Make OpTest check grad inplace even if forward has no inplace (#19847)

* make OpTest check grad inplace even if forward has no inplace, test=develop

* do not run PE when enable_inplace is False, test=develop

* add conv3d cuda kernel for float16 type, test=develop

* refactor OpTest for inplace, test=develop

* add comments, test=develop

paddle/fluid/operators/conv_cudnn_op.cu.cc

@@ -509,7 +509,8 @@ REGISTER_OP_KERNEL(conv3d, CUDNN, plat::CUDAPlace,
                    paddle::operators::CUDNNConvOpKernel<plat::float16>);
 REGISTER_OP_KERNEL(conv3d_grad, CUDNN, plat::CUDAPlace,
                    paddle::operators::CUDNNConvGradOpKernel<float>,
-                   paddle::operators::CUDNNConvGradOpKernel<double>);
+                   paddle::operators::CUDNNConvGradOpKernel<double>,
+                   paddle::operators::CUDNNConvGradOpKernel<plat::float16>);
 REGISTER_OP_KERNEL(
     conv3d_grad_grad, CUDNN, plat::CUDAPlace,
     paddle::operators::CUDNNConvDoubleGradOpKernel<float>,

paddle/fluid/operators/elementwise/elementwise_mul_op.h

@@ -186,8 +186,7 @@ class ElementwiseMulDoubleGradKernel : public framework::OpKernel<T> {
   }
 };
 
-DECLARE_INPLACE_OP_INFERER(ElementwiseMulDoubleGradOpInplace, {"DDX", "DDOut"},
-                           {"X", framework::GradVarName("X")},
-                           {"Y", framework::GradVarName("Y")});
+DECLARE_INPLACE_OP_INFERER(ElementwiseMulDoubleGradOpInplace, {"DDX", "DDOut"});
+
 }  // namespace operators
 }  // namespace paddle

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

@@ -318,33 +318,6 @@ class OpTest(unittest.TestCase):
                 attrs=self.attrs)
             return outputs
 
-    def _compare_expect_and_actual_outputs(self,
-                                           place,
-                                           fetch_list,
-                                           expect_outs,
-                                           actual_outs,
-                                           inplace_atol=None):
-        # compare expect_outs and actual_outs
-        for i, name in enumerate(fetch_list):
-            if inplace_atol is not None:
-                self.assertTrue(
-                    np.allclose(
-                        np.array(expect_outs[i]),
-                        np.array(actual_outs[i]),
-                        atol=inplace_atol),
-                    "Output (" + name + ") has diff at " + str(place) +
-                    " when using and not using inplace" + "\nExpect " +
-                    str(expect_outs[i]) + "\n" + "But Got" + str(actual_outs[i])
-                    + " in class " + self.__class__.__name__)
-            else:
-                self.assertTrue(
-                    np.array_equal(
-                        np.array(expect_outs[i]), np.array(actual_outs[i])),
-                    "Output (" + name + ") has diff at " + str(place) +
-                    " when using and not using inplace" + "\nExpect " +
-                    str(expect_outs[i]) + "\n" + "But Got" + str(actual_outs[i])
-                    + " in class " + self.__class__.__name__ + '\n')
-
     def _calc_output(self,
                      place,
                      parallel=False,
@@ -365,7 +338,8 @@ class OpTest(unittest.TestCase):
             # and the shapes of those variables contain 0 (eg. Xshape.shape = [0, 2, 5]). 
             # Set persistable for those variables in order to get them from global_scope for inplace grad test directly other than feed them,
             # since feed op calls check_memory_size() which fails when tensor's holder_ is NULL.
-            for name, var in block.vars.items():
+            for out_name in op.output_arg_names:
+                var = block.var(out_name)
                 if 0 in var.shape:
                     var.persistable = True
         original_program = program
@@ -411,13 +385,189 @@ class OpTest(unittest.TestCase):
         else:
             return outs, fetch_list
 
