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eager

broadcast_arrays(*args)

Broadcasts one or more arrays against one another.

Parameters

*args: array an arbitrary number of to-be broadcasted arrays.

Returns

out: List[array] a list of broadcasted arrays. Each array has the same shape. Element types are preserved.

Source code in src/finch/interface/eager.py 
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def broadcast_arrays(*args):
    """
    Broadcasts one or more arrays against one another.

    Parameters
    ----------
    *args: array
        an arbitrary number of to-be broadcasted arrays.

    Returns
    -------
    out: List[array]
        a list of broadcasted arrays. Each array has the same shape.
        Element types are preserved.
    """
    if builtins.any(isinstance(arg, lazy.LazyTensor) for arg in args):
        return lazy.broadcast_arrays(*args)
    # compute can take in a list of LazyTensors
    return compute(lazy.broadcast_arrays(*args))

broadcast_to(x, /, shape)

Broadcasts an array to a new shape.

Parameters

x: array The input tensor to be broadcasted. shape: Sequence[int] The target shape to which the input tensor should be broadcasted.

Returns

out: array A tensor with the same data as x, but with the specified shape.

Source code in src/finch/interface/eager.py 
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def broadcast_to(x, /, shape: Sequence[int]):
    """
    Broadcasts an array to a new shape.

    Parameters
    ----------
    x: array
        The input tensor to be broadcasted.
    shape: Sequence[int]
        The target shape to which the input tensor should be broadcasted.

    Returns
    -------
    out: array
        A tensor with the same data as `x`, but with the specified shape.
    """
    shape = tuple(shape)  # Ensure shape is a tuple for consistency
    if isinstance(x, lazy.LazyTensor):
        return lazy.broadcast_to(x, shape=shape)
    return compute(lazy.broadcast_to(x, shape=shape))

einop(prgm, /, **kwargs)

Execute an einsum expression using the specified array framework.

This function parses and executes einsum-like expressions with extended syntax that supports various operations beyond traditional Einstein summation notation.

Parameters:

Name Type Description Default
prgm str

Einsum program string specifying the computation. The syntax supports: - Assignment: "C[i,j] = A[i,j] + B[j,i]" - Increment: "C[i,j] += A[i,k] * B[k,j]" - Reductions: "C[i] += A[i,j]", "C[i] max= A[i,j]", "C[i] &= A[i,j]" - Arithmetic operations: +, -, *, /, //, %, ** - Comparison operations: ==, !=, <, <=, >, >= - Logical operations: and, or, not - Bitwise operations: &, |, ^, <<, >> - Function calls and complex expressions with parentheses - Mathematical functions: abs, sqrt, exp, log, sin, cos, tan, etc. - Literal values: integers, floats, booleans, and complex numbers - Python operator precedence and parentheses for grouping

 required
**kwargs Any

Named arrays referenced in the einsum expression. The keys should match the tensor names used in the program string.

 {}

Returns:

Type Description
Any

The result array from executing the einsum expression.

Examples:

>>> rng = np.random.default_rng(42)
>>> A = rng.integers(0, 10, size=(3, 4))
>>> B = rng.integers(0, 10, size=(4, 3))
>>> # Matrix addition with transpose
>>> C = einop("C[i,j] = A[i,j] + B[j,i]", A=A, B=B)
>>> # Matrix multiplication
>>> D = einop("D[i,j] += A[i,k] * B[k,j]", A=A, B=B)
>>> # Min-Plus multiplication with shift
>>> E = einop("E[i] min= A[k,i] + D[k,j] << 1", A=A, D=D)
Source code in src/finch/interface/eager.py 
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def einop(prgm: str, /, **kwargs: Any) -> Any:
    """Execute an einsum expression using the specified array framework.

    This function parses and executes einsum-like expressions with extended syntax
    that supports various operations beyond traditional Einstein summation notation.

