[1/N] Remove unused loop variables (#166258)

This PR removes unused loop variables.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/166258
Approved by: https://github.com/Lucaskabela, https://github.com/mlazos

torch/_dynamo/eval_frame.py

@@ -1791,7 +1791,7 @@ def rewrite_signature(
         for i, val in enumerate(sources):
             dict_of_source_vals[id(val)] = i
 
-        for i, val in enumerate(candidates):
+        for val in candidates:
             if isinstance(val, tuple(common_constant_types)):
                 matched_elements_positions.append(None)
             elif id(val) not in dict_of_source_vals:

torch/_dynamo/guards.py

@@ -319,7 +319,7 @@ class GuardManagerWrapper:
             is_diff_guard_node = (
                 node.get_source() in self.diff_guard_sources or node.fail_count() > 0
             )
-            for idx, (key_mgr, val_mgr) in sorted(
+            for _idx, (key_mgr, val_mgr) in sorted(
                 node.get_key_value_managers().items()
             ):
                 is_diff_guard_node |= visit(key_mgr) | visit(val_mgr)
@@ -442,7 +442,7 @@ class GuardManagerWrapper:
             is_subtree_tag_safe = True
 
             # Recurse to get the tag safe roots from subtree.
-            for idx, (key_mgr, val_mgr) in sorted(
+            for _idx, (key_mgr, val_mgr) in sorted(
                 node.get_key_value_managers().items()
             ):
                 if key_mgr is not None:
@@ -450,9 +450,7 @@ class GuardManagerWrapper:
                 if val_mgr is not None:
                     tag_safe_roots.extend(visit(val_mgr))
 
-            for idx, (key_mgr, val_mgr) in sorted(
-                node.get_key_value_managers().items()
-            ):
+            for key_mgr, val_mgr in node.get_key_value_managers().values():
                 if key_mgr:
                     is_subtree_tag_safe &= key_mgr.is_tag_safe()
 

torch/_dynamo/variables/optimizer.py

@@ -289,9 +289,7 @@ class OptimizerVariable(UserDefinedObjectVariable):
             params_vt = group_vt.getitem_const(tx, ConstantVariable.create("params"))
             all_static = True
             non_static_grads = []
-            for p_ind, (p, p_vt) in enumerate(
-                zip(group["params"], params_vt.unpack_var_sequence(tx))
-            ):
+            for p, p_vt in zip(group["params"], params_vt.unpack_var_sequence(tx)):
                 param_source = p_vt.source
                 self.tensor_to_source[p] = param_source
                 grad_source = GradSource(
@@ -322,12 +320,12 @@ class OptimizerVariable(UserDefinedObjectVariable):
 
         # We have to again iterate over the state dict to collect the
         # tensor_to_source dict. This is used for the finalizer.
-        for idx, (p, value) in enumerate(self.value.state.items()):
+        for idx, value in enumerate(self.value.state.values()):
             p_state_source = DictGetItemSource(
                 state_source, ConstDictKeySource(state_source, idx)
             )
             tx.output.guard_on_key_order.add(p_state_source)
-            for inner_idx, (k, v) in enumerate(value.items()):
+            for inner_idx, v in enumerate(value.values()):
                 if (
                     isinstance(v, torch.Tensor)
                     and v not in self.grad_to_source

torch/_functorch/_aot_autograd/collect_metadata_analysis.py

@@ -240,7 +240,7 @@ def run_functionalized_fw_and_collect_metadata(
 
         # Inspect the state of the input tensor functional wrapper to detect input mutation info
         # If inp[i] has a metadata-only mutation, then maybe_inputs_with_mutated_metadata[i] contains the updated version
-        for i, (arg, f_arg) in enumerate(zip(flat_args, flat_f_args)):
+        for arg, f_arg in zip(flat_args, flat_f_args):
             # NB: Mutation of non-contiguous tensor subclass input can result in a mismatch in
             # strides between the functionalized arg inner tensors and non-functionalized arg inner
             # tensors. This is a problem as the inner tensor stride change may not be reflected

