[EZ] Replace pytorch-labs with meta-pytorch

.ci/scripts/test_ios_ci.sh

@@ -36,7 +36,7 @@ say() {
 
 say "Cloning the Demo App"
 
-git clone --depth 1 https://github.com/pytorch-labs/executorch-examples.git
+git clone --depth 1 https://github.com/meta-pytorch/executorch-examples.git
 
 say "Installing CoreML Backend Requirements"
 

backends/apple/mps/setup.md

@@ -15,7 +15,7 @@ The MPS backend device maps machine learning computational graphs and primitives
 * [Introduction to ExecuTorch](../../../docs/source/intro-how-it-works.md)
 * [Setting up ExecuTorch](../../../docs/source/getting-started-setup.rst)
 * [Building ExecuTorch with CMake](../../../docs/source/using-executorch-cpp.md#building-with-cmake)
-* [ExecuTorch iOS Demo App](https://github.com/pytorch-labs/executorch-examples/tree/main/mv3/apple/ExecuTorchDemo)
+* [ExecuTorch iOS Demo App](https://github.com/meta-pytorch/executorch-examples/tree/main/mv3/apple/ExecuTorchDemo)
 * [ExecuTorch iOS LLaMA Demo App](../../../docs/source/llm/llama-demo-ios.md)
 :::
 ::::

backends/test/facto/test_facto.py

@@ -8,7 +8,7 @@
 
 #
 # This file contains logic to run generated operator tests using the FACTO
-# library (https://github.com/pytorch-labs/FACTO). To run the tests, first
+# library (https://github.com/meta-pytorch/FACTO). To run the tests, first
 # clone and install FACTO by running pip install . from the FACTO source
 # directory. Then, from the executorch root directory, run the following:
 #

docs/source/backends-mps.md

@@ -15,7 +15,7 @@ The MPS backend device maps machine learning computational graphs and primitives
 * [Introduction to ExecuTorch](intro-how-it-works.md)
 * [Getting Started](getting-started.md)
 * [Building ExecuTorch with CMake](using-executorch-building-from-source.md)
-* [ExecuTorch iOS Demo App](https://github.com/pytorch-labs/executorch-examples/tree/main/mv3/apple/ExecuTorchDemo)
+* [ExecuTorch iOS Demo App](https://github.com/meta-pytorch/executorch-examples/tree/main/mv3/apple/ExecuTorchDemo)
 * [ExecuTorch iOS LLaMA Demo App](llm/llama-demo-ios.md)
 :::
 ::::

docs/source/getting-started.md

@@ -101,7 +101,7 @@ print("Comparing against original PyTorch module")
 print(torch.allclose(output[0], eager_reference_output, rtol=1e-3, atol=1e-5))
 ```
 
-For complete examples of exporting and running the model, please refer to our [examples GitHub repository](https://github.com/pytorch-labs/executorch-examples/tree/main/mv2/python).
+For complete examples of exporting and running the model, please refer to our [examples GitHub repository](https://github.com/meta-pytorch/executorch-examples/tree/main/mv2/python).
 
 Additionally, if you work with Hugging Face models, the [*huggingface/optimum-executorch*](https://github.com/huggingface/optimum-executorch) library simplifies running these models end-to-end with ExecuTorch, using familiar Hugging Face APIs. Visit the repository for specific examples and supported models.
 
@@ -147,7 +147,7 @@ EValue[] output = model.forward(input_evalue);
 float[] scores = output[0].toTensor().getDataAsFloatArray();
 ```
 
-For a full example of running a model on Android, see the [DeepLabV3AndroidDemo](https://github.com/pytorch-labs/executorch-examples/tree/main/dl3/android/DeepLabV3Demo). For more information on Android development, including building from source, a full description of the Java APIs, and information on using ExecuTorch from Android native code, see [Using ExecuTorch on Android](using-executorch-android.md).
+For a full example of running a model on Android, see the [DeepLabV3AndroidDemo](https://github.com/meta-pytorch/executorch-examples/tree/main/dl3/android/DeepLabV3Demo). For more information on Android development, including building from source, a full description of the Java APIs, and information on using ExecuTorch from Android native code, see [Using ExecuTorch on Android](using-executorch-android.md).
 
