mirror of
https://github.com/ikawrakow/ik_llama.cpp.git
synced 2026-01-26 17:20:01 +00:00
874 lines
32 KiB
C++
874 lines
32 KiB
C++
//
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// Copyright (C) 2024 Iwan Kawrakow
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// Copyright (C) 2023-2024 The ggml authors
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// MIT license
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// SPDX-License-Identifier: MIT
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//
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#include "common.h"
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#include "llama.h"
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#include <cmath>
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#include <cstdio>
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#include <cstring>
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#include <ctime>
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#include <sstream>
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#include <thread>
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#include <mutex>
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#include <vector>
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#include <fstream>
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#include <unordered_map>
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#include <algorithm>
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#include <optional>
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#include <sstream>
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#if defined(_MSC_VER)
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#pragma warning(disable: 4244 4267) // possible loss of data
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#endif
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static void print_usage(int argc, char ** argv, const gpt_params & params) {
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gpt_params_print_usage(argc, argv, params);
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LOG_TEE("\nexample usage:\n");
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LOG_TEE("\n %s \\\n"
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" -m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] [--verbosity 1] \\\n"
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" [--no-ppl] [--chunk 123] [--output-frequency 10] [--save-frequency 0] \\\n"
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" [--in-file imatrix-prev-0.dat --in-file imatrix-prev-1.dat ...]\n" , argv[0]);
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LOG_TEE("\n");
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}
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struct Stats {
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std::vector<float> values;
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std::vector<int> counts;
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int ncall = 0;
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int n_as = 1;
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};
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class IMatrixCollector {
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public:
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IMatrixCollector() = default;
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void set_params(gpt_params params) { m_params = std::move(params); }
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bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data);
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void save_imatrix(int ncall = -1) const;
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bool load_imatrix(const char * file_name);
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void set_collect_lsim(bool yes_or_no) { m_collect_lsim = yes_or_no; }
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void print_layer_importance();
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private:
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std::unordered_map<std::string, Stats> m_stats;
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gpt_params m_params;
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std::mutex m_mutex;
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int m_last_call = 0;
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int m_last_layer = 9999;
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int m_last_ffn = -1;
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std::vector<char> m_src1_data;
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std::vector<char> m_ids; // the expert ids from ggml_mul_mat_id
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std::vector<float> m_last_input;
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std::vector<float> m_ffn_input;
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std::vector<std::pair<double,int>> m_layer_sim;
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std::vector<std::pair<double,int>> m_attn_sim;
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std::vector<std::pair<double,int>> m_ffn_sim;
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bool m_collect_lsim = false;
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std::optional<int> layer_index(const std::string& name) const {
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if (name == m_params.output_tensor_name && m_last_layer < 199) {
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return m_last_layer + 1;
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}
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if (auto pos = name.find("blk."); pos == 0) {
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pos += 4;
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if (auto pos1 = name.find('.', pos); pos1 != std::string::npos) {
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auto index_str = name.substr(pos, pos1 - pos);
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std::istringstream str(index_str);
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int index; str >> index;
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if (!str.fail()) return index;
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}
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}
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return std::nullopt;
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}
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static inline double cosine_similarity(int n, const float * x, const float * y) {
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double sumxy = 0, sumx2 = 0, sumy2 = 0;
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for (int j = 0; j < n; ++j) {
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sumxy += x[j]*y[j]; sumx2 += x[j]*x[j]; sumy2 += y[j]*y[j];
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}
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double cos_sim = sumx2 > 0 && sumy2 > 0 ? sumxy/sqrt(sumx2*sumy2) : 0;
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return cos_sim;
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}
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static inline void collect_cos_similarity(int nrow, int n, const float * x, const float * y, std::pair<double, int>& p) {
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for (int row = 0; row < nrow; ++row) {
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p.first += cosine_similarity(n, x, y);
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p.second += 1;
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x += n;
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y += n;
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}
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}
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static void print_layer_importance(const char * msg, const std::vector<std::pair<double, int>>& sim);
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};
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// remove any prefix and suffixes from the name
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// CUDA0#blk.0.attn_k.weight#0 => blk.0.attn_k.weight
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static std::string filter_tensor_name(const char * name) {
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std::string wname;
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const char * p = strchr(name, '#');
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if (p != NULL) {
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p = p + 1;
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const char * q = strchr(p, '#');
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if (q != NULL) {
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wname = std::string(p, q - p);
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} else {
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wname = p;
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}
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} else {
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wname = name;
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}
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return wname;
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}
