422 lines
14 KiB
C++
422 lines
14 KiB
C++
#include "arg.h"
|
|
#include "common.h"
|
|
#include "log.h"
|
|
#include "llama.h"
|
|
#include "ggml.h"
|
|
|
|
#include <cmath>
|
|
#include <cstdint>
|
|
#include <cstdlib>
|
|
#include <string>
|
|
#include <vector>
|
|
#include <filesystem>
|
|
#include <fstream>
|
|
#include <regex>
|
|
|
|
static void print_usage(int, char ** argv) {
|
|
const std::string usage_template = R"(
|
|
example usage:
|
|
|
|
Print tensors:
|
|
|
|
{prog} -m model.gguf -p "Hello my name is" --verbose
|
|
|
|
The tensors to be printed can be filtered with --tensor-filter option.
|
|
|
|
Save logits/embeddings:
|
|
|
|
{prog} -m model.gguf -p "Hello my name is" --save-logits
|
|
|
|
Add --embedding to save embeddings)" "\n";
|
|
|
|
// Fix the source code indentation above that is introduced by the raw string literal.
|
|
std::string usage = std::regex_replace(usage_template, std::regex("\\n {8}"), "\n");
|
|
usage = std::regex_replace(usage, std::regex("\\{prog\\}"), argv[0]);
|
|
LOG("%s\n", usage.c_str());
|
|
}
|
|
|
|
static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data);
|
|
|
|
struct callback_data {
|
|
std::vector<uint8_t> data;
|
|
std::vector<std::regex> tensor_filters;
|
|
|
|
callback_data() = default;
|
|
|
|
callback_data(common_params & params, const std::vector<std::string> & filter_patterns) {
|
|
for (const auto & pattern : filter_patterns) {
|
|
try {
|
|
std::string anchored_pattern = "^" + pattern;
|
|
tensor_filters.emplace_back(anchored_pattern, std::regex::optimize);
|
|
} catch (const std::regex_error & e) {
|
|
throw std::runtime_error("Invalid regex pattern '" + pattern + "': " + e.what());
|
|
}
|
|
}
|
|
params.cb_eval = ggml_debug;
|
|
params.cb_eval_user_data = this;
|
|
}
|
|
};
|
|
|
|
struct output_data {
|
|
float * data_ptr = nullptr;
|
|
int data_size = 0;
|
|
std::string type_suffix;
|
|
std::vector<float> storage;
|
|
std::string prompt;
|
|
std::vector<llama_token> tokens;
|
|
|
|
output_data(llama_context * ctx, const llama_model * model, const common_params & params) {
|
|
const llama_vocab * vocab = llama_model_get_vocab(model);
|
|
const bool add_bos = llama_vocab_get_add_bos(vocab);
|
|
|
|
tokens = common_tokenize(ctx, params.prompt, add_bos);
|
|
prompt = params.prompt;
|
|
|
|
if (params.embedding) {
|
|
const int n_embd = llama_model_n_embd_out(model);
|
|
const bool pooling_enabled = llama_pooling_type(ctx) != LLAMA_POOLING_TYPE_NONE;
|
|
const int n_embd_count = pooling_enabled ? 1 : tokens.size();
|
|
const int n_embeddings = n_embd * n_embd_count;
|
|
|
|
float * embeddings;
|
|
if (pooling_enabled) {
|
|
embeddings = llama_get_embeddings_seq(ctx, 0);
|
|
storage.resize(n_embeddings);
|
|
common_embd_normalize(embeddings, storage.data(), n_embeddings, params.embd_normalize);
|
|
embeddings = storage.data();
|
|
} else {
|
|
embeddings = llama_get_embeddings(ctx);
|
|
}
|
|
|
|
data_ptr = embeddings;
|
|
data_size = n_embeddings;
|
|
type_suffix = "-embeddings";
|
|
