[MPS/BE] @parametrize generation of pointwise_ops. (pytorch#149363)

Make this less error prone/reduces duplication.

Pull Request resolved: pytorch#149363
Approved by: https://github.com/malfet

Author: dcci

test/inductor/test_mps_basic.py

@@ -82,20 +82,30 @@ def foo(x):
     def test_cast(self, dtype):
         self.common(lambda a: a.to(dtype), (torch.rand(1024),))
 
-    def test_pointwise_i0(self):
-        self.common(torch.special.i0, (torch.rand(128, 128),), check_lowp=False)
-
-    def test_pointwise_i0e(self):
-        self.common(torch.special.i0e, (torch.rand(128, 128),), check_lowp=False)
-
-    def test_pointwise_i1(self):
-        self.common(torch.special.i1, (torch.rand(128, 128),), check_lowp=False)
-
-    def test_pointwise_i1e(self):
-        self.common(torch.special.i1e, (torch.rand(128, 128),), check_lowp=False)
-
-    def test_pointwise_erf(self):
-        self.common(torch.special.erf, (torch.rand(128, 128),), check_lowp=False)
+    pointwise_unary_ops = [
+        "i0",
+        "i0e",
+        "i1",
+        "i1e",
+        "erf",
+        "digamma",
+        "sinc",
+        "spherical_bessel_j0",
+        "bessel_j0",
+        "bessel_j1",
+        "bessel_y0",
+        "bessel_y1",
+        "modified_bessel_i0",
+        "entr",
+    ]
+
+    @parametrize("op_name", pointwise_unary_ops)
+    def test_pointwise_unary_op(self, op_name):
+        self.common(
+            lambda x: getattr(torch.special, op_name)(x),
+            (torch.rand(128, 128),),
+            check_lowp=False,
+        )
 
     def test_pointwise_polygamma(self):
         self.common(
@@ -107,51 +117,20 @@ def test_pointwise_polygamma(self):
             check_lowp=False,
         )
 
-    def test_pointwise_digamma(self):
-        self.common(torch.special.digamma, (torch.rand(128, 128),), check_lowp=False)
-
-    def test_pointwise_sinc(self):
-        self.common(torch.special.sinc, (torch.rand(128, 128),), check_lowp=False)
-
     def test_pointwise_zeta(self):
         self.common(
             torch.special.zeta,
             (torch.rand(128, 128), torch.rand(128, 128)),
             check_lowp=False,
         )
 
-    def test_pointwise_spherical_bessel_j0(self):
-        self.common(
-            torch.special.spherical_bessel_j0, (torch.rand(128, 128),), check_lowp=False
-        )
-
-    def test_pointwise_bessel_j0(self):
-        self.common(torch.special.bessel_j0, (torch.rand(128, 128),), check_lowp=True)
-
-    def test_pointwise_bessel_j1(self):
-        self.common(torch.special.bessel_j1, (torch.rand(128, 128),), check_lowp=True)
-
-    def test_pointwise_bessel_y0(self):
-        self.common(torch.special.bessel_y0, (torch.rand(128, 128),), check_lowp=False)
-
-    def test_pointwise_bessel_y1(self):
-        self.common(torch.special.bessel_y1, (torch.rand(128, 128),), check_lowp=True)
-
-    def test_pointwise_modified_bessel_i0(self):
-        self.common(
-            torch.special.modified_bessel_i0, (torch.rand(128, 128),), check_lowp=True
-        )
-
     def test_pointwise_xlog1py(self):
         self.common(
             torch.special.xlog1py,
             (torch.rand(128, 128), torch.rand(128, 128)),
             check_lowp=False,
         )
 
-    def test_pointwise_entr(self):
-        self.common(torch.special.entr, (torch.rand(128, 128),), check_lowp=False)
-
     def test_broadcast(self):
         self.common(torch.add, (torch.rand(32, 1024), torch.rand(1024)))