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using new device-agnostic api instead of old api like torch.cpu or torch.cuda (#134448)

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Pull Request resolved: https://github.com/pytorch/pytorch/pull/134448
Approved by: https://github.com/guangyey, https://github.com/shink, https://github.com/albanD
This commit is contained in:
FFFrog
2024-08-27 21:02:45 +00:00
committed by PyTorch MergeBot
parent 0c7856973b
commit f7467c3b95
1 changed files with 11 additions and 11 deletions
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22
test/test_autocast.py
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@ -45,9 +45,9 @@ class TestAutocastCPU(TestCase):
if add_kwargs is None:
add_kwargs = {}
self.assertFalse(torch.is_autocast_cpu_enabled())
with torch.cpu.amp.autocast(dtype=amp_dtype):
self.assertTrue(torch.is_autocast_cpu_enabled())
self.assertFalse(torch.is_autocast_enabled(device_type="cpu"))
with torch.amp.autocast(device_type="cpu", dtype=amp_dtype):
self.assertTrue(torch.is_autocast_enabled(device_type="cpu"))
out_type = out_type if out_type is not None else run_as_type
output = output_method = None
@ -94,8 +94,8 @@ class TestAutocastCPU(TestCase):
# Compare numerics to Python-side "autocasting" that (we expect) does the same thing
# as the C++-side autocasting, and should be bitwise accurate.
output_to_compare = output if output is not None else output_method
with torch.cpu.amp.autocast(enabled=False):
self.assertFalse(torch.is_autocast_cpu_enabled())
with torch.amp.autocast(device_type="cpu", enabled=False):
self.assertFalse(torch.is_autocast_enabled(device_type="cpu"))
if module is not None and hasattr(module, op):
control = getattr(module, op)(
@ -108,8 +108,8 @@ class TestAutocastCPU(TestCase):
self.assertTrue(type(output_to_compare) == type(control))
comparison = compare(output_to_compare, control)
self.assertTrue(comparison, f"torch.{op} result did not match control")
self.assertTrue(torch.is_autocast_cpu_enabled())
self.assertFalse(torch.is_autocast_cpu_enabled())
self.assertTrue(torch.is_autocast_enabled(device_type="cpu"))
self.assertFalse(torch.is_autocast_enabled(device_type="cpu"))
def args_maybe_kwargs(self, op_with_args):
if len(op_with_args) == 2:
@ -237,7 +237,7 @@ class TestAutocastCPU(TestCase):
m(x, (hx, cx))
# Should be able to run the below case with autocast
with torch.cpu.amp.autocast():
with torch.amp.autocast(device_type="cpu"):
m(x, (hx, cx))
def test_autocast_disabled_with_fp32_dtype(self):
@ -249,7 +249,7 @@ class TestAutocastCPU(TestCase):
op, args, maybe_kwargs = self.args_maybe_kwargs(op_with_args)
with torch.amp.autocast(device_type="cpu"):
generic_autocast_output = getattr(torch, op)(*args, **maybe_kwargs)
with torch.cpu.amp.autocast():
with torch.amp.autocast(device_type="cpu"):
cpu_autocast_output = getattr(torch, op)(*args, **maybe_kwargs)
self.assertEqual(generic_autocast_output, cpu_autocast_output)
@ -346,8 +346,8 @@ class TestAutocastGPU(TestCase):
class TestTorchAutocast(TestCase):
def test_autocast_fast_dtype(self):
gpu_fast_dtype = torch.get_autocast_gpu_dtype()
cpu_fast_dtype = torch.get_autocast_cpu_dtype()
gpu_fast_dtype = torch.get_autocast_dtype(device_type="cuda")
cpu_fast_dtype = torch.get_autocast_dtype(device_type="cpu")
self.assertEqual(gpu_fast_dtype, torch.half)
self.assertEqual(cpu_fast_dtype, torch.bfloat16)