!3115 Remove transdata and cast for internal outputs

Merge pull request !3115 from YuJianfeng/master

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

@@ -96,6 +96,7 @@
 #include "backend/optimizer/ascend/format_type/modify_ops_attrs.h"
 #include "backend/optimizer/ascend/format_type/remove_no_use_reshape_op.h"
 #include "backend/optimizer/ascend/ir_fusion/add_input_to_output.h"
+#include "backend/optimizer/ascend/format_type/remove_internal_output.h"
 #include "utils/context/ms_context.h"
 #include "utils/config_manager.h"
 #include "debug/anf_ir_dump.h"
@@ -199,6 +200,7 @@ void AscendDataLayout(const std::shared_ptr<session::KernelGraph> &kernel_graph)
   data_layout_pm->AddPass(std::make_shared<OptimizeDependence>());
   data_layout_pm->AddPass(std::make_shared<TransDataSplit>());
   data_layout_pm->AddPass(std::make_shared<EraseVisitAttr>());
+  data_layout_pm->AddPass(std::make_shared<RemoveInternalOutputTransOp>());
   optimizer->AddPassManager(data_layout_pm);
   (void)optimizer->Optimize(kernel_graph);
   kernel_graph->SetExecOrderByDefault();
@@ -220,6 +222,7 @@ void AscendMixPrecision(const std::shared_ptr<session::KernelGraph> &kernel_grap
   mixed_precision_pm->AddPass(std::make_shared<LayerNormBetaGammaBackpropFusion>());
   mixed_precision_pm->AddPass(std::make_shared<EraseVisitAttr>());
   mixed_precision_pm->AddPass(std::make_shared<ConvertUnSupportNodeToAICPU>());
+  mixed_precision_pm->AddPass(std::make_shared<RemoveInternalOutputCast>());
   optimizer->AddPassManager(mixed_precision_pm);
   (void)optimizer->Optimize(kernel_graph);
   kernel_graph->SetExecOrderByDefault();

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

@@ -142,6 +142,7 @@ AnfNodePtr InsertTransOpForMultipleOutput(const FuncGraphPtr &func_graph, const
   MS_EXCEPTION_IF_NULL(node);
   std::vector<AnfNodePtr> make_tuple_inputs;
   make_tuple_inputs.push_back(NewValueNode(prim::kPrimMakeTuple));
+  auto kernel_graph = func_graph->cast<KernelGraphPtr>();
   for (size_t output_idx = 0; output_idx < AnfAlgo::GetOutputTensorNum(node); ++output_idx) {
     std::string output_format = AnfAlgo::GetOutputFormat(node, output_idx);
     if (output_format == kOpFormat_NC1KHKWHWC0) {
@@ -151,7 +152,11 @@ AnfNodePtr InsertTransOpForMultipleOutput(const FuncGraphPtr &func_graph, const
     auto tuple_getitem = CreatTupleGetItemNode(func_graph, node, output_idx);
     std::vector<size_t> origin_shape = AnfAlgo::GetOutputInferShape(node, output_idx);
     if (kCommonFormatSet.find(output_format) == kCommonFormatSet.end() && origin_shape.size() > 1) {
-      make_tuple_inputs.emplace_back(AddTransOpNodeToGraph(func_graph, tuple_getitem, kernel_select, 0, false));
+      auto trans_op = AddTransOpNodeToGraph(func_graph, tuple_getitem, kernel_select, 0, false);
+      if (kernel_graph != nullptr && kernel_graph->IsInternalOutput(node)) {
+        kernel_graph->ReplaceInternalOutput(node, trans_op, output_idx, 0);
+      }
+      make_tuple_inputs.emplace_back(trans_op);
     } else {
       // No need insert trans op.
       make_tuple_inputs.push_back(tuple_getitem);
@@ -249,9 +254,14 @@ AnfNodePtr InsertTransOpForOutput(const FuncGraphPtr &func_graph, const AnfNodeP
   if (outputs_num == 0) {
     return node;
   }
+  auto kernel_graph = func_graph->cast<KernelGraphPtr>();
   // Single output
   if (outputs_num == 1 && (!AnfAlgo::IsTupleOutput(node))) {
-    return InsertTransOpForSingleOutput(func_graph, node, kernel_select);
+    auto new_node = InsertTransOpForSingleOutput(func_graph, node, kernel_select);
+    if (kernel_graph != nullptr && kernel_graph->IsInternalOutput(node)) {
+      kernel_graph->ReplaceInternalOutput(node, new_node);
+    }
+    return new_node;
   }
   // Multiple output
   return InsertTransOpForMultipleOutput(func_graph, node, kernel_select);

