fix qwen2_vl on plain-text data and plain-text-image-mixed data

Author: huangjiaming

verl/models/mcore/model_forward.py

@@ -79,6 +79,8 @@ def gptmodel_forward_qwen2_5_vl(
     assert mpu.get_context_parallel_world_size() == 1, "qwen2_5_vl's context parallel is not accurate yet"
     pre_process = unwrap_model(model).pre_process
     post_process = unwrap_model(model).post_process
+    pixel_values = multi_modal_inputs["pixel_values"].to(input_ids.device) if "pixel_values" in multi_modal_inputs else None
+    image_grid_thw = multi_modal_inputs["image_grid_thw"].to(input_ids.device) if "image_grid_thw" in multi_modal_inputs else None
     if pack_seqs:
         batch_size, seq_len = attention_mask.shape[:2]
         input_ids_rmpad, packed_seq_params = preprocess_packed_seqs(input_ids, attention_mask, pre_process=True)
@@ -88,8 +90,8 @@ def gptmodel_forward_qwen2_5_vl(
             attention_mask=None,
             position_ids=position_ids,
             packed_seq_params=packed_seq_params,
-            pixel_values=multi_modal_inputs["pixel_values"].to(input_ids.device),
-            image_grid_thw=multi_modal_inputs["image_grid_thw"].to(input_ids.device),
+            pixel_values=pixel_values,
+            image_grid_thw=image_grid_thw,
         )
 
         if post_process and logits_processor is not None:
@@ -105,8 +107,8 @@ def gptmodel_forward_qwen2_5_vl(
             input_ids=new_input_ids,
             position_ids=new_position_ids,
             attention_mask=new_attention_mask,
-            pixel_values=multi_modal_inputs["pixel_values"].to(input_ids.device),
-            image_grid_thw=multi_modal_inputs["image_grid_thw"].to(input_ids.device),
+            pixel_values=pixel_values,
+            image_grid_thw=image_grid_thw,
         )
         output = recover_left_padding(output, new_attention_mask, attention_mask, sequence_length, post_process=post_process)
     if value_model and post_process:

verl/workers/actor/megatron_actor.py

@@ -409,7 +409,7 @@ def forward_step(batch_iter, model):
             multi_modal_inputs = {}
             if "multi_modal_inputs" in batch:
                 for key in batch["multi_modal_inputs"][0].keys():
-                    multi_modal_inputs[key] = torch.cat([batch["multi_modal_inputs"][i][key] for i in batch["multi_modal_inputs_idx"]], dim=0)
+                    multi_modal_inputs[key] = torch.cat(list(filter(lambda x: x != None, [batch["multi_modal_inputs"][i].get(key) for i in batch["multi_modal_inputs_idx"]])), dim=0)
             responses = batch["responses"]
             response_length = responses.size(1)
             label = copy.deepcopy(position_ids)