[OpTest] Polish optest (#40879)

* 1. add the python api grad 2. add final and intermediate state vlog 3. change the python_api error logic

* add python api or close the check_eager=True

* fix the compatibility

* matmul

* disable unittests: test_elementwise_add_op test_scatter_nd_op test_gather_nd_op test_scatter_op test_index_sample_op test_elementwise_add_mkldnn_op

* refine the logic of prepara_parameter logic

* fix Tensor(gpu) 2 Scalar segment fault.

* add multi-attribute. (test_unsqueeze_op); add python_sig_out for customizing op sig out

* fix some bugs, support python_out_sig

python/paddle/fluid/tests/unittests/op_test.py

@@ -731,12 +731,14 @@ class OpTest(unittest.TestCase):
                 if name in op_proto_attrs:
                     return op_proto_attrs[name]
                 elif name in op_inputs:
-                    assert op_inputs[name].__len__(
-                    ) == 1, "currently don't support multi-input in attribute."
-                    # why don't use numpy().item() : if the Tensor is float64, we will change it to python.float32, where we loss accuracy: [allclose_op]
-                    # why we reconstruct a tensor: because we want the tensor in cpu. 
-                    return paddle.to_tensor(
-                        op_inputs[name][0].numpy(), place='cpu')
+                    if len(op_inputs[name]) == 1:
+                        # why don't use numpy().item() : if the Tensor is float64, we will change it to python.float32, where we loss accuracy: [allclose_op]
+                        # why we reconstruct a tensor: because we want the tensor in cpu. 
+                        return paddle.to_tensor(
+                            op_inputs[name][0].numpy(), place='cpu')
+                    else:
+                        # if this is a list (test_unsqueeze2_op): we just pass it into the python api.
+                        return op_inputs[name]
                 else:
                     return Empty()
 
@@ -786,6 +788,8 @@ class OpTest(unittest.TestCase):
             return results
 
         def construct_output_dict_by_kernel_sig(ret_tuple, output_sig):
+            if hasattr(self, "python_out_sig"):
+                output_sig = self.python_out_sig
             if not isinstance(ret_tuple, (tuple, list)):
                 ret_tuple = [ret_tuple]
             if len(output_sig) == len(ret_tuple):
@@ -795,7 +799,7 @@ class OpTest(unittest.TestCase):
                 # [assumption]: return multi-Tensor in a single output. such as paddle.split()
                 assert len(
                     output_sig
-                ) == 1, "Don't support multi-output with multi-tensor output."
+                ) == 1, "Don't support multi-output with multi-tensor output. (May be you can use set `python_out_sig`, see `test_squeeze2_op` as a example.)"
                 return {output_sig[0]: ret_tuple}
 
         def assumption_assert_and_transform(args, inp_num):
@@ -825,6 +829,9 @@ class OpTest(unittest.TestCase):
                 """ we think the kernel_sig is missing.
                 """
                 kernel_sig = None
+                print(
+                    "[Warning: op_test.py] Kernel Signature is not found for %s, fall back to intermediate state."
+                    % self.op_type)
             return kernel_sig
 
         def cal_python_api(python_api, args, kernel_sig):
@@ -1942,15 +1949,17 @@ class OpTest(unittest.TestCase):
                         attrs_outputs[attrs_name] = self.attrs[attrs_name]
 
             if check_eager:
-                outputs = self._calc_python_api_output(place, inputs, outputs)
-
+                eager_outputs = self._calc_python_api_output(place, inputs,
+                                                             outputs)
             # if outputs is None, kernel sig is empty or other error is happens.
-            if not check_eager or outputs is None:
+            if not check_eager or eager_outputs is None:
                 block.append_op(
                     type=self.op_type,
                     inputs=inputs,
                     outputs=outputs,
                     attrs=attrs_outputs if hasattr(self, "attrs") else None)
+            else:
+                outputs = eager_outputs
 
             if self.dtype == np.uint16:
                 cast_inputs = self._find_var_in_dygraph(outputs,