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Add pyrefly suppressions 2/n (#164513)

Browse Source 
Adds suppressions to pyrefly will typecheck clean: https://github.com/pytorch/pytorch/issues/163283

Test plan:
dmypy restart && python3 scripts/lintrunner.py -a
pyrefly check

---
step 1: uncomment lines in the `pyrefly.toml` file
before: https://gist.github.com/maggiemoss/911b4d0bc88bf8cf3ab91f67184e9d46

after:
```
 INFO Checking project configured at `/Users/maggiemoss/python_projects/pytorch/pyrefly.toml`
 INFO 0 errors (1,152 ignored)
 ```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/164513
Approved by: https://github.com/oulgen
This commit is contained in:
Maggie Moss 2025-10-03 02:46:09 +00:00 committed by PyTorch MergeBot
parent d1cbb74fb1
commit 1051c1de5c
26 changed files with 133 additions and 13 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
pyrefly.toml
View File

@ -40,16 +40,6 @@ project-excludes = [
"torch/autograd/**",
"torch/cuda/**",
"torch/export/**",
"torch/profiler/**",
"torch/_prims_common/**",
"torch/backends/**",
# "torch/testing/**",
"torch/_C/**",
"torch/sparse/**",
"torch/_library/**",
"torch/_prims/**",
"torch/_decomp/**",
"torch/_meta_registrations.py",
# formatting issues
"torch/linalg/__init__.py",
"torch/package/importer.py",

11
torch/_C/_distributed_c10d.pyi
View File

@ -112,17 +112,28 @@ class DebugLevel(Enum):
DETAIL = ...
class ReduceOp:
# pyrefly: ignore # unknown-name
def __init__(self, op: RedOpType) -> None: ...
# pyrefly: ignore # unknown-name
SUM: RedOpType = ...
# pyrefly: ignore # unknown-name
AVG: RedOpType = ...
# pyrefly: ignore # unknown-name
PRODUCT: RedOpType = ...
# pyrefly: ignore # unknown-name
MIN: RedOpType = ...
# pyrefly: ignore # unknown-name
MAX: RedOpType = ...
# pyrefly: ignore # unknown-name
BAND: RedOpType = ...
# pyrefly: ignore # unknown-name
BOR: RedOpType = ...
# pyrefly: ignore # unknown-name
BXOR: RedOpType = ...
# pyrefly: ignore # unknown-name
PREMUL_SUM: RedOpType = ...
# pyrefly: ignore # unknown-name
UNUSED: RedOpType = ...
# mypy error being ignored:

1
torch/_decomp/__init__.py
View File

@ -240,6 +240,7 @@ def get_decompositions(
registry = global_decomposition_table[type]
packets_to_overloads = defaultdict(list)
# pyrefly: ignore # bad-assignment
for opo in registry:
if isinstance(opo, (OpOverload, OpOverloadPacket)):
packets_to_overloads[opo.overloadpacket].append(opo)

5
torch/_decomp/decompositions.py
View File

@ -382,6 +382,7 @@ def to_real_dtype(dtype: torch.dtype):
def mse_loss(
self: Tensor, target: Tensor, reduction: int = Reduction.MEAN.value
) -> Tensor:
# pyrefly: ignore # unsupported-operation
loss = (self - target) ** 2
return apply_loss_reduction(loss, reduction)
@ -415,6 +416,7 @@ def smooth_l1_loss(
beta: float = 1.0,
):
loss = (self - target).abs()
# pyrefly: ignore # unsupported-operation
loss = torch.where(loss < beta, 0.5 * loss**2 / beta, loss - 0.5 * beta)
return apply_loss_reduction(loss, reduction)
@ -4893,7 +4895,9 @@ def _reflection_pad_backward(grad_output, x, padding):
@register_decomposition(aten.aminmax)
@out_wrapper("min", "max")
def aminmax(self, *, dim=None, keepdim=False):
# pyrefly: ignore # bad-argument-type
amin = torch.amin(self, dim=dim, keepdim=keepdim)
# pyrefly: ignore # bad-argument-type
amax = torch.amax(self, dim=dim, keepdim=keepdim)
return amin, amax
@ -5138,6 +5142,7 @@ def baddbmm(self, batch1, batch2, beta=1, alpha=1):
alpha = int(alpha)
result = torch.bmm(batch1, batch2)
if not isinstance(alpha, numbers.Number) or alpha != 1:
# pyrefly: ignore # unsupported-operation
result = result * alpha
if beta == 0:
return result

2
torch/_library/fake_profile.py
View File

@ -245,6 +245,7 @@ def save_op_profiles(op_profiles: dict[str, set[OpProfile]], f: FileLike) -> Non
yaml_str = generate_yaml_from_profiles(op_profiles)
if isinstance(f, (str, os.PathLike)):
# pyrefly: ignore # no-matching-overload
f = os.fspath(f)
with open(f, "w") as file:
@ -309,6 +310,7 @@ def load_op_profiles(f: FileLike) -> dict[str, set[OpProfile]]:
Loads the saved operator profiles from `save_op_profiles`.
"""
if isinstance(f, (str, os.PathLike)):
# pyrefly: ignore # no-matching-overload
f = os.fspath(f)
with open(f) as file:

5
torch/_library/infer_schema.py
View File

@ -159,6 +159,7 @@ def infer_schema(
schema_type = f"Tensor(a{idx}!){schema_type[len('Tensor') :]}"
seen_args.add(name)
if param.default is inspect.Parameter.empty:
# pyrefly: ignore # bad-argument-type
params.append(f"{schema_type} {name}")
else:
default_repr = None
@ -176,6 +177,7 @@ def infer_schema(
f"Parameter {name} has an unsupported default value type {type(param.default)}. "
f"Please file an issue on GitHub so we can prioritize this."
)
# pyrefly: ignore # bad-argument-type
params.append(f"{schema_type} {name}={default_repr}")
if mutates_args != UNKNOWN_MUTATES:
mutates_args_not_seen = set(mutates_args) - seen_args
@ -202,6 +204,7 @@ def derived_types(
):
result: list[tuple[Union[type, typing._SpecialForm, GenericAlias], str]] = [
(base_type, cpp_type),
# pyrefly: ignore # not-a-type
(typing.Optional[base_type], f"{cpp_type}?"),
]
@ -220,6 +223,7 @@ def derived_types(
if optional_base_list:
result.extend(
(seq_typ, f"{cpp_type}?[]")
# pyrefly: ignore # not-a-type
for seq_typ in derived_seq_types(typing.Optional[base_type])
)
if optional_list_base:
@ -273,6 +277,7 @@ def parse_return(annotation, error_fn):
f"Return has unsupported type {annotation}. "
f"The valid types are: {SUPPORTED_RETURN_TYPES}."
)
# pyrefly: ignore # index-error
return SUPPORTED_RETURN_TYPES[annotation]
args = typing.get_args(annotation)

10
torch/_meta_registrations.py
View File

@ -2341,16 +2341,19 @@ def calc_conv_nd_return_shape(
ret_shape = [input_tensor.shape[0], out_channels]
if isinstance(stride, IntLike):
# pyrefly: ignore # bad-assignment
stride = [stride] * len(dims)
elif len(stride) == 1:
stride = [stride[0]] * len(dims)
if isinstance(padding, IntLike):
# pyrefly: ignore # bad-assignment
padding = [padding] * len(dims)
elif len(padding) == 1:
padding = [padding[0]] * len(dims)
if isinstance(dilation, IntLike):
# pyrefly: ignore # bad-assignment
dilation = [dilation] * len(dims)
elif len(dilation) == 1:
dilation = [dilation[0]] * len(dims)
@ -2358,6 +2361,7 @@ def calc_conv_nd_return_shape(
output_padding_list: Optional[list[int]] = None
if output_padding:
if isinstance(output_padding, IntLike):
# pyrefly: ignore # bad-assignment
output_padding_list = [output_padding] * len(dims)
elif len(output_padding) == 1:
output_padding_list = [output_padding[0]] * len(dims)
@ -2370,15 +2374,19 @@ def calc_conv_nd_return_shape(
ret_shape.append(
_formula_transposed(
dims[i],
# pyrefly: ignore # index-error
padding[i],
# pyrefly: ignore # index-error
dilation[i],
kernel_size[i],
# pyrefly: ignore # index-error
stride[i],
output_padding_list[i],
)
)
else:
ret_shape.append(
# pyrefly: ignore # index-error
_formula(dims[i], padding[i], dilation[i], kernel_size[i], stride[i])
)
from torch.fx.experimental.symbolic_shapes import sym_or
@ -3444,6 +3452,7 @@ def meta_index_Tensor(self, indices):
"""
shape = before_shape + replacement_shape + after_shape
strides = list(self.stride())
# pyrefly: ignore # unsupported-operation
strides[len(before_shape) : len(self.shape) - len(after_shape)] = [0] * len(
replacement_shape
)
@ -6655,6 +6664,7 @@ def rnn_cell_checkSizes(
)
torch._check(
all(
# pyrefly: ignore # missing-attribute
x.device == input_gates.device
for x in [hidden_gates, input_bias, hidden_bias, prev_hidden]
),

