Fix self assignment (#165816)

This PR removes assignments of the form `var=var`.

Pull Request resolved: https://github.com/pytorch/pytorch/pull/165816
Approved by: https://github.com/jansel

torch/_functorch/vmap.py

@@ -293,7 +293,7 @@ def vmap_impl(func, in_dims, out_dims, randomness, chunk_size, *args, **kwargs):
 
 
 def get_chunk_sizes(total_elems, chunk_size):
-    n_chunks = n_chunks = total_elems // chunk_size
+    n_chunks = total_elems // chunk_size
     chunk_sizes = [chunk_size] * n_chunks
     # remainder chunk
     remainder = total_elems % chunk_size

torch/_inductor/fx_passes/efficient_conv_bn_eval.py

@@ -108,14 +108,10 @@ def efficient_conv_bn_eval_decomposed(
     else:
         bias_on_the_fly = torch.zeros_like(bn_running_var)
 
-    if bn_weight is not None:
-        bn_weight = bn_weight
-    else:
+    if bn_weight is None:
         bn_weight = torch.ones_like(bn_running_var)
 
-    if bn_bias is not None:
-        bn_bias = bn_bias
-    else:
+    if bn_bias is None:
         bn_bias = torch.zeros_like(bn_running_var)
 
     # shape of [C_out, 1, 1, 1] in Conv2d

torch/_inductor/tiling_utils.py

@@ -477,7 +477,6 @@ def extract_normalized_read_writes(
     (norm_pw_vars, norm_red_vars), ranges = index_vars_no_squeeze(
         pw_splits, red_splits, prefix="n"
     )
-    node = node
 
     for n in list(node.get_nodes()):
         if not isinstance(n, torch._inductor.scheduler.SchedulerNode):

torch/_inductor/utils.py

@@ -760,7 +760,6 @@ def get_fused_kernel_name(
         ]
     else:
         raise NotImplementedError
-    sources = sources
     return "_".join(["fused"] + sources)
 
 

torch/_numpy/_dtypes.py

@@ -408,8 +408,6 @@ def set_default_dtype(fp_dtype="numpy", int_dtype="numpy"):
 
     if int_dtype in ["numpy", "pytorch"]:
         int_dtype = torch.int64
-    else:
-        int_dtype = int_dtype
 
     new_defaults = _dtypes_impl.DefaultDTypes(
         float_dtype=float_dtype, complex_dtype=complex_dtype, int_dtype=int_dtype

torch/_prims/__init__.py

@@ -447,9 +447,7 @@ def _prim_elementwise_meta(
     # (but getting it wrong will cause too many casts to be inserted in traces!)
     if device is not None:
         assert dtype is not None
-        if type_promotion == ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.DEFAULT:
-            dtype = dtype
-        elif type_promotion == ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.ALWAYS_BOOL:
+        if type_promotion == ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.ALWAYS_BOOL:
             dtype = torch.bool
         elif type_promotion == ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.INT_TO_FLOAT:
             if utils.is_integer_dtype(dtype) or utils.is_boolean_dtype(dtype):
@@ -457,8 +455,6 @@ def _prim_elementwise_meta(
         elif type_promotion == ELEMENTWISE_PRIM_TYPE_PROMOTION_KIND.COMPLEX_TO_FLOAT:
             if utils.is_complex_dtype(dtype):
                 dtype = utils.corresponding_real_dtype(dtype)
-            else:
-                dtype = dtype
 
         assert shape is not None
         return torch.empty_permuted(shape, l2p_perm, device=device, dtype=dtype)  # type: ignore[return-value]

torch/nn/utils/stateless.py

@@ -103,9 +103,6 @@ def _reparametrize_module(
     strict: bool = False,
     stack_weights: bool = False,
 ):
-    parameters_and_buffers = parameters_and_buffers
-    stack_weights = stack_weights
-
     if tie_weights:
         untied_parameters_and_buffers = _untie_named_tensors_map(
             module, parameters_and_buffers

torch/testing/_internal/distributed/rpc/jit/rpc_test.py

@@ -85,7 +85,7 @@ class RRefAPITest:
         ):
             rref_local_value(rref)
 
-        ret = ret = rpc.rpc_sync(dst_worker_name, rref_local_value, (rref,))
+        ret = rpc.rpc_sync(dst_worker_name, rref_local_value, (rref,))
         self.assertEqual(ret, torch.add(torch.ones(2, 2), 1))
 
     @dist_init