haraldwolff/llama.cpp
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Allow quantize to only copy tensors, some other improvements (#2931)

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* Allow quantize tool to only copy tensors to allow repackaging models.

* Slightly better logic when requantizing.

* Change help message to go to `stdout`.
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Kerfuffle 2023-09-01 08:02:48 -06:00 committed by GitHub
parent 0d58936686
commit 5d6f19f16b
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3 changed files with 37 additions and 13 deletions
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24
examples/quantize/quantize.cpp
View file

@ -35,6 +35,8 @@ static const std::vector<struct quant_option> QUANT_OPTIONS = {
{ "Q8_0", LLAMA_FTYPE_MOSTLY_Q8_0, " 6.70G, +0.0004 ppl @ LLaMA-v1-7B", },
{ "F16", LLAMA_FTYPE_MOSTLY_F16, "13.00G @ 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", },
};
@ -71,12 +73,17 @@ bool try_parse_ftype(const std::string & ftype_str_in, llama_ftype & ftype, std:
// ./quantize [--allow-requantize] [--leave-output-tensor] models/llama/ggml-model.gguf [models/llama/ggml-model-quant.gguf] type [nthreads]
//
void usage(const char * executable) {
fprintf(stderr, "usage: %s [--help] [--allow-requantize] [--leave-output-tensor] model-f32.gguf [model-quant.gguf] type [nthreads]\n\n", executable);
fprintf(stderr, " --allow-requantize: Allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit\n");
fprintf(stderr, " --leave-output-tensor: Will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing\n");
fprintf(stderr, "\nAllowed quantization types:\n");
printf("usage: %s [--help] [--allow-requantize] [--leave-output-tensor] 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("\nAllowed quantization types:\n");
for (auto & it : QUANT_OPTIONS) {
printf(" %2d or %-6s : %s\n", it.ftype, it.name.c_str(), it.desc.c_str());
if (it.name != "COPY") {
printf(" %2d or ", it.ftype);
} else {
printf(" ");
}
printf("%-6s : %s\n", it.name.c_str(), it.desc.c_str());
}
exit(1);
}
@ -121,6 +128,9 @@ int main(int argc, char ** argv) {
// export as [inp path]/ggml-model-[ftype].gguf
fname_out = fpath + "ggml-model-" + ftype_str + ".gguf";
arg_idx++;
if (ftype_str == "COPY") {
params.only_copy = true;
}
}
else {
fname_out = argv[arg_idx];
@ -133,6 +143,10 @@ int main(int argc, char ** argv) {
if (!try_parse_ftype(argv[arg_idx], params.ftype, ftype_str)) {
fprintf(stderr, "%s: invalid ftype '%s'\n", __func__, argv[3]);
return 1;
} else {
if (ftype_str == "COPY") {
params.only_copy = true;
}
}
arg_idx++;
}

25
llama.cpp
View file

@ -4683,6 +4683,10 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
llm_load_arch(*ml, model);
llm_load_hparams(*ml, model, 0, 0, 0);
if (params->only_copy) {
ftype = model.ftype;
}
const size_t align = GGUF_DEFAULT_ALIGNMENT;
struct gguf_context * ctx_out = gguf_init_empty();
@ -4769,18 +4773,13 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
// quantize only 2D tensors
quantize &= (tensor->n_dims == 2);
quantize &= params->quantize_output_tensor || name != "output.weight";
quantize &= quantized_type != tensor->type;
quantize &= !params->only_copy;
enum ggml_type new_type;
void * new_data;
size_t new_size;
if (!quantize) {
new_type = tensor->type;
new_data = tensor->data;
new_size = ggml_nbytes(tensor);
LLAMA_LOG_INFO("size = %8.3f MB\n", ggml_nbytes(tensor)/1024.0/1024.0);
} else {
if (quantize) {
new_type = quantized_type;
#ifdef GGML_USE_K_QUANTS
// TODO: avoid hardcoded tensor names - use the TN_* constants
@ -4879,7 +4878,16 @@ static void llama_model_quantize_internal(const std::string & fname_inp, const s
}
}
#endif
// If we've decided to quantize to the same type the tensor is already
// in then there's nothing to do.
quantize = tensor->type != new_type;
}
if (!quantize) {
new_type = tensor->type;
new_data = tensor->data;
new_size = ggml_nbytes(tensor);
LLAMA_LOG_INFO("size = %8.3f MB\n", ggml_nbytes(tensor)/1024.0/1024.0);
} else {
const size_t nelements = ggml_nelements(tensor);
float * f32_data;
@ -5310,6 +5318,7 @@ struct llama_model_quantize_params llama_model_quantize_default_params() {
/*.ftype =*/ LLAMA_FTYPE_MOSTLY_Q5_1,
/*.allow_requantize =*/ false,
/*.quantize_output_tensor =*/ true,
/*.only_copy =*/ false,
};
return result;

1
llama.h
View file

@ -164,6 +164,7 @@ extern "C" {
enum llama_ftype ftype; // quantize to this llama_ftype
bool allow_requantize; // allow quantizing non-f32/f16 tensors
bool quantize_output_tensor; // quantize output.weight
bool only_copy; // only copy tensors - ftype, allow_requantize and quantize_output_tensor are ignored
} llama_model_quantize_params;
// grammar types