[3/N] fix typo in other folders (#166606)

fix typo in other folders

#166374
#166126

_typos.toml
```bash
[files]
extend-exclude = ["tools/linter/dictionary.txt"]
[default.extend-words]
nd = "nd"
arange = "arange"
Nd = "Nd"
GLOBALs = "GLOBALs"
hte = "hte"
iy = "iy"
PN = "PN"
Dout = "Dout"
optin = "optin"
gam = "gam"
PTD = "PTD"
Sur = "Sur"
nin = "nin"
tme = "tme"
inpt = "inpt"
mis = "mis"
Raison = "Raison"
ouput = "ouput"
nto = "nto"
Onwer = "Onwer"
callibrate = "callibrate"
ser = "ser"
Metdata = "Metdata"
```

Pull Request resolved: https://github.com/pytorch/pytorch/pull/166606
Approved by: https://github.com/ezyang

CMakeLists.txt

@@ -374,7 +374,7 @@ cmake_dependent_option(
   "Build the lazy Torchscript backend, not compatible with mobile builds" ON
   "NOT INTERN_BUILD_MOBILE" OFF)
 cmake_dependent_option(BUILD_FUNCTORCH "Build Functorch" ON "BUILD_PYTHON" OFF)
-cmake_dependent_option(BUILD_BUNDLE_PTXAS "Bundle PTX into torch/bin fodler"
+cmake_dependent_option(BUILD_BUNDLE_PTXAS "Bundle PTX into torch/bin folder"
                        OFF "USE_CUDA" OFF)
 cmake_dependent_option(USE_KLEIDIAI "Use KleidiAI for the ARM CPU & AARCH64 architecture." ON
                         "CPU_AARCH64" OFF)

c10/metal/special_math.h

@@ -1,4 +1,4 @@
-// Implementation of specal math functions for Metal
+// Implementation of special math functions for Metal
 #pragma once
 #include <c10/metal/expm1f.h>
 #include <c10/metal/igamma.h>

c10/mobile/CPUProfilingAllocator.cpp

@@ -34,7 +34,7 @@ struct MemEvent {
 bool overlaps(const MemBlock& a, const MemBlock& b) {
   // two blocks dont overlap if
   // |---a--------|--------------b--------|
-  // strat_a     end_a <= start_b       end_b
+  // start_a     end_a <= start_b       end_b
   return !(
       (a.end_offset <= b.start_offset) || (b.end_offset <= a.start_offset));
 }

c10/util/Bitset.h

@@ -33,7 +33,7 @@ struct bitset final {
   constexpr bitset() noexcept = default;
   constexpr bitset(const bitset&) noexcept = default;
   constexpr bitset(bitset&&) noexcept = default;
-  // there is an issure for gcc 5.3.0 when define default function as constexpr
+  // there is an issue for gcc 5.3.0 when define default function as constexpr
   // see https://gcc.gnu.org/bugzilla/show_bug.cgi?id=68754.
   bitset& operator=(const bitset&) noexcept = default;
   bitset& operator=(bitset&&) noexcept = default;

caffe2/serialize/crc_alt.h

@@ -38,7 +38,7 @@ uint32_t crc32_combine (uint32_t crcA, uint32_t crcB, size_t lengthB);
 
 /// compute CRC32 (bitwise algorithm)
 uint32_t crc32_bitwise (const void* data, size_t length, uint32_t previousCrc32 = 0);
-/// compute CRC32 (half-byte algoritm)
+/// compute CRC32 (half-byte algorithm)
 uint32_t crc32_halfbyte(const void* data, size_t length, uint32_t previousCrc32 = 0);
 
 #ifdef CRC32_USE_LOOKUP_TABLE_BYTE
@@ -96,7 +96,7 @@ uint32_t crc32_16bytes_prefetch(const void* data, size_t length, uint32_t previo
   #define __BIG_ENDIAN    4321
 #endif
 
-// define endianess and some integer data types
+// define endianness and some integer data types
 #if defined(_MSC_VER) || defined(__MINGW32__)
   // Windows always little endian
   #define __BYTE_ORDER __LITTLE_ENDIAN
@@ -168,7 +168,7 @@ namespace
   /// zlib's CRC32 polynomial
   const uint32_t Polynomial = 0xEDB88320;
 
-  /// swap endianess
+  /// swap endianness
   static inline uint32_t swap(uint32_t x)
   {
   #if defined(__GNUC__) || defined(__clang__)
@@ -229,7 +229,7 @@ uint32_t crc32_bitwise(const void* data, size_t length, uint32_t previousCrc32)
 }
 
 
-/// compute CRC32 (half-byte algoritm)
+/// compute CRC32 (half-byte algorithm)
 uint32_t crc32_halfbyte(const void* data, size_t length, uint32_t previousCrc32)
 {
   uint32_t crc = ~previousCrc32; // same as previousCrc32 ^ 0xFFFFFFFF
@@ -662,7 +662,7 @@ uint32_t crc32_combine(uint32_t crcA, uint32_t crcB, size_t lengthB)
   // - if you append length(B) zeros to A and call it A' (think of it as AAAA000)
   //   and   prepend length(A) zeros to B and call it B' (think of it as 0000BBB)
   //   then exists a C' = A' ^ B'
-  // - remember: if you XOR someting with zero, it remains unchanged: X ^ 0 = X
+  // - remember: if you XOR something with zero, it remains unchanged: X ^ 0 = X
   // - that means C' = A concat B so that crc(A concat B) = crc(C') = crc(A') ^ crc(B')
   // - the trick is to compute crc(A') based on crc(A)
   //                       and crc(B') based on crc(B)

caffe2/serialize/inline_container.h

@@ -76,7 +76,7 @@ typedef struct mz_zip_archive mz_zip_archive;
 // 2) Writing with 1-pass sequential access
 //      -> We must take care not to require updating values that have already
 //         been written. We place the variable-length index at the end and do
-//         not put any indicies into the header to fulfill this constraint.
+//         not put any index into the header to fulfill this constraint.
 
 // The model.json, which contains all the metadata information,
 // should be written as the last file. One reason is that the size of tensor

caffe2/serialize/inline_container_test.cc

@@ -519,7 +519,7 @@ TEST(PyTorchStreamWriterAndReader, SaveAndLoadWithAllocator) {
   std::tie(data_ptr, size) = reader.getRecord("key1", &overrideAllocator);
   EXPECT_EQ(overrideAllocator.getAllocatedBytes(), kBytes1);
   EXPECT_EQ(baseAllocator.getAllocatedBytes(), allocBytes);
-  // allcoate with base allocator
+  // allocate with base allocator
   std::tie(data_ptr, size) = reader.getRecord("key1");
   EXPECT_EQ(overrideAllocator.getAllocatedBytes(), kBytes1);
   EXPECT_EQ(baseAllocator.getAllocatedBytes(), allocBytes + kBytes1);

setup.py

@@ -1106,7 +1106,7 @@ class build_ext(setuptools.command.build_ext.build_ext):
                 continue
             self.copy_file(source_lib, target_lib)
             # Delete old rpath and add @loader_lib to the rpath
-            # This should prevent delocate from attempting to package another instance
+            # This should prevent deallocate from attempting to package another instance
             # of OpenMP library in torch wheel as well as loading two libomp.dylib into
             # the address space, as libraries are cached by their unresolved names
             install_name_tool_args = [

torch/_dynamo/output_graph.py

@@ -1060,7 +1060,7 @@ class OutputGraph(OutputGraphCommon):
     def module_key_name(*names: Any) -> str:
         # create a new unique name
         name = "_".join(map(str, names))
-        # Strip _buffers[..]/_parmeters[..]/_modules[..] names
+        # Strip _buffers[..]/_parameters[..]/_modules[..] names
         name = re.sub(
             r"\._(?:modules|parameters|buffers)\[(['\"])([^'\"\]]+)\1\]", r".\2", name
         )
@@ -2217,7 +2217,7 @@ class OutputGraph(OutputGraphCommon):
                     backend_fake_mode = torch._subclasses.FakeTensorMode(
                         shape_env=old_fake_mode.shape_env,
                     )
-                # TODO(voz): Ostensibily, this should be scoped and
+                # TODO(voz): Ostensibly, this should be scoped and
                 # restore back to old_fake_mode, but doing so currently violates
                 # a lot of fake_tensor ownership assumptions and runs afoul of detect_fake_mode
                 self.tracing_context.fake_mode = backend_fake_mode
@@ -3414,7 +3414,7 @@ class SubgraphTracer(fx.Tracer):
         if proxy in self.lifted_freevars:
             return self.lifted_freevars[proxy]
 
-        # We first lift proxy to parent's graph then lift to current grpah's input
+        # We first lift proxy to parent's graph then lift to current graph's input
         # so that when we bind symints of the sizes in current graph, those symints
         # would already be lifted as inputs to parent graph.
         if proxy.tracer != self.parent:
@@ -3462,7 +3462,7 @@ class SubgraphTracer(fx.Tracer):
     def track_produced_symints(
         self, example_value: Any, e_proxy: Union[LazyProxy, torch.fx.Proxy]
     ) -> None:
-        # When binding the symbols in an exmaple_value, we bind the symbols
+        # When binding the symbols in an example_value, we bind the symbols
         # to the proxy's associated Tracer instead of current tracer.
         # This is because:
         # 1. We may be calling wrap_tensors during speculate_subgraph because

torch/_dynamo/symbolic_convert.py

@@ -2089,7 +2089,7 @@ class InstructionTranslatorBase(
     def _raise_exception_variable(self, val: VariableTracker) -> NoReturn:
         # User can raise exception in 2 ways
         #   1) raise exception type - raise NotImplementedError
-        #   2) raise exception instance - raise NotImplemetedError("foo")
+        #   2) raise exception instance - raise NotImplementedError("foo")
 
         # 1) when user raises exception type
         val = self._create_exception_type(val)
@@ -2140,7 +2140,7 @@ class InstructionTranslatorBase(
             try:
                 self._raise_exception_variable(val)
             finally:
-                # Update __cause__/__supppress_context__ in the raised exception
+                # Update __cause__/__suppress_context__ in the raised exception
                 curr_exc = self.exn_vt_stack.get_current_exception()
                 cause = self._create_exception_type(from_vt)
                 curr_exc.call_setattr(self, ConstantVariable("__cause__"), cause)  # type: ignore[arg-type, union-attr, assignment]
@@ -2417,8 +2417,8 @@ class InstructionTranslatorBase(
 
         # Users can check exception in 3 ways
         # 1) except NotImplementedError --> BuiltinVariable
-        # 2) except CustomException --> UserDefinedExceptionClasVariable
+        # 2) except CustomException --> UserDefinedExceptionClassVariable
-        # 3) except (NotImplemetedError, AttributeError) -> TupleVariable
+        # 3) except (NotImplementedError, AttributeError) -> TupleVariable
 
         if not isinstance(
             expected_exc_types,

torch/_dynamo/variables/dicts.py

@@ -54,7 +54,7 @@ if TYPE_CHECKING:
     from torch._dynamo.symbolic_convert import InstructionTranslator
 
 
-# [Adding a new supported class within the keys of ConstDictVarialble]
+# [Adding a new supported class within the keys of ConstDictVariable]
 # - Add its tracker type to is_hashable
 # - (perhaps) Define how it is compared in _HashableTracker._eq_impl
 

torch/_export/converter.py

@@ -765,7 +765,7 @@ class TS2FXGraphConverter:
             raise ValueError(f"Unsupported JitType ({input_type}) when get device")
 
     def convert_prim_GetAttr(self, node: torch._C.Node):
-        # Build fully qulified name
+        # Build fully qualified name
         attr_fqn = get_attribute_fqn_from_ts_node(self.name_to_attribute_fqn, node)
         output_name = node.output().debugName()
         self.name_to_attribute_fqn[output_name] = attr_fqn
@@ -1455,7 +1455,7 @@ DEBUG: (TORCH_LOGS="+export" <cmd>), additionally
         )
         gm = graph_converter.convert()
 
-        # Post-proccessing step to deal with quantized operators.
+        # Post-processing step to deal with quantized operators.
         replace_quantized_ops_with_standard_ops(gm)
         log.info("GraphModule: %s", gm.print_readable(print_output=False))
 

torch/_export/utils.py

@@ -1477,7 +1477,7 @@ def register_module_as_pytree_input_node(cls: type[torch.nn.Module]) -> None:
         flattened, _ = flatten_fn(obj)
 
         # NOTE: This helper function will replicate an nn.Module in the exactly same
-        #       structure to be used together with _reparametrize_module. This will
+        #       structure to be used together with _reparameterize_module. This will
         #       create a clone of the module with the new parameters and buffers without
         #       affecting the original module.
         def copy_module(mod: torch.nn.Module):

torch/_functorch/_aot_autograd/autograd_cache.py

@@ -771,7 +771,7 @@ class GenericAOTAutogradCacheEntry(Generic[TForward, TBackward]):
     maybe_subclass_meta: Optional[SubclassMeta]
     num_fw_outs_saved_for_bw: Optional[int]
 
-    # Used by RuntimeWrapepr
+    # Used by RuntimeWrapper
     indices_of_inps_to_detach: list[int]
 
     # Time taken to trace/compile the forward

torch/_functorch/compile_utils.py

@@ -99,7 +99,7 @@ def fx_graph_cse(fx_g: torch.fx.graph.Graph):
             # so it's not worth CSEing.
             or get_aten_target(n) is aten.empty
             or n in nodes_that_alias_outputs
-            # This CSE pass currently doesn't handle re-propogation of unbacked
+            # This CSE pass currently doesn't handle re-propagation of unbacked
             # meta where it'll sometimes eliminate a _local_scalar_dense but not
             # replace the meta of downstream users. eg. one bug we've seen is:
             #

torch/_functorch/config.py

@@ -20,7 +20,7 @@ from torch.utils._config_module import Config, install_config_module
 
 # [@compile_ignored: debug]
 _save_config_ignore = [
-    # callable not serializeable
+    # callable not serializable
     "joint_custom_pass",
 ]
 

torch/_higher_order_ops/auto_functionalize.py

@@ -44,7 +44,7 @@ class SchemaHolder:
         return cls(pytree.tree_unflatten([], tree_spec).schema)
 
 
-# regsiter_constant allows us to get a tree_spec from pytree.tree_flatten(SchemaHolder(FunctionSchema)).
+# register_constant allows us to get a tree_spec from pytree.tree_flatten(SchemaHolder(FunctionSchema)).
 # The tree_spec is proxable in the graph and we can get back the schema via
 # schema = pytree.tree_unflatten([], tree_spec).schema
 pytree.register_constant(SchemaHolder)

torch/_higher_order_ops/scan.py

@@ -312,7 +312,7 @@ def generic_scan(operator, init, xs, dim=0, additional_inputs=()):
         out_tensor_mask = get_tensor_mask(dummy_out)
         dummy_out_masked = mask_list(out_tensor_mask, dummy_out)
 
-        # Pre-alocate
+        # Pre-allocate
         # outs -> Output matrix
         # idxs -> Index matrix for scatter_
         # out: (num_elems, M, N, ...)

torch/_higher_order_ops/utils.py

@@ -708,7 +708,7 @@ def _stack_pytree(pytrees):
 # is partitioned into in order to recover it in saved_tensors_and_symints.
 #
 # In saved_tensors_and_symints, we can recover the original args by:
-# iterating over the pos list and pop one item from the front of paritioned_args[pos[i]].
+# iterating over the pos list and pop one item from the front of partitioned_args[pos[i]].
 # We use t_idx and s_idx to keep track of the next index of the item we are going to pop for the two lists.
 def save_tensors_and_symints_for_backward(ctx, args):
     assert all(

torch/_higher_order_ops/while_loop.py

@@ -660,7 +660,7 @@ class WhileLoopStackOutputOp(HigherOrderOperator):
 #
 #     gx = gy0 * bw(y0, x),
 #
-# where gy0 denotes the graident of loss with respect to y0, and bw(y0, x) denotes the graident of y0 with
+# where gy0 denotes the gradient of loss with respect to y0, and bw(y0, x) denotes the gradient of y0 with
 # respect to x. Note that bw can be computed from forward body_fn easily using torch.autograd.grad.
 # We could substitute the unknowns gy0, gy1, ..., with chain rule until gy4:
 #
@@ -769,7 +769,7 @@ class WhileLoopAutogradOp(torch.autograd.Function):
         # Note [Handle inputs that're not differentiable]
         # When a forward input is non-differentiable e.g. a symint or an integer tensor, their gradients
         # will be None. However, we don't want to return None in the subgraph because this complicates the
-        # inductor codegen, where we need to do a non-unform treatment for None and tensors.
+        # inductor codegen, where we need to do a non-uniform 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 = [
             bool(isinstance(t, torch.Tensor) and t.dtype.is_floating_point)

torch/_inductor/fx_passes/ddp_fusion.py

@@ -348,7 +348,7 @@ def _scatter_fused_allreduce_waits(
         # Some descendant users of the orig_comm_blocks may be scheduled before
         # the fused all_reduce. For example, the user nodes of the very first
         # all_reduce may be scheduled before the second all_reduce. Since the
-        # fused all_reduce is inserted right after the last all_reudce, the
+        # fused all_reduce is inserted right after the last all_reduce, the
         # order can be wrong.
         # `incorrect_order_nodes` records these nodes.
 

