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mypy 1.16.0 (#155821)

Browse Source 
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155821
Approved by: https://github.com/ezyang, https://github.com/zou3519
This commit is contained in:
Aaron Orenstein
2025-06-13 21:30:46 -07:00
committed by PyTorch MergeBot
parent ce79056471
commit e95e8eed0a
65 changed files with 151 additions and 141 deletions
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4
.ci/docker/requirements-ci.txt
View File

@ -90,10 +90,10 @@ librosa>=0.6.2 ; python_version < "3.11"
#Pinned versions:
#test that import:
mypy==1.15.0
mypy==1.16.0
# Pin MyPy version because new errors are likely to appear with each release
#Description: linter
#Pinned versions: 1.14.0
#Pinned versions: 1.16.0
#test that import: test_typing.py, test_type_hints.py
networkx==2.8.8

2
.lintrunner.toml
View File

@ -154,7 +154,7 @@ init_command = [
'numpy==1.26.4 ; python_version >= "3.9" and python_version <= "3.11"',
'numpy==2.1.0 ; python_version >= "3.12"',
'expecttest==0.3.0',
'mypy==1.15.0',
'mypy==1.16.0',
'sympy==1.13.3',
'types-requests==2.27.25',
'types-PyYAML==6.0.7',

2
tools/extract_scripts.py
View File

@ -42,7 +42,7 @@ def extract(step: Step) -> Script | None:
"bash": f"#!/usr/bin/env bash\nset -eo pipefail\n{run}",
"sh": f"#!/usr/bin/env sh\nset -e\n{run}",
}.get(shell, run)
return {"extension": extension, "script": script}
return {"extension": extension, "script": script} # type: ignore[typeddict-item]
elif is_gh_script and gh_script is not None:
return {"extension": ".js", "script": gh_script}
else:

12
torch/_dynamo/device_interface.py
View File

@ -199,12 +199,12 @@ class DeviceGuard:
class CudaInterface(DeviceInterface):
device = torch.cuda.device
device = torch.cuda.device # type: ignore[assignment]
# register Event and Stream class into the backend interface
# make sure Event and Stream are implemented and inherited from the torch.Event and torch.Stream
Event = torch.cuda.Event
Stream = torch.cuda.Stream
Event = torch.cuda.Event # type: ignore[assignment]
Stream = torch.cuda.Stream # type: ignore[assignment]
class Worker:
@staticmethod
@ -297,9 +297,9 @@ else:
class XpuInterface(DeviceInterface):
device = torch.xpu.device
Event = torch.xpu.Event
Stream = torch.xpu.Stream
device = torch.xpu.device # type: ignore[assignment]
Event = torch.xpu.Event # type: ignore[assignment]
Stream = torch.xpu.Stream # type: ignore[assignment]
class Worker:
@staticmethod

2
torch/_dynamo/output_graph.py
View File

@ -1113,7 +1113,7 @@ class OutputGraph(OutputGraphGuardsState):
# A small codegen optimization because we might have different
# VariableTrackers that share the same source.
list_idx = x.source.index
list_idx = x.source.index # type: ignore[attr-defined]
if list_idx not in visited:
alias_name = self.new_var(
f"{list_name}_ref"

2
torch/_dynamo/pgo.py
View File

@ -663,7 +663,7 @@ def get_code_state() -> defaultdict[CodeId, CodeState]:
trace_structured_artifact(
f"get_{ty}_code_state",
"string",
lambda: render_code_state(_CODE_STATE),
lambda: render_code_state(_CODE_STATE), # type: ignore[arg-type]
)
set_feature_use("pgo", True)
_INIT_CODE_STATE = copy.deepcopy(_CODE_STATE)

6
torch/_higher_order_ops/auto_functionalize.py
View File

@ -238,7 +238,7 @@ def write_view_information_to_args(
write_single_view(
f"_{arg_name}",
kwargs[arg_name],
arg_to_base_index.get(arg_name, None),
arg_to_base_index.get(arg_name, None), # type: ignore[arg-type]
)
else:
raise RuntimeError(f"Unsupported type {arg_type}")
@ -389,7 +389,7 @@ class AutoFunctionalizedV2(HigherOrderOperator):
if isinstance(_mutable_op, HigherOrderOperator):
_op_to_check = HopInstance(
_mutable_op,
SchemaHolder.from_tree_spec(kwargs.get("_op_schema", None)).schema,
SchemaHolder.from_tree_spec(kwargs.get("_op_schema", None)).schema, # type: ignore[arg-type]
)
else:
_op_to_check = _mutable_op
@ -948,7 +948,7 @@ def auto_functionalized_v2_proxy(
if _only_clone_these_bases is None:
_only_clone_these_bases = tuple(range(len(all_bases)))
schema = pytree.tree_unflatten([], kwargs.get("_op_schema", None)).schema
schema = pytree.tree_unflatten([], kwargs.get("_op_schema", None)).schema # type: ignore[arg-type]
new_kwargs, _ = _generate_new_op_kwargs_from_bases(
schema,
{k: v for k, v in kwargs.items() if k not in ("_all_bases", "_op_schema")},

2
torch/_inductor/codegen/cpp.py
View File

@ -4986,7 +4986,7 @@ class CppScheduling(BaseScheduling):
layout=local_buffer_layout,
)
local_buffers.append(local_buffer_used)
local_to_global_buffers[local_buffer_used.name] = []
local_to_global_buffers[local_buffer_used.name] = [] # type: ignore[index]
local_to_global_buffers[local_buffer_used.name].append(
global_buffer,
)

12
torch/_inductor/codegen/triton.py
View File

@ -2742,13 +2742,19 @@ class TritonKernel(SIMDKernel[TritonCSEVariable]):
assert reduction_type == "welford_reduce"
result_mean, result_m2, result_weight = result_var
peer_mean = self.codegen_cooperative_reduction_peer_combine(
result_mean, upcast_acc_dtype(src_dtype), default[0]
result_mean,
upcast_acc_dtype(src_dtype),
default[0], # type: ignore[index]
)
peer_m2 = self.codegen_cooperative_reduction_peer_combine(
result_m2, upcast_acc_dtype(src_dtype), default[1]
result_m2,
upcast_acc_dtype(src_dtype),
default[1], # type: ignore[index]
)
peer_weight = self.codegen_cooperative_reduction_peer_combine(
result_weight, upcast_acc_dtype(src_dtype), default[2]
result_weight,
upcast_acc_dtype(src_dtype),
default[2], # type: ignore[index]
)
self.welford_reduce_final_reduction(
self.post_loop_store,

