Struct NdArray 

pub struct NdArray<E = f32, I = i64, Q = i8>
where
    NdArrayTensor: From<SharedArray<E>> + From<SharedArray<I>>,
{ /* private fields */ }

Tensor backend that uses the ndarray crate for executing tensor operations.

This backend is compatible with CPUs and can be compiled for almost any platform, including wasm, arm, and x86.

Trait Implementations

impl<E: FloatNdArrayElement, I: IntNdArrayElement, Q: QuantElement> ActivationOps<NdArray<E, I, Q>> for NdArray<E, I, Q>

where NdArrayTensor: From<SharedArray<E>> + From<SharedArray<I>>,

fn relu(tensor: FloatTensor<Self>) -> FloatTensor<Self>

Applies the ReLU 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 gelu( tensor: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

Applies the Gelu activation function.

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

Applies the PReLu activation function.

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

Applies the Gelu activation function backward.

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

Applies the Sigmoid 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( tensor: <B as Backend>::FloatTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

Applies the LogSigmoid 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: FloatNdArrayElement, I: IntNdArrayElement, Q: QuantElement> Backend for NdArray<E, I, Q>

where NdArrayTensor: From<SharedArray<E>> + From<SharedArray<I>>,

type Device = NdArrayDevice

Device type.

type FloatTensorPrimitive = NdArrayTensor

Tensor primitive to be used for all float operations.

type FloatElem = E

Default float element type.

type IntTensorPrimitive = NdArrayTensor

Tensor primitive to be used for all int operations.

type IntElem = I

Int element type.

type BoolTensorPrimitive = NdArrayTensor

Tensor primitive to be used for all bool operations.

type BoolElem = bool

Tensor primitive to be used for all bool operations.

type QuantizedTensorPrimitive = NdArrayQTensor

Tensor primitive to be used for all quantized operations.

fn ad_enabled() -> bool

If autodiff is enabled.

fn name(_device: &Self::Device) -> String

Name of the backend.

fn seed(_device: &Self::Device, seed: u64)

Seeds the backend on the specified device.

fn memory_persistent_allocations<Output, Input, Func>( device: &Self::Device, input: Input, func: Func, ) -> Output

where Func: Fn(Input) -> Output,

Sets the current allocation mode to persistent.

fn memory_cleanup(device: &Self::Device)

Manually triggers a memory cleanup on the given device.

fn sync(_device: &Self::Device)

Sync the backend, ensure that all computation are finished.

impl<E: FloatNdArrayElement, I: IntNdArrayElement, Q: QuantElement> BackendIr for NdArray<E, I, Q>

where NdArrayTensor: From<SharedArray<E>> + From<SharedArray<I>>,

type Handle = HandleKind<NdArray<E, I, Q>>

The type that can be used to point to a tensor of any kind.

fn float_tensor(handle: TensorHandle<Self::Handle>) -> FloatTensor<Self>

Convert a handle to a float tensor.

fn int_tensor(handle: TensorHandle<Self::Handle>) -> IntTensor<Self>

Convert a handle to an int tensor.

fn bool_tensor(handle: TensorHandle<Self::Handle>) -> BoolTensor<Self>

Convert a handle to a bool tensor.

fn quantized_tensor(handle: TensorHandle<Self::Handle>) -> QuantizedTensor<Self>

Convert a handle to a quantized tensor.

fn float_tensor_handle(tensor: FloatTensor<Self>) -> Self::Handle

Convert a float tensor to a handle.

fn int_tensor_handle(tensor: IntTensor<Self>) -> Self::Handle

Convert an int tensor to a handle.

fn bool_tensor_handle(tensor: BoolTensor<Self>) -> Self::Handle

Convert a bool tensor to a handle.

fn quantized_tensor_handle(tensor: QuantizedTensor<Self>) -> Self::Handle

Convert a quantized tensor to a handle.

impl<E: FloatNdArrayElement, I: IntNdArrayElement, Q: QuantElement> BoolTensorOps<NdArray<E, I, Q>> for NdArray<E, I, Q>

where NdArrayTensor: From<SharedArray<E>> + From<SharedArray<I>>,

fn bool_from_data(data: TensorData, _device: &NdArrayDevice) -> NdArrayTensor

Creates a tensor from the data structure.

async fn bool_into_data(tensor: NdArrayTensor) -> TensorData

Converts the tensor to a data structure.

fn bool_to_device( tensor: NdArrayTensor, _device: &NdArrayDevice, ) -> NdArrayTensor

Moves the tensor to the device.

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

Reshapes the tensor.

fn bool_slice(tensor: NdArrayTensor, slices: &[Slice]) -> NdArrayTensor

Gets the values from the tensor for the given ranges.

fn bool_into_int(tensor: NdArrayTensor) -> NdArrayTensor

Converts bool tensor to int tensor.

fn bool_device(_tensor: &NdArrayTensor) -> <NdArray<E> as Backend>::Device

Gets the device of the tensor.

fn bool_empty( shape: Shape, _device: &<NdArray<E> as Backend>::Device, ) -> NdArrayTensor

Creates a new bool tensor.

fn bool_zeros( shape: Shape, _device: &<NdArray<E> as Backend>::Device, ) -> NdArrayTensor

Creates a new bool tensor filled false.

fn bool_ones( shape: Shape, _device: &<NdArray<E> as Backend>::Device, ) -> NdArrayTensor

Creates a new bool tensor filled true.

fn bool_slice_assign( tensor: NdArrayTensor, slices: &[Slice], value: NdArrayTensor, ) -> NdArrayTensor

Sets the values in the tensor for the given ranges.