torch/_inductor/fx_passes/mkldnn_fusion.py

@@ -991,7 +991,7 @@ if torch._C._has_mkldnn:
 
     def _recover_linear():
         # convert reshape+linear+reshape to a single linear for applying fusion path.
-        # concat_linear (pass_number=0) -> mkldnn_linear_pack (pass_numer=1) -> _recover_linear(pass_number=2)
+        # concat_linear (pass_number=0) -> mkldnn_linear_pack (pass_number=1) -> _recover_linear(pass_number=2)
         @register_freezing_graph_pattern(
             CallFunction(
                 aten.reshape.default,

torch/_inductor/fx_passes/post_grad.py

@@ -585,7 +585,7 @@ def decompose_scan_to_while_loop(gm: torch.fx.GraphModule):
             # NOTE [Pre-allocate scan's output buffer]
             # In order to pre-allocate the output buffer for ys, we rely on the meta of scan's fx_node.
             # However, the meta consists of concrete symints, we need to bind those symints with
-            # proxies in order to trace the torch.empyt_strided call correctly.
+            # proxies in order to trace the torch.empty_strided call correctly.
             #
             # Also note that basic free symbols of tensor's shapes are guaranteed to be lifted as subgraph inputs
             # in dynamo so we can always re-construct the sym expression from placeholders.

torch/_inductor/output_code.py

@@ -677,7 +677,7 @@ class CompiledFxGraph(OutputCode):
                     ]
                 else:
                     # On the forward we don't know whether or not
-                    # boxed_foward_device_index is set yet
+                    # boxed_forward_device_index is set yet
                     boxed_forward_device_index = graph_kwargs.get(
                         "boxed_forward_device_index", None
                     )

torch/_inductor/runtime/triton_heuristics.py

@@ -530,7 +530,7 @@ class CachingAutotuner(KernelInterface):
                 #   = regs_per_multiprocessor / (nreg * 32 * num_warps)
                 #   < regs_per_multiprocessor / ((regs_per_multiprocessor / max_threads_per_multi_processor) * 32 * num_warps)
                 #   = max_threads_per_multi_processor / (32 * num_warps)
-                # Using a tigher upper bound can reveal more optimization opportunities.
+                # Using a tighter upper bound can reveal more optimization opportunities.
                 max_blocks_per_sm = max(
                     device_prop.regs_per_multiprocessor // nreg_per_block, 1
                 )

torch/_library/triton.py

@@ -215,7 +215,7 @@ def triton_op(
             # the exported program to be high-level and serializable. If we decompose
             # the custom op to a functional hop and make it a node in exported program,
             # we need to figure out ways of serializing the hop and its arguments, which can be triton.jited
-            # functions and triton dtypes. This is undesireble because:
+            # functions and triton dtypes. This is undesirable because:
             # - it can be tedious to maintain a layer that serializes the jited function (e.g. with a string) and dtypes.
             # - exported program will contain the implementation detail (e.g. triton source code) for a specific
             #   backend (GPU), which is probably at a wrong level of abstraction.

torch/_ops.py

@@ -530,7 +530,7 @@ class HigherOrderOperator(OperatorBase, abc.ABC):
         dispatch_key_set = _compute_keyset(args, kwargs, self.non_fallthrough_keys)
         return self.dispatch(dispatch_key_set.highestPriorityTypeId(), *args, **kwargs)
 
-    # NOTE [HigherOrderOprator Schema]
+    # NOTE [HigherOrderOperator Schema]
     # Each invocation of a HigherOrderOperator (hop) should have its own schema because
     # the subgraphs and the arguments can be different even for the same hop.
     #

torch/_refs/__init__.py

@@ -3155,7 +3155,7 @@ def flatten(a: TensorLikeType, start_dim: int = 0, end_dim: int = -1) -> TensorL
 
     # Tries to take a view
     # TODO: we could look at directing collapse_view to skip its meta function here (unsafe_collapse_view)
-    # Unbacked semnatics: if validty of in-place flattening is undecided we copy.
+    # Unbacked semantics: if validity of in-place flattening is undecided we copy.
     new_shape, _new_strides = prims._collapse_view_helper(
         a, start_dim, end_dim, must_be_valid=None
     )

torch/ao/quantization/fx/_lower_to_native_backend.py

@@ -523,7 +523,7 @@ def fold_weight(
                 del original_weights_lookup[str(lookup_counter)]
                 lookup_counter += 1
         elif prepack_node is not None:
-            # remove the foled node
+            # remove the fold node
             continue
         else:
             # copy other nodes

torch/ao/quantization/observer.py

@@ -1213,7 +1213,7 @@ class HistogramObserver(UniformQuantizationObserverBase):
         boundaries_new_histogram = torch.linspace(
             update_min, update_max, self.bins + 1, device=update_min.device
         ).to(histogram.device)
-        # this maps the mid-poits of the histogram to the new histogram's space
+        # this maps the mid-points of the histogram to the new histogram's space
         bucket_assignments = (
             torch.bucketize(mid_points_histogram, boundaries_new_histogram, right=True)
             - 1

torch/ao/quantization/pt2e/lowering.py

@@ -12,7 +12,7 @@ def lower_pt2e_quantized_to_x86(
     model: torch.fx.GraphModule,
     example_inputs: tuple[torch.Tensor, ...],
 ) -> torch.fx.GraphModule:
-    """Lower a PT2E-qantized model to x86 backend.
+    """Lower a PT2E-quantized model to x86 backend.
 
     Args:
     * `model` (torch.fx.GraphModule): a model quantized by PT2E quantization flow.

torch/csrc/autograd/FunctionsManual.cpp

@@ -4568,7 +4568,7 @@ std::tuple<Tensor, Tensor> linalg_solve_triangular_backward(
   if (!grad.defined() || (!A_requires_grad && !B_requires_grad)) {
     return std::make_tuple(Tensor{}, Tensor{});
   }
-  // We always need to comput G_B
+  // We always need to compute G_B
   const Tensor A_H = A.mH();
   const Tensor G_B =
       at::linalg_solve_triangular(A_H, grad, !upper, left, unitriangular);

torch/csrc/cuda/Module.cpp

@@ -1035,7 +1035,7 @@ PyObject* THCPModule_cudaGetSyncDebugMode(PyObject* self, PyObject* noargs) {
 ////////////////////////////////////////////////////////////////////////////////
 
 static void registerCudaDeviceProperties(PyObject* module) {
-  // Add _cudaDevicePropertires class to torch._C
+  // Add _cudaDeviceProperties class to torch._C
   auto m = py::handle(module).cast<py::module>();
   // CUuuid is defined in either cuda.h or driver_types.h
   // hipified to hipUUID which is defined in hip_runtime_api.h

torch/csrc/distributed/c10d/symm_mem/CUDASymmetricMemoryUtils.hpp

@@ -103,7 +103,7 @@ class StoreExchange {
   size_t seq_id_ = 0;
 };
 
