Cherry-pick PR 30103. Add Inplace strategy (Output reuse Input Varbase) in...

Cherry-pick PR 30103. Add Inplace strategy (Output reuse Input Varbase) in dygraph (#30103) (#30496)

* add view strategy on squeeze,unsqueeze,reshape,flatten

* add squeeze unittest

* add unittests

* use View strategy as name rather than Reuse Allacation

* fix view api doc

* fix format

* use core.ops when input of reshape2 is Tensor

* fix test_cross_entropy_loss error because of reshape2

* fix test_cross_entropy_loss error because of reshape2

* add inplace strategy

* add elementwise_add sub

* let backward op not use inplace

* grad op do not use inplace

* fix memory increase error and add leaf error message

* delete selected_rows

* change op_function

* little change

* solve HandleViewBetweenInputAndOutput

* add unittest and leaf error message

* merge view error

* optimize op_function_generator format and support sum inplace op

* fix format of basic_engine

* fix format for framework

* little change of variable wrapper

* add reshape, squeeze, unsqueeze, scatter api

* add relu elu tanh softmax inplace api

* fix test_squeeze_op unittest

* fix test_relu_op unittest

* fix comment problems

* delete sample code of inplace api

* add reference of grad_pending_nodes in basic_engine

* fix unittest name

* add inplace apis into wlist

* fix error message

* add PADDLE_ENFORCE for set grad op twice

* fix head file error

paddle/fluid/framework/details/op_registry.h

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 
+#include <map>
 #include <memory>
 #include <string>
 #include <tuple>
@@ -247,8 +248,9 @@ struct OpInfoFiller<T, kGradOpBaseMaker> {
         const std::string& type,
         const imperative::NameVarBaseMap& var_base_map_in,
         const imperative::NameVarBaseMap& var_base_map_out,
-        const framework::AttributeMap& attrs) {
-      T maker(type, var_base_map_in, var_base_map_out, attrs);
+        const framework::AttributeMap& attrs,
+        const std::map<std::string, std::string>& inplace_map) {
+      T maker(type, var_base_map_in, var_base_map_out, attrs, inplace_map);
       return maker();
     };
   }

paddle/fluid/framework/grad_op_desc_maker.h

@@ -221,6 +221,10 @@ class SingleGradOpMaker<imperative::OpBase>
 
   std::shared_ptr<imperative::GradOpNode> operator()() const final {
     auto node = this->NewGradNode();
+    auto& inplace_map = this->GetInplaceMap();
+    if (!inplace_map.empty()) {
+      node->SetInplaceGradNameMap(inplace_map);
+    }
     {
       imperative::TracedGradOp traced_grad_op(node);
       try {

paddle/fluid/framework/type_defs.h

@@ -59,7 +59,8 @@ using DygraphGradOpMakerFN =
         const std::string& /*op_type*/,
         const imperative::NameVarBaseMap& /*var_base_map_in*/,
         const imperative::NameVarBaseMap& /*var_base_map_out*/,
-        const framework::AttributeMap& /*attributes*/)>;
+        const framework::AttributeMap& /*attributes*/,
+        const std::map<std::string, std::string>& /*inplace_map*/)>;
 
 using InferVarTypeFN =
     std::function<void(framework::InferVarTypeContext* /*context*/)>;

paddle/fluid/imperative/basic_engine.cc

@@ -108,7 +108,9 @@ void BasicEngine::CheckBackwardInputs(const OpBase& op) {
   }
 }
 
-void BasicEngine::PrepareGradAccumulators(const OpBase& op) {
+void BasicEngine::PrepareGradAccumulators(
+    const OpBase& op,
+    const std::vector<std::shared_ptr<GradOpNode>>& grad_pending_nodes) {
   for (const auto& pair : op.GetOutsMap()) {
     if (!pair.second.IsGrad()) {
       continue;
@@ -117,29 +119,94 @@ void BasicEngine::PrepareGradAccumulators(const OpBase& op) {
     for (const auto& var : pair.second) {
       if (!var) continue;
 
-      auto& accumulator = accumulators_[var.get()];
-      if (!accumulator) {
-        if (FLAGS_sort_sum_gradient) {
-          accumulator.reset(new SortedGradientAccumulator(var.get()));
-        } else {
-          accumulator.reset(new EagerGradientAccumulator(var.get()));
+      if (!var->HasGradNode()) {
+        auto& accumulator = accumulators_[var.get()];
+        if (!accumulator) {
+          if (FLAGS_sort_sum_gradient) {
+            accumulator.reset(new SortedGradientAccumulator(var.get()));
+          } else {
+            accumulator.reset(new EagerGradientAccumulator(var.get()));
+          }
         }
-      }
 
-      accumulator->IncreaseRefCnt();
+        accumulator->IncreaseRefCnt();
 
-      VLOG(3) << "Prepare to acccumulate variable grad " << var->Name() << "("
-              << var.get() << ")  with reference count "
-              << accumulator->RefCnt();
+        VLOG(3) << "Prepare to acccumulate variable grad " << var->Name() << "("
+                << var.get()
+                << ") that don't have grad node  with reference count "
+                << accumulator->RefCnt();
+
+        if (var->HasLeafHooks()) {
+          VLOG(3) << "Grad variable wrapper (" << var->Name()
+                  << ") has leaf grad hooks.";
+          PADDLE_ENFORCE_NE(
+              var->HasGradNode(), true,
+              platform::errors::PermissionDenied(
+                  "Only leaf Tensor's gradient can append hook to "
+                  "Gradientaccumulator."));
+          accumulator->SetPostHooks(var->GetLeafHooks());
+        }
+      } else {
+        // Because Inplace op overwrites the grad_node of the input grad_var. So
+        // only the information of grad_pending_node can be used to find the
+        // grad_node of grad_var.
+        bool find_grad_node_of_var = false;
+        for (auto& grad_pending_node : grad_pending_nodes) {
+          PADDLE_ENFORCE_NOT_NULL(
+              grad_pending_node,
+              platform::errors::NotFound("Grad pending node is nullptr."));
+          for (auto& grad_pending_op : *grad_pending_node) {
+            VLOG(6) << "Determine whether var (" << var->Name()
+                    << ") is the input var of grad_pending_op ("
+                    << grad_pending_op.Type() << ").";
+            grad_pending_op.EnforceHasInOut();
+            for (const auto& grad_pending_op_ins_pair :
+                 grad_pending_op.GetInsMap()) {
+              if (!grad_pending_op_ins_pair.second.IsGrad()) {
+                continue;
+              }
+              for (const auto& pending_in_var :
+                   grad_pending_op_ins_pair.second) {
+                if (var == pending_in_var) {
+                  VLOG(6) << "Var (" << var->Name()
+                          << ") is the input var of grad_pending_op ("
+                          << grad_pending_op.Type() << ").";
+                  find_grad_node_of_var = true;
+                  break;
+                }
+              }
+              if (find_grad_node_of_var) {
+                break;
+              }
+            }
+          }
 
-      if (var->HasLeafHooks()) {
-        VLOG(3) << "Grad variable wrapper (" << var->Name()
-                << ") has leaf grad hooks.";
-        PADDLE_ENFORCE_NE(var->HasGradNode(), true,
-                          platform::errors::PermissionDenied(
-                              "Only leaf Tensor's gradient can append hook to "
-                              "Gradientaccumulator."));
-        accumulator->SetPostHooks(var->GetLeafHooks());
+          if (find_grad_node_of_var) {
+            auto& accumulator =
+                accumulators_with_grad_node_[grad_pending_node][var.get()];
+
+            if (!accumulator) {
+              if (FLAGS_sort_sum_gradient) {
+                accumulator.reset(new SortedGradientAccumulator(var.get()));
+              } else {
+                accumulator.reset(new EagerGradientAccumulator(var.get()));
+              }
+            }
+
+            accumulator->IncreaseRefCnt();
+
+            VLOG(3) << "Prepare to acccumulate variable grad " << var->Name()
+                    << "(" << var.get()
+                    << ") that has grad node with reference count "
+                    << accumulator->RefCnt();
+            break;
+          }
+        }
+        PADDLE_ENFORCE_EQ(
+            find_grad_node_of_var, true,
+            platform::errors::NotFound(
+                "No grad node corresponding to grad Tensor (%s) was found.",
+                var->Name()));
       }
     }
   }
@@ -148,10 +215,13 @@ void BasicEngine::PrepareGradAccumulators(const OpBase& op) {
 void BasicEngine::PrepareDeps() {
   PADDLE_ENFORCE_EQ(
       node_deps_.empty(), true,
-      platform::errors::AlreadyExists("Op deps must be initialized here"));
+      platform::errors::AlreadyExists("Op deps must be initialized."));
   PADDLE_ENFORCE_EQ(
       accumulators_.empty(), true,
-      platform::errors::AlreadyExists("Accumulators must be initialized here"));
+      platform::errors::AlreadyExists("Accumulators must be initialized."));
+  PADDLE_ENFORCE_EQ(
+      accumulators_with_grad_node_.empty(), true,
+      platform::errors::AlreadyExists("Accumulators must be initialized."));
 
   std::queue<GradOpNode*> q;
   std::unordered_set<GradOpNode*> visited;
@@ -163,16 +233,17 @@ void BasicEngine::PrepareDeps() {
     auto* cur_node = q.front();
     q.pop();
 
+    const auto& grad_pending_nodes = cur_node->GradPendingNodes();
+
     for (auto& cur_op : *cur_node) {
       cur_op.EnforceHasInOut();
-      PrepareGradAccumulators(cur_op);
+      PrepareGradAccumulators(cur_op, grad_pending_nodes);
     }
 
-    const auto& grad_pending_nodes = cur_node->GradPendingNodes();
     for (auto& grad_pending_node : grad_pending_nodes) {
       PADDLE_ENFORCE_NOT_NULL(
           grad_pending_node,
-          platform::errors::NotFound("Grad pending node should not be null"));
+          platform::errors::NotFound("Grad pending node is nullptr."));
       ++node_deps_[grad_pending_node.get()];
       if (visited.count(grad_pending_node.get()) == 0) {
         visited.insert(grad_pending_node.get());
@@ -198,6 +269,8 @@ void BasicEngine::Execute() {
     auto shared_cur_node = std::move(q.front());
     q.pop();
 
+    auto& inplace_grad_name_map = shared_cur_node->InplaceGradNameMap();
+
     for (auto& cur_op : *shared_cur_node) {
       ++op_num;
 
@@ -222,11 +295,38 @@ void BasicEngine::Execute() {
             continue;
           }
 
-          auto iter = accumulators_.find(var.get());
-          PADDLE_ENFORCE_EQ(
-              iter != accumulators_.end(), true,
-              platform::errors::NotFound("Cannot find gradient of variable %s",
-                                         var->Name()));
+          std::unordered_map<VariableWrapper*,
+                             std::unique_ptr<GradientAccumulator>>::iterator
+              iter;
+          if (!var->HasGradNode()) {
+            VLOG(10) << "Find gradient of var (" << var->Name()
+                     << ") with no grad_node.";
+            iter = accumulators_.find(var.get());
+            PADDLE_ENFORCE_EQ(
+                iter != accumulators_.end(), true,
+                platform::errors::NotFound(
+                    "Cannot find gradient of variable %s", var->Name()));
+          } else {
+            bool flag_find_grad = false;
+            VLOG(10) << "Find gradient of var (" << var->Name()
+                     << ") with grad_node.";
+            for (auto& grad_pending_node :
+                 shared_cur_node->GradPendingNodes()) {
+              const auto& iter_grad_node =
+                  accumulators_with_grad_node_.find(grad_pending_node);
+              if (iter_grad_node != accumulators_with_grad_node_.end()) {
+                iter = iter_grad_node->second.find(var.get());
+                if (iter != iter_grad_node->second.end()) {
+                  flag_find_grad = true;
+                  break;
+                }
+              }
+            }
+            PADDLE_ENFORCE_EQ(
+                flag_find_grad, true,
+                platform::errors::NotFound(
+                    "Cannot find gradient of variable %s", var->Name()));
+          }
 
