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llama : multi-threaded quantization (#1075)

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* Multi-threading quantization.

Not much gain for simple quantizations, bit it will be important
for quantizations that require more CPU cycles.

* Multi-threading for quantize-stats

It now does the job in ~14 seconds on my Mac for
Q4_0, Q4_1 and Q4_2. Single-threaded it was taking
more than 2 minutes after adding the more elaborate
version of Q4_2.

* Reviewer comments

* Avoiding compiler confusion

After changing chunk_size to const int as suggested by
@ggerganov, clang and GCC starting to warn me that I don't
need to capture it in the lambda. So, I removed it from the
capture list. But that makes the MSVC build fail. So,
making it a constexpr to make every compiler happy.

* Still fighting with lambda captures in MSVC

---------

Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
This commit is contained in:
Kawrakow
2023-04-20 19:42:27 +02:00
committed by GitHub
parent e0305ead3a
commit 38de86a711
6 changed files with 183 additions and 61 deletions
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137
examples/quantize-stats/quantize-stats.cpp
View File

@@ -15,6 +15,8 @@
#include <string>
#include <unordered_map>
#include <vector>
#include <thread>
#include <mutex>
struct quantize_stats_params {
std::string model = "models/7B/ggml-model-f16.bin";
@@ -27,7 +29,6 @@ struct quantize_stats_params {
std::vector<enum ggml_type> include_types;
};
const int64_t SCRATCH_ELEMENTS = 32*32;
const size_t HISTOGRAM_BUCKETS = 150;
const double HISTOGRAM_RANGE = 0.03;
@@ -90,6 +91,13 @@ void update_error_stats(int64_t nelements, const float * input, const float * ou
stats.num_samples += nelements;
}
void combine_error_stats(error_stats & into, const error_stats & from) {
into.num_samples += from.num_samples;
into.total_error += from.total_error;
if (from.max_error > into.max_error) into.max_error = from.max_error;
for (size_t i=0; i<HISTOGRAM_BUCKETS; ++i) into.error_histogram[i] += from.error_histogram[i];
}
double find_quantile(const error_stats & stats, double quantile) {
double sum = std::accumulate(std::begin(stats.error_histogram), std::end(stats.error_histogram), 0.0);
@@ -130,6 +138,36 @@ static bool tensor_is_contiguous(const struct ggml_tensor * tensor) {
tensor->nb[3] == tensor->nb[2]*tensor->ne[2];
}
void test_roundtrip_on_chunk(
const ggml_tensor * layer,
int64_t offset,
int64_t chunk_size,
const quantize_fns_t & qfns,
bool use_reference,
float * input_scratch,
char * quantized_scratch,
float * output_scratch,
error_stats & stats) {
if (layer->type == GGML_TYPE_F16) {
for (int i = 0; i < chunk_size; i++) {
input_scratch[i] = ggml_get_f32_1d(layer, i + offset);
}
} else {
input_scratch = ggml_get_data_f32(layer) + offset;
}
if (use_reference) {
qfns.quantize_row_q_reference(input_scratch, quantized_scratch, chunk_size);
} else {
qfns.quantize_row_q(input_scratch, quantized_scratch, chunk_size);
}
qfns.dequantize_row_q(quantized_scratch, output_scratch, chunk_size);
update_error_stats(chunk_size, input_scratch, output_scratch, stats);
}
// Run quantization function for a single layer and update error stats
void test_roundtrip_on_layer(
std::string & name,
@@ -137,40 +175,61 @@ void test_roundtrip_on_layer(
const quantize_fns_t & qfns,
bool use_reference,
const ggml_tensor * layer,
float * input_scratch,
char *quantized_scratch,
float * output_scratch,
error_stats & total_error) {
std::vector<float> & input_scratch,
std::vector<char> & quantized_scratch,
std::vector<float> & output_scratch,
error_stats & total_error,
int max_thread = 0) {
assert(tensor_is_contiguous(layer));
error_stats layer_error {};
int64_t nelements = ggml_nelements(layer);
uint64_t nelements = ggml_nelements(layer);
for (int64_t offset = 0; offset < nelements; offset += SCRATCH_ELEMENTS) {
