expand ClipByNormNoDivSum

mindspore/_extends/graph_kernel/expanders/__init__.py

@@ -34,3 +34,4 @@ from .logsoftmax_grad import expand_logsoftmaxgrad
 from .gkdropout import expand_gkdropout
 from .tile import expand_tile
 from .sqrt_grad import expand_sqrtgrad
+from .clip_by_norm_no_div_sum import expand_clipbynormnodivsum

mindspore/_extends/graph_kernel/expanders/clip_by_norm_no_div_sum.py

@@ -0,0 +1,51 @@
+# 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.
+# ===========================================================================
+"""generate json desc for ClipByNormNoDivSum"""
+from mindspore._extends.graph_kernel.model import model_builder as builder
+
+
+def expand_clipbynormnodivsum(expand_info):
+    """ClipByNormNoDivSum expander"""
+
+    # get op info.
+    input_desc_0 = expand_info['input_desc'][0]
+    input_desc_1 = expand_info['input_desc'][1]
+    input_desc_2 = expand_info['input_desc'][2]
+    input_desc_3 = expand_info['input_desc'][3]
+    graph_builder = builder.GraphBuilder()
+
+    # generate a graph.
+    with graph_builder.graph_scope('main') as graph_scope:
+        # create tensor input.
+        input_x0 = graph_builder.tensor(input_desc_0['shape'], input_desc_0['data_type'], input_desc_0['format'])
+        input_x1 = graph_builder.tensor(input_desc_1['shape'], input_desc_1['data_type'], input_desc_1['format'])
+        input_x2 = graph_builder.tensor(input_desc_2['shape'], input_desc_2['data_type'], input_desc_2['format'])
+        input_x3 = graph_builder.tensor(input_desc_3['shape'], input_desc_3['data_type'], input_desc_3['format'])
+        graph_scope.set_input(input_x0, input_x1, input_x2, input_x3)
+
+        # cal result
+        greater_res = graph_builder.emit('Greater', [input_x0, input_x1], attrs={'fusion': 'SelectGT_000'})
+        select_res0 = graph_builder.emit('Select', [greater_res, input_x0, input_x2],
+                                         attrs={'fusion': 'SelectGT_000_end'})
+        sqrt_res = graph_builder.emit('Sqrt', [select_res0])
+        select_res1 = graph_builder.emit('Select', [greater_res, sqrt_res, input_x0],
+                                         attrs={'fusion': 'SelectGT_000_end'})
+        result = graph_builder.emit('Maximum', [select_res1, input_x3])
+
+        # set graph output.
+        graph_scope.set_output(result)
+
+    graph = graph_builder.get()[0]
+    return graph

mindspore/ccsrc/backend/optimizer/graph_kernel/graph_kernel_helper.cc

@@ -705,6 +705,7 @@ std::unordered_set<PrimitivePtr> GetExpandOps() {
 #if ENABLE_D
     prim::kPrimTile,
     prim::kPrimSqrtGrad,
+    prim::kPrimClipByNormNoDivSum,
 #elif ENABLE_GPU
     prim::kPrimBiasAdd,
     prim::kPrimBiasAddGrad,

mindspore/core/base/core_ops.h

@@ -192,6 +192,7 @@ inline const PrimitivePtr kPrimSparseApplyProximalAdagrad = std::make_shared<Pri
 inline const PrimitivePtr kPrimFusedAdam = std::make_shared<Primitive>("FusedAdam");
 inline const PrimitivePtr kPrimFusedAdamWeightDecay = std::make_shared<Primitive>("FusedAdamWeightDecay");
 inline const PrimitivePtr kPrimSGD = std::make_shared<Primitive>("SGD");
+inline const PrimitivePtr kPrimClipByNormNoDivSum = std::make_shared<Primitive>("ClipByNormNoDivSum");
 
 // Comm ops
 inline const PrimitivePtr kPrimMirror = std::make_shared<Primitive>("_MirrorOperator");