Replace Executorch with ExecuTorch, Part 5/N (#469)

Summary:
Pull Request resolved: #469

In python files comments

Reviewed By: cccclai

Differential Revision: D49566736

fbshipit-source-id: 2094ff15f63484b29429013636651ba24bfc4738

Author: mergennachin

backends/xnnpack/test/tester/tester.py

@@ -359,7 +359,7 @@ def run_method(self, method="forward"):
         delegated_module = self.get_artifact(self._stage_name(Partition))
         self.reference_output = delegated_module(*self.inputs)
 
-        # Executorch
+        # ExecuTorch
         inputs_flattened, _ = tree_flatten(self.inputs)
         serialized_buffer = self.get_artifact(self._stage_name(Serialize))
         executorch_module = _load_for_executorch_from_buffer(serialized_buffer)

bundled_program/schema.py

@@ -84,7 +84,7 @@ class BundledExecutionPlanTest:
 
 @dataclass
 class BundledProgram:
-    """Executorch program bunlded with data for verification."""
+    """ExecuTorch program bunlded with data for verification."""
 
     # Schema version.
     version: int
@@ -94,5 +94,5 @@ class BundledProgram:
     # Its length should be equal to the number of execution plans in program.
     execution_plan_tests: List[BundledExecutionPlanTest]
 
-    # The binary data of a serialized Executorch program.
+    # The binary data of a serialized ExecuTorch program.
     program: bytes

codegen/tools/gen_all_oplist.py

@@ -16,8 +16,8 @@
 def main(argv: List[Any]) -> None:
     """This binary is a wrapper for //xplat/caffe2/tools/code_analyzer/gen_oplist.py.
     This is needed because we intend to error out for the case where `model_file_list_path`
-    is empty or invalid, so that the Executorch build will fail when no selective build target
-    is provided as a dependency to Executorch build.
+    is empty or invalid, so that the ExecuTorch build will fail when no selective build target
+    is provided as a dependency to ExecuTorch build.
     """
     parser = argparse.ArgumentParser(description="Generate operator lists")
     parser.add_argument(
@@ -66,7 +66,7 @@ def main(argv: List[Any]) -> None:
             model_file_names = model_list_file.read().split()
             assert (
                 len(model_file_names) > 0
-            ), "BUCK was not able to find any `et_operator_library` in the dependency graph of the current Executorch "
+            ), "BUCK was not able to find any `et_operator_library` in the dependency graph of the current ExecuTorch "
             "build. Please refer to Selective Build wiki page to add at least one."
     gen_oplist.main(argv)
 

codegen/tools/gen_ops_def.py

@@ -63,7 +63,7 @@ def dump_yaml(model_file: str, output_file: str) -> None:
 
 
 def main(args: List[Any]) -> None:
-    """This binary generates a template functions.yaml which will be consumed by Executorch codegen.
+    """This binary generates a template functions.yaml which will be consumed by ExecuTorch codegen.
     It reads the model file, deserialize it and dumps all the operators into a new functions.yaml.
     The generated file contains placeholder kernels, it needs to be updated with proper kernel names.
     """

docs/source_cpp/conf.py

@@ -90,7 +90,7 @@
     ############################################################################
     # Main library page layout example configuration.                          #
     ############################################################################
-    "afterTitleDescription": "Welcome to the Executorch's documentation.",
+    "afterTitleDescription": "Welcome to the ExecuTorch's documentation.",
 }
 
 # Tell sphinx what the primary language being documented is.

exir/backend/test/demos/rpc/test_rpc.py

@@ -24,7 +24,7 @@
 
 """
 Server can be an App Call, and will send delegate to client backend like DSP,
-DSP will reeive the rpc call, calls the Executorch instance (like on DSP),
+DSP will reeive the rpc call, calls the ExecuTorch instance (like on DSP),
 and return the result.
 
