From ca82cf7bac0c91d03e3d320b3a865dd006f854ac Mon Sep 17 00:00:00 2001 From: Kawrakow <48489457+ikawrakow@users.noreply.github.com> Date: Sun, 3 Sep 2023 11:06:22 +0300 Subject: [PATCH 01/16] metal : more optimizations (#2959) * Very minor speedup via simd-group synchronization in f16 x f32 * Another very minor speedup on metal * Quite significant PP speedup on metal * Another attempt * Minor * Massive improvement for TG for fp16 * ~4-5% improvement for Q8_0 TG on metal --------- Co-authored-by: Iwan Kawrakow Co-authored-by: Georgi Gerganov --- ggml-metal.m | 22 +++-- ggml-metal.metal | 226 ++++++++++++++++++++++++++++++----------------- 2 files changed, 163 insertions(+), 85 deletions(-) diff --git a/ggml-metal.m b/ggml-metal.m index 88e7e1356..d0d23442e 100644 --- a/ggml-metal.m +++ b/ggml-metal.m @@ -76,6 +76,7 @@ struct ggml_metal_context { GGML_METAL_DECL_KERNEL(rms_norm); GGML_METAL_DECL_KERNEL(norm); GGML_METAL_DECL_KERNEL(mul_mat_f16_f32); + GGML_METAL_DECL_KERNEL(mul_mat_f16_f32_1row); GGML_METAL_DECL_KERNEL(mul_mat_q4_0_f32); GGML_METAL_DECL_KERNEL(mul_mat_q4_1_f32); GGML_METAL_DECL_KERNEL(mul_mat_q8_0_f32); @@ -219,6 +220,7 @@ struct ggml_metal_context * ggml_metal_init(int n_cb) { GGML_METAL_ADD_KERNEL(rms_norm); GGML_METAL_ADD_KERNEL(norm); GGML_METAL_ADD_KERNEL(mul_mat_f16_f32); + GGML_METAL_ADD_KERNEL(mul_mat_f16_f32_1row); GGML_METAL_ADD_KERNEL(mul_mat_q4_0_f32); GGML_METAL_ADD_KERNEL(mul_mat_q4_1_f32); GGML_METAL_ADD_KERNEL(mul_mat_q8_0_f32); @@ -284,6 +286,7 @@ void ggml_metal_free(struct ggml_metal_context * ctx) { GGML_METAL_DEL_KERNEL(rms_norm); GGML_METAL_DEL_KERNEL(norm); GGML_METAL_DEL_KERNEL(mul_mat_f16_f32); + GGML_METAL_DEL_KERNEL(mul_mat_f16_f32_1row); GGML_METAL_DEL_KERNEL(mul_mat_q4_0_f32); GGML_METAL_DEL_KERNEL(mul_mat_q4_1_f32); GGML_METAL_DEL_KERNEL(mul_mat_q8_0_f32); @@ -868,7 +871,11 @@ void ggml_metal_graph_compute( { nth0 = 32; nth1 = 1; - [encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32]; + if (ne11 * ne12 < 4) { + [encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32_1row]; + } else { + [encoder setComputePipelineState:ctx->pipeline_mul_mat_f16_f32]; + } } break; case GGML_TYPE_Q4_0: { @@ -920,8 +927,8 @@ void ggml_metal_graph_compute( GGML_ASSERT(ne02 == 1); GGML_ASSERT(ne12 == 1); - nth0 = 2; - nth1 = 32; + nth0 = 4; //1; + nth1 = 8; //32; [encoder setComputePipelineState:ctx->pipeline_mul_mat_q4_K_f32]; } break; case GGML_TYPE_Q5_K: @@ -969,9 +976,12 @@ void ggml_metal_graph_compute( [encoder setBytes:&gqa length:sizeof(gqa) atIndex:17]; if (src0t == GGML_TYPE_Q4_0 || src0t == GGML_TYPE_Q4_1 || src0t == GGML_TYPE_Q8_0 || - src0t == GGML_TYPE_Q2_K || src0t == GGML_TYPE_Q4_K) { + src0t == GGML_TYPE_Q2_K) {// || src0t == GGML_TYPE_Q4_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 7)/8, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } + else if (src0t == GGML_TYPE_Q4_K) { + [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 3)/4, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + } else if (src0t == GGML_TYPE_Q3_K) { #ifdef GGML_QKK_64 [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; @@ -985,8 +995,8 @@ void ggml_metal_graph_compute( else if (src0t == GGML_TYPE_Q6_K) { [encoder dispatchThreadgroups:MTLSizeMake((ne01 + 1)/2, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } else { - [encoder setThreadgroupMemoryLength:nth0*sizeof(float) atIndex:0]; - [encoder dispatchThreadgroups:MTLSizeMake(ne01, ne11, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; + int64_t ny = (ne11 + 3)/4; + [encoder dispatchThreadgroups:MTLSizeMake(ne01, ny, ne12) threadsPerThreadgroup:MTLSizeMake(nth0, nth1, 1)]; } } } break; diff --git a/ggml-metal.metal b/ggml-metal.metal index 8cdf0b9d2..3fa311b40 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -133,19 +133,24 @@ kernel void kernel_soft_max( threadgroup_barrier(mem_flags::mem_threadgroup); } - // broadcast - if (tpitg[0] == 0) { - buf[0] = buf[0]; - } + //// broadcast - not needed. There is a threadgroup barrier above in the last iteration of + // the loop, and when that is done, buf[0] has the correct (synchronized) value + //if (tpitg[0] == 0) { + // buf[0] = buf[0]; + //} - threadgroup_barrier(mem_flags::mem_threadgroup); + //threadgroup_barrier(mem_flags::mem_threadgroup); const float max = buf[0]; // parallel sum buf[tpitg[0]] = 0.0f; for (int i00 = tpitg[0]; i00 < ne00; i00 += ntg[0]) { - buf[tpitg[0]] += exp(psrc0[i00] - max); + const float exp_psrc0 = exp(psrc0[i00] - max); + buf[tpitg[0]] += exp_psrc0; + // Remember the result of exp here. exp is expensive, so we really do not + // whish to compute it twice. + pdst[i00] = exp_psrc0; } // reduce @@ -157,17 +162,18 @@ kernel void kernel_soft_max( threadgroup_barrier(mem_flags::mem_threadgroup); } - // broadcast - if (tpitg[0] == 0) { - buf[0] = buf[0]; - } + // broadcast - not needed, see above + //// broadcast + //if (tpitg[0] == 0) { + // buf[0] = buf[0]; + //} - threadgroup_barrier(mem_flags::mem_threadgroup); + //threadgroup_barrier(mem_flags::mem_threadgroup); const float sum = buf[0]; for (int i00 = tpitg[0]; i00 < ne00; i00 += ntg[0]) { - pdst[i00] = exp(psrc0[i00] - max) / sum; + pdst[i00] /= sum; } } @@ -214,25 +220,27 @@ kernel void kernel_norm( } threadgroup_barrier(mem_flags::mem_threadgroup); } - // broadcast - if (tpitg == 0) { - sum[0] /= ne00; - } - threadgroup_barrier(mem_flags::mem_threadgroup); + //// broadcast + //if (tpitg == 0) { + // sum[0] /= ne00; + //} + //threadgroup_barrier(mem_flags::mem_threadgroup); const float mean = sum[0]; - // recenter + // recenter and VARIANCE device float * y = dst + tgpig*ne00; - for (int i00 = tpitg; i00 < ne00; i00 += ntg) { - y[i00] = x[i00] - mean; - } - - // VARIANCE - // parallel sum sum[tpitg] = 0.0f; for (int i00 = tpitg; i00 < ne00; i00 += ntg) { + y[i00] = x[i00] - mean; sum[tpitg] += y[i00] * y[i00]; } + + //// VARIANCE + //// parallel sum + //sum[tpitg] = 0.0f; + //for (int i00 = tpitg; i00 < ne00; i00 += ntg) { + // sum[tpitg] += y[i00] * y[i00]; + //} // reduce threadgroup_barrier(mem_flags::mem_threadgroup); for (uint i = ntg/2; i > 0; i /= 2) { @@ -241,11 +249,11 @@ kernel void kernel_norm( } threadgroup_barrier(mem_flags::mem_threadgroup); } - // broadcast - if (tpitg == 0) { - sum[0] /= ne00; - } - threadgroup_barrier(mem_flags::mem_threadgroup); + //// broadcast + //if (tpitg == 0) { + // sum[0] /= ne00; + //} + //threadgroup_barrier(mem_flags::mem_threadgroup); const float variance = sum[0]; const float scale = 1.0f/sqrt(variance + eps); @@ -435,6 +443,8 @@ kernel void kernel_mul_mat_q4_1_f32( mul_vec_q_n_f32(src0,src1,dst,ne00,ne01,ne02,ne10,ne12,ne0,ne1,gqa,tgpig,tiisg,sgitg); } +#define NB_Q8_0 8 + kernel void kernel_mul_mat_q8_0_f32( device const void * src0, device const float * src1, @@ -463,30 +473,30 @@ kernel void kernel_mul_mat_q8_0_f32( device const block_q8_0 * x = (device const block_q8_0 *) src0 + offset0; device const float * y = (device const float *) src1 + r1*ne10 + im*ne00*ne1; - float yl[16]; + float yl[NB_Q8_0]; float sumf[nr]={0.f}; - const int ix = tiisg/2; - const int il = tiisg%2; + const int ix = tiisg/4; + const int il = tiisg%4; - device const float * yb = y + ix * QK8_0 + 16*il; + device const float * yb = y + ix * QK8_0 + NB_Q8_0*il; - // each thread in a SIMD group deals with half a block. - for (int ib = ix; ib < nb; ib += nw/2) { - for (int i = 0; i < 16; ++i) { + // each thread in a SIMD group deals with NB_Q8_0 quants at a time + for (int ib = ix; ib < nb; ib += nw/4) { + for (int i = 0; i < NB_Q8_0; ++i) { yl[i] = yb[i]; } for (int row = 0; row < nr; row++) { - device const int8_t * qs = x[ib+row*nb].qs + 16*il; + device const int8_t * qs = x[ib+row*nb].qs + NB_Q8_0*il; float sumq = 0.f; - for (int iq = 0; iq < 16; ++iq) { + for (int iq = 0; iq < NB_Q8_0; ++iq) { sumq += qs[iq] * yl[iq]; } sumf[row] += sumq*x[ib+row*nb].d; } - yb += QK8_0 * 16; + yb += NB_Q8_0 * nw; } for (int row = 0; row < nr; ++row) { @@ -497,6 +507,60 @@ kernel void kernel_mul_mat_q8_0_f32( } } +kernel void kernel_mul_mat_f16_f32_1row( + device const char * src0, + device const char * src1, + device float * dst, + constant int64_t & ne00, + constant int64_t & ne01, + constant int64_t & ne02, + constant uint64_t & nb00, + constant uint64_t & nb01, + constant uint64_t & nb02, + constant int64_t & ne10, + constant int64_t & ne11, + constant int64_t & ne12, + constant uint64_t & nb10, + constant uint64_t & nb11, + constant uint64_t & nb12, + constant int64_t & ne0, + constant int64_t & ne1, + uint3 tgpig[[threadgroup_position_in_grid]], + uint