-    def check_inplace_output_with_place(self,
-                                        place,
-                                        no_check_set=None,
-                                        inplace_atol=None):
-        # can`t enable inplace 
-        if not fluid.core.has_infer_inplace(self.op_type):
-            return
+    def _compare_expect_and_actual_outputs(self,
+                                           place,
+                                           fetch_list,
+                                           expect_outs,
+                                           actual_outs,
+                                           inplace_atol=None):
+        """Compare expect outs and actual outs of an tested op.
+
+        Args:
+            place (CPUPlace | CUDAPlace): The place where the op runs. 
+            fetch_list (list): The outputs of tested op.
+            expect_outs (list): The expect outs of tested op.
+            actual_outs (list): The actual outs of tested op.
+            inplace_atol (float): The tolerable error, only set when tested op doesn't ensure computational consistency, like group_norm op.
+
+        Returns:
+            None.
+        """
+        # compare expect_outs and actual_outs
+        for i, name in enumerate(fetch_list):
+            if inplace_atol is not None:
+                self.assertTrue(
+                    np.allclose(
+                        np.array(expect_outs[i]),
+                        np.array(actual_outs[i]),
+                        atol=inplace_atol),
+                    "Output (" + name + ") has diff at " + str(place) +
+                    " when using and not using inplace" + "\nExpect " +
+                    str(expect_outs[i]) + "\n" + "But Got" + str(actual_outs[i])
+                    + " in class " + self.__class__.__name__)
+            else:
+                self.assertTrue(
+                    np.array_equal(
+                        np.array(expect_outs[i]), np.array(actual_outs[i])),
+                    "Output (" + name + ") has diff at " + str(place) +
+                    " when using and not using inplace" + "\nExpect " +
+                    str(expect_outs[i]) + "\n" + "But Got" + str(actual_outs[i])
+                    + " in class " + self.__class__.__name__ + '\n')
+
+    def _construct_grad_program_from_forward(self, fwd_program, grad_op_desc,
+                                             op_grad_to_var):
+        """Generate grad_program which contains the grad_op.
+
+        Args:
+            fwd_program (tuple): The program that contains grad_op_desc's corresponding forward op.
+            grad_op_desc (OpDesc): The OpDesc of grad op.
+            op_grad_to_var (dict): The relation of variables in grad op and its forward op. 
+
+        Returns:
+            grad_program (program): The program which contains the grad_op.
+        """
+        grad_program = Program()
+        grad_block = grad_program.global_block()
+        new_op_desc = grad_block.desc.append_op()
+        new_op_desc.copy_from(grad_op_desc)
+        grad_program._sync_with_cpp()
+
+        # Create grad vars based on fwd vars (shape and dtype)
+        for arg in grad_op_desc.input_arg_names(
+        ) + grad_op_desc.output_arg_names():
+            fwd_var_name = op_grad_to_var.get(arg, None)
+            if fwd_var_name is None:
+                fwd_var_name = arg
+            fwd_var = fwd_program.global_block().vars.get(fwd_var_name)
+            assert fwd_var is not None, "{} cannot be found".format(
+                fwd_var_name)
+            grad_var = grad_block.create_var(
+                name=arg,
+                dtype=fwd_var.dtype,
+                shape=fwd_var.shape,
+                type=fwd_var.type,
+                persistable=False)
+
+            # Some variables' tensors hold no buffer (tensor's _holder is NULL), like XShape in reshape2 op, 
+            # and the shapes of those variables contain 0 (eg. Xshape.shape = [0, 2, 5]). 
+            # Set persistable for those variables in order to get them from global_scope for inplace grad test directly other than feed them,
+            # since feed op calls check_memory_size() which fails when tensor's holder_ is NULL.
+            if 0 in grad_var.shape:
+                grad_var.persistable = True
+        grad_program._sync_with_cpp()
+        return grad_program
+
+    def _construct_grad_feed_map_from_forward(self, place, fwd_res,
+                                              grad_op_desc, op_grad_to_var):
+        """Generate grad_feed_map for grad_program.
+
+        since we don`t really check gradient accuracy, but check the consistency when using and not using inplace,
+        we use fwd outs (also inputs sometimes) to construct grad inputs.
+
+        Args:
+            place (CPUPlace | CUDAPlace): The place where the op runs. 
+            fwd_res (tuple): The outputs of its forward op, in the same form as returns of _calc_outputs() when for_inplace_test is True.
+                i.e., tuple(fwd_outs, fwd_fetch_list, fwd_feed_map, fwd_program, fwd_op_desc)
+            grad_op_desc (OpDesc): The OpDesc of grad op.
