vbatts/llama.cpp
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compilade 2025-02-10 12:34:02 +01:00 • committed by GitHub
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388
examples/imatrix/imatrix.cpp
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@ -2,6 +2,7 @@
#include "common.h"
#include "log.h"
#include "llama.h"
#include "gguf.h"
#include <cmath>
#include <cstdio>
@ -10,8 +11,8 @@
#include <thread>
#include <mutex>
#include <vector>
#include <fstream>
#include <unordered_map>
#include <map>
#include <algorithm>
#if defined(_MSC_VER)
@ -21,16 +22,27 @@
static void print_usage(int, char ** argv) {
LOG("\nexample usage:\n");
LOG("\n %s \\\n"
" -m model.gguf -f some-text.txt [-o imatrix.dat] [--process-output] \\\n"
" -m model.gguf -f some-text.txt [-o imatrix.gguf] [--process-output] \\\n"
" [--no-ppl] [--chunk 123] [--output-frequency 10] [--save-frequency 0] \\\n"
" [--in-file imatrix-prev-0.dat --in-file imatrix-prev-1.dat ...]\n" , argv[0]);
" [--in-file imatrix-prev-0.gguf --in-file imatrix-prev-1.gguf ...]\n" , argv[0]);
LOG("\n");
}
static bool str_remove_suffix(std::string & str, const std::string & suffix) {
bool has_suffix = str.size() >= suffix.size() && str.compare(str.size() - suffix.size(), str.size(), suffix) == 0;
if (has_suffix) {
str = str.substr(0, str.size() - suffix.size());
}
return has_suffix;
}
static const char * const LLM_KV_IMATRIX_DATASET = "imatrix.dataset";
static const char * const LLM_KV_IMATRIX_CHUNK_COUNT = "imatrix.chunk_count";
static const char * const LLM_KV_IMATRIX_CHUNK_SIZE = "imatrix.chunk_size";
struct Stats {
std::vector<float> values;
std::vector<int> counts;
int ncall = 0;
std::vector<float> values;
std::vector<int64_t> counts;
};
class IMatrixCollector {
@ -38,13 +50,13 @@ public:
IMatrixCollector() = default;
void set_params(common_params params) { m_params = std::move(params); }
bool collect_imatrix(struct ggml_tensor * t, bool ask, void * user_data);
void save_imatrix(int ncall = -1) const;
bool load_imatrix(const char * fname);
void save_imatrix(int32_t n_chunk = -1) const;
bool load_imatrix(const char * file_name);
private:
std::unordered_map<std::string, Stats> m_stats;
common_params m_params;
std::mutex m_mutex;
int m_last_call = 0;
int32_t m_last_chunk = 0;
std::vector<float> m_src1_data;
std::vector<char> m_ids; // the expert ids from ggml_mul_mat_id
};
@ -118,17 +130,23 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
auto & e = m_stats[wname];
++e.ncall;
if (e.counts.size() == 1 && n_as > 1) {
// broadcast, when loading an old imatrix
e.counts.resize(n_as, e.counts[0]);
}
if (e.values.empty()) {
e.values.resize(src1->ne[0]*n_as, 0);
e.counts.resize(src1->ne[0]*n_as, 0);
e.counts.resize(n_as, 0);
}
else if (e.values.size() != (size_t)src1->ne[0]*n_as) {
LOG_ERR("%s: inconsistent size for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.values.size(), (int)src1->ne[0]*n_as);
exit(1); //GGML_ABORT("fatal error");
}
LOG_DBGV(2, "%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);
else if (e.counts.size() != (size_t)n_as) {
LOG_ERR("Oops: inconsistent expert count for %s (%d vs %d)\n", wname.c_str(), (int)e.counts.size(), (int)n_as);
exit(1); //GGML_ABORT("fatal error");
}
LOG_DBGV(2, "%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);
// loop over all possible experts, regardless if they are used or not in the batch
for (int ex = 0; ex < n_as; ++ex) {
size_t e_start = ex*src1->ne[0];
@ -145,24 +163,26 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
const int64_t i12 = row;
const float * x = (const float *)((const char *)data + i11*src1->nb[1] + i12*src1->nb[2]);