} else {
|
|
const float * logits = llama_get_logits_ith(ctx, tokens.size() - 1);
|
|
const int n_logits = llama_vocab_n_tokens(vocab);
|
|
|
|
data_ptr = const_cast<float*>(logits);
|
|
data_size = n_logits;
|
|
type_suffix = "";
|
|
}
|
|
}
|
|
};
|
|
|
|
static std::string ggml_ne_string(const ggml_tensor * t) {
|
|
std::string str;
|
|
for (int i = 0; i < GGML_MAX_DIMS; ++i) {
|
|
str += std::to_string(t->ne[i]);
|
|
if (i + 1 < GGML_MAX_DIMS) {
|
|
str += ", ";
|
|
}
|
|
}
|
|
return str;
|
|
}
|
|
|
|
static inline float ggml_compute_bf16_to_fp32(ggml_bf16_t h) {
|
|
union {
|
|
float f;
|
|
uint32_t i;
|
|
} u;
|
|
u.i = (uint32_t)h.bits << 16;
|
|
return u.f;
|
|
}
|
|
|
|
static float ggml_get_float_value(const uint8_t * data, ggml_type type,
|
|
const size_t * nb, size_t i0, size_t i1, size_t i2, size_t i3) {
|
|
size_t i = i3 * nb[3] + i2 * nb[2] + i1 * nb[1] + i0 * nb[0];
|
|
switch (type) {
|
|
case GGML_TYPE_F16:
|
|
return ggml_fp16_to_fp32(*(const ggml_fp16_t *) &data[i]);
|
|
case GGML_TYPE_F32:
|
|
return *(const float *) &data[i];
|
|
case GGML_TYPE_I64:
|
|
return (float) *(const int64_t *) &data[i];
|
|
case GGML_TYPE_I32:
|
|
return (float) *(const int32_t *) &data[i];
|
|
case GGML_TYPE_I16:
|
|
return (float) *(const int16_t *) &data[i];
|
|
case GGML_TYPE_I8:
|
|
return (float) *(const int8_t *) &data[i];
|
|
case GGML_TYPE_BF16:
|
|
return ggml_compute_bf16_to_fp32(*(const ggml_bf16_t *) &data[i]);
|
|
default:
|
|
GGML_ABORT("fatal error");
|
|
}
|
|
}
|
|
|
|
static void ggml_print_tensor(uint8_t * data, ggml_type type, const int64_t * ne, const size_t * nb, int64_t n) {
|
|
GGML_ASSERT(n > 0);
|
|
float sum = 0;
|
|
float sum_sq = 0.0;
|
|
for (int64_t i3 = 0; i3 < ne[3]; i3++) {
|
|
for (int64_t i2 = 0; i2 < ne[2]; i2++) {
|
|
for (int64_t i1 = 0; i1 < ne[1]; i1++) {
|
|
for (int64_t i0 = 0; i0 < ne[0]; i0++) {
|
|
const float v = ggml_get_float_value(data, type, nb, i0, i1, i2, i3);
|
|
sum += v;
|
|
sum_sq += v * v;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
for (int64_t i3 = 0; i3 < ne[3]; i3++) {
|
|
LOG_DBG(" [\n");
|
|
for (int64_t i2 = 0; i2 < ne[2]; i2++) {
|
|
if (i2 == n && ne[2] > 2*n) {
|
|
LOG_DBG(" ..., \n");
|
|
i2 = ne[2] - n;
|
|
}
|
|
LOG_DBG(" [\n");
|
|
for (int64_t i1 = 0; i1 < ne[1]; i1++) {
|
|
if (i1 == n && ne[1] > 2*n) {
|
|
LOG_DBG(" ..., \n");
|
|
i1 = ne[1] - n;
|
|
}
|
|
LOG_DBG(" [");
|
|
for (int64_t i0 = 0; i0 < ne[0]; i0++) {
|
|
if (i0 == n && ne[0] > 2*n) {
|
|
LOG_DBG("..., ");
|
|
i0 = ne[0] - n;
|
|
}
|
|
const float v = ggml_get_float_value(data, type, nb, i0, i1, i2, i3);
|
|
LOG_DBG("%12.4f", v);
|
|
if (i0 < ne[0] - 1) {
|
|
LOG_DBG(", ");
|
|
}
|
|
}
|
|
LOG_DBG("],\n");
|
|
}
|
|
LOG_DBG(" ],\n");
|
|
}
|
|
LOG_DBG(" ]\n");
|
|
LOG_DBG(" sum = %f\n", sum);
|
|
LOG_DBG(" sum_sq = %f\n", sum_sq);
|
|
}
|
|
|
|
if (std::isnan(sum)) {
|
|
LOG_ERR("encountered NaN - aborting\n");
|
|
exit(0);
|
|
}
|
|
}
|
|
|
|
/**
|
|
* GGML operations callback during the graph execution.
