Struct burn::backend::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<E>) -> TchTensor<E>

Applies the ReLU activation function.

fn gelu(tensor: TchTensor<E>) -> TchTensor<E>

Applies the Gelu activation function.

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

Applies the Gelu activation function backward.

fn sigmoid(tensor: TchTensor<E>) -> TchTensor<E>

Applies the Sigmoid activation function.

fn log_sigmoid(tensor: TchTensor<E>) -> TchTensor<E>

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 FullPrecisionBridge = PrecisionBridge<f32>

A bridge that can cast tensors to full precision.

type FloatTensorPrimitive = TchTensor<E>

Tensor primitive to be used for all float operations.

type FloatElem = E

Float element type.

type IntTensorPrimitive = TchTensor<i64>

Tensor primitive to be used for all int operations.

type IntElem = i64

Int element type.

type BoolTensorPrimitive = TchTensor<bool>

Tensor primitive to be used for all bool operations.

type QuantizedTensorPrimitive = TchQTensor<Q>

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<TElem, OElem, QElem> BackendBridge<LibTorch<OElem, QElem>> for PrecisionBridge<TElem>

where TElem: TchElement, OElem: TchElement, QElem: QuantElement,

type Target = LibTorch<TElem>

The target backend

fn into_target( tensor: <LibTorch<OElem> as Backend>::FloatTensorPrimitive, device: Option<<<PrecisionBridge<TElem> as BackendBridge<LibTorch<OElem, QElem>>>::Target as Backend>::Device>, ) -> <<PrecisionBridge<TElem> as BackendBridge<LibTorch<OElem, QElem>>>::Target as Backend>::FloatTensorPrimitive

Transfer the tensor to the target backend.

fn from_target( tensor: <<PrecisionBridge<TElem> as BackendBridge<LibTorch<OElem, QElem>>>::Target as Backend>::FloatTensorPrimitive, device: Option<<LibTorch<OElem> as Backend>::Device>, ) -> <LibTorch<OElem> as Backend>::FloatTensorPrimitive

Transfer the tensor from the target backend.

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<bool>

Creates a tensor from the data structure.

fn bool_shape(tensor: &TchTensor<bool>) -> Shape

Returns the shape of the tensor.

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

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

async fn bool_into_data(tensor: TchTensor<bool>) -> TensorData

Converts the tensor to a data structure.

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

Moves the tensor to the device.

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

Reshapes the tensor.

fn bool_device(tensor: &TchTensor<bool>) -> LibTorchDevice

Gets the device of the tensor.

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

Creates a new bool tensor.

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

Gets the values from the tensor for the given ranges.

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

Sets the values in the tensor for the given ranges.

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

Concatenates the tensors along the given dimension.

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

Equates the two tensors.

fn bool_not(tensor: TchTensor<bool>) -> TchTensor<bool>

Inverses boolean values.

fn bool_into_int(tensor: TchTensor<bool>) -> TchTensor<i64>

Converts bool tensor to int tensor.

fn bool_into_float(tensor: TchTensor<bool>) -> TchTensor<E>

Converts bool tensor to float tensor.

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

Swaps two dimensions of a bool tensor.

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

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

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

Split the tensor along the given dimension into chunks.

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

Permutes the dimensions of a tensor.

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

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

async fn bool_argwhere(tensor: TchTensor<bool>) -> TchTensor<i64>

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

async fn bool_nonzero(tensor: TchTensor<bool>) -> Vec<TchTensor<i64>>

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

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

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<E>

Creates a new tensor from the data structure.

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

Creates a new tensor with random values.

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

Repeat the tensor along the given dimension.

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

Creates a new tensor with zeros.

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

Creates a new tensor with ones.

fn float_shape(tensor: &TchTensor<E>) -> Shape

Gets the shape of the tensor.

async fn float_into_data(tensor: TchTensor<E>) -> TensorData

Converts the tensor to a data structure.

fn float_device(tensor: &TchTensor<E>) -> LibTorchDevice

Gets the device of the tensor.

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

Moves the tensor to the given device.

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

Creates an empty tensor with the given shape.

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

Adds two tensors together.

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

Adds a scalar to a tensor.

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

Subtracts two tensors.

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

Subtracts a scalar from a tensor.

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

Multiplies two tensors together element-wise.

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

Multiplies a tensor by a scalar.

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

Divides two tensors element-wise.

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

Divides a tensor by a scalar.

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

Computes the modulus of a tensor given a scalar.

