fixed bug for no-interleave pipeline schedule

megatron/schedules.py

@@ -28,6 +28,10 @@ from megatron.model import DistributedDataParallel as LocalDDP
 from megatron.model import Float16Module
 from megatron.model import ModelType
 
+# >>>
+from lutil import pax, tp, KEY_RANK
+# <<<
+
 def get_forward_backward_func():
     args = get_args()
     if mpu.get_pipeline_model_parallel_world_size() > 1:
@@ -42,19 +46,52 @@ def get_forward_backward_func():
         forward_backward_func = forward_backward_no_pipelining
     return forward_backward_func
 
-def free_output_tensor(output_tensors, deallocate_pipeline_outputs):
+# >>>
+# def free_output_tensor(output_tensors, deallocate_pipeline_outputs):
+#     '''Pseudo-free (i.e., set to scalar) the output tensor's '.data' field.
+
+#     This method should be called right after the output tensor has been
+#     sent to the next pipeline stage. At this point, the output tensor is
+#     only useful for its '.grad_fn' field, and not its '.data'.
+#     '''
+#     # >>>
+#     # raise Exception("hi.")
+#     # <<<
+#     if not deallocate_pipeline_outputs or output_tensors is None:
+#         return
+#     if isinstance(output_tensors, torch.Tensor):
+#         output_tensors = [output_tensors]
+#     for output_tensor in output_tensors:
+#         # >>>
+#         # if output_tensor.nelement() < 10:
+#         #     # raise Exception("interesting.")
+#         #     continue
+#         # <<<
+#         # >>>
+#         # output_tensor.data = torch.cuda.FloatTensor([0])
+#         output_tensor.data = torch.empty(
+#             (1,),
+#             device = torch.cuda.current_device(),
+#             dtype = output_tensor.dtype,
+#         )
+#         # <<<
+# <<<
+def free_output_tensor(out, deallocate_pipeline_outputs):
     '''Pseudo-free (i.e., set to scalar) the output tensor's '.data' field.
 
     This method should be called right after the output tensor has been
     sent to the next pipeline stage. At this point, the output tensor is
     only useful for its '.grad_fn' field, and not its '.data'.
     '''
-    if not deallocate_pipeline_outputs or output_tensors is None:
-        return
-    if isinstance(output_tensors, torch.Tensor):
-        output_tensors = [output_tensors]
-    for output_tensor in output_tensors:
-        output_tensor.data = torch.cuda.FloatTensor([0])
+    assert isinstance(out, torch.Tensor), \
+        "expected Tensor, found %s." % type(out).__name__
+    assert out._base is None, \
+        "counter-productive to free a view of another tensor."
+    out.data = torch.empty(
+        (1,),
+        device = out.device,
+        dtype = out.dtype,
+    )
         
 def custom_backward(output, grad_output):
     '''Directly call C++ autograd engine.
@@ -81,6 +118,8 @@ def custom_backward(output, grad_output):
         )
 
     # Call c++ engine [ see torch/csrc/autograd/python_engine.cpp ]
+    # >>>
+    try:
         Variable._execution_engine.run_backward(
             tensors = (output,),
             grad_tensors = (grad_output,),
@@ -90,6 +129,11 @@ def custom_backward(output, grad_output):
             allow_unreachable=True,
             accumulate_grad=True,
         )
+    except Exception as e:
+        print(">>>> rank = %d. <<<<" % torch.distributed.get_rank())
+        raise e
+    # <<<
+        
 
 def forward_step(forward_step_func, data_iterator, model, input_tensor, losses_reduced):
     """Forward step for passed-in model.
@@ -119,6 +163,14 @@ def forward_step(forward_step_func, data_iterator, model, input_tensor, losses_r
         losses_reduced.append(loss_reduced)
     timers('forward-compute').stop()
 
+    # >>>
+    # if torch.distributed.get_rank() == 4:
+    #     pax(4, {
+    #         "output_tensor" : tp(output_tensor),
+    #         "input_tensor[-1]" : tp(input_tensor[-1]),
+    #     })
+    # <<<
+
     # If T5 model (or other model with encoder and decoder)
     # and in decoder stack, then send encoder_hidden_state
     # downstream as well.
@@ -165,6 +217,9 @@ def backward_step(optimizer, input_tensor, output_tensor, output_tensor_grad):
     if output_tensor_grad[0] is None:
         output_tensor = optimizer.scale_loss(output_tensor[0])
     if args.deallocate_pipeline_outputs:
+        # >>>
+        # pax(4, {"output_tensor": output_tensor})
+        # <<<
         custom_backward(output_tensor[0], output_tensor_grad[0])
     else:
         torch.autograd.backward(output_tensor[0],
@@ -617,7 +672,10 @@ def forward_backward_pipelining_without_interleaving(forward_step_func, data_ite
         if not forward_only:
             input_tensors.append(input_tensor)
             output_tensors.append(output_tensor)
-            free_output_tensor(output_tensor, args.deallocate_pipeline_outputs)
+            # >>>
+            # pax(2, {"output_tensor": output_tensor})
+            # <<<
+            free_output_tensor(output_tensor[0], args.deallocate_pipeline_outputs)
 
     # Before running 1F1B, need to receive first forward tensor.
     # If all microbatches are run in warmup / cooldown phase, then no need to
@@ -646,7 +704,14 @@ def forward_backward_pipelining_without_interleaving(forward_step_func, data_ite
             # Add input_tensor and output_tensor to end of list.
             input_tensors.append(input_tensor)
             output_tensors.append(output_tensor)
-            free_output_tensor(output_tensor, args.deallocate_pipeline_outputs)
+            # >>>
+            # if torch.distributed.get_rank() == 3:
+            #     pax({"output_tensor": output_tensor})
+            # <<<
+            # >>>
+            # free_output_tensor(output_tensor, args.deallocate_pipeline_outputs)
+            free_output_tensor(output_tensor[0], args.deallocate_pipeline_outputs)
+            # <<<
 
             # Pop input_tensor and output_tensor from the start of the list for
             # the backward pass.