mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-14 23:09:53 +00:00
504 lines
18 KiB
C++
504 lines
18 KiB
C++
#include "arg.h"
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#include "common.h"
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#include "llama.h"
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#include "ggml.h"
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#include "pca.hpp"
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#include "mean.hpp"
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#ifdef GGML_USE_CUDA
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#include "ggml-cuda.h"
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#endif
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#ifdef GGML_USE_METAL
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#include "ggml-metal.h"
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#endif
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#include <algorithm>
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#include <climits>
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#include <cstdio>
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#include <cstring>
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#include <fstream>
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#include <iostream>
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#include <string>
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#include <tuple>
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#include <vector>
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//////////////////////////////////////////////////
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// utils
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template <class Iter>
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static std::string tokens_to_str(llama_context * ctx, Iter begin, Iter end) {
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std::string ret;
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for (; begin != end; ++begin) {
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ret += llama_token_to_piece(ctx, *begin);
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}
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return ret;
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}
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static void print_usage(int, char ** argv) {
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printf("\nexample usage:\n");
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printf("\n CPU only: %s -m ./llama-3.Q4_K_M.gguf\n", argv[0]);
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printf("\n with GPU: %s -m ./llama-3.Q4_K_M.gguf -ngl 99\n", argv[0]);
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printf("\n advanced: %s -m ./llama-3.Q4_K_M.gguf -ngl 99 --pca-iter 2000 --pca-batch 100\n", argv[0]);
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printf("\n using mean: %s -m ./llama-3.Q4_K_M.gguf --method mean\n", argv[0]);
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printf("\n");
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}
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//////////////////////////////////////////////////
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// cb_eval is reused for each pair of positive - negative prompt
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struct callback_data {
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ggml_context * ctx_ggml = nullptr; // holds v_pos, v_neg, v_diff_filtered
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int n_layers = 0;
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int n_tokens = 0;
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bool is_eval_pos = true;
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// each element of the vector correspond to one layer
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std::vector<struct ggml_tensor *> v_pos; // vector of matrices of size [n_embd, n_tokens]
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std::vector<struct ggml_tensor *> v_neg; // vector of matrices of size [n_embd, n_tokens]
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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
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// save a tensor into either v_pos or v_neg (decided by is_eval_pos)
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void save_tensor_for_layer(struct ggml_tensor * t) {
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GGML_ASSERT(t->type == GGML_TYPE_F32);
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if (ctx_ggml == nullptr) {
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// alloc a new ctx_ggml if needed
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struct ggml_init_params params_ggml = {
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/*.mem_size =*/ ggml_tensor_overhead() * n_layers * 3u,
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/*.mem_buffer =*/ NULL,
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/*.no_alloc =*/ true,
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};
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ctx_ggml = ggml_init(params_ggml);
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}
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// copy tensor data
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auto n_bytes = ggml_nbytes(t);
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struct ggml_tensor * t_layer = ggml_new_tensor_2d(ctx_ggml, t->type, t->ne[0], t->ne[1]);
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t_layer->data = malloc(n_bytes); // TODO @ngxson : get rid of this malloc somehow
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ggml_backend_tensor_get(t, t_layer->data, 0, n_bytes);
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ggml_set_name(t_layer, ggml_get_name(t));
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//print_debug_tensor(t_layer);
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if (is_eval_pos) {
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v_pos.push_back(t_layer);
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} else {
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v_neg.push_back(t_layer);
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}
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}
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// calculate diff (v_pos - v_neg) and place the result back to v_pos
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// all zero rows in the diff tensor will also be removed
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// NOTE: final layer is ignored. we only have (n_layers - 1) to process
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std::vector<struct ggml_tensor *> calc_diff() {
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for (float il = 0; il < v_pos.size(); il++) {
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float * a = (float *) v_pos[il]->data;
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float * b = (float *) v_neg[il]->data;
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size_t n_elem = ggml_nelements(v_pos[il]);
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for (size_t j = 0; j < n_elem; j++) {
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a[j] -= b[j];
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}
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//print_debug_tensor(v_pos[i]);
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auto diff_filtered = filter_nonzero_rows(v_pos[il]);
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v_diff_filtered.push_back(diff_filtered);
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}
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return v_diff_filtered; // for convinient, we return the result std::vector
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}
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// delete zero rows from a given 2D tensor
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struct ggml_tensor * filter_nonzero_rows(struct ggml_tensor * a) {
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//printf("filter_nonzero_rows\n");
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auto is_row_all_zeros = [](struct ggml_tensor * t, int row, float eps) -> bool {
