[megatron, worker] fix: use extract_multi_modal_inputs method for handling multi_modal_inputs (volcengine#3641)

Follow up for volcengine#3553

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Without those changes in volcengine#3315, the error when we train the mixture
modal dataset will remain unresolved, so it would be a good idea to add
them back.

```logs
File "verl/workers/actor/megatron_actor.py", line 639, in update_policy
  metric_micro_batch = self.forward_backward_batch(
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "verl/workers/actor/megatron_actor.py", line 587, in forward_backward_batch
  losses_reduced = forward_backward_func(
                   ^^^^^^^^^^^^^^^^^^^^^^
File "megatron/core/pipeline_parallel/schedules.py", line 595, in forward_backward_no_pipelining
  output_tensor, num_tokens = forward_step(
                              ^^^^^^^^^^^^^
File "megatron/core/pipeline_parallel/schedules.py", line 402, in forward_step
  output_tensor, loss_func = forward_step_func(data_iterator, model)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "verl/workers/actor/megatron_actor.py", line 497, in forward_step
  multi_modal_inputs[key] = torch.cat(
                            ^^^^^^^^^^
RuntimeError: torch.cat(): expected a non-empty list of Tensors
```

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Signed-off-by: Hollow Man <[email protected]>

Author: HollowMan6

verl/workers/actor/megatron_actor.py

@@ -491,12 +491,10 @@ def forward_step(batch_iter, model):
 
             multi_modal_inputs = {}
             if "multi_modal_inputs" in batch:
-                for key in batch["multi_modal_inputs"][0].keys():
-                    idxs = batch["multi_modal_inputs_idx"]
-                    mmi = batch["multi_modal_inputs"]
-                    multi_modal_inputs[key] = torch.cat(
-                        [mmi[idx].get(key) for idx in idxs if mmi[idx].get(key) is not None], dim=0
-                    )
+                from verl.utils.model import extract_multi_modal_inputs
+
+                indices = batch.get("multi_modal_inputs_idx", None)
+                multi_modal_inputs = extract_multi_modal_inputs(batch["multi_modal_inputs"], indices)
             responses = batch["responses"]
             response_length = responses.size(1)
             label = position_ids.clone()