Trait burn::tensor::ops::QTensorOps

pub trait QTensorOps<B>
where
    B: Backend,
{
    // Required methods
    fn q_from_data<const D: usize>(
        data: TensorData,
        device: &<B as Backend>::Device,
    ) -> <B as Backend>::QuantizedTensorPrimitive<D>;
    fn quantize<const D: usize>(
        tensor: <B as Backend>::FloatTensorPrimitive<D>,
        scheme: &QuantizationScheme,
        qparams: QuantizationParametersPrimitive<B>,
    ) -> <B as Backend>::QuantizedTensorPrimitive<D>;
    fn dequantize<const D: usize>(
        tensor: <B as Backend>::QuantizedTensorPrimitive<D>,
    ) -> <B as Backend>::FloatTensorPrimitive<D>;
    fn q_shape<const D: usize>(
        tensor: &<B as Backend>::QuantizedTensorPrimitive<D>,
    ) -> Shape<D>;
    fn q_device<const D: usize>(
        tensor: &<B as Backend>::QuantizedTensorPrimitive<D>,
    ) -> <B as Backend>::Device;
    fn q_reshape<const D1: usize, const D2: usize>(
        tensor: <B as Backend>::QuantizedTensorPrimitive<D1>,
        shape: Shape<D2>,
    ) -> <B as Backend>::QuantizedTensorPrimitive<D2>;
    fn q_into_data<const D: usize>(
        tensor: <B as Backend>::QuantizedTensorPrimitive<D>,
    ) -> impl Future<Output = TensorData> + Send;

    // Provided methods
    fn q_set_require_grad<const D: usize>(
        tensor: <B as Backend>::QuantizedTensorPrimitive<D>,
        _require_grad: bool,
    ) -> <B as Backend>::QuantizedTensorPrimitive<D> { ... }
    fn q_is_require_grad<const D: usize>(
        _tensor: &<B as Backend>::QuantizedTensorPrimitive<D>,
    ) -> bool { ... }
}

Quantized Tensor API for basic operations, see tensor for documentation on each function.

Required Methods

fn q_from_data<const D: usize>( data: TensorData, device: &<B as Backend>::Device, ) -> <B as Backend>::QuantizedTensorPrimitive<D>

Creates a new tensor from the data structure.

Arguments

• data - The data structure.
• device - The device to create the tensor on.

Returns

The tensor with the given data.

fn quantize<const D: usize>( tensor: <B as Backend>::FloatTensorPrimitive<D>, scheme: &QuantizationScheme, qparams: QuantizationParametersPrimitive<B>, ) -> <B as Backend>::QuantizedTensorPrimitive<D>

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

fn dequantize<const D: usize>( tensor: <B as Backend>::QuantizedTensorPrimitive<D>, ) -> <B as Backend>::FloatTensorPrimitive<D>

Convert the tensor back to a higher precision data type.

fn q_shape<const D: usize>( tensor: &<B as Backend>::QuantizedTensorPrimitive<D>, ) -> Shape<D>

Gets the shape of the tensor.

Arguments

• tensor - The tensor.

Returns

The shape of the tensor.

fn q_device<const D: usize>( tensor: &<B as Backend>::QuantizedTensorPrimitive<D>, ) -> <B as Backend>::Device

Gets the device of the tensor.

Arguments

• tensor - The tensor.

Returns

The device of the tensor.

fn q_reshape<const D1: usize, const D2: usize>( tensor: <B as Backend>::QuantizedTensorPrimitive<D1>, shape: Shape<D2>, ) -> <B as Backend>::QuantizedTensorPrimitive<D2>

Reshapes a tensor.

Arguments

• tensor - The tensor to reshape.
• shape - The new shape of the tensor.

Returns

The tensor with the new shape.

fn q_into_data<const D: usize>( tensor: <B as Backend>::QuantizedTensorPrimitive<D>, ) -> impl Future<Output = TensorData> + Send

Converts the tensor to a data structure.

Arguments

• tensor - The tensor.

Returns

The data structure with the tensor’s data.

Provided Methods

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

Sets the require_grad flag of a tensor.

fn q_is_require_grad<const D: usize>( _tensor: &<B as Backend>::QuantizedTensorPrimitive<D>, ) -> bool

Returns the require_grad flag of a tensor.

Object Safety

This trait is not object safe.

Implementations on Foreign Types

impl<B> QTensorOps<Fusion<B>> for Fusion<B>

where B: FusionBackend,

fn q_from_data<const D: usize>( _data: TensorData, _device: &<Fusion<B> as Backend>::Device, ) -> <Fusion<B> as Backend>::QuantizedTensorPrimitive<D>

fn quantize<const D: usize>( _tensor: <Fusion<B> as Backend>::FloatTensorPrimitive<D>, _scheme: &QuantizationScheme, _qparams: QuantizationParametersPrimitive<Fusion<B>>, ) -> <Fusion<B> as Backend>::QuantizedTensorPrimitive<D>

fn dequantize<const D: usize>( _tensor: <Fusion<B> as Backend>::QuantizedTensorPrimitive<D>, ) -> <Fusion<B> as Backend>::FloatTensorPrimitive<D>

fn q_shape<const D: usize>( tensor: &<Fusion<B> as Backend>::QuantizedTensorPrimitive<D>, ) -> Shape<D>

fn q_device<const D: usize>( tensor: &<Fusion<B> as Backend>::QuantizedTensorPrimitive<D>, ) -> <Fusion<B> as Backend>::Device

fn q_reshape<const D1: usize, const D2: usize>( _tensor: <Fusion<B> as Backend>::QuantizedTensorPrimitive<D1>, _shape: Shape<D2>, ) -> <Fusion<B> as Backend>::QuantizedTensorPrimitive<D2>

async fn q_into_data<const D: usize>( _tensor: <Fusion<B> as Backend>::QuantizedTensorPrimitive<D>, ) -> TensorData

Implementors

impl<B, C> QTensorOps<Autodiff<B, C>> for Autodiff<B, C>

where B: Backend, C: CheckpointStrategy,

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

where E: TchElement, Q: QuantElement,

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

where E: FloatNdArrayElement, Q: QuantElement,

impl<F, I> QTensorOps<Candle<F, I>> for Candle<F, I>

where F: FloatCandleElement, I: IntCandleElement,

impl<R, F, I> QTensorOps<JitBackend<R, F, I>> for JitBackend<R, F, I>

where R: JitRuntime, F: FloatElement, I: IntElement,