[1/N] Simplify "in" operation for containers of a single item (#164224)

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/164224
Approved by: https://github.com/rec, https://github.com/Skylion007

torch/ao/nn/quantized/modules/utils.py

@@ -83,7 +83,7 @@ def _quantize_weight(float_wt, observer):
             torch.qint8,
         )
         qweight = _clamp_weights(qweight, observer, wt_scale, wt_zp)
-    elif observer.qscheme in [torch.per_channel_affine_float_qparams]:
+    elif observer.qscheme == torch.per_channel_affine_float_qparams:
         qweight = torch.quantize_per_channel(
             float_wt,
             wt_scale.to(torch.float),

torch/ao/quantization/fake_quantize.py

@@ -64,9 +64,7 @@ def _is_symmetric_quant(qscheme: "torch.qscheme") -> bool:
 
 
 def _is_float_qparams(qscheme: "torch.qscheme") -> bool:
-    return qscheme in [
-        torch.per_channel_affine_float_qparams,
-    ]
+    return qscheme == torch.per_channel_affine_float_qparams
 
 
 class FakeQuantizeBase(ABC, Module):

torch/ao/quantization/fx/_lower_to_native_backend.py

@@ -227,7 +227,7 @@ def is_getattr_tensor_metadata_node(node):
     return (
         node.op == "call_function"
         and node.target == getattr
-        and node.args[1] in ["shape"]
+        and node.args[1] == "shape"
     )
 
 

torch/ao/quantization/observer.py

@@ -388,7 +388,7 @@ class UniformQuantizationObserverBase(ObserverBase):
                     )
                 else:
                     zero_point = zero_point.new_full(zero_point.size(), 128)
-            elif self.dtype in [torch.uint16]:
+            elif self.dtype == torch.uint16:
                 zero_point = zero_point.new_full(zero_point.size(), 2**15)
         elif self.qscheme == torch.per_channel_affine_float_qparams:
             scale = (max_val - min_val) / float(quant_max - quant_min)

torch/ao/quantization/quantize.py

@@ -237,7 +237,7 @@ def _add_observer_(
 
     for name, child in module.named_children():
         # TODO remove Dropout special after codebase stable
-        if type_before_parametrizations(child) in [nn.Dropout]:
+        if type_before_parametrizations(child) is nn.Dropout:
             continue
         elif issubclass(
             type_before_parametrizations(child), (nnq.FloatFunctional, nnq.QFunctional)

torch/ao/quantization/quantizer/x86_inductor_quantizer.py

@@ -598,7 +598,7 @@ class X86InductorQuantizer(Quantizer):
             _annotate_nodes_not_quantize(linear_node)
             return
         input_qspec_map = {}
-        assert linear_node.target in (torch.ops.aten.linear.default,)
+        assert linear_node.target == torch.ops.aten.linear.default
         has_bias = len(linear_node.args) == 3
         input_index = 0
         weight_index = 1
@@ -1436,8 +1436,9 @@ class X86InductorQuantizer(Quantizer):
                     "Linear partition cannot have more than one output node"
                 )
             linear_node = partition.output_nodes[0]
-            if linear_node.op != "call_function" or linear_node.target not in (
-                torch.ops.aten.linear.default,
+            if (
+                linear_node.op != "call_function"
+                or linear_node.target != torch.ops.aten.linear.default
             ):
                 raise ValueError(f"{linear_node} is not an aten linear operator")
             # skip annotation if it is already annotated
@@ -1467,8 +1468,9 @@ class X86InductorQuantizer(Quantizer):
             linear_node, unary_node = self._get_output_nodes_of_partitions(
                 [linear_partition, unary_partition]
             )
-            if linear_node.op != "call_function" or linear_node.target not in (
-                torch.ops.aten.linear.default,
+            if (
+                linear_node.op != "call_function"
+                or linear_node.target != torch.ops.aten.linear.default
             ):
                 continue
             if _skip_annotate([unary_node, linear_node], filter_fn):

torch/ao/quantization/utils.py

@@ -501,9 +501,9 @@ def calculate_qmin_qmax(
                 quant_min, quant_max = 0, 255
         elif dtype in [torch.qint32, torch.int32]:
             quant_min, quant_max = -1 * (2**31), (2**31) - 1
-        elif dtype in [torch.uint16]:
+        elif dtype == torch.uint16:
             quant_min, quant_max = 0, 2**16 - 1
-        elif dtype in [torch.int16]:
+        elif dtype == torch.int16:
             quant_min, quant_max = -(2**15), 2**15 - 1
         else:
             quant_min, quant_max = 0, 15

torch/cuda/_utils.py

@@ -420,7 +420,7 @@ class _CudaKernel:
             # navi, CDNA1-CDNA3 allows a max of 64KB shared memory
             # CDNA4 allows a max of 160KB shared memory
             max_shared_mem = (
-                65536 if device_props.gcnArchName not in ["gfx950"] else 160 * 1024
+                65536 if device_props.gcnArchName != "gfx950" else 160 * 1024
             )
         else:
             max_shared_mem = getattr(

torch/distributed/tensor/examples/convnext_example.py

@@ -34,7 +34,7 @@ class LayerNorm(nn.Module):
         self.bias = nn.Parameter(torch.zeros(normalized_shape))
         self.eps = eps
         self.data_format = data_format
-        if self.data_format not in [torch.contiguous_format]:
+        if self.data_format != torch.contiguous_format:
             raise NotImplementedError
         self.normalized_shape = (normalized_shape,)
 

torch/masked/_ops.py

@@ -427,7 +427,7 @@ def _reduction_identity(op_name: str, input: Tensor, *args):
             return torch.tensor(-torch.inf, dtype=dtype, device=device)
         elif torch.is_signed(input) or dtype == torch.uint8:
             return torch.tensor(torch.iinfo(dtype).min, dtype=dtype, device=device)
-    elif op_name in {"logsumexp"}:
+    elif op_name == "logsumexp":
         if torch.is_floating_point(input):
             return torch.tensor(-torch.inf, dtype=dtype, device=device)
         elif torch.is_complex(input):

torch/testing/_internal/common_quantization.py

@@ -765,7 +765,7 @@ class QuantizationTestCase(TestCase):
             and not isinstance(module, _FusedModule)
         ):
             for child in module.children():
-                if type(child) in [nn.Dropout]:
+                if type(child) is nn.Dropout:
                     continue
                 self.checkObservers(
                     child, propagate_qconfig_list, prepare_custom_config_dict

torch/testing/_internal/distributed/ddp_under_dist_autograd_test.py

@@ -192,7 +192,7 @@ class Trainer:
         self.hybrid_module = HybridModel(
             self.remote_em_rref,
             self.remote_net_rref,
-            self.trainer_group if ddp_mode in (DdpMode.INSIDE,) else None,
+            self.trainer_group if ddp_mode == DdpMode.INSIDE else None,
         )
         self.ddp_params, self.non_ddp_params = (
             self.hybrid_module.ddp_params,

torch/testing/_internal/distributed/distributed_test.py

@@ -707,7 +707,7 @@ class DistributedTest:
                 self.assertNotEqual(args.get("dtype", ""), "")
 
                 per_coll_meta[collname].append(args)
-                if collname in {"wait"}:
+                if collname == "wait":
                     continue
 
                 self.assertEqual(args["Process Group Description"], "default_pg")
@@ -7029,7 +7029,7 @@ class DistributedTest:
                 self.assertNotEqual(attrs.get("dtype", ""), "")
 
                 per_coll_meta[collname].append(attrs)
-                if collname in {"wait"}:
+                if collname == "wait":
                     continue
 
                 self.assertEqual(attrs["pg_name"], "0")  # yes this is a string

torch/utils/_debug_mode.py

@@ -125,7 +125,7 @@ class DebugMode(TorchDispatchMode):
             _get_current_dispatch_mode(), FakeTensorMode
         ):
             if self.record_faketensor:
-                if func not in {torch.ops.prim.device.default}:
+                if func != torch.ops.prim.device.default:
                     self.operators.append((func, args, kwargs, self.call_depth + 1))
         elif len(types) == 0:
             if self.record_realtensor:

torch/utils/data/datapipes/dataframe/dataframes.py

@@ -103,7 +103,7 @@ class Capture:
     def __getattr__(self, attrname):
         if attrname == "kwarg" or attrname == "kwargs":
             raise RuntimeError("no kwargs!")
-        if attrname in ["__deepcopy__"]:
+        if attrname == "__deepcopy__":
             raise AttributeError
         result = CaptureGetAttr(self, attrname, ctx=self.ctx)
         return result

torch/utils/flop_counter.py

@@ -783,7 +783,7 @@ class _FlopCounterMode(TorchDispatchMode):
         return result, flop_counts
 
     def _handle_higher_order_ops(self, func, types, args, kwargs):
-        if func not in {torch.ops.higher_order.cond, }:
+        if func is not torch.ops.higher_order.cond:
             return NotImplemented
 
         # The flop counter for cond counts the upper bound of flops.