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Importance Matrix calculation (#4861)

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* imatrix: 1st version

* imatrix: WIP

* Cleanup

* Update examples/imatrix/imatrix.cpp

Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Kawrakow 2024-01-12 06:59:57 +01:00 committed by GitHub
parent 1d118386fe
commit 326b418b59
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6 changed files with 410 additions and 1 deletions
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5
Makefile
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@ -1,6 +1,6 @@
# Define the default target now so that it is always the first target
BUILD_TARGETS = \
main quantize quantize-stats perplexity embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
main quantize quantize-stats perplexity imatrix embedding vdot q8dot train-text-from-scratch convert-llama2c-to-ggml \
simple batched batched-bench save-load-state server gguf llama-bench libllava.a llava-cli baby-llama beam-search \
speculative infill tokenize benchmark-matmult parallel finetune export-lora lookahead lookup passkey tests/test-c.o
@ -614,6 +614,9 @@ quantize-stats: examples/quantize-stats/quantize-stats.cpp build-info.o ggml.
perplexity: examples/perplexity/perplexity.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
imatrix: examples/imatrix/imatrix.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)
embedding: examples/embedding/embedding.cpp ggml.o llama.o $(COMMON_DEPS) $(OBJS)
$(CXX) $(CXXFLAGS) $(filter-out %.h,$^) -o $@ $(LDFLAGS)

1
examples/CMakeLists.txt
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@ -36,6 +36,7 @@ else()
add_subdirectory(lookahead)
add_subdirectory(lookup)
add_subdirectory(train-text-from-scratch)
add_subdirectory(imatrix)
if (LLAMA_METAL)
add_subdirectory(metal)
endif()

5
examples/imatrix/CMakeLists.txt Normal file
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@ -0,0 +1,5 @@
set(TARGET imatrix)
add_executable(${TARGET} imatrix.cpp)
install(TARGETS ${TARGET} RUNTIME)
target_link_libraries(${TARGET} PRIVATE common llama ${CMAKE_THREAD_LIBS_INIT})
target_compile_features(${TARGET} PRIVATE cxx_std_11)