|
|
*
|
|
* @param t current tensor
|
|
* @param ask when ask is true, the scheduler wants to know if we are interested in data from this tensor
|
|
* if we return true, a follow-up call will be made with ask=false in which we can do the actual collection.
|
|
* see ggml_backend_sched_eval_callback
|
|
* @param user_data user data to pass at each call back
|
|
* @return true to receive data or continue the graph, false otherwise
|
|
*/
|
|
static bool ggml_debug(struct ggml_tensor * t, bool ask, void * user_data) {
|
|
auto * cb_data = (callback_data *) user_data;
|
|
|
|
const struct ggml_tensor * src0 = t->src[0];
|
|
const struct ggml_tensor * src1 = t->src[1];
|
|
|
|
if (ask) {
|
|
return true; // Always retrieve data
|
|
}
|
|
|
|
bool matches_filter = cb_data->tensor_filters.empty();
|
|
|
|
if (!matches_filter) {
|
|
for (const auto & filter : cb_data->tensor_filters) {
|
|
if (std::regex_search(t->name, filter)) {
|
|
matches_filter = true;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
char src1_str[128] = {0};
|
|
if (src1) {
|
|
snprintf(src1_str, sizeof(src1_str), "%s{%s}", src1->name, ggml_ne_string(src1).c_str());
|
|
}
|
|
|
|
if (matches_filter) {
|
|
LOG_DBG("%s: %24s = (%s) %10s(%s{%s}, %s}) = {%s}\n", __func__,
|
|
t->name,
|
|
ggml_type_name(t->type),
|
|
ggml_op_desc(t),
|
|
src0->name,
|
|
ggml_ne_string(src0).c_str(),
|
|
src1 ? src1_str : "",
|
|
ggml_ne_string(t).c_str());
|
|
}
|
|
|
|
const bool is_host = ggml_backend_buffer_is_host(t->buffer);
|
|
|
|
if (!is_host) {
|
|
auto n_bytes = ggml_nbytes(t);
|
|
cb_data->data.resize(n_bytes);
|
|
ggml_backend_tensor_get(t, cb_data->data.data(), 0, n_bytes);
|
|
}
|
|
|
|
if (!ggml_is_quantized(t->type) && matches_filter) {
|
|
uint8_t * data = is_host ? (uint8_t *) t->data : cb_data->data.data();
|
|
ggml_print_tensor(data, t->type, t->ne, t->nb, 3);
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
|
|
static void save_output_data(const output_data & output, const std::string & model_name, const std::string & output_dir) {
|
|
std::filesystem::create_directory(output_dir);
|
|
auto base_path = std::filesystem::path{output_dir} / ("llamacpp-" + model_name + output.type_suffix);
|
|
|
|
// Save logits/embeddings to binary file.
|
|
{
|
|
std::filesystem::path filepath{base_path.string() + ".bin"};
|
|
std::ofstream file{filepath, std::ios::binary};
|
|
if (!file) {
|
|
throw std::runtime_error("failed to open binary output file: " + filepath.string());
|
|
}
|
|
file.write(reinterpret_cast<const char*>(output.data_ptr), output.data_size * sizeof(float));
|
|
LOG("Data saved to %s\n", filepath.c_str());
|
|
}
|
|
|
|
// Save logits/embeddings to text file.
|
|
{
|
|
std::filesystem::path filepath{base_path.string() + ".txt"};
|
|
std::ofstream file{filepath};
|
|
if (!file) {
|
|
throw std::runtime_error("failed to open text output file: " + filepath.string());
|
|
}
|
|
for (int i = 0; i < output.data_size; i++) {
|
|
file << i << ": " << output.data_ptr[i] << '\n';
|
|
}
|
|
LOG("Data saved to %s\n", filepath.c_str());
|
|
}
|
|
|
|
// Save prompt and tokens to text file.