e.counts[ex]++;
for (int j = 0; j < (int)src1->ne[0]; ++j) {
e.values[e_start + j] += x[j]*x[j];
e.counts[e_start + j]++;
if (!std::isfinite(e.values[e_start + j])) {
LOG("\n");
LOG_ERR("%f detected in %s\n", e.values[e_start + j], wname.c_str());
e.values[e_start + j] = std::fma(x[j], x[j], e.values[e_start + j]);
if (!std::isfinite((float)e.values[e_start + j])) {
LOG_ERR("%f detected in %s\n", (float)e.values[e_start + j], wname.c_str());
exit(1);
}
}
}
}
if (e.ncall > m_last_call) {
m_last_call = e.ncall;
if (m_last_call % m_params.n_out_freq == 0) {
const int32_t n_chunk = e.counts[ex] / (m_params.n_ctx / m_params.n_parallel);
if (n_chunk > m_last_chunk) {
const int32_t chunk_step = n_chunk - m_last_chunk;
m_last_chunk = n_chunk;
if ((m_last_chunk % m_params.n_out_freq) / chunk_step == 0) {
save_imatrix();
}
if (m_params.n_save_freq > 0 && m_last_call%m_params.n_save_freq == 0) {
save_imatrix(m_last_call);
if (m_params.n_save_freq > 0 && (m_last_chunk % m_params.n_save_freq) / chunk_step == 0) {
save_imatrix(m_last_chunk);
}
}
}
@ -170,32 +190,38 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
auto & e = m_stats[wname];
if (e.values.empty()) {
e.values.resize(src1->ne[0], 0);
e.counts.resize(src1->ne[0], 0);
e.counts.resize(1, 0);
}
else if (e.values.size() != (size_t)src1->ne[0]) {
LOG_ERR("%s: inconsistent size for %s (%d vs %d)\n", __func__, wname.c_str(), (int)e.values.size(), (int)src1->ne[0]);
exit(1); //GGML_ABORT("fatal error");
}
++e.ncall;
LOG_DBGV(2, "%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);
else if (e.counts.size() != 1) {
LOG_ERR("Oops: inconsistent expert count for %s (%d vs %d)\n", wname.c_str(), (int)e.counts.size(), 1);
exit(1); //GGML_ABORT("fatal error");
}
LOG_DBGV(2, "%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);
// TODO: higher dimensions
for (int row = 0; row < (int)src1->ne[1]; ++row) {
const float * x = data + row * src1->ne[0];
e.counts[0]++;
for (int j = 0; j < (int)src1->ne[0]; ++j) {
e.values[j] += x[j]*x[j];
e.counts[j]++;
if (!std::isfinite(e.values[j])) {
LOG_ERR("%f detected in %s\n", e.values[j], wname.c_str());
e.values[j] = std::fma(x[j], x[j], e.values[j]);
if (!std::isfinite((float)e.values[j])) {
LOG_ERR("%f detected in %s\n", (float)e.values[j], wname.c_str());
exit(1);
}
}
}
if (e.ncall > m_last_call) {
m_last_call = e.ncall;
if (m_last_call % m_params.n_out_freq == 0) {
const int32_t n_chunk = e.counts[0] / (m_params.n_ctx / m_params.n_parallel);
if (n_chunk > m_last_chunk) {
const int32_t chunk_step = n_chunk - m_last_chunk;
m_last_chunk = n_chunk;
if ((m_last_chunk % m_params.n_out_freq) / chunk_step == 0) {
save_imatrix();
}
if (m_params.n_save_freq > 0 && m_last_call%m_params.n_save_freq == 0) {
save_imatrix(m_last_call);
if (m_params.n_save_freq > 0 && (m_last_chunk % m_params.n_save_freq) / chunk_step == 0) {
save_imatrix(m_last_chunk);
}
}
}
@ -203,22 +229,22 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
return true;
}
void IMatrixCollector::save_imatrix(int ncall) const {
void IMatrixCollector::save_imatrix(int32_t n_chunk) const {
auto fname = m_params.out_file;
if (fname.empty()) {
fname = "imatrix.dat";
fname = "imatrix.gguf";
}
if (ncall > 0) {
if (n_chunk > 0) {
fname += ".at_";
fname += std::to_string(ncall);
fname += std::to_string(n_chunk);
}
// avoid writing imatrix entries that do not have full data
// this can happen with MoE models where some of the experts end up not being exercised by the provided training data
int n_entries = 0;
std::vector<std::string> to_store;
size_t data_size = 0;
bool is_first = true; // for printing
for (const auto & kv : m_stats) {
@ -250,101 +276,157 @@ void IMatrixCollector::save_imatrix(int ncall) const {
continue;
}
n_entries++;
to_store.push_back(kv.first);
data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.values.size(), GGML_MEM_ALIGN);
data_size += GGML_PAD(ggml_tensor_overhead() + sizeof(float) * kv.second.counts.size(), GGML_MEM_ALIGN);
}
if (to_store.size() < m_stats.size()) {
LOG_WRN("%s: 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));
// deterministic tensor name order
std::sort(to_store.begin(), to_store.end());
struct ggml_init_params params = {
/* .mem_size = */ data_size,
/* .mem_buffer = */ NULL,
/* .no_alloc = */ false,
};
struct ggml_context * ctx = ggml_init(params);
struct gguf_context * ctx_gguf = gguf_init_empty();
gguf_set_val_str(ctx_gguf, "general.type", "imatrix");
// Write the input filename to later on specify it in quantize
gguf_set_val_str(ctx_gguf, LLM_KV_IMATRIX_DATASET, m_params.prompt_file.c_str());
// Write the number of chunks the matrix was computed with
gguf_set_val_u32(ctx_gguf, LLM_KV_IMATRIX_CHUNK_COUNT, m_last_chunk);
gguf_set_val_u32(ctx_gguf, LLM_KV_IMATRIX_CHUNK_SIZE, m_params.n_ctx / m_params.n_parallel);
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));
const int32_t nval = (int32_t) stat.values.size();
const int32_t nmat = (int32_t) stat.counts.size();
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);
struct ggml_tensor * sums = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, nval / nmat, nmat);
struct ggml_tensor * counts = ggml_new_tensor_2d(ctx, GGML_TYPE_F32, 1, nmat);
ggml_format_name(sums, "%s.sums", name.c_str());
ggml_format_name(counts, "%s.counts", name.c_str());
for (int32_t j = 0; j < nval; ++j) {
((float *) sums->data)[j] = (float) stat.values[j];
}
out.write((const char*)tmp.data(), nval*sizeof(float));
for (int32_t j = 0; j < nmat; ++j) {
((float *) counts->data)[j] = (float) stat.counts[j];
}
gguf_add_tensor(ctx_gguf, sums);
gguf_add_tensor(ctx_gguf, counts);
}
}
// 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);
}
gguf_write_to_file(ctx_gguf, fname.c_str(), false);
LOGV(1, "\n");
LOG_DBGV(1, "%s: stored collected data after %d chunks in %s\n", __func__, m_last_call, fname.c_str());
LOG_DBGV(1, "%s: stored collected data after %d chunks in %s\n", __func__, m_last_chunk, fname.c_str());
gguf_free(ctx_gguf);
ggml_free(ctx);
}
bool IMatrixCollector::load_imatrix(const char * fname) {
std::ifstream in(fname, std::ios::binary);
if (!in) {
LOG_ERR("%s: failed to open %s\n",__func__, fname);
bool IMatrixCollector::load_imatrix(const char * file_name) {
struct ggml_context * ctx = nullptr;
struct gguf_init_params meta_gguf_params = {
/* .no_alloc = */ false, // the data is needed
/* .ctx = */ &ctx,
};
struct gguf_context * ctx_gguf = gguf_init_from_file(file_name, meta_gguf_params);
if (!ctx_gguf) {
return false;
}
int n_entries;
in.read((char*)&n_entries, sizeof(n_entries));
if (in.fail() || n_entries < 1) {
LOG_ERR("%s: no data in file %s\n", __func__, fname);
const int32_t n_entries = gguf_get_n_tensors(ctx_gguf);
if (n_entries < 1) {
LOG_ERR("%s: no data in file %s\n", __func__, file_name);
gguf_free(ctx_gguf);
ggml_free(ctx);
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()) {
LOG_ERR("%s: failed reading name for entry %d from %s\n",__func__,i+1, fname);
const std::string sums_suffix{".sums"};
const std::string counts_suffix{".counts"};
// Could re-use m_stats instead, but this allows
// checking for completeness of *each* loaded imatrix file
// and also makes it easier to re-use a similar implementation in quantize.cpp
// Using an ordered map to get a deterministic iteration order.
std::map<std::string, std::pair<struct ggml_tensor *, struct ggml_tensor *>> sums_counts_for;
for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
std::string name = cur->name;
if (name.empty()) { continue; }
if (str_remove_suffix(name, sums_suffix)) {
// sums
sums_counts_for[name].first = cur;
} else if (str_remove_suffix(name, counts_suffix)) {
// counts
sums_counts_for[name].second = cur;
} else {
LOG_ERR("%s: invalid imatrix tensor name: %s\n", __func__, name.c_str());
gguf_free(ctx_gguf);
ggml_free(ctx);
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) {
LOG_ERR("%s: failed reading number of values for entry %d\n",__func__,i);
m_stats = {};
}
for (const auto & sc : sums_counts_for) {
const std::string & name = sc.first;
const struct ggml_tensor * sums = sc.second.first;
const struct ggml_tensor * counts = sc.second.second;
if (!sums || !counts) {
LOG_ERR("%s: mismatched sums and counts for %s\n", __func__, name.c_str());
gguf_free(ctx_gguf);
ggml_free(ctx);
return false;
}
auto & e = m_stats[name];
int64_t nval = ggml_nelements(sums);
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()) {
LOG_ERR("%s: failed reading data for entry %d\n",__func__,i);
m_stats = {};
} else if ((size_t) nval != e.values.size()) {
LOG_ERR("%s: mismatched sums size for %s: %zu != %zu\n", __func__, name.c_str(), (size_t) nval, e.values.size());
gguf_free(ctx_gguf);
ggml_free(ctx);
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;
int64_t ncounts = ggml_nelements(counts);
if (e.counts.empty()) {
e.counts.resize(ncounts, 0);
} else if (e.counts.size() == 1 && ncounts > 1) {
// broadcast, when loading an old imatrix
e.counts.resize(ncounts, e.counts[0]);
} else if ((size_t) ncounts != e.counts.size()) {
LOG_ERR("%s: mismatched counts size for %s: %zu != %zu\n", __func__, name.c_str(), (size_t) ncounts, e.counts.size());
gguf_free(ctx_gguf);
ggml_free(ctx);
return false;
}
e.ncall += ncall;
// Recreate the state as expected by save_imatrix()
for (int64_t j = 0; j < nval; j++) {
e.values[j] += ((const float *) sums->data)[j];
}
for (int64_t j = 0; j < ncounts; j++) {
e.counts[j] += std::lround(((const float *) counts->data)[j]);
}
}
gguf_free(ctx_gguf);
ggml_free(ctx);
return true;
}
@ -427,12 +509,11 @@ static void process_logits(
}
}
static bool compute_imatrix(llama_context * ctx, const common_params & params) {
static bool compute_imatrix(llama_context * ctx, const common_params & params, const int32_t n_ctx) {
const llama_model * model = llama_get_model(ctx);
const llama_vocab * vocab = llama_model_get_vocab(model);
const bool add_bos = llama_vocab_get_add_bos(vocab);
const int n_ctx = llama_n_ctx(ctx);
GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
@ -477,45 +558,61 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) {
double nll = 0.0;
double nll2 = 0.0;
LOG_INF("%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;
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);
}
for (int i = 0; i < n_chunk; ++i) {
LOG_INF("%s: computing over %d chunks, n_ctx=%d, batch_size=%d, n_seq=%d\n", __func__, n_chunk, n_ctx, n_batch, n_seq);
std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
for (int i = 0; i < n_chunk; i += n_seq) {
const int start = i * n_ctx;
const int end = start + n_ctx;
std::vector<float> logits;
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);
llama_batch batch = llama_batch_init(n_batch, 0, 1);
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_vocab_bos(vocab);
}
// clear the batch
common_batch_clear(batch);
for (int i = 0; i < batch_size; i++) {
common_batch_add(batch, tokens[batch_start + i], j*n_batch + i, {0}, true);
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_vocab_bos(vocab);
}
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)
common_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)) {
@ -524,23 +621,19 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) {
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);
}
}
llama_batch_free(batch);
const auto t_end = std::chrono::high_resolution_clock::now();
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();
LOG_INF("%s: %.2f seconds per pass - ETA ", __func__, t_total);
int total_seconds = (int)(t_total * n_chunk);
int total_seconds = (int)(t_total * n_chunk / n_seq);
if (total_seconds >= 60*60) {
