Crate ruda_tensor

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This library provides the core types that define how Ruda tensor data is represented, stored, and interpreted.

Re-exports§

apiapi: Public tensor API built on the shared backend contracts and primitives.
backend: Backend trait and required types.
cast: Tensor element casting.
device
distribution: Random value distributions used to initialize and populate tensor data.
element: Traits and helpers for working with element types and conversions.
indexing: Indexing utilities.
ops
primitive
quantization: Quantization data representation.
shape: Shape definition.
slice: Slice utilities.
tensor: Backend tensor primitives and operations.

dequant_op_flow: Automatically applies dequantization -> float operation [-> quantization].
dequant_op_quant: Automatically applies dequantization -> float operation -> quantization.
doc_tensor: Convenience macro to link to the ruda-tensor API docs.
make_element: Macro to implement the element trait for a type.

Bytes: A buffer similar to Box<[u8]> that supports custom memory alignment and allows trailing uninitialized bytes.
DeviceHandle: TODO: Docs
DistributionSampler: Distribution sampler for random value of a tensor.
StreamId: Unique identifier that can represent a stream based on the current thread id.
TensorData: Data structure for tensors.
Tolerance: The tolerance used to compare to floating point numbers.
bf16: A 16-bit floating point type implementing the bfloat16 format.
f16: A 16-bit floating point type implementing the IEEE 754-2008 standard binary16 a.k.a “half” format.

AllocationProperty: The kind of allocation behind the Bytes type.
BoolStore: Data type used to store boolean values.
DType
DataError: The things that can go wrong when manipulating tensor data.
Distribution: Distribution for random value of a tensor.
DistributionSamplerKind: Distribution sampler kind for random value of a tensor.
FloatDType
IntDType
Scalar: A scalar element.