4
torch/_inductor/compile_fx.py
View File

@ -1650,8 +1650,8 @@ def cudagraphify(
nonlocal compiled_fn
if compiled_fn is None:
with dynamo_utils.preserve_rng_state():
compiled_fn = cudagraphify_fn(model, new_inputs, static_input_idxs)
return compiled_fn(new_inputs)
compiled_fn = cudagraphify_fn(model, new_inputs, static_input_idxs) # type: ignore[arg-type]
return compiled_fn(new_inputs) # type: ignore[arg-type]
return run

14
torch/_inductor/cpu_vec_isa.py
View File

@ -169,7 +169,7 @@ class VecNEON(VecISA):
return "neon"
return "asimd" # detects the presence of advanced SIMD on armv8-a kernels
__hash__: Callable[[VecISA], Any] = VecISA.__hash__
__hash__: Callable[[VecISA], Any] = VecISA.__hash__ # type: ignore[assignment]
@dataclasses.dataclass
@ -191,7 +191,7 @@ class VecSVE256(VecISA):
return "neon"
return "asimd"
__hash__: Callable[[VecISA], Any] = VecISA.__hash__
__hash__: Callable[[VecISA], Any] = VecISA.__hash__ # type: ignore[assignment]
@dataclasses.dataclass
@ -208,7 +208,7 @@ class VecAVX512(VecISA):
def __str__(self) -> str:
return "avx512"
__hash__: Callable[[VecISA], Any] = VecISA.__hash__
__hash__: Callable[[VecISA], Any] = VecISA.__hash__ # type: ignore[assignment]
@dataclasses.dataclass
@ -263,7 +263,7 @@ class VecAVX2(VecISA):
def __str__(self) -> str:
return "avx2"
__hash__: Callable[[VecISA], Any] = VecISA.__hash__
__hash__: Callable[[VecISA], Any] = VecISA.__hash__ # type: ignore[assignment]
@dataclasses.dataclass
@ -280,7 +280,7 @@ class VecZVECTOR(VecISA):
def __str__(self) -> str:
return "zvector"
__hash__: Callable[[VecISA], Any] = VecISA.__hash__
__hash__: Callable[[VecISA], Any] = VecISA.__hash__ # type: ignore[assignment]
@dataclasses.dataclass
@ -293,7 +293,7 @@ class VecVSX(VecISA):
def __str__(self) -> str:
return "vsx"
__hash__: Callable[[VecISA], Any] = VecISA.__hash__
__hash__: Callable[[VecISA], Any] = VecISA.__hash__ # type: ignore[assignment]
class InvalidVecISA(VecISA):
@ -308,7 +308,7 @@ class InvalidVecISA(VecISA):
def __bool__(self) -> bool: # type: ignore[override]
return False
__hash__: Callable[[VecISA], Any] = VecISA.__hash__
__hash__: Callable[[VecISA], Any] = VecISA.__hash__ # type: ignore[assignment]
def x86_isa_checker() -> list[str]:

2
torch/_inductor/freezing.py
View File

@ -150,7 +150,7 @@ class ErasedTensor(torch.Tensor):
self.owning_mod_ref = weakref.ref(mod)
@classmethod
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
def __torch_dispatch__(cls, func, types, args=(), kwargs=None): # type: ignore[override]
erased_tensors = [
e
for e in pytree.arg_tree_leaves(*args, **kwargs)

2
torch/_inductor/fx_passes/reinplace.py
View File

@ -253,7 +253,7 @@ def canonicalize_view_scatter_ops(graph: torch.fx.Graph) -> None:
def handle_views(node: torch.fx.Node):
inp = node.args[0]
node_to_view_base[node] = node_to_view_base.get(inp, inp) # type: ignore[arg-type]
node_to_view_base[node] = node_to_view_base.get(inp, inp) # type: ignore[arg-type, assignment]
node_to_view_op[node] = [
*node_to_view_op[inp], # type: ignore[index]
ViewOp(

6
torch/_inductor/ir.py
View File

@ -2977,7 +2977,7 @@ class View(GenericView):
return idx
@classmethod
def create(cls, x, new_size): # type: ignore[no-untyped-def]
def create(cls, x, new_size): # type: ignore[no-untyped-def, override]
assert isinstance(new_size, (tuple, list))
old_size, new_size = cls.resolve_negative_size(x.get_size(), new_size)
@ -3305,7 +3305,7 @@ class SliceView(View):
return start, end
@classmethod
def create(cls, x, dim, start, end, step=1, clamp=True): # type: ignore[no-untyped-def]
def create(cls, x, dim, start, end, step=1, clamp=True): # type: ignore[no-untyped-def, override]
step = sympy.expand(step)
assert isinstance(step, sympy.Expr) or step > 0
try:
@ -3906,7 +3906,7 @@ class MutationLayoutSHOULDREMOVE(Layout):
def stride(self) -> list[Expr]:
return self.real_layout().stride
@stride.setter
@stride.setter # type: ignore[override]
def stride(self, value: Never) -> None:
pass # ignore setting of stride

2
torch/_inductor/pattern_matcher.py
View File

@ -1558,7 +1558,7 @@ def register_replacement(
normalize_args=normalize_args,
)
pattern.register(pass_dicts)
return pattern.pattern
return pattern.pattern # type: ignore[return-value]
_serialized_patterns: OrderedSet[str] = OrderedSet()

4
torch/_subclasses/functional_tensor.py
View File

@ -160,7 +160,7 @@ class FunctionalTensor(torch.Tensor):
assert out._inference_mode_base is not None
return out
def __torch_dispatch__(self, func, types, args=(), kwargs=None):
def __torch_dispatch__(self, func, types, args=(), kwargs=None): # type: ignore[override]
unrecognized_types = [
t
for t in types
@ -291,7 +291,7 @@ class FunctionalTensor(torch.Tensor):
return self.elem.to_dense()
@property
def layout(self):
def layout(self): # type: ignore[override]
return self.elem.layout
def __bool__(self):