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

Concatenates the tensors along the given dimension.

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

Equates the two tensors.

fn bool_not(tensor: NdArrayTensor) -> NdArrayTensor

Inverses boolean values.

fn bool_and(lhs: NdArrayTensor, rhs: NdArrayTensor) -> NdArrayTensor

Executes the logical and (&&) operation on two boolean tensors.

fn bool_or(lhs: NdArrayTensor, rhs: NdArrayTensor) -> NdArrayTensor

Executes the logical or (||) operation on two boolean tensors.

fn bool_into_float(tensor: NdArrayTensor) -> FloatTensor<Self>

Converts bool tensor to float tensor.

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

Swaps two dimensions of a bool tensor.

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

Permutes the dimensions of a tensor.

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

Broadcasts the bool tensor to the given shape.

fn bool_select( tensor: NdArrayTensor, dim: usize, indices: NdArrayTensor, ) -> NdArrayTensor

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

fn bool_select_assign( tensor: NdArrayTensor, dim: usize, indices: NdArrayTensor, value: NdArrayTensor, ) -> NdArrayTensor

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

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

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

fn bool_unfold( tensor: NdArrayTensor, dim: usize, size: usize, step: usize, ) -> NdArrayTensor

Unfold windows along a dimension.

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

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

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

Element-wise exclusive or.

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.

fn bool_argwhere( tensor: <B as Backend>::BoolTensorPrimitive, ) -> impl Future<Output = <B as Backend>::IntTensorPrimitive> + Send + 'static

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

impl<E: Clone, I: Clone, Q: Clone> Clone for NdArray<E, I, Q>

where NdArrayTensor: From<SharedArray<E>> + From<SharedArray<I>>,

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

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<E: Debug, I: Debug, Q: Debug> Debug for NdArray<E, I, Q>

where NdArrayTensor: From<SharedArray<E>> + From<SharedArray<I>>,

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

Formats the value using the given formatter.

impl<E: Default, I: Default, Q: Default> Default for NdArray<E, I, Q>

where NdArrayTensor: From<SharedArray<E>> + From<SharedArray<I>>,

fn default() -> NdArray<E, I, Q>

Returns the “default value” for a type.

impl<E: FloatNdArrayElement, I: IntNdArrayElement, Q: QuantElement> FloatTensorOps<NdArray<E, I, Q>> for NdArray<E, I, Q>

where NdArrayTensor: From<SharedArray<E>> + From<SharedArray<I>>,

fn float_from_data( data: TensorData, _device: &NdArrayDevice, ) -> FloatTensor<Self>

Creates a new tensor from the data structure.

fn float_random( shape: Shape, distribution: Distribution, device: &NdArrayDevice, ) -> FloatTensor<Self>

Creates a new tensor with random values.

async fn float_into_data(tensor: FloatTensor<Self>) -> TensorData

Converts the tensor to a data structure.

fn float_device(_tensor: &FloatTensor<Self>) -> NdArrayDevice

Gets the device of the tensor.

fn float_to_device( tensor: FloatTensor<Self>, _device: &NdArrayDevice, ) -> FloatTensor<Self>

Moves the tensor to the given device.

fn float_empty( shape: Shape, device: &<NdArray<E> as Backend>::Device, dtype: FloatDType, ) -> FloatTensor<Self>

Creates an empty tensor with the given shape.

fn float_add( lhs: FloatTensor<Self>, rhs: FloatTensor<Self>, ) -> FloatTensor<Self>

Adds two tensors together.

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

Adds a scalar to a tensor.

fn float_sub( lhs: FloatTensor<Self>, rhs: FloatTensor<Self>, ) -> FloatTensor<Self>

Subtracts two tensors.

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

Subtracts a scalar from a tensor.

fn float_mul( lhs: FloatTensor<Self>, rhs: FloatTensor<Self>, ) -> FloatTensor<Self>

Multiplies two tensors together element-wise.

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

Multiplies a tensor by a scalar.

fn float_div( lhs: FloatTensor<Self>, rhs: FloatTensor<Self>, ) -> FloatTensor<Self>

Divides two tensors element-wise.

