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replace warning w/ hard error in AutoUnit's fsdp + torch compile #460

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replace warning w/ hard error in AutoUnit's fsdp + torch compile #460

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17 changes: 9 additions & 8 deletions torchtnt/framework/auto_unit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -194,14 +194,14 @@ def __init__(
if torch_compile_params and strategy.static_graph is True:
# https://dev-discuss.pytorch.org/t/torchdynamo-update-9-making-ddp-work-with-torchdynamo/860
raise RuntimeError(
"Torch compile requires DDPStrategy's static_graph to be False"
"Torch compile requires DDPStrategy's static_graph to be False."
)
module = prepare_ddp(module, self.device, strategy)
elif isinstance(strategy, FSDPStrategy):
if torch_compile_params and strategy.use_orig_params is False:
# as stated here https://pytorch.org/get-started/pytorch-2.0/
rank_zero_warn(
"We recommend setting FSDPStrategy's use_orig_params to True when using torch compile."
raise RuntimeError(
"Torch compile requires FSDPStrategy's use_orig_params to be True, since AOTAutograd needs to be aware of the original parameters."
)
module = prepare_fsdp(
module,
Expand Down Expand Up @@ -435,18 +435,19 @@ def __init__(
if torch_compile_params and strategy.static_graph is True:
# https://dev-discuss.pytorch.org/t/torchdynamo-update-9-making-ddp-work-with-torchdynamo/860
raise RuntimeError(
"Torch compile requires DDPStrategy's static_graph to be False"
"Torch compile requires DDPStrategy's static_graph to be False."
)
module = prepare_ddp(module, self.device, strategy)
elif isinstance(strategy, FSDPStrategy):
if swa_params:
raise RuntimeError(
"Stochastic Weight Averaging is currently not supported with the FSDP strategy"
)
# as stated here https://pytorch.org/get-started/pytorch-2.0/
rank_zero_warn(
"We recommend setting FSDPStrategy's use_original_params to True when using torch compile."
)
if torch_compile_params and strategy.use_orig_params is False:
# as stated here https://pytorch.org/get-started/pytorch-2.0/
raise RuntimeError(
"Torch compile requires FSDPStrategy's use_orig_params to be True, since AOTAutograd needs to be aware of the original parameters."
)
module = prepare_fsdp(module, self.device, strategy, self.precision)
else:
module = module.to(self.device)
Expand Down