3
torch/_prims/__init__.py
View File

@ -427,6 +427,7 @@ def _prim_elementwise_meta(
# Acquires the device (if it exists) or number
device = None
number = None
# pyrefly: ignore # bad-assignment
for arg in args_:
if isinstance(arg, TensorLike):
if utils.is_cpu_scalar_tensor(arg):
@ -1015,8 +1016,10 @@ def _div_aten(a, b):
)
if is_integral:
# pyrefly: ignore # bad-argument-type
return torch.div(a, b, rounding_mode="trunc")
else:
# pyrefly: ignore # bad-argument-type
return torch.true_divide(a, b)

3
torch/_prims/context.py
View File

@ -125,6 +125,7 @@ class TorchRefsMode(torch.overrides.TorchFunctionMode):
# Unless we are in prims_mode, in which case we want to use nvprims
if orig_func in torch_function_passthrough or orig_func in all_prims():
with self.prims_mode_cls():
# pyrefly: ignore # invalid-param-spec
return orig_func(*args, **kwargs)
mapping = torch_to_refs_map()
func = mapping.get(orig_func, None)
@ -147,6 +148,7 @@ class TorchRefsMode(torch.overrides.TorchFunctionMode):
if func is not None:
# If the ref exists query whether we should use it or not
if self.should_fallback_fn(self, orig_func, func, args, kwargs):
# pyrefly: ignore # invalid-param-spec
return orig_func(*args, **kwargs)
# torch calls inside func should be interpreted as refs calls
with self:
@ -155,4 +157,5 @@ class TorchRefsMode(torch.overrides.TorchFunctionMode):
raise RuntimeError(
f"no _refs support for {torch.overrides.resolve_name(orig_func)}"
)
# pyrefly: ignore # invalid-param-spec
return orig_func(*args, **kwargs)

3
torch/_prims/rng_prims.py
View File

@ -29,6 +29,7 @@ def register_rng_prim(name, schema, impl_aten, impl_meta, doc, tags=None):
rngprim_def = torch.library.custom_op(
"rngprims::" + name, impl_aten, mutates_args=(), schema=schema
)
# pyrefly: ignore # missing-attribute
rngprim_def.register_fake(impl_meta)
prim_packet = getattr(torch._ops.ops.rngprims, name)
@ -329,9 +330,11 @@ def register_graphsafe_run_with_rng_state_op():
@graphsafe_run_with_rng_state.py_impl(DispatchKey.CUDA)
def impl_cuda(op, *args, rng_state=None, **kwargs):
# pyrefly: ignore # missing-attribute
device_idx = rng_state.device.index
generator = torch.cuda.default_generators[device_idx]
current_state = generator.graphsafe_get_state()
# pyrefly: ignore # bad-argument-type
generator.graphsafe_set_state(rng_state)
out = op(*args, **kwargs)
generator.graphsafe_set_state(current_state)

21
torch/_prims_common/__init__.py
View File

@ -113,6 +113,7 @@ def same_shape(a: ShapeType, b: ShapeType, *, allow_rhs_unbacked=False) -> bool:
if len(a) != len(b):
return False
# pyrefly: ignore # bad-assignment
for x, y in zip(a, b):
if allow_rhs_unbacked:
if isinstance(y, torch.SymInt):
@ -389,7 +390,11 @@ def validate_memory_format(memory_format: torch.memory_format):
def is_contiguous_for_memory_format( # type: ignore[return]
a: Tensor, *, memory_format: torch.memory_format, false_if_dde=False
a: Tensor,
*,
memory_format: torch.memory_format,
false_if_dde=False,
# pyrefly: ignore # bad-return
) -> bool:
validate_memory_format(memory_format)
@ -810,12 +815,16 @@ def canonicalize_dim(rank: int, idx: int, wrap_scalar: bool = True) -> int:
# mapping negative offsets to positive ones
@overload
def canonicalize_dims(
rank: int, indices: Sequence[int], wrap_scalar: bool = True
rank: int,
indices: Sequence[int],
wrap_scalar: bool = True,
# pyrefly: ignore # bad-return
) -> tuple[int, ...]:
pass
@overload
# pyrefly: ignore # bad-return
def canonicalize_dims(rank: int, indices: int, wrap_scalar: bool = True) -> int:
pass
@ -862,6 +871,7 @@ def check_same_device(*args, allow_cpu_scalar_tensors):
# Note: cannot initialize device to the first arg's device (it may not have one)
device = None
# pyrefly: ignore # bad-assignment
for arg in args:
if isinstance(arg, Number):
continue
@ -909,6 +919,7 @@ def check_same_shape(*args, allow_cpu_scalar_tensors: bool):
"""
shape = None
# pyrefly: ignore # bad-assignment
for arg in args:
if isinstance(arg, Number):
continue
@ -935,6 +946,7 @@ def extract_shape(*args, allow_cpu_scalar_tensors: bool) -> Optional[ShapeType]:
shape = None
scalar_shape = None
# pyrefly: ignore # bad-assignment
for arg in args:
if isinstance(arg, Number):
continue
@ -991,6 +1003,7 @@ def extract_shape_from_varargs(
# Handles tuple unwrapping
if len(shape) == 1 and isinstance(shape[0], Sequence):
# pyrefly: ignore # bad-assignment
shape = shape[0]
if validate:
@ -1292,6 +1305,7 @@ def get_higher_dtype(
raise RuntimeError("Unexpected type given to _extract_dtype!")
# pyrefly: ignore # bad-argument-type
a, b = _extract_dtype(a), _extract_dtype(b)
if a is b:
@ -1387,6 +1401,7 @@ def check_same_dtype(*args):
full_dtype = None
scalar_type = None
# pyrefly: ignore # bad-assignment
for arg in args:
if isinstance(arg, Number):
# Scalar type checking is disabled (and may be removed in the future)
@ -1657,8 +1672,10 @@ def elementwise_dtypes(
# Prefers dtype of tensors with one or more dimensions
if one_plus_dim_tensor_dtype is not None:
# pyrefly: ignore # bad-return
return one_plus_dim_tensor_dtype
# pyrefly: ignore # bad-return
return zero_dim_tensor_dtype
if highest_type is float:

13
torch/_prims_common/wrappers.py
View File

@ -28,16 +28,19 @@ _P = ParamSpec("_P")
@overload
# pyrefly: ignore # bad-return
def _maybe_convert_to_dtype(a: TensorLikeType, dtype: torch.dtype) -> TensorLikeType:
pass
@overload
# pyrefly: ignore # bad-return
def _maybe_convert_to_dtype(a: NumberType, dtype: torch.dtype) -> NumberType:
pass
@overload
# pyrefly: ignore # bad-return
def _maybe_convert_to_dtype(a: Sequence, dtype: torch.dtype) -> Sequence:
pass
@ -276,7 +279,10 @@ def out_wrapper(
TensorLikeType
if is_tensor
else NamedTuple(
f"return_types_{fn.__name__}", [(o, TensorLikeType) for o in out_names]
# pyrefly: ignore # bad-argument-count
f"return_types_{fn.__name__}",
# pyrefly: ignore # bad-argument-count
[(o, TensorLikeType) for o in out_names],
)
)
@ -294,6 +300,7 @@ def out_wrapper(
kwargs[k] = out_attr
def maybe_check_copy_devices(out):
# pyrefly: ignore # unsupported-operation
if isinstance(out, TensorLike) and isinstance(args[0], TensorLike):
check_copy_devices(copy_from=args[0], copy_to=out)
@ -429,6 +436,7 @@ def backwards_not_supported(prim):
class BackwardsNotSupported(torch.autograd.Function):
@staticmethod
# pyrefly: ignore # bad-override
def forward(ctx, args_spec, *flat_args):
args, kwargs = tree_unflatten(flat_args, args_spec) # type: ignore[arg-type]
return redispatch_prim(args, kwargs)
@ -477,11 +485,14 @@ def elementwise_unary_scalar_wrapper(
dtype = utils.type_to_dtype(type(args[0]))
args_ = list(args)
args_[0] = torch.tensor(args[0], dtype=dtype)
# pyrefly: ignore # invalid-param-spec
result = fn(*args_, **kwargs)
assert isinstance(result, torch.Tensor)
return result.item()
# pyrefly: ignore # invalid-param-spec
return fn(*args, **kwargs)
_fn.__signature__ = sig # type: ignore[attr-defined]
# pyrefly: ignore # bad-return
return _fn

3
torch/backends/_nnapi/serializer.py
View File

@ -414,6 +414,7 @@ class _NnapiSerializer:
) # noqa: TRY002
return Operand(
shape=tuple(tensor.shape),
# pyrefly: ignore # bad-argument-type
op_type=op_type,
dim_order=dim_order,
scale=scale,
@ -1734,11 +1735,13 @@ class _NnapiSerializer:
for dim in (2, 3): # h, w indices
if image_oper.shape[dim] == 0:
if size_ctype.kind() != "NoneType":
# pyrefly: ignore # unsupported-operation
self.compute_operand_shape(out_id, dim, size_arg[dim - 2])
elif scale_ctype.kind() != "NoneType":
self.compute_operand_shape(
out_id,
dim,
# pyrefly: ignore # unsupported-operation
f"int({scale_arg[dim - 2]} * {flex_name(image_id, dim)})",
)
else:

4
torch/backends/cudnn/__init__.py
View File

@ -34,8 +34,11 @@ if _cudnn is not None:
def _init():
global __cudnn_version
if __cudnn_version is None:
# pyrefly: ignore # missing-attribute
__cudnn_version = _cudnn.getVersionInt()
# pyrefly: ignore # missing-attribute
runtime_version = _cudnn.getRuntimeVersion()
# pyrefly: ignore # missing-attribute
compile_version = _cudnn.getCompileVersion()
runtime_major, runtime_minor, _ = runtime_version
compile_major, compile_minor, _ = compile_version
@ -44,6 +47,7 @@ if _cudnn is not None:
# Not sure about MIOpen (ROCm), so always do a strict check
if runtime_major != compile_major:
cudnn_compatible = False
# pyrefly: ignore # missing-attribute
elif runtime_major < 7 or not _cudnn.is_cuda:
cudnn_compatible = runtime_minor == compile_minor
else:

5
torch/backends/cudnn/rnn.py
View File

@ -12,12 +12,16 @@ except ImportError:
def get_cudnn_mode(mode):
if mode == "RNN_RELU":
# pyrefly: ignore # missing-attribute
return int(_cudnn.RNNMode.rnn_relu)
elif mode == "RNN_TANH":
# pyrefly: ignore # missing-attribute
return int(_cudnn.RNNMode.rnn_tanh)
elif mode == "LSTM":
# pyrefly: ignore # missing-attribute
return int(_cudnn.RNNMode.lstm)
elif mode == "GRU":
# pyrefly: ignore # missing-attribute
return int(_cudnn.RNNMode.gru)
else:
raise Exception(f"Unknown mode: {mode}") # noqa: TRY002
@ -56,6 +60,7 @@ def init_dropout_state(dropout, train, dropout_seed, dropout_state):
dropout_p,
train,
dropout_seed,
# pyrefly: ignore # unexpected-keyword
self_ty=torch.uint8,
device=torch.device("cuda"),
)

