[fix] fix stage_indices;

colossalai/pipeline/schedule/zero_bubble_pp.py

@@ -430,7 +430,7 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
         with self.stage_manager.switch_model_chunk_id(model_chunk_id):
             #  fwd calculate
             internal_inputs = {} if input_obj is None else input_obj
-            # internal_inputs["stage_index"] = self.stage_manager.stage_indices[model_chunk_id]
+            internal_inputs["stage_index"] = self.stage_manager.stage_indices[model_chunk_id]
             output_obj = model_forward(model_chunk[model_chunk_id], micro_batch, internal_inputs)
 
             # last layer in model
@@ -480,22 +480,26 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
 
         # For chunk 0 stage 0, use micro_batch as input_obj_
         if model_chunk_id == 0 and self.stage_manager.is_first_stage(ignore_chunk=True):
-            input_obj_, _ = tree_flatten(micro_batch)
+            input_obj_, _ = tree_flatten({k: v for k, v in micro_batch.items() if isinstance(v, torch.Tensor)})
-            output_obj_, _ = tree_flatten(output_obj)  # y
+            output_obj_, _ = tree_flatten({k: v for k, v in output_obj.items() if isinstance(v, torch.Tensor)})  # y
-            output_obj_grad_, _ = tree_flatten(output_obj_grad)  # dy
+            output_obj_grad_, _ = tree_flatten(
+                {k: v for k, v in output_obj_grad.items() if isinstance(v, torch.Tensor)}
+            )  # dy
 
         # For loss backward; output_obj is loss; output_obj_grad should be None
         elif model_chunk_id == 1 and self.stage_manager.is_first_stage(ignore_chunk=True):
             assert output_obj_grad is None
-            input_obj_, _ = tree_flatten(input_obj)
+            input_obj_, _ = tree_flatten({k: v for k, v in input_obj.items() if isinstance(v, torch.Tensor)})
             output_obj_.append(output_obj)  # LOSS
             output_obj_grad_.append(output_obj_grad)  # None
 
         # For other chunk stage, use input_obj as input_obj_;
         else:
-            input_obj_, _ = tree_flatten(input_obj)
+            input_obj_, _ = tree_flatten({k: v for k, v in input_obj.items() if isinstance(v, torch.Tensor)})
-            output_obj_, _ = tree_flatten(output_obj)  # y
+            output_obj_, _ = tree_flatten({k: v for k, v in output_obj.items() if isinstance(v, torch.Tensor)})  # y
-            output_obj_grad_, _ = tree_flatten(output_obj_grad)  # dy
+            output_obj_grad_, _ = tree_flatten(
+                {k: v for k, v in output_obj_grad.items() if isinstance(v, torch.Tensor)}
+            )  # dy
 
         optimizer.backward_by_grad(
             tensor=output_obj_,
@@ -547,8 +551,10 @@ class ZeroBubbleVPipeScheduler(PipelineSchedule):
             output_obj_.append(output_obj)  # LOSS
             output_obj_grad_.append(None)  # None
         else:
-            output_obj_, _ = tree_flatten(output_obj)  # y
+            output_obj_, _ = tree_flatten({k: v for k, v in output_obj.items() if isinstance(v, torch.Tensor)})  # y
-            output_obj_grad_, _ = tree_flatten(output_obj_grad)  # dy
+            output_obj_grad_, _ = tree_flatten(
+                {k: v for k, v in output_obj_grad.items() if isinstance(v, torch.Tensor)}
+            )  # dy
 
         optimizer.backward_by_grad(
             tensor=output_obj_,

tests/test_pipeline/test_schedule/test_zerobubble_pp.py

@@ -39,6 +39,7 @@ def pp_linear_fwd(
     forward,
     data: torch.Tensor = None,
     hidden_states: torch.Tensor = None,
+    stage_index=None,
     stage_mgr: PipelineStageManager = None,
     model_chunk_id: int = None,
 ):
@@ -605,6 +606,8 @@ def run_fwd_bwd_vschedule_with_optim(test_config):
     # input_base = [t.clone() for t in data_iter]
     input_base = {k: v.clone() for k, v in data_iter.items()}
     model_base = deepcopy(model)
+    layers_per_stage = stage_manager.distribute_layers(len(model.layers))
+    stage_manager.stage_indices = stage_manager.get_stage_index(layers_per_stage)
 
     if rank == 0:
         # layer 0 & 7 to chunk 0 on rank0