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// check if given row containing all zero elements
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int n_cols = t->ne[0]; // hint: should be equal to n_embd
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for (int col = 0; col < n_cols; ++col) {
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if (ggml_get_f32_nd(t, col, row, 0, 0) > eps) {
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return false;
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}
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}
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return true;
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};
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std::vector<int> rows_to_copy; // the idx of non-zero cols (to be copied to row of diff_filtered)
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for (int i_row = 0; i_row < a->ne[1]; i_row++) {
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if (!is_row_all_zeros(a, i_row, 1e-6)) {
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rows_to_copy.push_back(i_row);
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}
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}
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// get "n_nonzero_rows" for the output "diff_filtered"
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int n_nonzero_rows = rows_to_copy.size();
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//printf("n_nonzero_rows: %d\n", n_nonzero_rows);
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int n_embd = a->ne[0];
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GGML_ASSERT(n_nonzero_rows > 0);
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// diff_filtered: [n_embd, n_nonzero_rows]
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struct ggml_tensor * diff_filtered = ggml_new_tensor_2d(
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ctx_ggml, GGML_TYPE_F32, n_embd, n_nonzero_rows);
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ggml_format_name(diff_filtered, "diff_filtered_%s", a->name);
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diff_filtered->data = malloc(ggml_nbytes(diff_filtered));
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// copy non-zero rows
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for (int dest_row = 0; dest_row < n_nonzero_rows; dest_row++) {
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int src_row = rows_to_copy[dest_row];
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for (int i = 0; i < n_embd; i++) {
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float src_elem = ggml_get_f32_nd(a, i, src_row, 0, 0);
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ggml_set_f32_nd(diff_filtered, i, dest_row, 0, 0, src_elem);
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}
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}
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//print_debug_tensor(diff_filtered);
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return diff_filtered;
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}
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// we don't implement destructor, because we want to reuse callback_data. we just want to free the tensors
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void reset() {
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for (auto ptr : v_pos) free(ptr->data);
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for (auto ptr : v_neg) free(ptr->data);
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for (auto ptr : v_diff_filtered) free(ptr->data);
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v_pos.clear();
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v_neg.clear();
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v_diff_filtered.clear();
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if (ctx_ggml) {
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ggml_free(ctx_ggml);
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}
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ctx_ggml = nullptr;
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}
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};
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/**
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* process_ctx is used to store the ggml context for pre-post processing the diff vectors
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* in short, input => v_diff and output => v_final
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*/
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struct train_context {
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ggml_context * ctx_ggml;
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int n_embd;
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int n_layers;
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/* pair of prompts to be used for generating final vector */
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std::vector<std::string> positive_entries;
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std::vector<std::string> negative_entries;
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// each element of the vector correspond to one layer
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// NOTE: the last layer is discard. therefore, we will have (n_layers - 1) elements here
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// NOTE (2): v_diff is transposed from v_diff_tmp
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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)
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std::vector<struct ggml_tensor *> v_final; // vector of vectors of size [n_embd] to be written to file
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// to easily re-alloc when concat v_diff, we temporary store v_diff in a vector instead of a tensor
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// v_diff_tmp will get converted unto v_diff later on
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std::vector<std::vector<uint8_t>> v_diff_tmp;
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train_context(int n_embd_, int n_layers_) {
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n_embd = n_embd_;
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n_layers = n_layers_;
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struct ggml_init_params params_ggml = {
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/*.mem_size =*/ ggml_tensor_overhead() * (n_layers - 1) * 2u,
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/*.mem_buffer =*/ NULL,
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/*.no_alloc =*/ true,
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};
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ctx_ggml = ggml_init(params_ggml);
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for (int il = 0; il < n_layers - 1; il++) {
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std::vector<uint8_t> empty;
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v_diff_tmp.push_back(empty);
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auto t = ggml_new_tensor_1d(ctx_ggml, GGML_TYPE_F32, n_embd);
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t->data = malloc(ggml_nbytes(t)); // TODO: get rid of malloc if possible
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v_final.push_back(t);
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}
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}
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// add new rows into existing tensor in v_diff_tmp
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void concat_diff_tmp(const std::vector<struct ggml_tensor *> & diff_filtered) {
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GGML_ASSERT((int) diff_filtered.size() == n_layers - 1);
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for (int il = 0; il < n_layers - 1; il++) {
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auto t = diff_filtered[il];
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auto & diff_tmp = v_diff_tmp[il];
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size_t curr_size = diff_tmp.size();
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diff_tmp.resize(curr_size + ggml_nbytes(t));
