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[GPTQ Enhence] Support GPTQ for Baichuan2-13B & Falcon 7B & Phi-1.5 (#…
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| Original file line number | Diff line number | Diff line change |
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| #!/usr/bin/env python | ||
| # -*- coding: utf-8 -*- | ||
| # | ||
| # Copyright (c) 2023 Intel Corporation | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
|
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| import os | ||
| import json | ||
| import sys | ||
| import re | ||
| import argparse | ||
| from common import * | ||
| from sentencepiece import SentencePieceProcessor | ||
| from transformers import AutoModelForCausalLM, AutoTokenizer | ||
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| def load_vocab_for_baichuan(path: Path) -> SentencePieceVocab: | ||
| # Be extra-friendly and accept either a file or a directory. Also, if it's | ||
| # a directory, it might be the model directory, and tokenizer.model might | ||
| # be in the parent of that. | ||
| if path.is_dir(): | ||
| path2 = path / "tokenizer.model" | ||
| # Use `.parent` instead of /.. to handle the symlink case better. | ||
| path3 = path.parent / "tokenizer.model" | ||
| if path2.exists(): | ||
| path = path2 | ||
| elif path3.exists(): | ||
| path = path3 | ||
| else: | ||
| raise FileNotFoundError( | ||
| f"Could not find tokenizer.model in {path} or its parent; if it's in another directory, \ | ||
| pass the directory as --vocab-dir") | ||
| added_tokens_path = path.parent / "added_tokens.json" | ||
| print(f"Loading vocab file {path}") | ||
| return SentencePieceVocab(path, added_tokens_path if added_tokens_path.exists() else None) | ||
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| def main(args_in: Optional[List[str]] = None) -> None: | ||
| parser = argparse.ArgumentParser(description="Convert a model to a NE compatible file") | ||
| parser.add_argument("--outtype", choices=["f32", "f16"], help="output format (default: based on input)") | ||
| parser.add_argument("--outfile", type=Path, help="path to write to; default: based on input") | ||
| parser.add_argument("--model_hub", | ||
| choices=["huggingface", "modelscope"], | ||
| default="huggingface", | ||
| help="hub to load model") | ||
| parser.add_argument("model", type=Path, help="directory containing model file") | ||
| args = parser.parse_args(args_in) | ||
|
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| out_path = args.outfile.as_posix() | ||
| model_path = args.model.as_posix() | ||
|
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| model, hparams, quantize_config = load_quantized_safetensors(model_path) | ||
| list_vars = model | ||
|
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| print(hparams) | ||
|
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| tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) | ||
| fout = open(out_path, "wb") | ||
|
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| # possible data types | ||
| # ftype == 0 -> float32, ftype == 1 -> float16 | ||
| ftype = 0 | ||
| if args.outtype == "f16": | ||
| ftype = 1 | ||
|
|
||
| # 1. write hparams | ||
| print(hparams) | ||
| ne_file_magic = 0x67676d66 | ||
| fout.write(struct.pack("i", ne_file_magic)) # magic: ne in hex | ||
| fout.write(struct.pack("i", 1)) | ||
|
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| fout.write(struct.pack("i", hparams["vocab_size"])) | ||
| fout.write(struct.pack("i", hparams["hidden_size"])) | ||
| fout.write(struct.pack("i", 0)) | ||
| fout.write(struct.pack("i", hparams["num_attention_heads"])) | ||
| fout.write(struct.pack("i", 0)) | ||
| fout.write(struct.pack("i", hparams["num_hidden_layers"])) | ||
| fout.write(struct.pack("i", 0)) | ||
| fout.write(struct.pack("i", ftype)) | ||
| fout.write(struct.pack("i", hparams["model_max_length"])) | ||
| fout.write(struct.pack("f", 0)) | ||
| fout.write(struct.pack("f", 0)) | ||
| fout.write(struct.pack("i", 0)) | ||
|
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| fout.write(struct.pack("i", 0)) # word_embed_proj_dim (for opt) | ||
| fout.write(struct.pack("i", 0)) # do_layer_norm_before (for opt) | ||
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| fout.write(struct.pack("i", 0)) | ||
| fout.write(struct.pack("i", 0)) | ||
| fout.write(struct.pack("i", hparams["intermediate_size"])) | ||
| fout.write(struct.pack("i", 0)) # n_experts | ||
| fout.write(struct.pack("i", 0)) # n_expert_used | ||
| fout.write(struct.pack("f", hparams.get("rms_norm_eps", 1e-6))) # rms norm eps | ||
| fout.write(struct.pack("f", 10000.0)) # freq_base | ||
| fout.write(struct.pack("f", 1.0)) # rope_factor | ||