mindspore/ccsrc/backend/optimizer/ascend/format_type/insert_cast.cc

@@ -40,6 +40,7 @@ AnfNodePtr InsertCastForMultipleOutput(const FuncGraphPtr &func_graph, const CNo
   std::vector<AnfNodePtr> make_tuple_inputs;
   AbstractBasePtrList abstract_list;
   make_tuple_inputs.push_back(NewValueNode(prim::kPrimMakeTuple));
+  auto kernel_graph = func_graph->cast<KernelGraphPtr>();
   for (size_t output_idx = 0; output_idx < AnfAlgo::GetOutputTensorNum(cnode); ++output_idx) {
     AnfNodePtr replace_node = nullptr;
     const auto origin_shape = AnfAlgo::GetOutputInferShape(cnode, output_idx);
@@ -64,6 +65,9 @@ AnfNodePtr InsertCastForMultipleOutput(const FuncGraphPtr &func_graph, const CNo
         MS_EXCEPTION_IF_NULL(replace_node);
         replace_node->set_scope(cnode->scope());
         AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), replace_node);
+        if (kernel_graph != nullptr && kernel_graph->IsInternalOutput(cnode)) {
+          kernel_graph->ReplaceInternalOutput(cnode, replace_node, output_idx, 0);
+        }
       } else {
         replace_node = getitem;
       }
@@ -87,6 +91,7 @@ AnfNodePtr InsertCastForOutput(const FuncGraphPtr &func_graph, const CNodePtr &c
     return cnode;
   }
   MS_EXCEPTION_IF_NULL(cnode->Type());
+  auto kernel_graph = func_graph->cast<KernelGraphPtr>();
   // Single output
   if (!cnode->Type()->isa<Tuple>()) {
     if (!need_insert_cast[0]) {
@@ -109,6 +114,9 @@ AnfNodePtr InsertCastForOutput(const FuncGraphPtr &func_graph, const CNodePtr &c
       MS_EXCEPTION_IF_NULL(replace_node);
       replace_node->set_scope(cnode->scope());
       AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), replace_node);
+      if (kernel_graph != nullptr && kernel_graph->IsInternalOutput(cnode)) {
+        kernel_graph->ReplaceInternalOutput(cnode, replace_node);
+      }
     }
     return replace_node;
   }
@@ -188,6 +196,10 @@ const AnfNodePtr InsertCast::Process(const FuncGraphPtr &func_graph, const AnfNo
   CNodePtr cnode = node->cast<CNodePtr>();
   MS_EXCEPTION_IF_NULL(cnode);
   auto new_node = InsertCastForInput(func_graph, cnode);
+  auto kernel_graph = func_graph->cast<std::shared_ptr<session::KernelGraph>>();
+  if (kernel_graph != nullptr && kernel_graph->IsInternalOutput(node)) {
+    kernel_graph->ReplaceInternalOutput(node, new_node);
+  }
   // process output
   return InsertCastForOutput(func_graph, new_node, std::vector<bool>(AnfAlgo::GetOutputTensorNum(new_node), true));
 }

mindspore/ccsrc/backend/optimizer/ascend/format_type/insert_trans_op.cc

@@ -46,14 +46,13 @@ const AnfNodePtr InsertTransOp::Process(const FuncGraphPtr &func_graph, const An
   if (node == nullptr || !AnfAlgo::IsRealKernel(node)) {
     return nullptr;
   }
-  AnfNodePtr front_node;
+  AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node);
+  MS_LOG(DEBUG) << "process op: " << node->DebugString();
+  AnfNodePtr new_node = InsertTransOpForInput(func_graph, node, kernel_select_);
   auto kernel_graph = func_graph->cast<std::shared_ptr<session::KernelGraph>>();
   if (kernel_graph != nullptr && kernel_graph->IsInternalOutput(node)) {
-    front_node = kernel_graph->GetFrontNodeByInternalOutput(node);
+    kernel_graph->ReplaceInternalOutput(node, new_node);
   }
-  AnfAlgo::SetNodeAttr(kAttrVisited, MakeValue(true), node);
-  MS_LOG(DEBUG) << "====process op: " << node->DebugString();
-  AnfNodePtr new_node = InsertTransOpForInput(func_graph, node, kernel_select_);
   auto ms_context = MsContext::GetInstance();
   MS_EXCEPTION_IF_NULL(ms_context);
   if (ms_context->execution_mode() == kPynativeMode && !ms_context->enable_pynative_hook()) {
@@ -61,12 +60,7 @@ const AnfNodePtr InsertTransOp::Process(const FuncGraphPtr &func_graph, const An
       return new_node;
     }
   }
-  auto final_node = InsertTransOpForOutput(func_graph, new_node, kernel_select_);
-  if (kernel_graph != nullptr && front_node != nullptr) {
-    auto old_node = kernel_graph->GetInternalOutputByFrontNode(front_node);
-    kernel_graph->ReplaceInternalOutput(old_node, final_node);
-  }
-  return final_node;
+  return InsertTransOpForOutput(func_graph, new_node, kernel_select_);
 }
 }  // namespace opt
 }  // namespace mindspore