           // leaf_accumulators_ : hooks and accumulate-grad for leaf tensor
           if (var->IsLeafGrad()) {
@@ -245,6 +345,25 @@ void BasicEngine::Execute() {
             need_accu_var_list_.emplace_back(iter->second.get(), var);
             VLOG(10) << "create temporary var of " << var->Name()
                      << " for sum gradient within this graph!";
+          } else if (!inplace_grad_name_map.empty() &&
+                     inplace_grad_name_map.count(pair.first)) {
+            // When calculate Inplace grad op, create a new output var.
+            // If a tmp var has been created, there is no need to create it
+            // again.
+            for (auto& in_var :
+                 bwd_ins.at(inplace_grad_name_map.at(pair.first))) {
+              if (in_var == var) {
+                auto tmp_var = std::make_shared<VariableWrapper>(var->Name());
+                tmp_var->SetType(var->Type());
+                tmp_var->SetForwardDataType(var->ForwardDataType());
+                inplace_output_grad_var_list_.emplace_back(var, tmp_var);
+                var = tmp_var;
+                VLOG(10) << "Inplace grad op does not use the Inplace "
+                            "strategy, a temporary output var ("
+                         << var->Name() << ") will be created.";
+                break;
+              }
+            }
           }
         }
       }
@@ -280,6 +399,10 @@ void BasicEngine::Execute() {
                     cur_op.place());
       }
 
+      for (auto& pair : inplace_output_grad_var_list_) {
+        *pair.first = std::move(*pair.second);
+      }
+
       // Step 2: Sum Gradient of This graph
       for (auto& pair : need_accu_var_list_) {
         pair.first->SumGrad(std::move(pair.second), cur_op.id());
@@ -302,6 +425,7 @@ void BasicEngine::Execute() {
       }
 
       need_accu_var_list_.clear();
+      inplace_output_grad_var_list_.clear();
       leaf_accumulators_.clear();
 
       if (!retain_graph_) {
@@ -312,9 +436,9 @@ void BasicEngine::Execute() {
 
     // Step 3: Collect ready ops
     for (auto& grad_pending_node : shared_cur_node->GradPendingNodes()) {
-      PADDLE_ENFORCE_NOT_NULL(grad_pending_node,
-                              platform::errors::NotFound(
-                                  "Grad pending node should not be nullptr"));
+      PADDLE_ENFORCE_NOT_NULL(
+          grad_pending_node,
+          platform::errors::NotFound("Grad pending node is nullptr."));
       auto iter = node_deps_.find(grad_pending_node.get());
       if (iter == node_deps_.end()) {
         continue;
@@ -334,6 +458,7 @@ void BasicEngine::Clear() {
   init_node_.reset();
   node_deps_.clear();
   accumulators_.clear();
+  accumulators_with_grad_node_.clear();
   need_accu_var_list_.clear();
   leaf_accumulators_.clear();
 }

paddle/fluid/imperative/basic_engine.h

@@ -39,15 +39,33 @@ class BasicEngine : public Engine {
 
   void CheckBackwardInputs(const OpBase& op);
 
-  void PrepareGradAccumulators(const OpBase& op);
+  void PrepareGradAccumulators(
+      const OpBase& op,
+      const std::vector<std::shared_ptr<GradOpNode>>& grad_pending_nodes);
 
   void Clear();
 
  private:
   std::shared_ptr<GradOpNode> init_node_;
   std::unordered_map<GradOpNode*, size_t> node_deps_;
+  // The input and output of Inplace op are the same. If only `var` is used
+  // as the key, then the input and output of inplace op must be gradient
+  // accumulated. Therefore, add the `grad_node` as the key to prevent the
+  // problem of gradient accumulation in inplace op.
+  std::unordered_map<std::shared_ptr<GradOpNode>,
+                     std::unordered_map<VariableWrapper*,
+                                        std::unique_ptr<GradientAccumulator>>>
+      accumulators_with_grad_node_;
+  // Leaf var doesn't have grad_node, and leaf var with `stop_gradient=False`
+  // can't use Inplace strategy. If a var doesn't have grad_node, only use
+  // `var` as the key.
   std::unordered_map<VariableWrapper*, std::unique_ptr<GradientAccumulator>>
       accumulators_;
+  // The output grad var of Inplace grad op. Because Inplace grad op does not
+  // use the Inplace strategy, a new output grad var needs to be created.
+  std::vector<std::pair<std::shared_ptr<VariableWrapper>,
+                        std::shared_ptr<VariableWrapper>>>
+      inplace_output_grad_var_list_;
   std::vector<std::pair<GradientAccumulator*, std::shared_ptr<VariableWrapper>>>
       need_accu_var_list_;
   // leaf_accumulators_ is only for leaf tensor(hooks/accumulate grad)

paddle/fluid/imperative/dygraph_grad_maker.h

@@ -14,6 +14,7 @@
 
 #pragma once
 
+#include <map>
 #include <memory>
 #include <string>
 #include <unordered_map>
@@ -43,14 +44,16 @@ class TracedVarList : public std::vector<std::shared_ptr<T>> {
 
 class GradOpBaseMakerBase {
  public:
-  explicit GradOpBaseMakerBase(const std::string& type,
-                               const NameVarBaseMap& var_base_map_in,
-                               const NameVarBaseMap& var_base_map_out,
-                               const framework::AttributeMap& attrs)
+  explicit GradOpBaseMakerBase(
+      const std::string& type, const NameVarBaseMap& var_base_map_in,
+      const NameVarBaseMap& var_base_map_out,
+      const framework::AttributeMap& attrs,
+      const std::map<std::string, std::string>& inplace_map)
       : type_(type),
         var_base_map_in_(var_base_map_in),
         var_base_map_out_(var_base_map_out),
-        attrs_(attrs) {}
+        attrs_(attrs),
+        inplace_map_(inplace_map) {}
 
   virtual ~GradOpBaseMakerBase() = default;
 
@@ -141,6 +144,10 @@ class GradOpBaseMakerBase {
     return std::make_shared<GradOpNode>();
   }
 
+  const std::map<std::string, std::string>& GetInplaceMap() const {
+    return inplace_map_;
+  }
+
  private:
   template <TracedVarRole kRole>
   TracedVarList<VarBase, kRole> GetVarBaseList(const std::string& name,
@@ -192,6 +199,7 @@ class GradOpBaseMakerBase {
   const NameVarBaseMap& var_base_map_in_;
   const NameVarBaseMap& var_base_map_out_;
   const framework::AttributeMap& attrs_;
+  const std::map<std::string, std::string>& inplace_map_;
 };
 
 class TracedGradOp {
@@ -220,6 +228,10 @@ class TracedGradOp {
       for (auto& var : vars) {
         if (var && !var->OverridedStopGradient()) {
           var->SetGraphIsFreed(false);
+          auto dirty_grad_node = var->GradNode();
+          if (dirty_grad_node) {
+            map_dirty_grad_node_[var] = dirty_grad_node;
+          }
           var->SetGradNode(node_);
         }
       }
@@ -246,7 +258,11 @@ class TracedGradOp {
       } else {
         for (auto& var : vars) {
           if (var && !var->OverridedStopGradient() && var->GradNode()) {
-            node_->InsertGradPendingNode(var->GradNode());
+            if (map_dirty_grad_node_.find(var) != map_dirty_grad_node_.end()) {
+              node_->InsertGradPendingNode(map_dirty_grad_node_[var]);
+            } else {
+              node_->InsertGradPendingNode(var->GradNode());
+            }
           }
         }
       }
@@ -329,6 +345,12 @@ class TracedGradOp {
  private:
   const std::shared_ptr<GradOpNode>& node_;
   OpBase* op_;
+  // Inplace op has recursion problems when performing grad calculation.
+  // Because the input and output of inplace op are the same, the grad
+  // node of inplace var will be overwritten.
+  // This map is used to store the grad node of inplace var in temporary.
+  std::unordered_map<std::shared_ptr<VarBase>, std::shared_ptr<GradOpNode>>
+      map_dirty_grad_node_;
 };
 
 }  // namespace imperative

paddle/fluid/imperative/layer.cc

@@ -441,13 +441,15 @@ static void ClearNoNeedBufferInputs(OpBase* op) {
 std::shared_ptr<GradOpNode> CreateGradOpNode(
     const framework::OperatorBase& op, const NameVarBaseMap& ins,
     const NameVarBaseMap& outs, const framework::AttributeMap& attrs,
-    const platform::Place& place) {
+    const platform::Place& place,
+    const std::map<std::string, std::string>& inplace_map) {
   const auto& info = op.Info();
   if (!info.dygraph_grad_op_maker_) {
     return nullptr;
   }
 
-  auto grad_node = info.dygraph_grad_op_maker_(op.Type(), ins, outs, attrs);
+  auto grad_node =
+      info.dygraph_grad_op_maker_(op.Type(), ins, outs, attrs, inplace_map);
   if (grad_node && !grad_node->empty()) {
     for (auto& grad_op : *grad_node) {
       grad_op.SetId(OpBase::GenerateUniqueId());

paddle/fluid/imperative/layer.h

@@ -256,7 +256,8 @@ class Layer {
 std::shared_ptr<GradOpNode> CreateGradOpNode(
     const framework::OperatorBase& op, const NameVarBaseMap& ins,
     const NameVarBaseMap& outs, const framework::AttributeMap& attrs,
-    const platform::Place& place);
+    const platform::Place& place,
+    const std::map<std::string, std::string>& inplace_map);
 
 }  // namespace imperative
 }  // namespace paddle

paddle/fluid/imperative/op_base.h

@@ -15,11 +15,13 @@
 #pragma once
 
 #include <atomic>
+#include <map>
 #include <memory>
 #include <string>
 #include <utility>
 #include <vector>
 #include "paddle/fluid/framework/type_defs.h"
+#include "paddle/fluid/imperative/saved_variable_wrapper_list.h"
 #include "paddle/fluid/imperative/type_defs.h"
 #include "paddle/fluid/imperative/variable_wrapper.h"
 #include "paddle/fluid/platform/place.h"
@@ -227,6 +229,22 @@ class GradOpNode {
     }
   }
 
+  void SetInplaceGradNameMap(
+      const std::map<std::string, std::string>& inplace_input_map) {
+    for (auto& pair : inplace_input_map) {
+      VLOG(10) << "Set mapping relationship ("
+               << framework::GradVarName(pair.first) << ", "
+               << framework::GradVarName(pair.second)
+               << ") for Inplace grad node.";
+      inplace_grad_name_map_[framework::GradVarName(pair.first)] =
+          framework::GradVarName(pair.second);
+    }
+  }
+
+  const std::map<std::string, std::string>& InplaceGradNameMap() const {
+    return inplace_grad_name_map_;
+  }
+
   const std::vector<std::shared_ptr<GradOpNode>>& GradPendingNodes() const {
     return grad_pending_nodes_;
   }
@@ -237,6 +255,9 @@ class GradOpNode {
  private:
   std::vector<OpBase> ops_;
   std::vector<std::shared_ptr<GradOpNode>> grad_pending_nodes_;
+  // Mapping relationship between grad output and grad input of the grad node of
+  // Inplace op.
+  std::map<std::string, std::string> inplace_grad_name_map_;
 };
 
