Struct LibTorch

pub struct LibTorch<E = f32, Q = i8> { /* private fields */ }

Tensor backend that uses LibTorch with the [tch] crate for executing tensor operations.

This backend is compatible with a wide range of hardwares ranging from CPUs to GPUs, but requires LibTorch to be installed correctly. The CPU version can be downloaded automatically and the CUDA version as well by setting the TORCH_CUDA_VERSION environment variable. For more complex configurations, check out the manual installation for burn-tch.

Refer to the [tch] crate for more information.

Trait Implementations

impl<E, Q> ActivationOps<LibTorch<E, Q>> for LibTorch<E, Q>

where E: TchElement, Q: QuantElement,

fn relu(tensor: TchTensor) -> TchTensor

Applies the ReLU activation function.

fn gelu(tensor: TchTensor) -> TchTensor

Applies the Gelu activation function.

fn gelu_backward(tensor: TchTensor, grad: TchTensor) -> TchTensor

Applies the Gelu activation function backward.

fn sigmoid(tensor: TchTensor) -> TchTensor

Applies the Sigmoid activation function.

fn log_sigmoid(tensor: TchTensor) -> TchTensor

Applies the LogSigmoid activation function.

fn leaky_relu( tensor: <B as Backend>::FloatTensorPrimitive, negative_slope: <B as Backend>::FloatElem, ) -> <B as Backend>::FloatTensorPrimitive

Applies the LeakyReLU activation function.

fn relu_backward( output: <B as Backend>::FloatTensorPrimitive, grad: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

Applies the ReLU activation function backward.

fn prelu( tensor: <B as Backend>::FloatTensorPrimitive, alpha: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

Applies the PReLu activation function.

fn sigmoid_backward( output: <B as Backend>::FloatTensorPrimitive, grad: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

Applies the Sigmoid activation function backward.

fn hard_sigmoid( tensor: <B as Backend>::FloatTensorPrimitive, alpha: <B as Backend>::FloatElem, beta: <B as Backend>::FloatElem, ) -> <B as Backend>::FloatTensorPrimitive

Applies the hard Sigmoid activation function.

fn log_sigmoid_backward( x: <B as Backend>::FloatTensorPrimitive, grad: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

Applies the LogSigmoid activation function backward.

impl<E, Q> Backend for LibTorch<E, Q>

where E: TchElement, Q: QuantElement,

type Device = LibTorchDevice

Device type.

type FloatTensorPrimitive = TchTensor

Tensor primitive to be used for all float operations.

type FloatElem = E

Default float element type.

type IntTensorPrimitive = TchTensor

Tensor primitive to be used for all int operations.

type IntElem = i64

Int element type.

type BoolTensorPrimitive = TchTensor

Tensor primitive to be used for all bool operations.

type BoolElem = bool

Tensor primitive to be used for all bool operations.

type QuantizedTensorPrimitive = TchQTensor

Tensor primitive to be used for all quantized operations.

type QuantizedEncoding = Q

Quantized tensor encoding type.

fn seed(seed: u64)

Seed the backend.

fn ad_enabled() -> bool

If autodiff is enabled.

fn name() -> String

Name of the backend.

fn sync(device: &<LibTorch<E, Q> as Backend>::Device)

Sync the backend, ensure that all computation are finished.

impl<E, Q> BoolTensorOps<LibTorch<E, Q>> for LibTorch<E, Q>

where E: TchElement, Q: QuantElement,

fn bool_from_data(data: TensorData, device: &LibTorchDevice) -> TchTensor

Creates a tensor from the data structure.

fn bool_repeat_dim(tensor: TchTensor, dim: usize, times: usize) -> TchTensor

Repeats one dimension of the tensor a given number of times along that dimension.

async fn bool_into_data(tensor: TchTensor) -> TensorData

Converts the tensor to a data structure.

fn bool_to_device(tensor: TchTensor, device: &LibTorchDevice) -> TchTensor

Moves the tensor to the device.

fn bool_reshape(tensor: TchTensor, shape: Shape) -> TchTensor

Reshapes the tensor.

fn bool_device(tensor: &TchTensor) -> LibTorchDevice

Gets the device of the tensor.

fn bool_empty( shape: Shape, device: &<LibTorch<E> as Backend>::Device, ) -> TchTensor

Creates a new bool tensor.

fn bool_slice(tensor: TchTensor, ranges: &[Range<usize>]) -> TchTensor

Gets the values from the tensor for the given ranges.

fn bool_slice_assign( tensor: TchTensor, ranges: &[Range<usize>], value: TchTensor, ) -> TchTensor

Sets the values in the tensor for the given ranges.

fn bool_cat(tensors: Vec<TchTensor>, dim: usize) -> TchTensor

Concatenates the tensors along the given dimension.

fn bool_equal(lhs: TchTensor, rhs: TchTensor) -> TchTensor

Equates the two tensors.

fn bool_not(tensor: TchTensor) -> TchTensor

Inverses boolean values.

fn bool_into_int(tensor: TchTensor) -> TchTensor

Converts bool tensor to int tensor.

fn bool_into_float(tensor: TchTensor) -> TchTensor

Converts bool tensor to float tensor.

fn bool_swap_dims(tensor: TchTensor, dim1: usize, dim2: usize) -> TchTensor

Swaps two dimensions of a bool tensor.

fn bool_narrow( tensor: TchTensor, dim: usize, start: usize, length: usize, ) -> TchTensor

Returns a new tensor with the given dimension narrowed to the given range.

fn bool_chunk(tensor: TchTensor, chunks: usize, dim: usize) -> Vec<TchTensor>

Split the tensor along the given dimension into chunks.

fn bool_split( tensor: TchTensor, split_size: usize, dim: usize, ) -> Vec<TchTensor>

Split the tensor along the given dimension into chunks of split_size.

fn bool_split_with_sizes( tensor: TchTensor, split_sizes: Vec<usize>, dim: usize, ) -> Vec<TchTensor>

Split the tensor along the given dimension into chunks with sizes in dim according to split_sizes.

fn bool_permute(tensor: TchTensor, axes: &[usize]) -> TchTensor

Permutes the dimensions of a tensor.

fn bool_flip(tensor: TchTensor, axes: &[usize]) -> TchTensor

Reverse the order of elements in a tensor along the given axes.

async fn bool_argwhere(tensor: TchTensor) -> TchTensor

Compute the indices of the elements that are non-zero, grouped by element.

async fn bool_nonzero(tensor: TchTensor) -> Vec<TchTensor>

Compute the indices of the elements that are non-zero.

fn bool_expand(tensor: TchTensor, shape: Shape) -> TchTensor

Broadcasts the bool tensor to the given shape.

fn bool_not_equal( lhs: <B as Backend>::BoolTensorPrimitive, rhs: <B as Backend>::BoolTensorPrimitive, ) -> <B as Backend>::BoolTensorPrimitive

Element-wise non-equality comparison.

fn bool_transpose( tensor: <B as Backend>::BoolTensorPrimitive, ) -> <B as Backend>::BoolTensorPrimitive

Transposes a bool tensor.

fn bool_any( tensor: <B as Backend>::BoolTensorPrimitive, ) -> <B as Backend>::BoolTensorPrimitive

