Fix typos in comments (#2539)

Fix typos in comments

build_tools/build_libtorch.sh

@@ -47,7 +47,7 @@ install_requirements() {
 checkout_pytorch() {
   if [[ ! -d "$PYTORCH_ROOT" ]]; then
     # ${TORCH_MLIR_SRC_PYTORCH_BRANCH} could be a branch name or a commit hash.
-    # Althought `git clone` can accept a branch name, the same command does not
+    # Although `git clone` can accept a branch name, the same command does not
     # accept a commit hash, so we instead use `git fetch`.  The alternative is
     # to clone the entire repository and then `git checkout` the requested
     # branch or commit hash, but that's too expensive.

include/torch-mlir/Dialect/Torch/Transforms/Passes.td

@@ -56,7 +56,7 @@ def GlobalizeObjectGraph : Pass<"torch-globalize-object-graph", "ModuleOp"> {
     - Multiple instances of the same class type are allowed, as long as it is
       possible to monomorphize ("template instantiate") functions so that each
       argument of !torch.nn.Module type corresponds to a unique instance.
-      In pratice, this limitation is either 1) (fundamental) due to truly
+      In practice, this limitation is either 1) (fundamental) due to truly
       dynamic use of modules, such as `m1 if cond() else m2` in Python code,
       or 2) (incidental) imprecision of the static analysis used in this pass
       which is used to calculate when a single intance is relevant. In general,

include/torch-mlir/Dialect/Torch/Utils/Utils.h

@@ -60,7 +60,7 @@ Value convertTensorToDtype(PatternRewriter &rewriter, Location loc, Value input,
 
 bool isBuiltInType(Type type);
 
-// Helper funtion to get rank of `Base tensor type`.
+// Helper function to get rank of `Base tensor type`.
 // std::nullopt is returned if the tensorRank can't be determined.
 std::optional<unsigned> getTensorRank(Value tensor);
 

python/torch_mlir/csrc/base_lazy_backend/README.md

@@ -14,7 +14,7 @@ The components are subclasses of the backend API interface classes found under
 [torch/csrc/lazy/backend](https://github.com/pytorch/pytorch/tree/master/torch/csrc/lazy/backend).
 
 Importantly, the subclasses are still abstract classes. Pure virtual methods
-such as `Compile` were purposefully not overriden as Torch-MLIR does not know
+such as `Compile` were purposefully not overridden as Torch-MLIR does not know
 how to compile the model for the target hardware.
 
 The intent is that vendor hardware specific plugins will subclass the Torch-MLIR

python/torch_mlir/csrc/base_lazy_backend/mlir_native_functions.cpp

@@ -168,7 +168,7 @@ at::Tensor LazyNativeFunctions::_copy_from(
     // materializing a lazy tensor (self) and copying its value into eager
     // tensor (dst)
     // detached=false lets us skip a copy in `ToTensor`, which should be safe
-    // becuase we are only going to use the tensor for dst.copy_()
+    // because we are only going to use the tensor for dst.copy_()
     CHECK(self_tensor);
     at::Tensor tensor = self_tensor->ToTensor(/*detached=*/false);
     at::Tensor typed_tensor =

python/torch_mlir/csrc/base_lazy_backend/mlir_node.h

@@ -74,7 +74,7 @@ private:
 // Note: shape is undefined for TensorList.  We assert in some places that
 // #shapes matches #outputs and this stems from
 //       the fact that currently all IR nodes represent tensors (there is no
-//       type system for this IR).  Becuase of this, TensorList is a bit of a
+//       type system for this IR).  Because of this, TensorList is a bit of a
 //       hack.
 //
 // TODO(whc) once Shape() API is moved to Node base, also make it virtual, and

python/torch_mlir/csrc/base_lazy_backend/mlir_node_lowering.cpp

@@ -36,7 +36,7 @@ TorchMlirOpVector LowerTorchMlirBuiltin(
     const std::vector<c10::TypePtr> tensor_types,
     const std::vector<torch::jit::NamedValue>& arguments,
     const std::vector<torch::jit::NamedValue>& kwarguments) {
-  // Workaround for ListType::isSubtypeOfExt behavoir which leads to
+  // Workaround for ListType::isSubtypeOfExt behavior which leads to
   // the problems with JIT schema matching, so we need to keep
   // c10::ListType empty before magic_method->call function call.
   auto dummy_graph = torch::jit::Graph();

python/torch_mlir/csrc/base_lazy_backend/ops/ivalue.h

@@ -15,7 +15,7 @@ namespace torch {
 namespace lazy {
 
 // IValueConstant IR Node represents a `prim::Constant` constructed with IValue
-// parameter which is helpfull in different usecases when we need custom
+// parameter which is helpful in different usecases when we need custom
 // native ops lowering to torch-mlir IR nodes.
 class IValueConstant : public torch::lazy::TorchMlirNode {
  public: