Xilinx/brevitas: Release v0.9.0

Highlights

• Initial support for graph quantization to programmatically generate a quantized model from a floating-point one. ImageNet examples with PTQ can be found here: https://github.com/Xilinx/brevitas/tree/master/src/brevitas_examples/imagenet_classification/ptq .
• Initial support for QuantMultiheadAttention, which is leveraged for e.g. ViT support above.
• Various improvements to graph equalization, which are leveraged in the PTQ examples above.
• New accumulation-aware quantizers, to train for low-precision accumulation, based on our A2Q paper https://arxiv.org/abs/2301.13376 .
• Experimental support for BatchQuant quantizer, based on https://arxiv.org/abs/2105.08952 , currently still untested.
• Initial support for learned rounding.

Overview of changes Graph quantization

• Initial graph quantization support by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/549 https://github.com/Xilinx/brevitas/pull/574 https://github.com/Xilinx/brevitas/pull/532 https://github.com/Xilinx/brevitas/pull/579

Quantized layers

• Initial support for QuantMultiheadAttention https://github.com/Xilinx/brevitas/pull/568
• Breaking change: rename Quant(Adaptive)AvgPool to Trunc(Adaptive)AvgPool by @volcacius in https://github.com/Xilinx/brevitas/pull/562

Quantizers

• Weight normalization-based integer quantizers by @i-colbert in https://github.com/Xilinx/brevitas/pull/559
• Accumulator-aware weight quantization by @i-colbert in https://github.com/Xilinx/brevitas/pull/567
• BatchQuant quantizers support by @volcacius in https://github.com/Xilinx/brevitas/pull/563

QuantTensor

• Support to move QuantTensor across devices by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/528
• Initial support for interpolate and pixel_shuffle by @volcacius in https://github.com/Xilinx/brevitas/pull/578

PTQ

• Batch Norm support in graph equalization by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/531
• Mul support in graph equalization by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/530
• Learned round support by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/573
• MultiheadAttention and LayerNorm support in graph equalization by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/555
• Fix calibration over large number of batches by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/523

Export

• Itemize scalar quantize args only in TorchScript QCDQ by @volcacius in https://github.com/Xilinx/brevitas/pull/561
• Round avgpool export fixes by @volcacius in https://github.com/Xilinx/brevitas/pull/562

CI, linting

• Linter isort by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/505
• CI: bump isort from 5.10.1 to 5.11.5 by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/540
• Test: enable parallelism with pytest-xdist by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/513
• GHA workflow improvement by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/507
• Add support for yapf by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/511

FX

• Disable FX backport on 1.8.1+ by @volcacius in https://github.com/Xilinx/brevitas/pull/504

Examples

• Pretrained Resnet18 example on CIFAR10 targeting FINN by @volcacius in https://github.com/Xilinx/brevitas/pull/577
• Graph quantization + PTQ examples and benchmarking scripts by @Giuseppe5 in https://github.com/Xilinx/brevitas/pull/547 https://github.com/Xilinx/brevitas/pull/575 https://github.com/Xilinx/brevitas/pull/576

For the Full Changelog please check : https://github.com/Xilinx/brevitas/compare/v0.8.0...v0.9.0