6
torch/ao/nn/intrinsic/qat/modules/conv_fused.py
View File

@ -633,7 +633,7 @@ class ConvReLU1d(nnqat.Conv1d, nni._FusedModule):
)
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
def from_float(cls, mod, use_precomputed_fake_quant=False): # type: ignore[override]
return super().from_float(
mod, use_precomputed_fake_quant=use_precomputed_fake_quant
)
@ -833,7 +833,7 @@ class ConvReLU2d(nnqat.Conv2d, nni._FusedModule):
)
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
def from_float(cls, mod, use_precomputed_fake_quant=False): # type: ignore[override]
return super().from_float(
mod, use_precomputed_fake_quant=use_precomputed_fake_quant
)
@ -1034,7 +1034,7 @@ class ConvReLU3d(nnqat.Conv3d, nni._FusedModule):
)
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
def from_float(cls, mod, use_precomputed_fake_quant=False): # type: ignore[override]
return super().from_float(
mod, use_precomputed_fake_quant=use_precomputed_fake_quant
)

2
torch/ao/nn/intrinsic/quantized/dynamic/modules/linear_relu.py
View File

@ -57,5 +57,5 @@ class LinearReLU(nnqd.Linear):
)
@classmethod
def from_reference(cls, ref_qlinear_relu):
def from_reference(cls, ref_qlinear_relu): # type: ignore[override]
return super().from_reference(ref_qlinear_relu[0])

4
torch/ao/nn/intrinsic/quantized/modules/bn_relu.py
View File

@ -47,7 +47,7 @@ class BNReLU2d(nnq.BatchNorm2d):
return "QuantizedBNReLU2d"
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
def from_float(cls, mod, use_precomputed_fake_quant=False): # type: ignore[override]
# TODO: Add qat support for BNReLU2d
return super().from_float(
mod, use_precomputed_fake_quant=use_precomputed_fake_quant
@ -96,7 +96,7 @@ class BNReLU3d(nnq.BatchNorm3d):
return "QuantizedBNReLU3d"
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
def from_float(cls, mod, use_precomputed_fake_quant=False): # type: ignore[override]
# TODO: Add qat support for BNReLU3d
return super().from_float(
mod, use_precomputed_fake_quant=use_precomputed_fake_quant

4
torch/ao/nn/intrinsic/quantized/modules/conv_add.py
View File

@ -68,7 +68,7 @@ class ConvAdd2d(nnq.Conv2d):
return "QuantizedConvAdd2d"
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
def from_float(cls, mod, use_precomputed_fake_quant=False): # type: ignore[override]
return super().from_float(
mod, use_precomputed_fake_quant=use_precomputed_fake_quant
)
@ -137,7 +137,7 @@ class ConvAddReLU2d(nnq.Conv2d):
return "QuantizedConvAddReLU2d"
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
def from_float(cls, mod, use_precomputed_fake_quant=False): # type: ignore[override]
return super().from_float(
mod, use_precomputed_fake_quant=use_precomputed_fake_quant
)

6
torch/ao/nn/intrinsic/quantized/modules/conv_relu.py
View File

@ -78,7 +78,7 @@ class ConvReLU1d(nnq.Conv1d):
return "QuantizedConvReLU1d"
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
def from_float(cls, mod, use_precomputed_fake_quant=False): # type: ignore[override]
if type(mod) == torch.ao.nn.intrinsic.qat.ConvBnReLU1d:
assert mod.bn.running_var is not None and mod.bn.running_mean is not None
mod.weight, mod.bias = fuse_conv_bn_weights(
@ -159,7 +159,7 @@ class ConvReLU2d(nnq.Conv2d):
return "QuantizedConvReLU2d"
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
def from_float(cls, mod, use_precomputed_fake_quant=False): # type: ignore[override]
if type(mod) == torch.ao.nn.intrinsic.qat.ConvBnReLU2d:
assert mod.bn.running_var is not None and mod.bn.running_mean is not None
mod.weight, mod.bias = fuse_conv_bn_weights(
@ -242,7 +242,7 @@ class ConvReLU3d(nnq.Conv3d):
return "QuantizedConvReLU3d"
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
def from_float(cls, mod, use_precomputed_fake_quant=False): # type: ignore[override]
if type(mod) == torch.ao.nn.intrinsic.qat.ConvBnReLU3d:
assert mod.bn.running_var is not None and mod.bn.running_mean is not None
mod.weight, mod.bias = fuse_conv_bn_weights(

6
torch/ao/nn/qat/modules/conv.py
View File

@ -175,7 +175,7 @@ class Conv1d(_ConvNd, nn.Conv1d):
)
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
def from_float(cls, mod, use_precomputed_fake_quant=False): # type: ignore[override]
return super().from_float(
cls, mod, use_precomputed_fake_quant=use_precomputed_fake_quant
)
@ -240,7 +240,7 @@ class Conv2d(_ConvNd, nn.Conv2d):
return self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias)
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
def from_float(cls, mod, use_precomputed_fake_quant=False): # type: ignore[override]
return super().from_float(
cls, mod, use_precomputed_fake_quant=use_precomputed_fake_quant
)
@ -305,7 +305,7 @@ class Conv3d(_ConvNd, nn.Conv3d):
return self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias)
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
def from_float(cls, mod, use_precomputed_fake_quant=False): # type: ignore[override]
return super().from_float(
cls, mod, use_precomputed_fake_quant=use_precomputed_fake_quant
)

2
torch/ao/nn/quantized/dynamic/modules/linear.py
View File

@ -147,7 +147,7 @@ class Linear(nnq.Linear):
return qlinear
@classmethod
def from_reference(cls, ref_qlinear):
def from_reference(cls, ref_qlinear): # type: ignore[override]
"""Create a (fbgemm/qnnpack) dynamic quantized module from a reference quantized
module
Args:

4
torch/ao/nn/quantized/modules/batchnorm.py
View File

@ -83,7 +83,7 @@ class BatchNorm2d(_BatchNorm):
)
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
def from_float(cls, mod, use_precomputed_fake_quant=False): # type: ignore[override]
return _BatchNorm.from_float(
cls, mod, use_precomputed_fake_quant=use_precomputed_fake_quant
)
@ -122,7 +122,7 @@ class BatchNorm3d(_BatchNorm):
)
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
def from_float(cls, mod, use_precomputed_fake_quant=False): # type: ignore[override]
return _BatchNorm.from_float(
cls, mod, use_precomputed_fake_quant=use_precomputed_fake_quant
)

16
torch/ao/nn/quantized/modules/conv.py
View File

@ -467,7 +467,7 @@ class Conv1d(_ConvNd):
)
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
def from_float(cls, mod, use_precomputed_fake_quant=False): # type: ignore[override]
r"""Creates a quantized module from a float module or qparams_dict.
Args:
@ -597,7 +597,7 @@ class Conv2d(_ConvNd):
)
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
def from_float(cls, mod, use_precomputed_fake_quant=False): # type: ignore[override]
r"""Creates a quantized module from a float module or qparams_dict.
Args:
@ -728,7 +728,7 @@ class Conv3d(_ConvNd):
)
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
def from_float(cls, mod, use_precomputed_fake_quant=False): # type: ignore[override]
r"""Creates a quantized module from a float module or qparams_dict.
Args:
@ -794,7 +794,7 @@ class _ConvTransposeNd(_ConvNd):
return res
@classmethod
def from_float(cls, mod, use_precomputed_fake_quant=False):
def from_float(cls, mod, use_precomputed_fake_quant=False): # type: ignore[override]
r"""Creates a quantized module from a float module or qparams_dict.
Args:
mod (Module): a float module, either produced by torch.ao.quantization
@ -841,7 +841,7 @@ class _ConvTransposeNd(_ConvNd):
return qconv
@staticmethod
def from_reference(cls, ref_qconvt, output_scale, output_zero_point):
def from_reference(cls, ref_qconvt, output_scale, output_zero_point): # type: ignore[override]
r"""Create a (fbgemm/qnnpack) quantized module from a reference quantized module
Args:
ref_qconvt (Module): a reference quantized module, either produced by torch.ao.quantization
@ -989,7 +989,7 @@ class ConvTranspose1d(_ConvTransposeNd):
)
@classmethod
def from_reference(cls, ref_qconvt, output_scale, output_zero_point):
def from_reference(cls, ref_qconvt, output_scale, output_zero_point): # type: ignore[override]
return _ConvTransposeNd.from_reference(
cls, ref_qconvt, output_scale, output_zero_point
)
@ -1112,7 +1112,7 @@ class ConvTranspose2d(_ConvTransposeNd):
)
@classmethod
def from_reference(cls, ref_qconvt, output_scale, output_zero_point):
def from_reference(cls, ref_qconvt, output_scale, output_zero_point): # type: ignore[override]
return _ConvTransposeNd.from_reference(
cls, ref_qconvt, output_scale, output_zero_point
)
@ -1237,7 +1237,7 @@ class ConvTranspose3d(_ConvTransposeNd):
)
@classmethod
def from_reference(cls, ref_qconvt, output_scale, output_zero_point):
def from_reference(cls, ref_qconvt, output_scale, output_zero_point): # type: ignore[override]
return _ConvTransposeNd.from_reference(
cls, ref_qconvt, output_scale, output_zero_point
)

12
torch/ao/nn/quantized/reference/modules/conv.py
View File

@ -110,7 +110,7 @@ class Conv1d(_ConvNd, nn.Conv1d):
return "QuantizedConv1d(Reference)"
@classmethod
def from_float(cls, float_conv, weight_qparams):
def from_float(cls, float_conv, weight_qparams): # type: ignore[override]
return _ConvNd.from_float(cls, float_conv, weight_qparams)
@ -173,7 +173,7 @@ class Conv2d(_ConvNd, nn.Conv2d):
return "QuantizedConv2d(Reference)"
@classmethod
def from_float(cls, float_conv, weight_qparams):
def from_float(cls, float_conv, weight_qparams): # type: ignore[override]
return _ConvNd.from_float(cls, float_conv, weight_qparams)
@ -236,7 +236,7 @@ class Conv3d(_ConvNd, nn.Conv3d):
return "QuantizedConv3d(Reference)"
@classmethod
def from_float(cls, float_conv, weight_qparams):
def from_float(cls, float_conv, weight_qparams): # type: ignore[override]
return _ConvNd.from_float(cls, float_conv, weight_qparams)
@ -346,7 +346,7 @@ class ConvTranspose1d(_ConvTransposeNd, nn.ConvTranspose1d):
return "QuantizedConvTranspose1d(Reference)"
@classmethod
def from_float(cls, float_conv, weight_qparams):
def from_float(cls, float_conv, weight_qparams): # type: ignore[override]
return _ConvTransposeNd.from_float(cls, float_conv, weight_qparams)
@ -427,7 +427,7 @@ class ConvTranspose2d(_ConvTransposeNd, nn.ConvTranspose2d):
return "QuantizedConvTranspose2d(Reference)"
@classmethod
def from_float(cls, float_conv, weight_qparams):
def from_float(cls, float_conv, weight_qparams): # type: ignore[override]
return _ConvTransposeNd.from_float(cls, float_conv, weight_qparams)
@ -507,5 +507,5 @@ class ConvTranspose3d(_ConvTransposeNd, nn.ConvTranspose3d):
return "QuantizedConvTranspose3d(Reference)"
@classmethod
def from_float(cls, float_conv, weight_qparams):
def from_float(cls, float_conv, weight_qparams): # type: ignore[override]
return _ConvTransposeNd.from_float(cls, float_conv, weight_qparams)

2
torch/ao/pruning/_experimental/data_sparsifier/base_data_sparsifier.py
View File

@ -310,7 +310,7 @@ class BaseDataSparsifier(base_sparsifier.BaseSparsifier):
self.update_mask(name, data, **config)
@abc.abstractmethod
def update_mask(self, name, data, **kwargs):
def update_mask(self, name, data, **kwargs): # type: ignore[override]
pass
def _delete_data(self, name):

2
torch/ao/quantization/_learnable_fake_quantize.py
View File

@ -145,7 +145,7 @@ class _LearnableFakeQuantize(torch.ao.quantization.FakeQuantizeBase):
print(f"_LearnableFakeQuantize Zero Point: {self.zero_point.detach()}")
@torch.jit.export
def calculate_qparams(self):
def calculate_qparams(self): # type: ignore[override]
self.scale.data.clamp_(min=self.eps.item()) # type: ignore[operator]
scale = self.scale.detach()
zero_point = (

14
torch/ao/quantization/backend_config/backend_config.py
View File

@ -671,23 +671,23 @@ class BackendPatternConfig:
for d in backend_pattern_config_dict.get(DTYPE_CONFIGS_DICT_KEY, []):
conf.add_dtype_config(_get_dtype_config(d))
conf.set_root_module(
backend_pattern_config_dict.get(ROOT_MODULE_DICT_KEY, None)
backend_pattern_config_dict.get(ROOT_MODULE_DICT_KEY, None) # type: ignore[arg-type]
)
conf.set_qat_module(backend_pattern_config_dict.get(QAT_MODULE_DICT_KEY, None))
conf.set_qat_module(backend_pattern_config_dict.get(QAT_MODULE_DICT_KEY, None)) # type: ignore[arg-type]
conf.set_reference_quantized_module(
backend_pattern_config_dict.get(REFERENCE_QUANTIZED_MODULE_DICT_KEY, None)
backend_pattern_config_dict.get(REFERENCE_QUANTIZED_MODULE_DICT_KEY, None) # type: ignore[arg-type]
)
conf.set_fused_module(
backend_pattern_config_dict.get(FUSED_MODULE_DICT_KEY, None)
backend_pattern_config_dict.get(FUSED_MODULE_DICT_KEY, None) # type: ignore[arg-type]
)
conf.set_fuser_method(
backend_pattern_config_dict.get(FUSER_METHOD_DICT_KEY, None)
backend_pattern_config_dict.get(FUSER_METHOD_DICT_KEY, None) # type: ignore[arg-type]
)
conf._set_root_node_getter(
backend_pattern_config_dict.get(ROOT_NODE_GETTER_DICT_KEY, None)
backend_pattern_config_dict.get(ROOT_NODE_GETTER_DICT_KEY, None) # type: ignore[arg-type]
)
conf._set_extra_inputs_getter(
backend_pattern_config_dict.get(EXTRA_INPUTS_GETTER_DICT_KEY, None)
backend_pattern_config_dict.get(EXTRA_INPUTS_GETTER_DICT_KEY, None) # type: ignore[arg-type]
)
conf._set_num_tensor_args_to_observation_type(
backend_pattern_config_dict.get(

4
torch/ao/quantization/fake_quantize.py
View File

@ -218,7 +218,7 @@ class FakeQuantize(FakeQuantizeBase):
self.is_per_channel = _is_per_channel(self.qscheme)
@torch.jit.export
def calculate_qparams(self):
def calculate_qparams(self): # type: ignore[override]
return self.activation_post_process.calculate_qparams()
def forward(self, X):
@ -342,7 +342,7 @@ class FixedQParamsFakeQuantize(FakeQuantize):
)
@torch.jit.export
def calculate_qparams(self):
def calculate_qparams(self): # type: ignore[override]
return self.scale, self.zero_point
@torch.jit.export

4
torch/ao/quantization/fx/_equalize.py
View File

@ -364,7 +364,7 @@ def get_op_node_and_weight_eq_obs(
maybe_equalization_node_name_to_config # type: ignore[assignment]
)
assert equalization_node_name_to_qconfig.get(op_node.name, None) is not None
weight_eq_obs = equalization_node_name_to_qconfig.get(
weight_eq_obs = equalization_node_name_to_qconfig.get( # type: ignore[union-attr]
op_node.name, None
).weight()
@ -845,7 +845,7 @@ def convert_eq_obs(
# Erase the weight equalization observer node
prev_node = weight_eq_obs_node.args[0]
remove_node(model, weight_eq_obs_node, prev_node)
remove_node(model, weight_eq_obs_node, prev_node) # type: ignore[arg-type]
else:
raise ValueError(
"Expected operation node to be 'call_module' or 'call_function"

2
torch/ao/quantization/fx/_model_report/model_report_observer.py
View File

@ -279,7 +279,7 @@ class ModelReportObserver(ObserverBase):
self.constant_channels = torch.tensor([], device=device)
@torch.jit.export
def calculate_qparams(self):
def calculate_qparams(self): # type: ignore[override]
raise Exception( # noqa: TRY002
"calculate_qparams should not be called for ModelReportObserver"
)

16
torch/ao/quantization/observer.py
View File

@ -564,7 +564,7 @@ class MinMaxObserver(UniformQuantizationObserverBase):
return x_orig
@torch.jit.export
def calculate_qparams(self):
def calculate_qparams(self): # type: ignore[override]
r"""Calculates the quantization parameters."""
return self._calculate_qparams(self.min_val, self.max_val)
@ -787,7 +787,7 @@ class PerChannelMinMaxObserver(UniformQuantizationObserverBase):
return x_orig
@torch.jit.export
def calculate_qparams(self):
def calculate_qparams(self): # type: ignore[override]
return self._calculate_qparams(self.min_val, self.max_val)
def extra_repr(self):
@ -1335,7 +1335,7 @@ class HistogramObserver(UniformQuantizationObserverBase):
return x_orig
@torch.jit.export
def calculate_qparams(self):
def calculate_qparams(self): # type: ignore[override]
is_uninitialized = self.min_val == float("inf") and self.max_val == float(
"-inf"
)
@ -1448,7 +1448,7 @@ class FixedQParamsObserver(ObserverBase):
return X
@torch.jit.export
def calculate_qparams(self):
def calculate_qparams(self): # type: ignore[override]
return self.scale, self.zero_point
@ -1517,7 +1517,7 @@ class PlaceholderObserver(ObserverBase):
return f"dtype={self.dtype}, is_dynamic={self.is_dynamic}"
@torch.jit.export
def calculate_qparams(self):
def calculate_qparams(self): # type: ignore[override]
raise Exception( # noqa: TRY002
"calculate_qparams should not be called for PlaceholderObserver"
)
@ -1544,7 +1544,7 @@ class RecordingObserver(ObserverBase):
return x
@torch.jit.export
def calculate_qparams(self):
def calculate_qparams(self): # type: ignore[override]
raise Exception( # noqa: TRY002
"calculate_qparams should not be called for RecordingObserver"
)
@ -1577,7 +1577,7 @@ class NoopObserver(ObserverBase):
return x
@torch.jit.export
def calculate_qparams(self):
def calculate_qparams(self): # type: ignore[override]
raise Exception( # noqa: TRY002
"calculate_qparams should not be called for NoopObserver"
)
@ -1604,7 +1604,7 @@ class ReuseInputObserver(ObserverBase):
return x
@torch.jit.export
def calculate_qparams(self):
def calculate_qparams(self): # type: ignore[override]
raise Exception( # noqa: TRY002
"calculate_qparams should not be called for ReuseInputObserver"
)

2
torch/ao/quantization/pt2e/duplicate_dq_pass.py
View File

@ -33,7 +33,7 @@ def _maybe_duplicate_dq(
gm: torch.fx.GraphModule, dq_node: torch.fx.Node, user: torch.fx.Node
):
annotation = user.meta.get("quantization_annotation", None)
if not _is_valid_annotation(annotation):
if not _is_valid_annotation(annotation): # type: ignore[arg-type]
return
with gm.graph.inserting_after(dq_node):
new_node = gm.graph.node_copy(dq_node)

2
torch/autograd/__init__.py
View File

@ -138,7 +138,7 @@ def _make_grads(
shape_matches = expect_true(sym_eq(out_size, first_grad.size()))
if not shape_matches:
out = cast(Union[torch.Tensor, graph.GradientEdge], out)
out = cast(Union[torch.Tensor, graph.GradientEdge], out) # type: ignore[redundant-cast]
out_shape, grad_shape = _calculate_shape(
out, first_grad, is_grads_batched
)

2
torch/autograd/function.py
View File

@ -815,7 +815,7 @@ class NestedIOFunction(Function):
self._to_save_nested = args
@property
def saved_tensors(self):
def saved_tensors(self): # type: ignore[override]
r"""
See :meth:`Function.saved_tensors`.
"""

2
torch/distributed/_functional_collectives.py
View File

@ -635,7 +635,7 @@ class AsyncCollectiveTensor(torch.Tensor):
return self.elem
@classmethod
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
def __torch_dispatch__(cls, func, types, args=(), kwargs=None): # type: ignore[override]
if func == torch.ops.aten.view.default:
# Fast handle aten.view as a lot of view related op goes to aten.view
# eventually, this avoids pytree slowdown

2
torch/distributed/_shard/sharded_tensor/api.py
View File

@ -184,7 +184,7 @@ class ShardedTensorBase(torch.Tensor):
return sharded_tensor_base
@classmethod
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
def __torch_dispatch__(cls, func, types, args=(), kwargs=None): # type: ignore[override]
raise RuntimeError(
f"A {cls.__name__} object is being used from c++ while calling {func.__module__}.{func.__name__} "
"but the there is no custom __torch_dispatch__ implementation for it."

8
torch/distributed/checkpoint/filesystem.py
View File

@ -425,14 +425,14 @@ def _write_files_from_queue(
transforms,
stream,
tensor,
write_item,
write_item, # type: ignore[arg-type]
storage_key,
serialization_format,
)
)
tensor_dict[write_item.index.fqn] = tensor
metadata_dict[write_item.index.fqn] = {
"saved_offsets": write_item.tensor_data.chunk.offsets
tensor_dict[write_item.index.fqn] = tensor # type: ignore[attr-defined]
metadata_dict[write_item.index.fqn] = { # type: ignore[attr-defined]
"saved_offsets": write_item.tensor_data.chunk.offsets # type: ignore[attr-defined]
}
if serialization_format == SerializationFormat.SAFETENSORS:

2
torch/distributed/device_mesh.py
View File

@ -621,7 +621,7 @@ else:
f"Each device mesh dimension should get only one process group, but got {self.get_rank()} "
f"in {subgroup_ranks}!"
)
dim_group_names.append(dim_group.group_name)
dim_group_names.append(dim_group.group_name) # type: ignore[union-attr]
self._dim_group_names = dim_group_names
def __enter__(self) -> "DeviceMesh":

2
torch/distributed/optim/post_localSGD_optimizer.py
View File

@ -61,7 +61,7 @@ class PostLocalSGDOptimizer(torch.optim.Optimizer):
self.averager = averager
@property
def state(self):
def state(self): # type: ignore[override]
return self.optim.state
def __repr__(self):

2
torch/distributed/tensor/_api.py
View File

@ -346,7 +346,7 @@ class DTensor(torch.Tensor):
@torch._disable_dynamo
# pyre-fixme[3]: Return type must be annotated.
# pyre-fixme[2]: Parameter must be annotated.
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
def __torch_dispatch__(cls, func, types, args=(), kwargs=None): # type: ignore[override]
return DTensor._op_dispatcher.dispatch(
func,
args,

2
torch/distributed/tensor/examples/torchrec_sharding_example.py
View File

@ -84,7 +84,7 @@ class LocalShardsWrapper(torch.Tensor):
# necessary for ops dispatching from this subclass to its local shards
@classmethod
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
def __torch_dispatch__(cls, func, types, args=(), kwargs=None): # type: ignore[override]
kwargs = kwargs or {}
# TODO: we shall continually extend this function to support more ops if needed

6
torch/export/_remove_effect_tokens_pass.py
View File

@ -53,13 +53,13 @@ def _remove_effect_tokens_from_graph_helper(
assert isinstance(func, (torch._ops.OpOverload, torch._ops.HigherOrderOperator))
if func == torch.ops.higher_order.call_torchbind:
custom_obj_meta = node.args[2].meta["val"]
custom_obj_meta = node.args[2].meta["val"] # type: ignore[union-attr]
assert isinstance(custom_obj_meta, CustomObjArgument)
if custom_obj_meta.fake_val:
custom_obj = custom_obj_meta.fake_val
elif node.args[2].name in inputs_to_lifted_custom_objs:
elif node.args[2].name in inputs_to_lifted_custom_objs: # type: ignore[union-attr]
custom_obj = ep.constants[
inputs_to_lifted_custom_objs[node.args[2].name]
inputs_to_lifted_custom_objs[node.args[2].name] # type: ignore[union-attr]
]
else:
raise RuntimeError(f"Unable to find custom obj for node {node}")