 }  // namespace imperative

paddle/fluid/imperative/partial_grad_engine.cc

@@ -884,7 +884,7 @@ void PartialGradTask::RunEachOp(OpBase *op) {
 
   if (create_graph_) {
     auto double_grad_node = CreateGradOpNode(op->InnerOp(), tmp_ins, tmp_outs,
-                                             op->Attrs(), op->place());
+                                             op->Attrs(), op->place(), {});
     PADDLE_ENFORCE_NOT_NULL(
         double_grad_node,
         platform::errors::NotFound("The Op %s doesn't have any grad op. If you "

paddle/fluid/imperative/tracer.cc

@@ -12,6 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 #include "paddle/fluid/imperative/tracer.h"
+#include <map>
 #include <set>
 #include <unordered_set>
 #include <utility>
@@ -130,7 +131,8 @@ paddle::framework::GarbageCollector* Tracer::MutableGarbageCollectorIfNotExists(
 
 void Tracer::TraceOp(const std::string& type, const NameVarBaseMap& ins,
                      const NameVarBaseMap& outs, framework::AttributeMap attrs,
-                     const platform::Place& place, bool trace_backward) {
+                     const platform::Place& place, bool trace_backward,
+                     const std::map<std::string, std::string>& inplace_map) {
   VLOG(1) << "Trace Op: " << type;
   if (FLAGS_use_mkldnn) {
     // if both lists are empty all ops are enabled (default for
@@ -182,16 +184,17 @@ void Tracer::TraceOp(const std::string& type, const NameVarBaseMap& ins,
   }
 
   if (ComputeRequiredGrad(new_ins, outs, trace_backward)) {
-    CreateGradOpNode(*op, new_ins, outs, attrs, place);
+    CreateGradOpNode(*op, new_ins, outs, attrs, place, inplace_map);
   } else {
     VLOG(3) << "No Grad to track for Op: " << type;
   }
 }
 
 void Tracer::TraceOp(const std::string& type, const NameVarBaseMap& ins,
-                     const NameVarBaseMap& outs,
-                     framework::AttributeMap attrs) {
-  TraceOp(type, ins, outs, std::move(attrs), expected_place_, has_grad_);
+                     const NameVarBaseMap& outs, framework::AttributeMap attrs,
+                     const std::map<std::string, std::string>& inplace_map) {
+  TraceOp(type, ins, outs, std::move(attrs), expected_place_, has_grad_,
+          inplace_map);
 }
 
 bool Tracer::ComputeRequiredGrad(const NameVarBaseMap& ins,

paddle/fluid/imperative/tracer.h

@@ -21,7 +21,6 @@
 #include <string>
 #include <unordered_map>
 #include <vector>
-
 #include "ThreadPool.h"
 #include "paddle/fluid/framework/garbage_collector.h"
 #include "paddle/fluid/imperative/basic_engine.h"
@@ -63,10 +62,12 @@ class Tracer {
 
   void TraceOp(const std::string& type, const NameVarBaseMap& ins,
                const NameVarBaseMap& outs, framework::AttributeMap attrs,
-               const platform::Place& place, bool trace_bacward);
+               const platform::Place& place, bool trace_bacward,
+               const std::map<std::string, std::string>& inplace_map = {});
 
   void TraceOp(const std::string& type, const NameVarBaseMap& ins,
-               const NameVarBaseMap& outs, framework::AttributeMap attrs);
+               const NameVarBaseMap& outs, framework::AttributeMap attrs,
+               const std::map<std::string, std::string>& inplace_map = {});
 
   bool ComputeRequiredGrad(const NameVarBaseMap& ins,
                            const NameVarBaseMap& outs, bool trace_backward);

paddle/fluid/imperative/variable_wrapper.h

@@ -20,6 +20,7 @@
 
 #include "paddle/fluid/framework/variable.h"
 #include "paddle/fluid/imperative/hooks.h"
+#include "paddle/fluid/imperative/op_base.h"
 
 namespace paddle {
 namespace imperative {
@@ -257,8 +258,13 @@ class VariableWrapper {
     auto shared_node = grad_node_.lock();
     if (shared_node != grad_node) {
       PADDLE_ENFORCE_EQ(
-          shared_node, nullptr,
-          platform::errors::PermissionDenied("Cannot set gradient op twice"));
+          !shared_node || !grad_node->InplaceGradNameMap().empty(), true,
+          platform::errors::PermissionDenied(
+              "Cannot set gradient op twice unless using Inplace Strategy."));
+      if (shared_node) {
+        VLOG(3) << "The gradient op of Var (" << Name()
+                << ") has been set twice. Because Inplace Strategy is used.";
+      }
       grad_node_ = grad_node;
     }
   }

paddle/fluid/pybind/op_function_generator.cc

@@ -139,9 +139,9 @@ std::map<std::string, std::set<std::string>> op_passing_outs_map = {
 // NOTE(pangyoki): Tensor View Strategy.
 // In this case, a new output varbase will be created, and this varbase will
 // reuse the input varbase's allocation.
-// It's a 2-layer map. The key of outer map is the view op name, the value is
-// also a map which implies the mapping relationship between the output and
-// input varbase.
+// It's a map. The key of outer map is the view op name, the value is
+// a pair which implies the mapping relationship between the input and
+// output varbase.
 std::map<std::string, std::pair<std::string, std::string>> view_op_map = {
     {"squeeze2", {"X", "Out"}},  // "X" -> "Out"
     {"unsqueeze2", {"X", "Out"}},
@@ -149,6 +149,14 @@ std::map<std::string, std::pair<std::string, std::string>> view_op_map = {
     {"flatten_contiguous_range", {"X", "Out"}},
 };
 
+// NOTE(pangyoki): Inplace OP with duplicable input.
+// The set includes inplace ops that have duplicable input.
+// The first Varbase in input needs to be specified for the inplace strategy
+// and share Varbase with the output.
+std::set<std::string> inplace_op_duplicable_ins_set = {
+    "sum",
+};
+
 // clang-format off
 const char* OUT_INITIALIZER_TEMPLATE =
     R"({"%s", {std::shared_ptr<imperative::VarBase>(new imperative::VarBase(tracer->GenerateUniqueName()))}})";
@@ -204,11 +212,26 @@ const char* RETURN_TEMPLATE = R"(outs["%s"][0])";
 const char* FUNCTION_ARGS = R"(%s, const py::args& args)";
 const char* FUNCTION_ARGS_NO_INPUT = R"(const py::args& args)";
 
-const char* HandleViewBetweenInputAndOutput = R"(
+const char* HANDLE_VIEW_BETWEEN_INPUT_AND_OUTPUT = R"(
     if (ins.count("%s") && outs.count("%s")) {
       HandleViewBetweenInputAndOutput(ins["%s"][0], outs["%s"][0]);
     })";
 
+const char* INPLACE_DUPLICABLE_INPUT = R"([0])";
+
+const char* INPLACE_LEAF_ERROR_MESSAGE = R"(Leaf Var (%s) that doesn't stop gradient can't use inplace strategy.)";
+
+const char* INPLACE_STRATEGY_TEMPLATE =
+R"(
+    PADDLE_ENFORCE_EQ(
+      %s->IsLeaf() && !%s->OverridedStopGradient(), false,
+      platform::errors::InvalidArgument("%s", %s->Name()));
+    %s->BumpInplaceVersion();
+    VLOG(3) << "Var(" << %s->Name() << ") uses Inplace Strategy.";
+)";
+
+const char* INPLACE_MAPPING_TEMPLATE = R"({"%s", "%s"})";
+
 const char* OP_FUNCTION_TEMPLATE =
 R"(
 %s %s(%s)
@@ -219,10 +242,11 @@ R"(
   {
     py::gil_scoped_release release;
     auto tracer = imperative::GetCurrentTracer();
+    %s
     imperative::NameVarBaseMap outs = %s;
     imperative::NameVarBaseMap ins = %s;
     %s
-    tracer->TraceOp("%s", ins, outs, attrs);
+    tracer->TraceOp("%s", ins, outs, attrs, {%s});
     return %s; 
   }   
 })";
@@ -245,6 +269,10 @@ static inline bool FindPassingOutsMap(const std::string& op_type,
   return op_passing_outs_map[op_type].count(out_name);
 }
 
+static inline bool FindDuplicableInputInplaceOpSet(const std::string& op_type) {
+  return inplace_op_duplicable_ins_set.count(op_type);
+}
+
 static inline bool FindViewOpMap(const std::string& op_type) {
   return view_op_map.count(op_type);
 }
@@ -253,6 +281,202 @@ static inline std::string TempName(const std::string& name) {
   return name + '_';
 }
 