380
examples/imatrix/imatrix.cpp Normal file
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@ -0,0 +1,380 @@
#include "common.h"
#include "llama.h"
#include <cmath>
#include <cstdio>
#include <cstring>
#include <ctime>
#include <sstream>
#include <thread>
#include <mutex>
#include <vector>
#include <fstream>
#include <unordered_map>
#include <algorithm>
#if defined(_MSC_VER)
#pragma warning(disable: 4244 4267) // possible loss of data
#endif
struct Stats {
std::vector<float> values;
int ncall = 0;
};
struct StatParams {
std::string ofile = "imatrix.dat";
int n_output_frequency = 10;
int verbosity = 1;
bool collect_output_weight = false;
};
class IMatrixCollector {
public:
IMatrixCollector() = default;
void set_parameters(StatParams&& params) { m_params = std::move(params); }
void collect_imatrix(const struct ggml_tensor * src0, const struct ggml_tensor * src1);
void save_imatrix() const;
private:
std::unordered_map<std::string, Stats> m_stats;
StatParams m_params;
std::mutex m_mutex;
int m_last_call = 0;
};
void IMatrixCollector::collect_imatrix(const struct ggml_tensor * src0, const struct ggml_tensor * src1) {
if (src1->ne[1] < 16 || src1->type != GGML_TYPE_F32) return;
if (!(strncmp(src0->name, "blk.", 4) == 0 || (m_params.collect_output_weight && strcmp(src0->name, "output.weight") == 0))) return;
std::lock_guard<std::mutex> lock(m_mutex);
auto& e = m_stats[src0->name];
if (e.values.empty()) {
e.values.resize(src1->ne[0], 0);
}
else if (e.values.size() != (size_t)src1->ne[0]) {
fprintf(stderr, "Oops: inconsistent size for %s (%d vs %d)\n", src0->name, (int)e.values.size(), (int)src1->ne[0]);
exit(1); //GGML_ASSERT(false);
}
++e.ncall;
if (m_params.verbosity > 1) {
printf("%s[%d]: %s, %d x %d, %d\n",__func__,m_last_call,src0->name,(int)src1->ne[0],(int)src1->ne[1],(int)src1->type);
}
for (int row = 0; row < (int)src1->ne[1]; ++row) {
const float * x = (const float *)src1->data + row * src1->ne[0];
for (int j = 0; j < (int)src1->ne[0]; ++j) {
e.values[j] += x[j]*x[j];
}
}
if (e.ncall > m_last_call) {
m_last_call = e.ncall;
if (m_last_call % m_params.n_output_frequency == 0) {
save_imatrix();
}
}
}
void IMatrixCollector::save_imatrix() const {
const char * fname = m_params.ofile.empty() ? "imatrix.dat" : m_params.ofile.c_str();
std::ofstream out(fname, std::ios::binary);
int n_entries = m_stats.size();
out.write((const char*)&n_entries, sizeof(n_entries));
for (auto& p : m_stats) {
int len = p.first.size();
out.write((const char*)&len, sizeof(len));
out.write(p.first.c_str(), len);
out.write((const char*)&p.second.ncall, sizeof(p.second.ncall));
int nval = p.second.values.size();
out.write((const char*)&nval, sizeof(nval));
if (nval > 0) out.write((const char*)p.second.values.data(), nval*sizeof(float));
}
if (m_params.verbosity > 0) {
fprintf(stderr, "\n%s: stored collected data after %d chunks in %s\n",__func__,m_last_call,fname);
}
}
static IMatrixCollector g_collector;
static void ik_collect_imatrix(const struct ggml_tensor * src0, const struct ggml_tensor * src1) {
g_collector.collect_imatrix(src0, src1);
}
struct results_log_softmax {
double log_softmax;
float logit;
float prob;
};
static std::vector<float> softmax(const std::vector<float>& logits) {
std::vector<float> probs(logits.size());
float max_logit = logits[0];
for (float v : logits) {
max_logit = std::max(max_logit, v);
}
double sum_exp = 0.0;
for (size_t i = 0; i < logits.size(); i++) {
// Subtract the maximum logit value from the current logit value for numerical stability
const float logit = logits[i] - max_logit;
const float exp_logit = expf(logit);
sum_exp += exp_logit;
probs[i] = exp_logit;
}
for (size_t i = 0; i < probs.size(); i++) {
probs[i] /= sum_exp;
}
return probs;
}
static results_log_softmax log_softmax(int n_vocab, const float * logits, int tok) {
float max_logit = logits[0];
for (int i = 1; i < n_vocab; ++i) {
max_logit = std::max(max_logit, logits[i]);
}
double sum_exp = 0.0;
for (int i = 0; i < n_vocab; ++i) {
sum_exp += expf(logits[i] - max_logit);
}
return {logits[tok] - max_logit - log(sum_exp), logits[tok], expf(logits[tok] - max_logit) / (float) sum_exp};
}
static void process_logits(
int n_vocab, const float * logits, const int * tokens, int n_token, std::vector<std::thread> & workers,
double & nll, double & nll2, float * logit_history, float * prob_history
) {
std::mutex mutex;
int counter = 0;
auto compute = [&mutex, &counter, &nll, &nll2, logit_history, prob_history, n_vocab, logits, tokens, n_token] () {
double local_nll = 0;
double local_nll2 = 0;
while (true) {
std::unique_lock<std::mutex> lock(mutex);
int i = counter++;
if (i >= n_token) {
nll += local_nll; nll2 += local_nll2;
break;
}
lock.unlock();
const results_log_softmax results = log_softmax(n_vocab, logits + i*n_vocab, tokens[i+1]);
const double v = -results.log_softmax;
local_nll += v;
local_nll2 += v*v;
logit_history[i] = results.logit;
prob_history[i] = results.prob;
}
};
for (auto & w : workers) {
w = std::thread(compute);
}
compute();
for (auto & w : workers) {
w.join();
}
}
static bool compute_imatrix(llama_context * ctx, const gpt_params & params) {
const bool add_bos = llama_should_add_bos_token(llama_get_model(ctx));
const int n_ctx = llama_n_ctx(ctx);
auto tim1 = std::chrono::high_resolution_clock::now();
fprintf(stderr, "%s: tokenizing the input ..\n", __func__);
std::vector<llama_token> tokens = ::llama_tokenize(ctx, params.prompt, add_bos);
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 (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;
logit_history.resize(tokens.size());
std::vector<float> prob_history;
prob_history.resize(tokens.size());
const int n_chunk_max = tokens.size() / n_ctx;
const int n_chunk = params.n_chunks < 0 ? n_chunk_max : std::min(params.n_chunks, n_chunk_max);
const int n_vocab = llama_n_vocab(llama_get_model(ctx));
const int n_batch = params.n_batch;
int count = 0;
double nll = 0.0;
double nll2 = 0.0;
fprintf(stderr, "%s: computing over %d chunks with batch_size %d\n", __func__, n_chunk, n_batch);
std::vector<std::thread> workers(std::thread::hardware_concurrency() - 1);
for (int i = 0; i < n_chunk; ++i) {
const int start = i * n_ctx;
const int end = start + n_ctx;
const int num_batches = (n_ctx + n_batch - 1) / n_batch;
std::vector<float> logits;
const auto t_start = std::chrono::high_resolution_clock::now();
// clear the KV cache
llama_kv_cache_clear(ctx);
for (int j = 0; j < num_batches; ++j) {
const int batch_start = start + j * n_batch;
const int batch_size = std::min(end - batch_start, n_batch);
// save original token and restore it after eval
const auto token_org = tokens[batch_start];
// add BOS token for the first batch of each chunk
if (add_bos && j == 0) {
tokens[batch_start] = llama_token_bos(llama_get_model(ctx));
}
if (llama_decode(ctx, llama_batch_get_one(tokens.data() + batch_start, batch_size, j * n_batch, 0))) {
fprintf(stderr, "%s : failed to eval\n", __func__);
return false;
}
// restore the original token in case it was set to BOS
tokens[batch_start] = token_org;
const auto * batch_logits = llama_get_logits(ctx);
logits.insert(logits.end(), batch_logits, batch_logits + batch_size * n_vocab);
}
const auto t_end = std::chrono::high_resolution_clock::now();
if (i == 0) {
const float t_total = std::chrono::duration<float>(t_end - t_start).count();
fprintf(stderr, "%s: %.2f seconds per pass - ETA ", __func__, t_total);
int total_seconds = (int)(t_total * n_chunk);
if (total_seconds >= 60*60) {
fprintf(stderr, "%d hours ", total_seconds / (60*60));
total_seconds = total_seconds % (60*60);
}
fprintf(stderr, "%.2f minutes\n", total_seconds / 60.0);
}
const int first = n_ctx/2;
process_logits(n_vocab, logits.data() + 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);
}
printf("\n");
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::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 {
args.push_back(argv[iarg]);
}
}
if (iarg < argc) {
args.push_back(argv[iarg]);
}
gpt_params params;
params.n_batch = 512;
if (!gpt_params_parse(args.size(), args.data(), params)) {
return 1;
}
g_collector.set_parameters(std::move(sparams));
ggml_set_imatrix_collection(ik_collect_imatrix);
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(params.numa);
llama_model * model;
llama_context * ctx;
// load the model and apply lora adapter, if any
std::tie(model, ctx) = llama_init_from_gpt_params(params);
if (model == NULL) {
fprintf(stderr, "%s: error: unable to load model\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);
if (!OK) {
return 1;
}
g_collector.save_imatrix();
llama_print_timings(ctx);
llama_free(ctx);
llama_free_model(model);
llama_backend_free();
return 0;
}

