solve typo ExecutorTorch to ExecuTorch

Gasoonjia · web-flow · commit ab3100715afd · 2025-09-23T02:36:53.000-07:00
Differential Revision: D83006323 Pull Request resolved: #14491
diff --git a/.ci/scripts/test-cuda-build.sh b/.ci/scripts/test-cuda-build.sh
@@ -9,14 +9,14 @@ set -exu
 
 CUDA_VERSION=${1:-"12.6"}
 
-echo "=== Testing ExecutorTorch CUDA ${CUDA_VERSION} Build ==="
+echo "=== Testing ExecuTorch CUDA ${CUDA_VERSION} Build ==="
 
-# Function to build and test ExecutorTorch with CUDA support
+# Function to build and test ExecuTorch with CUDA support
 test_executorch_cuda_build() {
     local cuda_version=$1
 
-    echo "Building ExecutorTorch with CUDA ${cuda_version} support..."
-    echo "ExecutorTorch will automatically detect CUDA and install appropriate PyTorch wheel"
+    echo "Building ExecuTorch with CUDA ${cuda_version} support..."
+    echo "ExecuTorch will automatically detect CUDA and install appropriate PyTorch wheel"
 
     # Check available resources before starting
     echo "=== System Information ==="
@@ -27,11 +27,11 @@ test_executorch_cuda_build() {
     nvcc --version || echo "nvcc not found"
     nvidia-smi || echo "nvidia-smi not found"
 
-    # Set CMAKE_ARGS to enable CUDA build - ExecutorTorch will handle PyTorch installation automatically
+    # Set CMAKE_ARGS to enable CUDA build - ExecuTorch will handle PyTorch installation automatically
     export CMAKE_ARGS="-DEXECUTORCH_BUILD_CUDA=ON"
 
-    echo "=== Starting ExecutorTorch Installation ==="
-    # Install ExecutorTorch with CUDA support with timeout and error handling
+    echo "=== Starting ExecuTorch Installation ==="
+    # Install ExecuTorch with CUDA support with timeout and error handling
     timeout 5400 ./install_executorch.sh || {
         local exit_code=$?
         echo "ERROR: install_executorch.sh failed with exit code: $exit_code"
@@ -41,15 +41,15 @@ test_executorch_cuda_build() {
         exit $exit_code
     }
 
-    echo "SUCCESS: ExecutorTorch CUDA build completed"
+    echo "SUCCESS: ExecuTorch CUDA build completed"
 
     # Verify the installation
-    echo "=== Verifying ExecutorTorch CUDA Installation ==="
+    echo "=== Verifying ExecuTorch CUDA Installation ==="
 
-    # Test that ExecutorTorch was built successfully
+    # Test that ExecuTorch was built successfully
     python -c "
 import executorch
-print('SUCCESS: ExecutorTorch imported successfully')
+print('SUCCESS: ExecuTorch imported successfully')
 "
 
     # Test CUDA availability and show details
@@ -60,7 +60,7 @@ try:
     print('INFO: CUDA available:', torch.cuda.is_available())
 
     if torch.cuda.is_available():
-        print('SUCCESS: CUDA is available for ExecutorTorch')
+        print('SUCCESS: CUDA is available for ExecuTorch')
         print('INFO: CUDA version:', torch.version.cuda)
         print('INFO: GPU device count:', torch.cuda.device_count())
         print('INFO: Current GPU device:', torch.cuda.current_device())
@@ -74,16 +74,16 @@ try:
         print('SUCCESS: CUDA tensor operation completed on device:', z.device)
         print('INFO: Result tensor shape:', z.shape)
 
-        print('SUCCESS: ExecutorTorch CUDA integration verified')
+        print('SUCCESS: ExecuTorch CUDA integration verified')
     else:
-        print('WARNING: CUDA not detected, but ExecutorTorch built successfully')
+        print('WARNING: CUDA not detected, but ExecuTorch built successfully')
         exit(1)
 except Exception as e:
-    print('ERROR: ExecutorTorch CUDA test failed:', e)
+    print('ERROR: ExecuTorch CUDA test failed:', e)
     exit(1)
 "
 
-    echo "SUCCESS: ExecutorTorch CUDA ${cuda_version} build and verification completed successfully"
+    echo "SUCCESS: ExecuTorch CUDA ${cuda_version} build and verification completed successfully"
 }
 
 # Main execution
diff --git a/backends/cortex_m/test/test_quantize_op_fusion_pass.py b/backends/cortex_m/test/test_quantize_op_fusion_pass.py
@@ -313,7 +313,7 @@ def forward(self, x, y):
         # Apply passes
         transformed_program = self._apply_passes(edge_program)
 
-        # Generate ExecutorTorch program
+        # Generate ExecuTorch program
         executorch_program = transformed_program.to_executorch()
 
         # Verify the program contains the expected fused operator
diff --git a/backends/vulkan/test/utils.py b/backends/vulkan/test/utils.py
@@ -303,13 +303,13 @@ def run_and_check_output(
     Returns:
         bool: True if outputs match within tolerance, False otherwise
     """
-    # Load the ExecutorTorch program
+    # Load the ExecuTorch program
     executorch_module = _load_for_executorch_from_buffer(executorch_program.buffer)
 
     # Flatten inputs for execution
     inputs_flattened, _ = tree_flatten(sample_inputs)
 
-    # Run the ExecutorTorch program
+    # Run the ExecuTorch program
     model_output = executorch_module.run_method("forward", tuple(inputs_flattened))
 
     # Generate reference outputs using the reference model
diff --git a/devtools/scripts/profile_model.sh b/devtools/scripts/profile_model.sh
@@ -7,7 +7,7 @@
 
 #!/bin/bash
 
-# ExecutorTorch Model Profiling Script
+# ExecuTorch Model Profiling Script
 #
 # This script automates the process of building executor_runner with profiling enabled,
 # running model inference with ETDump collection, and generating CSV profiling reports.
diff --git a/docs/source/llm/export-llm.md b/docs/source/llm/export-llm.md
@@ -4,7 +4,7 @@ Instead of needing to manually write code to call torch.export(), use ExecuTorch
 
