v0.4.0

We're excited to announce the Beta release of ExecuTorch! This release includes many new features, improvements, and bug fixes.

API Stability and Runtime Compatibility Guarantees

Starting with this release, ExecuTorch's Python and C++ APIs will follow the API Lifecycle and Deprecation Policy, and the .pte file format will comply with the Runtime Compatibility Policy.

New Features

• Introduced exir.to_edge_transform_and_lower API for combining the functionality of to_edge, transform, and to_backend
  • Allows users to prevent specific op decompositions while lowering to backends that implement those ops
• Increased operator coverage for ExecuTorch’s portable library
• Added new experimental APIs:
  • LLM runner C++ APIs such as prefill_image(), prefill_prompt(), and generate_from_pos() with multimodal support
  • executorch.runtime python module for loading .pte files and running them with the underlying C++ runtime
• Added a new Tensor API to bundle the dynamic data and metadata within a Tensor object.
• Improved the Module API to share an ExecuTorch Program between several Modules and provide APIs to set inputs/outputs before execution
• Added find_package(executorch) for projects to easily link to ExecuTorch’s prebuilt library in CMake
• Introduced reproducible benchmarking infrastructure to measure, debug, and track performance, enabling on-demand and automated nightly benchmarking of models and backend delegates on modern smartphones
  • New benchmarking apps for Apple platforms to measure model performance on iOS/macOS and Android
• Added support for TikToken v5 vision tokenizer
• Improved parallelization for LLM prefill
• Added experimental capabilities for on-device training, along with an example prototype for LLM finetuning

Supported Models

• Added support for the following models:
  • LLaMA 3 models, including LLaMA 3 8B, 3.1 8B, and 3.2 1B/3B
  • [MultiModal] LLaVA (Large Language and Vision Assistant)
  • Phi-3-mini
  • Gemma 2B
• Added LLaMA 3, 3.1, and 3.2 to the Android Llama Demo app
• Added LLaVa multimodal support to the iOS iLLaMA and Android LLaMa Demo apps

Hardware Acceleration

• Delegate framework
  • Allow delegate to consume buffer mutations
• [New] MediaTek
  • Added support for a new MediaTek backend
  • Enabled LLaMa 3 acceleration on MediaTek’s NPU
  • Added export scripts and runners for 8 different OSS models
  • Implemented intermediate tensor logging
• CoreML
  • Added LLaMA support for in-place KV cache, fused SDPA kernel, and 4-bit per-block quantization
  • Added primitive support for dynamic shapes to work without torch._check
  • Expanded operator coverage to over 100 ops
  • Enabled stateful runtime execution
  • Implemented Intermediate tensor logging
• MPS
  • Added support for 4-bit linear kernels (iOS 18 only)
  • Enabled LLaMa 2 7B and LLaMa 3 8B
• Qualcomm (Qualcomm Neural Network)
  • Enabled LLaMa 3 8B with 4-bit linear kernel, SpinQuant, fused RMSNorm from QNN 2.25, and model sharding
  • Added support for the AI Hub model format
  • Implemented Intermediate tensor logging
• ARM
  • Added new operators
    • add, addmm, avg_pool2d, batch_norm, bmm, clone/cat, conv2d improvements, div, ecp, full, hardtanh, logaddm, mean_dim, mul, permute, relu, sigmoid, slice, softmax, sub, unsqueeze, view
  • Added/enabled lowering passes to improve network compatibility
  • Improved quantization support
    • Made quantization accuracy improvements for all models
    • Added quantization coverage for all available ops
  • Improved channel last support by reducing overhead and number of conversions
  • Added performance measurements on Corstone-300 FVP for Ethos-U55
  • Moved to new compilation flow in Vela to provide better performance and compatibility
  • Improved code documentation for third party contributors
• XNNPACK
  • Enhanced XNNPACK backend performance
  • Added support for new LLaMa models and other quantized LLMs on Android/iOS devices, including LLaMA 3 8B, 3.1 8B, and 3.2 1B/3B
  • Introduced major partitioner refactor to improve UX and stability
  • Improved model coverage to ensure better stability
• Vulkan
  • Made latency optimizations for Vulkan convolution and matrix multiplication compute shaders through various algorithmic improvements
  • Added quantizer for 8 bit weight-only quantization
  • Expanded operator coverage to 63 ops
  • Added 4-bit and 8-bit weight quantized linear kernels
  • Added support for view tensors in the Vulkan graph runtime, allowing for no-copy permutes, squeeze/unsqueeze etc.
  • Added support for symbolic integers in the Vulkan graph runtime
  • Integration with ExecuTorch SDK to track compute shader latencies
• Cadence
  • Added an x86 executor to sanity check and numerically verify models locally
  • Added multiple supported e2e models such as wav2vec2
  • Integrated low-level optimizations resulting in 10x+ performance improvements
  • Migrated more graph-level optimizations to the open source repository
  • Enabled more types in the CadenceQuantizer, and moved to int8 default for better performance