mindspore/ccsrc/backend/optimizer/ascend/format_type/remove_internal_output.cc

+/**
+ * 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.
+ */
+
+#include "backend/optimizer/ascend/format_type/remove_internal_output.h"
+#include <memory>
+#include "backend/session/anf_runtime_algorithm.h"
+
+namespace mindspore {
+namespace opt {
+namespace {
+bool UsedForOutputOnly(const FuncGraphPtr &func_graph, const AnfNodePtr &node) {
+  MS_EXCEPTION_IF_NULL(func_graph);
+  auto manager = func_graph->manager();
+  MS_EXCEPTION_IF_NULL(manager);
+  auto &node_users = manager->node_users();
+  auto iter = node_users.find(node);
+  if (iter == node_users.end()) {
+    return false;
+  }
+  const auto &node_set = iter->second;
+  for (const auto &node_index : node_set) {
+    if (!AnfAlgo::CheckPrimitiveType(node_index.first, prim::kPrimMakeTuple)) {
+      return false;
+    }
+  }
+  return true;
+}
+}  // namespace
+const BaseRef RemoveInternalOutputTransOp::DefinePattern() const {
+  VarPtr X = std::make_shared<Var>();
+  auto prim = std::make_shared<Primitive>(kTransDataOpName);
+  return VectorRef({prim, X});
+}
+
+const BaseRef RemoveInternalOutputCast::DefinePattern() const {
+  VarPtr X = std::make_shared<Var>();
+  return VectorRef({prim::kPrimCast, X});
+}
+
+const AnfNodePtr RemoveInternalOutput::Process(const FuncGraphPtr &func_graph, const AnfNodePtr &node,
+                                               const EquivPtr &) const {
+  MS_EXCEPTION_IF_NULL(func_graph);
+  MS_EXCEPTION_IF_NULL(node);
+  auto kernel_graph = func_graph->cast<KernelGraphPtr>();
+  if (kernel_graph == nullptr) {
+    return nullptr;
+  }
+  if (!kernel_graph->IsInternalOutput(node)) {
+    return nullptr;
+  }
+  if (!UsedForOutputOnly(func_graph, node)) {
+    return nullptr;
+  }
+  auto cnode = node->cast<CNodePtr>();
+  MS_EXCEPTION_IF_NULL(cnode);
+  CheckCNodeInputSize(cnode, kTransOpInputNum);
+  auto input_node = cnode->input(1);
+  if (!AnfAlgo::CheckPrimitiveType(input_node, prim::kPrimTupleGetItem)) {
+    kernel_graph->ReplaceInternalOutput(node, input_node);
+  } else {
+    auto tuple_getitem = input_node->cast<CNodePtr>();
+    MS_EXCEPTION_IF_NULL(tuple_getitem);
+    int idx = AnfAlgo::GetTupleGetItemOutIndex(tuple_getitem);
+    AnfNodePtr real_input_node = AnfAlgo::GetTupleGetItemRealInput(tuple_getitem);
+    kernel_graph->ReplaceInternalOutput(node, real_input_node, 0, idx);
+  }
+  return input_node;
+}
+}  // namespace opt
+}  // namespace mindspore

mindspore/ccsrc/backend/optimizer/ascend/format_type/remove_internal_output.h

+/**
+ * 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.
+ */
+
+#ifndef MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_FORMAT_TYPE_REMOVE_INTERNAL_OUTPUT_H_
+#define MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_FORMAT_TYPE_REMOVE_INTERNAL_OUTPUT_H_
+
+#include <string>
+#include "backend/optimizer/common/optimizer.h"
+
+namespace mindspore {
+namespace opt {
+class RemoveInternalOutput : public PatternProcessPass {
+ public:
+  explicit RemoveInternalOutput(const std::string &name, bool multigraph = true)
+      : PatternProcessPass(name, multigraph) {}
+  ~RemoveInternalOutput() override = default;
+  const AnfNodePtr Process(const FuncGraphPtr &, const AnfNodePtr &, const EquivPtr &) const override;
+};
+
+class RemoveInternalOutputTransOp : public RemoveInternalOutput {
+ public:
+  explicit RemoveInternalOutputTransOp(bool multigraph = true)
+      : RemoveInternalOutput("remove_internal_output_trans_op", multigraph) {}
+  ~RemoveInternalOutputTransOp() override = default;
+  const BaseRef DefinePattern() const override;
+};
+
+class RemoveInternalOutputCast : public RemoveInternalOutput {
+ public:
+  explicit RemoveInternalOutputCast(bool multigraph = true)
+      : RemoveInternalOutput("remove_internal_output_cast", multigraph) {}
+  ~RemoveInternalOutputCast() override = default;
+  const BaseRef DefinePattern() const override;
+};
+}  // namespace opt
+}  // namespace mindspore
+
+#endif  // MINDSPORE_CCSRC_PRE_ACTIVATE_ASCEND_FORMAT_TYPE_REMOVE_INTERNAL_OUTPUT_H_

