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.