v1.12

Features

• Support for backend operators implemented using PPL.
• TPUv7-runtime CModel integrated with TPU-MLIR for BM1690 model CModel inference.
• Optimized inference speed for BM1690 Stable Diffusion 3.0 at 512 resolution to 0.72 img/s (Mac utilization: 41.9%).
• Support for training graph compilation of ResNet50-v1 through FxGraphConverter.

Bug Fixes

• Performance: Fixed the issue of performance degradation in SegNet.
• Functionality: Resolved the compilation comparison issue for BM1688 DeppLabv3P.

Known Issues

• Performance: Slight performance degradation observed in BM1690 YOLOv5-6 with 4 batch INT8 on eight cores.

v1.12-beta.0

combine slice and concate to new Rope ConcatToRope

Change-Id: Ib15b12fe97117b96c6fe7267c96c3f714aac6ec4

v1.11

[python] distinguish data path model-zoo from regression

Change-Id: I98fa0df1524f0b38d91cda02ab5d49876f7caee8
(cherry picked from commit fa082d0b29df8a82af77839df86349aabab86949)

v1.11-beta.0

[soc_dump] add doc

Change-Id: Icaf313113415a9bf0ad9c75abdcb609d661c815b

TPU-MLIR v1.10 Release

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.

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.

TPU-MLIR v1.8.1

Full Changelog: v1.8...v1.8.1

TPU-MLIR v1.8 Release

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.

TPU-MLIR v1.7 Release

Change Log

New Features

• Added support for new operations including flash attention, custom op dynamic compile, and tpulang ops.
• Enabled AttnReorder and added support for dynamic indices in ops like onehot, scatterelements, and cumsum.
• Added --dump_dataframe option for bmodel_checker and support for transpose with order [1, 2, 3, 0].
• Introduced Watchpoint feature to TDB and added support for mixed-precision networks.
• Implemented optimizations for dma efficiency of flash attention and optimized backend for various models.
• Added support for local memory dump in pcie mode and added various quantization features like eva quant, swin quant, and detr quant.
• Enhanced multi-core support including support for LayerNorm and GroupNorm in coreParallel, and multi-core data slice in tensorLocation.
• Added new patterns for Cswin and Einsum operations.
• Improved support for LLM (Large Language Models) in bm1688.

Bug Fixes

• Fixed various bugs including kernel_module msg_id, SAM-VIT-encoder regression, and attention accuracy problems.
• Addressed logical issues in AddToScale pattern and issues in fp_forward.
• Resolved bugs in model info core dump, op's liveRange in coreParallel, and DevParallel bugs.
• Fixed issues in model combine with io alone and bugs in various ops like interp, RotaryPosEmbPattern, and efficient-lite4 permute.

Performance Improvements

• Improved the performance of TDB and the bmodel_checker for 1684x pcie.
• Optimized facenet and fixed performance issues of 1688 multicore.
• Enabled single-core mode optimizations where necessary.

Documentation and Testing

• Updated documentation, refined custom chapters, and ensured consistency in quick start docs.
• Added test cases for custom tpulang, multi-core with subnets, and custom cpuop.
• Fixed various documentation errors and updated the release note.

Other Changes

• Added restrictions to tpulang ops and net test cases.
• Adjusted descriptions and refined interfaces for better user experience.
• Updated backend .so files and addressed sensitive words in the codebase.
• Added support for int4 dtype in tpu_profile and ensured tool/scripts work in Python virtual environments.

Technical Preview

Features

• Added support for LLM Decoding by utilizing multi-cores to enhance processing efficiency.
• Introduced fx2mlir, a new functionality for enhanced MLIR conversion.
• Implemented nnvlc2.0 and nnvlc1.0 local activation and weight operations, respectively, for improved neural network performance.
• Enabled TPULANG support for operations like sort, argsort, and additional ops, enhancing the language's functionality and flexibility.
• Added cv186x support in run_sensitive_layer.py and for the TDB, expanding compatibility and debugging capabilities.
• Introduced new ops and features like Watchpoint in TDB and activation ops support for scale & zero_point, broadening the range of functionalities available in the tpu-mlir project.
• Supports BM1690.
• L2mem performs intermediate data exchange for active tensor.

Bug Fixes

• Resolved a variety of bugs affecting backend processes, including issues with the 1684x backend, permutefuse2, permutemulconstswap, and more, improving overall stability and performance.
• Fixed several critical issues across tpulang, including errors in sort_by_key operation, reshape operations, where operation, and more, enhancing the language's reliability for developers.
• Addressed bugs in model processing, including fixes for concat logic, scale2conv, scale2conv3d, instance norm, and several more, ensuring smoother model optimization and execution.
• Corrected errors in the documentation, providing clearer and more accurate information for users and developers.

Documentation Updates

• Updated tpulang documentation to include new functionalities and optimizations, making it easier for users to understand and utilize the language effectively.

Performance Improvements

• Optimized TDB and bmodel_checker for 1684x pcie mode, significantly reducing processing times and enhancing efficiency for model analysis.
• Improved the efficiency of DMA in flash attention operations, ensuring faster data handling and processing.
• Enabled IO tag mode and refined address mode for better memory management and operational flexibility.