1
torch/backends/cusparselt/__init__.py
View File

@ -23,6 +23,7 @@ if _cusparselt is not None:
global __cusparselt_version
global __MAX_ALG_ID
if __cusparselt_version is None:
# pyrefly: ignore # missing-attribute
__cusparselt_version = _cusparselt.getVersionInt()
if __cusparselt_version == 400:
__MAX_ALG_ID = 4

1
torch/backends/opt_einsum/__init__.py
View File

@ -70,6 +70,7 @@ def _set_strategy(_strategy: str) -> None:
def _get_strategy() -> str:
# pyrefly: ignore # bad-return
return strategy

5
torch/backends/xeon/run_cpu.py
View File

@ -835,6 +835,7 @@ def create_args(parser=None):
@retval ArgumentParser
"""
# pyrefly: ignore # missing-attribute
parser.add_argument(
"--multi-instance",
"--multi_instance",
@ -843,6 +844,7 @@ def create_args(parser=None):
help="Enable multi-instance, by default one instance per node",
)
# pyrefly: ignore # missing-attribute
parser.add_argument(
"-m",
"--module",
@ -853,6 +855,7 @@ def create_args(parser=None):
'"python -m".',
)
# pyrefly: ignore # missing-attribute
parser.add_argument(
"--no-python",
"--no_python",
@ -867,6 +870,7 @@ def create_args(parser=None):
_add_multi_instance_params(parser)
# positional
# pyrefly: ignore # missing-attribute
parser.add_argument(
"program",
type=str,
@ -875,6 +879,7 @@ def create_args(parser=None):
)
# rest from the training program
# pyrefly: ignore # missing-attribute
parser.add_argument("program_args", nargs=REMAINDER)

10
torch/profiler/_memory_profiler.py
View File

@ -230,6 +230,7 @@ class SchemaMatcher:
for schema in cls.match_schemas(t):
mutable = mutable or [False for _ in schema.arguments]
for i, arg in enumerate(schema.arguments):
# pyrefly: ignore # unsupported-operation
mutable[i] |= getattr(arg.alias_info, "is_write", False)
return tuple(mutable or (None for _ in t.inputs))
@ -671,6 +672,7 @@ class MemoryProfile:
output: list[tuple[int, Action, KeyAndID, int]] = []
allocation_times: dict[tuple[TensorKey, bool], int] = {}
live_unknown: dict[tuple[int, torch.device], Literal[True]] = {}
# pyrefly: ignore # bad-assignment
for event in self._op_tree.dfs():
if event.typed[0] == _EventType.Allocation:
alloc_fields = event.typed[1]
@ -772,11 +774,14 @@ class MemoryProfile:
for key, (_, version) in node.inputs.items()
if self._categories.get(key, version)
in (Category.GRADIENT, Category.PARAMETER)
# pyrefly: ignore # unsupported-operation
or key.id in depends_on_gradient
)
if ids:
# pyrefly: ignore # missing-attribute
depends_on_gradient.update(ids)
# pyrefly: ignore # missing-attribute
depends_on_gradient.update(key.id for key in node.outputs)
# We are guaranteed to exit because there is a finite set of
@ -785,6 +790,7 @@ class MemoryProfile:
# once to fold the first step into that loop, and a third time
# where no new elements are added.
if len(depends_on_gradient) == start_size:
# pyrefly: ignore # bad-return
return depends_on_gradient
def _set_gradients_and_temporaries(self) -> None:
@ -1081,6 +1087,7 @@ class MemoryProfileTimeline:
if action in (Action.PREEXISTING, Action.CREATE):
raw_events.append(
# pyrefly: ignore # bad-argument-type
(
t,
_ACTION_TO_INDEX[action],
@ -1091,6 +1098,7 @@ class MemoryProfileTimeline:
elif action == Action.INCREMENT_VERSION:
raw_events.append(
# pyrefly: ignore # bad-argument-type
(
t,
_ACTION_TO_INDEX[action],
@ -1099,6 +1107,7 @@ class MemoryProfileTimeline:
)
)
raw_events.append(
# pyrefly: ignore # bad-argument-type
(
t,
_ACTION_TO_INDEX[action],
@ -1109,6 +1118,7 @@ class MemoryProfileTimeline:
elif action == Action.DESTROY:
raw_events.append(
# pyrefly: ignore # bad-argument-type
(
t,
_ACTION_TO_INDEX[action],

