venv/lib/python3.11/site-packages/trl/trainer/prm_trainer.py

# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import inspect
import os
import textwrap
import warnings
from itertools import chain
from typing import Callable, Optional, Union

import torch
import torch.nn as nn
from accelerate import PartialState
from datasets import Dataset, features
from transformers import (
    BaseImageProcessor,
    DataCollator,
    DataCollatorForTokenClassification,
    FeatureExtractionMixin,
    PreTrainedModel,
    PreTrainedTokenizerBase,
    ProcessorMixin,
    Trainer,
    is_wandb_available,
)
from transformers.trainer_callback import TrainerCallback
from transformers.trainer_utils import EvalPrediction
from transformers.utils import is_peft_available

from .prm_config import PRMConfig
from .utils import compute_accuracy, disable_dropout_in_model, generate_model_card


if is_peft_available():
    from peft import PeftModel, get_peft_model, prepare_model_for_kbit_training

if is_wandb_available():
    import wandb


class PRMTrainer(Trainer):
    """
    Initialize PRMTrainer.

    Args:
        model (`transformers.PreTrainedModel`):
            The model to train, preferably an `AutoModelForTokenClassification`.
        args (`PRMConfig`):
            The arguments to use for training.
        data_collator (`transformers.DataCollator`):
            The data collator to use for training. If None is specified, the default data collator (`DataCollatorForTokenClassification`) will be used
            which will pad the sequences to the maximum length of the sequences in the batch, given a dataset of paired sequences.
        train_dataset (`datasets.Dataset`):
            The dataset to use for training.
        eval_dataset (`datasets.Dataset`):
            The dataset to use for evaluation.
        processing_class (`PreTrainedTokenizerBase` or `BaseImageProcessor` or `FeatureExtractionMixin` or `ProcessorMixin`, *optional*):
            Processing class used to process the data. If provided, will be used to automatically process the inputs
            for the model, and it will be saved along the model to make it easier to rerun an interrupted training or
            reuse the fine-tuned model.
        model_init (`Callable[[], transformers.PreTrainedModel]`):
            The model initializer to use for training. If None is specified, the default model initializer will be used.
        compute_metrics (`Callable[[transformers.EvalPrediction], dict]`, *optional* defaults to `compute_accuracy`):
            The metrics to use for evaluation. If no metrics are specified, the default metric (`compute_accuracy`) will be used.
        callbacks (`list[transformers.TrainerCallback]`):
            The callbacks to use for training.
        optimizers (`tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR]`):
            The optimizer and scheduler to use for training.
        preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`):
            The function to use to preprocess the logits before computing the metrics.
        peft_config (`dict`, defaults to `None`):
            The PEFT configuration to use for training. If you pass a PEFT configuration, the model will be wrapped in a PEFT model.
    """

    _tag_names = ["trl", "prm"]

    def __init__(
        self,
        model: Optional[Union[PreTrainedModel, nn.Module]] = None,
        args: Optional[PRMConfig] = None,
        data_collator: Optional[DataCollator] = None,
        train_dataset: Optional[Dataset] = None,
        eval_dataset: Optional[Union[Dataset, dict[str, Dataset]]] = None,
        processing_class: Optional[
            Union[PreTrainedTokenizerBase, BaseImageProcessor, FeatureExtractionMixin, ProcessorMixin]
        ] = None,
        model_init: Optional[Callable[[], PreTrainedModel]] = None,
        compute_metrics: Optional[Callable[[EvalPrediction], dict]] = None,
        callbacks: Optional[list[TrainerCallback]] = None,
        optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (
            None,
            None,
        ),
        preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None,
        peft_config: Optional[dict] = None,
    ):
        if not is_peft_available() and peft_config is not None:
            raise ValueError(
                "PEFT is not installed and you passed a `peft_config` in the trainer's kwargs, please install it to use the PEFT models"
            )
        elif is_peft_available() and peft_config is not None:
            if not isinstance(model, PeftModel):
                if getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_quantized", False):
                    _supports_gc_kwargs = "gradient_checkpointing_kwargs" in list(
                        inspect.signature(prepare_model_for_kbit_training).parameters
                    )

                    prepare_model_kwargs = {"use_gradient_checkpointing": args.gradient_checkpointing}

                    if not _supports_gc_kwargs and args.gradient_checkpointing_kwargs is not None:
                        warnings.warn(
                            "You passed `gradient_checkpointing_kwargs` in the trainer's kwargs, but your peft version does not support it. "
                            "please update to the latest version of peft to use `gradient_checkpointing_kwargs`."
                        )
                    elif _supports_gc_kwargs and args.gradient_checkpointing_kwargs is not None:
                        prepare_model_kwargs["gradient_checkpointing_kwargs"] = args.gradient_checkpointing_kwargs

                    model = prepare_model_for_kbit_training(model, **prepare_model_kwargs)

                model = get_peft_model(model, peft_config)

