chore: rebase and update in test

Author: keehyuna

tests/py/dynamo/conversion/test_eq_aten.py

@@ -75,6 +75,10 @@ class eq(nn.Module):
             def forward(self, lhs_val, rhs_val):
                 return torch.ops.aten.eq.Tensor(lhs_val, rhs_val)
 
+        class eq_operator(nn.Module):
+            def forward(self, lhs_val, rhs_val):
+                return lhs_val == rhs_val
+
         input_specs = [
             Input(
                 dtype=torch.float32,
@@ -93,6 +97,10 @@ def forward(self, lhs_val, rhs_val):
             eq(),
             input_specs,
         )
+        self.run_test_with_dynamic_shape(
+            eq_operator(),
+            input_specs,
+        )
 
     @parameterized.expand(
         [
@@ -107,6 +115,10 @@ class eq(nn.Module):
             def forward(self, lhs_val):
                 return torch.ops.aten.eq.Tensor(lhs_val, torch.tensor(1))
 
+        class eq_operator(nn.Module):
+            def forward(self, lhs_val):
+                return lhs_val == torch.tensor(1)
+
         input_specs = [
             Input(
                 dtype=torch.int32,
@@ -119,6 +131,10 @@ def forward(self, lhs_val):
             eq(),
             input_specs,
         )
+        self.run_test_with_dynamic_shape(
+            eq_operator(),
+            input_specs,
+        )
 
     @parameterized.expand(
         [
@@ -133,6 +149,10 @@ class eq(nn.Module):
             def forward(self, lhs_val):
                 return torch.ops.aten.eq.Scalar(lhs_val, 1.0)
 
+        class eq_operator(nn.Module):
+            def forward(self, lhs_val):
+                return lhs_val == 1.0
+
         input_specs = [
             Input(
                 dtype=torch.int32,
@@ -145,6 +165,10 @@ def forward(self, lhs_val):
             eq(),
             input_specs,
         )
+        self.run_test_with_dynamic_shape(
+            eq_operator(),
+            input_specs,
+        )
 
 
 if __name__ == "__main__":

tests/py/dynamo/conversion/test_pow_aten.py

@@ -84,7 +84,7 @@ def test_pow_dynamic_shape(
     ):
         class pow(nn.Module):
             def forward(self, lhs_val, rhs_val):
-                return torch.ops.aten.floor_divide.default(lhs_val, rhs_val)
+                return torch.ops.aten.pow.Tensor_Tensor(lhs_val, rhs_val)
 
         class pow_scalar(nn.Module):
             def forward(self, lhs_val, rhs_val):