Tests if any element in the boolean tensor evaluates to True.

fn bool_any_dim( tensor: <B as Backend>::BoolTensorPrimitive, dim: usize, ) -> <B as Backend>::BoolTensorPrimitive

Tests if any element in the boolean tensor evaluates to True along a given dimension dim.

fn bool_all( tensor: <B as Backend>::BoolTensorPrimitive, ) -> <B as Backend>::BoolTensorPrimitive

Tests if all elements in the boolean tensor evaluate to True.

fn bool_all_dim( tensor: <B as Backend>::BoolTensorPrimitive, dim: usize, ) -> <B as Backend>::BoolTensorPrimitive

Tests if all elements in the boolean tensor evaluate to True along a given dimension dim.

impl<E, Q> Clone for LibTorch<E, Q>

where E: Clone, Q: Clone,

fn clone(&self) -> LibTorch<E, Q>

Returns a copy of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<E, Q> Debug for LibTorch<E, Q>

where E: Debug, Q: Debug,

fn fmt(&self, f: &mut Formatter<'_>) -> Result<(), Error>

Formats the value using the given formatter.

impl<E, Q> Default for LibTorch<E, Q>

where E: Default, Q: Default,

fn default() -> LibTorch<E, Q>

Returns the “default value” for a type.

impl<E, Q> FloatTensorOps<LibTorch<E, Q>> for LibTorch<E, Q>

where E: TchElement, Q: QuantElement,

fn float_from_data(data: TensorData, device: &LibTorchDevice) -> TchTensor

Creates a new tensor from the data structure.

fn float_random( shape: Shape, distribution: Distribution, device: &LibTorchDevice, ) -> TchTensor

Creates a new tensor with random values.

fn float_repeat_dim(tensor: TchTensor, dim: usize, times: usize) -> TchTensor

Repeat the tensor along the given dimension.

fn float_zeros(shape: Shape, device: &LibTorchDevice) -> TchTensor

Creates a new tensor with zeros.

fn float_ones(shape: Shape, device: &LibTorchDevice) -> TchTensor

Creates a new tensor with ones.

async fn float_into_data(tensor: TchTensor) -> TensorData

Converts the tensor to a data structure.

fn float_device(tensor: &TchTensor) -> LibTorchDevice

Gets the device of the tensor.

fn float_to_device(tensor: TchTensor, device: &LibTorchDevice) -> TchTensor

Moves the tensor to the given device.

fn float_empty( shape: Shape, device: &<LibTorch<E> as Backend>::Device, ) -> TchTensor

Creates an empty tensor with the given shape.

fn float_add(lhs: TchTensor, rhs: TchTensor) -> TchTensor

Adds two tensors together.

fn float_add_scalar(lhs: TchTensor, rhs: E) -> TchTensor

Adds a scalar to a tensor.

fn float_sub(lhs: TchTensor, rhs: TchTensor) -> TchTensor

Subtracts two tensors.

fn float_sub_scalar(lhs: TchTensor, rhs: E) -> TchTensor

Subtracts a scalar from a tensor.

fn float_mul(lhs: TchTensor, rhs: TchTensor) -> TchTensor

Multiplies two tensors together element-wise.

fn float_mul_scalar(lhs: TchTensor, rhs: E) -> TchTensor

Multiplies a tensor by a scalar.

fn float_div(lhs: TchTensor, rhs: TchTensor) -> TchTensor

Divides two tensors element-wise.

fn float_div_scalar(lhs: TchTensor, rhs: E) -> TchTensor

Divides a tensor by a scalar.

fn float_remainder(lhs: TchTensor, rhs: TchTensor) -> TchTensor

Computes the remainder of division between two tensors element-wise.

fn float_remainder_scalar(lhs: TchTensor, rhs: E) -> TchTensor

Computes the modulus of a tensor given a scalar.

fn float_matmul(lhs: TchTensor, rhs: TchTensor) -> TchTensor

Multiplies two tensors together using matrix multiplication.

fn float_neg(tensor: TchTensor) -> TchTensor

Negates a tensor element-wise.

fn float_recip(tensor: TchTensor) -> TchTensor

Calculates the reciprocals element-wise

fn float_swap_dims(tensor: TchTensor, dim1: usize, dim2: usize) -> TchTensor

Swaps two dimensions of a tensor.

fn float_reshape(tensor: TchTensor, shape: Shape) -> TchTensor

Reshapes a tensor.

fn float_gather(dim: usize, tensor: TchTensor, indices: TchTensor) -> TchTensor

Gather elements from a tensor.

fn float_scatter( dim: usize, tensor: TchTensor, indices: TchTensor, value: TchTensor, ) -> TchTensor

Scatter elements into a tensor.

fn float_select(tensor: TchTensor, dim: usize, indices: TchTensor) -> TchTensor

Select tensor elements along the given dimension corresponding for the given indices.

fn float_select_assign( tensor: TchTensor, dim: usize, indices: TchTensor, value: TchTensor, ) -> TchTensor

Assign the selected elements along the given dimension corresponding for the given indices to the given value.

fn float_slice(tensor: TchTensor, ranges: &[Range<usize>]) -> TchTensor

Select tensor elements corresponding for the given ranges.

fn float_slice_assign( tensor: TchTensor, ranges: &[Range<usize>], value: TchTensor, ) -> TchTensor

Assign the selected elements corresponding for the given ranges to the given value.

fn float_mask_where( tensor: TchTensor, mask: TchTensor, value: TchTensor, ) -> TchTensor

Update the given tensor with the value tensor where the mask is true.

fn float_mask_fill(tensor: TchTensor, mask: TchTensor, value: E) -> TchTensor

Update the given tensor with the value where the mask is true.

fn float_equal(lhs: TchTensor, rhs: TchTensor) -> TchTensor

Equal comparison of two tensors.

fn float_equal_elem(lhs: TchTensor, rhs: E) -> TchTensor

Equal comparison of a tensor and a scalar.

fn float_greater(lhs: TchTensor, rhs: TchTensor) -> TchTensor

Greater than comparison of two tensors.

fn float_greater_elem(lhs: TchTensor, rhs: E) -> TchTensor

Greater than comparison of a tensor and a scalar.

fn float_greater_equal(lhs: TchTensor, rhs: TchTensor) -> TchTensor

Greater than or equal comparison of two tensors.

fn float_greater_equal_elem(lhs: TchTensor, rhs: E) -> TchTensor

Greater than or equal comparison of a tensor and a scalar.

fn float_lower(lhs: TchTensor, rhs: TchTensor) -> TchTensor

Less than comparison of two tensors.

fn float_lower_elem(lhs: TchTensor, rhs: E) -> TchTensor

Less than comparison of a tensor and a scalar.

fn float_lower_equal(lhs: TchTensor, rhs: TchTensor) -> TchTensor

Less than or equal comparison of two tensors.

fn float_lower_equal_elem(lhs: TchTensor, rhs: E) -> TchTensor

Less than or equal comparison of a tensor and a scalar.

fn float_mean(tensor: TchTensor) -> TchTensor

Mean of all elements in a tensor.

fn float_sum(tensor: TchTensor) -> TchTensor

Sum of all elements in a tensor.

fn float_sum_dim(tensor: TchTensor, dim: usize) -> TchTensor

Sum of all elements in a tensor along a dimension.

