DeepSparse v1.1.0

New Features:

• Python 3.10 support added.
• Zero-shot text classification pipeline implemented.
• Haystack Information Retrieval pipeline implemented.
• YOLACT pipeline native integration for deployments is available.
• DeepSparse pipelines now support dynamic batch, dynamic shape through bucketing, and asynchronous execution support.
• CustomTaskPipeline added to enable easier custom pipeline creation.

Changes:

• The behavior of the Multi-stream scheduler is now identical to the Elastic scheduler, and the old Multi-stream scheduler has been removed.
• NLP pipelines for question answering, text classification, and token classification upgraded to improve accuracy and better match the SparseML training pathways.
• Updates made across the repository for new SparseZoo Python APIs.
• Max torchvision version increased to 0.12.0 for computer vision deployment pathways.

Performance:

• Inference performance improvements for
  • unstructured sparse quantized Transformer models.
  • slow activation functions (such as Gelu or Swish) when they follow a QuantizeLinear operator.
  • some sparse 1D convolutions. Speedups of up to 3x are observed.
  • Squeeze, when operating on a single axis.

Resolved Issues:

• Assertion errors no longer when one node had multiple inputs, both coming from the same node no longer occurs.
• An assertion error no longer appears when a MatMul operator followed a Transpose or Reshape operator no longer occurs.
• Pipelines now support hyphenated versions of standard task names such as question-answering,

Known Issues:

• In the C++ interface, the engine will crash with a segmentation fault when the num_streams provided to the engine_context_t is greater than the number of physical CPU cores.