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Fix exception causes all over the codebase (#90271)

Browse Source 
This is the continuation to #90134 and hopefully the final PR in this series.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/90271
Approved by: https://github.com/kit1980
This commit is contained in:
Ram Rachum 2022-12-07 04:28:56 +00:00 committed by PyTorch MergeBot
parent 8f079b895b
commit 351d73b97f
63 changed files with 150 additions and 147 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

4
benchmarks/dynamo/common.py
View File

@ -1001,8 +1001,8 @@ class BenchmarkRunner:
try:
self.model_iter_fn(model, example_inputs)
except Exception:
raise NotImplementedError("Eager model failed to run")
except Exception as e:
raise NotImplementedError("Eager model failed to run") from e
def maybe_cast(self, model, example_inputs):
model = copy.deepcopy(model)

5
benchmarks/functional_autograd_benchmark/torchaudio_models.py
View File

@ -330,8 +330,9 @@ class TransformerModel(nn.Module):
super(TransformerModel, self).__init__()
try:
from torch.nn import TransformerEncoder, TransformerEncoderLayer
except Exception:
raise ImportError('TransformerEncoder module does not exist in PyTorch 1.1 or lower.')
except Exception as e:
raise ImportError('TransformerEncoder module does not exist in PyTorch 1.1 or '
'lower.') from e
self.model_type = 'Transformer'
self.src_mask = None
self.pos_encoder = PositionalEncoding(ninp, dropout)

4
caffe2/python/caffe_translator.py
View File

@ -210,9 +210,9 @@ class TranslatorRegistry(object):
try:
caffe_ops, params = cls.registry_[layer.type](
layer, pretrained_blobs, is_test, **kwargs)
except KeyError:
except KeyError as e:
raise KeyError('No translator registered for layer: %s yet.' %
str(layer))
str(layer)) from e
if caffe_ops is None:
caffe_ops = []
if type(caffe_ops) is not list:

4
caffe2/python/core.py
View File

@ -970,7 +970,7 @@ StopGradient. Op:\n\n{}""".format(op.output[0], str(op)))
input_name,
err
)
)
) from err
# Finally, let's create the sum operator.
sum_ops, g = self._MakeSumOps(input_name, input_version)
@ -1175,7 +1175,7 @@ class GradientRegistry(object):
raise Exception(
"Exception when creating gradient for [{}]:{}.\nOp: \n{}".
format(op.type, e, str(op))
)
) from e
if gradient_ops is None:
return [], g_input

8
caffe2/python/model_helper.py
View File

@ -540,8 +540,8 @@ def ExtractPredictorNet(
'StopGradient'
]
)
except ValueError:
raise Exception("No ops with input={}".format(input_blobs))
except ValueError as e:
raise Exception("No ops with input={}".format(input_blobs)) from e
try:
last_op_with_output = max(
[
@ -549,8 +549,8 @@ def ExtractPredictorNet(
if output_blobs.intersection(ops[j].output)
]
)
except ValueError:
raise Exception("No ops with output={}".format(output_blobs))
except ValueError as e:
raise Exception("No ops with output={}".format(output_blobs)) from e
def validate_op(op):
# Check that the op does not have is_test = 0 set. This is a common

4
caffe2/python/models/download.py
View File

@ -69,10 +69,10 @@ def downloadFromURLToFile(url, filename, show_progress=True):
print("") # New line to fix for progress bar
except HTTPError as e:
raise Exception("Could not download model. [HTTP Error] {code}: {reason}."
.format(code=e.code, reason=e.reason))
.format(code=e.code, reason=e.reason)) from e
except URLError as e:
raise Exception("Could not download model. [URL Error] {reason}."
.format(reason=e.reason))
.format(reason=e.reason)) from e
def getURLFromName(name, filename):

4
caffe2/python/operator_test/roi_align_rotated_op_test.py
View File

@ -150,9 +150,9 @@ class RoIAlignRotatedOp(hu.HypothesisTestCase):
indexer = [slice(None)] * m.ndim
try:
indexer[axis] = slice(None, None, -1)
except IndexError:
except IndexError as e:
raise ValueError("axis=%i is invalid for the %i-dimensional input array"
% (axis, m.ndim))
% (axis, m.ndim)) from e
return m[tuple(indexer)]
def roialign_ref(X, R):

4
caffe2/python/operator_test/video_input_op_test.py
View File

@ -13,8 +13,8 @@ from caffe2.python import model_helper, workspace
try:
import lmdb
except ImportError:
raise unittest.SkipTest("python-lmdb is not installed")
except ImportError as e:
raise unittest.SkipTest("python-lmdb is not installed") from e
class VideoInputOpTest(unittest.TestCase):

4
caffe2/python/schema.py
View File

@ -546,8 +546,8 @@ class Struct(Field):
raise AttributeError(item)
try:
return super(Struct, self).__getattribute__("fields")[item]
except KeyError:
raise AttributeError(item)
except KeyError as e:
raise AttributeError(item) from e
def __setattr__(self, key, value):
# Disable setting attributes after initialization to prevent false

