From 417cfc3cc6ce0a72c3c139cf8fd68c9718966ca1 Mon Sep 17 00:00:00 2001 From: bssrdf Date: Fri, 31 Oct 2025 19:57:28 -0400 Subject: [PATCH] added a test case to directly compare im2col and implicit gemm --- tests/CMakeLists.txt | 1 + tests/test-conv2d.cpp | 413 ++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 414 insertions(+) create mode 100644 tests/test-conv2d.cpp diff --git a/tests/CMakeLists.txt b/tests/CMakeLists.txt index 9171957756..aaabfe91b7 100644 --- a/tests/CMakeLists.txt +++ b/tests/CMakeLists.txt @@ -198,6 +198,7 @@ if (NOT LLAMA_SANITIZE_ADDRESS) endif() llama_build_and_test(test-gguf.cpp) llama_build_and_test(test-backend-ops.cpp) +llama_build_and_test(test-conv2d.cpp) llama_build_and_test(test-model-load-cancel.cpp LABEL "model") llama_build_and_test(test-autorelease.cpp LABEL "model") diff --git a/tests/test-conv2d.cpp b/tests/test-conv2d.cpp new file mode 100644 index 0000000000..c2cc1930cb --- /dev/null +++ b/tests/test-conv2d.cpp @@ -0,0 +1,413 @@ +#include "ggml.h" +#include "ggml-alloc.h" +#include "ggml-cpu.h" +#include "ggml-backend.h" + +#ifdef GGML_USE_CUDA +#include "ggml-cuda.h" +//#include +#endif + +#ifdef GGML_USE_METAL +#include "ggml-metal.h" +#endif + +#include +#include +#include +#include +#include +#include +#include +#include + +static void ggml_log_callback_default(ggml_log_level level, const char * text, void * user_data) { + (void) level; + (void) user_data; + fputs(text, stderr); + fflush(stderr); +} + +struct test_model { + struct ggml_tensor * a; + struct ggml_tensor * b; + ggml_backend_t backend = NULL; + ggml_backend_buffer_t buffer; + struct ggml_context * ctx; +}; + + + +void load_model(test_model & model, int ic, int oc, int iw, int ih, int kw = 3, int kh = 3, bool use_gpu = false ) { + // create data + int KW = kw, KH = kh, IC = ic, OC = oc; + int IW = iw, IH = ih, N = 1; + srand(time(NULL)); + + // printf(" input: IC = %d, OC = %d, IW = %d, IH = %d \n ", IC, OC, IW, IH); + + // Initialize adata + std::vector adata(KW * KH * IC * OC); + for (int i = 0; i < KW * KH * IC * OC; i++) { + // adata[i] = 2.f; + // adata[i] = (float)(i%KW)-1.f; + // adata[i] = (rand() % 255) / 255.0; + float r = -1.f + static_cast (rand()) /( static_cast (RAND_MAX/(1.f-(-1.f)))); + adata[i] = r; + } + + // Convert adata to fp16 format + std::vector hadata(KW * KH * IC * OC); + ggml_fp32_to_fp16_row(adata.data(), hadata.data(), KW * KH * IC * OC); + + // Initialize bdata + std::vector bdata(IW * IH * IC * N); + for (int i = 0; i < IW * IH * IC * N; i++) { + // bdata[i] = (float)(i%IW)/10.f; + // bdata[i] = 1.5f; + // bdata[i] = (rand() % 255) / 255.0; + float r = -1.f + static_cast (rand()) /( static_cast (RAND_MAX/(1.f-(-1.f)))); + bdata[i] = r; + } + + size_t buffer_size = 0; + { + // buffer_size += KW * KH * IC * OC * ggml_type_size(GGML_TYPE_F32); // tensor a + buffer_size += KW * KH * IC * OC * ggml_type_size(GGML_TYPE_F16); // tensor a + buffer_size += IW * IH * IC * N * ggml_type_size(GGML_TYPE_F32); // tensor b + buffer_size += 1024; // overhead + } + + // printf("%s: ggml tensor size = %d bytes\n", __func__, (int) sizeof(ggml_tensor)); + // printf("%s: backend buffer size = %0.2f MB\n", __func__, (buffer_size/ 1024.f/ 1024.f)); + + int num_tensors = 2; + struct ggml_init_params params { + /*.mem_size =*/ ggml_tensor_overhead() * num_tensors, + /*.mem_buffer =*/ NULL, + /*.no_alloc =*/ true, + }; + + // initialize the backend +#ifdef GGML_USE_CUDA + if (use_gpu) { + // fprintf(stderr, "%s: using CUDA backend\n", __func__); + model.backend = ggml_backend_cuda_init(0); + if (!model.backend) { + fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__); + } + } +#endif + +#ifdef GGML_USE_METAL + if (use_gpu) { + fprintf(stderr, "%s: using Metal backend\n", __func__); + ggml_backend_metal_log_set_callback(ggml_log_callback_default, nullptr); + model.backend = ggml_backend_metal_init(); + if (!model.backend) { + fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__); + } + } +#endif + + if(!model.backend) { + // fallback to CPU backend + model.backend = ggml_backend_cpu_init(); + } + + model.buffer = ggml_backend_alloc_buffer(model.backend, buffer_size); + + // create context + model.ctx = ggml_init(params); + + // create tensors + model.a = ggml_new_tensor_4d(model.ctx, GGML_TYPE_F16, KW, KH, IC, OC); + // model.a = ggml_new_tensor_4d(model.ctx, GGML_TYPE_F32, KW, KH, IC, OC); + model.b = ggml_new_tensor_4d(model.ctx, GGML_TYPE_F32, IW, IH, IC, N); + + // create a allocator + struct ggml_tallocr alloc = ggml_tallocr_new(model.buffer); + + // alloc memory + ggml_tallocr_alloc(&alloc, model.a); + + // load data to buffer + if(ggml_backend_is_cpu(model.backend)) { + memcpy(model.a->data, hadata.data(), ggml_nbytes(model.a)); + // memcpy(model.a->data, adata.data(), ggml_nbytes(model.a)); + } else { + ggml_backend_tensor_set(model.a, hadata.data(), 0, ggml_nbytes(model.a)); + // ggml_backend_tensor_set(model.a, adata.data(), 0, ggml_nbytes(model.a)); + } + + // alloc memory + ggml_tallocr_alloc(&alloc, model.b); + + if(ggml_backend_is_cpu(model.backend) +#ifdef GGML_USE_METAL + || ggml_backend_is_metal(model.backend) +#endif + ) { + memcpy(model.b->data, bdata.data(), ggml_nbytes(model.b)); + } else { + ggml_backend_tensor_set(model.b, bdata.data(), 0, ggml_nbytes(model.b)); + } +} + +typedef struct ggml_cgraph* (*build_graph_t)(const test_model& model); + +struct ggml_cgraph * build_graph_0(const test_model& model) { + static size_t buf_size = ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead(); + static std::vector buf(buf_size); + + struct ggml_init_params params0 = { + /*.mem_size =*/ buf_size, + /*.mem_buffer =*/ buf.data(), + /*.no_alloc =*/ true, // the tensors will be allocated later by ggml_gallocr_alloc_graph() + }; + + // create a temporally context to build the graph + struct ggml_context * ctx0 = ggml_init(params0); + + struct ggml_cgraph * gf = ggml_new_graph(ctx0); + + int s0 = 1; + int s1 = 1; + int p0 = 1; + int p1 = 1; + int d0 = 1; + int d1 = 1; + + + + // recalculate for avoid fragmentation + struct ggml_tensor* conv2d_res = ggml_conv_2d(ctx0, model.a, model.b, s0, s1, p0, p1, d0, d1); + ggml_set_name(conv2d_res, "conv2d_res"); + ggml_build_forward_expand(gf, conv2d_res); + // int64_t *ne = conv2d_res->ne; + // printf("conv2d: (%zu, %zu, %zu, %zu) \n", ne[0], ne[1], ne[2], ne[3]); + + + // struct ggml_tensor* wino_res = ggml_conv_2d_3x3(ctx0, model.a, model.b); + // ggml_set_name(wino_res, "wino_res"); + // ggml_build_forward_expand(gf, wino_res); + // ne = wino_res->ne; + // printf("wino: (%zu, %zu, %zu, %zu) \n", ne[0], ne[1], ne[2], ne[3]); + ggml_free(ctx0); + return gf; +} + +struct ggml_cgraph * build_graph_1(const test_model& model) { + static size_t buf_size = ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead(); + static std::vector buf(buf_size); + + struct ggml_init_params params0 = { + /*.mem_size =*/ buf_size, + /*.mem_buffer =*/ buf.data(), + /*.no_alloc =*/ true, // the tensors will be allocated later by ggml_gallocr_alloc_graph() + }; + + // create a temporally context to build the graph + struct ggml_context * ctx0 = ggml_init(params0); + + struct ggml_cgraph * gf = ggml_new_graph(ctx0); + + int s0 = 1; + int s1 = 1; + int p0 = 1; + int p1 = 1; + int d0 = 1; + int d1 = 1; + + + + // recalculate for avoid fragmentation + // struct ggml_tensor* conv2d_res = ggml_conv_2d(ctx0, model.a, model.b, s0, s1, p0, p1, d0, d1); + // ggml_set_name(conv2d_res, "conv2d_res"); + // ggml_build_forward_expand(gf, conv2d_res); + // int64_t *ne = conv2d_res->ne; + // printf("conv2d: (%zu, %zu, %zu, %zu) \n", ne[0], ne[1], ne[2], ne[3]); + + + // struct ggml_tensor* wino_res = ggml_conv_2d_implicitgemm(ctx0, model.a, model.b, s0, s1, p0, p1, d0, d1); + struct ggml_tensor* wino_res = ggml_conv_2d_direct(ctx0, model.a, model.b, s0, s1, p0, p1, d0, d1); + ggml_set_name(wino_res, "wino_res"); + ggml_build_forward_expand(gf, wino_res); + // ne = wino_res->ne; + // printf("wino: (%zu, %zu, %zu, %zu) \n", ne[0], ne[1], ne[2], ne[3]); + ggml_free(ctx0); + return gf; +} + + + + +std::vector compute_graph(const test_model & model, ggml_gallocr_t allocr, + build_graph_t build_graph, int iters, double *t) { + struct ggml_cgraph * gf = build_graph(model); + + + // allocate tensors + ggml_gallocr_alloc_graph(allocr, gf); + int n_threads = 1; + + if (ggml_backend_is_cpu(model.backend)) { + ggml_backend_cpu_set_n_threads(model.backend, n_threads); + } + +#ifdef GGML_USE_METAL + if (ggml_backend_is_metal(model.backend)) { + ggml_backend_metal_set_n_cb(model.backend, n_threads); + } +#endif + + + + ggml_backend_graph_compute(model.backend, gf); + + ggml_backend_synchronize(model.backend); + + int64_t start_time = ggml_time_us(); + + for(int iter=0; iter data(ggml_nelements(res)); + ggml_backend_tensor_get(res, data.data(), 0, ggml_nbytes(res)); + + *t = time_us/1000; + return data; + +} + + +int main(void) +{ + ggml_time_init(); + std::vector> configs = { + // std::make_tuple(64,64,48,64,3,3), + // std::make_tuple(320,320,104,152,3,3), + // std::make_tuple(640,640,52,76,3,3), + // std::make_tuple(640,640,104,152,3,3), + // std::make_tuple(960,320,104,152,3,3), + // std::make_tuple(1280,1280,26,38,3,3), + // std::make_tuple(1280,1280,26,38,1,1), + // std::make_tuple(256,128,768,1024,3,3), + // std::make_tuple(128,3,768,1024,3,3), + // std::make_tuple(256,128,768,1024,1,1), + // std::make_tuple(512,256,384,512,1,1), + // std::make_tuple(1280,640,52,76,3,3), + // std::make_tuple(1920,1280,26,38,3,3), + // std::make_tuple(2560,1280,26,38,3,3), + std::make_tuple(320,1280,26,38,3,3), + // std::make_tuple(512,512,104,152,3,3), + // std::make_tuple(512,512,208,304,3,3), + // std::make_tuple(512,256,416,608,3,3), + // std::make_tuple(256,128,832,1216,3,3), + // std::make_tuple(256,256,832,1216,3,3), + // std::make_tuple(320,256,1024,1920) + }; + + int