[2/N] Fix ruff warnings (#164460)

Apply ruff `SIM` rules.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/164460
Approved by: https://github.com/ezyang

torch/_custom_op/autograd.py

@@ -220,7 +220,7 @@ def validate_grad_inputs_dict(grad_inputs_dict, forward_op, args_info):
                     f"hold a list of gradients but got object of type "
                     f"{type(grad)}."
                 )
-            if not len(grad) == len(arg_info):
+            if len(grad) != len(arg_info):
                 error(
                     f"for input '{name}' expected the grad_input dict to "
                     f"hold a list of {len(arg_info)} gradients but got "

torch/_dynamo/output_graph.py

@@ -604,7 +604,7 @@ class OutputGraph(OutputGraphCommon):
             fake_mode = torch._subclasses.FakeTensorMode(
                 shape_env=shape_env,
                 # TODO (tmanlaibaatar) Remove this once we always lift params and buffers
-                allow_non_fake_inputs=True if self.export else False,
+                allow_non_fake_inputs=bool(self.export),
                 export=self.export,
             )
         self.tracing_context: TracingContext = TracingContext(fake_mode)

torch/_dynamo/variables/higher_order_ops.py

@@ -453,9 +453,7 @@ def _call_while_loop(
         cond_r_meta = _extract_tensor_metadata(
             cond_r.proxy.node.meta["example_value"], include_contiguity=False
         )
-        if not cond_r_meta.dtype == torch.bool or not cond_r_meta.shape == torch.Size(
-            []
-        ):
+        if cond_r_meta.dtype != torch.bool or cond_r_meta.shape != torch.Size([]):
             unimplemented(
                 f"Expected cond_fn to return a scalar tensor or a bool but got {cond_r_meta.shape}"
             )

torch/_higher_order_ops/cond.py

@@ -325,8 +325,7 @@ class CondAutogradOp(torch.autograd.Function):
 
                 true_outputs = fn(*args)
                 grads_tensor_masks = [
-                    True if isinstance(out, torch.Tensor) else False
-                    for out in true_outputs
+                    bool(isinstance(out, torch.Tensor)) for out in true_outputs
                 ]
                 return filter_with_masks(true_outputs, grads_tensor_masks)
 

torch/_higher_order_ops/local_map.py

@@ -134,9 +134,7 @@ def create_hop_fw_bw(
                         "Dynamo traced submodule should return tuple"
                     )
                     return fw_out, [
-                        True
-                        if isinstance(ret, torch.Tensor) and ret.requires_grad
-                        else False
+                        bool(isinstance(ret, torch.Tensor) and ret.requires_grad)
                         for ret in fw_out
                     ]
 

torch/_higher_order_ops/scan.py

@@ -675,8 +675,7 @@ class ScanAutogradImpl:
 
         grad_carry, grad_ys = grad_fw_outputs[:n_carry], grad_fw_outputs[n_carry:]
         additional_inputs_tensor_masks = [
-            True if isinstance(t, torch.Tensor) else False
-            for t in self.additional_inputs
+            bool(isinstance(t, torch.Tensor)) for t in self.additional_inputs
         ]
         grad_additional_inputs = [
             torch.zeros_like(t)

torch/_higher_order_ops/utils.py

@@ -500,8 +500,7 @@ def prepare_fw_with_masks(fn):
     def fw_with_masks(*args):
         fw_out = fn(*args)
         return fw_out, [
-            True if isinstance(ret, torch.Tensor) and ret.requires_grad else False
-            for ret in fw_out
+            bool(isinstance(ret, torch.Tensor) and ret.requires_grad) for ret in fw_out
         ]
 
     return fw_with_masks
@@ -857,7 +856,7 @@ def first_slice_copy(t: torch.Tensor, dim: int = 0) -> torch.Tensor:
 
