add scalar_tensor for ascendgraph

dicp/dicp/vendor/AscendGraph/conversion.py

@@ -1483,3 +1483,7 @@ def SliceScatter(self, operand, src, dim=0, start=None, end=None, step=1):
             indices_expanded = indices
         return self.get_proxy(ascend_op.ScatterElements,
                               (operand, indices_expanded, src, dim))
+
+    @register_conversion(torch.ops.aten.scalar_tensor.default)
+    def scalar_tensor(self, x, dtype=None, layout=None, device=None, pin_memory=None):
+        return self.get_const_proxy(x, dtype)

dicp/test/ascend_scripts/ops/static.ini

@@ -54,6 +54,7 @@ python_files =
                test_relu.py
                test_repeat.py
                test_rsqrt.py
+               test_scalar_tensor.py
                test_scatter.py
                test_select.py
                test_sigmoid.py

dicp/test/op/test_scalar_tensor.py

@@ -9,9 +9,9 @@
 
 
 class OpModule(torch.nn.Module):
-    def forward(self, a, dtype):
+    def forward(self, a, redundant_input, dtype):
         res_default = torch.ops.aten.scalar_tensor.default(a, dtype=dtype)
-        return res_default
+        return res_default + redundant_input, res_default
 
 
 model = OpModule()
@@ -24,9 +24,10 @@ class TestScalarTensor():
     @pytest.mark.parametrize("inputs", [1.0, 3.0, 0.0])
     @pytest.mark.parametrize("compiled_model", compiled_model)
     def test_torch_scalar_tensor(self, inputs, dtype, compiled_model):
-        output = model(inputs, dtype)
+        redundant_input = torch.randn(1, dtype=dtype)
+        _, output = model(inputs, redundant_input, dtype)
         dynamo.reset()
         update_dynamo_config(compiled_model.dynamic)
-        dicp_output = compiled_model.model(inputs, dtype)
+        _, dicp_output = compiled_model.model(inputs, redundant_input, dtype)
 
         assert torch.allclose(output, dicp_output.cpu(), equal_nan=True)