mindspore/ccsrc/backend/session/kernel_graph.cc

@@ -929,10 +929,15 @@ void KernelGraph::AddInternalOutput(const AnfNodePtr &front_node, const AnfNodeP
   }
   MS_LOG(INFO) << "Add internal node " << node->DebugString() << " with front node " << front_node->DebugString();
   front_to_internal_outputs_map_[front_node] = node;
-  internal_outputs_to_front_map_[node] = front_node;
+  int output_idx = 0;
+  if (AnfAlgo::CheckPrimitiveType(front_node, prim::kPrimTupleGetItem)) {
+    output_idx = AnfAlgo::GetTupleGetItemOutIndex(front_node->cast<CNodePtr>());
+  }
+  internal_outputs_to_front_map_[node][output_idx] = front_node;
 }
 
-void KernelGraph::ReplaceInternalOutput(const AnfNodePtr &node, const AnfNodePtr &new_node) {
+void KernelGraph::ReplaceInternalOutput(const AnfNodePtr &node, const AnfNodePtr &new_node, int src_output_idx,
+                                        int dst_output_idx) {
   if (new_node == nullptr || node == nullptr) {
     MS_LOG(INFO) << "New node or node is nullptr";
     return;
@@ -947,9 +952,30 @@ void KernelGraph::ReplaceInternalOutput(const AnfNodePtr &node, const AnfNodePtr
     return;
   }
   MS_LOG(INFO) << "Replace internal node " << node->DebugString() << " To " << new_node->DebugString();
-  internal_outputs_to_front_map_[new_node] = iter->second;
-  front_to_internal_outputs_map_[iter->second] = new_node;
-  internal_outputs_to_front_map_.erase(iter);
+  auto &front_nodes = iter->second;
+  // Move all front nodes to new node mapping
+  if (src_output_idx == -1) {
+    internal_outputs_to_front_map_[new_node] = front_nodes;
+    for (const auto &front_node_iter : front_nodes) {
+      front_to_internal_outputs_map_[front_node_iter.second] = new_node;
+    }
+    internal_outputs_to_front_map_.erase(iter);
+    return;
+  }
+  // Move specified front node to new node mapping
+  int index = SizeToInt(src_output_idx);
+  auto front_node_iter = front_nodes.find(index);
+  if (front_node_iter == front_nodes.end()) {
+    MS_LOG(INFO) << "The output " << src_output_idx << " of node " << node->DebugString() << " is not an internal node";
+    return;
+  }
+  auto front_node = front_node_iter->second;
+  internal_outputs_to_front_map_[new_node][dst_output_idx] = front_node;
+  front_to_internal_outputs_map_[front_node] = new_node;
+  front_nodes.erase(index);
+  if (front_nodes.empty()) {
+    internal_outputs_to_front_map_.erase(iter);
+  }
 }
 
 AnfNodePtr KernelGraph::GetInternalOutputByFrontNode(const AnfNodePtr &front_node) const {
@@ -967,14 +993,6 @@ bool KernelGraph::IsInternalOutput(const AnfNodePtr &node) const {
   return false;
 }
 
-AnfNodePtr KernelGraph::GetFrontNodeByInternalOutput(const AnfNodePtr &node) const {
-  auto iter = internal_outputs_to_front_map_.find(node);
-  if (iter != internal_outputs_to_front_map_.end()) {
-    return iter->second;
-  }
-  return nullptr;
-}
-
 void KernelGraph::AddFinalOutputKernel(const AnfNodePtr &node) {
   if (node == nullptr) {
     return;

mindspore/ccsrc/backend/session/kernel_graph.h

@@ -148,10 +148,10 @@ class KernelGraph : public FuncGraph {
   const std::map<std::string, std::pair<AnfNodePtr, int>> &summary_nodes() const { return summary_nodes_; }
   void set_summary_nodes(const std::map<std::string, std::pair<AnfNodePtr, int>> &nodes) { summary_nodes_ = nodes; }
   void AddInternalOutput(const AnfNodePtr &front_node, const AnfNodePtr &node);
-  void ReplaceInternalOutput(const AnfNodePtr &node, const AnfNodePtr &new_node);
+  void ReplaceInternalOutput(const AnfNodePtr &node, const AnfNodePtr &new_node, int src_output_idx = -1,
+                             int dst_output_idx = -1);
   AnfNodePtr GetInternalOutputByFrontNode(const AnfNodePtr &front_node) const;
   bool IsInternalOutput(const AnfNodePtr &node) const;
-  AnfNodePtr GetFrontNodeByInternalOutput(const AnfNodePtr &node) const;
   void AddFinalOutputKernel(const AnfNodePtr &node);
   bool IsFinalOutputKernel(const AnfNodePtr &node) const;
   uint32_t current_epoch() const { return current_epoch_; }
@@ -223,7 +223,7 @@ class KernelGraph : public FuncGraph {
   CNodePtr end_goto_;
   bool null_output_;
   std::unordered_map<AnfNodePtr, AnfNodePtr> front_to_internal_outputs_map_;
-  std::unordered_map<AnfNodePtr, AnfNodePtr> internal_outputs_to_front_map_;
+  std::unordered_map<AnfNodePtr, std::unordered_map<int, AnfNodePtr>> internal_outputs_to_front_map_;
   std::set<AnfNodePtr> final_output_kernels_;
   uint32_t current_epoch_;
 };

