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Merge branch 'master' into compilade/imatrix-batched-chunks

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This commit is contained in:
Francis Couture-Harpin
2025-02-09 12:06:15 -05:00
parent c7a32e761d 553f1e46e9
commit db502ddd0e
762 changed files with 149462 additions and 91773 deletions
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176
examples/imatrix/imatrix.cpp
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@@ -1,5 +1,8 @@
#include "arg.h"
#include "common.h"
#include "log.h"
#include "llama.h"
#include "gguf.h"
#include <cmath>
#include <cstdio>
@@ -17,12 +20,12 @@
#endif
static void print_usage(int, char ** argv) {
LOG_TEE("\nexample usage:\n");
LOG_TEE("\n %s \\\n"
" -m model.gguf -f some-text.txt [-o imatrix.gguf] [--process-output] [--verbosity 1] \\\n"
LOG("\nexample usage:\n");
LOG("\n %s \\\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.gguf --in-file imatrix-prev-1.gguf ...]\n" , argv[0]);
LOG_TEE("\n");
LOG("\n");
}
static bool str_remove_suffix(std::string & str, const std::string & suffix) {
@@ -45,13 +48,13 @@ struct Stats {
class IMatrixCollector {
public:
IMatrixCollector() = default;
void set_params(gpt_params params) { m_params = std::move(params); }
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(int32_t n_chunk = -1) const;
bool load_imatrix(const char * file_name);
private:
std::unordered_map<std::string, Stats> m_stats;
gpt_params m_params;
common_params m_params;
std::mutex m_mutex;
int32_t m_last_chunk = 0;
std::vector<float> m_src1_data;
@@ -136,16 +139,14 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
e.counts.resize(n_as, 0);
}
else if (e.values.size() != (size_t)src1->ne[0]*n_as) {
fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)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");
}
else if (e.counts.size() != (size_t)n_as) {
fprintf(stderr, "Oops: inconsistent expert count for %s (%d vs %d)\n", wname.c_str(), (int)e.counts.size(), (int)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");
}
if (m_params.verbosity > 1) {
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);
}
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];
@@ -167,7 +168,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
for (int j = 0; j < (int)src1->ne[0]; ++j) {
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])) {
fprintf(stderr, "%f detected in %s\n", (float)e.values[e_start + j], wname.c_str());
LOG_ERR("%f detected in %s\n", (float)e.values[e_start + j], wname.c_str());
exit(1);
}
}
@@ -192,16 +193,14 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
e.counts.resize(1, 0);
}
else if (e.values.size() != (size_t)src1->ne[0]) {
fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", wname.c_str(), (int)e.values.size(), (int)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");
}
else if (e.counts.size() != 1) {
fprintf(stderr, "Oops: inconsistent expert count for %s (%d vs %d)\n", wname.c_str(), (int)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");
}
if (m_params.verbosity > 1) {
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);
}
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];
@@ -209,7 +208,7 @@ bool IMatrixCollector::collect_imatrix(struct ggml_tensor * t, bool ask, void *
for (int j = 0; j < (int)src1->ne[0]; ++j) {
e.values[j] = std::fma(x[j], x[j], e.values[j]);
if (!std::isfinite((float)e.values[j])) {
fprintf(stderr, "%f detected in %s\n", (float)e.values[j], wname.c_str());
LOG_ERR("%f detected in %s\n", (float)e.values[j], wname.c_str());
exit(1);
}
}
@@ -263,17 +262,17 @@ void IMatrixCollector::save_imatrix(int32_t n_chunk) const {
}
if (n_zeros != 0 && is_first) {
fprintf(stderr, "\n");
LOG_INF("\n");
is_first = false;
}
if (n_zeros == n_all) {
fprintf(stderr, "%s: entry '%40s' has no data - skipping\n", __func__, kv.first.c_str());
LOG_WRN("%s: entry '%40s' has no data - skipping\n", __func__, kv.first.c_str());
continue;
}
if (n_zeros > 0) {
