From 0035858273ebe0694926bf4414d279f3e1cd109d Mon Sep 17 00:00:00 2001 From: johnson442 <56517414+johnson442@users.noreply.github.com> Date: Thu, 8 Jun 2023 08:02:48 +0100 Subject: [PATCH 01/12] k-quants : add missing compile definition to CMakeLists (#1748) --- CMakeLists.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/CMakeLists.txt b/CMakeLists.txt index 456875f90..41f5bb737 100644 --- a/CMakeLists.txt +++ b/CMakeLists.txt @@ -229,6 +229,7 @@ endif() if (LLAMA_K_QUANTS) set(GGML_SOURCES_EXTRA ${GGML_SOURCES_EXTRA} k_quants.c k_quants.h) + add_compile_definitions(GGML_USE_K_QUANTS) endif() if (LLAMA_CLBLAST) From 4161bdc04debb70bf5f275492b4d89fd9330087c Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Thu, 8 Jun 2023 10:08:23 +0300 Subject: [PATCH 02/12] metal : add Q4_K implementation (#1733) * Metal implementation for Q4_K Very slow for now: 42 ms / token, Q4_0 runs in 28 ms/token on my 30-core M2 Max GPU. * Optimizing Q4_K on metal The first token always takes longer, I guess because the metal kernel is being jit-compiled. So, using n = 128 to measure time. At this point Q4_K takes 29.5 ms / token compared to 27.2 ms / token for Q4_0. Quite a bit better than the initial attempt, but still not good enough. * Optimizing q4_K metal dot some more For n = 256 it is now 28.1 ms/token compared to 27 ms/token for q4_0. * Fix after merge with master --------- Co-authored-by: Iwan Kawrakow --- .clang-tidy | 18 ------ ggml-metal.m | 23 ++++++- ggml-metal.metal | 162 +++++++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 184 insertions(+), 19 deletions(-) delete mode 100644 .clang-tidy diff --git a/.clang-tidy b/.clang-tidy deleted file mode 100644 index 1a42b9abc..000000000 --- a/.clang-tidy +++ /dev/null @@ -1,18 +0,0 @@ ---- -Checks: > - bugprone-*, - -bugprone-easily-swappable-parameters, - -bugprone-implicit-widening-of-multiplication-result, - -bugprone-narrowing-conversions, - readability-*, - -readability-avoid-unconditional-preprocessor-if, - -readability-function-cognitive-complexity, - -readability-identifier-length, - -readability-implicit-bool-conversion, - -readability-magic-numbers, - -readability-uppercase-literal-suffix, - clang-analyzer-*, - -clang-analyzer-security.insecureAPI.DeprecatedOrUnsafeBufferHandling, - performance-*, - portability-*, -FormatStyle: none diff --git a/ggml-metal.m b/ggml-metal.m index 0953af6a4..f2a637b7a 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -49,9 +49,11 @@ struct ggml_metal_context { GGML_METAL_DECL_KERNEL(diag_mask_inf); GGML_METAL_DECL_KERNEL(get_rows_f16); GGML_METAL_DECL_KERNEL(get_rows_q4_0); + GGML_METAL_DECL_KERNEL(get_rows_q4_k); GGML_METAL_DECL_KERNEL(rms_norm); GGML_METAL_DECL_KERNEL(mul_mat_f16_f32); GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32); + GGML_METAL_DECL_KERNEL(mul_mat_q4_k_f32); GGML_METAL_DECL_KERNEL(rope); GGML_METAL_DECL_KERNEL(cpy_f32_f16); GGML_METAL_DECL_KERNEL(cpy_f32_f32); @@ -133,9 +135,11 @@ struct ggml_metal_context * ggml_metal_init(void) { GGML_METAL_ADD_KERNEL(diag_mask_inf); GGML_METAL_ADD_KERNEL(get_rows_f16); GGML_METAL_ADD_KERNEL(get_rows_q4_0); + GGML_METAL_ADD_KERNEL(get_rows_q4_k); GGML_METAL_ADD_KERNEL(rms_norm); GGML_METAL_ADD_KERNEL(mul_mat_f16_f32); GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32); + GGML_METAL_ADD_KERNEL(mul_mat_q4_k_f32); GGML_METAL_ADD_KERNEL(rope); GGML_METAL_ADD_KERNEL(cpy_f32_f16); GGML_METAL_ADD_KERNEL(cpy_f32_f32); @@ -517,7 +521,20 @@ void ggml_metal_graph_compute( nth1 = 4; [encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_0_f32]; } break; - default: GGML_ASSERT(false && "not implemented"); + case GGML_TYPE_Q4_K: + { + GGML_ASSERT(ne02 == 1); + GGML_ASSERT(ne12 == 1); + + nth0 = 4; + nth1 = 16; + [encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_k_f32]; + } break; + default: + { + fprintf(stderr, "Asserting on type %d\n",(int)src0t); + GGML_ASSERT(false && "not implemented"); + } }; @@ -540,6 +557,9 @@ void ggml_metal_graph_compute( if (src0t == GGML_TYPE_Q4_0) { [encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } else if (src0t == GGML_TYPE_Q4_K) { + [encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0]; + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else { [encoder setThreadgroupMemoryLength:nth0*sizeof(float) atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; @@ -555,6 +575,7 @@ void ggml_metal_graph_compute( switch (src0->type) { case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_get_rows_f16]; break; case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break; + case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_k]; break; default: GGML_ASSERT(false && "not implemented"); } diff --git a/ggml-metal.metal b/ggml-metal.metal index a359bebe2..cbcd59ad4 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -503,3 +503,165 @@ kernel void kernel_cpy_f32_f32( dst_data[i00] = src[0]; } } + +//============================================ k-quants ====================================================== + +#define QK_K 256 + +typedef struct { + half d; // super-block scale for quantized scales + half dmin; // super-block scale for quantized mins + uint8_t scales[3*QK_K/64]; // scales and mins, quantized with 6 bits + uint8_t qs[QK_K/2]; // 4--bit quants +} block_q4_k; + +static inline uchar4 get_scale_min_k4(int j, device const uint8_t * q) { + uchar4 r; + if (j < 4) { + r[0] = q[j+0] & 63; r[1] = q[j+4] & 63; + r[2] = q[j+1] & 63; r[3] = q[j+5] & 