8
torch/export/dynamic_shapes.py
View File

@ -199,11 +199,11 @@ class _StaticDim(Dim):
self.value = value
@property
def min(self):
def min(self): # type: ignore[override]
return self.value # type: ignore[attr-defined]
@property
def max(self):
def max(self): # type: ignore[override]
return self.value # type: ignore[attr-defined]
@ -229,7 +229,7 @@ class _DerivedDim(Dim):
self.fn = fn
@property
def min(self):
def min(self): # type: ignore[override]
# assume that self.fn is an increasing function
# TODO(avik): use sympy value range analysis instead?
from sympy import Integer
@ -249,7 +249,7 @@ class _DerivedDim(Dim):
return int(_min_symint)
@property
def max(self):
def max(self): # type: ignore[override]
# assume that self.fn is an increasing function
# TODO(avik): use sympy value range analysis instead?
from sympy import Integer

2
torch/export/exported_program.py
View File

@ -567,7 +567,7 @@ def _decompose_and_get_gm_with_new_signature_constants(
# TODO(zhxhchen17) Return the new graph_signature directly.
fake_mode = detect_fake_mode(fake_args)
fake_mode = contextlib.nullcontext() if fake_mode is None else fake_mode
fake_mode = contextlib.nullcontext() if fake_mode is None else fake_mode # type: ignore[assignment]
custom_triton_ops_decomposition_ctx = (
contextlib.nullcontext
if decompose_custom_triton_ops

4
torch/export/unflatten.py
View File

@ -143,7 +143,7 @@ class InterpreterModule(_SubmoduleBase, torch.nn.Module):
super().__init__()
self.graph = graph
self._ty = ty
self.graph.owning_module = self
self.graph.owning_module = self # type: ignore[assignment]
self._run_with_interpreter = RUN_WITH_INTERPRETER
def forward(self, *args, **kwargs):
@ -296,7 +296,7 @@ class UnflattenedModule(torch.nn.Module):
export_graph = deepcopy(export_module.graph)
self.graph_signature = deepcopy(export_module.graph_signature)
self.graph = torch.fx.Graph()
self.graph.owning_module = self
self.graph.owning_module = self # type: ignore[assignment]
self.module_call_graph = deepcopy(export_module.module_call_graph)
self.flat_args_adapter = flat_args_adapter

2
torch/fx/experimental/meta_tracer.py
View File

@ -126,7 +126,7 @@ class MetaAttribute(MetaProxy):
self._node = None
@property
def node(self):
def node(self): # type: ignore[override]
# the node for attributes is added lazily, since most will just be method calls
# which do not rely on the getitem call
if self._node is None:

2
torch/fx/experimental/optimization.py
View File

@ -471,7 +471,7 @@ def optimize_for_inference(
if not use_mkl_heuristic(graph):
for node in graph.start_nodes + graph.end_nodes:
prv = node.args[0]
node.replace_all_uses_with(prv)
node.replace_all_uses_with(prv) # type: ignore[arg-type]
fx_graph.erase_node(node)
reset_modules(graph.nodes, modules, old_modules)

2
torch/fx/experimental/proxy_tensor.py
View File

@ -1011,7 +1011,7 @@ class _SymNodeDict:
) -> _PySymProxyType:
# dict.get()'s annotation doesn't accept `None` when the value type
# isn't Optional.
return self.sym_node_dict.get(key.node, default) # type: ignore[arg-type]
return self.sym_node_dict.get(key.node, default) # type: ignore[arg-type, return-value]
def __iter__(self) -> Any:
raise NotImplementedError

2
torch/fx/experimental/sym_node.py
View File

@ -1415,7 +1415,7 @@ def _make_node_magic(method, func):
out,
self.shape_env,
pytype,
out_hint,
out_hint, # type: ignore[arg-type]
fx_node=fx_node,
optimized_summation=optimized_summation, # see Note [optimized_summation]
)

2
torch/fx/experimental/unification/multipledispatch/dispatcher.py
View File

@ -354,7 +354,7 @@ class Dispatcher:
self._cache = {}
@property
def __doc__(self):
def __doc__(self): # type: ignore[override]
docs = [f"Multiply dispatched method: {self.name}"]
if self.doc:

2
torch/masked/_ops.py
View File

@ -794,7 +794,7 @@ def _sparse_csr_segment_reduction_helper(
0,
)
new_nnz = new_crow_indices[-1]
new_col_indices = col_indices.new_zeros(new_nnz)
new_col_indices = col_indices.new_zeros(new_nnz) # type: ignore[call-overload]
new_values = torch._segment_reduce(values, reduce, offsets=crow_indices) # type: ignore[attr-defined]
new_shape = [mask_input.size(0), 1]
else:

4
torch/masked/maskedtensor/core.py
View File

@ -304,7 +304,7 @@ class MaskedTensor(torch.Tensor):
return MaskedTensor(fn(data), mask)
@classmethod
def __torch_dispatch__(cls, func, types, args, kwargs):
def __torch_dispatch__(cls, func, types, args, kwargs): # type: ignore[override]
func = func.overloadpacket
from ._ops_refs import _MASKEDTENSOR_DISPATCH_TABLE
@ -355,5 +355,5 @@ class MaskedTensor(torch.Tensor):
# Update later to support more sparse layouts
@property
def is_sparse(self):
def is_sparse(self): # type: ignore[override]
return self.is_sparse_coo() or self.is_sparse_csr()

2
torch/nested/_internal/nested_tensor.py
View File

@ -319,7 +319,7 @@ class NestedTensor(torch.Tensor):
)
@classmethod
def __torch_dispatch__(cls, func, types, args=(), kwargs=None):
def __torch_dispatch__(cls, func, types, args=(), kwargs=None): # type: ignore[override]
# If you're wondering why there's a nested tensor with one of its
# size = -1, see note: [NJT outer_size in AOTDispatcher]
kwargs = {} if kwargs is None else kwargs

