Clean up unused Pyrefly suppressions (#166178)

Cleaning up ignores that are no longer needed in the repo and adding select suppressions so the main branch is clean.

test plan:
`lintrunner -a`

Pull Request resolved: https://github.com/pytorch/pytorch/pull/166178
Approved by: https://github.com/oulgen

torch/_dynamo/eval_frame.py

@@ -2103,11 +2103,10 @@ def export(
             )
             and not trace_rules.check(call_to_inspect)
         ):
-            # pyrefly: ignore  # unbound-name
             dim_constraints.solve()
-            # pyrefly: ignore  # unbound-name
+
             forced_specializations = dim_constraints.forced_specializations()
-            # pyrefly: ignore  # unbound-name
+
             msg = dim_constraints.prettify_results(
                 original_signature,
                 dynamic_shapes,
@@ -2128,11 +2127,10 @@ def export(
                     )
 
             # Error if we have any constraints on static values
-            # pyrefly: ignore  # unbound-name
+
             for k in shape_env.var_to_range.keys():
                 if isinstance(k, sympy.Integer):
                     constraint_violation_error = ConstraintViolationError(
-                        # pyrefly: ignore  # unbound-name
                         f"{''.join(traceback.format_list(shape_env.var_to_stack[k]))}\n"
                         "It appears that you're trying to set a constraint on a "
                         f"value which we evaluated to have a static value of {k}. "

torch/_dynamo/functional_export.py

@@ -408,11 +408,10 @@ def _suggest_or_raise_constraint_violation(
             torch._ops.OpOverloadPacket | torch._ops.OpOverload,
         )
     ):
-        # pyrefly: ignore  # unbound-name
         dim_constraints.solve()
-        # pyrefly: ignore  # unbound-name
+
         forced_specializations = dim_constraints.forced_specializations()
-        # pyrefly: ignore  # unbound-name
+
         msg = dim_constraints.prettify_results(
             inspect.signature(orig_callable),  # type: ignore[attr-defined]
             dynamic_shapes,
@@ -433,11 +432,10 @@ def _suggest_or_raise_constraint_violation(
                 )
 
         # Error if we have any constraints on static values
-        # pyrefly: ignore  # unbound-name
+
         for k in shape_env.var_to_range.keys():
             if isinstance(k, sympy.Integer):
                 constraint_violation_error = ConstraintViolationError(
-                    # pyrefly: ignore  # unbound-name
                     f"{''.join(traceback.format_list(shape_env.var_to_stack[k]))}\n"
                     "It appears that you're trying to set a constraint on a "
                     f"value which we evaluated to have a static value of {k}. "

torch/_dynamo/graph_deduplication.py

@@ -456,8 +456,10 @@ def _add_mutation_dependencies(
             for user in mutated_arg.users:
                 if user is node:
                     continue
+                # pyrefly: ignore  # unsupported-operation
                 elif user < node:
                     node_to_additional_deps[node].add(user)
+                # pyrefly: ignore  # unsupported-operation
                 elif user > node:
                     node_to_additional_deps[user].add(node)
 

torch/_dynamo/guards.py

@@ -4100,13 +4100,12 @@ class CheckFunctionManager:
             and (cache_entry := self.guard_manager.cache_entry) is not None
             and (extra_state := self.guard_manager.extra_state) is not None
         ):
-            # pyrefly: ignore  # unbound-name
             assert isinstance(cache_entry, CacheEntry)
-            # pyrefly: ignore  # unbound-name
+
             assert isinstance(extra_state, ExtraState)
             reason = f"Cache line invalidated because {obj_str} got deallocated"
             deleted_guard_manager = DeletedGuardManagerWrapper(reason)
-            # pyrefly: ignore  # unbound-name
+
             extra_state.invalidate(cache_entry, deleted_guard_manager)
             self.guard_manager = deleted_guard_manager
 

torch/_dynamo/output_graph.py

@@ -2048,9 +2048,8 @@ class OutputGraph(OutputGraphCommon):
         tx = self.root_tx
         assert tx is not None
         if (ds := tx.distributed_state) is not None and ds.all_states is None:
-            # pyrefly: ignore  # unbound-name
             compile_pg = ds.compile_pg
-            # pyrefly: ignore  # unbound-name
+
             log.info("compiler_collective %s", ds.local_state)
             torch._logging.trace_structured(
                 "artifact",
@@ -2058,7 +2057,6 @@ class OutputGraph(OutputGraphCommon):
                     "name": "compiler_collective",
                     "encoding": "string",
                 },
-                # pyrefly: ignore  # unbound-name
                 payload_fn=lambda: ds.local_state.render(),
             )
             device_types = compile_pg._device_types
@@ -2072,9 +2070,9 @@ class OutputGraph(OutputGraphCommon):
                 dynamo_timed("compiler_collective", log_pt2_compile_event=True),
             ):
                 all_states: list[Any] = [None] * compile_pg.size()
-                # pyrefly: ignore  # unbound-name
+
                 dist.all_gather_object(all_states, ds.local_state, group=compile_pg)
-                # pyrefly: ignore  # unbound-name
+
                 ds.all_states = all_states
             # Clear speculation log, because are tracing may diverge due to
             # this information from the compiler collective
@@ -2468,7 +2466,6 @@ class OutputGraph(OutputGraphCommon):
                 isinstance(b, torch.SymBool)
                 and (r := b.node.maybe_as_bool()) is not None
             ):
-                # pyrefly: ignore  # unbound-name
                 return r
             # TODO: We can also technically remove all cases when the input
             # doesn't have unbacked inputs, since it's all in the ShapeEnv

torch/_dynamo/pgo.py

@@ -876,7 +876,6 @@ def get_code_state() -> defaultdict[CodeId, CodeState]:
         not _CODE_STATE
         and (sticky_read := torch.compiler.config.pgo_extra_read_key) is not None
     ):
-        # pyrefly: ignore  # unbound-name
         extra_read_key = get_extra_cache_key(sticky_read)
         if extra_read_key is not None:
             get_extra_remote_code_state(extra_read_key)

torch/_dynamo/symbolic_convert.py

@@ -4410,7 +4410,6 @@ class InstructionTranslator(InstructionTranslatorBase):
             and isinstance(tos, LocalGeneratorObjectVariable)
         ):
             self.stack[-1] = ListIteratorVariable(
-                # pyrefly: ignore  # unbound-name
                 tos.force_unpack_var_sequence(self),
                 mutation_type=ValueMutationNew(),
             )

torch/_dynamo/utils.py

@@ -4214,7 +4214,6 @@ def _extract_anchors_from_expr(segment: str) -> Optional[_Anchors]:
             # (x) + (y)
             # ~~^~~~~~~
             while (ch := lines[cur_lineno][cur_col]).isspace() or ch in ")\\#":
-                # pyrefly: ignore  # unbound-name
                 if ch in "\\#":
                     cur_lineno, cur_col = nextline(cur_lineno, cur_col)
                 else:

torch/_export/pass_base.py

@@ -317,7 +317,6 @@ class _ExportPassBaseDeprecatedDoNotUse(PassBase):
         )
         res_proxy.node.meta.update(meta.data)
         if self.fake_tensor_mode and (shape_env := self.fake_tensor_mode.shape_env):
-            # pyrefly: ignore  # unbound-name
             if symbol_to_path := compute_unbacked_bindings(shape_env, res_data):
                 res_proxy.node.meta["unbacked_bindings"] = symbol_to_path
         self.tracer.set_metadata(res_proxy.node, res_data)

torch/_export/serde/serialize.py

@@ -2183,7 +2183,6 @@ class GraphModuleDeserializer(metaclass=Final):
                     simplify=True,
                 )
             ):
-                # pyrefly: ignore  # unbound-name
                 node.meta["unbacked_bindings"] = unbacked_bindings
 
         assert len(self.unbacked_symbols) == 0

torch/_functorch/_aot_autograd/collect_metadata_analysis.py

@@ -204,7 +204,6 @@ def run_functionalized_fw_and_collect_metadata(
         suppress_pending = contextlib.nullcontext()
         fake_mode = detect_fake_mode()
         if fake_mode and (shape_env := fake_mode.shape_env):
-            # pyrefly: ignore  # unbound-name
             suppress_pending = shape_env.ignore_fresh_unbacked_symbols()
         with disable_above, mode, suppress_pending:
             # precondition: The passed in function already handles unflattening inputs + flattening outputs

torch/_higher_order_ops/while_loop.py

@@ -746,7 +746,6 @@ class WhileLoopAutogradOp(torch.autograd.Function):
             and (shape_env := loop_count.node.shape_env)
             and loop_count in shape_env.pending_fresh_unbacked_symbols
         ):
-            # pyrefly: ignore  # unbound-name
             shape_env.pending_fresh_unbacked_symbols.remove(loop_count)
 