 
@@ -65,12 +65,12 @@
 
 MCU
 ——
-1. MCU instantiate Executorch instance with DSPBackend
+1. MCU instantiate ExecuTorch instance with DSPBackend
 2. In DSPBackend init/execute, it'll invoke the implemented RPC calls on DSP
 
 DSP
 ——
-3. DSP receives the RPC call and construct the Executorch instance on the DSP
+3. DSP receives the RPC call and construct the ExecuTorch instance on the DSP
 4. When dsp executor runs, it can call any delegate (e.g. Accelerator) as needed.
 
 There’ll negligible overhead in binary size on the MCU, as the executor size is small.

exir/dialects/backend/_ops.py

@@ -26,7 +26,7 @@ class BackendOpOverload(EdgeOpOverload):
     2. backend op is stateless while delegation doesn't have to
     3. backend op stays in executor standard runtime but delegation doesn't have to
 
-    Examples for backend ops including fused ops for a specific backend, Executorch prim ops to handle symbolic shape.
+    Examples for backend ops including fused ops for a specific backend, ExecuTorch prim ops to handle symbolic shape.
 
     Note that the assumption here is that the backend op and the original callable / equivalent callable is 1 - 1 mapping.
 

exir/dialects/edge/_ops.py

@@ -230,7 +230,7 @@ def __init__(
         else:
             # Not get the info from edge.yaml
             # Create a dtype constraint for this operator that allows any dtype
-            # combinations as long as any dtype is legal in Executorch.
+            # combinations as long as any dtype is legal in ExecuTorch.
             type_alias = {
                 f"T{idx}": list(regular_tensor_str_to_dtypes.values())
                 for idx in range(len(all_tensor_io_names))

exir/emit/_emit_program.py

@@ -106,7 +106,7 @@ class EmitterOutput:
     a mapping of instruction ids to debug handles.
     """
 
-    # The Executorch program
+    # The ExecuTorch program
     program: Program
 
     # This dictionary maps the instruction ids to their corresponding

exir/emit/_emitter.py

@@ -5,7 +5,7 @@
 # LICENSE file in the root directory of this source tree.
 
 """Takes an ExportedArtifact, or a collection of ExportedArtifacts, in execution dialect, and turns
-them into a single Executorch Program.
+them into a single ExecuTorch Program.
 
 The provided ExportedArtifact's graph modules are in execution dialect and the emitter parses and
 converts them into executorch instructions. The emitter walks the provided graphs and as it
@@ -1015,7 +1015,7 @@ def _emit_delegate(
         return delegate_ret
 
     def _get_operator(self, name: str, overload: str) -> Tuple[int, Operator]:
-        """Given a fully qualified name, lookups the operator in the Executorch Program, or adds it
+        """Given a fully qualified name, lookups the operator in the ExecuTorch Program, or adds it
         if it is not already present"""
         key = (name, overload)
         op_index = self.emitter_state.operator_cache.get(key)

exir/passes/memory_format_ops_pass.py

@@ -22,7 +22,7 @@
 class MemoryFormatOpsPass(ExportPass):
     """
     This pass replaces ops which takes torch.memory_format as an argument with
-    'equivalent' op which takes dim_order. This is towards the larger Executorch
+    'equivalent' op which takes dim_order. This is towards the larger ExecuTorch
     goal to move away from torch.memory_format. There is a 1:1 mapping between
     the aten op and the new edge dialect dim_order op.
     """

exir/passes/sym_to_tensor_pass.py

@@ -17,7 +17,7 @@
 class SymToTensorPass(ExportPass):
     """
     The dispatcher implicitly converts SymInt/SymFloats to tensors, but
-    sometimes this doesn't comply with the operator's schema which Executorch
+    sometimes this doesn't comply with the operator's schema which ExecuTorch
     heavily relies on. So this pass inserts a
     torch.ops.aten.scalar_tensor.default operator before these SymInts are used
     so that it matches the schema of the operator.

exir/program/_program.py

@@ -626,12 +626,12 @@ def to_edge(
 class EdgeProgramManager:
     """
     Package of one or more :class:'ExportedPrograms' in Edge dialect. Designed to simplify
-    lowering to Executorch.
+    lowering to ExecuTorch.
 