4
caffe2/python/trt/transform.py
View File

@ -29,8 +29,8 @@ def _get_output_shapes(output_value_infos):
def check_gpu_():
try:
C.get_cuda_version()
except Exception as _:
raise Exception("TensorRT related functions require CUDA support")
except Exception as e:
raise Exception("TensorRT related functions require CUDA support") from e
def convert_onnx_model_to_trt_op(onnx_model,
max_batch_size=64,

4
setup.py
View File

@ -446,8 +446,8 @@ Please install it via `conda install {module}` or `pip install {module}`
def check_pydep(importname, module):
try:
importlib.import_module(importname)
except ImportError:
raise RuntimeError(missing_pydep.format(importname=importname, module=module))
except ImportError as e:
raise RuntimeError(missing_pydep.format(importname=importname, module=module)) from e
class build_ext(setuptools.command.build_ext.build_ext):

8
test/distributed/fsdp/test_fsdp_state_dict.py
View File

@ -611,8 +611,8 @@ class TestFSDPStateDict(FSDPTest):
def _state_dict(model: Module, state_dict_type: str):
try:
enum_val = STATE_DICT_MAPPING[state_dict_type]
except KeyError:
raise ValueError(f"No state_dict type for {state_dict_type}")
except KeyError as e:
raise ValueError(f"No state_dict type for {state_dict_type}") from e
with FSDP.state_dict_type(model, enum_val):
return model.state_dict()
@ -623,8 +623,8 @@ class TestFSDPStateDict(FSDPTest):
):
try:
enum_val = STATE_DICT_MAPPING[state_dict_type]
except KeyError:
raise ValueError(f"No state_dict for {state_dict_type}")
except KeyError as e:
raise ValueError(f"No state_dict for {state_dict_type}") from e
with FSDP.state_dict_type(model, enum_val):
return model.load_state_dict(state_dict, strict=True)

2
test/distributed/test_c10d_nccl.py
View File

@ -2598,7 +2598,7 @@ class NcclErrorHandlingTest(MultiProcessTestCase):
try:
pg_gloo.barrier().wait()
except Exception as e:
raise ValueError(f"Rank {self.rank} barrier timed out waiting for rank 0 with error: {str(e)}")
raise ValueError(f"Rank {self.rank} barrier timed out waiting for rank 0 with error: {str(e)}") from e
# Now verify communicators on this rank have
# been aborted by watchdog.
self._wait_for_comm_abort(process_group, failed_collective_timeout)

4
test/distributed/test_dynamo_distributed.py
View File

@ -101,8 +101,8 @@ def get_hf_bert(rank):
# in a multiprocessing test
try:
from transformers import BertConfig, AutoModelForMaskedLM
except ImportError:
raise unittest.SkipTest("Unable to import transformers")
except ImportError as e:
raise unittest.SkipTest("Unable to import transformers") from e
batch_size, max_length, config, device = 4, 512, BertConfig(), f"cuda:{rank}"
model = AutoModelForMaskedLM.from_config(config).to(device)

4
test/dynamo/test_repros.py
View File

@ -1856,8 +1856,8 @@ class ReproTests(torch._dynamo.test_case.TestCase):
def __getattr__(self, item: str):
try:
return self.data[item]
except KeyError:
raise AttributeError
except KeyError as e:
raise AttributeError from e
def tokenization(x):
encoding = BatchEncoding({"key": x})

2
test/inductor/test_torchinductor.py
View File

@ -65,7 +65,7 @@ except (ImportError, AssertionError) as e:
sys.stderr.write(f"{type(e)}: {e}\n")
if __name__ == "__main__":
sys.exit(0)
raise unittest.SkipTest("requires sympy/functorch/filelock")
raise unittest.SkipTest("requires sympy/functorch/filelock") from e
from torch.testing._internal.inductor_utils import HAS_CPU, HAS_CUDA

4
test/jit/test_dtype_analysis.py
View File

@ -128,9 +128,9 @@ class TestDtypeBase(JitTestCase):
inputs = [self.get_rand_tensor(s, d) for s, d in zip(in_shapes, in_dtypes)]
try:
self.assert_dtype_equal_custom_args(fn, inputs)
except Exception:
except Exception as e:
fail_text = f"Failed for shapes {in_shapes}, and dtypes {in_dtypes}"
raise AssertionError(fail_text)
raise AssertionError(fail_text) from e
def assert_dtype_equal_custom_args(self, fn, args):
try:

2
test/lazy/test_extract_compiled_graph.py
View File

@ -141,7 +141,7 @@ def verify_reusing_compiled_graph(mod, exception_msg_pattern, ncase=10):
raise e # reraise the exception
exception_message = str(e)
if not re.search(exception_msg_pattern, exception_message):
raise RuntimeError(f"Exception message does not match the required pattern: {exception_message}")
raise RuntimeError(f"Exception message does not match the required pattern: {exception_message}") from e
else:
# We are done for the test case that expects an exception
return

4
test/onnx/test_pytorch_onnx_onnxruntime_cuda.py
View File

@ -110,8 +110,8 @@ class TestONNXRuntime_cuda(onnx_test_common._TestONNXRuntime):
try:
from apex import amp
except Exception:
raise unittest.SkipTest("Apex is not available")
except Exception as e:
raise unittest.SkipTest("Apex is not available") from e
input = torch.randn(3, 3, device=torch.device("cuda"))
model = amp.initialize(LinearModel(), opt_level="O2")
self.run_test(model, input)

