121876a2cc Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Mon, 5 Feb 2024 12:32:27 +0200 Subject: [PATCH 117/143] iq3_xxs: quards for the no-imatrix situation (#5334) Co-authored-by: Iwan Kawrakow --- llama.cpp | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/llama.cpp b/llama.cpp index 4787a92fe..65e399adc 100644 --- a/llama.cpp +++ b/llama.cpp @@ -9456,8 +9456,8 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S && qs.model.hparams.n_gqa() >= 4) { new_type = GGML_TYPE_Q4_K; } - else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && qs.model.hparams.n_gqa() >= 4) { - new_type = GGML_TYPE_Q4_K; + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) { + new_type = qs.model.hparams.n_gqa() >= 4 ? GGML_TYPE_Q4_K : !qs.has_imatrix ? GGML_TYPE_Q3_K : GGML_TYPE_IQ3_XXS; } else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) { new_type = qs.i_attention_wv < 2 ? GGML_TYPE_Q5_K : GGML_TYPE_Q4_K; @@ -9496,9 +9496,9 @@ static ggml_type get_k_quant_type(quantize_state_internal & qs, ggml_type new_ty else if (ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_XS) { if (i_layer < n_layer/8) new_type = GGML_TYPE_Q4_K; } - //else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS) { - // if (i_layer < n_layer/8) new_type = GGML_TYPE_Q5_K; - //} + else if (ftype == LLAMA_FTYPE_MOSTLY_IQ3_XXS && !qs.has_imatrix) { + new_type = i_layer < n_layer/8 ? GGML_TYPE_Q4_K : GGML_TYPE_Q3_K; + } else if (ftype == LLAMA_FTYPE_MOSTLY_Q3_K_M) { new_type = i_layer < n_layer/16 ? GGML_TYPE_Q5_K : arch != LLM_ARCH_FALCON || use_more_bits(i_layer, n_layer) ? GGML_TYPE_Q4_K From abb61944a5f64dec62c893ed0db10790169b672a Mon Sep 17 00:00:00 2001 From: "Dr. Tom Murphy VII Ph.D" <499244+tom7@users.noreply.github.com> Date: Mon, 5 Feb 2024 06:13:57 -0500 Subject: [PATCH 118/143] ggml : avoid duplicating function calls using MIN/MAX macros (#5325) * Avoid duplicating function calls when using MIN/MAX macros. Since these copy "a" and "b" they ask the compiler to evaluate one of them twice. The compiler doesn't have a problem with removing the duplication in something like MAX(0, x + 2), but in some cases we're calling functions, and those calls just happen twice. By explicitly evaluating at the expression we get smaller and faster code without duplicate calls. See ggml_rope_yarn_corr_dims in Compiler Explorer: https://godbolt.org/z/Ee4KMrvKh Code behaves exactly the same. * Update ggml.c --------- Co-authored-by: Georgi Gerganov --- ggml.c | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/ggml.c b/ggml.c index ee994c875..b9ec0c981 100644 --- a/ggml.c +++ b/ggml.c @@ -2470,7 +2470,8 @@ size_t ggml_get_max_tensor_size(const struct ggml_context * ctx) { size_t max_size = 0; for (struct ggml_tensor * tensor = ggml_get_first_tensor(ctx); tensor != NULL; tensor = ggml_get_next_tensor(ctx, tensor)) { - max_size = MAX(max_size, ggml_nbytes(tensor)); + size_t bytes = ggml_nbytes(tensor); + max_size = MAX(max_size, bytes); } return max_size; @@ -11887,8 +11888,10 @@ GGML_CALL void ggml_rope_yarn_corr_dims( int n_dims, int n_orig_ctx, float freq_base, float beta_fast, float beta_slow, float dims[2] ) { // start and end correction dims - dims[0] = MAX(0, floorf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_fast, freq_base))); - dims[1] = MIN(n_dims - 1, ceilf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_slow, freq_base))); + float start = floorf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_fast, freq_base)); + float end = ceilf(ggml_rope_yarn_corr_dim(n_dims, n_orig_ctx, beta_slow, freq_base)); + dims[0] = MAX(0, start); + dims[1] = MIN(n_dims - 1, end); } static void ggml_compute_forward_rope_f32( From c6b395535a6874d749ef47c33eacd466cb252cd5 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Mon, 5 Feb 2024 14:09:47 +0200 Subject: [PATCH 119/143] ggml : make use of ggml-quants.h possible in C++ code (#5338) * Make use of ggml-quants.h possible in C++ code * One cannot possibly be defining static_assert in a C++ compilation --------- Co-authored-by: Iwan Kawrakow --- ggml-impl.h | 2 + ggml-quants.h | 117 +++++++++++++++++++++++++++----------------------- 2 files changed, 65 insertions(+), 54 deletions(-) diff --git a/ggml-impl.h b/ggml-impl.h index 2c58075ac..19df66bce 100644 --- a/ggml-impl.h +++ b/ggml-impl.h @@ -19,6 +19,7 @@ extern "C" { // fall back to the _Static_assert C11 keyword. // if C99 - static_assert is noop // ref: https://stackoverflow.com/a/53923785/4039976 +#ifndef __cplusplus #ifndef static_assert #if defined(__STDC_VERSION__) && (__STDC_VERSION__ >= 201100L) #define static_assert(cond, msg) _Static_assert(cond, msg) @@ -26,6 +27,7 @@ extern "C" { #define static_assert(cond, msg) struct global_scope_noop_trick #endif #endif +#endif // __FMA__ and __F16C__ are not defined in MSVC, however they are implied with AVX2/AVX512 #if defined(_MSC_VER) && (defined(__AVX2__) || defined(__AVX512F__)) diff --git a/ggml-quants.h b/ggml-quants.h index 5c9f63bd9..bfdf3c997 100644 --- a/ggml-quants.h +++ b/ggml-quants.h @@ -191,70 +191,74 @@ typedef struct { } block_iq3_xxs; static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_fp16_t) + 3*(QK_K/8), "wrong iq3_xxs block size/padding"); +#ifdef __cplusplus +extern "C" { +#endif + // Quantization -void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int k); -void quantize_row_q4_1_reference(const float * restrict x, block_q4_1 * restrict y, int k); -void quantize_row_q5_0_reference(const float * restrict x, block_q5_0 * restrict y, int k); -void quantize_row_q5_1_reference(const float * restrict x, block_q5_1 * restrict y, int k); -void quantize_row_q8_0_reference(const float * restrict x, block_q8_0 * restrict y, int k); -void quantize_row_q8_1_reference(const float * restrict x, block_q8_1 * restrict y, int k); +void quantize_row_q4_0_reference(const float * GGML_RESTRICT x, block_q4_0 * GGML_RESTRICT y, int k); +void quantize_row_q4_1_reference(const float * GGML_RESTRICT x, block_q4_1 * GGML_RESTRICT y, int k); +void quantize_row_q5_0_reference(const float * GGML_RESTRICT x, block_q5_0 * GGML_RESTRICT y, int k); +void quantize_row_q5_1_reference(const float * GGML_RESTRICT x, block_q5_1 * GGML_RESTRICT y, int k); +void quantize_row_q8_0_reference(const float * GGML_RESTRICT x, block_q8_0 * GGML_RESTRICT y, int k); +void quantize_row_q8_1_reference(const float * GGML_RESTRICT x, block_q8_1 * GGML_RESTRICT y, int k); -void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict y, int k); -void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict y, int k); -void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict y, int k); -void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict y, int k); -void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict y, int k); -void quantize_row_q8_K_reference(const float * restrict x, block_q8_K * restrict y, int k); -void quantize_row_iq3_xxs_reference(const float * restrict x, block_iq3_xxs * restrict y, int k); +void quantize_row_q2_K_reference(const float * GGML_RESTRICT x, block_q2_K * GGML_RESTRICT y, int k); +void quantize_row_q3_K_reference(const float * GGML_RESTRICT x, block_q3_K * GGML_RESTRICT y, int k); +void quantize_row_q4_K_reference(const float * GGML_RESTRICT x, block_q4_K * GGML_RESTRICT y, int k); +void quantize_row_q5_K_reference(const float * GGML_RESTRICT x, block_q5_K * GGML_RESTRICT y, int k); +void quantize_row_q6_K_reference(const float * GGML_RESTRICT x, block_q6_K * GGML_RESTRICT y, int k); +void quantize_row_q8_K_reference(const float * GGML_RESTRICT x, block_q8_K * GGML_RESTRICT y, int k); +void quantize_row_iq3_xxs_reference(const float * GGML_RESTRICT x, block_iq3_xxs * GGML_RESTRICT y, int k); -void quantize_row_q4_0(const float * restrict x, void * restrict y, int k); -void quantize_row_q4_1(const float * restrict x, void * restrict y, int k); -void quantize_row_q5_0(const float * restrict x, void * restrict y, int k); -void quantize_row_q5_1(const float * restrict x, void * restrict y, int k); -void quantize_row_q8_0(const float * restrict x, void * restrict y, int k); -void quantize_row_q8_1(const float * restrict x, void * restrict y, int k); +void quantize_row_q4_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q4_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q5_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q5_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q8_0(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q8_1(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); -void quantize_row_q2_K(const float * restrict x, void * restrict y, int k); -void quantize_row_q3_K(const float * restrict x, void * restrict y, int k); -void quantize_row_q4_K(const float * restrict x, void * restrict y, int k); -void quantize_row_q5_K(const float * restrict x, void * restrict y, int k); -void quantize_row_q6_K(const float * restrict x, void * restrict y, int k); -void quantize_row_q8_K(const float * restrict x, void * restrict y, int k); -void quantize_row_iq3_xxs(const float * restrict x, void * restrict y, int k); +void quantize_row_q2_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q3_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q4_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q5_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q6_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_q8_K(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); +void quantize_row_iq3_xxs(const float * GGML_RESTRICT x, void * GGML_RESTRICT y, int k); // Dequantization -void dequantize_row_q4_0(const block_q4_0 * restrict x, float * restrict y, int k); -void dequantize_row_q4_1(const block_q4_1 * restrict x, float * restrict y, int k); -void dequantize_row_q5_0(const block_q5_0 * restrict x, float * restrict y, int k); -void dequantize_row_q5_1(const block_q5_1 * restrict x, float * restrict y, int k); -void dequantize_row_q8_0(const block_q8_0 * restrict x, float * restrict y, int k); -//void dequantize_row_q8_1(const block_q8_1 * restrict x, float * restrict y, int k); +void dequantize_row_q4_0(const block_q4_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q4_1(const block_q4_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q5_0(const block_q5_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q5_1(const block_q5_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q8_0(const block_q8_0 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +//void dequantize_row_q8_1(const block_q8_1 * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); -void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int k); -void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int k); -void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int k); -void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int k); -void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int k); -void dequantize_row_q8_K(const block_q8_K * restrict x, float * restrict y, int k); -void dequantize_row_iq2_xxs(const block_iq2_xxs * restrict x, float * restrict y, int k); -void dequantize_row_iq2_xs (const block_iq2_xs * restrict x, float * restrict y, int k); -void dequantize_row_iq3_xxs(const block_iq3_xxs * restrict x, float * restrict y, int k); +void dequantize_row_q2_K(const block_q2_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q3_K(const block_q3_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q4_K(const block_q4_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q5_K(const block_q5_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q6_K(const block_q6_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_q8_K(const block_q8_K * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_iq2_xxs(const block_iq2_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_iq2_xs (const block_iq2_xs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); +void dequantize_row_iq3_xxs(const block_iq3_xxs * GGML_RESTRICT x, float * GGML_RESTRICT y, int k); // Dot product -void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, const void * restrict vx, const void * restrict vy); +void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q5_1_q8_1(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q8_0_q8_0(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); -void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_iq2_xs_q8_K (int n, float * restrict s, const void * restrict vx, const void * restrict vy); -void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, const void * restrict vx, const void * restrict vy); +void ggml_vec_dot_q2_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q3_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q4_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q5_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_q6_K_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_iq2_xxs_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_iq2_xs_q8_K (int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); +void ggml_vec_dot_iq3_xxs_q8_K(int n, float * GGML_RESTRICT s, const void * GGML_RESTRICT vx, const void * GGML_RESTRICT vy); // // Quantization utilizing an importance matrix (a.k.a. "Activation aWare Quantization") @@ -276,3 +280,8 @@ void iq2xs_init_impl(int grid_size); void iq2xs_free_impl(int grid_size); void iq3xs_init_impl(int grid_size); void iq3xs_free_impl(int grid_size); + +#ifdef __cplusplus +} +#endif + From 78b00dda6c0d62c34f5371d47718defff6ed2b22 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Mon, 5 Feb 2024 15:55:10 +0100 Subject: [PATCH 120/143] README: updated introduction (#5343) * README: updated introduction * readme : update --------- Co-authored-by: Georgi Gerganov --- README.md | 49 ++++++++++++++++++++++++++++++------------------- 1 file changed, 30 insertions(+), 19 deletions(-) diff --git a/README.md b/README.md index a6fe34629..bb6c49338 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ [Roadmap](https://github.com/users/ggerganov/projects/7) / [Project status](https://github.com/ggerganov/llama.cpp/discussions/3471) / [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++ +Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++ ### Hot topics @@ -58,18 +58,20 @@ Inference of [LLaMA](https://arxiv.org/abs/2302.13971) model in pure C/C++ ## Description -The main goal of `llama.cpp` is to run the LLaMA model using 4-bit integer quantization on a MacBook +The main goal of `llama.cpp` is to enable LLM inference with minimal setup and state-of-the-art performance on a wide +variety of hardware - locally and in the cloud. -- Plain C/C++ implementation without dependencies -- Apple silicon first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks +- Plain C/C++ implementation without any dependencies +- Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks - AVX, AVX2 and AVX512 support for x86 architectures -- Mixed F16 / F32 precision -- 2-bit, 3-bit, 4-bit, 5-bit, 6-bit and 8-bit integer quantization support -- CUDA, Metal, OpenCL, SYCL GPU backend support +- 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use +- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP) +- Vulkan, SYCL, and (partial) OpenCL backend support +- CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity -The original implementation of `llama.cpp` was [hacked in an evening](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022). -Since then, the project has improved significantly thanks to many contributions. This project is mainly for educational purposes and serves -as the main playground for developing new features for the [ggml](https://github.com/ggerganov/ggml) library. +Since its [inception](https://github.com/ggerganov/llama.cpp/issues/33#issuecomment-1465108022), the project has +improved significantly thanks to many contributions. It is the main playground for developing new features for the +[ggml](https://github.com/ggerganov/ggml) library. **Supported platforms:** @@ -77,11 +79,14 @@ as the main playground for developing new features for the [ggml](https://github - [X] Linux - [X] Windows (via CMake) - [X] Docker +- [X] FreeBSD **Supported models:** - [X] LLaMA 🦙 - [x] LLaMA 2 🦙🦙 +- [X] [Mistral AI v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) +- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral) - [X] Falcon - [X] [Alpaca](https://github.com/ggerganov/llama.cpp#instruction-mode-with-alpaca) - [X] [GPT4All](https://github.com/ggerganov/llama.cpp#using-gpt4all) @@ -95,7 +100,6 @@ as the main playground for developing new features for the [ggml](https://github - [X] [Baichuan 1 & 2](https://huggingface.co/models?search=baichuan-inc/Baichuan) + [derivations](https://huggingface.co/hiyouga/baichuan-7b-sft) - [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila) - [X] [Starcoder models](https://github.com/ggerganov/llama.cpp/pull/3187) -- [X] [Mistral AI v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) - [X] [Refact](https://huggingface.co/smallcloudai/Refact-1_6B-fim) - [X] [Persimmon 8B](https://github.com/ggerganov/llama.cpp/pull/3410) - [X] [MPT](https://github.com/ggerganov/llama.cpp/pull/3417) @@ -104,15 +108,14 @@ as the main playground for developing new features for the [ggml](https://github - [X] [StableLM-3b-4e1t](https://github.com/ggerganov/llama.cpp/pull/3586) - [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek) - [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen) -- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral) - [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557) - [x] [GPT-2](https://huggingface.co/gpt2) - [x] [CodeShell](https://github.com/WisdomShell/codeshell) **Multimodal models:** -- [x] [Llava 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e) -- [x] [Bakllava](https://huggingface.co/models?search=SkunkworksAI/Bakllava) +- [x] [LLaVA 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e) +- [x] [BakLLaVA](https://huggingface.co/models?search=SkunkworksAI/Bakllava) - [x] [Obsidian](https://huggingface.co/NousResearch/Obsidian-3B-V0.5) - [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V) - [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM) @@ -137,14 +140,22 @@ as the main playground for developing new features for the [ggml](https://github **UI:** +Unless otherwise noted these projects are open-source with permissive licensing: + +- [iohub/collama](https://github.com/iohub/coLLaMA) +- [janhq/jan](https://github.com/janhq/jan) (AGPL) - [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) -- [semperai/amica](https://github.com/semperai/amica) +- [LMStudio](https://lmstudio.ai/) (proprietary) +- [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL) +- [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile) +- [nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all) +- [ollama/ollama](https://github.com/ollama/ollama) +- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) (AGPL) - [psugihara/FreeChat](https://github.com/psugihara/FreeChat) - [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) -- [iohub/collama](https://github.com/iohub/coLLaMA) -- [pythops/tenere](https://github.com/pythops/tenere) +- [pythops/tenere](https://github.com/pythops/tenere) (AGPL) +- [semperai/amica](https://github.com/semperai/amica) +- [withcatai/catai](https://github.com/withcatai/catai) --- From 098f6d737b65134cf220d12b9b706e8cfc5e4610 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Mon, 5 Feb 2024 19:33:00 +0100 Subject: [PATCH 121/143] make: Use ccache for faster compilation (#5318) * make: Use ccache for faster compilation --- CMakeLists.txt | 4 +- Makefile | 169 ++++++++++++++++++++++++++++++++++--------------- 2 files changed, 121 insertions(+), 52 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 8c04e4c19..427015be5 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -809,9 +809,9 @@ if (LLAMA_CCACHE) if (LLAMA_CCACHE_FOUND) set_property(GLOBAL PROPERTY RULE_LAUNCH_COMPILE ccache) set(ENV{CCACHE_SLOPPINESS} time_macros) - message(STATUS "Using ccache") + message(STATUS "ccache found, compilation results will be cached. Disable with LLAMA_CCACHE=OFF.") else() - message(STATUS "Warning: ccache not found - consider installing it or use LLAMA_CCACHE=OFF") + message(STATUS "Warning: ccache not found - consider installing it for faster compilation or disable this warning with LLAMA_CCACHE=OFF") endif () endif() diff --git a/Makefile b/Makefile index 21d5e15ba..ba73f0637 100644 --- a/Makefile +++ b/Makefile @@ -112,6 +112,18 @@ MK_CXXFLAGS += -O3 MK_NVCCFLAGS += -O3 endif +ifndef LLAMA_NO_CCACHE +CCACHE := $(shell which ccache) +ifdef CCACHE +export CCACHE_SLOPPINESS = time_macros +$(info I ccache found, compilation results will be cached. Disable with LLAMA_NO_CCACHE.) +CC := $(CCACHE) $(CC) +CXX := $(CCACHE) $(CXX) +else +$(info I ccache not found. Consider installing it for faster compilation.) +endif # CCACHE +endif # LLAMA_NO_CCACHE + # clock_gettime came in POSIX.1b (1993) # CLOCK_MONOTONIC came in POSIX.1-2001 / SUSv3 as optional # posix_memalign came in POSIX.1-2001 / SUSv3 @@ -374,9 +386,9 @@ ifdef LLAMA_DEBUG MK_NVCCFLAGS += -lineinfo endif # LLAMA_DEBUG ifdef LLAMA_CUDA_NVCC - NVCC = $(LLAMA_CUDA_NVCC) + NVCC = $(CCACHE) $(LLAMA_CUDA_NVCC) else - NVCC = nvcc + NVCC = $(CCACHE) nvcc endif #LLAMA_CUDA_NVCC ifdef CUDA_DOCKER_ARCH MK_NVCCFLAGS += -Wno-deprecated-gpu-targets -arch=$(CUDA_DOCKER_ARCH) @@ -483,7 +495,7 @@ ifdef LLAMA_HIPBLAS ROCM_PATH ?= /opt/rocm GPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch) endif - HIPCC ?= $(ROCM_PATH)/bin/hipcc + HIPCC ?= $(CCACHE) $(ROCM_PATH)/bin/hipcc LLAMA_CUDA_DMMV_X ?= 32 LLAMA_CUDA_MMV_Y ?= 1 LLAMA_CUDA_KQUANTS_ITER ?= 2 @@ -607,97 +619,135 @@ libllama.a: llama.o ggml.o $(OBJS) $(COMMON_DEPS) clean: rm -vrf *.o tests/*.o *.so *.a *.dll benchmark-matmult common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) + find examples pocs -type f -name "*.o" -delete # # Examples # +# $< is the first prerequisite, i.e. the source file. +# Explicitly compile this to an object file so that it can be cached with ccache. +# The source file is then filtered out from $^ (the list of all prerequisites) and the object file is added instead. + +# Helper function that replaces .c, .cpp, and .cu file endings with .o: +GET_OBJ_FILE = $(patsubst %.c,%.o,$(patsubst %.cpp,%.o,$(patsubst %.cu,%.o,$(1)))) + main: examples/main/main.cpp ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) @echo @echo '==== Run ./main -h for help. ====' @echo infill: examples/infill/infill.cpp ggml.o llama.o $(COMMON_DEPS) console.o grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) simple: examples/simple/simple.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tokenize: examples/tokenize/tokenize.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) batched: examples/batched/batched.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) batched-bench: examples/batched-bench/batched-bench.cpp build-info.o ggml.o llama.o common.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) quantize: examples/quantize/quantize.cpp build-info.o ggml.o llama.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) quantize-stats: examples/quantize-stats/quantize-stats.cpp build-info.o ggml.o llama.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) perplexity: examples/perplexity/perplexity.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) imatrix: examples/imatrix/imatrix.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) embedding: examples/embedding/embedding.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) server: examples/server/server.cpp examples/server/oai.hpp examples/server/utils.hpp examples/server/httplib.h examples/server/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h,$(filter-out %.hpp,$^)) -o $@ $(LDFLAGS) $(LWINSOCK2) -Wno-cast-qual + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) -c examples/llava/clip.cpp -o $(call GET_OBJ_FILE, examples/llava/clip.cpp) -Wno-cast-qual + $(CXX) $(CXXFLAGS) -Iexamples/server $(filter-out %.h %.hpp $< examples/llava/clip.cpp,$^) $(call GET_OBJ_FILE, $<) $(call GET_OBJ_FILE, examples/llava/clip.cpp) -o $@ $(LDFLAGS) $(LWINSOCK2) gguf: examples/gguf/gguf.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) train-text-from-scratch: examples/train-text-from-scratch/train-text-from-scratch.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) 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) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) llama-bench: examples/llama-bench/llama-bench.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) libllava.a: examples/llava/llava.cpp examples/llava/llava.h examples/llava/clip.cpp examples/llava/clip.h common/stb_image.h common/base64.hpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) $(CXX) $(CXXFLAGS) -static -fPIC -c $< -o $@ -Wno-cast-qual llava-cli: examples/llava/llava-cli.cpp examples/llava/clip.h examples/llava/clip.cpp examples/llava/llava.h examples/llava/llava.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) -Wno-cast-qual + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) -c examples/llava/clip.cpp -o $(call GET_OBJ_FILE, examples/llava/clip.cpp) -Wno-cast-qual + $(CXX) $(CXXFLAGS) -c examples/llava/llava.cpp -o $(call GET_OBJ_FILE, examples/llava/llava.cpp) + $(CXX) $(CXXFLAGS) $(filter-out %.h $< examples/llava/clip.cpp examples/llava/llava.cpp,$^) $(call GET_OBJ_FILE, $<) $(call GET_OBJ_FILE, examples/llava/clip.cpp) $(call GET_OBJ_FILE, examples/llava/llava.cpp) -o $@ $(LDFLAGS) baby-llama: examples/baby-llama/baby-llama.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) beam-search: examples/beam-search/beam-search.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) finetune: examples/finetune/finetune.cpp ggml.o llama.o $(COMMON_DEPS) train.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) export-lora: examples/export-lora/export-lora.cpp ggml.o common/common.h $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) speculative: examples/speculative/speculative.cpp ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) parallel: examples/parallel/parallel.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) lookahead: examples/lookahead/lookahead.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) lookup: examples/lookup/lookup.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) passkey: examples/passkey/passkey.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) ifeq ($(UNAME_S),Darwin) swift: examples/batched.swift @@ -705,7 +755,7 @@ swift: examples/batched.swift endif common/build-info.cpp: $(wildcard .git/index) scripts/build-info.sh - @sh scripts/build-info.sh $(CC) > $@.tmp + @sh scripts/build-info.sh "$(CC)" > $@.tmp @if ! cmp -s $@.tmp $@; then \ mv $@.tmp $@; \ else \ @@ -722,7 +772,8 @@ build-info.o: common/build-info.cpp tests: $(TEST_TARGETS) benchmark-matmult: examples/benchmark/benchmark-matmult.cpp build-info.o ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) run-benchmark-matmult: benchmark-matmult ./$@ @@ -730,58 +781,76 @@ run-benchmark-matmult: benchmark-matmult .PHONY: run-benchmark-matmult swift vdot: pocs/vdot/vdot.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) q8dot: pocs/vdot/q8dot.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $^ -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-llama-grammar: tests/test-llama-grammar.cpp ggml.o grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-grammar-parser: tests/test-grammar-parser.cpp ggml.o llama.o grammar-parser.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-double-float: tests/test-double-float.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-grad0: tests/test-grad0.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-opt: tests/test-opt.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-quantize-fns: tests/test-quantize-fns.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-quantize-perf: tests/test-quantize-perf.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-sampling: tests/test-sampling.cpp ggml.o llama.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-tokenizer-0-falcon: tests/test-tokenizer-0-falcon.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-tokenizer-0-llama: tests/test-tokenizer-0-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-tokenizer-1-bpe: tests/test-tokenizer-1-bpe.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-tokenizer-1-llama: tests/test-tokenizer-1-llama.cpp ggml.o llama.o $(COMMON_DEPS) console.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-rope: tests/test-rope.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-c.o: tests/test-c.c llama.h $(CC) $(CFLAGS) -c $(filter-out %.h,$^) -o $@ tests/test-backend-ops: tests/test-backend-ops.cpp ggml.o $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-model-load-cancel: tests/test-model-load-cancel.cpp ggml.o llama.o tests/get-model.cpp $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) tests/test-autorelease: tests/test-autorelease.cpp ggml.o llama.o tests/get-model.cpp $(COMMON_DEPS) $(OBJS) - $(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS) + $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) + $(CXX) $(CXXFLAGS) $(filter-out %.h $<,$^) $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) From 906cff55c2848fda091d888a1585915ec0c9ea9e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 6 Feb 2024 07:47:22 +0200 Subject: [PATCH 122/143] py : handle byte tokens in `get_token_type` (#5341) * py : handle byte tokens in `get_token_type` * py : fix empty bytes arg --- convert.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/convert.py b/convert.py index 75c100118..4a2847a27 100755 --- a/convert.py +++ b/convert.py @@ -515,10 +515,14 @@ class HfVocab: # Yield token text, score, and type yield token_text, self.get_token_score(token_id), self.get_token_type( - token_id, self.special_ids # Reuse already stored special IDs + token_id, token_text, self.special_ids # Reuse already stored special IDs ) - def get_token_type(self, token_id: int, special_ids: set[int]) -> gguf.TokenType: + def get_token_type(self, token_id: int, token_text: bytes, special_ids: set[int]) -> gguf.TokenType: + # Special case for byte tokens + if re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text): + return gguf.TokenType.BYTE + # Determine token type based on whether it's a special token return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL @@ -530,7 +534,7 @@ class HfVocab: def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: for text in self.added_tokens_list: if text in self.specials: - toktype = self.get_token_type(self.specials[text], self.special_ids) + toktype = self.get_token_type(self.specials[text], b'', self.special_ids) score = self.get_token_score(self.specials[text]) else: toktype = gguf.TokenType.USER_DEFINED From 4ffc7a17d4e80c5f3f905139cb570ed9b6934fcb Mon Sep 17 00:00:00 2001 From: Niall Coates <1349685+Niall-@users.noreply.github.com> Date: Tue, 6 Feb 2024 08:16:23 +0000 Subject: [PATCH 123/143] server : various fixes for the prompt field in /completion (#5300) server : fix deadlock when prompt array contains strings and numbers server : removed an unnecessary generation when generating multi-prompts server : removed an unnecessary assert --- examples/server/server.cpp | 34 +++++++++++++++++++++++++++------- 1 file changed, 27 insertions(+), 7 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 8000fee5c..fc7e723a1 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1163,13 +1163,30 @@ struct llama_server_context task.multitask_id = multitask_id; // when a completion task's prompt array is not a singleton, we split it into multiple requests - if (task.data.count("prompt") && task.data.at("prompt").size() > 1) - { - split_multiprompt_task(task_id, task); - } - // otherwise, it's a single-prompt task, we actually queue it - queue_tasks.post(task); + // if there's numbers in the prompt array it will be treated as an array of tokens + if (task.data.count("prompt") != 0 && task.data.at("prompt").size() > 1) { + bool numbers = false; + for (const auto& e : task.data.at("prompt")) { + if (e.is_number()) { + numbers = true; + break; + } + } + + // NOTE: split_multiprompt_task() does not handle a mix of strings and numbers, + // it will completely stall the server. I don't know where the bug for this is. + // + // if there are numbers, it needs to be treated like a single prompt, + // queue_tasks handles a mix of strings and numbers just fine. + if (numbers) { + queue_tasks.post(task); + } else { + split_multiprompt_task(task_id, task); + } + } else { + queue_tasks.post(task); + } } // for multiple images processing @@ -1251,7 +1268,10 @@ struct llama_server_context void split_multiprompt_task(int multitask_id, task_server& multiprompt_task) { int prompt_count = multiprompt_task.data.at("prompt").size(); - assert(prompt_count > 1); + if (prompt_count <= 1) { + send_error(multiprompt_task, "error while handling multiple prompts"); + return; + } // generate all the ID for subtask std::vector subtask_ids(prompt_count); From 31e790322133a4b1d0684527ea446e765e8a96cf Mon Sep 17 00:00:00 2001 From: Michael Coppola Date: Tue, 6 Feb 2024 04:20:00 -0500 Subject: [PATCH 124/143] server : add `dynatemp_range` and `dynatemp_exponent` (#5352) * server: added `dynatemp_range` and `dynatemp_exponent` * Update README.md --------- Co-authored-by: Michael Coppola --- examples/server/README.md | 4 ++++ examples/server/server.cpp | 46 +++++++++++++++++++++----------------- 2 files changed, 29 insertions(+), 21 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index d8e7c313e..46d8f85ae 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -137,6 +137,10 @@ node index.js `temperature`: Adjust the randomness of the generated text (default: 0.8). + `dynatemp_range`: Dynamic temperature range (default: 0.0, 0.0 = disabled). + + `dynatemp_exponent`: Dynamic temperature exponent (default: 1.0). + `top_k`: Limit the next token selection to the K most probable tokens (default: 40). `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.95). diff --git a/examples/server/server.cpp b/examples/server/server.cpp index fc7e723a1..e48a1da75 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -524,27 +524,29 @@ struct llama_server_context slot->oaicompat_model = ""; } - slot->params.stream = json_value(data, "stream", false); - slot->params.cache_prompt = json_value(data, "cache_prompt", false); - slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict); - slot->sparams.top_k = json_value(data, "top_k", default_sparams.top_k); - slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p); - slot->sparams.min_p = json_value(data, "min_p", default_sparams.min_p); - slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z); - slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p); - slot->sparams.temp = json_value(data, "temperature", default_sparams.temp); - slot->sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n); - slot->sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat); - slot->sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq); - slot->sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present); - slot->sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat); - slot->sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau); - slot->sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta); - slot->sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl); - slot->params.n_keep = json_value(data, "n_keep", slot->params.n_keep); - slot->params.seed = json_value(data, "seed", default_params.seed); - slot->sparams.grammar = json_value(data, "grammar", default_sparams.grammar); - slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); + slot->params.stream = json_value(data, "stream", false); + slot->params.cache_prompt = json_value(data, "cache_prompt", false); + slot->params.n_predict = json_value(data, "n_predict", default_params.n_predict); + slot->sparams.top_k = json_value(data, "top_k", default_sparams.top_k); + slot->sparams.top_p = json_value(data, "top_p", default_sparams.top_p); + slot->sparams.min_p = json_value(data, "min_p", default_sparams.min_p); + slot->sparams.tfs_z = json_value(data, "tfs_z", default_sparams.tfs_z); + slot->sparams.typical_p = json_value(data, "typical_p", default_sparams.typical_p); + slot->sparams.temp = json_value(data, "temperature", default_sparams.temp); + slot->sparams.dynatemp_range = json_value(data, "dynatemp_range", default_sparams.dynatemp_range); + slot->sparams.dynatemp_exponent = json_value(data, "dynatemp_exponent", default_sparams.dynatemp_exponent); + slot->sparams.penalty_last_n = json_value(data, "repeat_last_n", default_sparams.penalty_last_n); + slot->sparams.penalty_repeat = json_value(data, "repeat_penalty", default_sparams.penalty_repeat); + slot->sparams.penalty_freq = json_value(data, "frequency_penalty", default_sparams.penalty_freq); + slot->sparams.penalty_present = json_value(data, "presence_penalty", default_sparams.penalty_present); + slot->sparams.mirostat = json_value(data, "mirostat", default_sparams.mirostat); + slot->sparams.mirostat_tau = json_value(data, "mirostat_tau", default_sparams.mirostat_tau); + slot->sparams.mirostat_eta = json_value(data, "mirostat_eta", default_sparams.mirostat_eta); + slot->sparams.penalize_nl = json_value(data, "penalize_nl", default_sparams.penalize_nl); + slot->params.n_keep = json_value(data, "n_keep", slot->params.n_keep); + slot->params.seed = json_value(data, "seed", default_params.seed); + slot->sparams.grammar = json_value(data, "grammar", default_sparams.grammar); + slot->sparams.n_probs = json_value(data, "n_probs", default_sparams.n_probs); // infill if (data.count("input_prefix") != 0) @@ -1002,6 +1004,8 @@ struct llama_server_context {"model", params.model_alias}, {"seed", slot.params.seed}, {"temperature", slot.sparams.temp}, + {"dynatemp_range", slot.sparams.dynatemp_range}, + {"dynatemp_exponent", slot.sparams.dynatemp_exponent}, {"top_k", slot.sparams.top_k}, {"top_p", slot.sparams.top_p}, {"min_p", slot.sparams.min_p}, From 8a79c591de9b7ff3242a94f68b7fb5a17ed8c2be Mon Sep 17 00:00:00 2001 From: Justin Parker Date: Tue, 6 Feb 2024 04:20:59 -0500 Subject: [PATCH 125/143] server : include total "num_slots" in props endpoint (#5349) --- examples/server/server.