llama.cpp/examples/cvector-generator/cvector-generator.cpp
Liu Jia 0a4ce78681
common : Changed tuple to struct (TODO fix) (#8823)
* common : Changed tuple to struct (TODO fix)

Use struct `llama_init_result` to replace the previous
std::tuple<struct llama_model *, struct llama_context *>

* delete llama_init_default_params()

* delete the extra whitespace
2024-08-05 18:14:10 +02:00

505 lines
18 KiB
C++

#include "common.h"
#include "llama.h"
#include "ggml.h"
#include "pca.hpp"
#include "mean.hpp"
#ifdef GGML_USE_CUDA
#include "ggml-cuda.h"
#endif
#ifdef GGML_USE_METAL
#include "ggml-metal.h"
#endif
#include <cstdio>
#include <string>
#include <tuple>
#include <vector>
#include <algorithm>
#include <iostream>
#include <fstream>
#include <climits>
//////////////////////////////////////////////////
// utils
template <class Iter>
static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
std::string ret;
for (; begin != end; ++begin) {
ret += llama_token_to_piece(ctx, *begin);
}
return ret;
}
static void print_usage(int argc, char ** argv, const gpt_params & params) {
gpt_params_print_usage(argc, argv, params);
printf("\nexample usage:\n");
printf("\n CPU only: %s -m ./llama-3.Q4_K_M.gguf\n", argv[0]);
printf("\n with GPU: %s -m ./llama-3.Q4_K_M.gguf -ngl 99\n", argv[0]);
printf("\n advanced: %s -m ./llama-3.Q4_K_M.gguf -ngl 99 --pca-iter 2000 --pca-batch 100\n", argv[0]);
printf("\n using mean: %s -m ./llama-3.Q4_K_M.gguf --method mean\n", argv[0]);
printf("\n");
}
//////////////////////////////////////////////////
// cb_eval is reused for each pair of positive - negative prompt
struct callback_data {
ggml_context * ctx_ggml = nullptr; // holds v_pos, v_neg, v_diff_filtered
int n_layers = 0;
int n_tokens = 0;
bool is_eval_pos = true;
// each element of the vector correspond to one layer
std::vector<struct ggml_tensor *> v_pos; // vector of matrices of size [n_embd, n_tokens]
std::vector<struct ggml_tensor *> v_neg; // vector of matrices of size [n_embd, n_tokens]
std::vector<struct ggml_tensor *> v_diff_filtered; // vector of matrices of size [n_embd, n_nonzero_rows]. NOTE: n_nonzero_rows maybe different for each layer
// save a tensor into either v_pos or v_neg (decided by is_eval_pos)
void save_tensor_for_layer(struct ggml_tensor * t) {
GGML_ASSERT(t->type == GGML_TYPE_F32);
if (ctx_ggml == nullptr) {
// alloc a new ctx_ggml if needed
struct ggml_init_params params_ggml = {
/*.mem_size =*/ ggml_tensor_overhead() * n_layers * 3u,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
ctx_ggml = ggml_init(params_ggml);
}
// copy tensor data
auto n_bytes = ggml_nbytes(t);
struct ggml_tensor * t_layer = ggml_new_tensor_2d(ctx_ggml, t->type, t->ne[0], t->ne[1]);
t_layer->data = malloc(n_bytes); // TODO @ngxson : get rid of this malloc somehow
ggml_backend_tensor_get(t, t_layer->data, 0, n_bytes);
ggml_set_name(t_layer, ggml_get_name(t));
//print_debug_tensor(t_layer);
if (is_eval_pos) {
v_pos.push_back(t_layer);
} else {
v_neg.push_back(t_layer);
}
}
// calculate diff (v_pos - v_neg) and place the result back to v_pos
// all zero rows in the diff tensor will also be removed
// NOTE: final layer is ignored. we only have (n_layers - 1) to process
std::vector<struct ggml_tensor *> calc_diff() {
for (float il = 0; il < v_pos.size(); il++) {
float * a = (float *) v_pos[il]->data;
float * b = (float *) v_neg[il]->data;
size_t n_elem = ggml_nelements(v_pos[il]);
for (size_t j = 0; j < n_elem; j++) {
a[j] -= b[j];
}
//print_debug_tensor(v_pos[i]);
auto diff_filtered = filter_nonzero_rows(v_pos[il]);
v_diff_filtered.push_back(diff_filtered);
}
return v_diff_filtered; // for convinient, we return the result std::vector
}
// delete zero rows from a given 2D tensor
struct ggml_tensor * filter_nonzero_rows(struct ggml_tensor * a) {
//printf("filter_nonzero_rows\n");
auto is_row_all_zeros = [](struct ggml_tensor * t, int row, float eps) -> bool {
// check if given row containing all zero elements