int64_t chunk_size = std::min(SCRATCH_ELEMENTS, nelements - offset);
if (layer->type == GGML_TYPE_F16) {
for (int i = 0; i < chunk_size; i++) {
input_scratch[i] = ggml_get_f32_1d(layer, i + offset);
}
} else {
input_scratch = ggml_get_data_f32(layer) + offset;
}
if (use_reference) {
qfns.quantize_row_q_reference(input_scratch, quantized_scratch, chunk_size);
} else {
qfns.quantize_row_q(input_scratch, quantized_scratch, chunk_size);
}
qfns.dequantize_row_q(quantized_scratch, output_scratch, chunk_size);
update_error_stats(chunk_size, input_scratch, output_scratch, total_error);
if (print_layer_stats) {
update_error_stats(chunk_size, input_scratch, output_scratch, layer_error);
}
float* input_scratch_ptr = nullptr;
if (layer->type == GGML_TYPE_F16) {
if (input_scratch.size() < nelements) input_scratch.resize(nelements);
input_scratch_ptr = input_scratch.data();
}
if (quantized_scratch.size() < 4*nelements) quantized_scratch.resize(4*nelements);
if (output_scratch.size() < nelements) output_scratch.resize(nelements);
if (max_thread < 1) max_thread = std::thread::hardware_concurrency();
int chunk_size = 32*512;
int num_chunks = (nelements + chunk_size - 1)/chunk_size;
if (num_chunks < 2 || max_thread < 2) {
test_roundtrip_on_chunk(layer, 0, nelements, qfns, use_reference, input_scratch_ptr, quantized_scratch.data(),
output_scratch.data(), print_layer_stats ? layer_error : total_error);
} else {
auto & stats = print_layer_stats ? layer_error : total_error;
std::mutex mutex;
uint64_t counter = 0;
auto compute = [&mutex, &counter, &stats, &qfns, nelements, layer, use_reference, input_scratch_ptr,
&quantized_scratch, &output_scratch, chunk_size] () {
error_stats local_stats {};
while (true) {
std::unique_lock<std::mutex> lock(mutex);
uint64_t offset = counter; counter += chunk_size;
if (offset >= nelements) {
combine_error_stats(stats, local_stats);
break;
}
lock.unlock();
uint64_t chunk = offset + chunk_size < nelements ? chunk_size : nelements - offset;
test_roundtrip_on_chunk(layer, offset, chunk, qfns, use_reference, input_scratch_ptr + offset,
quantized_scratch.data() + 4*offset, output_scratch.data() + offset, local_stats);
}
};
int nthread = std::min(num_chunks, max_thread);
std::vector<std::thread> workers(nthread-1);
for (auto& w : workers) w = std::thread(compute);
compute();
for (auto& w : workers) w.join();
}
if (print_layer_stats) {
print_error_stats(name, layer_error, false);
combine_error_stats(total_error, layer_error);
}
}
@@ -181,6 +240,7 @@ int main(int argc, char ** argv) {
// read command line
int max_thread = 0;
bool invalid_param = false;
std::string arg;
for (int i = 1; i < argc; i++) {
@@ -230,6 +290,12 @@ int main(int argc, char ** argv) {
fprintf(stderr, "error: %s not in list of types\n", argv[i]);
invalid_param = true;
}
} else if (arg == "-n" || arg == "--num-threads") {
if (++i >= argc) {
invalid_param = true;
break;
}
max_thread = atoi(argv[i]);
} else {
fprintf(stderr, "error: unknown argument: %s\n", arg.c_str());
quantize_stats_print_usage(argc, argv);
@@ -295,9 +361,9 @@ int main(int argc, char ** argv) {
}
printf("testing %d layers with max size %" PRId64 "\n", included_layers, max_nelements);
// allocate scratch space
std::vector<float> input_scratch(SCRATCH_ELEMENTS);
std::vector<char> quantized_scratch(SCRATCH_ELEMENTS*4);
std::vector<float> output_scratch(SCRATCH_ELEMENTS);
std::vector<float> input_scratch;
std::vector<char> quantized_scratch;
std::vector<float> output_scratch;
// loop throught quantization types
for (int i = 0; i < GGML_TYPE_COUNT; i++) {
@@ -328,10 +394,11 @@ int main(int argc, char ** argv) {
qfns,
params.reference,
kv_tensor.second,
input_scratch.data(),
quantized_scratch.data(),
output_scratch.data(),
global_stats
input_scratch,
quantized_scratch,
output_scratch,
global_stats,
max_thread
);
}