mindspore/ccsrc/backend/session/session_basic.cc

@@ -300,7 +300,11 @@ void SessionBasic::InitInternalOutputParameter(const AnfNodePtr &out_node, const
     MS_LOG(INFO) << "No corresponding internal output for output node";
     return;
   }
-  auto real_kernel = AnfAlgo::VisitKernel(ref_node, 0);
+  size_t output_idx = 0;
+  if (AnfAlgo::CheckPrimitiveType(out_node, prim::kPrimTupleGetItem)) {
+    output_idx = AnfAlgo::GetTupleGetItemOutIndex(out_node->cast<CNodePtr>());
+  }
+  auto real_kernel = AnfAlgo::VisitKernel(ref_node, output_idx);
   auto ref_real_node = real_kernel.first;
   auto ref_real_node_index = real_kernel.second;
   if (ref_real_node->isa<CNode>() && node_graph->IsInternalOutput(ref_real_node) &&
@@ -325,6 +329,7 @@ void SessionBasic::InitInternalOutputParameter(const AnfNodePtr &out_node, const
     builder.SetOutputsFormat({format});
     d_kernel_info->set_select_kernel_build_info(builder.Build());
     AnfAlgo::SetOutputAddr(address, 0, parameter.get());
+    AnfAlgo::SetOutputInferTypeAndShape({type}, {AnfAlgo::GetOutputInferShape(parameter, 0)}, parameter.get());
   }
 }
 

tests/st/host_device/test_host_device_lenet.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 pytest
+
+import mindspore.context as context
+import mindspore.nn as nn
+from mindspore import Tensor
+from mindspore.nn import TrainOneStepCell, WithLossCell
+from mindspore.nn.optim import Momentum
+from mindspore.ops import operations as P
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+
+class LeNet(nn.Cell):
+    def __init__(self):
+        super(LeNet, self).__init__()
+        self.relu = P.ReLU()
+        self.batch_size = 32
+
+        self.conv1 = nn.Conv2d(1, 6, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
+        self.conv2 = nn.Conv2d(6, 16, kernel_size=5, stride=1, padding=0, has_bias=False, pad_mode='valid')
+        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.reshape = P.Reshape()
+        self.fc1 = nn.Dense(400, 120)
+        self.fc1.matmul.add_prim_attr("primitive_target", "CPU")
+        self.fc1.bias_add.add_prim_attr("primitive_target", "CPU")
+        self.fc2 = nn.Dense(120, 84)
+        self.fc2.matmul.add_prim_attr("primitive_target", "CPU")
+        self.fc2.bias_add.add_prim_attr("primitive_target", "CPU")
+        self.fc3 = nn.Dense(84, 10)
+        self.fc3.matmul.add_prim_attr("primitive_target", "CPU")
+        self.fc3.bias_add.add_prim_attr("primitive_target", "CPU")
+
+    def construct(self, input_x):
+        output = self.conv1(input_x)
+        output = self.relu(output)
+        output = self.pool(output)
+        output = self.conv2(output)
+        output = self.relu(output)
+        output = self.pool(output)
+        output = self.reshape(output, (self.batch_size, -1))
+        output = self.fc1(output)
+        output = self.relu(output)
+        output = self.fc2(output)
+        output = self.relu(output)
+        output = self.fc3(output)
+        return output
+
+
+def train(net, data, label):
+    learning_rate = 0.01
+    momentum = 0.9
+
+    optimizer = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), learning_rate, momentum)
+    criterion = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True)
+    net_with_criterion = WithLossCell(net, criterion)
+    train_network = TrainOneStepCell(net_with_criterion, optimizer)  # optimizer
+    train_network.set_train()
+    res = train_network(data, label)
+    print("+++++++++Loss+++++++++++++")
+    print(res)
+    print("+++++++++++++++++++++++++++")
+    diff = res.asnumpy()[0] - 2.3025851
+    assert np.all(diff < 1.e-7)
+
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_lenet():
+    data = Tensor(np.ones([32, 1, 32, 32]).astype(np.float32) * 0.01)
+    label = Tensor(np.ones([32]).astype(np.int32))
+    net = LeNet()
+    train(net, data, label)

tests/st/ops/cpu/test_sparse_apply_adam_op.py

@@ -14,6 +14,7 @@
 # ============================================================================
 
 import numpy as np
+import pytest
 import mindspore.context as context
 import mindspore.nn as nn
 from mindspore import Tensor
@@ -43,6 +44,9 @@ class Net(nn.Cell):
         return out
 
