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[WIP]Use float64 to reduce differences between cpu and gpu result in model test #7114

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[WIP]Use float64 to reduce differences between cpu and gpu result in model test #7114

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0c43c59
Use float64 to reduce differences between cpu and gpu result
YosuaMichael Jan 19, 2023
55a25eb
Use torch.no_grad to save memory
YosuaMichael Jan 26, 2023
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15 changes: 9 additions & 6 deletions test/test_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -683,16 +683,18 @@ def test_classification_model(model_fn, dev):
real_image = kwargs.pop("real_image", False)

model = model_fn(**kwargs)
model.eval().to(device=dev)
x = _get_image(input_shape=input_shape, real_image=real_image, device=dev)
out = model(x)
# We use float64 (.double()) to reduce differences between cpu and gpu result
model.eval().to(device=dev).double()
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Suggested change
model.eval().to(device=dev).double()
model.eval().to(device=dev, dtype=torch.double)

x = _get_image(input_shape=input_shape, real_image=real_image, device=dev).double()
with torch.no_grad(), freeze_rng_state():
out = model(x)
_assert_expected(out.cpu(), model_name, prec=1e-3)
assert out.shape[-1] == num_classes
_check_jit_scriptable(model, (x,), unwrapper=script_model_unwrapper.get(model_name, None), eager_out=out)
_check_fx_compatible(model, x, eager_out=out)

if dev == "cuda":
with torch.cuda.amp.autocast():
with torch.cuda.amp.autocast(), torch.no_grad(), freeze_rng_state():
out = model(x)
# See autocast_flaky_numerics comment at top of file.
if model_name not in autocast_flaky_numerics:
Expand Down Expand Up @@ -782,8 +784,9 @@ def test_detection_model(model_fn, dev):
real_image = kwargs.pop("real_image", False)

model = model_fn(**kwargs)
model.eval().to(device=dev)
x = _get_image(input_shape=input_shape, real_image=real_image, device=dev)
# We use float64 (.double()) to reduce differences between cpu and gpu result
model.eval().to(device=dev).double()
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Suggested change
model.eval().to(device=dev).double()
model.eval().to(device=dev, dtype=torch.double)

x = _get_image(input_shape=input_shape, real_image=real_image, device=dev).double()
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Extend _get_image to also get a dtype arg.

model_input = [x]
with torch.no_grad(), freeze_rng_state():
out = model(model_input)
Expand Down