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index e48a1da75..d86d7e04a 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -432,6 +432,7 @@ struct llama_server_context } default_generation_settings_for_props = get_formated_generation(slots.front()); + default_generation_settings_for_props["num_slots"] = params.n_parallel; default_generation_settings_for_props["seed"] = -1; batch = llama_batch_init(n_ctx, 0, params.n_parallel); From 2c516611f1d0f1e5e9754f8ea1cf97cb1b17bf2c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Tue, 6 Feb 2024 14:44:06 +0100 Subject: [PATCH 126/143] CUDA: mul_mat_vec_q for batch sizes > 1 (#5351) --- ggml-cuda.cu | 240 +++++++++++++++++++++------------------------------ 1 file changed, 98 insertions(+), 142 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 3242a0b4a..95161b3f4 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -5310,41 +5310,50 @@ template static __global__ void #endif // __CUDA_ARCH__ >= CC_VOLTA } -template -static __global__ void mul_mat_vec_q(const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, const int ncols, const int nrows) { +template +static __global__ void mul_mat_vec_q( + const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, + const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y_par) { + + const int ncols_y = ncols_y_template != 0 ? ncols_y_template : ncols_y_par; + const int row = blockIdx.x*blockDim.y + threadIdx.y; - if (row >= nrows) { + if (row >= nrows_x) { return; } - const int blocks_per_row = ncols / qk; + const int blocks_per_row_x = ncols_x / qk; + const int blocks_per_col_y = nrows_y / QK8_1; const int blocks_per_warp = vdr * WARP_SIZE / qi; // partial sum for each thread - float tmp = 0.0f; + float tmp[ncols_y_template != 0 ? ncols_y_template : 8] = {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 = threadIdx.x / (qi/vdr); i < blocks_per_row; i += blocks_per_warp) { - const int ibx = row*blocks_per_row + i; // x block index + for (int i = threadIdx.x / (qi/vdr); i < blocks_per_row_x; i += blocks_per_warp) { + const int ibx = row*blocks_per_row_x + i; // x block index const int iby = i * (qk/QK8_1); // y block index that aligns with ibx const int iqs = vdr * (threadIdx.x % (qi/vdr)); // x block quant index when casting the quants to int - tmp += vec_dot_q_cuda(&x[ibx], &y[iby], iqs); +#pragma unroll + for (int j = 0; j < ncols_y; ++j) { + tmp[j] += vec_dot_q_cuda(&x[ibx], &y[j*blocks_per_col_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); - } + for (int j = 0; j < ncols_y; ++j) { + tmp[j] = warp_reduce_sum(tmp[j]); - if (threadIdx.x == 0) { - dst[row] = tmp; + if (threadIdx.x == 0) { + dst[j*nrows_x + row] = tmp[j]; + } } } @@ -6816,121 +6825,56 @@ static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, floa <<>>(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 % QK4_0 == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} +template +static void mul_mat_vec_q_cuda( + const void * vx, const void * vy, float * dst, + const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, cudaStream_t stream) { -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 % QK4_1 == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); -} + GGML_ASSERT(ncols_x % qk == 0); + GGML_ASSERT(ncols_y <= 8); -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 % QK5_0 == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; + const int block_num_y = (nrows_x + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const dim3 block_nums(block_num_y, 1, 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 % QK5_1 == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 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 % QK8_0 == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 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_q2_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 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_q3_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 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_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 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_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 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_q6_K_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 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_iq2_xxs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 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_iq2_xs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 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_iq3_xxs_q8_1_cuda(const void * vx, const void * vy, float * dst, const int ncols, const int nrows, cudaStream_t stream) { - GGML_ASSERT(ncols % QK_K == 0); - const int block_num_y = (nrows + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; - const dim3 block_nums(block_num_y, 1, 1); - const dim3 block_dims(WARP_SIZE, GGML_CUDA_MMV_Y, 1); - mul_mat_vec_q - <<>>(vx, vy, dst, ncols, nrows); + switch (ncols_y) { + case 1: + mul_mat_vec_q<1, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + case 2: + mul_mat_vec_q<2, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + case 3: + mul_mat_vec_q<3, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + case 4: + mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + case 5: + mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + case 6: + mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + case 7: + mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + case 8: + mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot> + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + default: + GGML_ASSERT(false); + // mul_mat_vec_q<0, qk, qi, block_q_t, vdr, vec_dot> + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + break; + } } static void ggml_mul_mat_q4_0_q8_1_cuda( @@ -8578,50 +8522,61 @@ static void ggml_cuda_op_mul_mat_vec_q( const char * src1_ddq_i, float * dst_dd_i, const int64_t row_low, const int64_t row_high, const int64_t src1_ncols, const int64_t src1_padded_row_size, cudaStream_t stream) { - GGML_ASSERT(ggml_nrows(src1) == 1); - const int64_t ne00 = src0->ne[0]; const int64_t row_diff = row_high - row_low; switch (src0->type) { case GGML_TYPE_Q4_0: - mul_mat_vec_q4_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q4_1: - mul_mat_vec_q4_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q5_0: - mul_mat_vec_q5_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q5_1: - mul_mat_vec_q5_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q8_0: - mul_mat_vec_q8_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q2_K: - mul_mat_vec_q2_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q3_K: - mul_mat_vec_q3_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q4_K: - mul_mat_vec_q4_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q5_K: - mul_mat_vec_q5_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_Q6_K: - mul_mat_vec_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_IQ2_XXS: - mul_mat_vec_iq2_xxs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_IQ2_XS: - mul_mat_vec_iq2_xs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; case GGML_TYPE_IQ3_XXS: - mul_mat_vec_iq3_xxs_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, stream); + mul_mat_vec_q_cuda + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); break; default: GGML_ASSERT(false); @@ -9945,17 +9900,18 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 #ifdef GGML_CUDA_FORCE_DMMV const bool use_mul_mat_vec_q = false; #else - const bool use_mul_mat_vec_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type) && ggml_nrows(src1) == 1; + const bool use_mul_mat_vec_q = min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type); #endif // GGML_CUDA_FORCE_DMMV if (use_mul_mat_vec_q) { - // NOTE: this kernel does not support ggml_nrows(src1) > 1 ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true); } else { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false); } } else { - if (use_mul_mat_q) { + if (src1->ne[1] <= 8 && min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type)) { + ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true); + } else if (use_mul_mat_q) { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true); } else { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_cublas, false); From 2e9c0bd6b301155ce749e162527fc55e9fb5b832 Mon Sep 17 00:00:00 2001 From: BarfingLemurs <128182951+BarfingLemurs@users.noreply.github.com> Date: Tue, 6 Feb 2024 09:06:48 -0500 Subject: [PATCH 127/143] readme : add phi, orion 14b, internlm2, and yi-VL to readme (#5362) --- README.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index bb6c49338..cc87ac797 100644 --- a/README.md +++ b/README.md @@ -105,11 +105,14 @@ improved significantly thanks to many contributions. It is the main playground f - [X] [MPT](https://github.com/ggerganov/llama.cpp/pull/3417) - [X] [Bloom](https://github.com/ggerganov/llama.cpp/pull/3553) - [x] [Yi models](https://huggingface.co/models?search=01-ai/Yi) -- [X] [StableLM-3b-4e1t](https://github.com/ggerganov/llama.cpp/pull/3586) +- [X] [StableLM models](https://huggingface.co/stabilityai) - [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek) - [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen) - [x] [PLaMo-13B](https://github.com/ggerganov/llama.cpp/pull/3557) +- [x] [Phi models](https://huggingface.co/models?search=microsoft/phi) - [x] [GPT-2](https://huggingface.co/gpt2) +- [x] [Orion 14B](https://github.com/ggerganov/llama.cpp/pull/5118) +- [x] [InternLM2](https://huggingface.co/models?search=internlm2) - [x] [CodeShell](https://github.com/WisdomShell/codeshell) **Multimodal models:** @@ -119,6 +122,7 @@ improved significantly thanks to many contributions. It is the main playground f - [x] [Obsidian](https://huggingface.co/NousResearch/Obsidian-3B-V0.5) - [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V) - [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM) +- [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL) **Bindings:** From f57fadc009cbff741a1961cb7896c47d73978d2c Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Tue, 6 Feb 2024 17:28:02 +0200 Subject: [PATCH 128/143] Slight quantization improvement for Q4_K and Q5_K (#5361) * Q4_K: slightly better quantization * Q5_K: slightly better quantization --------- Co-authored-by: Iwan Kawrakow --- ggml-quants.c | 75 +++++++++++++++++++++++---------------------------- 1 file changed, 33 insertions(+), 42 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 014c0525a..101d3e783 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -2381,19 +2381,20 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri uint8_t L[QK_K]; uint8_t Laux[32]; + uint8_t Ls[QK_K/32]; + uint8_t Lm[QK_K/32]; float weights[32]; - float mins[QK_K/32]; - float scales[QK_K/32]; + float sw[QK_K/32]; + float mins[QK_K/32]; + float scales[QK_K/32]; for (int i = 0; i < nb; i++) { float sum_x2 = 0; for (int l = 0; l < QK_K; ++l) sum_x2 += x[l] * x[l]; - float sigma2 = sum_x2/QK_K; + float sigma2 = 2*sum_x2/QK_K; float av_x = sqrtf(sigma2); - 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) { if (quant_weights) { const float * qw = quant_weights + QK_K*i + 32*j; @@ -2401,25 +2402,17 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri } else { for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]); } + float sumw = 0; + for (int l = 0; l < 32; ++l) sumw += weights[l]; + sw[j] = sumw; scales[j] = make_qkx3_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.9f, 0.05f, 36, false); - //scales[j] = make_qkx2_quants(32, 15, x + 32*j, weights, L + 32*j, &mins[j], Laux, -1.f, 0.1f, 20, false); - float scale = scales[j]; - if (scale > max_scale) { - max_scale = scale; - } - float min = mins[j]; - if (min > max_min) { - max_min = min; - } } - float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f; - float inv_min = max_min > 0 ? 63.f/max_min : 0.f; + float d_block = make_qp_quants(QK_K/32, 63, scales, Ls, sw); + float m_block = make_qp_quants(QK_K/32, 63, mins, Lm, sw); for (int j = 0; j < QK_K/32; ++j) { - uint8_t ls = nearest_int(inv_scale*scales[j]); - uint8_t lm = nearest_int(inv_min*mins[j]); - ls = MIN(63, ls); - lm = MIN(63, lm); + uint8_t ls = Ls[j]; + uint8_t lm = Lm[j]; if (j < 4) { y[i].scales[j] = ls; y[i].scales[j+4] = lm; @@ -2429,8 +2422,8 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri y[i].scales[j-0] |= ((lm >> 4) << 6); } } - y[i].d = GGML_FP32_TO_FP16(max_scale/63.f); - y[i].dmin = GGML_FP32_TO_FP16(max_min/63.f); + y[i].d = GGML_FP32_TO_FP16(d_block); + y[i].dmin = GGML_FP32_TO_FP16(m_block); uint8_t sc, m; for (int j = 0; j < QK_K/32; ++j) { @@ -2688,20 +2681,21 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri const int nb = n_per_row / QK_K; uint8_t L[QK_K]; - float mins[QK_K/32]; - float scales[QK_K/32]; - float weights[32]; uint8_t Laux[32]; + uint8_t Ls[QK_K/32]; + uint8_t Lm[QK_K/32]; + float mins[QK_K/32]; + float scales[QK_K/32]; + float sw[QK_K/32]; + float weights[32]; for (int i = 0; i < nb; i++) { float sum_x2 = 0; for (int l = 0; l < QK_K; ++l) sum_x2 += x[l] * x[l]; - float sigma2 = sum_x2/QK_K; + float sigma2 = 2*sum_x2/QK_K; float av_x = sqrtf(sigma2); - 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) { if (quant_weights) { const float * qw = quant_weights + QK_K*i + 32*j; @@ -2709,22 +2703,19 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri } else { for (int l = 0; l < 32; ++l) weights[l] = av_x + fabsf(x[32*j + l]); } + float sumw = 0; + for (int l = 0; l < 32; ++l) sumw += weights[l]; + sw[j] = sumw; + scales[j] = make_qkx3_quants(32, 31, x + 32*j, weights, L + 32*j, &mins[j], Laux, -0.9f, 0.05f, 36, false); - float scale = scales[j]; - if (scale > max_scale) { - max_scale = scale; - } - float min = mins[j]; - if (min > max_min) { - max_min = min; - } } - float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f; - float inv_min = max_min > 0 ? 63.f/max_min : 0.f; + float d_block = make_qp_quants(QK_K/32, 63, scales, Ls, sw); + float m_block = make_qp_quants(QK_K/32, 63, mins, Lm, sw); + for (int j = 0; j < QK_K/32; ++j) { - uint8_t ls = nearest_int(inv_scale*scales[j]); - uint8_t lm = nearest_int(inv_min*mins[j]); + uint8_t ls = Ls[j]; + uint8_t lm = Lm[j]; ls = MIN(63, ls); lm = MIN(63, lm); if (j < 4) { @@ -2736,8 +2727,8 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri y[i].scales[j-0] |= ((lm >> 4) << 6); } } - y[i].d = GGML_FP32_TO_FP16(max_scale/63.f); - y[i].dmin = GGML_FP32_TO_FP16(max_min/63.f); + y[i].d = GGML_FP32_TO_FP16(d_block); + y[i].dmin = GGML_FP32_TO_FP16(m_block); uint8_t sc, m; for (int j = 0; j < QK_K/32; ++j) { From b08f22c882a1443e6b97081f3ce718a4d1a741f8 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Tue, 6 Feb 2024 19:00:16 +0200 Subject: [PATCH 129/143] Update README.md (#5366) Add some links to quantization related PRs --- README.md | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index cc87ac797..34f2021f9 100644 --- a/README.md +++ b/README.md @@ -736,9 +736,21 @@ Several quantization methods are supported. They differ in the resulting model d | 13B | bits/weight | 16.0 | 4.5 | 5.0 | 5.5 | 6.0 | 8.5 | - [k-quants](https://github.com/ggerganov/llama.cpp/pull/1684) -- recent k-quants improvements +- recent k-quants improvements and new i-quants - [#2707](https://github.com/ggerganov/llama.cpp/pull/2707) - [#2807](https://github.com/ggerganov/llama.cpp/pull/2807) + - [#4773 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4773) + - [#4856 - 2-bit i-quants (inference)](https://github.com/ggerganov/llama.cpp/pull/4856) + - [#4861 - importance matrix](https://github.com/ggerganov/llama.cpp/pull/4861) + - [#4872 - MoE models](https://github.com/ggerganov/llama.cpp/pull/4872) + - [#4897 - 2-bit quantization](https://github.com/ggerganov/llama.cpp/pull/4897) + - [#4930 - imatrix for all k-quants](https://github.com/ggerganov/llama.cpp/pull/4930) + - [#4951 - imatrix on the GPU](https://github.com/ggerganov/llama.cpp/pull/4957) + - [#4969 - imatrix for legacy quants](https://github.com/ggerganov/llama.cpp/pull/4969) + - [#4996 - k-qunats tuning](https://github.com/ggerganov/llama.cpp/pull/4996) + - [#5060 - Q3_K_XS](https://github.com/ggerganov/llama.cpp/pull/5060) + - [#5196 - 3-bit i-quants](https://github.com/ggerganov/llama.cpp/pull/5196) + - [quantization tuning](https://github.com/ggerganov/llama.cpp/pull/5320), [another one](https://github.com/ggerganov/llama.cpp/pull/5334), and [another one](https://github.com/ggerganov/llama.cpp/pull/5361) ### Perplexity (measuring model quality) From 17c97fb0620448b37516a3f53fea6c482b0a30a4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Tue, 6 Feb 2024 18:43:06 +0100 Subject: [PATCH 130/143] CUDA: mul_mat_vec_q max. batch size 8 -> 4 (#5370) --- ggml-cuda.cu | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 95161b3f4..3b828375e 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -6831,7 +6831,7 @@ static void mul_mat_vec_q_cuda( const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, cudaStream_t stream) { GGML_ASSERT(ncols_x % qk == 0); - GGML_ASSERT(ncols_y <= 8); + GGML_ASSERT(ncols_y <= 4); const int block_num_y = (nrows_x + GGML_CUDA_MMV_Y - 1) / GGML_CUDA_MMV_Y; const dim3 block_nums(block_num_y, 1, 1); @@ -6853,22 +6853,22 @@ static void mul_mat_vec_q_cuda( mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot> <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); break; - case 5: - mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); - break; - case 6: - mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); - break; - case 7: - mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); - break; - case 8: - mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); - break; + // case 5: + // mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot> + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // break; + // case 6: + // mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot> + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // break; + // case 7: + // mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot> + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // break; + // case 8: + // mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot> + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // break; default: GGML_ASSERT(false); // mul_mat_vec_q<0, qk, qi, block_q_t, vdr, vec_dot> @@ -9909,7 +9909,7 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false); } } else { - if (src1->ne[1] <= 8 && min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type)) { + if (src1->ne[1] <= 4 && min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type)) { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true); } else if (use_mul_mat_q) { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true); From 213d1439fadefe182f69c5f7e8dd3b4b6572ebcb Mon Sep 17 00:00:00 2001 From: Alexey Parfenov Date: Tue, 6 Feb 2024 18:08:38 +0000 Subject: [PATCH 131/143] server : remove model.json endpoint (#5371) --- examples/server/completion.js.hpp | 448 +++++++++++++++------------ examples/server/public/completion.js | 3 +- examples/server/server.cpp | 11 - 3 files changed, 244 insertions(+), 218 deletions(-) diff --git a/examples/server/completion.js.hpp b/examples/server/completion.js.hpp index fe5f81228..f5e696e17 100644 --- a/examples/server/completion.js.hpp +++ b/examples/server/completion.js.hpp @@ -236,214 +236,250 @@ unsigned char completion_js[] = { 0x20, 0x4a, 0x53, 0x4f, 0x4e, 0x2e, 0x70, 0x61, 0x72, 0x73, 0x65, 0x28, 0x72, 0x65, 0x73, 0x75, 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0x2e, 0x64, 0x65, 0x66, 0x61, 0x75, 0x6c, 0x74, + 0x5f, 0x67, 0x65, 0x6e, 0x65, 0x72, 0x61, 0x74, 0x69, 0x6f, 0x6e, 0x5f, + 0x73, 0x65, 0x74, 0x74, 0x69, 0x6e, 0x67, 0x73, 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 = 5346; +unsigned int completion_js_len = 5782; diff --git a/examples/server/public/completion.js b/examples/server/public/completion.js index baaec1d60..ab38a7b40 100644 --- a/examples/server/public/completion.js +++ b/examples/server/public/completion.js @@ -195,7 +195,8 @@ export const llamaComplete = async (params, controller, callback) => { // 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()); + const props = await fetch("/props").then(r => r.json()); + generation_settings = props.default_generation_settings; } return generation_settings; } diff --git a/examples/server/server.cpp b/examples/server/server.cpp index d86d7e04a..9481ce6b1 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -990,11 +990,6 @@ struct llama_server_context queue_results.send(res); } - json get_model_props() - { - return get_formated_generation(slots[0]); - } - json get_formated_generation(llama_client_slot &slot) { const auto eos_bias = slot.sparams.logit_bias.find(llama_token_eos(model)); @@ -2895,12 +2890,6 @@ int main(int argc, char **argv) } }); - svr.Get("/model.json", [&llama](const httplib::Request &, httplib::Response &res) - { - const json data = llama.get_model_props(); - return res.set_content(data.dump(), "application/json; charset=utf-8"); - }); - svr.Options(R"(/.*)", [](const httplib::Request &, httplib::Response &res) { return res.set_content("", "application/json; charset=utf-8"); }); From f68664ac241a6b5c233d8f1051eef20929b06008 Mon Sep 17 00:00:00 2001 From: Sang-Kil Park Date: Wed, 7 Feb 2024 13:28:00 +0900 Subject: [PATCH 132/143] convert : fix TypeError on GPT-2 vocab.json (#5288) --- convert.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/convert.py b/convert.py index 4a2847a27..323e8058d 100755 --- a/convert.py +++ b/convert.py @@ -334,9 +334,9 @@ class Params: class BpeVocab: 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()) - try: + if isinstance(self.bpe_tokenizer.get('model'), dict): self.vocab = self.bpe_tokenizer["model"]["vocab"] - except KeyError: + else: self.vocab = self.bpe_tokenizer added_tokens: dict[str, int] if fname_added_tokens is not None: From f3e2b4fa3f81a410ecb7dec929c259ef8d8dbb7d Mon Sep 17 00:00:00 2001 From: Justin Parker Date: Wed, 7 Feb 2024 01:15:19 -0500 Subject: [PATCH 133/143] server : update `/props` with "total_slots" value (#5373) * include total "num_slots" in default_generation_settings_for_props * cleanup total_slots return value in /props endpoint * update /props endpoint docs with total_slots * remove num_slots from default_generation_settings_for_props * update /props endpoint section --- examples/server/README.md | 4 +++- examples/server/server.cpp | 4 ++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 46d8f85ae..1db7cdf21 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -276,13 +276,15 @@ Notice that each `probs` is an array of length `n_probs`. { "assistant_name": "", "user_name": "", - "default_generation_settings": { ... } + "default_generation_settings": { ... }, + "total_slots": 1 } ``` - `assistant_name` - the required assistant name to generate the prompt in case you have specified a system prompt for all slots. - `user_name` - the required anti-prompt to generate the prompt in case you have specified a system prompt for all slots. - `default_generation_settings` - the default generation settings for the `/completion` endpoint, has the same fields as the `generation_settings` response object from the `/completion` endpoint. +- `total_slots` - the total number of slots for process requests (defined by `--parallel` option) - **POST** `/v1/chat/completions`: OpenAI-compatible Chat Completions API. Given a ChatML-formatted json description in `messages`, it returns the predicted completion. Both synchronous and streaming mode are supported, so scripted and interactive applications work fine. While no strong claims of compatibility with OpenAI API spec is being made, in our experience it suffices to support many apps. Only ChatML-tuned models, such as Dolphin, OpenOrca, OpenHermes, OpenChat-3.5, etc can be used with this endpoint. Compared to `api_like_OAI.py` this API implementation does not require a wrapper to be served. diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 9481ce6b1..eceda30d0 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -432,7 +432,6 @@ struct llama_server_context } default_generation_settings_for_props = get_formated_generation(slots.front()); - default_generation_settings_for_props["num_slots"] = params.n_parallel; default_generation_settings_for_props["seed"] = -1; batch = llama_batch_init(n_ctx, 0, params.n_parallel); @@ -2639,7 +2638,8 @@ int main(int argc, char **argv) json data = { { "user_name", llama.name_user.c_str() }, { "assistant_name", llama.name_assistant.c_str() }, - { "default_generation_settings", llama.default_generation_settings_for_props } + { "default_generation_settings", llama.default_generation_settings_for_props }, + { "total_slots", llama.params.n_parallel } }; res.set_content(data.dump(), "application/json; charset=utf-8"); }); From 316c7faf7740fa98ea68f1445f4505810f706b9e Mon Sep 17 00:00:00 2001 From: runfuture Date: Wed, 7 Feb 2024 14:15:56 +0800 Subject: [PATCH 134/143] llama : add MiniCPM support (#5346) * support minicpm arch. * fix tab/space typo. * convert minicpm model via convert-hf-gguf.py * try to make tokenizer work * fix bug for quantize minicpm * fix for flake8 lint * remove convert-minicpm.py * fix for editorconfig * correct minicpm model type (size) * constants expanded for minicpm * Minor change of the constant names for minicpm --- convert-hf-to-gguf.py | 49 ++++++++++ gguf-py/gguf/constants.py | 21 +++++ llama.cpp | 190 +++++++++++++++++++++++++++++++++++++- 3 files changed, 259 insertions(+), 1 deletion(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 5e343742d..829d68368 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -22,6 +22,8 @@ if 'NO_LOCAL_GGUF' not in os.environ: sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf +from convert import HfVocab + # check for any of the given keys in the dictionary and return the value of the first key found def get_key_opts(d, keys): @@ -205,6 +207,8 @@ class Model: return OrionModel if model_architecture == "InternLM2ForCausalLM": return InternLM2Model + if model_architecture == "MiniCPMForCausalLM": + return MiniCPMModel return Model def _is_model_safetensors(self) -> bool: @@ -258,6 +262,8 @@ class Model: return gguf.MODEL_ARCH.ORION if arch == "InternLM2ForCausalLM": return gguf.MODEL_ARCH.INTERNLM2 + if arch == "MiniCPMForCausalLM": + return gguf.MODEL_ARCH.MINICPM raise NotImplementedError(f'Architecture "{arch}" not supported!') @@ -402,6 +408,31 @@ class Model: special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) special_vocab.add_to_gguf(self.gguf_writer) + def _set_vocab_hf(self): + path = self.dir_model + added_tokens_path = self.dir_model + vocab = HfVocab( + path, added_tokens_path if added_tokens_path.exists() else None + ) + tokens = [] + scores = [] + toktypes = [] + + for text, score, toktype in vocab.all_tokens(): + tokens.append(text) + scores.append(score) + toktypes.append(toktype) + + assert len(tokens) == vocab.vocab_size + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_token_list(tokens) + self.gguf_writer.add_token_scores(scores) + self.gguf_writer.add_token_types(toktypes) + + special_vocab = gguf.SpecialVocab(self.dir_model, n_vocab=len(tokens)) + special_vocab.add_to_gguf(self.gguf_writer) + class GPTNeoXModel(Model): def set_gguf_parameters(self): @@ -1041,6 +1072,24 @@ class MixtralModel(Model): self._set_vocab_sentencepiece() +class MiniCPMModel(Model): + def set_gguf_parameters(self): + block_count = self.hparams["num_hidden_layers"] + self.gguf_writer.add_name("MiniCPM") + self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) + self.gguf_writer.add_embedding_length(self.hparams["hidden_size"]) + self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) + self.gguf_writer.add_block_count(block_count) + self.gguf_writer.add_head_count(self.hparams["num_attention_heads"]) + self.gguf_writer.add_head_count_kv(self.hparams["num_key_value_heads"]) + self.gguf_writer.add_layer_norm_rms_eps(self.hparams["rms_norm_eps"]) + self.gguf_writer.add_file_type(self.ftype) + self.gguf_writer.add_rope_dimension_count(self.hparams["hidden_size"] // self.hparams["num_attention_heads"]) + + def set_vocab(self): + self._set_vocab_hf() + + class QwenModel(Model): @staticmethod def token_bytes_to_string(b): diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index ed8e26f83..1cfd41c0b 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -104,6 +104,7 @@ class MODEL_ARCH(IntEnum): CODESHELL = auto() ORION = auto() INTERNLM2 = auto() + MINICPM = auto() class MODEL_TENSOR(IntEnum): @@ -156,6 +157,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.CODESHELL: "codeshell", MODEL_ARCH.ORION: "orion", MODEL_ARCH.INTERNLM2: "internlm2", + MODEL_ARCH.MINICPM: "minicpm", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -464,6 +466,25 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.MINICPM: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], # TODO } diff --git a/llama.cpp b/llama.cpp index 65e399adc..f3c5146d1 100644 --- a/llama.cpp +++ b/llama.cpp @@ -205,6 +205,7 @@ enum llm_arch { LLM_ARCH_CODESHELL, LLM_ARCH_ORION, LLM_ARCH_INTERNLM2, + LLM_ARCH_MINICPM, LLM_ARCH_UNKNOWN, }; @@ -228,6 +229,7 @@ static std::map LLM_ARCH_NAMES = { { LLM_ARCH_CODESHELL, "codeshell" }, { LLM_ARCH_ORION, "orion" }, { LLM_ARCH_INTERNLM2, "internlm2" }, + { LLM_ARCH_MINICPM, "minicpm" }, }; enum llm_kv { @@ -690,6 +692,29 @@ static std::map> LLM_TENSOR_NAMES = { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_MINICPM, + { + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ROPE_FREQS, "rope_freqs" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_K, "blk.%d.attn_k" }, + { LLM_TENSOR_ATTN_V, "blk.%d.attn_v" }, + { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output" }, + { LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd" }, + { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" }, + { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm" }, + { LLM_TENSOR_FFN_GATE, "blk.%d.ffn_gate" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_FFN_GATE_EXP, "blk.%d.ffn_gate.%d" }, + { LLM_TENSOR_FFN_DOWN_EXP, "blk.%d.ffn_down.%d" }, + { LLM_TENSOR_FFN_UP_EXP, "blk.%d.ffn_up.%d" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -1390,6 +1415,7 @@ enum e_model { MODEL_UNKNOWN, MODEL_0_5B, MODEL_1B, + MODEL_2B, MODEL_3B, MODEL_4B, MODEL_7B, @@ -2748,6 +2774,7 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { static const char * llama_model_type_name(e_model type) { switch (type) { case MODEL_1B: return "1B"; + case MODEL_2B: return "2B"; case MODEL_3B: return "3B"; case MODEL_7B: return "7B"; case MODEL_8B: return "8B"; @@ -2887,6 +2914,13 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_MINICPM: + { + switch (hparams.n_layer) { + case 40: model.type = e_model::MODEL_2B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; case LLM_ARCH_FALCON: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); @@ -3524,13 +3558,16 @@ static bool llm_load_tensors( switch (model.arch) { case LLM_ARCH_LLAMA: case LLM_ARCH_REFACT: + case LLM_ARCH_MINICPM: { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // output { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + if (model.arch != LLM_ARCH_MINICPM){ + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}); + } } for (int i = 0; i < n_layer; ++i) { @@ -6781,6 +6818,153 @@ struct llm_build_context { return gf; } + // ref: https://arxiv.org/abs/2203.03466 + // https://github.com/ggerganov/llama.cpp/issues/5276#issuecomment-1925774738 + // based on the original build_llama() function + struct ggml_cgraph * build_minicpm() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head = hparams.n_embd_head_v; + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + GGML_ASSERT(n_embd_head == hparams.n_rot); + + const int64_t n_embd = hparams.n_embd; + //TODO: if the model varies, these parameters need to be read from the model + const int64_t n_embd_base = 256; + const float scale_embd = 12.0f; + const float scale_depth = 1.4f; + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, hparams, batch, model.tok_embd, lctx.inp_tokens, lctx.inp_embd, cb); + cb(inpL, "inp_embd", -1); + + // scale the input embeddings + inpL = ggml_scale(ctx0, inpL, scale_embd); + cb(inpL, "inp_scaled", -1); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = ggml_view_1d(ctx0, lctx.inp_pos, n_tokens, 0); + cb(inp_pos, "inp_pos", -1); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = ggml_view_2d(ctx0, lctx.inp_KQ_mask, n_kv, n_tokens, n_kv*ggml_type_size(lctx.inp_KQ_mask->type), 0); + cb(KQ_mask, "KQ_mask", -1); + + // shift the entire K-cache if needed + if (do_rope_shift) { + llm_build_k_shift(ctx0, hparams, cparams, kv_self, gf, lctx.inp_K_shift, LLM_ROPE, n_ctx, freq_base, freq_scale, cb); + } + + for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * inpSA = inpL; + + // norm + cur = llm_build_norm(ctx0, inpL, hparams, + model.layers[il].attn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "attn_norm", il); + + // self-attention + { + // compute Q and K and RoPE them + struct ggml_tensor * Qcur = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(Qcur, "Qcur", il); + if (model.layers[il].bq) { + Qcur = ggml_add(ctx0, Qcur, model.layers[il].bq); + cb(Qcur, "Qcur", il); + } + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + if (model.layers[il].bk) { + Kcur = ggml_add(ctx0, Kcur, model.layers[il].bk); + cb(Kcur, "Kcur", il); + } + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + if (model.layers[il].bv) { + Vcur = ggml_add(ctx0, Vcur, model.layers[il].bv); + cb(Vcur, "Vcur", il); + } + + Qcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_custom( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + hparams.n_rot, 0, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Kcur, "Kcur", il); + + cur = llm_build_kv(ctx0, model, hparams, kv_self, gf, + model.layers[il].wo, model.layers[il].bo, + Kcur, Vcur, Qcur, KQ_mask, n_ctx, n_tokens, kv_head, n_kv, -1.0f, 1.0f/sqrtf(float(n_embd_head)), cb, il); + cb(cur, "kqv_out", il); + } + + // scale_res - scale the hidden states for residual connection + const float scale_res = scale_depth/sqrtf(float(n_layer)); + cur = ggml_scale(ctx0, cur, scale_res); + cb(cur, "hidden_scaled", -1); + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + // feed-forward network + { + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + cur = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up, NULL, + model.layers[il].ffn_gate, NULL, + model.layers[il].ffn_down, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(cur, "ffn_out", il); + } + + // scale the hidden states for residual connection + cur = ggml_scale(ctx0, cur, scale_res); + cb(cur, "hidden_scaled_ffn", -1); + + cur = ggml_add(ctx0, cur, ffn_inp); + cb(cur, "l_out", il); + + // input for next layer + inpL = cur; + } + + cur = inpL; + + cur = llm_build_norm(ctx0, cur, hparams, + model.output_norm, NULL, + LLM_NORM_RMS, cb, -1); + cb(cur, "result_norm", -1); + + // lm_head scaling + const float scale_lmhead = float(n_embd_base)/float(n_embd); + cur = ggml_scale(ctx0, cur, scale_lmhead); + cb(cur, "lmhead_scaling", -1); + + // lm_head + cur = ggml_mul_mat(ctx0, model.tok_embd, cur); + cb(cur, "result_output", -1); + + ggml_build_forward_expand(gf, cur); + + return gf; + } }; static struct ggml_cgraph * llama_build_graph( @@ -6943,6 +7127,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_internlm2(); } break; + case LLM_ARCH_MINICPM: + { + result = llm.build_minicpm(); + } break; default: GGML_ASSERT(false); } From 9a697d842bc0cfce8268ebd2ba703ffc1c904f98 Mon Sep 17 00:00:00 2001 From: Ben Williams Date: Tue, 6 Feb 2024 22:16:48 -0800 Subject: [PATCH 135/143] readme : update ui list (#5354) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 34f2021f9..672512d18 100644 --- a/README.md +++ b/README.md @@ -149,6 +149,7 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [iohub/collama](https://github.com/iohub/coLLaMA) - [janhq/jan](https://github.com/janhq/jan) (AGPL) - [nat/openplayground](https://github.com/nat/openplayground) +- [Faraday](https://faraday.dev/) (proprietary) - [LMStudio](https://lmstudio.ai/) (proprietary) - [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL) - [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile) From ed0bf32290ee5b30ffad5becd99cbecef74aedd7 Mon Sep 17 00:00:00 2001 From: Eve <139727413+netrunnereve@users.noreply.github.com> Date: Wed, 7 Feb 2024 06:21:30 +0000 Subject: [PATCH 136/143] readme : modernize (#5379) * first cleanup, update everything to Llama 2 and remove outdated content * Delete SHA256SUMS * make build instructions generic * recommend Q4_K_M quantization method * Update README.md --- README.md | 127 +++++++++++++++-------------------------------------- SHA256SUMS | 40 ----------------- 2 files changed, 36 insertions(+), 131 deletions(-) delete mode 100644 SHA256SUMS diff --git a/README.md b/README.md index 672512d18..0509b0ba1 100644 --- a/README.md +++ b/README.md @@ -33,17 +33,14 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others)
  • Get the Code
  • Build
  • BLAS Build
    • -
  • Prepare Data & Run
    • +
  • Prepare and Quantize
    • +
  • Run the quantized model
  • Memory/Disk Requirements
  • Quantization
  • Interactive mode
  • Constrained output with grammars
    • -
  • Instruction mode with Alpaca
    • -
  • Using OpenLLaMA
    • -
  • Using GPT4All
    • -
  • Using Pygmalion 7B & Metharme 7B
    • -
  • Obtaining the Facebook LLaMA original model and Stanford Alpaca model data
    • -
  • Verifying the model files
    • +
  • Instruct mode
    • +
  • Obtaining and using the Facebook LLaMA 2 model
  • Seminal papers and background on the models
  • Perplexity (measuring model quality)
  • Android
    • @@ -83,20 +80,16 @@ improved significantly thanks to many contributions. It is the main playground f **Supported models:** +Typically finetunes of the base models below are supported as well. + - [X] LLaMA 🦙 - [x] LLaMA 2 🦙🦙 -- [X] [Mistral AI v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) +- [X] [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) - [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral) - [X] Falcon -- [X] [Alpaca](https://github.com/ggerganov/llama.cpp#instruction-mode-with-alpaca) -- [X] [GPT4All](https://github.com/ggerganov/llama.cpp#using-gpt4all) - [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2) - [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne) -- [X] [Vicuna](https://github.com/ggerganov/llama.cpp/discussions/643#discussioncomment-5533894) - [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/) -- [X] [OpenBuddy 🐶 (Multilingual)](https://github.com/OpenBuddy/OpenBuddy) -- [X] [Pygmalion/Metharme](#using-pygmalion-7b--metharme-7b) -- [X] [WizardLM](https://github.com/nlpxucan/WizardLM) - [X] [Baichuan 1 & 2](https://huggingface.co/models?search=baichuan-inc/Baichuan) + [derivations](https://huggingface.co/hiyouga/baichuan-7b-sft) - [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila) - [X] [Starcoder models](https://github.com/ggerganov/llama.cpp/pull/3187) @@ -166,7 +159,7 @@ Unless otherwise noted these projects are open-source with permissive licensing: Here is a typical run using LLaMA v2 13B on M2 Ultra: -```java +``` $ make -j && ./main -m models/llama-13b-v2/ggml-model-q4_0.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e I llama.cpp build info: I UNAME_S: Darwin @@ -250,7 +243,7 @@ https://user-images.githubusercontent.com/1991296/224442907-7693d4be-acaa-4e01-8 ## Usage -Here are the end-to-end binary build and model conversion steps for the LLaMA-7B model. +Here are the end-to-end binary build and model conversion steps for most supported models. ### Get the Code @@ -635,7 +628,7 @@ Building the program with BLAS support may lead to some performance improvements **Without docker**: - Firstly, you need to make sure you installed [Vulkan SDK](https://vulkan.lunarg.com/doc/view/latest/linux/getting_started_ubuntu.html) + Firstly, you need to make sure you have installed [Vulkan SDK](https://vulkan.lunarg.com/doc/view/latest/linux/getting_started_ubuntu.html) For example, on Ubuntu 22.04 (jammy), use the command below: @@ -648,6 +641,8 @@ Building the program with BLAS support may lead to some performance improvements vulkaninfo ``` + Alternatively your package manager might be able to provide the appropiate libraries. For example for Ubuntu 22.04 you can install `libvulkan-dev` instead. + Then, build llama.cpp using the cmake command below: ```bash @@ -662,34 +657,42 @@ Building the program with BLAS support may lead to some performance improvements # ggml_vulkan: Using Intel(R) Graphics (ADL GT2) | uma: 1 | fp16: 1 | warp size: 32 ``` -### Prepare Data & Run +### Prepare and Quantize + +To obtain the official LLaMA 2 weights please see the Obtaining and using the Facebook LLaMA 2 model section. There is also a large selection of pre-quantized `gguf` models available on Hugging Face. ```bash -# obtain the original LLaMA model weights and place them in ./models +# obtain the official LLaMA model weights and place them in ./models ls ./models -65B 30B 13B 7B tokenizer_checklist.chk tokenizer.model +llama-2-7b tokenizer_checklist.chk tokenizer.model # [Optional] for models using BPE tokenizers ls ./models -65B 30B 13B 7B vocab.json + vocab.json +# [Optional] for PyTorch .bin models like Mistral-7B +ls ./models + # install Python dependencies python3 -m pip install -r requirements.txt -# convert the 7B model to ggml FP16 format -python3 convert.py models/7B/ +# convert the model to ggml FP16 format +python3 convert.py models/mymodel/ # [Optional] for models using BPE tokenizers -python convert.py models/7B/ --vocabtype bpe +python convert.py models/mymodel/ --vocabtype bpe -# quantize the model to 4-bits (using q4_0 method) -./quantize ./models/7B/ggml-model-f16.gguf ./models/7B/ggml-model-q4_0.gguf q4_0 +# quantize the model to 4-bits (using Q4_K_M method) +./quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M -# update the gguf filetype to current if older version is unsupported by another application -./quantize ./models/7B/ggml-model-q4_0.gguf ./models/7B/ggml-model-q4_0-v2.gguf COPY +# update the gguf filetype to current version if older version is now unsupported +./quantize ./models/mymodel/ggml-model-Q4_K_M.gguf ./models/mymodel/ggml-model-Q4_K_M-v2.gguf COPY +``` +### Run the quantized model -# run the inference -./main -m ./models/7B/ggml-model-q4_0.gguf -n 128 +```bash +# start inference on a gguf model +./main -m ./models/mymodel/ggml-model-Q4_K_M.gguf -n 128 ``` When running the larger models, make sure you have enough disk space to store all the intermediate files. @@ -710,7 +713,7 @@ From the unzipped folder, open a terminal/cmd window here and place a pre-conver As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same. -| Model | Original size | Quantized size (4-bit) | +| Model | Original size | Quantized size (Q4_0) | |------:|--------------:|-----------------------:| | 7B | 13 GB | 3.9 GB | | 13B | 24 GB | 7.8 GB | @@ -826,9 +829,9 @@ The `grammars/` folder contains a handful of sample grammars. To write your own, For authoring more complex JSON grammars, you can also check out https://grammar.intrinsiclabs.ai/, a browser app that lets you write TypeScript interfaces which it compiles to GBNF grammars that you can save for local use. Note that the app is built and maintained by members of the community, please file any issues or FRs on [its repo](http://github.com/intrinsiclabsai/gbnfgen) and not this one. -### Instruction mode with Alpaca +### Instruct mode -1. First, download the `ggml` Alpaca model into the `./models` folder +1. First, download and place the `ggml` model into the `./models` folder 2. Run the `main` tool like this: ``` @@ -854,50 +857,6 @@ 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) - -*Note: these instructions are likely obsoleted by the GGUF update* - -- Obtain the `tokenizer.model` file from LLaMA model and put it to `models` -- Obtain the `added_tokens.json` file from Alpaca model and put it to `models` -- Obtain the `gpt4all-lora-quantized.bin` file from GPT4All model and put it to `models/gpt4all-7B` -- It is distributed in the old `ggml` format which is now obsoleted -- You have to convert it to the new format using `convert.py`: - -```bash -python3 convert.py models/gpt4all-7B/gpt4all-lora-quantized.bin -``` - -- You can now use the newly generated `models/gpt4all-7B/ggml-model-q4_0.bin` model in exactly the same way as all other models - -- The newer GPT4All-J model is not yet supported! - -### Using Pygmalion 7B & Metharme 7B - -- Obtain the [LLaMA weights](#obtaining-the-facebook-llama-original-model-and-stanford-alpaca-model-data) -- Obtain the [Pygmalion 7B](https://huggingface.co/PygmalionAI/pygmalion-7b/) or [Metharme 7B](https://huggingface.co/PygmalionAI/metharme-7b) XOR encoded weights -- Convert the LLaMA model with [the latest HF convert script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py) -- Merge the XOR files with the converted LLaMA weights by running the [xor_codec](https://huggingface.co/PygmalionAI/pygmalion-7b/blob/main/xor_codec.py) script -- Convert to `ggml` format using the `convert.py` script in this repo: -```bash -python3 convert.py pygmalion-7b/ --outtype q4_1 -``` -> The Pygmalion 7B & Metharme 7B weights are saved in [bfloat16](https://en.wikipedia.org/wiki/Bfloat16_floating-point_format) precision. If you wish to convert to `ggml` without quantizating, please specify the `--outtype` as `f32` instead of `f16`. - - -### Obtaining the Facebook LLaMA original model and Stanford Alpaca model data - -- **Under no circumstances should IPFS, magnet links, or any other links to model downloads be shared anywhere in this repository, including in issues, discussions, or pull requests. They will be immediately deleted.** -- The LLaMA models are officially distributed by Facebook and will **never** be provided through this repository. -- Refer to [Facebook's LLaMA repository](https://github.com/facebookresearch/llama/pull/73/files) if you need to request access to the model data. - ### Obtaining and using the Facebook LLaMA 2 model - Refer to [Facebook's LLaMA download page](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) if you want to access the model data. @@ -909,20 +868,6 @@ python3 convert.py pygmalion-7b/ --outtype q4_1 - [LLaMA 2 13B chat](https://huggingface.co/TheBloke/Llama-2-13B-chat-GGUF) - [LLaMA 2 70B chat](https://huggingface.co/TheBloke/Llama-2-70B-chat-GGUF) -### Verifying the model files - -Please verify the [sha256 checksums](SHA256SUMS) of all downloaded model files to confirm that you have the correct model data files before creating an issue relating to your model files. -- The following python script will verify if you have all possible latest files in your self-installed `./models` subdirectory: - -```bash -# run the verification script -./scripts/verify-checksum-models.py -``` - -- On linux or macOS it is also possible to run the following commands to verify if you have all possible latest files in your self-installed `./models` subdirectory: - - On Linux: `sha256sum --ignore-missing -c SHA256SUMS` - - on macOS: `shasum -a 256 --ignore-missing -c SHA256SUMS` - ### Seminal papers and background on the models If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT: diff --git a/SHA256SUMS b/SHA256SUMS deleted file mode 100644 index ca4d5a4a5..000000000 --- a/SHA256SUMS +++ /dev/null @@ -1,40 +0,0 @@ -700df0d3013b703a806d2ae7f1bfb8e59814e3d06ae78be0c66368a50059f33d models/7B/consolidated.00.pth -666a4bb533b303bdaf89e1b6a3b6f93535d868de31d903afdc20983dc526c847 models/7B/ggml-model-f16.bin -ec2f2d1f0dfb73b72a4cbac7fa121abbe04c37ab327125a38248f930c0f09ddf models/7B/ggml-model-q4_0.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/7B/ggml-model-q4_1.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/7B/ggml-model-q5_0.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/7B/ggml-model-q5_1.bin -7e89e242ddc0dd6f060b43ca219ce8b3e8f08959a72cb3c0855df8bb04d46265 models/7B/params.json 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-e7babf7c5606f165a3756f527cb0fedc4f83e67ef1290391e52fb1cce5f26770 models/65B/consolidated.02.pth -73176ffb426b40482f2aa67ae1217ef79fbbd1fff5482bae5060cdc5a24ab70e models/65B/consolidated.03.pth -882e6431d0b08a8bc66261a0d3607da21cbaeafa96a24e7e59777632dbdac225 models/65B/consolidated.04.pth -a287c0dfe49081626567c7fe87f74cce5831f58e459b427b5e05567641f47b78 models/65B/consolidated.05.pth -72b4eba67a1a3b18cb67a85b70f8f1640caae9b40033ea943fb166bd80a7b36b models/65B/consolidated.06.pth -d27f5b0677d7ff129ceacd73fd461c4d06910ad7787cf217b249948c3f3bc638 models/65B/consolidated.07.pth -60758f2384d74e423dffddfd020ffed9d3bb186ebc54506f9c4a787d0f5367b0 models/65B/ggml-model-f16.bin -cde053439fa4910ae454407e2717cc46cc2c2b4995c00c93297a2b52e790fa92 models/65B/ggml-model-q4_0.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/65B/ggml-model-q4_1.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/65B/ggml-model-q5_0.bin -ffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffffff models/65B/ggml-model-q5_1.bin -999ed1659b469ccc2a941714c0a9656fa571d17c9f7c8c7589817ca90edef51b models/65B/params.json -9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347 models/tokenizer.model From ee1628bdfea8b0079fed0140ac2f00ef1b465b57 Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Wed, 7 Feb 2024 07:54:50 +0100 Subject: [PATCH 137/143] Basic Vulkan Multi-GPU implementation (#5321) * Initial Vulkan multi-gpu implementation Move most global variables into backend context * Add names to backend device functions * Add further missing cleanup code * Reduce code duplication in tensor split layer assignment * generalize LLAMA_SPLIT_LAYER for all backends, do not expose device count and memory in llama.h * Only do device info print in the beginning and initialize one backend for cpu assist Add missing cleanup code * Rework backend memory management to make sure devices and buffers get properly allocated and freed * Rename cpu assist free function --------- Co-authored-by: slaren --- common/common.cpp | 8 +- ggml-vulkan.cpp | 2639 ++++++++++++++++++++++++++------------------- ggml-vulkan.h | 23 +- ggml.c | 14 +- llama.cpp | 69 +- 5 files changed, 1587 insertions(+), 1166 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 8c1a60583..e0082a823 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -46,6 +46,10 @@ #define GGML_USE_CUBLAS_SYCL #endif +#if (defined(GGML_USE_CUBLAS) || defined(GGML_USE_SYCL)) || defined(GGML_USE_VULKAN) +#define GGML_USE_CUBLAS_SYCL_VULKAN +#endif + int32_t get_num_physical_cores() { #ifdef __linux__ // enumerate the set of thread siblings, num entries is num cores @@ -660,8 +664,8 @@ bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { params.tensor_split[i] = 0.0f; } } -#ifndef GGML_USE_CUBLAS_SYCL - fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL. Setting a tensor split has no effect.\n"); +#ifndef GGML_USE_CUBLAS_SYCL_VULKAN + fprintf(stderr, "warning: llama.cpp was compiled without cuBLAS/SYCL/Vulkan. Setting a tensor split has no effect.\n"); #endif // GGML_USE_CUBLAS_SYCL } else if (arg == "--no-mmap") { params.use_mmap = false; diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 14fb89e09..9e2846ee4 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -15,6 +15,7 @@ #include #include #include +#include #include "ggml.h" #include "ggml-backend-impl.h" @@ -37,6 +38,8 @@ #define GGML_VK_MAX_NODES 8192 +#define MAX_VK_BUFFERS 256 + #ifndef K_QUANTS_PER_ITERATION #define K_QUANTS_PER_ITERATION 1 #else @@ -53,15 +56,68 @@ static_assert(K_QUANTS_PER_ITERATION == 1 || K_QUANTS_PER_ITERATION == 2, "K_QUA } \ } while (0) -struct vk_buffer { +struct ggml_backend_vk_context; + +struct vk_queue { + uint32_t queue_family_index; + vk::Queue queue; + vk::CommandPool pool; + uint32_t cmd_buffer_idx; + std::vector cmd_buffers; + + vk::PipelineStageFlags stage_flags; +}; + +struct vk_device { + vk::PhysicalDevice physical_device; + vk::PhysicalDeviceProperties properties; + std::string name; + uint64_t max_memory_allocation_size; + bool fp16; + vk::Device device; + uint32_t vendor_id; + vk_queue compute_queue; + vk_queue transfer_queue; + bool single_queue; + uint32_t descriptor_set_mode; + uint32_t subgroup_size; + bool uma; + + ~vk_device() { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "destroy device " << name << std::endl; +#endif + device.destroy(); + } +}; + +struct vk_buffer_struct { vk::Buffer buffer; vk::DeviceMemory device_memory; vk::MemoryPropertyFlags memory_property_flags; void * ptr; size_t size = 0; - uint32_t qf_owner; + + ggml_backend_vk_context * ctx; + + std::shared_ptr device; + + ~vk_buffer_struct() { + if (size == 0) { + return; + } +#ifdef GGML_VULKAN_DEBUG + std::cerr << "~vk_buffer_struct(" << buffer << ", " << size << ")" << std::endl; +#endif + + device->device.freeMemory(device_memory); + device->device.destroyBuffer(buffer); + } }; +typedef std::shared_ptr vk_buffer; +typedef std::weak_ptr vk_buffer_ref; + struct vk_subbuffer { vk_buffer buffer; uint64_t offset; @@ -70,6 +126,7 @@ struct vk_subbuffer { struct vk_pipeline { std::string name; + vk::ShaderModule shader_module; vk::DescriptorSetLayout dsl; std::vector descriptor_pools; std::vector descriptor_sets; @@ -82,16 +139,6 @@ struct vk_pipeline { uint32_t align; }; -struct vk_queue { - uint32_t queue_family_index; - vk::Queue queue; - vk::CommandPool pool; - uint32_t cmd_buffer_idx; - std::vector cmd_buffers; - - vk::PipelineStageFlags stage_flags; -}; - struct vk_semaphore { vk::Semaphore s; uint64_t value; @@ -105,20 +152,6 @@ struct vk_submission { typedef std::vector vk_sequence; -struct vk_device { - vk::PhysicalDevice physical_device; - vk::PhysicalDeviceProperties properties; - uint64_t max_memory_allocation_size; - bool fp16; - vk::Device device; - uint32_t vendor_id; - vk_queue compute_queue; - vk_queue transfer_queue; - uint32_t descriptor_set_mode; - uint32_t subgroup_size; - bool uma; -}; - struct vk_op_push_constants { uint32_t KX; uint32_t KY; @@ -190,13 +223,13 @@ struct ggml_tensor_extra_gpu { size_t ctx_idx; - vk_buffer buffer_gpu; + vk_buffer_ref buffer_gpu; uint64_t offset; void reset() { ready = false; ctx_idx = 0; - buffer_gpu.size = 0; + buffer_gpu.reset(); offset = 0; } }; @@ -210,69 +243,96 @@ struct ggml_vk_garbage_collector { std::vector contexts; }; -typedef void (*ggml_vk_func_t)(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); +struct ggml_backend_vk_context { + std::string name; -vk::Instance vk_instance; -vk_device vk_device; -vk_pipeline vk_pipeline_matmul_f32_l, vk_pipeline_matmul_f32_m, vk_pipeline_matmul_f32_s; -vk_pipeline vk_pipeline_matmul_f32_aligned_l, vk_pipeline_matmul_f32_aligned_m, vk_pipeline_matmul_f32_aligned_s; -vk_pipeline vk_pipeline_matmul_f16_l, vk_pipeline_matmul_f16_m, vk_pipeline_matmul_f16_s; -vk_pipeline vk_pipeline_matmul_f16_aligned_l, vk_pipeline_matmul_f16_aligned_m, vk_pipeline_matmul_f16_aligned_s; -vk_pipeline vk_pipeline_matmul_f16_f32_l, vk_pipeline_matmul_f16_f32_m, vk_pipeline_matmul_f16_f32_s; -vk_pipeline vk_pipeline_matmul_f16_f32_aligned_l, vk_pipeline_matmul_f16_f32_aligned_m, vk_pipeline_matmul_f16_f32_aligned_s; -vk_pipeline vk_pipeline_matmul_split_k_reduce; -vk_pipeline vk_pipeline_dequant[VK_NUM_TYPES]; -vk_pipeline vk_pipeline_dequant_mul_mat_vec_f32[VK_NUM_TYPES]; -vk_pipeline vk_pipeline_mul_mat_vec_p021_f16_f32; -vk_pipeline vk_pipeline_mul_mat_vec_nc_f16_f32; -vk_pipeline vk_pipeline_get_rows[VK_NUM_TYPES]; -vk_pipeline vk_pipeline_get_rows_f32[VK_NUM_TYPES]; -vk_pipeline vk_pipeline_mul_f32; -vk_pipeline vk_pipeline_add_f32; -vk_pipeline vk_pipeline_scale_f32; -vk_pipeline vk_pipeline_sqr_f32; -vk_pipeline vk_pipeline_clamp_f32; -vk_pipeline vk_pipeline_cpy_f32_f32, vk_pipeline_cpy_f32_f16, vk_pipeline_cpy_f16_f16; -vk_pipeline vk_pipeline_norm_f32; -vk_pipeline vk_pipeline_rms_norm_f32; -vk_pipeline vk_pipeline_gelu_f32; -vk_pipeline vk_pipeline_silu_f32; -vk_pipeline vk_pipeline_relu_f32; -vk_pipeline vk_pipeline_diag_mask_inf_f32; -vk_pipeline vk_pipeline_soft_max_f32; -vk_pipeline vk_pipeline_rope_f32, vk_pipeline_rope_f16; -vk_pipeline vk_pipeline_rope_neox_f32, vk_pipeline_rope_neox_f16; + std::weak_ptr device; + vk_pipeline pipeline_matmul_f32_l, pipeline_matmul_f32_m, pipeline_matmul_f32_s; + vk_pipeline pipeline_matmul_f32_aligned_l, pipeline_matmul_f32_aligned_m, pipeline_matmul_f32_aligned_s; + vk_pipeline pipeline_matmul_f16_l, pipeline_matmul_f16_m, pipeline_matmul_f16_s; + vk_pipeline pipeline_matmul_f16_aligned_l, pipeline_matmul_f16_aligned_m, pipeline_matmul_f16_aligned_s; + vk_pipeline pipeline_matmul_f16_f32_l, pipeline_matmul_f16_f32_m, pipeline_matmul_f16_f32_s; + vk_pipeline pipeline_matmul_f16_f32_aligned_l, pipeline_matmul_f16_f32_aligned_m, pipeline_matmul_f16_f32_aligned_s; + vk_pipeline pipeline_matmul_split_k_reduce; + vk_pipeline pipeline_dequant[VK_NUM_TYPES]; + vk_pipeline pipeline_dequant_mul_mat_vec_f32[VK_NUM_TYPES]; + vk_pipeline pipeline_mul_mat_vec_p021_f16_f32; + vk_pipeline pipeline_mul_mat_vec_nc_f16_f32; + vk_pipeline pipeline_get_rows[VK_NUM_TYPES]; + vk_pipeline pipeline_get_rows_f32[VK_NUM_TYPES]; + vk_pipeline pipeline_mul_f32; + vk_pipeline pipeline_add_f32; + vk_pipeline pipeline_scale_f32; + vk_pipeline pipeline_sqr_f32; + vk_pipeline pipeline_clamp_f32; + vk_pipeline pipeline_cpy_f32_f32, pipeline_cpy_f32_f16, pipeline_cpy_f16_f16; + vk_pipeline pipeline_norm_f32; + vk_pipeline pipeline_rms_norm_f32; + vk_pipeline pipeline_gelu_f32; + vk_pipeline pipeline_silu_f32; + vk_pipeline pipeline_relu_f32; + vk_pipeline pipeline_diag_mask_inf_f32; + vk_pipeline pipeline_soft_max_f32; + vk_pipeline pipeline_rope_f32, pipeline_rope_f16; + vk_pipeline pipeline_rope_neox_f32, pipeline_rope_neox_f16; -static size_t vk_semaphore_idx, vk_event_idx; -static ggml_vk_garbage_collector vk_gc; -static std::vector> vk_pinned_memory; -static size_t vk_prealloc_size_qx, vk_prealloc_size_qy, vk_prealloc_size_x, vk_prealloc_size_y, vk_prealloc_size_split_k; -static vk_buffer vk_prealloc_qx, vk_prealloc_qy, vk_prealloc_x, vk_prealloc_y, vk_prealloc_split_k; -static vk::Fence vk_fence; -static vk_buffer vk_staging; -static size_t vk_staging_size; -static size_t vk_staging_offset; -static vk_buffer vk_sync_staging; + size_t semaphore_idx, event_idx; + ggml_vk_garbage_collector gc; + std::vector> pinned_memory; + size_t prealloc_size_qx, prealloc_size_qy, prealloc_size_x, prealloc_size_y, prealloc_size_split_k; + vk_buffer prealloc_qx, prealloc_qy, prealloc_x, prealloc_y, prealloc_split_k; + vk::Fence fence; + vk_buffer staging; + size_t staging_size; + size_t staging_offset; + vk_buffer sync_staging; -static vk_context * vk_ctx; -static vk_context * vk_transfer_ctx; + vk_buffer buffer_pool[MAX_VK_BUFFERS]; -static bool vk_disable; + vk_context * compute_ctx; + vk_context * transfer_ctx; + + bool disable; + bool initialized; + + size_t idx; +}; + +struct vk_instance { + vk::Instance instance; + + std::vector device_indices; + + std::shared_ptr devices[GGML_VK_MAX_DEVICES]; + ggml_backend_t backends[GGML_VK_MAX_DEVICES]; + ggml_backend_vk_context contexts[GGML_VK_MAX_DEVICES]; + ggml_backend_buffer_type buffer_types[GGML_VK_MAX_DEVICES]; + bool initialized[GGML_VK_MAX_DEVICES]; +}; #ifdef GGML_VULKAN_CHECK_RESULTS -size_t vk_skip_checks; -size_t vk_output_tensor; +static size_t vk_skip_checks; +static size_t vk_output_tensor; + +static void ggml_vk_print_tensor(ggml_backend * ctx, const ggml_tensor * tensor, const char * name); +static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor); +static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor); #endif -static vk_pipeline ggml_vk_create_pipeline(const std::string& name, size_t spv_size, const void* spv_data, const std::string& entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, std::vector&& specialization_constants, uint32_t align) { +typedef void (*ggml_vk_func_t)(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst); + +static bool vk_instance_initialized = false; +static vk_instance vk_instance; + +GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend); + +static void ggml_vk_create_pipeline(ggml_backend_vk_context * ctx, vk_pipeline& pipeline, const std::string& name, size_t spv_size, const void* spv_data, const std::string& entrypoint, uint32_t parameter_count, uint32_t push_constant_size, std::array wg_denoms, std::vector&& specialization_constants, uint32_t align) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_pipeline(" << name << ", " << entrypoint << ", " << parameter_count << ", " << push_constant_size << ", (" << wg_denoms[0] << "," << wg_denoms[1] << "," << wg_denoms[2] << "), specialization_constants, " << align << ")" << std::endl; #endif GGML_ASSERT(parameter_count > 0); GGML_ASSERT(wg_denoms[0] > 0 && wg_denoms[1] > 0 && wg_denoms[2] > 0); // NOLINT - vk_pipeline pipeline; - pipeline.name = name; pipeline.parameter_count = parameter_count; pipeline.push_constant_size = push_constant_size; @@ -280,7 +340,7 @@ static vk_pipeline ggml_vk_create_pipeline(const std::string& name, size_t spv_s pipeline.align = align; vk::ShaderModuleCreateInfo shader_module_create_info({}, spv_size, reinterpret_cast(spv_data)); - vk::ShaderModule shader_module = vk_device.device.createShaderModule(shader_module_create_info); + pipeline.shader_module = ctx->device.lock()->device.createShaderModule(shader_module_create_info); std::vector dsl_binding; std::vector dsl_binding_flags; @@ -301,17 +361,17 @@ static vk_pipeline ggml_vk_create_pipeline(const std::string& name, size_t spv_s {}, dsl_binding); descriptor_set_layout_create_info.setPNext(&dslbfci); - pipeline.dsl = vk_device.device.createDescriptorSetLayout(descriptor_set_layout_create_info); + pipeline.dsl = ctx->device.lock()->device.createDescriptorSetLayout(descriptor_set_layout_create_info); // Check if device supports multiple descriptors per pool - if (vk_device.descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN) { + if (ctx->device.lock()->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN) { const uint32_t alloc_count = 2; // Try allocating multiple sets from one pool // This fails on AMD for some reason, so add a fall back to allocating one pool per set vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline.parameter_count); vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, alloc_count, descriptor_pool_size); - vk::DescriptorPool pool = vk_device.device.createDescriptorPool(descriptor_pool_create_info); + vk::DescriptorPool pool = ctx->device.lock()->device.createDescriptorPool(descriptor_pool_create_info); std::vector layouts(alloc_count); for (uint32_t i = 0; i < alloc_count; i++) { @@ -319,24 +379,24 @@ static vk_pipeline ggml_vk_create_pipeline(const std::string& name, size_t spv_s } try { vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pool, alloc_count, layouts.data()); - std::vector sets = vk_device.device.allocateDescriptorSets(descriptor_set_alloc_info); + std::vector sets = ctx->device.lock()->device.allocateDescriptorSets(descriptor_set_alloc_info); } catch(vk::OutOfPoolMemoryError const&) { - vk_device.descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_SINGLE; + ctx->device.lock()->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_SINGLE; } - vk_device.device.destroyDescriptorPool(pool); + ctx->device.lock()->device.destroyDescriptorPool(pool); } - if (vk_device.descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { + if (ctx->device.lock()->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline.parameter_count); vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, 128, descriptor_pool_size); - pipeline.descriptor_pools.push_back(vk_device.device.createDescriptorPool(descriptor_pool_create_info)); + pipeline.descriptor_pools.push_back(ctx->device.lock()->device.createDescriptorPool(descriptor_pool_create_info)); } pipeline.descriptor_set_idx = 0; vk::PipelineLayoutCreateInfo pipeline_layout_create_info(vk::PipelineLayoutCreateFlags(), pipeline.dsl, pcr); - pipeline.layout = vk_device.device.createPipelineLayout(pipeline_layout_create_info); + pipeline.layout = ctx->device.lock()->device.createPipelineLayout(pipeline_layout_create_info); std::vector specialization_entries(specialization_constants.size()); @@ -356,41 +416,45 @@ static vk_pipeline ggml_vk_create_pipeline(const std::string& name, size_t spv_s vk::PipelineShaderStageCreateInfo pipeline_shader_create_info( vk::PipelineShaderStageCreateFlags(), vk::ShaderStageFlagBits::eCompute, - shader_module, + pipeline.shader_module, entrypoint.c_str(), &specialization_info); vk::ComputePipelineCreateInfo compute_pipeline_create_info( vk::PipelineCreateFlags(), pipeline_shader_create_info, pipeline.layout); - pipeline.pipeline = vk_device.device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value; + pipeline.pipeline = ctx->device.lock()->device.createComputePipeline(VK_NULL_HANDLE, compute_pipeline_create_info).value; - return pipeline; + ctx->gc.pipelines.push_back(&pipeline); } -static void ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline& pipeline, uint32_t n) { +static void ggml_vk_destroy_pipeline(ggml_backend_vk_context * ctx, vk_pipeline * pipeline) { + for (auto& pool : pipeline->descriptor_pools) { + ctx->device.lock()->device.destroyDescriptorPool(pool); + } + pipeline->descriptor_pools.clear(); + pipeline->descriptor_sets.clear(); + pipeline->descriptor_set_idx = 0; + + ctx->device.lock()->device.destroyDescriptorSetLayout(pipeline->dsl); + + ctx->device.lock()->device.destroyPipelineLayout(pipeline->layout); + + ctx->device.lock()->device.destroyShaderModule(pipeline->shader_module); + + ctx->device.lock()->device.destroyPipeline(pipeline->pipeline); +} + +static void ggml_pipeline_allocate_descriptor_sets(ggml_backend_vk_context * ctx, vk_pipeline& pipeline, uint32_t n) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_pipeline_allocate_descriptor_sets(" << pipeline.name << ", " << n << ")" << std::endl; + std::cerr << "ggml_pipeline_allocate_descriptor_sets(" << pipeline.name << ", " << n << ")" << std::endl; #endif - // Check if gc already contains pipeline before adding it - bool gc_found = false; - for (auto * pl : vk_gc.pipelines) { - if (&pipeline == pl) { - gc_found = true; - break; - } - } - - if (!gc_found) { - vk_gc.pipelines.push_back(&pipeline); - } - if (pipeline.descriptor_sets.size() >= pipeline.descriptor_set_idx + n) { // Enough descriptors are available return; } - if (vk_device.descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { + if (ctx->device.lock()->descriptor_set_mode == VK_DEVICE_DESCRIPTOR_POOL_MODE_MULTI) { const uint32_t alloc_count = pipeline.descriptor_set_idx + n - pipeline.descriptor_sets.size(); std::vector layouts(alloc_count); @@ -398,29 +462,29 @@ static void ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline& pipeline, uin layouts[i] = pipeline.dsl; } vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline.descriptor_pools[0], alloc_count, layouts.data()); - std::vector sets = vk_device.device.allocateDescriptorSets(descriptor_set_alloc_info); + std::vector sets = ctx->device.lock()->device.allocateDescriptorSets(descriptor_set_alloc_info); pipeline.descriptor_sets.insert(pipeline.descriptor_sets.end(), sets.begin(), sets.end()); } else { for (uint32_t i = pipeline.descriptor_sets.size(); i < pipeline.descriptor_set_idx + n; i++) { vk::DescriptorPoolSize descriptor_pool_size(vk::DescriptorType::eStorageBuffer, pipeline.parameter_count); vk::DescriptorPoolCreateInfo descriptor_pool_create_info({}, 1, descriptor_pool_size); - pipeline.descriptor_pools.push_back(vk_device.device.createDescriptorPool(descriptor_pool_create_info)); + pipeline.descriptor_pools.push_back(ctx->device.lock()->device.createDescriptorPool(descriptor_pool_create_info)); vk::DescriptorSetAllocateInfo descriptor_set_alloc_info(pipeline.descriptor_pools[i], 1, &pipeline.dsl); - std::vector sets = vk_device.device.allocateDescriptorSets(descriptor_set_alloc_info); + std::vector sets = ctx->device.lock()->device.allocateDescriptorSets(descriptor_set_alloc_info); pipeline.descriptor_sets.push_back(sets[0]); } } } -static void ggml_vk_pipeline_cleanup(vk_pipeline& pipeline) { +static void ggml_pipeline_cleanup(vk_pipeline& pipeline) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_pipeline_cleanup(" << pipeline.name << ")" << std::endl; + std::cerr << "ggml_pipeline_cleanup(" << pipeline.name << ")" << std::endl; #endif pipeline.descriptor_set_idx = 0; } -static vk::CommandBuffer ggml_vk_create_cmd_buffer(vk_queue& q) { +static vk::CommandBuffer ggml_vk_create_cmd_buffer(ggml_backend_vk_context * ctx, vk_queue& q) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_cmd_buffer()" << std::endl; #endif @@ -433,7 +497,7 @@ static vk::CommandBuffer ggml_vk_create_cmd_buffer(vk_queue& q) { q.pool, vk::CommandBufferLevel::ePrimary, 1); - const std::vector cmd_buffers = vk_device.device.allocateCommandBuffers(command_buffer_alloc_info); + const std::vector cmd_buffers = ctx->device.lock()->device.allocateCommandBuffers(command_buffer_alloc_info); auto buf = cmd_buffers.front(); q.cmd_buffers.push_back(buf); @@ -442,24 +506,17 @@ static vk::CommandBuffer ggml_vk_create_cmd_buffer(vk_queue& q) { return buf; } -static vk_submission ggml_vk_create_submission(vk_queue& q, std::vector wait_semaphores, std::vector signal_semaphores) { +static vk_submission ggml_vk_create_submission(ggml_backend_vk_context * ctx, vk_queue& q, std::vector wait_semaphores, std::vector signal_semaphores) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_submission()" << std::endl; #endif vk_submission s; - s.buffer = ggml_vk_create_cmd_buffer(q); + s.buffer = ggml_vk_create_cmd_buffer(ctx, q); s.wait_semaphores = std::move(wait_semaphores); s.signal_semaphores = std::move(signal_semaphores); return s; } -static vk_sequence ggml_vk_create_sequence_1(vk_queue& q, std::vector wait_semaphores, std::vector signal_semaphores) { -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_create_sequence_1()" << std::endl; -#endif - return { ggml_vk_create_submission(q, std::move(wait_semaphores), std::move(signal_semaphores)) }; -} - static void ggml_vk_submit(vk_context * ctx, vk::Fence fence) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_submit(" << ctx->seqs.size() << ", " << fence << ")" << std::endl; @@ -578,89 +635,89 @@ static uint32_t ggml_vk_find_queue_family_index(std::vectordevice.lock()->device.createCommandPool(command_pool_create_info_compute); q.cmd_buffer_idx = 0; - q.queue = vk_device.device.getQueue(queue_family_index, queue_index); + q.queue = ctx->device.lock()->device.getQueue(queue_family_index, queue_index); q.stage_flags = stage_flags; - - return q; } -static vk_context * ggml_vk_create_context(vk_queue& q) { +static vk_context * ggml_vk_create_context(ggml_backend_vk_context * ctx, vk_queue& q) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_context()" << std::endl; #endif - vk_gc.contexts.emplace_back(); - vk_context * result = &vk_gc.contexts[vk_gc.contexts.size() - 1]; + ctx->gc.contexts.emplace_back(); + vk_context * result = &ctx->gc.contexts[ctx->gc.contexts.size() - 1]; memset((void *) result, 0, sizeof(vk_context)); - result->idx = vk_gc.contexts.size() - 1; + result->idx = ctx->gc.contexts.size() - 1; result->q = &q; return result; } -static vk_semaphore * ggml_vk_create_binary_semaphore() { +static vk_semaphore * ggml_vk_create_binary_semaphore(ggml_backend_vk_context * ctx) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_timeline_semaphore()" << std::endl; #endif vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eBinary, 0 }; vk::SemaphoreCreateInfo ci{}; ci.setPNext(&tci); - vk::Semaphore semaphore = vk_device.device.createSemaphore(ci); - vk_gc.semaphores.push_back({ semaphore, 0 }); - return &vk_gc.semaphores[vk_gc.semaphores.size() - 1]; + vk::Semaphore semaphore = ctx->device.lock()->device.createSemaphore(ci); + ctx->gc.semaphores.push_back({ semaphore, 0 }); + return &ctx->gc.semaphores[ctx->gc.semaphores.size() - 1]; } -static vk_semaphore * ggml_vk_create_timeline_semaphore() { +static vk_semaphore * ggml_vk_create_timeline_semaphore(ggml_backend_vk_context * ctx) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_timeline_semaphore()" << std::endl; #endif - if (vk_semaphore_idx >= vk_gc.tl_semaphores.size()) { + if (ctx->semaphore_idx >= ctx->gc.tl_semaphores.size()) { vk::SemaphoreTypeCreateInfo tci{ vk::SemaphoreType::eTimeline, 0 }; vk::SemaphoreCreateInfo ci{}; ci.setPNext(&tci); - vk::Semaphore semaphore = vk_device.device.createSemaphore(ci); - vk_gc.tl_semaphores.push_back({ semaphore, 0 }); + vk::Semaphore semaphore = ctx->device.lock()->device.createSemaphore(ci); + ctx->gc.tl_semaphores.push_back({ semaphore, 0 }); } - return &vk_gc.tl_semaphores[vk_semaphore_idx++]; + return &ctx->gc.tl_semaphores[ctx->semaphore_idx++]; } -static vk::Event ggml_vk_create_event() { - if (vk_event_idx >= vk_gc.events.size()) { - vk_gc.events.push_back(vk_device.device.createEvent({})); +static vk::Event ggml_vk_create_event(ggml_backend_vk_context * ctx) { + if (ctx->event_idx >= ctx->gc.events.size()) { + ctx->gc.events.push_back(ctx->device.lock()->device.createEvent({})); } - return vk_gc.events[vk_event_idx++]; + return ctx->gc.events[ctx->event_idx++]; } -static void ggml_vk_queue_cleanup(vk_queue& q) { +static void ggml_vk_queue_cleanup(ggml_backend_vk_context * ctx, vk_queue& q) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_queue_cleanup()" << std::endl; #endif // Requires command buffers to be done - vk_device.device.resetCommandPool(q.pool); + ctx->device.lock()->device.resetCommandPool(q.pool); q.cmd_buffer_idx = 0; } -static vk_buffer ggml_vk_create_buffer(size_t size, vk::MemoryPropertyFlags req_flags) { +static vk_buffer ggml_vk_create_buffer(ggml_backend_vk_context * ctx, size_t size, vk::MemoryPropertyFlags req_flags) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_create_buffer(" << size << ", " << to_string(req_flags) << ")" << std::endl; #endif - GGML_ASSERT(size > 0); + vk_buffer buf = std::make_shared(); - vk_buffer buf; + if (size == 0) { + buf->size = 0; + return buf; + } - buf.size = size; + buf->size = size; vk::BufferCreateInfo buffer_create_info{ vk::BufferCreateFlags(), size, @@ -670,11 +727,11 @@ static vk_buffer ggml_vk_create_buffer(size_t size, vk::MemoryPropertyFlags req_ nullptr, }; - buf.buffer = vk_device.device.createBuffer(buffer_create_info); + buf->buffer = ctx->device.lock()->device.createBuffer(buffer_create_info); - vk::MemoryRequirements mem_req = vk_device.device.getBufferMemoryRequirements(buf.buffer); + vk::MemoryRequirements mem_req = ctx->device.lock()->device.getBufferMemoryRequirements(buf->buffer); - vk::PhysicalDeviceMemoryProperties mem_props = vk_device.physical_device.getMemoryProperties(); + vk::PhysicalDeviceMemoryProperties mem_props = ctx->device.lock()->physical_device.getMemoryProperties(); uint32_t memory_type_index = UINT32_MAX; @@ -691,30 +748,36 @@ static vk_buffer ggml_vk_create_buffer(size_t size, vk::MemoryPropertyFlags req_ } try { - buf.device_memory = vk_device.device.allocateMemory({ mem_req.size, memory_type_index }); + buf->device_memory = ctx->device.lock()->device.allocateMemory({ mem_req.size, memory_type_index }); } catch (const vk::SystemError& e) { // Out of Host/Device memory, clean up buffer - vk_device.device.destroyBuffer(buf.buffer); - buf.size = 0; + ctx->device.lock()->device.destroyBuffer(buf->buffer); + buf->size = 0; throw e; } - buf.memory_property_flags = req_flags; - buf.ptr = nullptr; + buf->memory_property_flags = req_flags; + buf->ptr = nullptr; if (req_flags & vk::MemoryPropertyFlagBits::eHostVisible) { - buf.ptr = vk_device.device.mapMemory(buf.device_memory, 0, VK_WHOLE_SIZE); + buf->ptr = ctx->device.lock()->device.mapMemory(buf->device_memory, 0, VK_WHOLE_SIZE); } - vk_device.device.bindBufferMemory(buf.buffer, buf.device_memory, 0); + ctx->device.lock()->device.bindBufferMemory(buf->buffer, buf->device_memory, 0); - buf.qf_owner = VK_QUEUE_FAMILY_IGNORED; + buf->ctx = ctx; + + buf->device = ctx->device.lock(); + +#ifdef GGML_VULKAN_DEBUG + std::cerr << "Created buffer " << buf->buffer << std::endl; +#endif return buf; } -static vk_buffer ggml_vk_create_buffer_check(size_t size, vk::MemoryPropertyFlags req_flags) { +static vk_buffer ggml_vk_create_buffer_check(ggml_backend_vk_context * ctx, size_t size, vk::MemoryPropertyFlags req_flags) { try { - return ggml_vk_create_buffer(size, req_flags); + return ggml_vk_create_buffer(ctx, size, req_flags); } catch (const vk::SystemError& e) { std::cerr << "ggml_vulkan: Memory allocation of size " << size << " failed." << std::endl; std::cerr << "ggml_vulkan: " << e.what() << std::endl; @@ -722,14 +785,14 @@ static vk_buffer ggml_vk_create_buffer_check(size_t size, vk::MemoryPropertyFlag } } -static vk_buffer ggml_vk_create_buffer_device(size_t size) { +static vk_buffer ggml_vk_create_buffer_device(ggml_backend_vk_context * ctx, size_t size) { vk_buffer buf; try { - buf = ggml_vk_create_buffer(size, vk::MemoryPropertyFlagBits::eDeviceLocal); + buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal); } catch (const vk::SystemError& e) { - if (vk_device.uma) { + if (ctx->device.lock()->uma) { // Fall back to host memory type - buf = ggml_vk_create_buffer_check(size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); + buf = ggml_vk_create_buffer_check(ctx, size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent); } else { std::cerr << "ggml_vulkan: Device memory allocation of size " << size << " failed." << std::endl; std::cerr << "ggml_vulkan: " << e.what() << std::endl; @@ -741,16 +804,7 @@ static vk_buffer ggml_vk_create_buffer_device(size_t size) { } static void ggml_vk_destroy_buffer(vk_buffer& buf) { - if (buf.size == 0) { - return; - } -#ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_destroy_buffer(" << buf.size << ")" << std::endl; -#endif - - buf.size = 0; - vk_device.device.freeMemory(buf.device_memory); - vk_device.device.destroyBuffer(buf.buffer); + buf.reset(); } static vk_subbuffer ggml_vk_subbuffer(vk_buffer& buf) { @@ -773,7 +827,7 @@ static void ggml_vk_sync_buffers(vk_context * ctx) { ); } -static void ggml_vk_wait_events(vk::CommandBuffer& cmd_buffer, std::vector&& events, vk::PipelineStageFlags src_stages, vk::PipelineStageFlags dst_stages) { +static void ggml_vk_wait_events(vk_context * ctx, std::vector&& events) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_wait_events()" << std::endl; #endif @@ -781,10 +835,10 @@ static void ggml_vk_wait_events(vk::CommandBuffer& cmd_buffer, std::vectors->buffer.waitEvents( events, - src_stages, - dst_stages, + ctx->q->stage_flags, + ctx->q->stage_flags, {}, {}, {} @@ -810,15 +864,15 @@ static bool ggml_vk_build_shader(ggml_type type) { } } -static void ggml_vk_load_shaders() { +static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_load_shaders()" << std::endl; + std::cerr << "ggml_vk_load_shaders(" << ctx->name << ")" << std::endl; #endif // mulmat - std::initializer_list warptile_l = { 128, 128, 128, 16, vk_device.subgroup_size * 2, 64, 2, 4, 4, vk_device.subgroup_size }; - std::initializer_list warptile_m = { 128, 64, 64, 16, vk_device.subgroup_size, 32, 2, 4, 2, vk_device.subgroup_size }; - std::initializer_list warptile_s = { vk_device.subgroup_size, 32, 32, 16, 32, 32, 2, 2, 2, vk_device.subgroup_size }; + std::initializer_list warptile_l = { 128, 128, 128, 16, ctx->device.lock()->subgroup_size * 2, 64, 2, 4, 4, ctx->device.lock()->subgroup_size }; + std::initializer_list warptile_m = { 128, 64, 64, 16, ctx->device.lock()->subgroup_size, 32, 2, 4, 2, ctx->device.lock()->subgroup_size }; + std::initializer_list warptile_s = { ctx->device.lock()->subgroup_size, 32, 32, 16, 32, 32, 2, 2, 2, ctx->device.lock()->subgroup_size }; std::array l_wg_denoms = {128, 128, 1 }; std::array m_wg_denoms = { 64, 64, 1 }; @@ -828,145 +882,208 @@ static void ggml_vk_load_shaders() { uint32_t m_align = 64; uint32_t s_align = 32; - if (vk_device.fp16) { - vk_pipeline_matmul_f32_l = ggml_vk_create_pipeline("matmul_f32_l", matmul_f32_l_len, matmul_f32_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - vk_pipeline_matmul_f32_m = ggml_vk_create_pipeline("matmul_f32_m", matmul_f32_m_len, matmul_f32_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - vk_pipeline_matmul_f32_s = ggml_vk_create_pipeline("matmul_f32_s", matmul_f32_s_len, matmul_f32_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - vk_pipeline_matmul_f32_aligned_l = ggml_vk_create_pipeline("matmul_f32_aligned_l", matmul_f32_aligned_l_len, matmul_f32_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - vk_pipeline_matmul_f32_aligned_m = ggml_vk_create_pipeline("matmul_f32_aligned_m", matmul_f32_aligned_m_len, matmul_f32_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - vk_pipeline_matmul_f32_aligned_s = ggml_vk_create_pipeline("matmul_f32_aligned_s", matmul_f32_aligned_s_len, matmul_f32_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + if (ctx->device.lock()->fp16) { + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_l, "matmul_f32_l", matmul_f32_l_len, matmul_f32_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_m, "matmul_f32_m", matmul_f32_m_len, matmul_f32_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_s, "matmul_f32_s", matmul_f32_s_len, matmul_f32_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_l, "matmul_f32_aligned_l", matmul_f32_aligned_l_len, matmul_f32_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_m, "matmul_f32_aligned_m", matmul_f32_aligned_m_len, matmul_f32_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_s, "matmul_f32_aligned_s", matmul_f32_aligned_s_len, matmul_f32_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - vk_pipeline_matmul_f16_l = ggml_vk_create_pipeline("matmul_f16_l", matmul_f16_l_len, matmul_f16_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - vk_pipeline_matmul_f16_m = ggml_vk_create_pipeline("matmul_f16_m", matmul_f16_m_len, matmul_f16_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - vk_pipeline_matmul_f16_s = ggml_vk_create_pipeline("matmul_f16_s", matmul_f16_s_len, matmul_f16_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_l, "matmul_f16_l", matmul_f16_l_len, matmul_f16_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_m, "matmul_f16_m", matmul_f16_m_len, matmul_f16_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_s, "matmul_f16_s", matmul_f16_s_len, matmul_f16_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_l, "matmul_f16_aligned_l", matmul_f16_aligned_l_len, matmul_f16_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_m, "matmul_f16_aligned_m", matmul_f16_aligned_m_len, matmul_f16_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_s, "matmul_f16_aligned_s", matmul_f16_aligned_s_len, matmul_f16_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - vk_pipeline_matmul_f16_aligned_l = ggml_vk_create_pipeline("matmul_f16_aligned_l", matmul_f16_aligned_l_len, matmul_f16_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - vk_pipeline_matmul_f16_aligned_m = ggml_vk_create_pipeline("matmul_f16_aligned_m", matmul_f16_aligned_m_len, matmul_f16_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - vk_pipeline_matmul_f16_aligned_s = ggml_vk_create_pipeline("matmul_f16_aligned_s", matmul_f16_aligned_s_len, matmul_f16_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - - vk_pipeline_matmul_f16_f32_l = ggml_vk_create_pipeline("matmul_f16_f32_l", matmul_f16_f32_l_len, matmul_f16_f32_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - vk_pipeline_matmul_f16_f32_m = ggml_vk_create_pipeline("matmul_f16_f32_m", matmul_f16_f32_m_len, matmul_f16_f32_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - vk_pipeline_matmul_f16_f32_s = ggml_vk_create_pipeline("matmul_f16_f32_s", matmul_f16_f32_s_len, matmul_f16_f32_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - vk_pipeline_matmul_f16_f32_aligned_l = ggml_vk_create_pipeline("matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_len, matmul_f16_f32_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - vk_pipeline_matmul_f16_f32_aligned_m = ggml_vk_create_pipeline("matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_len, matmul_f16_f32_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - vk_pipeline_matmul_f16_f32_aligned_s = ggml_vk_create_pipeline("matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_len, matmul_f16_f32_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_l, "matmul_f16_f32_l", matmul_f16_f32_l_len, matmul_f16_f32_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_m, "matmul_f16_f32_m", matmul_f16_f32_m_len, matmul_f16_f32_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_s, "matmul_f16_f32_s", matmul_f16_f32_s_len, matmul_f16_f32_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_len, matmul_f16_f32_aligned_l_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_len, matmul_f16_f32_aligned_m_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_len, matmul_f16_f32_aligned_s_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); } else { - vk_pipeline_matmul_f32_l = ggml_vk_create_pipeline("matmul_f32_l", matmul_f32_l_fp32_len, matmul_f32_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - vk_pipeline_matmul_f32_m = ggml_vk_create_pipeline("matmul_f32_m", matmul_f32_m_fp32_len, matmul_f32_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - vk_pipeline_matmul_f32_s = ggml_vk_create_pipeline("matmul_f32_s", matmul_f32_s_fp32_len, matmul_f32_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - vk_pipeline_matmul_f32_aligned_l = ggml_vk_create_pipeline("matmul_f32_aligned_l", matmul_f32_aligned_l_fp32_len, matmul_f32_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - vk_pipeline_matmul_f32_aligned_m = ggml_vk_create_pipeline("matmul_f32_aligned_m", matmul_f32_aligned_m_fp32_len, matmul_f32_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - vk_pipeline_matmul_f32_aligned_s = ggml_vk_create_pipeline("matmul_f32_aligned_s", matmul_f32_aligned_s_fp32_len, matmul_f32_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_l, "matmul_f32_l", matmul_f32_l_fp32_len, matmul_f32_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_m, "matmul_f32_m", matmul_f32_m_fp32_len, matmul_f32_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_s, "matmul_f32_s", matmul_f32_s_fp32_len, matmul_f32_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_l, "matmul_f32_aligned_l", matmul_f32_aligned_l_fp32_len, matmul_f32_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_m, "matmul_f32_aligned_m", matmul_f32_aligned_m_fp32_len, matmul_f32_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f32_aligned_s, "matmul_f32_aligned_s", matmul_f32_aligned_s_fp32_len, matmul_f32_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - vk_pipeline_matmul_f16_l = ggml_vk_create_pipeline("matmul_f16_l", matmul_f16_l_fp32_len, matmul_f16_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - vk_pipeline_matmul_f16_m = ggml_vk_create_pipeline("matmul_f16_m", matmul_f16_m_fp32_len, matmul_f16_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - vk_pipeline_matmul_f16_s = ggml_vk_create_pipeline("matmul_f16_s", matmul_f16_s_fp32_len, matmul_f16_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_l, "matmul_f16_l", matmul_f16_l_fp32_len, matmul_f16_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_m, "matmul_f16_m", matmul_f16_m_fp32_len, matmul_f16_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_s, "matmul_f16_s", matmul_f16_s_fp32_len, matmul_f16_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_l, "matmul_f16_aligned_l", matmul_f16_aligned_l_fp32_len, matmul_f16_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_m, "matmul_f16_aligned_m", matmul_f16_aligned_m_fp32_len, matmul_f16_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_aligned_s, "matmul_f16_aligned_s", matmul_f16_aligned_s_fp32_len, matmul_f16_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - vk_pipeline_matmul_f16_aligned_l = ggml_vk_create_pipeline("matmul_f16_aligned_l", matmul_f16_aligned_l_fp32_len, matmul_f16_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - vk_pipeline_matmul_f16_aligned_m = ggml_vk_create_pipeline("matmul_f16_aligned_m", matmul_f16_aligned_m_fp32_len, matmul_f16_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - vk_pipeline_matmul_f16_aligned_s = ggml_vk_create_pipeline("matmul_f16_aligned_s", matmul_f16_aligned_s_fp32_len, matmul_f16_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); - - vk_pipeline_matmul_f16_f32_l = ggml_vk_create_pipeline("matmul_f16_f32_l", matmul_f16_f32_l_fp32_len, matmul_f16_f32_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); - vk_pipeline_matmul_f16_f32_m = ggml_vk_create_pipeline("matmul_f16_f32_m", matmul_f16_f32_m_fp32_len, matmul_f16_f32_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); - vk_pipeline_matmul_f16_f32_s = ggml_vk_create_pipeline("matmul_f16_f32_s", matmul_f16_f32_s_fp32_len, matmul_f16_f32_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); - vk_pipeline_matmul_f16_f32_aligned_l = ggml_vk_create_pipeline("matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_fp32_len, matmul_f16_f32_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); - vk_pipeline_matmul_f16_f32_aligned_m = ggml_vk_create_pipeline("matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_fp32_len, matmul_f16_f32_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); - vk_pipeline_matmul_f16_f32_aligned_s = ggml_vk_create_pipeline("matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_fp32_len, matmul_f16_f32_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_l, "matmul_f16_f32_l", matmul_f16_f32_l_fp32_len, matmul_f16_f32_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_m, "matmul_f16_f32_m", matmul_f16_f32_m_fp32_len, matmul_f16_f32_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_s, "matmul_f16_f32_s", matmul_f16_f32_s_fp32_len, matmul_f16_f32_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_l, "matmul_f16_f32_aligned_l", matmul_f16_f32_aligned_l_fp32_len, matmul_f16_f32_aligned_l_fp32_data, "main", 3, 14 * sizeof(uint32_t), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_m, "matmul_f16_f32_aligned_m", matmul_f16_f32_aligned_m_fp32_len, matmul_f16_f32_aligned_m_fp32_data, "main", 3, 14 * sizeof(uint32_t), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_f16_f32_aligned_s, "matmul_f16_f32_aligned_s", matmul_f16_f32_aligned_s_fp32_len, matmul_f16_f32_aligned_s_fp32_data, "main", 3, 14 * sizeof(uint32_t), s_wg_denoms, warptile_s, s_align); } - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_F16] = ggml_vk_create_pipeline("mul_mat_vec_f16_f32", mul_mat_vec_f16_f32_len, mul_mat_vec_f16_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("mul_mat_vec_q4_0_f32", mul_mat_vec_q4_0_f32_len, mul_mat_vec_q4_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("mul_mat_vec_q4_1_f32", mul_mat_vec_q4_1_f32_len, mul_mat_vec_q4_1_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("mul_mat_vec_q5_0_f32", mul_mat_vec_q5_0_f32_len, mul_mat_vec_q5_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("mul_mat_vec_q5_1_f32", mul_mat_vec_q5_1_f32_len, mul_mat_vec_q5_1_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("mul_mat_vec_q8_0_f32", mul_mat_vec_q8_0_f32_len, mul_mat_vec_q8_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q2_K] = ggml_vk_create_pipeline("mul_mat_vec_q2_K_f32", mul_mat_vec_q2_K_f32_len, mul_mat_vec_q2_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q3_K] = ggml_vk_create_pipeline("mul_mat_vec_q3_K_f32", mul_mat_vec_q3_K_f32_len, mul_mat_vec_q3_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_K] = ggml_vk_create_pipeline("mul_mat_vec_q4_K_f32", mul_mat_vec_q4_K_f32_len, mul_mat_vec_q4_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_K] = ggml_vk_create_pipeline("mul_mat_vec_q5_K_f32", mul_mat_vec_q5_K_f32_len, mul_mat_vec_q5_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); - vk_pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q6_K] = ggml_vk_create_pipeline("mul_mat_vec_q6_K_f32", mul_mat_vec_q6_K_f32_len, mul_mat_vec_q6_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_F16 ], "mul_mat_vec_f16_f32", mul_mat_vec_f16_f32_len, mul_mat_vec_f16_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_0], "mul_mat_vec_q4_0_f32", mul_mat_vec_q4_0_f32_len, mul_mat_vec_q4_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_1], "mul_mat_vec_q4_1_f32", mul_mat_vec_q4_1_f32_len, mul_mat_vec_q4_1_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_0], "mul_mat_vec_q5_0_f32", mul_mat_vec_q5_0_f32_len, mul_mat_vec_q5_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_1], "mul_mat_vec_q5_1_f32", mul_mat_vec_q5_1_f32_len, mul_mat_vec_q5_1_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q8_0], "mul_mat_vec_q8_0_f32", mul_mat_vec_q8_0_f32_len, mul_mat_vec_q8_0_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q2_K], "mul_mat_vec_q2_K_f32", mul_mat_vec_q2_K_f32_len, mul_mat_vec_q2_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q3_K], "mul_mat_vec_q3_K_f32", mul_mat_vec_q3_K_f32_len, mul_mat_vec_q3_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q4_K], "mul_mat_vec_q4_K_f32", mul_mat_vec_q4_K_f32_len, mul_mat_vec_q4_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q5_K], "mul_mat_vec_q5_K_f32", mul_mat_vec_q5_K_f32_len, mul_mat_vec_q5_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant_mul_mat_vec_f32[GGML_TYPE_Q6_K], "mul_mat_vec_q6_K_f32", mul_mat_vec_q6_K_f32_len, mul_mat_vec_q6_K_f32_data, "main", 3, 3 * sizeof(int), {1, 1, 1}, {}, 1); // dequant shaders - vk_pipeline_dequant[GGML_TYPE_F32] = ggml_vk_create_pipeline("f32_to_f16", f32_to_f16_len, f32_to_f16_data, "main", 2, 4 * sizeof(int), {64, 1, 1}, {}, 1); - - vk_pipeline_dequant[GGML_TYPE_F16] = ggml_vk_create_pipeline("dequant_f16", dequant_f16_len, dequant_f16_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q2_K] = ggml_vk_create_pipeline("dequant_q2_K", dequant_q2_K_len, dequant_q2_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q3_K] = ggml_vk_create_pipeline("dequant_q3_K", dequant_q3_K_len, dequant_q3_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q4_K] = ggml_vk_create_pipeline("dequant_q4_K", dequant_q4_K_len, dequant_q4_K_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q5_K] = ggml_vk_create_pipeline("dequant_q5_K", dequant_q5_K_len, dequant_q5_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); - vk_pipeline_dequant[GGML_TYPE_Q6_K] = ggml_vk_create_pipeline("dequant_q6_K", dequant_q6_K_len, dequant_q6_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_F32 ], "f32_to_f16", f32_to_f16_len, f32_to_f16_data, "main", 2, 4 * sizeof(int), { 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_F16 ], "dequant_f16", dequant_f16_len, dequant_f16_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q4_0], "dequant_q4_0", dequant_q4_0_len, dequant_q4_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q4_1], "dequant_q4_1", dequant_q4_1_len, dequant_q4_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q5_0], "dequant_q5_0", dequant_q5_0_len, dequant_q5_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q5_1], "dequant_q5_1", dequant_q5_1_len, dequant_q5_1_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q8_0], "dequant_q8_0", dequant_q8_0_len, dequant_q8_0_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q2_K], "dequant_q2_K", dequant_q2_K_len, dequant_q2_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q3_K], "dequant_q3_K", dequant_q3_K_len, dequant_q3_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q4_K], "dequant_q4_K", dequant_q4_K_len, dequant_q4_K_data, "main", 2, 4 * sizeof(int), {256 * 32, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q5_K], "dequant_q5_K", dequant_q5_K_len, dequant_q5_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_dequant[GGML_TYPE_Q6_K], "dequant_q6_K", dequant_q6_K_len, dequant_q6_K_data, "main", 2, 4 * sizeof(int), {256 * 64, 1, 1}, {}, 1); // get_rows - vk_pipeline_get_rows[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16", get_rows_f16_len, get_rows_f16_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_F16 ], "get_rows_f16", get_rows_f16_len, get_rows_f16_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q4_0], "get_rows_q4_0", get_rows_q4_0_len, get_rows_q4_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q4_1], "get_rows_q4_1", get_rows_q4_1_len, get_rows_q4_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q5_0], "get_rows_q5_0", get_rows_q5_0_len, get_rows_q5_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q5_1], "get_rows_q5_1", get_rows_q5_1_len, get_rows_q5_1_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows[GGML_TYPE_Q8_0], "get_rows_q8_0", get_rows_q8_0_len, get_rows_q8_0_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_F16] = ggml_vk_create_pipeline("get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q4_0] = ggml_vk_create_pipeline("get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q4_1] = ggml_vk_create_pipeline("get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q5_0] = ggml_vk_create_pipeline("get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q5_1] = ggml_vk_create_pipeline("get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_get_rows_f32[GGML_TYPE_Q8_0] = ggml_vk_create_pipeline("get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_F32 ], "get_rows_f16_f32", get_rows_f16_f32_len, get_rows_f16_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q4_0], "get_rows_q4_0_f32", get_rows_q4_0_f32_len, get_rows_q4_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q4_1], "get_rows_q4_1_f32", get_rows_q4_1_f32_len, get_rows_q4_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q5_0], "get_rows_q5_0_f32", get_rows_q5_0_f32_len, get_rows_q5_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q5_1], "get_rows_q5_1_f32", get_rows_q5_1_f32_len, get_rows_q5_1_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_get_rows_f32[GGML_TYPE_Q8_0], "get_rows_q8_0_f32", get_rows_q8_0_f32_len, get_rows_q8_0_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_matmul_split_k_reduce = ggml_vk_create_pipeline("split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_matmul_split_k_reduce, "split_k_reduce", split_k_reduce_len, split_k_reduce_data, "main", 2, 2 * sizeof(uint32_t), {256, 1, 1}, {}, 1); - vk_pipeline_mul_mat_vec_p021_f16_f32 = ggml_vk_create_pipeline("mul_mat_vec_p021_f16_f32", mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {}, 1); - vk_pipeline_mul_mat_vec_nc_f16_f32 = ggml_vk_create_pipeline("mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 7 * sizeof(uint32_t), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_mul_mat_vec_p021_f16_f32, "mul_mat_vec_p021_f16_f32", mul_mat_vec_p021_f16_f32_len, mul_mat_vec_p021_f16_f32_data, "main", 3, 6 * sizeof(uint32_t), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_mul_mat_vec_nc_f16_f32, "mul_mat_vec_nc_f16_f32", mul_mat_vec_nc_f16_f32_len, mul_mat_vec_nc_f16_f32_data, "main", 3, 7 * sizeof(uint32_t), {1, 1, 1}, {}, 1); - vk_pipeline_norm_f32 = ggml_vk_create_pipeline("norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); - vk_pipeline_rms_norm_f32 = ggml_vk_create_pipeline("rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_norm_f32, "norm_f32", norm_f32_len, norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_rms_norm_f32, "rms_norm_f32", rms_norm_f32_len, rms_norm_f32_data, "main", 2, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); - vk_pipeline_cpy_f32_f32 = ggml_vk_create_pipeline("cpy_f32_f32", cpy_f32_f32_len, cpy_f32_f32_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_cpy_f32_f16 = ggml_vk_create_pipeline("cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_cpy_f16_f16 = ggml_vk_create_pipeline("cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_cpy_f32_f32, "cpy_f32_f32", cpy_f32_f32_len, cpy_f32_f32_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_cpy_f32_f16, "cpy_f32_f16", cpy_f32_f16_len, cpy_f32_f16_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_cpy_f16_f16, "cpy_f16_f16", cpy_f16_f16_len, cpy_f16_f16_data, "main", 2, sizeof(vk_op_cpy_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_add_f32 = ggml_vk_create_pipeline("add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_add_f32, "add_f32", add_f32_len, add_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_mul_f32 = ggml_vk_create_pipeline("mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_mul_f32, "mul_f32", mul_f32_len, mul_f32_data, "main", 3, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_scale_f32 = ggml_vk_create_pipeline("scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_scale_f32, "scale_f32", scale_f32_len, scale_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_sqr_f32 = ggml_vk_create_pipeline("sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_sqr_f32, "sqr_f32", sqr_f32_len, sqr_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_clamp_f32 = ggml_vk_create_pipeline("clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_clamp_f32, "clamp_f32", clamp_f32_len, clamp_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_gelu_f32 = ggml_vk_create_pipeline("gelu_f32", gelu_f32_len, gelu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_silu_f32 = ggml_vk_create_pipeline("silu_f32", silu_f32_len, silu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_relu_f32 = ggml_vk_create_pipeline("relu_f32", relu_f32_len, relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_gelu_f32, "gelu_f32", gelu_f32_len, gelu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_silu_f32, "silu_f32", silu_f32_len, silu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_relu_f32, "relu_f32", relu_f32_len, relu_f32_data, "main", 2, sizeof(vk_op_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_diag_mask_inf_f32 = ggml_vk_create_pipeline("diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_diag_mask_inf_f32, "diag_mask_inf_f32", diag_mask_inf_f32_len, diag_mask_inf_f32_data, "main", 2, sizeof(vk_op_diag_mask_push_constants), {512, 1, 1}, {}, 1); - vk_pipeline_soft_max_f32 = ggml_vk_create_pipeline("soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_push_constants), {1, 1, 1}, {}, 1); - vk_pipeline_rope_f32 = ggml_vk_create_pipeline("rope_f32", rope_f32_len, rope_f32_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); - vk_pipeline_rope_f16 = ggml_vk_create_pipeline("rope_f16", rope_f16_len, rope_f16_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_f32, "rope_f32", rope_f32_len, rope_f32_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_f16, "rope_f16", rope_f16_len, rope_f16_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); - vk_pipeline_rope_neox_f32 = ggml_vk_create_pipeline("rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); - vk_pipeline_rope_neox_f16 = ggml_vk_create_pipeline("rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 3, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); } -void ggml_vk_init() { +static void ggml_vk_print_gpu_info(size_t idx) { + GGML_ASSERT(idx < vk_instance.device_indices.size()); + size_t dev_num = vk_instance.device_indices[idx]; #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_init()" << std::endl; + std::cerr << "ggml_vk_print_gpu_info(" << dev_num << ")" << std::endl; #endif - static bool initialized = false; + GGML_ASSERT(vk_instance.initialized); - if (initialized) { - return; + std::vector devices = vk_instance.instance.enumeratePhysicalDevices(); + + if (dev_num >= devices.size()) { + std::cerr << "ggml_vulkan: Device with index " << dev_num << " does not exist." << std::endl; + throw std::runtime_error("Device not found"); } - initialized = true; + vk::PhysicalDevice physical_device = devices[dev_num]; + std::vector ext_props = physical_device.enumerateDeviceExtensionProperties(); - const char* GGML_VULKAN_DEVICE = getenv("GGML_VULKAN_DEVICE"); - int dev_num = (GGML_VULKAN_DEVICE == NULL ? 0 : atoi(GGML_VULKAN_DEVICE)); + vk::PhysicalDeviceProperties2 props2; + vk::PhysicalDeviceMaintenance3Properties props3; + vk::PhysicalDeviceSubgroupProperties subgroup_props; + props2.pNext = &props3; + props3.pNext = &subgroup_props; + physical_device.getProperties2(&props2); + + const size_t subgroup_size = subgroup_props.subgroupSize; + const bool uma = props2.properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu; + + bool fp16_storage = false; + bool fp16_compute = false; + + for (auto properties : ext_props) { + if (strcmp("VK_KHR_16bit_storage", properties.extensionName) == 0) { + fp16_storage = true; + } else if (strcmp("VK_KHR_shader_float16_int8", properties.extensionName) == 0) { + fp16_compute = true; + } + } + + const char* GGML_VULKAN_DISABLE_F16 = getenv("GGML_VULKAN_DISABLE_F16"); + bool force_disable_f16 = GGML_VULKAN_DISABLE_F16 != nullptr; + + bool fp16 = !force_disable_f16 && fp16_storage && fp16_compute; + + vk::PhysicalDeviceFeatures device_features = physical_device.getFeatures(); + + VkPhysicalDeviceFeatures2 device_features2; + device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2; + device_features2.pNext = nullptr; + device_features2.features = (VkPhysicalDeviceFeatures)device_features; + + VkPhysicalDeviceVulkan11Features vk11_features; + vk11_features.pNext = nullptr; + vk11_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_1_FEATURES; + device_features2.pNext = &vk11_features; + + VkPhysicalDeviceVulkan12Features vk12_features; + vk12_features.pNext = nullptr; + vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES; + vk11_features.pNext = &vk12_features; + + vkGetPhysicalDeviceFeatures2(physical_device, &device_features2); + + fp16 = fp16 && vk12_features.shaderFloat16; + + std::string device_name = props2.properties.deviceName.data(); + std::cerr << GGML_VK_NAME << idx << ": " << device_name << " | uma: " << uma << " | fp16: " << fp16 << " | warp size: " << subgroup_size << std::endl; + + if (props2.properties.deviceType == vk::PhysicalDeviceType::eCpu) { + std::cerr << "ggml_vulkan: Warning: Device type is CPU. This is probably not the device you want." << std::endl; + } +} + +void ggml_vk_instance_init() { + if (vk_instance_initialized) { + return; + } +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_instance_init()" << std::endl; +#endif vk::ApplicationInfo app_info{ "ggml-vulkan", 1, nullptr, 0, VK_API_VERSION }; const std::vector layers = { @@ -989,12 +1106,55 @@ void ggml_vk_init() { validation_features.setPNext(nullptr); instance_create_info.setPNext(&validation_features); -std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; + std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; #endif - vk_instance = vk::createInstance(instance_create_info); + vk_instance.instance = vk::createInstance(instance_create_info); - vk_device.physical_device = vk_instance.enumeratePhysicalDevices()[dev_num]; - std::vector ext_props = vk_device.physical_device.enumerateDeviceExtensionProperties(); + memset(vk_instance.initialized, 0, sizeof(bool) * GGML_VK_MAX_DEVICES); + + size_t num_available_devices = vk_instance.instance.enumeratePhysicalDevices().size(); + + // Emulate behavior of CUDA_VISIBLE_DEVICES for Vulkan + char * devices_env = getenv("GGML_VK_VISIBLE_DEVICES"); + if (devices_env != nullptr) { + std::string devices(devices_env); + std::replace(devices.begin(), devices.end(), ',', ' '); + + std::stringstream ss(devices); + size_t tmp; + while (ss >> tmp) { + if(tmp >= num_available_devices) { + std::cerr << "ggml_vulkan: Invalid device index " << tmp << " in GGML_VK_VISIBLE_DEVICES." << std::endl; + throw std::runtime_error("Invalid Vulkan device index"); + } + vk_instance.device_indices.push_back(tmp); + } + } else { + vk_instance.device_indices.push_back(0); + } + + vk_instance_initialized = true; +} + +void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { + GGML_ASSERT(idx < vk_instance.device_indices.size()); + size_t dev_num = vk_instance.device_indices[idx]; +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_init(" << ctx->name << ", " << dev_num << ")" << std::endl; +#endif + ggml_vk_instance_init(); + + std::vector devices = vk_instance.instance.enumeratePhysicalDevices(); + + if (dev_num >= devices.size()) { + std::cerr << "ggml_vulkan: Device with index " << dev_num << " does not exist." << std::endl; + throw std::runtime_error("Device not found"); + } + + vk_instance.devices[idx] = std::make_shared(); + ctx->device = vk_instance.devices[idx]; + ctx->device.lock()->physical_device = devices[dev_num]; + std::vector ext_props = ctx->device.lock()->physical_device.enumerateDeviceExtensionProperties(); bool maintenance4_support = false; @@ -1014,18 +1174,18 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; if (maintenance4_support) { subgroup_props.pNext = &props4; } - vk_device.physical_device.getProperties2(&props2); - vk_device.properties = props2.properties; + ctx->device.lock()->physical_device.getProperties2(&props2); + ctx->device.lock()->properties = props2.properties; if (maintenance4_support) { - vk_device.max_memory_allocation_size = std::min(props3.maxMemoryAllocationSize, props4.maxBufferSize); + ctx->device.lock()->max_memory_allocation_size = std::min(props3.maxMemoryAllocationSize, props4.maxBufferSize); } else { - vk_device.max_memory_allocation_size = props3.maxMemoryAllocationSize; + ctx->device.lock()->max_memory_allocation_size = props3.maxMemoryAllocationSize; } - vk_device.vendor_id = vk_device.properties.vendorID; - vk_device.subgroup_size = subgroup_props.subgroupSize; - vk_device.uma = vk_device.properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu; + ctx->device.lock()->vendor_id = ctx->device.lock()->properties.vendorID; + ctx->device.lock()->subgroup_size = subgroup_props.subgroupSize; + ctx->device.lock()->uma = ctx->device.lock()->properties.deviceType == vk::PhysicalDeviceType::eIntegratedGpu; bool fp16_storage = false; bool fp16_compute = false; @@ -1039,31 +1199,31 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; } const char* GGML_VULKAN_DISABLE_F16 = getenv("GGML_VULKAN_DISABLE_F16"); - bool force_disable_f16 = GGML_VULKAN_DISABLE_F16 != NULL; + bool force_disable_f16 = GGML_VULKAN_DISABLE_F16 != nullptr; - vk_device.fp16 = !force_disable_f16 && fp16_storage && fp16_compute; + ctx->device.lock()->fp16 = !force_disable_f16 && fp16_storage && fp16_compute; - std::vector queue_family_props = vk_device.physical_device.getQueueFamilyProperties(); + std::vector queue_family_props = ctx->device.lock()->physical_device.getQueueFamilyProperties(); // Try to find a non-graphics compute queue and transfer-focused queues const uint32_t compute_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eCompute, vk::QueueFlagBits::eGraphics, -1, 1); const uint32_t transfer_queue_family_index = ggml_vk_find_queue_family_index(queue_family_props, vk::QueueFlagBits::eTransfer, vk::QueueFlagBits::eCompute | vk::QueueFlagBits::eGraphics, compute_queue_family_index, 1); const float priorities[] = { 1.0f, 1.0f }; - const bool single_queue = compute_queue_family_index == transfer_queue_family_index && queue_family_props[compute_queue_family_index].queueCount == 1; + ctx->device.lock()->single_queue = compute_queue_family_index == transfer_queue_family_index && queue_family_props[compute_queue_family_index].queueCount == 1; std::vector device_queue_create_infos; if (compute_queue_family_index != transfer_queue_family_index) { device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), transfer_queue_family_index, 1, priorities + 1}); - } else if(!single_queue) { + } else if(!ctx->device.lock()->single_queue) { device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 2, priorities}); } else { device_queue_create_infos.push_back({vk::DeviceQueueCreateFlags(), compute_queue_family_index, 1, priorities}); } vk::DeviceCreateInfo device_create_info; std::vector device_extensions; - vk::PhysicalDeviceFeatures device_features = vk_device.physical_device.getFeatures(); + vk::PhysicalDeviceFeatures device_features = ctx->device.lock()->physical_device.getFeatures(); VkPhysicalDeviceFeatures2 device_features2; device_features2.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_FEATURES_2; @@ -1080,13 +1240,13 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; vk12_features.sType = VK_STRUCTURE_TYPE_PHYSICAL_DEVICE_VULKAN_1_2_FEATURES; vk11_features.pNext = &vk12_features; - vkGetPhysicalDeviceFeatures2(vk_device.physical_device, &device_features2); + vkGetPhysicalDeviceFeatures2(ctx->device.lock()->physical_device, &device_features2); - vk_device.fp16 = vk_device.fp16 && vk12_features.shaderFloat16; + ctx->device.lock()->fp16 = ctx->device.lock()->fp16 && vk12_features.shaderFloat16; if (!vk11_features.storageBuffer16BitAccess) { - std::cerr << "ggml_vulkan: device does not support 16-bit storage" << std::endl; - GGML_ASSERT(false); + std::cerr << "ggml_vulkan: device " << GGML_VK_NAME << idx << " does not support 16-bit storage." << std::endl; + throw std::runtime_error("Unsupported device"); } device_extensions.push_back("VK_KHR_16bit_storage"); @@ -1095,10 +1255,11 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; device_extensions.push_back("VK_KHR_shader_non_semantic_info"); #endif - if (vk_device.fp16) { + if (ctx->device.lock()->fp16) { device_extensions.push_back("VK_KHR_shader_float16_int8"); } - std::cerr << "ggml_vulkan: Using " << vk_device.properties.deviceName << " | uma: " << vk_device.uma << " | fp16: " << vk_device.fp16 << " | warp size: " << vk_device.subgroup_size << std::endl; + ctx->device.lock()->name = ctx->device.lock()->properties.deviceName.data(); + device_create_info = { vk::DeviceCreateFlags(), device_queue_create_infos, @@ -1106,28 +1267,32 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; device_extensions }; device_create_info.setPNext(&device_features2); - vk_device.device = vk_device.physical_device.createDevice(device_create_info); + ctx->device.lock()->device = ctx->device.lock()->physical_device.createDevice(device_create_info); - vk_device.descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN; + ctx->device.lock()->descriptor_set_mode = VK_DEVICE_DESCRIPTOR_POOL_MODE_UNKNOWN; // Shaders - ggml_vk_load_shaders(); + ggml_vk_load_shaders(ctx); // Queues - vk_device.compute_queue = ggml_vk_create_queue(compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer }); - if (!single_queue) { + ggml_vk_create_queue(ctx, ctx->device.lock()->compute_queue, compute_queue_family_index, 0, { vk::PipelineStageFlagBits::eComputeShader | vk::PipelineStageFlagBits::eTransfer }); + if (!ctx->device.lock()->single_queue) { const uint32_t transfer_queue_index = compute_queue_family_index == transfer_queue_family_index ? 1 : 0; - vk_device.transfer_queue = ggml_vk_create_queue(transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer }); + ggml_vk_create_queue(ctx, ctx->device.lock()->transfer_queue, transfer_queue_family_index, transfer_queue_index, { vk::PipelineStageFlagBits::eTransfer }); } else { - vk_device.transfer_queue = vk_device.compute_queue; + // TODO: Use pointer or reference to avoid copy + ctx->device.lock()->transfer_queue = ctx->device.lock()->compute_queue; } - vk_fence = vk_device.device.createFence({}); + ctx->fence = ctx->device.lock()->device.createFence({}); - vk_ctx = nullptr; - vk_transfer_ctx = nullptr; + ctx->compute_ctx = nullptr; + ctx->transfer_ctx = nullptr; - vk_disable = false; + ctx->disable = false; + ctx->initialized = true; + + ctx->idx = idx; #ifdef GGML_VULKAN_CHECK_RESULTS const char* skip_checks = getenv("GGML_VULKAN_SKIP_CHECKS"); @@ -1137,7 +1302,7 @@ std::cerr << "ggml_vulkan: Validation layers enabled" << std::endl; #endif } -static vk_pipeline* ggml_vk_get_to_fp16(ggml_type type) { +static vk_pipeline* ggml_vk_get_to_fp16(ggml_backend_vk_context * ctx, ggml_type type) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_get_to_fp16()" << std::endl; #endif @@ -1158,10 +1323,10 @@ static vk_pipeline* ggml_vk_get_to_fp16(ggml_type type) { return nullptr; } - return &vk_pipeline_dequant[type]; + return &ctx->pipeline_dequant[type]; } -static vk_pipeline* ggml_vk_get_dequantize_mul_mat_vec(ggml_type type) { +static vk_pipeline* ggml_vk_get_dequantize_mul_mat_vec(ggml_backend_vk_context * ctx, ggml_type type) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_get_dequantize_mul_mat_vec()" << std::endl; #endif @@ -1182,15 +1347,10 @@ static vk_pipeline* ggml_vk_get_dequantize_mul_mat_vec(ggml_type type) { return nullptr; } - return &vk_pipeline_dequant_mul_mat_vec_f32[type]; + return &ctx->pipeline_dequant_mul_mat_vec_f32[type]; } -// buffer pool for vulkan -#define MAX_VK_BUFFERS 256 - -static vk_buffer g_vk_buffer_pool[MAX_VK_BUFFERS]; - -static vk_buffer ggml_vk_pool_malloc(size_t size) { +static vk_buffer ggml_vk_pool_malloc(ggml_backend_vk_context * ctx, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_pool_malloc(" << size << ")" << std::endl; #endif @@ -1199,98 +1359,95 @@ static vk_buffer ggml_vk_pool_malloc(size_t size) { int worst_i = -1; size_t worst_size = 0; //largest unused buffer seen so far for (int i = 0; i < MAX_VK_BUFFERS; ++i) { - vk_buffer &b = g_vk_buffer_pool[i]; - if (b.size > 0 && b.size >= size && b.size < best_size) { + vk_buffer &b = ctx->buffer_pool[i]; + if (b != nullptr && b->size >= size && b->size < best_size) { best_i = i; - best_size = b.size; + best_size = b->size; } - if (b.size > 0 && b.size > worst_size) { + if (b != nullptr && b->size > worst_size) { worst_i = i; - worst_size = b.size; + worst_size = b->size; } } if(best_i != -1) { //found the smallest buffer that fits our needs - vk_buffer b = g_vk_buffer_pool[best_i]; - g_vk_buffer_pool[best_i].size = 0; + vk_buffer b = ctx->buffer_pool[best_i]; + ctx->buffer_pool[best_i].reset(); return b; } if(worst_i != -1) { //no buffer that fits our needs, resize largest one to save memory - vk_buffer& b = g_vk_buffer_pool[worst_i]; + vk_buffer& b = ctx->buffer_pool[worst_i]; ggml_vk_destroy_buffer(b); } - return ggml_vk_create_buffer_check(size, vk::MemoryPropertyFlagBits::eDeviceLocal); + return ggml_vk_create_buffer_check(ctx, size, vk::MemoryPropertyFlagBits::eDeviceLocal); } -static void ggml_vk_pool_free(vk_buffer& buffer) { +static void ggml_vk_pool_free(ggml_backend_vk_context * ctx, vk_buffer& buffer) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_pool_free(" << buffer.size << ")" << std::endl; + std::cerr << "ggml_vk_pool_free(" << buffer->size << ")" << std::endl; #endif for (int i = 0; i < MAX_VK_BUFFERS; ++i) { - vk_buffer& b = g_vk_buffer_pool[i]; - if (b.size == 0) { + vk_buffer& b = ctx->buffer_pool[i]; + if (b == nullptr) { b = buffer; - // Set owning queue family index to ignored to avoid synchronization on next use - b.qf_owner = VK_QUEUE_FAMILY_IGNORED; return; } } - fprintf(stderr, "WARNING: vk buffer pool full, increase MAX_VK_BUFFERS\n"); + std::cerr << "ggml_vulkan: WARNING: vk buffer pool full, increase MAX_VK_BUFFERS" << std::endl; ggml_vk_destroy_buffer(buffer); } // Returns an available temporary buffer that may only be used temporarily, it will be reused -static vk_buffer ggml_vk_create_buffer_temp(size_t size) { +static vk_buffer ggml_vk_create_buffer_temp(ggml_backend_vk_context * ctx, size_t size) { // Try to find existing temp buffer with enough capacity - for (auto& buffer : vk_gc.temp_buffers) { - if (buffer.size >= size) { + for (auto& buffer : ctx->gc.temp_buffers) { + if (buffer->size >= size) { return buffer; } } // Otherwise create new buffer - vk_buffer buf = ggml_vk_pool_malloc(size); - vk_gc.temp_buffers.push_back(buf); + vk_buffer buf = ggml_vk_pool_malloc(ctx, size); + ctx->gc.temp_buffers.push_back(buf); return buf; } -static void * ggml_vk_host_malloc(size_t size) { +static void * ggml_vk_host_malloc(ggml_backend_vk_context * ctx, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_host_malloc(" << size << ")" << std::endl; #endif - vk_buffer buf = ggml_vk_create_buffer(size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); + vk_buffer buf = ggml_vk_create_buffer(ctx, size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); - if(!(buf.memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible)) { + if(!(buf->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible)) { fprintf(stderr, "WARNING: failed to allocate %.2f MB of pinned memory\n", size/1024.0/1024.0); - buf.size = 0; - vk_device.device.freeMemory(buf.device_memory); - vk_device.device.destroyBuffer(buf.buffer); + ctx->device.lock()->device.freeMemory(buf->device_memory); + ctx->device.lock()->device.destroyBuffer(buf->buffer); return nullptr; } - vk_pinned_memory.push_back(std::make_tuple(buf.ptr, size, buf)); + ctx->pinned_memory.push_back(std::make_tuple(buf->ptr, size, buf)); - return buf.ptr; + return buf->ptr; } -static void ggml_vk_host_free(void* ptr) { +static void ggml_vk_host_free(ggml_backend_vk_context * ctx, void* ptr) { if (ptr == nullptr) { return; } #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_host_free(" << ptr << ")" << std::endl; #endif - vk_buffer* buf = nullptr; + vk_buffer buf; size_t index; - for (size_t i = 0; i < vk_pinned_memory.size(); i++) { - const uint8_t* addr = (const uint8_t*) std::get<0>(vk_pinned_memory[i]); - const uint8_t* endr = addr + std::get<1>(vk_pinned_memory[i]); + for (size_t i = 0; i < ctx->pinned_memory.size(); i++) { + const uint8_t* addr = (const uint8_t*) std::get<0>(ctx->pinned_memory[i]); + const uint8_t* endr = addr + std::get<1>(ctx->pinned_memory[i]); if (ptr >= addr && ptr < endr) { - buf = &std::get<2>(vk_pinned_memory[i]); + buf = std::get<2>(ctx->pinned_memory[i]); index = i; break; } @@ -1300,28 +1457,28 @@ static void ggml_vk_host_free(void* ptr) { return; } - ggml_vk_destroy_buffer(*buf); + ggml_vk_destroy_buffer(buf); - vk_pinned_memory.erase(vk_pinned_memory.begin() + index); + ctx->pinned_memory.erase(ctx->pinned_memory.begin() + index); } -static void ggml_vk_host_get(const void * ptr, vk_buffer *& buf, size_t& buf_offset) { +static void ggml_vk_host_get(ggml_backend_vk_context * ctx, const void * ptr, vk_buffer& buf, size_t& buf_offset) { buf = nullptr; buf_offset = 0; - for (size_t i = 0; i < vk_pinned_memory.size(); i++) { - const uint8_t* addr = (const uint8_t*) std::get<0>(vk_pinned_memory[i]); - const uint8_t* endr = addr + std::get<1>(vk_pinned_memory[i]); + for (size_t i = 0; i < ctx->pinned_memory.size(); i++) { + const uint8_t* addr = (const uint8_t*) std::get<0>(ctx->pinned_memory[i]); + const uint8_t* endr = addr + std::get<1>(ctx->pinned_memory[i]); if (ptr >= addr && ptr < endr) { - buf = &std::get<2>(vk_pinned_memory[i]); + buf = std::get<2>(ctx->pinned_memory[i]); buf_offset = ((const uint8_t *)ptr) - addr; break; } } } -static vk_submission ggml_vk_begin_submission(vk_queue& q, bool one_time = true) { +static vk_submission ggml_vk_begin_submission(ggml_backend_vk_context * ctx, vk_queue& q, bool one_time = true) { vk_submission s; - s.buffer = ggml_vk_create_cmd_buffer(q); + s.buffer = ggml_vk_create_cmd_buffer(ctx, q); if (one_time) { s.buffer.begin({ vk::CommandBufferUsageFlagBits::eOneTimeSubmit }); } else { @@ -1331,7 +1488,7 @@ static vk_submission ggml_vk_begin_submission(vk_queue& q, bool one_time = true) return s; } -static void ggml_vk_dispatch_pipeline(vk_context * ctx, vk_pipeline& pipeline, std::vector&& buffers, size_t push_constant_size, const void* push_constants, std::array elements) { +static void ggml_vk_dispatch_pipeline(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline, std::vector&& buffers, size_t push_constant_size, const void* push_constants, std::array elements) { const uint32_t wg0 = CEIL_DIV(elements[0], pipeline.wg_denoms[0]); const uint32_t wg1 = CEIL_DIV(elements[1], pipeline.wg_denoms[1]); const uint32_t wg2 = CEIL_DIV(elements[2], pipeline.wg_denoms[2]); @@ -1344,22 +1501,22 @@ static void ggml_vk_dispatch_pipeline(vk_context * ctx, vk_pipeline& pipeline, s GGML_ASSERT(buffers.size() == pipeline.parameter_count); vk::DescriptorSet& descriptor_set = pipeline.descriptor_sets[pipeline.descriptor_set_idx++]; for (uint32_t i = 0; i < pipeline.parameter_count; i++) { - descriptor_buffer_infos.push_back({buffers[i].buffer.buffer, buffers[i].offset, buffers[i].size}); + descriptor_buffer_infos.push_back({buffers[i].buffer->buffer, buffers[i].offset, buffers[i].size}); } for (uint32_t i = 0; i < pipeline.parameter_count; i++) { write_descriptor_sets.push_back({descriptor_set, i, 0, 1, vk::DescriptorType::eStorageBuffer, nullptr, &descriptor_buffer_infos[i]}); } - vk_device.device.updateDescriptorSets(write_descriptor_sets, {}); + ctx->device.lock()->device.updateDescriptorSets(write_descriptor_sets, {}); - ctx->s->buffer.pushConstants(pipeline.layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size, push_constants); - ctx->s->buffer.