        # Disable dropout in the model
        if args.disable_dropout:
            disable_dropout_in_model(model)

        if compute_metrics is None:
            compute_metrics = compute_accuracy

        if data_collator is None:
            if processing_class is None:
                raise ValueError(
                    "A processing_class must be specified when using the default DataCollatorForTokenClassification"
                )
            data_collator = DataCollatorForTokenClassification(processing_class, max_length=args.max_length)

        if "input_ids" not in train_dataset.column_names:
            with PartialState().main_process_first():
                fn_kwargs = {
                    "tokenizer": processing_class,
                    "step_separator": args.step_separator,
                    "max_length": args.max_length,
                    "max_prompt_length": args.max_prompt_length,
                    "max_completion_length": args.max_completion_length,
                    "train_on_last_step_only": args.train_on_last_step_only,
                }
                train_fn_kwargs = {**fn_kwargs, "is_eval": False}
                train_dataset = train_dataset.map(
                    self.tokenize_row,
                    fn_kwargs=train_fn_kwargs,
                    num_proc=args.dataset_num_proc,
                    remove_columns=train_dataset.features,
                    desc="Tokenizing train dataset",
                    features=features.Features(  # needed to avoid map to cast labels to bool
                        {
                            "labels": features.Sequence(features.Value("int64")),
                            "input_ids": features.Sequence(features.Value("int64")),
                        }
                    ),
                )

                eval_fn_kwargs = {**fn_kwargs, "is_eval": True}
                if eval_dataset is not None:
                    eval_dataset = eval_dataset.map(
                        self.tokenize_row,
                        fn_kwargs=eval_fn_kwargs,
                        num_proc=args.dataset_num_proc,
                        remove_columns=eval_dataset.features,
                        desc="Tokenizing eval dataset",
                        features=features.Features(  # needed to avoid map to cast labels to bool
                            {
                                "labels": features.Sequence(features.Value("int64")),
                                "input_ids": features.Sequence(features.Value("int64")),
                            }
                        ),
                    )

        super().__init__(
            model=model,
            args=args,
            data_collator=data_collator,
            train_dataset=train_dataset,
            eval_dataset=eval_dataset,
            processing_class=processing_class,
            model_init=model_init,
            compute_metrics=compute_metrics,
            callbacks=callbacks,
            optimizers=optimizers,
            preprocess_logits_for_metrics=preprocess_logits_for_metrics,
        )

        # Add tags for models that have been loaded with the correct transformers version
        if hasattr(self.model, "add_model_tags"):
            self.model.add_model_tags(self._tag_names)

    @staticmethod
    def tokenize_row(
        features,
        tokenizer,
        step_separator,
        max_length,
        max_prompt_length,
        max_completion_length,
        train_on_last_step_only,
        is_eval,
    ):
        r"""
        Tokenize a row of the dataset.

        Args:
            features (`dict[str, str]`):
                Row of the dataset, should contain the keys `"prompt"`, `"completions"`, and `"labels"`.
            tokenizer (`PreTrainedTokenizerBase`):
                Tokenizer used to process the data.
            step_separator (`str`):
                Separator between steps in the completion.
            max_length (`int` or `None`):
               Maximum length of the sequences (prompt + completion). If `None`, the sequences are not truncated.
            max_prompt_length (`int` or `None`):
                Maximum length of the prompt. If `None`, the prompt is not truncated.
            max_completion_length (`int` or `None`):
                Maximum length of the completion sequences. If `None`, the completion sequences are not truncated.
            train_on_last_step_only (`bool`):
                Whether to train only on the last step. If `True`, the labels are `-100` for all tokens except the last
                token of the completion.
            is_eval (`bool`):
                Whether the function is used to tokenize samples from a training or an evaluation dataset. Used only if `train_on_last_step_only` is set to `True`.

        Returns:
            `dict[str, list[int]]`:
                Tokenized sequences with the keys `"input_ids"`, and `"labels".

        Example:
        ```python
        >>> from transformers import AutoTokenizer
        >>> tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-0.5B")
        >>> features = {"prompt": "Which number is larger, 9.8 or 9.11?",
        ...             "completions": ["11 is greater than 8.",
        ...                             "Hence, 9.11 > 9.8."],
        ...             "labels": [True, False]}
        >>> PRMTrainer.tokenize_row(features, tokenizer, "\n", max_completion_length=None, train_on_last_step_only=False, is_eval=False)
        {'input_ids': [23085, 1372, 374, 8131, 11, 220, 24, 13, 23, 476, 220, 24, 13, 16, 16, 30, 16, 16, 374, 7046, 1091, 220, 23, 13, 198, 39, 763, 11, 220, 24, 13, 16, 16, 861, 220, 24, 13, 23, 13, 198],
         'labels': [-100, -100, -100, -100, -100, -100, -100, -100, 1, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, -100, 0]}
        ```
        """
        # Tokenize the prompt and completions
        prompt_ids = tokenizer(features["prompt"], add_special_tokens=False)["input_ids"]
        completions_ids = [
            tokenizer(completion, add_special_tokens=False)["input_ids"] for completion in features["completions"]
        ]
        if train_on_last_step_only and not is_eval:
            labels = [-100] * (len(features["labels"]) - 1) + [int(features["labels"][-1])]
        else:
            labels = [int(label) for label in features["labels"]]

        # Get the ID of the separator token and add it to the completions
        separator_ids = tokenizer.encode(step_separator, add_special_tokens=False)
        completions_ids = [completion + separator_ids for completion in completions_ids]

        # Create the label
        labels = [[-100] * (len(completion) - 1) + [label] for completion, label in zip(completions_ids, labels)]

        # Join the completions and labels steps
        completion_ids = list(chain(*completions_ids))
        labels = list(chain(*labels))

        if tokenizer.bos_token_id is not None:
            prompt_ids = [tokenizer.bos_token_id] + prompt_ids

        # Truncate prompt and completion sequences
        if max_prompt_length is not None:
            prompt_ids = prompt_ids[-max_prompt_length:]
        if max_completion_length is not None:
            completion_ids = completion_ids[:max_completion_length]
            labels = labels[:max_completion_length]

        input_ids = prompt_ids + completion_ids
        labels = [-100] * len(prompt_ids) + labels

        if max_length is not None:
            input_ids = input_ids[:max_length]
            labels = labels[:max_length]

        return {"input_ids": input_ids, "labels": labels}

    def create_model_card(
        self,
        model_name: Optional[str] = None,
        dataset_name: Optional[str] = None,
        tags: Union[str, list[str], None] = None,
    ):
        """
        Creates a draft of a model card using the information available to the `Trainer`.

        Args:
            model_name (`str` or `None`, *optional*, defaults to `None`):
                Name of the model.
            dataset_name (`str` or `None`, *optional*, defaults to `None`):
                Name of the dataset used for training.
            tags (`str`, `list[str]` or `None`, *optional*, defaults to `None`):
                Tags to be associated with the model card.
        """
        if not self.is_world_process_zero():
            return

        if hasattr(self.model.config, "_name_or_path") and not os.path.isdir(self.model.config._name_or_path):
            base_model = self.model.config._name_or_path
        else:
            base_model = None

        tags = tags or []
        if isinstance(tags, str):
            tags = [tags]

        if hasattr(self.model.config, "unsloth_version"):
            tags.append("unsloth")

        citation = textwrap.dedent("""\
        @article{uesato2022solving,
            title        = {{Solving Math Word Problems With Process- and Outcome-Based Feedback}},
            author       = {Uesato, Jonathan and Kushman, Nate and Kumar, Ramana and Song, Francis and Siegel, Noah and Wang, Lisa and Creswell, Antonia and Irving, Geoffrey and Higgins, Irina},
            year         = 2022,
            journal      = {arXiv preprint arXiv:2211.14275}
        }""")

        model_card = generate_model_card(
            base_model=base_model,
            model_name=model_name,
            hub_model_id=self.hub_model_id,
            dataset_name=dataset_name,
            tags=tags,
            wandb_url=wandb.run.get_url() if is_wandb_available() and wandb.run is not None else None,
            trainer_name="PRM",
            trainer_citation=citation,
            paper_title="Solving math word problems with process-and outcome-based feedback",
        )

        model_card.save(os.path.join(self.args.output_dir, "README.md"))