int n_cols = t->ne[0]; // hint: should be equal to n_embd
for (int col = 0; col < n_cols; ++col) {
if (ggml_get_f32_nd(t, col, row, 0, 0) > eps) {
return false;
}
}
return true;
};
std::vector<int> rows_to_copy; // the idx of non-zero cols (to be copied to row of diff_filtered)
for (int i_row = 0; i_row < a->ne[1]; i_row++) {
if (!is_row_all_zeros(a, i_row, 1e-6)) {
rows_to_copy.push_back(i_row);
}
}
// get "n_nonzero_rows" for the output "diff_filtered"
int n_nonzero_rows = rows_to_copy.size();
//printf("n_nonzero_rows: %d\n", n_nonzero_rows);
int n_embd = a->ne[0];
GGML_ASSERT(n_nonzero_rows > 0);
// diff_filtered: [n_embd, n_nonzero_rows]
struct ggml_tensor * diff_filtered = ggml_new_tensor_2d(
ctx_ggml, GGML_TYPE_F32, n_embd, n_nonzero_rows);
ggml_format_name(diff_filtered, "diff_filtered_%s", a->name);
diff_filtered->data = malloc(ggml_nbytes(diff_filtered));
// copy non-zero rows
for (int dest_row = 0; dest_row < n_nonzero_rows; dest_row++) {
int src_row = rows_to_copy[dest_row];
for (int i = 0; i < n_embd; i++) {
float src_elem = ggml_get_f32_nd(a, i, src_row, 0, 0);
ggml_set_f32_nd(diff_filtered, i, dest_row, 0, 0, src_elem);
}
}
//print_debug_tensor(diff_filtered);
return diff_filtered;
}
// we don't implement destructor, because we want to reuse callback_data. we just want to free the tensors
void reset() {
for (auto ptr : v_pos) free(ptr->data);
for (auto ptr : v_neg) free(ptr->data);
for (auto ptr : v_diff_filtered) free(ptr->data);
v_pos.clear();
v_neg.clear();
v_diff_filtered.clear();
if (ctx_ggml) {
ggml_free(ctx_ggml);
}
ctx_ggml = nullptr;
}
};
/**
* process_ctx is used to store the ggml context for pre-post processing the diff vectors
* in short, input => v_diff and output => v_final
*/
struct train_context {
ggml_context * ctx_ggml;
int n_embd;
int n_layers;
/* pair of prompts to be used for generating final vector */
std::vector<std::string> positive_entries;
std::vector<std::string> negative_entries;
// each element of the vector correspond to one layer
// NOTE: the last layer is discard. therefore, we will have (n_layers - 1) elements here
// NOTE (2): v_diff is transposed from v_diff_tmp
std::vector<struct ggml_tensor *> v_diff; // vector of matrices of size [m, n_embd] where m ~ n_tokens * n_completions (v_diff contains no zero-rows)
std::vector<struct ggml_tensor *> v_final; // vector of vectors of size [n_embd] to be written to file
// to easily re-alloc when concat v_diff, we temporary store v_diff in a vector instead of a tensor
// v_diff_tmp will get converted unto v_diff later on
std::vector<std::vector<uint8_t>> v_diff_tmp;
train_context(int n_embd_, int n_layers_) {
n_embd = n_embd_;
n_layers = n_layers_;
struct ggml_init_params params_ggml = {
/*.mem_size =*/ ggml_tensor_overhead() * (n_layers - 1) * 2u,
/*.mem_buffer =*/ NULL,
/*.no_alloc =*/ true,
};
ctx_ggml = ggml_init(params_ggml);
for (int il = 0; il < n_layers - 1; il++) {
std::vector<uint8_t> empty;
v_diff_tmp.push_back(empty);
auto t = ggml_new_tensor_1d(ctx_ggml, GGML_TYPE_F32, n_embd);
t->data = malloc(ggml_nbytes(t)); // TODO: get rid of malloc if possible
v_final.push_back(t);
}
}
// add new rows into existing tensor in v_diff_tmp
void concat_diff_tmp(const std::vector<struct ggml_tensor *> & diff_filtered) {
GGML_ASSERT((int) diff_filtered.size() == n_layers - 1);
for (int il = 0; il < n_layers - 1; il++) {
auto t = diff_filtered[il];
auto & diff_tmp = v_diff_tmp[il];
size_t curr_size = diff_tmp.size();
diff_tmp.resize(curr_size + ggml_nbytes(t));
memcpy(diff_tmp.data() + curr_size, t->data, ggml_nbytes(t));
}
}
// build the v_diff tensors from v_diff_tmp (v_diff need to be transposed)
// TODO @ngxson : maybe add option NOT to transpose v_diff; will be useful for "mean" method
void build_v_diff(bool transpose) {
printf("build_v_diff\n");
for (int il = 0; il < n_layers - 1; il++) {
auto & diff_tmp = v_diff_tmp[il];
int n_elem = diff_tmp.size() / sizeof(float);
GGML_ASSERT(n_elem % n_embd == 0);