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

Multiplies two tensors together using matrix multiplication.

fn float_neg(tensor: TchTensor<E>) -> TchTensor<E>

Negates a tensor element-wise.

fn float_recip(tensor: TchTensor<E>) -> TchTensor<E>

Calculates the reciprocals element-wise

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

Swaps two dimensions of a tensor.

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

Reshapes a tensor.

fn float_gather( dim: usize, tensor: TchTensor<E>, indices: TchTensor<i64>, ) -> TchTensor<E>

Gather elements from a tensor.

fn float_scatter( dim: usize, tensor: TchTensor<E>, indices: TchTensor<i64>, value: TchTensor<E>, ) -> TchTensor<E>

Scatter elements into a tensor.

fn float_select( tensor: TchTensor<E>, dim: usize, indices: TchTensor<i64>, ) -> TchTensor<E>

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

fn float_select_assign( tensor: TchTensor<E>, dim: usize, indices: TchTensor<i64>, value: TchTensor<E>, ) -> TchTensor<E>

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

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

Select tensor elements corresponding for the given ranges.

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

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

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

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

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

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

fn float_equal(lhs: TchTensor<E>, rhs: TchTensor<E>) -> TchTensor<bool>

Equal comparison of two tensors.

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

Equal comparison of a tensor and a scalar.

fn float_greater(lhs: TchTensor<E>, rhs: TchTensor<E>) -> TchTensor<bool>

Greater than comparison of two tensors.

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

Greater than comparison of a tensor and a scalar.

fn float_greater_equal(lhs: TchTensor<E>, rhs: TchTensor<E>) -> TchTensor<bool>

Greater than or equal comparison of two tensors.

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

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

fn float_lower(lhs: TchTensor<E>, rhs: TchTensor<E>) -> TchTensor<bool>

Less than comparison of two tensors.

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

Less than comparison of a tensor and a scalar.

fn float_lower_equal(lhs: TchTensor<E>, rhs: TchTensor<E>) -> TchTensor<bool>

Less than or equal comparison of two tensors.

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

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

fn float_mean(tensor: TchTensor<E>) -> TchTensor<E>

Mean of all elements in a tensor.

fn float_sum(tensor: TchTensor<E>) -> TchTensor<E>

Sum of all elements in a tensor.

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

Sum of all elements in a tensor along a dimension.

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

Mean of all elements in a tensor along a dimension.

fn float_prod(tensor: TchTensor<E>) -> TchTensor<E>

Product of all elements in a tensor.

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

Product of all elements in a tensor along a dimension.

fn float_argmax(tensor: TchTensor<E>, dim: usize) -> TchTensor<i64>

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

fn float_argmin(tensor: TchTensor<E>, dim: usize) -> TchTensor<i64>

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

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

Gets the maximum elements of a tensor along an axis.

fn float_max_dim_with_indices( tensor: TchTensor<E>, dim: usize, ) -> (TchTensor<E>, TchTensor<i64>)

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

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

Gets the minimum elements of a tensor along an axis.

fn float_min_dim_with_indices( tensor: TchTensor<E>, dim: usize, ) -> (TchTensor<E>, TchTensor<i64>)

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

fn float_exp(tensor: TchTensor<E>) -> TchTensor<E>

Returns a new tensor with exponential values.

fn float_log(tensor: TchTensor<E>) -> TchTensor<E>

Returns a new tensor with natural logarithm values.

fn float_log1p(tensor: TchTensor<E>) -> TchTensor<E>

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

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

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

fn float_sqrt(tensor: TchTensor<E>) -> TchTensor<E>

Returns a new tensor with square root values.

fn float_abs(tensor: TchTensor<E>) -> TchTensor<E>

Returns a new tensor with absolute values.

fn float_cos(tensor: TchTensor<E>) -> TchTensor<E>

Returns a new tensor with cosine values.

fn float_sin(tensor: TchTensor<E>) -> TchTensor<E>

Returns a new tensor with sine values.

fn float_tanh(tensor: TchTensor<E>) -> TchTensor<E>

Returns a new tensor with tangent values.

fn float_round(tensor: TchTensor<E>) -> TchTensor<E>

Returns a new tensor with rounded values.

fn float_floor(tensor: TchTensor<E>) -> TchTensor<E>

Returns a new tensor with floored values.

fn float_ceil(tensor: TchTensor<E>) -> TchTensor<E>

Returns a new tensor with ceiled values.

fn float_erf(tensor: TchTensor<E>) -> TchTensor<E>

Returns a new tensor with the error function values.

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

Concatenates tensors along a dimension.

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

Clamps a tensor under a minimum value.

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

Clamps a tensor over a maximum value.

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

Clamps a tensor between a minimum and maximum value.

fn float_into_int(tensor: TchTensor<E>) -> TchTensor<i64>

Converts float tensor to int tensor.

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

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

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

Split the tensor along the given dimension into chunks.

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

Element-wise power with a FloatTensor.

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

Permutes the dimensions of a tensor.

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

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

fn float_sign(tensor: TchTensor<E>) -> TchTensor<E>

Returns the signs of the float tensor.

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

Broadcasts the float tensor to the given shape.

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

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

fn float_sort_with_indices( tensor: TchTensor<E>, dim: usize, descending: bool, ) -> (TchTensor<E>, TchTensor<i64>)

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

fn float_argsort( tensor: TchTensor<E>, 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_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_into_full_precision( tensor: <B as Backend>::FloatTensorPrimitive, ) -> <<<B as Backend>::FullPrecisionBridge as BackendBridge<B>>::Target as Backend>::FloatTensorPrimitive

Converts a tensor to full precision.

fn float_from_full_precision( tensor: <<<B as Backend>::FullPrecisionBridge as BackendBridge<B>>::Target as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

Converts a tensor from full precision.

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<i64>

Creates a tensor from the data structure.

fn int_shape(tensor: &TchTensor<i64>) -> Shape

Returns the shape of the tensor.

fn int_repeat_dim( tensor: TchTensor<i64>, dim: usize, times: usize, ) -> TchTensor<i64>

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

async fn int_into_data(tensor: TchTensor<i64>) -> TensorData

Converts the tensor to a data structure.

fn int_to_device( tensor: TchTensor<i64>, device: &LibTorchDevice, ) -> TchTensor<i64>

Moves the tensor to the given device.

fn int_reshape(tensor: TchTensor<i64>, shape: Shape) -> TchTensor<i64>

Reshapes the tensor.

fn int_device(tensor: &TchTensor<i64>) -> LibTorchDevice

Gets the device of the tensor.

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

Creates a new int tensor.

fn int_slice(tensor: TchTensor<i64>, ranges: &[Range<usize>]) -> TchTensor<i64>

Gets the element at the given indices.

fn int_slice_assign( tensor: TchTensor<i64>, ranges: &[Range<usize>], value: TchTensor<i64>, ) -> TchTensor<i64>

Sets the element at the given indices.

fn int_cat(tensors: Vec<TchTensor<i64>>, dim: usize) -> TchTensor<i64>

Concatenates the given tensors along the given dimension.

fn int_equal(lhs: TchTensor<i64>, rhs: TchTensor<i64>) -> TchTensor<bool>

Element-wise equality comparison.

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

Element-wise equality comparison with a scalar.

fn int_greater(lhs: TchTensor<i64>, rhs: TchTensor<i64>) -> TchTensor<bool>

Element-wise greater than comparison.

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

Element-wise greater than comparison with a scalar.

fn int_greater_equal( lhs: TchTensor<i64>, rhs: TchTensor<i64>, ) -> TchTensor<bool>

Element-wise greater than or equal comparison.

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

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

fn int_lower(lhs: TchTensor<i64>, rhs: TchTensor<i64>) -> TchTensor<bool>

Element-wise less than comparison.

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

Element-wise less than comparison with a scalar.

fn int_lower_equal(lhs: TchTensor<i64>, rhs: TchTensor<i64>) -> TchTensor<bool>

Element-wise less than or equal comparison.

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

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

fn int_add(lhs: TchTensor<i64>, rhs: TchTensor<i64>) -> TchTensor<i64>

Element-wise addition.

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

Element-wise addition with a scalar.

fn int_sub(lhs: TchTensor<i64>, rhs: TchTensor<i64>) -> TchTensor<i64>

Element-wise subtraction.

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

Element-wise subtraction with a scalar.

fn int_mul(lhs: TchTensor<i64>, rhs: TchTensor<i64>) -> TchTensor<i64>

Element-wise multiplication.

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

Element-wise multiplication with a scalar.

fn int_div(lhs: TchTensor<i64>, rhs: TchTensor<i64>) -> TchTensor<i64>

Element-wise division.

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

Element-wise division with a scalar.

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

Element-wise modulus with a scalar.

fn int_neg(tensor: TchTensor<i64>) -> TchTensor<i64>

Element-wise negation.

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

Creates a tensor of zeros.

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

Creates a tensor of ones.

