Use direct imports in blas.py

pytensor/tensor/blas.py

@@ -103,7 +103,7 @@ from pytensor.link.c.op import COp
 from pytensor.link.c.params_type import ParamsType
 from pytensor.printing import FunctionPrinter, pprint
 from pytensor.scalar import bool as bool_t
-from pytensor.tensor import basic as ptb
+from pytensor.tensor.basic import as_tensor_variable, cast
 from pytensor.tensor.blas_headers import blas_header_text, blas_header_version
 from pytensor.tensor.math import dot, tensordot
 from pytensor.tensor.shape import specify_broadcastable
@@ -157,11 +157,11 @@ class Gemv(Op):
             return f"{self.__class__.__name__}{{no_inplace}}"
 
     def make_node(self, y, alpha, A, x, beta):
-        y = ptb.as_tensor_variable(y)
-        x = ptb.as_tensor_variable(x)
-        A = ptb.as_tensor_variable(A)
-        alpha = ptb.as_tensor_variable(alpha)
-        beta = ptb.as_tensor_variable(beta)
+        y = as_tensor_variable(y)
+        x = as_tensor_variable(x)
+        A = as_tensor_variable(A)
+        alpha = as_tensor_variable(alpha)
+        beta = as_tensor_variable(beta)
         if y.dtype != A.dtype or y.dtype != x.dtype:
             raise TypeError(
                 "Gemv requires matching dtypes", (y.dtype, A.dtype, x.dtype)
@@ -257,10 +257,10 @@ class Ger(Op):
             return f"{self.__class__.__name__}{{non-destructive}}"
 
     def make_node(self, A, alpha, x, y):
-        A = ptb.as_tensor_variable(A)
-        y = ptb.as_tensor_variable(y)
-        x = ptb.as_tensor_variable(x)
-        alpha = ptb.as_tensor_variable(alpha)
+        A = as_tensor_variable(A)
+        y = as_tensor_variable(y)
+        x = as_tensor_variable(x)
+        alpha = as_tensor_variable(alpha)
         if not (A.dtype == x.dtype == y.dtype == alpha.dtype):
             raise TypeError(
                 "ger requires matching dtypes", (A.dtype, alpha.dtype, x.dtype, y.dtype)
@@ -859,7 +859,7 @@ class Gemm(GemmRelated):
         return rval
 
     def make_node(self, *inputs):
-        inputs = list(map(ptb.as_tensor_variable, inputs))
+        inputs = list(map(as_tensor_variable, inputs))
 
         if any(not isinstance(i.type, DenseTensorType) for i in inputs):
             raise NotImplementedError("Only dense tensor types are supported")
@@ -1129,8 +1129,8 @@ class Dot22(GemmRelated):
     check_input = False
 
     def make_node(self, x, y):
-        x = ptb.as_tensor_variable(x)
-        y = ptb.as_tensor_variable(y)
+        x = as_tensor_variable(x)
+        y = as_tensor_variable(y)
 
         if any(not isinstance(i.type, DenseTensorType) for i in (x, y)):
             raise NotImplementedError("Only dense tensor types are supported")
@@ -1322,8 +1322,8 @@ class BatchedDot(COp):
     gufunc_signature = "(b,m,k),(b,k,n)->(b,m,n)"
 
     def make_node(self, x, y):
-        x = ptb.as_tensor_variable(x)
-        y = ptb.as_tensor_variable(y)
+        x = as_tensor_variable(x)
+        y = as_tensor_variable(y)
 
         if not (
             isinstance(x.type, DenseTensorType) and isinstance(y.type, DenseTensorType)
@@ -1357,7 +1357,7 @@ class BatchedDot(COp):
 
         # Change dtype if needed
         dtype = pytensor.scalar.upcast(x.type.dtype, y.type.dtype)
-        x, y = ptb.cast(x, dtype), ptb.cast(y, dtype)
+        x, y = cast(x, dtype), cast(y, dtype)
         out = tensor(dtype=dtype, shape=out_shape)
         return Apply(self, [x, y], [out])
 
@@ -1738,7 +1738,7 @@ def batched_dot(a, b):
         "Use `dot` in conjution with `tensor.vectorize` or `graph.replace.vectorize_graph`",
         FutureWarning,
     )
-    a, b = ptb.as_tensor_variable(a), ptb.as_tensor_variable(b)
+    a, b = as_tensor_variable(a), as_tensor_variable(b)
 
     if a.ndim == 0:
         raise TypeError("a must have at least one (batch) axis")