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finetune_scrolls.py
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finetune_scrolls.py
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import copy
import os
from typing import Dict, Optional
import torch
import transformers
from datasets import load_dataset
from model import DataArguments, ModelArguments, TrainingArguments, init_model
from torch.utils.data import Dataset
from tqdm import tqdm
from transformers import Trainer
from data import make_lloco_data_module
from utils import load_jsonl
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
IGNORE_INDEX = -100
truncation_seperator = "... [The rest of the story is omitted]\n\n"
local_rank = None
def rank0_print(*args):
if local_rank == 0:
print(*args)
B_INST, E_INST = "[INST]", "[/INST]"
B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
SPECIAL_TAGS = [B_INST, E_INST, "<<SYS>>", "<</SYS>>"]
quality_prompt = "You are provided a story from above. We will now give you a multiple-choice question with 4 possible answers (marked by A, B, C, D). Choose the best answer by writing its corresponding letter (either A, B, C, or D).\nExample question:\nWhere is the capital of France?\n\nChoices:\nA. Berlin\nB. Paris\nC. London\nD. Tokyo\nChoose the best answer by writing its corresponding letter (either A, B, C, or D).\n\nAnswer:\nB. Paris\n\n"
qasper_prompt = "You are just given an scientific article from above. I will now give you a question. Answer the question as concisely as you can, using a single phrase or sentence if possible. If the question cannot be answered based on the information in the article, write 'unanswerable'. If the question is a yes/no question, answer 'yes', 'no', or 'unanswerable'.\nQuestion: "
qmsum_prompt = "You are given a meeting transcript from above. I will now give you a query containing a question or instruction. Answer the query in one or more sentences. \nQuery: "
nqa_prompt = "You are given a story from above, which can be either a novel or a movie script, and a question. Answer the question as concisely as you can, using a single phrase if possible.\nQuestion:"
gov_prompt = "You are given a report by a government agency. Write a one-page summary of the report."
musique_prompt = "You are given several paragraphs from Wikipedia and a question. Answer the question as concisely as you can, using a single phrase if possible. If the question cannot be answered based on the information in the paragraphs, write 'unanswerable'."
sys_prompts = {
"quality": quality_prompt,
"qasper": qasper_prompt,
"qmsum": qmsum_prompt,
"narrative_qa": nqa_prompt,
"gov_report": gov_prompt,
}
scrolls_datasets = [
"gov_report",
"summ_screen_fd",
"qmsum",
"squality",
"qasper",
"narrative_qa",
"quality",
"musique",
"space_digest",
"book_sum_sort",
]
class LazyScrollsSFTDataset(Dataset):
"""Dataset for supervised fine-tuning."""
def __init__(
self,
tokenizer: transformers.PreTrainedTokenizer,
embedding_path: Optional[str] = None,
dataset_name: str = "qmsum",
split: str = "train",
mode: str = "baseline",
instruction_path: Optional[str] = None,
):
super(LazyScrollsSFTDataset, self).__init__()
assert dataset_name in scrolls_datasets
assert split in ["train", "validation"]
self.instruction_path = instruction_path
# if instruction data is provided, then use it as our dataset.
# otherwise, use the scrolls dataset to generate instruction pairs.
if self.instruction_path is not None:
rank0_print("Loading instruction data from", instruction_path)
self.dataset = load_jsonl(instruction_path)
else:
rank0_print(f"Loading {dataset_name}_{split}")
if split == "validation":
self.dataset = load_dataset("tau/scrolls", dataset_name)["validation"]
self.dataset = self.__preproc_dataset(self.dataset)
print("dataset size:", len(self.dataset))
else:
self.dataset = load_dataset("tau/scrolls", dataset_name)["train"]
if embedding_path is not None:
rank0_print("Loading context embeddings...")
self.context_embeddings_map = torch.load(embedding_path)
self.is_preprocessed = True
else:
rank0_print(
"No context embeddings provided, will use context data instead."
)
self.context_embeddings_map = None
self.is_preprocessed = False
self.tokenizer = tokenizer
self.cached_data_dict = {}
self.is_eval = True if split == "validation" else False
self.mode = mode
self.dataset_name = dataset_name
print("Current mode:", self.mode)
def __preproc_dataset(self, dataset):
ret = []
visited = set()
for data in tqdm(dataset):
example_id = data["id"]
if example_id not in visited:
visited.add(example_id)
ret.append(data)
return ret
def __process_entry(self, entry):
if self.instruction_path is not None:
article_id = entry["article_id"]
question = entry["question"]
answer = entry["answer"] + "\nExplanation:" + entry["explanation"]
context = ""
return article_id, question, answer, context
else:
article_id = entry["id"].split("_")[0]
input = entry["input"]
lines = input.splitlines()
question = lines[0]
context = "\n".join(lines[1:])
answer = entry["output"]
return article_id, question, answer, context
def __len__(self):
return len(self.dataset)
def __getitem__(self, i) -> Dict[str, torch.Tensor]:
entry = self.dataset[i]
article_id, question, answer, context = self.__process_entry(entry)
sys_prompt = sys_prompts[self.dataset_name]
question = sys_prompt + question + "\nAnswer:"
q_input_ids = self.tokenizer(question, add_special_tokens=False).input_ids
a_input_ids = self.tokenizer(answer, add_special_tokens=False).input_ids
a_input_ids += [self.tokenizer.eos_token_id]
if self.mode == "baseline":
context = B_SYS + context + truncation_seperator + E_SYS
c_input_ids = self.tokenizer(
context,
padding="longest",
truncation=True,
add_special_tokens=False,
max_length=4000 - len(q_input_ids),
).input_ids
decoder_input_ids = copy.deepcopy(c_input_ids + q_input_ids)
return {"decoder_input_ids": decoder_input_ids}
elif self.mode == "baseline_nocontext":
decoder_input_ids = copy.deepcopy(q_input_ids)
return {"decoder_input_ids": decoder_input_ids}
decoder_input_ids = copy.deepcopy(q_input_ids)
if not self.is_eval:
decoder_input_ids += a_input_ids
else:
print("----------------Question Prompt----------------\n" + question)
decoder_input_ids = torch.as_tensor(decoder_input_ids)
labels = copy.deepcopy(decoder_input_ids)
labels[: len(q_input_ids)] = IGNORE_INDEX
assert self.is_preprocessed
context_embeddings = self.context_embeddings_map[article_id]
if self.is_eval:
ret = dict(
decoder_input_ids=decoder_input_ids.to(device),
context_embeddings=context_embeddings.to(device),
)
return ret
else:
ret = dict(
input_ids=decoder_input_ids,
labels=labels,
inputs_embeds=context_embeddings,
)
return ret
def get_ground_truth(self, i):
return self.dataset[i]["output"]
def get_example_id(self, i):
return self.dataset[i]["id"]
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
if not os.path.exists(data_args.embedding_path):
rank0_print("Embedding file does not exist...")
exit()
else:
rank0_print("Embedding file exists, skipping preprocessing...")
model = init_model(model_args, data_args, training_args)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_args.model_name_or_path)
tokenizer.pad_token = '[PAD]'
model.config.use_cache = False # required for gradient checkpointing
model.base_model.enable_input_require_grads() # required for gradient checkpointing
model.base_model.gradient_checkpointing_enable() # enable gradient
data_module = make_lloco_data_module(model=model,
tokenizer=tokenizer,
dataset_cls=LazyScrollsSFTDataset,
data_args=data_args,
dataset_name=data_args.dataset_name,
)
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()