    Args:
        prgm (str): Einsum program string specifying the computation. The syntax
            supports:
            - Assignment: "C[i,j] = A[i,j] + B[j,i]"
            - Increment: "C[i,j] += A[i,k] * B[k,j]"
            - Reductions: "C[i] += A[i,j]", "C[i] max= A[i,j]", "C[i] &= A[i,j]"
            - Arithmetic operations: +, -, *, /, //, %, **
            - Comparison operations: ==, !=, <, <=, >, >=
            - Logical operations: and, or, not
            - Bitwise operations: &, |, ^, <<, >>
            - Function calls and complex expressions with parentheses
            - Mathematical functions: abs, sqrt, exp, log, sin, cos, tan, etc.
            - Literal values: integers, floats, booleans, and complex numbers
            - Python operator precedence and parentheses for grouping
        **kwargs: Named arrays referenced in the einsum expression. The keys
            should match the tensor names used in the program string.

    Returns:
        The result array from executing the einsum expression.

    Examples:
        >>> rng = np.random.default_rng(42)
        >>> A = rng.integers(0, 10, size=(3, 4))
        >>> B = rng.integers(0, 10, size=(4, 3))
        >>> # Matrix addition with transpose
        >>> C = einop("C[i,j] = A[i,j] + B[j,i]", A=A, B=B)
        >>> # Matrix multiplication
        >>> D = einop("D[i,j] += A[i,k] * B[k,j]", A=A, B=B)
        >>> # Min-Plus multiplication with shift
        >>> E = einop("E[i] min= A[k,i] + D[k,j] << 1", A=A, D=D)
    """
    if builtins.any(isinstance(v, lazy.LazyTensor) for v in kwargs.values()):
        return lazy.einop(prgm, **kwargs)
    return compute(lazy.einop(prgm, **kwargs))

einsum(*args, **kwargs)

einsum(subscripts, *operands)

Evaluates the Einstein summation convention on the operands.

Using the Einstein summation convention, many common multi-dimensional, linear algebraic array operations can be represented in a simple fashion. In implicit mode einsum computes these values.

In explicit mode, einsum provides further flexibility to compute other array operations that might not be considered classical Einstein summation operations, by disabling, or forcing summation over specified subscript labels.

See the notes and examples for clarification.

Parameters

subscripts : str Specifies the subscripts for summation as comma separated list of subscript labels. An implicit (classical Einstein summation) calculation is performed unless the explicit indicator '->' is included as well as subscript labels of the precise output form. operands : list of array_like These are the arrays for the operation.

Returns

output : ndarray The calculation based on the Einstein summation convention.

Notes

The Einstein summation convention can be used to compute many multi-dimensional, linear algebraic array operations. einsum provides a succinct way of representing these.

A non-exhaustive list of these operations, which can be computed by einsum, is shown below along with examples:

  • Trace of an array, :py:func:numpy.trace.
  • Return a diagonal, :py:func:numpy.diag.
  • Array axis summations, :py:func:numpy.sum.
  • Transpositions and permutations, :py:func:numpy.transpose.
  • Matrix multiplication and dot product, :py:func:numpy.matmul :py:func:numpy.dot.
  • Vector inner and outer products, :py:func:numpy.inner :py:func:numpy.outer.
  • Broadcasting, element-wise and scalar multiplication, :py:func:numpy.multiply.
  • Tensor contractions, :py:func:numpy.tensordot.
  • Chained array operations, in efficient calculation order, :py:func:numpy.einsum_path.

The subscripts string is a comma-separated list of subscript labels, where each label refers to a dimension of the corresponding operand. Whenever a label is repeated it is summed, so np.einsum('i,i', a, b) is equivalent to :py:func:np.inner(a,b) <numpy.inner>. If a label appears only once, it is not summed, so np.einsum('i', a) produces a view of a with no changes. A further example np.einsum('ij,jk', a, b) describes traditional matrix multiplication and is equivalent to :py:func:np.matmul(a,b) <numpy.matmul>. Repeated subscript labels in one operand take the diagonal. For example, np.einsum('ii', a) is equivalent to :py:func:np.trace(a) <numpy.trace>.

In implicit mode, the chosen subscripts are important since the axes of the output are reordered alphabetically. This means that np.einsum('ij', a) doesn't affect a 2D array, while np.einsum('ji', a) takes its transpose. Additionally, np.einsum('ij,jk', a, b) returns a matrix multiplication, while, np.einsum('ij,jh', a, b) returns the transpose of the multiplication since subscript 'h' precedes subscript 'i'.

In explicit mode the output can be directly controlled by specifying output subscript labels. This requires the identifier '->' as well as the list of output subscript labels. This feature increases the flexibility of the function since summing can be disabled or forced when required. The call np.einsum('i->', a) is like :py:func:np.sum(a) <numpy.sum> if a is a 1-D array, and np.einsum('ii->i', a) is like :py:func:np.diag(a) <numpy.diag> if a is a square 2-D array. The difference is that einsum does not allow broadcasting by default. Additionally np.einsum('ij,jh->ih', a, b) directly specifies the order of the output subscript labels and therefore returns matrix multiplication, unlike the example above in implicit mode.

To enable and control broadcasting, use an ellipsis. Default NumPy-style broadcasting is done by adding an ellipsis to the left of each term, like np.einsum('...ii->...i', a). np.einsum('...i->...', a) is like :py:func:np.sum(a, axis=-1) <numpy.sum> for array a of any shape. To take the trace along the first and last axes, you can do np.einsum('i...i', a), or to do a matrix-matrix product with the left-most indices instead of rightmost, one can do np.einsum('ij...,jk...->ik...', a, b).

einsum also provides an alternative way to provide the subscripts and operands as einsum(op0, sublist0, op1, sublist1, ..., [sublistout]). If the output shape is not provided in this format einsum will be calculated in implicit mode, otherwise it will be performed explicitly. The examples below have corresponding einsum calls with the two parameter methods.

Examples

a = np.arange(25).reshape(5, 5) b = np.arange(5) c = np.arange(6).reshape(2, 3)

Trace of a matrix:

np.einsum("ii", a) np.int64(60) np.einsum(a, [0, 0]) np.int64(60) np.trace(a) np.int64(60)

Extract the diagonal (requires explicit form):

np.einsum("ii->i", a) array([ 0, 6, 12, 18, 24]) np.einsum(a, [0, 0], [0]) array([ 0, 6, 12, 18, 24]) np.diag(a) array([ 0, 6, 12, 18, 24])

Sum over an axis (requires explicit form):

np.einsum("ij->i", a) array([ 10, 35, 60, 85, 110]) np.einsum(a, [0, 1], [0]) array([ 10, 35, 60, 85, 110]) np.sum(a, axis=1) array([ 10, 35, 60, 85, 110])

For higher dimensional arrays summing a single axis can be done with ellipsis:

np.einsum("...j->...", a) array([ 10, 35, 60, 85, 110]) np.einsum(a, [Ellipsis, 1], [Ellipsis]) array([ 10, 35, 60, 85, 110])

Compute a matrix transpose, or reorder any number of axes:

np.einsum("ji", c) array([[0, 3], [1, 4], [2, 5]]) np.einsum("ij->ji", c) array([[0, 3], [1, 4], [2, 5]]) np.einsum(c, [1, 0]) array([[0, 3], [1, 4], [2, 5]]) np.transpose(c) array([[0, 3], [1, 4], [2, 5]])

Vector inner products:

np.einsum("i,i", b, b) np.int64(30) np.einsum(b, [0], b, [0]) np.int64(30) np.inner(b, b) np.int64(30)

Matrix vector multiplication:

np.einsum("ij,j", a, b) array([ 30, 80, 130, 180, 230]) np.einsum(a, [0, 1], b, [1]) array([ 30, 80, 130, 180, 230]) np.dot(a, b) array([ 30, 80, 130, 180, 230]) np.einsum("...j,j", a, b) array([ 30, 80, 130, 180, 230])

Broadcasting and scalar multiplication:

np.einsum("..., ...", 3, c) array([[ 0, 3, 6], [ 9, 12, 15]]) np.einsum(",ij", 3, c) array([[ 0, 3, 6], [ 9, 12, 15]]) np.einsum(3, [Ellipsis], c, [Ellipsis]) array([[ 0, 3, 6], [ 9, 12, 15]]) np.multiply(3, c) array([[ 0, 3, 6], [ 9, 12, 15]])

