641 lines
22 KiB
C++
641 lines
22 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 <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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#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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struct Stats {
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std::vector<float> values;
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int ncall = 0;
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};
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struct StatParams {
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std::string ofile = "imatrix.dat";
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int n_output_frequency = 10;
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int verbosity = 1;
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int keep_every = 0;
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bool collect_output_weight = false;
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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_parameters(StatParams&& 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() const;
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bool load_imatrix(const char * file_name, bool add);
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static bool load_imatrix(const char * file_name, std::unordered_map<std::string, Stats>& imatrix);
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private:
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std::unordered_map<std::string, Stats> m_stats;
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StatParams m_params;
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std::mutex m_mutex;
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int m_last_call = 0;
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std::vector<float> m_src1_data;
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std::vector<int> m_ids; // the expert ids from ggml_mul_mat_id
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//
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void save_imatrix(const char * file_name) const;
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void keep_imatrix(int ncall) const;
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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;
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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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const char * p = strchr(src0->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 = src0->name;
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}
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}
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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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if (src1->ne[1] < 16 || src1->type != GGML_TYPE_F32) return false;
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if (!(wname.substr(0, 4) == "blk." || (m_params.collect_output_weight && 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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if (t->op == GGML_OP_MUL_MAT_ID) {
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const int idx = ((int32_t *) t->op_params)[0];
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const int n_as = ((int32_t *) t->op_params)[1];
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// the top-k selected expert ids are stored in the src0 tensor
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// for simplicity, always copy src0 to host, because it is small
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// take into account that src0 is not contiguous!
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GGML_ASSERT(src0->ne[1] == src1->ne[1]);
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GGML_ASSERT(n_as*ggml_nrows(src0)*sizeof(int) == GGML_PAD(ggml_nbytes(src0), n_as*sizeof(int)));
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m_ids.resize(ggml_nbytes(src0)/sizeof(int));
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ggml_backend_tensor_get(src0, m_ids.data(), 0, ggml_nbytes(src0));
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// loop over all possible experts, regardless if they are used or not in the batch
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// this is necessary to guarantee equal number of "ncall" for each tensor
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for (int ex = 0; ex < n_as; ++ex) {
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src0 = t->src[2 + ex];
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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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}
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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_ASSERT(false);
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}
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// NOTE: since we select top-k experts, the number of calls for the expert tensors will be k times larger
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// using the following line, we can correct for that if needed
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//if (idx == t->src[0]->ne[0] - 1) ++e.ncall;
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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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for (int row = 0; row < (int)src1->ne[1]; ++row) {
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const int excur = m_ids[row*n_as + idx];
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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 float * x = data + row * src1->ne[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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}
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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_output_frequency == 0) {
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save_imatrix();
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}
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if (m_params.keep_every > 0 && m_last_call%m_params.keep_every == 0) {
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keep_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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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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}
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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_ASSERT(false);
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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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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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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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}