torch/_functorch/_aot_autograd/runtime_wrappers.py

@@ -2041,7 +2041,7 @@ To fix this, your tensor subclass must implement the dunder method __force_to_sa
 
         assert len(meta.attrs) == len(runtime_subclass_keys)
         leaves = []
-        for i, (attr, attr_meta) in enumerate(meta.attrs.items()):
+        for attr, attr_meta in meta.attrs.items():
             elem = getattr(x, attr)
             new_elem, elem_leaves = AOTDispatchAutograd.process_runtime_tangent(
                 elem, attr_meta

torch/_functorch/_aot_autograd/subclass_parametrization.py

@@ -98,7 +98,7 @@ def unwrap_tensor_subclass_parameters(module: torch.nn.Module) -> torch.nn.Modul
                 module, name, UnwrapTensorSubclass()
             )
 
-    for name, child in module.named_children():
+    for child in module.children():
         unwrap_tensor_subclass_parameters(child)
 
     return module

torch/_functorch/partitioners.py

@@ -1481,9 +1481,7 @@ def functionalize_rng_ops(
         )
     )
 
-    for rng_count, (base_node, node_pair) in enumerate(
-        recomputable_rng_ops_map.items()
-    ):
+    for rng_count, node_pair in enumerate(recomputable_rng_ops_map.values()):
         # Step 2 - Modify the fwd pass such that
         fw_node = node_pair["fwd"]
         bw_node = node_pair["bwd"]
@@ -2714,9 +2712,7 @@ def thread_graphsafe_rng_from_hops(module, is_backward):
         subgraph = getattr(module, hop_node.args[0].target)
         if isinstance(subgraph, fx.GraphModule):
             new_rng_inputs = []
-            for idx, placeholder_node in enumerate(
-                subgraph.graph.find_nodes(op="placeholder")
-            ):
+            for placeholder_node in subgraph.graph.find_nodes(op="placeholder"):
                 if rng_string in placeholder_node.name:
                     # Found a rng state placeholder in the hop graph, lets add
                     # the corresponding node in the outer graph

torch/_functorch/pyfunctorch.py

@@ -116,7 +116,7 @@ def temporarily_restore_interpreter_stack(stack):
             pushed.append(s)
         yield
     finally:
-        for s in reversed(pushed):
+        for _ in reversed(pushed):
             # TODO: would be nice to assert that the layers are the same, but
             # Python object identity is not preserved
             pop_dynamic_layer_stack()

torch/_higher_order_ops/utils.py

@@ -907,7 +907,7 @@ def diff_tensor_meta(
         try:
             if val1 != val2:
                 pair_diffs.append(f"'{meta_name}: {val1} vs {val2}'")
-        except GuardOnDataDependentSymNode as _:
+        except GuardOnDataDependentSymNode:
             pair_diffs.append(f"'{meta_name}: {val1} vs {val2}'")
             continue
     return pair_diffs
@@ -1197,7 +1197,7 @@ def materialize_callable_in_args(op: HopInstance, args, kwargs):
 
     # call_op preserves ordering of proxies via schema
     materialized_args = []
-    for i, (proxy, arg) in enumerate(zip(arg_proxies, schema.arguments)):
+    for i, proxy in enumerate(arg_proxies):
         if (
             isinstance(proxy, torch.fx.Node)
             and proxy.op == "get_attr"

torch/_higher_order_ops/while_loop.py

@@ -316,7 +316,7 @@ def while_loop_dense(
 
     if stack_output:
         outs: list[torch.Tensor] = []
-        for i, out in enumerate(outputs):
+        for out in outputs:
             outs.append(torch.stack(out, dim=0))
         return tuple(outs)
 

