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[Enhancement] Enhance inputs_to_half in DeepSpeedStrategy #1400

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merged 5 commits into from
Nov 2, 2023
Merged

[Enhancement] Enhance inputs_to_half in DeepSpeedStrategy #1400

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06562f8
[Enhancement] Enhance inputs_to_half
fanqiNO1 Oct 25, 2023
bd7d007
[Fix] Fix dtype
fanqiNO1 Nov 2, 2023
19466d1
[Fix] Fix apply_to logic
fanqiNO1 Nov 2, 2023
fc0ef67
Merge branch 'main' into deepspeed-enhance
fanqiNO1 Nov 2, 2023
d878b43
[Enhancement] Enhance example
fanqiNO1 Nov 2, 2023
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3 changes: 3 additions & 0 deletions examples/distributed_training_with_flexible_runner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -94,6 +94,9 @@ def main():
initial_scale_power=15,
),
inputs_to_half=[0],
# bf16=dict(
# enabled=True,
# ),
zero_optimization=dict(
stage=3,
allgather_partitions=True,
Expand Down
10 changes: 7 additions & 3 deletions mmengine/_strategy/deepspeed.py
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Original file line number Diff line number Diff line change
Expand Up @@ -18,7 +18,7 @@
from mmengine.optim import BaseOptimWrapper, _ParamScheduler
from mmengine.registry import (MODEL_WRAPPERS, OPTIM_WRAPPERS, OPTIMIZERS,
STRATEGIES)
from mmengine.utils import digit_version, get_git_hash
from mmengine.utils import apply_to, digit_version, get_git_hash
from .base import BaseStrategy


Expand Down Expand Up @@ -188,18 +188,22 @@ def _cast_inputs_half(self, inputs: Union[list, tuple, dict, None]):
if self._inputs_to_half is None:
return inputs

dtype = next(self.model.parameters()).dtype
if isinstance(inputs, (list, tuple)):
new_inputs = []
for i, v in enumerate(inputs):
if i in self._inputs_to_half:
new_inputs.append(v.half())
new_inputs.append(
apply_to(v, lambda x: hasattr(x, 'to'),
lambda x: x.to(dtype)))
else:
new_inputs.append(v)
return inputs.__class__(new_inputs)
elif isinstance(inputs, dict):
for k, v in inputs.items():
if k in self._inputs_to_half:
inputs[k] = v.half()
inputs[k] = apply_to(v, lambda x: hasattr(x, 'to'),
lambda x: x.to(dtype))
return inputs
else:
raise TypeError('inputs should be list, tuple or dict, '
Expand Down
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