 # Returns a mask whether a list element is a tensor or not
 def get_tensor_mask(tensor_list: Iterable[Any]) -> list[bool]:
-    return [True if isinstance(v, torch.Tensor) else False for v in tensor_list]
+    return [bool(isinstance(v, torch.Tensor)) for v in tensor_list]
 
 
 def mask_list(

torch/_higher_order_ops/while_loop.py

@@ -767,11 +767,11 @@ class WhileLoopAutogradOp(torch.autograd.Function):
         # inductor codegen, where we need to do a non-unform treatment for None and tensors.
         # So we set up masks and filter the None gradients so that only tensors are returned from each step.
         carries_tensor_masks = [
-            True if isinstance(t, torch.Tensor) and t.dtype.is_floating_point else False
+            bool(isinstance(t, torch.Tensor) and t.dtype.is_floating_point)
             for t in ctx.carries
         ]
         additional_inputs_tensor_masks = [
-            True if isinstance(t, torch.Tensor) and t.dtype.is_floating_point else False
+            bool(isinstance(t, torch.Tensor) and t.dtype.is_floating_point)
             for t in ctx.additional_inputs
         ]
 

torch/_inductor/comms.py

@@ -1413,7 +1413,7 @@ Resize can only operate on graph inputs, but got {node} which is resizing non-gr
         )
         resized_to_0_idxes = graph_input_to_resized_to_0_node_idxes.get(graph_input, [])
 
-        if not len(resized_to_full_idxes) == len(resized_to_0_idxes):
+        if len(resized_to_full_idxes) != len(resized_to_0_idxes):
             log.warning(
                 f"""
 Unequal number of resize-to-full and resize-to-0 nodes for graph input {graph_input}:

torch/_inductor/fuzzer.py

@@ -383,7 +383,7 @@ class SamplingMethod(Enum):
         elif TypeExemplars.contains(type_hint):
             return TypeExemplars.example(type_hint)
         elif type_hint == Any:
-            return 1 if not default == 1 else 2
+            return 1 if default != 1 else 2
         else:
             raise ValueError(f"Unable to process type {type_hint}. PRs welcome :)")
 

torch/_inductor/fx_passes/binary_folding.py

@@ -19,18 +19,18 @@ def mark_mixed_dtype(computation_node):
     if computation_node_dtype not in (torch.float16, torch.bfloat16):
         return
 
-    if not len(computation_node.users) == 1:
+    if len(computation_node.users) != 1:
         return
 
     computation_node_user = next(iter(computation_node.users.keys()))
     if not isinstance(computation_node_user.meta["val"], torch.Tensor):
         return
 
-    if not computation_node_user.meta["val"].dtype == torch.float32:
+    if computation_node_user.meta["val"].dtype != torch.float32:
         return
 
     while computation_node_user.target in _binary_ops:
-        if not len(computation_node_user.users) == 1:
+        if len(computation_node_user.users) != 1:
             return
 
         computation_node_user = next(iter(computation_node_user.users.keys()))
@@ -188,7 +188,7 @@ def binary_folding_init():
         ):
             return False
 
-        if not len(conv_node.args[1].users) == 1:
+        if len(conv_node.args[1].users) != 1:
             return False
 
         weight_meta_value = conv_node.args[1].meta.get("val")
@@ -242,7 +242,7 @@ def binary_folding_init():
         ):
             return False
 
-        if not len(weight_node.users) == 1:
+        if len(weight_node.users) != 1:
             return False
 
         weight_meta_value = weight_node.meta.get("val")

torch/_inductor/fx_passes/overlap_scheduling.py

@@ -594,7 +594,7 @@ class OverlapScheduler:
 
             if is_wait_tensor(node):
                 info = self.collective_info[self.wait_to_start[node]]
-                assert not info.hiding_node == curr_compute_node
+                assert info.hiding_node != curr_compute_node
                 self._handle_wait(node)
                 continue
 

torch/_inductor/fx_passes/post_grad.py

@@ -705,7 +705,7 @@ def reorder_for_locality(graph: torch.fx.Graph):
         iter(graph.find_nodes(op="call_function", target=torch.ops.aten.copy_.default)),
         None,
     )
-    past_mutating_epilogue = True if first_copy is None else False
+    past_mutating_epilogue = first_copy is None
 
     for node in reversed(graph.nodes):
         seen_nodes.add(node)
@@ -1761,7 +1761,7 @@ class ConstructorMoverPass:
             if not torch._subclasses.fake_tensor._is_tensor_constructor(node.target):
                 continue
 