1
torch/profiler/_utils.py
View File

@ -211,6 +211,7 @@ class BasicEvaluation:
# Find latest cuda kernel event
if hasattr(event, "start_us"):
start_time = event.start_us() * 1000
# pyrefly: ignore # missing-attribute
end_time = (event.start_us() + event.duration_us()) * 1000
# Find current spawned cuda kernel event
if event in kernel_mapping and kernel_mapping[event] is not None:

6
torch/profiler/profiler.py
View File

@ -161,14 +161,19 @@ class _KinetoProfile:
self.mem_tl: Optional[MemoryProfileTimeline] = None
self.use_device = None
if ProfilerActivity.CUDA in self.activities:
# pyrefly: ignore # bad-assignment
self.use_device = "cuda"
elif ProfilerActivity.XPU in self.activities:
# pyrefly: ignore # bad-assignment
self.use_device = "xpu"
elif ProfilerActivity.MTIA in self.activities:
# pyrefly: ignore # bad-assignment
self.use_device = "mtia"
elif ProfilerActivity.HPU in self.activities:
# pyrefly: ignore # bad-assignment
self.use_device = "hpu"
elif ProfilerActivity.PrivateUse1 in self.activities:
# pyrefly: ignore # bad-assignment
self.use_device = _get_privateuse1_backend_name()
# user-defined metadata to be amended to the trace
@ -380,6 +385,7 @@ class _KinetoProfile:
}
if backend == "nccl":
nccl_version = torch.cuda.nccl.version()
# pyrefly: ignore # unsupported-operation
dist_info["nccl_version"] = ".".join(str(v) for v in nccl_version)
return dist_info

3
torch/sparse/__init__.py
View File

@ -623,17 +623,20 @@ def as_sparse_gradcheck(gradcheck):
)
obj = obj.to_dense().sparse_mask(full_mask)
if obj.layout is torch.sparse_coo:
# pyrefly: ignore # no-matching-overload
d.update(
indices=obj._indices(), is_coalesced=obj.is_coalesced()
)
values = obj._values()
elif obj.layout in {torch.sparse_csr, torch.sparse_bsr}:
# pyrefly: ignore # no-matching-overload
d.update(
compressed_indices=obj.crow_indices(),
plain_indices=obj.col_indices(),
)
values = obj.values()
else:
# pyrefly: ignore # no-matching-overload
d.update(
compressed_indices=obj.ccol_indices(),
plain_indices=obj.row_indices(),

7
torch/sparse/_triton_ops.py
View File

@ -385,6 +385,7 @@ def scatter_mm(blocks, others, indices_data, *, accumulators=None):
g1 = c_offsets[r + 1]
for g in range(g0, g1):
p, q = pq[g]
# pyrefly: ignore # unsupported-operation
accumulators[r] += blocks[p] @ others[q]
else:
_scatter_mm2(blocks, others, c_offsets, pq, accumulators)
@ -1296,6 +1297,7 @@ def bsr_dense_addmm(
assert alpha != 0
def kernel(grid, *sliced_tensors):
# pyrefly: ignore # unsupported-operation
_bsr_strided_addmm_kernel[grid](
*ptr_stride_extractor(*sliced_tensors),
beta,
@ -1425,6 +1427,7 @@ if has_triton():
mat1_block = tl.load(
mat1_block_ptrs + mat1_col_block_stride * k_offsets[None, :],
# pyrefly: ignore # index-error
mask=mask_k[None, :],
other=0.0,
)
@ -1433,6 +1436,7 @@ if has_triton():
mat2_block_ptrs
+ mat2_tiled_col_stride * col_block
+ mat2_row_block_stride * k_offsets[:, None],
# pyrefly: ignore # index-error
mask=mask_k[:, None],
other=0.0,
)
@ -1970,6 +1974,7 @@ if has_triton():
if attn_mask.dtype is not torch.bool:
check_dtype(f_name, attn_mask, query.dtype)
# pyrefly: ignore # not-callable
sdpa = sampled_addmm(
attn_mask, query, key.transpose(-2, -1), beta=0.0, skip_checks=False
)
@ -1981,8 +1986,10 @@ if has_triton():
)
scale_factor = 1 / math.sqrt(query.size(-1)) if scale is None else scale
sdpa.values().mul_(scale_factor)
# pyrefly: ignore # not-callable
sdpa = bsr_softmax(sdpa)
torch.nn.functional.dropout(sdpa.values(), p=dropout_p, inplace=True)
# pyrefly: ignore # not-callable
sdpa = bsr_dense_mm(sdpa, value)
return sdpa

