mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2025-10-31 08:51:55 +00:00
ecb217db4f
* 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 98c267fc77.
* metal : add comments
* metal : clean-up stuff, fix typos
* readme : add Metal instructions
* readme : add example for main
103 lines
2.7 KiB
C++
103 lines
2.7 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.bin --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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gf.n_threads = 1;
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// this allocates all Metal resources and memory buffers
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auto * ctx_metal = ggml_metal_init();
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ggml_metal_add_buffer(ctx_metal, "data", ggml_get_mem_buffer(ctx_data), ggml_get_mem_size(ctx_data));
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ggml_metal_add_buffer(ctx_metal, "eval", ggml_get_mem_buffer(ctx_eval), ggml_get_mem_size(ctx_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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