+std::string GenerateOpFunctionsBody(
+    const paddle::framework::proto::OpProto* op_proto, std::string func_name,
+    bool use_inplace_strategy = false,
+    std::map<std::string, std::string> inplace_map = {}) {
+  auto& op_type = op_proto->type();
+  std::string input_args = "";
+  std::string ins_initializer = "{";
+  std::string ins_initializer_with_null = "";
+  std::string py_arg = "";
+  int arg_idx = 0;
+  int input_args_num = 0;
+  std::string ins_cast_str = "";
+  std::string view_strategy_str = "";
+  std::string inplace_strategy_str = "";
+  for (auto& input : op_proto->inputs()) {
+    auto& in_name = input.name();
+    // skip those dispensable inputs, like ResidualData in conv2d
+    if (input.dispensable() && !FindInsMap(op_type, in_name)) {
+      continue;
+    }
+    const auto in_type = input.duplicable() ? IN_VAR_LIST_TYPE : IN_VAR_TYPE;
+    auto input_arg =
+        paddle::string::Sprintf(ARG_TEMPLATE, in_type, TempName(in_name));
+    input_args += input_arg;
+    input_args += ",";
+    input_args_num++;
+    const auto in_cast_type =
+        input.duplicable() ? CAST_VAR_LIST_TEMPLATE : CAST_VAR_TEMPLATE;
+    auto dispensable = input.dispensable() ? "true" : "false";
+    ins_cast_str +=
+        paddle::string::Sprintf(in_cast_type, in_name, op_type, in_name,
+                                arg_idx++, TempName(in_name), dispensable);
+
+    if (input.dispensable()) {
+      const auto in_template = input.duplicable()
+                                   ? INPUT_INITIALIZER_TEMPLATE_WITH_NULL_LIST
+                                   : INPUT_INITIALIZER_TEMPLATE_WITH_NULL;
+      ins_initializer_with_null +=
+          paddle::string::Sprintf(in_template, in_name, in_name, in_name);
+    } else {
+      const auto in_template = input.duplicable()
+                                   ? INPUT_LIST_INITIALIZER_TEMPLATE
+                                   : INPUT_INITIALIZER_TEMPLATE;
+      ins_initializer += paddle::string::Sprintf(in_template, in_name, in_name);
+      ins_initializer += ",";
+    }
+  }
+  if (ins_initializer.back() == ',') {
+    ins_initializer.pop_back();
+  }
+  ins_initializer += "}";
+
+  if (input_args.back() == ',') {
+    input_args.pop_back();
+  }
+
+  // Generate outs initializer
+  std::string outs_initializer = "{";
+  std::string outs_initializer_with_null = "";
+  std::string return_type = "";
+  std::string inplace_mapping_str = "";
+  std::string return_str = "";
+
+  int outs_num = 0;
+  for (auto& output : op_proto->outputs()) {
+    auto& out_name = output.name();
+    // skip those dispensable oututs
+    if (output.dispensable() && !FindOutsMap(op_type, out_name)) {
+      continue;
+    }
+    const auto out_type =
+        output.duplicable() ? OUT_VAR_LIST_TYPE : OUT_VAR_TYPE;
+    const auto return_template =
+        output.duplicable() ? RETURN_LIST_TEMPLATE : RETURN_TEMPLATE;
+
+    if (FindPassingOutsMap(op_type, out_name)) {
+      if (input_args != "") {
+        input_args += ",";
+      }
+      input_args += out_type;
+      input_args += out_name;
+      input_args_num++;
+
+      if (output.dispensable()) {
+        const auto out_template =
+            output.duplicable() ? OUTPUT_INITIALIZER_TEMPLATE_WITH_NULL_LIST
+                                : OUTPUT_INITIALIZER_TEMPLATE_WITH_NULL;
+        outs_initializer_with_null +=
+            paddle::string::Sprintf(out_template, out_name, out_name);
+      } else {
+        const auto out_template = output.duplicable()
+                                      ? INPUT_LIST_INITIALIZER_TEMPLATE
+                                      : INPUT_INITIALIZER_TEMPLATE;
+        outs_initializer +=
+            paddle::string::Sprintf(out_template, out_name, out_name);
+        outs_initializer += ",";
+      }
+    } else if (use_inplace_strategy && inplace_map.count(out_name)) {
+      PADDLE_ENFORCE_NE(
+          inplace_map[out_name], "",
+          paddle::platform::errors::InvalidArgument(
+              "Inplace op %s has no input corresponding to output %s.", op_type,
+              out_name));
+
+      // TODO(pangyoki): Inplace op don't have duplicable output in temporary,
+      // so don't support duplicable output now.
+      const auto out_template = INPUT_INITIALIZER_TEMPLATE;
+
+      auto inplace_input_name = inplace_map[out_name];
+      inplace_mapping_str += paddle::string::Sprintf(
+          INPLACE_MAPPING_TEMPLATE, inplace_input_name, out_name);
+      inplace_mapping_str += ",";
+
+      // If inplace op has duplicable input, the first Varbase in input will
+      // share Varbase with output.
+      if (FindDuplicableInputInplaceOpSet(op_type)) {
+        inplace_input_name += INPLACE_DUPLICABLE_INPUT;
+      }
+
+      // Leaf Var that doesn't stop gradient can't use inplace strategy.
+      // Increase inplace_version.
+      inplace_strategy_str += paddle::string::Sprintf(
+          INPLACE_STRATEGY_TEMPLATE, inplace_input_name, inplace_input_name,
+          INPLACE_LEAF_ERROR_MESSAGE, inplace_input_name, inplace_input_name,
+          inplace_input_name);
+      outs_initializer +=
+          paddle::string::Sprintf(out_template, out_name, inplace_input_name);
+      outs_initializer += ",";
+    } else {
+      // There are few Operators that have duplicable output, like `Out` in
+      // split op. We need to specify the number of variables for the
+      // duplicable output, as the argument OutNum;
+      if (output.duplicable()) {
+        if (input_args != "") {
+          input_args += ",";
+        }
+        auto out_num_str = paddle::string::Sprintf(ARG_OUT_NUM, out_name);
+        input_args += ARG_OUT_NUM_TYPE;
+        input_args += out_num_str;
+        input_args_num++;
+        outs_initializer += paddle::string::Sprintf(
+            OUT_DUPLICABLE_INITIALIZER_TEMPLATE, out_name, out_num_str);
+      } else {
+        outs_initializer +=
+            paddle::string::Sprintf(OUT_INITIALIZER_TEMPLATE, out_name);
+      }
+      outs_initializer += ",";
+    }
+
+    return_type += out_type;
+    return_type += ",";
+    return_str += paddle::string::Sprintf(return_template, out_name);
+    return_str += ",";
+    outs_num += 1;
+  }
+  if (outs_initializer.back() == ',') {
+    outs_initializer.pop_back();
+    return_type.pop_back();
+    return_str.pop_back();
+  }
+  outs_initializer += "}";
+  if (inplace_mapping_str.back() == ',') {
+    inplace_mapping_str.pop_back();
+  }
+  if (!use_inplace_strategy && FindViewOpMap(op_type)) {
+    std::string viwe_input_name = view_op_map[op_type].first;
+    std::string viwe_output_name = view_op_map[op_type].second;
+    view_strategy_str += paddle::string::Sprintf(
+        HANDLE_VIEW_BETWEEN_INPUT_AND_OUTPUT, viwe_input_name, viwe_output_name,
+        viwe_input_name, viwe_output_name);
+  }
+  if (outs_num == 0) {
+    return_type = "void";
+  }
+  if (outs_num > 1) {
+    return_str = paddle::string::Sprintf(RETURN_TUPLE_TEMPLATE, return_str);
+    return_type = paddle::string::Sprintf(RETURN_TUPLE_TYPE, return_type);
+  }
+  std::string function_args = "";
+  if (input_args == "") {
+    function_args = FUNCTION_ARGS_NO_INPUT;
+  } else {
+    function_args = paddle::string::Sprintf(FUNCTION_ARGS, input_args);
+  }
+
+  // generate op funtcion body
+  auto op_function_str = paddle::string::Sprintf(
+      OP_FUNCTION_TEMPLATE, return_type, func_name, function_args, ins_cast_str,
+      op_type, input_args_num, inplace_strategy_str, outs_initializer,
+      ins_initializer, ins_initializer_with_null + outs_initializer_with_null +
+                           view_strategy_str,
+      op_type, inplace_mapping_str, return_str);
+
+  return op_function_str;
+}
+
 static std::tuple<std::vector<std::string>, std::vector<std::string>>
 GenerateOpFunctions(const std::string& module_name) {
   auto& op_info_map = paddle::framework::OpInfoMap::Instance().map();
@@ -272,158 +496,26 @@ GenerateOpFunctions(const std::string& module_name) {
     if (!all_kernels.count(op_type)) {
       continue;
     }
-    std::string input_args = "";
-    std::string ins_initializer = "{";
-    std::string ins_initializer_with_null = "";
-    std::string py_arg = "";
-    int arg_idx = 0;
-    int input_args_num = 0;
-    std::string ins_cast_str = "";
-    std::string view_strategy_str = "";
-    for (auto& input : op_proto->inputs()) {
-      auto& in_name = input.name();
-      // skip those dispensable inputs, like ResidualData in conv2d
-      if (input.dispensable() && !FindInsMap(op_type, in_name)) {
-        continue;
-      }
-      const auto in_type = input.duplicable() ? IN_VAR_LIST_TYPE : IN_VAR_TYPE;
-      auto input_arg =
-          paddle::string::Sprintf(ARG_TEMPLATE, in_type, TempName(in_name));
-      input_args += input_arg;
-      input_args += ",";
-      input_args_num++;
-      const auto in_cast_type =
-          input.duplicable() ? CAST_VAR_LIST_TEMPLATE : CAST_VAR_TEMPLATE;
-      auto dispensable = input.dispensable() ? "true" : "false";
-      ins_cast_str +=
-          paddle::string::Sprintf(in_cast_type, in_name, op_type, in_name,
-                                  arg_idx++, TempName(in_name), dispensable);
-
-      if (input.dispensable()) {
-        const auto in_template = input.duplicable()
-                                     ? INPUT_INITIALIZER_TEMPLATE_WITH_NULL_LIST
-                                     : INPUT_INITIALIZER_TEMPLATE_WITH_NULL;
-        ins_initializer_with_null +=
-            paddle::string::Sprintf(in_template, in_name, in_name, in_name);
-      } else {
-        const auto in_template = input.duplicable()
-                                     ? INPUT_LIST_INITIALIZER_TEMPLATE
-                                     : INPUT_INITIALIZER_TEMPLATE;
-        ins_initializer +=
-            paddle::string::Sprintf(in_template, in_name, in_name);
-        ins_initializer += ",";
-      }
-    }
-    if (ins_initializer.back() == ',') {
-      ins_initializer.pop_back();
-    }
-    ins_initializer += "}";
-
-    if (input_args.back() == ',') {
-      input_args.pop_back();
-    }
 
-    // Generate outs initializer
-    std::string outs_initializer = "{";
-    std::string outs_initializer_with_null = "";
-    std::string return_type = "";
-    std::string return_str = "";
-
-    int outs_num = 0;
-    for (auto& output : op_proto->outputs()) {
-      auto& out_name = output.name();
-      // skip those dispensable oututs
-      if (output.dispensable() && !FindOutsMap(op_type, out_name)) {
-        continue;
+    // NOTE(pangyoki): Inplace Strategy.
+    // In this case, output will reuse input varbase.
+    // Dygraph mode needs to be aligned with the in-place strategy in static
+    // mode, and the mapping relationships between output and input that have
+    // been defined in static mode should be used in dygraph mode.
+    // Find which ops need to use Inplace strategy in static mode, and get the
+    // mapping relationship between Inplace output and input.
+    auto& infer_inplace =
+        paddle::framework::OpInfoMap::Instance().Get(op_type).infer_inplace_;
+    std::map<std::string, std::string> inplace_map;
+    if (infer_inplace) {
+      auto in_to_outs = infer_inplace(true);
+      for (auto& inplace_pair : in_to_outs) {
+        inplace_map[inplace_pair.second] = inplace_pair.first;
       }
-      const auto out_type =
-          output.duplicable() ? OUT_VAR_LIST_TYPE : OUT_VAR_TYPE;
-      const auto return_template =
-          output.duplicable() ? RETURN_LIST_TEMPLATE : RETURN_TEMPLATE;
-      if (FindPassingOutsMap(op_type, out_name)) {
-        if (input_args != "") {
-          input_args += ",";
-        }
-        input_args += out_type;
-        input_args += out_name;
-        input_args_num++;
-
-        if (output.dispensable()) {
-          const auto out_template =
-              output.duplicable() ? OUTPUT_INITIALIZER_TEMPLATE_WITH_NULL_LIST
-                                  : OUTPUT_INITIALIZER_TEMPLATE_WITH_NULL;
-          outs_initializer_with_null +=
-              paddle::string::Sprintf(out_template, out_name, out_name);
-        } else {
-          const auto out_template = output.duplicable()
-                                        ? INPUT_LIST_INITIALIZER_TEMPLATE
-                                        : INPUT_INITIALIZER_TEMPLATE;
-          outs_initializer +=
-              paddle::string::Sprintf(out_template, out_name, out_name);
-          outs_initializer += ",";
-        }
-      } else {
-        // There are few Operators that have duplicable output, like `Out` in
-        // split op. We need to specify the number of variables for the
-        // duplicable output, as the argument OutNum;
-        if (output.duplicable()) {
-          if (input_args != "") {
-            input_args += ",";
-          }
-          auto out_num_str = paddle::string::Sprintf(ARG_OUT_NUM, out_name);
-          input_args += ARG_OUT_NUM_TYPE;
-          input_args += out_num_str;
-          input_args_num++;
-          outs_initializer += paddle::string::Sprintf(
-              OUT_DUPLICABLE_INITIALIZER_TEMPLATE, out_name, out_num_str);
-        } else {
-          outs_initializer +=
-              paddle::string::Sprintf(OUT_INITIALIZER_TEMPLATE, out_name);
-        }
-        outs_initializer += ",";
-      }
-
-      return_type += out_type;
-      return_type += ",";
-      return_str += paddle::string::Sprintf(return_template, out_name);
-      return_str += ",";
-      outs_num += 1;
-    }
-    if (outs_initializer.back() == ',') {
-      outs_initializer.pop_back();
-      return_type.pop_back();
-      return_str.pop_back();
-    }
-    outs_initializer += "}";
-    if (FindViewOpMap(op_type)) {
-      std::string viwe_input_name = view_op_map[op_type].first;
-      std::string viwe_output_name = view_op_map[op_type].second;
-      view_strategy_str += paddle::string::Sprintf(
-          HandleViewBetweenInputAndOutput, viwe_input_name, viwe_output_name,
-          viwe_input_name, viwe_output_name);
-    }
-    if (outs_num == 0) {
-      return_type = "void";
-    }
-    if (outs_num > 1) {
-      return_str = paddle::string::Sprintf(RETURN_TUPLE_TEMPLATE, return_str);
-      return_type = paddle::string::Sprintf(RETURN_TUPLE_TYPE, return_type);
-    }
-    std::string function_args = "";
-    if (input_args == "") {
-      function_args = FUNCTION_ARGS_NO_INPUT;
-    } else {
-      function_args = paddle::string::Sprintf(FUNCTION_ARGS, input_args);
     }
 
     std::string func_name = "imperative_" + op_type;
-    // generate op funtcion body
-    auto op_function_str = paddle::string::Sprintf(
-        OP_FUNCTION_TEMPLATE, return_type, func_name, function_args,
-        ins_cast_str, op_type, input_args_num, outs_initializer,
-        ins_initializer, ins_initializer_with_null +
-                             outs_initializer_with_null + view_strategy_str,
-        op_type, return_str);
+    std::string op_function_str = GenerateOpFunctionsBody(op_proto, func_name);
 