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void IMatrixCollector::print_layer_importance(const char * msg, const std::vector<std::pair<double, int>>& sim) {
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if (sim.empty()) return;
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std::vector<std::pair<float, int>> layers;
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layers.reserve(sim.size());
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for (int i = 0; i < int(sim.size()); ++i) {
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if (sim[i].second > 0) layers.emplace_back(float(std::abs(sim[i].first/sim[i].second)), i);
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}
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if (layers.empty()) return;
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std::sort(layers.begin(), layers.end());
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printf("%s\n", msg);
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//printf("======================== sorted layer importances\n");
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int j = 0;
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for (auto& p : layers) {
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int i = p.second;
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printf("%3d: Layer %3d, <cos_sim> = %g\n", j++, i, sim[i].first/sim[i].second);
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}
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}
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void IMatrixCollector::print_layer_importance() {
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print_layer_importance("\n======================== sorted layer importances", m_layer_sim);
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print_layer_importance("\n======================== sorted attention importances", m_attn_sim);
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print_layer_importance("\n======================== sorted ffn importances", m_ffn_sim);
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//printf("%s: have %d layers\n", __func__, int(m_layer_sim.size()));
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//if (m_layer_sim.empty()) return;
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//std::vector<std::pair<float, int>> layers;
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//layers.reserve(m_layer_sim.size());
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//for (int i = 0; i < int(m_layer_sim.size()); ++i) {
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// if (m_layer_sim[i].second > 0) layers.emplace_back(float(std::abs(m_layer_sim[i].first/m_layer_sim[i].second)), i);
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//}
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//if (layers.empty()) return;
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//std::sort(layers.begin(), layers.end());
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//printf("======================== sorted layer importances\n");
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//int j = 0;
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//for (auto& p : layers) {
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// int i = p.second;
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// printf("%3d: Layer %3d, <cos_sim> = %g\n", j++, i, m_layer_sim[i].first/m_layer_sim[i].second);
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//}
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}
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bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) {
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GGML_UNUSED(user_data);
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const struct ggml_tensor * src0 = t->src[0];
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const struct ggml_tensor * src1 = t->src[1];
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std::string wname = filter_tensor_name(src0->name);
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// when ask is true, the scheduler wants to know if we are interested in data from this tensor
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// if we return true, a follow-up call will be made with ask=false in which we can do the actual collection
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if (ask) {
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if (t->op == GGML_OP_MUL_MAT_ID) return true; // collect all indirect matrix multiplications
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if (t->op != GGML_OP_MUL_MAT) return false;
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// why are small batches ignored (<16 tokens)?
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if (src1->ne[1] < 16 || src1->type != GGML_TYPE_F32) return false;
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//printf("wname = %s\n", wname.c_str());
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if (!(wname.substr(0, 4) == "blk." || ((m_params.process_output || m_collect_lsim) && wname == m_params.output_tensor_name))) return false;
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return true;
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}
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std::lock_guard<std::mutex> lock(m_mutex);
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// copy the data from the GPU memory if needed
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const bool is_host = ggml_backend_buffer_is_host(src1->buffer);
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if (!is_host) {
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auto nbytes = ggml_nbytes(src1);
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m_src1_data.resize(nbytes);
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ggml_backend_tensor_get(src1, m_src1_data.data(), 0, nbytes);
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}
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const float * data = is_host ? (const float *) src1->data : (const float *)m_src1_data.data();
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if (m_collect_lsim) {
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if (wname.find(".ffn_") != std::string::npos) {
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if (auto index = layer_index(wname); index.has_value() && *index == m_last_layer && *index != m_last_ffn) {
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int n = src1->ne[0];
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int nrow = t->op == GGML_OP_MUL_MAT_ID ? src1->ne[2] : src1->ne[1];
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if (t->op == GGML_OP_MUL_MAT_ID) {
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GGML_ASSERT(src1->ne[1] == 1);
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}
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if (m_ffn_input.empty()) {
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m_ffn_input.resize(nrow*n);
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} else {
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if ((int)m_ffn_input.size() != nrow*n) {
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printf("Oops, inconsistent ffn size\n"); exit(1);
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}
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}
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std::memcpy(m_ffn_input.data(), data, nrow*n*sizeof(float));
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if (m_ffn_input.size() != m_last_input.size()) {
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printf("Oops, inconsistent ffn vs last_input size\n"); exit(1);
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}
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if (m_attn_sim.size() < *index + 1) m_attn_sim.resize(*index + 1);
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auto& p = m_attn_sim[*index];
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collect_cos_similarity(nrow, n, m_ffn_input.data(), m_last_input.data(), p);
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m_last_ffn = *index;
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}
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}
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}
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// this has been adapted to the new format of storing merged experts in a single 3d tensor
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// ref: https://github.com/ggerganov/llama.cpp/pull/6387
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if (t->op == GGML_OP_MUL_MAT_ID) {
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// ids -> [n_experts_used, n_tokens]
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// src1 -> [cols, n_expert_used, n_tokens]
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const ggml_tensor * ids = t->src[2];
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const int n_as = src0->ne[2];
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const int n_ids = ids->ne[0];
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// the top-k selected expert ids are stored in the ids tensor
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// for simplicity, always copy ids to host, because it is small
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// take into account that ids is not contiguous!