63; + } else { + r[0] = (q[j+4] & 0xF) | ((q[j-4] >> 6) << 4); + r[1] = (q[j+4] >> 4) | ((q[j-0] >> 6) << 4); + r[2] = (q[j+5] & 0xF) | ((q[j-3] >> 6) << 4); + r[3] = (q[j+5] >> 4) | ((q[j+1] >> 6) << 4); + } + return r; +} + +static void dequantize_row_q4_k(device const block_q4_k * x, device float * y, int k) { + assert(k % QK_K == 0); + const int nb = k / QK_K; + + for (int i = 0; i < nb; i++) { + + const float d = x[i].d; + const float min = x[i].dmin; + + device const uint8_t * q = x[i].qs; + device const uint8_t * scales = x[i].scales; + + int is = 0; + for (int j = 0; j < QK_K; j += 64) { + const uchar4 sc = get_scale_min_k4(is, scales); + const float d1 = d * sc[0]; const float m1 = min * sc[1]; + const float d2 = d * sc[2]; const float m2 = min * sc[3]; + for (int l = 0; l < 32; ++l) *y++ = d1 * (q[l] & 0xF) - m1; + for (int l = 0; l < 32; ++l) *y++ = d2 * (q[l] >> 4) - m2; + q += 32; is += 2; + } + + } +} + +kernel void kernel_get_rows_q4_k( + device const void * src0, + device const int * src1, + device float * dst, + constant int64_t & ne00, + constant uint64_t & nb01, + constant uint64_t & nb1, + uint tpig[[thread_position_in_grid]]) { + const int i = tpig; + const int r = ((device int32_t *) src1)[i]; + + dequantize_row_q4_k( + (device const block_q4_k *) ((device char *) src0 + r*nb01), + (device float *) ((device char *) dst + i*nb1), ne00); +} + +kernel void kernel_mul_mat_q4_k_f32( + device const void * src0, + device const float * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + threadgroup float * sum [[threadgroup(0)]], + uint2 tgpig[[threadgroup_position_in_grid]], + uint2 tpig[[thread_position_in_grid]], // we don't use this for now + uint2 tpitg[[thread_position_in_threadgroup]], + uint2 tptg[[threads_per_threadgroup]]) { + + const int nb = ne00/QK_K; + + const int64_t r0 = tgpig.x; + const int64_t r1 = tgpig.y; + + device const block_q4_k * x = (device const block_q4_k *) src0 + r0*nb; + device const float * yy = (device const float *) src1 + r1*ne10; + + const uint nth = tptg.x*tptg.y; + const uint ith = tptg.y*tpitg.x + tpitg.y; + + const int tid = tpitg.y; // 0...16 + const int il = tid/4; // 0...3 + const int ir = tid%4; // 0...3 + const int n = 8; + const int is = 2*il; + + sum[ith] = 0.0f; + + float sumf = 0; + for (int i = tpitg.x; i < nb; i += tptg.x) { + + device const uint8_t * q = (x + i)->qs + 32*il + n*ir; + device const float * y = yy + i*QK_K + 64*il + n*ir; + device const uint8_t * scales = (x + i)->scales; + + const float dall = (float)((x + i)->d); + const float dmin = (float)((x + i)->dmin); + + const uchar4 sc = get_scale_min_k4(is, scales); + + float4 s = {0.f, 0.f, 0.f, 0.f}; + for (int l = 0; l < n; ++l) { + s[0] += y[l+ 0] * (q[l] & 0xF); s[1] += y[l+ 0]; + s[2] += y[l+32] * (q[l] >> 4); s[3] += y[l+32]; + } + sumf += dall * (s[0] * sc[0] + s[2] * sc[2]) - dmin * (s[1] * sc[1] + s[3] * sc[3]); + + } + sum[ith] = sumf; + + // + // Accumulate the sum from all threads in the threadgroup + // This version is slightly faster than the commented out one below, + // which I copy-pasted from ggerganov's q4_0 dot product for metal. + // + threadgroup_barrier(mem_flags::mem_threadgroup); + if (ith%4 == 0) { + for (int i = 1; i < 4; ++i) sum[ith] += sum[ith + i]; + } + threadgroup_barrier(mem_flags::mem_threadgroup); + if (ith%16 == 0) { + for (int i = 4; i < 16; i += 4) sum[ith] += sum[ith + i]; + } + threadgroup_barrier(mem_flags::mem_threadgroup); + if (ith == 0) { + for (int i = 16; i < nth; i += 16) sum[0] += sum[i]; + dst[r1*ne0 + r0] = sum[0]; + } + + //// accumulate the sum from all threads in the threadgroup + //threadgroup_barrier(mem_flags::mem_threadgroup); + //for (uint i = nth/2; i > 0; i /= 2) { + // if (ith < i) { + // sum[ith] += sum[ith + i]; + // } + // threadgroup_barrier(mem_flags::mem_threadgroup); + //} + + //if (ith == 0) { + // dst[r1*ne0 + r0] = sum[0]; + //} +} From 53aba3f393f2e02a78ddaba2e934893a8bbf3246 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 8 Jun 2023 10:09:08 +0300 Subject: [PATCH 03/12] clang-tidy : restore dot file from accidental deletion --- .clang-tidy | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) create mode 100644 .clang-tidy diff --git a/.clang-tidy b/.clang-tidy new file mode 100644 index 000000000..1a42b9abc --- /dev/null +++ b/.clang-tidy @@ -0,0 +1,18 @@ +--- +Checks: > + bugprone-*, + -bugprone-easily-swappable-parameters, + -bugprone-implicit-widening-of-multiplication-result, + -bugprone-narrowing-conversions, + readability-*, + -readability-avoid-unconditional-preprocessor-if, + -readability-function-cognitive-complexity, + -readability-identifier-length, + -readability-implicit-bool-conversion, + -readability-magic-numbers, + -readability-uppercase-literal-suffix, + clang-analyzer-*, + -clang-analyzer-security.insecureAPI.DeprecatedOrUnsafeBufferHandling, + performance-*, + portability-*, +FormatStyle: none From b50b570ed9d699d3d126d72fc02de92926bcd937 Mon Sep 17 00:00:00 2001 From: Steven Roussey Date: Thu, 8 Jun 2023 00:12:28 -0700 Subject: [PATCH 04/12] ggml : fix fprintf warnings (#1720) --- ggml.c | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/ggml.c b/ggml.c index 34212b841..567dbc1e1 100644 --- a/ggml.c +++ b/ggml.c @@ -14729,12 +14729,12 @@ static void ggml_graph_export_leaf(const struct ggml_tensor * tensor, FILE * fou const int64_t * ne = tensor->ne; const size_t * nb = tensor->nb; - fprintf(fout, "%-6s %-12s %8d %8d %d %d %d %16zu %16zu %16zu %16zu %16p %32s\n", + fprintf(fout, "%-6s %-12s %8d %" PRId64 " %" PRId64 " %" PRId64 " %" PRId64 " %16zu %16zu %16zu %16zu %16p %32s\n", ggml_type_name(tensor->type), ggml_op_name (tensor->op), tensor->n_dims, - (int) ne[0], (int) ne[1], (int) ne[2], (int) ne[3], - nb[0], nb[1], nb[2], nb[3], + ne[0], ne[1], ne[2], ne[3], + nb[0], nb[1], nb[2], nb[3], tensor->data, tensor->name); } @@ -14743,13 +14743,13 @@ static void ggml_graph_export_node(const struct ggml_tensor * tensor, const char const int64_t * ne = tensor->ne; const size_t * nb = tensor->nb; - fprintf(fout, "%-6s %-6s %-12s %8d %d %d %d %d %16zu %16zu %16zu %16zu %8d %16p %32s\n", + fprintf(fout, "%-6s %-6s %-12s %8d %" PRId64 " %" PRId64 " %" PRId64 " %" PRId64 " %16zu %16zu %16zu %16zu %8d %16p %32s\n", arg, ggml_type_name(tensor->type), ggml_op_name (tensor->op), tensor->n_dims, - (int) ne[0], (int) ne[1], (int) ne[2], (int) ne[3], - nb[0], nb[1], nb[2], nb[3], + ne[0], ne[1], ne[2], ne[3], + nb[0], nb[1], nb[2], nb[3], tensor->n_tasks, tensor->data, tensor->name); @@ -14772,11 +14772,11 @@ void ggml_graph_export(const struct ggml_cgraph * cgraph, const char * fname) { FILE * fout = stdout; fprintf(fout, "\n"); - fprintf(fout, "%-16s %8x\n", "magic", GGML_FILE_MAGIC); - fprintf(fout, "%-16s %8d\n", "version", GGML_FILE_VERSION); - fprintf(fout, "%-16s %8d\n", "leafs", cgraph->n_leafs); - fprintf(fout, "%-16s %8d\n", "nodes", cgraph->n_nodes); - fprintf(fout, "%-16s %8d\n", "eval", (int) size_eval); + fprintf(fout, "%-16s %8x\n", "magic", GGML_FILE_MAGIC); + fprintf(fout, "%-16s %8d\n", "version", GGML_FILE_VERSION); + fprintf(fout, "%-16s %8d\n", "leafs", cgraph->n_leafs); + fprintf(fout, "%-16s %8d\n", "nodes", cgraph->n_nodes); + fprintf(fout, "%-16s %" PRIu64 "\n", "eval", size_eval); // header fprintf(fout, "\n"); From 8fc8179919a11738910db07a800f2b176f8adf09 Mon Sep 17 00:00:00 2001 From: qingfengfenga <41416092+qingfengfenga@users.noreply.github.com> Date: Thu, 8 Jun 2023 15:58:53 +0800 Subject: [PATCH 05/12] Add llama.cpp docker support for non-latin languages (#1673) * Modify Dockerfile default character set to improve compatibility (#1673) --- .devops/full.Dockerfile | 2 ++ .devops/main.Dockerfile | 2 ++ 2 files changed, 4 insertions(+) diff --git a/.devops/full.Dockerfile b/.devops/full.Dockerfile index 01b3111d9..687628b35 100644 --- a/.devops/full.Dockerfile +++ b/.devops/full.Dockerfile @@ -16,4 +16,6 @@ COPY . . RUN make +ENV LC_ALL=C.utf8 + ENTRYPOINT ["/app/.devops/tools.sh"] diff --git a/.devops/main.Dockerfile b/.devops/main.Dockerfile index fc34a0c18..3ab1decd6 100644 --- a/.devops/main.Dockerfile +++ b/.devops/main.Dockerfile @@ -15,4 +15,6 @@ FROM ubuntu:$UBUNTU_VERSION as runtime COPY --from=build /app/main /main +ENV LC_ALL=C.utf8 + ENTRYPOINT [ "/main" ] From 0f291e1f65c1d68201e71ce99c89562a36686b6d Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Thu, 8 Jun 2023 19:46:22 +0300 Subject: [PATCH 06/12] metal : Q6_K implementation (#1752) * Metal implementation for Q4_K Very slow for now: 42 ms / token, Q4_0 runs in 28 ms/token on my 30-core M2 Max GPU. * Optimizing Q4_K on metal The first token always takes longer, I guess because the metal kernel is being jit-compiled. So, using n = 128 to measure time. At this point Q4_K takes 29.5 ms / token compared to 27.2 ms / token for Q4_0. Quite a bit better than the initial attempt, but still not good enough. * Optimizing q4_K metal dot some more For n = 256 it is now 28.1 ms/token compared to 27 ms/token for q4_0. * Fix after merge with master * Metal implementation for Q6_K Similar to the CUDA implementation. No idea if this is the optimum for Metal, but the few alternative variants I tried all had a lower performance. We get 36.5 ms / token on M2 Max with 30 GPU cores. This corresponds to ~200 GB/second throughput. * clang-tidy : add config back * Much better Q6_K implementation for metal 28.3 ms / token for 7B. Subtracting ~9 ms that is spent in other compute graph operations, we are left with ~19 ms for the matrix multiplications. The model is ~5.5 GB, so we are getting 1000 / 19 * 5.5 = 290 GB/s! --------- Co-authored-by: Iwan Kawrakow --- ggml-metal.m | 17 +++++ ggml-metal.metal | 177 +++++++++++++++++++++++++++++++++++++++++++++-- 2 files changed, 187 insertions(+), 7 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index f2a637b7a..626ca871c 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -50,10 +50,12 @@ struct ggml_metal_context { GGML_METAL_DECL_KERNEL(get_rows_f16); GGML_METAL_DECL_KERNEL(get_rows_q4_0); GGML_METAL_DECL_KERNEL(get_rows_q4_k); + GGML_METAL_DECL_KERNEL(get_rows_q6_k); GGML_METAL_DECL_KERNEL(rms_norm); GGML_METAL_DECL_KERNEL(mul_mat_f16_f32); GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32); GGML_METAL_DECL_KERNEL(mul_mat_q4_k_f32); + GGML_METAL_DECL_KERNEL(mul_mat_q6_k_f32); GGML_METAL_DECL_KERNEL(rope); GGML_METAL_DECL_KERNEL(cpy_f32_f16); GGML_METAL_DECL_KERNEL(cpy_f32_f32); @@ -136,10 +138,12 @@ struct ggml_metal_context * ggml_metal_init(void) { GGML_METAL_ADD_KERNEL(get_rows_f16); GGML_METAL_ADD_KERNEL(get_rows_q4_0); GGML_METAL_ADD_KERNEL(get_rows_q4_k); + GGML_METAL_ADD_KERNEL(get_rows_q6_k); GGML_METAL_ADD_KERNEL(rms_norm); GGML_METAL_ADD_KERNEL(mul_mat_f16_f32); GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32); GGML_METAL_ADD_KERNEL(mul_mat_q4_k_f32); + GGML_METAL_ADD_KERNEL(mul_mat_q6_k_f32); GGML_METAL_ADD_KERNEL(rope); GGML_METAL_ADD_KERNEL(cpy_f32_f16); GGML_METAL_ADD_KERNEL(cpy_f32_f32); @@ -530,6 +534,15 @@ void ggml_metal_graph_compute( nth1 = 16; [encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_k_f32]; } break; + case GGML_TYPE_Q6_K: + { + GGML_ASSERT(ne02 == 1); + GGML_ASSERT(ne12 == 1); + + nth0 = 4; + nth1 = 16; + [encoder setComputePipelineState:ctx->pipeline_mul_mat_q6_k_f32]; + } break; default: { fprintf(stderr, "Asserting on type %d\n",(int)src0t); @@ -560,6 +573,9 @@ void ggml_metal_graph_compute( } else if (src0t == GGML_TYPE_Q4_K) { [encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } else if (src0t == GGML_TYPE_Q6_K) { + [encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0]; + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else { [encoder setThreadgroupMemoryLength:nth0*sizeof(float) atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; @@ -576,6 +592,7 @@ void ggml_metal_graph_compute( case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_get_rows_f16]; break; case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break; case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_k]; break; + case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q6_k]; break; default: GGML_ASSERT(false && "not implemented"); } diff --git a/ggml-metal.metal b/ggml-metal.metal index cbcd59ad4..e851cbd4d 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -303,18 +303,37 @@ kernel void kernel_mul_mat_q4_0_f32( sum[ith] += acc*d; } - // accumulate the sum from all threads in the threadgroup + // + // Accumulate the sum from all threads in the threadgroup + // This version is slightly faster than the commented out one below, + // which I copy-pasted from ggerganov's q4_0 dot product for metal. + // threadgroup_barrier(mem_flags::mem_threadgroup); - for (uint i = nth/2; i > 0; i /= 2) { - if (ith < i) { - sum[ith] += sum[ith + i]; - } - threadgroup_barrier(mem_flags::mem_threadgroup); + if (ith%4 == 0) { + for (int i = 1; i < 4; ++i) sum[ith] += sum[ith + i]; } - + threadgroup_barrier(mem_flags::mem_threadgroup); + if (ith%16 == 0) { + for (int i = 4; i < 16; i += 4) sum[ith] += sum[ith + i]; + } + threadgroup_barrier(mem_flags::mem_threadgroup); if (ith == 0) { + for (int i = 16; i < nth; i += 16) sum[0] += sum[i]; dst[r1*ne0 + r0] = sum[0]; } + + //// accumulate the sum from all threads in the threadgroup + //threadgroup_barrier(mem_flags::mem_threadgroup); + //for (uint i = nth/2; i > 0; i /= 2) { + // if (ith < i) { + // sum[ith] += sum[ith + i]; + // } + // threadgroup_barrier(mem_flags::mem_threadgroup); + //} + + //if (ith == 0) { + // dst[r1*ne0 + r0] = sum[0]; + //} } kernel void kernel_mul_mat_f16_f32( @@ -515,6 +534,13 @@ typedef struct { uint8_t qs[QK_K/2]; // 4--bit quants } block_q4_k; +typedef struct { + uint8_t ql[QK_K/2]; // quants, lower 4 bits + uint8_t qh[QK_K/4]; // quants, upper 2 bits + int8_t scales[QK_K/16]; // scales, quantized with 8 bits + half d; // super-block scale +} block_q6_k; + static inline uchar4 get_scale_min_k4(int j, device const uint8_t * q) { uchar4 r; if (j < 4) { @@ -554,6 +580,38 @@ static void dequantize_row_q4_k(device const block_q4_k * x, device float * y, i } } +static void dequantize_row_q6_k(device const block_q6_k * x, device float * y, int k) { + assert(k % QK_K == 0); + const int nb = k / QK_K; + + for (int i = 0; i < nb; i++) { + + const float d = x[i].d; + + device const uint8_t * ql = x[i].ql; + device const uint8_t * qh = x[i].qh; + device const int8_t * sc = x[i].scales; + + for (int n = 0; n < QK_K; n += 128) { + for (int l = 0; l < 32; ++l) { + int is = l/16; + const int8_t q1 = (int8_t)((ql[l + 0] & 0xF) | (((qh[l] >> 0) & 3) << 4)) - 32; + const int8_t q2 = (int8_t)((ql[l + 32] & 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 + 32] >> 4) | (((qh[l] >> 6) & 3) << 4)) - 32; + y[l + 0] = d * sc[is + 0] * q1; + y[l + 32] = d * sc[is + 2] * q2; + y[l + 64] = d * sc[is + 4] * q3; + y[l + 96] = d * sc[is + 6] * q4; + } + y += 128; + ql += 64; + qh += 32; + sc += 8; + } + } +} + kernel void kernel_get_rows_q4_k( device const void * src0, device const int * src1, @@ -665,3 +723,108 @@ kernel void kernel_mul_mat_q4_k_f32( // dst[r1*ne0 + r0] = sum[0]; //} } + +kernel void kernel_get_rows_q6_k( + device const void * src0, + device const int * src1, + device float * dst, + constant int64_t & ne00, + constant uint64_t & nb01, + constant uint64_t & nb1, + uint tpig[[thread_position_in_grid]]) { + const int i = tpig; + const int r = ((device int32_t *) src1)[i]; + + dequantize_row_q6_k( + (device const block_q6_k *) ((device char *) src0 + r*nb01), + (device float *) ((device char *) dst + i*nb1), ne00); +} + +kernel void kernel_mul_mat_q6_k_f32( + device const void * src0, + device const float * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + threadgroup float * sum [[threadgroup(0)]], + uint2 tgpig[[threadgroup_position_in_grid]], + uint2 tpig[[thread_position_in_grid]], // we don't use this for now + uint2 tpitg[[thread_position_in_threadgroup]], + uint2 tptg[[threads_per_threadgroup]]) { + + const uint8_t kmask1 = 0x03; + const uint8_t kmask2 = 0x0C; + const uint8_t kmask3 = 0x30; + const uint8_t kmask4 = 0xC0; + + const int nb = ne00/QK_K; + + const int64_t r0 = tgpig.x; + const int64_t r1 = tgpig.y; + + device const block_q6_k * x = (device const block_q6_k *) src0 + r0*nb; + device const float * yy = (device const float *) src1 + r1*ne10; + + const uint nth = tptg.x*tptg.y; + const