tiisg[[thread_index_in_simdgroup]]) { + + const int64_t r0 = tgpig.x; + const int64_t r1 = tgpig.y; + const int64_t im = tgpig.z; + + device const half * x = (device const half *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02); + device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); + + float sumf = 0; + if (ne00 < 128) { + for (int i = tiisg; i < ne00; i += 32) { + sumf += (float) x[i] * (float) y[i]; + } + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } + } else { + device const half4 * x4 = (device const half4 *) x; + device const float4 * y4 = (device const float4 *) y; + for (int i = tiisg; i < ne00/4; i += 32) { + for (int k = 0; k < 4; ++k) sumf += (float)x4[i][k] * y4[i][k]; + } + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + for (int i = 4*(ne00/4); i < ne00; ++i) sumf += (float) x[i] * y[i]; + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } + } + +} + +#define N_F16_F32 4 + kernel void kernel_mul_mat_f16_f32( device const char * src0, device const char * src1, @@ -515,55 +579,58 @@ kernel void kernel_mul_mat_f16_f32( constant uint64_t & nb12, constant int64_t & ne0, constant int64_t & ne1, - threadgroup float * sum [[threadgroup(0)]], uint3 tgpig[[threadgroup_position_in_grid]], - uint3 tpig[[thread_position_in_grid]], - uint3 tpitg[[thread_position_in_threadgroup]], - uint3 tptg[[threads_per_threadgroup]]) { + uint tiisg[[thread_index_in_simdgroup]]) { const int64_t r0 = tgpig.x; - const int64_t r1 = tgpig.y; + const int64_t rb = N_F16_F32*tgpig.y; const int64_t im = tgpig.z; - device const half * x = (device const half *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02); - device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); + device const half * x = (device const half *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02); - uint ith = tpitg.x; - uint nth = tptg.x; + if (ne00 < 128) { + for (int row = 0; row < N_F16_F32; ++row) { + int r1 = rb + row; + if (r1 >= ne11) { + break; + } - sum[ith] = 0.0f; + device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); - for (int i = ith; i < ne00; i += nth) { - sum[ith] += (float) x[i] * (float) y[i]; + float sumf = 0; + for (int i = tiisg; i < ne00; i += 32) { + sumf += (float) x[i] * (float) y[i]; + } + + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } + } + } else { + device const half4 * x4 = (device const half4 *)x; + for (int row = 0; row < N_F16_F32; ++row) { + int r1 = rb + row; + if (r1 >= ne11) { + break; + } + + device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); + device const float4 * y4 = (device const float4 *) y; + + float sumf = 0; + for (int i = tiisg; i < ne00/4; i += 32) { + for (int k = 0; k < 4; ++k) sumf += (float) x4[i][k] * y4[i][k]; + } + + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + for (int i = 4*(ne00/4); i < ne00; ++i) sumf += (float) x[i] * y[i]; + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } + } } - // accumulate the sum from all threads in the threadgroup - threadgroup_barrier(mem_flags::mem_threadgroup); - if (ith%4 == 0) { - for (int i = 1; i < 4; ++i) sum[ith] += sum[ith + i]; - } - threadgroup_barrier(mem_flags::mem_threadgroup); - if (ith%16 == 0) { - for (int i = 4; i < 16; i += 4) sum[ith] += sum[ith + i]; - } - threadgroup_barrier(mem_flags::mem_threadgroup); - if (ith == 0) { - for (int i = 16; i < nth; i += 16) sum[0] += sum[i]; - dst[im*ne1*ne0 + r1*ne0 + r0] = sum[0]; - } - - // Original implementation. Left behind commented out for now - //threadgroup_barrier(mem_flags::mem_threadgroup); - //for (uint i = tptg.x/2; i > 0; i /= 2) { - // if (tpitg.x < i) { - // sum[tpitg.x] += sum[tpitg.x + i]; - // } - // threadgroup_barrier(mem_flags::mem_threadgroup); - //} - // - //if (tpitg.x == 0) { - // dst[im*ne1*ne0 + r1*ne0 + r0] = sum[0]; - //} } kernel void kernel_alibi_f32( @@ -1262,7 +1329,8 @@ kernel void kernel_mul_mat_q4_K_f32( const int r0 = tgpig.x; const int r1 = tgpig.y; const int r2 = tgpig.z; - const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; + //const int first_row = (r0 * N_SIMDGROUP + sgitg) * N_DST; + const int first_row = r0 * N_DST; const int ib_row = first_row * nb; const uint offset0 = r2/gqa*(nb*ne0); device const block_q4_K * x = (device const block_q4_K *) src0 + ib_row + offset0; From 6460f758dbd472653296044d36bed8c4554988f5 Mon Sep 17 00:00:00 2001 From: Wentai Zhang Date: Sun, 3 Sep 2023 16:46:44 +0800 Subject: [PATCH 02/16] opencl : fix a bug in ggml_cl_pool_malloc() for ggml_cl_mul_mat_f32() (#2955) Co-authored-by: Wentai Zhang --- ggml-opencl.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ggml-opencl.cpp b/ggml-opencl.cpp index eb214a836..3d50a7f08 100644 --- a/ggml-opencl.cpp +++ b/ggml-opencl.cpp @@ -1493,7 +1493,7 @@ static void ggml_cl_mul_mat_f32(const ggml_tensor * src0, const ggml_tensor * sr if (src0->backend == GGML_BACKEND_GPU) { // NOLINT d_X = (cl_mem) src0->data; } else { - d_X = ggml_cl_pool_malloc(sizeof(ggml_fp16_t) * x_ne, &x_size); + d_X = ggml_cl_pool_malloc(sizeof(float) * x_ne, &x_size); } cl_mem d_Y = ggml_cl_pool_malloc(sizeof(float) * y_ne, &y_size); cl_mem d_D = ggml_cl_pool_malloc(sizeof(float) * d_ne, &d_size); From afc43d5f82588d2ed71ea104e8262f5e5da13980 Mon Sep 17 00:00:00 2001 From: Alon Date: Sun, 3 Sep 2023 11:48:49 +0300 Subject: [PATCH 03/16] cov : add Code Coverage and codecov.io integration (#2928) * update .gitignore * makefile: add coverage support (lcov, gcovr) * add code-coverage workflow * update code coverage workflow * wun on ubuntu 20.04 * use gcc-8 * check why the job hang * add env vars * add LLAMA_CODE_COVERAGE=1 again * - add CODECOV_TOKEN - add missing make lcov-report * install lcov * update make file -pb flag * remove unused GGML_NITER from workflows * wrap coverage output files in COV_TARGETS --- .github/workflows/build.yml | 1 - .github/workflows/code-coverage.yml | 36 +++++++++++++++++++++++++++++ .gitignore | 7 ++++++ Makefile | 22 +++++++++++++++++- 4 files changed, 64 insertions(+), 2 deletions(-) create mode 100644 .github/workflows/code-coverage.yml diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 20fd8c2b5..9d0a6c222 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -18,7 +18,6 @@ on: env: BRANCH_NAME: ${{ github.head_ref || github.ref_name }} GGML_NLOOP: 3 - GGML_NITER: 1 GGML_N_THREADS: 1 jobs: diff --git a/.github/workflows/code-coverage.yml b/.github/workflows/code-coverage.yml new file mode 100644 index 000000000..392db8a08 --- /dev/null +++ b/.github/workflows/code-coverage.yml @@ -0,0 +1,36 @@ +name: Code Coverage +on: [push, pull_request] + +env: + GGML_NLOOP: 3 + GGML_N_THREADS: 1 + +jobs: + run: + runs-on: ubuntu-20.04 + steps: + - name: Checkout + uses: actions/checkout@v3 + + - name: Dependencies + run: | + sudo apt-get update + sudo apt-get install build-essential gcc-8 lcov + + - name: Build + run: CC=gcc-8 make -j LLAMA_CODE_COVERAGE=1 tests + + - name: Run tests + run: CC=gcc-8 make test + + - name: Generate coverage report + run: | + make coverage + make lcov-report + + - name: Upload coverage to Codecov + uses: codecov/codecov-action@v3 + env: + CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} + with: + files: lcov-report/coverage.info diff --git a/.gitignore b/.gitignore index 8b5f45a2d..f9244fadc 100644 --- a/.gitignore +++ b/.gitignore @@ -6,6 +6,10 @@ *.exe *.dll *.log +*.gcov +*.gcno +*.gcda +*.dot .DS_Store .build/ .cache/ @@ -17,6 +21,9 @@ .vs/ .vscode/ +lcov-report/ +gcovr-report/ + build*/ out/ tmp/ diff --git a/Makefile b/Makefile index e214970f8..c042bf0e5 100644 --- a/Makefile +++ b/Makefile @@ -4,6 +4,9 @@ BUILD_TARGETS = main quantize quantize-stats perplexity embedding vdot train-tex # Binaries only useful for tests TEST_TARGETS = tests/test-llama-grammar tests/test-grammar-parser tests/test-double-float tests/test-grad0 tests/test-opt tests/test-quantize-fns tests/test-quantize-perf tests/test-sampling tests/test-tokenizer-0-llama tests/test-tokenizer-0-falcon tests/test-tokenizer-1 +# Code coverage output files +COV_TARGETS = *.gcno tests/*.gcno *.gcda tests/*.gcda *.gcov tests/*.gcov lcov-report gcovr-report + default: $(BUILD_TARGETS) test: @@ -23,6 +26,18 @@ test: all: $(BUILD_TARGETS) $(TEST_TARGETS) +coverage: ## Run code coverage + gcov -pb tests/*.cpp + +lcov-report: coverage ## Generate lcov report + mkdir -p lcov-report + lcov --capture --directory . --output-file lcov-report/coverage.info + genhtml lcov-report/coverage.info --output-directory lcov-report + +gcovr-report: coverage ## Generate gcovr report + mkdir -p