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ZenDNN Release v4.2
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@kiriti-pendyala kiriti-pendyala released this 21 May 13:13
v4.2
a36ae00

The highlights of this release are as follows

  • The ZenDNN library is based on oneDNN v2.6.3, and provides optimizations tailored to enable performant AI inference on AMD EPYCTM servers.

  • The ZenDNN library can be used in the following frameworks through a plug-in:

    • TensorFlow v2.16 and later
    • PyTorch v2.0 and later

  • The ZenDNN library is integrated with ONNX Runtime v1.17.0.

  • Supports Environment Variables for Tuning Performance
    The following environment variables have been added to tune performance:

    • Memory Pooling (Persistent Memory Caching)
      • ZENDNN_ENABLE_MEMPOOL for all TensorFlow models
      • Added MEMPOOL support for BF16 models in TensorFlow models
    • Convolution Operation
      • ZENDNN_CONV_ALGO for all TensorFlow models
      • Added new options to ALGO paths
    • Matrix Multiplication Operation
      • ZENDNN_MATMUL_ALGO for TensorFlow, PyTorch, and ONNX Runtime models
      • Added new options, ALGO paths, and an experimental version of auto-tuner for TensorFlow

  • Embedding Bag and Embedding Operators

    • Support for Embedding operator
    • AVX512 support for Embedding and Embedding Bag kernel
    • Two new parallelization strategies for Embedding and Embedding bag operators, namely, Table threading and Hierarchical threading

  • Matrix Multiplication (MatMul) Operators

    • MatMul post-ops computation with BLIS kernels
    • Weight caching for FP32 JIT and BLIS kernels
    • BLIS BF16 kernel support
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