!10289 Ignore the graphs with defer_inline flag when specializing on graph arguments

From: @ginfung
Reviewed-by: @zh_qh
Signed-off-by: @zh_qh

mindspore/ccsrc/frontend/optimizer/irpass/specialize_transform.h

@@ -113,7 +113,7 @@ class SpecializeOnGraphArguments : public AnfVisitor {
     }
 
     auto inp0_fg = GetValueNode<FuncGraphPtr>(inputs[0]);
-    if (inp0_fg->recursive()) {
+    if (inp0_fg->has_flag(FUNC_GRAPH_FLAG_DEFER_INLINE) || inp0_fg->recursive()) {
       return nullptr;
     }
 

tests/ut/python/optimizer/test_auto_grad.py

@@ -0,0 +1,70 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+import numpy as np
+
+import mindspore.nn as nn
+from mindspore import context
+from mindspore import Tensor
+from mindspore.ops import operations as P
+from mindspore.ops import composite as C
+
+grad_all = C.GradOperation(get_all=True)
+
+
+class CropAndResizeNet(nn.Cell):
+    def __init__(self, crop_size):
+        super(CropAndResizeNet, self).__init__()
+        self.crop_and_resize = P.CropAndResize()
+        self.crop_size = crop_size
+
+    def construct(self, x, boxes, box_indices):
+        return self.crop_and_resize(x, boxes, box_indices, self.crop_size)
+
+    def bprop(self, x, boxes, box_indices, out, dout):
+        return x, boxes, box_indices
+
+
+class TestUserDefinedBpropNet(nn.Cell):
+    def __init__(self, in_channel, out_channel):
+        super(TestUserDefinedBpropNet, self).__init__()
+        self.relu = nn.ReLU()
+        self.conv = nn.Conv2d(in_channels=in_channel, out_channels=out_channel, kernel_size=2, stride=1, has_bias=False,
+                              weight_init='ones', pad_mode='same')
+        self.crop = CropAndResizeNet((10, 10))
+        self.boxes = Tensor(np.ones((128, 4)).astype(np.float32))
+        self.box_indices = Tensor(np.ones((128,)).astype(np.int32))
+
+    def construct(self, x):
+        x = self.relu(x)
+        x = self.conv(x)
+        x = self.crop(x, self.boxes, self.box_indices)
+        return x
+
+
+class TestUserDefinedBpropGradNet(nn.Cell):
+    def __init__(self, net):
+        super(TestUserDefinedBpropGradNet, self).__init__()
+        self.net = net
+
+    def construct(self, x):
+        return grad_all(self.net)(x)
+
+
+def test_user_defined_bprop():
+    context.set_context(mode=context.GRAPH_MODE)
+    net = TestUserDefinedBpropNet(3, 10)
+    grad_net = TestUserDefinedBpropGradNet(net)
+    x = Tensor(np.ones((128, 3, 12, 12)).astype(np.float32))
+    grad_net(x)