-// Teturns a pointer of virtual address that is mapped to the physical memory
+// Returns a pointer of virtual address that is mapped to the physical memory
 // held by the handle.
 void map_block(
     void** ptr,

torch/csrc/distributed/rpc/request_callback_no_python.cpp

@@ -72,7 +72,7 @@ c10::intrusive_ptr<JitFuture> RequestCallbackNoPython::processMessage(
 
     auto retFuture = rrefsReadyFuture->thenAsync(
         [this,
-         // std::function must be copyable, hence hae to cast the unique_ptr to
+         // std::function must be copyable, hence has to cast the unique_ptr to
          // a shared_ptr here.
          rpc = std::shared_ptr<RpcCommandBase>(std::move(rpc)),
          messageType = request.type(),

torch/csrc/distributed/rpc/rpc_agent.h

@@ -240,7 +240,7 @@ class TORCH_API RpcAgent {
   // should be profiled or not.
   void enableGILProfiling(bool flag);
 
-  // Retrieve wheher we should profile GIL wait times or not.
+  // Retrieve whether we should profile GIL wait times or not.
   bool isGILProfilingEnabled();
 
   // Set type resolver that will be passed to JIT pickler to resolver type Ptr

torch/csrc/dynamo/guards.cpp

@@ -3534,7 +3534,7 @@ class RootGuardManager : public GuardManager {
 
   void add_no_tensor_aliasing_guard(
       std::shared_ptr<RelationalGuard> no_tensor_aliasing_guard) {
-    // stash a pointer to the _no_tensor_alising_guard
+    // stash a pointer to the _no_tensor_aliasing_guard
     _no_tensor_aliasing_guard = no_tensor_aliasing_guard;
     this->add_relational_guard_resetter(std::move(no_tensor_aliasing_guard));
   }

torch/csrc/inductor/aoti_runtime/sycl_runtime_wrappers.h

@@ -143,7 +143,7 @@ static std::unique_ptr<sycl::kernel> _createKernel(
   sycl::range<3> localRange(localRangeZ, localRangeY, localRangeX);
   sycl::nd_range<3> parallelWorkSize(globalRange, localRange);
   if (sharedMemory) {
-    // numParams from sycl info  = user provided args + sharedMemroyBuffer
+    // numParams from sycl info  = user provided args + sharedMemoryBuffer
     numParams -= 1;
   }
   // Submit the imported kernel.

torch/csrc/inductor/cpp_prefix.h

@@ -14,7 +14,7 @@
 // Because AOTInductor generated code will copy-paste this cpp_prefix.h for
 // the CPU backend, we have to make sure the used headers are implemented
 // in a header-only way, i.e. all the function and class definitions are
-// in .h files instead of .cpp files, to avoid ABI backward-compatiblity
+// in .h files instead of .cpp files, to avoid ABI backward-compatibility
 // breakage.
 
 #include <ATen/NumericUtils.h>

torch/csrc/jit/OVERVIEW.md

@@ -441,7 +441,7 @@ The following sections look into each the stages in the script frontend in detai
 
 [frontend/tree.h](frontend/tree.h)
 
