🐾 Process-supervised RM Trainer

docs/source/_toctree.yml

@@ -44,6 +44,8 @@
       title: PPO
     - local: reward_trainer
       title: Reward
+    - local: prm_trainer
+      title: PRM
     - local: rloo_trainer
       title: RLOO
     - local: sft_trainer

docs/source/dataset_formats.mdx

@@ -254,21 +254,22 @@ stepwise_example = {
 
 Choosing the right dataset type depends on the task you are working on and the specific requirements of the TRL trainer you are using. Below is a brief overview of the dataset types supported by each TRL trainer.
 
-| Trainer                 | Expected dataset type                                                                                  |
-| ----------------------- | ------------------------------------------------------------------------------------------------------ |
-| [`BCOTrainer`]          | [Unpaired preference](#unpaired-preference)                                                            |
-| [`CPOTrainer`]          | [Preference (explicit prompt recommended)](#preference)                                                |
-| [`DPOTrainer`]          | [Preference (explicit prompt recommended)](#preference)                                                |
-| [`GKDTrainer`]          | [Prompt-completion](#prompt-completion)                                                                |
-| [`IterativeSFTTrainer`] | [Unpaired preference](#unpaired-preference)                                                            |
-| [`KTOTrainer`]          | [Unpaired preference](#unpaired-preference) or [Preference (explicit prompt recommended)](#preference) |
-| [`NashMDTrainer`]       | [Prompt-only](#prompt-only)                                                                            |
-| [`OnlineDPOTrainer`]    | [Prompt-only](#prompt-only)                                                                            |
-| [`ORPOTrainer`]         | [Preference (explicit prompt recommended)](#preference)                                                |
-| [`PPOTrainer`]          | Tokenized language modeling                                                                            |
-| [`RewardTrainer`]       | [Preference (implicit prompt recommended)](#preference)                                                |
-| [`SFTTrainer`]          | [Language modeling](#language-modeling)                                                                |
-| [`XPOTrainer`]          | [Prompt-only](#prompt-only)                                                                            |
+| Trainer                   | Expected dataset type                                                                                  |
+| ------------------------- | ------------------------------------------------------------------------------------------------------ |
+| [`BCOTrainer`]            | [Unpaired preference](#unpaired-preference)                                                            |
+| [`CPOTrainer`]            | [Preference (explicit prompt recommended)](#preference)                                                |
+| [`DPOTrainer`]            | [Preference (explicit prompt recommended)](#preference)                                                |
+| [`GKDTrainer`]            | [Prompt-completion](#prompt-completion)                                                                |
+| [`IterativeSFTTrainer`]   | [Unpaired preference](#unpaired-preference)                                                            |
+| [`KTOTrainer`]            | [Unpaired preference](#unpaired-preference) or [Preference (explicit prompt recommended)](#preference) |
+| [`NashMDTrainer`]         | [Prompt-only](#prompt-only)                                                                            |
+| [`OnlineDPOTrainer`]      | [Prompt-only](#prompt-only)                                                                            |
+| [`ORPOTrainer`]           | [Preference (explicit prompt recommended)](#preference)                                                |
+| [`PPOTrainer`]            | Tokenized language modeling                                                                            |
+| [`RewardTrainer`]         | [Preference (implicit prompt recommended)](#preference)                                                |
+| [`SFTTrainer`]            | [Language modeling](#language-modeling)                                                                |
+| [`PRMTrainer`] | [Stepwise supervision](#stepwise-supervision)                                                          |
+| [`XPOTrainer`]            | [Prompt-only](#prompt-only)                                                                            |
 