+            op_grad_to_var (dict): The relation of variables in grad op and its fwd_op. 
+
+        Returns:
+            grad_feed_map (dict): The feed_map of grad_op.
+        """
+        fwd_outs, fwd_fetch_list, fwd_feed_map, fwd_program, fwd_op_desc = fwd_res
+        p = core.Place()
+        p.set_place(place)
+        grad_feed_map = {}
+        for arg in grad_op_desc.input_arg_names():
+            if arg in fwd_feed_map.keys():
+                grad_feed_map[arg] = fwd_feed_map[arg]._copy(p)
+            else:
+                fwd_var_name = op_grad_to_var.get(arg, None)
+                if fwd_var_name is None:
+                    fwd_var_name = arg
+
+                for i, out_name in enumerate(fwd_fetch_list):
+                    if out_name == fwd_var_name:
+                        # don't feed variables whose tensors hold no buffer (shape contains 0 like shape = [0,2,5] and holder_ is NULL), like XShape in reshape2 op.
+                        # get them from global_scope directly since we have set them persistable in fwd execution
+                        if 0 in fwd_program.global_block().var(out_name).shape:
+                            continue
+                        else:
+                            grad_feed_map[arg] = fwd_outs[i]._copy(p)
+        return grad_feed_map
+
+    def _get_need_run_ops(self, op_desc, fwd_op_desc=None):
+        """Postorder traversal of the 'grad' tree to get all ops that need to run during inplace test.
+        An op needs to run druing inplace check if,
+        (1) it has infer_inplace,
+        (2) it has infer_inplace in its grad descendants. (since we need its outputs as to construct its grad's inputs)
+        
+        Args:
+            op_desc (OpDesc): The op_desc of current op. 
+            fwd_op_desc (OpDesc): The op_desc of current op's forward op, None if current op has no forward op. 
+                Eg. relu's fwd_op is None, relu_grad's fwd_op is relu, relu_grad_grad's fwd_op is relu_grad, etc.
+            
+        Returns:
+            need_run_ops (list[(op_desc, fwd_op_desc)]): The ops that need to run during inplace test.
+        """
+        need_run_ops = []
+        visited_ops = []
+
+        def _dfs_grad_op(op_desc, fwd_op_desc=None):
+            visited_ops.append(op_desc.type())
+            has_infer_inplace = fluid.core.has_infer_inplace(op_desc.type())
+            has_grad_op_maker = fluid.core.has_grad_op_maker(op_desc.type())
+            has_infer_inplace_in_grad_descendants = False
+            if not has_grad_op_maker:
+                has_infer_inplace_in_descendants = False
+            else:
+                # get grad_op_desc 
+                grad_op_desc_list, op_grad_to_var = core.get_grad_op_desc(
+                    op_desc, set(), [])
+                if not grad_op_desc_list:
+                    has_infer_inplace_in_grad_descendants = False
+                else:
+                    for i, grad_op_desc in enumerate(grad_op_desc_list):
+                        if grad_op_desc.type(
+                        ) not in visited_ops and _dfs_grad_op(
+                                grad_op_desc, fwd_op_desc=op_desc):
+                            has_infer_inplace_in_grad_descendants = True
+            if has_infer_inplace or has_infer_inplace_in_grad_descendants:
+                need_run_ops.append((op_desc, fwd_op_desc))
+                return True
+            else:
+                return False
+
+        _dfs_grad_op(op_desc, fwd_op_desc=fwd_op_desc)
+        return need_run_ops
+
+    def _check_forward_inplace(self,
+                               place,
+                               no_check_set=None,
+                               inplace_atol=None):
+        """Chech the inplace correctness of given op (self.op_type).
+        Run the op twice with same inputs, one enable inplace and another disable, compare their outputs.
+        
+        Args:
+            place (CPUPlace | CUDAPlace): The place where the op runs. 
+            no_check_set (list): The names of outputs that needn't check, like XShape of reshape op.
+            inplace_atol (float): The tolerable error, only set when op doesn't ensure computational consistency, like group_norm op.
+
+        Returns:
+            expect_res (tuple(outs, fetch_list, feed_map, program, op_desc)): The results of given op. 
+                We return this to construct grad_program and grad_feed_map for grad inplace check. 
+        """
+        # _calc_output() returns in the form tuple(outs, fetch_list, feed_map, program, op_desc) when for_inplace_test=True.
         expect_res = self._calc_output(
             place,
             no_check_set=no_check_set,
@@ -428,7 +578,6 @@ class OpTest(unittest.TestCase):
             no_check_set=no_check_set,
             enable_inplace=True,
             for_inplace_test=True)
-
         # compare expect_outs and actual_outs
         self._compare_expect_and_actual_outputs(
             place,
@@ -436,160 +585,149 @@ class OpTest(unittest.TestCase):
             expect_res[0],
             actual_res[0],
             inplace_atol=inplace_atol)
+        return expect_res
 
