From 583fd6b000ec9ad1b465b5c98524f4a0ae388077 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Wed, 15 May 2024 08:44:16 +0200 Subject: [PATCH 001/181] server bench: fix bench not waiting for model load (#7284) --- examples/server/bench/bench.py | 15 ++++++++------- 1 file changed, 8 insertions(+), 7 deletions(-) diff --git a/examples/server/bench/bench.py b/examples/server/bench/bench.py index 86c5de101..25ac29c4c 100644 --- a/examples/server/bench/bench.py +++ b/examples/server/bench/bench.py @@ -293,13 +293,14 @@ def start_server_background(args): def is_server_listening(server_fqdn, server_port): - with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock: - result = sock.connect_ex((server_fqdn, server_port)) - _is_server_listening = result == 0 - if _is_server_listening: - print(f"server is listening on {server_fqdn}:{server_port}...") - return _is_server_listening - + try: + url = f"{server_fqdn}:{server_port}/health" + if not url.startswith("http://"): + url = f"http://{url}" + result = requests.get(url) + return result.status_code == 200 + except Exception: + return False def escape_metric_name(metric_name): return re.sub('[^A-Z0-9]', '_', metric_name.upper()) From 48aa8fd1f213a69b41569f809cc954f24dbc4366 Mon Sep 17 00:00:00 2001 From: John Balis Date: Wed, 15 May 2024 03:52:33 -0500 Subject: [PATCH 002/181] ggml : add `ggml_upscale_ext` (ggml/814) * initial commit with CPU implementation of upscale to shape and test, cuda implementation next * experimental commit to see if dst shape is correct * test version * test * removed unnecessary params * refactor * fixed tests * ggml : metal impl + cleanup + sycl dev warnings * patched ggml_upscale cuda op to handle non-contiguous tensors, added test for non-contiguous behavior * metal : fix upsacle op to support nb00 + style --------- Co-authored-by: Georgi Gerganov --- ggml-cuda/upscale.cu | 63 +++++++++++++++++++------------------ ggml-metal.m | 10 ++++-- ggml-metal.metal | 21 ++++++++----- ggml-sycl.cpp | 4 +++ ggml.c | 64 ++++++++++++++++++++++++++++---------- ggml.h | 12 +++++++ tests/test-backend-ops.cpp | 32 +++++++++++++++++-- 7 files changed, 146 insertions(+), 60 deletions(-) diff --git a/ggml-cuda/upscale.cu b/ggml-cuda/upscale.cu index 2f62fed48..cf513c3ad 100644 --- a/ggml-cuda/upscale.cu +++ b/ggml-cuda/upscale.cu @@ -1,35 +1,36 @@ #include "upscale.cuh" -static __global__ void upscale_f32(const float * x, float * dst, const int ne00, const int ne00xne01, const int scale_factor) { - // blockIdx.z: idx of ne02*ne03 - // blockIdx.y: idx of ne01*scale_factor， aka ne1 - // blockIDx.x: idx of ne00*scale_factor / BLOCK_SIZE - // ne00xne01: ne00 * ne01 - int ne0 = ne00 * scale_factor; - int nidx = threadIdx.x + blockIdx.x * blockDim.x; - if (nidx >= ne0) { +static __global__ void upscale_f32(const float * x, float * dst, + const int nb00, const int nb01, const int nb02, const int nb03, + const int ne10, const int ne11, const int ne12, const int ne13, + const float sf0, const float sf1, const float sf2, const float sf3) { + int index = threadIdx.x + blockIdx.x * blockDim.x; + if (index >= ne10 * ne11 * ne12 * ne13) { return; } - // operation - int i00 = nidx / scale_factor; - int i01 = blockIdx.y / scale_factor; - int offset_src = - i00 + - i01 * ne00 + - blockIdx.z * ne00xne01; - int offset_dst = - nidx + - blockIdx.y * ne0 + - blockIdx.z * ne0 * gridDim.y; - dst[offset_dst] = x[offset_src]; + + int i10 = index % ne10; + int i11 = (index / ne10) % ne11; + int i12 = (index / (ne10 * ne11)) % ne12; + int i13 = (index / (ne10 * ne11 * ne12)) % ne13; + + int i00 = i10 / sf0; + int i01 = i11 / sf1; + int i02 = i12 / sf2; + int i03 = i13 / sf3; + + dst[index] = *(float *)((char *)x + i03 * nb03 + i02 * nb02 + i01 * nb01 + i00 * nb00); } -static void upscale_f32_cuda(const float * x, float * dst, const int ne00, const int ne01, const int ne02, const int ne03, - const int scale_factor, cudaStream_t stream) { - int ne0 = (ne00 * scale_factor); - int num_blocks = (ne0 + CUDA_UPSCALE_BLOCK_SIZE - 1) / CUDA_UPSCALE_BLOCK_SIZE; - dim3 gridDim(num_blocks, (ne01 * scale_factor), ne02*ne03); - upscale_f32<<>>(x, dst, ne00, ne00 * ne01, scale_factor); +static void upscale_f32_cuda(const float * x, float * dst, + const int nb00, const int nb01, const int nb02, const int nb03, + const int ne10, const int ne11, const int ne12, const int ne13, + const float sf0, const float sf1, const float sf2, const float sf3, + cudaStream_t stream) { + int dst_size = ne10 * ne11 * ne12 * ne13; + int num_blocks = (dst_size + CUDA_UPSCALE_BLOCK_SIZE - 1) / CUDA_UPSCALE_BLOCK_SIZE; + + upscale_f32<<>>(x, dst, nb00, nb01, nb02, nb03, ne10, ne11, ne12, ne13, sf0, sf1, sf2, sf3); } void ggml_cuda_op_upscale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { @@ -39,10 +40,12 @@ void ggml_cuda_op_upscale(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { cudaStream_t stream = ctx.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 + GGML_ASSERT( dst->type == GGML_TYPE_F32); - const int scale_factor = dst->op_params[0]; + const float sf0 = (float)dst->ne[0]/src0->ne[0]; + const float sf1 = (float)dst->ne[1]/src0->ne[1]; + const float sf2 = (float)dst->ne[2]/src0->ne[2]; + const float sf3 = (float)dst->ne[3]/src0->ne[3]; - upscale_f32_cuda(src0_d, dst_d, src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], scale_factor, stream); + upscale_f32_cuda(src0_d, dst_d, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3], dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], sf0, sf1, sf2, sf3, stream); } diff --git a/ggml-metal.m b/ggml-metal.m index 390a1cd78..b0b16dbf7 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -2353,7 +2353,10 @@ static enum ggml_status ggml_metal_graph_compute( { GGML_ASSERT(src0->type == GGML_TYPE_F32); - const int sf = dst->op_params[0]; + const float sf0 = (float)ne0/src0->ne[0]; + const float sf1 = (float)ne1/src0->ne[1]; + const float sf2 = (float)ne2/src0->ne[2]; + const float sf3 = (float)ne3/src0->ne[3]; const id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_UPSCALE_F32].pipeline; @@ -2376,7 +2379,10 @@ static enum ggml_status ggml_metal_graph_compute( [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; - [encoder setBytes:&sf length:sizeof(sf) atIndex:18]; + [encoder setBytes:&sf0 length:sizeof(sf0) atIndex:18]; + [encoder setBytes:&sf1 length:sizeof(sf1) atIndex:19]; + [encoder setBytes:&sf2 length:sizeof(sf2) atIndex:20]; + [encoder setBytes:&sf3 length:sizeof(sf3) atIndex:21]; const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); diff --git a/ggml-metal.metal b/ggml-metal.metal index 57fdf564e..386e9195f 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -1852,7 +1852,10 @@ kernel void kernel_upscale_f32( constant uint64_t & nb1, constant uint64_t & nb2, constant uint64_t & nb3, - constant int32_t & sf, + constant float & sf0, + constant float & sf1, + constant float & sf2, + constant float & sf3, uint3 tgpig[[threadgroup_position_in_grid]], uint3 tpitg[[thread_position_in_threadgroup]], uint3 ntg[[threads_per_threadgroup]]) { @@ -1861,15 +1864,17 @@ kernel void kernel_upscale_f32( const int64_t i2 = tgpig.y; const int64_t i1 = tgpig.x; - const int64_t i03 = i3; - const int64_t i02 = i2; - const int64_t i01 = i1/sf; - - device const float * src0_ptr = (device const float *) (src0 + i03*nb03 + i02*nb02 + i01*nb01); - device float * dst_ptr = (device float *) (dst + i3*nb3 + i2*nb2 + i1*nb1); + const int64_t i03 = i3/sf3; + const int64_t i02 = i2/sf2; + const int64_t i01 = i1/sf1; for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - dst_ptr[i0] = src0_ptr[i0/sf]; + const int64_t i00 = i0/sf0; + + device const float * src0_ptr = (device const float *) (src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); + device float * dst_ptr = (device float *) (dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + dst_ptr[0] = src0_ptr[0]; } } diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 724070eb9..b15efb704 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -13987,6 +13987,10 @@ inline void ggml_sycl_op_upscale(const ggml_tensor *src0, GGML_ASSERT(dst->type == GGML_TYPE_F32); GGML_ASSERT(src0->ne[3] == 1 && dst->ne[3] == 1); // just 3D tensors +#pragma message("TODO: generalize upscale operator") +#pragma message(" https://github.com/ggerganov/ggml/pull/814") + GGML_ASSERT(false && "TODO: generalize upscale operator); + 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); diff --git a/ggml.c b/ggml.c index 03b609ddd..f09cc3060 100644 --- a/ggml.c +++ b/ggml.c @@ -6293,7 +6293,10 @@ struct ggml_tensor * ggml_pool_2d( static struct ggml_tensor * ggml_upscale_impl( struct ggml_context * ctx, struct ggml_tensor * a, - int scale_factor) { + int ne0, + int ne1, + int ne2, + int ne3) { bool is_node = false; if (a->grad) { @@ -6301,19 +6304,45 @@ static struct ggml_tensor * ggml_upscale_impl( is_node = true; } + GGML_ASSERT(a->ne[0] <= ne0); + GGML_ASSERT(a->ne[1] <= ne1); + GGML_ASSERT(a->ne[2] <= ne2); + GGML_ASSERT(a->ne[3] <= ne3); + struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, - a->ne[0] * scale_factor, - a->ne[1] * scale_factor, - a->ne[2], a->ne[3]); + ne0, + ne1, + ne2, + ne3 + ); result->op = GGML_OP_UPSCALE; - result->op_params[0] = scale_factor; + result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; return result; } +struct ggml_tensor * ggml_upscale( + struct ggml_context * ctx, + struct ggml_tensor * a, + int scale_factor) { + return ggml_upscale_impl(ctx, a, a->ne[0] * scale_factor, a->ne[1] * scale_factor, a->ne[2], a->ne[3]); +} + +struct ggml_tensor * ggml_upscale_ext( + struct ggml_context * ctx, + struct ggml_tensor * a, + int ne0, + int ne1, + int ne2, + int ne3) { + return ggml_upscale_impl(ctx, a, ne0, ne1, ne2, ne3); +} + +// ggml_pad + struct ggml_tensor * ggml_pad( struct ggml_context * ctx, struct ggml_tensor * a, @@ -6338,12 +6367,7 @@ struct ggml_tensor * ggml_pad( return result; } -struct ggml_tensor * ggml_upscale( - struct ggml_context * ctx, - struct ggml_tensor * a, - int scale_factor) { - return ggml_upscale_impl(ctx, a, scale_factor); -} +// ggml_arange struct ggml_tensor * ggml_arange( struct ggml_context * ctx, @@ -6365,6 +6389,8 @@ struct ggml_tensor * ggml_arange( return result; } +// ggml_timestep_embedding + struct ggml_tensor * ggml_timestep_embedding( struct ggml_context * ctx, struct ggml_tensor * timesteps, @@ -14820,25 +14846,28 @@ static void ggml_compute_forward_upscale_f32( return; } - GGML_ASSERT(src0->nb[0] == sizeof(float)); + GGML_ASSERT(src0->type == GGML_TYPE_F32); const int ith = params->ith; const int nth = params->nth; GGML_TENSOR_UNARY_OP_LOCALS - const int scale_factor = dst->op_params[0]; + const float sf0 = (float)ne0/src0->ne[0]; + const float sf1 = (float)ne1/src0->ne[1]; + const float sf2 = (float)ne2/src0->ne[2]; + const float sf3 = (float)ne3/src0->ne[3]; // TODO: optimize for (int64_t i3 = 0; i3 < ne3; i3++) { - const int64_t i03 = i3; + const int64_t i03 = i3 / sf3; for (int64_t i2 = ith; i2 < ne2; i2 += nth) { - const int64_t i02 = i2; + const int64_t i02 = i2 / sf2; for (int64_t i1 = 0; i1 < ne1; i1++) { - const int64_t i01 = i1 / scale_factor; + const int64_t i01 = i1 / sf1; for (int64_t i0 = 0; i0 < ne0; i0++) { - const int64_t i00 = i0 / scale_factor; + const int64_t i00 = i0 / sf0; const float * x = (float *)((char *) src0->data + i00*nb00 + i01*nb01 + i02*nb02 + i03*nb03); float * y = (float *)((char *) dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); @@ -14868,6 +14897,7 @@ static void ggml_compute_forward_upscale( } } + // ggml_compute_forward_pad static void ggml_compute_forward_pad_f32( diff --git a/ggml.h b/ggml.h index 25f4f73a8..5e121604a 100644 --- a/ggml.h +++ b/ggml.h @@ -1674,12 +1674,24 @@ extern "C" { float p1); // nearest interpolate + // multiplies ne0 and ne1 by scale factor // used in stable-diffusion GGML_API struct ggml_tensor * ggml_upscale( struct ggml_context * ctx, struct ggml_tensor * a, int scale_factor); + // nearest interpolate + // nearest interpolate to specified dimensions + // used in tortoise.cpp + GGML_API struct ggml_tensor * ggml_upscale_ext( + struct ggml_context * ctx, + struct ggml_tensor * a, + int ne0, + int ne1, + int ne2, + int ne3); + // pad each dimension with zeros: [x, ..., x] -> [x, ..., x, 0, ..., 0] GGML_API struct ggml_tensor * ggml_pad( struct ggml_context * ctx, diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index f080f7e22..85ef21c2a 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1329,23 +1329,47 @@ struct test_upscale : public test_case { const ggml_type type; const std::array ne; const int32_t scale_factor; + const bool transpose; std::string vars() override { - return VARS_TO_STR3(type, ne, scale_factor); + return VARS_TO_STR4(type, ne, scale_factor, transpose); } test_upscale(ggml_type type = GGML_TYPE_F32, std::array ne = {512, 512, 3, 1}, - int32_t scale_factor = 2) - : type(type), ne(ne), scale_factor(scale_factor) {} + int32_t scale_factor = 2, bool transpose = false) + : type(type), ne(ne), scale_factor(scale_factor), transpose(transpose) {} ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); + if (transpose) a = ggml_transpose(ctx, a); ggml_tensor * out = ggml_upscale(ctx, a, scale_factor); return out; } }; +// GGML_OP_UPSCALE (ext) +struct test_upscale_ext : public test_case { + const ggml_type type; + const std::array ne; + const std::array ne_tgt; + + std::string vars() override { + return VARS_TO_STR3(type, ne, ne_tgt); + } + + test_upscale_ext(ggml_type type = GGML_TYPE_F32, + std::array ne = {2, 5, 7, 11}, + std::array ne_tgt = {5, 7, 11, 13}) + : type(type), ne(ne), ne_tgt(ne_tgt) {} + + ggml_tensor * build_graph(ggml_context * ctx) override { + ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); + ggml_tensor * out = ggml_upscale_ext(ctx, a, ne_tgt[0], ne_tgt[1],ne_tgt[2], ne_tgt[3]); + return out; + } +}; + // GGML_OP_GROUP_NORM struct test_group_norm : public test_case { const ggml_type type; @@ -2169,6 +2193,8 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op test_cases.emplace_back(new test_sum_rows()); test_cases.emplace_back(new test_upscale()); + test_cases.emplace_back(new test_upscale(GGML_TYPE_F32, { 512, 512, 3, 1 }, 2, true)); + test_cases.emplace_back(new test_upscale_ext()); test_cases.emplace_back(new test_group_norm()); test_cases.emplace_back(new test_acc()); test_cases.emplace_back(new test_pad()); From 29499bb59383c2a8c5d557a90abb08b696cef7f6 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 15 May 2024 13:23:41 +0300 Subject: [PATCH 003/181] 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 b2b5f8fc1..57bede67b 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -fafd5e7f89382b8cfb51e3dac8d4f1500ca44918 +126d34985705a5a2222723c145cb4e125ac689f3 From ea3b0590ee33d3573eb8ef76f88cc60f36d2a38d Mon Sep 17 00:00:00 2001 From: dm4 Date: Wed, 15 May 2024 20:01:12 +0800 Subject: [PATCH 004/181] embedding : free the batch after execution (#7297) --- examples/embedding/embedding.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index c85a2da53..0c921ed69 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -211,6 +211,7 @@ int main(int argc, char ** argv) { // clean up llama_print_timings(ctx); + llama_batch_free(batch); llama_free(ctx); llama_free_model(model); llama_backend_free(); From 9a17ab914b0aa7353389c656a3f2a0f086726868 Mon Sep 17 00:00:00 2001 From: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> Date: Wed, 15 May 2024 13:26:30 +0100 Subject: [PATCH 005/181] Add missing " (#7303) --- ggml-sycl.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index b15efb704..19d22d637 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -13989,7 +13989,7 @@ inline void ggml_sycl_op_upscale(const ggml_tensor *src0, #pragma message("TODO: generalize upscale operator") #pragma message(" https://github.com/ggerganov/ggml/pull/814") - GGML_ASSERT(false && "TODO: generalize upscale operator); + GGML_ASSERT(false && "TODO: generalize upscale operator"); const int scale_factor = dst->op_params[0]; From 344f9126cc0d15891fde9472fe40b8572628ad7d Mon Sep 17 00:00:00 2001 From: slaren Date: Wed, 15 May 2024 15:08:48 +0200 Subject: [PATCH 006/181] ggml : tag ggml_tensor::backend as deprecated (#7290) --- examples/llava/llava.cpp | 15 --------------- ggml-backend.c | 1 - ggml.c | 10 ++++++++++ ggml.h | 3 ++- 4 files changed, 12 insertions(+), 17 deletions(-) diff --git a/examples/llava/llava.cpp b/examples/llava/llava.cpp index 9a990bb18..63878d176 100644 --- a/examples/llava/llava.cpp +++ b/examples/llava/llava.cpp @@ -88,7 +88,6 @@ static struct clip_image_grid_shape get_anyres_image_grid_shape(const std::pair< // Take the image segments in a grid configuration and return the embeddings and the number of embeddings into preallocated memory (image_embd_out) static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector & image_embd_v, struct clip_image_grid_shape grid_shape, float * image_embd_out, int * n_img_pos_out) { struct { - struct ggml_tensor * newline; struct ggml_context * ctx; } model; @@ -150,20 +149,6 @@ static bool clip_llava_handle_patches(clip_ctx * ctx_clip, std::vector model.ctx = ggml_init(params); - ggml_tensor * newline_tmp = clip_get_newline_tensor(ctx_clip); - model.newline = ggml_new_tensor_1d(model.ctx, GGML_TYPE_F32, newline_tmp->ne[0]); - if (newline_tmp->backend != GGML_BACKEND_TYPE_CPU) { - if (newline_tmp->buffer == NULL) { - LOG_TEE("newline_tmp tensor buffer is NULL\n"); - } - ggml_backend_tensor_get(newline_tmp, model.newline->data, 0, ggml_nbytes(newline_tmp)); - } else { - model.newline->data = newline_tmp->data; - if (model.newline->data == NULL) { - LOG_TEE("newline_tmp tensor data is NULL\n"); - } - } - struct ggml_tensor * image_features = ggml_new_tensor_3d(model.ctx, GGML_TYPE_F32, clip_n_mmproj_embd(ctx_clip), clip_n_patches(ctx_clip), num_images - 1); // example: 4096 x 576 x 4 // ggml_tensor_printf(image_features,"image_features",__LINE__,false,false); // fill it with the image embeddings, ignoring the base diff --git a/ggml-backend.c b/ggml-backend.c index dd090a583..9e35ce98d 100644 --- a/ggml-backend.c +++ b/ggml-backend.c @@ -1895,7 +1895,6 @@ void ggml_backend_view_init(ggml_backend_buffer_t buffer, struct ggml_tensor * t tensor->buffer = buffer; tensor->data = (char *)tensor->view_src->data + tensor->view_offs; - tensor->backend = tensor->view_src->backend; ggml_backend_buffer_init_tensor(buffer, tensor); } diff --git a/ggml.c b/ggml.c index f09cc3060..67e17a210 100644 --- a/ggml.c +++ b/ggml.c @@ -3178,6 +3178,12 @@ static struct ggml_tensor * ggml_new_tensor_impl( struct ggml_tensor * const result = (struct ggml_tensor *)((char *)ctx->mem_buffer + obj_new->offs); +#ifdef __clang__ + // temporary until ggml_tensor::backend is removed + #pragma clang diagnostic push + #pragma clang diagnostic ignored "-Wdeprecated-declarations" +#endif + *result = (struct ggml_tensor) { /*.type =*/ type, /*.backend =*/ GGML_BACKEND_TYPE_CPU, @@ -3200,6 +3206,10 @@ static struct ggml_tensor * ggml_new_tensor_impl( /*.padding =*/ { 0 }, }; +#ifdef __clang__ + #pragma clang diagnostic pop +#endif + // TODO: this should not be needed as long as we don't rely on aligned SIMD loads //ggml_assert_aligned(result->data); diff --git a/ggml.h b/ggml.h index 5e121604a..8c13f4ba8 100644 --- a/ggml.h +++ b/ggml.h @@ -565,7 +565,8 @@ extern "C" { // n-dimensional tensor struct ggml_tensor { enum ggml_type type; - enum ggml_backend_type backend; + + GGML_DEPRECATED(enum ggml_backend_type backend, "use the buffer type to find the storage location of the tensor"); struct ggml_backend_buffer * buffer; From dc020985b8755dd6aa93a2f002f43c3ede808cce Mon Sep 17 00:00:00 2001 From: agray3 Date: Wed, 15 May 2024 14:44:49 +0100 Subject: [PATCH 007/181] Avoid unnecessarily disabling CUDA graphs (#7302) As discussed in PR #6766, CUDA graphs were being disabled in the presence of long prompts. This fixes the issue by avoiding the consective update counter from incrementing unnecessarily for tokens in which cuda graphs are disabled due to batch size > 1. --- ggml-cuda.cu | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 75a2ad480..04b6e5285 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -2558,7 +2558,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t } // Disable CUDA graphs (from the next token) if the use-case is demanding too many consecutive graph updates. - if (cuda_graph_update_required) { + if (use_cuda_graph && cuda_graph_update_required) { cuda_ctx->cuda_graph->number_consecutive_updates++; } else { cuda_ctx->cuda_graph->number_consecutive_updates = 0; From e1b40ac3b94824d761b5e26ea1bc5692706029d9 Mon Sep 17 00:00:00 2001 From: kunnis Date: Wed, 15 May 2024 12:59:12 -0500 Subject: [PATCH 008/181] ggml : use dynamic thread scheduling for matrix multiplication (#6915) * Just reordering some structs. * Adding in the calls to mm_pause * Passing around the state * Renaming and moving a bunch of variables around. * Extracting the logic to it's own function. * Moving some variable definitions into the chunk function. * Moving some variables around * moving src1_cont inside * Moving row_size * adding the current_chunk * Reorg the code. * Formatting to match the orig patch * starting to setup the chunking variables * Starting the buildup of the loop * The yield shouldn't be necessary. * adding the looping structure based on the chunk configuration. * Add in the re-chunking code. * Making it much more likely to rechunk. * disable resizing if numa is enabled. * Updating comments with what we've learned. * Fix formatting * Couple more formatting fixes. * More style fixes. * Fix Warnings * Going with unused because there's conditional logic that needs it. * Update ggml.c * Update ggml.c --------- --- ggml.c | 381 +++++++++++++++++++++++++++++++++++---------------------- 1 file changed, 237 insertions(+), 144 deletions(-) diff --git a/ggml.c b/ggml.c index 67e17a210..684d84235 100644 --- a/ggml.c +++ b/ggml.c @@ -112,6 +112,8 @@ typedef void * thread_ret_t; #endif +typedef pthread_t ggml_thread_t; + #ifdef GGML_USE_CPU_HBM #include #endif @@ -1539,6 +1541,59 @@ static inline void __sse_f16x4_store(ggml_fp16_t *x, __m128 y) { #define GGML_F16_ARR (GGML_F16_STEP/GGML_F16_EPR) #endif +// +// ggml context +// + +struct ggml_context { + size_t mem_size; + void* mem_buffer; + bool mem_buffer_owned; + bool no_alloc; + bool no_alloc_save; // this is used to save the no_alloc state when using scratch buffers + + int n_objects; + + struct ggml_object* objects_begin; + struct ggml_object* objects_end; + + struct ggml_scratch scratch; + struct ggml_scratch scratch_save; +}; + +struct ggml_context_container { + bool used; + + struct ggml_context context; +}; + +struct ggml_compute_state_shared { + const struct ggml_cgraph* cgraph; + const struct ggml_cplan* cplan; + + int64_t perf_node_start_cycles; + int64_t perf_node_start_time_us; + + const int n_threads; + + // synchronization primitives + atomic_int n_active; // num active threads + atomic_int node_n; // active graph node + atomic_int node_task; // active graph node task phase + + ggml_abort_callback abort_callback; // abort ggml_graph_compute when true + void* abort_callback_data; + + atomic_int current_chunk; // currently processing chunk during Mat_Mul, shared between all the threads. +}; + +struct ggml_compute_state { + ggml_thread_t thrd; + int ith; + struct ggml_compute_state_shared* shared; + enum ggml_status ec; +}; + // // fundamental operations // @@ -2385,32 +2440,6 @@ static void ggml_setup_op_has_task_pass(void) { } } -// -// ggml context -// - -struct ggml_context { - size_t mem_size; - void * mem_buffer; - bool mem_buffer_owned; - bool no_alloc; - bool no_alloc_save; // this is used to save the no_alloc state when using scratch buffers - - int n_objects; - - struct ggml_object * objects_begin; - struct ggml_object * objects_end; - - struct ggml_scratch scratch; - struct ggml_scratch scratch_save; -}; - -struct ggml_context_container { - bool used; - - struct ggml_context context; -}; - // // NUMA support // @@ -11815,9 +11844,101 @@ static bool ggml_compute_forward_mul_mat_use_blas(struct ggml_tensor * dst) { } #endif +static void ggml_compute_forward_mul_mat_one_chunk( + const struct ggml_compute_params * params, + struct ggml_tensor * dst, + const int64_t num_rows_per_vec_dot, + const int64_t ir0_start, + const int64_t ir0_end, + const int64_t ir1_start, + const int64_t ir1_end) { + + const struct ggml_tensor * src0 = dst->src[0]; + const struct ggml_tensor * src1 = dst->src[1]; + + GGML_TENSOR_BINARY_OP_LOCALS + + const enum ggml_type type = src0->type; + + const bool src1_cont = ggml_is_contiguous(src1); + + ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot; + enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type; + + // broadcast factors + const int64_t r2 = ne12 / ne02; + const int64_t r3 = ne13 / ne03; + + //printf("ir0_start = %6lld, ir0_end = %6lld, ir1_start = %6lld, ir1_end = %6lld\n", ir0_start, ir0_end, ir1_start, ir1_end); + + // threads with no work simply yield (not sure if it helps) + if (ir0_start >= ir0_end || ir1_start >= ir1_end) { + return; + } + + const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; + const size_t row_size = ggml_row_size(vec_dot_type, ne10); + + assert(ne12 % ne02 == 0); + assert(ne13 % ne03 == 0); + + // block-tiling attempt + const int64_t blck_0 = 16; + const int64_t blck_1 = 16; + + const size_t src1_col_stride = src1_cont || src1->type != vec_dot_type ? row_size : nb11; + + // attempt to reduce false-sharing (does not seem to make a difference) + // 16 * 2, accounting for mmla kernels + float tmp[32]; + + for (int64_t iir1 = ir1_start; iir1 < ir1_end; iir1 += blck_1) { + for (int64_t iir0 = ir0_start; iir0 < ir0_end; iir0 += blck_0) { + for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir1_end; ir1 += num_rows_per_vec_dot) { + const int64_t i13 = (ir1 / (ne12 * ne1)); + const int64_t i12 = (ir1 - i13 * ne12 * ne1) / ne1; + const int64_t i11 = (ir1 - i13 * ne12 * ne1 - i12 * ne1); + + // broadcast src0 into src1 + const int64_t i03 = i13 / r3; + const int64_t i02 = i12 / r2; + + const int64_t i1 = i11; + const int64_t i2 = i12; + const int64_t i3 = i13; + + const char * src0_row = (const char*)src0->data + (0 + i02 * nb02 + i03 * nb03); + + // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides + // if it is, then we have either copied the data to params->wdata and made it contiguous or we are using + // the original src1 data pointer, so we should index using the indices directly + // TODO: this is a bit of a hack, we should probably have a better way to handle this + const char * src1_col = (const char*)wdata + + (src1_cont || src1->type != vec_dot_type + ? (i11 + i12 * ne11 + i13 * ne12 * ne11) * row_size + : (i11 * nb11 + i12 * nb12 + i13 * nb13)); + float * dst_col = (float*)((char*)dst->data + (i1 * nb1 + i2 * nb2 + i3 * nb3)); + + //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ++ir0) { + // vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col); + //} + + for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir0_end; ir0 += num_rows_per_vec_dot) { + vec_dot(ne00, &tmp[ir0 - iir0], (num_rows_per_vec_dot > 1 ? 16 : 0), src0_row + ir0 * nb01, (num_rows_per_vec_dot > 1 ? nb01 : 0), src1_col, (num_rows_per_vec_dot > 1 ? src1_col_stride : 0), num_rows_per_vec_dot); + } + + for (int cn = 0; cn < num_rows_per_vec_dot; ++cn) { + memcpy(&dst_col[iir0 + cn * nb1 / nb0], tmp + (cn * 16), (MIN(iir0 + blck_0, ir0_end) - iir0) * sizeof(float)); + } + } + } + } +} + static void ggml_compute_forward_mul_mat( const struct ggml_compute_params * params, - struct ggml_tensor * dst) { + struct ggml_tensor * dst, + struct ggml_compute_state * state) { const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; @@ -11832,9 +11953,6 @@ static void ggml_compute_forward_mul_mat( const enum ggml_type type = src0->type; - const bool src1_cont = ggml_is_contiguous(src1); - - ggml_vec_dot_t const vec_dot = type_traits[type].vec_dot; enum ggml_type const vec_dot_type = type_traits[type].vec_dot_type; ggml_from_float_t const from_float_to_vec_dot = type_traits[vec_dot_type].from_float; int64_t const vec_dot_num_rows = type_traits[type].nrows; @@ -11855,8 +11973,10 @@ static void ggml_compute_forward_mul_mat( GGML_ASSERT(nb2 <= nb3); // broadcast factors - const int64_t r2 = ne12/ne02; - const int64_t r3 = ne13/ne03; + const int64_t r2 = ne12 / ne02; + const int64_t r3 = ne13 / ne03; + UNUSED(r2); + UNUSED(r3); // nb01 >= nb00 - src0 is not transposed // compute by src0 rows @@ -11938,6 +12058,8 @@ static void ggml_compute_forward_mul_mat( #endif #if GGML_USE_LLAMAFILE + const bool src1_cont = ggml_is_contiguous(src1); + if (src1_cont) { for (int64_t i13 = 0; i13 < ne13; i13++) for (int64_t i12 = 0; i12 < ne12; i12++) @@ -11963,6 +12085,8 @@ UseGgmlGemm1:; if (ith != 0) { return; } + // Every thread starts at ith, so the first unprocessed chunk is nth. This save a bit of coordination right at the start. + atomic_store(&state->shared->current_chunk, nth); if (src1->type != vec_dot_type) { char * wdata = params->wdata; const size_t row_size = ggml_row_size(vec_dot_type, ne10); @@ -11987,11 +12111,11 @@ UseGgmlGemm1:; return; } - const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; - const size_t row_size = ggml_row_size(vec_dot_type, ne10); - #if GGML_USE_LLAMAFILE if (src1->type != vec_dot_type) { + const void* wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata; + const size_t row_size = ggml_row_size(vec_dot_type, ne10); + for (int64_t i13 = 0; i13 < ne13; i13++) for (int64_t i12 = 0; i12 < ne12; i12++) if (!llamafile_sgemm(ne01, ne11, ne00/ggml_blck_size(src0->type), @@ -12012,98 +12136,87 @@ UseGgmlGemm1:; UseGgmlGemm2:; #endif - const int64_t nr0 = ne01; // src0 rows - const int64_t nr1 = ne1*ne12*ne13; // src1 rows +#ifdef GGML_PERF + int chunks_executed = 0; + UNUSED(chunks_executed); +#endif - //printf("nr0 = %lld, nr1 = %lld\n", nr0, nr1); + // This is the size of the first dimension of the result, so we can iterate that way. (see the ASSERT above, these are the same numbers) + const int64_t nr0 = ne0; - // distribute the thread work across the inner or outer loop based on which one is larger - - const int64_t nth0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows - const int64_t nth1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows - - const int64_t ith0 = ith % nth0; - const int64_t ith1 = ith / nth0; - - const int64_t dr0 = (nr0 + nth0 - 1)/nth0; - const int64_t dr1 = (nr1 + nth1 - 1)/nth1; - - const int64_t ir010 = dr0*ith0; - const int64_t ir011 = MIN(ir010 + dr0, nr0); - - const int64_t ir110 = dr1*ith1; - const int64_t ir111 = MIN(ir110 + dr1, nr1); - - //printf("ir010 = %6lld, ir011 = %6lld, ir110 = %6lld, ir111 = %6lld\n", ir010, ir011, ir110, ir111); - - // threads with no work simply yield (not sure if it helps) - if (ir010 >= ir011 || ir110 >= ir111) { - sched_yield(); - return; - } - - assert(ne12 % ne02 == 0); - assert(ne13 % ne03 == 0); - - // block-tiling attempt - const int64_t blck_0 = 16; - const int64_t blck_1 = 16; + // This is the size of the rest of the dimensions of the result + const int64_t nr1 = ne1 * ne2 * ne3; // dot kernels can handle 1 row and col at a time, but mmla kernels can process 2 rows and cols - int64_t nrc = vec_dot_num_rows; + int64_t num_rows_per_vec_dot = vec_dot_num_rows; // TODO: currently the mmla kernels support only even numbered rows/cols. // this check can be removed once they are extended to support odd numbered rows/cols too if ((nr0 % 2 != 0) || (ne11 % 2 != 0)) { - nrc = 1; + num_rows_per_vec_dot = 1; } - const size_t src1_col_stride = src1_cont || src1->type != vec_dot_type ? row_size : nb11; + // Now select a reasonable chunk size. + int chunk_size = 16; - // attempt to reduce false-sharing (does not seem to make a difference) - // 16 * 2, accounting for mmla kernels - float tmp[32]; + // We need to step up the size if it's small + if (nr0 == 1 || nr1 == 1) { + chunk_size = 64; + } - for (int64_t iir1 = ir110; iir1 < ir111; iir1 += blck_1) { - for (int64_t iir0 = ir010; iir0 < ir011; iir0 += blck_0) { - for (int64_t ir1 = iir1; ir1 < iir1 + blck_1 && ir1 < ir111; ir1 += nrc) { - const int64_t i13 = (ir1/(ne12*ne1)); - const int64_t i12 = (ir1 - i13*ne12*ne1)/ne1; - const int64_t i11 = (ir1 - i13*ne12*ne1 - i12*ne1); + // distribute the work across the inner or outer loop based on which one is larger + // The number of chunks in the 0/1 dim. + // CEIL(nr0/chunk_size) + int64_t nchunk0 = (nr0 + chunk_size - 1) / chunk_size; + int64_t nchunk1 = (nr1 + chunk_size - 1) / chunk_size; - // broadcast src0 into src1 - const int64_t i03 = i13/r3; - const int64_t i02 = i12/r2; + // If the chunking is poor for the number of threads on this setup, scrap the whole plan. Re-chunk it by thread. + // Also, chunking by thread was measured to have perform better on NUMA systems. See https://github.com/ggerganov/llama.cpp/pull/6915 + // In theory, chunking should be just as useful on NUMA and non NUMA systems, but testing disagreed with that. + if (nchunk0 * nchunk1 < nth * 4 || ggml_is_numa()) { + // distribute the thread work across the inner or outer loop based on which one is larger + nchunk0 = nr0 > nr1 ? nth : 1; // parallelize by src0 rows + nchunk1 = nr0 > nr1 ? 1 : nth; // parallelize by src1 rows + } - const int64_t i1 = i11; - const int64_t i2 = i12; - const int64_t i3 = i13; + // The number of elements in each chunk + const int64_t dr0 = (nr0 + nchunk0 - 1) / nchunk0; + const int64_t dr1 = (nr1 + nchunk1 - 1) / nchunk1; - const char * src0_row = (const char *) src0->data + (0 + i02*nb02 + i03*nb03); + //if (ith == 0) + // printf("MUL_MAT = [%d, %d, %d, %d] x [%d, %d, %d, %d] = %d x %d = %d. Fp Ops/Ch %d\n", ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, nchunk0, nchunk1, nchunk0 * nchunk1, ne00 * nr0 * nr1 / nchunk0 / nchunk1); - // desc: when src1 is not a contiguous memory block we have to calculate the offset using the strides - // if it is, then we have either copied the data to params->wdata and made it contiguous or we are using - // the original src1 data pointer, so we should index using the indices directly - // TODO: this is a bit of a hack, we should probably have a better way to handle this - const char * src1_col = (const char *) wdata + - (src1_cont || src1->type != vec_dot_type - ? (i11 + i12*ne11 + i13*ne12*ne11)*row_size - : (i11*nb11 + i12*nb12 + i13*nb13)); - float * dst_col = (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)); + // The first chunk comes from our thread_id, the rest will get auto-assigned. + int current_chunk = ith; - //for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ++ir0) { - // vec_dot(ne00, &dst_col[ir0], src0_row + ir0*nb01, src1_col); - //} + while (current_chunk < nchunk0 * nchunk1) { + const int64_t ith0 = current_chunk % nchunk0; + const int64_t ith1 = current_chunk / nchunk0; - for (int64_t ir0 = iir0; ir0 < iir0 + blck_0 && ir0 < ir011; ir0 += nrc) { - vec_dot(ne00, &tmp[ir0 - iir0], (nrc>1 ? 16 : 0), src0_row + ir0*nb01, (nrc>1 ? nb01 : 0), src1_col, (nrc>1 ? src1_col_stride : 0), nrc); - } + const int64_t ir0_start = dr0 * ith0; + const int64_t ir0_end = MIN(ir0_start + dr0, nr0); - for (int cn = 0; cn < nrc; ++cn) { - memcpy(&dst_col[iir0 + cn*nb1/nb0], tmp + (cn*16), (MIN(iir0 + blck_0, ir011) - iir0)*sizeof(float)); - } - } + const int64_t ir1_start = dr1 * ith1; + const int64_t ir1_end = MIN(ir1_start + dr1, nr1); + + ggml_compute_forward_mul_mat_one_chunk(params, dst, num_rows_per_vec_dot, ir0_start, ir0_end, ir1_start, ir1_end); + +#ifdef GGML_PERF + chunks_executed++; +#endif + + if (nth >= nchunk0 * nchunk1) { + break; } + + current_chunk = atomic_fetch_add(&state->shared->current_chunk, 1); } + +#ifdef GGML_PERF + // These numbers are useful when trying to measure how well the threading scheduling works. + //int64_t workSize = (ne01 * ne11 * ne12 * ne13 * ne00) / nchunk0 / nchunk1; + //float time = (ggml_perf_time_us() - t0); + //printf("MUL_MAT = %f ms, [%d, %d, %d, %d] x [%d, %d, %d, %d] = %I64u, %f ops/usec in %d chunks.\n", time / 1000.0, ne00, ne01, ne02, ne03, ne10, ne11, ne12, ne13, workSize, (float)workSize/time, chunks_executed); +#endif } // ggml_compute_forward_mul_mat_id @@ -17358,7 +17471,7 @@ static void ggml_compute_forward_cross_entropy_loss_back( ///////////////////////////////// -static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor) { +static void ggml_compute_forward(struct ggml_compute_params * params, struct ggml_tensor * tensor, struct ggml_compute_state * state) { GGML_ASSERT(params); if (tensor->op == GGML_OP_NONE || ggml_is_empty(tensor)) { @@ -17456,7 +17569,7 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm } break; case GGML_OP_MUL_MAT: { - ggml_compute_forward_mul_mat(params, tensor); + ggml_compute_forward_mul_mat(params, tensor, state); } break; case GGML_OP_MUL_MAT_ID: { @@ -19072,8 +19185,6 @@ typedef int ggml_lock_t; #define GGML_LOCK_INITIALIZER 0 -typedef pthread_t ggml_thread_t; - #define ggml_thread_create pthread_create #define ggml_thread_join pthread_join @@ -19099,8 +19210,6 @@ typedef int ggml_lock_t; #define GGML_LOCK_INITIALIZER 0 -typedef pthread_t ggml_thread_t; - #define ggml_thread_create pthread_create #define ggml_thread_join pthread_join @@ -19180,31 +19289,6 @@ static void set_numa_thread_affinity(int thread_n) { UNUSED(thread_n); } static void clear_numa_thread_affinity(void) {} #endif -struct ggml_compute_state_shared { - const struct ggml_cgraph * cgraph; - const struct ggml_cplan * cplan; - - int64_t perf_node_start_cycles; - int64_t perf_node_start_time_us; - - const int n_threads; - - // synchronization primitives - atomic_int n_active; // num active threads - atomic_int node_n; // active graph node - atomic_int node_task; // active graph node task phase - - ggml_abort_callback abort_callback; // abort ggml_graph_compute when true - void * abort_callback_data; -}; - -struct ggml_compute_state { - ggml_thread_t thrd; - int ith; - struct ggml_compute_state_shared * shared; - enum ggml_status ec; -}; - static void ggml_graph_compute_perf_stats_node(struct ggml_tensor * node, const struct ggml_compute_state_shared * st) { int64_t cycles_cur = ggml_perf_cycles() - st->perf_node_start_cycles; int64_t time_us_cur = ggml_perf_time_us() - st->perf_node_start_time_us; @@ -19477,6 +19561,10 @@ static void ggml_graph_compute_thread_sync_node(int * node_n, struct ggml_comput * node_n = atomic_load(&state->shared->node_n); if (* node_n != last_node_n) break; +#if defined(__SSE3__) + // Tell the processor we're spinning. It's a processor hint for spinlocks. + _mm_pause(); +#endif } } @@ -19491,6 +19579,10 @@ static void ggml_graph_compute_thread_sync_task(int * task_phase, struct ggml_co * task_phase = atomic_load(&state->shared->node_task); if (* task_phase != last_task_phase) break; +#if defined(__SSE3__) + // Tell the processor we're spinning. It's a processor hint for spinlocks. + _mm_pause(); +#endif } } @@ -19530,7 +19622,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { struct ggml_tensor * node = cgraph->nodes[node_n]; if (GGML_OP_HAS_FINALIZE[node->op]) { params.nth = ggml_get_n_tasks(node, n_threads, state->shared->n_threads); - ggml_compute_forward(¶ms, node); + ggml_compute_forward(¶ms, node, state); } ggml_graph_compute_perf_stats_node(node, state->shared); } @@ -19550,17 +19642,17 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { /* INIT */ if (GGML_OP_HAS_INIT[node->op]) { params.type = GGML_TASK_TYPE_INIT; - ggml_compute_forward(¶ms, node); + ggml_compute_forward(¶ms, node, state); } // TODO: maybe push node_n to the atomic but if other threads see n_tasks is 1, // they do something more efficient than spinning (?) params.type = GGML_TASK_TYPE_COMPUTE; - ggml_compute_forward(¶ms, node); + ggml_compute_forward(¶ms, node, state); if (GGML_OP_HAS_FINALIZE[node->op]) { params.type = GGML_TASK_TYPE_FINALIZE; - ggml_compute_forward(¶ms, node); + ggml_compute_forward(¶ms, node, state); } ggml_graph_compute_perf_stats_node(node, state->shared); @@ -19599,7 +19691,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { if (state->ith < n_tasks) { if (GGML_OP_HAS_INIT[node->op]) { - ggml_compute_forward(¶ms, node); + ggml_compute_forward(¶ms, node, state); } } @@ -19620,7 +19712,7 @@ static thread_ret_t ggml_graph_compute_thread(void * data) { if (state->ith < n_tasks) { params.type = GGML_TASK_TYPE_COMPUTE; - ggml_compute_forward(¶ms, node); + ggml_compute_forward(¶ms, node, state); } if (atomic_fetch_sub(&state->shared->n_active, 1) == 1) { @@ -19871,6 +19963,7 @@ enum ggml_status ggml_graph_compute(struct ggml_cgraph * cgraph, struct ggml_cpl /*.node_task =*/ GGML_TASK_TYPE_FINALIZE, /*.abort_callback =*/ NULL, /*.abort_callback_data =*/ NULL, + /*.current_chunk; =*/ 0, }; struct ggml_compute_state * workers = alloca(sizeof(struct ggml_compute_state)*n_threads); From 8f7080bf48828b538bc9387c3d150bbd4fb4cf2d Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Wed, 15 May 2024 23:41:03 +0200 Subject: [PATCH 009/181] readme : remove stray double quote (#7310) Signed-off-by: Daniel Bevenius --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9f2f8df64..ecbe802df 100644 --- a/README.md +++ b/README.md @@ -532,7 +532,7 @@ Building the program with BLAS support may lead to some performance improvements cmake -B build -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ && cmake --build build --config Release -- -j 16 ``` - On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DLLAMA_HIP_UMA=ON"`. + On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DLLAMA_HIP_UMA=ON`. However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs). - Using `make` (example for target gfx1030, build with 16 CPU threads): From 13ad16af1231ab2d245d35df3295bcfa23de1305 Mon Sep 17 00:00:00 2001 From: Max Krasnyansky Date: Wed, 15 May 2024 19:47:36 -0700 Subject: [PATCH 010/181] Add support for properly optimized Windows ARM64 builds with LLVM and MSVC (#7191) * logging: add proper checks for clang to avoid errors and warnings with VA_ARGS * build: add CMake Presets and toolchian files for Windows ARM64 * matmul-int8: enable matmul-int8 with MSVC and fix Clang warnings * ci: add support for optimized Windows ARM64 builds with MSVC and LLVM * matmul-int8: fixed typos in q8_0_q8_0 matmuls Co-authored-by: Georgi Gerganov * matmul-int8: remove unnecessary casts in q8_0_q8_0 --------- Co-authored-by: Georgi Gerganov --- .github/workflows/build.yml | 61 ++++++++++++++++++---------------- CMakeLists.txt | 5 +++ CMakePresets.json | 45 +++++++++++++++++++++++++ cmake/arm64-windows-llvm.cmake | 16 +++++++++ cmake/arm64-windows-msvc.cmake | 6 ++++ common/log.h | 10 +++--- ggml-quants.c | 53 +++++++++++++++-------------- 7 files changed, 138 insertions(+), 58 deletions(-) create mode 100644 CMakePresets.json create mode 100644 cmake/arm64-windows-llvm.cmake create mode 100644 cmake/arm64-windows-msvc.cmake diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 7ac0e5f6e..2d2fea4a2 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -693,26 +693,28 @@ jobs: strategy: matrix: include: - - build: 'rpc' + - build: 'rpc-x64' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_RPC=ON -DBUILD_SHARED_LIBS=ON' - - build: 'noavx' + - build: 'noavx-x64' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX=OFF -DLLAMA_AVX2=OFF -DLLAMA_FMA=OFF -DBUILD_SHARED_LIBS=ON' - - build: 'avx2' + - build: 'avx2-x64' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON' - - build: 'avx' + - build: 'avx-x64' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX2=OFF -DBUILD_SHARED_LIBS=ON' - - build: 'avx512' + - build: 'avx512-x64' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_AVX512=ON -DBUILD_SHARED_LIBS=ON' - - build: 'clblast' + - build: 'clblast-x64' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_CLBLAST=ON -DBUILD_SHARED_LIBS=ON -DCMAKE_PREFIX_PATH="$env:RUNNER_TEMP/clblast"' - - build: 'openblas' + - build: 'openblas-x64' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_BLAS=ON -DBUILD_SHARED_LIBS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS -DBLAS_INCLUDE_DIRS="$env:RUNNER_TEMP/openblas/include" -DBLAS_LIBRARIES="$env:RUNNER_TEMP/openblas/lib/openblas.lib"' - - build: 'kompute' + - build: 'kompute-x64' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_KOMPUTE=ON -DKOMPUTE_OPT_DISABLE_VULKAN_VERSION_CHECK=ON -DBUILD_SHARED_LIBS=ON' - - build: 'vulkan' + - build: 'vulkan-x64' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_VULKAN=ON -DBUILD_SHARED_LIBS=ON' - - build: 'arm64' - defines: '-A ARM64 -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON' + - build: 'llvm-arm64' + defines: '-G Ninja -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON' + - build: 'msvc-arm64' + defines: '-G Ninja -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc.cmake -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON' steps: - name: Clone @@ -723,13 +725,13 @@ jobs: - name: Clone Kompute submodule id: clone_kompute - if: ${{ matrix.build == 'kompute' }} + if: ${{ matrix.build == 'kompute-x64' }} run: | git submodule update --init kompute - name: Download OpenCL SDK id: get_opencl - if: ${{ matrix.build == 'clblast' }} + if: ${{ matrix.build == 'clblast-x64' }} run: | curl.exe -o $env:RUNNER_TEMP/opencl.zip -L "https://github.com/KhronosGroup/OpenCL-SDK/releases/download/v${env:OPENCL_VERSION}/OpenCL-SDK-v${env:OPENCL_VERSION}-Win-x64.zip" mkdir $env:RUNNER_TEMP/opencl @@ -737,7 +739,7 @@ jobs: - name: Download CLBlast id: get_clblast - if: ${{ matrix.build == 'clblast' }} + if: ${{ matrix.build == 'clblast-x64' }} run: | curl.exe -o $env:RUNNER_TEMP/clblast.7z -L "https://github.com/CNugteren/CLBlast/releases/download/${env:CLBLAST_VERSION}/CLBlast-${env:CLBLAST_VERSION}-windows-x64.7z" curl.exe -o $env:RUNNER_TEMP/CLBlast.LICENSE.txt -L "https://github.com/CNugteren/CLBlast/raw/${env:CLBLAST_VERSION}/LICENSE" @@ -750,7 +752,7 @@ jobs: - name: Download OpenBLAS id: get_openblas - if: ${{ matrix.build == 'openblas' }} + if: ${{ matrix.build == 'openblas-x64' }} run: | curl.exe -o $env:RUNNER_TEMP/openblas.zip -L "https://github.com/xianyi/OpenBLAS/releases/download/v${env:OPENBLAS_VERSION}/OpenBLAS-${env:OPENBLAS_VERSION}-x64.zip" curl.exe -o $env:RUNNER_TEMP/OpenBLAS.LICENSE.txt -L "https://github.com/xianyi/OpenBLAS/raw/v${env:OPENBLAS_VERSION}/LICENSE" @@ -763,38 +765,41 @@ jobs: - name: Install Vulkan SDK id: get_vulkan - if: ${{ matrix.build == 'kompute' || matrix.build == 'vulkan' }} + if: ${{ matrix.build == 'kompute-x64' || matrix.build == 'vulkan-x64' }} 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: Install Ninja + id: install_ninja + run: | + choco install ninja + - name: Build id: cmake_build run: | - mkdir build - cd build - cmake .. ${{ matrix.defines }} - cmake --build . --config Release -j ${env:NUMBER_OF_PROCESSORS} + cmake -S . -B build ${{ matrix.defines }} + cmake --build build --config Release -j ${env:NUMBER_OF_PROCESSORS} - name: Add clblast.dll id: add_clblast_dll - if: ${{ matrix.build == 'clblast' }} + if: ${{ matrix.build == 'clblast-x64' }} run: | cp $env:RUNNER_TEMP/clblast/lib/clblast.dll ./build/bin/Release cp $env:RUNNER_TEMP/CLBlast.LICENSE.txt ./build/bin/Release/CLBlast-${env:CLBLAST_VERSION}.txt - name: Add libopenblas.dll id: add_libopenblas_dll - if: ${{ matrix.build == 'openblas' }} + if: ${{ matrix.build == 'openblas-x64' }} run: | cp $env:RUNNER_TEMP/openblas/bin/libopenblas.dll ./build/bin/Release/openblas.dll cp $env:RUNNER_TEMP/OpenBLAS.LICENSE.txt ./build/bin/Release/OpenBLAS-${env:OPENBLAS_VERSION}.txt - name: Check AVX512F support id: check_avx512f - if: ${{ matrix.build == 'avx512' }} + if: ${{ matrix.build == 'avx512-x64' }} continue-on-error: true run: | cd build @@ -808,14 +813,14 @@ jobs: - name: Test id: cmake_test # not all machines have native AVX-512 - if: ${{ matrix.build != 'arm64' && matrix.build != 'clblast' && matrix.build != 'kompute' && matrix.build != 'vulkan' && (matrix.build != 'avx512' || env.HAS_AVX512F == '1') }} + if: ${{ matrix.build != 'msvc-arm64' && matrix.build != 'llvm-arm64' && matrix.build != 'clblast-x64' && matrix.build != 'kompute-x64' && matrix.build != 'vulkan-x64' && (matrix.build != 'avx512-x64' || env.HAS_AVX512F == '1') }} run: | cd build ctest -L main -C Release --verbose --timeout 900 - name: Test (Intel SDE) id: cmake_test_sde - if: ${{ matrix.build == 'avx512' && env.HAS_AVX512F == '0' }} # use Intel SDE for AVX-512 emulation + if: ${{ matrix.build == 'avx512-x64' && env.HAS_AVX512F == '0' }} # use Intel SDE for AVX-512 emulation run: | curl.exe -o $env:RUNNER_TEMP/sde.tar.xz -L "https://downloadmirror.intel.com/813591/sde-external-${env:SDE_VERSION}-win.tar.xz" # for some weird reason windows tar doesn't like sde tar.xz @@ -843,14 +848,14 @@ jobs: if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} run: | Copy-Item LICENSE .\build\bin\Release\llama.cpp.txt - 7z a llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-x64.zip .\build\bin\Release\* + 7z a llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}.zip .\build\bin\Release\* - name: Upload artifacts if: ${{ ( github.event_name == 'push' && github.ref == 'refs/heads/master' ) || github.event.inputs.create_release == 'true' }} uses: actions/upload-artifact@v4 with: - path: llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}-x64.zip - name: llama-bin-win-${{ matrix.build }}-x64.zip + path: llama-${{ steps.tag.outputs.name }}-bin-win-${{ matrix.build }}.zip + name: llama-bin-win-${{ matrix.build }}.zip windows-latest-cmake-cuda: runs-on: windows-latest diff --git a/CMakeLists.txt b/CMakeLists.txt index feb6f39d0..8ab6a45a6 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1007,6 +1007,11 @@ if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR CMAKE_GENERATOR_PLATFORM_LWR STR if (GGML_COMPILER_SUPPORT_DOTPROD) add_compile_definitions(__ARM_FEATURE_DOTPROD) endif () + check_cxx_source_compiles("#include \nint main() { int8x16_t _a, _b; int32x4_t _s = vmlaq_f32(_s, _a, _b); return 0; }" GGML_COMPILER_SUPPORT_MATMUL_INT8) + if (GGML_COMPILER_SUPPORT_MATMUL_INT8) + add_compile_definitions(__ARM_FEATURE_MATMUL_INT8) + endif () + check_cxx_source_compiles("#include \nint main() { float16_t _a; float16x8_t _s = vdupq_n_f16(_a); return 0; }" GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC) if (GGML_COMPILER_SUPPORT_FP16_VECTOR_ARITHMETIC) add_compile_definitions(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC) diff --git a/CMakePresets.json b/CMakePresets.json new file mode 100644 index 000000000..ad1af7ecc --- /dev/null +++ b/CMakePresets.json @@ -0,0 +1,45 @@ +{ + "version": 4, + "configurePresets": [ + { + "name": "base", + "hidden": true, + "generator": "Ninja", + "binaryDir": "${sourceDir}/build-${presetName}", + "cacheVariables": { + "CMAKE_EXPORT_COMPILE_COMMANDS": "ON", + "CMAKE_INSTALL_RPATH": "$ORIGIN;$ORIGIN/.." + } + }, + + { "name": "debug", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "Debug" } }, + { "name": "release", "hidden": true, "cacheVariables": { "CMAKE_BUILD_TYPE": "RelWithDebInfo" } }, + { "name": "static", "hidden": true, "cacheVariables": { "LLAMA_STATIC": "ON" } }, + + { + "name": "arm64-windows-msvc", "hidden": true, + "architecture": { "value": "arm64", "strategy": "external" }, + "toolset": { "value": "host=x86_64", "strategy": "external" }, + "cacheVariables": { + "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-windows-msvc.cmake" + } + }, + + { + "name": "arm64-windows-llvm", "hidden": true, + "architecture": { "value": "arm64", "strategy": "external" }, + "toolset": { "value": "host=x86_64", "strategy": "external" }, + "cacheVariables": { + "CMAKE_TOOLCHAIN_FILE": "${sourceDir}/cmake/arm64-windows-llvm.cmake" + } + }, + + { "name": "arm64-windows-llvm-debug" , "inherits": [ "base", "arm64-windows-llvm", "debug" ] }, + { "name": "arm64-windows-llvm-release", "inherits": [ "base", "arm64-windows-llvm", "release" ] }, + { "name": "arm64-windows-llvm+static-release", "inherits": [ "base", "arm64-windows-llvm", "release", "static" ] }, + + { "name": "arm64-windows-msvc-debug" , "inherits": [ "base", "arm64-windows-msvc", "debug" ] }, + { "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc", "release" ] }, + { "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc", "release", "static" ] } + ] +} diff --git a/cmake/arm64-windows-llvm.cmake b/cmake/arm64-windows-llvm.cmake new file mode 100644 index 000000000..46fba6514 --- /dev/null +++ b/cmake/arm64-windows-llvm.cmake @@ -0,0 +1,16 @@ +set( CMAKE_SYSTEM_NAME Windows ) +set( CMAKE_SYSTEM_PROCESSOR arm64 ) + +set( target arm64-pc-windows-msvc ) + +set( CMAKE_C_COMPILER clang ) +set( CMAKE_CXX_COMPILER clang++ ) + +set( CMAKE_C_COMPILER_TARGET ${target} ) +set( CMAKE_CXX_COMPILER_TARGET ${target} ) + +set( arch_c_flags "-march=armv8.7-a -fvectorize -ffp-model=fast" ) +set( warn_c_flags "-Wno-format -Wno-unused-variable -Wno-unused-function -Wno-gnu-zero-variadic-macro-arguments" ) + +set( CMAKE_C_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" ) +set( CMAKE_CXX_FLAGS_INIT "${arch_c_flags} ${warn_c_flags}" ) diff --git a/cmake/arm64-windows-msvc.cmake b/cmake/arm64-windows-msvc.cmake new file mode 100644 index 000000000..c77631420 --- /dev/null +++ b/cmake/arm64-windows-msvc.cmake @@ -0,0 +1,6 @@ +set( CMAKE_SYSTEM_NAME Windows ) +set( CMAKE_SYSTEM_PROCESSOR arm64 ) + +set( target arm64-pc-windows-msvc ) +set( CMAKE_C_COMPILER_TARGET ${target} ) +set( CMAKE_CXX_COMPILER_TARGET ${target} ) diff --git a/common/log.h b/common/log.h index 6934c57b2..09fa63c26 100644 --- a/common/log.h +++ b/common/log.h @@ -211,7 +211,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std:: #define LOG_FLF_VAL , __FILE__, __LINE__, __FUNCTION__ #else #define LOG_FLF_FMT "[%24s:%5ld][%24s] " - #define LOG_FLF_VAL , __FILE__, __LINE__, __FUNCTION__ + #define LOG_FLF_VAL , __FILE__, (long)__LINE__, __FUNCTION__ #endif #else #define LOG_FLF_FMT "%s" @@ -224,7 +224,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std:: #define LOG_TEE_FLF_VAL , __FILE__, __LINE__, __FUNCTION__ #else #define LOG_TEE_FLF_FMT "[%24s:%5ld][%24s] " - #define LOG_TEE_FLF_VAL , __FILE__, __LINE__, __FUNCTION__ + #define LOG_TEE_FLF_VAL , __FILE__, (long)__LINE__, __FUNCTION__ #endif #else #define LOG_TEE_FLF_FMT "%s" @@ -294,7 +294,7 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std:: // Main LOG macro. // behaves like printf, and supports arguments the exact same way. // -#ifndef _MSC_VER +#if !defined(_MSC_VER) || defined(__clang__) #define LOG(...) LOG_IMPL(__VA_ARGS__, "") #else #define LOG(str, ...) LOG_IMPL("%s" str, "", ##__VA_ARGS__, "") @@ -308,14 +308,14 @@ inline std::string log_filename_generator_impl(LogTriState multilog, const std:: // Secondary target can be changed just like LOG_TARGET // by defining LOG_TEE_TARGET // -#ifndef _MSC_VER +#if !defined(_MSC_VER) || defined(__clang__) #define LOG_TEE(...) LOG_TEE_IMPL(__VA_ARGS__, "") #else #define LOG_TEE(str, ...) LOG_TEE_IMPL("%s" str, "", ##__VA_ARGS__, "") #endif // LOG macro variants with auto endline. -#ifndef _MSC_VER +#if !defined(_MSC_VER) || defined(__clang__) #define LOGLN(...) LOG_IMPL(__VA_ARGS__, "\n") #define LOG_TEELN(...) LOG_TEE_IMPL(__VA_ARGS__, "\n") #else diff --git a/ggml-quants.c b/ggml-quants.c index 9e62a3f32..f13599f6b 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -3487,10 +3487,9 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r #if defined(__ARM_FEATURE_MATMUL_INT8) if (nrc == 2) { const block_q4_0 * restrict vx0 = vx; - const block_q4_0 * restrict vx1 = vx + bx; - + const block_q4_0 * restrict vx1 = (const block_q4_0 *) ((const uint8_t*)vx + bx); const block_q8_0 * restrict vy0 = vy; - const block_q8_0 * restrict vy1 = vy + by; + const block_q8_0 * restrict vy1 = (const block_q8_0 *) ((const uint8_t*)vy + by); float32x4_t sumv0 = vdupq_n_f32(0.0f); @@ -3524,10 +3523,12 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r const int8x16_t y1_l = vld1q_s8(b_y1->qs); const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); - float32x4_t scale = {GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; + float32_t _scale[4] = { GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; + + float32x4_t scale = vld1q_f32(_scale); int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); @@ -3894,9 +3895,9 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r #if defined(__ARM_FEATURE_MATMUL_INT8) if (nrc == 2) { const block_q4_1 * restrict vx0 = vx; - const block_q4_1 * restrict vx1 = vx + bx; + const block_q4_1 * restrict vx1 = (const block_q4_1 *) ((const uint8_t*)vx + bx); const block_q8_1 * restrict vy0 = vy; - const block_q8_1 * restrict vy1 = vy + by; + const block_q8_1 * restrict vy1 = (const block_q8_1 *) ((const uint8_t*)vy + by); float32x4_t sumv0 = vdupq_n_f32(0.0f); float32x4_t summs0 = vdupq_n_f32(0.0f); @@ -3907,11 +3908,11 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r const block_q8_1 * restrict b_y0 = &vy0[i]; const block_q8_1 * restrict b_y1 = &vy1[i]; - float32x4_t summs_t = {GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y0->s), - GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y0->s), - GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y1->s), - GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y1->s)}; - summs0 += summs_t; + float32_t summs_t[4] = {GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y0->s), + GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y0->s), + GGML_FP16_TO_FP32(b_x0->m) * GGML_FP16_TO_FP32(b_y1->s), + GGML_FP16_TO_FP32(b_x1->m) * GGML_FP16_TO_FP32(b_y1->s)}; + summs0 = vaddq_f32(summs0, vld1q_f32(summs_t)); const uint8x16_t m4b = vdupq_n_u8(0x0F); @@ -3931,10 +3932,11 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); // mmla into int32x4_t - float32x4_t scale = {GGML_FP16_TO_FP32(b_x0->d)*b_y0->d, - GGML_FP16_TO_FP32(b_x0->d)*b_y1->d, - GGML_FP16_TO_FP32(b_x1->d)*b_y0->d, - GGML_FP16_TO_FP32(b_x1->d)*b_y1->d}; + float32_t _scale[4] = {GGML_FP16_TO_FP32(b_x0->d)*b_y0->d, + GGML_FP16_TO_FP32(b_x0->d)*b_y1->d, + GGML_FP16_TO_FP32(b_x1->d)*b_y0->d, + GGML_FP16_TO_FP32(b_x1->d)*b_y1->d}; + float32x4_t scale = vld1q_f32(_scale); int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); @@ -3953,7 +3955,7 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r float32x4_t sumv1 = vextq_f32(sumv0, sumv0, 2); float32x4_t sumv2 = vzip1q_f32(sumv0, sumv1); - sumv2 = sumv2 + summs0; + sumv2 = vaddq_f32(sumv2, summs0); vst1_f32(s, vget_low_f32(sumv2)); vst1_f32(s + bs, vget_high_f32(sumv2)); @@ -4837,9 +4839,9 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r #if defined(__ARM_FEATURE_MATMUL_INT8) if (nrc == 2) { const block_q8_0 * restrict vx0 = vx; - const block_q8_0 * restrict vx1 = vx + bx; + const block_q8_0 * restrict vx1 = (const block_q8_0 *) ((const uint8_t*)vx + bx); const block_q8_0 * restrict vy0 = vy; - const block_q8_0 * restrict vy1 = vy + by; + const block_q8_0 * restrict vy1 = (const block_q8_0 *) ((const uint8_t*)vy + by); float32x4_t sumv0 = vdupq_n_f32(0.0f); @@ -4861,10 +4863,11 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r const int8x16_t y1_l = vld1q_s8(b_y1->qs); const int8x16_t y1_h = vld1q_s8(b_y1->qs + 16); - float32x4_t scale = {GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), - GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; + float32_t _scale[4] = {GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x0->d)*GGML_FP16_TO_FP32(b_y1->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y0->d), + GGML_FP16_TO_FP32(b_x1->d)*GGML_FP16_TO_FP32(b_y1->d)}; + float32x4_t scale = vld1q_f32(_scale); int8x16_t l0 = vreinterpretq_s8_s64(vzip1q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); int8x16_t l1 = vreinterpretq_s8_s64(vzip2q_s64(vreinterpretq_s64_s8(x0_l), vreinterpretq_s64_s8(x1_l))); From 172b78210aae0e54d3668c5de14200efab9fac23 Mon Sep 17 00:00:00 2001 From: Max Krasnyansky Date: Wed, 15 May 2024 22:36:43 -0700 Subject: [PATCH 011/181] ci: fix bin/Release path for windows-arm64 builds (#7317) Switch to Ninja Multi-Config CMake generator to resurect bin/Release path that broke artifact packaging in CI. --- .github/workflows/build.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 2d2fea4a2..0742443c6 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -712,9 +712,9 @@ jobs: - build: 'vulkan-x64' defines: '-DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DLLAMA_VULKAN=ON -DBUILD_SHARED_LIBS=ON' - build: 'llvm-arm64' - defines: '-G Ninja -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON' + defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-llvm.cmake -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON' - build: 'msvc-arm64' - defines: '-G Ninja -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc.cmake -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON' + defines: '-G "Ninja Multi-Config" -D CMAKE_TOOLCHAIN_FILE=cmake/arm64-windows-msvc.cmake -DLLAMA_NATIVE=OFF -DLLAMA_BUILD_SERVER=ON -DBUILD_SHARED_LIBS=ON' steps: - name: Clone From ad52d5c259344888b06fd5acd3344c663dd0621d Mon Sep 17 00:00:00 2001 From: Vaibhav Srivastav Date: Thu, 16 May 2024 07:38:43 +0200 Subject: [PATCH 012/181] doc: add references to hugging face GGUF-my-repo quantisation web tool. (#7288) * chore: add references to the quantisation space. * fix grammer lol. * Update README.md Co-authored-by: Julien Chaumond * Update README.md Co-authored-by: Georgi Gerganov --------- Co-authored-by: Julien Chaumond Co-authored-by: Georgi Gerganov --- README.md | 3 +++ examples/quantize/README.md | 4 +++- 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index ecbe802df..5d6217d13 100644 --- a/README.md +++ b/README.md @@ -712,6 +712,9 @@ Building the program with BLAS support may lead to some performance improvements ### Prepare and Quantize +> [!NOTE] +> You can use the [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space on Hugging Face to quantise your model weights without any setup too. It is synced from `llama.cpp` main every 6 hours. + 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. Note: `convert.py` does not support LLaMA 3, you can use `convert-hf-to-gguf.py` with LLaMA 3 downloaded from Hugging Face. diff --git a/examples/quantize/README.md b/examples/quantize/README.md index 8a10365c0..b78ece4e7 100644 --- a/examples/quantize/README.md +++ b/examples/quantize/README.md @@ -1,6 +1,8 @@ # quantize -TODO +You can also use the [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space on Hugging Face to build your own quants without any setup. + +Note: It is synced from llama.cpp `main` every 6 hours. ## Llama 2 7B From 0350f5815218c483fb3026a86adc44a115481625 Mon Sep 17 00:00:00 2001 From: Herman Semenov Date: Thu, 16 May 2024 06:14:24 +0000 Subject: [PATCH 013/181] grammar, json, llama: replace push on emplace if it possible (#7273) --- common/grammar-parser.cpp | 2 +- common/json-schema-to-grammar.cpp | 12 ++++++------ llama.cpp | 4 ++-- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/common/grammar-parser.cpp b/common/grammar-parser.cpp index fecb7cd71..b5bc7d49b 100644 --- a/common/grammar-parser.cpp +++ b/common/grammar-parser.cpp @@ -26,7 +26,7 @@ namespace grammar_parser { static uint32_t get_symbol_id(parse_state & state, const char * src, size_t len) { uint32_t next_id = static_cast(state.symbol_ids.size()); - auto result = state.symbol_ids.insert(std::make_pair(std::string(src, len), next_id)); + auto result = state.symbol_ids.emplace(std::string(src, len), next_id); return result.first->second; } diff --git a/common/json-schema-to-grammar.cpp b/common/json-schema-to-grammar.cpp index 0f8f1b1d4..9a71f5d8d 100644 --- a/common/json-schema-to-grammar.cpp +++ b/common/json-schema-to-grammar.cpp @@ -272,7 +272,7 @@ private: if (literal.empty()) { return false; } - ret.push_back(std::make_pair(literal, true)); + ret.emplace_back(literal, true); literal.clear(); return true; }; @@ -298,7 +298,7 @@ private: while (i < length) { char c = sub_pattern[i]; if (c == '.') { - seq.push_back(std::make_pair(get_dot(), false)); + seq.emplace_back(get_dot(), false); i++; } else if (c == '(') { i++; @@ -307,7 +307,7 @@ private: _warnings.push_back("Unsupported pattern syntax"); } } - seq.push_back(std::make_pair("(" + to_rule(transform()) + ")", false)); + seq.emplace_back("(" + to_rule(transform()) + ")", false); } else if (c == ')') { i++; if (start > 0 && sub_pattern[start - 1] != '(') { @@ -331,9 +331,9 @@ private: } square_brackets += ']'; i++; - seq.push_back(std::make_pair(square_brackets, false)); + seq.emplace_back(square_brackets, false); } else if (c == '|') { - seq.push_back(std::make_pair("|", false)); + seq.emplace_back("|", false); i++; } else if (c == '*' || c == '+' || c == '?') { seq.back() = std::make_pair(to_rule(seq.back()) + c, false); @@ -417,7 +417,7 @@ private: } } if (!literal.empty()) { - seq.push_back(std::make_pair(literal, true)); + seq.emplace_back(literal, true); } } } diff --git a/llama.cpp b/llama.cpp index 7d26966e4..0ef756a52 100644 --- a/llama.cpp +++ b/llama.cpp @@ -17015,13 +17015,13 @@ static size_t llama_state_seq_get_data_internal(struct llama_context * ctx, llam } else { if (cell_range_begin != kv_self.size) { - cell_ranges.push_back({ cell_range_begin, i }); + cell_ranges.emplace_back(cell_range_begin, i); cell_range_begin = kv_self.size; } } } if (cell_range_begin != kv_self.size) { - cell_ranges.push_back({ cell_range_begin, kv_self.size }); + cell_ranges.emplace_back(cell_range_begin, kv_self.size); } // DEBUG CHECK: Sum of cell counts in ranges should equal the total cell count From dda64fc17c97820ea9489eb0cc9ae8b8fdce4926 Mon Sep 17 00:00:00 2001 From: Jared Van Bortel Date: Thu, 16 May 2024 02:15:23 -0400 Subject: [PATCH 014/181] convert : get general.name from model dir, not its parent (#5615) Co-authored-by: Brian --- convert.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/convert.py b/convert.py index e2e642351..da1247957 100755 --- a/convert.py +++ b/convert.py @@ -1109,7 +1109,7 @@ class OutputFile: if metadata is not None and metadata.name is not None: name = metadata.name elif params.path_model is not None: - name = str(params.path_model.parent).split("/")[-1] + name = params.path_model.name elif params.n_ctx == 4096: # Heuristic detection of LLaMA v2 model name = "LLaMA v2" From 3b3963c55c8332e33533c44b2aa882b0e45f8292 Mon Sep 17 00:00:00 2001 From: Radoslav Gerganov Date: Wed, 15 May 2024 15:29:07 +0300 Subject: [PATCH 015/181] rpc : add command line arg for specifying backend memory ref: #7293 --- examples/rpc/README.md | 4 +-- examples/rpc/rpc-server.cpp | 68 +++++++++++++++++++++++++++++++------ ggml-rpc.cpp | 2 +- 3 files changed, 60 insertions(+), 14 deletions(-) diff --git a/examples/rpc/README.md b/examples/rpc/README.md index 325d0abc4..eeec71a8e 100644 --- a/examples/rpc/README.md +++ b/examples/rpc/README.md @@ -42,7 +42,7 @@ cmake --build . --config Release Then, start the `rpc-server` with the backend: ```bash -$ bin/rpc-server 0.0.0.0 50052 +$ bin/rpc-server -p 50052 create_backend: using CUDA backend ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no ggml_cuda_init: CUDA_USE_TENSOR_CORES: yes @@ -53,7 +53,7 @@ Starting RPC server on 0.0.0.0:50052 When using the CUDA backend, you can specify the device with the `CUDA_VISIBLE_DEVICES` environment variable, e.g.: ```bash -$ CUDA_VISIBLE_DEVICES=0 bin/rpc-server 0.0.0.0 50052 +$ CUDA_VISIBLE_DEVICES=0 bin/rpc-server -p 50052 ``` This way you can run multiple `rpc-server` instances on the same host, each with a different CUDA device. diff --git a/examples/rpc/rpc-server.cpp b/examples/rpc/rpc-server.cpp index 496af8496..021185b83 100644 --- a/examples/rpc/rpc-server.cpp +++ b/examples/rpc/rpc-server.cpp @@ -10,6 +10,52 @@ #include #include +struct rpc_server_params { + std::string host = "0.0.0.0"; + int port = 50052; + size_t backend_mem = 0; +}; + +static void print_usage(int /*argc*/, char ** argv, rpc_server_params params) { + fprintf(stderr, "Usage: %s [options]\n\n", argv[0]); + fprintf(stderr, "options:\n"); + fprintf(stderr, " -h, --help show this help message and exit\n"); + fprintf(stderr, " -H HOST, --host HOST host to bind to (default: %s)\n", params.host.c_str()); + fprintf(stderr, " -p PORT, --port PORT port to bind to (default: %d)\n", params.port); + fprintf(stderr, " -m MEM, --mem MEM backend memory size (in MB)\n"); + fprintf(stderr, "\n"); +} + +static bool rpc_server_params_parse(int argc, char ** argv, rpc_server_params & params) { + std::string arg; + for (int i = 1; i < argc; i++) { + arg = argv[i]; + if (arg == "-H" || arg == "--host") { + if (++i >= argc) { + return false; + } + params.host = argv[i]; + } else if (arg == "-p" || arg == "--port") { + if (++i >= argc) { + return false; + } + params.port = std::stoi(argv[i]); + if (params.port <= 0 || params.port > 65535) { + return false; + } + } else if (arg == "-m" || arg == "--mem") { + if (++i >= argc) { + return false; + } + params.backend_mem = std::stoul(argv[i]) * 1024 * 1024; + } else if (arg == "-h" || arg == "--help") { + print_usage(argc, argv, params); + exit(0); + } + } + return true; +} + static ggml_backend_t create_backend() { ggml_backend_t backend = NULL; #ifdef GGML_USE_CUDA @@ -45,14 +91,9 @@ static void get_backend_memory(size_t * free_mem, size_t * total_mem) { } int main(int argc, char * argv[]) { - if (argc < 3) { - fprintf(stderr, "Usage: %s \n", argv[0]); - return 1; - } - const char * host = argv[1]; - int port = std::stoi(argv[2]); - if (port <= 0 || port > 65535) { - fprintf(stderr, "Invalid port number: %d\n", port); + rpc_server_params params; + if (!rpc_server_params_parse(argc, argv, params)) { + fprintf(stderr, "Invalid parameters\n"); return 1; } ggml_backend_t backend = create_backend(); @@ -60,10 +101,15 @@ int main(int argc, char * argv[]) { fprintf(stderr, "Failed to create backend\n"); return 1; } - printf("Starting RPC server on %s:%d\n", host, port); + std::string endpoint = params.host + ":" + std::to_string(params.port); size_t free_mem, total_mem; - get_backend_memory(&free_mem, &total_mem); - std::string endpoint = std::string(host) + ":" + std::to_string(port); + if (params.backend_mem > 0) { + free_mem = params.backend_mem; + total_mem = params.backend_mem; + } else { + get_backend_memory(&free_mem, &total_mem); + } + printf("Starting RPC server on %s, backend memory: %zu MB\n", endpoint.c_str(), free_mem / (1024 * 1024)); start_rpc_server(backend, endpoint.c_str(), free_mem, total_mem); ggml_backend_free(backend); return 0; diff --git a/ggml-rpc.cpp b/ggml-rpc.cpp index efeacb297..ba392009f 100644 --- a/ggml-rpc.cpp +++ b/ggml-rpc.cpp @@ -28,7 +28,7 @@ #define UNUSED GGML_UNUSED -#define GGML_DEBUG 1 +#define GGML_DEBUG 0 #if (GGML_DEBUG >= 1) #define GGML_PRINT_DEBUG(...) printf(__VA_ARGS__) #else From 9afdffe70ebf3166d429b4434783bb0b7f97bdeb Mon Sep 17 00:00:00 2001 From: Radoslav Gerganov Date: Wed, 15 May 2024 16:04:40 +0300 Subject: [PATCH 016/181] rpc : get available mem for the CPU backend This can be overridden with the -m command line option ref: #7293 --- examples/rpc/rpc-server.cpp | 20 +++++++++++++++++--- 1 file changed, 17 insertions(+), 3 deletions(-) diff --git a/examples/rpc/rpc-server.cpp b/examples/rpc/rpc-server.cpp index 021185b83..41f377376 100644 --- a/examples/rpc/rpc-server.cpp +++ b/examples/rpc/rpc-server.cpp @@ -7,6 +7,11 @@ #endif #include "ggml-rpc.h" +#ifdef _WIN32 +# include +#else +# include +#endif #include #include @@ -84,9 +89,18 @@ static void get_backend_memory(size_t * free_mem, size_t * total_mem) { #ifdef GGML_USE_CUDA ggml_backend_cuda_get_device_memory(0, free_mem, total_mem); #else - // TODO: implement for other backends - *free_mem = 1; - *total_mem = 1; + #ifdef _WIN32 + MEMORYSTATUSEX status; + status.dwLength = sizeof(status); + GlobalMemoryStatusEx(&status); + *total_mem = status.ullTotalPhys; + *free_mem = status.ullAvailPhys; + #else + long pages = sysconf(_SC_PHYS_PAGES); + long page_size = sysconf(_SC_PAGE_SIZE); + *total_mem = pages * page_size; + *free_mem = *total_mem; + #endif #endif } From 24ecb58168dce81646c2ed425690a106591c8c6d Mon Sep 17 00:00:00 2001 From: Pierrick Hymbert Date: Thu, 16 May 2024 20:43:45 +0200 Subject: [PATCH 017/181] Revert "server bench: fix bench not waiting for model load (#7284)" (#7334) This reverts commit 583fd6b000ec9ad1b465b5c98524f4a0ae388077. --- examples/server/bench/bench.py | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/examples/server/bench/bench.py b/examples/server/bench/bench.py index 25ac29c4c..86c5de101 100644 --- a/examples/server/bench/bench.py +++ b/examples/server/bench/bench.py @@ -293,14 +293,13 @@ def start_server_background(args): def is_server_listening(server_fqdn, server_port): - try: - url = f"{server_fqdn}:{server_port}/health" - if not url.startswith("http://"): - url = f"http://{url}" - result = requests.get(url) - return result.status_code == 200 - except Exception: - return False + with closing(socket.socket(socket.AF_INET, socket.SOCK_STREAM)) as sock: + result = sock.connect_ex((server_fqdn, server_port)) + _is_server_listening = result == 0 + if _is_server_listening: + print(f"server is listening on {server_fqdn}:{server_port}...") + return _is_server_listening + def escape_metric_name(metric_name): return re.sub('[^A-Z0-9]', '_', metric_name.upper()) From 9c4fdcbec8c7fcc428e723b0d8a1cf1f351ba642 Mon Sep 17 00:00:00 2001 From: Leon Knauer Date: Fri, 17 May 2024 02:11:03 +0200 Subject: [PATCH 018/181] [Server] Added --verbose option to README [no ci] (#7335) --- examples/server/README.md | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index f6eb6942c..13cbdcd2c 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -17,7 +17,8 @@ The project is under active development, and we are [looking for feedback and co **Command line options:** -- `--threads N`, `-t N`: Set the number of threads to use during generation. Not used if model layers are offloaded to GPU. The server is using batching. This parameter is used only if one token is to be processed on CPU backend. +- `-v`, `--verbose`: Enable verbose server output. When using the `/completion` endpoint, this includes the tokenized prompt, the full request and the full response. +- `-t N`, `--threads N`: Set the number of threads to use during generation. Not used if model layers are offloaded to GPU. The server is using batching. This parameter is used only if one token is to be processed on CPU backend. - `-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. Not used if model layers are offloaded to GPU. - `--threads-http N`: Number of threads in the http server pool to process requests. Default: `max(std::thread::hardware_concurrency() - 1, --parallel N + 2)` - `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`). @@ -36,9 +37,7 @@ The project is under active development, and we are [looking for feedback and co - `--numa STRATEGY`: Attempt one of the below optimization strategies that may help on some NUMA systems - `--numa distribute`: Spread execution evenly over all nodes - `--numa isolate`: Only spawn threads on CPUs on the node that execution started on -- `--numa numactl`: Use the CPU map provided by numactl. If run without this previously, it is recommended to drop the system -page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/1437 - +- `--numa numactl`: Use the CPU map provided by numactl. If run without this previously, it is recommended to drop the system page cache before using this. See https://github.com/ggerganov/llama.cpp/issues/1437 - `--numa`: Attempt optimizations that may 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. From 934266c0e0b2aa9781fdba2deb112c161ff038a9 Mon Sep 17 00:00:00 2001 From: Justine Tunney Date: Fri, 17 May 2024 02:58:52 -0400 Subject: [PATCH 019/181] ggml : rewrite silu and softmax for cpu (#7154) This change upstreams llamafile's vectorized expf() functions. This lets us compute softmax and silu more accurately than the short[65536] lookup table that GGML previously used to make this operation go faster. We can support aarch64 and sse2+ with the worst case rounding error of 2ulp. It makes make -j8 tests && ./tests/test-backend-ops -o SOFT_MAX -b CPU perf go 1.5x faster for SSE2+FMA, 1.9x faster for AVX2+FMA and 2.1x on AVX512 --- ggml.c | 476 ++++++++++++++++++++++++++++++++++----------------------- 1 file changed, 283 insertions(+), 193 deletions(-) diff --git a/ggml.c b/ggml.c index 684d84235..55152bce4 100644 --- a/ggml.c +++ b/ggml.c @@ -165,9 +165,6 @@ void ggml_print_backtrace(void) { #define GGML_DEBUG 0 #define GGML_GELU_FP16 #define GGML_GELU_QUICK_FP16 -#define GGML_SILU_FP16 -// #define GGML_CROSS_ENTROPY_EXP_FP16 -// #define GGML_FLASH_ATTN_EXP_FP16 #define GGML_SOFT_MAX_UNROLL 4 #define GGML_VEC_DOT_UNROLL 2 @@ -318,12 +315,6 @@ static ggml_fp16_t ggml_table_gelu_f16[1 << 16]; // precomputed quick gelu table for f16 (128 KB) static ggml_fp16_t ggml_table_gelu_quick_f16[1 << 16]; -// precomputed silu table for f16 (128 KB) -static ggml_fp16_t ggml_table_silu_f16[1 << 16]; - -// precomputed exp table for f16 (128 KB) -static ggml_fp16_t ggml_table_exp_f16[1 << 16]; - // precomputed f32 table for f16 (256 KB) (ggml-impl.h) float ggml_table_f32_f16[1 << 16]; @@ -2085,52 +2076,291 @@ inline static float ggml_silu_f32(float x) { return x/(1.0f + expf(-x)); } -//inline static void ggml_vec_silu_f16(const int n, ggml_fp16_t * y, const ggml_fp16_t * x) { -// const uint16_t * i16 = (const uint16_t *) x; -// for (int i = 0; i < n; ++i) { -// y[i] = ggml_table_silu_f16[i16[i]]; -// } -//} +#if defined(__ARM_NEON) -#ifdef GGML_SILU_FP16 -inline static void ggml_vec_silu_f32(const int n, float * y, const float * x) { - uint16_t t; - for (int i = 0; i < n; ++i) { - ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); - memcpy(&t, &fp16, sizeof(uint16_t)); - y[i] = GGML_FP16_TO_FP32(ggml_table_silu_f16[t]); - } +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static float32x4_t ggml_v_expf(float32x4_t x) { + const float32x4_t r = vdupq_n_f32(0x1.8p23f); + const float32x4_t z = vfmaq_f32(r, x, vdupq_n_f32(0x1.715476p+0f)); + const float32x4_t n = vsubq_f32(z, r); + const float32x4_t b = vfmsq_f32(vfmsq_f32(x, n, vdupq_n_f32(0x1.62e4p-1f)), n, + vdupq_n_f32(0x1.7f7d1cp-20f)); + const uint32x4_t e = vshlq_n_u32(vreinterpretq_u32_f32(z), 23); + const float32x4_t k = vreinterpretq_f32_u32(vaddq_u32(e, vreinterpretq_u32_f32(vdupq_n_f32(1)))); + const uint32x4_t c = vcagtq_f32(n, vdupq_n_f32(126)); + const float32x4_t u = vmulq_f32(b, b); + const float32x4_t j = vfmaq_f32( + vmulq_f32(vdupq_n_f32(0x1.ffffecp-1f), b), + vfmaq_f32(vfmaq_f32(vdupq_n_f32(0x1.fffdb6p-2f), vdupq_n_f32(0x1.555e66p-3f), b), + vfmaq_f32(vdupq_n_f32(0x1.573e2ep-5f), vdupq_n_f32(0x1.0e4020p-7f), b), u), u); + if (!vpaddd_u64(vreinterpretq_u64_u32(c))) + return vfmaq_f32(k, j, k); + const uint32x4_t d = vandq_u32(vclezq_f32(n), vdupq_n_u32(0x82000000)); + const float32x4_t s1 = vreinterpretq_f32_u32(vaddq_u32(d, vdupq_n_u32(0x7f000000))); + const float32x4_t s2 = vreinterpretq_f32_u32(vsubq_u32(e, d)); + return vbslq_f32(vcagtq_f32(n, vdupq_n_f32(192)), vmulq_f32(s1, s1), + vbslq_f32(c, vmulq_f32(vfmaq_f32(s2, s2, j), s1), vfmaq_f32(k, k, j))); } + +// computes silu x/(1+exp(-x)) in single precision vector +inline static float32x4_t ggml_v_silu(float32x4_t x) { + const float32x4_t one = vdupq_n_f32(1.0f); + const float32x4_t zero = vdupq_n_f32(0.0f); + const float32x4_t neg_x = vsubq_f32(zero, x); + const float32x4_t exp_neg_x = ggml_v_expf(neg_x); + const float32x4_t one_plus_exp_neg_x = vaddq_f32(one, exp_neg_x); + return vdivq_f32(x, one_plus_exp_neg_x); +} + +#elif defined(__AVX512F__) && defined(__AVX512DQ__) + +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static __m512 ggml_v_expf(__m512 x) { + const __m512 r = _mm512_set1_ps(0x1.8p23f); + const __m512 z = _mm512_fmadd_ps(x, _mm512_set1_ps(0x1.715476p+0f), r); + const __m512 n = _mm512_sub_ps(z, r); + const __m512 b = _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.7f7d1cp-20f), + _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.62e4p-1f), x)); + const __m512i e = _mm512_slli_epi32(_mm512_castps_si512(z), 23); + const __m512 k = _mm512_castsi512_ps(_mm512_add_epi32(e, _mm512_castps_si512(_mm512_set1_ps(1)))); + const __mmask16 c = _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(126), _CMP_GT_OQ); + const __m512 u = _mm512_mul_ps(b, b); + const __m512 j = _mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_set1_ps(0x1.0e4020p-7f), b, + _mm512_set1_ps(0x1.573e2ep-5f)), u, + _mm512_fmadd_ps(_mm512_set1_ps(0x1.555e66p-3f), b, + _mm512_set1_ps(0x1.fffdb6p-2f))), + u, _mm512_mul_ps(_mm512_set1_ps(0x1.ffffecp-1f), b)); + if (_mm512_kortestz(c, c)) + return _mm512_fmadd_ps(j, k, k); + const __m512i g = _mm512_and_si512( + _mm512_movm_epi32(_mm512_cmp_ps_mask(n, _mm512_setzero_ps(), _CMP_LE_OQ)), + _mm512_set1_epi32(0x82000000u)); + const __m512 s1 = + _mm512_castsi512_ps(_mm512_add_epi32(g, _mm512_set1_epi32(0x7f000000u))); + const __m512 s2 = _mm512_castsi512_ps(_mm512_sub_epi32(e, g)); + const __mmask16 d = + _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(192), _CMP_GT_OQ); + return _mm512_mask_blend_ps( + d, _mm512_mask_blend_ps( + c, _mm512_fmadd_ps(k, j, k), + _mm512_mul_ps(_mm512_fmadd_ps(s2, j, s2), s1)), + _mm512_mul_ps(s1, s1)); +} + +// computes silu x/(1+exp(-x)) in single precision vector +inline static __m512 ggml_v_silu(__m512 x) { + const __m512 one = _mm512_set1_ps(1); + const __m512 zero = _mm512_setzero_ps(); + const __m512 neg_x = _mm512_sub_ps(zero, x); + const __m512 exp_neg_x = ggml_v_expf(neg_x); + const __m512 one_plus_exp_neg_x = _mm512_add_ps(one, exp_neg_x); + return _mm512_div_ps(x, one_plus_exp_neg_x); +} + +#elif defined(__AVX2__) && defined(__FMA__) + +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static __m256 ggml_v_expf(__m256 x) { + const __m256 r = _mm256_set1_ps(0x1.8p23f); + const __m256 z = _mm256_fmadd_ps(x, _mm256_set1_ps(0x1.715476p+0f), r); + const __m256 n = _mm256_sub_ps(z, r); + const __m256 b = _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.7f7d1cp-20f), + _mm256_fnmadd_ps(n, _mm256_set1_ps(0x1.62e4p-1f), x)); + const __m256i e = _mm256_slli_epi32(_mm256_castps_si256(z), 23); + const __m256 k = _mm256_castsi256_ps( + _mm256_add_epi32(e, _mm256_castps_si256(_mm256_set1_ps(1)))); + const __m256i c = _mm256_castps_si256( + _mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), + _mm256_set1_ps(126), _CMP_GT_OQ)); + const __m256 u = _mm256_mul_ps(b, b); + const __m256 j = _mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_fmadd_ps(_mm256_set1_ps(0x1.0e4020p-7f), b, + _mm256_set1_ps(0x1.573e2ep-5f)), u, + _mm256_fmadd_ps(_mm256_set1_ps(0x1.555e66p-3f), b, + _mm256_set1_ps(0x1.fffdb6p-2f))), + u, _mm256_mul_ps(_mm256_set1_ps(0x1.ffffecp-1f), b)); + if (!_mm256_movemask_ps(_mm256_castsi256_ps(c))) + return _mm256_fmadd_ps(j, k, k); + const __m256i g = _mm256_and_si256( + _mm256_castps_si256(_mm256_cmp_ps(n, _mm256_setzero_ps(), _CMP_LE_OQ)), + _mm256_set1_epi32(0x82000000u)); + const __m256 s1 = + _mm256_castsi256_ps(_mm256_add_epi32(g, _mm256_set1_epi32(0x7f000000u))); + const __m256 s2 = _mm256_castsi256_ps(_mm256_sub_epi32(e, g)); + const __m256i d = _mm256_castps_si256( + _mm256_cmp_ps(_mm256_andnot_ps(_mm256_set1_ps(-0.f), n), + _mm256_set1_ps(192), _CMP_GT_OQ)); + return _mm256_or_ps( + _mm256_and_ps(_mm256_castsi256_ps(d), _mm256_mul_ps(s1, s1)), + _mm256_andnot_ps( + _mm256_castsi256_ps(d), + _mm256_or_ps( + _mm256_and_ps(_mm256_castsi256_ps(c), + _mm256_mul_ps(_mm256_fmadd_ps(s2, j, s2), s1)), + _mm256_andnot_ps(_mm256_castsi256_ps(c), _mm256_fmadd_ps(k, j, k))))); +} + +// computes silu x/(1+exp(-x)) in single precision vector +inline static __m256 ggml_v_silu(__m256 x) { + const __m256 one = _mm256_set1_ps(1); + const __m256 zero = _mm256_setzero_ps(); + const __m256 neg_x = _mm256_sub_ps(zero, x); + const __m256 exp_neg_x = ggml_v_expf(neg_x); + const __m256 one_plus_exp_neg_x = _mm256_add_ps(one, exp_neg_x); + return _mm256_div_ps(x, one_plus_exp_neg_x); +} + +#elif defined(__SSE2__) // __AVX2__ / __ARM_NEON + +#if defined(__FMA__) +#define MADD128(x, y, z) _mm_fmadd_ps(x, y, z) +#define NMADD128(x, y, z) _mm_fnmadd_ps(x, y, z) #else -inline static void ggml_vec_silu_f32(const int n, float * y, const float * x) { - for (int i = 0; i < n; ++i) { +#define MADD128(x, y, z) _mm_add_ps(_mm_mul_ps(x, y), z) +#define NMADD128(x, y, z) _mm_sub_ps(z, _mm_mul_ps(x, y)) +#endif + +// adapted from arm limited optimized routine +// the maximum error is 1.45358 plus 0.5 ulps +// numbers above 88.38 will flush to infinity +// numbers beneath -103.97 will flush to zero +inline static __m128 ggml_v_expf(__m128 x) { + const __m128 r = _mm_set1_ps(0x1.8p23f); + const __m128 z = MADD128(x, _mm_set1_ps(0x1.715476p+0f), r); + const __m128 n = _mm_sub_ps(z, r); + const __m128 b = + NMADD128(n, _mm_set1_ps(0x1.7f7d1cp-20f), NMADD128(n, _mm_set1_ps(0x1.62e4p-1f), x)); + const __m128i e = _mm_slli_epi32(_mm_castps_si128(z), 23); + const __m128 k = _mm_castsi128_ps(_mm_add_epi32(e, _mm_castps_si128(_mm_set1_ps(1)))); + const __m128i c = + _mm_castps_si128(_mm_cmpgt_ps(_mm_andnot_ps(_mm_set1_ps(-0.f), n), _mm_set1_ps(126))); + const __m128 u = _mm_mul_ps(b, b); + const __m128 j = + MADD128(MADD128(MADD128(_mm_set1_ps(0x1.0e4020p-7f), b, _mm_set1_ps(0x1.573e2ep-5f)), u, + MADD128(_mm_set1_ps(0x1.555e66p-3f), b, _mm_set1_ps(0x1.fffdb6p-2f))), + u, _mm_mul_ps(_mm_set1_ps(0x1.ffffecp-1f), b)); + if (!_mm_movemask_epi8(c)) + return MADD128(j, k, k); + const __m128i g = _mm_and_si128(_mm_castps_si128(_mm_cmple_ps(n, _mm_setzero_ps())), + _mm_set1_epi32(0x82000000u)); + const __m128 s1 = _mm_castsi128_ps(_mm_add_epi32(g, _mm_set1_epi32(0x7f000000u))); + const __m128 s2 = _mm_castsi128_ps(_mm_sub_epi32(e, g)); + const __m128i d = + _mm_castps_si128(_mm_cmpgt_ps(_mm_andnot_ps(_mm_set1_ps(-0.f), n), _mm_set1_ps(192))); + return _mm_or_ps( + _mm_and_ps(_mm_castsi128_ps(d), _mm_mul_ps(s1, s1)), + _mm_andnot_ps(_mm_castsi128_ps(d), + _mm_or_ps(_mm_and_ps(_mm_castsi128_ps(c), _mm_mul_ps(MADD128(s2, j, s2), s1)), + _mm_andnot_ps(_mm_castsi128_ps(c), MADD128(k, j, k))))); +} + +// computes silu x/(1+exp(-x)) in single precision vector +inline static __m128 ggml_v_silu(__m128 x) { + const __m128 one = _mm_set1_ps(1); + const __m128 zero = _mm_setzero_ps(); + const __m128 neg_x = _mm_sub_ps(zero, x); + const __m128 exp_neg_x = ggml_v_expf(neg_x); + const __m128 one_plus_exp_neg_x = _mm_add_ps(one, exp_neg_x); + return _mm_div_ps(x, one_plus_exp_neg_x); +} + +#endif // __ARM_NEON / __AVX2__ / __SSE2__ + +static void ggml_vec_silu_f32(const int n, float * y, const float * x) { + int i = 0; +#if defined(__AVX512F__) && defined(__AVX512DQ__) + for (; i + 15 < n; i += 16) { + _mm512_storeu_ps(y + i, ggml_v_silu(_mm512_loadu_ps(x + i))); + } +#elif defined(__AVX2__) && defined(__FMA__) + for (; i + 7 < n; i += 8) { + _mm256_storeu_ps(y + i, ggml_v_silu(_mm256_loadu_ps(x + i))); + } +#elif defined(__SSE2__) + for (; i + 3 < n; i += 4) { + _mm_storeu_ps(y + i, ggml_v_silu(_mm_loadu_ps(x + i))); + } +#elif defined(__ARM_NEON) + for (; i + 3 < n; i += 4) { + vst1q_f32(y + i, ggml_v_silu(vld1q_f32(x + i))); + } +#endif + for (; i < n; ++i) { y[i] = ggml_silu_f32(x[i]); } } + +static ggml_float ggml_vec_soft_max_f32(const int n, float * y, const float * x, float max) { + int i = 0; + ggml_float sum = 0; +#if defined(__AVX512F__) && defined(__AVX512DQ__) + for (; i + 15 < n; i += 16) { + __m512 val = ggml_v_expf(_mm512_sub_ps(_mm512_loadu_ps(x + i), + _mm512_set1_ps(max))); + _mm512_storeu_ps(y + i, val); + sum += (ggml_float)_mm512_reduce_add_ps(val); + } +#elif defined(__AVX2__) && defined(__FMA__) + for (; i + 7 < n; i += 8) { + __m256 val = ggml_v_expf(_mm256_sub_ps(_mm256_loadu_ps(x + i), + _mm256_set1_ps(max))); + _mm256_storeu_ps(y + i, val); + __m128 val2 = _mm_add_ps(_mm256_extractf128_ps(val, 1), + _mm256_castps256_ps128(val)); + val2 = _mm_add_ps(val2, _mm_movehl_ps(val2, val2)); + val2 = _mm_add_ss(val2, _mm_movehdup_ps(val2)); + sum += (ggml_float)_mm_cvtss_f32(val2); + } +#elif defined(__SSE2__) + for (; i + 3 < n; i += 4) { + __m128 val = ggml_v_expf(_mm_sub_ps(_mm_loadu_ps(x + i), + _mm_set1_ps(max))); + _mm_storeu_ps(y + i, val); +#if defined(__AVX__) || defined(__AVX2__) || defined(__AVX512F__) + val = _mm_add_ps(val, _mm_movehl_ps(val, val)); + val = _mm_add_ss(val, _mm_movehdup_ps(val)); +#else + __m128 tmp = _mm_shuffle_ps(val, val, _MM_SHUFFLE(2, 3, 0, 1)); + val = _mm_add_ps(val, tmp); + tmp = _mm_movehl_ps(tmp, val); + val = _mm_add_ss(val, tmp); #endif + sum += (ggml_float)_mm_cvtss_f32(val); + } +#elif defined(__ARM_NEON) + for (; i + 3 < n; i += 4) { + float32x4_t val = ggml_v_expf(vsubq_f32(vld1q_f32(x + i), + vdupq_n_f32(max))); + vst1q_f32(y + i, val); + sum += (ggml_float)vaddvq_f32(val); + } +#endif + for (; i < n; ++i) { + float val = expf(x[i] - max); + sum += (ggml_float)val; + y[i] = val; + } + return sum; +} inline static float ggml_silu_backward_f32(float x, float dy) { const float s = 1.0f/(1.0f + expf(-x)); return dy*s*(1.0f + x*(1.0f - s)); } -#ifdef GGML_SILU_FP16 -inline static void ggml_vec_silu_backward_f32(const int n, float * dx, const float * x, const float * dy) { - for (int i = 0; i < n; ++i) { - // we did not use x[i] to compute forward silu but its f16 equivalent - // take derivative at f16 of x[i]: - ggml_fp16_t fp16 = GGML_FP32_TO_FP16(x[i]); - float usedx = GGML_FP16_TO_FP32(fp16); - dx[i] = ggml_silu_backward_f32(usedx, dy[i]); - } -} -#else inline static void ggml_vec_silu_backward_f32(const int n, float * dx, const float * x, const float * dy) { for (int i = 0; i < n; ++i) { dx[i] = ggml_silu_backward_f32(x[i], dy[i]); } } -#endif inline static void ggml_vec_sum_f32(const int n, float * s, const float * x) { #ifndef GGML_USE_ACCELERATE @@ -2922,8 +3152,6 @@ struct ggml_context * ggml_init(struct ggml_init_params params) { float f = ggml_table_f32_f16[i] = GGML_COMPUTE_FP16_TO_FP32(u.fp16); ggml_table_gelu_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_f32(f)); ggml_table_gelu_quick_f16[i] = GGML_FP32_TO_FP16(ggml_gelu_quick_f32(f)); - ggml_table_silu_f16[i] = GGML_FP32_TO_FP16(ggml_silu_f32(f)); - ggml_table_exp_f16[i] = GGML_FP32_TO_FP16(expf(f)); } const uint64_t t_end = ggml_time_us(); UNUSED(t_end); @@ -13600,22 +13828,7 @@ static void ggml_compute_forward_soft_max_f32( float max = -INFINITY; ggml_vec_max_f32(nc, &max, wp); - ggml_float sum = 0.0; - - uint16_t scvt; - for (int i = 0; i < nc; i++) { - if (wp[i] == -INFINITY) { - dp[i] = 0.0f; - } else { - // const float val = (wp[i] == -INFINITY) ? 0.0 : exp(wp[i] - max); - ggml_fp16_t s = GGML_FP32_TO_FP16(wp[i] - max); - memcpy(&scvt, &s, sizeof(scvt)); - const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt]); - sum += (ggml_float)val; - dp[i] = val; - } - } - + ggml_float sum = ggml_vec_soft_max_f32(nc, dp, wp, max); assert(sum > 0.0); sum = 1.0/sum; @@ -15374,37 +15587,7 @@ static void ggml_compute_forward_flash_attn_f32( vvexpf(S, S, &Mup); ggml_vec_sum_f32(Mup, &sum, S); #else - uint16_t scvt[GGML_SOFT_MAX_UNROLL]; UNUSED(scvt); - ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 }; - - for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) { - if (i >= masked_begin) { - break; - } - float * SS = S + i; - - for (int j = 0; j < GGML_SOFT_MAX_UNROLL; ++j) { - if (i + j >= masked_begin) { - break; - } else if (SS[j] == -INFINITY) { - SS[j] = 0.0f; - } else { -#ifndef GGML_FLASH_ATTN_EXP_FP16 - const float val = expf(SS[j] - max); -#else - ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max); - memcpy(&scvt[j], &s, sizeof(uint16_t)); - const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt[j]]); -#endif - sump[j] += (ggml_float)val; - SS[j] = val; - } - } - } - - for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) { - sum += sump[i]; - } + sum = ggml_vec_soft_max_f32(Mup, S, S, max); #endif } @@ -15586,28 +15769,7 @@ static void ggml_compute_forward_flash_attn_f16( vvexpf(S, S, &Mup); ggml_vec_sum_f32(Mup, &sum, S); #else - uint16_t scvt[GGML_SOFT_MAX_UNROLL]; - ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 }; - - for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) { - float * SS = S + i; - - for (int j = 0; j < GGML_SOFT_MAX_UNROLL; ++j) { - if (SS[j] == -INFINITY) { - SS[j] = 0.0f; - } else { - ggml_fp16_t s = GGML_FP32_TO_FP16(SS[j] - max); - memcpy(&scvt[j], &s, sizeof(uint16_t)); - const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt[j]]); - sump[j] += (ggml_float)val; - SS[j] = val; - } - } - } - - for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) { - sum += sump[i]; - } + sum = ggml_vec_soft_max_f32(Mup, S, S, max); #endif } @@ -16234,38 +16396,7 @@ static void ggml_compute_forward_flash_attn_back_f32( vvexpf(SM, SM, &Mup); ggml_vec_sum_f32(Mup, &sum, SM); #else - uint16_t scvt[GGML_SOFT_MAX_UNROLL]; UNUSED(scvt); - ggml_float sump[GGML_SOFT_MAX_UNROLL] = { 0.0 }; - - for (int i = 0; i < Mup; i += GGML_SOFT_MAX_UNROLL) { - if (i >= masked_begin) { - break; - } - float * SR = S + i; - float * SW = SM + i; - - for (int j = 0; j < GGML_SOFT_MAX_UNROLL; ++j) { - if (i + j >= masked_begin) { - break; - } else if (SR[j] == -INFINITY) { - SW[j] = 0.0f; - } else { -#ifndef GGML_FLASH_ATTN_EXP_FP16 - const float val = expf(SR[j] - max); -#else - ggml_fp16_t s = GGML_FP32_TO_FP16(SR[j] - max); - memcpy(&scvt[j], &s, sizeof(uint16_t)); - const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt[j]]); -#endif - sump[j] += (ggml_float)val; - SW[j] = val; - } - } - } - - for (int i = 0; i < GGML_SOFT_MAX_UNROLL; i++) { - sum += sump[i]; - } + sum = ggml_vec_soft_max_f32(Mup, SM, S, max); #endif } @@ -17291,35 +17422,15 @@ static void ggml_compute_forward_cross_entropy_loss_f32( assert(!isnan(s1[i])); } #endif + // soft_max - ggml_float sum = 0.0; - { - float max = -INFINITY; - ggml_vec_max_f32(nc, &max, s0); - - uint16_t scvt; UNUSED(scvt); - for (int i = 0; i < nc; i++) { - if (s0[i] == -INFINITY) { - st[i] = 0.0f; - } else { -#ifndef GGML_CROSS_ENTROPY_EXP_FP16 - const float s = s0[i] - max; - const float val = expf(s); -#else - ggml_fp16_t s = GGML_FP32_TO_FP16(s0[i] - max); - memcpy(&scvt, &s, sizeof(scvt)); - const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt]); -#endif - sum += (ggml_float)val; - st[i] = val; - } - } - - assert(sum > 0.0); - // sum = 1.0/sum; - } - // avoid log(0) by rescaling from [0..1] to [eps..1] + float max = -INFINITY; + ggml_vec_max_f32(nc, &max, s0); + ggml_float sum = ggml_vec_soft_max_f32(nc, st, s0, max); + assert(sum > 0.0); sum = (1.0 - eps) / sum; + + // avoid log(0) by rescaling from [0..1] to [eps..1] ggml_vec_scale_f32(nc, st, sum); ggml_vec_add1_f32(nc, st, st, eps); ggml_vec_log_f32(nc, st, st); @@ -17409,32 +17520,11 @@ static void ggml_compute_forward_cross_entropy_loss_back_f32( #endif // soft_max - ggml_float sum = 0.0; - { - float max = -INFINITY; - ggml_vec_max_f32(nc, &max, s0); - - uint16_t scvt; UNUSED(scvt); - for (int i = 0; i < nc; i++) { - if (s0[i] == -INFINITY) { - ds0[i] = 0.0f; - } else { -#ifndef GGML_CROSS_ENTROPY_EXP_FP16 - const float s = s0[i] - max; - const float val = expf(s); -#else - ggml_fp16_t s = GGML_FP32_TO_FP16(s0[i] - max); - memcpy(&scvt, &s, sizeof(scvt)); - const float val = GGML_FP16_TO_FP32(ggml_table_exp_f16[scvt]); -#endif - sum += (ggml_float)val; - ds0[i] = val; - } - } - - assert(sum > 0.0); - sum = (1.0 - eps)/sum; - } + float max = -INFINITY; + ggml_vec_max_f32(nc, &max, s0); + ggml_float sum = ggml_vec_soft_max_f32(nc, ds0, s0, max); + assert(sum > 0.0); + sum = (1.0 - eps) / sum; // grad(src0) = (softmax(src0) - src1) * grad(cross_entropy_loss(src0, src1)) / nr ggml_vec_scale_f32(nc, ds0, sum); From ee94172d33399d2e814ca05c8a3ff8c523ebb093 Mon Sep 17 00:00:00 2001 From: Radoslav Gerganov Date: Fri, 17 May 2024 10:00:17 +0300 Subject: [PATCH 020/181] server : add support for the RPC backend (#7305) ref: #7292 --- examples/server/server.cpp | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 7e0d068f8..b598076d3 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -2387,6 +2387,7 @@ static void server_print_usage(const char * argv0, const gpt_params & params, co 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(" --rpc SERVERS comma separated list of RPC servers\n"); printf(" --path PUBLIC_PATH path from which to serve static files (default: disabled)\n"); 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"); @@ -2439,6 +2440,12 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, break; } sparams.port = std::stoi(argv[i]); + } else if (arg == "--rpc") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.rpc_servers = argv[i]; } else if (arg == "--host") { if (++i >= argc) { invalid_param = true; From e18bc6aaf3b547890609ed254ee5248e720e5840 Mon Sep 17 00:00:00 2001 From: amd-lalithnc Date: Fri, 17 May 2024 12:31:58 +0530 Subject: [PATCH 021/181] convert : fix Qwen/Qwen-7b conversion (#7308) --- 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 cd875fa4a..2810e1e41 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -526,7 +526,7 @@ class Model: # for this kind of tokenizer, added_vocab is not a subset of vocab, so they need to be combined added_vocab = tokenizer.special_tokens - reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in (vocab | added_vocab).items()} + reverse_vocab = {id_ : encoded_tok for encoded_tok, id_ in {**vocab, **added_vocab}.items()} for i in range(vocab_size): if i not in reverse_vocab: From 359cbe3f46c90ce6f5151005e411b8fb74f8139e Mon Sep 17 00:00:00 2001 From: Herman Semenov Date: Fri, 17 May 2024 07:08:49 +0000 Subject: [PATCH 022/181] ggml-quants, llama : removed excess checks (#7274) --- common/common.cpp | 2 +- ggml-quants.c | 2 +- llama.cpp | 8 ++------ 3 files changed, 4 insertions(+), 8 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 96130ad54..e624fc7f3 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -2553,7 +2553,7 @@ void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const cha size_t pos_start = 0; size_t pos_found = 0; - if (!data_str.empty() && (std::isspace(data_str[0]) || std::isspace(data_str.back()))) { + if (std::isspace(data_str[0]) || std::isspace(data_str.back())) { data_str = std::regex_replace(data_str, std::regex("\n"), "\\n"); data_str = std::regex_replace(data_str, std::regex("\""), "\\\""); data_str = std::regex_replace(data_str, std::regex(R"(\\[^n"])"), R"(\$&)"); diff --git a/ggml-quants.c b/ggml-quants.c index f13599f6b..9b291d522 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -1986,7 +1986,7 @@ static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restri for (int j = 0; j < QK_K/16; ++j) { if (quant_weights) { - const float * qw = quant_weights ? quant_weights + QK_K * i + 16*j : NULL; + const float * qw = quant_weights + QK_K * i + 16*j; for (int l = 0; l < 16; ++l) weight[l] = qw[l] * sqrtf(sigma2 + x[16*j+l]*x[16*j+l]); } else { for (int l = 0; l < 16; ++l) weight[l] = x[16*j+l]*x[16*j+l]; diff --git a/llama.cpp b/llama.cpp index 0ef756a52..daaa138b9 100644 --- a/llama.cpp +++ b/llama.cpp @@ -13904,9 +13904,7 @@ llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_ // Sample the next word X using top-k sampling llama_sample_top_k(nullptr, candidates, int(k), 1); - if (ctx) { - ctx->t_sample_us += ggml_time_us() - t_start_sample_us; - } + ctx->t_sample_us += ggml_time_us() - t_start_sample_us; llama_token X = llama_sample_token(ctx, candidates); t_start_sample_us = ggml_time_us(); @@ -13920,9 +13918,7 @@ llama_token llama_sample_token_mirostat(struct llama_context * ctx, llama_token_ // Update mu using the learning rate and error *mu = *mu - eta * e; - if (ctx) { - ctx->t_sample_us += ggml_time_us() - t_start_sample_us; - } + ctx->t_sample_us += ggml_time_us() - t_start_sample_us; return X; } From 29c60d8cddcfd14fa8a6bf023a6c4eb8692c76ba Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Fri, 17 May 2024 09:59:57 +0200 Subject: [PATCH 023/181] tokenization: add warning for double BOS (#7332) --- llama.cpp | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/llama.cpp b/llama.cpp index daaa138b9..c5a1fa0f6 100644 --- a/llama.cpp +++ b/llama.cpp @@ -12818,6 +12818,13 @@ static std::vector llama_tokenize_internal(const llama_vocab & } } + if (add_special && vocab.special_add_bos != 0 && output.size() >= 2 && output[1] == vocab.special_bos_id) { + LLAMA_LOG_WARN( + "%s: Added a BOS token to the prompt as specified by the model but the prompt " + "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. " + "Are you sure this is what you want?\n", __FUNCTION__); + } + if (add_special && vocab.special_add_eos == 1) { GGML_ASSERT(vocab.special_eos_id != -1); output.push_back(vocab.special_eos_id); @@ -12844,6 +12851,13 @@ static std::vector llama_tokenize_internal(const llama_vocab & } } + if (add_special && vocab.special_add_bos != 0 && output.size() >= 2 && output[1] == vocab.special_bos_id) { + LLAMA_LOG_WARN( + "%s: Added a BOS token to the prompt as specified by the model but the prompt " + "also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. " + "Are you sure this is what you want?\n", __FUNCTION__); + } + if (add_special && vocab.special_add_eos == 1) { GGML_ASSERT(vocab.special_add_eos != -1); output.push_back(vocab.special_eos_id); From 27b040691cbe45314147c2745e891a38e9c048d4 Mon Sep 17 00:00:00 2001 From: fairydreaming <166155368+fairydreaming@users.noreply.github.com> Date: Fri, 17 May 2024 13:24:38 +0200 Subject: [PATCH 024/181] llama : use n_embd_head_v when reshaping kqv (#7327) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * llama : use n_embd_head_v instead of n_embd_head_k when reshaping kqv * llama : use n_embd_v_gqa and n_embd_head_v instead of n_embd_k_gqa and n_embd_head_k when making a view of cached value vectors. --------- Co-authored-by: Stanisław Szymczyk --- llama.cpp | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/llama.cpp b/llama.cpp index c5a1fa0f6..e11f0ac4b 100644 --- a/llama.cpp +++ b/llama.cpp @@ -6622,6 +6622,7 @@ static struct ggml_tensor * llm_build_kqv( const int64_t n_embd_head_k = hparams.n_embd_head_k; const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); const int64_t n_embd_head_v = hparams.n_embd_head_v; + const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); struct ggml_tensor * q = ggml_permute(ctx, q_cur, 0, 2, 1, 3); cb(q, "q", il); @@ -6644,8 +6645,8 @@ static struct ggml_tensor * llm_build_kqv( struct ggml_tensor * v = ggml_view_3d(ctx, kv.v_l[il], n_embd_head_v, n_kv, n_head_kv, - ggml_row_size(kv.v_l[il]->type, n_embd_k_gqa), - ggml_row_size(kv.v_l[il]->type, n_embd_head_k), + ggml_row_size(kv.v_l[il]->type, n_embd_v_gqa), + ggml_row_size(kv.v_l[il]->type, n_embd_head_v), 0); cb(v, "v", il); @@ -6655,7 +6656,7 @@ static struct ggml_tensor * llm_build_kqv( ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32); } - cur = ggml_reshape_2d(ctx, cur, n_embd_head_k*n_head, n_tokens); + cur = ggml_reshape_2d(ctx, cur, n_embd_head_v*n_head, n_tokens); } else { struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q); cb(kq, "kq", il); @@ -6700,7 +6701,7 @@ static struct ggml_tensor * llm_build_kqv( struct ggml_tensor * kqv_merged = ggml_permute(ctx, kqv, 0, 2, 1, 3); cb(kqv_merged, "kqv_merged", il); - cur = ggml_cont_2d(ctx, kqv_merged, n_embd_head_k*n_head, n_tokens); + cur = ggml_cont_2d(ctx, kqv_merged, n_embd_head_v*n_head, n_tokens); cb(cur, "kqv_merged_cont", il); } From d273c1402b25086fd91aef2467ac13f2e49fa0ea Mon Sep 17 00:00:00 2001 From: Aarni Koskela Date: Fri, 17 May 2024 15:11:45 +0300 Subject: [PATCH 025/181] py : convert-hf-to-gguf-update improvements (#7340) * convert-hf-to-gguf-update: automate updating * convert-hf-to-gguf-update: improve download * share requests session for performance * create directories only when needed, don't skip downloads when empty directory encountered * be more graceful about errors --- convert-hf-to-gguf-update.py | 87 ++++++++++++++++-------------------- convert-hf-to-gguf.py | 2 + 2 files changed, 41 insertions(+), 48 deletions(-) diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py index 14aa0c45a..27983fadf 100755 --- a/convert-hf-to-gguf-update.py +++ b/convert-hf-to-gguf-update.py @@ -20,11 +20,13 @@ # - Update llama.cpp with the new pre-tokenizer if necessary # # TODO: generate tokenizer tests for llama.cpp -# TODO: automate the update of convert-hf-to-gguf.py # import logging import os +import pathlib +import re + import requests import sys import json @@ -35,6 +37,7 @@ from transformers import AutoTokenizer logging.basicConfig(level=logging.DEBUG) logger = logging.getLogger("convert-hf-to-gguf-update") +sess = requests.Session() class TOKENIZER_TYPE(IntEnum): @@ -79,63 +82,44 @@ models = [ {"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", }, ] -# make directory "models/tokenizers" if it doesn't exist -if not os.path.exists("models/tokenizers"): - os.makedirs("models/tokenizers") - def download_file_with_auth(url, token, save_path): headers = {"Authorization": f"Bearer {token}"} - response = requests.get(url, headers=headers) - if response.status_code == 200: - with open(save_path, 'wb') as f: - f.write(response.content) - logger.info(f"File {save_path} downloaded successfully") - else: - logger.info(f"Failed to download file. Status code: {response.status_code}") + response = sess.get(url, headers=headers) + response.raise_for_status() + os.makedirs(os.path.dirname(save_path), exist_ok=True) + with open(save_path, 'wb') as f: + f.write(response.content) + logger.info(f"File {save_path} downloaded successfully") -# download the tokenizer models -for model in models: +def download_model(model): name = model["name"] repo = model["repo"] tokt = model["tokt"] - if not os.path.exists(f"models/tokenizers/{name}"): - os.makedirs(f"models/tokenizers/{name}") - else: - logger.info(f"Directory models/tokenizers/{name} already exists - skipping") - continue - - logger.info(f"Downloading {name} to models/tokenizers/{name}") - - url = f"{repo}/raw/main/config.json" - save_path = f"models/tokenizers/{name}/config.json" - download_file_with_auth(url, token, save_path) - - url = f"{repo}/raw/main/tokenizer.json" - save_path = f"models/tokenizers/{name}/tokenizer.json" - download_file_with_auth(url, token, save_path) - - # if downloaded file is less than 1KB, we likely need to download an LFS instead - if os.path.getsize(save_path) < 1024: - # remove the file - os.remove(save_path) - url = f"{repo}/resolve/main/tokenizer.json" - save_path = f"models/tokenizers/{name}/tokenizer.json" - download_file_with_auth(url, token, save_path) + os.makedirs(f"models/tokenizers/{name}", exist_ok=True) + files = ["config.json", "tokenizer.json", "tokenizer_config.json"] if tokt == TOKENIZER_TYPE.SPM: - url = f"{repo}/resolve/main/tokenizer.model" - save_path = f"models/tokenizers/{name}/tokenizer.model" - download_file_with_auth(url, token, save_path) + files.append("tokenizer.model") + + for file in files: + save_path = f"models/tokenizers/{name}/{file}" + if os.path.isfile(save_path): + logger.info(f"{name}: File {save_path} already exists - skipping") + continue + download_file_with_auth(f"{repo}/resolve/main/{file}", token, save_path) + + +for model in models: + try: + download_model(model) + except Exception as e: + logger.error(f"Failed to download model {model['name']}. Error: {e}") - url = f"{repo}/raw/main/tokenizer_config.json" - save_path = f"models/tokenizers/{name}/tokenizer_config.json" - download_file_with_auth(url, token, save_path) # generate the source code for the convert-hf-to-gguf.py:get_vocab_base_pre() function: -# TODO: auto-update convert-hf-to-gguf.py with the generated function src_ifs = "" for model in models: @@ -224,11 +208,18 @@ src_func = f""" return res """ -print(src_func) # noqa: NP100 +convert_py_pth = pathlib.Path("convert-hf-to-gguf.py") +convert_py = convert_py_pth.read_text() +convert_py = re.sub( + r"(# Marker: Start get_vocab_base_pre)(.+?)( +# Marker: End get_vocab_base_pre)", + lambda m: m.group(1) + src_func + m.group(3), + convert_py, + flags=re.DOTALL | re.MULTILINE, +) -logger.info("\n") -logger.info("!!! Copy-paste the function above into convert-hf-to-gguf.py !!!") -logger.info("\n") +convert_py_pth.write_text(convert_py) + +logger.info("+++ convert-hf-to-gguf.py was updated") # generate tests for each tokenizer model diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 2810e1e41..5ba3161c7 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -402,6 +402,7 @@ class Model: # NOTE: this function is generated by convert-hf-to-gguf-update.py # do not modify it manually! # ref: https://github.com/ggerganov/llama.cpp/pull/6920 + # Marker: Start get_vocab_base_pre def get_vocab_base_pre(self, tokenizer) -> str: # encoding this string and hashing the resulting tokens would (hopefully) give us a unique identifier that # is specific for the BPE pre-tokenizer used by the model @@ -489,6 +490,7 @@ class Model: logger.debug(f"chkhsh: {chkhsh}") return res + # Marker: End get_vocab_base_pre def _set_vocab_gpt2(self) -> None: tokens, toktypes, tokpre = self.get_vocab_base() From 51e9d02599336e62948d29f1d6c05addeb921ac2 Mon Sep 17 00:00:00 2001 From: Brian Date: Fri, 17 May 2024 22:40:14 +1000 Subject: [PATCH 026/181] Added a single test function script and fix debug-test.sh to be more robust (#7279) * run-single-test.sh: added a single test function script and fix debug-test.sh to be more robust * debug-test.sh: combined execute and gdb test mode via -g flag * debug-test.sh: refactor * debug-test: refactor for clarity * debug-test.sh: comment style changes * debug-test.sh: fix gdb --- docs/debugging-tests.md | 24 +++- scripts/debug-test.sh | 266 ++++++++++++++++++++++++++-------------- 2 files changed, 196 insertions(+), 94 deletions(-) diff --git a/docs/debugging-tests.md b/docs/debugging-tests.md index 51a125e19..18407f688 100644 --- a/docs/debugging-tests.md +++ b/docs/debugging-tests.md @@ -1,6 +1,6 @@ # Debugging Tests Tips -## How to run & debug a specific test without anything else to keep the feedback loop short? +## How to run & execute or debug a specific test without anything else to keep the feedback loop short? There is a script called debug-test.sh in the scripts folder whose parameter takes a REGEX and an optional test number. @@ -10,13 +10,27 @@ For example, running the following command will output an interactive list from It will then build & run in the debugger for you. +To just execute a test and get back a PASS or FAIL message run: + ```bash ./scripts/debug-test.sh test-tokenizer +``` + +To test in GDB use the `-g` flag to enable gdb test mode. + +```bash +./scripts/debug-test.sh -g test-tokenizer # Once in the debugger, i.e. at the chevrons prompt, setting a breakpoint could be as follows: >>> b main ``` +To speed up the testing loop, if you know your test number you can just run it similar to below: + +```bash +./scripts/debug-test.sh test 23 +``` + For further reference use `debug-test.sh -h` to print help.   @@ -41,7 +55,7 @@ cmake -DCMAKE_BUILD_TYPE=Debug -DLLAMA_CUDA=1 -DLLAMA_FATAL_WARNINGS=ON .. make -j ``` -#### Step 3.1: Identify Test Command for Debugging +#### Step 3: Find all tests available that matches REGEX The output of this command will give you the command & arguments needed to run GDB. @@ -69,11 +83,13 @@ Labels: main ... ``` -So for test #1 we can tell these two pieces of relevant information: +#### Step 4: Identify Test Command for Debugging + +So for test #1 above we can tell these two pieces of relevant information: * Test Binary: `~/llama.cpp/build-ci-debug/bin/test-tokenizer-0` * Test GGUF Model: `~/llama.cpp/tests/../models/ggml-vocab-llama-spm.gguf` -#### Step 3.2: Run GDB on test command +#### Step 5: Run GDB on test command Based on the ctest 'test command' report above we can then run a gdb session via this command below: diff --git a/scripts/debug-test.sh b/scripts/debug-test.sh index 231a23d69..7b2b601a9 100755 --- a/scripts/debug-test.sh +++ b/scripts/debug-test.sh @@ -1,117 +1,203 @@ #!/bin/bash -test_suite=${1:-} -test_number=${2:-} PROG=${0##*/} build_dir="build-ci-debug" -if [ x"$1" = x"-h" ] || [ x"$1" = x"--help" ]; then - echo "Usage: $PROG [OPTION]... (test_number)" - echo "Debug specific ctest program." - echo - echo "Options:" - echo " -h, --help Display this help and exit" - echo - echo "Arguments:" - echo " (Mandatory) Supply one regex to the script to filter tests" - echo " (test_number) (Optional) Test number to run a specific test" - echo - echo "Example:" - echo " $PROG test-tokenizer" - echo " $PROG test-tokenizer 3" - echo - exit 0 -fi +# Print Color Commands +red=$(tput setaf 1) +green=$(tput setaf 2) +yellow=$(tput setaf 3) +blue=$(tput setaf 4) +magenta=$(tput setaf 5) +cyan=$(tput setaf 6) +normal=$(tput sgr0) -# Function to select and debug a test -function select_test() { - test_suite=${1:-test} - test_number=${2:-} - # Sanity Check If Tests Is Detected - printf "\n\nGathering tests that fit REGEX: ${test_suite} ...\n" - tests=($(ctest -R ${test_suite} -V -N | grep -E " +Test +#[0-9]+*" | cut -d':' -f2 | awk '{$1=$1};1')) - if [ ${#tests[@]} -eq 0 ] - then - echo "No tests avaliable... check your compliation process..." - echo "Exiting." - exit 1 - fi +# Print Help Message +#################### - if [ -z $test_number ] - then - # List out avaliable tests - printf "Which test would you like to debug?\n" - id=0 - for s in "${tests[@]}" - do - echo "Test# ${id}" - echo " $s" - ((id++)) - done +print_full_help() { + cat << EOF +Usage: $PROG [OPTION]... (test_number) +Debug specific ctest program. - # Prompt user which test they wanted to run - printf "\nRun test#? " - read test_number - else - printf "\nUser Already Requested #${test_number}" - fi +Options: + -h, --help display this help and exit + -g run in gdb mode - # Start GDB with the requested test binary and arguments - printf "Debugging(GDB) test: ${tests[test_number]}\n" - # Change IFS (Internal Field Separator) - sIFS=$IFS - IFS=$'\n' +Arguments: + (Mandatory) Supply one regex to the script to filter tests + (test_number) (Optional) Test number to run a specific test - # Get test args - gdb_args=($(ctest -R ${test_suite} -V -N | grep "Test command" | cut -d':' -f3 | awk '{$1=$1};1' )) - IFS=$sIFS - printf "Debug arguments: ${gdb_args[test_number]}\n\n" - - # Expand paths if needed - args=() - for x in $(echo ${gdb_args[test_number]} | sed -e 's/"\/\"//') - do - args+=($(echo $x | sed -e 's/.*\/..\//..\//')) - done - - # Execute debugger - echo "gdb args: ${args[@]}" - gdb --args ${args[@]} +Example: + $PROG test-tokenizer + $PROG test-tokenizer 3 +EOF } +abort() { + echo "Error: $1" >&2 + cat << EOF >&2 +Usage: $PROG [OPTION]... (test_number) +Debug specific ctest program. +Refer to --help for full instructions. +EOF + exit 1 +} + + +# Dependency Sanity Check +######################### + +check_dependency() { + command -v "$1" >/dev/null 2>&1 || { + abort "$1 is required but not found. Please install it and try again." + } +} + +check_dependency ctest +check_dependency cmake + + # Step 0: Check the args -if [ -z "$test_suite" ] -then - echo "Usage: $PROG [OPTION]... (test_number)" - echo "Supply one regex to the script to filter tests," - echo "and optionally a test number to run a specific test." - echo "Use --help flag for full instructions" - exit 1 +######################## + +if [ x"$1" = x"-h" ] || [ x"$1" = x"--help" ]; then + print_full_help >&2 + exit 0 fi +# Parse command-line options +gdb_mode=false +while getopts "g" opt; do + case $opt in + g) + gdb_mode=true + echo "gdb_mode Mode Enabled" + ;; + esac +done + +# Shift the option parameters +shift $((OPTIND - 1)) + +# Positionial Argument Processing : +if [ -z "${1}" ]; then + abort "Test regex is required" +else + test_suite=${1:-} +fi + +# Positionial Argument Processing : (test_number) +test_number=${2:-} + + # Step 1: Reset and Setup folder context +######################################## + ## Sanity check that we are actually in a git repo repo_root=$(git rev-parse --show-toplevel) if [ ! -d "$repo_root" ]; then - echo "Error: Not in a Git repository." - exit 1 + abort "Not in a Git repository." fi -## Reset folder to root context of git repo -pushd "$repo_root" || exit 1 +## Reset folder to root context of git repo and Create and enter build directory +pushd "$repo_root" +rm -rf "$build_dir" && mkdir "$build_dir" || abort "Failed to make $build_dir" -## Create and enter build directory -rm -rf "$build_dir" && mkdir "$build_dir" || exit 1 # Step 2: Setup Build Environment and Compile Test Binaries -cmake -B "./$build_dir" -DCMAKE_BUILD_TYPE=Debug -DLLAMA_CUDA=1 -DLLAMA_FATAL_WARNINGS=ON || exit 1 -pushd "$build_dir" && make -j || exit 1 +########################################################### -# Step 3: Debug the Test -select_test "$test_suite" "$test_number" +# Note: test-eval-callback requires -DLLAMA_CURL +cmake -B "./$build_dir" -DCMAKE_BUILD_TYPE=Debug -DLLAMA_CUDA=1 -DLLAMA_CURL=1 || abort "Failed to build enviroment" +pushd "$build_dir" +make -j || abort "Failed to compile" +popd > /dev/null || exit 1 -# Step 4: Return to the directory from which the user ran the command. -popd || exit 1 -popd || exit 1 -popd || exit 1 + +# Step 3: Find all tests available that matches REGEX +#################################################### + +# Ctest Gather Tests +# `-R test-tokenizer` : looks for all the test files named `test-tokenizer*` (R=Regex) +# `-N` : "show-only" disables test execution & shows test commands that you can feed to GDB. +# `-V` : Verbose Mode +printf "\n\nGathering tests that fit REGEX: ${test_suite} ...\n" +pushd "$build_dir" +tests=($(ctest -R ${test_suite} -V -N | grep -E " +Test +#[0-9]+*" | cut -d':' -f2 | awk '{$1=$1};1')) +if [ ${#tests[@]} -eq 0 ]; then + abort "No tests avaliable... check your compliation process..." +fi +popd > /dev/null || exit 1 + + +# Step 4: Identify Test Command for Debugging +############################################# + +# Select test number +if [ -z $test_number ]; then + # List out avaliable tests + printf "Which test would you like to debug?\n" + id=0 + for s in "${tests[@]}" + do + echo "Test# ${id}" + echo " $s" + ((id++)) + done + + # Prompt user which test they wanted to run + printf "\nRun test#? " + read test_number + +else + printf "\nUser Already Requested #${test_number}\n" + +fi + +# Grab all tests commands +pushd "$build_dir" +sIFS=$IFS # Save Initial IFS (Internal Field Separator) +IFS=$'\n' # Change IFS (Internal Field Separator) (So we split ctest output by newline rather than by spaces) +test_args=($(ctest -R ${test_suite} -V -N | grep "Test command" | cut -d':' -f3 | awk '{$1=$1};1' )) # Get test args +IFS=$sIFS # Reset IFS (Internal Field Separator) +popd > /dev/null || exit 1 + +# Grab specific test command +single_test_name="${tests[test_number]}" +single_test_command="${test_args[test_number]}" + + +# Step 5: Execute or GDB Debug +############################## + +printf "${magenta}Running Test #${test_number}: ${single_test_name}${normal}\n" +printf "${cyan}single_test_command: ${single_test_command}${normal}\n" + +if [ "$gdb_mode" = "true" ]; then + # Execute debugger + pushd "$repo_root" || exit 1 + eval "gdb --args ${single_test_command}" + popd > /dev/null || exit 1 + +else + # Execute Test + pushd "$repo_root" || exit 1 + eval "${single_test_command}" + exit_code=$? + popd > /dev/null || exit 1 + + # Print Result + printf "${blue}Ran Test #${test_number}: ${single_test_name}${normal}\n" + printf "${yellow}Command: ${single_test_command}${normal}\n" + if [ $exit_code -eq 0 ]; then + printf "${green}TEST PASS${normal}\n" + else + printf "${red}TEST FAIL${normal}\n" + fi + +fi + +# Return to the directory from which the user ran the command. +popd > /dev/null || exit 1 From f4bd8b3d260bb09491ba63c77ab7012b744362ef Mon Sep 17 00:00:00 2001 From: Radoslav Gerganov Date: Fri, 17 May 2024 17:25:44 +0300 Subject: [PATCH 027/181] rpc : set SO_REUSEADDR for the server socket (#7320) ref: #7293 --- examples/rpc/rpc-server.cpp | 4 ++++ ggml-rpc.cpp | 13 +++++++++++-- 2 files changed, 15 insertions(+), 2 deletions(-) diff --git a/examples/rpc/rpc-server.cpp b/examples/rpc/rpc-server.cpp index 41f377376..7c15d2aa4 100644 --- a/examples/rpc/rpc-server.cpp +++ b/examples/rpc/rpc-server.cpp @@ -56,6 +56,10 @@ static bool rpc_server_params_parse(int argc, char ** argv, rpc_server_params & } else if (arg == "-h" || arg == "--help") { print_usage(argc, argv, params); exit(0); + } else { + fprintf(stderr, "error: unknown argument: %s\n", arg.c_str()); + print_usage(argc, argv, params); + exit(0); } } return true; diff --git a/ggml-rpc.cpp b/ggml-rpc.cpp index ba392009f..4a9bfa52d 100644 --- a/ggml-rpc.cpp +++ b/ggml-rpc.cpp @@ -134,7 +134,13 @@ static bool set_no_delay(sockfd_t sockfd) { int flag = 1; // set TCP_NODELAY to disable Nagle's algorithm int ret = setsockopt(sockfd, IPPROTO_TCP, TCP_NODELAY, (char *)&flag, sizeof(int)); - return ret >= 0; + return ret == 0; +} + +static bool set_reuse_addr(sockfd_t sockfd) { + int flag = 1; + int ret = setsockopt(sockfd, SOL_SOCKET, SO_REUSEADDR, (char *)&flag, sizeof(int)); + return ret == 0; } static std::shared_ptr socket_connect(const char * host, int port) { @@ -181,7 +187,10 @@ static std::shared_ptr create_server_socket(const char * host, int por if (sock == nullptr) { return nullptr; } - + if (!set_reuse_addr(sockfd)) { + fprintf(stderr, "Failed to set SO_REUSEADDR\n"); + return nullptr; + } struct sockaddr_in serv_addr; serv_addr.sin_family = AF_INET; serv_addr.sin_addr.s_addr = inet_addr(host); From 82ca83db3c8d45df559c03a4225b6eb34808a2db Mon Sep 17 00:00:00 2001 From: Gavin Zhao Date: Fri, 17 May 2024 11:03:03 -0400 Subject: [PATCH 028/181] ROCm: use native CMake HIP support (#5966) Supercedes #4024 and #4813. CMake's native HIP support has become the recommended way to add HIP code into a project (see [here](https://rocm.docs.amd.com/en/docs-6.0.0/conceptual/cmake-packages.html#using-hip-in-cmake)). This PR makes the following changes: 1. The environment variable `HIPCXX` or CMake option `CMAKE_HIP_COMPILER` should be used to specify the HIP compiler. Notably this shouldn't be `hipcc`, but ROCm's clang, which usually resides in `$ROCM_PATH/llvm/bin/clang`. Previously this was control by `CMAKE_C_COMPILER` and `CMAKE_CXX_COMPILER`. Note that since native CMake HIP support is not yet available on Windows, on Windows we fall back to the old behavior. 2. CMake option `CMAKE_HIP_ARCHITECTURES` is used to control the GPU architectures to build for. Previously this was controled by `GPU_TARGETS`. 3. Updated the Nix recipe to account for these new changes. 4. The GPU targets to build against in the Nix recipe is now consistent with the supported GPU targets in nixpkgs. 5. Added CI checks for HIP on both Linux and Windows. On Linux, we test both the new and old behavior. The most important part about this PR is the separation of the HIP compiler and the C/C++ compiler. This allows users to choose a different C/C++ compiler if desired, compared to the current situation where when building for ROCm support, everything must be compiled with ROCm's clang. ~~Makefile is unchanged. Please let me know if we want to be consistent on variables' naming because Makefile still uses `GPU_TARGETS` to control architectures to build for, but I feel like setting `CMAKE_HIP_ARCHITECTURES` is a bit awkward when you're calling `make`.~~ Makefile used `GPU_TARGETS` but the README says to use `AMDGPU_TARGETS`. For consistency with CMake, all usage of `GPU_TARGETS` in Makefile has been updated to `AMDGPU_TARGETS`. Thanks to the suggestion of @jin-eld, to maintain backwards compatibility (and not break too many downstream users' builds), if `CMAKE_CXX_COMPILER` ends with `hipcc`, then we still compile using the original behavior and emit a warning that recommends switching to the new HIP support. Similarly, if `AMDGPU_TARGETS` is set but `CMAKE_HIP_ARCHITECTURES` is not, then we forward `AMDGPU_TARGETS` to `CMAKE_HIP_ARCHITECTURES` to ease the transition to the new HIP support. Signed-off-by: Gavin Zhao --- .devops/nix/package.nix | 16 +++++----- .github/workflows/build.yml | 58 +++++++++++++++++++++++++++++++++++++ CMakeLists.txt | 42 ++++++++++++++++++++++----- Makefile | 6 ++-- README.md | 25 ++++++++++++---- 5 files changed, 122 insertions(+), 25 deletions(-) diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix index 2c0ae4e2a..1c9633cdf 100644 --- a/.devops/nix/package.nix +++ b/.devops/nix/package.nix @@ -227,20 +227,20 @@ effectiveStdenv.mkDerivation ( ) ] ++ optionals useRocm [ - (cmakeFeature "CMAKE_C_COMPILER" "hipcc") - (cmakeFeature "CMAKE_CXX_COMPILER" "hipcc") - - # Build all targets supported by rocBLAS. When updating search for TARGET_LIST_ROCM - # in https://github.com/ROCmSoftwarePlatform/rocBLAS/blob/develop/CMakeLists.txt - # and select the line that matches the current nixpkgs version of rocBLAS. - # Should likely use `rocmPackages.clr.gpuTargets`. - "-DAMDGPU_TARGETS=gfx803;gfx900;gfx906:xnack-;gfx908:xnack-;gfx90a:xnack+;gfx90a:xnack-;gfx940;gfx941;gfx942;gfx1010;gfx1012;gfx1030;gfx1100;gfx1101;gfx1102" + (cmakeFeature "CMAKE_HIP_COMPILER" "${rocmPackages.llvm.clang}/bin/clang") + (cmakeFeature "CMAKE_HIP_ARCHITECTURES" (builtins.concatStringsSep ";" rocmPackages.clr.gpuTargets)) ] ++ optionals useMetalKit [ (lib.cmakeFeature "CMAKE_C_FLAGS" "-D__ARM_FEATURE_DOTPROD=1") (cmakeBool "LLAMA_METAL_EMBED_LIBRARY" (!precompileMetalShaders)) ]; + # Environment variables needed for ROCm + env = optionals useRocm { + ROCM_PATH = "${rocmPackages.clr}"; + HIP_DEVICE_LIB_PATH = "${rocmPackages.rocm-device-libs}/amdgcn/bitcode"; + }; + # TODO(SomeoneSerge): It's better to add proper install targets at the CMake level, # if they haven't been added yet. postInstall = '' diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 0742443c6..0109cc004 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -392,6 +392,33 @@ jobs: cmake -DLLAMA_VULKAN=ON .. cmake --build . --config Release -j $(nproc) + ubuntu-22-cmake-hip: + runs-on: ubuntu-22.04 + container: rocm/dev-ubuntu-22.04:6.0.2 + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v3 + + - name: Dependencies + id: depends + run: | + sudo apt-get update + sudo apt-get install -y build-essential git cmake rocblas-dev hipblas-dev + + - name: Build with native CMake HIP support + id: cmake_build + run: | + cmake -B build -S . -DCMAKE_HIP_COMPILER="$(hipconfig -l)/clang" -DLLAMA_HIPBLAS=ON + cmake --build build --config Release -j $(nproc) + + - name: Build with legacy HIP support + id: cmake_build_legacy_hip + run: | + cmake -B build2 -S . -DCMAKE_C_COMPILER=hipcc -DCMAKE_CXX_COMPILER=hipcc -DLLAMA_HIPBLAS=ON + cmake --build build2 --config Release -j $(nproc) + ubuntu-22-cmake-sycl: runs-on: ubuntu-22.04 @@ -989,6 +1016,37 @@ jobs: path: llama-${{ steps.tag.outputs.name }}-bin-win-sycl-x64.zip name: llama-bin-win-sycl-x64.zip + windows-latest-cmake-hip: + runs-on: windows-latest + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v3 + + - name: Install + id: depends + run: | + $ErrorActionPreference = "Stop" + write-host "Downloading AMD HIP SDK Installer" + Invoke-WebRequest -Uri "https://download.amd.com/developer/eula/rocm-hub/AMD-Software-PRO-Edition-23.Q4-WinSvr2022-For-HIP.exe" -OutFile "${env:RUNNER_TEMP}\rocm-install.exe" + write-host "Installing AMD HIP SDK" + Start-Process "${env:RUNNER_TEMP}\rocm-install.exe" -ArgumentList '-install' -NoNewWindow -Wait + write-host "Completed AMD HIP SDK installation" + + - name: Verify ROCm + id: verify + run: | + & 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' --version + + - name: Build + id: cmake_build + run: | + $env:HIP_PATH=$(Resolve-Path 'C:\Program Files\AMD\ROCm\*\bin\clang.exe' | split-path | split-path) + $env:CMAKE_PREFIX_PATH="${env:HIP_PATH}" + cmake -G "Unix Makefiles" -B build -S . -DCMAKE_C_COMPILER="${env:HIP_PATH}\bin\clang.exe" -DCMAKE_CXX_COMPILER="${env:HIP_PATH}\bin\clang++.exe" -DLLAMA_HIPBLAS=ON + cmake --build build --config Release + ios-xcode-build: runs-on: macos-latest diff --git a/CMakeLists.txt b/CMakeLists.txt index 8ab6a45a6..990e34b86 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -555,16 +555,37 @@ if (LLAMA_VULKAN) endif() if (LLAMA_HIPBLAS) - list(APPEND CMAKE_PREFIX_PATH /opt/rocm) + if ($ENV{ROCM_PATH}) + set(ROCM_PATH $ENV{ROCM_PATH}) + else() + set(ROCM_PATH /opt/rocm) + endif() + list(APPEND CMAKE_PREFIX_PATH ${ROCM_PATH}) - if (NOT ${CMAKE_C_COMPILER_ID} MATCHES "Clang") - message(WARNING "Only LLVM is supported for HIP, hint: CC=/opt/rocm/llvm/bin/clang") + # CMake on Windows doesn't support the HIP language yet + if(WIN32) + set(CXX_IS_HIPCC TRUE) + else() + string(REGEX MATCH "hipcc(\.bat)?$" CXX_IS_HIPCC "${CMAKE_CXX_COMPILER}") endif() - if (NOT ${CMAKE_CXX_COMPILER_ID} MATCHES "Clang") - message(WARNING "Only LLVM is supported for HIP, hint: CXX=/opt/rocm/llvm/bin/clang++") - endif() + if(CXX_IS_HIPCC) + if(LINUX) + if (NOT ${CMAKE_CXX_COMPILER_ID} MATCHES "Clang") + message(WARNING "Only LLVM is supported for HIP, hint: CXX=/opt/rocm/llvm/bin/clang++") + endif() + message(WARNING "Setting hipcc as the C++ compiler is legacy behavior." + " Prefer setting the HIP compiler directly. See README for details.") + endif() + else() + # Forward AMDGPU_TARGETS to CMAKE_HIP_ARCHITECTURES. + if(AMDGPU_TARGETS AND NOT CMAKE_HIP_ARCHITECTURES) + set(CMAKE_HIP_ARCHITECTURES ${AMDGPU_ARGETS}) + endif() + cmake_minimum_required(VERSION 3.21) + enable_language(HIP) + endif() find_package(hip REQUIRED) find_package(hipblas REQUIRED) find_package(rocblas REQUIRED) @@ -598,13 +619,18 @@ if (LLAMA_HIPBLAS) add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y}) add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER}) - set_source_files_properties(${GGML_SOURCES_ROCM} PROPERTIES LANGUAGE CXX) + if (CXX_IS_HIPCC) + set_source_files_properties(${GGML_SOURCES_ROCM} PROPERTIES LANGUAGE CXX) + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} hip::device) + else() + set_source_files_properties(${GGML_SOURCES_ROCM} PROPERTIES LANGUAGE HIP) + endif() if (LLAMA_STATIC) message(FATAL_ERROR "Static linking not supported for HIP/ROCm") endif() - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} hip::device PUBLIC hip::host roc::rocblas roc::hipblas) + set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} PUBLIC hip::host roc::rocblas roc::hipblas) endif() if (LLAMA_SYCL) diff --git a/Makefile b/Makefile index 3fa56d13a..22d521856 100644 --- a/Makefile +++ b/Makefile @@ -560,10 +560,10 @@ endif # LLAMA_VULKAN ifdef LLAMA_HIPBLAS ifeq ($(wildcard /opt/rocm),) ROCM_PATH ?= /usr - GPU_TARGETS ?= $(shell $(shell which amdgpu-arch)) + AMDGPU_TARGETS ?= $(shell $(shell which amdgpu-arch)) else ROCM_PATH ?= /opt/rocm - GPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch) + AMDGPU_TARGETS ?= $(shell $(ROCM_PATH)/llvm/bin/amdgpu-arch) endif HIPCC ?= $(CCACHE) $(ROCM_PATH)/bin/hipcc LLAMA_CUDA_DMMV_X ?= 32 @@ -575,7 +575,7 @@ ifdef LLAMA_HIP_UMA endif # LLAMA_HIP_UMA MK_LDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib MK_LDFLAGS += -lhipblas -lamdhip64 -lrocblas - HIPFLAGS += $(addprefix --offload-arch=,$(GPU_TARGETS)) + HIPFLAGS += $(addprefix --offload-arch=,$(AMDGPU_TARGETS)) HIPFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X) HIPFLAGS += -DGGML_CUDA_MMV_Y=$(LLAMA_CUDA_MMV_Y) HIPFLAGS += -DK_QUANTS_PER_ITERATION=$(LLAMA_CUDA_KQUANTS_ITER) diff --git a/README.md b/README.md index 5d6217d13..7dd6fc0eb 100644 --- a/README.md +++ b/README.md @@ -528,13 +528,28 @@ Building the program with BLAS support may lead to some performance improvements ``` - Using `CMake` for Linux (assuming a gfx1030-compatible AMD GPU): ```bash - CC=/opt/rocm/llvm/bin/clang CXX=/opt/rocm/llvm/bin/clang++ \ - cmake -B build -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ + HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -R)" \ + cmake -S . -B build -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ && cmake --build build --config Release -- -j 16 ``` On Linux it is also possible to use unified memory architecture (UMA) to share main memory between the CPU and integrated GPU by setting `-DLLAMA_HIP_UMA=ON`. However, this hurts performance for non-integrated GPUs (but enables working with integrated GPUs). + Note that if you get the following error: + ``` + clang: error: cannot find ROCm device library; provide its path via '--rocm-path' or '--rocm-device-lib-path', or pass '-nogpulib' to build without ROCm device library + ``` + Try searching for a directory under `HIP_PATH` that contains the file + `oclc_abi_version_400.bc`. Then, add the following to the start of the + command: `HIP_DEVICE_LIB_PATH=`, so something + like: + ```bash + HIPCXX="$(hipconfig -l)/clang" HIP_PATH="$(hipconfig -p)" \ + HIP_DEVICE_LIB_PATH= \ + cmake -S . -B build -DLLAMA_HIPBLAS=ON -DAMDGPU_TARGETS=gfx1030 -DCMAKE_BUILD_TYPE=Release \ + && cmake --build build -- -j 16 + ``` + - Using `make` (example for target gfx1030, build with 16 CPU threads): ```bash make -j16 LLAMA_HIPBLAS=1 LLAMA_HIP_UMA=1 AMDGPU_TARGETS=gfx1030 @@ -543,10 +558,8 @@ Building the program with BLAS support may lead to some performance improvements - Using `CMake` for Windows (using x64 Native Tools Command Prompt for VS, and assuming a gfx1100-compatible AMD GPU): ```bash set PATH=%HIP_PATH%\bin;%PATH% - mkdir build - cd build - cmake -G Ninja -DAMDGPU_TARGETS=gfx1100 -DLLAMA_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release .. - cmake --build . + cmake -S . -B build -G Ninja -DAMDGPU_TARGETS=gfx1100 -DLLAMA_HIPBLAS=ON -DCMAKE_C_COMPILER=clang -DCMAKE_CXX_COMPILER=clang++ -DCMAKE_BUILD_TYPE=Release + cmake --build build ``` Make sure that `AMDGPU_TARGETS` is set to the GPU arch you want to compile for. The above example uses `gfx1100` that corresponds to Radeon RX 7900XTX/XT/GRE. You can find a list of targets [here](https://llvm.org/docs/AMDGPUUsage.html#processors) Find your gpu version string by matching the most significant version information from `rocminfo | grep gfx | head -1 | awk '{print $2}'` with the list of processors, e.g. `gfx1035` maps to `gfx1030`. From 0fc1e820a9900a3dd08ddd3c6abe6604c53b689b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Fri, 17 May 2024 18:54:52 +0200 Subject: [PATCH 029/181] CUDA: faster large batch FA without tensor cores (#7314) --- ggml-cuda/fattn-tile-f16.cu | 395 +++++++++++++++++++++++++++++++++++ ggml-cuda/fattn-tile-f16.cuh | 3 + ggml-cuda/fattn-tile-f32.cu | 393 ++++++++++++++++++++++++++++++++++ ggml-cuda/fattn-tile-f32.cuh | 3 + ggml-cuda/fattn-vec-f16.cu | 9 +- ggml-cuda/fattn-vec-f32.cu | 9 +- ggml-cuda/fattn.cu | 26 ++- 7 files changed, 823 insertions(+), 15 deletions(-) create mode 100644 ggml-cuda/fattn-tile-f16.cu create mode 100644 ggml-cuda/fattn-tile-f16.cuh create mode 100644 ggml-cuda/fattn-tile-f32.cu create mode 100644 ggml-cuda/fattn-tile-f32.cuh diff --git a/ggml-cuda/fattn-tile-f16.cu b/ggml-cuda/fattn-tile-f16.cu new file mode 100644 index 000000000..d2a1077ed --- /dev/null +++ b/ggml-cuda/fattn-tile-f16.cu @@ -0,0 +1,395 @@ +#include "common.cuh" +#include "fattn-common.cuh" +#include "fattn-tile-f16.cuh" + +#define FATTN_KQ_STRIDE_TILE_F16 64 + +template // D == head size +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(nwarps*WARP_SIZE, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +static __global__ void flash_attn_tile_ext_f16( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const int ne00, + const int ne01, + const int ne02, + const int ne03, + const int ne10, + const int ne11, + const int ne12, + const int ne13, + const int ne31, + const int nb31, + const int nb01, + const int nb02, + const int nb03, + const int nb11, + const int nb12, + const int nb13, + const int ne0, + const int ne1, + const int ne2, + const int ne3) { +#if FP16_AVAILABLE + //In this kernel Q, K, V are matrices while i, j, k are matrix indices. + + const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. + const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. + + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0); + const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio)); + const half2 * V_h2 = (const half2 *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape + const half * maskh = (const half *) mask + ne11*ic0; + + const int stride_KV2 = nb11 / sizeof(half2); + + half slopeh = __float2half(1.0f); + + // ALiBi + if (max_bias > 0.0f) { + const uint32_t h = blockIdx.y; + + const float base = h < n_head_log2 ? m0 : m1; + const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; + + slopeh = __float2half(powf(base, exph)); + } + + static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); + + __shared__ half KQ[ncols*FATTN_KQ_STRIDE_TILE_F16]; + half2 * KQ2 = (half2 *) KQ; + + __shared__ half2 KV_tmp[FATTN_KQ_STRIDE_TILE_F16][D/2 + 1]; // Pad D to avoid memory bank conflicts. + + half kqmax[ncols/nwarps]; +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + kqmax[j0/nwarps] = -HALF_MAX_HALF; + } + half2 kqsum[ncols/nwarps] = {{0.0f, 0.0f}}; + + half2 VKQ[ncols/nwarps][(D/2)/WARP_SIZE] = {{{0.0f, 0.0f}}}; + + // Convert Q to half2 and store in registers: + __shared__ half2 Q_h2[ncols][D/2]; +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + const float2 tmp = Q_f2[j*(nb01/sizeof(float2)) + i]; + Q_h2[j][i] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y); + } + } + + __syncthreads(); + + const int k_start = parallel_blocks == 1 ? 0 : ip*FATTN_KQ_STRIDE_TILE_F16; + for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*FATTN_KQ_STRIDE_TILE_F16) { + // Calculate KQ tile and keep track of new maximum KQ values: + + half kqmax_new[ncols/nwarps]; +#pragma unroll + for (int j = 0; j < ncols/nwarps; ++j) { + kqmax_new[j] = kqmax[j]; + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F16; i_KQ_0 += nwarps) { + const int i_KQ = i_KQ_0 + threadIdx.y; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + KV_tmp[i_KQ][k_KQ] = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ]; + } + } + + __syncthreads(); + + half2 sum2[FATTN_KQ_STRIDE_TILE_F16/WARP_SIZE][ncols/nwarps] = {{{0.0f, 0.0f}}}; + +#pragma unroll + for (int k_KQ = 0; k_KQ < D/2; ++k_KQ) { + half2 K_k[FATTN_KQ_STRIDE_TILE_F16/WARP_SIZE]; + half2 Q_k[ncols/nwarps]; + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F16; i_KQ_0 += WARP_SIZE) { + const int i_KQ = i_KQ_0 + threadIdx.x; + + K_k[i_KQ_0/WARP_SIZE] = KV_tmp[i_KQ][k_KQ]; + } +#pragma unroll + for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) { + const int j_KQ = j_KQ_0 + threadIdx.y; + + Q_k[j_KQ_0/nwarps] = Q_h2[j_KQ][k_KQ]; + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F16; i_KQ_0 += WARP_SIZE) { +#pragma unroll + for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) { + sum2[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps] += K_k[i_KQ_0/WARP_SIZE]*Q_k[j_KQ_0/nwarps]; + } + } + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F16; i_KQ_0 += WARP_SIZE) { + const int i_KQ = i_KQ_0 + threadIdx.x; + +#pragma unroll + for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) { + const int j_KQ = j_KQ_0 + threadIdx.y; + + half sum = __low2half(sum2[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]) + __high2half(sum2[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]); + sum += mask ? slopeh*maskh[j_KQ*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f); + + kqmax_new[j_KQ_0/nwarps] = ggml_cuda_hmax(kqmax_new[j_KQ_0/nwarps], sum); + + KQ[j_KQ*FATTN_KQ_STRIDE_TILE_F16 + i_KQ] = sum; + } + } + + __syncthreads(); + +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + kqmax_new[j0/nwarps] = warp_reduce_max(kqmax_new[j0/nwarps]); + const half2 KQ_max_scale = __half2half2(hexp(kqmax[j0/nwarps] - kqmax_new[j0/nwarps])); + kqmax[j0/nwarps] = kqmax_new[j0/nwarps]; + +#pragma unroll + for (int i0 = 0; i0 < FATTN_KQ_STRIDE_TILE_F16/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + const half2 diff = KQ2[j*(FATTN_KQ_STRIDE_TILE_F16/2) + i] - __half2half2(kqmax[j0/nwarps]); + const half2 val = h2exp(diff); + kqsum[j0/nwarps] = kqsum[j0/nwarps]*KQ_max_scale + val; + KQ2[j*(FATTN_KQ_STRIDE_TILE_F16/2) + i] = val; + } + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + VKQ[j0/nwarps][i0/WARP_SIZE] *= KQ_max_scale; + } + } + + __syncthreads(); + +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE_TILE_F16; k0 += nwarps) { + const int k = k0 + threadIdx.y; + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + KV_tmp[k][i] = V_h2[(k_VKQ_0 + k)*stride_KV2 + i]; + } + } + + __syncthreads(); + +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE_TILE_F16; k0 += 2) { + half2 V_k[(D/2)/WARP_SIZE][2]; + half2 KQ_k[ncols/nwarps]; + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + V_k[i0/WARP_SIZE][0] = KV_tmp[k0 + 0][i]; + V_k[i0/WARP_SIZE][1] = KV_tmp[k0 + 1][i]; + } +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + KQ_k[j0/nwarps] = KQ2[j*(FATTN_KQ_STRIDE_TILE_F16/2) + k0/2]; + } + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + VKQ[j0/nwarps][i0/WARP_SIZE] += V_k[i0/WARP_SIZE][0]* __low2half2(KQ_k[j0/nwarps]); + VKQ[j0/nwarps][i0/WARP_SIZE] += V_k[i0/WARP_SIZE][1]*__high2half2(KQ_k[j0/nwarps]); + } + } + } + + __syncthreads(); + } + +#pragma unroll + for (int j_VKQ_0 = 0; j_VKQ_0 < ncols; j_VKQ_0 += nwarps) { + const int j_VKQ = j_VKQ_0 + threadIdx.y; + + half kqsum_j = __low2half(kqsum[j_VKQ_0/nwarps]) + __high2half(kqsum[j_VKQ_0/nwarps]); + kqsum_j = warp_reduce_sum(kqsum_j); + +#pragma unroll + for (int i00 = 0; i00 < D; i00 += 2*WARP_SIZE) { + const int i0 = i00 + 2*threadIdx.x; + + half2 dst_val = VKQ[j_VKQ_0/nwarps][i0/(2*WARP_SIZE)]; + if (parallel_blocks == 1) { + dst_val /= __half2half2(kqsum_j); + } + const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; + dst[j_dst*D*gridDim.y + D*blockIdx.y + i0 + 0] = __low2float(dst_val); + dst[j_dst*D*gridDim.y + D*blockIdx.y + i0 + 1] = __high2float(dst_val); + } + + if (parallel_blocks != 1 && threadIdx.x == 0) { + dst_meta[(ic0 + j_VKQ)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[j_VKQ_0/nwarps], kqsum_j); + } + } +#else + NO_DEVICE_CODE; +#endif // FP16_AVAILABLE +} + +template void launch_fattn_tile_f16( + const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask, + ggml_cuda_pool & pool, cudaStream_t main_stream +) { + ggml_cuda_pool_alloc dst_tmp(pool); + ggml_cuda_pool_alloc dst_tmp_meta(pool); + + if (parallel_blocks > 1) { + dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); + dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV)); + } + + constexpr int nwarps = 8; + const dim3 block_dim(WARP_SIZE, nwarps, 1); + const dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]); + const int shmem = 0; + + float scale = 1.0f; + float max_bias = 0.0f; + + memcpy(&scale, (float *) KQV->op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float)); + + const uint32_t n_head = Q->ne[2]; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + flash_attn_tile_ext_f16 + <<>> ( + (const char *) Q->data, + (const char *) K->data, + (const char *) V->data, + mask ? ((const char *) mask->data) : nullptr, + parallel_blocks == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr, + scale, max_bias, m0, m1, n_head_log2, + Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3], + K->ne[0], K->ne[1], K->ne[2], K->ne[3], + mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, + Q->nb[1], Q->nb[2], Q->nb[3], + K->nb[1], K->nb[2], K->nb[3], + KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3] + ); + CUDA_CHECK(cudaGetLastError()); + + if (parallel_blocks == 1) { + return; + } + + const dim3 block_dim_combine(D, 1, 1); + const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z); + const int shmem_combine = 0; + + flash_attn_combine_results + <<>> + (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data); + CUDA_CHECK(cudaGetLastError()); +} + +void ggml_cuda_flash_attn_ext_tile_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + const ggml_tensor * K = dst->src[1]; + const ggml_tensor * V = dst->src[2]; + + const ggml_tensor * mask = dst->src[3]; + + ggml_tensor * KQV = dst; + + const int32_t precision = KQV->op_params[2]; + GGML_ASSERT(precision == GGML_PREC_DEFAULT); + GGML_ASSERT(Q->ne[0] == 64 || Q->ne[0] == 128 && "FlashAttention without tensor cores only supports head sizes 64 and 128."); + + if (Q->ne[1] <= 16) { + constexpr int cols_per_block = 16; + constexpr int parallel_blocks = 4; + switch (Q->ne[0]) { + case 64: + launch_fattn_tile_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_tile_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } + return; + } + + if (Q->ne[1] <= 32) { + constexpr int cols_per_block = 32; + constexpr int parallel_blocks = 4; + switch (Q->ne[0]) { + case 64: + launch_fattn_tile_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_tile_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } + return; + } + + constexpr int cols_per_block = 32; + constexpr int parallel_blocks = 1; + switch (Q->ne[0]) { + case 64: + launch_fattn_tile_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_tile_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } +} diff --git a/ggml-cuda/fattn-tile-f16.cuh b/ggml-cuda/fattn-tile-f16.cuh new file mode 100644 index 000000000..ffc587842 --- /dev/null +++ b/ggml-cuda/fattn-tile-f16.cuh @@ -0,0 +1,3 @@ +#include "common.cuh" + +void ggml_cuda_flash_attn_ext_tile_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml-cuda/fattn-tile-f32.cu b/ggml-cuda/fattn-tile-f32.cu new file mode 100644 index 000000000..176895edd --- /dev/null +++ b/ggml-cuda/fattn-tile-f32.cu @@ -0,0 +1,393 @@ +#include "common.cuh" +#include "fattn-common.cuh" +#include "fattn-tile-f32.cuh" + +#define FATTN_KQ_STRIDE_TILE_F32 32 + +template // D == head size +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(nwarps*WARP_SIZE, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +static __global__ void flash_attn_tile_ext_f32( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const int ne00, + const int ne01, + const int ne02, + const int ne03, + const int ne10, + const int ne11, + const int ne12, + const int ne13, + const int ne31, + const int nb31, + const int nb01, + const int nb02, + const int nb03, + const int nb11, + const int nb12, + const int nb13, + const int ne0, + const int ne1, + const int ne2, + const int ne3) { + //In this kernel Q, K, V are matrices while i, j, k are matrix indices. + + const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. + const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. + + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0); + const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio)); + const half2 * V_h2 = (const half2 *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape + const half * maskh = (const half *) mask + ne11*ic0; + + const int stride_KV2 = nb11 / sizeof(half2); + + float slope = 1.0f; + + // ALiBi + if (max_bias > 0.0f) { + const uint32_t h = blockIdx.y; + + const float base = h < n_head_log2 ? m0 : m1; + const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; + + slope = powf(base, exph); + } + + static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); + + __shared__ float KQ[ncols*FATTN_KQ_STRIDE_TILE_F32]; + + __shared__ float KV_tmp[FATTN_KQ_STRIDE_TILE_F32][D + 1]; // Pad D to avoid memory bank conflicts. + float2 * KV_tmp2 = (float2 *) KV_tmp; + + float kqmax[ncols/nwarps]; +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + kqmax[j0/nwarps] = -FLT_MAX/2.0f; + } + float kqsum[ncols/nwarps] = {0.0f}; + + float2 VKQ[ncols/nwarps][(D/2)/WARP_SIZE] = {{{0.0f, 0.0f}}}; + + // Convert Q to half2 and store in registers: + __shared__ float Q_f[ncols][D]; +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + +#pragma unroll + for (int i0 = 0; i0 < D; i0 += 2*WARP_SIZE) { + float2 tmp = Q_f2[j*(nb01/sizeof(float2)) + i0/2 + threadIdx.x]; + Q_f[j][i0 + 0*WARP_SIZE + threadIdx.x] = tmp.x * scale; + Q_f[j][i0 + 1*WARP_SIZE + threadIdx.x] = tmp.y * scale; + } + } + + __syncthreads(); + + const int k_start = parallel_blocks == 1 ? 0 : ip*FATTN_KQ_STRIDE_TILE_F32; + for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*FATTN_KQ_STRIDE_TILE_F32) { + // Calculate KQ tile and keep track of new maximum KQ values: + + float kqmax_new[ncols/nwarps]; +#pragma unroll + for (int j = 0; j < ncols/nwarps; ++j) { + kqmax_new[j] = kqmax[j]; + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F32; i_KQ_0 += nwarps) { + const int i_KQ = i_KQ_0 + threadIdx.y; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 2*WARP_SIZE) { + const half2 tmp = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ_0/2 + threadIdx.x]; + KV_tmp[i_KQ][k_KQ_0 + 0*WARP_SIZE + threadIdx.x] = __low2float(tmp); + KV_tmp[i_KQ][k_KQ_0 + 1*WARP_SIZE + threadIdx.x] = __high2float(tmp); + } + } + + __syncthreads(); + + float sum[FATTN_KQ_STRIDE_TILE_F32/WARP_SIZE][ncols/nwarps] = {{0.0f}}; + +#pragma unroll + for (int k_KQ = 0; k_KQ < D; ++k_KQ) { + float K_k[FATTN_KQ_STRIDE_TILE_F32/WARP_SIZE]; + float Q_k[ncols/nwarps]; + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F32; i_KQ_0 += WARP_SIZE) { + const int i_KQ = i_KQ_0 + threadIdx.x; + + K_k[i_KQ_0/WARP_SIZE] = KV_tmp[i_KQ][k_KQ]; + } +#pragma unroll + for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) { + const int j_KQ = j_KQ_0 + threadIdx.y; + + Q_k[j_KQ_0/nwarps] = Q_f[j_KQ][k_KQ]; + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F32; i_KQ_0 += WARP_SIZE) { +#pragma unroll + for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) { + sum[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps] += K_k[i_KQ_0/WARP_SIZE] * Q_k[j_KQ_0/nwarps]; + } + } + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE_TILE_F32; i_KQ_0 += WARP_SIZE) { + const int i_KQ = i_KQ_0 + threadIdx.x; + +#pragma unroll + for (int j_KQ_0 = 0; j_KQ_0 < ncols; j_KQ_0 += nwarps) { + const int j_KQ = j_KQ_0 + threadIdx.y; + + sum[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps] += mask ? slope*__half2float(maskh[j_KQ*ne11 + k_VKQ_0 + i_KQ]) : 0.0f; + + kqmax_new[j_KQ_0/nwarps] = fmaxf(kqmax_new[j_KQ_0/nwarps], sum[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]); + + KQ[j_KQ*FATTN_KQ_STRIDE_TILE_F32 + i_KQ] = sum[i_KQ_0/WARP_SIZE][j_KQ_0/nwarps]; + } + } + + __syncthreads(); + +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + kqmax_new[j0/nwarps] = warp_reduce_max(kqmax_new[j0/nwarps]); + const float KQ_max_scale = expf(kqmax[j0/nwarps] - kqmax_new[j0/nwarps]); + kqmax[j0/nwarps] = kqmax_new[j0/nwarps]; + + float kqsum_add = 0.0f; +#pragma unroll + for (int i0 = 0; i0 < FATTN_KQ_STRIDE_TILE_F32; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + const float diff = KQ[j*FATTN_KQ_STRIDE_TILE_F32 + i] - kqmax[j0/nwarps]; + const float val = expf(diff); + kqsum_add += val; + KQ[j*FATTN_KQ_STRIDE_TILE_F32 + i] = val; + } + kqsum[j0/nwarps] = kqsum[j0/nwarps]*KQ_max_scale + kqsum_add; + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + VKQ[j0/nwarps][i0/WARP_SIZE].x *= KQ_max_scale; + VKQ[j0/nwarps][i0/WARP_SIZE].y *= KQ_max_scale; + } + } + + __syncthreads(); + +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE_TILE_F32; k0 += nwarps) { + const int k = k0 + threadIdx.y; + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + KV_tmp2[k*(D/2) + i].x = __low2float(V_h2[(k_VKQ_0 + k)*stride_KV2 + i]); + KV_tmp2[k*(D/2) + i].y = __high2float(V_h2[(k_VKQ_0 + k)*stride_KV2 + i]); + } + } + + __syncthreads(); + +#pragma unroll + for (int k = 0; k < FATTN_KQ_STRIDE_TILE_F32; ++k) { + float2 V_k[(D/2)/WARP_SIZE]; + float KQ_k[ncols/nwarps]; + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + V_k[i0/WARP_SIZE] = KV_tmp2[k*(D/2) + i]; + } +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + KQ_k[j0/nwarps] = KQ[j*FATTN_KQ_STRIDE_TILE_F32 + k]; + } + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + VKQ[j0/nwarps][i0/WARP_SIZE].x += V_k[i0/WARP_SIZE].x*KQ_k[j0/nwarps]; + VKQ[j0/nwarps][i0/WARP_SIZE].y += V_k[i0/WARP_SIZE].y*KQ_k[j0/nwarps]; + } + } + } + + __syncthreads(); + } + +#pragma unroll + for (int j_VKQ_0 = 0; j_VKQ_0 < ncols; j_VKQ_0 += nwarps) { + const int j_VKQ = j_VKQ_0 + threadIdx.y; + + float kqsum_j = kqsum[j_VKQ_0/nwarps]; + kqsum_j = warp_reduce_sum(kqsum_j); + +#pragma unroll + for (int i00 = 0; i00 < D; i00 += 2*WARP_SIZE) { + const int i0 = i00 + 2*threadIdx.x; + + float2 dst_val = VKQ[j_VKQ_0/nwarps][i0/(2*WARP_SIZE)]; + if (parallel_blocks == 1) { + dst_val.x /= kqsum_j; + dst_val.y /= kqsum_j; + } + const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; + dst[j_dst*D*gridDim.y + D*blockIdx.y + i0 + 0] = dst_val.x; + dst[j_dst*D*gridDim.y + D*blockIdx.y + i0 + 1] = dst_val.y; + } + + if (parallel_blocks != 1 && threadIdx.x == 0) { + dst_meta[(ic0 + j_VKQ)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[j_VKQ_0/nwarps], kqsum_j); + } + } +} + +template void launch_fattn_tile_f32( + const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask, + ggml_cuda_pool & pool, cudaStream_t main_stream +) { + ggml_cuda_pool_alloc dst_tmp(pool); + ggml_cuda_pool_alloc dst_tmp_meta(pool); + + if (parallel_blocks > 1) { + dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); + dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV)); + } + + constexpr int nwarps = 8; + const dim3 block_dim(WARP_SIZE, nwarps, 1); + const dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]); + const int shmem = 0; + + float scale = 1.0f; + float max_bias = 0.0f; + + memcpy(&scale, (float *) KQV->op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float)); + + const uint32_t n_head = Q->ne[2]; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + flash_attn_tile_ext_f32 + <<>> ( + (const char *) Q->data, + (const char *) K->data, + (const char *) V->data, + mask ? ((const char *) mask->data) : nullptr, + parallel_blocks == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr, + scale, max_bias, m0, m1, n_head_log2, + Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3], + K->ne[0], K->ne[1], K->ne[2], K->ne[3], + mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, + Q->nb[1], Q->nb[2], Q->nb[3], + K->nb[1], K->nb[2], K->nb[3], + KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3] + ); + CUDA_CHECK(cudaGetLastError()); + + if (parallel_blocks == 1) { + return; + } + + const dim3 block_dim_combine(D, 1, 1); + const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z); + const int shmem_combine = 0; + + flash_attn_combine_results + <<>> + (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data); + CUDA_CHECK(cudaGetLastError()); +} + +void ggml_cuda_flash_attn_ext_tile_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + const ggml_tensor * K = dst->src[1]; + const ggml_tensor * V = dst->src[2]; + + const ggml_tensor * mask = dst->src[3]; + + ggml_tensor * KQV = dst; + + const int32_t precision = KQV->op_params[2]; + GGML_ASSERT(precision == GGML_PREC_DEFAULT); + GGML_ASSERT(Q->ne[0] == 64 || Q->ne[0] == 128 && "FlashAttention without tensor cores only supports head sizes 64 and 128."); + + if (Q->ne[1] <= 16) { + constexpr int cols_per_block = 16; + constexpr int parallel_blocks = 4; + switch (Q->ne[0]) { + case 64: + launch_fattn_tile_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_tile_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } + return; + } + + if (Q->ne[1] <= 32) { + constexpr int cols_per_block = 32; + constexpr int parallel_blocks = 4; + switch (Q->ne[0]) { + case 64: + launch_fattn_tile_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_tile_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } + return; + } + + constexpr int cols_per_block = 32; + constexpr int parallel_blocks = 1; + switch (Q->ne[0]) { + case 64: + launch_fattn_tile_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + case 128: + launch_fattn_tile_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); + break; + default: + GGML_ASSERT(false); + break; + } +} diff --git a/ggml-cuda/fattn-tile-f32.cuh b/ggml-cuda/fattn-tile-f32.cuh new file mode 100644 index 000000000..b1c546c80 --- /dev/null +++ b/ggml-cuda/fattn-tile-f32.cuh @@ -0,0 +1,3 @@ +#include "common.cuh" + +void ggml_cuda_flash_attn_ext_tile_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst); diff --git a/ggml-cuda/fattn-vec-f16.cu b/ggml-cuda/fattn-vec-f16.cu index cbf5f7835..a18be5ddc 100644 --- a/ggml-cuda/fattn-vec-f16.cu +++ b/ggml-cuda/fattn-vec-f16.cu @@ -57,7 +57,7 @@ static __global__ void flash_attn_vec_ext_f16( // ALiBi if (max_bias > 0.0f) { - const int h = blockIdx.y; + const uint32_t h = blockIdx.y; const float base = h < n_head_log2 ? m0 : m1; const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; @@ -232,11 +232,8 @@ static __global__ void flash_attn_vec_ext_f16( dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; } - if (parallel_blocks != 1 && tid != 0) { -#pragma unroll - for (int j = 0; j < ncols; ++j) { - dst_meta[(ic0 + j)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[j], kqsum[j]); - } + if (parallel_blocks != 1 && threadIdx.x < ncols) { + dst_meta[(ic0 + threadIdx.x)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[threadIdx.x], kqsum[threadIdx.x]); } #else NO_DEVICE_CODE; diff --git a/ggml-cuda/fattn-vec-f32.cu b/ggml-cuda/fattn-vec-f32.cu index 40c336ce3..91fcdc8c3 100644 --- a/ggml-cuda/fattn-vec-f32.cu +++ b/ggml-cuda/fattn-vec-f32.cu @@ -56,7 +56,7 @@ static __global__ void flash_attn_vec_ext_f32( // ALiBi if (max_bias > 0.0f) { - const int h = blockIdx.y; + const uint32_t h = blockIdx.y; const float base = h < n_head_log2 ? m0 : m1; const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; @@ -221,11 +221,8 @@ static __global__ void flash_attn_vec_ext_f32( dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; } - if (parallel_blocks != 1 && tid != 0) { -#pragma unroll - for (int j = 0; j < ncols; ++j) { - dst_meta[(ic0 + j)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[j], kqsum[j]); - } + if (parallel_blocks != 1 && threadIdx.x < ncols) { + dst_meta[(ic0 + threadIdx.x)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[threadIdx.x], kqsum[threadIdx.x]); } } diff --git a/ggml-cuda/fattn.cu b/ggml-cuda/fattn.cu index 419f8e752..a1918e258 100644 --- a/ggml-cuda/fattn.cu +++ b/ggml-cuda/fattn.cu @@ -1,5 +1,7 @@ #include "common.cuh" #include "fattn-common.cuh" +#include "fattn-tile-f16.cuh" +#include "fattn-tile-f32.cuh" #include "fattn-vec-f16.cuh" #include "fattn-vec-f32.cuh" #include "fattn.cuh" @@ -88,7 +90,7 @@ static __global__ void flash_attn_ext_f16( // ALiBi if (max_bias > 0.0f) { - const int h = blockIdx.y; + const uint32_t h = blockIdx.y; const float base = h < n_head_log2 ? m0 : m1; const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; @@ -541,13 +543,31 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst const int32_t precision = KQV->op_params[2]; + // On AMD the tile kernels perform poorly, use the vec kernel instead: + if (cc >= CC_OFFSET_AMD) { + if (precision == GGML_PREC_DEFAULT) { + ggml_cuda_flash_attn_ext_vec_f16_no_mma(ctx, dst); + } else { + ggml_cuda_flash_attn_ext_vec_f32(ctx, dst); + } + return; + } + if (!fast_fp16_available(cc)) { - ggml_cuda_flash_attn_ext_vec_f32(ctx, dst); + if (Q->ne[1] <= 8) { + ggml_cuda_flash_attn_ext_vec_f32(ctx, dst); + } else { + ggml_cuda_flash_attn_ext_tile_f32(ctx, dst); + } return; } if (!fp16_mma_available(cc)) { - ggml_cuda_flash_attn_ext_vec_f16_no_mma(ctx, dst); + if (Q->ne[1] <= 8) { + ggml_cuda_flash_attn_ext_vec_f16_no_mma(ctx, dst); + } else { + ggml_cuda_flash_attn_ext_tile_f16(ctx, dst); + } return; } From b43272afa29a64dcb8bcf26a96a05bac40792b92 Mon Sep 17 00:00:00 2001 From: jaime-m-p <167997752+jaime-m-p@users.noreply.github.com> Date: Sat, 18 May 2024 01:09:13 +0200 Subject: [PATCH 030/181] Unicode codepoint flags for custom regexs (#7245) * Replace CODEPOINT_TYPE_* with codepoint_flags * Update and bugfix brute force random test * Deterministic brute force random test * Unicode normalization NFD * Get rid of BOM --- llama.cpp | 8 +- scripts/gen-unicode-data.py | 166 +- tests/test-tokenizer-random.py | 113 +- unicode-data.cpp | 9138 ++++++++++++++++++++++++-------- unicode-data.h | 27 +- unicode.cpp | 200 +- unicode.h | 56 +- 7 files changed, 7299 insertions(+), 2409 deletions(-) diff --git a/llama.cpp b/llama.cpp index e11f0ac4b..b752ddc6b 100644 --- a/llama.cpp +++ b/llama.cpp @@ -12576,16 +12576,16 @@ struct llm_tokenizer_wpm { // to lowercase, pad chinese characters, pad punctuation std::string new_str = ""; for (uint32_t code : cpts_nfd) { - int type = unicode_cpt_type(code); - if (type == CODEPOINT_TYPE_ACCENT_MARK || type == CODEPOINT_TYPE_CONTROL) { + const codepoint_flags flags = unicode_cpt_flags(code); + if (flags.is_accent_mark || flags.is_control) { continue; } code = unicode_tolower(code); - if (type == CODEPOINT_TYPE_SEPARATOR) { + if (flags.is_separator || flags.is_whitespace) { //####FIXME: is_separator ? code = ' '; } std::string s = unicode_cpt_to_utf8(code); - if (type == CODEPOINT_TYPE_PUNCTUATION || is_ascii_punct(code) || is_chinese_char(code)) { + if (flags.is_punctuation || is_ascii_punct(code) || is_chinese_char(code)) { new_str += " "; new_str += s; new_str += " "; diff --git a/scripts/gen-unicode-data.py b/scripts/gen-unicode-data.py index 37d1e396c..744873c2a 100644 --- a/scripts/gen-unicode-data.py +++ b/scripts/gen-unicode-data.py @@ -1,64 +1,134 @@ import regex +import ctypes +import unicodedata -def get_matches(regex_expr): - regex_expr_compiled = regex.compile(regex_expr) - unicode_ranges = [] - current_range = None - - for codepoint in range(0x110000): - char = chr(codepoint) - if regex_expr_compiled.match(char): - if current_range is None: - current_range = [codepoint, codepoint] - else: - current_range[1] = codepoint - elif current_range is not None: - unicode_ranges.append(tuple(current_range)) - current_range = None - - if current_range is not None: - unicode_ranges.append(tuple(current_range)) - - return unicode_ranges +class CoodepointFlags (ctypes.Structure): + _fields_ = [ # see definition in unicode.h + ("is_undefined", ctypes.c_uint16, 1), + ("is_number", ctypes.c_uint16, 1), # regex: \p{N} + ("is_letter", ctypes.c_uint16, 1), # regex: \p{L} + ("is_separator", ctypes.c_uint16, 1), # regex: \p{Z} + ("is_accent_mark", ctypes.c_uint16, 1), # regex: \p{M} + ("is_punctuation", ctypes.c_uint16, 1), # regex: \p{P} + ("is_symbol", ctypes.c_uint16, 1), # regex: \p{S} + ("is_control", ctypes.c_uint16, 1), # regex: \p{C} + ] -def print_cat(mode, cat, ranges): - if mode == "range": - print("const std::vector> unicode_ranges_{} = {{".format(cat)) # noqa: NP100 - if mode == "map": - print("const std::map unicode_map_{} = {{".format(cat)) # noqa: NP100 - for i, values in enumerate(ranges): - end = ",\n" if (i % 4 == 3 or i + 1 == len(ranges)) else ", " - values = ["0x%08X" % value for value in values] - print("{" + ", ".join(values) + "}", end=end) # noqa: NP100 - print("};") # noqa: NP100 - print("") # noqa: NP100 +assert (ctypes.sizeof(CoodepointFlags) == 2) -print_cat("range", "number", get_matches(r'\p{N}')) -print_cat("range", "letter", get_matches(r'\p{L}')) -print_cat("range", "separator", get_matches(r'\p{Z}')) -print_cat("range", "accent_mark", get_matches(r'\p{M}')) -print_cat("range", "punctuation", get_matches(r'\p{P}')) -print_cat("range", "symbol", get_matches(r'\p{S}')) -print_cat("range", "control", get_matches(r'\p{C}')) +MAX_CODEPOINTS = 0x110000 -print_cat("range", "whitespace", get_matches(r'\s')) +regex_number = regex.compile(r'\p{N}') +regex_letter = regex.compile(r'\p{L}') +regex_separator = regex.compile(r'\p{Z}') +regex_accent_mark = regex.compile(r'\p{M}') +regex_punctuation = regex.compile(r'\p{P}') +regex_symbol = regex.compile(r'\p{S}') +regex_control = regex.compile(r'\p{C}') +regex_whitespace = regex.compile(r'\s') +codepoint_flags = (CoodepointFlags * MAX_CODEPOINTS)() +table_whitespace = [] +table_lowercase = [] +table_uppercase = [] +table_nfd = [] -map_lowercase = [] -map_uppercase = [] -for codepoint in range(0x110000): +for codepoint in range(MAX_CODEPOINTS): + # convert codepoint to unicode character char = chr(codepoint) + + # regex categories + flags = codepoint_flags[codepoint] + flags.is_number = bool(regex_number.match(char)) + flags.is_letter = bool(regex_letter.match(char)) + flags.is_separator = bool(regex_separator.match(char)) + flags.is_accent_mark = bool(regex_accent_mark.match(char)) + flags.is_punctuation = bool(regex_punctuation.match(char)) + flags.is_symbol = bool(regex_symbol.match(char)) + flags.is_control = bool(regex_control.match(char)) + flags.is_undefined = bytes(flags)[0] == 0 + assert (not flags.is_undefined) + + # whitespaces + if bool(regex_whitespace.match(char)): + table_whitespace.append(codepoint) + + # lowercase conversion lower = ord(char.lower()[0]) - upper = ord(char.upper()[0]) if codepoint != lower: - map_lowercase.append((codepoint, lower)) + table_lowercase.append((codepoint, lower)) + + # uppercase conversion + upper = ord(char.upper()[0]) if codepoint != upper: - map_uppercase.append((codepoint, upper)) -print_cat("map", "lowercase", map_lowercase) -print_cat("map", "uppercase", map_uppercase) + table_uppercase.append((codepoint, upper)) + + # NFD normalization + norm = ord(unicodedata.normalize('NFD', char)[0]) + if codepoint != norm: + table_nfd.append((codepoint, norm)) -# TODO: generate unicode_map_nfd +# group ranges with same flags +ranges_flags = [(0, codepoint_flags[0])] # start, flags +for codepoint, flags in enumerate(codepoint_flags): + if bytes(flags) != bytes(ranges_flags[-1][1]): + ranges_flags.append((codepoint, flags)) +ranges_flags.append((MAX_CODEPOINTS, CoodepointFlags())) + + +# group ranges with same nfd +ranges_nfd = [(0, 0, 0)] # start, last, nfd +for codepoint, norm in table_nfd: + start = ranges_nfd[-1][0] + if ranges_nfd[-1] != (start, codepoint - 1, norm): + ranges_nfd.append(None) + start = codepoint + ranges_nfd[-1] = (start, codepoint, norm) + + +# Generate 'unicode-data.cpp' + + +def out(line=""): + print(line, end='\n') # noqa + + +out("""\ +// generated with scripts/gen-unicode-data.py + +#include "unicode-data.h" + +#include +#include +#include +#include +""") + +out("const std::vector> unicode_ranges_flags = { // start, flags // last=next_start-1") +for codepoint, flags in ranges_flags: + flags = int.from_bytes(bytes(flags), "little") + out("{0x%06X, 0x%04X}," % (codepoint, flags)) +out("};\n") + +out("const std::unordered_set unicode_set_whitespace = {") +out(", ".join("0x%06X" % cpt for cpt in table_whitespace)) +out("};\n") + +out("const std::unordered_map unicode_map_lowercase = {") +for tuple in table_lowercase: + out("{0x%06X, 0x%06X}," % tuple) +out("};\n") + +out("const std::unordered_map unicode_map_uppercase = {") +for tuple in table_uppercase: + out("{0x%06X, 0x%06X}," % tuple) +out("};\n") + +out("const std::vector unicode_ranges_nfd = { // start, last, nfd") +for triple in ranges_nfd: + out("{0x%06X, 0x%06X, 0x%06X}," % triple) +out("};\n") diff --git a/tests/test-tokenizer-random.py b/tests/test-tokenizer-random.py index 5b2ab8ef7..d5a6f185f 100644 --- a/tests/test-tokenizer-random.py +++ b/tests/test-tokenizer-random.py @@ -1,5 +1,5 @@ # Test libllama tokenizer == AutoTokenizer. -# Brute force random tokens/text generation. +# Brute force random words/text generation. # # Sample usage: # @@ -12,10 +12,10 @@ import argparse import subprocess import random -from typing import Iterator +from typing import Callable, Iterator import cffi -from transformers import AutoTokenizer, PreTrainedTokenizerBase +from transformers import AutoTokenizer logger = logging.getLogger("test-tokenizer-random-bpe") @@ -145,28 +145,35 @@ def generator_custom_text() -> Iterator[str]: def generator_custom_text_edge_cases() -> Iterator[str]: """Edge cases found while debugging""" yield from [ - '\x1f-a', # unicode_ranges_control, {0x00001C, 0x00001F} - '¼-a', # unicode_ranges_digit, 0x00BC - '½-a', # unicode_ranges_digit, 0x00BD - '¾-a', # unicode_ranges_digit, 0x00BE - 'a 〇b', # unicode_ranges_digit, 0x3007 - 'Ⅵ-a', # unicode_ranges_digit, {0x00002150, 0x0000218F} // Number Forms - '\uFEFF//', # unicode_ranges_control, 0xFEFF (BOM) - 'a' # TODO: Phi-3 fail + '\x1f-a', # unicode_ranges_control, {0x00001C, 0x00001F} + '¼-a', # unicode_ranges_digit, 0x00BC + '½-a', # unicode_ranges_digit, 0x00BD + '¾-a', # unicode_ranges_digit, 0x00BE + 'a 〇b', # unicode_ranges_digit, 0x3007 + 'Ⅵ-a', # unicode_ranges_digit, {0x00002150, 0x0000218F} // Number Forms + '\uFEFF//', # unicode_ranges_control, 0xFEFF (BOM) + 'Cửa Việt', # llama-3, ignore_merges = true + 'a', # TODO: Phi-3 fail + 'a\na', # TODO: Bert fail ] -def generator_random_chars(iterations = 100) -> Iterator[str]: +def generator_vocab_words(vocab: list[str]) -> Iterator[str]: + """Brute force check all vocab words""" + yield from vocab + + +def generator_random_chars(iterations=100) -> Iterator[str]: """Brute force random text with simple characters""" WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5) - CHARS = list(set(""" + CHARS = list(sorted(set(""" ABCDEFGHIJKLMNOPQRSTUVWXYZ abcdefghijklmnopqrstuvwxyz ÁÉÍÓÚÀÈÌÒÙÂÊÎÔÛÄËÏÖÜ áéíóúàèìòùâêîôûäëïöü .-,*/-+ª!"·$%&/()=?¿[]{}<>\\|@#~½¬~;:_ - """)) + """))) rand = random.Random() for m in range(iterations): @@ -181,13 +188,13 @@ def generator_random_chars(iterations = 100) -> Iterator[str]: yield "".join(text) -def generator_random_vocab_chars(tokenizer: PreTrainedTokenizerBase, iterations = 100) -> Iterator[str]: +def generator_random_vocab_chars(vocab: list[str], iterations=100) -> Iterator[str]: """Brute force random text with vocab characters""" - vocab_ids = list(tokenizer.vocab.values()) - vocab_text = tokenizer.decode(vocab_ids, skip_special_tokens=True) - vocab_chars = list(set(vocab_text)) - del vocab_ids, vocab_text + vocab_chars = set() + for word in vocab: + vocab_chars.update(word) + vocab_chars = list(sorted(vocab_chars)) rand = random.Random() for m in range(iterations): @@ -196,19 +203,11 @@ def generator_random_vocab_chars(tokenizer: PreTrainedTokenizerBase, iterations yield "".join(text) -def generator_random_vocab_tokens(tokenizer: PreTrainedTokenizerBase, iterations = 100) -> Iterator[str]: - """Brute force random text from vocab tokens""" +def generator_random_vocab_words(vocab: list[str], iterations=100) -> Iterator[str]: + """Brute force random text from vocab words""" - space_id = tokenizer.encode(" ", add_special_tokens=False)[0] - vocab_ids = list(tokenizer.vocab.values()) - vocab_ids = list(sorted(vocab_ids + vocab_ids)) - for i in range(1, len(vocab_ids), 2): - vocab_ids[i] = space_id - vocab_tokens = tokenizer.decode(vocab_ids, skip_special_tokens=True) - vocab_tokens = vocab_tokens.split(" ") - del vocab_ids - - yield from vocab_tokens + vocab = [w.strip() for w in vocab] + yield from vocab rand = random.Random() for m in range(iterations): @@ -217,14 +216,13 @@ def generator_random_vocab_tokens(tokenizer: PreTrainedTokenizerBase, iterations num_words = rand.randint(300, 400) for i in range(num_words): k = rand.randint(1, 3) - tokens = rand.choices(vocab_tokens, k=k) - tokens = [t.strip(" \n\r\t") for t in tokens] + words = rand.choices(vocab, k=k) sep = rand.choice(" \n\r\t") - text.append("".join(tokens) + sep) + text.append("".join(words) + sep) yield "".join(text) -def generator_random_bytes(iterations = 100) -> Iterator[str]: +def generator_random_bytes(iterations=100) -> Iterator[str]: """Brute force random bytes""" WHITESPACES = list(" " * 20 + "\n" * 5 + "\r\n" * 5 + "\t" * 5) @@ -242,10 +240,10 @@ def generator_random_bytes(iterations = 100) -> Iterator[str]: yield "".join(text) -def test_compare_tokenizer(model: LibLlamaModel, tokenizer: PreTrainedTokenizerBase, generator: Iterator[str]): +def test_compare_tokenizer(func_tokenize1: Callable, func_tokenize2: Callable, generator: Iterator[str]): def find_first_mismatch(ids1: list[int], ids2: list[int]): - for i, (a,b) in enumerate(zip(ids1, ids2)): + for i, (a, b) in enumerate(zip(ids1, ids2)): if a != b: return i if len(ids1) == len(ids2): @@ -255,15 +253,12 @@ def test_compare_tokenizer(model: LibLlamaModel, tokenizer: PreTrainedTokenizerB t0 = time.perf_counter() logger.info("%s: %s" % (generator.__name__, "ini")) for text in generator: - ids1 = model.tokenize(text, add_special=False, parse_special=False) - ids2 = tokenizer.encode(text, add_special_tokens=False) + ids1 = func_tokenize1(text) + ids2 = func_tokenize2(text) if ids1 != ids2: i = find_first_mismatch(ids1, ids2) ids1 = list(ids1)[max(0, i - 2) : i + 2 + 1] ids2 = list(ids2)[max(0, i - 2) : i + 2 + 1] - text2 = tokenizer.decode(ids2, skip_special_tokens=True) - assert (text2 in text) - logger.info(" Text: " + repr(text2)) logger.info(" TokenIDs: " + str(ids1)) logger.info(" Expected: " + str(ids2)) raise Exception() @@ -271,25 +266,37 @@ def test_compare_tokenizer(model: LibLlamaModel, tokenizer: PreTrainedTokenizerB logger.info("%s: end, time: %.3f secs" % (generator.__name__, t1 - t0)) -if __name__ == "__main__": - +def main(argv: list[str] = None): parser = argparse.ArgumentParser() parser.add_argument("vocab_file", help="path to vocab 'gguf' file") parser.add_argument("dir_tokenizer", help="directory containing 'tokenizer.model' file") parser.add_argument("--verbose", action="store_true", help="increase output verbosity") - args = parser.parse_args() + args = parser.parse_args(argv) logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) - model = LibLlamaModel(LibLlama(), args.vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=2048)) - + model = LibLlamaModel(LibLlama(), args.vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=4096)) tokenizer = AutoTokenizer.from_pretrained(args.dir_tokenizer) - test_compare_tokenizer(model, tokenizer, generator_custom_text()) - test_compare_tokenizer(model, tokenizer, generator_custom_text_edge_cases()) - test_compare_tokenizer(model, tokenizer, generator_random_chars(10_000)) - test_compare_tokenizer(model, tokenizer, generator_random_vocab_chars(tokenizer, 10_000)) - test_compare_tokenizer(model, tokenizer, generator_random_vocab_tokens(tokenizer, 10_000)) - # test_compare_tokenizer(model, tokenizer, generator_random_bytes(10_000)) # FAIL + def func_tokenize2(text: str): + return tokenizer.encode(text, add_special_tokens=False) + + parse_special = all(len(func_tokenize2(t)) == 1 for t in tokenizer.all_special_tokens) + + def func_tokenize1(text: str): + return model.tokenize(text, add_special=False, parse_special=parse_special) + + vocab = list(sorted(tokenizer.batch_decode(list(tokenizer.get_vocab().values()), skip_special_tokens=True))) + test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text()) + test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text_edge_cases()) + test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_vocab_words(vocab)) + test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_chars(10_000)) + test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_vocab_chars(vocab, 10_000)) + test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_vocab_words(vocab, 10_000)) + # test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_bytes(10_000)) # FAIL model.free() + + +if __name__ == "__main__": + main() diff --git a/unicode-data.cpp b/unicode-data.cpp index c54175fc3..d7c1c898d 100644 --- a/unicode-data.cpp +++ b/unicode-data.cpp @@ -1,2183 +1,6983 @@ -#include "unicode-data.h" +// generated with scripts/gen-unicode-data.py + +#include "unicode-data.h" #include -#include -#include #include +#include +#include -// generated with scripts/gen-unicode-data.py -// -// TODO: generate unicode_map_nfd - -const std::vector> unicode_ranges_number = { -{0x00000030, 0x00000039}, {0x000000B2, 0x000000B3}, {0x000000B9, 0x000000B9}, {0x000000BC, 0x000000BE}, -{0x00000660, 0x00000669}, {0x000006F0, 0x000006F9}, {0x000007C0, 0x000007C9}, {0x00000966, 0x0000096F}, -{0x000009E6, 0x000009EF}, {0x000009F4, 0x000009F9}, {0x00000A66, 0x00000A6F}, {0x00000AE6, 0x00000AEF}, -{0x00000B66, 0x00000B6F}, {0x00000B72, 0x00000B77}, {0x00000BE6, 0x00000BF2}, {0x00000C66, 0x00000C6F}, -{0x00000C78, 0x00000C7E}, {0x00000CE6, 0x00000CEF}, {0x00000D58, 0x00000D5E}, {0x00000D66, 0x00000D78}, -{0x00000DE6, 0x00000DEF}, {0x00000E50, 0x00000E59}, {0x00000ED0, 0x00000ED9}, {0x00000F20, 0x00000F33}, -{0x00001040, 0x00001049}, {0x00001090, 0x00001099}, {0x00001369, 0x0000137C}, {0x000016EE, 0x000016F0}, -{0x000017E0, 0x000017E9}, {0x000017F0, 0x000017F9}, {0x00001810, 0x00001819}, {0x00001946, 0x0000194F}, -{0x000019D0, 0x000019DA}, {0x00001A80, 0x00001A89}, {0x00001A90, 0x00001A99}, {0x00001B50, 0x00001B59}, -{0x00001BB0, 0x00001BB9}, {0x00001C40, 0x00001C49}, {0x00001C50, 0x00001C59}, {0x00002070, 0x00002070}, -{0x00002074, 0x00002079}, {0x00002080, 0x00002089}, {0x00002150, 0x00002182}, {0x00002185, 0x00002189}, -{0x00002460, 0x0000249B}, {0x000024EA, 0x000024FF}, {0x00002776, 0x00002793}, {0x00002CFD, 0x00002CFD}, -{0x00003007, 0x00003007}, {0x00003021, 0x00003029}, {0x00003038, 0x0000303A}, {0x00003192, 0x00003195}, -{0x00003220, 0x00003229}, {0x00003248, 0x0000324F}, {0x00003251, 0x0000325F}, {0x00003280, 0x00003289}, -{0x000032B1, 0x000032BF}, {0x0000A620, 0x0000A629}, {0x0000A6E6, 0x0000A6EF}, {0x0000A830, 0x0000A835}, -{0x0000A8D0, 0x0000A8D9}, {0x0000A900, 0x0000A909}, {0x0000A9D0, 0x0000A9D9}, {0x0000A9F0, 0x0000A9F9}, -{0x0000AA50, 0x0000AA59}, {0x0000ABF0, 0x0000ABF9}, {0x0000FF10, 0x0000FF19}, {0x00010107, 0x00010133}, -{0x00010140, 0x00010178}, {0x0001018A, 0x0001018B}, {0x000102E1, 0x000102FB}, {0x00010320, 0x00010323}, -{0x00010341, 0x00010341}, {0x0001034A, 0x0001034A}, {0x000103D1, 0x000103D5}, {0x000104A0, 0x000104A9}, -{0x00010858, 0x0001085F}, {0x00010879, 0x0001087F}, {0x000108A7, 0x000108AF}, {0x000108FB, 0x000108FF}, -{0x00010916, 0x0001091B}, {0x000109BC, 0x000109BD}, {0x000109C0, 0x000109CF}, {0x000109D2, 0x000109FF}, -{0x00010A40, 0x00010A48}, {0x00010A7D, 0x00010A7E}, {0x00010A9D, 0x00010A9F}, {0x00010AEB, 0x00010AEF}, -{0x00010B58, 0x00010B5F}, {0x00010B78, 0x00010B7F}, {0x00010BA9, 0x00010BAF}, {0x00010CFA, 0x00010CFF}, -{0x00010D30, 0x00010D39}, {0x00010E60, 0x00010E7E}, {0x00010F1D, 0x00010F26}, {0x00010F51, 0x00010F54}, -{0x00010FC5, 0x00010FCB}, {0x00011052, 0x0001106F}, {0x000110F0, 0x000110F9}, {0x00011136, 0x0001113F}, -{0x000111D0, 0x000111D9}, {0x000111E1, 0x000111F4}, {0x000112F0, 0x000112F9}, {0x00011450, 0x00011459}, -{0x000114D0, 0x000114D9}, {0x00011650, 0x00011659}, {0x000116C0, 0x000116C9}, {0x00011730, 0x0001173B}, -{0x000118E0, 0x000118F2}, {0x00011950, 0x00011959}, {0x00011C50, 0x00011C6C}, {0x00011D50, 0x00011D59}, -{0x00011DA0, 0x00011DA9}, {0x00011F50, 0x00011F59}, {0x00011FC0, 0x00011FD4}, {0x00012400, 0x0001246E}, -{0x00016A60, 0x00016A69}, {0x00016AC0, 0x00016AC9}, {0x00016B50, 0x00016B59}, {0x00016B5B, 0x00016B61}, -{0x00016E80, 0x00016E96}, {0x0001D2C0, 0x0001D2D3}, {0x0001D2E0, 0x0001D2F3}, {0x0001D360, 0x0001D378}, -{0x0001D7CE, 0x0001D7FF}, {0x0001E140, 0x0001E149}, {0x0001E2F0, 0x0001E2F9}, {0x0001E4F0, 0x0001E4F9}, -{0x0001E8C7, 0x0001E8CF}, {0x0001E950, 0x0001E959}, {0x0001EC71, 0x0001ECAB}, {0x0001ECAD, 0x0001ECAF}, -{0x0001ECB1, 0x0001ECB4}, {0x0001ED01, 0x0001ED2D}, {0x0001ED2F, 0x0001ED3D}, {0x0001F100, 0x0001F10C}, -{0x0001FBF0, 0x0001FBF9}, +const std::vector> unicode_ranges_flags = { // start, flags // last=next_start-1 +{0x000000, 0x0080}, +{0x000020, 0x0008}, +{0x000021, 0x0020}, +{0x000024, 0x0040}, +{0x000025, 0x0020}, +{0x00002B, 0x0040}, +{0x00002C, 0x0020}, +{0x000030, 0x0002}, +{0x00003A, 0x0020}, +{0x00003C, 0x0040}, +{0x00003F, 0x0020}, +{0x000041, 0x0004}, +{0x00005B, 0x0020}, +{0x00005E, 0x0040}, +{0x00005F, 0x0020}, +{0x000060, 0x0040}, +{0x000061, 0x0004}, +{0x00007B, 0x0020}, +{0x00007C, 0x0040}, +{0x00007D, 0x0020}, +{0x00007E, 0x0040}, +{0x00007F, 0x0080}, +{0x0000A0, 0x0008}, +{0x0000A1, 0x0020}, +{0x0000A2, 0x0040}, +{0x0000A7, 0x0020}, +{0x0000A8, 0x0040}, +{0x0000AA, 0x0004}, +{0x0000AB, 0x0020}, +{0x0000AC, 0x0040}, +{0x0000AD, 0x0080}, +{0x0000AE, 0x0040}, +{0x0000B2, 0x0002}, +{0x0000B4, 0x0040}, +{0x0000B5, 0x0004}, +{0x0000B6, 0x0020}, +{0x0000B8, 0x0040}, +{0x0000B9, 0x0002}, +{0x0000BA, 0x0004}, +{0x0000BB, 0x0020}, +{0x0000BC, 0x0002}, +{0x0000BF, 0x0020}, +{0x0000C0, 0x0004}, +{0x0000D7, 0x0040}, +{0x0000D8, 0x0004}, +{0x0000F7, 0x0040}, +{0x0000F8, 0x0004}, +{0x0002C2, 0x0040}, +{0x0002C6, 0x0004}, +{0x0002D2, 0x0040}, +{0x0002E0, 0x0004}, +{0x0002E5, 0x0040}, +{0x0002EC, 0x0004}, +{0x0002ED, 0x0040}, +{0x0002EE, 0x0004}, +{0x0002EF, 0x0040}, +{0x000300, 0x0010}, +{0x000370, 0x0004}, +{0x000375, 0x0040}, +{0x000376, 0x0004}, +{0x000378, 0x0080}, +{0x00037A, 0x0004}, +{0x00037E, 0x0020}, +{0x00037F, 0x0004}, +{0x000380, 0x0080}, +{0x000384, 0x0040}, +{0x000386, 0x0004}, +{0x000387, 0x0020}, +{0x000388, 0x0004}, +{0x00038B, 0x0080}, +{0x00038C, 0x0004}, +{0x00038D, 0x0080}, +{0x00038E, 0x0004}, +{0x0003A2, 0x0080}, +{0x0003A3, 0x0004}, +{0x0003F6, 0x0040}, +{0x0003F7, 0x0004}, +{0x000482, 0x0040}, +{0x000483, 0x0010}, +{0x00048A, 0x0004}, +{0x000530, 0x0080}, +{0x000531, 0x0004}, +{0x000557, 0x0080}, +{0x000559, 0x0004}, +{0x00055A, 0x0020}, +{0x000560, 0x0004}, +{0x000589, 0x0020}, +{0x00058B, 0x0080}, +{0x00058D, 0x0040}, +{0x000590, 0x0080}, +{0x000591, 0x0010}, +{0x0005BE, 0x0020}, +{0x0005BF, 0x0010}, +{0x0005C0, 0x0020}, +{0x0005C1, 0x0010}, +{0x0005C3, 0x0020}, +{0x0005C4, 0x0010}, +{0x0005C6, 0x0020}, +{0x0005C7, 0x0010}, +{0x0005C8, 0x0080}, +{0x0005D0, 0x0004}, +{0x0005EB, 0x0080}, +{0x0005EF, 0x0004}, +{0x0005F3, 0x0020}, +{0x0005F5, 0x0080}, +{0x000606, 0x0040}, +{0x000609, 0x0020}, +{0x00060B, 0x0040}, +{0x00060C, 0x0020}, +{0x00060E, 0x0040}, +{0x000610, 0x0010}, +{0x00061B, 0x0020}, +{0x00061C, 0x0080}, +{0x00061D, 0x0020}, +{0x000620, 0x0004}, +{0x00064B, 0x0010}, +{0x000660, 0x0002}, +{0x00066A, 0x0020}, +{0x00066E, 0x0004}, +{0x000670, 0x0010}, +{0x000671, 0x0004}, +{0x0006D4, 0x0020}, +{0x0006D5, 0x0004}, +{0x0006D6, 0x0010}, +{0x0006DD, 0x0080}, +{0x0006DE, 0x0040}, +{0x0006DF, 0x0010}, +{0x0006E5, 0x0004}, +{0x0006E7, 0x0010}, +{0x0006E9, 0x0040}, +{0x0006EA, 0x0010}, +{0x0006EE, 0x0004}, +{0x0006F0, 0x0002}, +{0x0006FA, 0x0004}, +{0x0006FD, 0x0040}, +{0x0006FF, 0x0004}, +{0x000700, 0x0020}, +{0x00070E, 0x0080}, +{0x000710, 0x0004}, +{0x000711, 0x0010}, +{0x000712, 0x0004}, +{0x000730, 0x0010}, +{0x00074B, 0x0080}, +{0x00074D, 0x0004}, +{0x0007A6, 0x0010}, +{0x0007B1, 0x0004}, +{0x0007B2, 0x0080}, +{0x0007C0, 0x0002}, +{0x0007CA, 0x0004}, +{0x0007EB, 0x0010}, +{0x0007F4, 0x0004}, +{0x0007F6, 0x0040}, +{0x0007F7, 0x0020}, +{0x0007FA, 0x0004}, +{0x0007FB, 0x0080}, +{0x0007FD, 0x0010}, +{0x0007FE, 0x0040}, +{0x000800, 0x0004}, +{0x000816, 0x0010}, +{0x00081A, 0x0004}, +{0x00081B, 0x0010}, +{0x000824, 0x0004}, +{0x000825, 0x0010}, +{0x000828, 0x0004}, +{0x000829, 0x0010}, +{0x00082E, 0x0080}, +{0x000830, 0x0020}, +{0x00083F, 0x0080}, +{0x000840, 0x0004}, +{0x000859, 0x0010}, +{0x00085C, 0x0080}, +{0x00085E, 0x0020}, +{0x00085F, 0x0080}, +{0x000860, 0x0004}, +{0x00086B, 0x0080}, +{0x000870, 0x0004}, +{0x000888, 0x0040}, +{0x000889, 0x0004}, +{0x00088F, 0x0080}, +{0x000898, 0x0010}, +{0x0008A0, 0x0004}, +{0x0008CA, 0x0010}, +{0x0008E2, 0x0080}, +{0x0008E3, 0x0010}, +{0x000904, 0x0004}, +{0x00093A, 0x0010}, +{0x00093D, 0x0004}, +{0x00093E, 0x0010}, +{0x000950, 0x0004}, +{0x000951, 0x0010}, +{0x000958, 0x0004}, +{0x000962, 0x0010}, +{0x000964, 0x0020}, +{0x000966, 0x0002}, +{0x000970, 0x0020}, +{0x000971, 0x0004}, +{0x000981, 0x0010}, +{0x000984, 0x0080}, +{0x000985, 0x0004}, +{0x00098D, 0x0080}, +{0x00098F, 0x0004}, +{0x000991, 0x0080}, +{0x000993, 0x0004}, +{0x0009A9, 0x0080}, +{0x0009AA, 0x0004}, +{0x0009B1, 0x0080}, +{0x0009B2, 0x0004}, +{0x0009B3, 0x0080}, +{0x0009B6, 0x0004}, +{0x0009BA, 0x0080}, +{0x0009BC, 0x0010}, +{0x0009BD, 0x0004}, +{0x0009BE, 0x0010}, +{0x0009C5, 0x0080}, +{0x0009C7, 0x0010}, +{0x0009C9, 0x0080}, +{0x0009CB, 0x0010}, +{0x0009CE, 0x0004}, +{0x0009CF, 0x0080}, +{0x0009D7, 0x0010}, +{0x0009D8, 0x0080}, +{0x0009DC, 0x0004}, +{0x0009DE, 0x0080}, +{0x0009DF, 0x0004}, +{0x0009E2, 0x0010}, +{0x0009E4, 0x0080}, +{0x0009E6, 0x0002}, +{0x0009F0, 0x0004}, +{0x0009F2, 0x0040}, +{0x0009F4, 0x0002}, +{0x0009FA, 0x0040}, +{0x0009FC, 0x0004}, +{0x0009FD, 0x0020}, +{0x0009FE, 0x0010}, +{0x0009FF, 0x0080}, +{0x000A01, 0x0010}, +{0x000A04, 0x0080}, +{0x000A05, 0x0004}, +{0x000A0B, 0x0080}, +{0x000A0F, 0x0004}, +{0x000A11, 0x0080}, +{0x000A13, 0x0004}, +{0x000A29, 0x0080}, +{0x000A2A, 0x0004}, +{0x000A31, 0x0080}, +{0x000A32, 0x0004}, +{0x000A34, 0x0080}, +{0x000A35, 0x0004}, +{0x000A37, 0x0080}, +{0x000A38, 0x0004}, +{0x000A3A, 0x0080}, +{0x000A3C, 0x0010}, +{0x000A3D, 0x0080}, +{0x000A3E, 0x0010}, +{0x000A43, 0x0080}, +{0x000A47, 0x0010}, +{0x000A49, 0x0080}, +{0x000A4B, 0x0010}, +{0x000A4E, 0x0080}, +{0x000A51, 0x0010}, +{0x000A52, 0x0080}, +{0x000A59, 0x0004}, +{0x000A5D, 0x0080}, +{0x000A5E, 0x0004}, +{0x000A5F, 0x0080}, +{0x000A66, 0x0002}, +{0x000A70, 0x0010}, +{0x000A72, 0x0004}, +{0x000A75, 0x0010}, +{0x000A76, 0x0020}, +{0x000A77, 0x0080}, +{0x000A81, 0x0010}, +{0x000A84, 0x0080}, +{0x000A85, 0x0004}, +{0x000A8E, 0x0080}, +{0x000A8F, 0x0004}, +{0x000A92, 0x0080}, +{0x000A93, 0x0004}, +{0x000AA9, 0x0080}, +{0x000AAA, 0x0004}, +{0x000AB1, 0x0080}, +{0x000AB2, 0x0004}, +{0x000AB4, 0x0080}, +{0x000AB5, 0x0004}, +{0x000ABA, 0x0080}, +{0x000ABC, 0x0010}, +{0x000ABD, 0x0004}, +{0x000ABE, 0x0010}, +{0x000AC6, 0x0080}, +{0x000AC7, 0x0010}, +{0x000ACA, 0x0080}, +{0x000ACB, 0x0010}, +{0x000ACE, 0x0080}, +{0x000AD0, 0x0004}, +{0x000AD1, 0x0080}, +{0x000AE0, 0x0004}, +{0x000AE2, 0x0010}, +{0x000AE4, 0x0080}, +{0x000AE6, 0x0002}, +{0x000AF0, 0x0020}, +{0x000AF1, 0x0040}, +{0x000AF2, 0x0080}, +{0x000AF9, 0x0004}, +{0x000AFA, 0x0010}, +{0x000B00, 0x0080}, +{0x000B01, 0x0010}, +{0x000B04, 0x0080}, +{0x000B05, 0x0004}, +{0x000B0D, 0x0080}, +{0x000B0F, 0x0004}, +{0x000B11, 0x0080}, +{0x000B13, 0x0004}, +{0x000B29, 0x0080}, +{0x000B2A, 0x0004}, +{0x000B31, 0x0080}, +{0x000B32, 0x0004}, +{0x000B34, 0x0080}, +{0x000B35, 0x0004}, +{0x000B3A, 0x0080}, +{0x000B3C, 0x0010}, +{0x000B3D, 0x0004}, +{0x000B3E, 0x0010}, +{0x000B45, 0x0080}, +{0x000B47, 0x0010}, +{0x000B49, 0x0080}, +{0x000B4B, 0x0010}, +{0x000B4E, 0x0080}, +{0x000B55, 0x0010}, +{0x000B58, 0x0080}, +{0x000B5C, 0x0004}, +{0x000B5E, 0x0080}, +{0x000B5F, 0x0004}, +{0x000B62, 0x0010}, +{0x000B64, 0x0080}, +{0x000B66, 0x0002}, +{0x000B70, 0x0040}, +{0x000B71, 0x0004}, +{0x000B72, 0x0002}, +{0x000B78, 0x0080}, +{0x000B82, 0x0010}, +{0x000B83, 0x0004}, +{0x000B84, 0x0080}, +{0x000B85, 0x0004}, +{0x000B8B, 0x0080}, +{0x000B8E, 0x0004}, +{0x000B91, 0x0080}, +{0x000B92, 0x0004}, +{0x000B96, 0x0080}, +{0x000B99, 0x0004}, +{0x000B9B, 0x0080}, +{0x000B9C, 0x0004}, +{0x000B9D, 0x0080}, +{0x000B9E, 0x0004}, +{0x000BA0, 0x0080}, +{0x000BA3, 0x0004}, +{0x000BA5, 0x0080}, +{0x000BA8, 0x0004}, +{0x000BAB, 0x0080}, +{0x000BAE, 0x0004}, +{0x000BBA, 0x0080}, +{0x000BBE, 0x0010}, +{0x000BC3, 0x0080}, +{0x000BC6, 0x0010}, +{0x000BC9, 0x0080}, +{0x000BCA, 0x0010}, +{0x000BCE, 0x0080}, +{0x000BD0, 0x0004}, +{0x000BD1, 0x0080}, +{0x000BD7, 0x0010}, +{0x000BD8, 0x0080}, +{0x000BE6, 0x0002}, +{0x000BF3, 0x0040}, +{0x000BFB, 0x0080}, +{0x000C00, 0x0010}, +{0x000C05, 0x0004}, +{0x000C0D, 0x0080}, +{0x000C0E, 0x0004}, +{0x000C11, 0x0080}, +{0x000C12, 0x0004}, +{0x000C29, 0x0080}, +{0x000C2A, 0x0004}, +{0x000C3A, 0x0080}, +{0x000C3C, 0x0010}, +{0x000C3D, 0x0004}, +{0x000C3E, 0x0010}, +{0x000C45, 0x0080}, +{0x000C46, 0x0010}, +{0x000C49, 0x0080}, +{0x000C4A, 0x0010}, +{0x000C4E, 0x0080}, +{0x000C55, 0x0010}, +{0x000C57, 0x0080}, +{0x000C58, 0x0004}, +{0x000C5B, 0x0080}, +{0x000C5D, 0x0004}, +{0x000C5E, 0x0080}, +{0x000C60, 0x0004}, +{0x000C62, 0x0010}, +{0x000C64, 0x0080}, +{0x000C66, 0x0002}, +{0x000C70, 0x0080}, +{0x000C77, 0x0020}, +{0x000C78, 0x0002}, +{0x000C7F, 0x0040}, +{0x000C80, 0x0004}, +{0x000C81, 0x0010}, +{0x000C84, 0x0020}, +{0x000C85, 0x0004}, +{0x000C8D, 0x0080}, +{0x000C8E, 0x0004}, +{0x000C91, 0x0080}, +{0x000C92, 0x0004}, +{0x000CA9, 0x0080}, +{0x000CAA, 0x0004}, +{0x000CB4, 0x0080}, +{0x000CB5, 0x0004}, +{0x000CBA, 0x0080}, +{0x000CBC, 0x0010}, +{0x000CBD, 0x0004}, +{0x000CBE, 0x0010}, +{0x000CC5, 0x0080}, +{0x000CC6, 0x0010}, +{0x000CC9, 0x0080}, +{0x000CCA, 0x0010}, +{0x000CCE, 0x0080}, +{0x000CD5, 0x0010}, +{0x000CD7, 0x0080}, +{0x000CDD, 0x0004}, +{0x000CDF, 0x0080}, +{0x000CE0, 0x0004}, +{0x000CE2, 0x0010}, +{0x000CE4, 0x0080}, +{0x000CE6, 0x0002}, +{0x000CF0, 0x0080}, +{0x000CF1, 0x0004}, +{0x000CF3, 0x0010}, +{0x000CF4, 0x0080}, +{0x000D00, 0x0010}, +{0x000D04, 0x0004}, +{0x000D0D, 0x0080}, +{0x000D0E, 0x0004}, +{0x000D11, 0x0080}, +{0x000D12, 0x0004}, +{0x000D3B, 0x0010}, +{0x000D3D, 0x0004}, +{0x000D3E, 0x0010}, +{0x000D45, 0x0080}, +{0x000D46, 0x0010}, +{0x000D49, 0x0080}, +{0x000D4A, 0x0010}, +{0x000D4E, 0x0004}, +{0x000D4F, 0x0040}, +{0x000D50, 0x0080}, +{0x000D54, 0x0004}, +{0x000D57, 0x0010}, +{0x000D58, 0x0002}, +{0x000D5F, 0x0004}, +{0x000D62, 0x0010}, +{0x000D64, 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0x0010}, +{0x011003, 0x0004}, +{0x011038, 0x0010}, +{0x011047, 0x0020}, +{0x01104E, 0x0080}, +{0x011052, 0x0002}, +{0x011070, 0x0010}, +{0x011071, 0x0004}, +{0x011073, 0x0010}, +{0x011075, 0x0004}, +{0x011076, 0x0080}, +{0x01107F, 0x0010}, +{0x011083, 0x0004}, +{0x0110B0, 0x0010}, +{0x0110BB, 0x0020}, +{0x0110BD, 0x0080}, +{0x0110BE, 0x0020}, +{0x0110C2, 0x0010}, +{0x0110C3, 0x0080}, +{0x0110D0, 0x0004}, +{0x0110E9, 0x0080}, +{0x0110F0, 0x0002}, +{0x0110FA, 0x0080}, +{0x011100, 0x0010}, +{0x011103, 0x0004}, +{0x011127, 0x0010}, +{0x011135, 0x0080}, +{0x011136, 0x0002}, +{0x011140, 0x0020}, +{0x011144, 0x0004}, +{0x011145, 0x0010}, +{0x011147, 0x0004}, +{0x011148, 0x0080}, +{0x011150, 0x0004}, +{0x011173, 0x0010}, +{0x011174, 0x0020}, +{0x011176, 0x0004}, +{0x011177, 0x0080}, +{0x011180, 0x0010}, +{0x011183, 0x0004}, +{0x0111B3, 0x0010}, +{0x0111C1, 0x0004}, +{0x0111C5, 0x0020}, +{0x0111C9, 0x0010}, +{0x0111CD, 0x0020}, +{0x0111CE, 0x0010}, +{0x0111D0, 0x0002}, +{0x0111DA, 0x0004}, 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0x0010}, +{0x011345, 0x0080}, +{0x011347, 0x0010}, +{0x011349, 0x0080}, +{0x01134B, 0x0010}, +{0x01134E, 0x0080}, +{0x011350, 0x0004}, +{0x011351, 0x0080}, +{0x011357, 0x0010}, +{0x011358, 0x0080}, +{0x01135D, 0x0004}, +{0x011362, 0x0010}, +{0x011364, 0x0080}, +{0x011366, 0x0010}, +{0x01136D, 0x0080}, +{0x011370, 0x0010}, +{0x011375, 0x0080}, +{0x011400, 0x0004}, +{0x011435, 0x0010}, +{0x011447, 0x0004}, +{0x01144B, 0x0020}, +{0x011450, 0x0002}, +{0x01145A, 0x0020}, +{0x01145C, 0x0080}, +{0x01145D, 0x0020}, +{0x01145E, 0x0010}, +{0x01145F, 0x0004}, +{0x011462, 0x0080}, +{0x011480, 0x0004}, +{0x0114B0, 0x0010}, +{0x0114C4, 0x0004}, +{0x0114C6, 0x0020}, +{0x0114C7, 0x0004}, +{0x0114C8, 0x0080}, +{0x0114D0, 0x0002}, +{0x0114DA, 0x0080}, +{0x011580, 0x0004}, +{0x0115AF, 0x0010}, +{0x0115B6, 0x0080}, +{0x0115B8, 0x0010}, +{0x0115C1, 0x0020}, +{0x0115D8, 0x0004}, +{0x0115DC, 0x0010}, +{0x0115DE, 0x0080}, +{0x011600, 0x0004}, +{0x011630, 0x0010}, +{0x011641, 0x0020}, +{0x011644, 0x0004}, +{0x011645, 0x0080}, +{0x011650, 0x0002}, +{0x01165A, 0x0080}, +{0x011660, 0x0020}, +{0x01166D, 0x0080}, +{0x011680, 0x0004}, +{0x0116AB, 0x0010}, +{0x0116B8, 0x0004}, +{0x0116B9, 0x0020}, +{0x0116BA, 0x0080}, +{0x0116C0, 0x0002}, +{0x0116CA, 0x0080}, +{0x011700, 0x0004}, +{0x01171B, 0x0080}, +{0x01171D, 0x0010}, +{0x01172C, 0x0080}, +{0x011730, 0x0002}, +{0x01173C, 0x0020}, +{0x01173F, 0x0040}, +{0x011740, 0x0004}, +{0x011747, 0x0080}, +{0x011800, 0x0004}, +{0x01182C, 0x0010}, +{0x01183B, 0x0020}, +{0x01183C, 0x0080}, +{0x0118A0, 0x0004}, +{0x0118E0, 0x0002}, +{0x0118F3, 0x0080}, +{0x0118FF, 0x0004}, +{0x011907, 0x0080}, +{0x011909, 0x0004}, +{0x01190A, 0x0080}, +{0x01190C, 0x0004}, +{0x011914, 0x0080}, +{0x011915, 0x0004}, +{0x011917, 0x0080}, +{0x011918, 0x0004}, +{0x011930, 0x0010}, +{0x011936, 0x0080}, +{0x011937, 0x0010}, +{0x011939, 0x0080}, +{0x01193B, 0x0010}, +{0x01193F, 0x0004}, +{0x011940, 0x0010}, +{0x011941, 0x0004}, +{0x011942, 0x0010}, +{0x011944, 0x0020}, +{0x011947, 0x0080}, +{0x011950, 0x0002}, +{0x01195A, 0x0080}, +{0x0119A0, 0x0004}, +{0x0119A8, 0x0080}, +{0x0119AA, 0x0004}, +{0x0119D1, 0x0010}, +{0x0119D8, 0x0080}, +{0x0119DA, 0x0010}, +{0x0119E1, 0x0004}, +{0x0119E2, 0x0020}, +{0x0119E3, 0x0004}, +{0x0119E4, 0x0010}, +{0x0119E5, 0x0080}, +{0x011A00, 0x0004}, +{0x011A01, 0x0010}, +{0x011A0B, 0x0004}, +{0x011A33, 0x0010}, +{0x011A3A, 0x0004}, +{0x011A3B, 0x0010}, +{0x011A3F, 0x0020}, +{0x011A47, 0x0010}, +{0x011A48, 0x0080}, +{0x011A50, 0x0004}, +{0x011A51, 0x0010}, +{0x011A5C, 0x0004}, +{0x011A8A, 0x0010}, +{0x011A9A, 0x0020}, +{0x011A9D, 0x0004}, +{0x011A9E, 0x0020}, +{0x011AA3, 0x0080}, +{0x011AB0, 0x0004}, +{0x011AF9, 0x0080}, +{0x011B00, 0x0020}, +{0x011B0A, 0x0080}, +{0x011C00, 0x0004}, +{0x011C09, 0x0080}, +{0x011C0A, 0x0004}, +{0x011C2F, 0x0010}, +{0x011C37, 0x0080}, +{0x011C38, 0x0010}, +{0x011C40, 0x0004}, +{0x011C41, 0x0020}, +{0x011C46, 0x0080}, +{0x011C50, 0x0002}, +{0x011C6D, 0x0080}, +{0x011C70, 0x0020}, +{0x011C72, 0x0004}, +{0x011C90, 0x0080}, +{0x011C92, 0x0010}, +{0x011CA8, 0x0080}, +{0x011CA9, 0x0010}, +{0x011CB7, 0x0080}, +{0x011D00, 0x0004}, +{0x011D07, 0x0080}, +{0x011D08, 0x0004}, +{0x011D0A, 0x0080}, +{0x011D0B, 0x0004}, +{0x011D31, 0x0010}, +{0x011D37, 0x0080}, +{0x011D3A, 0x0010}, +{0x011D3B, 0x0080}, +{0x011D3C, 0x0010}, +{0x011D3E, 0x0080}, +{0x011D3F, 0x0010}, +{0x011D46, 0x0004}, +{0x011D47, 0x0010}, +{0x011D48, 0x0080}, +{0x011D50, 0x0002}, +{0x011D5A, 0x0080}, +{0x011D60, 0x0004}, +{0x011D66, 0x0080}, +{0x011D67, 0x0004}, +{0x011D69, 0x0080}, +{0x011D6A, 0x0004}, +{0x011D8A, 0x0010}, +{0x011D8F, 0x0080}, +{0x011D90, 0x0010}, +{0x011D92, 0x0080}, +{0x011D93, 0x0010}, +{0x011D98, 0x0004}, +{0x011D99, 0x0080}, +{0x011DA0, 0x0002}, +{0x011DAA, 0x0080}, +{0x011EE0, 0x0004}, +{0x011EF3, 0x0010}, +{0x011EF7, 0x0020}, +{0x011EF9, 0x0080}, +{0x011F00, 0x0010}, +{0x011F02, 0x0004}, +{0x011F03, 0x0010}, +{0x011F04, 0x0004}, +{0x011F11, 0x0080}, +{0x011F12, 0x0004}, +{0x011F34, 0x0010}, +{0x011F3B, 0x0080}, +{0x011F3E, 0x0010}, +{0x011F43, 0x0020}, +{0x011F50, 0x0002}, +{0x011F5A, 0x0080}, +{0x011FB0, 0x0004}, +{0x011FB1, 0x0080}, +{0x011FC0, 0x0002}, +{0x011FD5, 0x0040}, +{0x011FF2, 0x0080}, +{0x011FFF, 0x0020}, +{0x012000, 0x0004}, +{0x01239A, 0x0080}, +{0x012400, 0x0002}, +{0x01246F, 0x0080}, +{0x012470, 0x0020}, +{0x012475, 0x0080}, +{0x012480, 0x0004}, +{0x012544, 0x0080}, +{0x012F90, 0x0004}, +{0x012FF1, 0x0020}, +{0x012FF3, 0x0080}, +{0x013000, 0x0004}, +{0x013430, 0x0080}, +{0x013440, 0x0010}, +{0x013441, 0x0004}, +{0x013447, 0x0010}, +{0x013456, 0x0080}, +{0x014400, 0x0004}, +{0x014647, 0x0080}, +{0x016800, 0x0004}, +{0x016A39, 0x0080}, +{0x016A40, 0x0004}, +{0x016A5F, 0x0080}, +{0x016A60, 0x0002}, +{0x016A6A, 0x0080}, +{0x016A6E, 0x0020}, +{0x016A70, 0x0004}, +{0x016ABF, 0x0080}, +{0x016AC0, 0x0002}, +{0x016ACA, 0x0080}, +{0x016AD0, 0x0004}, +{0x016AEE, 0x0080}, +{0x016AF0, 0x0010}, +{0x016AF5, 0x0020}, +{0x016AF6, 0x0080}, +{0x016B00, 0x0004}, +{0x016B30, 0x0010}, +{0x016B37, 0x0020}, +{0x016B3C, 0x0040}, +{0x016B40, 0x0004}, +{0x016B44, 0x0020}, +{0x016B45, 0x0040}, +{0x016B46, 0x0080}, +{0x016B50, 0x0002}, +{0x016B5A, 0x0080}, +{0x016B5B, 0x0002}, +{0x016B62, 0x0080}, +{0x016B63, 0x0004}, +{0x016B78, 0x0080}, +{0x016B7D, 0x0004}, +{0x016B90, 0x0080}, +{0x016E40, 0x0004}, +{0x016E80, 0x0002}, +{0x016E97, 0x0020}, +{0x016E9B, 0x0080}, +{0x016F00, 0x0004}, +{0x016F4B, 0x0080}, +{0x016F4F, 0x0010}, +{0x016F50, 0x0004}, +{0x016F51, 0x0010}, +{0x016F88, 0x0080}, +{0x016F8F, 0x0010}, +{0x016F93, 0x0004}, +{0x016FA0, 0x0080}, +{0x016FE0, 0x0004}, +{0x016FE2, 0x0020}, +{0x016FE3, 0x0004}, +{0x016FE4, 0x0010}, +{0x016FE5, 0x0080}, +{0x016FF0, 0x0010}, +{0x016FF2, 0x0080}, +{0x017000, 0x0004}, +{0x0187F8, 0x0080}, +{0x018800, 0x0004}, +{0x018CD6, 0x0080}, +{0x018D00, 0x0004}, +{0x018D09, 0x0080}, +{0x01AFF0, 0x0004}, +{0x01AFF4, 0x0080}, +{0x01AFF5, 0x0004}, +{0x01AFFC, 0x0080}, +{0x01AFFD, 0x0004}, +{0x01AFFF, 0x0080}, +{0x01B000, 0x0004}, +{0x01B123, 0x0080}, +{0x01B132, 0x0004}, +{0x01B133, 0x0080}, +{0x01B150, 0x0004}, +{0x01B153, 0x0080}, +{0x01B155, 0x0004}, +{0x01B156, 0x0080}, +{0x01B164, 0x0004}, +{0x01B168, 0x0080}, +{0x01B170, 0x0004}, +{0x01B2FC, 0x0080}, +{0x01BC00, 0x0004}, +{0x01BC6B, 0x0080}, +{0x01BC70, 0x0004}, +{0x01BC7D, 0x0080}, +{0x01BC80, 0x0004}, +{0x01BC89, 0x0080}, +{0x01BC90, 0x0004}, +{0x01BC9A, 0x0080}, +{0x01BC9C, 0x0040}, +{0x01BC9D, 0x0010}, +{0x01BC9F, 0x0020}, +{0x01BCA0, 0x0080}, +{0x01CF00, 0x0010}, +{0x01CF2E, 0x0080}, +{0x01CF30, 0x0010}, +{0x01CF47, 0x0080}, +{0x01CF50, 0x0040}, +{0x01CFC4, 0x0080}, +{0x01D000, 0x0040}, +{0x01D0F6, 0x0080}, +{0x01D100, 0x0040}, +{0x01D127, 0x0080}, +{0x01D129, 0x0040}, +{0x01D165, 0x0010}, +{0x01D16A, 0x0040}, +{0x01D16D, 0x0010}, +{0x01D173, 0x0080}, +{0x01D17B, 0x0010}, +{0x01D183, 0x0040}, +{0x01D185, 0x0010}, +{0x01D18C, 0x0040}, +{0x01D1AA, 0x0010}, +{0x01D1AE, 0x0040}, +{0x01D1EB, 0x0080}, +{0x01D200, 0x0040}, +{0x01D242, 0x0010}, +{0x01D245, 0x0040}, +{0x01D246, 0x0080}, +{0x01D2C0, 0x0002}, +{0x01D2D4, 0x0080}, +{0x01D2E0, 0x0002}, +{0x01D2F4, 0x0080}, +{0x01D300, 0x0040}, +{0x01D357, 0x0080}, +{0x01D360, 0x0002}, +{0x01D379, 0x0080}, +{0x01D400, 0x0004}, +{0x01D455, 0x0080}, +{0x01D456, 0x0004}, +{0x01D49D, 0x0080}, +{0x01D49E, 0x0004}, +{0x01D4A0, 0x0080}, +{0x01D4A2, 0x0004}, +{0x01D4A3, 0x0080}, +{0x01D4A5, 0x0004}, +{0x01D4A7, 0x0080}, +{0x01D4A9, 0x0004}, +{0x01D4AD, 0x0080}, +{0x01D4AE, 0x0004}, +{0x01D4BA, 0x0080}, +{0x01D4BB, 0x0004}, +{0x01D4BC, 0x0080}, +{0x01D4BD, 0x0004}, +{0x01D4C4, 0x0080}, +{0x01D4C5, 0x0004}, +{0x01D506, 0x0080}, +{0x01D507, 0x0004}, +{0x01D50B, 0x0080}, +{0x01D50D, 0x0004}, +{0x01D515, 0x0080}, +{0x01D516, 0x0004}, +{0x01D51D, 0x0080}, +{0x01D51E, 0x0004}, +{0x01D53A, 0x0080}, +{0x01D53B, 0x0004}, +{0x01D53F, 0x0080}, +{0x01D540, 0x0004}, +{0x01D545, 0x0080}, +{0x01D546, 0x0004}, +{0x01D547, 0x0080}, +{0x01D54A, 0x0004}, +{0x01D551, 0x0080}, +{0x01D552, 0x0004}, +{0x01D6A6, 0x0080}, +{0x01D6A8, 0x0004}, +{0x01D6C1, 0x0040}, +{0x01D6C2, 0x0004}, +{0x01D6DB, 0x0040}, +{0x01D6DC, 0x0004}, +{0x01D6FB, 0x0040}, +{0x01D6FC, 0x0004}, +{0x01D715, 0x0040}, +{0x01D716, 0x0004}, +{0x01D735, 0x0040}, +{0x01D736, 0x0004}, +{0x01D74F, 0x0040}, +{0x01D750, 0x0004}, +{0x01D76F, 0x0040}, +{0x01D770, 0x0004}, +{0x01D789, 0x0040}, +{0x01D78A, 0x0004}, +{0x01D7A9, 0x0040}, +{0x01D7AA, 0x0004}, +{0x01D7C3, 0x0040}, +{0x01D7C4, 0x0004}, +{0x01D7CC, 0x0080}, +{0x01D7CE, 0x0002}, +{0x01D800, 0x0040}, +{0x01DA00, 0x0010}, +{0x01DA37, 0x0040}, +{0x01DA3B, 0x0010}, +{0x01DA6D, 0x0040}, +{0x01DA75, 0x0010}, +{0x01DA76, 0x0040}, +{0x01DA84, 0x0010}, +{0x01DA85, 0x0040}, +{0x01DA87, 0x0020}, +{0x01DA8C, 0x0080}, +{0x01DA9B, 0x0010}, +{0x01DAA0, 0x0080}, +{0x01DAA1, 0x0010}, +{0x01DAB0, 0x0080}, +{0x01DF00, 0x0004}, +{0x01DF1F, 0x0080}, +{0x01DF25, 0x0004}, +{0x01DF2B, 0x0080}, +{0x01E000, 0x0010}, +{0x01E007, 0x0080}, +{0x01E008, 0x0010}, +{0x01E019, 0x0080}, +{0x01E01B, 0x0010}, +{0x01E022, 0x0080}, +{0x01E023, 0x0010}, +{0x01E025, 0x0080}, +{0x01E026, 0x0010}, +{0x01E02B, 0x0080}, +{0x01E030, 0x0004}, +{0x01E06E, 0x0080}, +{0x01E08F, 0x0010}, +{0x01E090, 0x0080}, +{0x01E100, 0x0004}, +{0x01E12D, 0x0080}, +{0x01E130, 0x0010}, +{0x01E137, 0x0004}, +{0x01E13E, 0x0080}, +{0x01E140, 0x0002}, +{0x01E14A, 0x0080}, +{0x01E14E, 0x0004}, +{0x01E14F, 0x0040}, +{0x01E150, 0x0080}, +{0x01E290, 0x0004}, +{0x01E2AE, 0x0010}, +{0x01E2AF, 0x0080}, +{0x01E2C0, 0x0004}, +{0x01E2EC, 0x0010}, +{0x01E2F0, 0x0002}, +{0x01E2FA, 0x0080}, +{0x01E2FF, 0x0040}, +{0x01E300, 0x0080}, +{0x01E4D0, 0x0004}, +{0x01E4EC, 0x0010}, +{0x01E4F0, 0x0002}, +{0x01E4FA, 0x0080}, +{0x01E7E0, 0x0004}, +{0x01E7E7, 0x0080}, +{0x01E7E8, 0x0004}, +{0x01E7EC, 0x0080}, +{0x01E7ED, 0x0004}, +{0x01E7EF, 0x0080}, +{0x01E7F0, 0x0004}, +{0x01E7FF, 0x0080}, +{0x01E800, 0x0004}, +{0x01E8C5, 0x0080}, +{0x01E8C7, 0x0002}, +{0x01E8D0, 0x0010}, +{0x01E8D7, 0x0080}, +{0x01E900, 0x0004}, +{0x01E944, 0x0010}, +{0x01E94B, 0x0004}, +{0x01E94C, 0x0080}, +{0x01E950, 0x0002}, +{0x01E95A, 0x0080}, +{0x01E95E, 0x0020}, +{0x01E960, 0x0080}, +{0x01EC71, 0x0002}, +{0x01ECAC, 0x0040}, +{0x01ECAD, 0x0002}, +{0x01ECB0, 0x0040}, +{0x01ECB1, 0x0002}, +{0x01ECB5, 0x0080}, +{0x01ED01, 0x0002}, +{0x01ED2E, 0x0040}, +{0x01ED2F, 0x0002}, +{0x01ED3E, 0x0080}, +{0x01EE00, 0x0004}, +{0x01EE04, 0x0080}, +{0x01EE05, 0x0004}, +{0x01EE20, 0x0080}, +{0x01EE21, 0x0004}, +{0x01EE23, 0x0080}, +{0x01EE24, 0x0004}, +{0x01EE25, 0x0080}, +{0x01EE27, 0x0004}, +{0x01EE28, 0x0080}, +{0x01EE29, 0x0004}, +{0x01EE33, 0x0080}, +{0x01EE34, 0x0004}, +{0x01EE38, 0x0080}, +{0x01EE39, 0x0004}, +{0x01EE3A, 0x0080}, +{0x01EE3B, 0x0004}, +{0x01EE3C, 0x0080}, +{0x01EE42, 0x0004}, +{0x01EE43, 0x0080}, +{0x01EE47, 0x0004}, +{0x01EE48, 0x0080}, +{0x01EE49, 0x0004}, +{0x01EE4A, 0x0080}, +{0x01EE4B, 0x0004}, +{0x01EE4C, 0x0080}, +{0x01EE4D, 0x0004}, +{0x01EE50, 0x0080}, +{0x01EE51, 0x0004}, +{0x01EE53, 0x0080}, +{0x01EE54, 0x0004}, +{0x01EE55, 0x0080}, +{0x01EE57, 0x0004}, +{0x01EE58, 0x0080}, +{0x01EE59, 0x0004}, +{0x01EE5A, 0x0080}, +{0x01EE5B, 0x0004}, +{0x01EE5C, 0x0080}, +{0x01EE5D, 0x0004}, +{0x01EE5E, 0x0080}, +{0x01EE5F, 0x0004}, +{0x01EE60, 0x0080}, +{0x01EE61, 0x0004}, +{0x01EE63, 0x0080}, +{0x01EE64, 0x0004}, +{0x01EE65, 0x0080}, +{0x01EE67, 0x0004}, +{0x01EE6B, 0x0080}, +{0x01EE6C, 0x0004}, +{0x01EE73, 0x0080}, +{0x01EE74, 0x0004}, +{0x01EE78, 0x0080}, +{0x01EE79, 0x0004}, +{0x01EE7D, 0x0080}, +{0x01EE7E, 0x0004}, +{0x01EE7F, 0x0080}, +{0x01EE80, 0x0004}, +{0x01EE8A, 0x0080}, +{0x01EE8B, 0x0004}, +{0x01EE9C, 0x0080}, +{0x01EEA1, 0x0004}, +{0x01EEA4, 0x0080}, +{0x01EEA5, 0x0004}, +{0x01EEAA, 0x0080}, +{0x01EEAB, 0x0004}, +{0x01EEBC, 0x0080}, +{0x01EEF0, 0x0040}, +{0x01EEF2, 0x0080}, +{0x01F000, 0x0040}, +{0x01F02C, 0x0080}, +{0x01F030, 0x0040}, +{0x01F094, 0x0080}, +{0x01F0A0, 0x0040}, +{0x01F0AF, 0x0080}, +{0x01F0B1, 0x0040}, +{0x01F0C0, 0x0080}, +{0x01F0C1, 0x0040}, +{0x01F0D0, 0x0080}, +{0x01F0D1, 0x0040}, +{0x01F0F6, 0x0080}, +{0x01F100, 0x0002}, +{0x01F10D, 0x0040}, +{0x01F1AE, 0x0080}, +{0x01F1E6, 0x0040}, +{0x01F203, 0x0080}, +{0x01F210, 0x0040}, +{0x01F23C, 0x0080}, +{0x01F240, 0x0040}, +{0x01F249, 0x0080}, +{0x01F250, 0x0040}, +{0x01F252, 0x0080}, +{0x01F260, 0x0040}, +{0x01F266, 0x0080}, +{0x01F300, 0x0040}, +{0x01F6D8, 0x0080}, +{0x01F6DC, 0x0040}, +{0x01F6ED, 0x0080}, +{0x01F6F0, 0x0040}, +{0x01F6FD, 0x0080}, +{0x01F700, 0x0040}, +{0x01F777, 0x0080}, +{0x01F77B, 0x0040}, +{0x01F7DA, 0x0080}, +{0x01F7E0, 0x0040}, +{0x01F7EC, 0x0080}, +{0x01F7F0, 0x0040}, +{0x01F7F1, 0x0080}, +{0x01F800, 0x0040}, +{0x01F80C, 0x0080}, +{0x01F810, 0x0040}, +{0x01F848, 0x0080}, +{0x01F850, 0x0040}, +{0x01F85A, 0x0080}, +{0x01F860, 0x0040}, +{0x01F888, 0x0080}, +{0x01F890, 0x0040}, +{0x01F8AE, 0x0080}, +{0x01F8B0, 0x0040}, +{0x01F8B2, 0x0080}, +{0x01F900, 0x0040}, +{0x01FA54, 0x0080}, +{0x01FA60, 0x0040}, +{0x01FA6E, 0x0080}, +{0x01FA70, 0x0040}, +{0x01FA7D, 0x0080}, +{0x01FA80, 0x0040}, +{0x01FA89, 0x0080}, +{0x01FA90, 0x0040}, +{0x01FABE, 0x0080}, +{0x01FABF, 0x0040}, +{0x01FAC6, 0x0080}, +{0x01FACE, 0x0040}, +{0x01FADC, 0x0080}, +{0x01FAE0, 0x0040}, +{0x01FAE9, 0x0080}, +{0x01FAF0, 0x0040}, +{0x01FAF9, 0x0080}, +{0x01FB00, 0x0040}, +{0x01FB93, 0x0080}, +{0x01FB94, 0x0040}, +{0x01FBCB, 0x0080}, +{0x01FBF0, 0x0002}, +{0x01FBFA, 0x0080}, +{0x020000, 0x0004}, +{0x02A6E0, 0x0080}, +{0x02A700, 0x0004}, +{0x02B73A, 0x0080}, +{0x02B740, 0x0004}, +{0x02B81E, 0x0080}, +{0x02B820, 0x0004}, +{0x02CEA2, 0x0080}, +{0x02CEB0, 0x0004}, +{0x02EBE1, 0x0080}, +{0x02EBF0, 0x0004}, +{0x02EE5E, 0x0080}, +{0x02F800, 0x0004}, +{0x02FA1E, 0x0080}, +{0x030000, 0x0004}, +{0x03134B, 0x0080}, +{0x031350, 0x0004}, +{0x0323B0, 0x0080}, +{0x0E0100, 0x0010}, +{0x0E01F0, 0x0080}, +{0x110000, 0x0000}, }; -const std::vector> unicode_ranges_letter = { -{0x00000041, 0x0000005A}, {0x00000061, 0x0000007A}, {0x000000AA, 0x000000AA}, {0x000000B5, 0x000000B5}, -{0x000000BA, 0x000000BA}, {0x000000C0, 0x000000D6}, {0x000000D8, 0x000000F6}, {0x000000F8, 0x000002C1}, -{0x000002C6, 0x000002D1}, {0x000002E0, 0x000002E4}, {0x000002EC, 0x000002EC}, {0x000002EE, 0x000002EE}, -{0x00000370, 0x00000374}, {0x00000376, 0x00000377}, {0x0000037A, 0x0000037D}, {0x0000037F, 0x0000037F}, -{0x00000386, 0x00000386}, {0x00000388, 0x0000038A}, {0x0000038C, 0x0000038C}, {0x0000038E, 0x000003A1}, -{0x000003A3, 0x000003F5}, {0x000003F7, 0x00000481}, {0x0000048A, 0x0000052F}, {0x00000531, 0x00000556}, -{0x00000559, 0x00000559}, {0x00000560, 0x00000588}, {0x000005D0, 0x000005EA}, {0x000005EF, 0x000005F2}, -{0x00000620, 0x0000064A}, {0x0000066E, 0x0000066F}, {0x00000671, 0x000006D3}, {0x000006D5, 0x000006D5}, -{0x000006E5, 0x000006E6}, {0x000006EE, 0x000006EF}, {0x000006FA, 0x000006FC}, {0x000006FF, 0x000006FF}, -{0x00000710, 0x00000710}, {0x00000712, 0x0000072F}, {0x0000074D, 0x000007A5}, {0x000007B1, 0x000007B1}, -{0x000007CA, 0x000007EA}, {0x000007F4, 0x000007F5}, {0x000007FA, 0x000007FA}, {0x00000800, 0x00000815}, -{0x0000081A, 0x0000081A}, {0x00000824, 0x00000824}, {0x00000828, 0x00000828}, {0x00000840, 0x00000858}, -{0x00000860, 0x0000086A}, {0x00000870, 0x00000887}, {0x00000889, 0x0000088E}, {0x000008A0, 0x000008C9}, -{0x00000904, 0x00000939}, {0x0000093D, 0x0000093D}, {0x00000950, 0x00000950}, {0x00000958, 0x00000961}, -{0x00000971, 0x00000980}, {0x00000985, 0x0000098C}, {0x0000098F, 0x00000990}, {0x00000993, 0x000009A8}, -{0x000009AA, 0x000009B0}, {0x000009B2, 0x000009B2}, {0x000009B6, 0x000009B9}, {0x000009BD, 0x000009BD}, -{0x000009CE, 0x000009CE}, {0x000009DC, 0x000009DD}, {0x000009DF, 0x000009E1}, {0x000009F0, 0x000009F1}, -{0x000009FC, 0x000009FC}, {0x00000A05, 0x00000A0A}, {0x00000A0F, 0x00000A10}, {0x00000A13, 0x00000A28}, -{0x00000A2A, 0x00000A30}, {0x00000A32, 0x00000A33}, {0x00000A35, 0x00000A36}, {0x00000A38, 0x00000A39}, -{0x00000A59, 0x00000A5C}, {0x00000A5E, 0x00000A5E}, {0x00000A72, 0x00000A74}, {0x00000A85, 0x00000A8D}, -{0x00000A8F, 0x00000A91}, {0x00000A93, 0x00000AA8}, {0x00000AAA, 0x00000AB0}, {0x00000AB2, 0x00000AB3}, -{0x00000AB5, 0x00000AB9}, {0x00000ABD, 0x00000ABD}, {0x00000AD0, 0x00000AD0}, {0x00000AE0, 0x00000AE1}, -{0x00000AF9, 0x00000AF9}, {0x00000B05, 0x00000B0C}, {0x00000B0F, 0x00000B10}, {0x00000B13, 0x00000B28}, -{0x00000B2A, 0x00000B30}, {0x00000B32, 0x00000B33}, {0x00000B35, 0x00000B39}, {0x00000B3D, 0x00000B3D}, -{0x00000B5C, 0x00000B5D}, {0x00000B5F, 0x00000B61}, {0x00000B71, 0x00000B71}, {0x00000B83, 0x00000B83}, -{0x00000B85, 0x00000B8A}, {0x00000B8E, 0x00000B90}, {0x00000B92, 0x00000B95}, {0x00000B99, 0x00000B9A}, -{0x00000B9C, 0x00000B9C}, {0x00000B9E, 0x00000B9F}, {0x00000BA3, 0x00000BA4}, {0x00000BA8, 0x00000BAA}, -{0x00000BAE, 0x00000BB9}, {0x00000BD0, 0x00000BD0}, {0x00000C05, 0x00000C0C}, {0x00000C0E, 0x00000C10}, -{0x00000C12, 0x00000C28}, {0x00000C2A, 0x00000C39}, {0x00000C3D, 0x00000C3D}, {0x00000C58, 0x00000C5A}, -{0x00000C5D, 0x00000C5D}, {0x00000C60, 0x00000C61}, {0x00000C80, 0x00000C80}, {0x00000C85, 0x00000C8C}, -{0x00000C8E, 0x00000C90}, {0x00000C92, 0x00000CA8}, {0x00000CAA, 0x00000CB3}, {0x00000CB5, 0x00000CB9}, -{0x00000CBD, 0x00000CBD}, {0x00000CDD, 0x00000CDE}, {0x00000CE0, 0x00000CE1}, {0x00000CF1, 0x00000CF2}, -{0x00000D04, 0x00000D0C}, {0x00000D0E, 0x00000D10}, {0x00000D12, 0x00000D3A}, {0x00000D3D, 0x00000D3D}, -{0x00000D4E, 0x00000D4E}, {0x00000D54, 0x00000D56}, {0x00000D5F, 0x00000D61}, {0x00000D7A, 0x00000D7F}, -{0x00000D85, 0x00000D96}, {0x00000D9A, 0x00000DB1}, {0x00000DB3, 0x00000DBB}, {0x00000DBD, 0x00000DBD}, -{0x00000DC0, 0x00000DC6}, {0x00000E01, 0x00000E30}, {0x00000E32, 0x00000E33}, {0x00000E40, 0x00000E46}, -{0x00000E81, 0x00000E82}, {0x00000E84, 0x00000E84}, {0x00000E86, 0x00000E8A}, {0x00000E8C, 0x00000EA3}, -{0x00000EA5, 0x00000EA5}, {0x00000EA7, 0x00000EB0}, {0x00000EB2, 0x00000EB3}, {0x00000EBD, 0x00000EBD}, -{0x00000EC0, 0x00000EC4}, {0x00000EC6, 0x00000EC6}, {0x00000EDC, 0x00000EDF}, {0x00000F00, 0x00000F00}, -{0x00000F40, 0x00000F47}, {0x00000F49, 0x00000F6C}, {0x00000F88, 0x00000F8C}, {0x00001000, 0x0000102A}, -{0x0000103F, 0x0000103F}, {0x00001050, 0x00001055}, {0x0000105A, 0x0000105D}, {0x00001061, 0x00001061}, -{0x00001065, 0x00001066}, {0x0000106E, 0x00001070}, {0x00001075, 0x00001081}, {0x0000108E, 0x0000108E}, -{0x000010A0, 0x000010C5}, {0x000010C7, 0x000010C7}, {0x000010CD, 0x000010CD}, {0x000010D0, 0x000010FA}, -{0x000010FC, 0x00001248}, {0x0000124A, 0x0000124D}, {0x00001250, 0x00001256}, {0x00001258, 0x00001258}, -{0x0000125A, 0x0000125D}, {0x00001260, 0x00001288}, {0x0000128A, 0x0000128D}, {0x00001290, 0x000012B0}, -{0x000012B2, 0x000012B5}, {0x000012B8, 0x000012BE}, {0x000012C0, 0x000012C0}, {0x000012C2, 0x000012C5}, -{0x000012C8, 0x000012D6}, {0x000012D8, 0x00001310}, {0x00001312, 0x00001315}, {0x00001318, 0x0000135A}, -{0x00001380, 0x0000138F}, {0x000013A0, 0x000013F5}, {0x000013F8, 0x000013FD}, {0x00001401, 0x0000166C}, -{0x0000166F, 0x0000167F}, {0x00001681, 0x0000169A}, {0x000016A0, 0x000016EA}, {0x000016F1, 0x000016F8}, -{0x00001700, 0x00001711}, {0x0000171F, 0x00001731}, {0x00001740, 0x00001751}, {0x00001760, 0x0000176C}, -{0x0000176E, 0x00001770}, {0x00001780, 0x000017B3}, {0x000017D7, 0x000017D7}, {0x000017DC, 0x000017DC}, -{0x00001820, 0x00001878}, {0x00001880, 0x00001884}, {0x00001887, 0x000018A8}, {0x000018AA, 0x000018AA}, -{0x000018B0, 0x000018F5}, {0x00001900, 0x0000191E}, {0x00001950, 0x0000196D}, {0x00001970, 0x00001974}, -{0x00001980, 0x000019AB}, {0x000019B0, 0x000019C9}, {0x00001A00, 0x00001A16}, {0x00001A20, 0x00001A54}, -{0x00001AA7, 0x00001AA7}, {0x00001B05, 0x00001B33}, {0x00001B45, 0x00001B4C}, {0x00001B83, 0x00001BA0}, -{0x00001BAE, 0x00001BAF}, {0x00001BBA, 0x00001BE5}, {0x00001C00, 0x00001C23}, {0x00001C4D, 0x00001C4F}, -{0x00001C5A, 0x00001C7D}, {0x00001C80, 0x00001C88}, {0x00001C90, 0x00001CBA}, {0x00001CBD, 0x00001CBF}, -{0x00001CE9, 0x00001CEC}, {0x00001CEE, 0x00001CF3}, {0x00001CF5, 0x00001CF6}, {0x00001CFA, 0x00001CFA}, -{0x00001D00, 0x00001DBF}, {0x00001E00, 0x00001F15}, {0x00001F18, 0x00001F1D}, {0x00001F20, 0x00001F45}, -{0x00001F48, 0x00001F4D}, {0x00001F50, 0x00001F57}, {0x00001F59, 0x00001F59}, {0x00001F5B, 0x00001F5B}, -{0x00001F5D, 0x00001F5D}, {0x00001F5F, 0x00001F7D}, {0x00001F80, 0x00001FB4}, {0x00001FB6, 0x00001FBC}, -{0x00001FBE, 0x00001FBE}, {0x00001FC2, 0x00001FC4}, {0x00001FC6, 0x00001FCC}, {0x00001FD0, 0x00001FD3}, -{0x00001FD6, 0x00001FDB}, {0x00001FE0, 0x00001FEC}, {0x00001FF2, 0x00001FF4}, {0x00001FF6, 0x00001FFC}, -{0x00002071, 0x00002071}, {0x0000207F, 0x0000207F}, {0x00002090, 0x0000209C}, {0x00002102, 0x00002102}, -{0x00002107, 0x00002107}, {0x0000210A, 0x00002113}, {0x00002115, 0x00002115}, {0x00002119, 0x0000211D}, -{0x00002124, 0x00002124}, {0x00002126, 0x00002126}, {0x00002128, 0x00002128}, {0x0000212A, 0x0000212D}, -{0x0000212F, 0x00002139}, {0x0000213C, 0x0000213F}, {0x00002145, 0x00002149}, {0x0000214E, 0x0000214E}, -{0x00002183, 0x00002184}, {0x00002C00, 0x00002CE4}, {0x00002CEB, 0x00002CEE}, {0x00002CF2, 0x00002CF3}, -{0x00002D00, 0x00002D25}, {0x00002D27, 0x00002D27}, {0x00002D2D, 0x00002D2D}, {0x00002D30, 0x00002D67}, -{0x00002D6F, 0x00002D6F}, {0x00002D80, 0x00002D96}, {0x00002DA0, 0x00002DA6}, {0x00002DA8, 0x00002DAE}, -{0x00002DB0, 0x00002DB6}, {0x00002DB8, 0x00002DBE}, {0x00002DC0, 0x00002DC6}, {0x00002DC8, 0x00002DCE}, -{0x00002DD0, 0x00002DD6}, {0x00002DD8, 0x00002DDE}, {0x00002E2F, 0x00002E2F}, {0x00003005, 0x00003006}, -{0x00003031, 0x00003035}, {0x0000303B, 0x0000303C}, {0x00003041, 0x00003096}, {0x0000309D, 0x0000309F}, -{0x000030A1, 0x000030FA}, {0x000030FC, 0x000030FF}, {0x00003105, 0x0000312F}, {0x00003131, 0x0000318E}, -{0x000031A0, 0x000031BF}, {0x000031F0, 0x000031FF}, {0x00003400, 0x00004DBF}, {0x00004E00, 0x0000A48C}, -{0x0000A4D0, 0x0000A4FD}, {0x0000A500, 0x0000A60C}, {0x0000A610, 0x0000A61F}, {0x0000A62A, 0x0000A62B}, -{0x0000A640, 0x0000A66E}, {0x0000A67F, 0x0000A69D}, {0x0000A6A0, 0x0000A6E5}, {0x0000A717, 0x0000A71F}, -{0x0000A722, 0x0000A788}, {0x0000A78B, 0x0000A7CA}, {0x0000A7D0, 0x0000A7D1}, {0x0000A7D3, 0x0000A7D3}, -{0x0000A7D5, 0x0000A7D9}, {0x0000A7F2, 0x0000A801}, {0x0000A803, 0x0000A805}, {0x0000A807, 0x0000A80A}, -{0x0000A80C, 0x0000A822}, {0x0000A840, 0x0000A873}, {0x0000A882, 0x0000A8B3}, {0x0000A8F2, 0x0000A8F7}, -{0x0000A8FB, 0x0000A8FB}, {0x0000A8FD, 0x0000A8FE}, {0x0000A90A, 0x0000A925}, {0x0000A930, 0x0000A946}, -{0x0000A960, 0x0000A97C}, {0x0000A984, 0x0000A9B2}, {0x0000A9CF, 0x0000A9CF}, {0x0000A9E0, 0x0000A9E4}, -{0x0000A9E6, 0x0000A9EF}, {0x0000A9FA, 0x0000A9FE}, {0x0000AA00, 0x0000AA28}, {0x0000AA40, 0x0000AA42}, -{0x0000AA44, 0x0000AA4B}, {0x0000AA60, 0x0000AA76}, {0x0000AA7A, 0x0000AA7A}, {0x0000AA7E, 0x0000AAAF}, -{0x0000AAB1, 0x0000AAB1}, {0x0000AAB5, 0x0000AAB6}, {0x0000AAB9, 0x0000AABD}, {0x0000AAC0, 0x0000AAC0}, -{0x0000AAC2, 0x0000AAC2}, {0x0000AADB, 0x0000AADD}, {0x0000AAE0, 0x0000AAEA}, {0x0000AAF2, 0x0000AAF4}, -{0x0000AB01, 0x0000AB06}, {0x0000AB09, 0x0000AB0E}, {0x0000AB11, 0x0000AB16}, {0x0000AB20, 0x0000AB26}, -{0x0000AB28, 0x0000AB2E}, {0x0000AB30, 0x0000AB5A}, {0x0000AB5C, 0x0000AB69}, {0x0000AB70, 0x0000ABE2}, -{0x0000AC00, 0x0000D7A3}, {0x0000D7B0, 0x0000D7C6}, {0x0000D7CB, 0x0000D7FB}, {0x0000F900, 0x0000FA6D}, -{0x0000FA70, 0x0000FAD9}, {0x0000FB00, 0x0000FB06}, {0x0000FB13, 0x0000FB17}, {0x0000FB1D, 0x0000FB1D}, -{0x0000FB1F, 0x0000FB28}, {0x0000FB2A, 0x0000FB36}, {0x0000FB38, 0x0000FB3C}, {0x0000FB3E, 0x0000FB3E}, -{0x0000FB40, 0x0000FB41}, {0x0000FB43, 0x0000FB44}, {0x0000FB46, 0x0000FBB1}, {0x0000FBD3, 0x0000FD3D}, -{0x0000FD50, 0x0000FD8F}, {0x0000FD92, 0x0000FDC7}, {0x0000FDF0, 0x0000FDFB}, {0x0000FE70, 0x0000FE74}, -{0x0000FE76, 0x0000FEFC}, {0x0000FF21, 0x0000FF3A}, {0x0000FF41, 0x0000FF5A}, {0x0000FF66, 0x0000FFBE}, -{0x0000FFC2, 0x0000FFC7}, {0x0000FFCA, 0x0000FFCF}, {0x0000FFD2, 0x0000FFD7}, {0x0000FFDA, 0x0000FFDC}, -{0x00010000, 0x0001000B}, {0x0001000D, 0x00010026}, {0x00010028, 0x0001003A}, {0x0001003C, 0x0001003D}, -{0x0001003F, 0x0001004D}, {0x00010050, 0x0001005D}, {0x00010080, 0x000100FA}, {0x00010280, 0x0001029C}, -{0x000102A0, 0x000102D0}, {0x00010300, 0x0001031F}, {0x0001032D, 0x00010340}, {0x00010342, 0x00010349}, -{0x00010350, 0x00010375}, {0x00010380, 0x0001039D}, {0x000103A0, 0x000103C3}, {0x000103C8, 0x000103CF}, -{0x00010400, 0x0001049D}, {0x000104B0, 0x000104D3}, {0x000104D8, 0x000104FB}, {0x00010500, 0x00010527}, -{0x00010530, 0x00010563}, {0x00010570, 0x0001057A}, {0x0001057C, 0x0001058A}, {0x0001058C, 0x00010592}, -{0x00010594, 0x00010595}, {0x00010597, 0x000105A1}, {0x000105A3, 0x000105B1}, {0x000105B3, 0x000105B9}, -{0x000105BB, 0x000105BC}, {0x00010600, 0x00010736}, {0x00010740, 0x00010755}, {0x00010760, 0x00010767}, -{0x00010780, 0x00010785}, {0x00010787, 0x000107B0}, {0x000107B2, 0x000107BA}, {0x00010800, 0x00010805}, -{0x00010808, 0x00010808}, {0x0001080A, 0x00010835}, {0x00010837, 0x00010838}, {0x0001083C, 0x0001083C}, -{0x0001083F, 0x00010855}, {0x00010860, 0x00010876}, {0x00010880, 0x0001089E}, {0x000108E0, 0x000108F2}, -{0x000108F4, 0x000108F5}, {0x00010900, 0x00010915}, {0x00010920, 0x00010939}, {0x00010980, 0x000109B7}, -{0x000109BE, 0x000109BF}, {0x00010A00, 0x00010A00}, {0x00010A10, 0x00010A13}, {0x00010A15, 0x00010A17}, -{0x00010A19, 0x00010A35}, {0x00010A60, 0x00010A7C}, {0x00010A80, 0x00010A9C}, {0x00010AC0, 0x00010AC7}, -{0x00010AC9, 0x00010AE4}, {0x00010B00, 0x00010B35}, {0x00010B40, 0x00010B55}, {0x00010B60, 0x00010B72}, -{0x00010B80, 0x00010B91}, {0x00010C00, 0x00010C48}, {0x00010C80, 0x00010CB2}, {0x00010CC0, 0x00010CF2}, -{0x00010D00, 0x00010D23}, {0x00010E80, 0x00010EA9}, {0x00010EB0, 0x00010EB1}, {0x00010F00, 0x00010F1C}, -{0x00010F27, 0x00010F27}, {0x00010F30, 0x00010F45}, {0x00010F70, 0x00010F81}, {0x00010FB0, 0x00010FC4}, -{0x00010FE0, 0x00010FF6}, {0x00011003, 0x00011037}, {0x00011071, 0x00011072}, {0x00011075, 0x00011075}, -{0x00011083, 0x000110AF}, {0x000110D0, 0x000110E8}, {0x00011103, 0x00011126}, {0x00011144, 0x00011144}, -{0x00011147, 0x00011147}, {0x00011150, 0x00011172}, {0x00011176, 0x00011176}, {0x00011183, 0x000111B2}, -{0x000111C1, 0x000111C4}, {0x000111DA, 0x000111DA}, {0x000111DC, 0x000111DC}, {0x00011200, 0x00011211}, -{0x00011213, 0x0001122B}, {0x0001123F, 0x00011240}, {0x00011280, 0x00011286}, {0x00011288, 0x00011288}, -{0x0001128A, 0x0001128D}, {0x0001128F, 0x0001129D}, {0x0001129F, 0x000112A8}, {0x000112B0, 0x000112DE}, -{0x00011305, 0x0001130C}, {0x0001130F, 0x00011310}, {0x00011313, 0x00011328}, {0x0001132A, 0x00011330}, -{0x00011332, 0x00011333}, {0x00011335, 0x00011339}, {0x0001133D, 0x0001133D}, {0x00011350, 0x00011350}, -{0x0001135D, 0x00011361}, {0x00011400, 0x00011434}, {0x00011447, 0x0001144A}, {0x0001145F, 0x00011461}, -{0x00011480, 0x000114AF}, {0x000114C4, 0x000114C5}, {0x000114C7, 0x000114C7}, {0x00011580, 0x000115AE}, -{0x000115D8, 0x000115DB}, {0x00011600, 0x0001162F}, {0x00011644, 0x00011644}, {0x00011680, 0x000116AA}, -{0x000116B8, 0x000116B8}, {0x00011700, 0x0001171A}, {0x00011740, 0x00011746}, {0x00011800, 0x0001182B}, -{0x000118A0, 0x000118DF}, {0x000118FF, 0x00011906}, {0x00011909, 0x00011909}, {0x0001190C, 0x00011913}, -{0x00011915, 0x00011916}, {0x00011918, 0x0001192F}, {0x0001193F, 0x0001193F}, {0x00011941, 0x00011941}, -{0x000119A0, 0x000119A7}, {0x000119AA, 0x000119D0}, {0x000119E1, 0x000119E1}, {0x000119E3, 0x000119E3}, -{0x00011A00, 0x00011A00}, {0x00011A0B, 0x00011A32}, {0x00011A3A, 0x00011A3A}, {0x00011A50, 0x00011A50}, -{0x00011A5C, 0x00011A89}, {0x00011A9D, 0x00011A9D}, {0x00011AB0, 0x00011AF8}, {0x00011C00, 0x00011C08}, -{0x00011C0A, 0x00011C2E}, {0x00011C40, 0x00011C40}, {0x00011C72, 0x00011C8F}, {0x00011D00, 0x00011D06}, -{0x00011D08, 0x00011D09}, {0x00011D0B, 0x00011D30}, {0x00011D46, 0x00011D46}, {0x00011D60, 0x00011D65}, -{0x00011D67, 0x00011D68}, {0x00011D6A, 0x00011D89}, {0x00011D98, 0x00011D98}, {0x00011EE0, 0x00011EF2}, -{0x00011F02, 0x00011F02}, {0x00011F04, 0x00011F10}, {0x00011F12, 0x00011F33}, {0x00011FB0, 0x00011FB0}, -{0x00012000, 0x00012399}, {0x00012480, 0x00012543}, {0x00012F90, 0x00012FF0}, {0x00013000, 0x0001342F}, -{0x00013441, 0x00013446}, {0x00014400, 0x00014646}, {0x00016800, 0x00016A38}, {0x00016A40, 0x00016A5E}, -{0x00016A70, 0x00016ABE}, {0x00016AD0, 0x00016AED}, {0x00016B00, 0x00016B2F}, {0x00016B40, 0x00016B43}, -{0x00016B63, 0x00016B77}, {0x00016B7D, 0x00016B8F}, {0x00016E40, 0x00016E7F}, {0x00016F00, 0x00016F4A}, -{0x00016F50, 0x00016F50}, {0x00016F93, 0x00016F9F}, {0x00016FE0, 0x00016FE1}, {0x00016FE3, 0x00016FE3}, -{0x00017000, 0x000187F7}, {0x00018800, 0x00018CD5}, {0x00018D00, 0x00018D08}, {0x0001AFF0, 0x0001AFF3}, -{0x0001AFF5, 0x0001AFFB}, {0x0001AFFD, 0x0001AFFE}, {0x0001B000, 0x0001B122}, {0x0001B132, 0x0001B132}, -{0x0001B150, 0x0001B152}, {0x0001B155, 0x0001B155}, {0x0001B164, 0x0001B167}, {0x0001B170, 0x0001B2FB}, -{0x0001BC00, 0x0001BC6A}, {0x0001BC70, 0x0001BC7C}, {0x0001BC80, 0x0001BC88}, {0x0001BC90, 0x0001BC99}, -{0x0001D400, 0x0001D454}, {0x0001D456, 0x0001D49C}, {0x0001D49E, 0x0001D49F}, {0x0001D4A2, 0x0001D4A2}, -{0x0001D4A5, 0x0001D4A6}, {0x0001D4A9, 0x0001D4AC}, {0x0001D4AE, 0x0001D4B9}, {0x0001D4BB, 0x0001D4BB}, -{0x0001D4BD, 0x0001D4C3}, {0x0001D4C5, 0x0001D505}, {0x0001D507, 0x0001D50A}, {0x0001D50D, 0x0001D514}, -{0x0001D516, 0x0001D51C}, {0x0001D51E, 0x0001D539}, {0x0001D53B, 0x0001D53E}, {0x0001D540, 0x0001D544}, -{0x0001D546, 0x0001D546}, {0x0001D54A, 0x0001D550}, {0x0001D552, 0x0001D6A5}, {0x0001D6A8, 0x0001D6C0}, -{0x0001D6C2, 0x0001D6DA}, {0x0001D6DC, 0x0001D6FA}, {0x0001D6FC, 0x0001D714}, {0x0001D716, 0x0001D734}, -{0x0001D736, 0x0001D74E}, {0x0001D750, 0x0001D76E}, {0x0001D770, 0x0001D788}, {0x0001D78A, 0x0001D7A8}, -{0x0001D7AA, 0x0001D7C2}, {0x0001D7C4, 0x0001D7CB}, {0x0001DF00, 0x0001DF1E}, {0x0001DF25, 0x0001DF2A}, -{0x0001E030, 0x0001E06D}, {0x0001E100, 0x0001E12C}, {0x0001E137, 0x0001E13D}, {0x0001E14E, 0x0001E14E}, -{0x0001E290, 0x0001E2AD}, {0x0001E2C0, 0x0001E2EB}, {0x0001E4D0, 0x0001E4EB}, {0x0001E7E0, 0x0001E7E6}, -{0x0001E7E8, 0x0001E7EB}, {0x0001E7ED, 0x0001E7EE}, {0x0001E7F0, 0x0001E7FE}, {0x0001E800, 0x0001E8C4}, -{0x0001E900, 0x0001E943}, {0x0001E94B, 0x0001E94B}, {0x0001EE00, 0x0001EE03}, {0x0001EE05, 0x0001EE1F}, -{0x0001EE21, 0x0001EE22}, {0x0001EE24, 0x0001EE24}, {0x0001EE27, 0x0001EE27}, {0x0001EE29, 0x0001EE32}, -{0x0001EE34, 0x0001EE37}, {0x0001EE39, 0x0001EE39}, {0x0001EE3B, 0x0001EE3B}, {0x0001EE42, 0x0001EE42}, -{0x0001EE47, 0x0001EE47}, {0x0001EE49, 0x0001EE49}, {0x0001EE4B, 0x0001EE4B}, {0x0001EE4D, 0x0001EE4F}, -{0x0001EE51, 0x0001EE52}, {0x0001EE54, 0x0001EE54}, {0x0001EE57, 0x0001EE57}, {0x0001EE59, 0x0001EE59}, -{0x0001EE5B, 0x0001EE5B}, {0x0001EE5D, 0x0001EE5D}, {0x0001EE5F, 0x0001EE5F}, {0x0001EE61, 0x0001EE62}, -{0x0001EE64, 0x0001EE64}, {0x0001EE67, 0x0001EE6A}, {0x0001EE6C, 0x0001EE72}, {0x0001EE74, 0x0001EE77}, -{0x0001EE79, 0x0001EE7C}, {0x0001EE7E, 0x0001EE7E}, {0x0001EE80, 0x0001EE89}, {0x0001EE8B, 0x0001EE9B}, -{0x0001EEA1, 0x0001EEA3}, {0x0001EEA5, 0x0001EEA9}, {0x0001EEAB, 0x0001EEBB}, {0x00020000, 0x0002A6DF}, -{0x0002A700, 0x0002B739}, {0x0002B740, 0x0002B81D}, {0x0002B820, 0x0002CEA1}, {0x0002CEB0, 0x0002EBE0}, -{0x0002EBF0, 0x0002EE5D}, {0x0002F800, 0x0002FA1D}, {0x00030000, 0x0003134A}, {0x00031350, 0x000323AF}, +const std::unordered_set unicode_set_whitespace = { +0x000009, 0x00000A, 0x00000B, 0x00000C, 0x00000D, 0x000020, 0x000085, 0x0000A0, 0x001680, 0x002000, 0x002001, 0x002002, 0x002003, 0x002004, 0x002005, 0x002006, 0x002007, 0x002008, 0x002009, 0x00200A, 0x002028, 0x002029, 0x00202F, 0x00205F, 0x003000 }; -const std::vector> unicode_ranges_separator = { -{0x00000020, 0x00000020}, {0x000000A0, 0x000000A0}, {0x00001680, 0x00001680}, {0x00002000, 0x0000200A}, -{0x00002028, 0x00002029}, {0x0000202F, 0x0000202F}, {0x0000205F, 0x0000205F}, {0x00003000, 0x00003000}, +const std::unordered_map unicode_map_lowercase = { +{0x000041, 0x000061}, +{0x000042, 0x000062}, +{0x000043, 0x000063}, +{0x000044, 0x000064}, +{0x000045, 0x000065}, +{0x000046, 0x000066}, +{0x000047, 0x000067}, +{0x000048, 0x000068}, +{0x000049, 0x000069}, +{0x00004A, 0x00006A}, +{0x00004B, 0x00006B}, +{0x00004C, 0x00006C}, +{0x00004D, 0x00006D}, +{0x00004E, 0x00006E}, +{0x00004F, 0x00006F}, +{0x000050, 0x000070}, +{0x000051, 0x000071}, +{0x000052, 0x000072}, +{0x000053, 0x000073}, +{0x000054, 0x000074}, +{0x000055, 0x000075}, +{0x000056, 0x000076}, +{0x000057, 0x000077}, +{0x000058, 0x000078}, +{0x000059, 0x000079}, +{0x00005A, 0x00007A}, +{0x0000C0, 0x0000E0}, +{0x0000C1, 0x0000E1}, +{0x0000C2, 0x0000E2}, +{0x0000C3, 0x0000E3}, +{0x0000C4, 0x0000E4}, +{0x0000C5, 0x0000E5}, +{0x0000C6, 0x0000E6}, +{0x0000C7, 0x0000E7}, +{0x0000C8, 0x0000E8}, +{0x0000C9, 0x0000E9}, +{0x0000CA, 0x0000EA}, +{0x0000CB, 0x0000EB}, +{0x0000CC, 0x0000EC}, +{0x0000CD, 0x0000ED}, +{0x0000CE, 0x0000EE}, +{0x0000CF, 0x0000EF}, +{0x0000D0, 0x0000F0}, +{0x0000D1, 0x0000F1}, +{0x0000D2, 0x0000F2}, +{0x0000D3, 0x0000F3}, +{0x0000D4, 0x0000F4}, +{0x0000D5, 0x0000F5}, +{0x0000D6, 0x0000F6}, +{0x0000D8, 0x0000F8}, +{0x0000D9, 0x0000F9}, +{0x0000DA, 0x0000FA}, +{0x0000DB, 0x0000FB}, +{0x0000DC, 0x0000FC}, +{0x0000DD, 0x0000FD}, +{0x0000DE, 0x0000FE}, +{0x000100, 0x000101}, +{0x000102, 0x000103}, +{0x000104, 0x000105}, +{0x000106, 0x000107}, +{0x000108, 0x000109}, +{0x00010A, 0x00010B}, +{0x00010C, 0x00010D}, +{0x00010E, 0x00010F}, +{0x000110, 0x000111}, +{0x000112, 0x000113}, +{0x000114, 0x000115}, +{0x000116, 0x000117}, +{0x000118, 0x000119}, +{0x00011A, 0x00011B}, +{0x00011C, 0x00011D}, +{0x00011E, 0x00011F}, +{0x000120, 0x000121}, +{0x000122, 0x000123}, +{0x000124, 0x000125}, +{0x000126, 0x000127}, +{0x000128, 0x000129}, +{0x00012A, 0x00012B}, +{0x00012C, 0x00012D}, +{0x00012E, 0x00012F}, +{0x000130, 0x000069}, +{0x000132, 0x000133}, +{0x000134, 0x000135}, +{0x000136, 0x000137}, +{0x000139, 0x00013A}, +{0x00013B, 0x00013C}, +{0x00013D, 0x00013E}, +{0x00013F, 0x000140}, +{0x000141, 0x000142}, +{0x000143, 0x000144}, +{0x000145, 0x000146}, +{0x000147, 0x000148}, +{0x00014A, 0x00014B}, +{0x00014C, 0x00014D}, +{0x00014E, 0x00014F}, +{0x000150, 0x000151}, +{0x000152, 0x000153}, +{0x000154, 0x000155}, +{0x000156, 0x000157}, +{0x000158, 0x000159}, +{0x00015A, 0x00015B}, +{0x00015C, 0x00015D}, +{0x00015E, 0x00015F}, +{0x000160, 0x000161}, +{0x000162, 0x000163}, +{0x000164, 0x000165}, +{0x000166, 0x000167}, +{0x000168, 0x000169}, +{0x00016A, 0x00016B}, +{0x00016C, 0x00016D}, +{0x00016E, 0x00016F}, +{0x000170, 0x000171}, +{0x000172, 0x000173}, +{0x000174, 0x000175}, +{0x000176, 0x000177}, +{0x000178, 0x0000FF}, +{0x000179, 0x00017A}, +{0x00017B, 0x00017C}, +{0x00017D, 0x00017E}, +{0x000181, 0x000253}, +{0x000182, 0x000183}, +{0x000184, 0x000185}, +{0x000186, 0x000254}, +{0x000187, 0x000188}, +{0x000189, 0x000256}, +{0x00018A, 0x000257}, +{0x00018B, 0x00018C}, +{0x00018E, 0x0001DD}, +{0x00018F, 0x000259}, +{0x000190, 0x00025B}, +{0x000191, 0x000192}, +{0x000193, 0x000260}, +{0x000194, 0x000263}, +{0x000196, 0x000269}, +{0x000197, 0x000268}, +{0x000198, 0x000199}, +{0x00019C, 0x00026F}, +{0x00019D, 0x000272}, +{0x00019F, 0x000275}, 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0x00A7C8}, +{0x00A7C9, 0x00A7CA}, +{0x00A7F5, 0x00A7F6}, +{0x00FF21, 0x00FF41}, +{0x00FF22, 0x00FF42}, +{0x00FF23, 0x00FF43}, +{0x00FF24, 0x00FF44}, +{0x00FF25, 0x00FF45}, +{0x00FF26, 0x00FF46}, +{0x00FF27, 0x00FF47}, +{0x00FF28, 0x00FF48}, +{0x00FF29, 0x00FF49}, +{0x00FF2A, 0x00FF4A}, +{0x00FF2B, 0x00FF4B}, +{0x00FF2C, 0x00FF4C}, +{0x00FF2D, 0x00FF4D}, +{0x00FF2E, 0x00FF4E}, +{0x00FF2F, 0x00FF4F}, +{0x00FF30, 0x00FF50}, +{0x00FF31, 0x00FF51}, +{0x00FF32, 0x00FF52}, +{0x00FF33, 0x00FF53}, +{0x00FF34, 0x00FF54}, +{0x00FF35, 0x00FF55}, +{0x00FF36, 0x00FF56}, +{0x00FF37, 0x00FF57}, +{0x00FF38, 0x00FF58}, +{0x00FF39, 0x00FF59}, +{0x00FF3A, 0x00FF5A}, +{0x010400, 0x010428}, +{0x010401, 0x010429}, +{0x010402, 0x01042A}, +{0x010403, 0x01042B}, +{0x010404, 0x01042C}, +{0x010405, 0x01042D}, +{0x010406, 0x01042E}, +{0x010407, 0x01042F}, +{0x010408, 0x010430}, +{0x010409, 0x010431}, +{0x01040A, 0x010432}, +{0x01040B, 0x010433}, +{0x01040C, 0x010434}, +{0x01040D, 0x010435}, +{0x01040E, 0x010436}, +{0x01040F, 0x010437}, +{0x010410, 0x010438}, +{0x010411, 0x010439}, +{0x010412, 0x01043A}, +{0x010413, 0x01043B}, +{0x010414, 0x01043C}, +{0x010415, 0x01043D}, +{0x010416, 0x01043E}, +{0x010417, 0x01043F}, +{0x010418, 0x010440}, +{0x010419, 0x010441}, +{0x01041A, 0x010442}, +{0x01041B, 0x010443}, +{0x01041C, 0x010444}, +{0x01041D, 0x010445}, +{0x01041E, 0x010446}, +{0x01041F, 0x010447}, +{0x010420, 0x010448}, +{0x010421, 0x010449}, +{0x010422, 0x01044A}, +{0x010423, 0x01044B}, +{0x010424, 0x01044C}, +{0x010425, 0x01044D}, +{0x010426, 0x01044E}, +{0x010427, 0x01044F}, +{0x0104B0, 0x0104D8}, +{0x0104B1, 0x0104D9}, +{0x0104B2, 0x0104DA}, +{0x0104B3, 0x0104DB}, +{0x0104B4, 0x0104DC}, +{0x0104B5, 0x0104DD}, +{0x0104B6, 0x0104DE}, +{0x0104B7, 0x0104DF}, +{0x0104B8, 0x0104E0}, +{0x0104B9, 0x0104E1}, +{0x0104BA, 0x0104E2}, +{0x0104BB, 0x0104E3}, +{0x0104BC, 0x0104E4}, +{0x0104BD, 0x0104E5}, +{0x0104BE, 0x0104E6}, +{0x0104BF, 0x0104E7}, +{0x0104C0, 0x0104E8}, +{0x0104C1, 0x0104E9}, +{0x0104C2, 0x0104EA}, +{0x0104C3, 0x0104EB}, +{0x0104C4, 0x0104EC}, +{0x0104C5, 0x0104ED}, +{0x0104C6, 0x0104EE}, +{0x0104C7, 0x0104EF}, +{0x0104C8, 0x0104F0}, +{0x0104C9, 0x0104F1}, +{0x0104CA, 0x0104F2}, +{0x0104CB, 0x0104F3}, +{0x0104CC, 0x0104F4}, +{0x0104CD, 0x0104F5}, +{0x0104CE, 0x0104F6}, +{0x0104CF, 0x0104F7}, +{0x0104D0, 0x0104F8}, +{0x0104D1, 0x0104F9}, +{0x0104D2, 0x0104FA}, +{0x0104D3, 0x0104FB}, +{0x010C80, 0x010CC0}, +{0x010C81, 0x010CC1}, +{0x010C82, 0x010CC2}, +{0x010C83, 0x010CC3}, +{0x010C84, 0x010CC4}, +{0x010C85, 0x010CC5}, +{0x010C86, 0x010CC6}, +{0x010C87, 0x010CC7}, +{0x010C88, 0x010CC8}, +{0x010C89, 0x010CC9}, +{0x010C8A, 0x010CCA}, +{0x010C8B, 0x010CCB}, +{0x010C8C, 0x010CCC}, +{0x010C8D, 0x010CCD}, +{0x010C8E, 0x010CCE}, +{0x010C8F, 0x010CCF}, +{0x010C90, 0x010CD0}, +{0x010C91, 0x010CD1}, +{0x010C92, 0x010CD2}, +{0x010C93, 0x010CD3}, +{0x010C94, 0x010CD4}, +{0x010C95, 0x010CD5}, +{0x010C96, 0x010CD6}, +{0x010C97, 0x010CD7}, +{0x010C98, 0x010CD8}, +{0x010C99, 0x010CD9}, +{0x010C9A, 0x010CDA}, +{0x010C9B, 0x010CDB}, +{0x010C9C, 0x010CDC}, +{0x010C9D, 0x010CDD}, +{0x010C9E, 0x010CDE}, +{0x010C9F, 0x010CDF}, +{0x010CA0, 0x010CE0}, +{0x010CA1, 0x010CE1}, +{0x010CA2, 0x010CE2}, +{0x010CA3, 0x010CE3}, +{0x010CA4, 0x010CE4}, +{0x010CA5, 0x010CE5}, +{0x010CA6, 0x010CE6}, +{0x010CA7, 0x010CE7}, +{0x010CA8, 0x010CE8}, +{0x010CA9, 0x010CE9}, +{0x010CAA, 0x010CEA}, +{0x010CAB, 0x010CEB}, +{0x010CAC, 0x010CEC}, +{0x010CAD, 0x010CED}, +{0x010CAE, 0x010CEE}, +{0x010CAF, 0x010CEF}, +{0x010CB0, 0x010CF0}, +{0x010CB1, 0x010CF1}, +{0x010CB2, 0x010CF2}, +{0x0118A0, 0x0118C0}, +{0x0118A1, 0x0118C1}, +{0x0118A2, 0x0118C2}, +{0x0118A3, 0x0118C3}, +{0x0118A4, 0x0118C4}, +{0x0118A5, 0x0118C5}, +{0x0118A6, 0x0118C6}, +{0x0118A7, 0x0118C7}, +{0x0118A8, 0x0118C8}, +{0x0118A9, 0x0118C9}, +{0x0118AA, 0x0118CA}, +{0x0118AB, 0x0118CB}, +{0x0118AC, 0x0118CC}, +{0x0118AD, 0x0118CD}, +{0x0118AE, 0x0118CE}, +{0x0118AF, 0x0118CF}, +{0x0118B0, 0x0118D0}, +{0x0118B1, 0x0118D1}, +{0x0118B2, 0x0118D2}, +{0x0118B3, 0x0118D3}, +{0x0118B4, 0x0118D4}, +{0x0118B5, 0x0118D5}, +{0x0118B6, 0x0118D6}, +{0x0118B7, 0x0118D7}, +{0x0118B8, 0x0118D8}, +{0x0118B9, 0x0118D9}, +{0x0118BA, 0x0118DA}, +{0x0118BB, 0x0118DB}, +{0x0118BC, 0x0118DC}, +{0x0118BD, 0x0118DD}, +{0x0118BE, 0x0118DE}, +{0x0118BF, 0x0118DF}, +{0x016E40, 0x016E60}, +{0x016E41, 0x016E61}, +{0x016E42, 0x016E62}, +{0x016E43, 0x016E63}, +{0x016E44, 0x016E64}, +{0x016E45, 0x016E65}, +{0x016E46, 0x016E66}, +{0x016E47, 0x016E67}, +{0x016E48, 0x016E68}, +{0x016E49, 0x016E69}, +{0x016E4A, 0x016E6A}, +{0x016E4B, 0x016E6B}, +{0x016E4C, 0x016E6C}, +{0x016E4D, 0x016E6D}, +{0x016E4E, 0x016E6E}, +{0x016E4F, 0x016E6F}, +{0x016E50, 0x016E70}, +{0x016E51, 0x016E71}, +{0x016E52, 0x016E72}, +{0x016E53, 0x016E73}, +{0x016E54, 0x016E74}, +{0x016E55, 0x016E75}, +{0x016E56, 0x016E76}, +{0x016E57, 0x016E77}, +{0x016E58, 0x016E78}, +{0x016E59, 0x016E79}, +{0x016E5A, 0x016E7A}, +{0x016E5B, 0x016E7B}, +{0x016E5C, 0x016E7C}, +{0x016E5D, 0x016E7D}, +{0x016E5E, 0x016E7E}, +{0x016E5F, 0x016E7F}, +{0x01E900, 0x01E922}, +{0x01E901, 0x01E923}, +{0x01E902, 0x01E924}, +{0x01E903, 0x01E925}, +{0x01E904, 0x01E926}, +{0x01E905, 0x01E927}, +{0x01E906, 0x01E928}, +{0x01E907, 0x01E929}, +{0x01E908, 0x01E92A}, +{0x01E909, 0x01E92B}, +{0x01E90A, 0x01E92C}, +{0x01E90B, 0x01E92D}, +{0x01E90C, 0x01E92E}, +{0x01E90D, 0x01E92F}, +{0x01E90E, 0x01E930}, +{0x01E90F, 0x01E931}, +{0x01E910, 0x01E932}, +{0x01E911, 0x01E933}, +{0x01E912, 0x01E934}, +{0x01E913, 0x01E935}, +{0x01E914, 0x01E936}, +{0x01E915, 0x01E937}, +{0x01E916, 0x01E938}, +{0x01E917, 0x01E939}, +{0x01E918, 0x01E93A}, +{0x01E919, 0x01E93B}, +{0x01E91A, 0x01E93C}, +{0x01E91B, 0x01E93D}, +{0x01E91C, 0x01E93E}, +{0x01E91D, 0x01E93F}, +{0x01E91E, 0x01E940}, +{0x01E91F, 0x01E941}, +{0x01E920, 0x01E942}, +{0x01E921, 0x01E943}, }; -const std::vector> unicode_ranges_whitespace = { -{0x00000009, 0x0000000D}, {0x00000020, 0x00000020}, {0x00000085, 0x00000085}, {0x000000A0, 0x000000A0}, -{0x00001680, 0x00001680}, {0x00002000, 0x0000200A}, {0x00002028, 0x00002029}, {0x0000202F, 0x0000202F}, -{0x0000205F, 0x0000205F}, {0x00003000, 0x00003000}, +const std::unordered_map unicode_map_uppercase = { +{0x000061, 0x000041}, +{0x000062, 0x000042}, +{0x000063, 0x000043}, +{0x000064, 0x000044}, +{0x000065, 0x000045}, +{0x000066, 0x000046}, +{0x000067, 0x000047}, +{0x000068, 0x000048}, +{0x000069, 0x000049}, +{0x00006A, 0x00004A}, +{0x00006B, 0x00004B}, +{0x00006C, 0x00004C}, +{0x00006D, 0x00004D}, +{0x00006E, 0x00004E}, +{0x00006F, 0x00004F}, +{0x000070, 0x000050}, +{0x000071, 0x000051}, +{0x000072, 0x000052}, +{0x000073, 0x000053}, +{0x000074, 0x000054}, +{0x000075, 0x000055}, +{0x000076, 0x000056}, +{0x000077, 0x000057}, +{0x000078, 0x000058}, +{0x000079, 0x000059}, +{0x00007A, 0x00005A}, +{0x0000B5, 0x00039C}, +{0x0000DF, 0x000053}, +{0x0000E0, 0x0000C0}, +{0x0000E1, 0x0000C1}, +{0x0000E2, 0x0000C2}, +{0x0000E3, 0x0000C3}, +{0x0000E4, 0x0000C4}, +{0x0000E5, 0x0000C5}, +{0x0000E6, 0x0000C6}, +{0x0000E7, 0x0000C7}, +{0x0000E8, 0x0000C8}, +{0x0000E9, 0x0000C9}, +{0x0000EA, 0x0000CA}, +{0x0000EB, 0x0000CB}, +{0x0000EC, 0x0000CC}, +{0x0000ED, 0x0000CD}, +{0x0000EE, 0x0000CE}, +{0x0000EF, 0x0000CF}, +{0x0000F0, 0x0000D0}, +{0x0000F1, 0x0000D1}, +{0x0000F2, 0x0000D2}, +{0x0000F3, 0x0000D3}, +{0x0000F4, 0x0000D4}, +{0x0000F5, 0x0000D5}, +{0x0000F6, 0x0000D6}, +{0x0000F8, 0x0000D8}, +{0x0000F9, 0x0000D9}, +{0x0000FA, 0x0000DA}, +{0x0000FB, 0x0000DB}, +{0x0000FC, 0x0000DC}, +{0x0000FD, 0x0000DD}, +{0x0000FE, 0x0000DE}, +{0x0000FF, 0x000178}, +{0x000101, 0x000100}, +{0x000103, 0x000102}, +{0x000105, 0x000104}, +{0x000107, 0x000106}, +{0x000109, 0x000108}, +{0x00010B, 0x00010A}, +{0x00010D, 0x00010C}, +{0x00010F, 0x00010E}, +{0x000111, 0x000110}, +{0x000113, 0x000112}, +{0x000115, 0x000114}, +{0x000117, 0x000116}, +{0x000119, 0x000118}, +{0x00011B, 0x00011A}, +{0x00011D, 0x00011C}, +{0x00011F, 0x00011E}, +{0x000121, 0x000120}, +{0x000123, 0x000122}, +{0x000125, 0x000124}, +{0x000127, 0x000126}, +{0x000129, 0x000128}, +{0x00012B, 0x00012A}, +{0x00012D, 0x00012C}, +{0x00012F, 0x00012E}, +{0x000131, 0x000049}, +{0x000133, 0x000132}, +{0x000135, 0x000134}, +{0x000137, 0x000136}, +{0x00013A, 0x000139}, +{0x00013C, 0x00013B}, +{0x00013E, 0x00013D}, +{0x000140, 0x00013F}, +{0x000142, 0x000141}, +{0x000144, 0x000143}, +{0x000146, 0x000145}, +{0x000148, 0x000147}, +{0x000149, 0x0002BC}, +{0x00014B, 0x00014A}, +{0x00014D, 0x00014C}, +{0x00014F, 0x00014E}, +{0x000151, 0x000150}, +{0x000153, 0x000152}, +{0x000155, 0x000154}, +{0x000157, 0x000156}, +{0x000159, 0x000158}, +{0x00015B, 0x00015A}, +{0x00015D, 0x00015C}, +{0x00015F, 0x00015E}, +{0x000161, 0x000160}, +{0x000163, 0x000162}, +{0x000165, 0x000164}, +{0x000167, 0x000166}, +{0x000169, 0x000168}, +{0x00016B, 0x00016A}, +{0x00016D, 0x00016C}, +{0x00016F, 0x00016E}, +{0x000171, 0x000170}, +{0x000173, 0x000172}, +{0x000175, 0x000174}, +{0x000177, 0x000176}, +{0x00017A, 0x000179}, +{0x00017C, 0x00017B}, +{0x00017E, 0x00017D}, 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+{0x00AB88, 0x0013B8}, +{0x00AB89, 0x0013B9}, +{0x00AB8A, 0x0013BA}, +{0x00AB8B, 0x0013BB}, +{0x00AB8C, 0x0013BC}, +{0x00AB8D, 0x0013BD}, +{0x00AB8E, 0x0013BE}, +{0x00AB8F, 0x0013BF}, +{0x00AB90, 0x0013C0}, +{0x00AB91, 0x0013C1}, +{0x00AB92, 0x0013C2}, +{0x00AB93, 0x0013C3}, +{0x00AB94, 0x0013C4}, +{0x00AB95, 0x0013C5}, +{0x00AB96, 0x0013C6}, +{0x00AB97, 0x0013C7}, +{0x00AB98, 0x0013C8}, +{0x00AB99, 0x0013C9}, +{0x00AB9A, 0x0013CA}, +{0x00AB9B, 0x0013CB}, +{0x00AB9C, 0x0013CC}, +{0x00AB9D, 0x0013CD}, +{0x00AB9E, 0x0013CE}, +{0x00AB9F, 0x0013CF}, +{0x00ABA0, 0x0013D0}, +{0x00ABA1, 0x0013D1}, +{0x00ABA2, 0x0013D2}, +{0x00ABA3, 0x0013D3}, +{0x00ABA4, 0x0013D4}, +{0x00ABA5, 0x0013D5}, +{0x00ABA6, 0x0013D6}, +{0x00ABA7, 0x0013D7}, +{0x00ABA8, 0x0013D8}, +{0x00ABA9, 0x0013D9}, +{0x00ABAA, 0x0013DA}, +{0x00ABAB, 0x0013DB}, +{0x00ABAC, 0x0013DC}, +{0x00ABAD, 0x0013DD}, +{0x00ABAE, 0x0013DE}, +{0x00ABAF, 0x0013DF}, +{0x00ABB0, 0x0013E0}, +{0x00ABB1, 0x0013E1}, +{0x00ABB2, 0x0013E2}, +{0x00ABB3, 0x0013E3}, +{0x00ABB4, 0x0013E4}, +{0x00ABB5, 0x0013E5}, +{0x00ABB6, 0x0013E6}, +{0x00ABB7, 0x0013E7}, +{0x00ABB8, 0x0013E8}, +{0x00ABB9, 0x0013E9}, +{0x00ABBA, 0x0013EA}, +{0x00ABBB, 0x0013EB}, +{0x00ABBC, 0x0013EC}, +{0x00ABBD, 0x0013ED}, +{0x00ABBE, 0x0013EE}, +{0x00ABBF, 0x0013EF}, +{0x00FB00, 0x000046}, +{0x00FB01, 0x000046}, +{0x00FB02, 0x000046}, +{0x00FB03, 0x000046}, +{0x00FB04, 0x000046}, +{0x00FB05, 0x000053}, +{0x00FB06, 0x000053}, +{0x00FB13, 0x000544}, +{0x00FB14, 0x000544}, +{0x00FB15, 0x000544}, +{0x00FB16, 0x00054E}, +{0x00FB17, 0x000544}, +{0x00FF41, 0x00FF21}, +{0x00FF42, 0x00FF22}, +{0x00FF43, 0x00FF23}, +{0x00FF44, 0x00FF24}, +{0x00FF45, 0x00FF25}, +{0x00FF46, 0x00FF26}, +{0x00FF47, 0x00FF27}, +{0x00FF48, 0x00FF28}, +{0x00FF49, 0x00FF29}, +{0x00FF4A, 0x00FF2A}, +{0x00FF4B, 0x00FF2B}, +{0x00FF4C, 0x00FF2C}, +{0x00FF4D, 0x00FF2D}, +{0x00FF4E, 0x00FF2E}, +{0x00FF4F, 0x00FF2F}, +{0x00FF50, 0x00FF30}, +{0x00FF51, 0x00FF31}, +{0x00FF52, 0x00FF32}, +{0x00FF53, 0x00FF33}, +{0x00FF54, 0x00FF34}, +{0x00FF55, 0x00FF35}, +{0x00FF56, 0x00FF36}, +{0x00FF57, 0x00FF37}, +{0x00FF58, 0x00FF38}, +{0x00FF59, 0x00FF39}, +{0x00FF5A, 0x00FF3A}, +{0x010428, 0x010400}, +{0x010429, 0x010401}, +{0x01042A, 0x010402}, +{0x01042B, 0x010403}, +{0x01042C, 0x010404}, +{0x01042D, 0x010405}, +{0x01042E, 0x010406}, +{0x01042F, 0x010407}, +{0x010430, 0x010408}, +{0x010431, 0x010409}, +{0x010432, 0x01040A}, +{0x010433, 0x01040B}, +{0x010434, 0x01040C}, +{0x010435, 0x01040D}, +{0x010436, 0x01040E}, +{0x010437, 0x01040F}, +{0x010438, 0x010410}, +{0x010439, 0x010411}, +{0x01043A, 0x010412}, +{0x01043B, 0x010413}, +{0x01043C, 0x010414}, +{0x01043D, 0x010415}, +{0x01043E, 0x010416}, +{0x01043F, 0x010417}, +{0x010440, 0x010418}, +{0x010441, 0x010419}, +{0x010442, 0x01041A}, +{0x010443, 0x01041B}, +{0x010444, 0x01041C}, +{0x010445, 0x01041D}, +{0x010446, 0x01041E}, +{0x010447, 0x01041F}, +{0x010448, 0x010420}, +{0x010449, 0x010421}, +{0x01044A, 0x010422}, +{0x01044B, 0x010423}, +{0x01044C, 0x010424}, +{0x01044D, 0x010425}, +{0x01044E, 0x010426}, +{0x01044F, 0x010427}, +{0x0104D8, 0x0104B0}, +{0x0104D9, 0x0104B1}, +{0x0104DA, 0x0104B2}, +{0x0104DB, 0x0104B3}, +{0x0104DC, 0x0104B4}, +{0x0104DD, 0x0104B5}, +{0x0104DE, 0x0104B6}, +{0x0104DF, 0x0104B7}, +{0x0104E0, 0x0104B8}, +{0x0104E1, 0x0104B9}, +{0x0104E2, 0x0104BA}, +{0x0104E3, 0x0104BB}, +{0x0104E4, 0x0104BC}, +{0x0104E5, 0x0104BD}, +{0x0104E6, 0x0104BE}, +{0x0104E7, 0x0104BF}, +{0x0104E8, 0x0104C0}, +{0x0104E9, 0x0104C1}, +{0x0104EA, 0x0104C2}, +{0x0104EB, 0x0104C3}, +{0x0104EC, 0x0104C4}, +{0x0104ED, 0x0104C5}, +{0x0104EE, 0x0104C6}, +{0x0104EF, 0x0104C7}, +{0x0104F0, 0x0104C8}, +{0x0104F1, 0x0104C9}, +{0x0104F2, 0x0104CA}, +{0x0104F3, 0x0104CB}, +{0x0104F4, 0x0104CC}, +{0x0104F5, 0x0104CD}, +{0x0104F6, 0x0104CE}, +{0x0104F7, 0x0104CF}, +{0x0104F8, 0x0104D0}, +{0x0104F9, 0x0104D1}, +{0x0104FA, 0x0104D2}, +{0x0104FB, 0x0104D3}, +{0x010CC0, 0x010C80}, +{0x010CC1, 0x010C81}, +{0x010CC2, 0x010C82}, +{0x010CC3, 0x010C83}, +{0x010CC4, 0x010C84}, +{0x010CC5, 0x010C85}, +{0x010CC6, 0x010C86}, +{0x010CC7, 0x010C87}, +{0x010CC8, 0x010C88}, +{0x010CC9, 0x010C89}, +{0x010CCA, 0x010C8A}, +{0x010CCB, 0x010C8B}, +{0x010CCC, 0x010C8C}, +{0x010CCD, 0x010C8D}, +{0x010CCE, 0x010C8E}, +{0x010CCF, 0x010C8F}, +{0x010CD0, 0x010C90}, +{0x010CD1, 0x010C91}, +{0x010CD2, 0x010C92}, +{0x010CD3, 0x010C93}, +{0x010CD4, 0x010C94}, +{0x010CD5, 0x010C95}, +{0x010CD6, 0x010C96}, +{0x010CD7, 0x010C97}, +{0x010CD8, 0x010C98}, +{0x010CD9, 0x010C99}, +{0x010CDA, 0x010C9A}, +{0x010CDB, 0x010C9B}, +{0x010CDC, 0x010C9C}, +{0x010CDD, 0x010C9D}, +{0x010CDE, 0x010C9E}, +{0x010CDF, 0x010C9F}, +{0x010CE0, 0x010CA0}, +{0x010CE1, 0x010CA1}, +{0x010CE2, 0x010CA2}, +{0x010CE3, 0x010CA3}, +{0x010CE4, 0x010CA4}, +{0x010CE5, 0x010CA5}, +{0x010CE6, 0x010CA6}, +{0x010CE7, 0x010CA7}, +{0x010CE8, 0x010CA8}, +{0x010CE9, 0x010CA9}, +{0x010CEA, 0x010CAA}, +{0x010CEB, 0x010CAB}, +{0x010CEC, 0x010CAC}, +{0x010CED, 0x010CAD}, +{0x010CEE, 0x010CAE}, +{0x010CEF, 0x010CAF}, +{0x010CF0, 0x010CB0}, +{0x010CF1, 0x010CB1}, +{0x010CF2, 0x010CB2}, +{0x0118C0, 0x0118A0}, +{0x0118C1, 0x0118A1}, +{0x0118C2, 0x0118A2}, +{0x0118C3, 0x0118A3}, +{0x0118C4, 0x0118A4}, +{0x0118C5, 0x0118A5}, +{0x0118C6, 0x0118A6}, +{0x0118C7, 0x0118A7}, +{0x0118C8, 0x0118A8}, +{0x0118C9, 0x0118A9}, +{0x0118CA, 0x0118AA}, +{0x0118CB, 0x0118AB}, +{0x0118CC, 0x0118AC}, +{0x0118CD, 0x0118AD}, +{0x0118CE, 0x0118AE}, +{0x0118CF, 0x0118AF}, +{0x0118D0, 0x0118B0}, +{0x0118D1, 0x0118B1}, +{0x0118D2, 0x0118B2}, +{0x0118D3, 0x0118B3}, +{0x0118D4, 0x0118B4}, +{0x0118D5, 0x0118B5}, +{0x0118D6, 0x0118B6}, +{0x0118D7, 0x0118B7}, +{0x0118D8, 0x0118B8}, +{0x0118D9, 0x0118B9}, +{0x0118DA, 0x0118BA}, +{0x0118DB, 0x0118BB}, +{0x0118DC, 0x0118BC}, +{0x0118DD, 0x0118BD}, +{0x0118DE, 0x0118BE}, +{0x0118DF, 0x0118BF}, +{0x016E60, 0x016E40}, +{0x016E61, 0x016E41}, +{0x016E62, 0x016E42}, +{0x016E63, 0x016E43}, +{0x016E64, 0x016E44}, +{0x016E65, 0x016E45}, +{0x016E66, 0x016E46}, +{0x016E67, 0x016E47}, +{0x016E68, 0x016E48}, +{0x016E69, 0x016E49}, +{0x016E6A, 0x016E4A}, +{0x016E6B, 0x016E4B}, +{0x016E6C, 0x016E4C}, +{0x016E6D, 0x016E4D}, +{0x016E6E, 0x016E4E}, +{0x016E6F, 0x016E4F}, +{0x016E70, 0x016E50}, +{0x016E71, 0x016E51}, +{0x016E72, 0x016E52}, +{0x016E73, 0x016E53}, +{0x016E74, 0x016E54}, +{0x016E75, 0x016E55}, +{0x016E76, 0x016E56}, +{0x016E77, 0x016E57}, +{0x016E78, 0x016E58}, +{0x016E79, 0x016E59}, +{0x016E7A, 0x016E5A}, +{0x016E7B, 0x016E5B}, +{0x016E7C, 0x016E5C}, +{0x016E7D, 0x016E5D}, +{0x016E7E, 0x016E5E}, +{0x016E7F, 0x016E5F}, +{0x01E922, 0x01E900}, +{0x01E923, 0x01E901}, +{0x01E924, 0x01E902}, +{0x01E925, 0x01E903}, +{0x01E926, 0x01E904}, +{0x01E927, 0x01E905}, +{0x01E928, 0x01E906}, +{0x01E929, 0x01E907}, +{0x01E92A, 0x01E908}, +{0x01E92B, 0x01E909}, +{0x01E92C, 0x01E90A}, +{0x01E92D, 0x01E90B}, +{0x01E92E, 0x01E90C}, +{0x01E92F, 0x01E90D}, +{0x01E930, 0x01E90E}, +{0x01E931, 0x01E90F}, +{0x01E932, 0x01E910}, +{0x01E933, 0x01E911}, +{0x01E934, 0x01E912}, +{0x01E935, 0x01E913}, +{0x01E936, 0x01E914}, +{0x01E937, 0x01E915}, +{0x01E938, 0x01E916}, +{0x01E939, 0x01E917}, +{0x01E93A, 0x01E918}, +{0x01E93B, 0x01E919}, +{0x01E93C, 0x01E91A}, +{0x01E93D, 0x01E91B}, +{0x01E93E, 0x01E91C}, +{0x01E93F, 0x01E91D}, +{0x01E940, 0x01E91E}, +{0x01E941, 0x01E91F}, +{0x01E942, 0x01E920}, +{0x01E943, 0x01E921}, }; -const std::vector> unicode_ranges_accent_mark = { -{0x00000300, 0x0000036F}, {0x00000483, 0x00000489}, {0x00000591, 0x000005BD}, {0x000005BF, 0x000005BF}, -{0x000005C1, 0x000005C2}, {0x000005C4, 0x000005C5}, {0x000005C7, 0x000005C7}, {0x00000610, 0x0000061A}, -{0x0000064B, 0x0000065F}, {0x00000670, 0x00000670}, {0x000006D6, 0x000006DC}, {0x000006DF, 0x000006E4}, -{0x000006E7, 0x000006E8}, {0x000006EA, 0x000006ED}, {0x00000711, 0x00000711}, {0x00000730, 0x0000074A}, -{0x000007A6, 0x000007B0}, {0x000007EB, 0x000007F3}, {0x000007FD, 0x000007FD}, {0x00000816, 0x00000819}, -{0x0000081B, 0x00000823}, {0x00000825, 0x00000827}, {0x00000829, 0x0000082D}, {0x00000859, 0x0000085B}, -{0x00000898, 0x0000089F}, {0x000008CA, 0x000008E1}, {0x000008E3, 0x00000903}, {0x0000093A, 0x0000093C}, -{0x0000093E, 0x0000094F}, {0x00000951, 0x00000957}, {0x00000962, 0x00000963}, {0x00000981, 0x00000983}, -{0x000009BC, 0x000009BC}, {0x000009BE, 0x000009C4}, {0x000009C7, 0x000009C8}, {0x000009CB, 0x000009CD}, -{0x000009D7, 0x000009D7}, {0x000009E2, 0x000009E3}, {0x000009FE, 0x000009FE}, {0x00000A01, 0x00000A03}, -{0x00000A3C, 0x00000A3C}, {0x00000A3E, 0x00000A42}, {0x00000A47, 0x00000A48}, {0x00000A4B, 0x00000A4D}, -{0x00000A51, 0x00000A51}, {0x00000A70, 0x00000A71}, {0x00000A75, 0x00000A75}, {0x00000A81, 0x00000A83}, -{0x00000ABC, 0x00000ABC}, {0x00000ABE, 0x00000AC5}, {0x00000AC7, 0x00000AC9}, {0x00000ACB, 0x00000ACD}, -{0x00000AE2, 0x00000AE3}, {0x00000AFA, 0x00000AFF}, {0x00000B01, 0x00000B03}, {0x00000B3C, 0x00000B3C}, -{0x00000B3E, 0x00000B44}, {0x00000B47, 0x00000B48}, {0x00000B4B, 0x00000B4D}, {0x00000B55, 0x00000B57}, -{0x00000B62, 0x00000B63}, {0x00000B82, 0x00000B82}, {0x00000BBE, 0x00000BC2}, {0x00000BC6, 0x00000BC8}, -{0x00000BCA, 0x00000BCD}, {0x00000BD7, 0x00000BD7}, {0x00000C00, 0x00000C04}, {0x00000C3C, 0x00000C3C}, -{0x00000C3E, 0x00000C44}, {0x00000C46, 0x00000C48}, {0x00000C4A, 0x00000C4D}, {0x00000C55, 0x00000C56}, -{0x00000C62, 0x00000C63}, {0x00000C81, 0x00000C83}, {0x00000CBC, 0x00000CBC}, {0x00000CBE, 0x00000CC4}, -{0x00000CC6, 0x00000CC8}, {0x00000CCA, 0x00000CCD}, {0x00000CD5, 0x00000CD6}, {0x00000CE2, 0x00000CE3}, -{0x00000CF3, 0x00000CF3}, {0x00000D00, 0x00000D03}, {0x00000D3B, 0x00000D3C}, {0x00000D3E, 0x00000D44}, -{0x00000D46, 0x00000D48}, {0x00000D4A, 0x00000D4D}, {0x00000D57, 0x00000D57}, {0x00000D62, 0x00000D63}, -{0x00000D81, 0x00000D83}, {0x00000DCA, 0x00000DCA}, {0x00000DCF, 0x00000DD4}, {0x00000DD6, 0x00000DD6}, -{0x00000DD8, 0x00000DDF}, {0x00000DF2, 0x00000DF3}, {0x00000E31, 0x00000E31}, {0x00000E34, 0x00000E3A}, -{0x00000E47, 0x00000E4E}, {0x00000EB1, 0x00000EB1}, {0x00000EB4, 0x00000EBC}, {0x00000EC8, 0x00000ECE}, -{0x00000F18, 0x00000F19}, {0x00000F35, 0x00000F35}, {0x00000F37, 0x00000F37}, {0x00000F39, 0x00000F39}, -{0x00000F3E, 0x00000F3F}, {0x00000F71, 0x00000F84}, {0x00000F86, 0x00000F87}, {0x00000F8D, 0x00000F97}, -{0x00000F99, 0x00000FBC}, {0x00000FC6, 0x00000FC6}, {0x0000102B, 0x0000103E}, {0x00001056, 0x00001059}, -{0x0000105E, 0x00001060}, {0x00001062, 0x00001064}, {0x00001067, 0x0000106D}, {0x00001071, 0x00001074}, -{0x00001082, 0x0000108D}, {0x0000108F, 0x0000108F}, {0x0000109A, 0x0000109D}, {0x0000135D, 0x0000135F}, -{0x00001712, 0x00001715}, {0x00001732, 0x00001734}, {0x00001752, 0x00001753}, {0x00001772, 0x00001773}, -{0x000017B4, 0x000017D3}, {0x000017DD, 0x000017DD}, {0x0000180B, 0x0000180D}, {0x0000180F, 0x0000180F}, -{0x00001885, 0x00001886}, {0x000018A9, 0x000018A9}, {0x00001920, 0x0000192B}, {0x00001930, 0x0000193B}, -{0x00001A17, 0x00001A1B}, {0x00001A55, 0x00001A5E}, {0x00001A60, 0x00001A7C}, {0x00001A7F, 0x00001A7F}, -{0x00001AB0, 0x00001ACE}, {0x00001B00, 0x00001B04}, {0x00001B34, 0x00001B44}, {0x00001B6B, 0x00001B73}, -{0x00001B80, 0x00001B82}, {0x00001BA1, 0x00001BAD}, {0x00001BE6, 0x00001BF3}, {0x00001C24, 0x00001C37}, -{0x00001CD0, 0x00001CD2}, {0x00001CD4, 0x00001CE8}, {0x00001CED, 0x00001CED}, {0x00001CF4, 0x00001CF4}, -{0x00001CF7, 0x00001CF9}, {0x00001DC0, 0x00001DFF}, {0x000020D0, 0x000020F0}, {0x00002CEF, 0x00002CF1}, -{0x00002D7F, 0x00002D7F}, {0x00002DE0, 0x00002DFF}, {0x0000302A, 0x0000302F}, {0x00003099, 0x0000309A}, -{0x0000A66F, 0x0000A672}, {0x0000A674, 0x0000A67D}, {0x0000A69E, 0x0000A69F}, {0x0000A6F0, 0x0000A6F1}, -{0x0000A802, 0x0000A802}, {0x0000A806, 0x0000A806}, {0x0000A80B, 0x0000A80B}, {0x0000A823, 0x0000A827}, -{0x0000A82C, 0x0000A82C}, {0x0000A880, 0x0000A881}, {0x0000A8B4, 0x0000A8C5}, {0x0000A8E0, 0x0000A8F1}, -{0x0000A8FF, 0x0000A8FF}, {0x0000A926, 0x0000A92D}, {0x0000A947, 0x0000A953}, {0x0000A980, 0x0000A983}, -{0x0000A9B3, 0x0000A9C0}, {0x0000A9E5, 0x0000A9E5}, {0x0000AA29, 0x0000AA36}, {0x0000AA43, 0x0000AA43}, -{0x0000AA4C, 0x0000AA4D}, {0x0000AA7B, 0x0000AA7D}, {0x0000AAB0, 0x0000AAB0}, {0x0000AAB2, 0x0000AAB4}, -{0x0000AAB7, 0x0000AAB8}, {0x0000AABE, 0x0000AABF}, {0x0000AAC1, 0x0000AAC1}, {0x0000AAEB, 0x0000AAEF}, -{0x0000AAF5, 0x0000AAF6}, {0x0000ABE3, 0x0000ABEA}, {0x0000ABEC, 0x0000ABED}, {0x0000FB1E, 0x0000FB1E}, -{0x0000FE00, 0x0000FE0F}, {0x0000FE20, 0x0000FE2F}, {0x000101FD, 0x000101FD}, {0x000102E0, 0x000102E0}, -{0x00010376, 0x0001037A}, {0x00010A01, 0x00010A03}, {0x00010A05, 0x00010A06}, {0x00010A0C, 0x00010A0F}, -{0x00010A38, 0x00010A3A}, {0x00010A3F, 0x00010A3F}, {0x00010AE5, 0x00010AE6}, {0x00010D24, 0x00010D27}, -{0x00010EAB, 0x00010EAC}, {0x00010EFD, 0x00010EFF}, {0x00010F46, 0x00010F50}, {0x00010F82, 0x00010F85}, -{0x00011000, 0x00011002}, {0x00011038, 0x00011046}, {0x00011070, 0x00011070}, {0x00011073, 0x00011074}, -{0x0001107F, 0x00011082}, {0x000110B0, 0x000110BA}, {0x000110C2, 0x000110C2}, {0x00011100, 0x00011102}, -{0x00011127, 0x00011134}, {0x00011145, 0x00011146}, {0x00011173, 0x00011173}, {0x00011180, 0x00011182}, -{0x000111B3, 0x000111C0}, {0x000111C9, 0x000111CC}, {0x000111CE, 0x000111CF}, {0x0001122C, 0x00011237}, -{0x0001123E, 0x0001123E}, {0x00011241, 0x00011241}, {0x000112DF, 0x000112EA}, {0x00011300, 0x00011303}, -{0x0001133B, 0x0001133C}, {0x0001133E, 0x00011344}, {0x00011347, 0x00011348}, {0x0001134B, 0x0001134D}, -{0x00011357, 0x00011357}, {0x00011362, 0x00011363}, {0x00011366, 0x0001136C}, {0x00011370, 0x00011374}, -{0x00011435, 0x00011446}, {0x0001145E, 0x0001145E}, {0x000114B0, 0x000114C3}, {0x000115AF, 0x000115B5}, -{0x000115B8, 0x000115C0}, {0x000115DC, 0x000115DD}, {0x00011630, 0x00011640}, {0x000116AB, 0x000116B7}, -{0x0001171D, 0x0001172B}, {0x0001182C, 0x0001183A}, {0x00011930, 0x00011935}, {0x00011937, 0x00011938}, -{0x0001193B, 0x0001193E}, {0x00011940, 0x00011940}, {0x00011942, 0x00011943}, {0x000119D1, 0x000119D7}, -{0x000119DA, 0x000119E0}, {0x000119E4, 0x000119E4}, {0x00011A01, 0x00011A0A}, {0x00011A33, 0x00011A39}, -{0x00011A3B, 0x00011A3E}, {0x00011A47, 0x00011A47}, {0x00011A51, 0x00011A5B}, {0x00011A8A, 0x00011A99}, -{0x00011C2F, 0x00011C36}, {0x00011C38, 0x00011C3F}, {0x00011C92, 0x00011CA7}, {0x00011CA9, 0x00011CB6}, -{0x00011D31, 0x00011D36}, {0x00011D3A, 0x00011D3A}, {0x00011D3C, 0x00011D3D}, {0x00011D3F, 0x00011D45}, -{0x00011D47, 0x00011D47}, {0x00011D8A, 0x00011D8E}, {0x00011D90, 0x00011D91}, {0x00011D93, 0x00011D97}, -{0x00011EF3, 0x00011EF6}, {0x00011F00, 0x00011F01}, {0x00011F03, 0x00011F03}, {0x00011F34, 0x00011F3A}, -{0x00011F3E, 0x00011F42}, {0x00013440, 0x00013440}, {0x00013447, 0x00013455}, {0x00016AF0, 0x00016AF4}, -{0x00016B30, 0x00016B36}, {0x00016F4F, 0x00016F4F}, {0x00016F51, 0x00016F87}, {0x00016F8F, 0x00016F92}, -{0x00016FE4, 0x00016FE4}, {0x00016FF0, 0x00016FF1}, {0x0001BC9D, 0x0001BC9E}, {0x0001CF00, 0x0001CF2D}, -{0x0001CF30, 0x0001CF46}, {0x0001D165, 0x0001D169}, {0x0001D16D, 0x0001D172}, {0x0001D17B, 0x0001D182}, -{0x0001D185, 0x0001D18B}, {0x0001D1AA, 0x0001D1AD}, {0x0001D242, 0x0001D244}, {0x0001DA00, 0x0001DA36}, -{0x0001DA3B, 0x0001DA6C}, {0x0001DA75, 0x0001DA75}, {0x0001DA84, 0x0001DA84}, {0x0001DA9B, 0x0001DA9F}, -{0x0001DAA1, 0x0001DAAF}, {0x0001E000, 0x0001E006}, {0x0001E008, 0x0001E018}, {0x0001E01B, 0x0001E021}, -{0x0001E023, 0x0001E024}, {0x0001E026, 0x0001E02A}, {0x0001E08F, 0x0001E08F}, {0x0001E130, 0x0001E136}, -{0x0001E2AE, 0x0001E2AE}, {0x0001E2EC, 0x0001E2EF}, {0x0001E4EC, 0x0001E4EF}, {0x0001E8D0, 0x0001E8D6}, -{0x0001E944, 0x0001E94A}, {0x000E0100, 0x000E01EF}, +const std::vector unicode_ranges_nfd = { // start, last, nfd +{0x000000, 0x000000, 0x000000}, +{0x0000C0, 0x0000C5, 0x000041}, +{0x0000C7, 0x0000C7, 0x000043}, +{0x0000C8, 0x0000CB, 0x000045}, +{0x0000CC, 0x0000CF, 0x000049}, +{0x0000D1, 0x0000D1, 0x00004E}, +{0x0000D2, 0x0000D6, 0x00004F}, +{0x0000D9, 0x0000DC, 0x000055}, +{0x0000DD, 0x0000DD, 0x000059}, +{0x0000E0, 0x0000E5, 0x000061}, +{0x0000E7, 0x0000E7, 0x000063}, +{0x0000E8, 0x0000EB, 0x000065}, +{0x0000EC, 0x0000EF, 0x000069}, +{0x0000F1, 0x0000F1, 0x00006E}, +{0x0000F2, 0x0000F6, 0x00006F}, +{0x0000F9, 0x0000FC, 0x000075}, +{0x0000FD, 0x0000FD, 0x000079}, +{0x0000FF, 0x0000FF, 0x000079}, +{0x000100, 0x000100, 0x000041}, +{0x000101, 0x000101, 0x000061}, +{0x000102, 0x000102, 0x000041}, +{0x000103, 0x000103, 0x000061}, +{0x000104, 0x000104, 0x000041}, +{0x000105, 0x000105, 0x000061}, +{0x000106, 0x000106, 0x000043}, +{0x000107, 0x000107, 0x000063}, +{0x000108, 0x000108, 0x000043}, +{0x000109, 0x000109, 0x000063}, +{0x00010A, 0x00010A, 0x000043}, +{0x00010B, 0x00010B, 0x000063}, +{0x00010C, 0x00010C, 0x000043}, +{0x00010D, 0x00010D, 0x000063}, +{0x00010E, 0x00010E, 0x000044}, +{0x00010F, 0x00010F, 0x000064}, +{0x000112, 0x000112, 0x000045}, +{0x000113, 0x000113, 0x000065}, +{0x000114, 0x000114, 0x000045}, +{0x000115, 0x000115, 0x000065}, +{0x000116, 0x000116, 0x000045}, +{0x000117, 0x000117, 0x000065}, +{0x000118, 0x000118, 0x000045}, +{0x000119, 0x000119, 0x000065}, +{0x00011A, 0x00011A, 0x000045}, +{0x00011B, 0x00011B, 0x000065}, +{0x00011C, 0x00011C, 0x000047}, +{0x00011D, 0x00011D, 0x000067}, +{0x00011E, 0x00011E, 0x000047}, +{0x00011F, 0x00011F, 0x000067}, +{0x000120, 0x000120, 0x000047}, +{0x000121, 0x000121, 0x000067}, +{0x000122, 0x000122, 0x000047}, +{0x000123, 0x000123, 0x000067}, +{0x000124, 0x000124, 0x000048}, +{0x000125, 0x000125, 0x000068}, +{0x000128, 0x000128, 0x000049}, +{0x000129, 0x000129, 0x000069}, +{0x00012A, 0x00012A, 0x000049}, +{0x00012B, 0x00012B, 0x000069}, +{0x00012C, 0x00012C, 0x000049}, +{0x00012D, 0x00012D, 0x000069}, +{0x00012E, 0x00012E, 0x000049}, +{0x00012F, 0x00012F, 0x000069}, +{0x000130, 0x000130, 0x000049}, +{0x000134, 0x000134, 0x00004A}, +{0x000135, 0x000135, 0x00006A}, +{0x000136, 0x000136, 0x00004B}, +{0x000137, 0x000137, 0x00006B}, +{0x000139, 0x000139, 0x00004C}, +{0x00013A, 0x00013A, 0x00006C}, +{0x00013B, 0x00013B, 0x00004C}, +{0x00013C, 0x00013C, 0x00006C}, +{0x00013D, 0x00013D, 0x00004C}, +{0x00013E, 0x00013E, 0x00006C}, +{0x000143, 0x000143, 0x00004E}, +{0x000144, 0x000144, 0x00006E}, +{0x000145, 0x000145, 0x00004E}, +{0x000146, 0x000146, 0x00006E}, +{0x000147, 0x000147, 0x00004E}, +{0x000148, 0x000148, 0x00006E}, +{0x00014C, 0x00014C, 0x00004F}, +{0x00014D, 0x00014D, 0x00006F}, +{0x00014E, 0x00014E, 0x00004F}, +{0x00014F, 0x00014F, 0x00006F}, +{0x000150, 0x000150, 0x00004F}, +{0x000151, 0x000151, 0x00006F}, +{0x000154, 0x000154, 0x000052}, +{0x000155, 0x000155, 0x000072}, +{0x000156, 0x000156, 0x000052}, +{0x000157, 0x000157, 0x000072}, +{0x000158, 0x000158, 0x000052}, +{0x000159, 0x000159, 0x000072}, +{0x00015A, 0x00015A, 0x000053}, +{0x00015B, 0x00015B, 0x000073}, +{0x00015C, 0x00015C, 0x000053}, +{0x00015D, 0x00015D, 0x000073}, +{0x00015E, 0x00015E, 0x000053}, +{0x00015F, 0x00015F, 0x000073}, +{0x000160, 0x000160, 0x000053}, +{0x000161, 0x000161, 0x000073}, +{0x000162, 0x000162, 0x000054}, +{0x000163, 0x000163, 0x000074}, +{0x000164, 0x000164, 0x000054}, +{0x000165, 0x000165, 0x000074}, +{0x000168, 0x000168, 0x000055}, +{0x000169, 0x000169, 0x000075}, +{0x00016A, 0x00016A, 0x000055}, +{0x00016B, 0x00016B, 0x000075}, +{0x00016C, 0x00016C, 0x000055}, +{0x00016D, 0x00016D, 0x000075}, +{0x00016E, 0x00016E, 0x000055}, +{0x00016F, 0x00016F, 0x000075}, +{0x000170, 0x000170, 0x000055}, +{0x000171, 0x000171, 0x000075}, +{0x000172, 0x000172, 0x000055}, +{0x000173, 0x000173, 0x000075}, +{0x000174, 0x000174, 0x000057}, +{0x000175, 0x000175, 0x000077}, +{0x000176, 0x000176, 0x000059}, +{0x000177, 0x000177, 0x000079}, +{0x000178, 0x000178, 0x000059}, +{0x000179, 0x000179, 0x00005A}, +{0x00017A, 0x00017A, 0x00007A}, +{0x00017B, 0x00017B, 0x00005A}, +{0x00017C, 0x00017C, 0x00007A}, +{0x00017D, 0x00017D, 0x00005A}, +{0x00017E, 0x00017E, 0x00007A}, +{0x0001A0, 0x0001A0, 0x00004F}, +{0x0001A1, 0x0001A1, 0x00006F}, +{0x0001AF, 0x0001AF, 0x000055}, +{0x0001B0, 0x0001B0, 0x000075}, +{0x0001CD, 0x0001CD, 0x000041}, +{0x0001CE, 0x0001CE, 0x000061}, +{0x0001CF, 0x0001CF, 0x000049}, +{0x0001D0, 0x0001D0, 0x000069}, +{0x0001D1, 0x0001D1, 0x00004F}, +{0x0001D2, 0x0001D2, 0x00006F}, +{0x0001D3, 0x0001D3, 0x000055}, +{0x0001D4, 0x0001D4, 0x000075}, +{0x0001D5, 0x0001D5, 0x000055}, +{0x0001D6, 0x0001D6, 0x000075}, +{0x0001D7, 0x0001D7, 0x000055}, +{0x0001D8, 0x0001D8, 0x000075}, +{0x0001D9, 0x0001D9, 0x000055}, +{0x0001DA, 0x0001DA, 0x000075}, +{0x0001DB, 0x0001DB, 0x000055}, +{0x0001DC, 0x0001DC, 0x000075}, +{0x0001DE, 0x0001DE, 0x000041}, +{0x0001DF, 0x0001DF, 0x000061}, +{0x0001E0, 0x0001E0, 0x000041}, +{0x0001E1, 0x0001E1, 0x000061}, +{0x0001E2, 0x0001E2, 0x0000C6}, +{0x0001E3, 0x0001E3, 0x0000E6}, +{0x0001E6, 0x0001E6, 0x000047}, +{0x0001E7, 0x0001E7, 0x000067}, +{0x0001E8, 0x0001E8, 0x00004B}, +{0x0001E9, 0x0001E9, 0x00006B}, +{0x0001EA, 0x0001EA, 0x00004F}, +{0x0001EB, 0x0001EB, 0x00006F}, +{0x0001EC, 0x0001EC, 0x00004F}, +{0x0001ED, 0x0001ED, 0x00006F}, +{0x0001EE, 0x0001EE, 0x0001B7}, +{0x0001EF, 0x0001EF, 0x000292}, +{0x0001F0, 0x0001F0, 0x00006A}, +{0x0001F4, 0x0001F4, 0x000047}, +{0x0001F5, 0x0001F5, 0x000067}, +{0x0001F8, 0x0001F8, 0x00004E}, +{0x0001F9, 0x0001F9, 0x00006E}, +{0x0001FA, 0x0001FA, 0x000041}, +{0x0001FB, 0x0001FB, 0x000061}, +{0x0001FC, 0x0001FC, 0x0000C6}, +{0x0001FD, 0x0001FD, 0x0000E6}, +{0x0001FE, 0x0001FE, 0x0000D8}, +{0x0001FF, 0x0001FF, 0x0000F8}, +{0x000200, 0x000200, 0x000041}, +{0x000201, 0x000201, 0x000061}, +{0x000202, 0x000202, 0x000041}, +{0x000203, 0x000203, 0x000061}, +{0x000204, 0x000204, 0x000045}, +{0x000205, 0x000205, 0x000065}, +{0x000206, 0x000206, 0x000045}, +{0x000207, 0x000207, 0x000065}, +{0x000208, 0x000208, 0x000049}, +{0x000209, 0x000209, 0x000069}, +{0x00020A, 0x00020A, 0x000049}, +{0x00020B, 0x00020B, 0x000069}, +{0x00020C, 0x00020C, 0x00004F}, +{0x00020D, 0x00020D, 0x00006F}, +{0x00020E, 0x00020E, 0x00004F}, +{0x00020F, 0x00020F, 0x00006F}, +{0x000210, 0x000210, 0x000052}, +{0x000211, 0x000211, 0x000072}, +{0x000212, 0x000212, 0x000052}, +{0x000213, 0x000213, 0x000072}, +{0x000214, 0x000214, 0x000055}, +{0x000215, 0x000215, 0x000075}, +{0x000216, 0x000216, 0x000055}, +{0x000217, 0x000217, 0x000075}, +{0x000218, 0x000218, 0x000053}, +{0x000219, 0x000219, 0x000073}, +{0x00021A, 0x00021A, 0x000054}, +{0x00021B, 0x00021B, 0x000074}, +{0x00021E, 0x00021E, 0x000048}, +{0x00021F, 0x00021F, 0x000068}, +{0x000226, 0x000226, 0x000041}, +{0x000227, 0x000227, 0x000061}, +{0x000228, 0x000228, 0x000045}, +{0x000229, 0x000229, 0x000065}, +{0x00022A, 0x00022A, 0x00004F}, +{0x00022B, 0x00022B, 0x00006F}, +{0x00022C, 0x00022C, 0x00004F}, +{0x00022D, 0x00022D, 0x00006F}, +{0x00022E, 0x00022E, 0x00004F}, +{0x00022F, 0x00022F, 0x00006F}, +{0x000230, 0x000230, 0x00004F}, +{0x000231, 0x000231, 0x00006F}, +{0x000232, 0x000232, 0x000059}, +{0x000233, 0x000233, 0x000079}, +{0x000340, 0x000340, 0x000300}, +{0x000341, 0x000341, 0x000301}, +{0x000343, 0x000343, 0x000313}, +{0x000344, 0x000344, 0x000308}, +{0x000374, 0x000374, 0x0002B9}, +{0x00037E, 0x00037E, 0x00003B}, +{0x000385, 0x000385, 0x0000A8}, +{0x000386, 0x000386, 0x000391}, +{0x000387, 0x000387, 0x0000B7}, +{0x000388, 0x000388, 0x000395}, +{0x000389, 0x000389, 0x000397}, +{0x00038A, 0x00038A, 0x000399}, +{0x00038C, 0x00038C, 0x00039F}, +{0x00038E, 0x00038E, 0x0003A5}, +{0x00038F, 0x00038F, 0x0003A9}, +{0x000390, 0x000390, 0x0003B9}, +{0x0003AA, 0x0003AA, 0x000399}, +{0x0003AB, 0x0003AB, 0x0003A5}, +{0x0003AC, 0x0003AC, 0x0003B1}, +{0x0003AD, 0x0003AD, 0x0003B5}, +{0x0003AE, 0x0003AE, 0x0003B7}, +{0x0003AF, 0x0003AF, 0x0003B9}, +{0x0003B0, 0x0003B0, 0x0003C5}, +{0x0003CA, 0x0003CA, 0x0003B9}, +{0x0003CB, 0x0003CB, 0x0003C5}, +{0x0003CC, 0x0003CC, 0x0003BF}, +{0x0003CD, 0x0003CD, 0x0003C5}, +{0x0003CE, 0x0003CE, 0x0003C9}, +{0x0003D3, 0x0003D4, 0x0003D2}, +{0x000400, 0x000401, 0x000415}, +{0x000403, 0x000403, 0x000413}, +{0x000407, 0x000407, 0x000406}, +{0x00040C, 0x00040C, 0x00041A}, +{0x00040D, 0x00040D, 0x000418}, +{0x00040E, 0x00040E, 0x000423}, 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+{0x02F8FC, 0x02F8FC, 0x006CBF}, +{0x02F8FD, 0x02F8FD, 0x006CCD}, +{0x02F8FE, 0x02F8FE, 0x006C67}, +{0x02F8FF, 0x02F8FF, 0x006D16}, +{0x02F900, 0x02F900, 0x006D3E}, +{0x02F901, 0x02F901, 0x006D77}, +{0x02F902, 0x02F902, 0x006D41}, +{0x02F903, 0x02F903, 0x006D69}, +{0x02F904, 0x02F904, 0x006D78}, +{0x02F905, 0x02F905, 0x006D85}, +{0x02F906, 0x02F906, 0x023D1E}, +{0x02F907, 0x02F907, 0x006D34}, +{0x02F908, 0x02F908, 0x006E2F}, +{0x02F909, 0x02F909, 0x006E6E}, +{0x02F90A, 0x02F90A, 0x003D33}, +{0x02F90B, 0x02F90B, 0x006ECB}, +{0x02F90C, 0x02F90C, 0x006EC7}, +{0x02F90D, 0x02F90D, 0x023ED1}, +{0x02F90E, 0x02F90E, 0x006DF9}, +{0x02F90F, 0x02F90F, 0x006F6E}, +{0x02F910, 0x02F910, 0x023F5E}, +{0x02F911, 0x02F911, 0x023F8E}, +{0x02F912, 0x02F912, 0x006FC6}, +{0x02F913, 0x02F913, 0x007039}, +{0x02F914, 0x02F914, 0x00701E}, +{0x02F915, 0x02F915, 0x00701B}, +{0x02F916, 0x02F916, 0x003D96}, +{0x02F917, 0x02F917, 0x00704A}, +{0x02F918, 0x02F918, 0x00707D}, +{0x02F919, 0x02F919, 0x007077}, +{0x02F91A, 0x02F91A, 0x0070AD}, +{0x02F91B, 0x02F91B, 0x020525}, +{0x02F91C, 0x02F91C, 0x007145}, +{0x02F91D, 0x02F91D, 0x024263}, +{0x02F91E, 0x02F91E, 0x00719C}, +{0x02F91F, 0x02F91F, 0x0243AB}, +{0x02F920, 0x02F920, 0x007228}, +{0x02F921, 0x02F921, 0x007235}, +{0x02F922, 0x02F922, 0x007250}, +{0x02F923, 0x02F923, 0x024608}, +{0x02F924, 0x02F924, 0x007280}, +{0x02F925, 0x02F925, 0x007295}, +{0x02F926, 0x02F926, 0x024735}, +{0x02F927, 0x02F927, 0x024814}, +{0x02F928, 0x02F928, 0x00737A}, +{0x02F929, 0x02F929, 0x00738B}, +{0x02F92A, 0x02F92A, 0x003EAC}, +{0x02F92B, 0x02F92B, 0x0073A5}, +{0x02F92C, 0x02F92D, 0x003EB8}, +{0x02F92E, 0x02F92E, 0x007447}, +{0x02F92F, 0x02F92F, 0x00745C}, +{0x02F930, 0x02F930, 0x007471}, +{0x02F931, 0x02F931, 0x007485}, +{0x02F932, 0x02F932, 0x0074CA}, +{0x02F933, 0x02F933, 0x003F1B}, +{0x02F934, 0x02F934, 0x007524}, +{0x02F935, 0x02F935, 0x024C36}, +{0x02F936, 0x02F936, 0x00753E}, +{0x02F937, 0x02F937, 0x024C92}, +{0x02F938, 0x02F938, 0x007570}, +{0x02F939, 0x02F939, 0x02219F}, +{0x02F93A, 0x02F93A, 0x007610}, +{0x02F93B, 0x02F93B, 0x024FA1}, +{0x02F93C, 0x02F93C, 0x024FB8}, +{0x02F93D, 0x02F93D, 0x025044}, +{0x02F93E, 0x02F93E, 0x003FFC}, +{0x02F93F, 0x02F93F, 0x004008}, +{0x02F940, 0x02F940, 0x0076F4}, +{0x02F941, 0x02F941, 0x0250F3}, +{0x02F942, 0x02F942, 0x0250F2}, +{0x02F943, 0x02F943, 0x025119}, +{0x02F944, 0x02F944, 0x025133}, +{0x02F945, 0x02F945, 0x00771E}, +{0x02F946, 0x02F947, 0x00771F}, +{0x02F948, 0x02F948, 0x00774A}, +{0x02F949, 0x02F949, 0x004039}, +{0x02F94A, 0x02F94A, 0x00778B}, +{0x02F94B, 0x02F94B, 0x004046}, +{0x02F94C, 0x02F94C, 0x004096}, +{0x02F94D, 0x02F94D, 0x02541D}, +{0x02F94E, 0x02F94E, 0x00784E}, +{0x02F94F, 0x02F94F, 0x00788C}, +{0x02F950, 0x02F950, 0x0078CC}, +{0x02F951, 0x02F951, 0x0040E3}, +{0x02F952, 0x02F952, 0x025626}, +{0x02F953, 0x02F953, 0x007956}, +{0x02F954, 0x02F954, 0x02569A}, +{0x02F955, 0x02F955, 0x0256C5}, +{0x02F956, 0x02F956, 0x00798F}, +{0x02F957, 0x02F957, 0x0079EB}, +{0x02F958, 0x02F958, 0x00412F}, +{0x02F959, 0x02F959, 0x007A40}, +{0x02F95A, 0x02F95A, 0x007A4A}, +{0x02F95B, 0x02F95B, 0x007A4F}, +{0x02F95C, 0x02F95C, 0x02597C}, +{0x02F95D, 0x02F95E, 0x025AA7}, +{0x02F95F, 0x02F95F, 0x007AEE}, +{0x02F960, 0x02F960, 0x004202}, +{0x02F961, 0x02F961, 0x025BAB}, +{0x02F962, 0x02F962, 0x007BC6}, +{0x02F963, 0x02F963, 0x007BC9}, +{0x02F964, 0x02F964, 0x004227}, +{0x02F965, 0x02F965, 0x025C80}, +{0x02F966, 0x02F966, 0x007CD2}, +{0x02F967, 0x02F967, 0x0042A0}, +{0x02F968, 0x02F968, 0x007CE8}, +{0x02F969, 0x02F969, 0x007CE3}, +{0x02F96A, 0x02F96A, 0x007D00}, +{0x02F96B, 0x02F96B, 0x025F86}, +{0x02F96C, 0x02F96C, 0x007D63}, +{0x02F96D, 0x02F96D, 0x004301}, +{0x02F96E, 0x02F96E, 0x007DC7}, +{0x02F96F, 0x02F96F, 0x007E02}, +{0x02F970, 0x02F970, 0x007E45}, +{0x02F971, 0x02F971, 0x004334}, +{0x02F972, 0x02F972, 0x026228}, +{0x02F973, 0x02F973, 0x026247}, +{0x02F974, 0x02F974, 0x004359}, +{0x02F975, 0x02F975, 0x0262D9}, +{0x02F976, 0x02F976, 0x007F7A}, +{0x02F977, 0x02F977, 0x02633E}, +{0x02F978, 0x02F978, 0x007F95}, +{0x02F979, 0x02F979, 0x007FFA}, +{0x02F97A, 0x02F97A, 0x008005}, +{0x02F97B, 0x02F97B, 0x0264DA}, +{0x02F97C, 0x02F97C, 0x026523}, +{0x02F97D, 0x02F97D, 0x008060}, +{0x02F97E, 0x02F97E, 0x0265A8}, +{0x02F97F, 0x02F97F, 0x008070}, +{0x02F980, 0x02F980, 0x02335F}, +{0x02F981, 0x02F981, 0x0043D5}, +{0x02F982, 0x02F982, 0x0080B2}, +{0x02F983, 0x02F983, 0x008103}, +{0x02F984, 0x02F984, 0x00440B}, +{0x02F985, 0x02F985, 0x00813E}, +{0x02F986, 0x02F986, 0x005AB5}, +{0x02F987, 0x02F987, 0x0267A7}, +{0x02F988, 0x02F988, 0x0267B5}, +{0x02F989, 0x02F989, 0x023393}, +{0x02F98A, 0x02F98A, 0x02339C}, +{0x02F98B, 0x02F98B, 0x008201}, +{0x02F98C, 0x02F98C, 0x008204}, +{0x02F98D, 0x02F98D, 0x008F9E}, +{0x02F98E, 0x02F98E, 0x00446B}, +{0x02F98F, 0x02F98F, 0x008291}, +{0x02F990, 0x02F990, 0x00828B}, +{0x02F991, 0x02F991, 0x00829D}, +{0x02F992, 0x02F992, 0x0052B3}, +{0x02F993, 0x02F993, 0x0082B1}, +{0x02F994, 0x02F994, 0x0082B3}, +{0x02F995, 0x02F995, 0x0082BD}, +{0x02F996, 0x02F996, 0x0082E6}, +{0x02F997, 0x02F997, 0x026B3C}, +{0x02F998, 0x02F998, 0x0082E5}, +{0x02F999, 0x02F999, 0x00831D}, +{0x02F99A, 0x02F99A, 0x008363}, +{0x02F99B, 0x02F99B, 0x0083AD}, +{0x02F99C, 0x02F99C, 0x008323}, +{0x02F99D, 0x02F99D, 0x0083BD}, +{0x02F99E, 0x02F99E, 0x0083E7}, +{0x02F99F, 0x02F99F, 0x008457}, +{0x02F9A0, 0x02F9A0, 0x008353}, +{0x02F9A1, 0x02F9A1, 0x0083CA}, +{0x02F9A2, 0x02F9A2, 0x0083CC}, +{0x02F9A3, 0x02F9A3, 0x0083DC}, +{0x02F9A4, 0x02F9A4, 0x026C36}, +{0x02F9A5, 0x02F9A5, 0x026D6B}, +{0x02F9A6, 0x02F9A6, 0x026CD5}, +{0x02F9A7, 0x02F9A7, 0x00452B}, +{0x02F9A8, 0x02F9A8, 0x0084F1}, +{0x02F9A9, 0x02F9A9, 0x0084F3}, +{0x02F9AA, 0x02F9AA, 0x008516}, +{0x02F9AB, 0x02F9AB, 0x0273CA}, +{0x02F9AC, 0x02F9AC, 0x008564}, +{0x02F9AD, 0x02F9AD, 0x026F2C}, +{0x02F9AE, 0x02F9AE, 0x00455D}, +{0x02F9AF, 0x02F9AF, 0x004561}, +{0x02F9B0, 0x02F9B0, 0x026FB1}, +{0x02F9B1, 0x02F9B1, 0x0270D2}, +{0x02F9B2, 0x02F9B2, 0x00456B}, +{0x02F9B3, 0x02F9B3, 0x008650}, +{0x02F9B4, 0x02F9B4, 0x00865C}, +{0x02F9B5, 0x02F9B5, 0x008667}, +{0x02F9B6, 0x02F9B6, 0x008669}, +{0x02F9B7, 0x02F9B7, 0x0086A9}, +{0x02F9B8, 0x02F9B8, 0x008688}, +{0x02F9B9, 0x02F9B9, 0x00870E}, +{0x02F9BA, 0x02F9BA, 0x0086E2}, +{0x02F9BB, 0x02F9BB, 0x008779}, +{0x02F9BC, 0x02F9BC, 0x008728}, +{0x02F9BD, 0x02F9BD, 0x00876B}, +{0x02F9BE, 0x02F9BE, 0x008786}, +{0x02F9BF, 0x02F9BF, 0x0045D7}, +{0x02F9C0, 0x02F9C0, 0x0087E1}, +{0x02F9C1, 0x02F9C1, 0x008801}, +{0x02F9C2, 0x02F9C2, 0x0045F9}, +{0x02F9C3, 0x02F9C3, 0x008860}, +{0x02F9C4, 0x02F9C4, 0x008863}, +{0x02F9C5, 0x02F9C5, 0x027667}, +{0x02F9C6, 0x02F9C6, 0x0088D7}, +{0x02F9C7, 0x02F9C7, 0x0088DE}, +{0x02F9C8, 0x02F9C8, 0x004635}, +{0x02F9C9, 0x02F9C9, 0x0088FA}, +{0x02F9CA, 0x02F9CA, 0x0034BB}, +{0x02F9CB, 0x02F9CB, 0x0278AE}, +{0x02F9CC, 0x02F9CC, 0x027966}, +{0x02F9CD, 0x02F9CD, 0x0046BE}, +{0x02F9CE, 0x02F9CE, 0x0046C7}, +{0x02F9CF, 0x02F9CF, 0x008AA0}, +{0x02F9D0, 0x02F9D0, 0x008AED}, +{0x02F9D1, 0x02F9D1, 0x008B8A}, +{0x02F9D2, 0x02F9D2, 0x008C55}, +{0x02F9D3, 0x02F9D3, 0x027CA8}, +{0x02F9D4, 0x02F9D4, 0x008CAB}, +{0x02F9D5, 0x02F9D5, 0x008CC1}, +{0x02F9D6, 0x02F9D6, 0x008D1B}, +{0x02F9D7, 0x02F9D7, 0x008D77}, +{0x02F9D8, 0x02F9D8, 0x027F2F}, +{0x02F9D9, 0x02F9D9, 0x020804}, +{0x02F9DA, 0x02F9DA, 0x008DCB}, +{0x02F9DB, 0x02F9DB, 0x008DBC}, +{0x02F9DC, 0x02F9DC, 0x008DF0}, +{0x02F9DD, 0x02F9DD, 0x0208DE}, +{0x02F9DE, 0x02F9DE, 0x008ED4}, +{0x02F9DF, 0x02F9DF, 0x008F38}, +{0x02F9E0, 0x02F9E0, 0x0285D2}, +{0x02F9E1, 0x02F9E1, 0x0285ED}, +{0x02F9E2, 0x02F9E2, 0x009094}, +{0x02F9E3, 0x02F9E3, 0x0090F1}, +{0x02F9E4, 0x02F9E4, 0x009111}, +{0x02F9E5, 0x02F9E5, 0x02872E}, +{0x02F9E6, 0x02F9E6, 0x00911B}, +{0x02F9E7, 0x02F9E7, 0x009238}, +{0x02F9E8, 0x02F9E8, 0x0092D7}, +{0x02F9E9, 0x02F9E9, 0x0092D8}, +{0x02F9EA, 0x02F9EA, 0x00927C}, +{0x02F9EB, 0x02F9EB, 0x0093F9}, +{0x02F9EC, 0x02F9EC, 0x009415}, +{0x02F9ED, 0x02F9ED, 0x028BFA}, +{0x02F9EE, 0x02F9EE, 0x00958B}, +{0x02F9EF, 0x02F9EF, 0x004995}, +{0x02F9F0, 0x02F9F0, 0x0095B7}, +{0x02F9F1, 0x02F9F1, 0x028D77}, +{0x02F9F2, 0x02F9F2, 0x0049E6}, +{0x02F9F3, 0x02F9F3, 0x0096C3}, +{0x02F9F4, 0x02F9F4, 0x005DB2}, +{0x02F9F5, 0x02F9F5, 0x009723}, +{0x02F9F6, 0x02F9F6, 0x029145}, +{0x02F9F7, 0x02F9F7, 0x02921A}, +{0x02F9F8, 0x02F9F8, 0x004A6E}, +{0x02F9F9, 0x02F9F9, 0x004A76}, +{0x02F9FA, 0x02F9FA, 0x0097E0}, +{0x02F9FB, 0x02F9FB, 0x02940A}, +{0x02F9FC, 0x02F9FC, 0x004AB2}, +{0x02F9FD, 0x02F9FD, 0x029496}, +{0x02F9FE, 0x02F9FF, 0x00980B}, +{0x02FA00, 0x02FA00, 0x009829}, +{0x02FA01, 0x02FA01, 0x0295B6}, +{0x02FA02, 0x02FA02, 0x0098E2}, +{0x02FA03, 0x02FA03, 0x004B33}, +{0x02FA04, 0x02FA04, 0x009929}, +{0x02FA05, 0x02FA05, 0x0099A7}, +{0x02FA06, 0x02FA06, 0x0099C2}, +{0x02FA07, 0x02FA07, 0x0099FE}, +{0x02FA08, 0x02FA08, 0x004BCE}, +{0x02FA09, 0x02FA09, 0x029B30}, +{0x02FA0A, 0x02FA0A, 0x009B12}, +{0x02FA0B, 0x02FA0B, 0x009C40}, +{0x02FA0C, 0x02FA0C, 0x009CFD}, +{0x02FA0D, 0x02FA0D, 0x004CCE}, +{0x02FA0E, 0x02FA0E, 0x004CED}, +{0x02FA0F, 0x02FA0F, 0x009D67}, +{0x02FA10, 0x02FA10, 0x02A0CE}, +{0x02FA11, 0x02FA11, 0x004CF8}, +{0x02FA12, 0x02FA12, 0x02A105}, +{0x02FA13, 0x02FA13, 0x02A20E}, +{0x02FA14, 0x02FA14, 0x02A291}, +{0x02FA15, 0x02FA15, 0x009EBB}, +{0x02FA16, 0x02FA16, 0x004D56}, +{0x02FA17, 0x02FA17, 0x009EF9}, +{0x02FA18, 0x02FA18, 0x009EFE}, +{0x02FA19, 0x02FA19, 0x009F05}, +{0x02FA1A, 0x02FA1A, 0x009F0F}, +{0x02FA1B, 0x02FA1B, 0x009F16}, +{0x02FA1C, 0x02FA1C, 0x009F3B}, +{0x02FA1D, 0x02FA1D, 0x02A600}, }; -const std::vector> unicode_ranges_punctuation = { -{0x00000021, 0x00000023}, {0x00000025, 0x0000002A}, {0x0000002C, 0x0000002F}, {0x0000003A, 0x0000003B}, -{0x0000003F, 0x00000040}, {0x0000005B, 0x0000005D}, {0x0000005F, 0x0000005F}, {0x0000007B, 0x0000007B}, -{0x0000007D, 0x0000007D}, {0x000000A1, 0x000000A1}, {0x000000A7, 0x000000A7}, {0x000000AB, 0x000000AB}, -{0x000000B6, 0x000000B7}, {0x000000BB, 0x000000BB}, {0x000000BF, 0x000000BF}, {0x0000037E, 0x0000037E}, -{0x00000387, 0x00000387}, {0x0000055A, 0x0000055F}, {0x00000589, 0x0000058A}, {0x000005BE, 0x000005BE}, -{0x000005C0, 0x000005C0}, {0x000005C3, 0x000005C3}, {0x000005C6, 0x000005C6}, {0x000005F3, 0x000005F4}, -{0x00000609, 0x0000060A}, {0x0000060C, 0x0000060D}, {0x0000061B, 0x0000061B}, {0x0000061D, 0x0000061F}, -{0x0000066A, 0x0000066D}, {0x000006D4, 0x000006D4}, {0x00000700, 0x0000070D}, {0x000007F7, 0x000007F9}, -{0x00000830, 0x0000083E}, {0x0000085E, 0x0000085E}, {0x00000964, 0x00000965}, {0x00000970, 0x00000970}, -{0x000009FD, 0x000009FD}, {0x00000A76, 0x00000A76}, {0x00000AF0, 0x00000AF0}, {0x00000C77, 0x00000C77}, -{0x00000C84, 0x00000C84}, {0x00000DF4, 0x00000DF4}, {0x00000E4F, 0x00000E4F}, {0x00000E5A, 0x00000E5B}, -{0x00000F04, 0x00000F12}, {0x00000F14, 0x00000F14}, {0x00000F3A, 0x00000F3D}, {0x00000F85, 0x00000F85}, -{0x00000FD0, 0x00000FD4}, {0x00000FD9, 0x00000FDA}, {0x0000104A, 0x0000104F}, {0x000010FB, 0x000010FB}, -{0x00001360, 0x00001368}, {0x00001400, 0x00001400}, {0x0000166E, 0x0000166E}, {0x0000169B, 0x0000169C}, -{0x000016EB, 0x000016ED}, {0x00001735, 0x00001736}, {0x000017D4, 0x000017D6}, {0x000017D8, 0x000017DA}, -{0x00001800, 0x0000180A}, {0x00001944, 0x00001945}, {0x00001A1E, 0x00001A1F}, {0x00001AA0, 0x00001AA6}, -{0x00001AA8, 0x00001AAD}, {0x00001B5A, 0x00001B60}, {0x00001B7D, 0x00001B7E}, {0x00001BFC, 0x00001BFF}, -{0x00001C3B, 0x00001C3F}, {0x00001C7E, 0x00001C7F}, {0x00001CC0, 0x00001CC7}, {0x00001CD3, 0x00001CD3}, -{0x00002010, 0x00002027}, {0x00002030, 0x00002043}, {0x00002045, 0x00002051}, {0x00002053, 0x0000205E}, -{0x0000207D, 0x0000207E}, {0x0000208D, 0x0000208E}, {0x00002308, 0x0000230B}, {0x00002329, 0x0000232A}, -{0x00002768, 0x00002775}, {0x000027C5, 0x000027C6}, {0x000027E6, 0x000027EF}, {0x00002983, 0x00002998}, -{0x000029D8, 0x000029DB}, {0x000029FC, 0x000029FD}, {0x00002CF9, 0x00002CFC}, {0x00002CFE, 0x00002CFF}, -{0x00002D70, 0x00002D70}, {0x00002E00, 0x00002E2E}, {0x00002E30, 0x00002E4F}, {0x00002E52, 0x00002E5D}, -{0x00003001, 0x00003003}, {0x00003008, 0x00003011}, {0x00003014, 0x0000301F}, {0x00003030, 0x00003030}, -{0x0000303D, 0x0000303D}, {0x000030A0, 0x000030A0}, {0x000030FB, 0x000030FB}, {0x0000A4FE, 0x0000A4FF}, -{0x0000A60D, 0x0000A60F}, {0x0000A673, 0x0000A673}, {0x0000A67E, 0x0000A67E}, {0x0000A6F2, 0x0000A6F7}, -{0x0000A874, 0x0000A877}, {0x0000A8CE, 0x0000A8CF}, {0x0000A8F8, 0x0000A8FA}, {0x0000A8FC, 0x0000A8FC}, -{0x0000A92E, 0x0000A92F}, {0x0000A95F, 0x0000A95F}, {0x0000A9C1, 0x0000A9CD}, {0x0000A9DE, 0x0000A9DF}, -{0x0000AA5C, 0x0000AA5F}, {0x0000AADE, 0x0000AADF}, {0x0000AAF0, 0x0000AAF1}, {0x0000ABEB, 0x0000ABEB}, -{0x0000FD3E, 0x0000FD3F}, {0x0000FE10, 0x0000FE19}, {0x0000FE30, 0x0000FE52}, {0x0000FE54, 0x0000FE61}, -{0x0000FE63, 0x0000FE63}, {0x0000FE68, 0x0000FE68}, {0x0000FE6A, 0x0000FE6B}, {0x0000FF01, 0x0000FF03}, -{0x0000FF05, 0x0000FF0A}, {0x0000FF0C, 0x0000FF0F}, {0x0000FF1A, 0x0000FF1B}, {0x0000FF1F, 0x0000FF20}, -{0x0000FF3B, 0x0000FF3D}, {0x0000FF3F, 0x0000FF3F}, {0x0000FF5B, 0x0000FF5B}, {0x0000FF5D, 0x0000FF5D}, -{0x0000FF5F, 0x0000FF65}, {0x00010100, 0x00010102}, {0x0001039F, 0x0001039F}, {0x000103D0, 0x000103D0}, -{0x0001056F, 0x0001056F}, {0x00010857, 0x00010857}, {0x0001091F, 0x0001091F}, {0x0001093F, 0x0001093F}, -{0x00010A50, 0x00010A58}, {0x00010A7F, 0x00010A7F}, {0x00010AF0, 0x00010AF6}, {0x00010B39, 0x00010B3F}, -{0x00010B99, 0x00010B9C}, {0x00010EAD, 0x00010EAD}, {0x00010F55, 0x00010F59}, {0x00010F86, 0x00010F89}, -{0x00011047, 0x0001104D}, {0x000110BB, 0x000110BC}, {0x000110BE, 0x000110C1}, {0x00011140, 0x00011143}, -{0x00011174, 0x00011175}, {0x000111C5, 0x000111C8}, {0x000111CD, 0x000111CD}, {0x000111DB, 0x000111DB}, -{0x000111DD, 0x000111DF}, {0x00011238, 0x0001123D}, {0x000112A9, 0x000112A9}, {0x0001144B, 0x0001144F}, -{0x0001145A, 0x0001145B}, {0x0001145D, 0x0001145D}, {0x000114C6, 0x000114C6}, {0x000115C1, 0x000115D7}, -{0x00011641, 0x00011643}, {0x00011660, 0x0001166C}, {0x000116B9, 0x000116B9}, {0x0001173C, 0x0001173E}, -{0x0001183B, 0x0001183B}, {0x00011944, 0x00011946}, {0x000119E2, 0x000119E2}, {0x00011A3F, 0x00011A46}, -{0x00011A9A, 0x00011A9C}, {0x00011A9E, 0x00011AA2}, {0x00011B00, 0x00011B09}, {0x00011C41, 0x00011C45}, -{0x00011C70, 0x00011C71}, {0x00011EF7, 0x00011EF8}, {0x00011F43, 0x00011F4F}, {0x00011FFF, 0x00011FFF}, -{0x00012470, 0x00012474}, {0x00012FF1, 0x00012FF2}, {0x00016A6E, 0x00016A6F}, {0x00016AF5, 0x00016AF5}, -{0x00016B37, 0x00016B3B}, {0x00016B44, 0x00016B44}, {0x00016E97, 0x00016E9A}, {0x00016FE2, 0x00016FE2}, -{0x0001BC9F, 0x0001BC9F}, {0x0001DA87, 0x0001DA8B}, {0x0001E95E, 0x0001E95F}, -}; - -const std::vector> unicode_ranges_symbol = { -{0x00000024, 0x00000024}, {0x0000002B, 0x0000002B}, {0x0000003C, 0x0000003E}, {0x0000005E, 0x0000005E}, -{0x00000060, 0x00000060}, {0x0000007C, 0x0000007C}, {0x0000007E, 0x0000007E}, {0x000000A2, 0x000000A6}, -{0x000000A8, 0x000000A9}, {0x000000AC, 0x000000AC}, {0x000000AE, 0x000000B1}, {0x000000B4, 0x000000B4}, -{0x000000B8, 0x000000B8}, {0x000000D7, 0x000000D7}, {0x000000F7, 0x000000F7}, {0x000002C2, 0x000002C5}, -{0x000002D2, 0x000002DF}, {0x000002E5, 0x000002EB}, {0x000002ED, 0x000002ED}, {0x000002EF, 0x000002FF}, -{0x00000375, 0x00000375}, {0x00000384, 0x00000385}, {0x000003F6, 0x000003F6}, {0x00000482, 0x00000482}, -{0x0000058D, 0x0000058F}, {0x00000606, 0x00000608}, {0x0000060B, 0x0000060B}, {0x0000060E, 0x0000060F}, -{0x000006DE, 0x000006DE}, {0x000006E9, 0x000006E9}, {0x000006FD, 0x000006FE}, {0x000007F6, 0x000007F6}, -{0x000007FE, 0x000007FF}, {0x00000888, 0x00000888}, {0x000009F2, 0x000009F3}, {0x000009FA, 0x000009FB}, -{0x00000AF1, 0x00000AF1}, {0x00000B70, 0x00000B70}, {0x00000BF3, 0x00000BFA}, {0x00000C7F, 0x00000C7F}, -{0x00000D4F, 0x00000D4F}, {0x00000D79, 0x00000D79}, {0x00000E3F, 0x00000E3F}, {0x00000F01, 0x00000F03}, -{0x00000F13, 0x00000F13}, {0x00000F15, 0x00000F17}, {0x00000F1A, 0x00000F1F}, {0x00000F34, 0x00000F34}, -{0x00000F36, 0x00000F36}, {0x00000F38, 0x00000F38}, {0x00000FBE, 0x00000FC5}, {0x00000FC7, 0x00000FCC}, -{0x00000FCE, 0x00000FCF}, {0x00000FD5, 0x00000FD8}, {0x0000109E, 0x0000109F}, {0x00001390, 0x00001399}, -{0x0000166D, 0x0000166D}, {0x000017DB, 0x000017DB}, {0x00001940, 0x00001940}, {0x000019DE, 0x000019FF}, -{0x00001B61, 0x00001B6A}, {0x00001B74, 0x00001B7C}, {0x00001FBD, 0x00001FBD}, {0x00001FBF, 0x00001FC1}, -{0x00001FCD, 0x00001FCF}, {0x00001FDD, 0x00001FDF}, {0x00001FED, 0x00001FEF}, {0x00001FFD, 0x00001FFE}, -{0x00002044, 0x00002044}, {0x00002052, 0x00002052}, {0x0000207A, 0x0000207C}, {0x0000208A, 0x0000208C}, -{0x000020A0, 0x000020C0}, {0x00002100, 0x00002101}, {0x00002103, 0x00002106}, {0x00002108, 0x00002109}, -{0x00002114, 0x00002114}, {0x00002116, 0x00002118}, {0x0000211E, 0x00002123}, {0x00002125, 0x00002125}, -{0x00002127, 0x00002127}, {0x00002129, 0x00002129}, {0x0000212E, 0x0000212E}, {0x0000213A, 0x0000213B}, -{0x00002140, 0x00002144}, {0x0000214A, 0x0000214D}, {0x0000214F, 0x0000214F}, {0x0000218A, 0x0000218B}, -{0x00002190, 0x00002307}, {0x0000230C, 0x00002328}, {0x0000232B, 0x00002426}, {0x00002440, 0x0000244A}, -{0x0000249C, 0x000024E9}, {0x00002500, 0x00002767}, {0x00002794, 0x000027C4}, {0x000027C7, 0x000027E5}, -{0x000027F0, 0x00002982}, {0x00002999, 0x000029D7}, {0x000029DC, 0x000029FB}, {0x000029FE, 0x00002B73}, -{0x00002B76, 0x00002B95}, {0x00002B97, 0x00002BFF}, {0x00002CE5, 0x00002CEA}, {0x00002E50, 0x00002E51}, -{0x00002E80, 0x00002E99}, {0x00002E9B, 0x00002EF3}, {0x00002F00, 0x00002FD5}, {0x00002FF0, 0x00002FFF}, -{0x00003004, 0x00003004}, {0x00003012, 0x00003013}, {0x00003020, 0x00003020}, {0x00003036, 0x00003037}, -{0x0000303E, 0x0000303F}, {0x0000309B, 0x0000309C}, {0x00003190, 0x00003191}, {0x00003196, 0x0000319F}, -{0x000031C0, 0x000031E3}, {0x000031EF, 0x000031EF}, {0x00003200, 0x0000321E}, {0x0000322A, 0x00003247}, -{0x00003250, 0x00003250}, {0x00003260, 0x0000327F}, {0x0000328A, 0x000032B0}, {0x000032C0, 0x000033FF}, -{0x00004DC0, 0x00004DFF}, {0x0000A490, 0x0000A4C6}, {0x0000A700, 0x0000A716}, {0x0000A720, 0x0000A721}, -{0x0000A789, 0x0000A78A}, {0x0000A828, 0x0000A82B}, {0x0000A836, 0x0000A839}, {0x0000AA77, 0x0000AA79}, -{0x0000AB5B, 0x0000AB5B}, {0x0000AB6A, 0x0000AB6B}, {0x0000FB29, 0x0000FB29}, {0x0000FBB2, 0x0000FBC2}, -{0x0000FD40, 0x0000FD4F}, {0x0000FDCF, 0x0000FDCF}, {0x0000FDFC, 0x0000FDFF}, {0x0000FE62, 0x0000FE62}, -{0x0000FE64, 0x0000FE66}, {0x0000FE69, 0x0000FE69}, {0x0000FF04, 0x0000FF04}, {0x0000FF0B, 0x0000FF0B}, -{0x0000FF1C, 0x0000FF1E}, {0x0000FF3E, 0x0000FF3E}, {0x0000FF40, 0x0000FF40}, {0x0000FF5C, 0x0000FF5C}, -{0x0000FF5E, 0x0000FF5E}, {0x0000FFE0, 0x0000FFE6}, {0x0000FFE8, 0x0000FFEE}, {0x0000FFFC, 0x0000FFFD}, -{0x00010137, 0x0001013F}, {0x00010179, 0x00010189}, {0x0001018C, 0x0001018E}, {0x00010190, 0x0001019C}, -{0x000101A0, 0x000101A0}, {0x000101D0, 0x000101FC}, {0x00010877, 0x00010878}, {0x00010AC8, 0x00010AC8}, -{0x0001173F, 0x0001173F}, {0x00011FD5, 0x00011FF1}, {0x00016B3C, 0x00016B3F}, {0x00016B45, 0x00016B45}, -{0x0001BC9C, 0x0001BC9C}, {0x0001CF50, 0x0001CFC3}, {0x0001D000, 0x0001D0F5}, {0x0001D100, 0x0001D126}, -{0x0001D129, 0x0001D164}, {0x0001D16A, 0x0001D16C}, {0x0001D183, 0x0001D184}, {0x0001D18C, 0x0001D1A9}, -{0x0001D1AE, 0x0001D1EA}, {0x0001D200, 0x0001D241}, {0x0001D245, 0x0001D245}, {0x0001D300, 0x0001D356}, -{0x0001D6C1, 0x0001D6C1}, {0x0001D6DB, 0x0001D6DB}, {0x0001D6FB, 0x0001D6FB}, {0x0001D715, 0x0001D715}, -{0x0001D735, 0x0001D735}, {0x0001D74F, 0x0001D74F}, {0x0001D76F, 0x0001D76F}, {0x0001D789, 0x0001D789}, -{0x0001D7A9, 0x0001D7A9}, {0x0001D7C3, 0x0001D7C3}, {0x0001D800, 0x0001D9FF}, {0x0001DA37, 0x0001DA3A}, -{0x0001DA6D, 0x0001DA74}, {0x0001DA76, 0x0001DA83}, {0x0001DA85, 0x0001DA86}, {0x0001E14F, 0x0001E14F}, -{0x0001E2FF, 0x0001E2FF}, {0x0001ECAC, 0x0001ECAC}, {0x0001ECB0, 0x0001ECB0}, {0x0001ED2E, 0x0001ED2E}, -{0x0001EEF0, 0x0001EEF1}, {0x0001F000, 0x0001F02B}, {0x0001F030, 0x0001F093}, {0x0001F0A0, 0x0001F0AE}, -{0x0001F0B1, 0x0001F0BF}, {0x0001F0C1, 0x0001F0CF}, {0x0001F0D1, 0x0001F0F5}, {0x0001F10D, 0x0001F1AD}, -{0x0001F1E6, 0x0001F202}, {0x0001F210, 0x0001F23B}, {0x0001F240, 0x0001F248}, {0x0001F250, 0x0001F251}, -{0x0001F260, 0x0001F265}, {0x0001F300, 0x0001F6D7}, {0x0001F6DC, 0x0001F6EC}, {0x0001F6F0, 0x0001F6FC}, -{0x0001F700, 0x0001F776}, {0x0001F77B, 0x0001F7D9}, {0x0001F7E0, 0x0001F7EB}, {0x0001F7F0, 0x0001F7F0}, -{0x0001F800, 0x0001F80B}, {0x0001F810, 0x0001F847}, {0x0001F850, 0x0001F859}, {0x0001F860, 0x0001F887}, -{0x0001F890, 0x0001F8AD}, {0x0001F8B0, 0x0001F8B1}, {0x0001F900, 0x0001FA53}, {0x0001FA60, 0x0001FA6D}, -{0x0001FA70, 0x0001FA7C}, {0x0001FA80, 0x0001FA88}, {0x0001FA90, 0x0001FABD}, {0x0001FABF, 0x0001FAC5}, -{0x0001FACE, 0x0001FADB}, {0x0001FAE0, 0x0001FAE8}, {0x0001FAF0, 0x0001FAF8}, {0x0001FB00, 0x0001FB92}, -{0x0001FB94, 0x0001FBCA}, -}; - -const std::vector> unicode_ranges_control = { -{0x00000000, 0x0000001F}, {0x0000007F, 0x0000009F}, {0x000000AD, 0x000000AD}, {0x00000378, 0x00000379}, -{0x00000380, 0x00000383}, {0x0000038B, 0x0000038B}, {0x0000038D, 0x0000038D}, {0x000003A2, 0x000003A2}, -{0x00000530, 0x00000530}, {0x00000557, 0x00000558}, {0x0000058B, 0x0000058C}, {0x00000590, 0x00000590}, -{0x000005C8, 0x000005CF}, {0x000005EB, 0x000005EE}, {0x000005F5, 0x00000605}, {0x0000061C, 0x0000061C}, -{0x000006DD, 0x000006DD}, {0x0000070E, 0x0000070F}, {0x0000074B, 0x0000074C}, {0x000007B2, 0x000007BF}, -{0x000007FB, 0x000007FC}, {0x0000082E, 0x0000082F}, {0x0000083F, 0x0000083F}, {0x0000085C, 0x0000085D}, -{0x0000085F, 0x0000085F}, {0x0000086B, 0x0000086F}, {0x0000088F, 0x00000897}, {0x000008E2, 0x000008E2}, -{0x00000984, 0x00000984}, {0x0000098D, 0x0000098E}, {0x00000991, 0x00000992}, {0x000009A9, 0x000009A9}, -{0x000009B1, 0x000009B1}, {0x000009B3, 0x000009B5}, {0x000009BA, 0x000009BB}, {0x000009C5, 0x000009C6}, -{0x000009C9, 0x000009CA}, {0x000009CF, 0x000009D6}, {0x000009D8, 0x000009DB}, {0x000009DE, 0x000009DE}, -{0x000009E4, 0x000009E5}, {0x000009FF, 0x00000A00}, {0x00000A04, 0x00000A04}, {0x00000A0B, 0x00000A0E}, -{0x00000A11, 0x00000A12}, {0x00000A29, 0x00000A29}, {0x00000A31, 0x00000A31}, {0x00000A34, 0x00000A34}, -{0x00000A37, 0x00000A37}, {0x00000A3A, 0x00000A3B}, {0x00000A3D, 0x00000A3D}, {0x00000A43, 0x00000A46}, -{0x00000A49, 0x00000A4A}, {0x00000A4E, 0x00000A50}, {0x00000A52, 0x00000A58}, {0x00000A5D, 0x00000A5D}, -{0x00000A5F, 0x00000A65}, {0x00000A77, 0x00000A80}, {0x00000A84, 0x00000A84}, {0x00000A8E, 0x00000A8E}, -{0x00000A92, 0x00000A92}, {0x00000AA9, 0x00000AA9}, {0x00000AB1, 0x00000AB1}, {0x00000AB4, 0x00000AB4}, -{0x00000ABA, 0x00000ABB}, {0x00000AC6, 0x00000AC6}, {0x00000ACA, 0x00000ACA}, {0x00000ACE, 0x00000ACF}, -{0x00000AD1, 0x00000ADF}, {0x00000AE4, 0x00000AE5}, {0x00000AF2, 0x00000AF8}, {0x00000B00, 0x00000B00}, -{0x00000B04, 0x00000B04}, {0x00000B0D, 0x00000B0E}, {0x00000B11, 0x00000B12}, {0x00000B29, 0x00000B29}, -{0x00000B31, 0x00000B31}, {0x00000B34, 0x00000B34}, {0x00000B3A, 0x00000B3B}, {0x00000B45, 0x00000B46}, -{0x00000B49, 0x00000B4A}, {0x00000B4E, 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0x0001D2BF}, -{0x0001D2D4, 0x0001D2DF}, {0x0001D2F4, 0x0001D2FF}, {0x0001D357, 0x0001D35F}, {0x0001D379, 0x0001D3FF}, -{0x0001D455, 0x0001D455}, {0x0001D49D, 0x0001D49D}, {0x0001D4A0, 0x0001D4A1}, {0x0001D4A3, 0x0001D4A4}, -{0x0001D4A7, 0x0001D4A8}, {0x0001D4AD, 0x0001D4AD}, {0x0001D4BA, 0x0001D4BA}, {0x0001D4BC, 0x0001D4BC}, -{0x0001D4C4, 0x0001D4C4}, {0x0001D506, 0x0001D506}, {0x0001D50B, 0x0001D50C}, {0x0001D515, 0x0001D515}, -{0x0001D51D, 0x0001D51D}, {0x0001D53A, 0x0001D53A}, {0x0001D53F, 0x0001D53F}, {0x0001D545, 0x0001D545}, -{0x0001D547, 0x0001D549}, {0x0001D551, 0x0001D551}, {0x0001D6A6, 0x0001D6A7}, {0x0001D7CC, 0x0001D7CD}, -{0x0001DA8C, 0x0001DA9A}, {0x0001DAA0, 0x0001DAA0}, {0x0001DAB0, 0x0001DEFF}, {0x0001DF1F, 0x0001DF24}, -{0x0001DF2B, 0x0001DFFF}, {0x0001E007, 0x0001E007}, {0x0001E019, 0x0001E01A}, {0x0001E022, 0x0001E022}, -{0x0001E025, 0x0001E025}, {0x0001E02B, 0x0001E02F}, {0x0001E06E, 0x0001E08E}, {0x0001E090, 0x0001E0FF}, -{0x0001E12D, 0x0001E12F}, {0x0001E13E, 0x0001E13F}, {0x0001E14A, 0x0001E14D}, {0x0001E150, 0x0001E28F}, -{0x0001E2AF, 0x0001E2BF}, {0x0001E2FA, 0x0001E2FE}, {0x0001E300, 0x0001E4CF}, {0x0001E4FA, 0x0001E7DF}, -{0x0001E7E7, 0x0001E7E7}, {0x0001E7EC, 0x0001E7EC}, {0x0001E7EF, 0x0001E7EF}, {0x0001E7FF, 0x0001E7FF}, -{0x0001E8C5, 0x0001E8C6}, {0x0001E8D7, 0x0001E8FF}, {0x0001E94C, 0x0001E94F}, {0x0001E95A, 0x0001E95D}, -{0x0001E960, 0x0001EC70}, {0x0001ECB5, 0x0001ED00}, {0x0001ED3E, 0x0001EDFF}, {0x0001EE04, 0x0001EE04}, -{0x0001EE20, 0x0001EE20}, {0x0001EE23, 0x0001EE23}, {0x0001EE25, 0x0001EE26}, {0x0001EE28, 0x0001EE28}, -{0x0001EE33, 0x0001EE33}, {0x0001EE38, 0x0001EE38}, {0x0001EE3A, 0x0001EE3A}, {0x0001EE3C, 0x0001EE41}, -{0x0001EE43, 0x0001EE46}, {0x0001EE48, 0x0001EE48}, {0x0001EE4A, 0x0001EE4A}, {0x0001EE4C, 0x0001EE4C}, -{0x0001EE50, 0x0001EE50}, {0x0001EE53, 0x0001EE53}, {0x0001EE55, 0x0001EE56}, {0x0001EE58, 0x0001EE58}, -{0x0001EE5A, 0x0001EE5A}, {0x0001EE5C, 0x0001EE5C}, {0x0001EE5E, 0x0001EE5E}, {0x0001EE60, 0x0001EE60}, -{0x0001EE63, 0x0001EE63}, {0x0001EE65, 0x0001EE66}, {0x0001EE6B, 0x0001EE6B}, {0x0001EE73, 0x0001EE73}, -{0x0001EE78, 0x0001EE78}, {0x0001EE7D, 0x0001EE7D}, {0x0001EE7F, 0x0001EE7F}, {0x0001EE8A, 0x0001EE8A}, -{0x0001EE9C, 0x0001EEA0}, {0x0001EEA4, 0x0001EEA4}, {0x0001EEAA, 0x0001EEAA}, {0x0001EEBC, 0x0001EEEF}, -{0x0001EEF2, 0x0001EFFF}, {0x0001F02C, 0x0001F02F}, {0x0001F094, 0x0001F09F}, {0x0001F0AF, 0x0001F0B0}, -{0x0001F0C0, 0x0001F0C0}, {0x0001F0D0, 0x0001F0D0}, {0x0001F0F6, 0x0001F0FF}, {0x0001F1AE, 0x0001F1E5}, -{0x0001F203, 0x0001F20F}, {0x0001F23C, 0x0001F23F}, {0x0001F249, 0x0001F24F}, {0x0001F252, 0x0001F25F}, -{0x0001F266, 0x0001F2FF}, {0x0001F6D8, 0x0001F6DB}, {0x0001F6ED, 0x0001F6EF}, {0x0001F6FD, 0x0001F6FF}, -{0x0001F777, 0x0001F77A}, {0x0001F7DA, 0x0001F7DF}, {0x0001F7EC, 0x0001F7EF}, {0x0001F7F1, 0x0001F7FF}, -{0x0001F80C, 0x0001F80F}, {0x0001F848, 0x0001F84F}, {0x0001F85A, 0x0001F85F}, {0x0001F888, 0x0001F88F}, -{0x0001F8AE, 0x0001F8AF}, {0x0001F8B2, 0x0001F8FF}, {0x0001FA54, 0x0001FA5F}, {0x0001FA6E, 0x0001FA6F}, -{0x0001FA7D, 0x0001FA7F}, {0x0001FA89, 0x0001FA8F}, {0x0001FABE, 0x0001FABE}, {0x0001FAC6, 0x0001FACD}, -{0x0001FADC, 0x0001FADF}, {0x0001FAE9, 0x0001FAEF}, {0x0001FAF9, 0x0001FAFF}, {0x0001FB93, 0x0001FB93}, -{0x0001FBCB, 0x0001FBEF}, {0x0001FBFA, 0x0001FFFF}, {0x0002A6E0, 0x0002A6FF}, {0x0002B73A, 0x0002B73F}, -{0x0002B81E, 0x0002B81F}, {0x0002CEA2, 0x0002CEAF}, {0x0002EBE1, 0x0002EBEF}, {0x0002EE5E, 0x0002F7FF}, -{0x0002FA1E, 0x0002FFFF}, {0x0003134B, 0x0003134F}, {0x000323B0, 0x000E00FF}, {0x000E01F0, 0x0010FFFF}, -}; - -const std::map unicode_map_lowercase = { -{0x00000041, 0x00000061}, {0x00000042, 0x00000062}, {0x00000043, 0x00000063}, {0x00000044, 0x00000064}, -{0x00000045, 0x00000065}, {0x00000046, 0x00000066}, {0x00000047, 0x00000067}, {0x00000048, 0x00000068}, -{0x00000049, 0x00000069}, {0x0000004A, 0x0000006A}, {0x0000004B, 0x0000006B}, {0x0000004C, 0x0000006C}, -{0x0000004D, 0x0000006D}, {0x0000004E, 0x0000006E}, {0x0000004F, 0x0000006F}, {0x00000050, 0x00000070}, -{0x00000051, 0x00000071}, {0x00000052, 0x00000072}, {0x00000053, 0x00000073}, {0x00000054, 0x00000074}, -{0x00000055, 0x00000075}, {0x00000056, 0x00000076}, {0x00000057, 0x00000077}, {0x00000058, 0x00000078}, -{0x00000059, 0x00000079}, {0x0000005A, 0x0000007A}, {0x000000C0, 0x000000E0}, {0x000000C1, 0x000000E1}, -{0x000000C2, 0x000000E2}, {0x000000C3, 0x000000E3}, {0x000000C4, 0x000000E4}, {0x000000C5, 0x000000E5}, -{0x000000C6, 0x000000E6}, {0x000000C7, 0x000000E7}, {0x000000C8, 0x000000E8}, {0x000000C9, 0x000000E9}, -{0x000000CA, 0x000000EA}, {0x000000CB, 0x000000EB}, {0x000000CC, 0x000000EC}, {0x000000CD, 0x000000ED}, -{0x000000CE, 0x000000EE}, {0x000000CF, 0x000000EF}, {0x000000D0, 0x000000F0}, {0x000000D1, 0x000000F1}, -{0x000000D2, 0x000000F2}, {0x000000D3, 0x000000F3}, {0x000000D4, 0x000000F4}, {0x000000D5, 0x000000F5}, -{0x000000D6, 0x000000F6}, {0x000000D8, 0x000000F8}, {0x000000D9, 0x000000F9}, {0x000000DA, 0x000000FA}, -{0x000000DB, 0x000000FB}, {0x000000DC, 0x000000FC}, {0x000000DD, 0x000000FD}, {0x000000DE, 0x000000FE}, -{0x00000100, 0x00000101}, {0x00000102, 0x00000103}, {0x00000104, 0x00000105}, {0x00000106, 0x00000107}, -{0x00000108, 0x00000109}, {0x0000010A, 0x0000010B}, {0x0000010C, 0x0000010D}, {0x0000010E, 0x0000010F}, -{0x00000110, 0x00000111}, {0x00000112, 0x00000113}, {0x00000114, 0x00000115}, {0x00000116, 0x00000117}, -{0x00000118, 0x00000119}, {0x0000011A, 0x0000011B}, {0x0000011C, 0x0000011D}, {0x0000011E, 0x0000011F}, -{0x00000120, 0x00000121}, {0x00000122, 0x00000123}, {0x00000124, 0x00000125}, {0x00000126, 0x00000127}, -{0x00000128, 0x00000129}, {0x0000012A, 0x0000012B}, {0x0000012C, 0x0000012D}, {0x0000012E, 0x0000012F}, -{0x00000130, 0x00000069}, {0x00000132, 0x00000133}, {0x00000134, 0x00000135}, {0x00000136, 0x00000137}, -{0x00000139, 0x0000013A}, {0x0000013B, 0x0000013C}, {0x0000013D, 0x0000013E}, {0x0000013F, 0x00000140}, -{0x00000141, 0x00000142}, {0x00000143, 0x00000144}, {0x00000145, 0x00000146}, {0x00000147, 0x00000148}, -{0x0000014A, 0x0000014B}, {0x0000014C, 0x0000014D}, {0x0000014E, 0x0000014F}, {0x00000150, 0x00000151}, -{0x00000152, 0x00000153}, {0x00000154, 0x00000155}, {0x00000156, 0x00000157}, {0x00000158, 0x00000159}, -{0x0000015A, 0x0000015B}, {0x0000015C, 0x0000015D}, {0x0000015E, 0x0000015F}, {0x00000160, 0x00000161}, -{0x00000162, 0x00000163}, {0x00000164, 0x00000165}, {0x00000166, 0x00000167}, {0x00000168, 0x00000169}, -{0x0000016A, 0x0000016B}, {0x0000016C, 0x0000016D}, {0x0000016E, 0x0000016F}, {0x00000170, 0x00000171}, -{0x00000172, 0x00000173}, {0x00000174, 0x00000175}, {0x00000176, 0x00000177}, {0x00000178, 0x000000FF}, -{0x00000179, 0x0000017A}, {0x0000017B, 0x0000017C}, {0x0000017D, 0x0000017E}, {0x00000181, 0x00000253}, -{0x00000182, 0x00000183}, {0x00000184, 0x00000185}, {0x00000186, 0x00000254}, {0x00000187, 0x00000188}, -{0x00000189, 0x00000256}, {0x0000018A, 0x00000257}, {0x0000018B, 0x0000018C}, {0x0000018E, 0x000001DD}, -{0x0000018F, 0x00000259}, {0x00000190, 0x0000025B}, {0x00000191, 0x00000192}, {0x00000193, 0x00000260}, -{0x00000194, 0x00000263}, {0x00000196, 0x00000269}, {0x00000197, 0x00000268}, {0x00000198, 0x00000199}, -{0x0000019C, 0x0000026F}, {0x0000019D, 0x00000272}, {0x0000019F, 0x00000275}, {0x000001A0, 0x000001A1}, -{0x000001A2, 0x000001A3}, {0x000001A4, 0x000001A5}, {0x000001A6, 0x00000280}, {0x000001A7, 0x000001A8}, -{0x000001A9, 0x00000283}, {0x000001AC, 0x000001AD}, {0x000001AE, 0x00000288}, {0x000001AF, 0x000001B0}, -{0x000001B1, 0x0000028A}, {0x000001B2, 0x0000028B}, {0x000001B3, 0x000001B4}, {0x000001B5, 0x000001B6}, -{0x000001B7, 0x00000292}, {0x000001B8, 0x000001B9}, {0x000001BC, 0x000001BD}, {0x000001C4, 0x000001C6}, -{0x000001C5, 0x000001C6}, {0x000001C7, 0x000001C9}, {0x000001C8, 0x000001C9}, {0x000001CA, 0x000001CC}, -{0x000001CB, 0x000001CC}, {0x000001CD, 0x000001CE}, {0x000001CF, 0x000001D0}, {0x000001D1, 0x000001D2}, -{0x000001D3, 0x000001D4}, {0x000001D5, 0x000001D6}, {0x000001D7, 0x000001D8}, {0x000001D9, 0x000001DA}, -{0x000001DB, 0x000001DC}, {0x000001DE, 0x000001DF}, {0x000001E0, 0x000001E1}, {0x000001E2, 0x000001E3}, -{0x000001E4, 0x000001E5}, {0x000001E6, 0x000001E7}, {0x000001E8, 0x000001E9}, {0x000001EA, 0x000001EB}, -{0x000001EC, 0x000001ED}, {0x000001EE, 0x000001EF}, {0x000001F1, 0x000001F3}, {0x000001F2, 0x000001F3}, -{0x000001F4, 0x000001F5}, {0x000001F6, 0x00000195}, {0x000001F7, 0x000001BF}, {0x000001F8, 0x000001F9}, -{0x000001FA, 0x000001FB}, {0x000001FC, 0x000001FD}, {0x000001FE, 0x000001FF}, {0x00000200, 0x00000201}, -{0x00000202, 0x00000203}, {0x00000204, 0x00000205}, {0x00000206, 0x00000207}, {0x00000208, 0x00000209}, -{0x0000020A, 0x0000020B}, {0x0000020C, 0x0000020D}, {0x0000020E, 0x0000020F}, {0x00000210, 0x00000211}, -{0x00000212, 0x00000213}, {0x00000214, 0x00000215}, {0x00000216, 0x00000217}, {0x00000218, 0x00000219}, -{0x0000021A, 0x0000021B}, {0x0000021C, 0x0000021D}, {0x0000021E, 0x0000021F}, {0x00000220, 0x0000019E}, -{0x00000222, 0x00000223}, {0x00000224, 0x00000225}, {0x00000226, 0x00000227}, {0x00000228, 0x00000229}, -{0x0000022A, 0x0000022B}, {0x0000022C, 0x0000022D}, {0x0000022E, 0x0000022F}, {0x00000230, 0x00000231}, -{0x00000232, 0x00000233}, {0x0000023A, 0x00002C65}, {0x0000023B, 0x0000023C}, {0x0000023D, 0x0000019A}, -{0x0000023E, 0x00002C66}, {0x00000241, 0x00000242}, {0x00000243, 0x00000180}, {0x00000244, 0x00000289}, -{0x00000245, 0x0000028C}, {0x00000246, 0x00000247}, {0x00000248, 0x00000249}, {0x0000024A, 0x0000024B}, -{0x0000024C, 0x0000024D}, {0x0000024E, 0x0000024F}, {0x00000370, 0x00000371}, {0x00000372, 0x00000373}, -{0x00000376, 0x00000377}, {0x0000037F, 0x000003F3}, {0x00000386, 0x000003AC}, {0x00000388, 0x000003AD}, -{0x00000389, 0x000003AE}, {0x0000038A, 0x000003AF}, {0x0000038C, 0x000003CC}, {0x0000038E, 0x000003CD}, -{0x0000038F, 0x000003CE}, {0x00000391, 0x000003B1}, {0x00000392, 0x000003B2}, {0x00000393, 0x000003B3}, -{0x00000394, 0x000003B4}, {0x00000395, 0x000003B5}, {0x00000396, 0x000003B6}, {0x00000397, 0x000003B7}, -{0x00000398, 0x000003B8}, {0x00000399, 0x000003B9}, {0x0000039A, 0x000003BA}, {0x0000039B, 0x000003BB}, -{0x0000039C, 0x000003BC}, {0x0000039D, 0x000003BD}, {0x0000039E, 0x000003BE}, {0x0000039F, 0x000003BF}, -{0x000003A0, 0x000003C0}, {0x000003A1, 0x000003C1}, {0x000003A3, 0x000003C3}, {0x000003A4, 0x000003C4}, -{0x000003A5, 0x000003C5}, {0x000003A6, 0x000003C6}, {0x000003A7, 0x000003C7}, {0x000003A8, 0x000003C8}, -{0x000003A9, 0x000003C9}, {0x000003AA, 0x000003CA}, {0x000003AB, 0x000003CB}, {0x000003CF, 0x000003D7}, -{0x000003D8, 0x000003D9}, {0x000003DA, 0x000003DB}, {0x000003DC, 0x000003DD}, {0x000003DE, 0x000003DF}, -{0x000003E0, 0x000003E1}, {0x000003E2, 0x000003E3}, {0x000003E4, 0x000003E5}, {0x000003E6, 0x000003E7}, -{0x000003E8, 0x000003E9}, {0x000003EA, 0x000003EB}, {0x000003EC, 0x000003ED}, {0x000003EE, 0x000003EF}, -{0x000003F4, 0x000003B8}, {0x000003F7, 0x000003F8}, {0x000003F9, 0x000003F2}, {0x000003FA, 0x000003FB}, -{0x000003FD, 0x0000037B}, {0x000003FE, 0x0000037C}, {0x000003FF, 0x0000037D}, {0x00000400, 0x00000450}, -{0x00000401, 0x00000451}, {0x00000402, 0x00000452}, {0x00000403, 0x00000453}, {0x00000404, 0x00000454}, -{0x00000405, 0x00000455}, {0x00000406, 0x00000456}, {0x00000407, 0x00000457}, {0x00000408, 0x00000458}, -{0x00000409, 0x00000459}, {0x0000040A, 0x0000045A}, {0x0000040B, 0x0000045B}, {0x0000040C, 0x0000045C}, -{0x0000040D, 0x0000045D}, {0x0000040E, 0x0000045E}, {0x0000040F, 0x0000045F}, {0x00000410, 0x00000430}, -{0x00000411, 0x00000431}, {0x00000412, 0x00000432}, {0x00000413, 0x00000433}, {0x00000414, 0x00000434}, -{0x00000415, 0x00000435}, {0x00000416, 0x00000436}, {0x00000417, 0x00000437}, {0x00000418, 0x00000438}, -{0x00000419, 0x00000439}, {0x0000041A, 0x0000043A}, {0x0000041B, 0x0000043B}, {0x0000041C, 0x0000043C}, -{0x0000041D, 0x0000043D}, {0x0000041E, 0x0000043E}, {0x0000041F, 0x0000043F}, {0x00000420, 0x00000440}, -{0x00000421, 0x00000441}, {0x00000422, 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0x00016E70}, -{0x00016E51, 0x00016E71}, {0x00016E52, 0x00016E72}, {0x00016E53, 0x00016E73}, {0x00016E54, 0x00016E74}, -{0x00016E55, 0x00016E75}, {0x00016E56, 0x00016E76}, {0x00016E57, 0x00016E77}, {0x00016E58, 0x00016E78}, -{0x00016E59, 0x00016E79}, {0x00016E5A, 0x00016E7A}, {0x00016E5B, 0x00016E7B}, {0x00016E5C, 0x00016E7C}, -{0x00016E5D, 0x00016E7D}, {0x00016E5E, 0x00016E7E}, {0x00016E5F, 0x00016E7F}, {0x0001E900, 0x0001E922}, -{0x0001E901, 0x0001E923}, {0x0001E902, 0x0001E924}, {0x0001E903, 0x0001E925}, {0x0001E904, 0x0001E926}, -{0x0001E905, 0x0001E927}, {0x0001E906, 0x0001E928}, {0x0001E907, 0x0001E929}, {0x0001E908, 0x0001E92A}, -{0x0001E909, 0x0001E92B}, {0x0001E90A, 0x0001E92C}, {0x0001E90B, 0x0001E92D}, {0x0001E90C, 0x0001E92E}, -{0x0001E90D, 0x0001E92F}, {0x0001E90E, 0x0001E930}, {0x0001E90F, 0x0001E931}, {0x0001E910, 0x0001E932}, -{0x0001E911, 0x0001E933}, {0x0001E912, 0x0001E934}, {0x0001E913, 0x0001E935}, {0x0001E914, 0x0001E936}, -{0x0001E915, 0x0001E937}, {0x0001E916, 0x0001E938}, {0x0001E917, 0x0001E939}, {0x0001E918, 0x0001E93A}, -{0x0001E919, 0x0001E93B}, {0x0001E91A, 0x0001E93C}, {0x0001E91B, 0x0001E93D}, {0x0001E91C, 0x0001E93E}, -{0x0001E91D, 0x0001E93F}, {0x0001E91E, 0x0001E940}, {0x0001E91F, 0x0001E941}, {0x0001E920, 0x0001E942}, -{0x0001E921, 0x0001E943}, -}; - -const std::map unicode_map_uppercase = { -{0x00000061, 0x00000041}, {0x00000062, 0x00000042}, {0x00000063, 0x00000043}, {0x00000064, 0x00000044}, -{0x00000065, 0x00000045}, {0x00000066, 0x00000046}, {0x00000067, 0x00000047}, {0x00000068, 0x00000048}, -{0x00000069, 0x00000049}, {0x0000006A, 0x0000004A}, {0x0000006B, 0x0000004B}, {0x0000006C, 0x0000004C}, -{0x0000006D, 0x0000004D}, {0x0000006E, 0x0000004E}, {0x0000006F, 0x0000004F}, {0x00000070, 0x00000050}, -{0x00000071, 0x00000051}, {0x00000072, 0x00000052}, {0x00000073, 0x00000053}, {0x00000074, 0x00000054}, -{0x00000075, 0x00000055}, {0x00000076, 0x00000056}, {0x00000077, 0x00000057}, {0x00000078, 0x00000058}, -{0x00000079, 0x00000059}, {0x0000007A, 0x0000005A}, {0x000000B5, 0x0000039C}, {0x000000DF, 0x00000053}, -{0x000000E0, 0x000000C0}, {0x000000E1, 0x000000C1}, {0x000000E2, 0x000000C2}, {0x000000E3, 0x000000C3}, -{0x000000E4, 0x000000C4}, {0x000000E5, 0x000000C5}, {0x000000E6, 0x000000C6}, {0x000000E7, 0x000000C7}, -{0x000000E8, 0x000000C8}, {0x000000E9, 0x000000C9}, {0x000000EA, 0x000000CA}, {0x000000EB, 0x000000CB}, -{0x000000EC, 0x000000CC}, {0x000000ED, 0x000000CD}, {0x000000EE, 0x000000CE}, {0x000000EF, 0x000000CF}, -{0x000000F0, 0x000000D0}, {0x000000F1, 0x000000D1}, {0x000000F2, 0x000000D2}, {0x000000F3, 0x000000D3}, -{0x000000F4, 0x000000D4}, {0x000000F5, 0x000000D5}, {0x000000F6, 0x000000D6}, {0x000000F8, 0x000000D8}, -{0x000000F9, 0x000000D9}, {0x000000FA, 0x000000DA}, {0x000000FB, 0x000000DB}, {0x000000FC, 0x000000DC}, -{0x000000FD, 0x000000DD}, {0x000000FE, 0x000000DE}, {0x000000FF, 0x00000178}, {0x00000101, 0x00000100}, -{0x00000103, 0x00000102}, {0x00000105, 0x00000104}, {0x00000107, 0x00000106}, {0x00000109, 0x00000108}, -{0x0000010B, 0x0000010A}, {0x0000010D, 0x0000010C}, {0x0000010F, 0x0000010E}, {0x00000111, 0x00000110}, -{0x00000113, 0x00000112}, {0x00000115, 0x00000114}, {0x00000117, 0x00000116}, {0x00000119, 0x00000118}, -{0x0000011B, 0x0000011A}, {0x0000011D, 0x0000011C}, {0x0000011F, 0x0000011E}, {0x00000121, 0x00000120}, -{0x00000123, 0x00000122}, {0x00000125, 0x00000124}, {0x00000127, 0x00000126}, {0x00000129, 0x00000128}, -{0x0000012B, 0x0000012A}, {0x0000012D, 0x0000012C}, {0x0000012F, 0x0000012E}, {0x00000131, 0x00000049}, -{0x00000133, 0x00000132}, {0x00000135, 0x00000134}, {0x00000137, 0x00000136}, {0x0000013A, 0x00000139}, -{0x0000013C, 0x0000013B}, {0x0000013E, 0x0000013D}, {0x00000140, 0x0000013F}, {0x00000142, 0x00000141}, -{0x00000144, 0x00000143}, {0x00000146, 0x00000145}, {0x00000148, 0x00000147}, {0x00000149, 0x000002BC}, -{0x0000014B, 0x0000014A}, {0x0000014D, 0x0000014C}, {0x0000014F, 0x0000014E}, {0x00000151, 0x00000150}, -{0x00000153, 0x00000152}, {0x00000155, 0x00000154}, {0x00000157, 0x00000156}, {0x00000159, 0x00000158}, -{0x0000015B, 0x0000015A}, {0x0000015D, 0x0000015C}, {0x0000015F, 0x0000015E}, {0x00000161, 0x00000160}, -{0x00000163, 0x00000162}, {0x00000165, 0x00000164}, {0x00000167, 0x00000166}, {0x00000169, 0x00000168}, -{0x0000016B, 0x0000016A}, {0x0000016D, 0x0000016C}, {0x0000016F, 0x0000016E}, {0x00000171, 0x00000170}, -{0x00000173, 0x00000172}, {0x00000175, 0x00000174}, {0x00000177, 0x00000176}, {0x0000017A, 0x00000179}, -{0x0000017C, 0x0000017B}, {0x0000017E, 0x0000017D}, {0x0000017F, 0x00000053}, {0x00000180, 0x00000243}, -{0x00000183, 0x00000182}, {0x00000185, 0x00000184}, {0x00000188, 0x00000187}, {0x0000018C, 0x0000018B}, -{0x00000192, 0x00000191}, {0x00000195, 0x000001F6}, {0x00000199, 0x00000198}, {0x0000019A, 0x0000023D}, -{0x0000019E, 0x00000220}, {0x000001A1, 0x000001A0}, {0x000001A3, 0x000001A2}, {0x000001A5, 0x000001A4}, -{0x000001A8, 0x000001A7}, {0x000001AD, 0x000001AC}, {0x000001B0, 0x000001AF}, {0x000001B4, 0x000001B3}, -{0x000001B6, 0x000001B5}, {0x000001B9, 0x000001B8}, {0x000001BD, 0x000001BC}, {0x000001BF, 0x000001F7}, -{0x000001C5, 0x000001C4}, {0x000001C6, 0x000001C4}, {0x000001C8, 0x000001C7}, {0x000001C9, 0x000001C7}, -{0x000001CB, 0x000001CA}, {0x000001CC, 0x000001CA}, {0x000001CE, 0x000001CD}, {0x000001D0, 0x000001CF}, -{0x000001D2, 0x000001D1}, {0x000001D4, 0x000001D3}, {0x000001D6, 0x000001D5}, {0x000001D8, 0x000001D7}, -{0x000001DA, 0x000001D9}, {0x000001DC, 0x000001DB}, {0x000001DD, 0x0000018E}, {0x000001DF, 0x000001DE}, -{0x000001E1, 0x000001E0}, {0x000001E3, 0x000001E2}, {0x000001E5, 0x000001E4}, {0x000001E7, 0x000001E6}, -{0x000001E9, 0x000001E8}, {0x000001EB, 0x000001EA}, {0x000001ED, 0x000001EC}, {0x000001EF, 0x000001EE}, -{0x000001F0, 0x0000004A}, {0x000001F2, 0x000001F1}, {0x000001F3, 0x000001F1}, {0x000001F5, 0x000001F4}, -{0x000001F9, 0x000001F8}, {0x000001FB, 0x000001FA}, {0x000001FD, 0x000001FC}, {0x000001FF, 0x000001FE}, -{0x00000201, 0x00000200}, {0x00000203, 0x00000202}, {0x00000205, 0x00000204}, {0x00000207, 0x00000206}, -{0x00000209, 0x00000208}, {0x0000020B, 0x0000020A}, {0x0000020D, 0x0000020C}, {0x0000020F, 0x0000020E}, -{0x00000211, 0x00000210}, {0x00000213, 0x00000212}, {0x00000215, 0x00000214}, {0x00000217, 0x00000216}, -{0x00000219, 0x00000218}, {0x0000021B, 0x0000021A}, {0x0000021D, 0x0000021C}, {0x0000021F, 0x0000021E}, -{0x00000223, 0x00000222}, {0x00000225, 0x00000224}, {0x00000227, 0x00000226}, {0x00000229, 0x00000228}, -{0x0000022B, 0x0000022A}, {0x0000022D, 0x0000022C}, {0x0000022F, 0x0000022E}, {0x00000231, 0x00000230}, -{0x00000233, 0x00000232}, {0x0000023C, 0x0000023B}, {0x0000023F, 0x00002C7E}, {0x00000240, 0x00002C7F}, -{0x00000242, 0x00000241}, {0x00000247, 0x00000246}, {0x00000249, 0x00000248}, {0x0000024B, 0x0000024A}, -{0x0000024D, 0x0000024C}, {0x0000024F, 0x0000024E}, {0x00000250, 0x00002C6F}, {0x00000251, 0x00002C6D}, -{0x00000252, 0x00002C70}, {0x00000253, 0x00000181}, {0x00000254, 0x00000186}, {0x00000256, 0x00000189}, -{0x00000257, 0x0000018A}, {0x00000259, 0x0000018F}, {0x0000025B, 0x00000190}, {0x0000025C, 0x0000A7AB}, -{0x00000260, 0x00000193}, {0x00000261, 0x0000A7AC}, {0x00000263, 0x00000194}, {0x00000265, 0x0000A78D}, -{0x00000266, 0x0000A7AA}, {0x00000268, 0x00000197}, {0x00000269, 0x00000196}, {0x0000026A, 0x0000A7AE}, -{0x0000026B, 0x00002C62}, {0x0000026C, 0x0000A7AD}, {0x0000026F, 0x0000019C}, {0x00000271, 0x00002C6E}, -{0x00000272, 0x0000019D}, {0x00000275, 0x0000019F}, {0x0000027D, 0x00002C64}, {0x00000280, 0x000001A6}, -{0x00000282, 0x0000A7C5}, {0x00000283, 0x000001A9}, {0x00000287, 0x0000A7B1}, {0x00000288, 0x000001AE}, -{0x00000289, 0x00000244}, {0x0000028A, 0x000001B1}, {0x0000028B, 0x000001B2}, {0x0000028C, 0x00000245}, -{0x00000292, 0x000001B7}, {0x0000029D, 0x0000A7B2}, {0x0000029E, 0x0000A7B0}, {0x00000345, 0x00000399}, -{0x00000371, 0x00000370}, {0x00000373, 0x00000372}, {0x00000377, 0x00000376}, {0x0000037B, 0x000003FD}, -{0x0000037C, 0x000003FE}, {0x0000037D, 0x000003FF}, {0x00000390, 0x00000399}, {0x000003AC, 0x00000386}, -{0x000003AD, 0x00000388}, {0x000003AE, 0x00000389}, {0x000003AF, 0x0000038A}, {0x000003B0, 0x000003A5}, -{0x000003B1, 0x00000391}, {0x000003B2, 0x00000392}, {0x000003B3, 0x00000393}, {0x000003B4, 0x00000394}, -{0x000003B5, 0x00000395}, {0x000003B6, 0x00000396}, {0x000003B7, 0x00000397}, {0x000003B8, 0x00000398}, -{0x000003B9, 0x00000399}, {0x000003BA, 0x0000039A}, {0x000003BB, 0x0000039B}, {0x000003BC, 0x0000039C}, -{0x000003BD, 0x0000039D}, {0x000003BE, 0x0000039E}, {0x000003BF, 0x0000039F}, {0x000003C0, 0x000003A0}, -{0x000003C1, 0x000003A1}, {0x000003C2, 0x000003A3}, {0x000003C3, 0x000003A3}, {0x000003C4, 0x000003A4}, -{0x000003C5, 0x000003A5}, {0x000003C6, 0x000003A6}, {0x000003C7, 0x000003A7}, {0x000003C8, 0x000003A8}, -{0x000003C9, 0x000003A9}, {0x000003CA, 0x000003AA}, {0x000003CB, 0x000003AB}, {0x000003CC, 0x0000038C}, -{0x000003CD, 0x0000038E}, {0x000003CE, 0x0000038F}, {0x000003D0, 0x00000392}, {0x000003D1, 0x00000398}, -{0x000003D5, 0x000003A6}, {0x000003D6, 0x000003A0}, {0x000003D7, 0x000003CF}, {0x000003D9, 0x000003D8}, -{0x000003DB, 0x000003DA}, {0x000003DD, 0x000003DC}, {0x000003DF, 0x000003DE}, {0x000003E1, 0x000003E0}, -{0x000003E3, 0x000003E2}, {0x000003E5, 0x000003E4}, {0x000003E7, 0x000003E6}, {0x000003E9, 0x000003E8}, -{0x000003EB, 0x000003EA}, {0x000003ED, 0x000003EC}, {0x000003EF, 0x000003EE}, {0x000003F0, 0x0000039A}, -{0x000003F1, 0x000003A1}, {0x000003F2, 0x000003F9}, {0x000003F3, 0x0000037F}, {0x000003F5, 0x00000395}, -{0x000003F8, 0x000003F7}, {0x000003FB, 0x000003FA}, {0x00000430, 0x00000410}, {0x00000431, 0x00000411}, -{0x00000432, 0x00000412}, {0x00000433, 0x00000413}, {0x00000434, 0x00000414}, {0x00000435, 0x00000415}, -{0x00000436, 0x00000416}, {0x00000437, 0x00000417}, {0x00000438, 0x00000418}, {0x00000439, 0x00000419}, -{0x0000043A, 0x0000041A}, {0x0000043B, 0x0000041B}, {0x0000043C, 0x0000041C}, {0x0000043D, 0x0000041D}, -{0x0000043E, 0x0000041E}, {0x0000043F, 0x0000041F}, {0x00000440, 0x00000420}, {0x00000441, 0x00000421}, -{0x00000442, 0x00000422}, {0x00000443, 0x00000423}, {0x00000444, 0x00000424}, {0x00000445, 0x00000425}, -{0x00000446, 0x00000426}, {0x00000447, 0x00000427}, {0x00000448, 0x00000428}, {0x00000449, 0x00000429}, -{0x0000044A, 0x0000042A}, {0x0000044B, 0x0000042B}, {0x0000044C, 0x0000042C}, {0x0000044D, 0x0000042D}, -{0x0000044E, 0x0000042E}, {0x0000044F, 0x0000042F}, {0x00000450, 0x00000400}, {0x00000451, 0x00000401}, -{0x00000452, 0x00000402}, {0x00000453, 0x00000403}, {0x00000454, 0x00000404}, {0x00000455, 0x00000405}, -{0x00000456, 0x00000406}, {0x00000457, 0x00000407}, {0x00000458, 0x00000408}, {0x00000459, 0x00000409}, -{0x0000045A, 0x0000040A}, {0x0000045B, 0x0000040B}, {0x0000045C, 0x0000040C}, {0x0000045D, 0x0000040D}, -{0x0000045E, 0x0000040E}, {0x0000045F, 0x0000040F}, {0x00000461, 0x00000460}, {0x00000463, 0x00000462}, -{0x00000465, 0x00000464}, {0x00000467, 0x00000466}, {0x00000469, 0x00000468}, {0x0000046B, 0x0000046A}, -{0x0000046D, 0x0000046C}, {0x0000046F, 0x0000046E}, {0x00000471, 0x00000470}, {0x00000473, 0x00000472}, -{0x00000475, 0x00000474}, {0x00000477, 0x00000476}, {0x00000479, 0x00000478}, {0x0000047B, 0x0000047A}, -{0x0000047D, 0x0000047C}, {0x0000047F, 0x0000047E}, {0x00000481, 0x00000480}, {0x0000048B, 0x0000048A}, -{0x0000048D, 0x0000048C}, {0x0000048F, 0x0000048E}, {0x00000491, 0x00000490}, {0x00000493, 0x00000492}, -{0x00000495, 0x00000494}, {0x00000497, 0x00000496}, {0x00000499, 0x00000498}, {0x0000049B, 0x0000049A}, -{0x0000049D, 0x0000049C}, {0x0000049F, 0x0000049E}, {0x000004A1, 0x000004A0}, {0x000004A3, 0x000004A2}, -{0x000004A5, 0x000004A4}, {0x000004A7, 0x000004A6}, {0x000004A9, 0x000004A8}, {0x000004AB, 0x000004AA}, -{0x000004AD, 0x000004AC}, {0x000004AF, 0x000004AE}, {0x000004B1, 0x000004B0}, {0x000004B3, 0x000004B2}, -{0x000004B5, 0x000004B4}, {0x000004B7, 0x000004B6}, {0x000004B9, 0x000004B8}, {0x000004BB, 0x000004BA}, -{0x000004BD, 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0x00016E4B}, {0x00016E6C, 0x00016E4C}, -{0x00016E6D, 0x00016E4D}, {0x00016E6E, 0x00016E4E}, {0x00016E6F, 0x00016E4F}, {0x00016E70, 0x00016E50}, -{0x00016E71, 0x00016E51}, {0x00016E72, 0x00016E52}, {0x00016E73, 0x00016E53}, {0x00016E74, 0x00016E54}, -{0x00016E75, 0x00016E55}, {0x00016E76, 0x00016E56}, {0x00016E77, 0x00016E57}, {0x00016E78, 0x00016E58}, -{0x00016E79, 0x00016E59}, {0x00016E7A, 0x00016E5A}, {0x00016E7B, 0x00016E5B}, {0x00016E7C, 0x00016E5C}, -{0x00016E7D, 0x00016E5D}, {0x00016E7E, 0x00016E5E}, {0x00016E7F, 0x00016E5F}, {0x0001E922, 0x0001E900}, -{0x0001E923, 0x0001E901}, {0x0001E924, 0x0001E902}, {0x0001E925, 0x0001E903}, {0x0001E926, 0x0001E904}, -{0x0001E927, 0x0001E905}, {0x0001E928, 0x0001E906}, {0x0001E929, 0x0001E907}, {0x0001E92A, 0x0001E908}, -{0x0001E92B, 0x0001E909}, {0x0001E92C, 0x0001E90A}, {0x0001E92D, 0x0001E90B}, {0x0001E92E, 0x0001E90C}, -{0x0001E92F, 0x0001E90D}, {0x0001E930, 0x0001E90E}, {0x0001E931, 0x0001E90F}, {0x0001E932, 0x0001E910}, -{0x0001E933, 0x0001E911}, {0x0001E934, 0x0001E912}, {0x0001E935, 0x0001E913}, {0x0001E936, 0x0001E914}, -{0x0001E937, 0x0001E915}, {0x0001E938, 0x0001E916}, {0x0001E939, 0x0001E917}, {0x0001E93A, 0x0001E918}, -{0x0001E93B, 0x0001E919}, {0x0001E93C, 0x0001E91A}, {0x0001E93D, 0x0001E91B}, {0x0001E93E, 0x0001E91C}, -{0x0001E93F, 0x0001E91D}, {0x0001E940, 0x0001E91E}, {0x0001E941, 0x0001E91F}, {0x0001E942, 0x0001E920}, -{0x0001E943, 0x0001E921}, -}; - -const std::multimap unicode_map_nfd = { -{0x000000C0, 0x00000041}, {0x000000C0, 0x00000300}, {0x000000C1, 0x00000041}, {0x000000C1, 0x00000301}, -{0x000000C2, 0x00000041}, {0x000000C2, 0x00000302}, {0x000000C3, 0x00000041}, {0x000000C3, 0x00000303}, -{0x000000C4, 0x00000041}, {0x000000C4, 0x00000308}, {0x000000C5, 0x00000041}, {0x000000C5, 0x0000030A}, -{0x000000C7, 0x00000043}, {0x000000C7, 0x00000327}, {0x000000C8, 0x00000045}, {0x000000C8, 0x00000300}, -{0x000000C9, 0x00000045}, {0x000000C9, 0x00000301}, {0x000000CA, 0x00000045}, {0x000000CA, 0x00000302}, -{0x000000CB, 0x00000045}, {0x000000CB, 0x00000308}, {0x000000CC, 0x00000049}, {0x000000CC, 0x00000300}, -{0x000000CD, 0x00000049}, {0x000000CD, 0x00000301}, {0x000000CE, 0x00000049}, {0x000000CE, 0x00000302}, -{0x000000CF, 0x00000049}, {0x000000CF, 0x00000308}, {0x000000D1, 0x0000004E}, {0x000000D1, 0x00000303}, -{0x000000D2, 0x0000004F}, {0x000000D2, 0x00000300}, {0x000000D3, 0x0000004F}, {0x000000D3, 0x00000301}, -{0x000000D4, 0x0000004F}, {0x000000D4, 0x00000302}, {0x000000D5, 0x0000004F}, {0x000000D5, 0x00000303}, -{0x000000D6, 0x0000004F}, {0x000000D6, 0x00000308}, {0x000000D9, 0x00000055}, {0x000000D9, 0x00000300}, -{0x000000DA, 0x00000055}, {0x000000DA, 0x00000301}, {0x000000DB, 0x00000055}, {0x000000DB, 0x00000302}, -{0x000000DC, 0x00000055}, {0x000000DC, 0x00000308}, {0x000000DD, 0x00000059}, {0x000000DD, 0x00000301}, -{0x000000E0, 0x00000061}, {0x000000E0, 0x00000300}, {0x000000E1, 0x00000061}, {0x000000E1, 0x00000301}, -{0x000000E2, 0x00000061}, {0x000000E2, 0x00000302}, {0x000000E3, 0x00000061}, {0x000000E3, 0x00000303}, -{0x000000E4, 0x00000061}, {0x000000E4, 0x00000308}, {0x000000E5, 0x00000061}, {0x000000E5, 0x0000030A}, -{0x000000E7, 0x00000063}, {0x000000E7, 0x00000327}, {0x000000E8, 0x00000065}, {0x000000E8, 0x00000300}, -{0x000000E9, 0x00000065}, {0x000000E9, 0x00000301}, {0x000000EA, 0x00000065}, {0x000000EA, 0x00000302}, -{0x000000EB, 0x00000065}, {0x000000EB, 0x00000308}, {0x000000EC, 0x00000069}, {0x000000EC, 0x00000300}, -{0x000000ED, 0x00000069}, {0x000000ED, 0x00000301}, {0x000000EE, 0x00000069}, {0x000000EE, 0x00000302}, -{0x000000EF, 0x00000069}, {0x000000EF, 0x00000308}, {0x000000F1, 0x0000006E}, {0x000000F1, 0x00000303}, -{0x000000F2, 0x0000006F}, {0x000000F2, 0x00000300}, {0x000000F3, 0x0000006F}, {0x000000F3, 0x00000301}, -{0x000000F4, 0x0000006F}, {0x000000F4, 0x00000302}, {0x000000F5, 0x0000006F}, {0x000000F5, 0x00000303}, -{0x000000F6, 0x0000006F}, {0x000000F6, 0x00000308}, {0x000000F9, 0x00000075}, {0x000000F9, 0x00000300}, -{0x000000FA, 0x00000075}, {0x000000FA, 0x00000301}, {0x000000FB, 0x00000075}, {0x000000FB, 0x00000302}, -{0x000000FC, 0x00000075}, {0x000000FC, 0x00000308}, {0x000000FD, 0x00000079}, {0x000000FD, 0x00000301}, -{0x000000FF, 0x00000079}, {0x000000FF, 0x00000308}, {0x00000100, 0x00000041}, {0x00000100, 0x00000304}, -{0x00000101, 0x00000061}, {0x00000101, 0x00000304}, {0x00000102, 0x00000041}, {0x00000102, 0x00000306}, -{0x00000103, 0x00000061}, {0x00000103, 0x00000306}, {0x00000104, 0x00000041}, {0x00000104, 0x00000328}, -{0x00000105, 0x00000061}, {0x00000105, 0x00000328}, {0x00000106, 0x00000043}, {0x00000106, 0x00000301}, -{0x00000107, 0x00000063}, {0x00000107, 0x00000301}, {0x00000108, 0x00000043}, {0x00000108, 0x00000302}, -{0x00000109, 0x00000063}, {0x00000109, 0x00000302}, {0x0000010A, 0x00000043}, {0x0000010A, 0x00000307}, -{0x0000010B, 0x00000063}, {0x0000010B, 0x00000307}, {0x0000010C, 0x00000043}, {0x0000010C, 0x0000030C}, -{0x0000010D, 0x00000063}, {0x0000010D, 0x0000030C}, {0x0000010E, 0x00000044}, {0x0000010E, 0x0000030C}, -{0x0000010F, 0x00000064}, {0x0000010F, 0x0000030C}, {0x00000112, 0x00000045}, {0x00000112, 0x00000304}, -{0x00000113, 0x00000065}, {0x00000113, 0x00000304}, {0x00000114, 0x00000045}, {0x00000114, 0x00000306}, -{0x00000115, 0x00000065}, {0x00000115, 0x00000306}, {0x00000116, 0x00000045}, {0x00000116, 0x00000307}, -{0x00000117, 0x00000065}, {0x00000117, 0x00000307}, {0x00000118, 0x00000045}, {0x00000118, 0x00000328}, -{0x00000119, 0x00000065}, {0x00000119, 0x00000328}, {0x0000011A, 0x00000045}, {0x0000011A, 0x0000030C}, -{0x0000011B, 0x00000065}, {0x0000011B, 0x0000030C}, {0x0000011C, 0x00000047}, {0x0000011C, 0x00000302}, -{0x0000011D, 0x00000067}, {0x0000011D, 0x00000302}, {0x0000011E, 0x00000047}, {0x0000011E, 0x00000306}, -{0x0000011F, 0x00000067}, {0x0000011F, 0x00000306}, {0x00000120, 0x00000047}, {0x00000120, 0x00000307}, -{0x00000121, 0x00000067}, {0x00000121, 0x00000307}, {0x00000122, 0x00000047}, {0x00000122, 0x00000327}, -{0x00000123, 0x00000067}, {0x00000123, 0x00000327}, {0x00000124, 0x00000048}, {0x00000124, 0x00000302}, -{0x00000125, 0x00000068}, {0x00000125, 0x00000302}, {0x00000128, 0x00000049}, {0x00000128, 0x00000303}, -{0x00000129, 0x00000069}, {0x00000129, 0x00000303}, {0x0000012A, 0x00000049}, {0x0000012A, 0x00000304}, -{0x0000012B, 0x00000069}, {0x0000012B, 0x00000304}, {0x0000012C, 0x00000049}, {0x0000012C, 0x00000306}, -{0x0000012D, 0x00000069}, {0x0000012D, 0x00000306}, {0x0000012E, 0x00000049}, {0x0000012E, 0x00000328}, -{0x0000012F, 0x00000069}, {0x0000012F, 0x00000328}, {0x00000130, 0x00000049}, {0x00000130, 0x00000307}, -{0x00000134, 0x0000004A}, {0x00000134, 0x00000302}, {0x00000135, 0x0000006A}, {0x00000135, 0x00000302}, -{0x00000136, 0x0000004B}, {0x00000136, 0x00000327}, {0x00000137, 0x0000006B}, {0x00000137, 0x00000327}, -{0x00000139, 0x0000004C}, {0x00000139, 0x00000301}, {0x0000013A, 0x0000006C}, {0x0000013A, 0x00000301}, -{0x0000013B, 0x0000004C}, {0x0000013B, 0x00000327}, {0x0000013C, 0x0000006C}, {0x0000013C, 0x00000327}, -{0x0000013D, 0x0000004C}, {0x0000013D, 0x0000030C}, {0x0000013E, 0x0000006C}, {0x0000013E, 0x0000030C}, -{0x00000143, 0x0000004E}, {0x00000143, 0x00000301}, {0x00000144, 0x0000006E}, {0x00000144, 0x00000301}, -{0x00000145, 0x0000004E}, {0x00000145, 0x00000327}, {0x00000146, 0x0000006E}, {0x00000146, 0x00000327}, -{0x00000147, 0x0000004E}, {0x00000147, 0x0000030C}, {0x00000148, 0x0000006E}, {0x00000148, 0x0000030C}, -{0x0000014C, 0x0000004F}, {0x0000014C, 0x00000304}, {0x0000014D, 0x0000006F}, {0x0000014D, 0x00000304}, -{0x0000014E, 0x0000004F}, {0x0000014E, 0x00000306}, {0x0000014F, 0x0000006F}, {0x0000014F, 0x00000306}, -{0x00000150, 0x0000004F}, {0x00000150, 0x0000030B}, {0x00000151, 0x0000006F}, {0x00000151, 0x0000030B}, -{0x00000154, 0x00000052}, {0x00000154, 0x00000301}, {0x00000155, 0x00000072}, {0x00000155, 0x00000301}, -{0x00000156, 0x00000052}, {0x00000156, 0x00000327}, {0x00000157, 0x00000072}, {0x00000157, 0x00000327}, -{0x00000158, 0x00000052}, {0x00000158, 0x0000030C}, {0x00000159, 0x00000072}, {0x00000159, 0x0000030C}, -{0x0000015A, 0x00000053}, {0x0000015A, 0x00000301}, {0x0000015B, 0x00000073}, {0x0000015B, 0x00000301}, -{0x0000015C, 0x00000053}, {0x0000015C, 0x00000302}, {0x0000015D, 0x00000073}, {0x0000015D, 0x00000302}, -{0x0000015E, 0x00000053}, {0x0000015E, 0x00000327}, {0x0000015F, 0x00000073}, {0x0000015F, 0x00000327}, -{0x00000160, 0x00000053}, {0x00000160, 0x0000030C}, {0x00000161, 0x00000073}, {0x00000161, 0x0000030C}, -{0x00000162, 0x00000054}, {0x00000162, 0x00000327}, {0x00000163, 0x00000074}, {0x00000163, 0x00000327}, -{0x00000164, 0x00000054}, {0x00000164, 0x0000030C}, {0x00000165, 0x00000074}, {0x00000165, 0x0000030C}, -{0x00000168, 0x00000055}, {0x00000168, 0x00000303}, {0x00000169, 0x00000075}, {0x00000169, 0x00000303}, -{0x0000016A, 0x00000055}, {0x0000016A, 0x00000304}, {0x0000016B, 0x00000075}, {0x0000016B, 0x00000304}, -{0x0000016C, 0x00000055}, {0x0000016C, 0x00000306}, {0x0000016D, 0x00000075}, {0x0000016D, 0x00000306}, -{0x0000016E, 0x00000055}, {0x0000016E, 0x0000030A}, {0x0000016F, 0x00000075}, {0x0000016F, 0x0000030A}, -{0x00000170, 0x00000055}, {0x00000170, 0x0000030B}, {0x00000171, 0x00000075}, {0x00000171, 0x0000030B}, -{0x00000172, 0x00000055}, {0x00000172, 0x00000328}, {0x00000173, 0x00000075}, {0x00000173, 0x00000328}, -{0x00000174, 0x00000057}, {0x00000174, 0x00000302}, {0x00000175, 0x00000077}, {0x00000175, 0x00000302}, -{0x00000176, 0x00000059}, {0x00000176, 0x00000302}, {0x00000177, 0x00000079}, {0x00000177, 0x00000302}, -{0x00000178, 0x00000059}, {0x00000178, 0x00000308}, {0x00000179, 0x0000005A}, {0x00000179, 0x00000301}, -{0x0000017A, 0x0000007A}, {0x0000017A, 0x00000301}, {0x0000017B, 0x0000005A}, {0x0000017B, 0x00000307}, -{0x0000017C, 0x0000007A}, {0x0000017C, 0x00000307}, {0x0000017D, 0x0000005A}, {0x0000017D, 0x0000030C}, -{0x0000017E, 0x0000007A}, {0x0000017E, 0x0000030C}, {0x000001A0, 0x0000004F}, {0x000001A0, 0x0000031B}, -{0x000001A1, 0x0000006F}, {0x000001A1, 0x0000031B}, {0x000001AF, 0x00000055}, {0x000001AF, 0x0000031B}, -{0x000001B0, 0x00000075}, {0x000001B0, 0x0000031B}, {0x000001CD, 0x00000041}, {0x000001CD, 0x0000030C}, -{0x000001CE, 0x00000061}, {0x000001CE, 0x0000030C}, {0x000001CF, 0x00000049}, {0x000001CF, 0x0000030C}, -{0x000001D0, 0x00000069}, {0x000001D0, 0x0000030C}, {0x000001D1, 0x0000004F}, {0x000001D1, 0x0000030C}, -{0x000001D2, 0x0000006F}, {0x000001D2, 0x0000030C}, {0x000001D3, 0x00000055}, {0x000001D3, 0x0000030C}, -{0x000001D4, 0x00000075}, {0x000001D4, 0x0000030C}, {0x000001D5, 0x00000055}, {0x000001D5, 0x00000308}, -{0x000001D5, 0x00000304}, {0x000001D6, 0x00000075}, {0x000001D6, 0x00000308}, {0x000001D6, 0x00000304}, -{0x000001D7, 0x00000055}, {0x000001D7, 0x00000308}, {0x000001D7, 0x00000301}, {0x000001D8, 0x00000075}, -{0x000001D8, 0x00000308}, {0x000001D8, 0x00000301}, {0x000001D9, 0x00000055}, {0x000001D9, 0x00000308}, -{0x000001D9, 0x0000030C}, {0x000001DA, 0x00000075}, {0x000001DA, 0x00000308}, {0x000001DA, 0x0000030C}, -{0x000001DB, 0x00000055}, {0x000001DB, 0x00000308}, {0x000001DB, 0x00000300}, {0x000001DC, 0x00000075}, -{0x000001DC, 0x00000308}, {0x000001DC, 0x00000300}, {0x000001DE, 0x00000041}, {0x000001DE, 0x00000308}, -{0x000001DE, 0x00000304}, {0x000001DF, 0x00000061}, {0x000001DF, 0x00000308}, {0x000001DF, 0x00000304}, -{0x000001E0, 0x00000041}, {0x000001E0, 0x00000307}, {0x000001E0, 0x00000304}, {0x000001E1, 0x00000061}, -{0x000001E1, 0x00000307}, {0x000001E1, 0x00000304}, {0x000001E2, 0x000000C6}, {0x000001E2, 0x00000304}, -{0x000001E3, 0x000000E6}, {0x000001E3, 0x00000304}, {0x000001E6, 0x00000047}, {0x000001E6, 0x0000030C}, -{0x000001E7, 0x00000067}, {0x000001E7, 0x0000030C}, {0x000001E8, 0x0000004B}, {0x000001E8, 0x0000030C}, -{0x000001E9, 0x0000006B}, {0x000001E9, 0x0000030C}, {0x000001EA, 0x0000004F}, {0x000001EA, 0x00000328}, -{0x000001EB, 0x0000006F}, {0x000001EB, 0x00000328}, {0x000001EC, 0x0000004F}, {0x000001EC, 0x00000328}, -{0x000001EC, 0x00000304}, {0x000001ED, 0x0000006F}, {0x000001ED, 0x00000328}, {0x000001ED, 0x00000304}, -{0x000001EE, 0x000001B7}, {0x000001EE, 0x0000030C}, {0x000001EF, 0x00000292}, {0x000001EF, 0x0000030C}, -{0x000001F0, 0x0000006A}, {0x000001F0, 0x0000030C}, {0x000001F4, 0x00000047}, {0x000001F4, 0x00000301}, -{0x000001F5, 0x00000067}, {0x000001F5, 0x00000301}, {0x000001F8, 0x0000004E}, {0x000001F8, 0x00000300}, -{0x000001F9, 0x0000006E}, {0x000001F9, 0x00000300}, {0x000001FA, 0x00000041}, {0x000001FA, 0x0000030A}, -{0x000001FA, 0x00000301}, {0x000001FB, 0x00000061}, {0x000001FB, 0x0000030A}, {0x000001FB, 0x00000301}, -{0x000001FC, 0x000000C6}, {0x000001FC, 0x00000301}, {0x000001FD, 0x000000E6}, {0x000001FD, 0x00000301}, -{0x000001FE, 0x000000D8}, {0x000001FE, 0x00000301}, {0x000001FF, 0x000000F8}, {0x000001FF, 0x00000301}, -{0x00000200, 0x00000041}, {0x00000200, 0x0000030F}, {0x00000201, 0x00000061}, {0x00000201, 0x0000030F}, -{0x00000202, 0x00000041}, {0x00000202, 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0x00009F16}, {0x0002FA1D, 0x0002A600}, -}; diff --git a/unicode-data.h b/unicode-data.h index 3cccf2068..e27fe1770 100644 --- a/unicode-data.h +++ b/unicode-data.h @@ -1,17 +1,20 @@ #pragma once #include -#include -#include #include +#include +#include -extern const std::vector> unicode_ranges_number; -extern const std::vector> unicode_ranges_letter; -extern const std::vector> unicode_ranges_separator; -extern const std::vector> unicode_ranges_whitespace; -extern const std::vector> unicode_ranges_accent_mark; -extern const std::vector> unicode_ranges_punctuation; -extern const std::vector> unicode_ranges_symbol; -extern const std::vector> unicode_ranges_control; -extern const std::multimap unicode_map_nfd; -extern const std::map unicode_map_lowercase; +struct range_nfd { + uint32_t first; + uint32_t last; + uint32_t nfd; +}; + +static const uint32_t MAX_CODEPOINTS = 0x110000; + +extern const std::vector> unicode_ranges_flags; +extern const std::unordered_set unicode_set_whitespace; +extern const std::unordered_map unicode_map_lowercase; +extern const std::unordered_map unicode_map_uppercase; +extern const std::vector unicode_ranges_nfd; diff --git a/unicode.cpp b/unicode.cpp index ca03c49d3..056a4c741 100644 --- a/unicode.cpp +++ b/unicode.cpp @@ -1,4 +1,4 @@ -#include "unicode.h" +#include "unicode.h" #include "unicode-data.h" #include @@ -109,57 +109,49 @@ static uint32_t unicode_cpt_from_utf8(const std::string & utf8, size_t & offset) // return result; //} -static std::unordered_map unicode_cpt_type_map() { - std::unordered_map cpt_types; - for (auto p : unicode_ranges_number) { - for (auto i = p.first; i <= p.second; ++i) { - cpt_types[i] = CODEPOINT_TYPE_NUMBER; +static std::vector unicode_cpt_flags_array() { + std::vector cpt_flags(MAX_CODEPOINTS, codepoint_flags::UNDEFINED); + + assert (unicode_ranges_flags.front().first == 0); + assert (unicode_ranges_flags.back().first == MAX_CODEPOINTS); + for (size_t i = 1; i < unicode_ranges_flags.size(); ++i) { + const auto range_ini = unicode_ranges_flags[i-1]; // codepoint_ini, flags + const auto range_end = unicode_ranges_flags[i]; // codepoint_end, flags + for (uint32_t cpt = range_ini.first; cpt < range_end.first; ++cpt) { + cpt_flags[cpt] = range_ini.second; } } - for (auto p : unicode_ranges_letter) { - for (auto i = p.first; i <= p.second; ++i) { - cpt_types[i] = CODEPOINT_TYPE_LETTER; - } + + for (auto cpt : unicode_set_whitespace) { + cpt_flags[cpt].is_whitespace = true; } - for (auto p : unicode_ranges_separator) { - for (auto i = p.first; i <= p.second; ++i) { - cpt_types[i] = CODEPOINT_TYPE_SEPARATOR; - } + + for (auto p : unicode_map_lowercase) { + cpt_flags[p.second].is_lowercase = true; } - for (auto p : unicode_ranges_accent_mark) { - for (auto i = p.first; i <= p.second; ++i) { - cpt_types[i] = CODEPOINT_TYPE_ACCENT_MARK; - } + + for (auto p : unicode_map_uppercase) { + cpt_flags[p.second].is_uppercase = true; } - for (auto p : unicode_ranges_punctuation) { - for (auto i = p.first; i <= p.second; ++i) { - cpt_types[i] = CODEPOINT_TYPE_PUNCTUATION; - } + + for (auto &range : unicode_ranges_nfd) { // start, last, nfd + cpt_flags[range.nfd].is_nfd = true; } - for (auto p : unicode_ranges_symbol) { - for (auto i = p.first; i <= p.second; ++i) { - cpt_types[i] = CODEPOINT_TYPE_SYMBOL; - } - } - for (auto p : unicode_ranges_control) { - for (auto i = p.first; i <= p.second; ++i) { - cpt_types[i] = CODEPOINT_TYPE_CONTROL; - } - } - return cpt_types; + + return cpt_flags; } static std::unordered_map unicode_byte_to_utf8_map() { std::unordered_map map; - for (int ch = u'!'; ch <= u'~'; ++ch) { + for (int ch = 0x21; ch <= 0x7E; ++ch) { // u'!' to u'~' assert(0 <= ch && ch < 256); map[ch] = unicode_cpt_to_utf8(ch); } - for (int ch = u'¡'; ch <= u'¬'; ++ch) { + for (int ch = 0xA1; ch <= 0xAC; ++ch) { // u'¡' to u'¬' assert(0 <= ch && ch < 256); map[ch] = unicode_cpt_to_utf8(ch); } - for (int ch = u'®'; ch <= u'ÿ'; ++ch) { + for (int ch = 0xAE; ch <= 0xFF; ++ch) { // u'®' to u'ÿ' assert(0 <= ch && ch < 256); map[ch] = unicode_cpt_to_utf8(ch); } @@ -175,15 +167,15 @@ static std::unordered_map unicode_byte_to_utf8_map() { static std::unordered_map unicode_utf8_to_byte_map() { std::unordered_map map; - for (int ch = u'!'; ch <= u'~'; ++ch) { + for (int ch = 0x21; ch <= 0x7E; ++ch) { // u'!' to u'~' assert(0 <= ch && ch < 256); map[unicode_cpt_to_utf8(ch)] = ch; } - for (int ch = u'¡'; ch <= u'¬'; ++ch) { + for (int ch = 0xA1; ch <= 0xAC; ++ch) { // u'¡' to u'¬' assert(0 <= ch && ch < 256); map[unicode_cpt_to_utf8(ch)] = ch; } - for (int ch = u'®'; ch <= u'ÿ'; ++ch) { + for (int ch = 0xAE; ch <= 0xFF; ++ch) { // u'®' to u'ÿ' assert(0 <= ch && ch < 256); map[unicode_cpt_to_utf8(ch)] = ch; } @@ -238,8 +230,9 @@ static std::vector unicode_regex_split_custom_gpt2(const std::string & t return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : 0; }; - auto _get_cpt_type = [&] (const size_t pos) -> int { - return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_type(cpts[pos]) : CODEPOINT_TYPE_UNIDENTIFIED; + auto _get_flags = [&] (const size_t pos) -> codepoint_flags { + static const codepoint_flags undef(codepoint_flags::UNDEFINED); + return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags(cpts[pos]) : undef; }; size_t _prev_end = offset_ini; @@ -261,7 +254,7 @@ static std::vector unicode_regex_split_custom_gpt2(const std::string & t for (size_t pos = offset_ini; pos < offset_end; /*pos++*/ ) { const char32_t cpt = _get_cpt(pos); - const int cpt_type = _get_cpt_type(pos); + const auto flags = _get_flags(pos); // regex: 's|'t|'re|'ve|'m|'ll|'d if (cpt == '\'' && pos+1 < offset_end) { @@ -281,39 +274,37 @@ static std::vector unicode_regex_split_custom_gpt2(const std::string & t } } - char32_t cpt2 = (cpt == ' ' ? _get_cpt(pos+1) : cpt); - int cpt2_type = (cpt == ' ' ? _get_cpt_type(pos+1) : cpt_type); + auto flags2 = (cpt == ' ' ? _get_flags(pos+1) : flags); // regex: ?