Version 2.26.0

• Bug fixes and Improvements

  • ONNX

    • Implement onnxscript RMSNorm fusion for improved graph optimization (68710d9)
    • Propagate encoding through Concat during ONNX QDQ export (4811a34)
    • Export scale & offset as initializers instead of Constants in ONNX QDQ export (ea9a619)
    • Fix AdaScale (aimet-onnx) for Qwen3 models (beac8f8)
    • Fix BN fold for YOLO models (bae9953)

  • Torch

    • Support int2 ONNX QDQ export (5fa79cf)
    • Significantly improve ONNX export performance by eliminating O(N^2) iterations and redundant Q/DQ operations (695465e, cb1f9ae, abe0ef5, c547cfb, 310b43d, fb9629d, d54efa0)
    • Add native support for Qwen3 MoE models (389d71f, ab6e810)
    • Fix triton kernel bug upon transposed inputs (a1f6795)
    • Fix GPU memory leak in AdaScale optimization loop (f52f2e2)
    • Fix AdaScale device error with caching disabled (964d11f)
    • Work around torch.compile bugs and exclude internal quantization methods from compilation (b8bcb47, d518f35)
    • Fix tie quantizers removing relu encoding constraint (3cc7252)
    • Fail immediately without retrying upon torch.cuda.OutOfMemoryError (4f84eb1)
    • Release blockwise sampler input memory before yielding to reduce memory usage (ee3d193)
    • Add aimet_torch.v1 end-of-life warning (8fc52c6)
    • Use whitelist approach for enabling per-channel quantization in quantsim config (817d3b1)

  • Common

    • Tie concat and interpolation op quantizers by default with safe edge case handling (5ce7229, 5084af3)
    • Implement supergroup unrolling without name mangling (e351112)
    • Treat CG_split as grid-preserving op (738ee26)
    • Handle dynamic matmul add in connected graph passes (3c0de8e)

• Documentation

  • Add AdaScale documentation with HuggingFace LLM example (c403562)
  • Update doc code examples to use aimet_torch.onnx.export (fed2a06)