int n_rows = n_elem / n_embd;
struct ggml_tensor * diff = transpose
? ggml_new_tensor_2d(ctx_ggml, GGML_TYPE_F32, n_rows, n_embd)
: ggml_new_tensor_2d(ctx_ggml, GGML_TYPE_F32, n_embd, n_rows);
ggml_set_name(diff, (std::string("diff_") + std::to_string(il)).c_str());
diff->data = malloc(ggml_nbytes(diff)); // TODO: get rid of this malloc if possible
if (transpose) {
// copy data & transpose
float * arr = (float *) diff_tmp.data();
for (int ir = 0; ir < n_rows; ++ir) {
for (int ic = 0; ic < n_embd; ++ic) {
float f = arr[ir*n_embd + ic];
ggml_set_f32_nd(diff, ir, ic, 0, 0, f);
}
}
} else {
// only copy
memcpy(diff->data, diff_tmp.data(), ggml_nbytes(diff));
}
v_diff.push_back(diff);
print_debug_tensor(diff);
// free memory of diff_tmp
diff_tmp.resize(0);
}
}
~train_context() {
for (auto ptr : v_final) free(ptr->data);
for (auto ptr : v_diff) free(ptr->data);
// no need to free v_diff_tmp, since we didn't use malloc
ggml_free(ctx_ggml);
}
};
struct tokenized_prompt {
std::vector<llama_token> tokens_pos;
std::vector<llama_token> tokens_neg;
size_t max_seq_len;
tokenized_prompt(llama_context * ctx, std::string pos, std::string neg) {
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
tokens_pos = ::llama_tokenize(ctx, pos, add_bos, true);
tokens_neg = ::llama_tokenize(ctx, neg, add_bos, true);
max_seq_len = std::max(tokens_pos.size(), tokens_neg.size());
padding_seq(ctx, tokens_pos, max_seq_len);
padding_seq(ctx, tokens_neg, max_seq_len);
}
void padding_seq(llama_context * ctx, std::vector<llama_token> & tokens, size_t len) {
// TODO: customize padding token
std::vector<llama_token> pad_tokens = ::llama_tokenize(ctx, " ", false);
llama_token pad_tok = pad_tokens.back();
while (tokens.size() < len) {
tokens.push_back(pad_tok);
}
}
};
//////////////////////////////////////////////////
template <typename T>
static std::string to_string(const T & val) {
std::stringstream ss;
ss << val;
return ss.str();
}
static std::vector<std::string> ctrlvec_load_prompt_file(std::string path, bool skip_empty_lines) {
std::vector<std::string> output;
std::ifstream file(path);
if (!file.is_open()) {
fprintf(stderr, "error: unable to open file: %s\n", path.c_str());
exit(1);
}
std::string line;
while (std::getline(file, line)) {
bool is_skip = skip_empty_lines && line.empty();
if (!is_skip) {
string_process_escapes(line);
output.push_back(line);
}
}
file.close();
return output;
}
//////////////////////////////////////////////////
static bool cb_eval(struct ggml_tensor * t, bool ask, void * user_data) {
auto * cb_data = (callback_data *) user_data;
static const char * l_out_name = "l_out";
const bool is_l_out = strncmp(t->name, l_out_name, strlen(l_out_name)) == 0;
if (ask) {
return is_l_out;
}
if (!is_l_out || t->ne[1] != cb_data->n_tokens) {
return true;
}
// save the tensor to current context
cb_data->save_tensor_for_layer(t);
return true;
}
static bool get_hidden_layers(llama_context * ctx, std::vector<llama_token> & tokens) {
llama_kv_cache_clear(ctx);
if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), 0, 0))) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return false;
}
return true;
}
static void export_gguf(const std::vector<struct ggml_tensor *> & v_ctrl, const std::string fname, const std::string model_hint) {
struct gguf_context * ctx = gguf_init_empty();
const std::string arch = "controlvector";
gguf_set_val_str(ctx, "general.architecture", arch.c_str());
gguf_set_val_str(ctx, (arch + ".model_hint").c_str(), model_hint.c_str());
gguf_set_val_i32(ctx, (arch + ".layer_count").c_str(), v_ctrl.size());
for (size_t i = 0; i < v_ctrl.size(); ++i) {
gguf_add_tensor(ctx, v_ctrl[i]);
print_debug_tensor(v_ctrl[i]);
printf("Added tensor: %s\n", v_ctrl[i]->name);
}
printf("%s: writing file...\n", __func__);
gguf_write_to_file(ctx, fname.c_str(), false);
printf("%s: wrote file '%s'\n", __func__, fname.c_str());
gguf_free(ctx);
}
/**
* Load prompt files and completion file.