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GGML_ASSERT(ids->ne[1] == src1->ne[2]);
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m_ids.resize(ggml_nbytes(ids));
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ggml_backend_tensor_get(ids, m_ids.data(), 0, ggml_nbytes(ids));
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auto & e = m_stats[wname];
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++e.ncall;
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if (e.values.empty()) {
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e.values.resize(src1->ne[0]*n_as, 0);
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e.counts.resize(src1->ne[0]*n_as, 0);
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e.n_as = n_as;
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}
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else if (e.values.size() != (size_t)src1->ne[0]*n_as) {
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fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]*n_as);
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exit(1); //GGML_ABORT("fatal error");
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}
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else if (e.n_as != n_as) {
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fprintf(stderr, "Oops: inconsistent n_as for %s (%d vs %d)\n", wname.c_str(), e.n_as, n_as);
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}
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if (m_params.verbosity > 1) {
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printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[2], (int)src1->type);
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}
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// loop over all possible experts, regardless if they are used or not in the batch
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for (int ex = 0; ex < n_as; ++ex) {
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size_t e_start = ex*src1->ne[0];
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for (int idx = 0; idx < n_ids; ++idx) {
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for (int row = 0; row < (int)src1->ne[2]; ++row) {
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const int excur = *(const int32_t *) (m_ids.data() + row*ids->nb[1] + idx*ids->nb[0]);
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GGML_ASSERT(excur >= 0 && excur < n_as); // sanity check
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if (excur != ex) continue;
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const int64_t i11 = idx % src1->ne[1];
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const int64_t i12 = row;
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const float * x = (const float *)((const char *)data + i11*src1->nb[1] + i12*src1->nb[2]);
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for (int j = 0; j < (int)src1->ne[0]; ++j) {
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e.values[e_start + j] += x[j]*x[j];
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e.counts[e_start + j]++;
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if (!std::isfinite(e.values[e_start + j])) {
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fprintf(stderr, "%f detected in %s\n", e.values[e_start + j], wname.c_str());
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exit(1);
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}
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}
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}
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}
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if (e.ncall > m_last_call) {
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m_last_call = e.ncall;
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if (m_last_call % m_params.n_out_freq == 0) {
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save_imatrix();
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}
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if (m_params.n_save_freq > 0 && m_last_call%m_params.n_save_freq == 0) {
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save_imatrix(m_last_call);
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}
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}
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}
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} else {
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if (m_collect_lsim) {
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// We only need to do it here and not in the MoE branch above because the first tensor in a layer
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// never is a MoE tensor
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if (auto index = layer_index(wname); index.has_value()) {
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if (*index != m_last_layer) {
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if (*index > 0) {
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if (m_last_input.size() != src1->ne[0]*src1->ne[1]) {
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printf("Oops: different size (%d vs %d). Tensor name was %s, m_last_layer = %d\n",
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(int)(src1->ne[0]*src1->ne[1]), (int)m_last_input.size(), src0->name, m_last_layer);
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exit(1);
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}
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if (*index > m_layer_sim.size()) m_layer_sim.resize(*index);
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auto& p = m_layer_sim[*index - 1];
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collect_cos_similarity(src1->ne[1], src1->ne[0], m_last_input.data(), (const float *)data, p);
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if (*index == m_last_ffn + 1) {
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if (*index > m_ffn_sim.size()) m_ffn_sim.resize(*index);
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auto& p1 = m_ffn_sim[*index-1];
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collect_cos_similarity(src1->ne[1], src1->ne[0], m_ffn_input.data(), (const float *)data, p1);
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}
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}
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m_last_layer = *index;
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if (m_last_input.empty()) {
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m_last_input.resize(src1->ne[0]*src1->ne[1]);
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} else {
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if (m_last_input.size() != src1->ne[0]*src1->ne[1]) {
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printf("Oops\n"); exit(1);
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}
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}
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//printf("Copying src1 to m_last_input\n");
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std::memcpy(m_last_input.data(), data, src1->ne[0]*src1->ne[1]*sizeof(float));
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}
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}
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}
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auto & e = m_stats[wname];
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if (e.values.empty()) {
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if (src0->ne[3] > 1) {
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fprintf(stderr, "Unsupported 4D tensor %s\n", wname.c_str());
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exit(1);
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}
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// If we have a 3D tensor as it is the case for the attn_k_b and attn_v_b for DeepSeek MLA models,
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// than we need to compute the imatrix for each head, and not just one imatrx for all heads.