-Our frontends produce ASTs in the form of Tree objects. Trees are similar to [s-expressions](https://en.wikipedia.org/wiki/S-expression). Leafs (i.e. Atoms) are always strings. Compound trees have a `kind` (e.g `TK_CONST` or `TK_IDENT` defined in [lexer.h](frontend/lexer.h)) and a list of sub-trees.  For instance, the Tree for `z.sigmoid() - (x + y)` is:
+Our frontends produce ASTs in the form of Tree objects. Trees are similar to [s-expressions](https://en.wikipedia.org/wiki/S-expression). Leaves (i.e. Atoms) are always strings. Compound trees have a `kind` (e.g `TK_CONST` or `TK_IDENT` defined in [lexer.h](frontend/lexer.h)) and a list of sub-trees.  For instance, the Tree for `z.sigmoid() - (x + y)` is:
 
 ```
  (-

torch/csrc/jit/backends/nnapi/nnapi_backend_lib.cpp

@@ -121,7 +121,7 @@ class NnapiBackend : public PyTorchBackendInterface {
         shape_compute_module.run_method("prepare", ser_model, inputs)
             .toTensorList();
 
-    // Create and initialize NnapiComilation object
+    // Create and initialize NnapiCompilation object
     comp_ = std::make_unique<torch::nnapi::bind::NnapiCompilation>();
     auto weights = dict.at("weights").toTensorVector();
     comp_->init(ser_model, weights);

torch/csrc/jit/mobile/flatbuffer_loader.cpp

@@ -379,7 +379,7 @@ std::unique_ptr<mobile::Function> FlatbufferLoader::parseFunction(
     function->append_type(getOrCreateTypeAnnotations(i));
   }
 
-  // 3. If upgrader is needed, change change the OP instrunction to CALL
+  // 3. If upgrader is needed, change change the OP instruction to CALL
   // instruction (In next PR, use_upgrader will be parsed to parseInstruction
   // function and do the actual change)
   if (use_upgrader) {

torch/csrc/jit/mobile/import.cpp

@@ -391,7 +391,7 @@ void BytecodeDeserializer::parseMethods(
         debug_handles_m_tuple,
         function.get());
 
-    // 3. If upgrader is needed, change change the OP instrunction to CALL
+    // 3. If upgrader is needed, change change the OP instruction to CALL
     // instruction (In next PR, use_upgrader will be parsed to parseInstruction
     // function and do the actual change)
     if (use_upgrader) {

torch/csrc/jit/passes/bailout_graph.cpp

@@ -196,7 +196,7 @@ struct BailOutGraphBuilderForNode {
   std::shared_ptr<Graph> buildBailOutGraphFrom(Node* n) {
     // add graph inputs for guard's input
     // and loop counts for loops `n` is contained in
-    // to make sure we can line bailout grap's inputs up properly
+    // to make sure we can line bailout graph's inputs up properly
     // with arguments to this BailOut node.
     for (auto bi : n->inputs()) {
       getOrAddInputForValue(bi);

torch/csrc/jit/passes/quantization/insert_quant_dequant.cpp

@@ -1230,7 +1230,7 @@ void removeDequantizeFromInputs(const std::unordered_set<Value*>& inputs) {
     TORCH_INTERNAL_ASSERT(
         dequantized_val->uses().size() == 1,
         "Expect to have one dequantize node for each use");
-    // Replace useses of dequantized_val with the input of
+    // Replace uses of dequantized_val with the input of
     // dequantize node
     dequantized_val->replaceAllUsesWith(dequantize_node->inputs()[0]);
     dequantize_node->removeAllInputs();

torch/csrc/jit/serialization/callstack_debug_info_serialization.cpp

@@ -162,7 +162,7 @@ InlinedCallStackPtr InlinedCallStackDeserializer::deserialize(
   }
   cached_inlined_callstacks_[tup] = cs_ptr;
   // Invoking move constructor
-  // It is not clear if copy-ellision can happen since
+  // It is not clear if copy-elision can happen since
   // cs_ptr is copied into map above.
   // This is to help avoid ref count update
   return cs_ptr;

torch/csrc/jit/serialization/pickler_helper.cpp

@@ -106,7 +106,7 @@ std::array<
 GetBackendMetaSerialization() {
   // The array to save function pointer for BackendMeta serialization.
   // key is the DeviceType, value is std::pair obj.
-  // value.first represent get function and value.seconde represent set function
+  // value.first represent get function and value.second represent set function
   static std::array<
       std::optional<std::pair<BackendMetaPtr, BackendMetaPtr>>,
       at::COMPILE_TIME_MAX_DEVICE_TYPES>

torch/csrc/lazy/core/lazy_graph_executor.cpp

@@ -830,7 +830,7 @@ std::shared_ptr<LazyGraphExecutor::Async> LazyGraphExecutor::
         const SyncTensorsConfig& config) {
   SyncTensorCollection coll = CollectSyncTensors(*tensors, config);
   if (coll.indices.empty()) {
-    /* Enure previous execution is complete before exiting this
+    /* Ensure previous execution is complete before exiting this
      * function */
     TensorCollectionBarrier(&coll);
     return nullptr;

torch/csrc/profiler/collection.cpp

@@ -915,7 +915,7 @@ void passEventsToKineto(
       // on-demand Kineto activity handling. Enabling this path
       // for Profiler API could cause side effects as much has changed since.
       // Make a surgical fix here until we holistically assess the on-demand
-      // vs API path framentation, which has been snowballing in complexity
+      // vs API path fragmentation, which has been snowballing in complexity
       // and thus flakiness.
       if (config.global()) {
         e->kineto_activity_ = activity;

torch/csrc/xpu/Module.cpp

@@ -261,7 +261,7 @@ static PyObject* THXPModule_resetAccumulatedMemoryStats(
 // XPU module initialization
 
 static void registerXpuDeviceProperties(PyObject* module) {
-  // Add _xpuDevicePropertires class to torch._C
+  // Add _xpuDeviceProperties class to torch._C
   using namespace c10::xpu;
   auto get_device_type = [](const DeviceProp& prop) {
     std::ostringstream stream;

torch/distributed/checkpoint/_checkpointer.py

@@ -17,7 +17,7 @@ __all__: list[str] = []
 
 
 class _Checkpointer:
-    """This base class specefies a high level API for saving and loading
+    """This base class specifies a high level API for saving and loading
     distributed `state_dict` 's. It provides an abstraction over the low-level APIs
     provided by :py:mod:`torch.distributed.checkpoint.storage`, essentially calling
     :py:meth: `torch.distributed.state_dict_saver.save` and

torch/distributed/checkpoint/format_utils.py

@@ -80,7 +80,7 @@ class BroadcastingTorchSaveReader(StorageReader):
         planner = cast(DefaultLoadPlanner, planner)
 
         # data is read in on the coordinator rank, and broadcast afterwards
-        # this incurrs a communication cost, but it avoids having to load
+        # this incurs a communication cost, but it avoids having to load
         # the entire checkpoint on each rank, hopefully preventing OOM issues
         # TODO: read on each host, instead of only the coordinator