torch/_inductor/codecache.py

@@ -2606,7 +2606,7 @@ def custom_op_wrapper(op: str, *args: Any) -> list[c_void_p] | c_void_p | None:
     if isinstance(result, (list, tuple)):
         # unsafe_alloc_void_ptrs_from_tensors expects result contains tensor only
         result = [torch.tensor([]) if r is None else r for r in result]
-        for i, r in enumerate(result):
+        for r in result:
             assert isinstance(r, torch.Tensor), op + " returns a list of non-tensors"
         return torch._C._aoti.unsafe_alloc_void_ptrs_from_tensors(result)  # type: ignore[arg-type]
 

torch/_inductor/codegen/mps.py

@@ -895,7 +895,7 @@ class MetalKernel(SIMDKernel):
                     else:
                         dtype_str = self.dtype_to_str(dtype)
                     code.writeline(f"constant {dtype_str}* {inner},")
-                for outer, inner in self.args.sizevars.items():
+                for inner in self.args.sizevars.values():
                     code.writeline(f"constant long& {inner},")
 
                 # Write dynamic values as inputs

torch/_inductor/codegen/multi_kernel.py

@@ -218,7 +218,7 @@ class MultiKernel:
             # the multi call kernel.
             multi_call_args = call_args
             multi_call_arg_types = arg_types
-            for i, kernel in enumerate(self.kernels):
+            for kernel in self.kernels:
                 additional_call_args, additional_arg_types = (
                     kernel.additional_call_args_and_types()
                 )

torch/_inductor/codegen/triton_combo_kernel.py

@@ -717,7 +717,7 @@ class ComboKernel(Kernel):
         self, name: str, call_args: list[Any], arg_types: list[Any]
     ) -> None:
         for num, sub_kernel in enumerate(self.sub_kernels):
-            for i, tree in enumerate(sub_kernel.range_trees):
+            for tree in sub_kernel.range_trees:
                 numel_name = f"{tree.prefix}numel_{num}"
                 if numel_name not in self.dynamic_shape_args:
                     continue
@@ -735,7 +735,7 @@ class ComboKernel(Kernel):
     def kernel_benchmark_extra_args(self) -> list[str]:
         extra_args = []
         for num, sub_kernel in enumerate(self.sub_kernels):
-            for i, tree in enumerate(sub_kernel.range_trees):
+            for tree in sub_kernel.range_trees:
                 numel_name = f"{tree.prefix}numel_{num}"
                 if numel_name not in self.dynamic_shape_args:
                     continue
@@ -1018,7 +1018,7 @@ class ComboKernel(Kernel):
 
         for num, sub_kernel in enumerate(self.sub_kernels):
             meta[f"no_x_dim_{num}"] = sub_kernel.no_x_dim
-            for i, tree in enumerate(sub_kernel.range_trees):
+            for tree in sub_kernel.range_trees:
                 # pyrefly: ignore [missing-argument]
                 if not tree.is_reduction:
                     numel_name = f"{tree.prefix}numel_{num}"

torch/_inductor/codegen/wrapper.py

@@ -3604,16 +3604,12 @@ class PythonWrapperCodegen(CodeGen):
         self.writeline("if not should_loop:")
         if stack_output:
             # Handle the case when loop never executes
-            for i, (carried_input, carried_buf) in enumerate(
-                zip(outer_carried_inputs, while_loop.carried_inputs)
-            ):
+            for i, carried_input in enumerate(outer_carried_inputs):
                 self.writeline(EnterSubgraphLine(self, while_loop.body_subgraph.graph))
                 self.writeline(f"{name}[{i}] = {carried_input}.unsqueeze(0).clone()")
                 self.writeline(ExitSubgraphLine(self))
         else:
-            for i, (carried_input, carried_buf) in enumerate(
-                zip(outer_carried_inputs, while_loop.carried_inputs)
-            ):
+            for i, carried_input in enumerate(outer_carried_inputs):
                 self.writeline(EnterSubgraphLine(self, while_loop.body_subgraph.graph))
                 self.writeline(f"{name}[{i}] = {carried_input}.clone()")
                 self.writeline(ExitSubgraphLine(self))

torch/_inductor/comms.py

@@ -424,10 +424,7 @@ def _reorder_communication_preserving_peak_memory_internal(
             return
 