fn float_mean_dim(tensor: TchTensor, dim: usize) -> TchTensor

Mean of all elements in a tensor along a dimension.

fn float_prod(tensor: TchTensor) -> TchTensor

Product of all elements in a tensor.

fn float_prod_dim(tensor: TchTensor, dim: usize) -> TchTensor

Product of all elements in a tensor along a dimension.

fn float_argmax(tensor: TchTensor, dim: usize) -> TchTensor

Gets the indices of the maximum elements of a tensor along an axis.

fn float_argmin(tensor: TchTensor, dim: usize) -> TchTensor

Gets the indices of the minimum elements of a tensor along an axis.

fn float_max_dim(tensor: TchTensor, dim: usize) -> TchTensor

Gets the maximum elements of a tensor along an axis.

fn float_max_dim_with_indices( tensor: TchTensor, dim: usize, ) -> (TchTensor, TchTensor)

Gets the maximum elements of a tensor along an axis and their indices.

fn float_min_dim(tensor: TchTensor, dim: usize) -> TchTensor

Gets the minimum elements of a tensor along an axis.

fn float_min_dim_with_indices( tensor: TchTensor, dim: usize, ) -> (TchTensor, TchTensor)

Gets the minimum elements of a tensor along an axis and their indices.

fn float_exp(tensor: TchTensor) -> TchTensor

Returns a new tensor with exponential values.

fn float_log(tensor: TchTensor) -> TchTensor

Returns a new tensor with natural logarithm values.

fn float_log1p(tensor: TchTensor) -> TchTensor

Returns a new tensor with logarithm values of (1 + Xi).

fn float_powf_scalar(tensor: TchTensor, value: f32) -> TchTensor

Returns a new tensor with values raised to the power of float value.

fn float_sqrt(tensor: TchTensor) -> TchTensor

Returns a new tensor with square root values.

fn float_abs(tensor: TchTensor) -> TchTensor

Returns a new tensor with absolute values.

fn float_cos(tensor: TchTensor) -> TchTensor

Returns a new tensor with cosine values.

fn float_sin(tensor: TchTensor) -> TchTensor

Returns a new tensor with sine values.

fn float_tanh(tensor: TchTensor) -> TchTensor

Returns a new tensor with tangent values.

fn float_round(tensor: TchTensor) -> TchTensor

Returns a new tensor with rounded values.

fn float_floor(tensor: TchTensor) -> TchTensor

Returns a new tensor with floored values.

fn float_ceil(tensor: TchTensor) -> TchTensor

Returns a new tensor with ceiled values.

fn float_erf(tensor: TchTensor) -> TchTensor

Returns a new tensor with the error function values.

fn float_cat(tensors: Vec<TchTensor>, dim: usize) -> TchTensor

Concatenates tensors along a dimension.

fn float_clamp_min(tensor: TchTensor, min: E) -> TchTensor

Clamps a tensor under a minimum value.

fn float_clamp_max( tensor: TchTensor, max: <LibTorch<E> as Backend>::FloatElem, ) -> TchTensor

Clamps a tensor over a maximum value.

fn float_clamp( tensor: TchTensor, min: <LibTorch<E> as Backend>::FloatElem, max: <LibTorch<E> as Backend>::FloatElem, ) -> TchTensor

Clamps a tensor between a minimum and maximum value.

fn float_into_int(tensor: TchTensor) -> TchTensor

Converts float tensor to int tensor.

fn float_narrow( tensor: TchTensor, dim: usize, start: usize, length: usize, ) -> TchTensor

Returns a new tensor with the given dimension narrowed to the given range.

fn float_chunk(tensor: TchTensor, chunks: usize, dim: usize) -> Vec<TchTensor>

Split the tensor along the given dimension into chunks.

fn float_split( tensor: TchTensor, split_size: usize, dim: usize, ) -> Vec<TchTensor>

Split the tensor along the given dimension into chunks of split_size.

fn float_split_with_sizes( tensor: TchTensor, split_sizes: Vec<usize>, dim: usize, ) -> Vec<TchTensor>

Split the tensor along the given dimension into chunks with sizes in dim according to split_sizes.

fn float_powf(lhs: TchTensor, rhs: TchTensor) -> TchTensor

Element-wise power with a FloatTensor.

fn float_permute(tensor: TchTensor, axes: &[usize]) -> TchTensor

Permutes the dimensions of a tensor.

fn float_flip(tensor: TchTensor, axes: &[usize]) -> TchTensor

Reverse the order of elements in a tensor along the given axes.

fn float_sign(tensor: TchTensor) -> TchTensor

Returns the signs of the float tensor.

fn float_expand(tensor: TchTensor, shape: Shape) -> TchTensor

Broadcasts the float tensor to the given shape.

fn float_sort(tensor: TchTensor, dim: usize, descending: bool) -> TchTensor

Sort the elements of the input tensor by value in along a given dimension.

fn float_sort_with_indices( tensor: TchTensor, dim: usize, descending: bool, ) -> (TchTensor, TchTensor)

Sort the elements of the input tensor by value in along a given dimension.

fn float_argsort( tensor: TchTensor, dim: usize, descending: bool, ) -> <LibTorch<E, Q> as Backend>::IntTensorPrimitive

Returns the indices that sort the elements of the input tensor by value along a given dimension.

fn float_cast(tensor: TchTensor, dtype: FloatDType) -> TchTensor

Converts a tensor to another floating point data type.

fn float_full( shape: Shape, fill_value: <B as Backend>::FloatElem, device: &<B as Backend>::Device, ) -> <B as Backend>::FloatTensorPrimitive

Creates a tensor filled with given value.

fn float_transpose( tensor: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

Transposes a tensor.

fn float_not_equal( lhs: <B as Backend>::FloatTensorPrimitive, rhs: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::BoolTensorPrimitive

Element-wise non-equality comparison.

fn float_not_equal_elem( lhs: <B as Backend>::FloatTensorPrimitive, rhs: <B as Backend>::FloatElem, ) -> <B as Backend>::BoolTensorPrimitive

Element-wise non-equality comparison with a scalar.

fn float_detach( tensor: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

Detaches a tensor from the computation graph.

fn float_set_require_grad( tensor: <B as Backend>::FloatTensorPrimitive, _require_grad: bool, ) -> <B as Backend>::FloatTensorPrimitive

Sets the require_grad flag of a tensor.

fn float_is_require_grad(_tensor: &<B as Backend>::FloatTensorPrimitive) -> bool

Returns the require_grad flag of a tensor.

fn float_powi( lhs: <B as Backend>::FloatTensorPrimitive, rhs: <B as Backend>::IntTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

Element-wise power with an IntTensor.

fn float_powi_scalar( lhs: <B as Backend>::FloatTensorPrimitive, rhs: <B as Backend>::IntElem, ) -> <B as Backend>::FloatTensorPrimitive

raises a tensor to the power of an int scalar.

fn float_max( tensor: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

Gets the maximum element of a tensor.

fn float_min( tensor: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

Gets the minimum element of a tensor.

fn float_any( tensor: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::BoolTensorPrimitive

Tests if any element in the float tensor evaluates to True.