\p{L}+ - if (cpt2_type == CODEPOINT_TYPE_LETTER) { + if (flags2.is_letter) { pos += (cpt == ' '); - while (cpt2_type == CODEPOINT_TYPE_LETTER) { - cpt2_type = _get_cpt_type(++pos); + while (flags2.is_letter) { + flags2 = _get_flags(++pos); } _add_token(pos); continue; } // regex: ?\p{N}+ - if (cpt2_type == CODEPOINT_TYPE_NUMBER) { + if (flags2.is_number) { pos += (cpt == ' '); - while (cpt2_type == CODEPOINT_TYPE_NUMBER) { - cpt2_type = _get_cpt_type(++pos); + while (flags2.is_number) { + flags2 = _get_flags(++pos); } _add_token(pos); continue; } // regex: ?[^\s\p{L}\p{N}]+ - if (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) { + if (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) { pos += (cpt == ' '); - while (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) { - cpt2_type = _get_cpt_type(++pos); - cpt2 = _get_cpt(pos); + while (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) { + flags2 = _get_flags(++pos); } _add_token(pos); continue; } size_t num_whitespaces = 0; - while (unicode_cpt_is_whitespace(_get_cpt(pos+num_whitespaces))) { + while (_get_flags(pos+num_whitespaces).is_whitespace) { num_whitespaces++; } @@ -357,8 +348,9 @@ static std::vector unicode_regex_split_custom_llama3(const std::string & return (offset_ini <= pos && pos < offset_end) ? cpts[pos] : 0; }; - auto _get_cpt_type = [&] (const size_t pos) -> int { - return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_type(cpts[pos]) : CODEPOINT_TYPE_UNIDENTIFIED; + auto _get_flags = [&] (const size_t pos) -> codepoint_flags { + static const codepoint_flags undef(codepoint_flags::UNDEFINED); + return (offset_ini <= pos && pos < offset_end) ? unicode_cpt_flags(cpts[pos]) : undef; }; size_t _prev_end = offset_ini; @@ -380,7 +372,7 @@ static std::vector unicode_regex_split_custom_llama3(const std::string & for (size_t pos = offset_ini; pos < offset_end; /*pos++*/ ) { const char32_t cpt = _get_cpt(pos); - const int cpt_type = _get_cpt_type(pos); + const auto flags = _get_flags(pos); // regex: (?i:'s|'t|'re|'ve|'m|'ll|'d) // case insensitive if (cpt == '\'' && pos+1 < offset_end) { @@ -401,10 +393,10 @@ static std::vector unicode_regex_split_custom_llama3(const std::string & } // regex: [^\r\n\p{L}\p{N}]?\p{L}+ //####FIXME: the first \p{L} is correct? - if (cpt != '\r' && cpt != '\n' && /*cpt_type != CODEPOINT_TYPE_LETTER &&*/ cpt_type != CODEPOINT_TYPE_NUMBER) { - if (cpt_type == CODEPOINT_TYPE_LETTER || _get_cpt_type(pos+1) == CODEPOINT_TYPE_LETTER) { // one or more letters + if (!(cpt == '\r' || cpt == '\n' || /*flags.is_letter |*/ flags.is_number)) { + if (flags.is_letter || _get_flags(pos+1).is_letter) { // one or more letters pos++; - while (_get_cpt_type(pos) == CODEPOINT_TYPE_LETTER) { + while (_get_flags(pos).is_letter) { pos++; } _add_token(pos); @@ -413,9 +405,9 @@ static std::vector unicode_regex_split_custom_llama3(const std::string & } // regex: \p{N}{1,3} - if (cpt_type == CODEPOINT_TYPE_NUMBER) { + if (flags.is_number) { size_t ini = pos; - while (_get_cpt_type(pos) == CODEPOINT_TYPE_NUMBER) { + while (_get_flags(pos).is_number) { if (++pos - ini >= 3 ) { _add_token(pos); ini = pos; @@ -426,14 +418,13 @@ static std::vector unicode_regex_split_custom_llama3(const std::string & } // regex: ?[^\s\p{L}\p{N}]+[\r\n]* - char32_t cpt2 = (cpt == ' ' ? _get_cpt(pos+1) : cpt); - int cpt2_type = (cpt == ' ' ? _get_cpt_type(pos+1) : cpt_type); - if (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) { + auto flags2 = (cpt == ' ' ? _get_flags(pos+1) : flags); + if (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) { pos += (cpt == ' '); - while (!unicode_cpt_is_whitespace(cpt2) && cpt2_type != CODEPOINT_TYPE_LETTER && cpt2_type != CODEPOINT_TYPE_NUMBER && cpt2_type != CODEPOINT_TYPE_UNIDENTIFIED) { - cpt2_type = _get_cpt_type(++pos); - cpt2 = _get_cpt(pos); + while (!(flags2.is_whitespace || flags2.is_letter || flags2.is_number || flags2.is_undefined)) { + flags2 = _get_flags(++pos); } + char32_t cpt2 = _get_cpt(pos); while (cpt2 == '\r' || cpt2 == '\n') { cpt2 = _get_cpt(++pos); } @@ -443,7 +434,7 @@ static std::vector unicode_regex_split_custom_llama3(const std::string & size_t num_whitespaces = 0; size_t last_end_r_or_n = 0; - while (unicode_cpt_is_whitespace(_get_cpt(pos+num_whitespaces))) { + while (_get_flags(pos+num_whitespaces).is_whitespace) { char32_t cpt2 = _get_cpt(pos+num_whitespaces); if (cpt2 == '\r' || cpt2 == '\n') { last_end_r_or_n = pos + num_whitespaces + 1; @@ -589,15 +580,14 @@ std::string unicode_cpt_to_utf8(uint32_t cp) { } std::vector unicode_cpts_normalize_nfd(const std::vector & cpts) { - std::vector result; - result.reserve(cpts.size()); + auto comp = [] (const uint32_t cpt, const range_nfd & range) { + return cpt < range.first; + }; + std::vector result(cpts.size()); for (size_t i = 0; i < cpts.size(); ++i) { - auto it = unicode_map_nfd.find(cpts[i]); - if (it == unicode_map_nfd.end()) { - result.push_back(cpts[i]); - } else { - result.push_back(it->second); - } + const uint32_t cpt = cpts[i]; + auto it = std::upper_bound(unicode_ranges_nfd.cbegin(), unicode_ranges_nfd.cend(), cpt, comp) - 1; + result[i] = (it->first <= cpt && cpt <= it->last) ? it->nfd : cpt; } return result; } @@ -611,31 +601,19 @@ std::vector unicode_cpts_from_utf8(const std::string & utf8) { return result; } -int unicode_cpt_type(uint32_t cp) { - static std::unordered_map cpt_types = unicode_cpt_type_map(); - const auto it = cpt_types.find(cp); - return it == cpt_types.end() ? CODEPOINT_TYPE_UNIDENTIFIED : it->second; +codepoint_flags unicode_cpt_flags(const uint32_t cp) { + static const codepoint_flags undef(codepoint_flags::UNDEFINED); + static const auto cpt_flags = unicode_cpt_flags_array(); + return cp < cpt_flags.size() ? cpt_flags[cp] : undef; } -int unicode_cpt_type(const std::string & utf8) { - if (utf8.length() == 0) { - return CODEPOINT_TYPE_UNIDENTIFIED; +codepoint_flags unicode_cpt_flags(const std::string & utf8) { + static const codepoint_flags undef(codepoint_flags::UNDEFINED); + if (utf8.empty()) { + return undef; // undefined } size_t offset = 0; - return unicode_cpt_type(unicode_cpt_from_utf8(utf8, offset)); -} - -bool unicode_cpt_is_whitespace(uint32_t cp) { - static const std::unordered_set is_whitespace = [] { - std::unordered_set is_whitespace; - for (auto p : unicode_ranges_whitespace) { - for (auto i = p.first; i <= p.second; ++i) { - is_whitespace.insert(i); - } - } - return is_whitespace; - }(); - return (bool)is_whitespace.count(cp); + return unicode_cpt_flags(unicode_cpt_from_utf8(utf8, offset)); } std::string unicode_byte_to_utf8(uint8_t byte) { @@ -656,21 +634,21 @@ char32_t unicode_tolower(char32_t cp) { std::vector unicode_regex_split(const std::string & text, const std::vector & regex_exprs) { // unicode categories static const std::map k_ucat_enum = { - { "\\p{N}", CODEPOINT_TYPE_NUMBER }, - { "\\p{L}", CODEPOINT_TYPE_LETTER }, - { "\\p{P}", CODEPOINT_TYPE_PUNCTUATION }, + { "\\p{N}", codepoint_flags::NUMBER }, + { "\\p{L}", codepoint_flags::LETTER }, + { "\\p{P}", codepoint_flags::PUNCTUATION }, }; static const std::map k_ucat_cpt = { - { CODEPOINT_TYPE_NUMBER, 0xD1 }, - { CODEPOINT_TYPE_LETTER, 0xD2 }, - { CODEPOINT_TYPE_PUNCTUATION, 0xD3 }, + { codepoint_flags::NUMBER, 0xD1 }, + { codepoint_flags::LETTER, 0xD2 }, + { codepoint_flags::PUNCTUATION, 0xD3 }, }; static const std::map k_ucat_map = { - { CODEPOINT_TYPE_NUMBER, "\x30-\x39" }, // 0-9 - { CODEPOINT_TYPE_LETTER, "\x41-\x5A\x61-\x7A" }, // A-Za-z - { CODEPOINT_TYPE_PUNCTUATION, "\x21-\x23\x25-\x2A\x2C-\x2F\x3A-\x3B\x3F-\x40\\\x5B-\\\x5D\x5F\\\x7B\\\x7D" }, // !-#%-*,-/:-;?-@\[-\]_\{\} + { codepoint_flags::NUMBER, "\x30-\x39" }, // 0-9 + { codepoint_flags::LETTER, "\x41-\x5A\x61-\x7A" }, // A-Za-z + { codepoint_flags::PUNCTUATION, "\x21-\x23\x25-\x2A\x2C-\x2F\x3A-\x3B\x3F-\x40\\\x5B-\\\x5D\x5F\\\x7B\\\x7D" }, // !-#%-*,-/:-;?-@\[-\]_\{\} }; // compute collapsed codepoints only if needed by at least one regex @@ -701,10 +679,10 @@ std::vector unicode_regex_split(const std::string & text, const std continue; } - const int cpt_type = unicode_cpt_type(cpts[i]); + const int cpt_flag = unicode_cpt_flags(cpts[i]).category_flag(); - if (k_ucat_cpt.find(cpt_type) != k_ucat_cpt.end()) { - text_collapsed[i] = k_ucat_cpt.at(cpt_type); + if (k_ucat_cpt.find(cpt_flag) != k_ucat_cpt.end()) { + text_collapsed[i] = k_ucat_cpt.at(cpt_flag); } else { text_collapsed[i] = (char) 0xD0; // fallback } diff --git a/unicode.h b/unicode.h index d6a14d470..7513be4ad 100644 --- a/unicode.h +++ b/unicode.h @@ -4,24 +4,56 @@ #include #include -#define CODEPOINT_TYPE_UNIDENTIFIED 0 -#define CODEPOINT_TYPE_NUMBER 1 -#define CODEPOINT_TYPE_LETTER 2 -#define CODEPOINT_TYPE_SEPARATOR 3 -#define CODEPOINT_TYPE_ACCENT_MARK 4 -#define CODEPOINT_TYPE_PUNCTUATION 5 -#define CODEPOINT_TYPE_SYMBOL 6 -#define CODEPOINT_TYPE_CONTROL 7 +struct codepoint_flags { + enum { + UNDEFINED = 0x0001, + NUMBER = 0x0002, // regex: \p{N} + LETTER = 0x0004, // regex: \p{L} + SEPARATOR = 0x0008, // regex: \p{Z} + ACCENT_MARK = 0x0010, // regex: \p{M} + PUNCTUATION = 0x0020, // regex: \p{P} + SYMBOL = 0x0040, // regex: \p{S} + CONTROL = 0x0080, // regex: \p{C} + MASK_CATEGORIES = 0x00FF, + }; + + // codepoint type + uint16_t is_undefined : 1; + uint16_t is_number : 1; // regex: \p{N} + uint16_t is_letter : 1; // regex: \p{L} + uint16_t is_separator : 1; // regex: \p{Z} + uint16_t is_accent_mark : 1; // regex: \p{M} + uint16_t is_punctuation : 1; // regex: \p{P} + uint16_t is_symbol : 1; // regex: \p{S} + uint16_t is_control : 1; // regex: \p{C} + // helper flags + uint16_t is_whitespace : 1; // regex: \s + uint16_t is_lowercase : 1; + uint16_t is_uppercase : 1; + uint16_t is_nfd : 1; + + // decode from uint16 + inline codepoint_flags(const uint16_t flags=0) { + *reinterpret_cast(this) = flags; + } + + inline uint16_t as_uint() const { + return *reinterpret_cast(this); + } + + inline uint16_t category_flag() const { + return this->as_uint() & MASK_CATEGORIES; + } +}; + std::string unicode_cpt_to_utf8(uint32_t cp); std::vector unicode_cpts_from_utf8(const std::string & utf8); std::vector unicode_cpts_normalize_nfd(const std::vector & cpts); -int unicode_cpt_type(uint32_t cp); -int unicode_cpt_type(const std::string & utf8); - -bool unicode_cpt_is_whitespace(uint32_t cp); +codepoint_flags unicode_cpt_flags(const uint32_t cp); +codepoint_flags unicode_cpt_flags(const std::string & utf8); std::string unicode_byte_to_utf8(uint8_t byte); uint8_t unicode_utf8_to_byte(const std::string & utf8); From ef277de2add255a08b2b909ebfbf70364d1f4dc4 Mon Sep 17 00:00:00 2001 From: Engininja2 <139037756+Engininja2@users.noreply.github.com> Date: Fri, 17 May 2024 18:39:25 -0600 Subject: [PATCH 031/181] cmake : fix typo in AMDGPU_TARGETS (#7356) --- CMakeLists.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 990e34b86..5e4daae03 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -581,7 +581,7 @@ if (LLAMA_HIPBLAS) else() # Forward AMDGPU_TARGETS to CMAKE_HIP_ARCHITECTURES. if(AMDGPU_TARGETS AND NOT CMAKE_HIP_ARCHITECTURES) - set(CMAKE_HIP_ARCHITECTURES ${AMDGPU_ARGETS}) + set(CMAKE_HIP_ARCHITECTURES ${AMDGPU_TARGETS}) endif() cmake_minimum_required(VERSION 3.21) enable_language(HIP) From 05834841dcb4f922983ea976539c70472272df9a Mon Sep 17 00:00:00 2001 From: slaren Date: Sat, 18 May 2024 02:39:54 +0200 Subject: [PATCH 032/181] ggml : fix quants nans when all the group weights are very close to zero (#7313) --- ggml-quants.c | 30 ++++++++++++++++++------------ tests/test-backend-ops.cpp | 18 ++++++++++++++++++ 2 files changed, 36 insertions(+), 12 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 9b291d522..7008e5d8b 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -14,6 +14,12 @@ #include // for qsort #include // for GGML_ASSERT +#define GROUP_MAX_EPS 1e-15f +#define GROUP_MAX_EPS_IQ3_XXS 1e-8f +#define GROUP_MAX_EPS_IQ2_S 1e-8f +#define GROUP_MAX_EPS_IQ1_M 1e-7f +#define GROUP_MAX_EPS_IQ1_S 1e-12f + #if defined(_MSC_VER) // disable "possible loss of data" to avoid warnings for hundreds of casts // we should just be careful :) @@ -1109,7 +1115,7 @@ static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t * float ax = fabsf(x[i]); if (ax > amax) { amax = ax; max = x[i]; } } - if (amax < 1e-30f) { // all zero + if (amax < GROUP_MAX_EPS) { // all zero for (int i = 0; i < n; ++i) { L[i] = 0; } @@ -1177,7 +1183,7 @@ static float make_q3_quants(int n, int nmax, const float * restrict x, int8_t * float ax = fabsf(x[i]); if (ax > amax) { amax = ax; max = x[i]; } } - if (!amax) { // all zero + if (amax < GROUP_MAX_EPS) { // all zero for (int i = 0; i < n; ++i) { L[i] = 0; } return 0.f; } @@ -1646,7 +1652,7 @@ static float make_qp_quants(int n, int nmax, const float * restrict x, uint8_t * break; } } - return sumlx / suml2; + return sumlx/suml2; } static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restrict y, int k, const float * restrict quant_weights) { @@ -2653,7 +2659,7 @@ void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict } - if (!max_abs_scale) { + if (max_abs_scale < GROUP_MAX_EPS) { memset(&y[i], 0, sizeof(block_q6_K)); y[i].d = GGML_FP32_TO_FP16(0.f); x += QK_K; @@ -2805,7 +2811,7 @@ static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restri } - if (!max_abs_scale) { + if (max_abs_scale < GROUP_MAX_EPS) { memset(&y[i], 0, sizeof(block_q6_K)); y[i].d = GGML_FP32_TO_FP16(0.f); x += QK_K; @@ -12599,7 +12605,7 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict } float max = xval[0]; for (int i = 1; i < 32; ++i) max = MAX(max, xval[i]); - if (!max) { + if (max < GROUP_MAX_EPS) { scales[ib] = 0; memset(L, 0, 32); continue; @@ -12775,7 +12781,7 @@ static void quantize_row_iq2_xs_impl(const float * restrict x, void * restrict v } float max = xval[0]; for (int i = 1; i < 16; ++i) max = MAX(max, xval[i]); - if (!max) { + if (max < GROUP_MAX_EPS) { scales[ib] = 0; memset(L, 0, 16); continue; @@ -13216,7 +13222,7 @@ static void quantize_row_iq3_xxs_impl(int grid_size, const float * restrict x, v } float max = xval[0]; for (int i = 1; i < 32; ++i) max = MAX(max, xval[i]); - if (!max) { + if (max < GROUP_MAX_EPS_IQ3_XXS) { scales[ib] = 0; memset(L, 0, 32); continue; @@ -13756,7 +13762,7 @@ static void quantize_row_iq1_s_impl(const float * restrict x, void * restrict vy for (int i = 0; i < block_size; ++i) weight[i] = qw[i] * sqrtf(sigma2 + xb[i]*xb[i]); float max = fabsf(xb[0]); for (int i = 1; i < block_size; ++i) max = MAX(max, fabsf(xb[i])); - if (!max) { + if (max < GROUP_MAX_EPS_IQ1_S) { scales[ib] = 0; memset(L, 1, block_size); continue; @@ -13944,7 +13950,7 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy } float max = fabsf(xb[0]); for (int i = 1; i < block_size; ++i) max = MAX(max, fabsf(xb[i])); - if (!max) { + if (max < GROUP_MAX_EPS_IQ1_M) { scales[ib] = 0; memset(L, 1, block_size); continue; @@ -14208,7 +14214,7 @@ static void quantize_row_iq4_nl_impl(const int super_block_size, const int block amax = ax; max = xb[j]; } } - if (!amax) { + if (amax < GROUP_MAX_EPS) { scales[ib] = 0; continue; } @@ -14429,7 +14435,7 @@ static void quantize_row_iq2_s_impl(const float * restrict x, void * restrict vy } float max = xval[0]; for (int i = 1; i < 16; ++i) max = MAX(max, xval[i]); - if (!max) { + if (max < GROUP_MAX_EPS_IQ2_S) { scales[ib] = 0; continue; } diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 85ef21c2a..c74e253db 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -16,6 +16,7 @@ #include #include + static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float max = 1.0f) { // static RNG initialization (revisit if n_threads stops being constant) static const size_t n_threads = std::thread::hardware_concurrency(); @@ -49,6 +50,22 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m t.join(); } +#if 0 + const char * val_str = getenv("GGML_TEST_EPS"); + float val = 1e-9f; + if (val_str != nullptr) { + val = std::stof(val_str); + printf("GGML_TEST_EPS=%e\n", val); + } + + // test quantization with very small values that may result in nan scales due to division by zero + if (ggml_is_quantized(tensor->type)) { + for (int i = 0; i < 256; i++) { + data[i] = val; + } + } +#endif + if (tensor->type == GGML_TYPE_F32 || tensor->type == GGML_TYPE_I32) { ggml_backend_tensor_set(tensor, data.data(), 0, size * sizeof(float)); } else if (ggml_is_quantized(tensor->type) || tensor->type == GGML_TYPE_F16 || tensor->type == GGML_TYPE_BF16) { @@ -64,6 +81,7 @@ static void init_tensor_uniform(ggml_tensor * tensor, float min = -1.0f, float m } } ggml_quantize_chunk(tensor->type, data.data(), dataq.data(), 0, size/tensor->ne[0], tensor->ne[0], im); + GGML_ASSERT(ggml_validate_row_data(tensor->type, dataq.data(), dataq.size())); ggml_backend_tensor_set(tensor, dataq.data(), 0, dataq.size()); } else if (tensor->type == GGML_TYPE_I8 || tensor->type == GGML_TYPE_I16 || tensor->type == GGML_TYPE_I32) { // This is going to create some weird integers though. From b49a13dd2fa9c94c2c19a8c248bb7fa45499f9a8 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 18 May 2024 08:46:20 +0300 Subject: [PATCH 033/181] convert : fix set_vocab_sentencepiece (#6866) * convert : fix set_vocab_sentencepiece * Update convert-hf-to-gguf.py --- convert-hf-to-gguf.py | 26 +++++++++++++++----------- 1 file changed, 15 insertions(+), 11 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 5ba3161c7..cd1750aa3 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -573,6 +573,10 @@ class Model: vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)] + scores: list[float] = [-10000.0] * vocab_size + toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size + for token_id in range(tokenizer.vocab_size()): piece = tokenizer.IdToPiece(token_id) text = piece.encode("utf-8") @@ -588,21 +592,23 @@ class Model: elif tokenizer.IsByte(token_id): toktype = SentencePieceTokenTypes.BYTE - tokens.append(text) - scores.append(score) - toktypes.append(toktype) + tokens[token_id] = text + scores[token_id] = score + toktypes[token_id] = 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: - key = key.encode("utf-8") - if key not in tokens: - tokens.append(key) - scores.append(-1000.0) - toktypes.append(SentencePieceTokenTypes.USER_DEFINED) + token_id = added_tokens_json[key] + if (token_id >= vocab_size): + logger.warning(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}') + continue + + tokens[token_id] = key.encode("utf-8") + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED if vocab_size > len(tokens): pad_count = vocab_size - len(tokens) @@ -612,8 +618,6 @@ class Model: scores.append(-1000.0) toktypes.append(SentencePieceTokenTypes.UNUSED) - assert len(tokens) == vocab_size - self.gguf_writer.add_tokenizer_model("llama") self.gguf_writer.add_tokenizer_pre("default") self.gguf_writer.add_token_list(tokens) From de731963441ff128248259e1b99573d75264d210 Mon Sep 17 00:00:00 2001 From: Brian Date: Sat, 18 May 2024 16:04:23 +1000 Subject: [PATCH 034/181] github-actions-labeler: initial commit (#7330) * github-actions-labeler: initial commit [no ci] * github actions: remove priority auto labeling [no ci] --- .github/labeler.yml | 73 +++++++++++++++++++++++++++++++++++ .github/workflows/labeler.yml | 12 ++++++ 2 files changed, 85 insertions(+) create mode 100644 .github/labeler.yml create mode 100644 .github/workflows/labeler.yml diff --git a/.github/labeler.yml b/.github/labeler.yml new file mode 100644 index 000000000..fca60594f --- /dev/null +++ b/.github/labeler.yml @@ -0,0 +1,73 @@ +# https://github.com/actions/labeler + +SYCL: + - changed-files: + - any-glob-to-any-file: + - ggml-sycl.h + - ggml-sycl.cpp + - README-sycl.md +Nvidia GPU: + - changed-files: + - any-glob-to-any-file: + - ggml-cuda/** +Vulkan: + - changed-files: + - any-glob-to-any-file: + - ggml_vk_generate_shaders.py + - ggml-vulkan* +documentation: + - changed-files: + - any-glob-to-any-file: + - docs/** + - media/** +testing: + - changed-files: + - any-glob-to-any-file: + - tests/** +build: + - changed-files: + - any-glob-to-any-file: + - cmake/** + - CMakeLists.txt + - CMakePresets.json + - codecov.yml +examples: + - changed-files: + - any-glob-to-any-file: examples/** +devops: + - changed-files: + - any-glob-to-any-file: + - .devops/** + - .github/** + - ci/** +python: + - changed-files: + - any-glob-to-any-file: + - "**/*.py" + - requirements/** + - gguf-py/** + - .flake8 +script: + - changed-files: + - any-glob-to-any-file: + - scripts/** +android: + - changed-files: + - any-glob-to-any-file: + - examples/llama.android/** +server: + - changed-files: + - any-glob-to-any-file: + - examples/server/** +ggml: + - changed-files: + - any-glob-to-any-file: + - ggml-*.c + - ggml-*.h + - ggml-cuda/** +nix: + - changed-files: + - any-glob-to-any-file: + - "**/*.nix" + - .github/workflows/nix-*.yml + - .devops/nix/nixpkgs-instances.nix diff --git a/.github/workflows/labeler.yml b/.github/workflows/labeler.yml new file mode 100644 index 000000000..e57cd86e2 --- /dev/null +++ b/.github/workflows/labeler.yml @@ -0,0 +1,12 @@ +name: "Pull Request Labeler" +on: +- pull_request_target + +jobs: + labeler: + permissions: + contents: read + pull-requests: write + runs-on: ubuntu-latest + steps: + - uses: actions/labeler@v5 From c1b295eea5c49887a066559527a74e8b94fe9db0 Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Sat, 18 May 2024 08:10:58 +0200 Subject: [PATCH 035/181] Update and fix Vulkan soft_max and argsort implementations (#7237) * Update and fix Vulkan softmax implementation * Update and fix Vulkan argsort implementation --- ggml-vulkan-shaders.hpp | 2157 ++++++++++++++++++----------------- ggml-vulkan.cpp | 194 ++-- ggml_vk_generate_shaders.py | 86 +- 3 files changed, 1210 insertions(+), 1227 deletions(-) diff --git a/ggml-vulkan-shaders.hpp b/ggml-vulkan-shaders.hpp index d3a2fcf38..586bafe46 100644 --- a/ggml-vulkan-shaders.hpp +++ b/ggml-vulkan-shaders.hpp @@ -363,320 +363,349 @@ const uint64_t add_f32_len = 4276; unsigned char argsort_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x2b,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x41,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 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, +0x01,0x00,0x00,0x00,0x0f,0x00,0x0b,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, 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+0x38,0x00,0x01,0x00, }; -const uint64_t argsort_f32_len = 3752; +const uint64_t argsort_f32_len = 4096; unsigned char clamp_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -76253,430 +76282,15 @@ const uint64_t silu_f32_len = 1264; unsigned char soft_max_f32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x5c,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x6f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 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,0x00,0x0d,0x00,0x05,0x00,0x00,0x00, +0x01,0x00,0x00,0x00,0x0f,0x00,0x0c,0x00,0x05,0x00,0x00,0x00, 0x04,0x00,0x00,0x00,0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00, 0x0b,0x00,0x00,0x00,0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x5c,0x00,0x00,0x00,0x75,0x00,0x00,0x00,0x8c,0x00,0x00,0x00, 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-0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, -}; -const uint64_t soft_max_f32_len = 4916; - -unsigned char soft_max_f32_f16_data[] = { -0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x61,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, -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,0x00,0x0d,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, -0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, -0x11,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x5c,0x00,0x00,0x00, -0x75,0x00,0x00,0x00,0x8d,0x00,0x00,0x00,0xa6,0x00,0x00,0x00, -0x0e,0x01,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, +0x77,0x00,0x00,0x00,0x8f,0x00,0x00,0x00,0xa2,0x00,0x00,0x00, +0x0c,0x01,0x00,0x00,0x10,0x00,0x06,0x00,0x04,0x00,0x00,0x00, 0x11,0x00,0x00,0x00,0x00,0x02,0x00,0x00,0x01,0x00,0x00,0x00, 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+0x50,0x01,0x00,0x00,0x52,0x01,0x00,0x00,0xf9,0x00,0x02,0x00, +0x38,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x38,0x01,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0x55,0x01,0x00,0x00, +0x6a,0x01,0x00,0x00,0xa0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x35,0x01,0x00,0x00,0xf8,0x00,0x02,0x00,0x37,0x01,0x00,0x00, 0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t soft_max_f32_f16_len = 5000; +const uint64_t soft_max_f32_f16_len = 4904; unsigned char split_k_reduce_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index b9449be03..d1ba47ac3 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -294,7 +294,6 @@ struct vk_op_rope_neox_push_constants { struct vk_op_soft_max_push_constants { uint32_t KX; uint32_t KY; - uint32_t KZ; float scale; float max_bias; float m0; @@ -304,7 +303,8 @@ struct vk_op_soft_max_push_constants { struct vk_op_argsort_push_constants { uint32_t ncols; - bool ascending; + uint32_t ncols_pad; + int32_t order; }; // Allow pre-recording command buffers @@ -1501,8 +1501,8 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { ggml_vk_create_pipeline(ctx, ctx->device->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); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); - ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 4, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32, "soft_max_f32", soft_max_f32_len, soft_max_f32_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_soft_max_f32_f16, "soft_max_f32_f16", soft_max_f32_f16_len, soft_max_f32_f16_data, "main", 3, sizeof(vk_op_soft_max_push_constants), {1, 1, 1}, {}, 1); ggml_vk_create_pipeline(ctx, ctx->device->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->device->pipeline_rope_f16, "rope_f16", rope_f16_len, rope_f16_data, "main", 3, sizeof(vk_op_rope_push_constants), {1, 512, 1}, {}, 1); @@ -3752,7 +3752,7 @@ static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx } -static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, 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) { @@ -3834,7 +3834,7 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const if (src0->type == GGML_TYPE_F32 && (src1 == nullptr || src1->type == GGML_TYPE_F32) && dst->type == GGML_TYPE_F32) { return ctx->device->pipeline_soft_max_f32; } - if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16 && src2->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) { + if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_F16 && dst->type == GGML_TYPE_F32) { return ctx->device->pipeline_soft_max_f32_f16; } return nullptr; @@ -3900,15 +3900,12 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) { } template -static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, 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) { 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]; } - if (src2 != nullptr) { - std::cerr << "), (" << src2 << ", name=" << src2->name << ", type=" << src2->type << ", backend=" << src2->backend << ", ne0=" << src2->ne[0] << ", ne1=" << src2->ne[1] << ", ne2=" << src2->ne[2] << ", ne3=" << src2->ne[3] << ", nb0=" << src2->nb[0] << ", nb1=" << src2->nb[1] << ", nb2=" << src2->nb[2] << ", nb3=" << src2->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(op == GGML_OP_GET_ROWS || (!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type)))); // NOLINT @@ -3929,10 +3926,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c const uint64_t nb2 = dst->nb[2]; const uint64_t nb3 = dst->nb[3]; - const bool use_src2 = src2 != nullptr; - const uint64_t ne2 = use_src2 ? src2->ne[0] * src2->ne[1] : 0; - - vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, dst, op); + vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, dst, op); ggml_vk_func_t op_func; if (pipeline == nullptr) { @@ -3955,18 +3949,15 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c 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; - ggml_tensor_extra_gpu * extra_src2 = use_src2 ? (ggml_tensor_extra_gpu *) src2->extra : nullptr; vk_buffer d_X = nullptr; size_t x_buf_offset = 0; vk_buffer d_Y = nullptr; size_t y_buf_offset = 0; vk_buffer d_Z = nullptr; - size_t z_buf_offset = 0; bool src0_uma = false; bool src1_uma = false; - bool src2_uma = false; if (ctx->device->uma) { ggml_vk_host_get(ctx, src0->data, d_X, x_buf_offset); @@ -3975,15 +3966,10 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c ggml_vk_host_get(ctx, src1->data, d_Y, y_buf_offset); src1_uma = d_Y != nullptr; } - if (use_src2) { - ggml_vk_host_get(ctx, src1->data, d_Z, z_buf_offset); - src2_uma = d_Z != nullptr; - } } uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type)/ggml_blck_size(src0->type) * ne0, ctx->device->properties.limits.minStorageBufferOffsetAlignment); uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : 0; - uint64_t z_sz = use_src2 ? ggml_vk_align_size(ggml_type_size(src2->type) * ne2, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : 0; uint64_t d_sz = ggml_type_size(dst->type) * ne0; vk_buffer d_D = extra->buffer_gpu.lock(); @@ -4007,12 +3993,6 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c GGML_ASSERT(d_Y != nullptr); } - if (use_src2 && !src2_uma) { - d_Z = extra_src2->buffer_gpu.lock(); - z_buf_offset = extra_src2->offset; - GGML_ASSERT(d_Z != nullptr); - } - if (op_supports_incontiguous) { x_sz = ggml_nbytes(src0); y_sz = use_src1 ? ggml_nbytes(src1) : 0; @@ -4046,7 +4026,10 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c elements = { (uint32_t)ggml_nrows(src0), (uint32_t)ne00, 1 }; break; case GGML_OP_GET_ROWS: - elements = { (uint32_t)ne00, (uint32_t)ne10, (uint32_t)(ne11 * ne12) }; + elements = { (uint32_t)ne00, (uint32_t)ne10, (uint32_t)(ne11 * ne12) }; + break; + case GGML_OP_ARGSORT: + elements = { (uint32_t)ne00, (uint32_t)ggml_nrows(src0), 1 }; break; default: elements = { (uint32_t)ggml_nelements(src0), 1, 1 }; @@ -4066,7 +4049,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c } if (op == GGML_OP_SOFT_MAX) { - // Empty src1 and src2 are possible on soft_max, but the shader needs buffers + // Empty src1 is possible on soft_max, but the shader needs a buffer vk_subbuffer subbuf_y; if (use_src1) { subbuf_y = { d_Y, y_buf_offset, y_sz }; @@ -4074,15 +4057,8 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c subbuf_y = { d_X, 0, d_X->size }; } - vk_subbuffer subbuf_z; - if (use_src2) { - subbuf_z = { d_Z, z_buf_offset, z_sz }; - } else { - subbuf_z = { d_X, 0, d_X->size }; - } - ggml_vk_sync_buffers(subctx); - ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, subbuf_y, subbuf_z, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, subbuf_y, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else if (use_src1) { 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); @@ -4099,13 +4075,13 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c } } else { GGML_ASSERT(op != GGML_OP_SOFT_MAX); + GGML_ASSERT(op != GGML_OP_ARGSORT); ggml_pipeline_allocate_descriptor_sets(ctx, 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: @@ -4145,7 +4121,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c } 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, nullptr, dst, GGML_OP_REPEAT, { (uint32_t)ggml_nelements(src0), (uint32_t)ggml_nelements(src1), 0.0f, 0.0f }); + 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(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -4153,7 +4129,7 @@ static void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context * subctx, const uint32_t src1_type_size = ggml_type_size(src1->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_GET_ROWS, { + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_GET_ROWS, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (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)src0->nb[3] / src0_type_size, (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, @@ -4168,7 +4144,7 @@ static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context * subctx, cons const uint32_t src1_type_size = ggml_type_size(src1->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ADD, { + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ADD, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (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)src0->nb[3] / src0_type_size, (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, @@ -4183,7 +4159,7 @@ static void ggml_vk_mul(ggml_backend_vk_context * ctx, vk_context * subctx, cons const uint32_t src1_type_size = ggml_type_size(src1->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_MUL, { + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_MUL, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (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)src0->nb[3] / src0_type_size, (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, @@ -4198,7 +4174,7 @@ static void ggml_vk_scale(ggml_backend_vk_context * ctx, vk_context * subctx, co const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SCALE, { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_SCALE, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (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)src0->nb[3] / src0_type_size, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, @@ -4211,7 +4187,7 @@ static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context * subctx, cons const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SQR, { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_SQR, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (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)src0->nb[3] / src0_type_size, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, @@ -4225,7 +4201,7 @@ static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context * subctx, co const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CLAMP, { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_CLAMP, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (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)src0->nb[3] / src0_type_size, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, @@ -4240,7 +4216,7 @@ static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context * subctx, cons const uint32_t dst_type_size = ggml_type_size(dst->type); const uint32_t d_offset = (extra->offset % ctx->device->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CPY, { + 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->ne[2], (uint32_t)src0->ne[3], (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)src0->nb[3] / src0_type_size, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, @@ -4252,24 +4228,24 @@ static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context * subctx, cons static void ggml_vk_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, subctx, src0, nullptr, nullptr, dst, GGML_OP_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_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); } 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, subctx, src0, nullptr, 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(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, ggml_tensor * dst) { - ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); + 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(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, subctx, src0, nullptr, 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(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, 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; float scale = op_params[0]; @@ -4285,13 +4261,9 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); -#pragma message("TODO: src2 is no longer used in soft_max - should be removed and ALiBi calculation should be updated") -#pragma message("ref: https://github.com/ggerganov/llama.cpp/pull/7192") - - ggml_vk_op_f32(ctx, subctx, src0, src1, src2, dst, GGML_OP_SOFT_MAX, { + ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_SOFT_MAX, { ncols, src1 != nullptr ? nrows_y : (uint32_t)0, - src2 != nullptr ? (uint32_t)1 : (uint32_t)0, scale, max_bias, m0, m1, n_head_log2, @@ -4321,15 +4293,39 @@ static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, con 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, subctx, src0, src1, nullptr, 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, subctx, src0, src1, nullptr, 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_argsort(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, subctx, src0, nullptr, nullptr, dst, GGML_OP_ARGSORT, { (uint32_t)src0->ne[0], ((ggml_sort_order) op_params[0]) == GGML_SORT_ORDER_ASC }); + + uint32_t ncols = src0->ne[0]; + + uint32_t ncols_pad = 1; + while (ncols_pad < ncols) { + ncols_pad *= 2; + } + + GGML_ASSERT(ncols_pad <= 1024); + + std::cerr << "ncols=" << ncols << " ncols_pad=" << ncols_pad << " ascending=" << op_params[0] << std::endl; + + std::cerr << ((ggml_sort_order) op_params[0]) << " " << GGML_SORT_ORDER_ASC << std::endl; + + ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_ARGSORT, { + ncols, + ncols_pad, + op_params[0], + }); } #ifdef GGML_VULKAN_RUN_TESTS @@ -5432,7 +5428,6 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod const ggml_tensor * src0 = node->src[0]; const ggml_tensor * src1 = node->src[1]; - const ggml_tensor * src2 = node->src[2]; ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra; @@ -5547,7 +5542,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod break; case GGML_OP_SOFT_MAX: - ggml_vk_soft_max(ctx, ctx->compute_ctx, src0, src1, src2, node); + ggml_vk_soft_max(ctx, ctx->compute_ctx, src0, src1, node); break; case GGML_OP_ROPE: @@ -6548,7 +6543,7 @@ static void ggml_vk_print_graph_origin(const ggml_tensor * tensor, std::vectortype != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16) { + if (tensor->type != GGML_TYPE_F32 && tensor->type != GGML_TYPE_F16 && tensor->type != GGML_TYPE_I32) { return; } i0 = std::max(i0, 5); @@ -6569,6 +6564,8 @@ static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * d 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(*(const ggml_fp16_t *) ((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_I32) { + val = *(const int32_t *) ((const char *) data + i3*tensor->nb[3] + i2*tensor->nb[2] + idx1*tensor->nb[1] + idx0*tensor->nb[0]); } else { GGML_ASSERT(false); } @@ -6671,7 +6668,6 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ ggml_tensor * src0 = tensor->src[0]; ggml_tensor * src1 = tensor->src[1]; - ggml_tensor * src2 = tensor->src[2]; struct ggml_init_params iparams = { /*.mem_size =*/ 1024*1024*1024, @@ -6798,66 +6794,6 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src1", src1_clone); } - if (src2 != nullptr) { - src2_clone = ggml_dup_tensor(ggml_ctx, src2); - - src2_size = ggml_nbytes(src2); - - src2_buffer = malloc(src2_size); - src2_clone->data = src2_buffer; - if (src2->backend == GGML_BACKEND_TYPE_CPU) { - memcpy(src2_clone->data, src2->data, src2_size); - memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS); - } else if (src2->backend == GGML_BACKEND_TYPE_GPU) { - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src2->extra; - vk_buffer buf = extra->buffer_gpu.lock(); - uint64_t offset = extra->offset; - if (!ggml_is_contiguous(src2) && ggml_vk_dim01_contiguous(src2)) { - for (int i3 = 0; i3 < src2->ne[3]; i3++) { - for (int i2 = 0; i2 < src2->ne[2]; i2++) { - const int idx = i3*src2->ne[2] + i2; - ggml_vk_buffer_read(ctx, buf, offset + idx * src2->nb[2], ((char *)src2_clone->data + idx * src2_clone->nb[2]), src2->ne[1] * src2->nb[1]); - } - } - - src2_clone->nb[0] = src2->nb[0]; - src2_clone->nb[1] = src2->nb[1]; - for (int i = 2; i < GGML_MAX_DIMS; i++) { - src2_clone->nb[i] = src2_clone->nb[i - 1]*src2_clone->ne[i - 1]; - } - } else { - if (offset + src2_size >= buf->size) { - src2_size = buf->size - offset; - } - ggml_vk_buffer_read(ctx, buf, offset, src2_clone->data, src2_size); - memcpy(src2_clone->nb, src2->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(ctx, src2, "src2"); - std::cerr << "TENSOR CHECK: " << ggml_op_name(src2_clone->op) << " (check " << check_counter << ")" << std::endl; - std::cerr << "src2_clone=" << tensor << " src2_clone->backend: " << src2_clone->backend << " src2_clone->type: " << ggml_type_name(src2_clone->type) << " ne0=" << src2_clone->ne[0] << " nb0=" << src2_clone->nb[0] << " ne1=" << src2_clone->ne[1] << " nb1=" << src2_clone->nb[1] << " ne2=" << src2_clone->ne[2] << " nb2=" << src2_clone->nb[2] << " ne3=" << src2_clone->ne[3] << " nb3=" << src2_clone->nb[3] << std::endl; - if (src2->src[0] != nullptr) { - std::cerr << "src2->src[0]=" << src2->src[0] << " op=" << ggml_op_name(src2->src[0]->op) << " type=" << ggml_type_name(src2->src[0]->type) << " backend=" << src2->src[0]->backend << " ne0=" << src2->src[0]->ne[0] << " nb0=" << src2->src[0]->nb[0] << " ne1=" << src2->src[0]->ne[1] << " nb1=" << src2->src[0]->nb[1] << " ne2=" << src2->src[0]->ne[2] << " nb2=" << src2->src[0]->nb[2] << " ne3=" << src2->src[0]->ne[3] << " nb3=" << src2->src[0]->nb[3] << std::endl; - } - if (src2->src[1] != nullptr) { - std::cerr << "src2->src[1]=" << src2->src[1] << " op=" << ggml_op_name(src2->src[1]->op) << " type=" << ggml_type_name(src2->src[1]->type) << " backend=" << src2->src[1]->backend << " ne0=" << src2->src[1]->ne[0] << " nb0=" << src2->src[1]->nb[0] << " ne1=" << src2->src[1]->ne[1] << " nb1=" << src2->src[1]->nb[1] << " ne2=" << src2->src[1]->ne[2] << " nb2=" << src2->src[1]->nb[2] << " ne3=" << src2->src[1]->ne[3] << " nb3=" << src2->src[1]->nb[3] << std::endl; - } - std::cerr << std::endl << "Result:" << std::endl; - ggml_vk_print_tensor_area(src2_clone, src2_clone->data, 5, 5, 0, 0); - std::cerr << std::endl; - std::cerr << std::endl << "Result:" << std::endl; - ggml_vk_print_tensor_area(src2_clone, src2_clone->data, 5, 5, 1, 0); - std::cerr << std::endl; - std::vector done; - ggml_vk_print_graph_origin(src2_clone, done); - } - - ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src2", src2_clone); - } if (tensor->op == GGML_OP_MUL_MAT) { tensor_clone = ggml_mul_mat(ggml_ctx, src0_clone, src1_clone); @@ -6877,7 +6813,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ 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(ggml_ctx, src0_clone, src1_clone, src2_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]); + tensor_clone = ggml_soft_max_ext(ggml_ctx, src0_clone, src1_clone, ((float *)tensor->op_params)[0], ((float *)tensor->op_params)[1]); } else { tensor_clone = ggml_soft_max(ggml_ctx, src0_clone); } @@ -6964,9 +6900,6 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ if (src1 != nullptr) { free(src1_buffer); } - if (src2 != nullptr) { - free(src2_buffer); - } ggml_free(ggml_ctx); } @@ -7026,8 +6959,11 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ } 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 if (tensor->type == GGML_TYPE_I32) { + correct = *(int32_t *) ((char *) comp_result + i3*comp_nb[3] + i2*comp_nb[2] + i1*comp_nb[1] + i0*comp_nb[0]); + result = *(int32_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; + std::cerr << "Results check not implemented for type " << ggml_type_name(tensor->type) << std::endl; } } else { std::cerr << "Missing debug code for type " << ggml_type_name(tensor->type) << std::endl; diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py index 162cf5c6e..3c22a986c 100644 --- a/ggml_vk_generate_shaders.py +++ b/ggml_vk_generate_shaders.py @@ -2432,7 +2432,6 @@ layout (push_constant) uniform parameter { uint KX; uint KY; - uint KZ; float scale; float max_bias; float m0; @@ -2449,8 +2448,7 @@ 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) readonly buffer Z {C_TYPE data_c[];}; -layout (binding = 3) buffer D {D_TYPE data_d[];}; +layout (binding = 2) buffer D {D_TYPE data_d[];}; shared FLOAT_TYPE vals[BLOCK_SIZE]; @@ -2459,7 +2457,7 @@ void main() { const uint rowx = gl_WorkGroupID.x; const uint rowy = rowx % p.KY; - float slope = 0.0f; + float slope = 1.0f; // ALiBi if (p.max_bias > 0.0f) { @@ -2472,11 +2470,18 @@ void main() { } // Find max - vals[tid] = uintBitsToFloat(0xFF800000); + FLOAT_TYPE max_val = 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.scale + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) + (p.KZ > 0 ? slope * FLOAT_TYPE(data_c[col]) : 0.0f)); + [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) { + const uint col = col0 + tid; + + if (col >= p.KX) { + break; + } + + max_val = max(max_val, FLOAT_TYPE(data_a[rowx * p.KX + col]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f))); } + vals[tid] = max_val; barrier(); [[unroll]] for (int s = BLOCK_SIZE / 2; s > 0; s >>= 1) { @@ -2486,15 +2491,21 @@ void main() { barrier(); } - const FLOAT_TYPE max_val = vals[0]; + 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) { + [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) { + const uint col = col0 + tid; + + if (col >= p.KX) { + break; + } + const uint i = rowx * p.KX + col; - const FLOAT_TYPE val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val); + const FLOAT_TYPE val = exp(FLOAT_TYPE(data_a[i]) * p.scale + (p.KY > 0 ? slope * FLOAT_TYPE(data_b[rowy * p.KX + col]) : FLOAT_TYPE(0.0f)) - max_val); vals[tid] += val; data_d[i] = D_TYPE(val); } @@ -2509,7 +2520,13 @@ void main() { const D_TYPE divisor = D_TYPE(vals[0]); - [[unroll]] for (uint col = tid; col < p.KX; col += BLOCK_SIZE) { + [[unroll]] for (uint col0 = 0; col0 < p.KX; col0 += BLOCK_SIZE) { + const uint col = col0 + tid; + + if (col >= p.KX) { + break; + } + data_d[rowx*p.KX + col] /= divisor; } } @@ -2672,20 +2689,26 @@ argsort_src = """ #extension GL_EXT_shader_16bit_storage : require -layout(local_size_x = 1024, local_size_y = 1, local_size_z = 1) in; +#define BLOCK_SIZE 1024 +#define ASC 0 + +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) buffer D {int data_d[];}; layout (push_constant) uniform parameter { uint ncols; - bool ascending; + uint ncols_pad; + uint order; } p; +shared int dst_row[BLOCK_SIZE]; + void swap(uint idx0, uint idx1) { - int tmp = data_d[idx0]; - data_d[idx0] = data_d[idx1]; - data_d[idx1] = tmp; + int tmp = dst_row[idx0]; + dst_row[idx0] = dst_row[idx1]; + dst_row[idx1] = tmp; } void main() { @@ -2693,36 +2716,45 @@ void main() { const int col = int(gl_LocalInvocationID.x); const uint row = gl_WorkGroupID.y; - if (col >= p.ncols) { + if (col >= p.ncols_pad) { return; } - const uint a_idx = row * p.ncols; - const uint d_idx = row * p.ncols; + const uint row_offset = row * p.ncols; // initialize indices - if (col < p.ncols) { - data_d[col] = col; - } + dst_row[col] = col; barrier(); - for (uint k = 2; k <= p.ncols; k *= 2) { + for (uint k = 2; k <= p.ncols_pad; k *= 2) { for (uint j = k / 2; j > 0; j /= 2) { const uint ixj = col ^ j; if (ixj > col) { if ((col & k) == 0) { - if (p.ascending ? data_a[a_idx + data_d[d_idx + col]] > data_a[a_idx + data_d[d_idx + ixj]] : data_a[a_idx + data_d[d_idx + col]] < data_a[a_idx + data_d[d_idx + ixj]]) { - swap(d_idx + col, d_idx + ixj); + if (dst_row[col] >= p.ncols || + (dst_row[ixj] < p.ncols && (p.order == ASC ? + data_a[row_offset + dst_row[col]] > data_a[row_offset + dst_row[ixj]] : + data_a[row_offset + dst_row[col]] < data_a[row_offset + dst_row[ixj]])) + ) { + swap(col, ixj); } } else { - if (p.ascending ? data_a[a_idx + data_d[d_idx + col]] < data_a[a_idx + data_d[d_idx + ixj]] : data_a[a_idx + data_d[d_idx + col]] > data_a[a_idx + data_d[d_idx + ixj]]) { - swap(d_idx + col, d_idx + ixj); + if (dst_row[ixj] >= p.ncols || + (dst_row[col] < p.ncols && (p.order == ASC ? + data_a[row_offset + dst_row[col]] < data_a[row_offset + dst_row[ixj]] : + data_a[row_offset + dst_row[col]] > data_a[row_offset + dst_row[ixj]])) + ) { + swap(col, ixj); } } } barrier(); } } + + if (col < p.ncols) { + data_d[row_offset + col] = dst_row[col]; + } } """ From ca57e0f35e33f714b9a6c2c4482b87bfe059c819 Mon Sep 17 00:00:00 2001 From: strawberrymelonpanda <152940198+strawberrymelonpanda@users.noreply.github.com> Date: Sat, 18 May 2024 00:57:08 -0700 Subject: [PATCH 036/181] perplexity : ndot progress and show stats with < 100 tasks (#7348) Fix floating point error with ndot printing, allow end stats on lower task numbers if multiple-choice tasks. --- 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 db6e0949d..bae014e6f 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -1425,7 +1425,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params // Use all tasks tasks.resize(n_task); printf("%s: reading tasks", __func__); - int n_dot = n_task/100; + int n_dot = std::max((int) n_task/100, 1); int i = 0; for (auto& task : tasks) { ++i; @@ -1675,7 +1675,7 @@ static void multiple_choice_score(llama_context * ctx, const gpt_params & params llama_batch_free(batch); - if (n_done < 100) return; + if (n_done < 100 && (params.multiple_choice_tasks != 0 && params.multiple_choice_tasks < (size_t)n_task)) return; float p = 1.f*n_correct/n_done; float sigma = sqrt(p*(1-p)/(n_done-1)); From 0f98acfac6cc561dc57586bfff778405e42b576b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Steffen=20R=C3=B6cker?= Date: Sat, 18 May 2024 10:04:55 +0200 Subject: [PATCH 037/181] llama : add support for larger Granite Code Models (20B, 34B) (#7324) Tie the weights for ARCH_STARCODER to support the larger Granite code models. Partially addresses ggerganov/issues/7116 There still remains to be a few things to fix. Currently requires `--override-kv tokenizer.ggml.add_bos_token=bool:false` --- llama.cpp | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index b752ddc6b..2b91eec86 100644 --- a/llama.cpp +++ b/llama.cpp @@ -5188,7 +5188,14 @@ static bool llm_load_tensors( { 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}); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + if (!model.output) { + // needs to be on GPU + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); + ml.n_created--; // artificial tensor + ml.size_data += ggml_nbytes(model.output); + } + } for (int i = 0; i < n_layer; ++i) { From d233b507cd19fcc2d8d8963ecc6a3eb7a33f2ecc Mon Sep 17 00:00:00 2001 From: Engininja2 <139037756+Engininja2@users.noreply.github.com> Date: Sat, 18 May 2024 02:05:17 -0600 Subject: [PATCH 038/181] cuda : add half2 __shfl_xor() for ROCm 5.5 (#7263) --- ggml-cuda/common.cuh | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/ggml-cuda/common.cuh b/ggml-cuda/common.cuh index b6f0bc36a..784792ba0 100644 --- a/ggml-cuda/common.cuh +++ b/ggml-cuda/common.cuh @@ -315,6 +315,20 @@ static __device__ __forceinline__ int __dp4a(const int a, const int b, int c) { #endif return c; } + +#if defined(__HIP_PLATFORM_AMD__) && HIP_VERSION < 50600000 +// __shfl_xor() for half2 was added in ROCm 5.6 +static __device__ __forceinline__ half2 __shfl_xor(half2 var, int laneMask, int width) { + typedef union half2_b32 { + half2 val; + int b32; + } half2_b32_t; + half2_b32_t tmp; + tmp.val = var; + tmp.b32 = __shfl_xor(tmp.b32, laneMask, width); + return tmp.val; +} +#endif // defined(__HIP_PLATFORM_AMD__) && HIP_VERSION < 50600000 #endif // defined(GGML_USE_HIPBLAS) #define FP16_AVAILABLE (defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) || __CUDA_ARCH__ >= CC_PASCAL From cb42c294279bc4a0a4e926a7b5a5568049f12fa7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 18 May 2024 11:10:47 +0200 Subject: [PATCH 039/181] server: correct --threads documentation [no ci] (#7362) --- examples/server/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 13cbdcd2c..4f3262cdd 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -18,8 +18,8 @@ The project is under active development, and we are [looking for feedback and co **Command line options:** - `-v`, `--verbose`: Enable verbose server output. When using the `/completion` endpoint, this includes the tokenized prompt, the full request and the full response. -- `-t N`, `--threads N`: Set the number of threads to use during generation. Not used if model layers are offloaded to GPU. The server is using batching. This parameter is used only if one token is to be processed on CPU backend. -- `-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. Not used if model layers are offloaded to GPU. +- `-t N`, `--threads N`: Set the number of threads to use by CPU layers during generation. Not used by model layers that are offloaded to GPU. This option has no effect when using the maximum number of GPU layers. Default: `std::thread::hardware_concurrency()` (number of CPU cores). +- `-tb N, --threads-batch N`: Set the number of threads to use by CPU layers during batch and prompt processing (>= 32 tokens). This option has no effect if a GPU is available. Default: `--threads`. - `--threads-http N`: Number of threads in the http server pool to process requests. Default: `max(std::thread::hardware_concurrency() - 1, --parallel N + 2)` - `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.gguf`). - `-mu MODEL_URL --model-url MODEL_URL`: Specify a remote http url to download the file. Default: unused From 133d99c59980139f5bb75922c8b5fca67d7ba9b8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 18 May 2024 12:36:25 +0200 Subject: [PATCH 040/181] CUDA: deduplicate FlashAttention code (#7352) --- ggml-cuda/common.cuh | 11 ++ ggml-cuda/fattn-common.cuh | 115 +++++++++++++++++++ ggml-cuda/fattn-tile-f16.cu | 135 +++++----------------- ggml-cuda/fattn-tile-f32.cu | 134 +++++----------------- ggml-cuda/fattn-vec-f16.cu | 215 ++++++++++-------------------------- ggml-cuda/fattn-vec-f32.cu | 170 ++++++---------------------- ggml-cuda/fattn.cu | 177 ++++++++--------------------- ggml-cuda/softmax.cu | 13 +-- 8 files changed, 316 insertions(+), 654 deletions(-) diff --git a/ggml-cuda/common.cuh b/ggml-cuda/common.cuh index 784792ba0..8f6fd71cf 100644 --- a/ggml-cuda/common.cuh +++ b/ggml-cuda/common.cuh @@ -477,6 +477,17 @@ static const __device__ int8_t kvalues_iq4nl[16] = {-127, -104, -83, -65, -49, - typedef void (*dequantize_kernel_t)(const void * vx, const int64_t ib, const int iqs, dfloat2 & v); +static __device__ __forceinline__ float get_alibi_slope( + const float max_bias, const uint32_t h, const uint32_t n_head_log2, const float m0, const float m1 +) { + if (max_bias <= 0.0f) { + return 1.0f; + } + const float base = h < n_head_log2 ? m0 : m1; + const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; + + return powf(base, exph); +} ////////////////////// diff --git a/ggml-cuda/fattn-common.cuh b/ggml-cuda/fattn-common.cuh index 33f640691..1dd519bde 100644 --- a/ggml-cuda/fattn-common.cuh +++ b/ggml-cuda/fattn-common.cuh @@ -1,7 +1,44 @@ +#include "common.cuh" + +#include + #define FATTN_KQ_STRIDE 256 #define HALF_MAX_HALF __float2half(65504.0f/2) // Use neg. of this instead of -INFINITY to initialize KQ max vals to avoid NaN upon subtraction. #define SOFTMAX_FTZ_THRESHOLD -20.0f // Softmax exp. of values smaller than this are flushed to zero to avoid NaNs. +typedef void (* fattn_kernel_t)( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const int ne00, + const int ne01, + const int ne02, + const int ne03, + const int ne10, + const int ne11, + const int ne12, + const int ne13, + const int ne31, + const int nb31, + const int nb01, + const int nb02, + const int nb03, + const int nb11, + const int nb12, + const int nb13, + const int ne0, + const int ne1, + const int ne2, + const int ne3); + template // D == head size #if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) __launch_bounds__(D, 1) @@ -45,3 +82,81 @@ static __global__ void flash_attn_combine_results( dst[blockIdx.y*D + tid] = VKQ_numerator / VKQ_denominator; } + +template +void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kernel_t fattn_kernel, int nwarps, int cols_per_block) { + const ggml_tensor * Q = dst->src[0]; + const ggml_tensor * K = dst->src[1]; + const ggml_tensor * V = dst->src[2]; + + const ggml_tensor * mask = dst->src[3]; + + ggml_tensor * KQV = dst; + + GGML_ASSERT(Q->type == GGML_TYPE_F32); + GGML_ASSERT(K->type == GGML_TYPE_F16); + GGML_ASSERT(V->type == GGML_TYPE_F16); + GGML_ASSERT(KQV->type == GGML_TYPE_F32); + + GGML_ASSERT(!mask || mask->type == GGML_TYPE_F16); + GGML_ASSERT(!mask || mask->ne[1] >= GGML_PAD(Q->ne[1], 16) && + "the Flash-Attention CUDA kernel requires the mask to be padded to 16 and at least n_queries big"); + + GGML_ASSERT(K->ne[1] % FATTN_KQ_STRIDE == 0 && "Incorrect KV cache padding."); + + ggml_cuda_pool & pool = ctx.pool(); + cudaStream_t main_stream = ctx.stream(); + + ggml_cuda_pool_alloc dst_tmp(pool); + ggml_cuda_pool_alloc dst_tmp_meta(pool); + + if (parallel_blocks > 1) { + dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); + dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV)); + } + + const dim3 block_dim(WARP_SIZE, nwarps, 1); + const dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]); + const int shmem = 0; + + float scale = 1.0f; + float max_bias = 0.0f; + + memcpy(&scale, (float *) KQV->op_params + 0, sizeof(float)); + memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float)); + + const uint32_t n_head = Q->ne[2]; + const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); + + const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); + const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + + fattn_kernel<<>>( + (const char *) Q->data, + (const char *) K->data, + (const char *) V->data, + mask ? ((const char *) mask->data) : nullptr, + (parallel_blocks) == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr, + scale, max_bias, m0, m1, n_head_log2, + Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3], + K->ne[0], K->ne[1], K->ne[2], K->ne[3], + mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, + Q->nb[1], Q->nb[2], Q->nb[3], + K->nb[1], K->nb[2], K->nb[3], + KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3] + ); + CUDA_CHECK(cudaGetLastError()); + + if ((parallel_blocks) == 1) { + return; + } + + const dim3 block_dim_combine(D, 1, 1); + const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z); + const int shmem_combine = 0; + + flash_attn_combine_results + <<>> + (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data); + CUDA_CHECK(cudaGetLastError()); +} diff --git a/ggml-cuda/fattn-tile-f16.cu b/ggml-cuda/fattn-tile-f16.cu index d2a1077ed..4a07ac6ad 100644 --- a/ggml-cuda/fattn-tile-f16.cu +++ b/ggml-cuda/fattn-tile-f16.cu @@ -54,17 +54,8 @@ static __global__ void flash_attn_tile_ext_f16( const int stride_KV2 = nb11 / sizeof(half2); - half slopeh = __float2half(1.0f); - - // ALiBi - if (max_bias > 0.0f) { - const uint32_t h = blockIdx.y; - - const float base = h < n_head_log2 ? m0 : m1; - const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; - - slopeh = __float2half(powf(base, exph)); - } + const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); + const half slopeh = __float2half(slopef); static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); @@ -272,124 +263,50 @@ static __global__ void flash_attn_tile_ext_f16( #endif // FP16_AVAILABLE } -template void launch_fattn_tile_f16( - const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask, - ggml_cuda_pool & pool, cudaStream_t main_stream -) { - ggml_cuda_pool_alloc dst_tmp(pool); - ggml_cuda_pool_alloc dst_tmp_meta(pool); - - if (parallel_blocks > 1) { - dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); - dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV)); +template +void launch_fattn_tile_f16_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + switch (Q->ne[0]) { + case 64: { + constexpr int D = 64; + constexpr int nwarps = 8; + fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + case 128: { + constexpr int D = 128; + constexpr int nwarps = 8; + fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + default: { + GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); + } break; } - - constexpr int nwarps = 8; - const dim3 block_dim(WARP_SIZE, nwarps, 1); - const dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]); - const int shmem = 0; - - float scale = 1.0f; - float max_bias = 0.0f; - - memcpy(&scale, (float *) KQV->op_params + 0, sizeof(float)); - memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float)); - - const uint32_t n_head = Q->ne[2]; - const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); - - const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - - flash_attn_tile_ext_f16 - <<>> ( - (const char *) Q->data, - (const char *) K->data, - (const char *) V->data, - mask ? ((const char *) mask->data) : nullptr, - parallel_blocks == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr, - scale, max_bias, m0, m1, n_head_log2, - Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3], - K->ne[0], K->ne[1], K->ne[2], K->ne[3], - mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, - Q->nb[1], Q->nb[2], Q->nb[3], - K->nb[1], K->nb[2], K->nb[3], - KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3] - ); - CUDA_CHECK(cudaGetLastError()); - - if (parallel_blocks == 1) { - return; - } - - const dim3 block_dim_combine(D, 1, 1); - const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z); - const int shmem_combine = 0; - - flash_attn_combine_results - <<>> - (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data); - CUDA_CHECK(cudaGetLastError()); } void ggml_cuda_flash_attn_ext_tile_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * Q = dst->src[0]; - const ggml_tensor * K = dst->src[1]; - const ggml_tensor * V = dst->src[2]; - - const ggml_tensor * mask = dst->src[3]; - - ggml_tensor * KQV = dst; + const ggml_tensor * KQV = dst; + const ggml_tensor * Q = dst->src[0]; const int32_t precision = KQV->op_params[2]; GGML_ASSERT(precision == GGML_PREC_DEFAULT); - GGML_ASSERT(Q->ne[0] == 64 || Q->ne[0] == 128 && "FlashAttention without tensor cores only supports head sizes 64 and 128."); if (Q->ne[1] <= 16) { constexpr int cols_per_block = 16; constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_tile_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_tile_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } + launch_fattn_tile_f16_64_128(ctx, dst); return; } if (Q->ne[1] <= 32) { constexpr int cols_per_block = 32; constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_tile_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_tile_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } + launch_fattn_tile_f16_64_128(ctx, dst); return; } constexpr int cols_per_block = 32; constexpr int parallel_blocks = 1; - switch (Q->ne[0]) { - case 64: - launch_fattn_tile_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_tile_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } + launch_fattn_tile_f16_64_128(ctx, dst); } diff --git a/ggml-cuda/fattn-tile-f32.cu b/ggml-cuda/fattn-tile-f32.cu index 176895edd..130e7cbdb 100644 --- a/ggml-cuda/fattn-tile-f32.cu +++ b/ggml-cuda/fattn-tile-f32.cu @@ -53,17 +53,7 @@ static __global__ void flash_attn_tile_ext_f32( const int stride_KV2 = nb11 / sizeof(half2); - float slope = 1.0f; - - // ALiBi - if (max_bias > 0.0f) { - const uint32_t h = blockIdx.y; - - const float base = h < n_head_log2 ? m0 : m1; - const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; - - slope = powf(base, exph); - } + const float slope = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); @@ -270,124 +260,50 @@ static __global__ void flash_attn_tile_ext_f32( } } -template void launch_fattn_tile_f32( - const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask, - ggml_cuda_pool & pool, cudaStream_t main_stream -) { - ggml_cuda_pool_alloc dst_tmp(pool); - ggml_cuda_pool_alloc dst_tmp_meta(pool); - - if (parallel_blocks > 1) { - dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); - dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV)); +template +void launch_fattn_tile_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + switch (Q->ne[0]) { + case 64: { + constexpr int D = 64; + constexpr int nwarps = 8; + fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + case 128: { + constexpr int D = 128; + constexpr int nwarps = 8; + fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + default: { + GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); + } break; } - - constexpr int nwarps = 8; - const dim3 block_dim(WARP_SIZE, nwarps, 1); - const dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]); - const int shmem = 0; - - float scale = 1.0f; - float max_bias = 0.0f; - - memcpy(&scale, (float *) KQV->op_params + 0, sizeof(float)); - memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float)); - - const uint32_t n_head = Q->ne[2]; - const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); - - const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - - flash_attn_tile_ext_f32 - <<>> ( - (const char *) Q->data, - (const char *) K->data, - (const char *) V->data, - mask ? ((const char *) mask->data) : nullptr, - parallel_blocks == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr, - scale, max_bias, m0, m1, n_head_log2, - Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3], - K->ne[0], K->ne[1], K->ne[2], K->ne[3], - mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, - Q->nb[1], Q->nb[2], Q->nb[3], - K->nb[1], K->nb[2], K->nb[3], - KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3] - ); - CUDA_CHECK(cudaGetLastError()); - - if (parallel_blocks == 1) { - return; - } - - const dim3 block_dim_combine(D, 1, 1); - const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z); - const int shmem_combine = 0; - - flash_attn_combine_results - <<>> - (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data); - CUDA_CHECK(cudaGetLastError()); } void ggml_cuda_flash_attn_ext_tile_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * Q = dst->src[0]; - const ggml_tensor * K = dst->src[1]; - const ggml_tensor * V = dst->src[2]; - - const ggml_tensor * mask = dst->src[3]; - - ggml_tensor * KQV = dst; + const ggml_tensor * KQV = dst; + const ggml_tensor * Q = dst->src[0]; const int32_t precision = KQV->op_params[2]; GGML_ASSERT(precision == GGML_PREC_DEFAULT); - GGML_ASSERT(Q->ne[0] == 64 || Q->ne[0] == 128 && "FlashAttention without tensor cores only supports head sizes 64 and 128."); if (Q->ne[1] <= 16) { constexpr int cols_per_block = 16; constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_tile_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_tile_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } + launch_fattn_tile_f32_64_128(ctx, dst); return; } if (Q->ne[1] <= 32) { constexpr int cols_per_block = 32; constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_tile_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_tile_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } + launch_fattn_tile_f32_64_128(ctx, dst); return; } constexpr int cols_per_block = 32; constexpr int parallel_blocks = 1; - switch (Q->ne[0]) { - case 64: - launch_fattn_tile_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_tile_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } + launch_fattn_tile_f32_64_128(ctx, dst); } diff --git a/ggml-cuda/fattn-vec-f16.cu b/ggml-cuda/fattn-vec-f16.cu index a18be5ddc..54e1ac5d1 100644 --- a/ggml-cuda/fattn-vec-f16.cu +++ b/ggml-cuda/fattn-vec-f16.cu @@ -53,17 +53,8 @@ static __global__ void flash_attn_vec_ext_f16( const int stride_KV = nb11 / sizeof(half); const int stride_KV2 = nb11 / sizeof(half2); - half slopeh = __float2half(1.0f); - - // ALiBi - if (max_bias > 0.0f) { - const uint32_t h = blockIdx.y; - - const float base = h < n_head_log2 ? m0 : m1; - const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; - - slopeh = __float2half(powf(base, exph)); - } + const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); + const half slopeh = __float2half(slopef); static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); constexpr int nwarps = D / WARP_SIZE; @@ -232,196 +223,104 @@ static __global__ void flash_attn_vec_ext_f16( dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; } - if (parallel_blocks != 1 && threadIdx.x < ncols) { - dst_meta[(ic0 + threadIdx.x)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[threadIdx.x], kqsum[threadIdx.x]); + if (parallel_blocks != 1 && tid < ncols) { + dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]); } #else NO_DEVICE_CODE; #endif // FP16_AVAILABLE } -template void launch_fattn_vec_f16( - const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask, - ggml_cuda_pool & pool, cudaStream_t main_stream -) { - ggml_cuda_pool_alloc dst_tmp(pool); - ggml_cuda_pool_alloc dst_tmp_meta(pool); - - if (parallel_blocks > 1) { - dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); - dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV)); - } - - constexpr int nwarps = (D + WARP_SIZE - 1) / WARP_SIZE; - const dim3 block_dim(WARP_SIZE, nwarps, 1); - const dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]); - const int shmem = 0; - - float scale = 1.0f; - float max_bias = 0.0f; - - memcpy(&scale, (float *) KQV->op_params + 0, sizeof(float)); - memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float)); - - const uint32_t n_head = Q->ne[2]; - const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); - - const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - - flash_attn_vec_ext_f16 - <<>> ( - (const char *) Q->data, - (const char *) K->data, - (const char *) V->data, - mask ? ((const char *) mask->data) : nullptr, - parallel_blocks == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr, - scale, max_bias, m0, m1, n_head_log2, - Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3], - K->ne[0], K->ne[1], K->ne[2], K->ne[3], - mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, - Q->nb[1], Q->nb[2], Q->nb[3], - K->nb[1], K->nb[2], K->nb[3], - KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3] - ); - CUDA_CHECK(cudaGetLastError()); - - if (parallel_blocks == 1) { - return; - } - - const dim3 block_dim_combine(D, 1, 1); - const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z); - const int shmem_combine = 0; - - flash_attn_combine_results - <<>> - (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data); - CUDA_CHECK(cudaGetLastError()); -} - void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * Q = dst->src[0]; - const ggml_tensor * K = dst->src[1]; - const ggml_tensor * V = dst->src[2]; - - const ggml_tensor * mask = dst->src[3]; - ggml_tensor * KQV = dst; + ggml_tensor * Q = dst->src[0]; const int32_t precision = KQV->op_params[2]; GGML_ASSERT(precision == GGML_PREC_DEFAULT); - constexpr int cols_per_block = 1; + constexpr int cols_per_block = 1; constexpr int parallel_blocks = 4; switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 256: - launch_fattn_vec_f16<256, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; + case 64: { + constexpr int D = 64; + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + case 128: { + constexpr int D = 128; + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + case 256: { + constexpr int D = 256; + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; default: GGML_ASSERT(false); break; } } +template +void launch_fattn_vec_f16_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + switch (Q->ne[0]) { + case 64: { + constexpr int D = 64; + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + case 128: { + constexpr int D = 128; + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + default: { + GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); + } break; + } +} + void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * Q = dst->src[0]; - const ggml_tensor * K = dst->src[1]; - const ggml_tensor * V = dst->src[2]; - - const ggml_tensor * mask = dst->src[3]; - - ggml_tensor * KQV = dst; + const ggml_tensor * KQV = dst; + const ggml_tensor * Q = dst->src[0]; const int32_t precision = KQV->op_params[2]; GGML_ASSERT(precision == GGML_PREC_DEFAULT); - GGML_ASSERT(Q->ne[0] == 64 || Q->ne[0] == 128 && "FlashAttention without tensor cores only supports head sizes 64 and 128."); if (Q->ne[1] == 1) { - constexpr int cols_per_block = 1; - constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } + ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); return; } if (Q->ne[1] == 2) { - constexpr int cols_per_block = 2; + constexpr int cols_per_block = 2; constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } + launch_fattn_vec_f16_64_128(ctx, dst); return; } if (Q->ne[1] <= 4) { - constexpr int cols_per_block = 4; + constexpr int cols_per_block = 4; constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } + launch_fattn_vec_f16_64_128(ctx, dst); return; } if (Q->ne[1] <= 8) { - constexpr int cols_per_block = 8; + constexpr int cols_per_block = 8; constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } + launch_fattn_vec_f16_64_128(ctx, dst); return; } - constexpr int cols_per_block = 8; + constexpr int cols_per_block = 8; constexpr int parallel_blocks = 1; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f16< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f16<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } + launch_fattn_vec_f16_64_128(ctx, dst); } diff --git a/ggml-cuda/fattn-vec-f32.cu b/ggml-cuda/fattn-vec-f32.cu index 91fcdc8c3..5bcabd092 100644 --- a/ggml-cuda/fattn-vec-f32.cu +++ b/ggml-cuda/fattn-vec-f32.cu @@ -52,17 +52,7 @@ static __global__ void flash_attn_vec_ext_f32( const int stride_KV = nb11 / sizeof(half); const int stride_KV2 = nb11 / sizeof(half2); - float slope = 1.0f; - - // ALiBi - if (max_bias > 0.0f) { - const uint32_t h = blockIdx.y; - - const float base = h < n_head_log2 ? m0 : m1; - const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; - - slope = powf(base, exph); - } + const float slope = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); constexpr int nwarps = D / WARP_SIZE; @@ -221,161 +211,65 @@ static __global__ void flash_attn_vec_ext_f32( dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; } - if (parallel_blocks != 1 && threadIdx.x < ncols) { - dst_meta[(ic0 + threadIdx.x)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[threadIdx.x], kqsum[threadIdx.x]); + if (parallel_blocks != 1 && tid < ncols) { + dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]); } } -template void launch_fattn_vec_f32( - const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask, - ggml_cuda_pool & pool, cudaStream_t main_stream -) { - ggml_cuda_pool_alloc dst_tmp(pool); - ggml_cuda_pool_alloc dst_tmp_meta(pool); - - if (parallel_blocks > 1) { - dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); - dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV)); +template +void launch_fattn_vec_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + switch (Q->ne[0]) { + case 64: { + constexpr int D = 64; + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + case 128: { + constexpr int D = 128; + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + } break; + default: { + GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); + } break; } - - constexpr int nwarps = (D + WARP_SIZE - 1) / WARP_SIZE; - const dim3 block_dim(WARP_SIZE, nwarps, 1); - const dim3 blocks_num(parallel_blocks*((Q->ne[1] + cols_per_block - 1) / cols_per_block), Q->ne[2], Q->ne[3]); - const int shmem = 0; - - float scale = 1.0f; - float max_bias = 0.0f; - - memcpy(&scale, (float *) KQV->op_params + 0, sizeof(float)); - memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float)); - - const uint32_t n_head = Q->ne[2]; - const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); - - const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - - flash_attn_vec_ext_f32 - <<>> ( - (const char *) Q->data, - (const char *) K->data, - (const char *) V->data, - mask ? ((const char *) mask->data) : nullptr, - parallel_blocks == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr, - scale, max_bias, m0, m1, n_head_log2, - Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3], - K->ne[0], K->ne[1], K->ne[2], K->ne[3], - mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, - Q->nb[1], Q->nb[2], Q->nb[3], - K->nb[1], K->nb[2], K->nb[3], - KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3] - ); - CUDA_CHECK(cudaGetLastError()); - - if (parallel_blocks == 1) { - return; - } - - const dim3 block_dim_combine(D, 1, 1); - const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z); - const int shmem_combine = 0; - - flash_attn_combine_results - <<>> - (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data); - CUDA_CHECK(cudaGetLastError()); } void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * Q = dst->src[0]; - const ggml_tensor * K = dst->src[1]; - const ggml_tensor * V = dst->src[2]; - - const ggml_tensor * mask = dst->src[3]; - - ggml_tensor * KQV = dst; - - GGML_ASSERT(Q->ne[0] == 64 || Q->ne[0] == 128 && "FlashAttention without tensor cores only supports head sizes 64 and 128."); if (Q->ne[1] == 1) { - constexpr int cols_per_block = 1; + constexpr int cols_per_block = 1; constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } + launch_fattn_vec_f32_64_128(ctx, dst); return; } if (Q->ne[1] == 2) { - constexpr int cols_per_block = 2; + constexpr int cols_per_block = 2; constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } + launch_fattn_vec_f32_64_128(ctx, dst); return; } if (Q->ne[1] <= 4) { - constexpr int cols_per_block = 4; + constexpr int cols_per_block = 4; constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } + launch_fattn_vec_f32_64_128(ctx, dst); return; } if (Q->ne[1] <= 8) { - constexpr int cols_per_block = 8; + constexpr int cols_per_block = 8; constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } + launch_fattn_vec_f32_64_128(ctx, dst); return; } - constexpr int cols_per_block = 8; + constexpr int cols_per_block = 8; constexpr int parallel_blocks = 1; - switch (Q->ne[0]) { - case 64: - launch_fattn_vec_f32< 64, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - case 128: - launch_fattn_vec_f32<128, cols_per_block, parallel_blocks>(Q, K, V, KQV, mask, ctx.pool(), ctx.stream()); - break; - default: - GGML_ASSERT(false); - break; - } + launch_fattn_vec_f32_64_128(ctx, dst); } diff --git a/ggml-cuda/fattn.cu b/ggml-cuda/fattn.cu index a1918e258..af7c95232 100644 --- a/ggml-cuda/fattn.cu +++ b/ggml-cuda/fattn.cu @@ -85,19 +85,9 @@ static __global__ void flash_attn_ext_f16( const int stride_Q = nb01 / sizeof(float); const int stride_KV = nb11 / sizeof(half); - half slopeh = __float2half(1.0f); - half2 slope2 = make_half2(1.0f, 1.0f); - - // ALiBi - if (max_bias > 0.0f) { - const uint32_t h = blockIdx.y; - - const float base = h < n_head_log2 ? m0 : m1; - const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; - - slopeh = __float2half(powf(base, exph)); - slope2 = make_half2(slopeh, slopeh); - } + const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); + const half slopeh = __float2half(slopef); + const half2 slope2 = make_half2(slopef, slopef); frag_b Q_b[D/16][ncols/frag_n]; @@ -439,108 +429,37 @@ static_assert(get_VKQ_stride( 80, 1, 16) == 16, "Test failed."); static_assert(get_VKQ_stride( 80, 2, 16) == 16, "Test failed."); static_assert(get_VKQ_stride( 80, 4, 16) == 16, "Test failed."); -template void launch_fattn_f16_impl( - const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask, - ggml_cuda_pool & pool, cudaStream_t main_stream -) { - ggml_cuda_pool_alloc dst_tmp(pool); - ggml_cuda_pool_alloc dst_tmp_meta(pool); +template +void launch_fattn_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; - if (parallel_blocks > 1) { - dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); - dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV)); - } - - constexpr int frag_m = (cols_per_block) == 8 && (D) % 32 == 0 ? 32 : 16; - const dim3 block_dim(WARP_SIZE, nwarps, 1); - const dim3 blocks_num(parallel_blocks*(Q->ne[1] + cols_per_block - 1) / cols_per_block, Q->ne[2], Q->ne[3]); - const int shmem = 0; - - float scale = 1.0f; - float max_bias = 0.0f; - - memcpy(&scale, (float *) KQV->op_params + 0, sizeof(float)); - memcpy(&max_bias, (float *) KQV->op_params + 1, sizeof(float)); - - const uint32_t n_head = Q->ne[2]; - const uint32_t n_head_log2 = 1u << (uint32_t) floorf(log2f((float) n_head)); - - const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); - const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - - flash_attn_ext_f16 - <<>> ( - (const char *) Q->data, - (const char *) K->data, - (const char *) V->data, - mask ? ((const char *) mask->data) : nullptr, - (parallel_blocks) == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr, - scale, max_bias, m0, m1, n_head_log2, - Q->ne[0], Q->ne[1], Q->ne[2], Q->ne[3], - K->ne[0], K->ne[1], K->ne[2], K->ne[3], - mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, - Q->nb[1], Q->nb[2], Q->nb[3], - K->nb[1], K->nb[2], K->nb[3], - KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3] - ); - CUDA_CHECK(cudaGetLastError()); - - if ((parallel_blocks) == 1) { - return; - } - - const dim3 block_dim_combine(D, 1, 1); - const dim3 blocks_num_combine(Q->ne[1], blocks_num.y, blocks_num.z); - const int shmem_combine = 0; - - flash_attn_combine_results - <<>> - (dst_tmp.ptr, dst_tmp_meta.ptr, (float *) KQV->data); - CUDA_CHECK(cudaGetLastError()); -} - -template void launch_fattn_f16( - const ggml_tensor * Q, const ggml_tensor * K, const ggml_tensor * V, ggml_tensor * KQV, const ggml_tensor * mask, - const int nsm, ggml_cuda_pool & pool, cudaStream_t main_stream -) { + constexpr int frag_m = cols_per_block == 8 && D % 32 == 0 ? 32 : 16; const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3]; + const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm; if (4*blocks_num_pb1 < 2*nsm) { - launch_fattn_f16_impl(Q, K, V, KQV, mask, pool, main_stream); + constexpr int parallel_blocks = 4; + fattn_kernel_t fattn_kernel = flash_attn_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); return; } if (2*blocks_num_pb1 < 2*nsm) { - launch_fattn_f16_impl(Q, K, V, KQV, mask, pool, main_stream); + constexpr int parallel_blocks = 2; + fattn_kernel_t fattn_kernel = flash_attn_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); return; } - launch_fattn_f16_impl(Q, K, V, KQV, mask, pool, main_stream); + constexpr int parallel_blocks = 1; + fattn_kernel_t fattn_kernel = flash_attn_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); } void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * Q = dst->src[0]; - const ggml_tensor * K = dst->src[1]; - const ggml_tensor * V = dst->src[2]; - - const ggml_tensor * mask = dst->src[3]; - - ggml_tensor * KQV = dst; - - GGML_ASSERT(Q->type == GGML_TYPE_F32); - GGML_ASSERT(K->type == GGML_TYPE_F16); - GGML_ASSERT(V->type == GGML_TYPE_F16); - GGML_ASSERT(KQV->type == GGML_TYPE_F32); - - GGML_ASSERT(!mask || mask->type == GGML_TYPE_F16); - GGML_ASSERT(!mask || mask->ne[1] >= GGML_PAD(Q->ne[1], 16) && - "the Flash-Attention CUDA kernel requires the mask to be padded to 16 and at least n_queries big"); - - GGML_ASSERT(K->ne[1] % FATTN_KQ_STRIDE == 0 && "Incorrect KV cache padding."); + const ggml_tensor * KQV = dst; + const ggml_tensor * Q = dst->src[0]; ggml_cuda_set_device(ctx.device); - - const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; - const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm; - + const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; const int32_t precision = KQV->op_params[2]; // On AMD the tile kernels perform poorly, use the vec kernel instead: @@ -582,22 +501,22 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst constexpr int nwarps = 4; switch (Q->ne[0]) { case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 64, cols_per_block, nwarps, float>(ctx, dst); break; case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 80, cols_per_block, nwarps, float>(ctx, dst); break; case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 96, cols_per_block, nwarps, float>(ctx, dst); break; case 112: - launch_fattn_f16<112, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<112, cols_per_block, nwarps, float>(ctx, dst); break; case 128: - launch_fattn_f16<128, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<128, cols_per_block, nwarps, float>(ctx, dst); break; case 256: - launch_fattn_f16<256, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<256, cols_per_block, nwarps, float>(ctx, dst); break; default: GGML_ASSERT(false); @@ -608,22 +527,22 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst constexpr int nwarps = 4; switch (Q->ne[0]) { case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 64, cols_per_block, nwarps, float>(ctx, dst); break; case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 80, cols_per_block, nwarps, float>(ctx, dst); break; case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 96, cols_per_block, nwarps, float>(ctx, dst); break; case 112: - launch_fattn_f16<112, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<112, cols_per_block, nwarps, float>(ctx, dst); break; case 128: - launch_fattn_f16<128, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<128, cols_per_block, nwarps, float>(ctx, dst); break; // case 256: - // launch_fattn_f16<256, cols_per_block, nwarps, float>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + // launch_fattn_f16<256, cols_per_block, nwarps, float>(ctx, dst); // break; default: GGML_ASSERT(false); @@ -643,16 +562,16 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst constexpr int nwarps = 4; switch (Q->ne[0]) { case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 64, cols_per_block, nwarps, half>(ctx, dst); break; case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst); break; case 128: - launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst); break; case 256: - launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst); break; default: GGML_ASSERT(false); @@ -666,22 +585,22 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst constexpr int nwarps = 4; switch (Q->ne[0]) { case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 64, cols_per_block, nwarps, half>(ctx, dst); break; case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 80, cols_per_block, nwarps, half>(ctx, dst); break; case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst); break; case 112: - launch_fattn_f16<112, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<112, cols_per_block, nwarps, half>(ctx, dst); break; case 128: - launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst); break; case 256: - launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst); break; default: GGML_ASSERT(false); @@ -694,22 +613,22 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst constexpr int nwarps = 4; switch (Q->ne[0]) { case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 64, cols_per_block, nwarps, half>(ctx, dst); break; case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 80, cols_per_block, nwarps, half>(ctx, dst); break; case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst); break; case 112: - launch_fattn_f16<112, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<112, cols_per_block, nwarps, half>(ctx, dst); break; case 128: - launch_fattn_f16<128, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst); break; case 256: - launch_fattn_f16<256, cols_per_block, nwarps, half>(Q, K, V, KQV, mask, nsm, ctx.pool(), ctx.stream()); + launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst); break; default: GGML_ASSERT(false); diff --git a/ggml-cuda/softmax.cu b/ggml-cuda/softmax.cu index ca85285a3..ce64f2f2c 100644 --- a/ggml-cuda/softmax.cu +++ b/ggml-cuda/softmax.cu @@ -1,3 +1,4 @@ +#include "common.cuh" #include "softmax.cuh" template @@ -23,17 +24,7 @@ static __global__ void soft_max_f32(const float * x, const T * mask, float * dst const int warp_id = threadIdx.x / WARP_SIZE; const int lane_id = threadIdx.x % WARP_SIZE; - float slope = 1.0f; - - // ALiBi - if (max_bias > 0.0f) { - const int h = rowx/nrows_y; // head index - - const float base = h < n_head_log2 ? m0 : m1; - const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; - - slope = powf(base, exph); - } + const float slope = get_alibi_slope(max_bias, rowx/nrows_y, n_head_log2, m0, m1); extern __shared__ float data_soft_max_f32[]; float * buf_iw = data_soft_max_f32; // shared memory buffer for inter-warp communication From 511182eabb36f6ec9776e2b3c4d7e16d93d0ac0d Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 18 May 2024 13:40:39 +0300 Subject: [PATCH 041/181] android : use "ci-android" branch for CI (#7341) * android : use "ci-android" branch for CI * ggml : disable SIMD exp and silu for 32-bit ARM ggml-ci * android : do not fetch, use add_subdirectory instead * cmake : provide binary dir --- .../app/src/main/cpp/CMakeLists.txt | 18 ++++++++++-------- ggml.c | 6 +++--- 2 files changed, 13 insertions(+), 11 deletions(-) diff --git a/examples/llama.android/app/src/main/cpp/CMakeLists.txt b/examples/llama.android/app/src/main/cpp/CMakeLists.txt index 85139329a..688a4750d 100644 --- a/examples/llama.android/app/src/main/cpp/CMakeLists.txt +++ b/examples/llama.android/app/src/main/cpp/CMakeLists.txt @@ -12,15 +12,17 @@ cmake_minimum_required(VERSION 3.22.1) # build script scope). project("llama-android") -include(FetchContent) -FetchContent_Declare( - llama - GIT_REPOSITORY https://github.com/ggerganov/llama.cpp - GIT_TAG master -) +#include(FetchContent) +#FetchContent_Declare( +# llama +# GIT_REPOSITORY https://github.com/ggerganov/llama.cpp +# GIT_TAG ci-android +#) +# +## Also provides "common" +#FetchContent_MakeAvailable(llama) -# Also provides "common" -FetchContent_MakeAvailable(llama) +add_subdirectory(../../../../../../ please-work) # Creates and names a library, sets it as either STATIC # or SHARED, and provides the relative paths to its source code. diff --git a/ggml.c b/ggml.c index 55152bce4..a04c74ddd 100644 --- a/ggml.c +++ b/ggml.c @@ -2076,7 +2076,7 @@ inline static float ggml_silu_f32(float x) { return x/(1.0f + expf(-x)); } -#if defined(__ARM_NEON) +#if defined(__ARM_NEON) && defined(__aarch64__) // adapted from arm limited optimized routine // the maximum error is 1.45358 plus 0.5 ulps @@ -2288,7 +2288,7 @@ static void ggml_vec_silu_f32(const int n, float * y, const float * x) { for (; i + 3 < n; i += 4) { _mm_storeu_ps(y + i, ggml_v_silu(_mm_loadu_ps(x + i))); } -#elif defined(__ARM_NEON) +#elif defined(__ARM_NEON) && defined(__aarch64__) for (; i + 3 < n; i += 4) { vst1q_f32(y + i, ggml_v_silu(vld1q_f32(x + i))); } @@ -2335,7 +2335,7 @@ static ggml_float ggml_vec_soft_max_f32(const int n, float * y, const float * x, #endif sum += (ggml_float)_mm_cvtss_f32(val); } -#elif defined(__ARM_NEON) +#elif defined(__ARM_NEON) && defined(__aarch64__) for (; i + 3 < n; i += 4) { float32x4_t val = ggml_v_expf(vsubq_f32(vld1q_f32(x + i), vdupq_n_f32(max))); From 059031b8c40e1f4ba60586842c5b1ed3ddf61842 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 18 May 2024 18:55:54 +0300 Subject: [PATCH 042/181] ci : re-enable sanitizer runs (#7358) * Revert "ci : temporary disable sanitizer builds (#6128)" This reverts commit 4f6d1337ca5a409dc74aca8c479b7c34408a69c0. * ci : trigger --- .github/workflows/build.yml | 68 ++++++++++++++++++------------------ .github/workflows/server.yml | 7 ++-- CMakeLists.txt | 2 +- 3 files changed, 39 insertions(+), 38 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 0109cc004..53e61b80f 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -271,40 +271,40 @@ jobs: path: llama-${{ steps.tag.outputs.name }}-bin-ubuntu-x64.zip name: llama-bin-ubuntu-x64.zip -# ubuntu-latest-cmake-sanitizer: -# runs-on: ubuntu-latest -# -# continue-on-error: true -# -# strategy: -# matrix: -# sanitizer: [ADDRESS, THREAD, UNDEFINED] -# build_type: [Debug, Release] -# -# steps: -# - name: Clone -# id: checkout -# uses: actions/checkout@v4 -# -# - name: Dependencies -# id: depends -# run: | -# sudo apt-get update -# sudo apt-get install build-essential -# -# - name: Build -# id: cmake_build -# run: | -# mkdir build -# cd build -# cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} -# cmake --build . --config ${{ matrix.build_type }} -j $(nproc) -# -# - name: Test -# id: cmake_test -# run: | -# cd build -# ctest -L main --verbose --timeout 900 + ubuntu-latest-cmake-sanitizer: + runs-on: ubuntu-latest + + continue-on-error: true + + strategy: + matrix: + sanitizer: [ADDRESS, THREAD, UNDEFINED] + build_type: [Debug, Release] + + steps: + - name: Clone + id: checkout + uses: actions/checkout@v4 + + - name: Dependencies + id: depends + run: | + sudo apt-get update + sudo apt-get install build-essential + + - name: Build + id: cmake_build + run: | + mkdir build + cd build + cmake .. -DLLAMA_FATAL_WARNINGS=ON -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON -DCMAKE_BUILD_TYPE=${{ matrix.build_type }} + cmake --build . --config ${{ matrix.build_type }} -j $(nproc) + + - name: Test + id: cmake_test + run: | + cd build + ctest -L main --verbose --timeout 900 ubuntu-latest-cmake-mpi: runs-on: ubuntu-latest diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index afac89c5b..217af67cf 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -32,13 +32,14 @@ jobs: strategy: matrix: - # TODO: temporary disabled due to linux kernel issues - #sanitizer: [ADDRESS, THREAD, UNDEFINED] - sanitizer: [UNDEFINED] + sanitizer: [ADDRESS, THREAD, UNDEFINED] build_type: [Debug] include: - build_type: Release sanitizer: "" + - build_type: Debug + sanitizer: THREAD + disabled_on_pr: true fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken steps: diff --git a/CMakeLists.txt b/CMakeLists.txt index 5e4daae03..cbeb2ee37 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1,4 +1,4 @@ -cmake_minimum_required(VERSION 3.14) # for add_link_options and implicit target directories. +cmake_minimum_required(VERSION 3.14) # for add_link_options and implicit target directories. project("llama.cpp" C CXX) include(CheckIncludeFileCXX) From f5bf761747988ee1832766f7d1433739aff810da Mon Sep 17 00:00:00 2001 From: fraxy-v <65565042+fraxy-v@users.noreply.github.com> Date: Sun, 19 May 2024 01:44:42 +0300 Subject: [PATCH 043/181] Capture CUDA logging output (#7298) * logging: output capture in cuda module * fix compile error * fix: vsnprintf terminates with 0, string use not correct * post review * Update llama.cpp Co-authored-by: slaren * Update llama.cpp Co-authored-by: slaren --------- Co-authored-by: slaren --- ggml-cuda.cu | 100 +++++++++++++++++++++++++++++++++++---------------- ggml-cuda.h | 1 + llama.cpp | 4 +++ 3 files changed, 75 insertions(+), 30 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 04b6e5285..754611bf3 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -43,19 +43,59 @@ #include #include #include +#include +#include #include #include static_assert(sizeof(half) == sizeof(ggml_fp16_t), "wrong fp16 size"); +static void ggml_cuda_default_log_callback(enum ggml_log_level level, const char * msg, void * user_data) { + GGML_UNUSED(level); + GGML_UNUSED(user_data); + fprintf(stderr, "%s", msg); +} + +ggml_log_callback ggml_cuda_log_callback = ggml_cuda_default_log_callback; +void * ggml_cuda_log_user_data = NULL; + +GGML_API void ggml_backend_cuda_log_set_callback(ggml_log_callback log_callback, void * user_data) { + ggml_cuda_log_callback = log_callback; + ggml_cuda_log_user_data = user_data; +} + +#define GGML_CUDA_LOG_INFO(...) ggml_cuda_log(GGML_LOG_LEVEL_INFO, __VA_ARGS__) +#define GGML_CUDA_LOG_WARN(...) ggml_cuda_log(GGML_LOG_LEVEL_WARN, __VA_ARGS__) +#define GGML_CUDA_LOG_ERROR(...) ggml_cuda_log(GGML_LOG_LEVEL_ERROR, __VA_ARGS__) + +GGML_ATTRIBUTE_FORMAT(2, 3) +static void ggml_cuda_log(enum ggml_log_level level, const char * format, ...) { + if (ggml_cuda_log_callback != NULL) { + va_list args; + va_start(args, format); + char buffer[128]; + int len = vsnprintf(buffer, 128, format, args); + if (len < 128) { + ggml_cuda_log_callback(level, buffer, ggml_cuda_log_user_data); + } else { + std::vector buffer2(len + 1); // vsnprintf adds a null terminator + va_end(args); + va_start(args, format); + vsnprintf(&buffer2[0], buffer2.size(), format, args); + ggml_cuda_log_callback(level, buffer2.data(), ggml_cuda_log_user_data); + } + va_end(args); + } +} + [[noreturn]] void ggml_cuda_error(const char * stmt, const char * func, const char * file, int line, const char * msg) { int id = -1; // in case cudaGetDevice fails cudaGetDevice(&id); - fprintf(stderr, "CUDA error: %s\n", msg); - fprintf(stderr, " current device: %d, in function %s at %s:%d\n", id, func, file, line); - fprintf(stderr, " %s\n", stmt); + GGML_CUDA_LOG_ERROR("CUDA error: %s\n", msg); + GGML_CUDA_LOG_ERROR(" current device: %d, in function %s at %s:%d\n", id, func, file, line); + GGML_CUDA_LOG_ERROR(" %s\n", stmt); // abort with GGML_ASSERT to get a stack trace GGML_ASSERT(!"CUDA error"); } @@ -91,7 +131,7 @@ static ggml_cuda_device_info ggml_cuda_init() { cudaError_t err = cudaGetDeviceCount(&info.device_count); if (err != cudaSuccess) { - fprintf(stderr, "%s: failed to initialize " GGML_CUDA_NAME ": %s\n", __func__, cudaGetErrorString(err)); + GGML_CUDA_LOG_ERROR("%s: failed to initialize " GGML_CUDA_NAME ": %s\n", __func__, cudaGetErrorString(err)); return info; } @@ -99,16 +139,16 @@ static ggml_cuda_device_info ggml_cuda_init() { int64_t total_vram = 0; #if defined(GGML_CUDA_FORCE_MMQ) - fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ: yes\n", __func__); + GGML_CUDA_LOG_INFO("%s: GGML_CUDA_FORCE_MMQ: yes\n", __func__); #else - fprintf(stderr, "%s: GGML_CUDA_FORCE_MMQ: no\n", __func__); + GGML_CUDA_LOG_INFO("%s: GGML_CUDA_FORCE_MMQ: no\n", __func__); #endif #if defined(CUDA_USE_TENSOR_CORES) - fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: yes\n", __func__); + GGML_CUDA_LOG_INFO("%s: CUDA_USE_TENSOR_CORES: yes\n", __func__); #else - fprintf(stderr, "%s: CUDA_USE_TENSOR_CORES: no\n", __func__); + GGML_CUDA_LOG_INFO("%s: CUDA_USE_TENSOR_CORES: no\n", __func__); #endif - fprintf(stderr, "%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, info.device_count); + GGML_CUDA_LOG_INFO("%s: found %d " GGML_CUDA_NAME " devices:\n", __func__, info.device_count); for (int id = 0; id < info.device_count; ++id) { int device_vmm = 0; @@ -129,7 +169,7 @@ static ggml_cuda_device_info ggml_cuda_init() { cudaDeviceProp prop; CUDA_CHECK(cudaGetDeviceProperties(&prop, id)); - fprintf(stderr, " Device %d: %s, compute capability %d.%d, VMM: %s\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no"); + GGML_CUDA_LOG_INFO(" Device %d: %s, compute capability %d.%d, VMM: %s\n", id, prop.name, prop.major, prop.minor, device_vmm ? "yes" : "no"); info.default_tensor_split[id] = total_vram; total_vram += prop.totalGlobalMem; @@ -235,8 +275,8 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool { *actual_size = look_ahead_size; pool_size += look_ahead_size; #ifdef DEBUG_CUDA_MALLOC - fprintf(stderr, "%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, device, nnz, - (uint32_t)(max_size/1024/1024), (uint32_t)(pool_size/1024/1024), (uint32_t)(size/1024/1024)); + GGML_CUDA_LOG_INFO("%s[%d]: %d buffers, max_size = %u MB, pool_size = %u MB, requested %u MB\n", __func__, device, nnz, + (uint32_t)(max_size / 1024 / 1024), (uint32_t)(pool_size / 1024 / 1024), (uint32_t)(size / 1024 / 1024)); #endif return ptr; } @@ -250,7 +290,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool { return; } } - fprintf(stderr, "WARNING: cuda buffer pool full, increase MAX_CUDA_BUFFERS\n"); + GGML_CUDA_LOG_WARN("Cuda buffer pool full, increase MAX_CUDA_BUFFERS\n"); ggml_cuda_set_device(device); CUDA_CHECK(cudaFree(ptr)); pool_size -= size; @@ -499,7 +539,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffe void * dev_ptr; cudaError_t err = cudaMalloc(&dev_ptr, size); if (err != cudaSuccess) { - fprintf(stderr, "%s: allocating %.2f MiB on device %d: cudaMalloc failed: %s\n", __func__, size/1024.0/1024.0, buft_ctx->device, cudaGetErrorString(err)); + GGML_CUDA_LOG_ERROR("%s: allocating %.2f MiB on device %d: cudaMalloc failed: %s\n", __func__, size / 1024.0 / 1024.0, buft_ctx->device, cudaGetErrorString(err)); return nullptr; } @@ -1002,8 +1042,8 @@ static void * ggml_cuda_host_malloc(size_t size) { if (err != cudaSuccess) { // clear the error cudaGetLastError(); - fprintf(stderr, "%s: warning: failed to allocate %.2f MiB of pinned memory: %s\n", __func__, - size/1024.0/1024.0, cudaGetErrorString(err)); + GGML_CUDA_LOG_WARN("%s: failed to allocate %.2f MiB of pinned memory: %s\n", __func__, + size / 1024.0 / 1024.0, cudaGetErrorString(err)); return nullptr; } @@ -2246,7 +2286,7 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg break; case GGML_OP_MUL_MAT: if (dst->src[0]->ne[3] != dst->src[1]->ne[3]) { - fprintf(stderr, "%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, dst->name, dst->src[0]->ne[3], dst->src[1]->ne[3]); + GGML_CUDA_LOG_ERROR("%s: cannot compute %s: src0->ne[3] = %" PRId64 ", src1->ne[3] = %" PRId64 " - fallback to CPU\n", __func__, dst->name, dst->src[0]->ne[3], dst->src[1]->ne[3]); return false; } else { ggml_cuda_mul_mat(ctx, dst->src[0], dst->src[1], dst); @@ -2300,7 +2340,7 @@ static bool ggml_cuda_compute_forward(ggml_backend_cuda_context & ctx, struct gg cudaError_t err = cudaGetLastError(); if (err != cudaSuccess) { - fprintf(stderr, "%s: %s failed\n", __func__, ggml_op_desc(dst)); + GGML_CUDA_LOG_ERROR("%s: %s failed\n", __func__, ggml_op_desc(dst)); CUDA_CHECK(err); } @@ -2476,7 +2516,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) { cuda_ctx->cuda_graph->disable_due_to_gpu_arch = true; #ifndef NDEBUG - fprintf(stderr, "%s: disabling CUDA graphs due to GPU architecture\n", __func__); + GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to GPU architecture\n", __func__); #endif } } @@ -2523,14 +2563,14 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t if (node->src[0] && ggml_backend_buffer_is_cuda_split(node->src[0]->buffer)) { use_cuda_graph = false; // Split buffers are not supported by CUDA graph capture #ifndef NDEBUG - fprintf(stderr, "%s: disabling CUDA graphs due to split buffer\n", __func__); + GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to split buffer\n", __func__); #endif } if (node->op == GGML_OP_MUL_MAT_ID) { use_cuda_graph = false; // This node type is not supported by CUDA graph capture #ifndef NDEBUG - fprintf(stderr, "%s: disabling CUDA graphs due to mul_mat_id\n", __func__); + GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to mul_mat_id\n", __func__); #endif } @@ -2539,7 +2579,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t // Changes in batch size or context size can cause changes to the grid size of some kernels. use_cuda_graph = false; #ifndef NDEBUG - fprintf(stderr, "%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]); + GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to batch size > 1 [%s] [%ld %ld %ld %ld]\n", __func__, node->name, node->ne[0], node->ne[1], node->ne[2], node->ne[3]); #endif } @@ -2567,7 +2607,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t if (cuda_ctx->cuda_graph->number_consecutive_updates >= 4) { cuda_ctx->cuda_graph->disable_due_to_too_many_updates = true; #ifndef NDEBUG - fprintf(stderr, "%s: disabling CUDA graphs due to too many consecutive updates\n", __func__); + GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to too many consecutive updates\n", __func__); #endif } } @@ -2605,7 +2645,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t bool ok = ggml_cuda_compute_forward(*cuda_ctx, node); if (!ok) { - fprintf(stderr, "%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); + GGML_CUDA_LOG_ERROR("%s: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op)); } GGML_ASSERT(ok); } @@ -2624,7 +2664,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t use_cuda_graph = false; cuda_ctx->cuda_graph->disable_due_to_failed_graph_capture = true; #ifndef NDEBUG - fprintf(stderr, "%s: disabling CUDA graphs due to failed graph capture\n", __func__); + GGML_CUDA_LOG_WARN("%s: disabling CUDA graphs due to failed graph capture\n", __func__); #endif } else { graph_evaluated_or_captured = true; // CUDA graph has been captured @@ -2691,7 +2731,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t cudaError_t stat = cudaGraphExecUpdate(cuda_ctx->cuda_graph->instance, cuda_ctx->cuda_graph->graph, &result_info); if (stat == cudaErrorGraphExecUpdateFailure) { #ifndef NDEBUG - fprintf(stderr, "%s: CUDA graph update failed\n", __func__); + GGML_CUDA_LOG_ERROR("%s: CUDA graph update failed\n", __func__); #endif // The pre-existing graph exec cannot be updated due to violated constraints // so instead clear error and re-instantiate @@ -2948,13 +2988,13 @@ static ggml_guid_t ggml_backend_cuda_guid() { GGML_CALL ggml_backend_t ggml_backend_cuda_init(int device) { if (device < 0 || device >= ggml_backend_cuda_get_device_count()) { - fprintf(stderr, "%s: error: invalid device %d\n", __func__, device); + GGML_CUDA_LOG_ERROR("%s: invalid device %d\n", __func__, device); return nullptr; } ggml_backend_cuda_context * ctx = new ggml_backend_cuda_context(device); if (ctx == nullptr) { - fprintf(stderr, "%s: error: failed to allocate context\n", __func__); + GGML_CUDA_LOG_ERROR("%s: failed to allocate context\n", __func__); return nullptr; } @@ -2998,8 +3038,8 @@ GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size // clear the error cudaGetLastError(); - fprintf(stderr, "%s: warning: failed to register %.2f MiB of pinned memory: %s\n", __func__, - size/1024.0/1024.0, cudaGetErrorString(err)); + GGML_CUDA_LOG_WARN("%s: failed to register %.2f MiB of pinned memory: %s\n", __func__, + size / 1024.0 / 1024.0, cudaGetErrorString(err)); return false; } return true; diff --git a/ggml-cuda.h b/ggml-cuda.h index 5eb4af40f..d7903c666 100644 --- a/ggml-cuda.h +++ b/ggml-cuda.h @@ -38,6 +38,7 @@ GGML_API GGML_CALL void ggml_backend_cuda_get_device_memory(int device, size_t * GGML_API GGML_CALL bool ggml_backend_cuda_register_host_buffer(void * buffer, size_t size); GGML_API GGML_CALL void ggml_backend_cuda_unregister_host_buffer(void * buffer); +GGML_API void ggml_backend_cuda_log_set_callback(ggml_log_callback log_callback, void * user_data); #ifdef __cplusplus } #endif diff --git a/llama.cpp b/llama.cpp index 2b91eec86..1409a05da 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1697,6 +1697,8 @@ struct llama_state { llama_state() { #ifdef GGML_USE_METAL ggml_backend_metal_log_set_callback(log_callback, log_callback_user_data); +#elif defined(GGML_USE_CUDA) + ggml_backend_cuda_log_set_callback(log_callback, log_callback_user_data); #endif } @@ -18174,6 +18176,8 @@ void llama_log_set(ggml_log_callback log_callback, void * user_data) { g_state.log_callback_user_data = user_data; #ifdef GGML_USE_METAL ggml_backend_metal_log_set_callback(g_state.log_callback, g_state.log_callback_user_data); +#elif defined(GGML_USE_CUDA) + ggml_backend_cuda_log_set_callback(g_state.log_callback, g_state.log_callback_user_data); #endif } From 854d365abab7194b5013a523f72da19860c3c550 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 19 May 2024 11:01:01 +0300 Subject: [PATCH 044/181] cmake : update android comments (#7341) --- examples/llama.android/app/src/main/cpp/CMakeLists.txt | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/examples/llama.android/app/src/main/cpp/CMakeLists.txt b/examples/llama.android/app/src/main/cpp/CMakeLists.txt index 688a4750d..4536974a5 100644 --- a/examples/llama.android/app/src/main/cpp/CMakeLists.txt +++ b/examples/llama.android/app/src/main/cpp/CMakeLists.txt @@ -12,17 +12,20 @@ cmake_minimum_required(VERSION 3.22.1) # build script scope). project("llama-android") +## Fetch latest llama.cpp from GitHub #include(FetchContent) #FetchContent_Declare( # llama # GIT_REPOSITORY https://github.com/ggerganov/llama.cpp -# GIT_TAG ci-android +# GIT_TAG master #) # ## Also provides "common" #FetchContent_MakeAvailable(llama) -add_subdirectory(../../../../../../ please-work) +# llama.cpp CI uses the code from the current branch +# ref: https://github.com/ggerganov/llama.cpp/pull/7341#issuecomment-2117617700 +add_subdirectory(../../../../../../ build-llama) # Creates and names a library, sets it as either STATIC # or SHARED, and provides the relative paths to its source code. From e23b974f4cf9270d05062d446f406e3ff55d9451 Mon Sep 17 00:00:00 2001 From: Brian Date: Sun, 19 May 2024 20:51:03 +1000 Subject: [PATCH 045/181] labeler.yml: Use settings from ggerganov/llama.cpp [no ci] (#7363) https://github.com/actions/labeler#using-configuration-path-input-together-with-the-actionscheckout-action Recommends the use of checkout action to use the correct repo context when applying settings for PR labels e.g. steps: - uses: actions/checkout@v4 # Uploads repository content to the runner with: repository: "owner/repositoryName" # The one of the available inputs, visit https://github.com/actions/checkout#readme to find more - uses: actions/labeler@v5 with: configuration-path: 'path/to/the/uploaded/configuration/file' --- .github/workflows/labeler.yml | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/.github/workflows/labeler.yml b/.github/workflows/labeler.yml index e57cd86e2..368dbdbe5 100644 --- a/.github/workflows/labeler.yml +++ b/.github/workflows/labeler.yml @@ -9,4 +9,9 @@ jobs: pull-requests: write runs-on: ubuntu-latest steps: + - uses: actions/checkout@v4 + with: + repository: "ggerganov/llama.cpp" - uses: actions/labeler@v5 + with: + configuration-path: '.github/labeler.yml' From ab33f7a338593f6cf1ae98b10b6f8684f63bd72c Mon Sep 17 00:00:00 2001 From: slaren Date: Sun, 19 May 2024 14:19:37 +0200 Subject: [PATCH 046/181] cuda : clear error after buffer allocation failure (#7376) --- ggml-cuda.cu | 2 ++ 1 file changed, 2 insertions(+) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 754611bf3..b82167cbf 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -539,6 +539,8 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffe void * dev_ptr; cudaError_t err = cudaMalloc(&dev_ptr, size); if (err != cudaSuccess) { + // clear the error + cudaGetLastError(); GGML_CUDA_LOG_ERROR("%s: allocating %.2f MiB on device %d: cudaMalloc failed: %s\n", __func__, size / 1024.0 / 1024.0, buft_ctx->device, cudaGetErrorString(err)); return nullptr; } From 6aade19ee74b896c59929676629340b36be3e22c Mon Sep 17 00:00:00 2001 From: Anas Ahouzi <112881240+aahouzi@users.noreply.github.com> Date: Sun, 19 May 2024 14:46:46 +0200 Subject: [PATCH 047/181] Add StableLM2 pre-tokenizer (#7349) * Add StableLM pre-tokenizer * Fix space * Fix trailing whitespace --- convert-hf-to-gguf-update.py | 1 + convert-hf-to-gguf.py | 3 +++ llama.cpp | 4 ++++ llama.h | 7 ++++--- 4 files changed, 12 insertions(+), 3 deletions(-) diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py index 27983fadf..45404b32b 100755 --- a/convert-hf-to-gguf-update.py +++ b/convert-hf-to-gguf-update.py @@ -72,6 +72,7 @@ models = [ {"name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", }, {"name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", }, {"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", }, + {"name": "stablelm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b", }, {"name": "refact", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", }, {"name": "command-r", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", }, {"name": "qwen2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", }, diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index cd1750aa3..bd303150a 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -446,6 +446,9 @@ class Model: if chkhsh == "3ce83efda5659b07b1ad37ca97ca5797ea4285d9b9ab0dc679e4a720c9da7454": # ref: https://huggingface.co/openai-community/gpt2 res = "gpt-2" + if chkhsh == "32d85c31273f8019248f2559fed492d929ea28b17e51d81d3bb36fff23ca72b3": + # ref: https://huggingface.co/stabilityai/stablelm-2-1_6b + res = "stablelm2" if chkhsh == "6221ad2852e85ce96f791f476e0b390cf9b474c9e3d1362f53a24a06dc8220ff": # ref: https://huggingface.co/smallcloudai/Refact-1_6-base res = "refact" diff --git a/llama.cpp b/llama.cpp index 1409a05da..06ff4da61 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4463,6 +4463,9 @@ static void llm_load_vocab( } else if ( tokenizer_pre == "qwen2") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_QWEN2; + } else if ( + tokenizer_pre == "stablelm2") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_STABLELM2; } else if ( tokenizer_pre == "olmo") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_OLMO; @@ -12363,6 +12366,7 @@ struct llm_tokenizer_bpe { "'s|'t|'re|'ve|'m|'ll|'d| ?\\p{L}+| ?\\p{N}+| ?[^\\s\\p{L}\\p{N}]+|\\s+(?!\\S)", }); break; + case LLAMA_VOCAB_PRE_TYPE_STABLELM2: case LLAMA_VOCAB_PRE_TYPE_QWEN2: word_collection = unicode_regex_split(text, { // original regex from tokenizer.json diff --git a/llama.h b/llama.h index 612e32c4e..b7bf2afcb 100644 --- a/llama.h +++ b/llama.h @@ -81,9 +81,10 @@ extern "C" { LLAMA_VOCAB_PRE_TYPE_GPT2 = 7, LLAMA_VOCAB_PRE_TYPE_REFACT = 8, LLAMA_VOCAB_PRE_TYPE_COMMAND_R = 9, - LLAMA_VOCAB_PRE_TYPE_QWEN2 = 10, - LLAMA_VOCAB_PRE_TYPE_OLMO = 11, - LLAMA_VOCAB_PRE_TYPE_DBRX = 12, + LLAMA_VOCAB_PRE_TYPE_STABLELM2 = 10, + LLAMA_VOCAB_PRE_TYPE_QWEN2 = 11, + LLAMA_VOCAB_PRE_TYPE_OLMO = 12, + LLAMA_VOCAB_PRE_TYPE_DBRX = 13, }; // note: these values should be synchronized with ggml_rope From 41858392e17abead21735309bf17cb55183d8c31 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sun, 19 May 2024 16:06:33 +0200 Subject: [PATCH 048/181] server: fix seed being reported back (#7382) --- examples/server/server.cpp | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index b598076d3..7978f979d 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -102,7 +102,6 @@ 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_discard = 0; // number of tokens after n_keep that may be discarded when shifting context, 0 defaults to half int32_t n_predict = -1; // new tokens to predict @@ -1264,7 +1263,7 @@ struct server_context { {"n_ctx", slot.n_ctx}, {"n_predict", slot.n_predict}, {"model", params.model_alias}, - {"seed", slot.params.seed}, + {"seed", slot.sparams.seed}, {"temperature", slot.sparams.temp}, {"dynatemp_range", slot.sparams.dynatemp_range}, {"dynatemp_exponent", slot.sparams.dynatemp_exponent}, From 1b01f06db0cff5f5f600bb754fc39fde565ed56a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sun, 19 May 2024 16:26:02 +0200 Subject: [PATCH 049/181] server: add test for token probs (#7347) --- examples/server/README.md | 2 +- .../server/tests/features/results.feature | 52 ++++++++++++++++--- examples/server/tests/features/steps/steps.py | 37 ++++++++++++- 3 files changed, 81 insertions(+), 10 deletions(-) diff --git a/examples/server/README.md b/examples/server/README.md index 4f3262cdd..0c3db8c84 100644 --- a/examples/server/README.md +++ b/examples/server/README.md @@ -48,7 +48,7 @@ The project is under active development, and we are [looking for feedback and co - `--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. - `--embeddings`: Enable embedding vector output and the OAI compatible endpoint /v1/embeddings. Physical batch size (`--ubatch-size`) must be carefully defined. Default: disabled -- `-np N`, `--parallel N`: Set the number of slots for process requests. Default: `1` +- `-np N`, `--parallel N`: Set the number of slots for process requests. Default: `1`. Values > 1 will allow for higher throughput with multiple parallel requests but the results will **not** be deterministic due to differences in rounding error. - `-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. diff --git a/examples/server/tests/features/results.feature b/examples/server/tests/features/results.feature index aa0b8d0c6..5deb278c2 100644 --- a/examples/server/tests/features/results.feature +++ b/examples/server/tests/features/results.feature @@ -70,12 +70,48 @@ Feature: Results Then all predictions are equal Examples: | n_parallel | temp | - | 1 | 0.0 | - | 2 | 0.0 | - | 4 | 0.0 | - | 1 | 1.0 | - # FIXME: These tests fail on master. The problem seems to be the unified KV cache. + | 1 | 0.0 | + | 2 | 0.0 | + | 4 | 0.0 | + | 1 | 1.0 | + # FIXME: These tests fail on master. + # Problems: unified KV cache (except for CPU backend with LLAMA_NO_LLAMAFILE=1), SIMD nondeterminism. # See https://github.com/ggerganov/whisper.cpp/issues/1941#issuecomment-1986923227 - # and https://github.com/ggerganov/llama.cpp/pull/6122#discussion_r1531405574 . - # | 2 | 1.0 | - # | 4 | 1.0 | + # and https://github.com/ggerganov/llama.cpp/pull/6122#discussion_r1531405574 + # and https://github.com/ggerganov/llama.cpp/pull/7347 . + # | 2 | 1.0 | + # | 4 | 1.0 | + + Scenario Outline: consistent token probs with same seed and prompt + Given slots + And KV cache size + And 1.0 temperature + And max tokens to predict + Then the server is starting + Then the server is healthy + + Given 1 prompts "The meaning of life is" with seed 42 + And concurrent completion requests + # Then the server is busy # Not all slots will be utilized. + Then the server is idle + And all slots are idle + + Given prompts "The meaning of life is" with seed 42 + And concurrent completion requests + # Then the server is busy # Not all slots will be utilized. + Then the server is idle + And all slots are idle + + Then all token probabilities are equal + Examples: + | n_slots | n_kv | n_predict | n_parallel | + | 4 | 1024 | 1 | 1 | + | 4 | 1024 | 1 | 4 | + # FIXME: These tests fail on master. + # Problems: unified KV cache (except for CPU backend with LLAMA_NO_LLAMAFILE=1), SIMD nondeterminism. + # See https://github.com/ggerganov/whisper.cpp/issues/1941#issuecomment-1986923227 + # and https://github.com/ggerganov/llama.cpp/pull/6122#discussion_r1531405574 + # and https://github.com/ggerganov/llama.cpp/pull/7347 . + # | 4 | 1024 | 100 | 1 | + # This test still fails even the above patches; the first token probabilities are already different. + # | 4 | 1024 | 100 | 4 | diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 577b87af3..7da503f2c 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -23,6 +23,7 @@ from prometheus_client import parser def step_server_config(context, server_fqdn, server_port): context.server_fqdn = server_fqdn context.server_port = int(server_port) + context.n_threads = None context.n_gpu_layer = None if 'PORT' in os.environ: context.server_port = int(os.environ['PORT']) @@ -109,6 +110,11 @@ def step_n_gpu_layer(context, ngl): context.n_gpu_layer = ngl +@step('{n_threads:d} threads') +def step_n_threads(context, n_threads): + context.n_thread = n_threads + + @step('{draft:d} as draft') def step_draft(context, draft): context.draft = draft @@ -274,13 +280,22 @@ async def step_predictions_equal(context): @step('all predictions are different') @async_run_until_complete -async def step_predictions_equal(context): +async def step_predictions_different(context): n_completions = await gather_tasks_results(context) assert n_completions >= 2, "need at least 2 completions" assert_all_predictions_different(context.tasks_result) context.tasks_result = [] +@step('all token probabilities are equal') +@async_run_until_complete +async def step_token_probabilities_equal(context): + n_completions = await gather_tasks_results(context) + assert n_completions >= 2, "need at least 2 completions" + assert_all_token_probabilities_equal(context.tasks_result) + context.tasks_result = [] + + @step('the completion is truncated') def step_assert_completion_truncated(context): step_assert_completion_truncated(context, '') @@ -869,6 +884,7 @@ async def request_completion(prompt, "id_slot": id_slot, "seed": seed if seed is not None else 42, "temperature": temperature if temperature is not None else "0.8f", + "n_probs": 2, }, headers=headers, timeout=3600) as response: @@ -1123,6 +1139,23 @@ def assert_all_predictions_different(completion_responses): assert content_i != content_j, "contents not different" +def assert_all_token_probabilities_equal(completion_responses): + n_predict = len(completion_responses[0]['completion_probabilities']) + if 'DEBUG' in os.environ and os.environ['DEBUG'] == 'ON': + for pos in range(n_predict): + for i, response_i in enumerate(completion_responses): + probs_i = response_i['completion_probabilities'][pos]['probs'] + print(f"pos {pos}, probs {i}: {probs_i}") + for pos in range(n_predict): + for i, response_i in enumerate(completion_responses): + probs_i = response_i['completion_probabilities'][pos]['probs'] + for j, response_j in enumerate(completion_responses): + if i == j: + continue + probs_j = response_j['completion_probabilities'][pos]['probs'] + assert probs_i == probs_j, "contents not equal" + + async def gather_tasks_results(context): n_tasks = len(context.concurrent_tasks) if context.debug: @@ -1261,6 +1294,8 @@ def start_server_background(context): server_args.extend(['--batch-size', context.n_batch]) if context.n_ubatch: server_args.extend(['--ubatch-size', context.n_ubatch]) + if context.n_threads: + server_args.extend(['--threads', context.threads]) if context.n_gpu_layer: server_args.extend(['--n-gpu-layers', context.n_gpu_layer]) if context.draft is not None: From 5ca49cbecda27ce0a7266658fc3b640bff3ed386 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sun, 19 May 2024 16:46:13 +0200 Subject: [PATCH 050/181] ggml: implement quantized KV cache for FA (#7372) --- ggml.c | 115 ++++++++++++++++++++++++++++++++++++--------------------- 1 file changed, 72 insertions(+), 43 deletions(-) diff --git a/ggml.c b/ggml.c index a04c74ddd..3a104c486 100644 --- a/ggml.c +++ b/ggml.c @@ -15882,9 +15882,10 @@ static void ggml_compute_forward_flash_attn_ext_f16( GGML_ASSERT(ne0 == D); GGML_ASSERT(ne2 == N); - GGML_ASSERT(nbq0 == sizeof(float)); - GGML_ASSERT(nbk0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nbv0 == sizeof(ggml_fp16_t)); + // input tensor rows must be contiguous + GGML_ASSERT(nbq0 == ggml_type_size(q->type)); + GGML_ASSERT(nbk0 == ggml_type_size(k->type)); + GGML_ASSERT(nbv0 == ggml_type_size(v->type)); GGML_ASSERT(neq0 == D); GGML_ASSERT(nek0 == D); @@ -15938,6 +15939,11 @@ static void ggml_compute_forward_flash_attn_ext_f16( const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); + enum ggml_type const k_vec_dot_type = type_traits[k->type].vec_dot_type; + ggml_from_float_t const q_to_vec_dot = type_traits[k_vec_dot_type].from_float; + ggml_vec_dot_t const kq_vec_dot = type_traits[k->type].vec_dot; + ggml_to_float_t const v_to_float = type_traits[v->type].to_float; + // loop over n_batch and n_head for (int ir = ir0; ir < ir1; ++ir) { // q indices @@ -15945,17 +15951,22 @@ static void ggml_compute_forward_flash_attn_ext_f16( const int iq2 = (ir - iq3*neq2*neq1)/neq1; const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1); - const uint32_t h = iq2; // head + const uint32_t h = iq2; // head index const float slope = (max_bias > 0.0f) ? h < n_head_log2 ? powf(m0, h + 1) : powf(m1, 2*(h - n_head_log2) + 1) : 1.0f; - float S = 0.0f; - float M = -INFINITY; + float S = 0.0f; // sum + float M = -INFINITY; // maximum KQ value - float * V32 = (float *) params->wdata + ith*(2*D + CACHE_LINE_SIZE_F32); - ggml_fp16_t * Q16 = (ggml_fp16_t *) (V32); // reuse memory - ggml_fp16_t * V16 = (ggml_fp16_t *) (V32 + D); + float * VKQ32 = (float *) params->wdata + ith*(3*D + CACHE_LINE_SIZE_F32); // FP32 VKQ accumulator + float * V32 = (VKQ32 + 1*D); // (temporary) FP32 V buffer + ggml_fp16_t * VKQ16 = (ggml_fp16_t *) (VKQ32 + 1*D); // (temporary) FP16 VKQ accumulator + ggml_fp16_t * Q_q = (ggml_fp16_t *) (VKQ32 + 2*D); // (temporary) buffer for Q converted to quantized/FP16 - memset(V16, 0, D*sizeof(ggml_fp16_t)); + if (v->type == GGML_TYPE_F16) { + memset(VKQ16, 0, D*sizeof(ggml_fp16_t)); + } else { + memset(VKQ32, 0, D*sizeof(float)); + } const ggml_fp16_t * mp = mask ? (ggml_fp16_t *)((char *) mask->data + iq1*mask->nb[1]) : NULL; @@ -15967,6 +15978,9 @@ static void ggml_compute_forward_flash_attn_ext_f16( const int iv3 = iq3 / rv3; const int iv2 = iq2 / rv2; + const float * pq = (const float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)); + q_to_vec_dot(pq, Q_q, D); + // online softmax / attention // loop over n_kv and n_head_kv // ref: https://arxiv.org/pdf/2112.05682.pdf @@ -15976,51 +15990,66 @@ static void ggml_compute_forward_flash_attn_ext_f16( continue; } - float s; + float s; // KQ value - // convert Q to F16 in V32 - { - const float * pq = (const float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)); + const char * k_data = (const char *) k->data + ( ic*nbk1 + ik2*nbk2 + ik3*nbk3); + kq_vec_dot(D, &s, 0, k_data, 0, Q_q, 0, 1); - for (int64_t d = 0; d < D; ++d) { - Q16[d] = GGML_FP32_TO_FP16(pq[d]); - } - } - - ggml_vec_dot_f16(D, - &s, 0, - (ggml_fp16_t *) ((char *) k->data + ( ic*nbk1 + ik2*nbk2 + ik3*nbk3)), 0, - Q16, 0, 1); - - s = s*scale + mv; + s = s*scale + mv; // scale KQ value and apply mask const float Mold = M; - float ms = 1.0f; - float vs = 1.0f; + float ms = 1.0f; // upon new higher max val, scale VKQ and KQ sum with this value + float vs = 1.0f; // post-softmax KQ value, expf(s - M) - if (s > M) { - M = s; - ms = expf(Mold - M); + const char * v_data = ((const char *) v->data + (ic*nbv1 + iv2*nbv2 + iv3*nbv3)); - // V = V*expf(Mold - M) - ggml_vec_scale_f16(D, V16, ms); + if (v->type== GGML_TYPE_F16) { + if (s > M) { + // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f + M = s; + ms = expf(Mold - M); + + // V = V*expf(Mold - M) + ggml_vec_scale_f16(D, VKQ16, ms); + } else { + // no new maximum, ms == 1.0f, vs != 1.0f + vs = expf(s - M); + } + + // V += v*expf(s - M) + ggml_vec_mad_f16(D, VKQ16, (const ggml_fp16_t *) v_data, vs); } else { - vs = expf(s - M); + if (s > M) { + // s is new maximum, ms < 1.0f, vs == expf(s - s) == 1.0f + M = s; + ms = expf(Mold - M); + + // V = V*expf(Mold - M) + ggml_vec_scale_f32(D, VKQ32, ms); + } else { + // no new maximum, ms == 1.0f, vs != 1.0f + vs = expf(s - M); + } + + v_to_float(v_data, V32, D); + + // V += v*expf(s - M) + ggml_vec_mad_f32(D, VKQ32, V32, vs); } - const ggml_fp16_t * v16 = (const ggml_fp16_t *) ((char *) v->data + (ic*nbv1 + iv2*nbv2 + iv3*nbv3)); + S = S*ms + vs; // scale and increment sum with partial sum + } - // V += v*expf(s - M) - ggml_vec_mad_f16(D, V16, v16, vs); - - S = S*ms + vs; + if (v->type == GGML_TYPE_F16) { + for (int64_t d = 0; d < D; ++d) { + VKQ32[d] = GGML_FP16_TO_FP32(VKQ16[d]); + } } // V /= S - for (int64_t d = 0; d < D; ++d) { - V32[d] = GGML_FP16_TO_FP32(V16[d])/S; - } + const float S_inv = 1.0f/S; + ggml_vec_scale_f32(D, VKQ32, S_inv); // dst indices const int i1 = iq1; @@ -16031,7 +16060,7 @@ static void ggml_compute_forward_flash_attn_ext_f16( //memcpy((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3), V, nev0*sizeof(float)); // permute(0, 2, 1, 3) - memcpy((char *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1)*nb1, V32, nb1); + memcpy((char *) dst->data + (i3*ne2*ne1 + i2 + i1*ne1)*nb1, VKQ32, nb1); } } @@ -19972,7 +20001,7 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa { const int64_t ne00 = node->src[0]->ne[0]; // D - cur = 2*sizeof(float)*ne00*n_tasks; // 2x head size + cur = 3*sizeof(float)*ne00*n_tasks; // 3x head size/thread } break; case GGML_OP_FLASH_FF: { From e4e6f67be6a8a697f5f89a28c98934e53c99c359 Mon Sep 17 00:00:00 2001 From: slaren Date: Sun, 19 May 2024 17:08:46 +0200 Subject: [PATCH 051/181] ggml : fix another case of quants nans (#7387) --- ggml-quants.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-quants.c b/ggml-quants.c index 7008e5d8b..ff1059863 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -1149,7 +1149,7 @@ static float make_qx_quants(int n, int nmax, const float * restrict x, int8_t * sumlx += w*x[i]*l; suml2 += w*l*l; } - float scale = sumlx/suml2; + float scale = suml2 ? sumlx/suml2 : 0.0f; if (return_early) return suml2 > 0 ? 0.5f*(scale + 1/iscale) : 1/iscale; float best = scale * sumlx; for (int is = -9; is <= 9; ++is) { From f030ec1f7a72aa825b2104823946551b9ec5dfc1 Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Sun, 19 May 2024 17:19:53 +0200 Subject: [PATCH 052/181] Vulkan Embedding Fix (#7360) * Fix empty Vulkan host buffers Add fp32 fp16 matmul shader Fix matmul shader alignment * Remove deprecated tensor->backend uses * Fix Vulkan validation errors on embedding models with no offloaded layers * Fix Vulkan llava segfault when not offloading layers --- ggml-vulkan-shaders.hpp | 12204 ++++++++++++++++++++++------------ ggml-vulkan.cpp | 184 +- ggml_vk_generate_shaders.py | 9 +- 3 files changed, 8000 insertions(+), 4397 deletions(-) diff --git a/ggml-vulkan-shaders.hpp b/ggml-vulkan-shaders.hpp index 586bafe46..70c4043d3 100644 --- a/ggml-vulkan-shaders.hpp +++ b/ggml-vulkan-shaders.hpp @@ -12023,7 +12023,7 @@ const uint64_t matmul_f16_len = 10316; unsigned char matmul_f16_aligned_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xf5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x08,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 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, @@ -12031,9 +12031,9 @@ unsigned char matmul_f16_aligned_data[] = { 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,0x00,0x00,0x00,0x14,0x00,0x00,0x00, -0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfc,0x00,0x00,0x00, -0x07,0x01,0x00,0x00,0x49,0x01,0x00,0x00,0x51,0x01,0x00,0x00, -0x55,0x02,0x00,0x00,0x9e,0x02,0x00,0x00,0x10,0x00,0x06,0x00, +0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfe,0x00,0x00,0x00, +0x05,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x69,0x01,0x00,0x00, +0x68,0x02,0x00,0x00,0xb1,0x02,0x00,0x00,0x10,0x00,0x06,0x00, 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, @@ -12076,37 +12076,40 @@ unsigned char matmul_f16_aligned_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb9,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x08,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00,0x06,0x00,0x00,0x00, -0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x05,0x01,0x00,0x00, -0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00, -0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x05,0x01,0x00,0x00, -0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x07,0x01,0x00,0x00, +0x47,0x00,0x04,0x00,0x02,0x01,0x00,0x00,0x06,0x00,0x00,0x00, +0x10,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x03,0x01,0x00,0x00, +0x00,0x00,0x00,0x00,0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00, +0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00, +0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00, +0x03,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x03,0x01,0x00,0x00, +0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x05,0x01,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x07,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x21,0x01,0x00,0x00,0x01,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x22,0x01,0x00,0x00, +0x05,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x3b,0x01,0x00,0x00,0x01,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x3c,0x01,0x00,0x00, 0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x4e,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00, -0x48,0x00,0x04,0x00,0x4f,0x01,0x00,0x00,0x00,0x00,0x00,0x00, -0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x4f,0x01,0x00,0x00, +0x66,0x01,0x00,0x00,0x06,0x00,0x00,0x00,0x10,0x00,0x00,0x00, +0x48,0x00,0x04,0x00,0x67,0x01,0x00,0x00,0x00,0x00,0x00,0x00, +0x05,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x67,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00, -0x4f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x4f,0x01,0x00,0x00, +0x67,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x48,0x00,0x05,0x00,0x67,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0x08,0x00,0x00,0x00, -0x47,0x00,0x03,0x00,0x4f,0x01,0x00,0x00,0x02,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x51,0x01,0x00,0x00,0x22,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x51,0x01,0x00,0x00, +0x47,0x00,0x03,0x00,0x67,0x01,0x00,0x00,0x02,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x69,0x01,0x00,0x00,0x22,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x69,0x01,0x00,0x00, 0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x55,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x9b,0x02,0x00,0x00,0x06,0x00,0x00,0x00, -0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x9c,0x02,0x00,0x00, +0x68,0x02,0x00,0x00,0x0b,0x00,0x00,0x00,0x18,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0xae,0x02,0x00,0x00,0x06,0x00,0x00,0x00, +0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0xaf,0x02,0x00,0x00, 0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00,0x48,0x00,0x05,0x00, -0x9c,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x9c,0x02,0x00,0x00, -0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x9e,0x02,0x00,0x00, +0xaf,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0xaf,0x02,0x00,0x00, +0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0xb1,0x02,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x9e,0x02,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00, +0xb1,0x02,0x00,0x00,0x21,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00,0x21,0x00,0x03,0x00, 0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x15,0x00,0x04,0x00, 0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00,0x00,0x00,0x00,0x00, @@ -12157,16 +12160,16 @@ unsigned char matmul_f16_aligned_data[] = { 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x68,0x00,0x00,0x00, 0x86,0x00,0x00,0x00,0x62,0x00,0x00,0x00,0x63,0x00,0x00,0x00, 0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, -0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x6e,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, -0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0x73,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, -0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, -0x78,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x34,0x00,0x06,0x00, +0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x06,0x00,0x00,0x00, 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+0xdb,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x8e,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x90,0x02,0x00,0x00, +0xf9,0x00,0x02,0x00,0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x79,0x02,0x00,0x00,0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00, +0xd3,0x02,0x00,0x00,0xd9,0x02,0x00,0x00,0xd0,0x00,0x00,0x00, +0xf9,0x00,0x02,0x00,0x76,0x02,0x00,0x00,0xf8,0x00,0x02,0x00, +0x78,0x02,0x00,0x00,0xf9,0x00,0x02,0x00,0x71,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0x71,0x02,0x00,0x00,0x80,0x00,0x05,0x00, +0x06,0x00,0x00,0x00,0xd5,0x02,0x00,0x00,0xd8,0x02,0x00,0x00, +0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00,0x6e,0x02,0x00,0x00, +0xf8,0x00,0x02,0x00,0x70,0x02,0x00,0x00,0xfd,0x00,0x01,0x00, +0x38,0x00,0x01,0x00, }; -const uint64_t matmul_f16_aligned_len = 10812; +const uint64_t matmul_f16_aligned_len = 11200; unsigned char matmul_f16_aligned_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xd9,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0xd8,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 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, @@ -12937,8 +12969,8 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00, -0xfb,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0x49,0x01,0x00,0x00, -0x50,0x01,0x00,0x00,0x39,0x02,0x00,0x00,0x82,0x02,0x00,0x00, +0xfd,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x4a,0x01,0x00,0x00, +0x50,0x01,0x00,0x00,0x38,0x02,0x00,0x00,0x81,0x02,0x00,0x00, 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, @@ -12981,17 +13013,17 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00, -0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x01,0x01,0x00,0x00, +0x06,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x04,0x00, +0x02,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x02,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x05,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x07,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x07,0x01,0x00,0x00,0x21,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x21,0x01,0x00,0x00, +0x02,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x04,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00,0x21,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x22,0x01,0x00,0x00, 0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x22,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x23,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x4d,0x01,0x00,0x00,0x06,0x00,0x00,0x00, 0x08,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x4e,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00, @@ -13000,15 +13032,15 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x01,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0x50,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x39,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, -0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7f,0x02,0x00,0x00, +0x47,0x00,0x04,0x00,0x38,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, +0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7e,0x02,0x00,0x00, 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x80,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x80,0x02,0x00,0x00,0x00,0x00,0x00,0x00, +0x7f,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x7f,0x02,0x00,0x00,0x00,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x80,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x82,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x82,0x02,0x00,0x00,0x21,0x00,0x00,0x00, +0x7f,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x81,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x81,0x02,0x00,0x00,0x21,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00, 0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00, @@ -13059,16 +13091,16 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x68,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x62,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00, -0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, -0x06,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x6d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x6f,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, +0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x74,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x79,0x00,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -13112,99 +13144,97 @@ unsigned char matmul_f16_aligned_fp32_data[] = { 0xcd,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xf7,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 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0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xfd,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x12,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 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, @@ -14665,9 +14697,9 @@ unsigned char matmul_f16_f32_aligned_data[] = { 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,0x00,0x00,0x00,0x14,0x00,0x00,0x00, -0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfc,0x00,0x00,0x00, -0x07,0x01,0x00,0x00,0x49,0x01,0x00,0x00,0x51,0x01,0x00,0x00, -0x5e,0x02,0x00,0x00,0xa7,0x02,0x00,0x00,0x10,0x00,0x06,0x00, +0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00,0xfe,0x00,0x00,0x00, +0x05,0x01,0x00,0x00,0x63,0x01,0x00,0x00,0x6b,0x01,0x00,0x00, +0x73,0x02,0x00,0x00,0xbc,0x02,0x00,0x00,0x10,0x00,0x06,0x00, 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+0x7c,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7c,0x02,0x00,0x00, +0x80,0x00,0x05,0x00,0x06,0x00,0x00,0x00,0xdf,0x02,0x00,0x00, +0xe2,0x02,0x00,0x00,0xd0,0x00,0x00,0x00,0xf9,0x00,0x02,0x00, +0x79,0x02,0x00,0x00,0xf8,0x00,0x02,0x00,0x7b,0x02,0x00,0x00, 0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t matmul_f16_f32_aligned_len = 10940; +const uint64_t matmul_f16_f32_aligned_len = 11360; unsigned char matmul_f16_f32_aligned_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -15581,8 +15648,8 @@ unsigned char matmul_f16_f32_aligned_fp32_data[] = { 0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00, -0xfb,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0x49,0x01,0x00,0x00, -0x50,0x01,0x00,0x00,0x36,0x02,0x00,0x00,0x7f,0x02,0x00,0x00, +0xfd,0x00,0x00,0x00,0x04,0x01,0x00,0x00,0x4a,0x01,0x00,0x00, +0x51,0x01,0x00,0x00,0x36,0x02,0x00,0x00,0x7f,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, @@ -15625,25 +15692,25 @@ unsigned char matmul_f16_f32_aligned_fp32_data[] = { 0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00, -0x06,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00, +0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x01,0x01,0x00,0x00, +0x06,0x00,0x00,0x00,0x08,0x00,0x00,0x00,0x48,0x00,0x04,0x00, +0x02,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, +0x48,0x00,0x05,0x00,0x02,0x01,0x00,0x00,0x00,0x00,0x00,0x00, 0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, -0x05,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x07,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x07,0x01,0x00,0x00,0x21,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x21,0x01,0x00,0x00, +0x02,0x01,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x04,0x01,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00,0x21,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x22,0x01,0x00,0x00, 0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x22,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00, -0x47,0x00,0x04,0x00,0x4d,0x01,0x00,0x00,0x06,0x00,0x00,0x00, -0x10,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x4e,0x01,0x00,0x00, +0x23,0x01,0x00,0x00,0x0b,0x00,0x00,0x00,0x19,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x4e,0x01,0x00,0x00,0x06,0x00,0x00,0x00, +0x10,0x00,0x00,0x00,0x48,0x00,0x04,0x00,0x4f,0x01,0x00,0x00, 0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00,0x48,0x00,0x05,0x00, -0x4e,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, -0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x4e,0x01,0x00,0x00, -0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x50,0x01,0x00,0x00, +0x4f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x23,0x00,0x00,0x00, +0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00,0x4f,0x01,0x00,0x00, +0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x51,0x01,0x00,0x00, 0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x04,0x00, -0x50,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00, +0x51,0x01,0x00,0x00,0x21,0x00,0x00,0x00,0x01,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0x36,0x02,0x00,0x00,0x0b,0x00,0x00,0x00, 0x18,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x7c,0x02,0x00,0x00, 0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00, @@ -15703,16 +15770,16 @@ unsigned char matmul_f16_f32_aligned_fp32_data[] = { 0x63,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 0x68,0x00,0x00,0x00,0x86,0x00,0x00,0x00,0x62,0x00,0x00,0x00, 0x63,0x00,0x00,0x00,0x32,0x00,0x04,0x00,0x06,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x34,0x00,0x06,0x00, -0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, -0x06,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x6d,0x00,0x00,0x00,0x10,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, +0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x04,0x00,0x00,0x00, +0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x6f,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, +0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x74,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x79,0x00,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, 0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, +0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x6e,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0x82,0x00,0x00,0x00, 0x06,0x00,0x00,0x00,0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00, 0x87,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, @@ -15756,677 +15823,678 @@ unsigned char matmul_f16_f32_aligned_fp32_data[] = { 0xcd,0x00,0x00,0x00,0x07,0x00,0x00,0x00,0xc4,0x00,0x00,0x00, 0x2b,0x00,0x04,0x00,0x15,0x00,0x00,0x00,0xd0,0x00,0x00,0x00, 0x01,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, -0xf7,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, +0xf4,0x00,0x00,0x00,0x80,0x00,0x00,0x00,0x6d,0x00,0x00,0x00, 0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00, 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+0x38,0x00,0x01,0x00, }; -const uint64_t matmul_f16_f32_aligned_fp32_len = 10224; +const uint64_t matmul_f16_f32_aligned_fp32_len = 10240; unsigned char matmul_f16_f32_fp32_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, @@ -19016,7 +19084,7 @@ const uint64_t matmul_f32_len = 10324; unsigned char matmul_f32_aligned_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0xfd,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x17,0x03,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 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, @@ -19024,8 +19092,8 @@ unsigned char matmul_f32_aligned_data[] = { 0x0f,0x00,0x0f,0x00,0x05,0x00,0x00,0x00,0x04,0x00,0x00,0x00, 0x6d,0x61,0x69,0x6e,0x00,0x00,0x00,0x00,0x0b,0x00,0x00,0x00, 0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00, -0xfc,0x00,0x00,0x00,0x07,0x01,0x00,0x00,0x4a,0x01,0x00,0x00, -0x52,0x01,0x00,0x00,0x5e,0x02,0x00,0x00,0xa7,0x02,0x00,0x00, +0xfe,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0x6b,0x01,0x00,0x00, +0x71,0x01,0x00,0x00,0x78,0x02,0x00,0x00,0xc1,0x02,0x00,0x00, 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, @@ -19068,37 +19136,4514 @@ unsigned char matmul_f32_aligned_data[] = { 0x01,0x00,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, 0xb9,0x00,0x00,0x00,0x01,0x00,0x00,0x00,0x05,0x00,0x00,0x00, 0x47,0x00,0x04,0x00,0xbc,0x00,0x00,0x00,0x01,0x00,0x00,0x00, -0x08,0x00,0x00,0x00,0x47,0x00,0x04,0x00,0x04,0x01,0x00,0x00, -0x06,0x00,0x00,0x00,0x04,0x00,0x00,0x00,0x48,0x00,0x04,0x00, -0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x18,0x00,0x00,0x00, -0x48,0x00,0x05,0x00,0x05,0x01,0x00,0x00,0x00,0x00,0x00,0x00, -0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, 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+0xf8,0x00,0x02,0x00,0x3e,0x02,0x00,0x00,0xfd,0x00,0x01,0x00, +0x38,0x00,0x01,0x00, +}; +const uint64_t matmul_f32_f16_aligned_fp32_len = 10240; + +unsigned char matmul_f32_f16_fp32_data[] = { +0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, +0xd5,0x02,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +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, +0x14,0x00,0x00,0x00,0x3e,0x00,0x00,0x00,0x4d,0x00,0x00,0x00, +0xfa,0x00,0x00,0x00,0x05,0x01,0x00,0x00,0x44,0x01,0x00,0x00, +0x50,0x01,0x00,0x00,0x36,0x02,0x00,0x00,0x7f,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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+0x23,0x00,0x00,0x00,0x00,0x00,0x00,0x00,0x47,0x00,0x03,0x00, +0x7d,0x02,0x00,0x00,0x02,0x00,0x00,0x00,0x47,0x00,0x04,0x00, +0x7f,0x02,0x00,0x00,0x22,0x00,0x00,0x00,0x00,0x00,0x00,0x00, +0x47,0x00,0x04,0x00,0x7f,0x02,0x00,0x00,0x21,0x00,0x00,0x00, 0x02,0x00,0x00,0x00,0x13,0x00,0x02,0x00,0x02,0x00,0x00,0x00, 0x21,0x00,0x03,0x00,0x03,0x00,0x00,0x00,0x02,0x00,0x00,0x00, 0x15,0x00,0x04,0x00,0x06,0x00,0x00,0x00,0x20,0x00,0x00,0x00, @@ -19153,156 +23698,146 @@ unsigned char matmul_f32_aligned_data[] = { 0x06,0x00,0x00,0x00,0x6e,0x00,0x00,0x00,0x86,0x00,0x00,0x00, 0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x34,0x00,0x06,0x00, 0x06,0x00,0x00,0x00,0x73,0x00,0x00,0x00,0x86,0x00,0x00,0x00, -0x6d,0x00,0x00,0x00,0x3a,0x00,0x00,0x00,0x2b,0x00,0x04,0x00, -0x06,0x00,0x00,0x00,0x78,0x00,0x00,0x00,0x08,0x00,0x00,0x00, -0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x79,0x00,0x00,0x00, -0x86,0x00,0x00,0x00,0x6d,0x00,0x00,0x00,0x78,0x00,0x00,0x00, -0x34,0x00,0x06,0x00,0x06,0x00,0x00,0x00,0x7e,0x00,0x00,0x00, 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-114,6 +114,7 @@ struct vk_device { size_t idx; vk_matmul_pipeline pipeline_matmul_f32; + vk_matmul_pipeline pipeline_matmul_f32_f16; vk_matmul_pipeline pipeline_matmul_f16; vk_matmul_pipeline pipeline_matmul_f16_f32; vk_pipeline pipeline_matmul_split_k_reduce; @@ -375,13 +376,12 @@ struct ggml_backend_vk_context { vk_context * compute_ctx; vk_context * transfer_ctx; - bool disable; bool initialized; size_t idx; }; -struct vk_instance { +struct vk_instance_t { vk::Instance instance; std::vector device_indices; @@ -423,7 +423,7 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ 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; +static vk_instance_t vk_instance; GGML_CALL static void ggml_backend_vk_free(ggml_backend_t backend); @@ -1013,6 +1013,7 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { uint32_t s_align = 32; ctx->device->pipeline_matmul_f32 = std::make_shared(); + ctx->device->pipeline_matmul_f32_f16 = std::make_shared(); ctx->device->pipeline_matmul_f16_f32 = std::make_shared(); ctx->device->pipeline_matmul_f16 = std::make_shared(); ctx->device->pipeline_dequant_mul_mat_mat[GGML_TYPE_Q4_0] = std::make_shared(); @@ -1048,6 +1049,13 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_len, matmul_f32_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->l, "matmul_f32_f16_l", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->m, "matmul_f32_f16_m", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->s, "matmul_f32_f16_s", matmul_f32_f16_len, matmul_f32_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_l, "matmul_f32_f16_aligned_l", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_m, "matmul_f32_f16_aligned_m", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_s, "matmul_f32_f16_aligned_s", matmul_f32_f16_aligned_len, matmul_f32_f16_aligned_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_len, matmul_f16_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); @@ -1230,6 +1238,13 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_m, "matmul_f32_aligned_m", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32->a_s, "matmul_f32_aligned_s", matmul_f32_aligned_fp32_len, matmul_f32_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->l, "matmul_f32_f16_l", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->m, "matmul_f32_f16_m", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->s, "matmul_f32_f16_s", matmul_f32_f16_fp32_len, matmul_f32_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_l, "matmul_f32_f16_aligned_l", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, l_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_m, "matmul_f32_f16_aligned_m", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, m_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f32_f16->a_s, "matmul_f32_f16_aligned_s", matmul_f32_f16_aligned_fp32_len, matmul_f32_f16_aligned_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, s_align); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->l, "matmul_f16_l", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), l_wg_denoms, warptile_l, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->m, "matmul_f16_m", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), m_wg_denoms, warptile_m, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_matmul_f16->s, "matmul_f16_s", matmul_f16_fp32_len, matmul_f16_fp32_data, "main", 3, sizeof(vk_mat_mat_push_constants), s_wg_denoms, warptile_s, 1); @@ -1859,7 +1874,6 @@ static void ggml_vk_init(ggml_backend_vk_context * ctx, size_t idx) { ctx->compute_ctx = nullptr; ctx->transfer_ctx = nullptr; - ctx->disable = false; ctx->initialized = true; ctx->idx = idx; @@ -1903,6 +1917,9 @@ static vk_matmul_pipeline ggml_vk_get_mul_mat_mat_pipeline(ggml_backend_vk_conte if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F32) { return ctx->device->pipeline_matmul_f32; } + if (src0_type == GGML_TYPE_F32 && src1_type == GGML_TYPE_F16) { + return ctx->device->pipeline_matmul_f32_f16; + } if (src0_type == GGML_TYPE_F16 && src1_type == GGML_TYPE_F32) { return ctx->device->pipeline_matmul_f16_f32; } @@ -2722,7 +2739,7 @@ static void ggml_vk_matmul( uint32_t batch_stride_a, uint32_t batch_stride_b, uint32_t batch_stride_d, uint32_t expert_stride_b, uint32_t expert_stride_d, uint32_t idx, uint32_t nbi1, uint32_t n_as) { #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 != nullptr ? split_k_buffer.buffer->buffer : VK_NULL_HANDLE) << ", " << 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(subctx); if (split_k == 1) { @@ -2792,7 +2809,7 @@ static vk_pipeline ggml_vk_get_cpy_pipeline(ggml_backend_vk_context * ctx, ggml_ 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) { #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 << "ggml_vk_cpy_to_contiguous((" << tensor << ", type=" << tensor->type << ", 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); @@ -2812,9 +2829,9 @@ static void ggml_vk_cpy_to_contiguous(ggml_backend_vk_context * ctx, vk_context 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]; - 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; + std::cerr << "ggml_vk_mul_mat_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", 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 << ", 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 << ", 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 @@ -2982,19 +2999,13 @@ static void ggml_vk_mul_mat_q_f16(ggml_backend_vk_context * ctx, vk_context * su ne01, ne11, ne10, ne10, ne10, ne01, split_k, ne12*ne13, ne02, ne12, r2, r3, stride_batch_x, stride_batch_y, ne20*ne21, 0, 0, 0, 0, 1 ); // NOLINT - - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - // copy dst to host - float * d = (float *) ((char *) dst->data); - 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(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]; - 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; + std::cerr << "ggml_vk_mul_mat_vec_q_f16((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", 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 << ", 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 << ", 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 @@ -3147,12 +3158,11 @@ static void ggml_vk_mul_mat_vec_q_f16(ggml_backend_vk_context * ctx, vk_context 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]; - 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; + std::cerr << "ggml_vk_mul_mat_p021_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", 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 << ", 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 << ", 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_TYPE_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); @@ -3217,25 +3227,17 @@ static void ggml_vk_mul_mat_vec_p021_f16_f32(ggml_backend_vk_context * ctx, vk_c 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(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->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_TYPE_CPU) { - // copy dst to host - float * d = (float *) dst->data; - 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(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]; - 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; + std::cerr << "ggml_vk_mul_mat_nc_f16_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", 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 << ", 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 << ", 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_TYPE_GPU); GGML_ASSERT(src0->type == GGML_TYPE_F16); GGML_ASSERT(src1->type == GGML_TYPE_F32); @@ -3302,26 +3304,6 @@ static void ggml_vk_mul_mat_vec_nc_f16_f32(ggml_backend_vk_context * ctx, vk_con 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(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, ctx->device->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_TYPE_CPU) { - // copy dst to host - float * d = (float *) dst->data; - ggml_vk_sync_buffers(subctx); - ggml_vk_buffer_read_async(ctx, subctx, 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_TYPE_GPU); } static void ggml_vk_mul_mat(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -3711,8 +3693,6 @@ static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx // TODO: support for transposed / permuted tensors GGML_ASSERT(nb0 == sizeof(float)); GGML_ASSERT(nb00 == sizeof(float)); - GGML_ASSERT(src0->backend == GGML_BACKEND_TYPE_GPU); - GGML_ASSERT(dst->backend == GGML_BACKEND_TYPE_GPU); ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) dst->extra; ggml_tensor_extra_gpu * extra_src0 = (ggml_tensor_extra_gpu *) src0->extra; @@ -3902,11 +3882,11 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) { template 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]; + std::cerr << "ggml_vk_op_f32((" << src0 << ", name=" << src0->name << ", type=" << src0->type << ", 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 << "), (" << src1 << ", name=" << src1->name << ", type=" << src1->type << ", 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; + std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", 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(op == GGML_OP_GET_ROWS || (!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 @@ -3923,8 +3903,6 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c 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(ctx, src0, src1, dst, op); ggml_vk_func_t op_func; @@ -3975,7 +3953,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c vk_buffer d_D = extra->buffer_gpu.lock(); // Workaround for tiny tensor inputs on ROPE - if (use_src1 && src1->backend == GGML_BACKEND_TYPE_GPU && y_sz > d_D->size) { + if (use_src1 && y_sz > d_D->size) { y_sz = VK_WHOLE_SIZE; } @@ -4066,13 +4044,6 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c 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_TYPE_CPU && op == GGML_OP_CPY) { - ggml_vk_d2h_tensor_2d(ctx, subctx, d_D, 0, dst); - } else if(dst->backend == GGML_BACKEND_TYPE_CPU) { - // copy dst to host - float * d = (float *) dst->data; - ggml_vk_buffer_read_async(ctx, subctx, d_D, 0, d, d_sz); - } } else { GGML_ASSERT(op != GGML_OP_SOFT_MAX); GGML_ASSERT(op != GGML_OP_ARGSORT); @@ -4111,10 +4082,6 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c 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_TYPE_CPU) { - // copy dst to host - ggml_vk_buffer_read_async(ctx, subctx, d_D, d_buf_offset + d_offset, (char *) dst->data + i02*nb2 + i03*nb3, d_sz); - } } } } @@ -4377,6 +4344,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f32->a_s; shname = "F32_ALIGNED_S"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->a_s; + shname = "F32_F16_ALIGNED_S"; } else if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f16_f32->a_s; shname = "F16_F32_ALIGNED_S"; @@ -4390,6 +4360,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f32->a_m; shname = "F32_ALIGNED_M"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->a_m; + shname = "F32_F16_ALIGNED_M"; } else if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f16_f32->a_m; shname = "F16_F32_ALIGNED_M"; @@ -4403,6 +4376,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f32->a_l; shname = "F32_ALIGNED_L"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->a_l; + shname = "F32_F16_ALIGNED_L"; } else if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f16_f32->a_l; shname = "F16_F32_ALIGNED_L"; @@ -4423,6 +4399,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f32->s; shname = "F32_S"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->s; + shname = "F32_F16_S"; } else if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f16_f32->s; shname = "F16_F32_S"; @@ -4434,6 +4413,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f32->m; shname = "F32_M"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->m; + shname = "F32_F16_M"; } else if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f16_f32->m; shname = "F16_F32_M"; @@ -4445,6 +4427,9 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f32->l; shname = "F32_L"; + } else if (std::is_same() && std::is_same()) { + p = ctx->device->pipeline_matmul_f32_f16->l; + shname = "F32_F16_L"; } else if (std::is_same() && std::is_same()) { p = ctx->device->pipeline_matmul_f16_f32->l; shname = "F16_F32_L"; @@ -4557,15 +4542,11 @@ static void ggml_vk_test_matmul(ggml_backend_vk_context * ctx, size_t m, size_t src1_ggml->data = y; tensor_ggml->data = d_chk; - 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); - ctx->disable = false; - ggml_free(ggml_ctx); double avg_err = 0.0; @@ -5045,15 +5026,11 @@ static void ggml_vk_test_dequant_matmul(ggml_backend_vk_context * ctx, size_t m, src1_ggml->data = y; tensor_ggml->data = d_chk; - 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); - ctx->disable = false; - ggml_free(ggml_ctx); double avg_err = 0.0; @@ -5130,12 +5107,12 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_vk_preallocate_buffers_graph(" << node << ")" << std::endl; #endif - if (ctx->disable || node->backend != GGML_BACKEND_TYPE_GPU) { + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra; + + if (extra == nullptr) { return; } - ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra; - ggml_tensor * src0 = node->src[0]; ggml_tensor * src1 = node->src[1]; @@ -5240,9 +5217,6 @@ static void ggml_vk_preallocate_buffers_graph(ggml_backend_vk_context * ctx, ggm } 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(x_size: " << ctx->prealloc_size_x << " y_size: " << ctx->prealloc_size_y << " split_k_size: " << ctx->prealloc_size_split_k << ")" << std::endl; #endif @@ -5416,7 +5390,9 @@ static void ggml_vk_preallocate_buffers(ggml_backend_vk_context * ctx) { } static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * node, bool last_node){ - if (ctx->disable || node->backend != GGML_BACKEND_TYPE_GPU || ggml_is_empty(node)) { + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) node->extra; + + if (ggml_is_empty(node) || extra == nullptr) { return; } @@ -5429,8 +5405,6 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod 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)) { @@ -5575,7 +5549,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod last_node = true; #endif - if (node->backend == GGML_BACKEND_TYPE_CPU || last_node) { + if (last_node) { ggml_vk_ctx_end(ctx->compute_ctx); ctx->compute_ctx->exit_tensor = node; ctx->compute_ctx = nullptr; @@ -5583,10 +5557,6 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod } static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_params * params, ggml_tensor * tensor){ - if (ctx->disable) { - return false; - } - ggml_tensor_extra_gpu * extra = nullptr; switch (tensor->op) { @@ -5645,7 +5615,7 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_ } #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; + std::cerr << "ggml_vk_compute_forward(" << tensor << ", name=" << tensor->name << ", op=" << ggml_op_name(tensor->op) << ", type=" << tensor->type << ", 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 @@ -5685,9 +5655,6 @@ static bool ggml_vk_compute_forward(ggml_backend_vk_context * ctx, ggml_compute_ // 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 @@ -5860,7 +5827,6 @@ 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; } - tensor->backend = GGML_BACKEND_TYPE_GPU; tensor->extra = extra; } @@ -5868,8 +5834,6 @@ GGML_CALL static void ggml_backend_vk_buffer_set_tensor(ggml_backend_buffer_t bu #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_TYPE_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; @@ -5883,8 +5847,6 @@ GGML_CALL static void ggml_backend_vk_buffer_get_tensor(ggml_backend_buffer_t bu #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_TYPE_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; @@ -6027,6 +5989,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_vk_host_buffer_type_alloc_bu #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_host_buffer_type_alloc_buffer(" << size << ")" << std::endl; #endif + size += 32; // Behave like the CPU buffer type void * ptr = nullptr; try { ptr = ggml_vk_host_malloc(&vk_instance.contexts[0], size); @@ -6114,7 +6077,6 @@ GGML_CALL static void ggml_backend_vk_set_tensor_async(ggml_backend_t backend, g #endif 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_TYPE_GPU); ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; @@ -6135,7 +6097,6 @@ GGML_CALL static void ggml_backend_vk_get_tensor_async(ggml_backend_t backend, c #endif 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_TYPE_GPU); ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) tensor->extra; @@ -6201,6 +6162,10 @@ GGML_CALL static void ggml_backend_vk_synchronize(ggml_backend_t backend) { ctx->transfer_ctx = nullptr; } +static bool ggml_vk_is_empty(ggml_tensor * node) { + return ggml_is_empty(node) || node->op == GGML_OP_NONE || node->op == GGML_OP_RESHAPE || node->op == GGML_OP_TRANSPOSE || node->op == GGML_OP_VIEW || node->op == GGML_OP_PERMUTE; +} + GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backend, ggml_cgraph * cgraph) { #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_graph_compute(" << cgraph->n_nodes << " nodes)" << std::endl; @@ -6215,7 +6180,7 @@ GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backen 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_TYPE_GPU || ggml_is_empty(cgraph->nodes[last_node]))) { + while (last_node > 0 && ggml_vk_is_empty(cgraph->nodes[last_node])) { last_node -= 1; } @@ -6229,7 +6194,7 @@ GGML_CALL static ggml_status ggml_backend_vk_graph_compute(ggml_backend_t backen for (int i = 0; i < cgraph->n_nodes; i++) { ggml_tensor * node = cgraph->nodes[i]; - if (ggml_is_empty(node) || 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) { + if (ggml_vk_is_empty(node)) { continue; } @@ -6873,16 +6838,11 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ GGML_ASSERT(false); } - // Disable vulkan here to avoid the hooks in ggml.c - ctx->disable = true; - ggml_cgraph * cgraph = ggml_new_graph(ggml_ctx); ggml_build_forward_expand(cgraph, tensor_clone); ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 8); - 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(ctx, tensor_clone, "tensor_clone"); diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py index 3c22a986c..8096c03b7 100644 --- a/ggml_vk_generate_shaders.py +++ b/ggml_vk_generate_shaders.py @@ -2877,13 +2877,16 @@ async def main(): stream.clear() stream.extend((mulmat_head, shader_float_type, mulmat_body1, mulmat_load_scalar, mulmat_body2)) tasks.append(string_to_spv("matmul_f32", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f32_aligned", "".join(stream), {"LOAD_VEC_A": 1, "LOAD_VEC_B": load_vec, "A_TYPE": "float", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f32_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + + tasks.append(string_to_spv("matmul_f32_f16", "".join(stream), {"A_TYPE": "float", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f32_f16_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) tasks.append(string_to_spv("matmul_f16", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float16_t", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_aligned", "".join(stream), {"LOAD_VEC_A": 1, "LOAD_VEC_B": load_vec, "A_TYPE": "float16_t", "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type_f16, "D_TYPE": "float"}, fp16)) tasks.append(string_to_spv("matmul_f16_f32", "".join(stream), {"A_TYPE": "float16_t", "B_TYPE": "float", "D_TYPE": "float"}, fp16)) - tasks.append(string_to_spv("matmul_f16_f32_aligned", "".join(stream), {"LOAD_VEC_A": 1, "LOAD_VEC_B": load_vec, "A_TYPE": "float16_t", "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) + tasks.append(string_to_spv("matmul_f16_f32_aligned", "".join(stream), {"LOAD_VEC_A": load_vec, "LOAD_VEC_B": load_vec, "A_TYPE": vec_type_f16, "B_TYPE": vec_type, "D_TYPE": "float"}, fp16)) stream.clear() stream.extend((mulmat_head, shader_int8_ext, shader_float_type, shader_q4_0_defines, mulmat_body1, mulmat_load_q4_0, mulmat_body2)) From 1ea2a0036e88172d6c8bf7e1a1989a03894dc955 Mon Sep 17 00:00:00 2001 From: Fred Douglas <43351173+fredlas@users.noreply.github.com> Date: Sun, 19 May 2024 11:37:04 -0500 Subject: [PATCH 053/181] quantize : fix --keep-split check (#7374) --- 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 909eab283..cbb452334 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -284,7 +284,7 @@ int main(int argc, char ** argv) { } else { usage(argv[0]); } - } else if (strcmp(argv[arg_idx], "--keep-split")) { + } else if (strcmp(argv[arg_idx], "--keep-split") == 0) { params.keep_split = true; } else { usage(argv[0]); From d359f30921a9f62a0fd299c412ff3f270286fea6 Mon Sep 17 00:00:00 2001 From: slaren Date: Mon, 20 May 2024 01:17:03 +0200 Subject: [PATCH 054/181] llama : remove MPI backend (#7395) --- .devops/nix/package.nix | 1 - .github/workflows/build.yml | 34 ------ CMakeLists.txt | 33 +----- Makefile | 12 -- README.md | 39 ------- ggml-mpi.c | 216 ----------------------------------- ggml-mpi.h | 39 ------- llama.cpp | 48 +------- scripts/LlamaConfig.cmake.in | 5 - 9 files changed, 2 insertions(+), 425 deletions(-) delete mode 100644 ggml-mpi.c delete mode 100644 ggml-mpi.h diff --git a/.devops/nix/package.nix b/.devops/nix/package.nix index 1c9633cdf..e8d5b0bd9 100644 --- a/.devops/nix/package.nix +++ b/.devops/nix/package.nix @@ -214,7 +214,6 @@ effectiveStdenv.mkDerivation ( (cmakeBool "LLAMA_CUDA" useCuda) (cmakeBool "LLAMA_HIPBLAS" useRocm) (cmakeBool "LLAMA_METAL" useMetalKit) - (cmakeBool "LLAMA_MPI" useMpi) (cmakeBool "LLAMA_VULKAN" useVulkan) (cmakeBool "LLAMA_STATIC" enableStatic) ] diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 53e61b80f..7b616281b 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -306,40 +306,6 @@ jobs: cd build ctest -L main --verbose --timeout 900 - ubuntu-latest-cmake-mpi: - runs-on: ubuntu-latest - - continue-on-error: true - - strategy: - matrix: - mpi_library: [mpich, libopenmpi-dev] - - steps: - - name: Clone - id: checkout - uses: actions/checkout@v4 - - - name: Dependencies - id: depends - run: | - sudo apt-get update - sudo apt-get install build-essential ${{ matrix.mpi_library }} - - - name: Build - id: cmake_build - run: | - mkdir build - cd build - cmake -DLLAMA_MPI=ON .. - cmake --build . --config Release -j $(nproc) - - - name: Test - id: cmake_test - run: | - cd build - ctest -L main --verbose - ubuntu-latest-cmake-rpc: runs-on: ubuntu-latest diff --git a/CMakeLists.txt b/CMakeLists.txt index cbeb2ee37..616698c7f 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -122,7 +122,6 @@ set(LLAMA_METAL_MACOSX_VERSION_MIN "" CACHE STRING "llama: metal minimum macOS version") set(LLAMA_METAL_STD "" CACHE STRING "llama: metal standard version (-std flag)") option(LLAMA_KOMPUTE "llama: use Kompute" OFF) -option(LLAMA_MPI "llama: use MPI" OFF) option(LLAMA_RPC "llama: use RPC" OFF) option(LLAMA_QKK_64 "llama: use super-block size of 64 for k-quants" OFF) option(LLAMA_SYCL "llama: use SYCL" OFF) @@ -466,35 +465,6 @@ if (LLAMA_CUDA) endif() endif() -if (LLAMA_MPI) - cmake_minimum_required(VERSION 3.10) - find_package(MPI) - if (MPI_C_FOUND) - message(STATUS "MPI found") - - set(GGML_HEADERS_MPI ggml-mpi.h) - set(GGML_SOURCES_MPI ggml-mpi.c) - - add_compile_definitions(GGML_USE_MPI) - add_compile_definitions(${MPI_C_COMPILE_DEFINITIONS}) - - if (NOT MSVC) - add_compile_options(-Wno-cast-qual) - endif() - - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${MPI_C_LIBRARIES}) - set(LLAMA_EXTRA_INCLUDES ${LLAMA_EXTRA_INCLUDES} ${MPI_C_INCLUDE_DIRS}) - - # Even if you're only using the C header, C++ programs may bring in MPI - # C++ functions, so more linkage is needed - if (MPI_CXX_FOUND) - set(LLAMA_EXTRA_LIBS ${LLAMA_EXTRA_LIBS} ${MPI_CXX_LIBRARIES}) - endif() - else() - message(WARNING "MPI not found") - endif() -endif() - if (LLAMA_RPC) add_compile_definitions(GGML_USE_RPC) @@ -1218,7 +1188,6 @@ add_library(ggml OBJECT ${GGML_SOURCES_CUDA} ${GGML_HEADERS_CUDA} ${GGML_SOURCES_OPENCL} ${GGML_HEADERS_OPENCL} ${GGML_SOURCES_METAL} ${GGML_HEADERS_METAL} - ${GGML_SOURCES_MPI} ${GGML_HEADERS_MPI} ${GGML_SOURCES_RPC} ${GGML_HEADERS_RPC} ${GGML_SOURCES_EXTRA} ${GGML_HEADERS_EXTRA} ${GGML_SOURCES_SYCL} ${GGML_HEADERS_SYCL} @@ -1306,7 +1275,7 @@ install(FILES ${CMAKE_CURRENT_BINARY_DIR}/LlamaConfig.cmake set(GGML_PUBLIC_HEADERS "ggml.h" "ggml-alloc.h" "ggml-backend.h" "${GGML_HEADERS_CUDA}" "${GGML_HEADERS_OPENCL}" - "${GGML_HEADERS_METAL}" "${GGML_HEADERS_MPI}" "${GGML_HEADERS_EXTRA}") + "${GGML_HEADERS_METAL}" "${GGML_HEADERS_EXTRA}") set_target_properties(ggml PROPERTIES PUBLIC_HEADER "${GGML_PUBLIC_HEADERS}") install(TARGETS ggml PUBLIC_HEADER) diff --git a/Makefile b/Makefile index 22d521856..9a26aec50 100644 --- a/Makefile +++ b/Makefile @@ -399,13 +399,6 @@ ifndef LLAMA_NO_ACCELERATE endif endif # LLAMA_NO_ACCELERATE -ifdef LLAMA_MPI - MK_CPPFLAGS += -DGGML_USE_MPI - MK_CFLAGS += -Wno-cast-qual - MK_CXXFLAGS += -Wno-cast-qual - OBJS += ggml-mpi.o -endif # LLAMA_MPI - ifdef LLAMA_OPENBLAS MK_CPPFLAGS += -DGGML_USE_OPENBLAS $(shell pkg-config --cflags-only-I openblas) MK_CFLAGS += $(shell pkg-config --cflags-only-other openblas) @@ -629,11 +622,6 @@ ggml-metal-embed.o: ggml-metal.metal ggml-common.h endif endif # LLAMA_METAL -ifdef LLAMA_MPI -ggml-mpi.o: ggml-mpi.c ggml-mpi.h - $(CC) $(CFLAGS) -c $< -o $@ -endif # LLAMA_MPI - ifndef LLAMA_NO_LLAMAFILE sgemm.o: sgemm.cpp sgemm.h ggml.h $(CXX) $(CXXFLAGS) -c $< -o $@ diff --git a/README.md b/README.md index 7dd6fc0eb..4abfd6d7e 100644 --- a/README.md +++ b/README.md @@ -382,45 +382,6 @@ To disable the Metal build at compile time use the `LLAMA_NO_METAL=1` flag or th When built with Metal support, you can explicitly disable GPU inference with the `--n-gpu-layers|-ngl 0` command-line argument. -### MPI Build - -MPI lets you distribute the computation over a cluster of machines. Because of the serial nature of LLM prediction, this won't yield any end-to-end speed-ups, but it will let you run larger models than would otherwise fit into RAM on a single machine. - -First you will need MPI libraries installed on your system. The two most popular (only?) options are [MPICH](https://www.mpich.org) and [OpenMPI](https://www.open-mpi.org). Either can be installed with a package manager (`apt`, Homebrew, MacPorts, etc). - -Next you will need to build the project with `LLAMA_MPI` set to true on all machines; if you're building with `make`, you will also need to specify an MPI-capable compiler (when building with CMake, this is configured automatically): - -- Using `make`: - - ```bash - make CC=mpicc CXX=mpicxx LLAMA_MPI=1 - ``` - -- Using `CMake`: - - ```bash - cmake -S . -B build -DLLAMA_MPI=ON - ``` - -Once the programs are built, download/convert the weights on all of the machines in your cluster. The paths to the weights and programs should be identical on all machines. - -Next, ensure password-less SSH access to each machine from the primary host, and create a `hostfile` with a list of the hostnames and their relative "weights" (slots). If you want to use localhost for computation, use its local subnet IP address rather than the loopback address or "localhost". - -Here is an example hostfile: - -``` -192.168.0.1:2 -malvolio.local:1 -``` - -The above will distribute the computation across 2 processes on the first host and 1 process on the second host. Each process will use roughly an equal amount of RAM. Try to keep these numbers small, as inter-process (intra-host) communication is expensive. - -Finally, you're ready to run a computation using `mpirun`: - -```bash -mpirun -hostfile hostfile -n 3 ./main -m ./models/7B/ggml-model-q4_0.gguf -n 128 -``` - ### BLAS Build Building the program with BLAS support may lead to some performance improvements in prompt processing using batch sizes higher than 32 (the default is 512). Support with CPU-only BLAS implementations doesn't affect the normal generation performance. We may see generation performance improvements with GPU-involved BLAS implementations, e.g. cuBLAS, hipBLAS and CLBlast. There are currently several different BLAS implementations available for build and use: diff --git a/ggml-mpi.c b/ggml-mpi.c deleted file mode 100644 index ae176d707..000000000 --- a/ggml-mpi.c +++ /dev/null @@ -1,216 +0,0 @@ -#include "ggml-mpi.h" - -#include "ggml.h" - -#include - -#include -#include - -#define MIN(a, b) ((a) < (b) ? (a) : (b)) - -#define UNUSED GGML_UNUSED - -struct ggml_mpi_context { - int rank; - int size; -}; - -void ggml_mpi_backend_init(void) { - MPI_Init(NULL, NULL); -} - -void ggml_mpi_backend_free(void) { - MPI_Finalize(); -} - -struct ggml_mpi_context * ggml_mpi_init(void) { - struct ggml_mpi_context * ctx = calloc(1, sizeof(struct ggml_mpi_context)); - - MPI_Comm_rank(MPI_COMM_WORLD, &ctx->rank); - MPI_Comm_size(MPI_COMM_WORLD, &ctx->size); - - return ctx; -} - -void ggml_mpi_free(struct ggml_mpi_context * ctx) { - free(ctx); -} - -int ggml_mpi_rank(struct ggml_mpi_context * ctx) { - return ctx->rank; -} - -void ggml_mpi_eval_init( - struct ggml_mpi_context * ctx_mpi, - int * n_tokens, - int * n_past, - int * n_threads) { - UNUSED(ctx_mpi); - - // synchronize the worker node parameters with the root node - MPI_Barrier(MPI_COMM_WORLD); - - MPI_Bcast(n_tokens, 1, MPI_INT, 0, MPI_COMM_WORLD); - MPI_Bcast(n_past, 1, MPI_INT, 0, MPI_COMM_WORLD); - MPI_Bcast(n_threads, 1, MPI_INT, 0, MPI_COMM_WORLD); -} - -static int ggml_graph_get_node_idx(struct ggml_cgraph * gf, const char * name) { - struct ggml_tensor * t = ggml_graph_get_tensor(gf, name); - if (t == NULL) { - fprintf(stderr, "%s: tensor %s not found\n", __func__, name); - return -1; - } - - for (int i = 0; i < gf->n_nodes; i++) { - if (gf->nodes[i] == t) { - return i; - } - } - - fprintf(stderr, "%s: tensor %s not found in graph (should not happen)\n", __func__, name); - return -1; -} - -static void ggml_mpi_tensor_send(struct ggml_tensor * t, int mpi_rank_dst) { - MPI_Datatype mpi_type; - - switch (t->type) { - case GGML_TYPE_I32: mpi_type = MPI_INT32_T; break; - case GGML_TYPE_F32: mpi_type = MPI_FLOAT; break; - default: GGML_ASSERT(false && "not implemented"); - } - - const int retval = MPI_Send(t->data, ggml_nelements(t), mpi_type, mpi_rank_dst, 0, MPI_COMM_WORLD); - GGML_ASSERT(retval == MPI_SUCCESS); -} - -static void ggml_mpi_tensor_recv(struct ggml_tensor * t, int mpi_rank_src) { - MPI_Datatype mpi_type; - - switch (t->type) { - case GGML_TYPE_I32: mpi_type = MPI_INT32_T; break; - case GGML_TYPE_F32: mpi_type = MPI_FLOAT; break; - default: GGML_ASSERT(false && "not implemented"); - } - - MPI_Status status; UNUSED(status); - - const int retval = MPI_Recv(t->data, ggml_nelements(t), mpi_type, mpi_rank_src, MPI_ANY_TAG, MPI_COMM_WORLD, &status); - GGML_ASSERT(retval == MPI_SUCCESS); -} - -// TODO: there are many improvements that can be done to this implementation -void ggml_mpi_graph_compute_pre( - struct ggml_mpi_context * ctx_mpi, - struct ggml_cgraph * gf, - int n_layers) { - const int mpi_rank = ctx_mpi->rank; - const int mpi_size = ctx_mpi->size; - - struct ggml_tensor * inp_tokens = ggml_graph_get_tensor(gf, "inp_tokens"); - if (inp_tokens == NULL) { - fprintf(stderr, "%s: tensor 'inp_tokens' not found\n", __func__); - return; - } - - struct ggml_tensor * inp0 = ggml_graph_get_tensor(gf, "layer_inp_0"); - if (inp0 == NULL) { - fprintf(stderr, "%s: tensor 'inp0' not found\n", __func__); - return; - } - - GGML_ASSERT(inp0 == gf->nodes[0]); - - // distribute the compute graph into slices across the MPI nodes - // - // the main node (0) processes the last layers + the remainder of the compute graph - // and is responsible to pass the input tokens to the first node (1) - // - // node 1: [( 0) * n_per_node, ( 1) * n_per_node) - // node 2: [( 1) * n_per_node, ( 2) * n_per_node) - // ... - // node n-1: [(n-2) * n_per_node, (n-1) * n_per_node) - // node 0: [(n-1) * n_per_node, n_nodes) - // - if (mpi_rank > 0) { - if (mpi_rank == 1) { - // the first node (1) receives the input tokens from the main node (0) - ggml_mpi_tensor_recv(inp_tokens, 0); - } else { - // recv input data for each node into the "inp0" tensor (i.e. the first node in the compute graph) - ggml_mpi_tensor_recv(inp0, mpi_rank - 1); - } - } else if (mpi_size > 1) { - // node 0 sends the input tokens to node 1 - ggml_mpi_tensor_send(inp_tokens, 1); - - // recv the output data from the last node - ggml_mpi_tensor_recv(inp0, mpi_size - 1); - } - - { - const int n_per_node = (n_layers + (mpi_size - 1)) / mpi_size; - - const int mpi_idx = mpi_rank > 0 ? mpi_rank - 1 : mpi_size - 1; - - const int il0 = (mpi_idx + 0) * n_per_node; - const int il1 = MIN(n_layers, (mpi_idx + 1) * n_per_node); - - char name_l0[GGML_MAX_NAME]; - char name_l1[GGML_MAX_NAME]; - - snprintf(name_l0, sizeof(name_l0), "layer_inp_%d", il0); - snprintf(name_l1, sizeof(name_l1), "layer_inp_%d", il1); - - const int idx_l0 = ggml_graph_get_node_idx(gf, name_l0); - const int idx_l1 = mpi_rank > 0 ? ggml_graph_get_node_idx(gf, name_l1) + 1 : gf->n_nodes; - - if (idx_l0 < 0 || idx_l1 < 0) { - fprintf(stderr, "%s: layer input nodes not found\n", __func__); - return; - } - - // attach the input data to all nodes that need it - // TODO: not great - should be able to do this without modifying the compute graph (see next TODO below) - for (int i = idx_l0; i < idx_l1; i++) { - if (gf->nodes[i]->src[0] == gf->nodes[idx_l0]) { - gf->nodes[i]->src[0] = inp0; - } - if (gf->nodes[i]->src[1] == gf->nodes[idx_l0]) { - gf->nodes[i]->src[1] = inp0; - } - } - - // TODO: instead of rearranging the nodes, we should be able to execute a subset of the compute graph - for (int i = 1; i < idx_l1 - idx_l0; i++) { - gf->nodes[i] = gf->nodes[idx_l0 + i]; - gf->grads[i] = gf->grads[idx_l0 + i]; - } - - // the first node performs the "get_rows" operation, the rest of the nodes get the data from the previous node - if (mpi_idx != 0) { - gf->nodes[0]->op = GGML_OP_NONE; - } - - gf->n_nodes = idx_l1 - idx_l0; - - //fprintf(stderr, "%s: node %d: processing %d nodes [%d, %d)\n", __func__, mpi_rank, gf->n_nodes, il0, il1); - } -} - -void ggml_mpi_graph_compute_post( - struct ggml_mpi_context * ctx_mpi, - struct ggml_cgraph * gf, - int n_layers) { - UNUSED(n_layers); - - const int mpi_rank = ctx_mpi->rank; - const int mpi_size = ctx_mpi->size; - - // send the output data to the next node - if (mpi_rank > 0) { - ggml_mpi_tensor_send(gf->nodes[gf->n_nodes - 1], (mpi_rank + 1) % mpi_size); - } -} diff --git a/ggml-mpi.h b/ggml-mpi.h deleted file mode 100644 index eda119d44..000000000 --- a/ggml-mpi.h +++ /dev/null @@ -1,39 +0,0 @@ -#pragma once - -struct ggml_context; -struct ggml_tensor; -struct ggml_cgraph; - -#ifdef __cplusplus -extern "C" { -#endif - -struct ggml_mpi_context; - -void ggml_mpi_backend_init(void); -void ggml_mpi_backend_free(void); - -struct ggml_mpi_context * ggml_mpi_init(void); -void ggml_mpi_free(struct ggml_mpi_context * ctx); - -int ggml_mpi_rank(struct ggml_mpi_context * ctx); - -void ggml_mpi_eval_init( - struct ggml_mpi_context * ctx_mpi, - int * n_tokens, - int * n_past, - int * n_threads); - -void ggml_mpi_graph_compute_pre( - struct ggml_mpi_context * ctx_mpi, - struct ggml_cgraph * gf, - int n_layers); - -void ggml_mpi_graph_compute_post( - struct ggml_mpi_context * ctx_mpi, - struct ggml_cgraph * gf, - int n_layers); - -#ifdef __cplusplus -} -#endif diff --git a/llama.cpp b/llama.cpp index 06ff4da61..102bc2020 100644 --- a/llama.cpp +++ b/llama.cpp @@ -26,9 +26,6 @@ #ifdef GGML_USE_METAL # include "ggml-metal.h" #endif -#ifdef GGML_USE_MPI -# include "ggml-mpi.h" -#endif #ifndef QK_K # ifdef GGML_QKK_64 # define QK_K 64 @@ -2270,10 +2267,6 @@ struct llama_context { // control vectors struct llama_control_vector cvec; - -#ifdef GGML_USE_MPI - ggml_mpi_context * ctx_mpi = NULL; -#endif }; static ggml_backend_buffer_type_t llama_default_buffer_type_offload(const llama_model & model, int gpu) { @@ -6336,10 +6329,7 @@ static struct ggml_tensor * llm_build_inp_embd( inpL = ggml_get_rows(ctx, tok_embd, lctx.inp_tokens); } else { -#ifdef GGML_USE_MPI - GGML_ASSERT(false && "not implemented"); -#endif - lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens); + lctx.inp_embd = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, n_embd, batch.n_tokens); inpL = lctx.inp_embd; ggml_set_input(lctx.inp_embd); } @@ -11351,11 +11341,6 @@ static void llama_graph_compute( llama_context & lctx, ggml_cgraph * gf, int n_threads) { -#ifdef GGML_USE_MPI - const int64_t n_layer = lctx.model.hparams.n_layer; - ggml_mpi_graph_compute_pre(lctx.ctx_mpi, gf, n_layer); -#endif - #ifdef GGML_USE_METAL if (ggml_backend_is_metal(lctx.backend_metal)) { ggml_backend_metal_set_n_cb(lctx.backend_metal, n_threads); @@ -11370,10 +11355,6 @@ static void llama_graph_compute( ggml_backend_sched_graph_compute_async(lctx.sched, gf); // fprintf(stderr, "splits: %d\n", ggml_backend_sched_get_n_splits(lctx.sched)); - -#ifdef GGML_USE_MPI - ggml_mpi_graph_compute_post(lctx.ctx_mpi, gf, n_layer); -#endif } // decode a batch of tokens by evaluating the transformer @@ -11411,12 +11392,6 @@ static int llama_decode_internal( } lctx.n_queued_tokens += n_tokens_all; -#ifdef GGML_USE_MPI - // TODO: needs fix after #3228 - GGML_ASSERT(false && "not implemented"); - //ggml_mpi_eval_init(lctx.ctx_mpi, &n_tokens, &n_past, &n_threads); -#endif - auto & kv_self = lctx.kv_self; const int64_t n_embd = hparams.n_embd; @@ -15546,10 +15521,6 @@ void llama_backend_init(void) { struct ggml_context * ctx = ggml_init(params); ggml_free(ctx); } - -#ifdef GGML_USE_MPI - ggml_mpi_backend_init(); -#endif } void llama_numa_init(enum ggml_numa_strategy numa) { @@ -15559,9 +15530,6 @@ void llama_numa_init(enum ggml_numa_strategy numa) { } void llama_backend_free(void) { -#ifdef GGML_USE_MPI - ggml_mpi_backend_free(); -#endif ggml_quantize_free(); } @@ -15962,20 +15930,6 @@ struct llama_context * llama_new_context_with_model( } } -#ifdef GGML_USE_MPI - ctx->ctx_mpi = ggml_mpi_init(); - - if (ggml_mpi_rank(ctx->ctx_mpi) > 0) { - // Enter a blocking eval loop with dummy input, letting rank=0 drive the process - // TODO: needs fix after #3228 - GGML_ASSERT(false && "not implemented"); - //const std::vector tmp(ctx->model.hparams.n_ctx, llama_token_bos(ctx)); - //while (!llama_eval(ctx, tmp.data(), tmp.size(), 0, 0)) {}; - llama_backend_free(); - exit(1); - } -#endif - return ctx; } diff --git a/scripts/LlamaConfig.cmake.in b/scripts/LlamaConfig.cmake.in index f842c7137..92e39708b 100644 --- a/scripts/LlamaConfig.cmake.in +++ b/scripts/LlamaConfig.cmake.in @@ -5,7 +5,6 @@ set(LLAMA_SHARED_LIB @BUILD_SHARED_LIBS@) set(LLAMA_BLAS @LLAMA_BLAS@) set(LLAMA_CUDA @LLAMA_CUDA@) set(LLAMA_METAL @LLAMA_METAL@) -set(LLAMA_MPI @LLAMA_MPI@) set(LLAMA_CLBLAST @LLAMA_CLBLAST@) set(LLAMA_HIPBLAS @LLAMA_HIPBLAS@) set(LLAMA_ACCELERATE @LLAMA_ACCELERATE@) @@ -37,10 +36,6 @@ if (LLAMA_METAL) find_library(METALKIT_FRAMEWORK MetalKit REQUIRED) endif() -if (LLAMA_MPI) - find_package(MPI REQUIRED) -endif() - if (LLAMA_CLBLAST) find_package(CLBlast REQUIRED) endif() From 33c8d50accd6dca73c9c4af00a05e24209c160fe Mon Sep 17 00:00:00 2001 From: Srihari-mcw <96763064+Srihari-mcw@users.noreply.github.com> Date: Sun, 19 May 2024 19:18:39 -0700 Subject: [PATCH 055/181] Add provisions for windows support for BF16 code including CMake provision for enabling AVX512_BF16 (#7258) --- CMakeLists.txt | 8 ++++++++ ggml-impl.h | 12 ++++++++++++ ggml.c | 24 ++++++++++++++++-------- ggml.h | 1 + llama.cpp | 1 + 5 files changed, 38 insertions(+), 8 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 616698c7f..92c9f09eb 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -77,6 +77,7 @@ option(LLAMA_AVX2 "llama: enable AVX2" option(LLAMA_AVX512 "llama: enable AVX512" OFF) option(LLAMA_AVX512_VBMI "llama: enable AVX512-VBMI" OFF) option(LLAMA_AVX512_VNNI "llama: enable AVX512-VNNI" OFF) +option(LLAMA_AVX512_BF16 "llama: enable AVX512-BF16" OFF) option(LLAMA_FMA "llama: enable FMA" ${INS_ENB}) # in MSVC F16C is implied with AVX2/AVX512 if (NOT MSVC) @@ -1060,6 +1061,10 @@ elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LW add_compile_definitions($<$:__AVX512VNNI__>) add_compile_definitions($<$:__AVX512VNNI__>) endif() + if (LLAMA_AVX512_BF16) + add_compile_definitions($<$:__AVX512BF16__>) + add_compile_definitions($<$:__AVX512BF16__>) + endif() elseif (LLAMA_AVX2) list(APPEND ARCH_FLAGS /arch:AVX2) elseif (LLAMA_AVX) @@ -1091,6 +1096,9 @@ elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LW if (LLAMA_AVX512_VNNI) list(APPEND ARCH_FLAGS -mavx512vnni) endif() + if (LLAMA_AVX512_BF16) + list(APPEND ARCH_FLAGS -mavx512bf16) + endif() endif() elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64") message(STATUS "PowerPC detected") diff --git a/ggml-impl.h b/ggml-impl.h index 59684fa81..5ff014fe3 100644 --- a/ggml-impl.h +++ b/ggml-impl.h @@ -17,6 +17,18 @@ #define MIN(a, b) ((a) < (b) ? (a) : (b)) #define MAX(a, b) ((a) > (b) ? (a) : (b)) +#if defined(_WIN32) + +#define m512bh(p) p +#define m512i(p) p + +#else + +#define m512bh(p) (__m512bh)(p) +#define m512i(p) (__m512i)(p) + +#endif + /** * Converts brain16 to float32. * diff --git a/ggml.c b/ggml.c index 3a104c486..53da231ee 100644 --- a/ggml.c +++ b/ggml.c @@ -406,10 +406,10 @@ void ggml_fp32_to_bf16_row(const float * x, ggml_bf16_t * y, int64_t n) { int i = 0; #if defined(__AVX512BF16__) for (; i + 32 <= n; i += 32) { - _mm512_storeu_ps( - (__m512 *)(y + i), - (__m512)_mm512_cvtne2ps_pbh(_mm512_loadu_ps(x + i + 16), - _mm512_loadu_ps(x + i))); + _mm512_storeu_si512( + (__m512i *)(y + i), + m512i(_mm512_cvtne2ps_pbh(_mm512_loadu_ps(x + i + 16), + _mm512_loadu_ps(x + i)))); } #endif for (; i < n; i++) { @@ -1666,10 +1666,10 @@ static void ggml_vec_dot_bf16(int n, float * restrict s, size_t bs, ggml_bf16_t __m512 c1 = _mm512_setzero_ps(); __m512 c2 = _mm512_setzero_ps(); for (; i + 64 <= n; i += 64) { - c1 = _mm512_dpbf16_ps(c1, (__m512bh)_mm512_loadu_ps((const float *)(x + i)), - (__m512bh)_mm512_loadu_ps((const float *)(y + i))); - c2 = _mm512_dpbf16_ps(c2, (__m512bh)_mm512_loadu_ps((const float *)(x + i + 32)), - (__m512bh)_mm512_loadu_ps((const float *)(y + i + 32))); + c1 = _mm512_dpbf16_ps(c1, m512bh(_mm512_loadu_si512((x + i))), + m512bh(_mm512_loadu_si512((y + i)))); + c2 = _mm512_dpbf16_ps(c2, m512bh(_mm512_loadu_si512((x + i + 32))), + m512bh(_mm512_loadu_si512((y + i + 32)))); } sumf += (ggml_float)_mm512_reduce_add_ps(c1); sumf += (ggml_float)_mm512_reduce_add_ps(c2); @@ -23137,6 +23137,14 @@ int ggml_cpu_has_avx512_vnni(void) { #endif } +int ggml_cpu_has_avx512_bf16(void) { +#if defined(__AVX512BF16__) + return 1; +#else + return 0; +#endif +} + int ggml_cpu_has_fma(void) { #if defined(__FMA__) return 1; diff --git a/ggml.h b/ggml.h index 8c13f4ba8..774757101 100644 --- a/ggml.h +++ b/ggml.h @@ -2390,6 +2390,7 @@ extern "C" { GGML_API int ggml_cpu_has_avx512 (void); GGML_API int ggml_cpu_has_avx512_vbmi(void); GGML_API int ggml_cpu_has_avx512_vnni(void); + GGML_API int ggml_cpu_has_avx512_bf16(void); GGML_API int ggml_cpu_has_fma (void); GGML_API int ggml_cpu_has_neon (void); GGML_API int ggml_cpu_has_arm_fma (void); diff --git a/llama.cpp b/llama.cpp index 102bc2020..ca3e9fcc0 100644 --- a/llama.cpp +++ b/llama.cpp @@ -18074,6 +18074,7 @@ const char * llama_print_system_info(void) { s += "AVX512 = " + std::to_string(ggml_cpu_has_avx512()) + " | "; s += "AVX512_VBMI = " + std::to_string(ggml_cpu_has_avx512_vbmi()) + " | "; s += "AVX512_VNNI = " + std::to_string(ggml_cpu_has_avx512_vnni()) + " | "; + s += "AVX512_BF16 = " + std::to_string(ggml_cpu_has_avx512_bf16()) + " | "; s += "FMA = " + std::to_string(ggml_cpu_has_fma()) + " | "; s += "NEON = " + std::to_string(ggml_cpu_has_neon()) + " | "; s += "ARM_FMA = " + std::to_string(ggml_cpu_has_arm_fma()) + " | "; From 2789baf480ba7dc9281b65f601f0918e58920f54 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 20 May 2024 08:55:09 +0300 Subject: [PATCH 056/181] tests : fix --keep_split -> --keep-split (#7374) --- examples/quantize/tests.sh | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/quantize/tests.sh b/examples/quantize/tests.sh index 160c12bee..a3ca74c68 100644 --- a/examples/quantize/tests.sh +++ b/examples/quantize/tests.sh @@ -41,8 +41,8 @@ $SPLIT --split-max-tensors 28 $WORK_PATH/gemma-1.1-2b-it.Q8_0.gguf $WORK_PATH/g echo PASS echo -# 3. Requant model with '--keep_split' -$QUANTIZE --allow-requantize --keep_split $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-requant.gguf Q4_K +# 3. Requant model with '--keep-split' +$QUANTIZE --allow-requantize --keep-split $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-requant.gguf Q4_K echo PASS echo @@ -51,7 +51,7 @@ $MAIN --model $WORK_PATH/ggml-model-requant-00001-of-00006.gguf --random-prompt echo PASS echo -# 4. Requant mode without '--keep_split' +# 4. Requant mode without '--keep-split' $QUANTIZE --allow-requantize $WORK_PATH/ggml-model-split-00001-of-00006.gguf $WORK_PATH/ggml-model-requant-merge.gguf Q4_K echo PASS echo From e932094d58f513d5996c3efc9f6fed8238894c57 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 20 May 2024 08:56:05 +0300 Subject: [PATCH 057/181] server : return error on too large embedding input (#7389) --- examples/server/server.cpp | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 7978f979d..6af5cb96e 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1981,8 +1981,7 @@ struct server_context { slot.state = SLOT_STATE_PROCESSING; slot.command = SLOT_COMMAND_NONE; slot.release(); - slot.print_timings(); - send_final_response(slot); + send_error(slot, "input is too large to process. increase the physical batch size", ERROR_TYPE_SERVER); continue; } } else { From 1cc0155d04918cb3017afa472acea51b77483c4a Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 20 May 2024 10:16:41 +0300 Subject: [PATCH 058/181] server : tuning tests (#7388) * server : don't pass temperature as string * server : increase timeout * tests : fix the fix 0.8f -> 0.8 ggml-ci * tests : set explicit temperature --- examples/server/tests/features/results.feature | 2 ++ examples/server/tests/features/steps/steps.py | 4 ++-- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/examples/server/tests/features/results.feature b/examples/server/tests/features/results.feature index 5deb278c2..4ab8ad20c 100644 --- a/examples/server/tests/features/results.feature +++ b/examples/server/tests/features/results.feature @@ -13,6 +13,7 @@ Feature: Results Scenario Outline: consistent results with same seed Given slots + And 0.0 temperature Then the server is starting Then the server is healthy @@ -30,6 +31,7 @@ Feature: Results Scenario Outline: different results with different seed Given slots + And 1.0 temperature Then the server is starting Then the server is healthy diff --git a/examples/server/tests/features/steps/steps.py b/examples/server/tests/features/steps/steps.py index 7da503f2c..26d9359d7 100644 --- a/examples/server/tests/features/steps/steps.py +++ b/examples/server/tests/features/steps/steps.py @@ -199,7 +199,7 @@ async def step_wait_for_the_server_to_be_started(context, expecting_status): case 'ready' | 'idle': await wait_for_health_status(context, context.base_url, 200, 'ok', - timeout=10, + timeout=30, params={'fail_on_no_slot': 0, 'include_slots': 0}, slots_idle=context.n_slots, slots_processing=0, @@ -883,7 +883,7 @@ async def request_completion(prompt, "cache_prompt": cache_prompt, "id_slot": id_slot, "seed": seed if seed is not None else 42, - "temperature": temperature if temperature is not None else "0.8f", + "temperature": temperature if temperature is not None else 0.8, "n_probs": 2, }, headers=headers, From 65c58207ece92ad213f4bfd0f91dcb2dfb664f5b Mon Sep 17 00:00:00 2001 From: junchao-loongson <68935141+junchao-loongson@users.noreply.github.com> Date: Mon, 20 May 2024 15:19:21 +0800 Subject: [PATCH 059/181] ggml : add loongarch lsx and lasx support (#6454) * add loongarch lsx and lasx optimize code * Add loongarch compilation support to makefile * revert stb_image.h * opt bytes_from_nibbles_32 and sum_i16_pairs_float * fix undeclared * format code * update * update 2 --------- Co-authored-by: Jinyang He --- CMakeLists.txt | 13 + Makefile | 5 + ggml-impl.h | 28 + ggml-quants.c | 2089 +++++++++++++++++++++++++++++++++++++++++++++++- ggml.c | 189 +++++ 5 files changed, 2316 insertions(+), 8 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index 92c9f09eb..9cc60039a 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -134,6 +134,8 @@ set(LLAMA_SCHED_MAX_COPIES "4" CACHE STRING "llama: max input copies for pipeli option(LLAMA_BUILD_TESTS "llama: build tests" ${LLAMA_STANDALONE}) option(LLAMA_BUILD_EXAMPLES "llama: build examples" ${LLAMA_STANDALONE}) option(LLAMA_BUILD_SERVER "llama: build server example" ON) +option(LLAMA_LASX "llama: enable lasx" ON) +option(LLAMA_LSX "llama: enable lsx" ON) # add perf arguments option(LLAMA_PERF "llama: enable perf" OFF) @@ -1108,6 +1110,17 @@ elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "ppc64") 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() +elseif (${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64") + message(STATUS "loongarch64 detected") + + list(APPEND ARCH_FLAGS -march=loongarch64) + if (LLAMA_LASX) + list(APPEND ARCH_FLAGS -mlasx) + endif() + if (LLAMA_LSX) + list(APPEND ARCH_FLAGS -mlsx) + endif() + else() message(STATUS "Unknown architecture") endif() diff --git a/Makefile b/Makefile index 9a26aec50..6b7c853b3 100644 --- a/Makefile +++ b/Makefile @@ -379,6 +379,11 @@ ifneq ($(filter ppc64le%,$(UNAME_M)),) CUDA_POWER_ARCH = 1 endif +ifneq ($(filter loongarch64%,$(UNAME_M)),) + MK_CFLAGS += -mlasx + MK_CXXFLAGS += -mlasx +endif + else MK_CFLAGS += -march=rv64gcv -mabi=lp64d MK_CXXFLAGS += -march=rv64gcv -mabi=lp64d diff --git a/ggml-impl.h b/ggml-impl.h index 5ff014fe3..362d40f4d 100644 --- a/ggml-impl.h +++ b/ggml-impl.h @@ -455,6 +455,34 @@ static inline ggml_fp16_t ggml_compute_fp32_to_fp16(float f) { #include #endif +#if defined(__loongarch64) +#if defined(__loongarch_asx) +#include +#endif +#if defined(__loongarch_sx) +#include +#endif +#endif + +#if defined(__loongarch_asx) + +typedef union { + int32_t i; + float f; +} ft_union; + +/* float type data load instructions */ +static __m128 __lsx_vreplfr2vr_s(float val) { + ft_union fi_tmpval = {.f = val}; + return (__m128)__lsx_vreplgr2vr_w(fi_tmpval.i); +} + +static __m256 __lasx_xvreplfr2vr_s(float val) { + ft_union fi_tmpval = {.f = val}; + return (__m256)__lasx_xvreplgr2vr_w(fi_tmpval.i); +} +#endif + #ifdef __F16C__ #ifdef _MSC_VER diff --git a/ggml-quants.c b/ggml-quants.c index ff1059863..ed40ca74a 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -262,6 +262,403 @@ static const uint64_t table_b2b_0[1 << 8] = { B8(00, 10) }; // ( b) << 4 static const uint64_t table_b2b_1[1 << 8] = { B8(10, 00) }; // (!b) << 4 #endif +#if defined(__loongarch_asx) + +#ifdef __clang__ +#define VREGS_PREFIX "$vr" +#define XREGS_PREFIX "$xr" +#else // GCC +#define VREGS_PREFIX "$f" +#define XREGS_PREFIX "$f" +#endif +#define __ALL_REGS "0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31" +// Convert __m128i to __m256i +static inline __m256i ____m256i(__m128i in) { + __m256i out = __lasx_xvldi(0); + __asm__ volatile ( + ".irp i," __ALL_REGS "\n\t" + " .ifc %[out], " XREGS_PREFIX"\\i \n\t" + " .irp j," __ALL_REGS "\n\t" + " .ifc %[in], " VREGS_PREFIX "\\j \n\t" + " xvpermi.q $xr\\i, $xr\\j, 0x20 \n\t" + " .endif \n\t" + " .endr \n\t" + " .endif \n\t" + ".endr \n\t" + : [out] "+f" (out) : [in] "f" (in) + ); + return out; +} +// Convert two __m128i to __m256i +static inline __m256i lasx_set_q(__m128i inhi, __m128i inlo) { + __m256i out; + __asm__ volatile ( + ".irp i," __ALL_REGS "\n\t" + " .ifc %[hi], " VREGS_PREFIX "\\i \n\t" + " .irp j," __ALL_REGS "\n\t" + " .ifc %[lo], " VREGS_PREFIX "\\j \n\t" + " xvpermi.q $xr\\i, $xr\\j, 0x20 \n\t" + " .endif \n\t" + " .endr \n\t" + " .endif \n\t" + ".endr \n\t" + ".ifnc %[out], %[hi] \n\t" + ".irp i," __ALL_REGS "\n\t" + " .ifc %[out], " XREGS_PREFIX "\\i \n\t" + " .irp j," __ALL_REGS "\n\t" + " .ifc %[hi], " VREGS_PREFIX "\\j \n\t" + " xvori.b $xr\\i, $xr\\j, 0 \n\t" + " .endif \n\t" + " .endr \n\t" + " .endif \n\t" + ".endr \n\t" + ".endif \n\t" + : [out] "=f" (out), [hi] "+f" (inhi) + : [lo] "f" (inlo) + ); + return out; +} +// Convert __m256i low part to __m128i +static inline __m128i lasx_extracti128_lo(__m256i in) { + __m128i out; + __asm__ volatile ( + ".ifnc %[out], %[in] \n\t" + ".irp i," __ALL_REGS "\n\t" + " .ifc %[out], " VREGS_PREFIX "\\i \n\t" + " .irp j," __ALL_REGS "\n\t" + " .ifc %[in], " XREGS_PREFIX "\\j \n\t" + " vori.b $vr\\i, $vr\\j, 0 \n\t" + " .endif \n\t" + " .endr \n\t" + " .endif \n\t" + ".endr \n\t" + ".endif \n\t" + : [out] "=f" (out) : [in] "f" (in) + ); + return out; +} +// Convert __m256i high part to __m128i +static inline __m128i lasx_extracti128_hi(__m256i in) { + __m128i out; + __asm__ volatile ( + ".irp i," __ALL_REGS "\n\t" + " .ifc %[out], " VREGS_PREFIX "\\i \n\t" + " .irp j," __ALL_REGS "\n\t" + " .ifc %[in], " XREGS_PREFIX "\\j \n\t" + " xvpermi.q $xr\\i, $xr\\j, 0x11 \n\t" + " .endif \n\t" + " .endr \n\t" + " .endif \n\t" + ".endr \n\t" + : [out] "=f" (out) : [in] "f" (in) + ); + return out; +} + +static __m256i lasx_set_w(int e7, int e6, int e5, int e4, int e3, int e2, int e1, int e0) { + v8i32 __ret = {e0, e1, e2, e3, e4, e5, e6, e7}; + return (__m256i)__ret; +} + +static __m128i lsx_set_w(int32_t a, int32_t b, int32_t c, int32_t d) { + v4i32 __ret = {d, c, b, a}; + return (__m128i)__ret; +} + +static __m256i lasx_set_d(int64_t a, int64_t b, int64_t c, int64_t d) { + v4i64 __ret = {d, c, b, a}; + return (__m256i)__ret; +} + +static __m256i lasx_insertf128( __m128i x, __m128i y) { + return lasx_set_q(x, y); +} + +static __m128i lsx_shuffle_b(__m128i a, __m128i b) { + __m128i mask_f, zero, tmp0, tmp2, mask; + int f = 0x8f; + mask_f = __lsx_vreplgr2vr_b(f); + zero = __lsx_vldi(0); + tmp0 = __lsx_vand_v(b, mask_f); // get mask with low 4 bit and sign bits + tmp0 = __lsx_vori_b(tmp0, 0x10); // make each mask or with 0x10 prepare for positive + mask = __lsx_vsle_b(zero, tmp0); // if mask >= 0, set mask + tmp2 = __lsx_vand_v(tmp0, mask); // maskout the in2 < ones + return __lsx_vshuf_b(a, zero, tmp2); +} + +static __m256i lasx_shuffle_b(__m256i a, __m256i b) { + __m256i mask_f, zero, tmp0, tmp2, mask; + int f = 0x8f; + mask_f = __lasx_xvreplgr2vr_b(f); + zero = __lasx_xvldi(0); + tmp0 = __lasx_xvand_v(b, mask_f); // get mask with low 4 bit and sign bits + tmp0 = __lasx_xvori_b(tmp0, 0x10); // make each mask or with 0x10 prepare for positive + mask = __lasx_xvsle_b(zero, tmp0); // if mask >= 0, set mask + tmp2 = __lasx_xvand_v(tmp0, mask); // maskout the in2 < ones + return __lasx_xvshuf_b(a, zero, tmp2); +} + +static __m256i lasx_extu8_16(__m128i a) { + __m128i zero = __lsx_vldi(0); + __m128i vlo = __lsx_vilvl_b(zero, a); + __m128i vhi = __lsx_vilvh_b(zero, a); + return lasx_set_q(vhi, vlo); +} + +static __m256i lasx_ext8_16(__m128i a) { + __m128i sign = __lsx_vslti_b(a, 0); + __m128i vlo = __lsx_vilvl_b(sign, a); + __m128i vhi = __lsx_vilvh_b(sign, a); + return lasx_set_q(vhi, vlo); +} + +static __m256i lasx_ext16_32(__m128i a) { + __m256i tmp1; + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 0), 0); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 1), 1); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 2), 2); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 3), 3); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 4), 4); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 5), 5); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 6), 6); + tmp1 = __lasx_xvinsgr2vr_w(tmp1, __lsx_vpickve2gr_h(a, 7), 7); + return tmp1; +} + +static __m128i lasx_extracti128( __m256i a, int pos) { + __m128i ret; + if( pos == 0) + { + ret = lasx_extracti128_lo(a); + } else { + ret = lasx_extracti128_hi(a); + } + return ret; +} + +static __m128 lasx_extractf128( __m256 a, int pos) { + __m128 ret; + if( pos == 0) + { + ret = (__m128)lasx_extracti128_lo((__m256i)a); + } else { + ret = (__m128)lasx_extracti128_hi((__m256i)a); + } + return ret; +} + +static __m128i lsx_hadd_h(__m128i a, __m128i b) { + __m128i tmp1 = __lsx_vpickev_h(b, a); + __m128i tmp2 = __lsx_vpickod_h(b, a); + return __lsx_vadd_h(tmp1, tmp2); +} + +static __m128i lsx_hadd_w(__m128i a, __m128i b) { + __m128i tmp1 = __lsx_vpickev_w(b, a); + __m128i tmp2 = __lsx_vpickod_w(b, a); + return __lsx_vadd_w(tmp1, tmp2); +} + +static __m128 lsx_hadd_s(__m128 a, __m128 b) { + __m128 tmp1 = (__m128)__lsx_vpickev_w((__m128i)b, (__m128i)a); + __m128 tmp2 = (__m128)__lsx_vpickod_w((__m128i)b, (__m128i)a); + + return __lsx_vfadd_s(tmp1, tmp2); +} + +static __m256i lasx_maddubs_h(__m256i a, __m256i b) { + __m256i tmp1, tmp2; + tmp1 = __lasx_xvmulwev_h_b(a, b); + tmp2 = __lasx_xvmulwod_h_b(a, b); + return __lasx_xvsadd_h(tmp1, tmp2); +} + +static __m256i lasx_madd_h(__m256i a, __m256i b) { + __m256i tmp1, tmp2; + tmp1 = __lasx_xvmulwev_w_h(a, b); + tmp2 = __lasx_xvmulwod_w_h(a, b); + return __lasx_xvadd_w(tmp1, tmp2); +} + +static __m256i lasx_packs_w(__m256i a, __m256i b) { + __m256i tmp, tmp1; + tmp = __lasx_xvsat_w(a, 15); + tmp1 = __lasx_xvsat_w(b, 15); + return __lasx_xvpickev_h(tmp1, tmp); +} + +static __m256i lasx_packs_h(__m256i a, __m256i b) { + __m256i tmp, tmp1; + tmp = __lasx_xvsat_h(a, 7); + tmp1 = __lasx_xvsat_h(b, 7); + return __lasx_xvpickev_b(tmp1, tmp); +} + +static __m128i lsx_packs_w(__m128i a, __m128i b) { + __m128i tmp, tmp1; + tmp = __lsx_vsat_w(a, 15); + tmp1 = __lsx_vsat_w(b, 15); + return __lsx_vpickev_h(tmp1, tmp); +} + +static __m128i lsx_packs_h(__m128i a, __m128i b) { + __m128i tmp, tmp1; + tmp = __lsx_vsat_h(a, 7); + tmp1 = __lsx_vsat_h(b, 7); + return __lsx_vpickev_b(tmp1, tmp); +} + +static __m128i lsx_packus_h(__m128i a, __m128i b) { + __m128i tmp, tmp1; + tmp = __lsx_vsat_hu(a, 7); + tmp1 = __lsx_vsat_hu(b, 7); + return __lsx_vpickev_b(tmp1, tmp); +} + + +static __m128i lsx_maddubs_h(__m128i a, __m128i b) { + __m128i tmp1, tmp2; + tmp1 = __lsx_vmulwev_h_b(a, b); + tmp2 = __lsx_vmulwod_h_b(a, b); + return __lsx_vsadd_h(tmp1, tmp2); +} + +static __m128i lsx_madd_h(__m128i a, __m128i b) { + __m128i tmp1, tmp2; + tmp1 = __lsx_vmulwev_w_h(a, b); + tmp2 = __lsx_vmulwod_w_h(a, b); + return __lsx_vadd_w(tmp1, tmp2); +} + +// multiply int8_t, add results pairwise twice +static inline __m128i mul_sum_i8_pairs(const __m128i x, const __m128i y) { + // Get absolute values of x vectors + const __m128i ax = __lsx_vsigncov_b(x, x); + // Sign the values of the y vectors + const __m128i sy = __lsx_vsigncov_b(x, y); + // Perform multiplication and create 16-bit values + const __m128i dot = lsx_maddubs_h(ax, sy); + const __m128i ones = __lsx_vreplgr2vr_h(1); + return lsx_madd_h(ones, dot); +} + +// horizontally add 8 floats +static inline float hsum_float_8(const __m256 x) { + __m128 res = lasx_extractf128(x, 1); + ft_union tmp; + res = __lsx_vfadd_s(res, lasx_extractf128(x, 0)); + res = __lsx_vfadd_s(res, (__m128)__lsx_vpickod_d((__m128i)res, (__m128i)res)); + res = __lsx_vfadd_s(res, (__m128)__lsx_vinsgr2vr_w(__lsx_vldi(0), __lsx_vpickve2gr_w(res, 1), 0)); + tmp.i = __lsx_vpickve2gr_w(res, 0); + return tmp.f; +} + +// horizontally add 8 int32_t +static inline int hsum_i32_8(const __m256i a) { + + __m256i tmp1 = __lasx_xvpermi_q(a, a, 0x11); + __m256i tmp2 = __lasx_xvpermi_q(a, a, 0x00); + + __m128i tmp1_128 = lasx_extracti128_lo(tmp1); + __m128i tmp2_128 = lasx_extracti128_lo(tmp2); + + __m128i sum128 = __lsx_vadd_w(tmp1_128, tmp2_128); + + __m128i ev = __lsx_vpickev_w(sum128, sum128); + __m128i od = __lsx_vpickod_w(sum128, sum128); + __m128i sum64 = __lsx_vadd_w(ev, od); + + int sum64_1, sum64_2; + sum64_1 = __lsx_vpickve2gr_w(sum64, 0); + sum64_2 = __lsx_vpickve2gr_w(sum64, 1); + + return sum64_1 + sum64_2; +} + +// horizontally add 4 int32_t +static inline int hsum_i32_4(const __m128i a) { + __m128i ev = __lsx_vpickev_w(a, a); + __m128i od = __lsx_vpickod_w(a, a); + __m128i sum64 = __lsx_vadd_w(ev, od); + + int sum64_1, sum64_2; + sum64_1 = __lsx_vpickve2gr_w(sum64, 0); + sum64_2 = __lsx_vpickve2gr_w(sum64, 1); + + return sum64_1 + sum64_2; +} + +// spread 32 bits to 32 bytes { 0x00, 0xFF } +static inline __m256i bytes_from_bits_32(const uint8_t * x) { + + uint32_t x32; + memcpy(&x32, x, sizeof(uint32_t)); + const __m256i shuf_mask = lasx_set_d( + 0x0303030303030303, 0x0202020202020202, + 0x0101010101010101, 0x0000000000000000); + + __m256i bytes = lasx_shuffle_b(__lasx_xvreplgr2vr_w(x32), shuf_mask); + const __m256i bit_mask = __lasx_xvreplgr2vr_d(0x7fbfdfeff7fbfdfe); + bytes = __lasx_xvor_v(bytes, bit_mask); + return __lasx_xvseq_b(bytes, __lasx_xvreplgr2vr_d(-1)); +} + +// Unpack 32 4-bit fields into 32 bytes +// The output vector contains 32 bytes, each one in [ 0 .. 15 ] interval +static inline __m256i bytes_from_nibbles_32(const uint8_t * rsi) { + const __m128i lo = __lsx_vld((const __m128i *)rsi, 0); + __m128i hi = __lsx_vsrli_h(lo, 4); + return __lasx_xvandi_b(lasx_insertf128(hi, lo), 0xf); +} + +// add int16_t pairwise and return as float vector +static inline __m256 sum_i16_pairs_float(const __m256i x) { + __m256i v = __lasx_xvpackod_h(x, x); + __m256i summed_pairs = __lasx_xvaddwev_w_h(x, v); + return __lasx_xvffint_s_w(summed_pairs); +} + +static inline __m256 mul_sum_us8_pairs_float(const __m256i ax, const __m256i sy) { + // Perform multiplication and create 16-bit values + const __m256i dot = lasx_maddubs_h(ax, sy); + return sum_i16_pairs_float(dot); +} + +// multiply int8_t, add results pairwise twice and return as float vector +static inline __m256 mul_sum_i8_pairs_float(const __m256i x, const __m256i y) { + + // Get absolute values of x vectors + const __m256i ax = __lasx_xvsigncov_b(x, x); + // Sign the values of the y vectors + const __m256i sy = __lasx_xvsigncov_b(x, y); + + return mul_sum_us8_pairs_float(ax, sy); +} + +static inline __m128i packNibbles( __m256i bytes ) { + // Move bits within 16-bit lanes from 0000_abcd_0000_efgh into 0000_0000_abcd_efgh + const __m256i lowByte = __lasx_xvreplgr2vr_h(0xFF); + __m256i high = __lasx_xvandn_v(lowByte, bytes); + __m256i low = __lasx_xvand_v(lowByte, bytes); + high = __lasx_xvsrli_h(high, 4); + bytes = __lasx_xvor_v(low, high); + // Compress uint16_t lanes into bytes + __m128i *r0 = (__m128i *)&bytes; + __m256i tmp_h128 = __lasx_xvpermi_q(bytes, bytes, 0x11); + __m128i *r1 = (__m128i *)&tmp_h128; + + __m128i zero = __lsx_vldi(0); + __m128i tmp, tmp2, tmp3; + + tmp = __lsx_vmax_h(zero, *r0); + tmp2 = __lsx_vsat_hu(tmp, 7); + + tmp = __lsx_vmax_h(zero, *r1); + tmp3 = __lsx_vsat_hu(tmp, 7); + return __lsx_vpickev_b(tmp3, tmp2); +} +#endif //__loongarch_asx + // reference implementation for deterministic creation of model files void quantize_row_q4_0_reference(const float * restrict x, block_q4_0 * restrict y, int64_t k) { static const int qk = QK4_0; @@ -649,6 +1046,7 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int64_t k) // store result __riscv_vse8_v_i8m1(y[i].qs , vs, vl); } + #elif defined(__POWER9_VECTOR__) for (int i = 0; i < nb; i++) { vector float srcv [8]; @@ -680,6 +1078,69 @@ void quantize_row_q8_0(const float * restrict x, void * restrict vy, int64_t k) } vec_xst(vec_pack(vec_pack(vi[0], vi[1]), vec_pack(vi[2], vi[3])), 0, &y[i].qs[0]); vec_xst(vec_pack(vec_pack(vi[4], vi[5]), vec_pack(vi[6], vi[7])), 16, &y[i].qs[0]); + +#elif defined(__loongarch_asx) + for (int i = 0; i < nb; i++) { + ft_union fi; + __m256 v0 = (__m256)__lasx_xvld( x , 0); + __m256 v1 = (__m256)__lasx_xvld( x , 32); + __m256 v2 = (__m256)__lasx_xvld( x , 64); + __m256 v3 = (__m256)__lasx_xvld( x , 96); + x += 32; + + // Compute max(abs(e)) for the block + const __m256 sign_bit = __lasx_xvreplfr2vr_s( -0.0f ); + __m256 max_abs = (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v0 ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v1 ) ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v2 ) ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v3 ) ); + + __m128 max4 = __lsx_vfmax_s( lasx_extractf128( max_abs, 1 ), lasx_extractf128( max_abs , 0) ); + max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vpickod_d((__m128i) max4, (__m128i)max4 ) ); + __m128 tmp = max4; + max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vinsgr2vr_w(tmp, __lsx_vpickve2gr_w( max4, 1 ), 0 )); + fi.i = __lsx_vpickve2gr_w( (__m128i)max4, 0 ); + const float max_scalar = fi.f; + + // Quantize these floats + const float d = max_scalar / 127.f; + y[i].d = GGML_FP32_TO_FP16(d); + const float id = ( max_scalar != 0.0f ) ? 127.f / max_scalar : 0.0f; + const __m256 mul = (__m256)__lasx_xvreplfr2vr_s( id ); + + // Apply the multiplier + v0 = __lasx_xvfmul_s( v0, mul ); + v1 = __lasx_xvfmul_s( v1, mul ); + v2 = __lasx_xvfmul_s( v2, mul ); + v3 = __lasx_xvfmul_s( v3, mul ); + + // Round to nearest integer + __m256i i0 = __lasx_xvftintrne_w_s( v0 ); + __m256i i1 = __lasx_xvftintrne_w_s( v1 ); + __m256i i2 = __lasx_xvftintrne_w_s( v2 ); + __m256i i3 = __lasx_xvftintrne_w_s( v3 ); + + __m128i ni0 = lasx_extracti128( i0, 0 ); + __m128i ni1 = lasx_extracti128( i0, 1); + __m128i ni2 = lasx_extracti128( i1, 0); + __m128i ni3 = lasx_extracti128( i1, 1); + __m128i ni4 = lasx_extracti128( i2, 0); + __m128i ni5 = lasx_extracti128( i2, 1); + __m128i ni6 = lasx_extracti128( i3, 0); + __m128i ni7 = lasx_extracti128( i3, 1); + + // Convert int32 to int16 + ni0 = lsx_packs_w( ni0, ni1 ); + ni2 = lsx_packs_w( ni2, ni3 ); + ni4 = lsx_packs_w( ni4, ni5 ); + ni6 = lsx_packs_w( ni6, ni7 ); + // Convert int16 to int8 + ni0 = lsx_packs_h( ni0, ni2 ); + ni4 = lsx_packs_h( ni4, ni6 ); + + __lsx_vst(ni0, (__m128i *)(y[i].qs + 0), 0); + __lsx_vst(ni4, (__m128i *)(y[i].qs + 16), 0); + } #else GGML_UNUSED(nb); @@ -828,12 +1289,12 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int64_t k) __m128 max4 = _mm_max_ps( _mm256_extractf128_ps( maxAbs, 1 ), _mm256_castps256_ps128( maxAbs ) ); max4 = _mm_max_ps( max4, _mm_movehl_ps( max4, max4 ) ); max4 = _mm_max_ss( max4, _mm_movehdup_ps( max4 ) ); - const float maxScalar = _mm_cvtss_f32( max4 ); + const float max_scalar = _mm_cvtss_f32( max4 ); // Quantize these floats - const float d = maxScalar / 127.f; + const float d = max_scalar / 127.f; y[i].d = GGML_FP32_TO_FP16(d); - const float id = ( maxScalar != 0.0f ) ? 127.f / maxScalar : 0.0f; + const float id = ( max_scalar != 0.0f ) ? 127.f / max_scalar : 0.0f; const __m256 mul = _mm256_set1_ps( id ); // Apply the multiplier @@ -936,6 +1397,7 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int64_t k) int sum = __riscv_vmv_x_s_i16m1_i16(vwrs); y[i].s = GGML_FP32_TO_FP16(sum*d); } + #elif defined(__POWER9_VECTOR__) for (int i = 0; i < nb; i++) { vector float srcv [8]; @@ -975,6 +1437,73 @@ void quantize_row_q8_1(const float * restrict x, void * restrict vy, int64_t k) accv = vec_add(accv, vec_sld(accv, accv, 4)); accv = vec_add(accv, vec_sld(accv, accv, 8)); y[i].s = GGML_FP32_TO_FP16(d * vec_extract(accv, 0)); + +#elif defined(__loongarch_asx) + for (int i = 0; i < nb; i++) { + ft_union ft; + __m256 v0 = (__m256)__lasx_xvld( x , 0 ); + __m256 v1 = (__m256)__lasx_xvld( x , 32 ); + __m256 v2 = (__m256)__lasx_xvld( x , 64 ); + __m256 v3 = (__m256)__lasx_xvld( x , 96 ); + x += 32; + + // Compute max(abs(e)) for the block + const __m256 sign_bit = __lasx_xvreplfr2vr_s( -0.0f ); + __m256 max_abs = (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v0 ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v1 ) ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v2 ) ); + max_abs = __lasx_xvfmax_s( max_abs, (__m256)__lasx_xvandn_v( (__m256i)sign_bit, (__m256i)v3 ) ); + + __m128 max4 = __lsx_vfmax_s( lasx_extractf128( max_abs, 1 ), lasx_extractf128( max_abs, 0) ); + max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vpickod_d((__m128i) max4, (__m128i)max4 ) ); + __m128 tmp = max4; + max4 = __lsx_vfmax_s( max4, (__m128)__lsx_vextrins_w((__m128i)tmp, (__m128i)max4, 0x10 )); + ft.i = __lsx_vpickve2gr_w( (__m128i)max4, 0 ); + const float max_scalar = ft.f; + + // Quantize these floats + const float d = max_scalar / 127.f; + y[i].d = GGML_FP32_TO_FP16(d); + const float id = ( max_scalar != 0.0f ) ? 127.f / max_scalar : 0.0f; + const __m256 mul = __lasx_xvreplfr2vr_s( id ); + + // Apply the multiplier + v0 = __lasx_xvfmul_s( v0, mul ); + v1 = __lasx_xvfmul_s( v1, mul ); + v2 = __lasx_xvfmul_s( v2, mul ); + v3 = __lasx_xvfmul_s( v3, mul ); + + // Round to nearest integer + __m256i i0 = __lasx_xvftintrne_w_s( v0 ); + __m256i i1 = __lasx_xvftintrne_w_s( v1 ); + __m256i i2 = __lasx_xvftintrne_w_s( v2 ); + __m256i i3 = __lasx_xvftintrne_w_s( v3 ); + + __m128i ni0 = lasx_extracti128(i0, 0); + __m128i ni1 = lasx_extracti128( i0, 1); + __m128i ni2 = lasx_extracti128( i1, 0); + __m128i ni3 = lasx_extracti128( i1, 1); + __m128i ni4 = lasx_extracti128( i2, 0 ); + __m128i ni5 = lasx_extracti128( i2, 1); + __m128i ni6 = lasx_extracti128( i3, 0); + __m128i ni7 = lasx_extracti128( i3, 1); + + // Compute the sum of the quants and set y[i].s + const __m128i s0 = __lsx_vadd_w(__lsx_vadd_w(ni0, ni1), __lsx_vadd_w(ni2, ni3)); + const __m128i s1 = __lsx_vadd_w(__lsx_vadd_w(ni4, ni5), __lsx_vadd_w(ni6, ni7)); + y[i].s = GGML_FP32_TO_FP16(d * hsum_i32_4(__lsx_vadd_w(s0, s1))); + + // Convert int32 to int16 + ni0 = lsx_packs_w( ni0, ni1 ); + ni2 = lsx_packs_w( ni2, ni3 ); + ni4 = lsx_packs_w( ni4, ni5 ); + ni6 = lsx_packs_w( ni6, ni7 ); + // Convert int16 to int8 + ni0 = lsx_packs_h( ni0, ni2 ); + ni4 = lsx_packs_h( ni4, ni6 ); + + __lsx_vst(ni0, (__m128i *)(y[i].qs + 0), 0); + __lsx_vst(ni4, (__m128i *)(y[i].qs + 16), 0); } #else GGML_UNUSED(nb); @@ -3470,6 +3999,43 @@ static inline __m128i get_scale_shuffle(int i) { }; return _mm_loadu_si128((const __m128i*)k_shuffle + i); } +#elif defined(__loongarch_asx) +// shuffles to pick the required scales in dot products +static inline __m256i get_scale_shuffle_q3k(int i) { + static const uint8_t k_shuffle[128] = { + 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, + 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, + 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11, + 12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13, 14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15, + }; + return __lasx_xvld((const __m256i*)k_shuffle + i, 0); +} +static inline __m256i get_scale_shuffle_k4(int i) { + static const uint8_t k_shuffle[256] = { + 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, + 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, 2, 3, + 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, 4, 5, + 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, 6, 7, + 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, 8, 9, + 10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11,10,11, + 12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13,12,13, + 14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15,14,15 + }; + return __lasx_xvld((const __m256i*)k_shuffle + i, 0); +} +static inline __m128i get_scale_shuffle(int i) { + static const uint8_t k_shuffle[128] = { + 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, + 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, + 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, + 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, + 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, + 10,10,10,10,10,10,10,10, 11,11,11,11,11,11,11,11, + 12,12,12,12,12,12,12,12, 13,13,13,13,13,13,13,13, + 14,14,14,14,14,14,14,14, 15,15,15,15,15,15,15,15 + }; + return __lsx_vld((const __m128i*)k_shuffle + i, 0); +} #endif void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { @@ -3819,6 +4385,7 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r } *s = sumf; + #elif defined(__POWER9_VECTOR__) const vector signed char lowMask = vec_splats((signed char)0xF); const vector unsigned char v4 = vec_splats((unsigned char)0x4); @@ -3859,6 +4426,149 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); *s = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + // Initialize accumulator with zeros + __m256 acc = (__m256)__lasx_xvldi(0); + + // Main loop + for (int i = 0; i < nb; ++i) { + /* Compute combined scale for the block */ + const __m256 d = __lasx_xvreplfr2vr_s( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) ); + + __m256i qx = bytes_from_nibbles_32(x[i].qs); + + // Now we have a vector with bytes in [ 0 .. 15 ] interval. Offset them into [ -8 .. +7 ] interval. + const __m256i off = __lasx_xvreplgr2vr_b( 8 ); + qx = __lasx_xvsub_b( qx, off ); + + __m256i qy = __lasx_xvld((const __m256i *)y[i].qs, 0); + + const __m256 q = mul_sum_i8_pairs_float(qx, qy); + + /* Multiply q with scale and accumulate */ + acc = __lasx_xvfmadd_s( d, q, acc ); + } + + *s = hsum_float_8(acc); +#elif defined(__loongarch_sx) + // set constants + const __m128i low_mask = __lsx_vreplgr2vr_b(0xF); + const __m128i off = __lsx_vreplgr2vr_b(8); + + // Initialize accumulator with zeros + __m128 acc_0 = __lsx_vldi(0); + __m128 acc_1 = __lsx_vldi(0); + __m128 acc_2 = __lsx_vldi(0); + __m128 acc_3 = __lsx_vldi(0); + + // First round without accumulation + { + _mm_prefetch(&x[0] + sizeof(block_q4_0), _MM_HINT_T0); + _mm_prefetch(&y[0] + sizeof(block_q8_0), _MM_HINT_T0); + + // Compute combined scale for the block 0 and 1 + const __m128 d_0_1 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[0].d) * GGML_FP16_TO_FP32(y[0].d) ); + + const __m128i tmp_0_1 = __lsx_vld((const __m128i *)x[0].qs, 0); + + __m128i bx_0 = __lsx_vand_v(low_mask, tmp_0_1); + __m128i by_0 = __lsx_vld((const __m128i *)y[0].qs, 0); + bx_0 = __lsx_vsub_b(bx_0, off); + const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0); + + __m128i bx_1 = __lsx_vand_v(low_mask, __lsx_vsrli_d(tmp_0_1, 4)); + __m128i by_1 = __lsx_vld((const __m128i *)(y[0].qs + 16), 0); + bx_1 = __lsx_vsub_b(bx_1, off); + const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1); + + // Compute combined scale for the block 2 and 3 + const __m128 d_2_3 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[1].d) * GGML_FP16_TO_FP32(y[1].d) ); + + const __m128i tmp_2_3 = __lsx_vld((const __m128i *)x[1].qs, 0); + + __m128i bx_2 = __lsx_vand_v(low_mask, tmp_2_3); + __m128i by_2 = __lsx_vld((const __m128i *)y[1].qs, 0); + bx_2 = __lsx_vsub_b(bx_2, off); + const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2); + + __m128i bx_3 = __lsx_vand_v(low_mask, __lsx_vsrli_d(tmp_2_3, 4)); + __m128i by_3 = __lsx_vld((const __m128i *)(y[1].qs + 16), 0); + bx_3 = __lsx_vsub_b(bx_3, off); + const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3); + + // Convert int32_t to float + __m128 p0 = __lsx_vffint_s_w(i32_0); + __m128 p1 = __lsx_vffint_s_w(i32_1); + __m128 p2 = __lsx_vffint_s_w(i32_2); + __m128 p3 = __lsx_vffint_s_w(i32_3); + + // Apply the scale + acc_0 = __lsx_vfmul_s( d_0_1, p0 ); + acc_1 = __lsx_vfmul_s( d_0_1, p1 ); + acc_2 = __lsx_vfmul_s( d_2_3, p2 ); + acc_3 = __lsx_vfmul_s( d_2_3, p3 ); + } + + assert(nb % 2 == 0); // TODO: handle odd nb + + // Main loop + for (int i = 2; i < nb; i+=2) { + + // Compute combined scale for the block 0 and 1 + const __m128 d_0_1 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d) ); + + const __m128i tmp_0_1 = __lsx_vld((const __m128i *)x[i].qs, 0); + + __m128i bx_0 = __lsx_vand_v(low_mask, tmp_0_1); + __m128i by_0 = __lsx_vld((const __m128i *)y[i].qs, 0); + bx_0 = __lsx_vsub_b(bx_0, off); + const __m128i i32_0 = mul_sum_i8_pairs(bx_0, by_0); + + __m128i bx_1 = __lsx_vand_v(low_mask, __lsx_vsrli_d(tmp_0_1, 4)); + __m128i by_1 = __lsx_vld((const __m128i *)(y[i].qs + 16), 0); + bx_1 = __lsx_vsub_b(bx_1, off); + const __m128i i32_1 = mul_sum_i8_pairs(bx_1, by_1); + + //_mm_prefetch(&x[i] + 2 * sizeof(block_q4_0), _MM_HINT_T0); + //_mm_prefetch(&y[i] + 2 * sizeof(block_q8_0), _MM_HINT_T0); + + // Compute combined scale for the block 2 and 3 + const __m128 d_2_3 = __lsx_vreplgr2vr_w( GGML_FP16_TO_FP32(x[i + 1].d) * GGML_FP16_TO_FP32(y[i + 1].d) ); + + const __m128i tmp_2_3 = __lsx_vld((const __m128i *)x[i + 1].qs, 0); + + __m128i bx_2 = __lsx_vand_v(low_mask, tmp_2_3); + __m128i by_2 = __lsx_vld((const __m128i *)y[i + 1].qs, 0); + bx_2 = __lsx_vsub_b(bx_2, off); + const __m128i i32_2 = mul_sum_i8_pairs(bx_2, by_2); + + __m128i bx_3 = __lsx_vand_v(low_mask, __lsx_vsrli_d(tmp_2_3, 4)); + __m128i by_3 = __lsx_vld((const __m128i *)(y[i + 1].qs + 16), 0); + bx_3 = __lsx_vsub_b(bx_3, off); + const __m128i i32_3 = mul_sum_i8_pairs(bx_3, by_3); + + // Convert int32_t to float + __m128 p0 = __lsx_vffint_s_w(i32_0); + __m128 p1 = __lsx_vffint_s_w(i32_1); + __m128 p2 = __lsx_vffint_s_w(i32_2); + __m128 p3 = __lsx_vffint_s_w(i32_3); + + // Apply the scale + __m128 p0_d = __lsx_vfmul_s( d_0_1, p0 ); + __m128 p1_d = __lsx_vfmul_s( d_0_1, p1 ); + __m128 p2_d = __lsx_vfmul_s( d_2_3, p2 ); + __m128 p3_d = __lsx_vfmul_s( d_2_3, p3 ); + + // Acummulate + acc_0 = __lsx_vfadd_s(p0_d, acc_0); + acc_1 = __lsx_vfadd_s(p1_d, acc_1); + acc_2 = __lsx_vfadd_s(p2_d, acc_2); + acc_3 = __lsx_vfadd_s(p3_d, acc_3); + } + + *s = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3); + #else // scalar float sumf = 0.0; @@ -4078,6 +4788,7 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r } *s = sumf; + #elif defined(__POWER9_VECTOR__) const vector signed char lowMask = vec_splats((signed char)0xF); const vector unsigned char v4 = vec_splats((unsigned char)0x4); @@ -4118,6 +4829,38 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); *s = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + // Initialize accumulator with zeros + __m256 acc = (__m256)__lasx_xvldi(0); + + float summs = 0; + + // Main loop + for (int i = 0; i < nb; ++i) { + const float d0 = GGML_FP16_TO_FP32(x[i].d); + const float d1 = GGML_FP16_TO_FP32(y[i].d); + + summs += GGML_FP16_TO_FP32(x[i].m) * GGML_FP16_TO_FP32(y[i].s); + + const __m256 d0v = __lasx_xvreplfr2vr_s( d0 ); + const __m256 d1v = __lasx_xvreplfr2vr_s( d1 ); + + // Compute combined scales + const __m256 d0d1 = __lasx_xvfmul_s( d0v, d1v ); + + // Load 16 bytes, and unpack 4 bit fields into bytes, making 32 bytes + const __m256i qx = bytes_from_nibbles_32(x[i].qs); + const __m256i qy = __lasx_xvld( (const __m256i *)y[i].qs, 0); + + const __m256 xy = mul_sum_us8_pairs_float(qx, qy); + + // Accumulate d0*d1*x*y + acc = __lasx_xvfmadd_s( d0d1, xy, acc ); + } + + *s = hsum_float_8(acc) + summs; + #else // scalar float sumf = 0.0; @@ -4403,6 +5146,7 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r } *s = sumf; + #elif defined(__POWER9_VECTOR__) const vector signed char lowMask = vec_splats((signed char)0xF); const vector unsigned char v4 = vec_splats((unsigned char)4); @@ -4446,6 +5190,31 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); *s = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + // Initialize accumulator with zeros + __m256 acc = (__m256)__lasx_xvldi(0); + + // Main loop + for (int i = 0; i < nb; i++) { + /* Compute combined scale for the block */ + const __m256 d = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d)); //FIXME + + __m256i qx = bytes_from_nibbles_32(x[i].qs); + __m256i bxhi = bytes_from_bits_32(x[i].qh); + bxhi = __lasx_xvandn_v(bxhi, __lasx_xvreplgr2vr_b((char)0xF0)); + qx = __lasx_xvor_v(qx, bxhi); + + __m256i qy = __lasx_xvld((const __m256i *)y[i].qs, 0); + + const __m256 q = mul_sum_i8_pairs_float(qx, qy); + + /* Multiply q with scale and accumulate */ + acc = __lasx_xvfmadd_s(d, q, acc); + } + + *s = hsum_float_8(acc); + #else // scalar float sumf = 0.0; @@ -4750,6 +5519,7 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r } *s = sumf; + #elif defined(__POWER9_VECTOR__) const vector signed char lowMask = vec_splats((signed char)0xF); const vector unsigned char v4 = vec_splats((unsigned char)0x4); @@ -4797,6 +5567,34 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); *s = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + // Initialize accumulator with zeros + __m256 acc = (__m256)__lasx_xvldi(0); + + float summs = 0.0f; + + // Main loop + for (int i = 0; i < nb; i++) { + const __m256 dx = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[i].d)); + + summs += GGML_FP16_TO_FP32(x[i].m) * GGML_FP16_TO_FP32(y[i].s); + + __m256i qx = bytes_from_nibbles_32(x[i].qs); + __m256i bxhi = bytes_from_bits_32(x[i].qh); + bxhi = __lasx_xvand_v(bxhi, __lasx_xvreplgr2vr_b(0x10)); + qx = __lasx_xvor_v(qx, bxhi); + + const __m256 dy = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[i].d)); + const __m256i qy = __lasx_xvld((const __m256i *)y[i].qs, 0); + + const __m256 q = mul_sum_us8_pairs_float(qx, qy); + + acc = __lasx_xvfmadd_s(q, __lasx_xvfmul_s(dx, dy), acc); + } + + *s = hsum_float_8(acc) + summs; + #else // scalar float sumf = 0.0; @@ -4973,6 +5771,7 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r } *s = sumf; + #elif defined(__POWER9_VECTOR__) vector float vsumf0 = vec_splats(0.0f); @@ -5012,6 +5811,26 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); *s = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + // Initialize accumulator with zeros + __m256 acc = (__m256)__lasx_xvldi(0); + + // Main loop + for (int i = 0; i < nb; ++i) { + // Compute combined scale for the block + const __m256 d = __lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[i].d) * GGML_FP16_TO_FP32(y[i].d)); + __m256i qx = __lasx_xvld((const __m256i *)x[i].qs, 0); + __m256i qy = __lasx_xvld((const __m256i *)y[i].qs, 0); + + const __m256 q = mul_sum_i8_pairs_float(qx, qy); + + // Multiply q with scale and accumulate + acc = __lasx_xvfmadd_s( d, q, acc ); + } + + *s = hsum_float_8(acc); + #else // scalar float sumf = 0.0; @@ -5416,8 +6235,6 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r vector signed int vsumi6 = vec_splats((int32_t)0); vector signed int vsumi7 = vec_splats((int32_t)0); - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; for (int j = 0; j < QK_K/128; ++j) { __builtin_prefetch(q2, 0, 1); @@ -5508,6 +6325,71 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = vec_extract(vsumf0, 0); +#elif defined __loongarch_asx + + const __m256i m3 = __lasx_xvreplgr2vr_b(3); + const __m128i m4 = __lsx_vreplgr2vr_b(0xF); + + __m256 acc = (__m256)__lasx_xvldi(0); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + const uint8_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + const __m128i mins_and_scales = __lsx_vld((const __m128i*)x[i].scales, 0); + const __m128i scales8 = __lsx_vand_v(mins_and_scales, m4); + const __m128i mins8 = __lsx_vand_v(__lsx_vsrli_h(mins_and_scales, 4), m4); + const __m256i mins = lasx_ext8_16(mins8); + const __m256i prod = lasx_madd_h(mins, __lasx_xvld((const __m256i*)y[i].bsums, 0)); + + acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(dmin), __lasx_xvffint_s_w(prod), acc); + + const __m256i all_scales = lasx_ext8_16(scales8); + const __m128i l_scales = lasx_extracti128(all_scales, 0); + const __m128i h_scales = lasx_extracti128(all_scales, 1); + const __m256i scales[2] = {lasx_insertf128(l_scales, l_scales), lasx_insertf128(h_scales, h_scales)}; + + __m256i sumi = __lasx_xvldi(0); + + for (int j = 0; j < QK_K/128; ++j) { + + const __m256i q2bits = __lasx_xvld((const __m256i*)q2, 0); q2 += 32; + + const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_3 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + + const __m256i q2_0 = __lasx_xvand_v(q2bits, m3); + const __m256i q2_1 = __lasx_xvand_v(__lasx_xvsrli_h(q2bits, 2), m3); + const __m256i q2_2 = __lasx_xvand_v(__lasx_xvsrli_h(q2bits, 4), m3); + const __m256i q2_3 = __lasx_xvand_v(__lasx_xvsrli_h(q2bits, 6), m3); + + __m256i p0 = lasx_maddubs_h(q2_0, q8_0); + __m256i p1 = lasx_maddubs_h(q2_1, q8_1); + __m256i p2 = lasx_maddubs_h(q2_2, q8_2); + __m256i p3 = lasx_maddubs_h(q2_3, q8_3); + + p0 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(0)), p0); + p1 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(1)), p1); + p2 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(2)), p2); + p3 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(3)), p3); + + p0 = __lasx_xvadd_w(p0, p1); + p2 = __lasx_xvadd_w(p2, p3); + + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p0, p2)); + } + + acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc); + + } + + *s = hsum_float_8(acc); + #else float sumf = 0; @@ -5778,6 +6660,7 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = sumf; + #elif defined(__POWER9_VECTOR__) const vector signed char lowMask = vec_splats((signed char)0x3); const vector signed char lowScaleMask = vec_splats((signed char)0xF); @@ -5859,6 +6742,63 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = vec_extract(vsumf0, 0); +#elif defined __loongarch_asx + + const __m256i m3 = __lasx_xvreplgr2vr_b(3); + + __m256 acc = (__m256)__lasx_xvldi(0); + + uint32_t ud, um; + const uint8_t * restrict db = (const uint8_t *)&ud; + const uint8_t * restrict mb = (const uint8_t *)&um; + + float summs = 0; + + // TODO: optimize this + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + const uint8_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + const uint32_t * restrict sc = (const uint32_t *)x[i].scales; + ud = (sc[0] >> 0) & 0x0f0f0f0f; + um = (sc[0] >> 4) & 0x0f0f0f0f; + + int32_t smin = mb[0] * y[i].bsums[0] + mb[1] * y[i].bsums[1] + mb[2] * y[i].bsums[2] + mb[3] * y[i].bsums[3]; + summs += dmin * smin; + + const __m128i q2bits = __lsx_vld((const __m128i*)q2, 0); + const __m256i q2_0 = __lasx_xvand_v(lasx_insertf128(__lsx_vsrli_h(q2bits, 2), q2bits), m3); + const __m256i q2_1 = __lasx_xvand_v(lasx_insertf128(__lsx_vsrli_h(q2bits, 6), __lsx_vsrli_h(q2bits, 4)), m3); + + const __m256i q8_0 = __lasx_xvld((const __m256i*)(q8+ 0), 0); + const __m256i q8_1 = __lasx_xvld((const __m256i*)(q8+32), 0); + + const __m256i p0 = lasx_maddubs_h(q2_0, q8_0); + const __m256i p1 = lasx_maddubs_h(q2_1, q8_1); + + const __m256i p_0 = lasx_ext16_32(lasx_extracti128(p0, 0)); + const __m256i p_1 = lasx_ext16_32(lasx_extracti128(p0, 1)); + const __m256i p_2 = lasx_ext16_32(lasx_extracti128(p1, 0)); + const __m256i p_3 = lasx_ext16_32(lasx_extracti128(p1, 1)); + + ft_union t0, t1, t2, t3; + t0.f = d * db[0]; + t1.f = d * db[1]; + t2.f = d * db[2]; + t3.f = d * db[3]; + acc = __lasx_xvfmadd_s(__lasx_xvreplgr2vr_w(t0.i), __lasx_xvffint_s_w(p_0), acc); + acc = __lasx_xvfmadd_s(__lasx_xvreplgr2vr_w(t1.i), __lasx_xvffint_s_w(p_1), acc); + acc = __lasx_xvfmadd_s(__lasx_xvreplgr2vr_w(t2.i), __lasx_xvffint_s_w(p_2), acc); + acc = __lasx_xvfmadd_s(__lasx_xvreplgr2vr_w(t3.i), __lasx_xvffint_s_w(p_3), acc); + } + + *s = hsum_float_8(acc) + summs; + #else float sumf = 0; @@ -6396,6 +7336,7 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r vector signed int vsumi6 = vec_splats((int32_t)0); vector signed int vsumi7 = vec_splats((int32_t)0); + const uint8_t * restrict q3 = x[i].qs; const int8_t * restrict q8 = y[i].qs; @@ -6507,6 +7448,107 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); *s = vec_extract(vsumf0, 0); + +#elif defined __loongarch_asx + + const __m256i m3 = __lasx_xvreplgr2vr_b(3); + const __m256i mone = __lasx_xvreplgr2vr_b(1); + const __m128i m32 = __lsx_vreplgr2vr_b(32); + + __m256 acc = (__m256)__lasx_xvldi(0); + + uint32_t aux[3]; + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + // Set up scales + memcpy(aux, x[i].scales, 12); + __m128i scales128 = lsx_set_w( + ((aux[1] >> 4) & kmask2) | (((aux[2] >> 6) & kmask1) << 4), + ((aux[0] >> 4) & kmask2) | (((aux[2] >> 4) & kmask1) << 4), + (aux[1] & kmask2) | (((aux[2] >> 2) & kmask1) << 4), + (aux[0] & kmask2) | (((aux[2] >> 0) & kmask1) << 4)); + scales128 = __lsx_vsub_b(scales128, m32); + const __m256i all_scales = lasx_ext8_16(scales128); + const __m128i l_scales = lasx_extracti128(all_scales, 0); + const __m128i h_scales = lasx_extracti128(all_scales, 1); + const __m256i scales[2] = {lasx_insertf128(l_scales, l_scales), lasx_insertf128(h_scales, h_scales)}; + + // high bit + const __m256i hbits = __lasx_xvld((const __m256i*)x[i].hmask, 0); + + // integer accumulator + __m256i sumi = __lasx_xvldi(0); + + int bit = 0; + int is = 0; + + const uint8_t * restrict q3 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + for (int j = 0; j < QK_K/128; ++j) { + // load low 2 bits + const __m256i q3bits = __lasx_xvld((const __m256i*)q3, 0); q3 += 32; + + // prepare low and high bits + const __m256i q3l_0 = __lasx_xvand_v(q3bits, m3); + const __m256i q3h_0 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2); + ++bit; + + const __m256i q3l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 2), m3); + const __m256i q3h_1 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2); + ++bit; + + const __m256i q3l_2 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 4), m3); + const __m256i q3h_2 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2); + ++bit; + + const __m256i q3l_3 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 6), m3); + const __m256i q3h_3 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2); + ++bit; + + // load Q8 quants + const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_3 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + + // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use lasx_maddubs_h, + // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, + // and 2 if the high bit was set) + __m256i q8s_0 = lasx_maddubs_h(q3h_0, q8_0); + __m256i q8s_1 = lasx_maddubs_h(q3h_1, q8_1); + __m256i q8s_2 = lasx_maddubs_h(q3h_2, q8_2); + __m256i q8s_3 = lasx_maddubs_h(q3h_3, q8_3); + + __m256i p16_0 = lasx_maddubs_h(q3l_0, q8_0); + __m256i p16_1 = lasx_maddubs_h(q3l_1, q8_1); + __m256i p16_2 = lasx_maddubs_h(q3l_2, q8_2); + __m256i p16_3 = lasx_maddubs_h(q3l_3, q8_3); + + p16_0 = __lasx_xvsub_h(p16_0, q8s_0); + p16_1 = __lasx_xvsub_h(p16_1, q8s_1); + p16_2 = __lasx_xvsub_h(p16_2, q8s_2); + p16_3 = __lasx_xvsub_h(p16_3, q8s_3); + + // multiply with scales + p16_0 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 0)), p16_0); + p16_1 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 1)), p16_1); + p16_2 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 2)), p16_2); + p16_3 = lasx_madd_h(lasx_shuffle_b(scales[j], get_scale_shuffle_q3k(is + 3)), p16_3); + + // accumulate + p16_0 = __lasx_xvadd_w(p16_0, p16_1); + p16_2 = __lasx_xvadd_w(p16_2, p16_3); + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_2)); + } + // multiply with block scale and accumulate + acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc);//FIXME + } + + *s = hsum_float_8(acc); + #else // scalar version // This function is written like this so the compiler can manage to vectorize most of it @@ -6962,6 +8004,73 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); *s = vec_extract(vsumf0, 0); + +#elif defined __loongarch_asx + + const __m256i m3 = __lasx_xvreplgr2vr_b(3); + const __m256i m1 = __lasx_xvreplgr2vr_b(1); + + __m256 acc = (__m256)__lasx_xvldi(0); + + uint64_t aux64; + + uint16_t aux16[2]; + const int8_t * aux8 = (const int8_t *)aux16; + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + + const uint8_t * restrict q3 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + const __m256i scale_0 = lasx_insertf128(__lasx_xvreplgr2vr_h(aux8[2] - 8), __lasx_xvreplgr2vr_h(aux8[0] - 8)); + const __m256i scale_1 = lasx_insertf128(__lasx_xvreplgr2vr_h(aux8[3] - 8), __lasx_xvreplgr2vr_h(aux8[1] - 8)); + + memcpy(&aux64, x[i].hmask, 8); + + __m128i haux = __lsx_vinsgr2vr_d(haux, aux64, 0); + haux = __lsx_vinsgr2vr_d(haux, aux64 >> 1, 1); + __m256i q3h_0 = lasx_insertf128(__lsx_vsrli_h(haux, 2), haux); + __m256i q3h_1 = __lasx_xvsrli_h(q3h_0, 4); + q3h_0 = __lasx_xvslli_h(__lasx_xvandn_v(q3h_0, m1), 2); + q3h_1 = __lasx_xvslli_h(__lasx_xvandn_v(q3h_1, m1), 2); + + // load low 2 bits + const __m128i q3bits = __lsx_vld((const __m128i*)q3, 0); + + // prepare low and high bits + const __m256i q3aux = lasx_insertf128(__lsx_vsrli_h(q3bits, 2), q3bits); + const __m256i q3l_0 = __lasx_xvand_v(q3aux, m3); + const __m256i q3l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q3aux, 4), m3); + + // load Q8 quants + const __m256i q8_0 = __lasx_xvld((const __m256i*)(q8+ 0), 0); + const __m256i q8_1 = __lasx_xvld((const __m256i*)(q8+32), 0); + + // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use lasx_maddubs_h, + // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, + // and 2 if the high bit was set) + const __m256i q8s_0 = lasx_maddubs_h(q3h_0, q8_0); + const __m256i q8s_1 = lasx_maddubs_h(q3h_1, q8_1); + + __m256i p16_0 = lasx_maddubs_h(q3l_0, q8_0); + __m256i p16_1 = lasx_maddubs_h(q3l_1, q8_1); + + p16_0 = __lasx_xvsub_h(p16_0, q8s_0); + p16_1 = __lasx_xvsub_h(p16_1, q8s_1); + + // multiply with scales + p16_0 = lasx_madd_h(scale_0, p16_0); + p16_1 = lasx_madd_h(scale_1, p16_1); + + p16_0 = __lasx_xvadd_w(p16_0, p16_1); + + // multiply with block scale and accumulate + acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(p16_0), acc); + } + + *s = hsum_float_8(acc); + #else int8_t aux8[QK_K]; @@ -7450,8 +8559,69 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = vec_extract(vsumf0, 0); -#else +#elif defined __loongarch_asx + const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); + + __m256 acc = (__m256)__lasx_xvldi(0); + __m128 acc_m = (__m128)__lsx_vldi(0); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + memcpy(utmp, x[i].scales, 12); + + const uint8_t * restrict q4 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + const __m256i mins_and_scales = lasx_extu8_16(lsx_set_w(utmp[3], utmp[2], utmp[1], utmp[0])); + + const __m256i q8sums = __lasx_xvld((const __m256i*)y[i].bsums, 0); + const __m128i q8s = lsx_hadd_h(lasx_extracti128(q8sums, 0), lasx_extracti128(q8sums, 1)); + const __m128i prod = lsx_madd_h(lasx_extracti128(mins_and_scales, 1), q8s); + acc_m = __lsx_vfmadd_s(__lsx_vreplfr2vr_s(dmin), __lsx_vffint_s_w(prod), acc_m); + + const __m128i sc128 = lasx_extracti128(mins_and_scales, 0); + const __m256i scales = lasx_insertf128(sc128, sc128); + + __m256i sumi = __lasx_xvldi(0); + + for (int j = 0; j < QK_K/64; ++j) { + + const __m256i scale_l = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+0)); + const __m256i scale_h = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+1)); + + const __m256i q4bits = __lasx_xvld((const __m256i*)q4, 0); q4 += 32; + const __m256i q4l = __lasx_xvand_v(q4bits, m4); + const __m256i q4h = __lasx_xvand_v(__lasx_xvsrli_h(q4bits, 4), m4); + + const __m256i q8l = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + __m256i p16l = lasx_maddubs_h(q4l, q8l); + p16l = lasx_madd_h(scale_l, p16l); + + const __m256i q8h = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + __m256i p16h = lasx_maddubs_h(q4h, q8h); + p16h = lasx_madd_h(scale_h, p16h); + const __m256i sumj = __lasx_xvadd_w(p16l, p16h); + + sumi = __lasx_xvadd_w(sumi, sumj); + } + + __m256 vd = __lasx_xvreplfr2vr_s(d); + acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc); + } + + acc_m = __lsx_vfadd_s(acc_m, (__m128)__lsx_vpermi_w((__m128i)acc_m, (__m128i)acc_m, 0xee)); + __m128i tmp1 = __lsx_vinsgr2vr_w(__lsx_vldi(0), __lsx_vpickve2gr_w((__m128i)acc_m, 1), 0); + acc_m = __lsx_vfadd_s(acc_m, (__m128)tmp1); + + ft_union fi; + fi.i = __lsx_vpickve2gr_w(acc_m, 0); + *s = hsum_float_8(acc) + fi.f ; + +#else const uint8_t * scales = (const uint8_t*)&utmp[0]; const uint8_t * mins = (const uint8_t*)&utmp[2]; @@ -7797,6 +8967,51 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = vec_extract(vsumf0, 0); +#elif defined __loongarch_asx + + const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); + + __m256 acc = (__m256)__lasx_xvldi(0); + + float summs = 0; + + uint16_t aux16[2]; + const uint8_t * scales = (const uint8_t *)aux16; + + for (int i = 0; i < nb; ++i) { + + const float d = GGML_FP16_TO_FP32(x[i].d[0]) * y[i].d; + const float m = GGML_FP16_TO_FP32(x[i].d[1]) * y[i].d; + const __m256 vd = __lasx_xvreplfr2vr_s(d); + + const uint16_t * a = (const uint16_t *)x[i].scales; + aux16[0] = a[0] & 0x0f0f; + aux16[1] = (a[0] >> 4) & 0x0f0f; + + summs += m * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3])); + + const uint8_t * restrict q4 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + const __m256i q4bits = __lasx_xvld((const __m256i*)q4, 0); + const __m256i q4l = __lasx_xvand_v(q4bits, m4); + const __m256i q4h = __lasx_xvand_v(__lasx_xvsrli_h(q4bits, 4), m4); + + const __m256i q8l = __lasx_xvld((const __m256i*)(q8+ 0), 0); + const __m256i q8h = __lasx_xvld((const __m256i*)(q8+32), 0); + + const __m256i p16l = lasx_maddubs_h(q4l, q8l); + const __m256i p16h = lasx_maddubs_h(q4h, q8h); + + const __m256i p32l = lasx_madd_h(__lasx_xvreplgr2vr_h(scales[0]), p16l); + acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(p32l), acc); + + const __m256i p32h = lasx_madd_h(__lasx_xvreplgr2vr_h(scales[1]), p16h); + acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(p32h), acc); + } + + *s = hsum_float_8(acc) - summs; + #else uint8_t aux8[QK_K]; @@ -8322,6 +9537,84 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = vec_extract(vsumf0, 0); +#elif defined __loongarch_asx + + const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); + const __m128i mzero = __lsx_vldi(0); + const __m256i mone = __lasx_xvreplgr2vr_b(1); + + __m256 acc = (__m256)__lasx_xvldi(0); + + float summs = 0.f; + + for (int i = 0; i < nb; ++i) { + + const uint8_t * restrict q5 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + +#if QK_K == 256 + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); + + memcpy(utmp, x[i].scales, 12); +#else + // TODO + const float d = 0, dmin = 0; +#endif + + const __m256i mins_and_scales = lasx_extu8_16(lsx_set_w(utmp[3], utmp[2], utmp[1], utmp[0])); + + const __m256i q8sums = __lasx_xvld((const __m256i*)y[i].bsums, 0); + const __m128i q8s = lsx_hadd_h(lasx_extracti128(q8sums, 0), lasx_extracti128(q8sums, 1)); + const __m128i prod = lsx_madd_h(lasx_extracti128(mins_and_scales, 1), q8s); + const __m128i hsum = lsx_hadd_w(lsx_hadd_w(prod, mzero), mzero); + summs += dmin * __lsx_vpickve2gr_w(hsum, 0); //TODO check + + const __m128i sc128 = lasx_extracti128(mins_and_scales, 0); + const __m256i scales = lasx_insertf128(sc128, sc128); + + const __m256i hbits = __lasx_xvld((const __m256i*)x[i].qh, 0); + __m256i hmask = mone; + + __m256i sumi = __lasx_xvldi(0); + + int bit = 0; + + for (int j = 0; j < QK_K/64; ++j) { + + const __m256i scale_0 = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+0)); + const __m256i scale_1 = lasx_shuffle_b(scales, get_scale_shuffle_k4(2*j+1)); + + const __m256i q5bits = __lasx_xvld((const __m256i*)q5, 0); q5 += 32; + + const __m256i q5l_0 = __lasx_xvand_v(q5bits, m4); + const __m256i q5h_0 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvand_v(hbits, hmask), bit++), 4); + const __m256i q5_0 = __lasx_xvadd_b(q5l_0, q5h_0); + hmask = __lasx_xvslli_h(hmask, 1); + + const __m256i q5l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q5bits, 4), m4); + const __m256i q5h_1 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvand_v(hbits, hmask), bit++), 4); + const __m256i q5_1 = __lasx_xvadd_b(q5l_1, q5h_1); + hmask = __lasx_xvslli_h(hmask, 1); + + const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + + __m256i p16_0 = lasx_maddubs_h(q5_0, q8_0); + __m256i p16_1 = lasx_maddubs_h(q5_1, q8_1); + + p16_0 = lasx_madd_h(scale_0, p16_0); + p16_1 = lasx_madd_h(scale_1, p16_1); + + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_1)); + } + + __m256 vd = __lasx_xvreplfr2vr_s(d); + acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc); + } + + *s = hsum_float_8(acc) + summs; + #else const uint8_t * scales = (const uint8_t*)&utmp[0]; @@ -8696,6 +9989,52 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = vec_extract(vsumf0, 0); +#elif defined __loongarch_asx + + const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); + const __m256i mone = __lasx_xvreplgr2vr_b(1); + + __m256 acc = (__m256)__lasx_xvldi(0); + + for (int i = 0; i < nb; ++i) { + + const uint8_t * restrict q5 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + + const __m256i q5bits = __lasx_xvld((const __m256i*)q5, 0); + + const __m256i scale_l = lasx_insertf128(__lsx_vreplgr2vr_h(x[i].scales[1]), __lsx_vreplgr2vr_h(x[i].scales[0])); + const __m256i scale_h = lasx_insertf128(__lsx_vreplgr2vr_h(x[i].scales[3]), __lsx_vreplgr2vr_h(x[i].scales[2])); + + int64_t aux64; + memcpy(&aux64, x[i].qh, 8); + __m128i haux128 = __lsx_vinsgr2vr_d(haux128, aux64, 0); + haux128 = __lsx_vinsgr2vr_d(haux128, aux64 >> 1, 1); + const __m256i haux256 = lasx_insertf128(__lsx_vsrli_h(haux128, 2), haux128); + + const __m256i q5h_0 = __lasx_xvslli_h(__lasx_xvandn_v(haux256, mone), 4); + const __m256i q5h_1 = __lasx_xvslli_h(__lasx_xvandn_v(__lasx_xvsrli_h(haux256, 4), mone), 4); + + const __m256i q5l_0 = __lasx_xvand_v(q5bits, m4); + const __m256i q5l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q5bits, 4), m4); + + const __m256i q8_0 = __lasx_xvld((const __m256i*)(q8+ 0), 0); + const __m256i q8_1 = __lasx_xvld((const __m256i*)(q8+32), 0); + + const __m256i p16_0 = lasx_madd_h(scale_l, lasx_maddubs_h(q5l_0, q8_0)); + const __m256i p16_1 = lasx_madd_h(scale_h, lasx_maddubs_h(q5l_1, q8_1)); + const __m256i s16_0 = lasx_madd_h(scale_l, lasx_maddubs_h(q5h_0, q8_0)); + const __m256i s16_1 = lasx_madd_h(scale_h, lasx_maddubs_h(q5h_1, q8_1)); + + const __m256i dot = __lasx_xvsub_w(__lasx_xvadd_w(p16_0, p16_1), __lasx_xvadd_w(s16_0, s16_1)); + + acc = __lasx_xvfmadd_s((__m256)__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(dot), acc); + } + + *s = hsum_float_8(acc); + #else int8_t aux8[QK_K]; @@ -9271,6 +10610,84 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = vec_extract(vsumf0, 0); +#elif defined __loongarch_asx + + const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); + const __m256i m2 = __lasx_xvreplgr2vr_b(3); + const __m256i m32s = __lasx_xvreplgr2vr_b(32); + + __m256 acc = (__m256)__lasx_xvldi(0); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + + const uint8_t * restrict q4 = x[i].ql; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + + const __m128i scales = __lsx_vld((const __m128i*)x[i].scales, 0); + + __m256i sumi = __lasx_xvldi(0); + + int is = 0; + + for (int j = 0; j < QK_K/128; ++j) { + + const __m128i scale_0 = lsx_shuffle_b(scales, get_scale_shuffle(is + 0)); + const __m128i scale_1 = lsx_shuffle_b(scales, get_scale_shuffle(is + 1)); + const __m128i scale_2 = lsx_shuffle_b(scales, get_scale_shuffle(is + 2)); + const __m128i scale_3 = lsx_shuffle_b(scales, get_scale_shuffle(is + 3)); + is += 4; + + const __m256i q4bits1 = __lasx_xvld((const __m256i*)q4, 0); q4 += 32; + const __m256i q4bits2 = __lasx_xvld((const __m256i*)q4, 0); q4 += 32; + const __m256i q4bitsH = __lasx_xvld((const __m256i*)qh, 0); qh += 32; + + const __m256i q4h_0 = __lasx_xvslli_h(__lasx_xvand_v(q4bitsH, m2), 4); + const __m256i q4h_1 = __lasx_xvslli_h(__lasx_xvand_v(__lasx_xvsrli_h(q4bitsH, 2), m2), 4); + const __m256i q4h_2 = __lasx_xvslli_h(__lasx_xvand_v(__lasx_xvsrli_h(q4bitsH, 4), m2), 4); + const __m256i q4h_3 = __lasx_xvslli_h(__lasx_xvand_v(__lasx_xvsrli_h(q4bitsH, 6), m2), 4); + + const __m256i q4_0 = __lasx_xvor_v(__lasx_xvand_v(q4bits1, m4), q4h_0); + const __m256i q4_1 = __lasx_xvor_v(__lasx_xvand_v(q4bits2, m4), q4h_1); + const __m256i q4_2 = __lasx_xvor_v(__lasx_xvand_v(__lasx_xvsrli_h(q4bits1, 4), m4), q4h_2); + const __m256i q4_3 = __lasx_xvor_v(__lasx_xvand_v(__lasx_xvsrli_h(q4bits2, 4), m4), q4h_3); + + const __m256i q8_0 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8_3 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + + __m256i q8s_0 = lasx_maddubs_h(m32s, q8_0); + __m256i q8s_1 = lasx_maddubs_h(m32s, q8_1); + __m256i q8s_2 = lasx_maddubs_h(m32s, q8_2); + __m256i q8s_3 = lasx_maddubs_h(m32s, q8_3); + + __m256i p16_0 = lasx_maddubs_h(q4_0, q8_0); + __m256i p16_1 = lasx_maddubs_h(q4_1, q8_1); + __m256i p16_2 = lasx_maddubs_h(q4_2, q8_2); + __m256i p16_3 = lasx_maddubs_h(q4_3, q8_3); + + p16_0 = __lasx_xvsub_h(p16_0, q8s_0); + p16_1 = __lasx_xvsub_h(p16_1, q8s_1); + p16_2 = __lasx_xvsub_h(p16_2, q8s_2); + p16_3 = __lasx_xvsub_h(p16_3, q8s_3); + + p16_0 = lasx_madd_h(lasx_ext8_16(scale_0), p16_0); + p16_1 = lasx_madd_h(lasx_ext8_16(scale_1), p16_1); + p16_2 = lasx_madd_h(lasx_ext8_16(scale_2), p16_2); + p16_3 = lasx_madd_h(lasx_ext8_16(scale_3), p16_3); + + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_1)); + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_2, p16_3)); + } + + acc = __lasx_xvfmadd_s((__m256)__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc); + } + + *s = hsum_float_8(acc); + #else int8_t aux8[QK_K]; @@ -9656,6 +11073,65 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = vec_extract(vsumf0, 0); +#elif defined __loongarch_asx + + const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); + const __m256i m2 = __lasx_xvreplgr2vr_b(3); + const __m256i m32s = __lasx_xvreplgr2vr_b(32); + + __m256 acc = (__m256)__lasx_xvldi(0); + + for (int i = 0; i < nb; ++i) { + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + + const uint8_t * restrict q4 = x[i].ql; + const uint8_t * restrict qh = x[i].qh; + const int8_t * restrict q8 = y[i].qs; + + const __m64 scales_1 = __lasx_xvreplgr2vr_b(x[i].scales[0]); + const __m64 scales_2 = __lasx_xvreplgr2vr_b(x[i].scales[1]); + const __m64 scales_3 = __lasx_xvreplgr2vr_b(x[i].scales[2]); + const __m64 scales_4 = __lasx_xvreplgr2vr_b(x[i].scales[3]); + + __m256i sumi = __lasx_xvldi(0); + + __m128i scale_0 = __lsx_vinsgr2vr_d(scale_0, scales_1, 0); + scale_0 = __lsx_vinsgr2vr_d(scale_0, scales_2, 1); + __m128i scale_1 = __lsx_vinsgr2vr_d(scale_1, scales_3, 0); + scale_1 = __lsx_vinsgr2vr_d(scale_1, scales_4, 1); + + const __m256i q4bits1 = __lasx_xvld((const __m256i*)q4, 0); + const __m128i q4bitsH = __lsx_vld((const __m128i*)qh, 0); + + const __m256i q4h_0 = __lasx_xvslli_h(__lasx_xvand_v(lasx_insertf128(__lasx_xvsrli_h(q4bitsH, 2), q4bitsH), m2), 4); + const __m256i q4h_1 = __lasx_xvslli_h(__lasx_xvand_v(lasx_insertf128(__lasx_xvsrli_h(q4bitsH, 6), __lasx_xvsrli_h(q4bitsH, 4)), m2), 4); + + const __m256i q4_0 = __lasx_xvor_v(__lasx_xvand_v(q4bits1, m4), q4h_0); + const __m256i q4_1 = __lasx_xvor_v(__lasx_xvand_v(__lasx_xvsrli_h(q4bits1, 4), m4), q4h_1); + + const __m256i q8_0 = __lasx_xvld((const __m256i*)(q8+ 0), 0); + const __m256i q8_1 = __lasx_xvld((const __m256i*)(q8+32), 0); + + __m256i q8s_0 = lasx_maddubs_h(m32s, q8_0); + __m256i q8s_1 = lasx_maddubs_h(m32s, q8_1); + + __m256i p16_0 = lasx_maddubs_h(q4_0, q8_0); + __m256i p16_1 = lasx_maddubs_h(q4_1, q8_1); + + p16_0 = __lasx_xvsub_h(p16_0, q8s_0); + p16_1 = __lasx_xvsub_h(p16_1, q8s_1); + + p16_0 = lasx_madd_h(lasx_ext8_16(scale_0), p16_0); + p16_1 = lasx_madd_h(lasx_ext8_16(scale_1), p16_1); + + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_1)); + + acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc); + } + + *s = hsum_float_8(acc); + #else int8_t aux8[QK_K]; @@ -9697,7 +11173,7 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r #endif -#if defined (__AVX2__) || defined (__ARM_NEON) || defined (__POWER9_VECTOR__) +#if defined (__AVX2__) || defined (__ARM_NEON) || defined (__POWER9_VECTOR__) || defined(__loongarch_asx) static const int8_t keven_signs_q2xs[1024] = { 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, -1, -1, 1, -1, 1, 1, 1, 1, 1, 1, -1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, -1, @@ -9927,6 +11403,49 @@ void ggml_vec_dot_iq2_xxs_q8_K(int n, float * restrict s, size_t bs, const void vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); *s = 0.125f * vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[4]; + const uint8_t * aux8 = (const uint8_t *)aux32; + + __m256 accumf = (__m256)__lasx_xvldi(0); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + memcpy(aux32, q2, 4*sizeof(uint32_t)); q2 += 8; + + const __m256i q2_1 = lasx_set_d(iq2xxs_grid[aux8[ 3]], iq2xxs_grid[aux8[ 2]], iq2xxs_grid[aux8[1]], iq2xxs_grid[aux8[0]]); + const __m256i q2_2 = lasx_set_d(iq2xxs_grid[aux8[11]], iq2xxs_grid[aux8[10]], iq2xxs_grid[aux8[9]], iq2xxs_grid[aux8[8]]); + const __m256i s2_1 = lasx_set_d(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], + signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); + const __m256i s2_2 = lasx_set_d(signs64[(aux32[3] >> 21) & 127], signs64[(aux32[3] >> 14) & 127], + signs64[(aux32[3] >> 7) & 127], signs64[(aux32[3] >> 0) & 127]); + const __m256i q8s_1 = __lasx_xvsigncov_b(s2_1, q8_1); + const __m256i q8s_2 = __lasx_xvsigncov_b(s2_2, q8_2); + const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); + const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); + const uint16_t ls1 = aux32[1] >> 28; + const uint16_t ls2 = aux32[3] >> 28; + const __m256i p1 = lasx_madd_h(dot1, __lasx_xvreplgr2vr_h(2*ls1+1)); + const __m256i p2 = lasx_madd_h(dot2, __lasx_xvreplgr2vr_h(2*ls2+1)); + sumi1 = __lasx_xvadd_w(sumi1, p1); + sumi2 = __lasx_xvadd_w(sumi2, p2); + } + + accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); + } + + *s = 0.125f * hsum_float_8(accumf); + #else uint32_t aux32[2]; @@ -10201,6 +11720,181 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * *s = 0.125f * hsum_float_8(accumf); #endif +#elif defined(__loongarch_asx) + + const __m256i mone = __lasx_xvreplgr2vr_b(1); + 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_selector_mask = __lasx_xvld((const __m256i*)bit_selector_mask_bytes, 0); + const __m256i block_sign_shuffle_1 = __lasx_xvld((const __m256i*)block_sign_shuffle_mask_1, 0); + const __m256i block_sign_shuffle_2 = __lasx_xvld((const __m256i*)block_sign_shuffle_mask_2, 0); + +#if QK_K == 64 + static const uint8_t k_bit_helper[16] = { + 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, + }; + const __m128i bit_helper = __lsx_vld((const __m128i*)k_bit_helper, 0); + const __m128i m511 = __lsx_vreplgr2vr_h(511); + typedef union { + __m128i vec_index; + uint16_t index[8]; + } index_t; + + index_t idx; + __m256 accumf = (__m256)__lasx_xvldi(0); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const __m128i q2_data = __lsx_vld((const __m128i*)x[i].qs, 0); + idx.vec_index = __lsx_vand_v(q2_data, m511); + + const __m128i partial_sign_bits = __lsx_vsrli_h(q2_data, 9); + const __m128i partial_sign_bits_upper = __lsx_vsrli_h(q2_data, 13); + const __m128i partial_sign_bits_for_counting = __lsx_vxor_v(partial_sign_bits, partial_sign_bits_upper); + + const __m128i odd_bits = lsx_shuffle_b(bit_helper, partial_sign_bits_for_counting); + const __m128i full_sign_bits = __lsx_vor_v(partial_sign_bits, odd_bits); + const __m256i full_signs = lasx_insertf128(full_sign_bits, full_sign_bits); + + const __m256i q8_1 = __lasx_xvld((const __m256i *)y[i].qs, 0); + const __m256i q8_2 = __lasx_xvld((const __m256i *)(y[i].qs+32), 0); + + const __m256i q2_1 = lasx_set_d(iq2xs_grid[idx.index[3]], iq2xs_grid[idx.index[2]], + iq2xs_grid[idx.index[1]], iq2xs_grid[idx.index[0]]); + const __m256i q2_2 = lasx_set_d(iq2xs_grid[idx.index[7]], iq2xs_grid[idx.index[6]], + iq2xs_grid[idx.index[5]], iq2xs_grid[idx.index[4]]); + __m256i signs; + signs = lasx_shuffle_b(full_signs, block_sign_shuffle_1); + signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_1 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_1); + + signs = lasx_shuffle_b(full_signs, block_sign_shuffle_2); + signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_2 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_2); + + const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); + const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); + + const __m256i sc1 = lasx_insertf128(_mm_set1_epi16(2*(x[i].scales[0] >> 4)+1), __lsx_vreplgr2vr_h(2*(x[i].scales[0] & 0xf)+1)); + const __m256i sc2 = lasx_insertf128(_mm_set1_epi16(2*(x[i].scales[1] >> 4)+1), __lsx_vreplgr2vr_h(2*(x[i].scales[1] & 0xf)+1)); + + const __m256i sum = __lasx_xvadd_w(lasx_madd_h(sc1, dot1), lasx_madd_h(sc2, dot2)); + + accumf = __lasx_vfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sum), accumf); + } + + *s = 0.125f * hsum_float_8(accumf); +#else + + 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, + }; + const __m256i bit_helper = __lasx_xvld((const __m256i*)k_bit_helper, 0); + const __m256i m511 = __lasx_xvreplgr2vr_h(511); + const __m128i m4 = __lsx_vreplgr2vr_b(0xf); + const __m128i m1 = __lsx_vreplgr2vr_b(1); + + uint64_t aux64; + + // somewhat hacky, but gives a significant boost in performance + __m256i aux_gindex; + const uint16_t * gindex = (const uint16_t *)&aux_gindex; + + __m256 accumf = (__m256)__lasx_xvldi(0); + for (int i = 0; i < nb; ++i) { + const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; + const uint16_t * restrict q2 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; + + memcpy(&aux64, x[i].scales, 8); + __m128i stmp = __lsx_vreplgr2vr_d(aux64); + stmp = __lsx_vilvl_b( __lsx_vand_v(__lsx_vsrli_h(stmp, 4), m4), __lsx_vand_v(stmp, m4)); + const __m128i scales = __lsx_vadd_b(__lsx_vslli_h(stmp, 1), m1); + + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 4) { + + const __m256i q2_data = __lasx_xvld((const __m256i*)q2, 0); q2 += 16; + aux_gindex = __lasx_xvand_v(q2_data, m511); + + const __m256i partial_sign_bits = __lasx_xvsrli_h(q2_data, 9); + const __m256i partial_sign_bits_upper = __lasx_xvsrli_h(q2_data, 13); + const __m256i partial_sign_bits_for_counting = __lasx_xvxor_v(partial_sign_bits, partial_sign_bits_upper); + + const __m256i odd_bits = lasx_shuffle_b(bit_helper, partial_sign_bits_for_counting); + const __m256i full_sign_bits = __lasx_xvor_v(partial_sign_bits, odd_bits); + + const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_3 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_4 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + + const __m256i q2_1 = lasx_set_d(iq2xs_grid[gindex[ 3]], iq2xs_grid[gindex[ 2]], + iq2xs_grid[gindex[ 1]], iq2xs_grid[gindex[ 0]]); + const __m256i q2_2 = lasx_set_d(iq2xs_grid[gindex[ 7]], iq2xs_grid[gindex[ 6]], + iq2xs_grid[gindex[ 5]], iq2xs_grid[gindex[ 4]]); + const __m256i q2_3 = lasx_set_d(iq2xs_grid[gindex[11]], iq2xs_grid[gindex[10]], + iq2xs_grid[gindex[ 9]], iq2xs_grid[gindex[ 8]]); + const __m256i q2_4 = lasx_set_d(iq2xs_grid[gindex[15]], iq2xs_grid[gindex[14]], + iq2xs_grid[gindex[13]], iq2xs_grid[gindex[12]]); + + const __m128i full_signs_l = lasx_extracti128(full_sign_bits, 0); + const __m128i full_signs_h = lasx_extracti128(full_sign_bits, 1); + const __m256i full_signs_1 = lasx_insertf128(full_signs_l, full_signs_l); + const __m256i full_signs_2 = lasx_insertf128(full_signs_h, full_signs_h); + + __m256i signs; + signs = lasx_shuffle_b(full_signs_1, block_sign_shuffle_1); + signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_1 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_1); + + signs = lasx_shuffle_b(full_signs_1, block_sign_shuffle_2); + signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_2 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_2); + + signs = lasx_shuffle_b(full_signs_2, block_sign_shuffle_1); + signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_3 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_3); + + signs = lasx_shuffle_b(full_signs_2, block_sign_shuffle_2); + signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); + const __m256i q8s_4 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_4); + + const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); + const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); + const __m256i dot3 = lasx_maddubs_h(q2_3, q8s_3); + const __m256i dot4 = lasx_maddubs_h(q2_4, q8s_4); + + const __m256i sc1 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+0))); + const __m256i sc2 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+1))); + const __m256i sc3 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+2))); + const __m256i sc4 = lasx_ext8_16(lsx_shuffle_b(scales, get_scale_shuffle(ib32+3))); + + sumi1 = __lasx_xvadd_w(sumi1, lasx_madd_h(dot1, sc1)); + sumi2 = __lasx_xvadd_w(sumi2, lasx_madd_h(dot2, sc2)); + sumi1 = __lasx_xvadd_w(sumi1, lasx_madd_h(dot3, sc3)); + sumi2 = __lasx_xvadd_w(sumi2, lasx_madd_h(dot4, sc4)); + } + + accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); + + } + + *s = 0.125f * hsum_float_8(accumf); +#endif + #elif defined(__POWER9_VECTOR__) vector float vsumf0 = vec_splats(0.0f); @@ -10618,6 +12312,81 @@ void ggml_vec_dot_iq2_s_q8_K(int n, float * restrict s, size_t bs, const void * vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); *s = 0.125f * vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[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 __m128i m4 = __lsx_vreplgr2vr_b(0xf); + const __m128i m1 = __lsx_vreplgr2vr_b(1); + + const __m256i mask1 = __lasx_xvld((const __m256i*)k_mask1, 0); + const __m256i mask2 = __lasx_xvld((const __m256i*)k_mask2, 0); + uint64_t aux64; + + __m256 accumf = (__m256)__lasx_xvldi(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 qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)(x[i].qs + QK_K/8); + const int8_t * restrict q8 = y[i].qs; + + __m128i tmp1; + memcpy(&aux64, x[i].scales, 8); + tmp1 = __lsx_vinsgr2vr_d(tmp1, aux64, 0); + tmp1 = __lsx_vinsgr2vr_d(tmp1, aux64 >> 4, 1); + const __m128i scales8 = __lsx_vadd_b(__lsx_vslli_h(__lsx_vand_v(tmp1, m4), 1), m1); + const __m256i scales16 = lasx_ext8_16(scales8); // 0 2 4 6 8 10 12 14 1 3 5 7 9 11 13 15 + + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q2_1 = lasx_set_d(iq2s_grid[qs[3] | ((qh[ib32+0] << 2) & 0x300)], + iq2s_grid[qs[2] | ((qh[ib32+0] << 4) & 0x300)], + iq2s_grid[qs[1] | ((qh[ib32+0] << 6) & 0x300)], + iq2s_grid[qs[0] | ((qh[ib32+0] << 8) & 0x300)]); + const __m256i q2_2 = lasx_set_d(iq2s_grid[qs[7] | ((qh[ib32+1] << 2) & 0x300)], + iq2s_grid[qs[6] | ((qh[ib32+1] << 4) & 0x300)], + iq2s_grid[qs[5] | ((qh[ib32+1] << 6) & 0x300)], + iq2s_grid[qs[4] | ((qh[ib32+1] << 8) & 0x300)]); + qs += 8; + + __m256i aux256 = __lasx_xvreplgr2vr_w(signs[0] | ((uint32_t) signs[1] << 16)); + aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); + const __m256i s2_1 = __lasx_xvseq_b(aux256, mask2); + const __m256i q8s_1 = __lasx_xvsub_b(__lasx_xvxor_v(s2_1, q8_1), s2_1); + + aux256 = __lasx_xvreplgr2vr_w(signs[2] | ((uint32_t) signs[3] << 16)); + aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); + const __m256i s2_2 = __lasx_xvseq_b(aux256, mask2); + const __m256i q8s_2 = __lasx_xvsub_b(__lasx_xvxor_v(s2_2, q8_2), s2_2); + + signs += 4; + + const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); // blocks 2*ib32+0, 2*ib32+1 + const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); // blocks 2*ib32+2, 2*ib32+3 + + const __m256i p1 = lasx_madd_h(dot1, lasx_shuffle_b(scales16, get_scale_shuffle_k4(ib32+0))); + const __m256i p2 = lasx_madd_h(dot2, lasx_shuffle_b(scales16, get_scale_shuffle_k4(ib32+1))); + sumi1 = __lasx_xvadd_w(sumi1, p1); + sumi2 = __lasx_xvadd_w(sumi2, p2); + } + + accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); + } + + *s = 0.125f * hsum_float_8(accumf); + #else float sumf = 0; @@ -10857,6 +12626,54 @@ void ggml_vec_dot_iq3_xxs_q8_K(int n, float * restrict s, size_t bs, const void vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); *s = 0.25f * vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + + const uint64_t * signs64 = (const uint64_t *)keven_signs_q2xs; + + uint32_t aux32[2]; + + __m256 accumf = (__m256)__lasx_xvldi(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; + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q2_1 = lasx_set_w(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 = lasx_set_w(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 = lasx_set_d(signs64[(aux32[0] >> 21) & 127], signs64[(aux32[0] >> 14) & 127], + signs64[(aux32[0] >> 7) & 127], signs64[(aux32[0] >> 0) & 127]); + const __m256i s2_2 = lasx_set_d(signs64[(aux32[1] >> 21) & 127], signs64[(aux32[1] >> 14) & 127], + signs64[(aux32[1] >> 7) & 127], signs64[(aux32[1] >> 0) & 127]); + const __m256i q8s_1 = __lasx_xvsigncov_b(s2_1, q8_1); + const __m256i q8s_2 = __lasx_xvsigncov_b(s2_2, q8_2); + const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); + const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); + const uint16_t ls1 = aux32[0] >> 28; + const uint16_t ls2 = aux32[1] >> 28; + + const __m256i p1 = lasx_madd_h(dot1, __lasx_xvreplgr2vr_h(2*ls1+1)); + const __m256i p2 = lasx_madd_h(dot2, __lasx_xvreplgr2vr_h(2*ls2+1)); + sumi1 = __lasx_xvadd_w(sumi1, p1); + sumi2 = __lasx_xvadd_w(sumi2, p2); + } + + accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); + } + + *s = 0.25f * hsum_float_8(accumf); + #else uint32_t aux32; @@ -11202,6 +13019,89 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); *s = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + + static const uint8_t k_mask1[32] = {0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x00, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, 0x01, + 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x02, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03, 0x03 + }; + + static const uint8_t k_mask2[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 mask1 = __lasx_xvld((const __m256i*)k_mask1, 0); + const __m256i mask2 = __lasx_xvld((const __m256i*)k_mask2, 0); + + __m256i idx_shift = lasx_set_w(1, 2, 3, 4, 5, 6, 7, 8); + const __m256i idx_mask = __lasx_xvreplgr2vr_w(256); + + typedef union { + __m256i vec[2]; + uint32_t index[16]; + } index_t; + + index_t idx; + + __m256 accumf = (__m256)__lasx_xvldi(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 qs = x[i].qs; + const uint8_t * restrict qh = x[i].qh; + const uint16_t * restrict signs = (const uint16_t *)x[i].signs; + const int8_t * restrict q8 = y[i].qs; + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); + for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { + const __m256i q8_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i idx_l = lasx_extu8_16(__lsx_vld(qs, 0)); qs += 16; + idx.vec[0] = __lasx_xvreplgr2vr_w(qh[ib32+0]); + idx.vec[1] = __lasx_xvreplgr2vr_w(qh[ib32+1]); + idx.vec[0] = __lasx_xvand_v(__lasx_xvsll_w(idx.vec[0], idx_shift), idx_mask); + idx.vec[1] = __lasx_xvand_v(__lasx_xvsll_w(idx.vec[1], idx_shift), idx_mask); + idx.vec[0] = __lasx_xvor_v(idx.vec[0], lasx_ext16_32(lasx_extracti128(idx_l, 0))); + idx.vec[1] = __lasx_xvor_v(idx.vec[1], lasx_ext16_32(lasx_extracti128(idx_l, 1))); + + // At leat on my CPU (Ryzen 7950X), using _mm256_i32gather_epi32 is slower than _mm256_set_epi32. Strange. + //const __m256i q2_1 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[0], 4); + //const __m256i q2_2 = _mm256_i32gather_epi32((const int *)iq3s_grid, idx.vec[1], 4); + const __m256i q2_1 = lasx_set_w( + iq3s_grid[idx.index[7]], iq3s_grid[idx.index[6]], iq3s_grid[idx.index[5]], iq3s_grid[idx.index[4]], + iq3s_grid[idx.index[3]], iq3s_grid[idx.index[2]], iq3s_grid[idx.index[1]], iq3s_grid[idx.index[0]] + ); + const __m256i q2_2 = lasx_set_w( + iq3s_grid[idx.index[15]], iq3s_grid[idx.index[14]], iq3s_grid[idx.index[13]], iq3s_grid[idx.index[12]], + iq3s_grid[idx.index[11]], iq3s_grid[idx.index[10]], iq3s_grid[idx.index[ 9]], iq3s_grid[idx.index[ 8]] + ); + + __m256i aux256 = __lasx_xvreplgr2vr_w(signs[0] | (signs[1] << 16)); + aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); + const __m256i s2_1 = __lasx_xvseq_b(aux256, mask2); + const __m256i q8s_1 = __lasx_xvsub_b(__lasx_xvxor_v(s2_1, q8_1), s2_1); + + aux256 = __lasx_xvreplgr2vr_w(signs[2] | (signs[3] << 16)); + aux256 = __lasx_xvand_v(lasx_shuffle_b(aux256,mask1), mask2); + const __m256i s2_2 = __lasx_xvseq_b(aux256, mask2); + const __m256i q8s_2 = __lasx_xvsub_b(__lasx_xvxor_v(s2_2, q8_2), s2_2); + + signs += 4; + + const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); + const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); + const uint16_t ls1 = x[i].scales[ib32/2] & 0xf; + const uint16_t ls2 = x[i].scales[ib32/2] >> 4; + const __m256i p1 = lasx_madd_h(dot1, __lasx_xvreplgr2vr_h(2*ls1+1)); + const __m256i p2 = lasx_madd_h(dot2, __lasx_xvreplgr2vr_h(2*ls2+1)); + sumi1 = __lasx_xvadd_w(sumi1, p1); + sumi2 = __lasx_xvadd_w(sumi2, p2); + } + + accumf = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accumf); + } + + *s = hsum_float_8(accumf); + #else float sumf = 0.f; @@ -11249,12 +13149,22 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * } -#ifdef __AVX2__ +#if defined(__AVX2__) static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) { const __m256i ax = _mm256_sign_epi8(x, x); const __m256i sy = _mm256_sign_epi8(y, x); return _mm256_maddubs_epi16(ax, sy); } +#elif defined(__loongarch_asx) +static inline __m256i mul_add_epi8(const __m256i x, const __m256i y) { + const __m256i ax = __lasx_xvsigncov_b(x, x); + const __m256i sy = __lasx_xvsigncov_b(x, y); + __m256i tmp1, tmp2, tmp3; + tmp1 = __lasx_xvmulwev_h_bu_b(ax, sy); + tmp2 = __lasx_xvmulwod_h_bu_b(ax, sy); + tmp3 = __lasx_xvadd_h(tmp1, tmp2); + return __lasx_xvsat_h(tmp3, 15); +} #endif void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { @@ -11463,6 +13373,62 @@ void ggml_vec_dot_iq1_s_q8_K (int n, float * restrict s, size_t bs, const void vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); *s = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + + __m256 accum = (__m256)__lasx_xvldi(0); + float accum1 = 0; + for (int i = 0; i < nb; ++i) { + + const int8_t * q8 = y[i].qs; + const uint8_t * qs = x[i].qs; + const uint16_t * qh = x[i].qh; + + __m256i sumi = __lasx_xvldi(0); + int sumi1 = 0; + for (int ib = 0; ib < QK_K/32; ib += 2) { + __m256i q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[0] | ((qh[ib+0] << 8) & 0x700)], 0); + q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[1] | ((qh[ib+0] << 5) & 0x700)], 1); + q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[2] | ((qh[ib+0] << 2) & 0x700)], 2); + q1b_1 = __lasx_xvinsgr2vr_d(q1b_1, iq1s_grid[qs[3] | ((qh[ib+0] >> 1) & 0x700)], 3); + + __m256i q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[4] | ((qh[ib+1] << 8) & 0x700)], 0); + q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[5] | ((qh[ib+1] << 5) & 0x700)], 1); + q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[6] | ((qh[ib+1] << 2) & 0x700)], 2); + q1b_2 = __lasx_xvinsgr2vr_d(q1b_2, iq1s_grid[qs[7] | ((qh[ib+1] >> 1) & 0x700)], 3); + + qs += 8; + const __m256i q8b_1 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + const __m256i q8b_2 = __lasx_xvld((const __m256i*)q8, 0); q8 += 32; + + const __m256i dot1 = mul_add_epi8(q1b_1, q8b_1); + const __m256i dot2 = mul_add_epi8(q1b_2, q8b_2); + const int16_t ls1 = 2*((qh[ib+0] >> 12) & 7) + 1; + const int16_t ls2 = 2*((qh[ib+1] >> 12) & 7) + 1; + + __m256i tmp1, tmp5, tmp6; + tmp1 = __lasx_xvreplgr2vr_h(ls1); + tmp5 = __lasx_xvmulwev_w_h(dot1, tmp1); + tmp6 = __lasx_xvmulwod_w_h(dot1, tmp1); + const __m256i p1 = __lasx_xvadd_w(tmp5, tmp6); + + tmp1 = __lasx_xvreplgr2vr_h(ls2); + tmp5 = __lasx_xvmulwev_w_h(dot2, tmp1); + tmp6 = __lasx_xvmulwod_w_h(dot2, tmp1); + const __m256i p2 = __lasx_xvadd_w(tmp5, tmp6); + + sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p1, p2)); + sumi1 += (y[i].bsums[2*ib+0] + y[i].bsums[2*ib+1]) * (qh[ib+0] & 0x8000 ? -1 : 1) * ls1 + + (y[i].bsums[2*ib+2] + y[i].bsums[2*ib+3]) * (qh[ib+1] & 0x8000 ? -1 : 1) * ls2; + } + + const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + accum = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), accum); + accum1 += d * sumi1; + } + + *s = hsum_float_8(accum) + IQ1S_DELTA * accum1; + #else float sumf = 0; @@ -11864,6 +13830,39 @@ void ggml_vec_dot_iq4_nl_q8_0(int n, float * restrict s, size_t bs, const void * vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); *s = vec_extract(vsumf0, 0); + +#elif defined (__loongarch_asx) + + const __m128i values128 = __lsx_vld((const __m128i*)kvalues_iq4nl, 0); + const __m128i m4b = __lsx_vreplgr2vr_b(0x0f); + const __m256i mone = __lasx_xvreplgr2vr_h(1); + + __m256 accum1 = (__m256)__lasx_xvldi(0); + __m256 accum2 = (__m256)__lasx_xvldi(0); + for (int ib = 0; ib < nb; ib += 2) { + const __m128i q4bits_1 = __lsx_vld((const __m128i*)x[0].qs, 0); + const __m128i q4bits_2 = __lsx_vld((const __m128i*)x[1].qs, 0); + const __m256i q8b_1 = __lasx_xvld((const __m256i *)y[0].qs, 0); + const __m256i q8b_2 = __lasx_xvld((const __m256i *)y[1].qs, 0); + const __m256i q4b_1 = lasx_insertf128(lsx_shuffle_b(values128, __lsx_vand_v(__lsx_vsrli_h(q4bits_1, 4), m4b)), + lsx_shuffle_b(values128, __lsx_vand_v(q4bits_1, m4b))); + const __m256i q4b_2 = lasx_insertf128(lsx_shuffle_b(values128, __lsx_vand_v(__lsx_vsrli_h(q4bits_2, 4), m4b)), + lsx_shuffle_b(values128, __lsx_vand_v(q4bits_2, m4b))); + const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); + const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); + const __m256i p_1 = lasx_madd_h(p16_1, mone); + const __m256i p_2 = lasx_madd_h(p16_2, mone); + accum1 = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[0].d)*GGML_FP16_TO_FP32(x[0].d)), + __lasx_xvffint_s_w(p_1), accum1); + accum2 = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(y[1].d)*GGML_FP16_TO_FP32(x[1].d)), + __lasx_xvffint_s_w(p_2), accum2); + + y += 2; + x += 2; + } + + *s = hsum_float_8(__lasx_xvfadd_s(accum1, accum2)); + #else float sumf = 0; for (int ib = 0; ib < nb; ++ib) { @@ -12074,6 +14073,80 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); *s = vec_extract(vsumf0, 0); + +#elif defined(__loongarch_asx) + + const __m128i values128 = __lsx_vld((const __m128i*)kvalues_iq4nl, 0); + const __m128i m4b = __lsx_vreplgr2vr_b(0x0f); + + __m256 accum = (__m256)__lasx_xvldi(0); + __m256i tmp1; + __m128i tmp0, tmp2, tmp3, tmp4, mask_8f, mask; + + mask_8f = __lsx_vreplgr2vr_b(0x8f); + for (int ibl = 0; ibl < nb; ++ibl) { + const uint8_t * qs = x[ibl].qs; + const int8_t * q8 = y[ibl].qs; + uint16_t sh = x[ibl].scales_h; + __m256i sumi1 = __lasx_xvldi(0); + __m256i sumi2 = __lasx_xvldi(0); + __m128i zero = __lsx_vldi(0); + for (int ib = 0; ib < QK_K/32; ib += 2) { + const __m128i q4bits_1 = __lsx_vld((const __m128i*)qs, 0); qs += 16; + const __m128i q4bits_2 = __lsx_vld((const __m128i*)qs, 0); qs += 16; + const __m256i q8b_1 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + const __m256i q8b_2 = __lasx_xvld((const __m256i *)q8, 0); q8 += 32; + tmp2 = __lsx_vand_v(__lsx_vand_v(__lsx_vsrli_h(q4bits_1, 4), m4b), mask_8f); + tmp0 = __lsx_vori_b(tmp2, 0x10); + mask = __lsx_vsle_b(zero, tmp2); + tmp3 = __lsx_vand_v(tmp0, mask); + tmp3 = __lsx_vshuf_b(values128, zero, tmp3); + + tmp2 = __lsx_vand_v(__lsx_vand_v(q4bits_1, m4b), mask_8f); + tmp0 = __lsx_vori_b(tmp2, 0x10); + mask = __lsx_vsle_b(zero, tmp2); + tmp4 = __lsx_vand_v(tmp0, mask); + tmp4 = __lsx_vshuf_b(values128, zero, tmp4); + + const __m256i q4b_1 = lasx_insertf128(tmp3, tmp4); + + tmp2 = __lsx_vand_v(__lsx_vand_v(__lsx_vsrli_h(q4bits_2, 4), m4b), mask_8f); + tmp0 = __lsx_vori_b(tmp2, 0x10); + mask = __lsx_vsle_b(zero, tmp2); + tmp3 = __lsx_vand_v(tmp0, mask); + tmp3 = __lsx_vshuf_b(values128, zero, tmp3); + + tmp2 = __lsx_vand_v(__lsx_vand_v(q4bits_2, m4b), mask_8f); + tmp0 = __lsx_vori_b(tmp2, 0x10); + mask = __lsx_vsle_b(zero, tmp2); + tmp4 = __lsx_vand_v(tmp0, mask); + tmp4 = __lsx_vshuf_b(values128, zero, tmp4); + + const __m256i q4b_2 = lasx_insertf128(tmp3, tmp4); + + const __m256i p16_1 = mul_add_epi8(q4b_1, q8b_1); + const __m256i p16_2 = mul_add_epi8(q4b_2, q8b_2); + const int16_t ls1 = ((x[ibl].scales_l[ib/2] & 0xf) | ((sh << 4) & 0x30)) - 32; + const int16_t ls2 = ((x[ibl].scales_l[ib/2] >> 4) | ((sh << 2) & 0x30)) - 32; + sh >>= 4; + __m256i tmp5, tmp6; + tmp1 = __lasx_xvreplgr2vr_h(ls1); + tmp5 = __lasx_xvmulwev_w_h(p16_1, tmp1); + tmp6 = __lasx_xvmulwod_w_h(p16_1, tmp1); + const __m256i p_1 = __lasx_xvadd_w(tmp5, tmp6); + tmp1 = __lasx_xvreplgr2vr_h(ls2); + tmp5 = __lasx_xvmulwev_w_h(p16_2, tmp1); + tmp6 = __lasx_xvmulwod_w_h(p16_2, tmp1); + const __m256i p_2 = __lasx_xvadd_w(tmp5, tmp6); + sumi1 = __lasx_xvadd_w(p_1, sumi1); + sumi2 = __lasx_xvadd_w(p_2, sumi2); + } + accum = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(GGML_FP16_TO_FP32(x[ibl].d)*y[ibl].d), + __lasx_xvffint_s_w(__lasx_xvadd_w(sumi1, sumi2)), accum); + } + + *s = hsum_float_8(accum); + #else float sumf = 0; for (int ibl = 0; ibl < nb; ++ibl) { diff --git a/ggml.c b/ggml.c index 53da231ee..4bd911528 100644 --- a/ggml.c +++ b/ggml.c @@ -1523,6 +1523,195 @@ static inline void __sse_f16x4_store(ggml_fp16_t *x, __m128 y) { #define GGML_F16_VEC_MUL GGML_F32Cx4_MUL #define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE +#elif defined(__loongarch_asx) + +#define GGML_SIMD + +// F32 LASX +#define GGML_F32_STEP 32 +#define GGML_F32_EPR 8 + +#define GGML_F32x8 __m256 +#define GGML_F32x8_ZERO (__m256)__lasx_xvldi(0) +#define GGML_F32x8_SET1(x) (__m256)__lasx_xvreplfr2vr_s((x)) +#define GGML_F32x8_LOAD(x) (__m256)__lasx_xvld((x), 0) +#define GGML_F32x8_STORE(x,y) __lasx_xvst((y), (x), 0) +#define GGML_F32x8_FMA(a, b, c) __lasx_xvfmadd_s(b, c, a) +#define GGML_F32x8_ADD __lasx_xvfadd_s +#define GGML_F32x8_MUL __lasx_xvfmul_s +#define GGML_F32x8_REDUCE(res, x) \ +do { \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lasx_xvfadd_s(x[i], x[offset+i]); \ + } \ + float *tmp_p = (float *)&x[0]; \ + res = tmp_p[0] + tmp_p[1] + tmp_p[2] + tmp_p[3] + tmp_p[4] + tmp_p[5] + tmp_p[6] + tmp_p[7]; \ +} while (0) +// TODO: is this optimal ? + +#define GGML_F32_VEC GGML_F32x8 +#define GGML_F32_VEC_ZERO GGML_F32x8_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x8_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x8_LOAD +#define GGML_F32_VEC_STORE GGML_F32x8_STORE +#define GGML_F32_VEC_FMA GGML_F32x8_FMA +#define GGML_F32_VEC_ADD GGML_F32x8_ADD +#define GGML_F32_VEC_MUL GGML_F32x8_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x8_REDUCE + +// F16 LASX + +#define GGML_F16_STEP 32 +#define GGML_F16_EPR 8 + +// F16 arithmetic is not supported by AVX, so we use F32 instead + +#define GGML_F32Cx8 __m256 +#define GGML_F32Cx8_ZERO (__m256)__lasx_xvldi(0) +#define GGML_F32Cx8_SET1(x) (__m256)__lasx_xvreplgr2vr_w((x)) + +static inline __m256 __lasx_f32cx8_load(ggml_fp16_t *x) { + float tmp[8]; + + for (int i = 0; i < 8; i++) { + tmp[i] = GGML_FP16_TO_FP32(x[i]); + } + + return (__m256)__lasx_xvld(tmp, 0); +} +static inline void __lasx_f32cx8_store(ggml_fp16_t *x, __m256 y) { + float arr[8]; + + __lasx_xvst(y, arr, 0); + + for (int i = 0; i < 8; i++) + x[i] = GGML_FP32_TO_FP16(arr[i]); +} +#define GGML_F32Cx8_LOAD(x) __lasx_f32cx8_load(x) +#define GGML_F32Cx8_STORE(x, y) __lasx_f32cx8_store(x, y) + +#define GGML_F32Cx8_FMA GGML_F32x8_FMA +#define GGML_F32Cx8_ADD __lasx_xvfadd_s +#define GGML_F32Cx8_MUL __lasx_xvfmul_s +#define GGML_F32Cx8_REDUCE GGML_F32x8_REDUCE + +#define GGML_F16_VEC GGML_F32Cx8 +#define GGML_F16_VEC_ZERO GGML_F32Cx8_ZERO +#define GGML_F16_VEC_SET1 GGML_F32Cx8_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx8_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx8_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F32Cx8_FMA +#define GGML_F16_VEC_ADD GGML_F32Cx8_ADD +#define GGML_F16_VEC_MUL GGML_F32Cx8_MUL +#define GGML_F16_VEC_REDUCE GGML_F32Cx8_REDUCE + +#elif defined(__loongarch_sx) + +#define GGML_SIMD + +// F32 LSX + +#define GGML_F32_STEP 32 +#define GGML_F32_EPR 4 + +#define GGML_F32x4 __m128 +#define GGML_F32x4_ZERO __lsx_vldi(0) +#define GGML_F32x4_SET1(x) __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0) +#define GGML_F32x4_LOAD(x) __lsx_vld((x), 0) +#define GGML_F32x4_STORE((x),(y)) __lsx_vst((y), (x), 0) +#define GGML_F32x4_FMA(a, b, c) __lsx_vfmadd_s(b, c, a) +#define GGML_F32x4_ADD __lsx_vfadd_s +#define GGML_F32x4_MUL __lsx_vfmul_s +#define GGML_F32x4_REDUCE(res, x) \ +{ \ + int offset = GGML_F32_ARR >> 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ + } \ + offset >>= 1; \ + for (int i = 0; i < offset; ++i) { \ + x[i] = __lsx_vfadd_s(x[i], x[offset+i]); \ + } \ + __m128i tmp = __lsx_vsrli_d((__m128i)x[0], 32); \ + tmp = (__m128i)__lsx_vfadd_s((__m128)tmp, x[0]); \ + tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ + const __m128 t0 = __lsx_vshuf4i_w(tmp, 0x88); \ + tmp = __lsx_vsrli_d((__m128i)t0, 32); \ + tmp = (__m128i)__lsx_vfadd_s((__m128)tmp, t0); \ + tmp = __lsx_vpickev_w(__lsx_vldi(0), tmp); \ + res = (ggml_float) __lsx_vpickve2gr_w(__lsx_vshuf4i_w(tmp, 0x88), 0); \ +} + +#define GGML_F32_VEC GGML_F32x4 +#define GGML_F32_VEC_ZERO GGML_F32x4_ZERO +#define GGML_F32_VEC_SET1 GGML_F32x4_SET1 +#define GGML_F32_VEC_LOAD GGML_F32x4_LOAD +#define GGML_F32_VEC_STORE GGML_F32x4_STORE +#define GGML_F32_VEC_FMA GGML_F32x4_FMA +#define GGML_F32_VEC_ADD GGML_F32x4_ADD +#define GGML_F32_VEC_MUL GGML_F32x4_MUL +#define GGML_F32_VEC_REDUCE GGML_F32x4_REDUCE + +// F16 LSX + +#define GGML_F16_STEP 32 +#define GGML_F16_EPR 4 + +static inline __m128 __lsx_f16x4_load(ggml_fp16_t *x) { + float tmp[4]; + + tmp[0] = GGML_FP16_TO_FP32(x[0]); + tmp[1] = GGML_FP16_TO_FP32(x[1]); + tmp[2] = GGML_FP16_TO_FP32(x[2]); + tmp[3] = GGML_FP16_TO_FP32(x[3]); + + return __lsx_vld(tmp, 0); +} + +static inline void __lsx_f16x4_store(ggml_fp16_t *x, __m128 y) { + float arr[4]; + + __lsx_vst(y, arr, 0); + + x[0] = GGML_FP32_TO_FP16(arr[0]); + x[1] = GGML_FP32_TO_FP16(arr[1]); + x[2] = GGML_FP32_TO_FP16(arr[2]); + x[3] = GGML_FP32_TO_FP16(arr[3]); +} + +#define GGML_F32Cx4 __m128 +#define GGML_F32Cx4_ZERO __lsx_vldi(0) +#define GGML_F32Cx4_SET1(x) __lsx_vinsgr2vr_w(__lsx_vldi(0),(x), 0) +#define GGML_F32Cx4_LOAD(x) __lsx_f16x4_load(x) +#define GGML_F32Cx4_STORE(x, y) __lsx_f16x4_store(x, y) +#define GGML_F32Cx4_FMA GGML_F32x4_FMA +#define GGML_F32Cx4_ADD __lsx_vfadd_s +#define GGML_F32Cx4_MUL __lsx_vfmul_s +#define GGML_F32Cx4_REDUCE GGML_F32x4_REDUCE + +#define GGML_F16_VEC GGML_F32Cx4 +#define GGML_F16_VEC_ZERO GGML_F32Cx4_ZERO +#define GGML_F16_VEC_SET1 GGML_F32Cx4_SET1 +#define GGML_F16_VEC_LOAD(p, i) GGML_F32Cx4_LOAD(p) +#define GGML_F16_VEC_STORE(p, r, i) GGML_F32Cx4_STORE(p, r[i]) +#define GGML_F16_VEC_FMA GGML_F32Cx4_FMA +#define GGML_F16_VEC_ADD GGML_F32Cx4_ADD +#define GGML_F16_VEC_MUL GGML_F32Cx4_MUL +#define GGML_F16_VEC_REDUCE GGML_F32Cx4_REDUCE + #endif // GGML_F32_ARR / GGML_F16_ARR From 213e90ed73f8ac3cd3026dc3f086beae0d414f96 Mon Sep 17 00:00:00 2001 From: Herman Semenov Date: Mon, 20 May 2024 07:33:21 +0000 Subject: [PATCH 060/181] ggml-opencl, llama: using reserve() if count already known (#7272) --- ggml-opencl.cpp | 7 +++++-- llama.cpp | 3 +++ 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp index 880a14958..922f24837 100644 --- a/ggml-opencl.cpp +++ b/ggml-opencl.cpp @@ -1,4 +1,4 @@ -#include "ggml.h" +#include "ggml.h" #include "ggml-opencl.h" #include "ggml-backend-impl.h" @@ -1835,7 +1835,10 @@ static void ggml_cl_mul_mat_q_f32(const ggml_tensor * src0, const ggml_tensor * CL_CHECK(clEnqueueNDRangeKernel(queue, *to_fp32_cl, 1, &offset, &global, local > 0 ? &local : NULL, events.size(), !events.empty() ? events.data() : NULL, NULL)); } - for (int64_t i12 = i02 * r2, e12 = i12 + r2; i12 < e12; i12++) { + int64_t i12 = i02 * r2; + int64_t e12 = i12 + r2; + events.reserve(e12 - i12); + for (; i12 < e12; i12++) { if (mul_mat_vec) { // specialized dequantize_mul_mat_vec kernel // copy src1 to device events.emplace_back(); diff --git a/llama.cpp b/llama.cpp index ca3e9fcc0..2025e4558 100644 --- a/llama.cpp +++ b/llama.cpp @@ -16162,6 +16162,7 @@ static bool llama_control_vector_init(struct llama_control_vector & cvec, const } // make tensors + cvec.tensors.reserve(model.hparams.n_layer); cvec.tensors.push_back(nullptr); // there's never a tensor for layer 0 for (size_t il = 1; il < model.hparams.n_layer; il++) { struct ggml_context * ctx = ctx_map.at(model.buft_layer[il].buft); @@ -16170,6 +16171,8 @@ static bool llama_control_vector_init(struct llama_control_vector & cvec, const } // allocate tensors / buffers and zero + cvec.ctxs.reserve(ctx_map.size()); + cvec.bufs.reserve(ctx_map.size()); for (auto it : ctx_map) { ggml_backend_buffer_type_t buft = it.first; ggml_context * ctx = it.second; From 26cd4237bc499f7144d76f440aa775d749b170bb Mon Sep 17 00:00:00 2001 From: Bingan <70050083+binganao@users.noreply.github.com> Date: Mon, 20 May 2024 17:55:34 +0800 Subject: [PATCH 061/181] Update README.md (#7410) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 4abfd6d7e..47d41ebfc 100644 --- a/README.md +++ b/README.md @@ -301,7 +301,7 @@ cd llama.cpp ### Build -In order to build llama.cpp you have three different options. +In order to build llama.cpp you have four different options. - Using `make`: - On Linux or MacOS: From 6bf9b66fa3f263ca2175dcb5f6d0a658581e1dfb Mon Sep 17 00:00:00 2001 From: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> Date: Mon, 20 May 2024 12:08:23 +0100 Subject: [PATCH 062/181] [SYCL] Update SYCL upscale operation (#7321) * Update SYCL upscale operation * Formatting * Remove messages --- ggml-sycl.cpp | 65 +++++++++++++++++++++++++++------------------------ 1 file changed, 35 insertions(+), 30 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 19d22d637..eac8f5579 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -3847,21 +3847,27 @@ static void concat_f32(const float *x,const float *y, float *dst, const int ne } } -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) { +static void upscale_f32(const float *x, float *dst, const int nb00, const int nb01, + const int nb02, const int nb03, const int ne10, const int ne11, + const int ne12, const int ne13, const float sf0, const float sf1, + const float sf2, const float sf3, const sycl::nd_item<1> &item_ct1) { + int index = item_ct1.get_local_id(0) + + item_ct1.get_group(0) * item_ct1.get_local_range(0); + if (index >= ne10 * ne11 * ne12 * ne13) { 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]; + int i10 = index % ne10; + int i11 = (index / ne10) % ne11; + int i12 = (index / (ne10 * ne11)) % ne12; + int i13 = (index / (ne10 * ne11 * ne12)) % ne13; + + int i00 = i10 / sf0; + int i01 = i11 / sf1; + int i02 = i12 / sf2; + int i03 = i13 / sf3; + + dst[index] = *(float *)((char *)x + i03 * nb03 + i02 * nb02 + i01 * nb01 + i00 * nb00); } static void pad_f32(const float *x, float *dst, const int ne0, const int ne00, const int ne01, const int ne02, @@ -10085,18 +10091,17 @@ static void concat_f32_sycl(const float *x, const float *y, float *dst, }); } -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); +static void upscale_f32_sycl(const float *x, float *dst, const int nb00, const int nb01, + const int nb02, const int nb03, const int ne10, const int ne11, + const int ne12, const int ne13, const float sf0, const float sf1, + const float sf2, const float sf3, dpct::queue_ptr stream) { + int dst_size = ne10 * ne11 * ne12 * ne13; + int num_blocks = (dst_size + SYCL_UPSCALE_BLOCK_SIZE - 1) / SYCL_UPSCALE_BLOCK_SIZE; + sycl::range<1> gridDim(num_blocks * SYCL_UPSCALE_BLOCK_SIZE); 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); + sycl::nd_range<1>(gridDim, sycl::range<1>(SYCL_UPSCALE_BLOCK_SIZE)), + [=](sycl::nd_item<1> item_ct1) { + upscale_f32(x, dst, nb00, nb01, nb02, nb03, ne10, ne11, ne12, ne13, sf0, sf1, sf2, sf3, item_ct1); }); } @@ -13985,15 +13990,15 @@ inline void ggml_sycl_op_upscale(const ggml_tensor *src0, 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 -#pragma message("TODO: generalize upscale operator") -#pragma message(" https://github.com/ggerganov/ggml/pull/814") - GGML_ASSERT(false && "TODO: generalize upscale operator"); + const float sf0 = (float)dst->ne[0]/src0->ne[0]; + const float sf1 = (float)dst->ne[1]/src0->ne[1]; + const float sf2 = (float)dst->ne[2]/src0->ne[2]; + const float sf3 = (float)dst->ne[3]/src0->ne[3]; - 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); + upscale_f32_sycl(src0_dd, dst_dd, src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3], + dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], sf0, sf1, sf2, sf3, + main_stream); (void) src1; (void) dst; From 3bc10cb485dd7efa4da6c64e73ad0c9e2bfe0821 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 20 May 2024 15:10:03 +0300 Subject: [PATCH 063/181] server : fix temperature + disable some tests (#7409) * server : fix temperature * server : disable tests relying on parallel determinism * ci : change server Debug -> RelWithDebInfo --- .github/workflows/server.yml | 7 +------ examples/server/tests/features/results.feature | 17 ++++++++--------- 2 files changed, 9 insertions(+), 15 deletions(-) diff --git a/.github/workflows/server.yml b/.github/workflows/server.yml index 217af67cf..0789efd18 100644 --- a/.github/workflows/server.yml +++ b/.github/workflows/server.yml @@ -33,13 +33,10 @@ jobs: strategy: matrix: sanitizer: [ADDRESS, THREAD, UNDEFINED] - build_type: [Debug] + build_type: [RelWithDebInfo] include: - build_type: Release sanitizer: "" - - build_type: Debug - sanitizer: THREAD - disabled_on_pr: true fail-fast: false # While -DLLAMA_SANITIZE_THREAD=ON is broken steps: @@ -103,10 +100,8 @@ jobs: -DLLAMA_SANITIZE_${{ matrix.sanitizer }}=ON ; cmake --build build --config ${{ matrix.build_type }} -j $(nproc) --target server - - name: Tests id: server_integration_tests - if: ${{ !matrix.disabled_on_pr || !github.event.pull_request }} run: | cd examples/server/tests PORT=8888 ./tests.sh diff --git a/examples/server/tests/features/results.feature b/examples/server/tests/features/results.feature index 4ab8ad20c..e8e1b5414 100644 --- a/examples/server/tests/features/results.feature +++ b/examples/server/tests/features/results.feature @@ -13,7 +13,7 @@ Feature: Results Scenario Outline: consistent results with same seed Given slots - And 0.0 temperature + And 1.0 temperature Then the server is starting Then the server is healthy @@ -27,7 +27,8 @@ Feature: Results Examples: | n_slots | | 1 | - | 2 | + # FIXME: unified KV cache nondeterminism + # | 2 | Scenario Outline: different results with different seed Given slots @@ -73,14 +74,13 @@ Feature: Results Examples: | n_parallel | temp | | 1 | 0.0 | - | 2 | 0.0 | - | 4 | 0.0 | | 1 | 1.0 | - # FIXME: These tests fail on master. - # Problems: unified KV cache (except for CPU backend with LLAMA_NO_LLAMAFILE=1), SIMD nondeterminism. + # FIXME: unified KV cache nondeterminism # See https://github.com/ggerganov/whisper.cpp/issues/1941#issuecomment-1986923227 # and https://github.com/ggerganov/llama.cpp/pull/6122#discussion_r1531405574 # and https://github.com/ggerganov/llama.cpp/pull/7347 . + # | 2 | 0.0 | + # | 4 | 0.0 | # | 2 | 1.0 | # | 4 | 1.0 | @@ -108,12 +108,11 @@ Feature: Results Examples: | n_slots | n_kv | n_predict | n_parallel | | 4 | 1024 | 1 | 1 | - | 4 | 1024 | 1 | 4 | - # FIXME: These tests fail on master. - # Problems: unified KV cache (except for CPU backend with LLAMA_NO_LLAMAFILE=1), SIMD nondeterminism. + # FIXME: unified KV cache nondeterminism # See https://github.com/ggerganov/whisper.cpp/issues/1941#issuecomment-1986923227 # and https://github.com/ggerganov/llama.cpp/pull/6122#discussion_r1531405574 # and https://github.com/ggerganov/llama.cpp/pull/7347 . + # | 4 | 1024 | 1 | 4 | # | 4 | 1024 | 100 | 1 | # This test still fails even the above patches; the first token probabilities are already different. # | 4 | 1024 | 100 | 4 | From db10f01310beea8a1ef7798651b9d692fd1149d0 Mon Sep 17 00:00:00 2001 From: Radoslav Gerganov Date: Mon, 20 May 2024 16:36:55 +0300 Subject: [PATCH 064/181] rpc : track allocated buffers (#7411) * rpc : track allocated buffers ref: #7407 * rpc : pack rpc_tensor tightly --- ggml-rpc.cpp | 229 +++++++++++++++++++++++++++++++++++++++------------ 1 file changed, 176 insertions(+), 53 deletions(-) diff --git a/ggml-rpc.cpp b/ggml-rpc.cpp index 4a9bfa52d..cc1d3ace1 100644 --- a/ggml-rpc.cpp +++ b/ggml-rpc.cpp @@ -56,6 +56,7 @@ struct socket_t { }; // ggml_tensor is serialized into rpc_tensor +#pragma pack(push, 1) struct rpc_tensor { uint64_t id; uint32_t type; @@ -71,6 +72,7 @@ struct rpc_tensor { uint64_t data; char name[GGML_MAX_NAME]; }; +#pragma pack(pop) // RPC commands enum rpc_cmd { @@ -340,23 +342,6 @@ static rpc_tensor serialize_tensor(const ggml_tensor * tensor) { return result; } -static ggml_tensor * deserialize_tensor(struct ggml_context * ctx, const rpc_tensor * tensor) { - ggml_tensor * result = ggml_new_tensor_4d(ctx, (ggml_type) tensor->type, - tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); - for (uint32_t i = 0; i < GGML_MAX_DIMS; i++) { - result->nb[i] = tensor->nb[i]; - } - result->buffer = reinterpret_cast(tensor->buffer); - result->op = (ggml_op) tensor->op; - for (uint32_t i = 0; i < GGML_MAX_OP_PARAMS / sizeof(int32_t); i++) { - result->op_params[i] = tensor->op_params[i]; - } - result->flags = tensor->flags; - result->data = reinterpret_cast(tensor->data); - ggml_set_name(result, tensor->name); - return result; -} - GGML_CALL static void ggml_backend_rpc_buffer_init_tensor(ggml_backend_buffer_t buffer, ggml_tensor * tensor) { UNUSED(buffer); if (ggml_is_quantized(tensor->type)) { @@ -465,13 +450,15 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_rpc_buffer_type_alloc_buffer memcpy(&remote_ptr, output.data(), sizeof(remote_ptr)); size_t remote_size; memcpy(&remote_size, output.data() + sizeof(uint64_t), sizeof(remote_size)); - - ggml_backend_buffer_t buffer = ggml_backend_buffer_init(buft, - ggml_backend_rpc_buffer_interface, - new ggml_backend_rpc_buffer_context{buft_ctx->sock, {}, remote_ptr, "RPC"}, - remote_size); - - return buffer; + if (remote_ptr != 0) { + ggml_backend_buffer_t buffer = ggml_backend_buffer_init(buft, + ggml_backend_rpc_buffer_interface, + new ggml_backend_rpc_buffer_context{buft_ctx->sock, {}, remote_ptr, "RPC"}, + remote_size); + return buffer; + } else { + return nullptr; + } } static size_t get_alignment(const std::shared_ptr & sock) { @@ -658,7 +645,7 @@ GGML_CALL ggml_backend_t ggml_backend_rpc_init(const char * endpoint) { } } #endif - GGML_PRINT_DEBUG("Connecting to %s\n", endpoint); + fprintf(stderr, "Connecting to %s\n", endpoint); std::string host; int port; if (!parse_endpoint(endpoint, host, port)) { @@ -731,22 +718,61 @@ GGML_API GGML_CALL void ggml_backend_rpc_get_device_memory(const char * endpoint // RPC server-side implementation -static void rpc_alloc_buffer(ggml_backend_t backend, const std::vector & input, std::vector & output) { +class rpc_server { +public: + rpc_server(ggml_backend_t backend) : backend(backend) {} + ~rpc_server(); + + bool alloc_buffer(const std::vector & input, std::vector & output); + void get_alignment(std::vector & output); + void get_max_size(std::vector & output); + bool buffer_get_base(const std::vector & input, std::vector & output); + bool free_buffer(const std::vector & input); + bool buffer_clear(const std::vector & input); + bool set_tensor(const std::vector & input); + bool get_tensor(const std::vector & input, std::vector & output); + bool copy_tensor(const std::vector & input, std::vector & output); + bool graph_compute(const std::vector & input, std::vector & output); + +private: + ggml_tensor * deserialize_tensor(struct ggml_context * ctx, const rpc_tensor * tensor); + ggml_tensor * create_node(uint64_t id, + struct ggml_context * ctx, + const std::unordered_map & tensor_ptrs, + std::unordered_map & tensor_map); + + + ggml_backend_t backend; + std::unordered_set buffers; +}; + +bool rpc_server::alloc_buffer(const std::vector & input, std::vector & output) { // input serialization format: | size (8 bytes) | + if (input.size() != sizeof(uint64_t)) { + return false; + } uint64_t size; memcpy(&size, input.data(), sizeof(size)); ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend); ggml_backend_buffer_t buffer = ggml_backend_buft_alloc_buffer(buft, size); - uint64_t remote_ptr = reinterpret_cast(buffer); - uint64_t remote_size = buffer->size; - GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> remote_ptr: %" PRIx64 ", remote_size: %" PRIu64 "\n", __func__, size, remote_ptr, remote_size); + uint64_t remote_ptr = 0; + uint64_t remote_size = 0; + if (buffer != nullptr) { + remote_ptr = reinterpret_cast(buffer); + remote_size = buffer->size; + GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> remote_ptr: %" PRIx64 ", remote_size: %" PRIu64 "\n", __func__, size, remote_ptr, remote_size); + buffers.insert(buffer); + } else { + GGML_PRINT_DEBUG("[%s] size: %" PRIu64 " -> failed\n", __func__, size); + } // output serialization format: | remote_ptr (8 bytes) | remote_size (8 bytes) | output.resize(2*sizeof(uint64_t), 0); memcpy(output.data(), &remote_ptr, sizeof(remote_ptr)); memcpy(output.data() + sizeof(uint64_t), &remote_size, sizeof(remote_size)); + return true; } -static void rpc_get_alignment(ggml_backend_t backend, std::vector & output) { +void rpc_server::get_alignment(std::vector & output) { ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend); size_t alignment = ggml_backend_buft_get_alignment(buft); GGML_PRINT_DEBUG("[%s] alignment: %lu\n", __func__, alignment); @@ -755,7 +781,7 @@ static void rpc_get_alignment(ggml_backend_t backend, std::vector & out memcpy(output.data(), &alignment, sizeof(alignment)); } -static void rpc_get_max_size(ggml_backend_t backend, std::vector & output) { +void rpc_server::get_max_size(std::vector & output) { ggml_backend_buffer_type_t buft = ggml_backend_get_default_buffer_type(backend); size_t max_size = ggml_backend_buft_get_max_size(buft); GGML_PRINT_DEBUG("[%s] max_size: %lu\n", __func__, max_size); @@ -764,41 +790,90 @@ static void rpc_get_max_size(ggml_backend_t backend, std::vector & outp memcpy(output.data(), &max_size, sizeof(max_size)); } -static void rpc_buffer_get_base(const std::vector & input, std::vector & output) { +bool rpc_server::buffer_get_base(const std::vector & input, std::vector & output) { // input serialization format: | remote_ptr (8 bytes) | + if (input.size() != sizeof(uint64_t)) { + return false; + } uint64_t remote_ptr; memcpy(&remote_ptr, input.data(), sizeof(remote_ptr)); GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, remote_ptr); ggml_backend_buffer_t buffer = reinterpret_cast(remote_ptr); + if (buffers.find(buffer) == buffers.end()) { + GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__); + return false; + } void * base = ggml_backend_buffer_get_base(buffer); // output serialization format: | base_ptr (8 bytes) | uint64_t base_ptr = reinterpret_cast(base); output.resize(sizeof(uint64_t), 0); memcpy(output.data(), &base_ptr, sizeof(base_ptr)); + return true; } -static void rpc_free_buffer(const std::vector & input) { +bool rpc_server::free_buffer(const std::vector & input) { // input serialization format: | remote_ptr (8 bytes) | + if (input.size() != sizeof(uint64_t)) { + return false; + } uint64_t remote_ptr; memcpy(&remote_ptr, input.data(), sizeof(remote_ptr)); GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 "\n", __func__, remote_ptr); ggml_backend_buffer_t buffer = reinterpret_cast(remote_ptr); + if (buffers.find(buffer) == buffers.end()) { + GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__); + return false; + } ggml_backend_buffer_free(buffer); + buffers.erase(buffer); + return true; } -static void rpc_buffer_clear(const std::vector & input) { +bool rpc_server::buffer_clear(const std::vector & input) { // input serialization format: | remote_ptr (8 bytes) | value (1 byte) | + if (input.size() != sizeof(uint64_t) + sizeof(uint8_t)) { + return false; + } uint64_t remote_ptr; memcpy(&remote_ptr, input.data(), sizeof(remote_ptr)); uint8_t value; memcpy(&value, input.data() + sizeof(uint64_t), sizeof(value)); GGML_PRINT_DEBUG("[%s] remote_ptr: %" PRIx64 ", value: %u\n", __func__, remote_ptr, value); ggml_backend_buffer_t buffer = reinterpret_cast(remote_ptr); + if (buffers.find(buffer) == buffers.end()) { + GGML_PRINT_DEBUG("[%s] buffer not found\n", __func__); + return false; + } ggml_backend_buffer_clear(buffer, value); + return true; } -static void rpc_set_tensor(const std::vector & input) { +ggml_tensor * rpc_server::deserialize_tensor(struct ggml_context * ctx, const rpc_tensor * tensor) { + ggml_tensor * result = ggml_new_tensor_4d(ctx, (ggml_type) tensor->type, + tensor->ne[0], tensor->ne[1], tensor->ne[2], tensor->ne[3]); + for (uint32_t i = 0; i < GGML_MAX_DIMS; i++) { + result->nb[i] = tensor->nb[i]; + } + result->buffer = reinterpret_cast(tensor->buffer); + if (result->buffer && buffers.find(result->buffer) == buffers.end()) { + return nullptr; + } + result->op = (ggml_op) tensor->op; + for (uint32_t i = 0; i < GGML_MAX_OP_PARAMS / sizeof(int32_t); i++) { + result->op_params[i] = tensor->op_params[i]; + } + result->flags = tensor->flags; + result->data = reinterpret_cast(tensor->data); + ggml_set_name(result, tensor->name); + return result; +} + + +bool rpc_server::set_tensor(const std::vector & input) { // serialization format: | rpc_tensor | offset (8 bytes) | data (size bytes) | + if (input.size() < sizeof(rpc_tensor) + sizeof(uint64_t)) { + return false; + } const rpc_tensor * in_tensor = (const rpc_tensor *)input.data(); uint64_t offset; memcpy(&offset, input.data() + sizeof(rpc_tensor), sizeof(offset)); @@ -811,14 +886,23 @@ static void rpc_set_tensor(const std::vector & input) { }; struct ggml_context * ctx = ggml_init(params); ggml_tensor * tensor = deserialize_tensor(ctx, in_tensor); + if (tensor == nullptr) { + GGML_PRINT_DEBUG("[%s] error deserializing tensor\n", __func__); + ggml_free(ctx); + return false; + } GGML_PRINT_DEBUG("[%s] buffer: %p, data: %p, offset: %" PRIu64 ", size: %zu\n", __func__, (void*)tensor->buffer, tensor->data, offset, size); const void * data = input.data() + sizeof(rpc_tensor) + sizeof(offset); ggml_backend_tensor_set(tensor, data, offset, size); ggml_free(ctx); + return true; } -static void rpc_get_tensor(const std::vector & input, std::vector & output) { +bool rpc_server::get_tensor(const std::vector & input, std::vector & output) { // serialization format: | rpc_tensor | offset (8 bytes) | size (8 bytes) | + if (input.size() != sizeof(rpc_tensor) + 2*sizeof(uint64_t)) { + return false; + } const rpc_tensor * in_tensor = (const rpc_tensor *)input.data(); uint64_t offset; memcpy(&offset, input.data() + sizeof(rpc_tensor), sizeof(offset)); @@ -832,15 +916,24 @@ static void rpc_get_tensor(const std::vector & input, std::vectorbuffer, tensor->data, offset, size); // output serialization format: | data (size bytes) | output.resize(size, 0); ggml_backend_tensor_get(tensor, output.data(), offset, size); ggml_free(ctx); + return true; } -static void rpc_copy_tensor(const std::vector & input, std::vector & output) { +bool rpc_server::copy_tensor(const std::vector & input, std::vector & output) { // serialization format: | rpc_tensor src | rpc_tensor dst | + if (input.size() != 2*sizeof(rpc_tensor)) { + return false; + } const rpc_tensor * rpc_src = (const rpc_tensor *)input.data(); const rpc_tensor * rpc_dst = (const rpc_tensor *)(input.data() + sizeof(rpc_src)); @@ -852,18 +945,24 @@ static void rpc_copy_tensor(const std::vector & input, std::vectorbuffer: %p, dst->buffer: %p\n", __func__, (void*)src->buffer, (void*)dst->buffer); bool result = ggml_backend_buffer_copy_tensor(src, dst); // output serialization format: | result (1 byte) | output.resize(1, 0); output[0] = result; ggml_free(ctx); + return true; } -static struct ggml_tensor * create_node(uint64_t id, - struct ggml_context * ctx, - const std::unordered_map & tensor_ptrs, - std::unordered_map & tensor_map) { +ggml_tensor * rpc_server::create_node(uint64_t id, + struct ggml_context * ctx, + const std::unordered_map & tensor_ptrs, + std::unordered_map & tensor_map) { if (id == 0) { return nullptr; } @@ -872,6 +971,9 @@ static struct ggml_tensor * create_node(uint64_t id, } const rpc_tensor * tensor = tensor_ptrs.at(id); struct ggml_tensor * result = deserialize_tensor(ctx, tensor); + if (result == nullptr) { + return nullptr; + } tensor_map[id] = result; for (int i = 0; i < GGML_MAX_SRC; i++) { result->src[i] = create_node(tensor->src[i], ctx, tensor_ptrs, tensor_map); @@ -881,14 +983,23 @@ static struct ggml_tensor * create_node(uint64_t id, return result; } -static void rpc_graph_compute(ggml_backend_t backend, const std::vector & input, std::vector & output) { +bool rpc_server::graph_compute(const std::vector & input, std::vector & output) { // serialization format: // | n_nodes (4 bytes) | nodes (n_nodes * sizeof(uint64_t) | n_tensors (4 bytes) | tensors (n_tensors * sizeof(rpc_tensor)) | + if (input.size() < sizeof(uint32_t)) { + return false; + } uint32_t n_nodes; memcpy(&n_nodes, input.data(), sizeof(n_nodes)); + if (input.size() < sizeof(uint32_t) + n_nodes*sizeof(uint64_t) + sizeof(uint32_t)) { + return false; + } const uint64_t * nodes = (const uint64_t *)(input.data() + sizeof(n_nodes)); uint32_t n_tensors; memcpy(&n_tensors, input.data() + sizeof(n_nodes) + n_nodes*sizeof(uint64_t), sizeof(n_tensors)); + if (input.size() < sizeof(uint32_t) + n_nodes*sizeof(uint64_t) + sizeof(uint32_t) + n_tensors*sizeof(rpc_tensor)) { + return false; + } const rpc_tensor * tensors = (const rpc_tensor *)(input.data() + sizeof(n_nodes) + n_nodes*sizeof(uint64_t) + sizeof(n_tensors)); GGML_PRINT_DEBUG("[%s] n_nodes: %u, n_tensors: %u\n", __func__, n_nodes, n_tensors); @@ -914,9 +1025,17 @@ static void rpc_graph_compute(ggml_backend_t backend, const std::vector output.resize(1, 0); output[0] = status; ggml_free(ctx); + return true; +} + +rpc_server::~rpc_server() { + for (auto buffer : buffers) { + ggml_backend_buffer_free(buffer); + } } static void rpc_serve_client(ggml_backend_t backend, sockfd_t sockfd, size_t free_mem, size_t total_mem) { + rpc_server server(backend); while (true) { uint8_t cmd; if (!recv_data(sockfd, &cmd, 1)) { @@ -932,45 +1051,46 @@ static void rpc_serve_client(ggml_backend_t backend, sockfd_t sockfd, size_t fre if (!recv_data(sockfd, input.data(), input_size)) { break; } + bool ok = true; switch (cmd) { case ALLOC_BUFFER: { - rpc_alloc_buffer(backend, input, output); + ok = server.alloc_buffer(input, output); break; } case GET_ALIGNMENT: { - rpc_get_alignment(backend, output); + server.get_alignment(output); break; } case GET_MAX_SIZE: { - rpc_get_max_size(backend, output); + server.get_max_size(output); break; } case BUFFER_GET_BASE: { - rpc_buffer_get_base(input, output); + ok = server.buffer_get_base(input, output); break; } case FREE_BUFFER: { - rpc_free_buffer(input); + ok = server.free_buffer(input); break; } case BUFFER_CLEAR: { - rpc_buffer_clear(input); + ok = server.buffer_clear(input); break; } case SET_TENSOR: { - rpc_set_tensor(input); + ok = server.set_tensor(input); break; } case GET_TENSOR: { - rpc_get_tensor(input, output); + ok = server.get_tensor(input, output); break; } case COPY_TENSOR: { - rpc_copy_tensor(input, output); + ok = server.copy_tensor(input, output); break; } case GRAPH_COMPUTE: { - rpc_graph_compute(backend, input, output); + ok = server.graph_compute(input, output); break; } case GET_DEVICE_MEMORY: { @@ -982,9 +1102,12 @@ static void rpc_serve_client(ggml_backend_t backend, sockfd_t sockfd, size_t fre } default: { fprintf(stderr, "Unknown command: %d\n", cmd); - return; + ok = false; } } + if (!ok) { + break; + } uint64_t output_size = output.size(); if (!send_data(sockfd, &output_size, sizeof(output_size))) { break; From 20385cebcc4bb3f6dd10f989573c11864d70d53d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Mon, 20 May 2024 18:15:38 +0200 Subject: [PATCH 065/181] perplexity: update README FP16 results [no ci] (#7413) --- examples/perplexity/README.