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#include "ggml/ggml.h"
#include "common.h"
#include <cmath>
#include <cstdio>
#include <cstring>
#include <ctime>
#include <fstream>
#include <string>
#include <vector>
#include <algorithm>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
struct mnist_model {
struct ggml_tensor * conv2d_1_kernel;
struct ggml_tensor * conv2d_1_bias;
struct ggml_tensor * conv2d_2_kernel;
struct ggml_tensor * conv2d_2_bias;
struct ggml_tensor * dense_weight;
struct ggml_tensor * dense_bias;
struct ggml_context * ctx;
};
bool mnist_model_load(const std::string & fname, mnist_model & model) {
struct gguf_init_params params = {
/*.no_alloc =*/ false,
/*.ctx =*/ &model.ctx,
};
gguf_context * ctx = gguf_init_from_file(fname.c_str(), params);
if (!ctx) {
fprintf(stderr, "%s: gguf_init_from_file() failed\n", __func__);
return false;
}
model.conv2d_1_kernel = ggml_get_tensor(model.ctx, "kernel1");
model.conv2d_1_bias = ggml_get_tensor(model.ctx, "bias1");
model.conv2d_2_kernel = ggml_get_tensor(model.ctx, "kernel2");
model.conv2d_2_bias = ggml_get_tensor(model.ctx, "bias2");
model.dense_weight = ggml_get_tensor(model.ctx, "dense_w");
model.dense_bias = ggml_get_tensor(model.ctx, "dense_b");
return true;
}
int mnist_eval(
const mnist_model & model,
const int n_threads,
std::vector<float> digit,
const char * fname_cgraph
)
{
static size_t buf_size = 100000 * sizeof(float) * 4;
static void * buf = malloc(buf_size);
struct ggml_init_params params = {
/*.mem_size =*/ buf_size,
/*.mem_buffer =*/ buf,
/*.no_alloc =*/ false,
};
struct ggml_context * ctx0 = ggml_init(params);
struct ggml_cgraph gf = {};
struct ggml_tensor * input = ggml_new_tensor_4d(ctx0, GGML_TYPE_F32, 28, 28, 1, 1);
memcpy(input->data, digit.data(), ggml_nbytes(input));
ggml_set_name(input, "input");
ggml_tensor * cur = ggml_conv_2d(ctx0, model.conv2d_1_kernel, input, 1, 1, 0, 0, 1, 1);
cur = ggml_add(ctx0, cur, model.conv2d_1_bias);
cur = ggml_relu(ctx0, cur);
// Output shape after Conv2D: (26 26 32 1)
cur = ggml_pool_2d(ctx0, cur, GGML_OP_POOL_MAX, 2, 2, 2, 2, 0, 0);
// Output shape after MaxPooling2D: (13 13 32 1)
cur = ggml_conv_2d(ctx0, model.conv2d_2_kernel, cur, 1, 1, 0, 0, 1, 1);
cur = ggml_add(ctx0, cur, model.conv2d_2_bias);
cur = ggml_relu(ctx0, cur);
// Output shape after Conv2D: (11 11 64 1)
cur = ggml_pool_2d(ctx0, cur, GGML_OP_POOL_MAX, 2, 2, 2, 2, 0, 0);
// Output shape after MaxPooling2D: (5 5 64 1)
cur = ggml_cont(ctx0, ggml_permute(ctx0, cur, 1, 2, 0, 3));
// Output shape after permute: (64 5 5 1)
cur = ggml_reshape_2d(ctx0, cur, 1600, 1);
// Final Dense layer
cur = ggml_add(ctx0, ggml_mul_mat(ctx0, model.dense_weight, cur), model.dense_bias);
ggml_tensor * probs = ggml_soft_max(ctx0, cur);
ggml_set_name(probs, "probs");
ggml_build_forward_expand(&gf, probs);
ggml_graph_compute_with_ctx(ctx0, &gf, n_threads);
//ggml_graph_print(&gf);
ggml_graph_dump_dot(&gf, NULL, "mnist-cnn.dot");
if (fname_cgraph) {
// export the compute graph for later use
// see the "mnist-cpu" example
ggml_graph_export(&gf, fname_cgraph);
fprintf(stderr, "%s: exported compute graph to '%s'\n", __func__, fname_cgraph);
}
const float * probs_data = ggml_get_data_f32(probs);
const int prediction = std::max_element(probs_data, probs_data + 10) - probs_data;
ggml_free(ctx0);
return prediction;
}
int main(int argc, char ** argv) {
srand(time(NULL));
ggml_time_init();
if (argc != 3) {
fprintf(stderr, "Usage: %s models/mnist/mnist-cnn.gguf models/mnist/t10k-images.idx3-ubyte\n", argv[0]);
exit(0);
}
uint8_t buf[784];
mnist_model model;
std::vector<float> digit;
// load the model
{
const int64_t t_start_us = ggml_time_us();
if (!mnist_model_load(argv[1], model)) {
fprintf(stderr, "%s: failed to load model from '%s'\n", __func__, argv[1]);
return 1;
}
const int64_t t_load_us = ggml_time_us() - t_start_us;
fprintf(stdout, "%s: loaded model in %8.2f ms\n", __func__, t_load_us / 1000.0f);
}
// read a random digit from the test set
{
std::ifstream fin(argv[2], std::ios::binary);
if (!fin) {
fprintf(stderr, "%s: failed to open '%s'\n", __func__, argv[2]);
return 1;
}
// seek to a random digit: 16-byte header + 28*28 * (random 0 - 10000)
fin.seekg(16 + 784 * (rand() % 10000));
fin.read((char *) &buf, sizeof(buf));
}
// render the digit in ASCII
{
digit.resize(sizeof(buf));
for (int row = 0; row < 28; row++) {
for (int col = 0; col < 28; col++) {
fprintf(stderr, "%c ", (float)buf[row*28 + col] > 230 ? '*' : '_');
digit[row*28 + col] = ((float)buf[row*28 + col] / 255.0f);
}
fprintf(stderr, "\n");
}
fprintf(stderr, "\n");
}
const int prediction = mnist_eval(model, 1, digit, nullptr);
fprintf(stdout, "%s: predicted digit is %d\n", __func__, prediction);
ggml_free(model.ctx);
return 0;
}
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