Refactor RewardFn

examples/grpo_math/math.yaml

@@ -23,10 +23,12 @@ buffer:
         prompt_key: 'question'
         response_key: 'gt_answer'
       rollout_args:
-        n: 8
         temperature: 1.0
         logprobs: 0
-    default_workflow_type: 'math_workflow'
+      reward_fn_args:
+        reward_name: math_verify_reward
+    default_workflow_type: 'math_rm_workflow'
+    default_reward_fn_type: 'rm_gallery_reward'
   trainer_input:
     experience_buffer:
       name: math_buffer

pyproject.toml

@@ -52,6 +52,9 @@ data = [
 agent = [
     "agentscope"
 ]
+rm = [
+    "rm-gallery"
+]
 dev = [
     "pre-commit>=2.17.0",
     "black>=23.7.0",

tests/explorer/workflow_test.py

@@ -2,6 +2,7 @@
 """Test for the workflow module"""
 import unittest
 from dataclasses import dataclass
+from typing import Dict, Optional
 from unittest.mock import MagicMock
 
 from tests.tools import get_unittest_dataset_config
@@ -13,6 +14,7 @@
 class MockResponse:
     response_text: str
     reward: float = 0.0
+    metrics: Optional[Dict[str, float]] = None
 
 
 class DummyWorkflow(Workflow):

trinity/buffer/reader/file_reader.py

@@ -294,6 +294,7 @@ def read(
                 format_args=self.meta.format,
                 rollout_args=self.meta.rollout_args,
                 workflow_args=self.meta.workflow_args,
+                reward_fn_args=self.meta.reward_fn_args,
                 is_eval=self.meta.task_type == TaskType.EVAL,
                 reward_fn=reward_fn,
                 raw_task=sample,

trinity/common/config.py

@@ -94,6 +94,7 @@ class StorageConfig:
     default_reward_fn_type: Optional[str] = None
     rollout_args: GenerationConfig = field(default_factory=GenerationConfig)
     workflow_args: dict = field(default_factory=dict)
+    reward_fn_args: dict = field(default_factory=dict)
 
     # get storage from existing experiment
     ray_namespace: Optional[str] = None

trinity/common/rewards/__init__.py

@@ -1,11 +1,23 @@
 # -*- coding: utf-8 -*-
 """Reward functions for RFT"""
 
-from .reward_fn import REWARD_FUNCTIONS, AccuracyReward, FormatReward, RewardFn
+# isort: off
+from .reward_fn import REWARD_FUNCTIONS, RewardFn, RMGalleryFn
+
+from .accuracy_reward import AccuracyReward
+from .countdown_reward import CountDownRewardFn
+from .format_reward import FormatReward
+from .math_reward import MathBoxedRewardFn, MathRewardFn
+
+# isort: on
 
 __all__ = [
     "RewardFn",
+    "RMGalleryFn",
     "REWARD_FUNCTIONS",
     "AccuracyReward",
+    "CountDownRewardFn",
     "FormatReward",
+    "MathRewardFn",
+    "MathBoxedRewardFn",
 ]

trinity/common/rewards/accuracy_reward.py

@@ -1,33 +1,68 @@
-from typing import Any, Callable, Dict, List
+# -*- coding: utf-8 -*-
+"""Accuracy Reward Function Class."""
+from typing import Callable, Optional
 
-from .base import RewardShapper
+from latex2sympy2_extended import NormalizationConfig
+from math_verify import LatexExtractionConfig, parse, verify
 
+from trinity.common.rewards.reward_fn import REWARD_FUNCTIONS, RewardFn
+from trinity.utils.log import get_logger
 
-class AccuracyRewardShapper(RewardShapper):
-    """Shapper for accuracy-based rewards"""
+logger = get_logger(__name__)
 
-    def __init__(
-        self,
-        answer_parser: Callable[[str], str],
-        correct_reward: float = 1.0,
-        incorrect_reward: float = 0.0,
-        kwargs: Dict[str, Any] = {},
-    ):
+
+@REWARD_FUNCTIONS.register_module("accuracy_reward")
+class AccuracyReward(RewardFn):
+    """A reward function that rewards correct answers.
+    Ref: https://github.com/huggingface/open-r1/blob/main/src/open_r1/rewards.py
+    """
+
+    def __init__(self, answer_parser: Optional[Callable[[str], str]] = None):
         self.answer_parser = answer_parser
-        self.correct_reward = correct_reward
-        self.incorrect_reward = incorrect_reward
-        self.response_key = kwargs.get("response", "response")
-        self.truth_key = kwargs.get("ground_truth", "ground_truth")
 
-    def shape(self, sample: Dict[str, Any]) -> Dict[str, Any]:
-        response = sample[self.response_key]
-        truth = sample[self.truth_key]
+    def __call__(  # type: ignore
+        self,
+        response: str,
+        prompt: Optional[str] = None,
+        truth: Optional[str] = None,
+    ) -> dict[str, float]:
+        if self.answer_parser:
+            answer_parsed = self.answer_parser(response)
+            truth_parsed = self.answer_parser(truth)  # type: ignore [arg-type]
 
