Skip to Content
DocumentationFunctionalBinary Opsadd_op

add_op

View the code on GitHub 

Structs

Struct: Add

Fields

Methods

fwd(arg0: Array, arg1: Array) -> Array
Adds two arrays element-wise.
Args

  • arg0: Array The first input array.

  • arg1: Array The second input array.

Returns
  • Array - The element-wise sum of arg0 and arg1.

Examples:

a = Array([[1, 2], [3, 4]])
b = Array([[5, 6], [7, 8]])
result = add(a, b)
print(result)

This function supports

  • Broadcasting.
  • Automatic differentiation (forward and reverse modes).
  • Complex valued arguments.
jvp(primals: List[Array], tangents: List[Array]) -> Array
Compute Jacobian-vector product for array addition.
Args

  • primals: List[Array] Primal input arrays.

  • tangents: List[Array] Tangent vectors.

Returns
  • Array - Array: Jacobian-vector product.

Note: Implements forward-mode automatic differentiation for addition. The result represents how the output changes with respect to infinitesimal changes in the inputs along the directions specified by the tangents.

See Also:

add_vjp: Reverse-mode autodiff for addition.

vjp(primals: List[Array], grad: Array, out: Array) -> List[Array]
Compute vector-Jacobian product for array addition.
Args

  • primals: List[Array] Primal input arrays.

  • grad: Array Gradient of the output with respect to some scalar function.

  • out: Array The output of the forward pass.

Returns
  • List[Array] - List[Array]: Gradients with respect to each input.

Note: Implements reverse-mode automatic differentiation for addition. Returns arrays with shape zero for inputs that do not require gradients.

See Also:

add_jvp: Forward-mode autodiff for addition.

binary_simd_op(arg0_real: SIMD[float32, nelts[::DType]().__mul__(2).__floordiv__(2)], arg1_real: SIMD[float32, nelts[::DType]().__mul__(2).__floordiv__(2)], arg0_imag: SIMD[float32, nelts[::DType]().__mul__(2).__floordiv__(2)], arg1_imag: SIMD[float32, nelts[::DType]().__mul__(2).__floordiv__(2)]) -> Tuple[SIMD[float32, nelts[::DType]().__mul__(2).__floordiv__(2)], SIMD[float32, nelts[::DType]().__mul__(2).__floordiv__(2)]]
Low-level function to add two complex numbers represented as SIMD vectors.
Args

  • arg0_real: SIMD[float32, nelts[::DType]().__mul__(2).__floordiv__(2)] The real part of the first complex number.

  • arg1_real: SIMD[float32, nelts[::DType]().__mul__(2).__floordiv__(2)] The real part of the second complex number.

  • arg0_imag: SIMD[float32, nelts[::DType]().__mul__(2).__floordiv__(2)] The imaginary part of the first complex number.

  • arg1_imag: SIMD[float32, nelts[::DType]().__mul__(2).__floordiv__(2)] The imaginary part of the second complex number.

Returns
  • Tuple[SIMD[float32, nelts[::DType]().__mul__(2).__floordiv__(2)], SIMD[float32, nelts[::DType]().__mul__(2).__floordiv__(2)]] - The real and imaginary parts of the sum of the two complex numbers as a tuple.
__call__(mut curr: Array, args: List[Array])
Adds two arrays element-wise and stores the result in the current array (curr). The function assumes that the shape and data of the args are already set up. If the shape and data of the current array (curr) is not set up, the function will compute the shape based on the shapes of the args and set up the data accordingly.
Args

  • curr: Array The current array, must be mutable.

  • args: List[Array] The two arrays to add.

Functions

add

add(arg0: Array, arg1: Array) -> Array
Adds two arrays element-wise.
Args

  • arg0: Array The first input array.

  • arg1: Array The second input array.

Returns
  • Array - The element-wise sum of arg0 and arg1.

Examples:

a = Array([[1, 2], [3, 4]])
b = Array([[5, 6], [7, 8]])
result = add(a, b)
print(result)

This function supports

  • Broadcasting.
  • Automatic differentiation (forward and reverse modes).
  • Complex valued arguments.
Last updated on
max_utilsdiv_op