Crate burn_tensor

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This library provides multiple tensor implementations hidden behind an easy to use API that supports reverse mode automatic differentiation.

Modules§

activation: The activation module.
backend: The backend module.
cast: Tensor element casting.
container: The container module.
loss: The loss module.
module: The burn module.
ops: Operations on tensors module.
quantization: Tensor quantization module.
reprrepr: Burn Tensor representaton

Macros§

dequant_op_quant: Automatically applies dequantization -> float operation -> quantization.
make_element: Macro to implement the element trait for a type.

Structs§

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.
DataDeprecated: Data structure for tensors.
DataSerializeDeprecated: Data structure for serializing and deserializing tensor data.
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.
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.
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.

Enums§

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.

Traits§

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.
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.
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.

Functions§

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_closenessstd: Checks the closeness of two tensors and prints the results.
chunk: Split the tensor along the given dimension into chunks.
narrow: Returns a new tensor with the given dimension narrowed to the given range.
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.
split: Splits the tensor along the given dimension into equally sized chunks (if possible) with size split_size. Last chunk will be smaller if the tensor size along the given dimension dim is not divisible by split_size.
split_with_sizes: Splits the tensor along the given dimension into chunks with sizes in dim according to split_sizes.
try_read_sync: Read a future synchronously.

Type Aliases§

Device: Device type used by the backend.