mirror of
https://github.com/hiyouga/LLaMA-Factory.git
synced 2025-08-28 00:32:48 +08:00
104 lines
4.3 KiB
Python
104 lines
4.3 KiB
Python
import os
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import json
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import torch
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from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
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from transformers import Trainer
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from llmtuner.extras.logging import get_logger
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if TYPE_CHECKING:
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from transformers.trainer import PredictionOutput
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from transformers.modeling_utils import PreTrainedModel
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logger = get_logger(__name__)
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class PairwiseTrainer(Trainer):
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r"""
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Inherits PeftTrainer to compute pairwise loss.
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"""
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def __init__(self, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.can_return_loss = True # override property to return eval_loss
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def compute_loss(
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self,
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model: "PreTrainedModel",
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inputs: Dict[str, torch.Tensor],
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return_outputs: Optional[bool] = False
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) -> Union[torch.Tensor, Tuple[torch.Tensor, List[torch.Tensor]]]:
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r"""
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Computes pairwise loss. The first n examples are chosen and the last n examples are rejected.
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Subclass and override to inject custom behavior.
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Note that the first element will be removed from the output tuple.
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See: https://github.com/huggingface/transformers/blob/v4.30.2/src/transformers/trainer.py#L3509
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"""
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# Compute rewards
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_, _, values = model(**inputs, output_hidden_states=True, return_dict=True)
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unwrapped_model: "PreTrainedModel" = self.accelerator.unwrap_model(self.model)
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if getattr(unwrapped_model.config, "model_type", None) == "chatglm":
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values = torch.transpose(values, 0, 1)
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# Split the inputs and rewards into two parts, chosen and rejected
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batch_size = inputs["input_ids"].size(0) // 2
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chosen_input_ids, rejected_input_ids = inputs["input_ids"][:batch_size], inputs["input_ids"][batch_size:]
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chosen_rewards, rejected_rewards = values[:batch_size], values[batch_size:]
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chosen_scores, rejected_scores = [], []
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# Compute pairwise loss. Only backprop on the different tokens before padding
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# Inspired by: https://github.com/CarperAI/trlx/blob/main/examples/summarize_rlhf/reward_model/reward_model.py
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loss = 0
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for i in range(batch_size):
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chosen_length = (chosen_input_ids[i] != self.tokenizer.pad_token_id).nonzero()[-1] + 1
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rejected_length = (rejected_input_ids[i] != self.tokenizer.pad_token_id).nonzero()[-1] + 1
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check_divergence = (chosen_input_ids[i] != rejected_input_ids[i]).nonzero()
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if len(check_divergence) == 0:
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end_index = chosen_length
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div_index = end_index - 1
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else:
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end_index = max(chosen_length, rejected_length)
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div_index = check_divergence[0]
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assert div_index > 0
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chosen_trunc_rewards = chosen_rewards[i, div_index:end_index]
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rejected_trunc_rewards = rejected_rewards[i, div_index:end_index]
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if return_outputs: # use the score on the last token except pad token for inference
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chosen_scores.append(chosen_rewards[i, chosen_length-1])
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rejected_scores.append(rejected_rewards[i, rejected_length-1])
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loss += -torch.nn.functional.logsigmoid(chosen_trunc_rewards - rejected_trunc_rewards).mean()
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loss = loss / batch_size
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if return_outputs:
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chosen_scores, rejected_scores = torch.stack(chosen_scores), torch.stack(rejected_scores)
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return loss, [loss, chosen_scores, rejected_scores]
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return loss
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def save_predictions(
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self,
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predict_results: "PredictionOutput"
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) -> None:
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r"""
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Saves model predictions to `output_dir`.
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A custom behavior that not contained in Seq2SeqTrainer.
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"""
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if not self.is_world_process_zero():
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return
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output_prediction_file = os.path.join(self.args.output_dir, "generated_predictions.jsonl")
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logger.info(f"Saving prediction results to {output_prediction_file}")
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chosen_scores, rejected_scores = predict_results.predictions
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with open(output_prediction_file, "w", encoding="utf-8") as writer:
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res: List[str] = []
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for c_score, r_score in zip(chosen_scores, rejected_scores):
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res.append(json.dumps({"chosen": round(float(c_score), 2), "rejected": round(float(r_score), 2)}))
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writer.write("\n".join(res))
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