-        # check grad
-        # TODO(zhiqiu): enhance inplace_grad test for ops (sum and activation) using mkldnn
-        # skip use_mkldnn currently
-        flags_use_mkldnn = fluid.core.get_flags_use_mkldnn()
-        attrs_use_mkldnn = hasattr(
-            self, 'attrs') and bool(self.attrs.get('use_mkldnn', False))
-        if flags_use_mkldnn or attrs_use_mkldnn:
-            warnings.warn(
-                "check inplace_grad for ops using mkldnn is not supported")
-            return
-        use_ngraph = fluid.core.is_compiled_with_ngraph(
-        ) and fluid.core.get_flags_use_ngraph()
-        if use_ngraph:
-            warnings.warn(
-                "check inplace_grad for ops using ngraph is not supported")
-            return
-
-        fwd_outs = expect_res[0]
-        fwd_fetch_list = expect_res[1]
-        fwd_feed_map = expect_res[2]
-        fwd_program = expect_res[3]
-        fwd_op_desc = expect_res[4]
-        self.check_inplace_grad_output_using_fwd_inputs_outputs(
-            place,
-            fwd_feed_map,
-            fwd_fetch_list,
-            fwd_outs,
-            fwd_program,
-            fwd_op_desc,
-            no_check_set=no_check_set,
-            inplace_atol=inplace_atol,
-            depth=0)
-
-    def check_inplace_grad_output_using_fwd_inputs_outputs(self,
-                                                           place,
-                                                           fwd_feed_map,
-                                                           fwd_fetch_list,
-                                                           fwd_outs,
-                                                           fwd_program,
-                                                           fwd_op_desc,
-                                                           no_check_set=None,
-                                                           inplace_atol=None,
-                                                           depth=0):
-        # depth=0 means grad
-        # depth=1 means double_grad
-        # depth=2 means triple_grad, which is not supported yet
-        if depth >= 2:
-            return
-        # get grad_op 
-        if not fluid.core.has_grad_op_maker(fwd_op_desc.type()):
-            return
+    def _calc_grad_output(self,
+                          place,
+                          fwd_res,
+                          grad_op_desc,
+                          enable_inplace=None):
+        """Calculate grad_output for given grad_op_desc.
+
+        since we don`t really check gradient accuracy, but check the consistency when using and not using inplace,
+        we use fwd outs (also inputs sometimes) to construct grad inputs.
+
+        Args:
+            place (CPUPlace | CUDAPlace): The place where the op runs. 
+            fwd_res (tuple): The outputs of its forward op, in the same form as returns of _calc_outputs() when for_inplace_test is True.
+                i.e., tuple(fwd_outs, fwd_fetch_list, fwd_feed_map, fwd_program, fwd_op_desc).
+            grad_op_desc (OpDesc): The OpDesc of grad op.
+            enable_inplace (bool): Enable inplace or not.
+
+        Returns:
+            res (tuple(outs, fetch_list, feed_map, program, op_desc)): The results of given grad_op_desc.
+        """
+        fwd_outs, fwd_fetch_list, fwd_feed_map, fwd_program, fwd_op_desc = fwd_res
         grad_op_desc_list, op_grad_to_var = core.get_grad_op_desc(fwd_op_desc,
                                                                   set(), [])
-        # has grad_op_maker but no grad_op 
-        if not grad_op_desc_list:
-            return
-        for i, grad_op_desc in enumerate(grad_op_desc_list):
-            # grad_op can not inplace
-            if not fluid.core.has_infer_inplace(grad_op_desc.type()):
-                continue
+        grad_program = self._construct_grad_program_from_forward(
+            fwd_program, grad_op_desc, op_grad_to_var)
+        grad_feed_map = self._construct_grad_feed_map_from_forward(
+            place, fwd_res, grad_op_desc, op_grad_to_var)
+        grad_fetch_list = grad_op_desc.output_arg_names()
+        exe = Executor(place)
+        program = grad_program
+        if enable_inplace is not None:
+            build_strategy = fluid.BuildStrategy()
+            build_strategy.enable_inplace = enable_inplace
+            compiled_program = fluid.CompiledProgram(
+                grad_program).with_data_parallel(
+                    loss_name="", build_strategy=build_strategy, places=place)
+            program = compiled_program
+        outs = exe.run(program,
+                       feed=grad_feed_map,
+                       fetch_list=grad_fetch_list,
+                       return_numpy=False)
+        return outs, grad_fetch_list, grad_feed_map, grad_program, grad_op_desc
+
+    def _check_grad_inplace(self,
+                            place,
+                            fwd_res,
+                            grad_op_desc,
+                            inplace_atol=None):
+        """Chech the inplace correctness of given grad_op_desc.
+
+        Run the grad op twice with same inputs, one enable inplace and another disable, compare their outputs.
+        It works like _check_forward_inplace, but the way to construct program and feed_map differs.
+        So we define a new function for grad, grad_grad, etc.
 