         # Even when body function is not executed, we clone and unsqueeze the input

torch/_inductor/__init__.py

@@ -132,7 +132,6 @@ def aoti_compile_and_package(
         )
         or (
             isinstance(package_path, (str, os.PathLike))
-            # pyrefly: ignore  # no-matching-overload
             and os.fspath(package_path).endswith(".pt2")
         )
     ), (

torch/_inductor/autotune_process.py

@@ -557,7 +557,6 @@ class GPUDeviceBenchmarkMixin:
             res = benchmarker.benchmark_gpu(fn)
             device_interface.synchronize()  # shake out any CUDA errors
 
-        # pyrefly: ignore  # bad-return
         return res
 
 

torch/_inductor/codegen/common.py

@@ -1737,7 +1737,6 @@ class KernelArgs:
                 )
             )
         for outer, inner in chain(
-            # pyrefly: ignore  # bad-argument-type
             self.input_buffers.items(),
             # pyrefly: ignore  # bad-argument-type
             self.output_buffers.items(),

torch/_inductor/codegen/cpp_gemm_template.py

@@ -1478,7 +1478,6 @@ class CppGemmTemplate(CppTemplate):
             assert isinstance(template_buffer, ir.IRNode)
             gemm_output_name = f"{template_buffer.get_name()}_GemmOut"
             gemm_output_buffer = ir.Buffer(
-                # pyrefly: ignore  # missing-attribute
                 name=gemm_output_name,
                 # pyrefly: ignore  # missing-attribute
                 layout=template_buffer.layout,
@@ -1502,7 +1501,6 @@ class CppGemmTemplate(CppTemplate):
                 reindexers.append(None)
                 if i < len(epilogue_creators) - 1:
                     current_input_buffer = ir.Buffer(
-                        # pyrefly: ignore  # missing-attribute
                         name=buffer_name,
                         # pyrefly: ignore  # missing-attribute
                         layout=template_buffer.layout,

torch/_inductor/codegen/cpp_wrapper_gpu.py

@@ -822,7 +822,6 @@ class CppWrapperGpu(CppWrapperCpu):
 
         if triton:
             call_args, arg_types = self.prepare_triton_wrapper_args(
-                # pyrefly: ignore  # bad-argument-type
                 call_args,
                 # pyrefly: ignore  # bad-argument-type
                 arg_types,

torch/_inductor/codegen/mps.py

@@ -680,7 +680,6 @@ class MetalKernel(SIMDKernel):
                 )
                 idx_val = self._new_idxvar(dtype, default_value=0, is_threadgroup=False)  # type: ignore[assignment]
                 idx_var = next(
-                    # pyrefly: ignore  # missing-argument
                     t
                     for t in self.range_tree_nodes.values()
                     # pyrefly: ignore  # missing-argument
@@ -863,7 +862,6 @@ class MetalKernel(SIMDKernel):
 
             if self.inside_reduction:
                 total_reduction_size = math.prod(
-                    # pyrefly: ignore  # missing-argument
                     t.numel
                     for t in self.range_trees
                     # pyrefly: ignore  # missing-argument

torch/_inductor/codegen/simd.py

@@ -965,7 +965,6 @@ class SIMDKernel(Kernel[CSEVariableType], Generic[CSEVariableType]):
 
     def active_range_trees(self) -> list[IterationRangesRoot]:
         return [
-            # pyrefly: ignore  # missing-argument
             t
             for t in self.range_trees
             # pyrefly: ignore  # missing-argument

torch/_inductor/codegen/wrapper_fxir.py

@@ -1036,7 +1036,6 @@ class FxConverter:
             # Add constants stored as Triton metadata, in signature order.
             call_kwargs |= constants
             new_call_args = [
-                # pyrefly: ignore  # missing-attribute
                 call_kwargs[key]
                 for key in signature
                 # pyrefly: ignore  # missing-attribute

torch/_inductor/comms.py

@@ -826,11 +826,10 @@ def _schedule_for_comm(
             collective_cost > 0
             and (candidate := get_overlapping_candidate()) is not None
         ):
-            # pyrefly: ignore  # unbound-name
             ready.remove(candidate)
-            # pyrefly: ignore  # unbound-name
+
             schedule(candidate.snode)
-            # pyrefly: ignore  # unbound-name
+
             collective_cost -= snode_to_cost[candidate.snode]
         heapq.heapify(ready)
 
@@ -1098,7 +1097,7 @@ def _sink_waits_iterative_internal(
                         info.grouped_info = _group_names(gns)
                         candidate = _next[candidate]
                         continue
-                    # pyrefly: ignore  # unbound-name
+
                     elif (data_dep is None) and both_contain_comms:
                         info.limiting_factor = (
                             f"collective ordering {_group_names(gns)}"

torch/_inductor/compile_fx.py

@@ -271,7 +271,6 @@ def record_original_output_strides(gm: GraphModule) -> None:
             and (val := output.meta.get("val")) is not None
             and isinstance(val, torch.Tensor)
         ):
-            # pyrefly: ignore  # unbound-name
             output_strides.append(val.stride())
         else:
             # pyrefly: ignore  # bad-argument-type

torch/_inductor/compile_fx_ext.py

@@ -620,7 +620,6 @@ class _OutOfProcessFxCompile(_SerializedFxCompile):
 
         if output.warning_replay:
             for w in output.warning_replay:
-                # pyrefly: ignore  # no-matching-overload
                 warnings.warn_explicit(
                     message=w.message,
                     category=w.category,

torch/_inductor/decomposition.py

@@ -544,7 +544,6 @@ def amax(
     keepdim: bool = False,
 ) -> torch.Tensor:
     if self.dtype == torch.bool:
-        # pyrefly: ignore  # no-matching-overload
         return torch.any(self, dim=dim, keepdim=keepdim)
     return NotImplemented
 
@@ -556,7 +555,6 @@ def amin(
     keepdim: bool = False,
 ) -> torch.Tensor:
     if self.dtype == torch.bool:
-        # pyrefly: ignore  # no-matching-overload
         return torch.all(self, dim=dim, keepdim=keepdim)
     return NotImplemented
 

torch/_inductor/fx_utils.py

@@ -238,7 +238,7 @@ class FakeTensorUpdater:
                 symbol_to_path := compute_unbacked_bindings(shape_env, new_fake_tensor)
             ):
                 # Refresh the bindings to the new symbols
-                # pyrefly: ignore  # unbound-name
+
                 node.meta["unbacked_bindings"] = symbol_to_path
 
             existing_storages[get_node_storage(node)] += 1

torch/_inductor/lowering.py

@@ -6500,12 +6500,10 @@ def div_prim(a, b):
     # see https://github.com/pytorch/pytorch/issues/157959
     if (divisor := get_constant_value(b)) is not None and a.get_device().type != "cpu":
         # Replace divide by constant with multiply by reciprocal
-        # pyrefly: ignore  # unbound-name
+
         if divisor.value == 0:
-            # pyrefly: ignore  # unbound-name
             reciprocal = math.copysign(float("inf"), divisor.value)
         else:
-            # pyrefly: ignore  # unbound-name
             reciprocal = 1.0 / divisor.value
         return mul(a, reciprocal)
 

torch/_inductor/package/package.py

@@ -131,7 +131,6 @@ def load_package(
             )
             return AOTICompiledModel(loader)
 
-    # pyrefly: ignore  # no-matching-overload
     path = os.fspath(path)  # AOTIModelPackageLoader expects (str, str)
     loader = torch._C._aoti.AOTIModelPackageLoader(
         path, model_name, run_single_threaded, num_runners, device_index

torch/_inductor/scheduler.py

@@ -2676,7 +2676,6 @@ class Scheduler:
                 and (dep := next(iter(node.read_writes.writes)))
                 and isinstance(dep, MemoryDep)
             ):
-                # pyrefly: ignore  # unbound-name
                 node_mode = dep.mode
             else:
                 node_mode = None
@@ -4360,7 +4359,6 @@ class Scheduler:
         if config.expand_dimension_for_pointwise_nodes and (
             expand_analysis := self.get_expand_dim_for_pointwise_nodes(node1, node2)
         ):
-            # pyrefly: ignore  # unbound-name
             (expand_dim, smaller_node, expand_size) = expand_analysis
             smaller_node.expand_dimension_for_pointwise_node(expand_dim, expand_size)
             shared_data_score = self.score_fusion_memory(node1, node2)
@@ -4669,7 +4667,6 @@ class Scheduler:
                 device.type == "cuda"
                 and (device_props := torch.cuda.get_device_properties(device)).major < 7
             ):
-                # pyrefly: ignore  # unbound-name
                 raise GPUTooOldForTriton(device_props, inspect.currentframe())
             elif is_gpu(device.type) and not device.type == "mps":
                 raise TritonMissing(inspect.currentframe())
@@ -4967,7 +4964,6 @@ class Scheduler:
                 if isinstance(buf.node, ir.MutationOutput) and (
                     real_name := self.mutation_real_name.get(buf_name, None)
                 ):
-                    # pyrefly: ignore  # unbound-name
                     return is_none_layout(real_name)
 
                 return True

torch/_inductor/select_algorithm.py

@@ -3681,8 +3681,8 @@ class AlgorithmSelectorCache(PersistentCache):
             ),
             node.get_device(),
             node.get_dtype(),
-            # pyrefly: ignore  # missing-attribute
             V.graph.sizevars.atomically_apply_size_hint(
+                # pyrefly: ignore  # missing-attribute
                 node.layout.offset,
                 fallback=config.unbacked_symint_fallback,
                 hint_override=hint_override,

torch/_inductor/template_heuristics/triton.py

@@ -1652,7 +1652,7 @@ class MMTemplateConfigMixin(GemmMaxAutotuneTemplateConfigHeuristics):
         )
 
         # Build options dict
-        # pyrefly: ignore  # no-matching-overload
+
         options_dict = dict(
             EVEN_K=even_k_symbolic,
             USE_FAST_ACCUM=False,  # Option for _scaled_mm

torch/_inductor/utils.py

@@ -3764,7 +3764,6 @@ def maybe_log_cudagraph_partition(
         and (fx_node := ir_node.get_origin_node())
         and (stack_trace := fx_node.meta.get("stack_trace", None))
     ):
-        # pyrefly: ignore  # unbound-name
         warning_msg = f"{warning_msg}. Found from : \n {stack_trace}"
 
     perf_hint_log.warning(warning_msg)

torch/_inductor/wrapper_benchmark.py

@@ -144,7 +144,6 @@ def benchmark_all_kernels(
             launcher = triton_kernel.launchers[0]
             print(
                 get_info_str(
-                    # pyrefly: ignore  # bad-argument-type
                     ms,
                     launcher.n_regs,
                     launcher.n_spills,

torch/_library/fake_profile.py

@@ -246,7 +246,6 @@ 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:
@@ -312,7 +311,6 @@ 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:

torch/_library/opaque_object.py

@@ -173,7 +173,7 @@ def register_opaque_type(cls: Any, name: Optional[str] = None) -> None:
             f"Unable to accept name, {name}, for this opaque type as it contains a '.'"
         )
     _OPAQUE_TYPES[cls] = name
-    # pyrefly: ignore  # missing-attribute
+
     torch._C._register_opaque_type(name)
 
 
@@ -183,5 +183,5 @@ def is_opaque_type(cls: Any) -> bool:
     """
     if cls not in _OPAQUE_TYPES:
         return False
-    # pyrefly: ignore  # missing-attribute
+
     return torch._C._is_opaque_type_registered(_OPAQUE_TYPES[cls])

torch/_logging/_internal.py

@@ -914,7 +914,6 @@ class TorchLogsFormatter(logging.Formatter):
             and (trace_id := torch._guards.CompileContext.current_trace_id())
             is not None
         ):
-            # pyrefly: ignore  # unbound-name
             record.traceid = f" [{trace_id}]"
 
         glog_level_to_abbr = {

torch/_refs/__init__.py

@@ -1336,9 +1336,9 @@ def float_power(
 
     # Float power has the following contiguous cast behavior to be
     # consistent with its C++ impl
-    # pyrefly: ignore  # no-matching-overload
+
     a = _maybe_convert_to_dtype(a, dtype)
-    # pyrefly: ignore  # no-matching-overload
+
     b = _maybe_convert_to_dtype(b, dtype)
 
     a, b = _maybe_broadcast(a, b)
@@ -2348,7 +2348,6 @@ def all(
     dim: Optional[DimsType] = None,
     keepdim: bool = False,
 ) -> TensorLikeType:
-    # pyrefly: ignore  # no-matching-overload
     result = torch.logical_not(torch.any(torch.logical_not(a), dim, keepdim=keepdim))
 
     if a.dtype == torch.uint8:
@@ -3245,7 +3244,7 @@ def _normalize(
         mean (Tensor): mean of the tensor along norm_dims.
         rstd (Tensor): 1/std of the tensor along norm_dims.
     """
-    # pyrefly: ignore  # no-matching-overload
+
     norm_dims = utils.canonicalize_dims(a.ndim, norm_dims)
     computation_dtype = utils.get_computation_dtype(a.dtype)
     a_acc = _maybe_convert_to_dtype(a, computation_dtype)
@@ -3975,7 +3974,7 @@ def reshape_as(self: TensorLikeType, other: TensorLikeType) -> TensorLikeType:
 @out_wrapper()
 def roll(a: TensorLikeType, shifts: DimsType, dims: DimsType = ()) -> TensorLikeType:
     """Reference implementation of :func:`torch.roll`."""
-    # pyrefly: ignore  # no-matching-overload
+
     dims = utils.canonicalize_dims(a.ndim, dims)
     # ATen specifies int[1] type for shifts and dims which expands integers to tuples of length 1
     if not isinstance(shifts, Iterable):
@@ -4286,7 +4285,7 @@ def squeeze(a: TensorLikeType, dim: Optional[DimsType] = None) -> TensorLikeType
         return prims.squeeze(a, dims) if dims else prims.view_of(a)
 
     ndim = a.ndim
-    # pyrefly: ignore  # no-matching-overload
+
     dim = utils.canonicalize_dims(ndim, dim)
     dims = (dim,) if isinstance(dim, Dim) else dim
     # Short-circuits if the tensor has no dimensions

torch/_refs/linalg/__init__.py

@@ -216,7 +216,7 @@ def matrix_norm(
     # shape
     check_is_matrix(A, "linalg.matrix_norm")
     # dim
-    # pyrefly: ignore  # no-matching-overload
+
     dim = utils.canonicalize_dims(A.ndim, dim)
     if isinstance(dim, Dim):
         dim = (dim,)  # type: ignore[assignment]

torch/_subclasses/fake_tensor.py

@@ -2620,7 +2620,7 @@ class FakeTensorMode(TorchDispatchMode):
                         and s.rhs == 1
                     ):
                         assert self.shape_env is not None
-                        # pyrefly: ignore  # unbound-name
+
                         self.shape_env.set_unbacked_var_to_val(s, int(real_t))
 
             if real_out is not nil:

torch/_tensor.py

@@ -1110,7 +1110,6 @@ class Tensor(torch._C.TensorBase):
 