LOG("%d hours ", total_seconds / (60*60));
total_seconds = total_seconds % (60*60);
@ -550,17 +643,27 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) {
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;
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);
LOG("[%d]%.4lf,", i + 1, std::exp(nll / count));
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;
LOG("[%d]%.4lf,", i + seq + 1, std::exp(nll / count));
}
fflush(stdout);
logits.clear();
}
}
LOG("\n");
if (params.compute_ppl) {
@ -576,6 +679,8 @@ static bool compute_imatrix(llama_context * ctx, const common_params & params) {
}
}
llama_batch_free(batch);
return true;
}
@ -592,7 +697,22 @@ int main(int argc, char ** argv) {
common_init();
params.n_batch = std::min(params.n_batch, params.n_ctx);
const int32_t n_ctx = params.n_ctx;
if (n_ctx <= 0) {
LOG_ERR("%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);
@ -648,7 +768,7 @@ int main(int argc, char ** argv) {
}
LOG_INF("No prompt provided; combining precomputed matrices only.\n");
} else {
if (!compute_imatrix(ctx, params)) {
if (!compute_imatrix(ctx, params, n_ctx)) {
return 1;
}
}

152
examples/quantize/quantize.cpp
View file

@ -1,13 +1,13 @@
#include "common.h"
#include "llama.h"
#include "gguf.h"
#include <cstdio>
#include <cstring>
#include <vector>
#include <string>
#include <unordered_map>
#include <fstream>
#include <cmath>
#include <map>
struct quant_option {
std::string name;
@ -60,6 +60,11 @@ static const char * const LLM_KV_QUANTIZE_IMATRIX_DATASET = "quantize.imatrix
static const char * const LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES = "quantize.imatrix.entries_count";
static const char * const LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS = "quantize.imatrix.chunks_count";
// TODO: share with imatrix.cpp
static const char * const LLM_KV_IMATRIX_DATASET = "imatrix.dataset";
static const char * const LLM_KV_IMATRIX_CHUNK_COUNT = "imatrix.chunk_count";
static const char * const LLM_KV_IMATRIX_CHUNK_SIZE = "imatrix.chunk_size";
static bool striequals(const char * a, const char * b) {
while (*a && *b) {
if (std::tolower(*a) != std::tolower(*b)) {
@ -129,67 +134,114 @@ static void usage(const char * executable) {
exit(1);
}
// TODO: share with implementation in imatrix.cpp
static bool str_remove_suffix(std::string & str, const std::string & suffix) {
bool has_suffix = str.size() >= suffix.size() && str.compare(str.size() - suffix.size(), str.size(), suffix) == 0;
if (has_suffix) {
str = str.substr(0, str.size() - suffix.size());
}
return has_suffix;
}
static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_dataset, std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
std::ifstream in(imatrix_file.c_str(), std::ios::binary);
if (!in) {
printf("%s: failed to open %s\n",__func__, imatrix_file.c_str());
struct ggml_context * ctx = nullptr;
struct gguf_init_params meta_gguf_params = {
/* .no_alloc = */ false, // the data is needed
/* .ctx = */ &ctx,
};
struct gguf_context * ctx_gguf = gguf_init_from_file(imatrix_file.c_str(), meta_gguf_params);
if (!ctx_gguf) {
fprintf(stderr, "%s: if this is an older imatrix file, make sure to convert it to the GGUF-based imatrix format\n", __func__);
exit(1);
}
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__, imatrix_file.c_str());
const int32_t n_entries = gguf_get_n_tensors(ctx_gguf);
if (n_entries < 1) {
fprintf(stderr, "%s: no data in file %s\n", __func__, imatrix_file.c_str());
gguf_free(ctx_gguf);
ggml_free(ctx);
exit(1);
}
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, imatrix_file.c_str());
const int dataset_idx = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_DATASET);
const int chunk_count_idx = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_CHUNK_COUNT);
const int chunk_size_idx = gguf_find_key(ctx_gguf, LLM_KV_IMATRIX_CHUNK_SIZE);
if (dataset_idx < 0 || chunk_count_idx < 0 || chunk_size_idx < 0) {
fprintf(stderr, "%s: missing imatrix metadata in file %s\n", __func__, imatrix_file.c_str());
gguf_free(ctx_gguf);
ggml_free(ctx);
exit(1);
}
const uint32_t chunk_size = gguf_get_val_u32(ctx_gguf, chunk_size_idx);
const std::string sums_suffix{".sums"};
const std::string counts_suffix{".counts"};
// Using an ordered map to get a deterministic iteration order.