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

Divides a tensor by a scalar.

fn float_remainder( lhs: FloatTensor<Self>, rhs: FloatTensor<Self>, ) -> FloatTensor<Self>

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

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

Computes the modulus of a tensor given a scalar.

fn float_matmul( lhs: FloatTensor<Self>, rhs: FloatTensor<Self>, ) -> FloatTensor<Self>

Multiplies two tensors together using matrix multiplication.

fn float_cross( lhs: FloatTensor<Self>, rhs: FloatTensor<Self>, dim: usize, ) -> FloatTensor<Self>

Computes the cross product of two tensors along a given dimension.

fn float_neg(tensor: FloatTensor<Self>) -> FloatTensor<Self>

Negates a tensor element-wise.

fn float_recip(tensor: FloatTensor<Self>) -> FloatTensor<Self>

Calculates the reciprocals element-wise

fn float_swap_dims( tensor: FloatTensor<Self>, dim1: usize, dim2: usize, ) -> FloatTensor<Self>

Swaps two dimensions of a tensor.

fn float_reshape(tensor: FloatTensor<Self>, shape: Shape) -> FloatTensor<Self>

Reshapes a tensor.

fn float_gather( dim: usize, tensor: FloatTensor<Self>, indices: NdArrayTensor, ) -> FloatTensor<Self>

Gather elements from a tensor.

fn float_scatter( dim: usize, tensor: FloatTensor<Self>, indices: NdArrayTensor, value: FloatTensor<Self>, ) -> FloatTensor<Self>

Scatter elements into a tensor.

fn float_select( tensor: FloatTensor<Self>, dim: usize, indices: NdArrayTensor, ) -> FloatTensor<Self>

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

fn float_select_assign( tensor: FloatTensor<Self>, dim: usize, indices: NdArrayTensor, value: FloatTensor<Self>, ) -> FloatTensor<Self>

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

fn float_slice(tensor: FloatTensor<Self>, slices: &[Slice]) -> FloatTensor<Self>

Select tensor elements corresponding to the given slices.

fn float_slice_assign( tensor: FloatTensor<Self>, slices: &[Slice], value: FloatTensor<Self>, ) -> FloatTensor<Self>

Assign the selected elements corresponding to the given slices to the given value.

fn float_mask_where( tensor: FloatTensor<Self>, mask: NdArrayTensor, value: FloatTensor<Self>, ) -> FloatTensor<Self>

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

fn float_mask_fill( tensor: FloatTensor<Self>, mask: NdArrayTensor, value: E, ) -> FloatTensor<Self>

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

fn float_equal(lhs: FloatTensor<Self>, rhs: FloatTensor<Self>) -> NdArrayTensor

Equal comparison of two tensors.

fn float_equal_elem(lhs: FloatTensor<Self>, rhs: E) -> NdArrayTensor

Equal comparison of a tensor and a scalar.

fn float_greater( lhs: FloatTensor<Self>, rhs: FloatTensor<Self>, ) -> NdArrayTensor

Greater than comparison of two tensors.

fn float_greater_elem(lhs: FloatTensor<Self>, rhs: E) -> NdArrayTensor

Greater than comparison of a tensor and a scalar.

fn float_greater_equal( lhs: FloatTensor<Self>, rhs: FloatTensor<Self>, ) -> NdArrayTensor

Greater than or equal comparison of two tensors.

fn float_greater_equal_elem(lhs: FloatTensor<Self>, rhs: E) -> NdArrayTensor

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

fn float_lower(lhs: FloatTensor<Self>, rhs: FloatTensor<Self>) -> NdArrayTensor

Less than comparison of two tensors.

fn float_lower_elem(lhs: FloatTensor<Self>, rhs: E) -> NdArrayTensor

Less than comparison of a tensor and a scalar.

fn float_lower_equal( lhs: FloatTensor<Self>, rhs: FloatTensor<Self>, ) -> NdArrayTensor

Less than or equal comparison of two tensors.

fn float_lower_equal_elem(lhs: FloatTensor<Self>, rhs: E) -> NdArrayTensor

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

fn float_detach(tensor: FloatTensor<Self>) -> FloatTensor<Self>

Detaches a tensor from the computation graph.

fn float_mean(tensor: FloatTensor<Self>) -> FloatTensor<Self>

Mean of all elements in a tensor.

fn float_sum(tensor: FloatTensor<Self>) -> FloatTensor<Self>

Sum of all elements in a tensor.

fn float_mean_dim(tensor: FloatTensor<Self>, dim: usize) -> FloatTensor<Self>

Mean of all elements in a tensor along a dimension.

fn float_cumsum(tensor: FloatTensor<Self>, dim: usize) -> FloatTensor<Self>

Computes the cumulative sum of elements along a dimension.

fn float_cumprod(tensor: FloatTensor<Self>, dim: usize) -> FloatTensor<Self>

Computes the cumulative product of elements along a dimension.

fn float_cummin(tensor: FloatTensor<Self>, dim: usize) -> FloatTensor<Self>

Computes the cumulative minimum of elements along a dimension.

fn float_cummax(tensor: FloatTensor<Self>, dim: usize) -> FloatTensor<Self>

Computes the cumulative maximum of elements along a dimension.

fn float_sum_dim(tensor: FloatTensor<Self>, dim: usize) -> FloatTensor<Self>

Sum of all elements in a tensor along a dimension.

fn float_argmax(tensor: FloatTensor<Self>, dim: usize) -> NdArrayTensor

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

fn float_argmin(tensor: FloatTensor<Self>, dim: usize) -> NdArrayTensor

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

fn float_exp(tensor: FloatTensor<Self>) -> FloatTensor<Self>

Returns a new tensor with exponential values.

fn float_log(tensor: FloatTensor<Self>) -> FloatTensor<Self>

Returns a new tensor with natural logarithm values.

fn float_prod(tensor: FloatTensor<Self>) -> FloatTensor<Self>

Product of all elements in a tensor.

fn float_prod_dim(tensor: FloatTensor<Self>, dim: usize) -> FloatTensor<Self>

Product of all elements in a tensor along a dimension.

fn float_log1p(tensor: FloatTensor<Self>) -> FloatTensor<Self>

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

fn float_powf_scalar_impl( tensor: FloatTensor<Self>, value: f32, ) -> FloatTensor<Self>

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

fn float_sqrt(tensor: FloatTensor<Self>) -> FloatTensor<Self>

Returns a new tensor with square root values.

fn float_abs(tensor: FloatTensor<Self>) -> FloatTensor<Self>

Returns a new tensor with absolute values.

fn float_cos(tensor: FloatTensor<Self>) -> FloatTensor<Self>

Returns a new tensor with cosine values.

fn float_sin(tensor: FloatTensor<Self>) -> FloatTensor<Self>

Returns a new tensor with sine values.

fn float_tanh(tensor: FloatTensor<Self>) -> FloatTensor<Self>

Returns a new tensor with hyperbolic tangent values.

fn float_round(tensor: FloatTensor<Self>) -> FloatTensor<Self>

Returns a new tensor with rounded values.

fn float_floor(tensor: FloatTensor<Self>) -> FloatTensor<Self>

Returns a new tensor with floored values.

fn float_ceil(tensor: FloatTensor<Self>) -> FloatTensor<Self>

Returns a new tensor with ceiled values.

fn float_trunc(tensor: FloatTensor<Self>) -> FloatTensor<Self>

Returns a new tensor with truncated values.

fn float_erf(tensor: FloatTensor<Self>) -> FloatTensor<Self>

Returns a new tensor with the error function values.

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

Concatenates tensors along a dimension.

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

Clamps a tensor under a minimum value.

fn float_clamp_max(tensor: FloatTensor<Self>, max: E) -> FloatTensor<Self>

Clamps a tensor over a maximum value.

fn float_clamp(tensor: FloatTensor<Self>, min: E, max: E) -> FloatTensor<Self>

Clamps a tensor between a minimum and maximum value.

fn float_into_int(tensor: FloatTensor<Self>) -> NdArrayTensor

Converts float tensor to int tensor.

fn float_powf( lhs: FloatTensor<Self>, rhs: FloatTensor<Self>, ) -> FloatTensor<Self>

Element-wise power with a FloatTensor.

fn float_permute(tensor: FloatTensor<Self>, axes: &[usize]) -> FloatTensor<Self>

Permutes the dimensions of a tensor.

fn float_flip(tensor: FloatTensor<Self>, axes: &[usize]) -> FloatTensor<Self>

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

fn float_sign(tensor: FloatTensor<Self>) -> FloatTensor<Self>

Returns the signs of the float tensor.

fn float_expand(tensor: FloatTensor<Self>, shape: Shape) -> FloatTensor<Self>

Broadcasts the float tensor to the given shape.

fn float_cast(tensor: FloatTensor<Self>, dtype: FloatDType) -> FloatTensor<Self>

Converts a tensor to another floating point data type.

fn float_grid_sample_2d( tensor: FloatTensor<Self>, grid: FloatTensor<Self>, method: InterpolateMode, ) -> FloatTensor<Self>

Samples tensor as a two-dimensional spatial grid of (possibly multi-channel) values, using the given locations in [-1, 1].

fn float_unfold( tensor: FloatTensor<Self>, dim: usize, size: usize, step: usize, ) -> FloatTensor<Self>

Unfold windows along a dimension.

fn float_zeros( shape: Shape, device: &<B as Backend>::Device, dtype: FloatDType, ) -> <B as Backend>::FloatTensorPrimitive

Creates a new tensor with zeros.

fn float_ones( shape: Shape, device: &<B as Backend>::Device, dtype: FloatDType, ) -> <B as Backend>::FloatTensorPrimitive

Creates a new tensor with ones.

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

Creates a tensor filled with given value.

fn float_repeat_dim( tensor: <B as Backend>::FloatTensorPrimitive, dim: usize, times: usize, ) -> <B as Backend>::FloatTensorPrimitive

Repeat the tensor along the given dimension.

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_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_powi_scalar_impl( 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_powf_scalar( tensor: <B as Backend>::FloatTensorPrimitive, value: f32, ) -> <B as Backend>::FloatTensorPrimitive

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

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

Returns a new tensor with tangent values.

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

Returns a new tensor with hyperbolic cosine values.

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

Returns a new tensor with hyperbolic sine values.

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

Gets the maximum element of a tensor.

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

Gets the maximum elements of a tensor along an axis.

fn float_max_dim_with_indices( tensor: <B as Backend>::FloatTensorPrimitive, dim: usize, ) -> (<B as Backend>::FloatTensorPrimitive, <B as Backend>::IntTensorPrimitive)

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

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

Gets the minimum element of a tensor.

