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.

TPU-MLIR v1.6.1

Full Changelog: v1.6...v1.6.1

TPU-MLIR v1.6 release

Change Log

Bug Fixes

• Fixed documentation errors and added checks for documentation errors during build.
• Set workaround for ar.copy cycle issue to 0, avoiding potential data overwriting in inplacing operations.
• Addressed a bug in Caffe DetectionOutput and fixed a hang in cv186x.
• Corrected Mul buffer size alignment issues and various other buffer size corrections.
• Fixed issues with attention accuracy, RotaryPosEmbPattern, and op status validation before the matching process.
• Addressed a series of backend bugs, including daily build errors, performance declines, and incorrect return values.
• Fixed data_checker issues, api_conv bug, and a local slice calculation bug.
• Resolved incorrect affineMap for Pooling buffer and fixed reshape bug for inner products.
• Corrected Mul&Div dynamic support for local operations and fixed issues with Conv2d buffer size calculations.
• Addressed various matmul bugs, including fp8 support issues and quantization inconsistencies.

Features

• Enabled multicore optimizations and added support for multi-core model tests.
• Updated libbackend_1688.so and various backend updates for better performance and compatibility.
• Introduced groupParallel operation, support for dynamic input data generation.
• Added support for new patterns such as Permute fuse pattern and splitQuantizedMLP pattern.
• Implemented npz compare visualizer tool and added support for bm1688 backend.
• Added MatMul weight split case and improved permute performance.
• Added support for img2col pattern, attention interface, and several dialects for SG2260 operations.

Documentation Updates

• Updated release notes and resolved issues with document formatting.
• Standardized expression terminology and replaced sensitive words in documentation.

Performance Improvements

• Improved local softmax performance and optimized dataFlow checking in coreMatch.
• Enhanced performance for Vit L i8 4 batch operations and refined conv multi-core handling.
• Optimized VIT-B concurrency and addressed performance issues with MaxPool buffer sizes.

v1.6-beta.0

New Features

• Implemented SG2260 structureOp interface and structured transform, including a solver for finding transforms【ea234bc2†source】.
• Added OneHot converter and support for fp8 in the debugger【c03ba46c†source】【f87127bd†source】【fed7e68a†source】.
• Supported MatMulOp for special cases broadcast in batch dims and added interface for attention【90d4b327†source】【044c4fc3†source】.
• Provided "decompose linalg op" and "tile+fuse" pass for MatMul parallel supports more batch patterns【25f24e3d†source】.
• Unet single block test added【ea76f9c9†source】.
• Implemented fp8 support for Matmul and other ops including addconst, subconst, mul, add, sub, and abs【e09adbda†source】【7eaec57f†source】.

Performance Improvements

• Improved Matmul fp8 performance with new backend support【2b8dd03b†source】.
• Enabled distribute MLP and attention with improved performance for cascade_net input/output names and order【d5a42d7a†source】.
• Refactored tdb to improve disassembler serialize and resolve BM1688 decoding issue【e73450f8†source】【1457df29†source】.
• Improved weight reorder for ConvOp and optimized permute of attention matmul【a9045c3c†source】【91a353e3†source】.

Bug Fixes

• Resolved various bugs in MatMul, Conv, and other ops across multiple chipsets including SG2260, BM1688, and CV18xx【b809a8c1†source】【bfada4de†source】【9804e30c†source】.
• Fixed bugs related to ReduceOp, ArgOp, SliceOp, and others for better operation and tensor handling【2cdeb60d†source】【bbacf00f†source】.
• Addressed issues in SAM, daily test, and tdb related to core operations and functionality【83e1979c†source】【7c37e39d†source】.
• Fixed memory and data handling bugs for more accurate and stable operation of the models【2310cd8d†source】【0ed60f1f†source】.

Documentation Updates

• Updated documentation to remove sensitive words and improve clarity and comprehensiveness【43e0b428†source】【5d6c49fc†source】.

Miscellaneous

• Enhanced various backend libraries and supported new ops and patterns for more efficient and versatile model handling【1ca95d71†source】【8f1a2de8†source】.
• Improved scatterE and reduce dynamic shape_value handling for better model optimization【fa2ccf29†source】.
• Refinements in graph optimization, permute parallel indexMapping, and related areas for improved model processing【094f05da†source】【1ec6c16b†source】.

Technical Preview

TPU-MLIR Project Update

Bug Fixes and Dependency Updates

• Fix Dependency: Fixed the dependency of MLIRInputConversion.
• SDK Release Workflow: Fixed tpu-mlir tag for building and added workflow file for SDK release.
• Softplus LoweringINT8: Fixed 1684 Softplus LoweringINT8 issue.
• Slice Begin Index: Fixed bm1684 slice begin_index problem.
• Mul Conflict Resolution: Partially fixed the output data sign of mul conflict with chip restriction.

Feature Enhancements and Support

• Subgraph Split Support: Enhanced support for subgraph split.
• Quant IO List Note: Added quant io list note for better quantization handling.
• New Full Operation: Supported the aten::new_full operation.
• Torch Flip for bm1684x: Added support for torch.flip for bm1684x.
• Weight Input Shape Bind: Supported shape bind for weight input.