 ### iOS
 
@@ -214,7 +214,7 @@ if (result.ok()) {
 
 For more information on the C++ APIs, see [Running an ExecuTorch Model Using the Module Extension in C++](extension-module.md) and [Managing Tensor Memory in C++](extension-tensor.md).
 
-For complete examples of building and running C++ application, please refer to our [examples GitHub repository](https://github.com/pytorch-labs/executorch-examples/tree/main/mv2/cpp).
+For complete examples of building and running C++ application, please refer to our [examples GitHub repository](https://github.com/meta-pytorch/executorch-examples/tree/main/mv2/cpp).
 
 <hr/>
 

docs/source/index.md

@@ -41,8 +41,8 @@ ExecuTorch provides support for:
 - [Quantization](quantization-overview)
 - [FAQs](using-executorch-faqs)
 #### Examples
-- [Android Demo Apps](https://github.com/pytorch-labs/executorch-examples/tree/main/dl3/android/DeepLabV3Demo#executorch-android-demo-app)
-- [iOS Demo Apps](https://github.com/pytorch-labs/executorch-examples/tree/main/mv3/apple/ExecuTorchDemo)
+- [Android Demo Apps](https://github.com/meta-pytorch/executorch-examples/tree/main/dl3/android/DeepLabV3Demo#executorch-android-demo-app)
+- [iOS Demo Apps](https://github.com/meta-pytorch/executorch-examples/tree/main/mv3/apple/ExecuTorchDemo)
 - [Hugging Face Models](https://github.com/huggingface/optimum-executorch/blob/main/README.md)
 #### Backends
 - [Overview](backends-overview)
@@ -147,7 +147,7 @@ using-executorch-faqs
 :hidden:
 