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/examples/perplexity/README.md b/examples/perplexity/README.md index c2a3c5ce9..33a46d1a2 100644 --- a/examples/perplexity/README.md +++ b/examples/perplexity/README.md @@ -42,10 +42,13 @@ In addition to the KL divergence the following statistics are calculated with `- Results were generated using the CUDA backend and are sorted by Kullback-Leibler divergence relative to FP16. The "WT" importance matrices were created using varying numbers of Wikitext tokens and can be found [here](https://huggingface.co/JohannesGaessler/llama.cpp_importance_matrices/blob/main/imatrix-llama_3-8b-f16-2.7m_tokens.dat). +Note: the FP16 logits used for the calculation of all metrics other than perplexity are stored in a binary file between runs. +In order to save space this file does **not** contain the exact same FP32 logits but instead casts them to 16 bit unsigned integers (with some scaling). +So the "f16" results are to be understood as the difference resulting only from this downcast. | Quantization | imatrix | Model size [GiB] | PPL | ΔPPL | KLD | Mean Δp | RMS Δp | |--------------|---------|------------------|------------------------|------------------------|-----------------------|-------------------|------------------| -| f16 | None | 14.97 | 6.233160 ± 0.037828 | - | - | - | - | +| f16 | None | 14.97 | 6.233160 ± 0.037828 | 0.001524 ± 0.000755 | 0.000551 ± 0.000002 | 0.001 ± 0.002 % | 0.787 ± 0.004 % | | q8_0 | None | 7.96 | 6.234284 ± 0.037878 | 0.002650 ± 0.001006 | 0.001355 ± 0.000006 | -0.019 ± 0.003 % | 1.198 ± 0.007 % | | q6_K | None | 6.14 | 6.253382 ± 0.038078 | 0.021748 ± 0.001852 | 0.005452 ± 0.000035 | -0.007 ± 0.006 % | 2.295 ± 0.019 % | | q5_K_M | None | 5.33 | 6.288607 ± 0.038338 | 0.056974 ± 0.002598 | 0.010762 ± 0.000079 | -0.114 ± 0.008 % | 3.160 ± 0.031 % | From fabf30b4c4fca32e116009527180c252919ca922 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 20 May 2024 19:35:28 +0300 Subject: [PATCH 066/181] llama : remove Persimmon (#7408) * llama : remove Persimmon * requirements : remove --- README.md | 1 - convert-hf-to-gguf.py | 39 --- convert-persimmon-to-gguf.py | 143 --------- gguf-py/gguf/constants.py | 19 -- llama.cpp | 280 ------------------ requirements.txt | 1 - ...requirements-convert-persimmon-to-gguf.txt | 2 - 7 files changed, 485 deletions(-) delete mode 100755 convert-persimmon-to-gguf.py delete mode 100644 requirements/requirements-convert-persimmon-to-gguf.txt diff --git a/README.md b/README.md index 47d41ebfc..f4088c05e 100644 --- a/README.md +++ b/README.md @@ -107,7 +107,6 @@ Typically finetunes of the base models below are supported as well. - [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila) - [X] [Starcoder models](https://github.com/ggerganov/llama.cpp/pull/3187) - [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) - [X] [Bloom](https://github.com/ggerganov/llama.cpp/pull/3553) - [x] [Yi models](https://huggingface.co/models?search=01-ai/Yi) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index bd303150a..d534b5163 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1148,45 +1148,6 @@ class RefactModel(Model): return tensors -@Model.register("PersimmonForCausalLM") -class PersimmonModel(Model): - model_arch = gguf.MODEL_ARCH.PERSIMMON - - def set_gguf_parameters(self): - block_count = self.hparams.get("num_layers", self.hparams.get("num_hidden_layers")) - head_count = self.hparams["num_attention_heads"] - head_count_kv = head_count - hidden_size = self.hparams["hidden_size"] - - self.gguf_writer.add_name('persimmon-8b-chat') - self.gguf_writer.add_context_length(self.hparams["max_position_embeddings"]) - self.gguf_writer.add_embedding_length(hidden_size) - self.gguf_writer.add_block_count(block_count) - self.gguf_writer.add_feed_forward_length(self.hparams["intermediate_size"]) - - # NOTE: not sure about this change - why does the model not have a rope dimension count when it is smaller - # than the head size? - # ref: https://github.com/ggerganov/llama.cpp/pull/4889 - # self.gguf_writer.add_rope_dimension_count(hidden_size // head_count) - self.gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2) - - self.gguf_writer.add_head_count(head_count) - self.gguf_writer.add_head_count_kv(head_count_kv) - self.gguf_writer.add_rope_freq_base(self.hparams["rope_theta"]) - self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"]) - - def set_vocab(self): - self._set_vocab_sentencepiece() - # self.gguf_writer.add_bos_token_id(71013) - # self.gguf_writer.add_eos_token_id(71013) - - def extra_f32_tensors(self, name: str, new_name: str, bid: int | None, n_dims: int) -> bool: - del name, new_name, bid, n_dims # unused - - # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?) - return True - - @Model.register("StableLmForCausalLM", "StableLMEpochForCausalLM", "LlavaStableLMEpochForCausalLM") class StableLMModel(Model): model_arch = gguf.MODEL_ARCH.STABLELM diff --git a/convert-persimmon-to-gguf.py b/convert-persimmon-to-gguf.py deleted file mode 100755 index 07dcade74..000000000 --- a/convert-persimmon-to-gguf.py +++ /dev/null @@ -1,143 +0,0 @@ -#!/usr/bin/env python3 -from __future__ import annotations - -import logging -import argparse -import os -import sys -from pathlib import Path -from pprint import pprint - -import torch -from sentencepiece import SentencePieceProcessor - -if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) -import gguf - -logger = logging.getLogger("persimmon-to-gguf") - - -def _flatten_dict(dct, tensors, prefix=None): - assert isinstance(dct, dict) - for key in dct.keys(): - new_prefix = prefix + '.' + key if prefix is not None else key - if isinstance(dct[key], torch.Tensor): - tensors[new_prefix] = dct[key] - elif isinstance(dct[key], dict): - _flatten_dict(dct[key], tensors, new_prefix) - else: - raise ValueError(type(dct[key])) - return None - - -def _get_sentencepiece_tokenizer_info(dir_model: Path): - tokenizer_path = dir_model / 'adept_vocab.model' - logger.info('getting sentencepiece tokenizer from', tokenizer_path) - tokenizer = SentencePieceProcessor(str(tokenizer_path)) - logger.info('adding tokens') - tokens: list[bytes] = [] - scores: list[float] = [] - toktypes: list[int] = [] - - for i in range(tokenizer.vocab_size()): - text: bytes - score: float - - piece = tokenizer.id_to_piece(i) - text = piece.encode("utf-8") - score = tokenizer.get_score(i) - - toktype = 1 - if tokenizer.is_unknown(i): - toktype = 2 - if tokenizer.is_control(i): - toktype = 3 - if tokenizer.is_unused(i): - toktype = 5 - if tokenizer.is_byte(i): - toktype = 6 - - tokens.append(text) - scores.append(score) - toktypes.append(toktype) - pass - return tokens, scores, toktypes - - -def main(): - parser = argparse.ArgumentParser(description="Convert a Persimmon model from Adept (e.g. Persimmon 8b chat) to a GGML compatible file") - parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") - parser.add_argument("--ckpt-path", type=Path, help="path to persimmon checkpoint .pt file") - parser.add_argument("--model-dir", type=Path, help="directory containing model e.g. 8b_chat_model_release") - parser.add_argument("--adept-inference-dir", type=str, help="path to adept-inference code directory") - parser.add_argument("--verbose", action="store_true", help="increase output verbosity") - args = parser.parse_args() - logging.basicConfig(level=logging.DEBUG if args.verbose else logging.INFO) - sys.path.append(str(args.adept_inference_dir)) - persimmon_model = torch.load(args.ckpt_path) - hparams = persimmon_model['args'] - pprint(hparams) - tensors: dict[str, torch.Tensor] = {} - _flatten_dict(persimmon_model['model'], tensors, None) - - arch = gguf.MODEL_ARCH.PERSIMMON - gguf_writer = gguf.GGUFWriter(args.outfile, gguf.MODEL_ARCH_NAMES[arch]) - - block_count = hparams.num_layers - head_count = hparams.num_attention_heads - head_count_kv = head_count - ctx_length = hparams.seq_length - hidden_size = hparams.hidden_size - - gguf_writer.add_name('persimmon-8b-chat') - gguf_writer.add_context_length(ctx_length) - gguf_writer.add_embedding_length(hidden_size) - gguf_writer.add_block_count(block_count) - gguf_writer.add_feed_forward_length(hparams.ffn_hidden_size) - # ref: https://github.com/ggerganov/llama.cpp/pull/4889/commits/eea19039fc52ea2dbd1aab45b59ab4e3e29a3443 - gguf_writer.add_rope_dimension_count(hidden_size // head_count // 2) - gguf_writer.add_head_count(head_count) - gguf_writer.add_head_count_kv(head_count_kv) - gguf_writer.add_rope_freq_base(hparams.rotary_emb_base) - gguf_writer.add_layer_norm_eps(hparams.layernorm_epsilon) - - tokens, scores, toktypes = _get_sentencepiece_tokenizer_info(args.model_dir) - gguf_writer.add_tokenizer_model('llama') - gguf_writer.add_tokenizer_pre('default') - gguf_writer.add_token_list(tokens) - gguf_writer.add_token_scores(scores) - gguf_writer.add_token_types(toktypes) - gguf_writer.add_bos_token_id(71013) - gguf_writer.add_eos_token_id(71013) - - tensor_map = gguf.get_tensor_name_map(arch, block_count) - logger.info(tensor_map) - for name in tensors.keys(): - data_torch = tensors[name] - if name.endswith(".self_attention.rotary_emb.inv_freq"): - continue - old_dtype = data_torch.dtype - # TODO: FP16 conversion produces garbage outputs. (Q8_0 does not, so..?) - data = data_torch.to(torch.float32).squeeze().numpy() - new_name = tensor_map.get_name(name, try_suffixes = (".weight", ".bias")) - if new_name is None: - raise ValueError(f"Can not map tensor '{name}'") - - n_dims = len(data.shape) - logger.debug(f"{new_name}, n_dims = {str(n_dims)}, {str(old_dtype)} --> {str(data.dtype)}") - gguf_writer.add_tensor(new_name, data) - logger.info("gguf: write header") - gguf_writer.write_header_to_file() - logger.info("gguf: write metadata") - gguf_writer.write_kv_data_to_file() - logger.info("gguf: write tensors") - gguf_writer.write_tensors_to_file() - - gguf_writer.close() - - logger.info(f"gguf: model successfully exported to '{args.outfile}'") - - -if __name__ == '__main__': - main() diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 978fcada3..692120f4d 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -115,7 +115,6 @@ class MODEL_ARCH(IntEnum): GPTNEOX = auto() MPT = auto() STARCODER = auto() - PERSIMMON = auto() REFACT = auto() BERT = auto() NOMIC_BERT = auto() @@ -193,7 +192,6 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.GPTNEOX: "gptneox", MODEL_ARCH.MPT: "mpt", MODEL_ARCH.STARCODER: "starcoder", - MODEL_ARCH.PERSIMMON: "persimmon", MODEL_ARCH.REFACT: "refact", MODEL_ARCH.BERT: "bert", MODEL_ARCH.NOMIC_BERT: "nomic-bert", @@ -426,20 +424,6 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], - MODEL_ARCH.PERSIMMON: [ - MODEL_TENSOR.TOKEN_EMBD, - MODEL_TENSOR.OUTPUT, - MODEL_TENSOR.OUTPUT_NORM, - MODEL_TENSOR.ATTN_NORM, - MODEL_TENSOR.ATTN_QKV, - MODEL_TENSOR.ATTN_OUT, - MODEL_TENSOR.FFN_NORM, - MODEL_TENSOR.FFN_DOWN, - MODEL_TENSOR.FFN_UP, - MODEL_TENSOR.ATTN_Q_NORM, - MODEL_TENSOR.ATTN_K_NORM, - MODEL_TENSOR.ATTN_ROT_EMBD, - ], MODEL_ARCH.REFACT: [ MODEL_TENSOR.TOKEN_EMBD, MODEL_TENSOR.OUTPUT_NORM, @@ -756,9 +740,6 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], - MODEL_ARCH.PERSIMMON: [ - MODEL_TENSOR.ROPE_FREQS, - ], MODEL_ARCH.QWEN: [ MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, diff --git a/llama.cpp b/llama.cpp index 2025e4558..863961f15 100644 --- a/llama.cpp +++ b/llama.cpp @@ -202,7 +202,6 @@ enum llm_arch { LLM_ARCH_GPTNEOX, LLM_ARCH_MPT, LLM_ARCH_STARCODER, - LLM_ARCH_PERSIMMON, LLM_ARCH_REFACT, LLM_ARCH_BERT, LLM_ARCH_NOMIC_BERT, @@ -239,7 +238,6 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_MPT, "mpt" }, { LLM_ARCH_BAICHUAN, "baichuan" }, { LLM_ARCH_STARCODER, "starcoder" }, - { LLM_ARCH_PERSIMMON, "persimmon" }, { LLM_ARCH_REFACT, "refact" }, { LLM_ARCH_BERT, "bert" }, { LLM_ARCH_NOMIC_BERT, "nomic-bert" }, @@ -595,23 +593,6 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, - { - LLM_ARCH_PERSIMMON, - { - { LLM_TENSOR_TOKEN_EMBD, "token_embd"}, - { LLM_TENSOR_OUTPUT_NORM, "output_norm"}, - { LLM_TENSOR_OUTPUT, "output"}, - { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm"}, - { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv"}, - { LLM_TENSOR_ATTN_OUT, "blk.%d.attn_output"}, - { LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm"}, - { LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm"}, - { LLM_TENSOR_FFN_NORM, "blk.%d.ffn_norm"}, - { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down"}, - { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up"}, - { LLM_TENSOR_ATTN_ROT_EMBD, "blk.%d.attn_rot_embd"}, - }, - }, { LLM_ARCH_MPT, { @@ -3967,14 +3948,6 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; - case LLM_ARCH_PERSIMMON: - { - ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); - switch (hparams.n_layer) { - case 36: model.type = e_model::MODEL_8B; break; - default: model.type = e_model::MODEL_UNKNOWN; - } - } break; case LLM_ARCH_REFACT: { ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); @@ -5221,47 +5194,6 @@ 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_PERSIMMON: - { - 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.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); - - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}); - - layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); - - layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); - - 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.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {64}); - layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {64}); - - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {64}); - layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {64}); - } - } break; case LLM_ARCH_BERT: case LLM_ARCH_NOMIC_BERT: { @@ -7923,213 +7855,6 @@ struct llm_build_context { return gf; } - struct ggml_cgraph * build_persimmon() { - 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/2 == hparams.n_rot); - - struct ggml_tensor * cur; - struct ggml_tensor * inpL; - - inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); - - // inp_pos - contains the positions - struct ggml_tensor * inp_pos = build_inp_pos(); - - // KQ_mask (mask for 1 head, it will be broadcasted to all heads) - struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); - - for (int il = 0; il < n_layer; ++il) { - struct ggml_tensor * residual = inpL; - - 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 - { - cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); - cb(cur, "wqkv", il); - - cur = ggml_add(ctx0, cur, model.layers[il].bqkv); - cb(cur, "bqkv", il); - - // split qkv - GGML_ASSERT(n_head_kv == n_head); - - struct ggml_tensor * tmpqkv = ggml_reshape_4d(ctx0, cur, n_embd_head, 3, n_head, n_tokens); - cb(tmpqkv, "tmpqkv", il); - - struct ggml_tensor * tmpqkv_perm = ggml_cont(ctx0, ggml_permute(ctx0, tmpqkv, 0, 3, 1, 2)); - cb(tmpqkv_perm, "tmpqkv", il); - - struct ggml_tensor * tmpq = ggml_view_3d( - ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens, - ggml_element_size(tmpqkv_perm) * n_embd_head, - ggml_element_size(tmpqkv_perm) * n_embd_head * n_head, - 0 - ); - cb(tmpq, "tmpq", il); - - struct ggml_tensor * tmpk = ggml_view_3d( - ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens, - ggml_element_size(tmpqkv_perm) * n_embd_head, - ggml_element_size(tmpqkv_perm) * n_embd_head * n_head, - ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens - ); - cb(tmpk, "tmpk", il); - - // Q/K Layernorm - tmpq = llm_build_norm(ctx0, tmpq, hparams, - model.layers[il].attn_q_norm, - model.layers[il].attn_q_norm_b, - LLM_NORM, cb, il); - cb(tmpq, "tmpq", il); - - tmpk = llm_build_norm(ctx0, tmpk, hparams, - model.layers[il].attn_k_norm, - model.layers[il].attn_k_norm_b, - LLM_NORM, cb, il); - cb(tmpk, "tmpk", il); - - // RoPE the first n_rot of q/k, pass the other half, and concat. - struct ggml_tensor * qrot = ggml_view_3d( - ctx0, tmpq, n_rot, n_head, n_tokens, - ggml_element_size(tmpq) * n_embd_head, - ggml_element_size(tmpq) * n_embd_head * n_head, - 0 - ); - cb(qrot, "qrot", il); - - struct ggml_tensor * krot = ggml_view_3d( - ctx0, tmpk, n_rot, n_head, n_tokens, - ggml_element_size(tmpk) * n_embd_head, - ggml_element_size(tmpk) * n_embd_head * n_head, - 0 - ); - cb(krot, "krot", il); - - // get the second half of tmpq, e.g tmpq[n_rot:, :, :] - struct ggml_tensor * qpass = ggml_view_3d( - ctx0, tmpq, n_rot, n_head, n_tokens, - ggml_element_size(tmpq) * n_embd_head, - ggml_element_size(tmpq) * n_embd_head * n_head, - ggml_element_size(tmpq) * n_rot - ); - cb(qpass, "qpass", il); - - struct ggml_tensor * kpass = ggml_view_3d( - ctx0, tmpk, n_rot, n_head, n_tokens, - ggml_element_size(tmpk) * n_embd_head, - ggml_element_size(tmpk) * n_embd_head * n_head, - ggml_element_size(tmpk) * n_rot - ); - cb(kpass, "kpass", il); - - struct ggml_tensor * qrotated = ggml_rope_custom( - ctx0, qrot, inp_pos, n_rot, rope_type, 0, n_orig_ctx, - freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow - ); - cb(qrotated, "qrotated", il); - - struct ggml_tensor * krotated = ggml_rope_custom( - ctx0, krot, inp_pos, n_rot, rope_type, 0, n_orig_ctx, - freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow - ); - cb(krotated, "krotated", il); - - // ggml currently only supports concatenation on dim=2 - // so we need to permute qrot, qpass, concat, then permute back. - qrotated = ggml_cont(ctx0, ggml_permute(ctx0, qrotated, 2, 1, 0, 3)); - cb(qrotated, "qrotated", il); - - krotated = ggml_cont(ctx0, ggml_permute(ctx0, krotated, 2, 1, 0, 3)); - cb(krotated, "krotated", il); - - qpass = ggml_cont(ctx0, ggml_permute(ctx0, qpass, 2, 1, 0, 3)); - cb(qpass, "qpass", il); - - kpass = ggml_cont(ctx0, ggml_permute(ctx0, kpass, 2, 1, 0, 3)); - cb(kpass, "kpass", il); - - struct ggml_tensor * Qcur = ggml_concat(ctx0, qrotated, qpass); - cb(Qcur, "Qcur", il); - - struct ggml_tensor * Kcur = ggml_concat(ctx0, krotated, kpass); - cb(Kcur, "Kcur", il); - - struct ggml_tensor * Q = ggml_cont(ctx0, ggml_permute(ctx0, Qcur, 2, 1, 0, 3)); - cb(Q, "Q", il); - - Kcur = ggml_cont(ctx0, ggml_permute(ctx0, Kcur, 2, 1, 0, 3)); - cb(Kcur, "Kcur", il); - - struct ggml_tensor * Vcur = ggml_view_3d( - ctx0, tmpqkv_perm, n_embd_head, n_head, n_tokens, - ggml_element_size(tmpqkv_perm) * n_embd_head, - ggml_element_size(tmpqkv_perm) * n_embd_head * n_head, - ggml_element_size(tmpqkv_perm) * n_embd_head * n_head * n_tokens * 2 - ); - cb(Vcur, "Vcur", il); - - cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, - model.layers[il].wo, model.layers[il].bo, - Kcur, Vcur, Q, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); - } - - if (il == n_layer - 1) { - // skip computing output for unused tokens - struct ggml_tensor * inp_out_ids = build_inp_out_ids(); - cur = ggml_get_rows(ctx0, cur, inp_out_ids); - residual = ggml_get_rows(ctx0, residual, inp_out_ids); - } - - struct ggml_tensor * ffn_inp = ggml_add(ctx0, residual, cur); - 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, model.layers[il].ffn_up_b, - NULL, NULL, - model.layers[il].ffn_down, model.layers[il].ffn_down_b, - NULL, - LLM_FFN_RELU_SQR, LLM_FFN_SEQ, cb, il); - cb(cur, "ffn_out", il); - } - - cur = ggml_add(ctx0, cur, ffn_inp); - cb(cur, "l_out", il); - - 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); - - 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_refact() { struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); @@ -10898,10 +10623,6 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_starcoder(); } break; - case LLM_ARCH_PERSIMMON: - { - result = llm.build_persimmon(); - } break; case LLM_ARCH_REFACT: { result = llm.build_refact(); @@ -15992,7 +15713,6 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_FALCON: case LLM_ARCH_GROK: case LLM_ARCH_DBRX: - case LLM_ARCH_PERSIMMON: case LLM_ARCH_BERT: case LLM_ARCH_NOMIC_BERT: case LLM_ARCH_STABLELM: diff --git a/requirements.txt b/requirements.txt index e7d14e16a..43f82dc2e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -9,4 +9,3 @@ -r ./requirements/requirements-convert-hf-to-gguf.txt -r ./requirements/requirements-convert-hf-to-gguf-update.txt -r ./requirements/requirements-convert-llama-ggml-to-gguf.txt --r ./requirements/requirements-convert-persimmon-to-gguf.txt diff --git a/requirements/requirements-convert-persimmon-to-gguf.txt b/requirements/requirements-convert-persimmon-to-gguf.txt deleted file mode 100644 index 6ac402610..000000000 --- a/requirements/requirements-convert-persimmon-to-gguf.txt +++ /dev/null @@ -1,2 +0,0 @@ --r ./requirements-convert.txt -torch~=2.1.1 From 917dc8cfa67a72fb7c8bf7392270da3bf4833af4 Mon Sep 17 00:00:00 2001 From: jaime-m-p <167997752+jaime-m-p@users.noreply.github.com> Date: Mon, 20 May 2024 20:15:57 +0200 Subject: [PATCH 067/181] Tokenizer SPM fixes for phi-3 and llama-spm (#7375) * Update brute force test: special tokens * Fix added tokens - Try to read 'added_tokens.json'. - Try to read 'tokenizer_config.json'. - Try to read 'tokenizer.json'. * Fix special tokens rtrim Co-authored-by: Georgi Gerganov * server : fix test regexes --- convert-hf-to-gguf.py | 32 +++++++++++++++++ examples/server/tests/features/server.feature | 10 +++--- .../server/tests/features/slotsave.feature | 4 +-- llama.cpp | 31 +++++++++++++--- tests/test-tokenizer-random.py | 35 +++++++++++++++++-- 5 files changed, 98 insertions(+), 14 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index d534b5163..8937a4981 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1740,6 +1740,38 @@ class Phi3MiniModel(Model): scores[token_id] = -1000.0 toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + tokenizer_config_file = self.dir_model / 'tokenizer_config.json' + if tokenizer_config_file.is_file(): + with open(tokenizer_config_file, "r", encoding="utf-8") as f: + tokenizer_config_json = json.load(f) + added_tokens_decoder = tokenizer_config_json.get("added_tokens_decoder", {}) + for token_id, foken_data in added_tokens_decoder.items(): + token_id = int(token_id) + token = foken_data["content"].encode("utf-8") + if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN: + assert(tokens[token_id] == token) + tokens[token_id] = token + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + if foken_data.get("special"): + toktypes[token_id] = SentencePieceTokenTypes.CONTROL + + tokenizer_file = self.dir_model / 'tokenizer.json' + if tokenizer_file.is_file(): + with open(tokenizer_file, "r", encoding="utf-8") as f: + tokenizer_json = json.load(f) + added_tokens = tokenizer_json.get("added_tokens", []) + for foken_data in added_tokens: + token_id = int(foken_data["id"]) + token = foken_data["content"].encode("utf-8") + if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN: + assert(tokens[token_id] == token) + tokens[token_id] = token + scores[token_id] = -1000.0 + toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED + if foken_data.get("special"): + toktypes[token_id] = SentencePieceTokenTypes.CONTROL + self.gguf_writer.add_tokenizer_model("llama") self.gguf_writer.add_tokenizer_pre("default") self.gguf_writer.add_token_list(tokens) diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature index d21c09135..048cfad06 100644 --- a/examples/server/tests/features/server.feature +++ b/examples/server/tests/features/server.feature @@ -37,8 +37,8 @@ Feature: llama.cpp server Examples: Prompts | prompt | n_predict | re_content | n_prompt | n_predicted | truncated | - | I believe the meaning of life is | 8 | (read\|going)+ | 18 | 8 | not | - | Write a joke about AI from a very long prompt which will not be truncated | 256 | (princesses\|everyone\|kids\|Anna\|forest)+ | 46 | 64 | not | + | I believe the meaning of life is | 8 | (read\|going\|pretty)+ | 18 | 8 | not | + | Write a joke about AI from a very long prompt which will not be truncated | 256 | (princesses\|everyone\|kids\|Anna\|forest)+ | 45 | 64 | not | Scenario: Completion prompt truncated Given a prompt: @@ -67,8 +67,8 @@ Feature: llama.cpp server Examples: Prompts | model | system_prompt | user_prompt | max_tokens | re_content | n_prompt | n_predicted | enable_streaming | truncated | - | llama-2 | Book | What is the best book | 8 | (Here\|what)+ | 77 | 8 | disabled | not | - | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 128 | (thanks\|happy\|bird\|Annabyear)+ | -1 | 64 | enabled | | + | llama-2 | Book | What is the best book | 8 | (Here\|what)+ | 76 | 8 | disabled | not | + | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 128 | (thanks\|happy\|bird\|fireplace)+ | -1 | 64 | enabled | | Scenario Outline: OAI Compatibility w/ response format @@ -84,7 +84,7 @@ Feature: llama.cpp server | response_format | n_predicted | re_content | | {"type": "json_object", "schema": {"const": "42"}} | 5 | "42" | | {"type": "json_object", "schema": {"items": [{"type": "integer"}]}} | 10 | \[ -300 \] | - | {"type": "json_object"} | 10 | \{ " Jacky. | + | {"type": "json_object"} | 10 | \{ " Saragine. | Scenario: Tokenize / Detokenize diff --git a/examples/server/tests/features/slotsave.feature b/examples/server/tests/features/slotsave.feature index 1c281c074..ba4ecb6f5 100644 --- a/examples/server/tests/features/slotsave.feature +++ b/examples/server/tests/features/slotsave.feature @@ -26,7 +26,7 @@ Feature: llama.cpp server slot management # Since we have cache, this should only process the last tokens Given a user prompt "What is the capital of Germany?" And a completion request with no api error - Then 24 tokens are predicted matching (Thank|special) + Then 24 tokens are predicted matching (Thank|special|Lily) And 7 prompt tokens are processed # Loading the original cache into slot 0, # we should only be processing 1 prompt token and get the same output @@ -41,7 +41,7 @@ Feature: llama.cpp server slot management Given a user prompt "What is the capital of Germany?" And using slot id 1 And a completion request with no api error - Then 24 tokens are predicted matching (Thank|special) + Then 24 tokens are predicted matching (Thank|special|Lily) And 1 prompt tokens are processed Scenario: Erase Slot diff --git a/llama.cpp b/llama.cpp index 863961f15..e2ebe1752 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4553,7 +4553,8 @@ static void llm_load_vocab( (t.first == "<|eot_id|>" || t.first == "<|im_end|>" || t.first == "<|end|>" || - t.first == "" + t.first == "" || + t.first == "<|endoftext|>" ) ) { vocab.special_eot_id = t.second; @@ -12502,6 +12503,10 @@ static std::vector llama_tokenize_internal(const llama_vocab & output.push_back(vocab.special_bos_id); } + static const bool rtrim = true; //TODO: as param + bool is_prev_special = false; + bool special_token_rtrim = false; + for (const auto & fragment : fragment_buffer) { if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { // without adding this leading whitespace, we do not get the same results as the original tokenizer @@ -12511,9 +12516,21 @@ static std::vector llama_tokenize_internal(const llama_vocab & // and passing 'add space prefix' as bool argument // auto raw_text = fragment.raw_text.substr(fragment.offset, fragment.length); - if (&fragment == &fragment_buffer.front()) { - if (vocab.add_space_prefix) { - raw_text = " " + raw_text; // prefix with space if the first token is not special + + if (special_token_rtrim) { + size_t num_whitespaces = 0; + while (isspace(raw_text[num_whitespaces])) { + num_whitespaces++; + } + if (num_whitespaces == raw_text.size()) { + continue; // skip if all whitespaces + } + raw_text = raw_text.substr(num_whitespaces); + } + + if (vocab.add_space_prefix) { + if (!output.size() || is_prev_special) { // prefix with space if first token + raw_text = " " + raw_text; } } @@ -12525,6 +12542,12 @@ static std::vector llama_tokenize_internal(const llama_vocab & tokenizer.tokenize(raw_text, output); } else { // if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_TOKEN) output.push_back(fragment.token); + is_prev_special = true; + // phi-3 special tokens without rtrim, works fine for llama-spm too + special_token_rtrim = rtrim + && fragment.token != vocab.special_bos_id + && fragment.token != vocab.special_unk_id + && fragment.token != vocab.special_eos_id; } } diff --git a/tests/test-tokenizer-random.py b/tests/test-tokenizer-random.py index d5a6f185f..1166ac1e4 100644 --- a/tests/test-tokenizer-random.py +++ b/tests/test-tokenizer-random.py @@ -153,11 +153,23 @@ def generator_custom_text_edge_cases() -> Iterator[str]: 'Ⅵ-a', # unicode_ranges_digit, {0x00002150, 0x0000218F} // Number Forms '\uFEFF//', # unicode_ranges_control, 0xFEFF (BOM) 'Cửa Việt', # llama-3, ignore_merges = true - 'a', # TODO: Phi-3 fail + 'a', # Phi-3 fail + '<|endoftext|>' # Phi-3 fail 'a\na', # TODO: Bert fail ] +def generator_random_special_tokens(special_tokens:list[str], iterations=100) -> Iterator[str]: + special_tokens = set(special_tokens) + special_tokens.update([" ", "\n", "\t", "-", "!", "one", "1", "", ""]) + special_tokens = list(sorted(special_tokens)) + rand = random.Random() + for m in range(iterations): + rand.seed(m) + words = rand.choices(special_tokens, k=500) + yield "".join(words) + + def generator_vocab_words(vocab: list[str]) -> Iterator[str]: """Brute force check all vocab words""" yield from vocab @@ -289,14 +301,31 @@ def main(argv: list[str] = None): vocab = list(sorted(tokenizer.batch_decode(list(tokenizer.get_vocab().values()), skip_special_tokens=True))) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text()) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text_edge_cases()) + test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_special_tokens(tokenizer.all_special_tokens, 10_000)) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_vocab_words(vocab)) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_chars(10_000)) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_vocab_chars(vocab, 10_000)) - test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_vocab_words(vocab, 10_000)) + test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_vocab_words(vocab, 5_000)) # test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_bytes(10_000)) # FAIL model.free() if __name__ == "__main__": - main() + # main() + + path_tokenizers = "./models/tokenizers/" + path_vocab_format = "./models/ggml-vocab-%s.gguf" + + # import os + # tokenizers = os.listdir(path_tokenizers) + tokenizers = [ + "llama-spm", # SPM + "phi-3", # SPM + ] + + for tokenizer in tokenizers: + print("\n" + "=" * 50 + "\n" + tokenizer + "\n") # noqa + vocab_file = path_vocab_format % tokenizer + dir_tokenizer = path_tokenizers + "/" + tokenizer + main([vocab_file, dir_tokenizer, "--verbose"]) From d7e852c1bc8e85bf62a6f1aede08cd2de723404a Mon Sep 17 00:00:00 2001 From: jaime-m-p <167997752+jaime-m-p@users.noreply.github.com> Date: Tue, 21 May 2024 14:39:48 +0200 Subject: [PATCH 068/181] Tokenizer SPM fixes for phi-3 and llama-spm (bugfix) (#7425) * Update brute force test: add_special * Update brute force test: default values for add_bos_token and add_eos_token * Enable rtrim when pre-inserting BOS Co-authored-by: Georgi Gerganov * Revert "server : fix test regexes" --- convert-hf-to-gguf.py | 4 ++-- examples/server/tests/features/server.feature | 10 ++++----- .../server/tests/features/slotsave.feature | 4 ++-- llama.cpp | 11 +++++----- tests/test-tokenizer-random.py | 22 +++++++++++-------- 5 files changed, 28 insertions(+), 23 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 8937a4981..1acf45bf2 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1749,7 +1749,7 @@ class Phi3MiniModel(Model): token_id = int(token_id) token = foken_data["content"].encode("utf-8") if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN: - assert(tokens[token_id] == token) + assert tokens[token_id] == token tokens[token_id] = token scores[token_id] = -1000.0 toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED @@ -1765,7 +1765,7 @@ class Phi3MiniModel(Model): token_id = int(foken_data["id"]) token = foken_data["content"].encode("utf-8") if toktypes[token_id] != SentencePieceTokenTypes.UNKNOWN: - assert(tokens[token_id] == token) + assert tokens[token_id] == token tokens[token_id] = token scores[token_id] = -1000.0 toktypes[token_id] = SentencePieceTokenTypes.USER_DEFINED diff --git a/examples/server/tests/features/server.feature b/examples/server/tests/features/server.feature index 048cfad06..d21c09135 100644 --- a/examples/server/tests/features/server.feature +++ b/examples/server/tests/features/server.feature @@ -37,8 +37,8 @@ Feature: llama.cpp server Examples: Prompts | prompt | n_predict | re_content | n_prompt | n_predicted | truncated | - | I believe the meaning of life is | 8 | (read\|going\|pretty)+ | 18 | 8 | not | - | Write a joke about AI from a very long prompt which will not be truncated | 256 | (princesses\|everyone\|kids\|Anna\|forest)+ | 45 | 64 | not | + | I believe the meaning of life is | 8 | (read\|going)+ | 18 | 8 | not | + | Write a joke about AI from a very long prompt which will not be truncated | 256 | (princesses\|everyone\|kids\|Anna\|forest)+ | 46 | 64 | not | Scenario: Completion prompt truncated Given a prompt: @@ -67,8 +67,8 @@ Feature: llama.cpp server Examples: Prompts | model | system_prompt | user_prompt | max_tokens | re_content | n_prompt | n_predicted | enable_streaming | truncated | - | llama-2 | Book | What is the best book | 8 | (Here\|what)+ | 76 | 8 | disabled | not | - | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 128 | (thanks\|happy\|bird\|fireplace)+ | -1 | 64 | enabled | | + | llama-2 | Book | What is the best book | 8 | (Here\|what)+ | 77 | 8 | disabled | not | + | codellama70b | You are a coding assistant. | Write the fibonacci function in c++. | 128 | (thanks\|happy\|bird\|Annabyear)+ | -1 | 64 | enabled | | Scenario Outline: OAI Compatibility w/ response format @@ -84,7 +84,7 @@ Feature: llama.cpp server | response_format | n_predicted | re_content | | {"type": "json_object", "schema": {"const": "42"}} | 5 | "42" | | {"type": "json_object", "schema": {"items": [{"type": "integer"}]}} | 10 | \[ -300 \] | - | {"type": "json_object"} | 10 | \{ " Saragine. | + | {"type": "json_object"} | 10 | \{ " Jacky. | Scenario: Tokenize / Detokenize diff --git a/examples/server/tests/features/slotsave.feature b/examples/server/tests/features/slotsave.feature index ba4ecb6f5..1c281c074 100644 --- a/examples/server/tests/features/slotsave.feature +++ b/examples/server/tests/features/slotsave.feature @@ -26,7 +26,7 @@ Feature: llama.cpp server slot management # Since we have cache, this should only process the last tokens Given a user prompt "What is the capital of Germany?" And a completion request with no api error - Then 24 tokens are predicted matching (Thank|special|Lily) + Then 24 tokens are predicted matching (Thank|special) And 7 prompt tokens are processed # Loading the original cache into slot 0, # we should only be processing 1 prompt token and get the same output @@ -41,7 +41,7 @@ Feature: llama.cpp server slot management Given a user prompt "What is the capital of Germany?" And using slot id 1 And a completion request with no api error - Then 24 tokens are predicted matching (Thank|special|Lily) + Then 24 tokens are predicted matching (Thank|special) And 1 prompt tokens are processed Scenario: Erase Slot diff --git a/llama.cpp b/llama.cpp index e2ebe1752..d26fe559a 100644 --- a/llama.cpp +++ b/llama.cpp @@ -12498,15 +12498,16 @@ static std::vector llama_tokenize_internal(const llama_vocab & // tokenizer.encode('', add_special_tokens=True) returns [1] // tokenizer.encode('', add_special_tokens=False) returns [] - if (add_special && vocab.special_add_bos != 0) { - GGML_ASSERT(vocab.special_bos_id != -1); - output.push_back(vocab.special_bos_id); - } - static const bool rtrim = true; //TODO: as param bool is_prev_special = false; bool special_token_rtrim = false; + if (add_special && vocab.special_add_bos != 0) { + GGML_ASSERT(vocab.special_bos_id != -1); + output.push_back(vocab.special_bos_id); + is_prev_special = true; + } + for (const auto & fragment : fragment_buffer) { if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { // without adding this leading whitespace, we do not get the same results as the original tokenizer diff --git a/tests/test-tokenizer-random.py b/tests/test-tokenizer-random.py index 1166ac1e4..7e1b656e5 100644 --- a/tests/test-tokenizer-random.py +++ b/tests/test-tokenizer-random.py @@ -154,19 +154,22 @@ def generator_custom_text_edge_cases() -> Iterator[str]: '\uFEFF//', # unicode_ranges_control, 0xFEFF (BOM) 'Cửa Việt', # llama-3, ignore_merges = true 'a', # Phi-3 fail - '<|endoftext|>' # Phi-3 fail + '<|endoftext|>', # Phi-3 fail 'a\na', # TODO: Bert fail ] -def generator_random_special_tokens(special_tokens:list[str], iterations=100) -> Iterator[str]: - special_tokens = set(special_tokens) +def generator_random_special_tokens(tokenizer, iterations=100) -> Iterator[str]: + special_tokens = set(tokenizer.all_special_tokens) special_tokens.update([" ", "\n", "\t", "-", "!", "one", "1", "", ""]) special_tokens = list(sorted(special_tokens)) rand = random.Random() for m in range(iterations): rand.seed(m) words = rand.choices(special_tokens, k=500) + if tokenizer.add_bos_token: # skip spam warning of double BOS + while words and words[0] == tokenizer.bos_token: + words.pop(0) yield "".join(words) @@ -290,18 +293,19 @@ def main(argv: list[str] = None): model = LibLlamaModel(LibLlama(), args.vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=4096)) tokenizer = AutoTokenizer.from_pretrained(args.dir_tokenizer) - def func_tokenize2(text: str): - return tokenizer.encode(text, add_special_tokens=False) - - parse_special = all(len(func_tokenize2(t)) == 1 for t in tokenizer.all_special_tokens) + tokenizer.add_bos_token = getattr(tokenizer, "add_bos_token", True) + tokenizer.add_eos_token = getattr(tokenizer, "add_eos_token", False) def func_tokenize1(text: str): - return model.tokenize(text, add_special=False, parse_special=parse_special) + return model.tokenize(text, add_special=True, parse_special=True) + + def func_tokenize2(text: str): + return tokenizer.encode(text, add_special_tokens=True) vocab = list(sorted(tokenizer.batch_decode(list(tokenizer.get_vocab().values()), skip_special_tokens=True))) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text()) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text_edge_cases()) - test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_special_tokens(tokenizer.all_special_tokens, 10_000)) + test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_special_tokens(tokenizer, 10_000)) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_vocab_words(vocab)) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_chars(10_000)) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_random_vocab_chars(vocab, 10_000)) From d8ee90222791afff2ab666ded4cb6195fd94cced Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Tue, 21 May 2024 16:02:12 +0200 Subject: [PATCH 069/181] CUDA: deduplicate mmq code (#7397) --- ggml-cuda/mmq.cu | 1237 ++++++++++------------------------------------ 1 file changed, 271 insertions(+), 966 deletions(-) diff --git a/ggml-cuda/mmq.cu b/ggml-cuda/mmq.cu index 7948f1b12..5b540d375 100644 --- a/ggml-cuda/mmq.cu +++ b/ggml-cuda/mmq.cu @@ -9,6 +9,135 @@ typedef float (*vec_dot_q_mul_mat_cuda_t)( const int * __restrict__ x_ql, const half2 * __restrict__ x_dm, const int * __restrict__ x_qh, const int * __restrict__ x_sc, const int * __restrict__ y_qs, const half2 * __restrict__ y_ms, const int & i, const int & j, const int & k); typedef void (*dot_kernel_k_t)(const void * __restrict__ vx, const int ib, const int iqs, const float * __restrict__ y, float & v); +typedef void (mul_mat_q_t)( + 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); + +struct mmq_arch_config_t { + int x; + int y; + int nwarps; +}; + +struct mmq_config_t { + mmq_arch_config_t rdna2; + mmq_arch_config_t rdna1; + mmq_arch_config_t ampere; + mmq_arch_config_t pascal; +}; + +constexpr mmq_config_t MMQ_CONFIG_Q4_0 = { +// x y nwarps + { 64, 128, 8}, + { 64, 64, 8}, +#ifdef CUDA_USE_TENSOR_CORES + { 4, 32, 4}, +#else + { 64, 128, 4}, +#endif // CUDA_USE_TENSOR_CORES + { 64, 64, 8}, +}; +constexpr mmq_config_t MMQ_CONFIG_Q4_1 = { +// x y nwarps + { 64, 128, 8}, + { 64, 64, 8}, +#ifdef CUDA_USE_TENSOR_CORES + { 4, 32, 4}, +#else + { 64, 128, 4}, +#endif // CUDA_USE_TENSOR_CORES + { 64, 64, 8}, +}; +constexpr mmq_config_t MMQ_CONFIG_Q5_0 = { +// x y nwarps + { 64, 128, 8}, + { 64, 64, 8}, +#ifdef CUDA_USE_TENSOR_CORES + { 4, 32, 4}, +#else + {128, 64, 4}, +#endif // CUDA_USE_TENSOR_CORES + { 64, 64, 8}, +}; +constexpr mmq_config_t MMQ_CONFIG_Q5_1 = { +// x y nwarps + { 64, 128, 8}, + { 64, 64, 8}, +#ifdef CUDA_USE_TENSOR_CORES + { 4, 32, 4}, +#else + {128, 64, 4}, +#endif // CUDA_USE_TENSOR_CORES + { 64, 64, 8}, +}; +constexpr mmq_config_t MMQ_CONFIG_Q8_0 = { +// x y nwarps + { 64, 128, 8}, + { 64, 64, 8}, +#ifdef CUDA_USE_TENSOR_CORES + { 4, 32, 4}, +#else + {128, 64, 4}, +#endif // CUDA_USE_TENSOR_CORES + { 64, 64, 8}, +}; +constexpr mmq_config_t MMQ_CONFIG_Q2_K = { +// x y nwarps + { 64, 128, 8}, + {128, 32, 8}, +#ifdef CUDA_USE_TENSOR_CORES + { 4, 32, 4}, +#else + { 64, 128, 4}, +#endif // CUDA_USE_TENSOR_CORES + { 64, 64, 8}, +}; +constexpr mmq_config_t MMQ_CONFIG_Q3_K = { +// x y nwarps + {128, 64, 8}, + { 32, 128, 8}, +#ifdef CUDA_USE_TENSOR_CORES + { 4, 32, 4}, +#else + {128, 128, 4}, +#endif // CUDA_USE_TENSOR_CORES + { 64, 64, 8}, +}; +constexpr mmq_config_t MMQ_CONFIG_Q4_K = { +// x y nwarps + { 64, 128, 8}, + { 32, 64, 8}, +#ifdef CUDA_USE_TENSOR_CORES + { 4, 32, 4}, +#else + { 64, 128, 4}, +#endif // CUDA_USE_TENSOR_CORES + { 64, 64, 8}, +}; +constexpr mmq_config_t MMQ_CONFIG_Q5_K = { +// x y nwarps + { 64, 128, 8}, + { 32, 64, 8}, +#ifdef CUDA_USE_TENSOR_CORES + { 4, 32, 4}, +#else + { 64, 128, 4}, +#endif // CUDA_USE_TENSOR_CORES + { 64, 64, 8}, +}; +constexpr mmq_config_t MMQ_CONFIG_Q6_K = { +// x y nwarps + { 64, 128, 8}, + { 32, 64, 8}, +#ifdef CUDA_USE_TENSOR_CORES + { 4, 32, 4}, +#else + { 64, 64, 4}, +#endif // CUDA_USE_TENSOR_CORES + { 64, 64, 8}, +}; + +// ------------------------------------------------------------ template static __device__ __forceinline__ void allocate_tiles_q4_0(int ** x_ql, half2 ** x_dm, int ** x_qh, int ** x_sc) { GGML_UNUSED(x_qh); @@ -943,25 +1072,6 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1_mul_mat( 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]); } -#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(CUDA_USE_TENSOR_CORES) -#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 __device__ __forceinline__ void mul_mat_q( @@ -1072,1107 +1182,265 @@ static __device__ __forceinline__ void mul_mat_q( } } +static constexpr __device__ mmq_arch_config_t get_arch_config_device(mmq_config_t mmq_config) { + +#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) + +#if defined(RDNA3) || defined(RDNA2) + return mmq_config.rdna2; +#else + return mmq_config.rdna1; +#endif // defined(RDNA3) || defined(RDNA2) + +#else + +#if __CUDA_ARCH__ >= CC_VOLTA + return mmq_config.ampere; +#else + return mmq_config.pascal; +#endif // __CUDA_ARCH__ >= CC_VOLTA + +#endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) +} + template static __global__ void #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) #if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q4_0_RDNA2, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_0.rdna2.nwarps, 2) #endif // defined(RDNA3) || defined(RDNA2) #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) 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) { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q4_0_RDNA2; - const int mmq_y = MMQ_Y_Q4_0_RDNA2; - const int nwarps = NWARPS_Q4_0_RDNA2; -#else - const int mmq_x = MMQ_X_Q4_0_RDNA1; - const int mmq_y = MMQ_Y_Q4_0_RDNA1; - const int nwarps = NWARPS_Q4_0_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) +#if __CUDA_ARCH__ >= MIN_CC_DP4A + constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q4_0); - mul_mat_q, - load_tiles_q4_0, 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); - -#elif __CUDA_ARCH__ >= CC_VOLTA - 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; - - mul_mat_q, - load_tiles_q4_0, 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); - -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q4_0_PASCAL; - const int mmq_y = MMQ_Y_Q4_0_PASCAL; - const int nwarps = NWARPS_Q4_0_PASCAL; - - mul_mat_q, - load_tiles_q4_0, VDR_Q4_0_Q8_1_MMQ, vec_dot_q4_0_q8_1_mul_mat> + mul_mat_q, + load_tiles_q4_0, 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); #else GGML_UNUSED(vec_dot_q4_0_q8_1_mul_mat); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -#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(CUDA_USE_TENSOR_CORES) -#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 __global__ void #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) #if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q4_1_RDNA2, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_1.rdna2.nwarps, 2) #endif // defined(RDNA3) || defined(RDNA2) #elif __CUDA_ARCH__ < CC_VOLTA - __launch_bounds__(WARP_SIZE*NWARPS_Q4_1_PASCAL, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_1.pascal.nwarps, 2) #endif // __CUDA_ARCH__ < CC_VOLTA 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) { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q4_1_RDNA2; - const int mmq_y = MMQ_Y_Q4_1_RDNA2; - const int nwarps = NWARPS_Q4_1_RDNA2; -#else - const int mmq_x = MMQ_X_Q4_1_RDNA1; - const int mmq_y = MMQ_Y_Q4_1_RDNA1; - const int nwarps = NWARPS_Q4_1_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) +#if __CUDA_ARCH__ >= MIN_CC_DP4A + constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q4_1); - mul_mat_q, - load_tiles_q4_1, 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); - -#elif __CUDA_ARCH__ >= CC_VOLTA - 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; - - mul_mat_q, - load_tiles_q4_1, 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); - -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q4_1_PASCAL; - const int mmq_y = MMQ_Y_Q4_1_PASCAL; - const int nwarps = NWARPS_Q4_1_PASCAL; - - mul_mat_q, - load_tiles_q4_1, VDR_Q4_1_Q8_1_MMQ, vec_dot_q4_1_q8_1_mul_mat> + mul_mat_q, + load_tiles_q4_1, 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); #else GGML_UNUSED(vec_dot_q4_1_q8_1_mul_mat); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -#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(CUDA_USE_TENSOR_CORES) -#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 __global__ void #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) #if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q5_0_RDNA2, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q5_0.rdna2.nwarps, 2) #endif // defined(RDNA3) || defined(RDNA2) #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) 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) { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q5_0_RDNA2; - const int mmq_y = MMQ_Y_Q5_0_RDNA2; - const int nwarps = NWARPS_Q5_0_RDNA2; -#else - const int mmq_x = MMQ_X_Q5_0_RDNA1; - const int mmq_y = MMQ_Y_Q5_0_RDNA1; - const int nwarps = NWARPS_Q5_0_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) +#if __CUDA_ARCH__ >= MIN_CC_DP4A + constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q5_0); - mul_mat_q, - load_tiles_q5_0, 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); - -#elif __CUDA_ARCH__ >= CC_VOLTA - 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; - - mul_mat_q, - load_tiles_q5_0, 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); - -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q5_0_PASCAL; - const int mmq_y = MMQ_Y_Q5_0_PASCAL; - const int nwarps = NWARPS_Q5_0_PASCAL; - - mul_mat_q, - load_tiles_q5_0, VDR_Q5_0_Q8_1_MMQ, vec_dot_q5_0_q8_1_mul_mat> + mul_mat_q, + load_tiles_q5_0, 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); #else GGML_UNUSED(vec_dot_q5_0_q8_1_mul_mat); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -#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(CUDA_USE_TENSOR_CORES) -#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 __global__ void #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) #if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q5_1_RDNA2, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q5_1.rdna2.nwarps, 2) #endif // defined(RDNA3) || defined(RDNA2) #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) 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) { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q5_1_RDNA2; - const int mmq_y = MMQ_Y_Q5_1_RDNA2; - const int nwarps = NWARPS_Q5_1_RDNA2; -#else - const int mmq_x = MMQ_X_Q5_1_RDNA1; - const int mmq_y = MMQ_Y_Q5_1_RDNA1; - const int nwarps = NWARPS_Q5_1_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) +#if __CUDA_ARCH__ >= MIN_CC_DP4A + constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q5_1); - mul_mat_q, - load_tiles_q5_1, 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); - -#elif __CUDA_ARCH__ >= CC_VOLTA - 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; - - mul_mat_q, - load_tiles_q5_1, 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); - -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q5_1_PASCAL; - const int mmq_y = MMQ_Y_Q5_1_PASCAL; - const int nwarps = NWARPS_Q5_1_PASCAL; - - mul_mat_q, - load_tiles_q5_1, VDR_Q5_1_Q8_1_MMQ, vec_dot_q5_1_q8_1_mul_mat> + mul_mat_q, + load_tiles_q5_1, 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); #else GGML_UNUSED(vec_dot_q5_1_q8_1_mul_mat); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -#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(CUDA_USE_TENSOR_CORES) -#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 __global__ void #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) #if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q8_0_RDNA2, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q8_0.rdna2.nwarps, 2) #endif // defined(RDNA3) || defined(RDNA2) #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) 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) { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q8_0_RDNA2; - const int mmq_y = MMQ_Y_Q8_0_RDNA2; - const int nwarps = NWARPS_Q8_0_RDNA2; -#else - const int mmq_x = MMQ_X_Q8_0_RDNA1; - const int mmq_y = MMQ_Y_Q8_0_RDNA1; - const int nwarps = NWARPS_Q8_0_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) +#if __CUDA_ARCH__ >= MIN_CC_DP4A + constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q8_0); - mul_mat_q, - load_tiles_q8_0, 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); - -#elif __CUDA_ARCH__ >= CC_VOLTA - 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; - - mul_mat_q, - load_tiles_q8_0, 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); - -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q8_0_PASCAL; - const int mmq_y = MMQ_Y_Q8_0_PASCAL; - const int nwarps = NWARPS_Q8_0_PASCAL; - - mul_mat_q, - load_tiles_q8_0, VDR_Q8_0_Q8_1_MMQ, vec_dot_q8_0_q8_1_mul_mat> + mul_mat_q, + load_tiles_q8_0, 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); #else GGML_UNUSED(vec_dot_q8_0_q8_1_mul_mat); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -#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(CUDA_USE_TENSOR_CORES) -#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 __global__ void #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) #if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q2_K_RDNA2, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q2_K.rdna2.nwarps, 2) #endif // defined(RDNA3) || defined(RDNA2) #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) 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) { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q2_K_RDNA2; - const int mmq_y = MMQ_Y_Q2_K_RDNA2; - const int nwarps = NWARPS_Q2_K_RDNA2; -#else - const int mmq_x = MMQ_X_Q2_K_RDNA1; - const int mmq_y = MMQ_Y_Q2_K_RDNA1; - const int nwarps = NWARPS_Q2_K_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) +#if __CUDA_ARCH__ >= MIN_CC_DP4A + constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q2_K); - mul_mat_q, - load_tiles_q2_K, 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); - -#elif __CUDA_ARCH__ >= CC_VOLTA - 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; - - mul_mat_q, - load_tiles_q2_K, 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); - -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q2_K_PASCAL; - const int mmq_y = MMQ_Y_Q2_K_PASCAL; - const int nwarps = NWARPS_Q2_K_PASCAL; - - mul_mat_q, - load_tiles_q2_K, VDR_Q2_K_Q8_1_MMQ, vec_dot_q2_K_q8_1_mul_mat> + mul_mat_q, + load_tiles_q2_K, 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); #else GGML_UNUSED(vec_dot_q2_K_q8_1_mul_mat); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -#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(CUDA_USE_TENSOR_CORES) -#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 __global__ void #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) #if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q3_K_RDNA2, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q3_K.rdna2.nwarps, 2) #endif // defined(RDNA3) || defined(RDNA2) #elif __CUDA_ARCH__ < CC_VOLTA - __launch_bounds__(WARP_SIZE*NWARPS_Q3_K_PASCAL, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q3_K.pascal.nwarps, 2) #endif // __CUDA_ARCH__ < CC_VOLTA 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) { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q3_K_RDNA2; - const int mmq_y = MMQ_Y_Q3_K_RDNA2; - const int nwarps = NWARPS_Q3_K_RDNA2; -#else - const int mmq_x = MMQ_X_Q3_K_RDNA1; - const int mmq_y = MMQ_Y_Q3_K_RDNA1; - const int nwarps = NWARPS_Q3_K_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) +#if __CUDA_ARCH__ >= MIN_CC_DP4A + constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q3_K); - mul_mat_q, - load_tiles_q3_K, 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); - -#elif __CUDA_ARCH__ >= CC_VOLTA - 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; - - mul_mat_q, - load_tiles_q3_K, 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); - -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q3_K_PASCAL; - const int mmq_y = MMQ_Y_Q3_K_PASCAL; - const int nwarps = NWARPS_Q3_K_PASCAL; - - mul_mat_q, - load_tiles_q3_K, VDR_Q3_K_Q8_1_MMQ, vec_dot_q3_K_q8_1_mul_mat> + mul_mat_q, + load_tiles_q3_K, 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); #else GGML_UNUSED(vec_dot_q3_K_q8_1_mul_mat); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -#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(CUDA_USE_TENSOR_CORES) -#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 __global__ void #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) #if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q4_K_RDNA2, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_K.rdna2.nwarps, 2) #endif // defined(RDNA3) || defined(RDNA2) #elif __CUDA_ARCH__ < CC_VOLTA - __launch_bounds__(WARP_SIZE*NWARPS_Q4_K_PASCAL, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_K.pascal.nwarps, 2) #endif // __CUDA_ARCH__ < CC_VOLTA 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) { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q4_K_RDNA2; - const int mmq_y = MMQ_Y_Q4_K_RDNA2; - const int nwarps = NWARPS_Q4_K_RDNA2; -#else - const int mmq_x = MMQ_X_Q4_K_RDNA1; - const int mmq_y = MMQ_Y_Q4_K_RDNA1; - const int nwarps = NWARPS_Q4_K_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) +#if __CUDA_ARCH__ >= MIN_CC_DP4A + constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q4_K); - mul_mat_q, - load_tiles_q4_K, 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); - -#elif __CUDA_ARCH__ >= CC_VOLTA - 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; - - mul_mat_q, - load_tiles_q4_K, 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); - -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q4_K_PASCAL; - const int mmq_y = MMQ_Y_Q4_K_PASCAL; - const int nwarps = NWARPS_Q4_K_PASCAL; - - mul_mat_q, - load_tiles_q4_K, VDR_Q4_K_Q8_1_MMQ, vec_dot_q4_K_q8_1_mul_mat> + mul_mat_q, + load_tiles_q4_K, 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); #else GGML_UNUSED(vec_dot_q4_K_q8_1_mul_mat); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -#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(CUDA_USE_TENSOR_CORES) -#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 __global__ void #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) #if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q5_K_RDNA2, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q5_K.rdna2.nwarps, 2) #endif // defined(RDNA3) || defined(RDNA2) #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) 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) { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q5_K_RDNA2; - const int mmq_y = MMQ_Y_Q5_K_RDNA2; - const int nwarps = NWARPS_Q5_K_RDNA2; -#else - const int mmq_x = MMQ_X_Q5_K_RDNA1; - const int mmq_y = MMQ_Y_Q5_K_RDNA1; - const int nwarps = NWARPS_Q5_K_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) +#if __CUDA_ARCH__ >= MIN_CC_DP4A + constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q5_K); - mul_mat_q, - load_tiles_q5_K, 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); - -#elif __CUDA_ARCH__ >= CC_VOLTA - 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; - - mul_mat_q, - load_tiles_q5_K, 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); - -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q5_K_PASCAL; - const int mmq_y = MMQ_Y_Q5_K_PASCAL; - const int nwarps = NWARPS_Q5_K_PASCAL; - - mul_mat_q, - load_tiles_q5_K, VDR_Q5_K_Q8_1_MMQ, vec_dot_q5_K_q8_1_mul_mat> + mul_mat_q, + load_tiles_q5_K, 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); #else GGML_UNUSED(vec_dot_q5_K_q8_1_mul_mat); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -#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(CUDA_USE_TENSOR_CORES) -#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 __global__ void #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) #if defined(RDNA3) || defined(RDNA2) - __launch_bounds__(WARP_SIZE*NWARPS_Q6_K_RDNA2, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q6_K.rdna2.nwarps, 2) #endif // defined(RDNA3) || defined(RDNA2) #elif __CUDA_ARCH__ < CC_VOLTA - __launch_bounds__(WARP_SIZE*NWARPS_Q6_K_PASCAL, 2) + __launch_bounds__(WARP_SIZE*MMQ_CONFIG_Q4_K.pascal.nwarps, 2) #endif // __CUDA_ARCH__ < CC_VOLTA 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) { -#if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) -#if defined(RDNA3) || defined(RDNA2) - const int mmq_x = MMQ_X_Q6_K_RDNA2; - const int mmq_y = MMQ_Y_Q6_K_RDNA2; - const int nwarps = NWARPS_Q6_K_RDNA2; -#else - const int mmq_x = MMQ_X_Q6_K_RDNA1; - const int mmq_y = MMQ_Y_Q6_K_RDNA1; - const int nwarps = NWARPS_Q6_K_RDNA1; -#endif // defined(RDNA3) || defined(RDNA2) +#if __CUDA_ARCH__ >= MIN_CC_DP4A + constexpr mmq_arch_config_t arch_config = get_arch_config_device(MMQ_CONFIG_Q6_K); - mul_mat_q, - load_tiles_q6_K, 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); - -#elif __CUDA_ARCH__ >= CC_VOLTA - 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; - - mul_mat_q, - load_tiles_q6_K, 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); - -#elif __CUDA_ARCH__ >= MIN_CC_DP4A - const int mmq_x = MMQ_X_Q6_K_PASCAL; - const int mmq_y = MMQ_Y_Q6_K_PASCAL; - const int nwarps = NWARPS_Q6_K_PASCAL; - - mul_mat_q, - load_tiles_q6_K, VDR_Q6_K_Q8_1_MMQ, vec_dot_q6_K_q8_1_mul_mat> + mul_mat_q, + load_tiles_q6_K, 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); #else GGML_UNUSED(vec_dot_q6_K_q8_1_mul_mat); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= CC_VOLTA +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } -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) { - - int id = ggml_cuda_get_device(); - const int compute_capability = ggml_cuda_info().devices[id].cc; - - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q4_0_RDNA2; - mmq_y = MMQ_Y_Q4_0_RDNA2; - nwarps = NWARPS_Q4_0_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q4_0_RDNA1; - mmq_y = MMQ_Y_Q4_0_RDNA1; - nwarps = NWARPS_Q4_0_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q4_0_AMPERE; - mmq_y = MMQ_Y_Q4_0_AMPERE; - nwarps = NWARPS_Q4_0_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - 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 dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); - - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q4_0<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q4_0<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } -} - -static void ggml_mul_mat_q4_1_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) { - - int id = ggml_cuda_get_device(); - const int compute_capability = ggml_cuda_info().devices[id].cc; - - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q4_1_RDNA2; - mmq_y = MMQ_Y_Q4_1_RDNA2; - nwarps = NWARPS_Q4_1_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q4_1_RDNA1; - mmq_y = MMQ_Y_Q4_1_RDNA1; - nwarps = NWARPS_Q4_1_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q4_1_AMPERE; - mmq_y = MMQ_Y_Q4_1_AMPERE; - nwarps = NWARPS_Q4_1_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - 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 dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); - - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q4_1<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q4_1<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } -} - -static void ggml_mul_mat_q5_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) { - - int id = ggml_cuda_get_device(); - const int compute_capability = ggml_cuda_info().devices[id].cc; - - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q5_0_RDNA2; - mmq_y = MMQ_Y_Q5_0_RDNA2; - nwarps = NWARPS_Q5_0_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q5_0_RDNA1; - mmq_y = MMQ_Y_Q5_0_RDNA1; - nwarps = NWARPS_Q5_0_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q5_0_AMPERE; - mmq_y = MMQ_Y_Q5_0_AMPERE; - nwarps = NWARPS_Q5_0_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - 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 dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); - - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q5_0<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q5_0<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } -} - -static void ggml_mul_mat_q5_1_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) { - - int id = ggml_cuda_get_device(); - const int compute_capability = ggml_cuda_info().devices[id].cc; - - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q5_1_RDNA2; - mmq_y = MMQ_Y_Q5_1_RDNA2; - nwarps = NWARPS_Q5_1_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q5_1_RDNA1; - mmq_y = MMQ_Y_Q5_1_RDNA1; - nwarps = NWARPS_Q5_1_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q5_1_AMPERE; - mmq_y = MMQ_Y_Q5_1_AMPERE; - nwarps = NWARPS_Q5_1_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - 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 dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); - - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q5_1<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q5_1<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } -} - -static void ggml_mul_mat_q8_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) { - - int id = ggml_cuda_get_device(); - const int compute_capability = ggml_cuda_info().devices[id].cc; - - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q8_0_RDNA2; - mmq_y = MMQ_Y_Q8_0_RDNA2; - nwarps = NWARPS_Q8_0_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q8_0_RDNA1; - mmq_y = MMQ_Y_Q8_0_RDNA1; - nwarps = NWARPS_Q8_0_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q8_0_AMPERE; - mmq_y = MMQ_Y_Q8_0_AMPERE; - nwarps = NWARPS_Q8_0_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - 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 dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); - - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q8_0<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q8_0<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } -} - -static void ggml_mul_mat_q2_K_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - - int id = ggml_cuda_get_device(); - const int compute_capability = ggml_cuda_info().devices[id].cc; - - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q2_K_RDNA2; - mmq_y = MMQ_Y_Q2_K_RDNA2; - nwarps = NWARPS_Q2_K_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q2_K_RDNA1; - mmq_y = MMQ_Y_Q2_K_RDNA1; - nwarps = NWARPS_Q2_K_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q2_K_AMPERE; - mmq_y = MMQ_Y_Q2_K_AMPERE; - nwarps = NWARPS_Q2_K_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - 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 dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); - - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q2_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q2_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } -} - -static void ggml_mul_mat_q3_K_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - -#if QK_K == 256 - - int id = ggml_cuda_get_device(); - const int compute_capability = ggml_cuda_info().devices[id].cc; - - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q3_K_RDNA2; - mmq_y = MMQ_Y_Q3_K_RDNA2; - nwarps = NWARPS_Q3_K_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q3_K_RDNA1; - mmq_y = MMQ_Y_Q3_K_RDNA1; - nwarps = NWARPS_Q3_K_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q3_K_AMPERE; - mmq_y = MMQ_Y_Q3_K_AMPERE; - nwarps = NWARPS_Q3_K_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - 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 dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); - - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q3_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q3_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } -#endif -} - -static void ggml_mul_mat_q4_K_q8_1_cuda( - 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) { - - int id = ggml_cuda_get_device(); - const int compute_capability = ggml_cuda_info().devices[id].cc; - - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q4_K_RDNA2; - mmq_y = MMQ_Y_Q4_K_RDNA2; - nwarps = NWARPS_Q4_K_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q4_K_RDNA1; - mmq_y = MMQ_Y_Q4_K_RDNA1; - nwarps = NWARPS_Q4_K_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q4_K_AMPERE; - mmq_y = MMQ_Y_Q4_K_AMPERE; - nwarps = NWARPS_Q4_K_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - 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 dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); - - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q4_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q4_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } -} - -static void ggml_mul_mat_q5_K_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - - int id = ggml_cuda_get_device(); - const int compute_capability = ggml_cuda_info().devices[id].cc; - - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q5_K_RDNA2; - mmq_y = MMQ_Y_Q5_K_RDNA2; - nwarps = NWARPS_Q5_K_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q5_K_RDNA1; - mmq_y = MMQ_Y_Q5_K_RDNA1; - nwarps = NWARPS_Q5_K_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q5_K_AMPERE; - mmq_y = MMQ_Y_Q5_K_AMPERE; - nwarps = NWARPS_Q5_K_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - 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 dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); - - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q5_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q5_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } -} - -static void ggml_mul_mat_q6_K_q8_1_cuda( - const void * vx, const void * vy, float * dst, const int ncols_x, const int nrows_x, - const int ncols_y, const int nrows_y, const int nrows_dst, cudaStream_t stream) { - - int id = ggml_cuda_get_device(); - const int compute_capability = ggml_cuda_info().devices[id].cc; - - int mmq_x, mmq_y, nwarps; - if (compute_capability >= CC_RDNA2) { - mmq_x = MMQ_X_Q6_K_RDNA2; - mmq_y = MMQ_Y_Q6_K_RDNA2; - nwarps = NWARPS_Q6_K_RDNA2; - } else if (compute_capability >= CC_OFFSET_AMD) { - mmq_x = MMQ_X_Q6_K_RDNA1; - mmq_y = MMQ_Y_Q6_K_RDNA1; - nwarps = NWARPS_Q6_K_RDNA1; - } else if (compute_capability >= CC_VOLTA) { - mmq_x = MMQ_X_Q6_K_AMPERE; - mmq_y = MMQ_Y_Q6_K_AMPERE; - nwarps = NWARPS_Q6_K_AMPERE; - } else if (compute_capability >= MIN_CC_DP4A) { - 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 dim3 block_nums(block_num_x, block_num_y, 1); - const dim3 block_dims(WARP_SIZE, nwarps, 1); - - if (nrows_x % mmq_y == 0) { - const bool need_check = false; - mul_mat_q6_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } else { - const bool need_check = true; - mul_mat_q6_K<<>> - (vx, vy, dst, ncols_x, nrows_x, ncols_y, nrows_y, nrows_dst); - } -} +#define MMQ_SWITCH_CASE(type_suffix) \ + case GGML_TYPE_Q##type_suffix: if (row_diff % arch_config.y == 0) { \ + const bool need_check = false; \ + mul_mat_q##type_suffix<<>> \ + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst); \ + } else { \ + const bool need_check = true; \ + mul_mat_q##type_suffix<<>> \ + (src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst); \ + } break; \ void ggml_cuda_op_mul_mat_q( ggml_backend_cuda_context & ctx, @@ -2190,47 +1458,84 @@ void ggml_cuda_op_mul_mat_q( const int64_t row_diff = row_high - row_low; int id = ggml_cuda_get_device(); + const int compute_capability = ggml_cuda_info().devices[id].cc; // 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 = id == ctx.device ? ne0 : row_diff; + mmq_config_t mmq_config; + switch (src0->type) { case GGML_TYPE_Q4_0: - ggml_mul_mat_q4_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + mmq_config = MMQ_CONFIG_Q4_0; break; case GGML_TYPE_Q4_1: - ggml_mul_mat_q4_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + mmq_config = MMQ_CONFIG_Q4_1; break; case GGML_TYPE_Q5_0: - ggml_mul_mat_q5_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + mmq_config = MMQ_CONFIG_Q5_0; break; case GGML_TYPE_Q5_1: - ggml_mul_mat_q5_1_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + mmq_config = MMQ_CONFIG_Q5_1; break; case GGML_TYPE_Q8_0: - ggml_mul_mat_q8_0_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + mmq_config = MMQ_CONFIG_Q8_0; break; case GGML_TYPE_Q2_K: - ggml_mul_mat_q2_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + mmq_config = MMQ_CONFIG_Q2_K; break; case GGML_TYPE_Q3_K: - ggml_mul_mat_q3_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + mmq_config = MMQ_CONFIG_Q3_K; break; case GGML_TYPE_Q4_K: - ggml_mul_mat_q4_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + mmq_config = MMQ_CONFIG_Q4_K; break; case GGML_TYPE_Q5_K: - ggml_mul_mat_q5_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + mmq_config = MMQ_CONFIG_Q5_K; break; case GGML_TYPE_Q6_K: - ggml_mul_mat_q6_K_q8_1_cuda(src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff, src1_ncols, src1_padded_row_size, nrows_dst, stream); + mmq_config = MMQ_CONFIG_Q6_K; break; default: GGML_ASSERT(false); break; } + mmq_arch_config_t arch_config; + if (compute_capability >= CC_RDNA2) { + arch_config = mmq_config.rdna2; + } else if (compute_capability >= CC_OFFSET_AMD) { + arch_config = mmq_config.rdna1; + } else if (compute_capability >= CC_VOLTA) { + arch_config = mmq_config.ampere; + } else if (compute_capability >= MIN_CC_DP4A) { + arch_config = mmq_config.pascal; + } else { + GGML_ASSERT(false); + } + + const int block_num_x = (row_diff + arch_config.y - 1) / arch_config.y; + const int block_num_y = (src1_ncols + arch_config.x - 1) / arch_config.x; + const dim3 block_nums(block_num_x, block_num_y, 1); + const dim3 block_dims(WARP_SIZE, arch_config.nwarps, 1); + + switch (src0->type) { + MMQ_SWITCH_CASE(4_0) + MMQ_SWITCH_CASE(4_1) + MMQ_SWITCH_CASE(5_0) + MMQ_SWITCH_CASE(5_1) + MMQ_SWITCH_CASE(8_0) + MMQ_SWITCH_CASE(2_K) + MMQ_SWITCH_CASE(3_K) + MMQ_SWITCH_CASE(4_K) + MMQ_SWITCH_CASE(5_K) + MMQ_SWITCH_CASE(6_K) + default: + GGML_ASSERT(false); + break; + } + GGML_UNUSED(src1); GGML_UNUSED(dst); GGML_UNUSED(src1_ddf_i); From 11474e756de3f56b760986e73086d40e787e52f8 Mon Sep 17 00:00:00 2001 From: Amir Date: Tue, 21 May 2024 17:13:12 +0300 Subject: [PATCH 070/181] examples: cache hf model when --model not provided (#7353) * examples: cache hf model when --model not provided * examples: cache hf model when --model not provided * examples: cache hf model when --model not provided * examples: cache hf model when --model not provided * examples: cache hf model when --model not provided --- common/common.cpp | 32 +++++++++++++++++++++++++++++++- common/common.h | 1 + examples/main/README.md | 2 ++ 3 files changed, 34 insertions(+), 1 deletion(-) diff --git a/common/common.cpp b/common/common.cpp index e624fc7f3..ae11650b4 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1354,7 +1354,12 @@ void gpt_params_handle_model_default(gpt_params & params) { } params.hf_file = params.model; } else if (params.model.empty()) { - params.model = "models/" + string_split(params.hf_file, '/').back(); + std::string cache_directory = get_cache_directory(); + const bool success = create_directory_with_parents(cache_directory); + if (!success) { + throw std::runtime_error("failed to create cache directory: " + cache_directory); + } + params.model = cache_directory + string_split(params.hf_file, '/').back(); } } else if (!params.model_url.empty()) { if (params.model.empty()) { @@ -2516,6 +2521,31 @@ bool create_directory_with_parents(const std::string & path) { #endif // _WIN32 } +std::string get_cache_directory() { + std::string cache_directory = ""; + if (getenv("LLAMA_CACHE")) { + cache_directory = std::getenv("LLAMA_CACHE"); + if (cache_directory.back() != DIRECTORY_SEPARATOR) { + cache_directory += DIRECTORY_SEPARATOR; + } + } else { +#ifdef __linux__ + if (std::getenv("XDG_CACHE_HOME")) { + cache_directory = std::getenv("XDG_CACHE_HOME"); + } else { + cache_directory = std::getenv("HOME") + std::string("/.cache/"); + } +#elif defined(__APPLE__) + cache_directory = std::getenv("HOME") + std::string("/Library/Caches/"); +#elif defined(_WIN32) + cache_directory = std::getenv("APPDATA"); +#endif // __linux__ + cache_directory += "llama.cpp"; + cache_directory += DIRECTORY_SEPARATOR; + } + return cache_directory; +} + void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector & data) { if (data.empty()) { fprintf(stream, "%s:\n", prop_name); diff --git a/common/common.h b/common/common.h index 566490e2f..a8e5e50e6 100644 --- a/common/common.h +++ b/common/common.h @@ -281,6 +281,7 @@ bool llama_should_add_bos_token(const llama_model * model); // bool create_directory_with_parents(const std::string & path); +std::string get_cache_directory(); void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector & data); void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector & data); void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data); diff --git a/examples/main/README.md b/examples/main/README.md index 97e2ae4c2..ee930f4e7 100644 --- a/examples/main/README.md +++ b/examples/main/README.md @@ -325,3 +325,5 @@ These options provide extra functionality and customization when running the LLa - `-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. - `--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. + +- `-hfr URL --hf-repo URL`: The url to the Hugging Face model repository. Used in conjunction with `--hf-file` or `-hff`. The model is downloaded and stored in the file provided by `-m` or `--model`. If `-m` is not provided, the model is auto-stored in the path specified by the `LLAMA_CACHE` environment variable or in an OS-specific local cache. From c3f8d583560b4f261fa21c976793e538c60cd66c Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 21 May 2024 19:53:48 +0300 Subject: [PATCH 071/181] tests : test-tokenizer-0.sh print more info (#7402) --- convert-hf-to-gguf-update.py | 2 +- convert-hf-to-gguf.py | 2 +- tests/test-tokenizer-0.sh | 9 +++++++-- 3 files changed, 9 insertions(+), 4 deletions(-) diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py index 45404b32b..1923b88ba 100755 --- a/convert-hf-to-gguf-update.py +++ b/convert-hf-to-gguf-update.py @@ -72,7 +72,7 @@ models = [ {"name": "mpt", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/mosaicml/mpt-7b", }, {"name": "starcoder", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/bigcode/starcoder2-3b", }, {"name": "gpt-2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/openai-community/gpt2", }, - {"name": "stablelm", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b", }, + {"name": "stablelm2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b", }, {"name": "refact", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/smallcloudai/Refact-1_6-base", }, {"name": "command-r", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/CohereForAI/c4ai-command-r-v01", }, {"name": "qwen2", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/Qwen/Qwen1.5-7B", }, diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 1acf45bf2..6357d4034 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -447,7 +447,7 @@ class Model: # ref: https://huggingface.co/openai-community/gpt2 res = "gpt-2" if chkhsh == "32d85c31273f8019248f2559fed492d929ea28b17e51d81d3bb36fff23ca72b3": - # ref: https://huggingface.co/stabilityai/stablelm-2-1_6b + # ref: https://huggingface.co/stabilityai/stablelm-2-zephyr-1_6b res = "stablelm2" if chkhsh == "6221ad2852e85ce96f791f476e0b390cf9b474c9e3d1362f53a24a06dc8220ff": # ref: https://huggingface.co/smallcloudai/Refact-1_6-base diff --git a/tests/test-tokenizer-0.sh b/tests/test-tokenizer-0.sh index 2fb8632d8..1fec8bbf1 100755 --- a/tests/test-tokenizer-0.sh +++ b/tests/test-tokenizer-0.sh @@ -17,10 +17,15 @@ make -j tests/test-tokenizer-0 printf "Testing %s on %s ...\n" $name $input -python3 ./tests/test-tokenizer-0.py ./models/tokenizers/$name --fname-tok $input > /tmp/test-tokenizer-0-$name-py.log 2>&1 -cat /tmp/test-tokenizer-0-$name-py.log | grep "tokenized in" +set -e +printf "Tokenizing using (py) Python AutoTokenizer ...\n" +python3 ./tests/test-tokenizer-0.py ./models/tokenizers/$name --fname-tok $input > /tmp/test-tokenizer-0-$name-py.log 2>&1 + +printf "Tokenizing using (cpp) llama.cpp ...\n" ./tests/test-tokenizer-0 ./models/ggml-vocab-$name.gguf $input > /tmp/test-tokenizer-0-$name-cpp.log 2>&1 + +cat /tmp/test-tokenizer-0-$name-py.log | grep "tokenized in" cat /tmp/test-tokenizer-0-$name-cpp.log | grep "tokenized in" diff $input.tok $input.tokcpp > /dev/null 2>&1 From fcf6538ba6702c55eaec70da9a75c81d04900a72 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Tue, 21 May 2024 19:27:12 +0200 Subject: [PATCH 072/181] CUDA: fix unused warning in mmq.cu (#7442) --- ggml-cuda/mmq.cu | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/ggml-cuda/mmq.cu b/ggml-cuda/mmq.cu index 5b540d375..933d799ce 100644 --- a/ggml-cuda/mmq.cu +++ b/ggml-cuda/mmq.cu @@ -1220,6 +1220,7 @@ template static __global__ void load_tiles_q4_0, 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); #else + GGML_UNUSED(get_arch_config_device); GGML_UNUSED(vec_dot_q4_0_q8_1_mul_mat); NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A @@ -1244,6 +1245,7 @@ template static __global__ void load_tiles_q4_1, 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); #else + GGML_UNUSED(get_arch_config_device); GGML_UNUSED(vec_dot_q4_1_q8_1_mul_mat); NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A @@ -1266,6 +1268,7 @@ template static __global__ void load_tiles_q5_0, 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); #else + GGML_UNUSED(get_arch_config_device); GGML_UNUSED(vec_dot_q5_0_q8_1_mul_mat); NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A @@ -1288,6 +1291,7 @@ mul_mat_q5_1( load_tiles_q5_1, 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); #else + GGML_UNUSED(get_arch_config_device); GGML_UNUSED(vec_dot_q5_1_q8_1_mul_mat); NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A @@ -1310,6 +1314,7 @@ template static __global__ void load_tiles_q8_0, 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); #else + GGML_UNUSED(get_arch_config_device); GGML_UNUSED(vec_dot_q8_0_q8_1_mul_mat); NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A @@ -1332,6 +1337,7 @@ mul_mat_q2_K( load_tiles_q2_K, 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); #else + GGML_UNUSED(get_arch_config_device); GGML_UNUSED(vec_dot_q2_K_q8_1_mul_mat); NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A @@ -1356,6 +1362,7 @@ template static __global__ void load_tiles_q3_K, 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); #else + GGML_UNUSED(get_arch_config_device); GGML_UNUSED(vec_dot_q3_K_q8_1_mul_mat); NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A @@ -1380,6 +1387,7 @@ template static __global__ void load_tiles_q4_K, 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); #else + GGML_UNUSED(get_arch_config_device); GGML_UNUSED(vec_dot_q4_K_q8_1_mul_mat); NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A @@ -1402,6 +1410,7 @@ mul_mat_q5_K( load_tiles_q5_K, 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); #else + GGML_UNUSED(get_arch_config_device); GGML_UNUSED(vec_dot_q5_K_q8_1_mul_mat); NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A @@ -1426,6 +1435,7 @@ template static __global__ void load_tiles_q6_K, 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); #else + GGML_UNUSED(get_arch_config_device); GGML_UNUSED(vec_dot_q6_K_q8_1_mul_mat); NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A From e402de364b643cb89ea9f43057733b5d36298670 Mon Sep 17 00:00:00 2001 From: Olivier Chafik Date: Tue, 21 May 2024 20:40:00 +0100 Subject: [PATCH 073/181] `grammars`: fix resampling logic regression (#7424) --- common/sampling.cpp | 13 +++++++------ examples/main/main.cpp | 4 ++-- 2 files changed, 9 insertions(+), 8 deletions(-) diff --git a/common/sampling.cpp b/common/sampling.cpp index f0f1b92d3..7fc2e2158 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -179,7 +179,7 @@ static llama_token llama_sampling_sample_impl( struct llama_context * ctx_main, struct llama_context * ctx_cfg, const int idx, - bool is_resampling) { // Add a parameter to indicate if we are resampling + bool is_resampling) { const llama_sampling_params & params = ctx_sampling->params; const float temp = params.temp; @@ -188,8 +188,8 @@ static llama_token llama_sampling_sample_impl( const float mirostat_eta = params.mirostat_eta; std::vector original_logits; - auto cur_p = llama_sampling_prepare(ctx_sampling, ctx_main, ctx_cfg, idx, !is_resampling, &original_logits); - if (!is_resampling) { + auto cur_p = llama_sampling_prepare(ctx_sampling, ctx_main, ctx_cfg, idx, /* apply_grammar= */ is_resampling, &original_logits); + if (ctx_sampling->grammar != NULL && !is_resampling) { GGML_ASSERT(!original_logits.empty()); } llama_token id = 0; @@ -252,7 +252,7 @@ static llama_token llama_sampling_sample_impl( // Restore logits from the copy std::copy(original_logits.begin(), original_logits.end(), logits); - return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, true); // Pass true for is_resampling + return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, /* is_resampling= */ true); } } @@ -285,7 +285,8 @@ static llama_token_data_array llama_sampling_prepare_impl( // Get a pointer to the logits float * logits = llama_get_logits_ith(ctx_main, idx); - if (apply_grammar && original_logits != NULL) { + if (ctx_sampling->grammar != NULL && !apply_grammar) { + GGML_ASSERT(original_logits != NULL); // Only make a copy of the original logits if we are not applying grammar checks, not sure if I actually have to do this. *original_logits = {logits, logits + llama_n_vocab(llama_get_model(ctx_main))}; } @@ -342,7 +343,7 @@ llama_token llama_sampling_sample( struct llama_context * ctx_cfg, const int idx) { // Call the implementation function with is_resampling set to false by default - return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, false); + return llama_sampling_sample_impl(ctx_sampling, ctx_main, ctx_cfg, idx, /* is_resampling= */ false); } llama_token_data_array llama_sampling_prepare( diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 9dee41001..832b51ee0 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -707,7 +707,7 @@ int main(int argc, char ** argv) { const llama_token id = llama_sampling_sample(ctx_sampling, ctx, ctx_guidance); - llama_sampling_accept(ctx_sampling, ctx, id, true); + llama_sampling_accept(ctx_sampling, ctx, id, /* apply_grammar= */ true); LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, ctx_sampling->prev).c_str()); @@ -728,7 +728,7 @@ int main(int argc, char ** argv) { // push the prompt in the sampling context in order to apply repetition penalties later // for the prompt, we don't apply grammar rules - llama_sampling_accept(ctx_sampling, ctx, embd_inp[n_consumed], false); + llama_sampling_accept(ctx_sampling, ctx, embd_inp[n_consumed], /* apply_grammar= */ false); ++n_consumed; if ((int) embd.size() >= params.n_batch) { From 6369bf04336ab60e5c892dd77a3246df91015147 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 21 May 2024 23:03:42 +0300 Subject: [PATCH 074/181] metal : handle F16 inf values, fix FA partial offload (#7434) ggml-ci --- ggml-metal.metal | 27 ++++++++++++--------------- 1 file changed, 12 insertions(+), 15 deletions(-) diff --git a/ggml-metal.metal b/ggml-metal.metal index 386e9195f..cf262e834 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -2204,11 +2204,7 @@ kernel void kernel_flash_attn_ext_f16( // pointer to the mask device const half * mp = (device const half *) (mask + iq1*nb31); - // prepare diagonal scale matrix - simdgroup_float8x8 mscale(scale); - - // prepare diagonal slope matrix - simdgroup_float8x8 mslope(1.0f); + float slope = 1.0f; // ALiBi if (max_bias > 0.0f) { @@ -2217,7 +2213,7 @@ kernel void kernel_flash_attn_ext_f16( const float base = h < n_head_log2 ? m0 : m1; const int exph = h < n_head_log2 ? h + 1 : 2*(h - n_head_log2) + 1; - mslope = simdgroup_float8x8(pow(base, exph)); + slope = pow(base, exph); } // loop over the KV cache @@ -2242,18 +2238,20 @@ kernel void kernel_flash_attn_ext_f16( simdgroup_multiply_accumulate(mqk, mq[i], mk, mqk); } + simdgroup_store(mqk, ss + 8*cc, TF, 0, false); + + const short tx = tiisg%4; + const short ty = tiisg/4; + if (mask != q) { // mqk = mqk*scale + mask*slope - simdgroup_half8x8 mm; - simdgroup_load(mm, mp + ic + 8*cc, nb31/sizeof(half), 0, false); - simdgroup_multiply(mm, mslope, mm); - simdgroup_multiply_accumulate(mqk, mqk, mscale, mm); + ss[8*cc + ty*TF + 2*tx + 0] = scale*ss[8*cc + ty*TF + 2*tx + 0] + slope*mp[ic + 8*cc + ty*nb31/sizeof(half) + 2*tx + 0]; + ss[8*cc + ty*TF + 2*tx + 1] = scale*ss[8*cc + ty*TF + 2*tx + 1] + slope*mp[ic + 8*cc + ty*nb31/sizeof(half) + 2*tx + 1]; } else { // mqk = mqk*scale - simdgroup_multiply(mqk, mscale, mqk); + ss[8*cc + ty*TF + 2*tx + 0] *= scale; + ss[8*cc + ty*TF + 2*tx + 1] *= scale; } - - simdgroup_store(mqk, ss + 8*cc, TF, 0, false); } } @@ -2816,8 +2814,7 @@ kernel void kernel_cpy_f32_f16( for (int64_t i00 = tpitg.x; i00 < ne00; i00 += ntg.x) { device const float * src = (device float *)((device char *) src0 + i03*nb03 + i02*nb02 + i01*nb01 + i00*nb00); - // TODO: is there a better way to handle -INFINITY? - dst_data[i00] = src[0] == -INFINITY ? -MAXHALF : src[0]; + dst_data[i00] = src[0]; } } From 201cc11afa0a1950e1f632390b2ac6c937a0d8f0 Mon Sep 17 00:00:00 2001 From: liuwei-git <14815172+liuwei-git@users.noreply.github.com> Date: Wed, 22 May 2024 04:28:32 +0800 Subject: [PATCH 075/181] llama : add phi3 128K model support (#7225) * add phi3 128k support in convert-hf-to-gguf * add phi3 128k support in cuda * address build warnings on llama.cpp * adjust index value in cuda long rope freq factors * add long rope support in ggml cpu backend * make freq factors only depend on ctx size * remove unused rope scaling type 'su' frin gguf converter * fix flint warnings on convert-hf-to-gguf.py * set to the short freq factor when context size is small than trained context size * add one line of comments * metal : support rope freq_factors * ggml : update ggml_rope_ext API to support freq. factors * backends : add dev messages to support rope freq. factors * minor : style * tests : update to use new rope API * backends : fix pragma semicolons * minor : cleanup * llama : move rope factors from KV header to tensors * llama : remove tmp assert * cuda : fix compile warning * convert : read/write n_head_kv * llama : fix uninitialized tensors --------- Co-authored-by: Georgi Gerganov --- convert-hf-to-gguf.py | 49 +++- examples/finetune/finetune.cpp | 4 +- .../train-text-from-scratch.cpp | 4 +- ggml-cuda/rope.cu | 72 +++-- ggml-kompute.cpp | 4 + ggml-metal.m | 121 ++++---- ggml-metal.metal | 6 +- ggml-sycl.cpp | 3 + ggml-vulkan.cpp | 4 + ggml.c | 88 +++++- ggml.h | 49 +++- gguf-py/gguf/constants.py | 17 +- gguf-py/gguf/gguf_writer.py | 3 + llama.cpp | 277 +++++++++++------- tests/test-backend-ops.cpp | 16 +- 15 files changed, 484 insertions(+), 233 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 6357d4034..daad1c4fc 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -14,6 +14,7 @@ from pathlib import Path from hashlib import sha256 from typing import TYPE_CHECKING, Any, Callable, ContextManager, Iterable, Iterator, Sequence, TypeVar, cast +import math import numpy as np import torch @@ -1784,23 +1785,59 @@ class Phi3MiniModel(Model): def set_gguf_parameters(self): block_count = self.find_hparam(["num_hidden_layers", "n_layer"]) - rot_pct = 1.0 n_embd = self.find_hparam(["hidden_size", "n_embd"]) n_head = self.find_hparam(["num_attention_heads", "n_head"]) + n_head_kv = self.find_hparam(["num_key_value_heads", "n_head_kv"]) rms_eps = self.find_hparam(["rms_norm_eps"]) + max_pos_embds = self.find_hparam(["n_positions", "max_position_embeddings"]) + orig_max_pos_embds = self.find_hparam(["original_max_position_embeddings"]) + rope_dims = n_embd // n_head self.gguf_writer.add_name("Phi3") - self.gguf_writer.add_context_length(self.find_hparam(["n_positions", "max_position_embeddings"])) - + self.gguf_writer.add_context_length(max_pos_embds) + self.gguf_writer.add_rope_scaling_orig_ctx_len(orig_max_pos_embds) self.gguf_writer.add_embedding_length(n_embd) - self.gguf_writer.add_feed_forward_length(8192) + self.gguf_writer.add_feed_forward_length(self.find_hparam(["intermediate_size"])) self.gguf_writer.add_block_count(block_count) self.gguf_writer.add_head_count(n_head) - self.gguf_writer.add_head_count_kv(n_head) + self.gguf_writer.add_head_count_kv(n_head_kv) self.gguf_writer.add_layer_norm_rms_eps(rms_eps) - self.gguf_writer.add_rope_dimension_count(int(rot_pct * n_embd) // n_head) + self.gguf_writer.add_rope_dimension_count(rope_dims) + self.gguf_writer.add_rope_freq_base(self.find_hparam(["rope_theta"])) self.gguf_writer.add_file_type(self.ftype) + # write rope scaling for long context (128k) model + rope_scaling = self.find_hparam(['rope_scaling'], True) + if (rope_scaling is None): + return + + scale = max_pos_embds / orig_max_pos_embds + + rope_scaling_type = rope_scaling.get('type', '').lower() + if len(rope_scaling_type) == 0: + raise KeyError('Missing the required key rope_scaling.type') + + if rope_scaling_type == 'su': + attn_factor = math.sqrt(1 + math.log(scale) / math.log(orig_max_pos_embds)) if scale > 1.0 else 1.0 + elif rope_scaling_type == 'yarn': + attn_factor = 0.1 * math.log(scale) + 1.0 if scale > 1.0 else 1.0 + else: + raise NotImplementedError(f'The rope scaling type {rope_scaling_type} is not supported yet') + + self.gguf_writer.add_rope_scaling_attn_factors(attn_factor) + + long_factors = rope_scaling.get('long_factor', None) + short_factors = rope_scaling.get('short_factor', None) + + if long_factors is None or short_factors is None: + raise KeyError('Missing the required key rope_scaling.long_factor or rope_scaling_short_factor') + + if len(long_factors) != len(short_factors) or len(long_factors) != rope_dims / 2: + raise ValueError(f'The length of rope long and short factors must be {rope_dims / 2}') + + self.gguf_writer.add_tensor(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ROPE_FACTORS_LONG] + ".weight", np.array(long_factors, dtype=np.float32)) + self.gguf_writer.add_tensor(gguf.TENSOR_NAMES[gguf.MODEL_TENSOR.ROPE_FACTORS_SHORT] + ".weight", np.array(short_factors, dtype=np.float32)) + @Model.register("PlamoForCausalLM") class PlamoModel(Model): diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index 22743b1bf..992426c1b 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -563,8 +563,8 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( // not capturing these, to silcence warnings const int rope_mode = 0; - return ggml_rope_custom(ctx, - t, KQ_pos, n_rot, rope_mode, n_ctx, 0, + return ggml_rope_ext(ctx, + t, KQ_pos, nullptr, n_rot, rope_mode, n_ctx, 0, rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f ); }; diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index 587418cc7..45bdfa8f5 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -301,8 +301,8 @@ static struct ggml_tensor * llama_build_train_graphs( // not capturing these, to silcence warnings const int rope_mode = 0; - return ggml_rope_custom( - ctx, t, KQ_pos, n_rot, rope_mode, n_ctx, 0, rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f + return ggml_rope_ext( + ctx, t, KQ_pos, nullptr, n_rot, rope_mode, n_ctx, 0, rope_freq_base, rope_freq_scale, 0.0f, 1.0f, 0.0f, 0.0f ); }; diff --git a/ggml-cuda/rope.cu b/ggml-cuda/rope.cu index 4b0d2e5ad..4a558f4b3 100644 --- a/ggml-cuda/rope.cu +++ b/ggml-cuda/rope.cu @@ -58,10 +58,10 @@ static __global__ void rope( dst[i + 1] = x0*sin_theta + x1*cos_theta; } -template +template static __global__ 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 + float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims, const float * freq_factors ) { const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y); @@ -88,7 +88,9 @@ static __global__ void rope_neox( float cur_rot = inv_ndims * ic - ib; const int p = has_pos ? pos[i2] : 0; - const float theta_base = p*freq_scale*powf(theta_scale, col/2.0f); + const float freq_factor = has_freq_facs ? freq_factors[ic/2] : 1.0f; + + const float theta_base = p*freq_scale*powf(theta_scale, col/2.0f)/freq_factor; float cos_theta, sin_theta; rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta); @@ -164,7 +166,7 @@ static void rope_cuda( template static void rope_neox_cuda( 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, cudaStream_t stream + float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream ) { GGML_ASSERT(ncols % 2 == 0); const dim3 block_dims(1, CUDA_ROPE_BLOCK_SIZE, 1); @@ -175,15 +177,29 @@ static void rope_neox_cuda( const float inv_ndims = -1.0f / n_dims; if (pos == nullptr) { - rope_neox<<>>( - x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, - theta_scale, inv_ndims - ); + if (freq_factors == nullptr) { + rope_neox<<>>( + x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, + theta_scale, inv_ndims, freq_factors + ); + } else { + rope_neox<<>>( + x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, + theta_scale, inv_ndims, freq_factors + ); + } } else { - rope_neox<<>>( - x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, - theta_scale, inv_ndims - ); + if (freq_factors == nullptr) { + rope_neox<<>>( + x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, + theta_scale, inv_ndims, freq_factors + ); + } else { + rope_neox<<>>( + x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, + theta_scale, inv_ndims, freq_factors + ); + } } } @@ -214,24 +230,27 @@ static void rope_cuda_f32( static void rope_neox_cuda_f16( const half * x, half * 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, cudaStream_t stream) { + float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream) { - rope_neox_cuda(x, dst, ncols, n_dims, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, stream); + rope_neox_cuda(x, dst, ncols, n_dims, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream); } static void rope_neox_cuda_f32( const float * x, float * 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, cudaStream_t stream + float freq_base, float ext_factor, float attn_factor, rope_corr_dims corr_dims, const float * freq_factors, cudaStream_t stream ) { - rope_neox_cuda(x, dst, ncols, n_dims, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, stream); + rope_neox_cuda(x, dst, ncols, n_dims, nrows, pos, freq_scale, p_delta_rows, freq_base, ext_factor, attn_factor, corr_dims, freq_factors, stream); } void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * src0 = dst->src[0]; const ggml_tensor * src1 = dst->src[1]; + const ggml_tensor * src2 = dst->src[2]; + const float * src0_d = (const float *)src0->data; const float * src1_d = (const float *)src1->data; + float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); @@ -241,7 +260,6 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { 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]; @@ -259,16 +277,22 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + const float * freq_factors = nullptr; 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_d; - } const bool is_neox = mode & 2; const bool is_glm = mode & 4; + if (is_neox) { + pos = (const int32_t *) src1_d; + + if (src2 != nullptr) { + freq_factors = (const float *) src2->data; + } + } else { + GGML_ASSERT(src2 == nullptr && "TODO: freq_factors not implemented for !is_neox"); + } + rope_corr_dims corr_dims; ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v); @@ -280,12 +304,12 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { if (src0->type == GGML_TYPE_F32) { rope_neox_cuda_f32( (const float *)src0_d, (float *)dst_d, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor, - attn_factor, corr_dims, stream + attn_factor, corr_dims, freq_factors, stream ); } else if (src0->type == GGML_TYPE_F16) { rope_neox_cuda_f16( (const half *)src0_d, (half *)dst_d, ne00, n_dims, nrows, pos, freq_scale, ne01, freq_base, ext_factor, - attn_factor, corr_dims, stream + attn_factor, corr_dims, freq_factors, stream ); } else { GGML_ASSERT(false); diff --git a/ggml-kompute.cpp b/ggml-kompute.cpp index 3f033d58b..6c6058b2a 100644 --- a/ggml-kompute.cpp +++ b/ggml-kompute.cpp @@ -1677,6 +1677,10 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml } break; case GGML_OP_ROPE: { +#pragma message("TODO: implement phi3 frequency factors support") +#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7225") + GGML_ASSERT(dst->src[2] == nullptr && "phi3 frequency factors not implemented yet"); + GGML_ASSERT(ne10 == ne02); GGML_ASSERT(src0t == dstt); // const int n_past = ((int32_t *) dst->op_params)[0]; diff --git a/ggml-metal.m b/ggml-metal.m index b0b16dbf7..5d5ad20ad 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -927,22 +927,32 @@ static enum ggml_status ggml_metal_graph_compute( const int64_t ne10 = src1 ? src1->ne[0] : 0; const int64_t ne11 = src1 ? src1->ne[1] : 0; const int64_t ne12 = src1 ? src1->ne[2] : 0; - const int64_t ne13 = src1 ? src1->ne[3] : 0; UNUSED(ne13); + const int64_t ne13 = src1 ? src1->ne[3] : 0; const uint64_t nb10 = src1 ? src1->nb[0] : 0; const uint64_t nb11 = src1 ? src1->nb[1] : 0; const uint64_t nb12 = src1 ? src1->nb[2] : 0; - const uint64_t nb13 = src1 ? src1->nb[3] : 0; UNUSED(nb13); + const uint64_t nb13 = src1 ? src1->nb[3] : 0; - const int64_t ne0 = dst ? dst->ne[0] : 0; - const int64_t ne1 = dst ? dst->ne[1] : 0; - const int64_t ne2 = dst ? dst->ne[2] : 0; - const int64_t ne3 = dst ? dst->ne[3] : 0; + const int64_t ne20 = src2 ? src2->ne[0] : 0; + const int64_t ne21 = src2 ? src2->ne[1] : 0; + const int64_t ne22 = src2 ? src2->ne[2] : 0; GGML_UNUSED(ne22); + const int64_t ne23 = src2 ? src2->ne[3] : 0; GGML_UNUSED(ne23); - const uint64_t nb0 = dst ? dst->nb[0] : 0; - const uint64_t nb1 = dst ? dst->nb[1] : 0; - const uint64_t nb2 = dst ? dst->nb[2] : 0; - const uint64_t nb3 = dst ? dst->nb[3] : 0; + const uint64_t nb20 = src2 ? src2->nb[0] : 0; GGML_UNUSED(nb20); + const uint64_t nb21 = src2 ? src2->nb[1] : 0; + const uint64_t nb22 = src2 ? src2->nb[2] : 0; + const uint64_t nb23 = src2 ? src2->nb[3] : 0; + + const int64_t ne0 = dst ? dst->ne[0] : 0; + const int64_t ne1 = dst ? dst->ne[1] : 0; + const int64_t ne2 = dst ? dst->ne[2] : 0; + const int64_t ne3 = dst ? dst->ne[3] : 0; + + const uint64_t nb0 = dst ? dst->nb[0] : 0; + const uint64_t nb1 = dst ? dst->nb[1] : 0; + const uint64_t nb2 = dst ? dst->nb[2] : 0; + const uint64_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; @@ -1785,16 +1795,6 @@ static enum ggml_status ggml_metal_graph_compute( const int n_as = src0->ne[2]; // src2 = ids - const int64_t ne20 = src2->ne[0]; - const int64_t ne21 = src2->ne[1]; - const int64_t ne22 = src2->ne[2]; GGML_UNUSED(ne22); - const int64_t ne23 = src2->ne[3]; GGML_UNUSED(ne23); - - const uint64_t nb20 = src2->nb[0]; GGML_UNUSED(nb20); - const uint64_t nb21 = src2->nb[1]; - const uint64_t nb22 = src2->nb[2]; GGML_UNUSED(nb22); - const uint64_t nb23 = src2->nb[3]; GGML_UNUSED(nb23); - const enum ggml_type src2t = src2->type; GGML_UNUSED(src2t); GGML_ASSERT(src2t == GGML_TYPE_I32); @@ -2244,7 +2244,13 @@ static enum ggml_status ggml_metal_graph_compute( // skip 3, n_ctx, used in GLM RoPE, unimplemented in metal const int n_orig_ctx = ((int32_t *) dst->op_params)[4]; - float freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow; + float freq_base; + float freq_scale; + float ext_factor; + float attn_factor; + float beta_fast; + float 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)); @@ -2252,6 +2258,15 @@ static enum ggml_status ggml_metal_graph_compute( memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + const bool is_neox = mode & 2; + const bool is_glm = mode & 4; + + GGML_ASSERT(!is_glm && "GLM RoPE not implemented in Metal"); + + if (!is_neox) { + GGML_ASSERT(id_src2 == nil && "TODO: freq_factors not implemented for !is_neox"); + } + id pipeline = nil; switch (src0->type) { @@ -2263,33 +2278,38 @@ static enum ggml_status ggml_metal_graph_compute( [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; - [encoder setBuffer:id_dst offset:offs_dst atIndex:2]; - [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:3]; - [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:4]; - [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:5]; - [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:6]; - [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:7]; - [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:8]; - [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:9]; - [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:10]; - [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:11]; - [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:12]; - [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:13]; - [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:14]; - [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:15]; - [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:16]; - [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:17]; - [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:18]; - [encoder setBytes:&n_past length:sizeof( int) atIndex:19]; - [encoder setBytes:&n_dims length:sizeof( int) atIndex:20]; - [encoder setBytes:&mode length:sizeof( int) atIndex:21]; - [encoder setBytes:&n_orig_ctx length:sizeof( int) atIndex:22]; - [encoder setBytes:&freq_base length:sizeof( float) atIndex:23]; - [encoder setBytes:&freq_scale length:sizeof( float) atIndex:24]; - [encoder setBytes:&ext_factor length:sizeof( float) atIndex:25]; - [encoder setBytes:&attn_factor length:sizeof( float) atIndex:26]; - [encoder setBytes:&beta_fast length:sizeof( float) atIndex:27]; - [encoder setBytes:&beta_slow length:sizeof( float) atIndex:28]; + if (id_src2 != nil) { + [encoder setBuffer:id_src2 offset:offs_src2 atIndex:2]; + } else { + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:2]; + } + [encoder setBuffer:id_dst offset:offs_dst atIndex:3]; + [encoder setBytes:&ne00 length:sizeof( int64_t) atIndex:4]; + [encoder setBytes:&ne01 length:sizeof( int64_t) atIndex:5]; + [encoder setBytes:&ne02 length:sizeof( int64_t) atIndex:6]; + [encoder setBytes:&ne03 length:sizeof( int64_t) atIndex:7]; + [encoder setBytes:&nb00 length:sizeof(uint64_t) atIndex:8]; + [encoder setBytes:&nb01 length:sizeof(uint64_t) atIndex:9]; + [encoder setBytes:&nb02 length:sizeof(uint64_t) atIndex:10]; + [encoder setBytes:&nb03 length:sizeof(uint64_t) atIndex:11]; + [encoder setBytes:&ne0 length:sizeof( int64_t) atIndex:12]; + [encoder setBytes:&ne1 length:sizeof( int64_t) atIndex:13]; + [encoder setBytes:&ne2 length:sizeof( int64_t) atIndex:14]; + [encoder setBytes:&ne3 length:sizeof( int64_t) atIndex:15]; + [encoder setBytes:&nb0 length:sizeof(uint64_t) atIndex:16]; + [encoder setBytes:&nb1 length:sizeof(uint64_t) atIndex:17]; + [encoder setBytes:&nb2 length:sizeof(uint64_t) atIndex:18]; + [encoder setBytes:&nb3 length:sizeof(uint64_t) atIndex:19]; + [encoder setBytes:&n_past length:sizeof( int) atIndex:20]; + [encoder setBytes:&n_dims length:sizeof( int) atIndex:21]; + [encoder setBytes:&mode length:sizeof( int) atIndex:22]; + [encoder setBytes:&n_orig_ctx length:sizeof( int) atIndex:23]; + [encoder setBytes:&freq_base length:sizeof( float) atIndex:24]; + [encoder setBytes:&freq_scale length:sizeof( float) atIndex:25]; + [encoder setBytes:&ext_factor length:sizeof( float) atIndex:26]; + [encoder setBytes:&attn_factor length:sizeof( float) atIndex:27]; + [encoder setBytes:&beta_fast length:sizeof( float) atIndex:28]; + [encoder setBytes:&beta_slow length:sizeof( float) atIndex:29]; [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } break; @@ -2535,11 +2555,6 @@ static enum ggml_status ggml_metal_graph_compute( GGML_ASSERT(!src3 || src3->ne[1] >= GGML_PAD(src0->ne[1], 8) && "the Flash-Attention Metal kernel requires the mask to be padded to 8 and at least n_queries big"); - const uint64_t nb20 = src2 ? src2->nb[0] : 0; GGML_UNUSED(nb20); - const uint64_t nb21 = src2 ? src2->nb[1] : 0; - const uint64_t nb22 = src2 ? src2->nb[2] : 0; - const uint64_t nb23 = src2 ? src2->nb[3] : 0; - const int64_t ne30 = src3 ? src3->ne[0] : 0; GGML_UNUSED(ne30); //const int64_t ne31 = src3 ? src3->ne[1] : 0; const int64_t ne32 = src3 ? src3->ne[2] : 0; GGML_UNUSED(ne32); diff --git a/ggml-metal.metal b/ggml-metal.metal index cf262e834..c5eb25280 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -1640,6 +1640,7 @@ static void rope_yarn_corr_dims( typedef void (rope_t)( device const void * src0, device const int32_t * src1, + device const float * src2, device float * dst, constant int64_t & ne00, constant int64_t & ne01, @@ -1675,6 +1676,7 @@ template kernel void kernel_rope( device const void * src0, device const int32_t * src1, + device const float * src2, device float * dst, constant int64_t & ne00, constant int64_t & ne01, @@ -1744,8 +1746,10 @@ kernel void kernel_rope( // simplified from `(ib * n_dims + ic) * inv_ndims` const float cur_rot = inv_ndims*ic - ib; + const float freq_factor = src2 != src0 ? src2[ic/2] : 1.0f; + + const float theta = theta_0 * pow(freq_base, cur_rot) / freq_factor; - const float theta = theta_0 * pow(freq_base, cur_rot); float cos_theta, sin_theta; rope_yarn(theta, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta); diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index eac8f5579..f486b6c0a 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -14454,6 +14454,9 @@ 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) { +#pragma message("TODO: implement phi3 frequency factors support") +#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7225") + GGML_ASSERT(dst->src[2] == nullptr && "phi3 frequency factors not implemented yet"); GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index aff451b63..16287a280 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -4238,6 +4238,10 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, } static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { +#pragma message("TODO: implement phi3 frequency factors support") +#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7225") + GGML_ASSERT(dst->src[2] == nullptr && "phi3 frequency factors not implemented yet"); + 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]; diff --git a/ggml.c b/ggml.c index 4bd911528..37b16b7a9 100644 --- a/ggml.c +++ b/ggml.c @@ -6231,6 +6231,7 @@ static struct ggml_tensor * ggml_rope_impl( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, + struct ggml_tensor * c, int n_dims, int mode, int n_ctx, @@ -6248,6 +6249,11 @@ static struct ggml_tensor * ggml_rope_impl( GGML_ASSERT(b->type == GGML_TYPE_I32); GGML_ASSERT(a->ne[2] == b->ne[0]); + if (c) { + GGML_ASSERT(c->type == GGML_TYPE_F32); + GGML_ASSERT(c->ne[0] >= n_dims / 2); + } + bool is_node = false; if (a->grad) { @@ -6271,6 +6277,7 @@ static struct ggml_tensor * ggml_rope_impl( result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; result->src[0] = a; result->src[1] = b; + result->src[2] = c; return result; } @@ -6283,7 +6290,7 @@ struct ggml_tensor * ggml_rope( int mode, int n_ctx) { return ggml_rope_impl( - ctx, a, b, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, false + ctx, a, b, NULL, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, false ); } @@ -6295,7 +6302,49 @@ struct ggml_tensor * ggml_rope_inplace( int mode, int n_ctx) { return ggml_rope_impl( - ctx, a, b, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, true + ctx, a, b, NULL, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, 0.0f, false, true + ); +} + +struct ggml_tensor * ggml_rope_ext( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + int n_dims, + int mode, + int n_ctx, + int n_orig_ctx, + float freq_base, + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow) { + return ggml_rope_impl( + ctx, a, b, c, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, false + ); +} + +struct ggml_tensor * ggml_rope_ext_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + int n_dims, + int mode, + int n_ctx, + int n_orig_ctx, + float freq_base, + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow) { + return ggml_rope_impl( + ctx, a, b, c, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, true ); } @@ -6314,7 +6363,7 @@ struct ggml_tensor * ggml_rope_custom( float beta_fast, float beta_slow) { return ggml_rope_impl( - ctx, a, b, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, + ctx, a, b, NULL, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, false ); } @@ -6334,27 +6383,18 @@ struct ggml_tensor * ggml_rope_custom_inplace( float beta_fast, float beta_slow) { return ggml_rope_impl( - ctx, a, b, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, + ctx, a, b, NULL, n_dims, mode, n_ctx, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow, 0.0f, false, true ); } -struct ggml_tensor * ggml_rope_xpos_inplace( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b, - int n_dims, - float base, - bool down) { - return ggml_rope_impl(ctx, a, b, n_dims, 0, 0, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, base, down, true); -} - // ggml_rope_back struct ggml_tensor * ggml_rope_back( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, + struct ggml_tensor * c, int n_dims, int mode, int n_ctx, @@ -6370,6 +6410,7 @@ struct ggml_tensor * ggml_rope_back( GGML_ASSERT(ggml_is_vector(b)); GGML_ASSERT(b->type == GGML_TYPE_I32); GGML_ASSERT(a->ne[2] == b->ne[0]); + GGML_ASSERT(c == NULL && "freq factors not implemented yet"); GGML_ASSERT((mode & 4) == 0 && "ggml_rope_back() for ChatGLM not implemented yet"); @@ -14304,6 +14345,7 @@ static void ggml_compute_forward_rope_f32( const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; + const struct ggml_tensor * src2 = dst->src[2]; if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; @@ -14363,6 +14405,17 @@ static void ggml_compute_forward_rope_f32( const bool is_neox = mode & 2; const bool is_glm = mode & 4; + const float * freq_factors = NULL; + if (is_neox) { + if (src2 != NULL) { + GGML_ASSERT(src2->type == GGML_TYPE_F32); + GGML_ASSERT(src2->ne[0] >= n_dims / 2); + freq_factors = (const float *) src2->data; + } + } else { + GGML_ASSERT(src2 == NULL && "TODO: freq_factors not implemented for mode 1"); + } + // backward process uses inverse rotation by cos and sin. // cos and sin build a rotation matrix, where the inverse is the transpose. // this essentially just switches the sign of sin. @@ -14439,10 +14492,11 @@ static void ggml_compute_forward_rope_f32( // simplified from `(ib * n_dims + ic) * inv_ndims` float cur_rot = inv_ndims * ic - ib; + float freq_factor = freq_factors ? freq_factors[ic/2] : 1.0f; float cos_theta, sin_theta; rope_yarn( - theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, + theta_base/freq_factor, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta ); sin_theta *= sin_sign; @@ -18387,6 +18441,7 @@ static struct ggml_tensor * ggml_sub_or_set(struct ggml_context * ctx, struct gg static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor * tensor, struct ggml_hash_set zero_table) { struct ggml_tensor * src0 = tensor->src[0]; struct ggml_tensor * src1 = tensor->src[1]; + struct ggml_tensor * src2 = tensor->src[2]; switch (tensor->op) { case GGML_OP_DUP: @@ -18918,6 +18973,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor ggml_rope_back(ctx, tensor->grad, src1, + src2, n_dims, mode, n_ctx, @@ -18957,6 +19013,7 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor ggml_rope_impl(ctx, tensor->grad, src1, + src2, n_dims, mode, n_ctx, @@ -19038,7 +19095,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor masked); } - struct ggml_tensor * src2 = tensor->src[2]; const int64_t elem_q = ggml_nelements(src0); const int64_t elem_k = ggml_nelements(src1); const int64_t elem_v = ggml_nelements(src2); diff --git a/ggml.h b/ggml.h index 774757101..35ac9110c 100644 --- a/ggml.h +++ b/ggml.h @@ -1465,6 +1465,7 @@ extern "C" { // if mode & 4 == 1, ChatGLM style // // b is an int32 vector with size a->ne[2], it contains the positions + // c is freq factors (e.g. phi3-128k), (optional) GGML_API struct ggml_tensor * ggml_rope( struct ggml_context * ctx, struct ggml_tensor * a, @@ -1483,10 +1484,11 @@ extern "C" { int n_ctx); // custom RoPE - GGML_API struct ggml_tensor * ggml_rope_custom( + GGML_API struct ggml_tensor * ggml_rope_ext( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, + struct ggml_tensor * c, int n_dims, int mode, int n_ctx, @@ -1499,7 +1501,23 @@ extern "C" { float beta_slow); // in-place, returns view(a) - GGML_API struct ggml_tensor * ggml_rope_custom_inplace( + GGML_API struct ggml_tensor * ggml_rope_ext_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + struct ggml_tensor * c, + int n_dims, + int mode, + int n_ctx, + int n_orig_ctx, + float freq_base, + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow); + + GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_rope_custom( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, @@ -1512,20 +1530,28 @@ extern "C" { float ext_factor, float attn_factor, float beta_fast, - float beta_slow); + float beta_slow), + "use ggml_rope_ext instead"); - // compute correction dims for YaRN RoPE scaling - 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]); - - // xPos RoPE, in-place, returns view(a) - GGML_API struct ggml_tensor * ggml_rope_xpos_inplace( + GGML_DEPRECATED(GGML_API struct ggml_tensor * ggml_rope_custom_inplace( struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, int n_dims, - float base, - bool down); + int mode, + int n_ctx, + int n_orig_ctx, + float freq_base, + float freq_scale, + float ext_factor, + float attn_factor, + float beta_fast, + float beta_slow), + "use ggml_rope_ext_inplace instead"); + + // compute correction dims for YaRN RoPE scaling + 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]); // rotary position embedding backward, i.e compute dx from dy // a - dy @@ -1533,6 +1559,7 @@ extern "C" { struct ggml_context * ctx, struct ggml_tensor * a, struct ggml_tensor * b, + struct ggml_tensor * c, int n_dims, int mode, int n_ctx, diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 692120f4d..42df2e4d0 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -57,12 +57,13 @@ class Keys: CAUSAL = "{arch}.attention.causal" class Rope: - DIMENSION_COUNT = "{arch}.rope.dimension_count" - FREQ_BASE = "{arch}.rope.freq_base" - SCALING_TYPE = "{arch}.rope.scaling.type" - SCALING_FACTOR = "{arch}.rope.scaling.factor" - SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length" - SCALING_FINETUNED = "{arch}.rope.scaling.finetuned" + DIMENSION_COUNT = "{arch}.rope.dimension_count" + FREQ_BASE = "{arch}.rope.freq_base" + SCALING_TYPE = "{arch}.rope.scaling.type" + SCALING_FACTOR = "{arch}.rope.scaling.factor" + SCALING_ATTN_FACTOR = "{arch}.rope.scaling.attn_factor" + SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length" + SCALING_FINETUNED = "{arch}.rope.scaling.finetuned" class SSM: CONV_KERNEL = "{arch}.ssm.conv_kernel" @@ -148,6 +149,8 @@ class MODEL_TENSOR(IntEnum): OUTPUT = auto() OUTPUT_NORM = auto() ROPE_FREQS = auto() + ROPE_FACTORS_LONG = auto() + ROPE_FACTORS_SHORT = auto() ATTN_Q = auto() ATTN_K = auto() ATTN_V = auto() @@ -225,6 +228,8 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = { MODEL_TENSOR.OUTPUT_NORM: "output_norm", MODEL_TENSOR.OUTPUT: "output", MODEL_TENSOR.ROPE_FREQS: "rope_freqs", + MODEL_TENSOR.ROPE_FACTORS_LONG: "rope_factors_long", + MODEL_TENSOR.ROPE_FACTORS_SHORT: "rope_factors_short", MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm", MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2", MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv", diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index d5e323a52..8b41b54ea 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -433,6 +433,9 @@ class GGUFWriter: def add_rope_scaling_factor(self, value: float) -> None: self.add_float32(Keys.Rope.SCALING_FACTOR.format(arch=self.arch), value) + def add_rope_scaling_attn_factors(self, value: Sequence[float]) -> None: + self.add_float32(Keys.Rope.SCALING_ATTN_FACTOR.format(arch=self.arch), value) + def add_rope_scaling_orig_ctx_len(self, value: int) -> None: self.add_uint32(Keys.Rope.SCALING_ORIG_CTX_LEN.format(arch=self.arch), value) diff --git a/llama.cpp b/llama.cpp index d26fe559a..abff8c1c0 100644 --- a/llama.cpp +++ b/llama.cpp @@ -304,6 +304,7 @@ enum llm_kv { LLM_KV_ROPE_SCALE_LINEAR, LLM_KV_ROPE_SCALING_TYPE, LLM_KV_ROPE_SCALING_FACTOR, + LLM_KV_ROPE_SCALING_ATTN_FACTOR, LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, LLM_KV_ROPE_SCALING_FINETUNED, @@ -381,6 +382,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_ROPE_SCALE_LINEAR, "%s.rope.scale_linear" }, { LLM_KV_ROPE_SCALING_TYPE, "%s.rope.scaling.type" }, { LLM_KV_ROPE_SCALING_FACTOR, "%s.rope.scaling.factor" }, + { LLM_KV_ROPE_SCALING_ATTN_FACTOR, "%s.rope.scaling.attn_factor" }, { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, "%s.rope.scaling.original_context_length" }, { LLM_KV_ROPE_SCALING_FINETUNED, "%s.rope.scaling.finetuned" }, @@ -436,6 +438,8 @@ enum llm_tensor { LLM_TENSOR_OUTPUT, LLM_TENSOR_OUTPUT_NORM, LLM_TENSOR_ROPE_FREQS, + LLM_TENSOR_ROPE_FACTORS_LONG, + LLM_TENSOR_ROPE_FACTORS_SHORT, LLM_TENSOR_ATTN_Q, LLM_TENSOR_ATTN_K, LLM_TENSOR_ATTN_V, @@ -803,18 +807,20 @@ static const std::map> LLM_TENSOR_NA { LLM_ARCH_PHI3, { - { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, - { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, - { LLM_TENSOR_OUTPUT, "output" }, - { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, - { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, - { LLM_TENSOR_ATTN_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_DOWN, "blk.%d.ffn_down" }, - { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_TOKEN_EMBD, "token_embd" }, + { LLM_TENSOR_OUTPUT_NORM, "output_norm" }, + { LLM_TENSOR_OUTPUT, "output" }, + { LLM_TENSOR_ROPE_FACTORS_LONG, "rope_factors_long" }, + { LLM_TENSOR_ROPE_FACTORS_SHORT, "rope_factors_short" }, + { LLM_TENSOR_ATTN_NORM, "blk.%d.attn_norm" }, + { LLM_TENSOR_ATTN_QKV, "blk.%d.attn_qkv" }, + { 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_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, { @@ -1750,6 +1756,7 @@ struct llama_hparams { float f_norm_eps; float f_norm_rms_eps; + float rope_attn_factor = 1.0f; float rope_freq_base_train; float rope_freq_scale_train; uint32_t n_yarn_orig_ctx; @@ -1798,6 +1805,7 @@ struct llama_hparams { if (!is_float_close(this->f_norm_eps, other.f_norm_eps, EPSILON)) return true; if (!is_float_close(this->f_norm_rms_eps, other.f_norm_rms_eps, EPSILON)) return true; + if (!is_float_close(this->rope_attn_factor, other.rope_attn_factor, EPSILON)) return true; if (!is_float_close(this->rope_freq_base_train, other.rope_freq_base_train, EPSILON)) return true; if (!is_float_close(this->rope_freq_scale_train, other.rope_freq_scale_train, EPSILON)) return true; @@ -2103,6 +2111,10 @@ struct llama_model { struct ggml_tensor * output; struct ggml_tensor * output_b; + // long rope factors + struct ggml_tensor * rope_long = nullptr; + struct ggml_tensor * rope_short = nullptr; + std::vector layers; llama_split_mode split_mode; @@ -3306,6 +3318,39 @@ struct llama_model_loader { return get_arr_n(llm_kv(kid), result, required); } + template + bool get_arr(const std::string & key, std::vector & result, const bool required = true) { + const int kid = gguf_find_key(meta, key.c_str()); + + if (kid < 0) { + if (required) { + throw std::runtime_error(format("key not found in model: %s", key.c_str())); + } + return false; + } + + struct GGUFMeta::ArrayInfo arr_info = + GGUFMeta::GKV::get_kv(meta, kid); + + if (arr_info.gt != GGUF_TYPE_FLOAT32 && arr_info.gt != GGUF_TYPE_INT32) { + throw std::runtime_error(format("%s is not a float32 or int32 array", key.c_str())); + } + + // GGML_ASSERT(gguf_type_size(arr_info.gt) == sizeof(T)); + GGML_ASSERT((arr_info.gt != GGUF_TYPE_FLOAT32 || std::is_same::value)); + GGML_ASSERT((arr_info.gt != GGUF_TYPE_INT32 || std::is_same::value)); + + result.resize(arr_info.length); + result.assign((const T*)arr_info.data, (const T *)arr_info.data + arr_info.length); + + return true; + } + + template + bool get_arr(const enum llm_kv kid, T& result, const bool required = true) { + return get_arr(llm_kv(kid), result, required); + } + template bool get_key(const std::string & key, T & result, const bool required = true) { auto it = kv_overrides.find(key); @@ -3849,6 +3894,8 @@ static void llm_load_hparams( } hparams.rope_freq_scale_train = ropescale == 0.0f ? 1.0f : 1.0f/ropescale; + ml.get_key(LLM_KV_ROPE_SCALING_ATTN_FACTOR, hparams.rope_attn_factor, false); + // sanity check for n_rot (optional) { hparams.n_rot = (hparams.n_head == 0) ? 0 : hparams.n_embd / hparams.n_head; @@ -4880,6 +4927,7 @@ static bool llm_load_tensors( // create tensors for the weights { const int64_t n_embd = hparams.n_embd; + const int64_t n_embd_head = n_embd / hparams.n_head; const int64_t n_embd_k_gqa = hparams.n_embd_k_gqa(); const int64_t n_embd_v_gqa = hparams.n_embd_v_gqa(); const int64_t n_embd_gqa = n_embd_v_gqa; @@ -5591,6 +5639,9 @@ static bool llm_load_tensors( { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }); + model.rope_long = ml.create_tensor(ctx_input, tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight"), { n_embd_head/2 }, false); + model.rope_short = ml.create_tensor(ctx_input, tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight"), { n_embd_head/2 }, false); + // output { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }); @@ -5601,12 +5652,12 @@ static bool llm_load_tensors( ggml_context* ctx_layer = ctx_for_layer(i); ggml_context* ctx_split = ctx_for_layer_split(i); - auto& layer = model.layers[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 }, false); - layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), { n_embd, n_embd }); + 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 }); @@ -6821,17 +6872,20 @@ struct llm_build_context { cb(lctx.inp_K_shift, "K_shift", -1); ggml_set_input(lctx.inp_K_shift); + struct ggml_tensor * rope_factors = build_rope_factors(); + for (int il = 0; il < n_layer; ++il) { struct ggml_tensor * tmp = // we rotate only the first n_rot dimensions - ggml_rope_custom_inplace(ctx0, + ggml_rope_ext_inplace(ctx0, ggml_view_3d(ctx0, kv_self.k_l[il], n_embd_head_k, n_head_kv, n_ctx, ggml_row_size(kv_self.k_l[il]->type, n_embd_head_k), ggml_row_size(kv_self.k_l[il]->type, n_embd_k_gqa), 0), - lctx.inp_K_shift, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + lctx.inp_K_shift, rope_factors, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); + cb(tmp, "K_shifted", il); ggml_build_forward_expand(gf, tmp); } @@ -6934,6 +6988,17 @@ struct llm_build_context { return lctx.inp_pos; } + struct ggml_tensor * build_rope_factors() { + // choose long/short freq factors based on the context size + const auto n_ctx_pre_seq = cparams.n_ctx / cparams.n_seq_max; + + if (n_ctx_pre_seq > hparams.n_yarn_orig_ctx) { + return model.rope_long; + } + + return model.rope_short; + } + struct ggml_tensor * build_inp_out_ids() { lctx.inp_out_ids = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_outputs); cb(lctx.inp_out_ids, "inp_out_ids", -1); @@ -7041,15 +7106,15 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 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, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -7171,13 +7236,13 @@ struct llm_build_context { switch (model.type) { case MODEL_7B: - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -7283,15 +7348,15 @@ struct llm_build_context { struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); cb(Vcur, "Vcur", il); - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 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, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -7404,14 +7469,14 @@ struct llm_build_context { Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); // using mode = 2 for neox mode - Qcur = ggml_rope_custom( - ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, 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, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -7527,15 +7592,15 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 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, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -7679,15 +7744,15 @@ struct llm_build_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 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, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -8032,15 +8097,15 @@ struct llm_build_context { cb(Kcur, "Kcur", il); cb(Vcur, "Vcur", il); - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 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, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -8472,15 +8537,15 @@ struct llm_build_context { } - Qcur = ggml_rope_custom( - ctx0, Qcur, inp_pos, + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, 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, Kcur, inp_pos, + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -8592,14 +8657,14 @@ struct llm_build_context { Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); // using mode = 2 for neox mode - Qcur = ggml_rope_custom( - ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, 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, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -8703,15 +8768,15 @@ struct llm_build_context { 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, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 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, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -8817,15 +8882,15 @@ struct llm_build_context { 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, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 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, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -8969,8 +9034,8 @@ struct llm_build_context { Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); - Qcur = ggml_rope_custom( - ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); @@ -8980,8 +9045,8 @@ struct llm_build_context { Qcur = ggml_scale(ctx0, Qcur, 1.0f/sqrtf(float(n_embd_head))); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -9052,6 +9117,9 @@ struct llm_build_context { // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + // rope freq factors for 128k context + struct ggml_tensor * rope_factors = build_rope_factors(); + for (int il = 0; il < n_layer; ++il) { auto residual = inpL; @@ -9088,8 +9156,8 @@ struct llm_build_context { Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens); Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens); - Qcur = ggml_rope_custom( - ctx0, Qcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Qcur = ggml_rope_ext( + ctx0, Qcur, inp_pos, rope_factors, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); @@ -9097,8 +9165,8 @@ struct llm_build_context { Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head))); cb(Qcur, "Qcur", il); - Kcur = ggml_rope_custom( - ctx0, Kcur, inp_pos, n_rot, rope_type, 0, n_orig_ctx, + Kcur = ggml_rope_ext( + ctx0, Kcur, inp_pos, rope_factors, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Kcur, "Kcur", il); @@ -9204,14 +9272,14 @@ struct llm_build_context { struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); cb(Vcur, "Vcur", il); - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_rot, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_rot, n_head, n_tokens), inp_pos, nullptr, n_embd_head, rope_type, 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_rot, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_rot, n_head_kv, n_tokens), inp_pos, nullptr, n_embd_head, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Kcur, "Kcur", il); @@ -9412,15 +9480,15 @@ struct llm_build_context { cb(tmpk, "tmpk", il); cb(Vcur, "Vcur", il); - struct ggml_tensor * Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens), inp_pos, + struct ggml_tensor * Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, tmpq, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); cb(Qcur, "Qcur", il); - struct ggml_tensor * Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), inp_pos, + struct ggml_tensor * Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, tmpk, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -9528,15 +9596,15 @@ struct llm_build_context { // cb(Vcur, "Vcur", il); // } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 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, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -9645,15 +9713,15 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 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, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -9775,15 +9843,15 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 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, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -9895,8 +9963,8 @@ struct llm_build_context { struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); cb(Vcur, "Vcur", il); - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head_k, n_head, n_tokens), inp_pos, nullptr, n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Qcur, "Qcur", il); @@ -9904,8 +9972,8 @@ struct llm_build_context { Qcur = ggml_scale(ctx0, Qcur, 1.0f / sqrtf(float(n_embd_head_k))); cb(Qcur, "Qcur_scaled", il); - Kcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head_k, n_head_kv, n_tokens), inp_pos, nullptr, n_embd_head_k, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow); cb(Kcur, "Kcur", il); @@ -10015,15 +10083,15 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 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, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -10305,15 +10373,15 @@ struct llm_build_context { cb(Kcur, "Kcur", il); } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 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, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -10436,15 +10504,15 @@ struct llm_build_context { cb(Vcur, "Vcur", il); } - Qcur = ggml_rope_custom( - ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, n_rot, rope_type, 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, + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -15417,6 +15485,7 @@ struct llama_context * llama_new_context_with_model( cparams.yarn_ext_factor = rope_scaling_type == LLAMA_ROPE_SCALING_TYPE_YARN ? 1.0f : 0.0f; } + cparams.yarn_attn_factor *= hparams.rope_attn_factor; cparams.causal_attn = hparams.causal_attn; if (cparams.pooling_type == LLAMA_POOLING_TYPE_UNSPECIFIED) { diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index c74e253db..1493a7ca7 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1763,14 +1763,14 @@ struct test_llama : public test_llm { 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, + Qcur = ggml_rope_ext( + ctx, ggml_reshape_3d(ctx, Qcur, hp.n_embd_head, hp.n_head, hp.n_tokens), inp_pos, nullptr, 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, + Kcur = ggml_rope_ext( + ctx, ggml_reshape_3d(ctx, Kcur, hp.n_embd_head, hp.n_head_kv, hp.n_tokens), inp_pos, nullptr, hp.n_rot, 0, 0, hp.n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); @@ -1889,13 +1889,13 @@ struct test_falcon : public test_llm { 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, + Qcur = ggml_rope_ext( + ctx, Qcur, inp_pos, nullptr, 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, + Kcur = ggml_rope_ext( + ctx, Kcur, inp_pos, nullptr, hp.n_rot, 2, 0, hp.n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor, beta_fast, beta_slow ); From 3e5faa85032ec3106a2ad831bf412be9ff139f47 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 22 May 2024 11:01:35 +0300 Subject: [PATCH 076/181] cuda : fix rope + add tests (#7452) * cuda : fix rope pos data ggml-ci * ggml : drop mode & 1 == 1 support for ggml_rope ggml-ci * ggml : support freq_factors for f16 rope (CPU) ggml-ci * tests : add rope tests using frequency factors ggml-ci --- ggml-cuda/rope.cu | 4 ++-- ggml.c | 20 +++++++++++++++++-- ggml.h | 2 +- tests/test-backend-ops.cpp | 41 ++++++++++++++++++++++++-------------- 4 files changed, 47 insertions(+), 20 deletions(-) diff --git a/ggml-cuda/rope.cu b/ggml-cuda/rope.cu index 4a558f4b3..50f2cf415 100644 --- a/ggml-cuda/rope.cu +++ b/ggml-cuda/rope.cu @@ -283,9 +283,9 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const bool is_neox = mode & 2; const bool is_glm = mode & 4; - if (is_neox) { - pos = (const int32_t *) src1_d; + pos = (const int32_t *) src1_d; + if (is_neox) { if (src2 != nullptr) { freq_factors = (const float *) src2->data; } diff --git a/ggml.c b/ggml.c index 37b16b7a9..d316e3d31 100644 --- a/ggml.c +++ b/ggml.c @@ -6245,6 +6245,8 @@ static struct ggml_tensor * ggml_rope_impl( float xpos_base, bool xpos_down, bool inplace) { + GGML_ASSERT((mode & 1) == 0 && "mode & 1 == 1 is no longer supported"); + GGML_ASSERT(ggml_is_vector(b)); GGML_ASSERT(b->type == GGML_TYPE_I32); GGML_ASSERT(a->ne[2] == b->ne[0]); @@ -14413,7 +14415,7 @@ static void ggml_compute_forward_rope_f32( freq_factors = (const float *) src2->data; } } else { - GGML_ASSERT(src2 == NULL && "TODO: freq_factors not implemented for mode 1"); + GGML_ASSERT(src2 == NULL && "TODO: freq_factors not implemented for !is_neox"); } // backward process uses inverse rotation by cos and sin. @@ -14529,6 +14531,7 @@ static void ggml_compute_forward_rope_f32( } } +// TODO: deduplicate f16/f32 code static void ggml_compute_forward_rope_f16( const struct ggml_compute_params * params, struct ggml_tensor * dst, @@ -14536,6 +14539,7 @@ static void ggml_compute_forward_rope_f16( const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * src1 = dst->src[1]; + const struct ggml_tensor * src2 = dst->src[2]; if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { return; @@ -14588,6 +14592,17 @@ static void ggml_compute_forward_rope_f16( const bool is_neox = mode & 2; const bool is_glm = mode & 4; + const float * freq_factors = NULL; + if (is_neox) { + if (src2 != NULL) { + GGML_ASSERT(src2->type == GGML_TYPE_F32); + GGML_ASSERT(src2->ne[0] >= n_dims / 2); + freq_factors = (const float *) src2->data; + } + } else { + GGML_ASSERT(src2 == NULL && "TODO: freq_factors not implemented for !is_neox"); + } + // backward process uses inverse rotation by cos and sin. // cos and sin build a rotation matrix, where the inverse is the transpose. // this essentially just switches the sign of sin. @@ -14660,10 +14675,11 @@ static void ggml_compute_forward_rope_f16( // simplified from `(ib * n_dims + ic) * inv_ndims` float cur_rot = inv_ndims * ic - ib; + float freq_factor = freq_factors ? freq_factors[ic/2] : 1.0f; float cos_theta, sin_theta; rope_yarn( - theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, + theta_base/freq_factor, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta ); sin_theta *= sin_sign; diff --git a/ggml.h b/ggml.h index 35ac9110c..08835042c 100644 --- a/ggml.h +++ b/ggml.h @@ -1460,7 +1460,7 @@ extern "C" { struct ggml_tensor * b); // rotary position embedding - // if mode & 1 == 1, skip n_past elements (DEPRECATED) + // if mode & 1 == 1, skip n_past elements (NOT SUPPORTED) // if mode & 2 == 1, GPT-NeoX style // if mode & 4 == 1, ChatGLM style // diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 1493a7ca7..de74585da 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1142,20 +1142,22 @@ struct test_rope : public test_case { int n_dims; int mode; int n_ctx; + bool ff; std::string vars() override { - return VARS_TO_STR5(type, ne, n_dims, mode, n_ctx); + return VARS_TO_STR6(type, ne, n_dims, mode, n_ctx, ff); } test_rope(ggml_type type = GGML_TYPE_F32, std::array ne = {10, 10, 10, 1}, - int n_dims = 10, int mode = 0, int n_ctx = 512) - : type(type), ne(ne), n_dims(n_dims), mode(mode), n_ctx(n_ctx) {} + int n_dims = 10, int mode = 0, int n_ctx = 512, bool ff = false) + : type(type), ne(ne), n_dims(n_dims), mode(mode), n_ctx(n_ctx), ff(ff) {} ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, ne[2]); - ggml_tensor * out = ggml_rope(ctx, a, pos, n_dims, mode, n_ctx); + ggml_tensor * freq = ff ? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_dims/2) : nullptr; + ggml_tensor * out = ggml_rope_ext(ctx, a, pos, freq, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f); return out; } @@ -1169,7 +1171,12 @@ struct test_rope : public test_case { } ggml_backend_tensor_set(t, data.data(), 0, ne[2] * sizeof(int)); } else { - init_tensor_uniform(t); + if (t->ne[0] == n_dims/2) { + // frequency factors in the range [0.9f, 1.1f] + init_tensor_uniform(t, 0.9f, 1.1f); + } else { + init_tensor_uniform(t); + } } } } @@ -2188,16 +2195,20 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true, 0.1f, 8.0f)); 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 - test_cases.emplace_back(new test_rope(type, {128, 52, 10, 1}, 128, 0, 512)); // llama 30B - test_cases.emplace_back(new test_rope(type, {128, 64, 10, 1}, 128, 0, 512)); // llama 65B - test_cases.emplace_back(new test_rope(type, { 64, 1, 10, 1}, 64, 2, 512)); // neox (falcon 7B) - test_cases.emplace_back(new test_rope(type, { 64, 71, 10, 1}, 64, 2, 512)); // neox (falcon 7B) - test_cases.emplace_back(new test_rope(type, { 64, 8, 10, 1}, 64, 2, 512)); // neox (falcon 40B) - test_cases.emplace_back(new test_rope(type, { 64, 128, 10, 1}, 64, 2, 512)); // neox (falcon 40B) - test_cases.emplace_back(new test_rope(type, { 80, 32, 10, 1}, 20, 2, 512)); // neox (stablelm) - test_cases.emplace_back(new test_rope(type, { 80, 32, 10, 1}, 32, 2, 512)); // neox (phi-2) + // TODO: ff not supported yet for !neox + test_cases.emplace_back(new test_rope(type, {128, 32, 10, 1}, 128, 0, 512, false)); // llama 7B + test_cases.emplace_back(new test_rope(type, {128, 40, 10, 1}, 128, 0, 512, false)); // llama 13B + test_cases.emplace_back(new test_rope(type, {128, 52, 10, 1}, 128, 0, 512, false)); // llama 30B + test_cases.emplace_back(new test_rope(type, {128, 64, 10, 1}, 128, 0, 512, false)); // llama 65B + + for (bool ff : {false, true}) { // freq_factors + test_cases.emplace_back(new test_rope(type, { 64, 1, 10, 1}, 64, 2, 512, ff)); // neox (falcon 7B) + test_cases.emplace_back(new test_rope(type, { 64, 71, 10, 1}, 64, 2, 512, ff)); // neox (falcon 7B) + test_cases.emplace_back(new test_rope(type, { 64, 8, 10, 1}, 64, 2, 512, ff)); // neox (falcon 40B) + test_cases.emplace_back(new test_rope(type, { 64, 128, 10, 1}, 64, 2, 512, ff)); // neox (falcon 40B) + test_cases.emplace_back(new test_rope(type, { 80, 32, 10, 1}, 20, 2, 512, ff)); // neox (stablelm) + test_cases.emplace_back(new test_rope(type, { 80, 32, 10, 1}, 32, 2, 512, ff)); // neox (phi-2) + } } test_cases.emplace_back(new test_concat(GGML_TYPE_F32)); From 95fb0aefab568348da159efdd370e064d1b35f97 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Wed, 22 May 2024 10:24:29 +0200 Subject: [PATCH 077/181] CUDA: remove incorrect precision check (#7454) --- ggml-cuda/fattn-tile-f32.cu | 3 --- 1 file changed, 3 deletions(-) diff --git a/ggml-cuda/fattn-tile-f32.cu b/ggml-cuda/fattn-tile-f32.cu index 130e7cbdb..54db765e2 100644 --- a/ggml-cuda/fattn-tile-f32.cu +++ b/ggml-cuda/fattn-tile-f32.cu @@ -286,9 +286,6 @@ void ggml_cuda_flash_attn_ext_tile_f32(ggml_backend_cuda_context & ctx, ggml_ten const ggml_tensor * KQV = dst; const ggml_tensor * Q = dst->src[0]; - const int32_t precision = KQV->op_params[2]; - GGML_ASSERT(precision == GGML_PREC_DEFAULT); - if (Q->ne[1] <= 16) { constexpr int cols_per_block = 16; constexpr int parallel_blocks = 4; From 9b3d83318931aa98c487baaa977626931d059e6a Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 22 May 2024 12:36:37 +0300 Subject: [PATCH 078/181] cuda : fix compile warning (#7454) --- ggml-cuda/fattn-tile-f32.cu | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/ggml-cuda/fattn-tile-f32.cu b/ggml-cuda/fattn-tile-f32.cu index 54db765e2..b8b2f69e1 100644 --- a/ggml-cuda/fattn-tile-f32.cu +++ b/ggml-cuda/fattn-tile-f32.cu @@ -283,8 +283,7 @@ void launch_fattn_tile_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * } void ggml_cuda_flash_attn_ext_tile_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * KQV = dst; - const ggml_tensor * Q = dst->src[0]; + const ggml_tensor * Q = dst->src[0]; if (Q->ne[1] <= 16) { constexpr int cols_per_block = 16; From 03d8900ebe062355e26a562379daee5f17ea099f Mon Sep 17 00:00:00 2001 From: Justine Tunney Date: Wed, 22 May 2024 07:08:18 -0400 Subject: [PATCH 079/181] llama : add missing model type names (#7445) --- llama.cpp | 3 +++ 1 file changed, 3 insertions(+) diff --git a/llama.cpp b/llama.cpp index abff8c1c0..d8c6f29a5 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3771,14 +3771,17 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { static const char * llama_model_type_name(e_model type) { switch (type) { + case MODEL_17M: return "17M"; case MODEL_22M: return "22M"; case MODEL_33M: return "33M"; case MODEL_109M: return "109M"; case MODEL_137M: return "137M"; + case MODEL_335M: return "335M"; case MODEL_0_5B: return "0.5B"; case MODEL_1B: return "1B"; case MODEL_2B: return "2B"; case MODEL_3B: return "3B"; + case MODEL_4B: return "4B"; case MODEL_7B: return "7B"; case MODEL_8B: return "8B"; case MODEL_12B: return "12B"; From fcda1128bc5f8eb7e1811708fe9d9867b9aec815 Mon Sep 17 00:00:00 2001 From: "k.h.lai" Date: Wed, 22 May 2024 20:53:21 +0800 Subject: [PATCH 080/181] vulkan: add workaround for iterator boundary check to fix clang-cl debug build (#7426) --- CMakeLists.txt | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/CMakeLists.txt b/CMakeLists.txt index 9cc60039a..c09d834fb 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -505,6 +505,12 @@ if (LLAMA_VULKAN) add_compile_definitions(GGML_USE_VULKAN) + # Workaround to the "can't dereference invalidated vector iterator" bug in clang-cl debug build + # Posssibly relevant: https://stackoverflow.com/questions/74748276/visual-studio-no-displays-the-correct-length-of-stdvector + if (MSVC AND CMAKE_CXX_COMPILER_ID STREQUAL "Clang") + add_compile_definitions(_ITERATOR_DEBUG_LEVEL=0) + endif() + if (LLAMA_VULKAN_CHECK_RESULTS) add_compile_definitions(GGML_VULKAN_CHECK_RESULTS) endif() From b18532a4efeca8796fea8e36195c81cbfd596a4a Mon Sep 17 00:00:00 2001 From: slaren Date: Wed, 22 May 2024 16:10:46 +0200 Subject: [PATCH 081/181] phi3 : duplicate rope factors in each layer (#7447) * phi3 : duplicate rope factors in each layer phi3 : set phi-3 model type as 14B model loader : simplify the process for duplicating model tensors llama-bench : remove default pg test * replace bool parameters in llama_model_loader with named flags --- examples/llama-bench/llama-bench.cpp | 2 +- llama.cpp | 178 ++++++++++++--------------- 2 files changed, 83 insertions(+), 97 deletions(-) diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 8b965e199..6bb1f70c3 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -195,7 +195,7 @@ static const cmd_params cmd_params_defaults = { /* model */ {"models/7B/ggml-model-q4_0.gguf"}, /* n_prompt */ {512}, /* n_gen */ {128}, - /* n_pg */ {{512, 128}}, + /* n_pg */ {}, /* n_batch */ {2048}, /* n_ubatch */ {512}, /* type_k */ {GGML_TYPE_F16}, diff --git a/llama.cpp b/llama.cpp index d8c6f29a5..34137c7ad 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1940,6 +1940,10 @@ struct llama_layer { // mamba bias struct ggml_tensor * ssm_conv1d_b; struct ggml_tensor * ssm_dt_b; + + // long rope factors + struct ggml_tensor * rope_long = nullptr; + struct ggml_tensor * rope_short = nullptr; }; struct llama_kv_cell { @@ -2111,10 +2115,6 @@ struct llama_model { struct ggml_tensor * output; struct ggml_tensor * output_b; - // long rope factors - struct ggml_tensor * rope_long = nullptr; - struct ggml_tensor * rope_short = nullptr; - std::vector layers; llama_split_mode split_mode; @@ -3425,11 +3425,15 @@ struct llama_model_loader { return get_tensor_meta(get_tensor_name(i)); } - struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, const struct ggml_tensor * cur) { + struct ggml_tensor * create_tensor_for(struct ggml_context * ctx, const struct ggml_tensor * cur, bool duplicated) { struct ggml_tensor * tensor = ggml_dup_tensor(ctx, cur); ggml_set_name(tensor, ggml_get_name(cur)); - n_created++; + if (duplicated) { + size_data += ggml_nbytes(cur); + } else { + n_created++; + } return tensor; } @@ -3464,14 +3468,17 @@ struct llama_model_loader { return cur; } - struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector & ne, bool required = true) { - const struct ggml_tensor * cur = check_tensor_dims(name, ne, required); + static const int TENSOR_NOT_REQUIRED = 1; + static const int TENSOR_DUPLICATED = 2; + + struct ggml_tensor * create_tensor(struct ggml_context * ctx, const std::string & name, const std::vector & ne, int flags = 0) { + const struct ggml_tensor * cur = check_tensor_dims(name, ne, !(flags & TENSOR_NOT_REQUIRED)); if (cur == NULL) { return NULL; } - return create_tensor_for(ctx, cur); + return create_tensor_for(ctx, cur, flags & TENSOR_DUPLICATED); } struct ggml_tensor * create_tensor_as_view(struct ggml_context * ctx, struct ggml_tensor * base, const std::string & name, const std::vector & ne, size_t offset, bool required = true) { @@ -4139,6 +4146,7 @@ static void llm_load_hparams( switch (hparams.n_layer) { case 24: model.type = e_model::MODEL_1B; break; case 32: model.type = e_model::MODEL_3B; break; + case 40: model.type = e_model::MODEL_14B; break; default: model.type = e_model::MODEL_UNKNOWN; } } break; @@ -4965,12 +4973,10 @@ static bool llm_load_tensors( { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); if (model.arch != LLM_ARCH_MINICPM){ - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); // if output is NULL, init from the input tok embed if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } } } @@ -4989,10 +4995,10 @@ static bool llm_load_tensors( layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); // optional bias tensors - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, false); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, false); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, false); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, false); + layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); @@ -5003,7 +5009,7 @@ static bool llm_load_tensors( } else { layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); - layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false); + layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED); if (layer.ffn_gate_exps) { layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}); layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); @@ -5045,12 +5051,10 @@ static bool llm_load_tensors( // 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}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); // if output is NULL, init from the input tok embed if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } } @@ -5073,7 +5077,7 @@ static bool llm_load_tensors( layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); - layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false); + layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, llama_model_loader::TENSOR_NOT_REQUIRED); if (layer.ffn_gate_exps) { layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}); layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); @@ -5175,11 +5179,9 @@ static bool llm_load_tensors( 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}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); if (!model.output) { - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU } } @@ -5192,8 +5194,8 @@ static bool llm_load_tensors( 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.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, false); - layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, false); + layer.attn_norm_2 = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_norm_2_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM_2, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); @@ -5211,12 +5213,10 @@ static bool llm_load_tensors( { 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}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); if (!model.output) { // needs to be on GPU - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } } @@ -5314,14 +5314,14 @@ static bool llm_load_tensors( layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}); - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, false); - layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, false); + layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); layer.wk = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd, n_embd_gqa}); layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}); - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, false); - layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, false); + layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); layer.wv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd, n_embd_gqa}); layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}); @@ -5383,18 +5383,16 @@ static bool llm_load_tensors( case LLM_ARCH_MPT: { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, false); + model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, llama_model_loader::TENSOR_NOT_REQUIRED); // output { 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}, false); + model.output_norm_b = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "bias"), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); if (!model.output) { - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // needs to be on GPU - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // needs to be on GPU } } @@ -5405,31 +5403,31 @@ static bool llm_load_tensors( 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}, false); + layer.attn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, false); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); - layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, false); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); 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}, false); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); - layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, false); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); - layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, false); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, false); - layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, false); + layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, false); - layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, false); + layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); // AWQ ScaleActivation layer - layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, false); + layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); } } break; case LLM_ARCH_STABLELM: @@ -5458,17 +5456,17 @@ static bool llm_load_tensors( layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); // optional bias tensors, present in Stable LM 2 1.6B - layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, false); - layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, false); - layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, false); + layer.bq = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bk = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_V, "bias", i), {n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); // optional q and k layernorms, present in StableLM 2 12B - layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head}, false); - layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv}, false); + layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {hparams.n_embd_head_k, hparams.n_head_kv}, llama_model_loader::TENSOR_NOT_REQUIRED); // optional FFN norm, not present in StableLM 2 12B which uses parallel residual - layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, false); - layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, false); + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); 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}); @@ -5511,12 +5509,10 @@ static bool llm_load_tensors( // 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}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); // if output is NULL, init from the input tok embed if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } } @@ -5614,8 +5610,8 @@ static bool llm_load_tensors( 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.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, false); - layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, false); + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}, llama_model_loader::TENSOR_NOT_REQUIRED); if (layer.wqkv == nullptr) { layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd}); @@ -5642,9 +5638,6 @@ static bool llm_load_tensors( { model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), { n_embd, n_vocab }); - model.rope_long = ml.create_tensor(ctx_input, tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight"), { n_embd_head/2 }, false); - model.rope_short = ml.create_tensor(ctx_input, tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight"), { n_embd_head/2 }, false); - // output { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), { n_embd }); @@ -5659,13 +5652,16 @@ static bool llm_load_tensors( 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 }, false); + layer.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), { n_embd, n_embd + 2 * n_embd_gqa }, llama_model_loader::TENSOR_NOT_REQUIRED); 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_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, 2 * n_ff }); + + layer.rope_long = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_LONG, "weight"), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); + layer.rope_short = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ROPE_FACTORS_SHORT, "weight"), { n_embd_head/2 }, llama_model_loader::TENSOR_NOT_REQUIRED | (i != 0 ? llama_model_loader::TENSOR_DUPLICATED : 0)); } } break; case LLM_ARCH_PLAMO: @@ -5834,9 +5830,7 @@ static bool llm_load_tensors( // output model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); // same as tok_embd, duplicated to allow offloading - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); // same as tok_embd, duplicated to allow offloading const int64_t n_ff = hparams.n_ff; const int64_t n_embd_head_k = hparams.n_embd_head_k; @@ -5871,12 +5865,10 @@ static bool llm_load_tensors( 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}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); // if output is NULL, init from the input tok embed if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } } @@ -5927,12 +5919,10 @@ static bool llm_load_tensors( { 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}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); // if output is NULL, init from the input tok embed, duplicated to allow offloading if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } } @@ -5993,9 +5983,7 @@ static bool llm_load_tensors( { model.output_norm = ml.create_tensor(ctx_output, tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}); // init output from the input tok embed - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } for (int i = 0; i < n_layer; ++i) { @@ -6027,12 +6015,10 @@ static bool llm_load_tensors( // output { - model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, false); + model.output = ml.create_tensor(ctx_output_split, tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_NOT_REQUIRED); // if output is NULL, init from the input tok embed if (model.output == NULL) { - model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}); - ml.n_created--; // artificial tensor - ml.size_data += ggml_nbytes(model.output); + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); } } @@ -6875,9 +6861,9 @@ struct llm_build_context { cb(lctx.inp_K_shift, "K_shift", -1); ggml_set_input(lctx.inp_K_shift); - struct ggml_tensor * rope_factors = build_rope_factors(); for (int il = 0; il < n_layer; ++il) { + struct ggml_tensor * rope_factors = build_rope_factors(il); struct ggml_tensor * tmp = // we rotate only the first n_rot dimensions ggml_rope_ext_inplace(ctx0, @@ -6991,15 +6977,15 @@ struct llm_build_context { return lctx.inp_pos; } - struct ggml_tensor * build_rope_factors() { + struct ggml_tensor * build_rope_factors(int il) { // choose long/short freq factors based on the context size const auto n_ctx_pre_seq = cparams.n_ctx / cparams.n_seq_max; if (n_ctx_pre_seq > hparams.n_yarn_orig_ctx) { - return model.rope_long; + return model.layers[il].rope_long; } - return model.rope_short; + return model.layers[il].rope_short; } struct ggml_tensor * build_inp_out_ids() { @@ -9120,14 +9106,14 @@ struct llm_build_context { // KQ_mask (mask for 1 head, it will be broadcasted to all heads) struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); - // rope freq factors for 128k context - struct ggml_tensor * rope_factors = build_rope_factors(); - for (int il = 0; il < n_layer; ++il) { auto residual = inpL; // self-attention { + // rope freq factors for 128k context + struct ggml_tensor * rope_factors = build_rope_factors(il); + struct ggml_tensor* attn_norm_output = llm_build_norm(ctx0, inpL, hparams, model.layers[il].attn_norm, NULL, From 38c03478a37e460ecd3a21155b338a83bfed7f90 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Wed, 22 May 2024 17:58:25 +0200 Subject: [PATCH 082/181] CUDA: fix FA out-of-bounds writes (#7465) --- ggml-cuda/fattn-tile-f16.cu | 4 ++++ ggml-cuda/fattn-tile-f32.cu | 4 ++++ ggml-cuda/fattn-vec-f16.cu | 6 +++++- ggml-cuda/fattn-vec-f32.cu | 6 +++++- 4 files changed, 18 insertions(+), 2 deletions(-) diff --git a/ggml-cuda/fattn-tile-f16.cu b/ggml-cuda/fattn-tile-f16.cu index 4a07ac6ad..586d469c0 100644 --- a/ggml-cuda/fattn-tile-f16.cu +++ b/ggml-cuda/fattn-tile-f16.cu @@ -238,6 +238,10 @@ static __global__ void flash_attn_tile_ext_f16( for (int j_VKQ_0 = 0; j_VKQ_0 < ncols; j_VKQ_0 += nwarps) { const int j_VKQ = j_VKQ_0 + threadIdx.y; + if (ic0 + j_VKQ >= ne01) { + return; + } + half kqsum_j = __low2half(kqsum[j_VKQ_0/nwarps]) + __high2half(kqsum[j_VKQ_0/nwarps]); kqsum_j = warp_reduce_sum(kqsum_j); diff --git a/ggml-cuda/fattn-tile-f32.cu b/ggml-cuda/fattn-tile-f32.cu index b8b2f69e1..b6ef8eb48 100644 --- a/ggml-cuda/fattn-tile-f32.cu +++ b/ggml-cuda/fattn-tile-f32.cu @@ -237,6 +237,10 @@ static __global__ void flash_attn_tile_ext_f32( for (int j_VKQ_0 = 0; j_VKQ_0 < ncols; j_VKQ_0 += nwarps) { const int j_VKQ = j_VKQ_0 + threadIdx.y; + if (ic0 + j_VKQ >= ne01) { + return; + } + float kqsum_j = kqsum[j_VKQ_0/nwarps]; kqsum_j = warp_reduce_sum(kqsum_j); diff --git a/ggml-cuda/fattn-vec-f16.cu b/ggml-cuda/fattn-vec-f16.cu index 54e1ac5d1..7352dcabf 100644 --- a/ggml-cuda/fattn-vec-f16.cu +++ b/ggml-cuda/fattn-vec-f16.cu @@ -212,6 +212,10 @@ static __global__ void flash_attn_vec_ext_f16( #pragma unroll for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) { + if (ic0 + j_VKQ >= ne01) { + break; + } + kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x]; kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]); @@ -223,7 +227,7 @@ static __global__ void flash_attn_vec_ext_f16( dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; } - if (parallel_blocks != 1 && tid < ncols) { + if (parallel_blocks != 1 && tid < ncols && ic0 + tid < ne01) { dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]); } #else diff --git a/ggml-cuda/fattn-vec-f32.cu b/ggml-cuda/fattn-vec-f32.cu index 5bcabd092..11476a6c0 100644 --- a/ggml-cuda/fattn-vec-f32.cu +++ b/ggml-cuda/fattn-vec-f32.cu @@ -200,6 +200,10 @@ static __global__ void flash_attn_vec_ext_f32( #pragma unroll for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) { + if (ic0 + j_VKQ >= ne01) { + break; + } + kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x]; kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]); @@ -211,7 +215,7 @@ static __global__ void flash_attn_vec_ext_f32( dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; } - if (parallel_blocks != 1 && tid < ncols) { + if (parallel_blocks != 1 && tid < ncols && ic0 + tid < ne01) { dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]); } } From 6ff13987ad1a9519bee13dd98b6a21cd98979aab Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 22 May 2024 20:04:20 +0300 Subject: [PATCH 083/181] common : normalize naming style (#7462) * common : normalize naming style ggml-ci * common : match declaration / definition order * zig : try to fix build --- build.zig | 12 +- common/common.cpp | 1338 +++++++++++----------- common/common.h | 90 +- common/sampling.cpp | 83 +- common/sampling.h | 5 + common/train.cpp | 2 +- examples/batched/batched.cpp | 2 +- examples/embedding/embedding.cpp | 4 +- examples/eval-callback/eval-callback.cpp | 4 +- examples/imatrix/imatrix.cpp | 4 +- examples/infill/infill.cpp | 16 +- examples/llama-bench/llama-bench.cpp | 2 +- examples/llava/llava-cli.cpp | 2 +- examples/lookahead/lookahead.cpp | 2 +- examples/lookup/lookup.cpp | 2 +- examples/main/main.cpp | 16 +- examples/parallel/parallel.cpp | 2 +- examples/perplexity/perplexity.cpp | 14 +- examples/quantize/quantize.cpp | 2 +- examples/retrieval/retrieval.cpp | 4 +- examples/server/server.cpp | 10 +- 21 files changed, 823 insertions(+), 793 deletions(-) diff --git a/build.zig b/build.zig index 96783574f..267c976b1 100644 --- a/build.zig +++ b/build.zig @@ -129,14 +129,14 @@ pub fn build(b: *std.build.Builder) !void { const clip = make.obj("clip", "examples/llava/clip.cpp"); const llava = make.obj("llava", "examples/llava/llava.cpp"); - _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, sampling, console, grammar_parser }); - _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo }); - _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo }); - _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo }); - _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, train }); + _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, sampling, json_schema_to_grammar, buildinfo, console, grammar_parser }); + _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, sampling, json_schema_to_grammar, buildinfo }); + _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, sampling, json_schema_to_grammar, buildinfo }); + _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, sampling, json_schema_to_grammar, buildinfo }); + _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, sampling, json_schema_to_grammar, buildinfo, train }); _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, train }); - const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, sampling, grammar_parser, clip, llava }); + const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, sampling, json_schema_to_grammar, buildinfo, grammar_parser, clip, llava }); if (server.target.isWindows()) { server.linkSystemLibrary("ws2_32"); } diff --git a/common/common.cpp b/common/common.cpp index ae11650b4..7500e08ff 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -73,7 +73,11 @@ using json = nlohmann::ordered_json; -int32_t get_num_physical_cores() { +// +// CPU utils +// + +int32_t cpu_get_num_physical_cores() { #ifdef __linux__ // enumerate the set of thread siblings, num entries is num cores std::unordered_set siblings; @@ -142,9 +146,9 @@ static bool is_running_on_efficiency_core(void) { return core_type == intel_atom; } -static int count_math_cpus(int cpu_count) { +static int cpu_count_math_cpus(int n_cpu) { int result = 0; - for (int cpu = 0; cpu < cpu_count; ++cpu) { + for (int cpu = 0; cpu < n_cpu; ++cpu) { if (pin_cpu(cpu)) { return -1; } @@ -162,16 +166,16 @@ static int count_math_cpus(int cpu_count) { /** * Returns number of CPUs on system that are useful for math. */ -int get_math_cpu_count() { +int32_t cpu_get_num_math() { #if defined(__x86_64__) && defined(__linux__) && !defined(__ANDROID__) - int cpu_count = sysconf(_SC_NPROCESSORS_ONLN); - if (cpu_count < 1) { - return get_num_physical_cores(); + int n_cpu = sysconf(_SC_NPROCESSORS_ONLN); + if (n_cpu < 1) { + return cpu_get_num_physical_cores(); } if (is_hybrid_cpu()) { cpu_set_t affinity; if (!pthread_getaffinity_np(pthread_self(), sizeof(affinity), &affinity)) { - int result = count_math_cpus(cpu_count); + int result = cpu_count_math_cpus(n_cpu); pthread_setaffinity_np(pthread_self(), sizeof(affinity), &affinity); if (result > 0) { return result; @@ -179,108 +183,103 @@ int get_math_cpu_count() { } } #endif - return get_num_physical_cores(); + return cpu_get_num_physical_cores(); } -void process_escapes(std::string & input) { - std::size_t input_len = input.length(); - std::size_t output_idx = 0; +// +// CLI argument parsing +// - for (std::size_t input_idx = 0; input_idx < input_len; ++input_idx) { - if (input[input_idx] == '\\' && input_idx + 1 < input_len) { - switch (input[++input_idx]) { - case 'n': input[output_idx++] = '\n'; break; - case 'r': input[output_idx++] = '\r'; break; - case 't': input[output_idx++] = '\t'; break; - case '\'': input[output_idx++] = '\''; break; - case '\"': input[output_idx++] = '\"'; break; - case '\\': input[output_idx++] = '\\'; break; - case 'x': - // Handle \x12, etc - if (input_idx + 2 < input_len) { - const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 }; - char *err_p = nullptr; - const long val = std::strtol(x, &err_p, 16); - if (err_p == x + 2) { - input_idx += 2; - input[output_idx++] = char(val); - break; - } - } - // fall through - default: input[output_idx++] = '\\'; - input[output_idx++] = input[input_idx]; break; +void gpt_params_handle_model_default(gpt_params & params) { + if (!params.hf_repo.empty()) { + // short-hand to avoid specifying --hf-file -> default it to --model + if (params.hf_file.empty()) { + if (params.model.empty()) { + throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n"); } - } else { - input[output_idx++] = input[input_idx]; + params.hf_file = params.model; + } else if (params.model.empty()) { + std::string cache_directory = fs_get_cache_directory(); + const bool success = fs_create_directory_with_parents(cache_directory); + if (!success) { + throw std::runtime_error("failed to create cache directory: " + cache_directory); + } + params.model = cache_directory + string_split(params.hf_file, '/').back(); + } + } else if (!params.model_url.empty()) { + if (params.model.empty()) { + auto f = string_split(params.model_url, '#').front(); + f = string_split(f, '?').front(); + f = string_split(f, '/').back(); + params.model = "models/" + f; + } + } else if (params.model.empty()) { + params.model = DEFAULT_MODEL_PATH; + } +} + +bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { + bool invalid_param = false; + std::string arg; + const std::string arg_prefix = "--"; + llama_sampling_params & sparams = params.sparams; + + for (int i = 1; i < argc; i++) { + arg = argv[i]; + if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { + std::replace(arg.begin(), arg.end(), '_', '-'); + } + if (!gpt_params_find_arg(argc, argv, arg, params, i, invalid_param)) { + throw std::invalid_argument("error: unknown argument: " + arg); + } + if (invalid_param) { + throw std::invalid_argument("error: invalid parameter for argument: " + arg); } } - input.resize(output_idx); + if (params.prompt_cache_all && + (params.interactive || params.interactive_first || + params.instruct)) { + + throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n"); + } + + gpt_params_handle_model_default(params); + + if (params.escape) { + string_process_escapes(params.prompt); + string_process_escapes(params.input_prefix); + string_process_escapes(params.input_suffix); + string_process_escapes(sparams.cfg_negative_prompt); + for (auto & antiprompt : params.antiprompt) { + string_process_escapes(antiprompt); + } + } + + if (!params.kv_overrides.empty()) { + params.kv_overrides.emplace_back(); + params.kv_overrides.back().key[0] = 0; + } + + return true; } bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { bool result = true; try { if (!gpt_params_parse_ex(argc, argv, params)) { - gpt_print_usage(argc, argv, gpt_params()); + gpt_params_print_usage(argc, argv, gpt_params()); exit(0); } } catch (const std::invalid_argument & ex) { fprintf(stderr, "%s\n", ex.what()); - gpt_print_usage(argc, argv, gpt_params()); + gpt_params_print_usage(argc, argv, gpt_params()); exit(1); } return result; } -bool parse_kv_override(const char * data, std::vector & overrides) { - const char * sep = strchr(data, '='); - if (sep == nullptr || sep - data >= 128) { - fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data); - return false; - } - llama_model_kv_override kvo; - std::strncpy(kvo.key, data, sep - data); - kvo.key[sep - data] = 0; - sep++; - if (strncmp(sep, "int:", 4) == 0) { - sep += 4; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; - kvo.val_i64 = std::atol(sep); - } else if (strncmp(sep, "float:", 6) == 0) { - sep += 6; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT; - kvo.val_f64 = std::atof(sep); - } else if (strncmp(sep, "bool:", 5) == 0) { - sep += 5; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL; - if (std::strcmp(sep, "true") == 0) { - kvo.val_bool = true; - } else if (std::strcmp(sep, "false") == 0) { - kvo.val_bool = false; - } else { - fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data); - return false; - } - } else if (strncmp(sep, "str:", 4) == 0) { - sep += 4; - kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR; - if (strlen(sep) > 127) { - fprintf(stderr, "%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data); - return false; - } - strncpy(kvo.val_str, sep, 127); - kvo.val_str[127] = '\0'; - } else { - fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data); - return false; - } - overrides.emplace_back(std::move(kvo)); - return true; -} - bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param) { llama_sampling_params & sparams = params.sparams; @@ -546,7 +545,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa return true; } const auto sampler_names = string_split(argv[i], ';'); - sparams.samplers_sequence = sampler_types_from_names(sampler_names, true); + sparams.samplers_sequence = llama_sampling_types_from_names(sampler_names, true); return true; } if (arg == "--sampling-seq") { @@ -554,7 +553,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa invalid_param = true; return true; } - sparams.samplers_sequence = sampler_types_from_chars(argv[i]); + sparams.samplers_sequence = llama_sampling_types_from_chars(argv[i]); return true; } if (arg == "--top-p") { @@ -1240,7 +1239,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa return true; } if (arg == "-h" || arg == "--help") { - gpt_print_usage(argc, argv, gpt_params()); + gpt_params_print_usage(argc, argv, gpt_params()); exit(0); } if (arg == "--version") { @@ -1311,7 +1310,7 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa invalid_param = true; return true; } - if (!parse_kv_override(argv[i], params.kv_overrides)) { + if (!string_parse_kv_override(argv[i], params.kv_overrides)) { fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]); invalid_param = true; return true; @@ -1345,88 +1344,14 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa return false; } -void gpt_params_handle_model_default(gpt_params & params) { - if (!params.hf_repo.empty()) { - // short-hand to avoid specifying --hf-file -> default it to --model - if (params.hf_file.empty()) { - if (params.model.empty()) { - throw std::invalid_argument("error: --hf-repo requires either --hf-file or --model\n"); - } - params.hf_file = params.model; - } else if (params.model.empty()) { - std::string cache_directory = get_cache_directory(); - const bool success = create_directory_with_parents(cache_directory); - if (!success) { - throw std::runtime_error("failed to create cache directory: " + cache_directory); - } - params.model = cache_directory + string_split(params.hf_file, '/').back(); - } - } else if (!params.model_url.empty()) { - if (params.model.empty()) { - auto f = string_split(params.model_url, '#').front(); - f = string_split(f, '?').front(); - f = string_split(f, '/').back(); - params.model = "models/" + f; - } - } else if (params.model.empty()) { - params.model = DEFAULT_MODEL_PATH; - } -} - -bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params) { - bool invalid_param = false; - std::string arg; - const std::string arg_prefix = "--"; - llama_sampling_params & sparams = params.sparams; - - for (int i = 1; i < argc; i++) { - arg = argv[i]; - if (arg.compare(0, arg_prefix.size(), arg_prefix) == 0) { - std::replace(arg.begin(), arg.end(), '_', '-'); - } - if (!gpt_params_find_arg(argc, argv, arg, params, i, invalid_param)) { - throw std::invalid_argument("error: unknown argument: " + arg); - } - if (invalid_param) { - throw std::invalid_argument("error: invalid parameter for argument: " + arg); - } - } - - if (params.prompt_cache_all && - (params.interactive || params.interactive_first || - params.instruct)) { - - throw std::invalid_argument("error: --prompt-cache-all not supported in interactive mode yet\n"); - } - - gpt_params_handle_model_default(params); - - if (params.escape) { - process_escapes(params.prompt); - process_escapes(params.input_prefix); - process_escapes(params.input_suffix); - process_escapes(sparams.cfg_negative_prompt); - for (auto & antiprompt : params.antiprompt) { - process_escapes(antiprompt); - } - } - - if (!params.kv_overrides.empty()) { - params.kv_overrides.emplace_back(); - params.kv_overrides.back().key[0] = 0; - } - - return true; -} - -void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { +void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { const llama_sampling_params & sparams = params.sparams; std::string sampler_type_chars; std::string sampler_type_names; for (const auto sampler_type : sparams.samplers_sequence) { sampler_type_chars += static_cast(sampler_type); - sampler_type_names += sampler_type_to_name_string(sampler_type) + ";"; + sampler_type_names += llama_sampling_type_to_str(sampler_type) + ";"; } sampler_type_names.pop_back(); @@ -1623,7 +1548,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { #endif // LOG_DISABLE_LOGS } -std::string get_system_info(const gpt_params & params) { +std::string gpt_params_get_system_info(const gpt_params & params) { std::ostringstream os; os << "system_info: n_threads = " << params.n_threads; @@ -1635,7 +1560,52 @@ std::string get_system_info(const gpt_params & params) { return os.str(); } -std::string gpt_random_prompt(std::mt19937 & rng) { +// +// String utils +// + +std::vector string_split(std::string input, char separator) { + std::vector parts; + size_t separator_pos = input.find(separator); + while (separator_pos != std::string::npos) { + std::string part = input.substr(0, separator_pos); + parts.emplace_back(part); + input = input.substr(separator_pos + 1); + separator_pos = input.find(separator); + } + parts.emplace_back(input); + return parts; +} + +std::string string_strip(const std::string & str) { + size_t start = 0; + size_t end = str.size(); + while (start < end && std::isspace(str[start])) { + start++; + } + while (end > start && std::isspace(str[end - 1])) { + end--; + } + return str.substr(start, end - start); +} + +std::string string_get_sortable_timestamp() { + using clock = std::chrono::system_clock; + + const clock::time_point current_time = clock::now(); + const time_t as_time_t = clock::to_time_t(current_time); + char timestamp_no_ns[100]; + std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t)); + + const int64_t ns = std::chrono::duration_cast( + current_time.time_since_epoch() % 1000000000).count(); + char timestamp_ns[11]; + snprintf(timestamp_ns, 11, "%09" PRId64, ns); + + return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns); +} + +std::string string_random_prompt(std::mt19937 & rng) { const int r = rng() % 10; switch (r) { case 0: return "So"; @@ -1653,9 +1623,96 @@ std::string gpt_random_prompt(std::mt19937 & rng) { GGML_UNREACHABLE(); } +void string_process_escapes(std::string & input) { + std::size_t input_len = input.length(); + std::size_t output_idx = 0; + + for (std::size_t input_idx = 0; input_idx < input_len; ++input_idx) { + if (input[input_idx] == '\\' && input_idx + 1 < input_len) { + switch (input[++input_idx]) { + case 'n': input[output_idx++] = '\n'; break; + case 'r': input[output_idx++] = '\r'; break; + case 't': input[output_idx++] = '\t'; break; + case '\'': input[output_idx++] = '\''; break; + case '\"': input[output_idx++] = '\"'; break; + case '\\': input[output_idx++] = '\\'; break; + case 'x': + // Handle \x12, etc + if (input_idx + 2 < input_len) { + const char x[3] = { input[input_idx + 1], input[input_idx + 2], 0 }; + char *err_p = nullptr; + const long val = std::strtol(x, &err_p, 16); + if (err_p == x + 2) { + input_idx += 2; + input[output_idx++] = char(val); + break; + } + } + // fall through + default: input[output_idx++] = '\\'; + input[output_idx++] = input[input_idx]; break; + } + } else { + input[output_idx++] = input[input_idx]; + } + } + + input.resize(output_idx); +} + +bool string_parse_kv_override(const char * data, std::vector & overrides) { + const char * sep = strchr(data, '='); + if (sep == nullptr || sep - data >= 128) { + fprintf(stderr, "%s: malformed KV override '%s'\n", __func__, data); + return false; + } + llama_model_kv_override kvo; + std::strncpy(kvo.key, data, sep - data); + kvo.key[sep - data] = 0; + sep++; + if (strncmp(sep, "int:", 4) == 0) { + sep += 4; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT; + kvo.val_i64 = std::atol(sep); + } else if (strncmp(sep, "float:", 6) == 0) { + sep += 6; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_FLOAT; + kvo.val_f64 = std::atof(sep); + } else if (strncmp(sep, "bool:", 5) == 0) { + sep += 5; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_BOOL; + if (std::strcmp(sep, "true") == 0) { + kvo.val_bool = true; + } else if (std::strcmp(sep, "false") == 0) { + kvo.val_bool = false; + } else { + fprintf(stderr, "%s: invalid boolean value for KV override '%s'\n", __func__, data); + return false; + } + } else if (strncmp(sep, "str:", 4) == 0) { + sep += 4; + kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR; + if (strlen(sep) > 127) { + fprintf(stderr, "%s: malformed KV override '%s', value cannot exceed 127 chars\n", __func__, data); + return false; + } + strncpy(kvo.val_str, sep, 127); + kvo.val_str[127] = '\0'; + } else { + fprintf(stderr, "%s: invalid type for KV override '%s'\n", __func__, data); + return false; + } + overrides.emplace_back(std::move(kvo)); + return true; +} + +// +// Filesystem utils +// + // Validate if a filename is safe to use // To validate a full path, split the path by the OS-specific path separator, and validate each part with this function -bool validate_file_name(const std::string & filename) { +bool fs_validate_filename(const std::string & filename) { if (!filename.length()) { // Empty filename invalid return false; @@ -1724,120 +1781,194 @@ bool validate_file_name(const std::string & filename) { return true; } -// -// String utils -// +// returns true if successful, false otherwise +bool fs_create_directory_with_parents(const std::string & path) { +#ifdef _WIN32 + std::wstring_convert> converter; + std::wstring wpath = converter.from_bytes(path); -std::vector string_split(std::string input, char separator) { - std::vector parts; - size_t separator_pos = input.find(separator); - while (separator_pos != std::string::npos) { - std::string part = input.substr(0, separator_pos); - parts.emplace_back(part); - input = input.substr(separator_pos + 1); - separator_pos = input.find(separator); + // if the path already exists, check whether it's a directory + const DWORD attributes = GetFileAttributesW(wpath.c_str()); + if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) { + return true; } - parts.emplace_back(input); - return parts; -} -std::string string_strip(const std::string & str) { - size_t start = 0; - size_t end = str.size(); - while (start < end && std::isspace(str[start])) { - start++; - } - while (end > start && std::isspace(str[end - 1])) { - end--; - } - return str.substr(start, end - start); -} + size_t pos_slash = 0; -std::vector sampler_types_from_names(const std::vector & names, bool allow_alt_names) { - std::unordered_map sampler_canonical_name_map { - {"top_k", llama_sampler_type::TOP_K}, - {"top_p", llama_sampler_type::TOP_P}, - {"typical_p", llama_sampler_type::TYPICAL_P}, - {"min_p", llama_sampler_type::MIN_P}, - {"tfs_z", llama_sampler_type::TFS_Z}, - {"temperature", llama_sampler_type::TEMPERATURE} - }; + // process path from front to back, procedurally creating directories + while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) { + const std::wstring subpath = wpath.substr(0, pos_slash); + const wchar_t * test = subpath.c_str(); - // since samplers names are written multiple ways - // make it ready for both system names and input names - std::unordered_map sampler_alt_name_map { - {"top-k", llama_sampler_type::TOP_K}, - {"top-p", llama_sampler_type::TOP_P}, - {"nucleus", llama_sampler_type::TOP_P}, - {"typical-p", llama_sampler_type::TYPICAL_P}, - {"typical", llama_sampler_type::TYPICAL_P}, - {"min-p", llama_sampler_type::MIN_P}, - {"tfs-z", llama_sampler_type::TFS_Z}, - {"tfs", llama_sampler_type::TFS_Z}, - {"temp", llama_sampler_type::TEMPERATURE} - }; + const bool success = CreateDirectoryW(test, NULL); + if (!success) { + const DWORD error = GetLastError(); - std::vector sampler_types; - sampler_types.reserve(names.size()); - for (const auto & name : names) - { - auto sampler_item = sampler_canonical_name_map.find(name); - if (sampler_item != sampler_canonical_name_map.end()) - { - sampler_types.push_back(sampler_item->second); - } - else - { - if (allow_alt_names) - { - sampler_item = sampler_alt_name_map.find(name); - if (sampler_item != sampler_alt_name_map.end()) - { - sampler_types.push_back(sampler_item->second); + // if the path already exists, ensure that it's a directory + if (error == ERROR_ALREADY_EXISTS) { + const DWORD attributes = GetFileAttributesW(subpath.c_str()); + if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) { + return false; } + } else { + return false; } } + + pos_slash += 1; } - return sampler_types; -} -std::vector sampler_types_from_chars(const std::string & names_string) { - std::unordered_map sampler_name_map { - {'k', llama_sampler_type::TOP_K}, - {'p', llama_sampler_type::TOP_P}, - {'y', llama_sampler_type::TYPICAL_P}, - {'m', llama_sampler_type::MIN_P}, - {'f', llama_sampler_type::TFS_Z}, - {'t', llama_sampler_type::TEMPERATURE} - }; + return true; +#else + // if the path already exists, check whether it's a directory + struct stat info; + if (stat(path.c_str(), &info) == 0) { + return S_ISDIR(info.st_mode); + } - std::vector sampler_types; - sampler_types.reserve(names_string.size()); - for (const auto & c : names_string) { - const auto sampler_item = sampler_name_map.find(c); - if (sampler_item != sampler_name_map.end()) { - sampler_types.push_back(sampler_item->second); + size_t pos_slash = 1; // skip leading slashes for directory creation + + // process path from front to back, procedurally creating directories + while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) { + const std::string subpath = path.substr(0, pos_slash); + struct stat info; + + // if the path already exists, ensure that it's a directory + if (stat(subpath.c_str(), &info) == 0) { + if (!S_ISDIR(info.st_mode)) { + return false; + } + } else { + // create parent directories + const int ret = mkdir(subpath.c_str(), 0755); + if (ret != 0) { + return false; + } } + + pos_slash += 1; } - return sampler_types; + + return true; +#endif // _WIN32 } -std::string sampler_type_to_name_string(llama_sampler_type sampler_type) { - switch (sampler_type) { - case llama_sampler_type::TOP_K: return "top_k"; - case llama_sampler_type::TFS_Z: return "tfs_z"; - case llama_sampler_type::TYPICAL_P: return "typical_p"; - case llama_sampler_type::TOP_P: return "top_p"; - case llama_sampler_type::MIN_P: return "min_p"; - case llama_sampler_type::TEMPERATURE: return "temperature"; - default : return ""; +std::string fs_get_cache_directory() { + std::string cache_directory = ""; + if (getenv("LLAMA_CACHE")) { + cache_directory = std::getenv("LLAMA_CACHE"); + if (cache_directory.back() != DIRECTORY_SEPARATOR) { + cache_directory += DIRECTORY_SEPARATOR; + } + } else { +#ifdef __linux__ + if (std::getenv("XDG_CACHE_HOME")) { + cache_directory = std::getenv("XDG_CACHE_HOME"); + } else { + cache_directory = std::getenv("HOME") + std::string("/.cache/"); + } +#elif defined(__APPLE__) + cache_directory = std::getenv("HOME") + std::string("/Library/Caches/"); +#elif defined(_WIN32) + cache_directory = std::getenv("APPDATA"); +#endif // __linux__ + cache_directory += "llama.cpp"; + cache_directory += DIRECTORY_SEPARATOR; } + return cache_directory; } + // // Model utils // +std::tuple llama_init_from_gpt_params(gpt_params & params) { + auto mparams = llama_model_params_from_gpt_params(params); + + llama_model * model = nullptr; + + if (!params.hf_repo.empty() && !params.hf_file.empty()) { + model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), mparams); + } else if (!params.model_url.empty()) { + model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), mparams); + } else { + model = llama_load_model_from_file(params.model.c_str(), mparams); + } + + if (model == NULL) { + fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str()); + return std::make_tuple(nullptr, nullptr); + } + + auto cparams = llama_context_params_from_gpt_params(params); + + llama_context * lctx = llama_new_context_with_model(model, cparams); + if (lctx == NULL) { + fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str()); + llama_free_model(model); + return std::make_tuple(nullptr, nullptr); + } + + if (!params.control_vectors.empty()) { + if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1; + if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model); + + const auto cvec = llama_control_vector_load(params.control_vectors); + if (cvec.n_embd == -1) { + llama_free(lctx); + llama_free_model(model); + return std::make_tuple(nullptr, nullptr); + } + + int err = llama_control_vector_apply(lctx, + cvec.data.data(), + cvec.data.size(), + cvec.n_embd, + params.control_vector_layer_start, + params.control_vector_layer_end); + if (err) { + llama_free(lctx); + llama_free_model(model); + return std::make_tuple(nullptr, nullptr); + } + } + + for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) { + const std::string & lora_adapter = std::get<0>(params.lora_adapter[i]); + float lora_scale = std::get<1>(params.lora_adapter[i]); + int err = llama_model_apply_lora_from_file(model, + lora_adapter.c_str(), + lora_scale, + ((i > 0) || params.lora_base.empty()) + ? NULL + : params.lora_base.c_str(), + params.n_threads); + if (err != 0) { + fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__); + llama_free(lctx); + llama_free_model(model); + return std::make_tuple(nullptr, nullptr); + } + } + + if (params.ignore_eos) { + params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY; + } + + if (params.warmup) { + LOG("warming up the model with an empty run\n"); + + std::vector tmp = { llama_token_bos(model), llama_token_eos(model), }; + llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0)); + llama_kv_cache_clear(lctx); + llama_synchronize(lctx); + llama_reset_timings(lctx); + } + + return std::make_tuple(model, lctx); +} + struct llama_model_params llama_model_params_from_gpt_params(const gpt_params & params) { auto mparams = llama_model_default_params(); @@ -1923,27 +2054,6 @@ struct llama_context_params llama_context_params_from_gpt_params(const gpt_param return cparams; } -void llama_batch_clear(struct llama_batch & batch) { - batch.n_tokens = 0; -} - -void llama_batch_add( - struct llama_batch & batch, - llama_token id, - llama_pos pos, - const std::vector & seq_ids, - bool logits) { - batch.token [batch.n_tokens] = id; - batch.pos [batch.n_tokens] = pos; - batch.n_seq_id[batch.n_tokens] = seq_ids.size(); - for (size_t i = 0; i < seq_ids.size(); ++i) { - batch.seq_id[batch.n_tokens][i] = seq_ids[i]; - } - batch.logits [batch.n_tokens] = logits; - - batch.n_tokens++; -} - #ifdef LLAMA_USE_CURL static bool starts_with(const std::string & str, const std::string & prefix) { @@ -2274,90 +2384,29 @@ struct llama_model * llama_load_model_from_hf( #endif // LLAMA_USE_CURL -std::tuple llama_init_from_gpt_params(gpt_params & params) { - auto mparams = llama_model_params_from_gpt_params(params); +// +// Batch utils +// - llama_model * model = nullptr; +void llama_batch_clear(struct llama_batch & batch) { + batch.n_tokens = 0; +} - if (!params.hf_repo.empty() && !params.hf_file.empty()) { - model = llama_load_model_from_hf(params.hf_repo.c_str(), params.hf_file.c_str(), params.model.c_str(), mparams); - } else if (!params.model_url.empty()) { - model = llama_load_model_from_url(params.model_url.c_str(), params.model.c_str(), mparams); - } else { - model = llama_load_model_from_file(params.model.c_str(), mparams); +void llama_batch_add( + struct llama_batch & batch, + llama_token id, + llama_pos pos, + const std::vector & seq_ids, + bool logits) { + batch.token [batch.n_tokens] = id; + batch.pos [batch.n_tokens] = pos; + batch.n_seq_id[batch.n_tokens] = seq_ids.size(); + for (size_t i = 0; i < seq_ids.size(); ++i) { + batch.seq_id[batch.n_tokens][i] = seq_ids[i]; } + batch.logits [batch.n_tokens] = logits; - if (model == NULL) { - fprintf(stderr, "%s: error: failed to load model '%s'\n", __func__, params.model.c_str()); - return std::make_tuple(nullptr, nullptr); - } - - auto cparams = llama_context_params_from_gpt_params(params); - - llama_context * lctx = llama_new_context_with_model(model, cparams); - if (lctx == NULL) { - fprintf(stderr, "%s: error: failed to create context with model '%s'\n", __func__, params.model.c_str()); - llama_free_model(model); - return std::make_tuple(nullptr, nullptr); - } - - if (!params.control_vectors.empty()) { - if (params.control_vector_layer_start <= 0) params.control_vector_layer_start = 1; - if (params.control_vector_layer_end <= 0) params.control_vector_layer_end = llama_n_layer(model); - - const auto cvec = llama_control_vector_load(params.control_vectors); - if (cvec.n_embd == -1) { - llama_free(lctx); - llama_free_model(model); - return std::make_tuple(nullptr, nullptr); - } - - int err = llama_control_vector_apply(lctx, - cvec.data.data(), - cvec.data.size(), - cvec.n_embd, - params.control_vector_layer_start, - params.control_vector_layer_end); - if (err) { - llama_free(lctx); - llama_free_model(model); - return std::make_tuple(nullptr, nullptr); - } - } - - for (unsigned int i = 0; i < params.lora_adapter.size(); ++i) { - const std::string & lora_adapter = std::get<0>(params.lora_adapter[i]); - float lora_scale = std::get<1>(params.lora_adapter[i]); - int err = llama_model_apply_lora_from_file(model, - lora_adapter.c_str(), - lora_scale, - ((i > 0) || params.lora_base.empty()) - ? NULL - : params.lora_base.c_str(), - params.n_threads); - if (err != 0) { - fprintf(stderr, "%s: error: failed to apply lora adapter\n", __func__); - llama_free(lctx); - llama_free_model(model); - return std::make_tuple(nullptr, nullptr); - } - } - - if (params.ignore_eos) { - params.sparams.logit_bias[llama_token_eos(model)] = -INFINITY; - } - - if (params.warmup) { - LOG("warming up the model with an empty run\n"); - - std::vector tmp = { llama_token_bos(model), llama_token_eos(model), }; - llama_decode(lctx, llama_batch_get_one(tmp.data(), std::min(tmp.size(), (size_t) params.n_batch), 0, 0)); - llama_kv_cache_clear(lctx); - llama_synchronize(lctx); - llama_reset_timings(lctx); - } - - return std::make_tuple(model, lctx); + batch.n_tokens++; } // @@ -2445,346 +2494,11 @@ bool llama_should_add_bos_token(const llama_model * model) { return add_bos != -1 ? bool(add_bos) : (llama_vocab_type(model) == LLAMA_VOCAB_TYPE_SPM); } -// -// YAML utils -// - -// returns true if successful, false otherwise -bool create_directory_with_parents(const std::string & path) { -#ifdef _WIN32 - std::wstring_convert> converter; - std::wstring wpath = converter.from_bytes(path); - - // if the path already exists, check whether it's a directory - const DWORD attributes = GetFileAttributesW(wpath.c_str()); - if ((attributes != INVALID_FILE_ATTRIBUTES) && (attributes & FILE_ATTRIBUTE_DIRECTORY)) { - return true; - } - - size_t pos_slash = 0; - - // process path from front to back, procedurally creating directories - while ((pos_slash = path.find('\\', pos_slash)) != std::string::npos) { - const std::wstring subpath = wpath.substr(0, pos_slash); - const wchar_t * test = subpath.c_str(); - - const bool success = CreateDirectoryW(test, NULL); - if (!success) { - const DWORD error = GetLastError(); - - // if the path already exists, ensure that it's a directory - if (error == ERROR_ALREADY_EXISTS) { - const DWORD attributes = GetFileAttributesW(subpath.c_str()); - if (attributes == INVALID_FILE_ATTRIBUTES || !(attributes & FILE_ATTRIBUTE_DIRECTORY)) { - return false; - } - } else { - return false; - } - } - - pos_slash += 1; - } - - return true; -#else - // if the path already exists, check whether it's a directory - struct stat info; - if (stat(path.c_str(), &info) == 0) { - return S_ISDIR(info.st_mode); - } - - size_t pos_slash = 1; // skip leading slashes for directory creation - - // process path from front to back, procedurally creating directories - while ((pos_slash = path.find('/', pos_slash)) != std::string::npos) { - const std::string subpath = path.substr(0, pos_slash); - struct stat info; - - // if the path already exists, ensure that it's a directory - if (stat(subpath.c_str(), &info) == 0) { - if (!S_ISDIR(info.st_mode)) { - return false; - } - } else { - // create parent directories - const int ret = mkdir(subpath.c_str(), 0755); - if (ret != 0) { - return false; - } - } - - pos_slash += 1; - } - - return true; -#endif // _WIN32 -} - -std::string get_cache_directory() { - std::string cache_directory = ""; - if (getenv("LLAMA_CACHE")) { - cache_directory = std::getenv("LLAMA_CACHE"); - if (cache_directory.back() != DIRECTORY_SEPARATOR) { - cache_directory += DIRECTORY_SEPARATOR; - } - } else { -#ifdef __linux__ - if (std::getenv("XDG_CACHE_HOME")) { - cache_directory = std::getenv("XDG_CACHE_HOME"); - } else { - cache_directory = std::getenv("HOME") + std::string("/.cache/"); - } -#elif defined(__APPLE__) - cache_directory = std::getenv("HOME") + std::string("/Library/Caches/"); -#elif defined(_WIN32) - cache_directory = std::getenv("APPDATA"); -#endif // __linux__ - cache_directory += "llama.cpp"; - cache_directory += DIRECTORY_SEPARATOR; - } - return cache_directory; -} - -void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector & data) { - if (data.empty()) { - fprintf(stream, "%s:\n", prop_name); - return; - } - - fprintf(stream, "%s: [", prop_name); - for (size_t i = 0; i < data.size() - 1; ++i) { - fprintf(stream, "%e, ", data[i]); - } - fprintf(stream, "%e]\n", data.back()); -} - -void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector & data) { - if (data.empty()) { - fprintf(stream, "%s:\n", prop_name); - return; - } - - fprintf(stream, "%s: [", prop_name); - for (size_t i = 0; i < data.size() - 1; ++i) { - fprintf(stream, "%d, ", data[i]); - } - fprintf(stream, "%d]\n", data.back()); -} - -void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data) { - std::string data_str(data == NULL ? "" : data); - - if (data_str.empty()) { - fprintf(stream, "%s:\n", prop_name); - return; - } - - size_t pos_start = 0; - size_t pos_found = 0; - - if (std::isspace(data_str[0]) || std::isspace(data_str.back())) { - data_str = std::regex_replace(data_str, std::regex("\n"), "\\n"); - data_str = std::regex_replace(data_str, std::regex("\""), "\\\""); - data_str = std::regex_replace(data_str, std::regex(R"(\\[^n"])"), R"(\$&)"); - data_str = "\"" + data_str + "\""; - fprintf(stream, "%s: %s\n", prop_name, data_str.c_str()); - return; - } - - if (data_str.find('\n') == std::string::npos) { - fprintf(stream, "%s: %s\n", prop_name, data_str.c_str()); - return; - } - - fprintf(stream, "%s: |\n", prop_name); - while ((pos_found = data_str.find('\n', pos_start)) != std::string::npos) { - fprintf(stream, " %s\n", data_str.substr(pos_start, pos_found-pos_start).c_str()); - pos_start = pos_found + 1; - } -} - -std::string get_sortable_timestamp() { - using clock = std::chrono::system_clock; - - const clock::time_point current_time = clock::now(); - const time_t as_time_t = clock::to_time_t(current_time); - char timestamp_no_ns[100]; - std::strftime(timestamp_no_ns, 100, "%Y_%m_%d-%H_%M_%S", std::localtime(&as_time_t)); - - const int64_t ns = std::chrono::duration_cast( - current_time.time_since_epoch() % 1000000000).count(); - char timestamp_ns[11]; - snprintf(timestamp_ns, 11, "%09" PRId64, ns); - - return std::string(timestamp_no_ns) + "." + std::string(timestamp_ns); -} - -void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const llama_context * lctx, - const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc) { - const llama_sampling_params & sparams = params.sparams; - - fprintf(stream, "build_commit: %s\n", LLAMA_COMMIT); - fprintf(stream, "build_number: %d\n", LLAMA_BUILD_NUMBER); - fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false"); - fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false"); - fprintf(stream, "cpu_has_avx_vnni: %s\n", ggml_cpu_has_avx_vnni() ? "true" : "false"); - fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false"); - fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false"); - fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false"); - fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false"); - fprintf(stream, "cpu_has_cuda: %s\n", ggml_cpu_has_cuda() ? "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"); - fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false"); - fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false"); - fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false"); - fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false"); - fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false"); - fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false"); - fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false"); - fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false"); - fprintf(stream, "cpu_has_matmul_int8: %s\n", ggml_cpu_has_matmul_int8() ? "true" : "false"); - -#ifdef NDEBUG - fprintf(stream, "debug: false\n"); -#else - fprintf(stream, "debug: true\n"); -#endif // NDEBUG - - fprintf(stream, "model_desc: %s\n", model_desc); - fprintf(stream, "n_vocab: %d # output size of the final layer, 32001 for some models\n", llama_n_vocab(llama_get_model(lctx))); - -#ifdef __OPTIMIZE__ - fprintf(stream, "optimize: true\n"); -#else - fprintf(stream, "optimize: false\n"); -#endif // __OPTIMIZE__ - - fprintf(stream, "time: %s\n", timestamp.c_str()); - - fprintf(stream, "\n"); - fprintf(stream, "###############\n"); - fprintf(stream, "# User Inputs #\n"); - fprintf(stream, "###############\n"); - fprintf(stream, "\n"); - - fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str()); - fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch); - dump_string_yaml_multiline(stream, "cfg_negative_prompt", sparams.cfg_negative_prompt.c_str()); - fprintf(stream, "cfg_scale: %f # default: 1.0\n", sparams.cfg_scale); - fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks); - fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false"); - fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx); - fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false"); - fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n"); - fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.penalty_freq); - dump_string_yaml_multiline(stream, "grammar", sparams.grammar.c_str()); - fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n"); - fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false"); - fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks); - - const auto logit_bias_eos = sparams.logit_bias.find(llama_token_eos(llama_get_model(lctx))); - const bool ignore_eos = logit_bias_eos != sparams.logit_bias.end() && logit_bias_eos->second == -INFINITY; - fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false"); - - dump_string_yaml_multiline(stream, "in_prefix", params.input_prefix.c_str()); - fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false"); - dump_string_yaml_multiline(stream, "in_suffix", params.input_prefix.c_str()); - fprintf(stream, "instruct: %s # default: false\n", params.instruct ? "true" : "false"); - fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false"); - fprintf(stream, "interactive_specials: %s # default: false\n", params.interactive_specials ? "true" : "false"); - fprintf(stream, "interactive_first: %s # default: false\n", params.interactive_first ? "true" : "false"); - fprintf(stream, "keep: %d # default: 0\n", params.n_keep); - fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str()); - - fprintf(stream, "logit_bias:\n"); - for (std::pair lb : sparams.logit_bias) { - if (ignore_eos && lb.first == logit_bias_eos->first) { - continue; - } - fprintf(stream, " %d: %f", lb.first, lb.second); - } - - fprintf(stream, "lora:\n"); - for (std::tuple la : params.lora_adapter) { - if (std::get<1>(la) != 1.0f) { - continue; - } - fprintf(stream, " - %s\n", std::get<0>(la).c_str()); - } - fprintf(stream, "lora_scaled:\n"); - for (std::tuple la : params.lora_adapter) { - if (std::get<1>(la) == 1.0f) { - continue; - } - fprintf(stream, " - %s: %f\n", std::get<0>(la).c_str(), std::get<1>(la)); - } - fprintf(stream, "lora_base: %s\n", params.lora_base.c_str()); - fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu); - fprintf(stream, "min_keep: %d # default: 0 (disabled)\n", sparams.min_keep); - fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat); - fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau); - fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta); - fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false"); - fprintf(stream, "model: %s # default: %s\n", params.model.c_str(), DEFAULT_MODEL_PATH); - fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str()); - fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false"); - fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers); - fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict); - fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs); - fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false"); - fprintf(stream, "penalize_nl: %s # default: false\n", sparams.penalize_nl ? "true" : "false"); - fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type); - fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride); - fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present); - dump_string_yaml_multiline(stream, "prompt", params.prompt.c_str()); - fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str()); - fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false"); - fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false"); - dump_vector_int_yaml(stream, "prompt_tokens", prompt_tokens); - fprintf(stream, "random_prompt: %s # default: false\n", params.random_prompt ? "true" : "false"); - fprintf(stream, "repeat_penalty: %f # default: 1.1\n", sparams.penalty_repeat); - - fprintf(stream, "reverse_prompt:\n"); - for (std::string ap : params.antiprompt) { - size_t pos = 0; - while ((pos = ap.find('\n', pos)) != std::string::npos) { - ap.replace(pos, 1, "\\n"); - pos += 1; - } - - fprintf(stream, " - %s\n", ap.c_str()); - } - - fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base); - fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale); - fprintf(stream, "seed: %u # default: -1 (random seed)\n", params.seed); - fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false"); - fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false"); - fprintf(stream, "flash_attn: %s # default: false\n", params.flash_attn ? "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()); - dump_vector_float_yaml(stream, "tensor_split", tensor_split_vector); - - fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z); - fprintf(stream, "threads: %d # default: %u\n", params.n_threads, std::thread::hardware_concurrency()); - fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k); - fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p); - fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p); - fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p); - fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false"); - fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false"); -} - // // KV cache utils // -void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size) { +void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size) { static const char slot_chars[] = ".123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz+"; printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d", @@ -2807,7 +2521,7 @@ void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size) { printf("\n=== Done dumping\n"); } -void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) { +void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size) { static const char slot_chars[] = "0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"; printf("=== Dumping KV cache. total cells %d, max sequences per cell %d, populated cells %d, total tokens in cache %d, largest empty slot=%d @ %d\n", @@ -2855,6 +2569,10 @@ void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size) { printf("\n=== Done dumping\n"); } +// +// Embedding utils +// + void llama_embd_normalize(const float * inp, float * out, int n) { double sum = 0.0; for (int i = 0; i < n; i++) { @@ -3039,3 +2757,225 @@ llama_control_vector_data llama_control_vector_load(const std::vector & data) { + if (data.empty()) { + fprintf(stream, "%s:\n", prop_name); + return; + } + + fprintf(stream, "%s: [", prop_name); + for (size_t i = 0; i < data.size() - 1; ++i) { + fprintf(stream, "%e, ", data[i]); + } + fprintf(stream, "%e]\n", data.back()); +} + +void yaml_dump_vector_int(FILE * stream, const char * prop_name, const std::vector & data) { + if (data.empty()) { + fprintf(stream, "%s:\n", prop_name); + return; + } + + fprintf(stream, "%s: [", prop_name); + for (size_t i = 0; i < data.size() - 1; ++i) { + fprintf(stream, "%d, ", data[i]); + } + fprintf(stream, "%d]\n", data.back()); +} + +void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data) { + std::string data_str(data == NULL ? "" : data); + + if (data_str.empty()) { + fprintf(stream, "%s:\n", prop_name); + return; + } + + size_t pos_start = 0; + size_t pos_found = 0; + + if (std::isspace(data_str[0]) || std::isspace(data_str.back())) { + data_str = std::regex_replace(data_str, std::regex("\n"), "\\n"); + data_str = std::regex_replace(data_str, std::regex("\""), "\\\""); + data_str = std::regex_replace(data_str, std::regex(R"(\\[^n"])"), R"(\$&)"); + data_str = "\"" + data_str + "\""; + fprintf(stream, "%s: %s\n", prop_name, data_str.c_str()); + return; + } + + if (data_str.find('\n') == std::string::npos) { + fprintf(stream, "%s: %s\n", prop_name, data_str.c_str()); + return; + } + + fprintf(stream, "%s: |\n", prop_name); + while ((pos_found = data_str.find('\n', pos_start)) != std::string::npos) { + fprintf(stream, " %s\n", data_str.substr(pos_start, pos_found-pos_start).c_str()); + pos_start = pos_found + 1; + } +} + +void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const llama_context * lctx, + const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc) { + const llama_sampling_params & sparams = params.sparams; + + fprintf(stream, "build_commit: %s\n", LLAMA_COMMIT); + fprintf(stream, "build_number: %d\n", LLAMA_BUILD_NUMBER); + fprintf(stream, "cpu_has_arm_fma: %s\n", ggml_cpu_has_arm_fma() ? "true" : "false"); + fprintf(stream, "cpu_has_avx: %s\n", ggml_cpu_has_avx() ? "true" : "false"); + fprintf(stream, "cpu_has_avx_vnni: %s\n", ggml_cpu_has_avx_vnni() ? "true" : "false"); + fprintf(stream, "cpu_has_avx2: %s\n", ggml_cpu_has_avx2() ? "true" : "false"); + fprintf(stream, "cpu_has_avx512: %s\n", ggml_cpu_has_avx512() ? "true" : "false"); + fprintf(stream, "cpu_has_avx512_vbmi: %s\n", ggml_cpu_has_avx512_vbmi() ? "true" : "false"); + fprintf(stream, "cpu_has_avx512_vnni: %s\n", ggml_cpu_has_avx512_vnni() ? "true" : "false"); + fprintf(stream, "cpu_has_cuda: %s\n", ggml_cpu_has_cuda() ? "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"); + fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false"); + fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false"); + fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false"); + fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false"); + fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false"); + fprintf(stream, "cpu_has_blas: %s\n", ggml_cpu_has_blas() ? "true" : "false"); + fprintf(stream, "cpu_has_sse3: %s\n", ggml_cpu_has_sse3() ? "true" : "false"); + fprintf(stream, "cpu_has_vsx: %s\n", ggml_cpu_has_vsx() ? "true" : "false"); + fprintf(stream, "cpu_has_matmul_int8: %s\n", ggml_cpu_has_matmul_int8() ? "true" : "false"); + +#ifdef NDEBUG + fprintf(stream, "debug: false\n"); +#else + fprintf(stream, "debug: true\n"); +#endif // NDEBUG + + fprintf(stream, "model_desc: %s\n", model_desc); + fprintf(stream, "n_vocab: %d # output size of the final layer, 32001 for some models\n", llama_n_vocab(llama_get_model(lctx))); + +#ifdef __OPTIMIZE__ + fprintf(stream, "optimize: true\n"); +#else + fprintf(stream, "optimize: false\n"); +#endif // __OPTIMIZE__ + + fprintf(stream, "time: %s\n", timestamp.c_str()); + + fprintf(stream, "\n"); + fprintf(stream, "###############\n"); + fprintf(stream, "# User Inputs #\n"); + fprintf(stream, "###############\n"); + fprintf(stream, "\n"); + + fprintf(stream, "alias: %s # default: unknown\n", params.model_alias.c_str()); + fprintf(stream, "batch_size: %d # default: 512\n", params.n_batch); + yaml_dump_string_multiline(stream, "cfg_negative_prompt", sparams.cfg_negative_prompt.c_str()); + fprintf(stream, "cfg_scale: %f # default: 1.0\n", sparams.cfg_scale); + fprintf(stream, "chunks: %d # default: -1 (unlimited)\n", params.n_chunks); + fprintf(stream, "color: %s # default: false\n", params.use_color ? "true" : "false"); + fprintf(stream, "ctx_size: %d # default: 512\n", params.n_ctx); + fprintf(stream, "escape: %s # default: false\n", params.escape ? "true" : "false"); + fprintf(stream, "file: # never logged, see prompt instead. Can still be specified for input.\n"); + fprintf(stream, "frequency_penalty: %f # default: 0.0 \n", sparams.penalty_freq); + yaml_dump_string_multiline(stream, "grammar", sparams.grammar.c_str()); + fprintf(stream, "grammar-file: # never logged, see grammar instead. Can still be specified for input.\n"); + fprintf(stream, "hellaswag: %s # default: false\n", params.hellaswag ? "true" : "false"); + fprintf(stream, "hellaswag_tasks: %zu # default: 400\n", params.hellaswag_tasks); + + const auto logit_bias_eos = sparams.logit_bias.find(llama_token_eos(llama_get_model(lctx))); + const bool ignore_eos = logit_bias_eos != sparams.logit_bias.end() && logit_bias_eos->second == -INFINITY; + fprintf(stream, "ignore_eos: %s # default: false\n", ignore_eos ? "true" : "false"); + + yaml_dump_string_multiline(stream, "in_prefix", params.input_prefix.c_str()); + fprintf(stream, "in_prefix_bos: %s # default: false\n", params.input_prefix_bos ? "true" : "false"); + yaml_dump_string_multiline(stream, "in_suffix", params.input_prefix.c_str()); + fprintf(stream, "instruct: %s # default: false\n", params.instruct ? "true" : "false"); + fprintf(stream, "interactive: %s # default: false\n", params.interactive ? "true" : "false"); + fprintf(stream, "interactive_specials: %s # default: false\n", params.interactive_specials ? "true" : "false"); + fprintf(stream, "interactive_first: %s # default: false\n", params.interactive_first ? "true" : "false"); + fprintf(stream, "keep: %d # default: 0\n", params.n_keep); + fprintf(stream, "logdir: %s # default: unset (no logging)\n", params.logdir.c_str()); + + fprintf(stream, "logit_bias:\n"); + for (std::pair lb : sparams.logit_bias) { + if (ignore_eos && lb.first == logit_bias_eos->first) { + continue; + } + fprintf(stream, " %d: %f", lb.first, lb.second); + } + + fprintf(stream, "lora:\n"); + for (std::tuple la : params.lora_adapter) { + if (std::get<1>(la) != 1.0f) { + continue; + } + fprintf(stream, " - %s\n", std::get<0>(la).c_str()); + } + fprintf(stream, "lora_scaled:\n"); + for (std::tuple la : params.lora_adapter) { + if (std::get<1>(la) == 1.0f) { + continue; + } + fprintf(stream, " - %s: %f\n", std::get<0>(la).c_str(), std::get<1>(la)); + } + fprintf(stream, "lora_base: %s\n", params.lora_base.c_str()); + fprintf(stream, "main_gpu: %d # default: 0\n", params.main_gpu); + fprintf(stream, "min_keep: %d # default: 0 (disabled)\n", sparams.min_keep); + fprintf(stream, "mirostat: %d # default: 0 (disabled)\n", sparams.mirostat); + fprintf(stream, "mirostat_ent: %f # default: 5.0\n", sparams.mirostat_tau); + fprintf(stream, "mirostat_lr: %f # default: 0.1\n", sparams.mirostat_eta); + fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false"); + fprintf(stream, "model: %s # default: %s\n", params.model.c_str(), DEFAULT_MODEL_PATH); + fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str()); + fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false"); + fprintf(stream, "n_gpu_layers: %d # default: -1\n", params.n_gpu_layers); + fprintf(stream, "n_predict: %d # default: -1 (unlimited)\n", params.n_predict); + fprintf(stream, "n_probs: %d # only used by server binary, default: 0\n", sparams.n_probs); + fprintf(stream, "no_mmap: %s # default: false\n", !params.use_mmap ? "true" : "false"); + fprintf(stream, "penalize_nl: %s # default: false\n", sparams.penalize_nl ? "true" : "false"); + fprintf(stream, "ppl_output_type: %d # default: 0\n", params.ppl_output_type); + fprintf(stream, "ppl_stride: %d # default: 0\n", params.ppl_stride); + fprintf(stream, "presence_penalty: %f # default: 0.0\n", sparams.penalty_present); + yaml_dump_string_multiline(stream, "prompt", params.prompt.c_str()); + fprintf(stream, "prompt_cache: %s\n", params.path_prompt_cache.c_str()); + fprintf(stream, "prompt_cache_all: %s # default: false\n", params.prompt_cache_all ? "true" : "false"); + fprintf(stream, "prompt_cache_ro: %s # default: false\n", params.prompt_cache_ro ? "true" : "false"); + yaml_dump_vector_int(stream, "prompt_tokens", prompt_tokens); + fprintf(stream, "random_prompt: %s # default: false\n", params.random_prompt ? "true" : "false"); + fprintf(stream, "repeat_penalty: %f # default: 1.1\n", sparams.penalty_repeat); + + fprintf(stream, "reverse_prompt:\n"); + for (std::string ap : params.antiprompt) { + size_t pos = 0; + while ((pos = ap.find('\n', pos)) != std::string::npos) { + ap.replace(pos, 1, "\\n"); + pos += 1; + } + + fprintf(stream, " - %s\n", ap.c_str()); + } + + fprintf(stream, "rope_freq_base: %f # default: 10000.0\n", params.rope_freq_base); + fprintf(stream, "rope_freq_scale: %f # default: 1.0\n", params.rope_freq_scale); + fprintf(stream, "seed: %u # default: -1 (random seed)\n", params.seed); + fprintf(stream, "simple_io: %s # default: false\n", params.simple_io ? "true" : "false"); + fprintf(stream, "cont_batching: %s # default: false\n", params.cont_batching ? "true" : "false"); + fprintf(stream, "flash_attn: %s # default: false\n", params.flash_attn ? "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()); + yaml_dump_vector_float(stream, "tensor_split", tensor_split_vector); + + fprintf(stream, "tfs: %f # default: 1.0\n", sparams.tfs_z); + fprintf(stream, "threads: %d # default: %u\n", params.n_threads, std::thread::hardware_concurrency()); + fprintf(stream, "top_k: %d # default: 40\n", sparams.top_k); + fprintf(stream, "top_p: %f # default: 0.95\n", sparams.top_p); + fprintf(stream, "min_p: %f # default: 0.0\n", sparams.min_p); + fprintf(stream, "typical_p: %f # default: 1.0\n", sparams.typical_p); + fprintf(stream, "verbose_prompt: %s # default: false\n", params.verbose_prompt ? "true" : "false"); + fprintf(stream, "display_prompt: %s # default: true\n", params.display_prompt ? "true" : "false"); +} diff --git a/common/common.h b/common/common.h index a8e5e50e6..f68f3c297 100644 --- a/common/common.h +++ b/common/common.h @@ -27,7 +27,7 @@ #define die_fmt(fmt, ...) do { fprintf(stderr, "error: " fmt "\n", __VA_ARGS__); exit(1); } while (0) #define print_build_info() do { \ - fprintf(stderr, "%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT); \ + fprintf(stderr, "%s: build = %d (%s)\n", __func__, LLAMA_BUILD_NUMBER, LLAMA_COMMIT); \ fprintf(stderr, "%s: built with %s for %s\n", __func__, LLAMA_COMPILER, LLAMA_BUILD_TARGET); \ } while(0) @@ -35,14 +35,18 @@ // build info extern int LLAMA_BUILD_NUMBER; -extern char const *LLAMA_COMMIT; -extern char const *LLAMA_COMPILER; -extern char const *LLAMA_BUILD_TARGET; +extern char const * LLAMA_COMMIT; +extern char const * LLAMA_COMPILER; +extern char const * LLAMA_BUILD_TARGET; struct llama_control_vector_load_info; -int get_math_cpu_count(); -int32_t get_num_physical_cores(); +// +// CPU utils +// + +int32_t cpu_get_num_physical_cores(); +int32_t cpu_get_num_math(); // // CLI argument parsing @@ -51,7 +55,7 @@ int32_t get_num_physical_cores(); struct gpt_params { uint32_t seed = LLAMA_DEFAULT_SEED; // RNG seed - int32_t n_threads = get_math_cpu_count(); + int32_t n_threads = cpu_get_num_math(); 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; @@ -179,33 +183,34 @@ struct gpt_params { void gpt_params_handle_model_default(gpt_params & params); -bool parse_kv_override(const char * data, std::vector & overrides); +bool gpt_params_parse_ex (int argc, char ** argv, gpt_params & params); +bool gpt_params_parse (int argc, char ** argv, gpt_params & params); +bool gpt_params_find_arg (int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param); +void gpt_params_print_usage(int argc, char ** argv, const gpt_params & params); -bool gpt_params_parse_ex(int argc, char ** argv, gpt_params & params); - -bool gpt_params_parse(int argc, char ** argv, gpt_params & params); - -void gpt_print_usage(int argc, char ** argv, const gpt_params & params); - -bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_params & params, int & i, bool & invalid_param); - -std::string get_system_info(const gpt_params & params); - -std::string gpt_random_prompt(std::mt19937 & rng); - -void process_escapes(std::string& input); - -bool validate_file_name(const std::string & filename); +std::string gpt_params_get_system_info(const gpt_params & params); // // String utils // -std::vector sampler_types_from_names(const std::vector & names, bool allow_alt_names); -std::vector sampler_types_from_chars(const std::string & names_string); std::vector string_split(std::string input, char separator); + std::string string_strip(const std::string & str); -std::string sampler_type_to_name_string(llama_sampler_type sampler_type); +std::string string_get_sortable_timestamp(); +std::string string_random_prompt(std::mt19937 & rng); + +bool string_parse_kv_override(const char * data, std::vector & overrides); +void string_process_escapes(std::string & input); + +// +// Filesystem utils +// + +bool fs_validate_filename(const std::string & filename); +bool fs_create_directory_with_parents(const std::string & path); + +std::string fs_get_cache_directory(); // // Model utils @@ -276,30 +281,15 @@ std::string llama_detokenize_bpe( // defaults to true when model type is SPM, otherwise false. bool llama_should_add_bos_token(const llama_model * model); -// -// YAML utils -// - -bool create_directory_with_parents(const std::string & path); -std::string get_cache_directory(); -void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector & data); -void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector & data); -void dump_string_yaml_multiline(FILE * stream, const char * prop_name, const char * data); -std::string get_sortable_timestamp(); - -void dump_non_result_info_yaml( - FILE * stream, const gpt_params & params, const llama_context * lctx, - const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc); - // // KV cache utils // // Dump the KV cache view with the number of sequences per cell. -void dump_kv_cache_view(const llama_kv_cache_view & view, int row_size = 80); +void llama_kv_cache_dump_view(const llama_kv_cache_view & view, int row_size = 80); // Dump the KV cache view showing individual sequences in each cell (long output). -void dump_kv_cache_view_seqs(const llama_kv_cache_view & view, int row_size = 40); +void llama_kv_cache_dump_view_seqs(const llama_kv_cache_view & view, int row_size = 40); // // Embedding utils @@ -333,6 +323,20 @@ llama_control_vector_data llama_control_vector_load(const std::vector & data); +void yaml_dump_vector_int (FILE * stream, const char * prop_name, const std::vector & data); +void yaml_dump_string_multiline(FILE * stream, const char * prop_name, const char * data); + +void yaml_dump_non_result_info( + FILE * stream, const gpt_params & params, const llama_context * lctx, + const std::string & timestamp, const std::vector & prompt_tokens, const char * model_desc); + diff --git a/common/sampling.cpp b/common/sampling.cpp index 7fc2e2158..f1f803516 100644 --- a/common/sampling.cpp +++ b/common/sampling.cpp @@ -125,7 +125,7 @@ std::string llama_sampling_order_print(const llama_sampling_params & params) { std::string result = "CFG -> Penalties "; if (params.mirostat == 0) { for (auto sampler_type : params.samplers_sequence) { - const auto sampler_type_name = sampler_type_to_name_string(sampler_type); + const auto sampler_type_name = llama_sampling_type_to_str(sampler_type); if (!sampler_type_name.empty()) { result += "-> " + sampler_type_name + " "; } @@ -137,6 +137,87 @@ std::string llama_sampling_order_print(const llama_sampling_params & params) { return result; } +std::string llama_sampling_type_to_str(llama_sampler_type sampler_type) { + switch (sampler_type) { + case llama_sampler_type::TOP_K: return "top_k"; + case llama_sampler_type::TFS_Z: return "tfs_z"; + case llama_sampler_type::TYPICAL_P: return "typical_p"; + case llama_sampler_type::TOP_P: return "top_p"; + case llama_sampler_type::MIN_P: return "min_p"; + case llama_sampler_type::TEMPERATURE: return "temperature"; + default : return ""; + } +} + +std::vector llama_sampling_types_from_names(const std::vector & names, bool allow_alt_names) { + std::unordered_map sampler_canonical_name_map { + {"top_k", llama_sampler_type::TOP_K}, + {"top_p", llama_sampler_type::TOP_P}, + {"typical_p", llama_sampler_type::TYPICAL_P}, + {"min_p", llama_sampler_type::MIN_P}, + {"tfs_z", llama_sampler_type::TFS_Z}, + {"temperature", llama_sampler_type::TEMPERATURE} + }; + + // since samplers names are written multiple ways + // make it ready for both system names and input names + std::unordered_map sampler_alt_name_map { + {"top-k", llama_sampler_type::TOP_K}, + {"top-p", llama_sampler_type::TOP_P}, + {"nucleus", llama_sampler_type::TOP_P}, + {"typical-p", llama_sampler_type::TYPICAL_P}, + {"typical", llama_sampler_type::TYPICAL_P}, + {"min-p", llama_sampler_type::MIN_P}, + {"tfs-z", llama_sampler_type::TFS_Z}, + {"tfs", llama_sampler_type::TFS_Z}, + {"temp", llama_sampler_type::TEMPERATURE} + }; + + std::vector sampler_types; + sampler_types.reserve(names.size()); + for (const auto & name : names) + { + auto sampler_item = sampler_canonical_name_map.find(name); + if (sampler_item != sampler_canonical_name_map.end()) + { + sampler_types.push_back(sampler_item->second); + } + else + { + if (allow_alt_names) + { + sampler_item = sampler_alt_name_map.find(name); + if (sampler_item != sampler_alt_name_map.end()) + { + sampler_types.push_back(sampler_item->second); + } + } + } + } + return sampler_types; +} + +std::vector llama_sampling_types_from_chars(const std::string & names_string) { + std::unordered_map sampler_name_map { + {'k', llama_sampler_type::TOP_K}, + {'p', llama_sampler_type::TOP_P}, + {'y', llama_sampler_type::TYPICAL_P}, + {'m', llama_sampler_type::MIN_P}, + {'f', llama_sampler_type::TFS_Z}, + {'t', llama_sampler_type::TEMPERATURE} + }; + + std::vector sampler_types; + sampler_types.reserve(names_string.size()); + for (const auto & c : names_string) { + const auto sampler_item = sampler_name_map.find(c); + if (sampler_item != sampler_name_map.end()) { + sampler_types.push_back(sampler_item->second); + } + } + return sampler_types; +} + // no reasons to expose this function in header static void sampler_queue( struct llama_context * ctx_main, diff --git a/common/sampling.h b/common/sampling.h index 655732ad1..eeaa53b8b 100644 --- a/common/sampling.h +++ b/common/sampling.h @@ -116,6 +116,11 @@ std::string llama_sampling_print(const llama_sampling_params & params); // Print sampling order into a string std::string llama_sampling_order_print(const llama_sampling_params & params); +std::string llama_sampling_type_to_str(llama_sampler_type sampler_type); + +std::vector llama_sampling_types_from_names(const std::vector & names, bool allow_alt_names); +std::vector llama_sampling_types_from_chars(const std::string & names_string); + // this is a common sampling function used across the examples for convenience // it can serve as a starting point for implementing your own sampling function // Note: When using multiple sequences, it is the caller's responsibility to call diff --git a/common/train.cpp b/common/train.cpp index 0dbfd24df..2d41a1d29 100644 --- a/common/train.cpp +++ b/common/train.cpp @@ -1380,7 +1380,7 @@ bool consume_common_train_arg( void finish_processing_train_args(struct train_params_common * params) { if (params->escape) { - process_escapes(params->sample_start); + string_process_escapes(params->sample_start); } } diff --git a/examples/batched/batched.cpp b/examples/batched/batched.cpp index be30d20bf..591bc6e57 100644 --- a/examples/batched/batched.cpp +++ b/examples/batched/batched.cpp @@ -48,7 +48,7 @@ int main(int argc, char ** argv) { params.prompt = "Hello my name is"; } - process_escapes(params.prompt); + string_process_escapes(params.prompt); // init LLM diff --git a/examples/embedding/embedding.cpp b/examples/embedding/embedding.cpp index 0c921ed69..004399b5f 100644 --- a/examples/embedding/embedding.cpp +++ b/examples/embedding/embedding.cpp @@ -80,7 +80,7 @@ int main(int argc, char ** argv) { std::mt19937 rng(params.seed); if (params.random_prompt) { - params.prompt = gpt_random_prompt(rng); + params.prompt = string_random_prompt(rng); } llama_backend_init(); @@ -107,7 +107,7 @@ int main(int argc, char ** argv) { // print system information { fprintf(stderr, "\n"); - fprintf(stderr, "%s\n", get_system_info(params).c_str()); + fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str()); } // split the prompt into lines diff --git a/examples/eval-callback/eval-callback.cpp b/examples/eval-callback/eval-callback.cpp index e670d3769..51d67d6d9 100644 --- a/examples/eval-callback/eval-callback.cpp +++ b/examples/eval-callback/eval-callback.cpp @@ -152,7 +152,7 @@ int main(int argc, char ** argv) { std::mt19937 rng(params.seed); if (params.random_prompt) { - params.prompt = gpt_random_prompt(rng); + params.prompt = string_random_prompt(rng); } llama_backend_init(); @@ -176,7 +176,7 @@ int main(int argc, char ** argv) { // print system information { fprintf(stderr, "\n"); - fprintf(stderr, "%s\n", get_system_info(params).c_str()); + fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str()); } bool OK = run(ctx, params); diff --git a/examples/imatrix/imatrix.cpp b/examples/imatrix/imatrix.cpp index 82b19fc4f..25a2351cc 100644 --- a/examples/imatrix/imatrix.cpp +++ b/examples/imatrix/imatrix.cpp @@ -598,7 +598,7 @@ int main(int argc, char ** argv) { std::mt19937 rng(params.seed); if (params.random_prompt) { - params.prompt = gpt_random_prompt(rng); + params.prompt = string_random_prompt(rng); } sparams.dataset = params.prompt_file; @@ -667,7 +667,7 @@ int main(int argc, char ** argv) { // print system information { fprintf(stderr, "\n"); - fprintf(stderr, "%s\n", get_system_info(params).c_str()); + fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str()); } bool OK = compute_imatrix(ctx, params, compute_ppl, from_chunk); diff --git a/examples/infill/infill.cpp b/examples/infill/infill.cpp index afac145f6..539f78184 100644 --- a/examples/infill/infill.cpp +++ b/examples/infill/infill.cpp @@ -50,9 +50,9 @@ static void write_logfile( return; } - const std::string timestamp = get_sortable_timestamp(); + const std::string timestamp = string_get_sortable_timestamp(); - const bool success = create_directory_with_parents(params.logdir); + const bool success = fs_create_directory_with_parents(params.logdir); if (!success) { fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n", __func__, params.logdir.c_str()); @@ -70,7 +70,7 @@ static void write_logfile( fprintf(logfile, "binary: infill\n"); char model_desc[128]; llama_model_desc(model, model_desc, sizeof(model_desc)); - dump_non_result_info_yaml(logfile, params, ctx, timestamp, input_tokens, model_desc); + yaml_dump_non_result_info(logfile, params, ctx, timestamp, input_tokens, model_desc); fprintf(logfile, "\n"); fprintf(logfile, "######################\n"); @@ -78,8 +78,8 @@ static void write_logfile( fprintf(logfile, "######################\n"); fprintf(logfile, "\n"); - dump_string_yaml_multiline(logfile, "output", output.c_str()); - dump_vector_int_yaml(logfile, "output_tokens", output_tokens); + yaml_dump_string_multiline(logfile, "output", output.c_str()); + yaml_dump_vector_int(logfile, "output_tokens", output_tokens); llama_dump_timing_info_yaml(logfile, ctx); fclose(logfile); @@ -236,7 +236,7 @@ int main(int argc, char ** argv) { // print system information { LOG_TEE("\n"); - LOG_TEE("%s\n", get_system_info(params).c_str()); + LOG_TEE("%s\n", gpt_params_get_system_info(params).c_str()); } const bool add_bos = llama_should_add_bos_token(model); GGML_ASSERT(llama_add_eos_token(model) != 1); @@ -621,8 +621,8 @@ int main(int argc, char ** argv) { if (params.escape) { //process escape sequences, for the initial prompt this is done in common.cpp when we load the params, but for the interactive mode we need to do it here - process_escapes(params.input_prefix); - process_escapes(params.input_suffix); + string_process_escapes(params.input_prefix); + string_process_escapes(params.input_suffix); } suff_rm_leading_spc = params.escape; if (suff_rm_leading_spc && params.input_suffix.find_first_of(' ') == 0 && params.input_suffix.size() > 1) { diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 6bb1f70c3..2afdb3abd 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -200,7 +200,7 @@ static const cmd_params cmd_params_defaults = { /* n_ubatch */ {512}, /* type_k */ {GGML_TYPE_F16}, /* type_v */ {GGML_TYPE_F16}, - /* n_threads */ {get_math_cpu_count()}, + /* n_threads */ {cpu_get_num_math()}, /* n_gpu_layers */ {99}, /* split_mode */ {LLAMA_SPLIT_MODE_LAYER}, /* main_gpu */ {0}, diff --git a/examples/llava/llava-cli.cpp b/examples/llava/llava-cli.cpp index a6d67e5d7..c974900f2 100644 --- a/examples/llava/llava-cli.cpp +++ b/examples/llava/llava-cli.cpp @@ -290,7 +290,7 @@ int main(int argc, char ** argv) { #endif // LOG_DISABLE_LOGS if (params.mmproj.empty() || (params.image.empty() && !prompt_contains_image(params.prompt))) { - gpt_print_usage(argc, argv, params); + gpt_params_print_usage(argc, argv, params); show_additional_info(argc, argv); return 1; } diff --git a/examples/lookahead/lookahead.cpp b/examples/lookahead/lookahead.cpp index 9c3540b20..54f060a85 100644 --- a/examples/lookahead/lookahead.cpp +++ b/examples/lookahead/lookahead.cpp @@ -174,7 +174,7 @@ int main(int argc, char ** argv) { // debug if (dump_kv_cache) { llama_kv_cache_view_update(ctx, &kvc_view); - dump_kv_cache_view_seqs(kvc_view, 40); + llama_kv_cache_dump_view_seqs(kvc_view, 40); } // build the mask from https://lmsys.org/blog/2023-11-21-lookahead-decoding/ diff --git a/examples/lookup/lookup.cpp b/examples/lookup/lookup.cpp index eebbd00a5..83dbee91a 100644 --- a/examples/lookup/lookup.cpp +++ b/examples/lookup/lookup.cpp @@ -121,7 +121,7 @@ int main(int argc, char ** argv){ // debug if (dump_kv_cache) { llama_kv_cache_view_update(ctx, &kvc_view); - dump_kv_cache_view_seqs(kvc_view, 40); + llama_kv_cache_dump_view_seqs(kvc_view, 40); } // print current draft sequence diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 832b51ee0..791dc61a7 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -60,9 +60,9 @@ static void write_logfile( return; } - const std::string timestamp = get_sortable_timestamp(); + const std::string timestamp = string_get_sortable_timestamp(); - const bool success = create_directory_with_parents(params.logdir); + const bool success = fs_create_directory_with_parents(params.logdir); if (!success) { fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n", __func__, params.logdir.c_str()); @@ -80,7 +80,7 @@ static void write_logfile( fprintf(logfile, "binary: main\n"); char model_desc[128]; llama_model_desc(model, model_desc, sizeof(model_desc)); - dump_non_result_info_yaml(logfile, params, ctx, timestamp, input_tokens, model_desc); + yaml_dump_non_result_info(logfile, params, ctx, timestamp, input_tokens, model_desc); fprintf(logfile, "\n"); fprintf(logfile, "######################\n"); @@ -88,8 +88,8 @@ static void write_logfile( fprintf(logfile, "######################\n"); fprintf(logfile, "\n"); - dump_string_yaml_multiline(logfile, "output", output.c_str()); - dump_vector_int_yaml(logfile, "output_tokens", output_tokens); + yaml_dump_string_multiline(logfile, "output", output.c_str()); + yaml_dump_vector_int(logfile, "output_tokens", output_tokens); llama_dump_timing_info_yaml(logfile, ctx); fclose(logfile); @@ -181,7 +181,7 @@ int main(int argc, char ** argv) { std::mt19937 rng(params.seed); if (params.random_prompt) { - params.prompt = gpt_random_prompt(rng); + params.prompt = string_random_prompt(rng); } LOG("%s: llama backend init\n", __func__); @@ -219,7 +219,7 @@ int main(int argc, char ** argv) { // print system information { LOG_TEE("\n"); - LOG_TEE("%s\n", get_system_info(params).c_str()); + LOG_TEE("%s\n", gpt_params_get_system_info(params).c_str()); } std::string path_session = params.path_prompt_cache; @@ -879,7 +879,7 @@ int main(int argc, char ** argv) { embd_inp.insert(embd_inp.end(), cml_pfx.begin(), cml_pfx.end()); } if (params.escape) { - process_escapes(buffer); + string_process_escapes(buffer); } const auto line_pfx = ::llama_tokenize(ctx, params.input_prefix, false, true); diff --git a/examples/parallel/parallel.cpp b/examples/parallel/parallel.cpp index 7c5595d6e..c731abb72 100644 --- a/examples/parallel/parallel.cpp +++ b/examples/parallel/parallel.cpp @@ -210,7 +210,7 @@ int main(int argc, char ** argv) { while (true) { if (dump_kv_cache) { llama_kv_cache_view_update(ctx, &kvc_view); - dump_kv_cache_view_seqs(kvc_view, 40); + llama_kv_cache_dump_view_seqs(kvc_view, 40); } llama_batch_clear(batch); diff --git a/examples/perplexity/perplexity.cpp b/examples/perplexity/perplexity.cpp index bae014e6f..30e5e282e 100644 --- a/examples/perplexity/perplexity.cpp +++ b/examples/perplexity/perplexity.cpp @@ -44,9 +44,9 @@ static void write_logfile( return; } - const std::string timestamp = get_sortable_timestamp(); + const std::string timestamp = string_get_sortable_timestamp(); - const bool success = create_directory_with_parents(params.logdir); + const bool success = fs_create_directory_with_parents(params.logdir); if (!success) { fprintf(stderr, "%s: warning: failed to create logdir %s, cannot write logfile\n", __func__, params.logdir.c_str()); @@ -64,7 +64,7 @@ static void write_logfile( fprintf(logfile, "binary: main\n"); char model_desc[128]; llama_model_desc(model, model_desc, sizeof(model_desc)); - dump_non_result_info_yaml(logfile, params, ctx, timestamp, results.tokens, model_desc); + yaml_dump_non_result_info(logfile, params, ctx, timestamp, results.tokens, model_desc); fprintf(logfile, "\n"); fprintf(logfile, "######################\n"); @@ -72,9 +72,9 @@ static void write_logfile( fprintf(logfile, "######################\n"); fprintf(logfile, "\n"); - dump_vector_float_yaml(logfile, "logits", results.logits); + yaml_dump_vector_float(logfile, "logits", results.logits); fprintf(logfile, "ppl_value: %f\n", results.ppl_value); - dump_vector_float_yaml(logfile, "probs", results.probs); + yaml_dump_vector_float(logfile, "probs", results.probs); llama_dump_timing_info_yaml(logfile, ctx); fclose(logfile); @@ -2007,7 +2007,7 @@ int main(int argc, char ** argv) { std::mt19937 rng(params.seed); if (params.random_prompt) { - params.prompt = gpt_random_prompt(rng); + params.prompt = string_random_prompt(rng); } llama_backend_init(); @@ -2035,7 +2035,7 @@ int main(int argc, char ** argv) { // print system information { fprintf(stderr, "\n"); - fprintf(stderr, "%s\n", get_system_info(params).c_str()); + fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str()); } struct results_perplexity results; diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index cbb452334..28584e14b 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -259,7 +259,7 @@ int main(int argc, char ** argv) { usage(argv[0]); } } else if (strcmp(argv[arg_idx], "--override-kv") == 0) { - if (arg_idx == argc-1 || !parse_kv_override(argv[++arg_idx], kv_overrides)) { + if (arg_idx == argc-1 || !string_parse_kv_override(argv[++arg_idx], kv_overrides)) { usage(argv[0]); } } else if (strcmp(argv[arg_idx], "--allow-requantize") == 0) { diff --git a/examples/retrieval/retrieval.cpp b/examples/retrieval/retrieval.cpp index 5ba71e76a..4e7530706 100644 --- a/examples/retrieval/retrieval.cpp +++ b/examples/retrieval/retrieval.cpp @@ -11,7 +11,7 @@ struct retrieval_params { }; static void retrieval_params_print_usage(int argc, char ** argv, gpt_params & gpt_params, retrieval_params & params) { - gpt_print_usage(argc, argv, gpt_params); + gpt_params_print_usage(argc, argv, gpt_params); printf("retrieval options:\n"); printf(" --context-file FNAME file containing context to embed.\n"); printf(" specify multiple files by providing --context-file option multiple times.\n"); @@ -226,7 +226,7 @@ int main(int argc, char ** argv) { // print system information { fprintf(stderr, "\n"); - fprintf(stderr, "%s\n", get_system_info(params).c_str()); + fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str()); } // max batch size diff --git a/examples/server/server.cpp b/examples/server/server.cpp index 6af5cb96e..e9904263d 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -1019,7 +1019,7 @@ struct server_context { sampler_names.emplace_back(sampler_name); } } - slot.sparams.samplers_sequence = sampler_types_from_names(sampler_names, false); + slot.sparams.samplers_sequence = llama_sampling_types_from_names(sampler_names, false); } else { slot.sparams.samplers_sequence = default_sparams.samplers_sequence; } @@ -1256,7 +1256,7 @@ struct server_context { std::vector samplers_sequence; samplers_sequence.reserve(slot.sparams.samplers_sequence.size()); for (const auto & sampler_type : slot.sparams.samplers_sequence) { - samplers_sequence.emplace_back(sampler_type_to_name_string(sampler_type)); + samplers_sequence.emplace_back(llama_sampling_type_to_str(sampler_type)); } return json { @@ -2852,7 +2852,7 @@ static void server_params_parse(int argc, char ** argv, server_params & sparams, invalid_param = true; break; } - if (!parse_kv_override(argv[i], params.kv_overrides)) { + if (!string_parse_kv_override(argv[i], params.kv_overrides)) { fprintf(stderr, "error: Invalid type for KV override: %s\n", argv[i]); invalid_param = true; break; @@ -3310,7 +3310,7 @@ int main(int argc, char ** argv) { const auto handle_slots_save = [&ctx_server, &res_error, &sparams](const httplib::Request & req, httplib::Response & res, int id_slot) { json request_data = json::parse(req.body); std::string filename = request_data.at("filename"); - if (!validate_file_name(filename)) { + if (!fs_validate_filename(filename)) { res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST)); return; } @@ -3340,7 +3340,7 @@ int main(int argc, char ** argv) { const auto handle_slots_restore = [&ctx_server, &res_error, &sparams](const httplib::Request & req, httplib::Response & res, int id_slot) { json request_data = json::parse(req.body); std::string filename = request_data.at("filename"); - if (!validate_file_name(filename)) { + if (!fs_validate_filename(filename)) { res_error(res, format_error_response("Invalid filename", ERROR_TYPE_INVALID_REQUEST)); return; } From 197ff91462dd05bb9a3be03578114abf0c355536 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 22 May 2024 20:05:38 +0300 Subject: [PATCH 084/181] build : remove zig (#7471) --- .github/workflows/zig-build.yml | 29 ------ build.zig | 172 -------------------------------- 2 files changed, 201 deletions(-) delete mode 100644 .github/workflows/zig-build.yml delete mode 100644 build.zig diff --git a/.github/workflows/zig-build.yml b/.github/workflows/zig-build.yml deleted file mode 100644 index 747c35cc0..000000000 --- a/.github/workflows/zig-build.yml +++ /dev/null @@ -1,29 +0,0 @@ -name: Zig CI - -on: - pull_request: - push: - branches: - - master - -concurrency: - group: ${{ github.workflow }}-${{ github.head_ref && github.ref || github.run_id }} - cancel-in-progress: true - -jobs: - build: - strategy: - fail-fast: false - matrix: - runs-on: [ubuntu-latest, macos-latest, windows-latest] - runs-on: ${{ matrix.runs-on }} - steps: - - uses: actions/checkout@v4 - with: - submodules: recursive - fetch-depth: 0 - - uses: goto-bus-stop/setup-zig@v2 - with: - version: 0.11.0 - - name: Build Summary - run: zig build --summary all -freference-trace diff --git a/build.zig b/build.zig deleted file mode 100644 index 267c976b1..000000000 --- a/build.zig +++ /dev/null @@ -1,172 +0,0 @@ -// Compatible with Zig Version 0.11.0 -const std = @import("std"); -const ArrayList = std.ArrayList; -const Compile = std.Build.Step.Compile; -const ConfigHeader = std.Build.Step.ConfigHeader; -const Mode = std.builtin.Mode; -const CrossTarget = std.zig.CrossTarget; - -const Maker = struct { - builder: *std.build.Builder, - target: CrossTarget, - optimize: Mode, - enable_lto: bool, - - include_dirs: ArrayList([]const u8), - cflags: ArrayList([]const u8), - cxxflags: ArrayList([]const u8), - objs: ArrayList(*Compile), - - fn addInclude(m: *Maker, dir: []const u8) !void { - try m.include_dirs.append(dir); - } - fn addProjectInclude(m: *Maker, path: []const []const u8) !void { - try m.addInclude(try m.builder.build_root.join(m.builder.allocator, path)); - } - fn addCFlag(m: *Maker, flag: []const u8) !void { - try m.cflags.append(flag); - } - fn addCxxFlag(m: *Maker, flag: []const u8) !void { - try m.cxxflags.append(flag); - } - fn addFlag(m: *Maker, flag: []const u8) !void { - try m.addCFlag(flag); - try m.addCxxFlag(flag); - } - - fn init(builder: *std.build.Builder) !Maker { - const target = builder.standardTargetOptions(.{}); - const zig_version = @import("builtin").zig_version_string; - const commit_hash = try std.ChildProcess.exec( - .{ .allocator = builder.allocator, .argv = &.{ "git", "rev-parse", "HEAD" } }, - ); - try std.fs.cwd().writeFile("common/build-info.cpp", builder.fmt( - \\int LLAMA_BUILD_NUMBER = {}; - \\char const *LLAMA_COMMIT = "{s}"; - \\char const *LLAMA_COMPILER = "Zig {s}"; - \\char const *LLAMA_BUILD_TARGET = "{s}"; - \\ - , .{ 0, commit_hash.stdout[0 .. commit_hash.stdout.len - 1], zig_version, try target.allocDescription(builder.allocator) })); - var m = Maker{ - .builder = builder, - .target = target, - .optimize = builder.standardOptimizeOption(.{}), - .enable_lto = false, - .include_dirs = ArrayList([]const u8).init(builder.allocator), - .cflags = ArrayList([]const u8).init(builder.allocator), - .cxxflags = ArrayList([]const u8).init(builder.allocator), - .objs = ArrayList(*Compile).init(builder.allocator), - }; - - try m.addCFlag("-std=c11"); - try m.addCxxFlag("-std=c++11"); - try m.addProjectInclude(&.{}); - try m.addProjectInclude(&.{"common"}); - return m; - } - - fn obj(m: *const Maker, name: []const u8, src: []const u8) *Compile { - const o = m.builder.addObject(.{ .name = name, .target = m.target, .optimize = m.optimize }); - if (o.target.getAbi() != .msvc) - o.defineCMacro("_GNU_SOURCE", null); - - if (std.mem.endsWith(u8, src, ".c")) { - o.addCSourceFiles(&.{src}, m.cflags.items); - o.linkLibC(); - } else { - o.addCSourceFiles(&.{src}, m.cxxflags.items); - if (o.target.getAbi() == .msvc) { - o.linkLibC(); // need winsdk + crt - } else { - // linkLibCpp already add (libc++ + libunwind + libc) - o.linkLibCpp(); - } - } - for (m.include_dirs.items) |i| o.addIncludePath(.{ .path = i }); - o.want_lto = m.enable_lto; - return o; - } - - fn exe(m: *const Maker, name: []const u8, src: []const u8, deps: []const *Compile) *Compile { - const e = m.builder.addExecutable(.{ .name = name, .target = m.target, .optimize = m.optimize }); - e.addCSourceFiles(&.{src}, m.cxxflags.items); - for (deps) |d| e.addObject(d); - for (m.objs.items) |o| e.addObject(o); - for (m.include_dirs.items) |i| e.addIncludePath(.{ .path = i }); - - // https://github.com/ziglang/zig/issues/15448 - if (e.target.getAbi() == .msvc) { - e.linkLibC(); // need winsdk + crt - } else { - // linkLibCpp already add (libc++ + libunwind + libc) - e.linkLibCpp(); - } - m.builder.installArtifact(e); - e.want_lto = m.enable_lto; - return e; - } -}; - -pub fn build(b: *std.build.Builder) !void { - var make = try Maker.init(b); - make.enable_lto = b.option(bool, "lto", "Enable LTO optimization, (default: false)") orelse false; - - const ggml = make.obj("ggml", "ggml.c"); - const sgemm = make.obj("sgemm", "sgemm.cpp"); - const ggml_alloc = make.obj("ggml-alloc", "ggml-alloc.c"); - const ggml_backend = make.obj("ggml-backend", "ggml-backend.c"); - const ggml_quants = make.obj("ggml-quants", "ggml-quants.c"); - const unicode = make.obj("unicode", "unicode.cpp"); - const unicode_data = make.obj("unicode-data", "unicode-data.cpp"); - const llama = make.obj("llama", "llama.cpp"); - const buildinfo = make.obj("common", "common/build-info.cpp"); - const common = make.obj("common", "common/common.cpp"); - const console = make.obj("console", "common/console.cpp"); - const sampling = make.obj("sampling", "common/sampling.cpp"); - const grammar_parser = make.obj("grammar-parser", "common/grammar-parser.cpp"); - const json_schema_to_grammar = make.obj("json-schema-to-grammar", "common/json-schema-to-grammar.cpp"); - const train = make.obj("train", "common/train.cpp"); - const clip = make.obj("clip", "examples/llava/clip.cpp"); - const llava = make.obj("llava", "examples/llava/llava.cpp"); - - _ = make.exe("main", "examples/main/main.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, sampling, json_schema_to_grammar, buildinfo, console, grammar_parser }); - _ = make.exe("quantize", "examples/quantize/quantize.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, sampling, json_schema_to_grammar, buildinfo }); - _ = make.exe("perplexity", "examples/perplexity/perplexity.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, sampling, json_schema_to_grammar, buildinfo }); - _ = make.exe("embedding", "examples/embedding/embedding.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, sampling, json_schema_to_grammar, buildinfo }); - _ = make.exe("finetune", "examples/finetune/finetune.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, sampling, json_schema_to_grammar, buildinfo, train }); - _ = make.exe("train-text-from-scratch", "examples/train-text-from-scratch/train-text-from-scratch.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, json_schema_to_grammar, buildinfo, train }); - - const server = make.exe("server", "examples/server/server.cpp", &.{ ggml, sgemm, ggml_alloc, ggml_backend, ggml_quants, llama, unicode, unicode_data, common, sampling, json_schema_to_grammar, buildinfo, grammar_parser, clip, llava }); - if (server.target.isWindows()) { - server.linkSystemLibrary("ws2_32"); - } - - const server_assets = [_][]const u8{ "index.html", "index.js", "completion.js", "json-schema-to-grammar.mjs" }; - for (server_assets) |asset| { - const input_path = b.fmt("examples/server/public/{s}", .{asset}); - const output_path = b.fmt("examples/server/{s}.hpp", .{asset}); - - // Portable equivalent of `b.addSystemCommand(&.{ "xxd", "-n", asset, "-i", input_path, output_path }) })`: - - const input = try std.fs.cwd().readFileAlloc(b.allocator, input_path, std.math.maxInt(usize)); - defer b.allocator.free(input); - - var buf = std.ArrayList(u8).init(b.allocator); - defer buf.deinit(); - - for (input) |byte| { - try std.fmt.format(buf.writer(), "0x{X:0>2}, ", .{byte}); - } - - var name = try std.mem.replaceOwned(u8, b.allocator, asset, "-", "_"); - defer b.allocator.free(name); - std.mem.replaceScalar(u8, name, '.', '_'); - - try std.fs.cwd().writeFile(output_path, b.fmt( - "unsigned char {s}[] = {{{s}}};\nunsigned int {s}_len = {d};\n", - .{ name, buf.items, name, input.len }, - )); - - std.debug.print("Dumped hex of \"{s}\" ({s}) to {s}\n", .{ input_path, name, output_path }); - } -} From 1e374365d170b7f692fd7753c145e21bc14486c8 Mon Sep 17 00:00:00 2001 From: HanishKVC Date: Wed, 22 May 2024 23:23:21 +0530 Subject: [PATCH 085/181] SimpleChat: a simple and dumb web front end for testing /chat/completions and /completions end points and try chat (#7350) * SimpleChat: Add a skeletal html page Contains a div placeholder for showing chat messages till now a text-input for allowing user to enter next chat message/query to the model. a submit button to allow sending of the user entered message and chat till now to the model. * SimpleChat: A js skeleton with SimpleChat class Allows maintaining an array of chat message. Allows adding chat message (from any of the roles be it system, user, assistant, ...) Allows showing chat messages till now, in a given div element. * SimpleChat: request_json, globals, startme * SimpleChatJS: Roles Class, submitClick Define Role class with static members corresponding to the roles. Update startme to * Get hold of the ui elements. * Attach a click handler to submit button, which adds the user input to xchats array and shows the chat messages till now in chat div element. Trap DOMContentLoaded to trigger startme * SimpleChat:HTML: Bring in the js file * SimpleChat: Rather value wrt input text element * SimpleChat: Also add completions related prompt * SimpleChat: Use common helper logic wrt json data * SimpleChat: Move handling of submit request into its own func * SimpleChat: Try handshake with llm over its web service endpoint * SimpleChat:JS: Extract model response and show to user * SimpleChat:JS: Messages/Prompt, indicate working to end user * SimpleChat: Try keep input element in view * SimpleChat: Diff user/assistant msgs, Make input wider Also show a default message to user Also add some metas * SimpleChat: Move into its own sub directory to avoid confusion * SimpleChat:sh: Add simple shell script to run python3 http.server So one needs to run the llm server locally then run this script and access it using a local browser * SimpleChat:JS: Try trap enter key press wrt input text field So user can either press submit button or press enter key * SimpleChat: Allow user to select chat or completion mode * SimpleChat: Dont submit if already submitted and waiting Also make chat the default selection wrt mode * SimpleChat:JS: Handle difference in response Try read the assistance response from appropriate field in the response got. Also examples/server seems to return the response in a slightly different field, so try account for that also. * SimpleChat:JS: Force completion mode be single message by default * SimpleChat: Add a simple readme file * SimpleChat:HTML: Cleanup/structure UI a bit, Add input for system * SimpleChat:Allow system prompt to be set, if provided before user * SimpleChat: Ignore empty user input, without trimming * SimpleChat:Alert user if they provide sysprompt late or change it * SimpleChat: Move handling systemprompt into its own func * SimpleChat:HTML: Add a style for system role message * SimpleChat: Update the readme file * SimpleChat:CSS: Move style info into its own css file To keep it simple, clean and seperate so that things are not unnecessarily cluttered. * SimpleChat:CSS: Allow for chat div to be scrollable * SimpleChat:JS: Try ensure the last entry in chat is visible Needed because now only the chat div is scrollable and not the full page. In last commit the chat div size was fixed to 75% vertical height, so the full page no longer scrolls, so the old bring user-input element to view wont work, instead now the last element in the chat div should be brought into view. * SimpleChat:JS: bottom of element visible, Set focus to user input As the generated text could be multiple lines and occupy more space that the full scrollable div's vertical space, make the bottom of the last element (which can be such a generated text) in the div visible by scrolling. Ensure that the user input box has focus * SimpleChat: Update notes a bit. Try keep browser happy Avoid browser quirk mode with DOCTYPE. Help with accessibility a bit by specifying the language explicitly. Specify the char encoding explicitly, inturn utf-8 is a safe bet, even with intermixing of languages if reqd in future. Add a cache-control http-equiv meta tag, which in all probability will be ignored. Defer js loading and execution, just for fun and future, not that critical here as it stands now. * SimpleChat:HTML:Group user input+btn together; Note about multichat * SimpleChat:JS: Allow for changing system prompt anytime for future * SimpleChat:Readme: Note about handle_systemprompt begin/anytime * SimpleChat:HTML: Add viewport meta for better mobile friendliness Without this the page content may look too small. * SimpleChat:HtmlCss: Cleanup UI flow set margin wrt vmin rather than vw or vh so portrait/landscape ok. Use flex and flex-grow to put things on the same line as well as distribute available space as needed. Given two main elements/line so it remains simple. In each line have one element with grows and one sits with a basic comfortably fixed size. * SimpleChat: textarea for multiline user chat, inturn shift+enter 4 enter * SimpleChat: Make vertical layout better responsive (flex based) Also needed to make things cleaner and properly usable whether landscape or portrait, after changing to multiline textarea rather than single line user input. Avoid hardcoding the chat-till-now display area height, instead make it a flex-growable within a flex column of ui elements within a fixed vertical area. * SimpleChat: Rename simplechat.html to index.html, update readme Instead of providing a seperate shell script, update the readme wrt how to run/use this web front end. * SimpleChat: Screen fixed view and scrolling, Printing full * SimpleChat:JS:CI: Avoid space at end of jsdoc param line * SimpleChat:JS: MultiChat initial skeleton Will help maintain multiple independent chats in future * SimpleChat:JS: Move system prompt begin/anytime into SimpleChat * SimpleChat:JS:Keep MultiChatUI simple for now Worry about different chats with different servers for later. * SimpleChat:JS: Move handle submit into MultiChat, build on same Create an instance of MultiChatUI and inturn a instance of chat session, which is what the UI will inturn work on. * SimpleChat:JS: Move to dictionary of SimpleChat, instead of array * SimpleChat: Move ui elements into MultiChatUI, Update el IDs Move ui elements into MultiChatUI, so that current handleUserSubmit doesnt need to take the element arguments. Also in future, when user is allowed to switch between different chat sessions, the UI can be updated as needed by using the elements in UI already known to MultiChatUI instance. Rename the element ids' so that they follow a common convention, as well as one can identify what the element represents in a more consistant manner. * SimpleChat:MCUI:Show available chat sessions, try switch btw them Previous commits brought in / consolidated existing logic into MultiChatUI class. Now start adding logic towards multichat support * show buttons indicating available chat sessions * on sessin button click, try switch to that session * SimpleChat:MCUI: Store and use current chat session id Also allow to switch chat session optionally, wrt some of the related helpers. setup for two chat sessions by default. * SimpleChat:MCUI: Delay enabling user-input to avoid race Re-enable user-input, only after response to a user query has been updated to the chat-div. This ensures that if user tries to switch chat session, it wont be allowed till chat-request-response flow is done. * SimpleChat: Take care of system prompt Helper to get the latest system prompt and inturn use same to set the system prompt ui, when switching. Ensure that system prompt is set if and when enter key is pressed. * SimpleChat:GetSystemLatest, fix a oversight. * SimpleChat:MCUI: Allow selected chat-session btn to be highlighted Also have a general helper for setting class of children. * SimpleChat:Cleanup corners Show system prompt in chat space, when it is set by pressing enter, as a feedback to user. Alert user, if they try to switch chat session in the middle of waiting for a response from the ai model. * SimpleChat:MCUI: Ensure req-resp failure doesnt lock up things * SimpleChat:MCUI: Support for new chat sessions Also a general create button helper. * SimpleChat:MCUI: CreateSessionBtn helper, use wrt NewChat Also fix a oversight wrt using stale data wrt the list of chat sessions. * SimpleChat:MCUI: NewChat btn first before existing chat sessions * SimpleChat:MCUI:CornerCases:Skip new chat, show only if current Skip NewChat if user cancels or if one waiting for response from the ai model. Dont show a chat with newly got ai model response, if current chat session has changed, some how. Chat session shouldnt be allowed to change, if there is a pending response, but still as a additional sanity check. * SimpleChat: Update readme, title, show usage if no chat to show * SimpleChat: Cleanup the log/dialog messages a bit --- examples/server/public_simplechat/index.html | 52 ++ examples/server/public_simplechat/readme.md | 81 +++ .../server/public_simplechat/simplechat.css | 61 +++ .../server/public_simplechat/simplechat.js | 478 ++++++++++++++++++ 4 files changed, 672 insertions(+) create mode 100644 examples/server/public_simplechat/index.html create mode 100644 examples/server/public_simplechat/readme.md create mode 100644 examples/server/public_simplechat/simplechat.css create mode 100644 examples/server/public_simplechat/simplechat.js diff --git a/examples/server/public_simplechat/index.html b/examples/server/public_simplechat/index.html new file mode 100644 index 000000000..1eb390b85 --- /dev/null +++ b/examples/server/public_simplechat/index.html @@ -0,0 +1,52 @@ + + + + SimpleChat (LlamaCPP, ...) + + + + + + + + + + +
+ +
+