-            if not node.kwargs.get("device") == torch.device("cpu"):
+            if node.kwargs.get("device") != torch.device("cpu"):
                 continue
 
             constructors.append(node)
@@ -1922,14 +1922,10 @@ def move_constructors_to_gpu(graph: fx.Graph) -> None:
     # by explicitly moving cpu scalar tensors to gpu when profitable, relying on
     # graph partition to split off this data copy, and cudagraphifying
     # the remaining gpu ops.
-    allow_inputs_outputs = (
-        True
-        if (
+    allow_inputs_outputs = bool(
         torch._inductor.config.triton.cudagraphs
         and torch._inductor.config.graph_partition
     )
-        else False
-    )
     ConstructorMoverPass(
         get_gpu_type(),
         allow_inputs=allow_inputs_outputs,

torch/_inductor/fx_passes/reinplace.py

@@ -615,7 +615,7 @@ def reinplace_inplaceable_ops_core(graph: torch.fx.Graph) -> None:
                 copy_node = copy_args_to_copy_nodes.get((mutated_arg, node))
                 if copy_node is not None:
                     replace_dict[copy_node] = copy_node.args[0]
-                if not trigger == ReInplaceTrigger.AUTO_FUNC_V2:
+                if trigger != ReInplaceTrigger.AUTO_FUNC_V2:
                     for user in node.users:
                         # For auto_functionalize_v2, arg is the index of the base, where base at index i corresponds to
                         # output atindex size(out)+i.

torch/_inductor/ir.py

@@ -469,9 +469,7 @@ def significant_strides_equal(
 
         if not V.graph.sizevars.statically_known_equals(
             s1, s2
-        ) and not V.graph.sizevars.symbolic_hint(s1) == V.graph.sizevars.symbolic_hint(
-            s2
-        ):
+        ) and V.graph.sizevars.symbolic_hint(s1) != V.graph.sizevars.symbolic_hint(s2):
             return False
 
     return True
@@ -5443,7 +5441,7 @@ class ConcatKernel(NopKernel):
                 return True
 
             # otherwise, check equality of layouts
-            if not len(src.get_stride()) == len(dst.get_stride()):
+            if len(src.get_stride()) != len(dst.get_stride()):
                 return False
 
             return all(

torch/_inductor/kernel/flex/flex_cpu.py

@@ -35,7 +35,7 @@ def check_cpu_supported():
     supported = (
         requires_avx2_on_cpu
         and not torch.xpu.is_available()
-        and not sys.platform == "darwin"
+        and sys.platform != "darwin"
     )
     return supported
 

torch/_inductor/pattern_matcher.py

@@ -1398,7 +1398,7 @@ def check_and_add_duplicate_pattern(
 
     new_graph_str = str(graph)
     for graph_str in equiv_pattern_reprs:
-        if not new_graph_str == graph_str:
+        if new_graph_str != graph_str:
             continue
         if skip_duplicates:
             return True

torch/_inductor/runtime/triton_heuristics.py

@@ -237,7 +237,7 @@ def check_autotune_cache(
         not disabled
         and filename is not None
         and (len(configs) > 1 or inductor_meta.get("coordinate_descent_tuning"))
-        and not os.environ.get("TRITON_INTERPRET", "0") == "1"
+        and os.environ.get("TRITON_INTERPRET", "0") != "1"
     ):
         configs_hash = hash_configs(configs)
 

torch/_inductor/scheduler.py

@@ -4976,7 +4976,7 @@ class Scheduler:
                 if name in name_to_node
             }
             input_deallocation = {
-                name: True if name in buffer_names_to_free else False
+                name: name in buffer_names_to_free
                 for name in partition_input_names
                 if name in name_to_node
             }

torch/_inductor/tiling_utils.py

@@ -132,7 +132,7 @@ def solve_for_tiling(expr: sympy.Expr) -> Optional[sympy.Expr]:
 
     out = _solve_simple_expr(eq_1_expr_simplified)
     # since we approximated FloorDiv/ModularIndexing, double check here
-    if not out or not (sympy_subs(eq_1_expr, {free_symbol: out})) == 1:
+    if not out or sympy_subs(eq_1_expr, {free_symbol: out}) != 1:
         return None
 
     required_values.append(out)

torch/_inductor/utils.py

@@ -504,7 +504,7 @@ def is_pointwise_use(
     Uses in views ops will follow the views uses
     """
 