Developer Experience

• Introduced API to enable intermediate output logging in delegates
• Improved CMake build system and reduced reliance on Buck2
• Added override options for fallback PAL implementations through CMake flag (-DEXECUTORCH_PAL_DEFAULT)
• Changes to DimOrder (please see this issue for current progress and next steps)

Bug Fixes

• Fixed various issues related to quantization, tensor operations, and backend integrations
• Resolved memory allocation and management issues
• Fixed compatibility issues with different Python and dependency versions
• Fixed bundled program and plan_execute in pybindings

Breaking Changes

• Updated the minimum C++ version to C++17 for the core runtime
• Removed all C++ headers under //executorch/util (see extension/runner_util/inputs.h for a PrepareInputTensors replacement)
  • Users are expected now to provide their own read_file.h functionality
• Renamed instances of sdk to devtools for file names, function names, and CMake options

Deprecation

• Added new annotations and decorators for API lifecycle and deprecation management
  • New ET_EXPERIMENTAL annotation indicates C++ APIs that may change without notice
  • New @deprecated and @experimental python decorators indicate non-stable APIs
• Names under the torch:: namespace are deprecated in favor of names under the executorch:: namespace, please migrate code to use the new namespace and avoid adding new references to the torch:: namespace
• Constant buffers are no longer stored inside the .pte flatbuffer and are stored in a segment attached to the .pte moving forward
• All C++ macros beginning with underscores such as __ET_UNUSED are deprecated in favor of unprefixed names such as ET_UNUSED
• capture_pre_autograd_graph() is deprecated in lieu of the new torch.export_for_training() API

Thanks to the following open source contributors for their work on this release!

denisVieriu97, Erik-Lundell, Esteb37, SaoirseARM, benkli01, bigfootjon, chuntl, cymbalrush, derekxu, dulinriley, freddan80, haowhsu-quic, namanahuja, neuropilot-captain, oscarandersson8218, per, python3kgae, r-barnes, robell, salykova, shewu-quic, tom-arm, winskuo-quic, zingo

Full Changelog: v0.3.0...v0.4.0

v0.3.0

Key Updates

• Continued feature work and improvements on operator kernels and backends, including Apple (Core ML, MPS), Arm, Cadence, Qualcomm, Vulkan, XNNPACK.
• Various improvements to the CMake system and the Android and iOS artifacts build.
• Various improvements on the AOT export path; e.g., eliminating view_copy, and adding Llama quantizer.
• Introduce dim order for ExecuTorch. With dim order, tensors within a single graph can support multiple memory formats.
• Introduce new API to register custom ExecuTorch kernels into ATen.
• Binary size reductions to the portable library via compile-time optimizations.
• Consolidate tokenizer interface for LLM models.
• Add a colab notebook to show Llama E2E flow in ExecuTorch.
• Improved C++ test and Pytest coverage.
• Deprecate exir.capture in favor of torch.export.
• Update versions for flatbuffers (v24.3.25), flatcc (896db54) and coremltools (8.0b1).

Kudos to the following first time contributors

Andres Suarez, Ben Rogers, Carlos Fernandez, Catherine Lee, Chakri Uddaraju, Chris Hopman, Chris Thompson, David Lin, Di Xu, Edward Yang, Eric J Nguyen, Erik Lundell, Hardik Sharma, Ignacio Guridi, Jakob Degen, Kaichen Liu, Lunwen He, Masahiro Hiramori, Naman Ahuja, Nathanael See, Nikita Shulga, Richard Zou, Sicheng Jia, Stephen Bochinski, Val Tarasyuk, Will Li, Yanghan Wang, Yipu Miao, Yujie Hui, Yupeng Zhang, Zingo Andersen, Zonglin Peng, salykova

Full Changelog

Please see v0.2.1-rc5...v0.3.0-rc6 for all 735 commits since the previous release.

v0.2.1

This release is meant to fix the following documentations and bugs.