14
ggml.c
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@ -394,6 +394,12 @@ static const size_t CACHE_LINE_SIZE_F32 = CACHE_LINE_SIZE/sizeof(float);
static void ggml_vec_dot_f32(const int n, float * restrict s, const float * restrict x, const float * restrict y);
static void ggml_vec_dot_f16(const int n, float * restrict s, ggml_fp16_t * restrict x, ggml_fp16_t * restrict y);
ggml_collect_imatrix_t g_imatrix_collect = NULL;
void ggml_set_imatrix_collection(ggml_collect_imatrix_t imatrix_collect) {
g_imatrix_collect = imatrix_collect;
}
static const ggml_type_traits_t type_traits[GGML_TYPE_COUNT] = {
[GGML_TYPE_I8] = {
.type_name = "i8",
@ -9763,6 +9769,10 @@ static void ggml_compute_forward_mul_mat(
const int ith = params->ith;
const int nth = params->nth;
if (ith == 1 && g_imatrix_collect) {
g_imatrix_collect(src0, src1);
}
const enum ggml_type type = src0->type;
const bool src1_cont = ggml_is_contiguous(src1);
@ -10066,6 +10076,10 @@ static void ggml_compute_forward_mul_mat_id(
const struct ggml_tensor * src0_cur = dst->src[cur_a + 2];
if (ith == 1 && g_imatrix_collect) {
g_imatrix_collect(src0_cur, src1);
}
const void * wdata = (src1->type == vec_dot_type) ? src1->data : params->wdata;
const size_t row_size = ggml_row_size(vec_dot_type, ne10);

6
ggml.h
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@ -2067,6 +2067,12 @@ extern "C" {
GGML_API size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist);
//
// Importance matrix
//
typedef void(*ggml_collect_imatrix_t)(const struct ggml_tensor * src0, const struct ggml_tensor * src1);
GGML_API void ggml_set_imatrix_collection(ggml_collect_imatrix_t imatrix_collect);
//
// gguf
//