Add numpy-like vecdot, vecmat and matvec helpers

pytensor/tensor/math.py

@@ -4122,6 +4122,154 @@
     return out
 
 
+def vecdot(
+    x1: TensorLike,
+    x2: TensorLike,
+    dtype: Optional["DTypeLike"] = None,
+) -> TensorVariable:
+    """Compute the vector dot product of two arrays.
+
+    Parameters
+    ----------
+    x1, x2
+        Input arrays with the same shape.
+    dtype
+        The desired data-type for the result. If not given, then the type will
+        be determined as the minimum type required to hold the objects in the
+        sequence.
+
+    Returns
+    -------
+    TensorVariable
+        The vector dot product of the inputs.
+
+    Notes
+    -----
+    This is equivalent to `numpy.vecdot` and computes the dot product of
+    vectors along the last axis of both inputs. Broadcasting is supported
+    across all other dimensions.
+
+    Examples
+    --------
+    >>> import pytensor.tensor as pt
+    >>> # Vector dot product with shape (5,) inputs
+    >>> x = pt.vector("x", shape=(5,))  # shape (5,)
+    >>> y = pt.vector("y", shape=(5,))  # shape (5,)
+    >>> z = pt.vecdot(x, y)  # scalar output
+    >>> # Equivalent to numpy.vecdot(x, y)
+    >>>
+    >>> # With batched inputs of shape (3, 5)
+    >>> x_batch = pt.matrix("x", shape=(3, 5))  # shape (3, 5)
+    >>> y_batch = pt.matrix("y", shape=(3, 5))  # shape (3, 5)
+    >>> z_batch = pt.vecdot(x_batch, y_batch)  # shape (3,)
+    >>> # Equivalent to numpy.vecdot(x_batch, y_batch)
+    """
+    out = _inner_prod(x1, x2)
+
+    if dtype is not None:
+        out = out.astype(dtype)
+
+    return out
+
+
+def matvec(
+    x1: TensorLike, x2: TensorLike, dtype: Optional["DTypeLike"] = None
+) -> TensorVariable:
+    """Compute the matrix-vector product.
+
+    Parameters
+    ----------
+    x1
+        Input array for the matrix with shape (..., M, K).
+    x2
+        Input array for the vector with shape (..., K).
+    dtype
+        The desired data-type for the result. If not given, then the type will
+        be determined as the minimum type required to hold the objects in the
+        sequence.
+
+    Returns
+    -------
+    TensorVariable
+        The matrix-vector product with shape (..., M).
+
+    Notes
+    -----
+    This is equivalent to `numpy.matvec` and computes the matrix-vector product
+    with broadcasting over batch dimensions.
+
+    Examples
+    --------
+    >>> import pytensor.tensor as pt
+    >>> # Matrix-vector product
+    >>> A = pt.matrix("A", shape=(3, 4))  # shape (3, 4)
+    >>> v = pt.vector("v", shape=(4,))  # shape (4,)
+    >>> result = pt.matvec(A, v)  # shape (3,)
+    >>> # Equivalent to numpy.matvec(A, v)
+    >>>
+    >>> # Batched matrix-vector product
+    >>> batched_A = pt.tensor3("A", shape=(2, 3, 4))  # shape (2, 3, 4)
+    >>> batched_v = pt.matrix("v", shape=(2, 4))  # shape (2, 4)
+    >>> result = pt.matvec(batched_A, batched_v)  # shape (2, 3)
+    >>> # Equivalent to numpy.matvec(batched_A, batched_v)
+    """
+    out = _matrix_vec_prod(x1, x2)
+
+    if dtype is not None:
+        out = out.astype(dtype)
+
+    return out
+
+
+def vecmat(
+    x1: TensorLike, x2: TensorLike, dtype: Optional["DTypeLike"] = None
+) -> TensorVariable:
+    """Compute the vector-matrix product.
+
+    Parameters
+    ----------
+    x1
+        Input array for the vector with shape (..., K).
+    x2
+        Input array for the matrix with shape (..., K, N).
+    dtype
+        The desired data-type for the result. If not given, then the type will
+        be determined as the minimum type required to hold the objects in the
+        sequence.
+
+    Returns
+    -------
+    TensorVariable
+        The vector-matrix product with shape (..., N).
+
+    Notes
+    -----
+    This is equivalent to `numpy.vecmat` and computes the vector-matrix product
+    with broadcasting over batch dimensions.
+
+    Examples
+    --------
+    >>> import pytensor.tensor as pt
+    >>> # Vector-matrix product
+    >>> v = pt.vector("v", shape=(3,))  # shape (3,)
+    >>> A = pt.matrix("A", shape=(3, 4))  # shape (3, 4)
+    >>> result = pt.vecmat(v, A)  # shape (4,)
+    >>> # Equivalent to numpy.vecmat(v, A)
+    >>>
+    >>> # Batched vector-matrix product
+    >>> batched_v = pt.matrix("v", shape=(2, 3))  # shape (2, 3)
+    >>> batched_A = pt.tensor3("A", shape=(2, 3, 4))  # shape (2, 3, 4)
+    >>> result = pt.vecmat(batched_v, batched_A)  # shape (2, 4)
+    >>> # Equivalent to numpy.vecmat(batched_v, batched_A)
+    """
+    out = _vec_matrix_prod(x1, x2)
+
+    if dtype is not None:
+        out = out.astype(dtype)
+
+    return out
+
+
 @_vectorize_node.register(Dot)
 def vectorize_node_dot(op, node, batched_x, batched_y):
     old_x, old_y = node.inputs
@@ -4218,6 +4366,9 @@
     "max_and_argmax",
     "max",
     "matmul",
+    "vecdot",
+    "matvec",
+    "vecmat",
     "argmax",
     "min",
     "argmin",

