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llama.cpp/examples/quantize/quantize.cpp
Francis Couture-Harpin 3ad0603c65 Merge branch 'master' into compilade/imatrix-batched-chunks
2024-09-08 10:05:08 -04:00

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#include "common.h"
#include "llama.h"
#include <cstdio>
#include <cstring>
#include <vector>
#include <string>
#include <unordered_map>
struct quant_option {
std::string name;
llama_ftype ftype;
std::string desc;
};
static const std::vector<struct quant_option> QUANT_OPTIONS = {
{ "Q4_0", LLAMA_FTYPE_MOSTLY_Q4_0, " 4.34G, +0.4685 ppl @ Llama-3-8B", },
{ "Q4_1", LLAMA_FTYPE_MOSTLY_Q4_1, " 4.78G, +0.4511 ppl @ Llama-3-8B", },
{ "Q5_0", LLAMA_FTYPE_MOSTLY_Q5_0, " 5.21G, +0.1316 ppl @ Llama-3-8B", },
{ "Q5_1", LLAMA_FTYPE_MOSTLY_Q5_1, " 5.65G, +0.1062 ppl @ Llama-3-8B", },
{ "IQ2_XXS", LLAMA_FTYPE_MOSTLY_IQ2_XXS, " 2.06 bpw quantization", },
{ "IQ2_XS", LLAMA_FTYPE_MOSTLY_IQ2_XS, " 2.31 bpw quantization", },
{ "IQ2_S", LLAMA_FTYPE_MOSTLY_IQ2_S, " 2.5 bpw quantization", },
{ "IQ2_M", LLAMA_FTYPE_MOSTLY_IQ2_M, " 2.7 bpw quantization", },
{ "IQ1_S", LLAMA_FTYPE_MOSTLY_IQ1_S, " 1.56 bpw quantization", },
{ "IQ1_M", LLAMA_FTYPE_MOSTLY_IQ1_M, " 1.75 bpw quantization", },
{ "TQ1_0", LLAMA_FTYPE_MOSTLY_TQ1_0, " 1.69 bpw ternarization", },
{ "TQ2_0", LLAMA_FTYPE_MOSTLY_TQ2_0, " 2.06 bpw ternarization", },
{ "Q2_K", LLAMA_FTYPE_MOSTLY_Q2_K, " 2.96G, +3.5199 ppl @ Llama-3-8B", },
{ "Q2_K_S", LLAMA_FTYPE_MOSTLY_Q2_K_S, " 2.96G, +3.1836 ppl @ Llama-3-8B", },
{ "IQ3_XXS", LLAMA_FTYPE_MOSTLY_IQ3_XXS, " 3.06 bpw quantization", },
{ "IQ3_S", LLAMA_FTYPE_MOSTLY_IQ3_S, " 3.44 bpw quantization", },
{ "IQ3_M", LLAMA_FTYPE_MOSTLY_IQ3_M, " 3.66 bpw quantization mix", },
{ "Q3_K", LLAMA_FTYPE_MOSTLY_Q3_K_M, "alias for Q3_K_M" },
{ "IQ3_XS", LLAMA_FTYPE_MOSTLY_IQ3_XS, " 3.3 bpw quantization", },
{ "Q3_K_S", LLAMA_FTYPE_MOSTLY_Q3_K_S, " 3.41G, +1.6321 ppl @ Llama-3-8B", },
{ "Q3_K_M", LLAMA_FTYPE_MOSTLY_Q3_K_M, " 3.74G, +0.6569 ppl @ Llama-3-8B", },
{ "Q3_K_L", LLAMA_FTYPE_MOSTLY_Q3_K_L, " 4.03G, +0.5562 ppl @ Llama-3-8B", },
{ "IQ4_NL", LLAMA_FTYPE_MOSTLY_IQ4_NL, " 4.50 bpw non-linear quantization", },
{ "IQ4_XS", LLAMA_FTYPE_MOSTLY_IQ4_XS, " 4.25 bpw non-linear quantization", },
{ "Q4_K", LLAMA_FTYPE_MOSTLY_Q4_K_M, "alias for Q4_K_M", },
{ "Q4_K_S", LLAMA_FTYPE_MOSTLY_Q4_K_S, " 4.37G, +0.2689 ppl @ Llama-3-8B", },
{ "Q4_K_M", LLAMA_FTYPE_MOSTLY_Q4_K_M, " 4.58G, +0.1754 ppl @ Llama-3-8B", },
{ "Q5_K", LLAMA_FTYPE_MOSTLY_Q5_K_M, "alias for Q5_K_M", },
{ "Q5_K_S", LLAMA_FTYPE_MOSTLY_Q5_K_S, " 5.21G, +0.1049 ppl @ Llama-3-8B", },
{ "Q5_K_M", LLAMA_FTYPE_MOSTLY_Q5_K_M, " 5.33G, +0.0569 ppl @ Llama-3-8B", },
{ "Q6_K", LLAMA_FTYPE_MOSTLY_Q6_K, " 6.14G, +0.0217 ppl @ Llama-3-8B", },
{ "Q8_0", LLAMA_FTYPE_MOSTLY_Q8_0, " 7.96G, +0.0026 ppl @ Llama-3-8B", },
{ "Q4_0_4_4", LLAMA_FTYPE_MOSTLY_Q4_0_4_4, " 4.34G, +0.4685 ppl @ Llama-3-8B", },
{ "Q4_0_4_8", LLAMA_FTYPE_MOSTLY_Q4_0_4_8, " 4.34G, +0.4685 ppl @ Llama-3-8B", },
{ "Q4_0_8_8", LLAMA_FTYPE_MOSTLY_Q4_0_8_8, " 4.34G, +0.4685 ppl @ Llama-3-8B", },