gcovr-report + gcovr --root . --html --html-details --output gcovr-report/coverage.html + ifndef UNAME_S UNAME_S := $(shell uname -s) endif @@ -84,6 +99,11 @@ ifdef LLAMA_SERVER_VERBOSE MK_CPPFLAGS += -DSERVER_VERBOSE=$(LLAMA_SERVER_VERBOSE) endif + +ifdef LLAMA_CODE_COVERAGE + CXXFLAGS += -fprofile-arcs -ftest-coverage -dumpbase '' +endif + ifdef LLAMA_DISABLE_LOGS CFLAGS += -DLOG_DISABLE_LOGS CXXFLAGS += -DLOG_DISABLE_LOGS @@ -399,7 +419,7 @@ libllama.so: llama.o ggml.o $(OBJS) $(CXX) $(CXXFLAGS) -shared -fPIC -o $@ $^ $(LDFLAGS) clean: - rm -vf *.o tests/*.o *.so *.dll benchmark-matmult build-info.h $(BUILD_TARGETS) $(TEST_TARGETS) + rm -vrf *.o tests/*.o *.so *.dll benchmark-matmult build-info.h *.dot $(COV_TARGETS) $(BUILD_TARGETS) $(TEST_TARGETS) # # Examples From d9151e6f570eb20bfd54427bd8a337d9b1a08018 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 3 Sep 2023 12:40:56 +0300 Subject: [PATCH 04/16] metal : revert 6af0bab until we fix it This restores the generated text to be the same as before #2959 --- ggml-metal.metal | 74 ++++++++++++------------------------------------ 1 file changed, 18 insertions(+), 56 deletions(-) diff --git a/ggml-metal.metal b/ggml-metal.metal index 3fa311b40..1d324e466 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -536,27 +536,14 @@ kernel void kernel_mul_mat_f16_f32_1row( device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); float sumf = 0; - if (ne00 < 128) { - for (int i = tiisg; i < ne00; i += 32) { - sumf += (float) x[i] * (float) y[i]; - } - float all_sum = simd_sum(sumf); - if (tiisg == 0) { - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; - } - } else { - device const half4 * x4 = (device const half4 *) x; - device const float4 * y4 = (device const float4 *) y; - for (int i = tiisg; i < ne00/4; i += 32) { - for (int k = 0; k < 4; ++k) sumf += (float)x4[i][k] * y4[i][k]; - } - float all_sum = simd_sum(sumf); - if (tiisg == 0) { - for (int i = 4*(ne00/4); i < ne00; ++i) sumf += (float) x[i] * y[i]; - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; - } + for (int i = tiisg; i < ne00; i += 32) { + sumf += (float) x[i] * (float) y[i]; } + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } } #define N_F16_F32 4 @@ -588,49 +575,24 @@ kernel void kernel_mul_mat_f16_f32( device const half * x = (device const half *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02); - if (ne00 < 128) { - for (int row = 0; row < N_F16_F32; ++row) { - int r1 = rb + row; - if (r1 >= ne11) { - break; - } - - device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); - - float sumf = 0; - for (int i = tiisg; i < ne00; i += 32) { - sumf += (float) x[i] * (float) y[i]; - } - - float all_sum = simd_sum(sumf); - if (tiisg == 0) { - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; - } + for (int row = 0; row < N_F16_F32; ++row) { + int r1 = rb + row; + if (r1 >= ne11) { + break; } - } else { - device const half4 * x4 = (device const half4 *)x; - for (int row = 0; row < N_F16_F32; ++row) { - int r1 = rb + row; - if (r1 >= ne11) { - break; - } - device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); - device const float4 * y4 = (device const float4 *) y; + device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); - float sumf = 0; - for (int i = tiisg; i < ne00/4; i += 32) { - for (int k = 0; k < 4; ++k) sumf += (float) x4[i][k] * y4[i][k]; - } + float sumf = 0; + for (int i = tiisg; i < ne00; i += 32) { + sumf += (float) x[i] * (float) y[i]; + } - float all_sum = simd_sum(sumf); - if (tiisg == 0) { - for (int i = 4*(ne00/4); i < ne00; ++i) sumf += (float) x[i] * y[i]; - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; - } + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; } } - } kernel void kernel_alibi_f32( From 37301347767d555d0a66c043ce4ef6ead8e61c55 Mon Sep 17 00:00:00 2001 From: opparco Date: Sun, 3 Sep 2023 19:18:09 +0900 Subject: [PATCH 05/16] llama : fix bpe tokenize from byte (#2889) --- llama.cpp | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/llama.cpp b/llama.cpp index 2b0cf30f6..c97c1462f 100644 --- a/llama.cpp +++ b/llama.cpp @@ -3366,9 +3366,15 @@ struct llm_tokenizer_bpe { std::string byte_str(1, *j); auto token_multibyte = vocab.token_to_id.find(byte_str); if (token_multibyte == vocab.token_to_id.end()) { - fprintf(stderr,"ERROR: byte not found in vocab: '%s'\n", byte_str.c_str()); + try { + llama_token token_byte = llama_byte_to_token(vocab, *j); + output.push_back(token_byte); + } catch (const std::out_of_range & err) { + fprintf(stderr,"ERROR: byte not found in vocab: '%s'\n", byte_str.c_str()); + } + } else { + output.push_back((*token_multibyte).second); } - output.push_back((*token_multibyte).second); } } else { output.push_back((*token).second); From 73a12a6344d5da4d8e2eba5d12221b8bc6895931 Mon Sep 17 00:00:00 2001 From: Alon Date: Sun, 3 Sep 2023 13:19:01 +0300 Subject: [PATCH 06/16] cov : disable comment in PRs (#2989) --- codecov.yml | 14 ++++++++++++++ 1 file changed, 14 insertions(+) create mode 100644 codecov.yml diff --git a/codecov.yml b/codecov.yml new file mode 100644 index 000000000..a301c5b2c --- /dev/null +++ b/codecov.yml @@ -0,0 +1,14 @@ +comment: off + +coverage: + status: + project: + default: + target: auto + threshold: 0 + base: auto + patch: + default: + target: auto + threshold: 0 + base: auto From b7f2aa9e512c3be2e863d877cbb1056d7c4a03f8 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 3 Sep 2023 13:23:33 +0300 Subject: [PATCH 07/16] metal : restore 363f0bf and fix reduce in F16_F32 kernels (#2986) --- ggml-metal.metal | 76 ++++++++++++++++++++++++++++++++++++------------ 1 file changed, 57 insertions(+), 19 deletions(-) diff --git a/ggml-metal.metal b/ggml-metal.metal index 1d324e466..119fcbeb6 100644 --- a/ggml-metal.metal +++ b/ggml-metal.metal @@ -536,14 +536,27 @@ kernel void kernel_mul_mat_f16_f32_1row( device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); float sumf = 0; - for (int i = tiisg; i < ne00; i += 32) { - sumf += (float) x[i] * (float) y[i]; + if (ne00 < 128) { + for (int i = tiisg; i < ne00; i += 32) { + sumf += (float) x[i] * (float) y[i]; + } + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } + } else { + device const half4 * x4 = (device const half4 *) x; + device const float4 * y4 = (device const float4 *) y; + for (int i = tiisg; i < ne00/4; i += 32) { + for (int k = 0; k < 4; ++k) sumf += (float)x4[i][k] * y4[i][k]; + } + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) x[i] * y[i]; + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } } - float all_sum = simd_sum(sumf); - if (tiisg == 0) { - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; - } } #define N_F16_F32 4 @@ -570,29 +583,54 @@ kernel void kernel_mul_mat_f16_f32( uint tiisg[[thread_index_in_simdgroup]]) { const int64_t r0 = tgpig.x; - const int64_t rb = N_F16_F32*tgpig.y; + const int64_t rb = tgpig.y*N_F16_F32; const int64_t im = tgpig.z; device const half * x = (device const half *) (src0 + r0*nb01 + im/(ne12/ne02)*nb02); - for (int row = 0; row < N_F16_F32; ++row) { - int r1 = rb + row; - if (r1 >= ne11) { - break; + if (ne00 < 128) { + for (int row = 0; row < N_F16_F32; ++row) { + int r1 = rb + row; + if (r1 >= ne11) { + break; + } + + device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); + + float sumf = 0; + for (int i = tiisg; i < ne00; i += 32) { + sumf += (float) x[i] * (float) y[i]; + } + + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } } + } else { + device const half4 * x4 = (device const half4 *)x; + for (int row = 0; row < N_F16_F32; ++row) { + int r1 = rb + row; + if (r1 >= ne11) { + break; + } - device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); + device const float * y = (device const float *) (src1 + r1*nb11 + im*nb12); + device const float4 * y4 = (device const float4 *) y; - float sumf = 0; - for (int i = tiisg; i < ne00; i += 32) { - sumf += (float) x[i] * (float) y[i]; - } + float sumf = 0; + for (int i = tiisg; i < ne00/4; i += 32) { + for (int k = 0; k < 4; ++k) sumf += (float) x4[i][k] * y4[i][k]; + } - float all_sum = simd_sum(sumf); - if (tiisg == 0) { - dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + float all_sum = simd_sum(sumf); + if (tiisg == 0) { + for (int i = 4*(ne00/4); i < ne00; ++i) all_sum += (float) x[i] * y[i]; + dst[im*ne1*ne0 + r1*ne0 + r0] = all_sum; + } } } + } kernel void kernel_alibi_f32( From 6519e9c99cffbad19b31bcba86df48c500628c09 Mon Sep 17 00:00:00 2001 From: Kerfuffle <44031344+KerfuffleV2@users.noreply.github.com> Date: Sun, 3 Sep 2023 04:38:43 -0600 Subject: [PATCH 