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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_output_frequency == 0) {
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save_imatrix();
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}
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if (m_params.keep_every > 0 && m_last_call%m_params.keep_every == 0) {
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keep_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() const {
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save_imatrix(m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str());
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}
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void IMatrixCollector::keep_imatrix(int ncall) const {
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auto file_name = m_params.ofile;
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if (file_name.empty()) file_name = "imatrix.dat";
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file_name += ".at_";
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file_name += std::to_string(ncall);
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save_imatrix(file_name.c_str());
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}
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void IMatrixCollector::save_imatrix(const char * fname) const {
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std::ofstream out(fname, std::ios::binary);
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int n_entries = m_stats.size();
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out.write((const char*)&n_entries, sizeof(n_entries));
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for (auto& p : m_stats) {
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int len = p.first.size();
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out.write((const char*)&len, sizeof(len));
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out.write(p.first.c_str(), len);
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out.write((const char*)&p.second.ncall, sizeof(p.second.ncall));
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int nval = p.second.values.size();
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out.write((const char*)&nval, sizeof(nval));
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if (nval > 0) out.write((const char*)p.second.values.data(), nval*sizeof(float));
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}
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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_call,fname);
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}
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}
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bool IMatrixCollector::load_imatrix(const char * imatrix_file, std::unordered_map<std::string, Stats>& imatrix_data) {
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std::ifstream in(imatrix_file, std::ios::binary);
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if (!in) {
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printf("%s: failed to open %s\n",__func__,imatrix_file);
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return false;
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}
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int n_entries;
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in.read((char*)&n_entries, sizeof(n_entries));
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if (in.fail() || n_entries < 1) {
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printf("%s: no data in file %s\n", __func__, imatrix_file);
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return false;
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}
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for (int i = 0; i < n_entries; ++i) {
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int len; in.read((char *)&len, sizeof(len));
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std::vector<char> name_as_vec(len+1);
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in.read((char *)name_as_vec.data(), len);
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if (in.fail()) {
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printf("%s: failed reading name for entry %d from %s\n",__func__,i+1,imatrix_file);
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return false;
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}
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name_as_vec[len] = 0;
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std::string name{name_as_vec.data()};
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auto& e = imatrix_data[std::move(name)];
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int ncall;
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in.read((char*)&ncall, sizeof(ncall));
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int nval;
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in.read((char *)&nval, sizeof(nval));
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if (in.fail() || nval < 1) {
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printf("%s: failed reading number of values for entry %d\n",__func__,i);
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imatrix_data = {};
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return false;
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}
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e.values.resize(nval);
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in.read((char*)e.values.data(), nval*sizeof(float));
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if (in.fail()) {
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printf("%s: failed reading data for entry %d\n",__func__,i);
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imatrix_data = {};
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return false;
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}
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e.ncall = ncall;
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}
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return true;
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}
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bool IMatrixCollector::load_imatrix(const char * file_name, bool add) {
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if (!add) {
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m_stats.clear();
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}
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return load_imatrix(file_name, m_stats);
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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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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);
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}
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double sum_exp = 0.0;
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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
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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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}
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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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}
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static results_log_softmax log_softmax(int n_vocab, const float * logits, int tok) {
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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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}
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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,
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double & nll, double & nll2, float * logit_history, float * prob_history
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) {
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std::mutex mutex;
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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;
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double local_nll2 = 0;
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while (true) {
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std::unique_lock<std::mutex> lock(mutex);
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int i = counter++;
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if (i >= n_token) {
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nll += local_nll; nll2 += local_nll2;
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break;
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}
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lock.unlock();
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const results_log_softmax results = log_softmax(n_vocab, logits + i*n_vocab, tokens[i+1]);
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const double v = -results.log_softmax;
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local_nll += v;
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local_nll2 += v*v;
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logit_history[i] = results.logit;
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prob_history[i] = results.prob;
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}
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};
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for (auto & w : workers) {
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w = std::thread(compute);
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}
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compute();
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for (auto & w : workers) {
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w.join();
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}
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}
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static bool compute_imatrix(llama_context * ctx, const gpt_params & params, bool compute_ppl, int from_chunk) {
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const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
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const int n_ctx = llama_n_ctx(ctx);
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auto tim1 = std::chrono::high_resolution_clock::now();
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fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
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std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
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auto tim2 = std::chrono::high_resolution_clock::now();
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fprintf(stderr, "%s: tokenization took %g ms\n",__func__,1e-3*std::chrono::duration_cast<std::chrono::microseconds>(tim2-tim1).count());
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if (from_chunk > 0) {
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if (size_t((from_chunk + 2)*n_ctx) >= tokens.size()) {
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fprintf(stderr, "%s: there will be not enough tokens left after removing %d chunks\n", __func__, from_chunk);
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return false;
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}
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fprintf(stderr, "%s: removing initial %d chunks (%d tokens)\n", __func__, from_chunk, from_chunk*n_ctx);
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tokens.erase(tokens.begin(), tokens.begin() + from_chunk*n_ctx);
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}
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if (int(tokens.size()) < 2*n_ctx) {
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fprintf(stderr, "%s: you need at least %d tokens for a context of %d tokens\n",__func__,2*n_ctx,
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n_ctx);
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fprintf(stderr, "%s: the data file you provided tokenizes to only %zu tokens\n",__func__,tokens.size());
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return false;
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}
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std::vector<float> logit_history;
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std::vector<float> prob_history;
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if (compute_ppl) {
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logit_history.resize(tokens.size());
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prob_history.resize(tokens.size());
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}
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const int n_chunk_max = tokens.size() / n_ctx;
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const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
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const int n_vocab = llama_n_vocab(llama_get_model(ctx));
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const int n_batch = params.n_batch;
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int count = 0;
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double nll = 0.0;
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double nll2 = 0.0;
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fprintf(stderr, "%s: computing over %d chunks with batch_size %d\n", __func__, n_chunk, n_batch);
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std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
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const int num_batches = (n_ctx + n_batch - 1) / n_batch;
