add tbe op info of BNInfer and BNInferGrad for VM

mindspore/ccsrc/kernel/tbe/tbe_adapter.cc

@@ -56,6 +56,8 @@ static std::map<string, string> tbe_func_adapter_map = {
   {"b_n_training_update", "bn_training_update"},
   {"b_n_training_reduce_grad", "bn_training_reduce_grad"},
   {"b_n_training_update_grad", "bn_training_update_grad"},
+  {"b_n_infer", "bn_infer"},
+  {"b_n_infer_grad", "bn_infer_grad"},
   {"n_pu_clear_float_status", "n_p_u_clear_float_status"},
   {"n_pu_get_float_status", "n_p_u_get_float_status"},
   {"n_pu_alloc_float_status", "n_p_u_alloc_float_status"},

mindspore/ops/_op_impl/tbe/__init__.py

@@ -71,6 +71,8 @@ from .bn_training_reduce import _bn_training_reduce_tbe
 from .bn_training_reduce_grad import _bn_training_reduce_grad_tbe
 from .bn_training_update import _bn_training_update_tbe
 from .bn_training_update_grad import _bn_training_update_grad_tbe
+from .bn_infer import _bn_infer_tbe
+from .bn_infer_grad import _bn_infer_grad_tbe
 from .reciprocal import _reciprocal_tbe
 from .strided_slice_d import _strided_slice_d_tbe
 from .strided_slice_grad_d import _strided_slice_grad_d_tbe

mindspore/ops/_op_impl/tbe/bn_infer.py

+# 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.
+# ============================================================================
+
+"""BNInfer op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+bn_infer_op_info = TBERegOp("BNInfer") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("bn_infer.so") \
+    .compute_cost(10) \
+    .kernel_name("bn_infer") \
+    .partial_flag(True) \
+    .attr("epsilon", "required", "float", "all") \
+    .input(0, "x", False, "required", "all", reshape_type="NC") \
+    .input(1, "scale", False, "required", "all") \
+    .input(2, "offset", False, "required", "all") \
+    .input(3, "mean", False, "required", "all") \
+    .input(4, "variance", False, "required", "all") \
+    .output(0, "y", False, "required", "all", reshape_type="NC") \
+    .dtype_format(DataType.F16_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD,
+                  DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
+
+
+@op_info_register(bn_infer_op_info)
+def _bn_infer_tbe():
+    """BNInfer TBE register"""
+    return

mindspore/ops/_op_impl/tbe/bn_infer_grad.py

+# 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.
+# ============================================================================
+
+"""BNInferGrad op"""
+from mindspore.ops.op_info_register import op_info_register, TBERegOp, DataType
+
+bn_infer_grad_op_info = TBERegOp("BNInferGrad") \
+    .fusion_type("OPAQUE") \
+    .async_flag(False) \
+    .binfile_name("bn_infer_grad.so") \
+    .compute_cost(10) \
+    .kernel_name("bn_infer_grad") \
+    .partial_flag(True) \
+    .attr("epsilon", "optional", "float", "all") \
+    .input(0, "grads", False, "required", "all", reshape_type="NC") \
+    .input(1, "scale", False, "required", "all") \
+    .input(2, "batch_variance", False, "required", "all") \
+    .output(0, "x_backprop", False, "required", "all", reshape_type="NC") \
+    .dtype_format(DataType.F16_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F16_5HD) \
+    .dtype_format(DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD, DataType.F32_5HD) \
+    .get_op_info()
+
+
+@op_info_register(bn_infer_grad_op_info)
+def _bn_infer_grad_tbe():
+    """BNInferGrad TBE register"""
+    return