         if self.is_coordinator:

torch/distributed/pipelining/stage.py

@@ -252,7 +252,7 @@ class _PipelineStageBase(ABC):
         self._outputs_meta = tuple(outputs_meta)  # type: ignore[assignment]
 
     def get_outputs_meta(self) -> tuple[torch.Tensor, ...]:
-        """Get the output metadata (meta tensors) reprensenting the outputs of this stage"""
+        """Get the output metadata (meta tensors) representing the outputs of this stage"""
         assert self._outputs_meta is not None, (
             "Attempted to get_outputs_meta() without configuring output meta"
         )
@@ -723,7 +723,7 @@ class _PipelineStageBase(ABC):
         )
         self._validate_fwd_outputs(output_tuple)
 
-        # We return the original user-provied output, not normalized to tuple.
+        # We return the original user-provided output, not normalized to tuple.
         # See [Note: pipeline model output type]
         return output
 
@@ -1188,7 +1188,7 @@ class _PipelineStage(_PipelineStageBase):
             #   No need to send back to rank 0
             # - If user.target is stage_backward:
             #   No need to send assuming submod output is stored locally or
-            #   should be re-calucated in case of activation checkpointing
+            #   should be re-calculated in case of activation checkpointing
             return None
 
     def _create_act_send_info(self):

torch/distributed/tensor/_ops/_einsum_strategy.py

@@ -45,7 +45,7 @@ class EinsumDims:
         for input_dim in input_dims:
             dim_char_set.update(input_dim)
 
-        # get a determinisitc order of all dim chars
+        # get a deterministic order of all dim chars
         all_dim_chars = sorted(dim_char_set)
 
         # parse input and output dimensions

torch/distributed/tensor/_ops/_tensor_ops.py

@@ -484,7 +484,7 @@ def replicate_tensor_dim(
 def gen_slice_scatter_strategy(op_schema: OpSchema) -> StrategyType:
     # 1. number of dimensions in input and src need to match.
     # 2. number of elements on all non-dim need to match between input and src.
-    # 3. numer of elements in src in dim need to match the slice size.
+    # 3. number of elements in src in dim need to match the slice size.
     # Given the above:
     # - We suggest for src to follow the sharding of input, except on the scatter dimension,
     #   where our best bet for now is to make them replicated as a fall-back.

torch/distributed/tensor/_redistribute.py

@@ -592,7 +592,7 @@ class DTensorRedistributePlanner:
                 current = current_placements[mesh_dim]
                 target = target_placements[mesh_dim]
                 # If target is not Shard, we can directly redistribute since we
-                # are traversing from innner to outer placements here
+                # are traversing from inner to outer placements here
                 if isinstance(target, Shard):
                     # If target is Shard, check for nested sharding on the
                     # tensor dim BEFORE the current mesh_dim

torch/export/_trace.py

@@ -922,7 +922,7 @@ def _export_to_aten_ir(
         if decompose_custom_triton_ops
         else _disable_custom_triton_op_functional_decomposition
     )
-    # This _reparametrize_module makes sure inputs and module.params/buffers have the same fake_mode,
+    # This _reparameterize_module makes sure inputs and module.params/buffers have the same fake_mode,
     # otherwise aot_export_module will error out because it sees a mix of fake_modes.
     # And we want aot_export_module to use the fake_tensor mode in dynamo to keep the pipeline easy to reason about.
     with ExitStack() as stack:
@@ -1843,7 +1843,7 @@ def _export_to_aten_ir_make_fx(
         )
         return gm, sig
 
-    # This _reparametrize_module makes sure inputs and module.params/buffers have the same fake_mode,
+    # This _reparameterize_module makes sure inputs and module.params/buffers have the same fake_mode,
     # otherwise aot_export_module will error out because it sees a mix of fake_modes.
     # And we want aot_export_module to use the fake_tensor mode in dynamo to keep the pipeline easy to reason about.
     with ExitStack() as stack:

torch/export/exported_program.py

@@ -281,7 +281,7 @@ def _split_decomp_table_to_cia_and_python_decomp(
     for op in list(decomp_table.keys()):
         # TODO we are silently allowing non-safe(non-functional) ops through a crack
         # due to core aten decomp table having non-functional entries. Once we have
-        # a tigher check around core aten decomp, we should warn users about them.
+        # a tighter check around core aten decomp, we should warn users about them.
         # Tracking issue: (https://github.com/pytorch/pytorch/issues/135759)
 
         # if it is a valid CIA op we can mess with in export, we check if it is:

torch/functional.py

@@ -1829,7 +1829,7 @@ def norm(  # noqa: F811
             return _VF.norm(input, p, dim=_dim, keepdim=keepdim)  # type: ignore[attr-defined]
 
     # TODO: when https://github.com/pytorch/pytorch/issues/33782 is fixed
-    # remove the overloads where dim is an int and replace with BraodcastingList1
+    # remove the overloads where dim is an int and replace with BroadcastingList1
     # and remove next four lines, replace _dim with dim
     if dim is not None:
         if isinstance(dim, (int, torch.SymInt)):

torch/fx/experimental/symbolic_shapes.py

@@ -4522,7 +4522,7 @@ class ShapeEnv:
 
     # The order of checking the guards matters. In this specific example:
     # If True branch guard check precedes False branch and for True branch, y.size(0) check precedes x == True,
-    # we may have an unnecessary shape speciliazation for y.
+    # we may have an unnecessary shape specialization for y.
     def _maybe_specialize_sym_int_with_hint(
         self, maybe_sym: IntLikeType
     ) -> IntLikeType:
@@ -5830,7 +5830,7 @@ class ShapeEnv:
         def issue_guard(guard: ShapeGuard) -> None:
             expr = self.simplify(guard.expr)
 
-            # Avoid re-issueing the same guard.
+            # Avoid re-issuing the same guard.
             if expr in issued:
                 return
 

torch/fx/experimental/unification/multipledispatch/conflict.py

@@ -118,7 +118,7 @@ def edge(a, b, tie_breaker=hash):
     """A should be checked before B
     Tie broken by tie_breaker, defaults to ``hash``
     """
-    # A either supercedes B and B does not supercede A or if B does then call
+    # A either supersedes B and B does not supersede A or if B does then call
     # tie_breaker
     return supercedes(a, b) and (
         not supercedes(b, a) or tie_breaker(a) > tie_breaker(b)

torch/nativert/OVERVIEW.md

@@ -282,7 +282,7 @@ RuntimeConfigs {
 
 Constant folding is the process of finding all of the constant-evaluable
 subgraphs, evaluating them at startup, and then storing their results as
-constants as opposed to re-evaluting them every time.
+constants as opposed to re-evaluating them every time.
 
 To enable constant folding, you can set the following configurations.
 

torch/nested/_internal/sdpa.py

@@ -438,7 +438,7 @@ def _view_as_dense(
 #     #     this is because needs_broadcast indicates that the batch_size is 1
 #     #     and hence there is only 1 value for seq_len
 #     # (2) The cum_seq_lens are given by [0, {*}_t.size(1), 2 * {*}_t.size(1),
-#     # ..., outut_batch_size * {*}_t.size(1)]
+#     # ..., output_batch_size * {*}_t.size(1)]
 #     # (3) Nnz_{*} is given by output_batch_size * {*}_t.size(1)
 
 #     if q_batch_size_needs_broadcast or not q_t.is_nested:

torch/nn/functional.py

@@ -2229,7 +2229,7 @@ def gumbel_softmax(
         ).scatter_(dim, index, 1.0)
         ret = y_hard - y_soft.detach() + y_soft
     else:
-        # Reparametrization trick.
+        # Reparameterization trick.
         ret = y_soft
     return ret
 

torch/nn/modules/conv.py

@@ -1471,7 +1471,7 @@ class _LazyConvXdMixin(LazyModuleMixin):
         raise NotImplementedError
 
 
-# LazyConv1d defines weight as a Tensor but derived class defines it as UnitializeParameter
+# LazyConv1d defines weight as a Tensor but derived class defines it as UninitializeParameter
 class LazyConv1d(_LazyConvXdMixin, Conv1d):  # type: ignore[misc]
     r"""A :class:`torch.nn.Conv1d` module with lazy initialization of the ``in_channels`` argument.
 
@@ -1543,7 +1543,7 @@ class LazyConv1d(_LazyConvXdMixin, Conv1d):  # type: ignore[misc]
         return 1
 
 
-# LazyConv2d defines weight as a Tensor but derived class defines it as UnitializeParameter
+# LazyConv2d defines weight as a Tensor but derived class defines it as UninitializeParameter
 class LazyConv2d(_LazyConvXdMixin, Conv2d):  # type: ignore[misc]
     r"""A :class:`torch.nn.Conv2d` module with lazy initialization of the ``in_channels`` argument.
 
@@ -1615,7 +1615,7 @@ class LazyConv2d(_LazyConvXdMixin, Conv2d):  # type: ignore[misc]
         return 2
 
 
-# LazyConv3d defines weight as a Tensor but derived class defines it as UnitializeParameter
+# LazyConv3d defines weight as a Tensor but derived class defines it as UninitializeParameter
 class LazyConv3d(_LazyConvXdMixin, Conv3d):  # type: ignore[misc]
     r"""A :class:`torch.nn.Conv3d` module with lazy initialization of the ``in_channels`` argument.
 
@@ -1688,7 +1688,7 @@ class LazyConv3d(_LazyConvXdMixin, Conv3d):  # type: ignore[misc]
         return 3
 
 
-# LazyConvTranspose1d defines weight as a Tensor but derived class defines it as UnitializeParameter
+# LazyConvTranspose1d defines weight as a Tensor but derived class defines it as UninitializeParameter
 class LazyConvTranspose1d(_LazyConvXdMixin, ConvTranspose1d):  # type: ignore[misc]
     r"""A :class:`torch.nn.ConvTranspose1d` module with lazy initialization of the ``in_channels`` argument.
 
@@ -1760,7 +1760,7 @@ class LazyConvTranspose1d(_LazyConvXdMixin, ConvTranspose1d):  # type: ignore[mi
         return 1
 
 
-# LazyConvTranspose2d defines weight as a Tensor but derived class defines it as UnitializeParameter
+# LazyConvTranspose2d defines weight as a Tensor but derived class defines it as UninitializeParameter
 class LazyConvTranspose2d(_LazyConvXdMixin, ConvTranspose2d):  # type: ignore[misc]
     r"""A :class:`torch.nn.ConvTranspose2d` module with lazy initialization of the ``in_channels`` argument.
 
@@ -1832,7 +1832,7 @@ class LazyConvTranspose2d(_LazyConvXdMixin, ConvTranspose2d):  # type: ignore[mi
         return 2
 
 
-# LazyConvTranspose3d defines weight as a Tensor but derived class defines it as UnitializeParameter
+# LazyConvTranspose3d defines weight as a Tensor but derived class defines it as UninitializeParameter
 class LazyConvTranspose3d(_LazyConvXdMixin, ConvTranspose3d):  # type: ignore[misc]
     r"""A :class:`torch.nn.ConvTranspose3d` module with lazy initialization of the ``in_channels`` argument.
 

torch/nn/utils/prune.py

@@ -144,7 +144,7 @@ class BasePruningMethod(ABC):
 
         method = _get_composite_method(cls, module, name, *args, **kwargs)
         # at this point we have no forward_pre_hooks but we could have an
-        # active reparametrization of the tensor if another pruning method
+        # active reparameterization of the tensor if another pruning method
         # had been applied (in which case `method` would be a PruningContainer
         # and not a simple pruning method).
 

torch/onnx/_internal/fx/type_utils.py

@@ -164,7 +164,7 @@ _TORCH_DTYPE_TO_ABBREVIATION = {
 
 SYM_VALUE_TYPE = Union[torch.SymInt, torch.SymFloat, torch.SymBool]
 META_VALUE_TYPE = Union[fake_tensor.FakeTensor, SYM_VALUE_TYPE, int, float, bool]
-# NOTE: Belows are from torch/fx/node.py
+# NOTE: Below are from torch/fx/node.py
 BaseArgumentTypes = Union[
     str,
     int,

torch/testing/_internal/common_quantization.py

@@ -810,7 +810,7 @@ class QuantizationTestCase(TestCase):
         b = io.BytesIO()
         torch.save(model_dict, b)
         b.seek(0)
-        # weights_only=False as we sometimes get a ScriptObect here (weird)
+        # weights_only=False as we sometimes get a ScriptObject here (weird)
         loaded_dict = torch.load(b, weights_only=False)
         loaded_model.load_state_dict(loaded_dict)
         ref_out = ref_model(*x)

torchgen/_autoheuristic/README.md

@@ -3,7 +3,7 @@ AutoHeuristic is a framework that allows one to use results from autotuning to l
 
 ## How to use AutoHeuristic
 In general, the following steps have to performed:
-- The AutoHeursitic constructor has to be called.
+- The AutoHeuristic constructor has to be called.
 - A script that runs benchmarks in order to collect training data has to be implemented.
 - The train_decision.py (if you want to learn a decision tree) or train_regression.py (if you want to learn a regression tree) script has to be run in order to learn the heuristic and generate it to code.
 

torchgen/gen_aoti_c_shim.py

@@ -678,7 +678,7 @@ def gen_aoti_c_shim_files(
         # Use "aten" as the device name when dispatch_key is Generic
         device_name = "aten" if dispatch_key is None else dispatch_key.lower()
 
-        # header files were checked in for ABI-compatiblilty checking
+        # header files were checked in for ABI-compatibility checking
         header_file_name = f"c_shim_{device_name}.h"
         new_header = gen_aoti_c_shim(
             fallback_native_functions,