-            # create grad program
-            grad_program = Program()
-            grad_block = grad_program.global_block()
-            new_op_desc = grad_block.desc.append_op()
-            new_op_desc.copy_from(grad_op_desc)
-            grad_program._sync_with_cpp()
+        Args:
+            place (CPUPlace | CUDAPlace): The place where the op runs. 
+            fwd_res (tuple): The outputs of its forward op, in the same form as returns of _calc_outputs() when for_inplace_test is True.
+                i.e., tuple(fwd_outs, fwd_fetch_list, fwd_feed_map, fwd_program, fwd_op_desc).
+            grad_op_desc (OpDesc): The OpDesc of grad op.
+            inplace_atol (float): The tolerable error, only set when op doesn't ensure computational consistency, like group_norm op.
 
-            # create grad vars based on fwd vars (shape and dtype)
-            for arg in grad_op_desc.input_arg_names(
-            ) + grad_op_desc.output_arg_names():
-                fwd_var_name = op_grad_to_var.get(arg, None)
-                if fwd_var_name is None:
-                    fwd_var_name = arg
-                fwd_var = fwd_program.global_block().vars.get(fwd_var_name)
-                assert fwd_var is not None, "{} cannot be found".format(
-                    fwd_var_name)
-                grad_var = grad_block.create_var(
-                    name=arg,
-                    dtype=fwd_var.dtype,
-                    shape=fwd_var.shape,
-                    type=fwd_var.type,
-                    persistable=False)
-                # some variables' tensors hold no buffer (tensor's _holder is NULL), like XShape in reshape2 op, 
-                # and the shapes of those variables contain 0 (eg. Xshape.shape = [0, 2, 5]). 
-                # set persistable for those variables in order to get them from global_scope for inplace grad test directly other than feed them,
-                # since feed op calls check_memory_size() which fails when tensor's holder_ is NULL.
-                if 0 in grad_var.shape:
-                    grad_var.persistable = True
-            grad_program._sync_with_cpp()
-            grad_fetch_list = grad_op_desc.output_arg_names()
-
-            # generate grad_feed_map for grad_program
-            # since we don`t really check gradient accuracy, but the consistency when using and not using inplace
-            # we use fwd outs (also inputs sometimes) as grad (fake) feeds
-            p = core.Place()
-            p.set_place(place)
-            grad_feed_map = {}
-            for arg in grad_op_desc.input_arg_names():
-                if arg in fwd_feed_map.keys():
-                    grad_feed_map[arg] = fwd_feed_map[arg]._copy(p)
-                else:
-                    fwd_var_name = op_grad_to_var.get(arg, None)
-                    if fwd_var_name is None:
-                        fwd_var_name = arg
-
-                    for i, out_name in enumerate(fwd_fetch_list):
-                        if out_name == fwd_var_name:
-                            # don't feed variables whose tensors hold no buffer (shape contains 0 like shape = [0,2,5] and holder_ is NULL), like XShape in reshape2 op.
-                            # get them from global_scope directly since we have set them persistable in fwd execution
-                            if 0 in fwd_program.global_block().var(
-                                    out_name).shape:
-                                continue
-                            else:
-                                grad_feed_map[arg] = fwd_outs[i]._copy(p)
-
-            def _calc_grad_output(enable_inplace=None):
-                exe = Executor(place)
-                build_strategy = fluid.BuildStrategy()
-                build_strategy.enable_inplace = enable_inplace
-                compiled_program = fluid.CompiledProgram(
-                    grad_program).with_data_parallel(
-                        loss_name="",
-                        build_strategy=build_strategy,
-                        places=place)
-                outs = exe.run(compiled_program,
-                               feed=grad_feed_map,
-                               fetch_list=grad_fetch_list,
-                               return_numpy=False)
-                return outs
-
-            expect_outs = _calc_grad_output(enable_inplace=False)
-            actual_outs = _calc_grad_output(enable_inplace=True)
-
-            # compare expect_outs and actual_outs
-            self._compare_expect_and_actual_outputs(
-                place,
-                grad_fetch_list,
-                expect_outs,
-                actual_outs,
-                inplace_atol=inplace_atol)
+        Returns:
+            expect_res (tuple(outs, fetch_list, feed_map, program, op_desc)): The results of given op. 
+                We return this to construct grad_program and grad_feed_map for grad inplace check. 
+        """
+        expect_res = self._calc_grad_output(
+            place, fwd_res, grad_op_desc, enable_inplace=False)
+        actual_res = self._calc_grad_output(
+            place, fwd_res, grad_op_desc, enable_inplace=True)
+        self._compare_expect_and_actual_outputs(
+            place,
+            expect_res[1],
+            expect_res[0],
+            actual_res[0],
+            inplace_atol=inplace_atol)
+        return expect_res
 
