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Fix upcycling state dict conversion for mixed dense/MoE models #3646

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Fix upcycling state dict conversion for mixed dense/MoE models #3646

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10 changes: 8 additions & 2 deletions megatron/core/transformer/moe/upcycling_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -218,7 +218,10 @@ def _convert_key_value(
value = src_dict[key]
new_value = value_process_func(value)
new_key = key.replace(key_replace_old, key_replace_new)
dist_dict[new_key] = new_value.clone() if hasattr(new_value, 'clone') else new_value
if (
new_key in dist_dict
): # Skip keys absent in target (e.g. dense layers in mixed dense/MoE)
dist_dict[new_key] = new_value.clone() if hasattr(new_value, 'clone') else new_value
return

_convert_key_value(
Expand Down Expand Up @@ -257,7 +260,10 @@ def _expand_key_value(
params = value_process_func(param)
for idx in range(num_local_experts):
new_key = key.replace(key_replace_old, key_replace_new).format(idx)
dist_dict[new_key] = params[ep_rank * num_local_experts + idx]
if (
new_key in dist_dict
): # Skip keys absent in target (e.g. dense layers in mixed dense/MoE)
dist_dict[new_key] = params[ep_rank * num_local_experts + idx]
return

if experts_type == ExpertsType.SequentialMLP:
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2 changes: 1 addition & 1 deletion megatron/training/training.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1569,7 +1569,7 @@ def setup_model_and_optimizer(
unwrapped_model,
dense_model_for_upcycling,
load_kwargs={
'model': dense_model_for_upcycling,
'ddp_model': dense_model_for_upcycling,
'optimizer': None,
'opt_param_scheduler': None,
},
Expand Down