Highlights

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• Expanded Cortex-M support across additional CPU variants, model tests, scratch-buffer planning, CMSIS-NN integration, and CMSIS Pack builds.
• Added broad Arm backend coverage for TOSA dialect lowering, dynamic shapes, FP8/MXFP/BFloat16 paths, VGF profiling, and LLM export workflows.
• Added Vulkan support for Linux and Windows desktop GPUs, opt-in wheel builds, automatic build enablement, and a functional backend availability check.
• Expanded Qualcomm QNN support with 2-bit 16a2w quantization, faster VLM/LLM PTQ flows, multi-batch quantization and evaluation, LPAI tooling, profiling, and additional operators.
• NXP Backend finished MLIR based Neutron Converter flow rollout - all operators using the MLIR flow, Neutron backend now support 48 ATen operators.
• Continued Android API migration to Kotlin and added Android model E2E coverage and Kotlin examples.

Runtime and Core

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• Removed the legacy _use_edge_ops toggles from the runtime/export path.
• Added explicit dim-order legalization for channels-last portable fallbacks.
• Hardened tensor-layout validation and segment loading against null metadata.
• Improved symbolic input metadata preservation during exported-program pass replay.
• Added direct bundled-program serialization improvements for embedded flows.

Backends

---

Arm and Cortex-M

• Added builds for more Cortex-M variants on Corstone-300, including M0+.
• Added AoT scratch-buffer planning and broader MLPerf Tiny model coverage.
• Expanded TOSA dialect lowering for activation, binary, conversion, data layout, FFT, reduction, shape, and unary operators.
• Added or improved VGF support for profiling, shader segments, BFloat16, MXFP, grid sampling, and persistent IO mappings.
• Ethos-U and VGF dependencies can now be installed with ExecuTorch using optional packages “pip install executorch[ethos_u,vgf]”
• Added SmolLM2 and Qwen3-VL export and evaluation coverage.
• Added CMSIS Pack infrastructure and an ExecuTorch 1.4 public API manifest.

Qualcomm

• Added additional QNN operators, including addmm, select_scatter, variance, diagonal, fill, cdist, LayerNorm variants, and hyperbolic functions.
• Added 2-bit weight quantization support and multi-batch LLM quantization.
• Decoupled calibration, quantization, compilation, and evaluation stages for faster LLM and VLM workflows.
• Added LPAI partitioning, tensor dumping, and runtime heap profiling.

NXP

• Updated the Neutron software stack to 3.1.3.
• Added backend profiling and MLPerf Tiny profiling tests.
• Expanded Neutron operator coverage and removed several broadcasting restrictions.
• Fixed partition-output handling, format inference, BatchNorm fusion, and multiprocessing conversion issues.

CoreML and MLX

• Added a Core ML compute-plan reporting tool for ANE, GPU, and CPU dispatch.
• Added MLX handlers for hardtanh, bitwise_or, and leaky_relu.
• Improved MLX submodule build isolation.

Vulkan

• Added Vulkan desktop support for Linux and Windows and opt-in wheel builds.

XNNPack

• Added quantized same-padding convolution support in XNNPACK, including fixes for even Conv1d kernels and ViT concatenation quantization parameters.
• Expanded RISC-V XNNPACK coverage across multiple RVV QEMU configurations.

Operators and Kernels

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• Added BFloat16 and FP16 activation support to CPU SDPA.
• Added BFloat16-output quantized embeddings on CPU.
• Improved numerical accuracy by accumulating Half and BFloat16 softmax, log-softmax, mean, and sum operations in FP32.
• Fixed portable anti-aliased bilinear upsampling correctness.

LLM, Mobile, and Examples

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• Added grammar and grammar_type fields to GenerationConfig.
• Added an unoptimized ExecuTorch runner example for Espressif ESP32.
• Migrated Android Module, TrainingModule, SGD, and LLM extension APIs from Java to Kotlin.
• Added Kotlin examples and Android model end-to-end testing.

Notable Fixes

---

• Fixed channels-last handling for portable fallbacks and Cortex-M DS-CNN.
• Fixed a QNN runtime null dereference when op-package options are absent.
• Fixed quantized Conv1d same padding for even kernels in XNNPACK.
• Fixed several Arm partitioning, dynamic-shape, quantization, and runner integration issues.
• Fixed multiple NXP Neutron partition, format, and conversion issues.

Contributors

We welcome 29 first-time human contributors to ExecuTorch in this release: @beomwookang, @karan1508, @atharvjairath, @devin-lai, @derekdixu, @jpiat, @doggeral, @arnavnagzirkar, @wirthual, @youxie, @nanookclaw, @blood-orange, @uddeshsingh, @johnny90, @KushalAdhvaryu, @MatthiasHertelArm, @mohankumarkumar, @yctwo, @jansel, @nlutsenko, @Reubend, @goutamadwant, @JaynouOliver, @RanjithRagavan, @arkadip-maitra, @durvesh1992, @corey-cole, @SakshamKapoor2911, @iamorlando

## Full Changelog

https://github.com/pytorch/executorch/compare/v1.3.1...v1.4.0