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from datasets import load_dataset, Features, Value, ClassLabel, Sequence | |
from peft import LoraConfig, get_peft_model, prepare_model_for_int8_training, TaskType | |
from random import randrange | |
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Seq2SeqTrainer, Seq2SeqTrainingArguments, HfArgumentParser, TrainingArguments | |
from datasets import concatenate_datasets | |
import evaluate | |
import numpy as np | |
import argparse | |
import sys | |
from dataclasses import dataclass, field | |
from src.train_utils import load_instruction_dataset, compute_metrics, postprocess_text, preprocess_function | |
from torch import nn | |
import torch.distributed as dist | |
@dataclass | |
class OtherArgs: | |
""" | |
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. | |
""" | |
train_file_path: str = field( | |
metadata={"help": "Path of training data"} | |
) | |
valid_file_path: str = field( | |
default=None, metadata={"help": "Path of val data"} | |
) | |
if __name__ == "__main__": | |
parser = HfArgumentParser((OtherArgs, TrainingArguments)) | |
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): | |
# If we pass only one argument to the script and it's the path to a json file, | |
# let's parse it to get our arguments. | |
args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) | |
else: | |
args, training_args = parser.parse_args_into_dataclasses() | |
print(args) | |
print("*"*30) | |
print(training_args) | |
dataset = load_instruction_dataset(train_path=args.train_file_path, | |
valid_path=args.valid_file_path) | |
print(f"Train dataset size: {len(dataset['train'])}") | |
print(f"Test dataset size: {len(dataset['valid'])}") | |
model_id = "google/flan-t5-large" | |
tokenizer = AutoTokenizer.from_pretrained(model_id) | |
print(dataset["train"][0]) | |
tokenized_inputs = concatenate_datasets([dataset["train"], dataset["valid"]]).map(lambda x: tokenizer(x["prompt"] + [" "]*(len(x)) + x["input_text"], truncation=True), batched=True, remove_columns=["input_text", "output_text", "prompt"]) | |
max_source_length = max([len(x) for x in tokenized_inputs["input_ids"]]) | |
print(f"Max source length: {max_source_length}") | |
tokenized_targets = concatenate_datasets([dataset["train"], dataset["valid"]]).map(lambda x: tokenizer(x["output_text"], truncation=True), batched=True, remove_columns=["input_text", "output_text", "prompt"]) | |
max_target_length = max([len(x) for x in tokenized_targets["input_ids"]]) | |
print(f"Max target length: {max_target_length}") | |
tokenized_dataset = dataset.map(preprocess_function, | |
fn_kwargs={"tokenizer" : tokenizer, | |
"max_source_length" : max_source_length, | |
"max_target_length" : max_target_length}, | |
batched=True, | |
remove_columns=["prompt", "input_text", "output_text"]) | |
print(f"Keys of tokenized dataset: {list(tokenized_dataset['train'].features)}") | |
metric = evaluate.load("rouge") | |
model = AutoModelForSeq2SeqLM.from_pretrained(model_id, load_in_8bit=True, device_map="auto") | |
lora_config = LoraConfig( | |
r=8, | |
lora_alpha=32, | |
target_modules=["q", "v"], | |
lora_dropout=0.05, | |
bias="none", | |
task_type=TaskType.SEQ_2_SEQ_LM | |
) | |
model = prepare_model_for_int8_training(model) | |
model = get_peft_model(model, lora_config) | |
model.print_trainable_parameters() | |
# we want to ignore tokenizer pad token in the loss | |
label_pad_token_id = -100 | |
# Data collator | |
data_collator = DataCollatorForSeq2Seq( | |
tokenizer, | |
model=model, | |
label_pad_token_id=label_pad_token_id, | |
pad_to_multiple_of=8 | |
) | |
training_args = Seq2SeqTrainingArguments( | |
output_dir=training_args.output_dir, | |
per_device_train_batch_size=2, | |
per_device_eval_batch_size=2, | |
predict_with_generate=True, | |
fp16=False, # Overflows with fp16 | |
learning_rate=1e-3, | |
num_train_epochs=5, | |
# logging & evaluation strategies | |
evaluation_strategy="epoch", | |
save_strategy="no", | |
push_to_hub=False, | |
) | |
trainer = Seq2SeqTrainer( | |
model=model, | |
args=training_args, | |
data_collator=data_collator, | |
train_dataset=tokenized_dataset["train"], | |
eval_dataset=tokenized_dataset["valid"], | |
compute_metrics=lambda x: compute_metrics(tokenizer, metric, x), | |
) | |
trainer.train() | |
# Save LoRA model | |
peft_model_id = training_args.output_dir | |
trainer.model.save_pretrained(peft_model_id) | |
tokenizer.save_pretrained(peft_model_id) |
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