Vector outer product:

np.einsum("i,j", np.arange(2) + 1, b) array([[0, 1, 2, 3, 4], [0, 2, 4, 6, 8]]) np.einsum(np.arange(2) + 1, [0], b, [1]) array([[0, 1, 2, 3, 4], [0, 2, 4, 6, 8]]) np.outer(np.arange(2) + 1, b) array([[0, 1, 2, 3, 4], [0, 2, 4, 6, 8]])

Tensor contraction:

a = np.arange(60.0).reshape(3, 4, 5) b = np.arange(24.0).reshape(4, 3, 2) np.einsum("ijk,jil->kl", a, b) array([[4400., 4730.], [4532., 4874.], [4664., 5018.], [4796., 5162.], [4928., 5306.]]) np.einsum(a, [0, 1, 2], b, [1, 0, 3], [2, 3]) array([[4400., 4730.], [4532., 4874.], [4664., 5018.], [4796., 5162.], [4928., 5306.]]) np.tensordot(a, b, axes=([1, 0], [0, 1])) array([[4400., 4730.], [4532., 4874.], [4664., 5018.], [4796., 5162.], [4928., 5306.]])

Example of ellipsis use:

a = np.arange(6).reshape((3, 2)) b = np.arange(12).reshape((4, 3)) np.einsum("ki,jk->ij", a, b) array([[10, 28, 46, 64], [13, 40, 67, 94]]) np.einsum("ki,...k->i...", a, b) array([[10, 28, 46, 64], [13, 40, 67, 94]]) np.einsum("k...,jk", a, b) array([[10, 28, 46, 64], [13, 40, 67, 94]])

Source code in src/finch/interface/eager.py 
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def einsum(*args, **kwargs):
    """
    einsum(subscripts, *operands)

    Evaluates the Einstein summation convention on the operands.

    Using the Einstein summation convention, many common multi-dimensional,
    linear algebraic array operations can be represented in a simple fashion.
    In *implicit* mode `einsum` computes these values.

    In *explicit* mode, `einsum` provides further flexibility to compute
    other array operations that might not be considered classical Einstein
    summation operations, by disabling, or forcing summation over specified
    subscript labels.

    See the notes and examples for clarification.

    Parameters
    ----------
    subscripts : str
        Specifies the subscripts for summation as comma separated list of
        subscript labels. An implicit (classical Einstein summation)
        calculation is performed unless the explicit indicator '->' is
        included as well as subscript labels of the precise output form.
    operands : list of array_like
        These are the arrays for the operation.

    Returns
    -------
    output : ndarray
        The calculation based on the Einstein summation convention.

    Notes
    -----
    The Einstein summation convention can be used to compute
    many multi-dimensional, linear algebraic array operations. `einsum`
    provides a succinct way of representing these.

    A non-exhaustive list of these operations,
    which can be computed by `einsum`, is shown below along with examples:

    * Trace of an array, :py:func:`numpy.trace`.
    * Return a diagonal, :py:func:`numpy.diag`.
    * Array axis summations, :py:func:`numpy.sum`.
    * Transpositions and permutations, :py:func:`numpy.transpose`.
    * Matrix multiplication and dot product, :py:func:`numpy.matmul`
        :py:func:`numpy.dot`.
    * Vector inner and outer products, :py:func:`numpy.inner`
        :py:func:`numpy.outer`.
    * Broadcasting, element-wise and scalar multiplication,
        :py:func:`numpy.multiply`.
    * Tensor contractions, :py:func:`numpy.tensordot`.
    * Chained array operations, in efficient calculation order,
        :py:func:`numpy.einsum_path`.