{ "F16", LLAMA_FTYPE_MOSTLY_F16, "14.00G, +0.0020 ppl @ Mistral-7B", },
{ "BF16", LLAMA_FTYPE_MOSTLY_BF16, "14.00G, -0.0050 ppl @ Mistral-7B", },
{ "F32", LLAMA_FTYPE_ALL_F32, "26.00G @ 7B", },
// Note: Ensure COPY comes after F32 to avoid ftype 0 from matching.
{ "COPY", LLAMA_FTYPE_ALL_F32, "only copy tensors, no quantizing", },
};
static const char * const LLM_KV_QUANTIZE_IMATRIX_FILE = "quantize.imatrix.file";
static const char * const LLM_KV_QUANTIZE_IMATRIX_DATASET = "quantize.imatrix.dataset";
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 try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftype, std::string & ftype_str_out) {
std::string ftype_str;
for (auto ch : ftype_str_in) {
ftype_str.push_back(std::toupper(ch));
}
for (auto & it : QUANT_OPTIONS) {
if (it.name == ftype_str) {
ftype = it.ftype;
ftype_str_out = it.name;
return true;
}
}
try {
int ftype_int = std::stoi(ftype_str);
for (auto & it : QUANT_OPTIONS) {
if (it.ftype == ftype_int) {
ftype = it.ftype;
ftype_str_out = it.name;
return true;
}
}
}
catch (...) {
// stoi failed
}
return false;
}
// usage:
// ./llama-quantize [--allow-requantize] [--leave-output-tensor] [--pure] models/llama/ggml-model.gguf [models/llama/ggml-model-quant.gguf] type [nthreads]
//
[[noreturn]]
static void usage(const char * executable) {
printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] [--pure] [--imatrix] [--include-weights] [--exclude-weights] [--output-tensor-type] [--token-embedding-type] [--override-kv] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable);
printf(" --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n");
printf(" --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n");
printf(" --pure: Disable k-quant mixtures and quantize all tensors to the same type\n");
printf(" --imatrix file_name: use data in file_name as importance matrix for quant optimizations\n");
printf(" --include-weights tensor_name: use importance matrix for this/these tensor(s)\n");
printf(" --exclude-weights tensor_name: use importance matrix for this/these tensor(s)\n");
printf(" --output-tensor-type ggml_type: use this ggml_type for the output.weight tensor\n");
printf(" --token-embedding-type ggml_type: use this ggml_type for the token embeddings tensor\n");
printf(" --keep-split: will generate quantized model in the same shards as input\n");
printf(" --override-kv KEY=TYPE:VALUE\n");
printf(" Advanced option to override model metadata by key in the quantized model. May be specified multiple times.\n");
printf("Note: --include-weights and --exclude-weights cannot be used together\n");
printf("\nAllowed quantization types:\n");
for (auto & it : QUANT_OPTIONS) {
if (it.name != "COPY") {
printf(" %2d or ", it.ftype);
} else {
printf(" ");
}
printf("%-7s : %s\n", it.name.c_str(), it.desc.c_str());
}
exit(1);
}
static int load_imatrix(const std::string & imatrix_file, std::string & imatrix_dataset, std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
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);
}
const int32_t n_entries = gguf_get_n_tensors(ctx_gguf);
if (n_entries < 2) {