PyPi package can be downloaded via https://pypi.org/project/executorch/0.2.1/

Docs

• Fix the broken cmake commands in sdk integration tutorial (#3432)
• Simplify SDK tutorial by moving cmake commands to a script (#3492)
• Fix Docs on how to perform cross compilation for Android and iOS (#3722)
• Fix top-level documentation ordering (#3738)
• Change docs to use CMake instead of Buck (#3778)
• Improve Docs for fresh M1 Setup (#3791)
• Update README.md to double check python environment (#3806)
• Improve Docs on Module Extension (#3807)
• Update top level README.md (#3817)
• Add documentation for Android prebuilt workflow (#3841)
• Update Quant Overview Documentation (#3857)
• Update docs for when flatbuffer is not found (#3862)
• Update Llama README for Llama3 (#3871)
• Fix docs for tokenizer.model in Llama2 Readme (#3881)
• Add colab/jupyter notebook in getting started page (#3885)

Bug Fixes

• Fix the sdk_example_runner.sh script (#3431)
• Fix op_split_with_sizes_copy to support dynamic shape (#3175)
• Fix the temp allocator for backend (#3506)
• Fix Buck 2 Error on running ./install_requirements.sh (#3512)
• Fix a .pte export issue in some environments (#3813)
• Fix python dispatcher so that expand_copy and view_copy will go through the correct meta kernels (#3809)
• Fix bug in examples/demos to build prebuilt android package (#3820)
• Update Ethos-u software to version 24.05 (#3852)

Release tracker #3409 contains all relevant pull requests related to this release as well as links to related issues.

v0.2.0

Full Changelog: v0.1.0...v0.2.0

Foundational Improvements

Large generative AI model support

• Support generative AI models like Meta Llama 3 8B and Llama 2 7B on Android and iOS phones
• 4-bit group-wise weight quantization
• XNNPACK Delegate and kernels for best performance on CPU (WIP on other backends)
• KV Cache support through PyTorch mutable buffer
• Custom ops for SDPA, with kv cache and multi-query attention
• ExecuTorch Runtime + tokenizer and sampler

Core ExecuTorch improvements

• Simplified setup experience
• Support for PyTorch mutable buffers
• Support for multi-gigabyte models
• Constant data moved to its own .pte segment for more efficient serialization
• Better kernel coverage in portable lib, XNNPACK, ARM, CoreML, MPS and HTP delegates.
• SDK - better profiling and debugging within delegates
• API improvements/simplification
• Dozens of fixes to fuzzer-identified .pte file-parsing issues
• Vulkan delegate for mobile GPU
• Data-type based selective build for optimizing binary size
• Compatibility with torchtune
• More models supported across different backends
• Python code now available as the "executorch" pip package in PyPI

Hardware Acceleration Improvements

Arm

• Significant boost in operator test coverage thought the use of TOSA reference model, as well as improved CI coverage
• Added support for quantization with the ArmQuantizer
• Added support for MobileNet v2 TOSA generation
• Working towards MobileNet v2 execution on Ethos-U
• Added support for multiple new operators on Ethos-U compiler
• Added NCHW/NHWC conversion for Ethos-U targets until NHWC is supported by ExecuTorch
• Arm backend example now works on MacOS

Apple Core ML

• [SDK] ExecuTorch SDK Integration for better debugging and profiling experience
• [SDK] ExecuTorch SDK integration using the new MLComputePlan API released in iOS 17.4 and macOS 14.4
• [SDK] A model lowered to the CoreML backend can be profiled using the ExecuTorch Inspector without additional setup
• [SDK] Profiling surfaces Core ML specific information for each operation in the model, including: supported compute devices, preferred compute device, and estimated cost for each compute device.
• [SDK] The Core ML delegate backend also supports logging intermediate tensors for model debugging.
• [Partitioner] Enables a developer to lower a model even if Core ML doesn’t support all the operations in the model.
• [Partitioner] A developer will now be able to specify the operations that should be skipped by the Core ML backend when lowering the model.
• [Quantizer] Leverages PyTorch 2.0 export-based quantization APIs.
• [Quantizer] Encodes specific quantization rules in order to optimize the model for execution on Apple silicon
• [Quantizer] Integrated with ExecuTorch Core ML delegate conversion pipeline

Apple MPS

• Support for over 100 ops (parity with PyTorch MPS backend supported ops)
• Support for iOS/iPadOS>=14.4+ / macOS>=12.4
• Support for MPSPartitioner
• Support for following dtypes: fp16, fp32, bfloat16, int8, int16, int32, int64, uint8, bool
• Support for profiling (etrecord, etdump) through Inspector API
• Full unit testing coverage for AOT and runtime for all supported operators
• Enabled storiesllama (floating point) on MPS

