Fix typos and spelling mistakes across docs, comments, and logs (pytorch#20758)

Fixes multiple typos and spelling mistakes across several files
(including program.fbs, external_constants_pass.py, test_partitioner.py,
training_module_test.cpp, clamp_hardswish_pass.py, replacement_pass.py,
vulkan_preprocess.py, vulkan_compute_api_test.cpp, XNNCompiler.cpp,
upsample_bilinear2d_converter.py, test_fuse_batchnorm_pass.py,
conftest.py, README.md, tester.py, test_reporting.py, reporting.py) to
improve code readability and documentation quality.

cc @digantdesai @freddan80 @per @zingo @oscarandersson8218 @mansnils
@Sebastian-Larsson @robell @rascani

Author: RanjithRagavan

backends/arm/test/passes/test_fuse_batchnorm_pass.py

@@ -143,7 +143,7 @@ def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
         z = self.conv2d2(x)
         a = self.batch_norm2d(
             y
-        )  # Can be fused despite paramters of conv2d2 having multiple users.
+        )  # Can be fused despite parameters of conv2d2 having multiple users.
 
         return z, a
 

backends/arm/test/quantizer/test_selective_quantization.py

@@ -324,7 +324,7 @@ def test_multiple_folded_get_attr():
     node targets are deleted as soon as there is one get_attr node w/o users
     using the target.
 
-    If there are multiple get_attr nodes refering the same target such as in
+    If there are multiple get_attr nodes referring the same target such as in
     this test, the function crashes if no workaround is present.
 
     """

backends/cortex_m/passes/clamp_hardswish_pass.py

@@ -16,7 +16,7 @@ class ClampHardswishPass(ExportPass):
     """
     Adds a clamp operation before hardswish to ensure input is in the range [-3, inf).
 
-    By doing this before quantization the output range of the preceeding op is minimized,
+    By doing this before quantization the output range of the preceding op is minimized,
     potentially improving accuracy.
     """
 

backends/cuda/triton/replacement_pass.py

@@ -97,7 +97,7 @@ def _pick_sdpa_kernel(node: Node):
         allocating partial buffers and running the reduction kernel.
 
         TODO(gasoonjia): Benchmarking to determine the optimal
-        implmentation for each shape.
+        implementation for each shape.
         """
         q_shape = node.args[0].meta["val"].shape
         k_shape = node.args[1].meta["val"].shape

backends/nxp/backend/ir/converter/node_converters/ops_converters/upsample_bilinear2d_converter.py

@@ -113,7 +113,7 @@ def convert(self, node: Node):
         x = t_op.tmp_inputs[0]
         y = t_op.tmp_outputs[0]
 
-        # ExecuTorch has 1 paramter (align_corners). NeutronIR has 2 parameters (align_corners, half_pixel_centers).
+        # ExecuTorch has 1 parameter (align_corners). NeutronIR has 2 parameters (align_corners, half_pixel_centers).
         # In ExecuTorch, the pixel compute scale is:
         #  `(input_size - 1) / (output_size - 1)` if align_corners else `input_size / output_size`
         # https://github.com/pytorch/executorch/blob/v1.1.0/kernels/portable/cpu/util/upsample_util.h#L65

backends/nxp/quantizer/patterns.py

@@ -43,11 +43,11 @@
 @dataclass
 class NodeArgsIdx:
     """
-    Specifies indexes to args paramater of Node in node input annotation.
+    Specifies indexes to args parameter of Node in node input annotation.
 
 
     Attributes:
-        idx (int): Index to Node's args paramater (list). Selects an input Node or a list of Nodes at the index.
+        idx (int): Index to Node's args parameter (list). Selects an input Node or a list of Nodes at the index.
         inner_idx (int): If specified, index to a list pointed by 'idx' attribute. Selects an input Node at the index.
                          Default: None.
     """

backends/openvino/README.md

@@ -126,7 +126,7 @@ Follow the steps below to setup your build environment:
    ```bash
    openvino_build.sh
    ```
-- Optionally, `openvino_build.sh` script can be used to build python package or C++ libraries/binaries seperately.
+- Optionally, `openvino_build.sh` script can be used to build python package or C++ libraries/binaries separately.
 
    **Build OpenVINO Backend Python Package with Pybindings**: To build and install the OpenVINO backend Python package with Python bindings, run the `openvino_build.sh` script with the `--enable_python` argument as shown in the below command. This will compile and install the ExecuTorch Python package with the OpenVINO backend into your Python environment. This option will also enable python bindings required to execute OpenVINO backend tests and `aot_optimize_and_infer.py` script inside `executorch/examples/openvino` folder.
      ```bash

backends/qualcomm/tests/rework/conftest.py

@@ -244,8 +244,8 @@ def temp_attribute(obj, attr, new_value):
         )
 
 
-# No direct depedency between qnn_config and global_setup.
-# Keep following depedency for better practice so global_setup is done first.
+# No direct dependency between qnn_config and global_setup.
+# Keep following dependency for better practice so global_setup is done first.
 @pytest.fixture(autouse=True, scope="session")
 def qnn_config(global_setup, request):
     # generate QnnConfig for on-device test

backends/test/harness/tester.py

@@ -287,7 +287,7 @@ def check_count(self, input: Dict[Any, int]):
         return self
 
     def check_node_count(self, input: Dict[Any, int]):
-        # Count the occurances of each target in the graph.
+        # Count the occurrences of each target in the graph.
         target_ops = [
             node.target
             for node in self.stages[self.cur].graph_module.graph.nodes

backends/test/suite/reporting.py

@@ -196,10 +196,10 @@ class TestCaseSummary:
     """ The total runtime of the to_edge_transform_and_lower stage, or none, if the test did not run the quantize stage. """
 
     delegated_op_counts: Counter | None = None
-    """ The number of delegated occurances of each operator in the graph. """
+    """ The number of delegated occurrences of each operator in the graph. """
 
     undelegated_op_counts: Counter | None = None
-    """ The number of undelegated occurances of each operator in the graph. """
+    """ The number of undelegated occurrences of each operator in the graph. """
 
     pte_size_bytes: int | None = None
     """ The size of the PTE file in bytes. """