Allow KD loss in val mode for MLM plugin (#331)

Signed-off-by: Asha Anoosheh <[email protected]>

Author: AAnoosheh

modelopt/torch/distill/plugins/megatron.py

@@ -532,10 +532,6 @@ def _set_input_tensor(self, input_tensors: list[Tensor]):
 
     # HACK: Concatenate output tensors when PP>1 so they can be passed between ranks.
     def _forward(self, *args, **kwargs):
-        if not self.training:
-            with self.only_student_forward():
-                return type(self).forward(self, *args, **kwargs)
-
         with torch.no_grad():
             self._teacher_model.eval()
             teacher_output = self._teacher_model(*args, **kwargs)
@@ -551,20 +547,15 @@ def _forward(self, *args, **kwargs):
 
 
 def get_tensor_shapes_adjust_fn_for_distillation(
-    model: torch.nn.Module | list[torch.nn.Module],
-    seq_length: int,
-    micro_batch_size: int,
-    decoder_seq_length: int | None = None,
-    forward_only: bool = False,
+    model: torch.nn.Module | list[torch.nn.Module], **kwargs
 ) -> Callable | None:
     """Return the function to adjust tensor shapes for Distillation in Megatron-Core's forward pass.
 
     Currently only used during non-interleaved pipelining for Distillation.
     Concatenates sizes of student and teacher output tensors for inter-process communication.
     """
     if (
-        forward_only
-        or parallel_state.get_pipeline_model_parallel_world_size() == 1
+        parallel_state.get_pipeline_model_parallel_world_size() == 1
         or parallel_state.get_virtual_pipeline_model_parallel_world_size() is not None
     ):
         return None
@@ -584,20 +575,10 @@ def adjust_tensor_shapes(
         cp_group = parallel_state.get_context_parallel_group()
 
         teacher_recv_tensor_shapes = get_tensor_shapes(
-            seq_length=seq_length,
-            micro_batch_size=micro_batch_size,
-            decoder_seq_length=decoder_seq_length,
-            config=teacher_config,
-            tp_group=tp_group,
-            cp_group=cp_group,
+            config=teacher_config, tp_group=tp_group, cp_group=cp_group, **kwargs
         )
         teacher_send_tensor_shapes = get_tensor_shapes(
-            seq_length=seq_length,
-            micro_batch_size=micro_batch_size,
-            decoder_seq_length=decoder_seq_length,
-            config=teacher_config,
-            tp_group=tp_group,
-            cp_group=cp_group,
+            config=teacher_config, tp_group=tp_group, cp_group=cp_group, **kwargs
         )
         model.set_student_input_tensor_shape(recv_tensor_shapes)