fn float_any_dim( tensor: <B as Backend>::FloatTensorPrimitive, dim: usize, ) -> <B as Backend>::BoolTensorPrimitive

Tests if any element in the float tensor evaluates to True along a given dimension dim.

fn float_all( tensor: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::BoolTensorPrimitive

Tests if all elements in the float tensor evaluate to True.

fn float_all_dim( tensor: <B as Backend>::FloatTensorPrimitive, dim: usize, ) -> <B as Backend>::BoolTensorPrimitive

Tests if all elements in the float tensor evaluate to True along a given dimension dim.

impl<E, Q> IntTensorOps<LibTorch<E, Q>> for LibTorch<E, Q>

where E: TchElement, Q: QuantElement,

fn int_from_data(data: TensorData, device: &LibTorchDevice) -> TchTensor

Creates a tensor from the data structure.

fn int_repeat_dim(tensor: TchTensor, dim: usize, times: usize) -> TchTensor

Repeats the tensor along the given dimension the given number of times.

async fn int_into_data(tensor: TchTensor) -> TensorData

Converts the tensor to a data structure.

fn int_to_device(tensor: TchTensor, device: &LibTorchDevice) -> TchTensor

Moves the tensor to the given device.

fn int_reshape(tensor: TchTensor, shape: Shape) -> TchTensor

Reshapes the tensor.

fn int_device(tensor: &TchTensor) -> LibTorchDevice

Gets the device of the tensor.

fn int_empty( shape: Shape, device: &<LibTorch<E> as Backend>::Device, ) -> TchTensor

Creates a new int tensor.

fn int_slice(tensor: TchTensor, ranges: &[Range<usize>]) -> TchTensor

Gets the element at the given indices.

fn int_slice_assign( tensor: TchTensor, ranges: &[Range<usize>], value: TchTensor, ) -> TchTensor

Sets the element at the given indices.

fn int_cat(tensors: Vec<TchTensor>, dim: usize) -> TchTensor

Concatenates the given tensors along the given dimension.

fn int_equal(lhs: TchTensor, rhs: TchTensor) -> TchTensor

Element-wise equality comparison.

fn int_equal_elem(lhs: TchTensor, rhs: i64) -> TchTensor

Element-wise equality comparison with a scalar.

fn int_greater(lhs: TchTensor, rhs: TchTensor) -> TchTensor

Element-wise greater than comparison.

fn int_greater_elem(lhs: TchTensor, rhs: i64) -> TchTensor

Element-wise greater than comparison with a scalar.

fn int_greater_equal(lhs: TchTensor, rhs: TchTensor) -> TchTensor

Element-wise greater than or equal comparison.

fn int_greater_equal_elem(lhs: TchTensor, rhs: i64) -> TchTensor

Element-wise greater than or equal comparison with a scalar.

fn int_lower(lhs: TchTensor, rhs: TchTensor) -> TchTensor

Element-wise less than comparison.

fn int_lower_elem(lhs: TchTensor, rhs: i64) -> TchTensor

Element-wise less than comparison with a scalar.

fn int_lower_equal(lhs: TchTensor, rhs: TchTensor) -> TchTensor

Element-wise less than or equal comparison.

fn int_lower_equal_elem(lhs: TchTensor, rhs: i64) -> TchTensor

Element-wise less than or equal comparison with a scalar.

fn int_add(lhs: TchTensor, rhs: TchTensor) -> TchTensor

Element-wise addition.

fn int_add_scalar(lhs: TchTensor, rhs: i64) -> TchTensor

Element-wise addition with a scalar.

fn int_sub(lhs: TchTensor, rhs: TchTensor) -> TchTensor

Element-wise subtraction.

fn int_sub_scalar(lhs: TchTensor, rhs: i64) -> TchTensor

Element-wise subtraction with a scalar.

fn int_mul(lhs: TchTensor, rhs: TchTensor) -> TchTensor

Element-wise multiplication.

fn int_mul_scalar(lhs: TchTensor, rhs: i64) -> TchTensor

Element-wise multiplication with a scalar.

fn int_div(lhs: TchTensor, rhs: TchTensor) -> TchTensor

Element-wise division.

fn int_div_scalar(lhs: TchTensor, rhs: i64) -> TchTensor

Element-wise division with a scalar.

fn int_remainder(lhs: TchTensor, rhs: TchTensor) -> TchTensor

Element-wise modulus.

fn int_remainder_scalar(lhs: TchTensor, rhs: i64) -> TchTensor

Element-wise modulus with a scalar.

fn int_neg(tensor: TchTensor) -> TchTensor

Element-wise negation.

fn int_zeros( shape: Shape, device: &<LibTorch<E> as Backend>::Device, ) -> TchTensor

Creates a tensor of zeros.

fn int_ones( shape: Shape, device: &<LibTorch<E> as Backend>::Device, ) -> TchTensor

Creates a tensor of ones.

fn int_full( shape: Shape, fill_value: i64, device: &<LibTorch<E> as Backend>::Device, ) -> TchTensor

Creates a tensor filled with given value.

fn int_sum(tensor: TchTensor) -> TchTensor

Sums all elements in the tensor.

fn int_sum_dim(tensor: TchTensor, dim: usize) -> TchTensor

Sums all elements in the tensor along a dimension.

fn int_prod(tensor: TchTensor) -> TchTensor

Computes the product of all elements in the tensor.

fn int_prod_dim(tensor: TchTensor, dim: usize) -> TchTensor

Computes the product of all elements in the tensor along a dimension.

fn int_mean(tensor: TchTensor) -> TchTensor

Computes the mean of all elements in the tensor.

fn int_mean_dim(tensor: TchTensor, dim: usize) -> TchTensor

Computes the mean of all elements in the tensor along a dimension.

fn int_gather(dim: usize, tensor: TchTensor, indices: TchTensor) -> TchTensor

Gather elements from the tensor at the given indices.

fn int_scatter( dim: usize, tensor: TchTensor, indices: TchTensor, value: TchTensor, ) -> TchTensor

Scatter a given value to the tensor at the given indices.

fn int_select(tensor: TchTensor, dim: usize, indices: TchTensor) -> TchTensor

Select tensor elements along the given dimension corresponding to the given indices.

fn int_select_assign( tensor: TchTensor, dim: usize, indices: TchTensor, value: TchTensor, ) -> TchTensor

Assign the selected elements along the given dimension corresponding to the given indices to the given value.

fn int_mask_where( tensor: TchTensor, mask: TchTensor, source: TchTensor, ) -> TchTensor

Fills the tensor with values from the source tensor if the mask is true at the given indices.

fn int_mask_fill(tensor: TchTensor, mask: TchTensor, value: i64) -> TchTensor

Fills the tensor with the given value if the mask is true at the given indices.

fn int_argmax(tensor: TchTensor, dim: usize) -> TchTensor

Gets the indices of the maximum elements along a dimension.

fn int_argmin(tensor: TchTensor, dim: usize) -> TchTensor

Gets the indices of the minimum elements along a dimension.

fn int_max_dim(tensor: TchTensor, dim: usize) -> TchTensor

Gets the maximum element in the tensor along a dimension.

fn int_max_dim_with_indices( tensor: TchTensor, dim: usize, ) -> (TchTensor, TchTensor)

