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June 5, 2023 23:22
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# Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li | |
# | |
# 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. | |
import copy | |
import logging | |
from dataclasses import dataclass, field | |
from typing import Dict, Optional, Sequence | |
import torch | |
import transformers | |
import utils | |
from torch.utils.data import Dataset | |
from transformers import Trainer | |
from transformers import GPTNeoXTokenizerFast | |
torch.manual_seed(1337) | |
IGNORE_INDEX = -100 | |
DEFAULT_PAD_TOKEN = "<|padding|>" | |
DEFAULT_EOS_TOKEN = "<|endoftext|>" | |
DEFAULT_BOS_TOKEN = "<|beginningoftext|>" | |
DEFAULT_UNK_TOKEN = "<unk>" | |
PROMPT_DICT = { | |
"prompt_input": ( | |
"### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:" | |
), | |
"prompt_no_input": ( | |
"### Instruction:\n{instruction}\n\n### Response:" | |
), | |
} | |
@dataclass | |
class ModelArguments: | |
model_name_or_path: Optional[str] = field(default="mosaicml/mpt-7b") | |
@dataclass | |
class DataArguments: | |
data_path: str = field(default=None, metadata={"help": "Path to the training data."}) | |
@dataclass | |
class TrainingArguments(transformers.TrainingArguments): | |
cache_dir: Optional[str] = field(default=None) | |
optim: str = field(default="adamw_torch") | |
model_max_length: int = field( | |
default=2048, | |
metadata={"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."}, | |
) | |
def smart_tokenizer_and_embedding_resize( | |
special_tokens_dict: Dict, | |
tokenizer: transformers.PreTrainedTokenizer, | |
model: transformers.PreTrainedModel, | |
): | |
"""Resize tokenizer and embedding. | |
Note: This is the unoptimized version that may make your embedding size not be divisible by 64. | |
""" | |
num_new_tokens = tokenizer.add_special_tokens(special_tokens_dict) | |
model.resize_token_embeddings(len(tokenizer)) | |
if num_new_tokens > 0: | |
input_embeddings = model.get_input_embeddings().weight.data | |
output_embeddings = model.get_output_embeddings().weight.data | |
input_embeddings_avg = input_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) | |
output_embeddings_avg = output_embeddings[:-num_new_tokens].mean(dim=0, keepdim=True) | |
input_embeddings[-num_new_tokens:] = input_embeddings_avg | |
output_embeddings[-num_new_tokens:] = output_embeddings_avg | |
def _tokenize_fn(strings: Sequence[str], tokenizer: transformers.PreTrainedTokenizer) -> Dict: | |
"""Tokenize a list of strings.""" | |
tokenized_list = [ | |
tokenizer( | |
text, | |
return_tensors="pt", | |
padding="longest", | |
max_length=tokenizer.model_max_length, | |
truncation=True, | |
) | |
for text in strings | |
] | |
input_ids = labels = [tokenized.input_ids[0] for tokenized in tokenized_list] | |
input_ids_lens = labels_lens = [ | |
tokenized.input_ids.ne(tokenizer.pad_token_id).sum().item() for tokenized in tokenized_list | |
] | |
return dict( | |
input_ids=input_ids, | |
labels=labels, | |
input_ids_lens=input_ids_lens, | |
labels_lens=labels_lens, | |
) | |
def preprocess( | |
sources: Sequence[str], | |
targets: Sequence[str], | |
tokenizer: transformers.PreTrainedTokenizer, | |
) -> Dict: | |
"""Preprocess the data by tokenizing.""" | |
examples = [s + t for s, t in zip(sources, targets)] | |
examples_tokenized, sources_tokenized = [_tokenize_fn(strings, tokenizer) for strings in (examples, sources)] | |
input_ids = examples_tokenized["input_ids"] | |
labels = copy.deepcopy(input_ids) | |
for label, source_len in zip(labels, sources_tokenized["input_ids_lens"]): | |
label[:source_len] = IGNORE_INDEX | |
return dict(input_ids=input_ids, labels=labels) | |
class SupervisedDataset(Dataset): | |
"""Dataset for supervised fine-tuning.""" | |