         # Candidate becomes last use of some bufs
-        for (
-            gn,
-            bufs,
-        ) in group_n_to_bufs_after_swap_dealloc_by_candidate.items():
+        for bufs in group_n_to_bufs_after_swap_dealloc_by_candidate.values():
             for buf in bufs:
                 buf_to_snode_last_use[buf] = candidate
 
@@ -840,7 +837,7 @@ def _schedule_for_comm(
         else:
             schedule(snode)
 
-    for snode, deps in unmet_deps.items():
+    for deps in unmet_deps.values():
         assert len(deps) == 0, (
             f"Detected unscheduled nodes. Nodes with unmet dependencies: {unmet_deps}"
         )
@@ -1552,11 +1549,8 @@ Graph: {graph}
                     node.args = new_args
 
     # Delete `fsdp.copy_(unsharded_param, Y)` nodes
-    for (
-        unsharded_param,
-        fsdp_copy_node_idxes,
-    ) in unsharded_param_to_fsdp_copy_node_idxes.items():
-        for i, fsdp_copy_node_idx in enumerate(fsdp_copy_node_idxes):
+    for fsdp_copy_node_idxes in unsharded_param_to_fsdp_copy_node_idxes.values():
+        for fsdp_copy_node_idx in fsdp_copy_node_idxes:
             fsdp_copy_node = node_list[fsdp_copy_node_idx]
             graph.erase_node(fsdp_copy_node)
 

torch/_inductor/comms_debug.py

@@ -46,7 +46,7 @@ def _debug_iterative_memory_recompute(
     if iter_cm != new_cm:
         log = "ITERATIVE CURR MEMORY CANDIDATE DOES NOT MATCH"
         iterative_recompute_error = True
-    for i, gn in enumerate(gns):
+    for gn in gns:
         iter_gnm = iter_curr_memory[gn]
         new_gnm = est_curr_memory[gn]
         if iter_gnm != new_gnm:
@@ -65,7 +65,7 @@ def _debug_iterative_memory_recompute(
             f"\nCANDIDATE_NEW_ALLOCFREE:{snodes_allocfree[candidate]}"
         )
         peak_log = ""
-        for i, (pre, post) in enumerate(snodes_curr_memory):
+        for i, (pre, _post) in enumerate(snodes_curr_memory):
             if est_peak_memory == pre:
                 n = snodes[i]
                 peak_log = (

torch/_inductor/fx_passes/post_grad.py

@@ -454,7 +454,7 @@ def decompose_map_to_while_loop(gm: torch.fx.GraphModule):
 
     graph_pass.apply(gm)
 
-    for node in gm.graph.find_nodes(
+    for _node in gm.graph.find_nodes(
         op="call_function", target=torch.ops.higher_order.map_impl
     ):
         raise AssertionError("map is not lowered to while_loop")
@@ -666,7 +666,7 @@ def decompose_scan_to_while_loop(gm: torch.fx.GraphModule):
 
     graph_pass.apply(gm)
 
-    for node in gm.graph.find_nodes(
+    for _node in gm.graph.find_nodes(
         op="call_function", target=torch.ops.higher_order.scan
     ):
         raise AssertionError("scan is not lowered to while_loop")
@@ -1265,7 +1265,7 @@ def decompose_triton_kernel_wrapper_functional(graph):
 
     graph_pass.apply(graph)
 
-    for node in graph.find_nodes(
+    for _ in graph.find_nodes(
         op="call_function",
         target=torch.ops.higher_order.triton_kernel_wrapper_functional,
     ):

torch/_inductor/ir.py

@@ -8770,9 +8770,7 @@ class WhileLoop(ExternKernel):
         seen_buffers: OrderedSet[int] = OrderedSet()
         result: list[Union[IRNode, TensorBox, ShapeAsConstantBuffer]] = []
 