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memcpy(diff_tmp.data() + curr_size, t->data, ggml_nbytes(t));
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}
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}
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// build the v_diff tensors from v_diff_tmp (v_diff need to be transposed)
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// TODO @ngxson : maybe add option NOT to transpose v_diff; will be useful for "mean" method
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void build_v_diff(bool transpose) {
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printf("build_v_diff\n");
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for (int il = 0; il < n_layers - 1; il++) {
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auto & diff_tmp = v_diff_tmp[il];
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int n_elem = diff_tmp.size() / sizeof(float);
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GGML_ASSERT(n_elem % n_embd == 0);
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int n_rows = n_elem / n_embd;
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struct ggml_tensor * diff = transpose
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? ggml_new_tensor_2d(ctx_ggml, GGML_TYPE_F32, n_rows, n_embd)
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: ggml_new_tensor_2d(ctx_ggml, GGML_TYPE_F32, n_embd, n_rows);
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ggml_set_name(diff, (std::string("diff_") + std::to_string(il)).c_str());
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diff->data = malloc(ggml_nbytes(diff)); // TODO: get rid of this malloc if possible
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if (transpose) {
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// copy data & transpose
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float * arr = (float *) diff_tmp.data();
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for (int ir = 0; ir < n_rows; ++ir) {
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for (int ic = 0; ic < n_embd; ++ic) {
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float f = arr[ir*n_embd + ic];
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ggml_set_f32_nd(diff, ir, ic, 0, 0, f);
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}
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}
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} else {
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// only copy
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memcpy(diff->data, diff_tmp.data(), ggml_nbytes(diff));
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}
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v_diff.push_back(diff);
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print_debug_tensor(diff);
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// free memory of diff_tmp
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diff_tmp.resize(0);
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}
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}
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~train_context() {
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for (auto ptr : v_final) free(ptr->data);
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for (auto ptr : v_diff) free(ptr->data);
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// no need to free v_diff_tmp, since we didn't use malloc
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ggml_free(ctx_ggml);
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}
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};
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struct tokenized_prompt {
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std::vector<llama_token> tokens_pos;
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std::vector<llama_token> tokens_neg;
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size_t max_seq_len;
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tokenized_prompt(llama_context * ctx, std::string pos, std::string neg) {
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const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
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tokens_pos = ::llama_tokenize(ctx, pos, add_bos, true);
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tokens_neg = ::llama_tokenize(ctx, neg, add_bos, true);
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max_seq_len = std::max(tokens_pos.size(), tokens_neg.size());
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padding_seq(ctx, tokens_pos, max_seq_len);
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padding_seq(ctx, tokens_neg, max_seq_len);
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}
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void padding_seq(llama_context * ctx, std::vector<llama_token> & tokens, size_t len) {
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// TODO: customize padding token
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std::vector<llama_token> pad_tokens = ::llama_tokenize(ctx, " ", false);
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llama_token pad_tok = pad_tokens.back();
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while (tokens.size() < len) {
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tokens.push_back(pad_tok);
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}
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}
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};
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//////////////////////////////////////////////////
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template <typename T>
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static std::string to_string(const T & val) {
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std::stringstream ss;
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ss << val;
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return ss.str();
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}
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static std::vector<std::string> ctrlvec_load_prompt_file(std::string path, bool skip_empty_lines) {
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std::vector<std::string> output;
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std::ifstream file(path);
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if (!file.is_open()) {
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fprintf(stderr, "error: unable to open file: %s\n", path.c_str());
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exit(1);
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}
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std::string line;
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while (std::getline(file, line)) {
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bool is_skip = skip_empty_lines && line.empty();
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if (!is_skip) {
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string_process_escapes(line);
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output.push_back(line);
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}
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}
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file.close();
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return output;
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}
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//////////////////////////////////////////////////
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static bool cb_eval(struct ggml_tensor * t, bool ask, void * user_data) {
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auto * cb_data = (callback_data *) user_data;
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static const char * l_out_name = "l_out";
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const bool is_l_out = strncmp(t->name, l_out_name, strlen(l_out_name)) == 0;
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if (ask) {
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return is_l_out;
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}
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if (!is_l_out || t->ne[1] != cb_data->n_tokens) {
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return true;
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}
