Test: Reduce number of parametrizations

tests/sparse/test_math.py

@@ -9,6 +9,7 @@ import pytensor
 import pytensor.sparse.math as psm
 import pytensor.tensor as pt
 from pytensor.configdefaults import config
+from pytensor.scalar import upcast
 from pytensor.sparse.basic import (
     CSR,
     CSMProperties,
@@ -367,7 +368,7 @@ class TestStructuredDot:
         )
         for dense_dtype in typenames:
             for sparse_dtype in typenames:
-                correct_dtype = pytensor.scalar.upcast(sparse_dtype, dense_dtype)
+                correct_dtype = upcast(sparse_dtype, dense_dtype)
                 a = SparseTensorType("csc", dtype=sparse_dtype)()
                 b = matrix(dtype=dense_dtype)
                 d = structured_dot(a, b)
@@ -736,11 +737,10 @@ class TestUsmm:
     @pytest.mark.slow
     @pytest.mark.parametrize("dtype1", ["float32", "float64", "int16", "complex64"])
     @pytest.mark.parametrize("dtype2", ["float32", "float64", "int16", "complex64"])
-    @pytest.mark.parametrize("dtype3", ["float32", "float64", "int16", "complex64"])
-    @pytest.mark.parametrize("dtype4", ["float32", "float64", "int16", "complex64"])
+    @pytest.mark.parametrize("can_inplace", [False, True])
     @pytest.mark.parametrize("format1", ["dense", "csc", "csr"])
     @pytest.mark.parametrize("format2", ["dense", "csc", "csr"])
-    def test_basic(self, dtype1, dtype2, dtype3, dtype4, format1, format2):
+    def test_basic(self, dtype1, dtype2, can_inplace, format1, format2):
         def mat(format, name, dtype):
             if format == "dense":
                 return matrix(name, dtype=dtype)
@@ -750,8 +750,13 @@ class TestUsmm:
         if format1 == "dense" and format2 == "dense":
             pytest.skip("Skipping dense-dense case")
 
+        dtype3 = upcast(dtype1, dtype2)
+        dtype4 = dtype3 if can_inplace else "int32"
+        inplace = can_inplace
+
         x = mat(format1, "x", dtype1)
         y = mat(format2, "y", dtype2)
+
         a = scalar("a", dtype=dtype3)
         z = pytensor.shared(np.asarray(self.z, dtype=dtype4).copy())
 
@@ -769,9 +774,6 @@ class TestUsmm:
 
         f_b_out = f_b(z_data, a_data, x_data, y_data)
 
-        # Can it work inplace?
-        inplace = dtype4 == pytensor.scalar.upcast(dtype1, dtype2, dtype3)
-
         # To make it easier to check the toposort
         mode = pytensor.compile.mode.get_default_mode().excluding("fusion")
 
@@ -782,17 +784,7 @@ class TestUsmm:
             f_a_out = z.get_value(borrow=True)
         else:
             f_a = pytensor.function([a, x, y], z - a * psm.dot(x, y), mode=mode)
-            # In DebugMode there is a strange difference with complex
-            # So we raise a little the threshold a little.
-            try:
-                orig_atol = pytensor.tensor.math.float64_atol
-                orig_rtol = pytensor.tensor.math.float64_rtol
-                pytensor.tensor.math.float64_atol = 1e-7
-                pytensor.tensor.math.float64_rtol = 1e-6
-                f_a_out = f_a(a_data, x_data, y_data)
-            finally:
-                pytensor.tensor.math.float64_atol = orig_atol
-                pytensor.tensor.math.float64_rtol = orig_rtol
+            f_a_out = f_a(a_data, x_data, y_data)
 
         # As we do a dot product of 2 vector of 100 element,
         # This mean we can have 2*100*eps abs error.
@@ -804,7 +796,7 @@ class TestUsmm:
             rtol = None
         utt.assert_allclose(f_a_out, f_b_out, rtol=rtol, atol=atol)
         topo = f_a.maker.fgraph.toposort()
-        up = pytensor.scalar.upcast(dtype1, dtype2, dtype3, dtype4)
+        up = upcast(dtype1, dtype2, dtype3, dtype4)
 
         fast_compile = pytensor.config.mode == "FAST_COMPILE"
 
@@ -906,9 +898,6 @@ class TestUsmm:
 
         f_b_out = f_b(z_data, a_data, x_data, y_data)
 
-        # Can it work inplace?
-        # inplace = dtype4 == pytensor.scalar.upcast(dtype1, dtype2, dtype3)
-
         # To make it easier to check the toposort
         mode = pytensor.compile.mode.get_default_mode().excluding("fusion")