-        parsed_response = self.answer_parser(response)
-        reward = self.correct_reward if parsed_response == truth else self.incorrect_reward
+        else:
+            truth_parsed = parse(
+                truth,
+                extraction_mode="first_match",
+                extraction_config=[LatexExtractionConfig()],
+            )
+            if len(truth_parsed) == 0:
+                truth_parsed = truth
 
-        sample["accuracy_reward"] = reward
-        return sample
+            answer_parsed = parse(
+                response,
+                extraction_config=[
+                    LatexExtractionConfig(
+                        normalization_config=NormalizationConfig(
+                            nits=False,
+                            malformed_operators=False,
+                            basic_latex=True,
+                            equations=True,
+                            boxed="all",
+                            units=True,
+                        ),
+                        # Ensures that boxed is tried first
+                        boxed_match_priority=0,
+                        try_extract_without_anchor=False,
+                    )
+                ],
+                extraction_mode="first_match",
+            )
 
-    def batch_shape(self, samples: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
-        return [self.shape(sample) for sample in samples]
+        # Reward 1 if the content is the same as the ground truth, 0 otherwise
+        try:
+            reward = float(verify(answer_parsed, truth_parsed))
+        except Exception as e:
+            logger.info(f"verify failed: {e}, answer: {answer_parsed}, gold: {truth_parsed}")
+            reward = 0.0
+        return {"accuracy": reward}

trinity/common/rewards/countdown_reward.py

@@ -0,0 +1,58 @@
+"""Base Reward Function Class."""
+import json
+from typing import Optional
+
+from trinity.common.rewards.reward_fn import REWARD_FUNCTIONS, RewardFn
+from trinity.utils.eval_utils import (
+    evaluate_equation,
+    extract_solution,
+    validate_equation,
+)
+from trinity.utils.log import get_logger
+
+logger = get_logger(__name__)
+
+
+@REWARD_FUNCTIONS.register_module("countdown_reward")
+class CountDownRewardFn(RewardFn):
+    """A reward function that rewards for countdown task.
+    Ref: Jiayi-Pan/TinyZero verl/utils/reward_score/countdown.py
+    """
+
+    def __init__(self):
+        pass
+
+    def __call__(  # type: ignore
+        self,
+        response: str,
+        prompt: Optional[str] = None,
+        truth: Optional[str] = None,
+    ) -> dict[str, float]:
+        truth = json.loads(truth)  # type: ignore
+        target = truth["target"]  # type: ignore
+        numbers = truth["numbers"]  # type: ignore
+
+        solution_str = response
+        equation = extract_solution(solution_str=solution_str)
+        format_score = 0.1
+        score = 1.0
+
+        if equation is None:
+            return {"score": 0}
+
+        # Validate equation uses correct numbers
+        if not validate_equation(equation, numbers):
+            return {"score": format_score}
+
+        # Evaluate equation
+        try:
+            result = evaluate_equation(equation)
+            if result is None:
+                return {"score": format_score}
+
+            if abs(result - target) < 1e-5:  # Account for floating point precision
+                return {"score": score}
+            else:
+                return {"score": format_score}
+        except Exception as e:  # noqa: F841
+            return {"score": format_score}

trinity/common/rewards/format_reward.py

@@ -1,29 +1,28 @@
-import re
-from typing import Any, Dict, List
+"""Base Reward Function Class."""
 
-from .base import RewardShapper
+import re
+from typing import Optional
 
+from trinity.common.rewards.reward_fn import REWARD_FUNCTIONS, RewardFn
+from trinity.utils.log import get_logger
 
-class FormatRewardShapper(RewardShapper):
-    """Shapper for format-based rewards"""
+logger = get_logger(__name__)
 
-    def __init__(
-        self, pattern: str, correct_format_reward: float = 1.0, incorrect_format_reward: float = 0.0
-    ):
-        self.pattern = re.compile(pattern, re.DOTALL | re.MULTILINE)
-        self.correct_format_reward = correct_format_reward
-        self.incorrect_format_reward = incorrect_format_reward
 
-    def shape(self, sample: Dict[str, Any]) -> Dict[str, Any]:
-        response = sample["response"]
-        reward = (
-            self.correct_format_reward
-            if self.pattern.match(response)
-            else self.incorrect_format_reward
-        )
+@REWARD_FUNCTIONS.register_module("format_reward")
+class FormatReward(RewardFn):
+    """A reward function that checks if the reasoning process is enclosed within <think> and </think> tags, while the final answer is enclosed within <answer> and </answer> tags.
+    Ref: https://github.com/huggingface/open-r1/blob/main/src/open_r1/rewards.py
+    """
 
-        sample["format_reward"] = reward
-        return sample
+    def __init__(self, pattern: Optional[str] = None):
+        self.pattern = pattern if pattern else r"^<think>\n.*?\n</think>\n<answer>\n.*?\n</answer>$"
 
-    def batch_shape(self, samples: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
-        return [self.shape(sample) for sample in samples]
+    def __call__(  # type: ignore
+        self,
+        response,
+    ) -> dict[str, float]:
+        if re.match(self.pattern, response, re.DOTALL | re.MULTILINE):
+            return {"format_score": 0.1}
+        else:
+            return {"format_score": -0.1}