7
examples/quantize/quantize.cpp
View File

@@ -10,8 +10,8 @@
int main(int argc, char ** argv) {
ggml_time_init();
if (argc != 4) {
fprintf(stderr, "usage: %s model-f32.bin model-quant.bin type\n", argv[0]);
if (argc < 4) {
fprintf(stderr, "usage: %s model-f32.bin model-quant.bin type [nthread]\n", argv[0]);
fprintf(stderr, " type = %d - q4_0\n", LLAMA_FTYPE_MOSTLY_Q4_0);
fprintf(stderr, " type = %d - q4_1\n", LLAMA_FTYPE_MOSTLY_Q4_1);
fprintf(stderr, " type = %d - q4_2\n", LLAMA_FTYPE_MOSTLY_Q4_2);
@@ -30,6 +30,7 @@ int main(int argc, char ** argv) {
const std::string fname_out = argv[2];
const enum llama_ftype ftype = (enum llama_ftype)atoi(argv[3]);
int nthread = argc > 4 ? atoi(argv[4]) : 0;
const int64_t t_main_start_us = ggml_time_us();
@@ -39,7 +40,7 @@ int main(int argc, char ** argv) {
{
const int64_t t_start_us = ggml_time_us();
if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), ftype)) {
if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), ftype, nthread)) {
fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str());
return 1;
}

27
ggml.c
View File

@@ -12189,6 +12189,33 @@ size_t ggml_quantize_q4_3(const float * src, void * dst, int n, int k, int64_t *
return (n/QK4_3*sizeof(block_q4_3));
}
size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist) {
size_t result = 0;
switch (type) {
case GGML_TYPE_Q4_0:
{
GGML_ASSERT(start % QK4_0 == 0);
block_q4_0 * block = (block_q4_0*)dst + start / QK4_0;
result = ggml_quantize_q4_0(src + start, block, n, n, hist);
} break;
case GGML_TYPE_Q4_1:
{
GGML_ASSERT(start % QK4_1 == 0);
block_q4_1 * block = (block_q4_1*)dst + start / QK4_1;
result = ggml_quantize_q4_1(src + start, block, n, n, hist);
} break;
case GGML_TYPE_Q4_2:
{
GGML_ASSERT(start % QK4_2 == 0);
block_q4_2 * block = (block_q4_2*)dst + start / QK4_2;
result = ggml_quantize_q4_2(src + start, block, n, n, hist);
} break;
default:
assert(false);
}
return result;
}
////////////////////////////////////////////////////////////////////////////////
int ggml_cpu_has_avx(void) {

2
ggml.h
View File

@@ -813,6 +813,8 @@ size_t ggml_quantize_q4_1(const float * src, void * dst, int n, int k, int64_t *
size_t ggml_quantize_q4_2(const float * src, void * dst, int n, int k, int64_t * hist);
size_t ggml_quantize_q4_3(const float * src, void * dst, int n, int k, int64_t * hist);
size_t ggml_quantize_chunk(enum ggml_type type, const float * src, void * dst, int start, int n, int64_t * hist);
//
// system info
//