Gets the maximum elements and corresponding indices along a dimension.

fn int_min_dim(tensor: TchTensor, dim: usize) -> TchTensor

Gets the minimum elements in the tensor along a dimension.

fn int_min_dim_with_indices( tensor: TchTensor, dim: usize, ) -> (TchTensor, TchTensor)

Gets the minimum elements and corresponding indices along a dimension.

fn int_clamp_min(tensor: TchTensor, min: i64) -> TchTensor

Clamps a tensor under a minimum value.

fn int_clamp_max(tensor: TchTensor, max: i64) -> TchTensor

Clamps a tensor over a maximum value.

fn int_clamp(tensor: TchTensor, min: i64, max: i64) -> TchTensor

Clamps a tensor between a minimum and maximum value.

fn int_abs(tensor: TchTensor) -> TchTensor

Returns a new tensor with absolute values.

fn int_into_float(tensor: TchTensor) -> TchTensor

Converts int tensor to float tensor.

fn int_swap_dims( tensor: <LibTorch<E, Q> as Backend>::IntTensorPrimitive, dim1: usize, dim2: usize, ) -> <LibTorch<E, Q> as Backend>::IntTensorPrimitive

Swaps two dimensions of an int tensor.

fn int_narrow( tensor: TchTensor, dim: usize, start: usize, length: usize, ) -> TchTensor

Returns a new tensor with the given dimension narrowed to the given range.

fn int_chunk(tensor: TchTensor, chunks: usize, dim: usize) -> Vec<TchTensor>

Split the tensor along the given dimension into chunks.

fn int_split(tensor: TchTensor, split_size: usize, dim: usize) -> Vec<TchTensor>

Split the tensor along the given dimension into chunks of split_size.

fn int_split_with_sizes( tensor: TchTensor, split_sizes: Vec<usize>, dim: usize, ) -> Vec<TchTensor>

Split the tensor along the given dimension into chunks with sizes in dim according to split_sizes.

fn int_random( shape: Shape, distribution: Distribution, device: &LibTorchDevice, ) -> TchTensor

Creates a new int tensor with random values.

fn int_arange(range: Range<i64>, device: &LibTorchDevice) -> TchTensor

Creates a new tensor with values from the given range.

fn int_permute( tensor: <LibTorch<E, Q> as Backend>::IntTensorPrimitive, axes: &[usize], ) -> <LibTorch<E, Q> as Backend>::IntTensorPrimitive

Permutes the dimensions of a tensor.

fn int_flip( tensor: <LibTorch<E, Q> as Backend>::IntTensorPrimitive, axes: &[usize], ) -> <LibTorch<E, Q> as Backend>::IntTensorPrimitive

Reverse the order of elements in a tensor along the given axes.

fn int_sign( tensor: <LibTorch<E, Q> as Backend>::IntTensorPrimitive, ) -> <LibTorch<E, Q> as Backend>::IntTensorPrimitive

Returns the signs of the int tensor.

fn int_expand( tensor: <LibTorch<E, Q> as Backend>::IntTensorPrimitive, shape: Shape, ) -> <LibTorch<E, Q> as Backend>::IntTensorPrimitive

Broadcasts the int tensor to the given shape.

fn int_sort( tensor: <LibTorch<E, Q> as Backend>::IntTensorPrimitive, dim: usize, descending: bool, ) -> <LibTorch<E, Q> as Backend>::IntTensorPrimitive

Sort the elements of the input tensor by value along a given dimension.

fn int_argsort( tensor: <LibTorch<E, Q> as Backend>::IntTensorPrimitive, dim: usize, descending: bool, ) -> <LibTorch<E, Q> as Backend>::IntTensorPrimitive

Returns the indices that sort the elements of the input tensor by value along a given dimension.

fn int_not_equal( lhs: <B as Backend>::IntTensorPrimitive, rhs: <B as Backend>::IntTensorPrimitive, ) -> <B as Backend>::BoolTensorPrimitive

Element-wise non-equality comparison.

fn int_not_equal_elem( lhs: <B as Backend>::IntTensorPrimitive, rhs: <B as Backend>::IntElem, ) -> <B as Backend>::BoolTensorPrimitive

Element-wise non-equality comparison with a scalar.

fn int_powi( lhs: <B as Backend>::IntTensorPrimitive, rhs: <B as Backend>::IntTensorPrimitive, ) -> <B as Backend>::IntTensorPrimitive

Element-wise power with a IntTensor.

fn int_powf( lhs: <B as Backend>::IntTensorPrimitive, rhs: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::IntTensorPrimitive

Element-wise power with a floatTensor.

fn int_powi_scalar( lhs: <B as Backend>::IntTensorPrimitive, rhs: <B as Backend>::IntElem, ) -> <B as Backend>::IntTensorPrimitive

Element-wise power with a scalar.

fn int_powf_scalar( lhs: <B as Backend>::IntTensorPrimitive, rhs: f32, ) -> <B as Backend>::IntTensorPrimitive

Element-wise power with a floatTensor.

fn int_max( tensor: <B as Backend>::IntTensorPrimitive, ) -> <B as Backend>::IntTensorPrimitive

Gets the maximum element in the tensor.

fn int_min( tensor: <B as Backend>::IntTensorPrimitive, ) -> <B as Backend>::IntTensorPrimitive

Gets the minimum element in the tensor.

fn int_transpose( tensor: <B as Backend>::IntTensorPrimitive, ) -> <B as Backend>::IntTensorPrimitive

Transposes an int tensor.

fn int_arange_step( range: Range<i64>, step: usize, device: &<B as Backend>::Device, ) -> <B as Backend>::IntTensorPrimitive

Creates a new tensor with values from the given range with the given step size.

fn int_any( tensor: <B as Backend>::IntTensorPrimitive, ) -> <B as Backend>::BoolTensorPrimitive

Tests if any element in the int tensor evaluates to True.

fn int_any_dim( tensor: <B as Backend>::IntTensorPrimitive, dim: usize, ) -> <B as Backend>::BoolTensorPrimitive

Tests if any element in the int tensor evaluates to True along a given dimension dim.

fn int_all( tensor: <B as Backend>::IntTensorPrimitive, ) -> <B as Backend>::BoolTensorPrimitive

Tests if all elements in the int tensor evaluate to True.

fn int_all_dim( tensor: <B as Backend>::IntTensorPrimitive, dim: usize, ) -> <B as Backend>::BoolTensorPrimitive

Tests if all elements in the int tensor evaluate to True along a given dimension dim.

fn int_sort_with_indices( tensor: <B as Backend>::IntTensorPrimitive, dim: usize, descending: bool, ) -> (<B as Backend>::IntTensorPrimitive, <B as Backend>::IntTensorPrimitive)

Sort the elements of the input tensor by value along a given dimension.

impl<E, Q> ModuleOps<LibTorch<E, Q>> for LibTorch<E, Q>

where E: TchElement, Q: QuantElement,

fn embedding(weights: TchTensor, indices: TchTensor) -> TchTensor

Embedding operation.

fn embedding_backward( weights: TchTensor, output: TchTensor, indices: TchTensor, ) -> TchTensor

Embedding backward operation.

fn conv1d( x: TchTensor, weight: TchTensor, bias: Option<TchTensor>, options: ConvOptions<1>, ) -> TchTensor