 ## Prerequisites
 
-The LLM export functionality requires the `pytorch_tokenizers` package. If you encounter a `ModuleNotFoundError: No module named 'pytorch_tokenizers'` error, install it from the ExecutorTorch source code:
+The LLM export functionality requires the `pytorch_tokenizers` package. If you encounter a `ModuleNotFoundError: No module named 'pytorch_tokenizers'` error, install it from the ExecuTorch source code:
 
 ```bash
 pip install -e ./extension/llm/tokenizers/
diff --git a/docs/source/ptd-file-format.md b/docs/source/ptd-file-format.md
@@ -111,7 +111,7 @@ The flatbuffer-encoded metadata follows the headers and contains:
 ### Tensor Layout
 
 If a data segment contains a canonical tensor, it may have associated layout information:
-- **Scalar type**: Data type (float32, int32, etc.) using ExecutorTorch scalar types.
+- **Scalar type**: Data type (float32, int32, etc.) using ExecuTorch scalar types.
 - **Sizes**: Dimensions of the tensor.
 - **Dim order**: Memory layout order specifying how dimensions are arranged in memory.
 
diff --git a/docs/source/using-executorch-faqs.md b/docs/source/using-executorch-faqs.md
@@ -16,7 +16,7 @@ if you are using Ubuntu, or use an equivalent install command.
 
 ### ModuleNotFoundError: No module named 'pytorch_tokenizers'
 
-The `pytorch_tokenizers` package is required for LLM export functionality. Install it from the ExecutorTorch source code:
+The `pytorch_tokenizers` package is required for LLM export functionality. Install it from the ExecuTorch source code:
 ```
 pip install -e ./extension/llm/tokenizers/
 ```
diff --git a/examples/vulkan/export.py b/examples/vulkan/export.py
@@ -210,7 +210,7 @@ def main() -> None:
         else:
             logging.error("✗ Model test FAILED - outputs do not match reference")
             raise RuntimeError(
-                "Model validation failed: ExecutorTorch outputs do not match reference model outputs"
+                "Model validation failed: ExecuTorch outputs do not match reference model outputs"
             )
 
     if args.bundled:
diff --git a/exir/emit/test/test_emit.py b/exir/emit/test/test_emit.py
@@ -1920,7 +1920,7 @@ def forward(self, x):
             program_buffer = et_program.buffer
             et_module = _load_for_executorch_from_buffer(program_buffer)
             for _, (inp, expected) in enumerate(zip(test_inputs, reference_outputs)):
-                # Execute with ExecutorTorch
+                # Execute with ExecuTorch
                 et_output = et_module.forward([inp])
                 et_result = et_output[0]  # Get first output
                 # Compare results
diff --git a/extension/audio/mel_spectrogram.py b/extension/audio/mel_spectrogram.py
@@ -213,7 +213,7 @@ def export_processor(model=None, output_file="whisper_preprocess.pte"):
 
 def main():
     parser = argparse.ArgumentParser(
-        description="Export WhisperAudioProcessor to ExecutorTorch"
+        description="Export WhisperAudioProcessor to ExecuTorch"
     )
     parser.add_argument(
         "--feature_size",
diff --git a/extension/llm/runner/README.md b/extension/llm/runner/README.md
@@ -1,6 +1,6 @@
-# LLM Runner Framework for ExecutorTorch
+# LLM Runner Framework for ExecuTorch
 
-This directory contains the LLM Runner framework for ExecutorTorch, providing high-level C++ APIs for running Large Language Models with both text-only and multimodal capabilities.
+This directory contains the LLM Runner framework for ExecuTorch, providing high-level C++ APIs for running Large Language Models with both text-only and multimodal capabilities.
 
 ## Overview
 
@@ -474,7 +474,7 @@ DeepFusion is another popular model architecture type where a pretrained encoder
 - **Llama 3.2 Vision**: Uses cross-attention layers for vision-text fusion
 - **Other cross-attention based multimodal models**
 
-For DeepFusion support, consider using the model's native inference framework or wait for future ExecutorTorch updates that may include DeepFusion architecture support.
+For DeepFusion support, consider using the model's native inference framework or wait for future ExecuTorch updates that may include DeepFusion architecture support.
 
 ## Building and Integration
 
diff --git a/extension/training/examples/CIFAR/data_utils.py b/extension/training/examples/CIFAR/data_utils.py
@@ -277,7 +277,7 @@ def parse_args() -> argparse.Namespace:
     Parse command line arguments for the CIFAR-10 training script.
 
     This function sets up an argument parser with various configuration options
-    for training a CIFAR-10 model with ExecutorTorch, including data paths,
+    for training a CIFAR-10 model with ExecuTorch, including data paths,
     training hyperparameters, and model save locations.
 
     Returns:
diff --git a/extension/training/examples/CIFAR/export.py b/extension/training/examples/CIFAR/export.py
@@ -27,12 +27,12 @@ def export_model_combined(
     with_external_tensor_data: bool = False,
 ) -> ExecuTorchModule:
     """
-    Export a PyTorch model to an ExecutorTorch module format, optionally with external tensor data.
+    Export a PyTorch model to an ExecuTorch module format, optionally with external tensor data.
 
     This function takes a PyTorch model and sample input/label tensors,
     wraps the model with a loss function, exports it using torch.export,
     applies forward-backward pass optimization, converts it to edge format,
-    and finally to ExecutorTorch format. If with_external_tensor_data is True,
+    and finally to ExecuTorch format. If with_external_tensor_data is True,
     the model will be exported with external constants and mutable weights.
 
     TODO: set dynamic shape for the batch size here.
@@ -45,7 +45,7 @@ def export_model_combined(