std::map<std::string, std::pair<struct ggml_tensor *, struct ggml_tensor *>> sums_counts_for;
for (struct ggml_tensor * cur = ggml_get_first_tensor(ctx); cur; cur = ggml_get_next_tensor(ctx, cur)) {
std::string name = cur->name;
if (name.empty()) { continue; }
if (str_remove_suffix(name, sums_suffix)) {
// sums
sums_counts_for[name].first = cur;
} else if (str_remove_suffix(name, counts_suffix)) {
// counts
sums_counts_for[name].second = cur;
} else {
fprintf(stderr, "%s: invalid imatrix tensor name: %s\n", __func__, name.c_str());
gguf_free(ctx_gguf);
ggml_free(ctx);
exit(1);
}
name_as_vec[len] = 0;
std::string name{name_as_vec.data()};
}
for (const auto & sc : sums_counts_for) {
const std::string & name = sc.first;
const struct ggml_tensor * sums = sc.second.first;
const struct ggml_tensor * counts = sc.second.second;
if (!sums || !counts) {
fprintf(stderr, "%s: mismatched sums and counts for %s\n", __func__, name.c_str());
gguf_free(ctx_gguf);
ggml_free(ctx);
exit(1);
}
const int64_t ne0 = sums->ne[0];
const int64_t ne1 = sums->ne[1];
auto & e = imatrix_data[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);
imatrix_data = {};
exit(1);
e.resize(ggml_nelements(sums));
float max_count = 0.0f;
for (int64_t j = 0; j < ne1; ++j) {
const float count = ((const float *) counts->data)[j];
for (int64_t i = 0; i < ne0; ++i) {
e[j*ne0 + i] = ((const float *) sums->data)[j*ne0 + i] / count;
}
if (count > max_count) {
max_count = count;
}
}
e.resize(nval);
in.read((char *)e.data(), nval*sizeof(float));
if (in.fail()) {
printf("%s: failed reading data for entry %d\n", __func__, i);
imatrix_data = {};
exit(1);
}
if (ncall > 0) {
for (auto& v : e) v /= ncall;
}
if (getenv("LLAMA_TRACE")) {
printf("%s: loaded data (size = %6d, ncall = %6d) for '%s'\n", __func__, int(e.size()), ncall, name.c_str());
printf("%s: loaded data (size = %6d, n_tokens = %6d, n_chunks = %6d) for '%s'\n", __func__, int(e.size()), int(max_count), int(max_count / chunk_size), name.c_str());
}
}
// latest imatrix version contains the dataset filename at the end of the file
int m_last_call = 0;
if (in.peek() != EOF) {
in.read((char *)&m_last_call, sizeof(m_last_call));
int dataset_len;
in.read((char *)&dataset_len, sizeof(dataset_len));
std::vector<char> dataset_as_vec(dataset_len);
in.read(dataset_as_vec.data(), dataset_len);
imatrix_dataset.assign(dataset_as_vec.begin(), dataset_as_vec.end());
printf("%s: imatrix dataset='%s'\n", __func__, imatrix_dataset.c_str());
}
printf("%s: loaded %d importance matrix entries from %s computed on %d chunks\n", __func__, int(imatrix_data.size()), imatrix_file.c_str(), m_last_call);
return m_last_call;
int m_last_chunk = gguf_get_val_u32(ctx_gguf, chunk_count_idx);
imatrix_dataset = gguf_get_val_str(ctx_gguf, dataset_idx);
printf("%s: imatrix dataset='%s'\n", __func__, imatrix_dataset.c_str());
printf("%s: loaded %d importance matrix entries from %s computed on %d chunks\n", __func__, int(imatrix_data.size()), imatrix_file.c_str(), m_last_chunk);
gguf_free(ctx_gguf);
ggml_free(ctx);
return m_last_chunk;
}
static int prepare_imatrix(const std::string & imatrix_file,