One dimensional convolution.

fn conv2d( x: TchTensor, weight: TchTensor, bias: Option<TchTensor>, options: ConvOptions<2>, ) -> TchTensor

Two dimensional convolution.

fn conv3d( x: TchTensor, weight: TchTensor, bias: Option<TchTensor>, options: ConvOptions<3>, ) -> TchTensor

Three dimensional convolution.

fn deform_conv2d( _x: TchTensor, _offset: TchTensor, _weight: TchTensor, _mask: Option<TchTensor>, _bias: Option<TchTensor>, _options: DeformConvOptions<2>, ) -> TchTensor

Two dimensional deformable convolution.

fn deform_conv2d_backward( _x: TchTensor, _offset: TchTensor, _weight: TchTensor, _mask: Option<TchTensor>, _bias: Option<TchTensor>, _out_grad: TchTensor, _options: DeformConvOptions<2>, ) -> DeformConv2dBackward<LibTorch<E, Q>>

Backward pass for the deform_conv2d operation.

fn conv_transpose1d( x: TchTensor, weight: TchTensor, bias: Option<TchTensor>, options: ConvTransposeOptions<1>, ) -> TchTensor

One dimensional transposed convolution.

fn conv_transpose2d( x: TchTensor, weight: TchTensor, bias: Option<TchTensor>, options: ConvTransposeOptions<2>, ) -> TchTensor

Two dimensional transposed convolution.

fn conv_transpose3d( x: TchTensor, weight: TchTensor, bias: Option<TchTensor>, options: ConvTransposeOptions<3>, ) -> TchTensor

Three dimensional transposed convolution.

fn avg_pool1d( x: TchTensor, kernel_size: usize, stride: usize, padding: usize, count_include_pad: bool, ) -> TchTensor

One dimensional avg pooling.

fn avg_pool2d( x: TchTensor, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], count_include_pad: bool, ) -> TchTensor

Two dimensional avg pooling.

fn avg_pool2d_backward( x: TchTensor, grad: TchTensor, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], count_include_pad: bool, ) -> TchTensor

Backward pass for the avg pooling 2d operation.

fn max_pool1d( x: TchTensor, kernel_size: usize, stride: usize, padding: usize, dilation: usize, ) -> TchTensor

One dimensional max pooling.

fn max_pool1d_with_indices( x: TchTensor, kernel_size: usize, stride: usize, padding: usize, dilation: usize, ) -> MaxPool1dWithIndices<LibTorch<E, Q>>

One dimensional max pooling with indices.

fn max_pool2d( x: TchTensor, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], dilation: [usize; 2], ) -> TchTensor

Two dimensional max pooling.

fn max_pool2d_with_indices( x: TchTensor, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], dilation: [usize; 2], ) -> MaxPool2dWithIndices<LibTorch<E, Q>>

Two dimensional max pooling with indices.

fn max_pool2d_with_indices_backward( x: TchTensor, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], dilation: [usize; 2], output_grad: TchTensor, indices: TchTensor, ) -> MaxPool2dBackward<LibTorch<E, Q>>

Backward pass for the max pooling 2d operation.

fn adaptive_avg_pool2d(x: TchTensor, output_size: [usize; 2]) -> TchTensor

Two dimensional adaptive avg pooling.

fn adaptive_avg_pool2d_backward(x: TchTensor, grad: TchTensor) -> TchTensor

Backward pass for the adaptive avg pooling 2d operation.

fn adaptive_avg_pool1d(x: TchTensor, output_size: usize) -> TchTensor

One dimensional adaptive avg pooling.

fn interpolate( x: TchTensor, output_size: [usize; 2], options: InterpolateOptions, ) -> TchTensor

Down/up samples the input.

fn interpolate_backward( x: TchTensor, grad: TchTensor, output_size: [usize; 2], options: InterpolateOptions, ) -> TchTensor

Backward pass for the interpolate operation.

fn conv1d_x_backward( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvOptions<1>, ) -> <B as Backend>::FloatTensorPrimitive

Backward pass for the conv1d operation, returning the gradient for x.

fn conv1d_weight_backward( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvOptions<1>, ) -> <B as Backend>::FloatTensorPrimitive

Backward pass for the conv1d operation, returning the gradient for weight.

fn conv1d_bias_backward( x: <B as Backend>::FloatTensorPrimitive, bias: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

Backward pass for the conv1d operation, returning the gradient for bias.

fn conv2d_x_backward( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvOptions<2>, ) -> <B as Backend>::FloatTensorPrimitive

Backward pass for the conv2d operation, returning the gradient for x.

fn conv2d_weight_backward( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvOptions<2>, ) -> <B as Backend>::FloatTensorPrimitive

Backward pass for the conv2d operation, returning the gradient for weight.

fn conv2d_bias_backward( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, bias: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

Backward pass for the conv2d operation, returning the gradient for bias.

fn conv3d_x_backward( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvOptions<3>, ) -> <B as Backend>::FloatTensorPrimitive

Backward pass for the conv3d operation, returning the gradient for x.

fn conv3d_weight_backward( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvOptions<3>, ) -> <B as Backend>::FloatTensorPrimitive

Backward pass for the conv3d operation, returning the gradient for weight.

fn conv3d_bias_backward( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, bias: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

Backward pass for the conv3d operation, returning the gradient for bias.

fn conv_transpose1d_x_backward( weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvTransposeOptions<1>, ) -> <B as Backend>::FloatTensorPrimitive

Backward pass for the conv transpose 1d operation, returning the gradient for x.

fn conv_transpose1d_weight_backward( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvTransposeOptions<1>, ) -> <B as Backend>::FloatTensorPrimitive

Backward pass for the conv transpose 1d operation, returning the gradient for weight.

fn conv_transpose1d_bias_backward( x: <B as Backend>::FloatTensorPrimitive, bias: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

Backward pass for the conv transpose 1d operation, returning the gradient for bias.

fn conv_transpose2d_x_backward( weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvTransposeOptions<2>, ) -> <B as Backend>::FloatTensorPrimitive

Backward pass for the conv transpose 2d operation, returning the gradient for x.

fn conv_transpose2d_weight_backward( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvTransposeOptions<2>, ) -> <B as Backend>::FloatTensorPrimitive

Backward pass for the conv transpose 2d operation, returning the gradient for weight.

fn conv_transpose2d_bias_backward( x: <B as Backend>::FloatTensorPrimitive, bias: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

Backward pass for the conv transpose 2d operation, returning the gradient for bias.

fn conv_transpose3d_x_backward( weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvTransposeOptions<3>, ) -> <B as Backend>::FloatTensorPrimitive

Backward pass for the conv transpose 3d operation, returning the gradient for x.

fn conv_transpose3d_weight_backward( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, options: ConvTransposeOptions<3>, ) -> <B as Backend>::FloatTensorPrimitive

Backward pass for the conv transpose 3d operation, returning the gradient for weight.

fn conv_transpose3d_bias_backward( x: <B as Backend>::FloatTensorPrimitive, bias: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

Backward pass for the conv transpose 3d operation, returning the gradient for bias.

fn unfold4d( x: <B as Backend>::FloatTensorPrimitive, kernel_size: [usize; 2], options: UnfoldOptions, ) -> <B as Backend>::FloatTensorPrimitive