fprintf(stderr, "%s: no data in file %s\n", __func__, imatrix_file.c_str());
gguf_free(ctx_gguf);
ggml_free(ctx);
exit(1);
}
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"};
// TODO: allow loading from mis-ordered imatrix files
for (int32_t i = 0; i < n_entries - 1; i += 2) {
std::string sums_name{gguf_get_tensor_name(ctx_gguf, i + 0)};
std::string counts_name{gguf_get_tensor_name(ctx_gguf, i + 1)};
if (sums_name.size() < sums_suffix.size() ||
counts_name.size() < counts_suffix.size() ||
!std::equal(sums_name.begin(), sums_name.end() - sums_suffix.size(), counts_name.begin()) ||
!std::equal(sums_suffix.rbegin(), sums_suffix.rend(), sums_name.rbegin()) ||
!std::equal(counts_suffix.rbegin(), counts_suffix.rend(), counts_name.rbegin())) {
fprintf(stderr, "%s: mismatched sums and counts for entry %d\n", __func__, i / 2);
gguf_free(ctx_gguf);
ggml_free(ctx);
exit(1);
}
struct ggml_tensor * sums = ggml_get_tensor(ctx, sums_name.c_str());
struct ggml_tensor * counts = ggml_get_tensor(ctx, counts_name.c_str());
if (!sums || !counts) {
fprintf(stderr, "%s: failed reading data for entry %d\n", __func__, i / 2);
gguf_free(ctx_gguf);
ggml_free(ctx);
exit(1);
}
const int64_t ne0 = sums->ne[0];
const int64_t ne1 = sums->ne[1];
std::string name = sums_name.substr(0, sums_name.size() - sums_suffix.size());
auto & e = imatrix_data[name];
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;
}
}
if (getenv("LLAMA_TRACE")) {
printf("%s: loaded data (size = %6d, ncall = %6d) for '%s'\n", __func__, int(e.size()), int(max_count / chunk_size), name.c_str());
}
}
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,
std::string & imatrix_dataset,
const std::vector<std::string> & included_weights,
const std::vector<std::string> & excluded_weights,
std::unordered_map<std::string, std::vector<float>> & imatrix_data) {
int m_last_call = -1;
if (!imatrix_file.empty()) {
m_last_call = load_imatrix(imatrix_file, imatrix_dataset, imatrix_data);
}
if (imatrix_data.empty()) {
return m_last_call;
}
if (!excluded_weights.empty()) {
for (auto& name : excluded_weights) {
for (auto it = imatrix_data.begin(); it != imatrix_data.end(); ) {
auto pos = it->first.find(name);
if (pos != std::string::npos) it = imatrix_data.erase(it);
else ++it;
}
}
}
if (!included_weights.empty()) {
std::unordered_map<std::string, std::vector<float>> tmp;
for (auto& name : included_weights) {
for (auto& e : imatrix_data) {
auto pos = e.first.find(name);
if (pos != std::string::npos) {
tmp.emplace(std::move(e));
}
}
}
imatrix_data = std::move(tmp);
}
if (!imatrix_data.empty()) {
printf("%s: have %d importance matrix entries\n", __func__, int(imatrix_data.size()));
}
return m_last_call;
}
static ggml_type parse_ggml_type(const char * arg) {
ggml_type result = GGML_TYPE_COUNT;
for (int j = 0; j < GGML_TYPE_COUNT; ++j) {
auto type = ggml_type(j);
const auto * name = ggml_type_name(type);
if (name && strcmp(arg, name) == 0) {
result = type; break;
}
}
return result;
}
int main(int argc, char ** argv) {
if (argc < 3) {
usage(argv[0]);
}
llama_model_quantize_params params = llama_model_quantize_default_params();
int arg_idx = 1;