 
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
 def test_net():
     gradient = Tensor(np.ones([3, 3, 3]).astype(np.float32))
     indices = Tensor([0, 1, 2], mstype.int32)

tests/st/ops/cpu/test_sparse_apply_ftrl_op.py

@@ -14,6 +14,7 @@
 # ============================================================================
 
 import numpy as np
+import pytest
 import mindspore.context as context
 import mindspore.nn as nn
 from mindspore import Tensor
@@ -35,6 +36,9 @@ class Net(nn.Cell):
         return out
 
 
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
 def test_net():
     gradient = Tensor(np.ones([3, 3, 3]).astype(np.float32))
     indices = Tensor([0, 1, 2], mstype.int32)

tests/st/ops/cpu/test_sparse_apply_proximal_adagrad_op.py

@@ -14,6 +14,7 @@
 # ============================================================================
 
 import numpy as np
+import pytest
 import mindspore.context as context
 import mindspore.nn as nn
 from mindspore import Tensor
@@ -37,6 +38,9 @@ class Net(nn.Cell):
         return out
 
 
+@pytest.mark.level0
+@pytest.mark.platform_x86_cpu
+@pytest.mark.env_onecard
 def test_net():
     gradient = Tensor(np.ones([3, 3, 3]).astype(np.float32))
     indices = Tensor([0, 1, 2], mstype.int32)

