burn_tensor/tensor/api/

int.rs

use burn_backend::Scalar;

use crate::{
    Float, Int, IntDType, Shape, Tensor, TensorData, TensorPrimitive, backend::Backend,
    cartesian_grid,
};

use core::ops::Range;

impl<B> Tensor<B, 1, Int>
where
    B: Backend,
{
    /// Returns a new integer tensor on the specified device.
    ///
    /// # Arguments
    ///
    /// * `range` - The range of values to generate.
    /// * `device` - The device to create the tensor on.
    pub fn arange(range: Range<i64>, device: &B::Device) -> Self {
        Tensor::new(B::int_arange(range, device))
    }

    /// Returns a new integer tensor on the specified device.
    ///
    /// # Arguments
    ///
    /// * `range` - The range of values to generate.
    /// * `step` - The step between each value.
    pub fn arange_step(range: Range<i64>, step: usize, device: &B::Device) -> Self {
        Tensor::new(B::int_arange_step(range, step, device))
    }
}

impl<const D: usize, B> Tensor<B, D, Int>
where
    B: Backend,
{
    /// Create a tensor from integers (i32), placing it on a given device.
    ///
    /// # Example
    ///
    /// ```rust
    /// use burn_tensor::backend::Backend;
    /// use burn_tensor::{Tensor, Int};
    ///
    /// fn example<B: Backend>() {
    ///     let device = B::Device::default();
    ///     let _x: Tensor<B, 1, Int> = Tensor::from_ints([1, 2], &device);
    ///     let _y: Tensor<B, 2, Int> = Tensor::from_ints([[1, 2], [3, 4]], &device);
    /// }
    /// ```
    pub fn from_ints<A: Into<TensorData>>(ints: A, device: &B::Device) -> Self {
        Self::from_data(ints.into().convert::<i32>(), device)
    }

    /// Returns a new tensor with the same shape and device as the current tensor and the data
    /// cast to Float.
    ///
    /// # Example
    ///
    /// ```rust
    /// use burn_tensor::backend::Backend;
    /// use burn_tensor::{Int, Tensor};
    ///
    /// fn example<B: Backend>() {
    ///     let device = Default::default();
    ///     let int_tensor = Tensor::<B, 1, Int>::arange(0..5, &device);
    ///     let float_tensor = int_tensor.float();
    /// }
    /// ```
    pub fn float(self) -> Tensor<B, D, Float> {
        Tensor::new(TensorPrimitive::Float(B::int_into_float(self.primitive)))
    }

    /// Generates a cartesian grid for the given tensor shape on the specified device.
    /// The generated tensor is of dimension `D2 = D + 1`, where each element at dimension D contains the cartesian grid coordinates for that element.
    ///
    /// # Arguments
    ///
    /// * `shape` - The shape specifying the dimensions of the tensor.
    /// * `device` - The device to create the tensor on.
    ///
    /// # Panics
    ///
    /// Panics if `D2` is not equal to `D+1`.
    ///
    /// # Examples
    ///
    /// ```rust
    ///    use burn_tensor::Int;
    ///    use burn_tensor::{backend::Backend, Shape, Tensor};
    ///    fn example<B: Backend>() {
    ///        let device = Default::default();
    ///        let result: Tensor<B, 3, _> = Tensor::<B, 2, Int>::cartesian_grid([2, 3], &device);
    ///        println!("{}", result);
    ///    }
    /// ```
    pub fn cartesian_grid<S: Into<Shape>, const D2: usize>(
        shape: S,
        device: &B::Device,
    ) -> Tensor<B, D2, Int> {
        cartesian_grid::<B, S, D, D2>(shape, device)
    }

    /// Applies the bitwise logical and operation with each bit representing the integer.
    pub fn bitwise_and(self, other: Self) -> Self {
        Self::new(B::bitwise_and(self.primitive, other.primitive))
    }

    /// Applies the bitwise logical or operation with another tensor.
    pub fn bitwise_or(self, other: Self) -> Self {
        Self::new(B::bitwise_or(self.primitive, other.primitive))
    }

    /// Applies the bitwise logical xor operation with another tensor.
    pub fn bitwise_xor(self, other: Self) -> Self {
        Self::new(B::bitwise_xor(self.primitive, other.primitive))
    }

    /// Applies the bitwise logical not operation.
    pub fn bitwise_not(self) -> Self {
        Self::new(B::bitwise_not(self.primitive))
    }

    /// Applies the bitwise logical and operation with each bit in the scalar and the integers in the tensor.
    pub fn bitwise_and_scalar(self, other: B::IntElem) -> Self {
        let other = Scalar::new(other, &self.dtype());
        Self::new(B::bitwise_and_scalar(self.primitive, other))
    }

    /// Applies the bitwise logical or operation with each bit in the scalar and the integers in the tensor.
    pub fn bitwise_or_scalar(self, other: B::IntElem) -> Self {
        let other = Scalar::new(other, &self.dtype());
        Self::new(B::bitwise_or_scalar(self.primitive, other))
    }

    /// Applies bitwise logical xor operation with each bit in the scalar and the integers in the tensor.
    pub fn bitwise_xor_scalar(self, other: B::IntElem) -> Self {
        let other = Scalar::new(other, &self.dtype());
        Self::new(B::bitwise_xor_scalar(self.primitive, other))
    }

    /// Applies the bitwise left shift operation with the integers in the tensor.
    pub fn bitwise_left_shift(self, other: Self) -> Self {
        Self::new(B::bitwise_left_shift(self.primitive, other.primitive))
    }

    /// Applies the bitwise right shift operation with the integers in the tensor.
    pub fn bitwise_right_shift(self, other: Self) -> Self {
        Self::new(B::bitwise_right_shift(self.primitive, other.primitive))
    }

    /// Applies the bitwise left shift operation with the scalar.
    pub fn bitwise_left_shift_scalar(self, other: B::IntElem) -> Self {
        let other = Scalar::new(other, &self.dtype());
        Self::new(B::bitwise_left_shift_scalar(self.primitive, other))
    }

    /// Applies the bitwise right shift operation with the scalar.
    pub fn bitwise_right_shift_scalar(self, other: B::IntElem) -> Self {
        let other = Scalar::new(other, &self.dtype());
        Self::new(B::bitwise_right_shift_scalar(self.primitive, other))
    }

    /// Converts a tensor to the specified integer data type.
    ///
    /// This is always a no-op when casting to the current dtype.
    ///
    /// # Warning
    /// Most backends don't have automatic type promotion at this time, so make sure that all tensors
    /// have the same integer data type for operations multiple input tensors (e.g., binary ops).
    pub fn cast<F: Into<IntDType>>(self, dtype: F) -> Tensor<B, D, Int> {
        let dtype = dtype.into();
        let self_dtype: IntDType = self.dtype().into();
        if dtype == self_dtype {
            // no-op.
            return self;
        }
        Tensor::new(B::int_cast(self.primitive, dtype))
    }
}