-    if not use.op == "call_function":
+    if use.op != "call_function":
         return False
     if not (
         isinstance(use.target, torch._ops.OpOverload) or use.target is operator.getitem
@@ -2020,7 +2020,7 @@ def use_ck_template(layout: Layout) -> bool:
     if not torch.version.hip:
         return False
     # tensors must be on GPU
-    if not layout.device.type == "cuda":
+    if layout.device.type != "cuda":
         return False
     # hardware check
     # if config arch list is not specified, get the native arch from the device properties

torch/_jit_internal.py

@@ -443,7 +443,7 @@ def get_callable_argument_names(fn) -> list[str]:
     for name, param in callable_signature.parameters.items():
         # All four other types of arguments do not map to individual values
         # with a keyword as name.
-        if not param.kind == param.POSITIONAL_OR_KEYWORD:
+        if param.kind != param.POSITIONAL_OR_KEYWORD:
             continue
 
         argument_names.append(name)

torch/_library/utils.py

@@ -135,7 +135,7 @@ def mutates_and_returns_first_arg(op: OpOverload):
     if op.namespace != "aten":
         return False
     schema = op._schema
-    if not len(schema.returns) == 1:
+    if len(schema.returns) != 1:
         return False
     if schema.returns[0].alias_info is None:
         return False

torch/_logging/_internal.py

@@ -1207,7 +1207,7 @@ def safe_grad_filter(message, category, filename, lineno, file=None, line=None)
 def user_warning_filter(
     message, category, filename, lineno, file=None, line=None
 ) -> bool:
-    return not category == UserWarning
+    return category != UserWarning
 
 
 @contextlib.contextmanager

torch/_numpy/testing/utils.py

@@ -317,7 +317,7 @@ def print_assert_equal(test_string, actual, desired):
     __tracebackhide__ = True  # Hide traceback for py.test
     import pprint
 
-    if not (actual == desired):
+    if actual != desired:
         msg = StringIO()
         msg.write(test_string)
         msg.write(" failed\nACTUAL: \n")
@@ -1505,7 +1505,7 @@ def _integer_repr(x, vdt, comp):
     # See also
     # https://randomascii.wordpress.com/2012/02/25/comparing-floating-point-numbers-2012-edition/
     rx = x.view(vdt)
-    if not (rx.size == 1):
+    if rx.size != 1:
         rx[rx < 0] = comp - rx[rx < 0]
     else:
         if rx < 0:

torch/_refs/__init__.py

@@ -4316,7 +4316,7 @@ def tensor_split(
 
     # If indices_or_sections is a tensor, it must be a CPU Long tensor
     if isinstance(indices_or_sections, TensorLike):
-        if not indices_or_sections.device.type == "cpu":
+        if indices_or_sections.device.type != "cpu":
             msg = (
                 f"tensor_split: if indices_or_sections is a tensor it must be on the CPU, "
                 f"but received one on {indices_or_sections.device}"

torch/_subclasses/fake_impls.py

@@ -1335,7 +1335,7 @@ def make_fast_binary_impl(
                 # Use elementwise_dtypes for the tricky case
                 has_different_input_dtypes = True
                 continue
-            if common_device == cpu and not op.device.type == "cpu":
+            if common_device == cpu and op.device.type != "cpu":
                 common_device = op.device
             # Slightly simplified here as target_dtype cannot vary
             if common_dtype is None:

torch/_subclasses/fake_tensor.py

@@ -3012,7 +3012,7 @@ class FakeTensorMode(TorchDispatchMode):
             t.numel() <= CONSTANT_NUMEL_LIMIT
             and not is_sparse_any(t)
             and not self.is_our_fake(t)
-            and not t.device.type == "meta"
+            and t.device.type != "meta"
         )
 
     def invalidate_written_to_constants(

torch/_subclasses/fake_utils.py

@@ -127,7 +127,7 @@ def try_convert_fake_to_real(
     key_to_real_storage = {v: k for k, v in desc.lookup_storage.items()}
     out = []
     for t in ten_list:
-        if not isinstance(t, FakeTensor) or not t.layout == torch.strided:
+        if not isinstance(t, FakeTensor) or t.layout != torch.strided:
             out.append(t)
             continue
 

torch/_subclasses/schema_check_mode.py

@@ -113,7 +113,7 @@ class SchemaCheckMode(TorchDispatchMode):
             return name if name != "self" else "input"
 
         def unwrap(e):
-            if isinstance(e, torch.Tensor) and not type(e) == torch.Tensor:
+            if isinstance(e, torch.Tensor) and type(e) != torch.Tensor:
                 try:
                     return e.elem
                 except AttributeError:
@@ -122,7 +122,7 @@ class SchemaCheckMode(TorchDispatchMode):
 