tests/ut/cpp/pre_activate/ascend/format_type/remove_internal_output_test.cc

+/**
+ * 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.
+ */
+#include "common/backend_common_test.h"
+#include "debug/anf_ir_dump.h"
+#include "common/py_func_graph_fetcher.h"
+#include "backend/optimizer/ascend/format_type/remove_internal_output.h"
+
+#define private public
+#define protected public
+#include "backend/optimizer/ascend/format_type/insert_trans_op.h"
+#undef private
+#undef protected
+
+namespace mindspore {
+namespace opt {
+using KernelBuildInfoBuilder = kernel::KernelBuildInfo::KernelBuildInfoBuilder;
+
+class TestHWRemoveInternalOutput : public BackendCommon {
+ public:
+  TestHWRemoveInternalOutput() : getPyFun_("gtest_input.pre_activate.remove_internal_output_test", true) {}
+  ~TestHWRemoveInternalOutput() override = default;
+
+  AnfNodePtr GetMakeTuple(const KernelGraphPtr &kg) {
+    auto ret = kg->get_return();
+    MS_EXCEPTION_IF_NULL(ret);
+    auto make_tuple = ret->input(1);
+    return make_tuple;
+  }
+
+  KernelGraphPtr GetSingleOutputGraph(const std::string &func_name, const std::string &sub_func_name) {
+    FuncGraphPtr g = getPyFun_.CallAndParseRet(func_name, sub_func_name);
+    std::vector<int> shp{2, 32, 224, 224};
+    auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp);
+    AbstractBasePtrList args_spec_list{x_abstract, x_abstract};
+    auto kg = GetKernelGraph(g, args_spec_list);
+    auto make_tuple = GetMakeTuple(kg);
+    auto add = make_tuple->cast<CNodePtr>()->input(1);
+    MS_EXCEPTION_IF_NULL(add);
+    kg->AddInternalOutput(add, add);
+    KernelBuildInfoBuilder builder;
+    builder.SetInputsFormat({kOpFormat_DEFAULT, kOpFormat_DEFAULT});
+    builder.SetInputsDeviceType({kFloat32->type_id(), kFloat32->type_id()});
+    builder.SetOutputsFormat({kOpFormat_NC1HWC0});
+    builder.SetOutputsDeviceType({kFloat16->type_id()});
+    add->set_kernel_info(std::make_shared<device::KernelInfo>());
+    AnfAlgo::SetSelectKernelBuildInfo(builder.Build(), add.get());
+    return kg;
+  }
+
+  KernelGraphPtr GetMutilpleOutputGraph(const std::string &func_name, const std::string &sub_func_name) {
+    FuncGraphPtr g = getPyFun_.CallAndParseRet(func_name, sub_func_name);
+    std::vector<int> shp{2, 32, 224, 224};
+    auto x_abstract = std::make_shared<abstract::AbstractTensor>(kFloat32, shp);
+    AbstractBasePtrList args_spec_list{x_abstract};
+    auto kg = GetKernelGraph(g, args_spec_list);
+    auto output_make_tuple = GetMakeTuple(kg);
+    auto make_tuple = output_make_tuple->cast<CNodePtr>()->input(1);
+    MS_EXCEPTION_IF_NULL(make_tuple);
+    auto tuple_getitem1 = make_tuple->cast<CNodePtr>()->input(1);
+    MS_EXCEPTION_IF_NULL(tuple_getitem1);
+    auto tuple_getitem2 = make_tuple->cast<CNodePtr>()->input(2);
+    MS_EXCEPTION_IF_NULL(tuple_getitem2);
+    auto max_pool = tuple_getitem1->cast<CNodePtr>()->input(1);
+    MS_EXCEPTION_IF_NULL(max_pool);
+    kg->AddInternalOutput(tuple_getitem1, max_pool);
+    kg->AddInternalOutput(tuple_getitem2, max_pool);
+    KernelBuildInfoBuilder builder;
+    builder.SetInputsFormat({kOpFormat_DEFAULT});
+    builder.SetInputsDeviceType({kFloat32->type_id()});
+    builder.SetOutputsFormat({kOpFormat_NC1HWC0, kOpFormat_NC1HWC0});
+    builder.SetOutputsDeviceType({kFloat16->type_id(), kFloat16->type_id()});
+    max_pool->set_kernel_info(std::make_shared<device::KernelInfo>());
+    AnfAlgo::SetSelectKernelBuildInfo(builder.Build(), max_pool.get());
+    return kg;
+  }
+  UT::PyFuncGraphFetcher getPyFun_;
+};
+
+class MockRemoveInternalOutputTransOpKernelSelect : public KernelSelect {
+ public:
+  MockRemoveInternalOutputTransOpKernelSelect() = default;
+  ~MockRemoveInternalOutputTransOpKernelSelect() override = default;
+  void SelectKernel(const CNodePtr &cnode) override {
+    KernelBuildInfoBuilder builder;
+    builder.SetInputsFormat({kOpFormat_NC1HWC0});
+    builder.SetInputsDeviceType({kFloat16->type_id()});
+    builder.SetOutputsFormat({kOpFormat_DEFAULT});
+    builder.SetOutputsDeviceType({kFloat32->type_id()});
+    AnfAlgo::SetSelectKernelBuildInfo(builder.Build(), cnode.get());
+  }
+};
+
+TEST_F(TestHWRemoveInternalOutput, test_remove_internal_output_trans_op_for_single_output) {
+  auto ms_context = MsContext::GetInstance();
+  MS_EXCEPTION_IF_NULL(ms_context);
+  ms_context->set_execution_mode(kGraphMode);
+  auto kg = GetSingleOutputGraph("test_remove_internal_output_trans_op_for_single_output", "before");
+  // insert trans op for output
+  auto graph_optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pass_manager = std::make_shared<opt::PassManager>();
+  auto insert_trans_op_pass = std::make_shared<opt::InsertTransOp>();
+  insert_trans_op_pass->kernel_select_ = std::make_shared<MockRemoveInternalOutputTransOpKernelSelect>();
+  pass_manager->AddPass(insert_trans_op_pass);
+  graph_optimizer->AddPassManager(pass_manager);
+  auto new_g = graph_optimizer->Optimize(kg);
+  FuncGraphPtr g_after =
+    getPyFun_.CallAndParseRet("test_remove_internal_output_trans_op_for_single_output", "after_insert_trans_op");
+  EXPECT_TRUE(CheckEqualGraph(g_after, new_g));
+
+  auto make_tuple = GetMakeTuple(kg);
+  auto trans_data = make_tuple->cast<CNodePtr>()->input(1);
+  EXPECT_TRUE(kg->IsInternalOutput(trans_data));
+
+  // remove trans op for internal output
+  auto graph_optimizer1 = std::make_shared<opt::GraphOptimizer>();
+  auto pass_manager1 = std::make_shared<opt::PassManager>();
+  auto remove_internal_output_trans_op_pass = std::make_shared<opt::RemoveInternalOutputTransOp>();
+  pass_manager1->AddPass(remove_internal_output_trans_op_pass);
+  graph_optimizer1->AddPassManager(pass_manager1);
+  auto new_g1 = graph_optimizer1->Optimize(new_g);
+  FuncGraphPtr g_after1 = getPyFun_.CallAndParseRet("test_remove_internal_output_trans_op_for_single_output",
+                                                    "after_remove_internal_output_trans_op");
+  EXPECT_TRUE(CheckEqualGraph(g_after1, new_g1));
+}
+
+TEST_F(TestHWRemoveInternalOutput, test_remove_internal_output_trans_op_for_multiple_output) {
+  auto kg = GetMutilpleOutputGraph("test_remove_internal_output_trans_op_for_multiple_output", "before");
+  // insert trans op for output
+  auto graph_optimizer = std::make_shared<opt::GraphOptimizer>();
+  auto pass_manager = std::make_shared<opt::PassManager>();
+  auto insert_trans_op_pass = std::make_shared<opt::InsertTransOp>();
+  insert_trans_op_pass->kernel_select_ = std::make_shared<MockRemoveInternalOutputTransOpKernelSelect>();
+  pass_manager->AddPass(insert_trans_op_pass);
+  graph_optimizer->AddPassManager(pass_manager);
+  auto new_g = graph_optimizer->Optimize(kg);
+  FuncGraphPtr g_after =
+    getPyFun_.CallAndParseRet("test_remove_internal_output_trans_op_for_multiple_output", "after_insert_trans_op");
+  EXPECT_TRUE(CheckEqualGraph(g_after, new_g));
+
+  auto output_make_tuple = GetMakeTuple(kg);
+  auto make_tuple = output_make_tuple->cast<CNodePtr>()->input(1);
+  auto tuple_getitem = make_tuple->cast<CNodePtr>()->input(1);
+  auto make_tuple1 = tuple_getitem->cast<CNodePtr>()->input(1);
+  auto trans_data1 = make_tuple1->cast<CNodePtr>()->input(1);
+  auto trans_data2 = make_tuple1->cast<CNodePtr>()->input(2);
+  EXPECT_TRUE(kg->IsInternalOutput(trans_data1));
+  EXPECT_TRUE(kg->IsInternalOutput(trans_data2));
+
+  // remove trans op for internal output
+  auto graph_optimizer1 = std::make_shared<opt::GraphOptimizer>();
+  auto pass_manager1 = std::make_shared<opt::PassManager>();
+  auto remove_internal_output_trans_op_pass = std::make_shared<opt::RemoveInternalOutputTransOp>();
+  pass_manager1->AddPass(remove_internal_output_trans_op_pass);
+  graph_optimizer1->AddPassManager(pass_manager1);
+  auto new_g1 = graph_optimizer1->Optimize(new_g);
+  FuncGraphPtr g_after1 = getPyFun_.CallAndParseRet("test_remove_internal_output_trans_op_for_multiple_output",
+                                                    "after_remove_internal_output_trans_op");
+  EXPECT_TRUE(CheckEqualGraph(g_after1, new_g1));
+}
+}  // namespace opt
+}  // namespace mindspore