     @_handle_torch_function_and_wrap_type_error_to_not_implemented
     def __rsub__(self, other: Union["Tensor", int, float, bool, complex]) -> "Tensor":
-        # pyrefly: ignore  # no-matching-overload
         return _C._VariableFunctions.rsub(self, other)
 
     @_handle_torch_function_and_wrap_type_error_to_not_implemented
@@ -1137,7 +1136,7 @@ class Tensor(torch._C.TensorBase):
 
     @_handle_torch_function_and_wrap_type_error_to_not_implemented
     def __rmod__(self, other: Union["Tensor", int, float, bool, complex]) -> "Tensor":
-        return torch.remainder(other, self)  # pyrefly: ignore  # no-matching-overload
+        return torch.remainder(other, self)
 
     def __format__(self, format_spec):
         if has_torch_function_unary(self):
@@ -1150,7 +1149,7 @@ class Tensor(torch._C.TensorBase):
 
     @_handle_torch_function_and_wrap_type_error_to_not_implemented
     def __rpow__(self, other: Union["Tensor", int, float, bool, complex]) -> "Tensor":
-        return torch.pow(other, self)  # pyrefly: ignore  # no-matching-overload
+        return torch.pow(other, self)
 
     @_handle_torch_function_and_wrap_type_error_to_not_implemented
     def __floordiv__(self, other: Union["Tensor", int, float, bool]) -> "Tensor":  # type: ignore[override]
@@ -1166,14 +1165,12 @@ class Tensor(torch._C.TensorBase):
     def __rlshift__(
         self, other: Union["Tensor", int, float, bool, complex]
     ) -> "Tensor":
-        # pyrefly: ignore  # no-matching-overload
         return torch.bitwise_left_shift(other, self)
 
     @_handle_torch_function_and_wrap_type_error_to_not_implemented
     def __rrshift__(
         self, other: Union["Tensor", int, float, bool, complex]
     ) -> "Tensor":
-        # pyrefly: ignore  # no-matching-overload
         return torch.bitwise_right_shift(other, self)
 
     @_handle_torch_function_and_wrap_type_error_to_not_implemented

torch/_utils.py

@@ -744,10 +744,7 @@ class ExceptionWrapper:
         if exc_info is None:
             exc_info = sys.exc_info()
         self.exc_type = exc_info[0]
-        self.exc_msg = "".join(
+        self.exc_msg = "".join(traceback.format_exception(*exc_info))
-            # pyrefly: ignore  # no-matching-overload
-            traceback.format_exception(*exc_info)
-        )
         self.where = where
 
     def reraise(self):

torch/_utils_internal.py

@@ -89,7 +89,6 @@ def compile_time_strobelight_meta(
                 skip := kwargs["skip"],
                 int,
             ):
-                # pyrefly: ignore  # unbound-name
                 kwargs["skip"] = skip + 1
 
             # This is not needed but we have it here to avoid having profile_compile_time

torch/ao/nn/quantized/reference/modules/conv.py

@@ -95,7 +95,7 @@ class Conv1d(_ConvNd, nn.Conv1d):
         and the backend should be able to fuse the ops with `*` into a quantized conv1d
         """
         weight_quant_dequant = self.get_weight()
-        # pyrefly: ignore  # no-matching-overload
+
         result = F.conv1d(
             x,
             weight_quant_dequant,
@@ -160,7 +160,7 @@ class Conv2d(_ConvNd, nn.Conv2d):
         and the backend should be able to fuse the ops with `*` into a quantized conv2d
         """
         weight_quant_dequant = self.get_weight()
-        # pyrefly: ignore  # no-matching-overload
+
         result = F.conv2d(
             x,
             weight_quant_dequant,
@@ -225,7 +225,7 @@ class Conv3d(_ConvNd, nn.Conv3d):
         and the backend should be able to fuse the ops with `*` into a quantized conv3d
         """
         weight_quant_dequant = self.get_weight()
-        # pyrefly: ignore  # no-matching-overload
+
         result = F.conv3d(
             x,
             weight_quant_dequant,

torch/ao/ns/fx/graph_passes.py

@@ -1095,6 +1095,7 @@ def create_a_shadows_b(
                         # pyrefly: ignore  # unbound-name
                         if not isinstance(input_logger, list):
                             raise AssertionError(
+                                # pyrefly: ignore  # unbound-name
                                 f"Expected list, got {type(input_logger)}"
                             )
                         # pyrefly: ignore  # unbound-name

torch/ao/quantization/experimental/adaround_optimization.py

@@ -127,7 +127,6 @@ class AdaptiveRoundingOptimizer:
     @torch.no_grad()
     def feed_forward(self, x, weight, module):
         if isinstance(module, torch.nn.Conv1d):
-            # pyrefly: ignore  # no-matching-overload
             out = torch.nn.functional.conv1d(
                 x,
                 weight,

torch/ao/quantization/pt2e/utils.py

@@ -90,7 +90,6 @@ def _find_q_dq_node_for_user(
         and arg.op == "call_function"
         and arg.target in _QUANTIZE_OPS
     ):
-        # pyrefly: ignore  # unbound-name
         q_node = arg
     return (q_node, dq_node)
 

torch/autograd/__init__.py

@@ -92,7 +92,7 @@ def _make_grads(
     is_grads_batched: bool,
 ) -> tuple[_OptionalTensor, ...]:
     new_grads: list[_OptionalTensor] = []
-    # pyrefly: ignore  # no-matching-overload
+
     for out, grad in zip(outputs, grads):
         out = cast(Union[torch.Tensor, graph.GradientEdge], out)
         out_size = None

torch/backends/cuda/__init__.py

@@ -155,25 +155,21 @@ class cuBLASModule:
         if name == "allow_tf32":
             return torch._C._get_cublas_allow_tf32()
         elif name == "allow_fp16_reduced_precision_reduction":
-            # pyrefly: ignore  # not-iterable
             allow_reduced_precision, _ = (
                 torch._C._get_cublas_allow_fp16_reduced_precision_reduction()
             )
             return allow_reduced_precision
         elif name == "allow_fp16_reduced_precision_reduction_split_k":
-            # pyrefly: ignore  # not-iterable
             _, allow_splitk = (
                 torch._C._get_cublas_allow_fp16_reduced_precision_reduction()
             )
             return allow_splitk
         elif name == "allow_bf16_reduced_precision_reduction":
-            # pyrefly: ignore  # not-iterable
             allow_reduced_precision, _ = (
                 torch._C._get_cublas_allow_bf16_reduced_precision_reduction()
             )
             return allow_reduced_precision
         elif name == "allow_bf16_reduced_precision_reduction_split_k":
-            # pyrefly: ignore  # not-iterable
             _, allow_splitk = (
                 torch._C._get_cublas_allow_bf16_reduced_precision_reduction()
             )
@@ -193,7 +189,6 @@ class cuBLASModule:
             )
             return torch._C._set_cublas_allow_fp16_reduced_precision_reduction(
                 allow_reduced_precision,
-                # pyrefly: ignore  # bad-argument-count
                 allow_splitk,
             )
         elif name == "allow_bf16_reduced_precision_reduction":
@@ -202,7 +197,6 @@ class cuBLASModule:
             )
             return torch._C._set_cublas_allow_bf16_reduced_precision_reduction(
                 allow_reduced_precision,
-                # pyrefly: ignore  # bad-argument-count
                 allow_splitk,
             )
         elif name == "allow_fp16_accumulation":

torch/cuda/green_contexts.py

@@ -10,6 +10,7 @@ if hasattr(torch._C, "_CUDAGreenContext"):
 