    The subscripts string is a comma-separated list of subscript labels,
    where each label refers to a dimension of the corresponding operand.
    Whenever a label is repeated it is summed, so ``np.einsum('i,i', a, b)``
    is equivalent to :py:func:`np.inner(a,b) <numpy.inner>`. If a label
    appears only once, it is not summed, so ``np.einsum('i', a)``
    produces a view of ``a`` with no changes. A further example
    ``np.einsum('ij,jk', a, b)`` describes traditional matrix multiplication
    and is equivalent to :py:func:`np.matmul(a,b) <numpy.matmul>`.
    Repeated subscript labels in one operand take the diagonal.
    For example, ``np.einsum('ii', a)`` is equivalent to
    :py:func:`np.trace(a) <numpy.trace>`.

    In *implicit mode*, the chosen subscripts are important
    since the axes of the output are reordered alphabetically.  This
    means that ``np.einsum('ij', a)`` doesn't affect a 2D array, while
    ``np.einsum('ji', a)`` takes its transpose. Additionally,
    ``np.einsum('ij,jk', a, b)`` returns a matrix multiplication, while,
    ``np.einsum('ij,jh', a, b)`` returns the transpose of the
    multiplication since subscript 'h' precedes subscript 'i'.

    In *explicit mode* the output can be directly controlled by
    specifying output subscript labels.  This requires the
    identifier '->' as well as the list of output subscript labels.
    This feature increases the flexibility of the function since
    summing can be disabled or forced when required. The call
    ``np.einsum('i->', a)`` is like :py:func:`np.sum(a) <numpy.sum>`
    if ``a`` is a 1-D array, and ``np.einsum('ii->i', a)``
    is like :py:func:`np.diag(a) <numpy.diag>` if ``a`` is a square 2-D array.
    The difference is that `einsum` does not allow broadcasting by default.
    Additionally ``np.einsum('ij,jh->ih', a, b)`` directly specifies the
    order of the output subscript labels and therefore returns matrix
    multiplication, unlike the example above in implicit mode.

    To enable and control broadcasting, use an ellipsis.  Default
    NumPy-style broadcasting is done by adding an ellipsis
    to the left of each term, like ``np.einsum('...ii->...i', a)``.
    ``np.einsum('...i->...', a)`` is like
    :py:func:`np.sum(a, axis=-1) <numpy.sum>` for array ``a`` of any shape.
    To take the trace along the first and last axes,
    you can do ``np.einsum('i...i', a)``, or to do a matrix-matrix
    product with the left-most indices instead of rightmost, one can do
    ``np.einsum('ij...,jk...->ik...', a, b)``.

    `einsum` also provides an alternative way to provide the subscripts and
    operands as ``einsum(op0, sublist0, op1, sublist1, ..., [sublistout])``.
    If the output shape is not provided in this format `einsum` will be
    calculated in implicit mode, otherwise it will be performed explicitly.
    The examples below have corresponding `einsum` calls with the two
    parameter methods.

    Examples
    --------
    >>> a = np.arange(25).reshape(5, 5)
    >>> b = np.arange(5)
    >>> c = np.arange(6).reshape(2, 3)

    Trace of a matrix:

    >>> np.einsum("ii", a)
    np.int64(60)
    >>> np.einsum(a, [0, 0])
    np.int64(60)
    >>> np.trace(a)
    np.int64(60)

    Extract the diagonal (requires explicit form):

    >>> np.einsum("ii->i", a)
    array([ 0,  6, 12, 18, 24])
    >>> np.einsum(a, [0, 0], [0])
    array([ 0,  6, 12, 18, 24])
    >>> np.diag(a)
    array([ 0,  6, 12, 18, 24])

    Sum over an axis (requires explicit form):

    >>> np.einsum("ij->i", a)
    array([ 10,  35,  60,  85, 110])
    >>> np.einsum(a, [0, 1], [0])
    array([ 10,  35,  60,  85, 110])
    >>> np.sum(a, axis=1)
    array([ 10,  35,  60,  85, 110])

    For higher dimensional arrays summing a single axis can be done
    with ellipsis:

    >>> np.einsum("...j->...", a)
    array([ 10,  35,  60,  85, 110])
    >>> np.einsum(a, [Ellipsis, 1], [Ellipsis])
    array([ 10,  35,  60,  85, 110])

    Compute a matrix transpose, or reorder any number of axes:

    >>> np.einsum("ji", c)
    array([[0, 3],
           [1, 4],
           [2, 5]])
    >>> np.einsum("ij->ji", c)
    array([[0, 3],
           [1, 4],
           [2, 5]])
    >>> np.einsum(c, [1, 0])
    array([[0, 3],
           [1, 4],
           [2, 5]])
    >>> np.transpose(c)
    array([[0, 3],
           [1, 4],
           [2, 5]])

    Vector inner products:

    >>> np.einsum("i,i", b, b)
    np.int64(30)
    >>> np.einsum(b, [0], b, [0])
    np.int64(30)
    >>> np.inner(b, b)
    np.int64(30)

    Matrix vector multiplication:

    >>> np.einsum("ij,j", a, b)
    array([ 30,  80, 130, 180, 230])
    >>> np.einsum(a, [0, 1], b, [1])
    array([ 30,  80, 130, 180, 230])
    >>> np.dot(a, b)
    array([ 30,  80, 130, 180, 230])
    >>> np.einsum("...j,j", a, b)
    array([ 30,  80, 130, 180, 230])

    Broadcasting and scalar multiplication:

    >>> np.einsum("..., ...", 3, c)
    array([[ 0,  3,  6],
           [ 9, 12, 15]])
    >>> np.einsum(",ij", 3, c)
    array([[ 0,  3,  6],
           [ 9, 12, 15]])
    >>> np.einsum(3, [Ellipsis], c, [Ellipsis])
    array([[ 0,  3,  6],
           [ 9, 12, 15]])
    >>> np.multiply(3, c)
    array([[ 0,  3,  6],
           [ 9, 12, 15]])

    Vector outer product:

    >>> np.einsum("i,j", np.arange(2) + 1, b)
    array([[0, 1, 2, 3, 4],
           [0, 2, 4, 6, 8]])
    >>> np.einsum(np.arange(2) + 1, [0], b, [1])
    array([[0, 1, 2, 3, 4],
           [0, 2, 4, 6, 8]])
    >>> np.outer(np.arange(2) + 1, b)
    array([[0, 1, 2, 3, 4],
           [0, 2, 4, 6, 8]])

    Tensor contraction:

    >>> a = np.arange(60.0).reshape(3, 4, 5)
    >>> b = np.arange(24.0).reshape(4, 3, 2)
    >>> np.einsum("ijk,jil->kl", a, b)
    array([[4400., 4730.],
           [4532., 4874.],
           [4664., 5018.],
           [4796., 5162.],
           [4928., 5306.]])
    >>> np.einsum(a, [0, 1, 2], b, [1, 0, 3], [2, 3])
    array([[4400., 4730.],
           [4532., 4874.],
           [4664., 5018.],
           [4796., 5162.],
           [4928., 5306.]])
    >>> np.tensordot(a, b, axes=([1, 0], [0, 1]))
    array([[4400., 4730.],
           [4532., 4874.],
           [4664., 5018.],
           [4796., 5162.],
           [4928., 5306.]])

    Example of ellipsis use:

    >>> a = np.arange(6).reshape((3, 2))
    >>> b = np.arange(12).reshape((4, 3))
    >>> np.einsum("ki,jk->ij", a, b)
    array([[10, 28, 46, 64],
           [13, 40, 67, 94]])
    >>> np.einsum("ki,...k->i...", a, b)
    array([[10, 28, 46, 64],
           [13, 40, 67, 94]])
    >>> np.einsum("k...,jk", a, b)
    array([[10, 28, 46, 64],
           [13, 40, 67, 94]])
    """

    if builtins.any(isinstance(v, lazy.LazyTensor) for v in args):
        return lazy.einsum(*args, **kwargs)
    return compute(lazy.einsum(*args, **kwargs))

matmul(x1, x2)

Computes the matrix product.

Returns a LazyTensor if either x1 or x2 is a LazyTensor. Otherwise, computes the result eagerly.

Source code in src/finch/interface/eager.py 
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def matmul(x1, x2, /):
    """
    Computes the matrix product.