-        for i, (original_input, unwrapped_buffer) in enumerate(
-            zip(carried_inputs, unwrapped_buffers)
-        ):
+        for original_input, unwrapped_buffer in zip(carried_inputs, unwrapped_buffers):
             if id(unwrapped_buffer) in seen_buffers:
                 result.append(ExternKernel.copy_input(original_input))
             else:

torch/_inductor/pattern_matcher.py

@@ -743,7 +743,7 @@ class _TargetArgsExpr(_TargetExpr):
         assert len(node_items) == len(self_items)
 
         m = Match(ctx, self)
-        for i, pattern, child_node in zip(itertools.count(), self_items, node_items):
+        for pattern, child_node in zip(self_items, node_items):
             if isinstance(pattern, PatternExpr):
                 child_match = ctx.match(pattern, child_node)
                 if not is_match(child_match):

torch/_inductor/scheduler.py

@@ -2850,7 +2850,7 @@ class Scheduler:
         # NB: None means that the dependency is on an input.  Don't actually
         # generate a dependency because if we do, Inductor will start trying
         # to free the unbacked int but that's pointless
-        for name, val in V.graph.graph_inputs.items():
+        for val in V.graph.graph_inputs.values():
             if isinstance(val, sympy.Expr):
                 for fs in val.free_symbols:
                     unbacked_symbol_to_origin_node[fs] = None
@@ -3550,9 +3550,7 @@ class Scheduler:
             future_choices: list[tuple[Any, Optional[LambdaFuture], ModuleType]] = []
             for hint_override in config.multi_kernel_hints:
                 choice_timings = multi_node.choice_timings(hint_override)
-                for choice, unfused_time in sorted(
-                    choice_timings.items(), key=lambda x: x[1]
-                ):
+                for choice, _ in sorted(choice_timings.items(), key=lambda x: x[1]):
                     if not isinstance(
                         choice, torch._inductor.select_algorithm.TritonTemplateCaller
                     ):

torch/_inductor/tiling_utils.py

@@ -425,7 +425,7 @@ def apply_var_mapping(
         new_ranges, norm_pw_vars + norm_red_vars, strict=True
     ):
         range_vars = []
-        for i in range(len(new_range)):
+        for _ in range(len(new_range)):
             range_vars.append(flat_vars[count])
             count += 1
 

torch/_inductor/utils.py

@@ -348,7 +348,7 @@ def _do_bench_using_profiling(
         ]
     ) as p:
         # Benchmark
-        for i in range(n_repeat):
+        for _ in range(n_repeat):
             # we clear the L2 cache before each run
             cache.zero_()
             # record time of `fn`

torch/_subclasses/fake_tensor.py

@@ -3118,7 +3118,7 @@ def _validate_symbolic_output_for_caching(
     if is_tracing:
         # Check for SymNode types in PROXY mode - this should bypass caching
         # regardless of whether symbols are known or not
-        for node in _iterate_nodes(output):
+        for _ in _iterate_nodes(output):
             raise _BypassDispatchCache("Proxy mode with SymNode output")
     else:
         # Check for unrepresented symbols in tensor expressions

torch/ao/ns/_numeric_suite.py

@@ -137,7 +137,7 @@ def _get_logger_dict_helper(
     def get_prefix(prefix):
         return prefix if prefix == "" else prefix + "."
 
-    for name, child in mod.named_children():
+    for child in mod.children():
         if isinstance(child, Logger):
             target_dict[get_prefix(prefix) + "stats"] = child.stats
             break

torch/ao/ns/fx/graph_passes.py

@@ -909,8 +909,7 @@ def create_a_shadows_b(
                         # is added
                         prev_node_c_list = [env_c[arg.name] for arg in prev_node_b]
 
