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// Use a pre-generated MNIST compute graph for inference on the M1 GPU via MPS
//
// You can generate a compute graph using the "mnist" tool:
//
// $ ./bin/mnist ./models/mnist/ggml-model-f32.bin ../examples/mnist/models/mnist/t10k-images.idx3-ubyte
//
// This command creates the "mnist.ggml" file, which contains the generated compute graph.
// Now, you can re-use the compute graph on the GPU with the "mnist-mtl" tool:
//
// $ ./bin/mnist-mtl ./models/mnist/mnist.ggml ../examples/mnist/models/mnist/t10k-images.idx3-ubyte
//
#include "ggml/ggml.h"
#include "main-mtl.h"
#include <cmath>
#include <cstdio>
#include <cstring>
#include <ctime>
#include <fstream>
#include <vector>
// evaluate the MNIST compute graph
//
// - fname_cgraph: path to the compute graph
// - digit: 784 pixel values
//
// returns 0 - 9 prediction
int mnist_eval(
const char * fname_cgraph,
std::vector<float> digit
) {
// load the compute graph
struct ggml_context * ctx_data = NULL;
struct ggml_context * ctx_eval = NULL;
struct ggml_cgraph gf = ggml_graph_import(fname_cgraph, &ctx_data, &ctx_eval);
// allocate work context
static size_t buf_size = 128ull*1024*1024; // TODO
static void * buf = malloc(buf_size);
struct ggml_init_params params = {
/*.mem_size =*/ buf_size,
/*.mem_buffer =*/ buf,
/*.no_alloc =*/ false,
};
struct ggml_context * ctx_work = ggml_init(params);
// this allocates all Metal resources and memory buffers
auto ctx_mtl = mnist_mtl_init(ctx_data, ctx_eval, ctx_work, &gf);
int prediction = -1;
for (int i = 0; i < 1; ++i) {
struct ggml_tensor * input = ggml_graph_get_tensor(&gf, "input");
if (i % 2 == 0) {
memcpy(input->data, digit.data(), ggml_nbytes(input));
} else {
memset(input->data, 0, ggml_nbytes(input));
}
// the actual inference happens here
prediction = mnist_mtl_eval(ctx_mtl, &gf);
}
mnist_mtl_free(ctx_mtl);
ggml_free(ctx_work);
ggml_free(ctx_data);
ggml_free(ctx_eval);
return prediction;
}
int main(int argc, char ** argv) {
srand(time(NULL));
ggml_time_init();
if (argc != 3) {
fprintf(stderr, "Usage: %s models/mnist/mnist.ggml models/mnist/t10k-images.idx3-ubyte\n", argv[0]);
exit(0);
}
uint8_t buf[784];
std::vector<float> digit;
// 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]);
}
fprintf(stderr, "\n");
}
fprintf(stderr, "\n");
}
const int prediction = mnist_eval(argv[1], digit);
fprintf(stdout, "%s: predicted digit is %d\n", __func__, prediction);
return 0;
}
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