 
 # Python shim helps Sphinx process docstrings more reliably.
+# pyrefly: ignore  # invalid-inheritance
 class GreenContext(_GreenContext):
     r"""Wrapper around a CUDA green context.
 

torch/distributed/checkpoint/logger.py

@@ -37,7 +37,6 @@ def _msg_dict_from_dcp_method_args(*args, **kwargs) -> dict[str, Any]:
 
     checkpoint_id = kwargs.get("checkpoint_id")
     if not checkpoint_id and (serializer := storage_writer or storage_reader):
-        # pyrefly: ignore  # unbound-name
         checkpoint_id = getattr(serializer, "checkpoint_id", None)
 
     msg_dict["checkpoint_id"] = (

torch/distributed/checkpoint/state_dict.py

@@ -1227,7 +1227,6 @@ def _unflatten_model_state_dict(
     if not state_dict:
         return {}
 
-    # pyrefly: ignore  # no-matching-overload
     if isinstance(next(iter(state_dict.keys())), nn.Module):
         warnings.warn(
             "Passing model_state_dict as a ``Dict[nn.Module, Dict[str, Any]]``"

torch/distributed/distributed_c10d.py

@@ -393,7 +393,7 @@ class BackendConfig:
             # e.g. "nccl", "gloo", "ucc", "mpi"
             supported_devices = Backend.backend_capability[backend.lower()]
             backend_val = Backend(backend)
-            # pyrefly: ignore  # bad-assignment
+
             self.device_backend_map = dict.fromkeys(supported_devices, backend_val)
         elif ":" in backend.lower():
             # Backend specified in "device:backend" format

torch/distributed/pipelining/microbatch.py

@@ -290,7 +290,6 @@ def _shard_dict_of_args(
                 f"Unsupported chunk spec: {spec} and value: {v} combination."
             )
 
-        # pyrefly: ignore  # no-matching-overload
         for _flat_split_result, _v_split in zip(
             flat_split_results, v_splits, strict=True
         ):

torch/distributed/tensor/experimental/_attention.py

@@ -1327,7 +1327,6 @@ class _ContextParallel(ParallelStyle):
         placement = [Shard(self.seq_dim)]
         all_args = []
 
-        # pyrefly: ignore  # bad-assignment, bad-argument-type
         for arg in itertools.chain(args, kwargs.values()):
             if isinstance(arg, torch.Tensor):
                 if isinstance(arg, DTensor):

torch/distributed/tensor/parallel/style.py

@@ -548,7 +548,7 @@ class PrepareModuleInput(ParallelStyle):
         assert self.desired_input_layouts is not None, (
             "desired module inputs should not be None!"
         )
-        # pyrefly: ignore  # no-matching-overload
+
         for inp, input_layout, desired_layout in zip(
             inputs, self.input_layouts, self.desired_input_layouts
         ):
@@ -664,7 +664,7 @@ class PrepareModuleOutput(ParallelStyle):
             raise ValueError(
                 "module outputs and output_layouts should have same length!"
             )
-        # pyrefly: ignore  # no-matching-overload
+
         for out, out_layout, desired_out_layout in zip(
             outputs, self.output_layouts, self.desired_output_layouts
         ):

torch/distributed/utils.py

@@ -59,7 +59,7 @@ def _cast_forward_inputs(
     def cast_fn(x: torch.Tensor) -> torch.Tensor:
         if not torch.is_floating_point(x) or x.dtype == dtype:
             return x
-        # pyrefly: ignore  # no-matching-overload
+
         return x.to(dtype)
 
     return (_apply_to_tensors(cast_fn, args), _apply_to_tensors(cast_fn, kwargs))

torch/export/__init__.py

@@ -436,7 +436,6 @@ def load(
         print(ep(torch.randn(5)))
     """
     if isinstance(f, (str, os.PathLike)):
-        # pyrefly: ignore  # no-matching-overload
         f = os.fspath(f)
 
     extra_files = extra_files or {}

torch/export/_trace.py

@@ -514,7 +514,6 @@ def _replace_unbacked_bindings(gm: torch.fx.GraphModule) -> None:
                 simplify=True,
             )
         ):
-            # pyrefly: ignore  # unbound-name
             node.meta["unbacked_bindings"] = unbacked_bindings
 
 

torch/export/pt2_archive/_package.py

@@ -683,7 +683,6 @@ def package_pt2(
 
     if not (
         (isinstance(f, (io.IOBase, IO)) and f.writable() and f.seekable())
-        # pyrefly: ignore  # no-matching-overload
         or (isinstance(f, (str, os.PathLike)) and os.fspath(f).endswith(".pt2"))
         or (isinstance(f, tempfile._TemporaryFileWrapper) and f.name.endswith(".pt2"))
     ):
@@ -695,7 +694,6 @@ def package_pt2(
         )
 
     if isinstance(f, (str, os.PathLike)):
-        # pyrefly: ignore  # no-matching-overload
         f = os.fspath(f)
 
     # pyrefly: ignore  # bad-argument-type
@@ -1086,7 +1084,6 @@ def load_pt2(
 
     if not (
         (isinstance(f, (io.IOBase, IO)) and f.readable() and f.seekable())
-        # pyrefly: ignore  # no-matching-overload
         or (isinstance(f, (str, os.PathLike)) and os.fspath(f).endswith(".pt2"))
     ):
         # TODO: turn this into an error in 2.9
@@ -1097,7 +1094,6 @@ def load_pt2(
         )
 
     if isinstance(f, (str, os.PathLike)):
-        # pyrefly: ignore  # no-matching-overload
         f = os.fspath(f)
 
     weights = {}
@@ -1167,7 +1163,6 @@ def load_pt2(
     else:
         aoti_runners = {
             model_name: _load_aoti(
-                # pyrefly: ignore  # bad-argument-type
                 f,
                 model_name,
                 run_single_threaded,

torch/fx/experimental/proxy_tensor.py

@@ -916,7 +916,6 @@ def fetch_object_proxy(
 def fetch_object_proxy(
     tracer: _ProxyTracer, t: Union[Tensor, _AnyScriptObjectType, PySymType]
 ) -> object:
-    # pyrefly: ignore  # no-matching-overload
     return get_proxy_slot(t, tracer, t)
 
 
@@ -965,7 +964,6 @@ def _fetch_proxies_and_all_constant_flag(
     """
     f_flat_args_kwargs = [
         (
-            # pyrefly: ignore  # no-matching-overload
             fetch_object_proxy(tracer, x)
             if isinstance(x, (Tensor, _AnyScriptObject))
             else x
@@ -2497,7 +2495,6 @@ class _MakefxTracer:
         ):
             from torch.fx.passes.runtime_assert import insert_deferred_runtime_asserts
 
-            # pyrefly: ignore  # unbound-name
             insert_deferred_runtime_asserts(t, fake_mode.shape_env, "reenter_make_fx")
             t.recompile()
         # TODO: kind of a bad way to do it, should maybe figure out a better way

torch/fx/experimental/symbolic_shapes.py

@@ -621,13 +621,12 @@ def rebind_unbacked(
             ):
                 # This is what the pattern match above is testing
                 repacked = _sympy_cast_symbool_to_symint_guardless(
-                    # pyrefly: ignore  # unbound-name
                     sympy.Eq(new_raw_u1, 1)
                 )
                 assert repacked == raw_u1, f"{repacked} != {raw_u1}"
                 # Cancel the to_int(to_bool(x)). This is sound because x in
                 # [0, 1]
-                # pyrefly: ignore  # unbound-name
+
                 raw_u1 = new_raw_u1
 
             if not isinstance(raw_u1, sympy.Symbol):
@@ -1055,7 +1054,6 @@ def find_symbol_binding_fx_nodes(
     # NB: Prefer first occurrence of symbol
     for node in graph.nodes:
         if (s := is_symbol_binding_fx_node(node)) is not None and s not in r:
-            # pyrefly: ignore  # unbound-name
             r[s] = node
     return r
 
@@ -1226,13 +1224,12 @@ def _free_unbacked_symbols_with_path(
         and isinstance(s := expr(a), sympy.Symbol)
         and s in pending
     ):
-        # pyrefly: ignore  # unbound-name
         r[s] = path
         if shape_env and real is not None:
             assert isinstance(real, (int, float))
-            # pyrefly: ignore  # unbound-name
+
             shape_env.set_unbacked_var_to_val(s, real)
-        # pyrefly: ignore  # unbound-name
+
         pending.remove(s)
     # When an unbacked SymInt is perfectly divisible by an integer
     # constant, we replace it with the integer constant to improve
@@ -1262,14 +1259,10 @@ def _free_unbacked_symbols_with_path(
                 source=shape_env.var_to_sources.get(s, [None])[0],  # type: ignore[union-attr]
             )
 
-        # pyrefly: ignore  # unbound-name
         unbacked = lhs if lhs in pending else rhs
         divisor: IntLikeType = (
-            # pyrefly: ignore  # unbound-name
             int(coeff)
-            # pyrefly: ignore  # unbound-name
             if shape_env and isinstance(coeff, sympy.Integer)
-            # pyrefly: ignore  # unbound-name
             else _symint_wrap(coeff)
         )
         # TODO: DivideByKey needs to test divisibility at runtime!
@@ -1278,11 +1271,8 @@ def _free_unbacked_symbols_with_path(
         if real is not None:
             assert isinstance(real, int)
             val = (
-                # pyrefly: ignore  # unbound-name
                 real // int(coeff)
-                # pyrefly: ignore  # unbound-name
                 if isinstance(coeff, sympy.Integer)
-                # pyrefly: ignore  # unbound-name
                 else CleanDiv(real, coeff)
             )
             if shape_env:
@@ -1299,14 +1289,12 @@ def _free_unbacked_symbols_with_path(
         and s.rhs == 1
         and s.lhs in pending
     ):
-        # pyrefly: ignore  # unsupported-operation
         r[s.lhs] = path + (ConvertIntKey(),)
         if real is not None:
             assert type(real) is bool
             if shape_env:
-                # pyrefly: ignore  # unbound-name
                 shape_env.set_unbacked_var_to_val(s, int(real))
-        # pyrefly: ignore  # unbound-name
+
         pending.remove(s.lhs)
 
     return r
@@ -1382,7 +1370,6 @@ def compute_unbacked_bindings(
             ):
                 if (
                     isinstance(old_sym, SymTypes)
-                    # pyrefly: ignore  # unbound-name
                     and (old_s := old_sym.node.expr) != new_s
                 ):
                     # If old_s is not an unbacked_symbol,
@@ -1392,15 +1379,12 @@ def compute_unbacked_bindings(
                     # and the original symbol gets replaced by the backed symbol.
                     # When this happens we just replace new_s by the old_s
                     # because we know the value is the same.
-                    # pyrefly: ignore  # unbound-name
+
                     if isinstance(old_s, sympy.Symbol) and free_unbacked_symbols(old_s):
-                        # pyrefly: ignore  # unbound-name
                         shape_env._rename_unbacked_to(new_s, old_s)
                     else:
-                        # pyrefly: ignore  # unbound-name
                         shape_env._eliminate_unbacked(new_s, old_s)
                 elif not isinstance(old_sym, SymTypes):
-                    # pyrefly: ignore  # unbound-name
                     shape_env._eliminate_unbacked(new_s, sympy.sympify(old_sym))
 
     return symbol_to_path
@@ -3365,7 +3349,7 @@ class DimConstraints:
                     and str(symbol := next(iter(c["eq"].free_symbols))) == old_root
                 ):  # derived dim with root = old_root
                     new_root_expr = results[str(old_root)]["eq"]  # dx=3*_dx+1
-                    # pyrefly: ignore  # unbound-name
+
                     new_expr = c["eq"].subs({symbol: new_root_expr})  # dy=(3*_dx+1)+1
                     c["eq"] = new_expr
 
@@ -7630,10 +7614,9 @@ class ShapeEnv:
                         log.info(
                             "oblivious_size %s -> %s (passed counterfactual)",
                             orig_expr,
-                            # pyrefly: ignore  # unbound-name
                             correct_hint,
                         )
-                        # pyrefly: ignore  # unbound-name
+
                         concrete_val = correct_hint
                         # NB: do NOT transmute into runtime assert
                         ok = True
@@ -7650,10 +7633,9 @@ class ShapeEnv:
                             ).xreplace(self.var_to_val)
                         ).free_symbols
                     ):
-                        # pyrefly: ignore  # unbound-name
                         self._log_real_tensor_propagation(orig_expr, unsound_result)
                         transmute_into_runtime_assert = True
-                        # pyrefly: ignore  # unbound-name
+
                         concrete_val = unsound_result
                         ok = True
 

torch/fx/graph.py

@@ -1314,6 +1314,7 @@ class Graph:
                 f(to_erase)
 
         self._find_nodes_lookup_table.remove(to_erase)
+        # pyrefly: ignore  # missing-attribute
         to_erase._remove_from_list()
         to_erase._erased = True  # iterators may retain handles to erased nodes
         self._len -= 1

torch/fx/node.py

@@ -385,6 +385,7 @@ class Node(_NodeBase):
         Args:
             x (Node): The node to put before this node. Must be a member of the same graph.
         """
+        # pyrefly: ignore  # missing-attribute
         self._prepend(x)
 
     @compatibility(is_backward_compatible=True)
@@ -396,6 +397,7 @@ class Node(_NodeBase):
         Args:
             x (Node): The node to put after this node. Must be a member of the same graph.
         """
+        # pyrefly: ignore  # missing-attribute
         self._next._prepend(x)
 
     @property

torch/fx/passes/_tensorify_python_scalars.py

@@ -276,7 +276,6 @@ def tensorify_python_scalars(
                     ):
                         transform = True
                         try:
-                            # pyrefly: ignore  # unbound-name
                             proxy = _sympy_interp(zf.node.expr)
                         except NotImplementedError:
                             transform = False
@@ -303,7 +302,6 @@ def tensorify_python_scalars(
                         args.append(a)
 
                 if transform:
-                    # pyrefly: ignore  # unbound-name
                     replacement_proxy = replacement_op(*args)
 
                     # pyrefly: ignore  # missing-attribute

torch/fx/passes/fake_tensor_prop.py

@@ -93,7 +93,6 @@ class FakeTensorProp(torch.fx.Interpreter):
             if (shape_env := self._mode.shape_env) and (
                 symbol_to_path := compute_unbacked_bindings(shape_env, result)
             ):
-                # pyrefly: ignore  # unbound-name
                 n.meta["unbacked_bindings"] = symbol_to_path
 
         return result

torch/fx/passes/runtime_assert.py

@@ -298,14 +298,12 @@ def insert_deferred_runtime_asserts(
                         and s not in expr_to_proxy
                     ):
                         with _set_node_metadata_hook(gm, _node_metadata_hook):
-                            # pyrefly: ignore  # unbound-name
                             expr_to_proxy[s] = fx.Proxy(cb(), tracer=tracer)
-                        # pyrefly: ignore  # unbound-name
+
                         log.debug("expr_to_proxy[%s] = %s", s, expr_to_proxy[s])
 
