mirror of
https://github.com/ggerganov/llama.cpp.git
synced 2024-11-14 23:09:53 +00:00
743 lines
27 KiB
C++
743 lines
27 KiB
C++
#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 <thread>
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#include <mutex>
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#include <vector>
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#include <unordered_map>
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#include <algorithm>
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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.gguf] [--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.gguf --in-file imatrix-prev-1.gguf ...]\n" , argv[0]);
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LOG_TEE("\n");
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}
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static const char * const LLM_KV_IMATRIX_DATASET = "imatrix.dataset";
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static const char * const LLM_KV_IMATRIX_CHUNK_COUNT = "imatrix.chunk_count";
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static const char * const LLM_KV_IMATRIX_CHUNK_SIZE = "imatrix.chunk_size";
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struct Stats {
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std::vector<double> values;
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std::vector<int64_t> counts;
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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(int32_t n_chunk = -1) const;
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bool load_imatrix(const char * file_name);
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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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int32_t m_last_chunk = 0;
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std::vector<float> m_src1_data;
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std::vector<char> m_ids; // the expert ids from ggml_mul_mat_id
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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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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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if (!(wname.substr(0, 4) == "blk." || (m_params.process_output && wname == "output.weight"))) 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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m_src1_data.resize(ggml_nelements(src1));
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ggml_backend_tensor_get(src1, m_src1_data.data(), 0, ggml_nbytes(src1));
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}
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const float * data = is_host ? (const float *) src1->data : m_src1_data.data();
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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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if (e.counts.size() == 1 && n_as > 1) {
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// broadcast, when loading an old imatrix
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e.counts.resize(n_as, e.counts[0]);
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}
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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(n_as, 0);
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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.counts.size() != (size_t)n_as) {
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fprintf(stderr, "Oops: inconsistent expert count for %s (%d vs %d)\n", wname.c_str(), (int)e.counts.size(), (int)n_as);
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exit(1); //GGML_ABORT("fatal error");
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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_chunk, 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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e.counts[ex]++;
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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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if (!std::isfinite((float)e.values[e_start + j])) {
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fprintf(stderr, "%f detected in %s\n", (float)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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const int32_t n_chunk = e.counts[ex] / (m_params.n_ctx / m_params.n_parallel);
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if (n_chunk > m_last_chunk) {
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const int32_t chunk_step = n_chunk - m_last_chunk;
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m_last_chunk = n_chunk;
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if ((m_last_chunk % m_params.n_out_freq) / chunk_step == 0) {
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save_imatrix();
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}
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if (m_params.n_save_freq > 0 && (m_last_chunk % m_params.n_save_freq) / chunk_step == 0) {
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save_imatrix(m_last_chunk);
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}
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}
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}
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} else {
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auto & e = m_stats[wname];
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if (e.values.empty()) {
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e.values.resize(src1->ne[0], 0);
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e.counts.resize(1, 0);
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}
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else if (e.values.size() != (size_t)src1->ne[0]) {
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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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else if (e.counts.size() != 1) {
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fprintf(stderr, "Oops: inconsistent expert count for %s (%d vs %d)\n", wname.c_str(), (int)e.counts.size(), 1);
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exit(1); //GGML_ABORT("fatal error");
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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_chunk, 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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// TODO: higher dimensions
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for (int row = 0; row < (int)src1->ne[1]; ++row) {
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const float * x = data + row * src1->ne[0];
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e.counts[0]++;
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for (int j = 0; j < (int)src1->ne[0]; ++j) {
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e.values[j] += x[j]*x[j];
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if (!std::isfinite((float)e.values[j])) {
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fprintf(stderr, "%f detected in %s\n", (float)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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const int32_t n_chunk = e.counts[0] / (m_params.n_ctx / m_params.n_parallel);
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if (n_chunk > m_last_chunk) {
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const int32_t chunk_step = n_chunk - m_last_chunk;
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m_last_chunk = n_chunk;
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if ((m_last_chunk % m_params.n_out_freq) / chunk_step == 0) {
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save_imatrix();
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}
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if (m_params.n_save_freq > 0 && (m_last_chunk % m_params.n_save_freq) / chunk_step == 0) {
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save_imatrix(m_last_chunk);
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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(int32_t n_chunk) const {
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auto fname = m_params.out_file;
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if (fname.empty()) {
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fname = "imatrix.gguf";
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}
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if (n_chunk > 0) {
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fname += ".at_";
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fname += std::to_string(n_chunk);
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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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size_t data_size = 0;
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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) {
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continue;
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}
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int n_zeros = 0;
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for (const int c : kv.second.counts) {
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if (c == 0) {
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n_zeros++;
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}
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}
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if (n_zeros != 0 && is_first) {
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fprintf(stderr, "\n");
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is_first = false;
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}
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if (n_zeros == n_all) {
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fprintf(stderr, "%s: entry '%40s' has no data - skipping\n", __func__, kv.first.c_str());
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continue;
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}
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if (n_zeros > 0) {
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fprintf(stderr, "%s: entry '%40s' has partial data (%.2f%%) - skipping\n", __func__, kv.first.c_str(), 100.0f * (n_all - n_zeros) / n_all);
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continue;
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}
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n_entries++;
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to_store.push_back(kv.first);
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data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.values.size(), GGML_MEM_ALIGN);
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data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.counts.size(), GGML_MEM_ALIGN);
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}
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if (to_store.size() < m_stats.size()) {
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fprintf(stderr, "%s: warning: storing only %zu out of %zu entries\n", __func__, to_store.size(), m_stats.size());
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}
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struct ggml_init_params params = {
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.mem_size = data_size,
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.mem_buffer = NULL,
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.no_alloc = false,
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};
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struct ggml_context * ctx = ggml_init(params);
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struct gguf_context * ctx_gguf = gguf_init_empty();
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gguf_set_val_str(ctx_gguf, "general.type", "imatrix");
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// Write the input filename to later on specify it in quantize
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gguf_set_val_str(ctx_gguf, LLM_KV_IMATRIX_DATASET, m_params.prompt_file.c_str());
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// Write the number of chunks the matrix was computed with
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gguf_set_val_u32(ctx_gguf, LLM_KV_IMATRIX_CHUNK_COUNT, m_last_chunk);
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gguf_set_val_u32(ctx_gguf, LLM_KV_IMATRIX_CHUNK_SIZE, m_params.n_ctx / m_params.n_parallel);
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for (const auto & name : to_store) {
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const auto & stat = m_stats.at(name);
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const int32_t nval = (int32_t) stat.values.size();
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const int32_t nmat = (int32_t) stat.counts.size();
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if (nval > 0) {
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struct ggml_tensor * sums = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nval / nmat, nmat);
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struct ggml_tensor * counts = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, nmat);
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ggml_set_name(sums, (name + ".sums").c_str());
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ggml_set_name(counts, (name + ".counts").c_str());
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for (int32_t j = 0; j < nval; ++j) {
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((float *) sums->data)[j] = (float) stat.values[j];
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}
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for (int32_t j = 0; j < nmat; ++j) {
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((float *) counts->data)[j] = (float) stat.counts[j];
