Expose `vecdot`, `vecmat` and `matvec` helpers

[ricardoV94]

Description

Newer numpy versions introduced the vecmat and matvec (and they had vecdot for a while), which we have internally as different Blockwise versions of Dot, just like matmul. We should expose those as we do with matmul:

pytensor/pytensor/tensor/math.py

Lines 2760 to 2841 in 5d4e9e0

	# Predefine all batched variations of Dot
	_inner_prod = Blockwise(
	_dot,
	signature="(n),(n)->()",
	)
	_matrix_vec_prod = Blockwise(
	_dot,
	signature="(m,k),(k)->(m)",
	)
	_vec_matrix_prod = Blockwise(
	_dot,
	signature="(k),(k,n)->(n)",
	)
	_matrix_matrix_matmul = Blockwise(
	_dot,
	signature="(m,k),(k,n)->(m,n)",
	gufunc_spec=("numpy.matmul", 2, 1),
	)
	def matmul(x1: "ArrayLike", x2: "ArrayLike", dtype: Optional["DTypeLike"] = None):
	"""Compute the matrix product of two tensor variables.
	Parameters
	----------
	x1, x2
	Input arrays, scalars not allowed.
	dtype
	The desired data-type for the array. If not given, then the type will
	be determined as the minimum type required to hold the objects in the
	sequence.
	Returns
	-------
	out : ndarray
	The matrix product of the inputs. This is a scalar only when both
	`x1`, `x2` are 1-d vectors.
	Raises
	------
	ValueError
	If the last dimension of `x1` is not the same size as the second-to-last
	dimension of `x2`. If a scalar value is passed in.
	Notes
	-----
	The behavior depends on the arguments in the following way.
	- If both arguments are 2-D they are multiplied like conventional matrices.
	- If either argument is N-D, N > 2, it is treated as a stack of matrices
	residing in the last two indexes and broadcast accordingly.
	- If the first argument is 1-D, it is promoted to a matrix by prepending a
	1 to its dimensions. After matrix multiplication the prepended 1 is removed.
	- If the second argument is 1-D, it is promoted to a matrix by appending a
	1 to its dimensions. After matrix multiplication the appended 1 is removed.
	`matmul` differs from `dot` in two important ways:
	- Multiplication by scalars is not allowed, use `mul` instead.
	- Stacks of matrices are broadcast together as if the matrices were elements,
	respecting the signature ``(n, k), (k, m) -> (n, m)``:
	"""
	x1 = as_tensor_variable(x1)
	x2 = as_tensor_variable(x2)
	if x1.type.ndim == 0 or x2.type.ndim == 0:
	raise ValueError("matmul operand cannot be scalar")
	if x1.type.ndim == 1 and x2.type.ndim == 1:
	out = _dot(x1, x2)
	elif x1.type.ndim == 1:
	out = _matrix_matrix_matmul(x1[None], x2).squeeze(-2)
	elif x2.type.ndim == 1:
	out = _matrix_matrix_matmul(x1, x2[:, None]).squeeze(-1)
	else:
	out = _matrix_matrix_matmul(x1, x2)
	if dtype is not None:
	out = out.astype(dtype)
	return out

https://numpy.org/doc/stable/reference/generated/numpy.matvec.html#numpy.matvec
https://numpy.org/doc/stable/reference/generated/numpy.vecmat.html
https://numpy.org/doc/stable/reference/generated/numpy.vecdot.html#numpy.vecdot