     // generate pybind item
     auto bind_function_str = paddle::string::Sprintf(
@@ -431,6 +523,23 @@ GenerateOpFunctions(const std::string& module_name) {
 
     op_function_list.emplace_back(std::move(op_function_str));
     bind_function_list.emplace_back(std::move(bind_function_str));
+
+    if (infer_inplace) {
+      // Reuse Varbase Inplace OP: op_type_.
+      // The inplace OP needs a new implementation method.
+      std::string inplace_op_type = op_type + "_";
+      std::string inplace_func_name = "imperative_" + inplace_op_type;
+      std::string inplace_op_function_str = GenerateOpFunctionsBody(
+          op_proto, inplace_func_name, true, inplace_map);
+
+      // generate pybind item
+      auto inplace_bind_function_str =
+          paddle::string::Sprintf(PYBIND_ITEM_TEMPLATE, module_name,
+                                  inplace_op_type, inplace_func_name);
+
+      op_function_list.emplace_back(std::move(inplace_op_function_str));
+      bind_function_list.emplace_back(std::move(inplace_bind_function_str));
+    }
   }
   return std::make_tuple(op_function_list, bind_function_list);
 }

python/paddle/__init__.py

@@ -113,19 +113,23 @@ from .tensor.manipulation import flatten  #DEFINE_ALIAS
 from .tensor.manipulation import gather  #DEFINE_ALIAS
 from .tensor.manipulation import gather_nd  #DEFINE_ALIAS
 from .tensor.manipulation import reshape  #DEFINE_ALIAS
+from .tensor.manipulation import reshape_  #DEFINE_ALIAS
 from .tensor.manipulation import flip as reverse  #DEFINE_ALIAS
 from .tensor.manipulation import scatter  #DEFINE_ALIAS
+from .tensor.manipulation import scatter_  #DEFINE_ALIAS
 from .tensor.manipulation import scatter_nd_add  #DEFINE_ALIAS
 from .tensor.manipulation import scatter_nd  #DEFINE_ALIAS
 from .tensor.manipulation import shard_index  #DEFINE_ALIAS
 from .tensor.manipulation import slice  #DEFINE_ALIAS
 from .tensor.manipulation import split  #DEFINE_ALIAS
 from .tensor.manipulation import squeeze  #DEFINE_ALIAS
+from .tensor.manipulation import squeeze_  #DEFINE_ALIAS
 from .tensor.manipulation import stack  #DEFINE_ALIAS
 from .tensor.manipulation import strided_slice  #DEFINE_ALIAS
 from .tensor.manipulation import transpose  #DEFINE_ALIAS
 from .tensor.manipulation import unique  #DEFINE_ALIAS
 from .tensor.manipulation import unsqueeze  #DEFINE_ALIAS
+from .tensor.manipulation import unsqueeze_  #DEFINE_ALIAS
 from .tensor.manipulation import unstack  #DEFINE_ALIAS
 from .tensor.manipulation import flip  #DEFINE_ALIAS
 from .tensor.manipulation import unbind  #DEFINE_ALIAS
@@ -171,6 +175,7 @@ from .tensor.math import square  #DEFINE_ALIAS
 from .tensor.math import stanh  #DEFINE_ALIAS
 from .tensor.math import sum  #DEFINE_ALIAS
 from .tensor.math import tanh  #DEFINE_ALIAS
+from .tensor.math import tanh_  #DEFINE_ALIAS
 from .tensor.math import add_n  #DEFINE_ALIAS
 from .tensor.math import max  #DEFINE_ALIAS
 from .tensor.math import maximum  #DEFINE_ALIAS

python/paddle/fluid/tests/unittests/test_activation_op.py

@@ -220,12 +220,16 @@ class TestTanhAPI(unittest.TestCase):
         self.x_np = np.random.uniform(-1, 1, [10, 12]).astype(self.dtype)
         self.place = paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
             else paddle.CPUPlace()
+        self.executed_api()
+
+    def executed_api(self):
+        self.tanh = F.tanh
 
     def test_static_api(self):
         paddle.enable_static()
         with paddle.static.program_guard(paddle.static.Program()):
             x = paddle.fluid.data('X', [10, 12], self.dtype)
-            out1 = F.tanh(x)
+            out1 = self.tanh(x)
             th = paddle.nn.Tanh()
             out2 = th(x)
             exe = paddle.static.Executor(self.place)
@@ -260,15 +264,21 @@ class TestTanhAPI(unittest.TestCase):
         paddle.enable_static()
         with paddle.static.program_guard(paddle.static.Program()):
             # The input type must be Variable.
-            self.assertRaises(TypeError, F.tanh, 1)
+            self.assertRaises(TypeError, self.tanh, 1)
             # The input dtype must be float16, float32.
             x_int32 = paddle.fluid.data(
                 name='x_int32', shape=[12, 10], dtype='int32')
-            self.assertRaises(TypeError, F.tanh, x_int32)
+            self.assertRaises(TypeError, self.tanh, x_int32)
             # support the input dtype is float16
             x_fp16 = paddle.fluid.data(
                 name='x_fp16', shape=[12, 10], dtype='float16')
-            F.tanh(x_fp16)
+            self.tanh(x_fp16)
+
+
+class TestTanhInplaceAPI(TestTanhAPI):
+    # test paddle.tanh_
+    def executed_api(self):
+        self.tanh = paddle.tanh_
 
 
 class TestAtan(TestActivation, TestParameter):
@@ -992,12 +1002,16 @@ class TestReluAPI(unittest.TestCase):
         self.x_np = np.random.uniform(-1, 1, [10, 12]).astype('float32')
         self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
             else paddle.CPUPlace()
+        self.executed_api()
+
+    def executed_api(self):
+        self.relu = F.relu
 
     def test_static_api(self):
         paddle.enable_static()
         with paddle.static.program_guard(paddle.static.Program()):
             x = paddle.fluid.data('X', [10, 12])
-            out1 = F.relu(x)
+            out1 = self.relu(x)
             m = paddle.nn.ReLU()
             out2 = m(x)
             exe = paddle.static.Executor(self.place)
@@ -1009,9 +1023,9 @@ class TestReluAPI(unittest.TestCase):
     def test_dygraph_api(self):
         paddle.disable_static(self.place)
         x = paddle.to_tensor(self.x_np)
-        out1 = F.relu(x)
         m = paddle.nn.ReLU()
-        out2 = m(x)
+        out1 = m(x)
+        out2 = self.relu(x)
         out_ref = np.maximum(self.x_np, 0)
         for r in [out1, out2]:
             self.assertEqual(np.allclose(out_ref, r.numpy()), True)
@@ -1021,15 +1035,21 @@ class TestReluAPI(unittest.TestCase):
         paddle.enable_static()
         with paddle.static.program_guard(paddle.static.Program()):
             # The input type must be Variable.
-            self.assertRaises(TypeError, F.relu, 1)
+            self.assertRaises(TypeError, self.relu, 1)
             # The input dtype must be float16, float32, float64.
             x_int32 = paddle.fluid.data(
                 name='x_int32', shape=[10, 12], dtype='int32')
-            self.assertRaises(TypeError, F.relu, x_int32)
+            self.assertRaises(TypeError, self.relu, x_int32)
             # support the input dtype is float16
             x_fp16 = paddle.fluid.data(
                 name='x_fp16', shape=[10, 12], dtype='float16')
-            F.relu(x_fp16)
+            self.relu(x_fp16)
+
+
+class TestReluInplaceAPI(TestReluAPI):
+    # test paddle.nn.functional.relu_
+    def executed_api(self):
+        self.relu = F.relu_
 
 
 def ref_leaky_relu(x, alpha=0.01):
@@ -1557,12 +1577,16 @@ class TestELUAPI(unittest.TestCase):
         self.x_np = np.random.uniform(-3, 3, [10, 12]).astype('float32')
         self.place=paddle.CUDAPlace(0) if core.is_compiled_with_cuda() \
             else paddle.CPUPlace()
+        self.executed_api()
+
+    def executed_api(self):
+        self.elu = F.elu
 
     def test_static_api(self):
         paddle.enable_static()
         with paddle.static.program_guard(paddle.static.Program()):
             x = paddle.fluid.data('X', [10, 12])
-            out1 = F.elu(x)
+            out1 = self.elu(x)
             m = paddle.nn.ELU()
             out2 = m(x)
             exe = paddle.static.Executor(self.place)
@@ -1574,14 +1598,16 @@ class TestELUAPI(unittest.TestCase):
     def test_dygraph_api(self):
         paddle.disable_static(self.place)
         x = paddle.to_tensor(self.x_np)
-        out1 = F.elu(x)
+        out1 = self.elu(x)
+        x = paddle.to_tensor(self.x_np)
         m = paddle.nn.ELU()
         out2 = m(x)
         out_ref = elu(self.x_np, 1.0)
         for r in [out1, out2]:
             self.assertEqual(np.allclose(out_ref, r.numpy()), True)
 
-        out1 = F.elu(x, 0.2)
+        out1 = self.elu(x, 0.2)
+        x = paddle.to_tensor(self.x_np)
         m = paddle.nn.ELU(0.2)
         out2 = m(x)
         out_ref = elu(self.x_np, 0.2)
@@ -1593,15 +1619,21 @@ class TestELUAPI(unittest.TestCase):
         paddle.enable_static()
         with paddle.static.program_guard(paddle.static.Program()):
             # The input type must be Variable.
-            self.assertRaises(TypeError, F.elu, 1)
+            self.assertRaises(TypeError, self.elu, 1)
             # The input dtype must be float16, float32, float64.
             x_int32 = paddle.fluid.data(
                 name='x_int32', shape=[10, 12], dtype='int32')
-            self.assertRaises(TypeError, F.elu, x_int32)
+            self.assertRaises(TypeError, self.elu, x_int32)
             # support the input dtype is float16
             x_fp16 = paddle.fluid.data(
                 name='x_fp16', shape=[10, 12], dtype='float16')
-            F.elu(x_fp16)
+            self.elu(x_fp16)
+
+
+class TestELUInplaceAPI(TestELUAPI):
+    # test paddle.nn.functional.elu_
+    def executed_api(self):
+        self.elu = F.elu_
 
 
 class TestReciprocal(TestActivation):

python/paddle/fluid/tests/unittests/test_inplace.py

@@ -95,5 +95,206 @@ class TestInplace(unittest.TestCase):
             loss.backward()
 