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline.pipeline); - ctx->s->buffer.bindDescriptorSets(vk::PipelineBindPoint::eCompute, + subctx->s->buffer.pushConstants(pipeline.layout, vk::ShaderStageFlagBits::eCompute, 0, push_constant_size, push_constants); + subctx->s->buffer.bindPipeline(vk::PipelineBindPoint::eCompute, pipeline.pipeline); + subctx->s->buffer.bindDescriptorSets(vk::PipelineBindPoint::eCompute, pipeline.layout, 0, { descriptor_set }, {}); - ctx->s->buffer.dispatch(wg0, wg1, wg2); + subctx->s->buffer.dispatch(wg0, wg1, wg2); } static void ggml_vk_end_submission(vk_submission& s, std::vector wait_semaphores, std::vector signal_semaphores) { @@ -1381,16 +1538,16 @@ static void ggml_vk_ctx_end(vk_context * ctx) { ctx->s = nullptr; } -static void ggml_vk_ctx_begin(vk_context * ctx) { +static void ggml_vk_ctx_begin(ggml_backend_vk_context * ctx, vk_context * subctx) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_ctx_begin(" << ctx << ")" << std::endl; #endif - if (ctx->s != nullptr) { - ggml_vk_ctx_end(ctx); + if (subctx->s != nullptr) { + ggml_vk_ctx_end(subctx); } - ctx->seqs.push_back({ ggml_vk_begin_submission(*ctx->q) }); - ctx->s = ctx->seqs[ctx->seqs.size() - 1].data(); + subctx->seqs.push_back({ ggml_vk_begin_submission(ctx, *subctx->q) }); + subctx->s = subctx->seqs[subctx->seqs.size() - 1].data(); } static size_t ggml_vk_align_size(size_t width, size_t align) { @@ -1405,14 +1562,14 @@ static void deferred_memcpy(void * dst, const void * src, size_t size, std::vect } } -static void ensure_sync_staging_buffer(size_t size) { - if (vk_sync_staging.size < size) { - ggml_vk_destroy_buffer(vk_sync_staging); - vk_sync_staging = ggml_vk_create_buffer_check(size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); +static void ggml_vk_ensure_sync_staging_buffer(ggml_backend_vk_context * ctx, size_t size) { + if (ctx->sync_staging == nullptr || ctx->sync_staging->size < size) { + ggml_vk_destroy_buffer(ctx->sync_staging); + ctx->sync_staging = ggml_vk_create_buffer_check(ctx, size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); } } -static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size_t offset, const ggml_tensor * tensor, bool sync_staging = false) { +static void ggml_vk_buffer_write_nc_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const ggml_tensor * tensor, bool sync_staging = false) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_nc_async(" << tensor << ")" << std::endl; #endif @@ -1423,9 +1580,9 @@ static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size GGML_ASSERT(false); } // Check if src is pinned memory - vk_buffer * buf = nullptr; + vk_buffer buf; size_t buf_offset; - ggml_vk_host_get(tensor->data, buf, buf_offset); + ggml_vk_host_get(ctx, tensor->data, buf, buf_offset); const uint64_t ne0 = tensor->ne[0]; const uint64_t ne1 = tensor->ne[1]; @@ -1471,21 +1628,21 @@ static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size } } - ggml_vk_sync_buffers(ctx); - ctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices); + ggml_vk_sync_buffers(subctx); + subctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices); return; } // Staging buffer required - vk_buffer * staging = &vk_staging; - size_t staging_offset = vk_staging_offset; + vk_buffer staging = ctx->staging; + size_t staging_offset = ctx->staging_offset; const size_t copy_size = ts*ne/bs; - if (vk_staging.size < vk_staging_offset + copy_size) { + if (ctx->staging->size < ctx->staging_offset + copy_size) { if (sync_staging) { // Create temporary larger buffer - ensure_sync_staging_buffer(copy_size); + ggml_vk_ensure_sync_staging_buffer(ctx, copy_size); - staging = &vk_sync_staging; + staging = ctx->sync_staging; staging_offset = 0; } else { GGML_ASSERT(false); @@ -1494,23 +1651,23 @@ static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size VkBufferCopy buf_copy{ staging_offset, offset, copy_size }; - ggml_vk_sync_buffers(ctx); - vkCmdCopyBuffer(ctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy); + ggml_vk_sync_buffers(subctx); + vkCmdCopyBuffer(subctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy); for (uint64_t i3 = 0; i3 < ne3; i3++) { for (uint64_t i2 = 0; i2 < ne2; i2++) { // Find longest contiguous slice if (ne1*nb1 == dstnb2) { - deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i3*dstnb3 + i2*dstnb2, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2, dstnb2, &ctx->in_memcpys); + deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i3*dstnb3 + i2*dstnb2, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2, dstnb2, &subctx->in_memcpys); } else { for (uint64_t i1 = 0; i1 < ne1; i1++) { if (ne0*nb0/bs == dstnb1) { - deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2 + i1*nb1, dstnb1, &ctx->in_memcpys); + deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1, (const uint8_t *) tensor->data + buf_offset + i3*nb3 + i2*nb2 + i1*nb1, dstnb1, &subctx->in_memcpys); } else { const uint64_t s_off = buf_offset + i3*nb3 + i2*nb2 + i1*nb1; const uint64_t d_off = staging_offset + i3*dstnb3 + i2*dstnb2 + i1*dstnb1; for (uint64_t i0 = 0; i0 < ne0; i0++) { - deferred_memcpy((uint8_t *)staging->ptr + d_off + i0*dstnb0, (const uint8_t *) tensor->data + s_off + i0*nb0, dstnb0, &ctx->in_memcpys); + deferred_memcpy((uint8_t *)staging->ptr + d_off + i0*dstnb0, (const uint8_t *) tensor->data + s_off + i0*nb0, dstnb0, &subctx->in_memcpys); } } } @@ -1519,19 +1676,22 @@ static void ggml_vk_buffer_write_nc_async(vk_context * ctx, vk_buffer* dst, size } } -static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height, bool sync_staging = false) { +static void ggml_vk_buffer_write_2d_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height, bool sync_staging = false) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_2d_async(" << width << ", " << height << ")" << std::endl; #endif + // Make sure ctx owns the buffer + GGML_ASSERT(dst->ctx == ctx); + // Buffer is already mapped if(dst->memory_property_flags & vk::MemoryPropertyFlagBits::eHostVisible) { std::cerr << "ggml_vulkan: buffer_write_async dst buffer is host_visible. Use synchronous write." << std::endl; GGML_ASSERT(false); } // Check if src is pinned memory - vk_buffer * buf = nullptr; + vk_buffer buf = nullptr; size_t buf_offset; - ggml_vk_host_get(src, buf, buf_offset); + ggml_vk_host_get(ctx, src, buf, buf_offset); if (buf != nullptr) { // Memory is pinned, use as staging buffer @@ -1550,8 +1710,8 @@ static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size } } - ggml_vk_sync_buffers(ctx); - ctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices); + ggml_vk_sync_buffers(subctx); + subctx->s->buffer.copyBuffer(buf->buffer, dst->buffer, slices); return; } #ifdef GGML_VULKAN_DEBUG @@ -1559,14 +1719,14 @@ static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size #endif // Staging buffer required - vk_buffer * staging = &vk_staging; - size_t staging_offset = vk_staging_offset; + vk_buffer staging = ctx->staging; + size_t staging_offset = ctx->staging_offset; const size_t copy_size = width*height; - if (vk_staging.size < vk_staging_offset + copy_size) { + if (ctx->staging == nullptr || ctx->staging->size < ctx->staging_offset + copy_size) { if (sync_staging) { - ensure_sync_staging_buffer(copy_size); + ggml_vk_ensure_sync_staging_buffer(ctx, copy_size); - staging = &vk_sync_staging; + staging = ctx->sync_staging; staging_offset = 0; } else { GGML_ASSERT(false); @@ -1578,26 +1738,26 @@ static void ggml_vk_buffer_write_2d_async(vk_context * ctx, vk_buffer* dst, size offset, copy_size}; - ggml_vk_sync_buffers(ctx); - vkCmdCopyBuffer(ctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy); + ggml_vk_sync_buffers(subctx); + vkCmdCopyBuffer(subctx->s->buffer, staging->buffer, dst->buffer, 1, &buf_copy); if (width == spitch) { - deferred_memcpy((uint8_t *)staging->ptr + staging_offset, src, width * height, &ctx->in_memcpys); + deferred_memcpy((uint8_t *)staging->ptr + staging_offset, src, width * height, &subctx->in_memcpys); } else { for (size_t i = 0; i < height; i++) { - deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i * width, (const uint8_t *) src + i * spitch, width, &ctx->in_memcpys); + deferred_memcpy((uint8_t *)staging->ptr + staging_offset + i * width, (const uint8_t *) src + i * spitch, width, &subctx->in_memcpys); } } } -static void ggml_vk_buffer_write_async(vk_context * ctx, vk_buffer* dst, size_t offset, const void * src, size_t size, bool sync_staging = false) { +static void ggml_vk_buffer_write_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const void * src, size_t size, bool sync_staging = false) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_async(" << size << ")" << std::endl; #endif - return ggml_vk_buffer_write_2d_async(ctx, dst, offset, src, size, size, 1, sync_staging); + return ggml_vk_buffer_write_2d_async(ctx, subctx, dst, offset, src, size, size, 1, sync_staging); } -static void ggml_vk_buffer_write_2d(vk_buffer* dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height) { +static void ggml_vk_buffer_write_2d(ggml_backend_vk_context * ctx, vk_buffer& dst, size_t offset, const void * src, size_t spitch, size_t width, size_t height) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write_2d(" << width << ", " << height << ")" << std::endl; #endif @@ -1609,39 +1769,42 @@ static void ggml_vk_buffer_write_2d(vk_buffer* dst, size_t offset, const void * memcpy((uint8_t *)dst->ptr + offset + i * width, (const uint8_t *) src + i * spitch, width); } } else { - vk_context * ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(ctx); - ggml_vk_buffer_write_2d_async(ctx, dst, offset, src, spitch, width, height, true); - ggml_vk_ctx_end(ctx); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_ctx_begin(ctx, subctx); + ggml_vk_buffer_write_2d_async(ctx, subctx, dst, offset, src, spitch, width, height, true); + ggml_vk_ctx_end(subctx); - for (auto& cpy : ctx->in_memcpys) { + for (auto& cpy : subctx->in_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_write_2d waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); } } -static void ggml_vk_buffer_write(vk_buffer* dst, size_t offset, const void * src, size_t size) { +static void ggml_vk_buffer_write(ggml_backend_vk_context * ctx, vk_buffer& dst, size_t offset, const void * src, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_write(" << size << ")" << std::endl; #endif - ggml_vk_buffer_write_2d(dst, offset, src, 0, size, 1); + ggml_vk_buffer_write_2d(ctx, dst, offset, src, 0, size, 1); } -static void ggml_vk_buffer_read_2d_async(vk_context * ctx, vk_buffer* src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) { +static void ggml_vk_buffer_read_2d_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& src, size_t offset, void * dst, size_t spitch, size_t dpitch, size_t width, size_t height, bool sync_staging = false) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_read_2d_async(offset=" << offset << ", width=" << width << ", height=" << height << ")" << std::endl; #endif GGML_ASSERT(width > 0); GGML_ASSERT(height > 0); - GGML_ASSERT(src->size > 0); + GGML_ASSERT(src != nullptr); + // Make sure ctx owns the buffer + GGML_ASSERT(src->ctx == ctx); + // Check if dst is pinned memory - vk_buffer * buf = nullptr; + vk_buffer buf = nullptr; size_t buf_offset; - ggml_vk_host_get(dst, buf, buf_offset); + ggml_vk_host_get(ctx, dst, buf, buf_offset); std::vector slices(1); if (width == spitch && width == dpitch) { @@ -1660,8 +1823,8 @@ static void ggml_vk_buffer_read_2d_async(vk_context * ctx, vk_buffer* src, size_ if (buf != nullptr) { // Memory is pinned, use as staging buffer - ggml_vk_sync_buffers(ctx); - ctx->s->buffer.copyBuffer(src->buffer, buf->buffer, slices); + ggml_vk_sync_buffers(subctx); + subctx->s->buffer.copyBuffer(src->buffer, buf->buffer, slices); return; } @@ -1670,30 +1833,30 @@ static void ggml_vk_buffer_read_2d_async(vk_context * ctx, vk_buffer* src, size_ #endif // Fall back to staging buffer - vk_buffer * staging = &vk_staging; + vk_buffer staging = ctx->staging; const size_t copy_size = dpitch * height; - if (vk_staging.size < vk_staging_offset + copy_size) { + if (ctx->staging == nullptr || ctx->staging->size < ctx->staging_offset + copy_size) { if (sync_staging) { // Create temporary larger buffer - ensure_sync_staging_buffer(copy_size); + ggml_vk_ensure_sync_staging_buffer(ctx, copy_size); - staging = &vk_sync_staging; + staging = ctx->sync_staging; } else { GGML_ASSERT(false); } } - ggml_vk_sync_buffers(ctx); - ctx->s->buffer.copyBuffer(src->buffer, staging->buffer, slices); + ggml_vk_sync_buffers(subctx); + subctx->s->buffer.copyBuffer(src->buffer, staging->buffer, slices); - deferred_memcpy(dst, staging->ptr, copy_size, &ctx->out_memcpys); + deferred_memcpy(dst, staging->ptr, copy_size, &subctx->out_memcpys); } -static void ggml_vk_buffer_read_async(vk_context * ctx, vk_buffer* src, size_t offset, void * dst, size_t size, bool sync_staging = false) { - return ggml_vk_buffer_read_2d_async(ctx, src, offset, dst, size, size, size, 1, sync_staging); +static void ggml_vk_buffer_read_async(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& src, size_t offset, void * dst, size_t size, bool sync_staging = false) { + return ggml_vk_buffer_read_2d_async(ctx, subctx, src, offset, dst, size, size, size, 1, sync_staging); } -static void ggml_vk_buffer_read(vk_buffer* src, size_t offset, void * dst, size_t size) { +static void ggml_vk_buffer_read(ggml_backend_vk_context * ctx, vk_buffer& src, size_t offset, void * dst, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_read(" << offset << ", " << size << ")" << std::endl; #endif @@ -1702,61 +1865,88 @@ static void ggml_vk_buffer_read(vk_buffer* src, size_t offset, void * dst, size_ memcpy(dst, (uint8_t *) src->ptr + offset, size); } else { - vk_context * ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(ctx); - ggml_vk_buffer_read_async(ctx, src, offset, dst, size, true); - ggml_vk_ctx_end(ctx); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_ctx_begin(ctx, subctx); + ggml_vk_buffer_read_async(ctx, subctx, src, offset, dst, size, true); + ggml_vk_ctx_end(subctx); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "vk_buffer_read waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_read waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); - for (auto& cpy : ctx->out_memcpys) { + for (auto& cpy : subctx->out_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } } } -static void ggml_vk_buffer_copy_async(vk_context * ctx, vk_buffer * dst, size_t dst_offset, vk_buffer * src, size_t src_offset, size_t size) { +static void ggml_vk_buffer_copy_async(vk_context * ctx, vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_copy_async(" << size << ")" << std::endl; #endif + // Make sure both buffers are on same ctx + GGML_ASSERT(src->ctx == dst->ctx); + VkBufferCopy bc{ src_offset, dst_offset, size }; vkCmdCopyBuffer(ctx->s->buffer, src->buffer, dst->buffer, 1, &bc); } -static void ggml_vk_buffer_copy(vk_buffer * dst, size_t dst_offset, vk_buffer * src, size_t src_offset, size_t size) { +static void ggml_vk_buffer_copy(vk_buffer& dst, size_t dst_offset, vk_buffer& src, size_t src_offset, size_t size) { + if (src->ctx == dst->ctx) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_buffer_copy(" << size << ")" << std::endl; + std::cerr << "ggml_vk_buffer_copy(SINGLE_DEVICE, " << size << ")" << std::endl; #endif - VkBufferCopy bc{ src_offset, dst_offset, size }; + // Copy within the device + ggml_backend_vk_context * ctx = src->ctx; - vk_context * ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(ctx); - vkCmdCopyBuffer(ctx->s->buffer, src->buffer, dst->buffer, 1, &bc); - ggml_vk_buffer_copy_async(ctx, dst, dst_offset, src, src_offset, size); - ggml_vk_ctx_end(ctx); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences"); - vk_device.device.resetFences({ vk_fence }); + VkBufferCopy bc{ src_offset, dst_offset, size }; + + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_ctx_begin(ctx, subctx); + ggml_vk_buffer_copy_async(subctx, dst, dst_offset, src, src_offset, size); + ggml_vk_ctx_end(subctx); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_buffer_copy waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); + } else { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_vk_buffer_copy(MULTI_DEVICE, " << size << ")" << std::endl; +#endif + // Copy device to device + ggml_backend_vk_context * src_ctx = src->ctx; + ggml_backend_vk_context * dst_ctx = dst->ctx; + + ggml_vk_ensure_sync_staging_buffer(src_ctx, size); + ggml_vk_ensure_sync_staging_buffer(dst_ctx, size); + + // Copy to src staging buffer + ggml_vk_buffer_copy(src_ctx->sync_staging, 0, src, src_offset, size); + // memcpy to dst staging buffer + memcpy(dst_ctx->sync_staging->ptr, src_ctx->sync_staging->ptr, size); + // Copy to dst buffer + ggml_vk_buffer_copy(dst, dst_offset, dst_ctx->sync_staging, 0, size); + } } -static void ggml_vk_buffer_memset(vk_buffer* dst, size_t offset, uint32_t c, size_t size) { +static void ggml_vk_buffer_memset(ggml_backend_vk_context * ctx, vk_buffer& dst, size_t offset, uint32_t c, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_buffer_memset(" << offset << ", " << c << ", " << size << ")" << std::endl; #endif - vk_context * ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(ctx); - ctx->s->buffer.fillBuffer(dst->buffer, offset, size, c); - ggml_vk_ctx_end(ctx); + // Make sure ctx owns the buffer + GGML_ASSERT(dst->ctx == ctx); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "vk_memset waitForFences"); - vk_device.device.resetFences({ vk_fence }); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_ctx_begin(ctx, subctx); + subctx->s->buffer.fillBuffer(dst->buffer, offset, size, c); + ggml_vk_ctx_end(subctx); + + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "vk_memset waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); } -static void ggml_vk_h2d_tensor_2d(vk_context * ctx, vk_buffer * dst, size_t offset, const ggml_tensor * src, uint64_t i3, uint64_t i2, uint64_t i1) { +static void ggml_vk_h2d_tensor_2d(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& dst, size_t offset, const ggml_tensor * src, uint64_t i3, uint64_t i2, uint64_t i1) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_h2d_tensor_2d(dst=" << dst << ", offset=" << offset << ", src=" << src << ", i3=" << i3 << ", i2=" << i2 << ", i1=" << i1 << ")" << std::endl; #endif @@ -1773,20 +1963,20 @@ static void ggml_vk_h2d_tensor_2d(vk_context * ctx, vk_buffer * dst, size_t offs const void * x = (const void *) ((const char *) src->data + i2*nb2 + i3*nb3); if (nb0 == ts && nb1 == row_length) { - return ggml_vk_buffer_write_async(ctx, dst, offset, x, i1*nb1); + return ggml_vk_buffer_write_async(ctx, subctx, dst, offset, x, i1*nb1); } if (nb0 == ts && (i1 == ne1 || !ggml_is_permuted(src))) { - return ggml_vk_buffer_write_2d_async(ctx, dst, offset, x, nb1, row_length, i1); + return ggml_vk_buffer_write_2d_async(ctx, subctx, dst, offset, x, nb1, row_length, i1); } GGML_ASSERT(i3 == 0); GGML_ASSERT(i2 == 0); GGML_ASSERT(i1 == (uint64_t) ggml_nrows(src)); - return ggml_vk_buffer_write_nc_async(ctx, dst, offset, src); + return ggml_vk_buffer_write_nc_async(ctx, subctx, dst, offset, src); } -static void ggml_vk_d2h_tensor_2d(vk_context * ctx, vk_buffer * src, size_t offset, const ggml_tensor * dst) { +static void ggml_vk_d2h_tensor_2d(ggml_backend_vk_context * ctx, vk_context * subctx, vk_buffer& src, size_t offset, const ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_d2h_tensor_2d()" << std::endl; #endif @@ -1804,10 +1994,10 @@ static void ggml_vk_d2h_tensor_2d(vk_context * ctx, vk_buffer * src, size_t offs const size_t row_length = ts*ne0/bs; if (ggml_is_contiguous(dst)) { - return ggml_vk_buffer_read_async(ctx, src, offset, dst->data, ne1*nb1*ne2*ne3); + return ggml_vk_buffer_read_async(ctx, subctx, src, offset, dst->data, ne1*nb1*ne2*ne3); } if (nb0 == ts) { - return ggml_vk_buffer_read_2d_async(ctx, src, offset, dst->data, nb1, nb1, row_length, ne1*ne2*ne3); + return ggml_vk_buffer_read_2d_async(ctx, subctx, src, offset, dst->data, nb1, nb1, row_length, ne1*ne2*ne3); } GGML_ASSERT(false); } @@ -1829,89 +2019,89 @@ static uint32_t ggml_vk_guess_split_k(int m, int n, int k) { return 1; } -static uint32_t ggml_vk_guess_matmul_pipeline_align(int m, int n) { +static uint32_t ggml_vk_guess_matmul_pipeline_align(ggml_backend_vk_context * ctx, int m, int n) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_guess_matmul_pipeline_align(" << m << ", " << n << ")" << std::endl; #endif if (m <= 32 || n <= 32) { - return vk_pipeline_matmul_f32_aligned_s.align; + return ctx->pipeline_matmul_f32_aligned_s.align; } - if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { - return vk_pipeline_matmul_f32_aligned_m.align; + if (ctx->device.lock()->subgroup_size == 64 || m <= 64 || n <= 64) { + return ctx->pipeline_matmul_f32_aligned_m.align; } - return vk_pipeline_matmul_f32_aligned_l.align; + return ctx->pipeline_matmul_f32_aligned_l.align; } -static vk_pipeline* ggml_vk_guess_matmul_pipeline(bool bit16_x, bool bit16_y, int m, int n, bool aligned) { +static vk_pipeline* ggml_vk_guess_matmul_pipeline(ggml_backend_vk_context * ctx, bool bit16_x, bool bit16_y, int m, int n, bool aligned) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_guess_matmul_pipeline(" << bit16_x << ", " << bit16_y << ", " << m << ", " << n << ", " << aligned << ")"; #endif if (bit16_x && bit16_y) { - if (vk_device.vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { + if (ctx->device.lock()->vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { #ifdef GGML_VULKAN_DEBUG std::cerr << " S" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f16_aligned_s : &vk_pipeline_matmul_f16_s; + return aligned ? &ctx->pipeline_matmul_f16_aligned_s : &ctx->pipeline_matmul_f16_s; } - if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { + if (ctx->device.lock()->subgroup_size == 64 || m <= 64 || n <= 64) { #ifdef GGML_VULKAN_DEBUG std::cerr << " M" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f16_aligned_m : &vk_pipeline_matmul_f16_m; + return aligned ? &ctx->pipeline_matmul_f16_aligned_m : &ctx->pipeline_matmul_f16_m; } #ifdef GGML_VULKAN_DEBUG std::cerr << " L" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f16_aligned_l : &vk_pipeline_matmul_f16_l; + return aligned ? &ctx->pipeline_matmul_f16_aligned_l : &ctx->pipeline_matmul_f16_l; } if (bit16_x && !bit16_y) { - if (vk_device.vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { + if (ctx->device.lock()->vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { #ifdef GGML_VULKAN_DEBUG std::cerr << " S" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f16_f32_aligned_s : &vk_pipeline_matmul_f16_f32_s; + return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_s : &ctx->pipeline_matmul_f16_f32_s; } - if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { + if (ctx->device.lock()->subgroup_size == 64 || m <= 64 || n <= 64) { #ifdef GGML_VULKAN_DEBUG std::cerr << " M" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f16_f32_aligned_m : &vk_pipeline_matmul_f16_f32_m; + return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_m : &ctx->pipeline_matmul_f16_f32_m; } #ifdef GGML_VULKAN_DEBUG std::cerr << " L" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f16_f32_aligned_l : &vk_pipeline_matmul_f16_f32_l; + return aligned ? &ctx->pipeline_matmul_f16_f32_aligned_l : &ctx->pipeline_matmul_f16_f32_l; } if (!bit16_x && bit16_y) { GGML_ASSERT(false); } - if (vk_device.vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { + if (ctx->device.lock()->vendor_id == VK_VENDOR_ID_INTEL || m <= 32 || n <= 32) { #ifdef GGML_VULKAN_DEBUG std::cerr << " S" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f32_aligned_s : &vk_pipeline_matmul_f32_s; + return aligned ? &ctx->pipeline_matmul_f32_aligned_s : &ctx->pipeline_matmul_f32_s; } - if (vk_device.subgroup_size == 64 || m <= 64 || n <= 64) { + if (ctx->device.lock()->subgroup_size == 64 || m <= 64 || n <= 64) { #ifdef GGML_VULKAN_DEBUG std::cerr << " M" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f32_aligned_m : &vk_pipeline_matmul_f32_m; + return aligned ? &ctx->pipeline_matmul_f32_aligned_m : &ctx->pipeline_matmul_f32_m; } #ifdef GGML_VULKAN_DEBUG std::cerr << " L" << std::endl; #endif - return aligned ? &vk_pipeline_matmul_f32_aligned_l : &vk_pipeline_matmul_f32_l; + return aligned ? &ctx->pipeline_matmul_f32_aligned_l : &ctx->pipeline_matmul_f32_l; } -static void ggml_vk_matmul(vk_context * ctx, vk_pipeline& pipeline, vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer, uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3, uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d) { +static void ggml_vk_matmul(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline& pipeline, vk_subbuffer&& a, vk_subbuffer&& b, vk_subbuffer&& d, vk_subbuffer&& split_k_buffer, uint32_t m, uint32_t n, uint32_t k, uint32_t stride_a, uint32_t stride_b, uint32_t stride_d, uint32_t split_k, uint32_t batch, uint32_t ne02, uint32_t ne12, uint32_t broadcast2, uint32_t broadcast3, uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_matmul(a: (" << a.buffer.buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer.buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer.buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << split_k_buffer.buffer.buffer << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl; + std::cerr << "ggml_vk_matmul(a: (" << a.buffer->buffer << ", " << a.offset << ", " << a.size << "), b: (" << b.buffer->buffer << ", " << b.offset << ", " << b.size << "), c: (" << d.buffer->buffer << ", " << d.offset << ", " << d.size << "), split_k: (" << split_k_buffer.buffer->buffer << ", " << split_k_buffer.offset << ", " << split_k_buffer.size << "), m: " << m << ", n: " << n << ", k: " << k << ", stride_a: " << stride_a << ", stride_b: " << stride_b << ", stride_d: " << stride_d << ", split_k: " << split_k << ", batch: " << batch << ", ne02: " << ne02 << ", ne12: " << ne12 << ", broadcast2: " << broadcast2 << ", broadcast3: " << broadcast3 << ", batch_stride_a: " << batch_stride_a << ", batch_stride_b: " << batch_stride_b << ", batch_stride_d: " << batch_stride_d << ")" << std::endl; #endif - ggml_vk_sync_buffers(ctx); + ggml_vk_sync_buffers(subctx); if (split_k == 1) { const std::array pc = { m, n, k, stride_a, stride_b, stride_d, k, ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d }; - ggml_vk_dispatch_pipeline(ctx, pipeline, { a, b, d }, pc.size() * sizeof(uint32_t), pc.data(), { m, n, batch }); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, d }, pc.size() * sizeof(uint32_t), pc.data(), { m, n, batch }); return; } @@ -1919,10 +2109,10 @@ static void ggml_vk_matmul(vk_context * ctx, vk_pipeline& pipeline, vk_subbuffer const std::array pc1 = { m, n, k, stride_a, stride_b, stride_d, CEIL_DIV(k, split_k), ne02, ne12, broadcast2, broadcast3, batch_stride_a, batch_stride_b, batch_stride_d }; // Make sure enough workgroups get assigned for split k to work - ggml_vk_dispatch_pipeline(ctx, pipeline, { a, b, split_k_buffer }, pc1.size() * sizeof(uint32_t), pc1.data(), { (CEIL_DIV(m, pipeline.wg_denoms[0]) * pipeline.wg_denoms[0]) * split_k, n, batch }); - ggml_vk_sync_buffers(ctx); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { a, b, split_k_buffer }, pc1.size() * sizeof(uint32_t), pc1.data(), { (CEIL_DIV(m, pipeline.wg_denoms[0]) * pipeline.wg_denoms[0]) * split_k, n, batch }); + ggml_vk_sync_buffers(subctx); const std::array pc2 = { (uint32_t)(m * n * batch), split_k }; - ggml_vk_dispatch_pipeline(ctx, vk_pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 }); + ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_matmul_split_k_reduce, { split_k_buffer, d }, pc2.size() * sizeof(uint32_t), pc2.data(), { m * n * batch, 1, 1 }); } static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) { @@ -1932,32 +2122,32 @@ static bool ggml_vk_dim01_contiguous(const ggml_tensor * tensor) { tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } -static vk_pipeline * ggml_vk_get_cpy_pipeline(ggml_type from, ggml_type to) { +static vk_pipeline * ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, ggml_type from, ggml_type to) { if (from == GGML_TYPE_F32 && to == GGML_TYPE_F32) { - return &vk_pipeline_cpy_f32_f32; + return &ctx->pipeline_cpy_f32_f32; } if (from == GGML_TYPE_F32 && to == GGML_TYPE_F16) { - return &vk_pipeline_cpy_f32_f16; + return &ctx->pipeline_cpy_f32_f16; } if (from == GGML_TYPE_F16 && to == GGML_TYPE_F16) { - return &vk_pipeline_cpy_f16_f16; + return &ctx->pipeline_cpy_f16_f16; } std::cerr << "Missing CPY op for types: " << ggml_type_name(from) << " " << ggml_type_name(to) << std::endl; GGML_ASSERT(false); } -static void ggml_vk_cpy_to_contiguous(vk_context * ctx, vk_pipeline * pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out, ggml_type buffer_type, bool aligned=true) { +static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context * subctx, vk_pipeline * pipeline, const ggml_tensor * tensor, vk_subbuffer&& in, vk_subbuffer&& out, ggml_type buffer_type, bool aligned=true) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", backend=" << tensor->backend << ", ne0=" << tensor->ne[0] << ", ne1=" << tensor->ne[1] << ", ne2=" << tensor->ne[2] << ", ne3=" << tensor->ne[3] << ", nb0=" << tensor->nb[0] << ", nb1=" << tensor->nb[1] << ", nb2=" << tensor->nb[2] << ", nb3=" << tensor->nb[3] << "), "; - std::cerr << "buffer in size=" << in.buffer.size << ", buffer out size=" << out.buffer.size << ")" << std::endl; + std::cerr << "buffer in size=" << in.buffer->size << ", buffer out size=" << out.buffer->size << ")" << std::endl; #endif const int tensor_type_size = ggml_type_size(tensor->type); const int dst_type_size = ggml_type_size(buffer_type); const uint32_t ne = tensor->ne[0] * tensor->ne[1] * tensor->ne[2]; - const uint32_t nb2 = aligned ? ggml_vk_align_size(dst_type_size * tensor->ne[0] * tensor->ne[1], vk_device.properties.limits.minStorageBufferOffsetAlignment) / dst_type_size : tensor->ne[0] * tensor->ne[1]; + const uint32_t nb2 = aligned ? ggml_vk_align_size(dst_type_size * tensor->ne[0] * tensor->ne[1], ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size : tensor->ne[0] * tensor->ne[1]; const vk_op_cpy_push_constants pc = { (uint32_t)ne, @@ -1965,11 +2155,11 @@ static void ggml_vk_cpy_to_contiguous(vk_context * ctx, vk_pipeline * pipeline, (uint32_t)tensor->ne[0], (uint32_t)tensor->ne[1], 1 , (uint32_t)tensor->ne[0] , nb2, 0, }; - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *pipeline, { in, out }, sizeof(vk_op_cpy_push_constants), &pc, { ne, 1, 1 }); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { in, out }, sizeof(vk_op_cpy_push_constants), &pc, { ne, 1, 1 }); } -static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; @@ -1998,17 +2188,17 @@ static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, co ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; - vk_buffer * d_Qx = nullptr; + vk_buffer d_Qx; size_t qx_buf_offset = 0; - vk_buffer * d_Qy = nullptr; + vk_buffer d_Qy; size_t qy_buf_offset = 0; bool src0_uma = false; bool src1_uma = false; - if (vk_device.uma) { - ggml_vk_host_get(src0->data, d_Qx, qx_buf_offset); - ggml_vk_host_get(src1->data, d_Qy, qy_buf_offset); + if (ctx->device.lock()->uma) { + ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset); + ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); src0_uma = d_Qx != nullptr; src1_uma = d_Qy != nullptr; } @@ -2031,12 +2221,12 @@ static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, co const int y_ne = ne11 * ne10; const int d_ne = ne11 * ne01; - const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ne01, ne11)); + const uint32_t kpad = ggml_vk_align_size(ne10, ggml_vk_guess_matmul_pipeline_align(ctx, ne01, ne11)); const bool aligned = ne10 == kpad; const uint32_t split_k = ggml_vk_guess_split_k(ne01, ne11, ne10); - vk_pipeline * pipeline = ggml_vk_guess_matmul_pipeline(true, !f16_f32_kernel, ne01, ne11, aligned); + vk_pipeline * pipeline = ggml_vk_guess_matmul_pipeline(ctx, true, !f16_f32_kernel, ne01, ne11, aligned); const uint64_t qx_sz = ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type); const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); @@ -2044,30 +2234,30 @@ static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, co const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; const uint64_t d_sz = sizeof(float) * d_ne; - vk_buffer* d_D = &extra->buffer_gpu; + vk_buffer d_D = extra->buffer_gpu.lock(); const uint64_t d_buf_offset = extra->offset; GGML_ASSERT(d_D != nullptr); GGML_ASSERT(d_D->size >= d_buf_offset + d_sz * ne02 * ne03); - vk_buffer* d_X; + vk_buffer d_X; uint64_t x_buf_offset = 0; - vk_buffer* d_Y; + vk_buffer d_Y; uint64_t y_buf_offset = 0; if (load_x) { - d_Qx = &vk_prealloc_qx; + d_Qx = ctx->prealloc_qx; } else if (!src0_uma) { - d_Qx = &extra_src0->buffer_gpu; + d_Qx = extra_src0->buffer_gpu.lock(); qx_buf_offset = extra_src0->offset; GGML_ASSERT(d_Qx != nullptr); } if (load_y) { - d_Qy = &vk_prealloc_qy; + d_Qy = ctx->prealloc_qy; } else if (!src1_uma) { - d_Qy = &extra_src1->buffer_gpu; + d_Qy = extra_src1->buffer_gpu.lock(); qy_buf_offset = extra_src1->offset; GGML_ASSERT(d_Qy != nullptr); } if (qx_needs_dequant) { - d_X = &vk_prealloc_x; + d_X = ctx->prealloc_x; GGML_ASSERT(d_X->size >= x_sz * ne02 * ne03); } else { d_X = d_Qx; @@ -2075,7 +2265,7 @@ static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, co GGML_ASSERT(qx_sz == x_sz); // NOLINT } if (qy_needs_dequant) { - d_Y = &vk_prealloc_y; + d_Y = ctx->prealloc_y; GGML_ASSERT(d_Y->size >= y_sz * ne02 * ne03); } else { d_Y = d_Qy; @@ -2087,49 +2277,49 @@ static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, co vk_pipeline * to_fp16_vk_1 = nullptr; if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(src0->type, GGML_TYPE_F16); + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, GGML_TYPE_F16); } else { - to_fp16_vk_0 = ggml_vk_get_to_fp16(src0->type); + to_fp16_vk_0 = ggml_vk_get_to_fp16(ctx, src0->type); } if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(src1->type, GGML_TYPE_F16); + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, GGML_TYPE_F16); } else { - to_fp16_vk_1 = ggml_vk_get_to_fp16(src1->type); + to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); } GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT // Allocate descriptor sets - ggml_vk_pipeline_allocate_descriptor_sets(*pipeline, ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, *pipeline, ne12 * ne13); if (qx_needs_dequant) { - ggml_vk_pipeline_allocate_descriptor_sets(*to_fp16_vk_0, x_non_contig ? 1 : ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_0, x_non_contig ? 1 : ne12 * ne13); } if (qy_needs_dequant) { - ggml_vk_pipeline_allocate_descriptor_sets(*to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13); } if (split_k > 1) { - ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline_matmul_split_k_reduce, ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_matmul_split_k_reduce, ne12 * ne13); } if (x_non_contig) { - ggml_vk_cpy_to_contiguous(ctx, to_fp16_vk_0, src0, { *d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { *d_X, 0, VK_WHOLE_SIZE }, dst->type, false); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }, dst->type, false); } else if (load_x || qx_needs_dequant) { if (load_x) { // copy data to device - ggml_vk_h2d_tensor_2d(ctx, d_Qx, 0, src0, 0, 0, ggml_nrows(src0)); - vk_staging_offset = qx_sz * ne02 * ne03; + ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qx, 0, src0, 0, 0, ggml_nrows(src0)); + ctx->staging_offset = qx_sz * ne02 * ne03; } if (qx_needs_dequant) { const std::vector pc = { (int)ne01, (int)ne10, (int)ne10, (int)ne10 }; - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *to_fp16_vk_0, { { *d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, { *d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1}); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *to_fp16_vk_0, { { d_Qx, qx_buf_offset, qx_sz * ne02 * ne03 }, { d_X, 0, x_sz * ne02 * ne03 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)(x_ne * ne02 * ne03), 1, 1}); } } if (y_non_contig) { - ggml_vk_cpy_to_contiguous(ctx, to_fp16_vk_1, src1, { *d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { *d_Y, 0, VK_WHOLE_SIZE }, dst->type); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }, dst->type); } else if (load_y) { - ggml_vk_h2d_tensor_2d(ctx, d_Qy, 0, src1, 0, 0, ggml_nrows(src1)); + ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, 0, src1, 0, 0, ggml_nrows(src1)); } uint32_t stride_batch_x = ne00*ne01; @@ -2144,16 +2334,16 @@ static void ggml_vk_mul_mat_q_f16(vk_context * ctx, const ggml_tensor * src0, co } // compute - ggml_vk_matmul(ctx, *pipeline, { *d_X, x_buf_offset, x_sz * ne02 * ne03 }, { *d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { *d_D, d_buf_offset, d_sz * ne12 * ne13 }, { vk_prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k }, ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21); // NOLINT + ggml_vk_matmul(ctx, subctx, *pipeline, { d_X, x_buf_offset, x_sz * ne02 * ne03 }, { d_Y, y_buf_offset, y_sz * ne12 * ne13 }, { d_D, d_buf_offset, d_sz * ne12 * ne13 }, { ctx->prealloc_split_k, 0, d_sz * ne12 * ne13 * split_k }, ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21); // NOLINT if (dst->backend == GGML_BACKEND_CPU) { // copy dst to host float * d = (float *) ((char *) dst->data); - ggml_vk_buffer_read_async(ctx, d_D, 0, d, sizeof(float) * d_ne * ne12 * ne13); + ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, sizeof(float) * d_ne * ne12 * ne13); } } -static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; @@ -2184,17 +2374,17 @@ static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0 ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; - vk_buffer * d_Qx = nullptr; + vk_buffer d_Qx; size_t qx_buf_offset = 0; - vk_buffer * d_Qy = nullptr; + vk_buffer d_Qy; size_t qy_buf_offset = 0; bool src0_uma = false; bool src1_uma = false; - if (vk_device.uma) { - ggml_vk_host_get(src0->data, d_Qx, qx_buf_offset); - ggml_vk_host_get(src1->data, d_Qy, qy_buf_offset); + if (ctx->device.lock()->uma) { + ggml_vk_host_get(ctx, src0->data, d_Qx, qx_buf_offset); + ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); src0_uma = d_Qx != nullptr; src1_uma = d_Qy != nullptr; } @@ -2214,42 +2404,42 @@ static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0 const uint64_t y_ne = ne11 * ne10; const uint64_t d_ne = ne11 * ne01; - const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), vk_device.properties.limits.minStorageBufferOffsetAlignment); + const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment); const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); - const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, vk_device.properties.limits.minStorageBufferOffsetAlignment) : qx_sz; + const uint64_t x_sz = x_non_contig ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) : qx_sz; const uint64_t y_sz = f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne; const uint64_t d_sz = sizeof(float) * d_ne; - vk_buffer* d_D = &extra->buffer_gpu; + vk_buffer d_D = extra->buffer_gpu.lock(); const uint64_t d_buf_offset = extra->offset; GGML_ASSERT(d_D != nullptr); - vk_buffer* d_X; + vk_buffer d_X; uint64_t x_buf_offset = 0; - vk_buffer* d_Y; + vk_buffer d_Y; uint64_t y_buf_offset = 0; if (load_x) { - d_Qx = &vk_prealloc_qx; + d_Qx = ctx->prealloc_qx; } else if(!src1_uma) { - d_Qx = &extra_src0->buffer_gpu; + d_Qx = extra_src0->buffer_gpu.lock(); qx_buf_offset = extra_src0->offset; GGML_ASSERT(d_Qx != nullptr); } if (load_y) { - d_Qy = &vk_prealloc_qy; + d_Qy = ctx->prealloc_qy; } else if(!src1_uma) { - d_Qy = &extra_src1->buffer_gpu; + d_Qy = extra_src1->buffer_gpu.lock(); qy_buf_offset = extra_src1->offset; GGML_ASSERT(d_Qy != nullptr); } if (qx_needs_dequant) { - d_X = &vk_prealloc_x; + d_X = ctx->prealloc_x; } else { d_X = d_Qx; x_buf_offset = qx_buf_offset; GGML_ASSERT(qx_sz == x_sz); } if (qy_needs_dequant) { - d_Y = &vk_prealloc_y; + d_Y = ctx->prealloc_y; } else { d_Y = d_Qy; y_buf_offset = qy_buf_offset; @@ -2259,39 +2449,39 @@ static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0 vk_pipeline * to_fp16_vk_0 = nullptr; vk_pipeline* to_fp16_vk_1 = nullptr; if (x_non_contig) { - to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(src0->type, src0->type); + to_fp16_vk_0 = ggml_vk_get_cpy_pipeline(ctx, src0->type, src0->type); } if (y_non_contig) { - to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(src1->type, src1->type); + to_fp16_vk_1 = ggml_vk_get_cpy_pipeline(ctx, src1->type, src1->type); } else { - to_fp16_vk_1 = ggml_vk_get_to_fp16(src1->type); + to_fp16_vk_1 = ggml_vk_get_to_fp16(ctx, src1->type); } - vk_pipeline* dmmv = ggml_vk_get_dequantize_mul_mat_vec(src0->type); + vk_pipeline* dmmv = ggml_vk_get_dequantize_mul_mat_vec(ctx, src0->type); GGML_ASSERT(!qx_needs_dequant || to_fp16_vk_0 != nullptr); // NOLINT GGML_ASSERT(!qy_needs_dequant || to_fp16_vk_1 != nullptr); // NOLINT GGML_ASSERT(dmmv != nullptr); // Allocate descriptor sets if (qx_needs_dequant) { - ggml_vk_pipeline_allocate_descriptor_sets(*to_fp16_vk_0, 1); + ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_0, 1); } if (qy_needs_dequant) { - ggml_vk_pipeline_allocate_descriptor_sets(*to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, *to_fp16_vk_1, y_non_contig ? 