Module tensor

Expand description

Module for the tensor.

Modules§

dequant_op_flow: Automatically applies dequantization -> float operation [-> quantization].
dequant_op_quant: Automatically applies dequantization -> float operation -> quantization.
make_element: Macro to implement the element trait for a type.
s: Creates a slice specification for tensor indexing operations.

Bool: A type-level representation of the kind of a bool tensor.
Bytes: A sort of Box<[u8]> that remembers the original alignment and can contain trailing uninitialized bytes.
DimIter: Iterator given by (Tensor::iter_dim).
DistributionSampler: Distribution sampler for random value of a tensor.
Float: A type-level representation of the kind of a float tensor
Int: A type-level representation of the kind of a int tensor.
PrintOptions: Options for Tensor pretty printing
Shape: Shape of a tensor.
Slice: A slice (range).
T: Transpose marker (zero-size type). Used to sugar the transpose of a tensor, e.g.
Tensor: A tensor with a given backend, shape and data type.
TensorData: Data structure for tensors.
Tolerance: The tolerance used to compare to floating point numbers.
Transaction: A transaction can read multiple tensors at once with a single operation improving compute utilization with optimized laziness.
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.
flex32: A floating point type with relaxed precision, minimum f16, max [f32].

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
Precision: Element precision trait for tensor.
TensorPrimitive: A primitive tensor representation.

AsIndex: Helper trait for implementing indexing with support for negative indices.
BasicAutodiffOps: Trait that list all operations that can be applied on all tensors on an autodiff backend.
BasicOps: Trait that list all operations that can be applied on all tensors.
BroadcastArgs: Trait used for broadcast arguments.
Element: Element trait for tensor.
ElementComparison: Element ordering trait.
ElementConversion: Element conversion trait for tensor.
ElementLimits: Element ordering trait.
ElementPrecision: Element precision trait for tensor.
ElementRandom: Element trait for random value of a tensor.
MovedimArgs: Trait used for movedim arguments
Numeric: Trait that list all operations that can be applied on all numerical tensors.
RangeArg: Trait used for slice dim arguments.
RangesArg: Trait used for slice arguments
ReshapeArgs: Trait used for reshape arguments.
TensorKind: A type-level representation of the kind of a tensor. Metadata access is lazy.
TensorMetadata: Tensor metadata trait for tensor primitive.

argsort: Returns the indices that sort the elements of the input tensor along a given dimension.
argwhere_data: Compute the indices of the elements that are non-zero, grouped by element.
cartesian_grid: Generates a cartesian grid for the given tensor shape on the specified device. The generated tensor is of dimension D2 = D + 1, where each element at dimension D contains the cartesian grid coordinates for that element.
check_closeness: Checks the closeness of two tensors and prints the results.
read_sync: Read a future synchronously.
set_print_options: Set print options
sort: Sort the elements of the input tensor by value along a given dimension.
sort_with_indices: Sort the elements of the input tensor by value along a given dimension.
try_read_sync: Read a future synchronously.