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# Adopted from tatsu-lab@stanford_alpaca. Below is the original copyright:
# 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
from dataclasses import dataclass, field
import json
import pathlib
from typing import Dict, Optional, Sequence
import numpy as np
import torch
from torch.utils.data import Dataset
import transformers
from transformers import Trainer
from transformers.trainer_pt_utils import LabelSmoother
from fastchat.conversation import get_default_conv_template, SeparatorStyle
IGNORE_TOKEN_ID = LabelSmoother.ignore_index
@dataclass
class ModelArguments:
model_name_or_path: Optional[str] = field(default="facebook/opt-125m")
@dataclass
class DataArguments:
data_path: str = field(
default=None, metadata={"help": "Path to the training data."}
)
lazy_preprocess: bool = False
@dataclass
class TrainingArguments(transformers.TrainingArguments):
cache_dir: Optional[str] = field(default=None)
optim: str = field(default="adamw_torch")
model_max_length: int = field(
default=512,
metadata={
"help": "Maximum sequence length. Sequences will be right padded (and possibly truncated)."
},
)
local_rank = None
def rank0_print(*args):
if local_rank == 0:
print(*args)
def safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output_dir: str):
"""Collects the state dict and dump to disk."""
state_dict = trainer.model.state_dict()
if trainer.args.should_save:
cpu_state_dict = {key: value.cpu() for key, value in state_dict.items()}
del state_dict
trainer._save(output_dir, state_dict=cpu_state_dict) # noqa
def preprocess(
sources,
tokenizer: transformers.PreTrainedTokenizer,
) -> Dict:
conv = get_default_conv_template("vicuna").copy()
roles = {"human": conv.roles[0], "gpt": conv.roles[1]}
# Apply prompt templates
conversations = []
for i, source in enumerate(sources):
if roles[source[0]["from"]] != conv.roles[0]:
# Skip the first one if it is not from human
source = source[1:]
conv.messages = []
for j, sentence in enumerate(source):
role = roles[sentence["from"]]
assert role == conv.roles[j % 2], f"{i}"
conv.append_message(role, sentence["value"])
conversations.append(conv.get_prompt())
# Tokenize conversations
input_ids = tokenizer(
conversations,
return_tensors="pt",
padding="max_length",
max_length=tokenizer.model_max_length,
truncation=True,
).input_ids
targets = input_ids.clone()
assert conv.sep_style == SeparatorStyle.TWO
# Mask targets
sep = conv.sep + conv.roles[1] + ": "
for conversation, target in zip(conversations, targets):
total_len = int(target.ne(tokenizer.pad_token_id).sum())
rounds = conversation.split(conv.sep2)
cur_len = 1
target[:cur_len] = IGNORE_TOKEN_ID
for i, rou in enumerate(rounds):
if rou == "":
break
parts = rou.split(sep)
if len(parts) != 2:
break
parts[0] += sep
round_len = len(tokenizer(rou).input_ids)
instruction_len = len(tokenizer(parts[0]).input_ids) - 2
target[cur_len : cur_len + instruction_len] = IGNORE_TOKEN_ID
cur_len += round_len
target[cur_len:] = IGNORE_TOKEN_ID
if False:
z = target.clone()
z = torch.where(z == IGNORE_TOKEN_ID, tokenizer.unk_token_id, z)
rank0_print(tokenizer.decode(z))
if cur_len < tokenizer.model_max_length:
if cur_len != total_len:
target[:] = IGNORE_TOKEN_ID
rank0_print(
f"WARNING: tokenization mismatch: {cur_len} vs. {total_len}."
f" (ignored)"
)
return dict(
input_ids=input_ids,
labels=targets,
attention_mask=input_ids.ne(tokenizer.pad_token_id),
)
class SupervisedDataset(Dataset):
"""Dataset for supervised fine-tuning."""
def __init__(self, raw_data, tokenizer: transformers.PreTrainedTokenizer):
super(SupervisedDataset, self).__init__()
rank0_print("Formatting inputs...")
sources = [example["conversations"] for example in raw_data]
data_dict = preprocess(sources, tokenizer)
self.input_ids = data_dict["input_ids"]
self.labels = data_dict["labels"]
self.attention_mask = data_dict["attention_mask"]
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],
attention_mask=self.attention_mask[i],
)
class LazySupervisedDataset(Dataset):
"""Dataset for supervised fine-tuning."""
def __init__(self, raw_data, tokenizer: transformers.PreTrainedTokenizer):
super(LazySupervisedDataset, self).__init__()
self.tokenizer = tokenizer
rank0_print("Formatting inputs...Skip in lazy mode")
self.tokenizer = tokenizer
self.raw_data = raw_data
self.cached_data_dict = {}
def __len__(self):
return len(self.raw_data)
def __getitem__(self, i) -> Dict[str, torch.Tensor]:
if i in self.cached_data_dict:
return self.cached_data_dict[i]
ret = preprocess([self.raw_data[i]["conversations"]], self.tokenizer)
ret = dict(
input_ids=ret["input_ids"][0],
labels=ret["labels"][0],
attention_mask=ret["attention_mask"][0],
)
self.cached_data_dict[i] = ret
return ret
def make_supervised_data_module(
tokenizer: transformers.PreTrainedTokenizer, data_args
) -> Dict:
"""Make dataset and collator for supervised fine-tuning."""
dataset_cls = (
LazySupervisedDataset if data_args.lazy_preprocess else SupervisedDataset
)
rank0_print("Loading data...")
raw_data = json.load(open(data_args.data_path, "r"))
# Split train/test
perm = np.random.permutation(len(raw_data))
split = int(len(perm) * 0.98)
train_indices = perm[:split]
eval_indices = perm[split:]
train_raw_data = [raw_data[i] for i in train_indices]
eval_raw_data = [raw_data[i] for i in eval_indices]
rank0_print(f"#train {len(train_raw_data)}, #eval {len(eval_raw_data)}")
train_dataset = dataset_cls(train_raw_data, tokenizer=tokenizer)
eval_dataset = dataset_cls(eval_raw_data, tokenizer=tokenizer)
return dict(train_dataset=train_dataset, eval_dataset=eval_dataset)
def train():
global local_rank
parser = transformers.HfArgumentParser(
(ModelArguments, DataArguments, TrainingArguments)
)
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
local_rank = training_args.local_rank
model = transformers.AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
)
model.config.use_cache = False
tokenizer = transformers.AutoTokenizer.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
model_max_length=training_args.model_max_length,
padding_side="right",
use_fast=False,
)
tokenizer.pad_token = tokenizer.unk_token
data_module = make_supervised_data_module(tokenizer=tokenizer, data_args=data_args)
trainer = Trainer(
model=model, tokenizer=tokenizer, args=training_args, **data_module
)
if list(pathlib.Path(training_args.output_dir).glob("checkpoint-*")):
trainer.train(resume_from_checkpoint=True)
else:
trainer.train()
trainer.save_state()
safe_save_model_for_hf_trainer(trainer=trainer, output_dir=training_args.output_dir)
if __name__ == "__main__":
train()
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