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| Original file line number | Diff line number | Diff line change |
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| # 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. | ||
| # Convert Hugging Face fine-tuned gpt-neox-like models to ne format | ||
| # | ||
| # Usage: | ||
| # | ||
| # python3 models/convert-h5-to-ne.py | ||
| # | ||
| # This script is similar to "convert-pt-to-ne.py" | ||
| # | ||
|
|
||
| import io | ||
| import os | ||
| import sys | ||
| import struct | ||
| import json | ||
| import code | ||
| import torch | ||
| import numpy as np | ||
| from pathlib import Path | ||
| import argparse | ||
| from typing import (IO, TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Literal, Optional, Sequence, Tuple, TypeVar, | ||
| Union) | ||
|
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|
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|
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| # ref: https://github.com/openai/gpt-2/blob/master/src/encoder.py | ||
| def bytes_to_unicode(): | ||
| """ | ||
| Returns list of utf-8 byte and a corresponding list of unicode strings. | ||
| The reversible bpe codes work on unicode strings. | ||
| This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. | ||
| When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. | ||
| This is a significant percentage of your normal, say, 32K bpe vocab. | ||
| To avoid that, we want lookup tables between utf-8 bytes and unicode strings. | ||
| And avoids mapping to whitespace/control characters the bpe code barfs on. | ||
| """ | ||
| bs = list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1)) | ||
| cs = bs[:] | ||
| n = 0 | ||
| for b in range(2**8): | ||
| if b not in bs: | ||
| bs.append(b) | ||
| cs.append(2**8 + n) | ||
| n += 1 | ||
| cs = [chr(n) for n in cs] | ||
| return dict(zip(bs, cs)) | ||
|
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|
|
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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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| dir_model = args.model.as_posix() | ||
| fname_out = args.outfile.as_posix() | ||
|
|
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| # possible data types | ||
| # ftype == 0 -> float32 | ||
| # ftype == 1 -> float16 | ||
| ftype = 0 | ||
| if args.outtype == "f16": | ||
| ftype = 1 | ||
| if args.model_hub == "modelscope": | ||
| from modelscope import AutoModelForCausalLM, AutoTokenizer | ||
| else: | ||
| from transformers import AutoModelForCausalLM, AutoTokenizer | ||
| print("Loading model: ", dir_model) | ||
| model = AutoModelForCausalLM.from_pretrained(dir_model) | ||
| tokenizer = AutoTokenizer.from_pretrained(dir_model) | ||
| model.eval() | ||
| for p in model.parameters(): | ||
| p.requires_grad = False | ||
| hparams = model.config.to_dict() | ||
| print("Model loaded: ", dir_model) | ||
|
|
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| fout = open(fname_out, "wb") | ||
|
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| # 0x67676d6c is unversioned ne | ||
| # 0x67676d66 is versioned ggmf (requires token scores) | ||
| ne_file_magic = 0x67676d66 | ||
| #ne_file_version = 0x00000001 # v1 | ||
|
|
||
| fout.write(struct.pack("i", ne_file_magic)) # magic: ne in hex | ||
| fout.write(struct.pack("i", 1)) | ||
| fout.write(struct.pack("i", hparams["vocab_size"])) | ||
| fout.write(struct.pack("i", hparams["hidden_size"])) | ||
| fout.write(struct.pack("i", hparams["intermediate_size"])) # dummy data | ||
| fout.write(struct.pack("i", hparams["num_attention_heads"])) | ||
| fout.write(struct.pack("i", hparams["num_key_value_heads"])) # multi-query attention | ||
| fout.write(struct.pack("i", hparams["num_hidden_layers"])) | ||
| fout.write(struct.pack("i", hparams["head_dim"])) | ||
| fout.write(struct.pack("i", ftype)) | ||
| fout.write( | ||
| struct.pack("i", hparams["seq_length"] if "seq_length" in hparams else hparams["max_position_embeddings"])) | ||
| fout.write(struct.pack("f", 0.0)) | ||
| fout.write(struct.pack("f", 0.0)) | ||
| fout.write(struct.pack("i", 0)) | ||
| 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", hparams["intermediate_size"])) | ||
| fout.write(struct.pack("i", 0)) | ||
| fout.write(struct.pack("i", 0)) # n_experts | ||
| fout.write(struct.pack("i", 0)) # n_expert_used | ||
| fout.write(struct.pack("i", hparams["head_dim"])) # n_embd_head_k | ||
| 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 | ||
|
|
||
| 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)) | ||
| fout.write(struct.pack("i", hparams["bos_token_id"])) | ||
| fout.write(struct.pack("i", hparams["eos_token_id"])) | ||
| 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)) | ||
|
|
||
| for i in range(hparams["vocab_size"]): | ||
| if i < tokenizer.vocab_size: | ||
| text = tokenizer.decode([i]).encode('utf-8') | ||
| fout.write(struct.pack("i", len(text))) | ||
| fout.write(text) | ||
| fout.write(struct.pack("f", 0.0 - i)) | ||
| else: | ||
| text = tokenizer.decode([tokenizer.vocab_size - 1]).encode('utf-8') | ||
| fout.write(struct.pack("i", len(text))) | ||
| fout.write(text) | ||
| fout.write(struct.pack("f", -10000)) | ||
|
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| list_vars = model.state_dict() | ||
|
|
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| print(hparams) | ||
|
|
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| for name in list_vars.keys(): | ||
| # No gradients for these | ||
| list_vars[name].requires_grad = False | ||
| src = name | ||
| nn = name | ||
|
|
||
| print(src, ' -> ', name) | ||
| data = list_vars[src].squeeze().numpy() | ||
| data = data.astype(np.float32) | ||
|
|
||
| n_dims = len(data.shape) | ||
| print(name, n_dims, data.shape) | ||
|
|
||
| # default type is fp32 | ||
| ftype_cur = 0 | ||
| if ftype == 1 and n_dims > 1: | ||
| print(" Converting to float16", data.shape, data[:3, :3].tolist()) | ||
| data = data.astype(np.float16) | ||
| ftype_cur = 1 | ||
| else: | ||
| print(" Converting to float32", data.shape, data[:3, :3].tolist() if n_dims > 1 else data[:3].tolist()) | ||
| data = data.astype(np.float32) | ||
| # gemma_rms: | ||
| # output = self._norm(x.float()).type_as(x) | ||
| # return output * (1 + self.weight) | ||
| if "norm" in name: | ||
| data = data + 1 | ||
| str = 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])) | ||
| print(str) | ||
| fout.write(str) | ||
|
|
||
| # data | ||
| data.tofile(fout) | ||
|
|
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| fout.close() | ||
|
|
||
| print("Done. Output file: " + fname_out) | ||
| print("") | ||
|
|
||
|
|
||
| if __name__ == '__main__': | ||
| main() |
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