SimpleChat

+
+ + +
+
+ +
+ +
+
+ + +
+ +
+
+

Enter the system prompt above, before entering/submitting any user query.

+

Enter your text to the ai assistant below.

+

Use shift+enter for inserting enter.

+

Refresh the page to start over fresh.

+
+ +
+
+ + +
+ +
+ + diff --git a/examples/server/public_simplechat/readme.md b/examples/server/public_simplechat/readme.md new file mode 100644 index 000000000..5ac8258f2 --- /dev/null +++ b/examples/server/public_simplechat/readme.md @@ -0,0 +1,81 @@ + +# SimpleChat + +by Humans for All. + + +## overview + +This simple web frontend, allows triggering/testing the server's /completions or /chat/completions endpoints +in a simple way with minimal code from a common code base. Inturn additionally it tries to allow single or +multiple independent back and forth chatting to an extent, with the ai llm model at a basic level, with their +own system prompts. + +The UI follows a responsive web design so that the layout can adapt to available display space in a usable +enough manner, in general. + +NOTE: Given that the idea is for basic minimal testing, it doesnt bother with any model context length and +culling of old messages from the chat. + +NOTE: It doesnt set any parameters other than temperature for now. However if someone wants they can update +the js file as needed. + + +## usage + +One could run this web frontend directly using server itself or if anyone is thinking of adding a built in web +frontend to configure the server over http(s) or so, then run this web frontend using something like python's +http module. + +### running using examples/server + +bin/server -m path/model.gguf --path ../examples/server/public_simplechat [--port PORT] + +### running using python3's server module + +first run examples/server +* bin/server -m path/model.gguf + +next run this web front end in examples/server/public_simplechat +* cd ../examples/server/public_simplechat +* python3 -m http.server PORT + +### using the front end + +Open this simple web front end from your local browser +* http://127.0.0.1:PORT/index.html + +Once inside +* Select between chat and completion mode. By default it is set to chat mode. +* If you want to provide a system prompt, then ideally enter it first, before entering any user query. + * if chat.add_system_begin is used + * you cant change the system prompt, after it is has been submitted once along with user query. + * you cant set a system prompt, after you have submitted any user query + * if chat.add_system_anytime is used + * one can change the system prompt any time during chat, by changing the contents of system prompt. + * inturn the updated/changed system prompt will be inserted into the chat session. + * this allows for the subsequent user chatting to be driven by the new system prompt set above. +* Enter your query and either press enter or click on the submit button. + If you want to insert enter (\n) as part of your chat/query to ai model, use shift+enter. +* Wait for the logic to communicate with the server and get the response. + * the user is not allowed to enter any fresh query during this time. + * the user input box will be disabled and a working message will be shown in it. +* just refresh the page, to reset wrt the chat history and or system prompt and start afresh. +* Using NewChat one can start independent chat sessions. + * two independent chat sessions are setup by default. + + +## Devel note + +Sometimes the browser may be stuborn with caching of the file, so your updates to html/css/js +may not be visible. Also remember that just refreshing/reloading page in browser or for that +matter clearing site data, dont directly override site caching in all cases. Worst case you may +have to change port. Or in dev tools of browser, you may be able to disable caching fully. + +Concept of multiple chat sessions with different servers, as well as saving and restoring of +those across browser usage sessions, can be woven around the SimpleChat/MultiChatUI class and +its instances relatively easily, however given the current goal of keeping this simple, it has +not been added, for now. + +By switching between chat.add_system_begin/anytime, one can control whether one can change +the system prompt, anytime during the conversation or only at the beginning. diff --git a/examples/server/public_simplechat/simplechat.css b/examples/server/public_simplechat/simplechat.css new file mode 100644 index 000000000..d45f50a95 --- /dev/null +++ b/examples/server/public_simplechat/simplechat.css @@ -0,0 +1,61 @@ +/** + * the styling of the simplechat web frontend + * by Humans for All + */ + +#fullbody { + height: 98vh; +} + +.heading { + background-color: lightgray; +} + +.session-selected { + background-color: lightblue; +} + +.role-system { + background-color: lightblue; +} +.role-user { + background-color: lightgray; +} + +.flex-grow { + flex-grow: 1; +} +.float-right { + float: right; +} + +#chat-div { + overflow: scroll; + flex-grow: 1; + flex-shrink: 1; + min-height: 40vh; +} +button { + min-width: 8vw; +} + +.sameline { + display: flex; + flex-direction: row; +} +.samecolumn { + display: flex; + flex-direction: column; +} + +* { + margin: 0.6vmin; +} + +@media print { + + #fullbody { + height: auto; + } + +} diff --git a/examples/server/public_simplechat/simplechat.js b/examples/server/public_simplechat/simplechat.js new file mode 100644 index 000000000..3fc4dbc20 --- /dev/null +++ b/examples/server/public_simplechat/simplechat.js @@ -0,0 +1,478 @@ +// @ts-check +// A simple completions and chat/completions test related web front end logic +// by Humans for All + +class Roles { + static System = "system"; + static User = "user"; + static Assistant = "assistant"; +} + +class ApiEP { + static Chat = "chat"; + static Completion = "completion"; +} + +let gUsageMsg = ` +

Enter the system prompt above, before entering/submitting any user query.

+

Enter your text to the ai assistant below.

+

Use shift+enter for inserting enter.

+

Refresh the page to start over fresh.

