mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-30 08:42:00 +00:00
4760e7cc0b
* sync : ggml (backend v2) (wip) * sync : migrate examples and llama.cpp to dynamic graphs (wip) * sync : update tests + fix max op params to 64 ggml-ci * sync : ggml-cuda ggml-ci * llama : fix save/load state context size ggml-ci * sync : try to fix build on tvOS * sync : pass custom graph sizes in training examples * sync : update graph copies to new ggml API * sync : update sync-ggml.sh with new files * scripts : fix header in sync script * train : fix context size calculations * llama : increase inference graph size up to 4096 nodes * train : allocate grads for backward graphs * train : allocate grads for gb_tmp
104 lines
2.8 KiB
C++
104 lines
2.8 KiB
C++
// Evaluate a statically exported ggml computation graph with Metal
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//
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// - First, export a LLaMA graph:
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//
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// $ ./bin/main -m ../models/7B/ggml-model-q4_0.gguf --export
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//
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// - Run this tool to evaluate the exported graph:
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//
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// $ ./bin/metal llama.ggml
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//
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// The purpose of this tool is mostly for debugging and demonstration purposes.
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// The main limitation of exporting computation graphs is that their sizes are static which often
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// can be a problem for real-world applications.
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//
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#include "ggml.h"
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#include "ggml-metal.h"
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#include <cstdio>
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#include <cstring>
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#include <cstdlib>
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int main(int argc, char ** argv) {
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ggml_time_init();
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if (argc != 2) {
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fprintf(stderr, "Usage: %s llama.ggml\n", argv[0]);
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return -1;
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}
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const char * fname_cgraph = argv[1];
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// load the compute graph
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struct ggml_context * ctx_data = NULL;
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struct ggml_context * ctx_eval = NULL;
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struct ggml_cgraph * gf = ggml_graph_import(fname_cgraph, &ctx_data, &ctx_eval);
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// this allocates all Metal resources and memory buffers
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auto * ctx_metal = ggml_metal_init(1);
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const size_t max_size_data = ggml_get_max_tensor_size(ctx_data);
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const size_t max_size_eval = ggml_get_max_tensor_size(ctx_eval);
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ggml_metal_add_buffer(ctx_metal, "data", ggml_get_mem_buffer(ctx_data), ggml_get_mem_size(ctx_data), max_size_data);
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ggml_metal_add_buffer(ctx_metal, "eval", ggml_get_mem_buffer(ctx_eval), ggml_get_mem_size(ctx_eval), max_size_eval);
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// main
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{
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struct ggml_tensor * input = ggml_graph_get_tensor(gf, "embd");
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*(int32_t *) input->data = 1; // BOS
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ggml_metal_set_tensor(ctx_metal, input);
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// warmup
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ggml_metal_graph_compute(ctx_metal, gf);
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const int n_iter = 16;
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const int64_t t0 = ggml_time_us();
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// the actual inference happens here
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for (int i = 0; i < n_iter; ++i) {
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ggml_metal_graph_compute(ctx_metal, gf);
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}
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const int64_t t1 = ggml_time_us();
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printf("time: %.2f ms, %.2f ms/tok\n", (t1 - t0) / 1000.0, (t1 - t0) / 1000.0 / n_iter);
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}
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// debug output
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{
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struct ggml_tensor * logits = gf->nodes[gf->n_nodes - 1];
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ggml_metal_get_tensor(ctx_metal, logits);
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float * ptr = (float *) ggml_get_data(logits);
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printf("logits: ");
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for (int i = 0; i < 10; i++) {
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printf("%8.4f ", ptr[i]);
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}
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printf("\n");
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int imax = 0;
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double sum = 0.0;
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double vmax = -1e9;
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for (int i = 0; i < 32000; i++) {
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sum += (double) ptr[i];
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if (ptr[i] > vmax) {
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vmax = ptr[i];
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imax = i;
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}
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}
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printf("sum: %f, imax = %d, vmax = %f\n", sum, imax, vmax);
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}
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ggml_metal_free(ctx_metal);
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ggml_free(ctx_data);
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ggml_free(ctx_eval);
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return 0;
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}
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