removed several 'assert_viewless_tensor' calls.

megatron/schedules.py

@@ -307,7 +307,6 @@ def forward_backward_pipelining_with_interleaving(forward_step_func, data_iterat
                                      model[model_chunk_id],
                                      input_tensor, losses_reduced)
         output_tensors[model_chunk_id].append(output_tensor)
-        assert_viewless_tensor(output_tensor)
 
         # if forward-only, no need to save tensors for a backward pass
         if forward_only:
@@ -341,7 +340,6 @@ def forward_backward_pipelining_with_interleaving(forward_step_func, data_iterat
     mpu.set_virtual_pipeline_model_parallel_rank(0)
     input_tensors[0].append(
         p2p_communication.recv_forward(tensor_shape, timers=timers))
-    assert_viewless_tensor(input_tensors[0][-1])
     for k in range(num_warmup_microbatches):
         output_tensor = forward_step_helper(k)
 
@@ -373,7 +371,6 @@ def forward_backward_pipelining_with_interleaving(forward_step_func, data_iterat
                         tensor_shape=tensor_shape,
                         timers=timers)
             output_tensor_grads[num_model_chunks-1].append(output_tensor_grad)
-            assert_viewless_tensor(output_tensor_grad)
         else:
             input_tensor = \
                 p2p_communication.send_forward_recv_forward(
@@ -382,7 +379,6 @@ def forward_backward_pipelining_with_interleaving(forward_step_func, data_iterat
                     timers=timers)
         free_output_tensor(output_tensor, args.deallocate_pipeline_outputs)
         input_tensors[next_forward_model_chunk_id].append(input_tensor)
-        assert_viewless_tensor(input_tensor)
 
     # Run 1F1B in steady state.
     for k in range(num_microbatches_remaining):
@@ -452,18 +448,15 @@ def forward_backward_pipelining_with_interleaving(forward_step_func, data_iterat
         # right location.
         if recv_prev:
             input_tensors[next_forward_model_chunk_id].append(input_tensor)
-            assert_viewless_tensor(input_tensor)
         if recv_next:
             output_tensor_grads[next_backward_model_chunk_id].append(
                 output_tensor_grad)
-            assert_viewless_tensor(output_tensor_grad)
 
     # Run cooldown backward passes (flush out pipeline).
     if not forward_only:
         if all_warmup_microbatches:
             output_tensor_grads[num_model_chunks-1].append(
                 p2p_communication.recv_backward(tensor_shape, timers=timers))
-            assert_viewless_tensor(output_tensor_grads[num_model_chunks-1][-1])
         for k in range(num_microbatches_remaining, num_microbatches):
             input_tensor_grad = backward_step_helper(k)
             next_backward_model_chunk_id = get_model_chunk_id(k+1, forward=False)
@@ -478,7 +471,6 @@ def forward_backward_pipelining_with_interleaving(forward_step_func, data_iterat
                     input_tensor_grad, recv_next=recv_next,
                     tensor_shape=tensor_shape,
                     timers=timers))
-            assert_viewless_tensor(output_tensor_grads[next_backward_model_chunk_id][-1])
 
     return losses_reduced
 
@@ -624,8 +616,8 @@ def forward_backward_pipelining_without_interleaving(forward_step_func, data_ite
         send_forward(output_tensor, send_tensor_shapes, timers=timers)
 
         if not forward_only:
-            input_tensors.append(mpu.assert_viewless_tensor(input_tensor))
-            output_tensors.append(mpu.assert_viewless_tensor(output_tensor))
+            input_tensors.append(input_tensor)
+            output_tensors.append(output_tensor)
             free_output_tensor(output_tensor, args.deallocate_pipeline_outputs)
 
     # Before running 1F1B, need to receive first forward tensor.
@@ -653,8 +645,8 @@ def forward_backward_pipelining_without_interleaving(forward_step_func, data_ite
                                            timers=timers)
 
             # Add input_tensor and output_tensor to end of list.
-            input_tensors.append(mpu.assert_viewless_tensor(input_tensor))
-            output_tensors.append(mpu.assert_viewless_tensor(output_tensor))
+            input_tensors.append(input_tensor)
+            output_tensors.append(output_tensor)
             free_output_tensor(output_tensor, args.deallocate_pipeline_outputs)
 
             # Pop input_tensor and output_tensor from the start of the list for