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// Hence, the storage we need is src0->ne[0]*src0->ne[2].
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e.values.resize(src0->ne[0]*src0->ne[2], 0);
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e.counts.resize(src0->ne[0]*src0->ne[2], 0);
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}
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else if (e.values.size() != (size_t)(src0->ne[0]*src0->ne[2])) {
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fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)src1->ne[0]);
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exit(1); //GGML_ABORT("fatal error");
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}
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++e.ncall;
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if (m_params.verbosity > 1) {
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printf("%s[%d]: %32s, %s, %5d x %5d, %d\n", __func__, m_last_call, wname.c_str(), ggml_op_name(t->op), (int)src1->ne[0], (int)src1->ne[1], (int)src1->type);
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}
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int rk2 = src1->ne[2]/src0->ne[2];
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for (int i12 = 0; i12 < (int)src1->ne[2]; ++i12) { // i.e., loop over attention heads for MLA models
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int i02 = i12/rk2;
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auto values = e.values.data() + i02*src0->ne[0];
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auto counts = e.counts.data() + i02*src0->ne[0];
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for (int i11 = 0; i11 < (int)src1->ne[1]; ++i11) {
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const float * x = (const float *)((const char *)data + i11*src1->nb[1] + i12*src1->nb[2]);
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for (int j = 0; j < (int)src1->ne[0]; ++j) {
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values[j] += x[j]*x[j];
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counts[j]++;
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if (!std::isfinite(values[j])) {
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fprintf(stderr, "%f detected in %s\n", e.values[j], wname.c_str());
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exit(1);
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}
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}
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}
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}
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if (e.ncall > m_last_call) {
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m_last_call = e.ncall;
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if (m_last_call % m_params.n_out_freq == 0) {
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save_imatrix();
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}
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if (m_params.n_save_freq > 0 && m_last_call%m_params.n_save_freq == 0) {
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save_imatrix(m_last_call);
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}
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}
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}
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return true;
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}
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void IMatrixCollector::save_imatrix(int ncall) const {
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auto fname = m_params.out_file;
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if (fname.empty()) {
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fname = "imatrix.dat";
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}
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if (ncall > 0) {
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fname += ".at_";
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fname += std::to_string(ncall);
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}
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// avoid writing imatrix entries that do not have full data
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// this can happen with MoE models where some of the experts end up not being exercised by the provided training data
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int n_entries = 0;
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std::vector<std::string> to_store;
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bool is_first = true; // for printing
