Module tensor

Expand description

Module for the tensor.

Modules§

Bool: A type-level representation of the kind of a bool tensor.
Bytes: A buffer similar to Box<[u8]> that supports custom memory alignment and allows 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
IndexWrap: Wraps an index with negative indexing support.
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 specification for a single tensor dimension.
SliceIter: Defines an Iterator over a Slice.
StreamId: Unique identifier that can represent a stream based on the current thread id.
Tensor: A tensor with a given backend, shape and data type.
TensorCreationOptions: Options for tensor creation.
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.
TransactionPrimitive: Contains all tensor primitives that are going to be read.
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.
BoundsError: Access Bounds Error.
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.
ExpressionError: Common Expression Error.
FloatDType
IndexingUpdateOp: Computation to be used to update the existing values in indexed assignment operations (scatter/select).
IntDType
ShapeError: Error that can occur when attempting to modify shapes.
TensorPrimitive: A primitive tensor representation.

AsIndex: Helper trait for implementing indexing with support for negative indices.
AsSize: Types which can be converted to a usize Size.
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.
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.
ReshapeArgs: Trait used for reshape arguments.
SliceArg: Trait for slice arguments that can be converted into an array of slices. This allows the slice method to accept both single slices (from s![..]) and arrays of slices (from s![.., ..] or [0..5, 1..3]).
TensorKind: A type-level representation of the kind of a tensor. Metadata access is lazy.
TensorMetadata: Tensor metadata trait for tensor primitive.

calculate_matmul_output: Compute the output shape for matrix multiplication with broadcasting support.
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.
expect_wrap: Wraps an index with negative indexing support.
ravel_index: Compute the ravel index for the given coordinates.
read_sync: Read a future synchronously.
set_print_options: Set print options
try_read_sync: Read a future synchronously.
try_wrap: Wraps an index with negative indexing support.
wrap_index: Wraps a dimension index to be within the bounds of the dimension size.