-                # pyrefly: ignore  # unbound-name
                 match_symbol(example_value, lambda: node)
-                # pyrefly: ignore  # unbound-name
+
                 if isinstance(t := example_value, torch.Tensor):
                     for i, s in enumerate(t.size()):
                         match_symbol(
@@ -386,7 +384,6 @@ def insert_deferred_runtime_asserts(
 
                 # maybe re-reify expression, replace current node
                 if (
-                    # pyrefly: ignore  # unbound-name
                     sym_expr in expr_to_proxy
                     or (  # example value is redundant
                         _is_intermediate_tensor_sym_call(node)
@@ -405,10 +402,8 @@ def insert_deferred_runtime_asserts(
                                 nn_module_stack=node.meta.get("nn_module_stack"),
                             ),
                         ):
-                            # pyrefly: ignore  # unbound-name
                             expr_to_proxy[sym_expr] = _sympy_interp(
                                 expr_to_proxy,
-                                # pyrefly: ignore  # unbound-name
                                 sym_expr,
                             )  # type: ignore[arg-type]
                         # won't try DCE-ing tensor compute here
@@ -419,14 +414,12 @@ def insert_deferred_runtime_asserts(
                         "CSE node %s -> %s for expr %s",
                         node,
                         hash_node,
-                        # pyrefly: ignore  # unbound-name
                         sym_expr,
                     )
 
                 # store node in hash cons, don't delete/replace
-                # pyrefly: ignore  # unbound-name
+
                 elif sym_expr not in expr_to_proxy and not isinstance(
-                    # pyrefly: ignore  # unbound-name
                     sym_expr,
                     (sympy.Number, sympy.logic.boolalg.BooleanAtom),
                 ):  # don't hash cons primitives

torch/fx/passes/split_module.py

@@ -318,7 +318,6 @@ def split_module(
             and isinstance(s0 := val.node.expr, sympy.Symbol)
             and s0 not in symbol_to_node
         ):
-            # pyrefly: ignore  # unbound-name
             symbol_to_node[val.node.expr] = node
 
         if node.op in ["placeholder", "get_attr", "output"]:

torch/fx/passes/utils/source_matcher_utils.py

@@ -85,7 +85,6 @@ def get_source_partitions(
         if (source_fn_st := node.meta.get("source_fn_stack", None)) is None and (
             torch_fn := node.meta.get("torch_fn", None)
         ) is not None:
-            # pyrefly: ignore  # unbound-name
             node_fqn, source_fn = torch_fn
             source_fn_name = source_fn.split(".")[1]
             if source_fn_name in wanted_sources:

torch/hub.py

@@ -421,7 +421,7 @@ def set_dir(d: Union[str, os.PathLike]) -> None:
         d (str): path to a local folder to save downloaded models & weights.
     """
     global _hub_dir
-    _hub_dir = os.path.expanduser(d)  # pyrefly: ignore  # no-matching-overload
+    _hub_dir = os.path.expanduser(d)
 
 
 def list(

torch/jit/_serialization.py

@@ -167,7 +167,6 @@ def load(f, map_location=None, _extra_files=None, _restore_shapes=False):
     if isinstance(f, (str, os.PathLike)):
         cpp_module = torch._C.import_ir_module(
             cu,
-            # pyrefly: ignore  # no-matching-overload
             os.fspath(f),
             map_location,
             _extra_files,
@@ -208,7 +207,6 @@ def validate_map_location(map_location=None):
 
 def jit_module_from_flatbuffer(f):
     if isinstance(f, (str, os.PathLike)):
-        # pyrefly: ignore  # no-matching-overload
         f = os.fspath(f)
         return wrap_cpp_module(torch._C._load_jit_module_from_file(f))
     else:
@@ -258,7 +256,6 @@ def save_jit_module_to_flatbuffer(m, f, _extra_files=None):
         extra_files = {}
 
     if isinstance(f, (str, os.PathLike)):
-        # pyrefly: ignore  # no-matching-overload
         f = os.fspath(f)
         torch._C._save_jit_module(m._c, f, extra_files)
     else:

torch/jit/mobile/__init__.py

@@ -44,7 +44,6 @@ def _load_for_lite_interpreter(f, map_location=None):
     map_location = validate_map_location(map_location)
 
     if isinstance(f, (str, os.PathLike)):
-        # pyrefly: ignore  # no-matching-overload
         cpp_module = torch._C._load_for_lite_interpreter(os.fspath(f), map_location)
     else:
         cpp_module = torch._C._load_for_lite_interpreter_from_buffer(
@@ -106,7 +105,6 @@ def _get_model_bytecode_version(f_input) -> int:
             raise ValueError(f"The provided filename {f_input} is a directory")
 
     if isinstance(f_input, (str, os.PathLike)):
-        # pyrefly: ignore  # no-matching-overload
         return torch._C._get_model_bytecode_version(os.fspath(f_input))
     else:
         # pyrefly: ignore  # missing-attribute
@@ -140,7 +138,6 @@ def _get_mobile_model_contained_types(f_input) -> int:
             raise ValueError(f"The provided filename {f_input} is a directory")
 
     if isinstance(f_input, (str, os.PathLike)):
-        # pyrefly: ignore  # no-matching-overload
         return torch._C._get_mobile_model_contained_types(os.fspath(f_input))
     else:
         # pyrefly: ignore  # missing-attribute
@@ -168,9 +165,7 @@ def _backport_for_mobile(f_input, f_output, to_version):
         isinstance(f_output, (str, os.PathLike))
     ):
         return torch._C._backport_for_mobile(
-            # pyrefly: ignore  # no-matching-overload
             os.fspath(f_input),
-            # pyrefly: ignore  # no-matching-overload
             os.fspath(f_output),
             to_version,
         )
@@ -198,7 +193,6 @@ def _backport_for_mobile_to_buffer(f_input, to_version):
             raise ValueError(f"The provided filename {f_input} is a directory")
 
     if isinstance(f_input, (str, os.PathLike)):
-        # pyrefly: ignore  # no-matching-overload
         return torch._C._backport_for_mobile_to_buffer(os.fspath(f_input), to_version)
     else:
         return torch._C._backport_for_mobile_from_buffer_to_buffer(
@@ -244,7 +238,6 @@ def _get_model_ops_and_info(f_input):
             raise ValueError(f"The provided filename {f_input} is a directory")
 
     if isinstance(f_input, (str, os.PathLike)):
-        # pyrefly: ignore  # no-matching-overload
         return torch._C._get_model_ops_and_info(os.fspath(f_input))
     else:
         # pyrefly: ignore  # missing-attribute

torch/library.py

@@ -644,7 +644,7 @@ def impl(
         >>> y2 = torch.sin(x) + 1
         >>> assert torch.allclose(y1, y2)
     """
-    # pyrefly: ignore  # no-matching-overload
+
     return _impl(qualname, types, func, lib=lib, disable_dynamo=False)
 
 
@@ -831,7 +831,6 @@ def register_kernel(
     if device_types is None:
         device_types = "CompositeExplicitAutograd"
 
-    # pyrefly: ignore  # no-matching-overload
     return _impl(op, device_types, func, lib=lib, disable_dynamo=True)
 
