[2/N] Simplify "in" operation for containers of a single item (#164323)

These issues are detected by ruff [FURB171](https://docs.astral.sh/ruff/rules/single-item-membership-test/#single-item-membership-test-furb171).

Pull Request resolved: https://github.com/pytorch/pytorch/pull/164323
Approved by: https://github.com/justinchuby, https://github.com/Skylion007

torch/_dynamo/debug_utils.py

@@ -264,7 +264,7 @@ def _cuda_system_info_comment() -> str:
     try:
         cuda_version_out = subprocess.check_output(["nvcc", "--version"])
         cuda_version_lines = cuda_version_out.decode().split("\n")
-        comment = "".join([f"# {s} \n" for s in cuda_version_lines if s not in [""]])
+        comment = "".join([f"# {s} \n" for s in cuda_version_lines if s != ""])
         model_str += f"{comment}\n"
     except (FileNotFoundError, subprocess.CalledProcessError):
         model_str += "# nvcc not found\n"

torch/_dynamo/guards.py

@@ -3623,7 +3623,7 @@ class CheckFunctionManager:
                 # Leave out the builtins dict key, as we will special handle
                 # it later because the guarded code rarely use the entire
                 # builtin dict in the common case.
-                if name not in (builtins_dict_name,):
+                if name != builtins_dict_name:
                     used_global_vars.add(name)
             elif name := get_local_source_name(source):
                 assert isinstance(name, str)

torch/_dynamo/symbolic_convert.py

@@ -4228,9 +4228,10 @@ class InliningInstructionTranslator(InstructionTranslatorBase):
 
             # _origin marks this as coming from an internal dynamo known function that is safe to
             # trace through.
-            if hasattr(getattr(func, "fn", None), "_origin") and func.fn._origin in [
-                produce_trampoline_autograd_apply,
-            ]:
+            if (
+                hasattr(getattr(func, "fn", None), "_origin")
+                and func.fn._origin is produce_trampoline_autograd_apply
+            ):
                 # Known sound
                 return trace_rules.SkipResult(
                     False, "allowlist in dynamo known function"

torch/_dynamo/variables/builder.py

@@ -3017,7 +3017,7 @@ def handle_traced_output(example_value, tx, proxy, options, subclass_type, targe
         ]
         or (
             # TODO: this is a little sus, because we didn't check what the self is
-            proxy.node.op == "call_method" and proxy.node.target in ["bit_length"]
+            proxy.node.op == "call_method" and proxy.node.target == "bit_length"
         )
     ):
         set_example_value(proxy.node, example_value)

torch/_dynamo/variables/distributed.py

@@ -154,7 +154,7 @@ class PlacementClassVariable(DistributedVariable):
         kwargs: "dict[str, VariableTracker]",
     ) -> "VariableTracker":
         if (
-            inspect.getattr_static(self.value, "__new__", None) in (object.__new__,)
+            inspect.getattr_static(self.value, "__new__", None) == object.__new__
             and self.source
         ):
             # NOTE: we don't need to track mutations to the placement class as they

torch/_dynamo/variables/tensor.py

@@ -1536,7 +1536,7 @@ class NumpyNdarrayVariable(TensorVariable):
                 explanation=f"Dynamo currently does not support tracing `ndarray.{name}`.",
                 hints=[],
             )
-        elif name in ["__version__"]:
+        elif name == "__version__":
             unimplemented_v2(
                 gb_type="Unsupported ndarray.__version__ access",
                 context=f"var_getattr {self} {name}",

torch/_dynamo/variables/torch.py

@@ -497,7 +497,7 @@ class TorchInGraphFunctionVariable(BaseTorchVariable):
             self, tx: "InstructionTranslator", *args, **kwargs
         ):
             assert not kwargs
-            if self.value in (torch._C._dispatch_keys,):
+            if self.value is torch._C._dispatch_keys:
                 assert len(args) == 1
                 assert isinstance(args[0], variables.TensorVariable)
                 example_value = args[0].proxy.node.meta["example_value"]
@@ -1862,7 +1862,7 @@ class DispatchKeySetVariable(BaseTorchVariable):
         return cls(value, source=source)
 
     def is_constant_fold_method(self, name):
-        return name in ["has"]
+        return name == "has"
 
     def call_method(
         self,

torch/_inductor/comm_lowering.py

@@ -151,7 +151,7 @@ def _should_lower_as_one_shot_all_reduce(
         config._collective.auto_select
         and is_symm_mem_enabled_for_group(group_name)
         and can_realize_as_comm_buffer(inp, ir.CommBufferType.SYMM_MEM)
-        and reduce_op in ("sum",)
+        and reduce_op == "sum"
         and inp_size <= config._collective.one_shot_all_reduce_threshold_bytes
     )
 

torch/_inductor/fx_passes/micro_pipeline_tp.py

@@ -1037,7 +1037,7 @@ def _get_unexposed_collectives(graph: torch.fx.Graph) -> list[torch.fx.Node]:
     """
 