 Building an ExecuTorch Android Demo App <https://github.com/pytorch-labs/executorch-examples/tree/main/dl3/android/DeepLabV3Demo#executorch-android-demo-app>
-Building an ExecuTorch iOS Demo App <https://github.com/pytorch-labs/executorch-examples/tree/main/mv3/apple/ExecuTorchDemo>
+Building an ExecuTorch iOS Demo App <https://github.com/meta-pytorch/executorch-examples/tree/main/mv3/apple/ExecuTorchDemo>
 tutorial-arm.md
 ```
 

docs/source/llm/run-with-c-plus-plus.md

@@ -251,7 +251,7 @@ Supported tokenizer formats include:
 3. **TikToken**: BPE tokenizers
 4. **Llama2c**: BPE tokenizers in the Llama2.c format
 
-For custom tokenizers, you can find implementations in the [pytorch-labs/tokenizers](https://github.com/pytorch-labs/tokenizers) repository.
+For custom tokenizers, you can find implementations in the [meta-pytorch/tokenizers](https://github.com/meta-pytorch/tokenizers) repository.
 
 
 ## Other APIs

docs/source/using-executorch-android.md

@@ -201,7 +201,7 @@ adb push extension/module/test/resources/add.pte /data/local/tmp/
 
 This example loads an ExecuTorch module, prepares input data, runs inference, and processes the output data.
 
-Please use [DeepLabV3AndroidDemo](https://github.com/pytorch-labs/executorch-examples/tree/main/dl3/android/DeepLabV3Demo)
+Please use [DeepLabV3AndroidDemo](https://github.com/meta-pytorch/executorch-examples/tree/main/dl3/android/DeepLabV3Demo)
 and [LlamaDemo](https://github.com/pytorch/executorch/tree/main/examples/demo-apps/android/LlamaDemo) for the code examples
 using ExecuTorch AAR package.
 

docs/source/using-executorch-building-from-source.md

@@ -392,7 +392,7 @@ See backend-specific documentation for more details.
 2. Copy over the generated `.xcframework` bundles to your Xcode project, link them against
 your targets and don't forget to add an extra linker flag `-all_load`.
 
-Check out the [iOS Demo App](https://github.com/pytorch-labs/executorch-examples/tree/main/mv3/apple/ExecuTorchDemo) tutorial for more info.
+Check out the [iOS Demo App](https://github.com/meta-pytorch/executorch-examples/tree/main/mv3/apple/ExecuTorchDemo) tutorial for more info.
 
 <hr/>
 
@@ -499,5 +499,5 @@ Output 0: tensor(sizes=[1, 1000], [
 ## Next Steps
 
 * [Selective Build](kernel-library-selective-build.md) to link only kernels used by the program. This can provide significant binary size savings.
-* Tutorials on building [Android](https://github.com/pytorch-labs/executorch-examples/tree/main/dl3/android/DeepLabV3Demo#executorch-android-demo-app) and [iOS](https://github.com/pytorch-labs/executorch-examples/tree/main/mv3/apple/ExecuTorchDemo) demo apps.
+* Tutorials on building [Android](https://github.com/meta-pytorch/executorch-examples/tree/main/dl3/android/DeepLabV3Demo#executorch-android-demo-app) and [iOS](https://github.com/meta-pytorch/executorch-examples/tree/main/mv3/apple/ExecuTorchDemo) demo apps.
 * Tutorials on deploying applications to embedded devices such as [ARM Cortex-M/Ethos-U](backends-arm-ethos-u.md) and [XTensa HiFi DSP](backends-cadence.md).

docs/source/using-executorch-cpp.md

@@ -32,7 +32,7 @@ if (result.ok()) {
 
 For more information on the Module class, see [Running an ExecuTorch Model Using the Module Extension in C++](extension-module.md). For information on high-level tensor APIs, see [Managing Tensor Memory in C++](extension-tensor.md).
 
-For complete examples of building and running a C++ application using the Module API, refer to our [examples GitHub repository](https://github.com/pytorch-labs/executorch-examples/tree/main/mv2/cpp).
+For complete examples of building and running a C++ application using the Module API, refer to our [examples GitHub repository](https://github.com/meta-pytorch/executorch-examples/tree/main/mv2/cpp).
 
 ## Low-Level APIs
 

docs/source/using-executorch-export.md

@@ -194,7 +194,7 @@ method = program.load_method("forward")
 outputs = method.execute([input_tensor])
 ```
 
-Pybindings currently does not support loading program and data. To run a model with PTE and PTD components, please use the [Extension Module](extension-module.md). There is also an E2E demo in [executorch-examples](https://github.com/pytorch-labs/executorch-examples/tree/main/program-data-separation).
+Pybindings currently does not support loading program and data. To run a model with PTE and PTD components, please use the [Extension Module](extension-module.md). There is also an E2E demo in [executorch-examples](https://github.com/meta-pytorch/executorch-examples/tree/main/program-data-separation).
 
 For more information, see [Runtime API Reference](executorch-runtime-api-reference.md).
 

examples/models/llama/experimental/generate.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.
 
-# Adapted from gpt-fast: https://github.com/pytorch-labs/gpt-fast/blob/main/generate.py
+# Adapted from gpt-fast: https://github.com/meta-pytorch/gpt-fast/blob/main/generate.py
 import argparse
 
 from typing import Optional, Tuple

scripts/test_ios.sh

@@ -54,7 +54,7 @@ say "Installing Requirements"
 
 say "Cloning the Demo App"
 
-git clone --depth 1 https://github.com/pytorch-labs/executorch-examples.git
+git clone --depth 1 https://github.com/meta-pytorch/executorch-examples.git
 
 say "Installing CoreML Backend Requirements"