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@janifer112x janifer112x released this 13 Jan 10:04
· 53 commits to master since this release
v3.0
2057c58

Release Notes 3.0

Documentation and Github Repository

  • Migrated core documentation to Github.IO.

  • Incorporated offline HTML documentation for air-gapped users.

  • Restructured user documentation.

  • Restructured repository directory structure for clarity and ease-of-use.

Docker Containers and GPU Support

  • Migrated from multi-framework to per framework Docker containers.

  • Enabled Docker ROCm GPU support for quantization and pruning.

Model Zoo

  • Updated Model Zoo with commentary regarding dataset licensing restrictions

  • Added 14 new models and deprecated 28 models for a total of 130 models

  • Added super resolution 4x, as well as 2D and 3D semantic segmentation for Medical applications

  • Optimized models for benchmarks:

    • MLPerf: 3D-Unet

    • FAMBench: MaskRCNN

  • Provides optimized backbones supporting YoloX, v4, v5, v6 and EfficientNet-Lite

  • Ease-of-use enhancements, including replacing markdown-format performance tables with a downloadable Model Zoo spreadsheet

  • Added 72 PyTorch and TensorFlow models for AMD EPYC™ CPUs, targeting deployment with ZenDNN

  • Added models to support AMD GPU architectures based on ROCm and MLGraphX

TensorFlow 2 CNN Quantizer

  • Based on TensorFlow 2.10

  • Updated the Model Inspector to for improved accuracy of partitioning results expected from the DPU compiler.

  • Added support for datatype conversions for float models, including FP16, BFloat16, FP32, and double.

  • Added support for exporting quantized ONNX format models (to support the ONNX Runtime).

  • Added support for new layer types including SeparableConv2D and PReLU.

  • Added support for unsigned integer quantization.

  • Added support for automatic modification of input shapes for models with variable input shapes.

  • Added support to align the input and output quantize positions for Concat and Pooling layers.

  • Added error codes and improved the readability of the error and warning messages.

  • Various bug fixes.

TensorFlow 1 CNN Quantizer

  • Separated the quantizer code from the TensorFlow code, making it a plug-in module to the official TensorFlow code base.

  • Added support for exporting quantized ONNX format models (to support the ONNX Runtime).

  • Added support for datatype conversions for float models, including FP16, BFloat16, FP32 and double.

  • Added support for additional operations, including Max, Transpose, and DepthToSpace.

  • Added support for aligning input and output quantize positions of Concat and Pooling operations.

  • Added support for automatic replacement of Softmax with DPU-accelerated Softmax.

  • Added error codes and improved the readability of the error and warning messages.

  • Various bug fixes.

PyTorch CNN Quantizer

  • Support PyTorch 1.11 and 1.12.

  • Support exporting torch script format quantized model.

  • QAT supports exporting trained model to ONNX and torch script.

  • Support FP16 model quantization.

  • Optimized Inspector to support more pattern types, and backward compatible of device assignment.

  • Cover more PyTorch operators: more than 560 types of PyTorch operators are supported.

  • Enhanced parsing to support control flow parsing.

  • Enhanced message system with more useful message text.

  • Support fusing and quantization of BatchNorm without affine calculation.

Compiler

  • Added support for new operators, including: strided_slice, cost volume, correlation 1D & 2D, argmax, group conv2d, reduction_max, reduction_mean

  • Added support for Versal™ AIE-ML architectures DPUCV2DX8G (V70 and Versal AI Edge)

  • Focused effort to improve the intelligibility of error and partitioning messages

PyTorch Optimizer

  • Added support for fine-grained model pruning (sparsity)

  • OFA support for convolution layers with kernel sizes = (1,3) and dialation

  • OFA support for ConvTranspose2D

  • Added pruning configuration that allows users to specify pruning hyper-parameters

  • Specific exception types are defined for each type of error

  • Enhanced parallel model analysis with increased robustness

  • Support for PyTorch 1.11 and 1.12

TensorFlow 2 Optimizer

  • Added support for Keras ConvTranspose2D, Conv3D, ConvTranspose3D

  • Added support TFOpLambda operator

  • Added pruning configuration that allows users to specify pruning hyper-parameters

  • Specific exception types are defined for each type of error

  • Added support for TensorFlow 2.10

Runtime and Library

  • Added support for Versal AI Edge VEK280 evaluation kit and Alveo™ V70 accelerator cards (Early Access)

  • Added support for ONNX runtime, with eleven ONNX-specific examples

  • Added four new model libraries to the Vitis™ AI Library and support for fifteen additional models

  • Focused effort to improve the intelligibility of error messages

Profiler

  • Added Profiler support for DPUCV2DX8G (VEK280 Early Access)

  • Added Profiler support for Versal DDR bandwidth profiling

DPU IP - Zynq Ultrascale+ DPUCZDX8G

  • Upgraded to enable Vivado™ and Vitis 2022.2 release

  • Added support for 1D and 2D Correlation, Argmax and Max

  • Reduced resource utilization

  • Timing closure improvements

DPU IP - Versal AIE Targets DPUCVDX8G

  • Upgraded to enable Vivado and Vitis 2022.2 release

  • Added support for 1D and 2D Correlation

  • Added support for Argmax and Max along the channel dimension

  • Added support for Cost-Volume

  • Reduced resource utilization

  • Timing closure improvements

DPU IP - Versal AIE-ML Targets DPUCV2DX8G (Versal AI Edge)

  • Early access release supporting early adopters with an early, unoptimized AIE-ML DPU

  • Supports most 2D operators (currently does not support 3D operators)

  • Batch size support from 1~13

  • Supports more than 90 Model Zoo models

DPU IP - CNN - Alveo Data Center DPUCVDX8H

  • Upgraded to enable Vitis 2022.2 release

  • Timing closure improvements via scripts supplied for .xo workflows

DPU IP - CNN - V70 Data Center DPUCV2DX8G

  • Early access release supporting early adopters with an unoptimized DPU

  • Supports most 2D operators (currently does not support 3D operators)

  • Batch size 13 support

  • Supports more than 70 Model Zoo models

WeGO

  • Integrated WeGO with the Vitis-AI Quantizer to enable on-the-fly quantization and improve easy-of-use

  • Introduced serialization and deserialization with the WeGO flow to offer the capability of building once and running anytime

  • Incorporated AMD ZenDNN into WeGO, enabling additional optimization for AMD EPYC CPU targets

  • Improve WeGO robustness to offer a better developer experience and support a wider range of models

Known Issues

  • Bitstream loading error occurs when the AIE-ML DPU application running on the VEK280 kit is interrupted manually

  • HDMI not functional for the early access VEK280 image. The issue will be fixed in the next release

AMD, the AMD Arrow logo, and combinations thereof are trademarks of Advanced Micro Devices, Inc.

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