llama.cpp/examples/metal/metal.cpp

103 lines
2.7 KiB
C++
Raw Normal View History

llama : Metal inference (#1642) * mtl : export the LLaMA computation graph * ci : disable temporary * mtl : adapt the MNIST example as starter * mtl : no need for mtl-export tool, add cli arg for main instead * mtl : export just a small part of the graph for now to make it easier * mtl : move MSL code into separate file for easy editing * mtl : initial get_rows_q4_0 kernel * mtl : confirmed get_rows_q4_0 is working correctly * mtl : add rms_norm kernel + confirm working * mtl : add mul kernel + confirm working * mtl : initial mul_mat Q4 kernel (wrong results) * mtl : mul_mat fixes (still wrong) * mtl : another mul_mat Q4 (still does not work) * mtl : working mul_mat q4 * ggml : fix handling of "view" ops in ggml_graph_import() * mtl : add rope kernel * mtl : add reshape and transpose handling * ggml : store offset as opt arg for ggml_view_xd() operators * mtl : add cpy kernel + handle view ops * mtl : confirm f16 x f32 attention mul mat * mtl : add scale kernel * mtl : add diag_mask_inf kernel * mtl : fix soft_max kernel * ggml : update ggml_nbytes() to handle non-contiguous tensors * mtl : verify V tensor contents * mtl : add f32 -> f32 cpy kernel * mtl : add silu kernel * mtl : add non-broadcast mul kernel * mtl : full GPU inference of the computation graph * mtl : optimize rms_norm and soft_max kernels * mtl : add f16 mat x f32 vec multiplication kernel * mtl : fix bug in f16 x f32 mul mat + speed-up computation * mtl : faster mul_mat_q4_0_f32 kernel * mtl : fix kernel signature + roll inner loop * mtl : more threads for rms_norm + better timing * mtl : remove printfs from inner loop * mtl : simplify implementation * mtl : add save/load vocab to ggml file * mtl : plug Metal inference into llama.cpp (very quick-n-dirty) * mtl : make it work with main example Lots of hacks but at least now it generates text * mtl : preparing for merge * mtl : clean-up ggml mtl interface + suport scratch / inplace * mtl : remove temp / debug code * metal : final refactoring and simplification * Revert "ci : disable temporary" This reverts commit 98c267fc77fe811082f672538fc91bcfc9072d63. * metal : add comments * metal : clean-up stuff, fix typos * readme : add Metal instructions * readme : add example for main
2023-06-04 22:34:30 +02:00
// Evaluate a statically exported ggml computation graph with Metal
//
// - First, export a LLaMA graph:
//
// $ ./bin/main -m ../models/7B/ggml-model-q4_0.bin --export
//
// - Run this tool to evaluate the exported graph:
//
// $ ./bin/metal llama.ggml
//
// The purpose of this tool is mostly for debugging and demonstration purposes.
// The main limitation of exporting computation graphs is that their sizes are static which often
// can be a problem for real-world applications.
//
#include "ggml.h"
#include "ggml-metal.h"
#include <cstdio>
#include <cstring>
#include <cstdlib>
int main(int argc, char ** argv) {
ggml_time_init();
if (argc != 2) {
fprintf(stderr, "Usage: %s llama.ggml\n", argv[0]);
return -1;
}
const char * fname_cgraph = argv[1];
// 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);
gf.n_threads = 1;
// this allocates all Metal resources and memory buffers
auto * ctx_metal = ggml_metal_init();
ggml_metal_add_buffer(ctx_metal, "data", ggml_get_mem_buffer(ctx_data), ggml_get_mem_size(ctx_data));
ggml_metal_add_buffer(ctx_metal, "eval", ggml_get_mem_buffer(ctx_eval), ggml_get_mem_size(ctx_eval));
// main
{
struct ggml_tensor * input = ggml_graph_get_tensor(&gf, "embd");
*(int32_t *) input->data = 1; // BOS
ggml_metal_set_tensor(ctx_metal, input);
// warmup
ggml_metal_graph_compute(ctx_metal, &gf);
const int n_iter = 16;
const int64_t t0 = ggml_time_us();
// the actual inference happens here
for (int i = 0; i < n_iter; ++i) {
ggml_metal_graph_compute(ctx_metal, &gf);
}
const int64_t t1 = ggml_time_us();
printf("time: %.2f ms, %.2f ms/tok\n", (t1 - t0) / 1000.0, (t1 - t0) / 1000.0 / n_iter);
}
// debug output
{
struct ggml_tensor * logits = gf.nodes[gf.n_nodes - 1];
ggml_metal_get_tensor(ctx_metal, logits);
float * ptr = (float *) ggml_get_data(logits);
printf("logits: ");
for (int i = 0; i < 10; i++) {
printf("%8.4f ", ptr[i]);
}
printf("\n");
int imax = 0;
double sum = 0.0;
double vmax = -1e9;
for (int i = 0; i < 32000; i++) {
sum += (double) ptr[i];
if (ptr[i] > vmax) {
vmax = ptr[i];
imax = i;
}
}
printf("sum: %f, imax = %d, vmax = %f\n", sum, imax, vmax);
}
ggml_metal_free(ctx_metal);
ggml_free(ctx_data);
ggml_free(ctx_eval);
return 0;
}