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for (const auto & kv : m_stats) {
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const int n_all = kv.second.counts.size();
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if (n_all == 0) {
|
|
continue;
|
|
}
|
|
|
|
int n_zeros = 0;
|
|
for (const int c : kv.second.counts) {
|
|
if (c == 0) {
|
|
n_zeros++;
|
|
}
|
|
}
|
|
|
|
if (n_zeros != 0 && is_first) {
|
|
fprintf(stderr, "\n");
|
|
is_first = false;
|
|
}
|
|
|
|
if (n_zeros == n_all) {
|
|
fprintf(stderr, "%s: entry '%40s' has no data - skipping\n", __func__, kv.first.c_str());
|
|
continue;
|
|
}
|
|
|
|
if (n_zeros > 0) {
|
|
fprintf(stderr, "%s: entry '%40s' has partial data (%.2f%%)", __func__, kv.first.c_str(), 100.0f * (n_all - n_zeros) / n_all);
|
|
bool store_it = false;
|
|
if (kv.second.n_as > 1) {
|
|
int n_per_expert = n_all / kv.second.n_as;
|
|
std::vector<int> bad_experts;
|
|
bad_experts.reserve(kv.second.n_as);
|
|
for (int i = 0; i < kv.second.n_as; ++i) {
|
|
auto counts = kv.second.counts.data() + i*n_per_expert;
|
|
int nz_i = 0;
|
|
for (int j = 0; j < n_per_expert; ++j) {
|
|
if (counts[j] == 0) ++nz_i;
|
|
}
|
|
if (nz_i > 0) bad_experts.push_back(i);
|
|
}
|
|
fprintf(stderr, " %d out of %d experts are missing data", int(bad_experts.size()), kv.second.n_as);
|
|
if (bad_experts.size() < round(kv.second.n_as * 0.05)) {
|
|
fprintf(stderr, " Storing **but be aware**\n");
|
|
store_it = true;
|
|
for (auto i : bad_experts) {
|
|
auto counts = (int *)kv.second.counts.data() + i*n_per_expert;
|
|
auto values = (float *)kv.second.values.data() + i*n_per_expert;
|
|
for (int j = 0; j < n_per_expert; ++j) {
|
|
counts[j] = 1;
|
|
values[j] = 1;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
if (!store_it) {
|
|
fprintf(stderr, " - skipping\n");
|
|
continue;
|
|
}
|
|
}
|
|
|
|
n_entries++;
|
|
to_store.push_back(kv.first);
|
|
}
|
|
|
|
if (to_store.size() < m_stats.size()) {
|
|
fprintf(stderr, "%s: warning: storing only %zu out of %zu entries\n", __func__, to_store.size(), m_stats.size());
|
|
}
|
|
|
|
std::ofstream out(fname, std::ios::binary);
|
|
out.write((const char *) &n_entries, sizeof(n_entries));
|
|
for (const auto & name : to_store) {
|
|
const auto & stat = m_stats.at(name);
|
|
int len = name.size();
|
|
out.write((const char *) &len, sizeof(len));
|
|
out.write(name.c_str(), len);
|
|
out.write((const char *) &stat.ncall, sizeof(stat.ncall));
|
|
int nval = stat.values.size();
|
|
out.write((const char *) &nval, sizeof(nval));
|
|
if (nval > 0) {
|
|
std::vector<float> tmp(nval);
|
|
for (int i = 0; i < nval; i++) {
|
|
tmp[i] = (stat.values[i] / static_cast<float>(stat.counts[i])) * static_cast<float>(stat.ncall);
|
|
}
|
|
out.write((const char*)tmp.data(), nval*sizeof(float));
|
|
}
|
|
}
|
|
|
|
// Write the number of call the matrix was computed with
|
|
out.write((const char *) &m_last_call, sizeof(m_last_call));
|
|
|
|
// Write the input filename at the end of the file to later on specify it in quantize
|
|
{
|
|
int len = m_params.prompt_file.size();
|
|
out.write((const char *) &len, sizeof(len));
|
|
out.write(m_params.prompt_file.c_str(), len);
|
|
}
|
|
|
|
if (m_params.verbosity > 0) {
|
|
fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n", __func__, m_last_call, fname.c_str());
|
|
}
|
|
}
|
|
|
|
bool IMatrixCollector::load_imatrix(const char * fname) {
|
|
std::ifstream in(fname, std::ios::binary);
|
|
if (!in) {
|
|
printf("%s: failed to open %s\n",__func__, fname);
|
|
return false;
|
|
}
|
|
int n_entries;
|
|
in.read((char*)&n_entries, sizeof(n_entries));
|
|
if (in.fail() || n_entries < 1) {
|
|
printf("%s: no data in file %s\n", __func__, fname);
|
|
return false;
|
|
}
|
|
for (int i = 0; i < n_entries; ++i) {
|
|
int len; in.read((char *)&len, sizeof(len));
|
|
std::vector<char> name_as_vec(len+1);
|
|
in.read((char *)name_as_vec.data(), len);
|
|
if (in.fail()) {
|
|
printf("%s: failed reading name for entry %d from %s\n",__func__,i+1, fname);
|
|
return false;
|
|
}
|
|
name_as_vec[len] = 0;
|
|
std::string name{name_as_vec.data()};
|
|
auto & e = m_stats[std::move(name)];
|
|
int ncall;
|
|
in.read((char*)&ncall, sizeof(ncall));
|
|
int nval;
|
|
in.read((char *)&nval, sizeof(nval));
|
|
if (in.fail() || nval < 1) {
|
|
printf("%s: failed reading number of values for entry %d\n",__func__,i);
|
|
m_stats = {};
|
|
return false;
|
|
}
|
|
|
|
if (e.values.empty()) {
|
|
e.values.resize(nval, 0);
|
|
e.counts.resize(nval, 0);
|
|
}
|
|
|
|
std::vector<float> tmp(nval);
|
|
in.read((char*)tmp.data(), nval*sizeof(float));
|
|
if (in.fail()) {
|
|
printf("%s: failed reading data for entry %d\n",__func__,i);
|
|
m_stats = {};
|
|
return false;
|
|
}
|
|
|
|
// Recreate the state as expected by save_imatrix(), and corerct for weighted sum.