|
|
{
|
|
std::filesystem::path filepath{base_path.string() + "-prompt.txt"};
|
|
std::ofstream file{filepath};
|
|
if (!file) {
|
|
throw std::runtime_error("failed to open prompt output file: " + filepath.string());
|
|
}
|
|
|
|
file << "prompt: " << output.prompt << '\n';
|
|
file << "n_tokens: " << output.tokens.size() << '\n';
|
|
|
|
file << "token ids: ";
|
|
for (size_t i = 0; i < output.tokens.size(); i++) {
|
|
file << output.tokens[i];
|
|
if (i + 1 < output.tokens.size()) {
|
|
file << ", ";
|
|
}
|
|
}
|
|
file << '\n';
|
|
LOG("Prompt saved to %s\n", filepath.c_str());
|
|
}
|
|
|
|
// Save token ids to binary file.
|
|
{
|
|
std::filesystem::path filepath{base_path.string() + "-tokens.bin"};
|
|
std::ofstream file{filepath, std::ios::binary};
|
|
if (!file) {
|
|
throw std::runtime_error("failed to open tokens binary file: " + filepath.string());
|
|
}
|
|
file.write(reinterpret_cast<const char*>(output.tokens.data()), output.tokens.size() * sizeof(llama_token));
|
|
LOG("Tokens saved to %s\n", filepath.c_str());
|
|
}
|
|
|
|
}
|
|
|
|
static void print_tokenized_prompt(llama_context * ctx, const std::vector<llama_token> & tokens, const std::string & prompt) {
|
|
const llama_model * model = llama_get_model(ctx);
|
|
const llama_vocab * vocab = llama_model_get_vocab(model);
|
|
|
|
LOG("Model add_bos: %s\n", llama_vocab_get_add_bos(vocab) ? "true" : "false");
|
|
LOG("Input prompt: \"%s\"\n", prompt.c_str());
|
|
LOG("Token ids (%zu):\n", tokens.size());
|
|
|
|
for (auto id : tokens) {
|
|
std::string piece(128, '\0');
|
|
int n = llama_token_to_piece(vocab, id, piece.data(), piece.size(), 0, true);
|
|
if (n < 0) {
|
|
LOG_ERR("failed to convert token %d to piece\n", id);
|
|
continue;
|
|
}
|
|
piece.resize(n);
|
|
LOG("%s(%d) ", piece.c_str(), id);
|
|
}
|
|
LOG("\n");
|
|
}
|
|
|
|
static bool run(llama_context * ctx, const common_params & params) {
|
|
const llama_model * model = llama_get_model(ctx);
|
|
const llama_vocab * vocab = llama_model_get_vocab(model);
|
|
|
|
const bool add_bos = llama_vocab_get_add_bos(vocab);
|
|
|
|
std::vector<llama_token> tokens = common_tokenize(ctx, params.prompt, add_bos);
|
|
|
|
if (tokens.empty()) {
|
|
LOG_ERR("%s : there are not input tokens to process - (try to provide a prompt with '-p')\n", __func__);
|
|
return false;
|
|
}
|
|
|
|
if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size()))) {
|
|
LOG_ERR("%s : failed to eval\n", __func__);
|
|
return false;
|
|
}
|
|
|
|
print_tokenized_prompt(ctx, tokens, params.prompt);
|
|
|
|
if (params.save_logits) {
|
|
output_data output {ctx, model, params};
|
|
std::filesystem::path model_path{params.model.path};
|
|
std::string model_name{model_path.stem().string()};
|
|
save_output_data(output, model_name, params.logits_output_dir);
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
int main(int argc, char ** argv) {
|
|
common_params params;
|
|
|
|
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_DEBUG, print_usage)) {
|
|
return 1;
|
|
}
|
|
|
|
common_init();
|
|
|
|
llama_backend_init();
|
|
llama_numa_init(params.numa);
|
|
|
|
callback_data cb_data(params, params.tensor_filter);
|
|
|
|
auto llama_init = common_init_from_params(params);
|
|
|
|
auto * model = llama_init->model();
|
|
auto * ctx = llama_init->context();
|
|
|
|
if (model == nullptr || ctx == nullptr) {
|
|
LOG_ERR("%s : failed to init\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
{
|
|
LOG_INF("\n");
|
|
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
|
|
LOG_INF("\n");
|
|
}
|
|
|
|
if (!run(ctx, params)) {
|
|
return 1;
|
|
}
|
|
|
|
LOG("\n");
|
|
llama_perf_context_print(ctx);
|
|
|
|
llama_backend_free();
|
|
|
|
return 0;
|
|
}
|