uint ith = tptg.y*tpitg.x + tpitg.y; + + const int step = QK_K / tptg.y; // we expect this to be 16 + const int iqs = step * tpitg.y; // 0...240 in steps of 16 + const int ip = iqs / 128; // 0 or 1 + const int il = (iqs - 128*ip)/16; // 0...7 + const int n = 4; + const int is = 8*ip + (n*il)/16; + + float sumf = 0; + for (int i = tpitg.x; i < nb; i += tptg.x) { + + device const uint8_t * ql = x[i].ql + 64*ip + n*il; + device const uint8_t * qh = x[i].qh + 32*ip + n*il; + device const int8_t * sc = x[i].scales + is; + + device const float * y = yy + i * QK_K + 128*ip + n*il; + + const float dall = x[i].d; + + float4 sums = {0.f, 0.f, 0.f, 0.f}; + for (int l = 0; l < n; ++l) { + sums[0] += y[l+ 0] * ((int8_t)((ql[l+ 0] & 0xF) | ((qh[l] & kmask1) << 4)) - 32); + sums[1] += y[l+32] * ((int8_t)((ql[l+32] & 0xF) | ((qh[l] & kmask2) << 2)) - 32); + sums[2] += y[l+64] * ((int8_t)((ql[l+ 0] >> 4) | ((qh[l] & kmask3) << 0)) - 32); + sums[3] += y[l+96] * ((int8_t)((ql[l+32] >> 4) | ((qh[l] & kmask4) >> 2)) - 32); + } + + sumf += dall * (sums[0] * sc[0] + sums[1] * sc[2] + sums[2] * sc[4] + sums[3] * sc[6]); + + } + + sum[ith] = sumf; + + // + // Accumulate the sum from all threads in the threadgroup + // + threadgroup_barrier(mem_flags::mem_threadgroup); + if (ith%4 == 0) { + for (int i = 1; i < 4; ++i) sum[ith] += sum[ith + i]; + } + threadgroup_barrier(mem_flags::mem_threadgroup); + if (ith%16 == 0) { + for (int i = 4; i < 16; i += 4) sum[ith] += sum[ith + i]; + } + threadgroup_barrier(mem_flags::mem_threadgroup); + if (ith == 0) { + for (int i = 16; i < nth; i += 16) sum[0] += sum[i]; + dst[r1*ne0 + r0] = sum[0]; + } + +} From 8432d4d9f716b25133e3ed671d91e21f6f3be867 Mon Sep 17 00:00:00 2001 From: "le.chang" Date: Fri, 9 Jun 2023 00:47:56 +0800 Subject: [PATCH 07/12] ggml : load data into int8x16x4_t using vld4q_s8 on arm64 (#1738) --- k_quants.c | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/k_quants.c b/k_quants.c index 4d524494d..b3d6dc765 100644 --- a/k_quants.c +++ b/k_quants.c @@ -1259,8 +1259,8 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri for (int j = 0; j < QK_K/128; ++j) { const uint8x16x2_t q3bits = vld1q_u8_x2(q3); q3 += 32; - const int8x16x4_t q8bytes_1 = vld1q_s8_x4(q8); q8 += 64; - const int8x16x4_t q8bytes_2 = vld1q_s8_x4(q8); q8 += 64; + const int8x16x4_t q8bytes_1 = vld4q_s8(q8); q8 += 64; + const int8x16x4_t q8bytes_2 = vld4q_s8(q8); q8 += 64; q3h.val[0] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[0]), 2); q3h.val[1] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[1]), 2); @@ -1788,7 +1788,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri for (int j = 0; j < QK_K/64; ++j) { const uint8x16x2_t q5bits = vld1q_u8_x2(q5); q5 += 32; - const int8x16x4_t q8bytes = vld1q_s8_x4(q8); q8 += 64; + const int8x16x4_t q8bytes = vld4q_s8(q8); q8 += 64; q5h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits.val[0]), 4); q5h.val[1] = vshlq_n_u8(vandq_u8(mone, qhbits.val[1]), 4); @@ -2020,8 +2020,8 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri for (int j = 0; j < QK_K/128; ++j) { uint8x16x2_t qhbits = vld1q_u8_x2(qh); qh += 32; - uint8x16x4_t q6bits = vld1q_u8_x4(q6); q6 += 64; - int8x16x4_t q8bytes = vld1q_s8_x4(q8); q8 += 64; + uint8x16x4_t q6bits = vld4q_u8(q6); q6 += 64; + int8x16x4_t q8bytes = vld4q_s8(q8); q8 += 64; q6h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits.val[0]), 4); q6h.val[1] = vshlq_n_u8(vandq_u8(mone, qhbits.val[1]), 4); @@ -2064,7 +2064,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri scale += 2; #endif - q8bytes = vld1q_s8_x4(q8); q8 += 64; + q8bytes = vld4q_s8(q8); q8 += 64; shifted = vshrq_n_u8(qhbits.val[0], 4); q6h.val[0] = vshlq_n_u8(vandq_u8(mone, shifted), 4); From 0bf7cf1b296fc9fca05411b37afdf08a531487d2 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Thu, 8 Jun 2023 20:48:14 +0300 Subject: [PATCH 08/12] Revert "ggml : load data into int8x16x4_t using vld4q_s8 on arm64 (#1738)" This reverts commit 8432d4d9f716b25133e3ed671d91e21f6f3be867. --- k_quants.c | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/k_quants.c b/k_quants.c index b3d6dc765..4d524494d 100644 --- a/k_quants.c +++ b/k_quants.c @@ -1259,8 +1259,8 @@ void ggml_vec_dot_q3_K_q8_K(const int n, float * restrict s, const void * restri for (int j = 0; j < QK_K/128; ++j) { const uint8x16x2_t q3bits = vld1q_u8_x2(q3); q3 += 32; - const int8x16x4_t q8bytes_1 = vld4q_s8(q8); q8 += 64; - const int8x16x4_t q8bytes_2 = vld4q_s8(q8); q8 += 64; + const int8x16x4_t q8bytes_1 = vld1q_s8_x4(q8); q8 += 64; + const int8x16x4_t q8bytes_2 = vld1q_s8_x4(q8); q8 += 64; q3h.val[0] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[0]), 2); q3h.val[1] = vshlq_n_u8(vbicq_u8(m0, qhbits.val[1]), 2); @@ -1788,7 +1788,7 @@ void ggml_vec_dot_q5_K_q8_K(const int n, float * restrict s, const void * restri for (int j = 0; j < QK_K/64; ++j) { const uint8x16x2_t q5bits = vld1q_u8_x2(q5); q5 += 32; - const int8x16x4_t q8bytes = vld4q_s8(q8); q8 += 64; + const int8x16x4_t q8bytes = vld1q_s8_x4(q8); q8 += 64; q5h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits.val[0]), 4); q5h.val[1] = vshlq_n_u8(vandq_u8(mone, qhbits.val[1]), 4); @@ -2020,8 +2020,8 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri for (int j = 0; j < QK_K/128; ++j) { uint8x16x2_t qhbits = vld1q_u8_x2(qh); qh += 32; - uint8x16x4_t q6bits = vld4q_u8(q6); q6 += 64; - int8x16x4_t q8bytes = vld4q_s8(q8); q8 += 64; + uint8x16x4_t q6bits = vld1q_u8_x4(q6); q6 += 64; + int8x16x4_t q8bytes = vld1q_s8_x4(q8); q8 += 64; q6h.val[0] = vshlq_n_u8(vandq_u8(mone, qhbits.val[0]), 4); q6h.val[1] = vshlq_n_u8(vandq_u8(mone, qhbits.val[1]), 4); @@ -2064,7 +2064,7 @@ void ggml_vec_dot_q6_K_q8_K(const int n, float * restrict s, const void * restri scale += 2; #endif - q8bytes = vld4q_s8(q8); q8 += 64; + q8bytes = vld1q_s8_x4(q8); q8 += 64; shifted = vshrq_n_u8(qhbits.val[0], 4); q6h.val[0] = vshlq_n_u8(vandq_u8(mone, shifted), 4); From 72ff5282bf0388c60821f504c4c8cc2b1f491aa6 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Thu, 8 Jun 2023 22:28:21 +0300 Subject: [PATCH 09/12] metal : add Q2_K implementation (#1762) * metal : add Q2_K implementation 27.1 ms / token on M2 Max 30-core GPU, so about the same speed as Q4_0. Memory throughput is ~156 GB/s. The access pattern used in the Q2_K CUDA implementation resulted in significantly lower performance (~31 ms/token). * Fixing merge conflicts --------- Co-authored-by: Iwan Kawrakow --- ggml-metal.m | 17 ++++ ggml-metal.metal | 201 ++++++++++++++++++++++++++++++++++++++++++----- 2 files changed, 200 insertions(+), 18 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 626ca871c..ac4f1346c 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -49,11 +49,13 @@ struct ggml_metal_context { GGML_METAL_DECL_KERNEL(diag_mask_inf); GGML_METAL_DECL_KERNEL(get_rows_f16); GGML_METAL_DECL_KERNEL(get_rows_q4_0); + GGML_METAL_DECL_KERNEL(get_rows_q2_k); GGML_METAL_DECL_KERNEL(get_rows_q4_k); GGML_METAL_DECL_KERNEL(get_rows_q6_k); GGML_METAL_DECL_KERNEL(rms_norm); GGML_METAL_DECL_KERNEL(mul_mat_f16_f32); GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32); + GGML_METAL_DECL_KERNEL(mul_mat_q2_k_f32); GGML_METAL_DECL_KERNEL(mul_mat_q4_k_f32); GGML_METAL_DECL_KERNEL(mul_mat_q6_k_f32); GGML_METAL_DECL_KERNEL(rope); @@ -137,11 +139,13 @@ struct ggml_metal_context * ggml_metal_init(void) { GGML_METAL_ADD_KERNEL(diag_mask_inf); GGML_METAL_ADD_KERNEL(get_rows_f16); GGML_METAL_ADD_KERNEL(get_rows_q4_0); + GGML_METAL_ADD_KERNEL(get_rows_q2_k); GGML_METAL_ADD_KERNEL(get_rows_q4_k); GGML_METAL_ADD_KERNEL(get_rows_q6_k); GGML_METAL_ADD_KERNEL(rms_norm); GGML_METAL_ADD_KERNEL(mul_mat_f16_f32); GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32); + GGML_METAL_ADD_KERNEL(mul_mat_q2_k_f32); GGML_METAL_ADD_KERNEL(mul_mat_q4_k_f32); GGML_METAL_ADD_KERNEL(mul_mat_q6_k_f32); GGML_METAL_ADD_KERNEL(rope); @@ -525,6 +529,15 @@ void ggml_metal_graph_compute( nth1 = 4; [encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_0_f32]; } break; + case GGML_TYPE_Q2_K: + { + GGML_ASSERT(ne02 == 1); + GGML_ASSERT(ne12 == 1); + + nth0 = 4; + nth1 = 16; + [encoder setComputePipelineState:ctx->pipeline_mul_mat_q2_k_f32]; + } break; case GGML_TYPE_Q4_K: { GGML_ASSERT(ne02 == 1); @@ -570,6 +583,9 @@ void ggml_metal_graph_compute( if (src0t == GGML_TYPE_Q4_0) { [encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } else if (src0t == GGML_TYPE_Q2_K) { + [encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0]; + [encoder dispatchThreadgroups:MTLSizeMake(ne01, 1, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else if (src0t == GGML_TYPE_Q4_K) { [encoder setThreadgroupMemoryLength:nth0*nth1*sizeof(float) atIndex:0]; [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, 1) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; @@ -591,6 +607,7 @@ void ggml_metal_graph_compute( switch (src0->type) { case GGML_TYPE_F16: [encoder setComputePipelineState:ctx->pipeline_get_rows_f16]; break; case GGML_TYPE_Q4_0: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_0]; break; + case GGML_TYPE_Q2_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q2_k]; break; case GGML_TYPE_Q4_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q4_k]; break; case GGML_TYPE_Q6_K: [encoder setComputePipelineState:ctx->pipeline_get_rows_q6_k]; break; default: GGML_ASSERT(false && "not implemented"); diff --git a/ggml-metal.metal b/ggml-metal.metal index e851cbd4d..43814ed09 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -527,6 +527,13 @@ kernel void kernel_cpy_f32_f32( #define QK_K 256 +typedef struct { + uint8_t scales[QK_K/16]; // scales and mins, quantized with 4 bits + uint8_t qs[QK_K/4]; // quants + half d; // super-block scale for quantized scales + half dmin; // super-block scale for quantized mins +} block_q2_k; + typedef struct { half d; // super-block scale for quantized scales half dmin; // super-block scale for quantized mins @@ -555,6 +562,41 @@ static inline uchar4 get_scale_min_k4(int j, device const uint8_t * q) { return r; } +//========================================== dequantization ============================= + +static void dequantize_row_q2_k(device const block_q2_k * x, device float * y, int k) { + assert(k % QK_K == 0); + const int nb = k / QK_K; + + for (int i = 0; i < nb; i++) { + + const float d = x[i].d; + const float min = x[i].dmin; + + device const uint8_t * q = x[i].qs; + + int is = 0; + float dl, ml; + for (int n = 0; n < QK_K; n += 128) { + int shift = 0; + for (int j = 0; j < 4; ++j) { + + uint8_t sc = x[i].scales[is++]; + dl = d * (sc & 0xF); ml = min * (sc >> 4); + for (int l = 0; l < 16; ++l) *y++ = dl * ((int8_t)((q[l] >> shift) & 3)) - ml; + + sc = x[i].scales[is++]; + dl = d * (sc & 0xF); ml = min * (sc >> 4); + for (int l = 0; l < 16; ++l) *y++ = dl * ((int8_t)((q[l+16] >> shift) & 3)) - ml; + + shift += 2; + } + q += 32; + } + + } +} + static void