Add gdpo reward and reward_score

Author: Rhetee

verl/experimental/reward_loop/reward_manager/gdpo.py

@@ -0,0 +1,94 @@
+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+#
+# 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
+
+from verl import DataProto
+from verl.experimental.reward_loop.reward_manager import register
+from verl.experimental.reward_loop.reward_manager.base import RewardManagerBase
+from verl.utils.reward_score import default_compute_score
+
+
+@register("gdpo")
+class GDPOdRewardManager(RewardManagerBase):
+    """GDPO Reward Manager."""
+
+    def __init__(self, config, tokenizer, compute_score, reward_router_address=None, reward_model_tokenizer=None):
+        super().__init__(config, tokenizer, compute_score)
+        self.compute_score = compute_score or default_compute_score
+        self.is_async_reward_score = inspect.iscoroutinefunction(self.compute_score)
+
+        # GDPO Reward Config
+        self.max_resp_len = config.reward.get("reward_kwargs", {}).get("max_resp_len", None)
+
+        self.reward_router_address = reward_router_address
+        self.reward_model_tokenizer = reward_model_tokenizer
+
+    async def run_single(self, data: DataProto) -> dict:
+        assert len(data) == 1, "Only support single data item"
+        data_item = data[0]
+        response_ids = data_item.batch["responses"]
+        response_length = response_ids.shape[-1]
+        valid_response_length = data_item.batch["attention_mask"][-response_length:].sum()
+        valid_response_ids = response_ids[:valid_response_length]
+
+        data_source = data_item.non_tensor_batch["data_source"]
+        ground_truth = data_item.non_tensor_batch["reward_model"]["ground_truth"]
+        extra_info = data_item.non_tensor_batch.get("extra_info", {})
+
+        response_str = await self.loop.run_in_executor(
+            None, lambda: self.tokenizer.decode(valid_response_ids, skip_special_tokens=True)
+        )
+        extra_reward_kwargs = (
+            {
+                "reward_router_address": self.reward_router_address,
+                "reward_model_tokenizer": self.reward_model_tokenizer,
+            }
+            if self.reward_router_address is not None
+            else {}
+        )
+        if self.is_async_reward_score:
+            result = await self.compute_score(
+                data_source=data_source,
+                solution_str=response_str,
+                ground_truth=ground_truth,
+                extra_info=extra_info,
+                **extra_reward_kwargs,
+            )
+        else:
+            result = await self.loop.run_in_executor(
+                None,
+                lambda: self.compute_score(
+                    data_source=data_source,
+                    solution_str=response_str,
+                    ground_truth=ground_truth,
+                    extra_info=extra_info,
+                    **extra_reward_kwargs,
+                ),
+            )
+
+        reward_extra_info = {}
+
+        score: float
+        if isinstance(result, dict):
+            score = result["score"]
+            for key, value in result.items():
+                reward_extra_info[key] = value
+        else:
+            score = result
+            reward_extra_info["acc"] = score
+
+        reward = score
+
+        return {"reward_score": reward, "reward_extra_info": reward_extra_info}