         def parse_metadata(e):
             if isinstance(e, torch.Tensor):
-                if not type(e) == torch.Tensor:
+                if type(e) != torch.Tensor:
                     try:
                         current = e.elem
                         return (

torch/ao/quantization/fx/_model_report/model_report_visualizer.py

@@ -518,7 +518,7 @@ class ModelReportVisualizer:
                 # the index of the feature will the 0 + num non feature columns
                 tensor_feature_index = feature_column_offset
                 row_value = row[tensor_feature_index]
-                if not type(row_value) == str:
+                if type(row_value) != str:
                     x_data.append(x_val_to_append)
                     y_data.append(row_value)
         elif is_valid_per_channel_plot:
@@ -541,7 +541,7 @@ class ModelReportVisualizer:
                 # the index of the feature will the 0 + num non feature columns
                 tensor_feature_index = feature_column_offset
                 row_value = row[tensor_feature_index]
-                if not type(row_value) == str:
+                if type(row_value) != str:
                     # only append if new index we are appending
                     if len(x_data) == 0 or x_data[-1] != x_val_to_append:
                         x_data.append(x_val_to_append)

torch/ao/quantization/fx/match_utils.py

@@ -51,7 +51,7 @@ def _is_match(modules, node, pattern, max_uses=sys.maxsize):
     if isinstance(self_match, type) and issubclass(self_match, torch.nn.Module):
         if node.op != "call_module":
             return False
-        if not type_before_parametrizations(modules[node.target]) == self_match:
+        if type_before_parametrizations(modules[node.target]) != self_match:
             return False
     elif callable(self_match):
         if node.op != "call_function" or node.target is not self_match:

torch/ao/quantization/fx/prepare.py

@@ -1708,7 +1708,7 @@ def insert_observers_for_model(
 
             skip_inserting_observers = (
                 (qconfig is None) or not output_is_a_tensor
-            ) and (not node.op == "output")
+            ) and (node.op != "output")
 
             # TODO: take a closer look to see if we can remove this check
             # right now it is here because of `observed_node_names`, we are using

torch/autograd/gradcheck.py

@@ -733,9 +733,7 @@ def _stack_and_check_tensors(
             if tensor is None:
                 out_jacobian[:, j].zero_()
             else:
-                dense = (
-                    tensor.to_dense() if not tensor.layout == torch.strided else tensor
-                )
+                dense = tensor.to_dense() if tensor.layout != torch.strided else tensor
                 assert out_jacobian[:, j].numel() == dense.numel()
                 out_jacobian[:, j] = dense.reshape(-1)
     return out_jacobians, correct_grad_sizes, correct_grad_types

torch/backends/opt_einsum/__init__.py

@@ -116,5 +116,5 @@ class OptEinsumModule(PropModule):
 # https://stackoverflow.com/questions/2447353/getattr-on-a-module/7668273#7668273
 sys.modules[__name__] = OptEinsumModule(sys.modules[__name__], __name__)
 
-enabled = True if is_available() else False
+enabled = bool(is_available())
 strategy = "auto" if is_available() else None

torch/cuda/_sanitizer.py

@@ -200,7 +200,7 @@ class _TensorsAccessed:
         del self.accesses[data_ptr]
 
     def were_there_reads_since_last_write(self, data_ptr: DataPtr) -> bool:
-        return True if self.accesses[data_ptr].reads else False
+        return bool(self.accesses[data_ptr].reads)
 
     def get_allocation_stack_trace(
         self, data_ptr: DataPtr

torch/distributed/checkpoint/default_planner.py

@@ -548,7 +548,7 @@ def create_default_global_save_plan(
     for plan in all_plans:
         new_items = []
         for item in plan.items:
-            if not item.type == WriteItemType.SHARD:
+            if item.type != WriteItemType.SHARD:
                 assert item.index.fqn not in md
 
             if item.type == WriteItemType.BYTE_IO:

torch/distributed/fsdp/_optim_utils.py

@@ -1508,8 +1508,7 @@ def _allgather_orig_param_states(
         return output_states
 
     has_state_params: list[bool] = [
-        True if fqn in output_states else False
-        for fqn, idx in fsdp_param_info.param_indices.items()
+        fqn in output_states for fqn, idx in fsdp_param_info.param_indices.items()
     ]
 
     # Loop through the ``state_buffers`` and construct the flattened, concatenated,

torch/distributed/pipelining/schedules.py

@@ -1191,7 +1191,7 @@ def _add_send_recv(
         """
         if action is None:
             return True
-        elif action.computation_type == F and not action.stage_index == 0:
+        elif action.computation_type == F and action.stage_index != 0:
             if (
                 _Action(action.stage_index, RECV_F, action.microbatch_index)
                 in prev_actions
@@ -1205,7 +1205,7 @@ def _add_send_recv(
             return False
         elif (
             action.computation_type in (BACKWARD_INPUT, FULL_BACKWARD)
-            and not action.stage_index == num_stages - 1
+            and action.stage_index != num_stages - 1
         ):
             if (
                 _Action(action.stage_index, RECV_B, action.microbatch_index)

torch/distributed/rpc/backend_registry.py

@@ -350,7 +350,7 @@ def _tensorpipe_init_backend_handler(
 
     device_count = torch.cuda.device_count()
 
-    is_static_group = True if world_size else False
+    is_static_group = bool(world_size)
     # world_size is specified so this is a static group (ranks cannot join and leave)
     if is_static_group:
         # The agent's join method is required to behave like a barrier and perform

torch/distributed/tensor/_ops/_matrix_ops.py

@@ -1067,12 +1067,12 @@ def grouped_mm_strategy(op_schema: OpSchema) -> OpStrategy:
             if meta.stride[end_dim - 1] == 1 and meta.stride[end_dim] >= max(
                 1, meta.shape[end_dim - 1]
             ):
-                if not meta.stride[end_dim] % alignment == 0:
+                if meta.stride[end_dim] % alignment != 0:
                     return False
             elif meta.stride[end_dim] == 1 and meta.stride[end_dim - 1] >= max(
                 1, meta.shape[end_dim]
             ):
-                if not meta.stride[end_dim - 1] % alignment == 0:
+                if meta.stride[end_dim - 1] % alignment != 0:
                     return False
             else:
                 return False

torch/distributed/tensor/_shards_wrapper.py

@@ -187,7 +187,7 @@ class LocalShardsWrapper(torch.Tensor):
             aten.equal.default(x, y) for x, y in zip(a.local_shards(), b.local_shards())
         ):
             return False
-        if not a.storage_metadata() == b.storage_metadata():
+        if a.storage_metadata() != b.storage_metadata():
             return False
         return True
 

torch/distributed/tensor/experimental/_tp_transform.py

@@ -464,7 +464,7 @@ def _insert_reshard_gm(
             if reshard_node.op not in ["placeholder", "output"]:
                 reshard_node.meta["nn_module_stack"] = (
                     copy.copy(input_arg.meta["nn_module_stack"])
-                    if not input_arg.op == "placeholder"
+                    if input_arg.op != "placeholder"
                     else copy.copy(node.meta["nn_module_stack"])
                 )
         output_node = gm.graph.graph_copy(

torch/export/_swap.py

@@ -142,7 +142,7 @@ def _try_remove_connecting_pytrees(curr_module_node: torch.fx.Node) -> None:
         return
 
     next_module_node = next(iter(unflatten_getitem_getitem_users))
-    if not (next_module_node.op == "call_module"):
+    if next_module_node.op != "call_module":
         log.debug(
             "Unflatten node %s's user is not a call_module. "
             "Instead it is: %s. Passing...",

torch/export/exported_program.py

@@ -590,7 +590,7 @@ def _decompose_and_get_gm_with_new_signature_constants(
             ep.graph_module,
             fake_args,
             decompositions=python_decomp_table,
-            trace_joint=True if joint_loss_index is not None else False,
+            trace_joint=joint_loss_index is not None,
             output_loss_index=(
                 joint_loss_index if joint_loss_index is not None else None
             ),

torch/functional.py

@@ -1942,7 +1942,7 @@ def _unravel_index(indices: Tensor, shape: Union[int, Sequence[int]]) -> Tensor:
     torch._check_type(
         not indices.is_complex()
         and not indices.is_floating_point()
-        and not indices.dtype == torch.bool,
+        and indices.dtype != torch.bool,
         lambda: f"expected 'indices' to be integer dtype, but got {indices.dtype}",
     )
 