k = 0; + + for (auto c : configs){ + test_model model; + load_model(model, std::get<0>(c), std::get<1>(c), std::get<2>(c), + std::get<3>(c), std::get<4>(c), std::get<5>(c), true); + + ggml_gallocr_t allocr = NULL; + allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(model.backend)); + + //create the worst case graph for memory usage estimation + struct ggml_cgraph * gf = build_graph_0(model); + + // compute the required memory + ggml_gallocr_reserve(allocr, gf); + size_t mem_size0 = ggml_gallocr_get_buffer_size(allocr, 0); + // fprintf(stderr, "%s: compute buffer size: %.2f MB\n", __func__, mem_size/1024.0f/1024.0f); + + + struct ggml_cgraph * gf_res_0 = NULL; + int iterations = 20; + + double run_time0; + std::vector im2col_data = compute_graph(model, allocr, build_graph_0, iterations, &run_time0); + + ggml_gallocr_free(allocr); + + allocr = NULL; + + allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(model.backend)); + + //create the worst case graph for memory usage estimation + gf = build_graph_1(model); + + // compute the required memory + ggml_gallocr_reserve(allocr, gf); + size_t mem_size1 = ggml_gallocr_get_buffer_size(allocr, 0); + // fprintf(stderr, "%s: compute buffer size: %.2f MB\n", __func__, mem_size/1024.0f/1024.0f); + + + struct ggml_cgraph * gf_res_1 = NULL; + + double run_time1; + // std::vector wino_data = compute_graph(model, allocr, build_graph_1, iterations, &run_time1); + std::vector conv2d_data = compute_graph(model, allocr, build_graph_1, iterations, &run_time1); + + if(k==0) { + k = 1; + fprintf(stderr, "| (IC, OC, IW, IH) | im2col+GEMM TIME | im2col+GEMM VRAM | implicit GEMM TIME | implicit GEMM VRAM \n"); + fprintf(stderr, "| --- | --- | --- | --- | --- \n"); + } + + fprintf(stderr, " | (%d, %d, %d, %d, %d, %d) | %.2f ms | %.2f MB | %.2f ms | %.2f MB\n", + std::get<0>(c), std::get<1>(c), std::get<2>(c), std::get<3>(c), std::get<4>(c), std::get<5>(c), + run_time0, mem_size0/1024.0f/1024.0f, + run_time1, mem_size1/1024.0f/1024.0f); + + + // for(int i = 0; i < ggml_nelements(wino_res); i++) { + // for(int i = 0; i < 26*38; i++) { + // for(int i = 0; i < conv2d_data.size(); i++) { + // // float diff = fabs(conv2d_data[i] - wino_data[i]); + // float diff = fabs(im2col_data[i] - wino_data[i]); + // float diff1 = fabs(im2col_data[i] - conv2d_data[i]); + // // if(diff > 0.5) { + // printf("(%7.3f, %7.3f, %7.3f, %.2f, %.2f, %d) \n", + // im2col_data[i], conv2d_data[i], + // wino_data[i], diff, diff1, i); + // // break; + // // } + // } + + ggml_free(model.ctx); + ggml_backend_buffer_free(model.buffer); + ggml_backend_free(model.backend); + ggml_gallocr_free(allocr); + + } + + // printf("\nPerforming test:\n"); + return 0; +}