mirror of
https://github.com/deepseek-ai/ESFT
synced 2024-11-21 19:17:39 +00:00
117 lines
4.4 KiB
Python
117 lines
4.4 KiB
Python
import argparse
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import json
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import yaml
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import os
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import random
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import torch
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from torch.utils.data import TensorDataset
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from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer, logging
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from benchmarks import *
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from utils import get_formatted_input_and_target, get_examples_from_buffer_pad
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from esft import to_esft
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from deepseek.modeling_deepseek import DeepseekV2ForCausalLM
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--base_model_path", type=str, required=True)
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parser.add_argument("--expert_config", type=str, required=True)
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parser.add_argument("--train_dataset", type=str, required=True)
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parser.add_argument("--output_dir", type=str, required=True)
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parser.add_argument("--train_config", type=str, required=True)
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parser.add_argument("--wandb_api_key", type=str, required=False)
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args = parser.parse_args()
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expert_config = json.load(open(args.expert_config))
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output_dir = args.output_dir
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base_model_path = args.base_model_path
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config = yaml.safe_load(open(args.train_config))
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os.makedirs(args.output_dir, exist_ok=True)
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seed = config['seed']
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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random.seed(seed)
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if args.wandb_api_key is not None:
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import wandb
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wandb.login(key=args.wandb_api_key)
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# Prepare data
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tokenizer = AutoTokenizer.from_pretrained(base_model_path)
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samples = [json.loads(i) for i in open(f"datasets/train/{args.train_dataset}.jsonl").readlines()]
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buffer = []
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for instance in samples:
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input_ids, target_ids = get_formatted_input_and_target(instance['messages'], tokenizer, -100)
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buffer.append((input_ids, target_ids))
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seq_length = config['seq_length']
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random_concat_ratio = config['random_concat_ratio']
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concated_examples = get_examples_from_buffer_pad(buffer, seq_length, tokenizer, random_concat_ratio)
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dataset = TensorDataset(concated_examples['input_ids'], concated_examples['labels'])
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train_dataset, valid_dataset = torch.utils.data.random_split(dataset, [int(len(dataset) * 0.98), len(dataset) - int(len(dataset) * 0.98)])
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# Training arguments
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training_args = TrainingArguments(
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output_dir=output_dir,
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max_steps=config['steps'],
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per_device_train_batch_size=config['per_device_batch_size'],
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per_device_eval_batch_size=config['per_device_batch_size'],
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warmup_steps=config['warmup_steps'],
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weight_decay=config['weight_decay'],
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logging_dir=f"{output_dir}/logs",
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logging_steps=config['logging_steps'],
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save_steps=config['save_steps'],
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eval_strategy="steps",
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eval_steps=config['eval_steps'],
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gradient_accumulation_steps=config['gradient_accumulation_steps'],
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load_best_model_at_end=True,
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metric_for_best_model="loss",
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greater_is_better=False,
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bf16=True,
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lr_scheduler_type='constant',
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save_total_limit=5,
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learning_rate=config['learning_rate'],
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optim=config['optim'],
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adam_beta1=config['adam_beta1'],
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adam_beta2=config['adam_beta2'],
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gradient_checkpointing=config['gradient_checkpointing'],
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gradient_checkpointing_kwargs={"use_reentrant": False} if config['gradient_checkpointing'] else {}, # if set to True, backward will raise bug
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)
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def data_collator(data):
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input_ids = torch.stack([item[0] for item in data])
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labels = torch.stack([item[1] for item in data])
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return {"input_ids": input_ids, "labels": labels}
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model = DeepseekV2ForCausalLM.from_pretrained(base_model_path, trust_remote_code=True, torch_dtype=torch.bfloat16, attn_implementation="flash_attention_2")
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to_esft(model, expert_config)
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# Initialize Trainer
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trainer = Trainer(
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model=model,
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args=training_args,
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train_dataset=train_dataset,
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eval_dataset=valid_dataset,
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data_collator=data_collator,
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)
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# Training
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if os.path.exists(output_dir) and len(os.listdir(output_dir)) > 1: # has checkpoints already
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trainer.train(resume_from_checkpoint=True)
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else:
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trainer.train()
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# Save the model and tokenizer
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trainer.save_model(output_dir + "/last_checkpoint")
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tokenizer.save_pretrained(output_dir + "/last_checkpoint")
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print("Training complete")
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if __name__ == "__main__":
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main() |