std::string imatrix_file;
std::vector<std::string> included_weights, excluded_weights;
std::vector<llama_model_kv_override> kv_overrides;
for (; arg_idx < argc && strncmp(argv[arg_idx], "--", 2) == 0; arg_idx++) {
if (strcmp(argv[arg_idx], "--leave-output-tensor") == 0) {
params.quantize_output_tensor = false;
} else if (strcmp(argv[arg_idx], "--output-tensor-type") == 0) {
if (arg_idx < argc-1) {
params.output_tensor_type = parse_ggml_type(argv[++arg_idx]);
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--token-embedding-type") == 0) {
if (arg_idx < argc-1) {
params.token_embedding_type = parse_ggml_type(argv[++arg_idx]);
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--override-kv") == 0) {
if (arg_idx == argc-1 || !string_parse_kv_override(argv[++arg_idx], kv_overrides)) {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--allow-requantize") == 0) {
params.allow_requantize = true;
} else if (strcmp(argv[arg_idx], "--pure") == 0) {
params.pure = true;
} else if (strcmp(argv[arg_idx], "--imatrix") == 0) {
if (arg_idx < argc-1) {
imatrix_file = argv[++arg_idx];
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--include-weights") == 0) {
if (arg_idx < argc-1) {
included_weights.emplace_back(argv[++arg_idx]);
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--exclude-weights") == 0) {
if (arg_idx < argc-1) {
excluded_weights.emplace_back(argv[++arg_idx]);
} else {
usage(argv[0]);
}
} else if (strcmp(argv[arg_idx], "--keep-split") == 0) {
params.keep_split = true;
} else {
usage(argv[0]);
}
}
if (argc - arg_idx < 2) {
printf("%s: bad arguments\n", argv[0]);
usage(argv[0]);
}
if (!included_weights.empty() && !excluded_weights.empty()) {
usage(argv[0]);
}
std::string imatrix_dataset;
std::unordered_map<std::string, std::vector<float>> imatrix_data;
int m_last_call = prepare_imatrix(imatrix_file, imatrix_dataset, included_weights, excluded_weights, imatrix_data);
if (!imatrix_data.empty()) {
params.imatrix = &imatrix_data;
{
llama_model_kv_override kvo;
std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_FILE);
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
strncpy(kvo.val_str, imatrix_file.c_str(), 127);
kvo.val_str[127] = '\0';
kv_overrides.emplace_back(std::move(kvo));
}
if (!imatrix_dataset.empty()) {
llama_model_kv_override kvo;
std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_DATASET);
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_STR;
strncpy(kvo.val_str, imatrix_dataset.c_str(), 127);
kvo.val_str[127] = '\0';
kv_overrides.emplace_back(std::move(kvo));
}
{
llama_model_kv_override kvo;
std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_N_ENTRIES);
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
kvo.val_i64 = imatrix_data.size();
kv_overrides.emplace_back(std::move(kvo));
}
if (m_last_call > 0) {
llama_model_kv_override kvo;
std::strcpy(kvo.key, LLM_KV_QUANTIZE_IMATRIX_N_CHUNKS);
kvo.tag = LLAMA_KV_OVERRIDE_TYPE_INT;
kvo.val_i64 = m_last_call;
kv_overrides.emplace_back(std::move(kvo));
}
}
if (!kv_overrides.empty()) {
kv_overrides.emplace_back();
kv_overrides.back().key[0] = 0;