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}
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gguf_add_tensor(ctx_gguf, sums);
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gguf_add_tensor(ctx_gguf, counts);
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}
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}
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gguf_write_to_file(ctx_gguf, fname.c_str(), false);
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if (m_params.verbosity > 0) {
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fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n", __func__, m_last_chunk, fname.c_str());
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}
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gguf_free(ctx_gguf);
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ggml_free(ctx);
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}
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bool IMatrixCollector::load_imatrix(const char * file_name) {
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struct ggml_context * ctx = nullptr;
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struct gguf_init_params meta_gguf_params = {
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/* .no_alloc = */ false, // the data is needed
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/* .ctx = */ &ctx,
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};
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struct gguf_context * ctx_gguf = gguf_init_from_file(file_name, meta_gguf_params);
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if (!ctx_gguf) {
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return false;
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}
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const int32_t n_entries = gguf_get_n_tensors(ctx_gguf);
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if (n_entries < 2) {
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fprintf(stderr, "%s: no data in file %s\n", __func__, file_name);
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gguf_free(ctx_gguf);
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ggml_free(ctx);
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return false;
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}
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const std::string sums_suffix{".sums"};
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const std::string counts_suffix{".counts"};
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// TODO: allow loading from mis-ordered imatrix files
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for (int32_t i = 0; i < n_entries - 1; i += 2) {
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std::string sums_name{gguf_get_tensor_name(ctx_gguf, i + 0)};
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std::string counts_name{gguf_get_tensor_name(ctx_gguf, i + 1)};
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if (sums_name.size() < sums_suffix.size() ||
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counts_name.size() < counts_suffix.size() ||
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!std::equal(sums_name.begin(), sums_name.end() - sums_suffix.size(), counts_name.begin()) ||
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!std::equal(sums_suffix.rbegin(), sums_suffix.rend(), sums_name.rbegin()) ||
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!std::equal(counts_suffix.rbegin(), counts_suffix.rend(), counts_name.rbegin())) {
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fprintf(stderr, "%s: mismatched sums and counts for entry %d\n", __func__, i / 2);
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gguf_free(ctx_gguf);
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ggml_free(ctx);
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return false;
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}
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struct ggml_tensor * sums = ggml_get_tensor(ctx, sums_name.c_str());
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struct ggml_tensor * counts = ggml_get_tensor(ctx, counts_name.c_str());
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if (!sums || !counts) {
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fprintf(stderr, "%s: failed reading data for entry %d\n", __func__, i / 2);
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gguf_free(ctx_gguf);
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ggml_free(ctx);
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return false;
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}
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std::string name = sums_name.substr(0, sums_name.size() - sums_suffix.size());
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auto & e = m_stats[name];
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int32_t nval = ggml_nelements(sums);
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if (e.values.empty()) {
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e.values.resize(nval, 0);
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}
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int32_t ncounts = ggml_nelements(counts);
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if (e.counts.empty()) {
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e.counts.resize(ncounts, 0);
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} else if (e.counts.size() == 1 && ncounts > 1) {
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// broadcast, when loading an old imatrix
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e.counts.resize(ncounts, e.counts[0]);
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}
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// Recreate the state as expected by save_imatrix()
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for (int32_t j = 0; j < nval; j++) {
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e.values[j] += ((const float *) sums->data)[j];
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}
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for (int32_t j = 0; j < ncounts; j++) {
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e.counts[j] += std::lround(((const float *) counts->data)[j]);
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}
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}
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gguf_free(ctx_gguf);
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ggml_free(ctx);
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return true;
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}
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static IMatrixCollector g_collector;
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static bool ik_collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data) {
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return g_collector.collect_imatrix(t, ask, user_data);