|
|
for (int i = 0; i < nval; i++) {
|
|
e.values[i] += tmp[i];
|
|
e.counts[i] += ncall;
|
|
}
|
|
e.ncall += ncall;
|
|
|
|
}
|
|
return true;
|
|
}
|
|
|
|
static IMatrixCollector g_collector;
|
|
|
|
static bool ik_collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) {
|
|
return g_collector.collect_imatrix(t, ask, user_data);
|
|
}
|
|
|
|
|
|
struct results_log_softmax {
|
|
double log_softmax;
|
|
float logit;
|
|
float prob;
|
|
};
|
|
|
|
static std::vector<float> softmax(const std::vector<float> & logits) {
|
|
std::vector<float> probs(logits.size());
|
|
float max_logit = logits[0];
|
|
for (float v : logits) {
|
|
max_logit = std::max(max_logit, v);
|
|
}
|
|
double sum_exp = 0.0;
|
|
for (size_t i = 0; i < logits.size(); i++) {
|
|
// Subtract the maximum logit value from the current logit value for numerical stability
|
|
const float logit = logits[i] - max_logit;
|
|
const float exp_logit = expf(logit);
|
|
sum_exp += exp_logit;
|
|
probs[i] = exp_logit;
|
|
}
|
|
for (size_t i = 0; i < probs.size(); i++) {
|
|
probs[i] /= sum_exp;
|
|
}
|
|
return probs;
|
|
}
|
|
|
|
static results_log_softmax log_softmax(int n_vocab, const float * logits, int tok) {
|
|
float max_logit = logits[0];
|
|
for (int i = 1; i < n_vocab; ++i) {
|
|
max_logit = std::max(max_logit, logits[i]);
|
|
}
|
|
double sum_exp = 0.0;
|
|
for (int i = 0; i < n_vocab; ++i) {
|
|
sum_exp += expf(logits[i] - max_logit);
|
|
}
|
|
return {logits[tok] - max_logit - log(sum_exp), logits[tok], expf(logits[tok] - max_logit) / (float) sum_exp};
|
|
}
|
|
|
|
static void process_logits(
|
|
int n_vocab, const float * logits, const int * tokens, int n_token, std::vector<std::thread> & workers,
|
|
double & nll, double & nll2, float * logit_history, float * prob_history) {
|
|
std::mutex mutex;
|
|
int counter = 0;
|
|
auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () {
|
|
double local_nll = 0;
|
|
double local_nll2 = 0;
|
|
while (true) {
|
|
std::unique_lock<std::mutex> lock(mutex);
|
|
int i = counter++;
|
|
if (i >= n_token) {
|
|
nll += local_nll; nll2 += local_nll2;
|
|
break;
|
|
}
|
|
lock.unlock();
|
|
const results_log_softmax results = log_softmax(n_vocab, logits + i*n_vocab, tokens[i+1]);
|
|
const double v = -results.log_softmax;
|
|
local_nll += v;
|
|
local_nll2 += v*v;
|
|
|
|
logit_history[i] = results.logit;
|
|
prob_history[i] = results.prob;
|
|
}
|
|
};
|
|
for (auto & w : workers) {
|
|
w = std::thread(compute);
|
|
}
|
|
compute();
|
|
for (auto & w : workers) {
|
|
w.join();
|
|
}
|
|
}
|
|
|
|
static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
|
|
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
|
|
GGML_ASSERT(llama_add_eos_token(llama_get_model(ctx)) != 1);
|
|
const int n_ctx = llama_n_ctx(ctx);
|
|
|
|
auto tim1 = std::chrono::high_resolution_clock::now();
|
|
fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
|
|
|
|
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, true);
|
|
|
|
auto tim2 = std::chrono::high_resolution_clock::now();
|
|
fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
|
|
|
|
if (params.i_chunk > 0) {
|
|
if (size_t((params.i_chunk + 2)*n_ctx) >= tokens.size()) {
|
|
fprintf(stderr, "%s: there will be not enough tokens left after removing %d chunks\n", __func__, params.i_chunk);
|
|
return false;
|
|
}
|
|
fprintf(stderr, "%s: removing initial %d chunks (%d tokens)\n", __func__, params.i_chunk, params.i_chunk*n_ctx);
|
|
tokens.erase(tokens.begin(), tokens.begin() + params.i_chunk*n_ctx);
|
|
}
|
|
|
|
if (int(tokens.size()) < 2*n_ctx) {
|
|
fprintf(stderr, "%s: you need at least %d tokens for a context of %d tokens\n",__func__,2*n_ctx,
|
|
n_ctx);
|
|
fprintf(stderr, "%s: the data file you provided tokenizes to only %zu tokens\n",__func__,tokens.size());
