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Update AMP imports for PyTorch version compatibility #5454

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Update AMP imports for PyTorch version compatibility #5454

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14 changes: 12 additions & 2 deletions detectron2/engine/train_loop.py
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Original file line number Diff line number Diff line change
Expand Up @@ -469,7 +469,12 @@ def __init__(
)

if grad_scaler is None:
from torch.cuda.amp import GradScaler
if torch.__version__ < "2.4.0":
from torch.cuda.amp import GradScaler
else:
from torch.amp import GradScaler
from functools import partial
GradScaler = partial(GradScaler, device="cuda")

grad_scaler = GradScaler()
self.grad_scaler = grad_scaler
Expand All @@ -482,7 +487,12 @@ def run_step(self):
"""
assert self.model.training, "[AMPTrainer] model was changed to eval mode!"
assert torch.cuda.is_available(), "[AMPTrainer] CUDA is required for AMP training!"
from torch.cuda.amp import autocast
if torch.__version__ < "2.4.0":
from torch.cuda.amp import autocast
else:
from torch.amp import autocast
from functools import partial
autocast = partial(autocast, device_type="cuda")

start = time.perf_counter()
data = next(self._data_loader_iter)
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7 changes: 6 additions & 1 deletion tests/layers/test_blocks.py
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Expand Up @@ -24,7 +24,12 @@ def test_aspp(self):

@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
def test_frozen_batchnorm_fp16(self):
from torch.cuda.amp import autocast
if torch.__version__ < "2.4.0":
from torch.cuda.amp import autocast
else:
from torch.amp import autocast
from functools import partial
autocast = partial(autocast, device_type="cuda")

C = 10
input = torch.rand(1, C, 10, 10).cuda()
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14 changes: 12 additions & 2 deletions tests/modeling/test_model_e2e.py
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Original file line number Diff line number Diff line change
Expand Up @@ -155,7 +155,12 @@ def test_roiheads_inf_nan_data(self):

@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
def test_autocast(self):
from torch.cuda.amp import autocast
if torch.__version__ < "2.4.0":
from torch.cuda.amp import autocast
else:
from torch.amp import autocast
from functools import partial
autocast = partial(autocast, device_type="cuda")

inputs = [{"image": torch.rand(3, 100, 100)}]
self.model.eval()
Expand Down Expand Up @@ -195,7 +200,12 @@ def test_inf_nan_data(self):

@unittest.skipIf(not torch.cuda.is_available(), "CUDA not available")
def test_autocast(self):
from torch.cuda.amp import autocast
if torch.__version__ < "2.4.0":
from torch.cuda.amp import autocast
else:
from torch.amp import autocast
from functools import partial
autocast = partial(autocast, device_type="cuda")

inputs = [{"image": torch.rand(3, 100, 100)}]
self.model.eval()
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