2
test/test_autograd.py
View File

@ -1703,7 +1703,7 @@ class TestAutograd(TestCase):
self.assertTrue(torch.is_grad_enabled())
yield (-i if has_raised else i)
except UnrecoverableException:
except UnrecoverableException :
self.assertTrue(torch.is_grad_enabled())
raise SecondaryException

2
test/test_fx.py
View File

@ -3805,7 +3805,7 @@ class TestFXAPIBackwardCompatibility(JitTestCase):
f"unintended, please revert it. If it was intended, check with the FX " \
f"team to ensure that the proper deprecation protocols have been followed " \
f"and subsequently --accept the change."
raise AssertionError(msg)
raise AssertionError(msg) from e
def test_public_api_surface(self):
non_back_compat_objects = {}

34
test/test_jit_fuser_te.py
View File

@ -597,7 +597,7 @@ class TestTEFuser(JitTestCase):
except Exception as e:
raise RuntimeError(
" ".join(["Failed:", str(dtype), op.__name__, device])
)
) from e
def test_minmax_int_ops(self):
def apply(fn):
@ -627,7 +627,7 @@ class TestTEFuser(JitTestCase):
except Exception as e:
raise RuntimeError(
" ".join(["Failed:", str(dtype), op.__name__, device])
)
) from e
def test_comparison_eq_ne(self):
for device in self.devices:
@ -1288,7 +1288,7 @@ class TestTEFuser(JitTestCase):
except Exception as e:
raise RuntimeError(
" ".join(["Failed:", str(self_dtype), op.__name__, device, str(size)])
)
) from e
def test_isnan(self):
x = torch.rand([4])
@ -1321,7 +1321,7 @@ class TestTEFuser(JitTestCase):
except Exception as e:
raise RuntimeError(
" ".join(["Failed:", str(dtype), 'isnan', device])
)
) from e
def test_gelu(self):
def apply(fn):
@ -1352,7 +1352,7 @@ class TestTEFuser(JitTestCase):
except Exception as e:
raise RuntimeError(
" ".join(["Failed:", str(dtype), op.__name__, device, str(size)])
)
) from e
def test_unary_ops(self):
with torch._jit_internal._disable_emit_hooks():
@ -1435,7 +1435,7 @@ class TestTEFuser(JitTestCase):
except Exception as e:
raise RuntimeError(
" ".join(["Failed:", str(dtype), op.__name__, device, str(size)])
)
) from e
def test_binary_ops(self):
def apply(fn):
@ -1488,7 +1488,7 @@ class TestTEFuser(JitTestCase):
except Exception as e:
raise RuntimeError(
" ".join(["Failed:", str(dtype), op.__name__, device])
)
) from e
def test_binary_scalar_ops(self):
def apply(fn):
@ -1534,7 +1534,7 @@ class TestTEFuser(JitTestCase):
try:
k = torch._C._te.TensorExprKernel(graph)
except Exception as e:
raise RuntimeError(" ".join(["Compilation failed:", device, str(code)]))
raise RuntimeError(" ".join(["Compilation failed:", device, str(code)])) from e
# Run the graph
for x, y in product(values[dtype_x], values[dtype_y]):
@ -1543,7 +1543,7 @@ class TestTEFuser(JitTestCase):
res = k.run((x, y))
self.assertEqual(ref, res)
except Exception as e:
raise RuntimeError(" ".join(["Failed at runtime:", device, str(x), str(y), str(code)]))
raise RuntimeError(" ".join(["Failed at runtime:", device, str(x), str(y), str(code)])) from e
def test_matmul(self):
if self.dynamic_shapes:
@ -1599,7 +1599,7 @@ class TestTEFuser(JitTestCase):
except Exception as e:
raise RuntimeError(
" ".join(["Failed:", str(dtype), device])
)
) from e
def test_binary_tensor_scalar_ops(self):
with torch._jit_internal._disable_emit_hooks():
@ -1643,7 +1643,7 @@ class TestTEFuser(JitTestCase):
except Exception as e:
raise RuntimeError(
" ".join(["Failed:", str(dtype), op.__name__, device])
)
) from e
def test_binary_div_ops(self):
def apply_with_scalar(fn, scalar):
@ -1676,7 +1676,7 @@ class TestTEFuser(JitTestCase):
except Exception as e:
raise RuntimeError(
"Failed: {} {} {} {}".format(dtype, op.__name__, device, scalar)
)
) from e
def test_binary_pow(self):
def apply_with_scalar(fn, scalar):
@ -1714,7 +1714,7 @@ class TestTEFuser(JitTestCase):
except Exception as e:
raise RuntimeError(
" ".join(["Failed:", str(dtype), op.__name__, device])
)
) from e
def test_ternary_ops(self):
def apply(fn):
@ -1746,7 +1746,7 @@ class TestTEFuser(JitTestCase):
except Exception as e:
raise RuntimeError(
" ".join(["Failed:", str(dtype), op.__name__, device])
)
) from e
def test_ternary_norm_ops(self):
def apply(fn):
@ -1777,7 +1777,7 @@ class TestTEFuser(JitTestCase):
except Exception as e:
raise RuntimeError(
" ".join(["Failed:", str(dtype), op.__name__, device])
)
) from e
@unittest.skip("FIXME: fuser doesn't include ListConstruct nodes to the group causing a failure")
@ -1810,7 +1810,7 @@ class TestTEFuser(JitTestCase):
except Exception as e:
raise RuntimeError(
" ".join(["Failed:", str(dtype), op.__name__, device])
)
) from e
def test_where_ops(self):
def apply(fn):
@ -1843,7 +1843,7 @@ class TestTEFuser(JitTestCase):
except Exception as e:
raise RuntimeError(
" ".join(["Failed:", str(dtype), op.__name__, device])
)
) from e
def test_unsupported_dtypes(self):
for device in self.devices:

2
test/test_meta.py
View File

@ -467,7 +467,7 @@ def run_meta_crossref(
# they're not tested outside of gradcheck which only checks
# torch.float64 and torch.complex128 (which this second one
# often skipped as well).
raise unittest.SkipTest("Original OpInfo is broken")
raise unittest.SkipTest("Original OpInfo is broken") from e
# TODO: also handle cases where func raise an exception

4
tools/gen_vulkan_glsl.py
View File

@ -28,13 +28,13 @@ class UniqueKeyLoader(Loader):
key = self.construct_object(key_node, deep=deep) # type: ignore[no-untyped-call]
try:
hash(key)
except TypeError:
except TypeError as e:
raise ConstructorError(
"while constructing a mapping",
node.start_mark,
"found unacceptable key ",
key_node.start_mark,
)
) from e
# check for duplicate keys
if key in mapping:
raise ConstructorError(

4
tools/render_junit.py
View File

@ -12,10 +12,10 @@ try:
TestCase,
TestSuite,
)
except ImportError:
except ImportError as e:
raise ImportError(
"junitparser not found, please install with 'pip install junitparser'"
)
) from e
try:
import rich

2
torch/_dynamo/debug_utils.py
View File

@ -364,7 +364,7 @@ def helper_for_dump_minify(contents):
fd.write(contents)
except OSError as e:
log.exception(e)
raise NotImplementedError("Could not write to {minified_repro_path}")
raise NotImplementedError("Could not write to {minified_repro_path}") from e
def dump_to_minify(gm, args, compiler_name: str):

2
torch/_dynamo/symbolic_convert.py
View File

@ -283,7 +283,7 @@ def break_graph_if_unsupported(*, push):
if self.has_backedge():
msg = "Skipping frame because there is a graph break in a for/while loop"
log.debug(msg)
raise exc.SkipFrame(msg)
raise exc.SkipFrame(msg) from excp
if not self.should_compile_partial_graph():
raise

6
torch/_dynamo/utils.py
View File

@ -348,13 +348,13 @@ def proxy_args_kwargs(args, kwargs):
proxy_args = tuple(arg.as_proxy() for arg in args)
proxy_kwargs = {key: arg.as_proxy() for key, arg in kwargs.items()}
return proxy_args, proxy_kwargs
except NotImplementedError:
except NotImplementedError as e:
from .exc import unimplemented
from .variables.base import typestr
raise unimplemented(
f"call_function args: {typestr(*args)} {typestr(*list(kwargs.values()))}"
)
) from e
@dataclasses.dataclass
@ -745,7 +745,7 @@ def wrap_fake_exception(fn):
msg = f"Unsupported: {e.reason} with fake tensor propagation."
log.warning(msg)
raise unimplemented(msg)
raise unimplemented(msg) from e
def wrap_to_fake_tensor(e, fake_mode):

4
torch/_dynamo/variables/constant.py
View File

@ -56,8 +56,8 @@ class ConstantVariable(VariableTracker):
try:
options = VariableTracker.propagate([self])
return [ConstantVariable(x, **options) for x in self.as_python_constant()]
except TypeError:
raise NotImplementedError()
except TypeError as e:
raise NotImplementedError from e
def const_getattr(self, tx, name):
member = getattr(self.value, name)

4
torch/_dynamo/variables/nn_module.py
View File

@ -504,8 +504,8 @@ class UnspecializedNNModuleVariable(UserDefinedObjectVariable):
try:
fn = inspect.getattr_static(self.value_type, "__iter__")
except AttributeError:
raise NotImplementedError()
except AttributeError as e:
raise NotImplementedError from e
if fn in (
torch.nn.ModuleList.__iter__,

5
torch/_functorch/partitioners.py
View File

@ -277,8 +277,9 @@ def min_cut_rematerialization_partition(
"""
try:
import networkx as nx
except ImportError:
raise RuntimeError("Need networkx installed to perform smart recomputation heuristics")
except ImportError as e:
raise RuntimeError("Need networkx installed to perform smart recomputation "
"heuristics") from e
joint_module.graph.eliminate_dead_code()
joint_module.recompile()

2
torch/_inductor/codecache.py
View File

@ -435,7 +435,7 @@ class CppCodeCache:
try:
subprocess.check_output(cmd, stderr=subprocess.STDOUT)
except subprocess.CalledProcessError as e:
raise exc.CppCompileError(cmd, e.output)
raise exc.CppCompileError(cmd, e.output) from e
cls.cache[key] = cls._load_library(output_path)
cls.cache[key].key = key

