Releases: sophgo/tpu-mlir
Releases · sophgo/tpu-mlir
v1.14-beta.0
fix bug in build ppl Change-Id: Ib93341da7fa6b420f9fb9cd9e4b61dc21aeaf001
v1.13
add a16 matmul multi_core Change-Id: I10a9097ee52e324555f4a505ce18d7fe9b665803
v1.13-beta.0
[doc] layergroup opt intro Change-Id: I0797b73e4d020e9556da29d1c1a743b8c80a83ad
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