diff options
| -rw-r--r-- | examples/quantize/quantize.cpp | 24 | ||||
| -rw-r--r-- | llama.cpp | 25 | ||||
| -rw-r--r-- | llama.h | 1 |
3 files changed, 37 insertions, 13 deletions
diff --git a/examples/quantize/quantize.cpp b/examples/quantize/quantize.cpp index df9a214f..c174be06 100644 --- a/examples/quantize/quantize.cpp +++ b/examples/quantize/quantize.cpp @@ -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++; } @@ -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; @@ -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 |