Tpu-mlir

Release Note

Enhancements:
- Added CUDA support for various operations like conv2d, MatMul, dwconv, pool2d, and more.
- Improved performance for operations like MeanStdScale and softmax.
- Enhanced multi-core batch mm and added support for bm168x with CUDA.
- Refined CUDA code style and adjusted interfaces for various operations.

Bug Fixes:
- Fixed issues with matmul, calibration failures, conv pad problems, and various performance problems.
- Addressed bugs in model transformations, calibration, and various pattern issues.
- Resolved bugs in different model backends like ssd, vit, detr, and yolov5.

New Features:
- Added support for new models like resnet50, mobilenet_v2, shufflenet_v2, and yolox_s/alphapose_res50.
- Introduced new operations like RequantIntAxisOp and Depth2Space with CUDA support.
- Implemented new functionalities for better model inference and compilation.

Documentation Updates:
- Updated weight.md, calibration sections, and user interface details.
- Improved documentation for quick start, developer manual, and various tpulang interfaces.
- Enhanced documentation for model transformation parameters and tensor data arrangements.

Miscellaneous:
- Added new npz tools, modelzoo regression, and support for bmodel encryption.
- Fixed issues with various model performance, shape inference, and CUDA backend optimizations.
- Revived performance for models like yolov5s-6, bm1690 swin multicore, and more.

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.

**Full Changelog**: https://github.com/sophgo/tpu-mlir/compare/v1.8...v1.8.1

**Highlights:**
- **Enhancements:**
- Added support for dynamic shape inference in various operations.
- Optimized core operations for better performance on specific models.
- Improved backend support for multiple models like BM1684X, BM1688, BM1690, SG2380, etc.
- Introduced new operations and patterns for more efficient model processing.
- Updated documentation for better clarity and user guidance.

- **Bug Fixes:**
- Resolved issues related to input/output handling, kernel configurations, and model-specific bugs.
- Fixed bugs in dynamic compilation, core parallel processing, and various backend operations.
- Addressed errors in specific model post-processing steps like YOLOv5, EfficientNet, etc.

- **Performance Improvements:**
- Optimized cycle calculations for multi-core models.
- Enhanced bandwidth usage statistics for better resource management.
- Accelerated compilation processes for training models using a new layer-group scheme.

- **New Features:**
- Introduced new operations like attention quant block, prelu op, and various dynamic compile features.
- Added support for additional operations, weight location, and dynamic compile enhancements.

**Documentation Updates:**
- Updated developer manuals, quick start guides, and model-specific documentation for better understanding.

**Miscellaneous:**
- Streamlined workflows for faster commit checks and improved debugging processes.
- Added new test cases for regression testing and script-based model evaluations.
- Fine-tuned backend operations for improved model performance and accuracy.