dequantize_row_q4_k(device const block_q4_k * x, device float * y, int k) { assert(k % QK_K == 0); const int nb = k / QK_K; @@ -586,12 +628,12 @@ static void dequantize_row_q6_k(device const block_q6_k * x, device float * y, i for (int i = 0; i < nb; i++) { - const float d = x[i].d; - device const uint8_t * ql = x[i].ql; device const uint8_t * qh = x[i].qh; device const int8_t * sc = x[i].scales; + const float d = x[i].d; + for (int n = 0; n < QK_K; n += 128) { for (int l = 0; l < 32; ++l) { int is = l/16; @@ -612,6 +654,22 @@ static void dequantize_row_q6_k(device const block_q6_k * x, device float * y, i } } +kernel void kernel_get_rows_q2_k( + device const void * src0, + device const int * src1, + device float * dst, + constant int64_t & ne00, + constant uint64_t & nb01, + constant uint64_t & nb1, + uint tpig[[thread_position_in_grid]]) { + const int i = tpig; + const int r = ((device int32_t *) src1)[i]; + + dequantize_row_q2_k( + (device const block_q2_k *) ((device char *) src0 + r*nb01), + (device float *) ((device char *) dst + i*nb1), ne00); +} + kernel void kernel_get_rows_q4_k( device const void * src0, device const int * src1, @@ -628,6 +686,129 @@ kernel void kernel_get_rows_q4_k( (device float *) ((device char *) dst + i*nb1), ne00); } +kernel void kernel_get_rows_q6_k( + device const void * src0, + device const int * src1, + device float * dst, + constant int64_t & ne00, + constant uint64_t & nb01, + constant uint64_t & nb1, + uint tpig[[thread_position_in_grid]]) { + const int i = tpig; + const int r = ((device int32_t *) src1)[i]; + + dequantize_row_q6_k( + (device const block_q6_k *) ((device char *) src0 + r*nb01), + (device float *) ((device char *) dst + i*nb1), ne00); +} + +//====================================== dot products ========================= + +kernel void kernel_mul_mat_q2_k_f32( + device const void * src0, + device const float * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + threadgroup float * sum [[threadgroup(0)]], + uint2 tgpig[[threadgroup_position_in_grid]], + uint2 tpig[[thread_position_in_grid]], // we don't use this for now + uint2 tpitg[[thread_position_in_threadgroup]], + uint2 tptg[[threads_per_threadgroup]]) { + + const int nb = ne00/QK_K; + + const int64_t r0 = tgpig.x; + const int64_t r1 = tgpig.y; + + device const block_q2_k * x = (device const block_q2_k *) src0 + r0*nb; + device const float * yy = (device const float *) src1 + r1*ne10; + + const int nth = tptg.x*tptg.y; + const int ith = tptg.y*tpitg.x + tpitg.y; + + + const int tid = tpitg.y; // 0...16 + const int il = tid/4; // 0...3 + const int ir = tid%4; // 0...3 + const int ip = il/2; // 0 or 1 + const int shift1 = 4*(il%2);// 0 or 4 + const int shift2 = shift1+2;// 2 or 6 + const int n = 8; + const int is = 4*il + (n*ir)/16; + + sum[ith] = 0.0f; + + float sumf = 0; + for (int i = tpitg.x; i < nb; i += tptg.x) { + + device const uint8_t * q = x[i].qs + 32*ip + n*ir; + device const uint8_t * scales = x[i].scales + is; + + uint8_t d1 = scales[0] & 0xF; + uint8_t m1 = scales[0] >> 4; + uint8_t d2 = scales[2] & 0xF; + uint8_t m2 = scales[2] >> 4; + + device const float * y = yy + i*QK_K + 64*il + n*ir; + + const float dall = (float)x[i].d; + const float dmin = (float)x[i].dmin; + + float4 s = {0.f, 0.f, 0.f, 0.f}; + for (int l = 0; l < n; ++l) { + s[0] += y[l+ 0] * ((q[l] >> shift1) & 3); s[1] += y[l+ 0]; + s[2] += y[l+32] * ((q[l] >> shift2) & 3); s[3] += y[l+32]; + } + sumf += dall * (s[0] * d1 + s[2] * d2) - dmin * (s[1] * m1 + s[3] * m2); + + + } + sum[ith] = sumf; + + // + // Accumulate the sum from all threads in the threadgroup + // This version is slightly faster than the commented out one below, + // which I copy-pasted from ggerganov's q4_0 dot product for metal. + // + threadgroup_barrier(mem_flags::mem_threadgroup); + if (ith%4 == 0) { + for (int i = 1; i < 4; ++i) sum[ith] += sum[ith + i]; + } + threadgroup_barrier(mem_flags::mem_threadgroup); + if (ith%16 == 0) { + for (int i = 4; i < 16; i += 4) sum[ith] += sum[ith + i]; + } + threadgroup_barrier(mem_flags::mem_threadgroup); + if (ith == 0) { + for (int i = 16; i < nth; i += 16) sum[0] += sum[i]; + dst[r1*ne0 + r0] = sum[0]; + } + + //// accumulate the sum from all threads in the threadgroup + //threadgroup_barrier(mem_flags::mem_threadgroup); + //for (uint i = nth/2; i > 0; i /= 2) { + // if (ith < i) { + // sum[ith] += sum[ith + i]; + // } + // threadgroup_barrier(mem_flags::mem_threadgroup); + //} + + //if (ith == 0) { + // dst[r1*ne0 + r0] = sum[0]; + //} +} + kernel void kernel_mul_mat_q4_k_f32( device const void * src0, device const float * src1, @@ -724,22 +905,6 @@ kernel void kernel_mul_mat_q4_k_f32( //} } -kernel void kernel_get_rows_q6_k( - device const void * src0, - device const int * src1, - device float * dst, - constant int64_t & ne00, - constant uint64_t & nb01, - constant uint64_t & nb1, - uint tpig[[thread_position_in_grid]]) { - const int i = tpig; - const int r = ((device int32_t *) src1)[i]; - - dequantize_row_q6_k( - (device const block_q6_k *) ((device char *) src0 + r*nb01), - (device float *) ((device char *) dst + i*nb1), ne00); -} - kernel void kernel_mul_mat_q6_k_f32( device const void * src0, device const float * src1, From 245fc3c37da5ac5963f9f11a9f4f2ac08d96afc6 Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Fri, 9 Jun 2023 10:39:59 +0300 Subject: [PATCH 10/12] metal : faster q4_0 (#1775) * metal : 8% faster q4_0 Avoid copying into local uchar4 