fix ub-fusion

mindspore/ccsrc/backend/optimizer/ascend/ascend_backend_optimization.cc

@@ -81,6 +81,7 @@
 #include "backend/optimizer/ascend/buffer_fusion/conv_single_in_fusion_pass.h"
 #include "backend/optimizer/ascend/buffer_fusion/conv_double_in_fusion_pass.h"
 #include "backend/optimizer/ascend/buffer_fusion/matmul_eltwise_fusion_pass.h"
+#include "backend/optimizer/ascend/buffer_fusion/matmul_confusiontranspose_fusion_pass.h"
 #include "backend/optimizer/ascend/buffer_fusion/depthwiseconv_eltwise_fusion_pass.h"
 #include "backend/optimizer/ascend/buffer_fusion/bnupdate_eltwise_fusion_pass.h"
 #include "backend/optimizer/ascend/buffer_fusion/bnupdate_eltwise_eltwise_fusion_pass.h"
@@ -504,6 +505,7 @@ void AscendBackendUBFusionOptimization(const std::shared_ptr<session::KernelGrap
   ub_fusion_pm->AddPass(std::make_shared<MultiOutputFusionPass>(fusion_id_allocator));
   ub_fusion_pm->AddPass(std::make_shared<EltwiseFusionPass>(fusion_id_allocator));
   ub_fusion_pm->AddPass(std::make_shared<DepthwiseConvEltwiseFusionPass>(fusion_id_allocator));
+  ub_fusion_pm->AddPass(std::make_shared<MatmulConfusionTranposeFusionPass>(fusion_id_allocator));
   ub_fusion_pm->AddPass(std::make_shared<UbPatternFusion>());
   optimizer->AddPassManager(ub_fusion_pm);
   (void)optimizer->Optimize(kernel_graph);

tests/st/fusion/test_ub_fusion_matmul_confusion_transpose.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.
+# ============================================================================
+import numpy as np
+
+import mindspore
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.ops import operations as P
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+context.set_context(save_graphs=True)
+
+
+class Net(nn.Cell):
+    def __init__(self):
+        super(Net, self).__init__()
+        self.matmul = P.MatMul()
+        self.transpose = P.Transpose()
+        self.reshape = P.Reshape()
+        self.bias_add = P.BiasAdd()
+
+    def construct(self, x, y, z):
+        res = self.matmul(x, y)
+        res = self.bias_add(res, z)
+        res = self.reshape(res, (24, 512, 16, 64))
+        res = self.transpose(res, (0, 2, 1, 3))
+        return res
+
+
+def test_net():
+    x = Tensor(np.ones(shape=[12288, 1024]), mindspore.float16)
+    y = Tensor(np.ones(shape=[1024, 1024]), mindspore.float16)
+    z = Tensor(np.ones(shape=[1024]), mindspore.float16)
+    net = Net()
+    output = net(x, y, z)
+    print("result", output.asnumpy())
+
+
+if __name__ == "__main__":
+    test_net()