File size: 6,897 Bytes
8efb28e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 | #include "ggml-cpp.h"
#include "ggml.h"
#include "gguf.h"
#include "llama.h"
#include "common.h"
#include "arg.h"
#include "log.h"
#include <cstdint>
#include <string>
#include <vector>
// normalized mean squared error = mse(a, b) / mse(a, 0)
static double nmse(const std::vector<float> & a, const std::vector<float> & b) {
GGML_ASSERT(a.size() == b.size());
double mse_a_b = 0.0;
double mse_a_0 = 0.0;
for (size_t i = 0; i < a.size(); i++) {
float a_i = a[i];
float b_i = b[i];
mse_a_b += (a_i - b_i) * (a_i - b_i);
mse_a_0 += a_i * a_i;
}
return mse_a_b / mse_a_0;
}
static std::vector<float> get_logits(
llama_model * model, llama_context * lctx, const std::vector<llama_token> & tokens) {
const uint32_t n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(model));
const uint32_t n_ctx = llama_n_ctx(lctx);
const uint32_t n_tokens = tokens.size();
llama_batch batch = llama_batch_init(n_ctx, 0, 1);
GGML_ASSERT(n_tokens <= n_ctx);
for (uint32_t pos = 0; pos < n_tokens; pos++) {
common_batch_add(batch, tokens[pos], pos, {0}, true);
}
batch.n_tokens = n_tokens;
if (llama_decode(lctx, batch)) {
llama_batch_free(batch);
throw std::runtime_error("failed to decode batch");
}
std::vector<float> ret;
ret.reserve(n_tokens*n_vocab);
for (uint32_t i = 0; i < n_tokens; i++) {
const float * logits_ith = llama_get_logits_ith(lctx, i);
for (uint32_t j = 0; j < n_vocab; j++) {
ret.push_back(logits_ith[j]);
}
}
llama_batch_free(batch);
return ret;
}
int main(int argc, char ** argv) {
common_params params;
params.escape = false;
common_init();
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_RESULTS)) {
return 1;
}
if (params.out_file.empty()) {
LOG_ERR("%s: an output file must be specified", __func__);
return 1;
}
llama_backend_init();
llama_numa_init(params.numa);
common_init_result_ptr llama_init = common_init_from_params(params);
struct llama_model * model = llama_init->model();
struct llama_context * lctx = llama_init->context();
if (model == nullptr) {
LOG_ERR("%s: unable to load model\n", __func__);
return 1;
}
const uint32_t n_vocab = llama_vocab_n_tokens(llama_model_get_vocab(model));
const std::vector<llama_token> tokens_calc = common_tokenize(lctx, params.prompt, true);
const std::vector<float> logits_calc = get_logits(model, lctx, tokens_calc);
GGML_ASSERT(logits_calc.size() == tokens_calc.size()*n_vocab);
struct gguf_init_params gguf_params = {
/*.no_alloc =*/ true,
/*.ctx =*/ nullptr,
};
gguf_context_ptr gguf_ctx_model(gguf_init_from_file(params.model.path.c_str(), gguf_params));
if (params.check) {
LOG_INF("%s: loading results from %s...\n", __func__, params.out_file.c_str());
gguf_context_ptr gguf_ctx;
{
struct gguf_init_params gguf_params = {
/*no_alloc =*/ true,
/*ctx =*/ nullptr,
};
gguf_ctx.reset(gguf_init_from_file(params.out_file.c_str(), gguf_params));
}
const std::string path_model_disk = gguf_get_val_str(gguf_ctx.get(), gguf_find_key(gguf_ctx.get(), "path_model"));
GGML_ASSERT(path_model_disk == params.model.path); // TODO better checks
auto load_tensor_data = [&](const std::string & name, void * dst, const size_t size){
const int64_t tid = gguf_find_tensor(gguf_ctx.get(), name.c_str());
const size_t offset = gguf_get_data_offset(gguf_ctx.get()) + gguf_get_tensor_offset(gguf_ctx.get(), tid);
GGML_ASSERT(size == gguf_get_tensor_size(gguf_ctx.get(), tid));
FILE * file = ggml_fopen(params.out_file.c_str(), "rb");
if (file == nullptr) {
throw std::runtime_error("failed to open results file");
}
if (fseek(file, offset, SEEK_SET) != 0) {
throw std::runtime_error("fseek failed");
}
const size_t nbytes_read = fread(dst, 1, size, file);
if (nbytes_read != size) {
throw std::runtime_error("fread failed");
}
};
std::vector<llama_token> tokens_disk(tokens_calc.size());
load_tensor_data("tokens", tokens_disk.data(), tokens_disk.size()*sizeof(llama_token));
GGML_ASSERT(tokens_disk.size() == tokens_calc.size());
for (size_t i = 0; i < tokens_calc.size(); i++) {
GGML_ASSERT(tokens_disk[i] == tokens_calc[i]);
}
std::vector<float> logits_disk(logits_calc.size());
load_tensor_data("logits", logits_disk.data(), logits_disk.size()*sizeof(float));
const double nmse_val = nmse(logits_disk, logits_calc);
LOG_INF("%s: NMSE=%.3e\n", __func__, nmse_val);
if (nmse_val > 1e-6) {
printf("\033[1;31mFAIL\033[0m\n");
return 1;
}
printf("\033[1;32mOK\033[0m\n");
return 0;
}
ggml_context_ptr ggml_ctx_calc;
{
const size_t size_tokens = tokens_calc.size()*sizeof(llama_token) + ggml_tensor_overhead();
const size_t size_logits = logits_calc.size()*sizeof(float) + ggml_tensor_overhead();
struct ggml_init_params params = {
/*.mem_size =*/ size_tokens + size_logits,
/*.mem_buffer =*/ nullptr,
/*.no_alloc =*/ false,
};
ggml_ctx_calc.reset(ggml_init(params));
}
gguf_context_ptr gguf_ctx(gguf_init_empty());
gguf_set_val_str(gguf_ctx.get(), "path_model", params.model.path.c_str());
{
ggml_tensor * t_tokens = ggml_new_tensor_1d(ggml_ctx_calc.get(), GGML_TYPE_I32, tokens_calc.size());
ggml_set_name(t_tokens, "tokens");
int32_t * tokens_data = (int32_t *) t_tokens->data;
for (uint32_t i = 0; i < tokens_calc.size(); i++) {
tokens_data[i] = tokens_calc[i];
}
gguf_add_tensor(gguf_ctx.get(), t_tokens);
}
{
ggml_tensor * t_logits = ggml_new_tensor_2d(ggml_ctx_calc.get(), GGML_TYPE_F32, tokens_calc.size(), n_vocab);
ggml_set_name(t_logits, "logits");
float * logits_data = ggml_get_data_f32(t_logits);
for (uint32_t i = 0; i < tokens_calc.size(); i++) {
const float * logits_ith = llama_get_logits_ith(lctx, i);
for (uint32_t j = 0; j < n_vocab; j++) {
logits_data[i*n_vocab + j] = logits_ith[j];
}
}
gguf_add_tensor(gguf_ctx.get(), t_logits);
}
LOG_INF("%s: writing results to %s...\n", __func__, params.out_file.c_str());
gguf_write_to_file(gguf_ctx.get(), params.out_file.c_str(), /*only_meta =*/ false);
return 0;
}
|