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

Gets the minimum elements of a tensor along an axis.

fn float_min_dim_with_indices( tensor: <B as Backend>::FloatTensorPrimitive, dim: usize, ) -> (<B as Backend>::FloatTensorPrimitive, <B as Backend>::IntTensorPrimitive)

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

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

Gets the maximum absolute element of a tensor.

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

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

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.

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

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

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

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

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

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

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

Returns a new tensor with boolean elements indicating whether each element of the input is NaN.

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

Returns a new tensor with boolean elements indicating whether each element of the input is infinite (either +INF or -INF).

impl<E: FloatNdArrayElement, I: IntNdArrayElement, Q: QuantElement> IntTensorOps<NdArray<E, I, Q>> for NdArray<E, I, Q>

where NdArrayTensor: From<SharedArray<E>> + From<SharedArray<I>>,

fn int_from_data(data: TensorData, _device: &NdArrayDevice) -> NdArrayTensor

Creates a tensor from the data structure.

async fn int_into_data(tensor: NdArrayTensor) -> TensorData

Converts the tensor to a data structure.

fn int_to_device( tensor: NdArrayTensor, _device: &NdArrayDevice, ) -> NdArrayTensor

Moves the tensor to the given device.

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

Reshapes the tensor.

fn int_slice(tensor: NdArrayTensor, slices: &[Slice]) -> NdArrayTensor

Gets the element at the given indices.

fn int_device(_tensor: &NdArrayTensor) -> <NdArray<E> as Backend>::Device

Gets the device of the tensor.

fn int_empty( shape: Shape, device: &<NdArray<E> as Backend>::Device, dtype: IntDType, ) -> NdArrayTensor

Creates a new int tensor.

fn int_matmul(lhs: IntTensor<Self>, rhs: IntTensor<Self>) -> IntTensor<Self>

Multiplies two tensors together using matrix multiplication.

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

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

fn int_mask_fill( tensor: NdArrayTensor, mask: NdArrayTensor, value: I, ) -> NdArrayTensor

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

fn int_slice_assign( tensor: NdArrayTensor, slices: &[Slice], value: NdArrayTensor, ) -> NdArrayTensor

Sets the values in the tensor for the given ranges.

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

Concatenates the given tensors along the given dimension.

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

Element-wise equality comparison.

fn int_equal_elem(lhs: NdArrayTensor, rhs: I) -> NdArrayTensor

Element-wise equality comparison with a scalar.

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

Element-wise greater than comparison.

fn int_greater_elem(lhs: NdArrayTensor, rhs: I) -> NdArrayTensor

Element-wise greater than comparison with a scalar.

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

Element-wise greater than or equal comparison.

fn int_greater_equal_elem(lhs: NdArrayTensor, rhs: I) -> NdArrayTensor

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

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

Element-wise less than comparison.

fn int_lower_elem(lhs: NdArrayTensor, rhs: I) -> NdArrayTensor

Element-wise less than comparison with a scalar.

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

Element-wise less than or equal comparison.

fn int_lower_equal_elem(lhs: NdArrayTensor, rhs: I) -> NdArrayTensor

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

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

Element-wise addition.

fn int_add_scalar(lhs: NdArrayTensor, rhs: I) -> NdArrayTensor

Element-wise addition with a scalar.

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

Element-wise subtraction.

fn int_sub_scalar(lhs: NdArrayTensor, rhs: I) -> NdArrayTensor

Element-wise subtraction with a scalar.

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

Element-wise multiplication.

fn int_mul_scalar(lhs: NdArrayTensor, rhs: I) -> NdArrayTensor

Element-wise multiplication with a scalar.

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

Element-wise division.

fn int_div_scalar(lhs: NdArrayTensor, rhs: I) -> NdArrayTensor

Element-wise division with a scalar.

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

Element-wise modulus.

fn int_remainder_scalar(lhs: NdArrayTensor, rhs: I) -> NdArrayTensor

Element-wise modulus with a scalar.

fn int_neg(tensor: NdArrayTensor) -> NdArrayTensor

Element-wise negation.

fn int_sum(tensor: NdArrayTensor) -> NdArrayTensor

Sums all elements in the tensor.

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

Sums all elements in the tensor along a dimension.

fn int_prod(tensor: NdArrayTensor) -> NdArrayTensor

Computes the product of all elements in the tensor.

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

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

fn int_mean(tensor: NdArrayTensor) -> NdArrayTensor

Computes the mean of all elements in the tensor.

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

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

fn int_cumsum(tensor: NdArrayTensor, dim: usize) -> NdArrayTensor

Computes the cumulative sum of elements along a dimension.

fn int_cumprod(tensor: NdArrayTensor, dim: usize) -> NdArrayTensor

Computes the cumulative product of elements along a dimension.

fn int_cummin(tensor: NdArrayTensor, dim: usize) -> NdArrayTensor

Computes the cumulative minimum of elements along a dimension.

fn int_cummax(tensor: NdArrayTensor, dim: usize) -> NdArrayTensor

Computes the cumulative maximum of elements along a dimension.

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

Gather elements from the tensor at the given indices.

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

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

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

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

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

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

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

Gets the indices of the maximum elements along a dimension.

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

Gets the indices of the minimum elements along a dimension.

fn int_clamp_min(tensor: NdArrayTensor, min: I) -> NdArrayTensor

Clamps a tensor under a minimum value.

fn int_clamp_max(tensor: NdArrayTensor, max: I) -> NdArrayTensor

Clamps a tensor over a maximum value.

fn int_clamp(tensor: NdArrayTensor, min: I, max: I) -> NdArrayTensor

Clamps a tensor between a minimum and maximum value.

fn int_abs(tensor: NdArrayTensor) -> NdArrayTensor

Returns a new tensor with absolute values.

fn int_into_float(tensor: NdArrayTensor) -> FloatTensor<Self>

Converts int tensor to float tensor.

fn int_swap_dims( tensor: NdArrayTensor, dim1: usize, dim2: usize, ) -> NdArrayTensor

Swaps two dimensions of an int tensor.

fn int_random( shape: Shape, distribution: Distribution, device: &NdArrayDevice, ) -> NdArrayTensor

Creates a new int tensor with random values.

fn int_powi(lhs: NdArrayTensor, rhs: NdArrayTensor) -> NdArrayTensor

Element-wise power with a IntTensor.

fn int_powf(lhs: NdArrayTensor, rhs: FloatTensor<Self>) -> NdArrayTensor

Element-wise power with a floatTensor.

fn int_powf_scalar_impl(lhs: NdArrayTensor, rhs: f32) -> NdArrayTensor

Element-wise power with a floatTensor.

fn int_permute(tensor: NdArrayTensor, axes: &[usize]) -> NdArrayTensor

Permutes the dimensions of a tensor.

fn int_flip(tensor: NdArrayTensor, axes: &[usize]) -> NdArrayTensor

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

fn int_sign(tensor: NdArrayTensor) -> NdArrayTensor

Returns the signs of the int tensor.

fn int_expand(tensor: NdArrayTensor, shape: Shape) -> NdArrayTensor

Broadcasts the int tensor to the given shape.

fn bitwise_and(lhs: NdArrayTensor, rhs: NdArrayTensor) -> NdArrayTensor

Bitwise AND operation for Int Tensors

fn bitwise_and_scalar(lhs: NdArrayTensor, rhs: I) -> NdArrayTensor

Bitwise AND operation for Int Tensors with a scalar

fn bitwise_or(lhs: NdArrayTensor, rhs: NdArrayTensor) -> NdArrayTensor

Bitwise OR operation for Int Tensors

fn bitwise_or_scalar(lhs: NdArrayTensor, rhs: I) -> NdArrayTensor

Bitwise OR operation for Int Tensors with a scalar

fn bitwise_xor(lhs: NdArrayTensor, rhs: NdArrayTensor) -> NdArrayTensor

Bitwise XOR operation for Int Tensors

fn bitwise_xor_scalar(lhs: NdArrayTensor, rhs: I) -> NdArrayTensor

Bitwise XOR operation for Int Tensors with a scalar

fn bitwise_not(tensor: NdArrayTensor) -> NdArrayTensor

Bitwise NOT operation for Int Tensors

fn bitwise_left_shift(lhs: NdArrayTensor, rhs: NdArrayTensor) -> NdArrayTensor

Bitwise left shift operation for Int Tensors

fn bitwise_left_shift_scalar(lhs: NdArrayTensor, rhs: I) -> NdArrayTensor

Bitwise left shift operation for Int Tensors with a scalar

fn bitwise_right_shift(lhs: NdArrayTensor, rhs: NdArrayTensor) -> NdArrayTensor

Bitwise right shift operation for Int Tensors

fn bitwise_right_shift_scalar(lhs: NdArrayTensor, rhs: I) -> NdArrayTensor

Bitwise right shift operation for Int Tensors with a scalar

fn int_cast(tensor: IntTensor<Self>, dtype: IntDType) -> IntTensor<Self>

Converts a tensor to another integer data type.

fn int_unfold( tensor: IntTensor<Self>, dim: usize, size: usize, step: usize, ) -> IntTensor<Self>

Unfold windows along a dimension.

fn int_repeat_dim( tensor: <B as Backend>::IntTensorPrimitive, dim: usize, times: usize, ) -> <B as Backend>::IntTensorPrimitive

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

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

Element-wise power with a scalar.

fn int_powi_scalar_impl( 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_zeros( shape: Shape, device: &<B as Backend>::Device, dtype: IntDType, ) -> <B as Backend>::IntTensorPrimitive

Creates a tensor of zeros.

fn int_ones( shape: Shape, device: &<B as Backend>::Device, dtype: IntDType, ) -> <B as Backend>::IntTensorPrimitive

Creates a tensor of ones.

fn int_full( shape: Shape, fill_value: <B as Backend>::IntElem, device: &<B as Backend>::Device, dtype: IntDType, ) -> <B as Backend>::IntTensorPrimitive

Creates a tensor filled with given value.