def __init__(self, data_path: str, tokenizer: transformers.PreTrainedTokenizer): | |
super(SupervisedDataset, self).__init__() | |
logging.warning("Loading data...") | |
list_data_dict = utils.jload(data_path) | |
logging.warning("Formatting inputs...") | |
prompt_input, prompt_no_input = PROMPT_DICT["prompt_input"], PROMPT_DICT["prompt_no_input"] | |
sources = [ | |
prompt_input.format_map(example) if example.get("input", "") != "" else prompt_no_input.format_map(example) | |
for example in list_data_dict | |
] | |
targets = [f"{example['output']}{tokenizer.eos_token}" for example in list_data_dict] | |
logging.warning("Tokenizing inputs... This may take some time...") | |
data_dict = preprocess(sources, targets, tokenizer) | |
self.input_ids = data_dict["input_ids"] | |
self.labels = data_dict["labels"] | |
def __len__(self): | |
return len(self.input_ids) | |
def __getitem__(self, i) -> Dict[str, torch.Tensor]: | |
return dict(input_ids=self.input_ids[i], labels=self.labels[i]) | |
@dataclass | |
class DataCollatorForSupervisedDataset(object): | |
"""Collate examples for supervised fine-tuning.""" | |
tokenizer: transformers.PreTrainedTokenizer | |
def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]: | |
input_ids, labels = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels")) | |
input_ids = torch.nn.utils.rnn.pad_sequence( | |
input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id | |
) | |
labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX) | |
return dict( | |
input_ids=input_ids, | |
labels=labels, | |
attention_mask=input_ids.ne(self.tokenizer.pad_token_id), | |
) | |
def make_supervised_data_module(tokenizer: transformers.PreTrainedTokenizer, data_args) -> Dict: | |
"""Make dataset and collator for supervised fine-tuning.""" | |
train_dataset = SupervisedDataset(tokenizer=tokenizer, data_path=data_args.data_path) | |
data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer) | |
return dict(train_dataset=train_dataset, eval_dataset=None, data_collator=data_collator) | |
def train(): | |
parser = transformers.HfArgumentParser((ModelArguments, DataArguments, TrainingArguments)) | |
model_args, data_args, training_args = parser.parse_args_into_dataclasses() | |
config = transformers.AutoConfig.from_pretrained('mosaicml/mpt-7b', trust_remote_code=True) | |
config.attn_config['attn_impl'] = 'triton' | |
config.init_device = 'cuda:0' | |
model = transformers.AutoModelForCausalLM.from_pretrained( | |
model_args.model_name_or_path, | |
cache_dir=training_args.cache_dir, | |
trust_remote_code=True, | |
) | |
tokenizer = transformers.AutoTokenizer.from_pretrained( | |
'EleutherAI/gpt-neox-20b', | |
cache_dir=training_args.cache_dir, | |
model_max_length=training_args.model_max_length, | |
padding_side="right", | |
use_fast=True, | |
trust_remote_code=True, | |
) | |
special_tokens_dict = dict() | |
if tokenizer.pad_token is None: | |
special_tokens_dict["pad_token"] = DEFAULT_PAD_TOKEN | |
if tokenizer.eos_token is None: | |
special_tokens_dict["eos_token"] = DEFAULT_EOS_TOKEN | |
if tokenizer.bos_token is None: | |
special_tokens_dict["bos_token"] = DEFAULT_BOS_TOKEN | |
if tokenizer.unk_token is None: | |
special_tokens_dict["unk_token"] = DEFAULT_UNK_TOKEN | |
smart_tokenizer_and_embedding_resize( | |
special_tokens_dict=special_tokens_dict, | |
tokenizer=tokenizer, | |
model=model, | |
) | |
#Tell Trainer not to attempt DataParallel | |
model.is_parallelizable = True | |
model.model_parallel = True | |
data_module = make_supervised_data_module(tokenizer=tokenizer, data_args=data_args) | |
trainer = Trainer(model=model, tokenizer=tokenizer, args=training_args, **data_module) | |
trainer.train() | |
trainer.save_state() | |
trainer.save_model(output_dir=training_args.output_dir) | |
if __name__ == "__main__": | |
train() |
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