67
llama.cpp
View File

@@ -24,6 +24,9 @@
#include <memory>
#include <algorithm>
#include <initializer_list>
#include <thread>
#include <atomic>
#include <mutex>
#define LLAMA_USE_SCRATCH
#define LLAMA_MAX_SCRATCH_BUFFERS 16
@@ -1572,7 +1575,7 @@ static llama_vocab::id llama_sample_top_p_top_k(
// quantization
//
static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, enum llama_ftype ftype) {
static void llama_model_quantize_internal(const std::string & fname_inp, const std::string & fname_out, enum llama_ftype ftype, int nthread) {
ggml_type quantized_type;
switch (ftype) {
case LLAMA_FTYPE_MOSTLY_Q4_0: quantized_type = GGML_TYPE_Q4_0; break;
@@ -1582,6 +1585,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
default: throw format("invalid output file type %d\n", ftype);
};
if (nthread <= 0) {
nthread = std::thread::hardware_concurrency();
}
std::unique_ptr<llama_model_loader> model_loader(new llama_model_loader(fname_inp.c_str(), /*use_mmap*/ false,
/*vocab_only*/ false));
llama_file_saver file_saver(fname_out.c_str(), model_loader->file_loaders.at(0).get(), ftype);
@@ -1590,6 +1597,9 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
size_t total_size_new = 0;
std::vector<int64_t> hist_all(1 << 4, 0);
std::vector<std::thread> workers;
std::mutex mutex;
size_t idx = 0;
for (llama_load_tensor & tensor : model_loader->tensors_map.tensors) {
llama_buffer read_data;
@@ -1643,25 +1653,37 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
new_data = work.addr;
std::vector<int64_t> hist_cur(1 << 4, 0);
switch (new_type) {
case GGML_TYPE_Q4_0:
{
new_size = ggml_quantize_q4_0(f32_data, new_data, nelements, (int) tensor.ne.at(0), hist_cur.data());
} break;
case GGML_TYPE_Q4_1:
{
new_size = ggml_quantize_q4_1(f32_data, new_data, nelements, (int) tensor.ne.at(0), hist_cur.data());
} break;
case GGML_TYPE_Q4_2:
{
new_size = ggml_quantize_q4_2(f32_data, new_data, nelements, (int) tensor.ne.at(0), hist_cur.data());
} break;
case GGML_TYPE_Q4_3:
{
new_size = ggml_quantize_q4_3(f32_data, new_data, nelements, (int) tensor.ne.at(0), hist_cur.data());
} break;
default:
LLAMA_ASSERT(false);
int chunk_size = 32 * 512;
const int nchunk = (nelements + chunk_size - 1)/chunk_size;
const int nthread_use = nthread > 1 ? std::max(1, std::min(nthread, nchunk)) : 1;
if (nthread_use < 2) {
new_size = ggml_quantize_chunk(new_type, f32_data, new_data, 0, nelements, hist_cur.data());
} else {
size_t counter = 0;
new_size = 0;
auto compute = [&mutex, &counter, &hist_cur, &new_size, new_type, f32_data, new_data, nelements, chunk_size] () {
std::vector<int64_t> local_hist;
size_t local_size = 0;
while (true) {
std::unique_lock<std::mutex> lock(mutex);
size_t first = counter; counter += chunk_size;
if (first >= nelements) {
if (!local_hist.empty()) {
for (int j=0; j<int(local_hist.size()); ++j) hist_cur[j] += local_hist[j];
new_size += local_size;
}
break;
}
lock.unlock();
size_t last = std::min(nelements, first + chunk_size);
if (local_hist.empty()) local_hist.resize(hist_cur.size(), 0);
local_size += ggml_quantize_chunk(new_type, f32_data, new_data, first, last - first, local_hist.data());
}
};
if (int(workers.size()) < nthread_use - 1) workers.resize(nthread_use - 1);
for (int it = 0; it < nthread_use - 1; ++it) workers[it] = std::thread(compute);
compute();
for (int it = 0; it < nthread_use - 1; ++it) workers[it].join();
}
printf("size = %8.2f MB -> %8.2f MB | hist: ", tensor.size/1024.0/1024.0, new_size/1024.0/1024.0);
@@ -1783,9 +1805,10 @@ void llama_free(struct llama_context * ctx) {
int llama_model_quantize(
const char * fname_inp,
const char * fname_out,
enum llama_ftype ftype) {
enum llama_ftype ftype,
int nthread) {
try {
llama_model_quantize_internal(fname_inp, fname_out, ftype);
llama_model_quantize_internal(fname_inp, fname_out, ftype, nthread);
return 0;
} catch (const std::string & err) {
fprintf(stderr, "%s: failed to quantize: %s\n", __func__, err.c_str());

4
llama.h
View File

@@ -93,10 +93,12 @@ extern "C" {
// TODO: not great API - very likely to change
// Returns 0 on success
// nthread - how many threads to use. If <=0, will use std::thread::hardware_concurrency(), else the number given
LLAMA_API int llama_model_quantize(
const char * fname_inp,
const char * fname_out,
enum llama_ftype ftype);
enum llama_ftype ftype,
int nthread);
// Apply a LoRA adapter to a loaded model
// path_base_model is the path to a higher quality model to use as a base for