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| fout.write(struct.pack("f", 0.0)) # config.json "rope_scaling.factor", not enabled | ||
| fout.write(struct.pack("i", 0)) # rope_scaling.original_max_position_embeddings | ||
| fout.write(struct.pack("i", 0)) # params["rope_scaling"]["type"] =="yarn" else 0)) | ||
|
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| fout.write(struct.pack("i", tokenizer.bos_token_id if tokenizer.bos_token_id is not None else 1)) | ||
| fout.write(struct.pack("i", tokenizer.eos_token_id if tokenizer.eos_token_id is not None else 2)) | ||
| fout.write(struct.pack("i", tokenizer.pad_token_id if tokenizer.pad_token_id is not None else -1)) | ||
| fout.write(struct.pack("i", tokenizer.sep_token_id if tokenizer.sep_token_id is not None else -1)) | ||
|
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| # 2. vocab | ||
| tokenizer_path = Path(tokenizer.vocab_file).parent | ||
| vocab = load_vocab_for_baichuan(Path(tokenizer_path)) | ||
| counter = 0 | ||
| for text, score in vocab.all_tokens(): | ||
| fout.write(struct.pack("i", len(text))) | ||
| fout.write(text) | ||
| fout.write(struct.pack("f", score)) | ||
| counter += 1 | ||
|
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| while counter < hparams["vocab_size"]: | ||
| fout.write(struct.pack("i", len(text))) | ||
| fout.write(text) | ||
| fout.write(struct.pack("f", 0)) | ||
| counter += 1 | ||
|
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| def convert_qwen_to_fp32_tensor(src_name, dst_name, model, fout): | ||
| # qwen-gptq is torch.bfloat16 mostly. | ||
| if model[src_name].dtype == torch.float32: | ||
| data = model[src_name].squeeze().numpy() | ||
| else: | ||
| data = model[src_name].squeeze().to(torch.float32).numpy() | ||
| data = data.astype(np.float32) | ||
| shape = data.shape | ||
| n_dims = len(shape) | ||
| print("convert_qwen_to_fp32_tensor: %40s" % src_name + "-> %-40s" % dst_name + " shape: ", shape, " type: ", | ||
| data.dtype) | ||
|
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| #ftype_cur = {torch.float16: 1, torch.float32: 0}[data.dtype] | ||
| # default type is fp32 | ||
| ftype_cur = 0 | ||
| if ftype == 1 and n_dims > 1: | ||
| data = data.astype(np.float16) | ||
| ftype_cur = 1 | ||
| else: | ||
| data = data.astype(np.float32) | ||
|
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| # header | ||
| # write_header(fout, shape, dst_name, ftype_cur) | ||
| str = src_name.encode('utf-8') | ||
| fout.write(struct.pack("iii", n_dims, len(str), ftype_cur)) | ||
| for i in range(n_dims): | ||
| fout.write(struct.pack("i", data.shape[n_dims - 1 - i])) | ||
| fout.write(str) | ||
|
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||
| # data | ||
| data.tofile(fout) | ||
|
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| #3. write tensors | ||
| convert_qwen_to_fp32_tensor("model.embed_tokens.weight", "model.embed_tokens.weight", list_vars, fout) | ||
| convert_qwen_to_fp32_tensor("model.norm.weight", "model.norm.weight", list_vars, fout) | ||
| convert_qwen_to_fp32_tensor("lm_head.weight", "lm_head.weight", list_vars, fout) | ||
|
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| for i in range(hparams["num_hidden_layers"]): | ||
| prefix = "model.layers." + str(i) | ||
|
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| convert_qwen_to_fp32_tensor(f"{prefix}.input_layernorm.weight", f"{prefix}.input_layernorm.weight", list_vars, | ||
| fout) | ||
| convert_qwen_to_fp32_tensor(f"{prefix}.post_attention_layernorm.weight", | ||
| f"{prefix}.post_attention_layernorm.weight", list_vars, fout) | ||
| # qkv GEMM | ||
| convert_to_qx_bestla_tensor(f"{prefix}.self_attn.W_pack.weight", f"{prefix}.self_attn.W_pack.weight", list_vars, | ||
| fout, quantize_config) | ||
| convert_to_qx_bestla_tensor(f"{prefix}.self_attn.o_proj.weight", f"{prefix}.self_attn.o_proj.weight", list_vars, | ||
| fout, quantize_config) | ||
|
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| # ffn GEMM | ||
| convert_to_qx_bestla_tensor(f"{prefix}.mlp.gate_proj", f"{prefix}.mlp.gate_proj.weight", list_vars, fout, | ||
| quantize_config) | ||
| convert_to_qx_bestla_tensor(f"{prefix}.mlp.down_proj", f"{prefix}.mlp.down_proj.weight", list_vars, fout, | ||
| quantize_config) | ||
| convert_to_qx_bestla_tensor(f"{prefix}.mlp.up_proj", f"{prefix}.mlp.up_proj.weight", list_vars, fout, | ||
| quantize_config) | ||
|
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| fout.close() | ||
| print(f"Success! saved as {out_path}") | ||
|
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|
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| if __name__ == '__main__': | ||
| main() |
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