v0.2.0

• Removed async traits and methods.
• Core functionality reimplemented in krnl:
  • Only targets Vulkan, more portable than Metal / DX12.
  • Metal is supported via MoltenVK.
    • GPGPU kernels implemented inline in Rust:
      • Kernels can be defined in the same file, near where they are invoked.
      • Modules allow sharing code between host and device.
      • Kernel bindings are type safe, checked at compile time.
      • Simple iterator patterns can be implemented without unsafe.
      • Supports specialization constants provided at runtime.
      • DeviceInfo includes useful properties:
        • Max / default threads per group.
        • Max / min threads per subgroup.
      • With DebugPrintf, kernel panics produce errors on the host.
      • krnlc generates a device crate and invokes spirv-builder.
        • spirv-builder / spirv-tools are compiled once on install.
        • Significantly streamlines and accelerates workflow.
      • Kernels are compressed to reduce package and binary size.
  • Device operations readily execute:
    • Block until kernels / transfers can queue.
    • An operation can be queued while another is executing.
    • Reduced latency, better repeatability, reliability, and performance.
  • Device buffers can be copied by the host if host visible.
  • Large buffer copies are streamed rather than allocating a large temporary.
    • Reuses a few small buffers for transfers.
    • Overlaps host and device copies.
    • Performance significantly closer to CUDA.
    • Also streams between devices.
  • Device buffers can be i32::MAX bytes (~2 GB, up from 256 MB).
  • Scalar / ScalarBufferBase replaces Float / FloatBuffer:
    • Streamlined conversions between buffers.
  • Buffers can be sliced.
  • Supports wasm (without device feature).
• TensorBase and ScalarBufferBase implemented with krnl::BufferBase and krnl::ScalarBufferBase:
  • Streamlined conversions between tensor types.
  • Host ops accelerated with rayon.
  • Improved and streamlined device gemm kernel.
  • Device sum and sum_axis use subgroup reductions for improved performance.
• Replaced Criterion trait with Accuracy / CrossEntropyLoss traits.
• ops::AddAssign implemented by Tensor and Variable.
• Implement ndarray::linalg::Dot for Tensor and Variable.
• Direct convolution algorithm for better host performance.
• Removed learn::kmeans.
• Redesigned autograd:
  • Autograd replaced with VariableBuilder:
    • Nodes and edges applied when building a Variable.
    • Backward edges are simply f(output_grad) -> input_grad.
  • Gradients are automatically accumulated.
  • Parameter and Variable are separate types (instead of VertexBase).
    • Parameters can be converted to Variables.
• Redesigned Layer trait:
  • for_each_parameter fn's instead of returning a Vec.
  • Cast layers to a ScalarType.
  • Removed enumeration of child layers.
• Redesigned Forward trait:
  • Generic over input and output type.
• Derive improvements:
  • Removed layer attribute.
  • Supports enums.
  • Fields can be skipped.
• Redesigned Optimizer trait:
  • Added learning rate.
  • Accepts a single parameter instead of a slice.
• Parameter optimizer::State:
  • Can be serialized / deserialized with serde.
• Simplified Iris dataset.
• MNIST dataset:
  • Replaced downloader with curl.
  • Decompress in parallel with rayon.

MSRV: 1.70.0