 
+class TestDygraphInplace(unittest.TestCase):
+    def setUp(self):
+        self.init_data()
+
+    def init_data(self):
+        self.input_var_numpy = np.random.rand(2, 3, 1)
+        self.dtype = "float32"
+
+    def non_inplace_api_processing(self, var):
+        return paddle.squeeze(var)
+
+    def inplace_api_processing(self, var):
+        return paddle.squeeze_(var)
+
+    def test_inplace_api(self):
+        var = paddle.to_tensor(self.input_var_numpy).astype(self.dtype)
+        inplace_var = self.inplace_api_processing(var)
+        self.assertTrue(id(var) == id(inplace_var))
+
+        inplace_var[0] = 2.
+        self.assertTrue(np.array_equal(var.numpy(), inplace_var.numpy()))
+
+    def test_forward_version(self):
+        with paddle.fluid.dygraph.guard():
+            var = paddle.to_tensor(self.input_var_numpy).astype(self.dtype)
+            self.assertEqual(var.inplace_version, 0)
+
+            inplace_var = self.inplace_api_processing(var)
+            self.assertEqual(var.inplace_version, 1)
+
+            inplace_var[0] = 2.
+            self.assertEqual(var.inplace_version, 2)
+
+            inplace_var = self.inplace_api_processing(inplace_var)
+            self.assertEqual(var.inplace_version, 3)
+
+    def test_leaf_inplace_var_error(self):
+        with paddle.fluid.dygraph.guard():
+            var = paddle.to_tensor(self.input_var_numpy).astype(self.dtype)
+            var.stop_gradient = False
+
+            def leaf_inplace_error():
+                self.inplace_api_processing(var)
+
+            self.assertRaises(ValueError, leaf_inplace_error)
+
+    def test_backward_error(self):
+        # It raises an error because the inplace operator will result
+        # in incorrect gradient computation.
+        with paddle.fluid.dygraph.guard():
+            var_a = paddle.to_tensor(self.input_var_numpy).astype(self.dtype)
+            var_a.stop_gradient = False
+
+            var_b = var_a**2
+
+            # Here, the gradient computation will use the value of var_b
+            var_c = var_b**2
+            self.inplace_api_processing(var_b)
+
+            loss = paddle.nn.functional.relu(var_c)
+            with self.assertRaisesRegexp(
+                    RuntimeError,
+                    "received tensor_version:{} != wrapper_version_snapshot:{}".
+                    format(1, 0)):
+                loss.backward()
+
+    def test_backward_success_1(self):
+        # var_b is modified inplace before using it, the inplace operator doesn't result
+        # in incorrect gradient computation.
+        grad_var_a, grad_var_a_inplace = 0, 1
+        with paddle.fluid.dygraph.guard():
+            var_a = paddle.to_tensor(self.input_var_numpy).astype(self.dtype)
+            var_a.stop_gradient = False
+
+            var_b = var_a**2
+            var_c = self.inplace_api_processing(
+                var_b)  # var_b is modified inplace before using it
+
+            # Here, the gradient computation will use the value of var_b
+            var_d = var_c**2
+            loss = var_d.sum()
+            loss.backward()
+            grad_var_a_inplace = var_a.grad
+
+        with paddle.fluid.dygraph.guard():
+            var_a = paddle.to_tensor(self.input_var_numpy).astype(self.dtype)
+            var_a.stop_gradient = False
+
+            var_b = var_a**2
+            var_c = self.non_inplace_api_processing(var_b)
+            var_d = var_c**2
+            loss = var_d.sum()
+            loss.backward()
+            grad_var_a = var_a.grad
+
+        self.assertTrue(np.array_equal(grad_var_a_inplace, grad_var_a))
+
+    def test_backward_success_2(self):
+        # Although var_b is modified inplace after using it, it does not used in gradient computation.
+        # The inplace operator doesn't result in incorrect gradient computation.
+        grad_var_a, grad_var_a_inplace = 0, 1
+        with paddle.fluid.dygraph.guard():
+            var_a = paddle.to_tensor(self.input_var_numpy).astype(self.dtype)
+            var_a.stop_gradient = False
+
+            var_b = var_a**2
+
+            var_c = self.inplace_api_processing(
+                var_b)  # var_b is modified inplace before using it
+
+            var_d = var_c + var_c  # Here, the grad op of sum doesn't use the value of var_b
+            loss = var_d.sum()
+
+            loss.backward()
+            grad_var_a_inplace = var_a.grad
+
+        with paddle.fluid.dygraph.guard():
+            var_a = paddle.to_tensor(self.input_var_numpy).astype(self.dtype)
+            var_a.stop_gradient = False
+
+            var_b = var_a**2
+
+            var_c = self.non_inplace_api_processing(
+                var_b)  # var_b is modified inplace before using it
+
+            var_d = var_c + var_c  # Here, the grad op of sum doesn't use the value of var_b
+            loss = var_d.sum()
+
+            loss.backward()
+            grad_var_a = var_a.grad
+        self.assertTrue(np.array_equal(grad_var_a_inplace, grad_var_a))
+
+
+class TestDygraphInplaceUnsqueeze(TestDygraphInplace):
+    def non_inplace_api_processing(self, var):
+        return paddle.unsqueeze(var, -1)
+
+    def inplace_api_processing(self, var):
+        return paddle.unsqueeze_(var, -1)
+
+
+class TestDygraphInplaceReshape(TestDygraphInplace):
+    def non_inplace_api_processing(self, var):
+        return paddle.reshape(var, [-1])
+
+    def inplace_api_processing(self, var):
+        return paddle.reshape_(var, [-1])
+
+
+class TestDygraphInplaceScatter(TestDygraphInplace):
+    def init_data(self):
+        self.input_var_numpy = np.array([[1, 1], [2, 2], [3, 3]])
+        self.dtype = "float32"
+
+    def non_inplace_api_processing(self, var):
+        index = paddle.to_tensor([2, 1, 0, 1], dtype='int64')
+        updates = paddle.to_tensor(
+            [[1, 1], [2, 2], [3, 3], [4, 4]], dtype='float32')
+
+        return paddle.scatter(var, index, updates, overwrite=False)
+
+    def inplace_api_processing(self, var):
+        index = paddle.to_tensor([2, 1, 0, 1], dtype='int64')
+        updates = paddle.to_tensor(
+            [[1, 1], [2, 2], [3, 3], [4, 4]], dtype='float32')
+
+        return paddle.scatter_(var, index, updates, overwrite=False)
+
+
+class TestDygraphInplaceElu(TestDygraphInplace):
+    def non_inplace_api_processing(self, var):
+        return paddle.nn.functional.elu(var)
+
+    def inplace_api_processing(self, var):
+        return paddle.nn.functional.elu_(var)
+
+
+class TestDygraphInplaceRelu(TestDygraphInplace):
+    def non_inplace_api_processing(self, var):
+        return paddle.nn.functional.relu(var)
+
+    def inplace_api_processing(self, var):
+        return paddle.nn.functional.relu_(var)
+
+
+class TestDygraphInplaceSoftmax(TestDygraphInplace):
+    def non_inplace_api_processing(self, var):
+        return paddle.nn.functional.softmax(var)
+
+    def inplace_api_processing(self, var):
+        return paddle.nn.functional.softmax_(var)
+
+
+class TestDygraphInplaceTanh(TestDygraphInplace):
+    def non_inplace_api_processing(self, var):
+        return paddle.tanh(var)
+
+    def inplace_api_processing(self, var):
+        return paddle.tanh_(var)
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_reshape_op.py

@@ -250,8 +250,11 @@ class TestReshapeAPI(unittest.TestCase):
     def _set_paddle_api(self):
         self.fill_constant = paddle.fluid.layers.fill_constant
         self.data = paddle.static.data
-        self.reshape = paddle.reshape
         self.to_tensor = paddle.to_tensor
+        self._executed_api()
+
+    def _executed_api(self):
+        self.reshape = paddle.reshape
 
     def _set_fluid_api(self):
         self.fill_constant = fluid.layers.fill_constant
@@ -322,6 +325,30 @@ class TestReshapeAPI(unittest.TestCase):
         assert np.array_equal(out_3.numpy(), input.reshape(shape))
 
 
+class TestStaticReshape_(TestReshapeAPI):
+    def _executed_api(self):
+        self.reshape = paddle.reshape_
+
+    def test_imperative(self):
+        self._set_paddle_api()
+        input = np.random.random([2, 25]).astype("float32")
+        shape = [2, 5, 5]
+        with fluid.dygraph.guard():
+            x = self.to_tensor(input)
+            positive_five = self.fill_constant([1], "int32", 5)
+
+            out_1 = self.reshape(x, shape)
+
+            out_2 = self.reshape(x, shape=[positive_five, 10])
+
+            shape_tensor = self.to_tensor(np.array([2, 5, 5]).astype("int32"))
+            out_3 = self.reshape(x, shape=shape_tensor)
+
+        assert np.array_equal(out_1.numpy(), input.reshape(shape))
+        assert np.array_equal(out_2.numpy(), input.reshape(shape))
+        assert np.array_equal(out_3.numpy(), input.reshape(shape))
+
+
 # Test Input Error
 class TestReshapeOpError(unittest.TestCase):
     def _set_paddle_api(self):
@@ -397,12 +424,18 @@ class TestReshapeOpError(unittest.TestCase):
         self._test_errors()
 
 
-class API_TestDygraphReshape(unittest.TestCase):
+class TestDygraphReshapeAPI(unittest.TestCase):
+    def setUp(self):
+        self.executed_api()
+
+    def executed_api(self):
+        self.reshape = paddle.reshape
+
     def test_out(self):
         paddle.disable_static()
         input_1 = np.random.random([5, 1, 10]).astype("int32")
         input = paddle.to_tensor(input_1)
-        output = paddle.reshape(x=input, shape=[5, 10])
+        output = self.reshape(x=input, shape=[5, 10])
         out_np = output.numpy()
         expected_out = np.reshape(input_1, newshape=[5, 10])
         self.assertTrue(np.allclose(expected_out, out_np))
@@ -411,7 +444,7 @@ class API_TestDygraphReshape(unittest.TestCase):
         paddle.disable_static()
         input_1 = np.random.random([5, 1, 10]).astype("uint8")
         input = paddle.to_tensor(input_1)
-        output = paddle.reshape(x=input, shape=[5, 10])
+        output = self.reshape(x=input, shape=[5, 10])
         out_np = output.numpy()
         expected_out = np.reshape(input_1, newshape=[5, 10])
         self.assertTrue(np.allclose(expected_out, out_np))
@@ -420,11 +453,16 @@ class API_TestDygraphReshape(unittest.TestCase):
         paddle.disable_static()
         input_1 = np.random.random([5, 1, 10]).astype("float32")
         input = paddle.to_tensor(input_1)
-        output = paddle.reshape(x=input, shape=[5, 10])
+        output = self.reshape(x=input, shape=[5, 10])
         out_np = output.numpy()
         expected_out = np.reshape(input_1, newshape=[5, 10])
         self.assertTrue(np.allclose(expected_out, out_np))
 
 
+class TestDygraphReshapeInplaceAPI(TestDygraphReshapeAPI):
+    def executed_api(self):
+        self.reshape = paddle.reshape_
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_scatter_op.py

@@ -180,13 +180,17 @@ class TestScatterAPI(unittest.TestCase):
         self.places = [fluid.CPUPlace()]
         if core.is_compiled_with_cuda():
             self.places.append(fluid.CUDAPlace(0))
+        self.executed_api()
+
+    def executed_api(self):
+        self.scatter = paddle.scatter
 
     def check_static_result(self, place):
         with fluid.program_guard(fluid.Program(), fluid.Program()):
             input = fluid.data(name="input", shape=[3, 2], dtype="float64")
             index = fluid.data(name="index", shape=[4], dtype="int64")
             updates = fluid.data(name="updates", shape=[4, 2], dtype="float64")
-            result = paddle.scatter(input, index, updates, False)
+            result = self.scatter(input, index, updates, False)
 
             input_data = np.array([[1, 1], [2, 2], [3, 3]]).astype(np.float64)
             index_data = np.array([2, 1, 0, 1]).astype(np.int64)
@@ -220,10 +224,15 @@ class TestScatterAPI(unittest.TestCase):
                 index = fluid.dygraph.to_variable(index_data)
                 updates = fluid.dygraph.to_variable(updates_data)
 
-                output1 = paddle.scatter(x, index, updates, overwrite=False)
+                output1 = self.scatter(x, index, updates, overwrite=False)
                 self.assertEqual((output1.numpy() == \
                                   np.array([[3., 3.],[6., 6.],[1., 1.]])).all(), True)
 
 
+class TestScatterInplaceAPI(TestScatterAPI):
+    def executed_api(self):
+        self.scatter = paddle.scatter_
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_softmax_op.py

@@ -301,11 +301,15 @@ class TestSoftmaxAPI(unittest.TestCase):
         ) else paddle.CPUPlace()
         self.x_np = np.random.uniform(-1., 1., [2, 3, 4, 5]).astype('float32')
         self.out_ref = np.apply_along_axis(stable_softmax, -1, self.x_np)
+        self.executed_api()
+
+    def executed_api(self):
+        self.softmax = F.softmax
 
     def test_static_check(self):
         with paddle.static.program_guard(paddle.static.Program()):
             x = paddle.fluid.data('X', self.x_np.shape, 'float32')
-            out1 = F.softmax(x)
+            out1 = self.softmax(x)
             m = paddle.nn.Softmax()
             out2 = m(x)
             exe = paddle.static.Executor(self.place)
@@ -318,21 +322,23 @@ class TestSoftmaxAPI(unittest.TestCase):
         paddle.disable_static(self.place)
 
         x = paddle.to_tensor(self.x_np)
-        out1 = F.softmax(x)
+        out1 = self.softmax(x)
+        x = paddle.to_tensor(self.x_np)
         m = paddle.nn.Softmax()
         out2 = m(x)
         out_ref = ref_softmax(self.x_np, axis=-1, dtype=None)
         for r in [out1, out2]:
             self.assertEqual(np.allclose(out_ref, r.numpy()), True)
 
-        out1 = F.softmax(x, axis=0)
+        out1 = self.softmax(x, axis=0)
+        x = paddle.to_tensor(self.x_np)
         m = paddle.nn.Softmax(axis=0)
         out2 = m(x)
         out_ref = ref_softmax(self.x_np, axis=0, dtype=None)
         for r in [out1, out2]:
             self.assertEqual(np.allclose(out_ref, r.numpy()), True)
 
