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[fix] fix stage_indices;

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duanjunwen
2024-09-25 06:15:45 +00:00
parent 7e6f793c51
commit fc8b016887
2 changed files with 19 additions and 10 deletions
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26
colossalai/pipeline/schedule/zero_bubble_pp.py
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@@ -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)
output_obj_, _ = tree_flatten(output_obj) # y
output_obj_grad_, _ = tree_flatten(output_obj_grad) # dy
input_obj_, _ = tree_flatten({k: v for k, v in micro_batch.items() if isinstance(v, torch.Tensor)})
output_obj_, _ = tree_flatten({k: v for k, v in output_obj.items() if isinstance(v, torch.Tensor)}) # y
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)
output_obj_, _ = tree_flatten(output_obj) # y
output_obj_grad_, _ = tree_flatten(output_obj_grad) # dy
input_obj_, _ = tree_flatten({k: v for k, v in input_obj.items() if isinstance(v, torch.Tensor)})
output_obj_, _ = tree_flatten({k: v for k, v in output_obj.items() if isinstance(v, torch.Tensor)}) # y
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_grad_, _ = tree_flatten(output_obj_grad) # dy
output_obj_, _ = tree_flatten({k: v for k, v in output_obj.items() if isinstance(v, torch.Tensor)}) # y
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_,