 <Tip>
 

docs/source/prm_trainer.mdx

@@ -0,0 +1,117 @@
+# PRM
+
+## Overview
+
+Process reward models were proposed in [Solving math word problems with process- and outcome-based feedback](https://huggingface.co/papers/2211.14275) by Jonathan Uesato, Nate Kushman, Ramana Kumar, Francis Song, Noah Siegel, Lisa Wang, Antonia Creswell, Geoffrey Irving, and Irina Higgins.
+
+The abstract from the paper is the following:
+
+> Recent work has shown that asking language models to generate reasoning steps improves performance on many reasoning tasks. When moving beyond prompting, this raises the question of how we should supervise such models: outcome-based approaches which supervise the final result, or process-based approaches which supervise the reasoning process itself? Differences between these approaches might naturally be expected not just in final-answer errors but also in reasoning errors, which can be difficult to detect and are problematic in many real-world domains such as education. We run the first comprehensive comparison between process- and outcome-based approaches trained on a natural language task, GSM8K. We find that pure outcome-based supervision produces similar final-answer error rates with less label supervision. However, for correct reasoning steps we find it necessary to use processbased supervision or supervision from learned reward models that emulate process-based feedback. In total, we improve the previous best results from 16.8% → 12.7% final-answer error and 14.0% → 3.4% reasoning error among final-answer-correct solutions.
+
+This post-training method was contributed by [Gaetan Lopez](https://github.com/gaetanlop), [Lewis Tunstall](https://huggingface.co/lewtun), [Quentin Gallouédec](https://huggingface.co/qgallouedec) and [Agustín Piqueres](https://huggingface.co/plaguss).
+
+
+## Quick start
+
+This example demonstrates how to train a model using the PRM method. We use the [Qwen 0.5B model](https://huggingface.co/Qwen/Qwen2-0.5B) as the base model. We use the stepwise supervision data from the [Math Shepherd dataset](https://huggingface.co/datasets/trl-lib/math_shepherd). You can view the data in the dataset here:
+
+<iframe
+  src="https://huggingface.co/datasets/trl-lib/math_shepherd/embed/viewer/default/train?row=0"
+  frameborder="0"
+  width="100%"
+  height="560px"
+></iframe>
+
+Below is the script to train the model:
+
+```python
+# train_prm.py
+from datasets import load_dataset
+from trl import PRMConfig, PRMTrainer
+from transformers import AutoModelForTokenClassification, AutoTokenizer
+
+model = AutoModelForTokenClassification.from_pretrained("Qwen/Qwen2-0.5B", num_labels=2)
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B")
+train_dataset = load_dataset("trl-lib/math_shepherd", split="train[:10%]")
+
+training_args = PRMConfig(output_dir="Qwen2-0.5B-Reward-Math-Sheperd", logging_steps=10)
+trainer = PRMTrainer(model=model, args=training_args, processing_class=tokenizer, train_dataset=train_dataset)
+trainer.train()
+```
+
+Execute the script using the following command:
+
+```bash
+accelerate launch train_prm.py
+```
+
+Distributed across 8 GPUs, the training takes approximately 1 hour.
+
+To see how the [trained model](https://huggingface.co/trl-lib/Qwen2-0.5B-Reward-Math-Sheperd) performs, you can use the following script.
+
+
+```python
+from datasets import load_dataset
+from transformers import pipeline
+
+pipe = pipeline("token-classification", model="trl-lib/Qwen2-0.5B-Reward-Math-Sheperd")
+dataset = load_dataset("trl-lib/math_shepherd")
+example = {
+    "prompt": "Musa is the class teacher of a class of 45 students. He wants to split them into three groups by age. If a third of the class is under 11 years, and two-fifths are above 11 but under 13, how many students will be in the third group (13 years and above)?",
+    "completions": [
+        "Step 1: A third of the class is under 11 years because 11 - 1/3 = <<11-1/3=7>>7.",
+        "Step 2: Two-fifths of the class are above 11 but under 13 because 2/5 * 11 = <<2/5*11=8>>8.",
+        "Step 3: There are 45 students, so the third group will have 45 - 7 - 8 = <<45-7-8=20>>20 students. The answer is: 20",
+    ],
+    "labels": [True, False, False],
+}
+
+
+separator = "\n"  # It's important to use the same separator as the one used during training
+
+for idx in range(1, len(example["completions"]) + 1):
+    steps = example["completions"][0:idx]
+    text = separator.join((example["prompt"], *steps)) + separator  # Add a separator between the prompt and each steps
+    pred_entity = pipe(text)[-1]["entity"]
+    pred = {"LABEL_0": False, "LABEL_1": True}[pred_entity]
+    label = example["labels"][idx - 1]
+    print(f"Step {idx}\tPredicted: {pred} \tLabel: {label}")
+```
+
+```
+Step 1  Predicted: True         Label: True
+Step 2  Predicted: False        Label: False
+Step 3  Predicted: False        Label: False
+```
+
+It's a win!
+
+## Expected dataset type
+
+Process-supervised reward modeling requires a [stepwise supervision](dataset_formats#stepwise-supervision).
+The dataset should contain the following columns: `prompt`, `completions` and `labels`, where `completions` contains a list of reasoning steps and `labels` a list of booleans or floats indicating the correctness of each step.
+
+The [`PRMTrainer`] only supports [standard](dataset_formats#standard) dataset format.
+
+## Example script
+
+We provide an example script to train a model using the process-supervised reward modeling method. The script is available in [`examples/scripts/prm.py`](https://github.com/huggingface/trl/blob/main/examples/scripts/prm.py)
+
+To use the process-supervised reward modeling script with the [Qwen2 0.5B model](https://huggingface.co/Qwen/Qwen2-0.5B) on the [Math Shepherd dataset](https://huggingface.co/datasets/trl-lib/math_shepherd), run the following command:
+
+```bash
+accelerate launch examples/scripts/prm.py \
+    --model_name_or_path Qwen/Qwen2-0.5B \
+    --dataset_name trl-lib/math_shepherd \
+    --num_train_epochs 1 \
+    --logging_steps 25 \
+    --output_dir Qwen2-0.5B-Reward-Math-Sheperd
+```
+
+## PRMTrainer
+
+[[autodoc]] PRMTrainer
+
+## PRMConfig
+
+[[autodoc]] PRMConfig

