171 lines
7.3 KiB
Python
171 lines
7.3 KiB
Python
# Copyright 2024 the LlamaFactory team.
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#
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# This code is inspired by the CarperAI's trlx library.
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# https://github.com/CarperAI/trlx/blob/v0.7.0/examples/summarize_rlhf/reward_model/reward_model.py
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# MIT License
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#
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# Copyright (c) 2022 CarperAI
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#
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# Permission is hereby granted, free of charge, to any person obtaining a copy
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# of this software and associated documentation files (the "Software"), to deal
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# in the Software without restriction, including without limitation the rights
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# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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# copies of the Software, and to permit persons to whom the Software is
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# furnished to do so, subject to the following conditions:
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#
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# The above copyright notice and this permission notice shall be included in all
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# copies or substantial portions of the Software.
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#
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# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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# SOFTWARE.
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import json
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import os
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from types import MethodType
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from typing import TYPE_CHECKING, Dict, List, Optional, Tuple, Union
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import torch
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from transformers import Trainer
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from ...extras.logging import get_logger
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from ..callbacks import FixValueHeadModelCallback, PissaConvertCallback, SaveProcessorCallback
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from ..trainer_utils import create_custom_optimzer, create_custom_scheduler
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if TYPE_CHECKING:
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from transformers import PreTrainedModel, ProcessorMixin
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from transformers.trainer import PredictionOutput
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from ...hparams import FinetuningArguments
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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 Trainer to compute pairwise loss.
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"""
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def __init__(
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self, finetuning_args: "FinetuningArguments", processor: Optional["ProcessorMixin"], **kwargs
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) -> None:
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super().__init__(**kwargs)
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self.finetuning_args = finetuning_args
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self.can_return_loss = True # override property to return eval_loss
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self.add_callback(FixValueHeadModelCallback)
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if processor is not None:
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self.add_callback(SaveProcessorCallback(processor))
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if finetuning_args.pissa_convert:
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self.add_callback(PissaConvertCallback)
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if finetuning_args.use_badam:
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from badam import BAdamCallback, clip_grad_norm_old_version
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self.accelerator.clip_grad_norm_ = MethodType(clip_grad_norm_old_version, self.accelerator)
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self.add_callback(BAdamCallback)
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def create_optimizer(self) -> "torch.optim.Optimizer":
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if self.optimizer is None:
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self.optimizer = create_custom_optimzer(self.model, self.args, self.finetuning_args)
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return super().create_optimizer()
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def create_scheduler(
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self, num_training_steps: int, optimizer: Optional["torch.optim.Optimizer"] = None
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) -> "torch.optim.lr_scheduler.LRScheduler":
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create_custom_scheduler(self.args, num_training_steps, optimizer)
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return super().create_scheduler(num_training_steps, optimizer)
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def compute_loss(
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self, model: "PreTrainedModel", inputs: Dict[str, torch.Tensor], return_outputs: 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/v4.40.0/src/transformers/trainer.py#L3842
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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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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(self, predict_results: "PredictionOutput") -> 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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