1 : ne12 * ne13); } - ggml_vk_pipeline_allocate_descriptor_sets(*dmmv, ne12 * ne13); + ggml_pipeline_allocate_descriptor_sets(ctx, *dmmv, ne12 * ne13); if (x_non_contig) { - GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, vk_device.properties.limits.minStorageBufferOffsetAlignment)); - ggml_vk_cpy_to_contiguous(ctx, to_fp16_vk_0, src0, { *d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { *d_X, 0, VK_WHOLE_SIZE }, src0->type); + GGML_ASSERT(x_sz == ggml_vk_align_size(ggml_type_size(src0->type) * x_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment)); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_0, src0, { d_Qx, qx_buf_offset, VK_WHOLE_SIZE }, { d_X, 0, VK_WHOLE_SIZE }, src0->type); } else if (load_x) { // copy data to device - ggml_vk_h2d_tensor_2d(ctx, d_Qx, 0, src0, 0, 0, ggml_nrows(src0)); + ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qx, 0, src0, 0, 0, ggml_nrows(src0)); } if (y_non_contig) { GGML_ASSERT(y_sz == ggml_type_size(src1->type) * y_ne); - ggml_vk_cpy_to_contiguous(ctx, to_fp16_vk_1, src1, { *d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { *d_Y, 0, VK_WHOLE_SIZE }, src1->type); + ggml_vk_cpy_to_contiguous(ctx, subctx, to_fp16_vk_1, src1, { d_Qy, qy_buf_offset, VK_WHOLE_SIZE }, { d_Y, 0, VK_WHOLE_SIZE }, src1->type); } else if (load_y) { - ggml_vk_h2d_tensor_2d(ctx, d_Qy, 0, src1, 0, 0, ggml_nrows(src1)); + ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, 0, src1, 0, 0, ggml_nrows(src1)); } for (uint64_t i13 = 0; i13 < ne13; i13++) { @@ -2306,34 +2496,34 @@ static void ggml_vk_mul_mat_vec_q_f16(vk_context * ctx, const ggml_tensor * src0 const uint64_t y_offset = y_buf_offset + y_sz * it_idx1; const uint64_t d_offset = d_buf_offset + d_sz * it_idx1; - const uint64_t y_buffer_offset = (y_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t y_buffer_offset = (y_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; const uint64_t y_shader_offset = y_offset - y_buffer_offset; - const uint64_t d_buffer_offset = (d_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t d_buffer_offset = (d_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; const uint64_t d_shader_offset = d_offset - d_buffer_offset; if (!y_non_contig && qy_needs_dequant) { const std::vector pc = { (int)ne11, (int)ne10, (int)ne10, (int)ne10 }; - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *to_fp16_vk_1, { { *d_Qy, qy_offset, qy_sz }, { *d_Y, y_offset, y_sz } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)y_ne, 1, 1}); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *to_fp16_vk_1, { { d_Qy, qy_offset, qy_sz }, { d_Y, y_offset, y_sz } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)y_ne, 1, 1}); } // compute const std::array pc = { (int)ne00, (int)(y_shader_offset / ggml_type_size(src1->type)), (int)(d_shader_offset / ggml_type_size(dst->type))}; - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *dmmv, { { *d_X, x_offset, x_sz }, { *d_Y, y_buffer_offset, y_sz + y_shader_offset }, { *d_D, d_buffer_offset, d_sz + d_shader_offset } }, 3 * sizeof(int), &pc, { (uint32_t)ne01, 1, 1}); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *dmmv, { { d_X, x_offset, x_sz }, { d_Y, y_buffer_offset, y_sz + y_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 3 * sizeof(int), &pc, { (uint32_t)ne01, 1, 1}); if (dst->backend == GGML_BACKEND_CPU) { // copy dst to host float * d = (float *) ((char *) dst->data + i12*nb2 + i13*nb3); - ggml_vk_sync_buffers(ctx); - ggml_vk_buffer_read_async(ctx, d_D, d_offset, d, sizeof(float) * d_ne); + ggml_vk_sync_buffers(subctx); + ggml_vk_buffer_read_async(ctx, subctx, d_D, d_offset, d, sizeof(float) * d_ne); } } } } -static void ggml_vk_mul_mat_vec_p021_f16_f32(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_p021_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; @@ -2362,13 +2552,13 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(vk_context * ctx, const ggml_tensor ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; - vk_buffer * d_Qy = nullptr; + vk_buffer d_Qy; size_t qy_buf_offset = 0; bool src1_uma = false; - if (vk_device.uma) { - ggml_vk_host_get(src1->data, d_Qy, qy_buf_offset); + if (ctx->device.lock()->uma) { + ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); src1_uma = d_Qy != nullptr; } @@ -2378,51 +2568,51 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(vk_context * ctx, const ggml_tensor const uint64_t y_ne = ne10 * ne11 * ne12; const uint64_t d_ne = ne01 * ne11 * ne12; - const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), vk_device.properties.limits.minStorageBufferOffsetAlignment); + const uint64_t qx_sz = ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment); const uint64_t qy_sz = ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type); const uint64_t d_sz = sizeof(float) * d_ne; - vk_buffer* d_D = &extra->buffer_gpu; + vk_buffer d_D = extra->buffer_gpu.lock(); const uint64_t d_buf_offset = extra->offset; GGML_ASSERT(d_D != nullptr); - vk_buffer* d_Qx = &extra_src0->buffer_gpu; + vk_buffer d_Qx = extra_src0->buffer_gpu.lock(); const uint64_t qx_buf_offset = extra_src0->offset; GGML_ASSERT(d_Qx != nullptr); if (load_y) { - d_Qy = &vk_prealloc_qy; + d_Qy = ctx->prealloc_qy; } else if (!src1_uma) { - d_Qy = &extra_src1->buffer_gpu; + d_Qy = extra_src1->buffer_gpu.lock(); qy_buf_offset = extra_src1->offset; GGML_ASSERT(d_Qx != nullptr); } // Allocate descriptor sets - ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline_mul_mat_vec_p021_f16_f32, 1); + ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_mul_mat_vec_p021_f16_f32, 1); - const uint64_t qy_buffer_offset = (qy_buf_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset; - const uint64_t d_buffer_offset = (d_buf_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t d_buffer_offset = (d_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset; if (load_y) { - ggml_vk_h2d_tensor_2d(ctx, d_Qy, qy_buf_offset, src1, 0, 0, ggml_nrows(src1)); + ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, qy_buf_offset, src1, 0, 0, ggml_nrows(src1)); } // compute const std::array pc = { (uint32_t)ne00, (uint32_t)ne01, (uint32_t)ne02, (uint32_t)ne12, (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) }; - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, vk_pipeline_mul_mat_vec_p021_f16_f32, { { *d_Qx, qx_buf_offset, qx_sz }, { *d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { *d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_mul_mat_vec_p021_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 6 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); if (dst->backend == GGML_BACKEND_CPU) { // copy dst to host float * d = (float *) dst->data; - ggml_vk_sync_buffers(ctx); - ggml_vk_buffer_read_async(ctx, d_D, d_buf_offset, d, sizeof(float) * d_ne); + ggml_vk_sync_buffers(subctx); + ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset, d, sizeof(float) * d_ne); } } -static void ggml_vk_mul_mat_vec_nc_f16_f32(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat_nc_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; std::cerr << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", backend=" << src1->backend << ", ne0=" << src1->ne[0] << ", ne1=" << src1->ne[1] << ", ne2=" << src1->ne[2] << ", ne3=" << src1->ne[3] << ", nb0=" << src1->nb[0] << ", nb1=" << src1->nb[1] << ", nb2=" << src1->nb[2] << ", nb3=" << src1->nb[3]; @@ -2454,13 +2644,13 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(vk_context * ctx, const ggml_tensor * ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; ggml_tensor_extra_gpu * extra_src1 = (ggml_tensor_extra_gpu *) src1->extra; - vk_buffer * d_Qy = nullptr; + vk_buffer d_Qy = nullptr; size_t qy_buf_offset = 0; bool src1_uma = false; - if (vk_device.uma) { - ggml_vk_host_get(src1->data, d_Qy, qy_buf_offset); + if (ctx->device.lock()->uma) { + ggml_vk_host_get(ctx, src1->data, d_Qy, qy_buf_offset); src1_uma = d_Qy != nullptr; } @@ -2475,43 +2665,43 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(vk_context * ctx, const ggml_tensor * const uint64_t qy_sz = ggml_nbytes(src1); const uint64_t d_sz = sizeof(float) * d_ne; - vk_buffer* d_D = &extra->buffer_gpu; + vk_buffer d_D = extra->buffer_gpu.lock(); const uint64_t d_buf_offset = extra->offset; GGML_ASSERT(d_D != nullptr); - vk_buffer* d_Qx = &extra_src0->buffer_gpu; + vk_buffer d_Qx = extra_src0->buffer_gpu.lock(); const uint64_t qx_buf_offset = extra_src0->offset; GGML_ASSERT(d_Qx != nullptr); if (load_y) { - d_Qy = &vk_prealloc_qy; + d_Qy = ctx->prealloc_qy; } else { - d_Qy = &extra_src1->buffer_gpu; + d_Qy = extra_src1->buffer_gpu.lock(); qy_buf_offset = extra_src1->offset; GGML_ASSERT(d_Qx != nullptr); } // Allocate descriptor sets - ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline_mul_mat_vec_nc_f16_f32, 1); + ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_mul_mat_vec_nc_f16_f32, 1); - const uint64_t qy_buffer_offset = (qy_buf_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t qy_buffer_offset = (qy_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; const uint64_t qy_shader_offset = qy_buf_offset - qy_buffer_offset; - const uint64_t d_buffer_offset = (d_buf_offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + const uint64_t d_buffer_offset = (d_buf_offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; const uint64_t d_shader_offset = d_buf_offset - d_buffer_offset; if (load_y) { - ggml_vk_h2d_tensor_2d(ctx, d_Qy, qy_buf_offset, src1, 0, 0, ggml_nrows(src1)); + ggml_vk_h2d_tensor_2d(ctx, subctx, d_Qy, qy_buf_offset, src1, 0, 0, ggml_nrows(src1)); } // compute const std::array pc = { (uint32_t)ne00, (uint32_t)ne01, row_stride_x, channel_stride_x, (uint32_t)(ne12 / ne02), (uint32_t)(qy_shader_offset / ggml_type_size(src1->type)), (uint32_t)(d_shader_offset / ggml_type_size(dst->type)) }; - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, vk_pipeline_mul_mat_vec_nc_f16_f32, { { *d_Qx, qx_buf_offset, qx_sz }, { *d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { *d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, ctx->pipeline_mul_mat_vec_nc_f16_f32, { { d_Qx, qx_buf_offset, qx_sz }, { d_Qy, qy_buffer_offset, qy_sz + qy_shader_offset }, { d_D, d_buffer_offset, d_sz + d_shader_offset } }, 7 * sizeof(uint32_t), &pc, { 1, (uint32_t)ne01, (uint32_t)ne12 }); if (dst->backend == GGML_BACKEND_CPU) { // copy dst to host float * d = (float *) dst->data; - ggml_vk_sync_buffers(ctx); - ggml_vk_buffer_read_async(ctx, d_D, d_buf_offset, d, sizeof(float) * d_ne); + ggml_vk_sync_buffers(subctx); + ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset, d, sizeof(float) * d_ne); } } @@ -2528,22 +2718,22 @@ static bool ggml_vk_can_mul_mat(const ggml_tensor * src0, const ggml_tensor * sr ((ne0 >= 32 && ne1 >= 32 && ne10 >= 32) || src0->backend == GGML_BACKEND_GPU); } -static void ggml_vk_mul_mat(vk_context * ctx, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { +static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, const struct ggml_tensor * src0, const struct ggml_tensor * src1, struct ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_mul_mat(" << src0 << ", " << src1 << ", " << dst << ")" << std::endl; #endif if (src0->type == GGML_TYPE_F16 && ggml_is_permuted(src0) && ggml_is_permuted(src1) && src1->ne[1] == 1) { - ggml_vk_mul_mat_vec_p021_f16_f32(ctx, src0, src1, dst); + ggml_vk_mul_mat_vec_p021_f16_f32(ctx, subctx, src0, src1, dst); } else if (src0->type == GGML_TYPE_F16 && !ggml_is_contiguous(src0) && !ggml_is_transposed(src1) && src1->ne[1] == 1) { - ggml_vk_mul_mat_vec_nc_f16_f32(ctx, src0, src1, dst); + ggml_vk_mul_mat_vec_nc_f16_f32(ctx, subctx, src0, src1, dst); } else if (src1->ne[1] == 1 && (src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type))) { - ggml_vk_mul_mat_vec_q_f16(ctx, src0, src1, dst); + ggml_vk_mul_mat_vec_q_f16(ctx, subctx, src0, src1, dst); } else { - ggml_vk_mul_mat_q_f16(ctx, src0, src1, dst); + ggml_vk_mul_mat_q_f16(ctx, subctx, src0, src1, dst); } } -static void ggml_vk_op_repeat(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { // guaranteed to be an integer due to the check in ggml_can_repeat const uint64_t ne0 = dst->ne[0]; const uint64_t ne1 = dst->ne[1]; @@ -2579,9 +2769,9 @@ static void ggml_vk_op_repeat(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; - const vk_buffer* src_buf = &extra_src0->buffer_gpu; + const vk_buffer src_buf = extra_src0->buffer_gpu.lock(); const uint64_t src_offset = extra_src0->offset; - vk_buffer* dst_buf = &extra->buffer_gpu; + vk_buffer dst_buf = extra->buffer_gpu.lock(); const uint64_t dst_offset = extra->offset; std::vector copies; @@ -2606,78 +2796,79 @@ static void ggml_vk_op_repeat(vk_context * ctx, const ggml_tensor * src0, const } } - ggml_vk_sync_buffers(ctx); - ctx->s->buffer.copyBuffer(src_buf->buffer, dst_buf->buffer, copies); + ggml_vk_sync_buffers(subctx); + subctx->s->buffer.copyBuffer(src_buf->buffer, dst_buf->buffer, copies); - (void) src1; + GGML_UNUSED(ctx); + GGML_UNUSED(src1); } -static vk_pipeline* ggml_vk_op_get_pipeline(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op) { +static vk_pipeline* ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op) { switch (op) { case GGML_OP_ADD: if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_add_f32; + return &ctx->pipeline_add_f32; } return nullptr; case GGML_OP_GET_ROWS: GGML_ASSERT(src1->type == GGML_TYPE_I32); if (dst->type == GGML_TYPE_F16) { - return &vk_pipeline_get_rows[src0->type]; + return &ctx->pipeline_get_rows[src0->type]; } if (dst->type == GGML_TYPE_F32) { - return &vk_pipeline_get_rows_f32[src0->type]; + return &ctx->pipeline_get_rows_f32[src0->type]; } return nullptr; case GGML_OP_MUL: if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_mul_f32; + return &ctx->pipeline_mul_f32; } return nullptr; case GGML_OP_SCALE: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_scale_f32; + return &ctx->pipeline_scale_f32; } return nullptr; case GGML_OP_SQR: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_sqr_f32; + return &ctx->pipeline_sqr_f32; } return nullptr; case GGML_OP_CLAMP: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_clamp_f32; + return &ctx->pipeline_clamp_f32; } return nullptr; case GGML_OP_CPY: case GGML_OP_CONT: case GGML_OP_DUP: - return ggml_vk_get_cpy_pipeline(src0->type, dst->type); + return ggml_vk_get_cpy_pipeline(ctx, src0->type, dst->type); case GGML_OP_NORM: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_norm_f32; + return &ctx->pipeline_norm_f32; } return nullptr; case GGML_OP_RMS_NORM: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_rms_norm_f32; + return &ctx->pipeline_rms_norm_f32; } return nullptr; case GGML_OP_UNARY: switch (ggml_get_unary_op(dst)) { case GGML_UNARY_OP_SILU: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_silu_f32; + return &ctx->pipeline_silu_f32; } break; case GGML_UNARY_OP_GELU: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_gelu_f32; + return &ctx->pipeline_gelu_f32; } break; case GGML_UNARY_OP_RELU: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_relu_f32; + return &ctx->pipeline_relu_f32; } break; default: @@ -2686,12 +2877,12 @@ static vk_pipeline* ggml_vk_op_get_pipeline(const ggml_tensor * src0, const ggml return nullptr; case GGML_OP_DIAG_MASK_INF: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_diag_mask_inf_f32; + return &ctx->pipeline_diag_mask_inf_f32; } return nullptr; case GGML_OP_SOFT_MAX: if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_soft_max_f32; + return &ctx->pipeline_soft_max_f32; } return nullptr; case GGML_OP_ROPE: @@ -2706,17 +2897,17 @@ static vk_pipeline* ggml_vk_op_get_pipeline(const ggml_tensor * src0, const ggml if (is_neox) { if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_rope_neox_f32; + return &ctx->pipeline_rope_neox_f32; } if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { - return &vk_pipeline_rope_neox_f16; + return &ctx->pipeline_rope_neox_f16; } } else { if (src0->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32) { - return &vk_pipeline_rope_f32; + return &ctx->pipeline_rope_f32; } if (src0->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F16) { - return &vk_pipeline_rope_f16; + return &ctx->pipeline_rope_f16; } } return nullptr; @@ -2735,13 +2926,8 @@ static ggml_vk_func_t ggml_vk_op_get_func(ggml_op op) { } } -#ifdef GGML_VULKAN_CHECK_RESULTS -static void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name); -static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * tensor); -#endif - template -static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op, const PC&& pc) { +static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst, ggml_op op, const PC&& pc) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", backend=" << src0->backend << ", ne0=" << src0->ne[0] << ", ne1=" << src0->ne[1] << ", ne2=" << src0->ne[2] << ", ne3=" << src0->ne[3] << ", nb0=" << src0->nb[0] << ", nb1=" << src0->nb[1] << ", nb2=" << src0->nb[2] << ", nb3=" << src0->nb[3]; if (src1 != nullptr) { @@ -2768,7 +2954,7 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm const uint64_t nb2 = dst->nb[2]; const uint64_t nb3 = dst->nb[3]; - vk_pipeline * pipeline = ggml_vk_op_get_pipeline(src0, src1, dst, op); + vk_pipeline * pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, dst, op); ggml_vk_func_t op_func; if (pipeline == nullptr) { @@ -2782,7 +2968,7 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm GGML_ASSERT(false); } - op_func(ctx, src0, src1, dst); + op_func(ctx, subctx, src0, src1, dst); return; } @@ -2790,19 +2976,19 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; ggml_tensor_extra_gpu * extra_src1 = use_src1 ? (ggml_tensor_extra_gpu *) src1->extra : nullptr; - vk_buffer * d_X = nullptr; + vk_buffer d_X = nullptr; size_t x_buf_offset = 0; - vk_buffer * d_Y = nullptr; + vk_buffer d_Y = nullptr; size_t y_buf_offset = 0; bool src0_uma = false; bool src1_uma = false; - if (vk_device.uma) { - ggml_vk_host_get(src0->data, d_X, x_buf_offset); + if (ctx->device.lock()->uma) { + ggml_vk_host_get(ctx, src0->data, d_X, x_buf_offset); src0_uma = d_X != nullptr; if (use_src1) { - ggml_vk_host_get(src1->data, d_Y, y_buf_offset); + ggml_vk_host_get(ctx, src1->data, d_Y, y_buf_offset); src1_uma = d_Y != nullptr; } } @@ -2810,30 +2996,31 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm const bool transfer_src0 = src0->backend != GGML_BACKEND_GPU && !src0_uma; const bool transfer_src1 = use_src1 && src1->backend != GGML_BACKEND_GPU && !src1_uma; - uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type) * ne0, vk_device.properties.limits.minStorageBufferOffsetAlignment); - uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, vk_device.properties.limits.minStorageBufferOffsetAlignment) : 0; + uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type) * ne0, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment); + uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) : 0; uint64_t d_sz = ggml_type_size(dst->type) * ne0; + vk_buffer d_D = extra->buffer_gpu.lock(); + // Workaround for tiny tensor inputs on ROPE - if (use_src1 && src1->backend == GGML_BACKEND_GPU && y_sz > extra_src1->buffer_gpu.size) { + if (use_src1 && src1->backend == GGML_BACKEND_GPU && y_sz > d_D->size) { y_sz = VK_WHOLE_SIZE; } - vk_buffer* d_D = &extra->buffer_gpu; GGML_ASSERT(d_D != nullptr); - uint64_t d_buf_offset = (extra->offset / vk_device.properties.limits.minStorageBufferOffsetAlignment) * vk_device.properties.limits.minStorageBufferOffsetAlignment; + uint64_t d_buf_offset = (extra->offset / ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; GGML_ASSERT(d_buf_offset == extra->offset || op == GGML_OP_CPY); // NOLINT if (transfer_src0) { - d_X = &vk_prealloc_qx; + d_X = ctx->prealloc_qx; } else if(!src0_uma) { - d_X = &extra_src0->buffer_gpu; + d_X = extra_src0->buffer_gpu.lock(); x_buf_offset = extra_src0->offset; GGML_ASSERT(d_X != nullptr); } if (transfer_src1) { - d_Y = &vk_prealloc_qy; + d_Y = ctx->prealloc_qy; } else if (use_src1 && !src1_uma) { - d_Y = &extra_src1->buffer_gpu; + d_Y = extra_src1->buffer_gpu.lock(); y_buf_offset = extra_src1->offset; GGML_ASSERT(d_Y != nullptr); } @@ -2856,16 +3043,16 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm // copy src0 to device if (transfer_src0) { - ggml_vk_h2d_tensor_2d(ctx, d_X, 0, src0, 0, 0, ggml_nrows(src0)); - vk_staging_offset = x_sz * ne02 * ne03; + ggml_vk_h2d_tensor_2d(ctx, subctx, d_X, 0, src0, 0, 0, ggml_nrows(src0)); + ctx->staging_offset = x_sz * ne02 * ne03; } if (transfer_src1) { - ggml_vk_h2d_tensor_2d(ctx, d_Y, 0, src1, 0, 0, ggml_nrows(src1)); + ggml_vk_h2d_tensor_2d(ctx, subctx, d_Y, 0, src1, 0, 0, ggml_nrows(src1)); } // Single call if dimension 2 is contiguous if (op == GGML_OP_CPY || (ggml_is_contiguous(src0) && (src1 == nullptr || ggml_is_contiguous(src1)))) { - ggml_vk_pipeline_allocate_descriptor_sets(*pipeline, 1); + ggml_pipeline_allocate_descriptor_sets(ctx, *pipeline, 1); switch (dst->op) { case GGML_OP_NORM: @@ -2896,24 +3083,24 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm if (!use_src1 && op == GGML_OP_SOFT_MAX) { // Empty src1 is possible on soft_max, but the shader needs a buffer - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset, x_sz }, { vk_prealloc_y, 0, vk_prealloc_y.size }, { *d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { ctx->prealloc_y, 0, ctx->prealloc_y->size }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else if (use_src1) { - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset, x_sz }, { *d_Y, y_buf_offset, y_sz }, { *d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { d_Y, y_buf_offset, y_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else { - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset, x_sz }, { *d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } if (dst->backend == GGML_BACKEND_CPU && op == GGML_OP_CPY) { - ggml_vk_d2h_tensor_2d(ctx, d_D, 0, dst); + ggml_vk_d2h_tensor_2d(ctx, subctx, d_D, 0, dst); } else if(dst->backend == GGML_BACKEND_CPU) { // copy dst to host float * d = (float *) dst->data; - ggml_vk_buffer_read_async(ctx, d_D, 0, d, d_sz); + ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, d_sz); } } else { - ggml_vk_pipeline_allocate_descriptor_sets(*pipeline, ne02 * ne03); + ggml_pipeline_allocate_descriptor_sets(ctx, *pipeline, ne02 * ne03); switch (dst->op) { case GGML_OP_NORM: @@ -2940,60 +3127,60 @@ static void ggml_vk_op_f32(vk_context * ctx, const ggml_tensor * src0, const ggm if (!use_src1 && op == GGML_OP_SOFT_MAX) { // Empty src1 is possible on soft_max, but the shader needs a buffer - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset, x_sz }, { vk_prealloc_y, 0, vk_prealloc_y.size }, { *d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset, x_sz }, { ctx->prealloc_y, 0, ctx->prealloc_y->size }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else if (use_src1) { - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset + x_offset, x_sz }, { *d_Y, y_buf_offset + y_offset, y_sz }, { *d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_Y, y_buf_offset + y_offset, y_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); } else { - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, *pipeline, { { *d_X, x_buf_offset + x_offset, x_sz }, { *d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_sync_buffers(subctx); + ggml_vk_dispatch_pipeline(ctx, subctx, *pipeline, { { d_X, x_buf_offset + x_offset, x_sz }, { d_D, d_buf_offset + d_offset, d_sz } }, sizeof(PC), &pc, elements); } if (dst->backend == GGML_BACKEND_CPU) { // copy dst to host - ggml_vk_buffer_read_async(ctx, d_D, d_buf_offset + d_offset, (char *) dst->data + i02*nb2 + i03*nb3, d_sz); + ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset + d_offset, (char *) dst->data + i02*nb2 + i03*nb3, d_sz); } } } } } -static void ggml_vk_repeat(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_REPEAT, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); +static void ggml_vk_repeat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_REPEAT, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); } -static void ggml_vk_get_rows(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_GET_ROWS, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); +static void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_GET_ROWS, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); } -static void ggml_vk_add(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_ADD, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); +static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ADD, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); } -static void ggml_vk_mul(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_MUL, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); +static void ggml_vk_mul(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_MUL, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); } -static void ggml_vk_scale(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_scale(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_SCALE, { (uint32_t)ggml_nelements(src0), 0, op_params[0], 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_SCALE, { (uint32_t)ggml_nelements(src0), 0, op_params[0], 0.0f }); } -static void ggml_vk_sqr(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_SQR, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); +static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_SQR, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); } -static void ggml_vk_clamp(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_CLAMP, { (uint32_t)ggml_nelements(src0), 0, op_params[0], op_params[1] }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_CLAMP, { (uint32_t)ggml_nelements(src0), 0, op_params[0], op_params[1] }); } -static void ggml_vk_cpy(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; const int src0_type_size = ggml_type_size(src0->type); const int dst_type_size = ggml_type_size(dst->type); - const uint32_t d_offset = (extra->offset % vk_device.properties.limits.minStorageBufferOffsetAlignment) / dst_type_size; - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_CPY, { + const uint32_t d_offset = (extra->offset % ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size; + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_CPY, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, @@ -3001,30 +3188,30 @@ static void ggml_vk_cpy(vk_context * ctx, const ggml_tensor * src0, ggml_tensor }); } -static void ggml_vk_norm(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], 0.0f, 0.0f }); +static void ggml_vk_norm(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], 0.0f, 0.0f }); } -static void ggml_vk_rms_norm(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_rms_norm(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_RMS_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); } -static void ggml_vk_unary(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); +static void ggml_vk_unary(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); } -static void ggml_vk_diag_mask_inf(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_diag_mask_inf(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { int32_t * op_params = (int32_t *)dst->op_params; - ggml_vk_op_f32(ctx, src0, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_DIAG_MASK_INF, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0] }); } -static void ggml_vk_soft_max(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { float * op_params = (float *)dst->op_params; - ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_SOFT_MAX, { (uint32_t)src0->ne[0], (uint32_t)(src1 != nullptr ? ggml_nrows(src1) : 0), op_params[0], 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_SOFT_MAX, { (uint32_t)src0->ne[0], (uint32_t)(src1 != nullptr ? ggml_nrows(src1) : 0), op_params[0], 0.0f }); } -static void ggml_vk_rope(vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { const int n_dims = ((int32_t *) dst->op_params)[1]; const int mode = ((int32_t *) dst->op_params)[2]; // const int n_ctx = ((int32_t *) dst->op_params)[3]; @@ -3047,19 +3234,19 @@ static void ggml_vk_rope(vk_context * ctx, const ggml_tensor * src0, const ggml_ if (is_neox) { const float theta_scale = powf(freq_base, -2.0f/n_dims); const float inv_ndims = -1.0f / n_dims; - ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f, theta_scale, inv_ndims }); + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], (uint32_t)n_dims, freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f, theta_scale, inv_ndims }); } else { - ggml_vk_op_f32(ctx, src0, src1, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ROPE, { (uint32_t)src0->ne[0], freq_scale, (uint32_t)src0->ne[1], freq_base, ext_factor, attn_factor, corr_dims[0], corr_dims[1], 0.0f, 0.0f }); } } -static void ggml_vk_nop(vk_context * ctx, const ggml_tensor * src0, ggml_tensor * dst) { +static void ggml_vk_nop(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { // If backend is CPU, data from src0 has to be copied off the device if (dst->backend == GGML_BACKEND_CPU) { ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; - vk_buffer * d_D = &extra_src0->buffer_gpu; - ggml_vk_sync_buffers(ctx); - ggml_vk_buffer_read_async(ctx, d_D, 0, dst->data, d_D->size); + vk_buffer d_D = extra_src0->buffer_gpu.lock(); + ggml_vk_sync_buffers(subctx); + ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, dst->data, d_D->size); } } @@ -3096,7 +3283,7 @@ static void ggml_vk_print_matrix_area(const void * data, ggml_type type, int ne0 } template -static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size_t num_it, int split_k, int shader_size) { +static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t n, size_t k, size_t batch, size_t num_it, int split_k, int shader_size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_test_matmul(" << m << ", " << n << ", " << k << ", " << batch << ", " << num_it << ", " << split_k << ", " << shader_size << ")" << std::endl; #endif @@ -3108,39 +3295,39 @@ static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size std::string shname; if (shader_size == 0) { if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f32_aligned_s; + p = &ctx->pipeline_matmul_f32_aligned_s; shname = "F32_ALIGNED_S"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_f32_aligned_s; + p = &ctx->pipeline_matmul_f16_f32_aligned_s; shname = "F16_F32_ALIGNED_S"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_aligned_s; + p = &ctx->pipeline_matmul_f16_aligned_s; shname = "F16_ALIGNED_S"; } else { GGML_ASSERT(false); } } else if (shader_size == 1) { if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f32_aligned_m; + p = &ctx->pipeline_matmul_f32_aligned_m; shname = "F32_ALIGNED_M"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_f32_aligned_m; + p = &ctx->pipeline_matmul_f16_f32_aligned_m; shname = "F16_F32_ALIGNED_M"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_aligned_m; + p = &ctx->pipeline_matmul_f16_aligned_m; shname = "F16_ALIGNED_M"; } else { GGML_ASSERT(false); } } else if (shader_size == 2) { if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f32_aligned_l; + p = &ctx->pipeline_matmul_f32_aligned_l; shname = "F32_ALIGNED_L"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_f32_aligned_l; + p = &ctx->pipeline_matmul_f16_f32_aligned_l; shname = "F16_F32_ALIGNED_L"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_aligned_l; + p = &ctx->pipeline_matmul_f16_aligned_l; shname = "F16_ALIGNED_L"; } else { GGML_ASSERT(false); @@ -3154,56 +3341,56 @@ static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size if (k != kpad) { if (shader_size == 0) { if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f32_s; + p = &ctx->pipeline_matmul_f32_s; shname = "F32_S"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_f32_s; + p = &ctx->pipeline_matmul_f16_f32_s; shname = "F16_F32_S"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_s; + p = &ctx->pipeline_matmul_f16_s; shname = "F16_S"; } } else if (shader_size == 1) { if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f32_m; + p = &ctx->pipeline_matmul_f32_m; shname = "F32_M"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_f32_m; + p = &ctx->pipeline_matmul_f16_f32_m; shname = "F16_F32_M"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_m; + p = &ctx->pipeline_matmul_f16_m; shname = "F16_M"; } } else if (shader_size == 2) { if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f32_l; + p = &ctx->pipeline_matmul_f32_l; shname = "F32_L"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_f32_l; + p = &ctx->pipeline_matmul_f16_f32_l; shname = "F16_F32_L"; } else if (std::is_same() && std::is_same()) { - p = &vk_pipeline_matmul_f16_l; + p = &ctx->pipeline_matmul_f16_l; shname = "F16_L"; } } } - ggml_vk_pipeline_allocate_descriptor_sets(*p, num_it); + ggml_pipeline_allocate_descriptor_sets(ctx, *p, num_it); if (split_k > 1) { - ggml_vk_pipeline_allocate_descriptor_sets(vk_pipeline_matmul_split_k_reduce, num_it); + ggml_pipeline_allocate_descriptor_sets(ctx, ctx->pipeline_matmul_split_k_reduce, num_it); - if (vk_prealloc_split_k.size < sizeof(float) * d_ne * split_k) { + if (ctx->prealloc_split_k == nullptr || ctx->prealloc_split_k->size < sizeof(float) * d_ne * split_k) { // Resize buffer - if (vk_prealloc_split_k.size > 0) { - ggml_vk_destroy_buffer(vk_prealloc_split_k); + if (ctx->prealloc_split_k != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_split_k); } - vk_prealloc_split_k = ggml_vk_create_buffer_check(sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal); + ctx->prealloc_split_k = ggml_vk_create_buffer_check(ctx, sizeof(float) * d_ne * split_k, vk::MemoryPropertyFlagBits::eDeviceLocal); } } - vk_buffer d_X = ggml_vk_create_buffer_check(sizeof(X_TYPE) * x_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); - vk_buffer d_Y = ggml_vk_create_buffer_check(sizeof(Y_TYPE) * y_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); - vk_buffer d_D = ggml_vk_create_buffer_check(sizeof(float) * d_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer d_X = ggml_vk_create_buffer_check(ctx, sizeof(X_TYPE) * x_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer d_Y = ggml_vk_create_buffer_check(ctx, sizeof(Y_TYPE) * y_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer d_D = ggml_vk_create_buffer_check(ctx, sizeof(float) * d_ne, vk::MemoryPropertyFlagBits::eDeviceLocal); X_TYPE* x = (X_TYPE *) malloc(sizeof(X_TYPE) * x_ne); Y_TYPE* y = (Y_TYPE *) malloc(sizeof(Y_TYPE) * y_ne); @@ -3228,26 +3415,26 @@ static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size } } - ggml_vk_buffer_write(&d_X, 0, x, sizeof(X_TYPE) * k * m * batch); - ggml_vk_buffer_write(&d_Y, 0, y, sizeof(Y_TYPE) * k * n * batch); + ggml_vk_buffer_write(ctx, d_X, 0, x, sizeof(X_TYPE) * k * m * batch); + ggml_vk_buffer_write(ctx, d_Y, 0, y, sizeof(Y_TYPE) * k * n * batch); - vk_context * ctx = ggml_vk_create_context(vk_device.compute_queue); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); for (size_t i = 0; i < num_it; i++) { - ggml_vk_ctx_begin(ctx); - ggml_vk_matmul(ctx, *p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(vk_prealloc_split_k), m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n); - ggml_vk_ctx_end(ctx); + ggml_vk_ctx_begin(ctx, subctx); + ggml_vk_matmul(ctx, subctx, *p, ggml_vk_subbuffer(d_X), ggml_vk_subbuffer(d_Y), ggml_vk_subbuffer(d_D), ggml_vk_subbuffer(ctx->prealloc_split_k), m, n, k, k, k, m, split_k, batch, batch, batch, 1, 1, k*m, k*n, m*n); + ggml_vk_ctx_end(subctx); } auto begin = std::chrono::high_resolution_clock::now(); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_test_matmul waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_matmul waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); auto end = std::chrono::high_resolution_clock::now(); double time = std::chrono::duration_cast(end-begin).count() / 1000.0; // copy dst to host - ggml_vk_buffer_read(&d_D, 0, d, sizeof(float) * d_ne); + ggml_vk_buffer_read(ctx, d_D, 0, d, sizeof(float) * d_ne); float * d_chk = (float *) malloc(sizeof(float) * d_ne); @@ -3285,14 +3472,14 @@ static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size src1_ggml->data = y; tensor_ggml->data = d_chk; - vk_disable = true; + ctx->disable = true; ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx); ggml_build_forward_expand(cgraph, tensor_ggml); ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 1); - vk_disable = false; + ctx->disable = false; ggml_free(ggml_ctx); @@ -3325,7 +3512,7 @@ static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size if (split_k > 1) { float * split_k_buf = (float *) malloc(sizeof(float) * d_ne * split_k); - ggml_vk_buffer_read(&vk_prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k); + ggml_vk_buffer_read(ctx, ctx->prealloc_split_k, 0, split_k_buf, sizeof(float) * d_ne * split_k); std::cerr << "d_buf0: " << std::endl << std::endl; ggml_vk_print_matrix_area(split_k_buf, GGML_TYPE_F32, m, n, first_err_m, first_err_n, first_err_b); @@ -3345,15 +3532,15 @@ static void ggml_vk_test_matmul(size_t m, size_t n, size_t k, size_t batch, size free(d_chk); - ggml_vk_queue_cleanup(vk_device.transfer_queue); - ggml_vk_queue_cleanup(vk_device.compute_queue); + ggml_vk_queue_cleanup(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_queue_cleanup(ctx, ctx->device.lock()->compute_queue); ggml_vk_destroy_buffer(d_X); ggml_vk_destroy_buffer(d_Y); ggml_vk_destroy_buffer(d_D); - ggml_vk_pipeline_cleanup(*p); - ggml_vk_pipeline_cleanup(vk_pipeline_matmul_split_k_reduce); + ggml_pipeline_cleanup(*p); + ggml_pipeline_cleanup(ctx->pipeline_matmul_split_k_reduce); free(x); free(y); @@ -3392,7 +3579,7 @@ static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, int i0, int i1 } } -static void ggml_vk_test_h2d_nc(size_t ne0, size_t ne1, size_t ne2, size_t ne3) { +static void ggml_vk_test_h2d_nc(ggml_backend_vk_context * ctx, size_t ne0, size_t ne1, size_t ne2, size_t ne3) { const size_t ne = ne0 * ne1 * ne2 * ne3; ggml_init_params iparams = { @@ -3406,7 +3593,7 @@ static void ggml_vk_test_h2d_nc(size_t ne0, size_t ne1, size_t ne2, size_t ne3) ggml_tensor * tensor = ggml_new_tensor_4d(ggml_ctx, GGML_TYPE_F32, ne0, ne2, ne1, ne3); // NOLINT ggml_tensor * result_tensor = ggml_new_tensor_4d(ggml_ctx, GGML_TYPE_F32, ne0, ne1, ne2, ne3); - float * data = (float *) ggml_vk_host_malloc(ggml_nbytes(tensor)); + float * data = (float *) ggml_vk_host_malloc(ctx, ggml_nbytes(tensor)); tensor->data = data; float * result_data = (float *) malloc(ggml_nbytes(tensor)); @@ -3426,19 +3613,19 @@ static void ggml_vk_test_h2d_nc(size_t ne0, size_t ne1, size_t ne2, size_t ne3) data[i] = (rand() / (float)RAND_MAX) * 2.0f - 1.0f; } - vk_context * ctx = ggml_vk_create_context(vk_device.compute_queue); - ggml_vk_ctx_begin(ctx); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); + ggml_vk_ctx_begin(ctx, subctx); - vk_buffer buffer = ggml_vk_create_buffer_check(ggml_nbytes(tensor), vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer buffer = ggml_vk_create_buffer_check(ctx, ggml_nbytes(tensor), vk::MemoryPropertyFlagBits::eDeviceLocal); - ggml_vk_h2d_tensor_2d(ctx, &buffer, 0, tensor, 0, 0, ggml_nrows(tensor)); + ggml_vk_h2d_tensor_2d(ctx, subctx, buffer, 0, tensor, 0, 0, ggml_nrows(tensor)); - ggml_vk_ctx_end(ctx); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_ctx_end(subctx); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_h2d_nc waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); - ggml_vk_buffer_read(&buffer, 0, result_data, ggml_nbytes(tensor)); + ggml_vk_buffer_read(ctx, buffer, 0, result_data, ggml_nbytes(tensor)); double avg_err = 0.0; int first_err_i0 = -1; @@ -3483,22 +3670,22 @@ static void ggml_vk_test_h2d_nc(size_t ne0, size_t ne1, size_t ne2, size_t ne3) ggml_vk_destroy_buffer(buffer); - ggml_vk_host_free(data); + ggml_vk_host_free(ctx, data); free(result_data); } -static void ggml_vk_test_transfer(size_t ne, bool pinned) { +static void ggml_vk_test_transfer(ggml_backend_vk_context * ctx, size_t ne, bool pinned) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_test_transfer(" << ne << ")" << std::endl; #endif // Check transfers are correct - vk_buffer buffer = ggml_vk_create_buffer_check(sizeof(float) * ne, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer buffer = ggml_vk_create_buffer_check(ctx, sizeof(float) * ne, vk::MemoryPropertyFlagBits::eDeviceLocal); float * x; float * y; if (pinned) { - x = (float *) ggml_vk_host_malloc(sizeof(float) * ne); - y = (float *) ggml_vk_host_malloc(sizeof(float) * ne); + x = (float *) ggml_vk_host_malloc(ctx, sizeof(float) * ne); + y = (float *) ggml_vk_host_malloc(ctx, sizeof(float) * ne); } else { x = (float *) malloc(sizeof(float) * ne); y = (float *) malloc(sizeof(float) * ne); @@ -3508,42 +3695,42 @@ static void ggml_vk_test_transfer(size_t ne, bool pinned) { x[i] = rand() / (float)RAND_MAX; } - vk_context * ctx = ggml_vk_create_context(vk_device.compute_queue); - ggml_vk_ctx_begin(ctx); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); + ggml_vk_ctx_begin(ctx, subctx); auto begin = std::chrono::high_resolution_clock::now(); - ggml_vk_buffer_write_async(ctx, &buffer, 0, x, sizeof(float) * ne); + ggml_vk_buffer_write_async(ctx, subctx, buffer, 0, x, sizeof(float) * ne); - for (auto& cpy : ctx->in_memcpys) { + for (auto& cpy : subctx->in_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - ctx->in_memcpys.clear(); + subctx->in_memcpys.clear(); - ggml_vk_ctx_end(ctx); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_ctx_end(subctx); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); auto end = std::chrono::high_resolution_clock::now(); double ms_to_gpu = std::chrono::duration_cast(end-begin).count() / 1000.0; - ggml_vk_ctx_begin(ctx); + ggml_vk_ctx_begin(ctx, subctx); begin = std::chrono::high_resolution_clock::now(); - ggml_vk_buffer_read_async(ctx, &buffer, 0, y, sizeof(float) * ne); + ggml_vk_buffer_read_async(ctx, subctx, buffer, 0, y, sizeof(float) * ne); - ggml_vk_ctx_end(ctx); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_ctx_end(subctx); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_transfer waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); - for (auto& cpy : ctx->out_memcpys) { + for (auto& cpy : subctx->out_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - ctx->out_memcpys.clear(); + subctx->out_memcpys.clear(); end = std::chrono::high_resolution_clock::now(); @@ -3561,15 +3748,15 @@ static void ggml_vk_test_transfer(size_t ne, bool pinned) { ggml_vk_destroy_buffer(buffer); if (pinned) { - ggml_vk_host_free(x); - ggml_vk_host_free(y); + ggml_vk_host_free(ctx, x); + ggml_vk_host_free(ctx, y); } else { free(x); free(y); } } -static void ggml_vk_test_dequant(size_t ne, ggml_type quant) { +static void ggml_vk_test_dequant(ggml_backend_vk_context * ctx, size_t ne, ggml_type quant) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_test_dequant(" << ne << ")" << std::endl; #endif @@ -3578,8 +3765,8 @@ static void ggml_vk_test_dequant(size_t ne, ggml_type quant) { const size_t qx_sz = ne * ggml_type_size(quant)/ggml_blck_size(quant); float * x = (float *) malloc(x_sz); void * qx = malloc(qx_sz); - vk_buffer qx_buf = ggml_vk_create_buffer_check(qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal); - vk_buffer x_buf = ggml_vk_create_buffer_check(x_sz_f16, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer qx_buf = ggml_vk_create_buffer_check(ctx, qx_sz, vk::MemoryPropertyFlagBits::eDeviceLocal); + vk_buffer x_buf = ggml_vk_create_buffer_check(ctx, x_sz_f16, vk::MemoryPropertyFlagBits::eDeviceLocal); ggml_fp16_t * x_chk = (ggml_fp16_t *) malloc(x_sz_f16); for (size_t i = 0; i < ne; i++) { @@ -3588,7 +3775,7 @@ static void ggml_vk_test_dequant(size_t ne, ggml_type quant) { std::vector hist_cur(1 << 4, 0); - vk_pipeline& p = vk_pipeline_dequant[quant]; + vk_pipeline& p = ctx->pipeline_dequant[quant]; switch(quant) { case GGML_TYPE_Q4_0: @@ -3625,27 +3812,26 @@ static void ggml_vk_test_dequant(size_t ne, ggml_type quant) { GGML_ASSERT(false); } - ggml_vk_pipeline_allocate_descriptor_sets(p, 1); + ggml_pipeline_allocate_descriptor_sets(ctx, p, 1); - ggml_vk_buffer_write(&qx_buf, 0, qx, qx_sz); + ggml_vk_buffer_write(ctx, qx_buf, 0, qx, qx_sz); - vk_context * ctx = ggml_vk_create_context(vk_device.compute_queue); - ggml_vk_ctx_begin(ctx); + vk_context * subctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); + ggml_vk_ctx_begin(ctx, subctx); const std::vector pc = { 1, (int)ne, (int)ne, (int)ne }; - ggml_vk_sync_buffers(ctx); - ggml_vk_dispatch_pipeline(ctx, p, { { qx_buf, 0, qx_sz }, { x_buf, 0, x_sz_f16 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)ne, 1, 1}); - ggml_vk_ctx_end(ctx); + ggml_vk_dispatch_pipeline(ctx, subctx, p, { { qx_buf, 0, qx_sz }, { x_buf, 0, x_sz_f16 } }, pc.size() * sizeof(int), pc.data(), { (uint32_t)ne, 1, 1}); + ggml_vk_ctx_end(subctx); auto begin = std::chrono::high_resolution_clock::now(); - ggml_vk_submit(ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_submit(subctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_test_dequant waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); auto end = std::chrono::high_resolution_clock::now(); double ms_dequant = std::chrono::duration_cast(end-begin).count() / 1000.0; - ggml_vk_buffer_read(&x_buf, 0, x_chk, x_sz_f16); + ggml_vk_buffer_read(ctx, x_buf, 0, x_chk, x_sz_f16); double avg_err = 0.0; for (size_t i = 0; i < ne; i++) { @@ -3687,15 +3873,15 @@ static ggml_tensor * ggml_vk_find_last_use(const ggml_tensor * node, ggml_cgraph return nullptr; } -void ggml_vk_preallocate_buffers_graph(ggml_tensor * node){ +static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggml_tensor * node){ #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_preallocate_buffers_graph(" << node << ")" << std::endl; + std::cerr << "ggml_ctx->preallocate_buffers_graph(" << node << ")" << std::endl; #endif const bool any_on_device = node->backend == GGML_BACKEND_GPU || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_GPU || node->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) || (node->src[1] != nullptr && (node->src[1]->backend == GGML_BACKEND_GPU)); - if (vk_disable || (!any_on_device && node->op != GGML_OP_MUL_MAT)) { + if (ctx->disable || (!any_on_device && node->op != GGML_OP_MUL_MAT)) { return; } @@ -3735,16 +3921,16 @@ void ggml_vk_preallocate_buffers_graph(ggml_tensor * node){ const uint32_t y_ne = ne10 * ne11; const uint32_t d_ne = ne20 * ne21; - const uint64_t qx_sz = use_src0 ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), vk_device.properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0; - const uint64_t qy_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type), vk_device.properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0; - const uint64_t x_sz = use_src0 ? ggml_vk_align_size(sizeof(ggml_fp16_t) * x_ne, vk_device.properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0; - const uint64_t y_sz = use_src1 ? ggml_vk_align_size(f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne, vk_device.properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0; - uint64_t d_sz = ggml_vk_align_size(ggml_type_size(node->type) * d_ne, vk_device.properties.limits.minStorageBufferOffsetAlignment) * ne22 * ne23; + const uint64_t qx_sz = use_src0 ? ggml_vk_align_size(ggml_type_size(src0->type) * x_ne / ggml_blck_size(src0->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0; + const uint64_t qy_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * y_ne / ggml_blck_size(src1->type), ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0; + const uint64_t x_sz = use_src0 ? ggml_vk_align_size(sizeof(ggml_fp16_t) * x_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne02 * ne03 : 0; + const uint64_t y_sz = use_src1 ? ggml_vk_align_size(f16_f32_kernel ? sizeof(float) * y_ne : sizeof(ggml_fp16_t) * y_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne12 * ne13 : 0; + uint64_t d_sz = ggml_vk_align_size(ggml_type_size(node->type) * d_ne, ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment) * ne22 * ne23; const uint64_t split_k_size = split_k > 1 ? d_sz * 4 : 0; - if (extra->buffer_gpu.size == 0) { + if (extra->buffer_gpu.expired()) { // Workaround for CPU backend BLAS matmul calls - extra->buffer_gpu = ggml_vk_create_buffer_temp(d_sz); + extra->buffer_gpu = ggml_vk_create_buffer_temp(ctx, d_sz); } switch (node->op) { @@ -3779,23 +3965,23 @@ void ggml_vk_preallocate_buffers_graph(ggml_tensor * node){ } break; case GGML_OP_MUL_MAT: - if (vk_prealloc_size_qx < qx_sz) { - vk_prealloc_size_qx = qx_sz; + if (ctx->prealloc_size_qx < qx_sz) { + ctx->prealloc_size_qx = qx_sz; } - if (vk_prealloc_size_qy < qy_sz) { - vk_prealloc_size_qy = qy_sz; + if (ctx->prealloc_size_qy < qy_sz) { + ctx->prealloc_size_qy = qy_sz; } - if (vk_prealloc_size_x < x_sz) { - vk_prealloc_size_x = x_sz; + if (ctx->prealloc_size_x < x_sz) { + ctx->prealloc_size_x = x_sz; } - if (vk_prealloc_size_y < y_sz) { - vk_prealloc_size_y = y_sz; + if (ctx->prealloc_size_y < y_sz) { + ctx->prealloc_size_y = y_sz; } - if (vk_prealloc_size_split_k < split_k_size) { - vk_prealloc_size_split_k = split_k_size; + if (ctx->prealloc_size_split_k < split_k_size) { + ctx->prealloc_size_split_k = split_k_size; } - if (vk_staging_size < x_sz + y_sz) { - vk_staging_size = x_sz + y_sz; + if (ctx->staging_size < x_sz + y_sz) { + ctx->staging_size = x_sz + y_sz; } break; default: @@ -3803,29 +3989,29 @@ void ggml_vk_preallocate_buffers_graph(ggml_tensor * node){ } } -void ggml_vk_preallocate_buffers() { - if (vk_disable) { +static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { + if (ctx->disable) { return; } #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_preallocate_buffers()" << std::endl; - std::cerr << "qx_size: " << vk_prealloc_size_qx << " qy_size: " << vk_prealloc_size_qy << " x_size: " << vk_prealloc_size_x << " y_size: " << vk_prealloc_size_y << " split_k_size: " << vk_prealloc_size_split_k << std::endl; + std::cerr << "ggml_ctx->preallocate_buffers()" << std::endl; + std::cerr << "qx_size: " << ctx->prealloc_size_qx << " qy_size: " << ctx->prealloc_size_qy << " x_size: " << ctx->prealloc_size_x << " y_size: " << ctx->prealloc_size_y << " split_k_size: " << ctx->prealloc_size_split_k << std::endl; #endif #if defined(GGML_VULKAN_RUN_TESTS) - vk_staging = ggml_vk_create_buffer_check(100ul * 1024ul * 1024ul, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); - ggml_vk_test_transfer(8192 * 1000, false); - ggml_vk_test_transfer(8192 * 1000, true); + ctx->staging = ggml_vk_create_buffer_check(ctx, 100ul * 1024ul * 1024ul, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); + ggml_vk_test_transfer(ctx, 8192 * 1000, false); + ggml_vk_test_transfer(ctx, 8192 * 1000, true); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q4_0); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q4_1); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q5_0); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q5_1); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q8_0); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q2_K); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q3_K); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q4_K); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q5_K); - ggml_vk_test_dequant(2560 * 7680, GGML_TYPE_Q6_K); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q4_0); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q4_1); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q5_0); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q5_1); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q8_0); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q2_K); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q3_K); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q4_K); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q5_K); + ggml_vk_test_dequant(ctx, 2560 * 7680, GGML_TYPE_Q6_K); const std::vector vals { 8, 8, 8, @@ -3852,76 +4038,76 @@ void ggml_vk_preallocate_buffers() { }; const size_t num_it = 1; for (size_t i = 0; i < vals.size(); i += 3) { - ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 0); - ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 1); - ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 2); - ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 0); - ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 1); - ggml_vk_test_matmul(vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 2); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 0); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 1); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 1, 2); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 0); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 1); + ggml_vk_test_matmul(ctx, vals[i], vals[i + 1], vals[i + 2], 2, num_it, 4, 2); std::cerr << std::endl; } GGML_ASSERT(false); #endif - if (vk_prealloc_size_qx > 0 && vk_prealloc_qx.size < vk_prealloc_size_qx) { + if (ctx->prealloc_qx == nullptr || (ctx->prealloc_size_qx > 0 && ctx->prealloc_qx->size < ctx->prealloc_size_qx)) { // Resize buffer - if (vk_prealloc_qx.size > 0) { - ggml_vk_destroy_buffer(vk_prealloc_qx); + if (ctx->prealloc_qx != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_qx); } - vk_prealloc_qx = ggml_vk_create_buffer_device(vk_prealloc_size_qx); + ctx->prealloc_qx = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_qx); } - if (vk_prealloc_size_qy > 0 && vk_prealloc_qy.size < vk_prealloc_size_qy) { + if (ctx->prealloc_qy == nullptr || (ctx->prealloc_size_qy > 0 && ctx->prealloc_qy->size < ctx->prealloc_size_qy)) { // Resize buffer - if (vk_prealloc_qy.size > 0) { - ggml_vk_destroy_buffer(vk_prealloc_qy); + if (ctx->prealloc_qy != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_qy); } - vk_prealloc_qy = ggml_vk_create_buffer_device(vk_prealloc_size_qy); + ctx->prealloc_qy = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_qy); } - if (vk_prealloc_size_x > 0 && vk_prealloc_x.size < vk_prealloc_size_x) { + if (ctx->prealloc_x == nullptr || (ctx->prealloc_size_x > 0 && ctx->prealloc_x->size < ctx->prealloc_size_x)) { // Resize buffer - if (vk_prealloc_x.size > 0) { - ggml_vk_destroy_buffer(vk_prealloc_x); + if (ctx->prealloc_x != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_x); } - vk_prealloc_x = ggml_vk_create_buffer_device(vk_prealloc_size_x); + ctx->prealloc_x = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_x); } - if (vk_prealloc_size_y > 0 && vk_prealloc_y.size < vk_prealloc_size_y) { + if (ctx->prealloc_y == nullptr || (ctx->prealloc_size_y > 0 && ctx->prealloc_y->size < ctx->prealloc_size_y)) { // Resize buffer - if (vk_prealloc_y.size > 0) { - ggml_vk_destroy_buffer(vk_prealloc_y); + if (ctx->prealloc_y != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_y); } - vk_prealloc_y = ggml_vk_create_buffer_device(vk_prealloc_size_y); + ctx->prealloc_y = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_y); } - if (vk_prealloc_size_split_k > 0 && vk_prealloc_split_k.size < vk_prealloc_size_split_k) { + if (ctx->prealloc_split_k == nullptr || (ctx->prealloc_size_split_k > 0 && ctx->prealloc_split_k->size < ctx->prealloc_size_split_k)) { // Resize buffer - if (vk_prealloc_split_k.size > 0) { - ggml_vk_destroy_buffer(vk_prealloc_split_k); + if (ctx->prealloc_split_k != nullptr) { + ggml_vk_destroy_buffer(ctx->prealloc_split_k); } - vk_prealloc_split_k = ggml_vk_create_buffer_device(vk_prealloc_size_split_k); + ctx->prealloc_split_k = ggml_vk_create_buffer_device(ctx, ctx->prealloc_size_split_k); } - if (vk_staging_size > 0 && vk_staging.size < vk_staging_size) { + if (ctx->staging == nullptr || (ctx->staging_size > 0 && ctx->staging->size < ctx->staging_size)) { // Resize buffer - if (vk_staging.size > 0) { - ggml_vk_destroy_buffer(vk_staging); + if (ctx->staging != nullptr) { + ggml_vk_destroy_buffer(ctx->staging); } - vk_staging = ggml_vk_create_buffer_check(vk_staging_size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); + ctx->staging = ggml_vk_create_buffer_check(ctx, ctx->staging_size, vk::MemoryPropertyFlagBits::eHostVisible | vk::MemoryPropertyFlagBits::eHostCoherent | vk::MemoryPropertyFlagBits::eHostCached); } } -void ggml_vk_build_graph(ggml_tensor * node, bool last_node){ +static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, bool last_node){ const bool any_on_device = node->backend == GGML_BACKEND_GPU || (node->src[0] != nullptr && (node->src[0]->backend == GGML_BACKEND_GPU || node->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) || (node->src[1] != nullptr && node->src[1]->backend == GGML_BACKEND_GPU); - if (vk_disable || (!any_on_device && node->op != GGML_OP_MUL_MAT) || (node->op == GGML_OP_MUL_MAT && !any_on_device && !ggml_vk_can_mul_mat(node->src[0], node->src[1], node))) { + if (ctx->disable || (!any_on_device && node->op != GGML_OP_MUL_MAT) || (node->op == GGML_OP_MUL_MAT && !any_on_device && !ggml_vk_can_mul_mat(node->src[0], node->src[1], node))) { return; } #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_build_graph(" << node << ", " << ggml_op_name(node->op) << ")" << std::endl; #endif - vk_semaphore_idx = 0; - vk_staging_offset = 0; + ctx->semaphore_idx = 0; + ctx->staging_offset = 0; const ggml_tensor * src0 = node->src[0]; const ggml_tensor * src1 = node->src[1]; @@ -3969,44 +4155,44 @@ void ggml_vk_build_graph(ggml_tensor * node, bool last_node){ return; } - if (vk_ctx == nullptr) { - vk_ctx = ggml_vk_create_context(vk_device.compute_queue); - ggml_vk_ctx_begin(vk_ctx); + if (ctx->compute_ctx == nullptr) { + ctx->compute_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->compute_queue); + ggml_vk_ctx_begin(ctx, ctx->compute_ctx); } switch (node->op) { case GGML_OP_REPEAT: - ggml_vk_repeat(vk_ctx, src0, src1, node); + ggml_vk_repeat(ctx, ctx->compute_ctx, src0, src1, node); break; case GGML_OP_GET_ROWS: - ggml_vk_get_rows(vk_ctx, src0, src1, node); + ggml_vk_get_rows(ctx, ctx->compute_ctx, src0, src1, node); break; case GGML_OP_ADD: - ggml_vk_add(vk_ctx, src0, src1, node); + ggml_vk_add(ctx, ctx->compute_ctx, src0, src1, node); break; case GGML_OP_MUL: - ggml_vk_mul(vk_ctx, src0, src1, node); + ggml_vk_mul(ctx, ctx->compute_ctx, src0, src1, node); break; case GGML_OP_SCALE: - ggml_vk_scale(vk_ctx, src0, node); + ggml_vk_scale(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_SQR: - ggml_vk_sqr(vk_ctx, src0, node); + ggml_vk_sqr(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_CLAMP: - ggml_vk_clamp(vk_ctx, src0, node); + ggml_vk_clamp(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_CPY: case GGML_OP_CONT: case GGML_OP_DUP: - ggml_vk_cpy(vk_ctx, src0, node); + ggml_vk_cpy(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_RESHAPE: @@ -4014,15 +4200,15 @@ void ggml_vk_build_graph(ggml_tensor * node, bool last_node){ case GGML_OP_PERMUTE: case GGML_OP_TRANSPOSE: case GGML_OP_NONE: - ggml_vk_nop(vk_ctx, src0, node); + ggml_vk_nop(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_NORM: - ggml_vk_norm(vk_ctx, src0, node); + ggml_vk_norm(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_RMS_NORM: - ggml_vk_rms_norm(vk_ctx, src0, node); + ggml_vk_rms_norm(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_UNARY: @@ -4030,26 +4216,26 @@ void ggml_vk_build_graph(ggml_tensor * node, bool last_node){ case GGML_UNARY_OP_SILU: case GGML_UNARY_OP_GELU: case GGML_UNARY_OP_RELU: - ggml_vk_unary(vk_ctx, src0, node); + ggml_vk_unary(ctx, ctx->compute_ctx, src0, node); break; default: return; } break; case GGML_OP_DIAG_MASK_INF: - ggml_vk_diag_mask_inf(vk_ctx, src0, node); + ggml_vk_diag_mask_inf(ctx, ctx->compute_ctx, src0, node); break; case GGML_OP_SOFT_MAX: - ggml_vk_soft_max(vk_ctx, src0, src1, node); + ggml_vk_soft_max(ctx, ctx->compute_ctx, src0, src1, node); break; case GGML_OP_ROPE: - ggml_vk_rope(vk_ctx, src0, src1, node); + ggml_vk_rope(ctx, ctx->compute_ctx, src0, src1, node); break; case GGML_OP_MUL_MAT: - ggml_vk_mul_mat(vk_ctx, src0, src1, node); + ggml_vk_mul_mat(ctx, ctx->compute_ctx, src0, src1, node); break; default: @@ -4057,7 +4243,7 @@ void ggml_vk_build_graph(ggml_tensor * node, bool last_node){ } extra->ready = true; - extra->ctx_idx = vk_ctx->idx; + extra->ctx_idx = ctx->compute_ctx->idx; #ifdef GGML_VULKAN_CHECK_RESULTS // Force context reset on each node so that each tensor ends up in its own context @@ -4066,18 +4252,18 @@ void ggml_vk_build_graph(ggml_tensor * node, bool last_node){ #endif if (node->backend == GGML_BACKEND_CPU || last_node) { - ggml_vk_ctx_end(vk_ctx); - vk_ctx->exit_tensor = node; - vk_ctx = nullptr; + ggml_vk_ctx_end(ctx->compute_ctx); + ctx->compute_ctx->exit_tensor = node; + ctx->compute_ctx = nullptr; } } -bool ggml_vk_compute_forward(ggml_compute_params * params, ggml_tensor * tensor){ +static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor){ const bool any_on_device = tensor->backend == GGML_BACKEND_GPU || (tensor->src[0] != nullptr && (tensor->src[0]->backend == GGML_BACKEND_GPU || tensor->src[0]->backend == GGML_BACKEND_GPU_SPLIT)) || (tensor->src[1] != nullptr && tensor->src[1]->backend == GGML_BACKEND_GPU); - if (vk_disable || (!any_on_device && tensor->op != GGML_OP_MUL_MAT)) { + if (ctx->disable || (!any_on_device && tensor->op != GGML_OP_MUL_MAT)) { return false; } @@ -4145,33 +4331,33 @@ bool ggml_vk_compute_forward(ggml_compute_params * params, ggml_tensor * tensor) #endif #ifdef GGML_VULKAN_CHECK_RESULTS - ggml_vk_check_results_0(params, tensor); + ggml_vk_check_results_0(ctx, params, tensor); #endif GGML_ASSERT(extra->ready); - vk_context& ctx = vk_gc.contexts[extra->ctx_idx]; + vk_context& subctx = ctx->gc.contexts[extra->ctx_idx]; // Only run if ctx hasn't been submitted yet - if (!ctx.seqs.empty()) { + if (!subctx.seqs.empty()) { // Do staging buffer copies - for (auto& cpy : ctx.in_memcpys) { + for (auto& cpy : subctx.in_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - ggml_vk_submit(&ctx, vk_fence); + ggml_vk_submit(&subctx, ctx->fence); } - if (tensor == ctx.exit_tensor) { - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); - vk_device.device.resetFences({ vk_fence }); + if (tensor == subctx.exit_tensor) { + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_vk_compute_forward waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); // Do staging buffer copies - for (auto& cpy : ctx.out_memcpys) { + for (auto& cpy : subctx.out_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - ctx.in_memcpys.clear(); - ctx.out_memcpys.clear(); + subctx.in_memcpys.clear(); + subctx.out_memcpys.clear(); } extra->ready = false; @@ -4179,90 +4365,204 @@ bool ggml_vk_compute_forward(ggml_compute_params * params, ggml_tensor * tensor) return true; } -void ggml_vk_graph_cleanup() { - if (vk_disable) { +// Clean up after graph processing is done +static void ggml_vk_graph_cleanup(ggml_backend_vk_context * ctx) { + if (ctx->disable) { return; } #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_graph_cleanup()" << std::endl; #endif - for (auto& buffer : vk_gc.temp_buffers) { - ggml_vk_pool_free(buffer); + for (auto& buffer : ctx->gc.temp_buffers) { + ggml_vk_pool_free(ctx, buffer); } - vk_gc.temp_buffers.clear(); + ctx->gc.temp_buffers.clear(); - for (auto * pipeline : vk_gc.pipelines) { - ggml_vk_pipeline_cleanup(*pipeline); - } - vk_gc.pipelines.clear(); - - ggml_vk_queue_cleanup(vk_device.compute_queue); - ggml_vk_queue_cleanup(vk_device.transfer_queue); - - for (size_t i = 0; i < vk_gc.semaphores.size(); i++) { - vk_device.device.destroySemaphore({ vk_gc.semaphores[i].s }); - } - vk_gc.semaphores.clear(); - - for (size_t i = 0; i < vk_gc.tl_semaphores.size(); i++) { - vk_device.device.destroySemaphore({ vk_gc.tl_semaphores[i].s }); - } - vk_gc.tl_semaphores.clear(); - - vk_event_idx = 0; - - for (auto& event : vk_gc.events) { - vk_device.device.resetEvent(event); + for (auto * pipeline : ctx->gc.pipelines) { + ggml_pipeline_cleanup(*pipeline); } - vk_staging_offset = 0; + ggml_vk_queue_cleanup(ctx, ctx->device.lock()->compute_queue); + ggml_vk_queue_cleanup(ctx, ctx->device.lock()->transfer_queue); - vk_ctx = nullptr; - vk_gc.contexts.clear(); + for (size_t i = 0; i < ctx->gc.semaphores.size(); i++) { + ctx->device.lock()->device.destroySemaphore({ ctx->gc.semaphores[i].s }); + } + ctx->gc.semaphores.clear(); + + for (size_t i = 0; i < ctx->gc.tl_semaphores.size(); i++) { + ctx->device.lock()->device.destroySemaphore({ ctx->gc.tl_semaphores[i].s }); + } + ctx->gc.tl_semaphores.clear(); + ctx->semaphore_idx = 0; + + ctx->event_idx = 0; + + for (auto& event : ctx->gc.events) { + ctx->device.lock()->device.resetEvent(event); + } + + ctx->staging_offset = 0; + + ctx->compute_ctx = nullptr; + ctx->transfer_ctx = nullptr; + ctx->gc.contexts.clear(); } -static void ggml_vk_cleanup() { +// Clean up on backend free +static void ggml_vk_cleanup(ggml_backend_vk_context * ctx) { #ifdef GGML_VULKAN_DEBUG - std::cerr << "ggml_vk_cleanup()" << std::endl; + std::cerr << "ggml_vk_cleanup(" << ctx->idx << ")" << std::endl; #endif - ggml_vk_destroy_buffer(vk_prealloc_x); - ggml_vk_destroy_buffer(vk_prealloc_y); - ggml_vk_destroy_buffer(vk_prealloc_split_k); - ggml_vk_destroy_buffer(vk_staging); - ggml_vk_destroy_buffer(vk_sync_staging); + ggml_vk_graph_cleanup(ctx); - vk_prealloc_size_x = 0; - vk_prealloc_size_y = 0; - vk_prealloc_size_split_k = 0; - vk_staging_size = 0; + ggml_vk_destroy_buffer(ctx->prealloc_qx); + ggml_vk_destroy_buffer(ctx->prealloc_qy); + ggml_vk_destroy_buffer(ctx->prealloc_x); + ggml_vk_destroy_buffer(ctx->prealloc_y); + ggml_vk_destroy_buffer(ctx->prealloc_split_k); + ggml_vk_destroy_buffer(ctx->staging); + ggml_vk_destroy_buffer(ctx->sync_staging); - for (auto& event : vk_gc.events) { - vk_device.device.destroyEvent(event); + for (auto& buffer : ctx->buffer_pool) { + ggml_vk_destroy_buffer(buffer); } - vk_gc.events.clear(); + + ctx->prealloc_size_qx = 0; + ctx->prealloc_size_qy = 0; + ctx->prealloc_size_x = 0; + ctx->prealloc_size_y = 0; + ctx->prealloc_size_split_k = 0; + ctx->staging_size = 0; + + for (auto& event : ctx->gc.events) { + ctx->device.lock()->device.destroyEvent(event); + } + ctx->gc.events.clear(); + + for (auto* pipeline : ctx->gc.pipelines) { + ggml_vk_destroy_pipeline(ctx, pipeline); + } + ctx->gc.pipelines.clear(); + + ctx->device.lock()->device.destroyFence(ctx->fence); + + ctx->device.lock()->device.destroyCommandPool(ctx->device.lock()->compute_queue.pool); + if (!ctx->device.lock()->single_queue) { + ctx->device.lock()->device.destroyCommandPool(ctx->device.lock()->transfer_queue.pool); + } +} + +GGML_CALL int ggml_vk_get_device_count() { + ggml_vk_instance_init(); + + return vk_instance.device_indices.size(); +} + +GGML_CALL void ggml_vk_get_device_description(int device, char * description, size_t description_size) { + ggml_vk_instance_init(); + + std::vector devices = vk_instance.instance.enumeratePhysicalDevices(); + + vk::PhysicalDeviceProperties props; + devices[device].getProperties(&props); + + snprintf(description, description_size, "%s", props.deviceName.data()); +} + +// CPU assist interface + +void ggml_vk_init_cpu_assist() { + ggml_vk_instance_init(); + + std::cerr << "ggml_vulkan: Found " << ggml_vk_get_device_count() << " Vulkan devices:" << std::endl; + + for (size_t i = 0; i < ggml_vk_get_device_count(); i++) { + ggml_vk_print_gpu_info(i); + } + // Initialize the first backend to make sure CPU matrix multiplications can be offloaded. + ggml_backend_vk_init(0); +} + +void ggml_vk_preallocate_buffers_graph_cpu_assist(ggml_tensor * node) { + ggml_backend_vk_context * ctx = &vk_instance.contexts[0]; + + if (!ctx->initialized) { + return; + } + + ggml_vk_preallocate_buffers_graph(ctx, node); +} + +void ggml_vk_preallocate_buffers_cpu_assist() { + ggml_backend_vk_context * ctx = &vk_instance.contexts[0]; + + if (!ctx->initialized) { + return; + } + + ggml_vk_preallocate_buffers(ctx); +} + +void ggml_vk_build_graph_cpu_assist(ggml_tensor * node, bool last_node) { + ggml_backend_vk_context * ctx = &vk_instance.contexts[0]; + + if (!ctx->initialized) { + return; + } + + ggml_vk_build_graph(ctx, node, last_node); +} + +bool ggml_vk_compute_forward_cpu_assist(ggml_compute_params * params, ggml_tensor * tensor){ + ggml_backend_vk_context * ctx = &vk_instance.contexts[0]; + + if (!ctx->initialized) { + return false; + } + + return ggml_vk_compute_forward(ctx, params, tensor); +} + +void ggml_vk_graph_cleanup_cpu_assist() { + ggml_backend_vk_context * ctx = &vk_instance.contexts[0]; + + if (!ctx->initialized) { + return; + } + + ggml_vk_graph_cleanup(ctx); +} + +void ggml_vk_free_cpu_assist() { + ggml_backend_vk_context * ctx = &vk_instance.contexts[0]; + + if (!ctx->initialized || vk_instance.backends[0] == nullptr) { + return; + } + + ggml_backend_vk_free(vk_instance.backends[0]); } // backend interface #define UNUSED GGML_UNUSED -struct ggml_backend_vk_context { - std::string name; -}; - // device backend static void * const vk_ptr_base = (void *)(uintptr_t) 0x1000; // NOLINT struct ggml_backend_vk_buffer_context { + ggml_backend_vk_context * ctx; vk_buffer dev_buffer; ggml_tensor_extra_gpu * temp_tensor_extras = nullptr; size_t temp_tensor_extra_index = 0; std::string name; - ggml_backend_vk_buffer_context(vk_buffer dev_buffer) : + ggml_backend_vk_buffer_context(ggml_backend_vk_context * ctx, vk_buffer&& dev_buffer, std::string& name) : + ctx(ctx), dev_buffer(dev_buffer), - name(GGML_VK_NAME) { + name(name) { } ~ggml_backend_vk_buffer_context() { @@ -4294,6 +4594,9 @@ GGML_CALL static bool ggml_backend_buffer_is_vk(ggml_backend_buffer_t buffer) { } GGML_CALL static void ggml_backend_vk_buffer_free_buffer(ggml_backend_buffer_t buffer) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_backend_vk_buffer_free_buffer()" << std::endl; +#endif ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; ggml_vk_destroy_buffer(ctx->dev_buffer); delete ctx; @@ -4313,6 +4616,7 @@ GGML_CALL static void ggml_backend_vk_buffer_init_tensor(ggml_backend_buffer_t b ggml_tensor_extra_gpu * extra = ctx->ggml_vk_alloc_temp_tensor_extra(); if (tensor->view_src != nullptr && tensor->view_src->extra != nullptr) { + GGML_ASSERT(tensor->view_src->buffer->buft == buffer->buft); ggml_tensor_extra_gpu * extra_view = (ggml_tensor_extra_gpu *) tensor->view_src->extra; extra->buffer_gpu = extra_view->buffer_gpu; extra->offset = extra_view->offset + tensor->view_offs; @@ -4331,11 +4635,13 @@ GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t bu #endif GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - ggml_vk_buffer_write(&extra->buffer_gpu, extra->offset + offset, data, size); + vk_buffer buf = extra->buffer_gpu.lock(); - UNUSED(buffer); + ggml_vk_buffer_write(ctx->ctx, buf, extra->offset + offset, data, size); } GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { @@ -4344,31 +4650,35 @@ GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t bu #endif GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); + ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - ggml_vk_buffer_read(&extra->buffer_gpu, extra->offset + offset, data, size); + vk_buffer buf = extra->buffer_gpu.lock(); - UNUSED(buffer); + ggml_vk_buffer_read(ctx->ctx, buf, extra->offset + offset, data, size); } GGML_CALL static bool ggml_backend_vk_buffer_cpy_tensor(ggml_backend_buffer_t buffer, const ggml_tensor * src, ggml_tensor * dst) { if (ggml_backend_buffer_is_vk(src->buffer)) { + ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - ggml_vk_buffer_copy(&src_extra->buffer_gpu, src_extra->offset, &dst_extra->buffer_gpu, dst_extra->offset, ggml_nbytes(src)); + vk_buffer src_buf = src_extra->buffer_gpu.lock(); + vk_buffer dst_buf = dst_extra->buffer_gpu.lock(); + + ggml_vk_buffer_copy(dst_buf, dst_extra->offset, src_buf, src_extra->offset, ggml_nbytes(src)); return true; } return false; - - UNUSED(buffer); } GGML_CALL static void ggml_backend_vk_buffer_clear(ggml_backend_buffer_t buffer, uint8_t value) { ggml_backend_vk_buffer_context * ctx = (ggml_backend_vk_buffer_context *)buffer->context; - ggml_vk_buffer_memset(&ctx->dev_buffer, 0, value, buffer->size); + ggml_vk_buffer_memset(ctx->ctx, ctx->dev_buffer, 0, value, buffer->size); } static ggml_backend_buffer_i ggml_backend_vk_buffer_interface = { @@ -4386,6 +4696,7 @@ static ggml_backend_buffer_i ggml_backend_vk_buffer_interface = { // vk buffer type struct ggml_backend_vk_buffer_type_context { std::string name; + ggml_backend_vk_context * ctx; }; GGML_CALL static const char * ggml_backend_vk_buffer_type_name(ggml_backend_buffer_type_t buft) { @@ -4398,25 +4709,22 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_buffer_type_alloc_buffer( #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_buffer_type_alloc_buffer(" << size << ")" << std::endl; #endif - vk_buffer dev_buffer = ggml_vk_create_buffer_device(size); + ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context; + vk_buffer dev_buffer = ggml_vk_create_buffer_device(ctx->ctx, size); - ggml_backend_vk_buffer_context * ctx = new ggml_backend_vk_buffer_context(dev_buffer); + ggml_backend_vk_buffer_context * bufctx = new ggml_backend_vk_buffer_context(ctx->ctx, std::move(dev_buffer), ctx->name); - return ggml_backend_buffer_init(buft, ggml_backend_vk_buffer_interface, ctx, size); - - UNUSED(buft); + return ggml_backend_buffer_init(buft, ggml_backend_vk_buffer_interface, bufctx, size); } GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return vk_device.properties.limits.minStorageBufferOffsetAlignment; - - UNUSED(buft); + ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context; + return ctx->ctx->device.lock()->properties.limits.minStorageBufferOffsetAlignment; } GGML_CALL static size_t ggml_backend_vk_buffer_type_get_max_size(ggml_backend_buffer_type_t buft) { - return vk_device.max_memory_allocation_size; - - UNUSED(buft); + ggml_backend_vk_buffer_type_context * ctx = (ggml_backend_vk_buffer_type_context *) buft->context; + return ctx->ctx->device.lock()->max_memory_allocation_size; } GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_buffer_type_t buft, const ggml_tensor * tensor) { @@ -4426,9 +4734,14 @@ GGML_CALL static size_t ggml_backend_vk_buffer_type_get_alloc_size(ggml_backend_ } GGML_CALL static bool ggml_backend_vk_buffer_type_supports_backend(ggml_backend_buffer_type_t buft, ggml_backend_t backend) { - return ggml_backend_is_vk(backend); + if (!ggml_backend_is_vk(backend)) { + return false; + } - UNUSED(buft); + ggml_backend_vk_buffer_type_context * buft_ctx = (ggml_backend_vk_buffer_type_context *)buft->context; + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + + return buft_ctx->ctx->idx == ctx->idx; } static ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface = { @@ -4441,20 +4754,16 @@ static ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface = { /* .is_host = */ NULL, }; -GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type() { - static ggml_backend_buffer_type ggml_backend_vk_buffer_type; +GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t idx) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_backend_vk_buffer_type(" << idx << ")" << std::endl; +#endif - static bool ggml_backend_vk_buffer_type_initialized = false; + GGML_ASSERT(idx < vk_instance.device_indices.size()); - if (!ggml_backend_vk_buffer_type_initialized) { - ggml_backend_vk_buffer_type = { - /* .iface = */ ggml_backend_vk_buffer_type_interface, - /* .context = */ new ggml_backend_vk_buffer_type_context{GGML_VK_NAME}, - }; - ggml_backend_vk_buffer_type_initialized = true; - } + ggml_backend_vk_init(idx); - return &ggml_backend_vk_buffer_type; + return &vk_instance.buffer_types[idx]; } // host buffer type @@ -4472,13 +4781,19 @@ GGML_CALL static const char * ggml_backend_vk_host_buffer_name(ggml_backend_buff } GGML_CALL static void ggml_backend_vk_host_buffer_free_buffer(ggml_backend_buffer_t buffer) { - ggml_vk_host_free(buffer->context); +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_backend_vk_host_buffer_free_buffer()" << std::endl; +#endif + ggml_vk_host_free(&vk_instance.contexts[0], buffer->context); } GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_buffer(ggml_backend_buffer_type_t buft, size_t size) { +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_backend_vk_host_buffer_type_alloc_buffer(" << size << ")" << std::endl; +#endif void * ptr = nullptr; try { - ptr = ggml_vk_host_malloc(size); + ptr = ggml_vk_host_malloc(&vk_instance.contexts[0], size); } catch (vk::SystemError& e) { std::cerr << "ggml_vulkan: Failed to allocate pinned memory." << std::endl; std::cerr << "ggml_vulkan: " << e.what() << std::endl; @@ -4495,7 +4810,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_bu } GGML_CALL static size_t ggml_backend_vk_host_buffer_type_get_alignment(ggml_backend_buffer_type_t buft) { - return vk_device.properties.limits.minMemoryMapAlignment; + return vk_instance.contexts[0].device.lock()->properties.limits.minMemoryMapAlignment; UNUSED(buft); } @@ -4514,127 +4829,150 @@ GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type() { /* .context = */ nullptr, }; + if (!vk_instance.contexts[0].initialized) { + // Fall back to CPU + return ggml_backend_cpu_buffer_type(); + } + return &ggml_backend_vk_buffer_type_host; } // backend GGML_CALL static const char * ggml_backend_vk_name(ggml_backend_t backend) { - ggml_backend_vk_context * vk_ctx = (ggml_backend_vk_context *)backend->context; + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; - return vk_ctx->name.c_str(); + return ctx->name.c_str(); } GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend) { - ggml_backend_vk_context * vk_ctx = (ggml_backend_vk_context *)backend->context; + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_backend_vk_free(" << ctx->name << ")" << std::endl; +#endif - delete vk_ctx; + size_t idx = ctx->idx; + + ggml_vk_cleanup(ctx); + + // Release device + vk_instance.devices[ctx->idx].reset(); + ctx->initialized = false; + + vk_instance.initialized[idx] = false; + vk_instance.backends[idx] = nullptr; + memset(&vk_instance.buffer_types[idx], 0, sizeof(ggml_backend_buffer_type)); delete backend; } GGML_CALL static ggml_backend_buffer_type_t ggml_backend_vk_get_default_buffer_type(ggml_backend_t backend) { - return ggml_backend_vk_buffer_type(); + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; - UNUSED(backend); + GGML_ASSERT(ctx->initialized); + + return ggml_backend_vk_buffer_type(ctx->idx); } GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, ggml_tensor * tensor, const void * data, size_t offset, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_set_tensor_async(" << size << ")" << std::endl; #endif - GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type() || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - if (vk_transfer_ctx == nullptr) { + if (ctx->transfer_ctx == nullptr) { // Initialize new transfer context - vk_transfer_ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(vk_transfer_ctx); + ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_ctx_begin(ctx, ctx->transfer_ctx); } - ggml_vk_buffer_write_async(vk_transfer_ctx, &extra->buffer_gpu, extra->offset + offset, data, size); + vk_buffer buf = extra->buffer_gpu.lock(); - UNUSED(backend); + ggml_vk_buffer_write_async(ctx, ctx->transfer_ctx, buf, extra->offset + offset, data, size); } GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, const ggml_tensor * tensor, void * data, size_t offset, size_t size) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_get_tensor_async(" << size << ")" << std::endl; #endif - GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type() || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + GGML_ASSERT((tensor->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || tensor->buffer->buft == ggml_backend_vk_host_buffer_type()) && "unsupported buffer type"); GGML_ASSERT(tensor->backend == GGML_BACKEND_GPU); ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - if (vk_transfer_ctx == nullptr) { + if (ctx->transfer_ctx == nullptr) { // Initialize new transfer context - vk_transfer_ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(vk_transfer_ctx); + ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_ctx_begin(ctx, ctx->transfer_ctx); } - ggml_vk_buffer_read_async(vk_transfer_ctx, &extra->buffer_gpu, extra->offset + offset, data, size); + vk_buffer buf = extra->buffer_gpu.lock(); - UNUSED(backend); + ggml_vk_buffer_read_async(ctx, ctx->transfer_ctx, buf, extra->offset + offset, data, size); } GGML_CALL static bool ggml_backend_vk_cpy_tensor_async(ggml_backend_t backend, const ggml_tensor * src, ggml_tensor * dst) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_cpy_tensor_async()" << std::endl; #endif - if ((dst->buffer->buft == ggml_backend_vk_buffer_type() || dst->buffer->buft == ggml_backend_vk_host_buffer_type()) && ggml_backend_buffer_is_vk(src->buffer)) { + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + if ((dst->buffer->buft == ggml_backend_vk_buffer_type(ctx->idx) || dst->buffer->buft == ggml_backend_vk_host_buffer_type()) && ggml_backend_buffer_is_vk(src->buffer)) { ggml_tensor_extra_gpu * src_extra = (ggml_tensor_extra_gpu *) src->extra; ggml_tensor_extra_gpu * dst_extra = (ggml_tensor_extra_gpu *) dst->extra; - if (vk_transfer_ctx == nullptr) { + if (ctx->transfer_ctx == nullptr) { // Initialize new transfer context - vk_transfer_ctx = ggml_vk_create_context(vk_device.transfer_queue); - ggml_vk_ctx_begin(vk_transfer_ctx); + ctx->transfer_ctx = ggml_vk_create_context(ctx, ctx->device.lock()->transfer_queue); + ggml_vk_ctx_begin(ctx, ctx->transfer_ctx); } - ggml_vk_buffer_copy_async(vk_transfer_ctx, &src_extra->buffer_gpu, src_extra->offset, &dst_extra->buffer_gpu, dst_extra->offset, ggml_nbytes(src)); + vk_buffer src_buf = src_extra->buffer_gpu.lock(); + vk_buffer dst_buf = dst_extra->buffer_gpu.lock(); + + ggml_vk_buffer_copy_async(ctx->transfer_ctx, src_buf, src_extra->offset, dst_buf, dst_extra->offset, ggml_nbytes(src)); return true; } return false; - - UNUSED(backend); } GGML_CALL static void ggml_backend_vk_synchronize(ggml_backend_t backend) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_synchronize()" << std::endl; #endif - if(vk_transfer_ctx == nullptr) { + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; + if(ctx->transfer_ctx == nullptr) { return; } - ggml_vk_ctx_end(vk_transfer_ctx); + ggml_vk_ctx_end(ctx->transfer_ctx); - for (auto& cpy : vk_transfer_ctx->in_memcpys) { + for (auto& cpy : ctx->transfer_ctx->in_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - ggml_vk_submit(vk_transfer_ctx, vk_fence); - VK_CHECK(vk_device.device.waitForFences({ vk_fence }, true, UINT64_MAX), "ggml_backend_vk_synchronize waitForFences"); - vk_device.device.resetFences({ vk_fence }); + ggml_vk_submit(ctx->transfer_ctx, ctx->fence); + VK_CHECK(ctx->device.lock()->device.waitForFences({ ctx->fence }, true, UINT64_MAX), "ggml_backend_vk_synchronize waitForFences"); + ctx->device.lock()->device.resetFences({ ctx->fence }); - for (auto& cpy : vk_transfer_ctx->out_memcpys) { + for (auto& cpy : ctx->transfer_ctx->out_memcpys) { memcpy(cpy.dst, cpy.src, cpy.n); } - vk_transfer_ctx = nullptr; - - UNUSED(backend); + ctx->transfer_ctx = nullptr; } GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { - // ggml_backend_vk_context * vk_ctx = (ggml_backend_vk_context *)backend->context; + ggml_backend_vk_context * ctx = (ggml_backend_vk_context *)backend->context; for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_vk_preallocate_buffers_graph(cgraph->nodes[i]); + ggml_vk_preallocate_buffers_graph(ctx, cgraph->nodes[i]); } - ggml_vk_preallocate_buffers(); + ggml_vk_preallocate_buffers(ctx); int last_node = cgraph->n_nodes - 1; @@ -4644,7 +4982,7 @@ GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml } for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_vk_build_graph(cgraph->nodes[i], i == last_node); + ggml_vk_build_graph(ctx,cgraph->nodes[i], i == last_node); } ggml_compute_params params = {}; @@ -4657,19 +4995,19 @@ GGML_CALL static bool ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml continue; } - bool ok = ggml_vk_compute_forward(¶ms, node); + bool ok = ggml_vk_compute_forward(ctx, ¶ms, node); if (!ok) { fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); } #ifdef GGML_VULKAN_CHECK_RESULTS else { - ggml_vk_check_results_1(¶ms, node); + ggml_vk_check_results_1(ctx, ¶ms, node); } #endif GGML_ASSERT(ok); } - ggml_vk_graph_cleanup(); + ggml_vk_graph_cleanup(ctx); return true; @@ -4734,7 +5072,7 @@ GGML_CALL static bool ggml_backend_vk_supports_op(ggml_backend_t backend, const } return false; } break; - // case GGML_OP_DUP: + case GGML_OP_DUP: // case GGML_OP_REPEAT: // { // ggml_type src0_type = op->src[0]->type; @@ -4786,18 +5124,30 @@ static ggml_backend_i ggml_backend_vk_interface = { /* .supports_op = */ ggml_backend_vk_supports_op, }; -GGML_CALL ggml_backend_t ggml_backend_vk_init() { - ggml_vk_init(); // TODO: remove from ggml.c +GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t idx) { + if (vk_instance.initialized[idx]) { + return vk_instance.backends[idx]; + } +#ifdef GGML_VULKAN_DEBUG + std::cerr << "ggml_backend_vk_init(" << idx << ")" << std::endl; +#endif - ggml_backend_vk_context * ctx = new ggml_backend_vk_context { - /* .name = */ GGML_VK_NAME, + ggml_backend_vk_context * ctx = &vk_instance.contexts[idx]; + ggml_vk_init(ctx, idx); + ctx->name = GGML_VK_NAME + std::to_string(idx); + vk_instance.buffer_types[idx] = { + /* .iface = */ ggml_backend_vk_buffer_type_interface, + /* .context = */ new ggml_backend_vk_buffer_type_context{ ctx->name, ctx }, }; + vk_instance.initialized[idx] = true; ggml_backend_t vk_backend = new ggml_backend { /* .interface = */ ggml_backend_vk_interface, - /* .context = */ ctx + /* .context = */ &vk_instance.contexts[ctx->idx], }; + vk_instance.backends[idx] = vk_backend; + return vk_backend; } @@ -4805,20 +5155,47 @@ GGML_CALL bool ggml_backend_is_vk(ggml_backend_t backend) { return backend && backend->iface.get_name == ggml_backend_vk_name; } +GGML_CALL int ggml_backend_vk_get_device_count() { + return ggml_vk_get_device_count(); +} + +GGML_CALL void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size) { + ggml_vk_get_device_description(device, description, description_size); +} + +GGML_CALL void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total) { + GGML_ASSERT(device < vk_instance.device_indices.size()); + + vk::PhysicalDevice vkdev = vk_instance.instance.enumeratePhysicalDevices()[vk_instance.device_indices[device]]; + + vk::PhysicalDeviceMemoryProperties memprops = vkdev.getMemoryProperties(); + + for (const vk::MemoryHeap& heap : memprops.memoryHeaps) { + if (heap.flags & vk::MemoryHeapFlagBits::eDeviceLocal) { + *total = heap.size; + *free = heap.size; + break; + } + } +} + // backend registry GGML_CALL static ggml_backend_t ggml_backend_reg_vk_init(const char * params, void * user_data) { - ggml_backend_t vk_backend = ggml_backend_vk_init(); + ggml_backend_t vk_backend = ggml_backend_vk_init((int) (intptr_t) user_data); return vk_backend; UNUSED(params); - UNUSED(user_data); } extern "C" GGML_CALL int ggml_backend_vk_reg_devices(); GGML_CALL int ggml_backend_vk_reg_devices() { - ggml_backend_register(GGML_VK_NAME, ggml_backend_reg_vk_init, ggml_backend_vk_buffer_type(), nullptr); - return 1; + for (auto idx : vk_instance.device_indices) { + char name[128]; + snprintf(name, sizeof(name), "%s%ld", GGML_VK_NAME, idx); + ggml_backend_register(name, ggml_backend_reg_vk_init, ggml_backend_vk_buffer_type(idx), (void *) (intptr_t) idx); + } + return vk_instance.device_indices.size(); } // checks @@ -4874,7 +5251,7 @@ static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * d } } -static void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name) { +static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tensor * tensor, const char * name) { void * tensor_data = tensor->data; if (tensor->backend == GGML_BACKEND_GPU) { @@ -4883,7 +5260,7 @@ static void ggml_vk_print_tensor(const ggml_tensor * tensor, const char * name) ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - ggml_vk_buffer_read(&extra->buffer_gpu, extra->offset, tensor_data, tensor_size); + ggml_vk_buffer_read(ctx, extra->buffer_gpu, extra->offset, tensor_data, tensor_size); } std::cerr << "TENSOR CHECK " << name << " (" << tensor->name << "): " << ggml_op_name(tensor->op) << std::endl; @@ -4944,7 +5321,7 @@ void * comp_result; size_t comp_size; size_t comp_nb[GGML_MAX_DIMS]; size_t check_counter = 0; -static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * tensor) { +static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor) { if (params->ith != 0) { return; } @@ -4966,7 +5343,7 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * /*.no_alloc =*/ false, }; - struct ggml_context * ctx = ggml_init(iparams); + struct ggml_context * ggml_ctx = ggml_init(iparams); struct ggml_tensor * src0_clone = nullptr; struct ggml_tensor * src1_clone = nullptr; @@ -4979,7 +5356,7 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * void * src1_buffer; if (src0 != nullptr) { - src0_clone = ggml_dup_tensor(ctx, src0); + src0_clone = ggml_dup_tensor(ggml_ctx, src0); src0_size = ggml_nbytes(src0); @@ -4995,7 +5372,7 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * for (int i3 = 0; i3 < src0->ne[3]; i3++) { for (int i2 = 0; i2 < src0->ne[2]; i2++) { const int idx = i3*src0->ne[2] + i2; - ggml_vk_buffer_read(&extra->buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]); + ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset + idx * src0->nb[2], ((char *)src0_clone->data + idx * src0_clone->nb[2]), src0->ne[1] * src0->nb[1]); } } @@ -5005,10 +5382,10 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * src0_clone->nb[i] = src0_clone->nb[i - 1]*src0_clone->ne[i - 1]; } } else { - if (offset + src0_size >= extra->buffer_gpu.size) { - src0_size = extra->buffer_gpu.size - offset; + if (offset + src0_size >= extra->buffer_gpu->size) { + src0_size = extra->buffer_gpu->size - offset; } - ggml_vk_buffer_read(&extra->buffer_gpu, offset, src0_clone->data, src0_size); + ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset, src0_clone->data, src0_size); memcpy(src0_clone->nb, src0->nb, sizeof(size_t) * GGML_MAX_DIMS); } } else { @@ -5016,13 +5393,13 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * } if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { - ggml_vk_print_tensor(src0, "src0"); + ggml_vk_print_tensor(ctx, src0, "src0"); } ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src0", src0_clone); } if (src1 != nullptr) { - src1_clone = ggml_dup_tensor(ctx, src1); + src1_clone = ggml_dup_tensor(ggml_ctx, src1); src1_size = ggml_nbytes(src1); @@ -5038,7 +5415,7 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * for (int i3 = 0; i3 < src1->ne[3]; i3++) { for (int i2 = 0; i2 < src1->ne[2]; i2++) { const int idx = i3*src1->ne[2] + i2; - ggml_vk_buffer_read(&extra->buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]); + ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset + idx * src1->nb[2], ((char *)src1_clone->data + idx * src1_clone->nb[2]), src1->ne[1] * src1->nb[1]); } } @@ -5048,10 +5425,10 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * src1_clone->nb[i] = src1_clone->nb[i - 1]*src1_clone->ne[i - 1]; } } else { - if (offset + src1_size >= extra->buffer_gpu.size) { - src1_size = extra->buffer_gpu.size - offset; + if (offset + src1_size >= extra->buffer_gpu->size) { + src1_size = extra->buffer_gpu->size - offset; } - ggml_vk_buffer_read(&extra->buffer_gpu, offset, src1_clone->data, src1_size); + ggml_vk_buffer_read(ctx, extra->buffer_gpu, offset, src1_clone->data, src1_size); memcpy(src1_clone->nb, src1->nb, sizeof(size_t) * GGML_MAX_DIMS); } } else { @@ -5059,7 +5436,7 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * } if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { - ggml_vk_print_tensor(src1, "src1"); + ggml_vk_print_tensor(ctx, src1, "src1"); std::cerr << "TENSOR CHECK: " << ggml_op_name(src1_clone->op) << " (check " << check_counter << ")" << std::endl; std::cerr << "src1_clone=" << tensor << " src1_clone->backend: " << src1_clone->backend << " src1_clone->type: " << ggml_type_name(src1_clone->type) << " ne0=" << src1_clone->ne[0] << " nb0=" << src1_clone->nb[0] << " ne1=" << src1_clone->ne[1] << " nb1=" << src1_clone->nb[1] << " ne2=" << src1_clone->ne[2] << " nb2=" << src1_clone->nb[2] << " ne3=" << src1_clone->ne[3] << " nb3=" << src1_clone->nb[3] << std::endl; if (src1->src[0] != nullptr) { @@ -5082,51 +5459,51 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * } if (tensor->op == GGML_OP_MUL_MAT) { - tensor_clone = ggml_mul_mat(ctx, src0_clone, src1_clone); + tensor_clone = ggml_mul_mat(ggml_ctx, src0_clone, src1_clone); } else if (tensor->op == GGML_OP_MUL) { - tensor_clone = ggml_mul(ctx, src0_clone, src1_clone); + tensor_clone = ggml_mul(ggml_ctx, src0_clone, src1_clone); } else if (tensor->op == GGML_OP_SCALE) { - tensor_clone = ggml_scale(ctx, src0_clone, ((float *)tensor->op_params)[0]); + tensor_clone = ggml_scale(ggml_ctx, src0_clone, ((float *)tensor->op_params)[0]); } else if (tensor->op == GGML_OP_SQR) { - tensor_clone = ggml_sqr(ctx, src0_clone); + tensor_clone = ggml_sqr(ggml_ctx, src0_clone); } else if (tensor->op == GGML_OP_CLAMP) { - tensor_clone = ggml_clamp(ctx, src0_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]); + tensor_clone = ggml_clamp(ggml_ctx, src0_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]); } else if (tensor->op == GGML_OP_ADD) { - tensor_clone = ggml_add(ctx, src0_clone, src1_clone); + tensor_clone = ggml_add(ggml_ctx, src0_clone, src1_clone); } else if (tensor->op == GGML_OP_NORM) { - tensor_clone = ggml_norm(ctx, src0_clone, *(float *)tensor->op_params); + tensor_clone = ggml_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_RMS_NORM) { - tensor_clone = ggml_rms_norm(ctx, src0_clone, *(float *)tensor->op_params); + tensor_clone = ggml_rms_norm(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_SOFT_MAX) { if (src1 != nullptr) { - tensor_clone = ggml_soft_max_ext(ctx, src0_clone, src1_clone, *(float *)tensor->op_params); + tensor_clone = ggml_soft_max_ext(ggml_ctx, src0_clone, src1_clone, *(float *)tensor->op_params); } else { - tensor_clone = ggml_soft_max(ctx, src0_clone); + tensor_clone = ggml_soft_max(ggml_ctx, src0_clone); } } else if (tensor->op == GGML_OP_DIAG_MASK_INF) { - tensor_clone = ggml_diag_mask_inf(ctx, src0_clone, *(float *)tensor->op_params); + tensor_clone = ggml_diag_mask_inf(ggml_ctx, src0_clone, *(float *)tensor->op_params); } else if (tensor->op == GGML_OP_ROPE) { const int n_dims = ((int32_t *) tensor->op_params)[1]; const int mode = ((int32_t *) tensor->op_params)[2]; - const int n_ctx = ((int32_t *) tensor->op_params)[3]; - const int n_orig_ctx = ((int32_t *) tensor->op_params)[4]; + const int n_ggml_ctx = ((int32_t *) tensor->op_params)[3]; + const int n_orig_ggml_ctx = ((int32_t *) tensor->op_params)[4]; float freq_base = ((float *) tensor->op_params)[5]; float freq_scale = ((float *) tensor->op_params)[6]; float ext_factor = ((float *) tensor->op_params)[7]; float attn_factor = ((float *) tensor->op_params)[8]; float beta_fast = ((float *) tensor->op_params)[9]; float beta_slow = ((float *) tensor->op_params)[10]; - tensor_clone = ggml_rope_custom(ctx, src0_clone, src1_clone, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); + tensor_clone = ggml_rope_custom(ggml_ctx, src0_clone, src1_clone, n_dims, mode, n_ggml_ctx, n_orig_ggml_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); } else if (tensor->op == GGML_OP_UNARY) { switch (ggml_get_unary_op(tensor)) { case GGML_UNARY_OP_SILU: - tensor_clone = ggml_silu(ctx, src0_clone); + tensor_clone = ggml_silu(ggml_ctx, src0_clone); break; case GGML_UNARY_OP_GELU: - tensor_clone = ggml_gelu(ctx, src0_clone); + tensor_clone = ggml_gelu(ggml_ctx, src0_clone); break; case GGML_UNARY_OP_RELU: - tensor_clone = ggml_relu(ctx, src0_clone); + tensor_clone = ggml_relu(ggml_ctx, src0_clone); break; default: std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl; @@ -5134,40 +5511,40 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * } } else if (tensor->op == GGML_OP_CPY || tensor->op == GGML_OP_DUP) { if (src1 == nullptr) { - tensor_clone = ggml_dup(ctx, src0_clone); + tensor_clone = ggml_dup(ggml_ctx, src0_clone); tensor_clone->type = tensor->type; } else { - tensor_clone = ggml_cpy(ctx, src0_clone, src1_clone); + tensor_clone = ggml_cpy(ggml_ctx, src0_clone, src1_clone); } } else if (tensor->op == GGML_OP_CONT) { - tensor_clone = ggml_cont_4d(ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); + tensor_clone = ggml_cont_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); } else if (tensor->op == GGML_OP_RESHAPE) { - tensor_clone = ggml_reshape_4d(ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); + tensor_clone = ggml_reshape_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); } else if (tensor->op == GGML_OP_VIEW) { - tensor_clone = ggml_view_4d(ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], tensor->nb[1], tensor->nb[2], tensor->nb[3], ((int32_t *) tensor->op_params)[0]); + tensor_clone = ggml_view_4d(ggml_ctx, src0_clone, tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3], tensor->nb[1], tensor->nb[2], tensor->nb[3], ((int32_t *) tensor->op_params)[0]); } else if (tensor->op == GGML_OP_PERMUTE) { int32_t * params = (int32_t *)tensor->op_params; - tensor_clone = ggml_permute(ctx, src0_clone, params[0], params[1], params[2], params[3]); + tensor_clone = ggml_permute(ggml_ctx, src0_clone, params[0], params[1], params[2], params[3]); } else if (tensor->op == GGML_OP_TRANSPOSE) { - tensor_clone = ggml_transpose(ctx, src0_clone); + tensor_clone = ggml_transpose(ggml_ctx, src0_clone); } else { std::cerr << "Missing vk_check_results OP: " << ggml_op_name(tensor->op) << std::endl; GGML_ASSERT(false); } // Disable vulkan here to avoid the hooks in ggml.c - vk_disable = true; + ctx->disable = true; - ggml_cgraph * cgraph = ggml_new_graph(ctx); + ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx); ggml_build_forward_expand(cgraph, tensor_clone); - ggml_graph_compute_with_ctx(ctx, cgraph, 8); + ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 8); - vk_disable = false; + ctx->disable = false; ggml_vk_check_tensor(ggml_op_name(tensor->op), tensor_clone); if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { - ggml_vk_print_tensor(tensor_clone, "tensor_clone"); + ggml_vk_print_tensor(ctx, tensor_clone, "tensor_clone"); } comp_size = ggml_nbytes(tensor_clone); @@ -5183,10 +5560,10 @@ static void ggml_vk_check_results_0(ggml_compute_params * params, ggml_tensor * free(src1_buffer); } - ggml_free(ctx); + ggml_free(ggml_ctx); } -void ggml_vk_check_results_1(ggml_compute_params * params, ggml_tensor * tensor) { +static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor) { if (params->ith != 0) { return; } @@ -5208,11 +5585,11 @@ void ggml_vk_check_results_1(ggml_compute_params * params, ggml_tensor * tensor) ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; - if (extra->offset + tensor_size >= extra->buffer_gpu.size) { - tensor_size = extra->buffer_gpu.size - (extra->offset); + if (extra->offset + tensor_size >= extra->buffer_gpu->size) { + tensor_size = extra->buffer_gpu->size - (extra->offset); } - ggml_vk_buffer_read(&extra->buffer_gpu, extra->offset, tensor_data, tensor_size); + ggml_vk_buffer_read(ctx, extra->buffer_gpu, extra->offset, tensor_data, tensor_size); } float first_error_result = -1.0f; @@ -5339,4 +5716,10 @@ void ggml_vk_check_results_1(ggml_compute_params * params, ggml_tensor * tensor) free(tensor_data); } } + +void ggml_vk_check_results_1_cpu_assist(struct ggml_compute_params * params, struct ggml_tensor * tensor) { + ggml_backend_vk_context * ctx = &vk_instance.contexts[0]; + + ggml_vk_check_results_0(ctx, params, tensor); +} #endif diff --git a/ggml-vulkan.h b/ggml-vulkan.h index eb8a148e2..9645126b4 100644 --- a/ggml-vulkan.h +++ b/ggml-vulkan.h @@ -8,24 +8,29 @@ extern "C" { #endif #define GGML_VK_NAME "Vulkan" +#define GGML_VK_MAX_DEVICES 16 -GGML_API void ggml_vk_init(void); +GGML_API void ggml_vk_init_cpu_assist(void); -GGML_API void ggml_vk_preallocate_buffers_graph(struct ggml_tensor * node); -GGML_API void ggml_vk_preallocate_buffers(void); -GGML_API void ggml_vk_build_graph(struct ggml_tensor * node, bool last_node); -GGML_API bool ggml_vk_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor); +GGML_API void ggml_vk_preallocate_buffers_graph_cpu_assist(struct ggml_tensor * node); +GGML_API void ggml_vk_preallocate_buffers_cpu_assist(void); +GGML_API void ggml_vk_build_graph_cpu_assist(struct ggml_tensor * node, bool last_node); +GGML_API bool ggml_vk_compute_forward_cpu_assist(struct ggml_compute_params * params, struct ggml_tensor * tensor); #ifdef GGML_VULKAN_CHECK_RESULTS -void ggml_vk_check_results_1(struct ggml_compute_params * params, struct ggml_tensor * tensor); +void ggml_vk_check_results_1_cpu_assist(struct ggml_compute_params * params, struct ggml_tensor * tensor); #endif -GGML_API void ggml_vk_graph_cleanup(void); +GGML_API void ggml_vk_graph_cleanup_cpu_assist(void); +GGML_API void ggml_vk_free_cpu_assist(void); // backend API -GGML_API GGML_CALL ggml_backend_t ggml_backend_vk_init(void); +GGML_API GGML_CALL ggml_backend_t ggml_backend_vk_init(size_t dev_num); GGML_API GGML_CALL bool ggml_backend_is_vk(ggml_backend_t backend); +GGML_API GGML_CALL int ggml_backend_vk_get_device_count(void); +GGML_API GGML_CALL void ggml_backend_vk_get_device_description(int device, char * description, size_t description_size); +GGML_API GGML_CALL void ggml_backend_vk_get_device_memory(int device, size_t * free, size_t * total); -GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(void); +GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num); // pinned host buffer for use with the CPU backend for faster copies between CPU and GPU GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_host_buffer_type(void); diff --git a/ggml.c b/ggml.c index b9ec0c981..f783a6fd3 100644 --- a/ggml.c +++ b/ggml.c @@ -2343,7 +2343,7 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { #elif defined(GGML_USE_CLBLAST) ggml_cl_init(); #elif defined(GGML_USE_VULKAN) - ggml_vk_init(); + ggml_vk_init_cpu_assist(); #elif defined(GGML_USE_SYCL) ggml_init_sycl(); #endif @@ -14850,10 +14850,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm GGML_ASSERT(tensor->src[0] == NULL || tensor->src[0]->backend == GGML_BACKEND_CPU); GGML_ASSERT(tensor->src[1] == NULL || tensor->src[1]->backend == GGML_BACKEND_CPU); #elif defined(GGML_USE_VULKAN) - const bool skip_cpu = ggml_vk_compute_forward(params, tensor); + const bool skip_cpu = ggml_vk_compute_forward_cpu_assist(params, tensor); #ifdef GGML_VULKAN_CHECK_RESULTS if (skip_cpu) { - ggml_vk_check_results_1(params, tensor); + ggml_vk_check_results_1_cpu_assist(params, tensor); } #endif if (skip_cpu) { @@ -17269,12 +17269,12 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { #ifdef GGML_USE_VULKAN for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_vk_preallocate_buffers_graph(cgraph->nodes[i]); + ggml_vk_preallocate_buffers_graph_cpu_assist(cgraph->nodes[i]); } - ggml_vk_preallocate_buffers(); + ggml_vk_preallocate_buffers_cpu_assist(); for (int i = 0; i < cgraph->n_nodes; i++) { - ggml_vk_build_graph(cgraph->nodes[i], i == cgraph->n_nodes - 1); + ggml_vk_build_graph_cpu_assist(cgraph->nodes[i], i == cgraph->n_nodes - 1); } #endif @@ -17330,7 +17330,7 @@ int ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cplan * cplan) { } #ifdef GGML_USE_VULKAN - ggml_vk_graph_cleanup(); + ggml_vk_graph_cleanup_cpu_assist(); #endif // performance stats (graph) diff --git a/llama.cpp b/llama.cpp index f3c5146d1..c45ae1d50 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1355,7 +1355,7 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_offload(int gpu) { #elif defined(GGML_USE_CUBLAS) buft = ggml_backend_cuda_buffer_type(gpu); #elif defined(GGML_USE_VULKAN) - buft = ggml_backend_vk_buffer_type(); + buft = ggml_backend_vk_buffer_type(gpu); #elif defined(GGML_USE_SYCL) buft = ggml_backend_sycl_buffer_type(gpu); #elif defined(GGML_USE_CLBLAST) @@ -1392,6 +1392,33 @@ static ggml_backend_buffer_type_t llama_default_buffer_type_split(int fallback_g GGML_UNUSED(tensor_split); } +static size_t llama_get_device_count() { +#if defined(GGML_USE_CUBLAS) + return ggml_backend_cuda_get_device_count(); +#elif defined(GGML_USE_VULKAN) + return ggml_backend_vk_get_device_count(); +#else + return 1; +#endif +} + +static size_t llama_get_device_memory(int device) { +#if defined(GGML_USE_CUBLAS) + size_t total; + size_t free; + ggml_backend_cuda_get_device_memory(device, &total, &free); + return free; +#elif defined(GGML_USE_VULKAN) + size_t total; + size_t free; + ggml_backend_vk_get_device_memory(device, &total, &free); + return free; +#else + return 1; + GGML_UNUSED(device); +#endif +} + // // globals // @@ -1763,6 +1790,10 @@ struct llama_context { ggml_backend_free(backend); } +#ifdef GGML_USE_VULKAN + ggml_vk_free_cpu_assist(); +#endif + ggml_backend_buffer_free(buf_input); ggml_free(ctx_input); } @@ -3436,22 +3467,18 @@ static bool llm_load_tensors( model.buft_layer[i] = llama_default_buffer_type_cpu(true); } -#ifdef GGML_USE_CUBLAS if (split_mode == LLAMA_SPLIT_LAYER) { // calculate the split points - int device_count = ggml_backend_cuda_get_device_count(); + int device_count = llama_get_device_count(); bool all_zero = tensor_split == nullptr || std::all_of(tensor_split, tensor_split + device_count, [](float x) { return x == 0.0f; }); - float splits[GGML_CUDA_MAX_DEVICES]; + std::vector splits(device_count); if (all_zero) { // default split, by free memory for (int i = 0; i < device_count; ++i) { - size_t total; - size_t free; - ggml_backend_cuda_get_device_memory(i, &total, &free); - splits[i] = free; + splits[i] = llama_get_device_memory(i); } } else { - std::copy(tensor_split, tensor_split + device_count, splits); + std::copy(tensor_split, tensor_split + device_count, splits.begin()); } // sum and normalize the splits to get the split points @@ -3467,19 +3494,17 @@ static bool llm_load_tensors( // assign the repeating layers to the devices according to the splits int act_gpu_layers = std::min(n_gpu_layers, (int)n_layer + 1); for (int64_t i = i_gpu_start; i < n_layer; ++i) { - int layer_gpu = std::upper_bound(splits, splits + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits; + int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(i - i_gpu_start)/act_gpu_layers) - splits.begin(); model.buft_layer[i] = llama_default_buffer_type_offload(layer_gpu); } // assign the output layer if (n_gpu_layers > n_layer) { - int layer_gpu = std::upper_bound(splits, splits + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits; + int layer_gpu = std::upper_bound(splits.begin(), splits.begin() + device_count, float(act_gpu_layers - 1)/act_gpu_layers) - splits.begin(); model.buft_output = llama_default_buffer_type_offload(layer_gpu); } else { model.buft_output = llama_default_buffer_type_cpu(true); } - } else -#endif - { + } else { ggml_backend_buffer_type_t split_buft; if (split_mode == LLAMA_SPLIT_ROW) { split_buft = llama_default_buffer_type_split(main_gpu, tensor_split); @@ -10483,6 +10508,8 @@ size_t llama_max_devices(void) { return GGML_CUDA_MAX_DEVICES; #elif defined(GGML_USE_SYCL) return GGML_SYCL_MAX_DEVICES; +#elif defined(GGML_USE_VULKAN) + return GGML_VK_MAX_DEVICES; #else return 1; #endif @@ -10690,13 +10717,15 @@ struct llama_context * llama_new_context_with_model( } #elif defined(GGML_USE_VULKAN) if (model->n_gpu_layers > 0) { - ggml_backend_t backend = ggml_backend_vk_init(); - if (backend == nullptr) { - LLAMA_LOG_ERROR("%s: failed to initialize Vulkan backend\n", __func__); - llama_free(ctx); - return nullptr; + for (int device = 0; device < ggml_backend_vk_get_device_count(); ++device) { + ggml_backend_t backend = ggml_backend_vk_init(device); + if (backend == nullptr) { + LLAMA_LOG_ERROR("%s: failed to initialize Vulkan%d backend\n", __func__, device); + llama_free(ctx); + return nullptr; + } + ctx->backends.push_back(backend); } - ctx->backends.push_back(backend); } #elif defined(GGML_USE_SYCL) if (model->n_gpu_layers > 0) { From 0ef46da632c32faa1a538e5dc180994e8bbb46e1 Mon Sep 17 00:00:00 2001 From: Xiao-Yong Jin Date: Wed, 7 Feb 2024 02:17:25 -0600 Subject: [PATCH 138/143] llava-cli : always tokenize special tokens (#5382) * llava-cli: tokenize special tokens in prompt * llava-cli: use the escape CLI argument, remove incomplete separate escaping process --- examples/llava/llava-cli.cpp | 14 +------------- 1 file changed, 1 insertion(+), 13 deletions(-) diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index 6ac70ba69..031e9806d 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -34,7 +34,7 @@ static bool eval_id(struct llama_context * ctx_llama, int id, int * n_past) { static bool eval_string(struct llama_context * ctx_llama, const char* str, int n_batch, int * n_past, bool add_bos){ std::string str2 = str; - std::vector embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos); + std::vector embd_inp = ::llama_tokenize(ctx_llama, str2, add_bos, true); eval_tokens(ctx_llama, embd_inp, n_batch, n_past); return true; } @@ -152,20 +152,8 @@ static void process_prompt(struct llava_context * ctx_llava, struct llava_image_ size_t image_pos = prompt.find(""); if (image_pos != std::string::npos) { // new templating mode: Provide the full prompt including system message and use as a placeholder for the image - system_prompt = prompt.substr(0, image_pos); user_prompt = prompt.substr(image_pos + std::string("").length()); - // We replace \n with actual newlines in user_prompt, just in case -e was not used in templating string - size_t pos = 0; - while ((pos = user_prompt.find("\\n", pos)) != std::string::npos) { - user_prompt.replace(pos, 2, "\n"); - pos += 1; // Advance past the replaced newline - } - while ((pos = system_prompt.find("\\n", pos)) != std::string::npos) { - system_prompt.replace(pos, 2, "\n"); - pos += 1; // Advance past the replaced newline - } - printf("system_prompt: %s\n", system_prompt.c_str()); printf("user_prompt: %s\n", user_prompt.c_str()); } else { From 10afa6f1d11ebc9fcc1085f468170002cbf6e2b5 Mon Sep 17 00:00:00 2001 From: Neo Zhang Jianyu Date: Wed, 7 Feb 2024 18:16:55 +0800 Subject: [PATCH 139/143] [SYCL] update install make by w64devkit (#5297) --- README-sycl.md | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/README-sycl.md b/README-sycl.md index 7aa4274a9..e3a8e726e 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -311,15 +311,13 @@ Output (example): a. Download & install cmake for Windows: https://cmake.org/download/ -b. Download & install make for Windows provided by mingw-w64 +b. Download & install mingw-w64 make for Windows provided by w64devkit -- Download binary package for Windows in https://github.com/niXman/mingw-builds-binaries/releases. +- Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases). - Like [x86_64-13.2.0-release-win32-seh-msvcrt-rt_v11-rev1.7z](https://github.com/niXman/mingw-builds-binaries/releases/download/13.2.0-rt_v11-rev1/x86_64-13.2.0-release-win32-seh-msvcrt-rt_v11-rev1.7z). +- Extract `w64devkit` on your pc. -- Unzip the binary package. In the **bin** sub-folder and rename **xxx-make.exe** to **make.exe**. - -- Add the **bin** folder path in the Windows system PATH environment. +- Add the **bin** folder path in the Windows system PATH environment, like `C:\xxx\w64devkit\bin\`. ### Build locally: From aa7ab99be29b633263803f2e185265734c2d9427 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Wed, 7 Feb 2024 12:40:26 +0100 Subject: [PATCH 140/143] CUDA: fixed mmvq kernel for bs 2,3,4 and -sm row (#5386) --- ggml-cuda.cu | 66 +++++++++++++++++++++++++++++++--------------------- 1 file changed, 39 insertions(+), 27 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 3b828375e..db9da2459 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -5313,7 +5313,7 @@ template static __global__ void template static __global__ void mul_mat_vec_q( const void * __restrict__ vx, const void * __restrict__ vy, float * __restrict__ dst, - const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y_par) { + const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y_par, const int nrows_dst) { const int ncols_y = ncols_y_template != 0 ? ncols_y_template : ncols_y_par; @@ -5352,7 +5352,7 @@ static __global__ void mul_mat_vec_q( tmp[j] = warp_reduce_sum(tmp[j]); if (threadIdx.x == 0) { - dst[j*nrows_x + row] = tmp[j]; + dst[j*nrows_dst + row] = tmp[j]; } } } @@ -6828,7 +6828,7 @@ static void convert_mul_mat_vec_f16_cuda(const void * vx, const dfloat * y, floa template static void mul_mat_vec_q_cuda( const void * vx, const void * vy, float * dst, - const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, cudaStream_t stream) { + const int ncols_x, const int nrows_x, const int nrows_y, const int ncols_y, const int nrows_dst, cudaStream_t stream) { GGML_ASSERT(ncols_x % qk == 0); GGML_ASSERT(ncols_y <= 4); @@ -6839,40 +6839,40 @@ static void mul_mat_vec_q_cuda( switch (ncols_y) { case 1: mul_mat_vec_q<1, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); break; case 2: mul_mat_vec_q<2, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); break; case 3: mul_mat_vec_q<3, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); break; case 4: mul_mat_vec_q<4, qk, qi, block_q_t, vdr, vec_dot> - <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); break; // case 5: // mul_mat_vec_q<5, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); // break; // case 6: // mul_mat_vec_q<6, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); // break; // case 7: // mul_mat_vec_q<7, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); // break; // case 8: // mul_mat_vec_q<8, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); // break; default: GGML_ASSERT(false); // mul_mat_vec_q<0, qk, qi, block_q_t, vdr, vec_dot> - // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y); + // <<>>(vx, vy, dst, ncols_x, nrows_x, nrows_y, ncols_y, nrows_dst); break; } } @@ -8391,7 +8391,7 @@ static void ggml_cuda_op_mul_mat_q( CUDA_CHECK(cudaGetDevice(&id)); // the main device has a larger memory buffer to hold the results from all GPUs - // nrows_dst == nrows of the matrix that the dequantize_mul_mat kernel writes into + // nrows_dst == nrows of the matrix that the kernel writes into const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff; switch (src0->type) { @@ -8525,58 +8525,70 @@ static void ggml_cuda_op_mul_mat_vec_q( const int64_t ne00 = src0->ne[0]; const int64_t row_diff = row_high - row_low; + const int64_t ne10 = src1->ne[0]; + GGML_ASSERT(ne10 % QK8_1 == 0); + + const int64_t ne0 = dst->ne[0]; + + int id; + CUDA_CHECK(cudaGetDevice(&id)); + + // the main device has a larger memory buffer to hold the results from all GPUs + // nrows_dst == nrows of the matrix that the kernel writes into + const int64_t nrows_dst = dst->backend == GGML_BACKEND_GPU && id == g_main_device ? ne0 : row_diff; + switch (src0->type) { case GGML_TYPE_Q4_0: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q4_1: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q5_0: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q5_1: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q8_0: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q2_K: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q3_K: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q4_K: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q5_K: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_Q6_K: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_IQ2_XXS: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_IQ2_XS: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; case GGML_TYPE_IQ3_XXS: mul_mat_vec_q_cuda - (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, stream); + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_padded_row_size, src1_ncols, nrows_dst, stream); break; default: GGML_ASSERT(false); @@ -9909,7 +9921,7 @@ static void ggml_cuda_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_dequantize_mul_mat_vec, false); } } else { - if (src1->ne[1] <= 4 && min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type)) { + if (src1->ne[1] <= 4 && min_compute_capability >= MIN_CC_DP4A && ggml_is_quantized(src0->type) && src1->type == GGML_TYPE_F32) { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_vec_q, true); } else if (use_mul_mat_q) { ggml_cuda_op_mul_mat(src0, src1, dst, ggml_cuda_op_mul_mat_q, true); From b906596bb775b17656c2e51d5ab1b347faab6860 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Kamil=20Tom=C5=A1=C3=ADk?= Date: Wed, 7 Feb 2024 19:44:52 +0100 Subject: [PATCH 141/143] Add Ava in the list of llama.cpp UIs (#4362) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 0509b0ba1..7e1187349 100644 --- a/README.md +++ b/README.md @@ -150,6 +150,7 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [ollama/ollama](https://github.com/ollama/ollama) - [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) (AGPL) - [psugihara/FreeChat](https://github.com/psugihara/FreeChat) +- [cztomsik/ava](https://github.com/cztomsik/ava) (MIT) - [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) - [pythops/tenere](https://github.com/pythops/tenere) (AGPL) - [semperai/amica](https://github.com/semperai/amica) From 8c933b70c21e05b685d476d0a1f36b34cbda7365 Mon Sep 17 00:00:00 2001 From: Ebey Abraham Date: Wed, 7 Feb 2024 21:11:30 +0000 Subject: [PATCH 142/143] fix typo in readme (#5399) Co-authored-by: Ebey Abraham --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7e1187349..66166c01b 100644 --- a/README.md +++ b/README.md @@ -680,7 +680,7 @@ python3 -m pip install -r requirements.txt python3 convert.py models/mymodel/ # [Optional] for models using BPE tokenizers -python convert.py models/mymodel/ --vocabtype bpe +python convert.py models/mymodel/ --vocab-type bpe # quantize the model to 4-bits (using Q4_K_M method) ./quantize ./models/mymodel/ggml-model-f16.gguf ./models/mymodel/ggml-model-Q4_K_M.gguf Q4_K_M From c4fbb6717c684196bd13b72d21747557130914e8 Mon Sep 17 00:00:00 2001 From: Michael Podvitskiy Date: Wed, 7 Feb 2024 22:39:23 +0100 Subject: [PATCH 143/143] CMAKE_OSX_ARCHITECTURES for MacOS cross compilation (#5393) Co-authored-by: Jared Van Bortel --- CMakeLists.txt | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 427015be5..a544f2da6 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -850,7 +850,9 @@ endif() set(ARCH_FLAGS "") -if ((${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm") OR (${CMAKE_SYSTEM_PROCESSOR} MATCHES "aarch64") OR ("${CMAKE_GENERATOR_PLATFORM_LWR}" MATCHES "arm64")) +if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR CMAKE_GENERATOR_PLATFORM_LWR STREQUAL "arm64" OR + (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND + CMAKE_SYSTEM_PROCESSOR MATCHES "^(aarch64|arm.*|ARM64)$")) message(STATUS "ARM detected") if (MSVC) add_compile_definitions(__ARM_NEON) @@ -876,7 +878,9 @@ if ((${CMAKE_SYSTEM_PROCESSOR} MATCHES "arm") OR (${CMAKE_SYSTEM_PROCESSOR} MATC list(APPEND ARCH_FLAGS -mno-unaligned-access) endif() endif() -elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "^(x86_64|i686|AMD64)$" OR "${CMAKE_GENERATOR_PLATFORM_LWR}" MATCHES "^(x86_64|i686|amd64|x64)$" ) +elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR + (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND + CMAKE_SYSTEM_PROCESSOR MATCHES "^(x86_64|i686|AMD64)$")) message(STATUS "x86 detected") if (MSVC) # instruction set detection for MSVC only