[skip ci] Fix "arugment" typos (pytorch#61459)

Summary:
Fixes pytorch#61455.

Pull Request resolved: pytorch#61459

Reviewed By: soulitzer

Differential Revision: D29636559

Pulled By: samestep

fbshipit-source-id: 9ad65265c0491d9e81bb303abe3a07c6843bfa4a

Author: samestep

aten/src/ATen/core/adaption.cpp

@@ -7,7 +7,7 @@ void common_device_check_failure(optional<Device>& common_device, const at::Tens
   TORCH_CHECK(false,
     "Expected all tensors to be on the same device, but "
     "found at least two devices, ", common_device.value(), " and ", tensor.device(), "! "
-    "(when checking arugment for argument ", argName, " in method ", methodName, ")");
+    "(when checking argument for argument ", argName, " in method ", methodName, ")");
 }
 
 } // namespace impl

binaries/compare_models_torch.cc

@@ -50,7 +50,7 @@ C10_DEFINE_string(
 C10_DEFINE_bool(
     no_inputs,
     false,
-    "Whether the model has any input. Will ignore other input arugments if true");
+    "Whether the model has any input. Will ignore other input arguments if true");
 C10_DEFINE_bool(
     use_caching_allocator,
     false,

binaries/speed_benchmark_torch.cc

@@ -48,7 +48,7 @@ C10_DEFINE_string(
 C10_DEFINE_bool(
   no_inputs,
   false,
-  "Whether the model has any input. Will ignore other input arugments if true");
+  "Whether the model has any input. Will ignore other input arguments if true");
 C10_DEFINE_bool(
   use_caching_allocator,
   false,

docs/source/fx.rst

@@ -247,7 +247,7 @@ multiplication after the ``F.relu``, and then clean up the original
 objects to automatically record operations into the :class:`Graph`.
 
 To use this method, we write the operations that we want inserted as regular
-PyTorch code and invoke that code with :class:`Proxy` objects as arugments.
+PyTorch code and invoke that code with :class:`Proxy` objects as arguments.
 These :class:`Proxy` objects will capture the operations that are performed
 on them and append them to the :class:`Graph`.
 

test/test_overrides.py

@@ -407,7 +407,7 @@ def test_mm_semantics(self):
 
     def test_precedence_semantics(self):
         """Test semantics for __torch_function__ for functions that take
-        multiple arugments
+        multiple arguments
 
         For functions that take multiple arguments, the appropriate
         __torch_function__ implementation to call is determined by

torch/csrc/jit/tensorexpr/dim_arg.h

@@ -5,7 +5,7 @@ namespace torch {
 namespace jit {
 namespace tensorexpr {
 // A helper structure to store the arguments to specify dimensions. In the
-// Compute arugments for dim_args, all of the following is supported. For
+// Compute arguments for dim_args, all of the following is supported. For
 // example:
 //    dim_args: {1, 2, 3, 4}
 //    dim_args: {{1, "x"}, {2, "y"}, {3, "z"}}

torch/fx/operator_schemas.py

@@ -317,7 +317,7 @@ def _args_kwargs_to_normalized_args_kwargs(sig : inspect.Signature, args : Tuple
 
         target (inspect.Signature): Signature object for the target
         args (Tuple): Arguments that appear at the callsite for `target`
-        kwargs (Dict): Keyword arugments that appear at the callsite for `target`
+        kwargs (Dict): Keyword arguments that appear at the callsite for `target`
         normalize_to_only_use_kwargs (bool): Whether to normalize to only use kwargs.
 
     Returns: