mirror of
https://github.com/ggerganov/ggml
synced 2026-03-02 05:00:27 +01:00
244 lines
7.0 KiB
C++
244 lines
7.0 KiB
C++
#include "ggml.h"
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#include "ggml-cpu.h"
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#include "ggml-alloc.h"
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#include "ggml-backend.h"
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#ifdef GGML_USE_CUDA
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#include "ggml-cuda.h"
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#endif
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#ifdef GGML_USE_METAL
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#include "ggml-metal.h"
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#endif
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#include <cassert>
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <fstream>
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#include <map>
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#include <string>
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#include <vector>
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static void ggml_log_callback_default(ggml_log_level level, const char * text, void * user_data) {
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(void) level;
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(void) user_data;
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fputs(text, stderr);
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fflush(stderr);
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}
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struct test_model {
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struct ggml_tensor * weight;
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struct ggml_tensor * input;
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ggml_backend_t backend = NULL;
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ggml_backend_buffer_t buffer;
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struct ggml_context * ctx;
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};
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void load_model(test_model & model, bool use_gpu = false) {
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// create data
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int K = 3, IC = 2, OC = 2;
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int IL = 6, N = 1;
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// Initialize adata
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float weight_data[6] = {10.0f, 20.0f, 30.0f, 0.1f, 0.2f, 0.3f};
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// Convert adata to fp16 format
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std::vector<ggml_fp16_t> h_weight_data(K * IC);
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ggml_fp32_to_fp16_row(weight_data, h_weight_data.data(), K * IC);
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// Initialize input data, 2 channels, 6 timesteps, 1 batch
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float input_data[12] = {
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
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};
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size_t buffer_size = 0;
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{
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buffer_size += K * IC * ggml_type_size(GGML_TYPE_F16); // tensor weight
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buffer_size += IL * IC * N * ggml_type_size(GGML_TYPE_F32); // tensor input
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buffer_size += 1024; // overhead
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}
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printf("%s: ggml tensor size = %d bytes\n", __func__, (int) sizeof(ggml_tensor));
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printf("%s: backend buffer size = %0.2f MB\n", __func__, (buffer_size/ 1024.f/ 1024.f));
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ggml_log_set(ggml_log_callback_default, nullptr);
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int num_tensors = 2;
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struct ggml_init_params params {
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/*.mem_size =*/ ggml_tensor_overhead() * num_tensors,
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/*.mem_buffer =*/ NULL,
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/*.no_alloc =*/ true,
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};
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// initialize the backend
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#ifdef GGML_USE_CUDA
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if (use_gpu) {
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fprintf(stderr, "%s: using CUDA backend\n", __func__);
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model.backend = ggml_backend_cuda_init(0);
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if (!model.backend) {
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fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
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}
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}
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#endif
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#ifdef GGML_USE_METAL
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if (use_gpu) {
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fprintf(stderr, "%s: using Metal backend\n", __func__);
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model.backend = ggml_backend_metal_init();
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if (!model.backend) {
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fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__);
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}
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}
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#endif
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if(!model.backend) {
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// fallback to CPU backend
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model.backend = ggml_backend_cpu_init();
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}
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model.buffer = ggml_backend_alloc_buffer(model.backend, buffer_size);
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// create context
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model.ctx = ggml_init(params);
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// create tensors
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// A Pytorch grouped Conv1d weight parameter is of shape (out_channels, input_channels/groups, kernel_size)
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model.weight = ggml_new_tensor_3d(model.ctx, GGML_TYPE_F16, K, 1, IC);
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model.input = ggml_new_tensor_3d(model.ctx, GGML_TYPE_F32, IL, IC, N);
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// create a allocator
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ggml_tallocr alloc = ggml_tallocr_new(model.buffer);
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// alloc memory
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ggml_tallocr_alloc(&alloc, model.weight);
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// load data to buffer
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if(ggml_backend_is_cpu(model.backend)) {
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memcpy(model.weight->data, h_weight_data.data(), ggml_nbytes(model.weight));
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} else {
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ggml_backend_tensor_set(model.weight, h_weight_data.data(), 0, ggml_nbytes(model.weight));