* Then format each pair of prompt + completion to make an entry.
*/
static int prepare_entries(gpt_params & params, train_context & ctx_train) {
// load prompts
std::vector<std::string> positive_prompts = ctrlvec_load_prompt_file(params.cvector_positive_file, true);
std::vector<std::string> negative_prompts = ctrlvec_load_prompt_file(params.cvector_negative_file, true);
if (positive_prompts.size() != negative_prompts.size()) {
fprintf(stderr, "number of positive and negative prompts must be equal\n");
return 1;
}
if (positive_prompts.empty()) {
fprintf(stderr, "must provide at least one prompt pair\n");
return 1;
}
ctx_train.positive_entries = positive_prompts;
ctx_train.negative_entries = negative_prompts;
return 0;
}
int main(int argc, char ** argv) {
gpt_params params;
if (!gpt_params_parse(argc, argv, params)) {
print_usage(argc, argv, params);
return 1;
}
if (params.n_pca_iterations % params.n_pca_batch != 0) {
fprintf(stderr, "PCA iterations must by multiply of PCA batch size\n");
return 1;
}
callback_data cb_data;
// pass the callback to the backend scheduler
// it will be executed for each node during the graph computation
params.cb_eval = cb_eval;
params.cb_eval_user_data = &cb_data;
params.warmup = false;
print_build_info();
llama_backend_init();
llama_numa_init(params.numa);
// load the model to get hparams
llama_init_result llama_init = llama_init_from_gpt_params(params);
llama_model * model = llama_init.model;
llama_context * ctx = llama_init.context;
// int n_ctx = llama_n_ctx(ctx);
int n_layers = llama_n_layer(model);
int n_embd = llama_n_embd(model);
// get model hint param (a.k.a model arch name)
char model_hint[128];
llama_model_meta_val_str(model, "general.architecture", model_hint, 128);
// init train_context
train_context ctx_train(n_embd, n_layers);
// load and prepare entries for training
prepare_entries(params, ctx_train);
// we have to pretokenize everything because otherwise we don't know how much overhead to allocate ctx_diffs_wrapped
std::vector<tokenized_prompt> tokenized_prompts;
size_t n_total_tokens = 0;
for (size_t i = 0; i < ctx_train.positive_entries.size(); ++i) {
tokenized_prompt t(ctx, ctx_train.positive_entries[i], ctx_train.negative_entries[i]);
n_total_tokens += 2 * t.max_seq_len;
tokenized_prompts.push_back(std::move(t));
}
std::cout << "n_total_tokens: " << n_total_tokens << std::endl;
for(size_t i = 0; i < ctx_train.positive_entries.size(); ++i) {
bool success = false;
tokenized_prompt t = tokenized_prompts[i];
cb_data.n_layers = n_layers;
cb_data.n_tokens = t.max_seq_len;
printf("Evaluating prompt[%d/%d]: \"%s\" - \"%s\" (%d tokens)\n",
(int) i+1, (int) ctx_train.positive_entries.size(),
tokens_to_str(ctx, t.tokens_pos.cbegin(), t.tokens_pos.cend()).c_str(),
tokens_to_str(ctx, t.tokens_neg.cbegin(), t.tokens_neg.cend()).c_str(),
(int) t.max_seq_len);
cb_data.is_eval_pos = true;
success = get_hidden_layers(ctx, t.tokens_pos);
if (!success) break;
cb_data.is_eval_pos = false;
success = get_hidden_layers(ctx, t.tokens_neg);
if (!success) break;
// calculate diff and remove all zero rows
auto v_diff_filtered = cb_data.calc_diff();
// save & concat the filtered v_diff to ctx_train
ctx_train.concat_diff_tmp(v_diff_filtered);
// reset for next iteration
cb_data.reset();
}
// done with the model, we can now free it to make gain some memory
printf("Done evaluate prompts, unload model...\n");
llama_free(ctx);
llama_free_model(model);
bool use_pca = params.cvector_dimre_method == DIMRE_METHOD_PCA;
// prepare ctx_train for PCA
ctx_train.build_v_diff(use_pca);
if (use_pca) {
// run PCA
PCA::pca_params pca_params;
pca_params.n_threads = params.n_threads;
pca_params.n_batch = params.n_pca_batch;
pca_params.n_iterations = params.n_pca_iterations;
PCA::run_pca(pca_params, ctx_train.v_diff, ctx_train.v_final);
} else {
// run mean
mean::run(ctx_train.v_diff, ctx_train.v_final);
}
// write output vectors to gguf
export_gguf(ctx_train.v_final, params.cvector_outfile, model_hint);
llama_backend_free();
return 0;
}