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// save the tensor to current context
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cb_data->save_tensor_for_layer(t);
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return true;
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}
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static bool get_hidden_layers(llama_context * ctx, std::vector<llama_token> & tokens) {
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llama_kv_cache_clear(ctx);
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if (llama_decode(ctx, llama_batch_get_one(tokens.data(), tokens.size(), 0, 0))) {
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fprintf(stderr, "%s : failed to eval\n", __func__);
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return false;
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}
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return true;
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}
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static void export_gguf(const std::vector<struct ggml_tensor *> & v_ctrl, const std::string fname, const std::string model_hint) {
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struct gguf_context * ctx = gguf_init_empty();
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const std::string arch = "controlvector";
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gguf_set_val_str(ctx, "general.architecture", arch.c_str());
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gguf_set_val_str(ctx, (arch + ".model_hint").c_str(), model_hint.c_str());
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gguf_set_val_i32(ctx, (arch + ".layer_count").c_str(), v_ctrl.size());
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for (size_t i = 0; i < v_ctrl.size(); ++i) {
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gguf_add_tensor(ctx, v_ctrl[i]);
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print_debug_tensor(v_ctrl[i]);
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printf("Added tensor: %s\n", v_ctrl[i]->name);
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}
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printf("%s: writing file...\n", __func__);
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gguf_write_to_file(ctx, fname.c_str(), false);
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printf("%s: wrote file '%s'\n", __func__, fname.c_str());
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gguf_free(ctx);
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}
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/**
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* Load prompt files and completion file.
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* Then format each pair of prompt + completion to make an entry.
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*/
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static int prepare_entries(gpt_params & params, train_context & ctx_train) {
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// load prompts
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std::vector<std::string> positive_prompts = ctrlvec_load_prompt_file(params.cvector_positive_file, true);
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std::vector<std::string> negative_prompts = ctrlvec_load_prompt_file(params.cvector_negative_file, true);
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if (positive_prompts.size() != negative_prompts.size()) {
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fprintf(stderr, "number of positive and negative prompts must be equal\n");
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return 1;
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}
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if (positive_prompts.empty()) {
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fprintf(stderr, "must provide at least one prompt pair\n");
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return 1;
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}
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ctx_train.positive_entries = positive_prompts;
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ctx_train.negative_entries = negative_prompts;
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return 0;
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}
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int main(int argc, char ** argv) {
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gpt_params params;
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if (!gpt_params_parse(argc, argv, params, LLAMA_EXAMPLE_CVECTOR_GENERATOR, print_usage)) {
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return 1;
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}
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if (params.n_pca_iterations % params.n_pca_batch != 0) {
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fprintf(stderr, "PCA iterations must by multiply of PCA batch size\n");
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return 1;
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}
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callback_data cb_data;
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// pass the callback to the backend scheduler
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// it will be executed for each node during the graph computation
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params.cb_eval = cb_eval;
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params.cb_eval_user_data = &cb_data;
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params.warmup = false;
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print_build_info();
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llama_backend_init();
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llama_numa_init(params.numa);
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// load the model to get hparams
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llama_init_result llama_init = llama_init_from_gpt_params(params);
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llama_model * model = llama_init.model;
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llama_context * ctx = llama_init.context;
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// int n_ctx = llama_n_ctx(ctx);
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int n_layers = llama_n_layer(model);
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int n_embd = llama_n_embd(model);
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// get model hint param (a.k.a model arch name)
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char model_hint[128];
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llama_model_meta_val_str(model, "general.architecture", model_hint, 128);
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// init train_context
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train_context ctx_train(n_embd, n_layers);
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// load and prepare entries for training
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prepare_entries(params, ctx_train);
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// we have to pretokenize everything because otherwise we don't know how much overhead to allocate ctx_diffs_wrapped
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std::vector<tokenized_prompt> tokenized_prompts;
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size_t n_total_tokens = 0;
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for (size_t i = 0; i < ctx_train.positive_entries.size(); ++i) {
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tokenized_prompt t(ctx, ctx_train.positive_entries[i], ctx_train.negative_entries[i]);
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n_total_tokens += 2 * t.max_seq_len;
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tokenized_prompts.push_back(std::move(t));
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}
|
|
|
|
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",
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(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(),
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|
(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.cpuparams.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;
|
|
}
|