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}
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// alloc memory
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ggml_tallocr_alloc(&alloc, model.input);
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if(ggml_backend_is_cpu(model.backend)
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#ifdef GGML_USE_METAL
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|| ggml_backend_is_metal(model.backend)
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#endif
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) {
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memcpy(model.input->data, input_data, ggml_nbytes(model.input));
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} else {
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ggml_backend_tensor_set(model.input, input_data, 0, ggml_nbytes(model.input));
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}
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}
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struct ggml_cgraph * build_graph(const test_model& model) {
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static size_t buf_size = ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead();
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static std::vector<uint8_t> buf(buf_size);
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struct ggml_init_params params0 = {
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/*.mem_size =*/ buf_size,
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/*.mem_buffer =*/ buf.data(),
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/*.no_alloc =*/ true, // the tensors will be allocated later by ggml_gallocr_alloc_graph()
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};
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// create a temporally context to build the graph
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struct ggml_context * ctx0 = ggml_init(params0);
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struct ggml_cgraph * gf = ggml_new_graph(ctx0);
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int s0 = 3;
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int p0 = 0;
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int d0 = 1;
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struct ggml_tensor* conv1d_dw_res = ggml_conv_1d_dw(ctx0, model.weight, model.input, s0, p0, d0);
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ggml_set_name(conv1d_dw_res, "conv1d_dw_res");
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ggml_build_forward_expand(gf, conv1d_dw_res);
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// delete the temporally context used to build the graph
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ggml_free(ctx0);
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return gf;
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}
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struct ggml_cgraph* compute_graph(const test_model & model, ggml_gallocr_t allocr) {
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struct ggml_cgraph * gf = build_graph(model);
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// allocate tensors
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ggml_gallocr_alloc_graph(allocr, gf);
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int n_threads = 1;
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if (ggml_backend_is_cpu(model.backend)) {
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ggml_backend_cpu_set_n_threads(model.backend, n_threads);
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}
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ggml_backend_graph_compute(model.backend, gf);
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//ggml_graph_print(gf);
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return gf;
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}
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int main(void)
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{
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ggml_time_init();
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test_model model;
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load_model(model, true);
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ggml_gallocr_t allocr = NULL;
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{
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allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(model.backend));
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//create the worst case graph for memory usage estimation
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struct ggml_cgraph * gf = build_graph(model);
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// compute the required memory
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ggml_gallocr_reserve(allocr, gf);
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size_t mem_size = ggml_gallocr_get_buffer_size(allocr, 0);
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fprintf(stderr, "%s: compute buffer size: %.2f MB\n", __func__, mem_size/1024.0f/1024.0f);
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}
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struct ggml_cgraph * gf_res = compute_graph(model, allocr);
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struct ggml_tensor * conv1d_dw_res = NULL;
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for(int i = 0; i < ggml_graph_n_nodes(gf_res); i++) {
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if(strcmp(ggml_get_name(ggml_graph_node(gf_res, i)), "conv1d_dw_res") == 0) {
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conv1d_dw_res = ggml_graph_node(gf_res, i);
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}
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}
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std::vector<float> conv2d_data(ggml_nelements(conv1d_dw_res));
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ggml_backend_tensor_get(conv1d_dw_res, conv2d_data.data(), 0, ggml_nbytes(conv1d_dw_res));
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const int n_conv1d_dw_test = 4;
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float expected_conv1d_dw[n_conv1d_dw_test] = {
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60.0f, 60.0f, 0.6f, 0.6f
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};
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printf("\nPerforming test:\n");
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bool passed = true;
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passed = true;
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for(int i = 0; i < n_conv1d_dw_test; i++) {
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if(std::abs(conv2d_data[i] - expected_conv1d_dw[i]) > 1e-4) {
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passed = false;
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break;
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}
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}
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printf("ggml_conv1d (%d): %s\n", (int) ggml_nelements(conv1d_dw_res), passed && (ggml_nelements(conv1d_dw_res) == n_conv1d_dw_test) ? "\033[32mPASSED\033[0m" : "\033[31mFAILED\033[0m");
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ggml_free(model.ctx);
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ggml_backend_buffer_free(model.buffer);
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ggml_backend_free(model.backend);
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ggml_gallocr_free(allocr);
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return 0;
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}
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