Updates and Implementations for Specific Operations

• Backend Update for sg2260: Updated sg2260 for backend for tag31.
• ScatterElements Implementation: Implemented ScatterElements for any axis.
• Unary Indexing Map: Added unary indexing map.
• Binary Indexing Map: Added binary (add/sub/mul/div/min/max) indexing map.
• Dynamic NMS Support: Featured support for dynamic nms for bm1684x.

Codebase and Documentation Refinements

• Cleanup: Removed test/sg2260 dialect.
• Documentation Update: Updated nntoolchain README and lib.
• Codegen Documentation: Added documentation for codegen.
• Template Format Update: Updated import mlir file template format.
• Quick Start Docs Modification: Modified quick start docs for tpu-mlir.

Optimizations and Performance Improvements

• Kernel Module Usage: Reverted to using the old kernel module.
• MLIR Conv2D Optimization: Improved 1684 mlir conv2d with 3ic optimization.
• SWINT Quantization: Added swint quant for better performance.
• Opt Parameter Addition: Added an optimization parameter.
• Loop and Fusion Enhancements: Supported interchange of inner loop, padOp transform, tensor op collapse, fusion on linalg-on-tensor, etc.

Technical Preview

🐳 Docker Image Update

Changed required Docker image from sophgo/tpuc_dev:v2.2 to sophgo/tpuc_dev:v3.1, which is based on Ubuntu 22.04.

📖 Documentation

Updated docs to add more parameters in model deployment.

🐛 Bug Fixes

Fixed TPU-MLIR dialect Python binding for DEBUG mode.
Resolved backward training bug.
Addressed average pooling and max pooling issues.
Several other bug fixes related to Winograd inference, training, and more.

🚀 Feature Additions

Added Deconv3D backend support.
Support for dynamic tile added for bm1684x.
Added Winograd feature.
Several other feature additions, including dual-core support in debugger, MatMulSliceMerge support for int8/int4, and more.

🔧 Code Maintenance

Code renaming and cleaning.
Regression adjustments and tests.

⚙️ Backend Updates

Backend updates for various architectures including BM1684 and sg2260.

Technical Preview

New Features and Enhancements

• Support for Various Operations: Added support for exp, erf, gelu, loopop, and other operations for specific platforms.
• Tooling and Visualization: Renamed profile.py, added visual tools for weights, and enhanced debugging capabilities.
• Model Support and Adjustments: Added daily release models, scripts, and support for specific model types like yolov8, yolov4s.
• Distribution and Multicore Support: Implemented distribution steps, multicore support, and group convolution transformation.

Bug Fixes and Resolutions

• Model and Parsing Fixes: Resolved issues in emvd models, parsing errors, slice bugs, and fixed specific issues in bm1684 and bm1686.
• Codegen and Canonicalization Fixes: Addressed type errors, canonicalization failures, and operand kind checks.
• Inference and Optimization Fixes: Fixed inference issues in max, where, and slice operations, and refined canonicalization.

Documentation and Cleanup

• Documentation Updates: Refined tpu-mlir docs, added supposed ops document, and updated specific documents.
• Code Cleanup and Refactoring: Removed unnecessary code, reconstructed permute move canonicalization, and prepared for LLVM upgrade.

Other Changes

• Testing and Calibration: Added test cases, calibration updates, and support for regression and tag in TDB.
• Backend and Runtime Adjustments: Updated backend, added support for auto-increase op, and fixed minor bugs.

Technical Preview

Features:
BM1686: support post handle op, provided parallelOp codegen, add DivOp for f16/bf16.
BM1684: Support dynamic compilation load tensor from L2mem, implement GROUP_3D local layer function, support more dynamic ops, like MinConst, MaxConst, Lut; and some static ops, like deform_conv2d.
CV18XX: Support more ops like equalOp.
Support IfOp for f16/bf16/int8 mode.
Implement post process function of sensitive layer, unranked tensor and dynamic tensor at frontend, add empty and baddbmm torch converter/interpreter.
Support weight split when layer group if op is broadcastbinary, suppoprt parse ops of each layer in top.mlir, support int32 to i/u8 inference for modeol_runner.py.
Remove onnx-sim and use unranked_type for all ops.
Implement more graph opimize: merge matmul + add to matmul if float type, fuse same operation pass, weight trans when permute+add.
Support more torch ops, like rmsnorm, ceil, remainder.
Other new operations: lowering of GatherElements, multi-input Add.

Bug Fixes:
Fix chatglm2 rmsnorm untransformed prob, ScaleOp inference error, bmodel_dis format bin, shape inference of matmul, subnet output order mismatch cause error in dynamic runtime.
Avoid duplicate name of inserted CastOp, distinguish caffe matmul shape.

Code Refactoring:
Use llvm::md5, llvm::sha256.
Use Clang to speed up code compilation.
Remove some unused header files.
Use rewriter.eraseOp instead of op->earse, use string to define padding mode.
Refine disassembler, refactor mix_precision.

Documentation Updates:
Update document version and change some model-zoo requirements.
Modified English part and modified developer_manual doc for visual.py part.

Testing and Verification:
Updated list of test models supported by BM1684X.