 

torch/masked/_ops.py

@@ -642,7 +642,6 @@ def _sparse_coo_scatter_reduction_helper(
 
     # promote dtype if specified
     if values.dtype != output_dtype:
-        # pyrefly: ignore  # no-matching-overload
         values = values.to(output_dtype)
 
     if keepdim:
@@ -767,7 +766,6 @@ def _sparse_csr_segment_reduction_helper(
 
     # promote dtype if specified
     if values.dtype != output_dtype:
-        # pyrefly: ignore  # no-matching-overload
         values = values.to(output_dtype)
 
     if len(dims) == 0:

torch/nn/modules/container.py

@@ -473,7 +473,6 @@ class ModuleList(Module):
         return self
 
     def pop(self, key: Union[int, slice]) -> Module:
-        # pyrefly: ignore  # index-error
         v = self[key]
         del self[key]
         return v

torch/nn/modules/conv.py

@@ -363,7 +363,7 @@ class Conv1d(_ConvNd):
                 self.dilation,
                 self.groups,
             )
-        # pyrefly: ignore  # no-matching-overload
+
         return F.conv1d(
             input, weight, bias, self.stride, self.padding, self.dilation, self.groups
         )
@@ -541,7 +541,7 @@ class Conv2d(_ConvNd):
                 self.dilation,
                 self.groups,
             )
-        # pyrefly: ignore  # no-matching-overload
+
         return F.conv2d(
             input, weight, bias, self.stride, self.padding, self.dilation, self.groups
         )
@@ -711,7 +711,7 @@ class Conv3d(_ConvNd):
                 self.dilation,
                 self.groups,
             )
-        # pyrefly: ignore  # no-matching-overload
+
         return F.conv3d(
             input, weight, bias, self.stride, self.padding, self.dilation, self.groups
         )

torch/onnx/_internal/torchscript_exporter/symbolic_helper.py

@@ -364,7 +364,6 @@ def parse_args(
                 fn_name = None
             args = [
                 _parse_arg(arg, arg_desc, arg_name, fn_name)  # type: ignore[method-assign]
-                # pyrefly: ignore  # no-matching-overload
                 for arg, arg_desc, arg_name in zip(args, arg_descriptors, arg_names)
             ]
             # only support _outputs in kwargs

torch/optim/adam.py

@@ -453,7 +453,7 @@ def _single_tensor_adam(
             device_beta1 = beta1
 
         # Decay the first and second moment running average coefficient
-        # pyrefly: ignore  # no-matching-overload
+
         exp_avg.lerp_(grad, 1 - device_beta1)
 
         # Nested if is necessary to bypass jitscript rules

torch/optim/optimizer.py

@@ -398,7 +398,6 @@ class Optimizer:
         self.state: defaultdict[torch.Tensor, Any] = defaultdict(dict)
         self.param_groups: list[dict[str, Any]] = []
 
-        # pyrefly: ignore  # no-matching-overload
         param_groups = list(params)
         if len(param_groups) == 0:
             raise ValueError("optimizer got an empty parameter list")

torch/package/package_exporter.py

@@ -219,7 +219,7 @@ class PackageExporter:
         torch._C._log_api_usage_once("torch.package.PackageExporter")
         self.debug = debug
         if isinstance(f, (str, os.PathLike)):
-            f = os.fspath(f)  # pyrefly: ignore  # no-matching-overload
+            f = os.fspath(f)
             self.buffer: Optional[IO[bytes]] = None
         else:  # is a byte buffer
             self.buffer = f

torch/package/package_importer.py

@@ -108,7 +108,6 @@ class PackageImporter(Importer):
             self.filename = "<pytorch_file_reader>"
             self.zip_reader = file_or_buffer
         elif isinstance(file_or_buffer, (os.PathLike, str)):
-            # pyrefly: ignore  # no-matching-overload
             self.filename = os.fspath(file_or_buffer)
             if not os.path.isdir(self.filename):
                 self.zip_reader = torch._C.PyTorchFileReader(self.filename)

torch/serialization.py

@@ -774,10 +774,7 @@ def _open_file_like(name_or_buffer: FileLike, mode: str) -> _opener[IO[bytes]]:
 
 class _open_zipfile_reader(_opener[torch._C.PyTorchFileReader]):
     def __init__(self, name_or_buffer: Union[str, IO[bytes]]) -> None:
-        super().__init__(
+        super().__init__(torch._C.PyTorchFileReader(name_or_buffer))
-            # pyrefly: ignore  # no-matching-overload
-            torch._C.PyTorchFileReader(name_or_buffer)
-        )
 
 
 class _open_zipfile_writer_file(_opener[torch._C.PyTorchFileWriter]):
@@ -970,7 +967,7 @@ def save(
     _check_save_filelike(f)
 
     if isinstance(f, (str, os.PathLike)):
-        f = os.fspath(f)  # pyrefly: ignore  # no-matching-overload
+        f = os.fspath(f)
 
     if _use_new_zipfile_serialization:
         with _open_zipfile_writer(f) as opened_zipfile:
@@ -1524,7 +1521,6 @@ def load(
                     else:
                         shared = False
                     overall_storage = torch.UntypedStorage.from_file(
-                        # pyrefly: ignore  # no-matching-overload
                         os.fspath(f),
                         shared,
                         size,

torch/utils/_cxx_pytree.py

@@ -701,7 +701,6 @@ def tree_map_only(
     tree: PyTree,
     is_leaf: Optional[Callable[[PyTree], bool]] = None,
 ) -> PyTree:
-    # pyrefly: ignore  # no-matching-overload
     return tree_map(map_only(type_or_types_or_pred)(func), tree, is_leaf=is_leaf)
 
 
@@ -762,7 +761,6 @@ def tree_map_only_(
     tree: PyTree,
     is_leaf: Optional[Callable[[PyTree], bool]] = None,
 ) -> PyTree:
-    # pyrefly: ignore  # no-matching-overload
     return tree_map_(map_only(type_or_types_or_pred)(func), tree, is_leaf=is_leaf)
 
 

torch/utils/_pytree.py

@@ -1555,7 +1555,6 @@ def tree_map_only(
     tree: PyTree,
     is_leaf: Optional[Callable[[PyTree], bool]] = None,
 ) -> PyTree:
-    # pyrefly: ignore  # no-matching-overload
     return tree_map(map_only(type_or_types_or_pred)(func), tree, is_leaf=is_leaf)
 
 
@@ -1616,7 +1615,6 @@ def tree_map_only_(
     tree: PyTree,
     is_leaf: Optional[Callable[[PyTree], bool]] = None,
 ) -> PyTree:
-    # pyrefly: ignore  # no-matching-overload
     return tree_map_(map_only(type_or_types_or_pred)(func), tree, is_leaf=is_leaf)
 
 

torch/utils/cpp_extension.py

@@ -1531,7 +1531,7 @@ def include_paths(device_type: str = "cpu", torch_include_dirs=True) -> list[str
         # Support CUDA_INC_PATH env variable supported by CMake files
         if (cuda_inc_path := os.environ.get("CUDA_INC_PATH", None)) and \
                 cuda_inc_path != '/usr/include':
-            # pyrefly: ignore  # unbound-name
+
             paths.append(cuda_inc_path)
         if CUDNN_HOME is not None:
             paths.append(os.path.join(CUDNN_HOME, 'include'))

torch/utils/data/dataloader.py

@@ -678,7 +678,6 @@ class _BaseDataLoaderIter:
 
         # Set pin memory device based on the current accelerator.
         self._pin_memory_device = (
-            # pyrefly: ignore  # unbound-name
             acc.type
             if self._pin_memory
             and (acc := torch.accelerator.current_accelerator()) is not None

torch/xpu/__init__.py

@@ -251,7 +251,6 @@ def get_device_capability(device: Optional[_device_t] = None) -> dict[str, Any]:
     # Only keep attributes that are safe for dictionary serialization.
     serializable_types = (int, float, bool, str, type(None), list, tuple, dict)
     return {
-        # pyrefly: ignore  # unbound-name
         key: value
         for key in dir(props)
         if not key.startswith("__")