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

Gets the maximum element in the tensor.

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

Gets the maximum element in the tensor along a dimension.

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

Gets the maximum elements and corresponding indices along a dimension.

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

Gets the maximum absolute element in the tensor.

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

Gets the maximum absolute element in the tensor along a dimension.

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

Gets the minimum element in the tensor.

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

Gets the minimum elements in the tensor along a dimension.

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

Gets the minimum elements and corresponding indices along a dimension.

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_arange( range: Range<i64>, device: &<B as Backend>::Device, ) -> <B as Backend>::IntTensorPrimitive

Creates a new tensor with values from the given range.

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

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

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.

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

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

impl<E: FloatNdArrayElement, I: IntNdArrayElement, Q: QuantElement> ModuleOps<NdArray<E, I, Q>> for NdArray<E, I, Q>

where NdArrayTensor: From<SharedArray<E>> + From<SharedArray<I>>,

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

Two dimensional convolution.

fn deform_conv2d( x: FloatTensor<Self>, offset: FloatTensor<Self>, weight: FloatTensor<Self>, mask: Option<FloatTensor<Self>>, bias: Option<FloatTensor<Self>>, options: DeformConvOptions<2>, ) -> FloatTensor<Self>

Two dimensional deformable convolution.

fn deform_conv2d_backward( x: FloatTensor<Self>, offset: FloatTensor<Self>, weight: FloatTensor<Self>, mask: Option<FloatTensor<Self>>, bias: Option<FloatTensor<Self>>, output_grad: FloatTensor<Self>, options: DeformConvOptions<2>, ) -> DeformConv2dBackward<Self>

Backward pass for the deform_conv2d operation.

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

Two dimensional transposed convolution.

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

Two dimensional avg pooling.

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

Backward pass for the avg pooling 2d operation.

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

Two dimensional max pooling.

fn max_pool2d_with_indices( x: FloatTensor<Self>, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], dilation: [usize; 2], ) -> MaxPool2dWithIndices<NdArray<E, I, Q>>

Two dimensional max pooling with indices.

fn max_pool2d_with_indices_backward( x: FloatTensor<Self>, kernel_size: [usize; 2], stride: [usize; 2], padding: [usize; 2], dilation: [usize; 2], output_grad: FloatTensor<Self>, indices: NdArrayTensor, ) -> MaxPool2dBackward<NdArray<E, I, Q>>

Backward pass for the max pooling 2d operation.

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

Two dimensional adaptive avg pooling.

fn adaptive_avg_pool2d_backward( x: FloatTensor<Self>, grad: FloatTensor<Self>, ) -> FloatTensor<Self>

Backward pass for the adaptive avg pooling 2d operation.

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

Down/up samples the input.

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

Backward pass for the interpolate operation.

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

Three dimensional convolution.

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

Three dimensional transposed convolution.

fn embedding( weights: <B as Backend>::FloatTensorPrimitive, indices: <B as Backend>::IntTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

Embedding operation.

fn embedding_backward( weights: <B as Backend>::FloatTensorPrimitive, output_grad: <B as Backend>::FloatTensorPrimitive, indices: <B as Backend>::IntTensorPrimitive, ) -> <B as Backend>::FloatTensorPrimitive

Embedding backward operation.

fn conv1d( x: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, bias: Option<<B as Backend>::FloatTensorPrimitive>, options: ConvOptions<1>, ) -> <B as Backend>::FloatTensorPrimitive

One dimensional convolution.

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: <B as Backend>::FloatTensorPrimitive, weight: <B as Backend>::FloatTensorPrimitive, bias: Option<<B as Backend>::FloatTensorPrimitive>, options: ConvTransposeOptions<1>, ) -> <B as Backend>::FloatTensorPrimitive

One dimensional transposed convolution.

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

One dimensional avg pooling.

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( x: <B as Backend>::FloatTensorPrimitive, output_size: usize, ) -> <B as Backend>::FloatTensorPrimitive

One dimensional adaptive avg pooling.

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( x: <B as Backend>::FloatTensorPrimitive, kernel_size: usize, stride: usize, padding: usize, dilation: usize, ) -> <B as Backend>::FloatTensorPrimitive

One dimensional max pooling.

fn max_pool1d_with_indices( x: <B as Backend>::FloatTensorPrimitive, kernel_size: usize, stride: usize, padding: usize, dilation: usize, ) -> MaxPool1dWithIndices<B>

One dimensional max pooling with indices.