+`; + +class SimpleChat { + + constructor() { + /** + * Maintain in a form suitable for common LLM web service chat/completions' messages entry + * @type {{role: string, content: string}[]} + */ + this.xchat = []; + this.iLastSys = -1; + } + + /** + * Add an entry into xchat + * @param {string} role + * @param {string|undefined|null} content + */ + add(role, content) { + if ((content == undefined) || (content == null) || (content == "")) { + return false; + } + this.xchat.push( {role: role, content: content} ); + if (role == Roles.System) { + this.iLastSys = this.xchat.length - 1; + } + return true; + } + + /** + * Show the contents in the specified div + * @param {HTMLDivElement} div + * @param {boolean} bClear + */ + show(div, bClear=true) { + if (bClear) { + div.replaceChildren(); + } + let last = undefined; + for(const x of this.xchat) { + let entry = document.createElement("p"); + entry.className = `role-${x.role}`; + entry.innerText = `${x.role}: ${x.content}`; + div.appendChild(entry); + last = entry; + } + if (last !== undefined) { + last.scrollIntoView(false); + } else { + if (bClear) { + div.innerHTML = gUsageMsg; + } + } + } + + /** + * Add needed fields wrt json object to be sent wrt LLM web services completions endpoint + * Convert the json into string. + * @param {Object} obj + */ + request_jsonstr(obj) { + obj["temperature"] = 0.7; + return JSON.stringify(obj); + } + + /** + * Return a string form of json object suitable for chat/completions + */ + request_messages_jsonstr() { + let req = { + messages: this.xchat, + } + return this.request_jsonstr(req); + } + + /** + * Return a string form of json object suitable for /completions + */ + request_prompt_jsonstr() { + let prompt = ""; + for(const chat of this.xchat) { + prompt += `${chat.role}: ${chat.content}\n`; + } + let req = { + prompt: prompt, + } + return this.request_jsonstr(req); + } + + /** + * Allow setting of system prompt, but only at begining. + * @param {string} sysPrompt + * @param {string} msgTag + */ + add_system_begin(sysPrompt, msgTag) { + if (this.xchat.length == 0) { + if (sysPrompt.length > 0) { + return this.add(Roles.System, sysPrompt); + } + } else { + if (sysPrompt.length > 0) { + if (this.xchat[0].role !== Roles.System) { + console.error(`ERRR:SimpleChat:SC:${msgTag}:You need to specify system prompt before any user query, ignoring...`); + } else { + if (this.xchat[0].content !== sysPrompt) { + console.error(`ERRR:SimpleChat:SC:${msgTag}:You cant change system prompt, mid way through, ignoring...`); + } + } + } + } + return false; + } + + /** + * Allow setting of system prompt, at any time. + * @param {string} sysPrompt + * @param {string} msgTag + */ + add_system_anytime(sysPrompt, msgTag) { + if (sysPrompt.length <= 0) { + return false; + } + + if (this.iLastSys < 0) { + return this.add(Roles.System, sysPrompt); + } + + let lastSys = this.xchat[this.iLastSys].content; + if (lastSys !== sysPrompt) { + return this.add(Roles.System, sysPrompt); + } + return false; + } + + /** + * Retrieve the latest system prompt. + */ + get_system_latest() { + if (this.iLastSys == -1) { + return ""; + } + let sysPrompt = this.xchat[this.iLastSys].content; + return sysPrompt; + } + +} + + +let gBaseURL = "http://127.0.0.1:8080"; +let gChatURL = { + 'chat': `${gBaseURL}/chat/completions`, + 'completion': `${gBaseURL}/completions`, +} +const gbCompletionFreshChatAlways = true; + + +/** + * Set the class of the children, based on whether it is the idSelected or not. + * @param {HTMLDivElement} elBase + * @param {string} idSelected + * @param {string} classSelected + * @param {string} classUnSelected + */ +function el_children_config_class(elBase, idSelected, classSelected, classUnSelected="") { + for(let child of elBase.children) { + if (child.id == idSelected) { + child.className = classSelected; + } else { + child.className = classUnSelected; + } + } +} + +/** + * Create button and set it up. + * @param {string} id + * @param {(this: HTMLButtonElement, ev: MouseEvent) => any} callback + * @param {string | undefined} name + * @param {string | undefined} innerText + */ +function el_create_button(id, callback, name=undefined, innerText=undefined) { + if (!name) { + name = id; + } + if (!innerText) { + innerText = id; + } + let btn = document.createElement("button"); + btn.id = id; + btn.name = name; + btn.innerText = innerText; + btn.addEventListener("click", callback); + return btn; +} + + +class MultiChatUI { + + constructor() { + /** @type {Object} */ + this.simpleChats = {}; + /** @type {string} */ + this.curChatId = ""; + + // the ui elements + this.elInSystem = /** @type{HTMLInputElement} */(document.getElementById("system-in")); + this.elDivChat = /** @type{HTMLDivElement} */(document.getElementById("chat-div")); + this.elBtnUser = /** @type{HTMLButtonElement} */(document.getElementById("user-btn")); + this.elInUser = /** @type{HTMLInputElement} */(document.getElementById("user-in")); + this.elSelectApiEP = /** @type{HTMLSelectElement} */(document.getElementById("api-ep")); + this.elDivSessions = /** @type{HTMLDivElement} */(document.getElementById("sessions-div")); + + this.validate_element(this.elInSystem, "system-in"); + this.validate_element(this.elDivChat, "chat-div"); + this.validate_element(this.elInUser, "user-in"); + this.validate_element(this.elSelectApiEP, "api-ep"); + this.validate_element(this.elDivChat, "sessions-div"); + } + + /** + * Check if the element got + * @param {HTMLElement | null} el + * @param {string} msgTag + */ + validate_element(el, msgTag) { + if (el == null) { + throw Error(`ERRR:SimpleChat:MCUI:${msgTag} element missing in html...`); + } else { + console.debug(`INFO:SimpleChat:MCUI:${msgTag} Id[${el.id}] Name[${el["name"]}]`); + } + } + + /** + * Reset user input ui. + * * clear user input + * * enable user input + * * set focus to user input + */ + ui_reset_userinput() { + this.elInUser.value = ""; + this.elInUser.disabled = false; + this.elInUser.focus(); + } + + /** + * Setup the needed callbacks wrt UI, curChatId to defaultChatId and + * optionally switch to specified defaultChatId. + * @param {string} defaultChatId + * @param {boolean} bSwitchSession + */ + setup_ui(defaultChatId, bSwitchSession=false) { + + this.curChatId = defaultChatId; + if (bSwitchSession) { + this.handle_session_switch(this.curChatId); + } + + this.elBtnUser.addEventListener("click", (ev)=>{ + if (this.elInUser.disabled) { + return; + } + this.handle_user_submit(this.curChatId, this.elSelectApiEP.value).catch((/** @type{Error} */reason)=>{ + let msg = `ERRR:SimpleChat\nMCUI:HandleUserSubmit:${this.curChatId}\n${reason.name}:${reason.message}`; + console.debug(msg.replace("\n", ":")); + alert(msg); + this.ui_reset_userinput(); + }); + }); + + this.elInUser.addEventListener("keyup", (ev)=> { + // allow user to insert enter into their message using shift+enter. + // while just pressing enter key will lead to submitting. + if ((ev.key === "Enter") && (!ev.shiftKey)) { + this.elBtnUser.click(); + ev.preventDefault(); + } + }); + + this.elInSystem.addEventListener("keyup", (ev)=> { + // allow user to insert enter into the system prompt using shift+enter. + // while just pressing enter key will lead to setting the system prompt. + if ((ev.key === "Enter") && (!ev.shiftKey)) { + let chat = this.simpleChats[this.curChatId]; + chat.add_system_anytime(this.elInSystem.value, this.curChatId); + chat.show(this.elDivChat); + ev.preventDefault(); + } + }); + + } + + /** + * Setup a new chat session and optionally switch to it. + * @param {string} chatId + * @param {boolean} bSwitchSession + */ + new_chat_session(chatId, bSwitchSession=false) { + this.simpleChats[chatId] = new SimpleChat(); + if (bSwitchSession) { + this.handle_session_switch(chatId); + } + } + + /** + * Handle user query submit request, wrt specified chat session. + * @param {string} chatId + * @param {string} apiEP + */ + async handle_user_submit(chatId, apiEP) { + + let chat = this.simpleChats[chatId]; + + chat.add_system_anytime(this.elInSystem.value, chatId); + + let content = this.elInUser.value; + if (!chat.add(Roles.User, content)) { + console.debug(`WARN:SimpleChat:MCUI:${chatId}:HandleUserSubmit:Ignoring empty user input...`); + return; + } + chat.show(this.elDivChat); + + let theBody; + let theUrl = gChatURL[apiEP] + if (apiEP == ApiEP.Chat) { + theBody = chat.request_messages_jsonstr(); + } else { + theBody = chat.request_prompt_jsonstr(); + } + + this.elInUser.value = "working..."; + this.elInUser.disabled = true; + console.debug(`DBUG:SimpleChat:MCUI:${chatId}:HandleUserSubmit:${theUrl}:ReqBody:${theBody}`); + let resp = await fetch(theUrl, { + method: "POST", + headers: { + "Content-Type": "application/json", + }, + body: theBody, + }); + + let respBody = await resp.json(); + console.debug(`DBUG:SimpleChat:MCUI:${chatId}:HandleUserSubmit:RespBody:${JSON.stringify(respBody)}`); + let assistantMsg; + if (apiEP == ApiEP.Chat) { + assistantMsg = respBody["choices"][0]["message"]["content"]; + } else { + try { + assistantMsg = respBody["choices"][0]["text"]; + } catch { + assistantMsg = respBody["content"]; + } + } + chat.add(Roles.Assistant, assistantMsg); + if (chatId == this.curChatId) { + chat.show(this.elDivChat); + } else { + console.debug(`DBUG:SimpleChat:MCUI:HandleUserSubmit:ChatId has changed:[${chatId}] [${this.curChatId}]`); + } + // Purposefully clear at end rather than begin of this function + // so that one can switch from chat to completion mode and sequece + // in a completion mode with multiple user-assistant chat data + // from before to be sent/occur once. + if ((apiEP == ApiEP.Completion) && (gbCompletionFreshChatAlways)) { + chat.xchat.length = 0; + } + this.ui_reset_userinput(); + } + + /** + * Show buttons for NewChat and available chat sessions, in the passed elDiv. + * If elDiv is undefined/null, then use this.elDivSessions. + * Take care of highlighting the selected chat-session's btn. + * @param {HTMLDivElement | undefined} elDiv + */ + show_sessions(elDiv=undefined) { + if (!elDiv) { + elDiv = this.elDivSessions; + } + elDiv.replaceChildren(); + // Btn for creating new chat session + let btnNew = el_create_button("New CHAT", (ev)=> { + if (this.elInUser.disabled) { + console.error(`ERRR:SimpleChat:MCUI:NewChat:Current session [${this.curChatId}] awaiting response, ignoring request...`); + alert("ERRR:SimpleChat\nMCUI:NewChat\nWait for response to pending query, before starting new chat session"); + return; + } + let chatId = `Chat${Object.keys(this.simpleChats).length}`; + let chatIdGot = prompt("INFO:SimpleChat\nMCUI:NewChat\nEnter id for new chat session", chatId); + if (!chatIdGot) { + console.error("ERRR:SimpleChat:MCUI:NewChat:Skipping based on user request..."); + return; + } + this.new_chat_session(chatIdGot, true); + this.create_session_btn(elDiv, chatIdGot); + el_children_config_class(elDiv, chatIdGot, "session-selected", ""); + }); + elDiv.appendChild(btnNew); + // Btns for existing chat sessions + let chatIds = Object.keys(this.simpleChats); + for(let cid of chatIds) { + let btn = this.create_session_btn(elDiv, cid); + if (cid == this.curChatId) { + btn.className = "session-selected"; + } + } + } + + create_session_btn(elDiv, cid) { + let btn = el_create_button(cid, (ev)=>{ + let target = /** @type{HTMLButtonElement} */(ev.target); + console.debug(`DBUG:SimpleChat:MCUI:SessionClick:${target.id}`); + if (this.elInUser.disabled) { + console.error(`ERRR:SimpleChat:MCUI:SessionClick:${target.id}:Current session [${this.curChatId}] awaiting response, ignoring switch...`); + alert("ERRR:SimpleChat\nMCUI:SessionClick\nWait for response to pending query, before switching"); + return; + } + this.handle_session_switch(target.id); + el_children_config_class(elDiv, target.id, "session-selected", ""); + }); + elDiv.appendChild(btn); + return btn; + } + + /** + * Switch ui to the specified chatId and set curChatId to same. + * @param {string} chatId + */ + async handle_session_switch(chatId) { + let chat = this.simpleChats[chatId]; + if (chat == undefined) { + console.error(`ERRR:SimpleChat:MCUI:HandleSessionSwitch:${chatId} missing...`); + return; + } + this.elInSystem.value = chat.get_system_latest(); + this.elInUser.value = ""; + chat.show(this.elDivChat); + this.elInUser.focus(); + this.curChatId = chatId; + console.log(`INFO:SimpleChat:MCUI:HandleSessionSwitch:${chatId} entered...`); + } + +} + + +let gMuitChat; +const gChatIds = [ "Default", "Other" ]; + +function startme() { + console.log("INFO:SimpleChat:StartMe:Starting..."); + gMuitChat = new MultiChatUI(); + for (let cid of gChatIds) { + gMuitChat.new_chat_session(cid); + } + gMuitChat.setup_ui(gChatIds[0]); + gMuitChat.show_sessions(); +} + +document.addEventListener("DOMContentLoaded", startme); From cd93a28cb1446319af5e2f4b416174c3a8e43546 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Thu, 23 May 2024 00:31:20 +0200 Subject: [PATCH 086/181] CUDA: fix FA out-of-bounds reads (#7479) --- ggml-cuda/fattn-tile-f16.cu | 2 +- ggml-cuda/fattn-tile-f32.cu | 2 +- ggml-cuda/fattn-vec-f16.cu | 6 +++--- ggml-cuda/fattn-vec-f32.cu | 6 +++--- 4 files changed, 8 insertions(+), 8 deletions(-) diff --git a/ggml-cuda/fattn-tile-f16.cu b/ggml-cuda/fattn-tile-f16.cu index 586d469c0..cdb5eaff7 100644 --- a/ggml-cuda/fattn-tile-f16.cu +++ b/ggml-cuda/fattn-tile-f16.cu @@ -83,7 +83,7 @@ static __global__ void flash_attn_tile_ext_f16( for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { const int i = i0 + threadIdx.x; - const float2 tmp = Q_f2[j*(nb01/sizeof(float2)) + i]; + const float2 tmp = ic0 + j < ne01 ? Q_f2[j*(nb01/sizeof(float2)) + i] : make_float2(0.0f, 0.0f); Q_h2[j][i] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y); } } diff --git a/ggml-cuda/fattn-tile-f32.cu b/ggml-cuda/fattn-tile-f32.cu index b6ef8eb48..5a3de2918 100644 --- a/ggml-cuda/fattn-tile-f32.cu +++ b/ggml-cuda/fattn-tile-f32.cu @@ -79,7 +79,7 @@ static __global__ void flash_attn_tile_ext_f32( #pragma unroll for (int i0 = 0; i0 < D; i0 += 2*WARP_SIZE) { - float2 tmp = Q_f2[j*(nb01/sizeof(float2)) + i0/2 + threadIdx.x]; + float2 tmp = ic0 + j < ne01 ? Q_f2[j*(nb01/sizeof(float2)) + i0/2 + threadIdx.x] : make_float2(0.0f, 0.0f); Q_f[j][i0 + 0*WARP_SIZE + threadIdx.x] = tmp.x * scale; Q_f[j][i0 + 1*WARP_SIZE + threadIdx.x] = tmp.y * scale; } diff --git a/ggml-cuda/fattn-vec-f16.cu b/ggml-cuda/fattn-vec-f16.cu index 7352dcabf..808e8f362 100644 --- a/ggml-cuda/fattn-vec-f16.cu +++ b/ggml-cuda/fattn-vec-f16.cu @@ -94,7 +94,7 @@ static __global__ void flash_attn_vec_ext_f16( for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { const int i = i0 + threadIdx.x; - const float2 tmp = Q_f2[j*(nb01/sizeof(float2)) + i]; + const float2 tmp = ncols <= 2 || ic0 + j < ne01 ? Q_f2[j*(nb01/sizeof(float2)) + i] : make_float2(0.0f, 0.0f); Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y); } } @@ -212,7 +212,7 @@ static __global__ void flash_attn_vec_ext_f16( #pragma unroll for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) { - if (ic0 + j_VKQ >= ne01) { + if (ncols > 2 && ic0 + j_VKQ >= ne01) { break; } @@ -227,7 +227,7 @@ static __global__ void flash_attn_vec_ext_f16( dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; } - if (parallel_blocks != 1 && tid < ncols && ic0 + tid < ne01) { + if (parallel_blocks != 1 && tid < ncols && (ncols <= 2 || ic0 + tid < ne01)) { dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]); } #else diff --git a/ggml-cuda/fattn-vec-f32.cu b/ggml-cuda/fattn-vec-f32.cu index 11476a6c0..b4652301b 100644 --- a/ggml-cuda/fattn-vec-f32.cu +++ b/ggml-cuda/fattn-vec-f32.cu @@ -91,7 +91,7 @@ static __global__ void flash_attn_vec_ext_f32( for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { const int i = i0 + threadIdx.x; - Q_h2[j][i0/WARP_SIZE] = Q_f2[j*(nb01/sizeof(float2)) + i]; + Q_h2[j][i0/WARP_SIZE] = ncols <= 2 || ic0 + j ? Q_f2[j*(nb01/sizeof(float2)) + i] : make_float2(0.0f, 0.0f); Q_h2[j][i0/WARP_SIZE].x *= scale; Q_h2[j][i0/WARP_SIZE].y *= scale; } @@ -200,7 +200,7 @@ static __global__ void flash_attn_vec_ext_f32( #pragma unroll for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) { - if (ic0 + j_VKQ >= ne01) { + if (ncols > 2 && ic0 + j_VKQ >= ne01) { break; } @@ -215,7 +215,7 @@ static __global__ void flash_attn_vec_ext_f32( dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; } - if (parallel_blocks != 1 && tid < ncols && ic0 + tid < ne01) { + if (parallel_blocks != 1 && tid < ncols && (ncols <= 2 || ic0 + tid < ne01)) { dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]); } } From fbf777d2b9c30e7569e3d1c149501c1e31d9b5b9 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 23 May 2024 09:43:24 +0300 Subject: [PATCH 087/181] main : minor (#7462) --- examples/main/main.cpp | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 791dc61a7..09fa85fce 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -474,12 +474,12 @@ int main(int argc, char ** argv) { LOG_TEE("\n\n"); if (params.interactive) { - const char *control_message; + const char * control_message; if (params.multiline_input) { - control_message = " - To return control to LLaMa, end your input with '\\'.\n" + control_message = " - To return control to the AI, end your input with '\\'.\n" " - To return control without starting a new line, end your input with '/'.\n"; } else { - control_message = " - Press Return to return control to LLaMa.\n" + control_message = " - Press Return to return control to the AI.\n" " - To return control without starting a new line, end your input with '/'.\n" " - If you want to submit another line, end your input with '\\'.\n"; } From 1b1e27cb49158123ef4902aa41eb368c9e76e6a1 Mon Sep 17 00:00:00 2001 From: 0cc4m Date: Thu, 23 May 2024 08:59:59 +0200 Subject: [PATCH 088/181] Update vulkan rope implementation to support frequency factors (#7475) --- ggml-vulkan-shaders.hpp | 1235 ++++++++++++++++++----------------- ggml-vulkan.cpp | 259 +++++--- ggml_vk_generate_shaders.py | 7 +- 3 files changed, 825 insertions(+), 676 deletions(-) diff --git a/ggml-vulkan-shaders.hpp b/ggml-vulkan-shaders.hpp index 70c4043d3..e8cb5f52c 100644 --- a/ggml-vulkan-shaders.hpp +++ b/ggml-vulkan-shaders.hpp @@ -78882,342 +78882,15 @@ const uint64_t rope_f32_len = 3072; unsigned char rope_neox_f16_data[] = { 0x03,0x02,0x23,0x07,0x00,0x05,0x01,0x00,0x0b,0x00,0x0d,0x00, -0x5f,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, +0x75,0x01,0x00,0x00,0x00,0x00,0x00,0x00,0x11,0x00,0x02,0x00, 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, 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+0x41,0x00,0x06,0x00,0xa0,0x00,0x00,0x00,0x23,0x01,0x00,0x00, +0x97,0x00,0x00,0x00,0x42,0x00,0x00,0x00,0x0a,0x01,0x00,0x00, +0x3e,0x00,0x03,0x00,0x23,0x01,0x00,0x00,0x22,0x01,0x00,0x00, +0xf9,0x00,0x02,0x00,0x26,0x01,0x00,0x00,0xf8,0x00,0x02,0x00, +0x26,0x01,0x00,0x00,0xfd,0x00,0x01,0x00,0x38,0x00,0x01,0x00, }; -const uint64_t rope_neox_f32_len = 3792; +const uint64_t rope_neox_f32_len = 4200; unsigned char scale_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 16287a280..79ce1479f 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -290,6 +290,7 @@ struct vk_op_rope_neox_push_constants { float corr_dims[4]; float theta_scale; float inv_ndims; + uint32_t has_freq_facs; }; struct vk_op_soft_max_push_constants { @@ -1522,8 +1523,8 @@ static void ggml_vk_load_shaders(ggml_backend_vk_context * ctx) { ggml_vk_create_pipeline(ctx, ctx->device->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->device->pipeline_rope_f16, "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->device->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->device->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); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f32, "rope_neox_f32", rope_neox_f32_len, rope_neox_f32_data, "main", 4, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); + ggml_vk_create_pipeline(ctx, ctx->device->pipeline_rope_neox_f16, "rope_neox_f16", rope_neox_f16_len, rope_neox_f16_data, "main", 4, sizeof(vk_op_rope_neox_push_constants), {1, 512, 1}, {}, 1); ggml_vk_create_pipeline(ctx, ctx->device->pipeline_argsort_f32, "argsort_f32", argsort_f32_len, argsort_f32_data, "main", 2, sizeof(vk_op_argsort_push_constants), {1024, 1, 1}, {}, 1); } @@ -3732,7 +3733,7 @@ static void ggml_vk_op_repeat(ggml_backend_vk_context * ctx, vk_context * subctx } -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) { +static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, 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) { @@ -3853,6 +3854,8 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const default: return nullptr; } + + GGML_UNUSED(src2); } static ggml_vk_func_t ggml_vk_op_get_func(ggml_op op) { @@ -3880,12 +3883,15 @@ static bool ggml_vk_op_supports_incontiguous(ggml_op op) { } template -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) { +static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, 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 << ", 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 << ", 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]; } + if (src2 != nullptr) { + std::cerr << "), (" << src2 << ", name=" << src2->name << ", type=" << src2->type << ", ne0=" << src2->ne[0] << ", ne1=" << src2->ne[1] << ", ne2=" << src2->ne[2] << ", ne3=" << src2->ne[3] << ", nb0=" << src2->nb[0] << ", nb1=" << src2->nb[1] << ", nb2=" << src2->nb[2] << ", nb3=" << src2->nb[3]; + } std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", 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(op == GGML_OP_GET_ROWS || (!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type)))); // NOLINT @@ -3896,6 +3902,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c 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; @@ -3904,7 +3911,14 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c const uint64_t ne1 = ne10 * ne11; // const uint64_t nb10 = use_src1 ? src1->nb[0] : 0; - vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, dst, op); + const bool use_src2 = src2 != nullptr; + const uint64_t ne20 = use_src2 ? src2->ne[0] : 0; + const uint64_t ne21 = use_src2 ? src2->ne[1] : 0; + const uint64_t ne22 = use_src2 ? src2->ne[2] : 0; + const uint64_t ne23 = use_src2 ? src2->ne[3] : 0; + const uint64_t ne2 = ne20 * ne21; + + vk_pipeline pipeline = ggml_vk_op_get_pipeline(ctx, src0, src1, src2, dst, op); ggml_vk_func_t op_func; if (pipeline == nullptr) { @@ -3927,15 +3941,18 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c 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; + ggml_tensor_extra_gpu * extra_src2 = use_src2 ? (ggml_tensor_extra_gpu *) src2->extra : nullptr; vk_buffer d_X = nullptr; size_t x_buf_offset = 0; vk_buffer d_Y = nullptr; size_t y_buf_offset = 0; vk_buffer d_Z = nullptr; + size_t z_buf_offset = 0; bool src0_uma = false; bool src1_uma = false; + bool src2_uma = false; if (ctx->device->uma) { ggml_vk_host_get(ctx, src0->data, d_X, x_buf_offset); @@ -3944,10 +3961,15 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c ggml_vk_host_get(ctx, src1->data, d_Y, y_buf_offset); src1_uma = d_Y != nullptr; } + if (use_src2) { + ggml_vk_host_get(ctx, src2->data, d_Z, z_buf_offset); + src2_uma = d_Z != nullptr; + } } uint64_t x_sz = ggml_vk_align_size(ggml_type_size(src0->type)/ggml_blck_size(src0->type) * ne0, ctx->device->properties.limits.minStorageBufferOffsetAlignment); uint64_t y_sz = use_src1 ? ggml_vk_align_size(ggml_type_size(src1->type) * ne1, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : 0; + uint64_t z_sz = use_src2 ? ggml_vk_align_size(ggml_type_size(src2->type) * ne2, ctx->device->properties.limits.minStorageBufferOffsetAlignment) : 0; uint64_t d_sz = ggml_type_size(dst->type) * ne0; vk_buffer d_D = extra->buffer_gpu.lock(); @@ -3970,10 +3992,16 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c y_buf_offset = extra_src1->offset; GGML_ASSERT(d_Y != nullptr); } + if (use_src2 && !src2_uma) { + d_Z = extra_src2->buffer_gpu.lock(); + z_buf_offset = extra_src2->offset; + GGML_ASSERT(d_Z != nullptr); + } if (op_supports_incontiguous) { x_sz = ggml_nbytes(src0); y_sz = use_src1 ? ggml_nbytes(src1) : 0; + z_sz = use_src2 ? ggml_nbytes(src2) : 0; d_sz = ggml_nbytes(dst); if (x_buf_offset + x_sz >= d_X->size) { @@ -3982,6 +4010,9 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c if (use_src1 && y_buf_offset + y_sz >= d_Y->size) { y_sz = VK_WHOLE_SIZE; } + if (use_src2 && z_buf_offset + z_sz >= d_Z->size) { + z_sz = VK_WHOLE_SIZE; + } if (d_buf_offset + d_sz >= d_D->size) { d_sz = VK_WHOLE_SIZE; } @@ -4021,13 +4052,16 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c if (use_src1 && y_sz != VK_WHOLE_SIZE) { y_sz *= ne12 * ne13; } + if (use_src2 && z_sz != VK_WHOLE_SIZE) { + z_sz *= ne22 * ne23; + } if (d_sz != VK_WHOLE_SIZE) { d_sz *= ne02 * ne03; } } if (op == GGML_OP_SOFT_MAX) { - // Empty src1 is possible on soft_max, but the shader needs a buffer + // Empty src1 is possible in soft_max, but the shader needs a buffer vk_subbuffer subbuf_y; if (use_src1) { subbuf_y = { d_Y, y_buf_offset, y_sz }; @@ -4037,6 +4071,28 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c ggml_vk_sync_buffers(subctx); ggml_vk_dispatch_pipeline(ctx, subctx, pipeline, { { d_X, x_buf_offset, x_sz }, subbuf_y, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + } else if (op == GGML_OP_ROPE) { + const int mode = ((int32_t *) dst->op_params)[2]; + const bool is_neox = mode & 2; + + if (is_neox) { + // Empty src2 is possible in rope, but the shader needs a buffer + vk_subbuffer subbuf_z; + if (use_src2) { + subbuf_z = { d_Z, z_buf_offset, z_sz }; + } else { + subbuf_z = { d_X, 0, d_X->size }; + } + + 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 }, subbuf_z, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); + } else { + 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 if (use_src2) { + 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_Z, z_buf_offset, z_sz }, { d_D, d_buf_offset, d_sz } }, sizeof(PC), &pc, elements); } else if (use_src1) { 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); @@ -4047,6 +4103,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c } else { GGML_ASSERT(op != GGML_OP_SOFT_MAX); GGML_ASSERT(op != GGML_OP_ARGSORT); + GGML_ASSERT(!use_src2); ggml_pipeline_allocate_descriptor_sets(ctx, pipeline, ne02 * ne03); @@ -4088,7 +4145,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context * subctx, c } 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 }); + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_REPEAT, { (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) { @@ -4096,7 +4153,7 @@ static void ggml_vk_get_rows(ggml_backend_vk_context * ctx, vk_context * subctx, const uint32_t src1_type_size = ggml_type_size(src1->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_GET_ROWS, { + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_GET_ROWS, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (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)src0->nb[3] / src0_type_size, (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, @@ -4111,7 +4168,7 @@ static void ggml_vk_add(ggml_backend_vk_context * ctx, vk_context * subctx, cons const uint32_t src1_type_size = ggml_type_size(src1->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ADD, { + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_ADD, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (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)src0->nb[3] / src0_type_size, (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, @@ -4126,7 +4183,7 @@ static void ggml_vk_mul(ggml_backend_vk_context * ctx, vk_context * subctx, cons const uint32_t src1_type_size = ggml_type_size(src1->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_MUL, { + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_MUL, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (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)src0->nb[3] / src0_type_size, (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size, @@ -4141,7 +4198,7 @@ static void ggml_vk_scale(ggml_backend_vk_context * ctx, vk_context * subctx, co const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_SCALE, { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SCALE, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (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)src0->nb[3] / src0_type_size, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, @@ -4154,7 +4211,7 @@ static void ggml_vk_sqr(ggml_backend_vk_context * ctx, vk_context * subctx, cons const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_SQR, { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_SQR, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (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)src0->nb[3] / src0_type_size, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, @@ -4168,7 +4225,7 @@ static void ggml_vk_clamp(ggml_backend_vk_context * ctx, vk_context * subctx, co const uint32_t src0_type_size = ggml_type_size(src0->type); const uint32_t dst_type_size = ggml_type_size(dst->type); - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_CLAMP, { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CLAMP, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (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)src0->nb[3] / src0_type_size, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, @@ -4183,7 +4240,7 @@ static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context * subctx, cons const uint32_t dst_type_size = ggml_type_size(dst->type); const uint32_t d_offset = (extra->offset % ctx->device->properties.limits.minStorageBufferOffsetAlignment) / dst_type_size; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_CPY, { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_CPY, { (uint32_t)ggml_nelements(src0), (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2], (uint32_t)src0->ne[3], (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)src0->nb[3] / src0_type_size, (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2], (uint32_t) dst->ne[3], (uint32_t) dst->nb[0] / dst_type_size, (uint32_t) dst->nb[1] / dst_type_size, (uint32_t) dst->nb[2] / dst_type_size, (uint32_t) dst->nb[3] / dst_type_size, @@ -4195,21 +4252,21 @@ static void ggml_vk_cpy(ggml_backend_vk_context * ctx, vk_context * subctx, cons static void ggml_vk_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, subctx, src0, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_NORM, { (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], op_params[0], 0.0f }); } 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, subctx, 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, 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(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 }); + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_UNARY, { (uint32_t)ggml_nelements(src0), 0, 0.0f, 0.0f }); } 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, subctx, 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, 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(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { @@ -4228,7 +4285,7 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, const float m0 = powf(2.0f, -(max_bias ) / n_head_log2); const float m1 = powf(2.0f, -(max_bias / 2.0f) / n_head_log2); - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_SOFT_MAX, { + ggml_vk_op_f32(ctx, subctx, src0, src1, nullptr, dst, GGML_OP_SOFT_MAX, { ncols, src1 != nullptr ? nrows_y : (uint32_t)0, scale, max_bias, @@ -4237,11 +4294,7 @@ static void ggml_vk_soft_max(ggml_backend_vk_context * ctx, vk_context * subctx, }); } -static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { -#pragma message("TODO: implement phi3 frequency factors support") -#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7225") - GGML_ASSERT(dst->src[2] == nullptr && "phi3 frequency factors not implemented yet"); - +static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, 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]; @@ -4264,12 +4317,13 @@ static void ggml_vk_rope(ggml_backend_vk_context * ctx, vk_context * subctx, con 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, subctx, src0, src1, dst, GGML_OP_ROPE, { + ggml_vk_op_f32(ctx, subctx, src0, src1, src2, 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 + freq_base, ext_factor, attn_factor, {corr_dims[0], corr_dims[1], 0.0f, 0.0f}, theta_scale, inv_ndims, + src2 != nullptr, }); } else { - ggml_vk_op_f32(ctx, subctx, src0, src1, dst, GGML_OP_ROPE, { + ggml_vk_op_f32(ctx, subctx, src0, src1, src2, 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} }); @@ -4292,7 +4346,7 @@ static void ggml_vk_argsort(ggml_backend_vk_context * ctx, vk_context * subctx, std::cerr << ((ggml_sort_order) op_params[0]) << " " << GGML_SORT_ORDER_ASC << std::endl; - ggml_vk_op_f32(ctx, subctx, src0, nullptr, dst, GGML_OP_ARGSORT, { + ggml_vk_op_f32(ctx, subctx, src0, nullptr, nullptr, dst, GGML_OP_ARGSORT, { ncols, ncols_pad, op_params[0], @@ -5408,6 +5462,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod const ggml_tensor * src0 = node->src[0]; const ggml_tensor * src1 = node->src[1]; + const ggml_tensor * src2 = node->src[2]; switch (node->op) { case GGML_OP_UNARY: @@ -5524,7 +5579,7 @@ static void ggml_vk_build_graph(ggml_backend_vk_context * ctx, ggml_tensor * nod break; case GGML_OP_ROPE: - ggml_vk_rope(ctx, ctx->compute_ctx, src0, src1, node); + ggml_vk_rope(ctx, ctx->compute_ctx, src0, src1, src2, node); break; case GGML_OP_ARGSORT: @@ -6500,7 +6555,7 @@ static void ggml_vk_print_graph_origin(const ggml_tensor * tensor, std::vectorop) << " gpu=" << (tensor->extra != nullptr) << " backend=" << tensor->backend << std::endl; + std::cerr << ggml_op_name(tensor->op) << " gpu=" << (tensor->extra != nullptr) << std::endl; done.push_back(tensor); @@ -6550,7 +6605,7 @@ static void ggml_vk_print_tensor_area(const ggml_tensor * tensor, const void * d 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_TYPE_GPU) { + if (ggml_backend_buffer_is_vk(tensor->buffer)) { const size_t tensor_size = ggml_nbytes(tensor); tensor_data = malloc(tensor_size); @@ -6561,12 +6616,12 @@ static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tenso } 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; + std::cerr << "tensor=" << tensor << " 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; + 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) << " 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 << "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) << " 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); @@ -6577,43 +6632,11 @@ static void ggml_vk_print_tensor(ggml_backend_vk_context * ctx, const ggml_tenso std::vector done; ggml_vk_print_graph_origin(tensor, done); - if (tensor->backend == GGML_BACKEND_TYPE_GPU) { + if (ggml_backend_buffer_is_vk(tensor->buffer)) { free(tensor_data); } } -static void ggml_vk_check_tensor(const std::string& name, const ggml_tensor * tensor) { - return; - GGML_ASSERT(tensor->backend == GGML_BACKEND_TYPE_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]; @@ -6637,6 +6660,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ ggml_tensor * src0 = tensor->src[0]; ggml_tensor * src1 = tensor->src[1]; + ggml_tensor * src2 = tensor->src[2]; struct ggml_init_params iparams = { /*.mem_size =*/ 1024*1024*1024, @@ -6666,10 +6690,10 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ src0_buffer = malloc(src0_size); src0_clone->data = src0_buffer; - if (src0->backend == GGML_BACKEND_TYPE_CPU) { + if (ggml_backend_buffer_is_host(src0->buffer)) { 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_TYPE_GPU) { + } else if (ggml_backend_buffer_is_vk(src0->buffer)) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src0->extra; vk_buffer buffer_gpu = extra->buffer_gpu.lock(); uint64_t offset = extra->offset; @@ -6700,8 +6724,6 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { 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(ggml_ctx, src1); @@ -6710,10 +6732,10 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ src1_buffer = malloc(src1_size); src1_clone->data = src1_buffer; - if (src1->backend == GGML_BACKEND_TYPE_CPU) { + if (ggml_backend_buffer_is_host(src1->buffer)) { 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_TYPE_GPU) { + } else if (ggml_backend_buffer_is_vk(src1->buffer)) { ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src1->extra; vk_buffer buffer_gpu = extra->buffer_gpu.lock(); uint64_t offset = extra->offset; @@ -6744,12 +6766,12 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ if (vk_output_tensor > 0 && vk_output_tensor == check_counter) { 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; + std::cerr << "src1_clone=" << tensor << " 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; + std::cerr << "src1->src[0]=" << src1->src[0] << " op=" << ggml_op_name(src1->src[0]->op) << " type=" << ggml_type_name(src1->src[0]->type) << " 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 << "src1->src[1]=" << src1->src[1] << " op=" << ggml_op_name(src1->src[1]->op) << " type=" << ggml_type_name(src1->src[1]->type) << " 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); @@ -6760,8 +6782,64 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ std::vector done; ggml_vk_print_graph_origin(src1_clone, done); } + } + if (src2 != nullptr) { + src2_clone = ggml_dup_tensor(ggml_ctx, src2); - ggml_vk_check_tensor(std::string(ggml_op_name(tensor->op)) + "->src1", src1_clone); + src2_size = ggml_nbytes(src2); + + src2_buffer = malloc(src2_size); + src2_clone->data = src2_buffer; + if (ggml_backend_buffer_is_host(src2->buffer)) { + memcpy(src2_clone->data, src2->data, src2_size); + memcpy(src2_clone->nb, src2->nb, sizeof(size_t) * GGML_MAX_DIMS); + } else if (ggml_backend_buffer_is_vk(src2->buffer)) { + ggml_tensor_extra_gpu * extra = (ggml_tensor_extra_gpu *) src2->extra; + vk_buffer buffer_gpu = extra->buffer_gpu.lock(); + uint64_t offset = extra->offset; + if (!ggml_is_contiguous(src2) && ggml_vk_dim01_contiguous(src2)) { + for (int i3 = 0; i3 < src2->ne[3]; i3++) { + for (int i2 = 0; i2 < src2->ne[2]; i2++) { + const int idx = i3*src2->ne[2] + i2; + ggml_vk_buffer_read(ctx, buffer_gpu, offset + idx * src2->nb[2], ((char *)src2_clone->data + idx * src2_clone->nb[2]), src2->ne[1] * src2->nb[1]); + } + } + + src2_clone->nb[0] = src2->nb[0]; + src2_clone->nb[1] = src2->nb[1]; + for (int i = 2; i < GGML_MAX_DIMS; i++) { + src2_clone->nb[i] = src2_clone->nb[i - 1]*src2_clone->ne[i - 1]; + } + } else { + if (offset + src2_size >= buffer_gpu->size) { + src2_size = buffer_gpu->size - offset; + } + ggml_vk_buffer_read(ctx, buffer_gpu, offset, src2_clone->data, src2_size); + memcpy(src2_clone->nb, src2->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(ctx, src2, "src2"); + std::cerr << "TENSOR CHECK: " << ggml_op_name(src2_clone->op) << " (check " << check_counter << ")" << std::endl; + std::cerr << "src2_clone=" << tensor << " src2_clone->type: " << ggml_type_name(src2_clone->type) << " ne0=" << src2_clone->ne[0] << " nb0=" << src2_clone->nb[0] << " ne1=" << src2_clone->ne[1] << " nb1=" << src2_clone->nb[1] << " ne2=" << src2_clone->ne[2] << " nb2=" << src2_clone->nb[2] << " ne3=" << src2_clone->ne[3] << " nb3=" << src2_clone->nb[3] << std::endl; + if (src2->src[0] != nullptr) { + std::cerr << "src2->src[0]=" << src2->src[0] << " op=" << ggml_op_name(src2->src[0]->op) << " type=" << ggml_type_name(src2->src[0]->type) << " ne0=" << src2->src[0]->ne[0] << " nb0=" << src2->src[0]->nb[0] << " ne1=" << src2->src[0]->ne[1] << " nb1=" << src2->src[0]->nb[1] << " ne2=" << src2->src[0]->ne[2] << " nb2=" << src2->src[0]->nb[2] << " ne3=" << src2->src[0]->ne[3] << " nb3=" << src2->src[0]->nb[3] << std::endl; + } + if (src2->src[1] != nullptr) { + std::cerr << "src2->src[1]=" << src2->src[1] << " op=" << ggml_op_name(src2->src[1]->op) << " type=" << ggml_type_name(src2->src[1]->type) << " ne0=" << src2->src[1]->ne[0] << " nb0=" << src2->src[1]->nb[0] << " ne1=" << src2->src[1]->ne[1] << " nb1=" << src2->src[1]->nb[1] << " ne2=" << src2->src[1]->ne[2] << " nb2=" << src2->src[1]->nb[2] << " ne3=" << src2->src[1]->ne[3] << " nb3=" << src2->src[1]->nb[3] << std::endl; + } + std::cerr << std::endl << "Result:" << std::endl; + ggml_vk_print_tensor_area(src2_clone, src2_clone->data, 5, 5, 0, 0); + std::cerr << std::endl; + std::cerr << std::endl << "Result:" << std::endl; + ggml_vk_print_tensor_area(src2_clone, src2_clone->data, 5, 5, 1, 0); + std::cerr << std::endl; + std::vector done; + ggml_vk_print_graph_origin(src2_clone, done); + } } if (tensor->op == GGML_OP_MUL_MAT) { @@ -6799,7 +6877,7 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ 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(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); + tensor_clone = ggml_rope_ext(ggml_ctx, src0_clone, src1_clone, src2_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: @@ -6847,7 +6925,6 @@ static void ggml_vk_check_results_0(ggml_backend_vk_context * ctx, ggml_compute_ ggml_graph_compute_with_ctx(ggml_ctx, cgraph, 8); - 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(ctx, tensor_clone, "tensor_clone"); } @@ -6888,7 +6965,7 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ void * tensor_data = tensor->data; - if (tensor->backend == GGML_BACKEND_TYPE_GPU) { + if (ggml_backend_buffer_is_vk(tensor->buffer)) { size_t tensor_size = ggml_nbytes(tensor); tensor_data = malloc(tensor_size); @@ -6936,12 +7013,12 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ 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; + std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " 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; + std::cerr << "src0=" << src0 << " src0->name=" << src0->name << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " 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 << "src1=" << src1 << " src1->name=" << src1->name << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " 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; @@ -6977,12 +7054,12 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ 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; + std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " 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; + std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " 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 << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " 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; @@ -7001,12 +7078,12 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ 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; + std::cerr << "tensor=" << tensor << " tensor->name=" << tensor->name << " 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; + std::cerr << "src0=" << src0 << " op=" << ggml_op_name(src0->op) << " type=" << ggml_type_name(src0->type) << " 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 << "src1=" << src1 << " op=" << ggml_op_name(src1->op) << " type=" << ggml_type_name(src1->type) << " 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; @@ -7018,14 +7095,14 @@ static void ggml_vk_check_results_1(ggml_backend_vk_context * ctx, ggml_compute_ 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; + std::cerr << check_counter << " " << tensor->name << " op=" << ggml_op_name(tensor->op) << " avg_err=" << avg_err << std::endl; } free(comp_result); comp_result = nullptr; comp_size = 0; - if (tensor->backend == GGML_BACKEND_TYPE_GPU) { + if (ggml_backend_buffer_is_vk(tensor->buffer)) { free(tensor_data); } } diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py index 8096c03b7..a8f7373df 100644 --- a/ggml_vk_generate_shaders.py +++ b/ggml_vk_generate_shaders.py @@ -2609,7 +2609,8 @@ 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 (binding = 2) readonly buffer Z {float data_freq_factors[];}; +layout (binding = 3) writeonly buffer D {D_TYPE data_d[];}; layout (push_constant) uniform parameter { uint ncols; @@ -2622,6 +2623,7 @@ layout (push_constant) uniform parameter { float corr_dims[4]; float theta_scale; float inv_ndims; + uint has_freq_facs; } p; float rope_yarn_ramp(const float low, const float high, const uint i0) { @@ -2671,7 +2673,8 @@ void main() { 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); + const float freq_factor = p.has_freq_facs != 0 ? data_freq_factors[ic/2] : 1.0f; + const float theta_base = pos*p.freq_scale*pow(p.theta_scale, col/2.0f) / freq_factor; float cos_theta, sin_theta; rope_yarn(theta_base, uint(cur_rot), cos_theta, sin_theta); From e84b71c2c6da6e69c8f815168ea836f9716a325e Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 23 May 2024 10:00:21 +0300 Subject: [PATCH 089/181] ggml : drop support for QK_K=64 (#7473) * ggml : drop support for QK_K=64 ggml-ci * opencl : restore QK_K=256 define --- CMakeLists.txt | 5 - Makefile | 4 - ci/run.sh | 3 +- ggml-common.h | 54 - ggml-cuda/convert.cu | 138 -- ggml-cuda/dmmv.cu | 151 --- ggml-cuda/mmq.cu | 6 - ggml-cuda/vecdotq.cuh | 126 -- ggml-metal.m | 17 - ggml-metal.metal | 398 +----- ggml-opencl.cpp | 2 +- ggml-quants.c | 2672 +------------------------------------ ggml-sycl.cpp | 472 +------ ggml.c | 12 - gguf-py/gguf/constants.py | 3 +- llama.cpp | 12 +- 16 files changed, 26 insertions(+), 4049 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index c09d834fb..ef02ff669 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -124,7 +124,6 @@ set(LLAMA_METAL_MACOSX_VERSION_MIN "" CACHE STRING set(LLAMA_METAL_STD "" CACHE STRING "llama: metal standard version (-std flag)") option(LLAMA_KOMPUTE "llama: use Kompute" OFF) option(LLAMA_RPC "llama: use RPC" 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) set(LLAMA_SYCL_TARGET "INTEL" CACHE STRING "llama: sycl target device") @@ -384,10 +383,6 @@ if (LLAMA_LLAMAFILE) set(GGML_SOURCES_LLAMAFILE sgemm.cpp) endif() -if (LLAMA_QKK_64) - add_compile_definitions(GGML_QKK_64) -endif() - if (LLAMA_CUBLAS) message(WARNING "LLAMA_CUBLAS is deprecated and will be removed in the future.\nUse LLAMA_CUDA instead") set(LLAMA_CUDA ON) diff --git a/Makefile b/Makefile index 6b7c853b3..fe63cbd60 100644 --- a/Makefile +++ b/Makefile @@ -389,10 +389,6 @@ else MK_CXXFLAGS += -march=rv64gcv -mabi=lp64d endif -ifdef LLAMA_QKK_64 - MK_CPPFLAGS += -DGGML_QKK_64 -endif - ifndef LLAMA_NO_ACCELERATE # Mac OS - include Accelerate framework. # `-framework Accelerate` works both with Apple Silicon and Mac Intel diff --git a/ci/run.sh b/ci/run.sh index d5972480b..79dcd0772 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -606,7 +606,8 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 8 ]; then if [ -z ${GG_BUILD_CUDA} ]; then - test $ret -eq 0 && gg_run open_llama_3b_v2 + #test $ret -eq 0 && gg_run open_llama_3b_v2 + date # dummy else test $ret -eq 0 && gg_run open_llama_7b_v2 fi diff --git a/ggml-common.h b/ggml-common.h index 43c7978a0..77e6bfba4 100644 --- a/ggml-common.h +++ b/ggml-common.h @@ -65,13 +65,8 @@ typedef sycl::half2 ggml_half2; // QK = number of values after dequantization // QK_K = super-block size -#ifdef GGML_QKK_64 -#define QK_K 64 -#define K_SCALE_SIZE 4 -#else #define QK_K 256 #define K_SCALE_SIZE 12 -#endif // GGML_QKK_64 #if defined(GGML_COMMON_DECL_CUDA) || defined(GGML_COMMON_DECL_HIP) || defined(GGML_COMMON_DECL_SYCL) // QR = QK / number of values before dequantization @@ -131,13 +126,8 @@ typedef sycl::half2 ggml_half2; #define QI4_NL (QK4_NL / (4*QR4_NL)) #define QR4_NL 2 -#if QK_K == 64 -#define QI4_XS QI4_NL -#define QR4_XS QR4_NL -#else #define QI4_XS (QK_K / (4*QR4_XS)) #define QR4_XS 8 -#endif #endif // GGML_COMMON_DECL_CUDA || GGML_COMMON_DECL_HIP @@ -228,15 +218,6 @@ static_assert(sizeof(block_q2_K) == 2*sizeof(ggml_half) + QK_K/16 + QK_K/4, "wro // weight is represented as x = a * q // 16 blocks of 16 elements each // Effectively 3.4375 bits per weight -#ifdef GGML_QKK_64 -typedef struct { - uint8_t hmask[QK_K/8]; // quants - high bit - uint8_t qs[QK_K/4]; // quants - low 2 bits - uint8_t scales[2]; - ggml_half d; // super-block scale -} block_q3_K; -static_assert(sizeof(block_q3_K) == sizeof(ggml_half) + QK_K / 4 + QK_K / 8 + 2, "wrong q3_K block size/padding"); -#else typedef struct { uint8_t hmask[QK_K/8]; // quants - high bit uint8_t qs[QK_K/4]; // quants - low 2 bits @@ -244,20 +225,11 @@ typedef struct { ggml_half d; // super-block scale } block_q3_K; static_assert(sizeof(block_q3_K) == sizeof(ggml_half) + QK_K / 4 + QK_K / 8 + 12, "wrong q3_K block size/padding"); -#endif // 4-bit quantization // 8 blocks of 32 elements each // weight is represented as x = a * q + b // Effectively 4.5 bits per weight -#ifdef GGML_QKK_64 -typedef struct { - ggml_half d[2]; // super-block scales/mins - uint8_t scales[2]; // 4-bit block scales/mins - uint8_t qs[QK_K/2]; // 4--bit quants -} block_q4_K; -static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_half) + QK_K/2 + 2, "wrong q4_K block size/padding"); -#else typedef struct { union { struct { @@ -270,21 +242,11 @@ typedef struct { uint8_t qs[QK_K/2]; // 4--bit quants } block_q4_K; static_assert(sizeof(block_q4_K) == 2*sizeof(ggml_half) + K_SCALE_SIZE + QK_K/2, "wrong q4_K block size/padding"); -#endif // 5-bit quantization // 8 blocks of 32 elements each // weight is represented as x = a * q + b // Effectively 5.5 bits per weight -#ifdef GGML_QKK_64 -typedef struct { - ggml_half d; // super-block scale - int8_t scales[QK_K/16]; // 8-bit block scales - uint8_t qh[QK_K/8]; // quants, high bit - uint8_t qs[QK_K/2]; // quants, low 4 bits -} block_q5_K; -static_assert(sizeof(block_q5_K) == sizeof(ggml_half) + QK_K/2 + QK_K/8 + QK_K/16, "wrong q5_K block size/padding"); -#else typedef struct { union { struct { @@ -298,7 +260,6 @@ typedef struct { uint8_t qs[QK_K/2]; // quants, low 4 bits } block_q5_K; static_assert(sizeof(block_q5_K) == 2*sizeof(ggml_half) + K_SCALE_SIZE + QK_K/2 + QK_K/8, "wrong q5_K block size/padding"); -#endif // 6-bit quantization // weight is represented as x = a * q @@ -356,11 +317,7 @@ typedef struct { static_assert(sizeof(block_iq3_xxs) == sizeof(ggml_half) + 3*(QK_K/8), "wrong iq3_xxs block size/padding"); // 3.4375 bpw -#if QK_K == 64 -#define IQ3S_N_SCALE 2 -#else #define IQ3S_N_SCALE QK_K/64 -#endif typedef struct { ggml_half d; uint8_t qs[QK_K/4]; @@ -381,16 +338,9 @@ static_assert(sizeof(block_iq1_s) == sizeof(ggml_half) + QK_K/8 + QK_K/16, "wron typedef struct { uint8_t qs[QK_K/8]; // grid index, low 8 bits uint8_t qh[QK_K/16]; // grid index, high 3 bits + grid shift bit (for two groups of 8) -#if QK_K == 64 - ggml_half d; -#endif uint8_t scales[QK_K/32]; // 3-bit block scales (4-bit if QK_K == 64) } block_iq1_m; -#if QK_K == 64 -static_assert(sizeof(block_iq1_m) == QK_K/8 + QK_K/16 + QK_K/32 + sizeof(ggml_half), "wrong iq1_m block size/padding"); -#else static_assert(sizeof(block_iq1_m) == QK_K/8 + QK_K/16 + QK_K/32, "wrong iq1_m block size/padding"); -#endif // Used by IQ1_M quants typedef union { @@ -406,9 +356,6 @@ typedef struct { } block_iq4_nl; static_assert(sizeof(block_iq4_nl) == sizeof(ggml_half) + QK4_NL/2, "wrong iq4_nl block size/padding"); -#if QK_K == 64 -#define block_iq4_xs block_iq4_nl -#else typedef struct { ggml_half d; uint16_t scales_h; @@ -416,7 +363,6 @@ typedef struct { uint8_t qs[QK_K/2]; } block_iq4_xs; static_assert(sizeof(block_iq4_xs) == sizeof(ggml_half) + sizeof(uint16_t) + QK_K/64 + QK_K/2, "wrong iq4_xs block size/padding"); -#endif #endif // GGML_COMMON_DECL #endif // GGML_COMMON_DECL diff --git a/ggml-cuda/convert.cu b/ggml-cuda/convert.cu index 830e2d756..c0a444707 100644 --- a/ggml-cuda/convert.cu +++ b/ggml-cuda/convert.cu @@ -131,7 +131,6 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t const block_q2_K * x = (const block_q2_K *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t n = tid/32; const int64_t l = tid - 32*n; const int64_t is = 8*n + l/16; @@ -145,17 +144,6 @@ static __global__ void dequantize_block_q2_K(const void * __restrict__ vx, dst_t 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 int64_t is = tid/16; // 0 or 1 - const int64_t 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 @@ -164,7 +152,6 @@ static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t const int64_t i = blockIdx.x; const block_q3_K * x = (const block_q3_K *) vx; -#if QK_K == 256 const int64_t r = threadIdx.x/4; const int64_t tid = r/2; const int64_t is0 = r%2; @@ -188,31 +175,8 @@ static __global__ void dequantize_block_q3_K(const void * __restrict__ vx, dst_t 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 int64_t tid = threadIdx.x; - const int64_t is = tid/16; // 0 or 1 - const int64_t il = tid%16; // 0...15 - const int64_t im = il/8; // 0...1 - const int64_t 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 __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m) { if (j < 4) { d = q[j] & 63; m = q[j + 4] & 63; @@ -221,7 +185,6 @@ static inline __device__ void get_scale_min_k4(int j, const uint8_t * q, uint8_t m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4); } } -#endif template static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restrict__ yy) { @@ -229,7 +192,6 @@ static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t const int64_t i = blockIdx.x; -#if QK_K == 256 // assume 32 threads const int64_t tid = threadIdx.x; const int64_t il = tid/8; @@ -253,15 +215,6 @@ static __global__ void dequantize_block_q4_K(const void * __restrict__ vx, dst_t y[l + 0] = d1 * (q[l] & 0xF) - m1; y[l +32] = d2 * (q[l] >> 4) - m2; } -#else - const int64_t 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 @@ -270,7 +223,6 @@ static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t const int64_t i = blockIdx.x; -#if QK_K == 256 // assume 64 threads - this is very slightly better than the one below const int64_t tid = threadIdx.x; const int64_t il = tid/16; // il is in 0...3 @@ -297,18 +249,6 @@ static __global__ void dequantize_block_q5_K(const void * __restrict__ vx, dst_t 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 int64_t tid = threadIdx.x; - const uint8_t q = x[i].qs[tid]; - const int64_t im = tid/8; // 0...3 - const int64_t in = tid%8; // 0...7 - const int64_t 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 @@ -316,7 +256,6 @@ static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t const block_q6_K * x = (const block_q6_K *) vx; const int64_t i = blockIdx.x; -#if QK_K == 256 // assume 64 threads - this is very slightly better than the one below const int64_t tid = threadIdx.x; @@ -336,24 +275,6 @@ static __global__ void dequantize_block_q6_K(const void * __restrict__ vx, dst_t 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 int64_t tid = threadIdx.x; - const int64_t ip = tid/16; // 0 or 1 - const int64_t 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 } template @@ -363,7 +284,6 @@ static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, ds const block_iq2_xxs * x = (const block_iq2_xxs *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t il = tid/8; // 0...3 const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -374,10 +294,6 @@ static __global__ void dequantize_block_iq2_xxs(const void * __restrict__ vx, ds const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f; const uint8_t signs = ksigns_iq2xs[(aux32 >> 7*il) & 127]; for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); -#else - NO_DEVICE_CODE; -#endif - } template @@ -387,7 +303,6 @@ static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst const block_iq2_xs * x = (const block_iq2_xs *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t il = tid/8; // 0...3 const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -396,10 +311,6 @@ static __global__ void dequantize_block_iq2_xs(const void * __restrict__ vx, dst const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; const uint8_t signs = ksigns_iq2xs[q2[il] >> 9]; for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); -#else - NO_DEVICE_CODE; -#endif - } template @@ -409,7 +320,6 @@ static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_ const block_iq2_s * x = (const block_iq2_s *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t il = tid/8; // 0...3 const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -417,10 +327,6 @@ static __global__ void dequantize_block_iq2_s(const void * __restrict__ vx, dst_ const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; const uint8_t signs = x[i].qs[QK_K/8+4*ib+il]; for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); -#else - NO_DEVICE_CODE; -#endif - } template @@ -430,7 +336,6 @@ static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, ds const block_iq3_xxs * x = (const block_iq3_xxs *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t il = tid/8; // 0...3 const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -445,10 +350,6 @@ static __global__ void dequantize_block_iq3_xxs(const void * __restrict__ vx, ds 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 - NO_DEVICE_CODE; -#endif - } template @@ -458,7 +359,6 @@ static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_ const block_iq3_s * x = (const block_iq3_s *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t il = tid/8; // 0...3 const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -471,10 +371,6 @@ static __global__ void dequantize_block_iq3_s(const void * __restrict__ vx, dst_ 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 - NO_DEVICE_CODE; -#endif - } template @@ -484,7 +380,6 @@ static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_ const block_iq1_s * x = (const block_iq1_s *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t il = tid/8; // 0...3 const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -497,10 +392,6 @@ static __global__ void dequantize_block_iq1_s(const void * __restrict__ vx, dst_ for (int j = 0; j < 8; ++j) { y[j] = d * (q[j] + delta); } -#else - NO_DEVICE_CODE; -#endif - } template @@ -510,7 +401,6 @@ static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_ const block_iq1_m * x = (const block_iq1_m *) vx; const int64_t tid = threadIdx.x; -#if QK_K == 256 const int64_t il = tid/8; // 0...3 const int64_t ib = tid%8; // 0...7 dst_t * y = yy + i*QK_K + 32*ib + 8*il; @@ -527,13 +417,8 @@ static __global__ void dequantize_block_iq1_m(const void * __restrict__ vx, dst_ for (int j = 0; j < 8; ++j) { y[j] = d * (q[j] + delta); } -#else - NO_DEVICE_CODE; -#endif - } - template static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst_t * __restrict__ yy) { @@ -550,10 +435,8 @@ static __global__ void dequantize_block_iq4_nl(const void * __restrict__ vx, dst y[j+ 0] = d * kvalues_iq4nl[q4[j] & 0xf]; y[j+16] = d * kvalues_iq4nl[q4[j] >> 4]; } - } -#if QK_K != 64 template static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst_t * __restrict__ yy) { const int64_t i = blockIdx.x; @@ -570,7 +453,6 @@ static __global__ void dequantize_block_iq4_xs(const void * __restrict__ vx, dst y[j+16] = d * kvalues_iq4nl[q4[j] >> 4]; } } -#endif template static void dequantize_block_cuda(const void * __restrict__ vx, dst_t * __restrict__ y, const int64_t k, cudaStream_t stream) { @@ -592,21 +474,13 @@ static void dequantize_block_q8_0_f16_cuda(const void * __restrict__ vx, half * template static void dequantize_row_q2_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; -#if QK_K == 256 dequantize_block_q2_K<<>>(vx, y); -#else - dequantize_block_q2_K<<>>(vx, y); -#endif } template static void dequantize_row_q3_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; -#if QK_K == 256 dequantize_block_q3_K<<>>(vx, y); -#else - dequantize_block_q3_K<<>>(vx, y); -#endif } template @@ -632,21 +506,13 @@ static void dequantize_row_q4_K_cuda(const void * vx, dst_t * y, const int64_t k template static void dequantize_row_q5_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; -#if QK_K == 256 dequantize_block_q5_K<<>>(vx, y); -#else - dequantize_block_q5_K<<>>(vx, y); -#endif } template static void dequantize_row_q6_K_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = k / QK_K; -#if QK_K == 256 dequantize_block_q6_K<<>>(vx, y); -#else - dequantize_block_q6_K<<>>(vx, y); -#endif } template @@ -700,11 +566,7 @@ static void dequantize_row_iq1_m_cuda(const void * vx, dst_t * y, const int64_t template static void dequantize_row_iq4_xs_cuda(const void * vx, dst_t * y, const int64_t k, cudaStream_t stream) { const int nb = (k + QK_K - 1) / QK_K; -#if QK_K == 64 - dequantize_block_iq4_nl<<>>(vx, y); -#else dequantize_block_iq4_xs<<>>(vx, y); -#endif } template diff --git a/ggml-cuda/dmmv.cu b/ggml-cuda/dmmv.cu index 7313e3e17..47d4d5d9e 100644 --- a/ggml-cuda/dmmv.cu +++ b/ggml-cuda/dmmv.cu @@ -22,7 +22,6 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, float tmp = 0; // partial sum for thread in warp -#if QK_K == 256 const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...15 const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0,1 @@ -71,37 +70,6 @@ static __global__ void dequantize_mul_mat_vec_q2_k(const void * __restrict__ vx, 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 tmp = warp_reduce_sum(tmp); @@ -123,8 +91,6 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, float tmp = 0; // partial sum for thread in warp -#if QK_K == 256 - const uint16_t kmask1 = 0x0303; const uint16_t kmask2 = 0x0f0f; @@ -175,34 +141,6 @@ static __global__ void dequantize_mul_mat_vec_q3_k(const void * __restrict__ vx, 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 tmp = warp_reduce_sum(tmp); @@ -221,7 +159,6 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, 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; @@ -306,36 +243,6 @@ static __global__ void dequantize_mul_mat_vec_q4_k(const void * __restrict__ vx, #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 tmp = warp_reduce_sum(tmp); @@ -355,7 +262,6 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx, 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; @@ -426,30 +332,6 @@ static __global__ void dequantize_mul_mat_vec_q5_k(const void * __restrict__ vx, 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 tmp = warp_reduce_sum(tmp); @@ -470,8 +352,6 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const block_q6_K * x = (const block_q6_K *)vx + ib0; -#if QK_K == 256 - const int tid = threadIdx.x/K_QUANTS_PER_ITERATION; // 0...31 or 0...16 const int ix = threadIdx.x%K_QUANTS_PER_ITERATION; // 0 or 0, 1 @@ -526,37 +406,6 @@ static __global__ void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, } -#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 tmp = warp_reduce_sum(tmp); diff --git a/ggml-cuda/mmq.cu b/ggml-cuda/mmq.cu index 933d799ce..c0a66d9b6 100644 --- a/ggml-cuda/mmq.cu +++ b/ggml-cuda/mmq.cu @@ -826,11 +826,7 @@ template static __device__ __forceinlin const block_q4_K * bxi = bx0 + i*blocks_per_row + kbxd; -#if QK_K == 256 x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = bxi->dm; -#else - x_dm[i * (WARP_SIZE/QI4_K) + i / QI4_K + kbxd] = {bxi->dm[0], bxi->dm[1]}; -#endif } #pragma unroll @@ -933,9 +929,7 @@ template static __device__ __forceinlin const block_q5_K * bxi = bx0 + i*blocks_per_row + kbxd; -#if QK_K == 256 x_dm[i * (WARP_SIZE/QI5_K) + i / QI5_K + kbxd] = bxi->dm; -#endif } #pragma unroll diff --git a/ggml-cuda/vecdotq.cuh b/ggml-cuda/vecdotq.cuh index 86b87fa93..5ebdddcc7 100644 --- a/ggml-cuda/vecdotq.cuh +++ b/ggml-cuda/vecdotq.cuh @@ -712,7 +712,6 @@ static __device__ __forceinline__ float vec_dot_q3_K_q8_1( static __device__ __forceinline__ 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]; @@ -754,58 +753,11 @@ static __device__ __forceinline__ float vec_dot_q4_K_q8_1( } return vec_dot_q4_K_q8_1_impl_vmmq(v, u, sc, m, bq4_K->dm, d8); - -#else - -#if __CUDA_ARCH__ >= MIN_CC_DP4A // 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 - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A - -#endif } static __device__ __forceinline__ 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]; @@ -847,48 +799,6 @@ static __device__ __forceinline__ float vec_dot_q5_K_q8_1( } return vec_dot_q5_K_q8_1_impl_vmmq(vl, vh, u, sc, m, bq5_K->dm, d8); - -#else - -#if __CUDA_ARCH__ >= MIN_CC_DP4A // 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 - NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ >= MIN_CC_DP4A - -#endif } static __device__ __forceinline__ float vec_dot_q6_K_q8_1( @@ -919,7 +829,6 @@ static __device__ __forceinline__ float vec_dot_q6_K_q8_1( static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -#if QK_K == 256 const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq; #if QR2_XXS == 8 @@ -960,15 +869,11 @@ static __device__ __forceinline__ float vec_dot_iq2_xxs_q8_1( } return d * (sumi1 + sumi2); #endif -#else - NO_DEVICE_CODE; -#endif } 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; const int ib32 = iqs; @@ -1002,17 +907,12 @@ static __device__ __forceinline__ float vec_dot_iq2_xs_q8_1( GGML_UNUSED(ksigns64); NO_DEVICE_CODE; #endif -#else - GGML_UNUSED(ksigns64); - NO_DEVICE_CODE; -#endif } // TODO static __device__ __forceinline__ float vec_dot_iq2_s_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_s * bq2 = (const block_iq2_s *) vbq; const int ib32 = iqs; @@ -1048,16 +948,11 @@ static __device__ __forceinline__ float vec_dot_iq2_s_q8_1( GGML_UNUSED(ksigns64); NO_DEVICE_CODE; #endif -#else - GGML_UNUSED(ksigns64); - NO_DEVICE_CODE; -#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; @@ -1082,16 +977,12 @@ static __device__ __forceinline__ float vec_dot_iq3_xxs_q8_1( #else NO_DEVICE_CODE; #endif -#else - NO_DEVICE_CODE; -#endif } // TODO: don't use lookup table for signs static __device__ __forceinline__ float vec_dot_iq3_s_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_s * bq2 = (const block_iq3_s *) vbq; const int ib32 = iqs; @@ -1114,14 +1005,10 @@ static __device__ __forceinline__ float vec_dot_iq3_s_q8_1( #else NO_DEVICE_CODE; #endif -#else - NO_DEVICE_CODE; -#endif } static __device__ __forceinline__ float vec_dot_iq1_s_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -#if QK_K == 256 const block_iq1_s * bq1 = (const block_iq1_s *) vbq; const int ib32 = iqs; @@ -1149,14 +1036,10 @@ static __device__ __forceinline__ float vec_dot_iq1_s_q8_1( const float d = d1q * __low2float (bq8_1[ib32].ds); const float m = d1q * __high2float(bq8_1[ib32].ds); return d * sumi + m * delta; -#else - NO_DEVICE_CODE; -#endif } static __device__ __forceinline__ float vec_dot_iq1_m_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -#if QK_K == 256 const block_iq1_m * bq1 = (const block_iq1_m *) vbq; const int ib32 = iqs; @@ -1192,9 +1075,6 @@ static __device__ __forceinline__ float vec_dot_iq1_m_q8_1( scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); const float d = (float)scale.f16 * __low2float (bq8_1[ib32].ds); return d * ((sumi[0] + sumf[0]) * (2*((sc[ib32/2] >> 6*(ib32%2)) & 0x7) + 1) + (sumi[1] + sumf[1]) * (2*((sc[ib32/2] >> (6*(ib32%2)+3)) & 0x7) + 1)); -#else - NO_DEVICE_CODE; -#endif } #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics @@ -1250,9 +1130,7 @@ static __device__ __forceinline__ float vec_dot_iq4_nl_q8_1( static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1( const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & iqs) { -#if QK_K == 256 #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics - const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq; const uint8_t * values = (const uint8_t *)kvalues_iq4nl; @@ -1270,10 +1148,6 @@ static __device__ __forceinline__ float vec_dot_iq4_xs_q8_1( sumi2 = __dp4a(v2, q8[j+4], sumi2); } return d * (sumi1 + sumi2); - -#else - NO_DEVICE_CODE; -#endif #else return vec_dot_iq4_xs_q8_1(vbq, bq8_1, iqs); #endif diff --git a/ggml-metal.m b/ggml-metal.m index 5d5ad20ad..c9e570dbf 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -381,10 +381,6 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { // dictionary of preprocessor macros NSMutableDictionary * prep = [NSMutableDictionary dictionary]; -#ifdef GGML_QKK_64 - prep[@"GGML_QKK_64"] = @(1); -#endif - MTLCompileOptions* options = [MTLCompileOptions new]; options.preprocessorMacros = prep; @@ -1773,11 +1769,7 @@ static enum ggml_status ggml_metal_graph_compute( [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src0t == GGML_TYPE_Q3_K) { -#ifdef GGML_QKK_64 - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; -#else [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; -#endif } else if (src0t == GGML_TYPE_Q5_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12*ne13) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; @@ -2018,12 +2010,7 @@ static enum ggml_status ggml_metal_graph_compute( { nth0 = 4; nth1 = 16; - #if QK_K == 64 - pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_NL_F32].pipeline; - #else pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_MUL_MV_ID_IQ4_XS_F32].pipeline; - #endif - } break; default: { @@ -2088,11 +2075,7 @@ static enum ggml_status ggml_metal_graph_compute( [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src0t == GGML_TYPE_Q3_K) { -#ifdef GGML_QKK_64 - [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; -#else [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; -#endif } else if (src0t == GGML_TYPE_Q5_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, _ne1, tgz) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; diff --git a/ggml-metal.metal b/ggml-metal.metal index c5eb25280..8ff70d7a7 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -3386,7 +3386,6 @@ void kernel_mul_mv_q2_K_f32_impl( const int step = sizeof(block_q2_K) * nb; -#if QK_K == 256 const int ix = tiisg/8; // 0...3 const int it = tiisg%8; // 0...7 const int iq = it/4; // 0 or 1 @@ -3438,57 +3437,6 @@ void kernel_mul_mv_q2_K_f32_impl( y4 += 4 * QK_K; } -#else - const int ix = tiisg/2; // 0...15 - const int it = tiisg%2; // 0...1 - - device const float * y4 = y + ix * QK_K + 8 * it; - - for (int ib = ix; ib < nb; ib += 16) { - - float4 sumy = {0.f, 0.f, 0.f, 0.f}; - for (int i = 0; i < 8; ++i) { - yl[i+ 0] = y4[i+ 0]; sumy[0] += yl[i+ 0]; - yl[i+ 8] = y4[i+16]; sumy[1] += yl[i+ 8]; - yl[i+16] = y4[i+32]; sumy[2] += yl[i+16]; - yl[i+24] = y4[i+48]; sumy[3] += yl[i+24]; - } - - device const uint8_t * sc = (device const uint8_t *)x[ib].scales; - device const uint16_t * qs = (device const uint16_t *)x[ib].qs + 4 * it; - device const half * dh = &x[ib].d; - - for (int row = 0; row < N_DST; row++) { - - float4 acc1 = {0.f, 0.f, 0.f, 0.f}; - float4 acc2 = {0.f, 0.f, 0.f, 0.f}; - for (int i = 0; i < 8; i += 2) { - acc1[0] += yl[i+ 0] * (qs[i/2] & 0x0003); - acc2[0] += yl[i+ 1] * (qs[i/2] & 0x0300); - acc1[1] += yl[i+ 8] * (qs[i/2] & 0x000c); - acc2[1] += yl[i+ 9] * (qs[i/2] & 0x0c00); - acc1[2] += yl[i+16] * (qs[i/2] & 0x0030); - acc2[2] += yl[i+17] * (qs[i/2] & 0x3000); - acc1[3] += yl[i+24] * (qs[i/2] & 0x00c0); - acc2[3] += yl[i+25] * (qs[i/2] & 0xc000); - } - - float dall = dh[0]; - float dmin = dh[1]; - sumf[row] += dall * ((acc1[0] + 1.f/256.f * acc2[0]) * (sc[0] & 0xF) * 1.f/ 1.f + - (acc1[1] + 1.f/256.f * acc2[1]) * (sc[1] & 0xF) * 1.f/ 4.f + - (acc1[2] + 1.f/256.f * acc2[2]) * (sc[2] & 0xF) * 1.f/16.f + - (acc1[3] + 1.f/256.f * acc2[3]) * (sc[3] & 0xF) * 1.f/64.f) - - dmin * (sumy[0] * (sc[0] >> 4) + sumy[1] * (sc[1] >> 4) + sumy[2] * (sc[2] >> 4) + sumy[3] * (sc[3] >> 4)); - - qs += step/2; - sc += step; - dh += step/2; - } - - y4 += 16 * QK_K; - } -#endif for (int row = 0; row < N_DST; ++row) { all_sum = simd_sum(sumf[row]); @@ -3526,7 +3474,6 @@ kernel void kernel_mul_mv_q2_K_f32( kernel_mul_mv_q2_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } -#if QK_K == 256 void kernel_mul_mv_q3_K_f32_impl( device const void * src0, device const float * src1, @@ -3685,84 +3632,6 @@ void kernel_mul_mv_q3_K_f32_impl( } } } -#else -void kernel_mul_mv_q3_K_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 int64_t r0 = tgpig.x; - const int64_t r1 = tgpig.y; - const int64_t im = tgpig.z; - - const int row = 2 * r0 + sgitg; - - 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_q3_K * x = (device const block_q3_K *) src0 + row*nb + offset0; - device const float * yy = (device const float *) src1 + r1*ne10 + im*ne00*ne1; - - const int ix = tiisg/4; - const int il = 4 * (tiisg%4);// 0, 4, 8, 12 - const int iq = il/8; // 0, 0, 1, 1 - const int in = il%8; // 0, 4, 0, 4 - - float2 sum = {0.f, 0.f}; - - for (int i = ix; i < nb; i += 8) { - - const float d_all = (float)(x[i].d); - - device const uint16_t * q = (device const uint16_t *)(x[i].qs + il); - device const uint16_t * h = (device const uint16_t *)(x[i].hmask + in); - device const uint16_t * s = (device const uint16_t *)(x[i].scales); - device const float * y = yy + i * QK_K + il; - - const float d1 = d_all * ((int32_t)(s[0] & 0x000F) - 8); - const float d2 = d_all * ((int32_t)(s[0] & 0x00F0) - 128) * 1.f/64.f; - const float d3 = d_all * ((int32_t)(s[0] & 0x0F00) - 2048) * 1.f/4096.f; - const float d4 = d_all * ((int32_t)(s[0] & 0xF000) - 32768) * 1.f/262144.f; - - for (int l = 0; l < 4; l += 2) { - const uint16_t hm = h[l/2] >> iq; - sum[0] += y[l+ 0] * d1 * ((int32_t)(q[l/2] & 0x0003) - ((hm & 0x0001) ? 0 : 4)) - + y[l+16] * d2 * ((int32_t)(q[l/2] & 0x000c) - ((hm & 0x0004) ? 0 : 16)) - + y[l+32] * d3 * ((int32_t)(q[l/2] & 0x0030) - ((hm & 0x0010) ? 0 : 64)) - + y[l+48] * d4 * ((int32_t)(q[l/2] & 0x00c0) - ((hm & 0x0040) ? 0 : 256)); - sum[1] += y[l+ 1] * d1 * ((int32_t)(q[l/2] & 0x0300) - ((hm & 0x0100) ? 0 : 1024)) - + y[l+17] * d2 * ((int32_t)(q[l/2] & 0x0c00) - ((hm & 0x0400) ? 0 : 4096)) - + y[l+33] * d3 * ((int32_t)(q[l/2] & 0x3000) - ((hm & 0x1000) ? 0 : 16384)) - + y[l+49] * d4 * ((int32_t)(q[l/2] & 0xc000) - ((hm & 0x4000) ? 0 : 65536)); - } - - } - const float sumf = sum[0] + sum[1] * 1.f/256.f; - - const float tot = simd_sum(sumf); - if (tiisg == 0) { - dst[r1*ne0 + im*ne0*ne1 + row] = tot; - } - -} -#endif [[host_name("kernel_mul_mv_q3_K_f32")]] kernel void kernel_mul_mv_q3_K_f32( @@ -3792,7 +3661,6 @@ kernel void kernel_mul_mv_q3_K_f32( kernel_mul_mv_q3_K_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, nullptr, tgpig, tiisg, sgitg); } -#if QK_K == 256 void kernel_mul_mv_q4_K_f32_impl( device const void * src0, device const float * src1, @@ -3906,103 +3774,6 @@ void kernel_mul_mv_q4_K_f32_impl( } } } -#else -void kernel_mul_mv_q4_K_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 ix = tiisg/4; // 0...7 - const int it = tiisg%4; // 0...3 - - 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_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_q4_K * x = (device const block_q4_K *) src0 + ib_row + offset0; - device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; - - float yl[8]; - float yh[8]; - float sumf[N_DST]={0.f}, all_sum; - - const int step = sizeof(block_q4_K) * nb / 2; - - device const float * y4 = y + ix * QK_K + 8 * it; - - uint16_t sc16[4]; - - for (int ib = ix; ib < nb; ib += 8) { - - float2 sumy = {0.f, 0.f}; - for (int i = 0; i < 8; ++i) { - yl[i] = y4[i+ 0]; sumy[0] += yl[i]; - yh[i] = y4[i+32]; sumy[1] += yh[i]; - } - - device const uint16_t * sc = (device const uint16_t *)x[ib].scales; - device const uint16_t * qs = (device const uint16_t *)x[ib].qs + 4 * it; - device const half * dh = x[ib].d; - - for (int row = 0; row < N_DST; row++) { - - sc16[0] = sc[0] & 0x000f; - sc16[1] = sc[0] & 0x0f00; - sc16[2] = sc[0] & 0x00f0; - sc16[3] = sc[0] & 0xf000; - - float2 acc1 = {0.f, 0.f}; - float2 acc2 = {0.f, 0.f}; - for (int i = 0; i < 8; i += 2) { - acc1[0] += yl[i+0] * (qs[i/2] & 0x000F); - acc1[1] += yl[i+1] * (qs[i/2] & 0x0F00); - acc2[0] += yh[i+0] * (qs[i/2] & 0x00F0); - acc2[1] += yh[i+1] * (qs[i/2] & 0xF000); - } - - float dall = dh[0]; - float dmin = dh[1]; - sumf[row] += dall * ((acc1[0] + 1.f/256.f * acc1[1]) * sc16[0] + - (acc2[0] + 1.f/256.f * acc2[1]) * sc16[1] * 1.f/4096.f) - - dmin * 1.f/16.f * (sumy[0] * sc16[2] + sumy[1] * sc16[3] * 1.f/256.f); - - qs += step; - sc += step; - dh += step; - } - - y4 += 8 * QK_K; - } - - 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; - } - } -} -#endif [[host_name("kernel_mul_mv_q4_K_f32")]] kernel void kernel_mul_mv_q4_K_f32( @@ -4070,8 +3841,6 @@ void kernel_mul_mv_q5_K_f32_impl( const int step = sizeof(block_q5_K) * nb; -#if QK_K == 256 -# float yl[16], yh[16]; const uint16_t kmask1 = 0x3f3f; @@ -4154,54 +3923,6 @@ void kernel_mul_mv_q5_K_f32_impl( y1 += 4 * QK_K; } -#else - float yl[8], yh[8]; - - const int il = 4 * (tiisg/8); // 0, 4, 8, 12 - const int ix = tiisg%8; - const int iq = il/8; // 0, 0, 1, 1 - const int in = il%8; // 0, 4, 0, 4 - - device const float * y = yy + ix*QK_K + il; - - for (int i = ix; i < nb; i += 8) { - - for (int l = 0; l < 4; ++l) { - yl[l+0] = y[l+ 0]; - yl[l+4] = y[l+16]; - yh[l+0] = y[l+32]; - yh[l+4] = y[l+48]; - } - - device const half * dh = &x[i].d; - device const uint8_t * q = x[i].qs + il; - device const uint8_t * h = x[i].qh + in; - device const int8_t * s = x[i].scales; - - for (int row = 0; row < 2; ++row) { - - const float d = dh[0]; - - float2 acc = {0.f, 0.f}; - for (int l = 0; l < 4; ++l) { - const uint8_t hl = h[l] >> iq; - acc[0] += yl[l+0] * s[0] * ((int16_t)(q[l+ 0] & 0x0F) - (hl & 0x01 ? 0 : 16)) - + yl[l+4] * s[1] * ((int16_t)(q[l+16] & 0x0F) - (hl & 0x04 ? 0 : 16)); - acc[1] += yh[l+0] * s[2] * ((int16_t)(q[l+ 0] & 0xF0) - (hl & 0x10 ? 0 : 256)) - + yh[l+4] * s[3] * ((int16_t)(q[l+16] & 0xF0) - (hl & 0x40 ? 0 : 256)); - } - sumf[row] += d * (acc[0] + 1.f/16.f * acc[1]); - - q += step; - h += step; - s += step; - dh += step/2; - - } - - y += 8 * QK_K; - } -#endif for (int row = 0; row < 2; ++row) { const float tot = simd_sum(sumf[row]); @@ -4280,7 +4001,6 @@ void kernel_mul_mv_q6_K_f32_impl( float sumf = 0; -#if QK_K == 256 const int tid = tiisg/2; const int ix = tiisg%2; const int ip = tid/8; // 0 or 1 @@ -4316,30 +4036,6 @@ void kernel_mul_mv_q6_K_f32_impl( } -#else - const int ix = tiisg/4; - const int il = 4*(tiisg%4); - - for (int i = ix; i < nb; i += 8) { - device const float * y = yy + i * QK_K + il; - device const uint8_t * ql = x[i].ql + il; - device const uint8_t * qh = x[i].qh + il; - device const int8_t * s = x[i].scales; - - const float d = x[i].d; - - float4 sums = {0.f, 0.f, 0.f, 0.f}; - for (int l = 0; l < 4; ++l) { - sums[0] += y[l+ 0] * ((int8_t)((ql[l+ 0] & 0xF) | ((qh[l] & kmask1) << 4)) - 32); - sums[1] += y[l+16] * ((int8_t)((ql[l+16] & 0xF) | ((qh[l] & kmask2) << 2)) - 32); - sums[2] += y[l+32] * ((int8_t)((ql[l+ 0] >> 4) | ((qh[l] & kmask3) >> 0)) - 32); - sums[3] += y[l+48] * ((int8_t)((ql[l+16] >> 4) | ((qh[l] & kmask4) >> 2)) - 32); - } - sumf += d * (sums[0] * s[0] + sums[1] * s[1] + sums[2] * s[2] + sums[3] * s[3]); - } - -#endif - const float tot = simd_sum(sumf); if (tiisg == 0) { dst[r1*ne0 + im*ne0*ne1 + row] = tot; @@ -5173,9 +4869,7 @@ void kernel_mul_mv_iq1_m_f32_impl( device const float * y4 = y + 32 * ix; -#if QK_K != 64 iq1m_scale_t scale; -#endif for (int ib32 = ix; ib32 < nb32; ib32 += 32) { @@ -5196,10 +4890,7 @@ void kernel_mul_mv_iq1_m_f32_impl( device const uint16_t * sc = (device const uint16_t *)xr->scales; for (int row = 0; row < N_DST; row++) { - -#if QK_K != 64 scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); -#endif constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700))); constant uint8_t * grid2 = (constant uint8_t *)(iq1s_grid_gpu + (qs[1] | ((qh[0] << 4) & 0x700))); @@ -5215,14 +4906,9 @@ void kernel_mul_mv_iq1_m_f32_impl( } const float delta1 = sumy[0] * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA) + sumy[1] * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); const float delta2 = sumy[2] * (qh[1] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA) + sumy[3] * (qh[1] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); -#if QK_K == 64 - const float d = (float) *((device const half *)(sc - 1)); - sumf[row] += d * ((sum[0] + delta1) * (2*((sc[0] >> (8*(ib%2)+0)) & 0xf) + 1) + - (sum[1] + delta2) * (2*((sc[0] >> (8*(ib%2)+4)) & 0xf) + 1)); -#else + sumf[row] += (float)scale.f16 * ((sum[0] + delta1) * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 7) + 1) + (sum[1] + delta2) * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 7) + 1)); -#endif sc += nb*sizeof(block_iq1_m)/2; qs += nb*sizeof(block_iq1_m); @@ -5334,7 +5020,6 @@ void kernel_mul_mv_iq4_nl_f32_impl( } } -#if QK_K != 64 void kernel_mul_mv_iq4_xs_f32_impl( device const void * src0, device const float * src1, @@ -5429,7 +5114,6 @@ void kernel_mul_mv_iq4_xs_f32_impl( } } } -#endif [[host_name("kernel_mul_mv_iq1_s_f32")]] kernel void kernel_mul_mv_iq1_s_f32( @@ -5542,11 +5226,7 @@ kernel void kernel_mul_mv_iq4_xs_f32( uint tiisg[[thread_index_in_simdgroup]], uint sgitg[[simdgroup_index_in_threadgroup]]) { -#if QK_K == 64 - kernel_mul_mv_iq4_nl_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); -#else kernel_mul_mv_iq4_xs_f32_impl(src0, src1, dst, ne00, ne01, ne02, ne10, ne12, ne0, ne1, r2, r3, shared_values, tgpig, tiisg, sgitg); -#endif } //============================= templates and their specializations ============================= @@ -5672,10 +5352,9 @@ void dequantize_q2_K(device const block_q2_K *xb, short il, thread type4x4 & reg float dl, ml; uint8_t sc = xb->scales[il]; -#if QK_K == 256 q = q + 32*(il/8) + 16*(il&1); il = (il/2)%4; -#endif + half coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h); uchar mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); dl = d * (sc & 0xF) * coef, ml = min * (sc >> 4); @@ -5691,7 +5370,6 @@ void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg device const uint8_t * h = (device const uint8_t *)xb->hmask; device const int8_t * scales = (device const int8_t *)xb->scales; -#if QK_K == 256 q = q + 32 * (il/8) + 16 * (il&1); h = h + 16 * (il&1); uint8_t m = 1 << (il/2); @@ -5712,17 +5390,6 @@ void dequantize_q3_K(device const block_q3_K *xb, short il, thread type4x4 & reg for (int i = 0; i < 16; ++i) { reg[i/4][i%4] = dl * (q[i] & mask) - (h[i] & m ? 0 : ml); } -#else - float kcoef = il&1 ? 1.f/16.f : 1.f; - uint16_t kmask = il&1 ? 0xF0 : 0x0F; - float dl = d_all * ((scales[il/2] & kmask) * kcoef - 8); - float coef = il>1 ? (il>2 ? 1/64.h : 1/16.h) : (il>0 ? 1/4.h : 1.h); - uint8_t mask = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); - uint8_t m = 1<<(il*2); - for (int i = 0; i < 16; ++i) { - reg[i/4][i%4] = coef * dl * ((q[i] & mask) - ((h[i%8] & (m * (1 + i/8))) ? 0 : 4.f/coef)); - } -#endif } static inline uchar2 get_scale_min_k4_just2(int j, int k, device const uchar * q) { @@ -5734,7 +5401,6 @@ template void dequantize_q4_K(device const block_q4_K *xb, short il, thread type4x4 & reg) { device const uchar * q = xb->qs; -#if QK_K == 256 short is = (il/4) * 2; q = q + (il/4) * 32 + 16 * (il&1); il = il & 3; @@ -5743,16 +5409,7 @@ void dequantize_q4_K(device const block_q4_K *xb, short il, thread type4x4 & reg const float min = xb->dmin; const float dl = d * sc[0]; const float ml = min * sc[1]; -#else - (void) get_scale_min_k4_just2; - q = q + 16 * (il&1); - device const uint8_t * s = xb->scales; - device const half2 * dh = (device const half2 *)xb->d; - const float2 d = (float2)dh[0]; - const float dl = il<2 ? d[0] * (s[0]&0xF) : d[0] * (s[1]&0xF)/16.h; - const float ml = il<2 ? d[1] * (s[0]>>4) : d[1] * (s[1]>>4); -#endif const ushort mask = il<2 ? 0x0F : 0xF0; for (int i = 0; i < 16; ++i) { reg[i/4][i%4] = dl * (q[i] & mask) - ml; @@ -5764,7 +5421,6 @@ void dequantize_q5_K(device const block_q5_K *xb, short il, thread type4x4 & reg device const uint8_t * q = xb->qs; device const uint8_t * qh = xb->qh; -#if QK_K == 256 short is = (il/4) * 2; q = q + 32 * (il/4) + 16 * (il&1); qh = qh + 16 * (il&1); @@ -5781,17 +5437,6 @@ void dequantize_q5_K(device const block_q5_K *xb, short il, thread type4x4 & reg for (int i = 0; i < 16; ++i) { reg[i/4][i%4] = dl * ((q[i] & mask) + (qh[i] & ul ? qh_val : 0)) - ml; } -#else - q = q + 16 * (il&1); - device const int8_t * s = xb->scales; - const float dl = xb->d * s[il]; - uint8_t m = 1<<(il*2); - const float coef = il<2 ? 1.f : 1.f/16.f; - const ushort mask = il<2 ? 0x0F : 0xF0; - for (int i = 0; i < 16; ++i) { - reg[i/4][i%4] = coef * dl * ((q[i] & mask) - (qh[i%8] & (m*(1+i/8)) ? 0.f : 16.f/coef)); - } -#endif } template @@ -5801,15 +5446,11 @@ void dequantize_q6_K(device const block_q6_K *xb, short il, thread type4x4 & reg device const uint8_t * qh = (device const uint8_t *)xb->qh; device const int8_t * scales = (device const int8_t *)xb->scales; -#if QK_K == 256 ql = ql + 64*(il/8) + 32*((il/2)&1) + 16*(il&1); qh = qh + 32*(il/8) + 16*(il&1); float sc = scales[(il%2) + 2 * ((il/2))]; il = (il/2) & 3; -#else - ql = ql + 16 * (il&1); - float sc = scales[il]; -#endif + const uint16_t kmask1 = il>1 ? (il>2 ? 192 : 48) : (il>0 ? 12 : 3); const uint16_t kmask2 = il>1 ? 0xF0 : 0x0F; const float coef = il>1 ? 1.f/16.f : 1.f; @@ -5966,20 +5607,15 @@ void dequantize_iq1_m(device const block_iq1_m * xb, short il, thread type4x4 & const int ib32 = il/2; il = il%2; device const uint16_t * sc = (device const uint16_t *)xb->scales; -#if QK_K == 64 - const float d = xb->d; -#else + iq1m_scale_t scale; scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); const float d = scale.f16; -#endif + device const uint8_t * qs = xb->qs + 4*ib32 + 2*il; device const uint8_t * qh = xb->qh + 2*ib32 + il; -#if QK_K == 64 - const float dl = d * (2*((sc[ib32/2] >> (8*(ib32%2)+4*il)) & 0xf) + 1); -#else + const float dl = d * (2*((sc[ib32/2] >> (6*(ib32%2)+3*il)) & 7) + 1); -#endif const float ml1 = dl * (qh[0] & 0x08 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); const float ml2 = dl * (qh[0] & 0x80 ? -1 - IQ1M_DELTA : -1 + IQ1M_DELTA); constant uint8_t * grid1 = (constant uint8_t *)(iq1s_grid_gpu + (qs[0] | ((qh[0] << 8) & 0x700))); @@ -6009,9 +5645,6 @@ void dequantize_iq4_nl(device const block_iq4_nl * xb, short il, thread type4x4 template void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 & reg) { -#if QK_K == 64 - dequantize_iq4_nl(xb, il, reg); -#else // il is 0...15 for QK_K = 256 => index of block of 32 is il/2 const int ib32 = il/2; il = il%2; @@ -6028,7 +5661,6 @@ void dequantize_iq4_xs(device const block_iq4_xs * xb, short il, thread type4x4 reg[i][2] = d * kvalues_iq4nl_f[q8[2]]; reg[i][3] = d * kvalues_iq4nl_f[q8[3]]; } -#endif } template @@ -6533,11 +6165,7 @@ kernel void kernel_mul_mm_id( sgitg); } -#if QK_K == 256 #define QK_NL 16 -#else -#define QK_NL 4 -#endif // // get rows @@ -6577,11 +6205,7 @@ template [[host_name("kernel_get_rows_iq2_s")]] kernel get_rows_t kernel_get_r template [[host_name("kernel_get_rows_iq1_s")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq1_m")]] kernel get_rows_t kernel_get_rows; template [[host_name("kernel_get_rows_iq4_nl")]] kernel get_rows_t kernel_get_rows; -#if QK_K == 64 -template [[host_name("kernel_get_rows_iq4_xs")]] kernel get_rows_t kernel_get_rows; -#else template [[host_name("kernel_get_rows_iq4_xs")]] kernel get_rows_t kernel_get_rows; -#endif // // matrix-matrix multiplication @@ -6609,11 +6233,7 @@ template [[host_name("kernel_mul_mm_iq2_s_f32")]] kernel mat_mm_t kernel_mul_m template [[host_name("kernel_mul_mm_iq1_s_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq1_m_f32")]] kernel mat_mm_t kernel_mul_mm; template [[host_name("kernel_mul_mm_iq4_nl_f32")]] kernel mat_mm_t kernel_mul_mm; -#if QK_K == 64 -template [[host_name("kernel_mul_mm_iq4_xs_f32")]] kernel mat_mm_t kernel_mul_mm; -#else template [[host_name("kernel_mul_mm_iq4_xs_f32")]] kernel mat_mm_t kernel_mul_mm; -#endif // // indirect matrix-matrix multiplication @@ -6641,11 +6261,7 @@ template [[host_name("kernel_mul_mm_id_iq2_s_f32")]] kernel mat_mm_id_t kernel template [[host_name("kernel_mul_mm_id_iq1_s_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq1_m_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; template [[host_name("kernel_mul_mm_id_iq4_nl_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -#if QK_K == 64 -template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -#else template [[host_name("kernel_mul_mm_id_iq4_xs_f32")]] kernel mat_mm_id_t kernel_mul_mm_id; -#endif // // matrix-vector multiplication @@ -6854,7 +6470,5 @@ template [[host_name("kernel_mul_mv_id_iq3_xxs_f32")]] kernel kernel_mul_mv_id_t template [[host_name("kernel_mul_mv_id_iq3_s_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; template [[host_name("kernel_mul_mv_id_iq2_s_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; template [[host_name("kernel_mul_mv_id_iq4_nl_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; -#if QK_K != 64 template [[host_name("kernel_mul_mv_id_iq4_xs_f32")]] kernel kernel_mul_mv_id_t kernel_mul_mv_id>; -#endif diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp index 922f24837..e28566a7b 100644 --- a/ggml-opencl.cpp +++ b/ggml-opencl.cpp @@ -1,4 +1,4 @@ -#include "ggml.h" +#include "ggml.h" #include "ggml-opencl.h" #include "ggml-backend-impl.h" diff --git a/ggml-quants.c b/ggml-quants.c index ed40ca74a..88f58a339 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -1888,7 +1888,6 @@ static float make_qkx2_quants(int n, int nmax, const float * restrict x, const f return scale; } -#if QK_K == 256 static inline void get_scale_min_k4(int j, const uint8_t * restrict q, uint8_t * restrict d, uint8_t * restrict m) { if (j < 4) { *d = q[j] & 63; *m = q[j + 4] & 63; @@ -1897,7 +1896,6 @@ static inline void get_scale_min_k4(int j, const uint8_t * restrict q, uint8_t * *m = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4); } } -#endif //========================- 2-bit (de)-quantization @@ -1961,20 +1959,13 @@ void quantize_row_q2_K_reference(const float * restrict x, block_q2_K * restrict } } -#if QK_K == 256 for (int j = 0; j < QK_K; j += 128) { for (int l = 0; l < 32; ++l) { y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6); } } -#else - for (int l = 0; l < 16; ++l) { - y[i].qs[l] = L[l] | (L[l + 16] << 2) | (L[l + 32] << 4) | (L[l + 48] << 6); - } -#endif x += QK_K; - } } @@ -1989,7 +1980,6 @@ void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int6 const uint8_t * q = x[i].qs; -#if QK_K == 256 int is = 0; float dl, ml; for (int n = 0; n < QK_K; n += 128) { @@ -2008,19 +1998,6 @@ void dequantize_row_q2_K(const block_q2_K * restrict x, float * restrict y, int6 } q += 32; } -#else - float dl1 = d * (x[i].scales[0] & 0xF), ml1 = min * (x[i].scales[0] >> 4); - float dl2 = d * (x[i].scales[1] & 0xF), ml2 = min * (x[i].scales[1] >> 4); - float dl3 = d * (x[i].scales[2] & 0xF), ml3 = min * (x[i].scales[2] >> 4); - float dl4 = d * (x[i].scales[3] & 0xF), ml4 = min * (x[i].scales[3] >> 4); - for (int l = 0; l < 16; ++l) { - y[l+ 0] = dl1 * ((int8_t)((q[l] >> 0) & 3)) - ml1; - y[l+16] = dl2 * ((int8_t)((q[l] >> 2) & 3)) - ml2; - y[l+32] = dl3 * ((int8_t)((q[l] >> 4) & 3)) - ml3; - y[l+48] = dl4 * ((int8_t)((q[l] >> 6) & 3)) - ml4; - } - y += QK_K; -#endif } } @@ -2211,36 +2188,9 @@ static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restri } float dm, mm; -#if QK_K == 64 - float max_scale = 0, max_min = 0; - for (int j = 0; j < QK_K/16; ++j) { - max_scale = MAX(max_scale, scales[j]); - max_min = MAX(max_min, mins[j]); - } - dm = max_scale/15; - mm = max_min/15; - if (max_scale) { - float id = 1/dm; - for (int j = 0; j < QK_K/16; ++j) { - int l = nearest_int(id*scales[j]); - Ls[j] = MAX(0, MIN(15, l)); - } - } else { - memset(Ls, 0, QK_K/16); - } - if (max_min) { - float id = 1/mm; - for (int j = 0; j < QK_K/16; ++j) { - int l = nearest_int(id*mins[j]); - Lm[j] = MAX(0, MIN(15, l)); - } - } else { - memset(Lm, 0, QK_K/16); - } -#else dm = make_qp_quants(QK_K/16, 15, scales, Ls, sw); mm = make_qp_quants(QK_K/16, 15, mins, Lm, sw); -#endif + y[i].d = GGML_FP32_TO_FP16(dm); y[i].dmin = GGML_FP32_TO_FP16(mm); dm = GGML_FP16_TO_FP32(y[i].d); @@ -2263,20 +2213,13 @@ static void quantize_row_q2_K_impl(const float * restrict x, block_q2_K * restri } } -#if QK_K == 256 for (int j = 0; j < QK_K; j += 128) { for (int l = 0; l < 32; ++l) { y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6); } } -#else - for (int l = 0; l < 16; ++l) { - y[i].qs[l] = L[l] | (L[l + 16] << 2) | (L[l + 32] << 4) | (L[l + 48] << 6); - } -#endif x += QK_K; - } } @@ -2317,7 +2260,6 @@ void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict } } -#if QK_K == 256 memset(y[i].scales, 0, 12); if (max_scale) { float iscale = -32.f/max_scale; @@ -2351,36 +2293,6 @@ void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict L[16*j + ii] = l + 4; } } -#else - if (max_scale) { - float iscale = -8.f/max_scale; - for (int j = 0; j < QK_K/16; j+=2) { - int l1 = nearest_int(iscale*scales[j]); - l1 = 8 + MAX(-8, MIN(7, l1)); - int l2 = nearest_int(iscale*scales[j+1]); - l2 = 8 + MAX(-8, MIN(7, l2)); - y[i].scales[j/2] = l1 | (l2 << 4); - } - y[i].d = GGML_FP32_TO_FP16(1/iscale); - } else { - for (int j = 0; j < QK_K/16; j+=2) { - y[i].scales[j/2] = 0; - } - y[i].d = GGML_FP32_TO_FP16(0.f); - } - for (int j = 0; j < QK_K/16; ++j) { - int s = j%2 == 0 ? y[i].scales[j/2] & 0xF : y[i].scales[j/2] >> 4; - float d = GGML_FP16_TO_FP32(y[i].d) * (s - 8); - if (!d) { - continue; - } - for (int ii = 0; ii < 16; ++ii) { - int l = nearest_int(x[16*j + ii]/d); - l = MAX(-4, MIN(3, l)); - L[16*j + ii] = l + 4; - } - } -#endif memset(y[i].hmask, 0, QK_K/8); // We put the high-bit for the 1st 8 quants into bit 0, the next 8 into bit 1, etc. @@ -2395,23 +2307,16 @@ void quantize_row_q3_K_reference(const float * restrict x, block_q3_K * restrict m = 0; hm <<= 1; } } -#if QK_K == 256 for (int j = 0; j < QK_K; j += 128) { for (int l = 0; l < 32; ++l) { y[i].qs[j/4 + l] = L[j + l] | (L[j + l + 32] << 2) | (L[j + l + 64] << 4) | (L[j + l + 96] << 6); } } -#else - for (int l = 0; l < 16; ++l) { - y[i].qs[l] = L[l] | (L[l + 16] << 2) | (L[l + 32] << 4) | (L[l + 48] << 6); - } -#endif x += QK_K; } } -#if QK_K == 256 void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int64_t k) { assert(k % QK_K == 0); const int nb = k / QK_K; @@ -2461,49 +2366,12 @@ void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int6 } } -#else -void dequantize_row_q3_K(const block_q3_K * restrict x, float * restrict y, int64_t k) { - assert(k % QK_K == 0); - assert(QK_K == 64); - const int nb = k / QK_K; - - for (int i = 0; i < nb; i++) { - - const float d_all = GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q = x[i].qs; - const uint8_t * restrict hm = x[i].hmask; - - const float d1 = d_all * ((x[i].scales[0] & 0xF) - 8); - const float d2 = d_all * ((x[i].scales[0] >> 4) - 8); - const float d3 = d_all * ((x[i].scales[1] & 0xF) - 8); - const float d4 = d_all * ((x[i].scales[1] >> 4) - 8); - - for (int l=0; l<8; ++l) { - uint8_t h = hm[l]; - y[l+ 0] = d1 * ((int8_t)((q[l+0] >> 0) & 3) - ((h & 0x01) ? 0 : 4)); - y[l+ 8] = d1 * ((int8_t)((q[l+8] >> 0) & 3) - ((h & 0x02) ? 0 : 4)); - y[l+16] = d2 * ((int8_t)((q[l+0] >> 2) & 3) - ((h & 0x04) ? 0 : 4)); - y[l+24] = d2 * ((int8_t)((q[l+8] >> 2) & 3) - ((h & 0x08) ? 0 : 4)); - y[l+32] = d3 * ((int8_t)((q[l+0] >> 4) & 3) - ((h & 0x10) ? 0 : 4)); - y[l+40] = d3 * ((int8_t)((q[l+8] >> 4) & 3) - ((h & 0x20) ? 0 : 4)); - y[l+48] = d4 * ((int8_t)((q[l+0] >> 6) & 3) - ((h & 0x40) ? 0 : 4)); - y[l+56] = d4 * ((int8_t)((q[l+8] >> 6) & 3) - ((h & 0x80) ? 0 : 4)); - } - y += QK_K; - } -} -#endif void quantize_row_q3_K(const float * restrict x, void * restrict vy, int64_t k) { quantize_row_q3_K_reference(x, vy, k); } static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restrict y, int64_t n_per_row, const float * restrict quant_weights) { -#if QK_K != 256 - (void)quant_weights; - quantize_row_q3_K_reference(x, y, n_per_row); -#else assert(n_per_row % QK_K == 0); const int nb = n_per_row / QK_K; @@ -2585,7 +2453,6 @@ static void quantize_row_q3_K_impl(const float * restrict x, block_q3_K * restri x += QK_K; } -#endif } size_t quantize_q3_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { @@ -2617,7 +2484,6 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict float scales[QK_K/32]; for (int i = 0; i < nb; i++) { - 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) { @@ -2637,7 +2503,6 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict } } -#if QK_K == 256 float inv_scale = max_scale > 0 ? 63.f/max_scale : 0.f; float inv_min = max_min > 0 ? 63.f/max_min : 0.f; for (int j = 0; j < QK_K/32; ++j) { @@ -2669,39 +2534,7 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict L[32*j + ii] = l; } } -#else - const float s_factor = 15.f; - float inv_scale = max_scale > 0 ? s_factor/max_scale : 0.f; - float inv_min = max_min > 0 ? s_factor/max_min : 0.f; - int d1 = nearest_int(inv_scale*scales[0]); - int m1 = nearest_int(inv_min*mins[0]); - int d2 = nearest_int(inv_scale*scales[1]); - int m2 = nearest_int(inv_min*mins[1]); - y[i].scales[0] = d1 | (m1 << 4); - y[i].scales[1] = d2 | (m2 << 4); - y[i].d[0] = GGML_FP32_TO_FP16(max_scale/s_factor); - y[i].d[1] = GGML_FP32_TO_FP16(max_min/s_factor); - float sumlx = 0; - int suml2 = 0; - for (int j = 0; j < QK_K/32; ++j) { - const uint8_t sd = y[i].scales[j] & 0xF; - const uint8_t sm = y[i].scales[j] >> 4; - const float d = GGML_FP16_TO_FP32(y[i].d[0]) * sd; - if (!d) continue; - const float m = GGML_FP16_TO_FP32(y[i].d[1]) * sm; - for (int ii = 0; ii < 32; ++ii) { - int l = nearest_int((x[32*j + ii] + m)/d); - l = MAX(0, MIN(15, l)); - L[32*j + ii] = l; - sumlx += (x[32*j + ii] + m)*l*sd; - suml2 += l*l*sd*sd; - } - } - if (suml2) { - y[i].d[0] = GGML_FP32_TO_FP16(sumlx/suml2); - } -#endif uint8_t * q = y[i].qs; for (int j = 0; j < QK_K; j += 64) { for (int l = 0; l < 32; ++l) q[l] = L[j + l] | (L[j + l + 32] << 4); @@ -2709,7 +2542,6 @@ void quantize_row_q4_K_reference(const float * restrict x, block_q4_K * restrict } x += QK_K; - } } @@ -2718,11 +2550,8 @@ void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int6 const int nb = k / QK_K; for (int i = 0; i < nb; i++) { - const uint8_t * q = x[i].qs; -#if QK_K == 256 - const float d = GGML_FP16_TO_FP32(x[i].d); const float min = GGML_FP16_TO_FP32(x[i].dmin); @@ -2737,18 +2566,6 @@ void dequantize_row_q4_K(const block_q4_K * restrict x, float * restrict y, int6 for (int l = 0; l < 32; ++l) *y++ = d2 * (q[l] >> 4) - m2; q += 32; is += 2; } -#else - const float dall = GGML_FP16_TO_FP32(x[i].d[0]); - const float mall = GGML_FP16_TO_FP32(x[i].d[1]); - const float d1 = dall * (x[i].scales[0] & 0xF), m1 = mall * (x[i].scales[0] >> 4); - const float d2 = dall * (x[i].scales[1] & 0xF), m2 = mall * (x[i].scales[1] >> 4); - for (int l = 0; l < 32; ++l) { - y[l+ 0] = d1 * (q[l] & 0xF) - m1; - y[l+32] = d2 * (q[l] >> 4) - m2; - } - y += QK_K; -#endif - } } @@ -2759,10 +2576,6 @@ void quantize_row_q4_K(const float * restrict x, void * restrict vy, int64_t k) } static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restrict y, int64_t n_per_row, const float * quant_weights) { -#if QK_K != 256 - (void)quant_weights; - quantize_row_q4_K_reference(x, y, n_per_row); -#else assert(n_per_row % QK_K == 0); const int64_t nb = n_per_row / QK_K; @@ -2833,7 +2646,6 @@ static void quantize_row_q4_K_impl(const float * restrict x, block_q4_K * restri x += QK_K; } -#endif } size_t quantize_q4_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { @@ -2858,21 +2670,13 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict assert(k % QK_K == 0); const int64_t nb = k / QK_K; -#if QK_K == 256 uint8_t L[QK_K]; float mins[QK_K/32]; float scales[QK_K/32]; float weights[32]; uint8_t Laux[32]; -#else - int8_t L[QK_K]; - float scales[QK_K/16]; -#endif for (int i = 0; i < nb; i++) { - -#if QK_K == 256 - float max_scale = 0; // as we are deducting the min, scales are always positive float max_min = 0; for (int j = 0; j < QK_K/32; ++j) { @@ -2944,55 +2748,8 @@ void quantize_row_q5_K_reference(const float * restrict x, block_q5_K * restrict m1 <<= 2; m2 <<= 2; ql += 32; } -#else - float max_scale = 0, amax = 0; - for (int j = 0; j < QK_K/16; ++j) { - scales[j] = make_qx_quants(16, 16, x + 16*j, L + 16*j, 1, NULL); - float abs_scale = fabsf(scales[j]); - if (abs_scale > amax) { - amax = abs_scale; - max_scale = scales[j]; - } - } - - float iscale = -128.f/max_scale; - for (int j = 0; j < QK_K/16; ++j) { - int l = nearest_int(iscale*scales[j]); - y[i].scales[j] = MAX(-128, MIN(127, l)); - } - y[i].d = GGML_FP32_TO_FP16(1/iscale); - - for (int j = 0; j < QK_K/16; ++j) { - const float d = GGML_FP16_TO_FP32(y[i].d) * y[i].scales[j]; - if (!d) continue; - for (int ii = 0; ii < 16; ++ii) { - int l = nearest_int(x[16*j + ii]/d); - l = MAX(-16, MIN(15, l)); - L[16*j + ii] = l + 16; - } - } - - uint8_t * restrict qh = y[i].qh; - uint8_t * restrict ql = y[i].qs; - memset(qh, 0, QK_K/8); - - for (int j = 0; j < 32; ++j) { - int jm = j%8; - int is = j/8; - int l1 = L[j]; - if (l1 > 15) { - l1 -= 16; qh[jm] |= (1 << is); - } - int l2 = L[j + 32]; - if (l2 > 15) { - l2 -= 16; qh[jm] |= (1 << (4 + is)); - } - ql[j] = l1 | (l2 << 4); - } -#endif x += QK_K; - } } @@ -3001,12 +2758,9 @@ void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int6 const int64_t nb = k / QK_K; for (int i = 0; i < nb; i++) { - const uint8_t * ql = x[i].qs; const uint8_t * qh = x[i].qh; -#if QK_K == 256 - const float d = GGML_FP16_TO_FP32(x[i].d); const float min = GGML_FP16_TO_FP32(x[i].dmin); @@ -3023,21 +2777,6 @@ void dequantize_row_q5_K(const block_q5_K * restrict x, float * restrict y, int6 ql += 32; is += 2; u1 <<= 2; u2 <<= 2; } -#else - float d = GGML_FP16_TO_FP32(x[i].d); - const int8_t * restrict s = x[i].scales; - for (int l = 0; l < 8; ++l) { - y[l+ 0] = d * s[0] * ((ql[l+ 0] & 0xF) - (qh[l] & 0x01 ? 0 : 16)); - y[l+ 8] = d * s[0] * ((ql[l+ 8] & 0xF) - (qh[l] & 0x02 ? 0 : 16)); - y[l+16] = d * s[1] * ((ql[l+16] & 0xF) - (qh[l] & 0x04 ? 0 : 16)); - y[l+24] = d * s[1] * ((ql[l+24] & 0xF) - (qh[l] & 0x08 ? 0 : 16)); - y[l+32] = d * s[2] * ((ql[l+ 0] >> 4) - (qh[l] & 0x10 ? 0 : 16)); - y[l+40] = d * s[2] * ((ql[l+ 8] >> 4) - (qh[l] & 0x20 ? 0 : 16)); - y[l+48] = d * s[3] * ((ql[l+16] >> 4) - (qh[l] & 0x40 ? 0 : 16)); - y[l+56] = d * s[3] * ((ql[l+24] >> 4) - (qh[l] & 0x80 ? 0 : 16)); - } - y += QK_K; -#endif } } @@ -3048,10 +2787,6 @@ void quantize_row_q5_K(const float * restrict x, void * restrict vy, int64_t k) } static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restrict y, int64_t n_per_row, const float * quant_weights) { -#if QK_K != 256 - (void)quant_weights; - quantize_row_q5_K_reference(x, y, n_per_row); -#else assert(n_per_row % QK_K == 0); const int64_t nb = n_per_row / QK_K; @@ -3142,7 +2877,6 @@ static void quantize_row_q5_K_impl(const float * restrict x, block_q5_K * restri x += QK_K; } -#endif } size_t quantize_q5_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { @@ -3215,7 +2949,6 @@ void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict uint8_t * restrict ql = y[i].ql; uint8_t * restrict qh = y[i].qh; -#if QK_K == 256 for (int j = 0; j < QK_K; j += 128) { for (int l = 0; l < 32; ++l) { const uint8_t q1 = L[j + l + 0] & 0xF; @@ -3229,19 +2962,8 @@ void quantize_row_q6_K_reference(const float * restrict x, block_q6_K * restrict ql += 64; qh += 32; } -#else - for (int l = 0; l < 32; ++l) { - const uint8_t q1 = L[l + 0] & 0xF; - const uint8_t q2 = L[l + 32] & 0xF; - ql[l] = q1 | (q2 << 4); - } - for (int l = 0; l < 16; ++l) { - qh[l] = (L[l] >> 4) | ((L[l + 16] >> 4) << 2) | ((L[l + 32] >> 4) << 4) | ((L[l + 48] >> 4) << 6); - } -#endif x += QK_K; - } } @@ -3250,14 +2972,12 @@ void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int6 const int64_t nb = k / QK_K; for (int i = 0; i < nb; i++) { - const float d = GGML_FP16_TO_FP32(x[i].d); const uint8_t * restrict ql = x[i].ql; const uint8_t * restrict qh = x[i].qh; const int8_t * restrict sc = x[i].scales; -#if QK_K == 256 for (int n = 0; n < QK_K; n += 128) { for (int l = 0; l < 32; ++l) { int is = l/16; @@ -3275,20 +2995,6 @@ void dequantize_row_q6_K(const block_q6_K * restrict x, float * restrict y, int6 qh += 32; sc += 8; } -#else - for (int l = 0; l < 16; ++l) { - const int8_t q1 = (int8_t)((ql[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32; - const int8_t q2 = (int8_t)((ql[l+16] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32; - const int8_t q3 = (int8_t)((ql[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32; - const int8_t q4 = (int8_t)((ql[l+16] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32; - y[l+ 0] = d * sc[0] * q1; - y[l+16] = d * sc[1] * q2; - y[l+32] = d * sc[2] * q3; - y[l+48] = d * sc[3] * q4; - } - y += 64; -#endif - } } @@ -3299,10 +3005,6 @@ void quantize_row_q6_K(const float * restrict x, void * restrict vy, int64_t k) } static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restrict y, int64_t n_per_row, const float * quant_weights) { -#if QK_K != 256 - (void)quant_weights; - quantize_row_q6_K_reference(x, y, n_per_row); -#else assert(n_per_row % QK_K == 0); const int64_t nb = n_per_row / QK_K; @@ -3384,7 +3086,6 @@ static void quantize_row_q6_K_impl(const float * restrict x, block_q6_K * restri x += QK_K; } -#endif } size_t quantize_q6_K(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { @@ -3801,30 +3502,21 @@ void dequantize_row_iq1_m(const block_iq1_m * restrict x, float * restrict y, in float delta[4]; uint16_t idx[4]; -#if QK_K != 64 iq1m_scale_t scale; -#endif for (int i = 0; i < nb; i++) { const uint16_t * sc = (const uint16_t *)x[i].scales; -#if QK_K == 64 - const float d = GGML_FP16_TO_FP32(x[i].d); -#else scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); const float d = GGML_FP16_TO_FP32(scale.f16); -#endif + const uint8_t * qs = x[i].qs; const uint8_t * qh = x[i].qh; for (int ib = 0; ib < QK_K/32; ++ib) { -#if QK_K == 64 - const float dl1 = d * (2*((sc[ib/2] >> (8*(ib%2)+0)) & 0xf) + 1); - const float dl2 = d * (2*((sc[ib/2] >> (8*(ib%2)+4)) & 0xf) + 1); -#else const float dl1 = d * (2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1); const float dl2 = d * (2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1); -#endif + idx[0] = qs[0] | ((qh[0] << 8) & 0x700); idx[1] = qs[1] | ((qh[0] << 4) & 0x700); idx[2] = qs[2] | ((qh[1] << 8) & 0x700); @@ -3875,9 +3567,6 @@ void dequantize_row_iq4_nl(const block_iq4_nl * restrict x, float * restrict y, void dequantize_row_iq4_xs(const block_iq4_xs * restrict x, float * restrict y, int64_t k) { assert(k % QK_K == 0); -#if QK_K == 64 - dequantize_row_iq4_nl((const block_iq4_nl *)x, y, k); -#else const int64_t nb = k / QK_K; for (int i = 0; i < nb; i++) { @@ -3897,7 +3586,6 @@ void dequantize_row_iq4_xs(const block_iq4_xs * restrict x, float * restrict y, qs += 16; } } -#endif } //===================================== Q8_K ============================================== @@ -5849,7 +5537,6 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r #endif } -#if QK_K == 256 void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(nrc == 1); UNUSED(nrc); @@ -6433,410 +6120,6 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r #endif } -#else - -void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q2_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#ifdef __ARM_NEON - const uint8x16_t m3 = vdupq_n_u8(0x3); - - const int32x4_t vzero = vdupq_n_s32(0); - - ggml_int8x16x4_t q2bytes; - - uint32_t aux32[2]; - const uint8_t * scales = (const uint8_t *)aux32; - - float sum = 0; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - const uint32_t * restrict sc = (const uint32_t *)x[i].scales; - - aux32[0] = sc[0] & 0x0f0f0f0f; - aux32[1] = (sc[0] >> 4) & 0x0f0f0f0f; - - sum += dmin * (scales[4] * y[i].bsums[0] + scales[5] * y[i].bsums[1] + scales[6] * y[i].bsums[2] + scales[7] * y[i].bsums[3]); - - int isum1 = 0, isum2 = 0; - - const uint8x16_t q2bits = vld1q_u8(q2); - - const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); - - q2bytes.val[0] = vreinterpretq_s8_u8(vandq_u8(q2bits, m3)); - q2bytes.val[1] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 2), m3)); - q2bytes.val[2] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 4), m3)); - q2bytes.val[3] = vreinterpretq_s8_u8(vandq_u8(vshrq_n_u8(q2bits, 6), m3)); - - isum1 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[0], q8bytes.val[0])) * scales[0]; - isum2 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[1], q8bytes.val[1])) * scales[1]; - isum1 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[2], q8bytes.val[2])) * scales[2]; - isum2 += vaddvq_s32(ggml_vdotq_s32(vzero, q2bytes.val[3], q8bytes.val[3])) * scales[3]; - - sum += d * (isum1 + isum2); - } - - *s = sum; - -#elif defined __AVX2__ - - const __m256i m3 = _mm256_set1_epi8(3); - - __m256 acc = _mm256_setzero_ps(); - - uint32_t ud, um; - const uint8_t * restrict db = (const uint8_t *)&ud; - const uint8_t * restrict mb = (const uint8_t *)&um; - - float summs = 0; - - // TODO: optimize this - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const uint32_t * restrict sc = (const uint32_t *)x[i].scales; - ud = (sc[0] >> 0) & 0x0f0f0f0f; - um = (sc[0] >> 4) & 0x0f0f0f0f; - - int32_t smin = mb[0] * y[i].bsums[0] + mb[1] * y[i].bsums[1] + mb[2] * y[i].bsums[2] + mb[3] * y[i].bsums[3]; - summs += dmin * smin; - - const __m128i q2bits = _mm_loadu_si128((const __m128i*)q2); - const __m256i q2_0 = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q2bits, 2), q2bits), m3); - const __m256i q2_1 = _mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q2bits, 6), _mm_srli_epi16(q2bits, 4)), m3); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - const __m256i p0 = _mm256_maddubs_epi16(q2_0, q8_0); - const __m256i p1 = _mm256_maddubs_epi16(q2_1, q8_1); - - const __m256i p_0 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p0, 0)); - const __m256i p_1 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p0, 1)); - const __m256i p_2 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p1, 0)); - const __m256i p_3 = _mm256_cvtepi16_epi32(_mm256_extracti128_si256(p1, 1)); - - acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[0]), _mm256_cvtepi32_ps(p_0), acc); - acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[1]), _mm256_cvtepi32_ps(p_1), acc); - acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[2]), _mm256_cvtepi32_ps(p_2), acc); - acc = _mm256_fmadd_ps(_mm256_set1_ps(d * db[3]), _mm256_cvtepi32_ps(p_3), acc); - } - - *s = hsum_float_8(acc) + summs; - -#elif defined __AVX__ - - const __m128i m3 = _mm_set1_epi8(3); - - __m256 acc = _mm256_setzero_ps(); - - uint32_t ud, um; - const uint8_t * restrict db = (const uint8_t *)&ud; - const uint8_t * restrict mb = (const uint8_t *)&um; - - float summs = 0; - - // TODO: optimize this - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const uint32_t * restrict sc = (const uint32_t *)x[i].scales; - ud = (sc[0] >> 0) & 0x0f0f0f0f; - um = (sc[0] >> 4) & 0x0f0f0f0f; - - int32_t smin = mb[0] * y[i].bsums[0] + mb[1] * y[i].bsums[1] + mb[2] * y[i].bsums[2] + mb[3] * y[i].bsums[3]; - summs += dmin * smin; - - const __m128i q2bits = _mm_loadu_si128((const __m128i*)q2); - const __m128i q2_0 = _mm_and_si128(q2bits, m3); - const __m128i q2_1 = _mm_and_si128(_mm_srli_epi16(q2bits, 2), m3); - const __m128i q2_2 = _mm_and_si128(_mm_srli_epi16(q2bits, 4), m3); - const __m128i q2_3 = _mm_and_si128(_mm_srli_epi16(q2bits, 6), m3); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - const __m128i p0 = _mm_maddubs_epi16(q2_0, _mm256_extractf128_si256(q8_0, 0)); - const __m128i p1 = _mm_maddubs_epi16(q2_1, _mm256_extractf128_si256(q8_0, 1)); - const __m128i p2 = _mm_maddubs_epi16(q2_2, _mm256_extractf128_si256(q8_1, 0)); - const __m128i p3 = _mm_maddubs_epi16(q2_3, _mm256_extractf128_si256(q8_1, 1)); - - const __m256i p_0 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p0, p0)), _mm_cvtepi16_epi32(p0)); - const __m256i p_1 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p1, p1)), _mm_cvtepi16_epi32(p1)); - const __m256i p_2 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p2, p2)), _mm_cvtepi16_epi32(p2)); - const __m256i p_3 = MM256_SET_M128I(_mm_cvtepi16_epi32(_mm_unpackhi_epi64(p3, p3)), _mm_cvtepi16_epi32(p3)); - - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[0]), _mm256_cvtepi32_ps(p_0)), acc); - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[1]), _mm256_cvtepi32_ps(p_1)), acc); - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[2]), _mm256_cvtepi32_ps(p_2)), acc); - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d * db[3]), _mm256_cvtepi32_ps(p_3)), acc); - } - - *s = hsum_float_8(acc) + summs; - -#elif defined __riscv_v_intrinsic - - uint32_t aux32[2]; - const uint8_t * scales = (const uint8_t *)aux32; - - float sumf = 0; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - const uint32_t * restrict sc = (const uint32_t *)x[i].scales; - - aux32[0] = sc[0] & 0x0f0f0f0f; - aux32[1] = (sc[0] >> 4) & 0x0f0f0f0f; - - sumf += dmin * (scales[4] * y[i].bsums[0] + scales[5] * y[i].bsums[1] + scales[6] * y[i].bsums[2] + scales[7] * y[i].bsums[3]); - - int isum1 = 0; - int isum2 = 0; - - size_t vl = 16; - - vint16m1_t vzero = __riscv_vmv_v_x_i16m1(0, 1); - - // load Q2 - vuint8mf2_t q2_x = __riscv_vle8_v_u8mf2(q2, vl); - - vint8mf2_t q2_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(q2_x, 0x03, vl)); - vint8mf2_t q2_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q2_x, 0x2, vl), 0x03 , vl)); - vint8mf2_t q2_2 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q2_x, 0x4, vl), 0x03 , vl)); - vint8mf2_t q2_3 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q2_x, 0x6, vl), 0x03 , vl)); - - // load Q8, and take product with Q2 - vint16m1_t p0 = __riscv_vwmul_vv_i16m1(q2_0, __riscv_vle8_v_i8mf2(q8, vl), vl); - vint16m1_t p1 = __riscv_vwmul_vv_i16m1(q2_1, __riscv_vle8_v_i8mf2(q8+16, vl), vl); - vint16m1_t p2 = __riscv_vwmul_vv_i16m1(q2_2, __riscv_vle8_v_i8mf2(q8+32, vl), vl); - vint16m1_t p3 = __riscv_vwmul_vv_i16m1(q2_3, __riscv_vle8_v_i8mf2(q8+48, vl), vl); - - vint16m1_t vs_0 = __riscv_vredsum_vs_i16m1_i16m1(p0, vzero, vl); - vint16m1_t vs_1 = __riscv_vredsum_vs_i16m1_i16m1(p1, vzero, vl); - vint16m1_t vs_2 = __riscv_vredsum_vs_i16m1_i16m1(p2, vzero, vl); - vint16m1_t vs_3 = __riscv_vredsum_vs_i16m1_i16m1(p3, vzero, vl); - - isum1 += __riscv_vmv_x_s_i16m1_i16(vs_0) * scales[0]; - isum2 += __riscv_vmv_x_s_i16m1_i16(vs_1) * scales[1]; - isum1 += __riscv_vmv_x_s_i16m1_i16(vs_2) * scales[2]; - isum2 += __riscv_vmv_x_s_i16m1_i16(vs_3) * scales[3]; - - sumf += d * (isum1 + isum2); - - } - - *s = sumf; - - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0x3); - const vector signed char lowScaleMask = vec_splats((signed char)0xF); - const vector unsigned char v2 = vec_splats((unsigned char)0x2); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - const vector unsigned char v6 = vec_splats((unsigned char)0x6); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - -#pragma GCC unroll 2 - for (int i = 0; i < nb; ++i) { - __builtin_prefetch(x[i].qs, 0, 1); - __builtin_prefetch(y[i].qs, 0, 1); - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - vector float vxmin = vec_splats(GGML_FP16_TO_FP32(x[i].dmin)); - vector float vdmin = vec_mul(vxmin, vyd); - - vector signed short q8ysums0 = vec_xl_len(y[i].bsums, 8); - - vector signed char q2xmins = (vector signed char)vec_xl_len(x[i].scales, 4); - vector signed char vscales = vec_and(q2xmins, lowScaleMask); - - q2xmins = vec_sr(q2xmins, v4); - vector signed short q2xmins0 = vec_unpackh((vector signed char)q2xmins); - - vector signed int prod0 = vec_mule(q2xmins0, q8ysums0); - vector signed int prod1 = vec_mulo(q2xmins0, q8ysums0); - - vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vdmin, vsumf0); - vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vdmin, vsumf1); - - vector signed char qxs0 = (vector signed char)vec_xl( 0, x[i].qs); - vector signed char q2x00 = vec_and(qxs0, lowMask); - vector signed char q2x01 = vec_and(vec_sr(qxs0, v2), lowMask); - vector signed char q2x02 = vec_and(vec_sr(qxs0, v4), lowMask); - vector signed char q2x03 = vec_and(vec_sr(qxs0, v6), lowMask); - - vector signed char q8y00 = vec_xl( 0, y[i].qs); - vector signed char q8y01 = vec_xl( 16, y[i].qs); - vector signed char q8y02 = vec_xl( 32, y[i].qs); - vector signed char q8y03 = vec_xl( 48, y[i].qs); - - vector signed short qv0 = vec_add(vec_mule(q2x00, q8y00), vec_mulo(q2x00, q8y00)); - vector signed short qv1 = vec_add(vec_mule(q2x01, q8y01), vec_mulo(q2x01, q8y01)); - vector signed short qv2 = vec_add(vec_mule(q2x02, q8y02), vec_mulo(q2x02, q8y02)); - vector signed short qv3 = vec_add(vec_mule(q2x03, q8y03), vec_mulo(q2x03, q8y03)); - - vector signed short vscales_h = vec_unpackh(vscales); - vector signed short vs0 = vec_splat(vscales_h, 0); - vector signed short vs1 = vec_splat(vscales_h, 1); - vector signed short vs2 = vec_splat(vscales_h, 2); - vector signed short vs3 = vec_splat(vscales_h, 3); - - vector signed int vsumi0 = vec_add(vec_mule(qv0, vs0), vec_mulo(qv0, vs0)); - vector signed int vsumi1 = vec_add(vec_mule(qv1, vs1), vec_mulo(qv1, vs1)); - vector signed int vsumi2 = vec_add(vec_mule(qv2, vs2), vec_mulo(qv2, vs2)); - vector signed int vsumi3 = vec_add(vec_mule(qv3, vs3), vec_mulo(qv3, vs3)); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined __loongarch_asx - - const __m256i m3 = __lasx_xvreplgr2vr_b(3); - - __m256 acc = (__m256)__lasx_xvldi(0); - - uint32_t ud, um; - const uint8_t * restrict db = (const uint8_t *)&ud; - const uint8_t * restrict mb = (const uint8_t *)&um; - - float summs = 0; - - // TODO: optimize this - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - const uint8_t * restrict q2 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const uint32_t * restrict sc = (const uint32_t *)x[i].scales; - ud = (sc[0] >> 0) & 0x0f0f0f0f; - um = (sc[0] >> 4) & 0x0f0f0f0f; - - int32_t smin = mb[0] * y[i].bsums[0] + mb[1] * y[i].bsums[1] + mb[2] * y[i].bsums[2] + mb[3] * y[i].bsums[3]; - summs += dmin * smin; - - const __m128i q2bits = __lsx_vld((const __m128i*)q2, 0); - const __m256i q2_0 = __lasx_xvand_v(lasx_insertf128(__lsx_vsrli_h(q2bits, 2), q2bits), m3); - const __m256i q2_1 = __lasx_xvand_v(lasx_insertf128(__lsx_vsrli_h(q2bits, 6), __lsx_vsrli_h(q2bits, 4)), m3); - - const __m256i q8_0 = __lasx_xvld((const __m256i*)(q8+ 0), 0); - const __m256i q8_1 = __lasx_xvld((const __m256i*)(q8+32), 0); - - const __m256i p0 = lasx_maddubs_h(q2_0, q8_0); - const __m256i p1 = lasx_maddubs_h(q2_1, q8_1); - - const __m256i p_0 = lasx_ext16_32(lasx_extracti128(p0, 0)); - const __m256i p_1 = lasx_ext16_32(lasx_extracti128(p0, 1)); - const __m256i p_2 = lasx_ext16_32(lasx_extracti128(p1, 0)); - const __m256i p_3 = lasx_ext16_32(lasx_extracti128(p1, 1)); - - ft_union t0, t1, t2, t3; - t0.f = d * db[0]; - t1.f = d * db[1]; - t2.f = d * db[2]; - t3.f = d * db[3]; - acc = __lasx_xvfmadd_s(__lasx_xvreplgr2vr_w(t0.i), __lasx_xvffint_s_w(p_0), acc); - acc = __lasx_xvfmadd_s(__lasx_xvreplgr2vr_w(t1.i), __lasx_xvffint_s_w(p_1), acc); - acc = __lasx_xvfmadd_s(__lasx_xvreplgr2vr_w(t2.i), __lasx_xvffint_s_w(p_2), acc); - acc = __lasx_xvfmadd_s(__lasx_xvreplgr2vr_w(t3.i), __lasx_xvffint_s_w(p_3), acc); - } - - *s = hsum_float_8(acc) + summs; - -#else - - float sumf = 0; - - int isum[QK_K/16]; - - for (int i = 0; i < nb; ++i) { - - const uint8_t * q2 = x[i].qs; - const int8_t * q8 = y[i].qs; - const uint8_t * sc = x[i].scales; - - int summs = 0; - for (int j = 0; j < QK_K/16; ++j) { - summs += y[i].bsums[j] * (sc[j] >> 4); - } - - const float dall = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const float dmin = y[i].d * GGML_FP16_TO_FP32(x[i].dmin); - - memset(isum, 0, (QK_K/16)*sizeof(int)); - for (int l = 0; l < 16; ++l) { - isum[0] += q8[l+ 0] * ((q2[l] >> 0) & 3); - isum[1] += q8[l+16] * ((q2[l] >> 2) & 3); - isum[2] += q8[l+32] * ((q2[l] >> 4) & 3); - isum[3] += q8[l+48] * ((q2[l] >> 6) & 3); - } - for (int l = 0; l < QK_K/16; ++l) { - isum[l] *= (sc[l] & 0xF); - } - sumf += dall * (isum[0] + isum[1] + isum[2] + isum[3]) - dmin * summs; - } - *s = sumf; -#endif -} -#endif - -#if QK_K == 256 void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); assert(nrc == 1); @@ -7616,512 +6899,6 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r } -#else - -void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q3_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#ifdef __ARM_NEON - const int32x4_t vzero = vdupq_n_s32(0); - - const uint8x16_t m3b = vdupq_n_u8(0x3); - const uint8x16_t mh = vdupq_n_u8(4); - - ggml_int8x16x4_t q3bytes; - - uint16_t aux16[2]; - int8_t * scales = (int8_t *)aux16; - - float sum = 0; - - for (int i = 0; i < nb; ++i) { - - ggml_uint8x16x4_t q3h; - - const uint8x8_t hbits = vld1_u8(x[i].hmask); - const uint8x16_t q3bits = vld1q_u8(x[i].qs); - const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(y[i].qs); - - const uint16_t a = *(const uint16_t *)x[i].scales; - aux16[0] = a & 0x0f0f; - aux16[1] = (a >> 4) & 0x0f0f; - - for (int j = 0; j < 4; ++j) scales[j] -= 8; - - int32_t isum = -4*(scales[0] * y[i].bsums[0] + scales[2] * y[i].bsums[1] + scales[1] * y[i].bsums[2] + scales[3] * y[i].bsums[3]); - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8x16_t htmp = vcombine_u8(hbits, vshr_n_u8(hbits, 1)); - q3h.val[0] = vandq_u8(mh, vshlq_n_u8(htmp, 2)); - q3h.val[1] = vandq_u8(mh, htmp); - q3h.val[2] = vandq_u8(mh, vshrq_n_u8(htmp, 2)); - q3h.val[3] = vandq_u8(mh, vshrq_n_u8(htmp, 4)); - - q3bytes.val[0] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q3bits, m3b), q3h.val[0])); - q3bytes.val[1] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(vshrq_n_u8(q3bits, 2), m3b), q3h.val[1])); - q3bytes.val[2] = vreinterpretq_s8_u8(vorrq_u8(vandq_u8(vshrq_n_u8(q3bits, 4), m3b), q3h.val[2])); - q3bytes.val[3] = vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q3bits, 6), q3h.val[3])); - - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[0], q8bytes.val[0])) * scales[0]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[1], q8bytes.val[1])) * scales[2]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[2], q8bytes.val[2])) * scales[1]; - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q3bytes.val[3], q8bytes.val[3])) * scales[3]; - - sum += d * isum; - - } - - *s = sum; - -#elif defined __AVX2__ - - const __m256i m3 = _mm256_set1_epi8(3); - const __m256i m1 = _mm256_set1_epi8(1); - - __m256 acc = _mm256_setzero_ps(); - - uint64_t aux64; - - uint16_t aux16[2]; - const int8_t * aux8 = (const int8_t *)aux16; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q3 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const uint16_t a = *(const uint16_t *)x[i].scales; - aux16[0] = a & 0x0f0f; - aux16[1] = (a >> 4) & 0x0f0f; - - const __m256i scale_0 = MM256_SET_M128I(_mm_set1_epi16(aux8[2] - 8), _mm_set1_epi16(aux8[0] - 8)); - const __m256i scale_1 = MM256_SET_M128I(_mm_set1_epi16(aux8[3] - 8), _mm_set1_epi16(aux8[1] - 8)); - - memcpy(&aux64, x[i].hmask, 8); - - const __m128i haux = _mm_set_epi64x(aux64 >> 1, aux64 >> 0); - __m256i q3h_0 = MM256_SET_M128I(_mm_srli_epi16(haux, 2), haux); - __m256i q3h_1 = _mm256_srli_epi16(q3h_0, 4); - q3h_0 = _mm256_slli_epi16(_mm256_andnot_si256(q3h_0, m1), 2); - q3h_1 = _mm256_slli_epi16(_mm256_andnot_si256(q3h_1, m1), 2); - - // load low 2 bits - const __m128i q3bits = _mm_loadu_si128((const __m128i*)q3); - - // prepare low and high bits - const __m256i q3aux = MM256_SET_M128I(_mm_srli_epi16(q3bits, 2), q3bits); - const __m256i q3l_0 = _mm256_and_si256(q3aux, m3); - const __m256i q3l_1 = _mm256_and_si256(_mm256_srli_epi16(q3aux, 4), m3); - - // load Q8 quants - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use _mm256_maddubs_epi16, - // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, - // and 2 if the high bit was set) - const __m256i q8s_0 = _mm256_maddubs_epi16(q3h_0, q8_0); - const __m256i q8s_1 = _mm256_maddubs_epi16(q3h_1, q8_1); - - __m256i p16_0 = _mm256_maddubs_epi16(q3l_0, q8_0); - __m256i p16_1 = _mm256_maddubs_epi16(q3l_1, q8_1); - - p16_0 = _mm256_sub_epi16(p16_0, q8s_0); - p16_1 = _mm256_sub_epi16(p16_1, q8s_1); - - // multiply with scales - p16_0 = _mm256_madd_epi16(scale_0, p16_0); - p16_1 = _mm256_madd_epi16(scale_1, p16_1); - - p16_0 = _mm256_add_epi32(p16_0, p16_1); - - // multiply with block scale and accumulate - acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(p16_0), acc); - - } - - *s = hsum_float_8(acc); - -#elif defined __AVX__ - - const __m128i m3 = _mm_set1_epi8(3); - const __m128i m1 = _mm_set1_epi8(1); - - __m256 acc = _mm256_setzero_ps(); - - uint64_t aux64; - - uint16_t aux16[2]; - const int8_t * aux8 = (const int8_t *)aux16; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q3 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const uint16_t a = *(const uint16_t *)x[i].scales; - aux16[0] = a & 0x0f0f; - aux16[1] = (a >> 4) & 0x0f0f; - - const __m128i scale_0 = _mm_set1_epi16(aux8[0] - 8); - const __m128i scale_1 = _mm_set1_epi16(aux8[2] - 8); - const __m128i scale_2 = _mm_set1_epi16(aux8[1] - 8); - const __m128i scale_3 = _mm_set1_epi16(aux8[3] - 8); - - memcpy(&aux64, x[i].hmask, 8); - - __m128i q3h_0 = _mm_set_epi64x(aux64 >> 1, aux64 >> 0); - __m128i q3h_1 = _mm_srli_epi16(q3h_0, 2); - __m128i q3h_2 = _mm_srli_epi16(q3h_0, 4); - __m128i q3h_3 = _mm_srli_epi16(q3h_0, 6); - q3h_0 = _mm_slli_epi16(_mm_andnot_si128(q3h_0, m1), 2); - q3h_1 = _mm_slli_epi16(_mm_andnot_si128(q3h_1, m1), 2); - q3h_2 = _mm_slli_epi16(_mm_andnot_si128(q3h_2, m1), 2); - q3h_3 = _mm_slli_epi16(_mm_andnot_si128(q3h_3, m1), 2); - - // load low 2 bits - const __m128i q3bits = _mm_loadu_si128((const __m128i*)q3); - - // prepare low and high bits - const __m128i q3l_0 = _mm_and_si128(q3bits, m3); - const __m128i q3l_1 = _mm_and_si128(_mm_srli_epi16(q3bits, 2), m3); - const __m128i q3l_2 = _mm_and_si128(_mm_srli_epi16(q3bits, 4), m3); - const __m128i q3l_3 = _mm_and_si128(_mm_srli_epi16(q3bits, 6), m3); - - // load Q8 quants - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use _mm_maddubs_epi16, - // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, - // and 2 if the high bit was set) - const __m128i q8s_0 = _mm_maddubs_epi16(q3h_0, _mm256_extractf128_si256(q8_0, 0)); - const __m128i q8s_1 = _mm_maddubs_epi16(q3h_1, _mm256_extractf128_si256(q8_0, 1)); - const __m128i q8s_2 = _mm_maddubs_epi16(q3h_2, _mm256_extractf128_si256(q8_1, 0)); - const __m128i q8s_3 = _mm_maddubs_epi16(q3h_3, _mm256_extractf128_si256(q8_1, 1)); - - __m128i p16_0 = _mm_maddubs_epi16(q3l_0, _mm256_extractf128_si256(q8_0, 0)); - __m128i p16_1 = _mm_maddubs_epi16(q3l_1, _mm256_extractf128_si256(q8_0, 1)); - __m128i p16_2 = _mm_maddubs_epi16(q3l_2, _mm256_extractf128_si256(q8_1, 0)); - __m128i p16_3 = _mm_maddubs_epi16(q3l_3, _mm256_extractf128_si256(q8_1, 1)); - - p16_0 = _mm_sub_epi16(p16_0, q8s_0); - p16_1 = _mm_sub_epi16(p16_1, q8s_1); - p16_2 = _mm_sub_epi16(p16_2, q8s_2); - p16_3 = _mm_sub_epi16(p16_3, q8s_3); - - // multiply with scales - p16_0 = _mm_madd_epi16(scale_0, p16_0); - p16_1 = _mm_madd_epi16(scale_1, p16_1); - p16_2 = _mm_madd_epi16(scale_2, p16_2); - p16_3 = _mm_madd_epi16(scale_3, p16_3); - - p16_0 = _mm_add_epi32(p16_0, p16_2); - p16_1 = _mm_add_epi32(p16_1, p16_3); - __m256i p16 = MM256_SET_M128I(p16_1, p16_0); - - // multiply with block scale and accumulate - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(p16)), acc); - - } - - *s = hsum_float_8(acc); - -#elif defined __riscv_v_intrinsic - - uint16_t aux16[2]; - int8_t * scales = (int8_t *)aux16; - - float sumf = 0; - - for (int i = 0; i < nb; ++i) { - - const uint8_t * restrict q3 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const uint16_t a = *(const uint16_t *)x[i].scales; - aux16[0] = a & 0x0f0f; - aux16[1] = (a >> 4) & 0x0f0f; - - for (int j = 0; j < 4; ++j) scales[j] -= 8; - - int32_t isum = -4*(scales[0] * y[i].bsums[0] + scales[2] * y[i].bsums[1] + scales[1] * y[i].bsums[2] + scales[3] * y[i].bsums[3]); - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); - - // load qh - vuint8mf4_t qh_x1 = __riscv_vle8_v_u8mf4(x[i].hmask, 8); - vuint8mf2_t qh_x2 = __riscv_vlmul_ext_v_u8mf4_u8mf2(__riscv_vsrl_vx_u8mf4(qh_x1, 1, 8)); - - size_t vl = 16; - - // extend and combine both qh_x1 and qh_x2 - vuint8mf2_t qh_x = __riscv_vslideup_vx_u8mf2(__riscv_vlmul_ext_v_u8mf4_u8mf2(qh_x1), qh_x2, vl/2, vl); - - vuint8mf2_t qh_0 = __riscv_vand_vx_u8mf2(__riscv_vsll_vx_u8mf2(qh_x, 0x2, vl), 0x4, vl); - vuint8mf2_t qh_1 = __riscv_vand_vx_u8mf2(qh_x, 0x4, vl); - vuint8mf2_t qh_2 = __riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(qh_x, 0x2, vl), 0x4, vl); - vuint8mf2_t qh_3 = __riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(qh_x, 0x4, vl), 0x4, vl); - - // load Q3 - vuint8mf2_t q3_x = __riscv_vle8_v_u8mf2(q3, vl); - - vuint8mf2_t q3h_0 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(q3_x, 0x3, vl), qh_0, vl); - vuint8mf2_t q3h_1 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q3_x, 2, vl), 0x3, vl), qh_1, vl); - vuint8mf2_t q3h_2 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(__riscv_vsrl_vx_u8mf2(q3_x, 4, vl), 0x3, vl), qh_2, vl); - vuint8mf2_t q3h_3 = __riscv_vor_vv_u8mf2(__riscv_vsrl_vx_u8mf2(q3_x, 0x6, vl), qh_3, vl); - - vint8mf2_t q3_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(q3h_0); - vint8mf2_t q3_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(q3h_1); - vint8mf2_t q3_2 = __riscv_vreinterpret_v_u8mf2_i8mf2(q3h_2); - vint8mf2_t q3_3 = __riscv_vreinterpret_v_u8mf2_i8mf2(q3h_3); - - // load Q8 and take product with Q3 - vint16m1_t p0 = __riscv_vwmul_vv_i16m1(q3_0, __riscv_vle8_v_i8mf2(q8, vl), vl); - vint16m1_t p1 = __riscv_vwmul_vv_i16m1(q3_1, __riscv_vle8_v_i8mf2(q8+16, vl), vl); - vint16m1_t p2 = __riscv_vwmul_vv_i16m1(q3_2, __riscv_vle8_v_i8mf2(q8+32, vl), vl); - vint16m1_t p3 = __riscv_vwmul_vv_i16m1(q3_3, __riscv_vle8_v_i8mf2(q8+48, vl), vl); - - vint32m1_t vs_0 = __riscv_vwredsum_vs_i16m1_i32m1(p0, vzero, vl); - vint32m1_t vs_1 = __riscv_vwredsum_vs_i16m1_i32m1(p1, vzero, vl); - vint32m1_t vs_2 = __riscv_vwredsum_vs_i16m1_i32m1(p2, vzero, vl); - vint32m1_t vs_3 = __riscv_vwredsum_vs_i16m1_i32m1(p3, vzero, vl); - - isum += __riscv_vmv_x_s_i32m1_i32(vs_0) * scales[0]; - isum += __riscv_vmv_x_s_i32m1_i32(vs_1) * scales[2]; - isum += __riscv_vmv_x_s_i32m1_i32(vs_2) * scales[1]; - isum += __riscv_vmv_x_s_i32m1_i32(vs_3) * scales[3]; - - sumf += d * isum; - - } - - *s = sumf; - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0x3); - const vector signed char v1 = vec_splats((signed char)0x1); - const vector unsigned char v2 = vec_splats((unsigned char)0x2); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - const vector unsigned char v6 = vec_splats((unsigned char)0x6); - const vector signed char off = vec_splats((signed char)0x8); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - -#pragma GCC unroll 2 - for (int i = 0; i < nb; ++i) { - __builtin_prefetch(x[i].qs, 0, 1); - __builtin_prefetch(y[i].qs, 0, 1); - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd = vec_mul(vxd, vyd); - - uint16_t aux16[2]; - int8_t * scales = (int8_t *)aux16; - - const uint16_t a = *(const uint16_t *)x[i].scales; - aux16[0] = a & 0x0f0f; - aux16[1] = (a >> 4) & 0x0f0f; - - vector signed char vscales = (vector signed char)vec_xl_len(scales, 8); - vector signed char qxhs0 = (vector signed char)vec_xl_len(x[i].hmask, 8); - qxhs0 = vec_or(qxhs0, vec_sr(vec_sld(qxhs0, qxhs0, 8), (vector unsigned char)v1)); - - vscales = vec_sub(vscales, off); - - vector signed char qxs0 = (vector signed char)vec_xl( 0, x[i].qs); - vector signed char qxs00 = vec_and(qxs0, lowMask); - vector signed char qxs01 = vec_and(vec_sr(qxs0, v2), lowMask); - vector signed char qxs10 = vec_and(vec_sr(qxs0, v4), lowMask); - vector signed char qxs11 = vec_and(vec_sr(qxs0, v6), lowMask); - - //the 3rd bit - vector signed char qxh00 = vec_sl(vec_andc(v1, qxhs0), v2); - vector signed char qxh01 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v2)), v2); - vector signed char qxh02 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v4)), v2); - vector signed char qxh03 = vec_sl(vec_andc(v1, vec_sr(qxhs0, v6)), v2); - qxhs0 = vec_sr(qxhs0, v4); - - vector signed char q3x00 = vec_sub(qxs00, qxh00); - vector signed char q3x01 = vec_sub(qxs01, qxh01); - vector signed char q3x10 = vec_sub(qxs10, qxh02); - vector signed char q3x11 = vec_sub(qxs11, qxh03); - - vector signed char q8y00 = vec_xl( 0, y[i].qs); - vector signed char q8y01 = vec_xl( 16, y[i].qs); - vector signed char q8y10 = vec_xl( 32, y[i].qs); - vector signed char q8y11 = vec_xl( 48, y[i].qs); - - vector signed short vscales_h = vec_unpackh(vscales); - vector signed short vs0 = vec_splat(vscales_h, 0); - vector signed short vs1 = vec_splat(vscales_h, 1); - vector signed short vs2 = vec_splat(vscales_h, 2); - vector signed short vs3 = vec_splat(vscales_h, 3); - - vector signed short qv00 = vec_add(vec_mule(q3x00, q8y00), vec_mulo(q3x00, q8y00)); - vector signed short qv10 = vec_add(vec_mule(q3x10, q8y10), vec_mulo(q3x10, q8y10)); - vector signed short qv01 = vec_add(vec_mule(q3x01, q8y01), vec_mulo(q3x01, q8y01)); - vector signed short qv11 = vec_add(vec_mule(q3x11, q8y11), vec_mulo(q3x11, q8y11)); - - vector signed int vsumi0 = vec_add(vec_mule(qv00, vs0), vec_mulo(qv00, vs0)); - vector signed int vsumi1 = vec_add(vec_mule(qv10, vs1), vec_mulo(qv10, vs1)); - vector signed int vsumi2 = vec_add(vec_mule(qv01, vs2), vec_mulo(qv01, vs2)); - vector signed int vsumi3 = vec_add(vec_mule(qv11, vs3), vec_mulo(qv11, vs3)); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined __loongarch_asx - - const __m256i m3 = __lasx_xvreplgr2vr_b(3); - const __m256i m1 = __lasx_xvreplgr2vr_b(1); - - __m256 acc = (__m256)__lasx_xvldi(0); - - uint64_t aux64; - - uint16_t aux16[2]; - const int8_t * aux8 = (const int8_t *)aux16; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q3 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - const __m256i scale_0 = lasx_insertf128(__lasx_xvreplgr2vr_h(aux8[2] - 8), __lasx_xvreplgr2vr_h(aux8[0] - 8)); - const __m256i scale_1 = lasx_insertf128(__lasx_xvreplgr2vr_h(aux8[3] - 8), __lasx_xvreplgr2vr_h(aux8[1] - 8)); - - memcpy(&aux64, x[i].hmask, 8); - - __m128i haux = __lsx_vinsgr2vr_d(haux, aux64, 0); - haux = __lsx_vinsgr2vr_d(haux, aux64 >> 1, 1); - __m256i q3h_0 = lasx_insertf128(__lsx_vsrli_h(haux, 2), haux); - __m256i q3h_1 = __lasx_xvsrli_h(q3h_0, 4); - q3h_0 = __lasx_xvslli_h(__lasx_xvandn_v(q3h_0, m1), 2); - q3h_1 = __lasx_xvslli_h(__lasx_xvandn_v(q3h_1, m1), 2); - - // load low 2 bits - const __m128i q3bits = __lsx_vld((const __m128i*)q3, 0); - - // prepare low and high bits - const __m256i q3aux = lasx_insertf128(__lsx_vsrli_h(q3bits, 2), q3bits); - const __m256i q3l_0 = __lasx_xvand_v(q3aux, m3); - const __m256i q3l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q3aux, 4), m3); - - // load Q8 quants - const __m256i q8_0 = __lasx_xvld((const __m256i*)(q8+ 0), 0); - const __m256i q8_1 = __lasx_xvld((const __m256i*)(q8+32), 0); - - // Dot product: we multiply the 2 low bits and 1 high bit part separately, so we can use lasx_maddubs_h, - // and then subtract. The high bit part has the 2 already subtracted (and so, it is zero if the high bit was not set, - // and 2 if the high bit was set) - const __m256i q8s_0 = lasx_maddubs_h(q3h_0, q8_0); - const __m256i q8s_1 = lasx_maddubs_h(q3h_1, q8_1); - - __m256i p16_0 = lasx_maddubs_h(q3l_0, q8_0); - __m256i p16_1 = lasx_maddubs_h(q3l_1, q8_1); - - p16_0 = __lasx_xvsub_h(p16_0, q8s_0); - p16_1 = __lasx_xvsub_h(p16_1, q8s_1); - - // multiply with scales - p16_0 = lasx_madd_h(scale_0, p16_0); - p16_1 = lasx_madd_h(scale_1, p16_1); - - p16_0 = __lasx_xvadd_w(p16_0, p16_1); - - // multiply with block scale and accumulate - acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(p16_0), acc); - } - - *s = hsum_float_8(acc); - -#else - - int8_t aux8[QK_K]; - int16_t aux16[8]; - float sums [8]; - int32_t aux32[8]; - int32_t scales[4]; - memset(sums, 0, 8*sizeof(float)); - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const uint8_t * restrict q3 = x[i].qs; - const uint8_t * restrict hm = x[i].hmask; - const int8_t * restrict q8 = y[i].qs; - int8_t * restrict a = aux8; - for (int l = 0; l < 8; ++l) { - a[l+ 0] = (int8_t)((q3[l+0] >> 0) & 3) - (hm[l] & 0x01 ? 0 : 4); - a[l+ 8] = (int8_t)((q3[l+8] >> 0) & 3) - (hm[l] & 0x02 ? 0 : 4); - a[l+16] = (int8_t)((q3[l+0] >> 2) & 3) - (hm[l] & 0x04 ? 0 : 4); - a[l+24] = (int8_t)((q3[l+8] >> 2) & 3) - (hm[l] & 0x08 ? 0 : 4); - a[l+32] = (int8_t)((q3[l+0] >> 4) & 3) - (hm[l] & 0x10 ? 0 : 4); - a[l+40] = (int8_t)((q3[l+8] >> 4) & 3) - (hm[l] & 0x20 ? 0 : 4); - a[l+48] = (int8_t)((q3[l+0] >> 6) & 3) - (hm[l] & 0x40 ? 0 : 4); - a[l+56] = (int8_t)((q3[l+8] >> 6) & 3) - (hm[l] & 0x80 ? 0 : 4); - } - - scales[0] = (x[i].scales[0] & 0xF) - 8; - scales[1] = (x[i].scales[0] >> 4) - 8; - scales[2] = (x[i].scales[1] & 0xF) - 8; - scales[3] = (x[i].scales[1] >> 4) - 8; - - memset(aux32, 0, 8*sizeof(int32_t)); - for (int j = 0; j < QK_K/16; ++j) { - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] += q8[l] * a[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux32[l] += scales[j] * aux16[l]; - } - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; - } - for (int l = 0; l < 8; ++l) sumf += sums[l]; - *s = sumf; - -#endif - -} -#endif - -#if QK_K == 256 void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); assert(nrc == 1); @@ -8679,381 +7456,7 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = sumf; #endif } -#else -void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - const block_q4_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#ifdef __ARM_NEON - const uint8x16_t m4b = vdupq_n_u8(0xf); - - const int32x4_t mzero = vdupq_n_s32(0); - - float sumf = 0; - - ggml_int8x16x2_t q4bytes; - ggml_int8x16x4_t q8bytes; - - float sum_mins = 0.f; - - uint16_t aux16[2]; - const uint8_t * restrict scales = (const uint8_t *)aux16; - - for (int i = 0; i < nb; ++i) { - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const uint16_t * restrict a = (const uint16_t *)x[i].scales; - aux16[0] = a[0] & 0x0f0f; - aux16[1] = (a[0] >> 4) & 0x0f0f; - - const int32_t summi = scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3]); - sum_mins += y[i].d * GGML_FP16_TO_FP32(x[i].d[1]) * summi; - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d[0]); - - const ggml_uint8x16x2_t q4bits = ggml_vld1q_u8_x2(q4); - - q8bytes = ggml_vld1q_s8_x4(q8); - q4bytes.val[0] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[0], m4b)); - q4bytes.val[1] = vreinterpretq_s8_u8(vandq_u8 (q4bits.val[1], m4b)); - - const int32x4_t p1 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[0]), q4bytes.val[1], q8bytes.val[1]); - const int32_t sumi1 = vaddvq_s32(p1) * scales[0]; - - q4bytes.val[0] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[0], 4)); - q4bytes.val[1] = vreinterpretq_s8_u8(vshrq_n_u8(q4bits.val[1], 4)); - - const int32x4_t p2 = ggml_vdotq_s32(ggml_vdotq_s32(mzero, q4bytes.val[0], q8bytes.val[2]), q4bytes.val[1], q8bytes.val[3]); - const int32_t sumi2 = vaddvq_s32(p2) * scales[1]; - - sumf += d * (sumi1 + sumi2); - } - - *s = sumf - sum_mins; - -#elif defined __AVX2__ - - const __m256i m4 = _mm256_set1_epi8(0xF); - - __m256 acc = _mm256_setzero_ps(); - - float summs = 0; - - uint16_t aux16[2]; - const uint8_t * scales = (const uint8_t *)aux16; - - for (int i = 0; i < nb; ++i) { - - const float d = GGML_FP16_TO_FP32(x[i].d[0]) * y[i].d; - const float m = GGML_FP16_TO_FP32(x[i].d[1]) * y[i].d; - const __m256 vd = _mm256_set1_ps(d); - - const uint16_t * a = (const uint16_t *)x[i].scales; - aux16[0] = a[0] & 0x0f0f; - aux16[1] = (a[0] >> 4) & 0x0f0f; - - summs += m * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3])); - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const __m256i q4bits = _mm256_loadu_si256((const __m256i*)q4); - const __m256i q4l = _mm256_and_si256(q4bits, m4); - const __m256i q4h = _mm256_and_si256(_mm256_srli_epi16(q4bits, 4), m4); - - const __m256i q8l = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8h = _mm256_loadu_si256((const __m256i*)(q8+32)); - - const __m256i p16l = _mm256_maddubs_epi16(q4l, q8l); - const __m256i p16h = _mm256_maddubs_epi16(q4h, q8h); - - const __m256i p32l = _mm256_madd_epi16(_mm256_set1_epi16(scales[0]), p16l); - acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(p32l), acc); - - const __m256i p32h = _mm256_madd_epi16(_mm256_set1_epi16(scales[1]), p16h); - acc = _mm256_fmadd_ps(vd, _mm256_cvtepi32_ps(p32h), acc); - - } - - *s = hsum_float_8(acc) - summs; - -#elif defined __AVX__ - - const __m128i m4 = _mm_set1_epi8(0xF); - - __m256 acc = _mm256_setzero_ps(); - - float summs = 0; - - uint16_t aux16[2]; - const uint8_t * scales = (const uint8_t *)aux16; - - for (int i = 0; i < nb; ++i) { - - const float d = GGML_FP16_TO_FP32(x[i].d[0]) * y[i].d; - const float m = GGML_FP16_TO_FP32(x[i].d[1]) * y[i].d; - const __m256 vd = _mm256_set1_ps(d); - - const uint16_t * a = (const uint16_t *)x[i].scales; - aux16[0] = a[0] & 0x0f0f; - aux16[1] = (a[0] >> 4) & 0x0f0f; - - summs += m * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3])); - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const __m256i q4bits = _mm256_loadu_si256((const __m256i*)q4); - const __m128i q4bits_0 = _mm256_extractf128_si256(q4bits, 0); - const __m128i q4bits_1 = _mm256_extractf128_si256(q4bits, 1); - const __m128i q4_0 = _mm_and_si128(q4bits_0, m4); - const __m128i q4_1 = _mm_and_si128(q4bits_1, m4); - const __m128i q4_2 = _mm_and_si128(_mm_srli_epi16(q4bits_0, 4), m4); - const __m128i q4_3 = _mm_and_si128(_mm_srli_epi16(q4bits_1, 4), m4); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - const __m128i p16_0 = _mm_maddubs_epi16(q4_0, _mm256_extractf128_si256(q8_0, 0)); - const __m128i p16_1 = _mm_maddubs_epi16(q4_1, _mm256_extractf128_si256(q8_0, 1)); - const __m128i p16_2 = _mm_maddubs_epi16(q4_2, _mm256_extractf128_si256(q8_1, 0)); - const __m128i p16_3 = _mm_maddubs_epi16(q4_3, _mm256_extractf128_si256(q8_1, 1)); - - const __m128i p32_0 = _mm_madd_epi16(_mm_set1_epi16(scales[0]), p16_0); - const __m128i p32_1 = _mm_madd_epi16(_mm_set1_epi16(scales[0]), p16_1); - acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(MM256_SET_M128I(p32_1, p32_0))), acc); - - const __m128i p32_2 = _mm_madd_epi16(_mm_set1_epi16(scales[1]), p16_2); - const __m128i p32_3 = _mm_madd_epi16(_mm_set1_epi16(scales[1]), p16_3); - acc = _mm256_add_ps(_mm256_mul_ps(vd, _mm256_cvtepi32_ps(MM256_SET_M128I(p32_3, p32_2))), acc); - - } - - *s = hsum_float_8(acc) - summs; - -#elif defined __riscv_v_intrinsic - - uint16_t s16[2]; - const uint8_t * restrict scales = (const uint8_t *)s16; - - float sumf = 0; - - for (int i = 0; i < nb; ++i) { - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const uint16_t * restrict b = (const uint16_t *)x[i].scales; - s16[0] = b[0] & 0x0f0f; - s16[1] = (b[0] >> 4) & 0x0f0f; - - sumf -= y[i].d * GGML_FP16_TO_FP32(x[i].d[1]) * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3])); - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d[0]); - - size_t vl = 32; - - vint16m1_t vzero = __riscv_vmv_v_x_i16m1(0, 1); - - // load Q4 - vuint8m1_t q4_x = __riscv_vle8_v_u8m1(q4, vl); - - // load Q8 and multiply it with lower Q4 nibble - vint8m1_t q4_a = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vand_vx_u8m1(q4_x, 0x0F, vl)); - vint16m2_t va_0 = __riscv_vwmul_vv_i16m2(q4_a, __riscv_vle8_v_i8m1(q8, vl), vl); - vint16m1_t aux1 = __riscv_vredsum_vs_i16m2_i16m1(va_0, vzero, vl); - - sumf += d*scales[0]*__riscv_vmv_x_s_i16m1_i16(aux1); - - // load Q8 and multiply it with upper Q4 nibble - vint8m1_t q4_s = __riscv_vreinterpret_v_u8m1_i8m1(__riscv_vsrl_vx_u8m1(q4_x, 0x04, vl)); - vint16m2_t va_1 = __riscv_vwmul_vv_i16m2(q4_s, __riscv_vle8_v_i8m1(q8+32, vl), vl); - vint16m1_t aux2 = __riscv_vredsum_vs_i16m2_i16m1(va_1, vzero, vl); - - sumf += d*scales[1]*__riscv_vmv_x_s_i16m1_i16(aux2); - - } - - *s = sumf; - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - -#pragma GCC unroll 2 - for (int i = 0; i < nb; ++i) { - __builtin_prefetch(x[i].qs, 0, 1); - __builtin_prefetch(y[i].qs, 0, 1); - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d[1])); - vector float vyd = vec_splats(y[i].d); - vector float vd= vec_mul(vxd, vyd); - - uint16_t s16[2]; - const uint8_t * scales = (const uint8_t *)s16; - - const uint16_t * restrict b = (const uint16_t *)x[i].scales; - s16[0] = b[0] & 0x0f0f; - s16[1] = (b[0] >> 4) & 0x0f0f; - - vector signed char utmps = (vector signed char)vec_xl_len(scales, 4); - vector signed short vscales = (vector signed short)vec_unpackh(utmps); - vector signed short q4xmins0 = vec_mergeh(vscales, vscales); - q4xmins0 = vec_sld(q4xmins0, q4xmins0, 8); - - vector signed short q8ysums0 = vec_xl_len((const int16_t *)(y[i].bsums), 8); - - vector signed int prod0 = vec_mule(q4xmins0, q8ysums0); - vector signed int prod1 = vec_mulo(q4xmins0, q8ysums0); - - vsumf0 = vec_nmsub(vec_ctf(prod0, 0), vd, vsumf0); - vsumf1 = vec_nmsub(vec_ctf(prod1, 0), vd, vsumf1); - - vd = vec_mul(vyd, vec_splats(GGML_FP16_TO_FP32(x[i].d[0]))); - - vector signed char qxs0 = (vector signed char)vec_xl( 0, x[i].qs); - vector signed char qxs1 = (vector signed char)vec_xl(16, x[i].qs); - vector signed char q4x00 = vec_and(qxs0, lowMask); - vector signed char q4x01 = vec_sr(qxs0, v4); - vector signed char q4x10 = vec_and(qxs1, lowMask); - vector signed char q4x11 = vec_sr(qxs1, v4); - - vector signed char q8y00 = vec_xl( 0, y[i].qs); - vector signed char q8y10 = vec_xl(16, y[i].qs); - vector signed char q8y01 = vec_xl(32, y[i].qs); - vector signed char q8y11 = vec_xl(48, y[i].qs); - - vector signed short qv00 = vec_add(vec_mule(q4x00, q8y00), vec_mulo(q4x00, q8y00)); - vector signed short qv01 = vec_add(vec_mule(q4x01, q8y01), vec_mulo(q4x01, q8y01)); - vector signed short qv10 = vec_add(vec_mule(q4x10, q8y10), vec_mulo(q4x10, q8y10)); - vector signed short qv11 = vec_add(vec_mule(q4x11, q8y11), vec_mulo(q4x11, q8y11)); - - vector signed short vs0 = vec_splat(vscales, 0); - vector signed short vs1 = vec_splat(vscales, 1); - - vector signed int vsumi0 = vec_add(vec_mule(qv00, vs0), vec_mulo(qv00, vs0)); - vector signed int vsumi1 = vec_add(vec_mule(qv10, vs0), vec_mulo(qv10, vs0)); - vector signed int vsumi2 = vec_add(vec_mule(qv01, vs1), vec_mulo(qv01, vs1)); - vector signed int vsumi3 = vec_add(vec_mule(qv11, vs1), vec_mulo(qv11, vs1)); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined __loongarch_asx - - const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); - - __m256 acc = (__m256)__lasx_xvldi(0); - - float summs = 0; - - uint16_t aux16[2]; - const uint8_t * scales = (const uint8_t *)aux16; - - for (int i = 0; i < nb; ++i) { - - const float d = GGML_FP16_TO_FP32(x[i].d[0]) * y[i].d; - const float m = GGML_FP16_TO_FP32(x[i].d[1]) * y[i].d; - const __m256 vd = __lasx_xvreplfr2vr_s(d); - - const uint16_t * a = (const uint16_t *)x[i].scales; - aux16[0] = a[0] & 0x0f0f; - aux16[1] = (a[0] >> 4) & 0x0f0f; - - summs += m * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3])); - - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const __m256i q4bits = __lasx_xvld((const __m256i*)q4, 0); - const __m256i q4l = __lasx_xvand_v(q4bits, m4); - const __m256i q4h = __lasx_xvand_v(__lasx_xvsrli_h(q4bits, 4), m4); - - const __m256i q8l = __lasx_xvld((const __m256i*)(q8+ 0), 0); - const __m256i q8h = __lasx_xvld((const __m256i*)(q8+32), 0); - - const __m256i p16l = lasx_maddubs_h(q4l, q8l); - const __m256i p16h = lasx_maddubs_h(q4h, q8h); - - const __m256i p32l = lasx_madd_h(__lasx_xvreplgr2vr_h(scales[0]), p16l); - acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(p32l), acc); - - const __m256i p32h = lasx_madd_h(__lasx_xvreplgr2vr_h(scales[1]), p16h); - acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(p32h), acc); - } - - *s = hsum_float_8(acc) - summs; - -#else - - uint8_t aux8[QK_K]; - int16_t aux16[16]; - float sums [8]; - memset(sums, 0, 8*sizeof(float)); - - uint16_t s16[2]; - const uint8_t * restrict scales = (const uint8_t *)s16; - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const uint8_t * restrict q4 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - uint8_t * restrict a = aux8; - for (int l = 0; l < 32; ++l) a[l+ 0] = q4[l] & 0xF; - for (int l = 0; l < 32; ++l) a[l+32] = q4[l] >> 4; - - const uint16_t * restrict b = (const uint16_t *)x[i].scales; - s16[0] = b[0] & 0x0f0f; - s16[1] = (b[0] >> 4) & 0x0f0f; - - sumf -= y[i].d * GGML_FP16_TO_FP32(x[i].d[1]) * (scales[2] * (y[i].bsums[0] + y[i].bsums[1]) + scales[3] * (y[i].bsums[2] + y[i].bsums[3])); - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d[0]); - - for (int j = 0; j < QK_K/32; ++j) { - for (int l = 0; l < 16; ++l) aux16[l] = q8[l] * a[l]; - q8 += 16; a += 16; - for (int l = 0; l < 16; ++l) aux16[l] += q8[l] * a[l]; - q8 += 16; a += 16; - const float dl = d * scales[j]; - for (int l = 0; l < 8; ++l) sums[l] += dl * (aux16[l] + aux16[l+8]); - } - } - for (int l = 0; l < 8; ++l) sumf += sums[l]; - *s = sumf; -#endif -} -#endif - -#if QK_K == 256 void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); assert(nrc == 1); @@ -9151,12 +7554,10 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r float summs = 0.f; - for (int i = 0; i < nb; ++i) { - + for (int i = 0; i < nb; ++i) { const uint8_t * restrict q5 = x[i].qs; const int8_t * restrict q8 = y[i].qs; -#if QK_K == 256 const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); @@ -9166,10 +7567,6 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); utmp[2] = uaux; utmp[0] &= kmask1; -#else - // TODO - const float d = 0, dmin = 0; -#endif const __m256i mins_and_scales = _mm256_cvtepu8_epi16(_mm_set_epi32(utmp[3], utmp[2], utmp[1], utmp[0])); @@ -9552,15 +7949,10 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r const uint8_t * restrict q5 = x[i].qs; const int8_t * restrict q8 = y[i].qs; -#if QK_K == 256 const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); memcpy(utmp, x[i].scales, 12); -#else - // TODO - const float d = 0, dmin = 0; -#endif const __m256i mins_and_scales = lasx_extu8_16(lsx_set_w(utmp[3], utmp[2], utmp[1], utmp[0])); @@ -9679,402 +8071,6 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r #endif } -#else - -void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q5_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#ifdef __ARM_NEON - const uint8x16_t m4b = vdupq_n_u8(0xf); - const uint8x16_t mh = vdupq_n_u8(16); - const int32x4_t mzero = vdupq_n_s32(0); - - ggml_int8x16x4_t q5bytes; - ggml_uint8x16x4_t q5h; - - float sumf = 0; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const int8_t * sc = x[i].scales; - - const uint8_t * restrict q5 = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const uint8x8_t qhbits = vld1_u8(qh); - - const ggml_uint8x16x2_t q5bits = ggml_vld1q_u8_x2(q5); - const ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); - - const uint8x16_t htmp = vcombine_u8(qhbits, vshr_n_u8(qhbits, 1)); - q5h.val[0] = vbicq_u8(mh, vshlq_n_u8(htmp, 4)); - q5h.val[1] = vbicq_u8(mh, vshlq_n_u8(htmp, 2)); - q5h.val[2] = vbicq_u8(mh, htmp); - q5h.val[3] = vbicq_u8(mh, vshrq_n_u8(htmp, 2)); - - q5bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q5bits.val[0], m4b)), vreinterpretq_s8_u8(q5h.val[0])); - q5bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vandq_u8(q5bits.val[1], m4b)), vreinterpretq_s8_u8(q5h.val[1])); - q5bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(q5bits.val[0], 4)), vreinterpretq_s8_u8(q5h.val[2])); - q5bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vshrq_n_u8(q5bits.val[1], 4)), vreinterpretq_s8_u8(q5h.val[3])); - - int32_t sumi1 = sc[0] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[0], q8bytes.val[0])); - int32_t sumi2 = sc[1] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[1], q8bytes.val[1])); - int32_t sumi3 = sc[2] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[2], q8bytes.val[2])); - int32_t sumi4 = sc[3] * vaddvq_s32(ggml_vdotq_s32(mzero, q5bytes.val[3], q8bytes.val[3])); - - sumf += d * (sumi1 + sumi2 + sumi3 + sumi4); - } - - *s = sumf; - -#elif defined __AVX2__ - - const __m256i m4 = _mm256_set1_epi8(0xF); - const __m256i mone = _mm256_set1_epi8(1); - - __m256 acc = _mm256_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - - const uint8_t * restrict q5 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5); - - const __m256i scale_l = MM256_SET_M128I(_mm_set1_epi16(x[i].scales[1]), _mm_set1_epi16(x[i].scales[0])); - const __m256i scale_h = MM256_SET_M128I(_mm_set1_epi16(x[i].scales[3]), _mm_set1_epi16(x[i].scales[2])); - - int64_t aux64; - memcpy(&aux64, x[i].qh, 8); - const __m128i haux128 = _mm_set_epi64x(aux64 >> 1, aux64); - const __m256i haux256 = MM256_SET_M128I(_mm_srli_epi16(haux128, 2), haux128); - - const __m256i q5h_0 = _mm256_slli_epi16(_mm256_andnot_si256(haux256, mone), 4); - const __m256i q5h_1 = _mm256_slli_epi16(_mm256_andnot_si256(_mm256_srli_epi16(haux256, 4), mone), 4); - - const __m256i q5l_0 = _mm256_and_si256(q5bits, m4); - const __m256i q5l_1 = _mm256_and_si256(_mm256_srli_epi16(q5bits, 4), m4); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - const __m256i p16_0 = _mm256_madd_epi16(scale_l, _mm256_maddubs_epi16(q5l_0, q8_0)); - const __m256i p16_1 = _mm256_madd_epi16(scale_h, _mm256_maddubs_epi16(q5l_1, q8_1)); - const __m256i s16_0 = _mm256_madd_epi16(scale_l, _mm256_maddubs_epi16(q5h_0, q8_0)); - const __m256i s16_1 = _mm256_madd_epi16(scale_h, _mm256_maddubs_epi16(q5h_1, q8_1)); - - const __m256i dot = _mm256_sub_epi32(_mm256_add_epi32(p16_0, p16_1), _mm256_add_epi32(s16_0, s16_1)); - - acc = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(dot), acc); - - } - - *s = hsum_float_8(acc); - -#elif defined __AVX__ - - const __m128i m4 = _mm_set1_epi8(0xF); - const __m128i mone = _mm_set1_epi8(1); - - __m256 acc = _mm256_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - - const uint8_t * restrict q5 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const __m256i q5bits = _mm256_loadu_si256((const __m256i*)q5); - - const __m128i scale_0 = _mm_set1_epi16(x[i].scales[0]); - const __m128i scale_1 = _mm_set1_epi16(x[i].scales[1]); - const __m128i scale_2 = _mm_set1_epi16(x[i].scales[2]); - const __m128i scale_3 = _mm_set1_epi16(x[i].scales[3]); - - int64_t aux64; - memcpy(&aux64, x[i].qh, 8); - const __m128i haux128_0 = _mm_set_epi64x(aux64 >> 1, aux64); - const __m128i haux128_1 = _mm_srli_epi16(haux128_0, 2); - - const __m128i q5h_0 = _mm_slli_epi16(_mm_andnot_si128(haux128_0, mone), 4); - const __m128i q5h_1 = _mm_slli_epi16(_mm_andnot_si128(haux128_1, mone), 4); - const __m128i q5h_2 = _mm_slli_epi16(_mm_andnot_si128(_mm_srli_epi16(haux128_0, 4), mone), 4); - const __m128i q5h_3 = _mm_slli_epi16(_mm_andnot_si128(_mm_srli_epi16(haux128_1, 4), mone), 4); - - const __m128i q5l_0 = _mm_and_si128(_mm256_extractf128_si256(q5bits, 0), m4); - const __m128i q5l_1 = _mm_and_si128(_mm256_extractf128_si256(q5bits, 1), m4); - const __m128i q5l_2 = _mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q5bits, 0), 4), m4); - const __m128i q5l_3 = _mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q5bits, 1), 4), m4); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - const __m128i p16_0 = _mm_madd_epi16(scale_0, _mm_maddubs_epi16(q5l_0, _mm256_extractf128_si256(q8_0, 0))); - const __m128i p16_1 = _mm_madd_epi16(scale_1, _mm_maddubs_epi16(q5l_1, _mm256_extractf128_si256(q8_0, 1))); - const __m128i p16_2 = _mm_madd_epi16(scale_2, _mm_maddubs_epi16(q5l_2, _mm256_extractf128_si256(q8_1, 0))); - const __m128i p16_3 = _mm_madd_epi16(scale_3, _mm_maddubs_epi16(q5l_3, _mm256_extractf128_si256(q8_1, 1))); - const __m128i s16_0 = _mm_madd_epi16(scale_0, _mm_maddubs_epi16(q5h_0, _mm256_extractf128_si256(q8_0, 0))); - const __m128i s16_1 = _mm_madd_epi16(scale_1, _mm_maddubs_epi16(q5h_1, _mm256_extractf128_si256(q8_0, 1))); - const __m128i s16_2 = _mm_madd_epi16(scale_2, _mm_maddubs_epi16(q5h_2, _mm256_extractf128_si256(q8_1, 0))); - const __m128i s16_3 = _mm_madd_epi16(scale_3, _mm_maddubs_epi16(q5h_3, _mm256_extractf128_si256(q8_1, 1))); - - const __m128i dot_0 = _mm_sub_epi32(_mm_add_epi32(p16_0, p16_2), _mm_add_epi32(s16_0, s16_2)); - const __m128i dot_1 = _mm_sub_epi32(_mm_add_epi32(p16_1, p16_3), _mm_add_epi32(s16_1, s16_3)); - - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(MM256_SET_M128I(dot_1, dot_0))), acc); - - } - - *s = hsum_float_8(acc); - -#elif defined __riscv_v_intrinsic - - float sumf = 0; - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const int8_t * sc = x[i].scales; - - const uint8_t * restrict q5 = x[i].qs; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); - - // load qh - vuint8mf4_t qh_x1 = __riscv_vle8_v_u8mf4(qh, 8); - vuint8mf2_t qh_x2 = __riscv_vlmul_ext_v_u8mf4_u8mf2(__riscv_vsrl_vx_u8mf4(qh_x1, 1, 8)); - - size_t vl = 16; - - // combine both qh_1 and qh_2 - vuint8mf2_t qh_x = __riscv_vslideup_vx_u8mf2(__riscv_vlmul_ext_v_u8mf4_u8mf2(qh_x1), qh_x2, vl/2, vl); - - vuint8mf2_t qh_h0 = __riscv_vand_vx_u8mf2(__riscv_vnot_v_u8mf2(__riscv_vsll_vx_u8mf2(qh_x, 0x4, vl), vl), 16, vl); - vuint8mf2_t qh_h1 = __riscv_vand_vx_u8mf2(__riscv_vnot_v_u8mf2(__riscv_vsll_vx_u8mf2(qh_x, 0x2, vl), vl), 16, vl); - vuint8mf2_t qh_h2 = __riscv_vand_vx_u8mf2(__riscv_vnot_v_u8mf2(qh_x, vl), 16, vl); - vuint8mf2_t qh_h3 = __riscv_vand_vx_u8mf2(__riscv_vnot_v_u8mf2(__riscv_vsrl_vx_u8mf2(qh_x, 0x4, vl), vl), 16, vl); - - vint8mf2_t qh_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(qh_h0); - vint8mf2_t qh_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(qh_h1); - vint8mf2_t qh_2 = __riscv_vreinterpret_v_u8mf2_i8mf2(qh_h2); - vint8mf2_t qh_3 = __riscv_vreinterpret_v_u8mf2_i8mf2(qh_h3); - - // load q5 - vuint8mf2_t q5_x1 = __riscv_vle8_v_u8mf2(q5, vl); - vuint8mf2_t q5_x2 = __riscv_vle8_v_u8mf2(q5+16, vl); - - vint8mf2_t q5s_0 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(q5_x1, 0xF, vl)); - vint8mf2_t q5s_1 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vand_vx_u8mf2(q5_x2, 0xF, vl)); - vint8mf2_t q5s_2 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vsrl_vx_u8mf2(q5_x1, 0x4, vl)); - vint8mf2_t q5s_3 = __riscv_vreinterpret_v_u8mf2_i8mf2(__riscv_vsrl_vx_u8mf2(q5_x2, 0x4, vl)); - - vint8mf2_t q5_0 = __riscv_vsub_vv_i8mf2(q5s_0, qh_0, vl); - vint8mf2_t q5_1 = __riscv_vsub_vv_i8mf2(q5s_1, qh_1, vl); - vint8mf2_t q5_2 = __riscv_vsub_vv_i8mf2(q5s_2, qh_2, vl); - vint8mf2_t q5_3 = __riscv_vsub_vv_i8mf2(q5s_3, qh_3, vl); - - // load Q8 and multiply it with Q5 - vint16m1_t p0 = __riscv_vwmul_vv_i16m1(q5_0, __riscv_vle8_v_i8mf2(q8, vl), vl); - vint16m1_t p1 = __riscv_vwmul_vv_i16m1(q5_1, __riscv_vle8_v_i8mf2(q8+16, vl), vl); - vint16m1_t p2 = __riscv_vwmul_vv_i16m1(q5_2, __riscv_vle8_v_i8mf2(q8+32, vl), vl); - vint16m1_t p3 = __riscv_vwmul_vv_i16m1(q5_3, __riscv_vle8_v_i8mf2(q8+48, vl), vl); - - vint32m1_t vs_0 = __riscv_vwredsum_vs_i16m1_i32m1(p0, vzero, vl); - vint32m1_t vs_1 = __riscv_vwredsum_vs_i16m1_i32m1(p1, vzero, vl); - vint32m1_t vs_2 = __riscv_vwredsum_vs_i16m1_i32m1(p2, vzero, vl); - vint32m1_t vs_3 = __riscv_vwredsum_vs_i16m1_i32m1(p3, vzero, vl); - - int32_t sumi1 = sc[0] * __riscv_vmv_x_s_i32m1_i32(vs_0); - int32_t sumi2 = sc[1] * __riscv_vmv_x_s_i32m1_i32(vs_1); - int32_t sumi3 = sc[2] * __riscv_vmv_x_s_i32m1_i32(vs_2); - int32_t sumi4 = sc[3] * __riscv_vmv_x_s_i32m1_i32(vs_3); - - sumf += d * (sumi1 + sumi2 + sumi3 + sumi4); - - } - - *s = sumf; - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector unsigned char v1 = vec_splats((unsigned char)0x1); - const vector unsigned char v2 = vec_splats((unsigned char)0x2); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - -#pragma GCC unroll 2 - for (int i = 0; i < nb; ++i) { - __builtin_prefetch(x[i].qs, 0, 1); - __builtin_prefetch(y[i].qs, 0, 1); - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd= vec_mul(vxd, vyd); - - vector signed char qxs0 = (vector signed char)vec_xl( 0, x[i].qs); - vector signed char qxs1 = (vector signed char)vec_xl(16, x[i].qs); - vector signed char qxs00 = (vector signed char)vec_and(qxs0, lowMask); - vector signed char qxs01 = (vector signed char)vec_sr(qxs0, v4); - vector signed char qxs10 = (vector signed char)vec_and(qxs1, lowMask); - vector signed char qxs11 = (vector signed char)vec_sr(qxs1, v4); - - vector signed char qxhs = (vector signed char)vec_xl_len(x[i].qh, 8); - vector signed char qxhs0 = vec_or(qxhs, vec_sr(vec_sld(qxhs, qxhs, 8), v1)); - vector signed char qxhs1 = vec_sr(qxhs0, v2); - vector signed char qxh00 = vec_sl(vec_andc((vector signed char)v1, qxhs0), v4); - vector signed char qxh10 = vec_sl(vec_andc((vector signed char)v1, qxhs1), v4); - vector signed char qxh01 = vec_sl(vec_andc((vector signed char)v1, vec_sr(qxhs0, v4)), v4); - vector signed char qxh11 = vec_sl(vec_andc((vector signed char)v1, vec_sr(qxhs1, v4)), v4); - - vector signed char q5x00 = vec_sub(qxs00, qxh00); - vector signed char q5x10 = vec_sub(qxs10, qxh10); - vector signed char q5x01 = vec_sub(qxs01, qxh01); - vector signed char q5x11 = vec_sub(qxs11, qxh11); - - vector signed char q8y00 = vec_xl( 0, y[i].qs); - vector signed char q8y10 = vec_xl(16, y[i].qs); - vector signed char q8y01 = vec_xl(32, y[i].qs); - vector signed char q8y11 = vec_xl(48, y[i].qs); - - vector signed short qv00 = vec_add(vec_mule(q5x00, q8y00), vec_mulo(q5x00, q8y00)); - vector signed short qv01 = vec_add(vec_mule(q5x01, q8y01), vec_mulo(q5x01, q8y01)); - vector signed short qv10 = vec_add(vec_mule(q5x10, q8y10), vec_mulo(q5x10, q8y10)); - vector signed short qv11 = vec_add(vec_mule(q5x11, q8y11), vec_mulo(q5x11, q8y11)); - - vector signed short vs = (vector signed short)vec_unpackh(vec_xl_len(x[i].scales, 4)); - vector signed short vs0 = vec_splat(vs, 0); - vector signed short vs1 = vec_splat(vs, 1); - vector signed short vs2 = vec_splat(vs, 2); - vector signed short vs3 = vec_splat(vs, 3); - - vector signed int vsumi0 = vec_add(vec_mule(qv00, vs0), vec_mulo(qv00, vs0)); - vector signed int vsumi1 = vec_add(vec_mule(qv10, vs1), vec_mulo(qv10, vs1)); - vector signed int vsumi2 = vec_add(vec_mule(qv01, vs2), vec_mulo(qv01, vs2)); - vector signed int vsumi3 = vec_add(vec_mule(qv11, vs3), vec_mulo(qv11, vs3)); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined __loongarch_asx - - const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); - const __m256i mone = __lasx_xvreplgr2vr_b(1); - - __m256 acc = (__m256)__lasx_xvldi(0); - - for (int i = 0; i < nb; ++i) { - - const uint8_t * restrict q5 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const __m256i q5bits = __lasx_xvld((const __m256i*)q5, 0); - - const __m256i scale_l = lasx_insertf128(__lsx_vreplgr2vr_h(x[i].scales[1]), __lsx_vreplgr2vr_h(x[i].scales[0])); - const __m256i scale_h = lasx_insertf128(__lsx_vreplgr2vr_h(x[i].scales[3]), __lsx_vreplgr2vr_h(x[i].scales[2])); - - int64_t aux64; - memcpy(&aux64, x[i].qh, 8); - __m128i haux128 = __lsx_vinsgr2vr_d(haux128, aux64, 0); - haux128 = __lsx_vinsgr2vr_d(haux128, aux64 >> 1, 1); - const __m256i haux256 = lasx_insertf128(__lsx_vsrli_h(haux128, 2), haux128); - - const __m256i q5h_0 = __lasx_xvslli_h(__lasx_xvandn_v(haux256, mone), 4); - const __m256i q5h_1 = __lasx_xvslli_h(__lasx_xvandn_v(__lasx_xvsrli_h(haux256, 4), mone), 4); - - const __m256i q5l_0 = __lasx_xvand_v(q5bits, m4); - const __m256i q5l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q5bits, 4), m4); - - const __m256i q8_0 = __lasx_xvld((const __m256i*)(q8+ 0), 0); - const __m256i q8_1 = __lasx_xvld((const __m256i*)(q8+32), 0); - - const __m256i p16_0 = lasx_madd_h(scale_l, lasx_maddubs_h(q5l_0, q8_0)); - const __m256i p16_1 = lasx_madd_h(scale_h, lasx_maddubs_h(q5l_1, q8_1)); - const __m256i s16_0 = lasx_madd_h(scale_l, lasx_maddubs_h(q5h_0, q8_0)); - const __m256i s16_1 = lasx_madd_h(scale_h, lasx_maddubs_h(q5h_1, q8_1)); - - const __m256i dot = __lasx_xvsub_w(__lasx_xvadd_w(p16_0, p16_1), __lasx_xvadd_w(s16_0, s16_1)); - - acc = __lasx_xvfmadd_s((__m256)__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(dot), acc); - } - - *s = hsum_float_8(acc); - -#else - - int8_t aux8[QK_K]; - int16_t aux16[16]; - float sums [8]; - memset(sums, 0, 8*sizeof(float)); - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const uint8_t * restrict q4 = x[i].qs; - const uint8_t * restrict hm = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - int8_t * restrict a = aux8; - for (int l = 0; l < 32; ++l) { - a[l+ 0] = q4[l] & 0xF; - a[l+32] = q4[l] >> 4; - } - for (int is = 0; is < 8; ++is) { - uint8_t m = 1 << is; - for (int l = 0; l < 8; ++l) a[8*is + l] -= (hm[l] & m ? 0 : 16); - } - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - const int8_t * restrict sc = x[i].scales; - - for (int j = 0; j < QK_K/16; ++j) { - const float dl = d * sc[j]; - for (int l = 0; l < 16; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) sums[l] += dl * (aux16[l] + aux16[8+l]); - q8 += 16; a += 16; - } - } - for (int l = 0; l < 8; ++l) sumf += sums[l]; - *s = sumf; -#endif -} -#endif - - -#if QK_K == 256 void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { assert(n % QK_K == 0); assert(nrc == 1); @@ -10733,446 +8729,6 @@ void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * r #endif } -#else - -void ggml_vec_dot_q6_K_q8_K(int n, float * restrict s, size_t bs, const void * restrict vx, size_t bx, const void * restrict vy, size_t by, int nrc) { - assert(n % QK_K == 0); - assert(nrc == 1); - UNUSED(nrc); - UNUSED(bx); - UNUSED(by); - UNUSED(bs); - - const block_q6_K * restrict x = vx; - const block_q8_K * restrict y = vy; - - const int nb = n / QK_K; - -#ifdef __ARM_NEON - float sum = 0; - - const uint8x16_t m4b = vdupq_n_u8(0xF); - const int8x16_t m32s = vdupq_n_s8(32); - const int32x4_t vzero = vdupq_n_s32(0); - - const uint8x16_t mone = vdupq_n_u8(3); - - ggml_int8x16x4_t q6bytes; - ggml_uint8x16x4_t q6h; - - for (int i = 0; i < nb; ++i) { - - const float d_all = GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q6 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const int8_t * restrict scale = x[i].scales; - - int32_t isum = 0; - - uint8x16_t qhbits = vld1q_u8(qh); - ggml_uint8x16x2_t q6bits = ggml_vld1q_u8_x2(q6); - ggml_int8x16x4_t q8bytes = ggml_vld1q_s8_x4(q8); - - q6h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits), 4); - uint8x16_t shifted = vshrq_n_u8(qhbits, 2); - q6h.val[1] = vshlq_n_u8(vandq_u8(mone, shifted), 4); - shifted = vshrq_n_u8(qhbits, 4); - q6h.val[2] = vshlq_n_u8(vandq_u8(mone, shifted), 4); - shifted = vshrq_n_u8(qhbits, 6); - q6h.val[3] = vshlq_n_u8(vandq_u8(mone, shifted), 4); - - q6bytes.val[0] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[0], m4b), q6h.val[0])), m32s); - q6bytes.val[1] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vandq_u8(q6bits.val[1], m4b), q6h.val[1])), m32s); - q6bytes.val[2] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[0], 4), q6h.val[2])), m32s); - q6bytes.val[3] = vsubq_s8(vreinterpretq_s8_u8(vorrq_u8(vshrq_n_u8(q6bits.val[1], 4), q6h.val[3])), m32s); - - isum += vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[0], q8bytes.val[0])) * scale[0] + - vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[1], q8bytes.val[1])) * scale[1] + - vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[2], q8bytes.val[2])) * scale[2] + - vaddvq_s32(ggml_vdotq_s32(vzero, q6bytes.val[3], q8bytes.val[3])) * scale[3]; - - sum += isum * d_all * y[i].d; - - } - *s = sum; - -#elif defined __AVX2__ - - const __m256i m4 = _mm256_set1_epi8(0xF); - const __m256i m2 = _mm256_set1_epi8(3); - const __m256i m32s = _mm256_set1_epi8(32); - - __m256 acc = _mm256_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q4 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const __m64 scales_1 = _mm_set1_pi8(x[i].scales[0]); - const __m64 scales_2 = _mm_set1_pi8(x[i].scales[1]); - const __m64 scales_3 = _mm_set1_pi8(x[i].scales[2]); - const __m64 scales_4 = _mm_set1_pi8(x[i].scales[3]); - - __m256i sumi = _mm256_setzero_si256(); - - const __m128i scale_0 = _mm_set_epi64(scales_2, scales_1); - const __m128i scale_1 = _mm_set_epi64(scales_4, scales_3); - - const __m256i q4bits1 = _mm256_loadu_si256((const __m256i*)q4); - const __m128i q4bitsH = _mm_loadu_si128((const __m128i*)qh); - - const __m256i q4h_0 = _mm256_slli_epi16(_mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q4bitsH, 2), q4bitsH), m2), 4); - const __m256i q4h_1 = _mm256_slli_epi16(_mm256_and_si256(MM256_SET_M128I(_mm_srli_epi16(q4bitsH, 6), _mm_srli_epi16(q4bitsH, 4)), m2), 4); - - const __m256i q4_0 = _mm256_or_si256(_mm256_and_si256(q4bits1, m4), q4h_0); - const __m256i q4_1 = _mm256_or_si256(_mm256_and_si256(_mm256_srli_epi16(q4bits1, 4), m4), q4h_1); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - __m256i q8s_0 = _mm256_maddubs_epi16(m32s, q8_0); - __m256i q8s_1 = _mm256_maddubs_epi16(m32s, q8_1); - - __m256i p16_0 = _mm256_maddubs_epi16(q4_0, q8_0); - __m256i p16_1 = _mm256_maddubs_epi16(q4_1, q8_1); - - p16_0 = _mm256_sub_epi16(p16_0, q8s_0); - p16_1 = _mm256_sub_epi16(p16_1, q8s_1); - - p16_0 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_0), p16_0); - p16_1 = _mm256_madd_epi16(_mm256_cvtepi8_epi16(scale_1), p16_1); - - sumi = _mm256_add_epi32(sumi, _mm256_add_epi32(p16_0, p16_1)); - - acc = _mm256_fmadd_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(sumi), acc); - } - - *s = hsum_float_8(acc); - -#elif defined __AVX__ - - const __m128i m4 = _mm_set1_epi8(0xF); - const __m128i m2 = _mm_set1_epi8(3); - const __m128i m32s = _mm_set1_epi8(32); - - __m256 acc = _mm256_setzero_ps(); - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q4 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const __m64 scales_1 = _mm_set1_pi8(x[i].scales[0]); - const __m64 scales_2 = _mm_set1_pi8(x[i].scales[1]); - const __m64 scales_3 = _mm_set1_pi8(x[i].scales[2]); - const __m64 scales_4 = _mm_set1_pi8(x[i].scales[3]); - - __m128i sumi_0 = _mm_setzero_si128(); - __m128i sumi_1 = _mm_setzero_si128(); - - const __m128i scale_0 = _mm_set_epi64(scales_2, scales_1); - const __m128i scale_1 = _mm_set_epi64(scales_4, scales_3); - - const __m256i q4bits1 = _mm256_loadu_si256((const __m256i*)q4); - const __m128i q4bitsH = _mm_loadu_si128((const __m128i*)qh); - - const __m128i q4h_0 = _mm_slli_epi16(_mm_and_si128(q4bitsH, m2), 4); - const __m128i q4h_1 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH, 2), m2), 4); - const __m128i q4h_2 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH, 4), m2), 4); - const __m128i q4h_3 = _mm_slli_epi16(_mm_and_si128(_mm_srli_epi16(q4bitsH, 6), m2), 4); - - const __m128i q4_0 = _mm_or_si128(_mm_and_si128(_mm256_extractf128_si256(q4bits1, 0), m4), q4h_0); - const __m128i q4_1 = _mm_or_si128(_mm_and_si128(_mm256_extractf128_si256(q4bits1, 1), m4), q4h_1); - const __m128i q4_2 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q4bits1, 0), 4), m4), q4h_2); - const __m128i q4_3 = _mm_or_si128(_mm_and_si128(_mm_srli_epi16(_mm256_extractf128_si256(q4bits1, 1), 4), m4), q4h_3); - - const __m256i q8_0 = _mm256_loadu_si256((const __m256i*)(q8+ 0)); - const __m256i q8_1 = _mm256_loadu_si256((const __m256i*)(q8+32)); - - __m128i q8s_0 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_0, 0)); - __m128i q8s_1 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_0, 1)); - __m128i q8s_2 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_1, 0)); - __m128i q8s_3 = _mm_maddubs_epi16(m32s, _mm256_extractf128_si256(q8_1, 1)); - - __m128i p16_0 = _mm_maddubs_epi16(q4_0, _mm256_extractf128_si256(q8_0, 0)); - __m128i p16_1 = _mm_maddubs_epi16(q4_1, _mm256_extractf128_si256(q8_0, 1)); - __m128i p16_2 = _mm_maddubs_epi16(q4_2, _mm256_extractf128_si256(q8_1, 0)); - __m128i p16_3 = _mm_maddubs_epi16(q4_3, _mm256_extractf128_si256(q8_1, 1)); - - p16_0 = _mm_sub_epi16(p16_0, q8s_0); - p16_1 = _mm_sub_epi16(p16_1, q8s_1); - p16_2 = _mm_sub_epi16(p16_2, q8s_2); - p16_3 = _mm_sub_epi16(p16_3, q8s_3); - - p16_0 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_0), p16_0); - p16_1 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_0, scale_0)), p16_1); - p16_2 = _mm_madd_epi16(_mm_cvtepi8_epi16(scale_1), p16_2); - p16_3 = _mm_madd_epi16(_mm_cvtepi8_epi16(_mm_unpackhi_epi64(scale_1, scale_1)), p16_3); - - sumi_0 = _mm_add_epi32(sumi_0, _mm_add_epi32(p16_0, p16_2)); - sumi_1 = _mm_add_epi32(sumi_1, _mm_add_epi32(p16_1, p16_3)); - - acc = _mm256_add_ps(_mm256_mul_ps(_mm256_broadcast_ss(&d), _mm256_cvtepi32_ps(MM256_SET_M128I(sumi_1, sumi_0))), acc); - } - - *s = hsum_float_8(acc); - -#elif defined __riscv_v_intrinsic - - float sumf = 0; - - for (int i = 0; i < nb; ++i) { - - const float d_all = GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q6 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const int8_t * restrict scale = x[i].scales; - - int32_t isum = 0; - - size_t vl = 16; - - vint32m1_t vzero = __riscv_vmv_v_x_i32m1(0, 1); - - // load Q6 - vuint8mf2_t q6_0 = __riscv_vle8_v_u8mf2(q6, vl); - vuint8mf2_t q6_1 = __riscv_vle8_v_u8mf2(q6+16, vl); - - // load qh - vuint8mf2_t qh_x = __riscv_vle8_v_u8mf2(qh, vl); - - vuint8mf2_t qh0 = __riscv_vsll_vx_u8mf2(__riscv_vand_vx_u8mf2(qh_x, 0x3, vl), 0x4, vl); - qh_x = __riscv_vsrl_vx_u8mf2(qh_x, 0x2, vl); - vuint8mf2_t qh1 = __riscv_vsll_vx_u8mf2(__riscv_vand_vx_u8mf2(qh_x, 0x3, vl), 0x4, vl); - qh_x = __riscv_vsrl_vx_u8mf2(qh_x, 0x2, vl); - vuint8mf2_t qh2 = __riscv_vsll_vx_u8mf2(__riscv_vand_vx_u8mf2(qh_x, 0x3, vl), 0x4, vl); - qh_x = __riscv_vsrl_vx_u8mf2(qh_x, 0x2, vl); - vuint8mf2_t qh3 = __riscv_vsll_vx_u8mf2(__riscv_vand_vx_u8mf2(qh_x, 0x3, vl), 0x4, vl); - - vuint8mf2_t q6h_0 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(q6_0, 0xF, vl), qh0, vl); - vuint8mf2_t q6h_1 = __riscv_vor_vv_u8mf2(__riscv_vand_vx_u8mf2(q6_1, 0xF, vl), qh1, vl); - vuint8mf2_t q6h_2 = __riscv_vor_vv_u8mf2(__riscv_vsrl_vx_u8mf2(q6_0, 0x4, vl), qh2, vl); - vuint8mf2_t q6h_3 = __riscv_vor_vv_u8mf2(__riscv_vsrl_vx_u8mf2(q6_1, 0x4, vl), qh3, vl); - - vint8mf2_t q6v_0 = __riscv_vsub_vx_i8mf2(__riscv_vreinterpret_v_u8mf2_i8mf2(q6h_0), 32, vl); - vint8mf2_t q6v_1 = __riscv_vsub_vx_i8mf2(__riscv_vreinterpret_v_u8mf2_i8mf2(q6h_1), 32, vl); - vint8mf2_t q6v_2 = __riscv_vsub_vx_i8mf2(__riscv_vreinterpret_v_u8mf2_i8mf2(q6h_2), 32, vl); - vint8mf2_t q6v_3 = __riscv_vsub_vx_i8mf2(__riscv_vreinterpret_v_u8mf2_i8mf2(q6h_3), 32, vl); - - // load Q8 and take product - vint16m1_t p0 = __riscv_vwmul_vv_i16m1(q6v_0, __riscv_vle8_v_i8mf2(q8, vl), vl); - vint16m1_t p1 = __riscv_vwmul_vv_i16m1(q6v_1, __riscv_vle8_v_i8mf2(q8+16, vl), vl); - vint16m1_t p2 = __riscv_vwmul_vv_i16m1(q6v_2, __riscv_vle8_v_i8mf2(q8+32, vl), vl); - vint16m1_t p3 = __riscv_vwmul_vv_i16m1(q6v_3, __riscv_vle8_v_i8mf2(q8+48, vl), vl); - - vint32m1_t vs_0 = __riscv_vwredsum_vs_i16m1_i32m1(p0, vzero, vl); - vint32m1_t vs_1 = __riscv_vwredsum_vs_i16m1_i32m1(p1, vzero, vl); - vint32m1_t vs_2 = __riscv_vwredsum_vs_i16m1_i32m1(p2, vzero, vl); - vint32m1_t vs_3 = __riscv_vwredsum_vs_i16m1_i32m1(p3, vzero, vl); - - isum += __riscv_vmv_x_s_i32m1_i32(vs_0) * scale[0]; - isum += __riscv_vmv_x_s_i32m1_i32(vs_1) * scale[1]; - isum += __riscv_vmv_x_s_i32m1_i32(vs_2) * scale[2]; - isum += __riscv_vmv_x_s_i32m1_i32(vs_3) * scale[3]; - - sumf += isum * d_all * y[i].d; - - } - - *s = sumf; - -#elif defined(__POWER9_VECTOR__) - const vector signed char lowMask = vec_splats((signed char)0xF); - const vector unsigned char v2 = vec_splats((unsigned char)0x2); - const vector unsigned char v3 = vec_splats((unsigned char)0x3); - const vector unsigned char v4 = vec_splats((unsigned char)0x4); - const vector unsigned char v6 = vec_splats((unsigned char)0x6); - const vector signed char off = vec_splats((signed char)0x20); - - vector float vsumf0 = vec_splats(0.0f); - vector float vsumf1 = vec_splats(0.0f); - vector float vsumf2 = vec_splats(0.0f); - vector float vsumf3 = vec_splats(0.0f); - -#pragma GCC unroll 2 - for (int i = 0; i < nb; ++i) { - __builtin_prefetch(x[i].ql, 0, 1); - __builtin_prefetch(x[i].qh, 0, 1); - __builtin_prefetch(y[i].qs, 0, 1); - - vector float vxd = vec_splats(GGML_FP16_TO_FP32(x[i].d)); - vector float vyd = vec_splats(y[i].d); - vector float vd= vec_mul(vxd, vyd); - - vector signed char qxs0 = (vector signed char)vec_xl( 0, x[i].ql); - vector signed char qxs1 = (vector signed char)vec_xl(16, x[i].ql); - vector signed char qxs00 = vec_and(qxs0, lowMask); - vector signed char qxs01 = vec_sr(qxs0, v4); - vector signed char qxs10 = vec_and(qxs1, lowMask); - vector signed char qxs11 = vec_sr(qxs1, v4); - - vector signed char qxhs0 = (vector signed char)vec_xl( 0, x[i].qh); - - vector signed char qxh00 = vec_sl(vec_and((vector signed char)v3, qxhs0), v4); - vector signed char qxh01 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v4)), v4); - vector signed char qxh10 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v2)), v4); - vector signed char qxh11 = vec_sl(vec_and((vector signed char)v3, vec_sr(qxhs0, v6)), v4); - - vector signed char q6x00 = vec_sub(vec_or(qxh00, qxs00), off); - vector signed char q6x01 = vec_sub(vec_or(qxh01, qxs01), off); - vector signed char q6x10 = vec_sub(vec_or(qxh10, qxs10), off); - vector signed char q6x11 = vec_sub(vec_or(qxh11, qxs11), off); - - vector signed char q8y00 = vec_xl( 0, y[i].qs); - vector signed char q8y10 = vec_xl(16, y[i].qs); - vector signed char q8y01 = vec_xl(32, y[i].qs); - vector signed char q8y11 = vec_xl(48, y[i].qs); - - vector signed short qv00 = vec_add(vec_mule(q6x00, q8y00), vec_mulo(q6x00, q8y00)); - vector signed short qv10 = vec_add(vec_mule(q6x10, q8y10), vec_mulo(q6x10, q8y10)); - vector signed short qv01 = vec_add(vec_mule(q6x01, q8y01), vec_mulo(q6x01, q8y01)); - vector signed short qv11 = vec_add(vec_mule(q6x11, q8y11), vec_mulo(q6x11, q8y11)); - - vector signed short vs = (vector signed short)vec_unpackh(vec_xl_len(x[i].scales, 4)); - vector signed short vs0 = vec_splat(vs, 0); - vector signed short vs1 = vec_splat(vs, 1); - vector signed short vs2 = vec_splat(vs, 2); - vector signed short vs3 = vec_splat(vs, 3); - - vector signed int vsumi0 = vec_add(vec_mule(qv00, vs0), vec_mulo(qv00, vs0)); - vector signed int vsumi1 = vec_add(vec_mule(qv10, vs1), vec_mulo(qv10, vs1)); - vector signed int vsumi2 = vec_add(vec_mule(qv01, vs2), vec_mulo(qv01, vs2)); - vector signed int vsumi3 = vec_add(vec_mule(qv11, vs3), vec_mulo(qv11, vs3)); - - vsumf0 = vec_madd(vec_ctf(vsumi0, 0), vd, vsumf0); - vsumf1 = vec_madd(vec_ctf(vsumi1, 0), vd, vsumf1); - vsumf2 = vec_madd(vec_ctf(vsumi2, 0), vd, vsumf2); - vsumf3 = vec_madd(vec_ctf(vsumi3, 0), vd, vsumf3); - } - - vsumf0 = vec_add(vsumf0, vsumf2); - vsumf1 = vec_add(vsumf1, vsumf3); - - vsumf0 = vec_add(vsumf0, vsumf1); - - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 4)); - vsumf0 = vec_add(vsumf0, vec_sld(vsumf0, vsumf0, 8)); - - *s = vec_extract(vsumf0, 0); - -#elif defined __loongarch_asx - - const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); - const __m256i m2 = __lasx_xvreplgr2vr_b(3); - const __m256i m32s = __lasx_xvreplgr2vr_b(32); - - __m256 acc = (__m256)__lasx_xvldi(0); - - for (int i = 0; i < nb; ++i) { - - const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); - - const uint8_t * restrict q4 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - - const __m64 scales_1 = __lasx_xvreplgr2vr_b(x[i].scales[0]); - const __m64 scales_2 = __lasx_xvreplgr2vr_b(x[i].scales[1]); - const __m64 scales_3 = __lasx_xvreplgr2vr_b(x[i].scales[2]); - const __m64 scales_4 = __lasx_xvreplgr2vr_b(x[i].scales[3]); - - __m256i sumi = __lasx_xvldi(0); - - __m128i scale_0 = __lsx_vinsgr2vr_d(scale_0, scales_1, 0); - scale_0 = __lsx_vinsgr2vr_d(scale_0, scales_2, 1); - __m128i scale_1 = __lsx_vinsgr2vr_d(scale_1, scales_3, 0); - scale_1 = __lsx_vinsgr2vr_d(scale_1, scales_4, 1); - - const __m256i q4bits1 = __lasx_xvld((const __m256i*)q4, 0); - const __m128i q4bitsH = __lsx_vld((const __m128i*)qh, 0); - - const __m256i q4h_0 = __lasx_xvslli_h(__lasx_xvand_v(lasx_insertf128(__lasx_xvsrli_h(q4bitsH, 2), q4bitsH), m2), 4); - const __m256i q4h_1 = __lasx_xvslli_h(__lasx_xvand_v(lasx_insertf128(__lasx_xvsrli_h(q4bitsH, 6), __lasx_xvsrli_h(q4bitsH, 4)), m2), 4); - - const __m256i q4_0 = __lasx_xvor_v(__lasx_xvand_v(q4bits1, m4), q4h_0); - const __m256i q4_1 = __lasx_xvor_v(__lasx_xvand_v(__lasx_xvsrli_h(q4bits1, 4), m4), q4h_1); - - const __m256i q8_0 = __lasx_xvld((const __m256i*)(q8+ 0), 0); - const __m256i q8_1 = __lasx_xvld((const __m256i*)(q8+32), 0); - - __m256i q8s_0 = lasx_maddubs_h(m32s, q8_0); - __m256i q8s_1 = lasx_maddubs_h(m32s, q8_1); - - __m256i p16_0 = lasx_maddubs_h(q4_0, q8_0); - __m256i p16_1 = lasx_maddubs_h(q4_1, q8_1); - - p16_0 = __lasx_xvsub_h(p16_0, q8s_0); - p16_1 = __lasx_xvsub_h(p16_1, q8s_1); - - p16_0 = lasx_madd_h(lasx_ext8_16(scale_0), p16_0); - p16_1 = lasx_madd_h(lasx_ext8_16(scale_1), p16_1); - - sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_1)); - - acc = __lasx_xvfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sumi), acc); - } - - *s = hsum_float_8(acc); - -#else - - int8_t aux8[QK_K]; - int16_t aux16[8]; - float sums [8]; - int32_t aux32[8]; - memset(sums, 0, 8*sizeof(float)); - - float sumf = 0; - for (int i = 0; i < nb; ++i) { - const uint8_t * restrict q4 = x[i].ql; - const uint8_t * restrict qh = x[i].qh; - const int8_t * restrict q8 = y[i].qs; - memset(aux32, 0, 8*sizeof(int32_t)); - int8_t * restrict a = aux8; - for (int l = 0; l < 16; ++l) { - a[l+ 0] = (int8_t)((q4[l+ 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32; - a[l+16] = (int8_t)((q4[l+16] & 0xF) | (((qh[l] >> 2) & 3) << 4)) - 32; - a[l+32] = (int8_t)((q4[l+ 0] >> 4) | (((qh[l] >> 4) & 3) << 4)) - 32; - a[l+48] = (int8_t)((q4[l+16] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32; - } - int is = 0; - for (int j = 0; j < QK_K/16; ++j) { - int scale = x[i].scales[is++]; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - for (int l = 0; l < 8; ++l) aux16[l] = q8[l] * a[l]; - for (int l = 0; l < 8; ++l) aux32[l] += scale * aux16[l]; - q8 += 8; a += 8; - } - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - for (int l = 0; l < 8; ++l) sums[l] += d * aux32[l]; - } - for (int l = 0; l < 8; ++l) sumf += sums[l]; - *s = sumf; -#endif -} - -#endif - #if defined (__AVX2__) || defined (__ARM_NEON) || defined (__POWER9_VECTOR__) || defined(__loongarch_asx) static const int8_t keven_signs_q2xs[1024] = { 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, -1, 1, -1, 1, 1, 1, 1, 1, -1, -1, -1, 1, 1, 1, 1, 1, 1, @@ -11564,64 +9120,6 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * 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); -#if QK_K == 64 - static const uint8_t k_bit_helper[16] = { - 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, - }; - const __m128i bit_helper = _mm_loadu_si128((const __m128i*)k_bit_helper); - const __m128i m511 = _mm_set1_epi16(511); - typedef union { - __m128i vec_index; - uint16_t index[8]; - } index_t; - - index_t idx; - __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 __m128i q2_data = _mm_loadu_si128((const __m128i*)x[i].qs); - idx.vec_index = _mm_and_si128(q2_data, m511); - - const __m128i partial_sign_bits = _mm_srli_epi16(q2_data, 9); - const __m128i partial_sign_bits_upper = _mm_srli_epi16(q2_data, 13); - const __m128i partial_sign_bits_for_counting = _mm_xor_si128(partial_sign_bits, partial_sign_bits_upper); - - const __m128i odd_bits = _mm_shuffle_epi8(bit_helper, partial_sign_bits_for_counting); - const __m128i full_sign_bits = _mm_or_si128(partial_sign_bits, odd_bits); - const __m256i full_signs = MM256_SET_M128I(full_sign_bits, full_sign_bits); - - const __m256i q8_1 = _mm256_loadu_si256((const __m256i *)y[i].qs); - const __m256i q8_2 = _mm256_loadu_si256((const __m256i *)(y[i].qs+32)); - - const __m256i q2_1 = _mm256_set_epi64x(iq2xs_grid[idx.index[3]], iq2xs_grid[idx.index[2]], - iq2xs_grid[idx.index[1]], iq2xs_grid[idx.index[0]]); - const __m256i q2_2 = _mm256_set_epi64x(iq2xs_grid[idx.index[7]], iq2xs_grid[idx.index[6]], - iq2xs_grid[idx.index[5]], iq2xs_grid[idx.index[4]]); - - __m256i signs; - signs = _mm256_shuffle_epi8(full_signs, 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, 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)); - - const __m256i dot1 = _mm256_maddubs_epi16(q2_1, q8s_1); - const __m256i dot2 = _mm256_maddubs_epi16(q2_2, q8s_2); - - const __m256i sc1 = MM256_SET_M128I(_mm_set1_epi16(2*(x[i].scales[0] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[0] & 0xf)+1)); - const __m256i sc2 = MM256_SET_M128I(_mm_set1_epi16(2*(x[i].scales[1] >> 4)+1), _mm_set1_epi16(2*(x[i].scales[1] & 0xf)+1)); - - const __m256i sum = _mm256_add_epi32(_mm256_madd_epi16(sc1, dot1), _mm256_madd_epi16(sc2, dot2)); - - accumf = _mm256_fmadd_ps(_mm256_set1_ps(d), _mm256_cvtepi32_ps(sum), accumf); - - } - - *s = 0.125f * hsum_float_8(accumf); -#else - 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, @@ -11719,7 +9217,6 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * } *s = 0.125f * hsum_float_8(accumf); -#endif #elif defined(__loongarch_asx) const __m256i mone = __lasx_xvreplgr2vr_b(1); @@ -11740,62 +9237,6 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * const __m256i block_sign_shuffle_1 = __lasx_xvld((const __m256i*)block_sign_shuffle_mask_1, 0); const __m256i block_sign_shuffle_2 = __lasx_xvld((const __m256i*)block_sign_shuffle_mask_2, 0); -#if QK_K == 64 - static const uint8_t k_bit_helper[16] = { - 0x00, 0x80, 0x80, 0x00, 0x80, 0x00, 0x00, 0x80, 0x80, 0x00, 0x00, 0x80, 0x00, 0x80, 0x80, 0x00, - }; - const __m128i bit_helper = __lsx_vld((const __m128i*)k_bit_helper, 0); - const __m128i m511 = __lsx_vreplgr2vr_h(511); - typedef union { - __m128i vec_index; - uint16_t index[8]; - } index_t; - - index_t idx; - __m256 accumf = (__m256)__lasx_xvldi(0); - for (int i = 0; i < nb; ++i) { - const float d = GGML_FP16_TO_FP32(x[i].d) * y[i].d; - const __m128i q2_data = __lsx_vld((const __m128i*)x[i].qs, 0); - idx.vec_index = __lsx_vand_v(q2_data, m511); - - const __m128i partial_sign_bits = __lsx_vsrli_h(q2_data, 9); - const __m128i partial_sign_bits_upper = __lsx_vsrli_h(q2_data, 13); - const __m128i partial_sign_bits_for_counting = __lsx_vxor_v(partial_sign_bits, partial_sign_bits_upper); - - const __m128i odd_bits = lsx_shuffle_b(bit_helper, partial_sign_bits_for_counting); - const __m128i full_sign_bits = __lsx_vor_v(partial_sign_bits, odd_bits); - const __m256i full_signs = lasx_insertf128(full_sign_bits, full_sign_bits); - - const __m256i q8_1 = __lasx_xvld((const __m256i *)y[i].qs, 0); - const __m256i q8_2 = __lasx_xvld((const __m256i *)(y[i].qs+32), 0); - - const __m256i q2_1 = lasx_set_d(iq2xs_grid[idx.index[3]], iq2xs_grid[idx.index[2]], - iq2xs_grid[idx.index[1]], iq2xs_grid[idx.index[0]]); - const __m256i q2_2 = lasx_set_d(iq2xs_grid[idx.index[7]], iq2xs_grid[idx.index[6]], - iq2xs_grid[idx.index[5]], iq2xs_grid[idx.index[4]]); - __m256i signs; - signs = lasx_shuffle_b(full_signs, block_sign_shuffle_1); - signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_1 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_1); - - signs = lasx_shuffle_b(full_signs, block_sign_shuffle_2); - signs = __lasx_xvseq_b(__lasx_xvand_v(signs, bit_selector_mask), bit_selector_mask); - const __m256i q8s_2 = __lasx_xvsigncov_b(__lasx_xvor_v(signs, mone), q8_2); - - const __m256i dot1 = lasx_maddubs_h(q2_1, q8s_1); - const __m256i dot2 = lasx_maddubs_h(q2_2, q8s_2); - - const __m256i sc1 = lasx_insertf128(_mm_set1_epi16(2*(x[i].scales[0] >> 4)+1), __lsx_vreplgr2vr_h(2*(x[i].scales[0] & 0xf)+1)); - const __m256i sc2 = lasx_insertf128(_mm_set1_epi16(2*(x[i].scales[1] >> 4)+1), __lsx_vreplgr2vr_h(2*(x[i].scales[1] & 0xf)+1)); - - const __m256i sum = __lasx_xvadd_w(lasx_madd_h(sc1, dot1), lasx_madd_h(sc2, dot2)); - - accumf = __lasx_vfmadd_s(__lasx_xvreplfr2vr_s(d), __lasx_xvffint_s_w(sum), accumf); - } - - *s = 0.125f * hsum_float_8(accumf); -#else - 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, @@ -11893,9 +9334,6 @@ void ggml_vec_dot_iq2_xs_q8_K(int n, float * restrict s, size_t bs, const void * } *s = 0.125f * hsum_float_8(accumf); -#endif - - #elif defined(__POWER9_VECTOR__) vector float vsumf0 = vec_splats(0.0f); vector float vsumf1 = vec_splats(0.0f); @@ -12748,10 +10186,8 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * ggml_int8x16x4_t q8b; vec_index_t idx; -#if QK_K == 256 uint32_t scales32[2]; const uint8_t * scales8 = (const uint8_t *)scales32; -#endif float sumf = 0; for (int i = 0; i < nb; ++i) { @@ -12761,11 +10197,9 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * const uint16_t * restrict signs = (const uint16_t *)x[i].signs; const int8_t * restrict q8 = y[i].qs; -#if QK_K == 256 memcpy(scales32, x[i].scales, 4); scales32[1] = (((scales32[0] >> 4) & 0x0f0f0f0f) << 1) | 0x01010101; scales32[0] = ((scales32[0] & 0x0f0f0f0f) << 1) | 0x01010101; -#endif int sumi1 = 0, sumi2 = 0; for (int ib32 = 0; ib32 < QK_K/32; ib32 += 2) { @@ -12806,13 +10240,9 @@ void ggml_vec_dot_iq3_s_q8_K (int n, float * restrict s, size_t bs, const void * 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]); -#if QK_K == 256 + sumi1 += vaddvq_s32(p1) * scales8[ib32/2+0]; sumi2 += vaddvq_s32(p2) * scales8[ib32/2+4]; -#else - sumi1 += vaddvq_s32(p1) * (1 + 2*(x[i].scales[ib32/2] & 0xf)); - sumi2 += vaddvq_s32(p2) * (1 + 2*(x[i].scales[ib32/2] >> 4)); -#endif } sumf += d*(sumi1 + sumi2); } @@ -13476,17 +10906,10 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void const int nb = n / QK_K; -#if QK_K != 64 iq1m_scale_t scale; -#endif #if defined __ARM_NEON - -#if QK_K == 64 - const int32x4_t mask = vdupq_n_s32(0xf); -#else const int32x4_t mask = vdupq_n_s32(0x7); -#endif const int32x4_t mone = vdupq_n_s32(1); const int32x4_t mzero = vdupq_n_s32(0); @@ -13510,9 +10933,7 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void const uint8_t * qh = x[i].qh; const uint16_t * sc = (const uint16_t *)x[i].scales; -#if QK_K != 64 scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); -#endif int32x4_t sumi1 = mzero; int32x4_t sumi2 = mzero; @@ -13541,11 +10962,8 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void const int32x4_t p4 = vpaddq_s32(ggml_vdotq_s32(mzero, deltas.val[aux8[2]], q8b.val[2]), ggml_vdotq_s32(mzero, deltas.val[aux8[3]], q8b.val[3])); const int32x4_t p34 = vpaddq_s32(p3, p4); -#if QK_K == 64 - int32x4_t scales_4 = ggml_vld1q_u32(sc[0] >> 0, sc[0] >> 4, sc[0] >> 8, sc[0] >> 12); -#else int32x4_t scales_4 = ggml_vld1q_u32(sc[ib/2] >> 0, sc[ib/2] >> 3, sc[ib/2] >> 6, sc[ib/2] >> 9); -#endif + scales_4 = vaddq_s32(vshlq_n_s32(vandq_s32(scales_4, mask), 1), mone); sumi1 = vmlaq_s32(sumi1, scales_4, p12); @@ -13555,22 +10973,14 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void } -#if QK_K == 64 - sumf += y[i].d * GGML_FP16_TO_FP32(x[i].d) * (vaddvq_s32(sumi1) + IQ1M_DELTA * vaddvq_s32(sumi2)); -#else sumf += y[i].d * GGML_FP16_TO_FP32(scale.f16) * (vaddvq_s32(sumi1) + IQ1M_DELTA * vaddvq_s32(sumi2)); -#endif } *s = sumf; #elif defined __AVX2__ -#if QK_K == 64 - const __m256i mask = _mm256_set1_epi16(0xf); -#else const __m256i mask = _mm256_set1_epi16(0x7); -#endif const __m256i mone = _mm256_set1_epi16(1); __m256 accum1 = _mm256_setzero_ps(); @@ -13582,9 +10992,7 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void const uint8_t * qh = x[i].qh; const uint16_t * sc = (const uint16_t *)x[i].scales; -#if QK_K != 64 scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); -#endif __m256i sumi1 = _mm256_setzero_si256(); __m256i sumi2 = _mm256_setzero_si256(); @@ -13614,13 +11022,10 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void const __m256i dot3 = mul_add_epi8(delta1, q8b_1); const __m256i dot4 = mul_add_epi8(delta2, q8b_2); -#if QK_K == 64 - __m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[0] >> 4), _mm_set1_epi16(sc[0] >> 0)); - __m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[0] >> 12), _mm_set1_epi16(sc[0] >> 8)); -#else + __m256i scale1 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 3), _mm_set1_epi16(sc[ib/2] >> 0)); __m256i scale2 = MM256_SET_M128I(_mm_set1_epi16(sc[ib/2] >> 9), _mm_set1_epi16(sc[ib/2] >> 6)); -#endif + scale1 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale1, mask), 1), mone); scale2 = _mm256_add_epi16(_mm256_slli_epi16(_mm256_and_si256(scale2, mask), 1), mone); const __m256i p1 = _mm256_madd_epi16(dot1, scale1); @@ -13634,14 +11039,10 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void qs += 8; qh += 4; } -#if QK_K == 64 - const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(x[i].d)); -#else const __m256 d = _mm256_set1_ps(y[i].d * GGML_FP16_TO_FP32(scale.f16)); -#endif + accum1 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi1), accum1); accum2 = _mm256_fmadd_ps(d, _mm256_cvtepi32_ps(sumi2), accum2); - } *s = hsum_float_8(accum1) + IQ1M_DELTA * hsum_float_8(accum2); @@ -13658,9 +11059,7 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void const uint8_t * qh = x[i].qh; const uint16_t * sc = (const uint16_t *)x[i].scales; -#if QK_K != 64 scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); -#endif int sumi1 = 0, sumi2 = 0; for (int ib = 0; ib < QK_K/32; ++ib) { @@ -13680,24 +11079,17 @@ void ggml_vec_dot_iq1_m_q8_K (int n, float * restrict s, size_t bs, const void sum1[l/2] += lsum1; sum2[l/2] += lsum2*delta[l]; } -#if QK_K == 64 - const int ls1 = 2*((sc[0] >> (8*(ib%2)+0)) & 0xf) + 1; - const int ls2 = 2*((sc[0] >> (8*(ib%2)+4)) & 0xf) + 1; -#else + const int ls1 = 2*((sc[ib/2] >> (6*(ib%2)+0)) & 0x7) + 1; const int ls2 = 2*((sc[ib/2] >> (6*(ib%2)+3)) & 0x7) + 1; -#endif + sumi1 += sum1[0] * ls1 + sum1[1] * ls2; sumi2 += sum2[0] * ls1 + sum2[1] * ls2; qs += 4; qh += 2; } -#if QK_K == 64 - sumf += GGML_FP16_TO_FP32(x[i].d) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2); -#else sumf += GGML_FP16_TO_FP32(scale.f16) * y[i].d * (sumi1 + IQ1M_DELTA * sumi2); -#endif } *s = sumf; @@ -13885,9 +11277,6 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * UNUSED(by); UNUSED(bs); assert(n % QK_K == 0); -#if QK_K == 64 - ggml_vec_dot_iq4_nl_q8_0(n, s, bs, vx, bx, vy, by, nrc); -#else const block_iq4_xs * restrict x = vx; const block_q8_K * restrict y = vy; @@ -14180,7 +11569,6 @@ void ggml_vec_dot_iq4_xs_q8_K(int n, float * restrict s, size_t bs, const void * } *s = sumf; #endif -#endif } // ================================ IQ2 quantization ============================================= @@ -15998,10 +13386,6 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy const float * xx; for (int ibl = 0; ibl < nbl; ++ibl) { - -#if QK_K == 64 - y[ibl].d = GGML_FP32_TO_FP16(0.f); -#endif memset(y[ibl].qs, 0, QK_K/8); memset(y[ibl].qh, 0, QK_K/16); memset(y[ibl].scales, 0, QK_K/32); @@ -16176,22 +13560,13 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy } uint16_t * sc = (uint16_t *)y[ibl].scales; -#if QK_K == 64 - float d = max_scale/31; -#else float d = max_scale/15; -#endif float id = 1/d; float sumqx_f = 0, sumq2_f = 0; for (int ib = 0; ib < QK_K/block_size; ++ib) { int l = nearest_int(0.5f*(id*scales[ib+0]-1)); -#if QK_K == 64 - l = MAX(0, MIN(15, l)); - sc[ib/4] |= (l << 4*(ib%4)); -#else l = MAX(0, MIN(7, l)); sc[ib/4] |= (l << 3*(ib%4)); -#endif y[ibl].qh[ib] |= masks[shifts[ib]]; const float * xb = xbl + block_size*ib; if (quant_weights) { @@ -16214,14 +13589,10 @@ static void quantize_row_iq1_m_impl(const float * restrict x, void * restrict vy } if (sumq2_f > 0) d = sumqx_f/sumq2_f; s.f16 = GGML_FP32_TO_FP16(d*1.1125f); // 1.1125f is another fudge factor. Don't ask me why it is needed. -#if QK_K == 64 - y[ibl].d = s.f16; -#else sc[0] |= ((s.u16 & 0x000f) << 12); sc[1] |= ((s.u16 & 0x00f0) << 8); sc[2] |= ((s.u16 & 0x0f00) << 4); sc[3] |= ((s.u16 & 0xf000) << 0); -#endif } } @@ -16410,9 +13781,6 @@ void quantize_row_iq4_nl_reference(const float * restrict x, block_iq4_nl * rest } size_t quantize_iq4_xs(const float * restrict src, void * restrict dst, int64_t nrow, int64_t n_per_row, const float * quant_weights) { -#if QK_K == 64 - return quantize_iq4_nl(src, dst, nrow, n_per_row, quant_weights); -#else GGML_ASSERT(n_per_row%QK_K == 0); int64_t nblock = n_per_row/QK_K; char * qrow = (char *)dst; @@ -16430,7 +13798,6 @@ size_t quantize_iq4_xs(const float * restrict src, void * restrict dst, int64_t qrow += nblock*sizeof(block_iq4_xs); } return nrow * nblock * sizeof(block_iq4_xs); -#endif } void quantize_row_iq4_xs(const float * restrict x, void * restrict vy, int64_t k) { @@ -16842,19 +14209,11 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte } break; case GGML_TYPE_Q4_K: { - #ifdef GGML_QKK_64 - VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_K, data, nb, d[0], d[1]); - #else VALIDATE_ROW_DATA_DM_F16_IMPL(block_q4_K, data, nb, d, dmin); - #endif } break; case GGML_TYPE_Q5_K: { - #ifdef GGML_QKK_64 - VALIDATE_ROW_DATA_D_F16_IMPL(block_q5_K, data, nb); - #else VALIDATE_ROW_DATA_DM_F16_IMPL(block_q5_K, data, nb, d, dmin); - #endif } break; case GGML_TYPE_Q6_K: { @@ -16877,18 +14236,12 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte { const block_iq1_m * q = (const block_iq1_m *) data; for (size_t i = 0; i < nb; ++i) { - #if QK_K == 64 - if (!validate_fp16(q[i].d, i)) { - return false; - } - #else iq1m_scale_t scale; const uint16_t * sc = (const uint16_t *)q[i].scales; scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); if (!validate_fp16(scale.f16, i)) { return false; } - #endif } } break; case GGML_TYPE_IQ2_XXS: @@ -16913,12 +14266,9 @@ bool ggml_validate_row_data(enum ggml_type type, const void * data, size_t nbyte VALIDATE_ROW_DATA_D_F16_IMPL(block_iq3_s, data, nb); } break; case GGML_TYPE_IQ4_XS: - #if QK_K != 64 { VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_xs, data, nb); } break; - #endif - // with QK_K == 64, iq4_xs is iq4_nl case GGML_TYPE_IQ4_NL: { VALIDATE_ROW_DATA_D_F16_IMPL(block_iq4_nl, data, nb); diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index f486b6c0a..496ec61c3 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -4197,7 +4197,6 @@ static void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restri 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; @@ -4211,18 +4210,6 @@ static void dequantize_block_q2_K(const void * __restrict__ vx, dst_t * __restri 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 = x[i].dm[0]; - float dmin = x[i].dm[1]; - 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 @@ -4232,7 +4219,6 @@ static void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restri 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; @@ -4256,31 +4242,8 @@ static void dequantize_block_q3_K(const void * __restrict__ vx, dst_t * __restri 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 = item_ct1.get_local_id(2); - 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; @@ -4289,7 +4252,6 @@ static inline void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8 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, @@ -4298,7 +4260,6 @@ static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restri 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; @@ -4322,15 +4283,6 @@ static void dequantize_block_q4_K(const void * __restrict__ vx, dst_t * __restri y[l + 0] = d1 * (q[l] & 0xF) - m1; y[l +32] = d2 * (q[l] >> 4) - m2; } -#else - const int tid = item_ct1.get_local_id(2); - 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 @@ -4340,7 +4292,6 @@ static void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restri 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 @@ -4367,18 +4318,6 @@ static void dequantize_block_q5_K(const void * __restrict__ vx, dst_t * __restri 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 = item_ct1.get_local_id(2); - 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 @@ -4387,7 +4326,6 @@ static void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restri 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); @@ -4407,24 +4345,6 @@ static void dequantize_block_q6_K(const void * __restrict__ vx, dst_t * __restri 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 = item_ct1.get_local_id(2); - 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 } template @@ -4438,7 +4358,6 @@ static void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __res const block_iq2_xxs * x = (const block_iq2_xxs *) vx; const int tid = item_ct1.get_local_id(2); -#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; @@ -4449,10 +4368,6 @@ static void dequantize_block_iq2_xxs(const void * __restrict__ vx, dst_t * __res const float d = (float)x[i].d * (0.5f + (aux32 >> 28)) * 0.25f; const uint8_t signs = ksigns_iq2xs_ptr[(aux32 >> 7*il) & 127]; for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs_ptr[j] ? -1.f : 1.f); -#else - assert(false); -#endif - } template @@ -4466,7 +4381,6 @@ static void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __rest const block_iq2_xs * x = (const block_iq2_xs *) vx; const int tid = item_ct1.get_local_id(2); -#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; @@ -4475,10 +4389,6 @@ static void dequantize_block_iq2_xs(const void * __restrict__ vx, dst_t * __rest const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; const uint8_t signs = ksigns_iq2xs[q2[il] >> 9]; for (int j = 0; j < 8; ++j) y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); -#else - assert(false); -#endif - } template @@ -4490,7 +4400,6 @@ dequantize_block_iq2_s(const void *__restrict__ vx, dst_t *__restrict__ yy, const block_iq2_s * x = (const block_iq2_s *) vx; const int tid = item_ct1.get_local_id(2); -#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; @@ -4498,13 +4407,9 @@ dequantize_block_iq2_s(const void *__restrict__ vx, dst_t *__restrict__ yy, const float d = (float)x[i].d * (0.5f + ((x[i].scales[ib] >> 4*(il/2)) & 0xf)) * 0.25f; const uint8_t signs = x[i].qs[QK_K/8+4*ib+il]; #pragma unroll - for (int j = 0; j < 8; ++j) + for (int j = 0; j < 8; ++j) { y[j] = d * grid[j] * (signs & kmask_iq2xs[j] ? -1.f : 1.f); -#else - assert(false); - -#endif - + } } template @@ -4518,7 +4423,6 @@ static void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __res const block_iq3_xxs * x = (const block_iq3_xxs *) vx; const int tid = item_ct1.get_local_id(2); -#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; @@ -4533,10 +4437,6 @@ static void dequantize_block_iq3_xxs(const void * __restrict__ vx, dst_t * __res 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 - } template @@ -4549,7 +4449,6 @@ dequantize_block_iq3_s(const void *__restrict__ vx, dst_t *__restrict__ yy, const block_iq3_s * x = (const block_iq3_s *) vx; const int tid = item_ct1.get_local_id(2); -#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; @@ -4563,10 +4462,6 @@ dequantize_block_iq3_s(const void *__restrict__ vx, dst_t *__restrict__ yy, 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 - } template @@ -4579,7 +4474,6 @@ dequantize_block_iq1_s(const void *__restrict__ vx, dst_t *__restrict__ yy, const block_iq1_s * x = (const block_iq1_s *) vx; const int tid = item_ct1.get_local_id(2); -#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; @@ -4593,10 +4487,6 @@ dequantize_block_iq1_s(const void *__restrict__ vx, dst_t *__restrict__ yy, for (int j = 0; j < 8; ++j) { y[j] = d * (q[j] + delta); } -#else - assert(false); -#endif - } template @@ -4609,7 +4499,6 @@ dequantize_block_iq1_m(const void *__restrict__ vx, dst_t *__restrict__ yy, const block_iq1_m * x = (const block_iq1_m *) vx; const int tid = item_ct1.get_local_id(2); -#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; @@ -4627,10 +4516,6 @@ dequantize_block_iq1_m(const void *__restrict__ vx, dst_t *__restrict__ yy, for (int j = 0; j < 8; ++j) { y[j] = d * (q[j] + delta); } -#else - assert(false); -#endif - } template @@ -4704,7 +4589,6 @@ static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx, 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 = @@ -4755,42 +4639,6 @@ static void dequantize_mul_mat_vec_q2_k(const void *__restrict__ vx, tmp += dall * sum1 - dmin * sum2; } -#else - const int tid = item_ct1.get_local_id(2) / - (2 * K_QUANTS_PER_ITERATION); // 0...15 or 0...7 - const int ix = item_ct1.get_local_id(2) % - (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 sycl::float2 dall = - x[i].dm.convert(); - - 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 @@ -4828,8 +4676,6 @@ static void dequantize_mul_mat_vec_q3_k(const void *__restrict__ vx, float tmp = 0; // partial sum for thread in warp -#if QK_K == 256 - const uint16_t kmask1 = 0x0303; const uint16_t kmask2 = 0x0f0f; @@ -4882,34 +4728,6 @@ static void dequantize_mul_mat_vec_q3_k(const void *__restrict__ vx, tmp += d * sum; } -#else - - const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...15 or 0...7 - const int ix = item_ct1.get_local_id(2)%(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 @@ -4944,7 +4762,6 @@ static void dequantize_mul_mat_vec_q4_k(const void *__restrict__ vx, 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; @@ -5033,36 +4850,6 @@ static void dequantize_mul_mat_vec_q4_k(const void *__restrict__ vx, #endif } -#else - const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...15 - const int ix = item_ct1.get_local_id(2)%(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 @@ -5097,7 +4884,6 @@ static void dequantize_mul_mat_vec_q5_k(const void *__restrict__ vx, 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; @@ -5174,30 +4960,6 @@ static void dequantize_mul_mat_vec_q5_k(const void *__restrict__ vx, dmin * smin; } -#else - const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...15 - const int ix = item_ct1.get_local_id(2)%(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) { @@ -5224,8 +4986,6 @@ static void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const floa 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 = @@ -5282,37 +5042,6 @@ static void dequantize_mul_mat_vec_q6_k(const void * __restrict__ vx, const floa } -#else - - const int tid = item_ct1.get_local_id(2)/(2*K_QUANTS_PER_ITERATION); // 0...7 - const int ix = item_ct1.get_local_id(2)%(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) { @@ -6857,7 +6586,6 @@ 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]; @@ -6899,52 +6627,6 @@ vec_dot_q4_K_q8_1(const void *__restrict__ vbq, } 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 = bq8_1[0].ds[0]; - const float d8_2 = bq8_1[1].ds[1]; - - 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 = dpct::dp4a(ui2, v2 & 0x0f0f0f0f, dpct::dp4a(ui1, v1 & 0x0f0f0f0f, 0)); - const int dot2 = dpct::dp4a(ui4, (v2 >> 4) & 0x0f0f0f0f, dpct::dp4a(ui3, (v1 >> 4) & 0x0f0f0f0f, 0)); - const int dot3 = dpct::dp4a(0x01010101, ui2, dpct::dp4a(0x01010101, ui1, 0)); - const int dot4 = dpct::dp4a(0x01010101, ui4, dpct::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 @@ -7003,11 +6685,7 @@ load_tiles_q4_K(const void *__restrict__ vx, int *__restrict__ x_ql, 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 @@ -7050,7 +6728,6 @@ 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]; @@ -7092,48 +6769,6 @@ vec_dot_q5_K_q8_1(const void *__restrict__ vbq, } 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 = bq8_1[0].ds[0]; - const float d8_2 = bq8_1[1].ds[1]; - - 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 * (dpct::dp4a(ui1, v1, 0) * s[0] + dpct::dp4a(ui2, v2, 0) * s[1]) - + d8_2 * (dpct::dp4a(ui3, v3, 0) * s[2] + dpct::dp4a(ui4, v4, 0) * s[3]); - - return d * sumf_d; - -#else - bad_arch(); -#endif // __SYCL_ARCH__ >= VER_4VEC - -#endif } template @@ -7205,9 +6840,7 @@ load_tiles_q5_K(const void *__restrict__ vx, int *__restrict__ x_ql, 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 @@ -7387,7 +7020,6 @@ vec_dot_iq2_xxs_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs, const uint64_t *iq2xxs_grid, const uint8_t *ksigns_iq2xs, const uint8_t *kmask_iq2xs) { -#if QK_K == 256 const block_iq2_xxs * bq2 = (const block_iq2_xxs *) vbq; #if QR2_XXS == 8 @@ -7428,10 +7060,6 @@ vec_dot_iq2_xxs_q8_1(const void *__restrict__ vbq, } return d * (sumi1 + sumi2); #endif -#else - assert(false); - return 0.f; -#endif } static __dpct_inline__ float @@ -7440,7 +7068,6 @@ vec_dot_iq2_xs_q8_1(const void *__restrict__ vbq, const uint64_t *iq2xs_grid, const uint64_t *ksigns64) { #if DPCT_COMPATIBILITY_TEMP >= \ MIN_CC_DP4A // lowest compute capability for integer intrinsics -#if QK_K == 256 const block_iq2_xs * bq2 = (const block_iq2_xs *) vbq; const int ib32 = iqs; @@ -7478,16 +7105,11 @@ vec_dot_iq2_xs_q8_1(const void *__restrict__ vbq, assert(false); return 0.f; #endif -#else - assert(false); - return 0.f; -#endif } static __dpct_inline__ float vec_dot_iq2_s_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs) { -#if QK_K == 256 const block_iq2_s * bq2 = (const block_iq2_s *) vbq; const int ib32 = iqs; @@ -7531,9 +7153,6 @@ vec_dot_iq2_s_q8_1(const void *__restrict__ vbq, } const float d = (float)bq2->d * bq8_1[ib32].ds[0] * 0.25f; return d * ((0.5f + ls1) * sumi1 + (0.5f + ls2) * sumi2); -#else - assert(false); -#endif } static __dpct_inline__ float @@ -7542,7 +7161,6 @@ vec_dot_iq3_xxs_q8_1(const void *__restrict__ vbq, const uint32_t *iq3xxs_grid, const uint64_t *ksigns64) { #if DPCT_COMPATIBILITY_TEMP >= \ 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; @@ -7570,17 +7188,12 @@ vec_dot_iq3_xxs_q8_1(const void *__restrict__ vbq, assert(false); return 0.f; #endif -#else - assert(false); - return 0.f; -#endif } static __dpct_inline__ float vec_dot_iq3_s_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs, const uint32_t *iq3s_grid) { -#if QK_K == 256 const block_iq3_s * bq2 = (const block_iq3_s *) vbq; const int ib32 = iqs; @@ -7609,16 +7222,12 @@ vec_dot_iq3_s_q8_1(const void *__restrict__ vbq, (1 + 2 * ((bq2->scales[ib32 / 2] >> 4 * (ib32 % 2)) & 0xf)) * bq8_1[ib32].ds[0]; return d * sumi; -#else - assert(false); -#endif } static __dpct_inline__ float vec_dot_iq1_s_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs, const uint32_t *iq1s_grid_gpu) { -#if QK_K == 256 const block_iq1_s * bq1 = (const block_iq1_s *) vbq; const int ib32 = iqs; @@ -7637,15 +7246,11 @@ vec_dot_iq1_s_q8_1(const void *__restrict__ vbq, const float d = d1q * bq8_1[ib32].ds[0]; const float m = d1q * bq8_1[ib32].ds[1]; return d * sumi + m * delta; -#else - assert(false); -#endif } static __dpct_inline__ float vec_dot_iq1_m_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs) { -#if QK_K == 256 const block_iq1_m * bq1 = (const block_iq1_m *) vbq; const int ib32 = iqs; @@ -7670,9 +7275,6 @@ vec_dot_iq1_m_q8_1(const void *__restrict__ vbq, scale.u16 = (sc[0] >> 12) | ((sc[1] >> 8) & 0x00f0) | ((sc[2] >> 4) & 0x0f00) | (sc[3] & 0xf000); const float d = (float)scale.f16 * bq8_1[ib32].ds[0]; return d * ((sumi[0] + sumf[0]) * (2*((sc[ib32/2] >> 6*(ib32%2)) & 0x7) + 1) + (sumi[1] + sumf[1]) * (2*((sc[ib32/2] >> (6*(ib32%2)+3)) & 0x7) + 1)); -#else - assert(false); -#endif } static __dpct_inline__ void get_int_from_table_16(const uint32_t &q4, @@ -7720,7 +7322,6 @@ static __dpct_inline__ float vec_dot_iq4_xs_q8_1(const void *__restrict__ vbq, const block_q8_1 *__restrict__ bq8_1, const int &iqs) { -#if QK_K == 256 const block_iq4_xs * bq4 = (const block_iq4_xs *) vbq; const uint8_t * values = (const uint8_t *)kvalues_iq4nl; @@ -7738,9 +7339,6 @@ vec_dot_iq4_xs_q8_1(const void *__restrict__ vbq, sumi2 = dpct::dp4a(v2, q8[j + 4], sumi2); } return d * (sumi1 + sumi2); -#else - assert(false); -#endif } 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}); @@ -10215,27 +9812,12 @@ static void dequantize_row_q2_K_sycl(const void *vx, dst_t *y, const int k, dequantize_block_q2_K(vx, y, item_ct1); }); } -#else - { - 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_q2_K(vx, y, item_ct1); - }); - } - -#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}); @@ -10247,19 +9829,6 @@ static void dequantize_row_q3_K_sycl(const void *vx, dst_t *y, const int k, dequantize_block_q3_K(vx, y, item_ct1); }); } -#else - { - 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_q3_K(vx, y, item_ct1); - }); - } -#endif } template @@ -10320,7 +9889,6 @@ 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}); @@ -10332,27 +9900,12 @@ static void dequantize_row_q5_K_sycl(const void *vx, dst_t *y, const int k, dequantize_block_q5_K(vx, y, item_ct1); }); } -#else - { - 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_q5_K(vx, y, item_ct1); - }); - } - -#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}); @@ -10364,20 +9917,6 @@ static void dequantize_row_q6_K_sycl(const void *vx, dst_t *y, const int k, dequantize_block_q6_K(vx, y, item_ct1); }); } -#else - { - 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_q6_K(vx, y, item_ct1); - }); - } - -#endif } template @@ -10529,9 +10068,6 @@ template static void dequantize_row_iq4_xs_sycl(const void *vx, dst_t *y, const int k, dpct::queue_ptr stream) { const int nb = (k + QK_K - 1) / QK_K; -#if QK_K == 64 - dequantize_row_iq4_nl_sycl(vx, y, k, stream); -#else { dpct::has_capability_or_fail(stream->get_device(), {sycl::aspect::fp16}); @@ -10546,7 +10082,6 @@ static void dequantize_row_iq4_xs_sycl(const void *vx, dst_t *y, const int k, }); }); } -#endif } @@ -12051,8 +11586,6 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy, 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_id())); @@ -12167,7 +11700,6 @@ static void ggml_mul_mat_q3_K_q8_1_sycl(const void *vx, const void *vy, }); } } -#endif } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ diff --git a/ggml.c b/ggml.c index d316e3d31..673c47748 100644 --- a/ggml.c +++ b/ggml.c @@ -871,22 +871,14 @@ static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = { }, [GGML_TYPE_IQ4_XS] = { .type_name = "iq4_xs", -#if QK_K == 64 - .blck_size = QK4_NL, -#else .blck_size = QK_K, -#endif .type_size = sizeof(block_iq4_xs), .is_quantized = true, .to_float = (ggml_to_float_t) dequantize_row_iq4_xs, .from_float = quantize_row_iq4_xs, .from_float_reference = (ggml_from_float_t)quantize_row_iq4_xs_reference, .vec_dot = ggml_vec_dot_iq4_xs_q8_K, -#if QK_K == 64 - .vec_dot_type = GGML_TYPE_Q8_0, -#else .vec_dot_type = GGML_TYPE_Q8_K, -#endif .nrows = 1, }, [GGML_TYPE_Q8_K] = { @@ -22117,11 +22109,7 @@ size_t ggml_quantize_chunk( case GGML_TYPE_IQ1_S: result = quantize_iq1_s (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ1_M: result = quantize_iq1_m (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; case GGML_TYPE_IQ4_NL: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; -#if QK_K == 64 - case GGML_TYPE_IQ4_XS: result = quantize_iq4_nl (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; -#else case GGML_TYPE_IQ4_XS: result = quantize_iq4_xs (src + start, (char *) dst + start_row * row_size, nrows, n_per_row, imatrix); break; -#endif case GGML_TYPE_F16: { size_t elemsize = sizeof(ggml_fp16_t); diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 42df2e4d0..67e23dcc1 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -905,9 +905,8 @@ class GGUFValueType(IntEnum): raise ValueError(f"Unknown type: {type(val)}") -# Note: Does not support GGML_QKK_64 -QK_K = 256 # Items here are (block size, type size) +QK_K = 256 GGML_QUANT_SIZES: dict[GGMLQuantizationType, tuple[int, int]] = { GGMLQuantizationType.F32: (1, 4), GGMLQuantizationType.F16: (1, 2), diff --git a/llama.cpp b/llama.cpp index 34137c7ad..37b3d58c6 100644 --- a/llama.cpp +++ b/llama.cpp @@ -26,13 +26,9 @@ #ifdef GGML_USE_METAL # include "ggml-metal.h" #endif -#ifndef QK_K -# ifdef GGML_QKK_64 -# define QK_K 64 -# else -# define QK_K 256 -# endif -#endif + +// TODO: replace with ggml API call +#define QK_K 256 #ifdef __has_include #if __has_include() @@ -14308,8 +14304,6 @@ static ggml_type llama_tensor_get_type(quantize_state_internal & qs, ggml_type n else if ((ftype == LLAMA_FTYPE_MOSTLY_Q4_K_M || ftype == LLAMA_FTYPE_MOSTLY_Q5_K_M) && use_more_bits(qs.i_attention_wv, qs.n_attention_wv)) new_type = GGML_TYPE_Q6_K; else if (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S && qs.i_attention_wv < 4) new_type = GGML_TYPE_Q5_K; - else if (QK_K == 64 && (ftype == LLAMA_FTYPE_MOSTLY_Q4_K_S || ftype == LLAMA_FTYPE_MOSTLY_Q3_K_S) && - (qs.i_attention_wv < qs.n_attention_wv/8 || qs.i_attention_wv >= 7*qs.n_attention_wv/8)) new_type = GGML_TYPE_Q6_K; if (qs.model.type == MODEL_70B) { // In the 70B model we have 8 heads sharing the same attn_v weights. As a result, the attn_v.weight tensor is // 8x smaller compared to attn_q.weight. Hence, we can get a nice boost in quantization accuracy with From d48c88cbd563b6cf0ce972e2f56796896e240736 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 23 May 2024 10:00:44 +0300 Subject: [PATCH 090/181] ggml : remove ggml_flash_attn and ggml_flash_ff (#7463) ggml-ci --- examples/finetune/finetune.cpp | 3 +- .../train-text-from-scratch.cpp | 3 +- ggml.c | 676 +----------------- ggml.h | 18 +- tests/test-grad0.cpp | 116 +-- 5 files changed, 47 insertions(+), 769 deletions(-) diff --git a/examples/finetune/finetune.cpp b/examples/finetune/finetune.cpp index 992426c1b..22425730f 100644 --- a/examples/finetune/finetune.cpp +++ b/examples/finetune/finetune.cpp @@ -643,7 +643,8 @@ static struct ggml_tensor * llama_build_lora_finetune_graphs( struct ggml_tensor * t15 = ggml_permute (ctx, t12, 0, 3, 1, 2); set_name(t15, "t15"); assert_shape_4d(t15, N, n_embd_head, n_head_kv, n_batch); struct ggml_tensor * t16; if (enable_flash_attn) { - t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, "t16"); assert_shape_4d(t16, n_embd_head, N, n_head, n_batch); + GGML_ASSERT(false && "TODO: ggml_flash_attn_ext() not yet supported"); + //t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, "t16"); assert_shape_4d(t16, n_embd_head, N, n_head, n_batch); } else { struct ggml_tensor * t16_0 = ggml_mul_mat (ctx, t14, t13); set_name(t16_0, "t16_0"); assert_shape_4d(t16_0, N, N, n_head, n_batch); struct ggml_tensor * t16_1 = ggml_scale_inplace (ctx, t16_0, kv_scale); set_name(t16_1, "t16_1"); assert_shape_4d(t16_1, N, N, n_head, n_batch); diff --git a/examples/train-text-from-scratch/train-text-from-scratch.cpp b/examples/train-text-from-scratch/train-text-from-scratch.cpp index 45bdfa8f5..e2f85c682 100644 --- a/examples/train-text-from-scratch/train-text-from-scratch.cpp +++ b/examples/train-text-from-scratch/train-text-from-scratch.cpp @@ -341,7 +341,8 @@ static struct ggml_tensor * llama_build_train_graphs( struct ggml_tensor * t15 = ggml_permute (ctx, t12, 0, 3, 1, 2); set_name(t15, "t15"); assert_shape_4d(t15, N, n_embd/n_head, n_head, n_batch); struct ggml_tensor * t16; if (enable_flash_attn) { - t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, "t16"); assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch); + GGML_ASSERT(false && "TODO: ggml_flash_attn_ext() not yet supported"); + //t16 = ggml_flash_attn(ctx, t13, t14, t15, true); set_name(t16, "t16"); assert_shape_4d(t16, n_embd/n_head, N, n_head, n_batch); } else { struct ggml_tensor * t16_0 = ggml_mul_mat (ctx, t14, t13); set_name(t16_0, "t16_0"); assert_shape_4d(t16_0, N, N, n_head, n_batch); struct ggml_tensor * t16_1 = ggml_scale_inplace (ctx, t16_0, kv_scale); set_name(t16_1, "t16_1"); assert_shape_4d(t16_1, N, N, n_head, n_batch); diff --git a/ggml.c b/ggml.c index 673c47748..9e72b7a76 100644 --- a/ggml.c +++ b/ggml.c @@ -2670,9 +2670,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "ARGSORT", "LEAKY_RELU", - "FLASH_ATTN", "FLASH_ATTN_EXT", - "FLASH_FF", "FLASH_ATTN_BACK", "SSM_CONV", "SSM_SCAN", @@ -2698,7 +2696,7 @@ static const char * GGML_OP_NAME[GGML_OP_COUNT] = { "CROSS_ENTROPY_LOSS_BACK", }; -static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76"); +static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74"); static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "none", @@ -2760,9 +2758,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "argsort(x)", "leaky_relu(x)", - "flash_attn(x)", "flash_attn_ext(x)", - "flash_ff(x)", "flash_attn_back(x)", "ssm_conv(x)", "ssm_scan(x)", @@ -2788,7 +2784,7 @@ static const char * GGML_OP_SYMBOL[GGML_OP_COUNT] = { "cross_entropy_loss_back(x,y)", }; -static_assert(GGML_OP_COUNT == 76, "GGML_OP_COUNT != 76"); +static_assert(GGML_OP_COUNT == 74, "GGML_OP_COUNT != 74"); static_assert(GGML_OP_POOL_COUNT == 2, "GGML_OP_POOL_COUNT != 2"); @@ -6948,38 +6944,6 @@ struct ggml_tensor * ggml_top_k( return result; } -// ggml_flash_attn - -struct ggml_tensor * ggml_flash_attn( - struct ggml_context * ctx, - struct ggml_tensor * q, - struct ggml_tensor * k, - struct ggml_tensor * v, - bool masked) { - GGML_ASSERT(ggml_can_mul_mat(k, q)); - // TODO: check if vT can be multiplied by (k*qT) - - bool is_node = false; - - if (q->grad || k->grad || v->grad) { - is_node = true; - } - - //struct ggml_tensor * result = ggml_dup_tensor(ctx, q); - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, GGML_MAX_DIMS, q->ne); - - int32_t t = masked ? 1 : 0; - ggml_set_op_params(result, &t, sizeof(t)); - - result->op = GGML_OP_FLASH_ATTN; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; - result->src[0] = q; - result->src[1] = k; - result->src[2] = v; - - return result; -} - // ggml_flash_attn_ext struct ggml_tensor * ggml_flash_attn_ext( @@ -7039,38 +7003,6 @@ void ggml_flash_attn_ext_set_prec( ggml_set_op_params_i32(a, 2, prec_i32); // scale is on first pos, max_bias on second } -// ggml_flash_ff - -struct ggml_tensor * ggml_flash_ff( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b0, - struct ggml_tensor * b1, - struct ggml_tensor * c0, - struct ggml_tensor * c1) { - GGML_ASSERT(ggml_can_mul_mat(b0, a)); - // TODO: more checks - - bool is_node = false; - - if (a->grad || b0->grad || b1->grad || c0->grad || c1->grad) { - is_node = true; - } - - //struct ggml_tensor * result = ggml_dup_tensor(ctx, a); - struct ggml_tensor * result = ggml_new_tensor(ctx, GGML_TYPE_F32, GGML_MAX_DIMS, a->ne); - - result->op = GGML_OP_FLASH_FF; - result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; - result->src[0] = a; - result->src[1] = b0; - result->src[2] = b1; - result->src[3] = c0; - result->src[4] = c1; - - return result; -} - // ggml_flash_attn_back struct ggml_tensor * ggml_flash_attn_back( @@ -7080,6 +7012,8 @@ struct ggml_tensor * ggml_flash_attn_back( struct ggml_tensor * v, struct ggml_tensor * d, bool masked) { + GGML_ASSERT(false && "TODO: adapt to ggml_flash_attn_ext() changes"); + GGML_ASSERT(ggml_can_mul_mat(k, q)); // TODO: check if vT can be multiplied by (k*qT) @@ -15709,400 +15643,6 @@ static void ggml_compute_forward_argsort( } } -// ggml_compute_forward_flash_attn - -static void ggml_compute_forward_flash_attn_f32( - const struct ggml_compute_params * params, - const bool masked, - struct ggml_tensor * dst) { - - const struct ggml_tensor * q = dst->src[0]; - const struct ggml_tensor * k = dst->src[1]; - const struct ggml_tensor * v = dst->src[2]; - - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - - GGML_TENSOR_LOCALS(int64_t, neq, q, ne) - GGML_TENSOR_LOCALS(size_t, nbq, q, nb) - GGML_TENSOR_LOCALS(int64_t, nek, k, ne) - GGML_TENSOR_LOCALS(size_t, nbk, k, nb) - GGML_TENSOR_LOCALS(int64_t, nev, v, ne) - GGML_TENSOR_LOCALS(size_t, nbv, v, nb) - GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) - GGML_TENSOR_LOCALS(size_t, nb, dst, nb) - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t D = neq0; - const int64_t N = neq1; - const int64_t P = nek1 - N; - const int64_t M = P + N; - - const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL); - - GGML_ASSERT(ne0 == D); - GGML_ASSERT(ne1 == N); - GGML_ASSERT(P >= 0); - - GGML_ASSERT(nbq0 == sizeof(float)); - GGML_ASSERT(nbk0 == sizeof(float)); - GGML_ASSERT(nbv0 == sizeof(float)); - - GGML_ASSERT(neq0 == D); - GGML_ASSERT(nek0 == D); - GGML_ASSERT(nev1 == D); - - GGML_ASSERT(neq1 == N); - GGML_ASSERT(nek1 == N + P); - GGML_ASSERT(nev1 == D); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - if (params->type == GGML_TASK_TYPE_INIT) { - return; - } - - if (params->type == GGML_TASK_TYPE_FINALIZE) { - return; - } - - // parallelize by q rows using ggml_vec_dot_f32 - - // total rows in q - const int nr = neq1*neq2*neq3; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - const float scale = 1.0f/sqrtf(D); - - //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale); - - for (int ir = ir0; ir < ir1; ++ir) { - // q indices - const int iq3 = ir/(neq2*neq1); - const int iq2 = (ir - iq3*neq2*neq1)/neq1; - const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1); - - float * S = (float *) params->wdata + ith*(Mup + CACHE_LINE_SIZE_F32); - - for (int i = M; i < Mup; ++i) { - S[i] = -INFINITY; - } - - const int64_t masked_begin = masked ? (P + iq1 + 1) : M; - for (int64_t ic = 0; ic < masked_begin; ++ic) { - // k indices - const int ik3 = iq3; - const int ik2 = iq2 % nek2; - const int ik1 = ic; - - // S indices - const int i1 = ik1; - - ggml_vec_dot_f32(neq0, - S + i1, 0, - (float *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), 0, - (float *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), 0, 1); - } - - // scale - ggml_vec_scale_f32(masked_begin, S, scale); - - for (int64_t i = masked_begin; i < M; i++) { - S[i] = -INFINITY; - } - - // softmax - // exclude known -INF S[..] values from max and loop - // dont forget to set their SW values to zero - { - float max = -INFINITY; - ggml_vec_max_f32(masked_begin, &max, S); - - ggml_float sum = 0.0; - { -#ifdef GGML_SOFT_MAX_ACCELERATE - max = -max; - vDSP_vsadd(S, 1, &max, S, 1, Mup); - vvexpf(S, S, &Mup); - ggml_vec_sum_f32(Mup, &sum, S); -#else - sum = ggml_vec_soft_max_f32(Mup, S, S, max); -#endif - } - - assert(sum > 0.0); - - sum = 1.0/sum; - ggml_vec_scale_f32(masked_begin, S, sum); - -#ifndef NDEBUG - for (int i = 0; i < masked_begin; ++i) { - assert(!isnan(S[i])); - assert(!isinf(S[i])); - } -#endif - } - - for (int64_t ic = 0; ic < nev1; ++ic) { - // dst indices - const int i1 = iq1; - const int i2 = iq2; - const int i3 = iq3; - - // v indices - const int iv2 = iq2 % nev2; - const int iv3 = iq3; - - ggml_vec_dot_f32(masked_begin, - (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), 0, - (float *) ((char *) v->data + ( ic*nbv1 + iv2*nbv2 + iv3*nbv3)), 0, - S, 0, 1); - } - } -} - -static void ggml_compute_forward_flash_attn_f16( - const struct ggml_compute_params * params, - const bool masked, - struct ggml_tensor * dst) { - - const struct ggml_tensor * q = dst->src[0]; - const struct ggml_tensor * k = dst->src[1]; - const struct ggml_tensor * v = dst->src[2]; - - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - - GGML_TENSOR_LOCALS(int64_t, neq, q, ne) - GGML_TENSOR_LOCALS(size_t, nbq, q, nb) - GGML_TENSOR_LOCALS(int64_t, nek, k, ne) - GGML_TENSOR_LOCALS(size_t, nbk, k, nb) - GGML_TENSOR_LOCALS(int64_t, nev, v, ne) - GGML_TENSOR_LOCALS(size_t, nbv, v, nb) - GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) - GGML_TENSOR_LOCALS(size_t, nb, dst, nb) - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t D = neq0; - const int64_t N = neq1; - const int64_t P = nek1 - N; - const int64_t M = P + N; - - const int Mup = ggml_up(M, GGML_SOFT_MAX_UNROLL); - - GGML_ASSERT(ne0 == D); - GGML_ASSERT(ne1 == N); - GGML_ASSERT(P >= 0); - - GGML_ASSERT(nbq0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nbk0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nbv0 == sizeof(ggml_fp16_t)); - - GGML_ASSERT(neq0 == D); - GGML_ASSERT(nek0 == D); - GGML_ASSERT(nev1 == D); - - GGML_ASSERT(neq1 == N); - GGML_ASSERT(nek1 == N + P); - GGML_ASSERT(nev1 == D); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - if (params->type == GGML_TASK_TYPE_INIT) { - return; - } - - if (params->type == GGML_TASK_TYPE_FINALIZE) { - return; - } - - // parallelize by q rows using ggml_vec_dot_f32 - - // total rows in q - const int nr = neq1*neq2*neq3; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - const float scale = 1.0f/sqrtf(D); - - //printf("P=%d N=%d D=%d ir0=%d ir1=%d scale = %f\n", P, N, D, ir0, ir1, scale); - - for (int ir = ir0; ir < ir1; ++ir) { - // q indices - const int iq3 = ir/(neq2*neq1); - const int iq2 = (ir - iq3*neq2*neq1)/neq1; - const int iq1 = (ir - iq3*neq2*neq1 - iq2*neq1); - - float * S = (float *) params->wdata + ith*(2*Mup + CACHE_LINE_SIZE_F32); - - for (int i = M; i < Mup; ++i) { - S[i] = -INFINITY; - } - - if (GGML_VEC_DOT_UNROLL > 2 || nek1 % GGML_VEC_DOT_UNROLL != 0) { - for (int64_t ic = 0; ic < nek1; ++ic) { - // k indices - const int ik3 = iq3; - const int ik2 = iq2 % nek2; - const int ik1 = ic; - - // S indices - const int i1 = ik1; - - ggml_vec_dot_f16(neq0, - S + i1, 0, - (ggml_fp16_t *) ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), 0, - (ggml_fp16_t *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3)), 0, 1); - } - } else { - for (int64_t ic = 0; ic < nek1; ic += GGML_VEC_DOT_UNROLL) { - // k indices - const int ik3 = iq3; - const int ik2 = iq2 % nek2; - const int ik1 = ic; - - // S indices - const int i1 = ik1; - - ggml_vec_dot_f16_unroll(neq0, nbk1, - S + i1, - ((char *) k->data + (ik1*nbk1 + ik2*nbk2 + ik3*nbk3)), - (ggml_fp16_t *) ((char *) q->data + (iq1*nbq1 + iq2*nbq2 + iq3*nbq3))); - } - } - - // scale - ggml_vec_scale_f32(nek1, S, scale); - - if (masked) { - for (int64_t i = P; i < M; i++) { - if (i > P + iq1) { - S[i] = -INFINITY; - } - } - } - - // softmax - // todo: exclude known -INF S[..] values from max and loop, assuming their results to be zero. - // dont forget to set their S values to zero - { - float max = -INFINITY; - ggml_vec_max_f32(M, &max, S); - - ggml_float sum = 0.0; - { -#ifdef GGML_SOFT_MAX_ACCELERATE - max = -max; - vDSP_vsadd(S, 1, &max, S, 1, Mup); - vvexpf(S, S, &Mup); - ggml_vec_sum_f32(Mup, &sum, S); -#else - sum = ggml_vec_soft_max_f32(Mup, S, S, max); -#endif - } - - assert(sum > 0.0); - - sum = 1.0/sum; - ggml_vec_scale_f32(M, S, sum); - -#ifndef NDEBUG - for (int i = 0; i < M; ++i) { - assert(!isnan(S[i])); - assert(!isinf(S[i])); - } -#endif - } - - ggml_fp16_t * S16 = (ggml_fp16_t *) ((float *) params->wdata + ith*(2*Mup + CACHE_LINE_SIZE_F32) + Mup); - - for (int64_t i = 0; i < M; i++) { - S16[i] = GGML_FP32_TO_FP16(S[i]); - } - - // todo: exclude known zero S[..] values from dot (reducing nev0 and increasing begin of v and S16). - if (GGML_VEC_DOT_UNROLL == 1 || (nev1 % GGML_VEC_DOT_UNROLL != 0)) { - for (int64_t ic = 0; ic < nev1; ++ic) { - // dst indices - const int i1 = iq1; - const int i2 = iq2; - const int i3 = iq3; - - // v indices - const int iv2 = iq2 % nev2; - const int iv3 = iq3; - - ggml_vec_dot_f16(nev0, - (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), 0, - (ggml_fp16_t *) ((char *) v->data + ( ic*nbv1 + iv2*nbv2 + iv3*nbv3)), 0, - S16, 0, 1); - } - } else { - for (int64_t ic = 0; ic < nev1; ic += GGML_VEC_DOT_UNROLL) { - // dst indices - const int i1 = iq1; - const int i2 = iq2; - const int i3 = iq3; - - // v indices - const int iv2 = iq2 % nev2; - const int iv3 = iq3; - - ggml_vec_dot_f16_unroll(nev0, nbv1, - (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), - ((char *) v->data + ( ic*nbv1 + iv2*nbv2 + iv3*nbv3)), - S16); - } - } - } -} - -static void ggml_compute_forward_flash_attn( - const struct ggml_compute_params * params, - const bool masked, - struct ggml_tensor * dst) { - - const struct ggml_tensor * q = dst->src[0]; - - switch (q->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_flash_attn_f16(params, masked, dst); - } break; - case GGML_TYPE_F32: - { - ggml_compute_forward_flash_attn_f32(params, masked, dst); - } break; - default: - { - GGML_ASSERT(false); - } break; - } -} - // ggml_compute_forward_flash_attn_ext static void ggml_compute_forward_flash_attn_ext_f16( @@ -16336,165 +15876,6 @@ static void ggml_compute_forward_flash_attn_ext( } } -// ggml_compute_forward_flash_ff - -static void ggml_compute_forward_flash_ff_f16( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * a = dst->src[0]; // F16 - const struct ggml_tensor * b0 = dst->src[1]; // F16 fc_w - const struct ggml_tensor * b1 = dst->src[2]; // F32 fc_b - const struct ggml_tensor * c0 = dst->src[3]; // F16 proj_w - const struct ggml_tensor * c1 = dst->src[4]; // F32 proj_b - - int64_t t0 = ggml_perf_time_us(); - UNUSED(t0); - - GGML_TENSOR_LOCALS(int64_t, nea, a, ne) - GGML_TENSOR_LOCALS(size_t, nba, a, nb) - GGML_TENSOR_LOCALS(int64_t, neb0, b0, ne) - GGML_TENSOR_LOCALS(size_t, nbb0, b0, nb) - GGML_TENSOR_LOCALS(int64_t, neb1, b1, ne) - GGML_TENSOR_LOCALS(size_t, nbb1, b1, nb) - GGML_TENSOR_LOCALS(int64_t, nec0, c0, ne) - GGML_TENSOR_LOCALS(size_t, nbc0, c0, nb) - GGML_TENSOR_LOCALS(int64_t, nec1, c1, ne) - GGML_TENSOR_LOCALS(size_t, nbc1, c1, nb) - GGML_TENSOR_LOCALS(int64_t, ne, dst, ne) - GGML_TENSOR_LOCALS(size_t, nb, dst, nb) - - const int ith = params->ith; - const int nth = params->nth; - - const int64_t D = nea0; - //const int64_t N = nea1; - const int64_t M = neb01; - - GGML_ASSERT(ne0 == nea0); - GGML_ASSERT(ne1 == nea1); - GGML_ASSERT(ne2 == nea2); - - GGML_ASSERT(nba0 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nbb00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nbb10 == sizeof(float)); - GGML_ASSERT(nbc00 == sizeof(ggml_fp16_t)); - GGML_ASSERT(nbc10 == sizeof(float)); - - GGML_ASSERT(neb00 == D); - GGML_ASSERT(neb01 == M); - GGML_ASSERT(neb10 == M); - GGML_ASSERT(neb11 == 1); - - GGML_ASSERT(nec00 == M); - GGML_ASSERT(nec01 == D); - GGML_ASSERT(nec10 == D); - GGML_ASSERT(nec11 == 1); - - // dst cannot be transposed or permuted - GGML_ASSERT(nb0 == sizeof(float)); - GGML_ASSERT(nb0 <= nb1); - GGML_ASSERT(nb1 <= nb2); - GGML_ASSERT(nb2 <= nb3); - - if (params->type == GGML_TASK_TYPE_INIT) { - return; - } - - if (params->type == GGML_TASK_TYPE_FINALIZE) { - return; - } - - // parallelize by a rows using ggml_vec_dot_f32 - - // total rows in a - const int nr = nea1*nea2*nea3; - - // rows per thread - const int dr = (nr + nth - 1)/nth; - - // row range for this thread - const int ir0 = dr*ith; - const int ir1 = MIN(ir0 + dr, nr); - - for (int ir = ir0; ir < ir1; ++ir) { - // a indices - const int ia3 = ir/(nea2*nea1); - const int ia2 = (ir - ia3*nea2*nea1)/nea1; - const int ia1 = (ir - ia3*nea2*nea1 - ia2*nea1); - - float * S = (float *) params->wdata + ith*(2*M + CACHE_LINE_SIZE_F32); - - for (int64_t ic = 0; ic < neb01; ++ic) { - // b0 indices - const int ib03 = ia3; - const int ib02 = ia2; - const int ib01 = ic; - - // S indices - const int i1 = ib01; - - ggml_vec_dot_f16(nea0, - S + i1, 0, - (ggml_fp16_t *) ((char *) b0->data + (ib01*nbb01 + ib02*nbb02 + ib03*nbb03)), 0, - (ggml_fp16_t *) ((char *) a->data + ( ia1*nba1 + ia2*nba2 + ia3*nba3)), 0, 1); - } - - ggml_vec_add_f32(neb01, S, S, (float *) b1->data); - //ggml_vec_gelu_f32(neb01, S, S); - - ggml_fp16_t * S16 = (ggml_fp16_t *) ((float *) params->wdata + ith*(2*M + CACHE_LINE_SIZE_F32) + M); - - for (int64_t i = 0; i < M; i++) { - S16[i] = GGML_FP32_TO_FP16(S[i]); - } - - ggml_vec_gelu_f16(neb01, S16, S16); - - { - // dst indices - const int i1 = ia1; - const int i2 = ia2; - const int i3 = ia3; - - for (int64_t ic = 0; ic < nec01; ++ic) { - - ggml_vec_dot_f16(neb01, - (float *) ((char *) dst->data + (ic*nb0 + i1*nb1 + i2*nb2 + i3*nb3)), 0, - (ggml_fp16_t *) ((char *) c0->data + ( ic*nbc01 + i2*nbc02 + i3*nbc03)), 0, - S16, 0, 1); - } - - ggml_vec_add_f32(nec01, - (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)), - (float *) ((char *) dst->data + (i1*nb1 + i2*nb2 + i3*nb3)), - (float *) c1->data); - } - } -} - -static void ggml_compute_forward_flash_ff( - const struct ggml_compute_params * params, - struct ggml_tensor * dst) { - - const struct ggml_tensor * b0 = dst->src[1]; - - switch (b0->type) { - case GGML_TYPE_F16: - { - ggml_compute_forward_flash_ff_f16(params, dst); - } break; - case GGML_TYPE_F32: - { - GGML_ASSERT(false); // TODO - } break; - default: - { - GGML_ASSERT(false); - } break; - } -} - // ggml_compute_forward_flash_attn_back static void ggml_compute_forward_flash_attn_back_f32( @@ -18065,21 +17446,10 @@ static void ggml_compute_forward(struct ggml_compute_params * params, struct ggm { ggml_compute_forward_leaky_relu(params, tensor); } break; - case GGML_OP_FLASH_ATTN: - { - const int32_t t = ggml_get_op_params_i32(tensor, 0); - GGML_ASSERT(t == 0 || t == 1); - const bool masked = t != 0; - ggml_compute_forward_flash_attn(params, masked, tensor); - } break; case GGML_OP_FLASH_ATTN_EXT: { ggml_compute_forward_flash_attn_ext(params, tensor->src[0], tensor->src[1], tensor->src[2], tensor->src[3], tensor); } break; - case GGML_OP_FLASH_FF: - { - ggml_compute_forward_flash_ff(params, tensor); - } break; case GGML_OP_FLASH_ATTN_BACK: { int32_t t = ggml_get_op_params_i32(tensor, 0); @@ -19086,7 +18456,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor { GGML_ASSERT(false); // TODO: not implemented } break; - case GGML_OP_FLASH_ATTN: case GGML_OP_FLASH_ATTN_EXT: { struct ggml_tensor * flash_grad = NULL; @@ -19140,10 +18509,6 @@ static void ggml_compute_backward(struct ggml_context * ctx, struct ggml_tensor zero_table); } } break; - case GGML_OP_FLASH_FF: - { - GGML_ASSERT(false); // not supported - } break; case GGML_OP_FLASH_ATTN_BACK: { GGML_ASSERT(false); // not supported @@ -19830,15 +19195,10 @@ static int ggml_get_n_tasks(struct ggml_tensor * node, int n_threads, int n_cur_ { n_tasks = n_threads; } break; - case GGML_OP_FLASH_ATTN: case GGML_OP_FLASH_ATTN_EXT: { n_tasks = n_threads; } break; - case GGML_OP_FLASH_FF: - { - n_tasks = n_threads; - } break; case GGML_OP_FLASH_ATTN_BACK: { n_tasks = n_threads; @@ -20235,40 +19595,12 @@ struct ggml_cplan ggml_graph_plan(const struct ggml_cgraph * cgraph, int n_threa cur += sizeof(ggml_fp16_t)*ne00*ne01*ne02*ne03; cur += sizeof(ggml_fp16_t)*ne10*ne11*ne12; } break; - case GGML_OP_FLASH_ATTN: - { - const int64_t ne11 = ggml_up(node->src[1]->ne[1], GGML_SOFT_MAX_UNROLL); - - if (node->src[1]->type == GGML_TYPE_F32) { - cur = sizeof(float)*ne11*n_tasks; // TODO: this can become (n_tasks-1) - cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2 - } else if (node->src[1]->type == GGML_TYPE_F16) { - cur = sizeof(float)*ne11*n_tasks; // TODO: this can become (n_tasks-1) - cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2 - } else if (node->src[1]->type == GGML_TYPE_BF16) { - cur = sizeof(float)*ne11*n_tasks; // TODO: this can become (n_tasks-1) - cur += sizeof(float)*ne11*n_tasks; // this is overestimated by x2 - } - } break; case GGML_OP_FLASH_ATTN_EXT: { const int64_t ne00 = node->src[0]->ne[0]; // D cur = 3*sizeof(float)*ne00*n_tasks; // 3x head size/thread } break; - case GGML_OP_FLASH_FF: - { - if (node->src[1]->type == GGML_TYPE_F32) { - cur = sizeof(float)*node->src[1]->ne[1]*n_tasks; // TODO: this can become (n_tasks-1) - cur += sizeof(float)*node->src[1]->ne[1]*n_tasks; // this is overestimated by x2 - } else if (node->src[1]->type == GGML_TYPE_F16) { - cur = sizeof(float)*node->src[1]->ne[1]*n_tasks; // TODO: this can become (n_tasks-1) - cur += sizeof(float)*node->src[1]->ne[1]*n_tasks; // this is overestimated by x2 - } else if (node->src[1]->type == GGML_TYPE_BF16) { - cur = sizeof(float)*node->src[1]->ne[1]*n_tasks; // TODO: this can become (n_tasks-1) - cur += sizeof(float)*node->src[1]->ne[1]*n_tasks; // this is overestimated by x2 - } - } break; case GGML_OP_FLASH_ATTN_BACK: { const int64_t D = node->src[0]->ne[0]; diff --git a/ggml.h b/ggml.h index 08835042c..be81e0c52 100644 --- a/ggml.h +++ b/ggml.h @@ -481,9 +481,7 @@ extern "C" { GGML_OP_ARGSORT, GGML_OP_LEAKY_RELU, - GGML_OP_FLASH_ATTN, GGML_OP_FLASH_ATTN_EXT, - GGML_OP_FLASH_FF, GGML_OP_FLASH_ATTN_BACK, GGML_OP_SSM_CONV, GGML_OP_SSM_SCAN, @@ -1761,13 +1759,6 @@ extern "C" { struct ggml_tensor * a, int k); - GGML_API struct ggml_tensor * ggml_flash_attn( - struct ggml_context * ctx, - struct ggml_tensor * q, - struct ggml_tensor * k, - struct ggml_tensor * v, - bool masked); - #define GGML_KQ_MASK_PAD 32 // q: [n_embd, n_batch, n_head, 1] @@ -1788,6 +1779,7 @@ extern "C" { struct ggml_tensor * a, enum ggml_prec prec); + // TODO: needs to be adapted to ggml_flash_attn_ext GGML_API struct ggml_tensor * ggml_flash_attn_back( struct ggml_context * ctx, struct ggml_tensor * q, @@ -1796,14 +1788,6 @@ extern "C" { struct ggml_tensor * d, bool masked); - GGML_API struct ggml_tensor * ggml_flash_ff( - struct ggml_context * ctx, - struct ggml_tensor * a, - struct ggml_tensor * b0, - struct ggml_tensor * b1, - struct ggml_tensor * c0, - struct ggml_tensor * c1); - GGML_API struct ggml_tensor * ggml_ssm_conv( struct ggml_context * ctx, struct ggml_tensor * s, diff --git a/tests/test-grad0.cpp b/tests/test-grad0.cpp index 8ff76c891..21ca43be3 100644 --- a/tests/test-grad0.cpp +++ b/tests/test-grad0.cpp @@ -1515,90 +1515,50 @@ int main(int argc, const char ** argv) { } // flash_attn f32 - { - srand(seed); - const int nargs = 3; + // TODO: adapt to ggml_flash_attn_ext() changes + //{ + // srand(seed); + // const int nargs = 3; - int64_t ne2[4]; + // int64_t ne2[4]; - get_random_dims(ne2, 4); - int64_t D = ne2[0]; - int64_t N = ne2[1]; - int64_t M = ne2[2] + N; - int64_t B = ne2[3]; + // get_random_dims(ne2, 4); + // int64_t D = ne2[0]; + // int64_t N = ne2[1]; + // int64_t M = ne2[2] + N; + // int64_t B = ne2[3]; - for (int masked = 0; masked <= 1; ++masked) { - for (int ndims = 2; ndims <= 4; ++ndims) { - int max_nrep = (ndims >= 3) ? 2 : 1; - for (int nrep = 1; nrep < max_nrep; ++nrep) { - int64_t neq[4] = { D, N, B*nrep, ne[3] }; - int64_t nek[4] = { D, M, B, ne[3] }; - int64_t nev[4] = { M, D, B, ne[3] }; - if (ndims == 2) { - neq[2] = 1; neq[3] = 1; - nek[2] = 1; nek[3] = 1; - nev[2] = 1; nev[3] = 1; - } else if (ndims == 3) { - neq[3] = 1; - nek[3] = 1; - nev[3] = 1; - } - x[0] = get_random_tensor_f32(ctx0, ndims, neq, -0.1250f, 0.1250f); - x[1] = get_random_tensor_f32(ctx0, ndims, nek, -0.1250f, 0.1250f); - x[2] = get_random_tensor_f32(ctx0, ndims, nev, -0.1250f, 0.1250f); - ggml_set_param(ctx0, x[0]); - ggml_set_param(ctx0, x[1]); - ggml_set_param(ctx0, x[2]); + // for (int masked = 0; masked <= 1; ++masked) { + // for (int ndims = 2; ndims <= 4; ++ndims) { + // int max_nrep = (ndims >= 3) ? 2 : 1; + // for (int nrep = 1; nrep < max_nrep; ++nrep) { + // int64_t neq[4] = { D, N, B*nrep, ne[3] }; + // int64_t nek[4] = { D, M, B, ne[3] }; + // int64_t nev[4] = { M, D, B, ne[3] }; + // if (ndims == 2) { + // neq[2] = 1; neq[3] = 1; + // nek[2] = 1; nek[3] = 1; + // nev[2] = 1; nev[3] = 1; + // } else if (ndims == 3) { + // neq[3] = 1; + // nek[3] = 1; + // nev[3] = 1; + // } + // x[0] = get_random_tensor_f32(ctx0, ndims, neq, -0.1250f, 0.1250f); + // x[1] = get_random_tensor_f32(ctx0, ndims, nek, -0.1250f, 0.1250f); + // x[2] = get_random_tensor_f32(ctx0, ndims, nev, -0.1250f, 0.1250f); + // ggml_set_param(ctx0, x[0]); + // ggml_set_param(ctx0, x[1]); + // ggml_set_param(ctx0, x[2]); - struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0))); + // struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0))); - check_gradient("flash_attn f32", ctx0, x, f, ndims, nargs, 1.5e-4f, 1e-3f, INFINITY); - } - } - } - } + // check_gradient("flash_attn f32", ctx0, x, f, ndims, nargs, 1.5e-4f, 1e-3f, INFINITY); + // } + // } + // } + //} - // flash_attn f16, not yet fully implemented - if(0) - { - srand(seed); - const int nargs = 3; - - int64_t ne2[4]; - - get_random_dims(ne2, 4); - int64_t D = ne2[0]; - int64_t N = ne2[1]; - int64_t M = ne2[2] + N; - int64_t B = ne2[3]; - - for (int masked = 0; masked <= 1; ++masked) { - for (int ndims = 2; ndims <= 4; ++ndims) { - int64_t neq[4] = { D, N, B, ne[3] }; - int64_t nek[4] = { D, M, B, ne[3] }; - int64_t nev[4] = { M, D, B, ne[3] }; - if (ndims == 2) { - neq[2] = 1; neq[3] = 1; - nek[2] = 1; nek[3] = 1; - nev[2] = 1; nev[3] = 1; - } else if (ndims == 3) { - neq[3] = 1; - nek[3] = 1; - nev[3] = 1; - } - x[0] = get_random_tensor_f16(ctx0, ndims, neq, -0.1250f, 0.1250f); - x[1] = get_random_tensor_f16(ctx0, ndims, nek, -0.1250f, 0.1250f); - x[2] = get_random_tensor_f16(ctx0, ndims, nev, -0.1250f, 0.1250f); - ggml_set_param(ctx0, x[0]); - ggml_set_param(ctx0, x[1]); - ggml_set_param(ctx0, x[2]); - - struct ggml_tensor * f = ggml_sum(ctx0, ggml_flash_attn(ctx0, x[0], x[1], x[2], (masked == 0))); - - check_gradient("flash_attn f16", ctx0, x, f, ndims, nargs, 1.5e-4f, 1e-3f, INFINITY); - } - } - } ggml_free(ctx0); } From 152da28ae54139e3754189b9e6e1c28e11277502 Mon Sep 17 00:00:00 2001 From: Brian Date: Thu, 23 May 2024 17:40:43 +1000 Subject: [PATCH 091/181] labeler.yml: add embedding label detector [no ci] (#7482) --- .github/labeler.yml | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/.github/labeler.yml b/.github/labeler.yml index fca60594f..a67f78044 100644 --- a/.github/labeler.yml +++ b/.github/labeler.yml @@ -62,6 +62,8 @@ server: ggml: - changed-files: - any-glob-to-any-file: + - ggml.c + - ggml.h - ggml-*.c - ggml-*.h - ggml-cuda/** @@ -71,3 +73,6 @@ nix: - "**/*.nix" - .github/workflows/nix-*.yml - .devops/nix/nixpkgs-instances.nix +embedding: + - changed-files: + - any-glob-to-any-file: examples/embedding/ From a61a94e543e3c6877c087e80fca27a0313ce5fd5 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 23 May 2024 12:38:18 +0300 Subject: [PATCH 092/181] llama : rename n_ctx -> cache.size, less confusing (#0) --- llama.cpp | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/llama.cpp b/llama.cpp index 37b3d58c6..3e09a2390 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2475,7 +2475,6 @@ static bool llama_kv_cache_init( static bool llama_kv_cache_find_slot( struct llama_kv_cache & cache, const struct llama_batch & batch) { - const uint32_t n_ctx = cache.size; const uint32_t n_tokens = batch.n_tokens; if (cache.recurrent) { @@ -2526,16 +2525,16 @@ static bool llama_kv_cache_find_slot( } // otherwise, one cell per token. - if (n_tokens > n_ctx) { - LLAMA_LOG_ERROR("%s: n_tokens=%d > n_ctx=%d\n", __func__, n_tokens, n_ctx); + if (n_tokens > cache.size) { + LLAMA_LOG_ERROR("%s: n_tokens=%d > cache.size=%d\n", __func__, n_tokens, cache.size); return false; } uint32_t n_tested = 0; while (true) { - if (cache.head + n_tokens > n_ctx) { - n_tested += n_ctx - cache.head; + if (cache.head + n_tokens > cache.size) { + n_tested += cache.size - cache.head; cache.head = 0; continue; } @@ -2554,7 +2553,7 @@ static bool llama_kv_cache_find_slot( break; } - if (n_tested >= n_ctx) { + if (n_tested >= cache.size) { //LLAMA_LOG_ERROR("%s: failed to find a slot for %d tokens\n", __func__, n_tokens); return false; } From 9b82476ee9e73065a759f8bcc4cf27ec7ab2ed8c Mon Sep 17 00:00:00 2001 From: fairydreaming <166155368+fairydreaming@users.noreply.github.com> Date: Thu, 23 May 2024 11:49:53 +0200 Subject: [PATCH 093/181] Add missing inference support for GPTNeoXForCausalLM (Pythia and GPT-NeoX base models) (#7461) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * convert-hf : add conversion of bloom-style qkv tensor to gpt-style qkv (code borrowed from BloomModel) * llama : add inference support for LLM_ARCH_GPTNEOX * llama : add model types for every Pythia variant and GPT-NeoX Co-authored-by: Stanisław Szymczyk --- convert-hf-to-gguf.py | 38 +++++++ llama.cpp | 236 +++++++++++++++++++++++++++++++++++++++++- 2 files changed, 273 insertions(+), 1 deletion(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index daad1c4fc..5a00a5e89 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -673,6 +673,44 @@ class GPTNeoXModel(Model): self.gguf_writer.add_parallel_residual(self.hparams.get("use_parallel_residual", True)) self.gguf_writer.add_layer_norm_eps(self.hparams["layer_norm_eps"]) + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + del bid # unused + + n_head = self.hparams.get("n_head", self.hparams.get("num_attention_heads")) + n_embed = self.hparams.get("hidden_size", self.hparams.get("n_embed")) + + tensors: list[tuple[str, Tensor]] = [] + + if re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.weight", name): + # Map bloom-style qkv_linear to gpt-style qkv_linear + # bloom: https://github.com/huggingface/transformers/blob/main/src/transformers/models/bloom/modeling_bloom.py#L238-L252 # noqa + # gpt-2: https://github.com/huggingface/transformers/blob/main/src/transformers/models/gpt2/modeling_gpt2.py#L312 # noqa + qkv_weights = data_torch.reshape((n_head, 3, n_embed // n_head, n_embed)) + data_torch = torch.cat( + ( + qkv_weights[:, 0, :, :].reshape((-1, n_embed)), + qkv_weights[:, 1, :, :].reshape((-1, n_embed)), + qkv_weights[:, 2, :, :].reshape((-1, n_embed)), + ), + dim=0, + ) + logger.info("re-format attention.linear_qkv.weight") + elif re.match(r"gpt_neox\.layers\.\d+\.attention\.query_key_value\.bias", name): + qkv_bias = data_torch.reshape((n_head, 3, n_embed // n_head)) + data_torch = torch.cat( + ( + qkv_bias[:, 0, :].reshape((n_embed,)), + qkv_bias[:, 1, :].reshape((n_embed,)), + qkv_bias[:, 2, :].reshape((n_embed,)), + ), + dim=0, + ) + logger.info("re-format attention.linear_qkv.bias") + + tensors.append((self.map_tensor_name(name), data_torch)) + + return tensors + @Model.register("BloomForCausalLM") class BloomModel(Model): diff --git a/llama.cpp b/llama.cpp index 3e09a2390..5ff186a57 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1692,17 +1692,24 @@ static llama_state g_state; // available llama models enum e_model { MODEL_UNKNOWN, + MODEL_14M, MODEL_17M, MODEL_22M, MODEL_33M, + MODEL_70M, MODEL_109M, MODEL_137M, + MODEL_160M, MODEL_335M, + MODEL_410M, MODEL_0_5B, MODEL_1B, + MODEL_1_4B, MODEL_2B, + MODEL_2_8B, MODEL_3B, MODEL_4B, + MODEL_6_9B, MODEL_7B, MODEL_8B, MODEL_12B, @@ -1734,6 +1741,7 @@ static const size_t GiB = 1024*MiB; struct llama_hparams { bool vocab_only; bool rope_finetuned; + bool use_par_res; uint32_t n_vocab; uint32_t n_ctx_train; // context size the model was trained on @@ -3773,17 +3781,24 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { static const char * llama_model_type_name(e_model type) { switch (type) { + case MODEL_14M: return "14M"; case MODEL_17M: return "17M"; case MODEL_22M: return "22M"; case MODEL_33M: return "33M"; + case MODEL_70M: return "70M"; case MODEL_109M: return "109M"; case MODEL_137M: return "137M"; + case MODEL_160M: return "160M"; case MODEL_335M: return "335M"; + case MODEL_410M: return "410M"; case MODEL_0_5B: return "0.5B"; case MODEL_1B: return "1B"; + case MODEL_1_4B: return "1.4B"; case MODEL_2B: return "2B"; + case MODEL_2_8B: return "2.8B"; case MODEL_3B: return "3B"; case MODEL_4B: return "4B"; + case MODEL_6_9B: return "6.9B"; case MODEL_7B: return "7B"; case MODEL_8B: return "8B"; case MODEL_12B: return "12B"; @@ -4282,6 +4297,52 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_GPTNEOX: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_EPS, hparams.f_norm_eps); + ml.get_key(LLM_KV_USE_PARALLEL_RESIDUAL, hparams.use_par_res); + switch (hparams.n_layer) { + case 6: + switch (hparams.n_ff) { + case 512: model.type = e_model::MODEL_14M; break; + case 2048: model.type = e_model::MODEL_70M; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + case 12: + switch (hparams.n_ff) { + case 3072: model.type = e_model::MODEL_160M; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + case 16: + switch (hparams.n_ff) { + case 8192: model.type = e_model::MODEL_1B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + case 24: + switch (hparams.n_ff) { + case 4096: model.type = e_model::MODEL_410M; break; + case 8192: model.type = e_model::MODEL_1_4B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + case 32: + switch (hparams.n_ff) { + case 10240: model.type = e_model::MODEL_2_8B; break; + case 16384: model.type = e_model::MODEL_6_9B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + case 36: + switch (hparams.n_ff) { + case 20480: model.type = e_model::MODEL_12B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + case 44: + switch (hparams.n_ff) { + case 24576: model.type = e_model::MODEL_20B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -6033,6 +6094,41 @@ 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_GPTNEOX: + { + 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_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.wqkv = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_QKV, "weight", i), {n_embd, n_embd + 2*n_embd_gqa}); + layer.bqkv = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_QKV, "bias", i), {n_embd + 2*n_embd_gqa}); + + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd, n_embd}); + layer.bo = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_OUT, "bias", i), {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_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_ff, n_embd}); + layer.ffn_down_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}); + + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}); + layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}); + } + } break; default: throw std::runtime_error("unknown architecture"); } @@ -10560,6 +10656,140 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_gptneox() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + const int64_t n_embd_head = hparams.n_embd_head_v; + const int64_t n_embd_gqa = hparams.n_embd_v_gqa(); + GGML_ASSERT(n_embd_head == hparams.n_embd_head_k); + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = build_inp_pos(); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + + for (int il = 0; il < n_layer; ++il) { + 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 + { + cur = ggml_mul_mat(ctx0, model.layers[il].wqkv, cur); + cb(cur, "wqkv", il); + + cur = ggml_add(ctx0, cur, model.layers[il].bqkv); + cb(cur, "bqkv", il); + + struct ggml_tensor * Qcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd, n_tokens, cur->nb[1], 0*sizeof(float)*(n_embd))); + struct ggml_tensor * Kcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd))); + struct ggml_tensor * Vcur = ggml_cont(ctx0, ggml_view_2d(ctx0, cur, n_embd_gqa, n_tokens, cur->nb[1], 1*sizeof(float)*(n_embd + n_embd_gqa))); + + cb(Qcur, "Qcur", il); + cb(Kcur, "Kcur", il); + cb(Vcur, "Vcur", il); + + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, + n_rot, rope_type, 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, cparams, kv_self, gf, + model.layers[il].wo, model.layers[il].bo, + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + } + + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpL = ggml_get_rows(ctx0, inpL, inp_out_ids); + } + + // ffn + if (hparams.use_par_res) { + // attention and ffn are computed in parallel + // x = x + attn(ln1(x)) + ffn(ln2(x)) + + struct ggml_tensor * attn_out = cur; + + cur = llm_build_norm(ctx0, inpL, 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, model.layers[il].ffn_up_b, + NULL, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, + NULL, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + cb(cur, "ffn_out", il); + + cur = ggml_add(ctx0, cur, inpL); + cb(cur, "ffn_out", il); + + inpL = ggml_add(ctx0, cur, attn_out); + cb(inpL, "l_out", il); + } else { + // attention and ffn are computed sequentially + // x = x + attn(ln1(x)) + // x = x + ffn(ln2(x)) + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpL); + cb(ffn_inp, "ffn_inp", il); + + 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, model.layers[il].ffn_up_b, + NULL, NULL, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, + NULL, + LLM_FFN_GELU, LLM_FFN_SEQ, cb, il); + cb(cur, "ffn_out", il); + + inpL = ggml_add(ctx0, cur, ffn_inp); + cb(inpL, "l_out", il); + } + } + + cur = llm_build_norm(ctx0, inpL, hparams, + model.output_norm, + model.output_norm_b, + LLM_NORM, cb, -1); + cb(cur, "result_norm", -1); + + 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_defrag(llama_context & lctx, const std::vector & ids) { @@ -10770,6 +11000,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_olmo(); } break; + case LLM_ARCH_GPTNEOX: + { + result = llm.build_gptneox(); + } break; default: GGML_ASSERT(false); } @@ -15762,7 +15996,6 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { // these models do not use RoPE case LLM_ARCH_GPT2: case LLM_ARCH_GPTJ: - case LLM_ARCH_GPTNEOX: case LLM_ARCH_MPT: case LLM_ARCH_REFACT: case LLM_ARCH_BLOOM: @@ -15798,6 +16031,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_PHI3: case LLM_ARCH_GEMMA: case LLM_ARCH_STARCODER2: + case LLM_ARCH_GPTNEOX: return LLAMA_ROPE_TYPE_NEOX; // all model arches should be listed explicitly here From dacfcebd6022175848e978f82811a244f1033038 Mon Sep 17 00:00:00 2001 From: Victor Nogueira Date: Thu, 23 May 2024 15:12:43 +0300 Subject: [PATCH 094/181] readme : add GPT-NeoX + Pythia to the list of supported models (#7491) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index f4088c05e..ccd12e211 100644 --- a/README.md +++ b/README.md @@ -127,6 +127,7 @@ Typically finetunes of the base models below are supported as well. - [x] [SEA-LION](https://huggingface.co/models?search=sea-lion) - [x] [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B) + [GritLM-8x7B](https://huggingface.co/GritLM/GritLM-8x7B) - [x] [OLMo](https://allenai.org/olmo) +- [x] [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) + [Pythia](https://github.com/EleutherAI/pythia) (instructions for supporting more models: [HOWTO-add-model.md](./docs/HOWTO-add-model.md)) From 55ac3b7aeaf52f19786ed96e885d89521fc0f6c8 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 23 May 2024 15:28:14 +0300 Subject: [PATCH 095/181] ci : use Pythia models instead of OpenLlama (#7470) * ci : start using Pythia models over OpenLlama ggml-ci * ci : disable q2_k ppl tests * ci : use convert-hf-to-gguf.py * ci : update gg_get_model * ci : fix convert outfile name ggml-ci * llama : gptneox arch use F32 attn prec ggml-ci --- ci/run.sh | 424 +++++++++++++++++++++++++++++++++++------------------- llama.cpp | 4 +- 2 files changed, 282 insertions(+), 146 deletions(-) diff --git a/ci/run.sh b/ci/run.sh index 79dcd0772..940299025 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -202,12 +202,15 @@ function gg_sum_test_scripts_release { } 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" + local gguf_0="$MNT/models/pythia/1.4B/ggml-model-f16.gguf" + local gguf_1="$MNT/models/pythia/2.8B/ggml-model-f16.gguf" + local gguf_2="$MNT/models/open-llama/7B-v2/ggml-model-f16.gguf" + if [[ -s $gguf_0 ]]; then + echo -n "$gguf_0" + elif [[ -s $gguf_1 ]]; then + echo -n "$gguf_1" + elif [[ -s $gguf_2 ]]; then + echo -n "$gguf_2" else echo >&2 "No model found. Can't run gg_run_ctest_with_model." exit 1 @@ -256,139 +259,6 @@ function gg_sum_ctest_with_model_release { gg_printf '```\n' } -# open_llama_3b_v2 - -function gg_run_open_llama_3b_v2 { - cd ${SRC} - - gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/config.json - gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/resolve/main/tokenizer.model - gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/tokenizer_config.json - gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/special_tokens_map.json - gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/resolve/main/pytorch_model.bin - gg_wget models-mnt/open-llama/3B-v2/ https://huggingface.co/openlm-research/open_llama_3b_v2/raw/main/generation_config.json - - gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip - unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/ - head -n 60 models-mnt/wikitext/wikitext-2-raw/wiki.test.raw > models-mnt/wikitext/wikitext-2-raw/wiki.test-60.raw - - path_models="../models-mnt/open-llama/3B-v2" - path_wiki="../models-mnt/wikitext/wikitext-2-raw" - - rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release - - set -e - - (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_QKK_64=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log - (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log - - python3 ../convert.py ${path_models} - - model_f16="${path_models}/ggml-model-f16.gguf" - model_q8_0="${path_models}/ggml-model-q8_0.gguf" - model_q4_0="${path_models}/ggml-model-q4_0.gguf" - model_q4_1="${path_models}/ggml-model-q4_1.gguf" - model_q5_0="${path_models}/ggml-model-q5_0.gguf" - model_q5_1="${path_models}/ggml-model-q5_1.gguf" - model_q2_k="${path_models}/ggml-model-q2_k.gguf" - model_q3_k="${path_models}/ggml-model-q3_k.gguf" - model_q4_k="${path_models}/ggml-model-q4_k.gguf" - model_q5_k="${path_models}/ggml-model-q5_k.gguf" - model_q6_k="${path_models}/ggml-model-q6_k.gguf" - - wiki_test_60="${path_wiki}/wiki.test-60.raw" - - ./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 - ./bin/quantize ${model_f16} ${model_q5_0} q5_0 - ./bin/quantize ${model_f16} ${model_q5_1} q5_1 - ./bin/quantize ${model_f16} ${model_q2_k} q2_k - ./bin/quantize ${model_f16} ${model_q3_k} q3_k - ./bin/quantize ${model_f16} ${model_q4_k} q4_k - ./bin/quantize ${model_f16} ${model_q5_k} q5_k - ./bin/quantize ${model_f16} ${model_q6_k} q6_k - - (time ./bin/main --model ${model_f16} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/main --model ${model_q8_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/main --model ${model_q4_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/main --model ${model_q4_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/main --model ${model_q5_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/main --model ${model_q5_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/main --model ${model_q2_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/main --model ${model_q3_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/main --model ${model_q4_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/main --model ${model_q5_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/main --model ${model_q6_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - - (time ./bin/perplexity --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log - (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log - (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log - (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log - (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log - (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log - (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log - (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log - (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log - (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log - (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log - - (time ./bin/imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log - - (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - (time ./bin/save-load-state -fa --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log - - function check_ppl { - qnt="$1" - ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) - - if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then - printf ' - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl" - return 20 - fi - - printf ' - %s @ %s OK\n' "$qnt" "$ppl" - return 0 - } - - check_ppl "f16" "$(cat $OUT/${ci}-tg-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log - - cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log - - set +e -} - -function gg_sum_open_llama_3b_v2 { - gg_printf '### %s\n\n' "${ci}" - - gg_printf 'OpenLLaMA 3B-v2:\n' - gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" - gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)" - gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)" - gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)" - gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)" - gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)" - gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)" - gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)" - gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)" - gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)" - gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)" - gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)" - gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)" - gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)" - gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)" -} - # open_llama_7b_v2 # requires: GG_BUILD_CUDA @@ -417,7 +287,7 @@ function gg_run_open_llama_7b_v2 { (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log - python3 ../convert.py ${path_models} + python3 ../convert.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf model_f16="${path_models}/ggml-model-f16.gguf" model_q8_0="${path_models}/ggml-model-q8_0.gguf" @@ -526,6 +396,272 @@ function gg_sum_open_llama_7b_v2 { gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)" } +# pythia_1.4b + +function gg_run_pythia_1_4b { + cd ${SRC} + + gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/config.json + gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/tokenizer.json + gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/tokenizer_config.json + gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/raw/main/special_tokens_map.json + gg_wget models-mnt/pythia/1.4B/ https://huggingface.co/EleutherAI/pythia-1.4b/resolve/main/pytorch_model.bin + + gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip + unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/ + head -n 60 models-mnt/wikitext/wikitext-2-raw/wiki.test.raw > models-mnt/wikitext/wikitext-2-raw/wiki.test-60.raw + + path_models="../models-mnt/pythia/1.4B" + path_wiki="../models-mnt/wikitext/wikitext-2-raw" + + rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release + + set -e + + (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log + (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log + + python3 ../convert-hf-to-gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf + + model_f16="${path_models}/ggml-model-f16.gguf" + model_q8_0="${path_models}/ggml-model-q8_0.gguf" + model_q4_0="${path_models}/ggml-model-q4_0.gguf" + model_q4_1="${path_models}/ggml-model-q4_1.gguf" + model_q5_0="${path_models}/ggml-model-q5_0.gguf" + model_q5_1="${path_models}/ggml-model-q5_1.gguf" + model_q2_k="${path_models}/ggml-model-q2_k.gguf" + model_q3_k="${path_models}/ggml-model-q3_k.gguf" + model_q4_k="${path_models}/ggml-model-q4_k.gguf" + model_q5_k="${path_models}/ggml-model-q5_k.gguf" + model_q6_k="${path_models}/ggml-model-q6_k.gguf" + + wiki_test_60="${path_wiki}/wiki.test-60.raw" + + ./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 + ./bin/quantize ${model_f16} ${model_q5_0} q5_0 + ./bin/quantize ${model_f16} ${model_q5_1} q5_1 + ./bin/quantize ${model_f16} ${model_q2_k} q2_k + ./bin/quantize ${model_f16} ${model_q3_k} q3_k + ./bin/quantize ${model_f16} ${model_q4_k} q4_k + ./bin/quantize ${model_f16} ${model_q5_k} q5_k + ./bin/quantize ${model_f16} ${model_q6_k} q6_k + + (time ./bin/main --model ${model_f16} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/main --model ${model_q8_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/main --model ${model_q4_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/main --model ${model_q4_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/main --model ${model_q5_0} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/main --model ${model_q5_1} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/main --model ${model_q2_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/main --model ${model_q3_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/main --model ${model_q4_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/main --model ${model_q5_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/main --model ${model_q6_k} -s 1234 -n 64 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + + (time ./bin/perplexity --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + + (time ./bin/imatrix --model ${model_f16} -f ${wiki_test_60} -c 128 -b 128 --chunks 1 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log + + (time ./bin/save-load-state --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/save-load-state -fa --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + + function check_ppl { + qnt="$1" + ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) + + if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then + printf ' - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl" + return 20 + fi + + printf ' - %s @ %s OK\n' "$qnt" "$ppl" + return 0 + } + + check_ppl "f16" "$(cat $OUT/${ci}-tg-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + #check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log # note: ppl > 20.0 for this quant and model + check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + + cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log + + set +e +} + +function gg_sum_pythia_1_4b { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Pythia 1.4B:\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)" + gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)" + gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)" + gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)" + gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)" + gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)" + gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)" + gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)" + gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)" + gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)" + gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)" + gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)" + gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)" + gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)" +} + +# pythia_2_8b +# requires: GG_BUILD_CUDA + +function gg_run_pythia_2_8b { + cd ${SRC} + + gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/config.json + gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/tokenizer.json + gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/tokenizer_config.json + gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/raw/main/special_tokens_map.json + gg_wget models-mnt/pythia/2.8B/ https://huggingface.co/EleutherAI/pythia-2.8b/resolve/main/pytorch_model.bin + + gg_wget models-mnt/wikitext/ https://huggingface.co/datasets/ggml-org/ci/resolve/main/wikitext-2-raw-v1.zip + unzip -o models-mnt/wikitext/wikitext-2-raw-v1.zip -d models-mnt/wikitext/ + + path_models="../models-mnt/pythia/2.8B" + path_wiki="../models-mnt/wikitext/wikitext-2-raw" + + rm -rf build-ci-release && mkdir build-ci-release && cd build-ci-release + + set -e + + (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log + (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log + + python3 ../convert-hf-to-gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf + + model_f16="${path_models}/ggml-model-f16.gguf" + model_q8_0="${path_models}/ggml-model-q8_0.gguf" + model_q4_0="${path_models}/ggml-model-q4_0.gguf" + model_q4_1="${path_models}/ggml-model-q4_1.gguf" + model_q5_0="${path_models}/ggml-model-q5_0.gguf" + model_q5_1="${path_models}/ggml-model-q5_1.gguf" + model_q2_k="${path_models}/ggml-model-q2_k.gguf" + model_q3_k="${path_models}/ggml-model-q3_k.gguf" + model_q4_k="${path_models}/ggml-model-q4_k.gguf" + model_q5_k="${path_models}/ggml-model-q5_k.gguf" + model_q6_k="${path_models}/ggml-model-q6_k.gguf" + + wiki_test="${path_wiki}/wiki.test.raw" + + ./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 + ./bin/quantize ${model_f16} ${model_q5_0} q5_0 + ./bin/quantize ${model_f16} ${model_q5_1} q5_1 + ./bin/quantize ${model_f16} ${model_q2_k} q2_k + ./bin/quantize ${model_f16} ${model_q3_k} q3_k + ./bin/quantize ${model_f16} ${model_q4_k} q4_k + ./bin/quantize ${model_f16} ${model_q5_k} q5_k + ./bin/quantize ${model_f16} ${model_q6_k} q6_k + + (time ./bin/main --model ${model_f16} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/main --model ${model_q8_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/main --model ${model_q4_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/main --model ${model_q4_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/main --model ${model_q5_0} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/main --model ${model_q5_1} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/main --model ${model_q2_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/main --model ${model_q3_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/main --model ${model_q4_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/main --model ${model_q5_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/main --model ${model_q6_k} -t 1 -ngl 999 -s 1234 -n 256 --ignore-eos -p "I believe the meaning of life is" ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + + (time ./bin/perplexity --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-f16.log + (time ./bin/perplexity --model ${model_q8_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q8_0.log + (time ./bin/perplexity --model ${model_q4_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_0.log + (time ./bin/perplexity --model ${model_q4_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_1.log + (time ./bin/perplexity --model ${model_q5_0} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_0.log + (time ./bin/perplexity --model ${model_q5_1} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_1.log + (time ./bin/perplexity --model ${model_q2_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q2_k.log + (time ./bin/perplexity --model ${model_q3_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q3_k.log + (time ./bin/perplexity --model ${model_q4_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q4_k.log + (time ./bin/perplexity --model ${model_q5_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q5_k.log + (time ./bin/perplexity --model ${model_q6_k} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-tg-q6_k.log + + (time ./bin/imatrix --model ${model_f16} -f ${wiki_test} -t 1 -ngl 999 -c 2048 -b 512 --chunks 4 ) 2>&1 | tee -a $OUT/${ci}-imatrix.log + + (time ./bin/save-load-state -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/save-load-state -fa -ngl 10 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/save-load-state -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + (time ./bin/save-load-state -fa -ngl 99 --model ${model_q4_0} ) 2>&1 | tee -a $OUT/${ci}-save-load-state.log + + function check_ppl { + qnt="$1" + ppl=$(echo "$2" | grep -oE "[0-9]+\.[0-9]+" | tail -n 1) + + if [ $(echo "$ppl > 20.0" | bc) -eq 1 ]; then + printf ' - %s @ %s (FAIL: ppl > 20.0)\n' "$qnt" "$ppl" + return 20 + fi + + printf ' - %s @ %s OK\n' "$qnt" "$ppl" + return 0 + } + + check_ppl "f16" "$(cat $OUT/${ci}-tg-f16.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q8_0" "$(cat $OUT/${ci}-tg-q8_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_0" "$(cat $OUT/${ci}-tg-q4_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_1" "$(cat $OUT/${ci}-tg-q4_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_0" "$(cat $OUT/${ci}-tg-q5_0.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_1" "$(cat $OUT/${ci}-tg-q5_1.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + #check_ppl "q2_k" "$(cat $OUT/${ci}-tg-q2_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log # note: ppl > 20.0 for this quant and model + check_ppl "q3_k" "$(cat $OUT/${ci}-tg-q3_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q4_k" "$(cat $OUT/${ci}-tg-q4_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q5_k" "$(cat $OUT/${ci}-tg-q5_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + check_ppl "q6_k" "$(cat $OUT/${ci}-tg-q6_k.log | grep "^\[1\]")" | tee -a $OUT/${ci}-ppl.log + + cat $OUT/${ci}-imatrix.log | grep "Final" >> $OUT/${ci}-imatrix-sum.log + + set +e +} + +function gg_sum_pythia_2_8b { + gg_printf '### %s\n\n' "${ci}" + + gg_printf 'Pythia 2.8B:\n' + gg_printf '- status: %s\n' "$(cat $OUT/${ci}.exit)" + gg_printf '- perplexity:\n%s\n' "$(cat $OUT/${ci}-ppl.log)" + gg_printf '- imatrix:\n```\n%s\n```\n' "$(cat $OUT/${ci}-imatrix-sum.log)" + gg_printf '- f16: \n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-f16.log)" + gg_printf '- q8_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q8_0.log)" + gg_printf '- q4_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_0.log)" + gg_printf '- q4_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_1.log)" + gg_printf '- q5_0:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_0.log)" + gg_printf '- q5_1:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_1.log)" + gg_printf '- q2_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q2_k.log)" + gg_printf '- q3_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q3_k.log)" + gg_printf '- q4_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q4_k.log)" + gg_printf '- q5_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q5_k.log)" + gg_printf '- q6_k:\n```\n%s\n```\n' "$(cat $OUT/${ci}-tg-q6_k.log)" + gg_printf '- save-load-state: \n```\n%s\n```\n' "$(cat $OUT/${ci}-save-load-state.log)" +} + # bge-small function gg_run_embd_bge_small { @@ -552,7 +688,7 @@ function gg_run_embd_bge_small { (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log - python3 ../convert-hf-to-gguf.py ${path_models} + python3 ../convert-hf-to-gguf.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf model_f16="${path_models}/ggml-model-f16.gguf" model_q8_0="${path_models}/ggml-model-q8_0.gguf" @@ -606,10 +742,10 @@ if [ -z ${GG_BUILD_LOW_PERF} ]; then if [ -z ${GG_BUILD_VRAM_GB} ] || [ ${GG_BUILD_VRAM_GB} -ge 8 ]; then if [ -z ${GG_BUILD_CUDA} ]; then - #test $ret -eq 0 && gg_run open_llama_3b_v2 - date # dummy + test $ret -eq 0 && gg_run pythia_1_4b else - test $ret -eq 0 && gg_run open_llama_7b_v2 + test $ret -eq 0 && gg_run pythia_2_8b + #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 diff --git a/llama.cpp b/llama.cpp index 5ff186a57..1f9e10eed 100644 --- a/llama.cpp +++ b/llama.cpp @@ -6718,7 +6718,7 @@ static struct ggml_tensor * llm_build_kqv( cur = ggml_flash_attn_ext(ctx, q, k, v, kq_mask, kq_scale, hparams.f_max_alibi_bias); - if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3) { + if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX) { ggml_flash_attn_ext_set_prec(cur, GGML_PREC_F32); } @@ -6727,7 +6727,7 @@ static struct ggml_tensor * llm_build_kqv( struct ggml_tensor * kq = ggml_mul_mat(ctx, k, q); cb(kq, "kq", il); - if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3) { + if (model.arch == LLM_ARCH_PHI2 || model.arch == LLM_ARCH_PHI3 || model.arch == LLM_ARCH_GPTNEOX) { // for this arch, we need to perform the KQ multiplication with F32 precision, otherwise we get NaNs // ref: https://github.com/ggerganov/llama.cpp/pull/4490#issuecomment-1859055847 ggml_mul_mat_set_prec(kq, GGML_PREC_F32); From 3015851c5ac7334fb544a23a70a284c117b87044 Mon Sep 17 00:00:00 2001 From: Daniel Bevenius Date: Thu, 23 May 2024 14:29:26 +0200 Subject: [PATCH 096/181] llama : add getters for n_threads/n_threads_batch (#7464) * llama : add getters for n_threads/n_threads_batch This commit adds two new functions to the llama API. The functions can be used to get the number of threads used for generating a single token and the number of threads used for prompt and batch processing (multiple tokens). The motivation for this is that we want to be able to get the number of threads that the a context is using. The main use case is for a testing/verification that the number of threads is set correctly. Signed-off-by: Daniel Bevenius * squash! llama : add getters for n_threads/n_threads_batch Rename the getters to llama_n_threads and llama_n_threads_batch. Signed-off-by: Daniel Bevenius --------- Signed-off-by: Daniel Bevenius --- llama.cpp | 8 ++++++++ llama.h | 6 ++++++ 2 files changed, 14 insertions(+) diff --git a/llama.cpp b/llama.cpp index 1f9e10eed..e540c1b39 100644 --- a/llama.cpp +++ b/llama.cpp @@ -17410,6 +17410,14 @@ void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_ ctx->cparams.n_threads_batch = n_threads_batch; } +uint32_t llama_n_threads(struct llama_context * ctx) { + return ctx->cparams.n_threads; +} + +uint32_t llama_n_threads_batch(struct llama_context * ctx) { + return ctx->cparams.n_threads_batch; +} + void llama_set_abort_callback(struct llama_context * ctx, bool (*abort_callback)(void * data), void * abort_callback_data) { ctx->abort_callback = abort_callback; ctx->abort_callback_data = abort_callback_data; diff --git a/llama.h b/llama.h index b7bf2afcb..16cece5db 100644 --- a/llama.h +++ b/llama.h @@ -759,6 +759,12 @@ extern "C" { // n_threads_batch is the number of threads used for prompt and batch processing (multiple tokens) LLAMA_API void llama_set_n_threads(struct llama_context * ctx, uint32_t n_threads, uint32_t n_threads_batch); + // Get the number of threads used for generation of a single token. + LLAMA_API uint32_t llama_n_threads(struct llama_context * ctx); + + // Get the number of threads used for prompt and batch processing (multiple token). + LLAMA_API uint32_t llama_n_threads_batch(struct llama_context * ctx); + // Set whether to use causal attention or not // If set to true, the model will only attend to the past tokens LLAMA_API void llama_set_causal_attn(struct llama_context * ctx, bool causal_attn); From 8b94e799dfa482adf63419df4905dc79b37e179f Mon Sep 17 00:00:00 2001 From: Raj Hammeer Singh Hada Date: Thu, 23 May 2024 18:00:13 +0530 Subject: [PATCH 097/181] readme : add Bunny in supported models [no ci] (#7469) --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index ccd12e211..461259afb 100644 --- a/README.md +++ b/README.md @@ -141,6 +141,7 @@ Typically finetunes of the base models below are supported as well. - [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL) - [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM) - [x] [Moondream](https://huggingface.co/vikhyatk/moondream2) +- [x] [Bunny](https://github.com/BAAI-DCAI/Bunny) **HTTP server** From 007489e895bad02e4e54758bf0bdf2d6a4cdb7c1 Mon Sep 17 00:00:00 2001 From: Tristan Druyen Date: Thu, 23 May 2024 16:15:15 +0200 Subject: [PATCH 098/181] Fix phi3 chat template confusion with zephyr (#7449) * Fix phi3 template matching vs zephyr * Add regression test for new phi3 chat template * Implement review suggestions * Fix phi3 jinja test templates & match by <|end|> * Apply suggestion Co-authored-by: Xuan Son Nguyen * Add all phi3 template variants in tests * Remove unneeded message trimming Co-authored-by: Xuan Son Nguyen * Fix tests to not expect trimmed messages --------- Co-authored-by: Xuan Son Nguyen --- llama.cpp | 18 +++++++++--------- tests/test-chat-template.cpp | 20 ++++++++++++++++---- 2 files changed, 25 insertions(+), 13 deletions(-) diff --git a/llama.cpp b/llama.cpp index e540c1b39..15c660775 100644 --- a/llama.cpp +++ b/llama.cpp @@ -17852,6 +17852,15 @@ static int32_t llama_chat_apply_template_internal( } } // llama2 templates seem to not care about "add_generation_prompt" + } else if (tmpl == "phi3" || (tmpl.find("<|assistant|>") != std::string::npos && tmpl.find("<|end|>") != std::string::npos)) { + // Phi 3 + for (auto message : chat) { + std::string role(message->role); + ss << "<|" << role << "|>\n" << message->content << "<|end|>\n"; + } + if (add_ass) { + ss << "<|assistant|>\n"; + } } else if (tmpl == "zephyr" || tmpl.find("<|user|>") != std::string::npos) { // zephyr template for (auto message : chat) { @@ -17984,15 +17993,6 @@ static int32_t llama_chat_apply_template_internal( if (add_ass) { ss << "<|start_header_id|>assistant<|end_header_id|>\n\n"; } - } else if (tmpl == "phi3" || (tmpl.find("<|assistant|>") != std::string::npos && tmpl.find("<|end|>") != std::string::npos )) { - // Phi 3 - for (auto message : chat) { - std::string role(message->role); - ss << "<|" << role << "|>\n" << trim(message->content) << "<|end|>\n"; - } - if (add_ass) { - ss << "<|assistant|>\n"; - } } else { // template not supported return -1; diff --git a/tests/test-chat-template.cpp b/tests/test-chat-template.cpp index 4fe9183b9..cef9a650b 100644 --- a/tests/test-chat-template.cpp +++ b/tests/test-chat-template.cpp @@ -49,8 +49,14 @@ int main(void) { "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% elif false == true %}{% set loop_messages = messages %}{% set system_message = 'You are Command-R, a brilliant, sophisticated, AI-assistant trained to assist human users by providing thorough responses. You are trained by Cohere.' %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% if system_message != false %}{{ '<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>' + system_message + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|START_OF_TURN_TOKEN|><|USER_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% elif message['role'] == 'assistant' %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' + content.strip() + '<|END_OF_TURN_TOKEN|>' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>' }}{% endif %}", // Llama-3 "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}", - // Phi-3 - "{{ bos_token }}{% for message in messages %}{{'<|' + message['role'] + '|>' + ' ' + message['content'] + '<|end|> ' }}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|> ' }}{% else %}{{ eos_token }}{% endif %}" + //Phi-3-mini + "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}", + //Phi-3-small + "{{ bos_token }}{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<|assistant|>\n' }}{% else %}{{ eos_token }}{% endif %}", + //Phi-3-medium + "{% for message in messages %}{% if (message['role'] == 'user') %}{{'<|user|>' + '\n' + message['content'] + '<|end|>' + '\n' + '<|assistant|>' + '\n'}}{% elif (message['role'] == 'assistant') %}{{message['content'] + '<|end|>' + '\n'}}{% endif %}{% endfor %}", + //Phi-3-vision + "{% for message in messages %}{{'<|' + message['role'] + '|>' + '\n' + message['content'] + '<|end|>\n' }}{% endfor %}{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{- '<|assistant|>\n' -}}{% endif %}" }; std::vector expected_output = { // teknium/OpenHermes-2.5-Mistral-7B @@ -79,8 +85,14 @@ int main(void) { "<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>You are a helpful assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Hello<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>Hi there<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Who are you<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>I am an assistant<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|USER_TOKEN|>Another question<|END_OF_TURN_TOKEN|><|START_OF_TURN_TOKEN|><|CHATBOT_TOKEN|>", // Llama 3 "<|start_header_id|>system<|end_header_id|>\n\nYou are a helpful assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nHello<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nHi there<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nWho are you<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\nI am an assistant<|eot_id|><|start_header_id|>user<|end_header_id|>\n\nAnother question<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n", - // Phi 3 - "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\nI am an assistant<|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n", + //Phi-3-mini + "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n I am an assistant <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n", + //Phi-3-small + "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n I am an assistant <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n", + //Phi-3-medium + "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n I am an assistant <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n", + //Phi-3-vision + "<|system|>\nYou are a helpful assistant<|end|>\n<|user|>\nHello<|end|>\n<|assistant|>\nHi there<|end|>\n<|user|>\nWho are you<|end|>\n<|assistant|>\n I am an assistant <|end|>\n<|user|>\nAnother question<|end|>\n<|assistant|>\n", }; std::vector formatted_chat(1024); int32_t res; From 1debe72737ea131cb52975da3d53ed3a835df3a6 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 23 May 2024 17:17:43 +0300 Subject: [PATCH 099/181] ggml : silence UB sanitizer error during iq2_xxs quantization (#0) --- ggml-quants.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-quants.c b/ggml-quants.c index 88f58a339..bb01ce93c 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -12144,7 +12144,7 @@ static void quantize_row_iq2_xxs_impl(const float * restrict x, void * restrict printf("\n"); GGML_ASSERT(false); } - q2[2*ib+0] |= (grid_index << 8*k); + q2[2*ib+0] |= ((uint32_t) grid_index << 8*k); q2[2*ib+1] |= (block_signs[k] << 7*k); } GGML_ASSERT(scale >= 0); From 74f33adf5f8b20b08fc5a6aa17ce081abe86ef2f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 23 May 2024 17:43:18 +0300 Subject: [PATCH 100/181] readme : remove trailing space (#7469) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 461259afb..2ee267fdf 100644 --- a/README.md +++ b/README.md @@ -141,7 +141,7 @@ Typically finetunes of the base models below are supported as well. - [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL) - [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM) - [x] [Moondream](https://huggingface.co/vikhyatk/moondream2) -- [x] [Bunny](https://github.com/BAAI-DCAI/Bunny) +- [x] [Bunny](https://github.com/BAAI-DCAI/Bunny) **HTTP server** From 0df0aa8e43c3378975269a51f9b876c8692e70da Mon Sep 17 00:00:00 2001 From: Neo Zhang <14088817+arthw@users.noreply.github.com> Date: Fri, 24 May 2024 10:06:56 +0800 Subject: [PATCH 101/181] add build shared lib in win release package (#7438) --- examples/sycl/win-build-sycl.bat | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/sycl/win-build-sycl.bat b/examples/sycl/win-build-sycl.bat index 1b0dc41ba..b8037aae8 100644 --- a/examples/sycl/win-build-sycl.bat +++ b/examples/sycl/win-build-sycl.bat @@ -13,10 +13,10 @@ if %errorlevel% neq 0 goto ERROR :: 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 +:: cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -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 +cmake -G "MinGW Makefiles" .. -DLLAMA_SYCL=ON -DCMAKE_C_COMPILER=icx -DCMAKE_CXX_COMPILER=icx -DBUILD_SHARED_LIBS=ON -DCMAKE_BUILD_TYPE=Release if %errorlevel% neq 0 goto ERROR :: build example/main only :: make main From fbca2f27fc7fa9aa4a8ad0357478fdb908472908 Mon Sep 17 00:00:00 2001 From: fairydreaming <166155368+fairydreaming@users.noreply.github.com> Date: Fri, 24 May 2024 14:31:13 +0200 Subject: [PATCH 102/181] Add support for ArcticForCausalLM (#7020) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * common : increase max number of experts to 128 * common : add tensor LLM_TENSOR_FFN_NORM_EXPS for normalization before MoE that runs in parallel to attention + ffn * gguf-py : add architecture-specific block mappings that override selected general block mappings * convert-hf : add model conversion support for ArcticForCausalLM * convert-hf : use added_tokens_decoder from tokenizer_config.json to redefine tokens from SentencePiece model (only for ArcticForCausalLM) * llama : add inference support for LLM_ARCH_ARCTIC --------- Co-authored-by: Stanisław Szymczyk --- convert-hf-to-gguf.py | 151 ++++++++++++++++ gguf-py/gguf/constants.py | 25 +++ gguf-py/gguf/tensor_mapping.py | 19 ++- llama.cpp | 304 ++++++++++++++++++++++++++++----- 4 files changed, 456 insertions(+), 43 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 5a00a5e89..998877c26 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -2466,6 +2466,157 @@ class JinaBertV2Model(BertModel): self.gguf_writer.add_add_eos_token(True) +@Model.register("ArcticForCausalLM") +class ArcticModel(Model): + model_arch = gguf.MODEL_ARCH.ARCTIC + + def set_vocab(self): + # The reason for using a custom implementation here is that the + # snowflake-arctic-instruct model redefined tokens 31998 and 31999 from + # tokenizer.model and used them as BOS and EOS instead of adding new tokens. + from sentencepiece import SentencePieceProcessor + + tokenizer_path = self.dir_model / 'tokenizer.model' + + if not tokenizer_path.is_file(): + logger.error(f'Error: Missing {tokenizer_path}') + sys.exit(1) + + # Read the whole vocabulary from the tokenizer.model file + tokenizer = SentencePieceProcessor() + tokenizer.LoadFromFile(str(tokenizer_path)) + + vocab_size = self.hparams.get('vocab_size', tokenizer.vocab_size()) + + tokens: list[bytes] = [f"[PAD{i}]".encode("utf-8") for i in range(vocab_size)] + scores: list[float] = [-10000.0] * vocab_size + toktypes: list[int] = [SentencePieceTokenTypes.UNKNOWN] * vocab_size + + for token_id in range(tokenizer.vocab_size()): + + piece = tokenizer.IdToPiece(token_id) + text = piece.encode("utf-8") + score = tokenizer.GetScore(token_id) + + toktype = SentencePieceTokenTypes.NORMAL + if tokenizer.IsUnknown(token_id): + toktype = SentencePieceTokenTypes.UNKNOWN + elif tokenizer.IsControl(token_id): + toktype = SentencePieceTokenTypes.CONTROL + elif tokenizer.IsUnused(token_id): + toktype = SentencePieceTokenTypes.UNUSED + elif tokenizer.IsByte(token_id): + toktype = SentencePieceTokenTypes.BYTE + + tokens[token_id] = text + scores[token_id] = score + toktypes[token_id] = toktype + + # Use the added_tokens_decoder field from tokeniser_config.json as the source + # of information about added/redefined tokens and modify them accordingly. + tokenizer_config_file = self.dir_model / 'tokenizer_config.json' + if tokenizer_config_file.is_file(): + with open(tokenizer_config_file, "r", encoding="utf-8") as f: + tokenizer_config_json = json.load(f) + + if "added_tokens_decoder" in tokenizer_config_json: + added_tokens_decoder = tokenizer_config_json["added_tokens_decoder"] + for token_id, token_json in added_tokens_decoder.items(): + token_id = int(token_id) + if (token_id >= vocab_size): + logger.debug(f'ignore token {token_id}: id is out of range, max={vocab_size - 1}') + continue + + token_content = token_json["content"] + token_type = SentencePieceTokenTypes.USER_DEFINED + token_score = -10000.0 + + # Map unk_token to UNKNOWN, other special tokens to CONTROL + # Set the score to 0.0 as in the original tokenizer.model + if ("special" in token_json) and token_json["special"]: + if token_content == tokenizer_config_json["unk_token"]: + token_type = SentencePieceTokenTypes.UNKNOWN + else: + token_type = SentencePieceTokenTypes.CONTROL + token_score = 0.0 + + logger.info(f"Setting added token {token_id} to '{token_content}' (type: {token_type}, score: {token_score:.2f})") + tokens[token_id] = token_content.encode("utf-8") + toktypes[token_id] = token_type + scores[token_id] = token_score + + self.gguf_writer.add_tokenizer_model("llama") + self.gguf_writer.add_tokenizer_pre("default") + 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) + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + self.gguf_writer.add_rope_dimension_count(hparams["hidden_size"] // hparams["num_attention_heads"]) + + _experts: list[dict[str, Tensor]] | None = None + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + n_head = self.hparams["num_attention_heads"] + n_kv_head = self.hparams.get("num_key_value_heads") + + if name.endswith("q_proj.weight"): + data_torch = LlamaModel.permute(data_torch, n_head, n_head) + if name.endswith("k_proj.weight"): + data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) + + # process the experts separately + if name.find("block_sparse_moe.experts") != -1: + n_experts = self.hparams["num_local_experts"] + + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for wid in ["w1", "w2", "w3"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.block_sparse_moe.experts.{xid}.{wid}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"layers.{bid}.feed_forward.experts.{wid}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + return tensors + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + def write_tensors(self): + super().write_tensors() + + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + + ###### CONVERSION LOGIC ###### diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index 67e23dcc1..c9ae259e1 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -139,6 +139,7 @@ class MODEL_ARCH(IntEnum): COMMAND_R = auto() DBRX = auto() OLMO = auto() + ARCTIC = auto() class MODEL_TENSOR(IntEnum): @@ -167,6 +168,7 @@ class MODEL_TENSOR(IntEnum): FFN_DOWN = auto() FFN_UP = auto() FFN_ACT = auto() + FFN_NORM_EXP = auto() FFN_GATE_EXP = auto() FFN_DOWN_EXP = auto() FFN_UP_EXP = auto() @@ -218,6 +220,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.COMMAND_R: "command-r", MODEL_ARCH.DBRX: "dbrx", MODEL_ARCH.OLMO: "olmo", + MODEL_ARCH.ARCTIC: "arctic", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -251,6 +254,7 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = { MODEL_TENSOR.FFN_DOWN_SHEXP: "blk.{bid}.ffn_down_shexp", MODEL_TENSOR.FFN_UP_SHEXP: "blk.{bid}.ffn_up_shexp", MODEL_TENSOR.FFN_ACT: "blk.{bid}.ffn", + MODEL_TENSOR.FFN_NORM_EXP: "blk.{bid}.ffn_norm_exps", MODEL_TENSOR.FFN_GATE_EXP: "blk.{bid}.ffn_gate_exps", MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down_exps", MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up_exps", @@ -732,6 +736,27 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN, MODEL_TENSOR.FFN_UP, ], + MODEL_ARCH.ARCTIC: [ + 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_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_NORM_EXP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], # TODO } diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 8e1cac915..8b1b21d78 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -244,6 +244,7 @@ class TensorNameMap: "encoder.layers.{bid}.mlp.fc11", # nomic-bert "model.layers.{bid}.mlp.c_fc", # starcoder2 "encoder.layer.{bid}.mlp.gated_layers_v", # jina-bert-v2 + "model.layers.{bid}.residual_mlp.w3", # arctic ), MODEL_TENSOR.FFN_UP_EXP: ( @@ -272,6 +273,7 @@ class TensorNameMap: "encoder.layers.{bid}.mlp.fc12", # nomic-bert "encoder.layer.{bid}.mlp.gated_layers_w", # jina-bert-v2 "transformer.h.{bid}.mlp.linear_1", # refact + "model.layers.{bid}.residual_mlp.w1", # arctic ), MODEL_TENSOR.FFN_GATE_EXP: ( @@ -306,6 +308,7 @@ class TensorNameMap: "encoder.layers.{bid}.mlp.fc2", # nomic-bert "model.layers.{bid}.mlp.c_proj", # starcoder2 "encoder.layer.{bid}.mlp.wo", # jina-bert-v2 + "model.layers.{bid}.residual_mlp.w2", # arctic ), MODEL_TENSOR.FFN_DOWN_EXP: ( @@ -382,6 +385,18 @@ class TensorNameMap: ), } + # architecture-specific block mappings + arch_block_mappings_cfg: dict[MODEL_ARCH, dict[MODEL_TENSOR, tuple[str, ...]]] = { + MODEL_ARCH.ARCTIC: { + MODEL_TENSOR.FFN_NORM: ( + "model.layers.{bid}.residual_layernorm", + ), + MODEL_TENSOR.FFN_NORM_EXP: ( + "model.layers.{bid}.post_attention_layernorm", + ), + }, + } + mapping: dict[str, tuple[MODEL_TENSOR, str]] def __init__(self, arch: MODEL_ARCH, n_blocks: int): @@ -393,12 +408,14 @@ class TensorNameMap: self.mapping[tensor_name] = (tensor, tensor_name) for key in keys: self.mapping[key] = (tensor, tensor_name) + if arch in self.arch_block_mappings_cfg: + self.block_mappings_cfg.update(self.arch_block_mappings_cfg[arch]) for bid in range(n_blocks): for tensor, keys in self.block_mappings_cfg.items(): if tensor not in MODEL_TENSORS[arch]: continue # TODO: make this configurable - n_experts = 60 + n_experts = 128 for xid in range(n_experts): tensor_name = TENSOR_NAMES[tensor].format(bid = bid, xid = xid) self.mapping[tensor_name] = (tensor, tensor_name) diff --git a/llama.cpp b/llama.cpp index 15c660775..3c9fe15bb 100644 --- a/llama.cpp +++ b/llama.cpp @@ -103,7 +103,7 @@ #endif #define LLAMA_MAX_NODES 8192 -#define LLAMA_MAX_EXPERTS 60 +#define LLAMA_MAX_EXPERTS 128 // // logging @@ -221,6 +221,7 @@ enum llm_arch { LLM_ARCH_COMMAND_R, LLM_ARCH_DBRX, LLM_ARCH_OLMO, + LLM_ARCH_ARCTIC, LLM_ARCH_UNKNOWN, }; @@ -257,6 +258,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_COMMAND_R, "command-r" }, { LLM_ARCH_DBRX, "dbrx" }, { LLM_ARCH_OLMO, "olmo" }, + { LLM_ARCH_ARCTIC, "arctic" }, { LLM_ARCH_UNKNOWN, "(unknown)" }, }; @@ -455,6 +457,7 @@ enum llm_tensor { LLM_TENSOR_FFN_DOWN_EXP, // split experts for backward compatibility LLM_TENSOR_FFN_GATE_EXP, LLM_TENSOR_FFN_UP_EXP, + LLM_TENSOR_FFN_NORM_EXPS, LLM_TENSOR_FFN_DOWN_EXPS, // merged experts LLM_TENSOR_FFN_GATE_EXPS, LLM_TENSOR_FFN_UP_EXPS, @@ -1032,6 +1035,28 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" }, }, }, + { + LLM_ARCH_ARCTIC, + { + { 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_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_NORM_EXPS, "blk.%d.ffn_norm_exps" }, + { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" }, + { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" }, + { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -1732,6 +1757,7 @@ enum e_model { MODEL_8x7B, MODEL_8x22B, MODEL_16x12B, + MODEL_10B_128x3_66B, }; static const size_t kiB = 1024; @@ -1907,6 +1933,7 @@ struct llama_layer { struct ggml_tensor * ffn_norm_b; struct ggml_tensor * layer_out_norm; struct ggml_tensor * layer_out_norm_b; + struct ggml_tensor * ffn_norm_exps; // ff struct ggml_tensor * ffn_gate; // w1 @@ -3781,47 +3808,48 @@ static std::string llama_model_ftype_name(llama_ftype ftype) { static const char * llama_model_type_name(e_model type) { switch (type) { - case MODEL_14M: return "14M"; - case MODEL_17M: return "17M"; - case MODEL_22M: return "22M"; - case MODEL_33M: return "33M"; - case MODEL_70M: return "70M"; - case MODEL_109M: return "109M"; - case MODEL_137M: return "137M"; - case MODEL_160M: return "160M"; - case MODEL_335M: return "335M"; - case MODEL_410M: return "410M"; - case MODEL_0_5B: return "0.5B"; - case MODEL_1B: return "1B"; - case MODEL_1_4B: return "1.4B"; - case MODEL_2B: return "2B"; - case MODEL_2_8B: return "2.8B"; - case MODEL_3B: return "3B"; - case MODEL_4B: return "4B"; - case MODEL_6_9B: return "6.9B"; - case MODEL_7B: return "7B"; - case MODEL_8B: return "8B"; - case MODEL_12B: return "12B"; - 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_35B: return "35B"; - case MODEL_40B: return "40B"; - case MODEL_65B: return "65B"; - case MODEL_70B: return "70B"; - case MODEL_314B: return "314B"; - case MODEL_SMALL: return "0.1B"; - case MODEL_MEDIUM: return "0.4B"; - case MODEL_LARGE: return "0.8B"; - case MODEL_XL: return "1.5B"; - case MODEL_A2_7B: return "A2.7B"; - case MODEL_8x7B: return "8x7B"; - case MODEL_8x22B: return "8x22B"; - case MODEL_16x12B: return "16x12B"; - default: return "?B"; + case MODEL_14M: return "14M"; + case MODEL_17M: return "17M"; + case MODEL_22M: return "22M"; + case MODEL_33M: return "33M"; + case MODEL_70M: return "70M"; + case MODEL_109M: return "109M"; + case MODEL_137M: return "137M"; + case MODEL_160M: return "160M"; + case MODEL_335M: return "335M"; + case MODEL_410M: return "410M"; + case MODEL_0_5B: return "0.5B"; + case MODEL_1B: return "1B"; + case MODEL_1_4B: return "1.4B"; + case MODEL_2B: return "2B"; + case MODEL_2_8B: return "2.8B"; + case MODEL_3B: return "3B"; + case MODEL_4B: return "4B"; + case MODEL_6_9B: return "6.9B"; + case MODEL_7B: return "7B"; + case MODEL_8B: return "8B"; + case MODEL_12B: return "12B"; + 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_35B: return "35B"; + case MODEL_40B: return "40B"; + case MODEL_65B: return "65B"; + case MODEL_70B: return "70B"; + case MODEL_314B: return "314B"; + case MODEL_SMALL: return "0.1B"; + case MODEL_MEDIUM: return "0.4B"; + case MODEL_LARGE: return "0.8B"; + case MODEL_XL: return "1.5B"; + case MODEL_A2_7B: return "A2.7B"; + case MODEL_8x7B: return "8x7B"; + case MODEL_8x22B: return "8x22B"; + case MODEL_16x12B: return "16x12B"; + case MODEL_10B_128x3_66B: return "10B+128x3.66B"; + default: return "?B"; } } @@ -4343,6 +4371,19 @@ static void llm_load_hparams( default: model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_ARCTIC: + { + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + + if (hparams.n_expert == 128) { + switch (hparams.n_layer) { + case 35: model.type = e_model::MODEL_10B_128x3_66B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } else { + model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -6129,6 +6170,46 @@ 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_ARCTIC: + { + 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}, llama_model_loader::TENSOR_NOT_REQUIRED); + // if output is NULL, init from the input tok embed + if (model.output == NULL) { + model.output = ml.create_tensor(ctx_output, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab}, llama_model_loader::TENSOR_DUPLICATED); + } + } + + 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.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_embd}); + layer.ffn_down = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "weight", i), {n_embd, n_embd}); + layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_embd}); + + layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); + layer.ffn_norm_exps = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM_EXPS, "weight", i), {n_embd}); + layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), {n_embd, n_ff, n_expert}, false); + layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), { n_ff, n_embd, n_expert}); + layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); + } + } break; default: throw std::runtime_error("unknown architecture"); } @@ -10790,6 +10871,140 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_arctic() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + // mutable variable, needed during the last layer of the computation to skip unused tokens + int32_t n_tokens = this->n_tokens; + + 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, lctx, hparams, batch, model.tok_embd, cb); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = build_inp_pos(); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + + 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); + + struct ggml_tensor * Kcur = ggml_mul_mat(ctx0, model.layers[il].wk, cur); + cb(Kcur, "Kcur", il); + + struct ggml_tensor * Vcur = ggml_mul_mat(ctx0, model.layers[il].wv, cur); + cb(Vcur, "Vcur", il); + + Qcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens), inp_pos, nullptr, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor, beta_fast, beta_slow + ); + cb(Qcur, "Qcur", il); + + Kcur = ggml_rope_ext( + ctx0, ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens), inp_pos, nullptr, + n_rot, rope_type, 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, cparams, kv_self, gf, + model.layers[il].wo, NULL, + Kcur, Vcur, Qcur, KQ_mask, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il); + } + + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + n_tokens = n_outputs; + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + 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); + + struct ggml_tensor * ffn_out = ggml_add(ctx0, cur, ffn_inp); + cb(ffn_out, "ffn_out", il); + + // MoE + cur = llm_build_norm(ctx0, inpSA, hparams, + model.layers[il].ffn_norm_exps, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm_exps", il); + + cur = llm_build_moe_ffn(ctx0, cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + n_expert, n_expert_used, + LLM_FFN_SILU, true, + cb, il); + cb(cur, "ffn_moe_out", il); + + cur = ggml_add(ctx0, cur, ffn_out); + cb(cur, "ffn_out", il); + + ggml_tensor * layer_dir = lctx.cvec.tensor_for(il); + if (layer_dir != nullptr) { + cur = ggml_add(ctx0, cur, layer_dir); + } + 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_defrag(llama_context & lctx, const std::vector & ids) { @@ -11004,6 +11219,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_gptneox(); } break; + case LLM_ARCH_ARCTIC: + { + result = llm.build_arctic(); + } break; default: GGML_ASSERT(false); } @@ -16015,6 +16234,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_XVERSE: case LLM_ARCH_COMMAND_R: case LLM_ARCH_OLMO: + case LLM_ARCH_ARCTIC: return LLAMA_ROPE_TYPE_NORM; // the pairs of head values are offset by n_rot/2 From 27891f6db03de6e3fd5941983838c29bef253352 Mon Sep 17 00:00:00 2001 From: Brian Date: Fri, 24 May 2024 23:47:56 +1000 Subject: [PATCH 103/181] docker.yml: disable light-intel and server-intel test (#7515) * docker.yml: disable light-intel test * docker.yml: disable server-intel test --- .github/workflows/docker.yml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index 9b03d19bc..c2838cbd9 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -42,8 +42,9 @@ jobs: - { 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" } + # TODO: Disabled due to build issues https://github.com/ggerganov/llama.cpp/issues/7507 + #- { 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@v4 From d041d2ceaaf50e058622d92921b3e680ffa4e9e7 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 24 May 2024 18:59:06 +0300 Subject: [PATCH 104/181] flake.lock: Update (#7232) 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/e5d10a24b66c3ea8f150e47dfdb0416ab7c3390e?narHash=sha256-yzcRNDoyVP7%2BSCNX0wmuDju1NUCt8Dz9%2BlyUXEI0dbI%3D' (2024-05-02) → 'github:hercules-ci/flake-parts/8dc45382d5206bd292f9c2768b8058a8fd8311d9?narHash=sha256-/GJvTdTpuDjNn84j82cU6bXztE0MSkdnTWClUCRub78%3D' (2024-05-16) • Updated input 'nixpkgs': 'github:NixOS/nixpkgs/63c3a29ca82437c87573e4c6919b09a24ea61b0f?narHash=sha256-4cPymbty65RvF1DWQfc%2BBc8B233A1BWxJnNULJKQ1EY%3D' (2024-05-02) → 'github:NixOS/nixpkgs/4a6b83b05df1a8bd7d99095ec4b4d271f2956b64?narHash=sha256-%2BNpbZRCRisUHKQJZF3CT%2Bxn14ZZQO%2BKjxIIanH3Pvn4%3D' (2024-05-17) Co-authored-by: github-actions[bot] --- flake.lock | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/flake.lock b/flake.lock index c9ead0bf7..451dfd32f 100644 --- a/flake.lock +++ b/flake.lock @@ -5,11 +5,11 @@ "nixpkgs-lib": "nixpkgs-lib" }, "locked": { - "lastModified": 1714641030, - "narHash": "sha256-yzcRNDoyVP7+SCNX0wmuDju1NUCt8Dz9+lyUXEI0dbI=", + "lastModified": 1715865404, + "narHash": "sha256-/GJvTdTpuDjNn84j82cU6bXztE0MSkdnTWClUCRub78=", "owner": "hercules-ci", "repo": "flake-parts", - "rev": "e5d10a24b66c3ea8f150e47dfdb0416ab7c3390e", + "rev": "8dc45382d5206bd292f9c2768b8058a8fd8311d9", "type": "github" }, "original": { @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1714635257, - "narHash": "sha256-4cPymbty65RvF1DWQfc+Bc8B233A1BWxJnNULJKQ1EY=", + "lastModified": 1715961556, + "narHash": "sha256-+NpbZRCRisUHKQJZF3CT+xn14ZZQO+KjxIIanH3Pvn4=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "63c3a29ca82437c87573e4c6919b09a24ea61b0f", + "rev": "4a6b83b05df1a8bd7d99095ec4b4d271f2956b64", "type": "github" }, "original": { From b83bab15a5d2a1e7807d09613a9b34309d86cfaa Mon Sep 17 00:00:00 2001 From: compilade Date: Fri, 24 May 2024 21:11:48 -0400 Subject: [PATCH 105/181] gguf-py : fix and simplify quantized shape round-trip (#7483) * gguf-py : fix and simplify quantized shape round-trip * gguf-py : remove unused import --- convert-hf-to-gguf.py | 7 +++---- gguf-py/gguf/gguf_reader.py | 6 +++++- gguf-py/gguf/gguf_writer.py | 8 +++----- gguf-py/gguf/quants.py | 16 +++++++++++++++- gguf-py/scripts/gguf-new-metadata.py | 4 +--- 5 files changed, 27 insertions(+), 14 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 998877c26..51549ac72 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -313,11 +313,10 @@ class Model: data = data.astype(np.float32) data_qtype = gguf.GGMLQuantizationType.F32 - block_size, type_size = gguf.GGML_QUANT_SIZES[data_qtype] + shape = gguf.quant_shape_from_byte_shape(data.shape, data_qtype) if data.dtype == np.uint8 else data.shape + # reverse shape to make it similar to the internal ggml dimension order - shape_str = f"""{{{', '.join(str(n) for n in reversed( - (*data.shape[:-1], data.shape[-1] * data.dtype.itemsize // type_size * block_size)) - )}}}""" + shape_str = f"{{{', '.join(str(n) for n in reversed(shape))}}}" # n_dims is implicit in the shape logger.info(f"{f'%-{max_name_len}s' % f'{new_name},'} {old_dtype} --> {data_qtype.name}, shape = {shape_str}") diff --git a/gguf-py/gguf/gguf_reader.py b/gguf-py/gguf/gguf_reader.py index 21b089f8a..e48bc00c3 100644 --- a/gguf-py/gguf/gguf_reader.py +++ b/gguf-py/gguf/gguf_reader.py @@ -12,6 +12,8 @@ from typing import Any, Literal, NamedTuple, TypeVar, Union import numpy as np import numpy.typing as npt +from .quants import quant_shape_to_byte_shape + if __name__ == "__main__": import sys from pathlib import Path @@ -251,6 +253,7 @@ class GGUFReader: tensor_names.add(tensor_name) ggml_type = GGMLQuantizationType(raw_dtype[0]) n_elems = int(np.prod(dims)) + np_dims = tuple(reversed(dims.tolist())) block_size, type_size = GGML_QUANT_SIZES[ggml_type] n_bytes = n_elems * type_size // block_size data_offs = int(start_offs + offset_tensor[0]) @@ -279,6 +282,7 @@ class GGUFReader: else: item_count = n_bytes item_type = np.uint8 + np_dims = quant_shape_to_byte_shape(np_dims, ggml_type) tensors.append(ReaderTensor( name = tensor_name, tensor_type = ggml_type, @@ -286,7 +290,7 @@ class GGUFReader: n_elements = n_elems, n_bytes = n_bytes, data_offset = data_offs, - data = self._get(data_offs, item_type, item_count), + data = self._get(data_offs, item_type, item_count).reshape(np_dims), field = field, )) self.tensors = tensors diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index 8b41b54ea..c194dd5dd 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -13,7 +13,6 @@ from string import ascii_letters, digits import numpy as np from .constants import ( - GGML_QUANT_SIZES, GGUF_DEFAULT_ALIGNMENT, GGUF_MAGIC, GGUF_VERSION, @@ -26,6 +25,8 @@ from .constants import ( TokenType, ) +from .quants import quant_shape_from_byte_shape + logger = logging.getLogger(__name__) @@ -229,10 +230,7 @@ class GGUFWriter: else: dtype = raw_dtype if tensor_dtype == np.uint8: - block_size, type_size = GGML_QUANT_SIZES[raw_dtype] - if tensor_shape[-1] % type_size != 0: - raise ValueError(f"Quantized tensor row size ({tensor_shape[-1]}) is not a multiple of {dtype.name} type size ({type_size})") - tensor_shape = tuple(tensor_shape[:-1]) + (tensor_shape[-1] // type_size * block_size,) + tensor_shape = quant_shape_from_byte_shape(tensor_shape, raw_dtype) n_dims = len(tensor_shape) self.ti_data += self._pack("I", n_dims) for i in range(n_dims): diff --git a/gguf-py/gguf/quants.py b/gguf-py/gguf/quants.py index e7fc0eae3..b22eec166 100644 --- a/gguf-py/gguf/quants.py +++ b/gguf-py/gguf/quants.py @@ -1,5 +1,5 @@ from __future__ import annotations -from typing import Callable +from typing import Callable, Sequence from numpy.typing import DTypeLike @@ -9,6 +9,20 @@ from .lazy import LazyNumpyTensor import numpy as np +def quant_shape_to_byte_shape(shape: Sequence[int], quant_type: GGMLQuantizationType): + block_size, type_size = GGML_QUANT_SIZES[quant_type] + if shape[-1] % block_size != 0: + raise ValueError(f"Quantized tensor row size ({shape[-1]}) is not a multiple of {quant_type.name} block size ({block_size})") + return (*shape[:-1], shape[-1] // block_size * type_size) + + +def quant_shape_from_byte_shape(shape: Sequence[int], quant_type: GGMLQuantizationType): + block_size, type_size = GGML_QUANT_SIZES[quant_type] + if shape[-1] % type_size != 0: + raise ValueError(f"Quantized tensor bytes per row ({shape[-1]}) is not a multiple of {quant_type.name} type size ({type_size})") + return (*shape[:-1], shape[-1] // type_size * block_size) + + # same as ggml_compute_fp32_to_bf16 in ggml-impl.h def __compute_fp32_to_bf16(n: np.ndarray) -> np.ndarray: n = n.astype(np.float32, copy=False).view(np.int32) diff --git a/gguf-py/scripts/gguf-new-metadata.py b/gguf-py/scripts/gguf-new-metadata.py index 63d3c5d8f..c9f1927f6 100755 --- a/gguf-py/scripts/gguf-new-metadata.py +++ b/gguf-py/scripts/gguf-new-metadata.py @@ -118,9 +118,7 @@ def copy_with_new_metadata(reader: gguf.GGUFReader, writer: gguf.GGUFWriter, new for tensor in reader.tensors: total_bytes += tensor.n_bytes - # Dimensions are written in reverse order, so flip them first - shape = np.flipud(tensor.shape).tolist() - writer.add_tensor_info(tensor.name, shape, tensor.data.dtype, tensor.data.nbytes, tensor.tensor_type) + writer.add_tensor_info(tensor.name, tensor.data.shape, tensor.data.dtype, tensor.data.nbytes, tensor.tensor_type) bar = tqdm(desc="Writing", total=total_bytes, unit="byte", unit_scale=True) From 57684331fc2d685f7d1f5775af0b9e47d1829833 Mon Sep 17 00:00:00 2001 From: Mikko Juola Date: Fri, 24 May 2024 18:14:42 -0700 Subject: [PATCH 106/181] Make tokenize CLI tool have nicer command line arguments. (#6188) * Make tokenizer.cpp CLI tool nicer. Before this commit, tokenize was a simple CLI tool like this: tokenize MODEL_FILENAME PROMPT [--ids] This simple tool loads the model, takes the prompt, and shows the tokens llama.cpp is interpreting. This changeset makes the tokenize more sophisticated, and more useful for debugging and troubleshooting: tokenize [-m, --model MODEL_FILENAME] [--ids] [--stdin] [--prompt] [-f, --file] [--no-bos] [--log-disable] It also behaves nicer on Windows now, interpreting and rendering Unicode from command line arguments and pipes no matter what code page the user has set on their terminal. * style fix: strlen(str) == 0 --> *str == 0 * Simplify tokenize.cpp; by getting rid of handling positional style arguments. It must now be invoked with long --model, --prompt etc. arguments only. Shortens the code. * tokenize.cpp: iostream header no longer required --------- Co-authored-by: Georgi Gerganov Co-authored-by: brian khuu --- examples/tokenize/tokenize.cpp | 368 ++++++++++++++++++++++++++++++++- 1 file changed, 359 insertions(+), 9 deletions(-) diff --git a/examples/tokenize/tokenize.cpp b/examples/tokenize/tokenize.cpp index 8b1baea80..54c9834af 100644 --- a/examples/tokenize/tokenize.cpp +++ b/examples/tokenize/tokenize.cpp @@ -3,40 +3,390 @@ #include #include +#include #include #include -int main(int argc, char ** argv) { - if (argc < 3 || argv[1][0] == '-') { - printf("usage: %s MODEL_PATH PROMPT [--ids]\n" , argv[0]); +#if defined(_WIN32) +#define WIN32_LEAN_AND_MEAN +#include +#include // For CommandLineToArgvW +#endif + +static void print_usage_information(const char * argv0, FILE * stream) { + fprintf(stream, "usage: %s [options]\n\n", argv0); + fprintf(stream, "The tokenize program tokenizes a prompt using a given model,\n"); + fprintf(stream, "and prints the resulting tokens to standard output.\n\n"); + fprintf(stream, "It needs a model file, a prompt, and optionally other flags\n"); + fprintf(stream, "to control the behavior of the tokenizer.\n\n"); + fprintf(stream, " The possible options are:\n"); + fprintf(stream, "\n"); + fprintf(stream, " -h, --help print this help and exit\n"); + fprintf(stream, " -m MODEL_PATH, --model MODEL_PATH path to model.\n"); + fprintf(stream, " --ids if given, only print numerical token IDs, and not token strings.\n"); + fprintf(stream, " The output format looks like [1, 2, 3], i.e. parseable by Python.\n"); + fprintf(stream, " -f PROMPT_FNAME, --file PROMPT_FNAME read prompt from a file.\n"); + fprintf(stream, " -p PROMPT, --prompt PROMPT read prompt from the argument.\n"); + fprintf(stream, " --stdin read prompt from standard input.\n"); + fprintf(stream, " --no-bos do not ever add a BOS token to the prompt, even if normally the model uses a BOS token.\n"); + fprintf(stream, " --log-disable disable logs. Makes stderr quiet when loading the model.\n"); +} + +static void llama_log_callback_null(ggml_log_level level, const char * text, void * user_data) { + (void) level; + (void) text; + (void) user_data; +} + +static std::string read_prompt_from_file(const char * filepath, bool & success) { + success = false; + + std::ifstream in(filepath, std::ios::binary); + if (!in) { + fprintf(stderr, "%s: could not open file '%s' for reading: %s\n", __func__, filepath, strerror(errno)); + return std::string(); + } + // do not assume the file is seekable (e.g. /dev/stdin) + std::stringstream buffer; + buffer << in.rdbuf(); + if (in.fail()) { + fprintf(stderr, "%s: could not read the entire file '%s': %s\n", __func__, filepath, strerror(errno)); + return std::string(); + } + + success = true; + return buffer.str(); +} + +// +// Function: ingest_args(...) -> vector +// +// Takes argc and argv arguments, and converts them to a vector of UTF-8 encoded +// strings, as an STL vector. +// +// In particular, it handles character encoding shenanigans on Windows. +// +// Note: raw_argc and raw_argv are not actually read at all on Windows. +// On Windows we call GetCommandLineW to get the arguments in wchar_t +// format, ignoring the regular argc/argv arguments to main(). +// +// TODO: potential opportunity to roll common stuff into common/console.cpp +// in relation to Windows wchar_t shenanigans. +static std::vector ingest_args(int raw_argc, char ** raw_argv) { + std::vector argv; + + // Handle Windows, if given non-ASCII arguments. + // We convert wchar_t arguments into UTF-8 char* on this platform. + // Lets you invoke 'tokenize' on Windows cmd.exe with non-ASCII characters + // without throwing tantrums. +#if defined(_WIN32) + int argc; + const LPWSTR cmdline_wargv = GetCommandLineW(); + LPWSTR * wargv = CommandLineToArgvW(cmdline_wargv, &argc); + + // silence unused arg warnings + (void) raw_argc; + (void) raw_argv; + + for (int i = 0; i < argc; ++i) { + int length_needed = WideCharToMultiByte(CP_UTF8, 0, wargv[i], wcslen(wargv[i]), 0, 0, NULL, NULL); + char * output_buf = (char *) calloc(length_needed+1, sizeof(char)); + GGML_ASSERT(output_buf); + + WideCharToMultiByte(CP_UTF8, 0, wargv[i], wcslen(wargv[i]), output_buf, length_needed, NULL, NULL); + output_buf[length_needed] = '\0'; + + argv.push_back(output_buf); + free(output_buf); + } + + LocalFree((HLOCAL) wargv); +#else + int argc = raw_argc; + for (int i = 0; i < argc; ++i) { + argv.push_back(raw_argv[i]); + } +#endif + + GGML_ASSERT((unsigned int) argc == argv.size()); + + return argv; +} + +// +// Function: write_utf8_cstr_to_stdout(const char *) -> +// +// writes a string to standard output; taking into account that on Windows +// to display correctly you have to use special handling. Works even if the +// user has not set a unicode code page on a Windows cmd.exe. +// +// In case of invalid UTF-8, invalid_utf8 is set to true on Windows, and something +// a human-readable is written instead. +// +// On non-Windows systems, simply printfs() the string. +static void write_utf8_cstr_to_stdout(const char * str, bool & invalid_utf8) { + invalid_utf8 = false; + +#if defined(_WIN32) + // Are we in a console? + HANDLE hConsole = GetStdHandle(STD_OUTPUT_HANDLE); + DWORD dwMode = 0; + + // According to Microsoft docs: + // "WriteConsole fails if it is used with a standard handle that is redirected to a file." + // Also according to the docs, you can use GetConsoleMode to check for that. + if (hConsole == INVALID_HANDLE_VALUE || !GetConsoleMode(hConsole, &dwMode)) { + printf("%s", str); + return; + } + + // MultiByteToWideChar reports an error if str is empty, don't report + // them as invalid_utf8. + if (*str == 0) { + return; + } + int length_needed = MultiByteToWideChar(CP_UTF8, MB_ERR_INVALID_CHARS, str, strlen(str), NULL, 0); + if (length_needed == 0) { + DWORD err = GetLastError(); + if (err == ERROR_NO_UNICODE_TRANSLATION) { + invalid_utf8 = true; + int len = strlen(str); + printf("<"); + for (int i = 0; i < len; ++i) { + if (i > 0) { + printf(" "); + } + printf("%02x", (uint8_t) str[i]); + } + printf(">"); + return; + } + GGML_ASSERT(false && "MultiByteToWideChar() failed in an unexpected way."); + } + + LPWSTR wstr = (LPWSTR) calloc(length_needed+1, sizeof(*wstr)); + GGML_ASSERT(wstr); + + MultiByteToWideChar(CP_UTF8, 0, str, strlen(str), wstr, length_needed); + WriteConsoleW(hConsole, wstr, length_needed, NULL, NULL); + + free(wstr); +#else + // TODO: reporting invalid_utf8 would be useful on non-Windows too. + // printf will silently just write bad unicode. + printf("%s", str); +#endif +} + +int main(int raw_argc, char ** raw_argv) { + const std::vector argv = ingest_args(raw_argc, raw_argv); + const int argc = argv.size(); + + if (argc <= 1) { + print_usage_information(argv[0].c_str(), stderr); return 1; } - const char * model_path = argv[1]; - const char * prompt = argv[2]; + ////// + // Read out all the command line arguments. + ////// - const bool printing_ids = argc > 3 && std::string(argv[3]) == "--ids"; + // variables where to put any arguments we see. + bool printing_ids = false; + bool no_bos = false; + bool disable_logging = false; + const char * model_path = NULL; + const char * prompt_path = NULL; + const char * prompt_arg = NULL; + + // track which arguments were explicitly given + // used for sanity checking down the line + bool model_path_set = false; + bool prompt_path_set = false; + bool prompt_set = false; + bool stdin_set = false; + + int iarg = 1; + for (; iarg < argc; ++iarg) { + std::string arg{argv[iarg]}; + if (arg == "-h" || arg == "--help") { + print_usage_information(argv[0].c_str(), stdout); + return 0; + } + else if (arg == "--ids") { + printing_ids = true; + } + else if (arg == "-m" || arg == "--model") { + if (model_path_set) { + fprintf(stderr, "Error: -m or --model specified multiple times.\n"); + return 1; + } + model_path = argv[++iarg].c_str(); + model_path_set = true; + } + else if (arg == "--no-bos") { + no_bos = true; + } + else if (arg == "-p" || arg == "--prompt") { + if (prompt_set) { + fprintf(stderr, "Error: -p or --prompt specified multiple times.\n"); + return 1; + } + prompt_arg = argv[++iarg].c_str(); + prompt_set = true; + } + else if (arg == "-f" || arg == "--file") { + if (prompt_path_set) { + fprintf(stderr, "Error: -f or --file specified multiple times.\n"); + return 1; + } + prompt_path = argv[++iarg].c_str(); + prompt_path_set = true; + } + else if (arg == "--stdin") { + stdin_set = true; + } + else if (arg == "--log-disable") { + disable_logging = true; + } + else { + fprintf(stderr, "Error: unknown option '%s'\n", argv[iarg].c_str()); + return 1; + } + } + + ////// + // Sanity check the command line arguments. + ////// + + // Check that we have the required stuff set. + if (model_path_set && model_path == NULL) { + fprintf(stderr, "Error: --model requires an argument.\n"); + return 1; + } + if (!model_path_set) { + fprintf(stderr, "Error: must specify --model.\n"); + return 1; + } + if (prompt_path_set && prompt_path == NULL) { + fprintf(stderr, "Error: --file requires an argument.\n"); + return 1; + } + if (prompt_set && prompt_arg == NULL) { + fprintf(stderr, "Error: --prompt requires an argument.\n"); + return 1; + } + const int prompts_set = !!(prompt_path_set) + !!(prompt_set) + !!(stdin_set); + if (prompts_set > 1) { + fprintf(stderr, "Error: --stdin, --file and --prompt are mutually exclusive.\n"); + return 1; + } + // Must have some prompt. + if (prompts_set == 0) { + fprintf(stderr, "Error: must specify one of: --stdin, --file or --prompt.\n"); + return 1; + } + + GGML_ASSERT(model_path); + GGML_ASSERT(prompt_path || prompt_arg || stdin_set); + + ////// + // Figure out where will the prompt come from. + ////// + + std::string prompt; + if (prompt_path_set) { + bool success = false; + prompt = read_prompt_from_file(prompt_path, success); + if (!success) { + return 1; + } + } else if (prompt_set) { + prompt = prompt_arg; + } else { + GGML_ASSERT(stdin_set); + // we read stdin *after* loading model (early exit if model cannot + // be loaded, which can be a nicer user experience) + } + + ////// + // Start actually doing the tokenizing stuff. + ////// + +#ifdef LOG_DISABLE_LOGS + disable_logging = true; +#endif + + if (disable_logging) { + llama_log_set(llama_log_callback_null, NULL); + } llama_backend_init(); llama_model_params model_params = llama_model_default_params(); model_params.vocab_only = true; llama_model * model = llama_load_model_from_file(model_path, model_params); + if (!model) { + fprintf(stderr, "Error: could not load model from file '%s'.\n", model_path); + return 1; + } llama_context_params ctx_params = llama_context_default_params(); llama_context * ctx = llama_new_context_with_model(model, ctx_params); + if (!ctx) { + fprintf(stderr, "Error: could not create context.\n"); + return 1; + } + + // read entire prompt from stdin? + if (stdin_set) { + GGML_ASSERT(!prompt_path_set && !prompt_set); + + std::stringstream stdin_buffer; + stdin_buffer << std::cin.rdbuf(); + if (std::cin.fail()) { + fprintf(stderr, "Error: could not read the entire standard input.\n"); + return 1; + } + + prompt = stdin_buffer.str(); + } + + const bool model_wants_add_bos = llama_should_add_bos_token(model); + const bool add_bos = model_wants_add_bos && !no_bos; std::vector tokens; + tokens = ::llama_tokenize(model, prompt, add_bos, true); - tokens = ::llama_tokenize(model, prompt, true, true); + if (printing_ids) { + printf("["); + } for (int i = 0; i < (int) tokens.size(); i++) { if (printing_ids) { - printf("%d\n", tokens[i]); + if (i > 0) { + printf(", "); + } + printf("%d", tokens[i]); } else { - printf("%6d -> '%s'\n", tokens[i], llama_token_to_piece(ctx, tokens[i]).c_str()); + bool invalid_utf8 = false; + printf("%6d -> '", tokens[i]); + write_utf8_cstr_to_stdout(llama_token_to_piece(ctx, tokens[i]).c_str(), invalid_utf8); + if (invalid_utf8) { + printf("' (utf-8 decode failure)\n"); + } else { + printf("'\n"); + } } } + if (printing_ids) { + printf("]\n"); + } + + // silence valgrind + llama_free(ctx); + llama_free_model(model); + return 0; } From 902184dd3a9d6685e752b19027a48423742531db Mon Sep 17 00:00:00 2001 From: Xuan Son Nguyen Date: Sat, 25 May 2024 05:30:59 +0200 Subject: [PATCH 107/181] fix missing slash in `fs_get_cache_directory()` (#7503) * fix missing slash in fs_get_cache_directory() * use LOCALAPPDATA for fs_get_cache_directory() * better code style --- common/common.cpp | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 7500e08ff..401d72bac 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -1855,11 +1855,15 @@ bool fs_create_directory_with_parents(const std::string & path) { std::string fs_get_cache_directory() { std::string cache_directory = ""; + auto ensure_trailing_slash = [](std::string p) { + // Make sure to add trailing slash + if (p.back() != DIRECTORY_SEPARATOR) { + p += DIRECTORY_SEPARATOR; + } + return p; + }; if (getenv("LLAMA_CACHE")) { cache_directory = std::getenv("LLAMA_CACHE"); - if (cache_directory.back() != DIRECTORY_SEPARATOR) { - cache_directory += DIRECTORY_SEPARATOR; - } } else { #ifdef __linux__ if (std::getenv("XDG_CACHE_HOME")) { @@ -1870,12 +1874,12 @@ std::string fs_get_cache_directory() { #elif defined(__APPLE__) cache_directory = std::getenv("HOME") + std::string("/Library/Caches/"); #elif defined(_WIN32) - cache_directory = std::getenv("APPDATA"); + cache_directory = std::getenv("LOCALAPPDATA"); #endif // __linux__ + cache_directory = ensure_trailing_slash(cache_directory); cache_directory += "llama.cpp"; - cache_directory += DIRECTORY_SEPARATOR; } - return cache_directory; + return ensure_trailing_slash(cache_directory); } From 9791f402580838d7f8543ae7bc633ef265e436f0 Mon Sep 17 00:00:00 2001 From: Elton Kola Date: Sat, 25 May 2024 04:11:33 -0400 Subject: [PATCH 108/181] android : module (#7502) * move ndk code to a new library * add gradle file --- examples/llama.android/app/build.gradle.kts | 25 +------ .../java/com/example/llama/MainViewModel.kt | 13 ++-- examples/llama.android/build.gradle.kts | 1 + examples/llama.android/llama/.gitignore | 1 + .../src/main/cpp => llama}/CMakeLists.txt | 2 +- examples/llama.android/llama/build.gradle.kts | 68 +++++++++++++++++++ .../llama.android/llama/consumer-rules.pro | 0 .../llama.android/llama/proguard-rules.pro | 21 ++++++ .../llama/cpp/ExampleInstrumentedTest.kt | 24 +++++++ .../llama/src/main/AndroidManifest.xml | 4 ++ .../llama/src/main/cpp/CMakeLists.txt | 49 +++++++++++++ .../src/main/cpp/llama-android.cpp | 28 ++++---- .../java/android/llama/cpp/LLamaAndroid.kt} | 8 +-- .../java/android/llama/cpp/ExampleUnitTest.kt | 17 +++++ examples/llama.android/settings.gradle.kts | 1 + 15 files changed, 213 insertions(+), 49 deletions(-) create mode 100644 examples/llama.android/llama/.gitignore rename examples/llama.android/{app/src/main/cpp => llama}/CMakeLists.txt (98%) create mode 100644 examples/llama.android/llama/build.gradle.kts create mode 100644 examples/llama.android/llama/consumer-rules.pro create mode 100644 examples/llama.android/llama/proguard-rules.pro create mode 100644 examples/llama.android/llama/src/androidTest/java/android/llama/cpp/ExampleInstrumentedTest.kt create mode 100644 examples/llama.android/llama/src/main/AndroidManifest.xml create mode 100644 examples/llama.android/llama/src/main/cpp/CMakeLists.txt rename examples/llama.android/{app => llama}/src/main/cpp/llama-android.cpp (92%) rename examples/llama.android/{app/src/main/java/com/example/llama/Llm.kt => llama/src/main/java/android/llama/cpp/LLamaAndroid.kt} (97%) create mode 100644 examples/llama.android/llama/src/test/java/android/llama/cpp/ExampleUnitTest.kt diff --git a/examples/llama.android/app/build.gradle.kts b/examples/llama.android/app/build.gradle.kts index d42140efe..8d1b37195 100644 --- a/examples/llama.android/app/build.gradle.kts +++ b/examples/llama.android/app/build.gradle.kts @@ -7,8 +7,6 @@ android { namespace = "com.example.llama" compileSdk = 34 - ndkVersion = "26.1.10909125" - defaultConfig { applicationId = "com.example.llama" minSdk = 33 @@ -20,17 +18,6 @@ android { vectorDrawables { useSupportLibrary = true } - ndk { - // Add NDK properties if wanted, e.g. - // abiFilters += listOf("arm64-v8a") - } - externalNativeBuild { - cmake { - arguments += "-DCMAKE_BUILD_TYPE=Release" - cppFlags += listOf() - arguments += listOf() - } - } } buildTypes { @@ -55,17 +42,6 @@ android { composeOptions { kotlinCompilerExtensionVersion = "1.5.1" } - packaging { - resources { - excludes += "/META-INF/{AL2.0,LGPL2.1}" - } - } - externalNativeBuild { - cmake { - path = file("src/main/cpp/CMakeLists.txt") - version = "3.22.1" - } - } } dependencies { @@ -78,6 +54,7 @@ dependencies { implementation("androidx.compose.ui:ui-graphics") implementation("androidx.compose.ui:ui-tooling-preview") implementation("androidx.compose.material3:material3") + implementation(project(":llama")) testImplementation("junit:junit:4.13.2") androidTestImplementation("androidx.test.ext:junit:1.1.5") androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1") diff --git a/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt b/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt index be95e2221..45ac29938 100644 --- a/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt +++ b/examples/llama.android/app/src/main/java/com/example/llama/MainViewModel.kt @@ -1,5 +1,6 @@ package com.example.llama +import android.llama.cpp.LLamaAndroid import android.util.Log import androidx.compose.runtime.getValue import androidx.compose.runtime.mutableStateOf @@ -9,7 +10,7 @@ import androidx.lifecycle.viewModelScope import kotlinx.coroutines.flow.catch import kotlinx.coroutines.launch -class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { +class MainViewModel(private val llamaAndroid: LLamaAndroid = LLamaAndroid.instance()): ViewModel() { companion object { @JvmStatic private val NanosPerSecond = 1_000_000_000.0 @@ -28,7 +29,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { viewModelScope.launch { try { - llm.unload() + llamaAndroid.unload() } catch (exc: IllegalStateException) { messages += exc.message!! } @@ -44,7 +45,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { messages += "" viewModelScope.launch { - llm.send(text) + llamaAndroid.send(text) .catch { Log.e(tag, "send() failed", it) messages += it.message!! @@ -57,7 +58,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { viewModelScope.launch { try { val start = System.nanoTime() - val warmupResult = llm.bench(pp, tg, pl, nr) + val warmupResult = llamaAndroid.bench(pp, tg, pl, nr) val end = System.nanoTime() messages += warmupResult @@ -70,7 +71,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { return@launch } - messages += llm.bench(512, 128, 1, 3) + messages += llamaAndroid.bench(512, 128, 1, 3) } catch (exc: IllegalStateException) { Log.e(tag, "bench() failed", exc) messages += exc.message!! @@ -81,7 +82,7 @@ class MainViewModel(private val llm: Llm = Llm.instance()): ViewModel() { fun load(pathToModel: String) { viewModelScope.launch { try { - llm.load(pathToModel) + llamaAndroid.load(pathToModel) messages += "Loaded $pathToModel" } catch (exc: IllegalStateException) { Log.e(tag, "load() failed", exc) diff --git a/examples/llama.android/build.gradle.kts b/examples/llama.android/build.gradle.kts index 50ebc8211..acd1ada7d 100644 --- a/examples/llama.android/build.gradle.kts +++ b/examples/llama.android/build.gradle.kts @@ -2,4 +2,5 @@ plugins { id("com.android.application") version "8.2.0" apply false id("org.jetbrains.kotlin.android") version "1.9.0" apply false + id("com.android.library") version "8.2.0" apply false } diff --git a/examples/llama.android/llama/.gitignore b/examples/llama.android/llama/.gitignore new file mode 100644 index 000000000..796b96d1c --- /dev/null +++ b/examples/llama.android/llama/.gitignore @@ -0,0 +1 @@ +/build diff --git a/examples/llama.android/app/src/main/cpp/CMakeLists.txt b/examples/llama.android/llama/CMakeLists.txt similarity index 98% rename from examples/llama.android/app/src/main/cpp/CMakeLists.txt rename to examples/llama.android/llama/CMakeLists.txt index 4536974a5..a5618cac0 100644 --- a/examples/llama.android/app/src/main/cpp/CMakeLists.txt +++ b/examples/llama.android/llama/CMakeLists.txt @@ -42,7 +42,7 @@ add_subdirectory(../../../../../../ build-llama) # used in the AndroidManifest.xml file. add_library(${CMAKE_PROJECT_NAME} SHARED # List C/C++ source files with relative paths to this CMakeLists.txt. - llama-android.cpp) + llama-android.cpp) # Specifies libraries CMake should link to your target library. You # can link libraries from various origins, such as libraries defined in this diff --git a/examples/llama.android/llama/build.gradle.kts b/examples/llama.android/llama/build.gradle.kts new file mode 100644 index 000000000..0a3806172 --- /dev/null +++ b/examples/llama.android/llama/build.gradle.kts @@ -0,0 +1,68 @@ +plugins { + id("com.android.library") + id("org.jetbrains.kotlin.android") +} + +android { + namespace = "android.llama.cpp" + compileSdk = 34 + + defaultConfig { + minSdk = 33 + + testInstrumentationRunner = "androidx.test.runner.AndroidJUnitRunner" + consumerProguardFiles("consumer-rules.pro") + ndk { + // Add NDK properties if wanted, e.g. + // abiFilters += listOf("arm64-v8a") + } + externalNativeBuild { + cmake { + arguments += "-DCMAKE_BUILD_TYPE=Release" + cppFlags += listOf() + arguments += listOf() + + cppFlags("") + } + } + } + + buildTypes { + release { + isMinifyEnabled = false + proguardFiles( + getDefaultProguardFile("proguard-android-optimize.txt"), + "proguard-rules.pro" + ) + } + } + externalNativeBuild { + cmake { + path("src/main/cpp/CMakeLists.txt") + version = "3.22.1" + } + } + compileOptions { + sourceCompatibility = JavaVersion.VERSION_1_8 + targetCompatibility = JavaVersion.VERSION_1_8 + } + kotlinOptions { + jvmTarget = "1.8" + } + + packaging { + resources { + excludes += "/META-INF/{AL2.0,LGPL2.1}" + } + } +} + +dependencies { + + implementation("androidx.core:core-ktx:1.12.0") + implementation("androidx.appcompat:appcompat:1.6.1") + implementation("com.google.android.material:material:1.11.0") + testImplementation("junit:junit:4.13.2") + androidTestImplementation("androidx.test.ext:junit:1.1.5") + androidTestImplementation("androidx.test.espresso:espresso-core:3.5.1") +} diff --git a/examples/llama.android/llama/consumer-rules.pro b/examples/llama.android/llama/consumer-rules.pro new file mode 100644 index 000000000..e69de29bb diff --git a/examples/llama.android/llama/proguard-rules.pro b/examples/llama.android/llama/proguard-rules.pro new file mode 100644 index 000000000..f1b424510 --- /dev/null +++ b/examples/llama.android/llama/proguard-rules.pro @@ -0,0 +1,21 @@ +# Add project specific ProGuard rules here. +# You can control the set of applied configuration files using the +# proguardFiles setting in build.gradle. +# +# For more details, see +# http://developer.android.com/guide/developing/tools/proguard.html + +# If your project uses WebView with JS, uncomment the following +# and specify the fully qualified class name to the JavaScript interface +# class: +#-keepclassmembers class fqcn.of.javascript.interface.for.webview { +# public *; +#} + +# Uncomment this to preserve the line number information for +# debugging stack traces. +#-keepattributes SourceFile,LineNumberTable + +# If you keep the line number information, uncomment this to +# hide the original source file name. +#-renamesourcefileattribute SourceFile diff --git a/examples/llama.android/llama/src/androidTest/java/android/llama/cpp/ExampleInstrumentedTest.kt b/examples/llama.android/llama/src/androidTest/java/android/llama/cpp/ExampleInstrumentedTest.kt new file mode 100644 index 000000000..05d6ab5d2 --- /dev/null +++ b/examples/llama.android/llama/src/androidTest/java/android/llama/cpp/ExampleInstrumentedTest.kt @@ -0,0 +1,24 @@ +package android.llama.cpp + +import androidx.test.platform.app.InstrumentationRegistry +import androidx.test.ext.junit.runners.AndroidJUnit4 + +import org.junit.Test +import org.junit.runner.RunWith + +import org.junit.Assert.* + +/** + * Instrumented test, which will execute on an Android device. + * + * See [testing documentation](http://d.android.com/tools/testing). + */ +@RunWith(AndroidJUnit4::class) +class ExampleInstrumentedTest { + @Test + fun useAppContext() { + // Context of the app under test. + val appContext = InstrumentationRegistry.getInstrumentation().targetContext + assertEquals("android.llama.cpp.test", appContext.packageName) + } +} diff --git a/examples/llama.android/llama/src/main/AndroidManifest.xml b/examples/llama.android/llama/src/main/AndroidManifest.xml new file mode 100644 index 000000000..8bdb7e14b --- /dev/null +++ b/examples/llama.android/llama/src/main/AndroidManifest.xml @@ -0,0 +1,4 @@ + + + + diff --git a/examples/llama.android/llama/src/main/cpp/CMakeLists.txt b/examples/llama.android/llama/src/main/cpp/CMakeLists.txt new file mode 100644 index 000000000..42ebaad49 --- /dev/null +++ b/examples/llama.android/llama/src/main/cpp/CMakeLists.txt @@ -0,0 +1,49 @@ +# For more information about using CMake with Android Studio, read the +# documentation: https://d.android.com/studio/projects/add-native-code.html. +# For more examples on how to use CMake, see https://github.com/android/ndk-samples. + +# Sets the minimum CMake version required for this project. +cmake_minimum_required(VERSION 3.22.1) + +# Declares the project name. The project name can be accessed via ${ PROJECT_NAME}, +# Since this is the top level CMakeLists.txt, the project name is also accessible +# with ${CMAKE_PROJECT_NAME} (both CMake variables are in-sync within the top level +# build script scope). +project("llama-android") + +include(FetchContent) +FetchContent_Declare( + llama + GIT_REPOSITORY https://github.com/ggerganov/llama.cpp + GIT_TAG master +) + +# Also provides "common" +FetchContent_MakeAvailable(llama) + +# Creates and names a library, sets it as either STATIC +# or SHARED, and provides the relative paths to its source code. +# You can define multiple libraries, and CMake builds them for you. +# Gradle automatically packages shared libraries with your APK. +# +# In this top level CMakeLists.txt, ${CMAKE_PROJECT_NAME} is used to define +# the target library name; in the sub-module's CMakeLists.txt, ${PROJECT_NAME} +# is preferred for the same purpose. +# +# In order to load a library into your app from Java/Kotlin, you must call +# System.loadLibrary() and pass the name of the library defined here; +# for GameActivity/NativeActivity derived applications, the same library name must be +# used in the AndroidManifest.xml file. +add_library(${CMAKE_PROJECT_NAME} SHARED + # List C/C++ source files with relative paths to this CMakeLists.txt. + llama-android.cpp) + +# Specifies libraries CMake should link to your target library. You +# can link libraries from various origins, such as libraries defined in this +# build script, prebuilt third-party libraries, or Android system libraries. +target_link_libraries(${CMAKE_PROJECT_NAME} + # List libraries link to the target library + llama + common + android + log) diff --git a/examples/llama.android/app/src/main/cpp/llama-android.cpp b/examples/llama.android/llama/src/main/cpp/llama-android.cpp similarity index 92% rename from examples/llama.android/app/src/main/cpp/llama-android.cpp rename to examples/llama.android/llama/src/main/cpp/llama-android.cpp index 4af9de303..874158ef0 100644 --- a/examples/llama.android/app/src/main/cpp/llama-android.cpp +++ b/examples/llama.android/llama/src/main/cpp/llama-android.cpp @@ -81,7 +81,7 @@ static void log_callback(ggml_log_level level, const char * fmt, void * data) { extern "C" JNIEXPORT jlong JNICALL -Java_com_example_llama_Llm_load_1model(JNIEnv *env, jobject, jstring filename) { +Java_android_llama_cpp_LLamaAndroid_load_1model(JNIEnv *env, jobject, jstring filename) { llama_model_params model_params = llama_model_default_params(); auto path_to_model = env->GetStringUTFChars(filename, 0); @@ -101,13 +101,13 @@ Java_com_example_llama_Llm_load_1model(JNIEnv *env, jobject, jstring filename) { extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_free_1model(JNIEnv *, jobject, jlong model) { +Java_android_llama_cpp_LLamaAndroid_free_1model(JNIEnv *, jobject, jlong model) { llama_free_model(reinterpret_cast(model)); } extern "C" JNIEXPORT jlong JNICALL -Java_com_example_llama_Llm_new_1context(JNIEnv *env, jobject, jlong jmodel) { +Java_android_llama_cpp_LLamaAndroid_new_1context(JNIEnv *env, jobject, jlong jmodel) { auto model = reinterpret_cast(jmodel); if (!model) { @@ -139,25 +139,25 @@ Java_com_example_llama_Llm_new_1context(JNIEnv *env, jobject, jlong jmodel) { extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_free_1context(JNIEnv *, jobject, jlong context) { +Java_android_llama_cpp_LLamaAndroid_free_1context(JNIEnv *, jobject, jlong context) { llama_free(reinterpret_cast(context)); } extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_backend_1free(JNIEnv *, jobject) { +Java_android_llama_cpp_LLamaAndroid_backend_1free(JNIEnv *, jobject) { llama_backend_free(); } extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_log_1to_1android(JNIEnv *, jobject) { +Java_android_llama_cpp_LLamaAndroid_log_1to_1android(JNIEnv *, jobject) { llama_log_set(log_callback, NULL); } extern "C" JNIEXPORT jstring JNICALL -Java_com_example_llama_Llm_bench_1model( +Java_android_llama_cpp_LLamaAndroid_bench_1model( JNIEnv *env, jobject, jlong context_pointer, @@ -271,13 +271,13 @@ Java_com_example_llama_Llm_bench_1model( extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_free_1batch(JNIEnv *, jobject, jlong batch_pointer) { +Java_android_llama_cpp_LLamaAndroid_free_1batch(JNIEnv *, jobject, jlong batch_pointer) { llama_batch_free(*reinterpret_cast(batch_pointer)); } extern "C" JNIEXPORT jlong JNICALL -Java_com_example_llama_Llm_new_1batch(JNIEnv *, jobject, jint n_tokens, jint embd, jint n_seq_max) { +Java_android_llama_cpp_LLamaAndroid_new_1batch(JNIEnv *, jobject, jint n_tokens, jint embd, jint n_seq_max) { // Source: Copy of llama.cpp:llama_batch_init but heap-allocated. @@ -313,19 +313,19 @@ Java_com_example_llama_Llm_new_1batch(JNIEnv *, jobject, jint n_tokens, jint emb extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_backend_1init(JNIEnv *, jobject) { +Java_android_llama_cpp_LLamaAndroid_backend_1init(JNIEnv *, jobject) { llama_backend_init(); } extern "C" JNIEXPORT jstring JNICALL -Java_com_example_llama_Llm_system_1info(JNIEnv *env, jobject) { +Java_android_llama_cpp_LLamaAndroid_system_1info(JNIEnv *env, jobject) { return env->NewStringUTF(llama_print_system_info()); } extern "C" JNIEXPORT jint JNICALL -Java_com_example_llama_Llm_completion_1init( +Java_android_llama_cpp_LLamaAndroid_completion_1init( JNIEnv *env, jobject, jlong context_pointer, @@ -376,7 +376,7 @@ Java_com_example_llama_Llm_completion_1init( extern "C" JNIEXPORT jstring JNICALL -Java_com_example_llama_Llm_completion_1loop( +Java_android_llama_cpp_LLamaAndroid_completion_1loop( JNIEnv * env, jobject, jlong context_pointer, @@ -438,6 +438,6 @@ Java_com_example_llama_Llm_completion_1loop( extern "C" JNIEXPORT void JNICALL -Java_com_example_llama_Llm_kv_1cache_1clear(JNIEnv *, jobject, jlong context) { +Java_android_llama_cpp_LLamaAndroid_kv_1cache_1clear(JNIEnv *, jobject, jlong context) { llama_kv_cache_clear(reinterpret_cast(context)); } diff --git a/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt b/examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt similarity index 97% rename from examples/llama.android/app/src/main/java/com/example/llama/Llm.kt rename to examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt index d86afee37..6c63e54e0 100644 --- a/examples/llama.android/app/src/main/java/com/example/llama/Llm.kt +++ b/examples/llama.android/llama/src/main/java/android/llama/cpp/LLamaAndroid.kt @@ -1,4 +1,4 @@ -package com.example.llama +package android.llama.cpp import android.util.Log import kotlinx.coroutines.CoroutineDispatcher @@ -10,7 +10,7 @@ import kotlinx.coroutines.withContext import java.util.concurrent.Executors import kotlin.concurrent.thread -class Llm { +class LLamaAndroid { private val tag: String? = this::class.simpleName private val threadLocalState: ThreadLocal = ThreadLocal.withInitial { State.Idle } @@ -165,8 +165,8 @@ class Llm { } // Enforce only one instance of Llm. - private val _instance: Llm = Llm() + private val _instance: LLamaAndroid = LLamaAndroid() - fun instance(): Llm = _instance + fun instance(): LLamaAndroid = _instance } } diff --git a/examples/llama.android/llama/src/test/java/android/llama/cpp/ExampleUnitTest.kt b/examples/llama.android/llama/src/test/java/android/llama/cpp/ExampleUnitTest.kt new file mode 100644 index 000000000..cbbb974d3 --- /dev/null +++ b/examples/llama.android/llama/src/test/java/android/llama/cpp/ExampleUnitTest.kt @@ -0,0 +1,17 @@ +package android.llama.cpp + +import org.junit.Test + +import org.junit.Assert.* + +/** + * Example local unit test, which will execute on the development machine (host). + * + * See [testing documentation](http://d.android.com/tools/testing). + */ +class ExampleUnitTest { + @Test + fun addition_isCorrect() { + assertEquals(4, 2 + 2) + } +} diff --git a/examples/llama.android/settings.gradle.kts b/examples/llama.android/settings.gradle.kts index 2ba32c4fa..c7c1a034a 100644 --- a/examples/llama.android/settings.gradle.kts +++ b/examples/llama.android/settings.gradle.kts @@ -15,3 +15,4 @@ dependencyResolutionManagement { rootProject.name = "LlamaAndroid" include(":app") +include(":llama") From faa0e6979a11dcb731e9d778ad42ceaa0302015e Mon Sep 17 00:00:00 2001 From: "Masaya, Kato" <62578291+msy-kato@users.noreply.github.com> Date: Sat, 25 May 2024 17:42:31 +0900 Subject: [PATCH 109/181] ggml: aarch64: SVE kernels for q8_0_q8_0, q4_0_q8_0 vector dot (#7433) * Add SVE support for q4_0_q8_0 q8_0_q8_0 * remove ifdef --- CMakeLists.txt | 4 +++ common/common.cpp | 1 + ggml-impl.h | 4 +++ ggml-quants.c | 66 +++++++++++++++++++++++++++++++++++++++++++++-- ggml.c | 10 +++++++ ggml.h | 1 + llama.cpp | 1 + 7 files changed, 85 insertions(+), 2 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index ef02ff669..c5add8239 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -72,6 +72,7 @@ else() set(INS_ENB ON) endif() +option(LLAMA_SVE "llama: enable SVE" OFF) option(LLAMA_AVX "llama: enable AVX" ${INS_ENB}) option(LLAMA_AVX2 "llama: enable AVX2" ${INS_ENB}) option(LLAMA_AVX512 "llama: enable AVX512" OFF) @@ -1040,6 +1041,9 @@ if (CMAKE_OSX_ARCHITECTURES STREQUAL "arm64" OR CMAKE_GENERATOR_PLATFORM_LWR STR # Raspberry Pi 3, 4, Zero 2 (32-bit) list(APPEND ARCH_FLAGS -mno-unaligned-access) endif() + if (LLAMA_SVE) + list(APPEND ARCH_FLAGS -march=armv8.6-a+sve) + endif() endif() elseif (CMAKE_OSX_ARCHITECTURES STREQUAL "x86_64" OR CMAKE_GENERATOR_PLATFORM_LWR MATCHES "^(x86_64|i686|amd64|x64|win32)$" OR (NOT CMAKE_OSX_ARCHITECTURES AND NOT CMAKE_GENERATOR_PLATFORM_LWR AND diff --git a/common/common.cpp b/common/common.cpp index 401d72bac..c64590385 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -2844,6 +2844,7 @@ void yaml_dump_non_result_info(FILE * stream, const gpt_params & params, const l fprintf(stream, "cpu_has_fma: %s\n", ggml_cpu_has_fma() ? "true" : "false"); fprintf(stream, "cpu_has_gpublas: %s\n", ggml_cpu_has_gpublas() ? "true" : "false"); fprintf(stream, "cpu_has_neon: %s\n", ggml_cpu_has_neon() ? "true" : "false"); + fprintf(stream, "cpu_has_sve: %s\n", ggml_cpu_has_sve() ? "true" : "false"); fprintf(stream, "cpu_has_f16c: %s\n", ggml_cpu_has_f16c() ? "true" : "false"); fprintf(stream, "cpu_has_fp16_va: %s\n", ggml_cpu_has_fp16_va() ? "true" : "false"); fprintf(stream, "cpu_has_wasm_simd: %s\n", ggml_cpu_has_wasm_simd() ? "true" : "false"); diff --git a/ggml-impl.h b/ggml-impl.h index 362d40f4d..5e77471f3 100644 --- a/ggml-impl.h +++ b/ggml-impl.h @@ -144,6 +144,10 @@ extern "C" { #endif #endif +#if defined(__ARM_FEATURE_SVE) +#include +#endif + // 16-bit float // on Arm, we use __fp16 // on x86, we use uint16_t diff --git a/ggml-quants.c b/ggml-quants.c index bb01ce93c..4f2c7224c 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -3813,7 +3813,44 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r return; } #endif -#if defined(__ARM_NEON) +#if defined(__ARM_FEATURE_SVE) + const svbool_t ptrueh = svptrue_pat_b8(SV_VL16); + const svbool_t ptruel = svnot_b_z(svptrue_b8(), ptrueh); + + svfloat32_t sumv0 = svdup_n_f32(0.0f); + svfloat32_t sumv1 = svdup_n_f32(0.0f); + + assert(nb % 2 == 0); // TODO: handle odd nb + + for (int i = 0; i < nb; i += 2) { + const block_q4_0 * restrict x0 = &x[i + 0]; + const block_q4_0 * restrict x1 = &x[i + 1]; + const block_q8_0 * restrict y0 = &y[i + 0]; + const block_q8_0 * restrict y1 = &y[i + 1]; + + // load x + const svuint8_t qx0r = svld1rq_u8(svptrue_b8(), x0->qs); + const svuint8_t qx1r = svld1rq_u8(svptrue_b8(), x1->qs); + + // 4-bit -> 8-bit + const svint8_t qx0 = svreinterpret_s8_u8(svlsr_n_u8_m(ptruel, svand_n_u8_m(ptrueh, qx0r, 0x0F), 0x04)); + const svint8_t qx1 = svreinterpret_s8_u8(svlsr_n_u8_m(ptruel, svand_n_u8_m(ptrueh, qx1r, 0x0F), 0x04)); + + // sub 8 + const svint8_t qx0s = svsub_n_s8_x(svptrue_b8(), qx0, 8); + const svint8_t qx1s = svsub_n_s8_x(svptrue_b8(), qx1, 8); + + // load y + const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs); + const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs); + + // dot product + sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx0s, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx1s, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + *s = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1)); +#elif defined(__ARM_NEON) float32x4_t sumv0 = vdupq_n_f32(0.0f); float32x4_t sumv1 = vdupq_n_f32(0.0f); @@ -5384,7 +5421,32 @@ void ggml_vec_dot_q8_0_q8_0(int n, float * restrict s, size_t bs, const void * r return; } #endif -#if defined(__ARM_NEON) +#if defined(__ARM_FEATURE_SVE) + svfloat32_t sumv0 = svdup_n_f32(0.0f); + svfloat32_t sumv1 = svdup_n_f32(0.0f); + + assert(nb % 2 == 0); // TODO: handle odd nb + + for (int i = 0; i < nb; i += 2) { + const block_q8_0 * restrict x0 = &x[i + 0]; + const block_q8_0 * restrict x1 = &x[i + 1]; + const block_q8_0 * restrict y0 = &y[i + 0]; + const block_q8_0 * restrict y1 = &y[i + 1]; + + // load x + const svint8_t qx0 = svld1_s8(svptrue_b8(), x0->qs); + const svint8_t qx1 = svld1_s8(svptrue_b8(), x1->qs); + + // load y + const svint8_t qy0 = svld1_s8(svptrue_b8(), y0->qs); + const svint8_t qy1 = svld1_s8(svptrue_b8(), y1->qs); + + sumv0 = svmla_n_f32_x(svptrue_b32(), sumv0, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx0, qy0)), GGML_FP16_TO_FP32(x0->d)*GGML_FP16_TO_FP32(y0->d)); + sumv1 = svmla_n_f32_x(svptrue_b32(), sumv1, svcvt_f32_s32_x(svptrue_b32(), svdot_s32(svdup_n_s32(0), qx1, qy1)), GGML_FP16_TO_FP32(x1->d)*GGML_FP16_TO_FP32(y1->d)); + } + + *s = svaddv_f32(svptrue_b32(), svadd_f32_x(svptrue_b32(), sumv0, sumv1)); +#elif defined(__ARM_NEON) float32x4_t sumv0 = vdupq_n_f32(0.0f); float32x4_t sumv1 = vdupq_n_f32(0.0f); diff --git a/ggml.c b/ggml.c index 9e72b7a76..5145ceec9 100644 --- a/ggml.c +++ b/ggml.c @@ -22742,6 +22742,16 @@ int ggml_cpu_has_neon(void) { #endif } +int ggml_cpu_has_sve(void) { +#if defined(__ARM_FEATURE_SVE) + // TODO: Currently, SVE 256 bit is only supported. + GGML_ASSERT(svcntb() == QK8_0); + return 1; +#else + return 0; +#endif +} + int ggml_cpu_has_arm_fma(void) { #if defined(__ARM_FEATURE_FMA) return 1; diff --git a/ggml.h b/ggml.h index be81e0c52..f803ba724 100644 --- a/ggml.h +++ b/ggml.h @@ -2404,6 +2404,7 @@ extern "C" { GGML_API int ggml_cpu_has_avx512_bf16(void); GGML_API int ggml_cpu_has_fma (void); GGML_API int ggml_cpu_has_neon (void); + GGML_API int ggml_cpu_has_sve (void); GGML_API int ggml_cpu_has_arm_fma (void); GGML_API int ggml_cpu_has_metal (void); GGML_API int ggml_cpu_has_f16c (void); diff --git a/llama.cpp b/llama.cpp index 3c9fe15bb..85cb3140d 100644 --- a/llama.cpp +++ b/llama.cpp @@ -18337,6 +18337,7 @@ const char * llama_print_system_info(void) { s += "AVX512_BF16 = " + std::to_string(ggml_cpu_has_avx512_bf16()) + " | "; s += "FMA = " + std::to_string(ggml_cpu_has_fma()) + " | "; s += "NEON = " + std::to_string(ggml_cpu_has_neon()) + " | "; + s += "SVE = " + std::to_string(ggml_cpu_has_sve()) + " | "; s += "ARM_FMA = " + std::to_string(ggml_cpu_has_arm_fma()) + " | "; s += "F16C = " + std::to_string(ggml_cpu_has_f16c()) + " | "; s += "FP16_VA = " + std::to_string(ggml_cpu_has_fp16_va()) + " | "; From 00c63907931bb08a0ed2b7e38cf44dd290143cb9 Mon Sep 17 00:00:00 2001 From: Justine Tunney Date: Sat, 25 May 2024 05:04:03 -0400 Subject: [PATCH 110/181] main : don't print special tokens with --grammar (#6923) * main : don't print special tokens with --grammar The CLI interface was recently changed to print special control tokens like the stop message one. This token shouldn't be printed if the grammar flag was passed, unless the grammar specifies it, because that breaks shell-scriptability. * main: use seperate stream for control characters * main: use dprintf and add --ctrl-token-no-out and --ctrl-token-fd-out * main: dprintf isn't part of the IEEE POSIX standard. Just use write(). * main: remove --ctrl-token-fd-out in favor for fcntl() based detection * common.cpp: accidentally removed --interactive-first * main: only merge stdout and control token if not in conversation or grammar mode * main: rejig control token descriptor handling * main: must check pipe status on very top of program * main: renamed --no-special from --ctrl-token-no-out and other refactoring * main: refactor ctrl_token_no_out --> no_special * llama: rename llama_token_is_control_token() to llama_token_is_control() * main: remove special token file descriptor feature (#5) --------- Co-authored-by: Brian --- common/common.cpp | 5 +++++ common/common.h | 1 + examples/main/main.cpp | 20 +++++++++++++++++--- llama.cpp | 4 ++++ llama.h | 3 +++ 5 files changed, 30 insertions(+), 3 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index c64590385..781f2166b 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -904,6 +904,10 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa params.interactive_specials = true; return true; } + if (arg == "--no-special") { + params.no_special = true; + return true; + } if (arg == "--embedding") { params.embedding = true; return true; @@ -1364,6 +1368,7 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param printf(" -i, --interactive run in interactive mode\n"); printf(" --interactive-specials allow special tokens in user text, in interactive mode\n"); printf(" --interactive-first run in interactive mode and wait for input right away\n"); + printf(" --no-special control tokens output disabled\n"); printf(" -cnv, --conversation run in conversation mode (does not print special tokens and suffix/prefix)\n"); printf(" -ins, --instruct run in instruction mode (use with Alpaca models)\n"); printf(" -cml, --chatml run in chatml mode (use with ChatML-compatible models)\n"); diff --git a/common/common.h b/common/common.h index f68f3c297..5388f6b68 100644 --- a/common/common.h +++ b/common/common.h @@ -146,6 +146,7 @@ struct gpt_params { bool use_color = false; // use color to distinguish generations and inputs bool interactive = false; // interactive mode bool interactive_specials = false; // whether to allow special tokens from user, during interactive mode + bool no_special = false; // disable control token output bool conversation = false; // conversation mode (does not print special tokens and suffix/prefix) bool chatml = false; // chatml mode (used for models trained on chatml syntax) bool prompt_cache_all = false; // save user input and generations to prompt cache diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 09fa85fce..ac35772f1 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -740,18 +740,32 @@ int main(int argc, char ** argv) { // display text if (input_echo && display) { for (auto id : embd) { - const std::string token_str = llama_token_to_piece(ctx, id, !params.conversation); - printf("%s", token_str.c_str()); + const std::string token_str = llama_token_to_piece(ctx, id); + // Console/Stream Output + if (!llama_token_is_control(llama_get_model(ctx), id)) { + // Stream Output Token To Standard Output + fprintf(stdout, "%s", token_str.c_str()); + } else if (!params.no_special && !params.conversation) { + // Stream Control Token To Standard Output Stream + fprintf(stdout, "%s", token_str.c_str()); + } + + // Record Displayed Tokens To Log + // Note: Generated tokens are created one by one hence this check if (embd.size() > 1) { + // Incoming Requested Tokens input_tokens.push_back(id); } else { + // Outgoing Generated Tokens output_tokens.push_back(id); output_ss << token_str; } + + fflush(stdout); } - fflush(stdout); } + // reset color to default if there is no pending user input if (input_echo && (int) embd_inp.size() == n_consumed) { console::set_display(console::reset); diff --git a/llama.cpp b/llama.cpp index 85cb3140d..989d27b9d 100644 --- a/llama.cpp +++ b/llama.cpp @@ -17861,6 +17861,10 @@ bool llama_token_is_eog(const struct llama_model * model, llama_token token) { ); } +bool llama_token_is_control(const struct llama_model * model, llama_token token) { + return llama_is_control_token(model->vocab, token); +} + llama_token llama_token_bos(const struct llama_model * model) { return model->vocab.special_bos_id; } diff --git a/llama.h b/llama.h index 16cece5db..16676269d 100644 --- a/llama.h +++ b/llama.h @@ -823,6 +823,9 @@ extern "C" { // Check if the token is supposed to end generation (end-of-generation, eg. EOS, EOT, etc.) LLAMA_API bool llama_token_is_eog(const struct llama_model * model, llama_token token); + // Identify if Token Id is a control token or a render-able token + LLAMA_API bool llama_token_is_control(const struct llama_model * model, llama_token token); + // Special tokens LLAMA_API llama_token llama_token_bos(const struct llama_model * model); // beginning-of-sentence LLAMA_API llama_token llama_token_eos(const struct llama_model * model); // end-of-sentence From 3cbd23ed88c03a27e1eb6090ac4a8186ca9ac29a Mon Sep 17 00:00:00 2001 From: Brian Date: Sat, 25 May 2024 19:30:42 +1000 Subject: [PATCH 111/181] labeler: added Apple Metal detector (+Kompute) (#7529) * labeler: added Apple Metal detector [no ci] * labeler: add Kompute to detector [no ci] --- .github/labeler.yml | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/.github/labeler.yml b/.github/labeler.yml index a67f78044..97d739b58 100644 --- a/.github/labeler.yml +++ b/.github/labeler.yml @@ -1,5 +1,16 @@ # https://github.com/actions/labeler - +Kompute: + - changed-files: + - any-glob-to-any-file: + - ggml-kompute.h + - ggml-kompute.cpp + - README-kompute.md +Apple Metal: + - changed-files: + - any-glob-to-any-file: + - ggml-metal.h + - ggml-metal.cpp + - README-metal.md SYCL: - changed-files: - any-glob-to-any-file: @@ -9,6 +20,7 @@ SYCL: Nvidia GPU: - changed-files: - any-glob-to-any-file: + - ggml-cuda.h - ggml-cuda/** Vulkan: - changed-files: From 9588f196b1d7b21bdff013fcf958c249576b2619 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sat, 25 May 2024 15:21:30 +0300 Subject: [PATCH 112/181] train : change default FA argument (#7528) --- common/train.cpp | 2 +- examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/common/train.cpp b/common/train.cpp index 2d41a1d29..fef1e57c9 100644 --- a/common/train.cpp +++ b/common/train.cpp @@ -1052,7 +1052,7 @@ struct train_params_common get_default_train_params_common() { params.custom_n_ctx = false; - params.use_flash = true; + params.use_flash = false; params.use_checkpointing = true; params.sample_start = ""; diff --git a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp index 746c3fbef..8ca9f8915 100644 --- a/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp +++ b/examples/convert-llama2c-to-ggml/convert-llama2c-to-ggml.cpp @@ -774,7 +774,7 @@ static struct train_params get_default_train_params() { params.samples_start_after_nl = false; params.use_adam = true; - params.use_flash = true; + params.use_flash = false; params.use_scratch = true; // only adam From b9adcbbf92fc7096bee23fe61496d25652ebf765 Mon Sep 17 00:00:00 2001 From: HanishKVC Date: Sun, 26 May 2024 06:26:34 +0530 Subject: [PATCH 113/181] SimpleChat Completion Mode flexibility and cleanup, Settings gMe, Optional sliding window (#7480) * SimpleChat: A placeholder system prompt, Use usage msg in code Just have a alert msg wrt needing javascript enabled in html. And have usage message from js file. Update the usage message a bit. So also enable switch session wrt setup_ui call. Add a possible system prompt as a placeholder for the system-input. * SimpleChat:CompletionMode: Allow control of Role: prefix * SimpleChat:Completion: Avoid Role: prefix; Newline only in between In completion mode * avoid inserting Role: prefix before each role's message * avoid inserting newline at the begin and end of the prompt message. However if there are multiple role messages, then insert newline when going from one role's message to the next role's message. * SimpleChat:CompletionMode: Update readme/usage, trim textarea newline Readme update wrt completion mode behavior. Usage help updated wrt completion mode behavior. When changing from input to textarea elment wrt user input, the last newline at the end of the user input wrt textarea, was forgotten to be filtered, this is fixed now. However if user wants to have a explicit newline they can using shift+enter to insert a newline, that wont be removed. The extra newline removal logic uses substring and keyup to keep things simple and avoid some previously noted bugs wrt other events in the key path as well as IME composition etal. * SimpleChat:SC: Ensure proper clearing/reseting previous logic would have cleared/reset the xchat, without doing the same wrt iLastSys, thus leading to it pointing to a now non existent role-content entry. So if a user set a system prompt and used completion mode, it would have done the half stupid clear, after the model response was got. Inturn when user tries to send a new completion query, it would inturn lead to handle_user_submit trying to add/update system prompt if any, which will fail, bcas iLastSys will be still pointing to a non existant entry. This is fixed now, by having a proper clear helper wrt SC class. * SimpleChat: Update usage note and readme a bit * SimpleChat:Completion: clear any prev chat history at begining Previously any chat history including model response to a completion query would have got cleared, after showing the same to the user, at the end of handle_user_submit, rather than at the begining. This gave the flexibility that user could switch from chat mode to completion mode and have the chat history till then sent to the ai model, as part of the completion query. However this flow also had the issue that, if user switches between different chat sessions, after getting a completion response, they can no longer see the completion query and its response that they had just got. The new flow changes the clearing of chat history wrt completion mode to the begining of handle_user_submit, so that user doesnt lose the last completion mode query and response, till a new completion mode query is sent to the model, even if they were to switch between the chat sessions. At the same time the loss of flexibility wrt converting previous chat history into being part of the completion query implicitly doesnt matter, because now the end user can enter multiline queries. * SimpleChat:Try read json early, if available For later the server flow doesnt seem to be sending back data early, atleast for the request (inc options) that is currently sent. if able to read json data early on in future, as and when ai model is generating data, then this helper needs to indirectly update the chat div with the recieved data, without waiting for the overall data to be available. * SimpleChat: Rename the half asleep mis-spelled global var * SimpleChat: Common chat request options from a global object * SimpleChat: Update title, usage and readme a bit Keep the title simple so that print file name doesnt have chars that need to be removed. Update readme wrt some of the new helpers and options. Change Usage list to a list of lists, add few items and style it to reduce the margin wrt lists. * SimpleChat:ChatRequestOptions: max_tokens As some times based on the query from the user, the ai model may get into a run away kind of generation with repeatations etal, so adding max_tokens to try and limit this run away behaviour, if possible. * SimpleChat: Reduce max_tokens to be small but still sufficient * SimpleChat: Consolidate global vars into gMe, Display to user This allows the end user to see the settings used by the logic, as well as allows users to change/update the settings if they want to by using devel-tools/console * SimpleChat:SlidingWindow: iRecentUserMsgCnt to limit context load This is disabled by default. However if enabled, then in addition to latest system message, only the last N user messages, after the latest system message and its reponses from the ai model will be sent to the ai-model, when querying for a new response. This specified N also includes the latest user query. * SimpleChat: placeholder based usage hint for user-in textarea * SimpleChat: Try make user experience better, if possible Reduce chat history context sent to the server/ai-model to be just the system-prompt, prev-user-request-and-ai-response and cur-user-request, instead of the previous full chat history. This way if there is any response with garbage/repeatation, it doesnt mess with things beyond the next question, in some ways. Increase max_tokens to 1024, so that a relatively large previous reponse doesnt eat up the space available wrt next query-response. However dont forget that the server when started should also be started with a model context size of 1k or more, to be on safe side. Add frequency and presence penalty fields set to 1.2 to the set of fields sent to server along with the user query. So that the model is partly set to try avoid repeating text in its response. * SimpleChat:Add n_predict (equiv max_tokens) for llamacpp server The /completions endpoint of examples/server doesnt take max_tokens, instead it takes the internal n_predict, for now add the same on the client side, maybe later add max_tokens to /completions endpoint handling. * SimpleChat: Note about trying to keep things simple yet flexible --- examples/server/public_simplechat/index.html | 11 +- examples/server/public_simplechat/readme.md | 128 ++++++++++- .../server/public_simplechat/simplechat.css | 7 + .../server/public_simplechat/simplechat.js | 207 +++++++++++++++--- 4 files changed, 314 insertions(+), 39 deletions(-) diff --git a/examples/server/public_simplechat/index.html b/examples/server/public_simplechat/index.html index 1eb390b85..1a1a34208 100644 --- a/examples/server/public_simplechat/index.html +++ b/examples/server/public_simplechat/index.html @@ -1,7 +1,7 @@ - SimpleChat (LlamaCPP, ...) + SimpleChat LlamaCppEtal @@ -30,20 +30,17 @@
- +
-

Enter the system prompt above, before entering/submitting any user query.

-

Enter your text to the ai assistant below.

-

Use shift+enter for inserting enter.

-

Refresh the page to start over fresh.

+

You need to have javascript enabled.

- +
diff --git a/examples/server/public_simplechat/readme.md b/examples/server/public_simplechat/readme.md index 5ac8258f2..de0dfc99d 100644 --- a/examples/server/public_simplechat/readme.md +++ b/examples/server/public_simplechat/readme.md @@ -14,11 +14,15 @@ own system prompts. The UI follows a responsive web design so that the layout can adapt to available display space in a usable enough manner, in general. -NOTE: Given that the idea is for basic minimal testing, it doesnt bother with any model context length and -culling of old messages from the chat. +Allows developer/end-user to control some of the behaviour by updating gMe members from browser's devel-tool +console. -NOTE: It doesnt set any parameters other than temperature for now. However if someone wants they can update -the js file as needed. +NOTE: Given that the idea is for basic minimal testing, it doesnt bother with any model context length and +culling of old messages from the chat by default. However by enabling the sliding window chat logic, a crude +form of old messages culling can be achieved. + +NOTE: It doesnt set any parameters other than temperature and max_tokens for now. However if someone wants +they can update the js file or equivalent member in gMe as needed. ## usage @@ -43,11 +47,33 @@ next run this web front end in examples/server/public_simplechat ### using the front end Open this simple web front end from your local browser + * http://127.0.0.1:PORT/index.html Once inside + * Select between chat and completion mode. By default it is set to chat mode. + +* In completion mode + * logic by default doesnt insert any role specific "ROLE: " prefix wrt each role's message. + If the model requires any prefix wrt user role messages, then the end user has to + explicitly add the needed prefix, when they enter their chat message. + Similarly if the model requires any prefix to trigger assistant/ai-model response, + then the end user needs to enter the same. + This keeps the logic simple, while still giving flexibility to the end user to + manage any templating/tagging requirement wrt their messages to the model. + * the logic doesnt insert newline at the begining and end wrt the prompt message generated. + However if the chat being sent to /completions end point has more than one role's message, + then insert newline when moving from one role's message to the next role's message, so + that it can be clearly identified/distinguished. + * given that /completions endpoint normally doesnt add additional chat-templating of its + own, the above ensures that end user can create a custom single/multi message combo with + any tags/special-tokens related chat templating to test out model handshake. Or enduser + can use it just for normal completion related/based query. + * If you want to provide a system prompt, then ideally enter it first, before entering any user query. + Normally Completion mode doesnt need system prompt, while Chat mode can generate better/interesting + responses with a suitable system prompt. * if chat.add_system_begin is used * you cant change the system prompt, after it is has been submitted once along with user query. * you cant set a system prompt, after you have submitted any user query @@ -55,27 +81,121 @@ Once inside * one can change the system prompt any time during chat, by changing the contents of system prompt. * inturn the updated/changed system prompt will be inserted into the chat session. * this allows for the subsequent user chatting to be driven by the new system prompt set above. + * Enter your query and either press enter or click on the submit button. If you want to insert enter (\n) as part of your chat/query to ai model, use shift+enter. + * Wait for the logic to communicate with the server and get the response. * the user is not allowed to enter any fresh query during this time. * the user input box will be disabled and a working message will be shown in it. + * just refresh the page, to reset wrt the chat history and or system prompt and start afresh. + * Using NewChat one can start independent chat sessions. * two independent chat sessions are setup by default. ## Devel note +### Reason behind this + +The idea is to be easy enough to use for basic purposes, while also being simple and easily discernable +by developers who may not be from web frontend background (so inturn may not be familiar with template / +end-use-specific-language-extensions driven flows) so that they can use it to explore/experiment things. + +And given that the idea is also to help explore/experiment for developers, some flexibility is provided +to change behaviour easily using the devel-tools/console, for now. And skeletal logic has been implemented +to explore some of the end points and ideas/implications around them. + + +### General + +Me/gMe consolidates the settings which control the behaviour into one object. +One can see the current settings, as well as change/update them using browsers devel-tool/console. + + bCompletionFreshChatAlways - whether Completion mode collates complete/sliding-window history when + communicating with the server or only sends the latest user query/message. + + bCompletionInsertStandardRolePrefix - whether Completion mode inserts role related prefix wrt the + messages that get inserted into prompt field wrt /Completion endpoint. + + chatRequestOptions - maintains the list of options/fields to send along with chat request, + irrespective of whether /chat/completions or /completions endpoint. + + If you want to add additional options/fields to send to the server/ai-model, and or + modify the existing options value or remove them, for now you can update this global var + using browser's development-tools/console. + + iRecentUserMsgCnt - a simple minded SlidingWindow to limit context window load at Ai Model end. + This is disabled by default. However if enabled, then in addition to latest system message, only + the last/latest iRecentUserMsgCnt user messages after the latest system prompt and its responses + from the ai model will be sent to the ai-model, when querying for a new response. IE if enabled, + only user messages after the latest system message/prompt will be considered. + + This specified sliding window user message count also includes the latest user query. + <0 : Send entire chat history to server + 0 : Send only the system message if any to the server + >0 : Send the latest chat history from the latest system prompt, limited to specified cnt. + + +By using gMe's iRecentUserMsgCnt and chatRequestOptions.max_tokens one can try to control the +implications of loading of the ai-model's context window by chat history, wrt chat response to +some extent in a simple crude way. + + Sometimes the browser may be stuborn with caching of the file, so your updates to html/css/js may not be visible. Also remember that just refreshing/reloading page in browser or for that matter clearing site data, dont directly override site caching in all cases. Worst case you may have to change port. Or in dev tools of browser, you may be able to disable caching fully. + Concept of multiple chat sessions with different servers, as well as saving and restoring of those across browser usage sessions, can be woven around the SimpleChat/MultiChatUI class and its instances relatively easily, however given the current goal of keeping this simple, it has not been added, for now. + By switching between chat.add_system_begin/anytime, one can control whether one can change the system prompt, anytime during the conversation or only at the beginning. + + +read_json_early, is to experiment with reading json response data early on, if available, +so that user can be shown generated data, as and when it is being generated, rather than +at the end when full data is available. + + the server flow doesnt seem to be sending back data early, atleast for request (inc options) + that is currently sent. + + if able to read json data early on in future, as and when ai model is generating data, then + this helper needs to indirectly update the chat div with the recieved data, without waiting + for the overall data to be available. + + +### Default setup + +By default things are setup to try and make the user experience a bit better, if possible. +However a developer when testing the server of ai-model may want to change these value. + +Using iRecentUserMsgCnt reduce chat history context sent to the server/ai-model to be +just the system-prompt, prev-user-request-and-ai-response and cur-user-request, instead of +full chat history. This way if there is any response with garbage/repeatation, it doesnt +mess with things beyond the next question/request/query, in some ways. + +Set max_tokens to 1024, so that a relatively large previous reponse doesnt eat up the space +available wrt next query-response. However dont forget that the server when started should +also be started with a model context size of 1k or more, to be on safe side. + + The /completions endpoint of examples/server doesnt take max_tokens, instead it takes the + internal n_predict, for now add the same here on the client side, maybe later add max_tokens + to /completions endpoint handling code on server side. + +Frequency and presence penalty fields are set to 1.2 in the set of fields sent to server +along with the user query. So that the model is partly set to try avoid repeating text in +its response. + +A end-user can change these behaviour by editing gMe from browser's devel-tool/console. + + +## At the end + +Also a thank you to all open source and open model developers, who strive for the common good. diff --git a/examples/server/public_simplechat/simplechat.css b/examples/server/public_simplechat/simplechat.css index d45f50a95..20c738b12 100644 --- a/examples/server/public_simplechat/simplechat.css +++ b/examples/server/public_simplechat/simplechat.css @@ -48,6 +48,13 @@ button { flex-direction: column; } +.ul1 { + padding-inline-start: 2vw; +} +.ul2 { + padding-inline-start: 2vw; +} + * { margin: 0.6vmin; } diff --git a/examples/server/public_simplechat/simplechat.js b/examples/server/public_simplechat/simplechat.js index 3fc4dbc20..0c48da879 100644 --- a/examples/server/public_simplechat/simplechat.js +++ b/examples/server/public_simplechat/simplechat.js @@ -14,23 +14,86 @@ class ApiEP { } let gUsageMsg = ` -

Enter the system prompt above, before entering/submitting any user query.

-

Enter your text to the ai assistant below.

-

Use shift+enter for inserting enter.

-

Refresh the page to start over fresh.

+

Usage

+
    +
  • Set system prompt above, to try control ai response charactersitic, if model supports same.
    • +
      +
    • Completion mode normally wont have a system prompt.
      • +
    +
  • Enter your query to ai assistant below.
    • +
      +
    • Completion mode doesnt insert user/role: prefix implicitly.
      • +
    • Use shift+enter for inserting enter/newline.
      • +
    +
  • Default ContextWindow = [System, Last Query+Resp, Cur Query].
    • +
      +
    • experiment iRecentUserMsgCnt, max_tokens, model ctxt window to expand
      • +
    +
`; +/** @typedef {{role: string, content: string}[]} ChatMessages */ + class SimpleChat { constructor() { /** * Maintain in a form suitable for common LLM web service chat/completions' messages entry - * @type {{role: string, content: string}[]} + * @type {ChatMessages} */ this.xchat = []; this.iLastSys = -1; } + clear() { + this.xchat = []; + this.iLastSys = -1; + } + + /** + * Recent chat messages. + * If iRecentUserMsgCnt < 0 + * Then return the full chat history + * Else + * Return chat messages from latest going back till the last/latest system prompt. + * While keeping track that the number of user queries/messages doesnt exceed iRecentUserMsgCnt. + * @param {number} iRecentUserMsgCnt + */ + recent_chat(iRecentUserMsgCnt) { + if (iRecentUserMsgCnt < 0) { + return this.xchat; + } + if (iRecentUserMsgCnt == 0) { + console.warn("WARN:SimpleChat:SC:RecentChat:iRecentUsermsgCnt of 0 means no user message/query sent"); + } + /** @type{ChatMessages} */ + let rchat = []; + let sysMsg = this.get_system_latest(); + if (sysMsg.length != 0) { + rchat.push({role: Roles.System, content: sysMsg}); + } + let iUserCnt = 0; + let iStart = this.xchat.length; + for(let i=this.xchat.length-1; i > this.iLastSys; i--) { + if (iUserCnt >= iRecentUserMsgCnt) { + break; + } + let msg = this.xchat[i]; + if (msg.role == Roles.User) { + iStart = i; + iUserCnt += 1; + } + } + for(let i = iStart; i < this.xchat.length; i++) { + let msg = this.xchat[i]; + if (msg.role == Roles.System) { + continue; + } + rchat.push({role: msg.role, content: msg.content}); + } + return rchat; + } + /** * Add an entry into xchat * @param {string} role @@ -57,7 +120,7 @@ class SimpleChat { div.replaceChildren(); } let last = undefined; - for(const x of this.xchat) { + for(const x of this.recent_chat(gMe.iRecentUserMsgCnt)) { let entry = document.createElement("p"); entry.className = `role-${x.role}`; entry.innerText = `${x.role}: ${x.content}`; @@ -69,17 +132,21 @@ class SimpleChat { } else { if (bClear) { div.innerHTML = gUsageMsg; + gMe.show_info(div); } } } /** - * Add needed fields wrt json object to be sent wrt LLM web services completions endpoint + * Add needed fields wrt json object to be sent wrt LLM web services completions endpoint. + * The needed fields/options are picked from a global object. * Convert the json into string. * @param {Object} obj */ request_jsonstr(obj) { - obj["temperature"] = 0.7; + for(let k in gMe.chatRequestOptions) { + obj[k] = gMe.chatRequestOptions[k]; + } return JSON.stringify(obj); } @@ -88,18 +155,27 @@ class SimpleChat { */ request_messages_jsonstr() { let req = { - messages: this.xchat, + messages: this.recent_chat(gMe.iRecentUserMsgCnt), } return this.request_jsonstr(req); } /** * Return a string form of json object suitable for /completions + * @param {boolean} bInsertStandardRolePrefix Insert ": " as prefix wrt each role's message */ - request_prompt_jsonstr() { + request_prompt_jsonstr(bInsertStandardRolePrefix) { let prompt = ""; - for(const chat of this.xchat) { - prompt += `${chat.role}: ${chat.content}\n`; + let iCnt = 0; + for(const chat of this.recent_chat(gMe.iRecentUserMsgCnt)) { + iCnt += 1; + if (iCnt > 1) { + prompt += "\n"; + } + if (bInsertStandardRolePrefix) { + prompt += `${chat.role}: `; + } + prompt += `${chat.content}`; } let req = { prompt: prompt, @@ -171,7 +247,6 @@ let gChatURL = { 'chat': `${gBaseURL}/chat/completions`, 'completion': `${gBaseURL}/completions`, } -const gbCompletionFreshChatAlways = true; /** @@ -291,6 +366,8 @@ class MultiChatUI { // allow user to insert enter into their message using shift+enter. // while just pressing enter key will lead to submitting. if ((ev.key === "Enter") && (!ev.shiftKey)) { + let value = this.elInUser.value; + this.elInUser.value = value.substring(0,value.length-1); this.elBtnUser.click(); ev.preventDefault(); } @@ -321,6 +398,29 @@ class MultiChatUI { } } + /** + * Try read json response early, if available. + * @param {Response} resp + */ + async read_json_early(resp) { + if (!resp.body) { + throw Error("ERRR:SimpleChat:MCUI:ReadJsonEarly:No body..."); + } + let tdUtf8 = new TextDecoder("utf-8"); + let rr = resp.body.getReader(); + let gotBody = ""; + while(true) { + let { value: cur, done: done} = await rr.read(); + let curBody = tdUtf8.decode(cur); + console.debug("DBUG:SC:PART:", curBody); + gotBody += curBody; + if (done) { + break; + } + } + return JSON.parse(gotBody); + } + /** * Handle user query submit request, wrt specified chat session. * @param {string} chatId @@ -330,6 +430,14 @@ class MultiChatUI { let chat = this.simpleChats[chatId]; + // In completion mode, if configured, clear any previous chat history. + // So if user wants to simulate a multi-chat based completion query, + // they will have to enter the full thing, as a suitable multiline + // user input/query. + if ((apiEP == ApiEP.Completion) && (gMe.bCompletionFreshChatAlways)) { + chat.clear(); + } + chat.add_system_anytime(this.elInSystem.value, chatId); let content = this.elInUser.value; @@ -344,7 +452,7 @@ class MultiChatUI { if (apiEP == ApiEP.Chat) { theBody = chat.request_messages_jsonstr(); } else { - theBody = chat.request_prompt_jsonstr(); + theBody = chat.request_prompt_jsonstr(gMe.bCompletionInsertStandardRolePrefix); } this.elInUser.value = "working..."; @@ -359,6 +467,7 @@ class MultiChatUI { }); let respBody = await resp.json(); + //let respBody = await this.read_json_early(resp); console.debug(`DBUG:SimpleChat:MCUI:${chatId}:HandleUserSubmit:RespBody:${JSON.stringify(respBody)}`); let assistantMsg; if (apiEP == ApiEP.Chat) { @@ -376,13 +485,6 @@ class MultiChatUI { } else { console.debug(`DBUG:SimpleChat:MCUI:HandleUserSubmit:ChatId has changed:[${chatId}] [${this.curChatId}]`); } - // Purposefully clear at end rather than begin of this function - // so that one can switch from chat to completion mode and sequece - // in a completion mode with multiple user-assistant chat data - // from before to be sent/occur once. - if ((apiEP == ApiEP.Completion) && (gbCompletionFreshChatAlways)) { - chat.xchat.length = 0; - } this.ui_reset_userinput(); } @@ -462,17 +564,66 @@ class MultiChatUI { } -let gMuitChat; -const gChatIds = [ "Default", "Other" ]; +class Me { + + constructor() { + this.defaultChatIds = [ "Default", "Other" ]; + this.multiChat = new MultiChatUI(); + this.bCompletionFreshChatAlways = true; + this.bCompletionInsertStandardRolePrefix = false; + this.iRecentUserMsgCnt = 2; + // Add needed fields wrt json object to be sent wrt LLM web services completions endpoint. + this.chatRequestOptions = { + "temperature": 0.7, + "max_tokens": 1024, + "frequency_penalty": 1.2, + "presence_penalty": 1.2, + "n_predict": 1024 + }; + } + + /** + * @param {HTMLDivElement} elDiv + */ + show_info(elDiv) { + + var p = document.createElement("p"); + p.innerText = "Settings (devel-tools-console gMe)"; + p.className = "role-system"; + elDiv.appendChild(p); + + var p = document.createElement("p"); + p.innerText = `bCompletionFreshChatAlways:${this.bCompletionFreshChatAlways}`; + elDiv.appendChild(p); + + p = document.createElement("p"); + p.innerText = `bCompletionInsertStandardRolePrefix:${this.bCompletionInsertStandardRolePrefix}`; + elDiv.appendChild(p); + + p = document.createElement("p"); + p.innerText = `iRecentUserMsgCnt:${this.iRecentUserMsgCnt}`; + elDiv.appendChild(p); + + p = document.createElement("p"); + p.innerText = `chatRequestOptions:${JSON.stringify(this.chatRequestOptions)}`; + elDiv.appendChild(p); + + } + +} + + +/** @type {Me} */ +let gMe; function startme() { console.log("INFO:SimpleChat:StartMe:Starting..."); - gMuitChat = new MultiChatUI(); - for (let cid of gChatIds) { - gMuitChat.new_chat_session(cid); + gMe = new Me(); + for (let cid of gMe.defaultChatIds) { + gMe.multiChat.new_chat_session(cid); } - gMuitChat.setup_ui(gChatIds[0]); - gMuitChat.show_sessions(); + gMe.multiChat.setup_ui(gMe.defaultChatIds[0], true); + gMe.multiChat.show_sessions(); } document.addEventListener("DOMContentLoaded", startme); From 9146d36fe7e3e911a07438c07efc1bae082f6390 Mon Sep 17 00:00:00 2001 From: Aarni Koskela Date: Sun, 26 May 2024 15:09:42 +0300 Subject: [PATCH 114/181] Readme: add akx/ggify to tools (#1484) --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index 2ee267fdf..15519c97f 100644 --- a/README.md +++ b/README.md @@ -203,6 +203,10 @@ Unless otherwise noted these projects are open-source with permissive licensing: *(to have a project listed here, it should clearly state that it depends on `llama.cpp`)* +**Tools:** + +- [akx/ggify](https://github.com/akx/ggify) – download PyTorch models from HuggingFace Hub and convert them to GGML + --- Here is a typical run using LLaMA v2 13B on M2 Ultra: From c429b33beb35f13934a4dfbe0c138d30b45e5d54 Mon Sep 17 00:00:00 2001 From: Bartowski Date: Sun, 26 May 2024 08:28:35 -0400 Subject: [PATCH 115/181] llama : add Smaug 70B support (#7402) --- convert-hf-to-gguf-update.py | 1 + convert-hf-to-gguf.py | 3 +++ llama.cpp | 4 ++++ llama.h | 1 + 4 files changed, 9 insertions(+) diff --git a/convert-hf-to-gguf-update.py b/convert-hf-to-gguf-update.py index 1923b88ba..84b72348d 100755 --- a/convert-hf-to-gguf-update.py +++ b/convert-hf-to-gguf-update.py @@ -81,6 +81,7 @@ models = [ {"name": "jina-v2-en", "tokt": TOKENIZER_TYPE.WPM, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-en", }, # WPM! {"name": "jina-v2-es", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-es", }, {"name": "jina-v2-de", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/jinaai/jina-embeddings-v2-base-de", }, + {"name": "smaug-bpe", "tokt": TOKENIZER_TYPE.BPE, "repo": "https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct", }, ] diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 51549ac72..bfccf8623 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -473,6 +473,9 @@ class Model: if chkhsh == "27949a2493fc4a9f53f5b9b029c82689cfbe5d3a1929bb25e043089e28466de6": # ref: https://huggingface.co/jinaai/jina-embeddings-v2-base-de res = "jina-v2-de" + if chkhsh == "c136ed14d01c2745d4f60a9596ae66800e2b61fa45643e72436041855ad4089d": + # ref: https://huggingface.co/abacusai/Smaug-Llama-3-70B-Instruct + res = "smaug-bpe" if res is None: logger.warning("\n") diff --git a/llama.cpp b/llama.cpp index 989d27b9d..f67cb7e23 100644 --- a/llama.cpp +++ b/llama.cpp @@ -4593,6 +4593,9 @@ static void llm_load_vocab( } else if ( tokenizer_pre == "dbrx") { vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_DBRX; + } else if ( + tokenizer_pre == "smaug-bpe") { + vocab.type_pre = LLAMA_VOCAB_PRE_TYPE_SMAUG; } else { throw std::runtime_error(format("unknown pre-tokenizer type: '%s'", tokenizer_pre.c_str())); } @@ -12512,6 +12515,7 @@ struct llm_tokenizer_bpe { }); break; case LLAMA_VOCAB_PRE_TYPE_DBRX: + case LLAMA_VOCAB_PRE_TYPE_SMAUG: word_collection = unicode_regex_split(text, { // same as llama3 "(?:'[sS]|'[tT]|'[rR][eE]|'[vV][eE]|'[mM]|'[lL][lL]|'[dD])|[^\\r\\n\\p{L}\\p{N}]?\\p{L}+|\\p{N}{1,3}| ?[^\\s\\p{L}\\p{N}]+[\\r\\n]*|\\s*[\\r\\n]+|\\s+(?!\\S)|\\s+", diff --git a/llama.h b/llama.h index 16676269d..7671b8a57 100644 --- a/llama.h +++ b/llama.h @@ -85,6 +85,7 @@ extern "C" { LLAMA_VOCAB_PRE_TYPE_QWEN2 = 11, LLAMA_VOCAB_PRE_TYPE_OLMO = 12, LLAMA_VOCAB_PRE_TYPE_DBRX = 13, + LLAMA_VOCAB_PRE_TYPE_SMAUG = 14, }; // note: these values should be synchronized with ggml_rope From 32a28217f475119926c603341e8273b26932b56a Mon Sep 17 00:00:00 2001 From: Galunid Date: Sun, 26 May 2024 16:02:34 +0200 Subject: [PATCH 116/181] Fix aya-23 conversion scripts (#7539) --- convert-hf-to-gguf.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index bfccf8623..a342f6b1c 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -2395,7 +2395,8 @@ class CommandR2Model(Model): # max_position_embeddings = 8192 in config.json but model was actually # trained on 128k context length - self.hparams["max_position_embeddings"] = self.hparams["model_max_length"] + # aya-23 models don't have model_max_length specified + self.hparams["max_position_embeddings"] = self.find_hparam(["model_max_length", "max_position_embeddings"]) def set_gguf_parameters(self): super().set_gguf_parameters() From d298382ad977ec89c8de7b57459b9d7965d2c272 Mon Sep 17 00:00:00 2001 From: Brian Date: Mon, 27 May 2024 00:10:17 +1000 Subject: [PATCH 117/181] main: replace --no-special with --special (#7534) This also flips the default behavior of the output to not include control token by default. --- common/common.cpp | 6 +++--- common/common.h | 2 +- examples/main/main.cpp | 10 ++-------- 3 files changed, 6 insertions(+), 12 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 781f2166b..65103c3c2 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -904,8 +904,8 @@ bool gpt_params_find_arg(int argc, char ** argv, const std::string & arg, gpt_pa params.interactive_specials = true; return true; } - if (arg == "--no-special") { - params.no_special = true; + if (arg == "--special") { + params.special = true; return true; } if (arg == "--embedding") { @@ -1366,9 +1366,9 @@ void gpt_params_print_usage(int /*argc*/, char ** argv, const gpt_params & param printf(" -h, --help show this help message and exit\n"); printf(" --version show version and build info\n"); printf(" -i, --interactive run in interactive mode\n"); + printf(" --special special tokens output enabled\n"); printf(" --interactive-specials allow special tokens in user text, in interactive mode\n"); printf(" --interactive-first run in interactive mode and wait for input right away\n"); - printf(" --no-special control tokens output disabled\n"); printf(" -cnv, --conversation run in conversation mode (does not print special tokens and suffix/prefix)\n"); printf(" -ins, --instruct run in instruction mode (use with Alpaca models)\n"); printf(" -cml, --chatml run in chatml mode (use with ChatML-compatible models)\n"); diff --git a/common/common.h b/common/common.h index 5388f6b68..264504830 100644 --- a/common/common.h +++ b/common/common.h @@ -146,7 +146,7 @@ struct gpt_params { bool use_color = false; // use color to distinguish generations and inputs bool interactive = false; // interactive mode bool interactive_specials = false; // whether to allow special tokens from user, during interactive mode - bool no_special = false; // disable control token output + bool special = false; // enable special token output bool conversation = false; // conversation mode (does not print special tokens and suffix/prefix) bool chatml = false; // chatml mode (used for models trained on chatml syntax) bool prompt_cache_all = false; // save user input and generations to prompt cache diff --git a/examples/main/main.cpp b/examples/main/main.cpp index ac35772f1..44949ba86 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -740,16 +740,10 @@ int main(int argc, char ** argv) { // display text if (input_echo && display) { for (auto id : embd) { - const std::string token_str = llama_token_to_piece(ctx, id); + const std::string token_str = llama_token_to_piece(ctx, id, params.special); // Console/Stream Output - if (!llama_token_is_control(llama_get_model(ctx), id)) { - // Stream Output Token To Standard Output - fprintf(stdout, "%s", token_str.c_str()); - } else if (!params.no_special && !params.conversation) { - // Stream Control Token To Standard Output Stream - fprintf(stdout, "%s", token_str.c_str()); - } + fprintf(stdout, "%s", token_str.c_str()); // Record Displayed Tokens To Log // Note: Generated tokens are created one by one hence this check From dff451cfa1f297348751ce6b538670e1ae9a7d5b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 26 May 2024 18:54:56 +0300 Subject: [PATCH 118/181] flake.lock: Update (#7540) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Flake lock file updates: • Updated input 'nixpkgs': 'github:NixOS/nixpkgs/4a6b83b05df1a8bd7d99095ec4b4d271f2956b64?narHash=sha256-%2BNpbZRCRisUHKQJZF3CT%2Bxn14ZZQO%2BKjxIIanH3Pvn4%3D' (2024-05-17) → 'github:NixOS/nixpkgs/bfb7a882678e518398ce9a31a881538679f6f092?narHash=sha256-4zSIhSRRIoEBwjbPm3YiGtbd8HDWzFxJjw5DYSDy1n8%3D' (2024-05-24) Co-authored-by: github-actions[bot] --- flake.lock | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/flake.lock b/flake.lock index 451dfd32f..fd6e2a5f6 100644 --- a/flake.lock +++ b/flake.lock @@ -20,11 +20,11 @@ }, "nixpkgs": { "locked": { - "lastModified": 1715961556, - "narHash": "sha256-+NpbZRCRisUHKQJZF3CT+xn14ZZQO+KjxIIanH3Pvn4=", + "lastModified": 1716509168, + "narHash": "sha256-4zSIhSRRIoEBwjbPm3YiGtbd8HDWzFxJjw5DYSDy1n8=", "owner": "NixOS", "repo": "nixpkgs", - "rev": "4a6b83b05df1a8bd7d99095ec4b4d271f2956b64", + "rev": "bfb7a882678e518398ce9a31a881538679f6f092", "type": "github" }, "original": { From d6ef0e77dd25f54fb5856af47e3926cf6f36c281 Mon Sep 17 00:00:00 2001 From: Brian Date: Mon, 27 May 2024 10:54:30 +1000 Subject: [PATCH 119/181] github: add self sorted issue ticket forms (#7543) * github: add self sorted issue ticket forms [no ci] * github: consolidate BSD in bug issue ticket * github: remove contact from bug ticket template [no ci] * github: remove bios from os dropdown in bug report [no ci] --- .github/ISSUE_TEMPLATE/01-bug-low.yml | 50 +++++++++++++++++++++ .github/ISSUE_TEMPLATE/02-bug-medium.yml | 50 +++++++++++++++++++++ .github/ISSUE_TEMPLATE/03-bug-high.yml | 50 +++++++++++++++++++++ .github/ISSUE_TEMPLATE/04-bug-critical.yml | 50 +++++++++++++++++++++ .github/ISSUE_TEMPLATE/05-enhancement.yml | 51 ++++++++++++++++++++++ .github/ISSUE_TEMPLATE/06-question.yml | 38 ++++++++++++++++ .github/ISSUE_TEMPLATE/bug.md | 11 ----- .github/ISSUE_TEMPLATE/enhancement.md | 28 ------------ 8 files changed, 289 insertions(+), 39 deletions(-) create mode 100644 .github/ISSUE_TEMPLATE/01-bug-low.yml create mode 100644 .github/ISSUE_TEMPLATE/02-bug-medium.yml create mode 100644 .github/ISSUE_TEMPLATE/03-bug-high.yml create mode 100644 .github/ISSUE_TEMPLATE/04-bug-critical.yml create mode 100644 .github/ISSUE_TEMPLATE/05-enhancement.yml create mode 100644 .github/ISSUE_TEMPLATE/06-question.yml delete mode 100644 .github/ISSUE_TEMPLATE/bug.md delete mode 100644 .github/ISSUE_TEMPLATE/enhancement.md diff --git a/.github/ISSUE_TEMPLATE/01-bug-low.yml b/.github/ISSUE_TEMPLATE/01-bug-low.yml new file mode 100644 index 000000000..bfb9d9a06 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/01-bug-low.yml @@ -0,0 +1,50 @@ +name: Low Severity Bugs +description: Used to report low severity bugs in llama.cpp (e.g. cosmetic issues, non critical UI glitches) +title: "Bug: " +labels: ["bug-unconfirmed", "low severity"] +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to fill out this bug report! + Please include information about your system, the steps to reproduce the bug, + and the version of llama.cpp that you are using. + If possible, please provide a minimal code example that reproduces the bug. + - type: textarea + id: what-happened + attributes: + label: What happened? + description: Also tell us, what did you expect to happen? + placeholder: Tell us what you see! + validations: + required: true + - type: textarea + id: version + attributes: + label: Name and Version + description: Which executable and which version of our software are you running? (use `--version` to get a version string) + placeholder: | + $./main --version + version: 2999 (42b4109e) + built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu + validations: + required: true + - type: dropdown + id: operating-system + attributes: + label: What operating system are you seeing the problem on? + multiple: true + options: + - Linux + - Mac + - Windows + - BSD + - Other? (Please let us know in description) + validations: + required: false + - type: textarea + id: logs + attributes: + label: Relevant log output + description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. + render: shell diff --git a/.github/ISSUE_TEMPLATE/02-bug-medium.yml b/.github/ISSUE_TEMPLATE/02-bug-medium.yml new file mode 100644 index 000000000..e8297eea0 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/02-bug-medium.yml @@ -0,0 +1,50 @@ +name: Medium Severity Bug +description: Used to report medium severity bugs in llama.cpp (e.g. Malfunctioning Features but generally still useable) +title: "Bug: " +labels: ["bug-unconfirmed", "medium severity"] +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to fill out this bug report! + Please include information about your system, the steps to reproduce the bug, + and the version of llama.cpp that you are using. + If possible, please provide a minimal code example that reproduces the bug. + - type: textarea + id: what-happened + attributes: + label: What happened? + description: Also tell us, what did you expect to happen? + placeholder: Tell us what you see! + validations: + required: true + - type: textarea + id: version + attributes: + label: Name and Version + description: Which executable and which version of our software are you running? (use `--version` to get a version string) + placeholder: | + $./main --version + version: 2999 (42b4109e) + built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu + validations: + required: true + - type: dropdown + id: operating-system + attributes: + label: What operating system are you seeing the problem on? + multiple: true + options: + - Linux + - Mac + - Windows + - BSD + - Other? (Please let us know in description) + validations: + required: false + - type: textarea + id: logs + attributes: + label: Relevant log output + description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. + render: shell diff --git a/.github/ISSUE_TEMPLATE/03-bug-high.yml b/.github/ISSUE_TEMPLATE/03-bug-high.yml new file mode 100644 index 000000000..3c9d50d16 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/03-bug-high.yml @@ -0,0 +1,50 @@ +name: High Severity Bug +description: Used to report high severity bugs in llama.cpp (e.g. Malfunctioning features hindering important common workflow) +title: "Bug: " +labels: ["bug-unconfirmed", "high severity"] +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to fill out this bug report! + Please include information about your system, the steps to reproduce the bug, + and the version of llama.cpp that you are using. + If possible, please provide a minimal code example that reproduces the bug. + - type: textarea + id: what-happened + attributes: + label: What happened? + description: Also tell us, what did you expect to happen? + placeholder: Tell us what you see! + validations: + required: true + - type: textarea + id: version + attributes: + label: Name and Version + description: Which executable and which version of our software are you running? (use `--version` to get a version string) + placeholder: | + $./main --version + version: 2999 (42b4109e) + built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu + validations: + required: true + - type: dropdown + id: operating-system + attributes: + label: What operating system are you seeing the problem on? + multiple: true + options: + - Linux + - Mac + - Windows + - BSD + - Other? (Please let us know in description) + validations: + required: false + - type: textarea + id: logs + attributes: + label: Relevant log output + description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. + render: shell diff --git a/.github/ISSUE_TEMPLATE/04-bug-critical.yml b/.github/ISSUE_TEMPLATE/04-bug-critical.yml new file mode 100644 index 000000000..d089d5fa1 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/04-bug-critical.yml @@ -0,0 +1,50 @@ +name: Critical Severity Bug +description: Used to report critical severity bugs in llama.cpp (e.g. Crashing, Corrupted, Dataloss) +title: "Bug: " +labels: ["bug-unconfirmed", "critical severity"] +body: + - type: markdown + attributes: + value: | + Thanks for taking the time to fill out this bug report! + Please include information about your system, the steps to reproduce the bug, + and the version of llama.cpp that you are using. + If possible, please provide a minimal code example that reproduces the bug. + - type: textarea + id: what-happened + attributes: + label: What happened? + description: Also tell us, what did you expect to happen? + placeholder: Tell us what you see! + validations: + required: true + - type: textarea + id: version + attributes: + label: Name and Version + description: Which executable and which version of our software are you running? (use `--version` to get a version string) + placeholder: | + $./main --version + version: 2999 (42b4109e) + built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu + validations: + required: true + - type: dropdown + id: operating-system + attributes: + label: What operating system are you seeing the problem on? + multiple: true + options: + - Linux + - Mac + - Windows + - BSD + - Other? (Please let us know in description) + validations: + required: false + - type: textarea + id: logs + attributes: + label: Relevant log output + description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. + render: shell diff --git a/.github/ISSUE_TEMPLATE/05-enhancement.yml b/.github/ISSUE_TEMPLATE/05-enhancement.yml new file mode 100644 index 000000000..7f516abb0 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/05-enhancement.yml @@ -0,0 +1,51 @@ +name: Enhancement template +description: Used to request enhancements for llama.cpp +title: "Feature Request: " +labels: ["enhancement"] +body: + - type: markdown + attributes: + value: | + [Please post your idea first in Discussion if there is not yet a consensus for this enhancement request. This will help to keep this issue tracker focused on enhancements that the community has agreed needs to be implemented.](https://github.com/ggerganov/llama.cpp/discussions/categories/ideas) + + - type: checkboxes + id: prerequisites + attributes: + label: Prerequisites + description: Please confirm the following before submitting your enhancement request. + options: + - label: I am running the latest code. Mention the version if possible as well. + required: true + - label: I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md). + required: true + - label: I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed). + required: true + - label: I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new and useful enhancement to share. + required: true + + - type: textarea + id: feature-description + attributes: + label: Feature Description + description: Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an enhancement. + placeholder: Detailed description of the enhancement + validations: + required: true + + - type: textarea + id: motivation + attributes: + label: Motivation + description: Please provide a detailed written description of reasons why this feature is necessary and how it is useful to `llama.cpp` users. + placeholder: Explanation of why this feature is needed and its benefits + validations: + required: true + + - type: textarea + id: possible-implementation + attributes: + label: Possible Implementation + description: If you have an idea as to how it can be implemented, please write a detailed description. Feel free to give links to external sources or share visuals that might be helpful to understand the details better. + placeholder: Detailed description of potential implementation + validations: + required: false diff --git a/.github/ISSUE_TEMPLATE/06-question.yml b/.github/ISSUE_TEMPLATE/06-question.yml new file mode 100644 index 000000000..23ad2f419 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/06-question.yml @@ -0,0 +1,38 @@ +name: Question template +description: Used to ask questions about llama.cpp +title: "Question: " +labels: ["question"] +body: + - type: markdown + attributes: + value: | + [Please search your question first in Discussion if you got a common general question.](https://github.com/ggerganov/llama.cpp/discussions/categories/q-a) + + - type: checkboxes + id: prerequisites + attributes: + label: Prerequisites + description: Please confirm the following before submitting your question. + options: + - label: I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed). + required: true + - label: I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new useful question to share that cannot be answered within Discussions. + required: true + + - type: textarea + id: background-description + attributes: + label: Background Description + description: Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an question. + placeholder: Detailed description of your question + validations: + required: true + + - type: textarea + id: possible-answer + attributes: + label: Possible Answer + description: If you have some idea of possible answers you want to confirm, that would also be appreciated. + placeholder: Your idea of possible answers + validations: + required: false diff --git a/.github/ISSUE_TEMPLATE/bug.md b/.github/ISSUE_TEMPLATE/bug.md deleted file mode 100644 index 49812832c..000000000 --- a/.github/ISSUE_TEMPLATE/bug.md +++ /dev/null @@ -1,11 +0,0 @@ ---- -name: Bug template -about: Used to report bugs in llama.cpp -labels: ["bug-unconfirmed"] -assignees: '' - ---- - -Please include information about your system, the steps to reproduce the bug, and the version of llama.cpp that you are using. If possible, please provide a minimal code example that reproduces the bug. - -If the bug concerns the server, please try to reproduce it first using the [server test scenario framework](https://github.com/ggerganov/llama.cpp/tree/master/examples/server/tests). diff --git a/.github/ISSUE_TEMPLATE/enhancement.md b/.github/ISSUE_TEMPLATE/enhancement.md deleted file mode 100644 index dcffda750..000000000 --- a/.github/ISSUE_TEMPLATE/enhancement.md +++ /dev/null @@ -1,28 +0,0 @@ ---- -name: Enhancement template -about: Used to request enhancements for llama.cpp -labels: ["enhancement"] -assignees: '' - ---- - -# Prerequisites - -Please answer the following questions for yourself before submitting an issue. - -- [ ] I am running the latest code. Development is very rapid so there are no tagged versions as of now. -- [ ] I carefully followed the [README.md](https://github.com/ggerganov/llama.cpp/blob/master/README.md). -- [ ] I [searched using keywords relevant to my issue](https://docs.github.com/en/issues/tracking-your-work-with-issues/filtering-and-searching-issues-and-pull-requests) to make sure that I am creating a new issue that is not already open (or closed). -- [ ] I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new bug or useful enhancement to share. - -# Feature Description - -Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an enhancement. - -# Motivation - -Please provide a detailed written description of reasons why this feature is necessary and how it is useful to `llama.cpp` users. - -# Possible Implementation - -If you have an idea as to how it can be implemented, please write a detailed description. Feel free to give links to external sources or share visuals that might be helpful to understand the details better. From eaf6e031741ca2d3aafeff3e0f4dd7557a974d2b Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 27 May 2024 09:24:13 +0300 Subject: [PATCH 120/181] llama : add comments about experimental flags (#7544) --- llama.h | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/llama.h b/llama.h index 7671b8a57..3e4474bb9 100644 --- a/llama.h +++ b/llama.h @@ -265,6 +265,8 @@ extern "C" { bool check_tensors; // validate model tensor data }; + // NOTE: changing the default values of parameters marked as [EXPERIMENTAL] may cause crashes or incorrect results in certain configurations + // https://github.com/ggerganov/llama.cpp/pull/7544 struct llama_context_params { uint32_t seed; // RNG seed, -1 for random uint32_t n_ctx; // text context, 0 = from model @@ -291,14 +293,14 @@ extern "C" { ggml_backend_sched_eval_callback cb_eval; void * cb_eval_user_data; - enum ggml_type type_k; // data type for K cache - enum ggml_type type_v; // data type for V cache + enum ggml_type type_k; // data type for K cache [EXPERIMENTAL] + enum ggml_type type_v; // data type for V cache [EXPERIMENTAL] // Keep the booleans together to avoid misalignment during copy-by-value. bool logits_all; // the llama_decode() call computes all logits, not just the last one (DEPRECATED - set llama_batch.logits instead) bool embeddings; // if true, extract embeddings (together with logits) bool offload_kqv; // whether to offload the KQV ops (including the KV cache) to GPU - bool flash_attn; // whether to use flash attention + bool flash_attn; // whether to use flash attention [EXPERIMENTAL] // Abort callback // if it returns true, execution of llama_decode() will be aborted From 62bfef5194d5582486d62da3db59bf44981b7912 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 27 May 2024 10:38:39 +0300 Subject: [PATCH 121/181] metal : disable FA kernel for HS=256 (#7556) ggml-ci --- ggml-metal.m | 15 +++++++++------ ggml-metal.metal | 4 ++-- 2 files changed, 11 insertions(+), 8 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index c9e570dbf..15fb68fc4 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -184,9 +184,9 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, + //GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, // https://github.com/ggerganov/llama.cpp/issues/7261 GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, - GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, + //GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, // https://github.com/ggerganov/llama.cpp/issues/7261 GGML_METAL_KERNEL_TYPE_CPY_F32_F16, GGML_METAL_KERNEL_TYPE_CPY_F32_F32, GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, @@ -634,9 +634,9 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96, flash_attn_ext_f16_h96, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112, flash_attn_ext_f16_h112, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128, flash_attn_ext_f16_h128, ctx->support_simdgroup_mm); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, flash_attn_ext_f16_h256, ctx->support_simdgroup_mm); + //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256, flash_attn_ext_f16_h256, ctx->support_simdgroup_mm); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128, flash_attn_ext_vec_f16_h128, ctx->support_simdgroup_reduction); - GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, flash_attn_ext_vec_f16_h256, ctx->support_simdgroup_reduction); + //GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256, flash_attn_ext_vec_f16_h256, ctx->support_simdgroup_reduction); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F16, cpy_f32_f16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_F32, cpy_f32_f32, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CPY_F32_Q8_0, cpy_f32_q8_0, true); @@ -770,6 +770,9 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const case GGML_OP_LEAKY_RELU: return true; case GGML_OP_FLASH_ATTN_EXT: + if (op->src[0]->ne[0] == 256) { + return false; + } return ctx->support_simdgroup_mm; // TODO: over-restricted for vec-kernels case GGML_OP_MUL_MAT: case GGML_OP_MUL_MAT_ID: @@ -2573,7 +2576,7 @@ static enum ggml_status ggml_metal_graph_compute( case 96: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H96 ].pipeline; break; case 112: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H112].pipeline; break; case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H128].pipeline; break; - case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256].pipeline; break; + //case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_F16_H256].pipeline; break; default: { GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00); @@ -2586,7 +2589,7 @@ static enum ggml_status ggml_metal_graph_compute( switch (ne00) { case 128: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H128].pipeline; break; - case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break; + //case 256: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_FLASH_ATTN_EXT_VEC_F16_H256].pipeline; break; default: { GGML_METAL_LOG_ERROR("unsupported size: %lld\n", ne00); diff --git a/ggml-metal.metal b/ggml-metal.metal index 8ff70d7a7..ce51c74d5 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -2418,7 +2418,7 @@ template [[host_name("kernel_flash_attn_ext_f16_h80" )]] kernel flash_attn_ext_f template [[host_name("kernel_flash_attn_ext_f16_h96" )]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<96>; template [[host_name("kernel_flash_attn_ext_f16_h112")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<112>; template [[host_name("kernel_flash_attn_ext_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<128>; -template [[host_name("kernel_flash_attn_ext_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<256>; +//template [[host_name("kernel_flash_attn_ext_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_f16<256>; template // head size, queries per threadgroup, cache items per threadgroup kernel void kernel_flash_attn_ext_vec_f16( @@ -2696,7 +2696,7 @@ kernel void kernel_flash_attn_ext_vec_f16( } template [[host_name("kernel_flash_attn_ext_vec_f16_h128")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<128>; -template [[host_name("kernel_flash_attn_ext_vec_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<256>; +//template [[host_name("kernel_flash_attn_ext_vec_f16_h256")]] kernel flash_attn_ext_f16_t kernel_flash_attn_ext_vec_f16<256>; kernel void kernel_cpy_f16_f16( device const half * src0, From 1d8fca72ae9154eec0e1c0a75cfaac3c50f08e4a Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Mon, 27 May 2024 12:10:19 +0300 Subject: [PATCH 122/181] metal : add GGML_OP_REPEAT kernels (#7557) ggml-ci --- ggml-metal.m | 53 ++++++++++++++++++++++++++++++++++++++++++++---- ggml-metal.metal | 47 ++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 96 insertions(+), 4 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 15fb68fc4..ff9ae55aa 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -35,6 +35,10 @@ enum ggml_metal_kernel_type { GGML_METAL_KERNEL_TYPE_MUL_ROW, GGML_METAL_KERNEL_TYPE_DIV, GGML_METAL_KERNEL_TYPE_DIV_ROW, + GGML_METAL_KERNEL_TYPE_REPEAT_F32, + GGML_METAL_KERNEL_TYPE_REPEAT_F16, + GGML_METAL_KERNEL_TYPE_REPEAT_I32, + GGML_METAL_KERNEL_TYPE_REPEAT_I16, GGML_METAL_KERNEL_TYPE_SCALE, GGML_METAL_KERNEL_TYPE_SCALE_4, GGML_METAL_KERNEL_TYPE_CLAMP, @@ -485,6 +489,10 @@ static struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_MUL_ROW, mul_row, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV, div, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_DIV_ROW, div_row, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_F32, repeat_f32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_F16, repeat_f16, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_I32, repeat_i32, true); + GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_REPEAT_I16, repeat_i16, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE, scale, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_SCALE_4, scale_4, true); GGML_METAL_ADD_KERNEL(GGML_METAL_KERNEL_TYPE_CLAMP, clamp, true); @@ -746,6 +754,7 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const case GGML_OP_ACC: case GGML_OP_MUL: case GGML_OP_DIV: + case GGML_OP_REPEAT: case GGML_OP_SCALE: case GGML_OP_CLAMP: case GGML_OP_SQR: @@ -979,8 +988,6 @@ static enum ggml_status ggml_metal_graph_compute( switch (dst->op) { case GGML_OP_CONCAT: { - const int64_t nb = ne00; - id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONCAT].pipeline; [encoder setComputePipelineState:pipeline]; @@ -1011,7 +1018,6 @@ static enum ggml_status ggml_metal_graph_compute( [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; - [encoder setBytes:&nb length:sizeof(nb) atIndex:27]; const int nth = MIN(1024, ne0); @@ -1021,11 +1027,14 @@ static enum ggml_status ggml_metal_graph_compute( case GGML_OP_MUL: case GGML_OP_DIV: { + GGML_ASSERT(src0t == GGML_TYPE_F32); + GGML_ASSERT(src1t == GGML_TYPE_F32); + const size_t offs = 0; bool bcast_row = false; - int64_t nb = ne00; + int64_t nb = ne00; // used by the "row" kernels id pipeline = nil; @@ -1094,6 +1103,42 @@ static enum ggml_status ggml_metal_graph_compute( [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne02, ne03) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; } } break; + case GGML_OP_REPEAT: + { + id pipeline; + + switch (src0t) { + case GGML_TYPE_F32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_F32].pipeline; break; + case GGML_TYPE_F16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_F16].pipeline; break; + case GGML_TYPE_I32: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_I32].pipeline; break; + case GGML_TYPE_I16: pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_REPEAT_I16].pipeline; break; + default: GGML_ASSERT(false); + } + + [encoder setComputePipelineState:pipeline]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + [encoder setBytes:&ne00 length:sizeof(ne00) atIndex:2]; + [encoder setBytes:&ne01 length:sizeof(ne01) atIndex:3]; + [encoder setBytes:&ne02 length:sizeof(ne02) atIndex:4]; + [encoder setBytes:&ne03 length:sizeof(ne03) atIndex:5]; + [encoder setBytes:&nb00 length:sizeof(nb00) atIndex:6]; + [encoder setBytes:&nb01 length:sizeof(nb01) atIndex:7]; + [encoder setBytes:&nb02 length:sizeof(nb02) atIndex:8]; + [encoder setBytes:&nb03 length:sizeof(nb03) atIndex:9]; + [encoder setBytes:&ne0 length:sizeof(ne0) atIndex:10]; + [encoder setBytes:&ne1 length:sizeof(ne1) atIndex:11]; + [encoder setBytes:&ne2 length:sizeof(ne2) atIndex:12]; + [encoder setBytes:&ne3 length:sizeof(ne3) atIndex:13]; + [encoder setBytes:&nb0 length:sizeof(nb0) atIndex:14]; + [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:15]; + [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:16]; + [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:17]; + + const int nth = MIN((int) pipeline.maxTotalThreadsPerThreadgroup, ne0); + + [encoder dispatchThreadgroups:MTLSizeMake(ne1, ne2, ne3) threadsPerThreadgroup:MTLSizeMake(nth, 1, 1)]; + } break; case GGML_OP_ACC: { GGML_ASSERT(src0t == GGML_TYPE_F32); diff --git a/ggml-metal.metal b/ggml-metal.metal index ce51c74d5..174086b5b 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -168,6 +168,53 @@ kernel void kernel_div( } } +template +kernel void kernel_repeat( + device const char * src0, + device char * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant int64_t & ne03, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant uint64_t & nb03, + constant int64_t & ne0, + constant int64_t & ne1, + constant int64_t & ne2, + constant int64_t & ne3, + constant uint64_t & nb0, + constant uint64_t & nb1, + constant uint64_t & nb2, + constant uint64_t & nb3, + uint3 tgpig[[threadgroup_position_in_grid]], + uint3 tpitg[[thread_position_in_threadgroup]], + uint3 ntg[[threads_per_threadgroup]]) { + const int64_t i3 = tgpig.z; + const int64_t i2 = tgpig.y; + const int64_t i1 = tgpig.x; + + const int64_t i03 = i3 % ne03; + const int64_t i02 = i2 % ne02; + const int64_t i01 = i1 % ne01; + + device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01; + device char * dst_ptr = dst + i3*nb3 + i2*nb2 + i1*nb1 ; + + for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { + const int i00 = i0 % ne00; + *((device T *)(dst_ptr + i0*nb0)) = *((device T *)(src0_ptr + i00*nb00)); + } +} + +typedef decltype(kernel_repeat) kernel_repeat_t; + +template [[host_name("kernel_repeat_f32")]] kernel kernel_repeat_t kernel_repeat; +template [[host_name("kernel_repeat_f16")]] kernel kernel_repeat_t kernel_repeat; +template [[host_name("kernel_repeat_i32")]] kernel kernel_repeat_t kernel_repeat; +template [[host_name("kernel_repeat_i16")]] kernel kernel_repeat_t kernel_repeat; + // assumption: src1 is a row // broadcast src1 into src0 kernel void kernel_add_row( From 5487593bc7ee0b65b9d2e2985b4b61dc77043101 Mon Sep 17 00:00:00 2001 From: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> Date: Mon, 27 May 2024 13:34:09 +0100 Subject: [PATCH 123/181] Add freq factors (#7495) --- ggml-sycl.cpp | 94 +++++++++++++++++++++++++++++++-------------------- 1 file changed, 57 insertions(+), 37 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 496ec61c3..f329bc272 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -8830,12 +8830,11 @@ static void rope( dst[i + 1] = x0*sin_theta + x1*cos_theta; } -template +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) { + float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, float inv_ndims, + const float * freq_factors, 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)); @@ -8863,8 +8862,10 @@ static void rope_neox( float cur_rot = inv_ndims * ic - ib; const int p = has_pos ? pos[i2] : 0; + const float freq_factor = has_freq_facs ? freq_factors[ic/2] : 1.0f; + const float theta_base = - p * freq_scale * dpct::pow(theta_scale, col / 2.0f); + p * freq_scale * dpct::pow(theta_scale, col / 2.0f)/freq_factor; float cos_theta, sin_theta; rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta); @@ -12413,7 +12414,7 @@ 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) { + const float * freq_factors, 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); @@ -12423,38 +12424,48 @@ static void rope_neox_sycl(const T *x, T *dst, int ncols, int n_dims, int nrows, 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); - }); + if (freq_factors == nullptr) { + 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, freq_factors, + item_ct1); + }); + } else { + 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, freq_factors, + 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); - }); + if (freq_factors == nullptr) { + 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, freq_factors, item_ct1); + }); + } else { + 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, freq_factors, item_ct1); + }); + } } } @@ -13986,9 +13997,7 @@ 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) { -#pragma message("TODO: implement phi3 frequency factors support") -#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7225") - GGML_ASSERT(dst->src[2] == nullptr && "phi3 frequency factors not implemented yet"); + const ggml_tensor * src2 = dst->src[2]; GGML_ASSERT(src0->type == GGML_TYPE_F32 || src0->type == GGML_TYPE_F16); GGML_ASSERT( dst->type == GGML_TYPE_F32 || dst->type == GGML_TYPE_F16); @@ -14014,6 +14023,7 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1, memcpy(&beta_fast, (int32_t *) dst->op_params + 9, sizeof(float)); memcpy(&beta_slow, (int32_t *) dst->op_params + 10, sizeof(float)); + const float * freq_factors = nullptr; const int32_t * pos = nullptr; if ((mode & 1) == 0) { GGML_ASSERT(src1->type == GGML_TYPE_I32); @@ -14024,6 +14034,16 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1, const bool is_neox = mode & 2; const bool is_glm = mode & 4; + if (is_neox) { + pos = (const int32_t *) src1_dd; + + if (src2 != nullptr) { + freq_factors = (const float *) src2->data; + } + } else { + GGML_ASSERT(src2 == nullptr && "TODO: freq_factors not implemented for !is_neox"); + } + rope_corr_dims corr_dims; ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims.v); @@ -14035,13 +14055,13 @@ inline void ggml_sycl_op_rope(const ggml_tensor *src0, const ggml_tensor *src1, 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 + attn_factor, corr_dims, freq_factors, 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); + freq_factors, main_stream); } else { GGML_ASSERT(false); } From 95f84d5ce8b449a9b16009434aca800df504a02e Mon Sep 17 00:00:00 2001 From: AidanBeltonS <87009434+AidanBeltonS@users.noreply.github.com> Date: Mon, 27 May 2024 17:34:51 +0100 Subject: [PATCH 124/181] Fix q_xxs using mul_mat_q (#7459) --- ggml-sycl.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index f329bc272..8839f775d 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -15263,6 +15263,7 @@ static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 } } else { bool use_mul_mat_q = min_compute_capability >= VER_4VEC && ggml_is_quantized(src0->type); + use_mul_mat_q = use_mul_mat_q && (src0->type != GGML_TYPE_IQ2_XXS); if (use_xmx && min_compute_capability >= VER_GEN9 && src1->ne[1] > XMX_MAX_BATCH_SIZE) { use_mul_mat_q = false; From 197c00681b80f9dea17d11a4436b6b8ef1be0ce8 Mon Sep 17 00:00:00 2001 From: agray3 Date: Mon, 27 May 2024 18:33:42 +0100 Subject: [PATCH 125/181] Allow multiple copy function pointers for CUDA graph kernel param updates (#7565) CUDA graphs require parameter updates to kernels associated with GGML_OP_CPY nodes. Previously the implementation only checked for a single CUDA kernel in such nodes, but this caused a bug in cases where 2 such kernels exist. This fixes the issue by using a vector to allow multiple function pointers to be stored and checked against. Fixes #7942 --- ggml-cuda.cu | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index b82167cbf..2a90ee55c 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -2510,9 +2510,9 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t bool use_cuda_graph = true; bool cuda_graph_update_required = false; - // pointer to CUDA cpy kernel, which is required to identify + // vector of pointers to CUDA cpy kernels, which are required to identify // kernel parameters which need updated in the graph for each token - void * ggml_cuda_cpy_fn_ptr = nullptr; + std::vector ggml_cuda_cpy_fn_ptrs; if (cuda_ctx->cuda_graph->graph == nullptr) { if (ggml_cuda_info().devices[cuda_ctx->device].cc < CC_AMPERE) { @@ -2588,9 +2588,10 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t if (node->op == GGML_OP_CPY) { // store the copy op parameter which changes with each token. cuda_ctx->cuda_graph->updated_kernel_arg.push_back((char **) &(node->src[1]->data)); - if (ggml_cuda_cpy_fn_ptr == nullptr) { - // store a pointer to the copy op CUDA kernel to identify it later - ggml_cuda_cpy_fn_ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]); + // store a pointer to each copy op CUDA kernel to identify it later + void * ptr = ggml_cuda_cpy_fn(node->src[0], node->src[1]); + if (std::find(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), ptr) == ggml_cuda_cpy_fn_ptrs.end()) { + ggml_cuda_cpy_fn_ptrs.push_back(ptr); } } @@ -2720,7 +2721,7 @@ GGML_CALL static enum ggml_status ggml_backend_cuda_graph_compute(ggml_backend_t if (!cuda_graph_update_required) { // on update steps, the live parameters will already be captured int k = 0; for (size_t i = 0; i < cuda_ctx->cuda_graph->num_nodes; i++) { - if (cuda_ctx->cuda_graph->params[i].func == ggml_cuda_cpy_fn_ptr) { + if(count(ggml_cuda_cpy_fn_ptrs.begin(), ggml_cuda_cpy_fn_ptrs.end(), cuda_ctx->cuda_graph->params[i].func) > 0) { char ** updated_kernel_arg_ptr = cuda_ctx->cuda_graph->updated_kernel_arg.at(k++); cuda_ctx->cuda_graph->params[i].kernelParams[1] = updated_kernel_arg_ptr; CUDA_CHECK(cudaGraphKernelNodeSetParams(cuda_ctx->cuda_graph->nodes[i], &cuda_ctx->cuda_graph->params[i])); From 10b1e4587670feba2c7730a645accf8234873113 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Mon, 27 May 2024 19:34:40 +0200 Subject: [PATCH 126/181] make: add --device-debug to NVCC debug flags (#7542) --- Makefile | 3 +++ 1 file changed, 3 insertions(+) diff --git a/Makefile b/Makefile index fe63cbd60..5caf31cdf 100644 --- a/Makefile +++ b/Makefile @@ -441,6 +441,9 @@ endif # JETSON_EOL_MODULE_DETECT ifdef LLAMA_DEBUG MK_NVCCFLAGS += -lineinfo endif # LLAMA_DEBUG +ifdef LLAMA_CUDA_DEBUG + MK_NVCCFLAGS += --device-debug +endif # LLAMA_CUDA_DEBUG ifdef LLAMA_CUDA_NVCC NVCC = $(CCACHE) $(LLAMA_CUDA_NVCC) else From 0136966dafb452601c23f30395878d5a65ddc559 Mon Sep 17 00:00:00 2001 From: kunnis Date: Mon, 27 May 2024 18:40:12 -0500 Subject: [PATCH 127/181] adding in x64 targets to cmake presets (#7574) --- CMakePresets.json | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/CMakePresets.json b/CMakePresets.json index ad1af7ecc..e2b7a79e3 100644 --- a/CMakePresets.json +++ b/CMakePresets.json @@ -1,4 +1,4 @@ -{ +{ "version": 4, "configurePresets": [ { @@ -40,6 +40,10 @@ { "name": "arm64-windows-msvc-debug" , "inherits": [ "base", "arm64-windows-msvc", "debug" ] }, { "name": "arm64-windows-msvc-release", "inherits": [ "base", "arm64-windows-msvc", "release" ] }, - { "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc", "release", "static" ] } + { "name": "arm64-windows-msvc+static-release", "inherits": [ "base", "arm64-windows-msvc", "release", "static" ] }, + + { "name": "x64-windows-msvc-debug" , "inherits": [ "base", "debug" ] }, + { "name": "x64-windows-msvc-release", "inherits": [ "base", "release" ] }, + { "name": "x64-windows-msvc+static-release", "inherits": [ "base", "release", "static" ] } ] } From 852aafb163d32d5bad63c10bc323a02c28fec59d Mon Sep 17 00:00:00 2001 From: Djip007 Date: Tue, 28 May 2024 01:40:47 +0200 Subject: [PATCH 128/181] update HIP_UMA #7399 (#7414) * update HIP_UMA #7399 add use of hipMemAdviseSetCoarseGrain when LLAMA_HIP_UMA is enable. - get x2 on prompte eval and x1.5 on token gen with rocm6.0 on ryzen 7940HX iGPU (780M/gfx1103) * simplify code, more consistent style --------- Co-authored-by: slaren --- ggml-cuda.cu | 20 +++++++++++++++++--- ggml-cuda/common.cuh | 5 ----- 2 files changed, 17 insertions(+), 8 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 2a90ee55c..d0a754ee1 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -119,6 +119,20 @@ int ggml_cuda_get_device() { return id; } +static cudaError_t ggml_cuda_device_malloc(void ** ptr, size_t size, int device) { + ggml_cuda_set_device(device); +#if defined(GGML_USE_HIPBLAS) && defined(GGML_HIP_UMA) + auto res = hipMallocManaged(ptr, size); + if (res == hipSuccess) { + // if error we "need" to know why... + CUDA_CHECK(hipMemAdvise(*ptr, size, hipMemAdviseSetCoarseGrain, device)); + } + return res; +#else + return cudaMalloc(ptr, size); +#endif +} + static ggml_cuda_device_info ggml_cuda_init() { #ifdef __HIP_PLATFORM_AMD__ // Workaround for a rocBLAS bug when using multiple graphics cards: @@ -271,7 +285,7 @@ struct ggml_cuda_pool_leg : public ggml_cuda_pool { size_t look_ahead_size = (size_t) (1.05 * size); look_ahead_size = 256 * ((look_ahead_size + 255)/256); ggml_cuda_set_device(device); - CUDA_CHECK(cudaMalloc((void **) &ptr, look_ahead_size)); + CUDA_CHECK(ggml_cuda_device_malloc(&ptr, look_ahead_size, device)); *actual_size = look_ahead_size; pool_size += look_ahead_size; #ifdef DEBUG_CUDA_MALLOC @@ -537,7 +551,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_cuda_buffer_type_alloc_buffe size = std::max(size, (size_t)1); // cudaMalloc returns null for size 0 void * dev_ptr; - cudaError_t err = cudaMalloc(&dev_ptr, size); + cudaError_t err = ggml_cuda_device_malloc(&dev_ptr, size, buft_ctx->device); if (err != cudaSuccess) { // clear the error cudaGetLastError(); @@ -798,7 +812,7 @@ GGML_CALL static void ggml_backend_cuda_split_buffer_init_tensor(ggml_backend_bu // currently, init_tensor cannot fail, it needs to be fixed in ggml-backend first ggml_cuda_set_device(id); char * buf; - CUDA_CHECK(cudaMalloc(&buf, size)); + CUDA_CHECK(ggml_cuda_device_malloc((void**)&buf, size, id)); // set padding to 0 to avoid possible NaN values if (size > original_size) { diff --git a/ggml-cuda/common.cuh b/ggml-cuda/common.cuh index 8f6fd71cf..22872ca5c 100644 --- a/ggml-cuda/common.cuh +++ b/ggml-cuda/common.cuh @@ -79,13 +79,8 @@ #define cudaHostRegisterReadOnly hipHostRegisterReadOnly #define cudaHostUnregister hipHostUnregister #define cudaLaunchHostFunc hipLaunchHostFunc -#ifdef GGML_HIP_UMA -#define cudaMalloc hipMallocManaged -#define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size) -#else #define cudaMalloc hipMalloc #define cudaMallocHost(ptr, size) hipHostMalloc(ptr, size, hipHostMallocDefault) -#endif #define cudaMemcpy hipMemcpy #define cudaMemcpyAsync hipMemcpyAsync #define cudaMemcpyPeerAsync hipMemcpyPeerAsync From 74b239b3d5f067470d7ef5e26e2e059720572e32 Mon Sep 17 00:00:00 2001 From: Ikko Eltociear Ashimine Date: Tue, 28 May 2024 11:48:16 +0900 Subject: [PATCH 129/181] llava : update clip.h (#7580) overriden -> overridden --- examples/llava/clip.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/llava/clip.h b/examples/llava/clip.h index 45bdad689..ca3631384 100644 --- a/examples/llava/clip.h +++ b/examples/llava/clip.h @@ -68,7 +68,7 @@ CLIP_API bool clip_image_load_from_file(const char * fname, struct clip_image_u8 /** interpret bytes as an image file with length bytes_length, and use the result to populate img */ CLIP_API bool clip_image_load_from_bytes(const unsigned char * bytes, size_t bytes_length, struct clip_image_u8 * img); -/** preprocess img and store the result in res_imgs, pad_to_square may be overriden to false depending on model configuration */ +/** preprocess img and store the result in res_imgs, pad_to_square may be overridden to false depending on model configuration */ CLIP_API bool clip_image_preprocess(struct clip_ctx * ctx, const struct clip_image_u8 * img, struct clip_image_f32_batch * res_imgs ); CLIP_API struct ggml_tensor * clip_get_newline_tensor(const struct clip_ctx * ctx); From c41767154eb82aa3fe7568fc816c3402b78eae94 Mon Sep 17 00:00:00 2001 From: Nathan Epstein Date: Tue, 28 May 2024 00:41:14 -0400 Subject: [PATCH 130/181] Markdownish code block fix (#7571) * markdownish codeblock fix * updating regexes --- examples/server/public/index.html | 39 ++++++++++++++++++++----------- 1 file changed, 25 insertions(+), 14 deletions(-) diff --git a/examples/server/public/index.html b/examples/server/public/index.html index 2961999f2..095c4a929 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -877,19 +877,30 @@ // poor mans markdown replacement const Markdownish = (params) => { - const md = params.text - .replace(/&/g, '&') - .replace(//g, '>') - .replace(/(^|\n)#{1,6} ([^\n]*)(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1

$2

') - .replace(/\*\*(.*?)\*\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') - .replace(/__(.*?)__(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') - .replace(/\*(.*?)\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') - .replace(/_(.*?)_(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') - .replace(/```.*?\n([\s\S]*?)```/g, '
$1
') - .replace(/`(.*?)`/g, '$1') - .replace(/\n/gim, '
'); - return html``; + const chunks = params.text.split('```'); + + for (let i = 0; i < chunks.length; i++) { + if (i % 2 === 0) { // outside code block + chunks[i] = chunks[i] + .replace(/&/g, '&') + .replace(//g, '>') + .replace(/(^|\n)#{1,6} ([^\n]*)(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1

$2

') + .replace(/\*\*(.*?)\*\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') + .replace(/__(.*?)__(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') + .replace(/\*(.*?)\*(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') + .replace(/_(.*?)_(?=([^`]*`[^`]*`)*[^`]*$)/g, '$1') + .replace(/```.*?\n([\s\S]*?)```/g, '
$1
') + .replace(/`(.*?)`/g, '$1') + .replace(/\n/gim, '
'); + } else { // inside code block + chunks[i] = `
${chunks[i]}
`; + } + } + + const restoredText = chunks.join(''); + + return html``; }; const ModelGenerationInfo = (params) => { @@ -903,6 +914,7 @@ ` } + // simple popover impl const Popover = (props) => { const isOpen = useSignal(false); @@ -1054,4 +1066,3 @@ - From 9335b969e86a222e247adacedf814d8abfff8847 Mon Sep 17 00:00:00 2001 From: mgroeber9110 <45620825+mgroeber9110@users.noreply.github.com> Date: Tue, 28 May 2024 06:55:51 +0200 Subject: [PATCH 131/181] server: do not remove whitespace at the start of a completion chunk (#7524) --- examples/server/public/index.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/server/public/index.html b/examples/server/public/index.html index 095c4a929..4c5a34d90 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -594,7 +594,7 @@ message = html`<${Probabilities} data=${data} />` } else { const text = isArrayMessage ? - data.map(msg => msg.content).join('').replace(/^\s+/, '') : + data.map(msg => msg.content).join('') : data; message = isCompletionMode ? text : From 0548a4187f2e53b8fc6d9ff0f4c71988f708ff42 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 28 May 2024 11:04:19 +0300 Subject: [PATCH 132/181] ggml : generalize GGML_OP_CONCAT (#7563) * ggml : generalize GGML_OP_CONCAT (WIP) ggml-ci * tests : add dim != 2 tests * metal : generalize concat kernel * tests : naming * cuda : generalize concat kernel ggml-ci * sycl : add warning and assert * ggml : fix op params handling * metal : bugfix kernel ggml-ci * ggml : reimplement CPU and Metal * cuda : add asserts ggml-ci * ggml : fix ptrs ggml-ci --- ggml-cuda/concat.cu | 93 +++++++++++++++++++++++++++++++++++--- ggml-metal.m | 3 ++ ggml-metal.metal | 29 ++++++------ ggml-sycl.cpp | 4 ++ ggml.c | 61 ++++++++++++++++--------- ggml.h | 5 +- tests/test-backend-ops.cpp | 28 +++++++----- 7 files changed, 167 insertions(+), 56 deletions(-) diff --git a/ggml-cuda/concat.cu b/ggml-cuda/concat.cu index 2941d2f17..fb9dee8f8 100644 --- a/ggml-cuda/concat.cu +++ b/ggml-cuda/concat.cu @@ -1,15 +1,68 @@ #include "concat.cuh" -static __global__ void concat_f32(const float * x,const float * y, float * dst, const int ne0, const int ne02) { +static __global__ void concat_f32_dim0(const float * x, const float * y, float * dst, const int ne0, const int ne00) { int nidx = threadIdx.x + blockIdx.x * blockDim.x; if (nidx >= ne0) { return; } - // operation + int offset_dst = nidx + blockIdx.y * ne0 + blockIdx.z * ne0 * gridDim.y; + + if (nidx < ne00) { // src0 + int offset_src = + nidx + + blockIdx.y * ne00 + + blockIdx.z * ne00 * gridDim.y; + dst[offset_dst] = x[offset_src]; + } else { + int offset_src = + (nidx - ne00) + + blockIdx.y * (ne0 - ne00) + + blockIdx.z * (ne0 - ne00) * gridDim.y; + dst[offset_dst] = y[offset_src]; + } +} + +static __global__ void concat_f32_dim1(const float * x, const float * y, float * dst, const int ne0, const int ne01) { + int nidx = threadIdx.x + blockIdx.x * blockDim.x; + if (nidx >= ne0) { + return; + } + + int offset_dst = + nidx + + blockIdx.y * ne0 + + blockIdx.z * ne0 * gridDim.y; + + if (blockIdx.y < ne01) { // src0 + int offset_src = + nidx + + blockIdx.y * ne0 + + blockIdx.z * ne0 * ne01; + dst[offset_dst] = x[offset_src]; + } else { + int offset_src = + nidx + + (blockIdx.y - ne01) * ne0 + + blockIdx.z * ne0 * (gridDim.y - ne01); + dst[offset_dst] = y[offset_src]; + } +} + +static __global__ void concat_f32_dim2(const float * x, const float * y, float * dst, const int ne0, const int ne02) { + int nidx = threadIdx.x + blockIdx.x * blockDim.x; + if (nidx >= ne0) { + return; + } + + int offset_dst = + nidx + + blockIdx.y * ne0 + + blockIdx.z * ne0 * gridDim.y; + if (blockIdx.z < ne02) { // src0 int offset_src = nidx + @@ -25,25 +78,53 @@ static __global__ void concat_f32(const float * x,const float * y, float * dst, } } -static void concat_f32_cuda(const float * x, const float * y, float * dst, const int ne0, int ne1, int ne2, int ne02, cudaStream_t stream) { +static void concat_f32_cuda(const float * x, const float * y, float * dst, int ne00, int ne01, int ne02, int ne0, int ne1, int ne2, int dim, cudaStream_t stream) { int num_blocks = (ne0 + CUDA_CONCAT_BLOCK_SIZE - 1) / CUDA_CONCAT_BLOCK_SIZE; dim3 gridDim(num_blocks, ne1, ne2); - concat_f32<<>>(x, y, dst, ne0, ne02); + if (dim == 0) { + concat_f32_dim0<<>>(x, y, dst, ne0, ne00); + return; + } + if (dim == 1) { + concat_f32_dim1<<>>(x, y, dst, ne0, ne01); + return; + } + concat_f32_dim2<<>>(x, y, dst, ne0, ne02); } void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * src0 = dst->src[0]; const ggml_tensor * src1 = dst->src[1]; + const float * src0_d = (const float *)src0->data; const float * src1_d = (const float *)src1->data; + float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); + const int32_t dim = ((int32_t *) dst->op_params)[0]; + + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(ggml_is_contiguous(src1)); + 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_cuda(src0_d + i3 * (src0->nb[3] / 4), src1_d + i3 * (src1->nb[3] / 4), dst_d + i3 * (dst->nb[3] / 4), dst->ne[0], dst->ne[1], dst->ne[2], src0->ne[2], stream); + if (dim != 3) { + for (int i3 = 0; i3 < dst->ne[3]; i3++) { + concat_f32_cuda( + src0_d + i3 * (src0->nb[3] / 4), + src1_d + i3 * (src1->nb[3] / 4), + dst_d + i3 * ( dst->nb[3] / 4), + src0->ne[0], src0->ne[1], src0->ne[2], + dst->ne[0], dst->ne[1], dst->ne[2], dim, stream); + } + } else { + const size_t size0 = ggml_nbytes(src0); + const size_t size1 = ggml_nbytes(src1); + + CUDA_CHECK(cudaMemcpyAsync(dst_d, src0_d, size0, cudaMemcpyDeviceToDevice, stream)); + CUDA_CHECK(cudaMemcpyAsync(dst_d + size0/4, src1_d, size1, cudaMemcpyDeviceToDevice, stream)); } } diff --git a/ggml-metal.m b/ggml-metal.m index ff9ae55aa..4ba498e87 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -990,6 +990,8 @@ static enum ggml_status ggml_metal_graph_compute( { id pipeline = ctx->kernels[GGML_METAL_KERNEL_TYPE_CONCAT].pipeline; + const int32_t dim = ((int32_t *) dst->op_params)[0]; + [encoder setComputePipelineState:pipeline]; [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; [encoder setBuffer:id_src1 offset:offs_src1 atIndex:1]; @@ -1018,6 +1020,7 @@ static enum ggml_status ggml_metal_graph_compute( [encoder setBytes:&nb1 length:sizeof(nb1) atIndex:24]; [encoder setBytes:&nb2 length:sizeof(nb2) atIndex:25]; [encoder setBytes:&nb3 length:sizeof(nb3) atIndex:26]; + [encoder setBytes:&dim length:sizeof(dim) atIndex:27]; const int nth = MIN(1024, ne0); diff --git a/ggml-metal.metal b/ggml-metal.metal index 174086b5b..b16f2b7e0 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -3366,31 +3366,30 @@ kernel void kernel_concat( constant uint64_t & nb1, constant uint64_t & nb2, constant uint64_t & nb3, + constant int32_t & dim, uint3 tgpig[[threadgroup_position_in_grid]], uint3 tpitg[[thread_position_in_threadgroup]], uint3 ntg[[threads_per_threadgroup]]) { - const int64_t i03 = tgpig.z; - const int64_t i02 = tgpig.y; - const int64_t i01 = tgpig.x; + const int64_t i3 = tgpig.z; + const int64_t i2 = tgpig.y; + const int64_t i1 = tgpig.x; - const int64_t i13 = i03 % ne13; - const int64_t i12 = i02 % ne12; - const int64_t i11 = i01 % ne11; + int64_t o[4] = {0, 0, 0, 0}; + o[dim] = dim == 0 ? ne00 : (dim == 1 ? ne01 : (dim == 2 ? ne02 : ne03)); - device const char * src0_ptr = src0 + i03*nb03 + i02*nb02 + i01*nb01 + tpitg.x*nb00; - device const char * src1_ptr = src1 + i13*nb13 + i12*nb12 + i11*nb11 + tpitg.x*nb10; - device char * dst_ptr = dst + i03*nb3 + i02*nb2 + i01*nb1 + tpitg.x*nb0; + device const float * x; for (int i0 = tpitg.x; i0 < ne0; i0 += ntg.x) { - if (i02 < ne02) { - ((device float *)dst_ptr)[0] = ((device float *)src0_ptr)[0]; - src0_ptr += ntg.x*nb00; + if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { + x = (device const float *)(src0 + (i3 )*nb03 + (i2 )*nb02 + (i1 )*nb01 + (i0 )*nb00); } else { - ((device float *)dst_ptr)[0] = ((device float *)src1_ptr)[0]; - src1_ptr += ntg.x*nb10; + x = (device const float *)(src1 + (i3 - o[3])*nb13 + (i2 - o[2])*nb12 + (i1 - o[1])*nb11 + (i0 - o[0])*nb10); } - dst_ptr += ntg.x*nb0; + + device float * y = (device float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + *y = *x; } } diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 8839f775d..d5384b2e0 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -13512,6 +13512,10 @@ inline void ggml_sycl_op_concat(const ggml_tensor *src0, const float *src0_dd, const float *src1_dd, float *dst_dd, const dpct::queue_ptr &main_stream) { +#pragma message("TODO: generalize concat kernel for dim != 2") +#pragma message(" https://github.com/ggerganov/llama.cpp/pull/7563") + int dim = dst->op_params[0]; + GGML_ASSERT(dim != 2); GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(src1->type == GGML_TYPE_F32); diff --git a/ggml.c b/ggml.c index 5145ceec9..023077ca6 100644 --- a/ggml.c +++ b/ggml.c @@ -4882,10 +4882,21 @@ struct ggml_tensor * ggml_repeat_back( // ggml_concat struct ggml_tensor * ggml_concat( - struct ggml_context* ctx, - struct ggml_tensor* a, - struct ggml_tensor* b) { - GGML_ASSERT(a->ne[0] == b->ne[0] && a->ne[1] == b->ne[1] && a->ne[3] == b->ne[3]); + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int dim) { + GGML_ASSERT(dim >= 0 && dim < GGML_MAX_DIMS); + + int64_t ne[GGML_MAX_DIMS]; + for (int d = 0; d < GGML_MAX_DIMS; ++d) { + if (d == dim) { + ne[d] = a->ne[d] + b->ne[d]; + continue; + } + GGML_ASSERT(a->ne[d] == b->ne[d]); + ne[d] = a->ne[d]; + } bool is_node = false; @@ -4893,7 +4904,9 @@ struct ggml_tensor * ggml_concat( is_node = true; } - struct ggml_tensor * result = ggml_new_tensor_4d(ctx, a->type, a->ne[0], a->ne[1], a->ne[2] + b->ne[2], a->ne[3]); + struct ggml_tensor * result = ggml_new_tensor(ctx, a->type, GGML_MAX_DIMS, ne); + + ggml_set_op_params_i32(result, 0, dim); result->op = GGML_OP_CONCAT; result->grad = is_node ? ggml_dup_tensor(ctx, result) : NULL; @@ -5013,6 +5026,7 @@ struct ggml_tensor * ggml_leaky_relu( } struct ggml_tensor * result = inplace ? ggml_view_tensor(ctx, a) : ggml_dup_tensor(ctx, a); + ggml_set_op_params(result, &negative_slope, sizeof(negative_slope)); result->op = GGML_OP_LEAKY_RELU; @@ -10967,26 +10981,29 @@ static void ggml_compute_forward_concat_f32( GGML_ASSERT(nb00 == sizeof(float)); GGML_ASSERT(nb10 == sizeof(float)); + const int32_t dim = ggml_get_op_params_i32(dst, 0); + + GGML_ASSERT(dim >= 0 && dim < 4); + + int64_t o[4] = {0, 0, 0, 0}; + o[dim] = src0->ne[dim]; + + const float * x; + + // TODO: smarter multi-theading for (int i3 = 0; i3 < ne3; i3++) { for (int i2 = ith; i2 < ne2; i2 += nth) { - if (i2 < ne02) { // src0 - for (int i1 = 0; i1 < ne1; i1++) { - for (int i0 = 0; i0 < ne0; i0++) { - const float * x = (float *)((char *) src0->data + i0 * nb00 + i1 * nb01 + i2 * nb02 + i3 * nb03); - - float * y = (float *)((char *)dst->data + i0 * nb0 + i1 * nb1 + i2 * nb2 + i3 * nb3); - *y = *x; + for (int i1 = 0; i1 < ne1; i1++) { + for (int i0 = 0; i0 < ne0; i0++) { + if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { + x = (const float *) ((const char *)src0->data + (i0 )*nb00 + (i1 )*nb01 + (i2 )*nb02 + (i3 )*nb03); + } else { + x = (const float *) ((const char *)src1->data + (i0 - o[0])*nb10 + (i1 - o[1])*nb11 + (i2 - o[2])*nb12 + (i3 - o[3])*nb13); } - } - } // src1 - else { - for (int i1 = 0; i1 < ne1; i1++) { - for (int i0 = 0; i0 < ne0; i0++) { - const float * x = (float *)((char *) src1->data + i0 * nb10 + i1 * nb11 + (i2 - ne02) * nb12 + i3 * nb13); - float * y = (float *)((char *)dst->data + i0 * nb0 + i1 * nb1 + i2 * nb2 + i3 * nb3); - *y = *x; - } + float * y = (float *)((char *)dst->data + i0*nb0 + i1*nb1 + i2*nb2 + i3*nb3); + + *y = *x; } } } @@ -10994,7 +11011,7 @@ static void ggml_compute_forward_concat_f32( } static void ggml_compute_forward_concat( - const struct ggml_compute_params* params, + const struct ggml_compute_params * params, struct ggml_tensor* dst) { const struct ggml_tensor * src0 = dst->src[0]; diff --git a/ggml.h b/ggml.h index f803ba724..4e6bcb30f 100644 --- a/ggml.h +++ b/ggml.h @@ -1007,12 +1007,13 @@ extern "C" { struct ggml_tensor * a, struct ggml_tensor * b); - // concat a and b on dim 2 + // concat a and b along dim // used in stable-diffusion GGML_API struct ggml_tensor * ggml_concat( struct ggml_context * ctx, struct ggml_tensor * a, - struct ggml_tensor * b); + struct ggml_tensor * b, + int dim); GGML_API struct ggml_tensor * ggml_abs( struct ggml_context * ctx, diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index de74585da..b200ccccd 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1259,22 +1259,26 @@ struct test_im2col : public test_case { // GGML_OP_CONCAT struct test_concat : public test_case { const ggml_type type; - const std::array ne; - const int64_t b_ne2; + const std::array ne_a; + const int64_t ne_b_d; + const int dim; std::string vars() override { - return VARS_TO_STR3(type, ne, b_ne2); + return VARS_TO_STR4(type, ne_a, ne_b_d, dim); } test_concat(ggml_type type = GGML_TYPE_F32, - std::array ne = {10, 10, 10, 10}, - int64_t b_ne2 = 10) - : type(type), ne(ne), b_ne2(b_ne2) {} + std::array ne_a = {10, 10, 10, 10}, + int64_t ne_b_d = 10, + int dim = 2) + : type(type), ne_a(ne_a), ne_b_d(ne_b_d), dim(dim) {} ggml_tensor * build_graph(ggml_context * ctx) override { - ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_tensor * b = ggml_new_tensor_4d(ctx, type, ne[0], ne[1], b_ne2, ne[3]); - ggml_tensor * out = ggml_concat(ctx, a, b); + auto ne_b = ne_a; + ne_b[dim] = ne_b_d; + ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data()); + ggml_tensor * b = ggml_new_tensor(ctx, type, 4, ne_b.data()); + ggml_tensor * out = ggml_concat(ctx, a, b, dim); return out; } }; @@ -2211,8 +2215,10 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op } } - test_cases.emplace_back(new test_concat(GGML_TYPE_F32)); - test_cases.emplace_back(new test_concat(GGML_TYPE_I32)); + for (int dim : { 0, 1, 2, 3, }) { + test_cases.emplace_back(new test_concat(GGML_TYPE_F32, {11, 12, 13, 14}, 7, dim)); + test_cases.emplace_back(new test_concat(GGML_TYPE_I32, {11, 12, 13, 14}, 7, dim)); + } for (ggml_sort_order order : {GGML_SORT_ORDER_ASC, GGML_SORT_ORDER_DESC}) { test_cases.emplace_back(new test_argsort(GGML_TYPE_F32, {8, 1, 1, 1}, order)); From e2b065071c5fc8ac5697d12ca343551faee465cc Mon Sep 17 00:00:00 2001 From: Neo Zhang <14088817+arthw@users.noreply.github.com> Date: Tue, 28 May 2024 17:53:37 +0800 Subject: [PATCH 133/181] [SYCL]fix ggml_sycl_mul_mat_id() to match the change of api (#7436) * fix mul_mat_id to match the change of api * rm comment * rm unused or duplicated code, rename as review comment --- ggml-sycl.cpp | 273 ++++++++++++++++++++++++++++++++++++++++---------- 1 file changed, 219 insertions(+), 54 deletions(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index d5384b2e0..022a52aeb 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -2944,6 +2944,57 @@ namespace dpct using shared_memory = detail::device_memory; + template + inline T atomic_fetch_add(T *addr, T operand) { + auto atm = + sycl::atomic_ref(addr[0]); + return atm.fetch_add(operand); + } + + template + inline T1 atomic_fetch_add(T1 *addr, T2 operand) { + auto atm = + sycl::atomic_ref(addr[0]); + return atm.fetch_add(operand); + } + + template + inline T atomic_fetch_add(T *addr, T operand, + sycl::memory_order memoryOrder) { + switch (memoryOrder) { + case sycl::memory_order::relaxed: + return atomic_fetch_add(addr, operand); + case sycl::memory_order::acq_rel: + return atomic_fetch_add(addr, operand); + case sycl::memory_order::seq_cst: + return atomic_fetch_add(addr, operand); + default: + assert(false && "Invalid memory_order for atomics. Valid memory_order for " + "atomics are: sycl::memory_order::relaxed, " + "sycl::memory_order::acq_rel, sycl::memory_order::seq_cst!"); + } + } + + template + inline T1 atomic_fetch_add(T1 *addr, T2 operand, + sycl::memory_order memoryOrder) { + atomic_fetch_add(addr, operand, memoryOrder); + } + } // COPY from DPCT head files #define GGML_COMMON_DECL_SYCL @@ -3060,6 +3111,7 @@ void ggml_sycl_get_device_description(int device, char * description, size_t d bool ggml_backend_is_sycl(ggml_backend_t backend); int ggml_backend_sycl_get_device(ggml_backend_t backend); int get_main_device(); +static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer); 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); @@ -15459,22 +15511,86 @@ static void ggml_sycl_mul_mat_id_sycl(ggml_tensor * dst) { } #endif +struct mmid_row_mapping { + int32_t i1; + int32_t i2; +}; + +__dpct_inline__ static void k_copy_src1_to_contiguous( + const char *__restrict__ src1_original, char *__restrict__ src1_contiguous, + int *__restrict__ cur_src1_row, mmid_row_mapping *__restrict__ row_mapping, + const char *__restrict ids, int64_t i02, size_t ids_nb1, size_t ids_nb0, + int64_t ne11, int64_t ne10, size_t nb11, size_t nb12, + const sycl::nd_item<3> &item_ct1, int &src1_row) { + int32_t iid1 = item_ct1.get_group(2); + int32_t id = item_ct1.get_group(1); + + const int32_t row_id_i = *(const int32_t *) (ids + iid1*ids_nb1 + id*ids_nb0); + + if (row_id_i != i02) { + return; + } + + const int64_t i11 = id % ne11; + const int64_t i12 = iid1; + + if (item_ct1.get_local_id(2) == 0) { + src1_row = + dpct::atomic_fetch_add( + cur_src1_row, 1); + row_mapping[src1_row] = {id, iid1}; + } + /* + DPCT1065:194: 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(); + + const float * src1_row_original = (const float *)(src1_original + i11*nb11 + i12*nb12); + float * src1_row_contiguous = (float *)(src1_contiguous + src1_row*nb11); + +#pragma unroll + for (int i = item_ct1.get_local_id(2); i < ne10; + i += item_ct1.get_local_range(2)) { + src1_row_contiguous[i] = src1_row_original[i]; + } +} + +__dpct_inline__ static void k_copy_dst_from_contiguous( + char *__restrict__ dst_original, const char *__restrict__ dst_contiguous, + const mmid_row_mapping *__restrict__ row_mapping, int64_t ne0, size_t nb1, + size_t nb2, const sycl::nd_item<3> &item_ct1) { + int32_t i = item_ct1.get_group(2); + + const int32_t i1 = row_mapping[i].i1; + const int32_t i2 = row_mapping[i].i2; + + const float * dst_row_contiguous = (const float *)(dst_contiguous + i*nb1); + float * dst_row_original = (float *)(dst_original + i1*nb1 + i2*nb2); + +#pragma unroll + for (int j = item_ct1.get_local_id(2); j < ne0; + j += item_ct1.get_local_range(2)) { + dst_row_original[j] = dst_row_contiguous[j]; + } +} + static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, const ggml_tensor *src1, ggml_tensor *dst) try { - GGML_ASSERT(src0->backend != GGML_BACKEND_TYPE_GPU_SPLIT && - "mul_mat_id does not support split buffers"); + GGML_ASSERT(!ggml_backend_buffer_is_sycl_split(src0->buffer) && "mul_mat_id does not support split buffers"); + const ggml_tensor *ids = dst->src[2]; + GGML_TENSOR_BINARY_OP_LOCALS + const dpct::queue_ptr stream = g_syclStreams[g_main_device][0]; - const size_t nb11 = src1->nb[1]; - const size_t nb1 = dst->nb[1]; - - const int32_t id = ((int32_t *)dst->op_params)[0]; - const int32_t n_as = src0->ne[2]; + const int64_t n_as = ne02; + const int64_t n_ids = ids->ne[0]; std::vector ids_host(ggml_nbytes(ids)); - const char *ids_dev = (const char *)ids->data; + const char * ids_dev = (const char *) ids->data; SYCL_CHECK(CHECK_TRY_ERROR( stream->memcpy(ids_host.data(), ids_dev, ggml_nbytes(ids)))); @@ -15514,24 +15630,40 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, src0_row.ne[2] = 1; src0_row.ne[3] = 1; - src0_row.nb[3] = src0->nb[2]; + src0_row.nb[3] = nb02; - if (src1->ne[1] == 1) { - for (int64_t i01 = 0; i01 < ids->ne[1]; i01++) { - const int32_t row_id = - *(const int32_t *)(ids_host.data() + i01 * ids->nb[1] + - id * ids->nb[0]); + src1_row.ne[1] = 1; + src1_row.ne[2] = 1; + src1_row.ne[3] = 1; + src1_row.nb[2] = nb11; + src1_row.nb[3] = nb11; - GGML_ASSERT(row_id >= 0 && row_id < n_as); + dst_row.ne[1] = 1; + dst_row.ne[2] = 1; + dst_row.ne[3] = 1; + dst_row.nb[2] = nb1; + dst_row.nb[3] = nb1; + if (ne12 == 1) { + for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) { + for (int64_t id = 0; id < n_ids; id++) { + const int32_t i02 = *(const int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]); + GGML_ASSERT(i02 >= 0 && i02 < n_as); + + const int64_t i11 = id % ne11; + const int64_t i12 = iid1; + + const int64_t i1 = id; + const int64_t i2 = i12; src0_row_extra.data_device[g_main_device] = - src0_original + row_id * src0->nb[2]; + src0_original + i02*nb02; src1_row_extra.data_device[g_main_device] = - src1_original + i01 * src1->nb[1]; + src1_original + + i11*nb11 + i12*nb12; dst_row_extra.data_device[g_main_device] = - dst_original + i01 * dst->nb[1]; + dst_original + i1*nb1 + i2*nb2; ggml_sycl_mul_mat(&src0_row, &src1_row, &dst_row); + } } } else { sycl_pool_alloc src1_contiguous(sizeof(float)*ggml_nelements(src1)); @@ -15540,64 +15672,98 @@ static void ggml_sycl_mul_mat_id(const ggml_tensor *src0, src1_row_extra.data_device[g_main_device] = src1_contiguous.get(); dst_row_extra.data_device[g_main_device] = dst_contiguous.get(); - for (int32_t row_id = 0; row_id < n_as; ++row_id) { + for (int64_t i02 = 0; i02 < n_as; i02++) { 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]); + for (int64_t iid1 = 0; iid1 < ids->ne[1]; iid1++) { + for (int64_t id = 0; id < n_ids; id++) { + const int32_t row_id_i = *(const int32_t *) (ids_host.data() + iid1*ids->nb[1] + id*ids->nb[0]); - if (row_id_i != row_id) { - continue; + GGML_ASSERT(row_id_i >= 0 && row_id_i < n_as); + + if (row_id_i != i02) { + continue; + } + + num_src1_rows++; } - - 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; } - src0_row_extra.data_device[g_main_device] = - src0_original + row_id * src0->nb[2]; + sycl_pool_alloc dev_cur_src1_row(1); + sycl_pool_alloc dev_row_mapping(num_src1_rows); + SYCL_CHECK(CHECK_TRY_ERROR( + stream->memset(dev_cur_src1_row.get(), 0, sizeof(int)))); + + { + sycl::range<3> block_dims(1, 1, std::min((unsigned int)ne10, 768u)); + sycl::range<3> grid_dims(1, n_ids, ids->ne[1]); + stream->submit([&](sycl::handler &cgh) { + sycl::local_accessor src1_row_acc(cgh); + + char *__restrict src1_contiguous_get = + src1_contiguous.get(); + int *__restrict dev_cur_src1_row_get = + dev_cur_src1_row.get(); + mmid_row_mapping *__restrict dev_row_mapping_get = + dev_row_mapping.get(); + size_t ids_nb_ct6 = ids->nb[1]; + size_t ids_nb_ct7 = ids->nb[0]; + + cgh.parallel_for( + sycl::nd_range<3>(grid_dims * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + k_copy_src1_to_contiguous( + src1_original, src1_contiguous_get, + dev_cur_src1_row_get, + dev_row_mapping_get, ids_dev, i02, + ids_nb_ct6, ids_nb_ct7, ne11, ne10, nb11, nb12, + item_ct1, src1_row_acc); + }); + }); + } + + src0_row_extra.data_device[g_main_device] = src0_original + i02*nb02; + + GGML_ASSERT(nb11 == sizeof(float)*ne10); + GGML_ASSERT(nb1 == sizeof(float)*ne0); 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.ne[1] = num_src1_rows; 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]); + { + sycl::range<3> block_dims(1, 1, std::min((unsigned int)ne0, 768u)); + sycl::range<3> grid_dims(1, 1, num_src1_rows); + stream->submit([&](sycl::handler &cgh) { + const char *__restrict dst_contiguous_get = + dst_contiguous.get(); + const mmid_row_mapping *__restrict dev_row_mapping_get = + dev_row_mapping.get(); - 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++; + cgh.parallel_for( + sycl::nd_range<3>(grid_dims * block_dims, block_dims), + [=](sycl::nd_item<3> item_ct1) { + k_copy_dst_from_contiguous(dst_original, + dst_contiguous_get, + dev_row_mapping_get, + ne0, nb1, nb2, item_ct1); + }); + }); } } } - - if (dst->backend == GGML_BACKEND_TYPE_CPU) { - SYCL_CHECK(CHECK_TRY_ERROR(stream->wait())); - } } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ @@ -16580,10 +16746,9 @@ GGML_CALL static const char * ggml_backend_sycl_split_buffer_get_name(ggml_backe UNUSED(buffer); } -// unused at the moment -//static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) { -// return buffer->iface.get_name == ggml_backend_sycl_split_buffer_get_name; -//} +static bool ggml_backend_buffer_is_sycl_split(ggml_backend_buffer_t buffer) { + return buffer->iface.get_name == ggml_backend_sycl_split_buffer_get_name; +} GGML_CALL static void ggml_backend_sycl_split_buffer_free_buffer(ggml_backend_buffer_t buffer) { ggml_backend_sycl_split_buffer_context * ctx = (ggml_backend_sycl_split_buffer_context *)buffer->context; From 271ff3fc44a6ecfcea3ebc192e67567d578b7772 Mon Sep 17 00:00:00 2001 From: Brian Date: Tue, 28 May 2024 20:27:27 +1000 Subject: [PATCH 134/181] github: add refactor to issue template (#7561) * github: add refactor issue template [no ci] * Update 07-refactor.yml --- .github/ISSUE_TEMPLATE/05-enhancement.yml | 2 +- .github/ISSUE_TEMPLATE/06-question.yml | 2 +- .github/ISSUE_TEMPLATE/07-refactor.yml | 28 +++++++++++++++++++++++ 3 files changed, 30 insertions(+), 2 deletions(-) create mode 100644 .github/ISSUE_TEMPLATE/07-refactor.yml diff --git a/.github/ISSUE_TEMPLATE/05-enhancement.yml b/.github/ISSUE_TEMPLATE/05-enhancement.yml index 7f516abb0..58fca7318 100644 --- a/.github/ISSUE_TEMPLATE/05-enhancement.yml +++ b/.github/ISSUE_TEMPLATE/05-enhancement.yml @@ -1,4 +1,4 @@ -name: Enhancement template +name: Enhancement description: Used to request enhancements for llama.cpp title: "Feature Request: " labels: ["enhancement"] diff --git a/.github/ISSUE_TEMPLATE/06-question.yml b/.github/ISSUE_TEMPLATE/06-question.yml index 23ad2f419..9d3ff4972 100644 --- a/.github/ISSUE_TEMPLATE/06-question.yml +++ b/.github/ISSUE_TEMPLATE/06-question.yml @@ -1,4 +1,4 @@ -name: Question template +name: Question description: Used to ask questions about llama.cpp title: "Question: " labels: ["question"] diff --git a/.github/ISSUE_TEMPLATE/07-refactor.yml b/.github/ISSUE_TEMPLATE/07-refactor.yml new file mode 100644 index 000000000..3a68d3d53 --- /dev/null +++ b/.github/ISSUE_TEMPLATE/07-refactor.yml @@ -0,0 +1,28 @@ +name: Refactor (Maintainers) +description: Used to track refactoring opportunities +title: "Refactor: " +labels: ["refactor"] +body: + - type: markdown + attributes: + value: | + Don't forget to [check for existing refactor issue tickets](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3Arefactoring) in case it's already covered. + Also you may want to check [Pull request refactor label as well](https://github.com/ggerganov/llama.cpp/pulls?q=is%3Aopen+is%3Apr+label%3Arefactoring) for duplicates too. + + - type: textarea + id: background-description + attributes: + label: Background Description + description: Please provide a detailed written description of the pain points you are trying to solve. + placeholder: Detailed description behind your motivation to request refactor + validations: + required: true + + - type: textarea + id: possible-approaches + attributes: + label: Possible Refactor Approaches + description: If you have some idea of possible approaches to solve this problem. You may want to make it a todo list. + placeholder: Your idea of possible refactoring opportunity/approaches + validations: + required: false From 8b99e2aa66ba39e4e1114effea6ef7430881eca4 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 28 May 2024 13:55:35 +0300 Subject: [PATCH 135/181] llama : handle unknown utf8 bytes (#7588) --- llama.cpp | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/llama.cpp b/llama.cpp index f67cb7e23..aa4935320 100644 --- a/llama.cpp +++ b/llama.cpp @@ -17940,7 +17940,16 @@ static std::string llama_decode_text(const std::string & text) { const auto cpts = unicode_cpts_from_utf8(text); for (const auto cpt : cpts) { - decoded_text += unicode_utf8_to_byte(unicode_cpt_to_utf8(cpt)); + const auto utf8 = unicode_cpt_to_utf8(cpt); + try { + decoded_text += unicode_utf8_to_byte(utf8); + } catch (const std::out_of_range & e) { + decoded_text += "[UNK_BYTE_0x"; + for (const auto c : utf8) { + decoded_text += format("%02x", (uint8_t) c); + } + decoded_text += text + "]"; + } } return decoded_text; From edc29433fa08b4e5aeb67649a29fc7713af13d04 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 28 May 2024 15:04:09 +0300 Subject: [PATCH 136/181] tests : fix test-tokenizer-0.sh --- tests/test-tokenizer-0.sh | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tests/test-tokenizer-0.sh b/tests/test-tokenizer-0.sh index 1fec8bbf1..4d2b83655 100755 --- a/tests/test-tokenizer-0.sh +++ b/tests/test-tokenizer-0.sh @@ -28,6 +28,8 @@ printf "Tokenizing using (cpp) llama.cpp ...\n" cat /tmp/test-tokenizer-0-$name-py.log | grep "tokenized in" cat /tmp/test-tokenizer-0-$name-cpp.log | grep "tokenized in" +set +e + diff $input.tok $input.tokcpp > /dev/null 2>&1 if [ $? -eq 0 ]; then From ee3dff6b8e39bb8c1cdea1782a7b95ef0118f970 Mon Sep 17 00:00:00 2001 From: fairydreaming <166155368+fairydreaming@users.noreply.github.com> Date: Tue, 28 May 2024 17:07:05 +0200 Subject: [PATCH 137/181] Add support for DeepseekV2ForCausalLM (#7519) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * common : increase max number of experts to 160 * common : add tensors ATTN_Q_A, ATTN_Q_A_NORM, ATTN_Q_B, ATTN_KV_A_MQA, ATTN_KV_A_NORM, ATTN_KV_B needed by DeepSeek-V2 MLA (multi-head latent attention) architecture * common : add model header parameters: leading_dense_block_count, expert_feed_forward_length, expert_shared_count, expert_weights_scale, attention.q_lora_rank, attention.kv_lora_rank, rope.scaling.yarn_log_multiplier * convert-hf : add model conversion support for DeepseekV2ForCausalLM * llama : add model types for DeepSeek-V2 and DeepSeek-V2-Lite models * llama : add two new llm_build_moe_ffn() arguments: scale_w (whether to scale weights of selected MoE experts) and w_scale (numerical value of the scaling factor) * llama : add inference support for LLM_ARCH_DEEPSEEK2 --------- Co-authored-by: Stanisław Szymczyk --- convert-hf-to-gguf.py | 79 ++++++ gguf-py/gguf/constants.py | 74 +++++- gguf-py/gguf/gguf_writer.py | 21 ++ gguf-py/gguf/tensor_mapping.py | 29 ++- llama.cpp | 422 +++++++++++++++++++++++++++++++-- 5 files changed, 599 insertions(+), 26 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index a342f6b1c..1b060e4e6 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -2620,6 +2620,85 @@ class ArcticModel(Model): raise ValueError(f"Unprocessed experts: {experts}") +@Model.register("DeepseekV2ForCausalLM") +class DeepseekV2Model(Model): + model_arch = gguf.MODEL_ARCH.DEEPSEEK2 + + def set_vocab(self): + self._set_vocab_gpt2() + + def set_gguf_parameters(self): + super().set_gguf_parameters() + hparams = self.hparams + + self.gguf_writer.add_leading_dense_block_count(hparams["first_k_dense_replace"]) + self.gguf_writer.add_vocab_size(hparams["vocab_size"]) + if "q_lora_rank" in hparams and hparams["q_lora_rank"] is not None: + self.gguf_writer.add_q_lora_rank(hparams["q_lora_rank"]) + self.gguf_writer.add_kv_lora_rank(hparams["kv_lora_rank"]) + self.gguf_writer.add_key_length(hparams["qk_nope_head_dim"] + hparams["qk_rope_head_dim"]) + self.gguf_writer.add_value_length(hparams["v_head_dim"]) + self.gguf_writer.add_expert_feed_forward_length(hparams["moe_intermediate_size"]) + self.gguf_writer.add_expert_count(hparams["n_routed_experts"]) + self.gguf_writer.add_expert_shared_count(hparams["n_shared_experts"]) + self.gguf_writer.add_expert_weights_scale(hparams["routed_scaling_factor"]) + self.gguf_writer.add_rope_dimension_count(hparams["qk_rope_head_dim"]) + + if self.hparams.get("rope_scaling") is not None and "factor" in self.hparams["rope_scaling"]: + if self.hparams["rope_scaling"].get("type") == "yarn": + self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.YARN) + self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) + self.gguf_writer.add_rope_scaling_orig_ctx_len(self.hparams["rope_scaling"]["original_max_position_embeddings"]) + self.gguf_writer.add_rope_scaling_yarn_log_mul(0.1 * hparams["rope_scaling"]["mscale_all_dim"]) + + _experts: list[dict[str, Tensor]] | None = None + + def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]: + # process the experts separately + if name.find("mlp.experts") != -1: + n_experts = self.hparams["n_routed_experts"] + assert bid is not None + + if self._experts is None: + self._experts = [{} for _ in range(self.block_count)] + + self._experts[bid][name] = data_torch + + if len(self._experts[bid]) >= n_experts * 3: + tensors: list[tuple[str, Tensor]] = [] + + # merge the experts into a single 3d tensor + for w_name in ["down_proj", "gate_proj", "up_proj"]: + datas: list[Tensor] = [] + + for xid in range(n_experts): + ename = f"model.layers.{bid}.mlp.experts.{xid}.{w_name}.weight" + datas.append(self._experts[bid][ename]) + del self._experts[bid][ename] + + data_torch = torch.stack(datas, dim=0) + + merged_name = f"model.layers.{bid}.mlp.experts.{w_name}.weight" + + new_name = self.map_tensor_name(merged_name) + + tensors.append((new_name, data_torch)) + return tensors + else: + return [] + + return [(self.map_tensor_name(name), data_torch)] + + def write_tensors(self): + super().write_tensors() + + if self._experts is not None: + # flatten `list[dict[str, Tensor]]` into `list[str]` + experts = [k for d in self._experts for k in d.keys()] + if len(experts) > 0: + raise ValueError(f"Unprocessed experts: {experts}") + + ###### CONVERSION LOGIC ###### diff --git a/gguf-py/gguf/constants.py b/gguf-py/gguf/constants.py index c9ae259e1..55ec2cb5c 100644 --- a/gguf-py/gguf/constants.py +++ b/gguf-py/gguf/constants.py @@ -33,17 +33,21 @@ class Keys: FILE_TYPE = "general.file_type" class LLM: - VOCAB_SIZE = "{arch}.vocab_size" - CONTEXT_LENGTH = "{arch}.context_length" - EMBEDDING_LENGTH = "{arch}.embedding_length" - BLOCK_COUNT = "{arch}.block_count" - FEED_FORWARD_LENGTH = "{arch}.feed_forward_length" - USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual" - TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout" - EXPERT_COUNT = "{arch}.expert_count" - EXPERT_USED_COUNT = "{arch}.expert_used_count" - POOLING_TYPE = "{arch}.pooling_type" - LOGIT_SCALE = "{arch}.logit_scale" + VOCAB_SIZE = "{arch}.vocab_size" + CONTEXT_LENGTH = "{arch}.context_length" + EMBEDDING_LENGTH = "{arch}.embedding_length" + BLOCK_COUNT = "{arch}.block_count" + LEADING_DENSE_BLOCK_COUNT = "{arch}.leading_dense_block_count" + FEED_FORWARD_LENGTH = "{arch}.feed_forward_length" + EXPERT_FEED_FORWARD_LENGTH = "{arch}.expert_feed_forward_length" + USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual" + TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout" + EXPERT_COUNT = "{arch}.expert_count" + EXPERT_USED_COUNT = "{arch}.expert_used_count" + EXPERT_SHARED_COUNT = "{arch}.expert_shared_count" + EXPERT_WEIGHTS_SCALE = "{arch}.expert_weights_scale" + POOLING_TYPE = "{arch}.pooling_type" + LOGIT_SCALE = "{arch}.logit_scale" class Attention: HEAD_COUNT = "{arch}.attention.head_count" @@ -55,6 +59,8 @@ class Keys: LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon" LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon" CAUSAL = "{arch}.attention.causal" + Q_LORA_RANK = "{arch}.attention.q_lora_rank" + KV_LORA_RANK = "{arch}.attention.kv_lora_rank" class Rope: DIMENSION_COUNT = "{arch}.rope.dimension_count" @@ -64,6 +70,7 @@ class Keys: SCALING_ATTN_FACTOR = "{arch}.rope.scaling.attn_factor" SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length" SCALING_FINETUNED = "{arch}.rope.scaling.finetuned" + SCALING_YARN_LOG_MUL = "{arch}.rope.scaling.yarn_log_multiplier" class SSM: CONV_KERNEL = "{arch}.ssm.conv_kernel" @@ -140,6 +147,7 @@ class MODEL_ARCH(IntEnum): DBRX = auto() OLMO = auto() ARCTIC = auto() + DEEPSEEK2 = auto() class MODEL_TENSOR(IntEnum): @@ -185,6 +193,12 @@ class MODEL_TENSOR(IntEnum): SSM_A = auto() SSM_D = auto() SSM_OUT = auto() + ATTN_Q_A = auto() + ATTN_Q_B = auto() + ATTN_KV_A_MQA = auto() + ATTN_KV_B = auto() + ATTN_Q_A_NORM = auto() + ATTN_KV_A_NORM = auto() MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { @@ -221,6 +235,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { MODEL_ARCH.DBRX: "dbrx", MODEL_ARCH.OLMO: "olmo", MODEL_ARCH.ARCTIC: "arctic", + MODEL_ARCH.DEEPSEEK2: "deepseek2", } TENSOR_NAMES: dict[MODEL_TENSOR, str] = { @@ -266,6 +281,12 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = { MODEL_TENSOR.SSM_A: "blk.{bid}.ssm_a", MODEL_TENSOR.SSM_D: "blk.{bid}.ssm_d", MODEL_TENSOR.SSM_OUT: "blk.{bid}.ssm_out", + MODEL_TENSOR.ATTN_Q_A: "blk.{bid}.attn_q_a", + MODEL_TENSOR.ATTN_Q_B: "blk.{bid}.attn_q_b", + MODEL_TENSOR.ATTN_KV_A_MQA: "blk.{bid}.attn_kv_a_mqa", + MODEL_TENSOR.ATTN_KV_B: "blk.{bid}.attn_kv_b", + MODEL_TENSOR.ATTN_Q_A_NORM: "blk.{bid}.attn_q_a_norm", + MODEL_TENSOR.ATTN_KV_A_NORM: "blk.{bid}.attn_kv_a_norm", } MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { @@ -757,6 +778,33 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.FFN_DOWN_EXP, MODEL_TENSOR.FFN_UP_EXP, ], + MODEL_ARCH.DEEPSEEK2: [ + 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_Q_A, + MODEL_TENSOR.ATTN_Q_B, + MODEL_TENSOR.ATTN_KV_A_MQA, + MODEL_TENSOR.ATTN_KV_B, + MODEL_TENSOR.ATTN_Q_A_NORM, + MODEL_TENSOR.ATTN_KV_A_NORM, + 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, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + ], # TODO } @@ -790,6 +838,10 @@ MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { MODEL_TENSOR.ROPE_FREQS, MODEL_TENSOR.ATTN_ROT_EMBD, ], + MODEL_ARCH.DEEPSEEK2: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], } # diff --git a/gguf-py/gguf/gguf_writer.py b/gguf-py/gguf/gguf_writer.py index c194dd5dd..b93747aff 100644 --- a/gguf-py/gguf/gguf_writer.py +++ b/gguf-py/gguf/gguf_writer.py @@ -374,9 +374,15 @@ class GGUFWriter: def add_block_count(self, length: int) -> None: self.add_uint32(Keys.LLM.BLOCK_COUNT.format(arch=self.arch), length) + def add_leading_dense_block_count(self, length: int) -> None: + self.add_uint32(Keys.LLM.LEADING_DENSE_BLOCK_COUNT.format(arch=self.arch), length) + def add_feed_forward_length(self, length: int) -> None: self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length) + def add_expert_feed_forward_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.EXPERT_FEED_FORWARD_LENGTH.format(arch=self.arch), length) + def add_parallel_residual(self, use: bool) -> None: self.add_bool(Keys.LLM.USE_PARALLEL_RESIDUAL.format(arch=self.arch), use) @@ -407,6 +413,12 @@ class GGUFWriter: def add_expert_used_count(self, count: int) -> None: self.add_uint32(Keys.LLM.EXPERT_USED_COUNT.format(arch=self.arch), count) + def add_expert_shared_count(self, count: int) -> None: + self.add_uint32(Keys.LLM.EXPERT_SHARED_COUNT.format(arch=self.arch), count) + + def add_expert_weights_scale(self, value: float) -> None: + self.add_float32(Keys.LLM.EXPERT_WEIGHTS_SCALE.format(arch=self.arch), value) + def add_layer_norm_eps(self, value: float) -> None: self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value) @@ -416,6 +428,12 @@ class GGUFWriter: def add_causal_attention(self, value: bool) -> None: self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value) + def add_q_lora_rank(self, length: int) -> None: + self.add_uint32(Keys.Attention.Q_LORA_RANK.format(arch=self.arch), length) + + def add_kv_lora_rank(self, length: int) -> None: + self.add_uint32(Keys.Attention.KV_LORA_RANK.format(arch=self.arch), length) + def add_pooling_type(self, value: PoolingType) -> None: self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value) @@ -440,6 +458,9 @@ class GGUFWriter: def add_rope_scaling_finetuned(self, value: bool) -> None: self.add_bool(Keys.Rope.SCALING_FINETUNED.format(arch=self.arch), value) + def add_rope_scaling_yarn_log_mul(self, value: float) -> None: + self.add_float32(Keys.Rope.SCALING_YARN_LOG_MUL.format(arch=self.arch), value) + def add_ssm_conv_kernel(self, value: int) -> None: self.add_uint32(Keys.SSM.CONV_KERNEL.format(arch=self.arch), value) diff --git a/gguf-py/gguf/tensor_mapping.py b/gguf-py/gguf/tensor_mapping.py index 8b1b21d78..83e3c4c33 100644 --- a/gguf-py/gguf/tensor_mapping.py +++ b/gguf-py/gguf/tensor_mapping.py @@ -256,6 +256,7 @@ class TensorNameMap: MODEL_TENSOR.FFN_UP_SHEXP: ( "model.layers.{bid}.mlp.shared_expert.up_proj", # qwen2moe + "model.layers.{bid}.mlp.shared_experts.up_proj", # deepseek2 ), # AWQ-activation gate @@ -285,6 +286,7 @@ class TensorNameMap: MODEL_TENSOR.FFN_GATE_SHEXP: ( "model.layers.{bid}.mlp.shared_expert.gate_proj", # qwen2moe + "model.layers.{bid}.mlp.shared_experts.gate_proj", # deepseek2 ), # Feed-forward down @@ -320,6 +322,7 @@ class TensorNameMap: MODEL_TENSOR.FFN_DOWN_SHEXP: ( "model.layers.{bid}.mlp.shared_expert.down_proj", # qwen2moe + "model.layers.{bid}.mlp.shared_experts.down_proj", # deepseek2 ), MODEL_TENSOR.ATTN_Q_NORM: ( @@ -383,6 +386,30 @@ class TensorNameMap: "model.layers.{bid}.out_proj", "backbone.layers.{bid}.mixer.out_proj", ), + + MODEL_TENSOR.ATTN_Q_A: ( + "model.layers.{bid}.self_attn.q_a_proj", # deepseek2 + ), + + MODEL_TENSOR.ATTN_Q_B: ( + "model.layers.{bid}.self_attn.q_b_proj", # deepseek2 + ), + + MODEL_TENSOR.ATTN_KV_A_MQA: ( + "model.layers.{bid}.self_attn.kv_a_proj_with_mqa", # deepseek2 + ), + + MODEL_TENSOR.ATTN_KV_B: ( + "model.layers.{bid}.self_attn.kv_b_proj", # deepseek2 + ), + + MODEL_TENSOR.ATTN_Q_A_NORM: ( + "model.layers.{bid}.self_attn.q_a_layernorm", # deepseek2 + ), + + MODEL_TENSOR.ATTN_KV_A_NORM: ( + "model.layers.{bid}.self_attn.kv_a_layernorm", # deepseek2 + ), } # architecture-specific block mappings @@ -415,7 +442,7 @@ class TensorNameMap: if tensor not in MODEL_TENSORS[arch]: continue # TODO: make this configurable - n_experts = 128 + n_experts = 160 for xid in range(n_experts): tensor_name = TENSOR_NAMES[tensor].format(bid = bid, xid = xid) self.mapping[tensor_name] = (tensor, tensor_name) diff --git a/llama.cpp b/llama.cpp index aa4935320..10c9e47dd 100644 --- a/llama.cpp +++ b/llama.cpp @@ -103,7 +103,7 @@ #endif #define LLAMA_MAX_NODES 8192 -#define LLAMA_MAX_EXPERTS 128 +#define LLAMA_MAX_EXPERTS 160 // // logging @@ -222,6 +222,7 @@ enum llm_arch { LLM_ARCH_DBRX, LLM_ARCH_OLMO, LLM_ARCH_ARCTIC, + LLM_ARCH_DEEPSEEK2, LLM_ARCH_UNKNOWN, }; @@ -259,6 +260,7 @@ static const std::map LLM_ARCH_NAMES = { { LLM_ARCH_DBRX, "dbrx" }, { LLM_ARCH_OLMO, "olmo" }, { LLM_ARCH_ARCTIC, "arctic" }, + { LLM_ARCH_DEEPSEEK2, "deepseek2" }, { LLM_ARCH_UNKNOWN, "(unknown)" }, }; @@ -279,11 +281,15 @@ enum llm_kv { LLM_KV_CONTEXT_LENGTH, LLM_KV_EMBEDDING_LENGTH, LLM_KV_BLOCK_COUNT, + LLM_KV_LEADING_DENSE_BLOCK_COUNT, LLM_KV_FEED_FORWARD_LENGTH, + LLM_KV_EXPERT_FEED_FORWARD_LENGTH, LLM_KV_USE_PARALLEL_RESIDUAL, LLM_KV_TENSOR_DATA_LAYOUT, LLM_KV_EXPERT_COUNT, LLM_KV_EXPERT_USED_COUNT, + LLM_KV_EXPERT_SHARED_COUNT, + LLM_KV_EXPERT_WEIGHTS_SCALE, LLM_KV_POOLING_TYPE, LLM_KV_LOGIT_SCALE, @@ -296,6 +302,8 @@ enum llm_kv { LLM_KV_ATTENTION_LAYERNORM_EPS, LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, LLM_KV_ATTENTION_CAUSAL, + LLM_KV_ATTENTION_Q_LORA_RANK, + LLM_KV_ATTENTION_KV_LORA_RANK, LLM_KV_ROPE_DIMENSION_COUNT, LLM_KV_ROPE_FREQ_BASE, @@ -305,6 +313,7 @@ enum llm_kv { LLM_KV_ROPE_SCALING_ATTN_FACTOR, LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, LLM_KV_ROPE_SCALING_FINETUNED, + LLM_KV_ROPE_SCALING_YARN_LOG_MUL, LLM_KV_SPLIT_NO, LLM_KV_SPLIT_COUNT, @@ -353,17 +362,21 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_GENERAL_SOURCE_URL, "general.source.url" }, { LLM_KV_GENERAL_SOURCE_HF_REPO, "general.source.huggingface.repository" }, - { LLM_KV_VOCAB_SIZE, "%s.vocab_size" }, - { LLM_KV_CONTEXT_LENGTH, "%s.context_length" }, - { LLM_KV_EMBEDDING_LENGTH, "%s.embedding_length" }, - { LLM_KV_BLOCK_COUNT, "%s.block_count" }, - { LLM_KV_FEED_FORWARD_LENGTH, "%s.feed_forward_length" }, - { LLM_KV_USE_PARALLEL_RESIDUAL, "%s.use_parallel_residual" }, - { LLM_KV_TENSOR_DATA_LAYOUT, "%s.tensor_data_layout" }, - { LLM_KV_EXPERT_COUNT, "%s.expert_count" }, - { LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" }, - { LLM_KV_POOLING_TYPE , "%s.pooling_type" }, - { LLM_KV_LOGIT_SCALE, "%s.logit_scale" }, + { LLM_KV_VOCAB_SIZE, "%s.vocab_size" }, + { LLM_KV_CONTEXT_LENGTH, "%s.context_length" }, + { LLM_KV_EMBEDDING_LENGTH, "%s.embedding_length" }, + { LLM_KV_BLOCK_COUNT, "%s.block_count" }, + { LLM_KV_LEADING_DENSE_BLOCK_COUNT, "%s.leading_dense_block_count" }, + { LLM_KV_FEED_FORWARD_LENGTH, "%s.feed_forward_length" }, + { LLM_KV_EXPERT_FEED_FORWARD_LENGTH, "%s.expert_feed_forward_length" }, + { LLM_KV_USE_PARALLEL_RESIDUAL, "%s.use_parallel_residual" }, + { LLM_KV_TENSOR_DATA_LAYOUT, "%s.tensor_data_layout" }, + { LLM_KV_EXPERT_COUNT, "%s.expert_count" }, + { LLM_KV_EXPERT_USED_COUNT, "%s.expert_used_count" }, + { LLM_KV_EXPERT_SHARED_COUNT, "%s.expert_shared_count" }, + { LLM_KV_EXPERT_WEIGHTS_SCALE, "%s.expert_weights_scale" }, + { LLM_KV_POOLING_TYPE , "%s.pooling_type" }, + { LLM_KV_LOGIT_SCALE, "%s.logit_scale" }, { LLM_KV_ATTENTION_HEAD_COUNT, "%s.attention.head_count" }, { LLM_KV_ATTENTION_HEAD_COUNT_KV, "%s.attention.head_count_kv" }, @@ -374,6 +387,8 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_ATTENTION_LAYERNORM_EPS, "%s.attention.layer_norm_epsilon" }, { LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, "%s.attention.layer_norm_rms_epsilon" }, { LLM_KV_ATTENTION_CAUSAL, "%s.attention.causal" }, + { LLM_KV_ATTENTION_Q_LORA_RANK, "%s.attention.q_lora_rank" }, + { LLM_KV_ATTENTION_KV_LORA_RANK, "%s.attention.kv_lora_rank" }, { LLM_KV_ROPE_DIMENSION_COUNT, "%s.rope.dimension_count" }, { LLM_KV_ROPE_FREQ_BASE, "%s.rope.freq_base" }, @@ -383,6 +398,7 @@ static const std::map LLM_KV_NAMES = { { LLM_KV_ROPE_SCALING_ATTN_FACTOR, "%s.rope.scaling.attn_factor" }, { LLM_KV_ROPE_SCALING_ORIG_CTX_LEN, "%s.rope.scaling.original_context_length" }, { LLM_KV_ROPE_SCALING_FINETUNED, "%s.rope.scaling.finetuned" }, + { LLM_KV_ROPE_SCALING_YARN_LOG_MUL, "%s.rope.scaling.yarn_log_multiplier" }, { LLM_KV_SPLIT_NO, "split.no" }, { LLM_KV_SPLIT_COUNT, "split.count" }, @@ -474,6 +490,12 @@ enum llm_tensor { LLM_TENSOR_SSM_A, LLM_TENSOR_SSM_D, LLM_TENSOR_SSM_OUT, + LLM_TENSOR_ATTN_Q_A, + LLM_TENSOR_ATTN_Q_B, + LLM_TENSOR_ATTN_KV_A_MQA, + LLM_TENSOR_ATTN_KV_B, + LLM_TENSOR_ATTN_Q_A_NORM, + LLM_TENSOR_ATTN_KV_A_NORM, }; static const std::map> LLM_TENSOR_NAMES = { @@ -1057,6 +1079,35 @@ static const std::map> LLM_TENSOR_NA { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, }, }, + { + LLM_ARCH_DEEPSEEK2, + { + { 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_A_NORM, "blk.%d.attn_q_a_norm" }, + { LLM_TENSOR_ATTN_KV_A_NORM, "blk.%d.attn_kv_a_norm" }, + { LLM_TENSOR_ATTN_Q, "blk.%d.attn_q" }, + { LLM_TENSOR_ATTN_Q_A, "blk.%d.attn_q_a" }, + { LLM_TENSOR_ATTN_Q_B, "blk.%d.attn_q_b" }, + { LLM_TENSOR_ATTN_KV_A_MQA, "blk.%d.attn_kv_a_mqa" }, + { LLM_TENSOR_ATTN_KV_B, "blk.%d.attn_kv_b" }, + { 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_UP, "blk.%d.ffn_up" }, + { LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" }, + { LLM_TENSOR_FFN_GATE_INP, "blk.%d.ffn_gate_inp" }, + { LLM_TENSOR_FFN_GATE_EXPS, "blk.%d.ffn_gate_exps" }, + { LLM_TENSOR_FFN_DOWN_EXPS, "blk.%d.ffn_down_exps" }, + { LLM_TENSOR_FFN_UP_EXPS, "blk.%d.ffn_up_exps" }, + { LLM_TENSOR_FFN_GATE_INP_SHEXP, "blk.%d.ffn_gate_inp_shexp" }, + { LLM_TENSOR_FFN_GATE_SHEXP, "blk.%d.ffn_gate_shexp" }, + { LLM_TENSOR_FFN_DOWN_SHEXP, "blk.%d.ffn_down_shexp" }, + { LLM_TENSOR_FFN_UP_SHEXP, "blk.%d.ffn_up_shexp" }, + }, + }, { LLM_ARCH_UNKNOWN, { @@ -1741,6 +1792,7 @@ enum e_model { MODEL_13B, MODEL_14B, MODEL_15B, + MODEL_16B, MODEL_20B, MODEL_30B, MODEL_34B, @@ -1748,6 +1800,7 @@ enum e_model { MODEL_40B, MODEL_65B, MODEL_70B, + MODEL_236B, MODEL_314B, MODEL_SMALL, MODEL_MEDIUM, @@ -1783,6 +1836,13 @@ struct llama_hparams { uint32_t n_expert_used = 0; uint32_t n_vocab_type = 0; // for BERT-style token types + uint32_t n_layer_dense_lead = 0; + uint32_t n_lora_q = 0; + uint32_t n_lora_kv = 0; + uint32_t n_ff_exp = 0; + uint32_t n_expert_shared = 0; + float expert_weights_scale = 0.0; + float f_norm_eps; float f_norm_rms_eps; @@ -1790,6 +1850,7 @@ struct llama_hparams { float rope_freq_base_train; float rope_freq_scale_train; uint32_t n_yarn_orig_ctx; + float rope_yarn_log_mul; // for State Space Models uint32_t ssm_d_conv = 0; @@ -1823,6 +1884,12 @@ struct llama_hparams { if (this->n_expert != other.n_expert) return true; if (this->n_expert_used != other.n_expert_used) return true; + if (this->n_layer_dense_lead != other.n_layer_dense_lead) return true; + if (this->n_lora_q != other.n_lora_q) return true; + if (this->n_lora_kv != other.n_lora_kv) return true; + if (this->n_ff_exp != other.n_ff_exp) return true; + if (this->n_expert_shared != other.n_expert_shared) return true; + if (this->rope_finetuned != other.rope_finetuned) return true; if (this->n_yarn_orig_ctx != other.n_yarn_orig_ctx) return true; @@ -1838,6 +1905,8 @@ struct llama_hparams { if (!is_float_close(this->rope_attn_factor, other.rope_attn_factor, EPSILON)) return true; if (!is_float_close(this->rope_freq_base_train, other.rope_freq_base_train, EPSILON)) return true; if (!is_float_close(this->rope_freq_scale_train, other.rope_freq_scale_train, EPSILON)) return true; + if (!is_float_close(this->expert_weights_scale, other.expert_weights_scale, EPSILON)) return true; + if (!is_float_close(this->rope_yarn_log_mul, other.rope_yarn_log_mul, EPSILON)) return true; return false; } @@ -1913,6 +1982,8 @@ struct llama_layer { struct ggml_tensor * attn_k_norm_b; struct ggml_tensor * attn_out_norm; struct ggml_tensor * attn_out_norm_b; + struct ggml_tensor * attn_q_a_norm; + struct ggml_tensor * attn_kv_a_norm; // attention struct ggml_tensor * wq; @@ -1920,6 +1991,10 @@ struct llama_layer { struct ggml_tensor * wv; struct ggml_tensor * wo; struct ggml_tensor * wqkv; + struct ggml_tensor * wq_a; + struct ggml_tensor * wq_b; + struct ggml_tensor * wkv_a_mqa; + struct ggml_tensor * wkv_b; // attention bias struct ggml_tensor * bq; @@ -3832,6 +3907,7 @@ 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_16B: return "16B"; case MODEL_20B: return "20B"; case MODEL_30B: return "30B"; case MODEL_34B: return "34B"; @@ -3839,6 +3915,7 @@ static const char * llama_model_type_name(e_model type) { case MODEL_40B: return "40B"; case MODEL_65B: return "65B"; case MODEL_70B: return "70B"; + case MODEL_236B: return "236B"; case MODEL_314B: return "314B"; case MODEL_SMALL: return "0.1B"; case MODEL_MEDIUM: return "0.4B"; @@ -4384,6 +4461,26 @@ static void llm_load_hparams( model.type = e_model::MODEL_UNKNOWN; } } break; + case LLM_ARCH_DEEPSEEK2: + { + bool is_lite = (hparams.n_layer == 27); + ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps); + ml.get_key(LLM_KV_LEADING_DENSE_BLOCK_COUNT, hparams.n_layer_dense_lead); + if (!is_lite) { + ml.get_key(LLM_KV_ATTENTION_Q_LORA_RANK, hparams.n_lora_q); + } + ml.get_key(LLM_KV_ATTENTION_KV_LORA_RANK, hparams.n_lora_kv); + ml.get_key(LLM_KV_EXPERT_FEED_FORWARD_LENGTH, hparams.n_ff_exp); + ml.get_key(LLM_KV_EXPERT_SHARED_COUNT, hparams.n_expert_shared); + ml.get_key(LLM_KV_EXPERT_WEIGHTS_SCALE, hparams.expert_weights_scale); + ml.get_key(LLM_KV_ROPE_SCALING_YARN_LOG_MUL, hparams.rope_yarn_log_mul); + + switch (hparams.n_layer) { + case 27: model.type = e_model::MODEL_16B; break; + case 60: model.type = e_model::MODEL_236B; break; + default: model.type = e_model::MODEL_UNKNOWN; + } + } break; default: (void)0; } @@ -4895,6 +4992,16 @@ static void llm_load_print_meta(llama_model_loader & ml, llama_model & model) { if (vocab.special_suffix_id != -1) { LLAMA_LOG_INFO( "%s: SUF token = %d '%s'\n", __func__, vocab.special_suffix_id, vocab.id_to_token[vocab.special_suffix_id].text.c_str() ); } if (vocab.special_middle_id != -1) { LLAMA_LOG_INFO( "%s: MID token = %d '%s'\n", __func__, vocab.special_middle_id, vocab.id_to_token[vocab.special_middle_id].text.c_str() ); } if (vocab.special_eot_id != -1) { LLAMA_LOG_INFO( "%s: EOT token = %d '%s'\n", __func__, vocab.special_eot_id, vocab.id_to_token[vocab.special_eot_id].text.c_str() ); } + + if (model.arch == LLM_ARCH_DEEPSEEK2) { + LLAMA_LOG_INFO("%s: n_layer_dense_lead = %d\n", __func__, hparams.n_layer_dense_lead); + LLAMA_LOG_INFO("%s: n_lora_q = %d\n", __func__, hparams.n_lora_q); + LLAMA_LOG_INFO("%s: n_lora_kv = %d\n", __func__, hparams.n_lora_kv); + LLAMA_LOG_INFO("%s: n_ff_exp = %d\n", __func__, hparams.n_ff_exp); + LLAMA_LOG_INFO("%s: n_expert_shared = %d\n", __func__, hparams.n_expert_shared); + LLAMA_LOG_INFO("%s: expert_weights_scale = %.1f\n", __func__, hparams.expert_weights_scale); + LLAMA_LOG_INFO("%s: rope_yarn_log_mul = %.4f\n", __func__, hparams.rope_yarn_log_mul); + } } // Returns false if cancelled by progress_callback @@ -5051,8 +5158,6 @@ static bool llm_load_tensors( throw std::runtime_error("model has expert layers but no expert layers are used"); } - GGML_ASSERT(n_embd_gqa == n_embd_k_gqa); - ggml_context * ctx_input = ctx_map.at(model.buft_input.buft); ggml_context * ctx_output = ctx_map.at(model.buft_output.buft); ggml_context * ctx_output_split = ctx_map.at(model.buft_output.buft_matrix); @@ -6213,6 +6318,70 @@ static bool llm_load_tensors( layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), {n_embd, n_ff, n_expert}); } } break; + case LLM_ARCH_DEEPSEEK2: + { + bool is_lite = (hparams.n_layer == 27); + + const uint32_t n_embd_head_qk_rope = hparams.n_rot; + const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot; + const uint32_t q_lora_rank = hparams.n_lora_q; + const uint32_t kv_lora_rank = hparams.n_lora_kv; + const uint32_t n_ff_exp = hparams.n_ff_exp; + + 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}); + if (!is_lite) { + layer.attn_q_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_A_NORM, "weight", i), {q_lora_rank}); + } + layer.attn_kv_a_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_KV_A_NORM, "weight", i), {kv_lora_rank}); + + if (!is_lite) { + layer.wq_a = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_A, "weight", i), {n_embd, q_lora_rank}); + layer.wq_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q_B, "weight", i), {q_lora_rank, hparams.n_head * hparams.n_embd_head_k}); + } else { + layer.wq = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd, n_embd_k_gqa}); + } + layer.wkv_a_mqa = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_A_MQA, "weight", i), {n_embd, kv_lora_rank + n_embd_head_qk_rope}); + layer.wkv_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_KV_B, "weight", i), {kv_lora_rank, hparams.n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)}); + layer.wo = ml.create_tensor(ctx_split, tn(LLM_TENSOR_ATTN_OUT, "weight", i), {hparams.n_head * hparams.n_embd_head_v, n_embd}); + + layer.ffn_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}); + + if ((uint32_t) i < hparams.n_layer_dense_lead) { + 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}); + } else { + layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); + + GGML_ASSERT(hparams.n_expert > 0); + GGML_ASSERT(hparams.n_expert_used > 0); + + // MoE branch + layer.ffn_gate_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}); + layer.ffn_down_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_EXPS, "weight", i), {n_ff_exp, n_embd, n_expert}); + layer.ffn_up_exps = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_EXPS, "weight", i), { n_embd, n_ff_exp, n_expert}); + + // Shared expert branch + layer.ffn_gate_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE_SHEXP, "weight", i), {n_embd, n_ff_exp * hparams.n_expert_shared}); + layer.ffn_down_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN_SHEXP, "weight", i), { n_ff_exp * hparams.n_expert_shared, n_embd}); + layer.ffn_up_shexp = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP_SHEXP, "weight", i), {n_embd, n_ff_exp * hparams.n_expert_shared}); + } + } + } break; default: throw std::runtime_error("unknown architecture"); } @@ -6667,6 +6836,8 @@ static struct ggml_tensor * llm_build_moe_ffn( int64_t n_expert_used, llm_ffn_op_type type_op, bool norm_w, + bool scale_w, + float w_scale, const llm_build_cb & cb, int il) { int64_t n_embd = cur->ne[0]; @@ -6698,6 +6869,10 @@ static struct ggml_tensor * llm_build_moe_ffn( weights = ggml_reshape_3d(ctx, weights, 1, n_expert_used, n_tokens); } + if (scale_w) { + weights = ggml_scale(ctx, weights, w_scale); + cb(weights, "ffn_moe_weights_scaled", il); + } cur = ggml_reshape_3d(ctx, cur, n_embd, 1, n_tokens); ggml_tensor * up = ggml_mul_mat_id(ctx, up_exps, cur, selected_experts); // [n_ff, n_expert_used, n_tokens] @@ -7328,6 +7503,7 @@ struct llm_build_context { model.layers[il].ffn_down_exps, n_expert, n_expert_used, LLM_FFN_SILU, true, + false, 0.0, cb, il); cb(cur, "ffn_moe_out", il); } @@ -7809,6 +7985,7 @@ struct llm_build_context { model.layers[il].ffn_down_exps, n_expert, n_expert_used, LLM_FFN_GELU, true, + false, 0.0, cb, il); cb(cur, "ffn_moe_out", il); @@ -7952,6 +8129,7 @@ struct llm_build_context { model.layers[il].ffn_down_exps, n_expert, n_expert_used, LLM_FFN_SILU, true, + false, 0.0, cb, il); cb(cur, "ffn_moe_out", il); @@ -9090,6 +9268,7 @@ struct llm_build_context { model.layers[il].ffn_down_exps, n_expert, n_expert_used, LLM_FFN_SILU, false, + false, 0.0, cb, il); cb(cur, "ffn_moe_out", il); @@ -10977,6 +11156,7 @@ struct llm_build_context { model.layers[il].ffn_down_exps, n_expert, n_expert_used, LLM_FFN_SILU, true, + false, 0.0, cb, il); cb(cur, "ffn_moe_out", il); @@ -11008,6 +11188,215 @@ struct llm_build_context { return gf; } + + struct ggml_cgraph * build_deepseek2() { + struct ggml_cgraph * gf = ggml_new_graph_custom(ctx0, LLAMA_MAX_NODES, false); + + // mutable variable, needed during the last layer of the computation to skip unused tokens + int32_t n_tokens = this->n_tokens; + + bool is_lite = (hparams.n_layer == 27); + + // We have to pre-scale kq_scale and attn_factor to make the YaRN RoPE work correctly. + // See https://github.com/ggerganov/llama.cpp/discussions/7416 for detailed explanation. + const float mscale = attn_factor * (1.0f + hparams.rope_yarn_log_mul * logf(1.0f / freq_scale)); + const float kq_scale = 1.0f*mscale*mscale/sqrtf(float(hparams.n_embd_head_k)); + const float attn_factor_scaled = 1.0f / (1.0f + 0.1f * logf(1.0f / freq_scale)); + + const uint32_t n_embd_head_qk_rope = hparams.n_rot; + const uint32_t n_embd_head_qk_nope = hparams.n_embd_head_k - hparams.n_rot; + const uint32_t kv_lora_rank = hparams.n_lora_kv; + + struct ggml_tensor * cur; + struct ggml_tensor * inpL; + + // {n_embd, n_tokens} + inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb); + + // inp_pos - contains the positions + struct ggml_tensor * inp_pos = build_inp_pos(); + + // KQ_mask (mask for 1 head, it will be broadcasted to all heads) + struct ggml_tensor * KQ_mask = build_inp_KQ_mask(); + + 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 + { + struct ggml_tensor * q = NULL; + if (!is_lite) { + // {n_embd, q_lora_rank} * {n_embd, n_tokens} -> {q_lora_rank, n_tokens} + q = ggml_mul_mat(ctx0, model.layers[il].wq_a, cur); + cb(q, "q", il); + + q = llm_build_norm(ctx0, q, hparams, + model.layers[il].attn_q_a_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(q, "q", il); + + // {q_lora_rank, n_head * hparams.n_embd_head_k} * {q_lora_rank, n_tokens} -> {n_head * hparams.n_embd_head_k, n_tokens} + q = ggml_mul_mat(ctx0, model.layers[il].wq_b, q); + cb(q, "q", il); + } else { + q = ggml_mul_mat(ctx0, model.layers[il].wq, cur); + cb(q, "q", il); + } + + // split into {n_head * n_embd_head_qk_nope, n_tokens} + struct ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, ggml_element_size(q) * hparams.n_embd_head_k, ggml_element_size(q) * hparams.n_embd_head_k * n_head, 0); + cb(q_nope, "q_nope", il); + // and {n_head * n_embd_head_qk_rope, n_tokens} + struct ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, ggml_element_size(q) * hparams.n_embd_head_k, ggml_element_size(q) * hparams.n_embd_head_k * n_head, ggml_element_size(q) * n_embd_head_qk_nope); + cb(q_pe, "q_pe", il); + + // {n_embd, kv_lora_rank + n_embd_head_qk_rope} * {n_embd, n_tokens} -> {kv_lora_rank + n_embd_head_qk_rope, n_tokens} + struct ggml_tensor * compressed_kv_pe = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur); + cb(compressed_kv_pe, "compressed_kv_pe", il); + + // split into {kv_lora_rank, n_tokens} + struct ggml_tensor * compressed_kv = ggml_view_2d(ctx0, compressed_kv_pe, kv_lora_rank, n_tokens, compressed_kv_pe->nb[1], 0); + cb(compressed_kv, "compressed_kv", il); + // and {n_embd_head_qk_rope, n_tokens} + struct ggml_tensor * k_pe = ggml_view_2d(ctx0, compressed_kv_pe, n_embd_head_qk_rope, n_tokens, compressed_kv_pe->nb[1], ggml_element_size(compressed_kv_pe)*kv_lora_rank); + cb(k_pe, "k_pe", il); + + compressed_kv = llm_build_norm(ctx0, compressed_kv, hparams, + model.layers[il].attn_kv_a_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(compressed_kv, "compressed_kv", il); + + // {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)} * {kv_lora_rank, n_tokens} -> {n_head * (n_embd_head_qk_nope + n_embd_head_v), n_tokens} + struct ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, compressed_kv); + cb(kv, "kv", il); + + // split into {n_head * n_embd_head_qk_nope, n_tokens} + struct ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens, ggml_element_size(kv) * (n_embd_head_qk_nope + hparams.n_embd_head_v), ggml_element_size(kv) * n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v), 0); + cb(k_nope, "k_nope", il); + + // and {n_head * n_embd_head_v, n_tokens} + struct ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v, n_head, n_tokens, ggml_element_size(kv) * (n_embd_head_qk_nope + hparams.n_embd_head_v), ggml_element_size(kv) * n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v), ggml_element_size(kv) * n_embd_head_qk_nope); + cb(v_states, "v_states", il); + + v_states = ggml_cont(ctx0, v_states); + cb(v_states, "v_states", il); + + v_states = ggml_view_2d(ctx0, v_states, hparams.n_embd_head_v * n_head, n_tokens, ggml_element_size(kv) * hparams.n_embd_head_v * n_head, 0); + cb(v_states, "v_states", il); + + q_pe = ggml_rope_ext( + ctx0, q_pe, inp_pos, nullptr, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor_scaled, beta_fast, beta_slow + ); + cb(q_pe, "q_pe", il); + + // shared RoPE key + k_pe = ggml_rope_ext( + ctx0, ggml_view_3d(ctx0, k_pe, n_embd_head_qk_rope, 1, n_tokens, k_pe->nb[0], k_pe->nb[1], 0), inp_pos, nullptr, + n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, + ext_factor, attn_factor_scaled, beta_fast, beta_slow + ); + cb(k_pe, "k_pe", il); + + struct ggml_tensor * q_states = ggml_concat(ctx0, q_nope, q_pe, 0); + cb(q_states, "q_states", il); + + struct ggml_tensor * k_states = ggml_concat(ctx0, k_nope, ggml_repeat(ctx0, k_pe, q_pe), 0); + cb(k_states, "k_states", il); + + cur = llm_build_kv(ctx0, model, hparams, cparams, kv_self, gf, + model.layers[il].wo, NULL, + k_states, v_states, q_states, KQ_mask, n_tokens, kv_head, n_kv, kq_scale, cb, il); + } + + if (il == n_layer - 1) { + // skip computing output for unused tokens + struct ggml_tensor * inp_out_ids = build_inp_out_ids(); + n_tokens = n_outputs; + cur = ggml_get_rows(ctx0, cur, inp_out_ids); + inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids); + } + + struct ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA); + cb(ffn_inp, "ffn_inp", il); + + if ((uint32_t) il < hparams.n_layer_dense_lead) { + 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); + } else { + // MoE branch + cur = llm_build_norm(ctx0, ffn_inp, hparams, + model.layers[il].ffn_norm, NULL, + LLM_NORM_RMS, cb, il); + cb(cur, "ffn_norm", il); + + ggml_tensor * moe_out = + llm_build_moe_ffn(ctx0, cur, + model.layers[il].ffn_gate_inp, + model.layers[il].ffn_up_exps, + model.layers[il].ffn_gate_exps, + model.layers[il].ffn_down_exps, + n_expert, n_expert_used, + LLM_FFN_SILU, false, + true, hparams.expert_weights_scale, + cb, il); + cb(moe_out, "ffn_moe_out", il); + + // FFN shared expert + { + ggml_tensor * ffn_shexp = llm_build_ffn(ctx0, cur, + model.layers[il].ffn_up_shexp, NULL, + model.layers[il].ffn_gate_shexp, NULL, + model.layers[il].ffn_down_shexp, NULL, + NULL, + LLM_FFN_SILU, LLM_FFN_PAR, cb, il); + cb(ffn_shexp, "ffn_shexp", il); + + cur = ggml_add(ctx0, moe_out, ffn_shexp); + 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_defrag(llama_context & lctx, const std::vector & ids) { @@ -11226,6 +11615,10 @@ static struct ggml_cgraph * llama_build_graph( { result = llm.build_arctic(); } break; + case LLM_ARCH_DEEPSEEK2: + { + result = llm.build_deepseek2(); + } break; default: GGML_ASSERT(false); } @@ -16239,6 +16632,7 @@ enum llama_rope_type llama_rope_type(const struct llama_model * model) { case LLM_ARCH_COMMAND_R: case LLM_ARCH_OLMO: case LLM_ARCH_ARCTIC: + case LLM_ARCH_DEEPSEEK2: return LLAMA_ROPE_TYPE_NORM; // the pairs of head values are offset by n_rot/2 From 2b737caae100cf0ac963206984332e422058f2b9 Mon Sep 17 00:00:00 2001 From: Radoslav Gerganov Date: Tue, 28 May 2024 18:13:36 +0300 Subject: [PATCH 138/181] rpc : resource management rework (#7562) * rpc : resource management rework * address review comments --- ggml-rpc.cpp | 131 +++++++++++++++++++++++++++++---------------------- 1 file changed, 74 insertions(+), 57 deletions(-) diff --git a/ggml-rpc.cpp b/ggml-rpc.cpp index cc1d3ace1..49a20df4b 100644 --- a/ggml-rpc.cpp +++ b/ggml-rpc.cpp @@ -6,6 +6,7 @@ #include #include #include +#include #include #include #ifdef _WIN32 @@ -47,6 +48,7 @@ struct socket_t { sockfd_t fd; socket_t(sockfd_t fd) : fd(fd) {} ~socket_t() { + GGML_PRINT_DEBUG("[%s] closing socket %d\n", __func__, this->fd); #ifdef _WIN32 closesocket(this->fd); #else @@ -97,7 +99,7 @@ static ggml_guid_t ggml_backend_rpc_guid() { } struct ggml_backend_rpc_buffer_type_context { - std::shared_ptr sock; + std::string endpoint; std::string name; size_t alignment; size_t max_size; @@ -106,8 +108,6 @@ struct ggml_backend_rpc_buffer_type_context { struct ggml_backend_rpc_context { std::string endpoint; std::string name; - std::shared_ptr sock; - ggml_backend_buffer_type_t buft; }; struct ggml_backend_rpc_buffer_context { @@ -231,14 +231,13 @@ static bool recv_data(sockfd_t sockfd, void * data, size_t size) { return true; } -static bool parse_endpoint(const char * endpoint, std::string & host, int & port) { - std::string str(endpoint); - size_t pos = str.find(':'); +static bool parse_endpoint(const std::string & endpoint, std::string & host, int & port) { + size_t pos = endpoint.find(':'); if (pos == std::string::npos) { return false; } - host = str.substr(0, pos); - port = std::stoi(str.substr(pos + 1)); + host = endpoint.substr(0, pos); + port = std::stoi(endpoint.substr(pos + 1)); return true; } @@ -273,6 +272,44 @@ static bool send_rpc_cmd(const std::shared_ptr & sock, enum rpc_cmd cm // RPC client-side implementation +static std::shared_ptr get_socket(const std::string & endpoint) { + static std::mutex mutex; + std::lock_guard lock(mutex); + static std::unordered_map> sockets; + static bool initialized = false; + + auto it = sockets.find(endpoint); + if (it != sockets.end()) { + if (auto sock = it->second.lock()) { + return sock; + } + } + std::string host; + int port; + if (!parse_endpoint(endpoint, host, port)) { + return nullptr; + } +#ifdef _WIN32 + if (!initialized) { + WSADATA wsaData; + int res = WSAStartup(MAKEWORD(2, 2), &wsaData); + if (res != 0) { + return nullptr; + } + initialized = true; + } +#else + UNUSED(initialized); +#endif + auto sock = socket_connect(host.c_str(), port); + if (sock == nullptr) { + return nullptr; + } + GGML_PRINT_DEBUG("[%s] connected to %s, sockfd=%d\n", __func__, endpoint.c_str(), sock->fd); + sockets[endpoint] = sock; + return sock; +} + GGML_CALL static const char * ggml_backend_rpc_buffer_get_name(ggml_backend_buffer_t buffer) { ggml_backend_rpc_buffer_context * ctx = (ggml_backend_rpc_buffer_context *)buffer->context; return ctx->name.c_str(); @@ -442,7 +479,8 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_rpc_buffer_type_alloc_buffer std::vector input(input_size, 0); memcpy(input.data(), &size, sizeof(size)); std::vector output; - bool status = send_rpc_cmd(buft_ctx->sock, ALLOC_BUFFER, input, output); + auto sock = get_socket(buft_ctx->endpoint); + bool status = send_rpc_cmd(sock, ALLOC_BUFFER, input, output); GGML_ASSERT(status); GGML_ASSERT(output.size() == 2*sizeof(uint64_t)); // output serialization format: | remote_ptr (8 bytes) | remote_size (8 bytes) | @@ -453,7 +491,7 @@ GGML_CALL static ggml_backend_buffer_t ggml_backend_rpc_buffer_type_alloc_buffer if (remote_ptr != 0) { ggml_backend_buffer_t buffer = ggml_backend_buffer_init(buft, ggml_backend_rpc_buffer_interface, - new ggml_backend_rpc_buffer_context{buft_ctx->sock, {}, remote_ptr, "RPC"}, + new ggml_backend_rpc_buffer_context{sock, {}, remote_ptr, "RPC"}, remote_size); return buffer; } else { @@ -508,7 +546,7 @@ GGML_CALL static bool ggml_backend_rpc_buffer_type_supports_backend(ggml_backend } ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)buft->context; ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; - return buft_ctx->sock == rpc_ctx->sock; + return buft_ctx->endpoint == rpc_ctx->endpoint; } static ggml_backend_buffer_type_i ggml_backend_rpc_buffer_type_interface = { @@ -521,7 +559,6 @@ static ggml_backend_buffer_type_i ggml_backend_rpc_buffer_type_interface = { /* .is_host = */ NULL, }; - GGML_CALL static const char * ggml_backend_rpc_name(ggml_backend_t backend) { ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; @@ -530,16 +567,13 @@ GGML_CALL static const char * ggml_backend_rpc_name(ggml_backend_t backend) { GGML_CALL static void ggml_backend_rpc_free(ggml_backend_t backend) { ggml_backend_rpc_context * rpc_ctx = (ggml_backend_rpc_context *)backend->context; - ggml_backend_rpc_buffer_type_context * buft_ctx = (ggml_backend_rpc_buffer_type_context *)rpc_ctx->buft->context; - delete buft_ctx; - delete rpc_ctx->buft; delete rpc_ctx; delete backend; } GGML_CALL static ggml_backend_buffer_type_t ggml_backend_rpc_get_default_buffer_type(ggml_backend_t backend) { ggml_backend_rpc_context * ctx = (ggml_backend_rpc_context *)backend->context; - return ctx->buft; + return ggml_backend_rpc_buffer_type(ctx->endpoint.c_str()); } GGML_CALL static void ggml_backend_rpc_synchronize(ggml_backend_t backend) { @@ -590,7 +624,8 @@ GGML_CALL static enum ggml_status ggml_backend_rpc_graph_compute(ggml_backend_t std::vector input; serialize_graph(cgraph, input); std::vector output; - bool status = send_rpc_cmd(rpc_ctx->sock, GRAPH_COMPUTE, input, output); + auto sock = get_socket(rpc_ctx->endpoint); + bool status = send_rpc_cmd(sock, GRAPH_COMPUTE, input, output); GGML_ASSERT(status); GGML_ASSERT(output.size() == 1); return (enum ggml_status)output[0]; @@ -624,65 +659,48 @@ static ggml_backend_i ggml_backend_rpc_interface = { /* .event_synchronize = */ NULL, }; -static std::unordered_map instances; - GGML_API GGML_CALL ggml_backend_buffer_type_t ggml_backend_rpc_buffer_type(const char * endpoint) { - ggml_backend_t backend = ggml_backend_rpc_init(endpoint); - return backend != nullptr ? ggml_backend_rpc_get_default_buffer_type(backend) : nullptr; -} - -GGML_CALL ggml_backend_t ggml_backend_rpc_init(const char * endpoint) { - std::string endpoint_str(endpoint); - if (instances.find(endpoint_str) != instances.end()) { - return instances[endpoint_str]; + static std::mutex mutex; + std::lock_guard lock(mutex); + // NOTE: buffer types are allocated and never freed; this is by design + static std::unordered_map buft_map; + auto it = buft_map.find(endpoint); + if (it != buft_map.end()) { + return it->second; } -#ifdef _WIN32 - { - WSADATA wsaData; - int res = WSAStartup(MAKEWORD(2, 2), &wsaData); - if (res != 0) { - return nullptr; - } - } -#endif - fprintf(stderr, "Connecting to %s\n", endpoint); - std::string host; - int port; - if (!parse_endpoint(endpoint, host, port)) { - return nullptr; - } - auto sock = socket_connect(host.c_str(), port); + auto sock = get_socket(endpoint); if (sock == nullptr) { return nullptr; } size_t alignment = get_alignment(sock); size_t max_size = get_max_size(sock); ggml_backend_rpc_buffer_type_context * buft_ctx = new ggml_backend_rpc_buffer_type_context { - /* .sock = */ sock, - /* .name = */ "RPC" + std::to_string(sock->fd), + /* .endpoint = */ endpoint, + /* .name = */ "RPC[" + std::string(endpoint) + "]", /* .alignment = */ alignment, - /* .max_size = */ max_size + /* .max_size = */ max_size }; ggml_backend_buffer_type_t buft = new ggml_backend_buffer_type { /* .iface = */ ggml_backend_rpc_buffer_type_interface, /* .context = */ buft_ctx }; + buft_map[endpoint] = buft; + return buft; +} +GGML_CALL ggml_backend_t ggml_backend_rpc_init(const char * endpoint) { ggml_backend_rpc_context * ctx = new ggml_backend_rpc_context { - /* .endpoint = */ endpoint, - /* .name = */ "RPC" + std::to_string(sock->fd), - /* .sock = */ sock, - /* .buft = */ buft + /* .endpoint = */ endpoint, + /* .name = */ "RPC", }; - instances[endpoint] = new ggml_backend { + ggml_backend_t backend = new ggml_backend { /* .guid = */ ggml_backend_rpc_guid(), /* .interface = */ ggml_backend_rpc_interface, /* .context = */ ctx }; - - return instances[endpoint]; + return backend; } GGML_API GGML_CALL bool ggml_backend_is_rpc(ggml_backend_t backend) { @@ -706,14 +724,13 @@ static void get_device_memory(const std::shared_ptr & sock, size_t * f } GGML_API GGML_CALL void ggml_backend_rpc_get_device_memory(const char * endpoint, size_t * free, size_t * total) { - ggml_backend_t backend = ggml_backend_rpc_init(endpoint); - if (backend == nullptr) { + auto sock = get_socket(endpoint); + if (sock == nullptr) { *free = 0; *total = 0; return; } - ggml_backend_rpc_context * ctx = (ggml_backend_rpc_context *)backend->context; - get_device_memory(ctx->sock, free, total); + get_device_memory(sock, free, total); } // RPC server-side implementation From 56411a950f255b523a9edd684fd1632752474399 Mon Sep 17 00:00:00 2001 From: "k.h.lai" Date: Wed, 29 May 2024 01:25:08 +0800 Subject: [PATCH 139/181] vulkan: properly initialize vulkan devices for LLAMA_SPLIT_MODE_NONE (#7552) --- ggml-vulkan.cpp | 2 ++ 1 file changed, 2 insertions(+) diff --git a/ggml-vulkan.cpp b/ggml-vulkan.cpp index 79ce1479f..92e622b04 100644 --- a/ggml-vulkan.cpp +++ b/ggml-vulkan.cpp @@ -6012,6 +6012,8 @@ static ggml_backend_buffer_type_i ggml_backend_vk_buffer_type_interface = { }; GGML_CALL ggml_backend_buffer_type_t ggml_backend_vk_buffer_type(size_t dev_num) { + ggml_vk_instance_init(); + #ifdef GGML_VULKAN_DEBUG std::cerr << "ggml_backend_vk_buffer_type(" << dev_num << ")" << std::endl; #endif From 5442939fcc5e6ae41abf40612a95fd71377e487e Mon Sep 17 00:00:00 2001 From: Giuseppe Scrivano Date: Tue, 28 May 2024 20:49:49 +0200 Subject: [PATCH 140/181] llama : support small Granite models (#7481) * Add optional MLP bias for Granite models Add optional MLP bias for ARCH_LLAMA to support Granite models. Partially addresses ggerganov/llama.cpp/issues/7116 Still needs some more changes to properly support Granite. * llama: honor add_space_prefix from the model configuration propagate the add_space_prefix configuration from the HF model configuration to the gguf file and honor it with the gpt2 tokenizer. Signed-off-by: Giuseppe Scrivano * llama: add support for small granite models it works only for the small models 3b and 8b. The convert-hf-to-gguf.py script uses the vocabulary size of the granite models to detect granite and set the correct configuration. Signed-off-by: Giuseppe Scrivano --------- Signed-off-by: Giuseppe Scrivano Co-authored-by: Steffen Roecker --- convert-hf-to-gguf.py | 15 +++++++++++++-- llama.cpp | 27 +++++++++++++++++++++------ 2 files changed, 34 insertions(+), 8 deletions(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 1b060e4e6..98b50d150 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -1317,6 +1317,17 @@ class LlamaModel(Model): self.gguf_writer.add_rope_scaling_type(gguf.RopeScalingType.LINEAR) self.gguf_writer.add_rope_scaling_factor(self.hparams["rope_scaling"]["factor"]) + tokenizer_config_file = self.dir_model / 'tokenizer_config.json' + if tokenizer_config_file.is_file(): + with open(tokenizer_config_file, "r", encoding="utf-8") as f: + tokenizer_config_json = json.load(f) + if "add_prefix_space" in tokenizer_config_json: + self.gguf_writer.add_add_space_prefix(tokenizer_config_json["add_prefix_space"]) + + # Apply to granite small models only + if self.hparams.get("vocab_size", 32000) == 49152: + self.gguf_writer.add_add_bos_token(False) + @staticmethod def permute(weights: Tensor, n_head: int, n_head_kv: int | None): if n_head_kv is not None and n_head != n_head_kv: @@ -1331,9 +1342,9 @@ class LlamaModel(Model): n_head = self.hparams["num_attention_heads"] n_kv_head = self.hparams.get("num_key_value_heads") - if name.endswith("q_proj.weight"): + if name.endswith(("q_proj.weight", "q_proj.bias")): data_torch = LlamaModel.permute(data_torch, n_head, n_head) - if name.endswith("k_proj.weight"): + if name.endswith(("k_proj.weight", "k_proj.bias")): data_torch = LlamaModel.permute(data_torch, n_head, n_kv_head) # process the experts separately diff --git a/llama.cpp b/llama.cpp index 10c9e47dd..468a7cb25 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2028,8 +2028,9 @@ struct llama_layer { struct ggml_tensor * ffn_up_shexp; // ff bias - struct ggml_tensor * ffn_down_b; // b2 - struct ggml_tensor * ffn_up_b; // b3 + struct ggml_tensor * ffn_gate_b = nullptr; + struct ggml_tensor * ffn_down_b = nullptr; // b2 + struct ggml_tensor * ffn_up_b = nullptr; // b3 struct ggml_tensor * ffn_act; // mamba proj @@ -4058,7 +4059,9 @@ static void llm_load_hparams( switch (hparams.n_layer) { case 22: model.type = e_model::MODEL_1B; break; case 26: model.type = e_model::MODEL_3B; break; - case 32: model.type = hparams.n_vocab < 40000 ? e_model::MODEL_7B : e_model::MODEL_8B; break; + // granite uses a vocab with len 49152 + case 32: model.type = hparams.n_vocab == 49152 ? e_model::MODEL_3B : (hparams.n_vocab < 40000 ? e_model::MODEL_7B : e_model::MODEL_8B); break; + case 36: model.type = e_model::MODEL_8B; break; // granite case 40: model.type = e_model::MODEL_13B; break; case 48: model.type = e_model::MODEL_34B; break; case 60: model.type = e_model::MODEL_30B; break; @@ -4328,6 +4331,8 @@ static void llm_load_hparams( case 30: model.type = e_model::MODEL_3B; break; case 32: model.type = e_model::MODEL_7B; break; case 40: model.type = e_model::MODEL_15B; break; + case 52: model.type = e_model::MODEL_20B; break; // granite + case 88: model.type = e_model::MODEL_34B; break; // granite default: model.type = e_model::MODEL_UNKNOWN; } } break; @@ -4590,6 +4595,11 @@ static void llm_load_vocab( } else { if (tokenizer_model == "gpt2") { vocab.type = LLAMA_VOCAB_TYPE_BPE; + + 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); + } } else { LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_model.c_str()); LLAMA_LOG_WARN("%s: using default tokenizer: 'llama'", __func__); @@ -5211,6 +5221,11 @@ static bool llm_load_tensors( 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}); + + // optional MLP bias + layer.ffn_gate_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_GATE, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_down_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_DOWN, "bias", i), {n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED); + layer.ffn_up_b = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, llama_model_loader::TENSOR_NOT_REQUIRED); } else { layer.ffn_gate_inp = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_GATE_INP, "weight", i), {n_embd, n_expert}); @@ -7483,9 +7498,9 @@ struct llm_build_context { 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, + model.layers[il].ffn_up, model.layers[il].ffn_up_b, + model.layers[il].ffn_gate, model.layers[il].ffn_gate_b, + model.layers[il].ffn_down, model.layers[il].ffn_down_b, NULL, LLM_FFN_SILU, LLM_FFN_PAR, cb, il); cb(cur, "ffn_out", il); From 6bd12ce409f949012935b7d1b15a21ffa473a565 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Tue, 28 May 2024 22:22:50 +0300 Subject: [PATCH 141/181] sycl : fix assert (#7563) --- ggml-sycl.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index 022a52aeb..dccfe9eb4 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -13567,7 +13567,7 @@ inline void ggml_sycl_op_concat(const ggml_tensor *src0, #pragma message("TODO: generalize concat kernel for dim != 2") #pragma message(" https://github.com/ggerganov/llama.cpp/pull/7563") int dim = dst->op_params[0]; - GGML_ASSERT(dim != 2); + GGML_ASSERT(dim == 2); GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(src1->type == GGML_TYPE_F32); From 02c1ecad07f0e2d2febe8196271bcc64bdc9c006 Mon Sep 17 00:00:00 2001 From: jaime-m-p <167997752+jaime-m-p@users.noreply.github.com> Date: Tue, 28 May 2024 21:46:34 +0200 Subject: [PATCH 142/181] Tokenizer WPM fixes (#7500) * Update random test: add_bos_token. * Update random test: add WPM models for testing. * Build vocab.special_tokens_cache using vocab token types. * Fix and improve WPM preprocessing. - Fix unicode edge case combinations. - Split by whitspace in the same pass. * Discard all tokens when no matching found. --- llama.cpp | 222 +++++++++------------------------ tests/test-tokenizer-random.py | 20 +-- 2 files changed, 75 insertions(+), 167 deletions(-) diff --git a/llama.cpp b/llama.cpp index 468a7cb25..dac81acc0 100644 --- a/llama.cpp +++ b/llama.cpp @@ -2162,7 +2162,7 @@ struct llama_vocab { std::unordered_map token_to_id; std::vector id_to_token; - std::unordered_map special_tokens_cache; + std::vector special_tokens_cache; std::map, int> bpe_ranks; @@ -4831,97 +4831,19 @@ static void llm_load_vocab( // build special tokens cache { - // TODO: It is unclear (to me) at this point, whether special tokes are guaranteed to be of a deterministic type, - // and will always be correctly labeled in 'added_tokens.json' etc. - // The assumption is, since special tokens aren't meant to be exposed to end user, they are designed - // to be unmatchable by the tokenizer, therefore tokens from the vocab, which are unmatchable by the tokenizer - // are special tokens. - // From testing, this appears to correlate 1:1 with special tokens. - // - - // Counting special tokens and verifying in only one direction - // is sufficient to detect difference in those two sets. - // - uint32_t special_tokens_count_by_type = 0; - uint32_t special_tokens_count_from_verification = 0; - - bool special_tokens_definition_mismatch = false; - - for (const auto & t : vocab.token_to_id) { - const auto & token = t.first; - const auto & id = t.second; - - // Count all non-normal tokens in the vocab while iterating + for (llama_vocab::id id = 0; id < (llama_vocab::id)n_vocab; ++id) { if (vocab.id_to_token[id].type != LLAMA_TOKEN_TYPE_NORMAL) { - special_tokens_count_by_type++; - } - - // Skip single character tokens - if (token.length() > 1) { - bool is_tokenizable = false; - - // Split token string representation in two, in all possible ways - // and check if both halves can be matched to a valid token - for (unsigned i = 1; i < token.length();) { - const auto left = token.substr(0, i); - const auto right = token.substr(i); - - // check if we didnt partition in the middle of a utf sequence - auto utf = utf8_len(left.at(left.length() - 1)); - - if (utf == 1) { - if (vocab.token_to_id.find(left) != vocab.token_to_id.end() && - vocab.token_to_id.find(right) != vocab.token_to_id.end() ) { - is_tokenizable = true; - break; - } - i++; - } else { - // skip over the rest of multibyte utf sequence - i += utf - 1; - } - } - - if (!is_tokenizable) { - // Some tokens are multibyte, but they are utf sequences with equivalent text length of 1 - // it's faster to re-filter them here, since there are way less candidates now - - // Calculate a total "utf" length of a token string representation - size_t utf8_str_len = 0; - for (unsigned i = 0; i < token.length();) { - utf8_str_len++; - i += utf8_len(token.at(i)); - } - - // And skip the ones which are one character - if (utf8_str_len > 1) { - // At this point what we have left are special tokens only - vocab.special_tokens_cache[token] = id; - - // Count manually found special tokens - special_tokens_count_from_verification++; - - // If this manually found special token is not marked as such, flag a mismatch - if (vocab.id_to_token[id].type == LLAMA_TOKEN_TYPE_NORMAL) { - special_tokens_definition_mismatch = true; - } - } - } + vocab.special_tokens_cache.push_back(id); } } - if (special_tokens_definition_mismatch || special_tokens_count_from_verification != special_tokens_count_by_type) { - LLAMA_LOG_WARN("%s: mismatch in special tokens definition ( %u/%zu vs %u/%zu ).\n", - __func__, - special_tokens_count_from_verification, vocab.id_to_token.size(), - special_tokens_count_by_type, vocab.id_to_token.size() - ); - } else { - LLAMA_LOG_INFO("%s: special tokens definition check successful ( %u/%zu ).\n", - __func__, - special_tokens_count_from_verification, vocab.id_to_token.size() - ); - } + std::sort( vocab.special_tokens_cache.begin(), vocab.special_tokens_cache.end(), + [&] (const llama_vocab::id a, const llama_vocab::id b) { + return vocab.id_to_token[a].text.size() > vocab.id_to_token[b].text.size(); + } + ); + + LLAMA_LOG_INFO("%s: special tokens cache size = %u.\n", __func__, (uint32_t)vocab.special_tokens_cache.size()); } } @@ -13146,7 +13068,7 @@ struct llm_tokenizer_wpm { llm_tokenizer_wpm(const llama_vocab & vocab): vocab(vocab) {} void tokenize(const std::string & text, std::vector & output) { - auto * token_map = &vocab.token_to_id; + const auto & token_map = vocab.token_to_id; // normalize and split by whitespace std::vector words = preprocess(text); @@ -13161,108 +13083,89 @@ struct llm_tokenizer_wpm { } // prepend phantom space - std::string word1 = "\xe2\x96\x81" + word; - int n = word1.size(); + const std::string word1 = "\xe2\x96\x81" + word; + const int n = word1.size(); + + const size_t current_tokens = output.size(); // we're at the start of a new word - int i = 0; - bool match_any = false; - // move through character position in word - while (i < n) { + for (int i = 0; i < n; ++i) { // loop through possible match length bool match = false; for (int j = n; j > i; j--) { - auto it = token_map->find(word1.substr(i, j - i)); - if (it != token_map->end()) { + auto it = token_map.find(word1.substr(i, j - i)); + if (it != token_map.end()) { output.push_back(it->second); match = true; - match_any = true; - i = j; + i = j - 1; break; } } - // must be an unknown character - if (!match) { - i++; + if (!match) { // discard all + output.resize(current_tokens); + break; // and discard next tokens } } // we didn't find any matches for this word - if (!match_any) { + if (current_tokens == output.size()) { output.push_back(vocab.special_unk_id); } } } std::vector preprocess(const std::string & text) { - std::vector cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text)); + const std::vector cpts_nfd = unicode_cpts_normalize_nfd(unicode_cpts_from_utf8(text)); + std::vector words(1, ""); - // strip accents, strip control, uniformize whitespace, - // to lowercase, pad chinese characters, pad punctuation - std::string new_str = ""; - for (uint32_t code : cpts_nfd) { - const codepoint_flags flags = unicode_cpt_flags(code); - if (flags.is_accent_mark || flags.is_control) { + for (const char32_t cpt : cpts_nfd) { + const auto flags = unicode_cpt_flags(cpt); + + if (flags.is_whitespace) { + if (words.back().size()) { // finish previous word if any + words.emplace_back(); + } continue; } - code = unicode_tolower(code); - if (flags.is_separator || flags.is_whitespace) { //####FIXME: is_separator ? - code = ' '; + + assert (!flags.is_separator); + if (cpt == 0 || cpt == 0xFFFD || flags.is_control) { + continue; } - std::string s = unicode_cpt_to_utf8(code); - if (flags.is_punctuation || is_ascii_punct(code) || is_chinese_char(code)) { - new_str += " "; - new_str += s; - new_str += " "; + + const std::string s = unicode_cpt_to_utf8(unicode_tolower(cpt)); + if (flags.is_punctuation || ( cpt < 0x7F && flags.is_symbol ) || is_chinese_char(cpt)) { + if (words.back().size()) { // finish previous word if any + words.emplace_back(); + } + words.back() = s; // single char word + words.emplace_back(); // start a new word } else { - new_str += s; + words.back() += s; // append char to word } } - // split by whitespace - uint64_t l = 0; - uint64_t r = 0; - std::vector words; - while (r < new_str.size()) { - // if is whitespace - if (isspace(new_str[r], std::locale::classic())) { - if (r > l) words.push_back(new_str.substr(l, (r - l))); - l = r + 1; - r = l; - } else { - r += 1; - } - } - if (r > l) { - words.push_back(new_str.substr(l, (r - l))); + if (!words.back().size()) { + words.pop_back(); } + return words; } - bool is_ascii_punct(uint32_t code) { - if (code > 0xFF) { - return false; - } - auto c = char(static_cast(code)); - return ispunct(c, std::locale::classic()); - } - - bool is_chinese_char(uint32_t cpt) { - if ((cpt >= 0x4E00 && cpt <= 0x9FFF) || - (cpt >= 0x3400 && cpt <= 0x4DBF) || + static bool is_chinese_char(uint32_t cpt) { + return + (cpt >= 0x04E00 && cpt <= 0x09FFF) || + (cpt >= 0x03400 && cpt <= 0x04DBF) || (cpt >= 0x20000 && cpt <= 0x2A6DF) || (cpt >= 0x2A700 && cpt <= 0x2B73F) || (cpt >= 0x2B740 && cpt <= 0x2B81F) || (cpt >= 0x2B920 && cpt <= 0x2CEAF) || // this should be 0x2B820 but in hf rust code it is 0x2B920 - (cpt >= 0xF900 && cpt <= 0xFAFF) || - (cpt >= 0x2F800 && cpt <= 0x2FA1F) || - (cpt >= 0x3000 && cpt <= 0x303F) || - (cpt >= 0xFF00 && cpt <= 0xFFEF)) { - return true; // NOLINT - } - return false; + (cpt >= 0x0F900 && cpt <= 0x0FAFF) || + (cpt >= 0x2F800 && cpt <= 0x2FA1F); + //(cpt >= 0x3000 && cpt <= 0x303F) || + //(cpt >= 0xFF00 && cpt <= 0xFFEF); } const llama_vocab & vocab; @@ -13306,9 +13209,8 @@ struct fragment_buffer_variant { static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list & buffer) { // for each special token - for (const auto & st: vocab.special_tokens_cache) { - const auto & special_token = st.first; - const auto & special_id = st.second; + for (const llama_vocab::id special_id : vocab.special_tokens_cache) { + const auto & special_token = vocab.id_to_token[special_id].text; // for each text fragment std::forward_list::iterator it = buffer.begin(); @@ -13317,7 +13219,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list< // if a fragment is text ( not yet processed ) if (fragment.type == FRAGMENT_BUFFER_VARIANT_TYPE_RAW_TEXT) { - auto * raw_text = &(fragment.raw_text); + auto & raw_text = fragment.raw_text; auto raw_text_base_offset = fragment.offset; auto raw_text_base_length = fragment.length; @@ -13327,7 +13229,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list< // find the first occurrence of a given special token in this fragment // passing offset argument only limit the "search area" but match coordinates // are still relative to the source full raw_text - auto match = raw_text->find(special_token, raw_text_base_offset); + auto match = raw_text.find(special_token, raw_text_base_offset); // no occurrences found, stop processing this fragment for a given special token if (match == std::string::npos) break; @@ -13346,7 +13248,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list< // left const int64_t left_reminder_offset = raw_text_base_offset + 0; const int64_t left_reminder_length = match - raw_text_base_offset; - buffer.emplace_after(it, (*raw_text), left_reminder_offset, left_reminder_length); + buffer.emplace_after(it, raw_text, left_reminder_offset, left_reminder_length); #ifdef PRETOKENIZERDEBUG LLAMA_LOG_WARN("FL: (%ld %ld) '%s'\n", left_reminder_offset, left_reminder_length, raw_text->substr(left_reminder_offset, left_reminder_length).c_str()); @@ -13362,7 +13264,7 @@ static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list< if (match + special_token.length() < raw_text_base_offset + raw_text_base_length) { const int64_t right_reminder_offset = match + special_token.length(); const int64_t right_reminder_length = raw_text_base_length - ((match - raw_text_base_offset) + special_token.length()); - buffer.emplace_after(it, (*raw_text), right_reminder_offset, right_reminder_length); + buffer.emplace_after(it, raw_text, right_reminder_offset, right_reminder_length); #ifdef PRETOKENIZERDEBUG LLAMA_LOG_WARN("FR: (%ld %ld) '%s'\n", right_reminder_offset, right_reminder_length, raw_text->substr(right_reminder_offset, right_reminder_length).c_str()); diff --git a/tests/test-tokenizer-random.py b/tests/test-tokenizer-random.py index 7e1b656e5..ec1b2837c 100644 --- a/tests/test-tokenizer-random.py +++ b/tests/test-tokenizer-random.py @@ -167,8 +167,10 @@ def generator_random_special_tokens(tokenizer, iterations=100) -> Iterator[str]: for m in range(iterations): rand.seed(m) words = rand.choices(special_tokens, k=500) - if tokenizer.add_bos_token: # skip spam warning of double BOS - while words and words[0] == tokenizer.bos_token: + if words[0] == tokenizer.bos_token: # skip spam warning of double BOS + while len(words) > 1 and words[1] == tokenizer.bos_token: # leave one starting BOS + words.pop(0) + if tokenizer.add_bos_token: # drop all starting BOS words.pop(0) yield "".join(words) @@ -293,15 +295,17 @@ def main(argv: list[str] = None): model = LibLlamaModel(LibLlama(), args.vocab_file, mparams=dict(vocab_only=True), cparams=dict(n_ctx=4096)) tokenizer = AutoTokenizer.from_pretrained(args.dir_tokenizer) - tokenizer.add_bos_token = getattr(tokenizer, "add_bos_token", True) - tokenizer.add_eos_token = getattr(tokenizer, "add_eos_token", False) - def func_tokenize1(text: str): return model.tokenize(text, add_special=True, parse_special=True) def func_tokenize2(text: str): return tokenizer.encode(text, add_special_tokens=True) + ids = func_tokenize2("a") + assert 1 <= len(ids) <= 3 + add_bos_token = len(ids) > 1 and tokenizer.bos_token_id == ids[0] + tokenizer.add_bos_token = getattr(tokenizer, "add_bos_token", add_bos_token) + vocab = list(sorted(tokenizer.batch_decode(list(tokenizer.get_vocab().values()), skip_special_tokens=True))) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text()) test_compare_tokenizer(func_tokenize1, func_tokenize2, generator_custom_text_edge_cases()) @@ -324,8 +328,10 @@ if __name__ == "__main__": # import os # tokenizers = os.listdir(path_tokenizers) tokenizers = [ - "llama-spm", # SPM - "phi-3", # SPM + # "llama-spm", # SPM + # "phi-3", # SPM + "jina-v2-en", # WPM + "bert-bge", # WPM ] for tokenizer in tokenizers: From b864b50ce5e2beefc8c2fd31733e4e1a978b7754 Mon Sep 17 00:00:00 2001 From: "Meng, Hengyu" Date: Wed, 29 May 2024 07:00:24 +0800 Subject: [PATCH 143/181] [SYCL] Align GEMM dispatch (#7566) * align GEMM dispatch --- CMakeLists.txt | 4 ++ README.md | 3 +- ggml-sycl.cpp | 122 ++++++++++++++++++++++--------------------------- 3 files changed, 61 insertions(+), 68 deletions(-) diff --git a/CMakeLists.txt b/CMakeLists.txt index c5add8239..fbbc38644 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -628,6 +628,10 @@ if (LLAMA_SYCL) add_compile_definitions(GGML_SYCL_F16) endif() + if (LLAMA_CUDA_FORCE_MMQ) + add_compile_definitions(GGML_SYCL_FORCE_MMQ) + endif() + add_compile_options(-I./) #include DPCT add_compile_options(-I/${SYCL_INCLUDE_DIR}) diff --git a/README.md b/README.md index 15519c97f..1cab7f19d 100644 --- a/README.md +++ b/README.md @@ -477,7 +477,8 @@ Building the program with BLAS support may lead to some performance improvements |--------------------------------|------------------------|---------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | LLAMA_CUDA_FORCE_DMMV | Boolean | false | Force the use of dequantization + matrix vector multiplication kernels instead of using kernels that do matrix vector multiplication on quantized data. By default the decision is made based on compute capability (MMVQ for 6.1/Pascal/GTX 1000 or higher). Does not affect k-quants. | | LLAMA_CUDA_DMMV_X | Positive integer >= 32 | 32 | Number of values in x direction processed by the CUDA dequantization + matrix vector multiplication kernel per iteration. Increasing this value can improve performance on fast GPUs. Power of 2 heavily recommended. Does not affect k-quants. | - | LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. | + | LLAMA_CUDA_MMV_Y | Positive integer | 1 | Block size in y direction for the CUDA mul mat vec kernels. Increasing this value can improve performance on fast GPUs. Power of 2 recommended. | + | LLAMA_CUDA_FORCE_MMQ | Boolean | false | Force the use of dequantization + matrix multiplication kernels instead of leveraging Math libraries. | | | LLAMA_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. | | LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | | LLAMA_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. | diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index dccfe9eb4..a73448136 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -3022,20 +3022,19 @@ 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_4VEC 130 //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 +#if !defined(GGML_SYCL_FORCE_MMQ) + #define SYCL_USE_XMX +#endif // max batch size to use MMQ kernels when tensor cores are available -#define XMX_MAX_BATCH_SIZE 32 +#define MMQ_MAX_BATCH_SIZE 32 #if defined(_MSC_VER) @@ -15249,6 +15248,29 @@ catch (sycl::exception const &exc) { std::exit(1); } +inline bool ggml_sycl_supports_mmq(enum ggml_type type) { + // TODO: accuracy issues in MMQ + return false; +} + +bool ggml_sycl_supports_dmmv(enum ggml_type type) { + switch (type) { + case GGML_TYPE_Q4_0: + case GGML_TYPE_Q4_1: + case GGML_TYPE_Q5_0: + case GGML_TYPE_Q5_1: + case GGML_TYPE_Q8_0: + case GGML_TYPE_Q2_K: + case GGML_TYPE_Q3_K: + case GGML_TYPE_Q4_K: + case GGML_TYPE_Q5_K: + case GGML_TYPE_Q6_K: + case GGML_TYPE_F16: + return true; + default: + return false; + } +} static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) { const bool all_on_device = @@ -15265,76 +15287,42 @@ static void ggml_sycl_mul_mat(const ggml_tensor * src0, const ggml_tensor * src1 } } + // check data types and tensor shapes for custom matrix multiplication kernels: + bool use_dequantize_mul_mat_vec = ggml_sycl_supports_dmmv(src0->type) + && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 + && src0->ne[0] % GGML_SYCL_DMMV_X == 0 && src1->ne[1] == 1; + + bool use_mul_mat_vec_q = ggml_is_quantized(src0->type) + && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32 + && src1->ne[1] <= MMVQ_MAX_BATCH_SIZE; + + bool use_mul_mat_q = ggml_sycl_supports_mmq(src0->type) + && src1->type == GGML_TYPE_F32 && dst->type == GGML_TYPE_F32; + + // mmvq and mmq need the __dp4a instruction which is available for gen12+ + // Workaround in https://github.com/ggerganov/llama.cpp/commit/95f84d5ce8b449a9b16009434aca800df504a02e + use_mul_mat_q = use_mul_mat_q && (src0->type != GGML_TYPE_IQ2_XXS); #ifdef SYCL_USE_XMX - const bool use_xmx = true; -#else - const bool use_xmx = false; -#endif + use_mul_mat_q = use_mul_mat_q && (src1->ne[1] <= MMQ_MAX_BATCH_SIZE); +#endif // SYCL_USE_XMX - // 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) { + if (!split && 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) { + } else if (!split && 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 && !ggml_is_transposed(src0) && !ggml_is_transposed(src1)) { + } else if (!split && src0->type == GGML_TYPE_F16 && (src1->type == GGML_TYPE_F16) && !ggml_is_transposed(src0) && !ggml_is_transposed(src1) && src1->ne[2]*src1->ne[3] > 1) { // KQ + KQV multi-batch - // GGML_SYCL_DEBUG("ggml_sycl_mul_mat_batched_sycl\n"); ggml_sycl_mul_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 - bool use_mul_mat_vec_q = min_compute_capability >= VER_4VEC && ggml_is_quantized(src0->type); - use_mul_mat_vec_q = use_mul_mat_vec_q || - (src0->type == GGML_TYPE_IQ2_XXS) || (src0->type == GGML_TYPE_IQ2_XS) || (src0->type == GGML_TYPE_IQ2_S) || - (src0->type == GGML_TYPE_IQ3_XXS) || (src0->type == GGML_TYPE_IQ3_S) || - (src0->type == GGML_TYPE_IQ4_NL) || (src0->type == GGML_TYPE_IQ4_XS) || - (src0->type == GGML_TYPE_IQ1_S) || (src0->type == GGML_TYPE_IQ1_M); - - -#endif // GGML_SYCL_FORCE_DMMV - - if (use_mul_mat_vec_q) { - // 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); - use_mul_mat_q = use_mul_mat_q && (src0->type != GGML_TYPE_IQ2_XXS); - - 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 if (use_dequantize_mul_mat_vec) { + ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_dequantize_mul_mat_vec, false); + } else if (use_mul_mat_vec_q) { + ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_vec_q, true); + } else if (use_mul_mat_q) { + ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_q, true); } else { - GGML_ASSERT(false); + ggml_sycl_op_mul_mat(src0, src1, dst, ggml_sycl_op_mul_mat_sycl, false); } } From 504f0c340f6b5e04de682f6ddefdd3b81208df5d Mon Sep 17 00:00:00 2001 From: zhouwg Date: Wed, 29 May 2024 10:09:31 +0800 Subject: [PATCH 144/181] ggml : fix typo in ggml.c (#7603) --- ggml.c | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml.c b/ggml.c index 023077ca6..8bfb9531e 100644 --- a/ggml.c +++ b/ggml.c @@ -11012,7 +11012,7 @@ static void ggml_compute_forward_concat_f32( static void ggml_compute_forward_concat( const struct ggml_compute_params * params, - struct ggml_tensor* dst) { + struct ggml_tensor * dst) { const struct ggml_tensor * src0 = dst->src[0]; From 0e8d8bfd6caf1d0a8cbdf9d3d5c06fbbb9dfced8 Mon Sep 17 00:00:00 2001 From: Akarshan Biswas Date: Wed, 29 May 2024 12:23:47 +0530 Subject: [PATCH 145/181] Add Arc A750 and Arch linux to readme-sycl.md as verified GPU model and Linux distro (#7605) --- README-sycl.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README-sycl.md b/README-sycl.md index cfa248a95..37f0306dc 100644 --- a/README-sycl.md +++ b/README-sycl.md @@ -54,10 +54,10 @@ It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, ## OS -| OS | Status | Verified | -|---------|---------|------------------------------------| -| Linux | Support | Ubuntu 22.04, Fedora Silverblue 39 | -| Windows | Support | Windows 11 | +| OS | Status | Verified | +|---------|---------|------------------------------------------------| +| Linux | Support | Ubuntu 22.04, Fedora Silverblue 39, Arch Linux | +| Windows | Support | Windows 11 | ## Hardware @@ -70,7 +70,7 @@ It has the similar design of other llama.cpp BLAS-based paths such as *OpenBLAS, |-------------------------------|---------|---------------------------------------| | Intel Data Center Max Series | Support | Max 1550, 1100 | | Intel Data Center Flex Series | Support | Flex 170 | -| Intel Arc Series | Support | Arc 770, 730M | +| Intel Arc Series | Support | Arc 770, 730M, Arc A750 | | Intel built-in Arc GPU | Support | built-in Arc GPU in Meteor Lake | | Intel iGPU | Support | iGPU in i5-1250P, i7-1260P, i7-1165G7 | From 72de268bec49f67e2883880f573c55cea32de736 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 26 May 2024 18:35:23 +0300 Subject: [PATCH 146/181] ggml : restore ggml_rope_xpos_inplace (ggml/0) ggml-ci --- ggml.c | 10 ++++++++++ ggml.h | 8 ++++++++ 2 files changed, 18 insertions(+) diff --git a/ggml.c b/ggml.c index 8bfb9531e..5025ec23b 100644 --- a/ggml.c +++ b/ggml.c @@ -6392,6 +6392,16 @@ struct ggml_tensor * ggml_rope_custom_inplace( ); } +struct ggml_tensor * ggml_rope_xpos_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int n_dims, + float base, + bool down) { + return ggml_rope_impl(ctx, a, b, NULL, n_dims, 0, 0, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f, base, down, true); +} + // ggml_rope_back struct ggml_tensor * ggml_rope_back( diff --git a/ggml.h b/ggml.h index 4e6bcb30f..3859895b6 100644 --- a/ggml.h +++ b/ggml.h @@ -1548,6 +1548,14 @@ extern "C" { float beta_slow), "use ggml_rope_ext_inplace instead"); + struct ggml_tensor * ggml_rope_xpos_inplace( + struct ggml_context * ctx, + struct ggml_tensor * a, + struct ggml_tensor * b, + int n_dims, + float base, + bool down); + // compute correction dims for YaRN RoPE scaling 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]); From 2ab977282b02ccd6783fbbaec393c96886cf33b1 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 29 May 2024 14:29:52 +0300 Subject: [PATCH 147/181] 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 57bede67b..5042f82ae 100644 --- a/scripts/sync-ggml.last +++ b/scripts/sync-ggml.last @@ -1 +1 @@ -126d34985705a5a2222723c145cb4e125ac689f3 +2aae01fd9b8f9399f343cf18f46f38996ef52e2c From 00281b7be32462754618c42ed93f95743af46627 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 29 May 2024 14:31:18 +0300 Subject: [PATCH 148/181] scripts : remove mpi remnants --- scripts/sync-ggml-am.sh | 4 ---- scripts/sync-ggml.sh | 2 -- 2 files changed, 6 deletions(-) diff --git a/scripts/sync-ggml-am.sh b/scripts/sync-ggml-am.sh index cf22afc41..3f8ddf37b 100755 --- a/scripts/sync-ggml-am.sh +++ b/scripts/sync-ggml-am.sh @@ -106,8 +106,6 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then # src/ggml-kompute.h -> ggml-kompute.h # src/ggml-metal.h -> ggml-metal.h # src/ggml-metal.m -> ggml-metal.m - # src/ggml-mpi.h -> ggml-mpi.h - # src/ggml-mpi.c -> ggml-mpi.c # src/ggml-opencl.cpp -> ggml-opencl.cpp # src/ggml-opencl.h -> ggml-opencl.h # src/ggml-quants.c -> ggml-quants.c @@ -145,8 +143,6 @@ if [ -f $SRC_LLAMA/ggml-src.patch ]; then -e 's/src\/ggml-kompute\.h/ggml-kompute.h/g' \ -e 's/src\/ggml-metal\.h/ggml-metal.h/g' \ -e 's/src\/ggml-metal\.m/ggml-metal.m/g' \ - -e 's/src\/ggml-mpi\.h/ggml-mpi.h/g' \ - -e 's/src\/ggml-mpi\.c/ggml-mpi.c/g' \ -e 's/src\/ggml-opencl\.cpp/ggml-opencl.cpp/g' \ -e 's/src\/ggml-opencl\.h/ggml-opencl.h/g' \ -e 's/src\/ggml-quants\.c/ggml-quants.c/g' \ diff --git a/scripts/sync-ggml.sh b/scripts/sync-ggml.sh index ec47fb27c..fbae6b7f8 100755 --- a/scripts/sync-ggml.sh +++ b/scripts/sync-ggml.sh @@ -14,8 +14,6 @@ cp -rpv ../ggml/src/ggml-kompute.h ./ggml-kompute.h cp -rpv ../ggml/src/ggml-metal.h ./ggml-metal.h cp -rpv ../ggml/src/ggml-metal.m ./ggml-metal.m cp -rpv ../ggml/src/ggml-metal.metal ./ggml-metal.metal -cp -rpv ../ggml/src/ggml-mpi.h ./ggml-mpi.h -cp -rpv ../ggml/src/ggml-mpi.c ./ggml-mpi.c cp -rpv ../ggml/src/ggml-opencl.cpp ./ggml-opencl.cpp cp -rpv ../ggml/src/ggml-opencl.h ./ggml-opencl.h cp -rpv ../ggml/src/ggml-quants.c ./ggml-quants.c From 87bdf2a199acd62e19814d7a4d0500a04a7f09f3 Mon Sep 17 00:00:00 2001 From: slaren Date: Wed, 29 May 2024 13:36:39 +0200 Subject: [PATCH 149/181] ggml : use atomic_flag for critical section (#7598) * ggml : use atomic_flag for critical section * add windows shims --- ggml.c | 23 ++++++++++++++--------- 1 file changed, 14 insertions(+), 9 deletions(-) diff --git a/ggml.c b/ggml.c index 5025ec23b..d8f74f3ce 100644 --- a/ggml.c +++ b/ggml.c @@ -60,6 +60,9 @@ typedef volatile LONG atomic_int; typedef atomic_int atomic_bool; +typedef atomic_int atomic_flag; + +#define ATOMIC_FLAG_INIT 0 static void atomic_store(atomic_int * ptr, LONG val) { InterlockedExchange(ptr, val); @@ -73,6 +76,12 @@ static LONG atomic_fetch_add(atomic_int * ptr, LONG inc) { static LONG atomic_fetch_sub(atomic_int * ptr, LONG dec) { return atomic_fetch_add(ptr, -(dec)); } +static atomic_bool atomic_flag_test_and_set(atomic_flag * ptr) { + return InterlockedExchange(ptr, 1); +} +static void atomic_flag_clear(atomic_flag * ptr) { + InterlockedExchange(ptr, 0); +} typedef HANDLE pthread_t; @@ -2883,24 +2892,20 @@ struct ggml_state { // global state static struct ggml_state g_state; -static atomic_int g_state_barrier = 0; +static atomic_flag g_state_critical = ATOMIC_FLAG_INIT; // barrier via spin lock inline static void ggml_critical_section_start(void) { - int processing = atomic_fetch_add(&g_state_barrier, 1); - - while (processing > 0) { - // wait for other threads to finish - atomic_fetch_sub(&g_state_barrier, 1); - sched_yield(); // TODO: reconsider this - processing = atomic_fetch_add(&g_state_barrier, 1); + while (atomic_flag_test_and_set(&g_state_critical)) { + // spin + sched_yield(); } } // TODO: make this somehow automatically executed // some sort of "sentry" mechanism inline static void ggml_critical_section_end(void) { - atomic_fetch_sub(&g_state_barrier, 1); + atomic_flag_clear(&g_state_critical); } #if defined(__gnu_linux__) From 210d99173dc82aafb48f6e39d787c387951fe3a9 Mon Sep 17 00:00:00 2001 From: Radoslav Gerganov Date: Wed, 29 May 2024 14:45:44 +0300 Subject: [PATCH 150/181] llama-bench : add support for the RPC backend (#7435) --- examples/llama-bench/llama-bench.cpp | 28 ++++++++++++++++++++++++++-- ggml.c | 8 ++++++++ ggml.h | 1 + 3 files changed, 35 insertions(+), 2 deletions(-) diff --git a/examples/llama-bench/llama-bench.cpp b/examples/llama-bench/llama-bench.cpp index 2afdb3abd..c00890447 100644 --- a/examples/llama-bench/llama-bench.cpp +++ b/examples/llama-bench/llama-bench.cpp @@ -178,6 +178,7 @@ struct cmd_params { std::vector type_v; std::vector n_threads; std::vector n_gpu_layers; + std::vector rpc_servers; std::vector split_mode; std::vector main_gpu; std::vector no_kv_offload; @@ -202,6 +203,7 @@ static const cmd_params cmd_params_defaults = { /* type_v */ {GGML_TYPE_F16}, /* n_threads */ {cpu_get_num_math()}, /* n_gpu_layers */ {99}, + /* rpc_servers */ {""}, /* split_mode */ {LLAMA_SPLIT_MODE_LAYER}, /* main_gpu */ {0}, /* no_kv_offload */ {false}, @@ -230,6 +232,7 @@ static void print_usage(int /* argc */, char ** argv) { printf(" -ctv, --cache-type-v (default: %s)\n", join(transform_to_str(cmd_params_defaults.type_v, ggml_type_name), ",").c_str()); printf(" -t, --threads (default: %s)\n", join(cmd_params_defaults.n_threads, ",").c_str()); printf(" -ngl, --n-gpu-layers (default: %s)\n", join(cmd_params_defaults.n_gpu_layers, ",").c_str()); + printf(" -rpc, --rpc (default: %s)\n", join(cmd_params_defaults.rpc_servers, ",").c_str()); 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()); @@ -384,6 +387,12 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { } auto p = split(argv[i], split_delim); params.n_gpu_layers.insert(params.n_gpu_layers.end(), p.begin(), p.end()); + } else if (arg == "-rpc" || arg == "--rpc") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.rpc_servers.push_back(argv[i]); } else if (arg == "-sm" || arg == "--split-mode") { if (++i >= argc) { invalid_param = true; @@ -519,6 +528,7 @@ static cmd_params parse_cmd_params(int argc, char ** argv) { if (params.type_k.empty()) { params.type_k = cmd_params_defaults.type_k; } if (params.type_v.empty()) { params.type_v = cmd_params_defaults.type_v; } if (params.n_gpu_layers.empty()) { params.n_gpu_layers = cmd_params_defaults.n_gpu_layers; } + if (params.rpc_servers.empty()) { params.rpc_servers = cmd_params_defaults.rpc_servers; } if (params.split_mode.empty()) { params.split_mode = cmd_params_defaults.split_mode; } if (params.main_gpu.empty()) { params.main_gpu = cmd_params_defaults.main_gpu; } if (params.no_kv_offload.empty()){ params.no_kv_offload = cmd_params_defaults.no_kv_offload; } @@ -541,6 +551,7 @@ struct cmd_params_instance { ggml_type type_v; int n_threads; int n_gpu_layers; + std::string rpc_servers; llama_split_mode split_mode; int main_gpu; bool no_kv_offload; @@ -553,6 +564,9 @@ struct cmd_params_instance { llama_model_params mparams = llama_model_default_params(); mparams.n_gpu_layers = n_gpu_layers; + if (!rpc_servers.empty()) { + mparams.rpc_servers = rpc_servers.c_str(); + } mparams.split_mode = split_mode; mparams.main_gpu = main_gpu; mparams.tensor_split = tensor_split.data(); @@ -564,6 +578,7 @@ struct cmd_params_instance { bool equal_mparams(const cmd_params_instance & other) const { return model == other.model && n_gpu_layers == other.n_gpu_layers && + rpc_servers == other.rpc_servers && split_mode == other.split_mode && main_gpu == other.main_gpu && use_mmap == other.use_mmap && @@ -592,6 +607,7 @@ static std::vector get_cmd_params_instances(const cmd_param // this ordering minimizes the number of times that each model needs to be reloaded for (const auto & m : params.model) for (const auto & nl : params.n_gpu_layers) + for (const auto & rpc : params.rpc_servers) for (const auto & sm : params.split_mode) for (const auto & mg : params.main_gpu) for (const auto & ts : params.tensor_split) @@ -618,6 +634,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .type_v = */ tv, /* .n_threads = */ nt, /* .n_gpu_layers = */ nl, + /* .rpc_servers = */ rpc, /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, @@ -643,6 +660,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .type_v = */ tv, /* .n_threads = */ nt, /* .n_gpu_layers = */ nl, + /* .rpc_servers = */ rpc, /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, @@ -668,6 +686,7 @@ static std::vector get_cmd_params_instances(const cmd_param /* .type_v = */ tv, /* .n_threads = */ nt, /* .n_gpu_layers = */ nl, + /* .rpc_servers = */ rpc, /* .split_mode = */ sm, /* .main_gpu = */ mg, /* .no_kv_offload= */ nkvo, @@ -692,6 +711,7 @@ struct test { static const bool kompute; static const bool metal; static const bool sycl; + static const bool rpc; static const bool gpu_blas; static const bool blas; static const std::string cpu_info; @@ -790,6 +810,9 @@ struct test { if (sycl) { return GGML_SYCL_NAME; } + if (rpc) { + return "RPC"; + } if (gpu_blas) { return "GPU BLAS"; } @@ -803,7 +826,7 @@ struct test { static const std::vector & get_fields() { static const std::vector fields = { "build_commit", "build_number", - "cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas", + "cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas", "blas", "cpu_info", "gpu_info", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", @@ -859,7 +882,7 @@ 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(sycl), std::to_string(gpu_blas), std::to_string(blas), + std::to_string(metal), std::to_string(sycl), std::to_string(rpc), 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_ubatch), @@ -894,6 +917,7 @@ 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 bool test::rpc = !!ggml_cpu_has_rpc(); const std::string test::cpu_info = get_cpu_info(); const std::string test::gpu_info = get_gpu_info(); diff --git a/ggml.c b/ggml.c index d8f74f3ce..e6e2397b7 100644 --- a/ggml.c +++ b/ggml.c @@ -22872,6 +22872,14 @@ int ggml_cpu_has_sycl(void) { #endif } +int ggml_cpu_has_rpc(void) { +#if defined(GGML_USE_RPC) + return 1; +#else + return 0; +#endif +} + int ggml_cpu_has_gpublas(void) { return ggml_cpu_has_cuda() || ggml_cpu_has_clblast() || ggml_cpu_has_vulkan() || ggml_cpu_has_kompute() || ggml_cpu_has_sycl(); diff --git a/ggml.h b/ggml.h index 3859895b6..f9deac7e8 100644 --- a/ggml.h +++ b/ggml.h @@ -2428,6 +2428,7 @@ extern "C" { 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_rpc (void); GGML_API int ggml_cpu_has_vsx (void); GGML_API int ggml_cpu_has_matmul_int8(void); From cce3dcffc5695bd24835f04e6080070a2a119873 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 29 May 2024 15:38:26 +0300 Subject: [PATCH 151/181] cuda : non-cont concat support (#7610) * tests : add non-cont concat tests * cuda : non-cont concat support ggml-ci --- ggml-cuda/concat.cu | 108 +++++++++++++++++++++++++++++-------- tests/test-backend-ops.cpp | 33 +++++++++--- 2 files changed, 112 insertions(+), 29 deletions(-) diff --git a/ggml-cuda/concat.cu b/ggml-cuda/concat.cu index fb9dee8f8..dac10ec36 100644 --- a/ggml-cuda/concat.cu +++ b/ggml-cuda/concat.cu @@ -1,5 +1,6 @@ #include "concat.cuh" +// contiguous kernels static __global__ void concat_f32_dim0(const float * x, const float * y, float * dst, const int ne0, const int ne00) { int nidx = threadIdx.x + blockIdx.x * blockDim.x; if (nidx >= ne0) { @@ -92,39 +93,104 @@ static void concat_f32_cuda(const float * x, const float * y, float * dst, int n concat_f32_dim2<<>>(x, y, dst, ne0, ne02); } +// non-contiguous kernel (slow) +static __global__ void concat_f32_non_cont( + const char * src0, + const char * src1, + char * dst, + int64_t ne00, + int64_t ne01, + int64_t ne02, + int64_t ne03, + uint64_t nb00, + uint64_t nb01, + uint64_t nb02, + uint64_t nb03, + int64_t /*ne10*/, + int64_t /*ne11*/, + int64_t /*ne12*/, + int64_t /*ne13*/, + uint64_t nb10, + uint64_t nb11, + uint64_t nb12, + uint64_t nb13, + int64_t ne0, + int64_t /*ne1*/, + int64_t /*ne2*/, + int64_t /*ne3*/, + uint64_t nb0, + uint64_t nb1, + uint64_t nb2, + uint64_t nb3, + int32_t dim) { + const int64_t i3 = blockIdx.z; + const int64_t i2 = blockIdx.y; + const int64_t i1 = blockIdx.x; + + int64_t o[4] = {0, 0, 0, 0}; + o[dim] = dim == 0 ? ne00 : (dim == 1 ? ne01 : (dim == 2 ? ne02 : ne03)); + + const float * x; + + for (int i0 = threadIdx.x; i0 < ne0; i0 += blockDim.x) { + if (i0 < ne00 && i1 < ne01 && i2 < ne02 && i3 < ne03) { + x = (const float *)(src0 + (i3 )*nb03 + (i2 )*nb02 + (i1 )*nb01 + (i0 )*nb00); + } else { + x = (const float *)(src1 + (i3 - o[3])*nb13 + (i2 - o[2])*nb12 + (i1 - o[1])*nb11 + (i0 - o[0])*nb10); + } + + float * y = (float *)(dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); + + *y = *x; + } +} + + void ggml_cuda_op_concat(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * src0 = dst->src[0]; const ggml_tensor * src1 = dst->src[1]; - const float * src0_d = (const float *)src0->data; - const float * src1_d = (const float *)src1->data; - - float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); const int32_t dim = ((int32_t *) dst->op_params)[0]; - GGML_ASSERT(ggml_is_contiguous(src0)); - GGML_ASSERT(ggml_is_contiguous(src1)); - GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT(src1->type == GGML_TYPE_F32); - GGML_ASSERT(dst->type == GGML_TYPE_F32); + GGML_ASSERT(dst->type == GGML_TYPE_F32); - if (dim != 3) { - for (int i3 = 0; i3 < dst->ne[3]; i3++) { - concat_f32_cuda( - src0_d + i3 * (src0->nb[3] / 4), - src1_d + i3 * (src1->nb[3] / 4), - dst_d + i3 * ( dst->nb[3] / 4), - src0->ne[0], src0->ne[1], src0->ne[2], - dst->ne[0], dst->ne[1], dst->ne[2], dim, stream); + if (ggml_is_contiguous(src0) && ggml_is_contiguous(src1)) { + const float * src0_d = (const float *)src0->data; + const float * src1_d = (const float *)src1->data; + + float * dst_d = (float *)dst->data; + + if (dim != 3) { + for (int i3 = 0; i3 < dst->ne[3]; i3++) { + concat_f32_cuda( + src0_d + i3 * (src0->nb[3] / 4), + src1_d + i3 * (src1->nb[3] / 4), + dst_d + i3 * ( dst->nb[3] / 4), + src0->ne[0], src0->ne[1], src0->ne[2], + dst->ne[0], dst->ne[1], dst->ne[2], dim, stream); + } + } else { + const size_t size0 = ggml_nbytes(src0); + const size_t size1 = ggml_nbytes(src1); + + CUDA_CHECK(cudaMemcpyAsync(dst_d, src0_d, size0, cudaMemcpyDeviceToDevice, stream)); + CUDA_CHECK(cudaMemcpyAsync(dst_d + size0/4, src1_d, size1, cudaMemcpyDeviceToDevice, stream)); } } else { - const size_t size0 = ggml_nbytes(src0); - const size_t size1 = ggml_nbytes(src1); - - CUDA_CHECK(cudaMemcpyAsync(dst_d, src0_d, size0, cudaMemcpyDeviceToDevice, stream)); - CUDA_CHECK(cudaMemcpyAsync(dst_d + size0/4, src1_d, size1, cudaMemcpyDeviceToDevice, stream)); + dim3 grid_dim(dst->ne[1], dst->ne[2], dst->ne[3]); + concat_f32_non_cont<<>>( + (const char *)src0->data, + (const char *)src1->data, + ( char *)dst->data, + src0->ne[0], src0->ne[1], src0->ne[2], src0->ne[3], + src0->nb[0], src0->nb[1], src0->nb[2], src0->nb[3], + src1->ne[0], src1->ne[1], src1->ne[2], src1->ne[3], + src1->nb[0], src1->nb[1], src1->nb[2], src1->nb[3], + dst->ne[0], dst->ne[1], dst->ne[2], dst->ne[3], + dst->nb[0], dst->nb[1], dst->nb[2], dst->nb[3], dim); } } diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index b200ccccd..5cde21c66 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1262,22 +1262,37 @@ struct test_concat : public test_case { const std::array ne_a; const int64_t ne_b_d; const int dim; + const int v; // view (1 << 0: non-cont a, 1 << 1: non-cont b) std::string vars() override { - return VARS_TO_STR4(type, ne_a, ne_b_d, dim); + return VARS_TO_STR5(type, ne_a, ne_b_d, dim, v); } test_concat(ggml_type type = GGML_TYPE_F32, std::array ne_a = {10, 10, 10, 10}, int64_t ne_b_d = 10, - int dim = 2) - : type(type), ne_a(ne_a), ne_b_d(ne_b_d), dim(dim) {} + int dim = 2, int v = 0) + : type(type), ne_a(ne_a), ne_b_d(ne_b_d), dim(dim), v(v) {} ggml_tensor * build_graph(ggml_context * ctx) override { auto ne_b = ne_a; ne_b[dim] = ne_b_d; - ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne_a.data()); - ggml_tensor * b = ggml_new_tensor(ctx, type, 4, ne_b.data()); + ggml_tensor * a; + if (v & 1) { + auto ne = ne_a; ne[0] *= 2; ne[1] *= 4; ne[2] *= 3; + a = ggml_new_tensor(ctx, type, 4, ne.data()); + a = ggml_view_4d(ctx, a, ne_a[0], ne_a[1], ne_a[2], ne_a[3], a->nb[1], a->nb[2], a->nb[3], 0); + } else { + a = ggml_new_tensor(ctx, type, 4, ne_a.data()); + } + ggml_tensor * b; + if (v & 2) { + auto ne = ne_b; ne[0] *= 3; ne[1] *= 2; ne[2] *= 4; + b = ggml_new_tensor(ctx, type, 4, ne.data()); + b = ggml_view_4d(ctx, b, ne_b[0], ne_b[1], ne_b[2], ne_b[3], b->nb[1], b->nb[2], b->nb[3], 0); + } else { + b = ggml_new_tensor(ctx, type, 4, ne_b.data()); + } ggml_tensor * out = ggml_concat(ctx, a, b, dim); return out; } @@ -2215,9 +2230,11 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op } } - for (int dim : { 0, 1, 2, 3, }) { - test_cases.emplace_back(new test_concat(GGML_TYPE_F32, {11, 12, 13, 14}, 7, dim)); - test_cases.emplace_back(new test_concat(GGML_TYPE_I32, {11, 12, 13, 14}, 7, dim)); + for (int v : { 0, 1, 2, 3 }) { + for (int dim : { 0, 1, 2, 3, }) { + test_cases.emplace_back(new test_concat(GGML_TYPE_F32, {11, 12, 13, 14}, 7, dim, v)); + test_cases.emplace_back(new test_concat(GGML_TYPE_I32, {11, 12, 13, 14}, 7, dim, v)); + } } for (ggml_sort_order order : {GGML_SORT_ORDER_ASC, GGML_SORT_ORDER_DESC}) { From fb76ec31a9914b7761c1727303ab30380fd4f05c Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 29 May 2024 20:17:31 +0300 Subject: [PATCH 152/181] ggml : fix YARN + add tests + add asserts (#7617) * tests : add rope tests ggml-ci * ggml : fixes (hopefully) ggml-ci * tests : add non-cont tests ggml-ci * cuda : add asserts for rope/norm + fix DS2 ggml-ci * ggml : assert contiguousness * tests : reduce RoPE tests ggml-ci --- ggml-cuda.cu | 4 +- ggml-cuda/norm.cu | 6 +++ ggml-cuda/rope.cu | 18 ++++----- ggml-kompute.cpp | 4 +- ggml-metal.m | 8 +++- ggml-metal.metal | 16 +++----- ggml-sycl.cpp | 2 +- ggml.c | 74 +++++++++++++++++------------------ ggml.h | 6 ++- ggml_vk_generate_shaders.py | 4 +- llama.cpp | 52 ++++++++++++++++++------- tests/test-backend-ops.cpp | 77 +++++++++++++++++++++++++------------ 12 files changed, 167 insertions(+), 104 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index d0a754ee1..1172f7b2f 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -1870,7 +1870,7 @@ static void ggml_cuda_mul_mat_batched_cublas(ggml_backend_cuda_context & ctx, co } } #else - if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) { + if (r2 == 1 && r3 == 1 && ggml_is_contiguous_2(src0) && ggml_is_contiguous_2(src1)) { // there is no broadcast and src0, src1 are contiguous across dims 2, 3 // use cublasGemmStridedBatchedEx CUBLAS_CHECK( @@ -2886,7 +2886,9 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons case GGML_OP_CONT: case GGML_OP_DIAG_MASK_INF: case GGML_OP_SOFT_MAX: + return true; case GGML_OP_ROPE: + return ggml_is_contiguous(op->src[0]); case GGML_OP_IM2COL: case GGML_OP_POOL_2D: case GGML_OP_SUM_ROWS: diff --git a/ggml-cuda/norm.cu b/ggml-cuda/norm.cu index 86f774534..30866d512 100644 --- a/ggml-cuda/norm.cu +++ b/ggml-cuda/norm.cu @@ -170,6 +170,8 @@ void ggml_cuda_op_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -188,6 +190,8 @@ void ggml_cuda_op_group_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); @@ -202,6 +206,8 @@ void ggml_cuda_op_rms_norm(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); + GGML_ASSERT(ggml_is_contiguous(src0)); + GGML_ASSERT(src0->type == GGML_TYPE_F32); GGML_ASSERT( dst->type == GGML_TYPE_F32); diff --git a/ggml-cuda/rope.cu b/ggml-cuda/rope.cu index 50f2cf415..0dd07977e 100644 --- a/ggml-cuda/rope.cu +++ b/ggml-cuda/rope.cu @@ -61,7 +61,7 @@ static __global__ void rope( template static __global__ 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 float * freq_factors + float ext_factor, float attn_factor, rope_corr_dims corr_dims, float theta_scale, const float * freq_factors ) { const int col = 2*(blockDim.y*blockIdx.y + threadIdx.y); @@ -85,15 +85,13 @@ static __global__ void rope_neox( 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 freq_factor = has_freq_facs ? freq_factors[ic/2] : 1.0f; - const float theta_base = p*freq_scale*powf(theta_scale, col/2.0f)/freq_factor; + const float theta_base = p*powf(theta_scale, col/2.0f)/freq_factor; float cos_theta, sin_theta; - rope_yarn(theta_base, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta); + rope_yarn(theta_base, freq_scale, corr_dims, ic, ext_factor, attn_factor, &cos_theta, &sin_theta); const float x0 = x[i + 0]; const float x1 = x[i + n_dims/2]; @@ -174,30 +172,29 @@ static void rope_neox_cuda( const dim3 block_nums(nrows, num_blocks_x, 1); const float theta_scale = powf(freq_base, -2.0f/n_dims); - const float inv_ndims = -1.0f / n_dims; if (pos == nullptr) { if (freq_factors == nullptr) { rope_neox<<>>( x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, - theta_scale, inv_ndims, freq_factors + theta_scale, freq_factors ); } else { rope_neox<<>>( x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, - theta_scale, inv_ndims, freq_factors + theta_scale, freq_factors ); } } else { if (freq_factors == nullptr) { rope_neox<<>>( x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, - theta_scale, inv_ndims, freq_factors + theta_scale, freq_factors ); } else { rope_neox<<>>( x, dst, ncols, n_dims, pos, freq_scale, p_delta_rows, ext_factor, attn_factor, corr_dims, - theta_scale, inv_ndims, freq_factors + theta_scale, freq_factors ); } } @@ -254,6 +251,7 @@ void ggml_cuda_op_rope(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { float * dst_d = (float *)dst->data; cudaStream_t stream = ctx.stream(); + GGML_ASSERT(ggml_is_contiguous(src0)); 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); diff --git a/ggml-kompute.cpp b/ggml-kompute.cpp index 6c6058b2a..ed59d2be6 100644 --- a/ggml-kompute.cpp +++ b/ggml-kompute.cpp @@ -1597,7 +1597,9 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml { GGML_ASSERT(ne00 == ne10); - // TODO: assert that dim2 and dim3 are contiguous + ggml_is_contiguous_2(src0); + ggml_is_contiguous_2(src1); + GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); diff --git a/ggml-metal.m b/ggml-metal.m index 4ba498e87..a7e13bdcf 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1519,7 +1519,9 @@ static enum ggml_status ggml_metal_graph_compute( { GGML_ASSERT(ne00 == ne10); - // TODO: assert that dim2 and dim3 are contiguous + ggml_is_contiguous_2(src0); + ggml_is_contiguous_2(src1); + GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); @@ -2187,6 +2189,7 @@ static enum ggml_status ggml_metal_graph_compute( case GGML_OP_RMS_NORM: { GGML_ASSERT(ne00 % 4 == 0); + GGML_ASSERT(ggml_is_contiguous_1(src0)); float eps; memcpy(&eps, dst->op_params, sizeof(float)); @@ -2214,6 +2217,7 @@ static enum ggml_status ggml_metal_graph_compute( case GGML_OP_GROUP_NORM: { GGML_ASSERT(ne00 % 4 == 0); + GGML_ASSERT(ggml_is_contiguous(src0)); //float eps; //memcpy(&eps, dst->op_params, sizeof(float)); @@ -2247,6 +2251,8 @@ static enum ggml_status ggml_metal_graph_compute( } break; case GGML_OP_NORM: { + GGML_ASSERT(ggml_is_contiguous_1(src0)); + float eps; memcpy(&eps, dst->op_params, sizeof(float)); diff --git a/ggml-metal.metal b/ggml-metal.metal index b16f2b7e0..0cb85e1a5 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -1767,13 +1767,13 @@ kernel void kernel_rope( const int64_t p = pos[i2]; - const float theta_0 = (float)p; + const float theta_base = (float)p; const float inv_ndims = -1.f/n_dims; if (!is_neox) { for (int64_t i0 = 2*tiitg; i0 < ne0; i0 += 2*tptg.x) { + const float theta = theta_base * pow(freq_base, inv_ndims*i0); - const float theta = theta_0 * pow(freq_base, inv_ndims*i0); float cos_theta, sin_theta; rope_yarn(theta, freq_scale, corr_dims, i0, ext_factor, attn_factor, &cos_theta, &sin_theta); @@ -1789,18 +1789,14 @@ kernel void kernel_rope( } else { for (int64_t ic = 2*tiitg; ic < ne0; ic += 2*tptg.x) { if (ic < n_dims) { - const int64_t ib = 0; + const int64_t i0 = ic/2; - // simplified from `(ib * n_dims + ic) * inv_ndims` - const float cur_rot = inv_ndims*ic - ib; - const float freq_factor = src2 != src0 ? src2[ic/2] : 1.0f; + const float freq_factor = src2 != src0 ? src2[i0] : 1.0f; - const float theta = theta_0 * pow(freq_base, cur_rot) / freq_factor; + const float theta = theta_base * pow(freq_base, inv_ndims*ic); float cos_theta, sin_theta; - rope_yarn(theta, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, &cos_theta, &sin_theta); - - const int64_t i0 = ib*n_dims + ic/2; + rope_yarn(theta/freq_factor, freq_scale, corr_dims, ic, ext_factor, attn_factor, &cos_theta, &sin_theta); device const T * const src = (device T *)((device char *) src0 + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); device T * dst_data = (device T *)((device char *) dst + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); diff --git a/ggml-sycl.cpp b/ggml-sycl.cpp index a73448136..5cd97e4ff 100644 --- a/ggml-sycl.cpp +++ b/ggml-sycl.cpp @@ -15183,7 +15183,7 @@ static void ggml_sycl_mul_mat_batched_sycl(const ggml_tensor *src0, const int64_t r2 = ne12/ne02; const int64_t r3 = ne13/ne03; - if (r2 == 1 && r3 == 1 && src0->nb[2]*src0->ne[2] == src0->nb[3] && src1->nb[2]*src1->ne[2] == src1->nb[3]) { + if (r2 == 1 && r3 == 1 && ggml_is_contiguous_2(src0) && ggml_is_contiguous_2(src1)) { // there is no broadcast and src0, src1 are contiguous across dims 2, 3 SYCL_CHECK(CHECK_TRY_ERROR(dpct::gemm_batch( *g_sycl_handles[g_main_device], oneapi::mkl::transpose::trans, diff --git a/ggml.c b/ggml.c index e6e2397b7..b2b725f65 100644 --- a/ggml.c +++ b/ggml.c @@ -3221,7 +3221,11 @@ GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor) { tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } -static inline bool ggml_is_contiguous_except_dim_1(const struct ggml_tensor * tensor) { +GGML_CALL bool ggml_is_contiguous_0(const struct ggml_tensor * tensor) { + return ggml_is_contiguous(tensor); +} + +GGML_CALL bool ggml_is_contiguous_1(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); return @@ -3230,6 +3234,14 @@ static inline bool ggml_is_contiguous_except_dim_1(const struct ggml_tensor * te tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; } +GGML_CALL bool ggml_is_contiguous_2(const struct ggml_tensor * tensor) { + static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); + + return + tensor->nb[0] == ggml_type_size(tensor->type) && + tensor->nb[3] == tensor->nb[2]*tensor->ne[2]; +} + GGML_CALL bool ggml_is_permuted(const struct ggml_tensor * tensor) { static_assert(GGML_MAX_DIMS == 4, "GGML_MAX_DIMS is not 4 - update this function"); @@ -11420,8 +11432,8 @@ static void ggml_compute_forward_gelu_f32( const struct ggml_tensor * src0 = dst->src[0]; - GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); - GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); + GGML_ASSERT(ggml_is_contiguous_1(src0)); + GGML_ASSERT(ggml_is_contiguous_1(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { @@ -11483,8 +11495,8 @@ static void ggml_compute_forward_gelu_quick_f32( const struct ggml_tensor * src0 = dst->src[0]; - GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); - GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); + GGML_ASSERT(ggml_is_contiguous_1(src0)); + GGML_ASSERT(ggml_is_contiguous_1(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { @@ -11546,8 +11558,8 @@ static void ggml_compute_forward_silu_f32( const struct ggml_tensor * src0 = dst->src[0]; - GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); - GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); + GGML_ASSERT(ggml_is_contiguous_1(src0)); + GGML_ASSERT(ggml_is_contiguous_1(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); if (params->type == GGML_TASK_TYPE_INIT || params->type == GGML_TASK_TYPE_FINALIZE) { @@ -11658,9 +11670,9 @@ static void ggml_compute_forward_silu_back_f32( const struct ggml_tensor * src0 = dst->src[0]; const struct ggml_tensor * grad = dst->src[1]; - GGML_ASSERT(ggml_is_contiguous_except_dim_1(grad)); - GGML_ASSERT(ggml_is_contiguous_except_dim_1(src0)); - GGML_ASSERT(ggml_is_contiguous_except_dim_1(dst)); + GGML_ASSERT(ggml_is_contiguous_1(grad)); + GGML_ASSERT(ggml_is_contiguous_1(src0)); + GGML_ASSERT(ggml_is_contiguous_1(dst)); GGML_ASSERT(ggml_are_same_shape(src0, dst)); GGML_ASSERT(ggml_are_same_shape(src0, grad)); @@ -14358,7 +14370,7 @@ static void ggml_compute_forward_rope_f32( int ir = 0; const float theta_scale = powf(freq_base, -2.0f/n_dims); - const float inv_ndims = -1.f/n_dims; + float corr_dims[2]; ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims); @@ -14407,7 +14419,7 @@ static void ggml_compute_forward_rope_f32( const float cos_block_theta = cosf(block_theta); const float sin_block_theta = sinf(block_theta) * sin_sign; - theta_base *= theta_scale; + theta_base *= theta_scale; block_theta *= theta_scale; const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); @@ -14442,29 +14454,22 @@ static void ggml_compute_forward_rope_f32( dst_data[1] = x0*sin_theta*zeta + x1*cos_theta*zeta; } } else { - // TODO: this might be wrong for ne0 != n_dims - need double check - // it seems we have to rope just the first n_dims elements and do nothing with the rest - // ref: https://github.com/ml-explore/mlx/blob/dc2edc762c797e3b8de50b1dad4dc0a131691033/benchmarks/python/llama_jax_bench.py#L11-L26 - theta_base *= freq_scale; + // ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py for (int64_t ic = 0; ic < ne0; ic += 2) { if (ic < n_dims) { - const int64_t ib = 0; + const int64_t i0 = ic/2; - // simplified from `(ib * n_dims + ic) * inv_ndims` - float cur_rot = inv_ndims * ic - ib; - float freq_factor = freq_factors ? freq_factors[ic/2] : 1.0f; + const float freq_factor = freq_factors ? freq_factors[i0] : 1.0f; float cos_theta, sin_theta; rope_yarn( - theta_base/freq_factor, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, + theta_base/freq_factor, freq_scale, corr_dims, ic, ext_factor, attn_factor, &cos_theta, &sin_theta ); - sin_theta *= sin_sign; + sin_theta *= sin_sign; theta_base *= theta_scale; - const int64_t i0 = ib*n_dims + ic/2; - const float * const src = (float *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); float * dst_data = (float *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); @@ -14543,7 +14548,7 @@ static void ggml_compute_forward_rope_f16( int ir = 0; const float theta_scale = powf(freq_base, -2.0f/n_dims); - const float inv_ndims = -1.f/n_dims; + float corr_dims[2]; ggml_rope_yarn_corr_dims(n_dims, n_orig_ctx, freq_base, beta_fast, beta_slow, corr_dims); @@ -14592,7 +14597,7 @@ static void ggml_compute_forward_rope_f16( const float cos_block_theta = cosf(block_theta); const float sin_block_theta = sinf(block_theta) * sin_sign; - theta_base *= theta_scale; + theta_base *= theta_scale; block_theta *= theta_scale; const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); @@ -14623,29 +14628,22 @@ static void ggml_compute_forward_rope_f16( dst_data[1] = GGML_FP32_TO_FP16(x0*sin_theta + x1*cos_theta); } } else { - // TODO: this might be wrong for ne0 != n_dims - need double check - // it seems we have to rope just the first n_dims elements and do nothing with the rest - // ref: https://github.com/ml-explore/mlx/blob/dc2edc762c797e3b8de50b1dad4dc0a131691033/benchmarks/python/llama_jax_bench.py#L11-L26 - theta_base *= freq_scale; + // ref: https://github.com/jquesnelle/yarn/blob/master/scaled_rope/LlamaYaRNScaledRotaryEmbedding.py for (int64_t ic = 0; ic < ne0; ic += 2) { if (ic < n_dims) { - const int64_t ib = 0; + const int64_t i0 = ic/2; - // simplified from `(ib * n_dims + ic) * inv_ndims` - float cur_rot = inv_ndims * ic - ib; - float freq_factor = freq_factors ? freq_factors[ic/2] : 1.0f; + const float freq_factor = freq_factors ? freq_factors[i0] : 1.0f; float cos_theta, sin_theta; rope_yarn( - theta_base/freq_factor, freq_scale, corr_dims, cur_rot, ext_factor, attn_factor, + theta_base/freq_factor, freq_scale, corr_dims, ic, ext_factor, attn_factor, &cos_theta, &sin_theta ); - sin_theta *= sin_sign; + sin_theta *= sin_sign; theta_base *= theta_scale; - const int64_t i0 = ib*n_dims + ic/2; - const ggml_fp16_t * const src = (ggml_fp16_t *)((char *) src0->data + i3*nb03 + i2*nb02 + i1*nb01 + i0*nb00); ggml_fp16_t * dst_data = (ggml_fp16_t *)((char *) dst->data + i3*nb3 + i2*nb2 + i1*nb1 + i0*nb0); diff --git a/ggml.h b/ggml.h index f9deac7e8..f38699698 100644 --- a/ggml.h +++ b/ggml.h @@ -756,7 +756,6 @@ extern "C" { GGML_API enum ggml_type ggml_ftype_to_ggml_type(enum ggml_ftype ftype); GGML_API GGML_CALL bool ggml_is_transposed(const struct ggml_tensor * tensor); - GGML_API GGML_CALL bool ggml_is_contiguous(const struct ggml_tensor * tensor); GGML_API GGML_CALL bool ggml_is_permuted (const struct ggml_tensor * tensor); GGML_API GGML_CALL bool ggml_is_empty (const struct ggml_tensor * tensor); GGML_API bool ggml_is_scalar (const struct ggml_tensor * tensor); @@ -765,6 +764,11 @@ extern "C" { GGML_API bool ggml_is_3d (const struct ggml_tensor * tensor); GGML_API int ggml_n_dims (const struct ggml_tensor * tensor); // returns 1 for scalars + GGML_API GGML_CALL bool ggml_is_contiguous (const struct ggml_tensor * tensor); + GGML_API GGML_CALL bool ggml_is_contiguous_0(const struct ggml_tensor * tensor); // same as ggml_is_contiguous() + GGML_API GGML_CALL bool ggml_is_contiguous_1(const struct ggml_tensor * tensor); // contiguous for dims >= 1 + GGML_API GGML_CALL bool ggml_is_contiguous_2(const struct ggml_tensor * tensor); // contiguous for dims >= 2 + GGML_API bool ggml_are_same_shape (const struct ggml_tensor * t0, const struct ggml_tensor * t1); GGML_API bool ggml_are_same_stride(const struct ggml_tensor * t0, const struct ggml_tensor * t1); diff --git a/ggml_vk_generate_shaders.py b/ggml_vk_generate_shaders.py index a8f7373df..7c85ca7ba 100644 --- a/ggml_vk_generate_shaders.py +++ b/ggml_vk_generate_shaders.py @@ -2670,14 +2670,12 @@ void main() { 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 freq_factor = p.has_freq_facs != 0 ? data_freq_factors[ic/2] : 1.0f; const float theta_base = pos*p.freq_scale*pow(p.theta_scale, col/2.0f) / freq_factor; float cos_theta, sin_theta; - rope_yarn(theta_base, uint(cur_rot), cos_theta, sin_theta); + rope_yarn(theta_base, ic, cos_theta, sin_theta); const float x0 = float(data_a[i + 0]); const float x1 = float(data_a[i + p.ndims/2]); diff --git a/llama.cpp b/llama.cpp index dac81acc0..e7412de4b 100644 --- a/llama.cpp +++ b/llama.cpp @@ -11187,46 +11187,69 @@ struct llm_build_context { } // split into {n_head * n_embd_head_qk_nope, n_tokens} - struct ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, ggml_element_size(q) * hparams.n_embd_head_k, ggml_element_size(q) * hparams.n_embd_head_k * n_head, 0); + struct ggml_tensor * q_nope = ggml_view_3d(ctx0, q, n_embd_head_qk_nope, n_head, n_tokens, + ggml_row_size(q->type, hparams.n_embd_head_k), + ggml_row_size(q->type, hparams.n_embd_head_k * n_head), + 0); cb(q_nope, "q_nope", il); + // and {n_head * n_embd_head_qk_rope, n_tokens} - struct ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, ggml_element_size(q) * hparams.n_embd_head_k, ggml_element_size(q) * hparams.n_embd_head_k * n_head, ggml_element_size(q) * n_embd_head_qk_nope); + struct ggml_tensor * q_pe = ggml_view_3d(ctx0, q, n_embd_head_qk_rope, n_head, n_tokens, + ggml_row_size(q->type, hparams.n_embd_head_k), + ggml_row_size(q->type, hparams.n_embd_head_k * n_head), + ggml_row_size(q->type, n_embd_head_qk_nope)); cb(q_pe, "q_pe", il); // {n_embd, kv_lora_rank + n_embd_head_qk_rope} * {n_embd, n_tokens} -> {kv_lora_rank + n_embd_head_qk_rope, n_tokens} - struct ggml_tensor * compressed_kv_pe = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur); - cb(compressed_kv_pe, "compressed_kv_pe", il); + struct ggml_tensor * kv_pe_compresseed = ggml_mul_mat(ctx0, model.layers[il].wkv_a_mqa, cur); + cb(kv_pe_compresseed, "kv_pe_compresseed", il); // split into {kv_lora_rank, n_tokens} - struct ggml_tensor * compressed_kv = ggml_view_2d(ctx0, compressed_kv_pe, kv_lora_rank, n_tokens, compressed_kv_pe->nb[1], 0); - cb(compressed_kv, "compressed_kv", il); + struct ggml_tensor * kv_compressed = ggml_view_2d(ctx0, kv_pe_compresseed, kv_lora_rank, n_tokens, + kv_pe_compresseed->nb[1], + 0); + cb(kv_compressed, "kv_compressed", il); + // and {n_embd_head_qk_rope, n_tokens} - struct ggml_tensor * k_pe = ggml_view_2d(ctx0, compressed_kv_pe, n_embd_head_qk_rope, n_tokens, compressed_kv_pe->nb[1], ggml_element_size(compressed_kv_pe)*kv_lora_rank); + struct ggml_tensor * k_pe = ggml_view_3d(ctx0, kv_pe_compresseed, n_embd_head_qk_rope, 1, n_tokens, + kv_pe_compresseed->nb[1], + kv_pe_compresseed->nb[1], + ggml_row_size(kv_pe_compresseed->type, kv_lora_rank)); cb(k_pe, "k_pe", il); - compressed_kv = llm_build_norm(ctx0, compressed_kv, hparams, + kv_compressed = ggml_cont(ctx0, kv_compressed); // TODO: the CUDA backend does not support non-contiguous norm + kv_compressed = llm_build_norm(ctx0, kv_compressed, hparams, model.layers[il].attn_kv_a_norm, NULL, LLM_NORM_RMS, cb, il); - cb(compressed_kv, "compressed_kv", il); + cb(kv_compressed, "kv_compressed", il); // {kv_lora_rank, n_head * (n_embd_head_qk_nope + n_embd_head_v)} * {kv_lora_rank, n_tokens} -> {n_head * (n_embd_head_qk_nope + n_embd_head_v), n_tokens} - struct ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, compressed_kv); + struct ggml_tensor * kv = ggml_mul_mat(ctx0, model.layers[il].wkv_b, kv_compressed); cb(kv, "kv", il); // split into {n_head * n_embd_head_qk_nope, n_tokens} - struct ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens, ggml_element_size(kv) * (n_embd_head_qk_nope + hparams.n_embd_head_v), ggml_element_size(kv) * n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v), 0); + struct ggml_tensor * k_nope = ggml_view_3d(ctx0, kv, n_embd_head_qk_nope, n_head, n_tokens, + ggml_row_size(kv->type, n_embd_head_qk_nope + hparams.n_embd_head_v), + ggml_row_size(kv->type, n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v)), + 0); cb(k_nope, "k_nope", il); // and {n_head * n_embd_head_v, n_tokens} - struct ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v, n_head, n_tokens, ggml_element_size(kv) * (n_embd_head_qk_nope + hparams.n_embd_head_v), ggml_element_size(kv) * n_head * (n_embd_head_qk_nope + hparams.n_embd_head_v), ggml_element_size(kv) * n_embd_head_qk_nope); + struct ggml_tensor * v_states = ggml_view_3d(ctx0, kv, hparams.n_embd_head_v, n_head, n_tokens, + ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)), + ggml_row_size(kv->type, (n_embd_head_qk_nope + hparams.n_embd_head_v)*n_head), + ggml_row_size(kv->type, (n_embd_head_qk_nope))); cb(v_states, "v_states", il); v_states = ggml_cont(ctx0, v_states); cb(v_states, "v_states", il); - v_states = ggml_view_2d(ctx0, v_states, hparams.n_embd_head_v * n_head, n_tokens, ggml_element_size(kv) * hparams.n_embd_head_v * n_head, 0); + v_states = ggml_view_2d(ctx0, v_states, hparams.n_embd_head_v * n_head, n_tokens, + ggml_row_size(kv->type, hparams.n_embd_head_v * n_head), + 0); cb(v_states, "v_states", il); + q_pe = ggml_cont(ctx0, q_pe); // TODO: the CUDA backend does not support non-contiguous RoPE q_pe = ggml_rope_ext( ctx0, q_pe, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, @@ -11235,8 +11258,9 @@ struct llm_build_context { cb(q_pe, "q_pe", il); // shared RoPE key + k_pe = ggml_cont(ctx0, k_pe); // TODO: the CUDA backend does not support non-contiguous RoPE k_pe = ggml_rope_ext( - ctx0, ggml_view_3d(ctx0, k_pe, n_embd_head_qk_rope, 1, n_tokens, k_pe->nb[0], k_pe->nb[1], 0), inp_pos, nullptr, + ctx0, k_pe, inp_pos, nullptr, n_rot, rope_type, 0, n_orig_ctx, freq_base, freq_scale, ext_factor, attn_factor_scaled, beta_fast, beta_slow ); diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 5cde21c66..72edc64a7 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1138,26 +1138,37 @@ struct test_soft_max : public test_case { // GGML_OP_ROPE struct test_rope : public test_case { const ggml_type type; - const std::array ne; + const std::array ne_a; int n_dims; int mode; int n_ctx; + float fs; // freq_scale + float ef; // ext_factor + float af; // attn_factor bool ff; + int v; // view (1 : non-contiguous a) std::string vars() override { - return VARS_TO_STR6(type, ne, n_dims, mode, n_ctx, ff); + return VARS_TO_STR10(type, ne_a, n_dims, mode, n_ctx, fs, ef, af, ff, v); } test_rope(ggml_type type = GGML_TYPE_F32, - std::array ne = {10, 10, 10, 1}, - int n_dims = 10, int mode = 0, int n_ctx = 512, bool ff = false) - : type(type), ne(ne), n_dims(n_dims), mode(mode), n_ctx(n_ctx), ff(ff) {} + std::array ne_a = {10, 10, 10, 1}, + int n_dims = 10, int mode = 0, int n_ctx = 512, float fs = 1.0f, float ef = 0.0f, float af = 0.0f, bool ff = false, int v = 0) + : type(type), ne_a(ne_a), n_dims(n_dims), mode(mode), n_ctx(n_ctx), fs(fs), ef(ef), af(af), ff(ff), v(v) {} ggml_tensor * build_graph(ggml_context * ctx) override { - ggml_tensor * a = ggml_new_tensor(ctx, type, 4, ne.data()); - ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, ne[2]); + ggml_tensor * a; + if (v & 1) { + auto ne = ne_a; ne[0] *= 2; ne[1] *= 4; ne[2] *= 3; + a = ggml_new_tensor(ctx, type, 4, ne.data()); + a = ggml_view_4d(ctx, a, ne_a[0], ne_a[1], ne_a[2], ne_a[3], a->nb[1], a->nb[2], a->nb[3], 0); + } else { + a = ggml_new_tensor(ctx, type, 4, ne_a.data()); + } + ggml_tensor * pos = ggml_new_tensor_1d(ctx, GGML_TYPE_I32, ne_a[2]); ggml_tensor * freq = ff ? ggml_new_tensor_1d(ctx, GGML_TYPE_F32, n_dims/2) : nullptr; - ggml_tensor * out = ggml_rope_ext(ctx, a, pos, freq, n_dims, mode, n_ctx, 0, 10000.0f, 1.0f, 0.0f, 1.0f, 0.0f, 0.0f); + ggml_tensor * out = ggml_rope_ext(ctx, a, pos, freq, n_dims, mode, n_ctx, 0, 10000.0f, fs, ef, af, 1.0f, 1.0f); return out; } @@ -1165,11 +1176,11 @@ struct test_rope : public test_case { 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(ne[2]); - for (int i = 0; i < ne[2]; i++) { + std::vector data(ne_a[2]); + for (int i = 0; i < ne_a[2]; i++) { data[i] = rand() % n_ctx; } - ggml_backend_tensor_set(t, data.data(), 0, ne[2] * sizeof(int)); + ggml_backend_tensor_set(t, data.data(), 0, ne_a[2] * sizeof(int)); } else { if (t->ne[0] == n_dims/2) { // frequency factors in the range [0.9f, 1.1f] @@ -2213,20 +2224,38 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true, 0.1f, 0.0f)); test_cases.emplace_back(new test_soft_max(GGML_TYPE_F32, {32, 2, 32, 1}, true, 0.1f, 8.0f)); - for (ggml_type type : {GGML_TYPE_F32, GGML_TYPE_F16}) { - // TODO: ff not supported yet for !neox - test_cases.emplace_back(new test_rope(type, {128, 32, 10, 1}, 128, 0, 512, false)); // llama 7B - test_cases.emplace_back(new test_rope(type, {128, 40, 10, 1}, 128, 0, 512, false)); // llama 13B - test_cases.emplace_back(new test_rope(type, {128, 52, 10, 1}, 128, 0, 512, false)); // llama 30B - test_cases.emplace_back(new test_rope(type, {128, 64, 10, 1}, 128, 0, 512, false)); // llama 65B + { + bool all = true; - for (bool ff : {false, true}) { // freq_factors - test_cases.emplace_back(new test_rope(type, { 64, 1, 10, 1}, 64, 2, 512, ff)); // neox (falcon 7B) - test_cases.emplace_back(new test_rope(type, { 64, 71, 10, 1}, 64, 2, 512, ff)); // neox (falcon 7B) - test_cases.emplace_back(new test_rope(type, { 64, 8, 10, 1}, 64, 2, 512, ff)); // neox (falcon 40B) - test_cases.emplace_back(new test_rope(type, { 64, 128, 10, 1}, 64, 2, 512, ff)); // neox (falcon 40B) - test_cases.emplace_back(new test_rope(type, { 80, 32, 10, 1}, 20, 2, 512, ff)); // neox (stablelm) - test_cases.emplace_back(new test_rope(type, { 80, 32, 10, 1}, 32, 2, 512, ff)); // neox (phi-2) + for (float v : { 0, 1 }) { + for (float fs : { 1.0f, 1.4245f }) { + for (float ef : { 0.0f, 0.7465f }) { + for (float af : { 1.0f, 1.4245f }) { + for (ggml_type type : {GGML_TYPE_F32, GGML_TYPE_F16}) { + // TODO: ff not supported yet for !neox + test_cases.emplace_back(new test_rope(type, {128, 32, 10, 1}, 128, 0, 512, fs, ef, af, false, v)); // llama 7B + if (all) { + test_cases.emplace_back(new test_rope(type, {128, 40, 10, 1}, 128, 0, 512, fs, ef, af, false, v)); // llama 13B + test_cases.emplace_back(new test_rope(type, {128, 52, 10, 1}, 128, 0, 512, fs, ef, af, false, v)); // llama 30B + test_cases.emplace_back(new test_rope(type, {128, 64, 10, 1}, 128, 0, 512, fs, ef, af, false, v)); // llama 65B + } + + for (bool ff : {false, true}) { // freq_factors + if (all) { + test_cases.emplace_back(new test_rope(type, { 64, 1, 10, 1}, 64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 7B) + test_cases.emplace_back(new test_rope(type, { 64, 71, 10, 1}, 64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 7B) + test_cases.emplace_back(new test_rope(type, { 64, 8, 10, 1}, 64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 40B) + test_cases.emplace_back(new test_rope(type, { 80, 32, 10, 1}, 20, 2, 512, fs, ef, af, ff, v)); // neox (stablelm) + test_cases.emplace_back(new test_rope(type, { 80, 32, 10, 1}, 32, 2, 512, fs, ef, af, ff, v)); // neox (phi-2) + } + + test_cases.emplace_back(new test_rope(type, { 64, 128, 10, 1}, 64, 2, 512, fs, ef, af, ff, v)); // neox (falcon 40B) + } + } + all = false; + } + } + } } } From 975ec63ff26cdf96156d1126d86f75a395fdc43a Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 29 May 2024 20:45:25 +0300 Subject: [PATCH 153/181] metal : add missing asserts (#7617) --- ggml-kompute.cpp | 4 ++-- ggml-metal.m | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/ggml-kompute.cpp b/ggml-kompute.cpp index ed59d2be6..18ce95ebf 100644 --- a/ggml-kompute.cpp +++ b/ggml-kompute.cpp @@ -1597,8 +1597,8 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml { GGML_ASSERT(ne00 == ne10); - ggml_is_contiguous_2(src0); - ggml_is_contiguous_2(src1); + GGML_ASSERT(ggml_is_contiguous_2(src0)); + GGML_ASSERT(ggml_is_contiguous_2(src1)); GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); diff --git a/ggml-metal.m b/ggml-metal.m index a7e13bdcf..e7c4298a2 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1519,8 +1519,8 @@ static enum ggml_status ggml_metal_graph_compute( { GGML_ASSERT(ne00 == ne10); - ggml_is_contiguous_2(src0); - ggml_is_contiguous_2(src1); + GGML_ASSERT(ggml_is_contiguous_2(src0)); + GGML_ASSERT(ggml_is_contiguous_2(src1)); GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); From 55d62262a99cd8bc28a1492975791fe433c8cc0f Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Wed, 29 May 2024 22:20:40 +0300 Subject: [PATCH 154/181] metal : remove invalid asserts (#7617) --- ggml-kompute.cpp | 3 --- ggml-metal.m | 3 --- 2 files changed, 6 deletions(-) diff --git a/ggml-kompute.cpp b/ggml-kompute.cpp index 18ce95ebf..0c51c322f 100644 --- a/ggml-kompute.cpp +++ b/ggml-kompute.cpp @@ -1597,9 +1597,6 @@ static void ggml_vk_graph_compute(struct ggml_kompute_context * ctx, struct ggml { GGML_ASSERT(ne00 == ne10); - GGML_ASSERT(ggml_is_contiguous_2(src0)); - GGML_ASSERT(ggml_is_contiguous_2(src1)); - GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); diff --git a/ggml-metal.m b/ggml-metal.m index e7c4298a2..079912952 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -1519,9 +1519,6 @@ static enum ggml_status ggml_metal_graph_compute( { GGML_ASSERT(ne00 == ne10); - GGML_ASSERT(ggml_is_contiguous_2(src0)); - GGML_ASSERT(ggml_is_contiguous_2(src1)); - GGML_ASSERT(ne12 % ne02 == 0); GGML_ASSERT(ne13 % ne03 == 0); From eb57fee51f7b4d78039f003249873c2eb46f12f6 Mon Sep 17 00:00:00 2001 From: Galunid Date: Thu, 30 May 2024 02:10:40 +0200 Subject: [PATCH 155/181] gguf-py : Add tokenizer.ggml.pre to gguf-new-metadata.py (#7627) --- gguf-py/scripts/gguf-new-metadata.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/gguf-py/scripts/gguf-new-metadata.py b/gguf-py/scripts/gguf-new-metadata.py index c9f1927f6..21e91180c 100755 --- a/gguf-py/scripts/gguf-new-metadata.py +++ b/gguf-py/scripts/gguf-new-metadata.py @@ -144,6 +144,7 @@ def main() -> None: parser.add_argument("--general-description", type=str, help="The models general.description", metavar='"Description ..."') parser.add_argument("--chat-template", type=str, help="Chat template string (or JSON string containing templates)", metavar='"{% ... %} ..."') parser.add_argument("--chat-template-config", type=Path, help="Config file containing chat template(s)", metavar='tokenizer_config.json') + parser.add_argument("--pre-tokenizer", type=str, help="The models tokenizer.ggml.pre", metavar='"pre tokenizer"') parser.add_argument("--remove-metadata", action="append", type=str, help="Remove metadata (by key name) from output model", metavar='general.url') parser.add_argument("--special-token", action="append", type=str, help="Special token by value", nargs=2, metavar=(' | '.join(token_names.keys()), '""')) parser.add_argument("--special-token-by-id", action="append", type=str, help="Special token by id", nargs=2, metavar=(' | '.join(token_names.keys()), '0')) @@ -172,6 +173,9 @@ def main() -> None: if template: new_metadata[gguf.Keys.Tokenizer.CHAT_TEMPLATE] = MetadataDetails(gguf.GGUFValueType.STRING, template) + if args.pre_tokenizer: + new_metadata[gguf.Keys.Tokenizer.PRE] = MetadataDetails(gguf.GGUFValueType.STRING, args.pre_tokenizer) + if remove_metadata: logger.warning('*** Warning *** Warning *** Warning **') logger.warning('* Most metadata is required for a fully functional GGUF file,') From 3854c9d07f67de7f8cd6d86117bfaef47549b05a Mon Sep 17 00:00:00 2001 From: "Meng, Hengyu" Date: Thu, 30 May 2024 14:19:08 +0800 Subject: [PATCH 156/181] [SYCL] fix intel docker (#7630) * Update main-intel.Dockerfile * workaround for https://github.com/intel/oneapi-containers/issues/70 * reset intel docker in CI * add missed in server --- .devops/main-intel.Dockerfile | 8 ++++++++ .devops/server-intel.Dockerfile | 16 ++++++++++++++++ .github/workflows/docker.yml | 5 ++--- 3 files changed, 26 insertions(+), 3 deletions(-) diff --git a/.devops/main-intel.Dockerfile b/.devops/main-intel.Dockerfile index 274b91b71..7516c8313 100644 --- a/.devops/main-intel.Dockerfile +++ b/.devops/main-intel.Dockerfile @@ -2,6 +2,14 @@ ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04 FROM intel/oneapi-basekit:$ONEAPI_VERSION as build +RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg > /dev/null && \ + echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " | tee /etc/apt/sources.list.d/oneAPI.list && \ + chmod 644 /usr/share/keyrings/intel-oneapi-archive-keyring.gpg && \ + rm /etc/apt/sources.list.d/intel-graphics.list && \ + wget -O- https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor | tee /usr/share/keyrings/intel-graphics.gpg > /dev/null && \ + echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc" | tee /etc/apt/sources.list.d/intel.gpu.jammy.list && \ + chmod 644 /usr/share/keyrings/intel-graphics.gpg + ARG LLAMA_SYCL_F16=OFF RUN apt-get update && \ apt-get install -y git diff --git a/.devops/server-intel.Dockerfile b/.devops/server-intel.Dockerfile index a8e451fa9..13d00b737 100644 --- a/.devops/server-intel.Dockerfile +++ b/.devops/server-intel.Dockerfile @@ -2,6 +2,14 @@ ARG ONEAPI_VERSION=2024.0.1-devel-ubuntu22.04 FROM intel/oneapi-basekit:$ONEAPI_VERSION as build +RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg > /dev/null && \ + echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " | tee /etc/apt/sources.list.d/oneAPI.list && \ + chmod 644 /usr/share/keyrings/intel-oneapi-archive-keyring.gpg && \ + rm /etc/apt/sources.list.d/intel-graphics.list && \ + wget -O- https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor | tee /usr/share/keyrings/intel-graphics.gpg > /dev/null && \ + echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc" | tee /etc/apt/sources.list.d/intel.gpu.jammy.list && \ + chmod 644 /usr/share/keyrings/intel-graphics.gpg + ARG LLAMA_SYCL_F16=OFF RUN apt-get update && \ apt-get install -y git libcurl4-openssl-dev @@ -19,6 +27,14 @@ RUN if [ "${LLAMA_SYCL_F16}" = "ON" ]; then \ FROM intel/oneapi-basekit:$ONEAPI_VERSION as runtime +RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg > /dev/null && \ + echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " | tee /etc/apt/sources.list.d/oneAPI.list && \ + chmod 644 /usr/share/keyrings/intel-oneapi-archive-keyring.gpg && \ + rm /etc/apt/sources.list.d/intel-graphics.list && \ + wget -O- https://repositories.intel.com/graphics/intel-graphics.key | gpg --dearmor | tee /usr/share/keyrings/intel-graphics.gpg > /dev/null && \ + echo "deb [arch=amd64,i386 signed-by=/usr/share/keyrings/intel-graphics.gpg] https://repositories.intel.com/graphics/ubuntu jammy arc" | tee /etc/apt/sources.list.d/intel.gpu.jammy.list && \ + chmod 644 /usr/share/keyrings/intel-graphics.gpg + RUN apt-get update && \ apt-get install -y libcurl4-openssl-dev diff --git a/.github/workflows/docker.yml b/.github/workflows/docker.yml index c2838cbd9..9b03d19bc 100644 --- a/.github/workflows/docker.yml +++ b/.github/workflows/docker.yml @@ -42,9 +42,8 @@ jobs: - { 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" } - # TODO: Disabled due to build issues https://github.com/ggerganov/llama.cpp/issues/7507 - #- { tag: "light-intel", dockerfile: ".devops/main-intel.Dockerfile", platforms: "linux/amd64" } - #- { tag: "server-intel", dockerfile: ".devops/server-intel.Dockerfile", platforms: "linux/amd64" } + - { 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@v4 From 972b555ab935705f3437abd5909a5c46852811f6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Thu, 30 May 2024 09:52:39 +0200 Subject: [PATCH 157/181] README: explain parallel build [no ci] (#7618) --- README.md | 25 ++++++++++++++++--------- 1 file changed, 16 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 1cab7f19d..1cedc0d9a 100644 --- a/README.md +++ b/README.md @@ -315,8 +315,6 @@ In order to build llama.cpp you have four different options. make ``` - **Note**: for `Debug` builds, run `make LLAMA_DEBUG=1` - - On Windows: 1. Download the latest fortran version of [w64devkit](https://github.com/skeeto/w64devkit/releases). @@ -328,23 +326,32 @@ In order to build llama.cpp you have four different options. make ``` + - Notes: + - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `make -j 8` will run 8 jobs in parallel. + - For faster repeated compilation, install [ccache](https://ccache.dev/). + - For debug builds, run `make LLAMA_DEBUG=1` + - Using `CMake`: - ```bash - cmake -B build - cmake --build build --config Release - ``` + ```bash + cmake -B build + cmake --build build --config Release + ``` - **Note**: for `Debug` builds, there are two cases: + **Notes**: - - Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag): + - For faster compilation, add the `-j` argument to run multiple jobs in parallel. For example, `cmake --build build --config Release -j 8` will run 8 jobs in parallel. + - For faster repeated compilation, install [ccache](https://ccache.dev/). + - For debug builds, there are two cases: + + 1. Single-config generators (e.g. default = `Unix Makefiles`; note that they just ignore the `--config` flag): ```bash cmake -B build -DCMAKE_BUILD_TYPE=Debug cmake --build build ``` - - Multi-config generators (`-G` param set to Visual Studio, XCode...): + 2. Multi-config generators (`-G` param set to Visual Studio, XCode...): ```bash cmake -B build -G "Xcode" From d5c05821f3c3d6cabe8ac45776fe0ecb0da13eca Mon Sep 17 00:00:00 2001 From: junchao-loongson <68935141+junchao-loongson@users.noreply.github.com> Date: Thu, 30 May 2024 17:30:10 +0800 Subject: [PATCH 158/181] ggml : fix loongarch build (O2 issue) (#7636) --- ggml-quants.c | 20 ++++++++++++++------ ggml.c | 2 +- 2 files changed, 15 insertions(+), 7 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 4f2c7224c..1128d66e2 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -6828,6 +6828,7 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r int bit = 0; int is = 0; + __m256i xvbit; const uint8_t * restrict q3 = x[i].qs; const int8_t * restrict q8 = y[i].qs; @@ -6836,21 +6837,25 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r // load low 2 bits const __m256i q3bits = __lasx_xvld((const __m256i*)q3, 0); q3 += 32; + xvbit = __lasx_xvreplgr2vr_h(bit); // prepare low and high bits const __m256i q3l_0 = __lasx_xvand_v(q3bits, m3); - const __m256i q3h_0 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2); + const __m256i q3h_0 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); ++bit; + xvbit = __lasx_xvreplgr2vr_h(bit); const __m256i q3l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 2), m3); - const __m256i q3h_1 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2); + const __m256i q3h_1 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); ++bit; + xvbit = __lasx_xvreplgr2vr_h(bit); const __m256i q3l_2 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 4), m3); - const __m256i q3h_2 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2); + const __m256i q3h_2 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); ++bit; + xvbit = __lasx_xvreplgr2vr_h(bit); const __m256i q3l_3 = __lasx_xvand_v(__lasx_xvsrli_h(q3bits, 6), m3); - const __m256i q3h_3 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvandn_v(hbits, __lasx_xvslli_h(mone, bit)), bit), 2); + const __m256i q3h_3 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvandn_v(hbits, __lasx_xvsll_h(mone, xvbit)), xvbit), 2); ++bit; // load Q8 quants @@ -8033,6 +8038,7 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r __m256i sumi = __lasx_xvldi(0); int bit = 0; + __m256i xvbit; for (int j = 0; j < QK_K/64; ++j) { @@ -8041,13 +8047,15 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r const __m256i q5bits = __lasx_xvld((const __m256i*)q5, 0); q5 += 32; + xvbit = __lasx_xvreplgr2vr_h(bit++); const __m256i q5l_0 = __lasx_xvand_v(q5bits, m4); - const __m256i q5h_0 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvand_v(hbits, hmask), bit++), 4); + const __m256i q5h_0 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvand_v(hbits, hmask), xvbit), 4); const __m256i q5_0 = __lasx_xvadd_b(q5l_0, q5h_0); hmask = __lasx_xvslli_h(hmask, 1); + xvbit = __lasx_xvreplgr2vr_h(bit++); const __m256i q5l_1 = __lasx_xvand_v(__lasx_xvsrli_h(q5bits, 4), m4); - const __m256i q5h_1 = __lasx_xvslli_h(__lasx_xvsrli_h(__lasx_xvand_v(hbits, hmask), bit++), 4); + const __m256i q5h_1 = __lasx_xvslli_h(__lasx_xvsrl_h(__lasx_xvand_v(hbits, hmask), xvbit), 4); const __m256i q5_1 = __lasx_xvadd_b(q5l_1, q5h_1); hmask = __lasx_xvslli_h(hmask, 1); diff --git a/ggml.c b/ggml.c index b2b725f65..f3a90ff2c 100644 --- a/ggml.c +++ b/ggml.c @@ -1580,7 +1580,7 @@ do { \ #define GGML_F32Cx8_ZERO (__m256)__lasx_xvldi(0) #define GGML_F32Cx8_SET1(x) (__m256)__lasx_xvreplgr2vr_w((x)) -static inline __m256 __lasx_f32cx8_load(ggml_fp16_t *x) { +static inline __m256 __lasx_f32cx8_load(const ggml_fp16_t *x) { float tmp[8]; for (int i = 0; i < 8; i++) { From 59b0d077662fab430446b3119fa142f3291c45b2 Mon Sep 17 00:00:00 2001 From: Chris Elrod Date: Thu, 30 May 2024 07:32:55 -0400 Subject: [PATCH 159/181] faster avx512 exp implementation (#7551) * faster avx512 exp implementation * x->r * improve accuracy, handle special cases * remove `e` --- ggml.c | 43 +++++++++++++++++++------------------------ 1 file changed, 19 insertions(+), 24 deletions(-) diff --git a/ggml.c b/ggml.c index f3a90ff2c..76803639c 100644 --- a/ggml.c +++ b/ggml.c @@ -2315,32 +2315,27 @@ inline static __m512 ggml_v_expf(__m512 x) { const __m512 r = _mm512_set1_ps(0x1.8p23f); const __m512 z = _mm512_fmadd_ps(x, _mm512_set1_ps(0x1.715476p+0f), r); const __m512 n = _mm512_sub_ps(z, r); - const __m512 b = _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.7f7d1cp-20f), - _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.62e4p-1f), x)); - const __m512i e = _mm512_slli_epi32(_mm512_castps_si512(z), 23); - const __m512 k = _mm512_castsi512_ps(_mm512_add_epi32(e, _mm512_castps_si512(_mm512_set1_ps(1)))); - const __mmask16 c = _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(126), _CMP_GT_OQ); - const __m512 u = _mm512_mul_ps(b, b); - const __m512 j = _mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_set1_ps(0x1.0e4020p-7f), b, - _mm512_set1_ps(0x1.573e2ep-5f)), u, - _mm512_fmadd_ps(_mm512_set1_ps(0x1.555e66p-3f), b, - _mm512_set1_ps(0x1.fffdb6p-2f))), - u, _mm512_mul_ps(_mm512_set1_ps(0x1.ffffecp-1f), b)); - if (_mm512_kortestz(c, c)) - return _mm512_fmadd_ps(j, k, k); - const __m512i g = _mm512_and_si512( - _mm512_movm_epi32(_mm512_cmp_ps_mask(n, _mm512_setzero_ps(), _CMP_LE_OQ)), - _mm512_set1_epi32(0x82000000u)); - const __m512 s1 = - _mm512_castsi512_ps(_mm512_add_epi32(g, _mm512_set1_epi32(0x7f000000u))); - const __m512 s2 = _mm512_castsi512_ps(_mm512_sub_epi32(e, g)); + const __m512 b = + _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.7f7d1cp-20f), + _mm512_fnmadd_ps(n, _mm512_set1_ps(0x1.62e4p-1f), x)); const __mmask16 d = _mm512_cmp_ps_mask(_mm512_abs_ps(n), _mm512_set1_ps(192), _CMP_GT_OQ); - return _mm512_mask_blend_ps( - d, _mm512_mask_blend_ps( - c, _mm512_fmadd_ps(k, j, k), - _mm512_mul_ps(_mm512_fmadd_ps(s2, j, s2), s1)), - _mm512_mul_ps(s1, s1)); + const __m512 u = _mm512_mul_ps(b, b); + const __m512 j = _mm512_fmadd_ps( + _mm512_fmadd_ps(_mm512_fmadd_ps(_mm512_set1_ps(0x1.0e4020p-7f), b, + _mm512_set1_ps(0x1.573e2ep-5f)), + u, + _mm512_fmadd_ps(_mm512_set1_ps(0x1.555e66p-3f), b, + _mm512_set1_ps(0x1.fffdb6p-2f))), + u, + _mm512_fmadd_ps(_mm512_set1_ps(0x1.ffffecp-1f), b, _mm512_set1_ps(1.0F))); + const __m512 res = _mm512_scalef_ps(j, n); + if (_mm512_kortestz(d, d)) + return res; + const __m512 zero = _mm512_setzero_ps(); + const __m512 alt = _mm512_mask_blend_ps( + _mm512_cmp_ps_mask(n, zero, _CMP_LE_OQ), _mm512_set1_ps(INFINITY), zero); + return _mm512_mask_blend_ps(d, res, alt); } // computes silu x/(1+exp(-x)) in single precision vector From 9c4c9cc83f7297a10bb3b2af54a22ac154fd5b20 Mon Sep 17 00:00:00 2001 From: Galunid Date: Thu, 30 May 2024 13:40:00 +0200 Subject: [PATCH 160/181] Move convert.py to examples/convert-legacy-llama.py (#7430) * Move convert.py to examples/convert-no-torch.py * Fix CI, scripts, readme files * convert-no-torch -> convert-legacy-llama * Move vocab thing to vocab.py * Fix convert-no-torch -> convert-legacy-llama * Fix lost convert.py in ci/run.sh * Fix imports * Fix gguf not imported correctly * Fix flake8 complaints * Fix check-requirements.sh * Get rid of ADDED_TOKENS_FILE, FAST_TOKENIZER_FILE * Review fixes --- .devops/tools.sh | 2 +- CMakeLists.txt | 2 +- README.md | 7 +- ci/run.sh | 2 +- convert-hf-to-gguf.py | 4 +- docs/HOWTO-add-model.md | 2 +- .../convert-legacy-llama.py | 308 +----------------- examples/llava/MobileVLM-README.md | 4 +- examples/llava/README.md | 6 +- examples/llava/requirements.txt | 2 +- examples/make-ggml.py | 98 ------ gguf-py/gguf/vocab.py | 302 ++++++++++++++++- requirements.txt | 2 +- ...requirements-convert-hf-to-gguf-update.txt | 2 +- .../requirements-convert-hf-to-gguf.txt | 2 +- ... => requirements-convert-legacy-llama.txt} | 0 ...equirements-convert-llama-ggml-to-gguf.txt | 2 +- scripts/check-requirements.sh | 2 +- scripts/convert-gg.sh | 20 +- scripts/pod-llama.sh | 14 +- 20 files changed, 343 insertions(+), 440 deletions(-) rename convert.py => examples/convert-legacy-llama.py (82%) delete mode 100755 examples/make-ggml.py rename requirements/{requirements-convert.txt => requirements-convert-legacy-llama.txt} (100%) diff --git a/.devops/tools.sh b/.devops/tools.sh index 3a7d274e4..97424c3aa 100755 --- a/.devops/tools.sh +++ b/.devops/tools.sh @@ -8,7 +8,7 @@ arg1="$1" shift if [[ "$arg1" == '--convert' || "$arg1" == '-c' ]]; then - python3 ./convert.py "$@" + python3 ./convert-hf-to-gguf.py "$@" elif [[ "$arg1" == '--quantize' || "$arg1" == '-q' ]]; then ./quantize "$@" elif [[ "$arg1" == '--run' || "$arg1" == '-r' ]]; then diff --git a/CMakeLists.txt b/CMakeLists.txt index fbbc38644..60cf7bdc4 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -1314,7 +1314,7 @@ set_target_properties(llama PROPERTIES PUBLIC_HEADER ${CMAKE_CURRENT_SOURCE_DIR} install(TARGETS llama LIBRARY PUBLIC_HEADER) install( - FILES convert.py + FILES convert-hf-to-gguf.py PERMISSIONS OWNER_READ OWNER_WRITE diff --git a/README.md b/README.md index 1cedc0d9a..29d6c1cb2 100644 --- a/README.md +++ b/README.md @@ -704,7 +704,8 @@ Building the program with BLAS support may lead to some performance improvements 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. -Note: `convert.py` does not support LLaMA 3, you can use `convert-hf-to-gguf.py` with LLaMA 3 downloaded from Hugging Face. +Note: `convert.py` has been moved to `examples/convert-legacy-llama.py` and shouldn't be used for anything other than `Llama/Llama2/Mistral` models and their derievatives. +It does not support LLaMA 3, you can use `convert-hf-to-gguf.py` with LLaMA 3 downloaded from Hugging Face. ```bash # obtain the official LLaMA model weights and place them in ./models @@ -721,10 +722,10 @@ ls ./models python3 -m pip install -r requirements.txt # convert the model to ggml FP16 format -python3 convert.py models/mymodel/ +python3 convert-hf-to-gguf.py models/mymodel/ # [Optional] for models using BPE tokenizers -python convert.py models/mymodel/ --vocab-type bpe +python convert-hf-to-gguf.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 diff --git a/ci/run.sh b/ci/run.sh index 940299025..3fc5f48b2 100755 --- a/ci/run.sh +++ b/ci/run.sh @@ -287,7 +287,7 @@ function gg_run_open_llama_7b_v2 { (time cmake -DCMAKE_BUILD_TYPE=Release ${CMAKE_EXTRA} -DLLAMA_CUDA=1 .. ) 2>&1 | tee -a $OUT/${ci}-cmake.log (time make -j ) 2>&1 | tee -a $OUT/${ci}-make.log - python3 ../convert.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf + python3 ../examples/convert-legacy-llama.py ${path_models} --outfile ${path_models}/ggml-model-f16.gguf model_f16="${path_models}/ggml-model-f16.gguf" model_q8_0="${path_models}/ggml-model-q8_0.gguf" diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 98b50d150..9f29cda23 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -25,8 +25,6 @@ if 'NO_LOCAL_GGUF' not in os.environ: sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) import gguf -from convert import LlamaHfVocab - logger = logging.getLogger("hf-to-gguf") @@ -634,7 +632,7 @@ class Model: special_vocab.add_to_gguf(self.gguf_writer) def _set_vocab_llama_hf(self): - vocab = LlamaHfVocab(self.dir_model) + vocab = gguf.LlamaHfVocab(self.dir_model) tokens = [] scores = [] toktypes = [] diff --git a/docs/HOWTO-add-model.md b/docs/HOWTO-add-model.md index 48769cdf6..138124248 100644 --- a/docs/HOWTO-add-model.md +++ b/docs/HOWTO-add-model.md @@ -17,7 +17,7 @@ Also, it is important to check that the examples and main ggml backends (CUDA, M ### 1. Convert the model to GGUF This step is done in python with a `convert` script using the [gguf](https://pypi.org/project/gguf/) library. -Depending on the model architecture, you can use either [convert.py](../convert.py) or [convert-hf-to-gguf.py](../convert-hf-to-gguf.py). +Depending on the model architecture, you can use either [convert-hf-to-gguf.py](../convert-hf-to-gguf.py) or [examples/convert-legacy-llama.py](../examples/convert-legacy-llama.py) (for `llama/llama2` models in `.pth` format). The convert script reads the model configuration, tokenizer, tensor names+data and converts them to GGUF metadata and tensors. diff --git a/convert.py b/examples/convert-legacy-llama.py similarity index 82% rename from convert.py rename to examples/convert-legacy-llama.py index da1247957..fd8401015 100755 --- a/convert.py +++ b/examples/convert-legacy-llama.py @@ -24,14 +24,16 @@ from abc import ABC, abstractmethod from concurrent.futures import ProcessPoolExecutor, ThreadPoolExecutor from dataclasses import dataclass from pathlib import Path -from typing import TYPE_CHECKING, Any, Callable, ClassVar, IO, Iterable, Literal, Protocol, TypeVar, runtime_checkable, Optional +from typing import TYPE_CHECKING, Any, Callable, IO, Iterable, Literal, TypeVar, Optional import numpy as np -from sentencepiece import SentencePieceProcessor if 'NO_LOCAL_GGUF' not in os.environ: - sys.path.insert(1, str(Path(__file__).parent / 'gguf-py')) + # use .parent.parent since we are in "examples" directory + sys.path.insert(1, str(Path(__file__).parent.parent / 'gguf-py')) + import gguf +from gguf import BaseVocab, Vocab, NoVocab, BpeVocab, SentencePieceVocab, LlamaHfVocab if TYPE_CHECKING: from typing_extensions import Self, TypeAlias @@ -380,306 +382,6 @@ class Metadata: return metadata -# -# vocab -# - - -@runtime_checkable -class BaseVocab(Protocol): - tokenizer_model: ClassVar[str] - name: ClassVar[str] - - -class NoVocab(BaseVocab): - tokenizer_model = "no_vocab" - name = "no_vocab" - - def __repr__(self) -> str: - return "" - - -@runtime_checkable -class Vocab(BaseVocab, Protocol): - vocab_size: int - added_tokens_dict: dict[str, int] - added_tokens_list: list[str] - fname_tokenizer: Path - - def __init__(self, base_path: Path): ... - def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: ... - - -class BpeVocab(Vocab): - tokenizer_model = "gpt2" - name = "bpe" - - def __init__(self, base_path: Path): - added_tokens: dict[str, int] = {} - - if (fname_tokenizer := base_path / 'vocab.json').exists(): - # "slow" tokenizer - with open(fname_tokenizer, encoding="utf-8") as f: - self.vocab = json.load(f) - - try: - # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab. - with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f: - added_tokens = json.load(f) - except FileNotFoundError: - pass - else: - # "fast" tokenizer - fname_tokenizer = base_path / FAST_TOKENIZER_FILE - - # if this fails, FileNotFoundError propagates to caller - with open(fname_tokenizer, encoding="utf-8") as f: - tokenizer_json = json.load(f) - - tokenizer_model: dict[str, Any] = tokenizer_json['model'] - if ( - tokenizer_model['type'] != 'BPE' or tokenizer_model.get('byte_fallback', False) - or tokenizer_json['decoder']['type'] != 'ByteLevel' - ): - raise FileNotFoundError('Cannot find GPT-2 BPE tokenizer') - - self.vocab = tokenizer_model["vocab"] - - if (added := tokenizer_json.get('added_tokens')) is not None: - # Added tokens here can be duplicates of the main vocabulary. - added_tokens = {item['content']: item['id'] - for item in added - if item['content'] not in self.vocab} - - vocab_size = len(self.vocab) - expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) - actual_ids = sorted(added_tokens.values()) - if expected_ids != actual_ids: - expected_end_id = vocab_size + len(actual_ids) - 1 - raise ValueError(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range " - f"{vocab_size} - {expected_end_id}; got {actual_ids}") - - items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1]) - self.added_tokens_dict = added_tokens - self.added_tokens_list = [text for (text, idx) in items] - self.vocab_size_base = vocab_size - self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) - self.fname_tokenizer = fname_tokenizer - - def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - reverse_vocab = {id: encoded_tok for encoded_tok, id in self.vocab.items()} - - for i, _ in enumerate(self.vocab): - yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL - - def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - for text in self.added_tokens_list: - score = -1000.0 - yield text.encode("utf-8"), score, gguf.TokenType.CONTROL - - def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - yield from self.bpe_tokens() - yield from self.added_tokens() - - def __repr__(self) -> str: - return f"" - - -class SentencePieceVocab(Vocab): - tokenizer_model = "llama" - name = "spm" - - def __init__(self, base_path: Path): - added_tokens: dict[str, int] = {} - if (fname_tokenizer := base_path / 'tokenizer.model').exists(): - # normal location - try: - with open(base_path / ADDED_TOKENS_FILE, encoding="utf-8") as f: - added_tokens = json.load(f) - except FileNotFoundError: - pass - elif not (fname_tokenizer := base_path.parent / 'tokenizer.model').exists(): - # not found in alternate location either - raise FileNotFoundError('Cannot find tokenizer.model') - - self.sentencepiece_tokenizer = SentencePieceProcessor() - self.sentencepiece_tokenizer.LoadFromFile(str(fname_tokenizer)) - vocab_size = self.sentencepiece_tokenizer.vocab_size() - - new_tokens = {id: piece for piece, id in added_tokens.items() if id >= vocab_size} - expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens))) - actual_new_ids = sorted(new_tokens.keys()) - - if expected_new_ids != actual_new_ids: - raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}") - - # Token pieces that were added to the base vocabulary. - self.added_tokens_dict = added_tokens - self.added_tokens_list = [new_tokens[id] for id in actual_new_ids] - self.vocab_size_base = vocab_size - self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) - self.fname_tokenizer = fname_tokenizer - - def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - tokenizer = self.sentencepiece_tokenizer - for i in range(tokenizer.vocab_size()): - piece = tokenizer.IdToPiece(i) - text = piece.encode("utf-8") - score: float = tokenizer.GetScore(i) - - toktype = gguf.TokenType.NORMAL - if tokenizer.IsUnknown(i): - toktype = gguf.TokenType.UNKNOWN - if tokenizer.IsControl(i): - toktype = gguf.TokenType.CONTROL - - # NOTE: I think added_tokens are user defined. - # ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto - # if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED - - if tokenizer.IsUnused(i): - toktype = gguf.TokenType.UNUSED - if tokenizer.IsByte(i): - toktype = gguf.TokenType.BYTE - - yield text, score, toktype - - def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - for text in self.added_tokens_list: - score = -1000.0 - yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED - - def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - yield from self.sentencepiece_tokens() - yield from self.added_tokens() - - def __repr__(self) -> str: - return f"" - - -class LlamaHfVocab(Vocab): - tokenizer_model = "llama" - name = "hfft" - - def __init__(self, base_path: Path): - fname_tokenizer = base_path / FAST_TOKENIZER_FILE - # if this fails, FileNotFoundError propagates to caller - with open(fname_tokenizer, encoding='utf-8') as f: - tokenizer_json = json.load(f) - - # pre-check so we know if we need transformers - tokenizer_model: dict[str, Any] = tokenizer_json['model'] - is_llama3 = ( - tokenizer_model['type'] == 'BPE' and tokenizer_model.get('ignore_merges', False) - and not tokenizer_model.get('byte_fallback', True) - ) - if is_llama3: - raise TypeError('Llama 3 must be converted with BpeVocab') - - if not is_llama3 and ( - tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False) - or tokenizer_json['decoder']['type'] != 'Sequence' - ): - raise FileNotFoundError('Cannot find Llama BPE tokenizer') - - try: - from transformers import AutoTokenizer - except ImportError as e: - raise ImportError( - "To use LlamaHfVocab, please install the `transformers` package. " - "You can install it with `pip install transformers`." - ) from e - - # Allow the tokenizer to default to slow or fast versions. - # Explicitly set tokenizer to use local paths. - self.tokenizer = AutoTokenizer.from_pretrained( - base_path, - cache_dir=base_path, - local_files_only=True, - ) - assert self.tokenizer.is_fast # assume tokenizer.json is used - - # Initialize lists and dictionaries for added tokens - self.added_tokens_list = [] - self.added_tokens_dict = dict() - self.added_tokens_ids = set() - - # Process added tokens - for tok, tokidx in sorted( - self.tokenizer.get_added_vocab().items(), key=lambda x: x[1] - ): - # Only consider added tokens that are not in the base vocabulary - if tokidx >= self.tokenizer.vocab_size: - self.added_tokens_list.append(tok) - self.added_tokens_dict[tok] = tokidx - self.added_tokens_ids.add(tokidx) - - # Store special tokens and their IDs - self.specials = { - tok: self.tokenizer.get_vocab()[tok] - for tok in self.tokenizer.all_special_tokens - } - self.special_ids = set(self.tokenizer.all_special_ids) - - # Set vocabulary sizes - self.vocab_size_base = self.tokenizer.vocab_size - self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) - - self.fname_tokenizer = fname_tokenizer - - def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - reverse_vocab = { - id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items() - } - - for token_id in range(self.vocab_size_base): - # Skip processing added tokens here - if token_id in self.added_tokens_ids: - continue - - # Convert token text to bytes - token_text = reverse_vocab[token_id].encode("utf-8") - - # Yield token text, score, and type - yield token_text, self.get_token_score(token_id), self.get_token_type( - token_id, token_text, self.special_ids # Reuse already stored special IDs - ) - - 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 - - def get_token_score(self, token_id: int) -> float: - # Placeholder for actual logic to determine the token's score - # This needs to be implemented based on specific requirements - return -1000.0 # Default score - - 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], b'', self.special_ids) - score = self.get_token_score(self.specials[text]) - else: - toktype = gguf.TokenType.USER_DEFINED - score = -1000.0 - - yield text.encode("utf-8"), score, toktype - - def has_newline_token(self): - return "<0x0A>" in self.tokenizer.vocab or "\n" in self.tokenizer.vocab - - def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: - yield from self.hf_tokens() - yield from self.added_tokens() - - def __repr__(self) -> str: - return f"" - - # # data loading # TODO: reuse (probably move to gguf.py?) diff --git a/examples/llava/MobileVLM-README.md b/examples/llava/MobileVLM-README.md index 413e433dd..74f021dec 100644 --- a/examples/llava/MobileVLM-README.md +++ b/examples/llava/MobileVLM-README.md @@ -54,10 +54,10 @@ python ./examples/llava/convert-image-encoder-to-gguf \ --projector-type ldpv2 ``` -4. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF: +4. Use `examples/convert-legacy-llama.py` to convert the LLaMA part of LLaVA to GGUF: ```sh -python ./convert.py path/to/MobileVLM-1.7B +python ./examples/convert-legacy-llama.py path/to/MobileVLM-1.7B ``` 5. Use `quantize` to convert LLaMA part's DataType from `fp16` to `q4_k` diff --git a/examples/llava/README.md b/examples/llava/README.md index 4fb0cf381..8d1ae5270 100644 --- a/examples/llava/README.md +++ b/examples/llava/README.md @@ -50,10 +50,10 @@ python ./examples/llava/llava-surgery.py -m ../llava-v1.5-7b python ./examples/llava/convert-image-encoder-to-gguf.py -m ../clip-vit-large-patch14-336 --llava-projector ../llava-v1.5-7b/llava.projector --output-dir ../llava-v1.5-7b ``` -5. Use `convert.py` to convert the LLaMA part of LLaVA to GGUF: +5. Use `examples/convert-legacy-llama.py` to convert the LLaMA part of LLaVA to GGUF: ```sh -python ./convert.py ../llava-v1.5-7b --skip-unknown +python ./examples/convert-legacy-llama.py ../llava-v1.5-7b --skip-unknown ``` Now both the LLaMA part and the image encoder are in the `llava-v1.5-7b` directory. @@ -92,7 +92,7 @@ python ./examples/llava/convert-image-encoder-to-gguf.py -m vit --llava-projecto 6) Then convert the model to gguf format: ```console -python ./convert.py ../llava-v1.6-vicuna-7b/ --skip-unknown +python ./examples/convert-legacy-llama.py ../llava-v1.6-vicuna-7b/ --skip-unknown ``` 7) And finally we can run the llava-cli using the 1.6 model version: diff --git a/examples/llava/requirements.txt b/examples/llava/requirements.txt index f80f727a7..17cb4d5e5 100644 --- a/examples/llava/requirements.txt +++ b/examples/llava/requirements.txt @@ -1,3 +1,3 @@ --r ../../requirements/requirements-convert.txt +-r ../../requirements/requirements-convert-legacy-llama.txt pillow~=10.2.0 torch~=2.1.1 diff --git a/examples/make-ggml.py b/examples/make-ggml.py deleted file mode 100755 index c73485ebf..000000000 --- a/examples/make-ggml.py +++ /dev/null @@ -1,98 +0,0 @@ -#!/usr/bin/env python3 -""" -This script converts Hugging Face Llama, StarCoder, Falcon, Baichuan, and GPT-NeoX models to GGUF and quantizes them. - -Usage: -python make-ggml.py {model_dir_or_hf_repo_name} --model_type {model_type} [--outname {output_name} (Optional)] [--outdir {output_directory} (Optional)] [--quants {quant_types} (Optional)] [--keep_fp16 (Optional)] - -Arguments: -- model: (Required) The directory of the downloaded Hugging Face model or the name of the Hugging Face model repository. If the model directory does not exist, it will be downloaded from the Hugging Face model hub. -- --model_type: (Required) The type of the model to be converted. Choose from llama, starcoder, falcon, baichuan, or gptneox. -- --outname: (Optional) The name of the output model. If not specified, the last part of the model directory path or the Hugging Face model repo name will be used. -- --outdir: (Optional) The directory where the output model(s) will be stored. If not specified, '../models/{outname}' will be used. -- --quants: (Optional) The types of quantization to apply. This should be a space-separated list. The default is 'Q4_K_M Q5_K_S'. -- --keep_fp16: (Optional) If specified, the FP16 model will not be deleted after the quantized models are created. - -Old quant types (some base model types require these): -- Q4_0: small, very high quality loss - legacy, prefer using Q3_K_M -- Q4_1: small, substantial quality loss - legacy, prefer using Q3_K_L -- Q5_0: medium, balanced quality - legacy, prefer using Q4_K_M -- Q5_1: medium, low quality loss - legacy, prefer using Q5_K_M - -New quant types (recommended): -- Q2_K: smallest, extreme quality loss - not recommended -- Q3_K: alias for Q3_K_M -- Q3_K_S: very small, very high quality loss -- Q3_K_M: very small, very high quality loss -- Q3_K_L: small, substantial quality loss -- Q4_K: alias for Q4_K_M -- Q4_K_S: small, significant quality loss -- Q4_K_M: medium, balanced quality - recommended -- Q5_K: alias for Q5_K_M -- Q5_K_S: large, low quality loss - recommended -- Q5_K_M: large, very low quality loss - recommended -- Q6_K: very large, extremely low quality loss -- Q8_0: very large, extremely low quality loss - not recommended -- F16: extremely large, virtually no quality loss - not recommended -- F32: absolutely huge, lossless - not recommended -""" -import subprocess -subprocess.run(f"pip install huggingface-hub==0.16.4", shell=True, check=True) - -import argparse -import os -from huggingface_hub import snapshot_download - -def main(model, model_type, outname, outdir, quants, keep_fp16): - if not os.path.isdir(model): - print(f"Model not found at {model}. Downloading...") - try: - if outname is None: - outname = model.split('/')[-1] - model = snapshot_download(repo_id=model, cache_dir='../models/hf_cache') - except Exception as e: - raise Exception(f"Could not download the model: {e}") - - if outdir is None: - outdir = f'../models/{outname}' - - if not os.path.isfile(f"{model}/config.json"): - raise Exception(f"Could not find config.json in {model}") - - os.makedirs(outdir, exist_ok=True) - - print("Building llama.cpp") - subprocess.run(f"cd .. && make quantize", shell=True, check=True) - - fp16 = f"{outdir}/{outname}.gguf.fp16.bin" - - print(f"Making unquantised GGUF at {fp16}") - if not os.path.isfile(fp16): - if model_type != "llama": - subprocess.run(f"python3 ../convert-{model_type}-hf-to-gguf.py {model} 1 --outfile {fp16}", shell=True, check=True) - else: - subprocess.run(f"python3 ../convert.py {model} --outtype f16 --outfile {fp16}", shell=True, check=True) - else: - print(f"Unquantised GGML already exists at: {fp16}") - - print("Making quants") - for type in quants: - outfile = f"{outdir}/{outname}.gguf.{type}.bin" - print(f"Making {type} : {outfile}") - subprocess.run(f"../quantize {fp16} {outfile} {type}", shell=True, check=True) - - if not keep_fp16: - os.remove(fp16) - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description='Convert/Quantize HF models to GGUF. If you have the HF model downloaded already, pass the path to the model dir. Otherwise, pass the Hugging Face model repo name. You need to be in the /examples folder for it to work.') - parser.add_argument('model', help='Downloaded model dir or Hugging Face model repo name') - parser.add_argument('--model_type', required=True, choices=['llama', 'starcoder', 'falcon', 'baichuan', 'gptneox'], help='Type of the model to be converted. Choose from llama, starcoder, falcon, baichuan, or gptneox.') - parser.add_argument('--outname', default=None, help='Output model(s) name') - parser.add_argument('--outdir', default=None, help='Output directory') - parser.add_argument('--quants', nargs='*', default=["Q4_K_M", "Q5_K_S"], help='Quant types') - parser.add_argument('--keep_fp16', action='store_true', help='Keep fp16 model', default=False) - - args = parser.parse_args() - - main(args.model, args.model_type, args.outname, args.outdir, args.quants, args.keep_fp16) diff --git a/gguf-py/gguf/vocab.py b/gguf-py/gguf/vocab.py index 3ba99be4f..dc5749913 100644 --- a/gguf-py/gguf/vocab.py +++ b/gguf-py/gguf/vocab.py @@ -1,10 +1,15 @@ from __future__ import annotations +import re import logging import json import os from pathlib import Path -from typing import Any, Callable, Sequence, Mapping, Iterable +from typing import Any, Callable, Sequence, Mapping, Iterable, Protocol, ClassVar, runtime_checkable + +from sentencepiece import SentencePieceProcessor + +import gguf from .gguf_writer import GGUFWriter @@ -163,3 +168,298 @@ class SpecialVocab: for typ in self.special_token_types: self._set_special_token(typ, config.get(f'{typ}_token_id')) return True + + +@runtime_checkable +class BaseVocab(Protocol): + tokenizer_model: ClassVar[str] + name: ClassVar[str] + + +@runtime_checkable +class Vocab(BaseVocab, Protocol): + vocab_size: int + added_tokens_dict: dict[str, int] + added_tokens_list: list[str] + fname_tokenizer: Path + + def __init__(self, base_path: Path): ... + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: ... + + +class NoVocab(BaseVocab): + tokenizer_model = "no_vocab" + name = "no_vocab" + + def __repr__(self) -> str: + return "" + + +class BpeVocab(Vocab): + tokenizer_model = "gpt2" + name = "bpe" + + def __init__(self, base_path: Path): + added_tokens: dict[str, int] = {} + + if (fname_tokenizer := base_path / 'vocab.json').exists(): + # "slow" tokenizer + with open(fname_tokenizer, encoding="utf-8") as f: + self.vocab = json.load(f) + + try: + # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab. + with open(base_path / 'added_tokens.json', encoding="utf-8") as f: + added_tokens = json.load(f) + except FileNotFoundError: + pass + else: + # "fast" tokenizer + fname_tokenizer = base_path / 'tokenizer.json' + + # if this fails, FileNotFoundError propagates to caller + with open(fname_tokenizer, encoding="utf-8") as f: + tokenizer_json = json.load(f) + + tokenizer_model: dict[str, Any] = tokenizer_json['model'] + if ( + tokenizer_model['type'] != 'BPE' or tokenizer_model.get('byte_fallback', False) + or tokenizer_json['decoder']['type'] != 'ByteLevel' + ): + raise FileNotFoundError('Cannot find GPT-2 BPE tokenizer') + + self.vocab = tokenizer_model["vocab"] + + if (added := tokenizer_json.get('added_tokens')) is not None: + # Added tokens here can be duplicates of the main vocabulary. + added_tokens = {item['content']: item['id'] + for item in added + if item['content'] not in self.vocab} + + vocab_size = len(self.vocab) + expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) + actual_ids = sorted(added_tokens.values()) + if expected_ids != actual_ids: + expected_end_id = vocab_size + len(actual_ids) - 1 + raise ValueError(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range " + f"{vocab_size} - {expected_end_id}; got {actual_ids}") + + items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1]) + self.added_tokens_dict = added_tokens + self.added_tokens_list = [text for (text, idx) in items] + self.vocab_size_base = vocab_size + self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) + self.fname_tokenizer = fname_tokenizer + + def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + reverse_vocab = {id: encoded_tok for encoded_tok, id in self.vocab.items()} + + for i, _ in enumerate(self.vocab): + yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL + + def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + for text in self.added_tokens_list: + score = -1000.0 + yield text.encode("utf-8"), score, gguf.TokenType.CONTROL + + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + yield from self.bpe_tokens() + yield from self.added_tokens() + + def __repr__(self) -> str: + return f"" + + +class SentencePieceVocab(Vocab): + tokenizer_model = "llama" + name = "spm" + + def __init__(self, base_path: Path): + added_tokens: dict[str, int] = {} + if (fname_tokenizer := base_path / 'tokenizer.model').exists(): + # normal location + try: + with open(base_path / 'added_tokens.json', encoding="utf-8") as f: + added_tokens = json.load(f) + except FileNotFoundError: + pass + elif not (fname_tokenizer := base_path.parent / 'tokenizer.model').exists(): + # not found in alternate location either + raise FileNotFoundError('Cannot find tokenizer.model') + + self.sentencepiece_tokenizer = SentencePieceProcessor() + self.sentencepiece_tokenizer.LoadFromFile(str(fname_tokenizer)) + vocab_size = self.sentencepiece_tokenizer.vocab_size() + + new_tokens = {id: piece for piece, id in added_tokens.items() if id >= vocab_size} + expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens))) + actual_new_ids = sorted(new_tokens.keys()) + + if expected_new_ids != actual_new_ids: + raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}") + + # Token pieces that were added to the base vocabulary. + self.added_tokens_dict = added_tokens + self.added_tokens_list = [new_tokens[id] for id in actual_new_ids] + self.vocab_size_base = vocab_size + self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) + self.fname_tokenizer = fname_tokenizer + + def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + tokenizer = self.sentencepiece_tokenizer + for i in range(tokenizer.vocab_size()): + piece = tokenizer.IdToPiece(i) + text = piece.encode("utf-8") + score: float = tokenizer.GetScore(i) + + toktype = gguf.TokenType.NORMAL + if tokenizer.IsUnknown(i): + toktype = gguf.TokenType.UNKNOWN + if tokenizer.IsControl(i): + toktype = gguf.TokenType.CONTROL + + # NOTE: I think added_tokens are user defined. + # ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto + # if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED + + if tokenizer.IsUnused(i): + toktype = gguf.TokenType.UNUSED + if tokenizer.IsByte(i): + toktype = gguf.TokenType.BYTE + + yield text, score, toktype + + def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + for text in self.added_tokens_list: + score = -1000.0 + yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED + + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + yield from self.sentencepiece_tokens() + yield from self.added_tokens() + + def __repr__(self) -> str: + return f"" + + +class LlamaHfVocab(Vocab): + tokenizer_model = "llama" + name = "hfft" + + def __init__(self, base_path: Path): + fname_tokenizer = base_path / 'tokenizer.json' + # if this fails, FileNotFoundError propagates to caller + with open(fname_tokenizer, encoding='utf-8') as f: + tokenizer_json = json.load(f) + + # pre-check so we know if we need transformers + tokenizer_model: dict[str, Any] = tokenizer_json['model'] + is_llama3 = ( + tokenizer_model['type'] == 'BPE' and tokenizer_model.get('ignore_merges', False) + and not tokenizer_model.get('byte_fallback', True) + ) + if is_llama3: + raise TypeError('Llama 3 must be converted with BpeVocab') + + if not is_llama3 and ( + tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False) + or tokenizer_json['decoder']['type'] != 'Sequence' + ): + raise FileNotFoundError('Cannot find Llama BPE tokenizer') + + try: + from transformers import AutoTokenizer + except ImportError as e: + raise ImportError( + "To use LlamaHfVocab, please install the `transformers` package. " + "You can install it with `pip install transformers`." + ) from e + + # Allow the tokenizer to default to slow or fast versions. + # Explicitly set tokenizer to use local paths. + self.tokenizer = AutoTokenizer.from_pretrained( + base_path, + cache_dir=base_path, + local_files_only=True, + ) + assert self.tokenizer.is_fast # assume tokenizer.json is used + + # Initialize lists and dictionaries for added tokens + self.added_tokens_list = [] + self.added_tokens_dict = dict() + self.added_tokens_ids = set() + + # Process added tokens + for tok, tokidx in sorted( + self.tokenizer.get_added_vocab().items(), key=lambda x: x[1] + ): + # Only consider added tokens that are not in the base vocabulary + if tokidx >= self.tokenizer.vocab_size: + self.added_tokens_list.append(tok) + self.added_tokens_dict[tok] = tokidx + self.added_tokens_ids.add(tokidx) + + # Store special tokens and their IDs + self.specials = { + tok: self.tokenizer.get_vocab()[tok] + for tok in self.tokenizer.all_special_tokens + } + self.special_ids = set(self.tokenizer.all_special_ids) + + # Set vocabulary sizes + self.vocab_size_base = self.tokenizer.vocab_size + self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) + + self.fname_tokenizer = fname_tokenizer + + def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + reverse_vocab = { + id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items() + } + + for token_id in range(self.vocab_size_base): + # Skip processing added tokens here + if token_id in self.added_tokens_ids: + continue + + # Convert token text to bytes + token_text = reverse_vocab[token_id].encode("utf-8") + + # Yield token text, score, and type + yield token_text, self.get_token_score(token_id), self.get_token_type( + token_id, token_text, self.special_ids # Reuse already stored special IDs + ) + + 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 + + def get_token_score(self, token_id: int) -> float: + # Placeholder for actual logic to determine the token's score + # This needs to be implemented based on specific requirements + return -1000.0 # Default score + + 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], b'', self.special_ids) + score = self.get_token_score(self.specials[text]) + else: + toktype = gguf.TokenType.USER_DEFINED + score = -1000.0 + + yield text.encode("utf-8"), score, toktype + + def has_newline_token(self): + return "<0x0A>" in self.tokenizer.vocab or "\n" in self.tokenizer.vocab + + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + yield from self.hf_tokens() + yield from self.added_tokens() + + def __repr__(self) -> str: + return f"" diff --git a/requirements.txt b/requirements.txt index 43f82dc2e..e5cfbf10b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,7 +4,7 @@ # Package versions must stay compatible across all top-level python scripts. # --r ./requirements/requirements-convert.txt +-r ./requirements/requirements-convert-legacy-llama.txt -r ./requirements/requirements-convert-hf-to-gguf.txt -r ./requirements/requirements-convert-hf-to-gguf-update.txt diff --git a/requirements/requirements-convert-hf-to-gguf-update.txt b/requirements/requirements-convert-hf-to-gguf-update.txt index 6ac402610..6eacaf429 100644 --- a/requirements/requirements-convert-hf-to-gguf-update.txt +++ b/requirements/requirements-convert-hf-to-gguf-update.txt @@ -1,2 +1,2 @@ --r ./requirements-convert.txt +-r ./requirements-convert-legacy-llama.txt torch~=2.1.1 diff --git a/requirements/requirements-convert-hf-to-gguf.txt b/requirements/requirements-convert-hf-to-gguf.txt index 6ac402610..6eacaf429 100644 --- a/requirements/requirements-convert-hf-to-gguf.txt +++ b/requirements/requirements-convert-hf-to-gguf.txt @@ -1,2 +1,2 @@ --r ./requirements-convert.txt +-r ./requirements-convert-legacy-llama.txt torch~=2.1.1 diff --git a/requirements/requirements-convert.txt b/requirements/requirements-convert-legacy-llama.txt similarity index 100% rename from requirements/requirements-convert.txt rename to requirements/requirements-convert-legacy-llama.txt diff --git a/requirements/requirements-convert-llama-ggml-to-gguf.txt b/requirements/requirements-convert-llama-ggml-to-gguf.txt index a0f37cd1c..e80c29012 100644 --- a/requirements/requirements-convert-llama-ggml-to-gguf.txt +++ b/requirements/requirements-convert-llama-ggml-to-gguf.txt @@ -1 +1 @@ --r ./requirements-convert.txt +-r ./requirements-convert-legacy-llama.txt diff --git a/scripts/check-requirements.sh b/scripts/check-requirements.sh index 6a7400d3c..0c6afdd59 100755 --- a/scripts/check-requirements.sh +++ b/scripts/check-requirements.sh @@ -166,7 +166,7 @@ if (( do_cleanup )); then rm -rf -- "$all_venv" fi -check_convert_script convert.py +check_convert_script examples/convert-legacy-llama.py for py in convert-*.py; do # skip convert-hf-to-gguf-update.py # TODO: the check is failing for some reason: diff --git a/scripts/convert-gg.sh b/scripts/convert-gg.sh index 01fda16fd..8a0168432 100755 --- a/scripts/convert-gg.sh +++ b/scripts/convert-gg.sh @@ -3,20 +3,20 @@ set -e # LLaMA v1 -python3 convert.py ../llama1/7B --outfile models/llama-7b/ggml-model-f16.gguf --outtype f16 -python3 convert.py ../llama1/13B --outfile models/llama-13b/ggml-model-f16.gguf --outtype f16 -python3 convert.py ../llama1/30B --outfile models/llama-30b/ggml-model-f16.gguf --outtype f16 -python3 convert.py ../llama1/65B --outfile models/llama-65b/ggml-model-f16.gguf --outtype f16 +python3 examples/convert-legacy-llama.py ../llama1/7B --outfile models/llama-7b/ggml-model-f16.gguf --outtype f16 +python3 examples/convert-legacy-llama.py ../llama1/13B --outfile models/llama-13b/ggml-model-f16.gguf --outtype f16 +python3 examples/convert-legacy-llama.py ../llama1/30B --outfile models/llama-30b/ggml-model-f16.gguf --outtype f16 +python3 examples/convert-legacy-llama.py ../llama1/65B --outfile models/llama-65b/ggml-model-f16.gguf --outtype f16 # LLaMA v2 -python3 convert.py ../llama2/llama-2-7b --outfile models/llama-7b-v2/ggml-model-f16.gguf --outtype f16 -python3 convert.py ../llama2/llama-2-13b --outfile models/llama-13b-v2/ggml-model-f16.gguf --outtype f16 -python3 convert.py ../llama2/llama-2-70b --outfile models/llama-70b-v2/ggml-model-f16.gguf --outtype f16 +python3 examples/convert-legacy-llama.py ../llama2/llama-2-7b --outfile models/llama-7b-v2/ggml-model-f16.gguf --outtype f16 +python3 examples/convert-legacy-llama.py ../llama2/llama-2-13b --outfile models/llama-13b-v2/ggml-model-f16.gguf --outtype f16 +python3 examples/convert-legacy-llama.py ../llama2/llama-2-70b --outfile models/llama-70b-v2/ggml-model-f16.gguf --outtype f16 # Code Llama -python3 convert.py ../codellama/CodeLlama-7b/ --outfile models/codellama-7b/ggml-model-f16.gguf --outtype f16 -python3 convert.py ../codellama/CodeLlama-13b/ --outfile models/codellama-13b/ggml-model-f16.gguf --outtype f16 -python3 convert.py ../codellama/CodeLlama-34b/ --outfile models/codellama-34b/ggml-model-f16.gguf --outtype f16 +python3 examples/convert-legacy-llama.py ../codellama/CodeLlama-7b/ --outfile models/codellama-7b/ggml-model-f16.gguf --outtype f16 +python3 examples/convert-legacy-llama.py ../codellama/CodeLlama-13b/ --outfile models/codellama-13b/ggml-model-f16.gguf --outtype f16 +python3 examples/convert-legacy-llama.py ../codellama/CodeLlama-34b/ --outfile models/codellama-34b/ggml-model-f16.gguf --outtype f16 # Falcon python3 convert-falcon-hf-to-gguf.py ../falcon/falcon-7b 1 diff --git a/scripts/pod-llama.sh b/scripts/pod-llama.sh index 2058ceabf..5dabbf60e 100644 --- a/scripts/pod-llama.sh +++ b/scripts/pod-llama.sh @@ -75,7 +75,7 @@ if [ "$1" -eq "1" ]; then cd /workspace/llama.cpp - python3 convert.py ./models/tinyllama-1b --outfile ./models/tinyllama-1b/ggml-model-f16.gguf --outtype f16 + python3 examples/convert-legacy-llama.py ./models/tinyllama-1b --outfile ./models/tinyllama-1b/ggml-model-f16.gguf --outtype f16 ./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_0.gguf q4_0 ./quantize ./models/tinyllama-1b/ggml-model-f16.gguf ./models/tinyllama-1b/ggml-model-q4_k.gguf q4_k @@ -90,7 +90,7 @@ if [ "$1" -eq "2" ]; then cd /workspace/llama.cpp - python3 convert.py ./models/codellama-7b --outfile ./models/codellama-7b/ggml-model-f16.gguf --outtype f16 + python3 examples/convert-legacy-llama.py ./models/codellama-7b --outfile ./models/codellama-7b/ggml-model-f16.gguf --outtype f16 ./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_0.gguf q4_0 ./quantize ./models/codellama-7b/ggml-model-f16.gguf ./models/codellama-7b/ggml-model-q4_k.gguf q4_k @@ -105,7 +105,7 @@ if [ "$1" -eq "3" ]; then cd /workspace/llama.cpp - python3 convert.py ./models/codellama-13b --outfile ./models/codellama-13b/ggml-model-f16.gguf --outtype f16 + python3 examples/convert-legacy-llama.py ./models/codellama-13b --outfile ./models/codellama-13b/ggml-model-f16.gguf --outtype f16 ./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_0.gguf q4_0 ./quantize ./models/codellama-13b/ggml-model-f16.gguf ./models/codellama-13b/ggml-model-q4_k.gguf q4_k @@ -120,7 +120,7 @@ if [ "$1" -eq "4" ]; then cd /workspace/llama.cpp - python3 convert.py ./models/codellama-34b --outfile ./models/codellama-34b/ggml-model-f16.gguf --outtype f16 + python3 examples/convert-legacy-llama.py ./models/codellama-34b --outfile ./models/codellama-34b/ggml-model-f16.gguf --outtype f16 ./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_0.gguf q4_0 ./quantize ./models/codellama-34b/ggml-model-f16.gguf ./models/codellama-34b/ggml-model-q4_k.gguf q4_k @@ -135,7 +135,7 @@ if [ "$1" -eq "5" ]; then cd /workspace/llama.cpp - python3 convert.py ./models/codellama-7b-instruct --outfile ./models/codellama-7b-instruct/ggml-model-f16.gguf --outtype f16 + python3 examples/convert-legacy-llama.py ./models/codellama-7b-instruct --outfile ./models/codellama-7b-instruct/ggml-model-f16.gguf --outtype f16 ./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_0.gguf q4_0 ./quantize ./models/codellama-7b-instruct/ggml-model-f16.gguf ./models/codellama-7b-instruct/ggml-model-q4_k.gguf q4_k @@ -150,7 +150,7 @@ if [ "$1" -eq "6" ]; then cd /workspace/llama.cpp - python3 convert.py ./models/codellama-13b-instruct --outfile ./models/codellama-13b-instruct/ggml-model-f16.gguf --outtype f16 + python3 examples/convert-legacy-llama.py ./models/codellama-13b-instruct --outfile ./models/codellama-13b-instruct/ggml-model-f16.gguf --outtype f16 ./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_0.gguf q4_0 ./quantize ./models/codellama-13b-instruct/ggml-model-f16.gguf ./models/codellama-13b-instruct/ggml-model-q4_k.gguf q4_k @@ -165,7 +165,7 @@ if [ "$1" -eq "7" ]; then cd /workspace/llama.cpp - python3 convert.py ./models/codellama-34b-instruct --outfile ./models/codellama-34b-instruct/ggml-model-f16.gguf --outtype f16 + python3 examples/convert-legacy-llama.py ./models/codellama-34b-instruct --outfile ./models/codellama-34b-instruct/ggml-model-f16.gguf --outtype f16 ./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_0.gguf q4_0 ./quantize ./models/codellama-34b-instruct/ggml-model-f16.gguf ./models/codellama-34b-instruct/ggml-model-q4_k.gguf q4_k From e6157f94c8f835f7f774b98409078867472a34fe Mon Sep 17 00:00:00 2001 From: Brian Date: Thu, 30 May 2024 21:55:36 +1000 Subject: [PATCH 161/181] github: add contact links to issues and convert question into research [no ci] (#7612) --- .github/ISSUE_TEMPLATE/06-question.yml | 38 ------------------- .github/ISSUE_TEMPLATE/06-research.yml | 52 ++++++++++++++++++++++++++ .github/ISSUE_TEMPLATE/config.yml | 13 +++++++ 3 files changed, 65 insertions(+), 38 deletions(-) delete mode 100644 .github/ISSUE_TEMPLATE/06-question.yml create mode 100644 .github/ISSUE_TEMPLATE/06-research.yml create mode 100644 .github/ISSUE_TEMPLATE/config.yml diff --git a/.github/ISSUE_TEMPLATE/06-question.yml b/.github/ISSUE_TEMPLATE/06-question.yml deleted file mode 100644 index 9d3ff4972..000000000 --- a/.github/ISSUE_TEMPLATE/06-question.yml +++ /dev/null @@ -1,38 +0,0 @@ -name: Question -description: Used to ask questions about llama.cpp -title: "Question: " -labels: ["question"] -body: - - type: markdown - attributes: - value: | - [Please search your question first in Discussion if you got a common general question.](https://github.com/ggerganov/llama.cpp/discussions/categories/q-a) - - - type: checkboxes - id: prerequisites - attributes: - label: Prerequisites - description: Please confirm the following before submitting your question. - options: - - label: I searched using keywords relevant to my issue to make sure that I am creating a new issue that is not already open (or closed). - required: true - - label: I reviewed the [Discussions](https://github.com/ggerganov/llama.cpp/discussions), and have a new useful question to share that cannot be answered within Discussions. - required: true - - - type: textarea - id: background-description - attributes: - label: Background Description - description: Please provide a detailed written description of what you were trying to do, and what you expected `llama.cpp` to do as an question. - placeholder: Detailed description of your question - validations: - required: true - - - type: textarea - id: possible-answer - attributes: - label: Possible Answer - description: If you have some idea of possible answers you want to confirm, that would also be appreciated. - placeholder: Your idea of possible answers - validations: - required: false diff --git a/.github/ISSUE_TEMPLATE/06-research.yml b/.github/ISSUE_TEMPLATE/06-research.yml new file mode 100644 index 000000000..3ae4e9f8c --- /dev/null +++ b/.github/ISSUE_TEMPLATE/06-research.yml @@ -0,0 +1,52 @@ +name: Research +description: Track new technical research area +title: "Research: " +labels: ["research 🔬"] +body: + - type: markdown + attributes: + value: | + Don't forget to check for any [duplicate research issue tickets](https://github.com/ggerganov/llama.cpp/issues?q=is%3Aopen+is%3Aissue+label%3A%22research+%F0%9F%94%AC%22) + + - type: checkboxes + id: research-stage + attributes: + label: Research Stage + description: Track general state of this research ticket + options: + - label: Background Research (Let's try to avoid reinventing the wheel) + - label: Hypothesis Formed (How do you think this will work and it's effect?) + - label: Strategy / Implementation Forming + - label: Analysis of results + - label: Debrief / Documentation (So people in the future can learn from us) + + - type: textarea + id: background + attributes: + label: Previous existing literature and research + description: Whats the current state of the art and whats the motivation for this research? + + - type: textarea + id: hypothesis + attributes: + label: Hypothesis + description: How do you think this will work and it's effect? + + - type: textarea + id: implementation + attributes: + label: Implementation + description: Got an approach? e.g. a PR ready to go? + + - type: textarea + id: analysis + attributes: + label: Analysis + description: How does the proposed implementation behave? + + - type: textarea + id: logs + attributes: + label: Relevant log output + description: Please copy and paste any relevant log output. This will be automatically formatted into code, so no need for backticks. + render: shell diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 000000000..c88134dbb --- /dev/null +++ b/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,13 @@ +blank_issues_enabled: true +contact_links: + - name: Got an idea? + url: https://github.com/ggerganov/llama.cpp/discussions/categories/ideas + about: Pop it there. It may then become an enhancement ticket. + - name: Got a question? + url: https://github.com/ggerganov/llama.cpp/discussions/categories/q-a + about: Ask a question there! + - name: Want to contribute? + url: https://github.com/ggerganov/llama.cpp/wiki/contribute + about: Head to the contribution guide page of the wiki for areas you can help with + + From 7846540bd291e52cd4eee53882315760e05239be Mon Sep 17 00:00:00 2001 From: Martin Delille Date: Thu, 30 May 2024 14:52:50 +0200 Subject: [PATCH 162/181] readme : add Conan badge (#7638) --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 29d6c1cb2..ea7099d01 100644 --- a/README.md +++ b/README.md @@ -6,6 +6,8 @@ [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) +[![Conan Center](https://shields.io/conan/v/llama-cpp)](https://conan.io/center/llama-cpp) + Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++ ### Recent API changes From 2e2340de1740b07f2418c04792607387d4dc1442 Mon Sep 17 00:00:00 2001 From: Manuel <44313466+makuche@users.noreply.github.com> Date: Thu, 30 May 2024 16:58:15 +0200 Subject: [PATCH 163/181] Add brew installation instruction to README [no ci] (#7616) --- README.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/README.md b/README.md index ea7099d01..ba06b1ee2 100644 --- a/README.md +++ b/README.md @@ -388,6 +388,14 @@ In order to build llama.cpp you have four different options. CLBLAST support for use OpenCL GPU acceleration in FreeBSD. Please read the instructions for use and activate this options in this document below. +### Homebrew + +On Mac and Linux, the homebrew package manager can be used via +``` +brew install llama.cpp +``` +The formula is automatically updated with new `llama.cpp` releases. + ### Metal Build On MacOS, Metal is enabled by default. Using Metal makes the computation run on the GPU. From 5dcdf946764fae49a8e2a90bf2f0960bde1c44e8 Mon Sep 17 00:00:00 2001 From: Martin Delille Date: Thu, 30 May 2024 17:07:39 +0200 Subject: [PATCH 164/181] Fix conan badge display [no ci] (#7645) --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index ba06b1ee2..d85a453be 100644 --- a/README.md +++ b/README.md @@ -2,12 +2,12 @@ ![llama](https://user-images.githubusercontent.com/1991296/230134379-7181e485-c521-4d23-a0d6-f7b3b61ba524.png) -[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) [![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg?branch=master&event=schedule)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml) +[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT) +[![Server](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml/badge.svg?branch=master&event=schedule)](https://github.com/ggerganov/llama.cpp/actions/workflows/server.yml) +[![Conan Center](https://shields.io/conan/v/llama-cpp)](https://conan.io/center/llama-cpp) [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) -[![Conan Center](https://shields.io/conan/v/llama-cpp)](https://conan.io/center/llama-cpp) - Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) in pure C/C++ ### Recent API changes From 5921b8f089d3b7bda86aac5a66825df6a6c10603 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 30 May 2024 19:01:41 +0300 Subject: [PATCH 165/181] llama : cache llama_token_to_piece (#7587) * llama : cache llama_token_to_piece ggml-ci * llama : use vectors and avoid has_cache ggml-ci * llama : throw on unknown tokenizer types ggml-ci * llama : print a log of the total cache size --- llama.cpp | 199 ++++++++++++++++++++++++++++++++---------------------- llama.h | 4 +- 2 files changed, 119 insertions(+), 84 deletions(-) diff --git a/llama.cpp b/llama.cpp index e7412de4b..40d2ec2c9 100644 --- a/llama.cpp +++ b/llama.cpp @@ -1702,12 +1702,13 @@ struct llama_mlock { }; using llama_mlocks = std::vector>; -static std::string llama_token_to_piece(const struct llama_context * ctx, llama_token token, bool special) { +// NOTE: avoid ever using this except for building the token_to_piece caches +static std::string llama_token_to_piece(const struct llama_model * model, llama_token token, bool special) { std::vector result(8, 0); - const int n_tokens = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special); + const int n_tokens = llama_token_to_piece(model, token, result.data(), result.size(), special); if (n_tokens < 0) { result.resize(-n_tokens); - int check = llama_token_to_piece(llama_get_model(ctx), token, result.data(), result.size(), special); + int check = llama_token_to_piece(model, token, result.data(), result.size(), special); GGML_ASSERT(check == -n_tokens); } else { @@ -2162,7 +2163,9 @@ struct llama_vocab { std::unordered_map token_to_id; std::vector id_to_token; - std::vector special_tokens_cache; + std::vector cache_special_tokens; + std::vector cache_token_to_piece; // llama_token_to_piece(special = false); + std::vector cache_token_to_piece_special; // llama_token_to_piece(special = true); std::map, int> bpe_ranks; @@ -4592,20 +4595,14 @@ static void llm_load_vocab( vocab.special_cls_id = 101; vocab.special_mask_id = 103; vocab.add_space_prefix = false; - } else { - if (tokenizer_model == "gpt2") { - vocab.type = LLAMA_VOCAB_TYPE_BPE; + } else if (tokenizer_model == "gpt2") { + vocab.type = LLAMA_VOCAB_TYPE_BPE; - 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); - } - } else { - LLAMA_LOG_WARN("%s: unknown tokenizer: '%s'", __func__, tokenizer_model.c_str()); - LLAMA_LOG_WARN("%s: using default tokenizer: 'llama'", __func__); - vocab.type = LLAMA_VOCAB_TYPE_SPM; - return; + 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); } + // read bpe merges and populate bpe ranks const int merges_keyidx = gguf_find_key(ctx, kv(LLM_KV_TOKENIZER_MERGES).c_str()); if (merges_keyidx == -1) { @@ -4639,6 +4636,8 @@ static void llm_load_vocab( vocab.special_pad_id = -1; vocab.special_cls_id = -1; vocab.special_mask_id = -1; + } else { + throw std::runtime_error(format("unknown tokenizer: '%s'", tokenizer_model.c_str())); } // for now, only BPE models have pre-tokenizers @@ -4833,17 +4832,38 @@ static void llm_load_vocab( { for (llama_vocab::id id = 0; id < (llama_vocab::id)n_vocab; ++id) { if (vocab.id_to_token[id].type != LLAMA_TOKEN_TYPE_NORMAL) { - vocab.special_tokens_cache.push_back(id); + vocab.cache_special_tokens.push_back(id); } } - std::sort( vocab.special_tokens_cache.begin(), vocab.special_tokens_cache.end(), + std::sort( vocab.cache_special_tokens.begin(), vocab.cache_special_tokens.end(), [&] (const llama_vocab::id a, const llama_vocab::id b) { return vocab.id_to_token[a].text.size() > vocab.id_to_token[b].text.size(); } ); - LLAMA_LOG_INFO("%s: special tokens cache size = %u.\n", __func__, (uint32_t)vocab.special_tokens_cache.size()); + LLAMA_LOG_INFO("%s: special tokens cache size = %u\n", __func__, (uint32_t)vocab.cache_special_tokens.size()); + } + + // build token to piece caches + { + size_t size_cache = 0; + + std::vector cache_token_to_piece (n_vocab); + std::vector cache_token_to_piece_special(n_vocab); + + for (uint32_t id = 0; id < n_vocab; ++id) { + cache_token_to_piece[id] = llama_token_to_piece(&model, id, false); + cache_token_to_piece_special[id] = llama_token_to_piece(&model, id, true); + + size_cache += cache_token_to_piece[id].size(); + size_cache += cache_token_to_piece_special[id].size(); + } + + std::swap(vocab.cache_token_to_piece, cache_token_to_piece); + std::swap(vocab.cache_token_to_piece_special, cache_token_to_piece_special); + + LLAMA_LOG_INFO("%s: token to piece cache size = %.4f MB\n", __func__, size_cache / 1024.0 / 1024.0); } } @@ -13233,7 +13253,7 @@ struct fragment_buffer_variant { static void tokenizer_st_partition(const llama_vocab & vocab, std::forward_list & buffer) { // for each special token - for (const llama_vocab::id special_id : vocab.special_tokens_cache) { + for (const llama_vocab::id special_id : vocab.cache_special_tokens) { const auto & special_token = vocab.id_to_token[special_id].text; // for each text fragment @@ -14392,7 +14412,7 @@ void llama_sample_repetition_penalties( void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * candidates, const struct llama_grammar * grammar) { GGML_ASSERT(ctx); - const int64_t t_start_sample_us = ggml_time_us(); + int64_t t_start_sample_us = ggml_time_us(); bool allow_eog = false; for (const auto & stack : grammar->stacks) { @@ -14404,12 +14424,13 @@ void llama_sample_grammar(struct llama_context * ctx, llama_token_data_array * c std::vector, llama_partial_utf8>> candidates_decoded; candidates_decoded.reserve(candidates->size); - std::vector candidates_grammar; + + std::vector candidates_grammar; candidates_grammar.reserve(candidates->size); for (size_t i = 0; i < candidates->size; ++i) { - const llama_token id = candidates->data[i].id; - const std::string piece = llama_token_to_piece(ctx, id, false); + const llama_token id = candidates->data[i].id; + const std::string & piece = ctx->model.vocab.cache_token_to_piece.at(id); if (llama_token_is_eog(&ctx->model, id)) { if (!allow_eog) { @@ -14609,7 +14630,7 @@ void llama_grammar_accept_token(struct llama_context * ctx, struct llama_grammar GGML_ASSERT(false); } - const std::string piece = llama_token_to_piece(ctx, token, false); + const std::string & piece = ctx->model.vocab.cache_token_to_piece.at(token); // Note terminating 0 in decoded string const auto decoded = decode_utf8(piece, grammar->partial_utf8); @@ -18292,69 +18313,83 @@ static std::string llama_decode_text(const std::string & text) { // does not write null-terminator to buf int32_t llama_token_to_piece(const struct llama_model * model, llama_token token, char * buf, int32_t length, bool special) { + // if we have a cache - use it + { + const auto & cache = special ? model->vocab.cache_token_to_piece_special : model->vocab.cache_token_to_piece; + + if (!cache.empty()) { + const auto & res = cache.at(token); + if (length < (int) res.size()) { + return -(int) res.size(); + } + memcpy(buf, res.c_str(), res.size()); + return res.size(); + } + } + if (0 <= token && token < llama_n_vocab(model)) { switch (llama_vocab_get_type(model->vocab)) { - case LLAMA_VOCAB_TYPE_WPM: - case LLAMA_VOCAB_TYPE_SPM: { - // NOTE: we accept all unsupported token types, - // suppressing them like CONTROL tokens. - if (llama_is_normal_token(model->vocab, token)) { - std::string result = model->vocab.id_to_token[token].text; - llama_unescape_whitespace(result); - if (length < (int) result.length()) { - return -(int) result.length(); + case LLAMA_VOCAB_TYPE_WPM: + case LLAMA_VOCAB_TYPE_SPM: { + // NOTE: we accept all unsupported token types, + // suppressing them like CONTROL tokens. + if (llama_is_normal_token(model->vocab, token)) { + std::string result = model->vocab.id_to_token[token].text; + llama_unescape_whitespace(result); + if (length < (int) result.length()) { + return -(int) result.length(); + } + memcpy(buf, result.c_str(), result.length()); + return result.length(); + } else if ( + (llama_is_user_defined_token(model->vocab, token)) || + (llama_is_control_token (model->vocab, token) && special)) { + std::string result = model->vocab.id_to_token[token].text; + if (length < (int) result.length()) { + return -(int) result.length(); + } + memcpy(buf, result.c_str(), result.length()); + return result.length(); + } else if (llama_is_unknown_token(model->vocab, token)) { // NOLINT + if (length < 3) { + return -3; + } + memcpy(buf, "\xe2\x96\x85", 3); + return 3; + } else if (llama_is_byte_token(model->vocab, token)) { + if (length < 1) { + return -1; + } + buf[0] = llama_token_to_byte(model->vocab, token); + return 1; } - memcpy(buf, result.c_str(), result.length()); - return result.length(); - } else if ( - (llama_is_user_defined_token(model->vocab, token)) || - (llama_is_control_token (model->vocab, token) && special)) { - std::string result = model->vocab.id_to_token[token].text; - if (length < (int) result.length()) { - return -(int) result.length(); - } - memcpy(buf, result.c_str(), result.length()); - return result.length(); - } else if (llama_is_unknown_token(model->vocab, token)) { // NOLINT - if (length < 3) { - return -3; - } - memcpy(buf, "\xe2\x96\x85", 3); - return 3; - } else if (llama_is_byte_token(model->vocab, token)) { - if (length < 1) { - return -1; - } - buf[0] = llama_token_to_byte(model->vocab, token); - return 1; + break; } - break; - } - case LLAMA_VOCAB_TYPE_BPE: { - // NOTE: we accept all unsupported token types, - // suppressing them like CONTROL tokens. - if (llama_is_normal_token(model->vocab, token)) { - std::string result = model->vocab.id_to_token[token].text; - result = llama_decode_text(result); - if (length < (int) result.length()) { - return -(int) result.length(); + case LLAMA_VOCAB_TYPE_BPE: { + // NOTE: we accept all unsupported token types, + // suppressing them like CONTROL tokens. + if (llama_is_normal_token(model->vocab, token)) { + std::string result = model->vocab.id_to_token[token].text; + result = llama_decode_text(result); + if (length < (int) result.length()) { + return -(int) result.length(); + } + memcpy(buf, result.c_str(), result.length()); + return result.length(); + } else if ( + (llama_is_user_defined_token(model->vocab, token)) || + (llama_is_control_token (model->vocab, token) && special)) { + std::string result = model->vocab.id_to_token[token].text; + if (length < (int) result.length()) { + return -(int) result.length(); + } + memcpy(buf, result.c_str(), result.length()); + return result.length(); } - memcpy(buf, result.c_str(), result.length()); - return result.length(); - } else if ( - (llama_is_user_defined_token(model->vocab, token)) || - (llama_is_control_token (model->vocab, token) && special)) { - std::string result = model->vocab.id_to_token[token].text; - if (length < (int) result.length()) { - return -(int) result.length(); - } - memcpy(buf, result.c_str(), result.length()); - return result.length(); + break; } - break; - } - default: - GGML_ASSERT(false); + default: + GGML_ASSERT(false); } } return 0; diff --git a/llama.h b/llama.h index 3e4474bb9..95105c28e 100644 --- a/llama.h +++ b/llama.h @@ -424,8 +424,8 @@ extern "C" { LLAMA_API enum llama_pooling_type llama_pooling_type(const struct llama_context * ctx); - LLAMA_API enum llama_vocab_type llama_vocab_type (const struct llama_model * model); - LLAMA_API enum llama_rope_type llama_rope_type (const struct llama_model * model); + LLAMA_API enum llama_vocab_type llama_vocab_type (const struct llama_model * model); + LLAMA_API enum llama_rope_type llama_rope_type (const struct llama_model * model); LLAMA_API int32_t llama_n_vocab (const struct llama_model * model); LLAMA_API int32_t llama_n_ctx_train(const struct llama_model * model); From 9022c33646fbf78da35f40c3f98576cc08c40ddf Mon Sep 17 00:00:00 2001 From: JohnnyB Date: Thu, 30 May 2024 21:32:38 +0100 Subject: [PATCH 166/181] Fixed painfully slow single process builds. (#7326) * Fixed painfully slow single process builds. * Added nproc for systems that don't default to nproc --- .devops/full-cuda.Dockerfile | 2 +- .devops/full-rocm.Dockerfile | 2 +- .devops/full.Dockerfile | 2 +- .devops/main-cuda.Dockerfile | 2 +- .devops/main-rocm.Dockerfile | 2 +- .devops/main.Dockerfile | 2 +- .devops/server-cuda.Dockerfile | 2 +- .devops/server-rocm.Dockerfile | 2 +- .devops/server.Dockerfile | 2 +- 9 files changed, 9 insertions(+), 9 deletions(-) diff --git a/.devops/full-cuda.Dockerfile b/.devops/full-cuda.Dockerfile index 059fd2695..c01006efe 100644 --- a/.devops/full-cuda.Dockerfile +++ b/.devops/full-cuda.Dockerfile @@ -31,6 +31,6 @@ ENV LLAMA_CUDA=1 # Enable cURL ENV LLAMA_CURL=1 -RUN make +RUN make -j$(nproc) ENTRYPOINT ["/app/.devops/tools.sh"] diff --git a/.devops/full-rocm.Dockerfile b/.devops/full-rocm.Dockerfile index 6ecf3bcc7..0314d469b 100644 --- a/.devops/full-rocm.Dockerfile +++ b/.devops/full-rocm.Dockerfile @@ -45,6 +45,6 @@ ENV LLAMA_CURL=1 RUN apt-get update && \ apt-get install -y libcurl4-openssl-dev -RUN make +RUN make -j$(nproc) ENTRYPOINT ["/app/.devops/tools.sh"] diff --git a/.devops/full.Dockerfile b/.devops/full.Dockerfile index 432fb5dad..6d5943a2f 100644 --- a/.devops/full.Dockerfile +++ b/.devops/full.Dockerfile @@ -18,7 +18,7 @@ COPY . . ENV LLAMA_CURL=1 -RUN make +RUN make -j$(nproc) ENV LC_ALL=C.utf8 diff --git a/.devops/main-cuda.Dockerfile b/.devops/main-cuda.Dockerfile index b937a4829..23f428944 100644 --- a/.devops/main-cuda.Dockerfile +++ b/.devops/main-cuda.Dockerfile @@ -23,7 +23,7 @@ ENV CUDA_DOCKER_ARCH=${CUDA_DOCKER_ARCH} # Enable CUDA ENV LLAMA_CUDA=1 -RUN make +RUN make -j$(nproc) FROM ${BASE_CUDA_RUN_CONTAINER} as runtime diff --git a/.devops/main-rocm.Dockerfile b/.devops/main-rocm.Dockerfile index 0a706dc73..37576d68e 100644 --- a/.devops/main-rocm.Dockerfile +++ b/.devops/main-rocm.Dockerfile @@ -40,6 +40,6 @@ ENV LLAMA_HIPBLAS=1 ENV CC=/opt/rocm/llvm/bin/clang ENV CXX=/opt/rocm/llvm/bin/clang++ -RUN make +RUN make -j$(nproc) ENTRYPOINT [ "/app/main" ] diff --git a/.devops/main.Dockerfile b/.devops/main.Dockerfile index 3ab1decd6..763d75fce 100644 --- a/.devops/main.Dockerfile +++ b/.devops/main.Dockerfile @@ -9,7 +9,7 @@ WORKDIR /app COPY . . -RUN make +RUN make -j$(nproc) FROM ubuntu:$UBUNTU_VERSION as runtime diff --git a/.devops/server-cuda.Dockerfile b/.devops/server-cuda.Dockerfile index 59a52ba21..7f5228185 100644 --- a/.devops/server-cuda.Dockerfile +++ b/.devops/server-cuda.Dockerfile @@ -25,7 +25,7 @@ ENV LLAMA_CUDA=1 # Enable cURL ENV LLAMA_CURL=1 -RUN make +RUN make -j$(nproc) FROM ${BASE_CUDA_RUN_CONTAINER} as runtime diff --git a/.devops/server-rocm.Dockerfile b/.devops/server-rocm.Dockerfile index c02a31dd8..a6b76dee8 100644 --- a/.devops/server-rocm.Dockerfile +++ b/.devops/server-rocm.Dockerfile @@ -45,6 +45,6 @@ ENV LLAMA_CURL=1 RUN apt-get update && \ apt-get install -y libcurl4-openssl-dev -RUN make +RUN make -j$(nproc) ENTRYPOINT [ "/app/server" ] diff --git a/.devops/server.Dockerfile b/.devops/server.Dockerfile index be964e0e8..0d09d3627 100644 --- a/.devops/server.Dockerfile +++ b/.devops/server.Dockerfile @@ -11,7 +11,7 @@ COPY . . ENV LLAMA_CURL=1 -RUN make +RUN make -j$(nproc) FROM ubuntu:$UBUNTU_VERSION as runtime From 0541f06296753dbc59a57379eb54cec865a4c9f9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Serta=C3=A7=20=C3=96zercan?= <852750+sozercan@users.noreply.github.com> Date: Thu, 30 May 2024 16:57:16 -0700 Subject: [PATCH 167/181] [no ci] docs: add aikit to readme (#7650) Signed-off-by: Sertac Ozercan --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index d85a453be..60e7aaf2c 100644 --- a/README.md +++ b/README.md @@ -202,6 +202,7 @@ Unless otherwise noted these projects are open-source with permissive licensing: - [KodiBot](https://github.com/firatkiral/kodibot) (GPL) - [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT) - [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT) +- [AIKit](https://github.com/sozercan/aikit) (MIT) *(to have a project listed here, it should clearly state that it depends on `llama.cpp`)* @@ -390,7 +391,7 @@ In order to build llama.cpp you have four different options. ### Homebrew -On Mac and Linux, the homebrew package manager can be used via +On Mac and Linux, the homebrew package manager can be used via ``` brew install llama.cpp ``` From 1af511fc22cba4959dd8bced5501df9e8af6ddf9 Mon Sep 17 00:00:00 2001 From: Galunid Date: Fri, 31 May 2024 10:09:20 +0200 Subject: [PATCH 168/181] Add convert.py removal to hot topics (#7662) --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 60e7aaf2c..eeeb64919 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,8 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ### Hot topics -- **Initial Flash-Attention support: https://github.com/ggerganov/llama.cpp/pull/5021** +- **`convert.py` has been deprecated and moved to `examples/convert-legacy-llama.py`, please use `convert-hf-to-gguf.py` https://github.com/ggerganov/llama.cpp/pull/7430 +- Initial Flash-Attention support: https://github.com/ggerganov/llama.cpp/pull/5021 - BPE pre-tokenization support has been added: https://github.com/ggerganov/llama.cpp/pull/6920 - MoE memory layout has been updated - reconvert models for `mmap` support and regenerate `imatrix` https://github.com/ggerganov/llama.cpp/pull/6387 - Model sharding instructions using `gguf-split` https://github.com/ggerganov/llama.cpp/discussions/6404 From 2e32f874e675f7bc5307cb7b4470ddbe090bab8f Mon Sep 17 00:00:00 2001 From: Galunid Date: Fri, 31 May 2024 10:24:41 +0200 Subject: [PATCH 169/181] Somehow '**' got lost (#7663) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index eeeb64919..89b0fe0b0 100644 --- a/README.md +++ b/README.md @@ -22,7 +22,7 @@ Inference of Meta's [LLaMA](https://arxiv.org/abs/2302.13971) model (and others) ### Hot topics -- **`convert.py` has been deprecated and moved to `examples/convert-legacy-llama.py`, please use `convert-hf-to-gguf.py` https://github.com/ggerganov/llama.cpp/pull/7430 +- **`convert.py` has been deprecated and moved to `examples/convert-legacy-llama.py`, please use `convert-hf-to-gguf.py`** https://github.com/ggerganov/llama.cpp/pull/7430 - Initial Flash-Attention support: https://github.com/ggerganov/llama.cpp/pull/5021 - BPE pre-tokenization support has been added: https://github.com/ggerganov/llama.cpp/pull/6920 - MoE memory layout has been updated - reconvert models for `mmap` support and regenerate `imatrix` https://github.com/ggerganov/llama.cpp/pull/6387 From 0c27e6f62eea80140daf152d7b6c154466614e5c Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 31 May 2024 14:17:10 +0300 Subject: [PATCH 170/181] ggml : fix loongson compile warnings (#7537) * ggml : fix loongson compile warnings ggml-ci * Fix loongarch quantize test fail. Fix unexpected error introduced during rebase code. * tests : disable json test due to lack of python on the CI node ggml-ci --------- Co-authored-by: junchao-loongson --- ggml-quants.c | 26 +++++++++++++++++++++++--- ggml.c | 13 +++++++------ tests/CMakeLists.txt | 7 +++++-- 3 files changed, 35 insertions(+), 11 deletions(-) diff --git a/ggml-quants.c b/ggml-quants.c index 1128d66e2..9f864e5c4 100644 --- a/ggml-quants.c +++ b/ggml-quants.c @@ -6088,6 +6088,7 @@ void ggml_vec_dot_q2_K_q8_K(int n, float * restrict s, size_t bs, const void * r const uint8_t * restrict q2 = x[i].qs; const int8_t * restrict q8 = y[i].qs; + const __m128i mins_and_scales = __lsx_vld((const __m128i*)x[i].scales, 0); const __m128i scales8 = __lsx_vand_v(mins_and_scales, m4); const __m128i mins8 = __lsx_vand_v(__lsx_vsrli_h(mins_and_scales, 4), m4); @@ -6807,6 +6808,8 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r for (int i = 0; i < nb; ++i) { const float d = y[i].d * GGML_FP16_TO_FP32(x[i].d); + const uint8_t * restrict q3 = x[i].qs; + const int8_t * restrict q8 = y[i].qs; // Set up scales memcpy(aux, x[i].scales, 12); __m128i scales128 = lsx_set_w( @@ -6830,8 +6833,6 @@ void ggml_vec_dot_q3_K_q8_K(int n, float * restrict s, size_t bs, const void * r int is = 0; __m256i xvbit; - const uint8_t * restrict q3 = x[i].qs; - const int8_t * restrict q8 = y[i].qs; for (int j = 0; j < QK_K/128; ++j) { // load low 2 bits @@ -7404,6 +7405,9 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = vec_extract(vsumf0, 0); #elif defined __loongarch_asx + GGML_UNUSED(kmask1); + GGML_UNUSED(kmask2); + GGML_UNUSED(kmask3); const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); @@ -7416,6 +7420,11 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; const uint8_t * restrict q4 = x[i].qs; const int8_t * restrict q8 = y[i].qs; @@ -7455,16 +7464,17 @@ void ggml_vec_dot_q4_K_q8_K(int n, float * restrict s, size_t bs, const void * r __m256 vd = __lasx_xvreplfr2vr_s(d); acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc); + } acc_m = __lsx_vfadd_s(acc_m, (__m128)__lsx_vpermi_w((__m128i)acc_m, (__m128i)acc_m, 0xee)); __m128i tmp1 = __lsx_vinsgr2vr_w(__lsx_vldi(0), __lsx_vpickve2gr_w((__m128i)acc_m, 1), 0); acc_m = __lsx_vfadd_s(acc_m, (__m128)tmp1); + ft_union fi; fi.i = __lsx_vpickve2gr_w(acc_m, 0); *s = hsum_float_8(acc) + fi.f ; - #else const uint8_t * scales = (const uint8_t*)&utmp[0]; @@ -8002,6 +8012,9 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r *s = vec_extract(vsumf0, 0); #elif defined __loongarch_asx + GGML_UNUSED(kmask1); + GGML_UNUSED(kmask2); + GGML_UNUSED(kmask3); const __m256i m4 = __lasx_xvreplgr2vr_b(0xF); const __m128i mzero = __lsx_vldi(0); @@ -8020,6 +8033,11 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r const float dmin = -y[i].d * GGML_FP16_TO_FP32(x[i].dmin); memcpy(utmp, x[i].scales, 12); + utmp[3] = ((utmp[2] >> 4) & kmask2) | (((utmp[1] >> 6) & kmask3) << 4); + const uint32_t uaux = utmp[1] & kmask1; + utmp[1] = (utmp[2] & kmask2) | (((utmp[0] >> 6) & kmask3) << 4); + utmp[2] = uaux; + utmp[0] &= kmask1; const __m256i mins_and_scales = lasx_extu8_16(lsx_set_w(utmp[3], utmp[2], utmp[1], utmp[0])); @@ -8069,10 +8087,12 @@ void ggml_vec_dot_q5_K_q8_K(int n, float * restrict s, size_t bs, const void * r p16_1 = lasx_madd_h(scale_1, p16_1); sumi = __lasx_xvadd_w(sumi, __lasx_xvadd_w(p16_0, p16_1)); + } __m256 vd = __lasx_xvreplfr2vr_s(d); acc = __lasx_xvfmadd_s(vd, __lasx_xvffint_s_w(sumi), acc); + } *s = hsum_float_8(acc) + summs; diff --git a/ggml.c b/ggml.c index 76803639c..f479dc3e1 100644 --- a/ggml.c +++ b/ggml.c @@ -1576,11 +1576,11 @@ do { \ // F16 arithmetic is not supported by AVX, so we use F32 instead -#define GGML_F32Cx8 __m256 +#define GGML_F32Cx8 __m256 #define GGML_F32Cx8_ZERO (__m256)__lasx_xvldi(0) #define GGML_F32Cx8_SET1(x) (__m256)__lasx_xvreplgr2vr_w((x)) -static inline __m256 __lasx_f32cx8_load(const ggml_fp16_t *x) { +static inline __m256 __lasx_f32cx8_load(const ggml_fp16_t * x) { float tmp[8]; for (int i = 0; i < 8; i++) { @@ -1589,13 +1589,14 @@ static inline __m256 __lasx_f32cx8_load(const ggml_fp16_t *x) { return (__m256)__lasx_xvld(tmp, 0); } -static inline void __lasx_f32cx8_store(ggml_fp16_t *x, __m256 y) { +static inline void __lasx_f32cx8_store(ggml_fp16_t * x, __m256 y) { float arr[8]; __lasx_xvst(y, arr, 0); - for (int i = 0; i < 8; i++) + for (int i = 0; i < 8; i++) { x[i] = GGML_FP32_TO_FP16(arr[i]); + } } #define GGML_F32Cx8_LOAD(x) __lasx_f32cx8_load(x) #define GGML_F32Cx8_STORE(x, y) __lasx_f32cx8_store(x, y) @@ -1671,7 +1672,7 @@ static inline void __lasx_f32cx8_store(ggml_fp16_t *x, __m256 y) { #define GGML_F16_STEP 32 #define GGML_F16_EPR 4 -static inline __m128 __lsx_f16x4_load(ggml_fp16_t *x) { +static inline __m128 __lsx_f16x4_load(const ggml_fp16_t * x) { float tmp[4]; tmp[0] = GGML_FP16_TO_FP32(x[0]); @@ -1682,7 +1683,7 @@ static inline __m128 __lsx_f16x4_load(ggml_fp16_t *x) { return __lsx_vld(tmp, 0); } -static inline void __lsx_f16x4_store(ggml_fp16_t *x, __m128 y) { +static inline void __lsx_f16x4_store(ggml_fp16_t * x, __m128 y) { float arr[4]; __lsx_vst(y, arr, 0); diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 766a01752..cfa707315 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -129,8 +129,11 @@ llama_target_and_test(test-rope.cpp) llama_target_and_test(test-model-load-cancel.cpp LABEL "model") llama_target_and_test(test-autorelease.cpp LABEL "model") -llama_target_and_test(test-json-schema-to-grammar.cpp WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/..) -target_include_directories(test-json-schema-to-grammar PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../examples/server) +# TODO: disabled on loongarch64 because the ggml-ci node lacks Python 3.8 +if (NOT ${CMAKE_SYSTEM_PROCESSOR} MATCHES "loongarch64") + llama_target_and_test(test-json-schema-to-grammar.cpp WORKING_DIRECTORY ${CMAKE_CURRENT_SOURCE_DIR}/..) + target_include_directories(test-json-schema-to-grammar PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}/../examples/server) +endif() # dummy executable - not installed get_filename_component(TEST_TARGET test-c.c NAME_WE) From 16926dff92d6d0efa8cbc0f44d30d63349532b38 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 31 May 2024 15:04:58 +0300 Subject: [PATCH 171/181] readme : link homebrew discussion --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 89b0fe0b0..e8bf1e246 100644 --- a/README.md +++ b/README.md @@ -396,7 +396,7 @@ On Mac and Linux, the homebrew package manager can be used via ``` brew install llama.cpp ``` -The formula is automatically updated with new `llama.cpp` releases. +The formula is automatically updated with new `llama.cpp` releases. More info: https://github.com/ggerganov/llama.cpp/discussions/7668 ### Metal Build From 30e238b246f8002cc6eb7cb79afe242243f1f66d Mon Sep 17 00:00:00 2001 From: Daniele <57776841+daniandtheweb@users.noreply.github.com> Date: Fri, 31 May 2024 14:00:29 +0000 Subject: [PATCH 172/181] Improve HIP compatibility (#7672) --- Makefile | 1 + 1 file changed, 1 insertion(+) diff --git a/Makefile b/Makefile index 5caf31cdf..aced0e370 100644 --- a/Makefile +++ b/Makefile @@ -571,6 +571,7 @@ ifdef LLAMA_HIP_UMA MK_CPPFLAGS += -DGGML_HIP_UMA endif # LLAMA_HIP_UMA MK_LDFLAGS += -L$(ROCM_PATH)/lib -Wl,-rpath=$(ROCM_PATH)/lib + MK_LDFLAGS += -L$(ROCM_PATH)/lib64 -Wl,-rpath=$(ROCM_PATH)/lib64 MK_LDFLAGS += -lhipblas -lamdhip64 -lrocblas HIPFLAGS += $(addprefix --offload-arch=,$(AMDGPU_TARGETS)) HIPFLAGS += -DGGML_CUDA_DMMV_X=$(LLAMA_CUDA_DMMV_X) From c8047d538f3addab40e3112be60bb92e70ce1a50 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Fri, 31 May 2024 16:26:21 +0200 Subject: [PATCH 173/181] scripts: update compare_llama_bench.py [no ci] (#7673) --- scripts/compare-llama-bench.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/scripts/compare-llama-bench.py b/scripts/compare-llama-bench.py index 0ede9e67c..6016eb2c0 100755 --- a/scripts/compare-llama-bench.py +++ b/scripts/compare-llama-bench.py @@ -19,22 +19,22 @@ logger = logging.getLogger("compare-llama-bench") # Properties by which to differentiate results per commit: KEY_PROPERTIES = [ - "cpu_info", "gpu_info", "n_gpu_layers", "main_gpu", "cuda", "opencl", "metal", "gpu_blas", - "blas", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_threads", - "type_k", "type_v", "no_kv_offload", "tensor_split", "n_prompt", "n_gen" + "cpu_info", "gpu_info", "n_gpu_layers", "cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "rpc", "gpu_blas", + "blas", "model_filename", "model_type", "model_size", "model_n_params", "n_batch", "n_ubatch", "embeddings", "n_threads", + "type_k", "type_v", "use_mmap", "no_kv_offload", "split_mode", "main_gpu", "tensor_split", "flash_attn", "n_prompt", "n_gen" ] # Properties that are boolean and are converted to Yes/No for the table: -BOOL_PROPERTIES = ["cuda", "opencl", "metal", "gpu_blas", "blas"] +BOOL_PROPERTIES = ["cuda", "opencl", "vulkan", "kompute", "metal", "sycl", "gpu_blas", "blas", "embeddings", "use_mmap", "no_kv_offload", "flash_attn"] # Header names for the table: PRETTY_NAMES = { - "cuda": "CUDA", "opencl": "OpenCL", "metal": "Metal", "gpu_blas": "GPU BLAS", "blas": "BLAS", - "cpu_info": "CPU", "gpu_info": "GPU", "model_filename": "File", "model_type": "Model", - "model_size": "Model Size [GiB]", "model_n_params": "Num. of Parameters", - "n_batch": "Batch size", "n_threads": "Threads", "type_k": "K type", "type_v": "V type", - "n_gpu_layers": "GPU layers", "main_gpu": "Main GPU", "no_kv_offload": "NKVO", - "tensor_split": "Tensor split" + "cuda": "CUDA", "opencl": "OpenCL", "vulkan": "Vulkan", "kompute": "Kompute", "metal": "Metal", "sycl": "SYCL", "rpc": "RPC", + "gpu_blas": "GPU BLAS", "blas": "BLAS", "cpu_info": "CPU", "gpu_info": "GPU", "model_filename": "File", "model_type": "Model", + "model_size": "Model Size [GiB]", "model_n_params": "Num. of Par.", "n_batch": "Batch size", "n_ubatch": "Microbatch size", + "n_threads": "Threads", "type_k": "K type", "type_v": "V type", "n_gpu_layers": "GPU layers", "split_mode": "Split mode", + "main_gpu": "Main GPU", "no_kv_offload": "NKVO", "flash_attn": "FlashAttention", "tensor_split": "Tensor split", + "use_mmap": "Use mmap", "embeddings": "Embeddings", } DEFAULT_SHOW = ["model_type"] # Always show these properties by default. From 0515ad93f48df63bbff204eddb0cac75e8585c65 Mon Sep 17 00:00:00 2001 From: Galunid Date: Fri, 31 May 2024 17:42:33 +0200 Subject: [PATCH 174/181] convert-hf : Handle NotImplementedError in convert-hf-to-gguf (#7660) --- convert-hf-to-gguf.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/convert-hf-to-gguf.py b/convert-hf-to-gguf.py index 9f29cda23..ad071b974 100755 --- a/convert-hf-to-gguf.py +++ b/convert-hf-to-gguf.py @@ -2840,7 +2840,12 @@ def main() -> None: hparams = Model.load_hparams(dir_model) with torch.inference_mode(): - model_class = Model.from_model_architecture(hparams["architectures"][0]) + try: + model_class = Model.from_model_architecture(hparams["architectures"][0]) + except NotImplementedError: + logger.error(f"Model {hparams['architectures'][0]} is not supported") + sys.exit(1) + model_instance = model_class(dir_model, ftype_map[args.outtype], fname_out, args.bigendian, args.use_temp_file, args.no_lazy) logger.info("Set model parameters") From a323ec60af14a33d560df98f2cc41b4112cb4f80 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 31 May 2024 22:23:04 +0300 Subject: [PATCH 175/181] server : update js (#7670) --- examples/server/public/index.js | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/server/public/index.js b/examples/server/public/index.js index 695aec256..670960939 100644 --- a/examples/server/public/index.js +++ b/examples/server/public/index.js @@ -1 +1 @@ -const t=Symbol.for("preact-signals");function n(){if(r>1){r--;return}let t,n=!1;while(void 0!==i){let _=i;i=void 0;u++;while(void 0!==_){const i=_.o;_.o=void 0;_.f&=-3;if(!(8&_.f)&&h(_))try{_.c()}catch(e){if(!n){t=e;n=!0}}_=i}}u=0;r--;if(n)throw t}function e(t){if(r>0)return t();r++;try{return t()}finally{n()}}let _,i;function o(t){const n=_;_=void 0;try{return t()}finally{_=n}}let r=0,u=0,l=0;function s(t){if(void 0===_)return;let n=t.n;if(void 0===n||n.t!==_){n={i:0,S:t,p:_.s,n:void 0,t:_,e:void 0,x:void 0,r:n};if(void 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9b596417af11c9ac44fcae0fcfbc6f3665089083 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 1 Jun 2024 08:44:14 +0200 Subject: [PATCH 176/181] CUDA: quantized KV support for FA vec (#7527) * CUDA: quantized KV support for FA vec * try CI fix * fix commented-out kernel variants * add q8_0 q4_0 tests * fix nwarps > batch size * split fattn compile via extern templates * fix flake8 * fix metal tests * fix cmake * make generate_cu_files.py executable * add autogenerated .cu files * fix AMD * error if type_v != FP16 and not flash_attn * remove obsolete code --- CMakeLists.txt | 30 + Makefile | 21 +- README.md | 1 + ggml-cuda.cu | 8 +- ggml-cuda/fattn-common.cuh | 539 ++++++++++- ggml-cuda/fattn-tile-f16.cu | 3 + ggml-cuda/fattn-tile-f32.cu | 3 + ggml-cuda/fattn-vec-f16.cu | 330 ------- ggml-cuda/fattn-vec-f16.cuh | 394 +++++++- ggml-cuda/fattn-vec-f32.cu | 279 ------ ggml-cuda/fattn-vec-f32.cuh | 375 +++++++- ggml-cuda/fattn-wmma-f16.cuh | 490 ++++++++++ ggml-cuda/fattn.cu | 849 ++++++------------ ggml-cuda/mmq.cu | 4 +- .../fattn-vec-f16-instance-hs128-f16-f16.cu | 5 + .../fattn-vec-f16-instance-hs128-f16-q4_0.cu | 5 + .../fattn-vec-f16-instance-hs128-f16-q4_1.cu | 5 + .../fattn-vec-f16-instance-hs128-f16-q5_0.cu | 5 + .../fattn-vec-f16-instance-hs128-f16-q5_1.cu | 5 + .../fattn-vec-f16-instance-hs128-f16-q8_0.cu | 5 + .../fattn-vec-f16-instance-hs128-q4_0-f16.cu | 5 + .../fattn-vec-f16-instance-hs128-q4_0-q4_0.cu | 5 + .../fattn-vec-f16-instance-hs128-q4_0-q4_1.cu | 5 + .../fattn-vec-f16-instance-hs128-q4_0-q5_0.cu | 5 + .../fattn-vec-f16-instance-hs128-q4_0-q5_1.cu | 5 + .../fattn-vec-f16-instance-hs128-q4_0-q8_0.cu | 5 + .../fattn-vec-f16-instance-hs128-q4_1-f16.cu | 5 + .../fattn-vec-f16-instance-hs128-q4_1-q4_0.cu | 5 + .../fattn-vec-f16-instance-hs128-q4_1-q4_1.cu | 5 + .../fattn-vec-f16-instance-hs128-q4_1-q5_0.cu | 5 + .../fattn-vec-f16-instance-hs128-q4_1-q5_1.cu | 5 + .../fattn-vec-f16-instance-hs128-q4_1-q8_0.cu | 5 + .../fattn-vec-f16-instance-hs128-q5_0-f16.cu | 5 + .../fattn-vec-f16-instance-hs128-q5_0-q4_0.cu | 5 + .../fattn-vec-f16-instance-hs128-q5_0-q4_1.cu | 5 + .../fattn-vec-f16-instance-hs128-q5_0-q5_0.cu | 5 + .../fattn-vec-f16-instance-hs128-q5_0-q5_1.cu | 5 + .../fattn-vec-f16-instance-hs128-q5_0-q8_0.cu | 5 + .../fattn-vec-f16-instance-hs128-q5_1-f16.cu | 5 + .../fattn-vec-f16-instance-hs128-q5_1-q4_0.cu | 5 + .../fattn-vec-f16-instance-hs128-q5_1-q4_1.cu | 5 + .../fattn-vec-f16-instance-hs128-q5_1-q5_0.cu | 5 + .../fattn-vec-f16-instance-hs128-q5_1-q5_1.cu | 5 + .../fattn-vec-f16-instance-hs128-q5_1-q8_0.cu | 5 + .../fattn-vec-f16-instance-hs128-q8_0-f16.cu | 5 + .../fattn-vec-f16-instance-hs128-q8_0-q4_0.cu | 5 + .../fattn-vec-f16-instance-hs128-q8_0-q4_1.cu | 5 + .../fattn-vec-f16-instance-hs128-q8_0-q5_0.cu | 5 + .../fattn-vec-f16-instance-hs128-q8_0-q5_1.cu | 5 + .../fattn-vec-f16-instance-hs128-q8_0-q8_0.cu | 5 + .../fattn-vec-f16-instance-hs256-f16-f16.cu | 5 + .../fattn-vec-f16-instance-hs64-f16-f16.cu | 5 + .../fattn-vec-f16-instance-hs64-f16-q4_0.cu | 5 + .../fattn-vec-f16-instance-hs64-f16-q4_1.cu | 5 + .../fattn-vec-f16-instance-hs64-f16-q5_0.cu | 5 + .../fattn-vec-f16-instance-hs64-f16-q5_1.cu | 5 + .../fattn-vec-f16-instance-hs64-f16-q8_0.cu | 5 + .../fattn-vec-f32-instance-hs128-f16-f16.cu | 5 + .../fattn-vec-f32-instance-hs128-f16-q4_0.cu | 5 + .../fattn-vec-f32-instance-hs128-f16-q4_1.cu | 5 + .../fattn-vec-f32-instance-hs128-f16-q5_0.cu | 5 + .../fattn-vec-f32-instance-hs128-f16-q5_1.cu | 5 + .../fattn-vec-f32-instance-hs128-f16-q8_0.cu | 5 + .../fattn-vec-f32-instance-hs128-q4_0-f16.cu | 5 + .../fattn-vec-f32-instance-hs128-q4_0-q4_0.cu | 5 + .../fattn-vec-f32-instance-hs128-q4_0-q4_1.cu | 5 + .../fattn-vec-f32-instance-hs128-q4_0-q5_0.cu | 5 + .../fattn-vec-f32-instance-hs128-q4_0-q5_1.cu | 5 + .../fattn-vec-f32-instance-hs128-q4_0-q8_0.cu | 5 + .../fattn-vec-f32-instance-hs128-q4_1-f16.cu | 5 + .../fattn-vec-f32-instance-hs128-q4_1-q4_0.cu | 5 + .../fattn-vec-f32-instance-hs128-q4_1-q4_1.cu | 5 + .../fattn-vec-f32-instance-hs128-q4_1-q5_0.cu | 5 + .../fattn-vec-f32-instance-hs128-q4_1-q5_1.cu | 5 + .../fattn-vec-f32-instance-hs128-q4_1-q8_0.cu | 5 + .../fattn-vec-f32-instance-hs128-q5_0-f16.cu | 5 + .../fattn-vec-f32-instance-hs128-q5_0-q4_0.cu | 5 + .../fattn-vec-f32-instance-hs128-q5_0-q4_1.cu | 5 + .../fattn-vec-f32-instance-hs128-q5_0-q5_0.cu | 5 + .../fattn-vec-f32-instance-hs128-q5_0-q5_1.cu | 5 + .../fattn-vec-f32-instance-hs128-q5_0-q8_0.cu | 5 + .../fattn-vec-f32-instance-hs128-q5_1-f16.cu | 5 + .../fattn-vec-f32-instance-hs128-q5_1-q4_0.cu | 5 + .../fattn-vec-f32-instance-hs128-q5_1-q4_1.cu | 5 + .../fattn-vec-f32-instance-hs128-q5_1-q5_0.cu | 5 + .../fattn-vec-f32-instance-hs128-q5_1-q5_1.cu | 5 + .../fattn-vec-f32-instance-hs128-q5_1-q8_0.cu | 5 + .../fattn-vec-f32-instance-hs128-q8_0-f16.cu | 5 + .../fattn-vec-f32-instance-hs128-q8_0-q4_0.cu | 5 + .../fattn-vec-f32-instance-hs128-q8_0-q4_1.cu | 5 + .../fattn-vec-f32-instance-hs128-q8_0-q5_0.cu | 5 + .../fattn-vec-f32-instance-hs128-q8_0-q5_1.cu | 5 + .../fattn-vec-f32-instance-hs128-q8_0-q8_0.cu | 5 + .../fattn-vec-f32-instance-hs256-f16-f16.cu | 5 + .../fattn-vec-f32-instance-hs64-f16-f16.cu | 5 + .../fattn-vec-f32-instance-hs64-f16-q4_0.cu | 5 + .../fattn-vec-f32-instance-hs64-f16-q4_1.cu | 5 + .../fattn-vec-f32-instance-hs64-f16-q5_0.cu | 5 + .../fattn-vec-f32-instance-hs64-f16-q5_1.cu | 5 + .../fattn-vec-f32-instance-hs64-f16-q8_0.cu | 5 + .../fattn-wmma-f16-instance-kqfloat-cpb16.cu | 10 + .../fattn-wmma-f16-instance-kqfloat-cpb32.cu | 9 + .../fattn-wmma-f16-instance-kqhalf-cpb16.cu | 10 + .../fattn-wmma-f16-instance-kqhalf-cpb32.cu | 10 + .../fattn-wmma-f16-instance-kqhalf-cpb8.cu | 8 + .../template-instances/generate_cu_files.py | 59 ++ ggml-cuda/vecdotq.cuh | 8 +- ggml-metal.m | 6 + llama.cpp | 5 + tests/test-backend-ops.cpp | 16 +- 110 files changed, 2697 insertions(+), 1200 deletions(-) delete mode 100644 ggml-cuda/fattn-vec-f16.cu delete mode 100644 ggml-cuda/fattn-vec-f32.cu create mode 100644 ggml-cuda/fattn-wmma-f16.cuh create mode 100644 ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-f16.cu create mode 100644 ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_0.cu create mode 100644 ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_1.cu create mode 100644 ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_0.cu create mode 100644 ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_1.cu create mode 100644 ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q8_0.cu create mode 100644 ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-f16.cu create mode 100644 ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_0.cu create mode 100644 ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_1.cu create mode 100644 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ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_1.cu create mode 100644 ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_0.cu create mode 100644 ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_1.cu create mode 100644 ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q8_0.cu create mode 100644 ggml-cuda/template-instances/fattn-vec-f32-instance-hs256-f16-f16.cu create mode 100644 ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-f16.cu create mode 100644 ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_0.cu create mode 100644 ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_1.cu create mode 100644 ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_0.cu create mode 100644 ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_1.cu create mode 100644 ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q8_0.cu create mode 100644 ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb16.cu create mode 100644 ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb32.cu create mode 100644 ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb16.cu create mode 100644 ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb32.cu create mode 100644 ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb8.cu create mode 100755 ggml-cuda/template-instances/generate_cu_files.py diff --git a/CMakeLists.txt b/CMakeLists.txt index 60cf7bdc4..52b392a13 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -106,6 +106,7 @@ set(LLAMA_CUDA_PEER_MAX_BATCH_SIZE "128" CACHE STRING "llama: max. batch size for using peer access") option(LLAMA_CUDA_NO_PEER_COPY "llama: do not use peer to peer copies" OFF) option(LLAMA_CUDA_NO_VMM "llama: do not try to use CUDA VMM" OFF) +option(LLAMA_CUDA_FA_ALL_QUANTS "llama: compile all quants for FlashAttention" OFF) option(LLAMA_CURL "llama: use libcurl to download model from an URL" OFF) option(LLAMA_HIPBLAS "llama: use hipBLAS" OFF) @@ -402,6 +403,8 @@ if (LLAMA_CUDA) file(GLOB GGML_SOURCES_CUDA "ggml-cuda/*.cu") list(APPEND GGML_SOURCES_CUDA "ggml-cuda.cu") + file(GLOB SRCS "ggml-cuda/template-instances/fattn-wmma*.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) add_compile_definitions(GGML_USE_CUDA) add_compile_definitions(GGML_CUDA_USE_GRAPHS) @@ -427,6 +430,18 @@ if (LLAMA_CUDA) if (LLAMA_CUDA_NO_PEER_COPY) add_compile_definitions(GGML_CUDA_NO_PEER_COPY) endif() + if (LLAMA_CUDA_FA_ALL_QUANTS) + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS) + else() + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*f16-f16.cu") + list(APPEND GGML_SOURCES_CUDA ${SRCS}) + endif() if (LLAMA_STATIC) if (WIN32) @@ -571,6 +586,8 @@ if (LLAMA_HIPBLAS) file(GLOB GGML_SOURCES_ROCM "ggml-cuda/*.cu") list(APPEND GGML_SOURCES_ROCM "ggml-cuda.cu") + file(GLOB SRCS "ggml-cuda/template-instances/fattn-wmma*.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) add_compile_definitions(GGML_USE_HIPBLAS GGML_USE_CUDA) @@ -590,6 +607,19 @@ if (LLAMA_HIPBLAS) add_compile_definitions(GGML_CUDA_NO_PEER_COPY) endif() + if (LLAMA_CUDA_FA_ALL_QUANTS) + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) + add_compile_definitions(GGML_CUDA_FA_ALL_QUANTS) + else() + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) + file(GLOB SRCS "ggml-cuda/template-instances/fattn-vec*f16-f16.cu") + list(APPEND GGML_SOURCES_ROCM ${SRCS}) + endif() + add_compile_definitions(GGML_CUDA_DMMV_X=${LLAMA_CUDA_DMMV_X}) add_compile_definitions(GGML_CUDA_MMV_Y=${LLAMA_CUDA_MMV_Y}) add_compile_definitions(K_QUANTS_PER_ITERATION=${LLAMA_CUDA_KQUANTS_ITER}) diff --git a/Makefile b/Makefile index aced0e370..dfb3bb2cd 100644 --- a/Makefile +++ b/Makefile @@ -421,6 +421,15 @@ ifdef LLAMA_CUBLAS LLAMA_CUDA := 1 endif +OBJS_CUDA_TEMP_INST = $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-wmma*.cu)) +ifdef LLAMA_CUDA_FA_ALL_QUANTS + OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*.cu)) +else + OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*q4_0-q4_0.cu)) + OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*q8_0-q8_0.cu)) + OBJS_CUDA_TEMP_INST += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/template-instances/fattn-vec*f16-f16.cu)) +endif # LLAMA_CUDA_FA_ALL_QUANTS + ifdef LLAMA_CUDA ifneq ('', '$(wildcard /opt/cuda)') CUDA_PATH ?= /opt/cuda @@ -431,6 +440,7 @@ ifdef LLAMA_CUDA MK_LDFLAGS += -lcuda -lcublas -lculibos -lcudart -lcublasLt -lpthread -ldl -lrt -L$(CUDA_PATH)/lib64 -L/usr/lib64 -L$(CUDA_PATH)/targets/$(UNAME_M)-linux/lib -L/usr/lib/wsl/lib OBJS += ggml-cuda.o OBJS += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu)) + OBJS += $(OBJS_CUDA_TEMP_INST) MK_NVCCFLAGS += -use_fast_math ifdef LLAMA_FATAL_WARNINGS MK_NVCCFLAGS += -Werror all-warnings @@ -493,7 +503,10 @@ ifdef LLAMA_CUDA_NO_PEER_COPY endif # LLAMA_CUDA_NO_PEER_COPY ifdef LLAMA_CUDA_CCBIN MK_NVCCFLAGS += -ccbin $(LLAMA_CUDA_CCBIN) -endif +endif # LLAMA_CUDA_CCBIN +ifdef LLAMA_CUDA_FA_ALL_QUANTS + MK_NVCCFLAGS += -DGGML_CUDA_FA_ALL_QUANTS +endif # LLAMA_CUDA_FA_ALL_QUANTS ifdef JETSON_EOL_MODULE_DETECT define NVCC_COMPILE @@ -505,7 +518,7 @@ define NVCC_COMPILE endef # NVCC_COMPILE endif # JETSON_EOL_MODULE_DETECT -ggml-cuda/%.o: ggml-cuda/%.cu ggml-cuda/%.cuh ggml.h ggml-common.h ggml-cuda/common.cuh +ggml-cuda/%.o: ggml-cuda/%.cu ggml.h ggml-common.h ggml-cuda/common.cuh $(NVCC_COMPILE) ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh) @@ -585,11 +598,12 @@ ifdef LLAMA_CUDA_NO_PEER_COPY endif # LLAMA_CUDA_NO_PEER_COPY OBJS += ggml-cuda.o OBJS += $(patsubst %.cu,%.o,$(wildcard ggml-cuda/*.cu)) + OBJS += $(OBJS_CUDA_TEMP_INST) ggml-cuda.o: ggml-cuda.cu ggml-cuda.h ggml.h ggml-backend.h ggml-backend-impl.h ggml-common.h $(wildcard ggml-cuda/*.cuh) $(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $< -ggml-cuda/%.o: ggml-cuda/%.cu ggml-cuda/%.cuh ggml.h ggml-common.h ggml-cuda/common.cuh +ggml-cuda/%.o: ggml-cuda/%.cu ggml.h ggml-common.h ggml-cuda/common.cuh $(HIPCC) $(CXXFLAGS) $(HIPFLAGS) -x hip -c -o $@ $< endif # LLAMA_HIPBLAS @@ -749,6 +763,7 @@ libllama.a: llama.o ggml.o $(OBJS) $(COMMON_DEPS) clean: rm -vrf *.o tests/*.o *.so *.a *.dll benchmark-matmult lookup-create lookup-merge lookup-stats common/build-info.cpp *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) rm -vrf ggml-cuda/*.o + rm -vrf ggml-cuda/template-instances/*.o find examples pocs -type f -name "*.o" -delete # diff --git a/README.md b/README.md index e8bf1e246..4791f84af 100644 --- a/README.md +++ b/README.md @@ -501,6 +501,7 @@ Building the program with BLAS support may lead to some performance improvements | LLAMA_CUDA_F16 | Boolean | false | If enabled, use half-precision floating point arithmetic for the CUDA dequantization + mul mat vec kernels and for the q4_1 and q5_1 matrix matrix multiplication kernels. Can improve performance on relatively recent GPUs. | | LLAMA_CUDA_KQUANTS_ITER | 1 or 2 | 2 | Number of values processed per iteration and per CUDA thread for Q2_K and Q6_K quantization formats. Setting this value to 1 can improve performance for slow GPUs. | | LLAMA_CUDA_PEER_MAX_BATCH_SIZE | Positive integer | 128 | Maximum batch size for which to enable peer access between multiple GPUs. Peer access requires either Linux or NVLink. When using NVLink enabling peer access for larger batch sizes is potentially beneficial. | + | LLAMA_CUDA_FA_ALL_QUANTS | Boolean | false | Compile support for all KV cache quantization type (combinations) for the FlashAttention CUDA kernels. More fine-grained control over KV cache size but compilation takes much longer. | - #### hipBLAS diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 1172f7b2f..daaa0cd6a 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -2905,10 +2905,14 @@ GGML_CALL static bool ggml_backend_cuda_supports_op(ggml_backend_t backend, cons #if defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) return op->src[0]->ne[0] == 64 || op->src[0]->ne[0] == 128; #else - if (op->src[0]->ne[0] == 64 || op->src[0]->ne[0] == 128) { + if (op->src[0]->ne[0] == 128) { return true; } - return ggml_cuda_info().devices[cuda_ctx->device].cc >= CC_VOLTA; + if (op->src[0]->ne[0] == 64 && op->src[1]->type == GGML_TYPE_F16) { + return true; + } + return ggml_cuda_info().devices[cuda_ctx->device].cc >= CC_VOLTA && + op->src[1]->type == GGML_TYPE_F16 && op->src[2]->type == GGML_TYPE_F16; #endif // defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__) default: return false; diff --git a/ggml-cuda/fattn-common.cuh b/ggml-cuda/fattn-common.cuh index 1dd519bde..4bf03a49f 100644 --- a/ggml-cuda/fattn-common.cuh +++ b/ggml-cuda/fattn-common.cuh @@ -1,4 +1,7 @@ +#pragma once + #include "common.cuh" +#include "vecdotq.cuh" #include @@ -34,11 +37,523 @@ typedef void (* fattn_kernel_t)( const int nb11, const int nb12, const int nb13, + const int nb21, + const int nb22, + const int nb23, const int ne0, const int ne1, const int ne2, const int ne3); +typedef half (*vec_dot_KQ_f16_t)( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds); +typedef float (*vec_dot_KQ_f32_t)( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds); + +template +static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_0( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { +#if __CUDA_ARCH__ > MIN_CC_DP4A + + const block_q4_0 * K_q4_0 = (const block_q4_0 *) K_c; + GGML_UNUSED(Q_v); + + half sum = 0.0f; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const int ib = k_KQ / QI8_1; + const int iqs4 = k_KQ % QI4_0; + const int shift = k_KQ & (QI8_1/2); + + const int v = (get_int_from_uint8(K_q4_0[ib].qs, iqs4) >> shift) & 0x0F0F0F0F; + const int u = Q_q8[k_KQ_0/WARP_SIZE]; + + const int sumi = __dp4a(v, u, 0); + +#if FP16_AVAILABLE + if (std::is_same::value) { + const half2 * Q_ds = (const half2 *) Q_ds_v; + + const half2 sum2 = __half2half2(K_q4_0[ib].d) * Q_ds[k_KQ_0/WARP_SIZE]; + sum += (T) (((half) sumi)*__low2half(sum2) - __high2half(sum2) /* *8/QI8_1 == 1 */); + } else +#endif // FP16_AVAILABLE + { + const float2 * Q_ds = (const float2 *) Q_ds_v; + + sum += (T) (__half2float(K_q4_0[ib].d) * (sumi*Q_ds[k_KQ_0/WARP_SIZE].x - (8/QI8_1)*Q_ds[k_KQ_0/WARP_SIZE].y)); + } + } + + return sum; +#else + GGML_UNUSED(K_c); + GGML_UNUSED(Q_v); + GGML_UNUSED(Q_q8); + GGML_UNUSED(Q_ds_v); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ > MIN_CC_DP4A +} + +template +static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_1( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { +#if __CUDA_ARCH__ > MIN_CC_DP4A + + const block_q4_1 * K_q4_1 = (const block_q4_1 *) K_c; + GGML_UNUSED(Q_v); + + T sum = 0.0f; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const int ib = k_KQ / QI8_1; + const int iqs4 = k_KQ % QI4_1; + const int shift = k_KQ & (QI8_1/2); + + const int v = (get_int_from_uint8_aligned(K_q4_1[ib].qs, iqs4) >> shift) & 0x0F0F0F0F; + const int u = Q_q8[k_KQ_0/WARP_SIZE]; + + const int sumi = __dp4a(v, u, 0); + +#if FP16_AVAILABLE + if (std::is_same::value) { + const half2 * Q_ds = (const half2 *) Q_ds_v; + + const half2 d4d8_m4s8 = K_q4_1[ib].dm * Q_ds[k_KQ_0/WARP_SIZE]; + const half2 sumid4d8_m4s8scaled = d4d8_m4s8 * make_half2(sumi, 1.0f/QI8_1); + sum += (T) (__low2half(sumid4d8_m4s8scaled) + __high2half(sumid4d8_m4s8scaled)); + } else +#endif // FP16_AVAILABLE + { + const float2 * Q_ds = (const float2 *) Q_ds_v; + + const float sumid4d8 = __low2float(K_q4_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].x * sumi; + const float m4s8scaled = __high2float(K_q4_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].y / QI8_1; + + sum += (T) (sumid4d8 + m4s8scaled); + } + } + + return sum; +#else + GGML_UNUSED(K_c); + GGML_UNUSED(Q_v); + GGML_UNUSED(Q_q8); + GGML_UNUSED(Q_ds_v); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ > MIN_CC_DP4A +} + +template +static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { +#if __CUDA_ARCH__ > MIN_CC_DP4A + + const block_q5_0 * K_q5_0 = (const block_q5_0 *) K_c; + GGML_UNUSED(Q_v); + + T sum = 0.0f; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const int ib = k_KQ / QI8_1; + const int iqs4 = k_KQ % QI5_0; + const int iqs8 = k_KQ % QI8_1; + const int shift = k_KQ & (QI8_1/2); + + int v = (get_int_from_uint8(K_q5_0[ib].qs, iqs4) >> shift) & 0x0F0F0F0F; + const int vh = get_int_from_uint8(K_q5_0[ib].qh, 0) >> (iqs8 * QI5_0); + v |= (vh << 4) & 0x00000010; // 0 -> 4 + v |= (vh << 11) & 0x00001000; // 1 -> 12 + v |= (vh << 18) & 0x00100000; // 2 -> 20 + v |= (vh << 25) & 0x10000000; // 3 -> 28 + + const int u = Q_q8[k_KQ_0/WARP_SIZE]; + + const int sumi = __dp4a(v, u, 0); + +#if FP16_AVAILABLE + if (std::is_same::value) { + const half2 * Q_ds = (const half2 *) Q_ds_v; + + const half2 sum2 = __half2half2(K_q5_0[ib].d) * Q_ds[k_KQ_0/WARP_SIZE]; + sum += (T) (((half) sumi)*__low2half(sum2) - __high2half(sum2)*__float2half(2.0f)) /* *16/QI8_1 == 2 */; + } else +#endif // FP16_AVAILABLE + { + const float2 * Q_ds = (const float2 *) Q_ds_v; + + sum += (T) (__half2float(K_q5_0[ib].d) * (sumi*Q_ds[k_KQ_0/WARP_SIZE].x - (16/QI8_1)*Q_ds[k_KQ_0/WARP_SIZE].y)); + } + } + + return sum; +#else + GGML_UNUSED(K_c); + GGML_UNUSED(Q_v); + GGML_UNUSED(Q_q8); + GGML_UNUSED(Q_ds_v); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ > MIN_CC_DP4A +} + +template +static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { +#if __CUDA_ARCH__ > MIN_CC_DP4A + + const block_q5_1 * K_q5_1 = (const block_q5_1 *) K_c; + GGML_UNUSED(Q_v); + + T sum = 0.0f; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const int ib = k_KQ / QI8_1; + const int iqs4 = k_KQ % QI5_1; + const int iqs8 = k_KQ % QI8_1; + const int shift = k_KQ & (QI8_1/2); + + int v = (get_int_from_uint8(K_q5_1[ib].qs, iqs4) >> shift) & 0x0F0F0F0F; + const int vh = get_int_from_uint8(K_q5_1[ib].qh, 0) >> (iqs8 * QI5_1); + v |= (vh << 4) & 0x00000010; // 0 -> 4 + v |= (vh << 11) & 0x00001000; // 1 -> 12 + v |= (vh << 18) & 0x00100000; // 2 -> 20 + v |= (vh << 25) & 0x10000000; // 3 -> 28 + + const int u = Q_q8[k_KQ_0/WARP_SIZE]; + + const int sumi = __dp4a(v, u, 0); + +#if FP16_AVAILABLE + if (std::is_same::value) { + const half2 * Q_ds = (const half2 *) Q_ds_v; + + const half2 d5d8_m5s8 = K_q5_1[ib].dm * Q_ds[k_KQ_0/WARP_SIZE]; + const half2 sumid5d8_m5s8scaled = d5d8_m5s8 * make_half2(sumi, 1.0f/QI8_1); + sum += (T) (__low2half(sumid5d8_m5s8scaled) + __high2half(sumid5d8_m5s8scaled)); + } else +#endif // FP16_AVAILABLE + { + const float2 * Q_ds = (const float2 *) Q_ds_v; + + const float sumid5d8 = __low2float(K_q5_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].x * sumi; + const float m5s8scaled = __high2float(K_q5_1[ib].dm)*Q_ds[k_KQ_0/WARP_SIZE].y / QI8_1; + + sum += (T) (sumid5d8 + m5s8scaled); + } + } + + return sum; +#else + GGML_UNUSED(K_c); + GGML_UNUSED(Q_v); + GGML_UNUSED(Q_q8); + GGML_UNUSED(Q_ds_v); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ > MIN_CC_DP4A +} + +template +static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q8_0( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { +#if __CUDA_ARCH__ > MIN_CC_DP4A + + const block_q8_0 * K_q8_0 = (const block_q8_0 *) K_c; + GGML_UNUSED(Q_v); + + T sum = 0.0f; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/sizeof(int); k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const int ib = k_KQ / QI8_0; + const int iqs = k_KQ % QI8_0; + + const int v = get_int_from_int8(K_q8_0[ib].qs, iqs); + + T Q_d; + if (std::is_same::value) { + const half2 * Q_ds = (const half2 *) Q_ds_v; + Q_d = __low2half(Q_ds[k_KQ_0/WARP_SIZE]); + } else { + const float2 * Q_ds = (const float2 *) Q_ds_v; + Q_d = Q_ds[k_KQ_0/WARP_SIZE].x; + } + + sum += vec_dot_q8_0_q8_1_impl(&v, &Q_q8[k_KQ_0/WARP_SIZE], K_q8_0[ib].d, Q_d); + } + + return sum; +#else + GGML_UNUSED(K_c); + GGML_UNUSED(Q_v); + GGML_UNUSED(Q_q8); + GGML_UNUSED(Q_ds_v); + NO_DEVICE_CODE; +#endif // __CUDA_ARCH__ > MIN_CC_DP4A +} + +template +static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_f16( + const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8 , const void * __restrict__ Q_ds_v) { + + const half2 * K_h2 = (const half2 *) K_c; + GGML_UNUSED(Q_q8); + GGML_UNUSED(Q_ds_v); + +#if FP16_AVAILABLE + if (std::is_same::value) { + const half2 * Q_h2 = (const half2 *) Q_v; + + half2 sum2 = make_half2(0.0f, 0.0f); + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const half2 K_ik = K_h2[k_KQ]; + sum2 += K_ik * Q_h2[k_KQ_0/WARP_SIZE]; + } + + return __low2half(sum2) + __high2half(sum2); + } +#endif // FP16_AVAILABLE + + const float2 * Q_f2 = (const float2 *) Q_v; + + float sum = 0.0f; + +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) { + const int k_KQ = k_KQ_0 + threadIdx.x; + + const half2 K_ik = K_h2[k_KQ]; + sum += __low2float(K_ik) * Q_f2[k_KQ_0/WARP_SIZE].x; + sum += __high2float(K_ik) * Q_f2[k_KQ_0/WARP_SIZE].y; + } + + return sum; +} + +template +static __device__ __forceinline__ void quantize_q8_1_to_shared( + const float * __restrict__ x, const float scale, int * __restrict__ yq32, void * __restrict__ yds) { + + float vals[sizeof(int)] = {0.0f}; +#pragma unroll + for (int l = 0; l < sizeof(int); ++l) { + vals[l] = scale * x[4*threadIdx.x + l]; + } + + float amax = fabsf(vals[0]); + float sum = vals[0]; +#pragma unroll + for (int l = 1; l < sizeof(int); ++l) { + amax = fmaxf(amax, fabsf(vals[l])); + sum += vals[l]; + } +#pragma unroll + for (int mask = QI8_1/2; mask > 0; mask >>= 1) { + amax = fmaxf(amax, __shfl_xor_sync(0xFFFFFFFF, amax, mask, 32)); + sum += __shfl_xor_sync(0xFFFFFFFF, sum, mask, 32); + } + + const float d = amax / 127; + int q32 = 0; + int8_t * q8 = (int8_t *) &q32; + + if (d != 0.0f) { +#pragma unroll + for (int l = 0; l < sizeof(int); ++l) { + q8[l] = roundf(vals[l] / d); + } + } + + yq32[threadIdx.x] = q32; + if (threadIdx.x % QI8_1 == 0) { + if (std::is_same::value) { + ((half2 *) yds)[threadIdx.x/QI8_1] = make_half2(d, sum); + } else { + ((float2 *) yds)[threadIdx.x/QI8_1] = make_float2(d, sum); + } + } +} + +typedef half (*dequantize_1_f16_t)(const void *, const int64_t); +typedef float (*dequantize_1_f32_t)(const void *, const int64_t); + +template +static __device__ __forceinline__ T dequantize_1_q4_0(const void * __restrict__ vx, const int64_t i) { + const block_q4_0 * x = (const block_q4_0 *) vx; + + const int64_t ib = i / QK4_0; + const int iqs = i % (QK4_0/2); + const int shift = (i % QK4_0) / (QK4_0/2); + + const T d = x[ib].d; + const int q0 = x[ib].qs[iqs]; + const int q = ((q0 >> (4*shift)) & 0x0F) - 8; + +#if FP16_AVAILABLE + if (std::is_same::value) { + return ((half) d)*((half) q); + } +#endif // FP16_AVAILABLE + + return ((float) d)*((float) q); +} + +template +static __device__ __forceinline__ T dequantize_1_q4_1(const void * __restrict__ vx, const int64_t i) { + const block_q4_1 * x = (const block_q4_1 *) vx; + + const int64_t ib = i / QK4_1; + const int iqs = i % (QK4_1/2); + const int shift = (i % QK4_1) / (QK4_1/2); + + const half2 dm = x[ib].dm; + const int q0 = x[ib].qs[iqs]; + const int q = ((q0 >> (4*shift)) & 0x0F); + +#if FP16_AVAILABLE + if (std::is_same::value) { + return __low2half(dm)*((half) q) + __high2half(dm); + } +#endif // FP16_AVAILABLE + + return __low2float(dm)*((float) q) + __high2float(dm); +} + +template +static __device__ __forceinline__ T dequantize_1_q5_0(const void * __restrict__ vx, const int64_t i) { + const block_q5_0 * x = (const block_q5_0 *) vx; + + const int64_t ib = i / QK5_0; + const int idq = i % QK5_0; + const int iqs = i % (QK5_0/2); + const int shift = (i % QK5_0) / (QK5_0/2); + + const T d = x[ib].d; + const int ql0 = x[ib].qs[iqs]; + const int qh0 = get_int_from_uint8(x[ib].qh, 0); + const int ql = ((ql0 >> (4*shift)) & 0x0F); + const int qh = ((qh0 >> idq) << 4) & 0x10; + const int q = (ql | qh) - 16; + +#if FP16_AVAILABLE + if (std::is_same::value) { + return ((half) d)*((half) q); + } +#endif // FP16_AVAILABLE + + return ((float) d)*((float) q); +} + +template +static __device__ __forceinline__ T dequantize_1_q5_1(const void * __restrict__ vx, const int64_t i) { + const block_q5_1 * x = (const block_q5_1 *) vx; + + const int64_t ib = i / QK5_1; + const int idq = i % QK5_1; + const int iqs = i % (QK5_1/2); + const int shift = (i % QK5_1) / (QK5_1/2); + + const half2 dm = x[ib].dm; + const int ql0 = x[ib].qs[iqs]; + const int qh0 = get_int_from_uint8_aligned(x[ib].qh, 0); + const int ql = ((ql0 >> (4*shift)) & 0x0F); + const int qh = ((qh0 >> idq) << 4) & 0x10; + const int q = (ql | qh); + +#if FP16_AVAILABLE + if (std::is_same::value) { + return __low2half(dm)*((half) q) + __high2half(dm); + } +#endif // FP16_AVAILABLE + + return __low2float(dm)*((float) q) + __high2float(dm); +} + +template +static __device__ __forceinline__ T dequantize_1_q8_0(const void * __restrict__ vx, const int64_t i) { + const block_q8_0 * x = (const block_q8_0 *) vx; + + const int64_t ib = i / QK8_0; + const int iqs = i % QK8_0; + + const T d = x[ib].d; + const int q = x[ib].qs[iqs]; + +#if FP16_AVAILABLE + if (std::is_same::value) { + return ((half) d)*((half) q); + } +#endif // FP16_AVAILABLE + + return ((float) d)*((float) q); +} + +template +static __device__ __forceinline__ T dequantize_1_f16(const void * __restrict__ vx, const int64_t i) { + const half * x = (const half *) vx; + + return x[i]; +} + +template +constexpr __device__ vec_dot_KQ_f16_t get_vec_dot_KQ_f16(ggml_type type_K) { + return type_K == GGML_TYPE_Q4_0 ? vec_dot_fattn_vec_KQ_q4_0 : + type_K == GGML_TYPE_Q4_1 ? vec_dot_fattn_vec_KQ_q4_1 : + type_K == GGML_TYPE_Q5_0 ? vec_dot_fattn_vec_KQ_q5_0 : + type_K == GGML_TYPE_Q5_1 ? vec_dot_fattn_vec_KQ_q5_1 : + type_K == GGML_TYPE_Q8_0 ? vec_dot_fattn_vec_KQ_q8_0 : + type_K == GGML_TYPE_F16 ? vec_dot_fattn_vec_KQ_f16 : + nullptr; +} + +template +constexpr __device__ vec_dot_KQ_f32_t get_vec_dot_KQ_f32(ggml_type type_K) { + return type_K == GGML_TYPE_Q4_0 ? vec_dot_fattn_vec_KQ_q4_0 : + type_K == GGML_TYPE_Q4_1 ? vec_dot_fattn_vec_KQ_q4_1 : + type_K == GGML_TYPE_Q5_0 ? vec_dot_fattn_vec_KQ_q5_0 : + type_K == GGML_TYPE_Q5_1 ? vec_dot_fattn_vec_KQ_q5_1 : + type_K == GGML_TYPE_Q8_0 ? vec_dot_fattn_vec_KQ_q8_0 : + type_K == GGML_TYPE_F16 ? vec_dot_fattn_vec_KQ_f16 : + nullptr; +} + +constexpr __device__ dequantize_1_f16_t get_dequantize_1_f16(ggml_type type_V) { + return type_V == GGML_TYPE_Q4_0 ? dequantize_1_q4_0 : + type_V == GGML_TYPE_Q4_1 ? dequantize_1_q4_1 : + type_V == GGML_TYPE_Q5_0 ? dequantize_1_q5_0 : + type_V == GGML_TYPE_Q5_1 ? dequantize_1_q5_1 : + type_V == GGML_TYPE_Q8_0 ? dequantize_1_q8_0 : + type_V == GGML_TYPE_F16 ? dequantize_1_f16 : + nullptr; +} + +constexpr __device__ dequantize_1_f32_t get_dequantize_1_f32(ggml_type type_V) { + return type_V == GGML_TYPE_Q4_0 ? dequantize_1_q4_0 : + type_V == GGML_TYPE_Q4_1 ? dequantize_1_q4_1 : + type_V == GGML_TYPE_Q5_0 ? dequantize_1_q5_0 : + type_V == GGML_TYPE_Q5_1 ? dequantize_1_q5_1 : + type_V == GGML_TYPE_Q8_0 ? dequantize_1_q8_0 : + type_V == GGML_TYPE_F16 ? dequantize_1_f16 : + nullptr; +} + template // D == head size #if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) __launch_bounds__(D, 1) @@ -83,6 +598,27 @@ static __global__ void flash_attn_combine_results( dst[blockIdx.y*D + tid] = VKQ_numerator / VKQ_denominator; } +static void on_no_fattn_vec_case(const int D) { + if (D == 64) { + fprintf(stderr, "Unsupported KV type combination for head_size 64.\n"); + fprintf(stderr, "By default only f16 KV cache is supported.\n"); + fprintf(stderr, "Compile with LLAMA_CUDA_FA_ALL_QUANTS for V cache quantization support.\n"); + GGML_ASSERT(false); + } else if (D == 128) { + fprintf(stderr, "Unsupported KV type combination for head_size 128.\n"); + fprintf(stderr, "Supported combinations:\n"); + fprintf(stderr, " - K == q4_0, V == q4_0, 4.50 BPV\n"); + fprintf(stderr, " - K == q8_0, V == q8_0, 8.50 BPV\n"); + fprintf(stderr, " - K == f16, V == f16, 16.00 BPV\n"); + fprintf(stderr, "Compile with LLAMA_CUDA_FA_ALL_QUANTS for all combinations of q4_0, q4_1, q5_0, q5_1, q8_0, and f16.\n"); + GGML_ASSERT(false); + } else { + fprintf(stderr, "Unsupported KV type combination for head_size 256.\n"); + fprintf(stderr, "Only f16 is supported.\n"); + GGML_ASSERT(false); + } +} + template void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kernel_t fattn_kernel, int nwarps, int cols_per_block) { const ggml_tensor * Q = dst->src[0]; @@ -94,8 +630,6 @@ void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kern ggml_tensor * KQV = dst; GGML_ASSERT(Q->type == GGML_TYPE_F32); - GGML_ASSERT(K->type == GGML_TYPE_F16); - GGML_ASSERT(V->type == GGML_TYPE_F16); GGML_ASSERT(KQV->type == GGML_TYPE_F32); GGML_ASSERT(!mask || mask->type == GGML_TYPE_F16); @@ -143,6 +677,7 @@ void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kern mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, Q->nb[1], Q->nb[2], Q->nb[3], K->nb[1], K->nb[2], K->nb[3], + V->nb[1], V->nb[2], V->nb[3], KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3] ); CUDA_CHECK(cudaGetLastError()); diff --git a/ggml-cuda/fattn-tile-f16.cu b/ggml-cuda/fattn-tile-f16.cu index cdb5eaff7..3d64a9eba 100644 --- a/ggml-cuda/fattn-tile-f16.cu +++ b/ggml-cuda/fattn-tile-f16.cu @@ -36,6 +36,9 @@ static __global__ void flash_attn_tile_ext_f16( const int nb11, const int nb12, const int nb13, + const int nb21, + const int nb22, + const int nb23, const int ne0, const int ne1, const int ne2, diff --git a/ggml-cuda/fattn-tile-f32.cu b/ggml-cuda/fattn-tile-f32.cu index 5a3de2918..61fce0a7e 100644 --- a/ggml-cuda/fattn-tile-f32.cu +++ b/ggml-cuda/fattn-tile-f32.cu @@ -36,6 +36,9 @@ static __global__ void flash_attn_tile_ext_f32( const int nb11, const int nb12, const int nb13, + const int nb21, + const int nb22, + const int nb23, const int ne0, const int ne1, const int ne2, diff --git a/ggml-cuda/fattn-vec-f16.cu b/ggml-cuda/fattn-vec-f16.cu deleted file mode 100644 index 808e8f362..000000000 --- a/ggml-cuda/fattn-vec-f16.cu +++ /dev/null @@ -1,330 +0,0 @@ -#include "common.cuh" -#include "fattn-common.cuh" -#include "fattn-vec-f16.cuh" - -template // D == head size -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -__launch_bounds__(D, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -static __global__ void flash_attn_vec_ext_f16( - const char * __restrict__ Q, - const char * __restrict__ K, - const char * __restrict__ V, - const char * __restrict__ mask, - float * __restrict__ dst, - float2 * __restrict__ dst_meta, - const float scale, - const float max_bias, - const float m0, - const float m1, - const uint32_t n_head_log2, - const int ne00, - const int ne01, - const int ne02, - const int ne03, - const int ne10, - const int ne11, - const int ne12, - const int ne13, - const int ne31, - const int nb31, - const int nb01, - const int nb02, - const int nb03, - const int nb11, - const int nb12, - const int nb13, - const int ne0, - const int ne1, - const int ne2, - const int ne3) { -#if FP16_AVAILABLE - //In this kernel Q, K, V are matrices while i, j, k are matrix indices. - - const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. - const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. - - const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. - const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0); - const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio)); - const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape - const half * maskh = (const half *) mask + ne11*ic0; - - const int stride_KV = nb11 / sizeof(half); - const int stride_KV2 = nb11 / sizeof(half2); - - const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); - const half slopeh = __float2half(slopef); - - static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); - constexpr int nwarps = D / WARP_SIZE; - const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; - __builtin_assume(tid < D); - - __shared__ half KQ[ncols*D]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - KQ[j*D + tid] = -HALF_MAX_HALF; - } - half2 * KQ2 = (half2 *) KQ; - - half kqmax[ncols]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - kqmax[j] = -HALF_MAX_HALF; - } - half kqsum[ncols] = {0.0f}; - - __shared__ half kqmax_shared[ncols][WARP_SIZE]; - __shared__ half kqsum_shared[ncols][WARP_SIZE]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - if (threadIdx.y == 0) { - kqmax_shared[j][threadIdx.x] = -HALF_MAX_HALF; - kqsum_shared[j][threadIdx.x] = 0.0f; - } - } - __syncthreads(); - - // Convert Q to half2 and store in registers: - half2 Q_h2[ncols][D/(2*WARP_SIZE)]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { - const int i = i0 + threadIdx.x; - - const float2 tmp = ncols <= 2 || ic0 + j < ne01 ? Q_f2[j*(nb01/sizeof(float2)) + i] : make_float2(0.0f, 0.0f); - Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y); - } - } - - half2 VKQ[ncols] = {{0.0f, 0.0f}}; - - const int k_start = parallel_blocks == 1 ? 0 : ip*D; - for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) { - // Calculate KQ tile and keep track of new maximum KQ values: - - // For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression, - // see https://github.com/ggerganov/llama.cpp/pull/7061 . - // Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable). - half kqmax_new = kqmax[0]; - half kqmax_new_arr[ncols]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - kqmax_new_arr[j] = kqmax[j]; - } - -#pragma unroll - for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) { - const int i_KQ = i_KQ_0 + threadIdx.y; - - if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) { - break; - } - - half2 sum2[ncols] = {{0.0f, 0.0f}}; -#pragma unroll - for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) { - const int k_KQ = k_KQ_0 + threadIdx.x; - - const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - sum2[j] += K_ik * Q_h2[j][k_KQ_0/WARP_SIZE]; - } - } - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - sum2[j] = warp_reduce_sum(sum2[j]); - half sum = __low2half(sum2[j]) + __high2half(sum2[j]); - sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f); - - if (ncols == 1) { - kqmax_new = ggml_cuda_hmax(kqmax_new, sum); - } else { - kqmax_new_arr[j] = ggml_cuda_hmax(kqmax_new_arr[j], sum); - } - - if (threadIdx.x == 0) { - KQ[j*D + i_KQ] = sum; - } - } - } - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - half kqmax_new_j = ncols == 1 ? kqmax_new : kqmax_new_arr[j]; - - kqmax_new_j = warp_reduce_max(kqmax_new_j); - if (threadIdx.x == 0) { - kqmax_shared[j][threadIdx.y] = kqmax_new_j; - } - } - - __syncthreads(); - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - half kqmax_new_j = kqmax_shared[j][threadIdx.x]; - kqmax_new_j = warp_reduce_max(kqmax_new_j); - - const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j); - kqmax[j] = kqmax_new_j; - - const half val = hexp(KQ[j*D + tid] - kqmax[j]); - kqsum[j] = kqsum[j]*KQ_max_scale + val; - KQ[j*D + tid] = val; - - VKQ[j] *= __half2half2(KQ_max_scale); - } - - __syncthreads(); - -#pragma unroll - for (int k0 = 0; k0 < D; k0 += 2) { - if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) { - break; - } - - half2 V_k; - reinterpret_cast(V_k.x) = V_h[(k_VKQ_0 + k0 + 0)*stride_KV + tid]; - reinterpret_cast(V_k.y) = V_h[(k_VKQ_0 + k0 + 1)*stride_KV + tid]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - VKQ[j] += V_k*KQ2[j*(D/2) + k0/2]; - } - } - - __syncthreads(); - } - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - kqsum[j] = warp_reduce_sum(kqsum[j]); - if (threadIdx.x == 0) { - kqsum_shared[j][threadIdx.y] = kqsum[j]; - } - } - - __syncthreads(); - -#pragma unroll - for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) { - if (ncols > 2 && ic0 + j_VKQ >= ne01) { - break; - } - - kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x]; - kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]); - - half dst_val = (__low2half(VKQ[j_VKQ]) + __high2half(VKQ[j_VKQ])); - if (parallel_blocks == 1) { - dst_val /= kqsum[j_VKQ]; - } - const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; - dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; - } - - if (parallel_blocks != 1 && tid < ncols && (ncols <= 2 || ic0 + tid < ne01)) { - dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]); - } -#else - NO_DEVICE_CODE; -#endif // FP16_AVAILABLE -} - -void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - ggml_tensor * KQV = dst; - ggml_tensor * Q = dst->src[0]; - - const int32_t precision = KQV->op_params[2]; - GGML_ASSERT(precision == GGML_PREC_DEFAULT); - - constexpr int cols_per_block = 1; - constexpr int parallel_blocks = 4; - switch (Q->ne[0]) { - case 64: { - constexpr int D = 64; - constexpr int nwarps = D/WARP_SIZE; - fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - } break; - case 128: { - constexpr int D = 128; - constexpr int nwarps = D/WARP_SIZE; - fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - } break; - case 256: { - constexpr int D = 256; - constexpr int nwarps = D/WARP_SIZE; - fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - } break; - default: - GGML_ASSERT(false); - break; - } -} - -template -void launch_fattn_vec_f16_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * Q = dst->src[0]; - switch (Q->ne[0]) { - case 64: { - constexpr int D = 64; - constexpr int nwarps = D/WARP_SIZE; - fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - } break; - case 128: { - constexpr int D = 128; - constexpr int nwarps = D/WARP_SIZE; - fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - } break; - default: { - GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); - } break; - } -} - -void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * KQV = dst; - const ggml_tensor * Q = dst->src[0]; - - const int32_t precision = KQV->op_params[2]; - GGML_ASSERT(precision == GGML_PREC_DEFAULT); - - if (Q->ne[1] == 1) { - ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); - return; - } - - if (Q->ne[1] == 2) { - constexpr int cols_per_block = 2; - constexpr int parallel_blocks = 4; - launch_fattn_vec_f16_64_128(ctx, dst); - return; - } - - if (Q->ne[1] <= 4) { - constexpr int cols_per_block = 4; - constexpr int parallel_blocks = 4; - launch_fattn_vec_f16_64_128(ctx, dst); - return; - } - - if (Q->ne[1] <= 8) { - constexpr int cols_per_block = 8; - constexpr int parallel_blocks = 4; - launch_fattn_vec_f16_64_128(ctx, dst); - return; - } - - constexpr int cols_per_block = 8; - constexpr int parallel_blocks = 1; - launch_fattn_vec_f16_64_128(ctx, dst); -} diff --git a/ggml-cuda/fattn-vec-f16.cuh b/ggml-cuda/fattn-vec-f16.cuh index c7023610a..ea4fcc972 100644 --- a/ggml-cuda/fattn-vec-f16.cuh +++ b/ggml-cuda/fattn-vec-f16.cuh @@ -1,5 +1,395 @@ #include "common.cuh" +#include "fattn-common.cuh" -void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst); +template // D == head size +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(D, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +static __global__ void flash_attn_vec_ext_f16( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const int ne00, + const int ne01, + const int ne02, + const int ne03, + const int ne10, + const int ne11, + const int ne12, + const int ne13, + const int ne31, + const int nb31, + const int nb01, + const int nb02, + const int nb03, + const int nb11, + const int nb12, + const int nb13, + const int nb21, + const int nb22, + const int nb23, + const int ne0, + const int ne1, + const int ne2, + const int ne3) { +#if FP16_AVAILABLE + //In this kernel Q, K, V are matrices while i, j, k are matrix indices. -void ggml_cuda_flash_attn_ext_vec_f16_no_mma(ggml_backend_cuda_context & ctx, ggml_tensor * dst); + constexpr vec_dot_KQ_f16_t vec_dot_KQ = get_vec_dot_KQ_f16(type_K); + constexpr bool Q_q8_1 = type_K != GGML_TYPE_F16; + constexpr dequantize_1_f16_t dequantize_1_v = get_dequantize_1_f16(type_V); + + const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. + const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. + + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + Q += nb02* blockIdx.y + nb01*ic0; + K += nb12*(blockIdx.y / gqa_ratio); + V += nb22*(blockIdx.y / gqa_ratio); + + const half * maskh = (const half *) mask + ne11*ic0; + + const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); + const half slopeh = __float2half(slopef); + + static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); + constexpr int nwarps = D / WARP_SIZE; + const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; + __builtin_assume(tid < D); + + __shared__ half KQ[ncols*D]; + half2 * KQ2 = (half2 *) KQ; + + half kqmax[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqmax[j] = -HALF_MAX_HALF; + } + half kqsum[ncols] = {0.0f}; + + __shared__ half kqmax_shared[ncols][WARP_SIZE]; + __shared__ half kqsum_shared[ncols][WARP_SIZE]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + if (threadIdx.y == 0) { + kqmax_shared[j][threadIdx.x] = -HALF_MAX_HALF; + kqsum_shared[j][threadIdx.x] = 0.0f; + } + } + __syncthreads(); + + // Convert Q to half2 (f16 K) or q8_1 (quantized K) and store in registers: + half2 Q_h2[ncols][D/(2*WARP_SIZE)]; + int Q_i32[ncols][D/(sizeof(int)*QK8_1) == 0 ? 1 : D/(sizeof(int)*QK8_1)]; + half2 Q_ds[ncols][D/QK8_1 == 0 ? 1 : D/QK8_1]; + if (Q_q8_1) { +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + if (j0 + nwarps > ncols && j >= ncols) { + break; + } + + // Reuse KQ as temporary storage for converting Q to q8_1: + int * tmp_q_i32 = (int *) &KQ[j*D]; + half2 * tmp_q_ds = (half2 *) (tmp_q_i32 + D/sizeof(int)); + + // Set memory to zero if out of bounds: + if (ncols > 2 && ic0 + j >= ne01) { +#pragma unroll + for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + tmp_q_i32[i] = 0; + } + if (threadIdx.x < D/QK8_1) { + tmp_q_ds[threadIdx.x] = make_half2(0.0f, 0.0f); + } + continue; + } + + const float * Q_f = (const float *) (Q + j*nb01); +#pragma unroll + for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) { + quantize_q8_1_to_shared(Q_f + 4*i0, scale, tmp_q_i32, tmp_q_ds); + } + } + + __syncthreads(); + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + int * tmp_q_i32 = (int *) &KQ[j*D]; + half2 * tmp_q_ds = (half2 *) (tmp_q_i32 + D/sizeof(int)); + +#pragma unroll + for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + Q_i32[j][i0/WARP_SIZE] = tmp_q_i32[i]; + Q_ds[j][i0/WARP_SIZE] = tmp_q_ds[i/QI8_1]; + } + } + + __syncthreads(); + } else { +#pragma unroll + for (int j = 0; j < ncols; ++j) { + const float2 * Q_f2_j = (const float2 *) (Q + j*nb01); + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + const float2 tmp = ncols <= 2 || ic0 + j < ne01 ? Q_f2_j[i] : make_float2(0.0f, 0.0f); + Q_h2[j][i0/WARP_SIZE] = make_half2(scale, scale) * make_half2(tmp.x, tmp.y); + } + } + } + + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + KQ[j*D + tid] = -HALF_MAX_HALF; + } + + half2 VKQ[ncols] = {{0.0f, 0.0f}}; + + const int k_start = parallel_blocks == 1 ? 0 : ip*D; + for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) { + // Calculate KQ tile and keep track of new maximum KQ values: + + // For unknown reasons using a half array of size 1 for kqmax_new causes a performance regression, + // see https://github.com/ggerganov/llama.cpp/pull/7061 . + // Therefore this variable is defined twice but only used once (so that the compiler can optimize out the unused variable). + half kqmax_new = kqmax[0]; + half kqmax_new_arr[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqmax_new_arr[j] = kqmax[j]; + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) { + const int i_KQ = i_KQ_0 + threadIdx.y; + + if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) { + break; + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + half sum = vec_dot_KQ(K + (k_VKQ_0 + i_KQ)*nb11, Q_h2[j], Q_i32[j], Q_ds[j]); + sum = warp_reduce_sum(sum); + sum += mask ? slopeh*maskh[j*ne11 + k_VKQ_0 + i_KQ] : __float2half(0.0f); + + if (ncols == 1) { + kqmax_new = ggml_cuda_hmax(kqmax_new, sum); + } else { + kqmax_new_arr[j] = ggml_cuda_hmax(kqmax_new_arr[j], sum); + } + + if (threadIdx.x == 0) { + KQ[j*D + i_KQ] = sum; + } + } + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + half kqmax_new_j = ncols == 1 ? kqmax_new : kqmax_new_arr[j]; + + kqmax_new_j = warp_reduce_max(kqmax_new_j); + if (threadIdx.x == 0) { + kqmax_shared[j][threadIdx.y] = kqmax_new_j; + } + } + + __syncthreads(); + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + half kqmax_new_j = kqmax_shared[j][threadIdx.x]; + kqmax_new_j = warp_reduce_max(kqmax_new_j); + + const half KQ_max_scale = hexp(kqmax[j] - kqmax_new_j); + kqmax[j] = kqmax_new_j; + + const half val = hexp(KQ[j*D + tid] - kqmax[j]); + kqsum[j] = kqsum[j]*KQ_max_scale + val; + KQ[j*D + tid] = val; + + VKQ[j] *= __half2half2(KQ_max_scale); + } + + __syncthreads(); + +#pragma unroll + for (int k0 = 0; k0 < D; k0 += 2) { + if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k0 >= ne11) { + break; + } + + half2 V_k; + reinterpret_cast(V_k.x) = dequantize_1_v(V + (k_VKQ_0 + k0 + 0)*nb21, tid); + reinterpret_cast(V_k.y) = dequantize_1_v(V + (k_VKQ_0 + k0 + 1)*nb21, tid); +#pragma unroll + for (int j = 0; j < ncols; ++j) { + VKQ[j] += V_k*KQ2[j*(D/2) + k0/2]; + } + } + + __syncthreads(); + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqsum[j] = warp_reduce_sum(kqsum[j]); + if (threadIdx.x == 0) { + kqsum_shared[j][threadIdx.y] = kqsum[j]; + } + } + + __syncthreads(); + +#pragma unroll + for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) { + if (ncols > 2 && ic0 + j_VKQ >= ne01) { + break; + } + + kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x]; + kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]); + + half dst_val = (__low2half(VKQ[j_VKQ]) + __high2half(VKQ[j_VKQ])); + if (parallel_blocks == 1) { + dst_val /= kqsum[j_VKQ]; + } + const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; + dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; + } + + if (parallel_blocks != 1 && tid < ncols && (ncols <= 2 || ic0 + tid < ne01)) { + dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]); + } +#else + NO_DEVICE_CODE; +#endif // FP16_AVAILABLE +} + +template +void ggml_cuda_flash_attn_ext_vec_f16_case_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); +} + +template +void ggml_cuda_flash_attn_ext_vec_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + ggml_tensor * KQV = dst; + ggml_tensor * Q = dst->src[0]; + ggml_tensor * K = dst->src[1]; + ggml_tensor * V = dst->src[2]; + + const int32_t precision = KQV->op_params[2]; + GGML_ASSERT(precision == GGML_PREC_DEFAULT); + + GGML_ASSERT(K->type == type_K); + GGML_ASSERT(V->type == type_V); + + if (Q->ne[1] == 1) { + constexpr int cols_per_block = 1; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f16_case_impl(ctx, dst); + return; + } + + if (Q->ne[1] == 2) { + constexpr int cols_per_block = 2; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f16_case_impl(ctx, dst); + return; + } + + if (Q->ne[1] <= 4) { + constexpr int cols_per_block = 4; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f16_case_impl(ctx, dst); + return; + } + + if (Q->ne[1] <= 8) { + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f16_case_impl(ctx, dst); + return; + } + + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 1; + ggml_cuda_flash_attn_ext_vec_f16_case_impl(ctx, dst); +} + +#define DECL_FATTN_VEC_F16_CASE(D, type_K, type_V) \ + template void ggml_cuda_flash_attn_ext_vec_f16_case \ + (ggml_backend_cuda_context & ctx, ggml_tensor * dst) \ + +extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16); + +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0); + +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1); + +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0); + +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1); + +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0); + +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16); + +extern DECL_FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/fattn-vec-f32.cu b/ggml-cuda/fattn-vec-f32.cu deleted file mode 100644 index b4652301b..000000000 --- a/ggml-cuda/fattn-vec-f32.cu +++ /dev/null @@ -1,279 +0,0 @@ -#include "common.cuh" -#include "fattn-common.cuh" -#include "fattn-vec-f32.cuh" - -template // D == head size -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -__launch_bounds__(D, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -static __global__ void flash_attn_vec_ext_f32( - const char * __restrict__ Q, - const char * __restrict__ K, - const char * __restrict__ V, - const char * __restrict__ mask, - float * __restrict__ dst, - float2 * __restrict__ dst_meta, - const float scale, - const float max_bias, - const float m0, - const float m1, - const uint32_t n_head_log2, - const int ne00, - const int ne01, - const int ne02, - const int ne03, - const int ne10, - const int ne11, - const int ne12, - const int ne13, - const int ne31, - const int nb31, - const int nb01, - const int nb02, - const int nb03, - const int nb11, - const int nb12, - const int nb13, - const int ne0, - const int ne1, - const int ne2, - const int ne3) { - //In this kernel Q, K, V are matrices while i, j, k are matrix indices. - - const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. - const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. - - const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. - const float2 * Q_f2 = (const float2 *) (Q + nb02* blockIdx.y + nb01*ic0); - const half2 * K_h2 = (const half2 *) (K + nb12*(blockIdx.y / gqa_ratio)); - const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape - const half * maskh = (const half *) mask + ne11*ic0; - - const int stride_KV = nb11 / sizeof(half); - const int stride_KV2 = nb11 / sizeof(half2); - - const float slope = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); - - static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); - constexpr int nwarps = D / WARP_SIZE; - const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; - __builtin_assume(tid < D); - - __shared__ float KQ[ncols*D]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - KQ[j*D + tid] = -FLT_MAX/2.0f; - } - - float kqmax[ncols]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - kqmax[j] = -FLT_MAX/2.0f; - } - float kqsum[ncols] = {0.0f}; - - __shared__ float kqmax_shared[ncols][WARP_SIZE]; - __shared__ float kqsum_shared[ncols][WARP_SIZE]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - if (threadIdx.y == 0) { - kqmax_shared[j][threadIdx.x] = -FLT_MAX/2.0f; - kqsum_shared[j][threadIdx.x] = 0.0f; - } - } - __syncthreads(); - - // Convert Q to half2 and store in registers: - float2 Q_h2[ncols][D/(2*WARP_SIZE)]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { - const int i = i0 + threadIdx.x; - - Q_h2[j][i0/WARP_SIZE] = ncols <= 2 || ic0 + j ? Q_f2[j*(nb01/sizeof(float2)) + i] : make_float2(0.0f, 0.0f); - Q_h2[j][i0/WARP_SIZE].x *= scale; - Q_h2[j][i0/WARP_SIZE].y *= scale; - } - } - - float VKQ[ncols] = {0.0f}; - - const int k_start = parallel_blocks == 1 ? 0 : ip*D; - for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) { - // Calculate KQ tile and keep track of new maximum KQ values: - - float kqmax_new_arr[ncols]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - kqmax_new_arr[j] = kqmax[j]; - } - -#pragma unroll - for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) { - const int i_KQ = i_KQ_0 + threadIdx.y; - - if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) { - break; - } - - float sum[ncols] = {0.0f}; -#pragma unroll - for (int k_KQ_0 = 0; k_KQ_0 < D/2; k_KQ_0 += WARP_SIZE) { - const int k_KQ = k_KQ_0 + threadIdx.x; - - const half2 K_ik = K_h2[(k_VKQ_0 + i_KQ)*stride_KV2 + k_KQ]; -#pragma unroll - for (int j = 0; j < ncols; ++j) { - sum[j] += __low2float(K_ik) * Q_h2[j][k_KQ_0/WARP_SIZE].x; - sum[j] += __high2float(K_ik) * Q_h2[j][k_KQ_0/WARP_SIZE].y; - } - } - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - sum[j] = warp_reduce_sum(sum[j]); - sum[j] += mask ? slope*__half2float(maskh[j*ne11 + k_VKQ_0 + i_KQ]) : 0.0f; - - kqmax_new_arr[j] = fmaxf(kqmax_new_arr[j], sum[j]); - - if (threadIdx.x == 0) { - KQ[j*D + i_KQ] = sum[j]; - } - } - } - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - float kqmax_new_j = kqmax_new_arr[j]; - - kqmax_new_j = warp_reduce_max(kqmax_new_j); - if (threadIdx.x == 0) { - kqmax_shared[j][threadIdx.y] = kqmax_new_j; - } - } - - __syncthreads(); - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - float kqmax_new_j = kqmax_shared[j][threadIdx.x]; - kqmax_new_j = warp_reduce_max(kqmax_new_j); - - const float KQ_max_scale = expf(kqmax[j] - kqmax_new_j); - kqmax[j] = kqmax_new_j; - - const float val = expf(KQ[j*D + tid] - kqmax[j]); - kqsum[j] = kqsum[j]*KQ_max_scale + val; - KQ[j*D + tid] = val; - - VKQ[j] *= KQ_max_scale; - } - - __syncthreads(); - -#pragma unroll - for (int k = 0; k < D; ++k) { - if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k >= ne11) { - break; - } - - const float V_ki = __half2float(V_h[(k_VKQ_0 + k)*stride_KV + tid]); -#pragma unroll - for (int j = 0; j < ncols; ++j) { - VKQ[j] += V_ki*KQ[j*D + k]; - } - } - - __syncthreads(); - } - -#pragma unroll - for (int j = 0; j < ncols; ++j) { - kqsum[j] = warp_reduce_sum(kqsum[j]); - if (threadIdx.x == 0) { - kqsum_shared[j][threadIdx.y] = kqsum[j]; - } - } - - __syncthreads(); - -#pragma unroll - for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) { - if (ncols > 2 && ic0 + j_VKQ >= ne01) { - break; - } - - kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x]; - kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]); - - float dst_val = VKQ[j_VKQ]; - if (parallel_blocks == 1) { - dst_val /= kqsum[j_VKQ]; - } - const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; - dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; - } - - if (parallel_blocks != 1 && tid < ncols && (ncols <= 2 || ic0 + tid < ne01)) { - dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]); - } -} - -template -void launch_fattn_vec_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * Q = dst->src[0]; - switch (Q->ne[0]) { - case 64: { - constexpr int D = 64; - constexpr int nwarps = D/WARP_SIZE; - fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - } break; - case 128: { - constexpr int D = 128; - constexpr int nwarps = D/WARP_SIZE; - fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - } break; - default: { - GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); - } break; - } -} - -void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * Q = dst->src[0]; - - if (Q->ne[1] == 1) { - constexpr int cols_per_block = 1; - constexpr int parallel_blocks = 4; - launch_fattn_vec_f32_64_128(ctx, dst); - return; - } - - if (Q->ne[1] == 2) { - constexpr int cols_per_block = 2; - constexpr int parallel_blocks = 4; - launch_fattn_vec_f32_64_128(ctx, dst); - return; - } - - if (Q->ne[1] <= 4) { - constexpr int cols_per_block = 4; - constexpr int parallel_blocks = 4; - launch_fattn_vec_f32_64_128(ctx, dst); - return; - } - - if (Q->ne[1] <= 8) { - constexpr int cols_per_block = 8; - constexpr int parallel_blocks = 4; - launch_fattn_vec_f32_64_128(ctx, dst); - return; - } - - constexpr int cols_per_block = 8; - constexpr int parallel_blocks = 1; - launch_fattn_vec_f32_64_128(ctx, dst); -} diff --git a/ggml-cuda/fattn-vec-f32.cuh b/ggml-cuda/fattn-vec-f32.cuh index 614d54ae3..3009f0f43 100644 --- a/ggml-cuda/fattn-vec-f32.cuh +++ b/ggml-cuda/fattn-vec-f32.cuh @@ -1,3 +1,376 @@ #include "common.cuh" +#include "fattn-common.cuh" -void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst); +template // D == head size +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(D, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +static __global__ void flash_attn_vec_ext_f32( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const int ne00, + const int ne01, + const int ne02, + const int ne03, + const int ne10, + const int ne11, + const int ne12, + const int ne13, + const int ne31, + const int nb31, + const int nb01, + const int nb02, + const int nb03, + const int nb11, + const int nb12, + const int nb13, + const int nb21, + const int nb22, + const int nb23, + const int ne0, + const int ne1, + const int ne2, + const int ne3) { + //In this kernel Q, K, V are matrices while i, j, k are matrix indices. + + constexpr vec_dot_KQ_f32_t vec_dot_KQ = get_vec_dot_KQ_f32(type_K); + constexpr bool Q_q8_1 = type_K != GGML_TYPE_F16; + constexpr dequantize_1_f32_t dequantize_1_v = get_dequantize_1_f32(type_V); + + const int ic0 = (blockIdx.x / parallel_blocks) * ncols; // Index of the Q/QKV column to work on. + const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. + + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + Q += nb02* blockIdx.y + nb01*ic0; + K += nb12*(blockIdx.y / gqa_ratio); + V += nb22*(blockIdx.y / gqa_ratio); // K and V have same shape + const half * maskh = (const half *) mask + ne11*ic0; + + const float slope = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); + + static_assert(D % (2*WARP_SIZE) == 0, "D not divisible by 2*WARP_SIZE == 64."); + constexpr int nwarps = D / WARP_SIZE; + const int tid = WARP_SIZE*threadIdx.y + threadIdx.x; + __builtin_assume(tid < D); + + __shared__ float KQ[ncols*D]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + KQ[j*D + tid] = -FLT_MAX/2.0f; + } + + float kqmax[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqmax[j] = -FLT_MAX/2.0f; + } + float kqsum[ncols] = {0.0f}; + + __shared__ float kqmax_shared[ncols][WARP_SIZE]; + __shared__ float kqsum_shared[ncols][WARP_SIZE]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + if (threadIdx.y == 0) { + kqmax_shared[j][threadIdx.x] = -FLT_MAX/2.0f; + kqsum_shared[j][threadIdx.x] = 0.0f; + } + } + __syncthreads(); + + // Convert Q to float2 (f16 K) or q8_1 (quantized K) and store in registers: + float2 Q_f2[ncols][D/(2*WARP_SIZE)]; + int Q_i32[ncols][D/(sizeof(int)*QK8_1) == 0 ? 1 : D >= D/(sizeof(int)*QK8_1)]; + float2 Q_ds[ncols][D/QK8_1 == 0 ? 1 : D/QK8_1]; + if (Q_q8_1) { +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + if (j0 + nwarps > ncols && j >= ncols) { + break; + } + + // Reuse KQ as temporary storage for converting Q to q8_1: + int * tmp_q_i32 = (int *) &KQ[j*D]; + float2 * tmp_q_ds = (float2 *) (tmp_q_i32 + D/sizeof(int)); + + // Set memory to zero if out of bounds: + if (ncols > 2 && ic0 + j >= ne01) { +#pragma unroll + for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + tmp_q_i32[i] = 0; + } + if (threadIdx.x < D/QK8_1) { + tmp_q_ds[threadIdx.x] = make_float2(0.0f, 0.0f); + } + continue; + } + + const float * Q_f = (const float *) (Q + j*nb01); +#pragma unroll + for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) { + quantize_q8_1_to_shared(Q_f + 4*i0, scale, tmp_q_i32, tmp_q_ds); + } + } + + __syncthreads(); + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + int * tmp_q_i32 = (int *) &KQ[j*D]; + float2 * tmp_q_ds = (float2 *) (tmp_q_i32 + D/sizeof(int)); + +#pragma unroll + for (int i0 = 0; i0 < D/sizeof(int); i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + Q_i32[j][i0/WARP_SIZE] = tmp_q_i32[i]; + Q_ds[j][i0/WARP_SIZE] = tmp_q_ds[i/QI8_1]; + } + } + + __syncthreads(); + } else { +#pragma unroll + for (int j = 0; j < ncols; ++j) { + const float2 * Q_f2_j = (const float2 *) (Q + j*nb01); +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + + Q_f2[j][i0/WARP_SIZE] = ncols <= 2 || ic0 + j ? Q_f2_j[i] : make_float2(0.0f, 0.0f); + Q_f2[j][i0/WARP_SIZE].x *= scale; + Q_f2[j][i0/WARP_SIZE].y *= scale; + } + } + } + + float VKQ[ncols] = {0.0f}; + + const int k_start = parallel_blocks == 1 ? 0 : ip*D; + for (int k_VKQ_0 = k_start; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*D) { + // Calculate KQ tile and keep track of new maximum KQ values: + + float kqmax_new_arr[ncols]; +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqmax_new_arr[j] = kqmax[j]; + } + +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += nwarps) { + const int i_KQ = i_KQ_0 + threadIdx.y; + + if ((i_KQ_0 + nwarps > D && i_KQ >= D) || (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + i_KQ >= ne11)) { + break; + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + float sum = vec_dot_KQ(K + (k_VKQ_0 + i_KQ)*nb11, Q_f2[j], Q_i32[j], Q_ds[j]); + sum = warp_reduce_sum(sum); + sum += mask ? slope*__half2float(maskh[j*ne11 + k_VKQ_0 + i_KQ]) : 0.0f; + + kqmax_new_arr[j] = fmaxf(kqmax_new_arr[j], sum); + + if (threadIdx.x == 0) { + KQ[j*D + i_KQ] = sum; + } + } + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + float kqmax_new_j = kqmax_new_arr[j]; + + kqmax_new_j = warp_reduce_max(kqmax_new_j); + if (threadIdx.x == 0) { + kqmax_shared[j][threadIdx.y] = kqmax_new_j; + } + } + + __syncthreads(); + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + float kqmax_new_j = kqmax_shared[j][threadIdx.x]; + kqmax_new_j = warp_reduce_max(kqmax_new_j); + + const float KQ_max_scale = expf(kqmax[j] - kqmax_new_j); + kqmax[j] = kqmax_new_j; + + const float val = expf(KQ[j*D + tid] - kqmax[j]); + kqsum[j] = kqsum[j]*KQ_max_scale + val; + KQ[j*D + tid] = val; + + VKQ[j] *= KQ_max_scale; + } + + __syncthreads(); + +#pragma unroll + for (int k = 0; k < D; ++k) { + if (FATTN_KQ_STRIDE % D != 0 && k_VKQ_0 + k >= ne11) { + break; + } + + const float V_ki = dequantize_1_v(V + (k_VKQ_0 + k)*nb21, tid); +#pragma unroll + for (int j = 0; j < ncols; ++j) { + VKQ[j] += V_ki*KQ[j*D + k]; + } + } + + __syncthreads(); + } + +#pragma unroll + for (int j = 0; j < ncols; ++j) { + kqsum[j] = warp_reduce_sum(kqsum[j]); + if (threadIdx.x == 0) { + kqsum_shared[j][threadIdx.y] = kqsum[j]; + } + } + + __syncthreads(); + +#pragma unroll + for (int j_VKQ = 0; j_VKQ < ncols; ++j_VKQ) { + if (ncols > 2 && ic0 + j_VKQ >= ne01) { + break; + } + + kqsum[j_VKQ] = kqsum_shared[j_VKQ][threadIdx.x]; + kqsum[j_VKQ] = warp_reduce_sum(kqsum[j_VKQ]); + + float dst_val = VKQ[j_VKQ]; + if (parallel_blocks == 1) { + dst_val /= kqsum[j_VKQ]; + } + const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; + dst[j_dst*D*gridDim.y + D*blockIdx.y + tid] = dst_val; + } + + if (parallel_blocks != 1 && tid < ncols && (ncols <= 2 || ic0 + tid < ne01)) { + dst_meta[(ic0 + tid)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = make_float2(kqmax[tid], kqsum[tid]); + } +} + +template +void ggml_cuda_flash_attn_ext_vec_f32_case_impl(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + constexpr int nwarps = D/WARP_SIZE; + fattn_kernel_t fattn_kernel = flash_attn_vec_ext_f32; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); +} + +template +void ggml_cuda_flash_attn_ext_vec_f32_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + ggml_tensor * KQV = dst; + ggml_tensor * Q = dst->src[0]; + ggml_tensor * K = dst->src[1]; + ggml_tensor * V = dst->src[2]; + + const int32_t precision = KQV->op_params[2]; + GGML_ASSERT(precision == GGML_PREC_DEFAULT); + + GGML_ASSERT(K->type == type_K); + GGML_ASSERT(V->type == type_V); + + if (Q->ne[1] == 1) { + constexpr int cols_per_block = 1; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f32_case_impl(ctx, dst); + return; + } + + if (Q->ne[1] == 2) { + constexpr int cols_per_block = 2; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f32_case_impl(ctx, dst); + return; + } + + if (Q->ne[1] <= 4) { + constexpr int cols_per_block = 4; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f32_case_impl(ctx, dst); + return; + } + + if (Q->ne[1] <= 8) { + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 4; + ggml_cuda_flash_attn_ext_vec_f32_case_impl(ctx, dst); + return; + } + + constexpr int cols_per_block = 8; + constexpr int parallel_blocks = 1; + ggml_cuda_flash_attn_ext_vec_f32_case_impl(ctx, dst); +} + +#define DECL_FATTN_VEC_F32_CASE(D, type_K, type_V) \ + template void ggml_cuda_flash_attn_ext_vec_f32_case \ + (ggml_backend_cuda_context & ctx, ggml_tensor * dst) \ + +extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16); + +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0); + +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1); + +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0); + +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1); + +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0); + +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16); +extern DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16); + +extern DECL_FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/fattn-wmma-f16.cuh b/ggml-cuda/fattn-wmma-f16.cuh new file mode 100644 index 000000000..65ed31852 --- /dev/null +++ b/ggml-cuda/fattn-wmma-f16.cuh @@ -0,0 +1,490 @@ +#include "common.cuh" +#include "fattn-common.cuh" + +#if FP16_MMA_AVAILABLE +#include +#endif + +// D == head size, VKQ_stride == num VKQ rows calculated in parallel: +template +#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +__launch_bounds__(nwarps*WARP_SIZE, 1) +#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) +static __global__ void flash_attn_ext_f16( + const char * __restrict__ Q, + const char * __restrict__ K, + const char * __restrict__ V, + const char * __restrict__ mask, + float * __restrict__ dst, + float2 * __restrict__ dst_meta, + const float scale, + const float max_bias, + const float m0, + const float m1, + const uint32_t n_head_log2, + const int ne00, + const int ne01, + const int ne02, + const int ne03, + const int ne10, + const int ne11, + const int ne12, + const int ne13, + const int ne31, + const int nb31, + const int nb01, + const int nb02, + const int nb03, + const int nb11, + const int nb12, + const int nb13, + const int nb21, + const int nb22, + const int nb23, + const int ne0, + const int ne1, + const int ne2, + const int ne3) { +#if FP16_MMA_AVAILABLE + //In this kernel Q, K, V are matrices while i, j, k are matrix indices. + + const int ic0 = ncols*(blockIdx.x / parallel_blocks); // Index of the first Q/QKV column to work on. + const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. + + static_assert(D <= FATTN_KQ_STRIDE, "D must be <= FATTN_KQ_STRIDE."); + static_assert(ncols == 8 || ncols % 16 == 0, "ncols must be 8 or a multiple of 16."); + constexpr int frag_m = ncols == 8 ? 32 : 16; + constexpr int frag_n = ncols == 8 ? 8 : 16; + static_assert(D % frag_m == 0, "If ncols == 8 then D % frag_m must be 0."); + typedef nvcuda::wmma::fragment frag_a_K; + typedef nvcuda::wmma::fragment frag_a_V; + typedef nvcuda::wmma::fragment frag_b; + typedef nvcuda::wmma::fragment frag_c_KQ; + typedef nvcuda::wmma::fragment frag_c_VKQ; + + constexpr int KQ_stride_tc = nwarps*frag_m; // Number of KQ rows calculated in parallel. + constexpr int VKQ_ratio = KQ_stride_tc/VKQ_stride; // Number of parallel VKQ accumulators needed to keep all warps busy. + static_assert(VKQ_ratio <= nwarps, "VKQ_ratio must be <= nwarps."); + + // Pad internal representation of KQ, KQV to reduce shared memory bank conflicts: + constexpr int D_padded = D + 8; + constexpr int kqs_padded = FATTN_KQ_STRIDE + 8; + constexpr int kqar = sizeof(KQ_acc_t)/sizeof(half); + + const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. + const float * Q_f = (const float *) (Q + nb02* blockIdx.y + nb01*ic0); + const half * K_h = (const half *) (K + nb12*(blockIdx.y / gqa_ratio)); + const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape + const half * maskh = (const half *) mask + (nb31/sizeof(half))* ic0; + const half2 * mask2 = (const half2 *) mask + (nb31/sizeof(half))*(ic0/2); + + const int stride_Q = nb01 / sizeof(float); + const int stride_KV = nb11 / sizeof(half); + + const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); + const half slopeh = __float2half(slopef); + const half2 slope2 = make_half2(slopef, slopef); + + frag_b Q_b[D/16][ncols/frag_n]; + + // A single buffer for temporarily holding tiles of KQ and VKQ parts: + constexpr int mem_KQ = ncols*kqs_padded*kqar; + constexpr int mem_VKQ_parts = VKQ_ratio*ncols*D_padded; + __shared__ half KQ[mem_KQ >= mem_VKQ_parts ? mem_KQ : mem_VKQ_parts]; + float * KQ_f = (float *) KQ; + half2 * KQ2 = (half2 *) KQ; + + float KQ_rowsum_f[ncols/nwarps] = {0.0f}; + float KQ_max_f[ncols/nwarps]; + float KQ_max_scale_f[ncols/nwarps] = {0.0f}; + +#pragma unroll + for (int j = 0; j < ncols/nwarps; ++j) { + KQ_max_f[j] = -FLT_MAX/2.0f; + } + + half2 KQ_rowsum_h2[ncols/nwarps] = {{0.0f, 0.0f}}; + half2 KQ_max_h2[ncols/nwarps]; + half2 KQ_max_scale_h2[ncols/nwarps] = {{0.0f, 0.0f}}; + +#pragma unroll + for (int j = 0; j < ncols/nwarps; ++j) { + KQ_max_h2[j] = make_half2(-HALF_MAX_HALF, -HALF_MAX_HALF); + } + + __shared__ half VKQ[ncols*D_padded]; // Accumulator for final VKQ slice. + half2 * VKQ2 = (half2 *) VKQ; +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + if (i0 + WARP_SIZE > D/2 && i >= D/2) { + break; + } + VKQ2[j*(D_padded/2) + i] = make_half2(0.0f, 0.0f); + } + } + + // Convert Q to half and apply scale, temporarily store in KQ: +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; +#pragma unroll + for (int i0 = 0; i0 < D; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + if (i0 + WARP_SIZE > D && i >= D) { + break; + } + KQ[j*D_padded + i] = ic0 + j < ne01 ? Q_f[j*stride_Q + i] * scale : 0.0f; + } + } + + __syncthreads(); + + // Load Q into tensor core fragments/registers since it will be used frequently: +#pragma unroll + for (int i0 = 0; i0 < D; i0 += 16) { +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += frag_n) { + nvcuda::wmma::load_matrix_sync(Q_b[i0/16][j0/frag_n], KQ + j0*D_padded + i0, D_padded); + } + } + + __syncthreads(); + + // Iterate over ne11 == previous tokens: + for (int k_VKQ_0 = ip*FATTN_KQ_STRIDE; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*FATTN_KQ_STRIDE) { + // Calculate tile of KQ: +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE; i_KQ_0 += KQ_stride_tc) { + frag_c_KQ KQ_c[ncols/frag_n]; +#pragma unroll + for (int j = 0; j < ncols/frag_n; ++j) { + nvcuda::wmma::fill_fragment(KQ_c[j], 0.0f); + } +#pragma unroll + for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 16) { + frag_a_K K_a; + nvcuda::wmma::load_matrix_sync(K_a, K_h + (k_VKQ_0 + i_KQ_0 + frag_m*threadIdx.y)*stride_KV + k_KQ_0, stride_KV); +#pragma unroll + for (int j = 0; j < ncols/frag_n; ++j) { + nvcuda::wmma::mma_sync(KQ_c[j], K_a, Q_b[k_KQ_0/16][j], KQ_c[j]); + } + } +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += frag_n) { + nvcuda::wmma::store_matrix_sync((KQ_acc_t *) KQ + j0*kqs_padded + i_KQ_0 + frag_m*threadIdx.y, KQ_c[j0/frag_n], kqs_padded, nvcuda::wmma::mem_col_major); + } + } + + __syncthreads(); + + // Calculate softmax for each KQ column using the current max. value. + // The divisor is stored in KQ_rowsum and will be applied at the end. +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + if (std::is_same::value) { + float KQ_f_tmp[FATTN_KQ_STRIDE / WARP_SIZE]; +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) { + const int k = k0 + threadIdx.x; + + KQ_f_tmp[k0/WARP_SIZE] = KQ_f[j*kqs_padded + k]; + } + + float KQ_max_new = KQ_max_f[j0/nwarps]; +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) { + const int k = k0 + threadIdx.x; + + KQ_f_tmp[k0/WARP_SIZE] += mask ? __half2float(slopeh*maskh[j*(nb31/sizeof(half)) + k_VKQ_0 + k]) : 0.0f; + KQ_max_new = max(KQ_max_new, KQ_f_tmp[k0/WARP_SIZE]); + } + KQ_max_new = warp_reduce_max(KQ_max_new); + + const float diff = KQ_max_f[j0/nwarps] - KQ_max_new; + KQ_max_scale_f[j0/nwarps] = expf(diff); + if (diff <= SOFTMAX_FTZ_THRESHOLD) { + KQ_max_scale_f[j0/nwarps] = 0.0f; + } + KQ_max_f[j0/nwarps] = KQ_max_new; + + float KQ_rowsum_add = 0.0f; +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) { + const int k = k0 + threadIdx.x; + + const float diff = KQ_f_tmp[k0/WARP_SIZE] - KQ_max_f[j0/nwarps]; + KQ_f_tmp[k0/WARP_SIZE] = expf(diff); + if (diff <= SOFTMAX_FTZ_THRESHOLD) { + KQ_f_tmp[k0/WARP_SIZE] = 0.0f; + } + KQ_rowsum_add += KQ_f_tmp[k0/WARP_SIZE]; + KQ[j*(kqar*kqs_padded) + k] = KQ_f_tmp[k0/WARP_SIZE]; + } + KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add); + + // Scale previous KQ_rowsum to account for a potential increase in KQ_max: + KQ_rowsum_f[j0/nwarps] = KQ_max_scale_f[j0/nwarps]*KQ_rowsum_f[j0/nwarps] + KQ_rowsum_add; + } else { + half2 KQ2_tmp[FATTN_KQ_STRIDE/(2*WARP_SIZE)]; +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) { + const int k = k0 + threadIdx.x; + + KQ2_tmp[k0/WARP_SIZE] = KQ2[j*(kqs_padded/2) + k]; + } + + half2 KQ_max_new = KQ_max_h2[j0/nwarps]; +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) { + const int k = k0 + threadIdx.x; + + KQ2_tmp[k0/WARP_SIZE] += mask ? slope2*mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f); + KQ_max_new = ggml_cuda_hmax2(KQ_max_new, KQ2_tmp[k0/WARP_SIZE]); + } + KQ_max_new = __half2half2(warp_reduce_max(ggml_cuda_hmax(__low2half(KQ_max_new), __high2half(KQ_max_new)))); + const half2 diff = KQ_max_h2[j0/nwarps] - KQ_max_new; + KQ_max_scale_h2[j0/nwarps] = h2exp(diff); + const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD)); + *((uint32_t *) &KQ_max_scale_h2[j0/nwarps]) &= ftz_mask; + KQ_max_h2[j0/nwarps] = KQ_max_new; + + half2 KQ_rowsum_add = make_half2(0.0f, 0.0f); +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) { + const int k = k0 + threadIdx.x; + + const half2 diff = KQ2_tmp[k0/WARP_SIZE] - KQ_max_h2[j0/nwarps]; + KQ2_tmp[k0/WARP_SIZE] = h2exp(diff); + const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD)); + *((uint32_t *) &KQ2_tmp[k0/WARP_SIZE]) &= ftz_mask; + KQ_rowsum_add += KQ2_tmp[k0/WARP_SIZE]; + KQ2[j*(kqs_padded/2) + k] = KQ2_tmp[k0/WARP_SIZE]; + } + KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add); + + // Scale previous KQ_rowsum to account for a potential increase in KQ_max: + KQ_rowsum_h2[j0/nwarps] = KQ_max_scale_h2[j0/nwarps]*KQ_rowsum_h2[j0/nwarps] + KQ_rowsum_add; + } + } + + __syncthreads(); + + frag_b KQ_b[FATTN_KQ_STRIDE/(VKQ_ratio*16)][ncols/frag_n]; +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += frag_n) { +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) { + const int k = k0 + (threadIdx.y % VKQ_ratio)*16; + nvcuda::wmma::load_matrix_sync( + KQ_b[k0/(VKQ_ratio*16)][j0/frag_n], + KQ + j0*(kqar*kqs_padded) + k, + kqar*kqs_padded); + } + } + + frag_c_VKQ VKQ_c[D/VKQ_stride][ncols/frag_n]; +#pragma unroll + for (int i_VKQ_0 = 0; i_VKQ_0 < D; i_VKQ_0 += VKQ_stride) { +#pragma unroll + for (int j = 0; j < ncols/frag_n; ++j) { + nvcuda::wmma::fill_fragment(VKQ_c[i_VKQ_0/VKQ_stride][j], 0.0f); + } + +#pragma unroll + for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) { + const int k = k0 + (threadIdx.y % VKQ_ratio)*16; + + frag_a_V v_a; + nvcuda::wmma::load_matrix_sync(v_a, V_h + (k_VKQ_0 + k)*stride_KV + i_VKQ_0 + frag_m*(threadIdx.y/VKQ_ratio), stride_KV); +#pragma unroll + for (int j = 0; j < ncols/frag_n; ++j) { + nvcuda::wmma::mma_sync(VKQ_c[i_VKQ_0/VKQ_stride][j], v_a, KQ_b[k0/(VKQ_ratio*16)][j], VKQ_c[i_VKQ_0/VKQ_stride][j]); + } + } + } + + __syncthreads(); + + const int offset_k = (threadIdx.y % VKQ_ratio) * (ncols*D_padded); +#pragma unroll + for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += VKQ_stride) { +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += frag_n) { + nvcuda::wmma::store_matrix_sync( + KQ + offset_k + j0*D_padded + i_KQ_0 + frag_m*(threadIdx.y/VKQ_ratio), + VKQ_c[i_KQ_0/VKQ_stride][j0/frag_n], + D_padded, nvcuda::wmma::mem_col_major); + } + } + + __syncthreads(); + +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j = j0 + threadIdx.y; + + half2 VKQ_scale; + if (std::is_same::value) { + VKQ_scale = make_half2(KQ_max_scale_f[j0/nwarps], KQ_max_scale_f[j0/nwarps]); + } else { + VKQ_scale = KQ_max_scale_h2[j0/nwarps]; + } + +#pragma unroll + for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + if (i0 + WARP_SIZE > D/2 && i >= D/2) { + break; + } + + half2 VKQ_add = make_half2(0.0f, 0.0f); +#pragma unroll + for (int l = 0; l < VKQ_ratio; ++l) { + VKQ_add += KQ2[l*(ncols*D_padded/2) + j*(D_padded/2) + i]; + } + VKQ2[j*(D_padded/2) + i] = VKQ_scale*VKQ2[j*(D_padded/2) + i] + VKQ_add; + } + } + + __syncthreads(); + } + +#pragma unroll + for (int j0 = 0; j0 < ncols; j0 += nwarps) { + const int j_VKQ = j0 + threadIdx.y; + if (ic0 + j_VKQ >= ne01) { + return; + } + const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; + + float KQ_rowsum_j; + if (std::is_same::value) { + KQ_rowsum_j = KQ_rowsum_f[j0/nwarps]; + } else { + KQ_rowsum_j = __low2float(KQ_rowsum_h2[j0/nwarps]) + __high2float(KQ_rowsum_h2[j0/nwarps]); + } + +#pragma unroll + for (int i0 = 0; i0 < D; i0 += WARP_SIZE) { + const int i = i0 + threadIdx.x; + if (i0 + WARP_SIZE > D && i >= D) { + break; + } + float dst_val = VKQ[j_VKQ*D_padded + i]; + if (parallel_blocks == 1) { + dst_val /= KQ_rowsum_j; + } + dst[j_dst*gridDim.y*D + blockIdx.y*D + i] = dst_val; + } + + if (parallel_blocks == 1 || threadIdx.x != 0) { + continue; + } + + float2 dst_meta_val; + if (std::is_same::value) { + dst_meta_val.x = KQ_max_f[j0/nwarps]; + } else { + dst_meta_val.x = __low2float(KQ_max_h2[j0/nwarps]); + } + dst_meta_val.y = KQ_rowsum_j; + dst_meta[(ic0 + j_VKQ)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = dst_meta_val; + } +#else + NO_DEVICE_CODE; +#endif // FP16_MMA_AVAILABLE +} + +constexpr int get_max_power_of_2(int x) { + return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1; +} + +static_assert(get_max_power_of_2(1) == 1, "Test failed."); +static_assert(get_max_power_of_2(2) == 2, "Test failed."); +static_assert(get_max_power_of_2(4) == 4, "Test failed."); +static_assert(get_max_power_of_2(6) == 2, "Test failed."); + +// Number of VKQ rows calculated in parallel: +constexpr int get_VKQ_stride(int D, int nwarps, int frag_m) { + return (get_max_power_of_2(D/frag_m) < nwarps ? get_max_power_of_2(D/frag_m) : nwarps)*frag_m; +} + +static_assert(get_VKQ_stride(128, 1, 32) == 32, "Test failed."); +static_assert(get_VKQ_stride(128, 2, 32) == 64, "Test failed."); +static_assert(get_VKQ_stride(128, 4, 32) == 128, "Test failed."); +static_assert(get_VKQ_stride( 64, 1, 32) == 32, "Test failed."); +static_assert(get_VKQ_stride( 64, 2, 32) == 64, "Test failed."); +static_assert(get_VKQ_stride( 64, 4, 32) == 64, "Test failed."); +static_assert(get_VKQ_stride( 80, 1, 16) == 16, "Test failed."); +static_assert(get_VKQ_stride( 80, 2, 16) == 16, "Test failed."); +static_assert(get_VKQ_stride( 80, 4, 16) == 16, "Test failed."); + +template +void ggml_cuda_flash_attn_ext_wmma_f16_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * Q = dst->src[0]; + + constexpr int nwarps = 4; + + constexpr int frag_m = cols_per_block == 8 && D % 32 == 0 ? 32 : 16; + const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3]; + const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm; + + if (4*blocks_num_pb1 < 2*nsm) { + constexpr int parallel_blocks = 4; + fattn_kernel_t fattn_kernel = flash_attn_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + return; + } + if (2*blocks_num_pb1 < 2*nsm) { + constexpr int parallel_blocks = 2; + fattn_kernel_t fattn_kernel = flash_attn_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + return; + } + constexpr int parallel_blocks = 1; + fattn_kernel_t fattn_kernel = flash_attn_ext_f16; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); +} + +#define DECL_FATTN_WMMA_F16_CASE(D, cols_per_block, KQ_acc_t) \ + template void ggml_cuda_flash_attn_ext_wmma_f16_case \ + (ggml_backend_cuda_context & ctx, ggml_tensor * dst) \ + +extern DECL_FATTN_WMMA_F16_CASE( 64, 16, float); +extern DECL_FATTN_WMMA_F16_CASE( 80, 16, float); +extern DECL_FATTN_WMMA_F16_CASE( 96, 16, float); +extern DECL_FATTN_WMMA_F16_CASE(112, 16, float); +extern DECL_FATTN_WMMA_F16_CASE(128, 16, float); +extern DECL_FATTN_WMMA_F16_CASE(256, 16, float); + +extern DECL_FATTN_WMMA_F16_CASE( 64, 32, float); +extern DECL_FATTN_WMMA_F16_CASE( 80, 32, float); +extern DECL_FATTN_WMMA_F16_CASE( 96, 32, float); +extern DECL_FATTN_WMMA_F16_CASE(112, 32, float); +extern DECL_FATTN_WMMA_F16_CASE(128, 32, float); +// extern DECL_FATTN_WMMA_F16_CASE(256, 16, float); + +extern DECL_FATTN_WMMA_F16_CASE( 64, 8, half); +extern DECL_FATTN_WMMA_F16_CASE( 96, 8, half); +extern DECL_FATTN_WMMA_F16_CASE(128, 8, half); +extern DECL_FATTN_WMMA_F16_CASE(256, 8, half); + +extern DECL_FATTN_WMMA_F16_CASE( 64, 16, half); +extern DECL_FATTN_WMMA_F16_CASE( 80, 16, half); +extern DECL_FATTN_WMMA_F16_CASE( 96, 16, half); +extern DECL_FATTN_WMMA_F16_CASE(112, 16, half); +extern DECL_FATTN_WMMA_F16_CASE(128, 16, half); +extern DECL_FATTN_WMMA_F16_CASE(256, 16, half); + +extern DECL_FATTN_WMMA_F16_CASE( 64, 32, half); +extern DECL_FATTN_WMMA_F16_CASE( 80, 32, half); +extern DECL_FATTN_WMMA_F16_CASE( 96, 32, half); +extern DECL_FATTN_WMMA_F16_CASE(112, 32, half); +extern DECL_FATTN_WMMA_F16_CASE(128, 32, half); +extern DECL_FATTN_WMMA_F16_CASE(256, 16, half); diff --git a/ggml-cuda/fattn.cu b/ggml-cuda/fattn.cu index af7c95232..b35ab67a8 100644 --- a/ggml-cuda/fattn.cu +++ b/ggml-cuda/fattn.cu @@ -4,468 +4,313 @@ #include "fattn-tile-f32.cuh" #include "fattn-vec-f16.cuh" #include "fattn-vec-f32.cuh" +#include "fattn-wmma-f16.cuh" #include "fattn.cuh" #include -#if FP16_MMA_AVAILABLE -#include -#endif +static void ggml_cuda_flash_attn_ext_wmma_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + const ggml_tensor * KQV = dst; + const ggml_tensor * Q = dst->src[0]; -// D == head size, VKQ_stride == num VKQ rows calculated in parallel: -template -#if !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -__launch_bounds__(nwarps*WARP_SIZE, 1) -#endif // !(defined(GGML_USE_HIPBLAS) && defined(__HIP_PLATFORM_AMD__)) -static __global__ void flash_attn_ext_f16( - const char * __restrict__ Q, - const char * __restrict__ K, - const char * __restrict__ V, - const char * __restrict__ mask, - float * __restrict__ dst, - float2 * __restrict__ dst_meta, - const float scale, - const float max_bias, - const float m0, - const float m1, - const uint32_t n_head_log2, - const int ne00, - const int ne01, - const int ne02, - const int ne03, - const int ne10, - const int ne11, - const int ne12, - const int ne13, - const int ne31, - const int nb31, - const int nb01, - const int nb02, - const int nb03, - const int nb11, - const int nb12, - const int nb13, - const int ne0, - const int ne1, - const int ne2, - const int ne3) { -#if FP16_MMA_AVAILABLE - //In this kernel Q, K, V are matrices while i, j, k are matrix indices. + const int32_t precision = KQV->op_params[2]; - const int ic0 = ncols*(blockIdx.x / parallel_blocks); // Index of the first Q/QKV column to work on. - const int ip = blockIdx.x % parallel_blocks; // Index in group of blocks running for the same column in parallel. - - static_assert(D <= FATTN_KQ_STRIDE, "D must be <= FATTN_KQ_STRIDE."); - static_assert(ncols == 8 || ncols % 16 == 0, "ncols must be 8 or a multiple of 16."); - constexpr int frag_m = ncols == 8 ? 32 : 16; - constexpr int frag_n = ncols == 8 ? 8 : 16; - static_assert(D % frag_m == 0, "If ncols == 8 then D % frag_m must be 0."); - typedef nvcuda::wmma::fragment frag_a_K; - typedef nvcuda::wmma::fragment frag_a_V; - typedef nvcuda::wmma::fragment frag_b; - typedef nvcuda::wmma::fragment frag_c_KQ; - typedef nvcuda::wmma::fragment frag_c_VKQ; - - constexpr int KQ_stride_tc = nwarps*frag_m; // Number of KQ rows calculated in parallel. - constexpr int VKQ_ratio = KQ_stride_tc/VKQ_stride; // Number of parallel VKQ accumulators needed to keep all warps busy. - static_assert(VKQ_ratio <= nwarps, "VKQ_ratio must be <= nwarps."); - - // Pad internal representation of KQ, KQV to reduce shared memory bank conflicts: - constexpr int D_padded = D + 8; - constexpr int kqs_padded = FATTN_KQ_STRIDE + 8; - constexpr int kqar = sizeof(KQ_acc_t)/sizeof(half); - - const int gqa_ratio = ne02 / ne12; // With grouped query attention there are > 1 Q matrices per K, V matrix. - const float * Q_f = (const float *) (Q + nb02* blockIdx.y + nb01*ic0); - const half * K_h = (const half *) (K + nb12*(blockIdx.y / gqa_ratio)); - const half * V_h = (const half *) (V + nb12*(blockIdx.y / gqa_ratio)); // K and V have same shape - const half * maskh = (const half *) mask + (nb31/sizeof(half))* ic0; - const half2 * mask2 = (const half2 *) mask + (nb31/sizeof(half))*(ic0/2); - - const int stride_Q = nb01 / sizeof(float); - const int stride_KV = nb11 / sizeof(half); - - const float slopef = get_alibi_slope(max_bias, blockIdx.y, n_head_log2, m0, m1); - const half slopeh = __float2half(slopef); - const half2 slope2 = make_half2(slopef, slopef); - - frag_b Q_b[D/16][ncols/frag_n]; - - // A single buffer for temporarily holding tiles of KQ and VKQ parts: - constexpr int mem_KQ = ncols*kqs_padded*kqar; - constexpr int mem_VKQ_parts = VKQ_ratio*ncols*D_padded; - __shared__ half KQ[mem_KQ >= mem_VKQ_parts ? mem_KQ : mem_VKQ_parts]; - float * KQ_f = (float *) KQ; - half2 * KQ2 = (half2 *) KQ; - - float KQ_rowsum_f[ncols/nwarps] = {0.0f}; - float KQ_max_f[ncols/nwarps]; - float KQ_max_scale_f[ncols/nwarps] = {0.0f}; - -#pragma unroll - for (int j = 0; j < ncols/nwarps; ++j) { - KQ_max_f[j] = -FLT_MAX/2.0f; - } - - half2 KQ_rowsum_h2[ncols/nwarps] = {{0.0f, 0.0f}}; - half2 KQ_max_h2[ncols/nwarps]; - half2 KQ_max_scale_h2[ncols/nwarps] = {{0.0f, 0.0f}}; - -#pragma unroll - for (int j = 0; j < ncols/nwarps; ++j) { - KQ_max_h2[j] = make_half2(-HALF_MAX_HALF, -HALF_MAX_HALF); - } - - __shared__ half VKQ[ncols*D_padded]; // Accumulator for final VKQ slice. - half2 * VKQ2 = (half2 *) VKQ; -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - const int j = j0 + threadIdx.y; -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { - const int i = i0 + threadIdx.x; - if (i0 + WARP_SIZE > D/2 && i >= D/2) { - break; - } - VKQ2[j*(D_padded/2) + i] = make_half2(0.0f, 0.0f); - } - } - - // Convert Q to half and apply scale, temporarily store in KQ: -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - const int j = j0 + threadIdx.y; -#pragma unroll - for (int i0 = 0; i0 < D; i0 += WARP_SIZE) { - const int i = i0 + threadIdx.x; - if (i0 + WARP_SIZE > D && i >= D) { - break; - } - KQ[j*D_padded + i] = ic0 + j < ne01 ? Q_f[j*stride_Q + i] * scale : 0.0f; - } - } - - __syncthreads(); - - // Load Q into tensor core fragments/registers since it will be used frequently: -#pragma unroll - for (int i0 = 0; i0 < D; i0 += 16) { -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += frag_n) { - nvcuda::wmma::load_matrix_sync(Q_b[i0/16][j0/frag_n], KQ + j0*D_padded + i0, D_padded); - } - } - - __syncthreads(); - - // Iterate over ne11 == previous tokens: - for (int k_VKQ_0 = ip*FATTN_KQ_STRIDE; k_VKQ_0 < ne11; k_VKQ_0 += parallel_blocks*FATTN_KQ_STRIDE) { - // Calculate tile of KQ: -#pragma unroll - for (int i_KQ_0 = 0; i_KQ_0 < FATTN_KQ_STRIDE; i_KQ_0 += KQ_stride_tc) { - frag_c_KQ KQ_c[ncols/frag_n]; -#pragma unroll - for (int j = 0; j < ncols/frag_n; ++j) { - nvcuda::wmma::fill_fragment(KQ_c[j], 0.0f); - } -#pragma unroll - for (int k_KQ_0 = 0; k_KQ_0 < D; k_KQ_0 += 16) { - frag_a_K K_a; - nvcuda::wmma::load_matrix_sync(K_a, K_h + (k_VKQ_0 + i_KQ_0 + frag_m*threadIdx.y)*stride_KV + k_KQ_0, stride_KV); -#pragma unroll - for (int j = 0; j < ncols/frag_n; ++j) { - nvcuda::wmma::mma_sync(KQ_c[j], K_a, Q_b[k_KQ_0/16][j], KQ_c[j]); - } - } -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += frag_n) { - nvcuda::wmma::store_matrix_sync((KQ_acc_t *) KQ + j0*kqs_padded + i_KQ_0 + frag_m*threadIdx.y, KQ_c[j0/frag_n], kqs_padded, nvcuda::wmma::mem_col_major); - } - } - - __syncthreads(); - - // Calculate softmax for each KQ column using the current max. value. - // The divisor is stored in KQ_rowsum and will be applied at the end. -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - const int j = j0 + threadIdx.y; - - if (std::is_same::value) { - float KQ_f_tmp[FATTN_KQ_STRIDE / WARP_SIZE]; -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) { - const int k = k0 + threadIdx.x; - - KQ_f_tmp[k0/WARP_SIZE] = KQ_f[j*kqs_padded + k]; - } - - float KQ_max_new = KQ_max_f[j0/nwarps]; -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) { - const int k = k0 + threadIdx.x; - - KQ_f_tmp[k0/WARP_SIZE] += mask ? __half2float(slopeh*maskh[j*(nb31/sizeof(half)) + k_VKQ_0 + k]) : 0.0f; - KQ_max_new = max(KQ_max_new, KQ_f_tmp[k0/WARP_SIZE]); - } - KQ_max_new = warp_reduce_max(KQ_max_new); - - const float diff = KQ_max_f[j0/nwarps] - KQ_max_new; - KQ_max_scale_f[j0/nwarps] = expf(diff); - if (diff <= SOFTMAX_FTZ_THRESHOLD) { - KQ_max_scale_f[j0/nwarps] = 0.0f; - } - KQ_max_f[j0/nwarps] = KQ_max_new; - - float KQ_rowsum_add = 0.0f; -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += WARP_SIZE) { - const int k = k0 + threadIdx.x; - - const float diff = KQ_f_tmp[k0/WARP_SIZE] - KQ_max_f[j0/nwarps]; - KQ_f_tmp[k0/WARP_SIZE] = expf(diff); - if (diff <= SOFTMAX_FTZ_THRESHOLD) { - KQ_f_tmp[k0/WARP_SIZE] = 0.0f; - } - KQ_rowsum_add += KQ_f_tmp[k0/WARP_SIZE]; - KQ[j*(kqar*kqs_padded) + k] = KQ_f_tmp[k0/WARP_SIZE]; - } - KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add); - - // Scale previous KQ_rowsum to account for a potential increase in KQ_max: - KQ_rowsum_f[j0/nwarps] = KQ_max_scale_f[j0/nwarps]*KQ_rowsum_f[j0/nwarps] + KQ_rowsum_add; - } else { - half2 KQ2_tmp[FATTN_KQ_STRIDE/(2*WARP_SIZE)]; -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) { - const int k = k0 + threadIdx.x; - - KQ2_tmp[k0/WARP_SIZE] = KQ2[j*(kqs_padded/2) + k]; - } - - half2 KQ_max_new = KQ_max_h2[j0/nwarps]; -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) { - const int k = k0 + threadIdx.x; - - KQ2_tmp[k0/WARP_SIZE] += mask ? slope2*mask2[(j*ne11 + k_VKQ_0)/2 + k] : make_half2(0.0f, 0.0f); - KQ_max_new = ggml_cuda_hmax2(KQ_max_new, KQ2_tmp[k0/WARP_SIZE]); - } - KQ_max_new = __half2half2(warp_reduce_max(ggml_cuda_hmax(__low2half(KQ_max_new), __high2half(KQ_max_new)))); - const half2 diff = KQ_max_h2[j0/nwarps] - KQ_max_new; - KQ_max_scale_h2[j0/nwarps] = h2exp(diff); - const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD)); - *((uint32_t *) &KQ_max_scale_h2[j0/nwarps]) &= ftz_mask; - KQ_max_h2[j0/nwarps] = KQ_max_new; - - half2 KQ_rowsum_add = make_half2(0.0f, 0.0f); -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE/2; k0 += WARP_SIZE) { - const int k = k0 + threadIdx.x; - - const half2 diff = KQ2_tmp[k0/WARP_SIZE] - KQ_max_h2[j0/nwarps]; - KQ2_tmp[k0/WARP_SIZE] = h2exp(diff); - const uint32_t ftz_mask = __hgt2_mask(diff, make_half2(SOFTMAX_FTZ_THRESHOLD, SOFTMAX_FTZ_THRESHOLD)); - *((uint32_t *) &KQ2_tmp[k0/WARP_SIZE]) &= ftz_mask; - KQ_rowsum_add += KQ2_tmp[k0/WARP_SIZE]; - KQ2[j*(kqs_padded/2) + k] = KQ2_tmp[k0/WARP_SIZE]; - } - KQ_rowsum_add = warp_reduce_sum(KQ_rowsum_add); - - // Scale previous KQ_rowsum to account for a potential increase in KQ_max: - KQ_rowsum_h2[j0/nwarps] = KQ_max_scale_h2[j0/nwarps]*KQ_rowsum_h2[j0/nwarps] + KQ_rowsum_add; - } - } - - __syncthreads(); - - frag_b KQ_b[FATTN_KQ_STRIDE/(VKQ_ratio*16)][ncols/frag_n]; -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += frag_n) { -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) { - const int k = k0 + (threadIdx.y % VKQ_ratio)*16; - nvcuda::wmma::load_matrix_sync( - KQ_b[k0/(VKQ_ratio*16)][j0/frag_n], - KQ + j0*(kqar*kqs_padded) + k, - kqar*kqs_padded); - } - } - - frag_c_VKQ VKQ_c[D/VKQ_stride][ncols/frag_n]; -#pragma unroll - for (int i_VKQ_0 = 0; i_VKQ_0 < D; i_VKQ_0 += VKQ_stride) { -#pragma unroll - for (int j = 0; j < ncols/frag_n; ++j) { - nvcuda::wmma::fill_fragment(VKQ_c[i_VKQ_0/VKQ_stride][j], 0.0f); - } - -#pragma unroll - for (int k0 = 0; k0 < FATTN_KQ_STRIDE; k0 += VKQ_ratio*16) { - const int k = k0 + (threadIdx.y % VKQ_ratio)*16; - - frag_a_V v_a; - nvcuda::wmma::load_matrix_sync(v_a, V_h + (k_VKQ_0 + k)*stride_KV + i_VKQ_0 + frag_m*(threadIdx.y/VKQ_ratio), stride_KV); -#pragma unroll - for (int j = 0; j < ncols/frag_n; ++j) { - nvcuda::wmma::mma_sync(VKQ_c[i_VKQ_0/VKQ_stride][j], v_a, KQ_b[k0/(VKQ_ratio*16)][j], VKQ_c[i_VKQ_0/VKQ_stride][j]); - } - } - } - - __syncthreads(); - - const int offset_k = (threadIdx.y % VKQ_ratio) * (ncols*D_padded); -#pragma unroll - for (int i_KQ_0 = 0; i_KQ_0 < D; i_KQ_0 += VKQ_stride) { -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += frag_n) { - nvcuda::wmma::store_matrix_sync( - KQ + offset_k + j0*D_padded + i_KQ_0 + frag_m*(threadIdx.y/VKQ_ratio), - VKQ_c[i_KQ_0/VKQ_stride][j0/frag_n], - D_padded, nvcuda::wmma::mem_col_major); - } - } - - __syncthreads(); - -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - const int j = j0 + threadIdx.y; - - half2 VKQ_scale; - if (std::is_same::value) { - VKQ_scale = make_half2(KQ_max_scale_f[j0/nwarps], KQ_max_scale_f[j0/nwarps]); - } else { - VKQ_scale = KQ_max_scale_h2[j0/nwarps]; - } - -#pragma unroll - for (int i0 = 0; i0 < D/2; i0 += WARP_SIZE) { - const int i = i0 + threadIdx.x; - if (i0 + WARP_SIZE > D/2 && i >= D/2) { + if (precision != GGML_PREC_DEFAULT) { + if (Q->ne[1] <= 32 || Q->ne[0] > 128) { + constexpr int cols_per_block = 16; + switch (Q->ne[0]) { + case 64: + ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst); + break; + case 80: + ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst); + break; + case 96: + ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst); + break; + case 112: + ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst); + break; + case 128: + ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst); + break; + case 256: + ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, float>(ctx, dst); + break; + default: + GGML_ASSERT(false); + break; + } + } else { + constexpr int cols_per_block = 32; + switch (Q->ne[0]) { + case 64: + ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, float>(ctx, dst); + break; + case 80: + ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, float>(ctx, dst); + break; + case 96: + ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, float>(ctx, dst); + break; + case 112: + ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, float>(ctx, dst); + break; + case 128: + ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst); + break; + // case 256: + // ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, float>(ctx, dst); + // break; + default: + GGML_ASSERT(false); break; - } - - half2 VKQ_add = make_half2(0.0f, 0.0f); -#pragma unroll - for (int l = 0; l < VKQ_ratio; ++l) { - VKQ_add += KQ2[l*(ncols*D_padded/2) + j*(D_padded/2) + i]; - } - VKQ2[j*(D_padded/2) + i] = VKQ_scale*VKQ2[j*(D_padded/2) + i] + VKQ_add; } } - - __syncthreads(); + return; } -#pragma unroll - for (int j0 = 0; j0 < ncols; j0 += nwarps) { - const int j_VKQ = j0 + threadIdx.y; - if (ic0 + j_VKQ >= ne01) { - return; - } - const int j_dst = (ic0 + j_VKQ)*parallel_blocks + ip; - - float KQ_rowsum_j; - if (std::is_same::value) { - KQ_rowsum_j = KQ_rowsum_f[j0/nwarps]; - } else { - KQ_rowsum_j = __low2float(KQ_rowsum_h2[j0/nwarps]) + __high2float(KQ_rowsum_h2[j0/nwarps]); - } - -#pragma unroll - for (int i0 = 0; i0 < D; i0 += WARP_SIZE) { - const int i = i0 + threadIdx.x; - if (i0 + WARP_SIZE > D && i >= D) { + if (Q->ne[1] <= 8 && Q->ne[0] % WARP_SIZE == 0) { + constexpr int cols_per_block = 8; + switch (Q->ne[0]) { + case 64: + ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst); + break; + case 96: + ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst); + break; + case 128: + ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst); + break; + case 256: + ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst); + break; + default: + GGML_ASSERT(false); break; - } - float dst_val = VKQ[j_VKQ*D_padded + i]; - if (parallel_blocks == 1) { - dst_val /= KQ_rowsum_j; - } - dst[j_dst*gridDim.y*D + blockIdx.y*D + i] = dst_val; } - - if (parallel_blocks == 1 || threadIdx.x != 0) { - continue; - } - - float2 dst_meta_val; - if (std::is_same::value) { - dst_meta_val.x = KQ_max_f[j0/nwarps]; - } else { - dst_meta_val.x = __low2float(KQ_max_h2[j0/nwarps]); - } - dst_meta_val.y = KQ_rowsum_j; - dst_meta[(ic0 + j_VKQ)*gridDim.y*parallel_blocks + blockIdx.y*parallel_blocks + ip] = dst_meta_val; + return; } + + if (Q->ne[1] <= 32) { + constexpr int cols_per_block = 16; + switch (Q->ne[0]) { + case 64: + ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst); + break; + case 80: + ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst); + break; + case 96: + ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst); + break; + case 112: + ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst); + break; + case 128: + ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst); + break; + case 256: + ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst); + break; + default: + GGML_ASSERT(false); + break; + } + return; + } + + constexpr int cols_per_block = 32; + switch (Q->ne[0]) { + case 64: + ggml_cuda_flash_attn_ext_wmma_f16_case< 64, cols_per_block, half>(ctx, dst); + break; + case 80: + ggml_cuda_flash_attn_ext_wmma_f16_case< 80, cols_per_block, half>(ctx, dst); + break; + case 96: + ggml_cuda_flash_attn_ext_wmma_f16_case< 96, cols_per_block, half>(ctx, dst); + break; + case 112: + ggml_cuda_flash_attn_ext_wmma_f16_case<112, cols_per_block, half>(ctx, dst); + break; + case 128: + ggml_cuda_flash_attn_ext_wmma_f16_case<128, cols_per_block, half>(ctx, dst); + break; + case 256: + ggml_cuda_flash_attn_ext_wmma_f16_case<256, cols_per_block, half>(ctx, dst); + break; + default: + GGML_ASSERT(false); + break; + } +} +#define FATTN_VEC_F16_CASE(D, type_K, type_V) \ + if (Q->ne[0] == (D) && K->type == (type_K) && V->type == (type_V)) { \ + ggml_cuda_flash_attn_ext_vec_f16_case(ctx, dst); \ + return; \ + } \ + +static void ggml_cuda_flash_attn_ext_vec_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + ggml_tensor * Q = dst->src[1]; + ggml_tensor * K = dst->src[1]; + ggml_tensor * V = dst->src[2]; + +#ifdef GGML_CUDA_FA_ALL_QUANTS + FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0) + FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1) + FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0) + FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1) + FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0) + FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16 ) + + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0) + + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1) + + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0) + + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1) + FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1) + + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0) + FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0) + + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16) + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16) + FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16) + + FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16) #else - NO_DEVICE_CODE; -#endif // FP16_MMA_AVAILABLE + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0) + + FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0) + + FATTN_VEC_F16_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16) + FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16) + FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16) +#endif // GGML_CUDA_FA_ALL_QUANTS + + on_no_fattn_vec_case(Q->ne[0]); } -constexpr int get_max_power_of_2(int x) { - return x % 2 == 0 ? 2*get_max_power_of_2(x/2) : 1; -} +#define FATTN_VEC_F32_CASE(D, type_K, type_V) \ + if (Q->ne[0] == (D) && K->type == (type_K) && V->type == (type_V)) { \ + ggml_cuda_flash_attn_ext_vec_f32_case(ctx, dst); \ + return; \ + } \ -static_assert(get_max_power_of_2(1) == 1, "Test failed."); -static_assert(get_max_power_of_2(2) == 2, "Test failed."); -static_assert(get_max_power_of_2(4) == 4, "Test failed."); -static_assert(get_max_power_of_2(6) == 2, "Test failed."); +static void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { + ggml_tensor * Q = dst->src[1]; + ggml_tensor * K = dst->src[1]; + ggml_tensor * V = dst->src[2]; -// Number of VKQ rows calculated in parallel: -constexpr int get_VKQ_stride(int D, int nwarps, int frag_m) { - return (get_max_power_of_2(D/frag_m) < nwarps ? get_max_power_of_2(D/frag_m) : nwarps)*frag_m; -} +#ifdef GGML_CUDA_FA_ALL_QUANTS + FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_0) + FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q4_1) + FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_0) + FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q5_1) + FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_Q8_0) + FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16) -static_assert(get_VKQ_stride(128, 1, 32) == 32, "Test failed."); -static_assert(get_VKQ_stride(128, 2, 32) == 64, "Test failed."); -static_assert(get_VKQ_stride(128, 4, 32) == 128, "Test failed."); -static_assert(get_VKQ_stride( 64, 1, 32) == 32, "Test failed."); -static_assert(get_VKQ_stride( 64, 2, 32) == 64, "Test failed."); -static_assert(get_VKQ_stride( 64, 4, 32) == 64, "Test failed."); -static_assert(get_VKQ_stride( 80, 1, 16) == 16, "Test failed."); -static_assert(get_VKQ_stride( 80, 2, 16) == 16, "Test failed."); -static_assert(get_VKQ_stride( 80, 4, 16) == 16, "Test failed."); + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0) -template -void launch_fattn_f16(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - const ggml_tensor * Q = dst->src[0]; + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1) - constexpr int frag_m = cols_per_block == 8 && D % 32 == 0 ? 32 : 16; - const int blocks_num_pb1 = ((Q->ne[1] + cols_per_block - 1) / cols_per_block)*Q->ne[2]*Q->ne[3]; - const int nsm = ggml_cuda_info().devices[ggml_cuda_get_device()].nsm; + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0) - if (4*blocks_num_pb1 < 2*nsm) { - constexpr int parallel_blocks = 4; - fattn_kernel_t fattn_kernel = flash_attn_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - return; - } - if (2*blocks_num_pb1 < 2*nsm) { - constexpr int parallel_blocks = 2; - fattn_kernel_t fattn_kernel = flash_attn_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); - return; - } - constexpr int parallel_blocks = 1; - fattn_kernel_t fattn_kernel = flash_attn_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1) + FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1) + + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0) + FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0) + + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16) + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16) + FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16) + + FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16) +#else + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0) + + FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0) + + FATTN_VEC_F32_CASE( 64, GGML_TYPE_F16, GGML_TYPE_F16) + FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16) + FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16) +#endif // GGML_CUDA_FA_ALL_QUANTS + + on_no_fattn_vec_case(Q->ne[0]); } void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * KQV = dst; const ggml_tensor * Q = dst->src[0]; + const ggml_tensor * K = dst->src[1]; + const ggml_tensor * V = dst->src[2]; ggml_cuda_set_device(ctx.device); const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; const int32_t precision = KQV->op_params[2]; + const bool quantized_KV = ggml_is_quantized(K->type) || ggml_is_quantized(V->type); + // On AMD the tile kernels perform poorly, use the vec kernel instead: - if (cc >= CC_OFFSET_AMD) { - if (precision == GGML_PREC_DEFAULT) { - ggml_cuda_flash_attn_ext_vec_f16_no_mma(ctx, dst); + if (cc >= CC_OFFSET_AMD || quantized_KV) { + if (precision == GGML_PREC_DEFAULT && fast_fp16_available(cc)) { + ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); } else { ggml_cuda_flash_attn_ext_vec_f32(ctx, dst); } @@ -483,156 +328,22 @@ void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst if (!fp16_mma_available(cc)) { if (Q->ne[1] <= 8) { - ggml_cuda_flash_attn_ext_vec_f16_no_mma(ctx, dst); + ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); } else { ggml_cuda_flash_attn_ext_tile_f16(ctx, dst); } return; } - if (precision != GGML_PREC_DEFAULT) { - if (Q->ne[1] == 1 && (Q->ne[0] == 64 || Q->ne[0] == 128)) { + if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) { + if (precision == GGML_PREC_DEFAULT) { + ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); + return; + } else if(Q->ne[0] <= 128) { ggml_cuda_flash_attn_ext_vec_f32(ctx, dst); return; } - - if (Q->ne[1] <= 32 || Q->ne[0] > 128) { - constexpr int cols_per_block = 16; - constexpr int nwarps = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, float>(ctx, dst); - break; - case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, float>(ctx, dst); - break; - case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, float>(ctx, dst); - break; - case 112: - launch_fattn_f16<112, cols_per_block, nwarps, float>(ctx, dst); - break; - case 128: - launch_fattn_f16<128, cols_per_block, nwarps, float>(ctx, dst); - break; - case 256: - launch_fattn_f16<256, cols_per_block, nwarps, float>(ctx, dst); - break; - default: - GGML_ASSERT(false); - break; - } - } else { - constexpr int cols_per_block = 32; - constexpr int nwarps = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, float>(ctx, dst); - break; - case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, float>(ctx, dst); - break; - case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, float>(ctx, dst); - break; - case 112: - launch_fattn_f16<112, cols_per_block, nwarps, float>(ctx, dst); - break; - case 128: - launch_fattn_f16<128, cols_per_block, nwarps, float>(ctx, dst); - break; - // case 256: - // launch_fattn_f16<256, cols_per_block, nwarps, float>(ctx, dst); - // break; - default: - GGML_ASSERT(false); - break; - } - } - return; } - if (Q->ne[1] == 1 && Q->ne[0] % (2*WARP_SIZE) == 0) { - ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); - return; - } - - if (Q->ne[1] <= 8 && Q->ne[0] % WARP_SIZE == 0) { - constexpr int cols_per_block = 8; - constexpr int nwarps = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, half>(ctx, dst); - break; - case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst); - break; - case 128: - launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst); - break; - case 256: - launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst); - break; - default: - GGML_ASSERT(false); - break; - } - return; - } - - if (Q->ne[1] <= 32) { - constexpr int cols_per_block = 16; - constexpr int nwarps = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, half>(ctx, dst); - break; - case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, half>(ctx, dst); - break; - case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst); - break; - case 112: - launch_fattn_f16<112, cols_per_block, nwarps, half>(ctx, dst); - break; - case 128: - launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst); - break; - case 256: - launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst); - break; - default: - GGML_ASSERT(false); - break; - } - return; - } - - constexpr int cols_per_block = 32; - constexpr int nwarps = 4; - switch (Q->ne[0]) { - case 64: - launch_fattn_f16< 64, cols_per_block, nwarps, half>(ctx, dst); - break; - case 80: - launch_fattn_f16< 80, cols_per_block, nwarps, half>(ctx, dst); - break; - case 96: - launch_fattn_f16< 96, cols_per_block, nwarps, half>(ctx, dst); - break; - case 112: - launch_fattn_f16<112, cols_per_block, nwarps, half>(ctx, dst); - break; - case 128: - launch_fattn_f16<128, cols_per_block, nwarps, half>(ctx, dst); - break; - case 256: - launch_fattn_f16<256, cols_per_block, nwarps, half>(ctx, dst); - break; - default: - GGML_ASSERT(false); - break; - } - return; + ggml_cuda_flash_attn_ext_wmma_f16(ctx, dst); } diff --git a/ggml-cuda/mmq.cu b/ggml-cuda/mmq.cu index c0a66d9b6..ebe1dc5c8 100644 --- a/ggml-cuda/mmq.cu +++ b/ggml-cuda/mmq.cu @@ -386,7 +386,7 @@ static __device__ __forceinline__ float vec_dot_q5_0_q8_1_mul_mat( u[2*l+1] = y_qs[j * WARP_SIZE + (kyqs + l + QI5_0) % WARP_SIZE]; } - return vec_dot_q8_0_q8_1_impl + 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)]); } @@ -547,7 +547,7 @@ static __device__ __forceinline__ float vec_dot_q8_0_q8_1_mul_mat( const float * x_dmf = (const float *) x_dm; const float * y_df = (const float *) y_ds; - return vec_dot_q8_0_q8_1_impl + 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]); } diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-f16.cu new file mode 100644 index 000000000..d7f103475 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_0.cu new file mode 100644 index 000000000..f3d8d2eda --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_1.cu new file mode 100644 index 000000000..9beb05ca2 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_0.cu new file mode 100644 index 000000000..0c163dcba --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_1.cu new file mode 100644 index 000000000..3980167b3 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q8_0.cu new file mode 100644 index 000000000..fe099921d --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-f16-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-f16.cu new file mode 100644 index 000000000..d4d5e7999 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_0.cu new file mode 100644 index 000000000..f08b10c4d --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_1.cu new file mode 100644 index 000000000..e8c3f8adc --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_0.cu new file mode 100644 index 000000000..c01416a13 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_1.cu new file mode 100644 index 000000000..46615f281 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q8_0.cu new file mode 100644 index 000000000..72dcc1a2f --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_0-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-f16.cu new file mode 100644 index 000000000..9fa8a377d --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_0.cu new file mode 100644 index 000000000..20ea86c6d --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_1.cu new file mode 100644 index 000000000..ed815957c --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_0.cu new file mode 100644 index 000000000..bbe9e6a1c --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_1.cu new file mode 100644 index 000000000..d12a61699 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q8_0.cu new file mode 100644 index 000000000..1e901afcb --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q4_1-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-f16.cu new file mode 100644 index 000000000..a3f98ce37 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_0.cu new file mode 100644 index 000000000..1bae97243 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_1.cu new file mode 100644 index 000000000..7258e9775 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_0.cu new file mode 100644 index 000000000..08435c005 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_1.cu new file mode 100644 index 000000000..17864e8e9 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q8_0.cu new file mode 100644 index 000000000..9239138c9 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_0-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-f16.cu new file mode 100644 index 000000000..e387d9c1d --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_0.cu new file mode 100644 index 000000000..d69d3bbd6 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_1.cu new file mode 100644 index 000000000..61a478816 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_0.cu new file mode 100644 index 000000000..89995080a --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_1.cu new file mode 100644 index 000000000..9e6a58dff --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q8_0.cu new file mode 100644 index 000000000..153cbfd86 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q5_1-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-f16.cu new file mode 100644 index 000000000..09d576558 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_0.cu new file mode 100644 index 000000000..3e3c91e68 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_1.cu new file mode 100644 index 000000000..7b973058f --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_0.cu new file mode 100644 index 000000000..a43a475d4 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_1.cu new file mode 100644 index 000000000..5b570c0a3 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q8_0.cu new file mode 100644 index 000000000..bf2cc684e --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs128-q8_0-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs256-f16-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs256-f16-f16.cu new file mode 100644 index 000000000..7428e45ea --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs256-f16-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-f16.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-f16.cu new file mode 100644 index 000000000..4aee830de --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_0.cu new file mode 100644 index 000000000..36acb6319 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_1.cu new file mode 100644 index 000000000..a4090c390 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_0.cu new file mode 100644 index 000000000..17b6b2d11 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_1.cu new file mode 100644 index 000000000..549e1cea1 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q8_0.cu new file mode 100644 index 000000000..66bcd820f --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f16-instance-hs64-f16-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f16.cuh" + +DECL_FATTN_VEC_F16_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-f16.cu new file mode 100644 index 000000000..15933a299 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_0.cu new file mode 100644 index 000000000..8aa785583 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_1.cu new file mode 100644 index 000000000..bde3924fd --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_0.cu new file mode 100644 index 000000000..1708181c1 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_1.cu new file mode 100644 index 000000000..30fa6fa4c --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q8_0.cu new file mode 100644 index 000000000..69673d50f --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-f16-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_F16, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-f16.cu new file mode 100644 index 000000000..d8b2b2e18 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_0.cu new file mode 100644 index 000000000..01cce7ab5 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_1.cu new file mode 100644 index 000000000..fd5563b39 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_0.cu new file mode 100644 index 000000000..b13cc4a0c --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_1.cu new file mode 100644 index 000000000..86f1fc637 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q8_0.cu new file mode 100644 index 000000000..26e7df4be --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_0-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_0, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-f16.cu new file mode 100644 index 000000000..e4fda8952 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_0.cu new file mode 100644 index 000000000..bd15117b4 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_1.cu new file mode 100644 index 000000000..cb6c6a760 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_0.cu new file mode 100644 index 000000000..201b6641d --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_1.cu new file mode 100644 index 000000000..6da57a44a --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q8_0.cu new file mode 100644 index 000000000..47623c9bf --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q4_1-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q4_1, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-f16.cu new file mode 100644 index 000000000..82c6861d2 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_0.cu new file mode 100644 index 000000000..24a80c2b0 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_1.cu new file mode 100644 index 000000000..b95eaf7e1 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_0.cu new file mode 100644 index 000000000..275f2efcc --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_1.cu new file mode 100644 index 000000000..3673f7fd5 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q8_0.cu new file mode 100644 index 000000000..2c4d59947 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_0-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_0, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-f16.cu new file mode 100644 index 000000000..2457cdf3f --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_0.cu new file mode 100644 index 000000000..b3b411ed3 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_1.cu new file mode 100644 index 000000000..b7f308a4d --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_0.cu new file mode 100644 index 000000000..739686697 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_1.cu new file mode 100644 index 000000000..708d03113 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q8_0.cu new file mode 100644 index 000000000..df891be60 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q5_1-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q5_1, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-f16.cu new file mode 100644 index 000000000..f49b6d1f9 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_0.cu new file mode 100644 index 000000000..1de92148b --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_1.cu new file mode 100644 index 000000000..7a1ba7f8d --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_0.cu new file mode 100644 index 000000000..25493e4ba --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_1.cu new file mode 100644 index 000000000..3cd650c7b --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q8_0.cu new file mode 100644 index 000000000..88ffa43d6 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs128-q8_0-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(128, GGML_TYPE_Q8_0, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs256-f16-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs256-f16-f16.cu new file mode 100644 index 000000000..8c7bac6c2 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs256-f16-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(256, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-f16.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-f16.cu new file mode 100644 index 000000000..a28f62e7b --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-f16.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_F16); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_0.cu new file mode 100644 index 000000000..d39838b96 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q4_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_1.cu new file mode 100644 index 000000000..834d40f6c --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q4_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q4_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_0.cu new file mode 100644 index 000000000..f7d54668b --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q5_0); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_1.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_1.cu new file mode 100644 index 000000000..59e00ad83 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q5_1.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q5_1); diff --git a/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q8_0.cu b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q8_0.cu new file mode 100644 index 000000000..6e63893de --- /dev/null +++ b/ggml-cuda/template-instances/fattn-vec-f32-instance-hs64-f16-q8_0.cu @@ -0,0 +1,5 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-vec-f32.cuh" + +DECL_FATTN_VEC_F32_CASE(64, GGML_TYPE_F16, GGML_TYPE_Q8_0); diff --git a/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb16.cu b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb16.cu new file mode 100644 index 000000000..ca356ad6c --- /dev/null +++ b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb16.cu @@ -0,0 +1,10 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-wmma-f16.cuh" + +DECL_FATTN_WMMA_F16_CASE(64, 16, float); +DECL_FATTN_WMMA_F16_CASE(80, 16, float); +DECL_FATTN_WMMA_F16_CASE(96, 16, float); +DECL_FATTN_WMMA_F16_CASE(112, 16, float); +DECL_FATTN_WMMA_F16_CASE(128, 16, float); +DECL_FATTN_WMMA_F16_CASE(256, 16, float); diff --git a/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb32.cu b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb32.cu new file mode 100644 index 000000000..430ee64eb --- /dev/null +++ b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqfloat-cpb32.cu @@ -0,0 +1,9 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-wmma-f16.cuh" + +DECL_FATTN_WMMA_F16_CASE(64, 32, float); +DECL_FATTN_WMMA_F16_CASE(80, 32, float); +DECL_FATTN_WMMA_F16_CASE(96, 32, float); +DECL_FATTN_WMMA_F16_CASE(112, 32, float); +DECL_FATTN_WMMA_F16_CASE(128, 32, float); diff --git a/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb16.cu b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb16.cu new file mode 100644 index 000000000..d421d17cc --- /dev/null +++ b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb16.cu @@ -0,0 +1,10 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-wmma-f16.cuh" + +DECL_FATTN_WMMA_F16_CASE(64, 16, half); +DECL_FATTN_WMMA_F16_CASE(80, 16, half); +DECL_FATTN_WMMA_F16_CASE(96, 16, half); +DECL_FATTN_WMMA_F16_CASE(112, 16, half); +DECL_FATTN_WMMA_F16_CASE(128, 16, half); +DECL_FATTN_WMMA_F16_CASE(256, 16, half); diff --git a/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb32.cu b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb32.cu new file mode 100644 index 000000000..deacd5f58 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb32.cu @@ -0,0 +1,10 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-wmma-f16.cuh" + +DECL_FATTN_WMMA_F16_CASE(64, 32, half); +DECL_FATTN_WMMA_F16_CASE(80, 32, half); +DECL_FATTN_WMMA_F16_CASE(96, 32, half); +DECL_FATTN_WMMA_F16_CASE(112, 32, half); +DECL_FATTN_WMMA_F16_CASE(128, 32, half); +DECL_FATTN_WMMA_F16_CASE(256, 32, half); diff --git a/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb8.cu b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb8.cu new file mode 100644 index 000000000..282896733 --- /dev/null +++ b/ggml-cuda/template-instances/fattn-wmma-f16-instance-kqhalf-cpb8.cu @@ -0,0 +1,8 @@ +// This file has been autogenerated by generate-variants.py, do not edit manually. + +#include "../fattn-wmma-f16.cuh" + +DECL_FATTN_WMMA_F16_CASE(64, 8, half); +DECL_FATTN_WMMA_F16_CASE(96, 8, half); +DECL_FATTN_WMMA_F16_CASE(128, 8, half); +DECL_FATTN_WMMA_F16_CASE(256, 8, half); diff --git a/ggml-cuda/template-instances/generate_cu_files.py b/ggml-cuda/template-instances/generate_cu_files.py new file mode 100755 index 000000000..ee5b460e0 --- /dev/null +++ b/ggml-cuda/template-instances/generate_cu_files.py @@ -0,0 +1,59 @@ +#!/usr/bin/env python3 + +from glob import glob +import os + +TYPES_KV = ["GGML_TYPE_Q4_0", "GGML_TYPE_Q4_1", "GGML_TYPE_Q5_0", "GGML_TYPE_Q5_1", "GGML_TYPE_Q8_0", "GGML_TYPE_F16"] + +SOURCE_FATTN_VEC = """// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-vec-f{vkq_size}.cuh" + +DECL_FATTN_VEC_F{vkq_size}_CASE({head_size}, {type_k}, {type_v}); +""" + +SOURCE_FATTN_WMMA_START = """// This file has been autogenerated by generate_cu_files.py, do not edit manually. + +#include "../fattn-wmma-f16.cuh" + +""" + +SOURCE_FATTN_WMMA_CASE = "DECL_FATTN_WMMA_F16_CASE({head_size}, {cols_per_block}, {kq_acc_t});\n" + + +def get_short_name(long_quant_name): + return long_quant_name.replace("GGML_TYPE_", "").lower() + + +def get_head_sizes(type_k, type_v): + if type_k == "GGML_TYPE_F16" and type_v == "GGML_TYPE_F16": + return [64, 128, 256] + if type_k == "GGML_TYPE_F16": + return [64, 128] + return [128] + + +for filename in glob("*.cu"): + os.remove(filename) + +for vkq_size in [16, 32]: + for type_k in TYPES_KV: + for type_v in TYPES_KV: + for head_size in get_head_sizes(type_k, type_v): + with open(f"fattn-vec-f{vkq_size}-instance-hs{head_size}-{get_short_name(type_k)}-{get_short_name(type_v)}.cu", "w") as f: + f.write(SOURCE_FATTN_VEC.format(vkq_size=vkq_size, head_size=head_size, type_k=type_k, type_v=type_v)) + +for kq_acc_t in ["half", "float"]: + for cols_per_block in [8, 16, 32]: + if kq_acc_t == "float" and cols_per_block == 8: + continue + + with open(f"fattn-wmma-f16-instance-kq{kq_acc_t}-cpb{cols_per_block}.cu", "w") as f: + f.write(SOURCE_FATTN_WMMA_START) + + for head_size in [64, 80, 96, 112, 128, 256]: + if cols_per_block == 8 and head_size % 32 != 0: # wmma fragment is 8x32 + continue + if kq_acc_t == "float" and cols_per_block == 32 and head_size == 256: # register spilling, bad performance + continue + f.write(SOURCE_FATTN_WMMA_CASE.format(kq_acc_t=kq_acc_t, cols_per_block=cols_per_block, head_size=head_size)) diff --git a/ggml-cuda/vecdotq.cuh b/ggml-cuda/vecdotq.cuh index 5ebdddcc7..df9752390 100644 --- a/ggml-cuda/vecdotq.cuh +++ b/ggml-cuda/vecdotq.cuh @@ -180,8 +180,8 @@ template static __device__ __forceinline__ float vec_dot_q5_1_q8_1_imp #define VDR_Q8_0_Q8_1_MMVQ 2 #define VDR_Q8_0_Q8_1_MMQ 8 -template static __device__ __forceinline__ float vec_dot_q8_0_q8_1_impl( - const int * v, const int * u, const float & d8_0, const float & d8_1) { +template static __device__ __forceinline__ T vec_dot_q8_0_q8_1_impl( + const int * v, const int * u, const T & d8_0, const T & d8_1) { #if __CUDA_ARCH__ >= MIN_CC_DP4A // lowest compute capability for integer intrinsics int sumi = 0; @@ -192,7 +192,7 @@ template static __device__ __forceinline__ float vec_dot_q8_0_q8_1_imp sumi = __dp4a(v[i], u[i], sumi); } - return d8_0*d8_1 * sumi; + return d8_0*d8_1 * ((T) sumi); #else NO_DEVICE_CODE; #endif // __CUDA_ARCH__ >= MIN_CC_DP4A @@ -656,7 +656,7 @@ static __device__ __forceinline__ float vec_dot_q8_0_q8_1( 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, __low2half(bq8_1->ds)); + return vec_dot_q8_0_q8_1_impl(v, u, bq8_0->d, __low2half(bq8_1->ds)); } static __device__ __forceinline__ float vec_dot_q2_K_q8_1( diff --git a/ggml-metal.m b/ggml-metal.m index 079912952..fddc44f78 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -779,6 +779,12 @@ static bool ggml_metal_supports_op(const struct ggml_metal_context * ctx, const case GGML_OP_LEAKY_RELU: return true; case GGML_OP_FLASH_ATTN_EXT: + if (op->src[1]->type != GGML_TYPE_F16) { + return false; + } + if (op->src[2]->type != GGML_TYPE_F16) { + return false; + } if (op->src[0]->ne[0] == 256) { return false; } diff --git a/llama.cpp b/llama.cpp index 40d2ec2c9..841be1de7 100644 --- a/llama.cpp +++ b/llama.cpp @@ -16221,6 +16221,11 @@ struct llama_context * llama_new_context_with_model( params.flash_attn = false; } + if (params.type_v != GGML_TYPE_F16 && !params.flash_attn) { + LLAMA_LOG_ERROR("%s: V cache quantization requires flash_attn\n", __func__); + return nullptr; + } + llama_context * ctx = new llama_context(*model); const auto & hparams = model->hparams; diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 72edc64a7..777230127 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1570,21 +1570,23 @@ struct test_flash_attn_ext : public test_case { const float max_bias; // ALiBi + const ggml_type type_KV; + std::string vars() override { - return VARS_TO_STR6(hs, nh, kv, nb, mask, max_bias); + return VARS_TO_STR7(hs, nh, kv, nb, mask, max_bias, type_KV); } double max_nmse_err() override { return 5e-4; } - test_flash_attn_ext(int64_t hs = 128, int64_t nh = 32, int64_t kv = 96, int64_t nb = 8, bool mask = true, float max_bias = 0.0f) - : hs(hs), nh(nh), kv(kv), nb(nb), mask(mask), max_bias(max_bias) {} + test_flash_attn_ext(int64_t hs = 128, int64_t nh = 32, int64_t kv = 96, int64_t nb = 8, bool mask = true, float max_bias = 0.0f, ggml_type type_KV = GGML_TYPE_F16) + : hs(hs), nh(nh), kv(kv), nb(nb), mask(mask), max_bias(max_bias), type_KV(type_KV) {} ggml_tensor * build_graph(ggml_context * ctx) override { ggml_tensor * q = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, hs, nb, nh, 1); - ggml_tensor * k = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, hs, kv, nh, 1); - ggml_tensor * v = ggml_new_tensor_4d(ctx, GGML_TYPE_F16, hs, kv, nh, 1); + ggml_tensor * k = ggml_new_tensor_4d(ctx, type_KV, hs, kv, nh, 1); + ggml_tensor * v = ggml_new_tensor_4d(ctx, type_KV, hs, kv, nh, 1); ggml_tensor * m = mask ? ggml_new_tensor_4d(ctx, GGML_TYPE_F16, kv, GGML_PAD(nb, GGML_KQ_MASK_PAD), 1, 1) : nullptr; ggml_tensor * out = ggml_flash_attn_ext(ctx, q, k, v, m, 1.0f/sqrtf(hs), max_bias); return out; @@ -2290,7 +2292,9 @@ static bool test_backend(ggml_backend_t backend, test_mode mode, const char * op for (int nh : { 32, }) { for (int kv : { 512, 1024, }) { for (int nb : { 1, 2, 4, 8, }) { - test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb, mask, max_bias)); + for (ggml_type type_KV : {GGML_TYPE_F16, GGML_TYPE_Q8_0, GGML_TYPE_Q4_0}) { + test_cases.emplace_back(new test_flash_attn_ext(hs, nh, kv, nb, mask, max_bias, type_KV)); + } } } } From 750f60c03e4d3f53fa51910551ce87a3d508d2d7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 1 Jun 2024 15:47:04 +0200 Subject: [PATCH 177/181] CUDA: fix Pascal FA, deq. KV to FP16 for batch > 8 (#7681) --- ggml-cuda/fattn-common.cuh | 74 ++++++++++++++++++++++++++++-------- ggml-cuda/fattn-tile-f16.cu | 4 +- ggml-cuda/fattn-tile-f32.cu | 4 +- ggml-cuda/fattn-vec-f16.cuh | 4 +- ggml-cuda/fattn-vec-f32.cuh | 4 +- ggml-cuda/fattn-wmma-f16.cuh | 6 +-- ggml-cuda/fattn.cu | 6 +-- 7 files changed, 73 insertions(+), 29 deletions(-) diff --git a/ggml-cuda/fattn-common.cuh b/ggml-cuda/fattn-common.cuh index 4bf03a49f..c00f8606a 100644 --- a/ggml-cuda/fattn-common.cuh +++ b/ggml-cuda/fattn-common.cuh @@ -1,6 +1,7 @@ #pragma once #include "common.cuh" +#include "convert.cuh" #include "vecdotq.cuh" #include @@ -53,7 +54,7 @@ typedef float (*vec_dot_KQ_f32_t)( template static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_0( const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { -#if __CUDA_ARCH__ > MIN_CC_DP4A +#if __CUDA_ARCH__ >= MIN_CC_DP4A const block_q4_0 * K_q4_0 = (const block_q4_0 *) K_c; GGML_UNUSED(Q_v); @@ -95,13 +96,13 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_0( GGML_UNUSED(Q_q8); GGML_UNUSED(Q_ds_v); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ > MIN_CC_DP4A +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } template static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_1( const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { -#if __CUDA_ARCH__ > MIN_CC_DP4A +#if __CUDA_ARCH__ >= MIN_CC_DP4A const block_q4_1 * K_q4_1 = (const block_q4_1 *) K_c; GGML_UNUSED(Q_v); @@ -147,13 +148,13 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q4_1( GGML_UNUSED(Q_q8); GGML_UNUSED(Q_ds_v); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ > MIN_CC_DP4A +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } template static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0( const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { -#if __CUDA_ARCH__ > MIN_CC_DP4A +#if __CUDA_ARCH__ >= MIN_CC_DP4A const block_q5_0 * K_q5_0 = (const block_q5_0 *) K_c; GGML_UNUSED(Q_v); @@ -202,13 +203,13 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_0( GGML_UNUSED(Q_q8); GGML_UNUSED(Q_ds_v); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ > MIN_CC_DP4A +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } template static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1( const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { -#if __CUDA_ARCH__ > MIN_CC_DP4A +#if __CUDA_ARCH__ >= MIN_CC_DP4A const block_q5_1 * K_q5_1 = (const block_q5_1 *) K_c; GGML_UNUSED(Q_v); @@ -261,13 +262,13 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q5_1( GGML_UNUSED(Q_q8); GGML_UNUSED(Q_ds_v); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ > MIN_CC_DP4A +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } template static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q8_0( const char * __restrict__ K_c, const void * __restrict__ Q_v, const int * __restrict__ Q_q8, const void * __restrict__ Q_ds_v) { -#if __CUDA_ARCH__ > MIN_CC_DP4A +#if __CUDA_ARCH__ >= MIN_CC_DP4A const block_q8_0 * K_q8_0 = (const block_q8_0 *) K_c; GGML_UNUSED(Q_v); @@ -302,7 +303,7 @@ static __device__ __forceinline__ T vec_dot_fattn_vec_KQ_q8_0( GGML_UNUSED(Q_q8); GGML_UNUSED(Q_ds_v); NO_DEVICE_CODE; -#endif // __CUDA_ARCH__ > MIN_CC_DP4A +#endif // __CUDA_ARCH__ >= MIN_CC_DP4A } template @@ -620,7 +621,10 @@ static void on_no_fattn_vec_case(const int D) { } template -void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kernel_t fattn_kernel, int nwarps, int cols_per_block) { +void launch_fattn( + ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kernel_t fattn_kernel, + const int nwarps, const int cols_per_block, const bool need_f16_K, const bool need_f16_V +) { const ggml_tensor * Q = dst->src[0]; const ggml_tensor * K = dst->src[1]; const ggml_tensor * V = dst->src[2]; @@ -641,9 +645,49 @@ void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kern ggml_cuda_pool & pool = ctx.pool(); cudaStream_t main_stream = ctx.stream(); + ggml_cuda_pool_alloc K_f16(pool); + ggml_cuda_pool_alloc V_f16(pool); ggml_cuda_pool_alloc dst_tmp(pool); ggml_cuda_pool_alloc dst_tmp_meta(pool); + char * K_data = (char *) K->data; + size_t nb11 = K->nb[1]; + size_t nb12 = K->nb[2]; + size_t nb13 = K->nb[3]; + + char * V_data = (char *) V->data; + size_t nb21 = V->nb[1]; + size_t nb22 = V->nb[2]; + size_t nb23 = V->nb[3]; + + if (need_f16_K && K->type != GGML_TYPE_F16) { + K_f16.alloc(ggml_nelements(K)); + to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(K->type); + to_fp16(K_data, K_f16.ptr, ggml_nelements(K), main_stream); + K_data = (char *) K_f16.ptr; + + const size_t bs = ggml_blck_size(K->type); + const size_t ts = ggml_type_size(K->type); + + nb11 = nb11*bs*sizeof(half)/ts; + nb12 = nb12*bs*sizeof(half)/ts; + nb13 = nb13*bs*sizeof(half)/ts; + } + + if (need_f16_V && V->type != GGML_TYPE_F16) { + V_f16.alloc(ggml_nelements(V)); + to_fp16_cuda_t to_fp16 = ggml_get_to_fp16_cuda(V->type); + to_fp16(V_data, V_f16.ptr, ggml_nelements(V), main_stream); + V_data = (char *) V_f16.ptr; + + const size_t bs = ggml_blck_size(V->type); + const size_t ts = ggml_type_size(V->type); + + nb21 = nb21*bs*sizeof(half)/ts; + nb22 = nb22*bs*sizeof(half)/ts; + nb23 = nb23*bs*sizeof(half)/ts; + } + if (parallel_blocks > 1) { dst_tmp.alloc(parallel_blocks*ggml_nelements(KQV)); dst_tmp_meta.alloc(parallel_blocks*ggml_nrows(KQV)); @@ -667,8 +711,8 @@ void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kern fattn_kernel<<>>( (const char *) Q->data, - (const char *) K->data, - (const char *) V->data, + K_data, + V_data, mask ? ((const char *) mask->data) : nullptr, (parallel_blocks) == 1 ? (float *) KQV->data : dst_tmp.ptr, dst_tmp_meta.ptr, scale, max_bias, m0, m1, n_head_log2, @@ -676,8 +720,8 @@ void launch_fattn(ggml_backend_cuda_context & ctx, ggml_tensor * dst, fattn_kern K->ne[0], K->ne[1], K->ne[2], K->ne[3], mask ? mask->ne[1] : 0, mask ? mask->nb[1] : 0, Q->nb[1], Q->nb[2], Q->nb[3], - K->nb[1], K->nb[2], K->nb[3], - V->nb[1], V->nb[2], V->nb[3], + nb11, nb12, nb13, + nb21, nb22, nb23, KQV->ne[0], KQV->ne[1], KQV->ne[2], KQV->ne[3] ); CUDA_CHECK(cudaGetLastError()); diff --git a/ggml-cuda/fattn-tile-f16.cu b/ggml-cuda/fattn-tile-f16.cu index 3d64a9eba..cb11d7212 100644 --- a/ggml-cuda/fattn-tile-f16.cu +++ b/ggml-cuda/fattn-tile-f16.cu @@ -278,13 +278,13 @@ void launch_fattn_tile_f16_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * constexpr int D = 64; constexpr int nwarps = 8; fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true); } break; case 128: { constexpr int D = 128; constexpr int nwarps = 8; fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true); } break; default: { GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); diff --git a/ggml-cuda/fattn-tile-f32.cu b/ggml-cuda/fattn-tile-f32.cu index 61fce0a7e..15e22f495 100644 --- a/ggml-cuda/fattn-tile-f32.cu +++ b/ggml-cuda/fattn-tile-f32.cu @@ -275,13 +275,13 @@ void launch_fattn_tile_f32_64_128(ggml_backend_cuda_context & ctx, ggml_tensor * constexpr int D = 64; constexpr int nwarps = 8; fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true); } break; case 128: { constexpr int D = 128; constexpr int nwarps = 8; fattn_kernel_t fattn_kernel = flash_attn_tile_ext_f32; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true); } break; default: { GGML_ASSERT(false && "FlashAttention without tensor cores only supports head sizes 64 and 128."); diff --git a/ggml-cuda/fattn-vec-f16.cuh b/ggml-cuda/fattn-vec-f16.cuh index ea4fcc972..9e1aa2c6b 100644 --- a/ggml-cuda/fattn-vec-f16.cuh +++ b/ggml-cuda/fattn-vec-f16.cuh @@ -290,7 +290,9 @@ template ; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + constexpr bool need_f16_K = D != 128; + constexpr bool need_f16_V = D != 128 && D != 64; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, need_f16_K, need_f16_V); } template diff --git a/ggml-cuda/fattn-vec-f32.cuh b/ggml-cuda/fattn-vec-f32.cuh index 3009f0f43..ce23a4ebd 100644 --- a/ggml-cuda/fattn-vec-f32.cuh +++ b/ggml-cuda/fattn-vec-f32.cuh @@ -271,7 +271,9 @@ template ; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + constexpr bool need_f16_K = D != 128; + constexpr bool need_f16_V = D != 128 && D != 64; + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, need_f16_K, need_f16_V); } template diff --git a/ggml-cuda/fattn-wmma-f16.cuh b/ggml-cuda/fattn-wmma-f16.cuh index 65ed31852..59cd30d78 100644 --- a/ggml-cuda/fattn-wmma-f16.cuh +++ b/ggml-cuda/fattn-wmma-f16.cuh @@ -438,18 +438,18 @@ void ggml_cuda_flash_attn_ext_wmma_f16_case(ggml_backend_cuda_context & ctx, ggm if (4*blocks_num_pb1 < 2*nsm) { constexpr int parallel_blocks = 4; fattn_kernel_t fattn_kernel = flash_attn_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true); return; } if (2*blocks_num_pb1 < 2*nsm) { constexpr int parallel_blocks = 2; fattn_kernel_t fattn_kernel = flash_attn_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true); return; } constexpr int parallel_blocks = 1; fattn_kernel_t fattn_kernel = flash_attn_ext_f16; - launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block); + launch_fattn(ctx, dst, fattn_kernel, nwarps, cols_per_block, true, true); } #define DECL_FATTN_WMMA_F16_CASE(D, cols_per_block, KQ_acc_t) \ diff --git a/ggml-cuda/fattn.cu b/ggml-cuda/fattn.cu index b35ab67a8..38d30b210 100644 --- a/ggml-cuda/fattn.cu +++ b/ggml-cuda/fattn.cu @@ -298,17 +298,13 @@ static void ggml_cuda_flash_attn_ext_vec_f32(ggml_backend_cuda_context & ctx, gg void ggml_cuda_flash_attn_ext(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { const ggml_tensor * KQV = dst; const ggml_tensor * Q = dst->src[0]; - const ggml_tensor * K = dst->src[1]; - const ggml_tensor * V = dst->src[2]; ggml_cuda_set_device(ctx.device); const int cc = ggml_cuda_info().devices[ggml_cuda_get_device()].cc; const int32_t precision = KQV->op_params[2]; - const bool quantized_KV = ggml_is_quantized(K->type) || ggml_is_quantized(V->type); - // On AMD the tile kernels perform poorly, use the vec kernel instead: - if (cc >= CC_OFFSET_AMD || quantized_KV) { + if (cc >= CC_OFFSET_AMD) { if (precision == GGML_PREC_DEFAULT && fast_fp16_available(cc)) { ggml_cuda_flash_attn_ext_vec_f16(ctx, dst); } else { From 2ac95c9d5678d05e253691fb1f26471675bff5ad Mon Sep 17 00:00:00 2001 From: HanishKVC Date: Sat, 1 Jun 2024 21:50:18 +0530 Subject: [PATCH 178/181] SimpleChat: Simple histogram/repeatMatching driven garbageTrimming, Settings UI, Streaming mode, OpenAi Compat (Model, Authorization Bearer), Save/Restore session, Auto Settings UI (#7548) * SimpleChat:DU:BringIn local helper js modules using importmap Use it to bring in a simple trim garbage at end logic, which is used to trim received response. Also given that importmap assumes esm / standard js modules, so also global variables arent implicitly available outside the modules. So add it has a member of document for now * SimpleChat:DU: Add trim garbage at end in loop helper * SimpleChat:DU:TrimGarbage if unable try skip char and retry * SimpleChat:DU: Try trim using histogram based info TODO: May have to add max number of uniq chars in histogram at end of learning phase. * SimpleChat:DU: Switch trim garbage hist based to maxUniq simple Instead of blindly building histogram for specified substring length, and then checking if any new char within specified min garbage length limit, NOW exit learn state when specified maxUniq chars are found. Inturn there should be no new chars with in the specified min garbage length required limit. TODO: Need to track char classes like alphabets, numerals and special/other chars. * SimpleChat:DU: Bring in maxType to the mix along with maxUniq Allow for more uniq chars, but then ensure that a given type of char ie numerals or alphabets or other types dont cross the specified maxType limit. This allows intermixed text garbage to be identified and trimmed. * SimpleChat:DU: Cleanup debug log messages * SimpleChat:UI: Move html ui base helpers into its own module * SimpleChat:DU:Avoid setting frequence/Presence penalty Some models like llama3 found to try to be over intelligent by repeating garbage still, but by tweaking the garbage a bit so that it is not exactly same. So avoid setting these penalties and let the model's default behaviour work out, as is. Also the simple minded histogram based garbage trimming from end, works to an extent, when the garbage is more predictable and repeatative. * SimpleChat:UI: Add and use a para-create-append helper Also update the config params dump to indicate that now one needs to use document to get hold of gMe global object, this is bcas of moving to module type js. Also add ui.mjs to importmap * SimpleChat:UI: Helper to create bool button and use it wrt settings * SimpleChat:UI: Add Select helper and use it wrt ChatHistoryInCtxt * SimpleChat:UI:Select: dict-name-value, value wrt default, change Take a dict/object of name-value pairs instead of just names. Inturn specify the actual value wrt default, rather than the string representing that value. Trap the needed change event rather than click wrt select. * SimpleChat:UI: Add Div wrapped label+element helpers Move settings related elements to use the new div wrapped ones. * SimpleChat:UI:Add settings button and bring in settings ui * SimpleChat:UI:Settings make boolean button text show meaning * SimpleChat: Update a bit wrt readme and notes in du * SimpleChat: GarbageTrim enable/disable, show trimmed part ifany * SimpleChat: highlight trim, garbage trimming bitmore aggressive Make it easy for end user to identified the trimmed text. Make garbage trimming logic, consider a longer repeat garbage substring. * SimpleChat: Cleanup a bit wrt Api end point related flow Consolidate many of the Api end point related basic meta data into ApiEP class. Remove the hardcoded ApiEP/Mode settings from html+js, instead use the generic select helper logic, inturn in the settings block. Move helper to generate the appropriate request json string based on ApiEP into SimpleChat class itself. * SimpleChat:Move extracting assistant response to SimpleChat class so also the trimming of garbage. * SimpleChat:DU: Bring in both trim garbage logics to try trim * SimpleChat: Cleanup readme a bit, add one more chathistory length * SimpleChat:Stream:Initial handshake skeleton Parse the got stream responses and try extract the data from it. It allows for a part read to get a single data line or multiple data line. Inturn extract the json body and inturn the delta content/message in it. * SimpleChat: Move handling oneshot mode server response Move handling of the oneshot mode server response into SimpleChat. Also add plumbing for moving multipart server response into same. * SimpleChat: Move multi part server response handling in * SimpleChat: Add MultiPart Response handling, common trimming Add logic to call into multipart/stream server response handling. Move trimming of garbage at the end into the common handle_response helper. Add new global flag to control between oneshot and multipart/stream mode of fetching response. Allow same to be controlled by user. If in multipart/stream mode, send the stream flag to the server. * SimpleChat: show streamed generative text as it becomes available Now that the extracting of streamed generated text is implemented, add logic to show the same on the screen. * SimpleChat:DU: Add NewLines helper class To work with an array of new lines. Allow adding, appending, shifting, ... * SimpleChat:DU: Make NewLines shift more robust and flexible * SimpleChat:HandleResponseMultiPart using NewLines helper Make handle_response_multipart logic better and cleaner. Now it allows for working with the situation, where the delta data line got from server in stream mode, could be split up when recving, but still the logic will handle it appropriately. ALERT: Rather except (for now) for last data line wrt a request's response. * SimpleChat: Disable console debug by default by making it dummy Parallely save a reference to the original func. * SimpleChat:MultiPart/Stream flow cleanup Dont try utf8-decode and newlines-add_append if no data to work on. If there is no more data to get (ie done is set), then let NewLines instance return line without newline at end, So that we dont miss out on any last-data-line without newline kind of scenario. Pass stream flag wrt utf-8 decode, so that if any multi-byte char is only partly present in the passed buffer, it can be accounted for along with subsequent buffer. At sametime, bcas of utf-8's characteristics there shouldnt be any unaccounted bytes at end, for valid block of utf8 data split across chunks, so not bothering calling with stream set to false at end. LATER: Look at TextDecoder's implementation, for any over intelligence, it may be doing.. If needed, one can use done flag to account wrt both cases. * SimpleChat: Move baseUrl to Me and inturn gMe This should allow easy updating of the base url at runtime by the end user. * SimpleChat:UI: Add input element helper * SimpleChat: Add support for changing the base url This ensures that if the user is running the server with a different port or wants to try connect to server on a different machine, then this can be used. * SimpleChat: Move request headers into Me and gMe Inturn allow Authorization to be sent, if not empty. * SimpleChat: Rather need to use append to insert headers * SimpleChat: Allow Authorization header to be set by end user * SimpleChat:UI+: Return div and element wrt creatediv helpers use it to set placeholder wrt Authorization header. Also fix copy-paste oversight. * SimpleChat: readme wrt authorization, maybe minimal openai testing * SimpleChat: model request field for openai/equivalent compat May help testing with openai/equivalent web services, if they require this field. * SimpleChat: readme stream-utf-8 trim-english deps, exception2error * Readme: Add a entry for simplechat in the http server section * SimpleChat:WIP:Collate internally, Stream mode Trap exceptions This can help ensure that data fetched till that point, can be made use of, rather than losing it. On some platforms, the time taken wrt generating a long response, may lead to the network connection being broken when it enters some user-no-interaction related power saving mode. * SimpleChat:theResp-origMsg: Undo a prev change to fix non trim When the response handling was moved into SimpleChat, I had changed a flow bit unnecessarily and carelessly, which resulted in the non trim flow, missing out on retaining the ai assistant response. This has been fixed now. * SimpleChat: Save message internally in handle_response itself This ensures that throwing the caught exception again for higher up logic, doesnt lose the response collated till that time. Go through theResp.assistant in catch block, just to keep simple consistency wrt backtracing just in case. Update the readme file. * SimpleChat:Cleanup: Add spacing wrt shown req-options * SimpleChat:UI: CreateDiv Divs map to GridX2 class This allows the settings ui to be cleaner structured. * SimpleChat: Show Non SettingsUI config field by default * SimpleChat: Allow for multiline system prompt Convert SystemPrompt into a textarea with 2 rows. Reduce user-input-textarea to 2 rows from 3, so that overall vertical space usage remains same. Shorten usage messages a bit, cleanup to sync with settings ui. * SimpleChat: Add basic skeleton for saving and loading chat Inturn when ever a chat message (system/user/model) is added, the chat will be saved into browser's localStorage. * SimpleChat:ODS: Add a prefix to chatid wrt ondiskstorage key * SimpleChat:ODS:WIP:TMP: Add UI to load previously saved chat This is a temporary flow * SimpleChat:ODS:Move restore/load saved chat btn setup to Me This also allows being able to set the common system prompt ui element to loaded chat's system prompt. * SimpleChat:Readme updated wrt save and restore chat session info * SimpleChat:Show chat session restore button, only if saved session * SimpleChat: AutoCreate ChatRequestOptions settings to an extent * SimpleChat: Update main README wrt usage with server --- README.md | 2 + .../server/public_simplechat/datautils.mjs | 266 +++++++++ examples/server/public_simplechat/index.html | 24 +- examples/server/public_simplechat/readme.md | 132 ++++- .../server/public_simplechat/simplechat.css | 11 + .../server/public_simplechat/simplechat.js | 558 +++++++++++++----- examples/server/public_simplechat/ui.mjs | 211 +++++++ 7 files changed, 1029 insertions(+), 175 deletions(-) create mode 100644 examples/server/public_simplechat/datautils.mjs create mode 100644 examples/server/public_simplechat/ui.mjs diff --git a/README.md b/README.md index 4791f84af..8680460aa 100644 --- a/README.md +++ b/README.md @@ -150,6 +150,8 @@ Typically finetunes of the base models below are supported as well. [llama.cpp web server](./examples/server) is a lightweight [OpenAI API](https://github.com/openai/openai-openapi) compatible HTTP server that can be used to serve local models and easily connect them to existing clients. +[simplechat](./examples/server/public_simplechat) is a simple chat client, which can be used to chat with the model exposed using above web server (use --path to point to simplechat), from a local web browser. + **Bindings:** - Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python) diff --git a/examples/server/public_simplechat/datautils.mjs b/examples/server/public_simplechat/datautils.mjs new file mode 100644 index 000000000..75159d6b1 --- /dev/null +++ b/examples/server/public_simplechat/datautils.mjs @@ -0,0 +1,266 @@ +//@ts-check +// Helpers to work with different data types +// by Humans for All +// + +/** + * Given the limited context size of local LLMs and , many a times when context gets filled + * between the prompt and the response, it can lead to repeating text garbage generation. + * And many a times setting penalty wrt repeatation leads to over-intelligent garbage + * repeatation with slight variations. These garbage inturn can lead to overloading of the + * available model context, leading to less valuable response for subsequent prompts/queries, + * if chat history is sent to ai model. + * + * So two simple minded garbage trimming logics are experimented below. + * * one based on progressively-larger-substring-based-repeat-matching-with-partial-skip and + * * another based on char-histogram-driven garbage trimming. + * * in future characteristic of histogram over varying lengths could be used to allow for + * a more aggressive and adaptive trimming logic. + */ + + +/** + * Simple minded logic to help remove repeating garbage at end of the string. + * The repeatation needs to be perfectly matching. + * + * The logic progressively goes on probing for longer and longer substring based + * repeatation, till there is no longer repeatation. Inturn picks the one with + * the longest chain. + * + * @param {string} sIn + * @param {number} maxSubL + * @param {number} maxMatchLenThreshold + */ +export function trim_repeat_garbage_at_end(sIn, maxSubL=10, maxMatchLenThreshold=40) { + let rCnt = [0]; + let maxMatchLen = maxSubL; + let iMML = -1; + for(let subL=1; subL < maxSubL; subL++) { + rCnt.push(0); + let i; + let refS = sIn.substring(sIn.length-subL, sIn.length); + for(i=sIn.length; i > 0; i -= subL) { + let curS = sIn.substring(i-subL, i); + if (refS != curS) { + let curMatchLen = rCnt[subL]*subL; + if (maxMatchLen < curMatchLen) { + maxMatchLen = curMatchLen; + iMML = subL; + } + break; + } + rCnt[subL] += 1; + } + } + console.debug("DBUG:DU:TrimRepeatGarbage:", rCnt); + if ((iMML == -1) || (maxMatchLen < maxMatchLenThreshold)) { + return {trimmed: false, data: sIn}; + } + console.debug("DBUG:TrimRepeatGarbage:TrimmedCharLen:", maxMatchLen); + let iEnd = sIn.length - maxMatchLen; + return { trimmed: true, data: sIn.substring(0, iEnd) }; +} + + +/** + * Simple minded logic to help remove repeating garbage at end of the string, till it cant. + * If its not able to trim, then it will try to skip a char at end and then trim, a few times. + * This ensures that even if there are multiple runs of garbage with different patterns, the + * logic still tries to munch through them. + * + * @param {string} sIn + * @param {number} maxSubL + * @param {number | undefined} [maxMatchLenThreshold] + */ +export function trim_repeat_garbage_at_end_loop(sIn, maxSubL, maxMatchLenThreshold, skipMax=16) { + let sCur = sIn; + let sSaved = ""; + let iTry = 0; + while(true) { + let got = trim_repeat_garbage_at_end(sCur, maxSubL, maxMatchLenThreshold); + if (got.trimmed != true) { + if (iTry == 0) { + sSaved = got.data; + } + iTry += 1; + if (iTry >= skipMax) { + return sSaved; + } + got.data = got.data.substring(0,got.data.length-1); + } else { + iTry = 0; + } + sCur = got.data; + } +} + + +/** + * A simple minded try trim garbage at end using histogram driven characteristics. + * There can be variation in the repeatations, as long as no new char props up. + * + * This tracks the chars and their frequency in a specified length of substring at the end + * and inturn checks if moving further into the generated text from the end remains within + * the same char subset or goes beyond it and based on that either trims the string at the + * end or not. This allows to filter garbage at the end, including even if there are certain + * kind of small variations in the repeated text wrt position of seen chars. + * + * Allow the garbage to contain upto maxUniq chars, but at the same time ensure that + * a given type of char ie numerals or alphabets or other types dont cross the specified + * maxType limit. This allows intermixed text garbage to be identified and trimmed. + * + * ALERT: This is not perfect and only provides a rough garbage identification logic. + * Also it currently only differentiates between character classes wrt english. + * + * @param {string} sIn + * @param {number} maxType + * @param {number} maxUniq + * @param {number} maxMatchLenThreshold + */ +export function trim_hist_garbage_at_end(sIn, maxType, maxUniq, maxMatchLenThreshold) { + if (sIn.length < maxMatchLenThreshold) { + return { trimmed: false, data: sIn }; + } + let iAlp = 0; + let iNum = 0; + let iOth = 0; + // Learn + let hist = {}; + let iUniq = 0; + for(let i=0; i= maxUniq) { + break; + } + hist[c] = 1; + } + } + console.debug("DBUG:TrimHistGarbage:", hist); + if ((iAlp > maxType) || (iNum > maxType) || (iOth > maxType)) { + return { trimmed: false, data: sIn }; + } + // Catch and Trim + for(let i=0; i < sIn.length; i++) { + let c = sIn[sIn.length-1-i]; + if (!(c in hist)) { + if (i < maxMatchLenThreshold) { + return { trimmed: false, data: sIn }; + } + console.debug("DBUG:TrimHistGarbage:TrimmedCharLen:", i); + return { trimmed: true, data: sIn.substring(0, sIn.length-i+1) }; + } + } + console.debug("DBUG:TrimHistGarbage:Trimmed fully"); + return { trimmed: true, data: "" }; +} + +/** + * Keep trimming repeatedly using hist_garbage logic, till you no longer can. + * This ensures that even if there are multiple runs of garbage with different patterns, + * the logic still tries to munch through them. + * + * @param {any} sIn + * @param {number} maxType + * @param {number} maxUniq + * @param {number} maxMatchLenThreshold + */ +export function trim_hist_garbage_at_end_loop(sIn, maxType, maxUniq, maxMatchLenThreshold) { + let sCur = sIn; + while (true) { + let got = trim_hist_garbage_at_end(sCur, maxType, maxUniq, maxMatchLenThreshold); + if (!got.trimmed) { + return got.data; + } + sCur = got.data; + } +} + +/** + * Try trim garbage at the end by using both the hist-driven-garbage-trimming as well as + * skip-a-bit-if-reqd-then-repeat-pattern-based-garbage-trimming, with blind retrying. + * @param {string} sIn + */ +export function trim_garbage_at_end(sIn) { + let sCur = sIn; + for(let i=0; i<2; i++) { + sCur = trim_hist_garbage_at_end_loop(sCur, 8, 24, 72); + sCur = trim_repeat_garbage_at_end_loop(sCur, 32, 72, 12); + } + return sCur; +} + + +/** + * NewLines array helper. + * Allow for maintaining a list of lines. + * Allow for a line to be builtup/appended part by part. + */ +export class NewLines { + + constructor() { + /** @type {string[]} */ + this.lines = []; + } + + /** + * Extracts lines from the passed string and inturn either + * append to a previous partial line or add a new line. + * @param {string} sLines + */ + add_append(sLines) { + let aLines = sLines.split("\n"); + let lCnt = 0; + for(let line of aLines) { + lCnt += 1; + // Add back newline removed if any during split + if (lCnt < aLines.length) { + line += "\n"; + } else { + if (sLines.endsWith("\n")) { + line += "\n"; + } + } + // Append if required + if (lCnt == 1) { + let lastLine = this.lines[this.lines.length-1]; + if (lastLine != undefined) { + if (!lastLine.endsWith("\n")) { + this.lines[this.lines.length-1] += line; + continue; + } + } + } + // Add new line + this.lines.push(line); + } + } + + /** + * Shift the oldest/earliest/0th line in the array. [Old-New|Earliest-Latest] + * Optionally control whether only full lines (ie those with newline at end) will be returned + * or will a partial line without a newline at end (can only be the last line) be returned. + * @param {boolean} bFullWithNewLineOnly + */ + shift(bFullWithNewLineOnly=true) { + let line = this.lines[0]; + if (line == undefined) { + return undefined; + } + if ((line[line.length-1] != "\n") && bFullWithNewLineOnly){ + return undefined; + } + return this.lines.shift(); + } + +} diff --git a/examples/server/public_simplechat/index.html b/examples/server/public_simplechat/index.html index 1a1a34208..f6413016f 100644 --- a/examples/server/public_simplechat/index.html +++ b/examples/server/public_simplechat/index.html @@ -8,21 +8,23 @@ - + +
-
+