|
|
return false;
|
|
}
|
|
|
|
std::vector<float> logit_history;
|
|
std::vector<float> prob_history;
|
|
|
|
if (params.compute_ppl) {
|
|
logit_history.resize(tokens.size());
|
|
prob_history.resize(tokens.size());
|
|
}
|
|
|
|
const int n_chunk_max = tokens.size() / n_ctx;
|
|
|
|
const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
|
|
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
|
|
const int n_batch = params.n_batch;
|
|
|
|
int count = 0;
|
|
double nll = 0.0;
|
|
double nll2 = 0.0;
|
|
|
|
fprintf(stderr, "%s: computing over %d chunks with batch_size %d\n", __func__, n_chunk, n_batch);
|
|
|
|
std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
|
|
|
|
const int num_batches = (n_ctx + n_batch - 1) / n_batch;
|
|
|
|
std::vector<float> logits;
|
|
if (params.compute_ppl && num_batches > 1) {
|
|
logits.reserve((size_t)n_ctx * n_vocab);
|
|
}
|
|
|
|
for (int i = 0; i < n_chunk; ++i) {
|
|
const int start = i * n_ctx;
|
|
const int end = start + n_ctx;
|
|
|
|
std::vector<float> logits;
|
|
|
|
const auto t_start = std::chrono::high_resolution_clock::now();
|
|
|
|
// clear the KV cache
|
|
llama_kv_cache_clear(ctx);
|
|
|
|
for (int j = 0; j < num_batches; ++j) {
|
|
const int batch_start = start + j * n_batch;
|
|
const int batch_size = std::min(end - batch_start, n_batch);
|
|
|
|
// save original token and restore it after eval
|
|
const auto token_org = tokens[batch_start];
|
|
|
|
// add BOS token for the first batch of each chunk
|
|
if (add_bos && j == 0) {
|
|
tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
|
|
}
|
|
|
|
// TODO: use batch.logits to save computations instead of relying on logits_all == true
|
|
if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
|
|
fprintf(stderr, "%s : failed to eval\n", __func__);
|
|
return false;
|
|
}
|
|
|
|
// restore the original token in case it was set to BOS
|
|
tokens[batch_start] = token_org;
|
|
|
|
if (params.compute_ppl && num_batches > 1) {
|
|
const auto * batch_logits = llama_get_logits(ctx);
|
|
logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
|
|
}
|
|
}
|
|
|
|
const auto t_end = std::chrono::high_resolution_clock::now();
|
|
|
|
if (i == 0) {
|
|
const float t_total = std::chrono::duration<float>(t_end - t_start).count();
|
|
fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total);
|
|
int total_seconds = (int)(t_total * n_chunk);
|
|
if (total_seconds >= 60*60) {
|
|
fprintf(stderr, "%d hours ", total_seconds / (60*60));
|
|
total_seconds = total_seconds % (60*60);
|
|
}
|
|
fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0);
|
|
}
|
|
|
|
if (params.compute_ppl) {
|
|
const int first = n_ctx/2;
|
|
const auto all_logits = num_batches > 1 ? logits.data() : llama_get_logits(ctx);
|
|
process_logits(n_vocab, all_logits + first*n_vocab, tokens.data() + start + first, n_ctx - 1 - first,
|
|
workers, nll, nll2, logit_history.data() + start + first, prob_history.data() + start + first);
|
|
count += n_ctx - first - 1;
|
|
|
|
printf("[%d]%.4lf,", i + 1, std::exp(nll / count));
|
|
fflush(stdout);
|
|
|
|
logits.clear();
|
|
}
|
|
}
|
|
printf("\n");
|
|
|
|
if (params.compute_ppl) {
|
|
nll2 /= count;
|
|
nll /= count;
|
|
const double ppl = exp(nll);
|
|
nll2 -= nll * nll;
|
|
if (nll2 > 0) {
|
|
nll2 = sqrt(nll2/(count-1));
|
|
printf("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl);
|
|
} else {
|
|
printf("Unexpected negative standard deviation of log(prob)\n");
|
|
}
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
int main(int argc, char ** argv) {
|
|
gpt_params params;
|
|
|
|
params.n_ctx = 512;
|
|
params.logits_all = true;
|
|
params.verbosity = 1;
|
|
|
|
bool lsim = false;
|
|
//
|
|
// Do not pollute common with totally imatrix specific arguments as it was done in mainline.