2
torch/nn/parallel/replicate.py
View File

@ -184,7 +184,7 @@ def replicate(
# so setattr them as non-parameter attributes
setattr(replica, key, param_copy)
# expose the parameter for DDP
replica._former_parameters[key] = param_copy
replica._former_parameters[key] = param_copy # type: ignore[operator, index]
for key, buf in module._buffers.items(): # type: ignore[assignment]
if buf is None:
for j in range(num_replicas):

10
torch/nn/utils/_expanded_weights/expanded_weights_impl.py
View File

@ -152,23 +152,23 @@ class ExpandedWeight(torch.Tensor):
)
@property
def dtype(self):
def dtype(self): # type: ignore[override]
return self.orig_weight.dtype
@property
def data(self):
def data(self): # type: ignore[override]
return self.orig_weight.data
@property
def shape(self):
def shape(self): # type: ignore[override]
return self.orig_weight.shape
@property
def device(self):
def device(self): # type: ignore[override]
return self.orig_weight.device
@property
def is_cuda(self):
def is_cuda(self): # type: ignore[override]
return self.orig_weight.is_cuda
def data_ptr(self):

12
torch/nn/utils/prune.py
View File

@ -417,7 +417,7 @@ class Identity(BasePruningMethod):
return mask
@classmethod
def apply(cls, module, name):
def apply(cls, module, name): # type: ignore[override]
r"""Add pruning on the fly and reparametrization of a tensor.
Adds the forward pre-hook that enables pruning on the fly and
@ -472,7 +472,7 @@ class RandomUnstructured(BasePruningMethod):
return mask
@classmethod
def apply(cls, module, name, amount):
def apply(cls, module, name, amount): # type: ignore[override]
r"""Add pruning on the fly and reparametrization of a tensor.
Adds the forward pre-hook that enables pruning on the fly and
@ -531,7 +531,7 @@ class L1Unstructured(BasePruningMethod):
return mask
@classmethod
def apply(cls, module, name, amount, importance_scores=None):
def apply(cls, module, name, amount, importance_scores=None): # type: ignore[override]
r"""Add pruning on the fly and reparametrization of a tensor.
Adds the forward pre-hook that enables pruning on the fly and
@ -642,7 +642,7 @@ class RandomStructured(BasePruningMethod):
return mask
@classmethod
def apply(cls, module, name, amount, dim=-1):
def apply(cls, module, name, amount, dim=-1): # type: ignore[override]
r"""Add pruning on the fly and reparametrization of a tensor.
Adds the forward pre-hook that enables pruning on the fly and
@ -758,7 +758,7 @@ class LnStructured(BasePruningMethod):
return mask
@classmethod
def apply(cls, module, name, amount, n, dim, importance_scores=None):
def apply(cls, module, name, amount, n, dim, importance_scores=None): # type: ignore[override]
r"""Add pruning on the fly and reparametrization of a tensor.
Adds the forward pre-hook that enables pruning on the fly and
@ -805,7 +805,7 @@ class CustomFromMask(BasePruningMethod):
return mask
@classmethod
def apply(cls, module, name, mask):
def apply(cls, module, name, mask): # type: ignore[override]
r"""Add pruning on the fly and reparametrization of a tensor.
Adds the forward pre-hook that enables pruning on the fly and

6
torch/onnx/_internal/exporter/_torchlib/ops/nn.py
View File

@ -7,7 +7,11 @@ from __future__ import annotations
from typing import Optional, TYPE_CHECKING
from onnxscript.onnx_opset import opset20 as op20, opset21 as op21, opset23 as op23
from onnxscript.onnx_opset import ( # type: ignore[attr-defined]
opset20 as op20,
opset21 as op21,
opset23 as op23,
)
import torch
from torch.onnx._internal._lazy_import import onnxscript_ir as ir

2
torch/optim/lbfgs.py
View File

@ -299,7 +299,7 @@ class LBFGS(Optimizer):
return loss, flat_grad
@torch.no_grad()
def step(self, closure):
def step(self, closure): # type: ignore[override]
"""Perform a single optimization step.
Args:

2
torch/serialization.py
View File

@ -825,7 +825,7 @@ def _open_zipfile_writer(name_or_buffer: Union[str, IO[bytes]]) -> _opener:
container = _open_zipfile_writer_file
else:
container = _open_zipfile_writer_buffer
return container(name_or_buffer)
return container(name_or_buffer) # type: ignore[arg-type]
def _is_compressed_file(f) -> bool:

4
torch/sparse/semi_structured.py
View File

@ -197,10 +197,10 @@ class SparseSemiStructuredTensor(torch.Tensor):
requires_grad=requires_grad,
)
__torch_function__ = torch._C._disabled_torch_function_impl
__torch_function__ = torch._C._disabled_torch_function_impl # type: ignore[assignment]
@classmethod
def __torch_dispatch__(cls, func, types, args, kwargs) -> Any:
def __torch_dispatch__(cls, func, types, args, kwargs) -> Any: # type: ignore[override]
if func._overloadpacket not in cls.SPARSE_DISPATCH:
raise NotImplementedError(
f"{cls.__name__} only supports a specific set of operations, "

2
torch/storage.py
View File

@ -1523,7 +1523,7 @@ class _LegacyStorageMeta(type):
class _LegacyStorage(TypedStorage, metaclass=_LegacyStorageMeta):
@classmethod
def _new_shared(cls, size):
def _new_shared(cls, size): # type: ignore[override]
"""Create a new storage in shared memory with the same data type."""
untyped_storage = torch.UntypedStorage._new_shared(size * cls()._element_size())
return cls(wrap_storage=untyped_storage)

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

@ -1180,7 +1180,7 @@ class FSDPTest(MultiProcessTestCase):
return run_subtests(self, *args, **kwargs)
@classmethod
def _run(cls, rank, test_name, file_name, pipe, **kwargs):
def _run(cls, rank, test_name, file_name, pipe, **kwargs): # type: ignore[override]
self = cls(test_name)
self.rank = rank
self.file_name = file_name

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

@ -3900,7 +3900,7 @@ class TestCase(expecttest.TestCase):
((0, 0), [(1, 2)], [()]),
]:
for blocksize in blocksizes:
for densesize in densesizes:
for densesize in densesizes: # type: ignore[attr-defined]
if layout == torch.strided:
indices = () # type: ignore[assignment]
values = torch.empty((basesize + densesize), device=device, dtype=dtype)