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}
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struct results_log_softmax {
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double log_softmax;
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float logit;
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float prob;
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};
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|
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static std::vector<float> softmax(const std::vector<float> & logits) {
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std::vector<float> probs(logits.size());
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float max_logit = logits[0];
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|
for (float v : logits) {
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|
max_logit = std::max(max_logit, v);
|
|
}
|
|
double sum_exp = 0.0;
|
|
for (size_t i = 0; i < logits.size(); i++) {
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|
// Subtract the maximum logit value from the current logit value for numerical stability
|
|
const float logit = logits[i] - max_logit;
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|
const float exp_logit = expf(logit);
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|
sum_exp += exp_logit;
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|
probs[i] = exp_logit;
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|
}
|
|
for (size_t i = 0; i < probs.size(); i++) {
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|
probs[i] /= sum_exp;
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|
}
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|
return probs;
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|
}
|
|
|
|
static results_log_softmax log_softmax(int n_vocab, const float * logits, int tok) {
|
|
float max_logit = logits[0];
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|
for (int i = 1; i < n_vocab; ++i) {
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|
max_logit = std::max(max_logit, logits[i]);
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|
}
|
|
double sum_exp = 0.0;
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|
for (int i = 0; i < n_vocab; ++i) {
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|
sum_exp += expf(logits[i] - max_logit);
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|
}
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|
return {logits[tok] - max_logit - log(sum_exp), logits[tok], expf(logits[tok] - max_logit) / (float) sum_exp};
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|
}
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|
|
|
static void process_logits(
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|
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) {
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|
std::mutex mutex;
|
|
int counter = 0;
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|
auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () {
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|
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 int32_t n_ctx) {
|
|
const bool add_bos = llama_add_bos_token(llama_get_model(ctx));
|
|
GGML_ASSERT(!llama_add_eos_token(llama_get_model(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;
|
|
|
|
std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
|
|
|
|
const int num_batches = (n_ctx + n_batch - 1) / n_batch;
|
|
const int n_seq = std::max(1, n_batch / n_ctx);
|
|
|
|
GGML_ASSERT(n_batch < n_ctx || n_batch % n_ctx == 0);
|
|
GGML_ASSERT(params.n_ctx == n_seq * n_ctx);
|
|
|
|
llama_batch batch = llama_batch_init(std::min(n_batch, n_ctx*n_seq), 0, 1);
|
|
|
|
std::vector<float> logits;
|
|
if (params.compute_ppl && num_batches > 1) {
|
|
logits.reserve((size_t)n_ctx * n_vocab);
|
|
}
|
|
|
|
fprintf(stderr, "%s: computing over %d chunks, n_ctx=%d, batch_size=%d, n_seq=%d\n", __func__, n_chunk, n_ctx, n_batch, n_seq);
|
|
|
|
for (int i = 0; i < n_chunk; i += n_seq) {
|
|
const int start = i * n_ctx;
|
|
const int end = start + n_ctx;
|
|
|
|
const int n_seq_batch = std::min(n_seq, n_chunk - i);
|
|
|
|
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);
|
|
|
|
// clear the batch
|
|
llama_batch_clear(batch);
|
|
|
|
for (int seq = 0; seq < n_seq_batch; seq++) {
|
|
int seq_start = batch_start + seq*n_ctx;
|
|
|
|
// save original token and restore it after eval
|
|
const auto token_org = tokens[seq_start];
|
|
|
|
// add BOS token for the first batch of each chunk
|
|
if (add_bos && j == 0) {
|
|
tokens[seq_start] = llama_token_bos(llama_get_model(ctx));
|
|
}
|
|
|
|
for (int k = 0; k < batch_size; ++k) {
|
|
// NOTE: specifying all logits to get activations for the output.weight tensor
|
|
// and also for the perplexity calculation.
|
|
// TODO: only get outputs when (params.process_output || params.compute_ppl)
|
|
// (not possible when this skips FFN computation of the last layer)
|
|
llama_batch_add(batch, tokens[seq_start + k], j*n_batch + k, { seq }, true);
|
|
}
|
|
|
|
// restore the original token in case it was set to BOS
|
|
tokens[seq_start] = token_org;
|
|
}
|
|
|
|
if (llama_decode(ctx, batch)) {
|
|
fprintf(stderr, "%s : failed to eval\n", __func__);
|
|
return false;
|
|
}
|
|
|
|
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);
|
|
}
|
|
}
|
|
|
|
|
|
if (i == 0) {
|
|
llama_synchronize(ctx);
|
|
const auto t_end = std::chrono::high_resolution_clock::now();
|
|
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/n_seq);
|
|
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;
|
|
for (int seq = 0; seq < n_seq_batch; seq++) {
|
|
const float * all_logits = num_batches > 1 ? logits.data() : llama_get_logits_ith(ctx, seq*n_ctx);
|
|
|
|
llama_token * tokens_data = tokens.data() + start + seq*n_ctx + first;
|
|
|
|
process_logits(n_vocab, all_logits + first*n_vocab,
|
|
tokens_data, n_ctx - 1 - first,
|
|
workers, nll, nll2,
|
|
logit_history.data() + start + seq*n_ctx + first,
|
|
prob_history.data() + start + seq*n_ctx + first);
|
|
|
|
count += n_ctx - first - 1;
|
|
|
|
printf("[%d]%.4lf,", i + seq + 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;
|
|
|
|
if (!gpt_params_parse(argc, argv, params)) {
|
|
print_usage(argc, argv, params);
|
|
return 1;
|
|
}
|
|
|
|
const int32_t n_ctx = params.n_ctx;
|
|
|
|
if (n_ctx <= 0) {
|
|
fprintf(stderr, "%s: imatrix tool requires '--ctx-size' > 0\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
{
|
|
const int32_t n_seq = std::max(1, params.n_batch / n_ctx);
|
|
const int32_t n_kv = n_seq * n_ctx;
|
|
|
|
params.n_parallel = n_seq;
|
|
params.n_ctx = n_kv;
|
|
|
|
params.n_batch = std::min(params.n_batch, n_kv);
|
|
}
|
|
|
|
g_collector.set_params(params);
|
|
|
|
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, n_ctx)) {
|
|
return 1;
|
|
}
|
|
|
|
g_collector.save_imatrix();
|
|
|
|
llama_print_timings(ctx);
|
|
|
|
llama_free(ctx);
|
|
llama_free_model(model);
|
|
|
|
llama_backend_free();
|
|
|
|
return 0;
|
|
}
|