Fix exclude tag cxx -> cxx_only

pytensor/link/basic.py

@@ -284,7 +284,7 @@ class PerformLinker(LocalLinker):
     """
 
     required_rewrites: tuple[str, ...] = ("minimum_compile", "py_only")
-    incompatible_rewrites: tuple[str, ...] = ("cxx",)
+    incompatible_rewrites: tuple[str, ...] = ("cxx_only",)
 
     def __init__(
         self, allow_gc: bool | None = None, schedule: Callable | None = None

pytensor/link/jax/linker.py

@@ -14,7 +14,7 @@ class JAXLinker(JITLinker):
         "jax",
     )  # TODO: Distinguish between optional "jax" and "minimum_compile_jax"
     incompatible_rewrites = (
-        "cxx",
+        "cxx_only",
         "BlasOpt",
         "local_careduce_fusion",
         "scan_save_mem_prealloc",

pytensor/link/numba/linker.py

@@ -7,7 +7,7 @@ class NumbaLinker(JITLinker):
         "numba",
     )  # TODO: Distinguish between optional "numba" and "minimum_compile_numba"
     incompatible_rewrites = (
-        "cxx",
+        "cxx_only",
         "BlasOpt",
         "local_careduce_fusion",
         "scan_save_mem_prealloc",

pytensor/link/vm.py

@@ -840,7 +840,7 @@ class VMLinker(LocalLinker):
             c_thunks = bool(config.cxx)
         if not c_thunks:
             self.required_rewrites: tuple[str, ...] = ("minimum_compile", "py_only")
-            self.incompatible_rewrites: tuple[str, ...] = ("cxx",)
+            self.incompatible_rewrites: tuple[str, ...] = ("cxx_only",)
         self.c_thunks = c_thunks
         self.allow_partial_eval = allow_partial_eval
         self.updated_vars = {}

pytensor/tensor/rewriting/blas_c.py

@@ -68,5 +68,6 @@ optdb.register(
     "fast_run",
     "inplace",
     "c_blas",
+    "cxx_only",
     position=70.0,
 )

tests/sparse/test_rewriting.py

@@ -69,13 +69,10 @@ def test_local_csm_grad_c():
     not pytensor.config.cxx, reason="G++ not available, so we need to skip this test."
 )
 def test_local_mul_s_d():
-    mode = get_default_mode()
-    mode = mode.including("specialize", "local_mul_s_d")
-
     for sp_format in sparse.sparse_formats:
         inputs = [getattr(pytensor.sparse, sp_format + "_matrix")(), matrix()]
 
-        f = pytensor.function(inputs, smath.mul_s_d(*inputs), mode=mode)
+        f = pytensor.function(inputs, smath.mul_s_d(*inputs), mode="CVM")
 
         assert not any(
             isinstance(node.op, smath.MulSD) for node in f.maker.fgraph.toposort()
@@ -92,7 +89,7 @@ def test_local_mul_s_v():
     for sp_format in ["csr"]:  # Not implemented for other format
         inputs = [getattr(pytensor.sparse, sp_format + "_matrix")(), vector()]
 
-        f = pytensor.function(inputs, smath.mul_s_v(*inputs), mode=mode)
+        f = pytensor.function(inputs, smath.mul_s_v(*inputs), mode="CVM")
 
         assert not any(
             isinstance(node.op, smath.MulSV) for node in f.maker.fgraph.toposort()
@@ -103,13 +100,10 @@ def test_local_mul_s_v():
     not pytensor.config.cxx, reason="G++ not available, so we need to skip this test."
 )
 def test_local_structured_add_s_v():
-    mode = get_default_mode()
-    mode = mode.including("specialize", "local_structured_add_s_v")
-
     for sp_format in ["csr"]:  # Not implemented for other format
         inputs = [getattr(pytensor.sparse, sp_format + "_matrix")(), vector()]
 
-        f = pytensor.function(inputs, smath.structured_add_s_v(*inputs), mode=mode)
+        f = pytensor.function(inputs, smath.structured_add_s_v(*inputs), mode="CVM")
 
         assert not any(
             isinstance(node.op, smath.StructuredAddSV)
@@ -121,9 +115,6 @@ def test_local_structured_add_s_v():
     not pytensor.config.cxx, reason="G++ not available, so we need to skip this test."
 )
 def test_local_sampling_dot_csr():
-    mode = get_default_mode()
-    mode = mode.including("specialize", "local_sampling_dot_csr")
-
     for sp_format in ["csr"]:  # Not implemented for other format
         inputs = [
             matrix(),
@@ -131,7 +122,7 @@ def test_local_sampling_dot_csr():
             getattr(pytensor.sparse, sp_format + "_matrix")(),
         ]
 
-        f = pytensor.function(inputs, smath.sampling_dot(*inputs), mode=mode)
+        f = pytensor.function(inputs, smath.sampling_dot(*inputs), mode="CVM")
 
         if pytensor.config.blas__ldflags:
             assert not any(