fprintf(stderr, "%s: entry '%40s' has partial data (%.2f%%) - skipping\n", __func__, kv.first.c_str(), 100.0f * (n_all - n_zeros) / n_all);
LOG_WRN("%s: entry '%40s' has partial data (%.2f%%) - skipping\n", __func__, kv.first.c_str(), 100.0f * (n_all - n_zeros) / n_all);
continue;
}
@@ -283,7 +282,7 @@ void IMatrixCollector::save_imatrix(int32_t n_chunk) const {
}
if (to_store.size() < m_stats.size()) {
fprintf(stderr, "%s: warning: storing only %zu out of %zu entries\n", __func__, to_store.size(), m_stats.size());
LOG_WRN("%s: storing only %zu out of %zu entries\n", __func__, to_store.size(), m_stats.size());
}
// deterministic tensor name order
@@ -328,9 +327,8 @@ void IMatrixCollector::save_imatrix(int32_t n_chunk) const {
gguf_write_to_file(ctx_gguf, fname.c_str(), false);
if (m_params.verbosity > 0) {
fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n", __func__, m_last_chunk, fname.c_str());
}
LOGV(1, "\n");
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);
@@ -348,7 +346,7 @@ bool IMatrixCollector::load_imatrix(const char * file_name) {
}
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__, file_name);
LOG_ERR("%s: no data in file %s\n", __func__, file_name);
gguf_free(ctx_gguf);
ggml_free(ctx);
return false;
@@ -375,7 +373,7 @@ bool IMatrixCollector::load_imatrix(const char * file_name) {
// counts
sums_counts_for[name].second = cur;
} else {
fprintf(stderr, "%s: invalid imatrix tensor name: %s\n", __func__, name.c_str());
LOG_ERR("%s: invalid imatrix tensor name: %s\n", __func__, name.c_str());
gguf_free(ctx_gguf);
ggml_free(ctx);
return false;
@@ -388,7 +386,7 @@ bool IMatrixCollector::load_imatrix(const char * file_name) {
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());
LOG_ERR("%s: mismatched sums and counts for %s\n", __func__, name.c_str());
gguf_free(ctx_gguf);
ggml_free(ctx);
return false;
@@ -400,7 +398,7 @@ bool IMatrixCollector::load_imatrix(const char * file_name) {
if (e.values.empty()) {
e.values.resize(nval, 0);
} else if ((size_t) nval != e.values.size()) {
fprintf(stderr, "%s: mismatched sums size for %s: %zu != %zu\n", __func__, name.c_str(), (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;
@@ -413,7 +411,7 @@ bool IMatrixCollector::load_imatrix(const char * file_name) {
// broadcast, when loading an old imatrix
e.counts.resize(ncounts, e.counts[0]);
} else if ((size_t) ncounts != e.counts.size()) {
fprintf(stderr, "%s: mismatched counts size for %s: %zu != %zu\n", __func__, name.c_str(), (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;
@@ -511,31 +509,34 @@ static void process_logits(
}
}
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)));
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);
GGML_ASSERT(!llama_vocab_get_add_eos(vocab));
auto tim1 = std::chrono::high_resolution_clock::now();
fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
LOG_INF("%s: tokenizing the input ..\n", __func__);
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, true);
std::vector<llama_token> tokens = common_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());
LOG_INF("%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);
LOG_ERR("%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);
LOG_INF("%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());
LOG_ERR("%s: you need at least %d tokens for a context of %d tokens\n", __func__, 2*n_ctx, n_ctx);
LOG_ERR("%s: the data file you provided tokenizes to only %zu tokens\n", __func__, tokens.size());
return false;
}
@@ -550,15 +551,13 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, cons
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_vocab = llama_vocab_n_tokens(vocab);
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);
@@ -572,7 +571,9 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, cons
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);
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;
@@ -590,7 +591,7 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, cons
const int batch_size = std::min(end - batch_start, n_batch);
// clear the batch
llama_batch_clear(batch);
common_batch_clear(batch);