2
torch/_inductor/triton_ops/autotune.py
View File

@ -184,7 +184,7 @@ class CachingAutotuner(KernelInterface):
raise RuntimeError(
"""Consider updating Triton with
`pip install -U "git+https://github.com/openai/triton@af76c989eb4799b015f8b288ccd8421558772e56#subdirectory=python"`"""
)
) from e
else:
raise e

2
torch/_subclasses/fake_utils.py
View File

@ -136,5 +136,5 @@ class CrossRefFakeMode(TorchDispatchMode):
r_out, fake_out, check_strides=self.check_strides
)
except Exception as e:
raise RuntimeError(f"Mismatch on {func}: {e}")
raise RuntimeError(f"Mismatch on {func}: {e}") from e
return r

8
torch/autograd/gradcheck.py
View File

@ -819,7 +819,7 @@ def _test_batched_grad_forward_ad(func, inputs) -> bool:
except RuntimeError as ex:
# Rethrow to provide a better error message
raise GradcheckError(
f'While computing batched gradients, got: {ex}\n\n{FAILED_BATCHED_GRAD_MSG_FWD_AD}')
f'While computing batched gradients, got: {ex}\n\n{FAILED_BATCHED_GRAD_MSG_FWD_AD}') from ex
for input_idx, (res, exp) in enumerate(zip(result, expected)):
if torch.allclose(res, exp):
@ -861,7 +861,7 @@ def _test_batched_grad(input, output, output_idx) -> bool:
# autograd.grad instead of the C++ traceback of what line in the
# backward formula
raise GradcheckError(
f'While computing batched gradients, got: {ex}\n\n{FAILED_BATCHED_GRAD_MSG}')
f'While computing batched gradients, got: {ex}\n\n{FAILED_BATCHED_GRAD_MSG}') from ex
for input_idx, (res, exp) in enumerate(zip(result, expected)):
if torch.allclose(res, exp):
@ -977,12 +977,12 @@ def _test_undefined_backward_mode(func, outputs, inputs) -> bool:
try:
grads_input = torch.autograd.grad(output_to_check, diff_input_list,
grads_output, allow_unused=True)
except RuntimeError:
except RuntimeError as e:
warn_bc_breaking()
raise GradcheckError((
'Expected backward function to handle undefined output grads. '
'Please look at "Notes about undefined output gradients" in '
'"tools/autograd/derivatives.yaml"'))
'"tools/autograd/derivatives.yaml"')) from e
for gi, i in zip(grads_input, diff_input_list):
if (gi is not None) and (not gi.eq(0).all()):

16
torch/cuda/__init__.py
View File

@ -669,13 +669,13 @@ def memory_usage(device: Optional[Union[Device, int]] = None) -> int:
"""
try:
import pynvml # type: ignore[import]
except ModuleNotFoundError:
raise ModuleNotFoundError("pynvml module not found, please install pynvml")
except ModuleNotFoundError as e:
raise ModuleNotFoundError("pynvml module not found, please install pynvml") from e
from pynvml import NVMLError_DriverNotLoaded
try:
pynvml.nvmlInit()
except NVMLError_DriverNotLoaded:
raise RuntimeError("cuda driver can't be loaded, is cuda enabled?")
except NVMLError_DriverNotLoaded as e:
raise RuntimeError("cuda driver can't be loaded, is cuda enabled?") from e
device = _get_device_index(device, optional=True)
handle = pynvml.nvmlDeviceGetHandleByIndex(device)
return pynvml.nvmlDeviceGetUtilizationRates(handle).memory
@ -695,13 +695,13 @@ def utilization(device: Optional[Union[Device, int]] = None) -> int:
"""
try:
import pynvml # type: ignore[import]
except ModuleNotFoundError:
raise ModuleNotFoundError("pynvml module not found, please install pynvml")
except ModuleNotFoundError as e:
raise ModuleNotFoundError("pynvml module not found, please install pynvml") from e
from pynvml import NVMLError_DriverNotLoaded
try:
pynvml.nvmlInit()
except NVMLError_DriverNotLoaded:
raise RuntimeError("cuda driver can't be loaded, is cuda enabled?")
except NVMLError_DriverNotLoaded as e:
raise RuntimeError("cuda driver can't be loaded, is cuda enabled?") from e
device = _get_device_index(device, optional=True)
handle = pynvml.nvmlDeviceGetHandleByIndex(device)
return pynvml.nvmlDeviceGetUtilizationRates(handle).gpu

4
torch/distributed/_composable/_ddp.py
View File

@ -1640,10 +1640,10 @@ class DistributedDataParallel(Module, Joinable):
)
try:
overlapped_optim.register_ddp(self)
except NotImplementedError:
except NotImplementedError as e:
raise RuntimeError(
f"{optim} does not support overlapped DDP. Please file an issue to PyTorch or the respective owner of {optim}."
)
) from e
def _distributed_broadcast_coalesced(
self, tensors, buffer_size, authoritative_rank=0