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: FloatNdArrayElement, I: IntNdArrayElement, Q: QuantElement> QTensorOps<NdArray<E, I, Q>> for NdArray<E, I, Q>

where NdArrayTensor: From<SharedArray<E>> + From<SharedArray<I>>,

fn q_from_data( data: TensorData, _device: &NdArrayDevice, ) -> QuantizedTensor<Self>

Creates a new tensor from the data structure.

fn quantize( tensor: FloatTensor<Self>, scheme: &QuantScheme, qparams: QuantizationParametersPrimitive<Self>, ) -> QuantizedTensor<Self>

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

fn dequantize(tensor: QuantizedTensor<Self>) -> FloatTensor<Self>

Convert the tensor back to a higher precision data type.

fn q_device(_tensor: &QuantizedTensor<Self>) -> NdArrayDevice

Gets the device of the tensor.

fn q_to_device( tensor: QuantizedTensor<Self>, _device: &NdArrayDevice, ) -> QuantizedTensor<Self>

Moves the tensor to the given device.

fn q_reshape( tensor: QuantizedTensor<Self>, shape: Shape, ) -> QuantizedTensor<Self>

Reshapes a tensor.

async fn q_into_data(tensor: QuantizedTensor<Self>) -> TensorData

Converts the tensor to a data structure.

fn q_swap_dims( tensor: QuantizedTensor<Self>, dim1: usize, dim2: usize, ) -> QuantizedTensor<Self>

Swaps two dimensions of a tensor.

fn q_permute( tensor: QuantizedTensor<Self>, axes: &[usize], ) -> QuantizedTensor<Self>

Permutes the dimensions of a tensor.

fn q_flip( tensor: QuantizedTensor<Self>, axes: &[usize], ) -> QuantizedTensor<Self>

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

fn q_gather( dim: usize, tensor: QuantizedTensor<Self>, indices: IntTensor<Self>, ) -> QuantizedTensor<Self>

Gather elements from a tensor.

fn q_select( tensor: QuantizedTensor<Self>, dim: usize, indices: IntTensor<Self>, ) -> QuantizedTensor<Self>

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

fn q_slice( tensor: QuantizedTensor<Self>, slices: &[Slice], ) -> QuantizedTensor<Self>

Select tensor elements corresponding to the given slices.

fn q_argmax(tensor: QuantizedTensor<Self>, dim: usize) -> IntTensor<Self>

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

fn q_argmin(tensor: QuantizedTensor<Self>, dim: usize) -> IntTensor<Self>

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

fn q_expand( tensor: QuantizedTensor<Self>, shape: Shape, ) -> QuantizedTensor<Self>

Broadcasts the tensor to the given shape.

fn quantize_dynamic( tensor: <B as Backend>::FloatTensorPrimitive, scheme: &QuantScheme, ) -> <B as Backend>::QuantizedTensorPrimitive

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

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

Transposes 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, ) -> TensorPrimitive<B>

Adds two tensors together.

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

Adds a scalar to a tensor.

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

Clamps a tensor under a minimum value.

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

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, ) -> TensorPrimitive<B>

Clamps a tensor between a minimum and maximum value.

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

Subtracts two tensors.

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

Subtracts a scalar from a tensor.

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

Multiplies two tensors together element-wise.

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

Multiplies a tensor by a scalar.

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

Divides two tensors element-wise.

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

Divides a tensor by a scalar.

fn q_matmul( lhs: TensorPrimitive<B>, rhs: TensorPrimitive<B>, ) -> TensorPrimitive<B>

Multiplies two tensors together using matrix multiplication.

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

Negates a tensor element-wise.

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

Calculates the reciprocals element-wise

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

Sum of all elements in a tensor.

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

Sum of all elements in a tensor along a dimension.

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

Product of all elements in a tensor.

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

Product of all elements in a tensor along a dimension.

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

Mean of all elements in a tensor.

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

Mean of all elements in a tensor along a dimension.

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

Computes the cumulative sum of elements along a dimension.

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

Computes the cumulative product of elements along a dimension.

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

Computes the cumulative minimum of elements along a dimension.

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

Computes the cumulative maximum of elements along a dimension.

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

Returns a new tensor with exponential values.

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

Returns a new tensor with natural logarithm values.

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

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

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

Element-wise power with another tensor.

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

Element-wise power with an IntTensor.

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

Element-wise power with an int scalar.

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

Element-wise power with a float scalar.

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

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) -> TensorPrimitive<B>

Returns a new tensor with cosine values.

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

Returns a new tensor with sine values.

fn q_tan(tensor: <B as Backend>::QuantizedTensorPrimitive) -> TensorPrimitive<B>

Returns a new tensor with tangent values.

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

Returns a new tensor with hyperbolic cosine values.

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

Returns a new tensor with hyperbolic sine values.

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

Returns a new tensor with hyperbolic tangent values.

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

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

Gets the maximum elements of a tensor along an axis.

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

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

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

Gets the minimum element of a tensor.

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

Gets the minimum elements of a tensor along an axis.

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

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

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

Gets the maximum element of a tensor.

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

Gets the maximum elements of a tensor along an axis.

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.

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

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

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

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

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

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

impl<E: FloatNdArrayElement, I: IntNdArrayElement, Q: QuantElement> TransactionOps<NdArray<E, I, Q>> for NdArray<E, I, Q>

where NdArrayTensor: From<SharedArray<E>> + From<SharedArray<I>>,

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

Executes a transaction and return its result.

impl<E: Copy, I: Copy, Q: Copy> Copy for NdArray<E, I, Q>

where NdArrayTensor: From<SharedArray<E>> + From<SharedArray<I>>,