     def _is_compute_intensive(node: torch.fx.Node) -> bool:
-        return node.target in [torch.ops.aten.mm.default]
+        return node.target is torch.ops.aten.mm.default
 
     collective_to_overlapping_candidates = defaultdict(list)
     available_nodes = OrderedSet[torch.fx.Node]()

torch/_inductor/fx_passes/pre_grad.py

@@ -614,7 +614,7 @@ def fuse_conv_bn(gm: torch.fx.GraphModule, inplace=False) -> torch.fx.GraphModul
 class NormalizedLinearNode:
     def __init__(self, node: torch.fx.Node) -> None:
         assert node.op == "call_function"
-        assert node.target in [torch.nn.functional.linear]
+        assert node.target is torch.nn.functional.linear
         self.node: torch.fx.Node = node
 
     def get_input(self) -> torch.fx.Node:

torch/_meta_registrations.py

@@ -3846,7 +3846,7 @@ def meta__dyn_quant_matmul_4bit(
 ):
     torch._check(inp.dim() == 2, lambda: "input must be a 2D tensor")
     torch._check(
-        inp.dtype in [torch.float32],
+        inp.dtype == torch.float32,
         lambda: f"expected input to be f32, got {inp.dtype}",
     )
     M = inp.size(0)

torch/_prims/__init__.py

@@ -352,12 +352,14 @@ def _make_prim(
 
     from torch._subclasses.fake_tensor import contains_tensor_types
 
-    if not any(contains_tensor_types(a.type) for a in _prim._schema.arguments) or str(
-        _prim
-    ) in [
-        # See https://github.com/pytorch/pytorch/issues/103532
-        "prims.device_put.default"
-    ]:
+    if (
+        not any(contains_tensor_types(a.type) for a in _prim._schema.arguments)
+        or str(
+            _prim
+            # See https://github.com/pytorch/pytorch/issues/103532
+        )
+        == "prims.device_put.default"
+    ):
         prim_backend_select_impl.impl(name, _backend_select_impl)
 
     for p in (_prim_packet, _prim):

torch/fx/_symbolic_trace.py

@@ -890,7 +890,7 @@ class Tracer(TracerBase):
         new_tracer = Tracer.__new__(Tracer)
 
         for k, v in self.__dict__.items():
-            if k in {"_autowrap_search"}:
+            if k == "_autowrap_search":
                 new_obj = copy.copy(v)
             else:
                 new_obj = copy.deepcopy(v, memo)

torch/fx/experimental/proxy_tensor.py

@@ -882,7 +882,7 @@ def proxy_call(
     def can_handle_tensor(x: Tensor) -> bool:
         r = type(x) in HANDLED_TYPES or has_proxy_slot(x, proxy_mode.tracer)
         if proxy_mode._allow_fake_constant:
-            r = r or type(x) in (torch._subclasses.FakeTensor,)
+            r = r or type(x) is torch._subclasses.FakeTensor
         if not r:
             unrecognized_types.append(type(x))
         return r
@@ -1534,7 +1534,7 @@ class ProxyTorchDispatchMode(TorchDispatchMode):
         with set_original_aten_op(func):
             kwargs = kwargs or {}
 
-            if func in (prim.device.default,):
+            if func == prim.device.default:
                 return func(*args, **kwargs)
 
             return proxy_call(self, func, self.pre_dispatch, args, kwargs)

torch/fx/passes/backends/cudagraphs.py

@@ -15,10 +15,10 @@ class CudaGraphsSupport(OperatorSupport):
         if node.op not in CALLABLE_NODE_OPS:
             return False
 
-        if node.target in [torch.ops.aten.embedding_dense_backward.default]:
+        if node.target == torch.ops.aten.embedding_dense_backward.default:
             return False
 
-        if node.target in [operator.getitem]:
+        if node.target == operator.getitem:
             return True
 
         found_not_cuda = False

torch/onnx/_internal/torchscript_exporter/symbolic_helper.py

@@ -815,9 +815,10 @@ def _is_fp(value) -> bool:
 
 
 def _is_bool(value) -> bool:
-    return _type_utils.JitScalarType.from_value(
-        value, _type_utils.JitScalarType.UNDEFINED
-    ) in {_type_utils.JitScalarType.BOOL}
+    return (
+        _type_utils.JitScalarType.from_value(value, _type_utils.JitScalarType.UNDEFINED)
+        == _type_utils.JitScalarType.BOOL
+    )
 
 
 def _generate_wrapped_number(g: jit_utils.GraphContext, scalar):

torch/testing/_internal/common_methods_invocations.py

@@ -3011,7 +3011,7 @@ def error_inputs_aminmax_amax_amin(op_info, device, is_ref=False, **kwargs):
     err_msg_aminmax = "cannot compute aminmax over an empty dimension as the operation has no identity"
     if op_info.name in ['amax', 'amin', '_refs.amax', '_refs.amin']:
         yield ErrorInput(SampleInput(torch.rand(shape, device=device)), error_regex=err_msg_amax_amin)
-    elif op_info.name in ['aminmax']:
+    elif op_info.name == 'aminmax':
         yield ErrorInput(SampleInput(torch.rand(shape, device=device)), error_regex=err_msg_aminmax)
 
