# Adapted From https://github.com/tatsu-lab/stanford_alpaca/blob/main/train.py
# 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 io
import json
import logging
from dataclasses import dataclass
from typing import Dict, Sequence
import torch
import transformers
from torch.utils.data import Dataset
from utils import *
if "A100" in torch.cuda.get_device_name():
from llama_flash_attn_monkey_patch import replace_llama_attn_with_flash_attn
replace_llama_attn_with_flash_attn()
IGNORE_INDEX = -100
PROMPT = """You are an intelligent clinical languge model.
Below is a snippet of patient's discharge summary and a following instruction from healthcare professional.
Write a response that appropriately completes the instruction.
The response should provide the accurate answer to the instruction, while being concise.
[Discharge Summary Begin]
{note}
[Discharge Summary End]
[Instruction Begin]
{question}
[Instruction End]
"""
def jload(f, mode="r"):
"""Load a .json file into a dictionary."""
if not isinstance(f, io.IOBase):
f = open(f, mode=mode)
jdict = json.load(f)
f.close()
return jdict
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 = jload(data_path)
# Preprocess start/end \n in the data
for i in range(len(list_data_dict)):
for k, v in list_data_dict[i].items():
if isinstance(v, str):
list_data_dict[i][k] = v.strip("\n")
logging.warning("Formatting inputs...")
sources = [PROMPT.format_map(example) for example in list_data_dict]
targets = [
f"{example['answer']}{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()
model = transformers.AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
cache_dir=training_args.cache_dir,
)
tokenizer = transformers.AutoTokenizer.from_pretrained(
"decapoda-research/llama-7b-hf",
revision="pr/7",
model_max_length=training_args.model_max_length,
padding_side="right",
use_fast=False,
)
tokenizer = modify_special_tokens(tokenizer)
data_module = make_supervised_data_module(tokenizer=tokenizer, data_args=data_args)
trainer = Trainer(model=model, args=training_args, **data_module)
trainer.train()
trainer.save_state()
trainer.save_model(training_args.output_dir)
if __name__ == "__main__":
train()
