Simplify matrix/vector helper functions

- Remove redundant dimension checks that Blockwise already handles
- Streamline test cases while keeping essential coverage
- Based on PR feedback from Ricardo

🤖 Generated with Claude Code
Co-Authored-By: Claude <[email protected]>

Author: twiecki

pytensor/tensor/math.py

@@ -4147,11 +4147,6 @@ def vecdot(
     out : ndarray
         The vector dot product of the inputs computed along the specified axes.
 
-    Raises
-    ------
-    ValueError
-        If either input is a scalar value.
-
     Notes
     -----
     This is similar to `dot` but with broadcasting. It computes the dot product
@@ -4161,9 +4156,6 @@ def vecdot(
     x1 = as_tensor_variable(x1)
     x2 = as_tensor_variable(x2)
 
-    if x1.type.ndim == 0 or x2.type.ndim == 0:
-        raise ValueError("vecdot operand cannot be scalar")
-
     # Handle negative axis
     if axis < 0:
         x1_axis = axis % x1.type.ndim
@@ -4209,12 +4201,6 @@ def matvec(x1: "ArrayLike", x2: "ArrayLike", dtype: Optional["DTypeLike"] = None
     out : ndarray
         The matrix-vector product with shape (..., M).
 
-    Raises
-    ------
-    ValueError
-        If any input is a scalar or if the trailing dimension of x2 does not match
-        the second-to-last dimension of x1.
-
     Notes
     -----
     This is similar to `matmul` where the second argument is a vector,
@@ -4224,15 +4210,6 @@ def matvec(x1: "ArrayLike", x2: "ArrayLike", dtype: Optional["DTypeLike"] = None
     x1 = as_tensor_variable(x1)
     x2 = as_tensor_variable(x2)
 
-    if x1.type.ndim == 0 or x2.type.ndim == 0:
-        raise ValueError("matvec operand cannot be scalar")
-
-    if x1.type.ndim < 2:
-        raise ValueError("First input to matvec must have at least 2 dimensions")
-
-    if x2.type.ndim < 1:
-        raise ValueError("Second input to matvec must have at least 1 dimension")
-
     out = _matrix_vec_prod(x1, x2)
 
     if dtype is not None:
@@ -4260,12 +4237,6 @@ def vecmat(x1: "ArrayLike", x2: "ArrayLike", dtype: Optional["DTypeLike"] = None
     out : ndarray
         The vector-matrix product with shape (..., N).
 
-    Raises
-    ------
-    ValueError
-        If any input is a scalar or if the last dimension of x1 does not match
-        the second-to-last dimension of x2.
-
     Notes
     -----
     This is similar to `matmul` where the first argument is a vector,
@@ -4275,15 +4246,6 @@ def vecmat(x1: "ArrayLike", x2: "ArrayLike", dtype: Optional["DTypeLike"] = None
     x1 = as_tensor_variable(x1)
     x2 = as_tensor_variable(x2)
 
-    if x1.type.ndim == 0 or x2.type.ndim == 0:
-        raise ValueError("vecmat operand cannot be scalar")
-
-    if x1.type.ndim < 1:
-        raise ValueError("First input to vecmat must have at least 1 dimension")
-
-    if x2.type.ndim < 2:
-        raise ValueError("Second input to vecmat must have at least 2 dimensions")
-
     out = _vec_matrix_prod(x1, x2)
 
     if dtype is not None:

tests/tensor/test_math.py

@@ -2116,12 +2116,6 @@ def test_vecdot(self):
         y_val = random(2, 3, 4, rng=rng).astype(config.floatX)
         np.testing.assert_allclose(f(x_val, y_val), np.sum(x_val * y_val, axis=2))
 
-        # Test error cases
-        x = scalar()
-        y = scalar()
-        with pytest.raises(ValueError):
-            vecdot(x, y)
-
     def test_matvec(self):
         """Test matvec function with various input shapes."""
         rng = np.random.default_rng(seed=utt.fetch_seed())
@@ -2147,17 +2141,6 @@ def test_matvec(self):
         expected = np.array([np.dot(x_val[i], y_val[i]) for i in range(2)])
         np.testing.assert_allclose(f(x_val, y_val), expected)
 
-        # Test error cases
-        x = vector()
-        y = vector()
-        with pytest.raises(ValueError):
-            matvec(x, y)
-
-        x = scalar()
-        y = vector()
-        with pytest.raises(ValueError):
-            matvec(x, y)
-
     def test_vecmat(self):
         """Test vecmat function with various input shapes."""
         rng = np.random.default_rng(seed=utt.fetch_seed())
@@ -2183,17 +2166,6 @@ def test_vecmat(self):
         expected = np.array([np.dot(x_val[i], y_val[i]) for i in range(2)])
         np.testing.assert_allclose(f(x_val, y_val), expected)
 
-        # Test error cases
-        x = matrix()
-        y = vector()
-        with pytest.raises(ValueError):
-            vecmat(x, y)
-
-        x = scalar()
-        y = matrix()
-        with pytest.raises(ValueError):
-            vecmat(x, y)
-
     def test_matmul(self):
         """Test matmul function with various input shapes."""
         rng = np.random.default_rng(seed=utt.fetch_seed())