Qualcomm

• Support for Snapdragon 8 Gen 3 is added.
• Enabled on-device compilation. (aka QNN online-prepare)
• Enabled 4-bit and 16-bit quantization.
• Qualcomm AI Studio QNN Profiling is integrated into ExecuTorch flow.
• Enabled storiesllama on HTP-fp16 (but this effort is mainly thanks to Chen Lai from Meta being the main contributor for this)
• Added more operators support
• Additional models validated since v0.1.0:
  • FbNet
  • W2l (Wav2LetterModel)
  • SSD300_VGG16
  • ViT
  • Quantized MobileBert (Quantized MobileBert contribution was submitted prior to v0.1.0 timeline, but merged afterwards)

Cadence HiFi

• Expanded operator support for Cadence HiFi targets
• Added first small model (RNNT-emformer predictor) to the Cadence HiFi examples

Model Support

Validated with one or more delegates

Meta Llama 2 7B	LearningToPaint	resnet50
Meta Llama 3 8B	lennard_jones	shufflenet_v2_x1_0
Conformer	LSTM	squeezenet1_1
dcgan	maml_omniglot	SqueezeSAM
Deeplab_v3	mnasnet1_0	timm_efficientnet
Edsr	Mobilebert	Torchvision_vit
Emformer_rnnt	Mobilenet_v2	Wav2letter
functorch_dp_cifar10	Mobilenet_v3	Yolo v5
Inception_v3	phlippe_resnet
Inception_v4	resnet18

Tested with torch.export but not optimized for performance

Aquila 1 7B	GPT-2	PLaMo 13B
Aquila 2 7B	GPT-J 6B	Qwen 1.5 7B
Baichuan 1 7B	InternLM2 7B	Refact
BioGPT	Koala	RWKV 5 world 1B5
BLOOM 7B1	MiniCPM 2B sft	Stable LM 2 1.6B
Chinese Alpaca 2 7B	Mistral 7B	Stable LM 3B
Chinese LLaMA 2 7B	Mixtral 8x7B MoE	Starcoder
CodeShell	Persimmon 8B chat	Starcoder 2
Deepseek	Phi 1	Vigogne (French)
GPT Neo 1.3B	Phi 1.5	Yi 6B
GPT NeoX 20B	Phi 2

v0.1.0

Initial public release of ExecuTorch. See https://pytorch.org/executorch for documentation.

Important: This is a preview release

This is a preview version of ExecuTorch and should be used for testing and evaluation purposes only. It is not yet recommended for use in production settings. We welcome any feedback, suggestions, and bug reports from the community to help us improve the technology. Please use the PyTorch Forums for discussion and feedback about ExecuTorch using the tag #executorch, and our GitHub repository for bug reporting.

stable-2023-09-19

New models enabled (e2e tested via portable lib):

• Emformer RNN-T Transcriber, Predictor, Joiner (as three modules)

Quantization:

• Enabled quantization for incpetion_v4 and deeplab_v3 in examples with XNNPACKQuantizer

API changes:

• Runtime API
  • Many runtime APIs changed to improve ergonomics and to better match the style guide. Most of these changes are non-breaking (unless indicated as breaking), since the old APIs are available but marked as deprecated. We recommend that users migrate off of the deprecated APIs before the next release.
    • For an example of how these API changes affected common use cases, see the edits made to examples/executor_runner/executor_runner.cpp under the "Files changed" tab of stable-2023-09-12...78f884f
  • Breaking behavioral change: MethodMeta
    • MethodMeta::num_non_const_buffers and MethodMeta::non_const_buffer_size no longer require adjusting by 1 to skip over the reserved zero index. This will require that users of MethodMeta remove adjustments while counting and iterating over non-const buffers.
    • Details about the change, including migration to adapt to the new behavior: 5762802
    • Also note that these methods have been renamed to num_memory_planned_buffers and memory_planned_buffer_size (see note below)
    • Note that the deprecated Program::num_non_const_buffers and Program::get_non_const_buffer_size methods did not change behavior re: skipping index zero. But they are deprecated, and will be removed in a future release, so we recommend that users migrate to the MethodMeta API and behavior.
  • MethodMeta method names changed from non_const to memory_planned
    • MethodMeta::num_non_const_buffers() is now MethodMeta::num_memory_planned_buffers()
    • MethodMeta::non_const_buffer_size(N) is now MethodMeta::memory_planned_buffer_size(N)
    • Changed in 6944c45
    • The old names are available but deprecated, and will be removed in a future release
  • Breaking code-compatibility change: Method's constructor and init() method are now private
    • Users should not have used these methods; Method instances should only be created by Program::load_method()
    • Changed in 4f3e5e6
  • MemoryManager constructor no longer requires const_allocator or kernel_temporary_allocator
    • A new constructor lets users avoid creating zero-sized allocators that they don't use
    • It also renames the parameters for the remaining allocators to make their uses more clear
    • Changed in 6944c45
    • Example migration to the new constructor: fedc04c
    • The old constructor is available but deprecated, and will be removed in a future release
  • Breaking code-compatibility change: MemoryManager is now final and cannot be subclassed
    • Changed in 6944c45
  • HierarchicalAllocator's constructor now takes an array of Span<uint8_t> instead of an array of MemoryAllocator
    • Changed in 58c8c92
    • Example migration to the new API: 0bce2cb
    • The old constructor is still available but deprecated, and will be removed in a future release
  • Breaking code-compatibility change: HierarchicalAllocator is now final and cannot be subclassed
    • Changed in 58c8c92
  • Program::Load() renamed to Program::load()
    • Changed in 8a5f3e8
    • The old name is still available but deprecated, and will be removed in a future release
  • FileDataLoader::From() renamed to FileDataLoader::from()
    • Changed in e2dd0be
    • The old name is still available but deprecated, and will be removed in a future release
  • MmapDataLoader::From() renamed to MmapDataLoader::from()
    • Changed in 395e51a
    • The old name is still available but deprecated, and will be removed in a future release
• Delegate API
  • File rename: runtime/backend/backend_registry.cpp -> runtime/backend/interface.cpp
  • Partition API update: Partitioner.partition function takes ExportedProgram instead of torch.nn.GraphModule. With this change we access the parameters and buffer in partition function.
    • How to rebase: access graphmodule by exported_program.graph_module
• SDK
  • BundledProgram updates APIs to enable user bundling test cases on specific method by using method name instead of method id in the past

    • AOT: class BundledConfig (method_names: List[str], inputs: List[List[Any]], expected_outputs: List[List[Any]]). method_names is the new added attribute.

    • Runtime: Replace the original method_idx with method_name

      • API for load bundled test input to ET program:
        __ET_NODISCARD Error LoadBundledInput( Method& method, serialized_bundled_program* bundled_program_ptr, MemoryAllocator* memory_allocator, const char* method_name, size_t testset_idx);
      • API for verify result with bundled expected output:
        __ET_NODISCARD Error VerifyResultWithBundledExpectedOutput( Method& method, serialized_bundled_program* bundled_program_ptr, MemoryAllocator* memory_allocator, const char* method_name, size_t testset_idx, double rtol = 1e-5, double atol = 1e-8);

    • Details and examples can be found https://github.com/pytorch/executorch/blob/stable/docs/website/docs/tutorials/bundled_program.md

Bug Fixes:

• When exporting with enable_aot=True, all constant tensors will be lifted as inputs to the graph (in addition to the parameters and buffers).
• Kwargs are now consistently placed in the call_spec of the exported program.

stable-2023-09-12

New models enabled (e2e tested via portable lib):

• MobileBert

Export API

• Two stage export API
  • We are in the process of moving away from exir.capture(): Please refer to this issue #290 for more details. Also look at the updated doc at https://github.com/pytorch/executorch/blob/stable/docs/website/docs/tutorials/exporting_to_executorch.md
• exir.serialize
  • The exir.serialize module was renamed to exir._serialize and is now private
• transform()
  • For perform passes on the same dialect, use transform()

Runtime API

• Method
  • Added set_output_data_ptr(), which is a simpler and safer way to set the output buffers if they were not memory-planned
  • Program::load_method() now accepts an optional EventTracer parameter for non-global profiling and event data collection

