XNNPack --> XNNPACK

backends/xnnpack/operators/op_to_copy.py

@@ -47,7 +47,7 @@ def define_node(
         to_contiguous = bool(memory_format_target == torch.contiguous_format)
         check_or_raise(
             to_channels_last or to_contiguous,
-            "Unsupported Memory Format for XNNPack",
+            "Unsupported Memory Format for XNNPACK",
         )
 
         input_node = get_input_node(node, 0)

backends/xnnpack/passes/__init__.py

@@ -32,7 +32,7 @@ def __init__(
         self, exported_program: ExportedProgram, passes: Optional[List[PassType]] = None
     ) -> None:
         """
-        A helper class to run multiple XNNPack passes on a program
+        A helper class to run multiple XNNPACK passes on a program
         If passes list is empty, all passes in XNNPACK will be run.
         Else only run passes in the list will be run.
         """

backends/xnnpack/passes/channels_last_tagged_reshape_pass.py

@@ -15,13 +15,13 @@
 # TODO(T151254305) use subgraph_rewriter
 class ChannelsLastTaggedReshapePass(XNNPACKPass):
     """
-    This pass is Internal to XNNPack only! It is meant to give a new representation
-    of the edge graph to be consumed by XNNPack Preprocess. All added operators
+    This pass is Internal to XNNPACK only! It is meant to give a new representation
+    of the edge graph to be consumed by XNNPACK Preprocess. All added operators
     will be consumed by delegate and turned to delegate blobs.
 
     Edge IR graph pass to add operator stubs that signal a change in
     memory format from contiguous to channels last. This is to help with
-    XNNPack Delegate to add transpose nodes to change input memory format
+    XNNPACK Delegate to add transpose nodes to change input memory format
     at runtime and run operators in Channels Last Format.
 
     During this pass, nhwc nodes are not converted back to nchw immediately.

backends/xnnpack/runtime/XNNCompiler.cpp

@@ -85,7 +85,7 @@ bool isQuantizedDataType(const xnn_datatype data_type) {
 
 /**
 Converts dims from uint32 to size_t. Takes in a flatbuffer vector
-of uint32_t and returns a std::vector of size_t. XNNPack takes in
+of uint32_t and returns a std::vector of size_t. XNNPACK takes in
 dims of size_t* but tensor shape is serialized in flatbuffer as
 int32_t. As a result, we need to static cast the shapes to size_t
 */
@@ -143,7 +143,7 @@ Error defineTensor(
   // to properly convert the uint32_t* to size_t*
   std::vector<size_t> dims_data = flatbufferDimsToVector(tensor_value->dims());
 
-  // XNNPack Id
+  // XNNPACK Id
   uint32_t id = XNN_INVALID_VALUE_ID;
 
   // Get Pointer to constant data from flatbuffer, if its non-constant

backends/xnnpack/runtime/XNNCompiler.h

@@ -21,7 +21,7 @@ namespace delegate {
 
 class XNNCompiler {
  public:
-  // Takes Flatbuffer Serialized XNNPack Model and rebuilds the xnn-subgraph
+  // Takes Flatbuffer Serialized XNNPACK Model and rebuilds the xnn-subgraph
   // returns an executor object that holds the xnn runtime object which we
   // can then use to set inputs and run inference using the xnn graph.
   __ET_NODISCARD static Error compileModel(

backends/xnnpack/test/test_xnnpack_passes.py

@@ -42,7 +42,7 @@
 from torch.testing import FileCheck
 
 
-class TestXNNPackPasses(unittest.TestCase):
+class TestXNNPACKPasses(unittest.TestCase):
     class TwoOutputs(OpSequencesAddConv2d):
         def __init__(self):
             super().__init__(1, 2)

backends/xnnpack/test/test_xnnpack_utils.py

@@ -126,7 +126,7 @@ def assert_outputs_equal(self, model_output, ref_output):
         """
         Helper testing function that asserts that the model output and the reference output
         are equal with some tolerance. Due to numerical differences between eager mode and
-        the XNNPack's backend, we relax the detal such that absolute tolerance is 1e-3. and
+        the XNNPACK's backend, we relax the detal such that absolute tolerance is 1e-3. and
         relative tolerance is 1e-3.
         """
 

backends/xnnpack/xnnpack_preprocess.py

@@ -225,7 +225,7 @@ def preprocess(
                     )
                 else:
                     raise RuntimeError(
-                        f"For {node}, {node.op}:{node.target.__name__} is not supported in XNNPack Delegate"
+                        f"For {node}, {node.op}:{node.target.__name__} is not supported in XNNPACK Delegate"
                     )
             elif node.op in [
                 "get_attr",
@@ -234,5 +234,5 @@ def preprocess(
             ]:
                 continue
             else:
-                raise RuntimeError(f"{node.op} is not supported in XNNPack")
+                raise RuntimeError(f"{node.op} is not supported in XNNPACK")
         return PreprocessResult(processed_bytes=convert_to_flatbuffer(xnnpack_graph))