for (int seq = 0; seq < n_seq_batch; seq++) {
int seq_start = batch_start + seq*n_ctx;
@@ -600,23 +601,23 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, cons
// 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));
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)
llama_batch_add(batch, tokens[seq_start + k], j*n_batch + k, { seq }, true);
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)) {
fprintf(stderr, "%s : failed to eval\n", __func__);
LOG_ERR("%s : failed to eval\n", __func__);
llama_batch_free(batch);
return false;
}
@@ -631,13 +632,13 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, cons
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);
LOG_INF("%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));
LOG("%d hours ", total_seconds / (60*60));
total_seconds = total_seconds % (60*60);
}
fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0);
LOG("%.2f minutes\n", total_seconds / 60.0);
}
if (params.compute_ppl) {
@@ -655,14 +656,15 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, cons
count += n_ctx - first - 1;
printf("[%d]%.4lf,", i + seq + 1, std::exp(nll / count));
LOG("[%d]%.4lf,", i + seq + 1, std::exp(nll / count));
}
fflush(stdout);
logits.clear();
}
}
printf("\n");
LOG("\n");
if (params.compute_ppl) {
nll2 /= count;
@@ -671,31 +673,34 @@ static bool compute_imatrix(llama_context * ctx, const gpt_params & params, cons
nll2 -= nll * nll;
if (nll2 > 0) {
nll2 = sqrt(nll2/(count-1));
printf("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl);
LOG("Final estimate: PPL = %.4lf +/- %.5lf\n", ppl, nll2*ppl);
} else {
printf("Unexpected negative standard deviation of log(prob)\n");
LOG("Unexpected negative standard deviation of log(prob)\n");
}
}
llama_batch_free(batch);
return true;
}
int main(int argc, char ** argv) {
gpt_params params;
common_params params;
params.n_ctx = 512;
params.logits_all = true;
params.verbosity = 1;
params.escape = false;
auto options = gpt_params_parser_init(params, LLAMA_EXAMPLE_IMATRIX, print_usage);
if (!gpt_params_parse(argc, argv, params, options)) {
if (!common_params_parse(argc, argv, params, LLAMA_EXAMPLE_IMATRIX, print_usage)) {
return 1;
}
common_init();
const int32_t n_ctx = params.n_ctx;
if (n_ctx <= 0) {
fprintf(stderr, "%s: imatrix tool requires '--ctx-size' > 0\n", __func__);
LOG_ERR("%s: imatrix tool requires '--ctx-size' > 0\n", __func__);
return 1;
}
@@ -712,15 +717,15 @@ int main(int argc, char ** argv) {
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());
LOG_INF("%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());
LOG_ERR("%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());
LOG_INF("%s : saving combined imatrix to '%s'\n", __func__, params.out_file.c_str());
g_collector.save_imatrix();
}
@@ -734,38 +739,45 @@ int main(int argc, char ** argv) {
params.warmup = false;
// init
llama_init_result llama_init = llama_init_from_gpt_params(params);
common_init_result llama_init = common_init_from_params(params);
llama_model * model = llama_init.model.get();
llama_context * ctx = llama_init.context.get();
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__);
LOG_ERR("%s : failed to init\n", __func__);
return 1;
}
const int n_ctx_train = llama_n_ctx_train(model);
const int n_ctx_train = llama_model_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",
LOG_WRN("%s: 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());
LOG_INF("\n");
LOG_INF("%s\n", common_params_get_system_info(params).c_str());
}
if (!compute_imatrix(ctx, params, n_ctx)) {
return 1;
if (params.prompt.empty()) {
if (params.in_files.empty()) {
LOG_ERR("Error: No prompt provided and no precomputed matrices (--in-file) to combine.\n");
return 1;
}
LOG_INF("No prompt provided; combining precomputed matrices only.\n");
} else {
if (!compute_imatrix(ctx, params, n_ctx)) {
return 1;
}
}
g_collector.save_imatrix();
LOG_TEE("\n");
llama_perf_print(ctx, LLAMA_PERF_TYPE_CONTEXT);
llama_free(ctx);
llama_free_model(model);
LOG("\n");
llama_perf_context_print(ctx);
llama_backend_free();