8
torch/distributed/elastic/rendezvous/api.py
View File

@ -195,11 +195,11 @@ class RendezvousParameters:
return value
try:
return int(value)
except ValueError:
except ValueError as e:
raise ValueError(
f"The rendezvous configuration option '{key}' does not represent a valid integer "
"value."
)
) from e
RendezvousHandlerCreator = Callable[[RendezvousParameters], RendezvousHandler]
@ -244,11 +244,11 @@ class RendezvousHandlerRegistry:
"""Creates a new :py:class:`RendezvousHandler`."""
try:
creator = self._registry[params.backend]
except KeyError:
except KeyError as e:
raise ValueError(
f"The rendezvous backend '{params.backend}' is not registered. Did you forget "
f"to call `{self.register.__name__}`?"
)
) from e
handler = creator(params)

4
torch/distributed/elastic/rendezvous/etcd_rendezvous.py
View File

@ -464,10 +464,10 @@ class EtcdRendezvous(object):
version_counter = self.client.get(self.get_path("/rdzv/version_counter"))
version_counter.value = str(int(version_counter.value) + 1)
self.client.update(version_counter)
except (etcd.EtcdKeyNotFound, etcd.EtcdCompareFailed):
except (etcd.EtcdKeyNotFound, etcd.EtcdCompareFailed) as e:
raise RendezvousError(
"Unexpected state of EtcdRendezvousHandler, worker needs to die."
)
) from e
# Any failure below results in declaring a retryable rendezvous failure.
# The ephemeral /rdzv/active_version will expire and someone can then

4
torch/distributed/fsdp/_init_utils.py
View File

@ -381,8 +381,8 @@ def _get_ignored_modules(
msg_prefix = "`ignored_modules` should be an iterable of `torch.nn.Module`s "
try:
ignored_root_modules = set(_ignored_modules)
except TypeError:
raise TypeError(msg_prefix + f"but got {type(_ignored_modules)}")
except TypeError as e:
raise TypeError(msg_prefix + f"but got {type(_ignored_modules)}") from e
for module in ignored_root_modules:
if not isinstance(module, torch.nn.Module):
raise TypeError(msg_prefix + f"but got an iterable with {type(module)}")

4
torch/distributed/fsdp/_optim_utils.py
View File

@ -1015,11 +1015,11 @@ def _get_param_id_to_param_from_optim_input(
return list(model.parameters())
try:
params = list(optim_input)
except TypeError:
except TypeError as e:
raise TypeError(
"Optimizer input should be an iterable of Tensors or dicts, "
f"but got {optim_input}"
)
) from e
if len(params) == 0:
raise ValueError("Optimizer input should not be empty")

5
torch/distributed/optim/utils.py
View File

@ -44,8 +44,9 @@ def register_functional_optim(key, optim):
def as_functional_optim(optim_cls: Type, *args, **kwargs):
try:
functional_cls = functional_optim_map[optim_cls]
except KeyError:
raise ValueError(f"Optimizer {optim_cls} does not have a functional counterpart!")
except KeyError as e:
raise ValueError(f"Optimizer {optim_cls} does not have a functional "
f"counterpart!") from e
return _create_functional_optim(functional_cls, *args, **kwargs)

4
torch/distributed/optim/zero_redundancy_optimizer.py
View File

@ -1323,9 +1323,9 @@ class ZeroRedundancyOptimizer(Optimizer, Joinable):
f"Tensors, but got {torch.typename(params)}")
try:
all_params = list(params)
except TypeError:
except TypeError as e:
raise TypeError("`params` argument should be an iterable of Tensors"
f" or dicts, but got {torch.typename(params)}")
f" or dicts, but got {torch.typename(params)}") from e
if len(all_params) == 0:
raise ValueError("ZeroRedundancyOptimizer got an empty parameter "
"list")

4
torch/distributed/pipeline/sync/skip/skippable.py
View File

@ -198,8 +198,8 @@ class Skippable(nn.Module):
for ns, name in self.poppable():
try:
poppable_tensors[name] = skip_tracker.load(batch, ns, name)
except KeyError:
raise RuntimeError(f"'{name}' has not been stashed")
except KeyError as e:
raise RuntimeError(f"'{name}' has not been stashed") from e
input = batch.values
# Handle skip commands.

4
torch/distributed/rendezvous.py
View File

@ -1,9 +1,9 @@
try:
from urllib.parse import urlparse, urlunparse
except ImportError:
except ImportError as e:
raise ImportError(
"urllib cannot be found, urlparse from python2 is no longer supported."
)
) from e
import numbers
import os

2
torch/distributed/rpc/internal.py
View File

@ -228,7 +228,7 @@ def _handle_exception(result):
raise RuntimeError( # noqa: B904
f"Failed to create original exception type. Error msg was {str(e)}"
f" Original exception on remote side was {exception_msg}"
)
) from e
if exc is not None:
raise exc