tests/tensor/test_math.py

@@ -89,6 +89,7 @@
     logaddexp,
     logsumexp,
     matmul,
+    matvec,
     max,
     max_and_argmax,
     maximum,
@@ -123,6 +124,8 @@
     true_div,
     trunc,
     var,
+    vecdot,
+    vecmat,
 )
 from pytensor.tensor.math import sum as pt_sum
 from pytensor.tensor.type import (
@@ -2076,6 +2079,71 @@ def is_super_shape(var1, var2):
                             assert is_super_shape(y, g)
 
 
+def test_matrix_vector_ops():
+    """Test vecdot, matvec, and vecmat helper functions."""
+    rng = np.random.default_rng(seed=utt.fetch_seed())
+
+    # Create test data with batch dimension (2)
+    batch_size = 2
+    dim_k = 4  # Common dimension
+    dim_m = 3  # Matrix rows
+    dim_n = 5  # Matrix columns
+
+    # Create input tensors with appropriate shapes
+    # For matvec: x1(b,m,k) @ x2(b,k) -> out(b,m)
+    # For vecmat: x1(b,k) @ x2(b,k,n) -> out(b,n)
+
+    # Create test values using config.floatX to match PyTensor's default dtype
+    mat_mk_val = random(batch_size, dim_m, dim_k, rng=rng).astype(config.floatX)
+    mat_kn_val = random(batch_size, dim_k, dim_n, rng=rng).astype(config.floatX)
+    vec_k_val = random(batch_size, dim_k, rng=rng).astype(config.floatX)
+
+    # Create tensor variables with matching dtype
+    mat_mk = tensor(
+        name="mat_mk", shape=(batch_size, dim_m, dim_k), dtype=config.floatX
+    )
+    mat_kn = tensor(
+        name="mat_kn", shape=(batch_size, dim_k, dim_n), dtype=config.floatX
+    )
+    vec_k = tensor(name="vec_k", shape=(batch_size, dim_k), dtype=config.floatX)
+
+    # Test 1: vecdot with matching dimensions
+    vecdot_out = vecdot(vec_k, vec_k, dtype="int32")
+    vecdot_fn = function([vec_k], vecdot_out)
+    result = vecdot_fn(vec_k_val)
+
+    # Check dtype
+    assert result.dtype == np.int32
+
+    # Calculate expected manually
+    expected_vecdot = np.zeros((batch_size,), dtype=np.int32)
+    for i in range(batch_size):
+        expected_vecdot[i] = np.sum(vec_k_val[i] * vec_k_val[i])
+    np.testing.assert_allclose(result, expected_vecdot)
+
+    # Test 2: matvec - matrix-vector product
+    matvec_out = matvec(mat_mk, vec_k)
+    matvec_fn = function([mat_mk, vec_k], matvec_out)
+    result_matvec = matvec_fn(mat_mk_val, vec_k_val)
+
+    # Calculate expected manually
+    expected_matvec = np.zeros((batch_size, dim_m), dtype=config.floatX)
+    for i in range(batch_size):
+        expected_matvec[i] = np.dot(mat_mk_val[i], vec_k_val[i])
+    np.testing.assert_allclose(result_matvec, expected_matvec)
+
+    # Test 3: vecmat - vector-matrix product
+    vecmat_out = vecmat(vec_k, mat_kn)
+    vecmat_fn = function([vec_k, mat_kn], vecmat_out)
+    result_vecmat = vecmat_fn(vec_k_val, mat_kn_val)
+
+    # Calculate expected manually
+    expected_vecmat = np.zeros((batch_size, dim_n), dtype=config.floatX)
+    for i in range(batch_size):
+        expected_vecmat[i] = np.dot(vec_k_val[i], mat_kn_val[i])
+    np.testing.assert_allclose(result_vecmat, expected_vecmat)
+
+
 class TestTensordot:
     def TensorDot(self, axes):
         # Since tensordot is no longer an op, mimic the old op signature