             Defaults to False.
 
     Returns:
-        ExecuTorchModule: The exported model in ExecutorTorch format ready for deployment
+        ExecuTorchModule: The exported model in ExecuTorch format ready for deployment
     """
     criterion = torch.nn.CrossEntropyLoss()
     model_with_loss = ModelWithLoss(net, criterion)
@@ -72,17 +72,17 @@ def export_model_combined(
 
 def get_pte_only(net: torch.nn.Module) -> ExecuTorchModule:
     """
-    Generate an ExecutorTorch module from a PyTorch model without external tensor data.
+    Generate an ExecuTorch module from a PyTorch model without external tensor data.
 
     This function retrieves a sample input and label tensor from the test data loader,
-    and uses them to export the given PyTorch model to an ExecutorTorch module format
+    and uses them to export the given PyTorch model to an ExecuTorch module format
     without external constants or mutable weights.
 
     Args:
         net (torch.nn.Module): The PyTorch model to be exported.
 
     Returns:
-        ExecuTorchModule: The exported model in ExecutorTorch format.
+        ExecuTorchModule: The exported model in ExecuTorch format.
     """
     _, test_loader = get_data_loaders()
     # get a sample input and label tensor
@@ -95,17 +95,17 @@ def get_pte_only(net: torch.nn.Module) -> ExecuTorchModule:
 
 def get_pte_with_ptd(net: torch.nn.Module) -> ExecuTorchModule:
     """
-    Generate an ExecutorTorch module from a PyTorch model with external tensor data.
+    Generate an ExecuTorch module from a PyTorch model with external tensor data.
 
     This function retrieves a sample input and label tensor from the test data loader,
-    and uses them to export the given PyTorch model to an ExecutorTorch module format
+    and uses them to export the given PyTorch model to an ExecuTorch module format
     with external constants and mutable weights.
 
     Args:
         net (torch.nn.Module): The PyTorch model to be exported.
 
     Returns:
-        ExecuTorchModule: The exported model in ExecutorTorch format with external tensor data.
+        ExecuTorchModule: The exported model in ExecuTorch format with external tensor data.
     """
     _, test_loader = get_data_loaders()
     # get a sample input and label tensor
@@ -121,7 +121,7 @@ def export_model(
     with_ptd: bool = False,
 ) -> ExecuTorchModule:
     """
-    Export a PyTorch model to ExecutorTorch format, optionally with external tensor data.
+    Export a PyTorch model to ExecuTorch format, optionally with external tensor data.
 
     This function is a high-level wrapper that handles getting sample data and
     calling the appropriate export function based on the with_ptd flag.
@@ -132,7 +132,7 @@ def export_model(
             Defaults to False.
 
     Returns:
-        ExecuTorchModule: The exported model in ExecutorTorch format
+        ExecuTorchModule: The exported model in ExecuTorch format
     """
     _, test_loader = get_data_loaders()
     validation_sample_data = next(iter(test_loader))
@@ -145,13 +145,13 @@ def export_model(
 
 def save_model(ep: ExecuTorchModule, model_path: str) -> None:
     """
-    Save an ExecutorTorch model to a specified file path.
+    Save an ExecuTorch model to a specified file path.
 
-    This function writes the buffer of an ExecutorTorchModule to a
+    This function writes the buffer of an ExecuTorchModule to a
     file in binary format.
 
     Args:
-        ep (ExecuTorchModule): The ExecutorTorch module to be saved.
+        ep (ExecuTorchModule): The ExecuTorch module to be saved.
         model_path (str): The file path where the model will be saved.
     """
     with open(model_path, "wb") as file:
diff --git a/extension/training/examples/CIFAR/main.py b/extension/training/examples/CIFAR/main.py
@@ -28,7 +28,7 @@ def parse_args() -> argparse.Namespace:
     Parse command line arguments for the CIFAR-10 training script.
 
     This function sets up an argument parser with various configuration options
-    for training a CIFAR-10 model with ExecutorTorch, including data paths,
+    for training a CIFAR-10 model with ExecuTorch, including data paths,
     training hyperparameters, and model save locations.
 
     Returns:
diff --git a/extension/training/examples/CIFAR/train_utils.py b/extension/training/examples/CIFAR/train_utils.py

Original file line number	Diff line number	Diff line change
`@@ -7,7 +7,7 @@`
`7`	`7`
`8`	`8`	`#!/bin/bash`
`9`	`9`
`10`		`-# ExecutorTorch Model Profiling Script`
	`10`	`+# ExecuTorch Model Profiling Script`
`11`	`11`	`#`
`12`	`12`	`# This script automates the process of building executor_runner with profiling enabled,`
`13`	`13`	`# running model inference with ETDump collection, and generating CSV profiling reports.`
Original file line number	Diff line number	Diff line change
`@@ -210,7 +210,7 @@ def main() -> None:`
`210`	`210`	`else:`
`211`	`211`	`logging.error("✗ Model test FAILED - outputs do not match reference")`
`212`	`212`	`raise RuntimeError(`
`213`		`- "Model validation failed: ExecutorTorch outputs do not match reference model outputs"`
	`213`	`+ "Model validation failed: ExecuTorch outputs do not match reference model outputs"`
`214`	`214`	`)`
`215`	`215`
`216`	`216`	`if args.bundled:`
Original file line number	Diff line number	Diff line change
`@@ -213,7 +213,7 @@ def export_processor(model=None, output_file="whisper_preprocess.pte"):`
`213`	`213`
`214`	`214`	`def main():`
`215`	`215`	`parser = argparse.ArgumentParser(`
`216`		`- description="Export WhisperAudioProcessor to ExecutorTorch"`
	`216`	`+ description="Export WhisperAudioProcessor to ExecuTorch"`
`217`	`217`	`)`
`218`	`218`	`parser.add_argument(`
`219`	`219`	`"--feature_size",`