Delegation API

• backend.init() and backend.execute() API changes.
  • BackendInitContext is a new added argument for backend.init and BackendExecutionContext is the new added argument for backend.execute().
  • How to rebase on these apis changes?
    • For backend.init, if runtime_allocator is not used, just mark context is not used with __ET_UNUSED. Otherwise, runtime_allocator can be accessed from the context.
    • For backend.execute, nothing has been added to context yet, just mark it with __ET_UNUSED directly. We’ll add event tracer for profiling via context soon.
• backend.preprocess() API changes
  • Updated backend.preprocess:
    • def preprocess( edge_program: ExportedProgram, compile_specs: List[CompileSpec], ) -> PreprocessResult
  • How to rebase on this API changes?
    • Wrap the result like PreprocessResult(processed_bytes=bytes)
• Partitioner.partition API changes
  • Updated Partition class definition. Move partition_tags from class attribute to be part of the ParititionResult.
    • def partition(self, graph_module: GraphModule) -> PartitionResult
  • How to rebase on this API change?
    • Wrap both partition_tags and the tagged_graph together as PartitionResult
• Example Quantizer and Delegate e2e demo
  • Added an example to show to add a quantizer and have it working with delegate to fully delegated a quantized MobileNetV2 model to the example backend.

XnnpackDelegate

• In an effort to align better with the rest of the Executorch AoT stack, XnnpackDelegate added preliminary support to also handle graphs exported with the canonical capture config (i.e. CaptureConfig.enable_aot=True and CaptureConfig._unlift=False)

SDK

• DelegateMappingBuilder to generate debug handle mapping AOT for delegates
  • https://github.com/pytorch/executorch/blob/stable/docs/website/docs/tutorials/profiling_and_debugging_delegates.md
• BundledProgram enabled for usage with examples (more API changes to come in subsequent releases to improve usability, these will be breaking API changes)** **
  • Documentation:
    • https://github.com/pytorch/executorch/blob/stable/docs/website/docs/tutorials/bundled_program.md
  • Example code pointers:
    • https://github.com/pytorch/executorch/tree/stable/examples/bundled_executor_runner
    • https://github.com/pytorch/executorch/blob/stable/examples/export/export_bundled_program.py

Misc

• Linter enabled
• pytest enabled. Rerun pip install . to install pytest and other deps
• gtest enabled via buck, for example, run gtest for runtime/core
  • /tmp/buck2 test runtime/core/test/…
• Index operator rewrite:
  • Fixed bug related to null indices.
  • Implemented full Numpy’s advanced indexing functionality (now it is possible to use multidimensional indices, and masks that only index a subspace).
• Build/CMake
  • CMake release build mode with size optimization flags. We have an example in examples/selective_build/test_selective_build.sh

stable-2023-08-29

New models enabled (e2e tested via portable lib):

• Wav2Letter
• Inception V3 and Inception V4
• Resnet18 and Resnet50

Quantization:

• Enabled E2E MobileNet V2:
  • Model can be quantized and run with portable + quantized op (for quantize/dequantize ops) lib.
    Follow, https://github.com/pytorch/executorch/blob/main/examples/README.md#quantization, to run a quantized model via portable lib.
• MobileNet V3:
  • Needs bumping up the pytorch nightly version (dev20230828) in order to enable MobileNet V3 quantization. However, this breaks ViT export, hence this cut will skip MobileNet V3 quantization until we resolve ViT export breakage.

Delegation:

• API update:
  • [breaking changes] delegate AOT APIs are moved from executorch/backends/ to executorch/exir/backend. To address the breakage: Update from executorch.backends.backend_details to from executorch.exir.backend.backend_details, and from executorch.backends.backend_api to from executorch.exir.backend.backend_api
• XNNPACK:
  • XNNPACK delegated models can run on Mac/Linux in OSS
  • XNNPACK lowering workflow examples have been added for MobileNet V2 (with quantization and delegation) and MobileNet V3 (with delegation)
  • Showcase preliminary XNNPACK perf stats on Linux x86 & Mac M1

Selective build:

• Added buck2 examples to demonstrate 3 APIs to do selective build on any executorch runtime build
• Run test_selective_build.sh

stable-2023-08-15

• New models in example folder:
  • Torchvision ViT. Run the example from executorch dir:
    • python3 -m examples.export.export_example --model_name="vit"
    • buck2 run //examples/executor_runner:executor_runner -- --model_path vit.pte
• Quantization workflow example added and validated to work with MV2:
  • python3 -m examples.quantization.example --model_name mv2
• CMake build:
  • executor_runner can be built via cmake. See cmake_build_system.md.
• Custom ops:
  • Add examples to register custom ops into EXIR and Executorch runtime.
  • Note: buck2 in test_custom_ops.sh should point to installed buck2 if it is not accessible in system’s PATH

stable-2023-08-01

Initial release to early users.