[BE]: Enable ruff rule TRY302 and apply fixes (#101874)
Removes useless try statements and unreachable code.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/101874
Approved by: https://github.com/malfet
diff --git a/test/distributed/test_dynamo_distributed.py b/test/distributed/test_dynamo_distributed.py
index 31a36e1..dc74b46 100644
--- a/test/distributed/test_dynamo_distributed.py
+++ b/test/distributed/test_dynamo_distributed.py
@@ -150,8 +150,6 @@
DDP._active_ddp_module = self
try:
yield
- except Exception:
- raise
finally:
DDP._active_ddp_module = None
diff --git a/test/onnx/verify.py b/test/onnx/verify.py
index ac6f374..0dca467 100644
--- a/test/onnx/verify.py
+++ b/test/onnx/verify.py
@@ -66,13 +66,9 @@
At the moment, only tests on "numpy.ndarray" are supported.
"""
if isinstance(x, np.ndarray) and isinstance(y, np.ndarray):
- try:
- np.testing.assert_allclose(
- x, y, rtol=self.rtol, atol=self.atol, equal_nan=True, verbose=True
- )
- except AssertionError as e:
- raise
- k(f"{colonize(msg)}{str(e).lstrip()}")
+ np.testing.assert_allclose(
+ x, y, rtol=self.rtol, atol=self.atol, equal_nan=True, verbose=True
+ )
else:
raise RuntimeError("Unsupported almost equal test")
@@ -105,11 +101,7 @@
new_msg = f"{colonize(msg)}In embedded parameter '{x.name}'"
self.equalAndThen(t1, t2, new_msg, k)
elif isinstance(x, np.ndarray) and isinstance(y, np.ndarray):
- try:
- np.testing.assert_equal(x, y)
- except AssertionError as e:
- raise
- k("{}{}".format(colonize(msg, ": "), str(e).lstrip()))
+ np.testing.assert_equal(x, y)
else:
if x != y:
# TODO: Better algorithm for lists
diff --git a/test/test_dynamic_shapes.py b/test/test_dynamic_shapes.py
index 4689795..2c30304 100644
--- a/test/test_dynamic_shapes.py
+++ b/test/test_dynamic_shapes.py
@@ -559,15 +559,12 @@
lambda_apply = getattr(operator, fn)
def guard_fn(v):
- try:
- if type(v) in (SymBool, bool):
- return guard_bool(v)
- elif type(v) in (SymFloat, float):
- return guard_float(v)
- else: # SymInt, int
- return guard_int(v)
- except Exception as e:
- raise e
+ if type(v) in (SymBool, bool):
+ return guard_bool(v)
+ elif type(v) in (SymFloat, float):
+ return guard_float(v)
+ else: # SymInt, int
+ return guard_int(v)
# Get reference result
with maybe_xfail(inp1, inp2):
diff --git a/test/test_mps.py b/test/test_mps.py
index d6e0564..1629da3 100644
--- a/test/test_mps.py
+++ b/test/test_mps.py
@@ -10487,52 +10487,47 @@
# Forward check
#
forward_failed = False
- try:
- mps_sample = cpu_sample.transform(
- lambda x: x.detach().to("mps").requires_grad_(x.requires_grad) if isinstance(x, torch.Tensor) else x)
+ mps_sample = cpu_sample.transform(
+ lambda x: x.detach().to("mps").requires_grad_(x.requires_grad) if isinstance(x, torch.Tensor) else x)
- cpu_args = [cpu_sample.input] + list(cpu_sample.args)
- cpu_kwargs = cpu_sample.kwargs
- mps_args = [mps_sample.input] + list(mps_sample.args)
- mps_kwargs = mps_sample.kwargs
+ cpu_args = [cpu_sample.input] + list(cpu_sample.args)
+ cpu_kwargs = cpu_sample.kwargs
+ mps_args = [mps_sample.input] + list(mps_sample.args)
+ mps_kwargs = mps_sample.kwargs
- # for tensor_split(), the second tensor arg ("tensor_indices_or_sections") must be on CPU only
- if (op.name == "tensor_split" and isinstance(mps_args[1], torch.Tensor)):
- mps_args[1] = cpu_args[1]
+ # for tensor_split(), the second tensor arg ("tensor_indices_or_sections") must be on CPU only
+ if (op.name == "tensor_split" and isinstance(mps_args[1], torch.Tensor)):
+ mps_args[1] = cpu_args[1]
- cpu_out = op(*cpu_args, **cpu_kwargs)
- mps_out = op(*mps_args, **mps_kwargs)
+ cpu_out = op(*cpu_args, **cpu_kwargs)
+ mps_out = op(*mps_args, **mps_kwargs)
- if (op.name in self.FP32_LOW_PRECISION_LIST) and dtype == torch.float32:
- atol = 1e-4
- rtol = 3e-5
- elif op.name == "nn.functional.conv2d" or op.name == "linalg.multi_dot" and dtype == torch.float32:
- atol = 1e-4
- rtol = 3e-5
- elif (op.name in self.FP16_LOW_PRECISION_LIST) and dtype == torch.float16:
- atol = 1e-2
- rtol = 1e-2
- elif (op.name == "masked.mean"):
- atol = 7e-4
- rtol = 2e-3
- elif (op.name == "native_layer_norm"):
- atol = 1e-4
- rtol = 1.3e-5
- elif (op.name == "norm" or op.name == "linalg.norm") and dtype == torch.float16:
- atol = 7e-4
- rtol = 1.5e-3
- elif op.name == "unique" and cpu_kwargs["sorted"] is False:
- continue
- else:
- atol = None
- rtol = None
+ if (op.name in self.FP32_LOW_PRECISION_LIST) and dtype == torch.float32:
+ atol = 1e-4
+ rtol = 3e-5
+ elif op.name == "nn.functional.conv2d" or op.name == "linalg.multi_dot" and dtype == torch.float32:
+ atol = 1e-4
+ rtol = 3e-5
+ elif (op.name in self.FP16_LOW_PRECISION_LIST) and dtype == torch.float16:
+ atol = 1e-2
+ rtol = 1e-2
+ elif (op.name == "masked.mean"):
+ atol = 7e-4
+ rtol = 2e-3
+ elif (op.name == "native_layer_norm"):
+ atol = 1e-4
+ rtol = 1.3e-5
+ elif (op.name == "norm" or op.name == "linalg.norm") and dtype == torch.float16:
+ atol = 7e-4
+ rtol = 1.5e-3
+ elif op.name == "unique" and cpu_kwargs["sorted"] is False:
+ continue
+ else:
+ atol = None
+ rtol = None
- self.assertEqual(cpu_out, mps_out, atol=atol, rtol=rtol)
+ self.assertEqual(cpu_out, mps_out, atol=atol, rtol=rtol)
- except Exception as e:
- raise e
- forward_failed = True
- all_forward_pass = False
#
# Backward check
diff --git a/test/test_transformers.py b/test/test_transformers.py
index bb27202..e4030fe 100644
--- a/test/test_transformers.py
+++ b/test/test_transformers.py
@@ -46,8 +46,6 @@
try:
torch.use_deterministic_algorithms(mode, warn_only=warn_only)
yield {}
- except RuntimeError as err:
- raise err
finally:
torch.use_deterministic_algorithms(previous_mode, warn_only=previous_warn_only)
