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| #include "ggml.h"
| #include "ggml/ggml-alloc.h"
| #include "ggml/ggml-backend.h"
|
| // #define GGML_USE_CUBLAS
|
| #ifdef GGML_USE_CUBLAS
| #include "ggml-cuda.h"
| #endif
|
| #ifdef GGML_USE_METAL
| #include "ggml-metal.h"
| #endif
|
| #include <cassert>
| #include <cmath>
| #include <cstdio>
| #include <cstring>
| #include <fstream>
| #include <map>
| #include <string>
| #include <vector>
|
| 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, bool use_gpu = false) {
| // create data
| int KW = 3, KH = 3, IC = 10, OC = 10;
| int IW = 8, IH = 6, N = 1;
|
| // Initialize adata
| float * adata = new float[KW * KH * IC * OC];
| for (int i = 0; i < KW * KH * IC * OC; i++) {
| adata[i] = 2.5f;
| }
|
| // Convert adata to fp16 format
| std::vector<ggml_fp16_t> hadata(KW * KH * IC * OC);
| ggml_fp32_to_fp16_row(adata, hadata.data(), KW * KH * IC * OC);
|
| // Initialize bdata
| float * bdata = new float[IW * IH * IC * N];
| for (int i = 0; i < IW * IH * IC * N; i++) {
| bdata[i] = 1.5f;
| }
|
| size_t buffer_size = 0;
| {
| 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_CUBLAS
| 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.b = ggml_new_tensor_4d(model.ctx, GGML_TYPE_F32, IW, IH, IC, N);
|
| // create a allocator
| ggml_allocr * alloc = ggml_allocr_new_from_buffer(model.buffer);
|
| // alloc memory
| ggml_allocr_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));
| } else {
| ggml_backend_tensor_set(model.a, hadata.data(), 0, ggml_nbytes(model.a));
| }
|
| // alloc memory
| ggml_allocr_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, ggml_nbytes(model.b));
| } else {
| ggml_backend_tensor_set(model.b, bdata, 0, ggml_nbytes(model.b));
| }
|
| ggml_allocr_free(alloc);
| }
|
| struct ggml_cgraph * build_graph(const test_model& model, struct ggml_allocr * allocr) {
| static size_t buf_size = ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead();
| static std::vector<uint8_t> 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_allocr_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;
|
| // split conv2d in fundamental methods for test unit
| struct ggml_tensor* im2col_0 = ggml_im2col(ctx0, model.a, model.b, s0, s1, p0, p1, d0, d1, true);
| ggml_set_name(im2col_0, "im2col_res");
| ggml_build_forward_expand(gf, im2col_0);
|
| // 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);
|
| ggml_free(ctx0);
| return gf;
| }
|
| struct ggml_cgraph * compute_graph(const test_model & model, struct ggml_allocr * allocr) {
| // reset the allocator to free all the memory allocated during the previous inference
| ggml_allocr_reset(allocr);
|
| struct ggml_cgraph * gf = build_graph(model, allocr);
|
| // allocate tensors
| ggml_allocr_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_graph_print(gf);
|
| return gf;
| }
|
| int main(void)
| {
| ggml_time_init();
|
| test_model model;
| load_model(model, true);
|
| ggml_backend_buffer_t buf_compute; // for compute
| struct ggml_allocr * allocr = NULL;
|
| {
| allocr = ggml_allocr_new_measure_from_backend(model.backend);
|
| //create the worst case graph for memory usage estimation
| struct ggml_cgraph * gf = build_graph(model, allocr);
| size_t mem_size = ggml_allocr_alloc_graph(allocr, gf);
| ggml_allocr_free(allocr);
|
| // compute the required memory
| buf_compute = ggml_backend_alloc_buffer(model.backend, mem_size);
| allocr = ggml_allocr_new_from_buffer(buf_compute);
| fprintf(stderr, "%s: compute buffer size: %.2f MB\n", __func__, mem_size/1024.0f/1024.0f);
| }
|
| struct ggml_cgraph * gf_res = compute_graph(model, allocr);
|
| struct ggml_tensor * im2col_res = NULL;
| struct ggml_tensor * conv2d_res = NULL;
|
| for(int i = 0; i < gf_res->n_nodes; i++) {
| if(strcmp(ggml_get_name(gf_res->nodes[i]), "im2col_res") == 0) {
| im2col_res = gf_res->nodes[i];
| } else if(strcmp(ggml_get_name(gf_res->nodes[i]), "conv2d_res") == 0) {
| conv2d_res = gf_res->nodes[i];
| }
| }
|
| uint16_t* im2col_data = new uint16_t[ggml_nelements(im2col_res)];
| float* conv2d_data = new float[ggml_nelements(conv2d_res)];
|
| ggml_backend_tensor_get(im2col_res, im2col_data, 0, ggml_nbytes(im2col_res));
| ggml_backend_tensor_get(conv2d_res, conv2d_data, 0, ggml_nbytes(conv2d_res));
|
| const int n_conv2d_test = 480;
| const int n_im2col_test = 4320;
|
| float expected_conv2d [n_conv2d_test] = {
| 150.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 150.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 150.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 150.00f,
| 150.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 150.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 150.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 150.00f,
| 150.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 150.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 150.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 150.00f,
| 150.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 150.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 150.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 150.00f,
| 150.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 150.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 150.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 150.00f,
| 150.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 150.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 150.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 150.00f,
| 150.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 150.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 150.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 150.00f,
| 150.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 150.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 150.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 150.00f,
| 150.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 150.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 150.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 150.00f,
| 150.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 150.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 225.00f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 337.50f, 225.00f,
| 150.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 225.00f, 150.00f };
|
| uint16_t expected_im2col[n_conv2d_test] = {
| 0, 0, 0, 0, 15872, 15872, 0, 15872,
| 15872, 0, 0, 0, 0, 15872, 15872, 0,
| 15872, 15872, 0, 0, 0, 0, 15872, 15872,
| 0, 15872, 15872, 0, 0, 0, 0, 15872,
| 15872, 0, 15872, 15872, 0, 0, 0, 0,
| 15872, 15872, 0, 15872, 15872, 0, 0, 0,
| 0, 15872, 15872, 0, 15872, 15872, 0, 0,