-        out = F.softmax(x, dtype=np.float64)
+        out = self.softmax(x, dtype=np.float64)
         out_ref = ref_softmax(self.x_np, axis=-1, dtype=np.float64)
         self.assertEqual(np.allclose(out_ref, out.numpy()), True)
 
@@ -341,15 +347,20 @@ class TestSoftmaxAPI(unittest.TestCase):
     def test_error(self):
         with paddle.static.program_guard(paddle.static.Program()):
             # The input type must be Variable.
-            self.assertRaises(TypeError, F.softmax, 1)
+            self.assertRaises(TypeError, self.softmax, 1)
             # The input dtype must be float16, float32, float64.
             x_int32 = paddle.fluid.data(
                 name='x_int32', shape=[2, 3], dtype='int32')
-            self.assertRaises(TypeError, F.softmax, x_int32)
+            self.assertRaises(TypeError, self.softmax, x_int32)
             # support the input dtype is float16
             x_fp16 = paddle.fluid.data(
                 name='x_fp16', shape=[2, 3], dtype='float16')
-            F.softmax(x_fp16)
+            self.softmax(x_fp16)
+
+
+class TestSoftmaxInplaceAPI(TestSoftmaxAPI):
+    def executed_api(self):
+        self.softmax = F.softmax_
 
 
 if __name__ == "__main__":

python/paddle/fluid/tests/unittests/test_squeeze_op.py

@@ -98,13 +98,19 @@ class TestSqueezeOpError(unittest.TestCase):
 
 
 class API_TestSqueeze(unittest.TestCase):
+    def setUp(self):
+        self.executed_api()
+
+    def executed_api(self):
+        self.squeeze = paddle.squeeze
+
     def test_out(self):
         paddle.enable_static()
         with paddle.static.program_guard(paddle.static.Program(),
                                          paddle.static.Program()):
             data1 = paddle.static.data(
                 'data1', shape=[-1, 1, 10], dtype='float64')
-            result_squeeze = paddle.squeeze(data1, axis=[1])
+            result_squeeze = self.squeeze(data1, axis=[1])
             place = paddle.CPUPlace()
             exe = paddle.static.Executor(place)
             input1 = np.random.random([5, 1, 10]).astype('float64')
@@ -114,12 +120,23 @@ class API_TestSqueeze(unittest.TestCase):
             self.assertTrue(np.allclose(expected_result, result))
 
 
+class API_TestStaticSqueeze_(API_TestSqueeze):
+    def executed_api(self):
+        self.squeeze = paddle.squeeze_
+
+
 class API_TestDygraphSqueeze(unittest.TestCase):
+    def setUp(self):
+        self.executed_api()
+
+    def executed_api(self):
+        self.squeeze = paddle.squeeze
+
     def test_out(self):
         paddle.disable_static()
         input_1 = np.random.random([5, 1, 10]).astype("int32")
         input = paddle.to_tensor(input_1)
-        output = paddle.squeeze(input, axis=[1])
+        output = self.squeeze(input, axis=[1])
         out_np = output.numpy()
         expected_out = np.squeeze(input_1, axis=1)
         self.assertTrue(np.allclose(expected_out, out_np))
@@ -128,7 +145,7 @@ class API_TestDygraphSqueeze(unittest.TestCase):
         paddle.disable_static()
         input_1 = np.random.random([5, 1, 10]).astype("int8")
         input = paddle.to_tensor(input_1)
-        output = paddle.squeeze(input, axis=[1])
+        output = self.squeeze(input, axis=[1])
         out_np = output.numpy()
         expected_out = np.squeeze(input_1, axis=1)
         self.assertTrue(np.allclose(expected_out, out_np))
@@ -137,7 +154,7 @@ class API_TestDygraphSqueeze(unittest.TestCase):
         paddle.disable_static()
         input_1 = np.random.random([5, 1, 10]).astype("uint8")
         input = paddle.to_tensor(input_1)
-        output = paddle.squeeze(input, axis=[1])
+        output = self.squeeze(input, axis=[1])
         out_np = output.numpy()
         expected_out = np.squeeze(input_1, axis=1)
         self.assertTrue(np.allclose(expected_out, out_np))
@@ -146,7 +163,7 @@ class API_TestDygraphSqueeze(unittest.TestCase):
         paddle.disable_static()
         input_1 = np.random.random([5, 1, 10]).astype("int32")
         input = paddle.to_tensor(input_1)
-        output = paddle.squeeze(input, axis=1)
+        output = self.squeeze(input, axis=1)
         out_np = output.numpy()
         expected_out = np.squeeze(input_1, axis=1)
         self.assertTrue(np.allclose(expected_out, out_np))
@@ -155,11 +172,16 @@ class API_TestDygraphSqueeze(unittest.TestCase):
         paddle.disable_static()
         input_1 = np.random.random([5, 1, 10]).astype("int32")
         input = paddle.to_tensor(input_1)
-        output = paddle.squeeze(input, axis=(1, 2))
+        output = self.squeeze(input, axis=(1, 0))
         out_np = output.numpy()
         expected_out = np.squeeze(input_1, axis=1)
         self.assertTrue(np.allclose(expected_out, out_np))
 
 
+class API_TestDygraphSqueezeInplace(API_TestDygraphSqueeze):
+    def executed_api(self):
+        self.squeeze = paddle.squeeze_
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_unsqueeze2_op.py

@@ -208,6 +208,12 @@ class TestUnsqueezeOp4_AxesTensor(TestUnsqueezeOp_AxesTensor):
 
 # test api
 class TestUnsqueezeAPI(unittest.TestCase):
+    def setUp(self):
+        self.executed_api()
+
+    def executed_api(self):
+        self.unsqueeze = paddle.unsqueeze
+
     def test_api(self):
         input = np.random.random([3, 2, 5]).astype("float64")
         x = paddle.static.data(name='x', shape=[3, 2, 5], dtype="float64")
@@ -218,12 +224,11 @@ class TestUnsqueezeAPI(unittest.TestCase):
         axes_tensor_int64 = paddle.static.data(
             name='axes_tensor_int64', shape=[3], dtype="int64")
 
-        out_1 = paddle.unsqueeze(x, axis=[3, 1, 1])
-        out_2 = paddle.unsqueeze(
-            x, axis=[positive_3_int32, positive_1_int64, 1])
-        out_3 = paddle.unsqueeze(x, axis=axes_tensor_int32)
-        out_4 = paddle.unsqueeze(x, axis=3)
-        out_5 = paddle.unsqueeze(x, axis=axes_tensor_int64)
+        out_1 = self.unsqueeze(x, axis=[3, 1, 1])
+        out_2 = self.unsqueeze(x, axis=[positive_3_int32, positive_1_int64, 1])
+        out_3 = self.unsqueeze(x, axis=axes_tensor_int32)
+        out_4 = self.unsqueeze(x, axis=3)
+        out_5 = self.unsqueeze(x, axis=axes_tensor_int64)
 
         exe = paddle.static.Executor(place=paddle.CPUPlace())
         res_1, res_2, res_3, res_4, res_5 = exe.run(
@@ -244,10 +249,15 @@ class TestUnsqueezeAPI(unittest.TestCase):
     def test_error(self):
         def test_axes_type():
             x2 = paddle.static.data(name="x2", shape=[2, 25], dtype="int32")
-            paddle.unsqueeze(x2, axis=2.1)
+            self.unsqueeze(x2, axis=2.1)
 
         self.assertRaises(TypeError, test_axes_type)
 
 
+class TestUnsqueezeInplaceAPI(TestUnsqueezeAPI):
+    def executed_api(self):
+        self.unsqueeze = paddle.unsqueeze_
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/test_unsqueeze_op.py

@@ -203,11 +203,17 @@ class API_TestDyUnsqueezeAxisTensorList(unittest.TestCase):
 
 
 class API_TestDygraphUnSqueeze(unittest.TestCase):
+    def setUp(self):
+        self.executed_api()
+
+    def executed_api(self):
+        self.unsqueeze = paddle.unsqueeze
+
     def test_out(self):
         paddle.disable_static()
         input_1 = np.random.random([5, 1, 10]).astype("int32")
         input = paddle.to_tensor(input_1)
-        output = paddle.unsqueeze(input, axis=[1])
+        output = self.unsqueeze(input, axis=[1])
         out_np = output.numpy()
         expected_out = np.expand_dims(input_1, axis=1)
         self.assertTrue(np.allclose(expected_out, out_np))
@@ -216,7 +222,7 @@ class API_TestDygraphUnSqueeze(unittest.TestCase):
         paddle.disable_static()
         input_1 = np.random.random([5, 1, 10]).astype("int8")
         input = paddle.to_tensor(input_1)
-        output = paddle.unsqueeze(input, axis=[1])
+        output = self.unsqueeze(input, axis=[1])
         out_np = output.numpy()
         expected_out = np.expand_dims(input_1, axis=1)
         self.assertTrue(np.allclose(expected_out, out_np))
@@ -225,7 +231,7 @@ class API_TestDygraphUnSqueeze(unittest.TestCase):
         paddle.disable_static()
         input_1 = np.random.random([5, 1, 10]).astype("uint8")
         input = paddle.to_tensor(input_1)
-        output = paddle.unsqueeze(input, axis=1)
+        output = self.unsqueeze(input, axis=1)
         out_np = output.numpy()
         expected_out = np.expand_dims(input_1, axis=1)
         self.assertTrue(np.allclose(expected_out, out_np))
@@ -234,7 +240,7 @@ class API_TestDygraphUnSqueeze(unittest.TestCase):
         paddle.disable_static()
         input_1 = np.random.random([5, 1, 10]).astype("int32")
         input = paddle.to_tensor(input_1)
-        output = paddle.unsqueeze(input, axis=1)
+        output = self.unsqueeze(input, axis=1)
         out_np = output.numpy()
         expected_out = np.expand_dims(input_1, axis=1)
         self.assertTrue(np.allclose(expected_out, out_np))
@@ -243,11 +249,16 @@ class API_TestDygraphUnSqueeze(unittest.TestCase):
         paddle.disable_static()
         input_1 = np.random.random([5, 1, 10]).astype("int32")
         input = paddle.to_tensor(input_1)
-        output = paddle.unsqueeze(input, axis=(1, 2))
+        output = self.unsqueeze(input, axis=(1, 2))
         out_np = output.numpy()
         expected_out = np.expand_dims(input_1, axis=1)
         self.assertTrue(np.allclose(expected_out, out_np))
 
 
+class API_TestDygraphUnSqueezeInplace(API_TestDygraphUnSqueeze):
+    def executed_api(self):
+        self.unsqueeze = paddle.unsqueeze_
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/nn/functional/__init__.py

@@ -30,6 +30,7 @@ __all__ += pooling.__all__
 from . import loss
 __all__ += loss.__all__
 from .activation import elu  #DEFINE_ALIAS
+from .activation import elu_  #DEFINE_ALIAS
 # from .activation import erf  #DEFINE_ALIAS
 from .activation import gelu  #DEFINE_ALIAS
 from .activation import hardshrink  #DEFINE_ALIAS
@@ -41,16 +42,19 @@ from .activation import log_sigmoid  #DEFINE_ALIAS
 from .activation import maxout  #DEFINE_ALIAS
 from .activation import prelu  #DEFINE_ALIAS
 from .activation import relu  #DEFINE_ALIAS
+from .activation import relu_  #DEFINE_ALIAS
 from .activation import relu6  #DEFINE_ALIAS
 from .activation import selu  #DEFINE_ALIAS
 from .activation import sigmoid  #DEFINE_ALIAS
 # from .activation import soft_relu  #DEFINE_ALIAS
 from .activation import softmax  #DEFINE_ALIAS
+from .activation import softmax_  #DEFINE_ALIAS
 from .activation import softplus  #DEFINE_ALIAS
 from .activation import softshrink  #DEFINE_ALIAS
 from .activation import softsign  #DEFINE_ALIAS
 from .activation import swish  #DEFINE_ALIAS
 from .activation import tanh  #DEFINE_ALIAS
+from .activation import tanh_  #DEFINE_ALIAS
 from .activation import tanhshrink  #DEFINE_ALIAS
 from .activation import thresholded_relu  #DEFINE_ALIAS
 from .activation import log_softmax  #DEFINE_ALIAS

python/paddle/nn/functional/activation.py

@@ -20,10 +20,14 @@ from ...fluid.layers import maxout  #DEFINE_ALIAS
 from ...fluid.layers import swish  #DEFINE_ALIAS
 from ...fluid.layers import sigmoid  #DEFINE_ALIAS
 from ...tensor.math import tanh  #DEFINE_ALIAS
+from ...tensor.math import tanh_  #DEFINE_ALIAS
+
+from ...tensor.manipulation import _print_warning_in_static_mode
 