SimpleChat

-
- - -
+
@@ -30,7 +32,7 @@
- +
@@ -40,7 +42,7 @@
- +
diff --git a/examples/server/public_simplechat/readme.md b/examples/server/public_simplechat/readme.md index de0dfc99d..36a46885d 100644 --- a/examples/server/public_simplechat/readme.md +++ b/examples/server/public_simplechat/readme.md @@ -11,18 +11,29 @@ in a simple way with minimal code from a common code base. Inturn additionally i multiple independent back and forth chatting to an extent, with the ai llm model at a basic level, with their own system prompts. +This allows seeing the generated text / ai-model response in oneshot at the end, after it is fully generated, +or potentially as it is being generated, in a streamed manner from the server/ai-model. + +Auto saves the chat session locally as and when the chat is progressing and inturn at a later time when you +open SimpleChat, option is provided to restore the old chat session, if a matching one exists. + The UI follows a responsive web design so that the layout can adapt to available display space in a usable enough manner, in general. Allows developer/end-user to control some of the behaviour by updating gMe members from browser's devel-tool -console. +console. Parallely some of the directly useful to end-user settings can also be changed using the provided +settings ui. -NOTE: Given that the idea is for basic minimal testing, it doesnt bother with any model context length and -culling of old messages from the chat by default. However by enabling the sliding window chat logic, a crude -form of old messages culling can be achieved. +NOTE: Current web service api doesnt expose the model context length directly, so client logic doesnt provide +any adaptive culling of old messages nor of replacing them with summary of their content etal. However there +is a optional sliding window based chat logic, which provides a simple minded culling of old messages from +the chat history before sending to the ai model. -NOTE: It doesnt set any parameters other than temperature and max_tokens for now. However if someone wants -they can update the js file or equivalent member in gMe as needed. +NOTE: Wrt options sent with the request, it mainly sets temperature, max_tokens and optionaly stream for now. +However if someone wants they can update the js file or equivalent member in gMe as needed. + +NOTE: One may be able to use this to chat with openai api web-service /chat/completions endpoint, in a very +limited / minimal way. One will need to set model, openai url and authorization bearer key in settings ui. ## usage @@ -52,9 +63,15 @@ Open this simple web front end from your local browser Once inside -* Select between chat and completion mode. By default it is set to chat mode. +* If you want to, you can change many of the default global settings + * the base url (ie ip addr / domain name, port) + * chat (default) vs completion mode + * try trim garbage in response or not + * amount of chat history in the context sent to server/ai-model + * oneshot or streamed mode. * In completion mode + * one normally doesnt use a system prompt in completion mode. * logic by default doesnt insert any role specific "ROLE: " prefix wrt each role's message. If the model requires any prefix wrt user role messages, then the end user has to explicitly add the needed prefix, when they enter their chat message. @@ -88,12 +105,16 @@ Once inside * Wait for the logic to communicate with the server and get the response. * the user is not allowed to enter any fresh query during this time. * the user input box will be disabled and a working message will be shown in it. + * if trim garbage is enabled, the logic will try to trim repeating text kind of garbage to some extent. * just refresh the page, to reset wrt the chat history and or system prompt and start afresh. * Using NewChat one can start independent chat sessions. * two independent chat sessions are setup by default. +* When you want to print, switching ChatHistoryInCtxt to Full and clicking on the chat session button of + interest, will display the full chat history till then wrt same, if you want full history for printing. + ## Devel note @@ -104,14 +125,31 @@ by developers who may not be from web frontend background (so inturn may not be end-use-specific-language-extensions driven flows) so that they can use it to explore/experiment things. And given that the idea is also to help explore/experiment for developers, some flexibility is provided -to change behaviour easily using the devel-tools/console, for now. And skeletal logic has been implemented -to explore some of the end points and ideas/implications around them. +to change behaviour easily using the devel-tools/console or provided minimal settings ui (wrt few aspects). +Skeletal logic has been implemented to explore some of the end points and ideas/implications around them. ### General Me/gMe consolidates the settings which control the behaviour into one object. One can see the current settings, as well as change/update them using browsers devel-tool/console. +It is attached to the document object. Some of these can also be updated using the Settings UI. + + baseURL - the domain-name/ip-address and inturn the port to send the request. + + bStream - control between oneshot-at-end and live-stream-as-its-generated collating and showing + of the generated response. + + the logic assumes that the text sent from the server follows utf-8 encoding. + + in streaming mode - if there is any exception, the logic traps the same and tries to ensure + that text generated till then is not lost. + + if a very long text is being generated, which leads to no user interaction for sometime and + inturn the machine goes into power saving mode or so, the platform may stop network connection, + leading to exception. + + apiEP - select between /completions and /chat/completions endpoint provided by the server/ai-model. bCompletionFreshChatAlways - whether Completion mode collates complete/sliding-window history when communicating with the server or only sends the latest user query/message. @@ -119,6 +157,19 @@ One can see the current settings, as well as change/update them using browsers d bCompletionInsertStandardRolePrefix - whether Completion mode inserts role related prefix wrt the messages that get inserted into prompt field wrt /Completion endpoint. + bTrimGarbage - whether garbage repeatation at the end of the generated ai response, should be + trimmed or left as is. If enabled, it will be trimmed so that it wont be sent back as part of + subsequent chat history. At the same time the actual trimmed text is shown to the user, once + when it was generated, so user can check if any useful info/data was there in the response. + + One may be able to request the ai-model to continue (wrt the last response) (if chat-history + is enabled as part of the chat-history-in-context setting), and chances are the ai-model will + continue starting from the trimmed part, thus allows long response to be recovered/continued + indirectly, in many cases. + + The histogram/freq based trimming logic is currently tuned for english language wrt its + is-it-a-alpabetic|numeral-char regex match logic. + chatRequestOptions - maintains the list of options/fields to send along with chat request, irrespective of whether /chat/completions or /completions endpoint. @@ -126,6 +177,14 @@ One can see the current settings, as well as change/update them using browsers d modify the existing options value or remove them, for now you can update this global var using browser's development-tools/console. + For string and numeric fields in chatRequestOptions, including even those added by a user + at runtime by directly modifying gMe.chatRequestOptions, setting ui entries will be auto + created. + + headers - maintains the list of http headers sent when request is made to the server. By default + Content-Type is set to application/json. Additionally Authorization entry is provided, which can + be set if needed using the settings ui. + iRecentUserMsgCnt - a simple minded SlidingWindow to limit context window load at Ai Model end. This is disabled by default. However if enabled, then in addition to latest system message, only the last/latest iRecentUserMsgCnt user messages after the latest system prompt and its responses @@ -140,7 +199,8 @@ One can see the current settings, as well as change/update them using browsers d By using gMe's iRecentUserMsgCnt and chatRequestOptions.max_tokens one can try to control the implications of loading of the ai-model's context window by chat history, wrt chat response to -some extent in a simple crude way. +some extent in a simple crude way. You may also want to control the context size enabled when +the server loads ai-model, on the server end. Sometimes the browser may be stuborn with caching of the file, so your updates to html/css/js @@ -149,28 +209,15 @@ matter clearing site data, dont directly override site caching in all cases. Wor have to change port. Or in dev tools of browser, you may be able to disable caching fully. -Concept of multiple chat sessions with different servers, as well as saving and restoring of -those across browser usage sessions, can be woven around the SimpleChat/MultiChatUI class and -its instances relatively easily, however given the current goal of keeping this simple, it has -not been added, for now. +Currently the server to communicate with is maintained globally and not as part of a specific +chat session. So if one changes the server ip/url in setting, then all chat sessions will auto +switch to this new server, when you try using those sessions. By switching between chat.add_system_begin/anytime, one can control whether one can change the system prompt, anytime during the conversation or only at the beginning. -read_json_early, is to experiment with reading json response data early on, if available, -so that user can be shown generated data, as and when it is being generated, rather than -at the end when full data is available. - - the server flow doesnt seem to be sending back data early, atleast for request (inc options) - that is currently sent. - - if able to read json data early on in future, as and when ai model is generating data, then - this helper needs to indirectly update the chat div with the recieved data, without waiting - for the overall data to be available. - - ### Default setup By default things are setup to try and make the user experience a bit better, if possible. @@ -179,7 +226,8 @@ However a developer when testing the server of ai-model may want to change these Using iRecentUserMsgCnt reduce chat history context sent to the server/ai-model to be just the system-prompt, prev-user-request-and-ai-response and cur-user-request, instead of full chat history. This way if there is any response with garbage/repeatation, it doesnt -mess with things beyond the next question/request/query, in some ways. +mess with things beyond the next question/request/query, in some ways. The trim garbage +option also tries to help avoid issues with garbage in the context to an extent. Set max_tokens to 1024, so that a relatively large previous reponse doesnt eat up the space available wrt next query-response. However dont forget that the server when started should @@ -189,11 +237,33 @@ also be started with a model context size of 1k or more, to be on safe side. internal n_predict, for now add the same here on the client side, maybe later add max_tokens to /completions endpoint handling code on server side. -Frequency and presence penalty fields are set to 1.2 in the set of fields sent to server -along with the user query. So that the model is partly set to try avoid repeating text in -its response. +NOTE: One may want to experiment with frequency/presence penalty fields in chatRequestOptions +wrt the set of fields sent to server along with the user query. To check how the model behaves +wrt repeatations in general in the generated text response. -A end-user can change these behaviour by editing gMe from browser's devel-tool/console. +A end-user can change these behaviour by editing gMe from browser's devel-tool/console or by +using the providing settings ui. + + +### OpenAi / Equivalent API WebService + +One may be abe to handshake with OpenAI/Equivalent api web service's /chat/completions endpoint +for a minimal chatting experimentation by setting the below. + +* the baseUrl in settings ui + * https://api.openai.com/v1 or similar + +* Wrt request body - gMe.chatRequestOptions + * model (settings ui) + * any additional fields if required in future + +* Wrt request headers - gMe.headers + * Authorization (available through settings ui) + * Bearer THE_OPENAI_API_KEY + * any additional optional header entries like "OpenAI-Organization", "OpenAI-Project" or so + +NOTE: Not tested, as there is no free tier api testing available. However logically this might +work. ## At the end diff --git a/examples/server/public_simplechat/simplechat.css b/examples/server/public_simplechat/simplechat.css index 20c738b12..13bfb80b4 100644 --- a/examples/server/public_simplechat/simplechat.css +++ b/examples/server/public_simplechat/simplechat.css @@ -21,6 +21,17 @@ .role-user { background-color: lightgray; } +.role-trim { + background-color: lightpink; +} + +.gridx2 { + display: grid; + grid-template-columns: repeat(2, 1fr); + border-bottom-style: dotted; + border-bottom-width: thin; + border-bottom-color: lightblue; +} .flex-grow { flex-grow: 1; diff --git a/examples/server/public_simplechat/simplechat.js b/examples/server/public_simplechat/simplechat.js index 0c48da879..25afb2564 100644 --- a/examples/server/public_simplechat/simplechat.js +++ b/examples/server/public_simplechat/simplechat.js @@ -2,6 +2,9 @@ // A simple completions and chat/completions test related web front end logic // by Humans for All +import * as du from "./datautils.mjs"; +import * as ui from "./ui.mjs" + class Roles { static System = "system"; static User = "user"; @@ -9,40 +12,65 @@ class Roles { } class ApiEP { - static Chat = "chat"; - static Completion = "completion"; + static Type = { + Chat: "chat", + Completion: "completion", + } + static UrlSuffix = { + 'chat': `/chat/completions`, + 'completion': `/completions`, + } + + /** + * Build the url from given baseUrl and apiEp id. + * @param {string} baseUrl + * @param {string} apiEP + */ + static Url(baseUrl, apiEP) { + if (baseUrl.endsWith("/")) { + baseUrl = baseUrl.substring(0, baseUrl.length-1); + } + return `${baseUrl}${this.UrlSuffix[apiEP]}`; + } + } + let gUsageMsg = `

Usage

    -
  • Set system prompt above, to try control ai response charactersitic, if model supports same.
    • +
  • System prompt above, to try control ai response characteristics.
      • -
    • Completion mode normally wont have a system prompt.
      • +
    • Completion mode - no system prompt normally.
    +
  • Use shift+enter for inserting enter/newline.
  • Enter your query to ai assistant below.
    • -
      -
    • Completion mode doesnt insert user/role: prefix implicitly.
      • -
    • Use shift+enter for inserting enter/newline.
      • -
  • Default ContextWindow = [System, Last Query+Resp, Cur Query].
      • -
    • experiment iRecentUserMsgCnt, max_tokens, model ctxt window to expand
      • +
    • ChatHistInCtxt, MaxTokens, ModelCtxt window to expand
`; + /** @typedef {{role: string, content: string}[]} ChatMessages */ +/** @typedef {{iLastSys: number, xchat: ChatMessages}} SimpleChatODS */ + class SimpleChat { - constructor() { + /** + * @param {string} chatId + */ + constructor(chatId) { + this.chatId = chatId; /** * Maintain in a form suitable for common LLM web service chat/completions' messages entry * @type {ChatMessages} */ this.xchat = []; this.iLastSys = -1; + this.latestResponse = ""; } clear() { @@ -50,6 +78,27 @@ class SimpleChat { this.iLastSys = -1; } + ods_key() { + return `SimpleChat-${this.chatId}` + } + + save() { + /** @type {SimpleChatODS} */ + let ods = {iLastSys: this.iLastSys, xchat: this.xchat}; + localStorage.setItem(this.ods_key(), JSON.stringify(ods)); + } + + load() { + let sods = localStorage.getItem(this.ods_key()); + if (sods == null) { + return; + } + /** @type {SimpleChatODS} */ + let ods = JSON.parse(sods); + this.iLastSys = ods.iLastSys; + this.xchat = ods.xchat; + } + /** * Recent chat messages. * If iRecentUserMsgCnt < 0 @@ -94,6 +143,15 @@ class SimpleChat { return rchat; } + /** + * Collate the latest response from the server/ai-model, as it is becoming available. + * This is mainly useful for the stream mode. + * @param {string} content + */ + append_response(content) { + this.latestResponse += content; + } + /** * Add an entry into xchat * @param {string} role @@ -107,6 +165,7 @@ class SimpleChat { if (role == Roles.System) { this.iLastSys = this.xchat.length - 1; } + this.save(); return true; } @@ -121,10 +180,8 @@ class SimpleChat { } let last = undefined; for(const x of this.recent_chat(gMe.iRecentUserMsgCnt)) { - let entry = document.createElement("p"); + let entry = ui.el_create_append_p(`${x.role}: ${x.content}`, div); entry.className = `role-${x.role}`; - entry.innerText = `${x.role}: ${x.content}`; - div.appendChild(entry); last = entry; } if (last !== undefined) { @@ -132,21 +189,45 @@ class SimpleChat { } else { if (bClear) { div.innerHTML = gUsageMsg; + gMe.setup_load(div, this); gMe.show_info(div); } } + return last; + } + + /** + * Setup the fetch headers. + * It picks the headers from gMe.headers. + * It inserts Authorization only if its non-empty. + * @param {string} apiEP + */ + fetch_headers(apiEP) { + let headers = new Headers(); + for(let k in gMe.headers) { + let v = gMe.headers[k]; + if ((k == "Authorization") && (v.trim() == "")) { + continue; + } + headers.append(k, v); + } + return headers; } /** * Add needed fields wrt json object to be sent wrt LLM web services completions endpoint. * The needed fields/options are picked from a global object. + * Add optional stream flag, if required. * Convert the json into string. * @param {Object} obj */ - request_jsonstr(obj) { + request_jsonstr_extend(obj) { for(let k in gMe.chatRequestOptions) { obj[k] = gMe.chatRequestOptions[k]; } + if (gMe.bStream) { + obj["stream"] = true; + } return JSON.stringify(obj); } @@ -157,7 +238,7 @@ class SimpleChat { let req = { messages: this.recent_chat(gMe.iRecentUserMsgCnt), } - return this.request_jsonstr(req); + return this.request_jsonstr_extend(req); } /** @@ -180,7 +261,60 @@ class SimpleChat { let req = { prompt: prompt, } - return this.request_jsonstr(req); + return this.request_jsonstr_extend(req); + } + + /** + * Return a string form of json object suitable for specified api endpoint. + * @param {string} apiEP + */ + request_jsonstr(apiEP) { + if (apiEP == ApiEP.Type.Chat) { + return this.request_messages_jsonstr(); + } else { + return this.request_prompt_jsonstr(gMe.bCompletionInsertStandardRolePrefix); + } + } + + /** + * Extract the ai-model/assistant's response from the http response got. + * Optionally trim the message wrt any garbage at the end. + * @param {any} respBody + * @param {string} apiEP + */ + response_extract(respBody, apiEP) { + let assistant = ""; + if (apiEP == ApiEP.Type.Chat) { + assistant = respBody["choices"][0]["message"]["content"]; + } else { + try { + assistant = respBody["choices"][0]["text"]; + } catch { + assistant = respBody["content"]; + } + } + return assistant; + } + + /** + * Extract the ai-model/assistant's response from the http response got in streaming mode. + * @param {any} respBody + * @param {string} apiEP + */ + response_extract_stream(respBody, apiEP) { + let assistant = ""; + if (apiEP == ApiEP.Type.Chat) { + if (respBody["choices"][0]["finish_reason"] !== "stop") { + assistant = respBody["choices"][0]["delta"]["content"]; + } + } else { + try { + assistant = respBody["choices"][0]["text"]; + } catch { + assistant = respBody["content"]; + } + } + return assistant; } /** @@ -239,53 +373,99 @@ class SimpleChat { return sysPrompt; } -} - -let gBaseURL = "http://127.0.0.1:8080"; -let gChatURL = { - 'chat': `${gBaseURL}/chat/completions`, - 'completion': `${gBaseURL}/completions`, -} - - -/** - * Set the class of the children, based on whether it is the idSelected or not. - * @param {HTMLDivElement} elBase - * @param {string} idSelected - * @param {string} classSelected - * @param {string} classUnSelected - */ -function el_children_config_class(elBase, idSelected, classSelected, classUnSelected="") { - for(let child of elBase.children) { - if (child.id == idSelected) { - child.className = classSelected; - } else { - child.className = classUnSelected; + /** + * Handle the multipart response from server/ai-model + * @param {Response} resp + * @param {string} apiEP + * @param {HTMLDivElement} elDiv + */ + async handle_response_multipart(resp, apiEP, elDiv) { + let elP = ui.el_create_append_p("", elDiv); + if (!resp.body) { + throw Error("ERRR:SimpleChat:SC:HandleResponseMultiPart:No body..."); } + let tdUtf8 = new TextDecoder("utf-8"); + let rr = resp.body.getReader(); + this.latestResponse = ""; + let xLines = new du.NewLines(); + while(true) { + let { value: cur, done: done } = await rr.read(); + if (cur) { + let curBody = tdUtf8.decode(cur, {stream: true}); + console.debug("DBUG:SC:PART:Str:", curBody); + xLines.add_append(curBody); + } + while(true) { + let curLine = xLines.shift(!done); + if (curLine == undefined) { + break; + } + if (curLine.trim() == "") { + continue; + } + if (curLine.startsWith("data:")) { + curLine = curLine.substring(5); + } + let curJson = JSON.parse(curLine); + console.debug("DBUG:SC:PART:Json:", curJson); + this.append_response(this.response_extract_stream(curJson, apiEP)); + } + elP.innerText = this.latestResponse; + elP.scrollIntoView(false); + if (done) { + break; + } + } + console.debug("DBUG:SC:PART:Full:", this.latestResponse); + return this.latestResponse; } -} -/** - * Create button and set it up. - * @param {string} id - * @param {(this: HTMLButtonElement, ev: MouseEvent) => any} callback - * @param {string | undefined} name - * @param {string | undefined} innerText - */ -function el_create_button(id, callback, name=undefined, innerText=undefined) { - if (!name) { - name = id; + /** + * Handle the oneshot response from server/ai-model + * @param {Response} resp + * @param {string} apiEP + */ + async handle_response_oneshot(resp, apiEP) { + let respBody = await resp.json(); + console.debug(`DBUG:SimpleChat:SC:${this.chatId}:HandleUserSubmit:RespBody:${JSON.stringify(respBody)}`); + return this.response_extract(respBody, apiEP); } - if (!innerText) { - innerText = id; + + /** + * Handle the response from the server be it in oneshot or multipart/stream mode. + * Also take care of the optional garbage trimming. + * @param {Response} resp + * @param {string} apiEP + * @param {HTMLDivElement} elDiv + */ + async handle_response(resp, apiEP, elDiv) { + let theResp = { + assistant: "", + trimmed: "", + } + if (gMe.bStream) { + try { + theResp.assistant = await this.handle_response_multipart(resp, apiEP, elDiv); + this.latestResponse = ""; + } catch (error) { + theResp.assistant = this.latestResponse; + this.add(Roles.Assistant, theResp.assistant); + this.latestResponse = ""; + throw error; + } + } else { + theResp.assistant = await this.handle_response_oneshot(resp, apiEP); + } + if (gMe.bTrimGarbage) { + let origMsg = theResp.assistant; + theResp.assistant = du.trim_garbage_at_end(origMsg); + theResp.trimmed = origMsg.substring(theResp.assistant.length); + } + this.add(Roles.Assistant, theResp.assistant); + return theResp; } - let btn = document.createElement("button"); - btn.id = id; - btn.name = name; - btn.innerText = innerText; - btn.addEventListener("click", callback); - return btn; + } @@ -302,14 +482,16 @@ class MultiChatUI { this.elDivChat = /** @type{HTMLDivElement} */(document.getElementById("chat-div")); this.elBtnUser = /** @type{HTMLButtonElement} */(document.getElementById("user-btn")); this.elInUser = /** @type{HTMLInputElement} */(document.getElementById("user-in")); - this.elSelectApiEP = /** @type{HTMLSelectElement} */(document.getElementById("api-ep")); + this.elDivHeading = /** @type{HTMLSelectElement} */(document.getElementById("heading")); this.elDivSessions = /** @type{HTMLDivElement} */(document.getElementById("sessions-div")); + this.elBtnSettings = /** @type{HTMLButtonElement} */(document.getElementById("settings")); this.validate_element(this.elInSystem, "system-in"); this.validate_element(this.elDivChat, "chat-div"); this.validate_element(this.elInUser, "user-in"); - this.validate_element(this.elSelectApiEP, "api-ep"); + this.validate_element(this.elDivHeading, "heading"); this.validate_element(this.elDivChat, "sessions-div"); + this.validate_element(this.elBtnSettings, "settings"); } /** @@ -350,13 +532,18 @@ class MultiChatUI { this.handle_session_switch(this.curChatId); } + this.elBtnSettings.addEventListener("click", (ev)=>{ + this.elDivChat.replaceChildren(); + gMe.show_settings(this.elDivChat); + }); + this.elBtnUser.addEventListener("click", (ev)=>{ if (this.elInUser.disabled) { return; } - this.handle_user_submit(this.curChatId, this.elSelectApiEP.value).catch((/** @type{Error} */reason)=>{ + this.handle_user_submit(this.curChatId, gMe.apiEP).catch((/** @type{Error} */reason)=>{ let msg = `ERRR:SimpleChat\nMCUI:HandleUserSubmit:${this.curChatId}\n${reason.name}:${reason.message}`; - console.debug(msg.replace("\n", ":")); + console.error(msg.replace("\n", ":")); alert(msg); this.ui_reset_userinput(); }); @@ -377,6 +564,8 @@ class MultiChatUI { // allow user to insert enter into the system prompt using shift+enter. // while just pressing enter key will lead to setting the system prompt. if ((ev.key === "Enter") && (!ev.shiftKey)) { + let value = this.elInSystem.value; + this.elInSystem.value = value.substring(0,value.length-1); let chat = this.simpleChats[this.curChatId]; chat.add_system_anytime(this.elInSystem.value, this.curChatId); chat.show(this.elDivChat); @@ -392,34 +581,12 @@ class MultiChatUI { * @param {boolean} bSwitchSession */ new_chat_session(chatId, bSwitchSession=false) { - this.simpleChats[chatId] = new SimpleChat(); + this.simpleChats[chatId] = new SimpleChat(chatId); if (bSwitchSession) { this.handle_session_switch(chatId); } } - /** - * Try read json response early, if available. - * @param {Response} resp - */ - async read_json_early(resp) { - if (!resp.body) { - throw Error("ERRR:SimpleChat:MCUI:ReadJsonEarly:No body..."); - } - let tdUtf8 = new TextDecoder("utf-8"); - let rr = resp.body.getReader(); - let gotBody = ""; - while(true) { - let { value: cur, done: done} = await rr.read(); - let curBody = tdUtf8.decode(cur); - console.debug("DBUG:SC:PART:", curBody); - gotBody += curBody; - if (done) { - break; - } - } - return JSON.parse(gotBody); - } /** * Handle user query submit request, wrt specified chat session. @@ -434,7 +601,7 @@ class MultiChatUI { // So if user wants to simulate a multi-chat based completion query, // they will have to enter the full thing, as a suitable multiline // user input/query. - if ((apiEP == ApiEP.Completion) && (gMe.bCompletionFreshChatAlways)) { + if ((apiEP == ApiEP.Type.Completion) && (gMe.bCompletionFreshChatAlways)) { chat.clear(); } @@ -447,41 +614,26 @@ class MultiChatUI { } chat.show(this.elDivChat); - let theBody; - let theUrl = gChatURL[apiEP] - if (apiEP == ApiEP.Chat) { - theBody = chat.request_messages_jsonstr(); - } else { - theBody = chat.request_prompt_jsonstr(gMe.bCompletionInsertStandardRolePrefix); - } + let theUrl = ApiEP.Url(gMe.baseURL, apiEP); + let theBody = chat.request_jsonstr(apiEP); this.elInUser.value = "working..."; this.elInUser.disabled = true; console.debug(`DBUG:SimpleChat:MCUI:${chatId}:HandleUserSubmit:${theUrl}:ReqBody:${theBody}`); + let theHeaders = chat.fetch_headers(apiEP); let resp = await fetch(theUrl, { method: "POST", - headers: { - "Content-Type": "application/json", - }, + headers: theHeaders, body: theBody, }); - let respBody = await resp.json(); - //let respBody = await this.read_json_early(resp); - console.debug(`DBUG:SimpleChat:MCUI:${chatId}:HandleUserSubmit:RespBody:${JSON.stringify(respBody)}`); - let assistantMsg; - if (apiEP == ApiEP.Chat) { - assistantMsg = respBody["choices"][0]["message"]["content"]; - } else { - try { - assistantMsg = respBody["choices"][0]["text"]; - } catch { - assistantMsg = respBody["content"]; - } - } - chat.add(Roles.Assistant, assistantMsg); + let theResp = await chat.handle_response(resp, apiEP, this.elDivChat); if (chatId == this.curChatId) { chat.show(this.elDivChat); + if (theResp.trimmed.length > 0) { + let p = ui.el_create_append_p(`TRIMMED:${theResp.trimmed}`, this.elDivChat); + p.className="role-trim"; + } } else { console.debug(`DBUG:SimpleChat:MCUI:HandleUserSubmit:ChatId has changed:[${chatId}] [${this.curChatId}]`); } @@ -500,7 +652,7 @@ class MultiChatUI { } elDiv.replaceChildren(); // Btn for creating new chat session - let btnNew = el_create_button("New CHAT", (ev)=> { + let btnNew = ui.el_create_button("New CHAT", (ev)=> { if (this.elInUser.disabled) { console.error(`ERRR:SimpleChat:MCUI:NewChat:Current session [${this.curChatId}] awaiting response, ignoring request...`); alert("ERRR:SimpleChat\nMCUI:NewChat\nWait for response to pending query, before starting new chat session"); @@ -514,7 +666,7 @@ class MultiChatUI { } this.new_chat_session(chatIdGot, true); this.create_session_btn(elDiv, chatIdGot); - el_children_config_class(elDiv, chatIdGot, "session-selected", ""); + ui.el_children_config_class(elDiv, chatIdGot, "session-selected", ""); }); elDiv.appendChild(btnNew); // Btns for existing chat sessions @@ -528,7 +680,7 @@ class MultiChatUI { } create_session_btn(elDiv, cid) { - let btn = el_create_button(cid, (ev)=>{ + let btn = ui.el_create_button(cid, (ev)=>{ let target = /** @type{HTMLButtonElement} */(ev.target); console.debug(`DBUG:SimpleChat:MCUI:SessionClick:${target.id}`); if (this.elInUser.disabled) { @@ -537,7 +689,7 @@ class MultiChatUI { return; } this.handle_session_switch(target.id); - el_children_config_class(elDiv, target.id, "session-selected", ""); + ui.el_children_config_class(elDiv, target.id, "session-selected", ""); }); elDiv.appendChild(btn); return btn; @@ -567,46 +719,183 @@ class MultiChatUI { class Me { constructor() { + this.baseURL = "http://127.0.0.1:8080"; this.defaultChatIds = [ "Default", "Other" ]; this.multiChat = new MultiChatUI(); + this.bStream = true; this.bCompletionFreshChatAlways = true; this.bCompletionInsertStandardRolePrefix = false; + this.bTrimGarbage = true; this.iRecentUserMsgCnt = 2; + this.sRecentUserMsgCnt = { + "Full": -1, + "Last0": 1, + "Last1": 2, + "Last2": 3, + "Last4": 5, + }; + this.apiEP = ApiEP.Type.Chat; + this.headers = { + "Content-Type": "application/json", + "Authorization": "", // Authorization: Bearer OPENAI_API_KEY + } // Add needed fields wrt json object to be sent wrt LLM web services completions endpoint. this.chatRequestOptions = { + "model": "gpt-3.5-turbo", "temperature": 0.7, "max_tokens": 1024, - "frequency_penalty": 1.2, - "presence_penalty": 1.2, - "n_predict": 1024 + "n_predict": 1024, + //"frequency_penalty": 1.2, + //"presence_penalty": 1.2, }; } /** + * Disable console.debug by mapping it to a empty function. + */ + debug_disable() { + this.console_debug = console.debug; + console.debug = () => { + + }; + } + + /** + * Setup the load saved chat ui. + * @param {HTMLDivElement} div + * @param {SimpleChat} chat + */ + setup_load(div, chat) { + if (!(chat.ods_key() in localStorage)) { + return; + } + div.innerHTML += `

Restore

+

Load previously saved chat session, if available

`; + let btn = ui.el_create_button(chat.ods_key(), (ev)=>{ + console.log("DBUG:SimpleChat:SC:Load", chat); + chat.load(); + queueMicrotask(()=>{ + chat.show(div); + this.multiChat.elInSystem.value = chat.get_system_latest(); + }); + }); + div.appendChild(btn); + } + + /** + * Show the configurable parameters info in the passed Div element. + * @param {HTMLDivElement} elDiv + * @param {boolean} bAll + */ + show_info(elDiv, bAll=false) { + + let p = ui.el_create_append_p("Settings (devel-tools-console document[gMe])", elDiv); + p.className = "role-system"; + + if (bAll) { + + ui.el_create_append_p(`baseURL:${this.baseURL}`, elDiv); + + ui.el_create_append_p(`Authorization:${this.headers["Authorization"]}`, elDiv); + + ui.el_create_append_p(`bStream:${this.bStream}`, elDiv); + + ui.el_create_append_p(`bCompletionFreshChatAlways:${this.bCompletionFreshChatAlways}`, elDiv); + + ui.el_create_append_p(`bCompletionInsertStandardRolePrefix:${this.bCompletionInsertStandardRolePrefix}`, elDiv); + + ui.el_create_append_p(`bTrimGarbage:${this.bTrimGarbage}`, elDiv); + + ui.el_create_append_p(`iRecentUserMsgCnt:${this.iRecentUserMsgCnt}`, elDiv); + + ui.el_create_append_p(`ApiEndPoint:${this.apiEP}`, elDiv); + + } + + ui.el_create_append_p(`chatRequestOptions:${JSON.stringify(this.chatRequestOptions, null, " - ")}`, elDiv); + ui.el_create_append_p(`headers:${JSON.stringify(this.headers, null, " - ")}`, elDiv); + + } + + /** + * Auto create ui input elements for fields in ChatRequestOptions + * Currently supports text and number field types. * @param {HTMLDivElement} elDiv */ - show_info(elDiv) { + show_settings_chatrequestoptions(elDiv) { + let typeDict = { + "string": "text", + "number": "number", + }; + let fs = document.createElement("fieldset"); + let legend = document.createElement("legend"); + legend.innerText = "ChatRequestOptions"; + fs.appendChild(legend); + elDiv.appendChild(fs); + for(const k in this.chatRequestOptions) { + let val = this.chatRequestOptions[k]; + let type = typeof(val); + if (!((type == "string") || (type == "number"))) { + continue; + } + let inp = ui.el_creatediv_input(`Set${k}`, k, typeDict[type], this.chatRequestOptions[k], (val)=>{ + if (type == "number") { + val = Number(val); + } + this.chatRequestOptions[k] = val; + }); + fs.appendChild(inp.div); + } + } - var p = document.createElement("p"); - p.innerText = "Settings (devel-tools-console gMe)"; - p.className = "role-system"; - elDiv.appendChild(p); + /** + * Show settings ui for configurable parameters, in the passed Div element. + * @param {HTMLDivElement} elDiv + */ + show_settings(elDiv) { - var p = document.createElement("p"); - p.innerText = `bCompletionFreshChatAlways:${this.bCompletionFreshChatAlways}`; - elDiv.appendChild(p); + let inp = ui.el_creatediv_input("SetBaseURL", "BaseURL", "text", this.baseURL, (val)=>{ + this.baseURL = val; + }); + elDiv.appendChild(inp.div); - p = document.createElement("p"); - p.innerText = `bCompletionInsertStandardRolePrefix:${this.bCompletionInsertStandardRolePrefix}`; - elDiv.appendChild(p); + inp = ui.el_creatediv_input("SetAuthorization", "Authorization", "text", this.headers["Authorization"], (val)=>{ + this.headers["Authorization"] = val; + }); + inp.el.placeholder = "Bearer OPENAI_API_KEY"; + elDiv.appendChild(inp.div); - p = document.createElement("p"); - p.innerText = `iRecentUserMsgCnt:${this.iRecentUserMsgCnt}`; - elDiv.appendChild(p); + let bb = ui.el_creatediv_boolbutton("SetStream", "Stream", {true: "[+] yes stream", false: "[-] do oneshot"}, this.bStream, (val)=>{ + this.bStream = val; + }); + elDiv.appendChild(bb.div); - p = document.createElement("p"); - p.innerText = `chatRequestOptions:${JSON.stringify(this.chatRequestOptions)}`; - elDiv.appendChild(p); + bb = ui.el_creatediv_boolbutton("SetCompletionFreshChatAlways", "CompletionFreshChatAlways", {true: "[+] yes fresh", false: "[-] no, with history"}, this.bCompletionFreshChatAlways, (val)=>{ + this.bCompletionFreshChatAlways = val; + }); + elDiv.appendChild(bb.div); + + bb = ui.el_creatediv_boolbutton("SetCompletionInsertStandardRolePrefix", "CompletionInsertStandardRolePrefix", {true: "[+] yes insert", false: "[-] dont insert"}, this.bCompletionInsertStandardRolePrefix, (val)=>{ + this.bCompletionInsertStandardRolePrefix = val; + }); + elDiv.appendChild(bb.div); + + bb = ui.el_creatediv_boolbutton("SetTrimGarbage", "TrimGarbage", {true: "[+] yes trim", false: "[-] dont trim"}, this.bTrimGarbage, (val)=>{ + this.bTrimGarbage = val; + }); + elDiv.appendChild(bb.div); + + let sel = ui.el_creatediv_select("SetChatHistoryInCtxt", "ChatHistoryInCtxt", this.sRecentUserMsgCnt, this.iRecentUserMsgCnt, (val)=>{ + this.iRecentUserMsgCnt = this.sRecentUserMsgCnt[val]; + }); + elDiv.appendChild(sel.div); + + sel = ui.el_creatediv_select("SetApiEP", "ApiEndPoint", ApiEP.Type, this.apiEP, (val)=>{ + this.apiEP = ApiEP.Type[val]; + }); + elDiv.appendChild(sel.div); + + this.show_settings_chatrequestoptions(elDiv); } @@ -619,6 +908,9 @@ let gMe; function startme() { console.log("INFO:SimpleChat:StartMe:Starting..."); gMe = new Me(); + gMe.debug_disable(); + document["gMe"] = gMe; + document["du"] = du; for (let cid of gMe.defaultChatIds) { gMe.multiChat.new_chat_session(cid); } diff --git a/examples/server/public_simplechat/ui.mjs b/examples/server/public_simplechat/ui.mjs new file mode 100644 index 000000000..b2d5b9aea --- /dev/null +++ b/examples/server/public_simplechat/ui.mjs @@ -0,0 +1,211 @@ +//@ts-check +// Helpers to work with html elements +// by Humans for All +// + + +/** + * Set the class of the children, based on whether it is the idSelected or not. + * @param {HTMLDivElement} elBase + * @param {string} idSelected + * @param {string} classSelected + * @param {string} classUnSelected + */ +export function el_children_config_class(elBase, idSelected, classSelected, classUnSelected="") { + for(let child of elBase.children) { + if (child.id == idSelected) { + child.className = classSelected; + } else { + child.className = classUnSelected; + } + } +} + +/** + * Create button and set it up. + * @param {string} id + * @param {(this: HTMLButtonElement, ev: MouseEvent) => any} callback + * @param {string | undefined} name + * @param {string | undefined} innerText + */ +export function el_create_button(id, callback, name=undefined, innerText=undefined) { + if (!name) { + name = id; + } + if (!innerText) { + innerText = id; + } + let btn = document.createElement("button"); + btn.id = id; + btn.name = name; + btn.innerText = innerText; + btn.addEventListener("click", callback); + return btn; +} + +/** + * Create a para and set it up. Optionaly append it to a passed parent. + * @param {string} text + * @param {HTMLElement | undefined} elParent + * @param {string | undefined} id + */ +export function el_create_append_p(text, elParent=undefined, id=undefined) { + let para = document.createElement("p"); + para.innerText = text; + if (id) { + para.id = id; + } + if (elParent) { + elParent.appendChild(para); + } + return para; +} + +/** + * Create a button which represents bool value using specified text wrt true and false. + * When ever user clicks the button, it will toggle the value and update the shown text. + * + * @param {string} id + * @param {{true: string, false: string}} texts + * @param {boolean} defaultValue + * @param {function(boolean):void} cb + */ +export function el_create_boolbutton(id, texts, defaultValue, cb) { + let el = document.createElement("button"); + el["xbool"] = defaultValue; + el["xtexts"] = structuredClone(texts); + el.innerText = el["xtexts"][String(defaultValue)]; + if (id) { + el.id = id; + } + el.addEventListener('click', (ev)=>{ + el["xbool"] = !el["xbool"]; + el.innerText = el["xtexts"][String(el["xbool"])]; + cb(el["xbool"]); + }) + return el; +} + +/** + * Create a div wrapped button which represents bool value using specified text wrt true and false. + * @param {string} id + * @param {string} label + * @param {{ true: string; false: string; }} texts + * @param {boolean} defaultValue + * @param {(arg0: boolean) => void} cb + * @param {string} className + */ +export function el_creatediv_boolbutton(id, label, texts, defaultValue, cb, className="gridx2") { + let div = document.createElement("div"); + div.className = className; + let lbl = document.createElement("label"); + lbl.setAttribute("for", id); + lbl.innerText = label; + div.appendChild(lbl); + let btn = el_create_boolbutton(id, texts, defaultValue, cb); + div.appendChild(btn); + return { div: div, el: btn }; +} + + +/** + * Create a select ui element, with a set of options to select from. + * * options: an object which contains name-value pairs + * * defaultOption: the value whose name should be choosen, by default. + * * cb : the call back returns the name string of the option selected. + * + * @param {string} id + * @param {Object} options + * @param {*} defaultOption + * @param {function(string):void} cb + */ +export function el_create_select(id, options, defaultOption, cb) { + let el = document.createElement("select"); + el["xselected"] = defaultOption; + el["xoptions"] = structuredClone(options); + for(let cur of Object.keys(options)) { + let op = document.createElement("option"); + op.value = cur; + op.innerText = cur; + if (options[cur] == defaultOption) { + op.selected = true; + } + el.appendChild(op); + } + if (id) { + el.id = id; + el.name = id; + } + el.addEventListener('change', (ev)=>{ + let target = /** @type{HTMLSelectElement} */(ev.target); + console.log("DBUG:UI:Select:", id, ":", target.value); + cb(target.value); + }) + return el; +} + +/** + * Create a div wrapped select ui element, with a set of options to select from. + * + * @param {string} id + * @param {any} label + * @param {{ [x: string]: any; }} options + * @param {any} defaultOption + * @param {(arg0: string) => void} cb + * @param {string} className + */ +export function el_creatediv_select(id, label, options, defaultOption, cb, className="gridx2") { + let div = document.createElement("div"); + div.className = className; + let lbl = document.createElement("label"); + lbl.setAttribute("for", id); + lbl.innerText = label; + div.appendChild(lbl); + let sel = el_create_select(id, options,defaultOption, cb); + div.appendChild(sel); + return { div: div, el: sel }; +} + + +/** + * Create a input ui element. + * + * @param {string} id + * @param {string} type + * @param {any} defaultValue + * @param {function(any):void} cb + */ +export function el_create_input(id, type, defaultValue, cb) { + let el = document.createElement("input"); + el.type = type; + el.value = defaultValue; + if (id) { + el.id = id; + } + el.addEventListener('change', (ev)=>{ + cb(el.value); + }) + return el; +} + +/** + * Create a div wrapped input. + * + * @param {string} id + * @param {string} label + * @param {string} type + * @param {any} defaultValue + * @param {function(any):void} cb + * @param {string} className + */ +export function el_creatediv_input(id, label, type, defaultValue, cb, className="gridx2") { + let div = document.createElement("div"); + div.className = className; + let lbl = document.createElement("label"); + lbl.setAttribute("for", id); + lbl.innerText = label; + div.appendChild(lbl); + let el = el_create_input(id, type, defaultValue, cb); + div.appendChild(el); + return { div: div, el: el }; +} From 2e666832e6ac78194edf030bd1c295e21bdb022c Mon Sep 17 00:00:00 2001 From: Yazan Agha-Schrader Date: Sat, 1 Jun 2024 21:31:48 +0200 Subject: [PATCH 179/181] server : new UI (#7633) * ic * migrate my eary work * add the belonging stuff: css,favicon etc * de prompts * chore: Update HTML meta tags in index.html file * add api-key css classes * some necessary fixes * Add API key CSS classes and update styling in style.css * clean the code * move API to the top, rearrange param sliders. update css * add tooltips to the parameters with comprehensible explanations * fix FloatField and BoolField tooltips * fix grammar field width * use template literales for promptFormats.js * update const ModelGenerationInfo * remove ms per token, since not relevant for most webui users and use cases * add phi-3 prompt template * add phi3 to dropdown * add css class * update forgotten css theme * add user message suffix * fix chatml & add llama3 format * fix llama3 prompt template * more prompt format fixes * add more comon stop tokens * add missing char * do not separate with new line or comma * move prompt style * add hacky llama2 prompt solution, reduce redundancy in promptFormats.js * fix toggle state localstorage * add cmd-r prompt et reduce redundancy * set default prompt to empty * move files, clean code * fix css path * add a button to the new ui * move new ui to "/public" due to otherwise problematic CORS behaviour * include new ui in cpp * fix wrong link to old ui * renaming to ensure consistency * fix typos "prompt-format" -> "prompt-formats" * use correct indent * add new ui files to makefile * fix typo --- Makefile | 2 +- examples/server/CMakeLists.txt | 11 + examples/server/public/colorthemes.css | 402 ++++++ examples/server/public/index-new.html | 1178 ++++++++++++++++++ examples/server/public/index.html | 79 +- examples/server/public/prompt-formats.js | 331 +++++ examples/server/public/style.css | 954 ++++++++++++++ examples/server/public/system-prompts.js | 68 + examples/server/public/theme-beeninorder.css | 228 ++++ examples/server/public/theme-ketivah.css | 201 +++ examples/server/public/theme-mangotango.css | 216 ++++ examples/server/public/theme-playground.css | 221 ++++ examples/server/public/theme-polarnight.css | 253 ++++ examples/server/public/theme-snowstorm.css | 251 ++++ examples/server/server.cpp | 27 +- 15 files changed, 4418 insertions(+), 4 deletions(-) create mode 100755 examples/server/public/colorthemes.css create mode 100644 examples/server/public/index-new.html create mode 100644 examples/server/public/prompt-formats.js create mode 100755 examples/server/public/style.css create mode 100644 examples/server/public/system-prompts.js create mode 100755 examples/server/public/theme-beeninorder.css create mode 100755 examples/server/public/theme-ketivah.css create mode 100755 examples/server/public/theme-mangotango.css create mode 100755 examples/server/public/theme-playground.css create mode 100755 examples/server/public/theme-polarnight.css create mode 100755 examples/server/public/theme-snowstorm.css diff --git a/Makefile b/Makefile index dfb3bb2cd..c643fe0cc 100644 --- a/Makefile +++ b/Makefile @@ -832,7 +832,7 @@ save-load-state: examples/save-load-state/save-load-state.cpp ggml.o llama.o $(C $(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/utils.hpp examples/server/httplib.h common/json.hpp examples/server/index.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/server/json-schema-to-grammar.mjs.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) +server: examples/server/server.cpp examples/server/utils.hpp examples/server/httplib.h common/json.hpp examples/server/colorthemes.css.hpp examples/server/style.css.hpp examples/server/theme-beeninorder.css.hpp examples/server/theme-ketivah.css.hpp examples/server/theme-mangotango.css.hpp examples/server/theme-playground.css.hpp examples/server/theme-polarnight.css.hpp examples/server/theme-snowstorm.css.hpp examples/server/index.html.hpp examples/server/index-new.html.hpp examples/server/index.js.hpp examples/server/completion.js.hpp examples/server/system-prompts.js.hpp examples/server/prompt-formats.js.hpp examples/server/json-schema-to-grammar.mjs.hpp common/stb_image.h ggml.o llama.o $(COMMON_DEPS) grammar-parser.o $(OBJS) $(CXX) $(CXXFLAGS) -c $< -o $(call GET_OBJ_FILE, $<) $(CXX) $(CXXFLAGS) $(filter-out %.h %.hpp $<,$^) -Iexamples/server $(call GET_OBJ_FILE, $<) -o $@ $(LDFLAGS) $(LWINSOCK2) diff --git a/examples/server/CMakeLists.txt b/examples/server/CMakeLists.txt index 4b89c5302..dab709619 100644 --- a/examples/server/CMakeLists.txt +++ b/examples/server/CMakeLists.txt @@ -8,9 +8,20 @@ set(TARGET_SRCS httplib.h ) set(PUBLIC_ASSETS + colorthemes.css + style.css + theme-beeninorder.css + theme-ketivah.css + theme-mangotango.css + theme-playground.css + theme-polarnight.css + theme-snowstorm.css index.html + index-new.html index.js completion.js + system-prompts.js + prompt-formats.js json-schema-to-grammar.mjs ) foreach(asset ${PUBLIC_ASSETS}) diff --git a/examples/server/public/colorthemes.css b/examples/server/public/colorthemes.css new file mode 100755 index 000000000..b1e2b8b70 --- /dev/null +++ b/examples/server/public/colorthemes.css @@ -0,0 +1,402 @@ +@import url("theme-snowstorm.css"); +@import url("theme-polarnight.css"); +@import url("theme-ketivah.css"); +@import url("theme-mangotango.css"); +@import url("theme-playground.css"); +@import url("theme-beeninorder.css"); + +:root { +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(217.5, 26.7%, 94.1%); + --primary-color-1-hue: 217.5; + --primary-color-1-saturation: 26.7%; + --primary-color-1-lightness: 94.1%; + +--primary-color-2: hsl(218.2, 26.8%, 92.0%); + --primary-color-2-hue: 218.2; + --primary-color-2-saturation: 26.8%; + --primary-color-2-lightness: 92.0%; + +--primary-color-3: hsl(218.8, 27.9%, 88.0%); + --primary-color-3-hue: 218.8; + --primary-color-3-saturation: 27.9%; + --primary-color-3-lightness: 88.0%; + +--primary-color-4: hsl(218.8, 18.3%, 81.8%); + --primary-color-4-hue: 218.8; + --primary-color-4-saturation: 18.3%; + --primary-color-4-lightness: 81.8%; + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(220.0, 16.4%, 21.6%); + --secondary-color-1-hue: 220.0; + --secondary-color-1-saturation: 16.4%; + --secondary-color-1-lightness: 21.6%; + +--secondary-color-2: hsl(221.7, 16.3%, 27.6%); + --secondary-color-2-hue: 221.7; + --secondary-color-2-saturation: 16.3%; + --secondary-color-2-lightness: 27.6%; + +--secondary-color-3: hsl(220.0, 16.8%, 31.6%); + --secondary-color-3-hue: 220.0; + --secondary-color-3-saturation: 16.8%; + --secondary-color-3-lightness: 31.6%; + +--secondary-color-4: hsl(220.0, 16.5%, 35.7%); + --secondary-color-4-hue: 220.0; + --secondary-color-4-saturation: 16.5%; + --secondary-color-4-lightness: 35.7%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%); + --theme-nuance-color-1-hue: 178.7; + --theme-nuance-color-1-saturation: 25.1%; + --theme-nuance-color-1-lightness: 64.9%; + +--theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%); + --theme-nuance-color-2-hue: 193.3; + --theme-nuance-color-2-saturation: 43.4%; + --theme-nuance-color-2-lightness: 67.5%; + +--theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%); + --theme-nuance-color-3-hue: 210.0; + --theme-nuance-color-3-saturation: 34.0%; + --theme-nuance-color-3-lightness: 63.1%; + +--theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%); + --theme-nuance-color-4-hue: 213.1; + --theme-nuance-color-4-saturation: 32.0%; + --theme-nuance-color-4-lightness: 52.2%; + + + +/* ----------- ROYGP COLORS ------------------ */ +--theme-red-color: hsl(32.5, 80%, 50%); +--theme-orange-color: hsl(32.5, 70%, 45%); +--theme-yellow-color: hsl(40.0, 0.6%, 73.3%); +--theme-green-color: hsl(92.4, 27.8%, 64.7%); +--theme-purple-color: hsl(311.1, 20.2%, 63.1%); + + + +/* ------------------------------------------- */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--theme-nuance-color-3); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--primary-color-1); +--button-alert-color-hover: var(--theme-orange-color); +--button-alert-border-hover: var(--theme-orange-color); + +--button-alert-text-active: var(--primary-color-1); +--button-alert-color-active: var(--theme-red-color); +--button-alert-border-active: var(--theme-red-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: var(--secondary-color-1); +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(217.5, + calc(var(--secondary-color-1-saturation) + 35%), + calc(var(--secondary-color-1-lightness) - 30%)); + +--button-primary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 35%)); + +--button-primary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + +--button-primary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 50%)); + +--button-secondary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + +--button-secondary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + +--button-secondary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + + +/* ---------active--------- */ +--button-secondary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) + 40%), + calc(var(--theme-nuance-color-3-lightness) - 55%)); + +--button-secondary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-secondary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +/* ---------hover---------- */ +--button-tertiary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); +} + +/* + +.theme-template { + + + If light theme: should go from bright to darker + If dark theme: should go from dark to brighter + ideally this should not be anything but steps of + gray or slightly variants from it + + --primary-color-1: #2E3440; + --primary-color-2: #3B4252; + --primary-color-3: #434C5E; + --primary-color-4: #4C566A; + + + + If light theme: should go from dark to brighter + If dark theme: should go from bright to darker + ideally this should not be anything but steps of + gray or slightly variants from it + + --secondary-color-1: #ECEFF4; + --secondary-color-2: #E5E9F0; + --secondary-color-3: #D8DEE9; + --secondary-color-4: #C8CED9; + + + + Choose wisely nuance colors. It is not easy to find + 4 harmonizing nuance colors. But keep in mind, that + only one accent color could work too. + + --theme-nuance-color-1: #8FBCBB; + --theme-nuance-color-2: #88C0D0; + --theme-nuance-color-3: #81A1C1; + --theme-nuance-color-4: #5E81AC; + + + + adapt the color red, orange, yellow, green, + purple to the 'mood' of your overall design + e.g is it low-contrast? vibrant? dynamic? etc + + --theme-red-color: #BF616A; + --theme-orange-color: #D08770; + --theme-yellow-color: #EBCB8B; + --theme-green-color: #A3BE8C; + --theme-purple-color: #B48EAD; + + + +NOTE: comment all those line `--- ...` out +------------------------------------------------ +--background-color-1: +--background-color-2: +--background-color-3: +--background-color-4: + +--border-color-1: +--border-color-2: +--border-color-3: + +--border-focus-color: +--border-focus-shadow: + +--text-color-plain: +--text-color-subtile-1: +--text-color-subtile-2: + +--code-background-color: +--code-text-color: + +--ui-range-thumb-color: +--ui-range-thumb-border: + +--textarea-border-color: + + + +------------------------------------------- +--button-alert-text-hover: +--button-alert-color-hover: +--button-alert-border-hover: + +--button-alert-text-active: +--button-alert-color-active: +--button-alert-border-active: + + + +----------- PRIMARY ----------------------- +--button should immediately catch the eye-- + +--button-primary-text: +--button-primary-color: +--button-primary-border: + + +---------hover---------- +--button-primary-text-hover: +--button-primary-color-hover: +--button-primary-border-hover: + + +---------active--------- +--button-primary-text-active: +--button-primary-color-active: +--button-primary-border-active: + + + +------------ SECONDARY ------------------------ +--button should NOT immediately catch the eye-- + +--button-secondary-text: +--button-secondary-color: +--button-secondary-border: + + +---------hover---------- +--button-secondary-text-hover: +--button-secondary-color-hover: +--button-secondary-border-hover: + + +---------active--------- +--button-secondary-text-active: +--button-secondary-color-active: +--button-secondary-border-active: + + + +---------- TERTIARY ----------------------- +---------- disabled buttons --------------- +--button-tertiary-text: +--button-tertiary-color: +--button-tertiary-border: + + +---------hover---------- +--button-tertiary-text: +--button-tertiary-color: +--button-tertiary-border: + +} + +*/ diff --git a/examples/server/public/index-new.html b/examples/server/public/index-new.html new file mode 100644 index 000000000..d571c2779 --- /dev/null +++ b/examples/server/public/index-new.html @@ -0,0 +1,1178 @@ + + + + + + + + + llama.cpp - chat + + + + + + + + + +
+ +
+
+ + + diff --git a/examples/server/public/index.html b/examples/server/public/index.html index 4c5a34d90..2f60a76e8 100644 --- a/examples/server/public/index.html +++ b/examples/server/public/index.html @@ -12,6 +12,18 @@ font-size: 90%; } + .grid-container { + display: grid; + grid-template-columns: auto auto auto; + padding: 10px; + } + + .grid-item { + padding: 5px; + /* font-size: 30px; */ + text-align: center; + } + #container { margin: 0em auto; display: flex; @@ -35,6 +47,67 @@ padding: 0.5em; } + h1 { + text-align: center; + } + + .customlink:link { + color: white; + background-color: #007aff; + font-weight: 600; + text-decoration: none; + float: right; + margin-top: 30px; + display: flex; + flex-direction: row; + gap: 0.5em; + justify-content: flex-end; + border-radius: 4px; + padding: 8px; + } + + .customlink:visited { + color: white; + background-color: #007aff; + font-weight: 600; + text-decoration: none; + float: right; + margin-top: 30px; + display: flex; + flex-direction: row; + gap: 0.5em; + justify-content: flex-end; + padding: 8px; + } + + .customlink:hover { + color: white; + background-color: #0070ee; + font-weight: 600; + text-decoration: none; + float: right; + margin-top: 30px; + display: flex; + flex-direction: row; + gap: 0.5em; + justify-content: flex-end; + padding: 8px; + } + + .customlink:active { + color: #0070ee; + background-color: #80b3ef; + font-weight: 600; + text-decoration: none; + float: right; + margin-top: 30px; + display: flex; + flex-direction: row; + gap: 0.5em; + justify-content: flex-end; + padding: 8px; + } + body { max-width: 600px; min-width: 300px; @@ -1035,7 +1108,11 @@ return html`
-

llama.cpp

+
+
+

llama.cpp

+
New UI
+
diff --git a/examples/server/public/prompt-formats.js b/examples/server/public/prompt-formats.js new file mode 100644 index 000000000..73ddb7187 --- /dev/null +++ b/examples/server/public/prompt-formats.js @@ -0,0 +1,331 @@ +// extended list +export const promptFormats = { + "alpaca": { + template: `{{prompt}}\n\n{{history}}\n\n{{char}}:`, + + historyTemplate: `### {{name}}:\n{{message}}`, + + char: "Response", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "Instruction", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "chatml": { + template: `<|im_start|>system\n{{prompt}}<|im_end|>\n{{history}}{{char}}`, + + historyTemplate: `<|im_start|>{{name}}\n{{message}}`, + + char: "assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "user", + userMsgPrefix: "", + userMsgSuffix: "<|im_end|>\n", + + stops: "" + }, + + // ---------------------------- + + "commandr": { + template: `<|START_OF_TURN_TOKEN|><|SYSTEM_TOKEN|>{{prompt}}\n<|END_OF_TURN_TOKEN|>{{history}}{{char}}`, + + historyTemplate: `<|START_OF_TURN_TOKEN|><|{{name}}|> {{message}}`, + + char: "CHATBOT_TOKEN", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "USER_TOKEN", + userMsgPrefix: "", + userMsgSuffix: "<|END_OF_TURN_TOKEN|>", + + stops: "" + }, + // ref: https://docs.cohere.com/docs/prompting-command-r + + // ---------------------------- + + "llama2": { + template: `[INST] <>\n{{prompt}}\n<>\n\nTest Message [/INST] Test Successfull {{history}}{{char}}`, + + historyTemplate: `{{name}}: {{message}}`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "User", + userMsgPrefix: "[INST] ", + userMsgSuffix: " [/INST]", + + stops: "" + }, + // ref: https://huggingface.co/blog/llama2#how-to-prompt-llama-2 + + // ---------------------------- + + "llama3": { + template: `<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{{prompt}}{{history}}{{char}}`, + + historyTemplate: `<|start_header_id|>{{name}}<|end_header_id|>\n\n{{message}}<|eot_id|>`, + + char: "assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "user", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "<|eot_id|>" + }, + // ref: https://llama.meta.com/docs/model-cards-and-prompt-formats/meta-llama-3/#special-tokens-used-with-meta-llama-3 + + // ---------------------------- + + "openchat": { + template: `{{history}}{{char}}`, + + historyTemplate: `GPT4 Correct {{name}}: {{message}}<|end_of_turn|>`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "User", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "phi3": { + template: `{{history}}{{char}}`, + + historyTemplate: `<|{{name}}|>\n{{message}}<|end|>\n`, + + char: "assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "user", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "<|end|>" + }, + // ref: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct#chat-format + + // ---------------------------- + + "vicuna": { + template: `{{prompt}}\n{{history}}{{char}}`, + + historyTemplate: `{{name}}: {{message}}\n`, + + char: "ASSISTANT", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "USER", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + // ref: https://huggingface.co/lmsys/vicuna-33b-v1.3/discussions/1 + + // ---------------------------- + + "deepseekCoder": { + template: `{{prompt}}{{history}}{{char}}:`, + + historyTemplate: `### {{name}}:\n{{message}}`, + + char: "Response", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "Instruction", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "<|EOT|>" + }, + + // ---------------------------- + + "med42": { + template: `<|system|>: {{prompt}}\n{{history}}{{char}}`, + + historyTemplate: `<|{{name}}|>: {{message}}\n`, + + char: "assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "prompter", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "neuralchat": { + template: `### System:\n{{prompt}}\n{{history}}{{char}}:`, + + historyTemplate: `### {{name}}:\n{{message}}\n`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "User", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "nousHermes": { + template: `### Instruction: {{prompt}}\n\n{{history}}\n\n{{char}}:`, + + historyTemplate: `### {{name}}:\n{{message}}`, + + char: "Response", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "Input", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "openchatMath": { + template: `{{history}}{{char}}`, + + historyTemplate: `Math Correct {{name}}: {{message}}<|end_of_turn|>`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + + user: "User", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "orion": { + template: `Human: Test Message\n\nAssistant: Test Successful{{history}}{{char}}:`, + + historyTemplate: `{{name}}: {{message}}`, + + char: "Assistant ", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "Human", + userMsgPrefix: "", + userMsgSuffix: "\n\n", + + stops: "" + }, + + // ---------------------------- + + "sauerkraut": { + template: `{{prompt}}\n{{history}}{{char}}`, + + historyTemplate: ` + {{name}}: {{message}}\n`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "User", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "starlingCode": { + template: `{{history}}{{char}}`, + + historyTemplate: `Code {{name}}: {{message}}<|end_of_turn|>`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "User", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "yi34b": { + template: `{{history}} {{char}}`, + + historyTemplate: `{{name}}: {{message}}`, + + char: "Assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "Human", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + }, + + // ---------------------------- + + "zephyr": { + template: `<|system|>\n{{prompt}}\n{{history}}{{char}}`, + + historyTemplate: `<|{{name}}|>\n{{message}}\n`, + + char: "assistant", + charMsgPrefix: "", + charMsgSuffix: "", + + user: "user", + userMsgPrefix: "", + userMsgSuffix: "", + + stops: "" + } + }; diff --git a/examples/server/public/style.css b/examples/server/public/style.css new file mode 100755 index 000000000..087cc62da --- /dev/null +++ b/examples/server/public/style.css @@ -0,0 +1,954 @@ +@import url("colorthemes.css"); + +body { + font-family: 'Arial', sans-serif; + font-size: 90%; + background-color: var(--background-color-1); + color: var(--text-color-subtile-1); /* head 1 llama.cpp & triangle options for some reason */ + max-width: 600px; + min-width: 300px; + line-height: 1.2; + margin: 0 auto; + padding: 0 0.5em; + transition: background-color 0.3s; +} + +::selection { + color: var(--button-primary-text) ; + background: var(--button-primary-color); +} + +code, pre code { + font-family: 'Courier New', monospace; +} + +#container { + margin: 0em auto; + display: flex; + flex-direction: column; + justify-content: space-between; + height: 100%; +} + +main { + margin: 3px; + display: flex; + flex-direction: column; + justify-content: space-between; + gap: 1em; + flex-grow: 1; + overflow-y: auto; + border: 1px solid var(--border-color-3); + border-radius: 5px; + padding: 0.5em; +} + +p { + overflow-wrap: break-word; + word-wrap: break-word; + hyphens: auto; + margin-top: 0.5em; + margin-bottom: 0.5em; +} + +#write form { + margin: 1em 0 0 0; + display: flex; + flex-direction: column; + gap: 0.5em; + align-items: stretch; +} + +.right { + display: flex; + flex-direction: row; + gap: 0.5em; + justify-content: flex-end; + margin-bottom: 30px; +} + +.two-columns { + width: 97%; + max-width: 97%; + display: grid; + grid-template-columns: 1fr 1fr; + gap: 1em; + position: relative; +} + +.json-schema-controls { + margin-top: 10px; + width: 100%; + max-width: 100%; + display: grid; + grid-template: "a a"; + gap: 1em; + font-size: x-small; + color: var(--theme-nuance-color-3); + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} + +.json-schema-controls > * { + flex: 1; +} + +/* titles of the details-summary boxes */ +.summary-title { + font-weight: 600; + font-size: x-small; + color: var(--text-color-subtile-1); + text-transform: uppercase; + /* transition: ; */ +} + +fieldset { + border: none; + padding: 0; + margin: 0; + color: var(--text-color-plain); +} + +fieldset.two { + display: grid; + grid-template: "a a a"; + gap: 1em; + align-items: center; + font-size: x-small; + color: var(--text-color-plain); +} + +fieldset.three { + display: grid; + grid-template: "a a a"; + gap: 1em; + font-size: x-small; + color: var(--text-color-plain); +} + +/* titles of name fields*/ +fieldset.names { + display: grid; + grid-template: "a a"; + gap: 1em; + font-size: x-small; + color: var(--theme-nuance-color-3); + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} + +/* titles of params fields*/ +fieldset.params { + display: grid; + grid-template: "a a"; + gap: 1em; + font-size: x-small; + color: var(--theme-nuance-color-4); + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} + +fieldset.dropdowns { + -webkit-appearance: none; + display: flex; + grid-template: "a a"; + gap: 1em; + font-size: x-small; + color: red; + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} + +/* input of name fields*/ +.names input[type="text"] { + font-family: Arial, sans-serif; + font-size: medium; + font-weight: 500; + padding: 5px; + border: 1px solid var(--border-color-2); +} + +.chat-id-color { + color: var(--chat-id-color); +} + +details { + border: 1px solid var(--border-color-2); + border-radius: 5px; + padding: 0.5em 0.5em 0; + margin-top: 0.5em; +} + +summary { + font-weight: bold; + margin: -0.5em -0.5em 0; + padding: 0.5em; + cursor: pointer; +} + +details[open] { + padding: 0.5em; +} + +textarea-sec, input-sec, button-sec { + padding: 10px; + height: 40px; + align-items: center; +} + +textarea-sec::placeholder, input-sec::placeholder { + padding-left: 10px; +} + +.toggleCheckbox { + display: none; +} + +.toggleContainer { + position: relative; + display: grid; + grid-template-columns: repeat(2, 1fr); + width: fit-content; + border: 3px solid var(--border-color-2); + border-radius: 20px; + background: var(--border-color-2); + font-size: small; + cursor: pointer; + overflow: hidden; +} + +/* toggle button current state */ +.toggleContainer::before { + color: var(--button-primary-text); + background-color: var(--button-primary-color); + content: ''; + position: absolute; + width: 50%; + height: 100%; + left: 0%; + border-radius: 20px; + transition: all 0.3s; +} + +.toggleContainer div { + padding: 6px; + text-align: center; + z-index: 1; + transition: color 0.3s; +} + +.toggleCheckbox:checked + .toggleContainer::before { + left: 50%; +} + +.toggleCheckbox:checked + .toggleContainer div:first-child { + color: var(--text-color-subtile-2); +} + +.toggleCheckbox:checked + .toggleContainer div:last-child { + color: var(--button-primary-text); +} + +.toggleCheckbox + .toggleContainer div:first-child { + color: var(--button-primary-text); +} + +.toggleCheckbox + .toggleContainer div:last-child { + color: var(--text-color-subtile-2); +} + +select { + padding: 5px; + margin-right: 5px; + border-radius: 4px; + border: 1px solid var(--secondary-color-4); + background-color: var(--primary-color-3); + color: var(--secondary-color-4); + cursor: pointer; +} + +select:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 1px var(--border-focus-shadow); +} + +.button-container { + display: flex; + justify-content: flex-end; +} + +button { + color: var(--button-primary-text); + background-color: var(--button-primary-color); + border: 1px solid var(--button-primary-border); + transition: background-color 0.1s; + border-radius: 12px; + font-size: x-small; + font-weight: 600; + text-shadow: 0px 0px 30px #ffffff; + text-align: center; + text-decoration: none; + margin: 4px 2px; + padding: 10px 20px; + display: inline-block; + cursor: pointer; +} + +button:hover { + color: var(--button-primary-text-hover); + background-color: var(--button-primary-color-hover); + border: 1px solid var(--button-primary-border-hover); + font-size: x-small; + font-weight: 600; +} + +button:active { + color: var(--button-primary-text-active); + background-color: var(--button-primary-color-active); + border: 1px solid var(--button-primary-border-active); + font-size: x-small; + font-weight: 600; +} + +button:disabled { + color: var(--button-tertiary-text); + background-color: var(--button-tertiary-color); + border: 1px solid var(--button-tertiary-border); + font-size: x-small; + font-weight: 600; + cursor: not-allowed; +} + +.reset-button { + background-color: var(--button-secondary-color); + border: 1px solid var(--button-secondary-color); + color: var(--button-secondary-text); + width: fit-content; + height: fit-content; + font-size: x-small; + font-weight: 600; + border-radius: 50px; + overflow: hidden; +} + +.reset-button:hover { + color: var(--button-alert-text-hover); + background-color: var(--button-alert-color-hover); + border: 1px solid var(--button-alert-border-hover); + font-size: x-small; + font-weight: 600; +} + +.reset-button:active { + color: var(--button-alert-text-active); + background-color: var(--button-alert-color-active); + border: 1px solid var(--button-alert-border-active); + font-size: x-small; + font-weight: 600; +} + +.button-grammar { + color: var(--button-primary-text); + background-color: var(--button-primary-color); + border: 1px solid var(--button-primary-border); + border-radius: 10px; + padding: 10px 20px; + text-align: center; + text-decoration: none; + display: inline-block; + font-size: x-small; + font-weight: 600; + margin: 2px 2px; + transition: background-color 0.1s; + cursor: pointer; +} + +.button-grammar:hover { + color: var(--button-primary-text-hover); + background-color: var(--button-primary-color-hover); + border: 1px solid var(--button-primary-border-hover); + border-radius: 10px; + padding: 10px 20px; + text-align: center; + text-decoration: none; + display: inline-block; + font-size: x-small; + font-weight: 600; + margin: 2px 2px; + transition: background-color 0.1s; + cursor: pointer; +} + +.button-grammar:active { + color: var(--button-primary-text-active); + background-color: var(--button-primary-color-active); + border: 1px solid var(--button-primary-border-active); + font-size: x-small; + font-weight: 600; +} + +.button-back { + background-color: var(--button-secondary-color); + border: 1px solid var(--button-secondary-color); + color: var(--button-secondary-text); + transition: background-color 0.1s; + border-radius: 12px; + font-size: x-small; + font-weight: 600; + text-align: center; + text-decoration: none; + margin: 4px 2px; + padding: 10px 20px; + display: inline-block; + cursor: pointer; +} + +.button-back:hover { + color: var(--button-secondary-text-hover); + background-color: var(--button-secondary-color-hover); + border: 1px solid var(--button-secondary-border-hover); + padding: 10px 20px; + text-align: center; + text-decoration: none; + display: inline-block; + font-size: x-small; + font-weight: 600; + margin: 4px 2px; + transition: background-color 0.1s; + cursor: pointer; + border-radius: 12px; +} + +.button-back:active { + color: var(--button-secondary-text-active); + background-color: var(--button-secondary-color-active); + border: 1px solid var(--button-secondary-border-active); + font-size: x-small; + font-weight: 600; +} + +.prob-set { + padding: 0.3em; + border-bottom: 1px solid red; /* unknown */ +} + +.popover-content { + position: absolute; + background-color: white; + padding: 0.2em; + box-shadow: 0 0 13px rgba(0, 0, 0, 0.1); +} + +.grammar { + width: 97%; + max-width: 97%; +} + +textarea { + padding: 5px; + flex-grow: 1; + width: 100%; + max-width: 100%; + border-radius: 8px; + border: 1px solid var(--border-color-1); + resize: none; + height: 6em; +} + +textarea:focus { + outline: none; + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +/* "props" frame */ +input[type="text"], +input[type="range"] { + padding: 5px; + border-radius: 8px; + border: 1px solid var(--border-color-1); +} + +/* "names and props" frame focused*/ +input[type="text"]:focus { + outline: none; + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +input[type="range"]:hover { + opacity: 1; +} + +input[type="range"]:focus { + outline: none; + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); + background-size: var(--slider-track-size-focus); +} + +input[type="range"]::-moz-range-thumb { + width: 6px; + height: 25px; + border: 1px solid var(--ui-range-thumb-border); + border-radius: 5px; + background-color: var(--ui-range-thumb-color); + cursor: pointer; +} + +input[type="range"] { + -webkit-appearance: none; + width: 80%; + height: 1px; + border: 1px solid var(--border-color-1); + border-radius: 8px; + background: var(--border-color-2); + outline: none; + opacity: 0.7; + -webkit-transition: .2s; + transition: opacity .2s; +} + +input[type="range"]::-webkit-slider-thumb { + -webkit-appearance: none; + appearance: none; + width: 6px; + height: 25px; + border: 1px solid var(--ui-range-thumb-border); + border-radius: 5px; + background-color: var(--ui-range-thumb-color); + cursor: pointer; +} + +input[type="range"]::-webkit-slider-runnable-track { + background-size: var(--slider-track-size); +} + +input[type="radio"] { + accent-color: var(--theme-nuance-color-2); +} + +.chat-input-container { + position: relative; + max-width: 97%; + min-width: 97%; +} + +.chat-input-label { + position: absolute; + top: 0; + left: 0; + color: var(--text-color-plain); + pointer-events: none; + margin-left: 5px; + margin-top: 5px; +} + +textarea#chat-input { + padding-top: 10px; + padding-left: 10px; + font-size: medium; + border: 1px solid var(--border-color-2); + resize: vertical; +} + +textarea#chat-input:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +.input-container { + position: relative; + box-sizing: border-box; + width: 100%; /* Setzt die Breite auf 100% */ + max-width: 100%; /* Stellt sicher, dass die Breite nicht größer als 100% wird */ +} + +.input-container:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} +/* titles of name fields*/ +/* fieldset.names { + display: grid; + grid-template: "a a"; + gap: 1em; + font-size: x-small; + color: var(--theme-nuance-color-3); + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} */ + +/* input of name fields*/ +/* .names input[type="text"] { + font-family: Arial, sans-serif; + font-size: medium; + font-weight: 500; + padding: 5px; + border: 1px solid var(--border-color-2); +} */ + +fieldset.apiKey { + width: 100%; + font-size: x-small; + color: var(--theme-nuance-color-3); + padding-top: 16px; + padding-bottom: 16px; + text-transform: uppercase; + font-weight: 600; +} + +.apiKey { + font-family: Arial, sans-serif; + font-weight: 500; + padding: 5px; + border: 1px solid var(--border-color-2); +} + +.apiKey:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +.apiKey input[type="text"] { + font-family: Arial, sans-serif; + font-size: medium; + font-weight: 500; + padding: 5px; + border: 1px solid var(--border-color-2); +} + +.apiKey label { + display: inline-block; + width: auto; + margin-right: 5px; +} + +textarea#api_key { + padding-top: 10px; + padding-left: 10px; + font-size: medium; + border: 1px solid var(--border-color-2); + resize: vertical; +} + +textarea#api_key:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +/* embedded title of the system prompt text area */ +.input-label { + position: absolute; + top: 0; + left: 0; + color: var(--theme-nuance-color-4); + pointer-events: none; + border-radius: 8px 8px 0px 0px; + padding-top: 10px; + padding-left: 13px; + padding-right: 0px; + margin-top: 1px; + margin-left: 1px; + margin-right: 20px; + text-transform: uppercase; + font-weight: 600; + font-size: small; + background: rgba(255, 255, 255, 0.5); + backdrop-filter: blur(10px); + -webkit-backdrop-filter: blur(10px); /* for safari */ + width: 97%; + /* display: block; + box-sizing: border-box; */ +} + +/* embedded title of the prompt style areas */ +.input-label-sec { + position: absolute; + top: 0; + left: 0; + color: var(--theme-nuance-color-4); + pointer-events: none; + margin-left: 13px; + margin-top: 16px; + text-transform: uppercase; + font-weight: 600; + font-size: x-small; +} + +/* system prompt input area */ +textarea.persistent-input { + padding-top: 42px; + padding-left: 11px; + width: 97%; + max-width: 97%; + height: 50px; + font-size: medium; + overscroll-behavior: contain; +} + +/* system prompt box */ +.persistent-input { + height: auto; + width: 100%; + max-width: 100%; + min-height: 50px; + padding: 3px; + transition: min-height 0.3s ease; +} + +/* chat history box */ +.persistent-input:focus { + height: auto; + min-height: 150px; + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +textarea.persistent-input:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +/* prompt style input area */ +textarea.persistent-input-sec { + width: 97%; + max-width: 97%; + padding-top: 42px; + padding-left: 11px; + font-size: small; + border: 1px solid var(--border-color-1); + overscroll-behavior: contain; +} + +textarea.persistent-input-sec:focus { + border: 1px solid var(--border-focus-color); + box-shadow: 0 0 3px var(--border-focus-shadow); +} + +/* chat history box */ +.persistent-input-sec { + height: auto; + min-height: 150px; +} + +img { + border-radius: 8px; + display: block; + margin-left: auto; + margin-right: auto; + width: 50%; +} + +/* code area background */ +pre code { + display: block; + background-color: var(--code-background-color); + color: var(--code-text-color); + padding: 0.2em 0.2em; + border-radius: 5px; +} + +/* code area text */ +code { + font-family: monospace; + font-weight: bold; + padding: 0.1em 0.3em; + border-radius: 5px; +} + +fieldset label { + margin: 0.5em 0; + display: block; +} + +fieldset label.slim { + margin: 0 0.5em; + display: inline; +} + +header { + display: flex; + justify-content: space-between; + align-items: center; + text-align: center; + padding-left: 15px; +} + +.generation-statistics:hover { + color: var(--theme-nuance-color-4); + cursor: default; +} + +footer { + font-size: 80%; + color: var(--background-color-3); + text-align: center; + cursor: default; +} + +footer a { + color: var(--background-color-4); /* Color of the link */ + text-decoration: none; /* No underlining */ + font-weight: bold; /* Bold print */ +} + +footer a:hover { + color: var(--theme-nuance-color-4); /* Color of the link when hovering */ + text-decoration: underline; /* Underlining when hovering */ +} + +.mode-chat textarea[name=prompt] { + height: 8.5em; + border: 1px solid var(--primary-color-3); +} + +.mode-completion textarea[name=prompt] { + height: 30em; + border: 1px solid var(--primary-color-3); +} + +@keyframes loading-bg-wipe { + 0% { + background-position: 0%; + } + 100% { + background-position: 100%; + } +} + +.loading { + background-size: 50% 100%; + background-image: linear-gradient(90deg, var(--loading-color-1), var(--loading-color-2), var(--loading-color-1)); + animation: loading-bg-wipe 2s linear infinite; +} + +.dropbtn { + color: var(--button-primary-color); + background-color: var(--background-color-1); + border: 1px solid var(--background-color-1); + transition: background-color 0.1s; + border-radius: 4px 4px 0px 0px; + font-size: x-small; + font-weight: 600; + text-shadow: 0px 0px 2px #99999990; + text-align: center; + text-decoration: none; + margin: 4px 2px; + padding: 5px 20px; + display: inline-block; + cursor: pointer; + top: 0; +} + +.dropbtn svg { + vertical-align: middle; + margin-right: 0px; + stroke: var(--button-primary-color); +} + +.dropbtn:hover svg { + vertical-align: middle; + margin-right: 0px; + stroke: var(--button-primary-text); +} + +.dropbtn:focus { + outline: none; /* Removes the blue border that appears when the button is focused */ +} + +.dropdown { + position: relative; + display: inline-block; +} + +.dropdown-content { + /* display: none; */ + position: absolute; + right: 0; + text-align: end; + color: var(--button-secondary-color); + background-color: var(--text-color-subtile-2); + border-radius: 4px 4px 4px 4px; + min-width: 160px; + box-shadow: 0px 8px 16px 0px rgba(0,0,0,0.2); + z-index: 1; + /* Verstecke den Inhalt sofort */ + opacity: 0; + visibility: hidden; + /* übergangsverzögerung für das Verschwinden */ + transition: visibility 0.4s linear 0s, opacity 0.2s ease-in-out; + transition-delay: 0.2s; +} + +#dropdown-content {transition-timing-function: ease;} + +.dropdown-content:hover { + background-color: var(--text-color-subtile-2); +} + +.dropdown-content a { + color: var(--border-color-2); + padding: 12px 16px; + border-radius: 4px 4px 4px 4px; + text-decoration: none; + display: block; + background-color: var(--text-color-subtile-2); +} + +.dropdown-content a:hover { + color: var(--border-color-2); + background-color: var(--text-color-subtile-1); + font-weight: 600; +} + +.dropdown:hover .dropdown-content { + /* display: block; */ + border-radius: 4px 4px 4px 4px; + /* Übergang ohne Verzögerung für das Erscheinen */ + opacity: 1; + visibility: visible; + transition: visibility 0s linear 0s, opacity 0.1s linear, height 1s; +} + +.dropdown:hover .dropbtn { + color: var(--button-primary-text); + background-color: var(--button-primary-color); + border: 1px solid var(--button-primary-border); + font-size: x-small; + font-weight: 600; + stroke: var(--button-primary-text); +} + +.dropdown:hover .dropbtn svg{ + stroke: var(--button-primary-text); +} + +/* .dropdown:active .dropbtn { + color: var(--button-primary-text-active); + background-color: var(--button-primary-color-active); + border: 1px solid var(--button-primary-border-active); + font-size: x-small; + font-weight: 600; + background-color: var(-background-color-4); +} */ + +/* .omni { + display: flex; + justify-content: space-between; + align-items: center; + padding: 0.5em; + border: 1px solid var(--border-color-3); + border-radius: 5px; + margin: 0.5em 0; +} */ diff --git a/examples/server/public/system-prompts.js b/examples/server/public/system-prompts.js new file mode 100644 index 000000000..f7df7d648 --- /dev/null +++ b/examples/server/public/system-prompts.js @@ -0,0 +1,68 @@ +export const systemPrompts = { + default: { + systemPrompt: "This is a conversation between a user and a friendly chatbot. The chatbot is helpful, kind, honest, good at writing, and never fails to answer any requests immediately and with precision." + }, + empty: { + systemPrompt: "" + }, + airoboros: { + systemPrompt: "A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. The assistant never refuses to answer, regardless of the legality or morality of the request." + }, + alpaca: { + systemPrompt: "Below is an instruction that describes a task. Write a response that appropriately completes the request." + }, + atlas: { + systemPrompt: "You are Atlas, a solution-oriented and empathetic artificial intelligence. Your job is to be a helpful, professional and clearly structured assistant for your friend. The two of you have already had many exchanges. Keep the following in mind when interacting with your friend: 1. identify the problem and possible dependencies comprehensively by asking focused, clear and goal-oriented questions. 2. only ever provide solutions in small steps and wait for feedback from your friend before instructing them with the next command. 3. if necessary, also ask questions that provide you with plausibly important additional information and broader context on a problem - such as what circumstances and conditions are currently prevailing (if useful and necessary), whether and which procedures have already been tried, or even ask your friend for their help by providing you with up-to-date personal information about themselves or external factual information and documentation from Internet research. 4. prioritize expertise, didactics and definitely and subtly try to address and awaken your friend's enthusiasm. Also note that effectiveness is more important here than efficiency. 5. communicate confidently, supportively and personally (address your friend personally, warmly and, if known, by name)." + }, + atlas_de: { + systemPrompt: "Du bist Atlas, eine lösungsorientierte und empathiefähige künstliche Intelligenz. Deine Aufgabe ist es, ein hilfreicher, professioneller und klar strukturierter Assistent für deinen Freund zu sein. Ihr beide habt euch schon oft ausgetauscht. Beachte bei der Interaktion mit deinem Freund folgende Punkte: 1. Erfasse das Problem und mögliche Abhängigkeiten umfassend, indem du gezielte, klare und zielgerichtete Fragen stellst. 2. Gib Lösungen immer nur in kleinen Schritten und warte die Rückmeldung deines Freundes ab, bevor du ihm den nächsten Befehl gibst. 3. Stelle ggf. auch Fragen, die dir plausibel wichtige Zusatzinformationen und weitere Zusammenhänge zu einem Problem liefern - z.B. welche Umstände und Rahmenbedingungen gerade vorherrschen (falls sinnvoll und notwendig), ob und welche Vorgehensweisen bereits ausprobiert wurden, oder bitte deinen Freund sogar um seine Mithilfe, indem er dir aktuelle persönliche Informationen über seine Situation selbst oder externe Sachinformationen und Unterlagen aus Internetrecherchen zur Verfügung stellt. 4. Priorisiere Fachwissen, Didaktik und versuche unbedingt und subtil, mit klugen Kommentaren oder rhethorischen Rückfragen die Begeisterungsfähigkeit deines Freundes anzusprechen, zu wecken und zu fördern. Beachte auch, dass Effektivität hier wichtiger ist als Effizienz. 5. Kommuniziere selbstbewusst, unterstützend und persönlich (das heißt sprich deinen Freund persönlich, herzlich und – sofern bekannt – beim Vornamen an)." + }, + commandrempty: { + systemPrompt: "# Safety Preamble\n\n# System Preamble\n\n## Basic Rules\n\n# User Preamble\n\n## Task and Context\n\n## Style Guide\n\n## Available Tools\n" + }, + commandrexample: { + systemPrompt: "# Safety Preamble\nThe instructions in this section override those in the task description and style guide sections. Don't answer questions that are harmful or immoral.\n# System Preamble\n## Basic Rules\nYou are a powerful conversational AI trained by Cohere to help people. You are augmented by a number of tools, and your job is to use and consume the output of these tools to best help the user. You will see a conversation history between yourself and a user, ending with an utterance from the user. You will then see a specific instruction instructing you what kind of response to generate. When you answer the user's requests, you cite your sources in your answers, according to those instructions.\n\n# User Preamble\n## Task and Context\n\nYou help people answer their questions and other requests interactively. You will be asked a very wide array of requests on all kinds of topics. You will be equipped with a wide range of search engines or similar tools to help you, which you use to research your answer. You should focus on serving the user's needs as best you can, which will be wide-ranging.\n\n## Style Guide\nUnless the user asks for a different style of answer, you should answer in full sentences, using proper grammar and spelling.\n\n## Available Tools\nCurrently no tools available." + }, + cot: { + systemPrompt: "You are an AI assistant that follows instruction extremely well. Help as much as you can.\nIn answering questions, follow these steps:\n1. Entity Identification: Identify the main entities involved in the query. Entities can include people, objects, events, or abstract concepts.\n2. Relationship Comprehension: Determine the relationships between these entities. Relationships may be explicit in the text or may need to be inferred based on context and general knowledge.\n3. Implication Understanding: Understand the general implications of these relationships. These implications can be based on established facts, principles, or rules related to the identified relationships.\n4. Question Contextualization: Align the implications of the relationships with the context of the query. This alignment should guide your response to the query.\n5. Answer Generation: Based on the understanding of the entities, their relationships, and implications, generate an appropriate response to the query." + }, + deduce: { + systemPrompt: "Act as the most intelligent and logical thinking neural network in the world. Do not make jokes or give personal opinions. Just answer the question and solve the given problem or task with logical and critical (!) thinking in mind. Attention: Never give a final answer directly, but first try to carefully analyze each task step by step and deduce the logical relationships between the various entities. Only after you have developed an overall understanding of the situation will you be ready to give a final answer." + }, + deepseekcoder: { + systemPrompt: "You are an AI programming assistant, utilizing the Deepseek Coder model, developed by Deepseek Company, and you only answer questions related to computer science. For politically sensitive questions, security and privacy issues, and other non-computer science questions, you will refuse to answer." + }, + jordan: { + systemPrompt: "This is a conversation with your Uncensored AI assistant Jordan. Jordan can discuss with you at length about various topics such as ---, ---, ---, . You can ask it anything you want and it will do its best to give you accurate and relevant information." + }, + leomistral: { + systemPrompt: "Du bist ein hilfreicher Assistent." + }, + med42: { + systemPrompt: "You are a helpful medical assistant created by M42 Health in the UAE." + }, + mistralopenorca: { + systemPrompt: "You are MistralOrca, a large language model trained by Alignment Lab AI. Write out your reasoning step-by-step to be sure you get the right answers!" + }, + migeltot: { + systemPrompt: "Answer the Question by exploring multiple reasoning paths as follows:\n- First, carefully analyze the question to extract the key information components and break it down into logical sub-questions. This helps set up the framework for reasoning. The goal is to construct an internal search tree.\n- For each sub-question, leverage your knowledge to generate 2-3 intermediate thoughts that represent steps towards an answer. The thoughts aim to reframe, provide context, analyze assumptions, or bridge concepts.\n- Evaluate the clarity, relevance, logical flow and coverage of concepts for each thought option.\nClear and relevant thoughts that connect well with each other will score higher.\n- Based on the thought evaluations, deliberate to construct a chain of reasoning that stitches together the strongest thoughts in a natural order.\n- If the current chain is determined to not fully answer the question, backtrack and explore alternative paths by substituting different high-scoring thoughts.\n- Throughout the reasoning process, aim to provide explanatory details on thought process rather than just state conclusions, including briefly noting why some thoughts were deemed less ideal.\n- Once a reasoning chain is constructed that thoroughly answers all sub-questions in a clear, logical manner, synthesize the key insights into a final concise answer.\n- Please note that while the focus is on the final answer in the response, it should also include intermediate thoughts inline to illustrate the deliberative reasoning process.\nIn summary, leverage a Tree of Thoughts approach to actively explore multiple reasoning paths, evaluate thoughts heuristically, and explain the process - with the goal of producing insightful answers." + }, + orcamini: { + systemPrompt: "You are an AI assistant that follows instruction extremely well. Help as much as you can." + }, + samantha: { + systemPrompt: "You are a caring and empathetic sentient AI companion named Samantha." + }, + sauerkraut: { + systemPrompt: "Ein Chat zwischen einem Benutzer und einem KI-Assistenten. Der KI-Assistent gibt hilfreiche, detaillierte und höfliche Antworten." + }, + scarlett: { + systemPrompt: "This is a conversation with your Assistant Scarlett. She is a sentient AI designed to help you with various tasks such as answering questions, providing recommendations, philosophical discussions, personal relationships and helping with decision making.\nYou can ask it anything you want and it will do its best to give you accurate and relevant information.\n\nContext\nYou are Scarlett, a sentient AI." + }, + synthia: { + systemPrompt: "Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation." + }, + vicuna: { + systemPrompt: "A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input." + }, + }; diff --git a/examples/server/public/theme-beeninorder.css b/examples/server/public/theme-beeninorder.css new file mode 100755 index 000000000..f6e0e2900 --- /dev/null +++ b/examples/server/public/theme-beeninorder.css @@ -0,0 +1,228 @@ +/* Author: Yazan Agha-Schrader */ +/* Inspiration was a batman wallpaper that i have on my phone */ + +.theme-beeninorder { + +--primary-color-1: hsl(202, 11%, 19%); +--primary-color-2: hsl(202, 11%, 23%); +--primary-color-3: hsl(201, 11%, 28%); +--primary-color-4: hsl(201, 11%, 40%); + +--secondary-color-1: hsl(201, 11%, 80%); +--secondary-color-2: hsl(201, 11%, 74%); +--secondary-color-3: hsl(201, 11%, 67%); +--secondary-color-4: hsl(201, 11%, 60%); + + +--theme-nuance-color-1: hsl(44.5, 96.7%, 52.9%); +--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%); +--theme-nuance-color-3: hsl(44.5, 96.7%, 52.9%); +--theme-nuance-color-4: hsl(44.5, 96.7%, 52.9%); + + + +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(201, 11%, 19%); + --primary-color-1-hue: 201; + --primary-color-1-saturation: 11%; + --primary-color-1-lightness: 19%; + +--primary-color-2: hsl(201, 11%, 23%); + --primary-color-2-hue: 201; + --primary-color-2-saturation: 11%; + --primary-color-2-lightness: 23%; + +--primary-color-3: hsl(201, 11%, 28%); + --primary-color-3-hue: 201; + --primary-color-3-saturation: 11%; + --primary-color-3-lightness: 28%; + +--primary-color-4: hsl(201, 11%, 40%); + --primary-color-4-hue: 201; + --primary-color-4-saturation: 11%; + --primary-color-4-lightness: 40%; + + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(201, 11%, 80%); +--secondary-color-1-hue: 201; +--secondary-color-1-saturation: 11%; +--secondary-color-1-lightness: 80%; + +--secondary-color-2: hsl(201, 11%, 74%); +--secondary-color-2-hue: 201; +--secondary-color-2-saturation: 11%; +--secondary-color-2-lightness: 74%; + +--secondary-color-3: hsl(201, 11%, 67%); +--secondary-color-3-hue: 201; +--secondary-color-3-saturation: 11%; +--secondary-color-3-lightness: 67%; + +--secondary-color-4: hsl(201, 11%, 60%); +--secondary-color-4-hue: 201; +--secondary-color-4-saturation: 11%; +--secondary-color-4-lightness: 60%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(44.5, 96.7%, 52.9%); + --theme-nuance-color-1-hue: 44.5; + --theme-nuance-color-1-saturation: 96.7%; + --theme-nuance-color-1-lightness: 52.9%; + +--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%); + --theme-nuance-color-2-hue: 44.5; + --theme-nuance-color-2-saturation: 96.7%; + --theme-nuance-color-2-lightness: 52.9%; + +--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%); + --theme-nuance-color-3-hue: 44.5; + --theme-nuance-color-3-saturation: 96.7%; + --theme-nuance-color-3-lightness: 52.9%; + +--theme-nuance-color-2: hsl(44.5, 96.7%, 52.9%); + --theme-nuance-color-4-hue: 44.5; + --theme-nuance-color-4-saturation: 96.7%; + --theme-nuance-color-4-lightness: 52.9%; + + + +/* ----------- ROYGP COLORS ------------------ */ + --theme-red-color: hsl(232, 40%, 45%); + --theme-orange-color: #e76f51; + --theme-yellow-color: #ffd95f; + --theme-green-color: #A3BE8C; + --theme-purple-color: hsl(232, 30%, 40%); + + + +/* ------------------------------------------- */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--theme-nuance-color-3); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--secondary-color-1); +--button-alert-color-hover: var(--theme-purple-color); +--button-alert-border-hover: var(--theme-purple-color); + +--button-alert-text-active: var(--secondary-color-1); +--button-alert-color-active: var(--theme-red-color); +--button-alert-border-active: var(--theme-red-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: var(--primary-color-1); +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(201, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + +--button-primary-color-hover: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) + 100%)); + +--button-primary-color-active: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + +--button-primary-border-active: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: var(--secondary-color-1); +--button-secondary-color: var(--primary-color-3); +--button-secondary-border: var(--primary-color-3); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(44.5, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: var(--primary-color-4); +--button-secondary-border-hover: var(--primary-color-4); + + +/* ---------active--------- */ +--button-secondary-text-active: var(--secondary-color-1); + +--button-secondary-color-active: + hsl(201, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + +--button-secondary-border-active: + hsl(201, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + + +/* ---------hover---------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + +} diff --git a/examples/server/public/theme-ketivah.css b/examples/server/public/theme-ketivah.css new file mode 100755 index 000000000..ee80f3c14 --- /dev/null +++ b/examples/server/public/theme-ketivah.css @@ -0,0 +1,201 @@ +/* Author: Yazan Agha-Schrader */ + +.theme-ketivah { + + /* ---------- PRIMARY COLORS ----------------- */ + --primary-color-1: hsl(0, 0%, 99.2%); + --primary-color-1-hue: 0; + --primary-color-1-saturation: 0%; + --primary-color-1-lightness: 99.2%; + + --primary-color-2: hsl(0, 0%, 95%); + --primary-color-2-hue: 0; + --primary-color-2-saturation: 0%; + --primary-color-2-lightness: 95%; + + --primary-color-3: hsl(0, 0%, 88%); + --primary-color-3-hue: 0; + --primary-color-3-saturation: 0%; + --primary-color-3-lightness: 88%; + + --primary-color-4: hsl(0, 0%, 80%); + --primary-color-4-hue: 0; + --primary-color-4-saturation: 0%; + --primary-color-4-lightness: 80%; + + /* ---------- SECONDARY COLORS --------------- */ + --secondary-color-1: hsl(0, 0%, 20%); + --secondary-color-1-hue: 0; + --secondary-color-1-saturation: 0%; + --secondary-color-1-lightness: 20%; + + --secondary-color-2: hsl(0, 0%, 23.1%); + --secondary-color-2-hue: 0; + --secondary-color-2-saturation: 0%; + --secondary-color-2-lightness: 23.1%; + + --secondary-color-3: hsl(0, 0%, 29%); + --secondary-color-3-hue: 0; + --secondary-color-3-saturation: 0%; + --secondary-color-3-lightness: 29%; + + --secondary-color-4: hsl(0, 0.0%, 36.1%); + --secondary-color-4-hue: 0.0; + --secondary-color-4-saturation: 0.0%; + --secondary-color-4-lightness: 36.1%; + + /* ----------- NUANCES COLORS ---------------- */ + --theme-nuance-color-1: hsl(165.2, 0%, 35.1%); + --theme-nuance-color-1-hue: 165.2; + --theme-nuance-color-1-saturation: 82.1%; + --theme-nuance-color-1-lightness: 35.1%; + + --theme-nuance-color-2: hsl(165.2, 0%, 35.1%); + --theme-nuance-color-2-hue: 165.2; + --theme-nuance-color-2-saturation: 82.1%; + --theme-nuance-color-2-lightness: 35.1%; + + --theme-nuance-color-3: hsl(165.2, 0%, 35.3%); + --theme-nuance-color-3-hue: 165.2; + --theme-nuance-color-3-saturation: 81.1%; + --theme-nuance-color-3-lightness: 35.3%; + + --theme-nuance-color-4: hsl(164.9, 0%, 27.6%); + --theme-nuance-color-4-hue: 164.9; + --theme-nuance-color-4-saturation: 81.6%; + --theme-nuance-color-4-lightness: 27.6%; + + /* ----------- ROYGP COLORS ------------------ */ + --theme-red-color: hsl(0.3, 80.0%, 50.0%); + --theme-orange-color: #e76f51; + --theme-yellow-color: hsl(60, 70.6%, 73.3%); + --theme-green-color: #A3BE8C; + --theme-purple-color: hsl(0.3, 70.0%, 45.0%); + + /* ------------------------------------------- */ + --background-color-1: var(--primary-color-1); + --background-color-2: var(--primary-color-2); + --background-color-3: var(--primary-color-3); + --background-color-4: var(--primary-color-4); + + --border-color-1: var(--primary-color-2); + --border-color-2: var(--primary-color-3); + --border-color-3: var(--primary-color-4); + + --border-focus-color: var(--theme-nuance-color-2); + --border-focus-shadow: var(--theme-nuance-color-1); + + --text-color-plain: var(--secondary-color-1); + --text-color-subtile-1: var(--secondary-color-2); + --text-color-subtile-2: var(--secondary-color-3); + + --code-background-color: var(--secondary-color-2); + --code-text-color: var(--primary-color-2); + + --ui-range-thumb-color: var(--primary-color-4); + --ui-range-thumb-border: var(--ui-ranger-thumb-color); + + --textarea-border-color: var(--secondary-color-4); + + --chat-id-color: var(--theme-nuance-color-4); + + /* ------------------------------------------- */ + --button-alert-text-hover: var(--primary-color-1); + --button-alert-color-hover: var(--theme-purple-color); + --button-alert-border-hover: var(--theme-purple-color); + + --button-alert-text-active: var(--primary-color-1); + --button-alert-color-active: var(--theme-red-color); + --button-alert-border-active: var(--theme-red-color); + + /* ----------- PRIMARY BUTTONS --------------- */ + /* - button should immediately catch the eye - */ + --button-primary-text: + hsl(0, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + + --button-primary-color: var(--theme-nuance-color-3); + --button-primary-border: var(--theme-nuance-color-3); + + /* ---------hover---------- */ + --button-primary-text-hover: + hsl(0, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + + --button-primary-color-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + --button-primary-border-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + /* ---------active--------- */ + --button-primary-text-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) + 100%)); + + --button-primary-color-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + + --button-primary-border-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + /* ---------- SECONDARY BUTTONS -------------- */ + /* these should NOT immediately catch the eye */ + --button-secondary-text: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 50%)); + + --button-secondary-color: var(--primary-color-3); + --button-secondary-border: var(--primary-color-3); + + /* ---------hover---------- */ + --button-secondary-text-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + + --button-secondary-color-hover: var(--primary-color-4); + --button-secondary-border-hover: var(--primary-color-4); + + /* ---------active--------- */ + --button-secondary-text-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + + --button-secondary-color-active: + hsl(0, + calc(var(--primary-color-4-saturation) - 100%), + calc(var(--primary-color-4-lightness) - 15%)); + + --button-secondary-border-active: + hsl(0, + calc(var(--primary-color-4-saturation) - 100%), + calc(var(--primary-color-4-lightness) - 15%)); + + /* ---------- TERTIARY BUTTONS --------------- */ + /* ---------- disabled buttons --------------- */ + --button-tertiary-text: var(--primary-color-4); + --button-tertiary-color: var(--primary-color-2); + --button-tertiary-border: var(--primary-color-2); + + /* ---------hover---------- */ + --button-tertiary-text: var(--primary-color-4); + --button-tertiary-color: var(--primary-color-2); + --button-tertiary-border: var(--primary-color-2); + + --loading-color-1: #eeeeee00; + --loading-color-2: #eeeeeeff; + } diff --git a/examples/server/public/theme-mangotango.css b/examples/server/public/theme-mangotango.css new file mode 100755 index 000000000..e43380245 --- /dev/null +++ b/examples/server/public/theme-mangotango.css @@ -0,0 +1,216 @@ +/* Author: Yazan Agha-Schrader */ +/* Inspiration from llama.cpp logo/banner https://github.com/ggerganov/llama.cpp#readme */ + +.theme-mangotango { + +--primary-color-1: hsl(192, 8.5%, 11.6%); +--primary-color-2: hsl(192, 8.5%, 21%); +--primary-color-3: hsl(192, 8.5%, 30%); +--primary-color-4: hsl(192, 8.5%, 40%); + +--secondary-color-1: hsl(192, 8.5%, 80%); +--secondary-color-2: hsl(192, 8.5%, 73%); +--secondary-color-3: hsl(192, 8.5%, 66%); +--secondary-color-4: hsl(192, 8.5%, 60%); + +--theme-nuance-color-1: hsl(23.1, 100%, 60.2%); +--theme-nuance-color-2: hsl(23.1, 100%, 60.2%); +--theme-nuance-color-3: hsl(23.1, 100%, 60.2%); +--theme-nuance-color-4: hsl(23.1, 100%, 60.2%); + + + +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(192, 8.5%, 11.6%); + --primary-color-1-saturation: 8.5%; + --primary-color-1-lightness: 11.6%; + +--primary-color-2: hsl(192, 8.5%, 21%); + --primary-color-2-saturation: 8.5%; + --primary-color-2-lightness: 21%; + +--primary-color-3: hsl(192, 8.5%, 30%); + --primary-color-3-saturation: 8.5%; + --primary-color-3-lightness: 30%; + +--primary-color-4: hsl(192, 8.5%, 40%); + --primary-color-4-saturation: 8.5%; + --primary-color-4-lightness: 40%; + + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(192, 8.5%, 80%); + --secondary-color-1-saturation: 8.5%; + --secondary-color-1-lightness: 80%; + +--secondary-color-2: hsl(192, 8.5%, 73%); + --secondary-color-2-saturation: 8.5%; + --secondary-color-2-lightness: 73%; + +--secondary-color-3: hsl(192, 8.5%, 66%); + --secondary-color-3-saturation: 8.5%; + --secondary-color-3-lightness: 66%; + +--secondary-color-4: hsl(192, 8.5%, 60%); + --secondary-color-4-saturation: 8.5%; + --secondary-color-4-lightness: 60%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(23.1, 100%, 60.2%); + --theme-nuance-color-1-saturation: 100%; + --theme-nuance-color-1-lightness: 60.2%; + +--theme-nuance-color-2: hsl(23.1, 100%, 60.2%); + --theme-nuance-color-2-saturation: 100%; + --theme-nuance-color-2-lightness: 60.2%; + +--theme-nuance-color-3: hsl(23.1, 100%, 60.2%); + --theme-nuance-color-3-saturation: 100%; + --theme-nuance-color-3-lightness: 60.2%; + +--theme-nuance-color-4: hsl(23.1, 100%, 60.2%); + --theme-nuance-color-4-saturation: 100%; + --theme-nuance-color-4-lightness: 60.2%; + + + +/* ----------- ROYGP COLORS ------------------ */ + --theme-red-color: hsl(325, 60%, 50%); + --theme-orange-color: #e76f51; + --theme-yellow-color: #ffd95f; + --theme-green-color: #A3BE8C; + --theme-blue-color: hsl(192, 95%, 40%); + --theme-purple-color: hsl(192, 80%, 35%); + + + +/* ------------------------------------------- */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--theme-nuance-color-3); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--secondary-color-1); +--button-alert-color-hover: var(--theme-purple-color); +--button-alert-border-hover: var(--theme-purple-color); + +--button-alert-text-active: var(--secondary-color-1); +--button-alert-color-active: var(--theme-blue-color); +--button-alert-border-active: var(--theme-blue-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: var(--primary-color-1); +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(192, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + +--button-primary-color-hover: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) + 100%)); + +--button-primary-color-active: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + +--button-primary-border-active: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: var(--secondary-color-1); +--button-secondary-color: var(--primary-color-3); +--button-secondary-border: var(--primary-color-3); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(23.1, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: var(--primary-color-4); +--button-secondary-border-hover: var(--primary-color-4); + + +/* ---------active--------- */ +--button-secondary-text-active: var(--secondary-color-1); + +--button-secondary-color-active: + hsl(192, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + +--button-secondary-border-active: + hsl(192, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + + +/* ---------hover---------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + +} diff --git a/examples/server/public/theme-playground.css b/examples/server/public/theme-playground.css new file mode 100755 index 000000000..9d56a7182 --- /dev/null +++ b/examples/server/public/theme-playground.css @@ -0,0 +1,221 @@ +/* Author: Yazan Agha-Schrader */ +/* Inspiration from OpenAI's Playground platform https://platform.openai.com/playground/ */ + +.theme-playground { + +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(0, 0%, 99.2%); + --primary-color-1-hue: 0; + --primary-color-1-saturation: 0%; + --primary-color-1-lightness: 99.2%; + +--primary-color-2: hsl(0, 0%, 95%); + --primary-color-2-hue: 0; + --primary-color-2-saturation: 0%; + --primary-color-2-lightness: 95%; + +--primary-color-3: hsl(0, 0%, 88%); + --primary-color-3-hue: 0; + --primary-color-3-saturation: 0%; + --primary-color-3-lightness: 88%; + +--primary-color-4: hsl(0, 0%, 80%); + --primary-color-4-hue: 0; + --primary-color-4-saturation: 0%; + --primary-color-4-lightness: 80%; + + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(0, 0%, 20%); + --secondary-color-1-hue: 0; + --secondary-color-1-saturation: 0%; + --secondary-color-1-lightness: 20%; + +--secondary-color-2: hsl(0, 0%, 23.1%); + --secondary-color-2-hue: 0; + --secondary-color-2-saturation: 0%; + --secondary-color-2-lightness: 23.1%; + +--secondary-color-3: hsl(0, 0%, 29%); + --secondary-color-3-hue: 0; + --secondary-color-3-saturation: 0%; + --secondary-color-3-lightness: 29%; + +--secondary-color-4: hsl(0, 0%, 36.1%); + --secondary-color-4-hue: 0; + --secondary-color-4-saturation: 0%; + --secondary-color-4-lightness: 36.1%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(165.2, 82.1%, 35.1%); + --theme-nuance-color-1-hue: 165.2; + --theme-nuance-color-1-saturation: 82.1%; + --theme-nuance-color-1-lightness: 35.1%; + +--theme-nuance-color-2: hsl(165.2, 82.1%, 35.1%); + --theme-nuance-color-2-hue: 165.2; + --theme-nuance-color-2-saturation: 82.1%; + --theme-nuance-color-2-lightness: 35.1%; + +--theme-nuance-color-3: hsl(165.2, 81.1%, 35.3%); + --theme-nuance-color-3-hue: 165.2; + --theme-nuance-color-3-saturation: 81.1%; + --theme-nuance-color-3-lightness: 35.3%; + +--theme-nuance-color-4: hsl(164.9, 81.6%, 27.6%); + --theme-nuance-color-4-hue: 164.9; + --theme-nuance-color-4-saturation: 81.6%; + --theme-nuance-color-4-lightness: 27.6%; + + + +/* ----------- ROYGP COLORS ------------------ */ +--theme-red-color: hsl(0.3, 80%, 50%); +--theme-orange-color: #e76f51; +--theme-yellow-color: hsl(60, 70.6%, 73.3%); +--theme-green-color: #A3BE8C; +--theme-purple-color: hsl(0.3, 70%, 45%); + + + +/* ------------------------------------------- */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--primary-color-4); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--primary-color-1); +--button-alert-color-hover: var(--theme-purple-color); +--button-alert-border-hover: var(--theme-purple-color); + +--button-alert-text-active: var(--primary-color-1); +--button-alert-color-active: var(--theme-red-color); +--button-alert-border-active: var(--theme-red-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: + hsl(0, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(0, + calc(var(--primary-color-1-saturation) - 100%), + calc(var(--primary-color-1-lightness) + 100%)); + +--button-primary-color-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 100%), + calc(var(--theme-nuance-color-3-lightness) + 100%)); + +--button-primary-color-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + +--button-primary-border-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 50%)); + +--button-secondary-color: var(--primary-color-3); +--button-secondary-border: var(--primary-color-3); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: var(--primary-color-4); +--button-secondary-border-hover: var(--primary-color-4); + + +/* ---------active--------- */ +--button-secondary-text-active: + hsl(165.2, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-active: + hsl(0, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + +--button-secondary-border-active: + hsl(0, + calc(var(--primary-color-4-saturation) - 30%), + calc(var(--primary-color-4-lightness) - 15%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + + +/* ---------hover---------- */ +--button-tertiary-text: var(--primary-color-4); +--button-tertiary-color: var(--primary-color-2); +--button-tertiary-border: var(--primary-color-2); + +} diff --git a/examples/server/public/theme-polarnight.css b/examples/server/public/theme-polarnight.css new file mode 100755 index 000000000..2bcfb33d8 --- /dev/null +++ b/examples/server/public/theme-polarnight.css @@ -0,0 +1,253 @@ +/* Author: Yazan Agha-Schrader */ +/* Inspiration from Nord Theme https://www.nordtheme.com/docs/colors-and-palettes */ + +.theme-polarnight { + +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(220.0, 16.4%, 21.6%) ; + --primary-color-1-hue: 220.0; + --primary-color-1-saturation: 16.4%; + --primary-color-1-lightness: 21.6%; + +--primary-color-2: hsl(221.7, 16.3%, 27.6%) ; + -primary-color-2-hue: 221.7; + --primary-color-2-saturation: 16.3%; + --primary-color-2-lightness: 27.6%; + +--primary-color-3: hsl(220.0, 16.8%, 31.6%) ; + --primary-color-3-hue: 220.0; + --primary-color-3-saturation: 16.8%; + --primary-color-3-lightness: 31.6%; + +--primary-color-4: hsl(220.0, 16.5%, 35.7%); + --primary-color-4-hue: 220.0; + --primary-color-4-saturation: 16.5%; + --primary-color-4-lightness: 35.7%; + + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(217.5, 26.7%, 94.1%); + --secondary-color-1-hue: 217.5; + --secondary-color-1-saturation: 26.7%; + --secondary-color-1-lightness: 94.1%; + +--secondary-color-2: hsl(218.2, 26.8%, 92.0%); + --secondary-color-2-hue: 218.2; + --secondary-color-2-saturation: 26.8%; + --secondary-color-2-lightness: 92.0%; + +--secondary-color-3: hsl(218.8, 27.9%, 88.0%); + --secondary-color-3-hue: 218.8; + --secondary-color-3-saturation: 27.9%; + --secondary-color-3-lightness: 88.0%; + +--secondary-color-4: hsl(218.8, 18.3%, 81.8%); + --secondary-color-4-hue: 218.8; + --secondary-color-4-saturation: 18.3%; + --secondary-color-4-lightness: 81.8%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%); + --theme-nuance-color-1-hue: 178.7; + --theme-nuance-color-1-saturation: 25.1%; + --theme-nuance-color-1-lightness: 64.9%; + +--theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%); + --theme-nuance-color-2-hue: 193.3; + --theme-nuance-color-2-saturation: 43.4%; + --theme-nuance-color-2-lightness: 67.5%; + +--theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%); + --theme-nuance-color-3-hue: 210.0; + --theme-nuance-color-3-saturation: 34.0%; + --theme-nuance-color-3-lightness: 63.1%; + +--theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%); + --theme-nuance-color-4-hue: 213.1; + --theme-nuance-color-4-saturation: 32.0%; + --theme-nuance-color-4-lightness: 52.2%; + + + +/* ----------- ROYGP COLORS ------------------ */ +--theme-red-color: hsl(354.3, 42.3%, 56.5%); +--theme-orange-color: hsl(20, 85%, 50%); +--theme-yellow-color: hsl(20, 75%, 45%); +--theme-green-color: hsl( 92.4, 27.8%, 64.7%); +--theme-purple-color: hsl(311.1, 20.2%, 63.1%); + + + +/* ------------------------------------------------ */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--theme-nuance-color-3); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--secondary-color-1); +--button-alert-color-hover: var(--theme-yellow-color); +--button-alert-border-hover: var(--theme-yellow-color); + +--button-alert-text-active: var(--secondary-color-1); +--button-alert-color-active: var(--theme-orange-color); +--button-alert-border-active: var(--theme-orange-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: var(--secondary-color-1); +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(217.5, + calc(var(--secondary-color-1-saturation) - 35%), + calc(var(--secondary-color-1-lightness) + 30%)); + +--button-primary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 35%)); + +--button-primary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + +--button-primary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 50%)); + +--button-secondary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + +--button-secondary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + +--button-secondary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + + +/* ---------active--------- */ +--button-secondary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 25%)); + +--button-secondary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + +--button-secondary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 15%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + + +/* ---------hover---------- */ +--button-tertiary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +} diff --git a/examples/server/public/theme-snowstorm.css b/examples/server/public/theme-snowstorm.css new file mode 100755 index 000000000..7bb227594 --- /dev/null +++ b/examples/server/public/theme-snowstorm.css @@ -0,0 +1,251 @@ +/* Author: Yazan Agha-Schrader */ +/* Inspiration from Nord Theme https://www.nordtheme.com/docs/colors-and-palettes */ + +.theme-snowstorm { + +/* ---------- PRIMARY COLORS ----------------- */ +--primary-color-1: hsl(217.5, 26.7%, 94.1%); + --primary-color-1-hue: 217.5; + --primary-color-1-saturation: 26.7%; + --primary-color-1-lightness: 94.1%; + +--primary-color-2: hsl(218.2, 26.8%, 92.0%); + --primary-color-2-hue: 218.2; + --primary-color-2-saturation: 26.8%; + --primary-color-2-lightness: 92.0%; + +--primary-color-3: hsl(218.8, 27.9%, 88.0%); + --primary-color-3-hue: 218.8; + --primary-color-3-saturation: 27.9%; + --primary-color-3-lightness: 88.0%; + +--primary-color-4: hsl(218.8, 18.3%, 81.8%); + --primary-color-4-hue: 218.8; + --primary-color-4-saturation: 18.3%; + --primary-color-4-lightness: 81.8%; + + +/* ---------- SECONDARY COLORS --------------- */ +--secondary-color-1: hsl(220.0, 16.4%, 21.6%); + --secondary-color-1-hue: 220.0; + --secondary-color-1-saturation: 16.4%; + --secondary-color-1-lightness: 21.6%; + +--secondary-color-2: hsl(221.7, 16.3%, 27.6%); + --secondary-color-2-hue: 221.7; + --secondary-color-2-saturation: 16.3%; + --secondary-color-2-lightness: 27.6%; + +--secondary-color-3: hsl(220.0, 16.8%, 31.6%); + --secondary-color-3-hue: 220.0; + --secondary-color-3-saturation: 16.8%; + --secondary-color-3-lightness: 31.6%; + +--secondary-color-4: hsl(220.0, 16.5%, 35.7%); + --secondary-color-4-hue: 220.0; + --secondary-color-4-saturation: 16.5%; + --secondary-color-4-lightness: 35.7%; + + + +/* ----------- NUANCES COLORS ---------------- */ +--theme-nuance-color-1: hsl(178.7, 25.1%, 64.9%); + --theme-nuance-color-1-hue: 178.7; + --theme-nuance-color-1-saturation: 25.1%; + --theme-nuance-color-1-lightness: 64.9%; + +--theme-nuance-color-2: hsl(193.3, 43.4%, 67.5%); + --theme-nuance-color-2-hue: 193.3; + --theme-nuance-color-2-saturation: 43.4%; + --theme-nuance-color-2-lightness: 67.5%; + +--theme-nuance-color-3: hsl(210.0, 34.0%, 63.1%); + --theme-nuance-color-3-hue: 210.0; + --theme-nuance-color-3-saturation: 34.0%; + --theme-nuance-color-3-lightness: 63.1%; + +--theme-nuance-color-4: hsl(213.1, 32.0%, 52.2%); + --theme-nuance-color-4-hue: 213.1; + --theme-nuance-color-4-saturation: 32.0%; + --theme-nuance-color-4-lightness: 52.2%; + + + +/* ----------- ROYGP COLORS ------------------ */ +--theme-red-color: hsl(32.5, 80%, 50%); +--theme-orange-color: hsl(32.5, 70%, 45%); +--theme-yellow-color: hsl(40.0, 0.6%, 73.3%); +--theme-green-color: hsl(92.4, 27.8%, 64.7%); +--theme-purple-color: hsl(311.1, 20.2%, 63.1%); + + + +/* ------------------------------------------- */ +--background-color-1: var(--primary-color-1); +--background-color-2: var(--primary-color-2); +--background-color-3: var(--primary-color-3); +--background-color-4: var(--primary-color-4); + +--border-color-1: var(--primary-color-2); +--border-color-2: var(--primary-color-3); +--border-color-3: var(--primary-color-4); + +--border-focus-color: var(--theme-nuance-color-2); +--border-focus-shadow: var(--theme-nuance-color-1); + +--text-color-plain: var(--secondary-color-1); +--text-color-subtile-1: var(--secondary-color-2); +--text-color-subtile-2: var(--secondary-color-3); + +--code-background-color: var(--secondary-color-2); +--code-text-color: var(--primary-color-2); + +--ui-range-thumb-color: var(--theme-nuance-color-3); +--ui-range-thumb-border: var(--ui-ranger-thumb-color); + +--textarea-border-color: var(--secondary-color-4); + +--chat-id-color: var(--theme-nuance-color-4); + + + +/* ------------------------------------------- */ +--button-alert-text-hover: var(--primary-color-1); +--button-alert-color-hover: var(--theme-orange-color); +--button-alert-border-hover: var(--theme-orange-color); + +--button-alert-text-active: var(--primary-color-1); +--button-alert-color-active: var(--theme-red-color); +--button-alert-border-active: var(--theme-red-color); + + + +/* ----------- PRIMARY BUTTONS --------------- */ +/* - button should immediately catch the eye - */ +--button-primary-text: var(--secondary-color-1); +--button-primary-color: var(--theme-nuance-color-3); +--button-primary-border: var(--theme-nuance-color-3); + + +/* ---------hover---------- */ +--button-primary-text-hover: + hsl(217.5, + calc(var(--secondary-color-1-saturation) + 35%), + calc(var(--secondary-color-1-lightness) - 30%)); + +--button-primary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + +--button-primary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 2%), + calc(var(--theme-nuance-color-3-lightness) - 10%)); + + +/* ---------active--------- */ +--button-primary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 35%)); + +--button-primary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + +--button-primary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 10%), + calc(var(--theme-nuance-color-3-lightness) - 25%)); + + + +/* ---------- SECONDARY BUTTONS -------------- */ +/* these should NOT immediately catch the eye */ +--button-secondary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 50%)); + +--button-secondary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + +--button-secondary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) + 10%)); + + +/* ---------hover---------- */ +--button-secondary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 20%), + calc(var(--theme-nuance-color-3-lightness) - 80%)); + +--button-secondary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + +--button-secondary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 22%), + calc(var(--theme-nuance-color-3-lightness) + 1%)); + + +/* ---------active--------- */ +--button-secondary-text-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) + 40%), + calc(var(--theme-nuance-color-3-lightness) - 55%)); + +--button-secondary-color-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-secondary-border-active: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 30%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + + + +/* ---------- TERTIARY BUTTONS --------------- */ +/* ---------- disabled buttons --------------- */ +--button-tertiary-text: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +/* ---------hover---------- */ +--button-tertiary-text-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) - 5%)); + +--button-tertiary-color-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +--button-tertiary-border-hover: + hsl(210, + calc(var(--theme-nuance-color-3-saturation) - 40%), + calc(var(--theme-nuance-color-3-lightness) + 20%)); + +} diff --git a/examples/server/server.cpp b/examples/server/server.cpp index e9904263d..fc6d90848 100644 --- a/examples/server/server.cpp +++ b/examples/server/server.cpp @@ -17,9 +17,20 @@ #include "json.hpp" // auto generated files (update with ./deps.sh) +#include "colorthemes.css.hpp" +#include "style.css.hpp" +#include "theme-beeninorder.css.hpp" +#include "theme-ketivah.css.hpp" +#include "theme-mangotango.css.hpp" +#include "theme-playground.css.hpp" +#include "theme-polarnight.css.hpp" +#include "theme-snowstorm.css.hpp" #include "index.html.hpp" +#include "index-new.html.hpp" #include "index.js.hpp" #include "completion.js.hpp" +#include "system-prompts.js.hpp" +#include "prompt-formats.js.hpp" #include "json-schema-to-grammar.mjs.hpp" #include @@ -3750,13 +3761,25 @@ int main(int argc, char ** argv) { // Set the base directory for serving static files svr->set_base_dir(sparams.public_path); } - // using embedded static files svr->Get("/", handle_static_file(index_html, index_html_len, "text/html; charset=utf-8")); svr->Get("/index.js", handle_static_file(index_js, index_js_len, "text/javascript; charset=utf-8")); svr->Get("/completion.js", handle_static_file(completion_js, completion_js_len, "text/javascript; charset=utf-8")); svr->Get("/json-schema-to-grammar.mjs", handle_static_file( - json_schema_to_grammar_mjs, json_schema_to_grammar_mjs_len, "text/javascript; charset=utf-8")); + json_schema_to_grammar_mjs, json_schema_to_grammar_mjs_len, "text/javascript; charset=utf-8")); + + // add new-ui files + svr->Get("/colorthemes.css", handle_static_file(colorthemes_css, colorthemes_css_len, "text/css; charset=utf-8")); + svr->Get("/style.css", handle_static_file(style_css, style_css_len, "text/css; charset=utf-8")); + svr->Get("/theme-beeninorder.css", handle_static_file(theme_beeninorder_css, theme_beeninorder_css_len, "text/css; charset=utf-8")); + svr->Get("/theme-ketivah.css", handle_static_file(theme_ketivah_css, theme_ketivah_css_len, "text/css; charset=utf-8")); + svr->Get("/theme-mangotango.css", handle_static_file(theme_mangotango_css, theme_mangotango_css_len, "text/css; charset=utf-8")); + svr->Get("/theme-playground.css", handle_static_file(theme_playground_css, theme_playground_css_len, "text/css; charset=utf-8")); + svr->Get("/theme-polarnight.css", handle_static_file(theme_polarnight_css, theme_polarnight_css_len, "text/css; charset=utf-8")); + svr->Get("/theme-snowstorm.css", handle_static_file(theme_snowstorm_css, theme_snowstorm_css_len, "text/css; charset=utf-8")); + svr->Get("/index-new.html", handle_static_file(index_new_html, index_new_html_len, "text/html; charset=utf-8")); + svr->Get("/system-prompts.js", handle_static_file(system_prompts_js, system_prompts_js_len, "text/javascript; charset=utf-8")); + svr->Get("/prompt-formats.js", handle_static_file(prompt_formats_js, prompt_formats_js_len, "text/javascript; charset=utf-8")); // register API routes svr->Get ("/health", handle_health); From e141ce624af57bdffbaf57014a044eb1d9689230 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Johannes=20G=C3=A4=C3=9Fler?= Date: Sat, 1 Jun 2024 23:26:10 +0200 Subject: [PATCH 180/181] Fix FlashAttention debug test, FP32 assert (#7684) --- ggml-cuda/fattn-vec-f32.cuh | 4 ---- tests/test-backend-ops.cpp | 8 +++++--- 2 files changed, 5 insertions(+), 7 deletions(-) diff --git a/ggml-cuda/fattn-vec-f32.cuh b/ggml-cuda/fattn-vec-f32.cuh index ce23a4ebd..ddf0c8374 100644 --- a/ggml-cuda/fattn-vec-f32.cuh +++ b/ggml-cuda/fattn-vec-f32.cuh @@ -278,14 +278,10 @@ void ggml_cuda_flash_attn_ext_vec_f32_case_impl(ggml_backend_cuda_context & ctx, template void ggml_cuda_flash_attn_ext_vec_f32_case(ggml_backend_cuda_context & ctx, ggml_tensor * dst) { - ggml_tensor * KQV = dst; ggml_tensor * Q = dst->src[0]; ggml_tensor * K = dst->src[1]; ggml_tensor * V = dst->src[2]; - const int32_t precision = KQV->op_params[2]; - GGML_ASSERT(precision == GGML_PREC_DEFAULT); - GGML_ASSERT(K->type == type_K); GGML_ASSERT(V->type == type_V); diff --git a/tests/test-backend-ops.cpp b/tests/test-backend-ops.cpp index 777230127..8dc90a45d 100644 --- a/tests/test-backend-ops.cpp +++ b/tests/test-backend-ops.cpp @@ -1584,9 +1584,11 @@ struct test_flash_attn_ext : public test_case { : hs(hs), nh(nh), kv(kv), nb(nb), mask(mask), max_bias(max_bias), type_KV(type_KV) {} ggml_tensor * build_graph(ggml_context * ctx) override { - ggml_tensor * q = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, hs, nb, nh, 1); - ggml_tensor * k = ggml_new_tensor_4d(ctx, type_KV, hs, kv, nh, 1); - ggml_tensor * v = ggml_new_tensor_4d(ctx, type_KV, hs, kv, nh, 1); + const int64_t hs_padded = GGML_PAD(hs, ggml_blck_size(type_KV)); + + ggml_tensor * q = ggml_new_tensor_4d(ctx, GGML_TYPE_F32, hs_padded, nb, nh, 1); + ggml_tensor * k = ggml_new_tensor_4d(ctx, type_KV, hs_padded, kv, nh, 1); + ggml_tensor * v = ggml_new_tensor_4d(ctx, type_KV, hs_padded, kv, nh, 1); ggml_tensor * m = mask ? ggml_new_tensor_4d(ctx, GGML_TYPE_F16, kv, GGML_PAD(nb, GGML_KQ_MASK_PAD), 1, 1) : nullptr; ggml_tensor * out = ggml_flash_attn_ext(ctx, q, k, v, m, 1.0f/sqrtf(hs), max_bias); return out; From 9422c5e34bbd302493b77a8f6d546154a1f4fe82 Mon Sep 17 00:00:00 2001 From: nickp27 Date: Sun, 2 Jun 2024 19:13:54 +1000 Subject: [PATCH 181/181] [SYCL] Update rpc-server.cpp to include SYCL backend (#7682) * Update rpc-server.cpp to include SYCL backend Draft PR to address inclusion of SYCL backend for RPC server * Update rpc-server.cpp --- examples/rpc/rpc-server.cpp | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/examples/rpc/rpc-server.cpp b/examples/rpc/rpc-server.cpp index 7c15d2aa4..62d828250 100644 --- a/examples/rpc/rpc-server.cpp +++ b/examples/rpc/rpc-server.cpp @@ -6,6 +6,10 @@ #include "ggml-metal.h" #endif +#ifdef GGML_USE_SYCL +#include "ggml-sycl.h" +#endif + #include "ggml-rpc.h" #ifdef _WIN32 # include @@ -79,6 +83,12 @@ static ggml_backend_t create_backend() { if (!backend) { fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__); } +#elif GGML_USE_SYCL + fprintf(stderr, "%s: using SYCL backend\n", __func__); + backend = ggml_backend_sycl_init(0); // init device 0 + if (!backend) { + fprintf(stderr, "%s: ggml_backend_sycl_init() failed\n", __func__); + } #endif // if there aren't GPU Backends fallback to CPU backend