-                        for arg_idx, arg in enumerate(prev_node_b):
-                            prev_node_c = prev_node_c_list[arg_idx]
+                        for arg_idx, prev_node_c in enumerate(prev_node_c_list):
                             env_c[prev_node_c.name] = _insert_logger_after_node(
                                 prev_node_c,
                                 gm_b,

torch/ao/quantization/_correct_bias.py

@@ -151,6 +151,6 @@ def bias_correction(
             bias.data = updated_bias
 
             # Resets the data contained in the loggers
-            for name, submodule in quantized_model.named_modules():
+            for submodule in quantized_model.modules():
                 if isinstance(submodule, MeanShadowLogger):
                     submodule.clear()

torch/autograd/gradcheck.py

@@ -297,7 +297,7 @@ def _get_numerical_jacobian(
     inp_indices = [
         i for i, a in enumerate(target) if is_tensor_like(a) and a.requires_grad
     ]
-    for i, (inp, inp_idx) in enumerate(zip(_iter_tensors(target, True), inp_indices)):
+    for inp, inp_idx in zip(_iter_tensors(target, True), inp_indices):
         jacobians += [
             get_numerical_jacobian_wrt_specific_input(
                 fn,
@@ -549,7 +549,7 @@ def _get_analytical_jacobian_forward_ad(
     with fwAD.dual_level():
         fw_grads = []
         dual_inputs = []
-        for i, inp in enumerate(inputs):
+        for inp in inputs:
             if is_tensor_like(inp) and inp.requires_grad:
                 if inp.layout == torch._mkldnn:  # type: ignore[attr-defined]
                     raise ValueError(
@@ -1275,7 +1275,7 @@ def _test_undefined_forward_mode(func, outputs, inputs):
                 tensor_indices.add(i)
             dual_inputs.append(inp)
 
-        for i, (fw_grad, u) in enumerate(zip(fw_grads, all_u)):
+        for fw_grad, u in zip(fw_grads, all_u):
             fw_grad.copy_(u.view_as(fw_grad))
 
         for idx, inp in enumerate(inputs):

torch/distributed/_serialization.py

@@ -41,7 +41,7 @@ class _PseudoZipFile:
 
         pickle.dump(entries, f, protocol=DEFAULT_PROTOCOL)
 
-        for key, (data, length) in self.records.items():
+        for data, _ in self.records.values():
             if isinstance(data, bytes):
                 f.write(data)
             elif isinstance(data, str):

torch/distributed/checkpoint/state_dict.py

@@ -578,7 +578,7 @@ def _load_model_state_dict(
     assign = False
     if info.broadcast_from_rank0 or info.full_state_dict:
         devices = set()
-        for key, value in local_state_dict.items():
+        for value in local_state_dict.values():
             if torch.is_tensor(value) and value.dim() > 0:
                 devices.add(value.device)
         # In lora state_dict, there could be multiple devices, with meta device inside.

torch/distributed/fsdp/_flat_param.py

@@ -2087,14 +2087,14 @@ class FlatParamHandle:
                 param.grad.data = view
             else:
                 param.grad = view
-        for i, (
+        for (
             param_name,
             module,
             module_name,
             prim_param_name,
             prim_module,
             _,
-        ) in enumerate(self.flat_param._shared_param_infos):
+        ) in self.flat_param._shared_param_infos:
             _p_assert(
                 hasattr(module, param_name),
                 f"{module_name + '.' + param_name if module_name else param_name} is missing",
@@ -2171,11 +2171,8 @@ class FlatParamHandle:
                 param.data = flat_param[offset : offset + numel_in_shard]
         if self.flat_param._shared_params is None:
             raise AssertionError("Expected _shared_params to be not None")
-        for i, (
-            param,
-            (param_name, module, _, prim_param_name, prim_module, _),
-        ) in enumerate(
-            zip(self.flat_param._shared_params, self.flat_param._shared_param_infos)
+        for param, (param_name, module, _, prim_param_name, prim_module, _) in zip(
+            self.flat_param._shared_params, self.flat_param._shared_param_infos
         ):
             self._setattr_param(module, param_name, param)
             prim_param = getattr(prim_module, prim_param_name)
@@ -2388,14 +2385,14 @@ class FlatParamHandle:
 
         # TODO: If we want to handle shared parameters, we need to re-generate
         # the shared parameter data structures in case sharedness changed.
-        for i, (
+        for (
             param_name,
             module,
             _,
             prim_param_name,
             prim_module,
             _,
-        ) in enumerate(flat_param._shared_param_infos):
+        ) in flat_param._shared_param_infos:
             if getattr(module, param_name) is not getattr(prim_module, prim_param_name):
                 raise NotImplementedError(
                     "Changing shared parameters is not supported yet"

torch/distributed/pipelining/_IR.py

@@ -924,7 +924,7 @@ class Pipe(torch.nn.Module):
                 pass
 
         # This is done by (1) `_sink_params` at each submodule;
-        for name, submod in split.named_children():
+        for submod in split.children():
             if isinstance(submod, fx.GraphModule):
                 _sink_params(submod, inputs_to_state, [])
                 submod.graph.lint()

torch/distributed/tensor/_api.py

@@ -967,7 +967,7 @@ def distribute_module(
     if partition_fn is None:
         # if partition_fn not specified, we by default replicate
         # all module params/buffers
-        for name, submod in module.named_modules():
+        for submod in module.modules():
             replicate_module_params_buffers(submod, device_mesh)
     else:
         # apply partition_fun to submodules

torch/distributed/tensor/_ops/_einsum_strategy.py

@@ -170,7 +170,7 @@ def gen_einsum_strategies(
     # linearity strategy
     if linearity:
         linearity_placement_list: list[Placement] = [Partial()]
-        for input_dim in input_dims:
+        for _ in input_dims:
             linearity_placement_list.append(Partial())
         strategies_over_one_mesh_dim.append(linearity_placement_list)
 

torch/export/dynamic_shapes.py

@@ -1332,7 +1332,7 @@ def refine_dynamic_shapes_from_suggested_fixes(
             roots.add(c.root.__name__)  # type: ignore[attr-defined]
 
     # check keys are existing dims or new roots
-    for k, c in shape_fixes.items():
+    for k in shape_fixes.keys():
         assert k in name_to_dim or k in roots
 
     # cache so we don't produce multiple derived dim objects

torch/fx/experimental/graph_gradual_typechecker.py

@@ -101,11 +101,11 @@ def broadcast_types(t1, t2):
         # We make the types the same length which is the first requirement
         # for consistency
         if s1 > s2:
-            for i in range(s1 - s2):
+            for _ in range(s1 - s2):
                 new_t2.insert(0, 1)
 
         elif s2 > s1:
-            for i in range(s2 - s1):
+            for _ in range(s2 - s1):
                 new_t1.insert(0, 1)
 
         # we replace occurrences of "1" with each tensor with

torch/fx/experimental/sym_node.py

@@ -1871,7 +1871,7 @@ def _make_user_magic(method, user_type):
             setattrs(user_type, f"__r{method_name}__", rbinary_magic_impl)
 
 
-for method, func in magic_methods.items():  # type: ignore[assignment]
+for method in magic_methods.keys():  # type: ignore[assignment]
     if method in only_bool_magic_methods:
         _make_user_magic(method, SymBool)
         continue

torch/fx/experimental/symbolic_shapes.py

@@ -3342,7 +3342,7 @@ class DimConstraints:
         # alter derivations that depend on old root, to unify to new root
         # e.g. dx=3*_dx+1, dy=dx+1 -> dy=3*_dx+2
         for old_root in introduced_roots.values():
-            for k, c in list(results.items()):
+            for c in results.values():
                 if (
                     "eq" in c
                     and isinstance(c["eq"], sympy.Expr)

torch/jit/_script.py

@@ -1066,7 +1066,7 @@ def call_prepare_scriptable_func_impl(obj, memo):
         else:
             new_obj_dict[name] = sub_module
 