params.kv_overrides = &kv_overrides;
}
llama_backend_init();
// parse command line arguments
const std::string fname_inp = argv[arg_idx];
arg_idx++;
std::string fname_out;
std::string ftype_str;
std::string suffix = ".gguf";
if (try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) {
std::string fpath;
const size_t pos = fname_inp.find_last_of("/\\");
if (pos != std::string::npos) {
fpath = fname_inp.substr(0, pos + 1);
}
// export as [inp path]/ggml-model-[ftype]. Only add extension if there is no splitting
fname_out = fpath + "ggml-model-" + ftype_str;
if (!params.keep_split) {
fname_out += suffix;
}
arg_idx++;
if (ftype_str == "COPY") {
params.only_copy = true;
}
} else {
fname_out = argv[arg_idx];
if (params.keep_split && fname_out.find(suffix) != std::string::npos) {
fname_out = fname_out.substr(0, fname_out.length() - suffix.length());
}
arg_idx++;
if (argc <= arg_idx) {
fprintf(stderr, "%s: missing ftype\n", __func__);
return 1;
}
if (!try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) {
fprintf(stderr, "%s: invalid ftype '%s'\n", __func__, argv[3]);
return 1;
}
if (ftype_str == "COPY") {
params.only_copy = true;
}
arg_idx++;
}
// parse nthreads
if (argc > arg_idx) {
try {
params.nthread = std::stoi(argv[arg_idx]);
}
catch (const std::exception & e) {
fprintf(stderr, "%s: invalid nthread '%s' (%s)\n", __func__, argv[arg_idx], e.what());
return 1;
}
}
if ((params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XS || params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_XXS ||
params.ftype == LLAMA_FTYPE_MOSTLY_IQ2_S ||
params.ftype == LLAMA_FTYPE_MOSTLY_Q2_K_S ||
params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_S ||
params.ftype == LLAMA_FTYPE_MOSTLY_IQ1_M) && imatrix_data.empty()) {
fprintf(stderr, "\n==========================================================================================================\n");
fprintf(stderr, "Please do not use IQ1_S, IQ1_M, IQ2_S, IQ2_XXS, IQ2_XS or Q2_K_S quantization without an importance matrix\n");
fprintf(stderr, "==========================================================================================================\n\n\n");
return 1;
}
print_build_info();
fprintf(stderr, "%s: quantizing '%s' to '%s' as %s", __func__, fname_inp.c_str(), fname_out.c_str(), ftype_str.c_str());
if (params.nthread > 0) {
fprintf(stderr, " using %d threads", params.nthread);
}
fprintf(stderr, "\n");
const int64_t t_main_start_us = llama_time_us();
int64_t t_quantize_us = 0;
// load the model
{
const int64_t t_start_us = llama_time_us();
if (llama_model_quantize(fname_inp.c_str(), fname_out.c_str(), &params)) {
fprintf(stderr, "%s: failed to quantize model from '%s'\n", __func__, fname_inp.c_str());
return 1;
}
t_quantize_us = llama_time_us() - t_start_us;
}
// report timing
{
const int64_t t_main_end_us = llama_time_us();
printf("\n");
printf("%s: quantize time = %8.2f ms\n", __func__, t_quantize_us/1000.0);
printf("%s: total time = %8.2f ms\n", __func__, (t_main_end_us - t_main_start_us)/1000.0);
}
llama_backend_free();
return 0;
}
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