text stringlengths 1 93.6k |
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start_epoch = training_state['epoch']
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total_steps_count = training_state['step']
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best_reward = training_state['reward']
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logger.info(f"Resuming from epoch {start_epoch}, step {total_steps_count}")
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else:
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start_epoch = 0
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total_steps_count = 0
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best_reward = float('-inf')
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model, optimizer, dataloader, lr_scheduler, ref_model = accelerator.prepare(
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model, optimizer, dataloader, lr_scheduler, ref_model)
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trainer = GRPOTrainer(
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config=config,
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model=model,
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ref_model=ref_model,
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tokenizer=tokenizer,
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optimizer=optimizer,
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lr_scheduler=lr_scheduler,
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accelerator=accelerator,
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)
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# define reward funcs
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perplexity_reward_func = partial(perplexity_reward, model=ref_model, tokenizer=tokenizer)
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perplexity_reward_func.__name__ = "perplexity_reward"
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reward_funcs = [
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perplexity_reward_func,
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llm_rater_reward,
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repetition_reward,
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length_reward,
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chinese_char_ratio_reward
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]
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max_train_steps = num_epochs * len(dataloader)
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progress_bar = tqdm(range(max_train_steps), desc="Training Steps", disable=not accelerator.is_local_main_process)
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progress_bar.update(total_steps_count) # 更新进度条到恢复的步数
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for epoch in range(start_epoch, num_epochs):
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metrics = {}
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for step, batch in enumerate(dataloader):
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prompts = batch["prompt"]
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prompt_ids = tokenizer(prompts, return_tensors="pt", add_special_tokens=True, padding=True)["input_ids"].to(accelerator.device)
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prompt_len = prompt_ids.shape[1]
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# reject sampling
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gen_config = {
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"max_new_tokens": 512,
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"temperature": 0.7,
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"top_p": 0.9,
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"top_k": 50,
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"repetition_penalty": 1.1,
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"do_sample": True,
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"pad_token_id": tokenizer.pad_token_id,
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"eos_token_id": tokenizer.eos_token_id,
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"use_cache": True,
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"num_beams": 1,
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"num_return_sequences": group_num,
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}
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completions = trainer.generate(
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prompt_ids,
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**gen_config
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)
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torch.cuda.empty_cache()
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completion_ids = completions[:, prompt_len:]
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completion_texts = tokenizer.batch_decode(completion_ids, skip_special_tokens=True) # group_num * batch_size
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# get reward kwargs
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reward_kwargs = {key: [] for key in batch.keys() if key not in ["prompt", "completions"]}
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for key in reward_kwargs:
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for example in batch[key]:
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# Repeat each value in the column for `num_generations` times
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reward_kwargs[key].extend([example] * config.group_num)
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# call reward funcs
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all_rewards = []
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for reward_func in reward_funcs:
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rewards = np.array(reward_func(completion_texts, **reward_kwargs)) # length: group_num * batch_size
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all_rewards.append(rewards) # length: func_num
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all_rewards = np.array(all_rewards) # func_num, group_num * batch_size
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reward_per_func = all_rewards.mean(axis=1) # func_num
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reward_all_funcs = all_rewards.sum(axis=0) # group_num * batch_size
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for i, reward_func in enumerate(reward_funcs):
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reward_func_name = reward_func.__name__
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metrics[f"rewards/{reward_func_name}"] = reward_per_func[i].item()
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# expand prompt_ids, align with length of completions
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prompt_ids = prompt_ids.unsqueeze(1).expand(-1, config.group_num, -1).reshape(-1, prompt_ids.size(-1))
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reward_all_funcs = torch.tensor(reward_all_funcs, device=accelerator.device)
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# grpo step
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trainer.step(
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prompt_ids=prompt_ids,
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completion_ids=completion_ids,
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reward_scores=reward_all_funcs
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)
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total_steps_count += 1
|
current_lr = lr_scheduler.get_last_lr()[0]
|
avg_reward_score = reward_all_funcs.mean().item()
|
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