-    for k, v in new_obj_dict.items():
+    for v in new_obj_dict.values():
         obj.__dict__[name] = v
 
     return obj

torch/onnx/_internal/torchscript_exporter/symbolic_opset9.py

@@ -6099,7 +6099,7 @@ def index_add(g: jit_utils.GraphContext, self, dim, index, other, alpha=None):
 
     if other_dim_rank != self_dim_rank:
         delta = self_dim_rank - other_dim_rank
-        for i in range(delta):
+        for _ in range(delta):
             other = symbolic_helper._unsqueeze_helper(
                 g, other, [symbolic_helper._get_tensor_rank(other)]
             )
@@ -6126,10 +6126,10 @@ def index_add(g: jit_utils.GraphContext, self, dim, index, other, alpha=None):
     )
     other = expand_as(g, other, new_shape)
 
-    for i in range(dim):
+    for _ in range(dim):
         index = symbolic_helper._unsqueeze_helper(g, index, [0])
 
-    for i in range(self_dim_rank - dim - 1):
+    for _ in range(self_dim_rank - dim - 1):
         index = symbolic_helper._unsqueeze_helper(
             g, index, [symbolic_helper._get_tensor_rank(index)]
         )

torch/onnx/ops/_symbolic_impl.py

@@ -78,7 +78,7 @@ class EncodedAttrs:
             attr_floats=[],
             attr_strs=[],
         )
-        for i, (k, v) in enumerate(attrs.items()):
+        for k, v in attrs.items():
             encoded.attr_keys.append(k)
             if isinstance(v, int):
                 start_pos = len(encoded.attr_ints)

torch/testing/_internal/common_methods_invocations.py

@@ -445,11 +445,9 @@ def sample_inputs_batch_norm(op_info, device, dtype, requires_grad, **kwargs):
         )
 
     # Checking for permutations of weights and biases as `None`
-    weights = [channels, None, None]
-    biases = [None, channels, None]
     is_training = [True, False, False]
 
-    for weight, bias, training in zip(weights, biases, is_training, strict=True):
+    for training in is_training:
         yield SampleInput(
             make_arg(input_shape),
             args=(

torch/testing/_internal/distributed/ddp_under_dist_autograd_test.py

@@ -465,7 +465,7 @@ class DdpUnderDistAutogradTest(RpcAgentTestFixture):
                     )
 
         # Destroy process groups
-        for idx, trainer_rref in enumerate(trainer_rrefs):
+        for trainer_rref in trainer_rrefs:
             _remote_method_async(Trainer.destroy_pg, trainer_rref).wait()
 
         # Send shutdown signals.

torch/testing/_internal/distributed/distributed_test.py

@@ -6094,7 +6094,7 @@ class DistributedTest:
                 dim=1,
             ).cuda(rank)
 
-            for i in range(100):
+            for _ in range(100):
                 y = model(input_var[rank].cuda(rank))
                 y.mean().backward()
 

torch/testing/_internal/distributed/rpc/dist_autograd_test.py

@@ -1988,7 +1988,7 @@ class DistAutogradTest(CommonDistAutogradTest):
         self.assertEqual(self.world_size - 1, len(known_context_ids))
 
         t1 = torch.rand((3, 3), requires_grad=True)
-        for i in range(100):
+        for _ in range(100):
             dst = self._next_rank()
             t1 = rpc.rpc_sync(worker_name(dst), torch.add, args=(t1, t1))
 

torch/testing/_internal/triton_utils.py

@@ -823,7 +823,7 @@ if has_triton():
         mask = offsets < n_elements
         x = tl.load(in_ptr0 + offsets, mask=mask)
         y = tl.load(in_ptr1 + offsets, mask=mask)
-        for i in range(2):
+        for _ in range(2):
             output = x + y
             tl.store(out_ptr + offsets, output, mask=mask)
         i = 2

torch/utils/data/dataframes_pipes.ipynb

@@ -355,7 +355,7 @@
     "dp = dp.shuffle()\n",
     "dp = dp.batch(2)\n",
     "print(\"Iterate over DataFrame batches\")\n",
-    "for i,v in enumerate(dp):\n",
+    "for v in dp:\n",
     "    print(v)\n",
     "\n",
     "# this is similar to batching of regular DataPipe\n",