Four-dimensional unfolding.

fn avg_pool1d_backward( x: <B as Backend>::FloatTensorPrimitive, grad: <B as Backend>::FloatTensorPrimitive, kernel_size: usize, stride: usize, padding: usize, count_include_pad: bool, ) -> <B as Backend>::FloatTensorPrimitive

Backward pass for the avg pooling 1d operation.

fn adaptive_avg_pool1d_backward( x: <B as Backend>::FloatTensorPrimitive, grad: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

Backward pass for the adaptive avg pooling 1d operation.

fn max_pool1d_with_indices_backward( x: <B as Backend>::FloatTensorPrimitive, kernel_size: usize, stride: usize, padding: usize, dilation: usize, output_grad: <B as Backend>::FloatTensorPrimitive, indices: <B as Backend>::IntTensorPrimitive, ) -> MaxPool1dBackward<B>

Backward pass for the max pooling 1d operation.

impl<E, Q> QTensorOps<LibTorch<E, Q>> for LibTorch<E, Q>

where E: TchElement, Q: QuantElement,

fn q_from_data( data: TensorData, device: &LibTorchDevice, ) -> <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive

Creates a new tensor from the data structure.

fn quantize( tensor: <LibTorch<E, Q> as Backend>::FloatTensorPrimitive, scheme: &QuantizationScheme, qparams: QuantizationParametersPrimitive<LibTorch<E, Q>>, ) -> <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive

Convert the tensor to a lower precision data type based on the quantization scheme and parameters.

fn quantize_dynamic( tensor: <LibTorch<E, Q> as Backend>::FloatTensorPrimitive, scheme: &QuantizationScheme, ) -> <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive

Dynamically convert the tensor to a lower precision data type based on the quantization scheme.

fn dequantize( tensor: <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, ) -> <LibTorch<E, Q> as Backend>::FloatTensorPrimitive

Convert the tensor back to a higher precision data type.

fn q_device( tensor: &<LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, ) -> LibTorchDevice

Gets the device of the tensor.

fn q_to_device( tensor: <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, device: &<LibTorch<E, Q> as Backend>::Device, ) -> <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive

Moves the tensor to the given device.

fn q_reshape( tensor: <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, shape: Shape, ) -> <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive

Reshapes a tensor.

async fn q_into_data( tensor: <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, ) -> TensorData

Converts the tensor to a data structure.

fn q_swap_dims( tensor: <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, dim1: usize, dim2: usize, ) -> <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive

Swaps two dimensions of a tensor.

fn q_permute( tensor: <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, axes: &[usize], ) -> <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive

Permutes the dimensions of a tensor.

fn q_flip( tensor: <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, axes: &[usize], ) -> <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive

Reverse the order of elements in a tensor along the given axes.

fn q_select( tensor: <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, dim: usize, indices: <LibTorch<E, Q> as Backend>::IntTensorPrimitive, ) -> <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive

Select tensor elements along the given dimension corresponding for the given indices.

fn q_slice( tensor: <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, ranges: &[Range<usize>], ) -> <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive

Select tensor elements corresponding for the given ranges.

fn q_argmax( tensor: <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, dim: usize, ) -> <LibTorch<E, Q> as Backend>::IntTensorPrimitive

Gets the indices of the maximum elements of a tensor along an axis.

fn q_argmin( tensor: <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, dim: usize, ) -> <LibTorch<E, Q> as Backend>::IntTensorPrimitive

Gets the indices of the minimum elements of a tensor along an axis.

fn q_max_dim_with_indices( tensor: <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, dim: usize, ) -> (<LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, <LibTorch<E, Q> as Backend>::IntTensorPrimitive)

Gets the maximum elements of a tensor along an axis and their indices.

fn q_max_dim( tensor: <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, dim: usize, ) -> <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive

Gets the maximum elements of a tensor along an axis.

fn q_min_dim( tensor: <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, dim: usize, ) -> <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive

Gets the minimum elements of a tensor along an axis.

fn q_min_dim_with_indices( tensor: <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, dim: usize, ) -> (<LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, <LibTorch<E, Q> as Backend>::IntTensorPrimitive)

Gets the minimum elements of a tensor along an axis and their indices.

fn q_narrow( tensor: <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, dim: usize, start: usize, length: usize, ) -> <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive

Returns a new tensor with the given dimension narrowed to the given range.

fn q_chunk( tensor: <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, chunks: usize, dim: usize, ) -> Vec<<LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive>

Split the tensor along the given dimension into chunks.

fn q_expand( tensor: <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, shape: Shape, ) -> <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive

Broadcasts the tensor to the given shape.

fn q_sort( tensor: <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, dim: usize, descending: bool, ) -> <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive

Sort the elements of the input tensor by value in along a given dimension.

fn q_sort_with_indices( tensor: <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, dim: usize, descending: bool, ) -> (<LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, <LibTorch<E, Q> as Backend>::IntTensorPrimitive)

Sort the elements of the input tensor by value in along a given dimension.

fn q_argsort( tensor: <LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, dim: usize, descending: bool, ) -> <LibTorch<E, Q> as Backend>::IntTensorPrimitive

Returns the indices that sort the elements of the input tensor by value along a given dimension.

fn q_detach( tensor: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Detaches a tensor from the computation graph.

fn q_set_require_grad( tensor: <B as Backend>::QuantizedTensorPrimitive, _require_grad: bool, ) -> <B as Backend>::QuantizedTensorPrimitive

Sets the require_grad flag of a tensor.

fn q_is_require_grad(_tensor: &<B as Backend>::QuantizedTensorPrimitive) -> bool

Returns the require_grad flag of a tensor.

fn q_repeat_dim( tensor: <B as Backend>::QuantizedTensorPrimitive, dim: usize, times: usize, ) -> <B as Backend>::QuantizedTensorPrimitive

Repeat the tensor along the given dimension.

fn q_add( lhs: <B as Backend>::QuantizedTensorPrimitive, rhs: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Adds two tensors together.

fn q_add_scalar( lhs: <B as Backend>::QuantizedTensorPrimitive, rhs: <B as Backend>::FloatElem, ) -> <B as Backend>::QuantizedTensorPrimitive

Adds a scalar to a tensor.

fn q_clamp_min( tensor: <B as Backend>::QuantizedTensorPrimitive, min: <B as Backend>::FloatElem, ) -> <B as Backend>::QuantizedTensorPrimitive

Clamps a tensor under a minimum value.

fn q_clamp_max( tensor: <B as Backend>::QuantizedTensorPrimitive, max: <B as Backend>::FloatElem, ) -> <B as Backend>::QuantizedTensorPrimitive

Clamps a tensor over a maximum value.

fn q_clamp( tensor: <B as Backend>::QuantizedTensorPrimitive, min: <B as Backend>::FloatElem, max: <B as Backend>::FloatElem, ) -> <B as Backend>::QuantizedTensorPrimitive

Clamps a tensor between a minimum and maximum value.

fn q_sub( lhs: <B as Backend>::QuantizedTensorPrimitive, rhs: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Subtracts two tensors.