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

Creates a tensor filled with given value.

fn int_sum(tensor: TchTensor<i64>) -> TchTensor<i64>

Sums all elements in the tensor.

fn int_sum_dim(tensor: TchTensor<i64>, dim: usize) -> TchTensor<i64>

Sums all elements in the tensor along a dimension.

fn int_prod(tensor: TchTensor<i64>) -> TchTensor<i64>

Computes the product of all elements in the tensor.

fn int_prod_dim(tensor: TchTensor<i64>, dim: usize) -> TchTensor<i64>

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

fn int_mean(tensor: TchTensor<i64>) -> TchTensor<i64>

Computes the mean of all elements in the tensor.

fn int_mean_dim(tensor: TchTensor<i64>, dim: usize) -> TchTensor<i64>

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

fn int_gather( dim: usize, tensor: TchTensor<i64>, indices: TchTensor<i64>, ) -> TchTensor<i64>

Gather elements from the tensor at the given indices.

fn int_scatter( dim: usize, tensor: TchTensor<i64>, indices: TchTensor<i64>, value: TchTensor<i64>, ) -> TchTensor<i64>

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

fn int_select( tensor: TchTensor<i64>, dim: usize, indices: TchTensor<i64>, ) -> TchTensor<i64>

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

fn int_select_assign( tensor: TchTensor<i64>, dim: usize, indices: TchTensor<i64>, value: TchTensor<i64>, ) -> TchTensor<i64>

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

fn int_mask_where( tensor: TchTensor<i64>, mask: TchTensor<bool>, source: TchTensor<i64>, ) -> TchTensor<i64>

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

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

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

fn int_argmax(tensor: TchTensor<i64>, dim: usize) -> TchTensor<i64>

Gets the indices of the maximum elements along a dimension.

fn int_argmin(tensor: TchTensor<i64>, dim: usize) -> TchTensor<i64>

Gets the indices of the minimum elements along a dimension.

fn int_max_dim(tensor: TchTensor<i64>, dim: usize) -> TchTensor<i64>

Gets the maximum element in the tensor along a dimension.

fn int_max_dim_with_indices( tensor: TchTensor<i64>, dim: usize, ) -> (TchTensor<i64>, TchTensor<i64>)

Gets the maximum elements and corresponding indices along a dimension.

fn int_min_dim(tensor: TchTensor<i64>, dim: usize) -> TchTensor<i64>

Gets the minimum elements in the tensor along a dimension.

fn int_min_dim_with_indices( tensor: TchTensor<i64>, dim: usize, ) -> (TchTensor<i64>, TchTensor<i64>)

Gets the minimum elements and corresponding indices along a dimension.

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

Clamps a tensor under a minimum value.

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

Clamps a tensor over a maximum value.

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

Clamps a tensor between a minimum and maximum value.

fn int_abs(tensor: TchTensor<i64>) -> TchTensor<i64>

Returns a new tensor with absolute values.

fn int_into_float(tensor: TchTensor<i64>) -> TchTensor<E>

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<i64>, dim: usize, start: usize, length: usize, ) -> TchTensor<i64>

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

fn int_chunk( tensor: TchTensor<i64>, chunks: usize, dim: usize, ) -> Vec<TchTensor<i64>>

Split the tensor along the given dimension into chunks.

fn int_random( shape: Shape, distribution: Distribution, device: &LibTorchDevice, ) -> TchTensor<i64>

Creates a new int tensor with random values.

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

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<E>, indices: TchTensor<i64>) -> TchTensor<E>

Embedding operation.

fn embedding_backward( weights: TchTensor<E>, output: TchTensor<E>, indices: TchTensor<i64>, ) -> TchTensor<E>

Embedding backward operation.

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

One dimensional convolution.

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

Two dimensional convolution.

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

Three dimensional convolution.

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

Two dimensional deformable convolution.

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

Backward pass for the deform_conv2d operation.

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

One dimensional transposed convolution.

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

Two dimensional transposed convolution.

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

Three dimensional transposed convolution.

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

One dimensional avg pooling.

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

Two dimensional avg pooling.

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

Backward pass for the avg pooling 2d operation.

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

One dimensional max pooling.

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

One dimensional max pooling with indices.

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

Two dimensional max pooling.

fn max_pool2d_with_indices( x: TchTensor<E>, 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<E>, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], dilation: [usize; 2], output_grad: TchTensor<E>, indices: TchTensor<i64>, ) -> MaxPool2dBackward<LibTorch<E, Q>>

Backward pass for the max pooling 2d operation.

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

Two dimensional adaptive avg pooling.

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

Backward pass for the adaptive avg pooling 2d operation.

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

One dimensional adaptive avg pooling.

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

Down/up samples the input.

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

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_shape( tensor: &<LibTorch<E, Q> as Backend>::QuantizedTensorPrimitive, ) -> Shape

Gets the shape of the tensor.

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_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_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> Copy for LibTorch<E, Q>

where E: Copy, Q: Copy,