 __all__ = [
     'brelu',
     'elu',
+    'elu_',
     'gelu',
     'hardshrink',
     'hardtanh',
@@ -34,15 +38,18 @@ __all__ = [
     'maxout',
     'prelu',
     'relu',
+    'relu_',
     'relu6',
     'selu',
     'softmax',
+    'softmax_',
     'softplus',
     'softshrink',
     'softsign',
     'sigmoid',
     'swish',
     'tanh',
+    'tanh_',
     'tanhshrink',
     'thresholded_relu',
     'log_softmax',
@@ -99,6 +106,19 @@ def elu(x, alpha=1.0, name=None):
     return out
 
 
+def elu_(x, alpha=1.0, name=None):
+    r"""
+    Inplace version of ``elu`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_nn_cn_elu`.
+    """
+
+    if in_dygraph_mode():
+        return core.ops.elu_(x, 'alpha', alpha)
+
+    _print_warning_in_static_mode("elu")
+    return elu(x, alpha, name)
+
+
 def gelu(x, approximate=False, name=None):
     r"""
     gelu activation.
@@ -514,6 +534,19 @@ def relu(x, name=None):
     return out
 
 
+def relu_(x, name=None):
+    """
+    Inplace version of ``relu`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_nn_cn_relu`.
+    """
+
+    if in_dygraph_mode():
+        return core.ops.relu_(x)
+
+    _print_warning_in_static_mode("relu")
+    return relu(x, name)
+
+
 def log_sigmoid(x, name=None):
     r"""
     log_sigmoid activation.
@@ -879,6 +912,23 @@ def softmax(x, axis=-1, dtype=None, name=None):
     return outs_softmax
 
 
+def softmax_(x, axis=-1, dtype=None, name=None):
+    r"""
+    Inplace version of ``softmax`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_nn_cn_softmax`.
+    """
+
+    if (dtype is not None) and (not isinstance(dtype, core.VarDesc.VarType)):
+        dtype = convert_np_dtype_to_dtype_(dtype)
+    use_cudnn = True
+
+    if in_dygraph_mode():
+        return core.ops.softmax_(x, 'axis', axis, 'use_cudnn', use_cudnn)
+
+    _print_warning_in_static_mode("softmax")
+    return softmax(x, axis, dtype, name)
+
+
 def softplus(x, beta=1, threshold=20, name=None):
     r"""
     softplus activation

python/paddle/tensor/__init__.py

@@ -82,19 +82,23 @@ from .manipulation import flatten  #DEFINE_ALIAS
 from .manipulation import gather  #DEFINE_ALIAS
 from .manipulation import gather_nd  #DEFINE_ALIAS
 from .manipulation import reshape  #DEFINE_ALIAS
+from .manipulation import reshape_  #DEFINE_ALIAS
 from .manipulation import flip as reverse  #DEFINE_ALIAS
 from .manipulation import scatter  #DEFINE_ALIAS
+from .manipulation import scatter_  #DEFINE_ALIAS
 from .manipulation import scatter_nd_add  #DEFINE_ALIAS
 from .manipulation import scatter_nd  #DEFINE_ALIAS
 from .manipulation import shard_index  #DEFINE_ALIAS
 from .manipulation import slice  #DEFINE_ALIAS
 from .manipulation import split  #DEFINE_ALIAS
 from .manipulation import squeeze  #DEFINE_ALIAS
+from .manipulation import squeeze_  #DEFINE_ALIAS
 from .manipulation import stack  #DEFINE_ALIAS
 from .manipulation import strided_slice  #DEFINE_ALIAS
 from .manipulation import transpose  #DEFINE_ALIAS
 from .manipulation import unique  #DEFINE_ALIAS
 from .manipulation import unsqueeze  #DEFINE_ALIAS
+from .manipulation import unsqueeze_  #DEFINE_ALIAS
 from .manipulation import unstack  #DEFINE_ALIAS
 from .manipulation import flip  #DEFINE_ALIAS
 from .manipulation import unbind  #DEFINE_ALIAS
@@ -137,6 +141,7 @@ from .math import square  #DEFINE_ALIAS
 from .math import stanh  #DEFINE_ALIAS
 from .math import sum  #DEFINE_ALIAS
 from .math import tanh  #DEFINE_ALIAS
+from .math import tanh_  #DEFINE_ALIAS
 from .math import add_n  #DEFINE_ALIAS
 from .math import max  #DEFINE_ALIAS
 from .math import maximum  #DEFINE_ALIAS

python/paddle/tensor/manipulation.py

@@ -32,6 +32,7 @@ from ..fluid.layers import scatter_nd  #DEFINE_ALIAS
 from ..fluid.layers import shard_index  #DEFINE_ALIAS
 from ..fluid import layers
 import paddle
+import warnings
 
 __all__ = [
     'cast',
@@ -43,8 +44,10 @@ __all__ = [
     'gather',
     'gather_nd',
     'reshape',
+    'reshape_',
     'reverse',
     'scatter',
+    'scatter_',
     'scatter_nd_add',
     'scatter_nd',
     'shard_index',
@@ -52,11 +55,13 @@ __all__ = [
     'split',
     'chunk',
     'squeeze',
+    'squeeze_',
     'stack',
     'strided_slice',
     'transpose',
     'unique',
     'unsqueeze',
+    'unsqueeze_',
     'unstack',
     'flip',
     'unbind',
@@ -65,6 +70,12 @@ __all__ = [
 ]
 
 
+def _print_warning_in_static_mode(api_name):
+    warnings.warn(
+        "In static mode, {}_() is the same as {}() and does not perform inplace operation.".
+        format(api_name, api_name))
+
+
 def concat(x, axis=0, name=None):
     """
 
@@ -567,6 +578,26 @@ def squeeze(x, axis=None, name=None):
     return layers.squeeze(x, axis, name)
 
 
+def squeeze_(x, axis=None, name=None):
+    """
+    Inplace version of ``squeeze`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_paddle_tensor_squeeze`.
+    """
+    if axis is None:
+        axis = []
+    elif isinstance(axis, int):
+        axis = [axis]
+    elif isinstance(axis, tuple):
+        axis = list(axis)
+
+    if in_dygraph_mode():
+        out, _ = core.ops.squeeze2_(x, 'axes', axis)
+        return out
+
+    _print_warning_in_static_mode("squeeze")
+    return squeeze(x, axis, name)
+
+
 def unique(x,
            return_index=False,
            return_inverse=False,
@@ -740,6 +771,28 @@ def unsqueeze(x, axis, name=None):
     return layers.unsqueeze(x, axis, name)
 
 
+def unsqueeze_(x, axis, name=None):
+    """
+    Inplace version of ``unsqueeze`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_paddle_tensor_unsqueeze`.
+    """
+    if in_dygraph_mode():
+        if isinstance(axis, int):
+            axis = [axis]
+        elif isinstance(axis, Variable):
+            axis = axis.numpy().tolist()
+        elif isinstance(axis, (list, tuple)):
+            axis = [
+                item.numpy().item(0) if isinstance(item, Variable) else item
+                for item in axis
+            ]
+        out, _ = core.ops.unsqueeze2_(x, 'axes', axis)
+        return out
+
+    _print_warning_in_static_mode("unsqueeze")
+    return unsqueeze(x, axis, name)
+
+
 def gather(x, index, axis=None, name=None):
     """
     Output is obtained by gathering entries of ``axis``
@@ -966,6 +1019,18 @@ def scatter(x, index, updates, overwrite=True, name=None):
     return out
 
 
+def scatter_(x, index, updates, overwrite=True, name=None):
+    """
+    Inplace version of ``scatter`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_paddle_tensor_scatter`.
+    """
+    if in_dygraph_mode():
+        return core.ops.scatter_(x, index, updates, 'overwrite', overwrite)
+
+    _print_warning_in_static_mode("scatter")
+    return scatter(x, index, updates, overwrite, name)
+
+
 def scatter_nd_add(x, index, updates, name=None):
     r"""
     **Scatter_nd_add Layer**
@@ -1485,6 +1550,28 @@ def reshape(x, shape, name=None):
     return paddle.fluid.layers.reshape(x=x, shape=shape, name=name)
 
 
+def reshape_(x, shape, name=None):
+    """
+    Inplace version of ``reshape`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_paddle_tensor_reshape`.
+    """
+    if in_dygraph_mode():
+        if isinstance(shape, (list, tuple)):
+            shape = [
+                item.numpy().item(0) if isinstance(item, Variable) else item
+                for item in shape
+            ]
+            out, _ = core.ops.reshape2_(x, None, 'shape', shape)
+            return out
+        elif isinstance(shape, Variable):
+            shape.stop_gradient = True
+            out, _ = core.ops.reshape2_(x, shape)
+            return out
+
+    _print_warning_in_static_mode("reshape")
+    return reshape(x, shape, name)
+
+
 def gather_nd(x, index, name=None):
     """
 

python/paddle/tensor/math.py

@@ -25,6 +25,7 @@ from ..fluid.framework import core, _varbase_creator, in_dygraph_mode, Variable,
 from ..fluid.layer_helper import LayerHelper
 from ..fluid.data_feeder import check_variable_and_dtype, check_type, check_dtype, convert_dtype
 from ..fluid.layers.layer_function_generator import _generate_doc_string_, generate_activation_fn, generate_layer_fn
+from .manipulation import _print_warning_in_static_mode
 
 # TODO: define math functions
 # yapf: disable
@@ -98,6 +99,7 @@ __all__ = [
         'stanh',
         'sum',
         'tanh',
+        'tanh_',
         'add_n',
         'max',
         'maximum',
@@ -1968,6 +1970,17 @@ def tanh(x, name=None):
     helper.append_op(type='tanh', inputs={'X': x}, outputs={'Out': out})
     return out
 
+def tanh_(x, name=None):
+    r"""
+    Inplace version of ``tanh`` API, the output Tensor will be inplaced with input ``x``.
+    Please refer to :ref:`api_tensor_tanh`.
+    """
+    if in_dygraph_mode():
+        return core.ops.tanh_(x)
+
+    _print_warning_in_static_mode("tanh")
+    return tanh(x, name)
+
 def increment(x, value=1.0, name=None):
     """
     The OP is usually used for control flow to increment the data of :attr:`x` by an amount :attr:`value`.

tools/wlist.json

@@ -21,6 +21,38 @@
         {
             "name":"xxxxx",
             "annotation":"not a real api, just for example"
+        },
+        {
+            "name":"squeeze_",
+            "annotation":"Inplace APIs don't need sample code. There is a special document introducing Inplace strategy"
+        },
+        {
+            "name":"unsqueeze_",
+            "annotation":"Inplace APIs don't need sample code. There is a special document introducing Inplace strategy"
+        },
+        {
+            "name":"reshape_",
+            "annotation":"Inplace APIs don't need sample code. There is a special document introducing Inplace strategy"
+        },
+        {
+            "name":"scatter_",
+            "annotation":"Inplace APIs don't need sample code. There is a special document introducing Inplace strategy"
+        },
+        {
+            "name":"elu_",
+            "annotation":"Inplace APIs don't need sample code. There is a special document introducing Inplace strategy"
+        },
+        {
+            "name":"relu_",
+            "annotation":"Inplace APIs don't need sample code. There is a special document introducing Inplace strategy"
+        },
+        {
+            "name":"softmax_",
+            "annotation":"Inplace APIs don't need sample code. There is a special document introducing Inplace strategy"
+        },
+        {
+            "name":"tanh_",
+            "annotation":"Inplace APIs don't need sample code. There is a special document introducing Inplace strategy"
         }
     ],
     "wlist_temp_api":[