[bugfix] fix grpo mllm multi turn

docs/source/Instruction/GRPO/DeveloperGuide/多轮训练.md

@@ -163,6 +163,15 @@ swift rollout \
 
 可以在kwargs中获取 trajectory_inputs 获取完整轨迹的数据，具体实现参考[MultiTurnThinkingTips类](https://github.com/modelscope/ms-swift/blob/main/examples/train/grpo/plugin/plugin.py)
 
+### 多模态数据修改
+在多模态多轮交互场景下，可能需要在对话过程中动态增删或修改多模态数据，并确保这些变更同步至 trainer。
+
+实现方式：借助 rollout_infos，通过指定键值覆盖原始数据集的多模态内容。
+
+现已支持覆盖的键：images、audios、videos。
+
+具体请参考[DeepEyes Schduler](https://github.com/modelscope/ms-swift/blob/main/examples/train/grpo/plugin/deepeyes/deepeyes_plugin.py#L403-L404)
+
 ### 返回 response token ids
 在默认的多轮交互流程中，规划器先把模型生成的文本字符串返回给 trainer，trainer 再将其重新 encode 为 token id，用于后续训练。为了避免这一步重复编码的开销，你可以让规划器直接返回 response_token_ids，省去 trainer 侧的再次 encode。
 

docs/source_en/Instruction/GRPO/DeveloperGuide/multi_turn.md

@@ -172,6 +172,15 @@ The complete trajectory can be accessed via `trajectory_inputs` in `kwargs`.
 
 For a concrete implementation, see the [MultiTurnThinkingTips class](https://github.com/modelscope/ms-swift/blob/main/examples/train/grpo/plugin/plugin.py)
 
+### Multimodal Data Override
+In multimodal, multi-turn interactions, you may need to dynamically add, delete, or modify multimodal data during the conversation and ensure these changes are synchronized to the trainer.
+
+Implementation: Use `rollout_infos` to override the original multimodal content in the dataset by specifying the corresponding keys.
+
+Supported override keys: images, audios, videos.
+
+For details, see [DeepEyes Scheduler](https://github.com/modelscope/ms-swift/blob/main/examples/train/grpo/plugin/deepeyes/deepeyes_plugin.py#L403-L404).
+
 ### Returning response token IDs
 
 In the default workflow the scheduler returns text, the trainer re-encodes it to token IDs for training.

examples/train/grpo/plugin/deepeyes/deepeyes_plugin.py

@@ -400,6 +400,7 @@ def step(self, infer_request, response_choice, current_turn):
         infer_request.messages.append({'role': 'user', 'content': query})
         if cropped_img:
             infer_request.images.append(cropped_img)
+        # override the images
         extra_info['images'] = infer_request.images
 
         # Return dictionary format according to new MultiTurnScheduler interface

swift/trainers/rlhf_trainer/grpo_trainer.py

@@ -1613,12 +1613,7 @@ def _compute_loss_chunked(self, model, inputs: DataType):
             end_idx = min(start_idx + new_chunk_size, batch_size)
 
             if start_idx < batch_size:
-                # Create chunk inputs
-                for key, value in inputs.items():
-                    if isinstance(value, torch.Tensor):
-                        chunk_inputs[key] = value[start_idx:end_idx]
-                    else:
-                        chunk_inputs[key] = value
+                chunk_inputs = self.get_chunked_inputs(inputs, start_idx, end_idx)
 
             # Compute loss and metrics for this chunk (without updating global metrics)
             chunk_loss, chunk_metrics_data = self._compute_loss_and_metrics(model, chunk_inputs)
@@ -1862,26 +1857,6 @@ def _get_per_token_logps_and_entropies_chunked(self,
             ``False``.
         """
 
-        def get_chunked_inputs(inputs, start_idx, end_idx):
-            chunk_inputs = {}
-            if not self.is_multimodal:
-                # for LLM, slice the inputs
-                for key, val in inputs.items():
-                    if isinstance(val, torch.Tensor):
-                        chunk_inputs[key] = val[start_idx:end_idx]
-                    else:
-                        chunk_inputs[key] = val
-            else:
-                # for MLLM, re-encode to get mm-related inputs
-                origin_data = inputs['_origin_data'][start_idx:end_idx]
-                template = self.template
-                with self._template_context(template):
-                    chunk_inputs = [template.encode(data) for data in origin_data]
-                    chunk_inputs = to_device(template.data_collator(chunk_inputs), self.model.device)
-                    chunk_inputs['logits_to_keep'] = inputs['logits_to_keep']
-                    chunk_inputs.pop('labels', None)
-            return chunk_inputs
-
         batch_size = inputs['input_ids'].shape[0]
         mode = 'train' if self.model.training else 'eval'
         chunk_size = self.args.per_device_train_batch_size if mode == 'train' else self.args.per_device_eval_batch_size
@@ -1901,7 +1876,7 @@ def get_chunked_inputs(inputs, start_idx, end_idx):
             end_idx = min(start_idx + new_chunk_size, batch_size)
 
             if start_idx < end_idx:
-                chunk_inputs = get_chunked_inputs(inputs, start_idx, end_idx)
+                chunk_inputs = self.get_chunked_inputs(inputs, start_idx, end_idx)
 
             chunk_logps, chunk_entropies = self._get_per_token_logps_and_entropies_single(
                 model, chunk_inputs, compute_entropy)
@@ -2594,6 +2569,14 @@ def merge_output_input_data(input_data: Dict[str, Union[torch.Tensor, Any]], out
             input_data['finish_reason'] = choice.finish_reason
             input_data['is_truncated'] = choice.finish_reason == 'length'
 
+            # Step 5: override multi-modal data from rollout_infos
+            if output.rollout_infos:
+                multi_modal_keys = ['images', 'videos', 'audios']
+                for key in multi_modal_keys:
+                    if key in output.rollout_infos:
+                        input_data[key] = output.rollout_infos[key]
+                        logger.info_once(f'Overriding multi-modal data from rollout_infos for key: {key}')
+
             return input_data
 
         if not self.dynamic_num_samples:
@@ -2892,3 +2875,21 @@ def _get_last_indices(self, request_ids: List[str]) -> torch.Tensor:
         for i, rid in enumerate(request_ids):
             seen[rid] = i
         return torch.tensor(list(seen.values()), dtype=torch.long, device=self.accelerator.device)
+
+    def get_chunked_inputs(self, inputs, start_idx, end_idx):
+        chunk_inputs = {}
+        # for LLM, slice the inputs
+        for key, val in inputs.items():
+            if isinstance(val, torch.Tensor):
+                chunk_inputs[key] = val[start_idx:end_idx]
+            else:
+                chunk_inputs[key] = val
+        if self.is_multimodal:
+            # for MLLM, re-encode to get mm-related inputs
+            origin_data = inputs['_origin_data'][start_idx:end_idx]
+            template = self.template
+            with self._template_context(template):
+                encoded_data = [template.encode(data) for data in origin_data]
+                chunk_inputs.update(to_device(template.data_collator(encoded_data), self.model.device))
+                chunk_inputs.pop('labels', None)
+        return chunk_inputs