fn q_sub_scalar( lhs: <B as Backend>::QuantizedTensorPrimitive, rhs: <B as Backend>::FloatElem, ) -> <B as Backend>::QuantizedTensorPrimitive

Subtracts a scalar from a tensor.

fn q_mul( lhs: <B as Backend>::QuantizedTensorPrimitive, rhs: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Multiplies two tensors together element-wise.

fn q_mul_scalar( lhs: <B as Backend>::QuantizedTensorPrimitive, rhs: <B as Backend>::FloatElem, ) -> <B as Backend>::QuantizedTensorPrimitive

Multiplies a tensor by a scalar.

fn q_div( lhs: <B as Backend>::QuantizedTensorPrimitive, rhs: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Divides two tensors element-wise.

fn q_div_scalar( lhs: <B as Backend>::QuantizedTensorPrimitive, rhs: <B as Backend>::FloatElem, ) -> <B as Backend>::QuantizedTensorPrimitive

Divides a tensor by a scalar.

fn q_remainder( lhs: <B as Backend>::QuantizedTensorPrimitive, rhs: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Computes the remainder of division between two tensors element-wise.

fn q_remainder_scalar( lhs: <B as Backend>::QuantizedTensorPrimitive, rhs: <B as Backend>::FloatElem, ) -> <B as Backend>::QuantizedTensorPrimitive

Computes the modulus of a tensor given a scalar.

fn q_matmul( lhs: <B as Backend>::QuantizedTensorPrimitive, rhs: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Multiplies two tensors together using matrix multiplication.

fn q_neg( tensor: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Negates a tensor element-wise.

fn q_recip( tensor: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Calculates the reciprocals element-wise

fn q_transpose( tensor: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Transposes a tensor.

fn q_gather( dim: usize, tensor: <B as Backend>::QuantizedTensorPrimitive, indices: <B as Backend>::IntTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Gather elements from a tensor.

fn q_scatter( dim: usize, tensor: <B as Backend>::QuantizedTensorPrimitive, indices: <B as Backend>::IntTensorPrimitive, value: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Scatter elements into a tensor.

fn q_select_assign( tensor: <B as Backend>::QuantizedTensorPrimitive, dim: usize, indices: <B as Backend>::IntTensorPrimitive, value: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Assign the selected elements along the given dimension corresponding for the given indices to the given value.

fn q_slice_assign( tensor: <B as Backend>::QuantizedTensorPrimitive, ranges: &[Range<usize>], value: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Assign the selected elements corresponding for the given ranges to the given value.

fn q_mask_where( tensor: <B as Backend>::QuantizedTensorPrimitive, mask: <B as Backend>::BoolTensorPrimitive, value: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Update the given tensor with the value tensor where the mask is true.

fn q_mask_fill( tensor: <B as Backend>::QuantizedTensorPrimitive, mask: <B as Backend>::BoolTensorPrimitive, value: <B as Backend>::FloatElem, ) -> <B as Backend>::QuantizedTensorPrimitive

Update the given tensor with the value where the mask is true.

fn q_sum( tensor: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Sum of all elements in a tensor.

fn q_sum_dim( tensor: <B as Backend>::QuantizedTensorPrimitive, dim: usize, ) -> <B as Backend>::QuantizedTensorPrimitive

Sum of all elements in a tensor along a dimension.

fn q_prod( tensor: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Product of all elements in a tensor.

fn q_prod_dim( tensor: <B as Backend>::QuantizedTensorPrimitive, dim: usize, ) -> <B as Backend>::QuantizedTensorPrimitive

Product of all elements in a tensor along a dimension.

fn q_mean( tensor: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Mean of all elements in a tensor.

fn q_mean_dim( tensor: <B as Backend>::QuantizedTensorPrimitive, dim: usize, ) -> <B as Backend>::QuantizedTensorPrimitive

Mean of all elements in a tensor along a dimension.

fn q_exp( tensor: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Returns a new tensor with exponential values.

fn q_log( tensor: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Returns a new tensor with natural logarithm values.

fn q_log1p( tensor: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Returns a new tensor with logarithm values of (1 + Xi).

fn q_powf( lhs: <B as Backend>::QuantizedTensorPrimitive, rhs: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Element-wise power with another tensor.

fn q_powi( lhs: <B as Backend>::QuantizedTensorPrimitive, rhs: <B as Backend>::IntTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Element-wise power with an IntTensor.

fn q_powi_scalar( lhs: <B as Backend>::QuantizedTensorPrimitive, rhs: <B as Backend>::IntElem, ) -> <B as Backend>::QuantizedTensorPrimitive

Element-wise power with an int scalar.

fn q_powf_scalar( tensor: <B as Backend>::QuantizedTensorPrimitive, value: f32, ) -> <B as Backend>::QuantizedTensorPrimitive

Element-wise power with a float scalar.

fn q_sqrt( tensor: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Returns a new tensor with square root values.

fn q_abs( tensor: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Returns a new tensor with absolute values.

fn q_cos( tensor: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Returns a new tensor with cosine values.

fn q_sin( tensor: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Returns a new tensor with sine values.

fn q_tanh( tensor: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Returns a new tensor with tangent values.

fn q_erf( tensor: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Returns a new tensor with the error function values.

fn q_cat( tensors: Vec<<B as Backend>::QuantizedTensorPrimitive>, dim: usize, ) -> <B as Backend>::QuantizedTensorPrimitive

Concatenates tensors along a dimension.

fn q_max( tensor: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Gets the maximum element of a tensor.

fn q_min( tensor: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::QuantizedTensorPrimitive

Gets the minimum element of a tensor.

fn q_split( tensor: <B as Backend>::QuantizedTensorPrimitive, split_size: usize, dim: usize, ) -> Vec<<B as Backend>::QuantizedTensorPrimitive>

Split the tensor along the given dimension into chunks of split_size.

fn q_split_with_sizes( tensor: <B as Backend>::QuantizedTensorPrimitive, split_sizes: Vec<usize>, dim: usize, ) -> Vec<<B as Backend>::QuantizedTensorPrimitive>

Split the tensor along the given dimension into chunks with sizes in dim according to split_sizes.

fn q_any( tensor: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::BoolTensorPrimitive

Tests if any element in the tensor evaluates to True.

fn q_any_dim( tensor: <B as Backend>::QuantizedTensorPrimitive, dim: usize, ) -> <B as Backend>::BoolTensorPrimitive

Tests if any element in the float tensor evaluates to True along a given dimension dim.

fn q_all( tensor: <B as Backend>::QuantizedTensorPrimitive, ) -> <B as Backend>::BoolTensorPrimitive

Tests if all elements in the tensor evaluate to True.

fn q_all_dim( tensor: <B as Backend>::QuantizedTensorPrimitive, dim: usize, ) -> <B as Backend>::BoolTensorPrimitive

Tests if all elements in the tensor evaluate to True along a given dimension dim.

impl<E, Q> TransactionOps<LibTorch<E, Q>> for LibTorch<E, Q>

where E: TchElement, Q: QuantElement,

fn tr_execute( transaction: TransactionPrimitive<B>, ) -> impl Future<Output = TransactionPrimitiveResult> + Send + 'static

Executes a transaction and return its result.

impl<E, Q> Copy for LibTorch<E, Q>

where E: Copy, Q: Copy,