tests/ut/cpp/python_input/gtest_input/pre_activate/remove_internal_output_test.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.
+# ============================================================================
+from mindspore.ops import Primitive
+from mindspore.ops import operations as P
+
+tuple_getitem = Primitive('tuple_getitem')
+add = P.TensorAdd()
+max_pool = P.MaxPoolWithArgmax(padding="same", ksize=3, strides=2)
+make_tuple = Primitive('make_tuple')
+trans_data = Primitive("TransData")
+
+
+class FnDict:
+    def __init__(self):
+        self.fnDict = {}
+
+    def __call__(self, fn):
+        self.fnDict[fn.__name__] = fn
+
+    def __getitem__(self, name):
+        return self.fnDict[name]
+
+
+def test_remove_internal_output_trans_op_for_single_output(tag):
+    fns = FnDict()
+
+    @fns
+    def before(x, y):
+        res = add(x, y)
+        return res
+
+    @fns
+    def after_insert_trans_op(x, y):
+        output = add(x, y)
+        res = trans_data(output)
+        return make_tuple(res)
+
+    @fns
+    def after_remove_internal_output_trans_op(x, y):
+        res = add(x, y)
+        return make_tuple(res)
+
+    return fns[tag]
+
+
+def test_remove_internal_output_trans_op_for_multiple_output(tag):
+    fns = FnDict()
+
+    @fns
+    def before(x):
+        max_pool_res = max_pool(x)
+        res = make_tuple(tuple_getitem(max_pool_res, 0), tuple_getitem(max_pool_res, 1))
+        return res
+
+    @fns
+    def after_insert_trans_op(x):
+        output = max_pool(x)
+        trans_data0 = trans_data(tuple_getitem(output, 0))
+        trans_data1 = trans_data(tuple_getitem(output, 1))
+        new_make_tuple = make_tuple(trans_data0, trans_data1)
+        res = make_tuple(tuple_getitem(new_make_tuple, 0), tuple_getitem(new_make_tuple, 1))
+        return make_tuple(res)
+
+    @fns
+    def after_remove_internal_output_trans_op(x):
+        output = max_pool(x)
+        new_make_tuple = make_tuple(tuple_getitem(output, 0), tuple_getitem(output, 1))
+        res = make_tuple(tuple_getitem(new_make_tuple, 0), tuple_getitem(new_make_tuple, 1))
+        return make_tuple(res)
+
+    return fns[tag]