|
|
// Instead, parse imatrix specific args here, push unknown args into a new array of args,
|
|
// and pass that to gpt_params_parse().
|
|
//
|
|
std::vector<char*> args;
|
|
args.reserve(argc);
|
|
args.push_back(argv[0]);
|
|
for (int i = 1; i < argc; ++i) {
|
|
std::string arg{argv[i]};
|
|
if (arg == "-lsim" || arg == "--layer-similarity") {
|
|
lsim = true;
|
|
} else {
|
|
args.push_back(argv[i]);
|
|
}
|
|
}
|
|
|
|
if (!gpt_params_parse(args.size(), args.data(), params)) {
|
|
print_usage(argc, argv, params);
|
|
return 1;
|
|
}
|
|
|
|
params.n_batch = std::min(params.n_batch, params.n_ctx);
|
|
|
|
g_collector.set_params(params);
|
|
g_collector.set_collect_lsim(lsim);
|
|
|
|
for (const auto & in_file : params.in_files) {
|
|
printf("%s : loading imatrix from '%s'\n", __func__, in_file.c_str());
|
|
if (!g_collector.load_imatrix(in_file.c_str())) {
|
|
fprintf(stderr, "%s : failed to load %s\n", __func__, in_file.c_str());
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
if (params.in_files.size() > 1) {
|
|
printf("%s : saving combined imatrix to '%s'\n", __func__, params.out_file.c_str());
|
|
g_collector.save_imatrix();
|
|
}
|
|
|
|
llama_backend_init();
|
|
llama_numa_init(params.numa);
|
|
|
|
// pass the callback to the backend scheduler
|
|
// it will be executed for each node during the graph computation
|
|
params.cb_eval = ik_collect_imatrix;
|
|
params.cb_eval_user_data = NULL;
|
|
params.warmup = false;
|
|
|
|
// init
|
|
llama_init_result llama_init = llama_init_from_gpt_params(params);
|
|
|
|
llama_model * model = llama_init.model;
|
|
llama_context * ctx = llama_init.context;
|
|
if (model == nullptr || ctx == nullptr) {
|
|
fprintf(stderr, "%s : failed to init\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
const int n_ctx_train = llama_n_ctx_train(model);
|
|
if (params.n_ctx > n_ctx_train) {
|
|
fprintf(stderr, "%s: warning: model was trained on only %d context tokens (%d specified)\n",
|
|
__func__, n_ctx_train, params.n_ctx);
|
|
}
|
|
|
|
// print system information
|
|
{
|
|
fprintf(stderr, "\n");
|
|
fprintf(stderr, "%s\n", gpt_params_get_system_info(params).c_str());
|
|
}
|
|
|
|
if (!compute_imatrix(ctx, params)) {
|
|
return 1;
|
|
}
|
|
|
|
g_collector.save_imatrix();
|
|
g_collector.print_layer_importance();
|
|
|
|
llama_print_timings(ctx);
|
|
|
|
llama_free(ctx);
|
|
llama_free_model(model);
|
|
|
|
llama_backend_free();
|
|
|
|
return 0;
|
|
}
|