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simple-backend.cpp
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simple-backend.cpp
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#include "ggml.h"
#include "ggml/ggml-alloc.h"
#include "ggml/ggml-backend.h"
#ifdef GGML_USE_CUDA
#include "ggml-cuda.h"
#endif
#ifdef GGML_USE_METAL
#include "ggml-metal.h"
#endif
#include <cassert>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <fstream>
#include <map>
#include <string>
#include <iostream>
#include <vector>
static void ggml_log_callback_default(ggml_log_level level, const char * text, void * user_data) {
(void) level;
(void) user_data;
fputs(text, stderr);
fflush(stderr);
}
// This is a simple model with two tensors a and b
struct simple_model {
struct ggml_tensor * a;
struct ggml_tensor * b;
// the backend to perform the computation (CPU, CUDA, METAL)
ggml_backend_t backend = NULL;
// the backend buffer to storage the tensors data of a and b
ggml_backend_buffer_t buffer;
// the context to define the tensor information (dimensions, size, memory address)
struct ggml_context * ctx;
};
// initialize the tensors of the model in this case two matrices 2x2
void load_model(simple_model & model, float * a, float * b, int rows_A, int cols_A, int rows_B, int cols_B) {
// initialize the backend
#ifdef GGML_USE_CUDA
fprintf(stderr, "%s: using CUDA backend\n", __func__);
model.backend = ggml_backend_cuda_init(0); // init device 0
if (!model.backend) {
fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
}
#endif
#ifdef GGML_USE_METAL
fprintf(stderr, "%s: using Metal backend\n", __func__);
ggml_backend_metal_log_set_callback(ggml_log_callback_default, nullptr);
model.backend = ggml_backend_metal_init();
if (!model.backend) {
fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__);
}
#endif
// if there aren't GPU Backends fallback to CPU backend
if (!model.backend) {
model.backend = ggml_backend_cpu_init();
}
int num_tensors = 2;
struct ggml_init_params params {
/*.mem_size =*/ ggml_tensor_overhead() * num_tensors,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
// create context
model.ctx = ggml_init(params);
// create tensors
model.a = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, cols_A, rows_A);
model.b = ggml_new_tensor_2d(model.ctx, GGML_TYPE_F32, cols_B, rows_B);
// create a backend buffer (backend memory) and alloc the tensors from the context
model.buffer = ggml_backend_alloc_ctx_tensors(model.ctx, model.backend);
// load data from cpu memory to backend buffer
ggml_backend_tensor_set(model.a, a, 0, ggml_nbytes(model.a));
ggml_backend_tensor_set(model.b, b, 0, ggml_nbytes(model.b));
}
// build the compute graph to perform a matrix multiplication
struct ggml_cgraph * build_graph(const simple_model& model) {
static size_t buf_size = ggml_tensor_overhead()*GGML_DEFAULT_GRAPH_SIZE + ggml_graph_overhead();
static std::vector<uint8_t> buf(buf_size);
struct ggml_init_params params0 = {
/*.mem_size =*/ buf_size,
/*.mem_buffer =*/ buf.data(),
/*.no_alloc =*/ true, // the tensors will be allocated later by ggml_allocr_alloc_graph()
};
// create a temporally context to build the graph
struct ggml_context * ctx0 = ggml_init(params0);
struct ggml_cgraph * gf = ggml_new_graph(ctx0);
// result = a*b^T
struct ggml_tensor * result = ggml_group_norm(ctx0, model.a, 32);
// build operations nodes
ggml_build_forward_expand(gf, result);
// delete the temporally context used to build the graph
ggml_free(ctx0);
return gf;
}
// compute with backend
struct ggml_tensor * compute(const simple_model & model, ggml_gallocr_t allocr) {
// reset the allocator to free all the memory allocated during the previous inference
struct ggml_cgraph * gf = build_graph(model);
// allocate tensors
ggml_gallocr_alloc_graph(allocr, gf);
int n_threads = 1; // number of threads to perform some operations with multi-threading
if (ggml_backend_is_cpu(model.backend)) {
ggml_backend_cpu_set_n_threads(model.backend, n_threads);
}
#ifdef GGML_USE_METAL
if (ggml_backend_is_metal(model.backend)) {
ggml_backend_metal_set_n_cb(model.backend, n_threads);
}
#endif
ggml_backend_graph_compute(model.backend, gf);
// in this case, the output tensor is the last one in the graph
return gf->nodes[gf->n_nodes - 1];
}
int main(void) {
ggml_time_init();
// initialize data of matrices to perform matrix multiplication
const int rows_A = 1024, cols_A = 43;
float matrix_A[rows_A * cols_A] = {0.7013666033744812, 0.8223229050636292, -0.12929187715053558, -0.7994263172149658, -0.6027005314826965, -0.586579442024231, -0.5107471346855164, -0.16000691056251526, -0.5602322220802307, -0.14685790240764618, -0.14688722789287567, 0.3761242926120758, 0.3780743479728699, 1.2616475820541382, 1.1066375970840454, 0.7158601880073547, 0.7106958031654358, 0.2430015206336975, 0.10244931280612946, 0.041106872260570526, 0.7568025588989258, 0.3314422369003296, -0.210140660405159, 0.5404788851737976, 0.9092957377433777, 0.036413438618183136, -0.7535380125045776, -0.5061773657798767, -0.2746049463748932, -0.5850591063499451, -1.1422518491744995, -0.2515627443790436, 0.17288653552532196, -0.40598976612091064, 0.3695950508117676, 0.4255625009536743, 0.4654455780982971, 0.9313895106315613, 1.001731276512146, 0.7712277173995972, 1.0257902145385742, 0.9098255038261414, 0.39022505283355713, 0.3544605076313019, -0.5796005129814148, 0.6159850358963013, 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const int rows_B = 3, cols_B = 2;
/* Transpose([
10, 9, 5,
5, 9, 4
]) 2 rows, 3 cols */
float matrix_B[rows_B * cols_B] = {
10, 5,
9, 9,
5, 4
};
simple_model model;
load_model(model, matrix_A, matrix_B, rows_A, cols_A, rows_B, cols_B);
// calculate the temporaly memory required to compute
ggml_gallocr_t allocr = NULL;
{
allocr = ggml_gallocr_new(ggml_backend_get_default_buffer_type(model.backend));
// create the worst case graph for memory usage estimation
struct ggml_cgraph * gf = build_graph(model);
ggml_gallocr_reserve(allocr, gf);
size_t mem_size = ggml_gallocr_get_buffer_size(allocr, 0);
fprintf(stderr, "%s: compute buffer size: %.4f KB\n", __func__, mem_size/1024.0);
}
// perform computation
struct ggml_tensor * result = compute(model, allocr);
// create a array to print result
std::vector<float> out_data(ggml_nelements(result));
// bring the data from the backend memory
ggml_backend_tensor_get(result, out_data.data(), 0, ggml_nbytes(result));
// expected result:
// [ 60.00 110.00 54.00 29.00
// 55.00 90.00 126.00 28.00
// 50.00 54.00 42.00 64.00 ]
ggml_tensor * test = result;
std::cout << "---------------------------------------------------" << std::endl;
std::cout << "NAME:" << std::endl;
std::cout << test->name << std::endl;
std::cout << "TYPE" << std::endl;
std::cout << test->type << std::endl;
std::cout << "SHAPE:" << std::endl;
std::cout << test->ne[0]<< std::endl;
std::cout << test->ne[1]<< std::endl;
std::cout << test->ne[2]<< std::endl;
std::cout << test->ne[3]<< std::endl;
std::cout << "DATA:" << std::endl;
if (!ggml_is_contiguous(test))
{
std::cout << "Skipped data; not contiguous" << std::endl;
exit(1);
}
if (ggml_is_transposed(test))
{
std::cout << "Transposed:" << std::endl;
}
int elements = test->ne[0] * test->ne[1] * test->ne[2] * test->ne[3];
//ggml_tensor * weights = gf->leafs[gf->n_leafs -2];
//ggml_tensor * tokens = gf->leafs[gf->n_leafs -1];
//ggml_graph_dump_dot(gf, NULL, "autoregressive.dot");
//std::cout << "made it here" << std::endl;
if (test->type == GGML_TYPE_F32)
{
std::vector<float> test_read( elements);
ggml_backend_tensor_get(test,test_read.data(), 0 ,sizeof(float)* elements);
//
for (int c = 0; c < elements ; c++)
{
if (c < 3 || c > elements-4 )
{
std::cout << (test_read.data()[c])<< std::endl;
}
}
}
else if(test->type == GGML_TYPE_F16)
{
std::vector<ggml_fp16_t> test_read( elements);
ggml_backend_tensor_get(test,test_read.data(), 0 ,sizeof(ggml_fp16_t)* elements);
//
for (int c = 0; c < elements ; c++)
{
if (c < 3 || c > elements-4)
{
std::cout << ggml_fp16_to_fp32(test_read.data()[c])<< std::endl;
}
}
}
else if(test->type == GGML_TYPE_I32){
std::vector<int32_t> test_read( elements);
ggml_backend_tensor_get(test,test_read.data(), 0 ,sizeof(int32_t)* elements);
//
for (int c = 0; c < elements ; c++)
{
if (c < 3 || c > elements-4)
{
std::cout << test_read.data()[c]<< std::endl;
}
}
}
// release backend memory used for computation
ggml_gallocr_free(allocr);
// free memory
ggml_free(model.ctx);
// release backend memory and free backend
ggml_backend_buffer_free(model.buffer);
ggml_backend_free(model.backend);
return 0;
}