     # Error Inputs for tensors with more than 64 dimension
@@ -3050,7 +3050,7 @@ def error_inputs_aminmax_amax_amin(op_info, device, is_ref=False, **kwargs):
     if op_info.name in ['amax', 'amin', '_refs.amax', '_refs.amin']:
         yield ErrorInput(SampleInput(input5, kwargs={'dim': 0, 'out': illegal_values}),
                          error_regex=err_msg_amax_amin2)
-    elif op_info.name in ['aminmax']:
+    elif op_info.name == 'aminmax':
         yield ErrorInput(SampleInput(input5, kwargs={'dim': 0, 'out': (max_values, min_values)}),
                          error_regex=err_msg_aminmax2)
 
@@ -4883,7 +4883,7 @@ def sample_inputs_upsample(mode, self, device, dtype, requires_grad, **kwargs):
 def reference_inputs_upsample(mode, self, device, dtype, requires_grad, **kwargs):
     yield from sample_inputs_upsample(mode, self, device, dtype, requires_grad, **kwargs)
 
-    if mode in ('bilinear', ):
+    if mode == 'bilinear':
         make_arg = partial(
             make_tensor,
             device=device,
@@ -9468,7 +9468,7 @@ class foreach_inputs_sample_func:
             # unary
             if opinfo.ref in (torch.abs, torch.neg):
                 return False
-            if opinfo.ref_inplace in (torch.Tensor.zero_,):
+            if opinfo.ref_inplace == torch.Tensor.zero_:
                 return False
             return dtype in integral_types_and(torch.bool)
         if self.arity < 2 or rightmost_arg_type == ForeachRightmostArgType.Tensor:
@@ -9698,7 +9698,7 @@ class foreach_pointwise_sample_func(foreach_inputs_sample_func):
         super().__init__(arity, rightmost_supports_scalar, rightmost_supports_scalarlist)
 
     def _should_disable_fastpath(self, opinfo, rightmost_arg, rightmost_arg_type, dtype):
-        return dtype in integral_types_and(torch.bool) and opinfo.ref in (torch.addcmul,)
+        return dtype in integral_types_and(torch.bool) and opinfo.ref == torch.addcmul
 
     def sample_zero_size_tensor_inputs(self, opinfo, device, dtype, requires_grad, **kwargs):
         assert "num_input_tensors" not in kwargs

torch/testing/_internal/common_optimizers.py

@@ -572,7 +572,7 @@ def optim_inputs_func_adam(device, dtype=None):
         + (cuda_supported_configs if _get_device_type(device) == "cuda" else [])
         + (mps_supported_configs if _get_device_type(device) == "mps" else [])
     )
-    if dtype in (torch.float16,):
+    if dtype == torch.float16:
         for input in total:
             """
             Too small eps will make denom to be zero for low precision dtype

torch/testing/_internal/opinfo/definitions/linalg.py

@@ -322,7 +322,7 @@ def sample_inputs_linalg_matrix_norm(op_info, device, dtype, requires_grad, **kw
 def sample_inputs_linalg_norm(
     op_info, device, dtype, requires_grad, *, variant=None, **kwargs
 ):
-    if variant is not None and variant not in ("subgradient_at_zero",):
+    if variant is not None and variant != "subgradient_at_zero":
         raise ValueError(
             f"Unsupported variant, expected variant to be 'subgradient_at_zero' but got: {variant}"
         )

torch/testing/_internal/opinfo/definitions/sparse.py

@@ -204,7 +204,7 @@ def _validate_sample_input_sparse_reduction(op_info, sample, check_validate=Fals
     if op_info.name == "sum":
         sample = _validate_sample_input_sparse_reduction_sum(sample)
 
-    if op_info.name in {"masked.sum"}:
+    if op_info.name == "masked.sum":
         mask = sample.kwargs.get("mask", UNSPECIFIED)
         if (
             mask not in {None, UNSPECIFIED}
@@ -792,12 +792,16 @@ def _sample_inputs_sparse_like_fns(
 
 
 def _validate_sample_input_sparse_like_fns(op_info, sample, check_validate=False):
-    if sample.input.layout in {
-        torch.sparse_csr,
-        torch.sparse_csc,
-        torch.sparse_bsr,
-        torch.sparse_bsc,
-    } and op_info.name not in {"zeros_like"}:
+    if (
+        sample.input.layout
+        in {
+            torch.sparse_csr,
+            torch.sparse_csc,
+            torch.sparse_bsr,
+            torch.sparse_bsc,
+        }
+        and op_info.name != "zeros_like"
+    ):
         if sample.kwargs.get("layout", sample.input.layout) != sample.input.layout:
             return ErrorInput(
                 sample,