08/16] gguf(python): Fix special vocab handling when id < 0 (#2984) --- gguf-py/gguf/gguf.py | 4 ++-- gguf-py/pyproject.toml | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/gguf-py/gguf/gguf.py b/gguf-py/gguf/gguf.py index b1bc4205b..d377cd56d 100644 --- a/gguf-py/gguf/gguf.py +++ b/gguf-py/gguf/gguf.py @@ -801,7 +801,7 @@ class SpecialVocab: else: continue for maybe_token_id in (atok.get('id') for atok in added_tokens if atok.get('content') == tc_content): - if isinstance(maybe_token_id, int): + if isinstance(maybe_token_id, int) and maybe_token_id >= 0: self.special_token_ids[typ] = maybe_token_id break return True @@ -814,7 +814,7 @@ class SpecialVocab: config = json.load(f) for typ in self.special_token_types: maybe_token_id = config.get(f'{typ}_token_id') - if isinstance(maybe_token_id, int): + if isinstance(maybe_token_id, int) and maybe_token_id >= 0: self.special_token_ids[typ] = maybe_token_id return True diff --git a/gguf-py/pyproject.toml b/gguf-py/pyproject.toml index 26f792b14..8da60de1b 100644 --- a/gguf-py/pyproject.toml +++ b/gguf-py/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "gguf" -version = "0.3.1" +version = "0.3.2" description = "Write ML models in GGUF for GGML" authors = ["GGML "] packages = [ From 8f429fa5111901f9646cf998643ac5310846d487 Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 3 Sep 2023 13:42:56 +0300 Subject: [PATCH 09/16] perplexity : fix ETA by warming up the model with an empty run --- common/common.cpp | 8 ++++++++ examples/main/main.cpp | 8 -------- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/common/common.cpp b/common/common.cpp index 41fc59ced..a1c3dc780 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -752,6 +752,14 @@ std::tuple llama_init_from_gpt_par params.logit_bias[llama_token_eos(lctx)] = -INFINITY; } + { + LOG("warming up the model with an empty run\n"); + + const std::vector tmp = { llama_token_bos(lctx), }; + llama_eval(lctx, tmp.data(), tmp.size(), 0, params.n_threads); + llama_reset_timings(lctx); + } + return std::make_tuple(model, lctx); } diff --git a/examples/main/main.cpp b/examples/main/main.cpp index 7117db4b0..db98312ca 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -492,14 +492,6 @@ int main(int argc, char ** argv) { std::vector embd; std::vector embd_guidance; - { - LOG("warming up the model with an empty run\n"); - - const std::vector tmp = { llama_token_bos(ctx), }; - llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads); - llama_reset_timings(ctx); - } - while ((n_remain != 0 && !is_antiprompt) || params.interactive) { // predict if (embd.size() > 0) { From 47068e517004d90f13c16352bb3b4cafd53a00cd Mon Sep 17 00:00:00 2001 From: Georgi Gerganov Date: Sun, 3 Sep 2023 15:12:08 +0300 Subject: [PATCH 10/16] speculative : PoC for speeding-up inference via speculative sampling (#2926) * speculative : initial example * speculative : print encoding speed * speculative : add --draft CLI arg --- common/common.cpp | 140 ++++++++++++++++ common/common.h | 36 +++++ examples/CMakeLists.txt | 1 + examples/main/main.cpp | 136 +++------------- examples/speculative/CMakeLists.txt | 8 + examples/speculative/speculative.cpp | 234 +++++++++++++++++++++++++++ 6 files changed, 440 insertions(+), 115 deletions(-) create mode 100644 examples/speculative/CMakeLists.txt create mode 100644 examples/speculative/speculative.cpp diff --git a/common/common.cpp b/common/common.cpp index a1c3dc780..313821375 100644 --- a/common/common.cpp +++ b/common/common.cpp @@ -305,6 +305,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } params.n_keep = std::stoi(argv[i]); + } else if (arg == "--draft") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.n_draft = std::stoi(argv[i]); } else if (arg == "--chunks") { if (++i >= argc) { invalid_param = true; @@ -317,6 +323,12 @@ bool gpt_params_parse(int argc, char ** argv, gpt_params & params) { break; } params.model = argv[i]; + } else if (arg == "-md" || arg == "--model-draft") { + if (++i >= argc) { + invalid_param = true; + break; + } + params.model_draft = argv[i]; } else if (arg == "-a" || arg == "--alias") { if (++i >= argc) { invalid_param = true; @@ -638,6 +650,7 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { fprintf(stdout, " --hellaswag compute HellaSwag score over random tasks from datafile supplied with -f\n"); fprintf(stdout, " --hellaswag-tasks N number of tasks to use when computing the HellaSwag score (default: %zu)\n", params.hellaswag_tasks); fprintf(stdout, " --keep N number of tokens to keep from the initial prompt (default: %d, -1 = all)\n", params.n_keep); + fprintf(stdout, " --draft N number of tokens to draft for speculative decoding (default: %d)\n", params.n_draft); fprintf(stdout, " --chunks N max number of chunks to process (default: %d, -1 = all)\n", params.n_chunks); if (llama_mlock_supported()) { fprintf(stdout, " --mlock force system to keep model in RAM rather than swapping or compressing\n"); @@ -669,6 +682,8 @@ void gpt_print_usage(int /*argc*/, char ** argv, const gpt_params & params) { fprintf(stdout, " --lora-base FNAME optional model to use as a base for the layers modified by the LoRA adapter\n"); fprintf(stdout, " -m FNAME, --model FNAME\n"); fprintf(stdout, " model path (default: %s)\n", params.model.c_str()); + fprintf(stdout, " -md FNAME, --model-draft FNAME\n"); + fprintf(stdout, " draft model for speculative decoding (default: %s)\n", params.model.c_str()); fprintf(stdout, " -ld LOGDIR, --logdir LOGDIR\n"); fprintf(stdout, " path under which to save YAML logs (no logging if unset)\n"); fprintf(stdout, "\n"); @@ -832,6 +847,130 @@ std::string llama_detokenize_bpe(llama_context * ctx, const std::vector & last_tokens, + std::vector & candidates, + int idx) { + const int n_ctx = llama_n_ctx(ctx); + const int n_vocab = llama_n_vocab(ctx); + + const float temp = params.temp; + const int32_t top_k = params.top_k <= 0 ? n_vocab : params.top_k; + const float top_p = params.top_p; + const float tfs_z = params.tfs_z; + const float typical_p = params.typical_p; + const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n; + const float repeat_penalty = params.repeat_penalty; + const float alpha_presence = params.presence_penalty; + const float alpha_frequency = params.frequency_penalty; + const int mirostat = params.mirostat; + const float mirostat_tau = params.mirostat_tau; + const float mirostat_eta = params.mirostat_eta; + const bool penalize_nl = params.penalize_nl; + + llama_token id = 0; + + float * logits = llama_get_logits(ctx) + idx * n_vocab; + + // Apply params.logit_bias map + for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) { + logits[it->first] += it->second; + } + + candidates.clear(); + for (llama_token token_id = 0; token_id < n_vocab; token_id++) { + candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); + } + + llama_token_data_array cur_p = { candidates.data(), candidates.size(), false }; + + if (ctx_guidance) { + llama_sample_classifier_free_guidance(ctx, &cur_p, ctx_guidance, params.cfg_scale); + } + + // apply penalties + if (!last_tokens.empty()) { + const float nl_logit = logits[llama_token_nl(ctx)]; + const int last_n_repeat = std::min(std::min((int)last_tokens.size(), repeat_last_n), n_ctx); + + llama_sample_repetition_penalty(ctx, &cur_p, + last_tokens.data() + last_tokens.size() - last_n_repeat, + last_n_repeat, repeat_penalty); + llama_sample_frequency_and_presence_penalties(ctx, &cur_p, + last_tokens.data() + last_tokens.size() - last_n_repeat, + last_n_repeat, alpha_frequency, alpha_presence); + + if (!penalize_nl) { + for (size_t idx = 0; idx < cur_p.size; idx++) { + if (cur_p.data[idx].id == llama_token_nl(ctx)) { + cur_p.data[idx].logit = nl_logit; + break; + } + } + } + } + + if (grammar != NULL) { + llama_sample_grammar(ctx, &cur_p, grammar); + } + + if (temp <= 0) { + // Greedy sampling + id = llama_sample_token_greedy(ctx, &cur_p); + } else { + if (mirostat == 1) { + static float mirostat_mu = 2.0f * mirostat_tau; + const int mirostat_m = 100; + llama_sample_temperature(ctx, &cur_p, temp); + id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); + } else if (mirostat == 2) { + static float mirostat_mu = 2.0f * mirostat_tau; + llama_sample_temperature(ctx, &cur_p, temp); + id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu); + } else { + // Temperature sampling + llama_sample_top_k (ctx, &cur_p, top_k, 1); + llama_sample_tail_free (ctx, &cur_p, tfs_z, 1); + llama_sample_typical (ctx, &cur_p, typical_p, 1); + llama_sample_top_p (ctx, &cur_p, top_p, 