-            # check grad of grad, recursively
-            self.check_inplace_grad_output_using_fwd_inputs_outputs(
-                place,
-                grad_feed_map,
-                grad_fetch_list,
-                expect_outs,
-                grad_program,
-                grad_op_desc,
-                no_check_set=no_check_set,
-                inplace_atol=inplace_atol,
-                depth=depth + 1)
+    def check_inplace_output_with_place(self,
+                                        place,
+                                        no_check_set=None,
+                                        inplace_atol=None):
+        """Chech the inplace correctness of given op, its grad op, its grad_grad op, etc.
+
+        (1) Get all ops need to run. (see conditions in _get_need_run_ops())
+        (2) Run op in need_run_ops, and do inplace check if it has infer_inplace.
+
+        Args:
+            place (CPUPlace | CUDAPlace): The place where the op runs. 
+            no_check_set (list): The names of outputs that needn't check, like XShape of reshape op.
+            inplace_atol (float): The tolerable error, only set when op doesn't ensure computational consistency, like group_norm op.
+
+        Returns:
+            None
+        """
+        has_infer_inplace = fluid.core.has_infer_inplace(self.op_type)
+        has_grad_op_maker = fluid.core.has_grad_op_maker(self.op_type)
+
+        fwd_res = self._calc_output(
+            place, no_check_set=no_check_set, for_inplace_test=True)
+        op_desc = fwd_res[4]
+        need_run_ops = self._get_need_run_ops(op_desc)
+
+        res = {}
+        for op_desc, father_op_desc in reversed(need_run_ops):
+            # The first one is the forward op
+            has_infer_inplace = fluid.core.has_infer_inplace(op_desc.type())
+            if op_desc.type() == self.op_type:
+                if has_infer_inplace:
+                    res[op_desc] = self._check_forward_inplace(
+                        place,
+                        no_check_set=no_check_set,
+                        inplace_atol=inplace_atol)
+                else:
+                    res[op_desc] = self._calc_output(
+                        place, no_check_set=no_check_set, for_inplace_test=True)
+            else:
+                # TODO(zhiqiu): enhance inplace_grad test for ops (sum and activation) using mkldnn/ngraph
+                # skip op that use_mkldnn and use_ngraph currently
+                flags_use_mkldnn = fluid.core.get_flags_use_mkldnn()
+                attrs_use_mkldnn = hasattr(
+                    self,
+                    'attrs') and bool(self.attrs.get('use_mkldnn', False))
+                if flags_use_mkldnn or attrs_use_mkldnn:
+                    warnings.warn(
+                        "check inplace_grad for ops using mkldnn is not supported"
+                    )
+                    continue
+                use_ngraph = fluid.core.is_compiled_with_ngraph(
+                ) and fluid.core.get_flags_use_ngraph()
+                if use_ngraph:
+                    warnings.warn(
+                        "check inplace_grad for ops using ngraph is not supported"
+                    )
+                    continue
+                if has_infer_inplace:
+                    fwd_res = res[father_op_desc]
+                    res[op_desc] = self._check_grad_inplace(
+                        place, fwd_res, op_desc, inplace_atol=inplace_atol)
+                else:
+                    res[op_desc] = self._calc_grad_output(place, fwd_res,
+                                                          op_desc)
 
     def check_output_with_place(self,
                                 place,
@@ -701,6 +839,8 @@ class OpTest(unittest.TestCase):
         if inplace_atol is not None:
             warnings.warn(
                 "By default, inplace_atol should not be set, please check it")
+        # Check inplace for given op, its grad op, its grad_grad op, etc.
+        # No effect on original OpTest 
         self.check_inplace_output_with_place(
             place, no_check_set=no_check_set, inplace_atol=inplace_atol)