    Returns a LazyTensor if either x1 or x2 is a LazyTensor.
    Otherwise, computes the result eagerly.
    """
    if isinstance(x1, lazy.LazyTensor) or isinstance(x2, lazy.LazyTensor):
        return lazy.matmul(x1, x2)
    c = lazy.matmul(x1, x2)
    return compute(c)

matrix_power(x, n)

Computes the power of a matrix.

Source code in src/finch/interface/eager.py 
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def matrix_power(x, n, /):
    """
    Computes the power of a matrix.
    """
    if isinstance(x, lazy.LazyTensor) and not (isinstance(n, int) and n < 0):
        return lazy.matrix_power(x, n)
    if isinstance(n, int) and n < 0:
        return matrix_power(inv(x), -n)
    try:
        return lazy.asarray(
            to_numpy(scipy_sparse_linalg.matrix_power(to_scipy(lazy.asarray(x)), n))
        )
    except NotImplementedError:
        pass
    return compute(lazy.matrix_power(x, n))

matrix_transpose(x)

Computes the transpose of a matrix or stack of matrices.

Source code in src/finch/interface/eager.py 
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def matrix_transpose(x, /):
    """
    Computes the transpose of a matrix or stack of matrices.
    """
    if isinstance(x, lazy.LazyTensor):
        return lazy.matrix_transpose(x)
    return compute(lazy.matrix_transpose(x))

moveaxis(x, source, destination)

Moves array axes (dimensions) to new positions, while leaving other axes in their original positions.

Args

  • x (array) - input array.
  • source - Axes to move.
  • destination - indices defining the desired positions for each respective source axis index.

Returns

  • out (array) - an array containing reordered axes.
Source code in src/finch/interface/eager.py 
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def moveaxis(x, source: int | tuple[int, ...], destination: int | tuple[int, ...], /):
    """
    Moves array axes (dimensions) to new positions,
    while leaving other axes in their original positions.

    Args
    ---------
    - x (array) - input array.
    - source - Axes to move.
    - destination - indices defining the desired
    positions for each respective source axis index.

    Returns
    --------
    - out (array) - an array containing reordered axes.
    """
    if isinstance(x, lazy.LazyTensor):
        return lazy.moveaxis(x, source, destination)
    return compute(lazy.moveaxis(x, source, destination))

tensordot(x1, x2, /, *, axes=2)

Computes the tensordot operation.

Returns a LazyTensor if either x1 or x2 is a LazyTensor. Otherwise, computes the result eagerly.

Source code in src/finch/interface/eager.py 
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def tensordot(x1, x2, /, *, axes: int | tuple[Sequence[int], Sequence[int]] = 2):
    """
    Computes the tensordot operation.

    Returns a LazyTensor if either x1 or x2 is a LazyTensor.
    Otherwise, computes the result eagerly.
    """
    if isinstance(x1, lazy.LazyTensor) or isinstance(x2, lazy.LazyTensor):
        return lazy.tensordot(x1, x2, axes=axes)
    return compute(lazy.tensordot(x1, x2, axes=axes))

vecdot(x1, x2, /, *, axis=-1)

Computes the (vector) dot product of two arrays.

Parameters

x1: array The first input tensor. x2: array The second input tensor. axis: int, optional The axis along which to compute the dot product. Default is -1 (last axis).

Returns

out: array A tensor containing the dot product of x1 and x2 along the specified axis.

Source code in src/finch/interface/eager.py 
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def vecdot(x1, x2, /, *, axis=-1):
    """
    Computes the (vector) dot product of two arrays.

    Parameters
    ----------
    x1: array
        The first input tensor.
    x2: array
        The second input tensor.
    axis: int, optional
        The axis along which to compute the dot product. Default is -1 (last axis).

    Returns
    -------
    out: array
        A tensor containing the dot product of `x1` and `x2` along the specified axis.
    """
    if isinstance(x1, lazy.LazyTensor) or isinstance(x2, lazy.LazyTensor):
        return lazy.vecdot(x1, x2, axis=axis)
    return compute(lazy.vecdot(x1, x2, axis=axis))