Remove scalar_ prefix from log1mexp, xlogx, xlogy0 and fix numba imports

Author: Copilot

pytensor/link/numba/dispatch/elemwise.py

@@ -459,7 +459,7 @@ def numba_funcify_Softmax(op, node, **kwargs):
     if axis is not None:
         axis = normalize_axis_index(axis, x_at.ndim)
         reduce_max_py = create_multiaxis_reducer(
-            scalar_maximum, -np.inf, axis, x_at.ndim, x_dtype, keepdims=True
+            maximum, -np.inf, axis, x_at.ndim, x_dtype, keepdims=True
         )
         reduce_sum_py = create_multiaxis_reducer(
             add_as, 0.0, (axis,), x_at.ndim, x_dtype, keepdims=True
@@ -523,7 +523,7 @@ def numba_funcify_LogSoftmax(op, node, **kwargs):
     if axis is not None:
         axis = normalize_axis_index(axis, x_at.ndim)
         reduce_max_py = create_multiaxis_reducer(
-            scalar_maximum,
+            maximum,
             -np.inf,
             (axis,),
             x_at.ndim,

pytensor/scalar/math.py

@@ -1360,7 +1360,7 @@ def c_code(self, node, name, inp, out, sub):
             raise NotImplementedError("only floating point is implemented")
 
 
-log1mexp = Log1mexp(upgrade_to_float, name="scalar_log1mexp")
+log1mexp = Log1mexp(upgrade_to_float)
 
 
 class BetaInc(ScalarOp):

pytensor/tensor/xlogx.py

@@ -31,7 +31,7 @@ def c_code(self, node, name, inputs, outputs, sub):
         raise NotImplementedError("only floatingpoint is implemented")
 
 
-scalar_xlogx = XlogX(ps.upgrade_to_float, name="scalar_xlogx")
+scalar_xlogx = XlogX(ps.upgrade_to_float)
 xlogx = Elemwise(scalar_xlogx, name="xlogx")
 
 
@@ -62,5 +62,5 @@ def c_code(self, node, name, inputs, outputs, sub):
         raise NotImplementedError("only floatingpoint is implemented")
 
 
-scalar_xlogy0 = XlogY0(ps.upgrade_to_float, name="scalar_xlogy0")
+scalar_xlogy0 = XlogY0(ps.upgrade_to_float)
 xlogy0 = Elemwise(scalar_xlogy0, name="xlogy0")

tests/tensor/rewriting/test_math.py

@@ -3887,10 +3887,7 @@ def check_max_log_sum_exp(x, axis, dimshuffle_op=None):
 
     fgraph = f.maker.fgraph.toposort()
     for node in fgraph:
-        if (
-            hasattr(node.op, "scalar_op")
-            and node.op.scalar_op == ps.basic.maximum
-        ):
+        if hasattr(node.op, "scalar_op") and node.op.scalar_op == ps.basic.maximum:
             return
 
         # In mode FAST_COMPILE, the rewrites don't replace the

tests/tensor/test_elemwise.py

@@ -619,12 +619,8 @@ def test_perform_nan(self):
         for dtype in ["floatX", "complex64", "complex128"]:
             self.with_mode(Mode(linker="py"), ps.add, dtype=dtype, test_nan=True)
             self.with_mode(Mode(linker="py"), ps.mul, dtype=dtype, test_nan=True)
-            self.with_mode(
-                Mode(linker="py"), ps.maximum, dtype=dtype, test_nan=True
-            )
-            self.with_mode(
-                Mode(linker="py"), ps.minimum, dtype=dtype, test_nan=True
-            )
+            self.with_mode(Mode(linker="py"), ps.maximum, dtype=dtype, test_nan=True)
+            self.with_mode(Mode(linker="py"), ps.minimum, dtype=dtype, test_nan=True)
             self.with_mode(
                 Mode(linker="py"),
                 ps.or_,
@@ -678,12 +674,8 @@ def test_c_nan(self):
             self.with_mode(Mode(linker="c"), ps.add, dtype=dtype, test_nan=True)
             self.with_mode(Mode(linker="c"), ps.mul, dtype=dtype, test_nan=True)
         for dtype in ["floatX"]:
-            self.with_mode(
-                Mode(linker="c"), ps.minimum, dtype=dtype, test_nan=True
-            )
-            self.with_mode(
-                Mode(linker="c"), ps.maximum, dtype=dtype, test_nan=True
-            )
+            self.with_mode(Mode(linker="c"), ps.minimum, dtype=dtype, test_nan=True)
+            self.with_mode(Mode(linker="c"), ps.maximum, dtype=dtype, test_nan=True)
 
     def test_infer_shape(self, dtype=None, pre_scalar_op=None):
         if dtype is None: