[Unity][Fix] Remove duplicated words from comments, NFC (#15875)

Removed instances of accidentally repeated words from comments. There
are cases where duplicated words appear legitimately, those cases remain
unmodified.

Author: Krzysztof Parzyszek

include/tvm/relax/transform.h

@@ -503,7 +503,7 @@ TVM_DLL Pass DecomposeOpsForTraining(Optional<String> func_name);
  * pass inserts the layout transformations in the call sites of PrimFuncs being replaced to
  * transform i/o buffers into expected layout.
  *
- * \param op_impl_map Map from from kOperatorName attr (e.g., relax.conv2d) to replacement PrimFunc
+ * \param op_impl_map Map from kOperatorName attr (e.g., relax.conv2d) to replacement PrimFunc
  * \param op_buffer_transforms Map from kOperatorName attr to layout transformations on each of the
  * PrimFunc i/o buffers.
  * \param axis_separators Map from kOperatorName attr to axis_separators of each buffer_transforms

python/tvm/relax/frontend/nn/op.py

@@ -256,7 +256,7 @@ def matmul(a: Tensor, b: Tensor, out_dtype: Optional[str] = None, name: str = "m
 
     out_dtype: Optional[Union[str, DataType]]
         The data type of the matmul result.
-        When it is not specified, the output dtype will be the the same as input dtype.
+        When it is not specified, the output dtype will be the same as input dtype.
 
     name : str
         Name hint.

python/tvm/relax/op/linear_algebra.py

@@ -41,7 +41,7 @@ def matmul(x1: Expr, x2: Expr, out_dtype: Optional[Union[str, DataType]] = None)
 
     out_dtype: Optional[Union[str, DataType]]
         The data type of the matmul result.
-        When it is not specified, the output dtype will be the the same as input dtype.
+        When it is not specified, the output dtype will be the same as input dtype.
 
     Returns
     -------
@@ -72,7 +72,7 @@ def linear(
 
     out_dtype: Optional[Union[str, DataType]]
         The data type of the matmul result.
-        When it is not specified, the output dtype will be the the same as input dtype.
+        When it is not specified, the output dtype will be the same as input dtype.
 
     Notes
     -----

python/tvm/relax/struct_info.py

@@ -148,7 +148,7 @@ class TensorStructInfo(StructInfo):
     dtype : Optional[str]
         The content data type.
 
-    vdevice : Optional[VDevice]
+    vdevice : Optional[Vdevice]
         The virtual device.
 
     ndim : Optional[int]
@@ -247,7 +247,7 @@ def opaque_func(
         Parameters
         ----------
         ret: Optional[StructInfo]
-           The struct info of the the function return value.
+           The struct info of the function return value.
 
         derive_func: Optional[EnvFunc]
            The environment function used for derivation

python/tvm/relax/training/utils.py

@@ -90,7 +90,7 @@ def backbone_loss(input_instances, parameters, states, targets):
         Specify the number of `prediction_outputs` of the backbone function. Default: 1.
 
     new_func_name : Optional[str]
-        Specify the name of the appended result. If is is not specified, the name will be
+        Specify the name of the appended result. If it is not specified, the name will be
         `func_name + "_loss"`.
 
     Returns

python/tvm/relax/transform/transform.py

@@ -448,8 +448,12 @@ def BindParams(
     func_name: str
         The function name to be bound
 
-    params : Dict[Union[str,relax.Var],Union[tvm.runtime.NDArray, np.ndarray]]
-        The map from parameter or parameter name name to constant
+    params : Dict[
+                Union[str,relax.Var],
+                Union[tvm.runtime.NDArray, np.ndarray],
+             ]
+
+        The map from parameter or parameter name to constant
         tensors.
 
     Returns

python/tvm/runtime/disco/process_pool.py

@@ -165,7 +165,7 @@ def _kill_child_processes(pid):
 
 @register_func("runtime.disco.create_process_pool")
 def _create_process_pool(num_workers: int):
-    """Create a process pool where the workers' are are [1, num_workers)."""
+    """Create a process pool where the workers' are [1, num_workers)."""
     pool = [DiscoPopenWorker(i, num_workers) for i in range(1, num_workers)]
 
     def result_func(worker_id: int):

src/relax/op/tensor/linear_algebra.h

@@ -38,7 +38,7 @@ namespace relax {
  * \param x1 The first input tensor.
  * \param x2 The second input tensor.
  * \param out_dtype The data type of the matmul result.
- * When it is not specified, the output dtype will be the the same as input dtype.
+ * When it is not specified, the output dtype will be the same as input dtype.
  * \return The computed result.
  */
 Expr matmul(Expr x1, Expr x2, DataType out_dtype);

src/relax/training/utils.h

@@ -46,7 +46,7 @@ namespace transform {
  * \param loss_function The loss function.
  * \param num_backbone_outputs Specify the number of `prediction_outputs` of the backbone function.
  * Default: 1.
- * \param new_func_name Specify the name of the appended result. If is is not specified, the name
+ * \param new_func_name Specify the name of the appended result. If it is not specified, the name
  * will be `func_name + "_loss"`.
  * \return The Pass.
  */

src/relax/transform/fuse_ops.cc

@@ -446,7 +446,7 @@ class FunctionCreator : public ExprMutator {
   }
 
   /*!
-   * \brief Create the grouped function according according to the collected bindings and parameters
+   * \brief Create the grouped function according to the collected bindings and parameters
    * \param composite_name The name to identify the pattern this function is created from, if any.
    * It will become the value of the kComposite attribute of the created function.
    * \note The created function won't be returned immediately. It's stored in the `function_` field.