1); + llama_sample_temperature(ctx, &cur_p, temp); + + { + const int n_top = 10; + LOG("top %d candidates:\n", n_top); + + for (int i = 0; i < n_top; i++) { + const llama_token id = cur_p.data[i].id; + LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx, id).c_str(), cur_p.data[i].p); + } + } + + id = llama_sample_token(ctx, &cur_p); + + LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx, id).c_str()); + } + } + // printf("`%d`", candidates_p.size); + + if (grammar != NULL) { + llama_grammar_accept_token(ctx, grammar, id); + } + + return id; +} + +// +// YAML utils +// + // returns true if successful, false otherwise bool create_directory_with_parents(const std::string & path) { #ifdef _WIN32 @@ -1070,6 +1209,7 @@ void dump_non_result_info_yaml(FILE * stream, const gpt_params & params, const l fprintf(stream, "mirostat_lr: %f # default: 0.1\n", params.mirostat_eta); fprintf(stream, "mlock: %s # default: false\n", params.use_mlock ? "true" : "false"); fprintf(stream, "model: %s # default: models/7B/ggml-model.bin\n", params.model.c_str()); + fprintf(stream, "model_draft: %s # default:\n", params.model_draft.c_str()); fprintf(stream, "mtest: %s # default: false\n", params.mem_test ? "true" : "false"); fprintf(stream, "multiline_input: %s # default: false\n", params.multiline_input ? "true" : "false"); fprintf(stream, "n_gpu_layers: %d # default: 0\n", params.n_gpu_layers); diff --git a/common/common.h b/common/common.h index 5a379688e..105fb09e4 100644 --- a/common/common.h +++ b/common/common.h @@ -32,6 +32,7 @@ struct gpt_params { int32_t n_ctx = 512; // context size int32_t n_batch = 512; // batch size for prompt processing (must be >=32 to use BLAS) int32_t n_keep = 0; // number of tokens to keep from initial prompt + int32_t n_draft = 16; // number of tokens to draft during speculative decoding int32_t n_chunks = -1; // max number of chunks to process (-1 = unlimited) int32_t n_gpu_layers = 0; // number of layers to store in VRAM int32_t main_gpu = 0; // the GPU that is used for scratch and small tensors @@ -63,6 +64,7 @@ struct gpt_params { float cfg_scale = 1.f; // How strong is guidance std::string model = "models/7B/ggml-model-f16.gguf"; // model path + std::string model_draft = ""; // draft model for speculative decoding std::string model_alias = "unknown"; // model alias std::string prompt = ""; std::string path_prompt_cache = ""; // path to file for saving/loading prompt eval state @@ -156,6 +158,40 @@ std::string llama_detokenize_bpe( llama_context * ctx, const std::vector & tokens); +// +// Sampling utils +// + +// this is a common sampling function used across the examples for convenience +// it can serve as a starting point for implementing your own sampling function +// +// required: +// - ctx: context to use for sampling +// - params: sampling parameters +// +// optional: +// - ctx_guidance: context to use for classifier-free guidance, ignore if NULL +// - grammar: grammar to use for sampling, ignore if NULL +// - last_tokens: needed for repetition penalty, ignore if empty +// - idx: sample from llama_get_logits(ctx) + idx * n_vocab +// +// returns: +// - token: sampled token +// - candidates: vector of candidate tokens +// +llama_token llama_sample_token( + struct llama_context * ctx, + struct llama_context * ctx_guidance, + struct llama_grammar * grammar, + const struct gpt_params & params, + const std::vector & last_tokens, + std::vector & candidates, + int idx = 0); + +// +// YAML utils +// + bool create_directory_with_parents(const std::string & path); void dump_vector_float_yaml(FILE * stream, const char * prop_name, const std::vector & data); void dump_vector_int_yaml(FILE * stream, const char * prop_name, const std::vector & data); diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt index 6e65eb087..884c42764 100644 --- a/examples/CMakeLists.txt +++ b/examples/CMakeLists.txt @@ -23,6 +23,7 @@ else() add_subdirectory(train-text-from-scratch) add_subdirectory(convert-llama2c-to-ggml) add_subdirectory(simple) + add_subdirectory(speculative) add_subdirectory(embd-input) add_subdirectory(llama-bench) add_subdirectory(beam-search) diff --git a/examples/main/main.cpp b/examples/main/main.cpp index db98312ca..922b9a980 100644 --- a/examples/main/main.cpp +++ b/examples/main/main.cpp @@ -116,7 +116,7 @@ int main(int argc, char ** argv) { #ifndef LOG_DISABLE_LOGS log_set_target(log_filename_generator("main", "log")); LOG_TEE("Log start\n"); - log_dump_cmdline(argc,argv); + log_dump_cmdline(argc, argv); #endif // LOG_DISABLE_LOGS // TODO: Dump params ? @@ -425,8 +425,9 @@ int main(int argc, char ** argv) { LOG_TEE("generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n", n_ctx, params.n_batch, params.n_predict, params.n_keep); LOG_TEE("\n\n"); + struct llama_grammar * grammar = NULL; grammar_parser::parse_state parsed_grammar; - llama_grammar * grammar = NULL; + if (!params.grammar.empty()) { parsed_grammar = grammar_parser::parse(params.grammar.c_str()); // will be empty (default) if there are parse errors @@ -450,8 +451,8 @@ int main(int argc, char ** argv) { } // TODO: replace with ring-buffer - std::vector last_n_tokens(n_ctx); - std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0); + std::vector last_tokens(n_ctx); + std::fill(last_tokens.begin(), last_tokens.end(), 0); if (params.interactive) { const char *control_message; @@ -492,6 +493,11 @@ int main(int argc, char ** argv) { std::vector embd; std::vector embd_guidance; + const int n_vocab = llama_n_vocab(ctx); + + std::vector candidates; + candidates.reserve(n_vocab); + while ((n_remain != 0 && !is_antiprompt) || params.interactive) { // predict if (embd.size() > 0) { @@ -529,8 +535,8 @@ int main(int argc, char ** argv) { LOG("after swap: n_past = %d, n_past_guidance = %d\n", n_past, n_past_guidance); - // insert n_left/2 tokens at the start of embd from last_n_tokens - embd.insert(embd.begin(), last_n_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_n_tokens.end() - embd.size()); + // insert n_left/2 tokens at the start of embd from last_tokens + embd.insert(embd.begin(), last_tokens.begin() + n_ctx - n_left/2 - embd.size(), last_tokens.end() - embd.size()); LOG("embd: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, embd)); @@ -629,20 +635,6 @@ int main(int argc, char ** argv) { embd_guidance.clear(); if ((int) embd_inp.size() <= n_consumed && !is_interacting) { - const float temp = params.temp; - const int32_t top_k = params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k; - const float top_p = params.top_p; - const float tfs_z = params.tfs_z; - const float typical_p = params.typical_p; - const int32_t repeat_last_n = params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n; - const float repeat_penalty = params.repeat_penalty; - const float alpha_presence = params.presence_penalty; - const float alpha_frequency = params.frequency_penalty; - const int mirostat = params.mirostat; - const float mirostat_tau = params.mirostat_tau; - const float mirostat_eta = params.mirostat_eta; - const bool penalize_nl = params.penalize_nl; - // optionally save the session on first sample (for faster prompt loading next time) if (!path_session.empty() && need_to_save_session && !params.prompt_cache_ro) { need_to_save_session = false; @@ -651,98 +643,12 @@ int main(int argc, char ** argv) { LOG("saved session to %s\n", path_session.c_str()); } - llama_token id = 0; + const llama_token id = llama_sample_token(ctx, ctx_guidance, grammar, params, last_tokens, candidates); - { - auto logits = llama_get_logits(ctx); - auto n_vocab = llama_n_vocab(ctx); + last_tokens.erase(last_tokens.begin()); + last_tokens.push_back(id); - // Apply params.logit_bias map - for (auto it = params.logit_bias.begin(); it != params.logit_bias.end(); it++) { - logits[it->first] += it->second; - } - - std::vector candidates; - candidates.reserve(n_vocab); - for (llama_token token_id = 0; token_id < n_vocab; token_id++) { - candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); - } - - llama_token_data_array cur_p = { candidates.data(), candidates.size(), false }; - - if (ctx_guidance) { - llama_sample_classifier_free_guidance(ctx, &cur_p, ctx_guidance, params.cfg_scale); - } - - // Apply penalties - float nl_logit = logits[llama_token_nl(ctx)]; - auto last_n_repeat = std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx); - llama_sample_repetition_penalty(ctx, &cur_p, - last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, - last_n_repeat, repeat_penalty); - llama_sample_frequency_and_presence_penalties(ctx, &cur_p, - last_n_tokens.data() + last_n_tokens.size() - last_n_repeat, - last_n_repeat, alpha_frequency, alpha_presence); - if (!penalize_nl) { - for (size_t idx = 0; idx < cur_p.size; idx++) { - if (cur_p.data[idx].id == llama_token_nl(ctx)) { - cur_p.data[idx].logit = nl_logit; - break; - } - } - } - - if (grammar != NULL) { - llama_sample_grammar(ctx, &cur_p, grammar); - } - - if (temp <= 0) { - // Greedy sampling - id = llama_sample_token_greedy(ctx, &cur_p); - } else { - if (mirostat == 1) { - static float mirostat_mu = 2.0f * mirostat_tau; - const int mirostat_m = 100; - llama_sample_temperature(ctx, &cur_p, temp); - id = llama_sample_token_mirostat(ctx, &cur_p, mirostat_tau, mirostat_eta, mirostat_m, &mirostat_mu); - } else if (mirostat == 2) { - static float mirostat_mu = 2.0f * mirostat_tau; - llama_sample_temperature(ctx, &cur_p, temp); - id = llama_sample_token_mirostat_v2(ctx, &cur_p, mirostat_tau, mirostat_eta, &mirostat_mu); - } else { - // Temperature sampling - llama_sample_top_k (ctx, &cur_p, top_k, 1); - llama_sample_tail_free (ctx, &cur_p, tfs_z, 1); - llama_sample_typical (ctx, &cur_p, typical_p, 1); - llama_sample_top_p (ctx, &cur_p, top_p, 1); - llama_sample_temperature(ctx, &cur_p, temp); - - { - const int n_top = 10; - LOG("top %d candidates:\n", n_top); - - for (int i = 0; i < n_top; i++) { - const llama_token id = cur_p.data[i].id; - LOG(" - %5d: '%12s' (%.3f)\n", id, llama_token_to_piece(ctx, id).c_str(), cur_p.data[i].p); - } - } - - id = llama_sample_token(ctx, &cur_p); - - LOG("sampled token: %5d: '%s'\n", id, llama_token_to_piece(ctx, id).c_str()); - } - } - // printf("`%d`", candidates_p.size); - - if (grammar != NULL) { - llama_grammar_accept_token(ctx, grammar, id); - } - - last_n_tokens.erase(last_n_tokens.begin()); - last_n_tokens.push_back(id); - - LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, last_n_tokens)); - } + LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx, last_tokens)); embd.push_back(id); @@ -758,8 +664,8 @@ int main(int argc, char ** argv) { LOG("embd_inp.size(): %d, n_consumed: %d\n", (int) embd_inp.size(), n_consumed); while ((int) embd_inp.size() > n_consumed) { embd.push_back(embd_inp[n_consumed]); - last_n_tokens.erase(last_n_tokens.begin()); - last_n_tokens.push_back(embd_inp[n_consumed]); + last_tokens.erase(last_tokens.begin()); + last_tokens.push_back(embd_inp[n_consumed]); ++n_consumed; if ((int) embd.size() >= params.n_batch) { break; @@ -792,7 +698,7 @@ int main(int argc, char ** argv) { // check for reverse prompt if (params.antiprompt.size()) { std::string last_output; - for (auto id : last_n_tokens) { + for (auto id : last_tokens) { last_output += llama_token_to_piece(ctx, id); } @@ -823,7 +729,7 @@ int main(int argc, char ** argv) { } // deal with end of text token in interactive mode - if (last_n_tokens.back() == llama_token_eos(ctx)) { + if (last_tokens.back() == llama_token_eos(ctx)) { LOG("found EOS token\n"); if (params.interactive) { @@ -925,7 +831,7 @@ int main(int argc, char ** argv) { if (grammar != NULL) { llama_grammar_free(grammar); - std::vector grammar_rules( parsed_grammar.c_rules()); + std::vector grammar_rules(parsed_grammar.c_rules()); grammar = llama_grammar_init( grammar_rules.data(), grammar_rules.size(), parsed_grammar.symbol_ids.at("root")); diff --git a/examples/speculative/CMakeLists.txt b/examples/speculative/CMakeLists.txt new file mode 100644 index 000000000..6c5c9456e --- /dev/null +++ b/examples/speculative/CMakeLists.txt @@ -0,0 +1,8 @@ +set(TARGET speculative) +add_executable(${TARGET} speculative.cpp) +install(TARGETS ${TARGET} RUNTIME) +target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT}) +target_compile_features(${TARGET} PRIVATE cxx_std_11) +if(TARGET BUILD_INFO) + add_dependencies(${TARGET} BUILD_INFO) +endif() diff --git a/examples/speculative/speculative.cpp b/examples/speculative/speculative.cpp new file mode 100644 index 000000000..f0400c13f --- /dev/null +++ b/examples/speculative/speculative.cpp @@ -0,0 +1,234 @@ +#ifndef _GNU_SOURCE +#define _GNU_SOURCE +#endif + +#include "build-info.h" + +#include "common.h" +#include "llama.h" + +#include +#include +#include +#include + +int main(int argc, char ** argv) { + gpt_params params; + + if (gpt_params_parse(argc, argv, params) == false) { + return 1; + } + + if (params.model_draft.empty()) { + fprintf(stderr, "%s: error: --model-draft is required\n", __func__); + return 1; + } + +#ifndef LOG_DISABLE_LOGS + log_set_target(log_filename_generator("speculative", "log")); + LOG_TEE("Log start\n"); + log_dump_cmdline(argc, argv); +#endif // LOG_DISABLE_LOGS + + // init llama.cpp + llama_backend_init(params.numa); + + llama_model * model_tgt = NULL; + llama_model * model_dft = NULL; + + llama_context * ctx_tgt = NULL; + llama_context * ctx_dft = NULL; + + // load the target model + params.perplexity = true; // HACK: enable logits_all = true + std::tie(model_tgt, ctx_tgt) = llama_init_from_gpt_params(params); + + // load the draft model + params.model = params.model_draft; + std::tie(model_dft, ctx_dft) = llama_init_from_gpt_params(params); + + // tokenize the prompt + std::vector inp; + inp = ::llama_tokenize(ctx_tgt, params.prompt, true); + + const int max_context_size = llama_n_ctx(ctx_tgt); + const int max_tokens_list_size = max_context_size - 4; + + if ((int) inp.size() > max_tokens_list_size) { + fprintf(stderr, "%s: error: prompt too long (%d tokens, max %d)\n", __func__, (int) inp.size(), max_tokens_list_size); + return 1; + } + + fprintf(stderr, "\n\n"); + + for (auto id : inp) { + fprintf(stderr, "%s", llama_token_to_piece(ctx_tgt, id).c_str()); + } + + fflush(stderr); + + const int n_input = inp.size(); + + const auto t_enc_start = ggml_time_us(); + + // eval the prompt with both models + llama_eval(ctx_tgt, inp.data(), int(inp.size() - 1), 0, params.n_threads); + llama_eval(ctx_tgt, &inp.back(), 1, inp.size() - 1, params.n_threads); + llama_eval(ctx_dft, inp.data(), int(inp.size()), 0, params.n_threads); + + const auto t_enc_end = ggml_time_us(); + + // the 2 models should have the same vocab + const int n_ctx = llama_n_ctx(ctx_tgt); + const int n_vocab = llama_n_vocab(ctx_tgt); + //GGML_ASSERT(n_vocab == llama_n_vocab(ctx_dft)); + + // how many tokens to draft each time + const int n_draft = params.n_draft; + + int n_predict = 0; + int n_drafted = 0; + int n_accept = 0; + + int n_past_tgt = inp.size(); + int n_past_dft = inp.size(); + + std::vector drafted; + + std::vector last_tokens(n_ctx); + std::fill(last_tokens.begin(), last_tokens.end(), 0); + + for (auto & id : inp) { + last_tokens.erase(last_tokens.begin()); + last_tokens.push_back(id); + } + + std::vector candidates; + candidates.reserve(n_vocab); + + // used to determine end of generation + bool has_eos = false; + + const auto t_dec_start = ggml_time_us(); + + while (true) { + LOG("drafted: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_dft, drafted)); + + // sample from the drafted tokens if any + int i_dft = 0; + while (true) { + const llama_token id = llama_sample_token(ctx_tgt, NULL, NULL, params, last_tokens, candidates, i_dft); + + last_tokens.erase(last_tokens.begin()); + last_tokens.push_back(id); + + //LOG("last: %s\n", LOG_TOKENS_TOSTR_PRETTY(ctx_tgt, last_tokens)); + + const std::string token_str = llama_token_to_piece(ctx_tgt, id); + printf("%s", token_str.c_str()); + fflush(stdout); + + if (id == llama_token_eos(ctx_tgt)) { + has_eos = true; + } + + ++n_predict; + + if (i_dft < (int) drafted.size() && id == drafted[i_dft]) { + LOG("drafted token %d accepted\n", id); + ++n_accept; + ++n_past_tgt; + ++n_past_dft; + ++i_dft; + + continue; + } + + // the drafted token was rejected or we are out of drafted tokens + llama_eval(ctx_dft, &id, 1, n_past_dft, params.n_threads); + ++n_past_dft; + + drafted.clear(); + drafted.push_back(id); + + break; + } + + if (n_predict > params.n_predict || has_eos) { + break; + } + + // sample n_draft tokens from the draft model picking the best token + int n_past_cur = n_past_dft; + for (int i = 0; i < n_draft; ++i) { + float * logits = llama_get_logits(ctx_dft); + + candidates.clear(); + for (llama_token token_id = 0; token_id < n_vocab; token_id++) { + candidates.emplace_back(llama_token_data{token_id, logits[token_id], 0.0f}); + } + + llama_token_data_array cur_p = { candidates.data(), candidates.size(), false }; + + // computes softmax and sorts the candidates + llama_sample_softmax(ctx_dft, &cur_p); + + for (int i = 0; i < 3; ++i) { + LOG(" - draft candidate %d: %d (%.3f)\n", i, cur_p.data[i].id, cur_p.data[i].p); + } + + // too low probability, stop drafting + if (cur_p.data[0].p < 2*cur_p.data[1].p) { + break; + } + + drafted.push_back(cur_p.data[0].id); + ++n_drafted; + + if (i < n_draft - 1) { + // evaluate the drafted token on the draft model + llama_eval(ctx_dft, &drafted.back(), 1, n_past_cur, params.n_threads); + ++n_past_cur; + } + } + + // evaluate the target model on the drafted tokens + llama_eval(ctx_tgt, drafted.data(), drafted.size(), n_past_tgt, params.n_threads); + ++n_past_tgt; + + drafted.erase(drafted.begin()); + } + + auto t_dec_end = ggml_time_us(); + + LOG_TEE("\n\n"); + + LOG_TEE("encoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_input, (t_enc_end - t_enc_start) / 1e6f, inp.size() / ((t_enc_end - t_enc_start) / 1e6f)); + LOG_TEE("decoded %4d tokens in %8.3f seconds, speed: %8.3f t/s\n", n_predict, (t_dec_end - t_dec_start) / 1e6f, n_predict / ((t_dec_end - t_dec_start) / 1e6f)); + + // TODO: make sure these numbers are computed correctly + LOG_TEE("\n"); + LOG_TEE("n_draft = %d\n", n_draft); + LOG_TEE("n_predict = %d\n", n_predict); + LOG_TEE("n_drafted = %d\n", n_drafted); + LOG_TEE("n_accept = %d\n", n_accept); + LOG_TEE("accept = %.3f%%\n", 100.0f * n_accept / n_drafted); + + LOG_TEE("\ndraft:\n"); + llama_print_timings(ctx_dft); + + LOG_TEE("\ntarget:\n"); + llama_print_timings(ctx_tgt); + + llama_free(ctx_tgt); + llama_free_model(model_tgt); + + llama_free(ctx_dft); + llama_free_model(model_dft); + + llama_backend_free(); + + fprintf(stderr, "\n\n"); + + return 0; +} From cf9b08485c4c2d4d945c6e74fe20f273a38b6104 Mon Sep 17 00:00:00 2001 From: slaren Date: Sun, 3 Sep 2023 20:34:09 +0200 Subject: [PATCH 11/16] ggml-alloc : use virtual memory for measurement (#2973) * ggml-alloc : use virtual memory for measurement * compatibility fixes for MAP_ANONYMOUS * fallback to fixed address for systems without virtual memory --- ggml-alloc.c | 123 ++++++++++++++++++++++++++++++++++++++++----------- 1 file changed, 97 insertions(+), 26 deletions(-) diff --git a/ggml-alloc.c b/ggml-alloc.c index 459f121ca..c1939a4b7 100644 --- a/ggml-alloc.c +++ b/ggml-alloc.c @@ -1,3 +1,8 @@ +// defines MAP_ANONYMOUS +#ifndef _GNU_SOURCE +#define _GNU_SOURCE +#endif + #include "ggml-alloc.h" #include "ggml.h" #include @@ -6,6 +11,26 @@ #include #include +#ifdef __has_include + #if __has_include() + #include + #if defined(_POSIX_MAPPED_FILES) + #include + #include + #endif + #endif +#endif + +#if defined(_WIN32) + #define WIN32_LEAN_AND_MEAN + #ifndef NOMINMAX + #define NOMINMAX + #endif + #include + #include +#endif + + #define UNUSED(x) (void)(x) #define MAX(a, b) ((a) > (b) ? (a) : (b)) #define GGML_MAX_CONCUR (2*GGML_MAX_NODES) @@ -99,19 +124,24 @@ static void remove_allocated_tensor(struct ggml_allocr * alloc, struct ggml_tens } #endif - -static size_t ggml_allocator_get_alloc_size(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { +static size_t ggml_allocr_get_alloc_size(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { return ggml_nbytes(tensor); UNUSED(alloc); } +// check if a tensor is allocated by this buffer +static bool ggml_allocr_is_own(struct ggml_allocr * alloc, const struct ggml_tensor * tensor) { + void * ptr = tensor->data; + return ptr >= alloc->data && (char *)ptr < (char *)alloc->data + alloc->max_size; +} + void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { #ifdef GGML_ALLOCATOR_DEBUG GGML_ASSERT(ggml_is_view(tensor) == false); // views generally get data pointer from one of their sources GGML_ASSERT(tensor->data == NULL); // avoid allocating tensor which already has memory allocated #endif - size_t size = ggml_allocator_get_alloc_size(alloc, tensor); + size_t size = ggml_allocr_get_alloc_size(alloc, tensor); size = aligned_offset(NULL, size, alloc->alignment); AT_PRINTF("%s: allocating %s (%zu bytes) - ", __func__, tensor->name, size); @@ -177,17 +207,17 @@ void ggml_allocr_alloc(struct ggml_allocr * alloc, struct ggml_tensor * tensor) } // this is a very naive implementation, but for our case the number of free blocks should be very small -static void ggml_allocator_free_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { +static void ggml_allocr_free_tensor(struct ggml_allocr * alloc, struct ggml_tensor * tensor) { void * ptr = tensor->data; - if (ptr < alloc->data || (char*)ptr >= (char*)alloc->data + alloc->max_size) { + if (ggml_allocr_is_own(alloc, tensor) == false) { // the tensor was not allocated in this buffer // this can happen because the graph allocator will try to free weights and other tensors from different buffers // the easiest way to deal with this is just to ignore it return; } - size_t size = ggml_allocator_get_alloc_size(alloc, tensor); + size_t size = ggml_allocr_get_alloc_size(alloc, tensor); size = aligned_offset(NULL, size, alloc->alignment); AT_PRINTF("%s: freeing %s (%zu bytes) - n_free_blocks = %d\n", __func__, tensor->name, size, alloc->n_free_blocks); @@ -281,24 +311,64 @@ struct ggml_allocr * ggml_allocr_new(void * data, size_t size, size_t alignment) return alloc; } -// address and size of the buffer when measuring -// it needs to be large enough to fit all the tensors, but it cannot overlap with other existing buffers -static void * const MEASURE_BASE_ADDR = (void *) 0x1000; -#if defined(__ARM_NEON) && !defined(__aarch64__) -// 32-bit -// TODO: Use for 32-bit x86 as well -static const size_t MEASURE_MAX_SIZE = (1ULL<<32) - 1; // 4 GB +// OS specific functions to allocate and free uncommitted virtual memory +static void * alloc_vmem(size_t size) { +#if defined(_WIN32) + return VirtualAlloc(NULL, size, MEM_RESERVE, PAGE_NOACCESS); +#elif defined(_POSIX_MAPPED_FILES) + return mmap(NULL, size, PROT_NONE, MAP_PRIVATE | MAP_ANON, -1, 0); #else -// 64-bit -static const size_t MEASURE_MAX_SIZE = 1ULL<<40; // 1 TB + // use a fixed address for other platforms + uintptr_t base_addr = (uintptr_t)-size - 0x100; + return (void *)base_addr; #endif +} + +static void free_vmem(void * base_addr, size_t size) { +#if defined(_WIN32) + VirtualFree(base_addr, 0, MEM_RELEASE); + UNUSED(size); +#elif defined(_POSIX_MAPPED_FILES) + munmap(base_addr, size); +#else + // nothing to do + UNUSED(base_addr); + UNUSED(size); +#endif +} + +// allocate uncommitted virtual memory to measure the size of the graph +static void alloc_measure_vmem(void ** base_addr, size_t * size) { + // 1TB for 64-bit, 1GB for 32-bit + *size = sizeof(void *) == 4 ? 1ULL<<30 : 1ULL<<40; + do { + *base_addr = alloc_vmem(*size); + if (*base_addr != NULL) { + AT_PRINTF("allocated %.2f GB of virtual memory for measure buffer at %p\n", *size / 1024.0 / 1024.0 / 1024.0, *base_addr); + return; + } + // try again with half the size + *size /= 2; + } while (*size > 0); + + GGML_ASSERT(!"failed to allocate virtual memory for measure buffer"); +} + +static void free_measure_vmem(void * base_addr, size_t size) { + free_vmem(base_addr, size); +} struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) { struct ggml_allocr * alloc = (struct ggml_allocr *)malloc(sizeof(struct ggml_allocr) /* + n_free_blocks * sizeof(struct free_block) */); + void * base_addr; + size_t size; + + alloc_measure_vmem(&base_addr, &size); + *alloc = (struct ggml_allocr){ - /*.data = */ MEASURE_BASE_ADDR, - /*.size = */ MEASURE_MAX_SIZE, + /*.data = */ base_addr, + /*.size = */ size, /*.alignment = */ alignment, /*.n_free_blocks = */ 0, /*.free_blocks = */ {{0}}, @@ -318,6 +388,9 @@ struct ggml_allocr * ggml_allocr_new_measure(size_t alignment) { } void ggml_allocr_free(struct ggml_allocr * alloc) { + if (alloc->measure) { + free_measure_vmem(alloc->data, alloc->size); + } free(alloc); } @@ -387,8 +460,7 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) } // if the node's data is external, then we cannot re-use it - if ((char *) parent->data < (char *) alloc->data || - (char *) parent->data >= ((char *) alloc->data + alloc->size)) { + if (ggml_allocr_is_own(alloc, parent) == false) { AT_PRINTF("not reusing parent %s for %s as %p is external\n", parent->name, node->name, parent->data); continue; } @@ -422,7 +494,7 @@ static void allocate_node(struct ggml_allocr * alloc, struct ggml_tensor * node) } } -static size_t ggml_allocator_alloc_graph_tensors_n( +static size_t ggml_allocr_alloc_graph_tensors_n( struct ggml_allocr * alloc, struct ggml_cgraph ** graphs, int n_graphs, struct ggml_tensor *** inputs, struct ggml_tensor *** outputs) { @@ -500,11 +572,10 @@ static size_t ggml_allocator_alloc_graph_tensors_n( AT_PRINTF("\n"); } - // update parents // update immediately if there is no parse_seq // update only at barriers if there is parse_seq - if ((alloc->parse_seq_len==0) || alloc->parse_seq[ind] == -1) { + if ((alloc->parse_seq_len == 0) || alloc->parse_seq[ind] == -1) { int update_start = alloc->parse_seq_len ? last_barrier_pos : ind; int update_end = alloc->parse_seq_len ? ind : ind + 1; for (int i = update_start; i < update_end; i++) { @@ -528,12 +599,12 @@ static size_t ggml_allocator_alloc_graph_tensors_n( view_src_hn->n_views -= 1; AT_PRINTF("view_src %s: %d children, %d views\n", view_src->name, view_src_hn->n_children, view_src_hn->n_views); if (view_src_hn->n_views == 0 && view_src_hn->n_children == 0 && view_src->data != node->data) { - ggml_allocator_free_tensor(alloc, view_src); + ggml_allocr_free_tensor(alloc, view_src); } } else { if (parent->data != node->data) { - ggml_allocator_free_tensor(alloc, parent); + ggml_allocr_free_tensor(alloc, parent); } } } @@ -550,7 +621,7 @@ static size_t ggml_allocator_alloc_graph_tensors_n( for (int i = 0; outputs[g][i] != NULL; i++) { struct ggml_tensor * output = outputs[g][i]; AT_PRINTF("output: %s\n", output->name); - ggml_allocator_free_tensor(alloc, output); + ggml_allocr_free_tensor(alloc, output); } } } @@ -559,5 +630,5 @@ static size_t ggml_allocator_alloc_graph_tensors_n( } size_t ggml_allocr_alloc_graph(struct ggml_allocr * alloc, struct ggml_cgraph * graph) { - return ggml_allocator_alloc_graph_tensors_n(alloc, &graph, 1, NULL, NULL); + return ggml_allocr_alloc_graph_tensors_n(alloc, &graph, 1, NULL, NULL); } From 35195689cd835464779c247b1c22ab9247418fd1 Mon Sep 17 00:00:00 2001 From: Jiahao Li Date: Mon, 4 Sep 2023 14:53:30 +0800 Subject: [PATCH 12/16] 2x faster (rms) norm cuda kernels (3.7% e2e improvement) (#2985) * 2x faster (rms) norm cuda kernels * Fix code style --- ggml-cuda.cu | 89 ++++++++++++++++++++++++++++++++++++++-------------- 1 file changed, 66 insertions(+), 23 deletions(-) diff --git a/ggml-cuda.cu b/ggml-cuda.cu index 8357f32f7..d2dbf824e 100644 --- a/ggml-cuda.cu +++ b/ggml-cuda.cu @@ -464,58 +464,91 @@ static __global__ void silu_f32(const float * x, float * dst, const int k) { dst[i] = x[i] / (1.0f + expf(-x[i])); } +static __device__ __forceinline__ float2 warp_reduce_sum(float2 a) { +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + a.x += __shfl_xor_sync(0xffffffff, a.x, mask, 32); + a.y += __shfl_xor_sync(0xffffffff, a.y, mask, 32); + } + return a; +} + +template static __global__ void norm_f32(const float * x, float * dst, const int ncols) { const int row = blockIdx.x*blockDim.y + threadIdx.y; const int tid = threadIdx.x; const float eps = 1e-5f; - float mean = 0.0f; - float var = 0.0f; + float2 mean_var = make_float2(0.f, 0.f); - for (int col = tid; col < ncols; col += WARP_SIZE) { + for (int col = tid; col < ncols; col += block_size) { const float xi = x[row*ncols + col]; - mean += xi; - var += xi * xi; + mean_var.x += xi; + mean_var.y += xi * xi; } // sum up partial sums -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - mean += __shfl_xor_sync(0xffffffff, mean, mask, 32); - var += __shfl_xor_sync(0xffffffff, var, mask, 32); + mean_var = warp_reduce_sum(mean_var); + if (block_size > WARP_SIZE) { + __shared__ float2 s_sum[32]; + int warp_id = threadIdx.x / WARP_SIZE; + int lane_id = threadIdx.x % WARP_SIZE; + if (lane_id == 0) { + s_sum[warp_id] = mean_var; + } + __syncthreads(); + mean_var = s_sum[lane_id]; + mean_var = warp_reduce_sum(mean_var); } - mean /= ncols; - var = var / ncols - mean * mean; - const float inv_var = rsqrtf(var + eps); + const float mean = mean_var.x / ncols; + const float var = mean_var.y / ncols - mean * mean; + const float inv_std = rsqrtf(var + eps); - for (int col = tid; col < ncols; col += WARP_SIZE) { - dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_var; + for (int col = tid; col < ncols; col += block_size) { + dst[row*ncols + col] = (x[row*ncols + col] - mean) * inv_std; } } +static __device__ __forceinline__ float warp_reduce_sum(float x) { +#pragma unroll + for (int mask = 16; mask > 0; mask >>= 1) { + x += __shfl_xor_sync(0xffffffff, x, mask, 32); + } + return x; +} + +template static __global__ void rms_norm_f32(const float * x, float * dst, const int ncols, const float eps) { const int row = blockIdx.x*blockDim.y + threadIdx.y; const int tid = threadIdx.x; float tmp = 0.0f; // partial sum for thread in warp - for (int col = tid; col < ncols; col += WARP_SIZE) { + for (int col = tid; col < ncols; col += block_size) { const float xi = x[row*ncols + col]; tmp += xi * xi; } // sum up partial sums -#pragma unroll - for (int mask = 16; mask > 0; mask >>= 1) { - tmp += __shfl_xor_sync(0xffffffff, tmp, mask, 32); + tmp = warp_reduce_sum(tmp); + if (block_size > WARP_SIZE) { + __shared__ float s_sum[32]; + int warp_id = threadIdx.x / WARP_SIZE; + int lane_id = threadIdx.x % WARP_SIZE; + if (lane_id == 0) { + s_sum[warp_id] = tmp; + } + __syncthreads(); + tmp = s_sum[lane_id]; + tmp = warp_reduce_sum(tmp); } const float mean = tmp / ncols; const float scale = rsqrtf(mean + eps); - for (int col = tid; col < ncols; col += WARP_SIZE) { + for (int col = tid; col < ncols; col += block_size) { dst[row*ncols + col] = scale * x[row*ncols + col]; } } @@ -4203,14 +4236,24 @@ static void silu_f32_cuda(const float * x, float * dst, const int k, cudaStream_ static void norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, cudaStream_t stream) { GGML_ASSERT(ncols % WARP_SIZE == 0); - const dim3 block_dims(WARP_SIZE, 1, 1); - norm_f32<<>>(x, dst, ncols); + if (ncols < 1024) { + const dim3 block_dims(WARP_SIZE, 1, 1); + norm_f32<<>>(x, dst, ncols); + } else { + const dim3 block_dims(1024, 1, 1); + norm_f32<1024><<>>(x, dst, ncols); + } } static void rms_norm_f32_cuda(const float * x, float * dst, const int ncols, const int nrows, const float eps, cudaStream_t stream) { GGML_ASSERT(ncols % WARP_SIZE == 0); - const dim3 block_dims(WARP_SIZE, 1, 1); - rms_norm_f32<<>>(x, dst, ncols, eps); + if (ncols < 1024) { + const dim3 block_dims(WARP_SIZE, 1, 1); + rms_norm_f32<<>>(x, dst, ncols, eps); + } else { + const dim3 block_dims(1024, 1, 1); + rms_norm_f32<1024><<>>(x, dst, ncols, eps); + } } static void quantize_row_q8_1_cuda(const float * x, void * vy, const int kx, const int ky, const int kx_padded, cudaStream_t stream) { From e4386f417faf894f6706eec005e24d142b577fcb Mon Sep 17 00:00:00 2001 From: Aarni Koskela Date: Mon, 4 Sep 2023 10:28:55 +0200 Subject: [PATCH 13/16] server : add a subtle loading animation to the edit box (#2466) * editorconfig: add override for the server HTML (which already is 2-space indented) * server: add a subtle loading animation to the edit box --- .editorconfig | 3 + examples/server/index.html.hpp | 3970 +++++++++++++++-------------- examples/server/public/index.html | 41 +- 3 files changed, 2058 insertions(+), 1956 deletions(-) diff --git a/.editorconfig b/.editorconfig index 135a7e4bc..f8245b85c 100644 --- a/.editorconfig +++ b/.editorconfig @@ -17,3 +17,6 @@ indent_style = tab [prompts/*.txt] insert_final_newline = unset + +[examples/server/public/*] +indent_size = 2 diff --git a/examples/server/index.html.hpp b/examples/server/index.html.hpp index 84e6f97ba..f30232929 100644 --- a/examples/server/index.html.hpp +++ b/examples/server/index.html.hpp @@ -210,1024 +210,1120 @@ unsigned char index_html[] = { 0x66, 0x6f, 0x6e, 0x74, 0x2d, 0x73, 0x69, 0x7a, 0x65, 0x3a, 0x20, 0x38, 0x30, 0x25, 0x3b, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x3a, 0x20, 0x23, 0x38, 0x38, 0x38, 0x3b, 0x0a, 0x20, - 0x20, 0x20, 0x20, 0x7d, 0x0a, 0x0a, 0x20, 0x20, 0x20, 0x20, 0x40, 0x6d, - 0x65, 0x64, 0x69, 0x61, 0x20, 0x28, 0x70, 0x72, 0x65, 0x66, 0x65, 0x72, - 0x73, 0x2d, 0x63, 0x6f, 0x6c, 0x6f, 0x72, 0x2d, 0x73, 0x63, 0x68, 0x65, - 0x6d, 0x65, 0x3a, 0x20, 0x64, 0x61, 0x72, 0x6b, 0x29, 0x20, 0x7b, 0x0a, - 0x20, 0x20, 0x20, 0x20, 0x20, 0x20, 0x2e, 0x70, 0x6f, 0x70, 0x6f, 0x76, - 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@media (prefers-color-scheme: dark) { + .loading { + --loading-color-1: #22222200; + --loading-color-2: #222222ff; + } .popover-content { background-color: black; } @@ -321,7 +343,10 @@ const llamaStats = signal(null) const controller = signal(null) - const generating = computed(() => controller.value == null ) + // currently generating a completion? + const generating = computed(() => controller.value != null) + + // has the user started a chat? const chatStarted = computed(() => session.value.transcript.length > 0) const transcriptUpdate = (transcript) => { @@ -430,11 +455,19 @@ return html`
-