     Allows easy applications of transforms across a collection of exported programs
     including the delegation of subgraphs.
 
-    Manages the second link in the lowering chain of ATen -> Edge -> Executorch.
+    Manages the second link in the lowering chain of ATen -> Edge -> ExecuTorch.
     """
 
     # TODO(T163717152): Link to Edge dialect docs here ^.
@@ -761,11 +761,11 @@ def to_executorch(
         self, config: Optional[ExecutorchBackendConfig] = None
     ) -> "ExecutorchProgramManager":
         """
-        Transforms the program to the Executorch backend.
+        Transforms the program to the ExecuTorch backend.
 
         Args:
             config: An optional argument used to provide greater control over
-            the transformation to the Executorch backend.
+            the transformation to the ExecuTorch backend.
 
         Returns:
             ExecutorchProgramManager: A manager representing the state of the EdgeProgramManager
@@ -786,13 +786,13 @@ def to_executorch(
 class ExecutorchProgramManager:
     """
     Package of one or more :class:'ExportedPrograms' in Execution dialect. Designed to simplify
-    lowering to Executorch.
+    lowering to ExecuTorch.
 
     When the ExecutorchProgramManager is constructed the ExportedPrograms in execution dialect
     are used to form the executorch binary (in a process called emission) and then serialized
     to a buffer.
 
-    Manages the final link in the lowering chain of ATen -> Edge -> Executorch.
+    Manages the final link in the lowering chain of ATen -> Edge -> ExecuTorch.
     """
 
     # TODO(T163717152): Link to Execution dialect docs here ^.
@@ -864,7 +864,7 @@ def exported_program(self, method_name: str = "forward") -> ExportedProgram:
 
     def dump_executorch_program(self, verbose: bool = False) -> None:
         """
-        Prints the Executorch binary in a human readable format.
+        Prints the ExecuTorch binary in a human readable format.
 
         Args:
             verbose (bool):
@@ -884,6 +884,6 @@ def debug_handle_map(self) -> Dict[int, Union[int, List[int]]]:
     @property
     def buffer(self) -> bytes:
         """
-        Returns a buffer containing the serialized Executorch binary.
+        Returns a buffer containing the serialized ExecuTorch binary.
         """
         return self._buffer

exir/serde/serialize.py

@@ -683,7 +683,7 @@ def deserialize(
 ) -> exir.ExportedProgram:
     exported_program_str = exported_program_bytes.decode("utf-8")
     exported_program_dict = json.loads(exported_program_str)
-    # Executorch tests save exported program on disk when we haven't
+    # ExecuTorch tests save exported program on disk when we haven't
     # provided any compatibility guarantees.
     if "module_call_graph" not in exported_program_dict["graph_module"]:
         exported_program_dict["graph_module"]["module_call_graph"] = []

extension/pybindings/test/make_test.py

@@ -60,7 +60,7 @@ def create_program(
         ) -> Tuple[Program, Tuple[Any, ...]]:
             """Returns an executorch program based on ModuleAdd, along with inputs."""
 
-            # Trace the test module and create a serialized Executorch program.
+            # Trace the test module and create a serialized ExecuTorch program.
             inputs = (torch.ones(2, 2), torch.ones(2, 2))
             input_map = {}
             for method in eager_module.get_methods_to_export():
@@ -74,14 +74,14 @@ def create_program(
                 .to_executorch()
             )
 
-            # Create the Executorch program from the graph.
+            # Create the ExecuTorch program from the graph.
             pretty_print(exec_prog.program)
             return (exec_prog, inputs)
 
         ######### TEST CASES #########
 
         def test_e2e(tester):
-            # Create an Executorch program from ModuleAdd.
+            # Create an ExecuTorch program from ModuleAdd.
             exported_program, inputs = create_program(ModuleAdd())
 
             # Use pybindings to load and execute the program.

scripts/file_size_compare.py

@@ -36,8 +36,8 @@
 TTY_RESET = "\033[0m"
 