6
torch/distributed/run.py
View File

@ -605,13 +605,13 @@ def determine_local_world_size(nproc_per_node: str):
try:
logging.info(f"Using nproc_per_node={nproc_per_node}.")
return int(nproc_per_node)
except ValueError:
except ValueError as e:
if nproc_per_node == "cpu":
num_proc = os.cpu_count()
device_type = "cpu"
elif nproc_per_node == "gpu":
if not torch.cuda.is_available():
raise ValueError("Cuda is not available.")
raise ValueError("Cuda is not available.") from e
device_type = "gpu"
num_proc = torch.cuda.device_count()
elif nproc_per_node == "auto":
@ -622,7 +622,7 @@ def determine_local_world_size(nproc_per_node: str):
num_proc = os.cpu_count()
device_type = "cpu"
else:
raise ValueError(f"Unsupported nproc_per_node value: {nproc_per_node}")
raise ValueError(f"Unsupported nproc_per_node value: {nproc_per_node}") from e
log.info(
f"Using nproc_per_node={nproc_per_node},"

4
torch/jit/_dataclass_impls.py
View File

@ -20,13 +20,13 @@ def compose_fn(cls, name: str, body_lines: List[str], signature: str) -> ParsedD
# Parse the function declaration
try:
py_ast = ast.parse(decl)
except SyntaxError:
except SyntaxError as e:
# This should only happen if there's some unforeseeable change
# in the dataclasses module that makes our synthesized code fail
raise RuntimeError(
f"TorchScript failed to synthesize dataclass method '{name}' for class '{cls.__name__}'. "
"Please file a bug report at <https://github.com/pytorch/pytorch/issues>"
)
) from e
fake_filename = _get_fake_filename(cls, name)
# Parse the function
return ParsedDef(

2
torch/jit/_recursive.py
View File

@ -185,7 +185,7 @@ def infer_concrete_type_builder(nn_module, share_types=True):
except RuntimeError as re:
raise RuntimeError(
"Error inferring type for {name}: {item}: {re}".format(name=name, item=item, re=re)
)
) from re
return attr_type, inferred

4
torch/profiler/profiler.py
View File

@ -292,8 +292,8 @@ def tensorboard_trace_handler(dir_name: str, worker_name: Optional[str] = None,
if not os.path.isdir(dir_name):
try:
os.makedirs(dir_name, exist_ok=True)
except Exception:
raise RuntimeError("Can't create directory: " + dir_name)
except Exception as e:
raise RuntimeError("Can't create directory: " + dir_name) from e
if not worker_name:
worker_name = "{}_{}".format(socket.gethostname(), str(os.getpid()))
file_name = "{}.{}.pt.trace.json".format(worker_name, int(time.time() * 1000))

4
torch/testing/_comparison.py
View File

@ -677,8 +677,8 @@ class TensorLikePair(Pair):
try:
return torch.as_tensor(tensor_like)
except Exception:
raise UnsupportedInputs()
except Exception as e:
raise UnsupportedInputs() from e
def _check_supported(self, tensor: torch.Tensor, *, id: Tuple[Any, ...]) -> None:
if tensor.layout not in {

2
torch/testing/_internal/common_fsdp.py
View File

@ -949,7 +949,7 @@ class FSDPTest(MultiProcessTestCase):
**init_kwargs,
)
except Exception as e:
raise ValueError(f"Initializing {model_class} raised error {str(e)}")
raise ValueError(f"Initializing {model_class} raised error {str(e)}") from e
if not isinstance(fsdp_model, FSDP):
# Enforce that we wrap with top-level FSDP since we are comparing
# assuming a data parallel reference and some test models may not

6
torch/testing/_internal/common_utils.py
View File

@ -3260,7 +3260,7 @@ def retry_on_connect_failures(func=None, connect_errors=(ADDRESS_IN_USE)):
if any(connect_error in str(error) for connect_error in connect_errors):
tries_remaining -= 1
if tries_remaining == 0:
raise RuntimeError(f"Failing after {n_retries} retries with error: {str(error)}")
raise RuntimeError(f"Failing after {n_retries} retries with error: {str(error)}") from error
time.sleep(random.random())
continue
raise
@ -4001,8 +4001,8 @@ def first_sample(self: unittest.TestCase, samples: Iterable[T]) -> T:
"""
try:
return next(iter(samples))
except StopIteration:
raise unittest.SkipTest('Skipped! Need at least 1 sample input')
except StopIteration as e:
raise unittest.SkipTest('Skipped! Need at least 1 sample input') from e
# this helper method is to recursively
# clone the tensor-type input of operators tested by OpInfo

4
torch/testing/_internal/distributed/_tensor/common_dtensor.py
View File

@ -290,8 +290,8 @@ class DTensorConverter(object):
tree_unflatten(new_args, self.flatten_args_spec),
tree_unflatten(new_kwargs, self.flatten_kwargs_spec),
)
except StopIteration:
raise StopIteration
except StopIteration as e:
raise StopIteration from e
def to_dist_tensor(
self, t: torch.Tensor, mesh: DeviceMesh, placements: List[Placement]

4
torch/utils/data/datapipes/datapipe.py
View File

@ -349,10 +349,10 @@ class _DataPipeSerializationWrapper:
def __len__(self):
try:
return len(self._datapipe)
except Exception:
except Exception as e:
raise TypeError(
"{} instance doesn't have valid length".format(type(self).__name__)
)
) from e
class _IterDataPipeSerializationWrapper(_DataPipeSerializationWrapper, IterDataPipe):

