fix MirrorPadGrad

mindspore/ops/_grad/grad_nn_ops.py

@@ -624,7 +624,7 @@ def get_bprop_mirror_pad(self):
     mirror_pad_grad = G.MirrorPadGrad(self.mode)
 
     def bprop(x, paddings, out, dout):
-        dx = mirror_pad_grad(dout, paddings, x)
+        dx = mirror_pad_grad(dout, paddings)
         return (dx, zeros_like(paddings))
 
     return bprop

mindspore/ops/_op_impl/aicpu/__init__.py

@@ -33,6 +33,7 @@ from .rnnt_loss import _rnnt_loss_aicpu
 from .random_categorical import _random_categorical_aicpu
 from .cast import _cast_aicpu
 from .mirror_pad import _mirror_pad_aicpu
+from .mirror_pad_grad import _mirror_pad_grad_aicpu
 from .normal import _normal_aicpu
 from .gamma import _gamma_aicpu
 from .poisson import _poisson_aicpu

mindspore/ops/_op_impl/aicpu/mirror_pad_grad.py

@@ -0,0 +1,52 @@
+# 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.
+# ============================================================================
+
+"""MirrorPadGrad op"""
+from mindspore.ops.op_info_register import op_info_register, AiCPURegOp, DataType
+mirror_pad_grad_op_info = AiCPURegOp("MirrorPadGrad") \
+    .fusion_type("OPAQUE") \
+    .input(0, "x", "required") \
+    .input(1, "paddings", "required") \
+    .output(0, "y", "required") \
+    .attr("mode", "str") \
+    .dtype_format(DataType.I8_Default, DataType.I32_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I16_Default, DataType.I32_Default, DataType.I16_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I32_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I64_Default, DataType.I32_Default, DataType.I64_Default) \
+    .dtype_format(DataType.U8_Default, DataType.I32_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U16_Default, DataType.I32_Default, DataType.U16_Default) \
+    .dtype_format(DataType.U32_Default, DataType.I32_Default, DataType.U32_Default) \
+    .dtype_format(DataType.U64_Default, DataType.I32_Default, DataType.U64_Default) \
+    .dtype_format(DataType.F16_Default, DataType.I32_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.I32_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F64_Default, DataType.I32_Default, DataType.F64_Default) \
+    .dtype_format(DataType.I8_Default, DataType.I64_Default, DataType.I8_Default) \
+    .dtype_format(DataType.I16_Default, DataType.I64_Default, DataType.I16_Default) \
+    .dtype_format(DataType.I32_Default, DataType.I64_Default, DataType.I32_Default) \
+    .dtype_format(DataType.I64_Default, DataType.I64_Default, DataType.I64_Default) \
+    .dtype_format(DataType.U8_Default, DataType.I64_Default, DataType.U8_Default) \
+    .dtype_format(DataType.U16_Default, DataType.I64_Default, DataType.U16_Default) \
+    .dtype_format(DataType.U32_Default, DataType.I64_Default, DataType.U32_Default) \
+    .dtype_format(DataType.U64_Default, DataType.I64_Default, DataType.U64_Default) \
+    .dtype_format(DataType.F16_Default, DataType.I64_Default, DataType.F16_Default) \
+    .dtype_format(DataType.F32_Default, DataType.I64_Default, DataType.F32_Default) \
+    .dtype_format(DataType.F64_Default, DataType.I64_Default, DataType.F64_Default) \
+    .get_op_info()
+
+
+@op_info_register(mirror_pad_grad_op_info)
+def _mirror_pad_grad_aicpu():
+    """MirrorPadGrad AiCPU register"""
+    return

mindspore/ops/operations/_grad_ops.py

@@ -1152,11 +1152,10 @@ class MirrorPadGrad(PrimitiveWithInfer):
         validator.check_string('mode', mode, ['REFLECT', 'SYMMETRIC'], self.name)
         self.mode = mode
 
-    def __infer__(self, dout, paddings, x):
+    def __infer__(self, dout, paddings):
         validator.check_subclass("dout", dout['dtype'], mstype.tensor, self.name)
         validator.check_subclass("paddings", paddings['dtype'], mstype.tensor, self.name)
-        validator.check_subclass("input_x", x['dtype'], mstype.tensor, self.name)
-        return {'shape': x['shape'],
+        return {'shape': dout['shape'],
                 'dtype': dout['dtype'],
                 'value': None}