torch/fx/experimental/symbolic_shapes.py

@@ -7056,7 +7056,7 @@ class ShapeEnv:
                 expr,
                 concrete_val,
                 # only print stack trace when debug mode is on (e.g. TORCH_LOGS="dynamic")
-                stack_info=True if log.getEffectiveLevel() < logging.WARNING else False,
+                stack_info=log.getEffectiveLevel() < logging.WARNING,
             )
 
     def _get_user_frame(self) -> Optional[types.FrameType]:

torch/fx/passes/net_min_base.py

@@ -668,7 +668,7 @@ class _MinimizerBase:
         final_start_idx: Optional[int] = start_idx
         final_end_idx: Optional[int] = end_idx
 
-        run_both = True if find_last_node is None else False
+        run_both = find_last_node is None
 
         # step 1: find (0, end_idx) of culprit block
         if run_both or find_last_node:

torch/nn/functional.py

@@ -3649,7 +3649,7 @@ def smooth_l1_loss(
             reduction=reduction,
             beta=beta,
         )
-    if not (target.size() == input.size()):
+    if target.size() != input.size():
         warnings.warn(
             f"Using a target size ({target.size()}) that is different to the input size ({input.size()}). "
             "This will likely lead to incorrect results due to broadcasting. "
@@ -3712,7 +3712,7 @@ def huber_loss(
             weight=weight,
         )
 
-    if not (target.size() == input.size()):
+    if target.size() != input.size():
         warnings.warn(
             f"Using a target size ({target.size()}) that is different to the input size ({input.size()}). "
             "This will likely lead to incorrect results due to broadcasting. "
@@ -3789,7 +3789,7 @@ def l1_loss(
             reduce=reduce,
             reduction=reduction,
         )
-    if not (target.size() == input.size()):
+    if target.size() != input.size():
         warnings.warn(
             f"Using a target size ({target.size()}) that is different to the input size ({input.size()}). "
             "This will likely lead to incorrect results due to broadcasting. "
@@ -3862,7 +3862,7 @@ def mse_loss(
             weight=weight,
         )
 
-    if not (target.size() == input.size()):
+    if target.size() != input.size():
         warnings.warn(
             f"Using a target size ({target.size()}) that is different to the input size ({input.size()}). "
             "This will likely lead to incorrect results due to broadcasting. "

torch/nn/modules/rnn.py

@@ -242,7 +242,7 @@ class RNNBase(Module):
         for fw in self._flat_weights:
             if (
                 not isinstance(fw, Tensor)
-                or not (fw.dtype == dtype)
+                or fw.dtype != dtype
                 or not fw.is_cuda
                 or not torch.backends.cudnn.is_acceptable(fw)
             ):

torch/nn/modules/transformer.py

@@ -382,7 +382,7 @@ class TransformerEncoder(Module):
             why_not_sparsity_fast_path = (
                 f"{enc_layer}.activation_relu_or_gelu was not True"
             )
-        elif not (encoder_layer.norm1.eps == encoder_layer.norm2.eps):
+        elif encoder_layer.norm1.eps != encoder_layer.norm2.eps:
             why_not_sparsity_fast_path = (
                 f"{enc_layer}.norm1.eps was not equal to {enc_layer}.norm2.eps"
             )
@@ -458,7 +458,7 @@ class TransformerEncoder(Module):
             )
         elif first_layer.training:
             why_not_sparsity_fast_path = f"{str_first_layer} was in training mode"
-        elif not src.dim() == 3:
+        elif src.dim() != 3:
             why_not_sparsity_fast_path = (
                 f"input not batched; expected src.dim() of 3 but got {src.dim()}"
             )
@@ -832,7 +832,7 @@ class TransformerEncoderLayer(Module):
             why_not_sparsity_fast_path = (
                 "torch.backends.mha.get_fastpath_enabled() was not True"
             )
-        elif not src.dim() == 3:
+        elif src.dim() != 3:
             why_not_sparsity_fast_path = (
                 f"input not batched; expected src.dim() of 3 but got {src.dim()}"
             )
@@ -846,7 +846,7 @@ class TransformerEncoderLayer(Module):
             why_not_sparsity_fast_path = "self_attn._qkv_same_embed_dim was not True"
         elif not self.activation_relu_or_gelu:
             why_not_sparsity_fast_path = "activation_relu_or_gelu was not True"
-        elif not (self.norm1.eps == self.norm2.eps):
+        elif self.norm1.eps != self.norm2.eps:
             why_not_sparsity_fast_path = "norm1.eps is not equal to norm2.eps"
         elif src.is_nested and (
             src_key_padding_mask is not None or src_mask is not None

torch/onnx/_internal/torchscript_exporter/utils.py

@@ -136,7 +136,7 @@ def select_model_mode_for_export(model, mode: _C_onnx.TrainingMode):
     try:
         yield
     finally:
-        if hasattr(model, "training") and not mode == _C_onnx.TrainingMode.PRESERVE:
+        if hasattr(model, "training") and mode != _C_onnx.TrainingMode.PRESERVE:
             model.train(originally_training)
 
 

torch/testing/_internal/common_methods_invocations.py

@@ -9346,7 +9346,7 @@ def sample_inputs_multi_head_attention_forward(opinfo, device, dtype, requires_g
             "k_proj_weight" : k_proj_weight,
             "v_proj_weight" : v_proj_weight,
             "attn_mask" : attn_mask,
-            "training" : True if dropout_p > 0.0 else False,
+            "training" : dropout_p > 0.0,
             "use_separate_proj_weight" : use_separate_proj_weight
         }
 

torch/testing/_internal/common_quantization.py

@@ -1287,7 +1287,7 @@ class QuantizationTestCase(TestCase):
                 prepare_custom_config=prepare_custom_config,
                 backend_config=backend_config,
             )
-            if not quant_type == QuantType.DYNAMIC:
+            if quant_type != QuantType.DYNAMIC:
                 prepared(*inputs)
 
             if print_debug_info:

torch/testing/_internal/common_utils.py

@@ -1470,7 +1470,7 @@ TEST_ACL = torch.backends.mkldnn.is_available() and torch.ops.mkldnn._is_mkldnn_
 TEST_MPS = torch.backends.mps.is_available()
 MACOS_VERSION = float('.'.join(platform.mac_ver()[0].split('.')[:2]) or -1)
 TEST_XPU = torch.xpu.is_available()
-TEST_HPU = True if (hasattr(torch, "hpu") and torch.hpu.is_available()) else False
+TEST_HPU = bool(hasattr(torch, "hpu") and torch.hpu.is_available())
 TEST_CUDA = torch.cuda.is_available()
 custom_device_mod = getattr(torch, torch._C._get_privateuse1_backend_name(), None)
 TEST_PRIVATEUSE1 = is_privateuse1_backend_available()
@@ -3195,7 +3195,7 @@ class TestCase(expecttest.TestCase):
 
     def remove_empty_lines(self, input_string):
         lines = input_string.split('\n')
-        filtered_lines = [line for line in lines if not line.strip() == '']
+        filtered_lines = [line for line in lines if line.strip() != '']
         return '\n'.join(filtered_lines)
 
     # ignore comments will ignore lines that starts with # after being stripped

torch/utils/checkpoint.py

@@ -132,7 +132,7 @@ def _infer_device_type(*args):
 
     def add_device_types(arg):
         nonlocal device_types
-        if isinstance(arg, torch.Tensor) and not arg.device.type == "cpu":
+        if isinstance(arg, torch.Tensor) and arg.device.type != "cpu":
             device_types.append(arg.device.type)
     tree_map(add_device_types, args)
 

torch/utils/collect_env.py

@@ -119,7 +119,7 @@ PIP_PATTERNS = [
 
 def run(command):
     """Return (return-code, stdout, stderr)."""
-    shell = True if type(command) is str else False
+    shell = type(command) is str
     p = subprocess.Popen(
         command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=shell
     )

torch/utils/cpp_extension.py

@@ -228,7 +228,7 @@ CUDA_NOT_FOUND_MESSAGE = (
 )
 ROCM_HOME = _find_rocm_home() if (torch.cuda._is_compiled() and torch.version.hip) else None
 HIP_HOME = _join_rocm_home('hip') if ROCM_HOME else None
-IS_HIP_EXTENSION = True if ((ROCM_HOME is not None) and (torch.version.hip is not None)) else False
+IS_HIP_EXTENSION = bool(ROCM_HOME is not None and torch.version.hip is not None)
 ROCM_VERSION = None
 if torch.version.hip is not None:
     ROCM_VERSION = tuple(int(v) for v in torch.version.hip.split('.')[:2])