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std::vector<float> logits;
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if (compute_ppl && num_batches > 1) {
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logits.reserve((size_t)n_ctx * n_vocab);
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}
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for (int i = 0; i < n_chunk; ++i) {
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const int start = i * n_ctx;
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const int end = start + n_ctx;
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std::vector<float> logits;
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const auto t_start = std::chrono::high_resolution_clock::now();
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// clear the KV cache
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llama_kv_cache_clear(ctx);
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for (int j = 0; j < num_batches; ++j) {
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const int batch_start = start + j * n_batch;
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const int batch_size = std::min(end - batch_start, n_batch);
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// save original token and restore it after eval
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const auto token_org = tokens[batch_start];
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// add BOS token for the first batch of each chunk
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if (add_bos && j == 0) {
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tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
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}
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if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 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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// restore the original token in case it was set to BOS
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tokens[batch_start] = token_org;
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if (compute_ppl && num_batches > 1) {
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const auto * batch_logits = llama_get_logits(ctx);
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logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
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}
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}
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|
|
|
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 (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 (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) {
|
|
|
|
StatParams sparams;
|
|
std::string prev_result_file;
|
|
std::string combine_files;
|
|
bool compute_ppl = true;
|
|
int from_chunk = 0;
|
|
std::vector<char*> args;
|
|
args.push_back(argv[0]);
|
|
int iarg = 1;
|
|
for (; iarg < argc-1; ++iarg) {
|
|
std::string arg{argv[iarg]};
|
|
if (arg == "-o" || arg == "--output-file") {
|
|
sparams.ofile = argv[++iarg];
|
|
}
|
|
else if (arg == "-ofreq" || arg == "--output-frequency") {
|
|
sparams.n_output_frequency = std::stoi(argv[++iarg]);
|
|
}
|
|
else if (arg == "-ow" || arg == "--output-weight") {
|
|
sparams.collect_output_weight = std::stoi(argv[++iarg]);
|
|
}
|
|
else if (arg == "--verbosity") {
|
|
sparams.verbosity = std::stoi(argv[++iarg]);
|
|
} else if (arg == "--no-ppl") {
|
|
compute_ppl = false;
|
|
} else if (arg == "--keep-imatrix") {
|
|
sparams.keep_every = std::stoi(argv[++iarg]);
|
|
} else if (arg == "--continue-from") {
|
|
prev_result_file = argv[++iarg];
|
|
} else if (arg == "--combine") {
|
|
combine_files = argv[++iarg];
|
|
}
|
|
else if (arg == "--from-chunk") {
|
|
from_chunk = std::stoi(argv[++iarg]);
|
|
} else {
|
|
args.push_back(argv[iarg]);
|
|
}
|
|
}
|
|
if (iarg < argc) {
|
|
std::string arg{argv[iarg]};
|
|
if (arg == "--no-ppl") {
|
|
compute_ppl = false;
|
|
} else {
|
|
args.push_back(argv[iarg]);
|
|
}
|
|
}
|
|
|
|
g_collector.set_parameters(std::move(sparams));
|
|
|
|
if (!combine_files.empty()) {
|
|
std::vector<std::string> files;
|
|
size_t pos = 0;
|
|
while (true) {
|
|
auto new_pos = combine_files.find(',', pos);
|
|
if (new_pos != std::string::npos) {
|
|
files.emplace_back(combine_files.substr(pos, new_pos - pos));
|
|
pos = new_pos + 1;
|
|
} else {
|
|
files.emplace_back(combine_files.substr(pos));
|
|
break;
|
|
}
|
|
}
|
|
if (files.size() < 2) {
|
|
fprintf(stderr, "You must provide at least two comma separated files to use --combine\n");
|
|
return 1;
|
|
}
|
|
printf("Combining the following %d files\n", int(files.size()));
|
|
for (auto& file : files) {
|
|
printf(" %s\n", file.c_str());
|
|
if (!g_collector.load_imatrix(file.c_str(), true)) {
|
|
fprintf(stderr, "Failed to load %s\n", file.c_str());
|
|
return 1;
|
|
}
|
|
}
|
|
g_collector.save_imatrix();
|
|
return 0;
|
|
}
|
|
|
|
if (!prev_result_file.empty()) {
|
|
if (!g_collector.load_imatrix(prev_result_file.c_str(), false)) {
|
|
fprintf(stderr, "=============== Failed to load %s\n", prev_result_file.c_str());
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
gpt_params params;
|
|
params.n_batch = 512;
|
|
if (!gpt_params_parse(args.size(), args.data(), params)) {
|
|
return 1;
|
|
}
|
|
|
|
params.logits_all = true;
|
|
params.n_batch = std::min(params.n_batch, params.n_ctx);
|
|
|
|
print_build_info();
|
|
|
|
if (params.seed == LLAMA_DEFAULT_SEED) {
|
|
params.seed = time(NULL);
|
|
}
|
|
|
|
fprintf(stderr, "%s: seed = %u\n", __func__, params.seed);
|
|
|
|
std::mt19937 rng(params.seed);
|
|
if (params.random_prompt) {
|
|
params.prompt = gpt_random_prompt(rng);
|
|
}
|
|
|
|
llama_backend_init();
|
|
llama_numa_init(params.numa);
|
|
|
|
llama_model_params mparams = llama_model_params_from_gpt_params(params);
|
|
|
|
llama_model * model = llama_load_model_from_file(params.model.c_str(), mparams);
|
|
if (model == NULL) {
|
|
fprintf(stderr, "%s: error: unable to load model\n", __func__);
|
|
return 1;
|
|
}
|
|
|
|
llama_context_params cparams = llama_context_params_from_gpt_params(params);
|
|
|
|
// pass the callback to the backend scheduler
|
|
// it will be executed for each node during the graph computation
|
|
cparams.cb_eval = ik_collect_imatrix;
|
|
cparams.cb_eval_user_data = NULL;
|
|
|
|
llama_context * ctx = llama_new_context_with_model(model, cparams);
|
|
if (ctx == NULL) {
|
|
fprintf(stderr, "%s: error: unable to create context\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", get_system_info(params).c_str());
|
|
}
|
|
|
|
bool OK = compute_imatrix(ctx, params, compute_ppl, from_chunk);
|
|
if (!OK) {
|
|
return 1;
|
|
}
|
|
|
|
g_collector.save_imatrix();
|
|
|
|
llama_print_timings(ctx);
|
|
|
|
llama_free(ctx);
|
|
llama_free_model(model);
|
|
|
|
llama_backend_free();
|
|
|
|
return 0;
|
|
}
|