TPU-MLIR v1.9 Release

Release Note

Enhancements:

• Implemented output order preservation in converters like ONNX, Caffe, Torch, and TFLite.
• Added support for resnet50-v2 bm1690 f8 regression.
• Improved ILP group mlir file sequences for resnet50 training.
• Updated chip libraries and performance AI for A2 profiling.
• Added a new dump mode "COMB" and refined abs/relu conversions.

Bug Fixes:

• Fixed issues with preprocess when source layout differs from target layout.
• Addressed bugs in various operations like softmax, concat, and weight reorder in conv2d.
• Resolved bugs in model training, model transformation, and various pattern issues.
• Fixed bugs related to CUDA inference, matmul with bias, and multi-output calibration.

New Features:

• Added support for multi-graph in TPULang.
• Introduced new options in TPULang for inference and model deployment.
• Implemented various optimizations and enhancements for dynamic operations and model transformations.

Documentation Updates:

• Refined documentation for quick start quantization and user interface sections.
• Updated backend information, docker image download methods, and model deployment details in the documentation.

Miscellaneous:

• Improved performance for various models like vit, yolov5s, and bm1690.
• Introduced new functionalities like embedding multi-device slice and groupnorm train operations.
• Added support for adaptive_avgpool inference and multiple Einsum modes.