examples/datasets/math_shepherd.py

@@ -0,0 +1,131 @@
+# Copyright 2024 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 re
+from dataclasses import dataclass
+from itertools import chain
+from typing import Optional
+
+from datasets import load_dataset
+from transformers import HfArgumentParser
+
+
+@dataclass
+class ScriptArguments:
+    r"""
+    Arguments for the script.
+
+    Args:
+        push_to_hub (`bool`, *optional*, defaults to `False`):
+            Whether to push the dataset to the Hugging Face Hub.
+        repo_id (`str`, *optional*, defaults to `"trl-lib/math_shepherd"`):
+            Hugging Face repository ID to push the dataset to.
+        dataset_num_proc (`Optional[int]`, *optional*, defaults to `None`):
+            Number of workers to use for dataset processing.
+    """
+
+    push_to_hub: bool = False
+    repo_id: str = "trl-lib/math_shepherd"
+    dataset_num_proc: Optional[int] = None
+
+
+def process_example(example):
+    # Replace "ки" with "ⶻ" so that the size of the "input" matches the size of the "label"
+    inputs = example["input"].replace("ки", "ⶻ")
+
+    # Find the indices of the "ⶻ" characters (that should match with the indexes of the "+" or "-" in the label)
+    indexes = [m.start() for m in re.finditer("ⶻ", inputs)]
+
+    # Sanity that all indexes are either "+" or "-"
+    assert all(example["label"][idx] in ["+", "-"] for idx in indexes)
+
+    # Get the labels
+    labels = [example["label"][idx] == "+" for idx in indexes]
+
+    # Split the inputs into steps (caution, the first step is missing here, it is the prompt)
+    steps = [inputs[i:j] for i, j in zip(chain([0], indexes), chain(indexes, [None]))]
+
+    # Remove the last step (single ⶻ)
+    steps = steps[:-1]
+
+    # Get the prompt (first part) and completions (rest)
+    prompt = steps[0]
+    completions = steps[1:]
+
+    # Remove the heading "ⶻ" and the final whitespace from the completions
+    assert all(completion.startswith("ⶻ") for completion in completions)
+    completions = [completion[1:].strip() for completion in completions]
+
+    # At this point, we need to retrieve the first step from the prompt.
+    # First, we handle particular cases (annotation error) where we have a first label before the end of the prompt.
+    if prompt.startswith(
+        (
+            "Mr. Rocky",
+            "Parker",
+            "What is the smallest positive",
+            " The Myth",
+            "Let $\\mathbf{a}$",
+            "Find the arithmetic",
+            "Determine an ordered pair",
+            "Determine the ordered pair",
+            "At the Quill and Scroll stationery",
+            "Round to the nearest",
+            r"Calculate $\sqrt{10p}",
+            r"Simplify $\sqrt{28x}",
+        )
+    ):
+        # Some spotted datasets errors where there is an annotation in the prompt: we remove it
+        labels = labels[1:]
+
+    # Then we handle the general case: we get the first step from the prompt by looking for "Step 1:" or "step 1:" or
+    # (less common) "?".
+    elif "Step 1:" in prompt:
+        prompt, first_step = prompt.split("Step 1:")
+        first_step = "Step 1:" + first_step
+        completions = [first_step.strip()] + completions
+    elif "step 1:" in prompt:
+        prompt, first_step = prompt.split("step 1:")
+        first_step = "step 1:" + first_step
+        completions = [first_step.strip()] + completions
+    elif "?" in prompt:
+        prompt, first_step = prompt.split("?")
+        prompt = prompt + "?"
+        completions = [first_step.strip()] + completions
+    else:
+        raise ValueError(f"Prompt can't be processed: {prompt}")
+
+    # Strip the prompt
+    prompt = prompt.strip()
+
+    # Sanity check that the length of the completions is the same as the length of the labels
+    assert len(completions) == len(labels)
+
+    return {"prompt": prompt, "completions": completions, "labels": labels}
+
+
+if __name__ == "__main__":
+    parser = HfArgumentParser(ScriptArguments)
+    script_args = parser.parse_args_into_dataclasses()[0]
+
+    dataset = load_dataset("peiyi9979/Math-Shepherd", split="train")
+
+    dataset = dataset.map(
+        process_example,
+        remove_columns=["input", "label", "task"],
+        num_proc=script_args.dataset_num_proc,
+    )
+    dataset = dataset.train_test_split(test_size=0.05, seed=42)
+
+    if script_args.push_to_hub:
+        dataset.push_to_hub(script_args.repo_id)