3
torch/sparse/_triton_ops_meta.py
View File

@ -232,8 +232,10 @@ def dump():
part2 = current_content[end_data_index:]
data_part = []
for op_key in sorted(_operation_device_version_data, key=sort_key):
# pyrefly: ignore # bad-argument-type
data_part.append(" " + repr(op_key).replace("'", '"') + ": {")
op_data = _operation_device_version_data[op_key]
# pyrefly: ignore # bad-argument-type
data_part.extend(f" {key}: {op_data[key]}," for key in sorted(op_data))
data_part.append(" },")
new_content = part1 + "\n".join(data_part) + "\n" + part2
@ -367,6 +369,7 @@ def minimize(
if next_target < minimal_target:
minimal_target = next_target
parameters = next_parameters
# pyrefly: ignore # unsupported-operation
pbar.total += i + 1
break
else:

2
torch/testing/__init__.py
View File

@ -1,5 +1,7 @@
from torch._C import FileCheck as FileCheck
from . import _utils
# pyrefly: ignore # deprecated
from ._comparison import assert_allclose, assert_close as assert_close
from ._creation import make_tensor as make_tensor

8
torch/testing/_comparison.py
View File

@ -241,6 +241,7 @@ def make_scalar_mismatch_msg(
Defaults to "Scalars".
"""
abs_diff = abs(actual - expected)
# pyrefly: ignore # bad-argument-type
rel_diff = float("inf") if expected == 0 else abs_diff / abs(expected)
return _make_mismatch_msg(
default_identifier="Scalars",
@ -484,6 +485,7 @@ class BooleanPair(Pair):
def _supported_types(self) -> tuple[type, ...]:
cls: list[type] = [bool]
if HAS_NUMPY:
# pyrefly: ignore # missing-attribute
cls.append(np.bool_)
return tuple(cls)
@ -499,6 +501,7 @@ class BooleanPair(Pair):
def _to_bool(self, bool_like: Any, *, id: tuple[Any, ...]) -> bool:
if isinstance(bool_like, bool):
return bool_like
# pyrefly: ignore # missing-attribute
elif isinstance(bool_like, np.bool_):
return bool_like.item()
else:
@ -578,6 +581,7 @@ class NumberPair(Pair):
def _supported_types(self) -> tuple[type, ...]:
cls = list(self._NUMBER_TYPES)
if HAS_NUMPY:
# pyrefly: ignore # missing-attribute
cls.append(np.number)
return tuple(cls)
@ -593,6 +597,7 @@ class NumberPair(Pair):
def _to_number(
self, number_like: Any, *, id: tuple[Any, ...]
) -> Union[int, float, complex]:
# pyrefly: ignore # missing-attribute
if HAS_NUMPY and isinstance(number_like, np.number):
return number_like.item()
elif isinstance(number_like, self._NUMBER_TYPES):
@ -1115,6 +1120,7 @@ def originate_pairs(
mapping_types: tuple[type, ...] = (collections.abc.Mapping,),
id: tuple[Any, ...] = (),
**options: Any,
# pyrefly: ignore # bad-return
) -> list[Pair]:
"""Originates pairs from the individual inputs.
@ -1310,7 +1316,9 @@ def not_close_error_metas(
# would not get freed until cycle collection, leaking cuda memory in tests.
# We break the cycle by removing the reference to the error_meta objects
# from this frame as it returns.
# pyrefly: ignore # bad-assignment
error_metas = [error_metas]
# pyrefly: ignore # bad-return
return error_metas.pop()