4
torch/utils/data/datapipes/iter/callable.py
View File

@ -154,8 +154,8 @@ def _collate_helper(conversion, item):
try:
import torcharrow.pytorch as tap # type: ignore[import]
collation_fn = tap.rec.Default()
except Exception:
raise Exception("unable to import default collation function from the TorchArrow")
except Exception as e:
raise Exception("unable to import default collation function from the TorchArrow") from e
tuple_names.append(str(name))
value = collation_fn(df[name])

4
torch/utils/data/datapipes/map/combining.py
View File

@ -93,8 +93,8 @@ class ZipperMapDataPipe(MapDataPipe[Tuple[T_co, ...]]):
for dp in self.datapipes:
try:
res.append(dp[index])
except IndexError:
raise IndexError(f"Index {index} is out of range for one of the input MapDataPipes {dp}.")
except IndexError as e:
raise IndexError(f"Index {index} is out of range for one of the input MapDataPipes {dp}.") from e
return tuple(res)
def __len__(self) -> int:

4
torch/utils/data/datapipes/map/grouping.py
View File

@ -53,11 +53,11 @@ class BatcherMapDataPipe(MapDataPipe[DataChunk]):
for i in indices:
batch.append(self.datapipe[i])
return self.wrapper_class(batch)
except IndexError:
except IndexError as e:
if not self.drop_last and len(batch) > 0:
return self.wrapper_class(batch)
else:
raise IndexError(f"Index {index} is out of bound.")
raise IndexError(f"Index {index} is out of bound.") from e
def __len__(self) -> int:
if self.length is not None:

10
torch/utils/data/datapipes/utils/decoder.py
View File

@ -148,13 +148,13 @@ class ImageHandler:
import numpy as np
except ImportError as e:
raise ModuleNotFoundError("Package `numpy` is required to be installed for default image decoder."
"Please use `pip install numpy` to install the package")
"Please use `pip install numpy` to install the package") from e
try:
import PIL.Image
except ImportError as e:
raise ModuleNotFoundError("Package `PIL` is required to be installed for default image decoder."
"Please use `pip install Pillow` to install the package")
"Please use `pip install Pillow` to install the package") from e
imagespec = self.imagespec
atype, etype, mode = imagespecs[imagespec]
@ -200,7 +200,7 @@ def videohandler(extension, data):
except ImportError as e:
raise ModuleNotFoundError("Package `torchvision` is required to be installed for default video file loader."
"Please use `pip install torchvision` or `conda install torchvision -c pytorch`"
"to install the package")
"to install the package") from e
with tempfile.TemporaryDirectory() as dirname:
fname = os.path.join(dirname, f"file.{extension}")
@ -221,7 +221,7 @@ def audiohandler(extension, data):
except ImportError as e:
raise ModuleNotFoundError("Package `torchaudio` is required to be installed for default audio file loader."
"Please use `pip install torchaudio` or `conda install torchaudio -c pytorch`"
"to install the package")
"to install the package") from e
with tempfile.TemporaryDirectory() as dirname:
fname = os.path.join(dirname, f"file.{extension}")
@ -240,7 +240,7 @@ class MatHandler:
except ImportError as e:
raise ModuleNotFoundError("Package `scipy` is required to be installed for mat file."
"Please use `pip install scipy` or `conda install scipy`"
"to install the package")
"to install the package") from e
self.sio = sio
self.loadmat_kwargs = loadmat_kwargs

4
torch/utils/data/datapipes/utils/snapshot.py
View File

@ -47,9 +47,9 @@ def _simple_graph_snapshot_restoration(datapipe: IterDataPipe, n_iterations: int
try:
next(it)
remainder -= 1
except StopIteration:
except StopIteration as e:
raise RuntimeError(f"Fast-forward {datapipe} by {n_iterations} iterations "
"exceeds the number of samples available.")
"exceeds the number of samples available.") from e
datapipe._fast_forward_iterator = it
# While the DataPipe has `_fast_forward_iterator`, `next()` will get result from there instead of elsewhere.

4
torchgen/gen_backend_stubs.py
View File

@ -262,10 +262,10 @@ def error_on_missing_kernels(
try:
with open(kernel_defn_file_path, "r") as f:
backend_defns = f.read()
except IOError:
except IOError as e:
raise AssertionError(
f"Unable to read from the specified impl_path file: {kernel_defn_file_path}"
)
) from e
if full_codegen is None:
full_codegen = []

4
torchgen/gen_lazy_tensor.py
View File

@ -137,10 +137,10 @@ def validate_shape_inference_header(
with open(shape_inference_hdr, "r") as f:
shape_infr_decls = f.read()
shape_infr_decl_lines = set(shape_infr_decls.split("\n"))
except IOError:
except IOError as e:
raise AssertionError(
f"Unable to read from the specified shape_inference_hdr file: {shape_inference_hdr}"
)
) from e
shape_infr_regex = r"compute_shape_(\w+)"
actual_shape_infr_name_counts = Counter(

4
torchgen/model.py
View File

@ -1719,8 +1719,8 @@ class Type:
return CustomClassType(m.group(1))
try:
return BaseType(BaseTy[t])
except KeyError:
raise RuntimeError(f"unrecognized type {t}")
except KeyError as e:
raise RuntimeError(f"unrecognized type {t}") from e
def __str__(self) -> str:
raise NotImplementedError