anf float4. * metal : 17% faster Q4_0 Use 64 threads in a thread group. --------- Co-authored-by: Iwan Kawrakow --- ggml-metal.m | 2 +- ggml-metal.metal | 34 +++++++++++++++++++--------------- 2 files changed, 20 insertions(+), 16 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index ac4f1346c..54cbaf860 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -526,7 +526,7 @@ void ggml_metal_graph_compute( GGML_ASSERT(ne12 == 1); nth0 = 8; - nth1 = 4; + nth1 = 8; [encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_0_f32]; } break; case GGML_TYPE_Q2_K: diff --git a/ggml-metal.metal b/ggml-metal.metal index 43814ed09..8e730eb9c 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -267,6 +267,8 @@ kernel void kernel_mul_mat_q4_0_f32( uint2 tptg[[threads_per_threadgroup]]) { const int nb = ne00/QK4_0; + const int8_t m8 = 8; + const int64_t r0 = tgpig.x; const int64_t r1 = tgpig.y; @@ -276,33 +278,34 @@ kernel void kernel_mul_mat_q4_0_f32( const uint nth = tptg.x*tptg.y; const uint ith = tptg.y*tpitg.x + tpitg.y; - sum[ith] = 0.0f; + const int ix = tpitg.y/4; // 0 or 1 + const int iy = tpitg.y - 4*ix; // 0...3 - for (int i = tpitg.x; i < nb; i += tptg.x) { - device const uchar4 * x0p = (device const uchar4 *) (x + i)->qs; - device const float4 * y0p = (device const float4 *) (y + i*QK4_0); + const int first = 4 * iy; - const float d = (float)((x + i)->d); + float sumf = 0; - const uchar4 x0v = *(x0p + tpitg.y); - const float4 y0v = *(y0p + tpitg.y + 0); - const float4 y1v = *(y0p + tpitg.y + 4); + for (int i = 2*tpitg.x + ix; i < nb; i += 2*tptg.x) { - float acc = 0.0f; + const float d = (float)x[i].d; + + device const uint8_t * xl = x[i].qs + first; + device const float * yl = y + i * QK4_0 + first; + + float2 acc = {0.0f, 0.0f}; for (int j = 0; j < 4; ++j) { - const int x0 = x0v[j] & 0x0F; - const int x1 = x0v[j] >> 4; - const float y0 = y0v[j]; - const float y1 = y1v[j]; + acc[0] += yl[j+ 0] * ((int8_t)(xl[j] & 0xF) - m8); + acc[1] += yl[j+16] * ((int8_t)(xl[j] >> 4) - m8); - acc += (x0 - 8)*y0 + (x1 - 8)*y1; } - sum[ith] += acc*d; + sumf += d * (acc[0] + acc[1]); } + sum[ith] = sumf; + // // Accumulate the sum from all threads in the threadgroup // This version is slightly faster than the commented out one below, @@ -357,6 +360,7 @@ kernel void kernel_mul_mat_f16_f32( uint3 tpig[[thread_position_in_grid]], uint3 tpitg[[thread_position_in_threadgroup]], uint3 tptg[[threads_per_threadgroup]]) { + const int64_t r0 = tgpig.x; const int64_t r1 = tgpig.y; const int64_t im = tgpig.z; From 92f44ff7f778ef1b94028b2ba6d39943b5ca0ada Mon Sep 17 00:00:00 2001 From: AT Date: Fri, 9 Jun 2023 04:00:51 -0400 Subject: [PATCH 11/12] metal : add GELU implementation (#1770) Co-authored-by: Adam Treat --- ggml-metal.m | 16 ++++++++++++++++ ggml-metal.metal | 11 +++++++++++ 2 files changed, 27 insertions(+) diff --git a/ggml-metal.m b/ggml-metal.m index 54cbaf860..5c9ecd76e 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -45,6 +45,7 @@ struct ggml_metal_context { GGML_METAL_DECL_KERNEL(scale); GGML_METAL_DECL_KERNEL(silu); GGML_METAL_DECL_KERNEL(relu); + GGML_METAL_DECL_KERNEL(gelu); GGML_METAL_DECL_KERNEL(soft_max); GGML_METAL_DECL_KERNEL(diag_mask_inf); GGML_METAL_DECL_KERNEL(get_rows_f16); @@ -135,6 +136,7 @@ struct ggml_metal_context * ggml_metal_init(void) { GGML_METAL_ADD_KERNEL(scale); GGML_METAL_ADD_KERNEL(silu); GGML_METAL_ADD_KERNEL(relu); + GGML_METAL_ADD_KERNEL(gelu); GGML_METAL_ADD_KERNEL(soft_max); GGML_METAL_ADD_KERNEL(diag_mask_inf); GGML_METAL_ADD_KERNEL(get_rows_f16); @@ -420,6 +422,20 @@ void ggml_metal_graph_compute( [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; } break; + case GGML_OP_GELU: + { + if (encoder == nil) { + encoder = [command_buffer computeCommandEncoder]; + } + + [encoder setComputePipelineState:ctx->pipeline_gelu]; + [encoder setBuffer:id_src0 offset:offs_src0 atIndex:0]; + [encoder setBuffer:id_dst offset:offs_dst atIndex:1]; + + const int64_t n = ggml_nelements(dst); + + [encoder dispatchThreadgroups:MTLSizeMake(n, 1, 1) threadsPerThreadgroup:MTLSizeMake(1, 1, 1)]; + } break; case GGML_OP_SOFT_MAX: { if (encoder == nil) { diff --git a/ggml-metal.metal b/ggml-metal.metal index 8e730eb9c..745fe8ad3 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -81,6 +81,17 @@ kernel void kernel_relu( dst[tpig] = max(0.0f, src0[tpig]); } +constant float GELU_COEF_A = 0.044715f; +constant float SQRT_2_OVER_PI = 0.79788456080286535587989211986876f; + +kernel void kernel_gelu( + device const float * src0, + device float * dst, + uint tpig[[thread_position_in_grid]]) { + float x = src0[tpig]; + dst[tpig] = 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x))); +} + kernel void kernel_soft_max( device const float * src0, device float * dst, From b33dee282f5d8032b5f780152732dc45cbf2d349 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Fri, 9 Jun 2023 11:11:04 +0300 Subject: [PATCH 12/12] metal : fix build "tanhf" -> "tanh" --- ggml-metal.metal | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-metal.metal b/ggml-metal.metal index 745fe8ad3..c94ef83f9 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -89,7 +89,7 @@ kernel void kernel_gelu( device float * dst, uint tpig[[thread_position_in_grid]]) { float x = src0[tpig]; - dst[tpig] = 0.5f*x*(1.0f + tanhf(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x))); + dst[tpig] = 0.5f*x*(1.0f + tanh(SQRT_2_OVER_PI*x*(1.0f + GELU_COEF_A*x*x))); } kernel void kernel_soft_max(