 # Error message printed if size is exceeded.
-SIZE_ERROR_MESSAGE = """This diff is increasing the binary size of Executorch (the PyTorch Edge model executor) by a large amount.
-Executorch has strict size requirements due to its embedded use case. Please follow these steps:
+SIZE_ERROR_MESSAGE = """This diff is increasing the binary size of ExecuTorch (the PyTorch Edge model executor) by a large amount.
+ExecuTorch has strict size requirements due to its embedded use case. Please follow these steps:
 1. Check the output of the two steps (Build ... with the base commit/diff version) and compare their executable section sizes.
 2. Contact a member of #pytorch_edge_portability so we can better help you.
 """

sdk/edir/et_schema.py

@@ -5,7 +5,7 @@
 # LICENSE file in the root directory of this source tree.
 
 """
-Intermediate Representation of Executorch Concepts in Productivity SDK
+Intermediate Representation of ExecuTorch Concepts in Productivity SDK
 """
 
 from __future__ import annotations
@@ -74,7 +74,7 @@ class PROFILE_STAT_HEADER(Enum):
     MAX_MS = "Max (ms)"
 
 
-# A single inference run extracted from Executorch SDK ET Dump
+# A single inference run extracted from ExecuTorch SDK ET Dump
 @dataclass
 class InferenceRun:
     # Maps from a node identifier (e.g. Debug Handle) to its associated metadata
@@ -733,7 +733,7 @@ def _extract_output_shapes(metadata: Dict[str, Any]) -> Optional[List[List[int]]
 
 # Representation of an Exported ExecuTorch Program as an OperatorGraph
 class ExportedETOperatorGraph(OperatorGraphWithStats):
-    # Given the path to an Executorch Program, parse it into an OperatorGraph
+    # Given the path to an ExecuTorch Program, parse it into an OperatorGraph
     # `include_constant_nodes`: Whether to include constant nodes in output graph
     @staticmethod
     def gen_operator_graph_from_path(
@@ -773,7 +773,7 @@ def _resolve_kernel_args(args, values):
 
         return unpacked_args, out_indices
 
-    # Given an Executorch Program, parse it into an OperatorGraph
+    # Given an ExecuTorch Program, parse it into an OperatorGraph
     # `include_constant_nodes`: Whether to include constant nodes in output graph
     @staticmethod
     def gen_operator_graph(

test/end2end/exported_module.py

@@ -4,7 +4,7 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-"""Test helper for exporting an nn.Module to an Executorch program."""
+"""Test helper for exporting an nn.Module to an ExecuTorch program."""
 
 import functools
 import inspect

test/models/export_delegated_program.py

@@ -20,7 +20,7 @@
 )
 from torch import nn
 
-"""Traces and exports delegated nn.Modules to Executorch .pte program files.
+"""Traces and exports delegated nn.Modules to ExecuTorch .pte program files.
 
 Creates two versions of each file:
 - <module-name>.pte: Delegate data stored in segments outside of the flatbuffer data.
@@ -133,7 +133,7 @@ def main() -> None:
     # when possible.
     parser = argparse.ArgumentParser(
         prog="export_delegated_program",
-        description="Exports delegated nn.Module models to Executorch .pte files",
+        description="Exports delegated nn.Module models to ExecuTorch .pte files",
     )
     parser.add_argument(
         "--modules",

test/models/export_program.py

@@ -17,7 +17,7 @@
 from torch import nn
 from torch._export import dynamic_dim
 
-"""Traces and exports nn.Modules to Executorch .pte program files.
+"""Traces and exports nn.Modules to ExecuTorch .pte program files.
 
 This tool mainly exists to export programs for C++ tests, but can also
 be used to export models manually.
@@ -177,7 +177,7 @@ def main() -> None:
     # when possible.
     parser = argparse.ArgumentParser(
         prog="export_program",
-        description="Exports nn.Module models to Executorch .pte files",
+        description="Exports nn.Module models to ExecuTorch .pte files",
     )
     parser.add_argument(
         "--modules",