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#include "ggml.h"
#include "ggml-cpu.h"
#include "ggml-alloc.h"
#include "ggml-backend.h"
#ifdef GGML_USE_CUDA
#include "ggml-cuda.h"
#endif
#ifdef GGML_USE_METAL
#include "ggml-metal.h"
#endif
#include <string.h>
#include <stdio.h>
#include <stdlib.h>
#include <vector>
#include <cmath>
void ggml_tensor_set_f32(struct ggml_tensor* tensor, float value, int l, int k = 0, int j = 0, int i = 0) {
GGML_ASSERT(tensor->nb[0] == sizeof(float));
*(float*)((char*)(tensor->data) + i * tensor->nb[3] + j * tensor->nb[2] + k * tensor->nb[1] + l * tensor->nb[0]) = value;
}
void set_timestep_embedding(struct ggml_tensor* timesteps, struct ggml_tensor* embedding, int dim, int max_period = 10000) {
// timesteps: [N,]
// embedding: [dim, N]
int half = dim / 2;
std::vector<float> freqs(half);
for (int i = 0; i < half; ++i) {
freqs[i] = (float)std::exp(-std::log(max_period) * i / half);
}
for (int i = 0; i < timesteps->ne[0]; ++i) {
for (int j = 0; j < half; ++j) {
float arg = ggml_get_f32_1d(timesteps, i) * freqs[j];
ggml_tensor_set_f32(embedding, std::cos(arg), j, i);
ggml_tensor_set_f32(embedding, std::sin(arg), j + half, i);
}
if (dim % 2 != 0) {
*(float*)((char*)embedding->data + i * embedding->nb[1] + dim * embedding->nb[0]) = 0;
}
}
}
static bool equalsf(float v1, float v2) {
if (fabs(v1 - v2) <= 0.00001) {
return true;
}
return false;
}
struct ggml_tensor* new_timestep_embedding(struct ggml_context* ctx,
struct ggml_tensor* timesteps,
int dim,
int max_period = 10000) {
// timesteps: [N,]
// embedding: [dim, N]
int actual_dim = dim;
struct ggml_tensor* embedding = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, actual_dim, timesteps->ne[0]);
set_timestep_embedding(timesteps, embedding, dim, max_period);
return embedding;
}
int main(int argc, const char** argv) {
std::vector<float> ts = {12, 24};
int dim = 15;
int max_period = 10000;
std::vector<float> expected_result;
{
struct ggml_init_params params;
params.mem_size = 16 * 1024 * 1024;
params.mem_buffer = NULL;
params.no_alloc = false;
// memory allocation happens here
struct ggml_context* ctx = ggml_init(params);
struct ggml_tensor* timesteps = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, ts.size());
memcpy(timesteps->data, ts.data(), ggml_nbytes(timesteps));
struct ggml_tensor* embedding = new_timestep_embedding(ctx, timesteps, dim, max_period);
expected_result.resize(ggml_nelements(embedding));
float* vec1 = ggml_get_data_f32(embedding);
for (int i = 0; i < ggml_nelements(embedding); i++) {
float value = vec1[i];
expected_result[i] = value;
printf("%.4f ", value);
}
printf("\n");
}
printf("-----------------------------------\n");
{
bool use_gpu = true; GGML_UNUSED(use_gpu);
ggml_backend_t backend = NULL;
ggml_backend_buffer_t params_buffer = NULL;
#ifdef GGML_USE_CUDA
if (use_gpu) {
fprintf(stderr, "%s: using CUDA backend\n", __func__);
backend = ggml_backend_cuda_init(0);
if (!backend) {
fprintf(stderr, "%s: ggml_backend_cuda_init() failed\n", __func__);
}
}
#endif
#ifdef GGML_USE_METAL
if (use_gpu) {
fprintf(stderr, "%s: using Metal backend\n", __func__);
backend = ggml_backend_metal_init();
if (!backend) {
fprintf(stderr, "%s: ggml_backend_metal_init() failed\n", __func__);
}
}
#endif
const int num_tensors = 2;
struct ggml_init_params params = {
/*.mem_size =*/ ggml_tensor_overhead() * num_tensors + 2 * 1024 * 1024,
/*.mem_size =*/ NULL,
/*.mem_size =*/ true,
};
if (!backend) {
// fallback to CPU backend
backend = ggml_backend_cpu_init();
}
struct ggml_context * ctx = ggml_init(params);
struct ggml_tensor * timesteps = ggml_new_tensor_1d(ctx, GGML_TYPE_F32, ts.size());
params_buffer = ggml_backend_alloc_ctx_tensors(ctx, backend);
// load data to buffer
if (ggml_backend_is_cpu(backend)) {
memcpy(timesteps->data, ts.data(), ggml_nbytes(timesteps));
} else {
ggml_backend_tensor_set(timesteps, ts.data(), 0, ggml_nbytes(timesteps));
}
struct ggml_tensor * t = ggml_timestep_embedding(ctx, timesteps, dim, max_period);
ggml_gallocr_t galloc = ggml_gallocr_new(ggml_backend_get_default_buffer_type(backend));
struct ggml_cgraph * graph = ggml_new_graph(ctx);
ggml_build_forward_expand(graph, t);
ggml_gallocr_alloc_graph(galloc, graph);
int n_threads = 4;
if (ggml_backend_is_cpu(backend)) {
ggml_backend_cpu_set_n_threads(backend, n_threads);
}
ggml_backend_graph_compute(backend, graph);
float * output = new float[ggml_nelements(t)];
ggml_backend_tensor_get(t, output, 0, ggml_nbytes(t));
GGML_ASSERT((size_t)ggml_nelements(t) == expected_result.size());
for (int i = 0; i < ggml_nelements(t); i++) {
printf("%.4f ", output[i]);
GGML_ASSERT(equalsf(output[i], expected_result[i]));
}
printf("\n");
delete[] output;
ggml_free(ctx);
ggml_backend_buffer_free(params_buffer);
ggml_backend_free(backend);
ggml_gallocr_free(galloc);
}
return 0;
}
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