| 0, 0, 15872, 15872, 0, 15872, 15872, 0,
| 0, 0, 0, 15872, 15872, 0, 15872, 15872,
| 0, 0, 0, 0, 15872, 15872, 0, 15872,
| 15872, 0, 0, 0, 0, 15872, 15872, 0,
| 15872, 15872, 0, 0, 0, 15872, 15872, 15872,
| 15872, 15872, 15872, 0, 0, 0, 15872, 15872,
| 15872, 15872, 15872, 15872, 0, 0, 0, 15872,
| 15872, 15872, 15872, 15872, 15872, 0, 0, 0,
| 15872, 15872, 15872, 15872, 15872, 15872, 0, 0,
| 0, 15872, 15872, 15872, 15872, 15872, 15872, 0,
| 0, 0, 15872, 15872, 15872, 15872, 15872, 15872,
| 0, 0, 0, 15872, 15872, 15872, 15872, 15872,
| 15872, 0, 0, 0, 15872, 15872, 15872, 15872,
| 15872, 15872, 0, 0, 0, 15872, 15872, 15872,
| 15872, 15872, 15872, 0, 0, 0, 15872, 15872,
| 15872, 15872, 15872, 15872, 0, 0, 0, 15872,
| 15872, 15872, 15872, 15872, 15872, 0, 0, 0,
| 15872, 15872, 15872, 15872, 15872, 15872, 0, 0,
| 0, 15872, 15872, 15872, 15872, 15872, 15872, 0,
| 0, 0, 15872, 15872, 15872, 15872, 15872, 15872,
| 0, 0, 0, 15872, 15872, 15872, 15872, 15872,
| 15872, 0, 0, 0, 15872, 15872, 15872, 15872,
| 15872, 15872, 0, 0, 0, 15872, 15872, 15872,
| 15872, 15872, 15872, 0, 0, 0, 15872, 15872,
| 15872, 15872, 15872, 15872, 0, 0, 0, 15872,
| 15872, 15872, 15872, 15872, 15872, 0, 0, 0,
| 15872, 15872, 15872, 15872, 15872, 15872, 0, 0,
| 0, 15872, 15872, 15872, 15872, 15872, 15872, 0,
| 0, 0, 15872, 15872, 15872, 15872, 15872, 15872,
| 0, 0, 0, 15872, 15872, 15872, 15872, 15872,
| 15872, 0, 0, 0, 15872, 15872, 15872, 15872,
| 15872, 15872, 0, 0, 0, 15872, 15872, 15872,
| 15872, 15872, 15872, 0, 0, 0, 15872, 15872,
| 15872, 15872, 15872, 15872, 0, 0, 0, 15872,
| 15872, 15872, 15872, 15872, 15872, 0, 0, 0,
| 15872, 15872, 15872, 15872, 15872, 15872, 0, 0,
| 0, 15872, 15872, 15872, 15872, 15872, 15872, 0,
| 0, 0, 15872, 15872, 15872, 15872, 15872, 15872,
| 0, 0, 0, 15872, 15872, 15872, 15872, 15872,
| 15872, 0, 0, 0, 15872, 15872, 15872, 15872,
| 15872, 15872, 0, 0, 0, 15872, 15872, 15872,
| 15872, 15872, 15872, 0, 0, 0, 15872, 15872,
| 15872, 15872, 15872, 15872, 0, 0, 0, 15872,
| 15872, 15872, 15872, 15872, 15872, 0, 0, 0,
| 15872, 15872, 15872, 15872, 15872, 15872, 0, 0,
| 0, 15872, 15872, 15872, 15872, 15872, 15872, 0,
| 0, 0, 15872, 15872, 15872, 15872, 15872, 15872,
| 0, 0, 0, 15872, 15872, 15872, 15872, 15872,
| 15872, 0, 0, 0, 15872, 15872, 15872, 15872,
| 15872, 15872, 0, 0, 0, 15872, 15872, 15872,
| 15872, 15872, 15872, 0, 0, 0, 15872, 15872,
| 15872, 15872, 15872, 15872, 0, 0, 0, 15872,
| 15872, 15872, 15872, 15872, 15872, 0, 0, 0
| };
|
| printf("\nPerforming test:\n");
|
| bool passed = true;
| for(int i = 0; i < n_conv2d_test; i++) {
| if(
| im2col_data[i] != expected_im2col[i]) {
| passed = false;
| break;
| }
| }
|
| printf("ggml_im2col (%d): %s\n", (int) ggml_nelements(im2col_res), passed && (ggml_nelements(im2col_res) == n_im2col_test) ? "\033[32mPASSED\033[0m" : "\033[31mFAILED\033[0m");
|
| passed = true;
| for(int i = 0; i < n_conv2d_test; i++) {
| if(conv2d_data[i] != expected_conv2d[i]) {
| passed = false;
| break;
| }
| }
|
| printf("ggml_conv2d (%d): %s\n", (int) ggml_nelements(conv2d_res), passed && (ggml_nelements(conv2d_res) == n_conv2d_test) ? "\033[32mPASSED\033[0m" : "\033[31mFAILED\033[0m");
|
| ggml_free(model.ctx);
|
| ggml_backend_buffer_free(model.buffer);
| ggml_backend_buffer_free(buf_compute);
| ggml_backend_free(model.backend);
| return 0;
| }
|
|
